{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8663faad-e1ba-47b5-8fc5-722cdfeb785e",
   "metadata": {},
   "source": [
    "# MESA Ecospatial Tutorials (Colorectal Cancer, CODEX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2182894-28cb-4550-b359-75c563e8ab39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/mesa/lib/python3.11/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n",
      "  warnings.warn(\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "/opt/miniconda3/envs/mesa/lib/python3.11/site-packages/spaghetti/network.py:41: FutureWarning: The next major release of pysal/spaghetti (2.0.0) will drop support for all ``libpysal.cg`` geometries. This change is a first step in refactoring ``spaghetti`` that is expected to result in dramatically reduced runtimes for network instantiation and operations. Users currently requiring network and point pattern input as ``libpysal.cg`` geometries should prepare for this simply by converting to ``shapely`` geometries.\n",
      "  warnings.warn(dep_msg, FutureWarning, stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import anndata as ad\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "from matplotlib.figure import figaspect\n",
    "\n",
    "import os\n",
    "os.sys.path.append('../../../')\n",
    "from mesa import ecospatial as eco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14b872d8-1b0e-46fd-9ba4-added3f587c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['svg.fonttype'] = 'none'  # To keep text as editable text in SVGs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e010a32d-0eda-4c7c-8fa7-c368d27bbc62",
   "metadata": {},
   "source": [
    "## Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b69707d-ca9d-458e-86ad-3742f51c2922",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '/Users/Emrys/Dropbox/spatial_augmentation/data/CRC_related/crc_codex/'\n",
    "protein = pd.read_csv(os.path.join(data_dir, 'CRC_clusters_neighborhoods_markersV2.csv')) # ~258,000 codex cells\n",
    "protein = protein[~protein['ClusterName'].str.contains('dirt')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b828cd2b-3189-4024-aefb-a5663f1acfe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create maps from region to patients/conditions\n",
    "region_to_patient = dict(zip(protein[\"File Name\"], protein[\"patients\"]))\n",
    "region_to_condition = dict(zip(protein[\"File Name\"], protein[\"groups\"]))\n",
    "condition_num2str = {1.0:'CLR', 2.0:'DII'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "191734fd-9e5a-40bd-a66a-5fd69e47cab7",
   "metadata": {},
   "source": [
    "## Customise colour palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6df6370a-b290-417b-acb6-98b681f32382",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import colors as mcolors\n",
    "from matplotlib import colormaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d596a422-f2c2-4251-8fe6-39827ecbbd59",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_names = sorted(protein['ClusterName'].unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b08f4d4d-15ff-4827-b8c1-30c272f724bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "tab10 = colormaps['tab10']\n",
    "tab20b = colormaps['tab20b']\n",
    "tab20c = colormaps['tab20c']\n",
    "\n",
    "# Extract specific colors by indexing into the colormap (values from 0 to 1)\n",
    "# From tab20b: indices 8 to 19\n",
    "colors_from_tab20b = [tab20b(i) for i in range(8, 20)] + [tab20b(0),tab20b(3),tab20b(4),tab20b(5)]\n",
    "# From tab20c: indices 0-3, 8-11, and 16-19\n",
    "colors_from_tab20c = [tab20c(i) for i in range(4)] + [tab20c(i) for i in range(8, 12)] + [tab20c(i) for i in range(16, 20)]\n",
    "\n",
    "\n",
    "# Combine the colors into a custom palette\n",
    "custom_palette = colors_from_tab20c + colors_from_tab20b\n",
    "\n",
    "colors_hex = [mcolors.rgb2hex(color) for color in custom_palette]\n",
    "color_dict = dict(zip(cell_names, colors_hex))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cae88b64-c585-4cc0-9ad7-38115caa9c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "for label, color in color_dict.items():\n",
    "    ax.scatter([], [], c=color, label=label, marker='o',edgecolors='black', linewidths=0.5)\n",
    "\n",
    "ax.legend(loc='center', ncol=1, fontsize='x-small')\n",
    "ax.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b49791f7-b5ab-49de-815b-6010a07b52e0",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Selecting the scale for spatial tessellation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8bf54900-cd45-4f57-b55f-c732d80eef84",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: reg001_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg001_A at scale 2.0 diversity is 3.006720259484636\n",
      "Processing region: reg001_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg001_B at scale 2.0 diversity is 3.0782022986901705\n",
      "Processing region: reg002_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg002_A at scale 2.0 diversity is 2.907866052919977\n",
      "Processing region: reg002_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg002_B at scale 2.0 diversity is 1.0846680190412177\n",
      "Processing region: reg003_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg003_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg003_A at scale 2.0 diversity is 2.7898455684853216\n",
      "Processing region: reg003_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg003_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg003_B at scale 2.0 diversity is 2.3570981431059788\n",
      "Processing region: reg004_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg004_A at scale 2.0 diversity is 2.9425252594897655\n",
      "Processing region: reg004_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg004_B at scale 2.0 diversity is 2.392806711502546\n",
      "Processing region: reg005_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg005_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg005_A at scale 2.0 diversity is 2.798898509128272\n",
      "Processing region: reg005_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg005_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg005_B at scale 2.0 diversity is 2.694506707221392\n",
      "Processing region: reg006_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg006_A at scale 2.0 diversity is 3.0073481335469676\n",
      "Processing region: reg006_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg006_B at scale 2.0 diversity is 2.8859110557062992\n",
      "Processing region: reg007_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg007_A at scale 2.0 diversity is 2.3577983784154317\n",
      "Processing region: reg007_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg007_B at scale 2.0 diversity is 2.607293813373253\n",
      "Processing region: reg008_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg008_A at scale 2.0 diversity is 2.334349986612683\n",
      "Processing region: reg008_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg008_B at scale 2.0 diversity is 2.3060865930088674\n",
      "Processing region: reg009_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg009_A at scale 2.0 diversity is 2.9203552400591923\n",
      "Processing region: reg009_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg009_B at scale 2.0 diversity is 2.530705606466985\n",
      "Processing region: reg010_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg010_A at scale 2.0 diversity is 3.1818384035654357\n",
      "Processing region: reg010_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg010_B at scale 2.0 diversity is 3.0994554983880036\n",
      "Processing region: reg011_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg011_A at scale 2.0 diversity is 2.9854848615599265\n",
      "Processing region: reg011_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg011_B at scale 2.0 diversity is 3.0188474188887655\n",
      "Processing region: reg012_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg012_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg012_A at scale 2.0 diversity is 2.7635252200318203\n",
      "Processing region: reg012_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg012_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg012_B at scale 2.0 diversity is 2.137662763876956\n",
      "Processing region: reg013_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg013_A at scale 2.0 diversity is 2.3833344322980783\n",
      "Processing region: reg013_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg013_B at scale 2.0 diversity is 2.75996980712337\n",
      "Processing region: reg014_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg014_A at scale 2.0 diversity is 2.7988658517832863\n",
      "Processing region: reg014_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg014_B at scale 2.0 diversity is 2.7205386288694506\n",
      "Processing region: reg015_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg015_A at scale 2.0 diversity is 2.7284531088320736\n",
      "Processing region: reg015_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg015_B at scale 2.0 diversity is 1.7994770590494373\n",
      "Processing region: reg016_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg016_A at scale 2.0 diversity is 2.617353044572593\n",
      "Processing region: reg016_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg016_B at scale 2.0 diversity is 2.645222265804777\n",
      "Processing region: reg017_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg017_A at scale 2.0 diversity is 2.8480838985469985\n",
      "Processing region: reg017_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg017_B at scale 2.0 diversity is 3.1261504679194556\n",
      "Processing region: reg018_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg018_A at scale 2.0 diversity is 3.0230092602584158\n",
      "Processing region: reg018_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg018_B at scale 2.0 diversity is 2.3669000326870404\n",
      "Processing region: reg019_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg019_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg019_A at scale 2.0 diversity is 2.3332992092423406\n",
      "Processing region: reg019_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg019_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg019_B at scale 2.0 diversity is 2.2237925560786556\n",
      "Processing region: reg020_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg020_A at scale 2.0 diversity is 2.971366154333284\n",
      "Processing region: reg020_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg020_B at scale 2.0 diversity is 2.992584946090993\n",
      "Processing region: reg021_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg021_A at scale 2.0 diversity is 2.8041743883930996\n",
      "Processing region: reg021_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg021_B at scale 2.0 diversity is 2.9243665627701203\n",
      "Processing region: reg022_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg022_A at scale 2.0 diversity is 2.752586411309329\n",
      "Processing region: reg022_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg022_B at scale 2.0 diversity is 3.1356093204505244\n",
      "Processing region: reg023_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg023_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg023_A at scale 2.0 diversity is 1.7961463433492457\n",
      "Processing region: reg023_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg023_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg023_B at scale 2.0 diversity is 3.3058528331339563\n",
      "Processing region: reg024_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg024_A at scale 2.0 diversity is 2.105757515721894\n",
      "Processing region: reg024_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg024_B at scale 2.0 diversity is 2.224181528346875\n",
      "Processing region: reg025_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg025_A at scale 2.0 diversity is 2.8848975740211067\n",
      "Processing region: reg025_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg025_B at scale 2.0 diversity is 3.029128263674023\n",
      "Processing region: reg026_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg026_A at scale 2.0 diversity is 3.137453402688342\n",
      "Processing region: reg026_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg026_B at scale 2.0 diversity is 2.7292429025198977\n",
      "Processing region: reg027_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg027_A at scale 2.0 diversity is 2.7966400356352548\n",
      "Processing region: reg027_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg027_B at scale 2.0 diversity is 2.2160987163767256\n",
      "Processing region: reg028_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg028_A at scale 2.0 diversity is 3.4178433255452196\n",
      "Processing region: reg028_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg028_B at scale 2.0 diversity is 3.141115259713353\n",
      "Processing region: reg029_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg029_A at scale 2.0 diversity is 2.3517201560879855\n",
      "Processing region: reg029_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg029_B at scale 2.0 diversity is 2.1135717504325555\n",
      "Processing region: reg030_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg030_A at scale 2.0 diversity is 2.9389710559037736\n",
      "Processing region: reg030_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg030_B at scale 2.0 diversity is 1.8245292609524024\n",
      "Processing region: reg031_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg031_A at scale 2.0 diversity is 2.8674431705406382\n",
      "Processing region: reg031_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg031_B at scale 2.0 diversity is 3.213790354369765\n",
      "Processing region: reg032_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg032_A at scale 2.0 diversity is 3.0489069439668826\n",
      "Processing region: reg032_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg032_B at scale 2.0 diversity is 3.143420702529476\n",
      "Processing region: reg033_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg033_A at scale 2.0 diversity is 2.527373044461171\n",
      "Processing region: reg033_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg033_B at scale 2.0 diversity is 2.5147141187950557\n",
      "Processing region: reg034_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg034_A at scale 2.0 diversity is 2.7074354481330007\n",
      "Processing region: reg034_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg034_B at scale 2.0 diversity is 2.4018042365312335\n",
      "Processing region: reg035_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg035_A at scale 2.0 diversity is 2.799613911754396\n",
      "Processing region: reg035_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg035_B at scale 2.0 diversity is 2.302022735036954\n",
      "Processing region: reg036_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg036_A at scale 2.0 diversity is 3.075899366038348\n",
      "Processing region: reg036_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg036_B at scale 2.0 diversity is 2.1891631617555354\n",
      "Processing region: reg037_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg037_A at scale 2.0 diversity is 2.927739991601529\n",
      "Processing region: reg037_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg037_B at scale 2.0 diversity is 2.5889106052694433\n",
      "Processing region: reg038_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg038_A at scale 2.0 diversity is 2.851257124628696\n",
      "Processing region: reg038_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg038_B at scale 2.0 diversity is 2.5110933688101302\n",
      "Processing region: reg039_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg039_A at scale 2.0 diversity is 2.7501398430064765\n",
      "Processing region: reg039_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg039_B at scale 2.0 diversity is 2.7722004138599106\n",
      "Processing region: reg040_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg040_A at scale 2.0 diversity is 2.801217367062181\n",
      "Processing region: reg040_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg040_B at scale 2.0 diversity is 2.6177864218387\n",
      "Processing region: reg041_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg041_A at scale 2.0 diversity is 2.820376527688662\n",
      "Processing region: reg041_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg041_B at scale 2.0 diversity is 1.9430788195399333\n",
      "Processing region: reg042_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg042_A at scale 2.0 diversity is 1.586478372245642\n",
      "Processing region: reg042_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg042_B at scale 2.0 diversity is 1.4927608714421754\n",
      "Processing region: reg043_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg043_A at scale 2.0 diversity is 2.1503898337025666\n",
      "Processing region: reg043_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg043_B at scale 2.0 diversity is 1.6724706540785874\n",
      "Processing region: reg044_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg044_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg044_A at scale 2.0 diversity is 2.6124029928819477\n",
      "Processing region: reg044_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg044_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg044_B at scale 2.0 diversity is 1.7951798871976072\n",
      "Processing region: reg045_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg045_A at scale 2.0 diversity is 2.2895773085377495\n",
      "Processing region: reg045_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg045_B at scale 2.0 diversity is 2.7768677210528883\n",
      "Processing region: reg046_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg046_A at scale 2.0 diversity is 3.315128845125775\n",
      "Processing region: reg046_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg046_B at scale 2.0 diversity is 3.053485127952065\n",
      "Processing region: reg047_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg047_A at scale 2.0 diversity is 2.8928484684974425\n",
      "Processing region: reg047_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg047_B at scale 2.0 diversity is 1.9153496856985486\n",
      "Processing region: reg048_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg048_A at scale 2.0 diversity is 3.0029544926585374\n",
      "Processing region: reg048_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg048_B at scale 2.0 diversity is 2.9804816820205104\n",
      "Processing region: reg049_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg049_A at scale 2.0 diversity is 2.505208100394996\n",
      "Processing region: reg049_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg049_B at scale 2.0 diversity is 2.9161399723350385\n",
      "Processing region: reg050_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg050_A at scale 2.0 diversity is 2.0832690615372083\n",
      "Processing region: reg050_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg050_B at scale 2.0 diversity is 2.4407015139232318\n",
      "Processing region: reg051_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg051_A at scale 2.0 diversity is 2.7723543832512014\n",
      "Processing region: reg051_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg051_B at scale 2.0 diversity is 2.3807047380847712\n",
      "Processing region: reg052_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg052_A at scale 2.0 diversity is 2.6181633078096036\n",
      "Processing region: reg052_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg052_B at scale 2.0 diversity is 2.3462190590052017\n",
      "Processing region: reg053_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg053_A at scale 2.0 diversity is 3.194901537175901\n",
      "Processing region: reg053_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg053_B at scale 2.0 diversity is 2.422953985950602\n",
      "Processing region: reg054_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg054_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg054_A at scale 2.0 diversity is 2.84122092784912\n",
      "Processing region: reg054_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg054_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg054_B at scale 2.0 diversity is 2.259640353792332\n",
      "Processing region: reg055_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg055_A at scale 2.0 diversity is 2.931977622263422\n",
      "Processing region: reg055_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg055_B at scale 2.0 diversity is 2.4241989335748277\n",
      "Processing region: reg056_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg056_A at scale 2.0 diversity is 3.3336913955365493\n",
      "Processing region: reg056_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg056_B at scale 2.0 diversity is 3.06121137431393\n",
      "Processing region: reg057_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg057_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg057_A at scale 2.0 diversity is 1.9311336648802577\n",
      "Processing region: reg057_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg057_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg057_B at scale 2.0 diversity is 2.8849230678783955\n",
      "Processing region: reg058_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg058_A at scale 2.0 diversity is 1.1193777883958766\n",
      "Processing region: reg058_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg058_B at scale 2.0 diversity is 0.7261372057812775\n",
      "Processing region: reg059_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg059_A at scale 2.0 diversity is 3.026656580317602\n",
      "Processing region: reg059_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg059_B at scale 2.0 diversity is 2.630511966264921\n",
      "Processing region: reg060_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg060_A at scale 2.0 diversity is 3.0753116864042886\n",
      "Processing region: reg060_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg060_B at scale 2.0 diversity is 2.914872093216821\n",
      "Processing region: reg061_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg061_A at scale 2.0 diversity is 2.7104564453687585\n",
      "Processing region: reg061_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg061_B at scale 2.0 diversity is 2.983874307208561\n",
      "Processing region: reg062_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg062_A at scale 2.0 diversity is 2.7188912135702408\n",
      "Processing region: reg062_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg062_B at scale 2.0 diversity is 3.025015486366876\n",
      "Processing region: reg063_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg063_A at scale 2.0 diversity is 2.3876113318737273\n",
      "Processing region: reg063_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg063_B at scale 2.0 diversity is 3.0699850404039104\n",
      "Processing region: reg064_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg064_A at scale 2.0 diversity is 2.3677793458693284\n",
      "Processing region: reg064_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg064_B at scale 2.0 diversity is 3.183636600785769\n",
      "Processing region: reg065_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg065_A at scale 2.0 diversity is 1.9524192173371937\n",
      "Processing region: reg065_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg065_B at scale 2.0 diversity is 2.8202450781019914\n",
      "Processing region: reg066_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg066_A at scale 2.0 diversity is 2.222492851829908\n",
      "Processing region: reg066_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg066_B at scale 2.0 diversity is 2.8589435189018477\n",
      "Processing region: reg067_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg067_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg067_A at scale 2.0 diversity is 3.174313126306071\n",
      "Processing region: reg067_B at scale 2.0\n",
      "25.000 per cent patches are empty\n",
      "reg067_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg067_B at scale 2.0 diversity is 1.8404742956555353\n",
      "Processing region: reg068_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg068_A at scale 2.0 diversity is 2.816110317659155\n",
      "Processing region: reg068_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg068_B at scale 2.0 diversity is 3.37063036600333\n",
      "Processing region: reg069_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg069_A at scale 2.0 diversity is 3.0535958241158574\n",
      "Processing region: reg069_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg069_B at scale 2.0 diversity is 3.0349520079645496\n",
      "Processing region: reg070_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg070_A at scale 2.0 diversity is 2.311330477230654\n",
      "Processing region: reg070_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg070_B at scale 2.0 diversity is 2.2190816001107243\n",
      "Processing region: reg001_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg001_A at scale 4.0 diversity is 2.6912257855375854\n",
      "Processing region: reg001_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg001_B at scale 4.0 diversity is 2.508511745312688\n",
      "Processing region: reg002_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg002_A at scale 4.0 diversity is 2.5763810374235856\n",
      "Processing region: reg002_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg002_B at scale 4.0 diversity is 1.0092913098737057\n",
      "Processing region: reg003_A at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg003_A at scale 4.0 has 1 patches with zero diveristy\n",
      "reg003_A at scale 4.0 diversity is 2.40299494392918\n",
      "Processing region: reg003_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg003_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg003_B at scale 4.0 diversity is 2.2352323000088967\n",
      "Processing region: reg004_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg004_A at scale 4.0 diversity is 2.7239538152784335\n",
      "Processing region: reg004_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg004_B at scale 4.0 diversity is 2.239496883238029\n",
      "Processing region: reg005_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg005_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg005_A at scale 4.0 diversity is 2.5058962999578607\n",
      "Processing region: reg005_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg005_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg005_B at scale 4.0 diversity is 2.2974603781476604\n",
      "Processing region: reg006_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg006_A at scale 4.0 diversity is 2.8049149639675326\n",
      "Processing region: reg006_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg006_B at scale 4.0 diversity is 2.661566961345321\n",
      "Processing region: reg007_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg007_A at scale 4.0 diversity is 2.0720087383758763\n",
      "Processing region: reg007_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg007_B at scale 4.0 diversity is 2.223769568863302\n",
      "Processing region: reg008_A at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg008_A at scale 4.0 has 1 patches with zero diveristy\n",
      "reg008_A at scale 4.0 diversity is 1.8819007929993086\n",
      "Processing region: reg008_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg008_B at scale 4.0 diversity is 2.1184621981095377\n",
      "Processing region: reg009_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg009_A at scale 4.0 diversity is 2.7187002813275343\n",
      "Processing region: reg009_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg009_B at scale 4.0 diversity is 2.448889454136096\n",
      "Processing region: reg010_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg010_A at scale 4.0 diversity is 2.901288804238678\n",
      "Processing region: reg010_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg010_B at scale 4.0 diversity is 2.765224550587904\n",
      "Processing region: reg011_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg011_A at scale 4.0 diversity is 2.744119864315903\n",
      "Processing region: reg011_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg011_B at scale 4.0 diversity is 2.470285925795813\n",
      "Processing region: reg012_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg012_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg012_A at scale 4.0 diversity is 2.4489169928974905\n",
      "Processing region: reg012_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg012_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg012_B at scale 4.0 diversity is 1.7471079857854763\n",
      "Processing region: reg013_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg013_A at scale 4.0 diversity is 2.1524305538204276\n",
      "Processing region: reg013_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg013_B at scale 4.0 diversity is 2.520180774088927\n",
      "Processing region: reg014_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg014_A at scale 4.0 diversity is 2.4812265005534124\n",
      "Processing region: reg014_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg014_B at scale 4.0 diversity is 2.518657583271807\n",
      "Processing region: reg015_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg015_A at scale 4.0 diversity is 2.485686640035546\n",
      "Processing region: reg015_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg015_B at scale 4.0 diversity is 1.6663483336287583\n",
      "Processing region: reg016_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg016_A at scale 4.0 diversity is 2.422934885830574\n",
      "Processing region: reg016_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg016_B at scale 4.0 diversity is 2.4286480207114938\n",
      "Processing region: reg017_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg017_A at scale 4.0 diversity is 2.5948491092335506\n",
      "Processing region: reg017_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg017_B at scale 4.0 diversity is 2.774326887094235\n",
      "Processing region: reg018_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg018_A at scale 4.0 diversity is 2.6183636107516435\n",
      "Processing region: reg018_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg018_B at scale 4.0 diversity is 2.123087714675039\n",
      "Processing region: reg019_A at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg019_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg019_A at scale 4.0 diversity is 2.017202288679072\n",
      "Processing region: reg019_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg019_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg019_B at scale 4.0 diversity is 1.420851519204359\n",
      "Processing region: reg020_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg020_A at scale 4.0 diversity is 2.7080420261090867\n",
      "Processing region: reg020_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg020_B at scale 4.0 diversity is 2.8570169499980596\n",
      "Processing region: reg021_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg021_A at scale 4.0 diversity is 2.4923208169420734\n",
      "Processing region: reg021_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg021_B at scale 4.0 diversity is 2.3797447253640964\n",
      "Processing region: reg022_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg022_A at scale 4.0 diversity is 2.576345618958105\n",
      "Processing region: reg022_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg022_B at scale 4.0 diversity is 2.5725024190202213\n",
      "Processing region: reg023_A at scale 4.0\n",
      "12.500 per cent patches are empty\n",
      "reg023_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg023_A at scale 4.0 diversity is 1.53640564884635\n",
      "Processing region: reg023_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg023_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg023_B at scale 4.0 diversity is 3.081867323706144\n",
      "Processing region: reg024_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg024_A at scale 4.0 diversity is 1.9594151536098638\n",
      "Processing region: reg024_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg024_B at scale 4.0 diversity is 2.0979048551559996\n",
      "Processing region: reg025_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg025_A at scale 4.0 diversity is 2.5788803350843272\n",
      "Processing region: reg025_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg025_B at scale 4.0 diversity is 2.7155616172343815\n",
      "Processing region: reg026_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg026_A at scale 4.0 diversity is 2.950520774164608\n",
      "Processing region: reg026_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg026_B at scale 4.0 diversity is 2.6096562884028285\n",
      "Processing region: reg027_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg027_A at scale 4.0 diversity is 2.575019364816742\n",
      "Processing region: reg027_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg027_B at scale 4.0 diversity is 2.073792659899871\n",
      "Processing region: reg028_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg028_A at scale 4.0 diversity is 3.2328667604962695\n",
      "Processing region: reg028_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg028_B at scale 4.0 diversity is 2.966955126711894\n",
      "Processing region: reg029_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg029_A at scale 4.0 diversity is 2.113785592308676\n",
      "Processing region: reg029_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg029_B at scale 4.0 diversity is 1.8867529938578085\n",
      "Processing region: reg030_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg030_A at scale 4.0 diversity is 2.806596484947666\n",
      "Processing region: reg030_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg030_B at scale 4.0 diversity is 1.5803046749028604\n",
      "Processing region: reg031_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg031_A at scale 4.0 diversity is 2.695873676870986\n",
      "Processing region: reg031_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg031_B at scale 4.0 diversity is 2.965007045210802\n",
      "Processing region: reg032_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg032_A at scale 4.0 diversity is 2.7857185816520222\n",
      "Processing region: reg032_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg032_B at scale 4.0 diversity is 2.8851103073158213\n",
      "Processing region: reg033_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg033_A at scale 4.0 diversity is 2.4607063715685733\n",
      "Processing region: reg033_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg033_B at scale 4.0 diversity is 2.3183053093324117\n",
      "Processing region: reg034_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg034_A at scale 4.0 diversity is 2.59907305044079\n",
      "Processing region: reg034_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg034_B at scale 4.0 diversity is 2.21874873320456\n",
      "Processing region: reg035_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg035_A at scale 4.0 diversity is 2.5400862187776205\n",
      "Processing region: reg035_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg035_B at scale 4.0 diversity is 2.009293801669852\n",
      "Processing region: reg036_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg036_A at scale 4.0 diversity is 2.7864922736282027\n",
      "Processing region: reg036_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg036_B at scale 4.0 diversity is 1.8871738352990746\n",
      "Processing region: reg037_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg037_A at scale 4.0 diversity is 2.5105760460246414\n",
      "Processing region: reg037_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg037_B at scale 4.0 diversity is 2.2257376228807306\n",
      "Processing region: reg038_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg038_A at scale 4.0 diversity is 2.728365610428482\n",
      "Processing region: reg038_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg038_B at scale 4.0 diversity is 2.319347338468458\n",
      "Processing region: reg039_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg039_A at scale 4.0 diversity is 2.4013971766317983\n",
      "Processing region: reg039_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg039_B at scale 4.0 diversity is 2.385567597186299\n",
      "Processing region: reg040_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg040_A at scale 4.0 diversity is 2.5982686405147026\n",
      "Processing region: reg040_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg040_B at scale 4.0 diversity is 2.3054714695658873\n",
      "Processing region: reg041_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg041_A at scale 4.0 diversity is 2.611499919520702\n",
      "Processing region: reg041_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg041_B at scale 4.0 diversity is 1.7086811150086967\n",
      "Processing region: reg042_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg042_A at scale 4.0 diversity is 1.412249731404053\n",
      "Processing region: reg042_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg042_B at scale 4.0 diversity is 1.303806474662546\n",
      "Processing region: reg043_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg043_A at scale 4.0 diversity is 1.9450520789934216\n",
      "Processing region: reg043_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg043_B at scale 4.0 diversity is 1.4005357925956483\n",
      "Processing region: reg044_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg044_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg044_A at scale 4.0 diversity is 2.3905182338732827\n",
      "Processing region: reg044_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg044_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg044_B at scale 4.0 diversity is 1.5325797491427111\n",
      "Processing region: reg045_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg045_A at scale 4.0 diversity is 2.25074395347798\n",
      "Processing region: reg045_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg045_B at scale 4.0 diversity is 2.5495458882176933\n",
      "Processing region: reg046_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg046_A at scale 4.0 diversity is 3.041041408118236\n",
      "Processing region: reg046_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg046_B at scale 4.0 diversity is 2.9011803978494575\n",
      "Processing region: reg047_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg047_A at scale 4.0 diversity is 2.6609796781855533\n",
      "Processing region: reg047_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg047_B at scale 4.0 diversity is 1.7465111988719086\n",
      "Processing region: reg048_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg048_A at scale 4.0 diversity is 2.7825572538071452\n",
      "Processing region: reg048_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg048_B at scale 4.0 diversity is 2.6498873372021574\n",
      "Processing region: reg049_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg049_A at scale 4.0 diversity is 2.394152390161228\n",
      "Processing region: reg049_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg049_B at scale 4.0 diversity is 2.6263290858294948\n",
      "Processing region: reg050_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg050_A at scale 4.0 diversity is 1.9345445976385542\n",
      "Processing region: reg050_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg050_B at scale 4.0 diversity is 2.1403816358349177\n",
      "Processing region: reg051_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_A at scale 4.0 has 1 patches with zero diveristy\n",
      "reg051_A at scale 4.0 diversity is 2.324936180281602\n",
      "Processing region: reg051_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg051_B at scale 4.0 diversity is 2.108391827246699\n",
      "Processing region: reg052_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg052_A at scale 4.0 diversity is 2.4396721924476563\n",
      "Processing region: reg052_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg052_B at scale 4.0 diversity is 2.090261131052345\n",
      "Processing region: reg053_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg053_A at scale 4.0 diversity is 3.0225355938624263\n",
      "Processing region: reg053_B at scale 4.0\n",
      "18.750 per cent patches are empty\n",
      "reg053_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg053_B at scale 4.0 diversity is 1.4844248383783252\n",
      "Processing region: reg054_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg054_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg054_A at scale 4.0 diversity is 2.4392078558145522\n",
      "Processing region: reg054_B at scale 4.0\n",
      "18.750 per cent patches are empty\n",
      "reg054_B at scale 4.0 has 2 patches with zero diveristy\n",
      "reg054_B at scale 4.0 diversity is 1.6058476621875657\n",
      "Processing region: reg055_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg055_A at scale 4.0 diversity is 2.7719133995710594\n",
      "Processing region: reg055_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg055_B at scale 4.0 diversity is 1.5813363571935615\n",
      "Processing region: reg056_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg056_A at scale 4.0 diversity is 2.915100249036336\n",
      "Processing region: reg056_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg056_B at scale 4.0 diversity is 2.7184854616067478\n",
      "Processing region: reg057_A at scale 4.0\n",
      "50.000 per cent patches are empty\n",
      "reg057_A at scale 4.0 has 1 patches with zero diveristy\n",
      "reg057_A at scale 4.0 diversity is 1.6455779296010529\n",
      "Processing region: reg057_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg057_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg057_B at scale 4.0 diversity is 2.1291835266558037\n",
      "Processing region: reg058_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg058_A at scale 4.0 diversity is 1.0642012832042715\n",
      "Processing region: reg058_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg058_B at scale 4.0 diversity is 0.6843698800485913\n",
      "Processing region: reg059_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg059_A at scale 4.0 diversity is 2.8615841709776877\n",
      "Processing region: reg059_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg059_B at scale 4.0 diversity is 2.514360016742207\n",
      "Processing region: reg060_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg060_A at scale 4.0 diversity is 2.867529302830965\n",
      "Processing region: reg060_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg060_B at scale 4.0 diversity is 2.7622734666888213\n",
      "Processing region: reg061_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg061_A at scale 4.0 diversity is 2.478541352827784\n",
      "Processing region: reg061_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg061_B at scale 4.0 diversity is 2.8172624622292366\n",
      "Processing region: reg062_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg062_A at scale 4.0 diversity is 2.5083520946201032\n",
      "Processing region: reg062_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg062_B at scale 4.0 diversity is 2.7621370474313585\n",
      "Processing region: reg063_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg063_A at scale 4.0 diversity is 2.1107133611387217\n",
      "Processing region: reg063_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg063_B at scale 4.0 diversity is 2.8692382444867506\n",
      "Processing region: reg064_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg064_A at scale 4.0 diversity is 2.227882243918364\n",
      "Processing region: reg064_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg064_B at scale 4.0 diversity is 2.8997041177159906\n",
      "Processing region: reg065_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg065_A at scale 4.0 diversity is 1.7187528024407746\n",
      "Processing region: reg065_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg065_B at scale 4.0 diversity is 2.4116814289829964\n",
      "Processing region: reg066_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg066_A at scale 4.0 diversity is 2.0830799816718177\n",
      "Processing region: reg066_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg066_B at scale 4.0 diversity is 2.471308644554643\n",
      "Processing region: reg067_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg067_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg067_A at scale 4.0 diversity is 2.6544874636685867\n",
      "Processing region: reg067_B at scale 4.0\n",
      "62.500 per cent patches are empty\n",
      "reg067_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg067_B at scale 4.0 diversity is 1.7687970435637457\n",
      "Processing region: reg068_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg068_A at scale 4.0 diversity is 2.5901497977414074\n",
      "Processing region: reg068_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg068_B at scale 4.0 diversity is 3.0512670951225274\n",
      "Processing region: reg069_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg069_A at scale 4.0 diversity is 2.8358913942039394\n",
      "Processing region: reg069_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg069_B at scale 4.0 diversity is 2.5605284050944186\n",
      "Processing region: reg070_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg070_A at scale 4.0 diversity is 2.1426240069396147\n",
      "Processing region: reg070_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg070_B at scale 4.0 diversity is 2.058367215553855\n",
      "Processing region: reg001_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg001_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg001_A at scale 8.0 diversity is 2.1844272169741714\n",
      "Processing region: reg001_B at scale 8.0\n",
      "7.812 per cent patches are empty\n",
      "reg001_B at scale 8.0 has 8 patches with zero diveristy\n",
      "reg001_B at scale 8.0 diversity is 1.3978867930267103\n",
      "Processing region: reg002_A at scale 8.0\n",
      "18.750 per cent patches are empty\n",
      "reg002_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg002_A at scale 8.0 diversity is 2.1686610202770744\n",
      "Processing region: reg002_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_B at scale 8.0 has 3 patches with zero diveristy\n",
      "reg002_B at scale 8.0 diversity is 0.8589620488081982\n",
      "Processing region: reg003_A at scale 8.0\n",
      "18.750 per cent patches are empty\n",
      "reg003_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg003_A at scale 8.0 diversity is 1.9660160489920295\n",
      "Processing region: reg003_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg003_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg003_B at scale 8.0 diversity is 1.766514129279135\n",
      "Processing region: reg004_A at scale 8.0\n",
      "12.500 per cent patches are empty\n",
      "reg004_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg004_A at scale 8.0 diversity is 2.467347624676027\n",
      "Processing region: reg004_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg004_B at scale 8.0 diversity is 1.894028687177677\n",
      "Processing region: reg005_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg005_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg005_A at scale 8.0 diversity is 2.200987499344523\n",
      "Processing region: reg005_B at scale 8.0\n",
      "20.312 per cent patches are empty\n",
      "reg005_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg005_B at scale 8.0 diversity is 1.956832928800155\n",
      "Processing region: reg006_A at scale 8.0\n",
      "7.812 per cent patches are empty\n",
      "reg006_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg006_A at scale 8.0 diversity is 2.4137160332365495\n",
      "Processing region: reg006_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg006_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg006_B at scale 8.0 diversity is 2.2385821627736804\n",
      "Processing region: reg007_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg007_A at scale 8.0 diversity is 1.7392528437222632\n",
      "Processing region: reg007_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg007_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg007_B at scale 8.0 diversity is 1.9477726334384857\n",
      "Processing region: reg008_A at scale 8.0\n",
      "21.875 per cent patches are empty\n",
      "reg008_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg008_A at scale 8.0 diversity is 1.5519903470247445\n",
      "Processing region: reg008_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg008_B at scale 8.0 diversity is 1.8035790645184098\n",
      "Processing region: reg009_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg009_A at scale 8.0 diversity is 2.3717123438726038\n",
      "Processing region: reg009_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg009_B at scale 8.0 diversity is 2.260042615486046\n",
      "Processing region: reg010_A at scale 8.0\n",
      "7.812 per cent patches are empty\n",
      "reg010_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg010_A at scale 8.0 diversity is 2.546231294895141\n",
      "Processing region: reg010_B at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg010_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg010_B at scale 8.0 diversity is 2.1873200225549008\n",
      "Processing region: reg011_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg011_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg011_A at scale 8.0 diversity is 2.340933599355627\n",
      "Processing region: reg011_B at scale 8.0\n",
      "12.500 per cent patches are empty\n",
      "reg011_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg011_B at scale 8.0 diversity is 2.0363682900440123\n",
      "Processing region: reg012_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg012_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg012_A at scale 8.0 diversity is 1.917499275375252\n",
      "Processing region: reg012_B at scale 8.0\n",
      "37.500 per cent patches are empty\n",
      "reg012_B at scale 8.0 has 12 patches with zero diveristy\n",
      "reg012_B at scale 8.0 diversity is 1.3289531453053525\n",
      "Processing region: reg013_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg013_A at scale 8.0 has 5 patches with zero diveristy\n",
      "reg013_A at scale 8.0 diversity is 1.785869563079486\n",
      "Processing region: reg013_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg013_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg013_B at scale 8.0 diversity is 2.103927205554886\n",
      "Processing region: reg014_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg014_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg014_A at scale 8.0 diversity is 1.8998253046435998\n",
      "Processing region: reg014_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg014_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg014_B at scale 8.0 diversity is 2.173592310185113\n",
      "Processing region: reg015_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg015_A at scale 8.0 diversity is 2.1624804753048217\n",
      "Processing region: reg015_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg015_B at scale 8.0 has 3 patches with zero diveristy\n",
      "reg015_B at scale 8.0 diversity is 1.2907618015193947\n",
      "Processing region: reg016_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg016_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg016_A at scale 8.0 diversity is 2.0398401179745016\n",
      "Processing region: reg016_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg016_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg016_B at scale 8.0 diversity is 2.1240018677958097\n",
      "Processing region: reg017_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg017_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg017_A at scale 8.0 diversity is 2.1564587987256476\n",
      "Processing region: reg017_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg017_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg017_B at scale 8.0 diversity is 2.3460520649678718\n",
      "Processing region: reg018_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg018_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg018_A at scale 8.0 diversity is 2.2327726782544333\n",
      "Processing region: reg018_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg018_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg018_B at scale 8.0 diversity is 1.8415130971603912\n",
      "Processing region: reg019_A at scale 8.0\n",
      "18.750 per cent patches are empty\n",
      "reg019_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg019_A at scale 8.0 diversity is 1.6343616995090693\n",
      "Processing region: reg019_B at scale 8.0\n",
      "39.062 per cent patches are empty\n",
      "reg019_B at scale 8.0 has 22 patches with zero diveristy\n",
      "reg019_B at scale 8.0 diversity is 0.537882232457474\n",
      "Processing region: reg020_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg020_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg020_A at scale 8.0 diversity is 2.340486520490724\n",
      "Processing region: reg020_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg020_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg020_B at scale 8.0 diversity is 2.4957924769019777\n",
      "Processing region: reg021_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg021_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg021_A at scale 8.0 diversity is 1.9846964659994617\n",
      "Processing region: reg021_B at scale 8.0\n",
      "14.062 per cent patches are empty\n",
      "reg021_B at scale 8.0 has 9 patches with zero diveristy\n",
      "reg021_B at scale 8.0 diversity is 1.4313677786986\n",
      "Processing region: reg022_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg022_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg022_A at scale 8.0 diversity is 2.270296075666552\n",
      "Processing region: reg022_B at scale 8.0\n",
      "21.875 per cent patches are empty\n",
      "reg022_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg022_B at scale 8.0 diversity is 2.0466363189053025\n",
      "Processing region: reg023_A at scale 8.0\n",
      "20.312 per cent patches are empty\n",
      "reg023_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg023_A at scale 8.0 diversity is 1.2862367285317442\n",
      "Processing region: reg023_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg023_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg023_B at scale 8.0 diversity is 2.6530052715197545\n",
      "Processing region: reg024_A at scale 8.0\n",
      "9.375 per cent patches are empty\n",
      "reg024_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg024_A at scale 8.0 diversity is 1.568394740694343\n",
      "Processing region: reg024_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg024_B at scale 8.0 diversity is 1.827138657514349\n",
      "Processing region: reg025_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg025_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg025_A at scale 8.0 diversity is 2.1007342019006034\n",
      "Processing region: reg025_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg025_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg025_B at scale 8.0 diversity is 2.141150049791231\n",
      "Processing region: reg026_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg026_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg026_A at scale 8.0 diversity is 2.5766448535620565\n",
      "Processing region: reg026_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg026_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg026_B at scale 8.0 diversity is 2.292976651311868\n",
      "Processing region: reg027_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg027_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg027_A at scale 8.0 diversity is 2.2303329095264304\n",
      "Processing region: reg027_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg027_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg027_B at scale 8.0 diversity is 1.8028071674281572\n",
      "Processing region: reg028_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg028_A at scale 8.0 diversity is 2.7937843331556027\n",
      "Processing region: reg028_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg028_B at scale 8.0 diversity is 2.506966268773661\n",
      "Processing region: reg029_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg029_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg029_A at scale 8.0 diversity is 1.7660372419492292\n",
      "Processing region: reg029_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg029_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg029_B at scale 8.0 diversity is 1.609525621451343\n",
      "Processing region: reg030_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg030_A at scale 8.0 diversity is 2.451537369342063\n",
      "Processing region: reg030_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg030_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg030_B at scale 8.0 diversity is 1.2784416266667322\n",
      "Processing region: reg031_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg031_A at scale 8.0 diversity is 2.409881749017643\n",
      "Processing region: reg031_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg031_B at scale 8.0 diversity is 2.5458386318148043\n",
      "Processing region: reg032_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg032_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg032_A at scale 8.0 diversity is 2.386455984077176\n",
      "Processing region: reg032_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg032_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg032_B at scale 8.0 diversity is 2.317567198803208\n",
      "Processing region: reg033_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg033_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg033_A at scale 8.0 diversity is 2.0397376000471623\n",
      "Processing region: reg033_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg033_B at scale 8.0 diversity is 1.9145125798983857\n",
      "Processing region: reg034_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg034_A at scale 8.0 diversity is 2.296432797510287\n",
      "Processing region: reg034_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg034_B at scale 8.0 diversity is 1.7945713861079937\n",
      "Processing region: reg035_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg035_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg035_A at scale 8.0 diversity is 2.154921161381134\n",
      "Processing region: reg035_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg035_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg035_B at scale 8.0 diversity is 1.6275061015154395\n",
      "Processing region: reg036_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg036_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg036_A at scale 8.0 diversity is 2.4714631267491907\n",
      "Processing region: reg036_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_B at scale 8.0 has 11 patches with zero diveristy\n",
      "reg036_B at scale 8.0 diversity is 1.4686328638834323\n",
      "Processing region: reg037_A at scale 8.0\n",
      "7.812 per cent patches are empty\n",
      "reg037_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg037_A at scale 8.0 diversity is 1.9792718131577443\n",
      "Processing region: reg037_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg037_B at scale 8.0 has 5 patches with zero diveristy\n",
      "reg037_B at scale 8.0 diversity is 1.4565913715127137\n",
      "Processing region: reg038_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg038_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg038_A at scale 8.0 diversity is 2.4201397922281718\n",
      "Processing region: reg038_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg038_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg038_B at scale 8.0 diversity is 1.8832661309192116\n",
      "Processing region: reg039_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg039_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg039_A at scale 8.0 diversity is 2.0359109849094237\n",
      "Processing region: reg039_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg039_B at scale 8.0 has 10 patches with zero diveristy\n",
      "reg039_B at scale 8.0 diversity is 1.5623724831737296\n",
      "Processing region: reg040_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg040_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg040_A at scale 8.0 diversity is 2.1566145065414486\n",
      "Processing region: reg040_B at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg040_B at scale 8.0 has 7 patches with zero diveristy\n",
      "reg040_B at scale 8.0 diversity is 1.6233385935274889\n",
      "Processing region: reg041_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg041_A at scale 8.0 diversity is 2.1350376399273765\n",
      "Processing region: reg041_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg041_B at scale 8.0 has 8 patches with zero diveristy\n",
      "reg041_B at scale 8.0 diversity is 1.4662163043560505\n",
      "Processing region: reg042_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg042_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg042_A at scale 8.0 diversity is 1.1735325221826658\n",
      "Processing region: reg042_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg042_B at scale 8.0 has 5 patches with zero diveristy\n",
      "reg042_B at scale 8.0 diversity is 1.0386160678778527\n",
      "Processing region: reg043_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg043_A at scale 8.0 diversity is 1.6249529236206899\n",
      "Processing region: reg043_B at scale 8.0\n",
      "14.062 per cent patches are empty\n",
      "reg043_B at scale 8.0 has 14 patches with zero diveristy\n",
      "reg043_B at scale 8.0 diversity is 0.9092168498502688\n",
      "Processing region: reg044_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg044_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg044_A at scale 8.0 diversity is 1.9686318039209392\n",
      "Processing region: reg044_B at scale 8.0\n",
      "23.438 per cent patches are empty\n",
      "reg044_B at scale 8.0 has 8 patches with zero diveristy\n",
      "reg044_B at scale 8.0 diversity is 1.039603560478042\n",
      "Processing region: reg045_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg045_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg045_A at scale 8.0 diversity is 2.0564353895660323\n",
      "Processing region: reg045_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg045_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg045_B at scale 8.0 diversity is 1.909072047275319\n",
      "Processing region: reg046_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg046_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg046_A at scale 8.0 diversity is 2.649575036203187\n",
      "Processing region: reg046_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg046_B at scale 8.0 diversity is 2.5295808722262114\n",
      "Processing region: reg047_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg047_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg047_A at scale 8.0 diversity is 2.1461897755316177\n",
      "Processing region: reg047_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg047_B at scale 8.0 has 6 patches with zero diveristy\n",
      "reg047_B at scale 8.0 diversity is 1.2274152576852821\n",
      "Processing region: reg048_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg048_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg048_A at scale 8.0 diversity is 2.3424060740114325\n",
      "Processing region: reg048_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg048_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg048_B at scale 8.0 diversity is 2.1213169428272276\n",
      "Processing region: reg049_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg049_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg049_A at scale 8.0 diversity is 2.091376664587831\n",
      "Processing region: reg049_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg049_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg049_B at scale 8.0 diversity is 2.144246860541955\n",
      "Processing region: reg050_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg050_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg050_A at scale 8.0 diversity is 1.578639552644022\n",
      "Processing region: reg050_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg050_B at scale 8.0 has 3 patches with zero diveristy\n",
      "reg050_B at scale 8.0 diversity is 1.8484003493690628\n",
      "Processing region: reg051_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg051_A at scale 8.0 has 7 patches with zero diveristy\n",
      "reg051_A at scale 8.0 diversity is 1.8742271471377558\n",
      "Processing region: reg051_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_B at scale 8.0 has 3 patches with zero diveristy\n",
      "reg051_B at scale 8.0 diversity is 1.7306770156280515\n",
      "Processing region: reg052_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg052_A at scale 8.0 diversity is 2.1339259409353835\n",
      "Processing region: reg052_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg052_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg052_B at scale 8.0 diversity is 1.7228303565490486\n",
      "Processing region: reg053_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg053_A at scale 8.0 diversity is 2.5801653049005715\n",
      "Processing region: reg053_B at scale 8.0\n",
      "48.438 per cent patches are empty\n",
      "reg053_B at scale 8.0 has 9 patches with zero diveristy\n",
      "reg053_B at scale 8.0 diversity is 0.9804557157960465\n",
      "Processing region: reg054_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg054_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg054_A at scale 8.0 diversity is 2.029116250648739\n",
      "Processing region: reg054_B at scale 8.0\n",
      "43.750 per cent patches are empty\n",
      "reg054_B at scale 8.0 has 5 patches with zero diveristy\n",
      "reg054_B at scale 8.0 diversity is 1.375080970052685\n",
      "Processing region: reg055_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg055_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg055_A at scale 8.0 diversity is 2.3991975739546154\n",
      "Processing region: reg055_B at scale 8.0\n",
      "31.250 per cent patches are empty\n",
      "reg055_B at scale 8.0 has 18 patches with zero diveristy\n",
      "reg055_B at scale 8.0 diversity is 0.7867317883899703\n",
      "Processing region: reg056_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg056_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg056_A at scale 8.0 diversity is 2.2279799509588916\n",
      "Processing region: reg056_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg056_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg056_B at scale 8.0 diversity is 2.1903005336339896\n",
      "Processing region: reg057_A at scale 8.0\n",
      "76.562 per cent patches are empty\n",
      "reg057_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg057_A at scale 8.0 diversity is 1.2169801804571563\n",
      "Processing region: reg057_B at scale 8.0\n",
      "14.062 per cent patches are empty\n",
      "reg057_B at scale 8.0 has 11 patches with zero diveristy\n",
      "reg057_B at scale 8.0 diversity is 1.4453835609616434\n",
      "Processing region: reg058_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg058_A at scale 8.0 diversity is 0.8668496598063347\n",
      "Processing region: reg058_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg058_B at scale 8.0 has 16 patches with zero diveristy\n",
      "reg058_B at scale 8.0 diversity is 0.5604308392498848\n",
      "Processing region: reg059_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg059_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg059_A at scale 8.0 diversity is 2.5176904599215706\n",
      "Processing region: reg059_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg059_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg059_B at scale 8.0 diversity is 2.1663726510673063\n",
      "Processing region: reg060_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg060_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg060_A at scale 8.0 diversity is 2.5510411028814772\n",
      "Processing region: reg060_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg060_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg060_B at scale 8.0 diversity is 2.433151544170732\n",
      "Processing region: reg061_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg061_A at scale 8.0 diversity is 2.012420328726132\n",
      "Processing region: reg061_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg061_B at scale 8.0 diversity is 2.339576169151224\n",
      "Processing region: reg062_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg062_A at scale 8.0 diversity is 2.213338064575086\n",
      "Processing region: reg062_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg062_B at scale 8.0 diversity is 2.2426836941906974\n",
      "Processing region: reg063_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg063_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg063_A at scale 8.0 diversity is 1.636285167788469\n",
      "Processing region: reg063_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg063_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg063_B at scale 8.0 diversity is 2.3824944658329787\n",
      "Processing region: reg064_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg064_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg064_A at scale 8.0 diversity is 1.9339250474298497\n",
      "Processing region: reg064_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg064_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg064_B at scale 8.0 diversity is 2.4828709300668557\n",
      "Processing region: reg065_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg065_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg065_A at scale 8.0 diversity is 1.4187735070782346\n",
      "Processing region: reg065_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg065_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg065_B at scale 8.0 diversity is 1.6188740999790014\n",
      "Processing region: reg066_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg066_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg066_A at scale 8.0 diversity is 1.8591794637994814\n",
      "Processing region: reg066_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg066_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg066_B at scale 8.0 diversity is 1.9159140019815124\n",
      "Processing region: reg067_A at scale 8.0\n",
      "9.375 per cent patches are empty\n",
      "reg067_A at scale 8.0 has 6 patches with zero diveristy\n",
      "reg067_A at scale 8.0 diversity is 1.8516808709131558\n",
      "Processing region: reg067_B at scale 8.0\n",
      "71.875 per cent patches are empty\n",
      "reg067_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg067_B at scale 8.0 diversity is 1.0356301830650487\n",
      "Processing region: reg068_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg068_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg068_A at scale 8.0 diversity is 2.0490342425284105\n",
      "Processing region: reg068_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg068_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg068_B at scale 8.0 diversity is 2.54129394742902\n",
      "Processing region: reg069_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg069_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg069_A at scale 8.0 diversity is 2.3381342141732704\n",
      "Processing region: reg069_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg069_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg069_B at scale 8.0 diversity is 1.9312533895637745\n",
      "Processing region: reg070_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg070_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg070_A at scale 8.0 diversity is 1.737921244232286\n",
      "Processing region: reg070_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg070_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg070_B at scale 8.0 diversity is 1.7132580623682974\n",
      "Processing region: reg001_A at scale 16.0\n",
      "15.234 per cent patches are empty\n",
      "reg001_A at scale 16.0 has 45 patches with zero diveristy\n",
      "reg001_A at scale 16.0 diversity is 1.278679150302349\n",
      "Processing region: reg001_B at scale 16.0\n",
      "41.406 per cent patches are empty\n",
      "reg001_B at scale 16.0 has 78 patches with zero diveristy\n",
      "reg001_B at scale 16.0 diversity is 0.6240721579656607\n",
      "Processing region: reg002_A at scale 16.0\n",
      "30.078 per cent patches are empty\n",
      "reg002_A at scale 16.0 has 23 patches with zero diveristy\n",
      "reg002_A at scale 16.0 diversity is 1.4970986834941094\n",
      "Processing region: reg002_B at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg002_B at scale 16.0 has 73 patches with zero diveristy\n",
      "reg002_B at scale 16.0 diversity is 0.6202060055465932\n",
      "Processing region: reg003_A at scale 16.0\n",
      "31.250 per cent patches are empty\n",
      "reg003_A at scale 16.0 has 25 patches with zero diveristy\n",
      "reg003_A at scale 16.0 diversity is 1.4418751957099802\n",
      "Processing region: reg003_B at scale 16.0\n",
      "19.141 per cent patches are empty\n",
      "reg003_B at scale 16.0 has 34 patches with zero diveristy\n",
      "reg003_B at scale 16.0 diversity is 1.1944332996125686\n",
      "Processing region: reg004_A at scale 16.0\n",
      "21.875 per cent patches are empty\n",
      "reg004_A at scale 16.0 has 11 patches with zero diveristy\n",
      "reg004_A at scale 16.0 diversity is 1.8446158095105079\n",
      "Processing region: reg004_B at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg004_B at scale 16.0 has 21 patches with zero diveristy\n",
      "reg004_B at scale 16.0 diversity is 1.4417590496213195\n",
      "Processing region: reg005_A at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg005_A at scale 16.0 has 14 patches with zero diveristy\n",
      "reg005_A at scale 16.0 diversity is 1.6963073014378922\n",
      "Processing region: reg005_B at scale 16.0\n",
      "30.078 per cent patches are empty\n",
      "reg005_B at scale 16.0 has 17 patches with zero diveristy\n",
      "reg005_B at scale 16.0 diversity is 1.4771946098424056\n",
      "Processing region: reg006_A at scale 16.0\n",
      "15.625 per cent patches are empty\n",
      "reg006_A at scale 16.0 has 21 patches with zero diveristy\n",
      "reg006_A at scale 16.0 diversity is 1.5497292853096414\n",
      "Processing region: reg006_B at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg006_B at scale 16.0 has 22 patches with zero diveristy\n",
      "reg006_B at scale 16.0 diversity is 1.605315435803384\n",
      "Processing region: reg007_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg007_A at scale 16.0 has 50 patches with zero diveristy\n",
      "reg007_A at scale 16.0 diversity is 1.3026700296008487\n",
      "Processing region: reg007_B at scale 16.0\n",
      "14.453 per cent patches are empty\n",
      "reg007_B at scale 16.0 has 18 patches with zero diveristy\n",
      "reg007_B at scale 16.0 diversity is 1.5410101460874208\n",
      "Processing region: reg008_A at scale 16.0\n",
      "35.156 per cent patches are empty\n",
      "reg008_A at scale 16.0 has 24 patches with zero diveristy\n",
      "reg008_A at scale 16.0 diversity is 1.1006825796645114\n",
      "Processing region: reg008_B at scale 16.0\n",
      "3.516 per cent patches are empty\n",
      "reg008_B at scale 16.0 has 21 patches with zero diveristy\n",
      "reg008_B at scale 16.0 diversity is 1.41175634951127\n",
      "Processing region: reg009_A at scale 16.0\n",
      "2.344 per cent patches are empty\n",
      "reg009_A at scale 16.0 has 16 patches with zero diveristy\n",
      "reg009_A at scale 16.0 diversity is 1.7564122104261255\n",
      "Processing region: reg009_B at scale 16.0\n",
      "2.344 per cent patches are empty\n",
      "reg009_B at scale 16.0 has 3 patches with zero diveristy\n",
      "reg009_B at scale 16.0 diversity is 1.9277855308916516\n",
      "Processing region: reg010_A at scale 16.0\n",
      "11.719 per cent patches are empty\n",
      "reg010_A at scale 16.0 has 17 patches with zero diveristy\n",
      "reg010_A at scale 16.0 diversity is 1.8394794825765015\n",
      "Processing region: reg010_B at scale 16.0\n",
      "18.359 per cent patches are empty\n",
      "reg010_B at scale 16.0 has 41 patches with zero diveristy\n",
      "reg010_B at scale 16.0 diversity is 1.3631180826498142\n",
      "Processing region: reg011_A at scale 16.0\n",
      "18.750 per cent patches are empty\n",
      "reg011_A at scale 16.0 has 12 patches with zero diveristy\n",
      "reg011_A at scale 16.0 diversity is 1.847736105394992\n",
      "Processing region: reg011_B at scale 16.0\n",
      "24.609 per cent patches are empty\n",
      "reg011_B at scale 16.0 has 43 patches with zero diveristy\n",
      "reg011_B at scale 16.0 diversity is 1.2212822884889498\n",
      "Processing region: reg012_A at scale 16.0\n",
      "19.531 per cent patches are empty\n",
      "reg012_A at scale 16.0 has 24 patches with zero diveristy\n",
      "reg012_A at scale 16.0 diversity is 1.481997924593933\n",
      "Processing region: reg012_B at scale 16.0\n",
      "59.766 per cent patches are empty\n",
      "reg012_B at scale 16.0 has 41 patches with zero diveristy\n",
      "reg012_B at scale 16.0 diversity is 0.9566239776006007\n",
      "Processing region: reg013_A at scale 16.0\n",
      "5.469 per cent patches are empty\n",
      "reg013_A at scale 16.0 has 46 patches with zero diveristy\n",
      "reg013_A at scale 16.0 diversity is 1.2768105900507631\n",
      "Processing region: reg013_B at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg013_B at scale 16.0 has 39 patches with zero diveristy\n",
      "reg013_B at scale 16.0 diversity is 1.4352149930136526\n",
      "Processing region: reg014_A at scale 16.0\n",
      "17.969 per cent patches are empty\n",
      "reg014_A at scale 16.0 has 48 patches with zero diveristy\n",
      "reg014_A at scale 16.0 diversity is 1.138418135594311\n",
      "Processing region: reg014_B at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg014_B at scale 16.0 has 10 patches with zero diveristy\n",
      "reg014_B at scale 16.0 diversity is 1.6750389336322482\n",
      "Processing region: reg015_A at scale 16.0\n",
      "4.297 per cent patches are empty\n",
      "reg015_A at scale 16.0 has 10 patches with zero diveristy\n",
      "reg015_A at scale 16.0 diversity is 1.6116024647341678\n",
      "Processing region: reg015_B at scale 16.0\n",
      "12.109 per cent patches are empty\n",
      "reg015_B at scale 16.0 has 61 patches with zero diveristy\n",
      "reg015_B at scale 16.0 diversity is 0.8426733859069256\n",
      "Processing region: reg016_A at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg016_A at scale 16.0 has 27 patches with zero diveristy\n",
      "reg016_A at scale 16.0 diversity is 1.4541928747877895\n",
      "Processing region: reg016_B at scale 16.0\n",
      "5.078 per cent patches are empty\n",
      "reg016_B at scale 16.0 has 15 patches with zero diveristy\n",
      "reg016_B at scale 16.0 diversity is 1.5092365453005614\n",
      "Processing region: reg017_A at scale 16.0\n",
      "7.422 per cent patches are empty\n",
      "reg017_A at scale 16.0 has 27 patches with zero diveristy\n",
      "reg017_A at scale 16.0 diversity is 1.6272477419039446\n",
      "Processing region: reg017_B at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg017_B at scale 16.0 has 24 patches with zero diveristy\n",
      "reg017_B at scale 16.0 diversity is 1.7413837731075117\n",
      "Processing region: reg018_A at scale 16.0\n",
      "13.281 per cent patches are empty\n",
      "reg018_A at scale 16.0 has 26 patches with zero diveristy\n",
      "reg018_A at scale 16.0 diversity is 1.629163270233039\n",
      "Processing region: reg018_B at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg018_B at scale 16.0 has 14 patches with zero diveristy\n",
      "reg018_B at scale 16.0 diversity is 1.383201668872702\n",
      "Processing region: reg019_A at scale 16.0\n",
      "37.109 per cent patches are empty\n",
      "reg019_A at scale 16.0 has 33 patches with zero diveristy\n",
      "reg019_A at scale 16.0 diversity is 1.1662445405647155\n",
      "Processing region: reg019_B at scale 16.0\n",
      "76.953 per cent patches are empty\n",
      "reg019_B at scale 16.0 has 46 patches with zero diveristy\n",
      "reg019_B at scale 16.0 diversity is 0.2703726843421868\n",
      "Processing region: reg020_A at scale 16.0\n",
      "10.156 per cent patches are empty\n",
      "reg020_A at scale 16.0 has 29 patches with zero diveristy\n",
      "reg020_A at scale 16.0 diversity is 1.7024251996425317\n",
      "Processing region: reg020_B at scale 16.0\n",
      "8.594 per cent patches are empty\n",
      "reg020_B at scale 16.0 has 14 patches with zero diveristy\n",
      "reg020_B at scale 16.0 diversity is 1.9192905618248894\n",
      "Processing region: reg021_A at scale 16.0\n",
      "22.656 per cent patches are empty\n",
      "reg021_A at scale 16.0 has 23 patches with zero diveristy\n",
      "reg021_A at scale 16.0 diversity is 1.5605304675261158\n",
      "Processing region: reg021_B at scale 16.0\n",
      "46.875 per cent patches are empty\n",
      "reg021_B at scale 16.0 has 67 patches with zero diveristy\n",
      "reg021_B at scale 16.0 diversity is 0.6851940872168735\n",
      "Processing region: reg022_A at scale 16.0\n",
      "10.938 per cent patches are empty\n",
      "reg022_A at scale 16.0 has 26 patches with zero diveristy\n",
      "reg022_A at scale 16.0 diversity is 1.5620330150167288\n",
      "Processing region: reg022_B at scale 16.0\n",
      "37.891 per cent patches are empty\n",
      "reg022_B at scale 16.0 has 40 patches with zero diveristy\n",
      "reg022_B at scale 16.0 diversity is 1.1889771693275561\n",
      "Processing region: reg023_A at scale 16.0\n",
      "32.812 per cent patches are empty\n",
      "reg023_A at scale 16.0 has 43 patches with zero diveristy\n",
      "reg023_A at scale 16.0 diversity is 0.912962857483075\n",
      "Processing region: reg023_B at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg023_B at scale 16.0 has 10 patches with zero diveristy\n",
      "reg023_B at scale 16.0 diversity is 1.9839027758865977\n",
      "Processing region: reg024_A at scale 16.0\n",
      "21.094 per cent patches are empty\n",
      "reg024_A at scale 16.0 has 44 patches with zero diveristy\n",
      "reg024_A at scale 16.0 diversity is 0.9715197891350915\n",
      "Processing region: reg024_B at scale 16.0\n",
      "3.906 per cent patches are empty\n",
      "reg024_B at scale 16.0 has 25 patches with zero diveristy\n",
      "reg024_B at scale 16.0 diversity is 1.2808671906135307\n",
      "Processing region: reg025_A at scale 16.0\n",
      "11.719 per cent patches are empty\n",
      "reg025_A at scale 16.0 has 44 patches with zero diveristy\n",
      "reg025_A at scale 16.0 diversity is 1.286289895239046\n",
      "Processing region: reg025_B at scale 16.0\n",
      "12.109 per cent patches are empty\n",
      "reg025_B at scale 16.0 has 23 patches with zero diveristy\n",
      "reg025_B at scale 16.0 diversity is 1.5076554409188117\n",
      "Processing region: reg026_A at scale 16.0\n",
      "5.469 per cent patches are empty\n",
      "reg026_A at scale 16.0 has 9 patches with zero diveristy\n",
      "reg026_A at scale 16.0 diversity is 1.8944919852963507\n",
      "Processing region: reg026_B at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg026_B at scale 16.0 has 6 patches with zero diveristy\n",
      "reg026_B at scale 16.0 diversity is 1.7822104705553874\n",
      "Processing region: reg027_A at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg027_A at scale 16.0 has 16 patches with zero diveristy\n",
      "reg027_A at scale 16.0 diversity is 1.6837597918536693\n",
      "Processing region: reg027_B at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg027_B at scale 16.0 has 15 patches with zero diveristy\n",
      "reg027_B at scale 16.0 diversity is 1.3750974273053123\n",
      "Processing region: reg028_A at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg028_A at scale 16.0 has 11 patches with zero diveristy\n",
      "reg028_A at scale 16.0 diversity is 2.1160793930026798\n",
      "Processing region: reg028_B at scale 16.0\n",
      "5.078 per cent patches are empty\n",
      "reg028_B at scale 16.0 has 7 patches with zero diveristy\n",
      "reg028_B at scale 16.0 diversity is 1.8708715921726855\n",
      "Processing region: reg029_A at scale 16.0\n",
      "5.469 per cent patches are empty\n",
      "reg029_A at scale 16.0 has 45 patches with zero diveristy\n",
      "reg029_A at scale 16.0 diversity is 1.3114582969118185\n",
      "Processing region: reg029_B at scale 16.0\n",
      "8.203 per cent patches are empty\n",
      "reg029_B at scale 16.0 has 19 patches with zero diveristy\n",
      "reg029_B at scale 16.0 diversity is 1.2006683905758153\n",
      "Processing region: reg030_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg030_A at scale 16.0 has 8 patches with zero diveristy\n",
      "reg030_A at scale 16.0 diversity is 1.7961171415644808\n",
      "Processing region: reg030_B at scale 16.0\n",
      "8.203 per cent patches are empty\n",
      "reg030_B at scale 16.0 has 61 patches with zero diveristy\n",
      "reg030_B at scale 16.0 diversity is 0.8758578124437192\n",
      "Processing region: reg031_A at scale 16.0\n",
      "2.344 per cent patches are empty\n",
      "reg031_A at scale 16.0 has 13 patches with zero diveristy\n",
      "reg031_A at scale 16.0 diversity is 1.821469253229501\n",
      "Processing region: reg031_B at scale 16.0\n",
      "5.078 per cent patches are empty\n",
      "reg031_B at scale 16.0 has 10 patches with zero diveristy\n",
      "reg031_B at scale 16.0 diversity is 1.8644379619567157\n",
      "Processing region: reg032_A at scale 16.0\n",
      "4.688 per cent patches are empty\n",
      "reg032_A at scale 16.0 has 8 patches with zero diveristy\n",
      "reg032_A at scale 16.0 diversity is 1.766443731100261\n",
      "Processing region: reg032_B at scale 16.0\n",
      "12.891 per cent patches are empty\n",
      "reg032_B at scale 16.0 has 26 patches with zero diveristy\n",
      "reg032_B at scale 16.0 diversity is 1.5988874815055838\n",
      "Processing region: reg033_A at scale 16.0\n",
      "13.672 per cent patches are empty\n",
      "reg033_A at scale 16.0 has 22 patches with zero diveristy\n",
      "reg033_A at scale 16.0 diversity is 1.4950908933447642\n",
      "Processing region: reg033_B at scale 16.0\n",
      "2.734 per cent patches are empty\n",
      "reg033_B at scale 16.0 has 25 patches with zero diveristy\n",
      "reg033_B at scale 16.0 diversity is 1.39668223493805\n",
      "Processing region: reg034_A at scale 16.0\n",
      "7.422 per cent patches are empty\n",
      "reg034_A at scale 16.0 has 5 patches with zero diveristy\n",
      "reg034_A at scale 16.0 diversity is 1.805989175800188\n",
      "Processing region: reg034_B at scale 16.0\n",
      "9.375 per cent patches are empty\n",
      "reg034_B at scale 16.0 has 25 patches with zero diveristy\n",
      "reg034_B at scale 16.0 diversity is 1.3296613300745606\n",
      "Processing region: reg035_A at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg035_A at scale 16.0 has 23 patches with zero diveristy\n",
      "reg035_A at scale 16.0 diversity is 1.5806298251223463\n",
      "Processing region: reg035_B at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg035_B at scale 16.0 has 41 patches with zero diveristy\n",
      "reg035_B at scale 16.0 diversity is 1.2016286929363773\n",
      "Processing region: reg036_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg036_A at scale 16.0 has 4 patches with zero diveristy\n",
      "reg036_A at scale 16.0 diversity is 1.9680403838736258\n",
      "Processing region: reg036_B at scale 16.0\n",
      "3.516 per cent patches are empty\n",
      "reg036_B at scale 16.0 has 70 patches with zero diveristy\n",
      "reg036_B at scale 16.0 diversity is 1.1095966581191898\n",
      "Processing region: reg037_A at scale 16.0\n",
      "23.438 per cent patches are empty\n",
      "reg037_A at scale 16.0 has 36 patches with zero diveristy\n",
      "reg037_A at scale 16.0 diversity is 1.4047305990975751\n",
      "Processing region: reg037_B at scale 16.0\n",
      "27.734 per cent patches are empty\n",
      "reg037_B at scale 16.0 has 104 patches with zero diveristy\n",
      "reg037_B at scale 16.0 diversity is 0.5498515718539568\n",
      "Processing region: reg038_A at scale 16.0\n",
      "3.516 per cent patches are empty\n",
      "reg038_A at scale 16.0 has 5 patches with zero diveristy\n",
      "reg038_A at scale 16.0 diversity is 1.8435324598746055\n",
      "Processing region: reg038_B at scale 16.0\n",
      "12.891 per cent patches are empty\n",
      "reg038_B at scale 16.0 has 38 patches with zero diveristy\n",
      "reg038_B at scale 16.0 diversity is 1.3237002393813107\n",
      "Processing region: reg039_A at scale 16.0\n",
      "16.406 per cent patches are empty\n",
      "reg039_A at scale 16.0 has 10 patches with zero diveristy\n",
      "reg039_A at scale 16.0 diversity is 1.5830801490646864\n",
      "Processing region: reg039_B at scale 16.0\n",
      "26.172 per cent patches are empty\n",
      "reg039_B at scale 16.0 has 59 patches with zero diveristy\n",
      "reg039_B at scale 16.0 diversity is 0.9548139293737187\n",
      "Processing region: reg040_A at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg040_A at scale 16.0 has 17 patches with zero diveristy\n",
      "reg040_A at scale 16.0 diversity is 1.5511215316331814\n",
      "Processing region: reg040_B at scale 16.0\n",
      "30.859 per cent patches are empty\n",
      "reg040_B at scale 16.0 has 45 patches with zero diveristy\n",
      "reg040_B at scale 16.0 diversity is 1.0575943641473295\n",
      "Processing region: reg041_A at scale 16.0\n",
      "4.297 per cent patches are empty\n",
      "reg041_A at scale 16.0 has 31 patches with zero diveristy\n",
      "reg041_A at scale 16.0 diversity is 1.4565673183329213\n",
      "Processing region: reg041_B at scale 16.0\n",
      "14.844 per cent patches are empty\n",
      "reg041_B at scale 16.0 has 71 patches with zero diveristy\n",
      "reg041_B at scale 16.0 diversity is 1.0363120071917764\n",
      "Processing region: reg042_A at scale 16.0\n",
      "7.422 per cent patches are empty\n",
      "reg042_A at scale 16.0 has 66 patches with zero diveristy\n",
      "reg042_A at scale 16.0 diversity is 0.759286183040036\n",
      "Processing region: reg042_B at scale 16.0\n",
      "9.766 per cent patches are empty\n",
      "reg042_B at scale 16.0 has 78 patches with zero diveristy\n",
      "reg042_B at scale 16.0 diversity is 0.689848033437531\n",
      "Processing region: reg043_A at scale 16.0\n",
      "3.125 per cent patches are empty\n",
      "reg043_A at scale 16.0 has 45 patches with zero diveristy\n",
      "reg043_A at scale 16.0 diversity is 1.1180553298288896\n",
      "Processing region: reg043_B at scale 16.0\n",
      "35.938 per cent patches are empty\n",
      "reg043_B at scale 16.0 has 86 patches with zero diveristy\n",
      "reg043_B at scale 16.0 diversity is 0.5346895605617722\n",
      "Processing region: reg044_A at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg044_A at scale 16.0 has 52 patches with zero diveristy\n",
      "reg044_A at scale 16.0 diversity is 1.1342625563333575\n",
      "Processing region: reg044_B at scale 16.0\n",
      "48.438 per cent patches are empty\n",
      "reg044_B at scale 16.0 has 70 patches with zero diveristy\n",
      "reg044_B at scale 16.0 diversity is 0.535590639344215\n",
      "Processing region: reg045_A at scale 16.0\n",
      "8.203 per cent patches are empty\n",
      "reg045_A at scale 16.0 has 7 patches with zero diveristy\n",
      "reg045_A at scale 16.0 diversity is 1.5799282366123537\n",
      "Processing region: reg045_B at scale 16.0\n",
      "16.016 per cent patches are empty\n",
      "reg045_B at scale 16.0 has 46 patches with zero diveristy\n",
      "reg045_B at scale 16.0 diversity is 1.1684881061695667\n",
      "Processing region: reg046_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg046_A at scale 16.0 has 7 patches with zero diveristy\n",
      "reg046_A at scale 16.0 diversity is 1.969037799740305\n",
      "Processing region: reg046_B at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg046_B at scale 16.0 has 12 patches with zero diveristy\n",
      "reg046_B at scale 16.0 diversity is 1.8436947313787408\n",
      "Processing region: reg047_A at scale 16.0\n",
      "14.062 per cent patches are empty\n",
      "reg047_A at scale 16.0 has 27 patches with zero diveristy\n",
      "reg047_A at scale 16.0 diversity is 1.609495838803236\n",
      "Processing region: reg047_B at scale 16.0\n",
      "16.406 per cent patches are empty\n",
      "reg047_B at scale 16.0 has 95 patches with zero diveristy\n",
      "reg047_B at scale 16.0 diversity is 0.6530865052102662\n",
      "Processing region: reg048_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg048_A at scale 16.0 has 17 patches with zero diveristy\n",
      "reg048_A at scale 16.0 diversity is 1.5852563690364594\n",
      "Processing region: reg048_B at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg048_B at scale 16.0 has 41 patches with zero diveristy\n",
      "reg048_B at scale 16.0 diversity is 1.3406049350276892\n",
      "Processing region: reg049_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg049_A at scale 16.0 has 12 patches with zero diveristy\n",
      "reg049_A at scale 16.0 diversity is 1.6165216368651798\n",
      "Processing region: reg049_B at scale 16.0\n",
      "12.500 per cent patches are empty\n",
      "reg049_B at scale 16.0 has 38 patches with zero diveristy\n",
      "reg049_B at scale 16.0 diversity is 1.2533039766157301\n",
      "Processing region: reg050_A at scale 16.0\n",
      "13.281 per cent patches are empty\n",
      "reg050_A at scale 16.0 has 44 patches with zero diveristy\n",
      "reg050_A at scale 16.0 diversity is 1.045985333218258\n",
      "Processing region: reg050_B at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg050_B at scale 16.0 has 31 patches with zero diveristy\n",
      "reg050_B at scale 16.0 diversity is 1.274441178015937\n",
      "Processing region: reg051_A at scale 16.0\n",
      "15.625 per cent patches are empty\n",
      "reg051_A at scale 16.0 has 50 patches with zero diveristy\n",
      "reg051_A at scale 16.0 diversity is 1.3737017086628374\n",
      "Processing region: reg051_B at scale 16.0\n",
      "7.031 per cent patches are empty\n",
      "reg051_B at scale 16.0 has 30 patches with zero diveristy\n",
      "reg051_B at scale 16.0 diversity is 1.3149655951658545\n",
      "Processing region: reg052_A at scale 16.0\n",
      "3.906 per cent patches are empty\n",
      "reg052_A at scale 16.0 has 21 patches with zero diveristy\n",
      "reg052_A at scale 16.0 diversity is 1.5149924189868127\n",
      "Processing region: reg052_B at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg052_B at scale 16.0 has 40 patches with zero diveristy\n",
      "reg052_B at scale 16.0 diversity is 1.2074496480795733\n",
      "Processing region: reg053_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg053_A at scale 16.0 has 19 patches with zero diveristy\n",
      "reg053_A at scale 16.0 diversity is 1.828181943385345\n",
      "Processing region: reg053_B at scale 16.0\n",
      "70.703 per cent patches are empty\n",
      "reg053_B at scale 16.0 has 48 patches with zero diveristy\n",
      "reg053_B at scale 16.0 diversity is 0.42990472146248787\n",
      "Processing region: reg054_A at scale 16.0\n",
      "18.359 per cent patches are empty\n",
      "reg054_A at scale 16.0 has 29 patches with zero diveristy\n",
      "reg054_A at scale 16.0 diversity is 1.5096096613617158\n",
      "Processing region: reg054_B at scale 16.0\n",
      "59.766 per cent patches are empty\n",
      "reg054_B at scale 16.0 has 36 patches with zero diveristy\n",
      "reg054_B at scale 16.0 diversity is 0.7921187180915548\n",
      "Processing region: reg055_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg055_A at scale 16.0 has 8 patches with zero diveristy\n",
      "reg055_A at scale 16.0 diversity is 1.9212566918689848\n",
      "Processing region: reg055_B at scale 16.0\n",
      "69.531 per cent patches are empty\n",
      "reg055_B at scale 16.0 has 51 patches with zero diveristy\n",
      "reg055_B at scale 16.0 diversity is 0.40933933688926766\n",
      "Processing region: reg056_A at scale 16.0\n",
      "23.047 per cent patches are empty\n",
      "reg056_A at scale 16.0 has 44 patches with zero diveristy\n",
      "reg056_A at scale 16.0 diversity is 1.2747267956517867\n",
      "Processing region: reg056_B at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg056_B at scale 16.0 has 38 patches with zero diveristy\n",
      "reg056_B at scale 16.0 diversity is 1.4029016678673227\n",
      "Processing region: reg057_A at scale 16.0\n",
      "87.500 per cent patches are empty\n",
      "reg057_A at scale 16.0 has 15 patches with zero diveristy\n",
      "reg057_A at scale 16.0 diversity is 0.6693315263855854\n",
      "Processing region: reg057_B at scale 16.0\n",
      "31.250 per cent patches are empty\n",
      "reg057_B at scale 16.0 has 58 patches with zero diveristy\n",
      "reg057_B at scale 16.0 diversity is 1.0136105580975299\n",
      "Processing region: reg058_A at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg058_A at scale 16.0 has 93 patches with zero diveristy\n",
      "reg058_A at scale 16.0 diversity is 0.597051939321446\n",
      "Processing region: reg058_B at scale 16.0\n",
      "14.453 per cent patches are empty\n",
      "reg058_B at scale 16.0 has 139 patches with zero diveristy\n",
      "reg058_B at scale 16.0 diversity is 0.3408536314425973\n",
      "Processing region: reg059_A at scale 16.0\n",
      "11.328 per cent patches are empty\n",
      "reg059_A at scale 16.0 has 9 patches with zero diveristy\n",
      "reg059_A at scale 16.0 diversity is 2.0891563560721482\n",
      "Processing region: reg059_B at scale 16.0\n",
      "8.594 per cent patches are empty\n",
      "reg059_B at scale 16.0 has 12 patches with zero diveristy\n",
      "reg059_B at scale 16.0 diversity is 1.5843132976275534\n",
      "Processing region: reg060_A at scale 16.0\n",
      "7.031 per cent patches are empty\n",
      "reg060_A at scale 16.0 has 11 patches with zero diveristy\n",
      "reg060_A at scale 16.0 diversity is 1.9823834022017208\n",
      "Processing region: reg060_B at scale 16.0\n",
      "8.203 per cent patches are empty\n",
      "reg060_B at scale 16.0 has 5 patches with zero diveristy\n",
      "reg060_B at scale 16.0 diversity is 1.916517030735813\n",
      "Processing region: reg061_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg061_A at scale 16.0 has 36 patches with zero diveristy\n",
      "reg061_A at scale 16.0 diversity is 1.4336904201745997\n",
      "Processing region: reg061_B at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg061_B at scale 16.0 has 15 patches with zero diveristy\n",
      "reg061_B at scale 16.0 diversity is 1.6479345872567748\n",
      "Processing region: reg062_A at scale 16.0\n",
      "2.344 per cent patches are empty\n",
      "reg062_A at scale 16.0 has 12 patches with zero diveristy\n",
      "reg062_A at scale 16.0 diversity is 1.667230459467464\n",
      "Processing region: reg062_B at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg062_B at scale 16.0 has 26 patches with zero diveristy\n",
      "reg062_B at scale 16.0 diversity is 1.461775327443554\n",
      "Processing region: reg063_A at scale 16.0\n",
      "24.219 per cent patches are empty\n",
      "reg063_A at scale 16.0 has 41 patches with zero diveristy\n",
      "reg063_A at scale 16.0 diversity is 1.0847166528206407\n",
      "Processing region: reg063_B at scale 16.0\n",
      "9.766 per cent patches are empty\n",
      "reg063_B at scale 16.0 has 22 patches with zero diveristy\n",
      "reg063_B at scale 16.0 diversity is 1.3759567260882974\n",
      "Processing region: reg064_A at scale 16.0\n",
      "8.594 per cent patches are empty\n",
      "reg064_A at scale 16.0 has 11 patches with zero diveristy\n",
      "reg064_A at scale 16.0 diversity is 1.4355060178845582\n",
      "Processing region: reg064_B at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg064_B at scale 16.0 has 20 patches with zero diveristy\n",
      "reg064_B at scale 16.0 diversity is 1.6946818144977838\n",
      "Processing region: reg065_A at scale 16.0\n",
      "11.328 per cent patches are empty\n",
      "reg065_A at scale 16.0 has 67 patches with zero diveristy\n",
      "reg065_A at scale 16.0 diversity is 1.0101402887124225\n",
      "Processing region: reg065_B at scale 16.0\n",
      "23.438 per cent patches are empty\n",
      "reg065_B at scale 16.0 has 94 patches with zero diveristy\n",
      "reg065_B at scale 16.0 diversity is 0.6448394289785003\n",
      "Processing region: reg066_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg066_A at scale 16.0 has 23 patches with zero diveristy\n",
      "reg066_A at scale 16.0 diversity is 1.3853495554427253\n",
      "Processing region: reg066_B at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg066_B at scale 16.0 has 54 patches with zero diveristy\n",
      "reg066_B at scale 16.0 diversity is 1.0393069690487353\n",
      "Processing region: reg067_A at scale 16.0\n",
      "37.500 per cent patches are empty\n",
      "reg067_A at scale 16.0 has 47 patches with zero diveristy\n",
      "reg067_A at scale 16.0 diversity is 1.1133641212890972\n",
      "Processing region: reg067_B at scale 16.0\n",
      "84.375 per cent patches are empty\n",
      "reg067_B at scale 16.0 has 24 patches with zero diveristy\n",
      "reg067_B at scale 16.0 diversity is 0.48788885823487665\n",
      "Processing region: reg068_A at scale 16.0\n",
      "8.594 per cent patches are empty\n",
      "reg068_A at scale 16.0 has 32 patches with zero diveristy\n",
      "reg068_A at scale 16.0 diversity is 1.3039123926387821\n",
      "Processing region: reg068_B at scale 16.0\n",
      "7.031 per cent patches are empty\n",
      "reg068_B at scale 16.0 has 19 patches with zero diveristy\n",
      "reg068_B at scale 16.0 diversity is 1.7179625275097257\n",
      "Processing region: reg069_A at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg069_A at scale 16.0 has 27 patches with zero diveristy\n",
      "reg069_A at scale 16.0 diversity is 1.5653739676202172\n",
      "Processing region: reg069_B at scale 16.0\n",
      "14.453 per cent patches are empty\n",
      "reg069_B at scale 16.0 has 67 patches with zero diveristy\n",
      "reg069_B at scale 16.0 diversity is 1.0116912721249247\n",
      "Processing region: reg070_A at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg070_A at scale 16.0 has 42 patches with zero diveristy\n",
      "reg070_A at scale 16.0 diversity is 1.2684280512692527\n",
      "Processing region: reg070_B at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg070_B at scale 16.0 has 45 patches with zero diveristy\n",
      "reg070_B at scale 16.0 diversity is 1.0081353626718108\n",
      "Processing region: reg001_A at scale 24.0\n",
      "32.639 per cent patches are empty\n",
      "reg001_A at scale 24.0 has 157 patches with zero diveristy\n",
      "reg001_A at scale 24.0 diversity is 0.8284505941121223\n",
      "Processing region: reg001_B at scale 24.0\n",
      "63.542 per cent patches are empty\n",
      "reg001_B at scale 24.0 has 133 patches with zero diveristy\n",
      "reg001_B at scale 24.0 diversity is 0.3950197024599362\n",
      "Processing region: reg002_A at scale 24.0\n",
      "37.326 per cent patches are empty\n",
      "reg002_A at scale 24.0 has 106 patches with zero diveristy\n",
      "reg002_A at scale 24.0 diversity is 1.0293981929780056\n",
      "Processing region: reg002_B at scale 24.0\n",
      "12.500 per cent patches are empty\n",
      "reg002_B at scale 24.0 has 254 patches with zero diveristy\n",
      "reg002_B at scale 24.0 diversity is 0.46778209612438026\n",
      "Processing region: reg003_A at scale 24.0\n",
      "39.931 per cent patches are empty\n",
      "reg003_A at scale 24.0 has 89 patches with zero diveristy\n",
      "reg003_A at scale 24.0 diversity is 1.0473806897103768\n",
      "Processing region: reg003_B at scale 24.0\n",
      "28.646 per cent patches are empty\n",
      "reg003_B at scale 24.0 has 172 patches with zero diveristy\n",
      "reg003_B at scale 24.0 diversity is 0.7300708927790964\n",
      "Processing region: reg004_A at scale 24.0\n",
      "27.951 per cent patches are empty\n",
      "reg004_A at scale 24.0 has 82 patches with zero diveristy\n",
      "reg004_A at scale 24.0 diversity is 1.2029731685285925\n",
      "Processing region: reg004_B at scale 24.0\n",
      "10.590 per cent patches are empty\n",
      "reg004_B at scale 24.0 has 129 patches with zero diveristy\n",
      "reg004_B at scale 24.0 diversity is 0.9584240748433808\n",
      "Processing region: reg005_A at scale 24.0\n",
      "12.500 per cent patches are empty\n",
      "reg005_A at scale 24.0 has 94 patches with zero diveristy\n",
      "reg005_A at scale 24.0 diversity is 1.2128860955501075\n",
      "Processing region: reg005_B at scale 24.0\n",
      "35.417 per cent patches are empty\n",
      "reg005_B at scale 24.0 has 74 patches with zero diveristy\n",
      "reg005_B at scale 24.0 diversity is 1.1025830968877908\n",
      "Processing region: reg006_A at scale 24.0\n",
      "25.000 per cent patches are empty\n",
      "reg006_A at scale 24.0 has 139 patches with zero diveristy\n",
      "reg006_A at scale 24.0 diversity is 0.9435749894320793\n",
      "Processing region: reg006_B at scale 24.0\n",
      "17.188 per cent patches are empty\n",
      "reg006_B at scale 24.0 has 93 patches with zero diveristy\n",
      "reg006_B at scale 24.0 diversity is 1.158044813671002\n",
      "Processing region: reg007_A at scale 24.0\n",
      "11.979 per cent patches are empty\n",
      "reg007_A at scale 24.0 has 150 patches with zero diveristy\n",
      "reg007_A at scale 24.0 diversity is 1.0179350528769018\n",
      "Processing region: reg007_B at scale 24.0\n",
      "21.528 per cent patches are empty\n",
      "reg007_B at scale 24.0 has 71 patches with zero diveristy\n",
      "reg007_B at scale 24.0 diversity is 1.1736089936834242\n",
      "Processing region: reg008_A at scale 24.0\n",
      "42.882 per cent patches are empty\n",
      "reg008_A at scale 24.0 has 126 patches with zero diveristy\n",
      "reg008_A at scale 24.0 diversity is 0.7459389572392305\n",
      "Processing region: reg008_B at scale 24.0\n",
      "6.076 per cent patches are empty\n",
      "reg008_B at scale 24.0 has 131 patches with zero diveristy\n",
      "reg008_B at scale 24.0 diversity is 1.0459618176773038\n",
      "Processing region: reg009_A at scale 24.0\n",
      "6.597 per cent patches are empty\n",
      "reg009_A at scale 24.0 has 93 patches with zero diveristy\n",
      "reg009_A at scale 24.0 diversity is 1.257882010541914\n",
      "Processing region: reg009_B at scale 24.0\n",
      "4.340 per cent patches are empty\n",
      "reg009_B at scale 24.0 has 17 patches with zero diveristy\n",
      "reg009_B at scale 24.0 diversity is 1.549610172077202\n",
      "Processing region: reg010_A at scale 24.0\n",
      "16.493 per cent patches are empty\n",
      "reg010_A at scale 24.0 has 92 patches with zero diveristy\n",
      "reg010_A at scale 24.0 diversity is 1.2768006179097073\n",
      "Processing region: reg010_B at scale 24.0\n",
      "30.556 per cent patches are empty\n",
      "reg010_B at scale 24.0 has 146 patches with zero diveristy\n",
      "reg010_B at scale 24.0 diversity is 0.8830162720428654\n",
      "Processing region: reg011_A at scale 24.0\n",
      "23.438 per cent patches are empty\n",
      "reg011_A at scale 24.0 has 58 patches with zero diveristy\n",
      "reg011_A at scale 24.0 diversity is 1.4384536657016174\n",
      "Processing region: reg011_B at scale 24.0\n",
      "39.236 per cent patches are empty\n",
      "reg011_B at scale 24.0 has 141 patches with zero diveristy\n",
      "reg011_B at scale 24.0 diversity is 0.7731300024528768\n",
      "Processing region: reg012_A at scale 24.0\n",
      "26.910 per cent patches are empty\n",
      "reg012_A at scale 24.0 has 99 patches with zero diveristy\n",
      "reg012_A at scale 24.0 diversity is 1.097468913506505\n",
      "Processing region: reg012_B at scale 24.0\n",
      "71.007 per cent patches are empty\n",
      "reg012_B at scale 24.0 has 76 patches with zero diveristy\n",
      "reg012_B at scale 24.0 diversity is 0.7283561952618558\n",
      "Processing region: reg013_A at scale 24.0\n",
      "15.104 per cent patches are empty\n",
      "reg013_A at scale 24.0 has 155 patches with zero diveristy\n",
      "reg013_A at scale 24.0 diversity is 0.9075724416600545\n",
      "Processing region: reg013_B at scale 24.0\n",
      "10.417 per cent patches are empty\n",
      "reg013_B at scale 24.0 has 178 patches with zero diveristy\n",
      "reg013_B at scale 24.0 diversity is 0.9086994506803739\n",
      "Processing region: reg014_A at scale 24.0\n",
      "35.938 per cent patches are empty\n",
      "reg014_A at scale 24.0 has 157 patches with zero diveristy\n",
      "reg014_A at scale 24.0 diversity is 0.722663848710838\n",
      "Processing region: reg014_B at scale 24.0\n",
      "14.236 per cent patches are empty\n",
      "reg014_B at scale 24.0 has 86 patches with zero diveristy\n",
      "reg014_B at scale 24.0 diversity is 1.1447344811460178\n",
      "Processing region: reg015_A at scale 24.0\n",
      "10.938 per cent patches are empty\n",
      "reg015_A at scale 24.0 has 82 patches with zero diveristy\n",
      "reg015_A at scale 24.0 diversity is 1.1644854509838523\n",
      "Processing region: reg015_B at scale 24.0\n",
      "18.924 per cent patches are empty\n",
      "reg015_B at scale 24.0 has 233 patches with zero diveristy\n",
      "reg015_B at scale 24.0 diversity is 0.5434586739411313\n",
      "Processing region: reg016_A at scale 24.0\n",
      "19.618 per cent patches are empty\n",
      "reg016_A at scale 24.0 has 107 patches with zero diveristy\n",
      "reg016_A at scale 24.0 diversity is 1.024668804451219\n",
      "Processing region: reg016_B at scale 24.0\n",
      "9.549 per cent patches are empty\n",
      "reg016_B at scale 24.0 has 125 patches with zero diveristy\n",
      "reg016_B at scale 24.0 diversity is 1.0162576155656335\n",
      "Processing region: reg017_A at scale 24.0\n",
      "11.806 per cent patches are empty\n",
      "reg017_A at scale 24.0 has 114 patches with zero diveristy\n",
      "reg017_A at scale 24.0 diversity is 1.1900008271999611\n",
      "Processing region: reg017_B at scale 24.0\n",
      "10.764 per cent patches are empty\n",
      "reg017_B at scale 24.0 has 113 patches with zero diveristy\n",
      "reg017_B at scale 24.0 diversity is 1.2485399854852213\n",
      "Processing region: reg018_A at scale 24.0\n",
      "20.833 per cent patches are empty\n",
      "reg018_A at scale 24.0 has 123 patches with zero diveristy\n",
      "reg018_A at scale 24.0 diversity is 1.1309752176179426\n",
      "Processing region: reg018_B at scale 24.0\n",
      "10.764 per cent patches are empty\n",
      "reg018_B at scale 24.0 has 98 patches with zero diveristy\n",
      "reg018_B at scale 24.0 diversity is 0.9931362736330651\n",
      "Processing region: reg019_A at scale 24.0\n",
      "47.396 per cent patches are empty\n",
      "reg019_A at scale 24.0 has 76 patches with zero diveristy\n",
      "reg019_A at scale 24.0 diversity is 0.9737772430416679\n",
      "Processing region: reg019_B at scale 24.0\n",
      "87.847 per cent patches are empty\n",
      "reg019_B at scale 24.0 has 57 patches with zero diveristy\n",
      "reg019_B at scale 24.0 diversity is 0.1917812198516992\n",
      "Processing region: reg020_A at scale 24.0\n",
      "18.750 per cent patches are empty\n",
      "reg020_A at scale 24.0 has 81 patches with zero diveristy\n",
      "reg020_A at scale 24.0 diversity is 1.2735537323919324\n",
      "Processing region: reg020_B at scale 24.0\n",
      "14.583 per cent patches are empty\n",
      "reg020_B at scale 24.0 has 56 patches with zero diveristy\n",
      "reg020_B at scale 24.0 diversity is 1.4903339485534708\n",
      "Processing region: reg021_A at scale 24.0\n",
      "30.382 per cent patches are empty\n",
      "reg021_A at scale 24.0 has 68 patches with zero diveristy\n",
      "reg021_A at scale 24.0 diversity is 1.2241227054864512\n",
      "Processing region: reg021_B at scale 24.0\n",
      "63.715 per cent patches are empty\n",
      "reg021_B at scale 24.0 has 145 patches with zero diveristy\n",
      "reg021_B at scale 24.0 diversity is 0.341555442493998\n",
      "Processing region: reg022_A at scale 24.0\n",
      "20.833 per cent patches are empty\n",
      "reg022_A at scale 24.0 has 101 patches with zero diveristy\n",
      "reg022_A at scale 24.0 diversity is 1.1135479120797163\n",
      "Processing region: reg022_B at scale 24.0\n",
      "52.083 per cent patches are empty\n",
      "reg022_B at scale 24.0 has 118 patches with zero diveristy\n",
      "reg022_B at scale 24.0 diversity is 0.727895777657331\n",
      "Processing region: reg023_A at scale 24.0\n",
      "42.708 per cent patches are empty\n",
      "reg023_A at scale 24.0 has 170 patches with zero diveristy\n",
      "reg023_A at scale 24.0 diversity is 0.5561051556307051\n",
      "Processing region: reg023_B at scale 24.0\n",
      "11.806 per cent patches are empty\n",
      "reg023_B at scale 24.0 has 64 patches with zero diveristy\n",
      "reg023_B at scale 24.0 diversity is 1.408084539267112\n",
      "Processing region: reg024_A at scale 24.0\n",
      "34.028 per cent patches are empty\n",
      "reg024_A at scale 24.0 has 175 patches with zero diveristy\n",
      "reg024_A at scale 24.0 diversity is 0.5907492002695396\n",
      "Processing region: reg024_B at scale 24.0\n",
      "9.028 per cent patches are empty\n",
      "reg024_B at scale 24.0 has 153 patches with zero diveristy\n",
      "reg024_B at scale 24.0 diversity is 0.8686953404711043\n",
      "Processing region: reg025_A at scale 24.0\n",
      "27.257 per cent patches are empty\n",
      "reg025_A at scale 24.0 has 156 patches with zero diveristy\n",
      "reg025_A at scale 24.0 diversity is 0.8863237391735171\n",
      "Processing region: reg025_B at scale 24.0\n",
      "22.396 per cent patches are empty\n",
      "reg025_B at scale 24.0 has 105 patches with zero diveristy\n",
      "reg025_B at scale 24.0 diversity is 1.0571178098196843\n",
      "Processing region: reg026_A at scale 24.0\n",
      "9.549 per cent patches are empty\n",
      "reg026_A at scale 24.0 has 75 patches with zero diveristy\n",
      "reg026_A at scale 24.0 diversity is 1.3102339711133748\n",
      "Processing region: reg026_B at scale 24.0\n",
      "8.333 per cent patches are empty\n",
      "reg026_B at scale 24.0 has 48 patches with zero diveristy\n",
      "reg026_B at scale 24.0 diversity is 1.3318627658620268\n",
      "Processing region: reg027_A at scale 24.0\n",
      "10.243 per cent patches are empty\n",
      "reg027_A at scale 24.0 has 91 patches with zero diveristy\n",
      "reg027_A at scale 24.0 diversity is 1.2104459636170817\n",
      "Processing region: reg027_B at scale 24.0\n",
      "10.764 per cent patches are empty\n",
      "reg027_B at scale 24.0 has 126 patches with zero diveristy\n",
      "reg027_B at scale 24.0 diversity is 0.9399002512629252\n",
      "Processing region: reg028_A at scale 24.0\n",
      "11.111 per cent patches are empty\n",
      "reg028_A at scale 24.0 has 62 patches with zero diveristy\n",
      "reg028_A at scale 24.0 diversity is 1.5013560816886207\n",
      "Processing region: reg028_B at scale 24.0\n",
      "7.986 per cent patches are empty\n",
      "reg028_B at scale 24.0 has 81 patches with zero diveristy\n",
      "reg028_B at scale 24.0 diversity is 1.2381085902208318\n",
      "Processing region: reg029_A at scale 24.0\n",
      "9.375 per cent patches are empty\n",
      "reg029_A at scale 24.0 has 182 patches with zero diveristy\n",
      "reg029_A at scale 24.0 diversity is 0.923300447821893\n",
      "Processing region: reg029_B at scale 24.0\n",
      "16.493 per cent patches are empty\n",
      "reg029_B at scale 24.0 has 131 patches with zero diveristy\n",
      "reg029_B at scale 24.0 diversity is 0.8484888861086078\n",
      "Processing region: reg030_A at scale 24.0\n",
      "10.590 per cent patches are empty\n",
      "reg030_A at scale 24.0 has 78 patches with zero diveristy\n",
      "reg030_A at scale 24.0 diversity is 1.2356161912915113\n",
      "Processing region: reg030_B at scale 24.0\n",
      "16.146 per cent patches are empty\n",
      "reg030_B at scale 24.0 has 203 patches with zero diveristy\n",
      "reg030_B at scale 24.0 diversity is 0.6505121505508273\n",
      "Processing region: reg031_A at scale 24.0\n",
      "6.597 per cent patches are empty\n",
      "reg031_A at scale 24.0 has 73 patches with zero diveristy\n",
      "reg031_A at scale 24.0 diversity is 1.3131145912126447\n",
      "Processing region: reg031_B at scale 24.0\n",
      "8.854 per cent patches are empty\n",
      "reg031_B at scale 24.0 has 95 patches with zero diveristy\n",
      "reg031_B at scale 24.0 diversity is 1.2096334817549035\n",
      "Processing region: reg032_A at scale 24.0\n",
      "8.681 per cent patches are empty\n",
      "reg032_A at scale 24.0 has 89 patches with zero diveristy\n",
      "reg032_A at scale 24.0 diversity is 1.2338937696261323\n",
      "Processing region: reg032_B at scale 24.0\n",
      "22.049 per cent patches are empty\n",
      "reg032_B at scale 24.0 has 127 patches with zero diveristy\n",
      "reg032_B at scale 24.0 diversity is 1.0522754805563403\n",
      "Processing region: reg033_A at scale 24.0\n",
      "21.007 per cent patches are empty\n",
      "reg033_A at scale 24.0 has 60 patches with zero diveristy\n",
      "reg033_A at scale 24.0 diversity is 1.197375892344608\n",
      "Processing region: reg033_B at scale 24.0\n",
      "5.903 per cent patches are empty\n",
      "reg033_B at scale 24.0 has 142 patches with zero diveristy\n",
      "reg033_B at scale 24.0 diversity is 0.9810978288848654\n",
      "Processing region: reg034_A at scale 24.0\n",
      "12.674 per cent patches are empty\n",
      "reg034_A at scale 24.0 has 53 patches with zero diveristy\n",
      "reg034_A at scale 24.0 diversity is 1.2811668472334383\n",
      "Processing region: reg034_B at scale 24.0\n",
      "16.146 per cent patches are empty\n",
      "reg034_B at scale 24.0 has 138 patches with zero diveristy\n",
      "reg034_B at scale 24.0 diversity is 0.901151991441695\n",
      "Processing region: reg035_A at scale 24.0\n",
      "16.319 per cent patches are empty\n",
      "reg035_A at scale 24.0 has 102 patches with zero diveristy\n",
      "reg035_A at scale 24.0 diversity is 1.1631141234274132\n",
      "Processing region: reg035_B at scale 24.0\n",
      "17.014 per cent patches are empty\n",
      "reg035_B at scale 24.0 has 134 patches with zero diveristy\n",
      "reg035_B at scale 24.0 diversity is 0.9187148135130051\n",
      "Processing region: reg036_A at scale 24.0\n",
      "10.417 per cent patches are empty\n",
      "reg036_A at scale 24.0 has 46 patches with zero diveristy\n",
      "reg036_A at scale 24.0 diversity is 1.4777523947166862\n",
      "Processing region: reg036_B at scale 24.0\n",
      "6.771 per cent patches are empty\n",
      "reg036_B at scale 24.0 has 209 patches with zero diveristy\n",
      "reg036_B at scale 24.0 diversity is 0.821394716496006\n",
      "Processing region: reg037_A at scale 24.0\n",
      "34.375 per cent patches are empty\n",
      "reg037_A at scale 24.0 has 99 patches with zero diveristy\n",
      "reg037_A at scale 24.0 diversity is 1.0844756291314581\n",
      "Processing region: reg037_B at scale 24.0\n",
      "56.771 per cent patches are empty\n",
      "reg037_B at scale 24.0 has 172 patches with zero diveristy\n",
      "reg037_B at scale 24.0 diversity is 0.36781466314631395\n",
      "Processing region: reg038_A at scale 24.0\n",
      "6.597 per cent patches are empty\n",
      "reg038_A at scale 24.0 has 53 patches with zero diveristy\n",
      "reg038_A at scale 24.0 diversity is 1.3172526657824049\n",
      "Processing region: reg038_B at scale 24.0\n",
      "21.875 per cent patches are empty\n",
      "reg038_B at scale 24.0 has 134 patches with zero diveristy\n",
      "reg038_B at scale 24.0 diversity is 0.9425408289215109\n",
      "Processing region: reg039_A at scale 24.0\n",
      "22.743 per cent patches are empty\n",
      "reg039_A at scale 24.0 has 84 patches with zero diveristy\n",
      "reg039_A at scale 24.0 diversity is 1.1847207859545925\n",
      "Processing region: reg039_B at scale 24.0\n",
      "41.667 per cent patches are empty\n",
      "reg039_B at scale 24.0 has 188 patches with zero diveristy\n",
      "reg039_B at scale 24.0 diversity is 0.522891056111485\n",
      "Processing region: reg040_A at scale 24.0\n",
      "12.153 per cent patches are empty\n",
      "reg040_A at scale 24.0 has 102 patches with zero diveristy\n",
      "reg040_A at scale 24.0 diversity is 1.0825558457399784\n",
      "Processing region: reg040_B at scale 24.0\n",
      "46.528 per cent patches are empty\n",
      "reg040_B at scale 24.0 has 127 patches with zero diveristy\n",
      "reg040_B at scale 24.0 diversity is 0.7040277372936177\n",
      "Processing region: reg041_A at scale 24.0\n",
      "12.500 per cent patches are empty\n",
      "reg041_A at scale 24.0 has 145 patches with zero diveristy\n",
      "reg041_A at scale 24.0 diversity is 0.9925365706785454\n",
      "Processing region: reg041_B at scale 24.0\n",
      "21.701 per cent patches are empty\n",
      "reg041_B at scale 24.0 has 222 patches with zero diveristy\n",
      "reg041_B at scale 24.0 diversity is 0.6862921259404647\n",
      "Processing region: reg042_A at scale 24.0\n",
      "16.319 per cent patches are empty\n",
      "reg042_A at scale 24.0 has 249 patches with zero diveristy\n",
      "reg042_A at scale 24.0 diversity is 0.49994797632224025\n",
      "Processing region: reg042_B at scale 24.0\n",
      "17.361 per cent patches are empty\n",
      "reg042_B at scale 24.0 has 262 patches with zero diveristy\n",
      "reg042_B at scale 24.0 diversity is 0.46990983093632005\n",
      "Processing region: reg043_A at scale 24.0\n",
      "12.326 per cent patches are empty\n",
      "reg043_A at scale 24.0 has 200 patches with zero diveristy\n",
      "reg043_A at scale 24.0 diversity is 0.7155851950548874\n",
      "Processing region: reg043_B at scale 24.0\n",
      "53.993 per cent patches are empty\n",
      "reg043_B at scale 24.0 has 197 patches with zero diveristy\n",
      "reg043_B at scale 24.0 diversity is 0.2805726047471234\n",
      "Processing region: reg044_A at scale 24.0\n",
      "25.000 per cent patches are empty\n",
      "reg044_A at scale 24.0 has 193 patches with zero diveristy\n",
      "reg044_A at scale 24.0 diversity is 0.6568778729166838\n",
      "Processing region: reg044_B at scale 24.0\n",
      "61.979 per cent patches are empty\n",
      "reg044_B at scale 24.0 has 162 patches with zero diveristy\n",
      "reg044_B at scale 24.0 diversity is 0.27095026407207234\n",
      "Processing region: reg045_A at scale 24.0\n",
      "9.722 per cent patches are empty\n",
      "reg045_A at scale 24.0 has 90 patches with zero diveristy\n",
      "reg045_A at scale 24.0 diversity is 1.112825864806947\n",
      "Processing region: reg045_B at scale 24.0\n",
      "28.819 per cent patches are empty\n",
      "reg045_B at scale 24.0 has 194 patches with zero diveristy\n",
      "reg045_B at scale 24.0 diversity is 0.6733141996806591\n",
      "Processing region: reg046_A at scale 24.0\n",
      "8.681 per cent patches are empty\n",
      "reg046_A at scale 24.0 has 88 patches with zero diveristy\n",
      "reg046_A at scale 24.0 diversity is 1.2839441990835827\n",
      "Processing region: reg046_B at scale 24.0\n",
      "9.028 per cent patches are empty\n",
      "reg046_B at scale 24.0 has 82 patches with zero diveristy\n",
      "reg046_B at scale 24.0 diversity is 1.2542193330938392\n",
      "Processing region: reg047_A at scale 24.0\n",
      "23.438 per cent patches are empty\n",
      "reg047_A at scale 24.0 has 116 patches with zero diveristy\n",
      "reg047_A at scale 24.0 diversity is 1.1504543597835055\n",
      "Processing region: reg047_B at scale 24.0\n",
      "30.556 per cent patches are empty\n",
      "reg047_B at scale 24.0 has 253 patches with zero diveristy\n",
      "reg047_B at scale 24.0 diversity is 0.3939873113176526\n",
      "Processing region: reg048_A at scale 24.0\n",
      "14.410 per cent patches are empty\n",
      "reg048_A at scale 24.0 has 132 patches with zero diveristy\n",
      "reg048_A at scale 24.0 diversity is 0.9720280449246064\n",
      "Processing region: reg048_B at scale 24.0\n",
      "21.701 per cent patches are empty\n",
      "reg048_B at scale 24.0 has 133 patches with zero diveristy\n",
      "reg048_B at scale 24.0 diversity is 0.9451761583465113\n",
      "Processing region: reg049_A at scale 24.0\n",
      "8.507 per cent patches are empty\n",
      "reg049_A at scale 24.0 has 106 patches with zero diveristy\n",
      "reg049_A at scale 24.0 diversity is 1.097073608695628\n",
      "Processing region: reg049_B at scale 24.0\n",
      "24.826 per cent patches are empty\n",
      "reg049_B at scale 24.0 has 184 patches with zero diveristy\n",
      "reg049_B at scale 24.0 diversity is 0.7348533747032948\n",
      "Processing region: reg050_A at scale 24.0\n",
      "27.257 per cent patches are empty\n",
      "reg050_A at scale 24.0 has 189 patches with zero diveristy\n",
      "reg050_A at scale 24.0 diversity is 0.6434345375902569\n",
      "Processing region: reg050_B at scale 24.0\n",
      "15.278 per cent patches are empty\n",
      "reg050_B at scale 24.0 has 165 patches with zero diveristy\n",
      "reg050_B at scale 24.0 diversity is 0.8225118312462827\n",
      "Processing region: reg051_A at scale 24.0\n",
      "25.694 per cent patches are empty\n",
      "reg051_A at scale 24.0 has 134 patches with zero diveristy\n",
      "reg051_A at scale 24.0 diversity is 0.9754073230575896\n",
      "Processing region: reg051_B at scale 24.0\n",
      "12.326 per cent patches are empty\n",
      "reg051_B at scale 24.0 has 161 patches with zero diveristy\n",
      "reg051_B at scale 24.0 diversity is 0.8596308014321368\n",
      "Processing region: reg052_A at scale 24.0\n",
      "8.854 per cent patches are empty\n",
      "reg052_A at scale 24.0 has 144 patches with zero diveristy\n",
      "reg052_A at scale 24.0 diversity is 0.9474753619676947\n",
      "Processing region: reg052_B at scale 24.0\n",
      "14.931 per cent patches are empty\n",
      "reg052_B at scale 24.0 has 192 patches with zero diveristy\n",
      "reg052_B at scale 24.0 diversity is 0.7926462673807007\n",
      "Processing region: reg053_A at scale 24.0\n",
      "13.542 per cent patches are empty\n",
      "reg053_A at scale 24.0 has 97 patches with zero diveristy\n",
      "reg053_A at scale 24.0 diversity is 1.250090702727466\n",
      "Processing region: reg053_B at scale 24.0\n",
      "82.812 per cent patches are empty\n",
      "reg053_B at scale 24.0 has 82 patches with zero diveristy\n",
      "reg053_B at scale 24.0 diversity is 0.2054530303321679\n",
      "Processing region: reg054_A at scale 24.0\n",
      "26.389 per cent patches are empty\n",
      "reg054_A at scale 24.0 has 102 patches with zero diveristy\n",
      "reg054_A at scale 24.0 diversity is 1.0540158278341583\n",
      "Processing region: reg054_B at scale 24.0\n",
      "69.792 per cent patches are empty\n",
      "reg054_B at scale 24.0 has 105 patches with zero diveristy\n",
      "reg054_B at scale 24.0 diversity is 0.41755694759554757\n",
      "Processing region: reg055_A at scale 24.0\n",
      "13.021 per cent patches are empty\n",
      "reg055_A at scale 24.0 has 39 patches with zero diveristy\n",
      "reg055_A at scale 24.0 diversity is 1.5409441066850884\n",
      "Processing region: reg055_B at scale 24.0\n",
      "82.292 per cent patches are empty\n",
      "reg055_B at scale 24.0 has 81 patches with zero diveristy\n",
      "reg055_B at scale 24.0 diversity is 0.22641899514753033\n",
      "Processing region: reg056_A at scale 24.0\n",
      "41.146 per cent patches are empty\n",
      "reg056_A at scale 24.0 has 153 patches with zero diveristy\n",
      "reg056_A at scale 24.0 diversity is 0.7616751414558022\n",
      "Processing region: reg056_B at scale 24.0\n",
      "16.667 per cent patches are empty\n",
      "reg056_B at scale 24.0 has 190 patches with zero diveristy\n",
      "reg056_B at scale 24.0 diversity is 0.7998915883122556\n",
      "Processing region: reg057_A at scale 24.0\n",
      "92.708 per cent patches are empty\n",
      "reg057_A at scale 24.0 has 26 patches with zero diveristy\n",
      "reg057_A at scale 24.0 diversity is 0.492577806222506\n",
      "Processing region: reg057_B at scale 24.0\n",
      "44.097 per cent patches are empty\n",
      "reg057_B at scale 24.0 has 146 patches with zero diveristy\n",
      "reg057_B at scale 24.0 diversity is 0.7042313360869066\n",
      "Processing region: reg058_A at scale 24.0\n",
      "13.021 per cent patches are empty\n",
      "reg058_A at scale 24.0 has 308 patches with zero diveristy\n",
      "reg058_A at scale 24.0 diversity is 0.388528452261716\n",
      "Processing region: reg058_B at scale 24.0\n",
      "25.347 per cent patches are empty\n",
      "reg058_B at scale 24.0 has 343 patches with zero diveristy\n",
      "reg058_B at scale 24.0 diversity is 0.20868086331182362\n",
      "Processing region: reg059_A at scale 24.0\n",
      "13.889 per cent patches are empty\n",
      "reg059_A at scale 24.0 has 39 patches with zero diveristy\n",
      "reg059_A at scale 24.0 diversity is 1.5941586039663904\n",
      "Processing region: reg059_B at scale 24.0\n",
      "12.326 per cent patches are empty\n",
      "reg059_B at scale 24.0 has 115 patches with zero diveristy\n",
      "reg059_B at scale 24.0 diversity is 1.0427045668544659\n",
      "Processing region: reg060_A at scale 24.0\n",
      "11.806 per cent patches are empty\n",
      "reg060_A at scale 24.0 has 45 patches with zero diveristy\n",
      "reg060_A at scale 24.0 diversity is 1.4294050203626314\n",
      "Processing region: reg060_B at scale 24.0\n",
      "10.243 per cent patches are empty\n",
      "reg060_B at scale 24.0 has 43 patches with zero diveristy\n",
      "reg060_B at scale 24.0 diversity is 1.3404072736358728\n",
      "Processing region: reg061_A at scale 24.0\n",
      "14.583 per cent patches are empty\n",
      "reg061_A at scale 24.0 has 145 patches with zero diveristy\n",
      "reg061_A at scale 24.0 diversity is 0.9833049064970755\n",
      "Processing region: reg061_B at scale 24.0\n",
      "11.806 per cent patches are empty\n",
      "reg061_B at scale 24.0 has 127 patches with zero diveristy\n",
      "reg061_B at scale 24.0 diversity is 1.0364332417678477\n",
      "Processing region: reg062_A at scale 24.0\n",
      "5.382 per cent patches are empty\n",
      "reg062_A at scale 24.0 has 95 patches with zero diveristy\n",
      "reg062_A at scale 24.0 diversity is 1.1655218992515564\n",
      "Processing region: reg062_B at scale 24.0\n",
      "17.535 per cent patches are empty\n",
      "reg062_B at scale 24.0 has 141 patches with zero diveristy\n",
      "reg062_B at scale 24.0 diversity is 0.9174945032050905\n",
      "Processing region: reg063_A at scale 24.0\n",
      "36.285 per cent patches are empty\n",
      "reg063_A at scale 24.0 has 154 patches with zero diveristy\n",
      "reg063_A at scale 24.0 diversity is 0.6906172827104561\n",
      "Processing region: reg063_B at scale 24.0\n",
      "22.396 per cent patches are empty\n",
      "reg063_B at scale 24.0 has 214 patches with zero diveristy\n",
      "reg063_B at scale 24.0 diversity is 0.6263416172883494\n",
      "Processing region: reg064_A at scale 24.0\n",
      "12.847 per cent patches are empty\n",
      "reg064_A at scale 24.0 has 107 patches with zero diveristy\n",
      "reg064_A at scale 24.0 diversity is 0.9925532005462994\n",
      "Processing region: reg064_B at scale 24.0\n",
      "12.674 per cent patches are empty\n",
      "reg064_B at scale 24.0 has 104 patches with zero diveristy\n",
      "reg064_B at scale 24.0 diversity is 1.1531765379051737\n",
      "Processing region: reg065_A at scale 24.0\n",
      "18.750 per cent patches are empty\n",
      "reg065_A at scale 24.0 has 203 patches with zero diveristy\n",
      "reg065_A at scale 24.0 diversity is 0.7516702315165112\n",
      "Processing region: reg065_B at scale 24.0\n",
      "51.042 per cent patches are empty\n",
      "reg065_B at scale 24.0 has 198 patches with zero diveristy\n",
      "reg065_B at scale 24.0 diversity is 0.323245259253897\n",
      "Processing region: reg066_A at scale 24.0\n",
      "13.889 per cent patches are empty\n",
      "reg066_A at scale 24.0 has 131 patches with zero diveristy\n",
      "reg066_A at scale 24.0 diversity is 0.9378134216257313\n",
      "Processing region: reg066_B at scale 24.0\n",
      "23.611 per cent patches are empty\n",
      "reg066_B at scale 24.0 has 232 patches with zero diveristy\n",
      "reg066_B at scale 24.0 diversity is 0.5468359911176398\n",
      "Processing region: reg067_A at scale 24.0\n",
      "56.424 per cent patches are empty\n",
      "reg067_A at scale 24.0 has 103 patches with zero diveristy\n",
      "reg067_A at scale 24.0 diversity is 0.7916188021363973\n",
      "Processing region: reg067_B at scale 24.0\n",
      "91.146 per cent patches are empty\n",
      "reg067_B at scale 24.0 has 34 patches with zero diveristy\n",
      "reg067_B at scale 24.0 diversity is 0.36490783445676916\n",
      "Processing region: reg068_A at scale 24.0\n",
      "18.229 per cent patches are empty\n",
      "reg068_A at scale 24.0 has 195 patches with zero diveristy\n",
      "reg068_A at scale 24.0 diversity is 0.7095151836960808\n",
      "Processing region: reg068_B at scale 24.0\n",
      "14.931 per cent patches are empty\n",
      "reg068_B at scale 24.0 has 131 patches with zero diveristy\n",
      "reg068_B at scale 24.0 diversity is 1.026815534221902\n",
      "Processing region: reg069_A at scale 24.0\n",
      "16.840 per cent patches are empty\n",
      "reg069_A at scale 24.0 has 153 patches with zero diveristy\n",
      "reg069_A at scale 24.0 diversity is 0.9510660021626588\n",
      "Processing region: reg069_B at scale 24.0\n",
      "32.986 per cent patches are empty\n",
      "reg069_B at scale 24.0 has 217 patches with zero diveristy\n",
      "reg069_B at scale 24.0 diversity is 0.5521837867234057\n",
      "Processing region: reg070_A at scale 24.0\n",
      "15.278 per cent patches are empty\n",
      "reg070_A at scale 24.0 has 173 patches with zero diveristy\n",
      "reg070_A at scale 24.0 diversity is 0.8440186853087793\n",
      "Processing region: reg070_B at scale 24.0\n",
      "26.910 per cent patches are empty\n",
      "reg070_B at scale 24.0 has 202 patches with zero diveristy\n",
      "reg070_B at scale 24.0 diversity is 0.5686889653312129\n",
      "Processing region: reg001_A at scale 32.0\n",
      "46.582 per cent patches are empty\n",
      "reg001_A at scale 32.0 has 302 patches with zero diveristy\n",
      "reg001_A at scale 32.0 diversity is 0.5557217232368197\n",
      "Processing region: reg001_B at scale 32.0\n",
      "75.293 per cent patches are empty\n",
      "reg001_B at scale 32.0 has 197 patches with zero diveristy\n",
      "reg001_B at scale 32.0 diversity is 0.23847465007070615\n",
      "Processing region: reg002_A at scale 32.0\n",
      "45.996 per cent patches are empty\n",
      "reg002_A at scale 32.0 has 245 patches with zero diveristy\n",
      "reg002_A at scale 32.0 diversity is 0.6994581913297628\n",
      "Processing region: reg002_B at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "reg002_B at scale 32.0 has 555 patches with zero diveristy\n",
      "reg002_B at scale 32.0 diversity is 0.31562102488293486\n",
      "Processing region: reg003_A at scale 32.0\n",
      "46.875 per cent patches are empty\n",
      "reg003_A at scale 32.0 has 233 patches with zero diveristy\n",
      "reg003_A at scale 32.0 diversity is 0.657804877104138\n",
      "Processing region: reg003_B at scale 32.0\n",
      "43.945 per cent patches are empty\n",
      "reg003_B at scale 32.0 has 312 patches with zero diveristy\n",
      "reg003_B at scale 32.0 diversity is 0.5146402418896122\n",
      "Processing region: reg004_A at scale 32.0\n",
      "35.254 per cent patches are empty\n",
      "reg004_A at scale 32.0 has 270 patches with zero diveristy\n",
      "reg004_A at scale 32.0 diversity is 0.7402479198378501\n",
      "Processing region: reg004_B at scale 32.0\n",
      "17.871 per cent patches are empty\n",
      "reg004_B at scale 32.0 has 396 patches with zero diveristy\n",
      "reg004_B at scale 32.0 diversity is 0.5949950790704666\n",
      "Processing region: reg005_A at scale 32.0\n",
      "18.359 per cent patches are empty\n",
      "reg005_A at scale 32.0 has 303 patches with zero diveristy\n",
      "reg005_A at scale 32.0 diversity is 0.7934137071575837\n",
      "Processing region: reg005_B at scale 32.0\n",
      "40.527 per cent patches are empty\n",
      "reg005_B at scale 32.0 has 235 patches with zero diveristy\n",
      "reg005_B at scale 32.0 diversity is 0.7466883213515009\n",
      "Processing region: reg006_A at scale 32.0\n",
      "37.402 per cent patches are empty\n",
      "reg006_A at scale 32.0 has 304 patches with zero diveristy\n",
      "reg006_A at scale 32.0 diversity is 0.6191400438725643\n",
      "Processing region: reg006_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "reg006_B at scale 32.0 has 306 patches with zero diveristy\n",
      "reg006_B at scale 32.0 diversity is 0.7358364002341685\n",
      "Processing region: reg007_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "reg007_A at scale 32.0 has 359 patches with zero diveristy\n",
      "reg007_A at scale 32.0 diversity is 0.7421368500862212\n",
      "Processing region: reg007_B at scale 32.0\n",
      "26.660 per cent patches are empty\n",
      "reg007_B at scale 32.0 has 255 patches with zero diveristy\n",
      "reg007_B at scale 32.0 diversity is 0.8171170853651124\n",
      "Processing region: reg008_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "reg008_A at scale 32.0 has 261 patches with zero diveristy\n",
      "reg008_A at scale 32.0 diversity is 0.5266790007306591\n",
      "Processing region: reg008_B at scale 32.0\n",
      "8.691 per cent patches are empty\n",
      "reg008_B at scale 32.0 has 367 patches with zero diveristy\n",
      "reg008_B at scale 32.0 diversity is 0.7249209437948831\n",
      "Processing region: reg009_A at scale 32.0\n",
      "12.793 per cent patches are empty\n",
      "reg009_A at scale 32.0 has 315 patches with zero diveristy\n",
      "reg009_A at scale 32.0 diversity is 0.8268542667139614\n",
      "Processing region: reg009_B at scale 32.0\n",
      "6.543 per cent patches are empty\n",
      "reg009_B at scale 32.0 has 133 patches with zero diveristy\n",
      "reg009_B at scale 32.0 diversity is 1.1522742349013497\n",
      "Processing region: reg010_A at scale 32.0\n",
      "25.391 per cent patches are empty\n",
      "reg010_A at scale 32.0 has 260 patches with zero diveristy\n",
      "reg010_A at scale 32.0 diversity is 0.8655377989146098\n",
      "Processing region: reg010_B at scale 32.0\n",
      "42.383 per cent patches are empty\n",
      "reg010_B at scale 32.0 has 318 patches with zero diveristy\n",
      "reg010_B at scale 32.0 diversity is 0.5597647637376048\n",
      "Processing region: reg011_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "reg011_A at scale 32.0 has 140 patches with zero diveristy\n",
      "reg011_A at scale 32.0 diversity is 1.0948781842246476\n",
      "Processing region: reg011_B at scale 32.0\n",
      "51.562 per cent patches are empty\n",
      "reg011_B at scale 32.0 has 294 patches with zero diveristy\n",
      "reg011_B at scale 32.0 diversity is 0.47114908705905273\n",
      "Processing region: reg012_A at scale 32.0\n",
      "32.910 per cent patches are empty\n",
      "reg012_A at scale 32.0 has 291 patches with zero diveristy\n",
      "reg012_A at scale 32.0 diversity is 0.715899417238251\n",
      "Processing region: reg012_B at scale 32.0\n",
      "77.051 per cent patches are empty\n",
      "reg012_B at scale 32.0 has 141 patches with zero diveristy\n",
      "reg012_B at scale 32.0 diversity is 0.46494303672351367\n",
      "Processing region: reg013_A at scale 32.0\n",
      "25.098 per cent patches are empty\n",
      "reg013_A at scale 32.0 has 399 patches with zero diveristy\n",
      "reg013_A at scale 32.0 diversity is 0.5717931888532037\n",
      "Processing region: reg013_B at scale 32.0\n",
      "19.922 per cent patches are empty\n",
      "reg013_B at scale 32.0 has 418 patches with zero diveristy\n",
      "reg013_B at scale 32.0 diversity is 0.5932263292627094\n",
      "Processing region: reg014_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "reg014_A at scale 32.0 has 305 patches with zero diveristy\n",
      "reg014_A at scale 32.0 diversity is 0.45247809666690697\n",
      "Processing region: reg014_B at scale 32.0\n",
      "21.191 per cent patches are empty\n",
      "reg014_B at scale 32.0 has 283 patches with zero diveristy\n",
      "reg014_B at scale 32.0 diversity is 0.7752757566055144\n",
      "Processing region: reg015_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "reg015_A at scale 32.0 has 313 patches with zero diveristy\n",
      "reg015_A at scale 32.0 diversity is 0.7413513744314911\n",
      "Processing region: reg015_B at scale 32.0\n",
      "31.055 per cent patches are empty\n",
      "reg015_B at scale 32.0 has 463 patches with zero diveristy\n",
      "reg015_B at scale 32.0 diversity is 0.36216571944242704\n",
      "Processing region: reg016_A at scale 32.0\n",
      "27.539 per cent patches are empty\n",
      "reg016_A at scale 32.0 has 326 patches with zero diveristy\n",
      "reg016_A at scale 32.0 diversity is 0.6797345134618556\n",
      "Processing region: reg016_B at scale 32.0\n",
      "17.773 per cent patches are empty\n",
      "reg016_B at scale 32.0 has 351 patches with zero diveristy\n",
      "reg016_B at scale 32.0 diversity is 0.6839141072927567\n",
      "Processing region: reg017_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "reg017_A at scale 32.0 has 315 patches with zero diveristy\n",
      "reg017_A at scale 32.0 diversity is 0.8149216127699066\n",
      "Processing region: reg017_B at scale 32.0\n",
      "18.066 per cent patches are empty\n",
      "reg017_B at scale 32.0 has 292 patches with zero diveristy\n",
      "reg017_B at scale 32.0 diversity is 0.8927257639894027\n",
      "Processing region: reg018_A at scale 32.0\n",
      "29.492 per cent patches are empty\n",
      "reg018_A at scale 32.0 has 283 patches with zero diveristy\n",
      "reg018_A at scale 32.0 diversity is 0.7870709176203579\n",
      "Processing region: reg018_B at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "reg018_B at scale 32.0 has 346 patches with zero diveristy\n",
      "reg018_B at scale 32.0 diversity is 0.6650753971585537\n",
      "Processing region: reg019_A at scale 32.0\n",
      "53.613 per cent patches are empty\n",
      "reg019_A at scale 32.0 has 164 patches with zero diveristy\n",
      "reg019_A at scale 32.0 diversity is 0.7941809439673526\n",
      "Processing region: reg019_B at scale 32.0\n",
      "92.090 per cent patches are empty\n",
      "reg019_B at scale 32.0 has 70 patches with zero diveristy\n",
      "reg019_B at scale 32.0 diversity is 0.1327763889155984\n",
      "Processing region: reg020_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "reg020_A at scale 32.0 has 294 patches with zero diveristy\n",
      "reg020_A at scale 32.0 diversity is 0.8202500829777284\n",
      "Processing region: reg020_B at scale 32.0\n",
      "19.434 per cent patches are empty\n",
      "reg020_B at scale 32.0 has 176 patches with zero diveristy\n",
      "reg020_B at scale 32.0 diversity is 1.0608140188257598\n",
      "Processing region: reg021_A at scale 32.0\n",
      "36.328 per cent patches are empty\n",
      "reg021_A at scale 32.0 has 164 patches with zero diveristy\n",
      "reg021_A at scale 32.0 diversity is 0.956050983418754\n",
      "Processing region: reg021_B at scale 32.0\n",
      "76.270 per cent patches are empty\n",
      "reg021_B at scale 32.0 has 195 patches with zero diveristy\n",
      "reg021_B at scale 32.0 diversity is 0.22551707626951734\n",
      "Processing region: reg022_A at scale 32.0\n",
      "31.934 per cent patches are empty\n",
      "reg022_A at scale 32.0 has 248 patches with zero diveristy\n",
      "reg022_A at scale 32.0 diversity is 0.8199605494643846\n",
      "Processing region: reg022_B at scale 32.0\n",
      "62.402 per cent patches are empty\n",
      "reg022_B at scale 32.0 has 247 patches with zero diveristy\n",
      "reg022_B at scale 32.0 diversity is 0.4188788755622188\n",
      "Processing region: reg023_A at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "reg023_A at scale 32.0 has 334 patches with zero diveristy\n",
      "reg023_A at scale 32.0 diversity is 0.34879789982334336\n",
      "Processing region: reg023_B at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "reg023_B at scale 32.0 has 260 patches with zero diveristy\n",
      "reg023_B at scale 32.0 diversity is 0.9160669109208432\n",
      "Processing region: reg024_A at scale 32.0\n",
      "46.191 per cent patches are empty\n",
      "reg024_A at scale 32.0 has 358 patches with zero diveristy\n",
      "reg024_A at scale 32.0 diversity is 0.364969293738499\n",
      "Processing region: reg024_B at scale 32.0\n",
      "15.625 per cent patches are empty\n",
      "reg024_B at scale 32.0 has 449 patches with zero diveristy\n",
      "reg024_B at scale 32.0 diversity is 0.5456811479117779\n",
      "Processing region: reg025_A at scale 32.0\n",
      "39.648 per cent patches are empty\n",
      "reg025_A at scale 32.0 has 323 patches with zero diveristy\n",
      "reg025_A at scale 32.0 diversity is 0.5890890340494715\n",
      "Processing region: reg025_B at scale 32.0\n",
      "31.348 per cent patches are empty\n",
      "reg025_B at scale 32.0 has 299 patches with zero diveristy\n",
      "reg025_B at scale 32.0 diversity is 0.7363894695016134\n",
      "Processing region: reg026_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "reg026_A at scale 32.0 has 263 patches with zero diveristy\n",
      "reg026_A at scale 32.0 diversity is 0.8776847062283261\n",
      "Processing region: reg026_B at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "reg026_B at scale 32.0 has 256 patches with zero diveristy\n",
      "reg026_B at scale 32.0 diversity is 0.9004768676847865\n",
      "Processing region: reg027_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "reg027_A at scale 32.0 has 303 patches with zero diveristy\n",
      "reg027_A at scale 32.0 diversity is 0.79723050865919\n",
      "Processing region: reg027_B at scale 32.0\n",
      "17.578 per cent patches are empty\n",
      "reg027_B at scale 32.0 has 394 patches with zero diveristy\n",
      "reg027_B at scale 32.0 diversity is 0.6004464067236152\n",
      "Processing region: reg028_A at scale 32.0\n",
      "16.016 per cent patches are empty\n",
      "reg028_A at scale 32.0 has 253 patches with zero diveristy\n",
      "reg028_A at scale 32.0 diversity is 0.9714370162005411\n",
      "Processing region: reg028_B at scale 32.0\n",
      "13.965 per cent patches are empty\n",
      "reg028_B at scale 32.0 has 342 patches with zero diveristy\n",
      "reg028_B at scale 32.0 diversity is 0.7582122104379305\n",
      "Processing region: reg029_A at scale 32.0\n",
      "15.039 per cent patches are empty\n",
      "reg029_A at scale 32.0 has 435 patches with zero diveristy\n",
      "reg029_A at scale 32.0 diversity is 0.6153299482473555\n",
      "Processing region: reg029_B at scale 32.0\n",
      "23.633 per cent patches are empty\n",
      "reg029_B at scale 32.0 has 379 patches with zero diveristy\n",
      "reg029_B at scale 32.0 diversity is 0.5455665967513191\n",
      "Processing region: reg030_A at scale 32.0\n",
      "17.969 per cent patches are empty\n",
      "reg030_A at scale 32.0 has 272 patches with zero diveristy\n",
      "reg030_A at scale 32.0 diversity is 0.8442162056036804\n",
      "Processing region: reg030_B at scale 32.0\n",
      "25.195 per cent patches are empty\n",
      "reg030_B at scale 32.0 has 436 patches with zero diveristy\n",
      "reg030_B at scale 32.0 diversity is 0.46990064731306624\n",
      "Processing region: reg031_A at scale 32.0\n",
      "11.426 per cent patches are empty\n",
      "reg031_A at scale 32.0 has 297 patches with zero diveristy\n",
      "reg031_A at scale 32.0 diversity is 0.8563650465552216\n",
      "Processing region: reg031_B at scale 32.0\n",
      "17.480 per cent patches are empty\n",
      "reg031_B at scale 32.0 has 301 patches with zero diveristy\n",
      "reg031_B at scale 32.0 diversity is 0.7979802932943109\n",
      "Processing region: reg032_A at scale 32.0\n",
      "11.230 per cent patches are empty\n",
      "reg032_A at scale 32.0 has 323 patches with zero diveristy\n",
      "reg032_A at scale 32.0 diversity is 0.7804075674761368\n",
      "Processing region: reg032_B at scale 32.0\n",
      "33.984 per cent patches are empty\n",
      "reg032_B at scale 32.0 has 272 patches with zero diveristy\n",
      "reg032_B at scale 32.0 diversity is 0.7500143014454015\n",
      "Processing region: reg033_A at scale 32.0\n",
      "26.367 per cent patches are empty\n",
      "reg033_A at scale 32.0 has 198 patches with zero diveristy\n",
      "reg033_A at scale 32.0 diversity is 0.9048044337514133\n",
      "Processing region: reg033_B at scale 32.0\n",
      "12.012 per cent patches are empty\n",
      "reg033_B at scale 32.0 has 394 patches with zero diveristy\n",
      "reg033_B at scale 32.0 diversity is 0.6896540868642623\n",
      "Processing region: reg034_A at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "reg034_A at scale 32.0 has 265 patches with zero diveristy\n",
      "reg034_A at scale 32.0 diversity is 0.8186291275482388\n",
      "Processing region: reg034_B at scale 32.0\n",
      "25.586 per cent patches are empty\n",
      "reg034_B at scale 32.0 has 373 patches with zero diveristy\n",
      "reg034_B at scale 32.0 diversity is 0.5952104538602976\n",
      "Processing region: reg035_A at scale 32.0\n",
      "21.777 per cent patches are empty\n",
      "reg035_A at scale 32.0 has 282 patches with zero diveristy\n",
      "reg035_A at scale 32.0 diversity is 0.8409628638917658\n",
      "Processing region: reg035_B at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "reg035_B at scale 32.0 has 324 patches with zero diveristy\n",
      "reg035_B at scale 32.0 diversity is 0.670885352693505\n",
      "Processing region: reg036_A at scale 32.0\n",
      "13.184 per cent patches are empty\n",
      "reg036_A at scale 32.0 has 184 patches with zero diveristy\n",
      "reg036_A at scale 32.0 diversity is 1.0632868714800234\n",
      "Processing region: reg036_B at scale 32.0\n",
      "10.742 per cent patches are empty\n",
      "reg036_B at scale 32.0 has 472 patches with zero diveristy\n",
      "reg036_B at scale 32.0 diversity is 0.5829003993727727\n",
      "Processing region: reg037_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "reg037_A at scale 32.0 has 236 patches with zero diveristy\n",
      "reg037_A at scale 32.0 diversity is 0.7665670072009133\n",
      "Processing region: reg037_B at scale 32.0\n",
      "70.020 per cent patches are empty\n",
      "reg037_B at scale 32.0 has 255 patches with zero diveristy\n",
      "reg037_B at scale 32.0 diversity is 0.188536565468344\n",
      "Processing region: reg038_A at scale 32.0\n",
      "11.719 per cent patches are empty\n",
      "reg038_A at scale 32.0 has 290 patches with zero diveristy\n",
      "reg038_A at scale 32.0 diversity is 0.8611117573150999\n",
      "Processing region: reg038_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "reg038_B at scale 32.0 has 334 patches with zero diveristy\n",
      "reg038_B at scale 32.0 diversity is 0.6321475317707992\n",
      "Processing region: reg039_A at scale 32.0\n",
      "30.273 per cent patches are empty\n",
      "reg039_A at scale 32.0 has 203 patches with zero diveristy\n",
      "reg039_A at scale 32.0 diversity is 0.9109905007254127\n",
      "Processing region: reg039_B at scale 32.0\n",
      "56.543 per cent patches are empty\n",
      "reg039_B at scale 32.0 has 304 patches with zero diveristy\n",
      "reg039_B at scale 32.0 diversity is 0.3608682315400152\n",
      "Processing region: reg040_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "reg040_A at scale 32.0 has 338 patches with zero diveristy\n",
      "reg040_A at scale 32.0 diversity is 0.6970079056544474\n",
      "Processing region: reg040_B at scale 32.0\n",
      "57.520 per cent patches are empty\n",
      "reg040_B at scale 32.0 has 244 patches with zero diveristy\n",
      "reg040_B at scale 32.0 diversity is 0.4863994130681796\n",
      "Processing region: reg041_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "reg041_A at scale 32.0 has 415 patches with zero diveristy\n",
      "reg041_A at scale 32.0 diversity is 0.6103192240635986\n",
      "Processing region: reg041_B at scale 32.0\n",
      "29.688 per cent patches are empty\n",
      "reg041_B at scale 32.0 has 453 patches with zero diveristy\n",
      "reg041_B at scale 32.0 diversity is 0.44274065377569166\n",
      "Processing region: reg042_A at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "reg042_A at scale 32.0 has 525 patches with zero diveristy\n",
      "reg042_A at scale 32.0 diversity is 0.3449762230060837\n",
      "Processing region: reg042_B at scale 32.0\n",
      "24.902 per cent patches are empty\n",
      "reg042_B at scale 32.0 has 534 patches with zero diveristy\n",
      "reg042_B at scale 32.0 diversity is 0.3104026185490923\n",
      "Processing region: reg043_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "reg043_A at scale 32.0 has 459 patches with zero diveristy\n",
      "reg043_A at scale 32.0 diversity is 0.4603516593074785\n",
      "Processing region: reg043_B at scale 32.0\n",
      "64.844 per cent patches are empty\n",
      "reg043_B at scale 32.0 has 298 patches with zero diveristy\n",
      "reg043_B at scale 32.0 diversity is 0.17661800225951405\n",
      "Processing region: reg044_A at scale 32.0\n",
      "43.164 per cent patches are empty\n",
      "reg044_A at scale 32.0 has 361 patches with zero diveristy\n",
      "reg044_A at scale 32.0 diversity is 0.4211034838504594\n",
      "Processing region: reg044_B at scale 32.0\n",
      "71.973 per cent patches are empty\n",
      "reg044_B at scale 32.0 has 239 patches with zero diveristy\n",
      "reg044_B at scale 32.0 diversity is 0.1811331518997645\n",
      "Processing region: reg045_A at scale 32.0\n",
      "13.672 per cent patches are empty\n",
      "reg045_A at scale 32.0 has 359 patches with zero diveristy\n",
      "reg045_A at scale 32.0 diversity is 0.7102307125802958\n",
      "Processing region: reg045_B at scale 32.0\n",
      "43.750 per cent patches are empty\n",
      "reg045_B at scale 32.0 has 370 patches with zero diveristy\n",
      "reg045_B at scale 32.0 diversity is 0.40808583092063516\n",
      "Processing region: reg046_A at scale 32.0\n",
      "15.723 per cent patches are empty\n",
      "reg046_A at scale 32.0 has 307 patches with zero diveristy\n",
      "reg046_A at scale 32.0 diversity is 0.824653080439295\n",
      "Processing region: reg046_B at scale 32.0\n",
      "15.234 per cent patches are empty\n",
      "reg046_B at scale 32.0 has 300 patches with zero diveristy\n",
      "reg046_B at scale 32.0 diversity is 0.8110346447308838\n",
      "Processing region: reg047_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "reg047_A at scale 32.0 has 256 patches with zero diveristy\n",
      "reg047_A at scale 32.0 diversity is 0.8265704496875337\n",
      "Processing region: reg047_B at scale 32.0\n",
      "45.117 per cent patches are empty\n",
      "reg047_B at scale 32.0 has 447 patches with zero diveristy\n",
      "reg047_B at scale 32.0 diversity is 0.217459352739393\n",
      "Processing region: reg048_A at scale 32.0\n",
      "24.512 per cent patches are empty\n",
      "reg048_A at scale 32.0 has 356 patches with zero diveristy\n",
      "reg048_A at scale 32.0 diversity is 0.632285241336113\n",
      "Processing region: reg048_B at scale 32.0\n",
      "33.203 per cent patches are empty\n",
      "reg048_B at scale 32.0 has 309 patches with zero diveristy\n",
      "reg048_B at scale 32.0 diversity is 0.6458676068956488\n",
      "Processing region: reg049_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "reg049_A at scale 32.0 has 329 patches with zero diveristy\n",
      "reg049_A at scale 32.0 diversity is 0.7307917431322573\n",
      "Processing region: reg049_B at scale 32.0\n",
      "38.086 per cent patches are empty\n",
      "reg049_B at scale 32.0 has 382 patches with zero diveristy\n",
      "reg049_B at scale 32.0 diversity is 0.44560107536559873\n",
      "Processing region: reg050_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "reg050_A at scale 32.0 has 353 patches with zero diveristy\n",
      "reg050_A at scale 32.0 diversity is 0.42630701809588917\n",
      "Processing region: reg050_B at scale 32.0\n",
      "25.000 per cent patches are empty\n",
      "reg050_B at scale 32.0 has 430 patches with zero diveristy\n",
      "reg050_B at scale 32.0 diversity is 0.49653871022115786\n",
      "Processing region: reg051_A at scale 32.0\n",
      "33.496 per cent patches are empty\n",
      "reg051_A at scale 32.0 has 329 patches with zero diveristy\n",
      "reg051_A at scale 32.0 diversity is 0.6293450103290034\n",
      "Processing region: reg051_B at scale 32.0\n",
      "23.047 per cent patches are empty\n",
      "reg051_B at scale 32.0 has 413 patches with zero diveristy\n",
      "reg051_B at scale 32.0 diversity is 0.5381406981089925\n",
      "Processing region: reg052_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "reg052_A at scale 32.0 has 404 patches with zero diveristy\n",
      "reg052_A at scale 32.0 diversity is 0.5983554029508932\n",
      "Processing region: reg052_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "reg052_B at scale 32.0 has 439 patches with zero diveristy\n",
      "reg052_B at scale 32.0 diversity is 0.5123369607711975\n",
      "Processing region: reg053_A at scale 32.0\n",
      "24.023 per cent patches are empty\n",
      "reg053_A at scale 32.0 has 270 patches with zero diveristy\n",
      "reg053_A at scale 32.0 diversity is 0.8474392211965895\n",
      "Processing region: reg053_B at scale 32.0\n",
      "88.477 per cent patches are empty\n",
      "reg053_B at scale 32.0 has 103 patches with zero diveristy\n",
      "reg053_B at scale 32.0 diversity is 0.13559322033898305\n",
      "Processing region: reg054_A at scale 32.0\n",
      "35.547 per cent patches are empty\n",
      "reg054_A at scale 32.0 has 263 patches with zero diveristy\n",
      "reg054_A at scale 32.0 diversity is 0.7302801732780377\n",
      "Processing region: reg054_B at scale 32.0\n",
      "78.320 per cent patches are empty\n",
      "reg054_B at scale 32.0 has 176 patches with zero diveristy\n",
      "reg054_B at scale 32.0 diversity is 0.21515613741662346\n",
      "Processing region: reg055_A at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "reg055_A at scale 32.0 has 150 patches with zero diveristy\n",
      "reg055_A at scale 32.0 diversity is 1.1737463544368825\n",
      "Processing region: reg055_B at scale 32.0\n",
      "89.062 per cent patches are empty\n",
      "reg055_B at scale 32.0 has 98 patches with zero diveristy\n",
      "reg055_B at scale 32.0 diversity is 0.1399391369305175\n",
      "Processing region: reg056_A at scale 32.0\n",
      "56.641 per cent patches are empty\n",
      "reg056_A at scale 32.0 has 251 patches with zero diveristy\n",
      "reg056_A at scale 32.0 diversity is 0.5368372306790336\n",
      "Processing region: reg056_B at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "reg056_B at scale 32.0 has 424 patches with zero diveristy\n",
      "reg056_B at scale 32.0 diversity is 0.4703016023428499\n",
      "Processing region: reg057_A at scale 32.0\n",
      "94.434 per cent patches are empty\n",
      "reg057_A at scale 32.0 has 43 patches with zero diveristy\n",
      "reg057_A at scale 32.0 diversity is 0.2720441520973911\n",
      "Processing region: reg057_B at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "reg057_B at scale 32.0 has 287 patches with zero diveristy\n",
      "reg057_B at scale 32.0 diversity is 0.4616586255070822\n",
      "Processing region: reg058_A at scale 32.0\n",
      "20.703 per cent patches are empty\n",
      "reg058_A at scale 32.0 has 616 patches with zero diveristy\n",
      "reg058_A at scale 32.0 diversity is 0.24497604843820361\n",
      "Processing region: reg058_B at scale 32.0\n",
      "36.719 per cent patches are empty\n",
      "reg058_B at scale 32.0 has 571 patches with zero diveristy\n",
      "reg058_B at scale 32.0 diversity is 0.1166579173232915\n",
      "Processing region: reg059_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "reg059_A at scale 32.0 has 156 patches with zero diveristy\n",
      "reg059_A at scale 32.0 diversity is 1.1330184879161005\n",
      "Processing region: reg059_B at scale 32.0\n",
      "21.875 per cent patches are empty\n",
      "reg059_B at scale 32.0 has 358 patches with zero diveristy\n",
      "reg059_B at scale 32.0 diversity is 0.6637857631949273\n",
      "Processing region: reg060_A at scale 32.0\n",
      "15.820 per cent patches are empty\n",
      "reg060_A at scale 32.0 has 220 patches with zero diveristy\n",
      "reg060_A at scale 32.0 diversity is 0.9501429530272408\n",
      "Processing region: reg060_B at scale 32.0\n",
      "14.355 per cent patches are empty\n",
      "reg060_B at scale 32.0 has 254 patches with zero diveristy\n",
      "reg060_B at scale 32.0 diversity is 0.862701243108512\n",
      "Processing region: reg061_A at scale 32.0\n",
      "25.488 per cent patches are empty\n",
      "reg061_A at scale 32.0 has 340 patches with zero diveristy\n",
      "reg061_A at scale 32.0 diversity is 0.6630208302428718\n",
      "Processing region: reg061_B at scale 32.0\n",
      "22.656 per cent patches are empty\n",
      "reg061_B at scale 32.0 has 354 patches with zero diveristy\n",
      "reg061_B at scale 32.0 diversity is 0.6594595749329318\n",
      "Processing region: reg062_A at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "reg062_A at scale 32.0 has 328 patches with zero diveristy\n",
      "reg062_A at scale 32.0 diversity is 0.7760791929900223\n",
      "Processing region: reg062_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "reg062_B at scale 32.0 has 365 patches with zero diveristy\n",
      "reg062_B at scale 32.0 diversity is 0.5599294374161436\n",
      "Processing region: reg063_A at scale 32.0\n",
      "50.879 per cent patches are empty\n",
      "reg063_A at scale 32.0 has 299 patches with zero diveristy\n",
      "reg063_A at scale 32.0 diversity is 0.4443646795632483\n",
      "Processing region: reg063_B at scale 32.0\n",
      "40.234 per cent patches are empty\n",
      "reg063_B at scale 32.0 has 424 patches with zero diveristy\n",
      "reg063_B at scale 32.0 diversity is 0.3359688445866314\n",
      "Processing region: reg064_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "reg064_A at scale 32.0 has 358 patches with zero diveristy\n",
      "reg064_A at scale 32.0 diversity is 0.637025867355701\n",
      "Processing region: reg064_B at scale 32.0\n",
      "19.141 per cent patches are empty\n",
      "reg064_B at scale 32.0 has 341 patches with zero diveristy\n",
      "reg064_B at scale 32.0 diversity is 0.7251007262551754\n",
      "Processing region: reg065_A at scale 32.0\n",
      "27.930 per cent patches are empty\n",
      "reg065_A at scale 32.0 has 419 patches with zero diveristy\n",
      "reg065_A at scale 32.0 diversity is 0.5237081980471305\n",
      "Processing region: reg065_B at scale 32.0\n",
      "66.895 per cent patches are empty\n",
      "reg065_B at scale 32.0 has 286 patches with zero diveristy\n",
      "reg065_B at scale 32.0 diversity is 0.16007275075660887\n",
      "Processing region: reg066_A at scale 32.0\n",
      "23.242 per cent patches are empty\n",
      "reg066_A at scale 32.0 has 362 patches with zero diveristy\n",
      "reg066_A at scale 32.0 diversity is 0.6228292576468444\n",
      "Processing region: reg066_B at scale 32.0\n",
      "39.941 per cent patches are empty\n",
      "reg066_B at scale 32.0 has 439 patches with zero diveristy\n",
      "reg066_B at scale 32.0 diversity is 0.3194654362038013\n",
      "Processing region: reg067_A at scale 32.0\n",
      "66.992 per cent patches are empty\n",
      "reg067_A at scale 32.0 has 185 patches with zero diveristy\n",
      "reg067_A at scale 32.0 diversity is 0.5395960404181045\n",
      "Processing region: reg067_B at scale 32.0\n",
      "93.945 per cent patches are empty\n",
      "reg067_B at scale 32.0 has 46 patches with zero diveristy\n",
      "reg067_B at scale 32.0 diversity is 0.2622281586603083\n",
      "Processing region: reg068_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "reg068_A at scale 32.0 has 402 patches with zero diveristy\n",
      "reg068_A at scale 32.0 diversity is 0.43936214997273054\n",
      "Processing region: reg068_B at scale 32.0\n",
      "27.148 per cent patches are empty\n",
      "reg068_B at scale 32.0 has 366 patches with zero diveristy\n",
      "reg068_B at scale 32.0 diversity is 0.6211276185185179\n",
      "Processing region: reg069_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "reg069_A at scale 32.0 has 371 patches with zero diveristy\n",
      "reg069_A at scale 32.0 diversity is 0.5914787152155453\n",
      "Processing region: reg069_B at scale 32.0\n",
      "49.121 per cent patches are empty\n",
      "reg069_B at scale 32.0 has 350 patches with zero diveristy\n",
      "reg069_B at scale 32.0 diversity is 0.36949704212580514\n",
      "Processing region: reg070_A at scale 32.0\n",
      "26.270 per cent patches are empty\n",
      "reg070_A at scale 32.0 has 399 patches with zero diveristy\n",
      "reg070_A at scale 32.0 diversity is 0.5562779234292821\n",
      "Processing region: reg070_B at scale 32.0\n",
      "41.699 per cent patches are empty\n",
      "reg070_B at scale 32.0 has 430 patches with zero diveristy\n",
      "reg070_B at scale 32.0 diversity is 0.29723632585509063\n",
      "Processing region: reg001_A at scale 48.0\n",
      "66.059 per cent patches are empty\n",
      "reg001_A at scale 48.0 has 575 patches with zero diveristy\n",
      "reg001_A at scale 48.0 diversity is 0.2834285259929188\n",
      "Processing region: reg001_B at scale 48.0\n",
      "87.066 per cent patches are empty\n",
      "reg001_B at scale 48.0 has 265 patches with zero diveristy\n",
      "reg001_B at scale 48.0 diversity is 0.11046407998004862\n",
      "Processing region: reg002_A at scale 48.0\n",
      "60.851 per cent patches are empty\n",
      "reg002_A at scale 48.0 has 616 patches with zero diveristy\n",
      "reg002_A at scale 48.0 diversity is 0.3357395776120953\n",
      "Processing region: reg002_B at scale 48.0\n",
      "35.503 per cent patches are empty\n",
      "reg002_B at scale 48.0 has 1279 patches with zero diveristy\n",
      "reg002_B at scale 48.0 diversity is 0.13581294927429127\n",
      "Processing region: reg003_A at scale 48.0\n",
      "61.892 per cent patches are empty\n",
      "reg003_A at scale 48.0 has 662 patches with zero diveristy\n",
      "reg003_A at scale 48.0 diversity is 0.2572596123231796\n",
      "Processing region: reg003_B at scale 48.0\n",
      "62.240 per cent patches are empty\n",
      "reg003_B at scale 48.0 has 678 patches with zero diveristy\n",
      "reg003_B at scale 48.0 diversity is 0.2263059997980448\n",
      "Processing region: reg004_A at scale 48.0\n",
      "55.599 per cent patches are empty\n",
      "reg004_A at scale 48.0 has 734 patches with zero diveristy\n",
      "reg004_A at scale 48.0 diversity is 0.30298792153697257\n",
      "Processing region: reg004_B at scale 48.0\n",
      "41.840 per cent patches are empty\n",
      "reg004_B at scale 48.0 has 1041 patches with zero diveristy\n",
      "reg004_B at scale 48.0 diversity is 0.2294938876211342\n",
      "Processing region: reg005_A at scale 48.0\n",
      "35.851 per cent patches are empty\n",
      "reg005_A at scale 48.0 has 1028 patches with zero diveristy\n",
      "reg005_A at scale 48.0 diversity is 0.32542192433095307\n",
      "Processing region: reg005_B at scale 48.0\n",
      "54.470 per cent patches are empty\n",
      "reg005_B at scale 48.0 has 729 patches with zero diveristy\n",
      "reg005_B at scale 48.0 diversity is 0.33165879925895475\n",
      "Processing region: reg006_A at scale 48.0\n",
      "58.160 per cent patches are empty\n",
      "reg006_A at scale 48.0 has 720 patches with zero diveristy\n",
      "reg006_A at scale 48.0 diversity is 0.27068111274234957\n",
      "Processing region: reg006_B at scale 48.0\n",
      "42.405 per cent patches are empty\n",
      "reg006_B at scale 48.0 has 928 patches with zero diveristy\n",
      "reg006_B at scale 48.0 diversity is 0.3213280427486596\n",
      "Processing region: reg007_A at scale 48.0\n",
      "30.556 per cent patches are empty\n",
      "reg007_A at scale 48.0 has 1084 patches with zero diveristy\n",
      "reg007_A at scale 48.0 diversity is 0.35123095394846215\n",
      "Processing region: reg007_B at scale 48.0\n",
      "40.842 per cent patches are empty\n",
      "reg007_B at scale 48.0 has 876 patches with zero diveristy\n",
      "reg007_B at scale 48.0 diversity is 0.3801621150061871\n",
      "Processing region: reg008_A at scale 48.0\n",
      "62.457 per cent patches are empty\n",
      "reg008_A at scale 48.0 has 667 patches with zero diveristy\n",
      "reg008_A at scale 48.0 diversity is 0.23591351469183486\n",
      "Processing region: reg008_B at scale 48.0\n",
      "21.701 per cent patches are empty\n",
      "reg008_B at scale 48.0 has 1288 patches with zero diveristy\n",
      "reg008_B at scale 48.0 diversity is 0.30660184528357215\n",
      "Processing region: reg009_A at scale 48.0\n",
      "33.724 per cent patches are empty\n",
      "reg009_A at scale 48.0 has 1033 patches with zero diveristy\n",
      "reg009_A at scale 48.0 diversity is 0.35040394538772146\n",
      "Processing region: reg009_B at scale 48.0\n",
      "18.186 per cent patches are empty\n",
      "reg009_B at scale 48.0 has 1007 patches with zero diveristy\n",
      "reg009_B at scale 48.0 diversity is 0.5025893711089601\n",
      "Processing region: reg010_A at scale 48.0\n",
      "43.273 per cent patches are empty\n",
      "reg010_A at scale 48.0 has 864 patches with zero diveristy\n",
      "reg010_A at scale 48.0 diversity is 0.3676775217211776\n",
      "Processing region: reg010_B at scale 48.0\n",
      "61.589 per cent patches are empty\n",
      "reg010_B at scale 48.0 has 690 patches with zero diveristy\n",
      "reg010_B at scale 48.0 diversity is 0.23988191388540323\n",
      "Processing region: reg011_A at scale 48.0\n",
      "39.757 per cent patches are empty\n",
      "reg011_A at scale 48.0 has 687 patches with zero diveristy\n",
      "reg011_A at scale 48.0 diversity is 0.5712364762115034\n",
      "Processing region: reg011_B at scale 48.0\n",
      "69.358 per cent patches are empty\n",
      "reg011_B at scale 48.0 has 568 patches with zero diveristy\n",
      "reg011_B at scale 48.0 diversity is 0.2043824185828204\n",
      "Processing region: reg012_A at scale 48.0\n",
      "49.045 per cent patches are empty\n",
      "reg012_A at scale 48.0 has 848 patches with zero diveristy\n",
      "reg012_A at scale 48.0 diversity is 0.3023623864809933\n",
      "Processing region: reg012_B at scale 48.0\n",
      "85.113 per cent patches are empty\n",
      "reg012_B at scale 48.0 has 275 patches with zero diveristy\n",
      "reg012_B at scale 48.0 diversity is 0.2111342535527983\n",
      "Processing region: reg013_A at scale 48.0\n",
      "48.220 per cent patches are empty\n",
      "reg013_A at scale 48.0 has 926 patches with zero diveristy\n",
      "reg013_A at scale 48.0 diversity is 0.23412765353116544\n",
      "Processing region: reg013_B at scale 48.0\n",
      "43.012 per cent patches are empty\n",
      "reg013_B at scale 48.0 has 1036 patches with zero diveristy\n",
      "reg013_B at scale 48.0 diversity is 0.2236425044273665\n",
      "Processing region: reg014_A at scale 48.0\n",
      "68.924 per cent patches are empty\n",
      "reg014_A at scale 48.0 has 566 patches with zero diveristy\n",
      "reg014_A at scale 48.0 diversity is 0.21671885705156685\n",
      "Processing region: reg014_B at scale 48.0\n",
      "40.278 per cent patches are empty\n",
      "reg014_B at scale 48.0 has 952 patches with zero diveristy\n",
      "reg014_B at scale 48.0 diversity is 0.32338233376611836\n",
      "Processing region: reg015_A at scale 48.0\n",
      "35.807 per cent patches are empty\n",
      "reg015_A at scale 48.0 has 1018 patches with zero diveristy\n",
      "reg015_A at scale 48.0 diversity is 0.32835210244666924\n",
      "Processing region: reg015_B at scale 48.0\n",
      "51.780 per cent patches are empty\n",
      "reg015_B at scale 48.0 has 958 patches with zero diveristy\n",
      "reg015_B at scale 48.0 diversity is 0.13997415056114995\n",
      "Processing region: reg016_A at scale 48.0\n",
      "46.615 per cent patches are empty\n",
      "reg016_A at scale 48.0 has 887 patches with zero diveristy\n",
      "reg016_A at scale 48.0 diversity is 0.29723893549048863\n",
      "Processing region: reg016_B at scale 48.0\n",
      "37.674 per cent patches are empty\n",
      "reg016_B at scale 48.0 has 1039 patches with zero diveristy\n",
      "reg016_B at scale 48.0 diversity is 0.2897067514556336\n",
      "Processing region: reg017_A at scale 48.0\n",
      "32.769 per cent patches are empty\n",
      "reg017_A at scale 48.0 has 1029 patches with zero diveristy\n",
      "reg017_A at scale 48.0 diversity is 0.37293620169093666\n",
      "Processing region: reg017_B at scale 48.0\n",
      "33.116 per cent patches are empty\n",
      "reg017_B at scale 48.0 has 937 patches with zero diveristy\n",
      "reg017_B at scale 48.0 diversity is 0.44221581435555807\n",
      "Processing region: reg018_A at scale 48.0\n",
      "46.658 per cent patches are empty\n",
      "reg018_A at scale 48.0 has 823 patches with zero diveristy\n",
      "reg018_A at scale 48.0 diversity is 0.3617311220067054\n",
      "Processing region: reg018_B at scale 48.0\n",
      "32.509 per cent patches are empty\n",
      "reg018_B at scale 48.0 has 1123 patches with zero diveristy\n",
      "reg018_B at scale 48.0 diversity is 0.28568271541476625\n",
      "Processing region: reg019_A at scale 48.0\n",
      "63.715 per cent patches are empty\n",
      "reg019_A at scale 48.0 has 499 patches with zero diveristy\n",
      "reg019_A at scale 48.0 diversity is 0.42928774825766275\n",
      "Processing region: reg019_B at scale 48.0\n",
      "96.181 per cent patches are empty\n",
      "reg019_B at scale 48.0 has 80 patches with zero diveristy\n",
      "reg019_B at scale 48.0 diversity is 0.09662793562245052\n",
      "Processing region: reg020_A at scale 48.0\n",
      "41.102 per cent patches are empty\n",
      "reg020_A at scale 48.0 has 924 patches with zero diveristy\n",
      "reg020_A at scale 48.0 diversity is 0.35068336483716306\n",
      "Processing region: reg020_B at scale 48.0\n",
      "32.161 per cent patches are empty\n",
      "reg020_B at scale 48.0 has 884 patches with zero diveristy\n",
      "reg020_B at scale 48.0 diversity is 0.4740709157083006\n",
      "Processing region: reg021_A at scale 48.0\n",
      "45.703 per cent patches are empty\n",
      "reg021_A at scale 48.0 has 658 patches with zero diveristy\n",
      "reg021_A at scale 48.0 diversity is 0.5116122096521224\n",
      "Processing region: reg021_B at scale 48.0\n",
      "87.457 per cent patches are empty\n",
      "reg021_B at scale 48.0 has 263 patches with zero diveristy\n",
      "reg021_B at scale 48.0 diversity is 0.09142406286792434\n",
      "Processing region: reg022_A at scale 48.0\n",
      "48.264 per cent patches are empty\n",
      "reg022_A at scale 48.0 has 726 patches with zero diveristy\n",
      "reg022_A at scale 48.0 diversity is 0.4298833217176485\n",
      "Processing region: reg022_B at scale 48.0\n",
      "78.125 per cent patches are empty\n",
      "reg022_B at scale 48.0 has 393 patches with zero diveristy\n",
      "reg022_B at scale 48.0 diversity is 0.23303745869406933\n",
      "Processing region: reg023_A at scale 48.0\n",
      "69.922 per cent patches are empty\n",
      "reg023_A at scale 48.0 has 596 patches with zero diveristy\n",
      "reg023_A at scale 48.0 diversity is 0.14420213897216796\n",
      "Processing region: reg023_B at scale 48.0\n",
      "36.806 per cent patches are empty\n",
      "reg023_B at scale 48.0 has 888 patches with zero diveristy\n",
      "reg023_B at scale 48.0 diversity is 0.4313548348739456\n",
      "Processing region: reg024_A at scale 48.0\n",
      "64.800 per cent patches are empty\n",
      "reg024_A at scale 48.0 has 689 patches with zero diveristy\n",
      "reg024_A at scale 48.0 diversity is 0.15377098875979622\n",
      "Processing region: reg024_B at scale 48.0\n",
      "35.156 per cent patches are empty\n",
      "reg024_B at scale 48.0 has 1183 patches with zero diveristy\n",
      "reg024_B at scale 48.0 diversity is 0.21328706983319237\n",
      "Processing region: reg025_A at scale 48.0\n",
      "60.026 per cent patches are empty\n",
      "reg025_A at scale 48.0 has 667 patches with zero diveristy\n",
      "reg025_A at scale 48.0 diversity is 0.2942545059866424\n",
      "Processing region: reg025_B at scale 48.0\n",
      "49.392 per cent patches are empty\n",
      "reg025_B at scale 48.0 has 784 patches with zero diveristy\n",
      "reg025_B at scale 48.0 diversity is 0.35619588347374564\n",
      "Processing region: reg026_A at scale 48.0\n",
      "35.460 per cent patches are empty\n",
      "reg026_A at scale 48.0 has 979 patches with zero diveristy\n",
      "reg026_A at scale 48.0 diversity is 0.3710939451421201\n",
      "Processing region: reg026_B at scale 48.0\n",
      "28.082 per cent patches are empty\n",
      "reg026_B at scale 48.0 has 1041 patches with zero diveristy\n",
      "reg026_B at scale 48.0 diversity is 0.3939052612100063\n",
      "Processing region: reg027_A at scale 48.0\n",
      "34.722 per cent patches are empty\n",
      "reg027_A at scale 48.0 has 1055 patches with zero diveristy\n",
      "reg027_A at scale 48.0 diversity is 0.326420557774896\n",
      "Processing region: reg027_B at scale 48.0\n",
      "40.668 per cent patches are empty\n",
      "reg027_B at scale 48.0 has 1042 patches with zero diveristy\n",
      "reg027_B at scale 48.0 diversity is 0.2424301638413002\n",
      "Processing region: reg028_A at scale 48.0\n",
      "34.852 per cent patches are empty\n",
      "reg028_A at scale 48.0 has 947 patches with zero diveristy\n",
      "reg028_A at scale 48.0 diversity is 0.4036134709330133\n",
      "Processing region: reg028_B at scale 48.0\n",
      "35.547 per cent patches are empty\n",
      "reg028_B at scale 48.0 has 1068 patches with zero diveristy\n",
      "reg028_B at scale 48.0 diversity is 0.3012848681728073\n",
      "Processing region: reg029_A at scale 48.0\n",
      "31.510 per cent patches are empty\n",
      "reg029_A at scale 48.0 has 1190 patches with zero diveristy\n",
      "reg029_A at scale 48.0 diversity is 0.26308857272181485\n",
      "Processing region: reg029_B at scale 48.0\n",
      "43.229 per cent patches are empty\n",
      "reg029_B at scale 48.0 has 988 patches with zero diveristy\n",
      "reg029_B at scale 48.0 diversity is 0.2512995086792233\n",
      "Processing region: reg030_A at scale 48.0\n",
      "38.108 per cent patches are empty\n",
      "reg030_A at scale 48.0 has 930 patches with zero diveristy\n",
      "reg030_A at scale 48.0 diversity is 0.37487277167306504\n",
      "Processing region: reg030_B at scale 48.0\n",
      "43.793 per cent patches are empty\n",
      "reg030_B at scale 48.0 has 1023 patches with zero diveristy\n",
      "reg030_B at scale 48.0 diversity is 0.225049707612215\n",
      "Processing region: reg031_A at scale 48.0\n",
      "30.859 per cent patches are empty\n",
      "reg031_A at scale 48.0 has 1069 patches with zero diveristy\n",
      "reg031_A at scale 48.0 diversity is 0.35208143058750774\n",
      "Processing region: reg031_B at scale 48.0\n",
      "38.542 per cent patches are empty\n",
      "reg031_B at scale 48.0 has 999 patches with zero diveristy\n",
      "reg031_B at scale 48.0 diversity is 0.3107588207661207\n",
      "Processing region: reg032_A at scale 48.0\n",
      "29.948 per cent patches are empty\n",
      "reg032_A at scale 48.0 has 1114 patches with zero diveristy\n",
      "reg032_A at scale 48.0 diversity is 0.3306633685092787\n",
      "Processing region: reg032_B at scale 48.0\n",
      "53.559 per cent patches are empty\n",
      "reg032_B at scale 48.0 has 758 patches with zero diveristy\n",
      "reg032_B at scale 48.0 diversity is 0.31830558514750723\n",
      "Processing region: reg033_A at scale 48.0\n",
      "37.196 per cent patches are empty\n",
      "reg033_A at scale 48.0 has 842 patches with zero diveristy\n",
      "reg033_A at scale 48.0 diversity is 0.44616577676212243\n",
      "Processing region: reg033_B at scale 48.0\n",
      "27.214 per cent patches are empty\n",
      "reg033_B at scale 48.0 has 1163 patches with zero diveristy\n",
      "reg033_B at scale 48.0 diversity is 0.3248612165205544\n",
      "Processing region: reg034_A at scale 48.0\n",
      "39.410 per cent patches are empty\n",
      "reg034_A at scale 48.0 has 944 patches with zero diveristy\n",
      "reg034_A at scale 48.0 diversity is 0.3387454353305254\n",
      "Processing region: reg034_B at scale 48.0\n",
      "43.750 per cent patches are empty\n",
      "reg034_B at scale 48.0 has 1012 patches with zero diveristy\n",
      "reg034_B at scale 48.0 diversity is 0.22954651094767375\n",
      "Processing region: reg035_A at scale 48.0\n",
      "34.679 per cent patches are empty\n",
      "reg035_A at scale 48.0 has 969 patches with zero diveristy\n",
      "reg035_A at scale 48.0 diversity is 0.3925547605760045\n",
      "Processing region: reg035_B at scale 48.0\n",
      "39.583 per cent patches are empty\n",
      "reg035_B at scale 48.0 has 983 patches with zero diveristy\n",
      "reg035_B at scale 48.0 diversity is 0.30940401072992607\n",
      "Processing region: reg036_A at scale 48.0\n",
      "24.566 per cent patches are empty\n",
      "reg036_A at scale 48.0 has 950 patches with zero diveristy\n",
      "reg036_A at scale 48.0 diversity is 0.49755072823615676\n",
      "Processing region: reg036_B at scale 48.0\n",
      "25.391 per cent patches are empty\n",
      "reg036_B at scale 48.0 has 1297 patches with zero diveristy\n",
      "reg036_B at scale 48.0 diversity is 0.2619191778961595\n",
      "Processing region: reg037_A at scale 48.0\n",
      "57.899 per cent patches are empty\n",
      "reg037_A at scale 48.0 has 656 patches with zero diveristy\n",
      "reg037_A at scale 48.0 diversity is 0.34959189209475156\n",
      "Processing region: reg037_B at scale 48.0\n",
      "84.245 per cent patches are empty\n",
      "reg037_B at scale 48.0 has 334 patches with zero diveristy\n",
      "reg037_B at scale 48.0 diversity is 0.08288766070384794\n",
      "Processing region: reg038_A at scale 48.0\n",
      "30.990 per cent patches are empty\n",
      "reg038_A at scale 48.0 has 1008 patches with zero diveristy\n",
      "reg038_A at scale 48.0 diversity is 0.40503768166413806\n",
      "Processing region: reg038_B at scale 48.0\n",
      "51.476 per cent patches are empty\n",
      "reg038_B at scale 48.0 has 811 patches with zero diveristy\n",
      "reg038_B at scale 48.0 diversity is 0.29159515593096247\n",
      "Processing region: reg039_A at scale 48.0\n",
      "42.752 per cent patches are empty\n",
      "reg039_A at scale 48.0 has 757 patches with zero diveristy\n",
      "reg039_A at scale 48.0 diversity is 0.47266266718070277\n",
      "Processing region: reg039_B at scale 48.0\n",
      "73.220 per cent patches are empty\n",
      "reg039_B at scale 48.0 has 547 patches with zero diveristy\n",
      "reg039_B at scale 48.0 diversity is 0.11657181929271601\n",
      "Processing region: reg040_A at scale 48.0\n",
      "39.019 per cent patches are empty\n",
      "reg040_A at scale 48.0 has 1003 patches with zero diveristy\n",
      "reg040_A at scale 48.0 diversity is 0.2997483971180906\n",
      "Processing region: reg040_B at scale 48.0\n",
      "72.526 per cent patches are empty\n",
      "reg040_B at scale 48.0 has 514 patches with zero diveristy\n",
      "reg040_B at scale 48.0 diversity is 0.19579042755527334\n",
      "Processing region: reg041_A at scale 48.0\n",
      "42.144 per cent patches are empty\n",
      "reg041_A at scale 48.0 has 994 patches with zero diveristy\n",
      "reg041_A at scale 48.0 diversity is 0.27672531066338585\n",
      "Processing region: reg041_B at scale 48.0\n",
      "45.052 per cent patches are empty\n",
      "reg041_B at scale 48.0 has 1056 patches with zero diveristy\n",
      "reg041_B at scale 48.0 diversity is 0.1744673693143399\n",
      "Processing region: reg042_A at scale 48.0\n",
      "40.191 per cent patches are empty\n",
      "reg042_A at scale 48.0 has 1177 patches with zero diveristy\n",
      "reg042_A at scale 48.0 diversity is 0.1457195357276264\n",
      "Processing region: reg042_B at scale 48.0\n",
      "45.573 per cent patches are empty\n",
      "reg042_B at scale 48.0 has 1081 patches with zero diveristy\n",
      "reg042_B at scale 48.0 diversity is 0.13825019001142716\n",
      "Processing region: reg043_A at scale 48.0\n",
      "48.090 per cent patches are empty\n",
      "reg043_A at scale 48.0 has 966 patches with zero diveristy\n",
      "reg043_A at scale 48.0 diversity is 0.20284113449443125\n",
      "Processing region: reg043_B at scale 48.0\n",
      "80.295 per cent patches are empty\n",
      "reg043_B at scale 48.0 has 416 patches with zero diveristy\n",
      "reg043_B at scale 48.0 diversity is 0.0839091134472425\n",
      "Processing region: reg044_A at scale 48.0\n",
      "65.278 per cent patches are empty\n",
      "reg044_A at scale 48.0 has 669 patches with zero diveristy\n",
      "reg044_A at scale 48.0 diversity is 0.1727255664254057\n",
      "Processing region: reg044_B at scale 48.0\n",
      "84.288 per cent patches are empty\n",
      "reg044_B at scale 48.0 has 338 patches with zero diveristy\n",
      "reg044_B at scale 48.0 diversity is 0.06539553407795017\n",
      "Processing region: reg045_A at scale 48.0\n",
      "30.078 per cent patches are empty\n",
      "reg045_A at scale 48.0 has 1181 patches with zero diveristy\n",
      "reg045_A at scale 48.0 diversity is 0.2808162593197106\n",
      "Processing region: reg045_B at scale 48.0\n",
      "65.929 per cent patches are empty\n",
      "reg045_B at scale 48.0 has 665 patches with zero diveristy\n",
      "reg045_B at scale 48.0 diversity is 0.15768274417881356\n",
      "Processing region: reg046_A at scale 48.0\n",
      "37.977 per cent patches are empty\n",
      "reg046_A at scale 48.0 has 982 patches with zero diveristy\n",
      "reg046_A at scale 48.0 diversity is 0.33660237537772386\n",
      "Processing region: reg046_B at scale 48.0\n",
      "33.724 per cent patches are empty\n",
      "reg046_B at scale 48.0 has 1062 patches with zero diveristy\n",
      "reg046_B at scale 48.0 diversity is 0.32843504080399716\n",
      "Processing region: reg047_A at scale 48.0\n",
      "50.651 per cent patches are empty\n",
      "reg047_A at scale 48.0 has 723 patches with zero diveristy\n",
      "reg047_A at scale 48.0 diversity is 0.4032584836256531\n",
      "Processing region: reg047_B at scale 48.0\n",
      "66.493 per cent patches are empty\n",
      "reg047_B at scale 48.0 has 701 patches with zero diveristy\n",
      "reg047_B at scale 48.0 diversity is 0.09348857945105954\n",
      "Processing region: reg048_A at scale 48.0\n",
      "47.005 per cent patches are empty\n",
      "reg048_A at scale 48.0 has 902 patches with zero diveristy\n",
      "reg048_A at scale 48.0 diversity is 0.2745744429485376\n",
      "Processing region: reg048_B at scale 48.0\n",
      "53.082 per cent patches are empty\n",
      "reg048_B at scale 48.0 has 816 patches with zero diveristy\n",
      "reg048_B at scale 48.0 diversity is 0.25933764070464244\n",
      "Processing region: reg049_A at scale 48.0\n",
      "34.852 per cent patches are empty\n",
      "reg049_A at scale 48.0 has 1076 patches with zero diveristy\n",
      "reg049_A at scale 48.0 diversity is 0.2968166938235441\n",
      "Processing region: reg049_B at scale 48.0\n",
      "60.764 per cent patches are empty\n",
      "reg049_B at scale 48.0 has 731 patches with zero diveristy\n",
      "reg049_B at scale 48.0 diversity is 0.1992438237327463\n",
      "Processing region: reg050_A at scale 48.0\n",
      "65.061 per cent patches are empty\n",
      "reg050_A at scale 48.0 has 652 patches with zero diveristy\n",
      "reg050_A at scale 48.0 diversity is 0.19609147388390377\n",
      "Processing region: reg050_B at scale 48.0\n",
      "48.828 per cent patches are empty\n",
      "reg050_B at scale 48.0 has 974 patches with zero diveristy\n",
      "reg050_B at scale 48.0 diversity is 0.18346470702664183\n",
      "Processing region: reg051_A at scale 48.0\n",
      "52.734 per cent patches are empty\n",
      "reg051_A at scale 48.0 has 835 patches with zero diveristy\n",
      "reg051_A at scale 48.0 diversity is 0.25187233895227784\n",
      "Processing region: reg051_B at scale 48.0\n",
      "46.007 per cent patches are empty\n",
      "reg051_B at scale 48.0 has 993 patches with zero diveristy\n",
      "reg051_B at scale 48.0 diversity is 0.21070463860955965\n",
      "Processing region: reg052_A at scale 48.0\n",
      "43.186 per cent patches are empty\n",
      "reg052_A at scale 48.0 has 1002 patches with zero diveristy\n",
      "reg052_A at scale 48.0 diversity is 0.24405638211261452\n",
      "Processing region: reg052_B at scale 48.0\n",
      "42.405 per cent patches are empty\n",
      "reg052_B at scale 48.0 has 1068 patches with zero diveristy\n",
      "reg052_B at scale 48.0 diversity is 0.20857815643107352\n",
      "Processing region: reg053_A at scale 48.0\n",
      "44.401 per cent patches are empty\n",
      "reg053_A at scale 48.0 has 833 patches with zero diveristy\n",
      "reg053_A at scale 48.0 diversity is 0.37709310758921294\n",
      "Processing region: reg053_B at scale 48.0\n",
      "94.184 per cent patches are empty\n",
      "reg053_B at scale 48.0 has 130 patches with zero diveristy\n",
      "reg053_B at scale 48.0 diversity is 0.029850746268656716\n",
      "Processing region: reg054_A at scale 48.0\n",
      "54.036 per cent patches are empty\n",
      "reg054_A at scale 48.0 has 743 patches with zero diveristy\n",
      "reg054_A at scale 48.0 diversity is 0.31266705915316795\n",
      "Processing region: reg054_B at scale 48.0\n",
      "88.064 per cent patches are empty\n",
      "reg054_B at scale 48.0 has 253 patches with zero diveristy\n",
      "reg054_B at scale 48.0 diversity is 0.08212713636625875\n",
      "Processing region: reg055_A at scale 48.0\n",
      "24.045 per cent patches are empty\n",
      "reg055_A at scale 48.0 has 802 patches with zero diveristy\n",
      "reg055_A at scale 48.0 diversity is 0.6168669937170991\n",
      "Processing region: reg055_B at scale 48.0\n",
      "94.661 per cent patches are empty\n",
      "reg055_B at scale 48.0 has 114 patches with zero diveristy\n",
      "reg055_B at scale 48.0 diversity is 0.07317073170731707\n",
      "Processing region: reg056_A at scale 48.0\n",
      "73.220 per cent patches are empty\n",
      "reg056_A at scale 48.0 has 477 patches with zero diveristy\n",
      "reg056_A at scale 48.0 diversity is 0.24628427204434722\n",
      "Processing region: reg056_B at scale 48.0\n",
      "54.688 per cent patches are empty\n",
      "reg056_B at scale 48.0 has 851 patches with zero diveristy\n",
      "reg056_B at scale 48.0 diversity is 0.1922531449746415\n",
      "Processing region: reg057_A at scale 48.0\n",
      "97.092 per cent patches are empty\n",
      "reg057_A at scale 48.0 has 57 patches with zero diveristy\n",
      "reg057_A at scale 48.0 diversity is 0.15798451493613666\n",
      "Processing region: reg057_B at scale 48.0\n",
      "67.665 per cent patches are empty\n",
      "reg057_B at scale 48.0 has 602 patches with zero diveristy\n",
      "reg057_B at scale 48.0 diversity is 0.19797547820776337\n",
      "Processing region: reg058_A at scale 48.0\n",
      "43.880 per cent patches are empty\n",
      "reg058_A at scale 48.0 has 1135 patches with zero diveristy\n",
      "reg058_A at scale 48.0 diversity is 0.12090166679046077\n",
      "Processing region: reg058_B at scale 48.0\n",
      "55.990 per cent patches are empty\n",
      "reg058_B at scale 48.0 has 969 patches with zero diveristy\n",
      "reg058_B at scale 48.0 diversity is 0.043674173579555435\n",
      "Processing region: reg059_A at scale 48.0\n",
      "29.167 per cent patches are empty\n",
      "reg059_A at scale 48.0 has 906 patches with zero diveristy\n",
      "reg059_A at scale 48.0 diversity is 0.4893117833750441\n",
      "Processing region: reg059_B at scale 48.0\n",
      "43.880 per cent patches are empty\n",
      "reg059_B at scale 48.0 has 989 patches with zero diveristy\n",
      "reg059_B at scale 48.0 diversity is 0.24796797065723075\n",
      "Processing region: reg060_A at scale 48.0\n",
      "32.856 per cent patches are empty\n",
      "reg060_A at scale 48.0 has 1010 patches with zero diveristy\n",
      "reg060_A at scale 48.0 diversity is 0.37515962621689225\n",
      "Processing region: reg060_B at scale 48.0\n",
      "33.681 per cent patches are empty\n",
      "reg060_B at scale 48.0 has 1058 patches with zero diveristy\n",
      "reg060_B at scale 48.0 diversity is 0.32738874893992415\n",
      "Processing region: reg061_A at scale 48.0\n",
      "45.790 per cent patches are empty\n",
      "reg061_A at scale 48.0 has 913 patches with zero diveristy\n",
      "reg061_A at scale 48.0 diversity is 0.28509478429976576\n",
      "Processing region: reg061_B at scale 48.0\n",
      "46.267 per cent patches are empty\n",
      "reg061_B at scale 48.0 has 889 patches with zero diveristy\n",
      "reg061_B at scale 48.0 diversity is 0.29407647250777347\n",
      "Processing region: reg062_A at scale 48.0\n",
      "31.858 per cent patches are empty\n",
      "reg062_A at scale 48.0 has 1082 patches with zero diveristy\n",
      "reg062_A at scale 48.0 diversity is 0.3300964415170156\n",
      "Processing region: reg062_B at scale 48.0\n",
      "54.644 per cent patches are empty\n",
      "reg062_B at scale 48.0 has 827 patches with zero diveristy\n",
      "reg062_B at scale 48.0 diversity is 0.2156650030147567\n",
      "Processing region: reg063_A at scale 48.0\n",
      "69.575 per cent patches are empty\n",
      "reg063_A at scale 48.0 has 589 patches with zero diveristy\n",
      "reg063_A at scale 48.0 diversity is 0.16719330720285133\n",
      "Processing region: reg063_B at scale 48.0\n",
      "66.797 per cent patches are empty\n",
      "reg063_B at scale 48.0 has 670 patches with zero diveristy\n",
      "reg063_B at scale 48.0 diversity is 0.1285488235331825\n",
      "Processing region: reg064_A at scale 48.0\n",
      "41.536 per cent patches are empty\n",
      "reg064_A at scale 48.0 has 980 patches with zero diveristy\n",
      "reg064_A at scale 48.0 diversity is 0.28470231035479693\n",
      "Processing region: reg064_B at scale 48.0\n",
      "41.840 per cent patches are empty\n",
      "reg064_B at scale 48.0 has 971 patches with zero diveristy\n",
      "reg064_B at scale 48.0 diversity is 0.2949435207175512\n",
      "Processing region: reg065_A at scale 48.0\n",
      "43.707 per cent patches are empty\n",
      "reg065_A at scale 48.0 has 1000 patches with zero diveristy\n",
      "reg065_A at scale 48.0 diversity is 0.24595395831311195\n",
      "Processing region: reg065_B at scale 48.0\n",
      "83.333 per cent patches are empty\n",
      "reg065_B at scale 48.0 has 358 patches with zero diveristy\n",
      "reg065_B at scale 48.0 diversity is 0.07206558160473427\n",
      "Processing region: reg066_A at scale 48.0\n",
      "46.007 per cent patches are empty\n",
      "reg066_A at scale 48.0 has 932 patches with zero diveristy\n",
      "reg066_A at scale 48.0 diversity is 0.2640486895502383\n",
      "Processing region: reg066_B at scale 48.0\n",
      "64.236 per cent patches are empty\n",
      "reg066_B at scale 48.0 has 716 patches with zero diveristy\n",
      "reg066_B at scale 48.0 diversity is 0.13454996967944835\n",
      "Processing region: reg067_A at scale 48.0\n",
      "79.036 per cent patches are empty\n",
      "reg067_A at scale 48.0 has 364 patches with zero diveristy\n",
      "reg067_A at scale 48.0 diversity is 0.2627095756201228\n",
      "Processing region: reg067_B at scale 48.0\n",
      "96.701 per cent patches are empty\n",
      "reg067_B at scale 48.0 has 72 patches with zero diveristy\n",
      "reg067_B at scale 48.0 diversity is 0.05263157894736842\n",
      "Processing region: reg068_A at scale 48.0\n",
      "58.420 per cent patches are empty\n",
      "reg068_A at scale 48.0 has 804 patches with zero diveristy\n",
      "reg068_A at scale 48.0 diversity is 0.16571570003832858\n",
      "Processing region: reg068_B at scale 48.0\n",
      "51.432 per cent patches are empty\n",
      "reg068_B at scale 48.0 has 877 patches with zero diveristy\n",
      "reg068_B at scale 48.0 diversity is 0.2321953269590953\n",
      "Processing region: reg069_A at scale 48.0\n",
      "54.861 per cent patches are empty\n",
      "reg069_A at scale 48.0 has 780 patches with zero diveristy\n",
      "reg069_A at scale 48.0 diversity is 0.26581176783389543\n",
      "Processing region: reg069_B at scale 48.0\n",
      "70.486 per cent patches are empty\n",
      "reg069_B at scale 48.0 has 573 patches with zero diveristy\n",
      "reg069_B at scale 48.0 diversity is 0.16228844364229833\n",
      "Processing region: reg070_A at scale 48.0\n",
      "47.092 per cent patches are empty\n",
      "reg070_A at scale 48.0 has 944 patches with zero diveristy\n",
      "reg070_A at scale 48.0 diversity is 0.23629521297883022\n",
      "Processing region: reg070_B at scale 48.0\n",
      "65.582 per cent patches are empty\n",
      "reg070_B at scale 48.0 has 696 patches with zero diveristy\n",
      "reg070_B at scale 48.0 diversity is 0.12401669291092174\n",
      "Processing region: reg001_A at scale 64.0\n",
      "77.344 per cent patches are empty\n",
      "reg001_A at scale 64.0 has 806 patches with zero diveristy\n",
      "reg001_A at scale 64.0 diversity is 0.13463129939947022\n",
      "Processing region: reg001_B at scale 64.0\n",
      "92.212 per cent patches are empty\n",
      "reg001_B at scale 64.0 has 302 patches with zero diveristy\n",
      "reg001_B at scale 64.0 diversity is 0.05303541013810185\n",
      "Processing region: reg002_A at scale 64.0\n",
      "72.192 per cent patches are empty\n",
      "reg002_A at scale 64.0 has 983 patches with zero diveristy\n",
      "reg002_A at scale 64.0 diversity is 0.14027034314421954\n",
      "Processing region: reg002_B at scale 64.0\n",
      "51.855 per cent patches are empty\n",
      "reg002_B at scale 64.0 has 1851 patches with zero diveristy\n",
      "reg002_B at scale 64.0 diversity is 0.060967073406842445\n",
      "Processing region: reg003_A at scale 64.0\n",
      "74.072 per cent patches are empty\n",
      "reg003_A at scale 64.0 has 948 patches with zero diveristy\n",
      "reg003_A at scale 64.0 diversity is 0.1090093416584426\n",
      "Processing region: reg003_B at scale 64.0\n",
      "74.731 per cent patches are empty\n",
      "reg003_B at scale 64.0 has 925 patches with zero diveristy\n",
      "reg003_B at scale 64.0 diversity is 0.1081343468295747\n",
      "Processing region: reg004_A at scale 64.0\n",
      "69.312 per cent patches are empty\n",
      "reg004_A at scale 64.0 has 1097 patches with zero diveristy\n",
      "reg004_A at scale 64.0 diversity is 0.13041474079010107\n",
      "Processing region: reg004_B at scale 64.0\n",
      "60.669 per cent patches are empty\n",
      "reg004_B at scale 64.0 has 1437 patches with zero diveristy\n",
      "reg004_B at scale 64.0 diversity is 0.11014345386560151\n",
      "Processing region: reg005_A at scale 64.0\n",
      "53.784 per cent patches are empty\n",
      "reg005_A at scale 64.0 has 1594 patches with zero diveristy\n",
      "reg005_A at scale 64.0 diversity is 0.16085638151589593\n",
      "Processing region: reg005_B at scale 64.0\n",
      "67.432 per cent patches are empty\n",
      "reg005_B at scale 64.0 has 1121 patches with zero diveristy\n",
      "reg005_B at scale 64.0 diversity is 0.16200471327863716\n",
      "Processing region: reg006_A at scale 64.0\n",
      "71.753 per cent patches are empty\n",
      "reg006_A at scale 64.0 has 1015 patches with zero diveristy\n",
      "reg006_A at scale 64.0 diversity is 0.12512915947348086\n",
      "Processing region: reg006_B at scale 64.0\n",
      "58.374 per cent patches are empty\n",
      "reg006_B at scale 64.0 has 1477 patches with zero diveristy\n",
      "reg006_B at scale 64.0 diversity is 0.13854666179346195\n",
      "Processing region: reg007_A at scale 64.0\n",
      "46.777 per cent patches are empty\n",
      "reg007_A at scale 64.0 has 1828 patches with zero diveristy\n",
      "reg007_A at scale 64.0 diversity is 0.16542307818915775\n",
      "Processing region: reg007_B at scale 64.0\n",
      "55.444 per cent patches are empty\n",
      "reg007_B at scale 64.0 has 1540 patches with zero diveristy\n",
      "reg007_B at scale 64.0 diversity is 0.15956700172783855\n",
      "Processing region: reg008_A at scale 64.0\n",
      "73.511 per cent patches are empty\n",
      "reg008_A at scale 64.0 has 975 patches with zero diveristy\n",
      "reg008_A at scale 64.0 diversity is 0.10246145450322362\n",
      "Processing region: reg008_B at scale 64.0\n",
      "39.160 per cent patches are empty\n",
      "reg008_B at scale 64.0 has 2163 patches with zero diveristy\n",
      "reg008_B at scale 64.0 diversity is 0.1351152780710614\n",
      "Processing region: reg009_A at scale 64.0\n",
      "52.344 per cent patches are empty\n",
      "reg009_A at scale 64.0 has 1653 patches with zero diveristy\n",
      "reg009_A at scale 64.0 diversity is 0.1553647194214124\n",
      "Processing region: reg009_B at scale 64.0\n",
      "36.841 per cent patches are empty\n",
      "reg009_B at scale 64.0 has 2003 patches with zero diveristy\n",
      "reg009_B at scale 64.0 diversity is 0.23210684514479457\n",
      "Processing region: reg010_A at scale 64.0\n",
      "58.960 per cent patches are empty\n",
      "reg010_A at scale 64.0 has 1430 patches with zero diveristy\n",
      "reg010_A at scale 64.0 diversity is 0.15459395202298695\n",
      "Processing region: reg010_B at scale 64.0\n",
      "74.194 per cent patches are empty\n",
      "reg010_B at scale 64.0 has 956 patches with zero diveristy\n",
      "reg010_B at scale 64.0 diversity is 0.09620883003299133\n",
      "Processing region: reg011_A at scale 64.0\n",
      "53.027 per cent patches are empty\n",
      "reg011_A at scale 64.0 has 1386 patches with zero diveristy\n",
      "reg011_A at scale 64.0 diversity is 0.2929889082210829\n",
      "Processing region: reg011_B at scale 64.0\n",
      "80.444 per cent patches are empty\n",
      "reg011_B at scale 64.0 has 728 patches with zero diveristy\n",
      "reg011_B at scale 64.0 diversity is 0.09312294528123902\n",
      "Processing region: reg012_A at scale 64.0\n",
      "63.721 per cent patches are empty\n",
      "reg012_A at scale 64.0 has 1303 patches with zero diveristy\n",
      "reg012_A at scale 64.0 diversity is 0.12546507683545358\n",
      "Processing region: reg012_B at scale 64.0\n",
      "89.844 per cent patches are empty\n",
      "reg012_B at scale 64.0 has 385 patches with zero diveristy\n",
      "reg012_B at scale 64.0 diversity is 0.07572898638167222\n",
      "Processing region: reg013_A at scale 64.0\n",
      "65.552 per cent patches are empty\n",
      "reg013_A at scale 64.0 has 1268 patches with zero diveristy\n",
      "reg013_A at scale 64.0 diversity is 0.10271532896751032\n",
      "Processing region: reg013_B at scale 64.0\n",
      "60.181 per cent patches are empty\n",
      "reg013_B at scale 64.0 has 1463 patches with zero diveristy\n",
      "reg013_B at scale 64.0 diversity is 0.10403814633679984\n",
      "Processing region: reg014_A at scale 64.0\n",
      "80.078 per cent patches are empty\n",
      "reg014_A at scale 64.0 has 726 patches with zero diveristy\n",
      "reg014_A at scale 64.0 diversity is 0.11089580653214261\n",
      "Processing region: reg014_B at scale 64.0\n",
      "57.471 per cent patches are empty\n",
      "reg014_B at scale 64.0 has 1510 patches with zero diveristy\n",
      "reg014_B at scale 64.0 diversity is 0.1371253922793577\n",
      "Processing region: reg015_A at scale 64.0\n",
      "53.540 per cent patches are empty\n",
      "reg015_A at scale 64.0 has 1631 patches with zero diveristy\n",
      "reg015_A at scale 64.0 diversity is 0.14706946215722352\n",
      "Processing region: reg015_B at scale 64.0\n",
      "67.773 per cent patches are empty\n",
      "reg015_B at scale 64.0 has 1218 patches with zero diveristy\n",
      "reg015_B at scale 64.0 diversity is 0.07784954861493543\n",
      "Processing region: reg016_A at scale 64.0\n",
      "62.085 per cent patches are empty\n",
      "reg016_A at scale 64.0 has 1337 patches with zero diveristy\n",
      "reg016_A at scale 64.0 diversity is 0.1423345889805918\n",
      "Processing region: reg016_B at scale 64.0\n",
      "54.199 per cent patches are empty\n",
      "reg016_B at scale 64.0 has 1639 patches with zero diveristy\n",
      "reg016_B at scale 64.0 diversity is 0.12713780817637646\n",
      "Processing region: reg017_A at scale 64.0\n",
      "49.927 per cent patches are empty\n",
      "reg017_A at scale 64.0 has 1703 patches with zero diveristy\n",
      "reg017_A at scale 64.0 diversity is 0.17500699761668406\n",
      "Processing region: reg017_B at scale 64.0\n",
      "49.414 per cent patches are empty\n",
      "reg017_B at scale 64.0 has 1668 patches with zero diveristy\n",
      "reg017_B at scale 64.0 diversity is 0.20140530186412167\n",
      "Processing region: reg018_A at scale 64.0\n",
      "60.693 per cent patches are empty\n",
      "reg018_A at scale 64.0 has 1372 patches with zero diveristy\n",
      "reg018_A at scale 64.0 diversity is 0.15162782868525046\n",
      "Processing region: reg018_B at scale 64.0\n",
      "50.952 per cent patches are empty\n",
      "reg018_B at scale 64.0 has 1758 patches with zero diveristy\n",
      "reg018_B at scale 64.0 diversity is 0.127083264904604\n",
      "Processing region: reg019_A at scale 64.0\n",
      "71.533 per cent patches are empty\n",
      "reg019_A at scale 64.0 has 925 patches with zero diveristy\n",
      "reg019_A at scale 64.0 diversity is 0.21180997357672798\n",
      "Processing region: reg019_B at scale 64.0\n",
      "97.583 per cent patches are empty\n",
      "reg019_B at scale 64.0 has 97 patches with zero diveristy\n",
      "reg019_B at scale 64.0 diversity is 0.020202020202020204\n",
      "Processing region: reg020_A at scale 64.0\n",
      "57.739 per cent patches are empty\n",
      "reg020_A at scale 64.0 has 1463 patches with zero diveristy\n",
      "reg020_A at scale 64.0 diversity is 0.15782929425141465\n",
      "Processing region: reg020_B at scale 64.0\n",
      "48.608 per cent patches are empty\n",
      "reg020_B at scale 64.0 has 1702 patches with zero diveristy\n",
      "reg020_B at scale 64.0 diversity is 0.19626929732135792\n",
      "Processing region: reg021_A at scale 64.0\n",
      "55.249 per cent patches are empty\n",
      "reg021_A at scale 64.0 has 1413 patches with zero diveristy\n",
      "reg021_A at scale 64.0 diversity is 0.23674161213883302\n",
      "Processing region: reg021_B at scale 64.0\n",
      "92.505 per cent patches are empty\n",
      "reg021_B at scale 64.0 has 291 patches with zero diveristy\n",
      "reg021_B at scale 64.0 diversity is 0.05211726384364821\n",
      "Processing region: reg022_A at scale 64.0\n",
      "60.986 per cent patches are empty\n",
      "reg022_A at scale 64.0 has 1276 patches with zero diveristy\n",
      "reg022_A at scale 64.0 diversity is 0.2081394067040307\n",
      "Processing region: reg022_B at scale 64.0\n",
      "85.498 per cent patches are empty\n",
      "reg022_B at scale 64.0 has 536 patches with zero diveristy\n",
      "reg022_B at scale 64.0 diversity is 0.09963461000091733\n",
      "Processing region: reg023_A at scale 64.0\n",
      "79.932 per cent patches are empty\n",
      "reg023_A at scale 64.0 has 766 patches with zero diveristy\n",
      "reg023_A at scale 64.0 diversity is 0.06824177312049647\n",
      "Processing region: reg023_B at scale 64.0\n",
      "54.761 per cent patches are empty\n",
      "reg023_B at scale 64.0 has 1508 patches with zero diveristy\n",
      "reg023_B at scale 64.0 diversity is 0.19576613151948682\n",
      "Processing region: reg024_A at scale 64.0\n",
      "77.148 per cent patches are empty\n",
      "reg024_A at scale 64.0 has 868 patches with zero diveristy\n",
      "reg024_A at scale 64.0 diversity is 0.07328497596693173\n",
      "Processing region: reg024_B at scale 64.0\n",
      "54.810 per cent patches are empty\n",
      "reg024_B at scale 64.0 has 1667 patches with zero diveristy\n",
      "reg024_B at scale 64.0 diversity is 0.10039834943417844\n",
      "Processing region: reg025_A at scale 64.0\n",
      "72.998 per cent patches are empty\n",
      "reg025_A at scale 64.0 has 950 patches with zero diveristy\n",
      "reg025_A at scale 64.0 diversity is 0.14242568566313699\n",
      "Processing region: reg025_B at scale 64.0\n",
      "63.379 per cent patches are empty\n",
      "reg025_B at scale 64.0 has 1255 patches with zero diveristy\n",
      "reg025_B at scale 64.0 diversity is 0.16644428058007488\n",
      "Processing region: reg026_A at scale 64.0\n",
      "53.687 per cent patches are empty\n",
      "reg026_A at scale 64.0 has 1602 patches with zero diveristy\n",
      "reg026_A at scale 64.0 diversity is 0.16067845063709743\n",
      "Processing region: reg026_B at scale 64.0\n",
      "46.875 per cent patches are empty\n",
      "reg026_B at scale 64.0 has 1814 patches with zero diveristy\n",
      "reg026_B at scale 64.0 diversity is 0.17096257496375802\n",
      "Processing region: reg027_A at scale 64.0\n",
      "52.808 per cent patches are empty\n",
      "reg027_A at scale 64.0 has 1665 patches with zero diveristy\n",
      "reg027_A at scale 64.0 diversity is 0.1408545950908417\n",
      "Processing region: reg027_B at scale 64.0\n",
      "58.936 per cent patches are empty\n",
      "reg027_B at scale 64.0 has 1503 patches with zero diveristy\n",
      "reg027_B at scale 64.0 diversity is 0.10633152497456097\n",
      "Processing region: reg028_A at scale 64.0\n",
      "53.906 per cent patches are empty\n",
      "reg028_A at scale 64.0 has 1563 patches with zero diveristy\n",
      "reg028_A at scale 64.0 diversity is 0.17613127207761237\n",
      "Processing region: reg028_B at scale 64.0\n",
      "55.298 per cent patches are empty\n",
      "reg028_B at scale 64.0 has 1602 patches with zero diveristy\n",
      "reg028_B at scale 64.0 diversity is 0.13022986301573508\n",
      "Processing region: reg029_A at scale 64.0\n",
      "49.976 per cent patches are empty\n",
      "reg029_A at scale 64.0 has 1823 patches with zero diveristy\n",
      "reg029_A at scale 64.0 diversity is 0.1140141730835809\n",
      "Processing region: reg029_B at scale 64.0\n",
      "60.059 per cent patches are empty\n",
      "reg029_B at scale 64.0 has 1464 patches with zero diveristy\n",
      "reg029_B at scale 64.0 diversity is 0.10550045780830017\n",
      "Processing region: reg030_A at scale 64.0\n",
      "55.127 per cent patches are empty\n",
      "reg030_A at scale 64.0 has 1559 patches with zero diveristy\n",
      "reg030_A at scale 64.0 diversity is 0.155863409064209\n",
      "Processing region: reg030_B at scale 64.0\n",
      "59.619 per cent patches are empty\n",
      "reg030_B at scale 64.0 has 1481 patches with zero diveristy\n",
      "reg030_B at scale 64.0 diversity is 0.1076968435233254\n",
      "Processing region: reg031_A at scale 64.0\n",
      "50.415 per cent patches are empty\n",
      "reg031_A at scale 64.0 has 1720 patches with zero diveristy\n",
      "reg031_A at scale 64.0 diversity is 0.1579491583722435\n",
      "Processing region: reg031_B at scale 64.0\n",
      "57.031 per cent patches are empty\n",
      "reg031_B at scale 64.0 has 1523 patches with zero diveristy\n",
      "reg031_B at scale 64.0 diversity is 0.1369986055957259\n",
      "Processing region: reg032_A at scale 64.0\n",
      "48.535 per cent patches are empty\n",
      "reg032_A at scale 64.0 has 1829 patches with zero diveristy\n",
      "reg032_A at scale 64.0 diversity is 0.1350867696588356\n",
      "Processing region: reg032_B at scale 64.0\n",
      "67.847 per cent patches are empty\n",
      "reg032_B at scale 64.0 has 1122 patches with zero diveristy\n",
      "reg032_B at scale 64.0 diversity is 0.15213812959839773\n",
      "Processing region: reg033_A at scale 64.0\n",
      "50.293 per cent patches are empty\n",
      "reg033_A at scale 64.0 has 1630 patches with zero diveristy\n",
      "reg033_A at scale 64.0 diversity is 0.20126076714644223\n",
      "Processing region: reg033_B at scale 64.0\n",
      "42.188 per cent patches are empty\n",
      "reg033_B at scale 64.0 has 2010 patches with zero diveristy\n",
      "reg033_B at scale 64.0 diversity is 0.15594868894921218\n",
      "Processing region: reg034_A at scale 64.0\n",
      "56.934 per cent patches are empty\n",
      "reg034_A at scale 64.0 has 1492 patches with zero diveristy\n",
      "reg034_A at scale 64.0 diversity is 0.15771671391923223\n",
      "Processing region: reg034_B at scale 64.0\n",
      "59.595 per cent patches are empty\n",
      "reg034_B at scale 64.0 has 1477 patches with zero diveristy\n",
      "reg034_B at scale 64.0 diversity is 0.10933589286690946\n",
      "Processing region: reg035_A at scale 64.0\n",
      "51.147 per cent patches are empty\n",
      "reg035_A at scale 64.0 has 1636 patches with zero diveristy\n",
      "reg035_A at scale 64.0 diversity is 0.19101464582318223\n",
      "Processing region: reg035_B at scale 64.0\n",
      "55.640 per cent patches are empty\n",
      "reg035_B at scale 64.0 has 1578 patches with zero diveristy\n",
      "reg035_B at scale 64.0 diversity is 0.1336235484421966\n",
      "Processing region: reg036_A at scale 64.0\n",
      "41.089 per cent patches are empty\n",
      "reg036_A at scale 64.0 has 1888 patches with zero diveristy\n",
      "reg036_A at scale 64.0 diversity is 0.22431379250469224\n",
      "Processing region: reg036_B at scale 64.0\n",
      "42.798 per cent patches are empty\n",
      "reg036_B at scale 64.0 has 2067 patches with zero diveristy\n",
      "reg036_B at scale 64.0 diversity is 0.11934788111681215\n",
      "Processing region: reg037_A at scale 64.0\n",
      "69.873 per cent patches are empty\n",
      "reg037_A at scale 64.0 has 1023 patches with zero diveristy\n",
      "reg037_A at scale 64.0 diversity is 0.1751436959909275\n",
      "Processing region: reg037_B at scale 64.0\n",
      "90.845 per cent patches are empty\n",
      "reg037_B at scale 64.0 has 358 patches with zero diveristy\n",
      "reg037_B at scale 64.0 diversity is 0.04533333333333334\n",
      "Processing region: reg038_A at scale 64.0\n",
      "50.342 per cent patches are empty\n",
      "reg038_A at scale 64.0 has 1645 patches with zero diveristy\n",
      "reg038_A at scale 64.0 diversity is 0.20029498834707812\n",
      "Processing region: reg038_B at scale 64.0\n",
      "66.504 per cent patches are empty\n",
      "reg038_B at scale 64.0 has 1193 patches with zero diveristy\n",
      "reg038_B at scale 64.0 diversity is 0.13302516628198416\n",
      "Processing region: reg039_A at scale 64.0\n",
      "56.323 per cent patches are empty\n",
      "reg039_A at scale 64.0 has 1400 patches with zero diveristy\n",
      "reg039_A at scale 64.0 diversity is 0.22586854446907992\n",
      "Processing region: reg039_B at scale 64.0\n",
      "83.423 per cent patches are empty\n",
      "reg039_B at scale 64.0 has 627 patches with zero diveristy\n",
      "reg039_B at scale 64.0 diversity is 0.07720405621329916\n",
      "Processing region: reg040_A at scale 64.0\n",
      "56.885 per cent patches are empty\n",
      "reg040_A at scale 64.0 has 1543 patches with zero diveristy\n",
      "reg040_A at scale 64.0 diversity is 0.1275142854726226\n",
      "Processing region: reg040_B at scale 64.0\n",
      "81.836 per cent patches are empty\n",
      "reg040_B at scale 64.0 has 661 patches with zero diveristy\n",
      "reg040_B at scale 64.0 diversity is 0.11269234991621004\n",
      "Processing region: reg041_A at scale 64.0\n",
      "58.887 per cent patches are empty\n",
      "reg041_A at scale 64.0 has 1474 patches with zero diveristy\n",
      "reg041_A at scale 64.0 diversity is 0.12832467278763604\n",
      "Processing region: reg041_B at scale 64.0\n",
      "59.839 per cent patches are empty\n",
      "reg041_B at scale 64.0 has 1517 patches with zero diveristy\n",
      "reg041_B at scale 64.0 diversity is 0.08016561297911312\n",
      "Processing region: reg042_A at scale 64.0\n",
      "55.054 per cent patches are empty\n",
      "reg042_A at scale 64.0 has 1697 patches with zero diveristy\n",
      "reg042_A at scale 64.0 diversity is 0.07794404649622995\n",
      "Processing region: reg042_B at scale 64.0\n",
      "61.060 per cent patches are empty\n",
      "reg042_B at scale 64.0 has 1482 patches with zero diveristy\n",
      "reg042_B at scale 64.0 diversity is 0.07004627612759728\n",
      "Processing region: reg043_A at scale 64.0\n",
      "63.721 per cent patches are empty\n",
      "reg043_A at scale 64.0 has 1337 patches with zero diveristy\n",
      "reg043_A at scale 64.0 diversity is 0.10189926467253395\n",
      "Processing region: reg043_B at scale 64.0\n",
      "87.646 per cent patches are empty\n",
      "reg043_B at scale 64.0 has 485 patches with zero diveristy\n",
      "reg043_B at scale 64.0 diversity is 0.04117903491721142\n",
      "Processing region: reg044_A at scale 64.0\n",
      "78.198 per cent patches are empty\n",
      "reg044_A at scale 64.0 has 818 patches with zero diveristy\n",
      "reg044_A at scale 64.0 diversity is 0.0841841452082795\n",
      "Processing region: reg044_B at scale 64.0\n",
      "90.234 per cent patches are empty\n",
      "reg044_B at scale 64.0 has 386 patches with zero diveristy\n",
      "reg044_B at scale 64.0 diversity is 0.036258145836939114\n",
      "Processing region: reg045_A at scale 64.0\n",
      "48.706 per cent patches are empty\n",
      "reg045_A at scale 64.0 has 1864 patches with zero diveristy\n",
      "reg045_A at scale 64.0 diversity is 0.11260349933378415\n",
      "Processing region: reg045_B at scale 64.0\n",
      "78.516 per cent patches are empty\n",
      "reg045_B at scale 64.0 has 813 patches with zero diveristy\n",
      "reg045_B at scale 64.0 diversity is 0.07870243845106717\n",
      "Processing region: reg046_A at scale 64.0\n",
      "56.421 per cent patches are empty\n",
      "reg046_A at scale 64.0 has 1539 patches with zero diveristy\n",
      "reg046_A at scale 64.0 diversity is 0.14049535715134134\n",
      "Processing region: reg046_B at scale 64.0\n",
      "53.564 per cent patches are empty\n",
      "reg046_B at scale 64.0 has 1648 patches with zero diveristy\n",
      "reg046_B at scale 64.0 diversity is 0.13649067594318104\n",
      "Processing region: reg047_A at scale 64.0\n",
      "64.380 per cent patches are empty\n",
      "reg047_A at scale 64.0 has 1204 patches with zero diveristy\n",
      "reg047_A at scale 64.0 diversity is 0.1803051176802693\n",
      "Processing region: reg047_B at scale 64.0\n",
      "78.687 per cent patches are empty\n",
      "reg047_B at scale 64.0 has 840 patches with zero diveristy\n",
      "reg047_B at scale 64.0 diversity is 0.03828356720370004\n",
      "Processing region: reg048_A at scale 64.0\n",
      "63.892 per cent patches are empty\n",
      "reg048_A at scale 64.0 has 1289 patches with zero diveristy\n",
      "reg048_A at scale 64.0 diversity is 0.12984388101237845\n",
      "Processing region: reg048_B at scale 64.0\n",
      "67.798 per cent patches are empty\n",
      "reg048_B at scale 64.0 has 1162 patches with zero diveristy\n",
      "reg048_B at scale 64.0 diversity is 0.12157649735522251\n",
      "Processing region: reg049_A at scale 64.0\n",
      "53.564 per cent patches are empty\n",
      "reg049_A at scale 64.0 has 1646 patches with zero diveristy\n",
      "reg049_A at scale 64.0 diversity is 0.1382019732629305\n",
      "Processing region: reg049_B at scale 64.0\n",
      "73.706 per cent patches are empty\n",
      "reg049_B at scale 64.0 has 993 patches with zero diveristy\n",
      "reg049_B at scale 64.0 diversity is 0.07916564918450766\n",
      "Processing region: reg050_A at scale 64.0\n",
      "77.173 per cent patches are empty\n",
      "reg050_A at scale 64.0 has 850 patches with zero diveristy\n",
      "reg050_A at scale 64.0 diversity is 0.09163604278691899\n",
      "Processing region: reg050_B at scale 64.0\n",
      "66.797 per cent patches are empty\n",
      "reg050_B at scale 64.0 has 1223 patches with zero diveristy\n",
      "reg050_B at scale 64.0 diversity is 0.10154503983426424\n",
      "Processing region: reg051_A at scale 64.0\n",
      "67.432 per cent patches are empty\n",
      "reg051_A at scale 64.0 has 1186 patches with zero diveristy\n",
      "reg051_A at scale 64.0 diversity is 0.112769555853023\n",
      "Processing region: reg051_B at scale 64.0\n",
      "63.501 per cent patches are empty\n",
      "reg051_B at scale 64.0 has 1333 patches with zero diveristy\n",
      "reg051_B at scale 64.0 diversity is 0.11026730769713147\n",
      "Processing region: reg052_A at scale 64.0\n",
      "61.401 per cent patches are empty\n",
      "reg052_A at scale 64.0 has 1394 patches with zero diveristy\n",
      "reg052_A at scale 64.0 diversity is 0.12198776618995204\n",
      "Processing region: reg052_B at scale 64.0\n",
      "59.082 per cent patches are empty\n",
      "reg052_B at scale 64.0 has 1506 patches with zero diveristy\n",
      "reg052_B at scale 64.0 diversity is 0.10173270374065094\n",
      "Processing region: reg053_A at scale 64.0\n",
      "60.522 per cent patches are empty\n",
      "reg053_A at scale 64.0 has 1345 patches with zero diveristy\n",
      "reg053_A at scale 64.0 diversity is 0.17203289722050025\n",
      "Processing region: reg053_B at scale 64.0\n",
      "96.484 per cent patches are empty\n",
      "reg053_B at scale 64.0 has 143 patches with zero diveristy\n",
      "reg053_B at scale 64.0 diversity is 0.006944444444444444\n",
      "Processing region: reg054_A at scale 64.0\n",
      "68.018 per cent patches are empty\n",
      "reg054_A at scale 64.0 has 1126 patches with zero diveristy\n",
      "reg054_A at scale 64.0 diversity is 0.14499320929578932\n",
      "Processing region: reg054_B at scale 64.0\n",
      "92.432 per cent patches are empty\n",
      "reg054_B at scale 64.0 has 302 patches with zero diveristy\n",
      "reg054_B at scale 64.0 diversity is 0.025806451612903226\n",
      "Processing region: reg055_A at scale 64.0\n",
      "36.694 per cent patches are empty\n",
      "reg055_A at scale 64.0 has 1880 patches with zero diveristy\n",
      "reg055_A at scale 64.0 diversity is 0.287099067639214\n",
      "Processing region: reg055_B at scale 64.0\n",
      "96.777 per cent patches are empty\n",
      "reg055_B at scale 64.0 has 130 patches with zero diveristy\n",
      "reg055_B at scale 64.0 diversity is 0.015151515151515152\n",
      "Processing region: reg056_A at scale 64.0\n",
      "82.544 per cent patches are empty\n",
      "reg056_A at scale 64.0 has 633 patches with zero diveristy\n",
      "reg056_A at scale 64.0 diversity is 0.11585537296239018\n",
      "Processing region: reg056_B at scale 64.0\n",
      "70.898 per cent patches are empty\n",
      "reg056_B at scale 64.0 has 1088 patches with zero diveristy\n",
      "reg056_B at scale 64.0 diversity is 0.09005567673862072\n",
      "Processing region: reg057_A at scale 64.0\n",
      "98.169 per cent patches are empty\n",
      "reg057_A at scale 64.0 has 70 patches with zero diveristy\n",
      "reg057_A at scale 64.0 diversity is 0.06666666666666667\n",
      "Processing region: reg057_B at scale 64.0\n",
      "77.490 per cent patches are empty\n",
      "reg057_B at scale 64.0 has 851 patches with zero diveristy\n",
      "reg057_B at scale 64.0 diversity is 0.07665204266401081\n",
      "Processing region: reg058_A at scale 64.0\n",
      "59.204 per cent patches are empty\n",
      "reg058_A at scale 64.0 has 1585 patches with zero diveristy\n",
      "reg058_A at scale 64.0 diversity is 0.05168126870828157\n",
      "Processing region: reg058_B at scale 64.0\n",
      "70.166 per cent patches are empty\n",
      "reg058_B at scale 64.0 has 1191 patches with zero diveristy\n",
      "reg058_B at scale 64.0 diversity is 0.025646358431165812\n",
      "Processing region: reg059_A at scale 64.0\n",
      "45.923 per cent patches are empty\n",
      "reg059_A at scale 64.0 has 1782 patches with zero diveristy\n",
      "reg059_A at scale 64.0 diversity is 0.20209825011106716\n",
      "Processing region: reg059_B at scale 64.0\n",
      "62.061 per cent patches are empty\n",
      "reg059_B at scale 64.0 has 1364 patches with zero diveristy\n",
      "reg059_B at scale 64.0 diversity is 0.12544262924241206\n",
      "Processing region: reg060_A at scale 64.0\n",
      "51.465 per cent patches are empty\n",
      "reg060_A at scale 64.0 has 1706 patches with zero diveristy\n",
      "reg060_A at scale 64.0 diversity is 0.14645631947162924\n",
      "Processing region: reg060_B at scale 64.0\n",
      "53.394 per cent patches are empty\n",
      "reg060_B at scale 64.0 has 1634 patches with zero diveristy\n",
      "reg060_B at scale 64.0 diversity is 0.1455574515536225\n",
      "Processing region: reg061_A at scale 64.0\n",
      "61.938 per cent patches are empty\n",
      "reg061_A at scale 64.0 has 1357 patches with zero diveristy\n",
      "reg061_A at scale 64.0 diversity is 0.13090391210117075\n",
      "Processing region: reg061_B at scale 64.0\n",
      "62.842 per cent patches are empty\n",
      "reg061_B at scale 64.0 has 1341 patches with zero diveristy\n",
      "reg061_B at scale 64.0 diversity is 0.12079476018870668\n",
      "Processing region: reg062_A at scale 64.0\n",
      "50.781 per cent patches are empty\n",
      "reg062_A at scale 64.0 has 1747 patches with zero diveristy\n",
      "reg062_A at scale 64.0 diversity is 0.13584009745860245\n",
      "Processing region: reg062_B at scale 64.0\n",
      "70.239 per cent patches are empty\n",
      "reg062_B at scale 64.0 has 1106 patches with zero diveristy\n",
      "reg062_B at scale 64.0 diversity is 0.09483018526646739\n",
      "Processing region: reg063_A at scale 64.0\n",
      "80.859 per cent patches are empty\n",
      "reg063_A at scale 64.0 has 713 patches with zero diveristy\n",
      "reg063_A at scale 64.0 diversity is 0.09323308355235725\n",
      "Processing region: reg063_B at scale 64.0\n",
      "79.834 per cent patches are empty\n",
      "reg063_B at scale 64.0 has 768 patches with zero diveristy\n",
      "reg063_B at scale 64.0 diversity is 0.07082718926728286\n",
      "Processing region: reg064_A at scale 64.0\n",
      "58.569 per cent patches are empty\n",
      "reg064_A at scale 64.0 has 1488 patches with zero diveristy\n",
      "reg064_A at scale 64.0 diversity is 0.12439865203852388\n",
      "Processing region: reg064_B at scale 64.0\n",
      "59.253 per cent patches are empty\n",
      "reg064_B at scale 64.0 has 1463 patches with zero diveristy\n",
      "reg064_B at scale 64.0 diversity is 0.12488591222764094\n",
      "Processing region: reg065_A at scale 64.0\n",
      "58.081 per cent patches are empty\n",
      "reg065_A at scale 64.0 has 1522 patches with zero diveristy\n",
      "reg065_A at scale 64.0 diversity is 0.11607294458604941\n",
      "Processing region: reg065_B at scale 64.0\n",
      "90.186 per cent patches are empty\n",
      "reg065_B at scale 64.0 has 390 patches with zero diveristy\n",
      "reg065_B at scale 64.0 diversity is 0.029647502074762414\n",
      "Processing region: reg066_A at scale 64.0\n",
      "63.672 per cent patches are empty\n",
      "reg066_A at scale 64.0 has 1302 patches with zero diveristy\n",
      "reg066_A at scale 64.0 diversity is 0.1268645413379149\n",
      "Processing region: reg066_B at scale 64.0\n",
      "76.929 per cent patches are empty\n",
      "reg066_B at scale 64.0 has 889 patches with zero diveristy\n",
      "reg066_B at scale 64.0 diversity is 0.05979180776166735\n",
      "Processing region: reg067_A at scale 64.0\n",
      "86.084 per cent patches are empty\n",
      "reg067_A at scale 64.0 has 500 patches with zero diveristy\n",
      "reg067_A at scale 64.0 diversity is 0.12399947369433202\n",
      "Processing region: reg067_B at scale 64.0\n",
      "97.998 per cent patches are empty\n",
      "reg067_B at scale 64.0 has 79 patches with zero diveristy\n",
      "reg067_B at scale 64.0 diversity is 0.036585365853658534\n",
      "Processing region: reg068_A at scale 64.0\n",
      "73.267 per cent patches are empty\n",
      "reg068_A at scale 64.0 has 1004 patches with zero diveristy\n",
      "reg068_A at scale 64.0 diversity is 0.08372277778172932\n",
      "Processing region: reg068_B at scale 64.0\n",
      "68.774 per cent patches are empty\n",
      "reg068_B at scale 64.0 has 1136 patches with zero diveristy\n",
      "reg068_B at scale 64.0 diversity is 0.11370960712113583\n",
      "Processing region: reg069_A at scale 64.0\n",
      "69.897 per cent patches are empty\n",
      "reg069_A at scale 64.0 has 1085 patches with zero diveristy\n",
      "reg069_A at scale 64.0 diversity is 0.12390231395141843\n",
      "Processing region: reg069_B at scale 64.0\n",
      "81.348 per cent patches are empty\n",
      "reg069_B at scale 64.0 has 705 patches with zero diveristy\n",
      "reg069_B at scale 64.0 diversity is 0.07804852476239471\n",
      "Processing region: reg070_A at scale 64.0\n",
      "63.770 per cent patches are empty\n",
      "reg070_A at scale 64.0 has 1326 patches with zero diveristy\n",
      "reg070_A at scale 64.0 diversity is 0.10839362646682131\n",
      "Processing region: reg070_B at scale 64.0\n",
      "77.686 per cent patches are empty\n",
      "reg070_B at scale 64.0 has 874 patches with zero diveristy\n",
      "reg070_B at scale 64.0 diversity is 0.043584892415874155\n",
      "Processing region: reg001_A at scale 72.0\n",
      "80.787 per cent patches are empty\n",
      "reg001_A at scale 72.0 has 919 patches with zero diveristy\n",
      "reg001_A at scale 72.0 diversity is 0.07890710760067514\n",
      "Processing region: reg001_B at scale 72.0\n",
      "93.576 per cent patches are empty\n",
      "reg001_B at scale 72.0 has 322 patches with zero diveristy\n",
      "reg001_B at scale 72.0 diversity is 0.03303303303303303\n",
      "Processing region: reg002_A at scale 72.0\n",
      "76.543 per cent patches are empty\n",
      "reg002_A at scale 72.0 has 1110 patches with zero diveristy\n",
      "reg002_A at scale 72.0 diversity is 0.08854702576991609\n",
      "Processing region: reg002_B at scale 72.0\n",
      "59.105 per cent patches are empty\n",
      "reg002_B at scale 72.0 has 2016 patches with zero diveristy\n",
      "reg002_B at scale 72.0 diversity is 0.049261597881100336\n",
      "Processing region: reg003_A at scale 72.0\n",
      "78.144 per cent patches are empty\n",
      "reg003_A at scale 72.0 has 1040 patches with zero diveristy\n",
      "reg003_A at scale 72.0 diversity is 0.08245503457090039\n",
      "Processing region: reg003_B at scale 72.0\n",
      "78.897 per cent patches are empty\n",
      "reg003_B at scale 72.0 has 1028 patches with zero diveristy\n",
      "reg003_B at scale 72.0 diversity is 0.06017969987944148\n",
      "Processing region: reg004_A at scale 72.0\n",
      "74.846 per cent patches are empty\n",
      "reg004_A at scale 72.0 has 1190 patches with zero diveristy\n",
      "reg004_A at scale 72.0 diversity is 0.08858111580086422\n",
      "Processing region: reg004_B at scale 72.0\n",
      "67.342 per cent patches are empty\n",
      "reg004_B at scale 72.0 has 1571 patches with zero diveristy\n",
      "reg004_B at scale 72.0 diversity is 0.0732504626929135\n",
      "Processing region: reg005_A at scale 72.0\n",
      "59.857 per cent patches are empty\n",
      "reg005_A at scale 72.0 has 1869 patches with zero diveristy\n",
      "reg005_A at scale 72.0 diversity is 0.1039328267732553\n",
      "Processing region: reg005_B at scale 72.0\n",
      "72.801 per cent patches are empty\n",
      "reg005_B at scale 72.0 has 1261 patches with zero diveristy\n",
      "reg005_B at scale 72.0 diversity is 0.10699248227768687\n",
      "Processing region: reg006_A at scale 72.0\n",
      "76.659 per cent patches are empty\n",
      "reg006_A at scale 72.0 has 1101 patches with zero diveristy\n",
      "reg006_A at scale 72.0 diversity is 0.09119534435738506\n",
      "Processing region: reg006_B at scale 72.0\n",
      "65.741 per cent patches are empty\n",
      "reg006_B at scale 72.0 has 1585 patches with zero diveristy\n",
      "reg006_B at scale 72.0 diversity is 0.10830863833151061\n",
      "Processing region: reg007_A at scale 72.0\n",
      "53.607 per cent patches are empty\n",
      "reg007_A at scale 72.0 has 2152 patches with zero diveristy\n",
      "reg007_A at scale 72.0 diversity is 0.10572877166136924\n",
      "Processing region: reg007_B at scale 72.0\n",
      "61.844 per cent patches are empty\n",
      "reg007_B at scale 72.0 has 1757 patches with zero diveristy\n",
      "reg007_B at scale 72.0 diversity is 0.11262775320808543\n",
      "Processing region: reg008_A at scale 72.0\n",
      "77.585 per cent patches are empty\n",
      "reg008_A at scale 72.0 has 1077 patches with zero diveristy\n",
      "reg008_A at scale 72.0 diversity is 0.07393224953896978\n",
      "Processing region: reg008_B at scale 72.0\n",
      "47.859 per cent patches are empty\n",
      "reg008_B at scale 72.0 has 2457 patches with zero diveristy\n",
      "reg008_B at scale 72.0 diversity is 0.09314416351306747\n",
      "Processing region: reg009_A at scale 72.0\n",
      "60.320 per cent patches are empty\n",
      "reg009_A at scale 72.0 has 1814 patches with zero diveristy\n",
      "reg009_A at scale 72.0 diversity is 0.11995898912820555\n",
      "Processing region: reg009_B at scale 72.0\n",
      "46.296 per cent patches are empty\n",
      "reg009_B at scale 72.0 has 2339 patches with zero diveristy\n",
      "reg009_B at scale 72.0 diversity is 0.16121409992732627\n",
      "Processing region: reg010_A at scale 72.0\n",
      "65.490 per cent patches are empty\n",
      "reg010_A at scale 72.0 has 1609 patches with zero diveristy\n",
      "reg010_A at scale 72.0 diversity is 0.10248539221488873\n",
      "Processing region: reg010_B at scale 72.0\n",
      "78.684 per cent patches are empty\n",
      "reg010_B at scale 72.0 has 1019 patches with zero diveristy\n",
      "reg010_B at scale 72.0 diversity is 0.07783984917565843\n",
      "Processing region: reg011_A at scale 72.0\n",
      "58.565 per cent patches are empty\n",
      "reg011_A at scale 72.0 has 1732 patches with zero diveristy\n",
      "reg011_A at scale 72.0 diversity is 0.20107193272727059\n",
      "Processing region: reg011_B at scale 72.0\n",
      "83.912 per cent patches are empty\n",
      "reg011_B at scale 72.0 has 781 patches with zero diveristy\n",
      "reg011_B at scale 72.0 diversity is 0.06415258793138566\n",
      "Processing region: reg012_A at scale 72.0\n",
      "70.235 per cent patches are empty\n",
      "reg012_A at scale 72.0 has 1387 patches with zero diveristy\n",
      "reg012_A at scale 72.0 diversity is 0.10126905109328523\n",
      "Processing region: reg012_B at scale 72.0\n",
      "91.860 per cent patches are empty\n",
      "reg012_B at scale 72.0 has 400 patches with zero diveristy\n",
      "reg012_B at scale 72.0 diversity is 0.05193908965415756\n",
      "Processing region: reg013_A at scale 72.0\n",
      "71.451 per cent patches are empty\n",
      "reg013_A at scale 72.0 has 1370 patches with zero diveristy\n",
      "reg013_A at scale 72.0 diversity is 0.07613493243633057\n",
      "Processing region: reg013_B at scale 72.0\n",
      "67.130 per cent patches are empty\n",
      "reg013_B at scale 72.0 has 1590 patches with zero diveristy\n",
      "reg013_B at scale 72.0 diversity is 0.0677246228255467\n",
      "Processing region: reg014_A at scale 72.0\n",
      "83.275 per cent patches are empty\n",
      "reg014_A at scale 72.0 has 810 patches with zero diveristy\n",
      "reg014_A at scale 72.0 diversity is 0.06641864186934389\n",
      "Processing region: reg014_B at scale 72.0\n",
      "64.371 per cent patches are empty\n",
      "reg014_B at scale 72.0 has 1670 patches with zero diveristy\n",
      "reg014_B at scale 72.0 diversity is 0.09678826250203902\n",
      "Processing region: reg015_A at scale 72.0\n",
      "60.243 per cent patches are empty\n",
      "reg015_A at scale 72.0 has 1858 patches with zero diveristy\n",
      "reg015_A at scale 72.0 diversity is 0.09862149392115208\n",
      "Processing region: reg015_B at scale 72.0\n",
      "73.110 per cent patches are empty\n",
      "reg015_B at scale 72.0 has 1325 patches with zero diveristy\n",
      "reg015_B at scale 72.0 diversity is 0.04950719751729023\n",
      "Processing region: reg016_A at scale 72.0\n",
      "68.152 per cent patches are empty\n",
      "reg016_A at scale 72.0 has 1494 patches with zero diveristy\n",
      "reg016_A at scale 72.0 diversity is 0.09717418988972303\n",
      "Processing region: reg016_B at scale 72.0\n",
      "61.265 per cent patches are empty\n",
      "reg016_B at scale 72.0 has 1840 patches with zero diveristy\n",
      "reg016_B at scale 72.0 diversity is 0.08346838272379235\n",
      "Processing region: reg017_A at scale 72.0\n",
      "57.350 per cent patches are empty\n",
      "reg017_A at scale 72.0 has 1947 patches with zero diveristy\n",
      "reg017_A at scale 72.0 diversity is 0.12186980439465697\n",
      "Processing region: reg017_B at scale 72.0\n",
      "56.539 per cent patches are empty\n",
      "reg017_B at scale 72.0 has 1942 patches with zero diveristy\n",
      "reg017_B at scale 72.0 diversity is 0.14360401826536312\n",
      "Processing region: reg018_A at scale 72.0\n",
      "66.628 per cent patches are empty\n",
      "reg018_A at scale 72.0 has 1551 patches with zero diveristy\n",
      "reg018_A at scale 72.0 diversity is 0.10390832611081778\n",
      "Processing region: reg018_B at scale 72.0\n",
      "58.353 per cent patches are empty\n",
      "reg018_B at scale 72.0 has 1979 patches with zero diveristy\n",
      "reg018_B at scale 72.0 diversity is 0.08407726185429806\n",
      "Processing region: reg019_A at scale 72.0\n",
      "75.714 per cent patches are empty\n",
      "reg019_A at scale 72.0 has 1085 patches with zero diveristy\n",
      "reg019_A at scale 72.0 diversity is 0.1387551495987862\n",
      "Processing region: reg019_B at scale 72.0\n",
      "98.052 per cent patches are empty\n",
      "reg019_B at scale 72.0 has 100 patches with zero diveristy\n",
      "reg019_B at scale 72.0 diversity is 0.009900990099009901\n",
      "Processing region: reg020_A at scale 72.0\n",
      "64.622 per cent patches are empty\n",
      "reg020_A at scale 72.0 has 1638 patches with zero diveristy\n",
      "reg020_A at scale 72.0 diversity is 0.10970159942468474\n",
      "Processing region: reg020_B at scale 72.0\n",
      "56.597 per cent patches are empty\n",
      "reg020_B at scale 72.0 has 1959 patches with zero diveristy\n",
      "reg020_B at scale 72.0 diversity is 0.13112269630142448\n",
      "Processing region: reg021_A at scale 72.0\n",
      "60.262 per cent patches are empty\n",
      "reg021_A at scale 72.0 has 1745 patches with zero diveristy\n",
      "reg021_A at scale 72.0 diversity is 0.15624753793709745\n",
      "Processing region: reg021_B at scale 72.0\n",
      "93.904 per cent patches are empty\n",
      "reg021_B at scale 72.0 has 307 patches with zero diveristy\n",
      "reg021_B at scale 72.0 diversity is 0.028481012658227847\n",
      "Processing region: reg022_A at scale 72.0\n",
      "66.667 per cent patches are empty\n",
      "reg022_A at scale 72.0 has 1472 patches with zero diveristy\n",
      "reg022_A at scale 72.0 diversity is 0.15109504184307165\n",
      "Processing region: reg022_B at scale 72.0\n",
      "88.117 per cent patches are empty\n",
      "reg022_B at scale 72.0 has 574 patches with zero diveristy\n",
      "reg022_B at scale 72.0 diversity is 0.06967255817216637\n",
      "Processing region: reg023_A at scale 72.0\n",
      "83.796 per cent patches are empty\n",
      "reg023_A at scale 72.0 has 795 patches with zero diveristy\n",
      "reg023_A at scale 72.0 diversity is 0.05407327877241683\n",
      "Processing region: reg023_B at scale 72.0\n",
      "61.362 per cent patches are empty\n",
      "reg023_B at scale 72.0 has 1740 patches with zero diveristy\n",
      "reg023_B at scale 72.0 diversity is 0.13414677567630928\n",
      "Processing region: reg024_A at scale 72.0\n",
      "81.404 per cent patches are empty\n",
      "reg024_A at scale 72.0 has 900 patches with zero diveristy\n",
      "reg024_A at scale 72.0 diversity is 0.06665782763167959\n",
      "Processing region: reg024_B at scale 72.0\n",
      "61.651 per cent patches are empty\n",
      "reg024_B at scale 72.0 has 1857 patches with zero diveristy\n",
      "reg024_B at scale 72.0 diversity is 0.0664904217004753\n",
      "Processing region: reg025_A at scale 72.0\n",
      "77.392 per cent patches are empty\n",
      "reg025_A at scale 72.0 has 1062 patches with zero diveristy\n",
      "reg025_A at scale 72.0 diversity is 0.09707160481336694\n",
      "Processing region: reg025_B at scale 72.0\n",
      "68.924 per cent patches are empty\n",
      "reg025_B at scale 72.0 has 1427 patches with zero diveristy\n",
      "reg025_B at scale 72.0 diversity is 0.11730008019271561\n",
      "Processing region: reg026_A at scale 72.0\n",
      "61.323 per cent patches are empty\n",
      "reg026_A at scale 72.0 has 1770 patches with zero diveristy\n",
      "reg026_A at scale 72.0 diversity is 0.1193844970978004\n",
      "Processing region: reg026_B at scale 72.0\n",
      "55.285 per cent patches are empty\n",
      "reg026_B at scale 72.0 has 2058 patches with zero diveristy\n",
      "reg026_B at scale 72.0 diversity is 0.11364876869832113\n",
      "Processing region: reg027_A at scale 72.0\n",
      "60.185 per cent patches are empty\n",
      "reg027_A at scale 72.0 has 1881 patches with zero diveristy\n",
      "reg027_A at scale 72.0 diversity is 0.09000699491803166\n",
      "Processing region: reg027_B at scale 72.0\n",
      "66.127 per cent patches are empty\n",
      "reg027_B at scale 72.0 has 1636 patches with zero diveristy\n",
      "reg027_B at scale 72.0 diversity is 0.06881725987338284\n",
      "Processing region: reg028_A at scale 72.0\n",
      "61.535 per cent patches are empty\n",
      "reg028_A at scale 72.0 has 1755 patches with zero diveristy\n",
      "reg028_A at scale 72.0 diversity is 0.12220031083311031\n",
      "Processing region: reg028_B at scale 72.0\n",
      "62.481 per cent patches are empty\n",
      "reg028_B at scale 72.0 has 1773 patches with zero diveristy\n",
      "reg028_B at scale 72.0 diversity is 0.08890735861367906\n",
      "Processing region: reg029_A at scale 72.0\n",
      "57.774 per cent patches are empty\n",
      "reg029_A at scale 72.0 has 2022 patches with zero diveristy\n",
      "reg029_A at scale 72.0 diversity is 0.07750909995487527\n",
      "Processing region: reg029_B at scale 72.0\n",
      "66.590 per cent patches are empty\n",
      "reg029_B at scale 72.0 has 1592 patches with zero diveristy\n",
      "reg029_B at scale 72.0 diversity is 0.08185026703607991\n",
      "Processing region: reg030_A at scale 72.0\n",
      "62.018 per cent patches are empty\n",
      "reg030_A at scale 72.0 has 1759 patches with zero diveristy\n",
      "reg030_A at scale 72.0 diversity is 0.1084406043720944\n",
      "Processing region: reg030_B at scale 72.0\n",
      "66.339 per cent patches are empty\n",
      "reg030_B at scale 72.0 has 1600 patches with zero diveristy\n",
      "reg030_B at scale 72.0 diversity is 0.084879247858855\n",
      "Processing region: reg031_A at scale 72.0\n",
      "58.083 per cent patches are empty\n",
      "reg031_A at scale 72.0 has 1956 patches with zero diveristy\n",
      "reg031_A at scale 72.0 diversity is 0.10113871951717318\n",
      "Processing region: reg031_B at scale 72.0\n",
      "64.178 per cent patches are empty\n",
      "reg031_B at scale 72.0 has 1697 patches with zero diveristy\n",
      "reg031_B at scale 72.0 diversity is 0.08742750853095703\n",
      "Processing region: reg032_A at scale 72.0\n",
      "55.845 per cent patches are empty\n",
      "reg032_A at scale 72.0 has 2069 patches with zero diveristy\n",
      "reg032_A at scale 72.0 diversity is 0.09787640891975001\n",
      "Processing region: reg032_B at scale 72.0\n",
      "73.013 per cent patches are empty\n",
      "reg032_B at scale 72.0 has 1249 patches with zero diveristy\n",
      "reg032_B at scale 72.0 diversity is 0.10686903145412933\n",
      "Processing region: reg033_A at scale 72.0\n",
      "57.079 per cent patches are empty\n",
      "reg033_A at scale 72.0 has 1930 patches with zero diveristy\n",
      "reg033_A at scale 72.0 diversity is 0.13507814607713917\n",
      "Processing region: reg033_B at scale 72.0\n",
      "49.691 per cent patches are empty\n",
      "reg033_B at scale 72.0 has 2325 patches with zero diveristy\n",
      "reg033_B at scale 72.0 diversity is 0.11159157641184489\n",
      "Processing region: reg034_A at scale 72.0\n",
      "64.294 per cent patches are empty\n",
      "reg034_A at scale 72.0 has 1651 patches with zero diveristy\n",
      "reg034_A at scale 72.0 diversity is 0.10878411669991275\n",
      "Processing region: reg034_B at scale 72.0\n",
      "66.184 per cent patches are empty\n",
      "reg034_B at scale 72.0 has 1635 patches with zero diveristy\n",
      "reg034_B at scale 72.0 diversity is 0.06728472678109276\n",
      "Processing region: reg035_A at scale 72.0\n",
      "57.716 per cent patches are empty\n",
      "reg035_A at scale 72.0 has 1915 patches with zero diveristy\n",
      "reg035_A at scale 72.0 diversity is 0.1285063141495442\n",
      "Processing region: reg035_B at scale 72.0\n",
      "61.593 per cent patches are empty\n",
      "reg035_B at scale 72.0 has 1794 patches with zero diveristy\n",
      "reg035_B at scale 72.0 diversity is 0.09929318600947858\n",
      "Processing region: reg036_A at scale 72.0\n",
      "48.881 per cent patches are empty\n",
      "reg036_A at scale 72.0 has 2257 patches with zero diveristy\n",
      "reg036_A at scale 72.0 diversity is 0.15147850944811325\n",
      "Processing region: reg036_B at scale 72.0\n",
      "50.984 per cent patches are empty\n",
      "reg036_B at scale 72.0 has 2321 patches with zero diveristy\n",
      "reg036_B at scale 72.0 diversity is 0.0886303731417967\n",
      "Processing region: reg037_A at scale 72.0\n",
      "74.556 per cent patches are empty\n",
      "reg037_A at scale 72.0 has 1168 patches with zero diveristy\n",
      "reg037_A at scale 72.0 diversity is 0.11702512636600672\n",
      "Processing region: reg037_B at scale 72.0\n",
      "92.650 per cent patches are empty\n",
      "reg037_B at scale 72.0 has 370 patches with zero diveristy\n",
      "reg037_B at scale 72.0 diversity is 0.030406725723677575\n",
      "Processing region: reg038_A at scale 72.0\n",
      "57.079 per cent patches are empty\n",
      "reg038_A at scale 72.0 has 1892 patches with zero diveristy\n",
      "reg038_A at scale 72.0 diversity is 0.15290619992038504\n",
      "Processing region: reg038_B at scale 72.0\n",
      "71.798 per cent patches are empty\n",
      "reg038_B at scale 72.0 has 1341 patches with zero diveristy\n",
      "reg038_B at scale 72.0 diversity is 0.08448001026287752\n",
      "Processing region: reg039_A at scale 72.0\n",
      "62.230 per cent patches are empty\n",
      "reg039_A at scale 72.0 has 1663 patches with zero diveristy\n",
      "reg039_A at scale 72.0 diversity is 0.15442961516097875\n",
      "Processing region: reg039_B at scale 72.0\n",
      "86.111 per cent patches are empty\n",
      "reg039_B at scale 72.0 has 693 patches with zero diveristy\n",
      "reg039_B at scale 72.0 diversity is 0.03738652199174235\n",
      "Processing region: reg040_A at scale 72.0\n",
      "64.178 per cent patches are empty\n",
      "reg040_A at scale 72.0 has 1695 patches with zero diveristy\n",
      "reg040_A at scale 72.0 diversity is 0.08824052684238781\n",
      "Processing region: reg040_B at scale 72.0\n",
      "85.185 per cent patches are empty\n",
      "reg040_B at scale 72.0 has 715 patches with zero diveristy\n",
      "reg040_B at scale 72.0 diversity is 0.06955931532399758\n",
      "Processing region: reg041_A at scale 72.0\n",
      "65.606 per cent patches are empty\n",
      "reg041_A at scale 72.0 has 1611 patches with zero diveristy\n",
      "reg041_A at scale 72.0 diversity is 0.09833473254801625\n",
      "Processing region: reg041_B at scale 72.0\n",
      "65.451 per cent patches are empty\n",
      "reg041_B at scale 72.0 has 1689 patches with zero diveristy\n",
      "reg041_B at scale 72.0 diversity is 0.058726800673237996\n",
      "Processing region: reg042_A at scale 72.0\n",
      "61.574 per cent patches are empty\n",
      "reg042_A at scale 72.0 has 1866 patches with zero diveristy\n",
      "reg042_A at scale 72.0 diversity is 0.06304830938997379\n",
      "Processing region: reg042_B at scale 72.0\n",
      "67.284 per cent patches are empty\n",
      "reg042_B at scale 72.0 has 1611 patches with zero diveristy\n",
      "reg042_B at scale 72.0 diversity is 0.0495880036406836\n",
      "Processing region: reg043_A at scale 72.0\n",
      "69.502 per cent patches are empty\n",
      "reg043_A at scale 72.0 has 1472 patches with zero diveristy\n",
      "reg043_A at scale 72.0 diversity is 0.06952866066009221\n",
      "Processing region: reg043_B at scale 72.0\n",
      "89.834 per cent patches are empty\n",
      "reg043_B at scale 72.0 has 514 patches with zero diveristy\n",
      "reg043_B at scale 72.0 diversity is 0.02577791745867392\n",
      "Processing region: reg044_A at scale 72.0\n",
      "82.079 per cent patches are empty\n",
      "reg044_A at scale 72.0 has 876 patches with zero diveristy\n",
      "reg044_A at scale 72.0 diversity is 0.05750436401381069\n",
      "Processing region: reg044_B at scale 72.0\n",
      "92.226 per cent patches are empty\n",
      "reg044_B at scale 72.0 has 393 patches with zero diveristy\n",
      "reg044_B at scale 72.0 diversity is 0.02481389578163772\n",
      "Processing region: reg045_A at scale 72.0\n",
      "56.076 per cent patches are empty\n",
      "reg045_A at scale 72.0 has 2131 patches with zero diveristy\n",
      "reg045_A at scale 72.0 diversity is 0.0641969440803661\n",
      "Processing region: reg045_B at scale 72.0\n",
      "82.022 per cent patches are empty\n",
      "reg045_B at scale 72.0 has 902 patches with zero diveristy\n",
      "reg045_B at scale 72.0 diversity is 0.03281648336987248\n",
      "Processing region: reg046_A at scale 72.0\n",
      "63.368 per cent patches are empty\n",
      "reg046_A at scale 72.0 has 1734 patches with zero diveristy\n",
      "reg046_A at scale 72.0 diversity is 0.08847790943057077\n",
      "Processing region: reg046_B at scale 72.0\n",
      "60.185 per cent patches are empty\n",
      "reg046_B at scale 72.0 has 1868 patches with zero diveristy\n",
      "reg046_B at scale 72.0 diversity is 0.09662844194645544\n",
      "Processing region: reg047_A at scale 72.0\n",
      "69.850 per cent patches are empty\n",
      "reg047_A at scale 72.0 has 1363 patches with zero diveristy\n",
      "reg047_A at scale 72.0 diversity is 0.13096144167938056\n",
      "Processing region: reg047_B at scale 72.0\n",
      "82.388 per cent patches are empty\n",
      "reg047_B at scale 72.0 has 890 patches with zero diveristy\n",
      "reg047_B at scale 72.0 diversity is 0.025102186017584325\n",
      "Processing region: reg048_A at scale 72.0\n",
      "69.618 per cent patches are empty\n",
      "reg048_A at scale 72.0 has 1445 patches with zero diveristy\n",
      "reg048_A at scale 72.0 diversity is 0.0824960820147029\n",
      "Processing region: reg048_B at scale 72.0\n",
      "73.206 per cent patches are empty\n",
      "reg048_B at scale 72.0 has 1278 patches with zero diveristy\n",
      "reg048_B at scale 72.0 diversity is 0.0814216972678532\n",
      "Processing region: reg049_A at scale 72.0\n",
      "60.725 per cent patches are empty\n",
      "reg049_A at scale 72.0 has 1863 patches with zero diveristy\n",
      "reg049_A at scale 72.0 diversity is 0.085011030108301\n",
      "Processing region: reg049_B at scale 72.0\n",
      "78.588 per cent patches are empty\n",
      "reg049_B at scale 72.0 has 1034 patches with zero diveristy\n",
      "reg049_B at scale 72.0 diversity is 0.0681740390416378\n",
      "Processing region: reg050_A at scale 72.0\n",
      "81.096 per cent patches are empty\n",
      "reg050_A at scale 72.0 has 929 patches with zero diveristy\n",
      "reg050_A at scale 72.0 diversity is 0.05195744472862703\n",
      "Processing region: reg050_B at scale 72.0\n",
      "71.779 per cent patches are empty\n",
      "reg050_B at scale 72.0 has 1386 patches with zero diveristy\n",
      "reg050_B at scale 72.0 diversity is 0.05331956026626882\n",
      "Processing region: reg051_A at scale 72.0\n",
      "72.936 per cent patches are empty\n",
      "reg051_A at scale 72.0 has 1291 patches with zero diveristy\n",
      "reg051_A at scale 72.0 diversity is 0.08060457650427427\n",
      "Processing region: reg051_B at scale 72.0\n",
      "69.579 per cent patches are empty\n",
      "reg051_B at scale 72.0 has 1468 patches with zero diveristy\n",
      "reg051_B at scale 72.0 diversity is 0.07007595117585728\n",
      "Processing region: reg052_A at scale 72.0\n",
      "67.766 per cent patches are empty\n",
      "reg052_A at scale 72.0 has 1541 patches with zero diveristy\n",
      "reg052_A at scale 72.0 diversity is 0.07885572761296593\n",
      "Processing region: reg052_B at scale 72.0\n",
      "65.008 per cent patches are empty\n",
      "reg052_B at scale 72.0 has 1702 patches with zero diveristy\n",
      "reg052_B at scale 72.0 diversity is 0.06257429713579213\n",
      "Processing region: reg053_A at scale 72.0\n",
      "66.782 per cent patches are empty\n",
      "reg053_A at scale 72.0 has 1518 patches with zero diveristy\n",
      "reg053_A at scale 72.0 diversity is 0.11973080236410778\n",
      "Processing region: reg053_B at scale 72.0\n",
      "97.261 per cent patches are empty\n",
      "reg053_B at scale 72.0 has 141 patches with zero diveristy\n",
      "reg053_B at scale 72.0 diversity is 0.007042253521126761\n",
      "Processing region: reg054_A at scale 72.0\n",
      "73.322 per cent patches are empty\n",
      "reg054_A at scale 72.0 has 1238 patches with zero diveristy\n",
      "reg054_A at scale 72.0 diversity is 0.10672306279090654\n",
      "Processing region: reg054_B at scale 72.0\n",
      "94.348 per cent patches are empty\n",
      "reg054_B at scale 72.0 has 287 patches with zero diveristy\n",
      "reg054_B at scale 72.0 diversity is 0.020477815699658702\n",
      "Processing region: reg055_A at scale 72.0\n",
      "44.522 per cent patches are empty\n",
      "reg055_A at scale 72.0 has 2290 patches with zero diveristy\n",
      "reg055_A at scale 72.0 diversity is 0.20741170702025313\n",
      "Processing region: reg055_B at scale 72.0\n",
      "97.473 per cent patches are empty\n",
      "reg055_B at scale 72.0 has 129 patches with zero diveristy\n",
      "reg055_B at scale 72.0 diversity is 0.015267175572519083\n",
      "Processing region: reg056_A at scale 72.0\n",
      "85.783 per cent patches are empty\n",
      "reg056_A at scale 72.0 has 679 patches with zero diveristy\n",
      "reg056_A at scale 72.0 diversity is 0.080967684309658\n",
      "Processing region: reg056_B at scale 72.0\n",
      "75.829 per cent patches are empty\n",
      "reg056_B at scale 72.0 has 1184 patches with zero diveristy\n",
      "reg056_B at scale 72.0 diversity is 0.05493742351804388\n",
      "Processing region: reg057_A at scale 72.0\n",
      "98.553 per cent patches are empty\n",
      "reg057_A at scale 72.0 has 70 patches with zero diveristy\n",
      "reg057_A at scale 72.0 diversity is 0.06666666666666667\n",
      "Processing region: reg057_B at scale 72.0\n",
      "81.752 per cent patches are empty\n",
      "reg057_B at scale 72.0 has 890 patches with zero diveristy\n",
      "reg057_B at scale 72.0 diversity is 0.059728602890883345\n",
      "Processing region: reg058_A at scale 72.0\n",
      "65.644 per cent patches are empty\n",
      "reg058_A at scale 72.0 has 1703 patches with zero diveristy\n",
      "reg058_A at scale 72.0 diversity is 0.04376438097263316\n",
      "Processing region: reg058_B at scale 72.0\n",
      "74.383 per cent patches are empty\n",
      "reg058_B at scale 72.0 has 1301 patches with zero diveristy\n",
      "reg058_B at scale 72.0 diversity is 0.020127691233820497\n",
      "Processing region: reg059_A at scale 72.0\n",
      "53.144 per cent patches are empty\n",
      "reg059_A at scale 72.0 has 2141 patches with zero diveristy\n",
      "reg059_A at scale 72.0 diversity is 0.11940679292602213\n",
      "Processing region: reg059_B at scale 72.0\n",
      "68.287 per cent patches are empty\n",
      "reg059_B at scale 72.0 has 1519 patches with zero diveristy\n",
      "reg059_B at scale 72.0 diversity is 0.077256554138885\n",
      "Processing region: reg060_A at scale 72.0\n",
      "58.893 per cent patches are empty\n",
      "reg060_A at scale 72.0 has 1906 patches with zero diveristy\n",
      "reg060_A at scale 72.0 diversity is 0.1072711109831291\n",
      "Processing region: reg060_B at scale 72.0\n",
      "60.957 per cent patches are empty\n",
      "reg060_B at scale 72.0 has 1829 patches with zero diveristy\n",
      "reg060_B at scale 72.0 diversity is 0.09676042902058314\n",
      "Processing region: reg061_A at scale 72.0\n",
      "67.863 per cent patches are empty\n",
      "reg061_A at scale 72.0 has 1515 patches with zero diveristy\n",
      "reg061_A at scale 72.0 diversity is 0.09219371999102527\n",
      "Processing region: reg061_B at scale 72.0\n",
      "68.846 per cent patches are empty\n",
      "reg061_B at scale 72.0 has 1495 patches with zero diveristy\n",
      "reg061_B at scale 72.0 diversity is 0.07570163828912638\n",
      "Processing region: reg062_A at scale 72.0\n",
      "59.028 per cent patches are empty\n",
      "reg062_A at scale 72.0 has 1925 patches with zero diveristy\n",
      "reg062_A at scale 72.0 diversity is 0.09464504080894781\n",
      "Processing region: reg062_B at scale 72.0\n",
      "75.791 per cent patches are empty\n",
      "reg062_B at scale 72.0 has 1171 patches with zero diveristy\n",
      "reg062_B at scale 72.0 diversity is 0.06806757968414803\n",
      "Processing region: reg063_A at scale 72.0\n",
      "84.549 per cent patches are empty\n",
      "reg063_A at scale 72.0 has 739 patches with zero diveristy\n",
      "reg063_A at scale 72.0 diversity is 0.07876182376466517\n",
      "Processing region: reg063_B at scale 72.0\n",
      "83.546 per cent patches are empty\n",
      "reg063_B at scale 72.0 has 819 patches with zero diveristy\n",
      "reg063_B at scale 72.0 diversity is 0.040449306371366525\n",
      "Processing region: reg064_A at scale 72.0\n",
      "65.606 per cent patches are empty\n",
      "reg064_A at scale 72.0 has 1612 patches with zero diveristy\n",
      "reg064_A at scale 72.0 diversity is 0.09553918489760849\n",
      "Processing region: reg064_B at scale 72.0\n",
      "66.030 per cent patches are empty\n",
      "reg064_B at scale 72.0 has 1619 patches with zero diveristy\n",
      "reg064_B at scale 72.0 diversity is 0.08144509511845871\n",
      "Processing region: reg065_A at scale 72.0\n",
      "63.792 per cent patches are empty\n",
      "reg065_A at scale 72.0 has 1754 patches with zero diveristy\n",
      "reg065_A at scale 72.0 diversity is 0.065972737417748\n",
      "Processing region: reg065_B at scale 72.0\n",
      "92.130 per cent patches are empty\n",
      "reg065_B at scale 72.0 has 400 patches with zero diveristy\n",
      "reg065_B at scale 72.0 diversity is 0.0196078431372549\n",
      "Processing region: reg066_A at scale 72.0\n",
      "69.059 per cent patches are empty\n",
      "reg066_A at scale 72.0 has 1483 patches with zero diveristy\n",
      "reg066_A at scale 72.0 diversity is 0.07752673525061547\n",
      "Processing region: reg066_B at scale 72.0\n",
      "81.559 per cent patches are empty\n",
      "reg066_B at scale 72.0 has 909 patches with zero diveristy\n",
      "reg066_B at scale 72.0 diversity is 0.04968960076859377\n",
      "Processing region: reg067_A at scale 72.0\n",
      "88.465 per cent patches are empty\n",
      "reg067_A at scale 72.0 has 545 patches with zero diveristy\n",
      "reg067_A at scale 72.0 diversity is 0.0883555044617207\n",
      "Processing region: reg067_B at scale 72.0\n",
      "98.399 per cent patches are empty\n",
      "reg067_B at scale 72.0 has 83 patches with zero diveristy\n",
      "reg067_B at scale 72.0 diversity is 0.0\n",
      "Processing region: reg068_A at scale 72.0\n",
      "78.376 per cent patches are empty\n",
      "reg068_A at scale 72.0 has 1059 patches with zero diveristy\n",
      "reg068_A at scale 72.0 diversity is 0.05501622063890987\n",
      "Processing region: reg068_B at scale 72.0\n",
      "74.209 per cent patches are empty\n",
      "reg068_B at scale 72.0 has 1235 patches with zero diveristy\n",
      "reg068_B at scale 72.0 diversity is 0.07704301919936521\n",
      "Processing region: reg069_A at scale 72.0\n",
      "75.077 per cent patches are empty\n",
      "reg069_A at scale 72.0 has 1176 patches with zero diveristy\n",
      "reg069_A at scale 72.0 diversity is 0.09082031733969398\n",
      "Processing region: reg069_B at scale 72.0\n",
      "84.703 per cent patches are empty\n",
      "reg069_B at scale 72.0 has 746 patches with zero diveristy\n",
      "reg069_B at scale 72.0 diversity is 0.059165568517092676\n",
      "Processing region: reg070_A at scale 72.0\n",
      "69.599 per cent patches are empty\n",
      "reg070_A at scale 72.0 has 1456 patches with zero diveristy\n",
      "reg070_A at scale 72.0 diversity is 0.07810993285190315\n",
      "Processing region: reg070_B at scale 72.0\n",
      "82.041 per cent patches are empty\n",
      "reg070_B at scale 72.0 has 886 patches with zero diveristy\n",
      "reg070_B at scale 72.0 diversity is 0.04824736394635284\n"
     ]
    }
   ],
   "source": [
    "# Define the sequence of scales\n",
    "scales = [2., 4., 8., 16., 24., 32., 48., 64., 72.]\n",
    "library_ids = protein['File Name'].unique().tolist()\n",
    "\n",
    "mdi_results = eco.calculate_MDI(spatial_data=protein,\n",
    "                                scales=scales,\n",
    "                                library_key='File Name',\n",
    "                                library_id=library_ids,\n",
    "                                spatial_key=['X:X','Y:Y'],\n",
    "                                cluster_key='ClusterName',\n",
    "                                selecting_scale=True,\n",
    "                                random_patch=False,\n",
    "                                plotfigs=False,\n",
    "                                savefigs=False,\n",
    "                                patch_kwargs={'max_overlap':0.5,'random_seed': 42, 'min_points':2},\n",
    "                                other_kwargs={'metric': 'Shannon Diversity'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1ed36af8-b116-4b5f-b10f-a7ef4a53d0a3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.161656</td>\n",
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       "      <td>0.429751</td>\n",
       "      <td>0.200143</td>\n",
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       "      <td>0.030135</td>\n",
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       "      <td>0.008667</td>\n",
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       "      <th>reg003_A</th>\n",
       "      <td>-0.333333</td>\n",
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       "      <td>0.570232</td>\n",
       "      <td>0.483367</td>\n",
       "      <td>0.304637</td>\n",
       "      <td>0.090609</td>\n",
       "      <td>0.020104</td>\n",
       "      <td>0.016932</td>\n",
       "      <td>-0.039729</td>\n",
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       "      <td>2</td>\n",
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       "      <th>reg068_B</th>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.130753</td>\n",
       "      <td>0.35423</td>\n",
       "      <td>0.488595</td>\n",
       "      <td>0.392329</td>\n",
       "      <td>0.245274</td>\n",
       "      <td>0.056565</td>\n",
       "      <td>0.009705</td>\n",
       "      <td>-0.005239</td>\n",
       "      <td>-0.028730</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>reg069_A</th>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.085201</td>\n",
       "      <td>0.055839</td>\n",
       "      <td>0.31447</td>\n",
       "      <td>0.264596</td>\n",
       "      <td>0.200747</td>\n",
       "      <td>0.087016</td>\n",
       "      <td>0.021755</td>\n",
       "      <td>0.020824</td>\n",
       "      <td>-0.047382</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>reg069_B</th>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.142856</td>\n",
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       "      <td>0.35052</td>\n",
       "      <td>0.28406</td>\n",
       "      <td>0.247712</td>\n",
       "      <td>0.08814</td>\n",
       "      <td>0.043693</td>\n",
       "      <td>0.017784</td>\n",
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       "      <th>reg070_A</th>\n",
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       "      <td>0.387595</td>\n",
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       "      <td>0.386674</td>\n",
       "      <td>0.265592</td>\n",
       "      <td>0.071962</td>\n",
       "      <td>0.026927</td>\n",
       "      <td>0.021085</td>\n",
       "      <td>-0.013169</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>reg070_B</th>\n",
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       "      <td>0.01328</td>\n",
       "      <td>-0.046790</td>\n",
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      "text/plain": [
       "               2.0       4.0       8.0      16.0      24.0      32.0  \\\n",
       "reg001_A -0.333333  0.265996  0.145956  0.368778  0.395528  0.408648   \n",
       "reg001_B -0.333333  0.046393  0.136096  0.156415  0.142116  0.077049   \n",
       "reg002_A -0.333333  0.161656   0.48133  0.653945  0.536351  0.429751   \n",
       "reg002_B -0.333333 -0.172175  0.009045  0.141441  0.198274   0.13304   \n",
       "reg003_A -0.333333  0.023371  0.468953  0.570232  0.483367  0.304637   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "reg068_B -0.333333  0.130753   0.35423  0.488595  0.392329  0.245274   \n",
       "reg069_A -0.333333  0.085201  0.055839   0.31447  0.264596  0.200747   \n",
       "reg069_B -0.333333  0.142856  0.258559   0.35052   0.28406  0.247712   \n",
       "reg070_A -0.333333  0.387595  0.335771  0.498839  0.386674  0.265592   \n",
       "reg070_B -0.333333 -0.052269  0.122844  0.238468  0.105399  0.083936   \n",
       "\n",
       "              48.0      64.0      72.0     Slope  Patients  Condition  \n",
       "reg001_A  0.249583  0.084402  0.021259 -0.050577         1          1  \n",
       "reg001_B -0.014528 -0.004171 -0.002126 -0.032809         1          1  \n",
       "reg002_A  0.200143  0.057148  0.030135 -0.041940         1          1  \n",
       "reg002_B  0.062402  0.026924  0.008667 -0.066184         1          1  \n",
       "reg003_A  0.090609  0.020104  0.016932 -0.039729         2          2  \n",
       "...            ...       ...       ...       ...       ...        ...  \n",
       "reg068_B  0.056565  0.009705 -0.005239 -0.028730        34          1  \n",
       "reg069_A  0.087016  0.021755  0.020824 -0.047382        35          1  \n",
       "reg069_B   0.08814  0.043693  0.017784 -0.036851        35          1  \n",
       "reg070_A  0.071962  0.026927  0.021085 -0.013169        35          1  \n",
       "reg070_B  0.052466  0.015476   0.01328 -0.046790        35          1  \n",
       "\n",
       "[140 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdi_results['Patients'] = mdi_results.index.map(region_to_patient)\n",
    "mdi_results['Condition'] = mdi_results.index.map(region_to_condition)\n",
    "mdi_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74777499-399e-4c96-8372-9f7dab8c0403",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.359607</td>\n",
       "      <td>0.468953</td>\n",
       "      <td>0.590012</td>\n",
       "      <td>0.483367</td>\n",
       "      <td>0.304637</td>\n",
       "      <td>0.110694</td>\n",
       "      <td>0.022115</td>\n",
       "      <td>0.018035</td>\n",
       "      <td>-0.004435</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.280703</td>\n",
       "      <td>0.502827</td>\n",
       "      <td>0.581019</td>\n",
       "      <td>0.554904</td>\n",
       "      <td>0.411825</td>\n",
       "      <td>0.173702</td>\n",
       "      <td>0.059353</td>\n",
       "      <td>0.033071</td>\n",
       "      <td>-0.022406</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.452881</td>\n",
       "      <td>0.552124</td>\n",
       "      <td>0.59934</td>\n",
       "      <td>0.596691</td>\n",
       "      <td>0.459507</td>\n",
       "      <td>0.204825</td>\n",
       "      <td>0.067756</td>\n",
       "      <td>0.030451</td>\n",
       "      <td>-0.017915</td>\n",
       "      <td>2</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.221698</td>\n",
       "      <td>0.531216</td>\n",
       "      <td>0.576677</td>\n",
       "      <td>0.491064</td>\n",
       "      <td>0.353425</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.030738</td>\n",
       "      <td>0.016664</td>\n",
       "      <td>-0.021299</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.385274</td>\n",
       "      <td>0.570914</td>\n",
       "      <td>0.61025</td>\n",
       "      <td>0.614265</td>\n",
       "      <td>0.506903</td>\n",
       "      <td>0.284822</td>\n",
       "      <td>0.10405</td>\n",
       "      <td>0.054788</td>\n",
       "      <td>-0.010830</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.251205</td>\n",
       "      <td>0.411464</td>\n",
       "      <td>0.530734</td>\n",
       "      <td>0.379842</td>\n",
       "      <td>0.271132</td>\n",
       "      <td>0.099825</td>\n",
       "      <td>0.025405</td>\n",
       "      <td>0.008861</td>\n",
       "      <td>-0.027129</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.548189</td>\n",
       "      <td>0.409619</td>\n",
       "      <td>0.420385</td>\n",
       "      <td>0.366728</td>\n",
       "      <td>0.256045</td>\n",
       "      <td>0.098434</td>\n",
       "      <td>0.038924</td>\n",
       "      <td>0.020294</td>\n",
       "      <td>0.010658</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.560025</td>\n",
       "      <td>0.545334</td>\n",
       "      <td>0.602347</td>\n",
       "      <td>0.572577</td>\n",
       "      <td>0.468931</td>\n",
       "      <td>0.24803</td>\n",
       "      <td>0.09625</td>\n",
       "      <td>0.047036</td>\n",
       "      <td>0.011478</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.375449</td>\n",
       "      <td>0.500484</td>\n",
       "      <td>0.616395</td>\n",
       "      <td>0.55893</td>\n",
       "      <td>0.530189</td>\n",
       "      <td>0.309713</td>\n",
       "      <td>0.12657</td>\n",
       "      <td>0.041742</td>\n",
       "      <td>-0.014243</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.254617</td>\n",
       "      <td>0.422783</td>\n",
       "      <td>0.595666</td>\n",
       "      <td>0.561323</td>\n",
       "      <td>0.512197</td>\n",
       "      <td>0.319585</td>\n",
       "      <td>0.132562</td>\n",
       "      <td>0.077044</td>\n",
       "      <td>-0.040075</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.290103</td>\n",
       "      <td>0.490346</td>\n",
       "      <td>0.47917</td>\n",
       "      <td>0.447034</td>\n",
       "      <td>0.360642</td>\n",
       "      <td>0.186975</td>\n",
       "      <td>0.05653</td>\n",
       "      <td>0.036553</td>\n",
       "      <td>-0.007438</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
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       "      <td>0.23178</td>\n",
       "      <td>0.320979</td>\n",
       "      <td>0.561386</td>\n",
       "      <td>0.537345</td>\n",
       "      <td>0.458554</td>\n",
       "      <td>0.241923</td>\n",
       "      <td>0.082513</td>\n",
       "      <td>0.063832</td>\n",
       "      <td>-0.042398</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.409296</td>\n",
       "      <td>0.352025</td>\n",
       "      <td>0.520332</td>\n",
       "      <td>0.460418</td>\n",
       "      <td>0.352456</td>\n",
       "      <td>0.134917</td>\n",
       "      <td>0.037089</td>\n",
       "      <td>0.015709</td>\n",
       "      <td>-0.001108</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.276053</td>\n",
       "      <td>0.45819</td>\n",
       "      <td>0.566384</td>\n",
       "      <td>0.494225</td>\n",
       "      <td>0.377591</td>\n",
       "      <td>0.20468</td>\n",
       "      <td>0.071407</td>\n",
       "      <td>0.048669</td>\n",
       "      <td>-0.024788</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.283969</td>\n",
       "      <td>0.540566</td>\n",
       "      <td>0.495531</td>\n",
       "      <td>0.448659</td>\n",
       "      <td>0.310434</td>\n",
       "      <td>0.124904</td>\n",
       "      <td>0.039163</td>\n",
       "      <td>0.025403</td>\n",
       "      <td>-0.007948</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.350666</td>\n",
       "      <td>0.40724</td>\n",
       "      <td>0.518177</td>\n",
       "      <td>0.504092</td>\n",
       "      <td>0.426257</td>\n",
       "      <td>0.196913</td>\n",
       "      <td>0.099487</td>\n",
       "      <td>0.057869</td>\n",
       "      <td>-0.019798</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.260239</td>\n",
       "      <td>0.621972</td>\n",
       "      <td>0.608379</td>\n",
       "      <td>0.495294</td>\n",
       "      <td>0.390612</td>\n",
       "      <td>0.180294</td>\n",
       "      <td>0.063906</td>\n",
       "      <td>0.052532</td>\n",
       "      <td>-0.025944</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.311724</td>\n",
       "      <td>0.414214</td>\n",
       "      <td>0.517657</td>\n",
       "      <td>0.530521</td>\n",
       "      <td>0.442624</td>\n",
       "      <td>0.24082</td>\n",
       "      <td>0.111727</td>\n",
       "      <td>0.079033</td>\n",
       "      <td>-0.040574</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.059724</td>\n",
       "      <td>0.323085</td>\n",
       "      <td>0.527343</td>\n",
       "      <td>0.541372</td>\n",
       "      <td>0.485991</td>\n",
       "      <td>0.273654</td>\n",
       "      <td>0.086025</td>\n",
       "      <td>0.05362</td>\n",
       "      <td>-0.041999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.517133</td>\n",
       "      <td>0.334926</td>\n",
       "      <td>0.459136</td>\n",
       "      <td>0.41447</td>\n",
       "      <td>0.329835</td>\n",
       "      <td>0.10952</td>\n",
       "      <td>0.045726</td>\n",
       "      <td>0.024866</td>\n",
       "      <td>-0.010565</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.328721</td>\n",
       "      <td>0.400408</td>\n",
       "      <td>0.329228</td>\n",
       "      <td>0.197669</td>\n",
       "      <td>0.150023</td>\n",
       "      <td>0.085127</td>\n",
       "      <td>0.032288</td>\n",
       "      <td>0.019706</td>\n",
       "      <td>-0.000962</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.495936</td>\n",
       "      <td>0.308265</td>\n",
       "      <td>0.443654</td>\n",
       "      <td>0.318034</td>\n",
       "      <td>0.242123</td>\n",
       "      <td>0.109818</td>\n",
       "      <td>0.022998</td>\n",
       "      <td>0.021065</td>\n",
       "      <td>-0.002791</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.503906</td>\n",
       "      <td>0.504972</td>\n",
       "      <td>0.572143</td>\n",
       "      <td>0.591163</td>\n",
       "      <td>0.479414</td>\n",
       "      <td>0.221795</td>\n",
       "      <td>0.072084</td>\n",
       "      <td>0.056701</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.178268</td>\n",
       "      <td>0.395445</td>\n",
       "      <td>0.424921</td>\n",
       "      <td>0.280041</td>\n",
       "      <td>0.177824</td>\n",
       "      <td>0.088362</td>\n",
       "      <td>0.023252</td>\n",
       "      <td>0.015276</td>\n",
       "      <td>-0.031601</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.369389</td>\n",
       "      <td>0.598151</td>\n",
       "      <td>0.659627</td>\n",
       "      <td>0.518624</td>\n",
       "      <td>0.339871</td>\n",
       "      <td>0.090942</td>\n",
       "      <td>0.037074</td>\n",
       "      <td>0.016611</td>\n",
       "      <td>-0.007486</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.119093</td>\n",
       "      <td>0.542421</td>\n",
       "      <td>0.601661</td>\n",
       "      <td>0.537176</td>\n",
       "      <td>0.35669</td>\n",
       "      <td>0.132044</td>\n",
       "      <td>0.038502</td>\n",
       "      <td>0.029162</td>\n",
       "      <td>-0.011060</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.345421</td>\n",
       "      <td>0.404147</td>\n",
       "      <td>0.536792</td>\n",
       "      <td>0.552994</td>\n",
       "      <td>0.469785</td>\n",
       "      <td>0.238784</td>\n",
       "      <td>0.064277</td>\n",
       "      <td>0.041063</td>\n",
       "      <td>-0.005439</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.189771</td>\n",
       "      <td>0.496115</td>\n",
       "      <td>0.576985</td>\n",
       "      <td>0.46641</td>\n",
       "      <td>0.321321</td>\n",
       "      <td>0.088229</td>\n",
       "      <td>0.048894</td>\n",
       "      <td>0.013782</td>\n",
       "      <td>-0.028873</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.186993</td>\n",
       "      <td>0.473751</td>\n",
       "      <td>0.536794</td>\n",
       "      <td>0.545659</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.102306</td>\n",
       "      <td>0.035329</td>\n",
       "      <td>0.00635</td>\n",
       "      <td>-0.029216</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.407866</td>\n",
       "      <td>0.469721</td>\n",
       "      <td>0.428933</td>\n",
       "      <td>0.421011</td>\n",
       "      <td>0.319593</td>\n",
       "      <td>0.135964</td>\n",
       "      <td>0.036085</td>\n",
       "      <td>0.024634</td>\n",
       "      <td>-0.012787</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.223802</td>\n",
       "      <td>0.358884</td>\n",
       "      <td>0.455946</td>\n",
       "      <td>0.315288</td>\n",
       "      <td>0.266565</td>\n",
       "      <td>0.064718</td>\n",
       "      <td>0.020492</td>\n",
       "      <td>0.012794</td>\n",
       "      <td>-0.022146</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.310102</td>\n",
       "      <td>0.581609</td>\n",
       "      <td>0.609703</td>\n",
       "      <td>0.553607</td>\n",
       "      <td>0.438596</td>\n",
       "      <td>0.231988</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.036104</td>\n",
       "      <td>-0.019679</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.243084</td>\n",
       "      <td>0.35423</td>\n",
       "      <td>0.488595</td>\n",
       "      <td>0.392329</td>\n",
       "      <td>0.292669</td>\n",
       "      <td>0.202761</td>\n",
       "      <td>0.097191</td>\n",
       "      <td>0.03282</td>\n",
       "      <td>-0.024894</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.387595</td>\n",
       "      <td>0.335771</td>\n",
       "      <td>0.498839</td>\n",
       "      <td>0.386674</td>\n",
       "      <td>0.265592</td>\n",
       "      <td>0.08814</td>\n",
       "      <td>0.043693</td>\n",
       "      <td>0.021085</td>\n",
       "      <td>-0.013169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Patients       2.0       4.0       8.0      16.0      24.0      32.0  \\\n",
       "0          1 -0.333333  0.265996   0.48133  0.653945  0.536351  0.429751   \n",
       "1          2 -0.333333  0.359607  0.468953  0.590012  0.483367  0.304637   \n",
       "2          3 -0.333333  0.280703  0.502827  0.581019  0.554904  0.411825   \n",
       "3          4 -0.333333  0.452881  0.552124   0.59934  0.596691  0.459507   \n",
       "4          5 -0.333333  0.221698  0.531216  0.576677  0.491064  0.353425   \n",
       "5          6 -0.333333  0.385274  0.570914   0.61025  0.614265  0.506903   \n",
       "6          7 -0.333333  0.251205  0.411464  0.530734  0.379842  0.271132   \n",
       "7          8 -0.333333  0.548189  0.409619  0.420385  0.366728  0.256045   \n",
       "8          9 -0.333333  0.560025  0.545334  0.602347  0.572577  0.468931   \n",
       "9         10 -0.333333  0.375449  0.500484  0.616395   0.55893  0.530189   \n",
       "10        11 -0.333333  0.254617  0.422783  0.595666  0.561323  0.512197   \n",
       "11        12 -0.333333  0.290103  0.490346   0.47917  0.447034  0.360642   \n",
       "12        13 -0.333333   0.23178  0.320979  0.561386  0.537345  0.458554   \n",
       "13        14 -0.333333  0.409296  0.352025  0.520332  0.460418  0.352456   \n",
       "14        15 -0.333333  0.276053   0.45819  0.566384  0.494225  0.377591   \n",
       "15        16 -0.333333  0.283969  0.540566  0.495531  0.448659  0.310434   \n",
       "16        17 -0.333333  0.350666   0.40724  0.518177  0.504092  0.426257   \n",
       "17        18 -0.333333  0.260239  0.621972  0.608379  0.495294  0.390612   \n",
       "18        19 -0.333333  0.311724  0.414214  0.517657  0.530521  0.442624   \n",
       "19        20 -0.333333  0.059724  0.323085  0.527343  0.541372  0.485991   \n",
       "20        21 -0.333333  0.517133  0.334926  0.459136   0.41447  0.329835   \n",
       "21        22 -0.333333  0.328721  0.400408  0.329228  0.197669  0.150023   \n",
       "22        23 -0.333333  0.495936  0.308265  0.443654  0.318034  0.242123   \n",
       "23        24 -0.333333  0.503906  0.504972  0.572143  0.591163  0.479414   \n",
       "24        25 -0.333333  0.178268  0.395445  0.424921  0.280041  0.177824   \n",
       "25        26 -0.333333  0.369389  0.598151  0.659627  0.518624  0.339871   \n",
       "26        27 -0.333333  0.119093  0.542421  0.601661  0.537176   0.35669   \n",
       "27        28 -0.333333  0.345421  0.404147  0.536792  0.552994  0.469785   \n",
       "28        29 -0.333333  0.189771  0.496115  0.576985   0.46641  0.321321   \n",
       "29        30 -0.333333  0.186993  0.473751  0.536794  0.545659  0.380952   \n",
       "30        31 -0.333333  0.407866  0.469721  0.428933  0.421011  0.319593   \n",
       "31        32 -0.333333  0.223802  0.358884  0.455946  0.315288  0.266565   \n",
       "32        33 -0.333333  0.310102  0.581609  0.609703  0.553607  0.438596   \n",
       "33        34 -0.333333  0.243084   0.35423  0.488595  0.392329  0.292669   \n",
       "34        35 -0.333333  0.387595  0.335771  0.498839  0.386674  0.265592   \n",
       "\n",
       "        48.0      64.0      72.0     Slope  Condition  \n",
       "0   0.249583  0.084402  0.030135 -0.032809          1  \n",
       "1   0.110694  0.022115  0.018035 -0.004435          2  \n",
       "2   0.173702  0.059353  0.033071 -0.022406          2  \n",
       "3   0.204825  0.067756  0.030451 -0.017915          2  \n",
       "4      0.133  0.030738  0.016664 -0.021299          2  \n",
       "5   0.284822   0.10405  0.054788 -0.010830          1  \n",
       "6   0.099825  0.025405  0.008861 -0.027129          2  \n",
       "7   0.098434  0.038924  0.020294  0.010658          2  \n",
       "8    0.24803   0.09625  0.047036  0.011478          2  \n",
       "9   0.309713   0.12657  0.041742 -0.014243          1  \n",
       "10  0.319585  0.132562  0.077044 -0.040075          1  \n",
       "11  0.186975   0.05653  0.036553 -0.007438          1  \n",
       "12  0.241923  0.082513  0.063832 -0.042398          1  \n",
       "13  0.134917  0.037089  0.015709 -0.001108          2  \n",
       "14   0.20468  0.071407  0.048669 -0.024788          2  \n",
       "15  0.124904  0.039163  0.025403 -0.007948          2  \n",
       "16  0.196913  0.099487  0.057869 -0.019798          1  \n",
       "17  0.180294  0.063906  0.052532 -0.025944          2  \n",
       "18   0.24082  0.111727  0.079033 -0.040574          1  \n",
       "19  0.273654  0.086025   0.05362 -0.041999          1  \n",
       "20   0.10952  0.045726  0.024866 -0.010565          1  \n",
       "21  0.085127  0.032288  0.019706 -0.000962          2  \n",
       "22  0.109818  0.022998  0.021065 -0.002791          2  \n",
       "23  0.221795  0.072084  0.056701  0.000581          1  \n",
       "24  0.088362  0.023252  0.015276 -0.031601          2  \n",
       "25  0.090942  0.037074  0.016611 -0.007486          2  \n",
       "26  0.132044  0.038502  0.029162 -0.011060          2  \n",
       "27  0.238784  0.064277  0.041063 -0.005439          1  \n",
       "28  0.088229  0.048894  0.013782 -0.028873          1  \n",
       "29  0.102306  0.035329   0.00635 -0.029216          2  \n",
       "30  0.135964  0.036085  0.024634 -0.012787          2  \n",
       "31  0.064718  0.020492  0.012794 -0.022146          1  \n",
       "32  0.231988  0.096801  0.036104 -0.019679          1  \n",
       "33  0.202761  0.097191   0.03282 -0.024894          1  \n",
       "34   0.08814  0.043693  0.021085 -0.013169          1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute averages by every patients\n",
    "mdi_avg_results = mdi_results.groupby(by=\"Patients\").max().reset_index()\n",
    "mdi_avg_results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c8227ff-c107-45d3-9e47-b7e9928d29dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patients</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Diversity Value</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.024634</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.012794</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.036104</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.03282</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.021085</td>\n",
       "      <td>Tissue Sample</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>315 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Patients  Condition Scale Diversity Value         sample\n",
       "0           1          1   2.0       -0.333333  Tissue Sample\n",
       "1           2          2   2.0       -0.333333  Tissue Sample\n",
       "2           3          2   2.0       -0.333333  Tissue Sample\n",
       "3           4          2   2.0       -0.333333  Tissue Sample\n",
       "4           5          2   2.0       -0.333333  Tissue Sample\n",
       "..        ...        ...   ...             ...            ...\n",
       "310        31          2  72.0        0.024634  Tissue Sample\n",
       "311        32          1  72.0        0.012794  Tissue Sample\n",
       "312        33          1  72.0        0.036104  Tissue Sample\n",
       "313        34          1  72.0         0.03282  Tissue Sample\n",
       "314        35          1  72.0        0.021085  Tissue Sample\n",
       "\n",
       "[315 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_melted = pd.melt(mdi_avg_results, id_vars=['Patients', 'Condition'], value_vars=scales, \n",
    "                    var_name='Scale', value_name='Diversity Value')\n",
    "df_melted['sample'] = 'Tissue Sample'\n",
    "df_melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0a2e31c4-c636-4920-a7b8-baefabb68ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xrange = []\n",
    "yrange = []\n",
    "for region in protein['File Name'].unique():\n",
    "    spatial_value = protein[protein['File Name']==region][['X:X','Y:Y']]\n",
    "    xrange.append(spatial_value.max(axis=0)['X:X'] - spatial_value.min(axis=0)['X:X'])\n",
    "    yrange.append(spatial_value.max(axis=0)['Y:Y'] - spatial_value.min(axis=0)['Y:Y'])\n",
    "mean_xrange = np.mean(xrange)\n",
    "std_xrange = np.std(xrange)\n",
    "mean_yrange = np.mean(yrange)\n",
    "std_yrange = np.std(yrange)\n",
    "\n",
    "# Calculate mean and confidence interval\n",
    "grouped = df_melted.groupby('Scale')\n",
    "mean_values = grouped['Diversity Value'].mean()\n",
    "conf_intervals = grouped['Diversity Value'].apply(lambda x: stats.sem(x) * stats.t.ppf((1 + 0.95) / 2., len(x)-1))\n",
    "\n",
    "# Plotting using sns.lineplot\n",
    "plt.figure(figsize=(6, 4))\n",
    "ax = sns.lineplot(data=df_melted, \n",
    "                  x='Scale', \n",
    "                  y='Diversity Value', \n",
    "                  style='sample',\n",
    "                  markers=True,\n",
    "                  estimator='mean', \n",
    "                  err_style='bars', \n",
    "                  errorbar=(\"ci\", 95),\n",
    "                  err_kws={\"capsize\":5.0}\n",
    "                 )\n",
    "\n",
    "# Annotating error bars with their value\n",
    "for i, (scale, mean, ci) in enumerate(zip(mean_values.index, mean_values, conf_intervals)):\n",
    "    ax.text(scale, mean + ci, f'{mean:.3f}±{ci:.3f}', color='black', ha='center', va='bottom')\n",
    "\n",
    "# Drawing red dashed horizontal lines at half the maximum of x and y axes\n",
    "mean_diversity_per_scale = df_melted.groupby('Scale')['Diversity Value'].mean()\n",
    "y_sep = mean_diversity_per_scale.median()\n",
    "x_sep = mean_diversity_per_scale.idxmax()\n",
    "\n",
    "ax.axhline(y_sep, color='red', linestyle='--')\n",
    "ax.axvline(x_sep, color='red', linestyle='--')\n",
    "ax.get_legend().remove()\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "plt.xlabel('', fontsize=0)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.ylabel(f\"GDI\", fontsize=16)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Add secondary x-axis\n",
    "xtick_labels = [tick.get_text() for tick in ax.get_xticklabels()][2:-1]\n",
    "scales = [int(label) for label in xtick_labels if label.strip() != '']\n",
    "x_sizes = [mean_xrange / scale for scale in scales]\n",
    "y_sizes = [mean_yrange / scale for scale in scales]\n",
    "size_labels = [f\"{int(x_size)}×{int(y_size)}\" for x_size, y_size in zip(x_sizes, y_sizes)]\n",
    "secax = ax.secondary_xaxis(location=-0.075)\n",
    "secax.set_xticks(scales)\n",
    "secax.set_xticklabels(size_labels)\n",
    "secax.tick_params('x', length=0)\n",
    "secax.spines['bottom'].set_linewidth(0)\n",
    "secax.set_xlabel(f'Scale \\n (Area μm²)', fontsize=12)\n",
    "\n",
    "plt.title(f'GDI per Scale with 95% Confidence Intervals')\n",
    "plt.grid(False)\n",
    "fig = plt.gcf()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfc3864-1e1b-41db-b191-b348cc4982f9",
   "metadata": {},
   "source": [
    "## Perform Ecospatial Analysis on one sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2725931d-dd8c-47f1-909f-4e6bbce388ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define sample \n",
    "sample_id = 'reg021_A'\n",
    "scale = 32.\n",
    "\n",
    "# Generate Quardrats\n",
    "patches_coordinates = eco.generate_patches(spatial_data=protein,\n",
    "                                           library_key='File Name',\n",
    "                                           library_id=sample_id,\n",
    "                                           scaling_factor=scale,\n",
    "                                           spatial_key=['X:X','Y:Y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db5989f9-7f99-4c8d-be2c-c242fbb94a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: reg021_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg021_A at scale 1.0 diversity is 2.789462693106172\n",
      "Processing region: reg021_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg021_A at scale 2.0 diversity is 2.8041743883930996\n",
      "Processing region: reg021_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg021_A at scale 4.0 diversity is 2.4923208169420734\n",
      "Processing region: reg021_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg021_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg021_A at scale 8.0 diversity is 1.9846964659994617\n",
      "Processing region: reg021_A at scale 16.0\n",
      "22.656 per cent patches are empty\n",
      "reg021_A at scale 16.0 has 23 patches with zero diveristy\n",
      "reg021_A at scale 16.0 diversity is 1.5605304675261158\n",
      "Processing region: reg021_A at scale 32.0\n",
      "36.328 per cent patches are empty\n",
      "reg021_A at scale 32.0 has 164 patches with zero diveristy\n",
      "reg021_A at scale 32.0 diversity is 0.956050983418754\n"
     ]
    }
   ],
   "source": [
    "# Define the sequence of scales\n",
    "scales = [1., 2., 4., 8., 16., 32.]\n",
    "\n",
    "df_results = eco.calculate_MDI(spatial_data=protein,\n",
    "                               scales=scales,\n",
    "                               library_key='File Name',\n",
    "                               library_id=[sample_id],\n",
    "                               spatial_key=['X:X','Y:Y'],\n",
    "                               cluster_key='ClusterName',\n",
    "                               selecting_scale=False,\n",
    "                               random_patch=False,\n",
    "                               plotfigs=False,\n",
    "                               savefigs=False,\n",
    "                               patch_kwargs={'random_seed': None, 'min_points':2},\n",
    "                               other_kwargs={'metric': 'Shannon Diversity'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "68b75abf-a0c3-4b90-a217-ff87914f4397",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MDI: 2.789\n"
     ]
    }
   ],
   "source": [
    "print(f\"MDI: {df_results.loc[sample_id].values[0]:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b888d2-0dff-48ce-8996-54caec5e75bf",
   "metadata": {},
   "source": [
    "### Diversity Heatmap Related Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6844217b-c24c-48b4-9805-9878dcae79d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.328 per cent patches are empty\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Caculate Shannon Diversity Index for each quadrat\n",
    "patch_indices, patches_comp = eco.calculate_diversity_index(spatial_data=protein, \n",
    "                                                            library_key='File Name', \n",
    "                                                            library_id=sample_id, \n",
    "                                                            spatial_key=['X:X','Y:Y'], \n",
    "                                                            patches=patches_coordinates, \n",
    "                                                            cluster_key='ClusterName', \n",
    "                                                            metric='Shannon Diversity', return_comp=True)\n",
    "\n",
    "\n",
    "# Visualize the diversity indices of quadrats\n",
    "grid, heatmap_fig = eco.diversity_heatmap(spatial_data=protein,\n",
    "                                          library_key='File Name', \n",
    "                                          library_id=sample_id,\n",
    "                                          spatial_key=['X:X','Y:Y'],\n",
    "                                          patches=patches_coordinates, \n",
    "                                          heterogeneity_indices=patch_indices,\n",
    "                                          tissue_only=False,\n",
    "                                          plot=True,\n",
    "                                          return_fig=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "77f11229-aa8a-4f0a-803d-be18e64f6441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDI: 0.512, p-value: 0.001\n"
     ]
    }
   ],
   "source": [
    "# Calculate GDI \n",
    "GDI, p_sim = eco.global_spatial_stats(grid, mode='MoranI', tissue_only=False, plot_weights=False)\n",
    "print(f\"GDI: {GDI:.3f}, p-value: {p_sim:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6dcc3dcf-51af-41fe-9294-e8a97a3ece3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using MoranI\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, _, lisa = eco.local_spatial_stats(grid=grid, \n",
    "                                     mode='MoranI', \n",
    "                                     tissue_only=False, \n",
    "                                     p_value=0.01, \n",
    "                                     seed=42, \n",
    "                                     plot_weights=False, \n",
    "                                     return_stats=True)\n",
    "\n",
    "heatmap_fig = eco.signif_heatmap(spatial_data=protein,\n",
    "                                 library_key='File Name', \n",
    "                                 library_id=sample_id,\n",
    "                                 spatial_key=['X:X','Y:Y'],\n",
    "                                 patches=patches_coordinates, \n",
    "                                 heterogeneity_indices=pd.Series(lisa.p_sim),\n",
    "                                 tissue_only=False,\n",
    "                                 plot=True,\n",
    "                                 discrete=True,\n",
    "                                 return_fig=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01b10ca6-8e43-4c0e-9a89-f3e53a91a177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using MoranI\n"
     ]
    }
   ],
   "source": [
    "# Calculate LDI and find hot/coldspots \n",
    "hotspots, coldspots = eco.local_spatial_stats(grid, mode='MoranI', p_value=0.05, tissue_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "958aa7f7-3a83-49a8-88ac-b48a8043af66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "width: 1677.000, height: 1434.000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x15f2b8b90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 561.339x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise hot/coldspots \n",
    "combined_spots = (hotspots * 1) + (coldspots * -1)\n",
    "seismic = plt.cm.seismic\n",
    "colors = [seismic(0), \"white\", seismic(0.999)]\n",
    "cmap = mcolors.LinearSegmentedColormap.from_list(\"custom_map\", colors)\n",
    "\n",
    "spatial_value = protein[protein['File Name']==sample_id][['X:X', 'Y:Y']]\n",
    "min_x, min_y = spatial_value.min(axis=0)['X:X'], spatial_value.min(axis=0)['Y:Y']\n",
    "max_x, max_y = spatial_value.max(axis=0)['X:X'], spatial_value.max(axis=0)['Y:Y']\n",
    "width = max_x - min_x\n",
    "height = max_y - min_y\n",
    "print(f\"width: {width:.3f}, height: {height:.3f}\")\n",
    "w, h = figaspect(height/width)\n",
    "\n",
    "spot_fig = plt.figure(figsize=(w, h))\n",
    "ax = spot_fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "# Create a 2D grid\n",
    "grid = np.zeros((int(max_y - min_y + 1), int(max_x - min_x + 1)))\n",
    "\n",
    "# Fill the grid with heterogeneity indices\n",
    "for patch, diversity_index in enumerate(combined_spots.flatten()):\n",
    "    x0, y0, x1, y1 = patches_coordinates[patch]\n",
    "    grid[int(y0-min_y):int(y1-min_y+1), int(x0-min_x):int(x1-min_x+1)] = diversity_index\n",
    "\n",
    "# Plot the heatmap\n",
    "ax.imshow(grid, cmap=cmap, interpolation='none', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "76be15a8-1390-4361-81d6-c3d335851240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: reg021_A at scale 32.0\n",
      "36.328 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg021_A contains 131 diversity hotspots\n",
      "11 islands identified\n",
      "DPI: 80.591\n"
     ]
    }
   ],
   "source": [
    "# Calculate DPI for hotspots\n",
    "Hot = True # Set True to investigate hot islands\n",
    "proximity_I = eco.calculate_DPI(spatial_data=protein, \n",
    "                                scale=32.0, \n",
    "                                library_key='File Name',\n",
    "                                library_id=[sample_id], \n",
    "                                spatial_key=['X:X', 'Y:Y'],\n",
    "                                cluster_key='ClusterName',\n",
    "                                hotspot=Hot,\n",
    "                                mode='MoranI',\n",
    "                                p_value=0.01,\n",
    "                                metric='Shannon Diversity')\n",
    "print(f\"DPI: {proximity_I.loc[sample_id,'DPI']:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "44ced068-204f-404f-aad6-efb102dbae2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aggregating hot islands composition\n"
     ]
    },
    {
     "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>B cells</th>\n",
       "      <th>CD11b+ monocytes</th>\n",
       "      <th>CD11c+ DCs</th>\n",
       "      <th>CD3+ T cells</th>\n",
       "      <th>CD4+ T cells</th>\n",
       "      <th>CD4+ T cells CD45RO+</th>\n",
       "      <th>CD68+ macrophages</th>\n",
       "      <th>CD68+CD163+ macrophages</th>\n",
       "      <th>CD8+ T cells</th>\n",
       "      <th>Tregs</th>\n",
       "      <th>...</th>\n",
       "      <th>granulocytes</th>\n",
       "      <th>immune cells</th>\n",
       "      <th>immune cells / vasculature</th>\n",
       "      <th>plasma cells</th>\n",
       "      <th>smooth muscle</th>\n",
       "      <th>stroma</th>\n",
       "      <th>tumor cells</th>\n",
       "      <th>tumor cells / immune cells</th>\n",
       "      <th>undefined</th>\n",
       "      <th>vasculature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Island_1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Island_2</th>\n",
       "      <td>189.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Island_3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Island_4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Island_5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          B cells  CD11b+ monocytes  CD11c+ DCs  CD3+ T cells  CD4+ T cells  \\\n",
       "Island_1      0.0               0.0         0.0           0.0           2.0   \n",
       "Island_2    189.0               0.0         3.0           5.0         173.0   \n",
       "Island_3      0.0               0.0         0.0           0.0           6.0   \n",
       "Island_4      1.0               0.0         0.0           0.0           0.0   \n",
       "Island_5      1.0               0.0         0.0           0.0           3.0   \n",
       "\n",
       "          CD4+ T cells CD45RO+  CD68+ macrophages  CD68+CD163+ macrophages  \\\n",
       "Island_1                   5.0                0.0                      0.0   \n",
       "Island_2                 104.0                1.0                     51.0   \n",
       "Island_3                   6.0                0.0                      0.0   \n",
       "Island_4                   1.0                0.0                      0.0   \n",
       "Island_5                   2.0                0.0                      0.0   \n",
       "\n",
       "          CD8+ T cells  Tregs  ...  granulocytes  immune cells  \\\n",
       "Island_1           0.0    0.0  ...           0.0           0.0   \n",
       "Island_2          80.0    2.0  ...           5.0          19.0   \n",
       "Island_3           1.0    0.0  ...           0.0           0.0   \n",
       "Island_4           0.0    0.0  ...           0.0           0.0   \n",
       "Island_5           0.0    0.0  ...           0.0           0.0   \n",
       "\n",
       "          immune cells / vasculature  plasma cells  smooth muscle  stroma  \\\n",
       "Island_1                         0.0           0.0            0.0     0.0   \n",
       "Island_2                         1.0          19.0            2.0    11.0   \n",
       "Island_3                         0.0           0.0            0.0     0.0   \n",
       "Island_4                         0.0           0.0            0.0     0.0   \n",
       "Island_5                         0.0           0.0            0.0     0.0   \n",
       "\n",
       "          tumor cells  tumor cells / immune cells  undefined  vasculature  \n",
       "Island_1          0.0                         0.0        0.0          0.0  \n",
       "Island_2         42.0                         0.0       11.0         14.0  \n",
       "Island_3          0.0                         0.0        0.0          0.0  \n",
       "Island_4          0.0                         0.0        0.0          1.0  \n",
       "Island_5          0.0                         0.0        0.0          0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualise Islands composition and similarity\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "labelled_hot, num_hot_islands = eco._utils._label_islands(hotspots, rook=True)\n",
    "labelled_cold, num_cold_islands = eco._utils._label_islands(coldspots, rook=True)\n",
    "if Hot:\n",
    "    print(f\"Aggregating hot islands composition\")\n",
    "    filtered_patches_coordinates = [patch for patch, is_hotspot in zip(patches_coordinates, hotspots.flatten()) if is_hotspot]\n",
    "    filtered_patches_comp = [patch for patch, is_hotspot in zip(patches_comp, hotspots.flatten()) if is_hotspot]\n",
    "    island_comp = eco._utils.aggregate_spot_compositions(labelled_hot, patches_comp)\n",
    "else:\n",
    "    print(f\"Aggregating cold islands composition\")\n",
    "    filtered_patches_coordinates = [patch for patch, is_coldspot in zip(patches_coordinates, coldspots.flatten()) if is_coldspot]\n",
    "    filtered_patches_comp = [patch for patch, is_coldspot in zip(patches_comp, coldspots.flatten()) if is_coldspot]\n",
    "    island_comp = eco._utils.aggregate_spot_compositions(labelled_cold, patches_comp)\n",
    "island_comp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3b0c97d0-655b-4c22-806e-3fd38547a212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1677 1434\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x15e729310>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 561.339x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise labelled islands (hot islands in red; cold islands in blue)\n",
    "from scipy.ndimage import center_of_mass\n",
    "combined_spots = (hotspots * 1) + (coldspots * 1) \n",
    "seismic = plt.cm.seismic\n",
    "colors = ['white', 'grey']\n",
    "cmap = mcolors.LinearSegmentedColormap.from_list(\"single_color_map\", colors)\n",
    "\n",
    "labelled_hot, num_hot_islands = eco._utils._label_islands(hotspots, rook=True)\n",
    "labelled_cold, num_cold_islands = eco._utils._label_islands(coldspots, rook=True)\n",
    "    \n",
    "min_x, min_y = spatial_value.min(axis=0)['X:X'], spatial_value.min(axis=0)['Y:Y']\n",
    "max_x, max_y = spatial_value.max(axis=0)['X:X'], spatial_value.max(axis=0)['Y:Y']\n",
    "width = max_x - min_x\n",
    "height = max_y - min_y\n",
    "print(width, height)\n",
    "w, h = figaspect(height/width)\n",
    "\n",
    "spot_fig = plt.figure(figsize=(w, h))\n",
    "ax = spot_fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "# Create a 2D grid\n",
    "grid = np.zeros((int(max_y - min_y + 1), int(max_x - min_x + 1)))\n",
    "\n",
    "# Fill the grid with diversity indices\n",
    "for patch, diversity_index in enumerate(combined_spots.flatten()):\n",
    "    x0, y0, x1, y1 = patches_coordinates[patch]\n",
    "    grid[int(y0-min_y):int(y1-min_y+1), int(x0-min_x):int(x1-min_x+1)] = diversity_index\n",
    "\n",
    "# Calculate centroids of hot/cold islands and label them\n",
    "for label in range(1, num_hot_islands + 1):\n",
    "    positions = np.where(labelled_hot == label)\n",
    "    if positions[0].size > 0:\n",
    "        centroid = center_of_mass(labelled_hot == label)\n",
    "        ax.text(centroid[1]*width/scale,\n",
    "                centroid[0]*height/scale, \n",
    "                str(label), \n",
    "                fontsize=12,\n",
    "                fontweight='semibold',\n",
    "                color=seismic(0.999),\n",
    "                alpha=1.0,\n",
    "                ha='center', \n",
    "                va='center')\n",
    "        \n",
    "for label in range(1, num_cold_islands + 1):\n",
    "    positions = np.where(labelled_cold == label)\n",
    "    if positions[0].size > 0:\n",
    "        centroid = center_of_mass(labelled_cold == label)\n",
    "        ax.text(centroid[1]*width/scale,\n",
    "                centroid[0]*height/scale,  \n",
    "                str(label), \n",
    "                fontsize=12,\n",
    "                fontweight='semibold',\n",
    "                color=seismic(0),\n",
    "                alpha=1.0,\n",
    "                ha='center', \n",
    "                va='center')\n",
    "\n",
    "plt.xlabel(\"X Coordinates\")\n",
    "plt.ylabel(\"Y Coordinates\")\n",
    "ax.imshow(grid, cmap=cmap, alpha=0.75, interpolation='none', vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "279e1d20-30a7-4f5c-b064-40d7e31278b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "471.65625"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9.0*width/scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e921b76-27b5-4e2e-a13c-be4db87f3962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "76c3650c-ff31-4544-9cdf-6362b3bd5174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 9.0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "center_of_mass(labelled_hot == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb2760d9-f9a0-4b2f-8ccc-9b351f2a27a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [color_dict[col] for col in island_comp.columns]\n",
    "ax = island_comp.plot(kind='barh', stacked=True, color=colors, figsize=(6,9))\n",
    "\n",
    "# Set plot title, axis labels, and legend\n",
    "plt.title('', fontsize=16)\n",
    "plt.xlabel('Number of Cells', fontsize=16)\n",
    "plt.ylabel('')\n",
    "plt.xticks(rotation=0, fontsize=12)\n",
    "plt.yticks(rotation=0, fontsize=12)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.invert_yaxis()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3afa056e-afbe-4745-9c58-9028eea77135",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "island_comp = island_comp.div(island_comp.sum(axis=1), axis=0)\n",
    "island_comp_filled = island_comp.fillna(0)\n",
    "\n",
    "# Calculate Bray-Curtis distance\n",
    "distances = pdist(island_comp_filled.values, metric='braycurtis')\n",
    "\n",
    "# Convert the condensed distance matrix to a square matrix\n",
    "distance_matrix = squareform(distances)\n",
    "\n",
    "# Create a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(distance_matrix, dtype=bool),k=1)\n",
    "\n",
    "plt.figure(figsize=(7, 6))\n",
    "axis_labels = np.arange(1, len(distance_matrix)+1)\n",
    "sns.heatmap(distance_matrix, \n",
    "            mask=mask, \n",
    "            cmap='rocket', \n",
    "            vmin=0.0,\n",
    "            vmax=1.0, \n",
    "            annot=False, \n",
    "            square=True, \n",
    "            linewidths=.5, \n",
    "            xticklabels=axis_labels, \n",
    "            yticklabels=axis_labels,\n",
    "            cbar_kws={\"shrink\": .5, \"label\": \"Bray-Curtis Dissimilarity\"})\n",
    "\n",
    "plt.title('')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65fa71d2-999c-4605-bc9a-a8cb6790e391",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Perform Ecospatial Analysis on all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf231a94-6929-44b5-bcad-05b3c33b6a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "library_ids = protein['File Name'].unique().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481125d6-47fd-4ad7-aaac-5a3aec9e18c1",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Calculate MDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4eb5dbc4-b043-4122-bba3-91a4ba7f878d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: reg001_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg001_A at scale 1.0 diversity is 3.3164164900586974\n",
      "Processing region: reg001_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg001_B at scale 1.0 diversity is 3.354147234904591\n",
      "Processing region: reg002_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg002_A at scale 1.0 diversity is 3.151848682137609\n",
      "Processing region: reg002_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg002_B at scale 1.0 diversity is 1.1192231346242458\n",
      "Processing region: reg003_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg003_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg003_A at scale 1.0 diversity is 3.0403492069039224\n",
      "Processing region: reg003_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg003_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg003_B at scale 1.0 diversity is 2.42381777792195\n",
      "Processing region: reg004_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg004_A at scale 1.0 diversity is 3.0079336436959943\n",
      "Processing region: reg004_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg004_B at scale 1.0 diversity is 2.468714308448628\n",
      "Processing region: reg005_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg005_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg005_A at scale 1.0 diversity is 3.06810933760685\n",
      "Processing region: reg005_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg005_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg005_B at scale 1.0 diversity is 2.844013997770987\n",
      "Processing region: reg006_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg006_A at scale 1.0 diversity is 3.0899516069206547\n",
      "Processing region: reg006_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg006_B at scale 1.0 diversity is 3.1236760246850523\n",
      "Processing region: reg007_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg007_A at scale 1.0 diversity is 2.74078281172878\n",
      "Processing region: reg007_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg007_B at scale 1.0 diversity is 2.798887762013511\n",
      "Processing region: reg008_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg008_A at scale 1.0 diversity is 2.6104304239252727\n",
      "Processing region: reg008_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg008_B at scale 1.0 diversity is 2.567882631756749\n",
      "Processing region: reg009_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg009_A at scale 1.0 diversity is 3.0456153545069626\n",
      "Processing region: reg009_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg009_B at scale 1.0 diversity is 2.563615845328034\n",
      "Processing region: reg010_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg010_A at scale 1.0 diversity is 3.2771440955567854\n",
      "Processing region: reg010_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg010_B at scale 1.0 diversity is 3.3323584944431874\n",
      "Processing region: reg011_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg011_A at scale 1.0 diversity is 3.1236111964254585\n",
      "Processing region: reg011_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg011_B at scale 1.0 diversity is 3.168378127484966\n",
      "Processing region: reg012_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg012_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg012_A at scale 1.0 diversity is 2.941107336589279\n",
      "Processing region: reg012_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg012_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg012_B at scale 1.0 diversity is 3.1331464065561105\n",
      "Processing region: reg013_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg013_A at scale 1.0 diversity is 2.4596939032646947\n",
      "Processing region: reg013_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg013_B at scale 1.0 diversity is 3.0135580062697387\n",
      "Processing region: reg014_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg014_A at scale 1.0 diversity is 3.1417393955785795\n",
      "Processing region: reg014_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg014_B at scale 1.0 diversity is 2.81193882657248\n",
      "Processing region: reg015_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg015_A at scale 1.0 diversity is 2.8529661156014514\n",
      "Processing region: reg015_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg015_B at scale 1.0 diversity is 1.806531407891682\n",
      "Processing region: reg016_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg016_A at scale 1.0 diversity is 2.786561841977708\n",
      "Processing region: reg016_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg016_B at scale 1.0 diversity is 2.8054524487499664\n",
      "Processing region: reg017_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg017_A at scale 1.0 diversity is 3.096315315227833\n",
      "Processing region: reg017_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg017_B at scale 1.0 diversity is 3.458139001857196\n",
      "Processing region: reg018_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg018_A at scale 1.0 diversity is 3.216787022649993\n",
      "Processing region: reg018_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg018_B at scale 1.0 diversity is 2.5923052990093316\n",
      "Processing region: reg019_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg019_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg019_A at scale 1.0 diversity is 2.335964124646809\n",
      "Processing region: reg019_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg019_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg019_B at scale 1.0 diversity is 2.8120964822760333\n",
      "Processing region: reg020_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg020_A at scale 1.0 diversity is 3.2420898953759583\n",
      "Processing region: reg020_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg020_B at scale 1.0 diversity is 3.0721558915203895\n",
      "Processing region: reg021_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg021_A at scale 1.0 diversity is 2.789462693106172\n",
      "Processing region: reg021_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg021_B at scale 1.0 diversity is 3.13269000949422\n",
      "Processing region: reg022_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg022_A at scale 1.0 diversity is 2.9252440657567584\n",
      "Processing region: reg022_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg022_B at scale 1.0 diversity is 3.2756985004238657\n",
      "Processing region: reg023_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg023_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg023_A at scale 1.0 diversity is 2.107546487138399\n",
      "Processing region: reg023_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg023_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg023_B at scale 1.0 diversity is 3.363958719443704\n",
      "Processing region: reg024_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg024_A at scale 1.0 diversity is 2.2368697128978017\n",
      "Processing region: reg024_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg024_B at scale 1.0 diversity is 2.276401251682321\n",
      "Processing region: reg025_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg025_A at scale 1.0 diversity is 3.0693147873296356\n",
      "Processing region: reg025_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg025_B at scale 1.0 diversity is 3.27580811497425\n",
      "Processing region: reg026_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg026_A at scale 1.0 diversity is 3.2308668009087373\n",
      "Processing region: reg026_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg026_B at scale 1.0 diversity is 2.8590106536899835\n",
      "Processing region: reg027_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg027_A at scale 1.0 diversity is 2.9054114170999514\n",
      "Processing region: reg027_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg027_B at scale 1.0 diversity is 2.3618271427345165\n",
      "Processing region: reg028_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg028_A at scale 1.0 diversity is 3.5199569189863804\n",
      "Processing region: reg028_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg028_B at scale 1.0 diversity is 3.2303548408160783\n",
      "Processing region: reg029_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg029_A at scale 1.0 diversity is 2.53141981690718\n",
      "Processing region: reg029_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg029_B at scale 1.0 diversity is 2.3077229439540745\n",
      "Processing region: reg030_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg030_A at scale 1.0 diversity is 3.0139653343960235\n",
      "Processing region: reg030_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg030_B at scale 1.0 diversity is 2.3153869475947237\n",
      "Processing region: reg031_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg031_A at scale 1.0 diversity is 2.993452562035349\n",
      "Processing region: reg031_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg031_B at scale 1.0 diversity is 3.3941254451548653\n",
      "Processing region: reg032_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg032_A at scale 1.0 diversity is 3.198107491234435\n",
      "Processing region: reg032_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg032_B at scale 1.0 diversity is 3.217669430878999\n",
      "Processing region: reg033_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg033_A at scale 1.0 diversity is 2.523257594385668\n",
      "Processing region: reg033_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg033_B at scale 1.0 diversity is 2.7193098169793317\n",
      "Processing region: reg034_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg034_A at scale 1.0 diversity is 2.75382600157632\n",
      "Processing region: reg034_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg034_B at scale 1.0 diversity is 2.5347126144018204\n",
      "Processing region: reg035_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg035_A at scale 1.0 diversity is 2.96267652939499\n",
      "Processing region: reg035_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg035_B at scale 1.0 diversity is 2.4223620219868\n",
      "Processing region: reg036_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg036_A at scale 1.0 diversity is 3.155803812298486\n",
      "Processing region: reg036_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg036_B at scale 1.0 diversity is 2.552456707354359\n",
      "Processing region: reg037_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg037_A at scale 1.0 diversity is 3.0487934648145134\n",
      "Processing region: reg037_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg037_B at scale 1.0 diversity is 2.7408985949500333\n",
      "Processing region: reg038_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg038_A at scale 1.0 diversity is 3.171661128478348\n",
      "Processing region: reg038_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg038_B at scale 1.0 diversity is 2.597042067113209\n",
      "Processing region: reg039_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg039_A at scale 1.0 diversity is 3.067876224610554\n",
      "Processing region: reg039_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg039_B at scale 1.0 diversity is 3.03730351832146\n",
      "Processing region: reg040_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg040_A at scale 1.0 diversity is 2.927096418417743\n",
      "Processing region: reg040_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg040_B at scale 1.0 diversity is 2.703845520771031\n",
      "Processing region: reg041_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg041_A at scale 1.0 diversity is 2.877191377786103\n",
      "Processing region: reg041_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg041_B at scale 1.0 diversity is 2.0857871387512272\n",
      "Processing region: reg042_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg042_A at scale 1.0 diversity is 1.6188909197393675\n",
      "Processing region: reg042_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg042_B at scale 1.0 diversity is 1.6846881497194053\n",
      "Processing region: reg043_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg043_A at scale 1.0 diversity is 2.366555800681793\n",
      "Processing region: reg043_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg043_B at scale 1.0 diversity is 1.8431742248939642\n",
      "Processing region: reg044_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg044_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg044_A at scale 1.0 diversity is 2.7423091465981138\n",
      "Processing region: reg044_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg044_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg044_B at scale 1.0 diversity is 2.4787524947746715\n",
      "Processing region: reg045_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg045_A at scale 1.0 diversity is 2.3129778444533526\n",
      "Processing region: reg045_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg045_B at scale 1.0 diversity is 2.943622825639336\n",
      "Processing region: reg046_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg046_A at scale 1.0 diversity is 3.428713438505502\n",
      "Processing region: reg046_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg046_B at scale 1.0 diversity is 3.20822669460183\n",
      "Processing region: reg047_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg047_A at scale 1.0 diversity is 3.1617263657176657\n",
      "Processing region: reg047_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg047_B at scale 1.0 diversity is 1.9189380533002915\n",
      "Processing region: reg048_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg048_A at scale 1.0 diversity is 3.1948753157739347\n",
      "Processing region: reg048_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg048_B at scale 1.0 diversity is 3.133588216464989\n",
      "Processing region: reg049_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg049_A at scale 1.0 diversity is 2.629976242108961\n",
      "Processing region: reg049_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg049_B at scale 1.0 diversity is 3.1556579444297492\n",
      "Processing region: reg050_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg050_A at scale 1.0 diversity is 2.166029309990272\n",
      "Processing region: reg050_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg050_B at scale 1.0 diversity is 2.7125971150623718\n",
      "Processing region: reg051_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg051_A at scale 1.0 diversity is 3.034073891548151\n",
      "Processing region: reg051_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg051_B at scale 1.0 diversity is 2.7042193630758464\n",
      "Processing region: reg052_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg052_A at scale 1.0 diversity is 2.90343534365315\n",
      "Processing region: reg052_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg052_B at scale 1.0 diversity is 2.5660934742918453\n",
      "Processing region: reg053_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg053_A at scale 1.0 diversity is 3.326551519627347\n",
      "Processing region: reg053_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg053_B at scale 1.0 diversity is 2.8995514435987273\n",
      "Processing region: reg054_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg054_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg054_A at scale 1.0 diversity is 2.9540587743051603\n",
      "Processing region: reg054_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg054_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg054_B at scale 1.0 diversity is 2.714286671022972\n",
      "Processing region: reg055_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg055_A at scale 1.0 diversity is 3.0400257137217213\n",
      "Processing region: reg055_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg055_B at scale 1.0 diversity is 2.7138492853375844\n",
      "Processing region: reg056_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg056_A at scale 1.0 diversity is 3.632798160738558\n",
      "Processing region: reg056_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg056_B at scale 1.0 diversity is 3.238947609496681\n",
      "Processing region: reg057_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg057_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg057_A at scale 1.0 diversity is 2.567724649126506\n",
      "Processing region: reg057_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg057_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg057_B at scale 1.0 diversity is 3.179058321303633\n",
      "Processing region: reg058_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg058_A at scale 1.0 diversity is 1.13671840573412\n",
      "Processing region: reg058_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg058_B at scale 1.0 diversity is 0.8184673974571768\n",
      "Processing region: reg059_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg059_A at scale 1.0 diversity is 3.163004841005448\n",
      "Processing region: reg059_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg059_B at scale 1.0 diversity is 2.742712488124099\n",
      "Processing region: reg060_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg060_A at scale 1.0 diversity is 3.1456892577421365\n",
      "Processing region: reg060_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg060_B at scale 1.0 diversity is 2.9712260813665314\n",
      "Processing region: reg061_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg061_A at scale 1.0 diversity is 2.96686360691106\n",
      "Processing region: reg061_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg061_B at scale 1.0 diversity is 3.056943561409033\n",
      "Processing region: reg062_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg062_A at scale 1.0 diversity is 3.0380976999368756\n",
      "Processing region: reg062_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg062_B at scale 1.0 diversity is 3.183534227033122\n",
      "Processing region: reg063_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg063_A at scale 1.0 diversity is 2.5045489183143967\n",
      "Processing region: reg063_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg063_B at scale 1.0 diversity is 3.1418020740409784\n",
      "Processing region: reg064_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg064_A at scale 1.0 diversity is 2.476319359307367\n",
      "Processing region: reg064_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg064_B at scale 1.0 diversity is 3.301837096761576\n",
      "Processing region: reg065_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg065_A at scale 1.0 diversity is 2.2003907634184245\n",
      "Processing region: reg065_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg065_B at scale 1.0 diversity is 2.9364151337003945\n",
      "Processing region: reg066_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg066_A at scale 1.0 diversity is 2.3067398618907617\n",
      "Processing region: reg066_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg066_B at scale 1.0 diversity is 2.9720614569765713\n",
      "Processing region: reg067_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg067_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg067_A at scale 1.0 diversity is 3.43379293061301\n",
      "Processing region: reg067_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg067_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg067_B at scale 1.0 diversity is 2.40392467074881\n",
      "Processing region: reg068_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg068_A at scale 1.0 diversity is 2.9468292923597827\n",
      "Processing region: reg068_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg068_B at scale 1.0 diversity is 3.656725823093597\n",
      "Processing region: reg069_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg069_A at scale 1.0 diversity is 3.205997636079812\n",
      "Processing region: reg069_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg069_B at scale 1.0 diversity is 3.553294725483734\n",
      "Processing region: reg070_A at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_A at scale 1.0 has 0 patches with zero diveristy\n",
      "reg070_A at scale 1.0 diversity is 2.5419240278328954\n",
      "Processing region: reg070_B at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_B at scale 1.0 has 0 patches with zero diveristy\n",
      "reg070_B at scale 1.0 diversity is 2.6825429533029617\n",
      "Processing region: reg001_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg001_A at scale 2.0 diversity is 3.006720259484636\n",
      "Processing region: reg001_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg001_B at scale 2.0 diversity is 3.0782022986901705\n",
      "Processing region: reg002_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg002_A at scale 2.0 diversity is 2.907866052919977\n",
      "Processing region: reg002_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg002_B at scale 2.0 diversity is 1.0846680190412177\n",
      "Processing region: reg003_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg003_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg003_A at scale 2.0 diversity is 2.7898455684853216\n",
      "Processing region: reg003_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg003_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg003_B at scale 2.0 diversity is 2.3570981431059788\n",
      "Processing region: reg004_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg004_A at scale 2.0 diversity is 2.9425252594897655\n",
      "Processing region: reg004_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg004_B at scale 2.0 diversity is 2.392806711502546\n",
      "Processing region: reg005_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg005_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg005_A at scale 2.0 diversity is 2.798898509128272\n",
      "Processing region: reg005_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg005_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg005_B at scale 2.0 diversity is 2.694506707221392\n",
      "Processing region: reg006_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg006_A at scale 2.0 diversity is 3.0073481335469676\n",
      "Processing region: reg006_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg006_B at scale 2.0 diversity is 2.8859110557062992\n",
      "Processing region: reg007_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg007_A at scale 2.0 diversity is 2.3577983784154317\n",
      "Processing region: reg007_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg007_B at scale 2.0 diversity is 2.607293813373253\n",
      "Processing region: reg008_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg008_A at scale 2.0 diversity is 2.334349986612683\n",
      "Processing region: reg008_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg008_B at scale 2.0 diversity is 2.3060865930088674\n",
      "Processing region: reg009_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg009_A at scale 2.0 diversity is 2.9203552400591923\n",
      "Processing region: reg009_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg009_B at scale 2.0 diversity is 2.530705606466985\n",
      "Processing region: reg010_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg010_A at scale 2.0 diversity is 3.1818384035654357\n",
      "Processing region: reg010_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg010_B at scale 2.0 diversity is 3.0994554983880036\n",
      "Processing region: reg011_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg011_A at scale 2.0 diversity is 2.9854848615599265\n",
      "Processing region: reg011_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg011_B at scale 2.0 diversity is 3.0188474188887655\n",
      "Processing region: reg012_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg012_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg012_A at scale 2.0 diversity is 2.7635252200318203\n",
      "Processing region: reg012_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg012_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg012_B at scale 2.0 diversity is 2.137662763876956\n",
      "Processing region: reg013_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg013_A at scale 2.0 diversity is 2.3833344322980783\n",
      "Processing region: reg013_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg013_B at scale 2.0 diversity is 2.75996980712337\n",
      "Processing region: reg014_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg014_A at scale 2.0 diversity is 2.7988658517832863\n",
      "Processing region: reg014_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg014_B at scale 2.0 diversity is 2.7205386288694506\n",
      "Processing region: reg015_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg015_A at scale 2.0 diversity is 2.7284531088320736\n",
      "Processing region: reg015_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg015_B at scale 2.0 diversity is 1.7994770590494373\n",
      "Processing region: reg016_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg016_A at scale 2.0 diversity is 2.617353044572593\n",
      "Processing region: reg016_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg016_B at scale 2.0 diversity is 2.645222265804777\n",
      "Processing region: reg017_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg017_A at scale 2.0 diversity is 2.8480838985469985\n",
      "Processing region: reg017_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg017_B at scale 2.0 diversity is 3.1261504679194556\n",
      "Processing region: reg018_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg018_A at scale 2.0 diversity is 3.0230092602584158\n",
      "Processing region: reg018_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg018_B at scale 2.0 diversity is 2.3669000326870404\n",
      "Processing region: reg019_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg019_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg019_A at scale 2.0 diversity is 2.3332992092423406\n",
      "Processing region: reg019_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg019_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg019_B at scale 2.0 diversity is 2.2237925560786556\n",
      "Processing region: reg020_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg020_A at scale 2.0 diversity is 2.971366154333284\n",
      "Processing region: reg020_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg020_B at scale 2.0 diversity is 2.992584946090993\n",
      "Processing region: reg021_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg021_A at scale 2.0 diversity is 2.8041743883930996\n",
      "Processing region: reg021_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg021_B at scale 2.0 diversity is 2.9243665627701203\n",
      "Processing region: reg022_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg022_A at scale 2.0 diversity is 2.752586411309329\n",
      "Processing region: reg022_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg022_B at scale 2.0 diversity is 3.1356093204505244\n",
      "Processing region: reg023_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg023_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg023_A at scale 2.0 diversity is 1.7961463433492457\n",
      "Processing region: reg023_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg023_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg023_B at scale 2.0 diversity is 3.3058528331339563\n",
      "Processing region: reg024_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg024_A at scale 2.0 diversity is 2.105757515721894\n",
      "Processing region: reg024_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg024_B at scale 2.0 diversity is 2.224181528346875\n",
      "Processing region: reg025_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg025_A at scale 2.0 diversity is 2.8848975740211067\n",
      "Processing region: reg025_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg025_B at scale 2.0 diversity is 3.029128263674023\n",
      "Processing region: reg026_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg026_A at scale 2.0 diversity is 3.137453402688342\n",
      "Processing region: reg026_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg026_B at scale 2.0 diversity is 2.7292429025198977\n",
      "Processing region: reg027_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg027_A at scale 2.0 diversity is 2.7966400356352548\n",
      "Processing region: reg027_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg027_B at scale 2.0 diversity is 2.2160987163767256\n",
      "Processing region: reg028_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg028_A at scale 2.0 diversity is 3.4178433255452196\n",
      "Processing region: reg028_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg028_B at scale 2.0 diversity is 3.141115259713353\n",
      "Processing region: reg029_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg029_A at scale 2.0 diversity is 2.3517201560879855\n",
      "Processing region: reg029_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg029_B at scale 2.0 diversity is 2.1135717504325555\n",
      "Processing region: reg030_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg030_A at scale 2.0 diversity is 2.9389710559037736\n",
      "Processing region: reg030_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg030_B at scale 2.0 diversity is 1.8245292609524024\n",
      "Processing region: reg031_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg031_A at scale 2.0 diversity is 2.8674431705406382\n",
      "Processing region: reg031_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg031_B at scale 2.0 diversity is 3.213790354369765\n",
      "Processing region: reg032_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg032_A at scale 2.0 diversity is 3.0489069439668826\n",
      "Processing region: reg032_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg032_B at scale 2.0 diversity is 3.143420702529476\n",
      "Processing region: reg033_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg033_A at scale 2.0 diversity is 2.527373044461171\n",
      "Processing region: reg033_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg033_B at scale 2.0 diversity is 2.5147141187950557\n",
      "Processing region: reg034_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg034_A at scale 2.0 diversity is 2.7074354481330007\n",
      "Processing region: reg034_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg034_B at scale 2.0 diversity is 2.4018042365312335\n",
      "Processing region: reg035_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg035_A at scale 2.0 diversity is 2.799613911754396\n",
      "Processing region: reg035_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg035_B at scale 2.0 diversity is 2.302022735036954\n",
      "Processing region: reg036_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg036_A at scale 2.0 diversity is 3.075899366038348\n",
      "Processing region: reg036_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg036_B at scale 2.0 diversity is 2.1891631617555354\n",
      "Processing region: reg037_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg037_A at scale 2.0 diversity is 2.927739991601529\n",
      "Processing region: reg037_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg037_B at scale 2.0 diversity is 2.5889106052694433\n",
      "Processing region: reg038_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg038_A at scale 2.0 diversity is 2.851257124628696\n",
      "Processing region: reg038_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg038_B at scale 2.0 diversity is 2.5110933688101302\n",
      "Processing region: reg039_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg039_A at scale 2.0 diversity is 2.7501398430064765\n",
      "Processing region: reg039_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg039_B at scale 2.0 diversity is 2.7722004138599106\n",
      "Processing region: reg040_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg040_A at scale 2.0 diversity is 2.801217367062181\n",
      "Processing region: reg040_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg040_B at scale 2.0 diversity is 2.6177864218387\n",
      "Processing region: reg041_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg041_A at scale 2.0 diversity is 2.820376527688662\n",
      "Processing region: reg041_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg041_B at scale 2.0 diversity is 1.9430788195399333\n",
      "Processing region: reg042_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg042_A at scale 2.0 diversity is 1.586478372245642\n",
      "Processing region: reg042_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg042_B at scale 2.0 diversity is 1.4927608714421754\n",
      "Processing region: reg043_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg043_A at scale 2.0 diversity is 2.1503898337025666\n",
      "Processing region: reg043_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg043_B at scale 2.0 diversity is 1.6724706540785874\n",
      "Processing region: reg044_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg044_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg044_A at scale 2.0 diversity is 2.6124029928819477\n",
      "Processing region: reg044_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg044_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg044_B at scale 2.0 diversity is 1.7951798871976072\n",
      "Processing region: reg045_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg045_A at scale 2.0 diversity is 2.2895773085377495\n",
      "Processing region: reg045_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg045_B at scale 2.0 diversity is 2.7768677210528883\n",
      "Processing region: reg046_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg046_A at scale 2.0 diversity is 3.315128845125775\n",
      "Processing region: reg046_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg046_B at scale 2.0 diversity is 3.053485127952065\n",
      "Processing region: reg047_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg047_A at scale 2.0 diversity is 2.8928484684974425\n",
      "Processing region: reg047_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg047_B at scale 2.0 diversity is 1.9153496856985486\n",
      "Processing region: reg048_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg048_A at scale 2.0 diversity is 3.0029544926585374\n",
      "Processing region: reg048_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg048_B at scale 2.0 diversity is 2.9804816820205104\n",
      "Processing region: reg049_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg049_A at scale 2.0 diversity is 2.505208100394996\n",
      "Processing region: reg049_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg049_B at scale 2.0 diversity is 2.9161399723350385\n",
      "Processing region: reg050_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg050_A at scale 2.0 diversity is 2.0832690615372083\n",
      "Processing region: reg050_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg050_B at scale 2.0 diversity is 2.4407015139232318\n",
      "Processing region: reg051_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg051_A at scale 2.0 diversity is 2.7723543832512014\n",
      "Processing region: reg051_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg051_B at scale 2.0 diversity is 2.3807047380847712\n",
      "Processing region: reg052_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg052_A at scale 2.0 diversity is 2.6181633078096036\n",
      "Processing region: reg052_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg052_B at scale 2.0 diversity is 2.3462190590052017\n",
      "Processing region: reg053_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg053_A at scale 2.0 diversity is 3.194901537175901\n",
      "Processing region: reg053_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg053_B at scale 2.0 diversity is 2.422953985950602\n",
      "Processing region: reg054_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg054_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg054_A at scale 2.0 diversity is 2.84122092784912\n",
      "Processing region: reg054_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg054_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg054_B at scale 2.0 diversity is 2.259640353792332\n",
      "Processing region: reg055_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg055_A at scale 2.0 diversity is 2.931977622263422\n",
      "Processing region: reg055_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg055_B at scale 2.0 diversity is 2.4241989335748277\n",
      "Processing region: reg056_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg056_A at scale 2.0 diversity is 3.3336913955365493\n",
      "Processing region: reg056_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg056_B at scale 2.0 diversity is 3.06121137431393\n",
      "Processing region: reg057_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg057_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg057_A at scale 2.0 diversity is 1.9311336648802577\n",
      "Processing region: reg057_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg057_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg057_B at scale 2.0 diversity is 2.8849230678783955\n",
      "Processing region: reg058_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg058_A at scale 2.0 diversity is 1.1193777883958766\n",
      "Processing region: reg058_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg058_B at scale 2.0 diversity is 0.7261372057812775\n",
      "Processing region: reg059_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg059_A at scale 2.0 diversity is 3.026656580317602\n",
      "Processing region: reg059_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg059_B at scale 2.0 diversity is 2.630511966264921\n",
      "Processing region: reg060_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg060_A at scale 2.0 diversity is 3.0753116864042886\n",
      "Processing region: reg060_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg060_B at scale 2.0 diversity is 2.914872093216821\n",
      "Processing region: reg061_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg061_A at scale 2.0 diversity is 2.7104564453687585\n",
      "Processing region: reg061_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg061_B at scale 2.0 diversity is 2.983874307208561\n",
      "Processing region: reg062_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg062_A at scale 2.0 diversity is 2.7188912135702408\n",
      "Processing region: reg062_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg062_B at scale 2.0 diversity is 3.025015486366876\n",
      "Processing region: reg063_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg063_A at scale 2.0 diversity is 2.3876113318737273\n",
      "Processing region: reg063_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg063_B at scale 2.0 diversity is 3.0699850404039104\n",
      "Processing region: reg064_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg064_A at scale 2.0 diversity is 2.3677793458693284\n",
      "Processing region: reg064_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg064_B at scale 2.0 diversity is 3.183636600785769\n",
      "Processing region: reg065_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg065_A at scale 2.0 diversity is 1.9524192173371937\n",
      "Processing region: reg065_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg065_B at scale 2.0 diversity is 2.8202450781019914\n",
      "Processing region: reg066_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg066_A at scale 2.0 diversity is 2.222492851829908\n",
      "Processing region: reg066_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg066_B at scale 2.0 diversity is 2.8589435189018477\n",
      "Processing region: reg067_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg067_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg067_A at scale 2.0 diversity is 3.174313126306071\n",
      "Processing region: reg067_B at scale 2.0\n",
      "25.000 per cent patches are empty\n",
      "reg067_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg067_B at scale 2.0 diversity is 1.8404742956555353\n",
      "Processing region: reg068_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg068_A at scale 2.0 diversity is 2.816110317659155\n",
      "Processing region: reg068_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg068_B at scale 2.0 diversity is 3.37063036600333\n",
      "Processing region: reg069_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg069_A at scale 2.0 diversity is 3.0535958241158574\n",
      "Processing region: reg069_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg069_B at scale 2.0 diversity is 3.0349520079645496\n",
      "Processing region: reg070_A at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_A at scale 2.0 has 0 patches with zero diveristy\n",
      "reg070_A at scale 2.0 diversity is 2.311330477230654\n",
      "Processing region: reg070_B at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_B at scale 2.0 has 0 patches with zero diveristy\n",
      "reg070_B at scale 2.0 diversity is 2.2190816001107243\n",
      "Processing region: reg001_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg001_A at scale 4.0 diversity is 2.6912257855375854\n",
      "Processing region: reg001_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg001_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg001_B at scale 4.0 diversity is 2.508511745312688\n",
      "Processing region: reg002_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg002_A at scale 4.0 diversity is 2.5763810374235856\n",
      "Processing region: reg002_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg002_B at scale 4.0 diversity is 1.0092913098737057\n",
      "Processing region: reg003_A at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg003_A at scale 4.0 has 1 patches with zero diveristy\n",
      "reg003_A at scale 4.0 diversity is 2.40299494392918\n",
      "Processing region: reg003_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg003_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg003_B at scale 4.0 diversity is 2.2352323000088967\n",
      "Processing region: reg004_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg004_A at scale 4.0 diversity is 2.7239538152784335\n",
      "Processing region: reg004_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg004_B at scale 4.0 diversity is 2.239496883238029\n",
      "Processing region: reg005_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg005_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg005_A at scale 4.0 diversity is 2.5058962999578607\n",
      "Processing region: reg005_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg005_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg005_B at scale 4.0 diversity is 2.2974603781476604\n",
      "Processing region: reg006_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg006_A at scale 4.0 diversity is 2.8049149639675326\n",
      "Processing region: reg006_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg006_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg006_B at scale 4.0 diversity is 2.661566961345321\n",
      "Processing region: reg007_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg007_A at scale 4.0 diversity is 2.0720087383758763\n",
      "Processing region: reg007_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg007_B at scale 4.0 diversity is 2.223769568863302\n",
      "Processing region: reg008_A at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg008_A at scale 4.0 has 1 patches with zero diveristy\n",
      "reg008_A at scale 4.0 diversity is 1.8819007929993086\n",
      "Processing region: reg008_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg008_B at scale 4.0 diversity is 2.1184621981095377\n",
      "Processing region: reg009_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg009_A at scale 4.0 diversity is 2.7187002813275343\n",
      "Processing region: reg009_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg009_B at scale 4.0 diversity is 2.448889454136096\n",
      "Processing region: reg010_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg010_A at scale 4.0 diversity is 2.901288804238678\n",
      "Processing region: reg010_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg010_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg010_B at scale 4.0 diversity is 2.765224550587904\n",
      "Processing region: reg011_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg011_A at scale 4.0 diversity is 2.744119864315903\n",
      "Processing region: reg011_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg011_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg011_B at scale 4.0 diversity is 2.470285925795813\n",
      "Processing region: reg012_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg012_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg012_A at scale 4.0 diversity is 2.4489169928974905\n",
      "Processing region: reg012_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg012_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg012_B at scale 4.0 diversity is 1.7471079857854763\n",
      "Processing region: reg013_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg013_A at scale 4.0 diversity is 2.1524305538204276\n",
      "Processing region: reg013_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg013_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg013_B at scale 4.0 diversity is 2.520180774088927\n",
      "Processing region: reg014_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg014_A at scale 4.0 diversity is 2.4812265005534124\n",
      "Processing region: reg014_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg014_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg014_B at scale 4.0 diversity is 2.518657583271807\n",
      "Processing region: reg015_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg015_A at scale 4.0 diversity is 2.485686640035546\n",
      "Processing region: reg015_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg015_B at scale 4.0 diversity is 1.6663483336287583\n",
      "Processing region: reg016_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg016_A at scale 4.0 diversity is 2.422934885830574\n",
      "Processing region: reg016_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg016_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg016_B at scale 4.0 diversity is 2.4286480207114938\n",
      "Processing region: reg017_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg017_A at scale 4.0 diversity is 2.5948491092335506\n",
      "Processing region: reg017_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg017_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg017_B at scale 4.0 diversity is 2.774326887094235\n",
      "Processing region: reg018_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg018_A at scale 4.0 diversity is 2.6183636107516435\n",
      "Processing region: reg018_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg018_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg018_B at scale 4.0 diversity is 2.123087714675039\n",
      "Processing region: reg019_A at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg019_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg019_A at scale 4.0 diversity is 2.017202288679072\n",
      "Processing region: reg019_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg019_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg019_B at scale 4.0 diversity is 1.420851519204359\n",
      "Processing region: reg020_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg020_A at scale 4.0 diversity is 2.7080420261090867\n",
      "Processing region: reg020_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg020_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg020_B at scale 4.0 diversity is 2.8570169499980596\n",
      "Processing region: reg021_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg021_A at scale 4.0 diversity is 2.4923208169420734\n",
      "Processing region: reg021_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg021_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg021_B at scale 4.0 diversity is 2.3797447253640964\n",
      "Processing region: reg022_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg022_A at scale 4.0 diversity is 2.576345618958105\n",
      "Processing region: reg022_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg022_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg022_B at scale 4.0 diversity is 2.5725024190202213\n",
      "Processing region: reg023_A at scale 4.0\n",
      "12.500 per cent patches are empty\n",
      "reg023_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg023_A at scale 4.0 diversity is 1.53640564884635\n",
      "Processing region: reg023_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg023_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg023_B at scale 4.0 diversity is 3.081867323706144\n",
      "Processing region: reg024_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg024_A at scale 4.0 diversity is 1.9594151536098638\n",
      "Processing region: reg024_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg024_B at scale 4.0 diversity is 2.0979048551559996\n",
      "Processing region: reg025_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg025_A at scale 4.0 diversity is 2.5788803350843272\n",
      "Processing region: reg025_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg025_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg025_B at scale 4.0 diversity is 2.7155616172343815\n",
      "Processing region: reg026_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg026_A at scale 4.0 diversity is 2.950520774164608\n",
      "Processing region: reg026_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg026_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg026_B at scale 4.0 diversity is 2.6096562884028285\n",
      "Processing region: reg027_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg027_A at scale 4.0 diversity is 2.575019364816742\n",
      "Processing region: reg027_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg027_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg027_B at scale 4.0 diversity is 2.073792659899871\n",
      "Processing region: reg028_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg028_A at scale 4.0 diversity is 3.2328667604962695\n",
      "Processing region: reg028_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg028_B at scale 4.0 diversity is 2.966955126711894\n",
      "Processing region: reg029_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg029_A at scale 4.0 diversity is 2.113785592308676\n",
      "Processing region: reg029_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg029_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg029_B at scale 4.0 diversity is 1.8867529938578085\n",
      "Processing region: reg030_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg030_A at scale 4.0 diversity is 2.806596484947666\n",
      "Processing region: reg030_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg030_B at scale 4.0 diversity is 1.5803046749028604\n",
      "Processing region: reg031_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg031_A at scale 4.0 diversity is 2.695873676870986\n",
      "Processing region: reg031_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg031_B at scale 4.0 diversity is 2.965007045210802\n",
      "Processing region: reg032_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg032_A at scale 4.0 diversity is 2.7857185816520222\n",
      "Processing region: reg032_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg032_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg032_B at scale 4.0 diversity is 2.8851103073158213\n",
      "Processing region: reg033_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg033_A at scale 4.0 diversity is 2.4607063715685733\n",
      "Processing region: reg033_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg033_B at scale 4.0 diversity is 2.3183053093324117\n",
      "Processing region: reg034_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg034_A at scale 4.0 diversity is 2.59907305044079\n",
      "Processing region: reg034_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg034_B at scale 4.0 diversity is 2.21874873320456\n",
      "Processing region: reg035_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg035_A at scale 4.0 diversity is 2.5400862187776205\n",
      "Processing region: reg035_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg035_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg035_B at scale 4.0 diversity is 2.009293801669852\n",
      "Processing region: reg036_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg036_A at scale 4.0 diversity is 2.7864922736282027\n",
      "Processing region: reg036_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg036_B at scale 4.0 diversity is 1.8871738352990746\n",
      "Processing region: reg037_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg037_A at scale 4.0 diversity is 2.5105760460246414\n",
      "Processing region: reg037_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg037_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg037_B at scale 4.0 diversity is 2.2257376228807306\n",
      "Processing region: reg038_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg038_A at scale 4.0 diversity is 2.728365610428482\n",
      "Processing region: reg038_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg038_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg038_B at scale 4.0 diversity is 2.319347338468458\n",
      "Processing region: reg039_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg039_A at scale 4.0 diversity is 2.4013971766317983\n",
      "Processing region: reg039_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg039_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg039_B at scale 4.0 diversity is 2.385567597186299\n",
      "Processing region: reg040_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg040_A at scale 4.0 diversity is 2.5982686405147026\n",
      "Processing region: reg040_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg040_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg040_B at scale 4.0 diversity is 2.3054714695658873\n",
      "Processing region: reg041_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg041_A at scale 4.0 diversity is 2.611499919520702\n",
      "Processing region: reg041_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg041_B at scale 4.0 diversity is 1.7086811150086967\n",
      "Processing region: reg042_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg042_A at scale 4.0 diversity is 1.412249731404053\n",
      "Processing region: reg042_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg042_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg042_B at scale 4.0 diversity is 1.303806474662546\n",
      "Processing region: reg043_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg043_A at scale 4.0 diversity is 1.9450520789934216\n",
      "Processing region: reg043_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg043_B at scale 4.0 diversity is 1.4005357925956483\n",
      "Processing region: reg044_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg044_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg044_A at scale 4.0 diversity is 2.3905182338732827\n",
      "Processing region: reg044_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg044_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg044_B at scale 4.0 diversity is 1.5325797491427111\n",
      "Processing region: reg045_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg045_A at scale 4.0 diversity is 2.25074395347798\n",
      "Processing region: reg045_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg045_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg045_B at scale 4.0 diversity is 2.5495458882176933\n",
      "Processing region: reg046_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg046_A at scale 4.0 diversity is 3.041041408118236\n",
      "Processing region: reg046_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg046_B at scale 4.0 diversity is 2.9011803978494575\n",
      "Processing region: reg047_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg047_A at scale 4.0 diversity is 2.6609796781855533\n",
      "Processing region: reg047_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg047_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg047_B at scale 4.0 diversity is 1.7465111988719086\n",
      "Processing region: reg048_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg048_A at scale 4.0 diversity is 2.7825572538071452\n",
      "Processing region: reg048_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg048_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg048_B at scale 4.0 diversity is 2.6498873372021574\n",
      "Processing region: reg049_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg049_A at scale 4.0 diversity is 2.394152390161228\n",
      "Processing region: reg049_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg049_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg049_B at scale 4.0 diversity is 2.6263290858294948\n",
      "Processing region: reg050_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg050_A at scale 4.0 diversity is 1.9345445976385542\n",
      "Processing region: reg050_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg050_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg050_B at scale 4.0 diversity is 2.1403816358349177\n",
      "Processing region: reg051_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_A at scale 4.0 has 1 patches with zero diveristy\n",
      "reg051_A at scale 4.0 diversity is 2.324936180281602\n",
      "Processing region: reg051_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg051_B at scale 4.0 diversity is 2.108391827246699\n",
      "Processing region: reg052_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg052_A at scale 4.0 diversity is 2.4396721924476563\n",
      "Processing region: reg052_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg052_B at scale 4.0 diversity is 2.090261131052345\n",
      "Processing region: reg053_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg053_A at scale 4.0 diversity is 3.0225355938624263\n",
      "Processing region: reg053_B at scale 4.0\n",
      "18.750 per cent patches are empty\n",
      "reg053_B at scale 4.0 has 1 patches with zero diveristy\n",
      "reg053_B at scale 4.0 diversity is 1.4844248383783252\n",
      "Processing region: reg054_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg054_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg054_A at scale 4.0 diversity is 2.4392078558145522\n",
      "Processing region: reg054_B at scale 4.0\n",
      "18.750 per cent patches are empty\n",
      "reg054_B at scale 4.0 has 2 patches with zero diveristy\n",
      "reg054_B at scale 4.0 diversity is 1.6058476621875657\n",
      "Processing region: reg055_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg055_A at scale 4.0 diversity is 2.7719133995710594\n",
      "Processing region: reg055_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg055_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg055_B at scale 4.0 diversity is 1.5813363571935615\n",
      "Processing region: reg056_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg056_A at scale 4.0 diversity is 2.915100249036336\n",
      "Processing region: reg056_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg056_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg056_B at scale 4.0 diversity is 2.7184854616067478\n",
      "Processing region: reg057_A at scale 4.0\n",
      "50.000 per cent patches are empty\n",
      "reg057_A at scale 4.0 has 1 patches with zero diveristy\n",
      "reg057_A at scale 4.0 diversity is 1.6455779296010529\n",
      "Processing region: reg057_B at scale 4.0\n",
      "6.250 per cent patches are empty\n",
      "reg057_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg057_B at scale 4.0 diversity is 2.1291835266558037\n",
      "Processing region: reg058_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg058_A at scale 4.0 diversity is 1.0642012832042715\n",
      "Processing region: reg058_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg058_B at scale 4.0 diversity is 0.6843698800485913\n",
      "Processing region: reg059_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg059_A at scale 4.0 diversity is 2.8615841709776877\n",
      "Processing region: reg059_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg059_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg059_B at scale 4.0 diversity is 2.514360016742207\n",
      "Processing region: reg060_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg060_A at scale 4.0 diversity is 2.867529302830965\n",
      "Processing region: reg060_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg060_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg060_B at scale 4.0 diversity is 2.7622734666888213\n",
      "Processing region: reg061_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg061_A at scale 4.0 diversity is 2.478541352827784\n",
      "Processing region: reg061_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg061_B at scale 4.0 diversity is 2.8172624622292366\n",
      "Processing region: reg062_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg062_A at scale 4.0 diversity is 2.5083520946201032\n",
      "Processing region: reg062_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg062_B at scale 4.0 diversity is 2.7621370474313585\n",
      "Processing region: reg063_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg063_A at scale 4.0 diversity is 2.1107133611387217\n",
      "Processing region: reg063_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg063_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg063_B at scale 4.0 diversity is 2.8692382444867506\n",
      "Processing region: reg064_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg064_A at scale 4.0 diversity is 2.227882243918364\n",
      "Processing region: reg064_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg064_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg064_B at scale 4.0 diversity is 2.8997041177159906\n",
      "Processing region: reg065_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg065_A at scale 4.0 diversity is 1.7187528024407746\n",
      "Processing region: reg065_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg065_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg065_B at scale 4.0 diversity is 2.4116814289829964\n",
      "Processing region: reg066_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg066_A at scale 4.0 diversity is 2.0830799816718177\n",
      "Processing region: reg066_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg066_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg066_B at scale 4.0 diversity is 2.471308644554643\n",
      "Processing region: reg067_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg067_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg067_A at scale 4.0 diversity is 2.6544874636685867\n",
      "Processing region: reg067_B at scale 4.0\n",
      "62.500 per cent patches are empty\n",
      "reg067_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg067_B at scale 4.0 diversity is 1.7687970435637457\n",
      "Processing region: reg068_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg068_A at scale 4.0 diversity is 2.5901497977414074\n",
      "Processing region: reg068_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg068_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg068_B at scale 4.0 diversity is 3.0512670951225274\n",
      "Processing region: reg069_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg069_A at scale 4.0 diversity is 2.8358913942039394\n",
      "Processing region: reg069_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg069_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg069_B at scale 4.0 diversity is 2.5605284050944186\n",
      "Processing region: reg070_A at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_A at scale 4.0 has 0 patches with zero diveristy\n",
      "reg070_A at scale 4.0 diversity is 2.1426240069396147\n",
      "Processing region: reg070_B at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "reg070_B at scale 4.0 has 0 patches with zero diveristy\n",
      "reg070_B at scale 4.0 diversity is 2.058367215553855\n",
      "Processing region: reg001_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg001_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg001_A at scale 8.0 diversity is 2.1844272169741714\n",
      "Processing region: reg001_B at scale 8.0\n",
      "7.812 per cent patches are empty\n",
      "reg001_B at scale 8.0 has 8 patches with zero diveristy\n",
      "reg001_B at scale 8.0 diversity is 1.3978867930267103\n",
      "Processing region: reg002_A at scale 8.0\n",
      "18.750 per cent patches are empty\n",
      "reg002_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg002_A at scale 8.0 diversity is 2.1686610202770744\n",
      "Processing region: reg002_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg002_B at scale 8.0 has 3 patches with zero diveristy\n",
      "reg002_B at scale 8.0 diversity is 0.8589620488081982\n",
      "Processing region: reg003_A at scale 8.0\n",
      "18.750 per cent patches are empty\n",
      "reg003_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg003_A at scale 8.0 diversity is 1.9660160489920295\n",
      "Processing region: reg003_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg003_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg003_B at scale 8.0 diversity is 1.766514129279135\n",
      "Processing region: reg004_A at scale 8.0\n",
      "12.500 per cent patches are empty\n",
      "reg004_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg004_A at scale 8.0 diversity is 2.467347624676027\n",
      "Processing region: reg004_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg004_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg004_B at scale 8.0 diversity is 1.894028687177677\n",
      "Processing region: reg005_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg005_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg005_A at scale 8.0 diversity is 2.200987499344523\n",
      "Processing region: reg005_B at scale 8.0\n",
      "20.312 per cent patches are empty\n",
      "reg005_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg005_B at scale 8.0 diversity is 1.956832928800155\n",
      "Processing region: reg006_A at scale 8.0\n",
      "7.812 per cent patches are empty\n",
      "reg006_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg006_A at scale 8.0 diversity is 2.4137160332365495\n",
      "Processing region: reg006_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg006_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg006_B at scale 8.0 diversity is 2.2385821627736804\n",
      "Processing region: reg007_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg007_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg007_A at scale 8.0 diversity is 1.7392528437222632\n",
      "Processing region: reg007_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg007_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg007_B at scale 8.0 diversity is 1.9477726334384857\n",
      "Processing region: reg008_A at scale 8.0\n",
      "21.875 per cent patches are empty\n",
      "reg008_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg008_A at scale 8.0 diversity is 1.5519903470247445\n",
      "Processing region: reg008_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg008_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg008_B at scale 8.0 diversity is 1.8035790645184098\n",
      "Processing region: reg009_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg009_A at scale 8.0 diversity is 2.3717123438726038\n",
      "Processing region: reg009_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg009_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg009_B at scale 8.0 diversity is 2.260042615486046\n",
      "Processing region: reg010_A at scale 8.0\n",
      "7.812 per cent patches are empty\n",
      "reg010_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg010_A at scale 8.0 diversity is 2.546231294895141\n",
      "Processing region: reg010_B at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg010_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg010_B at scale 8.0 diversity is 2.1873200225549008\n",
      "Processing region: reg011_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg011_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg011_A at scale 8.0 diversity is 2.340933599355627\n",
      "Processing region: reg011_B at scale 8.0\n",
      "12.500 per cent patches are empty\n",
      "reg011_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg011_B at scale 8.0 diversity is 2.0363682900440123\n",
      "Processing region: reg012_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg012_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg012_A at scale 8.0 diversity is 1.917499275375252\n",
      "Processing region: reg012_B at scale 8.0\n",
      "37.500 per cent patches are empty\n",
      "reg012_B at scale 8.0 has 12 patches with zero diveristy\n",
      "reg012_B at scale 8.0 diversity is 1.3289531453053525\n",
      "Processing region: reg013_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg013_A at scale 8.0 has 5 patches with zero diveristy\n",
      "reg013_A at scale 8.0 diversity is 1.785869563079486\n",
      "Processing region: reg013_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg013_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg013_B at scale 8.0 diversity is 2.103927205554886\n",
      "Processing region: reg014_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg014_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg014_A at scale 8.0 diversity is 1.8998253046435998\n",
      "Processing region: reg014_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg014_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg014_B at scale 8.0 diversity is 2.173592310185113\n",
      "Processing region: reg015_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg015_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg015_A at scale 8.0 diversity is 2.1624804753048217\n",
      "Processing region: reg015_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg015_B at scale 8.0 has 3 patches with zero diveristy\n",
      "reg015_B at scale 8.0 diversity is 1.2907618015193947\n",
      "Processing region: reg016_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg016_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg016_A at scale 8.0 diversity is 2.0398401179745016\n",
      "Processing region: reg016_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg016_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg016_B at scale 8.0 diversity is 2.1240018677958097\n",
      "Processing region: reg017_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg017_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg017_A at scale 8.0 diversity is 2.1564587987256476\n",
      "Processing region: reg017_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg017_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg017_B at scale 8.0 diversity is 2.3460520649678718\n",
      "Processing region: reg018_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg018_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg018_A at scale 8.0 diversity is 2.2327726782544333\n",
      "Processing region: reg018_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg018_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg018_B at scale 8.0 diversity is 1.8415130971603912\n",
      "Processing region: reg019_A at scale 8.0\n",
      "18.750 per cent patches are empty\n",
      "reg019_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg019_A at scale 8.0 diversity is 1.6343616995090693\n",
      "Processing region: reg019_B at scale 8.0\n",
      "39.062 per cent patches are empty\n",
      "reg019_B at scale 8.0 has 22 patches with zero diveristy\n",
      "reg019_B at scale 8.0 diversity is 0.537882232457474\n",
      "Processing region: reg020_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg020_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg020_A at scale 8.0 diversity is 2.340486520490724\n",
      "Processing region: reg020_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg020_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg020_B at scale 8.0 diversity is 2.4957924769019777\n",
      "Processing region: reg021_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg021_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg021_A at scale 8.0 diversity is 1.9846964659994617\n",
      "Processing region: reg021_B at scale 8.0\n",
      "14.062 per cent patches are empty\n",
      "reg021_B at scale 8.0 has 9 patches with zero diveristy\n",
      "reg021_B at scale 8.0 diversity is 1.4313677786986\n",
      "Processing region: reg022_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg022_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg022_A at scale 8.0 diversity is 2.270296075666552\n",
      "Processing region: reg022_B at scale 8.0\n",
      "21.875 per cent patches are empty\n",
      "reg022_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg022_B at scale 8.0 diversity is 2.0466363189053025\n",
      "Processing region: reg023_A at scale 8.0\n",
      "20.312 per cent patches are empty\n",
      "reg023_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg023_A at scale 8.0 diversity is 1.2862367285317442\n",
      "Processing region: reg023_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg023_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg023_B at scale 8.0 diversity is 2.6530052715197545\n",
      "Processing region: reg024_A at scale 8.0\n",
      "9.375 per cent patches are empty\n",
      "reg024_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg024_A at scale 8.0 diversity is 1.568394740694343\n",
      "Processing region: reg024_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg024_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg024_B at scale 8.0 diversity is 1.827138657514349\n",
      "Processing region: reg025_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg025_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg025_A at scale 8.0 diversity is 2.1007342019006034\n",
      "Processing region: reg025_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg025_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg025_B at scale 8.0 diversity is 2.141150049791231\n",
      "Processing region: reg026_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg026_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg026_A at scale 8.0 diversity is 2.5766448535620565\n",
      "Processing region: reg026_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg026_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg026_B at scale 8.0 diversity is 2.292976651311868\n",
      "Processing region: reg027_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg027_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg027_A at scale 8.0 diversity is 2.2303329095264304\n",
      "Processing region: reg027_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg027_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg027_B at scale 8.0 diversity is 1.8028071674281572\n",
      "Processing region: reg028_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg028_A at scale 8.0 diversity is 2.7937843331556027\n",
      "Processing region: reg028_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg028_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg028_B at scale 8.0 diversity is 2.506966268773661\n",
      "Processing region: reg029_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg029_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg029_A at scale 8.0 diversity is 1.7660372419492292\n",
      "Processing region: reg029_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg029_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg029_B at scale 8.0 diversity is 1.609525621451343\n",
      "Processing region: reg030_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg030_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg030_A at scale 8.0 diversity is 2.451537369342063\n",
      "Processing region: reg030_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg030_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg030_B at scale 8.0 diversity is 1.2784416266667322\n",
      "Processing region: reg031_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg031_A at scale 8.0 diversity is 2.409881749017643\n",
      "Processing region: reg031_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg031_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg031_B at scale 8.0 diversity is 2.5458386318148043\n",
      "Processing region: reg032_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg032_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg032_A at scale 8.0 diversity is 2.386455984077176\n",
      "Processing region: reg032_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg032_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg032_B at scale 8.0 diversity is 2.317567198803208\n",
      "Processing region: reg033_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg033_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg033_A at scale 8.0 diversity is 2.0397376000471623\n",
      "Processing region: reg033_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg033_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg033_B at scale 8.0 diversity is 1.9145125798983857\n",
      "Processing region: reg034_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg034_A at scale 8.0 diversity is 2.296432797510287\n",
      "Processing region: reg034_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg034_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg034_B at scale 8.0 diversity is 1.7945713861079937\n",
      "Processing region: reg035_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg035_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg035_A at scale 8.0 diversity is 2.154921161381134\n",
      "Processing region: reg035_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg035_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg035_B at scale 8.0 diversity is 1.6275061015154395\n",
      "Processing region: reg036_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg036_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg036_A at scale 8.0 diversity is 2.4714631267491907\n",
      "Processing region: reg036_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg036_B at scale 8.0 has 11 patches with zero diveristy\n",
      "reg036_B at scale 8.0 diversity is 1.4686328638834323\n",
      "Processing region: reg037_A at scale 8.0\n",
      "7.812 per cent patches are empty\n",
      "reg037_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg037_A at scale 8.0 diversity is 1.9792718131577443\n",
      "Processing region: reg037_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg037_B at scale 8.0 has 5 patches with zero diveristy\n",
      "reg037_B at scale 8.0 diversity is 1.4565913715127137\n",
      "Processing region: reg038_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg038_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg038_A at scale 8.0 diversity is 2.4201397922281718\n",
      "Processing region: reg038_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg038_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg038_B at scale 8.0 diversity is 1.8832661309192116\n",
      "Processing region: reg039_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg039_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg039_A at scale 8.0 diversity is 2.0359109849094237\n",
      "Processing region: reg039_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg039_B at scale 8.0 has 10 patches with zero diveristy\n",
      "reg039_B at scale 8.0 diversity is 1.5623724831737296\n",
      "Processing region: reg040_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg040_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg040_A at scale 8.0 diversity is 2.1566145065414486\n",
      "Processing region: reg040_B at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg040_B at scale 8.0 has 7 patches with zero diveristy\n",
      "reg040_B at scale 8.0 diversity is 1.6233385935274889\n",
      "Processing region: reg041_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg041_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg041_A at scale 8.0 diversity is 2.1350376399273765\n",
      "Processing region: reg041_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg041_B at scale 8.0 has 8 patches with zero diveristy\n",
      "reg041_B at scale 8.0 diversity is 1.4662163043560505\n",
      "Processing region: reg042_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg042_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg042_A at scale 8.0 diversity is 1.1735325221826658\n",
      "Processing region: reg042_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg042_B at scale 8.0 has 5 patches with zero diveristy\n",
      "reg042_B at scale 8.0 diversity is 1.0386160678778527\n",
      "Processing region: reg043_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg043_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg043_A at scale 8.0 diversity is 1.6249529236206899\n",
      "Processing region: reg043_B at scale 8.0\n",
      "14.062 per cent patches are empty\n",
      "reg043_B at scale 8.0 has 14 patches with zero diveristy\n",
      "reg043_B at scale 8.0 diversity is 0.9092168498502688\n",
      "Processing region: reg044_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg044_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg044_A at scale 8.0 diversity is 1.9686318039209392\n",
      "Processing region: reg044_B at scale 8.0\n",
      "23.438 per cent patches are empty\n",
      "reg044_B at scale 8.0 has 8 patches with zero diveristy\n",
      "reg044_B at scale 8.0 diversity is 1.039603560478042\n",
      "Processing region: reg045_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg045_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg045_A at scale 8.0 diversity is 2.0564353895660323\n",
      "Processing region: reg045_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg045_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg045_B at scale 8.0 diversity is 1.909072047275319\n",
      "Processing region: reg046_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg046_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg046_A at scale 8.0 diversity is 2.649575036203187\n",
      "Processing region: reg046_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg046_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg046_B at scale 8.0 diversity is 2.5295808722262114\n",
      "Processing region: reg047_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg047_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg047_A at scale 8.0 diversity is 2.1461897755316177\n",
      "Processing region: reg047_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg047_B at scale 8.0 has 6 patches with zero diveristy\n",
      "reg047_B at scale 8.0 diversity is 1.2274152576852821\n",
      "Processing region: reg048_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg048_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg048_A at scale 8.0 diversity is 2.3424060740114325\n",
      "Processing region: reg048_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg048_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg048_B at scale 8.0 diversity is 2.1213169428272276\n",
      "Processing region: reg049_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg049_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg049_A at scale 8.0 diversity is 2.091376664587831\n",
      "Processing region: reg049_B at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg049_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg049_B at scale 8.0 diversity is 2.144246860541955\n",
      "Processing region: reg050_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg050_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg050_A at scale 8.0 diversity is 1.578639552644022\n",
      "Processing region: reg050_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg050_B at scale 8.0 has 3 patches with zero diveristy\n",
      "reg050_B at scale 8.0 diversity is 1.8484003493690628\n",
      "Processing region: reg051_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg051_A at scale 8.0 has 7 patches with zero diveristy\n",
      "reg051_A at scale 8.0 diversity is 1.8742271471377558\n",
      "Processing region: reg051_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg051_B at scale 8.0 has 3 patches with zero diveristy\n",
      "reg051_B at scale 8.0 diversity is 1.7306770156280515\n",
      "Processing region: reg052_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg052_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg052_A at scale 8.0 diversity is 2.1339259409353835\n",
      "Processing region: reg052_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg052_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg052_B at scale 8.0 diversity is 1.7228303565490486\n",
      "Processing region: reg053_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg053_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg053_A at scale 8.0 diversity is 2.5801653049005715\n",
      "Processing region: reg053_B at scale 8.0\n",
      "48.438 per cent patches are empty\n",
      "reg053_B at scale 8.0 has 9 patches with zero diveristy\n",
      "reg053_B at scale 8.0 diversity is 0.9804557157960465\n",
      "Processing region: reg054_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg054_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg054_A at scale 8.0 diversity is 2.029116250648739\n",
      "Processing region: reg054_B at scale 8.0\n",
      "43.750 per cent patches are empty\n",
      "reg054_B at scale 8.0 has 5 patches with zero diveristy\n",
      "reg054_B at scale 8.0 diversity is 1.375080970052685\n",
      "Processing region: reg055_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg055_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg055_A at scale 8.0 diversity is 2.3991975739546154\n",
      "Processing region: reg055_B at scale 8.0\n",
      "31.250 per cent patches are empty\n",
      "reg055_B at scale 8.0 has 18 patches with zero diveristy\n",
      "reg055_B at scale 8.0 diversity is 0.7867317883899703\n",
      "Processing region: reg056_A at scale 8.0\n",
      "6.250 per cent patches are empty\n",
      "reg056_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg056_A at scale 8.0 diversity is 2.2279799509588916\n",
      "Processing region: reg056_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg056_B at scale 8.0 has 2 patches with zero diveristy\n",
      "reg056_B at scale 8.0 diversity is 2.1903005336339896\n",
      "Processing region: reg057_A at scale 8.0\n",
      "76.562 per cent patches are empty\n",
      "reg057_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg057_A at scale 8.0 diversity is 1.2169801804571563\n",
      "Processing region: reg057_B at scale 8.0\n",
      "14.062 per cent patches are empty\n",
      "reg057_B at scale 8.0 has 11 patches with zero diveristy\n",
      "reg057_B at scale 8.0 diversity is 1.4453835609616434\n",
      "Processing region: reg058_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg058_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg058_A at scale 8.0 diversity is 0.8668496598063347\n",
      "Processing region: reg058_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg058_B at scale 8.0 has 16 patches with zero diveristy\n",
      "reg058_B at scale 8.0 diversity is 0.5604308392498848\n",
      "Processing region: reg059_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg059_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg059_A at scale 8.0 diversity is 2.5176904599215706\n",
      "Processing region: reg059_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg059_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg059_B at scale 8.0 diversity is 2.1663726510673063\n",
      "Processing region: reg060_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg060_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg060_A at scale 8.0 diversity is 2.5510411028814772\n",
      "Processing region: reg060_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg060_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg060_B at scale 8.0 diversity is 2.433151544170732\n",
      "Processing region: reg061_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg061_A at scale 8.0 diversity is 2.012420328726132\n",
      "Processing region: reg061_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg061_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg061_B at scale 8.0 diversity is 2.339576169151224\n",
      "Processing region: reg062_A at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg062_A at scale 8.0 diversity is 2.213338064575086\n",
      "Processing region: reg062_B at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "reg062_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg062_B at scale 8.0 diversity is 2.2426836941906974\n",
      "Processing region: reg063_A at scale 8.0\n",
      "4.688 per cent patches are empty\n",
      "reg063_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg063_A at scale 8.0 diversity is 1.636285167788469\n",
      "Processing region: reg063_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg063_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg063_B at scale 8.0 diversity is 2.3824944658329787\n",
      "Processing region: reg064_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg064_A at scale 8.0 has 0 patches with zero diveristy\n",
      "reg064_A at scale 8.0 diversity is 1.9339250474298497\n",
      "Processing region: reg064_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg064_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg064_B at scale 8.0 diversity is 2.4828709300668557\n",
      "Processing region: reg065_A at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg065_A at scale 8.0 has 4 patches with zero diveristy\n",
      "reg065_A at scale 8.0 diversity is 1.4187735070782346\n",
      "Processing region: reg065_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg065_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg065_B at scale 8.0 diversity is 1.6188740999790014\n",
      "Processing region: reg066_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg066_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg066_A at scale 8.0 diversity is 1.8591794637994814\n",
      "Processing region: reg066_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg066_B at scale 8.0 has 0 patches with zero diveristy\n",
      "reg066_B at scale 8.0 diversity is 1.9159140019815124\n",
      "Processing region: reg067_A at scale 8.0\n",
      "9.375 per cent patches are empty\n",
      "reg067_A at scale 8.0 has 6 patches with zero diveristy\n",
      "reg067_A at scale 8.0 diversity is 1.8516808709131558\n",
      "Processing region: reg067_B at scale 8.0\n",
      "71.875 per cent patches are empty\n",
      "reg067_B at scale 8.0 has 4 patches with zero diveristy\n",
      "reg067_B at scale 8.0 diversity is 1.0356301830650487\n",
      "Processing region: reg068_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg068_A at scale 8.0 has 2 patches with zero diveristy\n",
      "reg068_A at scale 8.0 diversity is 2.0490342425284105\n",
      "Processing region: reg068_B at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg068_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg068_B at scale 8.0 diversity is 2.54129394742902\n",
      "Processing region: reg069_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg069_A at scale 8.0 has 1 patches with zero diveristy\n",
      "reg069_A at scale 8.0 diversity is 2.3381342141732704\n",
      "Processing region: reg069_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg069_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg069_B at scale 8.0 diversity is 1.9312533895637745\n",
      "Processing region: reg070_A at scale 8.0\n",
      "1.562 per cent patches are empty\n",
      "reg070_A at scale 8.0 has 3 patches with zero diveristy\n",
      "reg070_A at scale 8.0 diversity is 1.737921244232286\n",
      "Processing region: reg070_B at scale 8.0\n",
      "3.125 per cent patches are empty\n",
      "reg070_B at scale 8.0 has 1 patches with zero diveristy\n",
      "reg070_B at scale 8.0 diversity is 1.7132580623682974\n",
      "Processing region: reg001_A at scale 16.0\n",
      "15.234 per cent patches are empty\n",
      "reg001_A at scale 16.0 has 45 patches with zero diveristy\n",
      "reg001_A at scale 16.0 diversity is 1.278679150302349\n",
      "Processing region: reg001_B at scale 16.0\n",
      "41.406 per cent patches are empty\n",
      "reg001_B at scale 16.0 has 78 patches with zero diveristy\n",
      "reg001_B at scale 16.0 diversity is 0.6240721579656607\n",
      "Processing region: reg002_A at scale 16.0\n",
      "30.078 per cent patches are empty\n",
      "reg002_A at scale 16.0 has 23 patches with zero diveristy\n",
      "reg002_A at scale 16.0 diversity is 1.4970986834941094\n",
      "Processing region: reg002_B at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg002_B at scale 16.0 has 73 patches with zero diveristy\n",
      "reg002_B at scale 16.0 diversity is 0.6202060055465932\n",
      "Processing region: reg003_A at scale 16.0\n",
      "31.250 per cent patches are empty\n",
      "reg003_A at scale 16.0 has 25 patches with zero diveristy\n",
      "reg003_A at scale 16.0 diversity is 1.4418751957099802\n",
      "Processing region: reg003_B at scale 16.0\n",
      "19.141 per cent patches are empty\n",
      "reg003_B at scale 16.0 has 34 patches with zero diveristy\n",
      "reg003_B at scale 16.0 diversity is 1.1944332996125686\n",
      "Processing region: reg004_A at scale 16.0\n",
      "21.875 per cent patches are empty\n",
      "reg004_A at scale 16.0 has 11 patches with zero diveristy\n",
      "reg004_A at scale 16.0 diversity is 1.8446158095105079\n",
      "Processing region: reg004_B at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg004_B at scale 16.0 has 21 patches with zero diveristy\n",
      "reg004_B at scale 16.0 diversity is 1.4417590496213195\n",
      "Processing region: reg005_A at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg005_A at scale 16.0 has 14 patches with zero diveristy\n",
      "reg005_A at scale 16.0 diversity is 1.6963073014378922\n",
      "Processing region: reg005_B at scale 16.0\n",
      "30.078 per cent patches are empty\n",
      "reg005_B at scale 16.0 has 17 patches with zero diveristy\n",
      "reg005_B at scale 16.0 diversity is 1.4771946098424056\n",
      "Processing region: reg006_A at scale 16.0\n",
      "15.625 per cent patches are empty\n",
      "reg006_A at scale 16.0 has 21 patches with zero diveristy\n",
      "reg006_A at scale 16.0 diversity is 1.5497292853096414\n",
      "Processing region: reg006_B at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg006_B at scale 16.0 has 22 patches with zero diveristy\n",
      "reg006_B at scale 16.0 diversity is 1.605315435803384\n",
      "Processing region: reg007_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg007_A at scale 16.0 has 50 patches with zero diveristy\n",
      "reg007_A at scale 16.0 diversity is 1.3026700296008487\n",
      "Processing region: reg007_B at scale 16.0\n",
      "14.453 per cent patches are empty\n",
      "reg007_B at scale 16.0 has 18 patches with zero diveristy\n",
      "reg007_B at scale 16.0 diversity is 1.5410101460874208\n",
      "Processing region: reg008_A at scale 16.0\n",
      "35.156 per cent patches are empty\n",
      "reg008_A at scale 16.0 has 24 patches with zero diveristy\n",
      "reg008_A at scale 16.0 diversity is 1.1006825796645114\n",
      "Processing region: reg008_B at scale 16.0\n",
      "3.516 per cent patches are empty\n",
      "reg008_B at scale 16.0 has 21 patches with zero diveristy\n",
      "reg008_B at scale 16.0 diversity is 1.41175634951127\n",
      "Processing region: reg009_A at scale 16.0\n",
      "2.344 per cent patches are empty\n",
      "reg009_A at scale 16.0 has 16 patches with zero diveristy\n",
      "reg009_A at scale 16.0 diversity is 1.7564122104261255\n",
      "Processing region: reg009_B at scale 16.0\n",
      "2.344 per cent patches are empty\n",
      "reg009_B at scale 16.0 has 3 patches with zero diveristy\n",
      "reg009_B at scale 16.0 diversity is 1.9277855308916516\n",
      "Processing region: reg010_A at scale 16.0\n",
      "11.719 per cent patches are empty\n",
      "reg010_A at scale 16.0 has 17 patches with zero diveristy\n",
      "reg010_A at scale 16.0 diversity is 1.8394794825765015\n",
      "Processing region: reg010_B at scale 16.0\n",
      "18.359 per cent patches are empty\n",
      "reg010_B at scale 16.0 has 41 patches with zero diveristy\n",
      "reg010_B at scale 16.0 diversity is 1.3631180826498142\n",
      "Processing region: reg011_A at scale 16.0\n",
      "18.750 per cent patches are empty\n",
      "reg011_A at scale 16.0 has 12 patches with zero diveristy\n",
      "reg011_A at scale 16.0 diversity is 1.847736105394992\n",
      "Processing region: reg011_B at scale 16.0\n",
      "24.609 per cent patches are empty\n",
      "reg011_B at scale 16.0 has 43 patches with zero diveristy\n",
      "reg011_B at scale 16.0 diversity is 1.2212822884889498\n",
      "Processing region: reg012_A at scale 16.0\n",
      "19.531 per cent patches are empty\n",
      "reg012_A at scale 16.0 has 24 patches with zero diveristy\n",
      "reg012_A at scale 16.0 diversity is 1.481997924593933\n",
      "Processing region: reg012_B at scale 16.0\n",
      "59.766 per cent patches are empty\n",
      "reg012_B at scale 16.0 has 41 patches with zero diveristy\n",
      "reg012_B at scale 16.0 diversity is 0.9566239776006007\n",
      "Processing region: reg013_A at scale 16.0\n",
      "5.469 per cent patches are empty\n",
      "reg013_A at scale 16.0 has 46 patches with zero diveristy\n",
      "reg013_A at scale 16.0 diversity is 1.2768105900507631\n",
      "Processing region: reg013_B at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg013_B at scale 16.0 has 39 patches with zero diveristy\n",
      "reg013_B at scale 16.0 diversity is 1.4352149930136526\n",
      "Processing region: reg014_A at scale 16.0\n",
      "17.969 per cent patches are empty\n",
      "reg014_A at scale 16.0 has 48 patches with zero diveristy\n",
      "reg014_A at scale 16.0 diversity is 1.138418135594311\n",
      "Processing region: reg014_B at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg014_B at scale 16.0 has 10 patches with zero diveristy\n",
      "reg014_B at scale 16.0 diversity is 1.6750389336322482\n",
      "Processing region: reg015_A at scale 16.0\n",
      "4.297 per cent patches are empty\n",
      "reg015_A at scale 16.0 has 10 patches with zero diveristy\n",
      "reg015_A at scale 16.0 diversity is 1.6116024647341678\n",
      "Processing region: reg015_B at scale 16.0\n",
      "12.109 per cent patches are empty\n",
      "reg015_B at scale 16.0 has 61 patches with zero diveristy\n",
      "reg015_B at scale 16.0 diversity is 0.8426733859069256\n",
      "Processing region: reg016_A at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg016_A at scale 16.0 has 27 patches with zero diveristy\n",
      "reg016_A at scale 16.0 diversity is 1.4541928747877895\n",
      "Processing region: reg016_B at scale 16.0\n",
      "5.078 per cent patches are empty\n",
      "reg016_B at scale 16.0 has 15 patches with zero diveristy\n",
      "reg016_B at scale 16.0 diversity is 1.5092365453005614\n",
      "Processing region: reg017_A at scale 16.0\n",
      "7.422 per cent patches are empty\n",
      "reg017_A at scale 16.0 has 27 patches with zero diveristy\n",
      "reg017_A at scale 16.0 diversity is 1.6272477419039446\n",
      "Processing region: reg017_B at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg017_B at scale 16.0 has 24 patches with zero diveristy\n",
      "reg017_B at scale 16.0 diversity is 1.7413837731075117\n",
      "Processing region: reg018_A at scale 16.0\n",
      "13.281 per cent patches are empty\n",
      "reg018_A at scale 16.0 has 26 patches with zero diveristy\n",
      "reg018_A at scale 16.0 diversity is 1.629163270233039\n",
      "Processing region: reg018_B at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg018_B at scale 16.0 has 14 patches with zero diveristy\n",
      "reg018_B at scale 16.0 diversity is 1.383201668872702\n",
      "Processing region: reg019_A at scale 16.0\n",
      "37.109 per cent patches are empty\n",
      "reg019_A at scale 16.0 has 33 patches with zero diveristy\n",
      "reg019_A at scale 16.0 diversity is 1.1662445405647155\n",
      "Processing region: reg019_B at scale 16.0\n",
      "76.953 per cent patches are empty\n",
      "reg019_B at scale 16.0 has 46 patches with zero diveristy\n",
      "reg019_B at scale 16.0 diversity is 0.2703726843421868\n",
      "Processing region: reg020_A at scale 16.0\n",
      "10.156 per cent patches are empty\n",
      "reg020_A at scale 16.0 has 29 patches with zero diveristy\n",
      "reg020_A at scale 16.0 diversity is 1.7024251996425317\n",
      "Processing region: reg020_B at scale 16.0\n",
      "8.594 per cent patches are empty\n",
      "reg020_B at scale 16.0 has 14 patches with zero diveristy\n",
      "reg020_B at scale 16.0 diversity is 1.9192905618248894\n",
      "Processing region: reg021_A at scale 16.0\n",
      "22.656 per cent patches are empty\n",
      "reg021_A at scale 16.0 has 23 patches with zero diveristy\n",
      "reg021_A at scale 16.0 diversity is 1.5605304675261158\n",
      "Processing region: reg021_B at scale 16.0\n",
      "46.875 per cent patches are empty\n",
      "reg021_B at scale 16.0 has 67 patches with zero diveristy\n",
      "reg021_B at scale 16.0 diversity is 0.6851940872168735\n",
      "Processing region: reg022_A at scale 16.0\n",
      "10.938 per cent patches are empty\n",
      "reg022_A at scale 16.0 has 26 patches with zero diveristy\n",
      "reg022_A at scale 16.0 diversity is 1.5620330150167288\n",
      "Processing region: reg022_B at scale 16.0\n",
      "37.891 per cent patches are empty\n",
      "reg022_B at scale 16.0 has 40 patches with zero diveristy\n",
      "reg022_B at scale 16.0 diversity is 1.1889771693275561\n",
      "Processing region: reg023_A at scale 16.0\n",
      "32.812 per cent patches are empty\n",
      "reg023_A at scale 16.0 has 43 patches with zero diveristy\n",
      "reg023_A at scale 16.0 diversity is 0.912962857483075\n",
      "Processing region: reg023_B at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg023_B at scale 16.0 has 10 patches with zero diveristy\n",
      "reg023_B at scale 16.0 diversity is 1.9839027758865977\n",
      "Processing region: reg024_A at scale 16.0\n",
      "21.094 per cent patches are empty\n",
      "reg024_A at scale 16.0 has 44 patches with zero diveristy\n",
      "reg024_A at scale 16.0 diversity is 0.9715197891350915\n",
      "Processing region: reg024_B at scale 16.0\n",
      "3.906 per cent patches are empty\n",
      "reg024_B at scale 16.0 has 25 patches with zero diveristy\n",
      "reg024_B at scale 16.0 diversity is 1.2808671906135307\n",
      "Processing region: reg025_A at scale 16.0\n",
      "11.719 per cent patches are empty\n",
      "reg025_A at scale 16.0 has 44 patches with zero diveristy\n",
      "reg025_A at scale 16.0 diversity is 1.286289895239046\n",
      "Processing region: reg025_B at scale 16.0\n",
      "12.109 per cent patches are empty\n",
      "reg025_B at scale 16.0 has 23 patches with zero diveristy\n",
      "reg025_B at scale 16.0 diversity is 1.5076554409188117\n",
      "Processing region: reg026_A at scale 16.0\n",
      "5.469 per cent patches are empty\n",
      "reg026_A at scale 16.0 has 9 patches with zero diveristy\n",
      "reg026_A at scale 16.0 diversity is 1.8944919852963507\n",
      "Processing region: reg026_B at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg026_B at scale 16.0 has 6 patches with zero diveristy\n",
      "reg026_B at scale 16.0 diversity is 1.7822104705553874\n",
      "Processing region: reg027_A at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg027_A at scale 16.0 has 16 patches with zero diveristy\n",
      "reg027_A at scale 16.0 diversity is 1.6837597918536693\n",
      "Processing region: reg027_B at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg027_B at scale 16.0 has 15 patches with zero diveristy\n",
      "reg027_B at scale 16.0 diversity is 1.3750974273053123\n",
      "Processing region: reg028_A at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg028_A at scale 16.0 has 11 patches with zero diveristy\n",
      "reg028_A at scale 16.0 diversity is 2.1160793930026798\n",
      "Processing region: reg028_B at scale 16.0\n",
      "5.078 per cent patches are empty\n",
      "reg028_B at scale 16.0 has 7 patches with zero diveristy\n",
      "reg028_B at scale 16.0 diversity is 1.8708715921726855\n",
      "Processing region: reg029_A at scale 16.0\n",
      "5.469 per cent patches are empty\n",
      "reg029_A at scale 16.0 has 45 patches with zero diveristy\n",
      "reg029_A at scale 16.0 diversity is 1.3114582969118185\n",
      "Processing region: reg029_B at scale 16.0\n",
      "8.203 per cent patches are empty\n",
      "reg029_B at scale 16.0 has 19 patches with zero diveristy\n",
      "reg029_B at scale 16.0 diversity is 1.2006683905758153\n",
      "Processing region: reg030_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg030_A at scale 16.0 has 8 patches with zero diveristy\n",
      "reg030_A at scale 16.0 diversity is 1.7961171415644808\n",
      "Processing region: reg030_B at scale 16.0\n",
      "8.203 per cent patches are empty\n",
      "reg030_B at scale 16.0 has 61 patches with zero diveristy\n",
      "reg030_B at scale 16.0 diversity is 0.8758578124437192\n",
      "Processing region: reg031_A at scale 16.0\n",
      "2.344 per cent patches are empty\n",
      "reg031_A at scale 16.0 has 13 patches with zero diveristy\n",
      "reg031_A at scale 16.0 diversity is 1.821469253229501\n",
      "Processing region: reg031_B at scale 16.0\n",
      "5.078 per cent patches are empty\n",
      "reg031_B at scale 16.0 has 10 patches with zero diveristy\n",
      "reg031_B at scale 16.0 diversity is 1.8644379619567157\n",
      "Processing region: reg032_A at scale 16.0\n",
      "4.688 per cent patches are empty\n",
      "reg032_A at scale 16.0 has 8 patches with zero diveristy\n",
      "reg032_A at scale 16.0 diversity is 1.766443731100261\n",
      "Processing region: reg032_B at scale 16.0\n",
      "12.891 per cent patches are empty\n",
      "reg032_B at scale 16.0 has 26 patches with zero diveristy\n",
      "reg032_B at scale 16.0 diversity is 1.5988874815055838\n",
      "Processing region: reg033_A at scale 16.0\n",
      "13.672 per cent patches are empty\n",
      "reg033_A at scale 16.0 has 22 patches with zero diveristy\n",
      "reg033_A at scale 16.0 diversity is 1.4950908933447642\n",
      "Processing region: reg033_B at scale 16.0\n",
      "2.734 per cent patches are empty\n",
      "reg033_B at scale 16.0 has 25 patches with zero diveristy\n",
      "reg033_B at scale 16.0 diversity is 1.39668223493805\n",
      "Processing region: reg034_A at scale 16.0\n",
      "7.422 per cent patches are empty\n",
      "reg034_A at scale 16.0 has 5 patches with zero diveristy\n",
      "reg034_A at scale 16.0 diversity is 1.805989175800188\n",
      "Processing region: reg034_B at scale 16.0\n",
      "9.375 per cent patches are empty\n",
      "reg034_B at scale 16.0 has 25 patches with zero diveristy\n",
      "reg034_B at scale 16.0 diversity is 1.3296613300745606\n",
      "Processing region: reg035_A at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg035_A at scale 16.0 has 23 patches with zero diveristy\n",
      "reg035_A at scale 16.0 diversity is 1.5806298251223463\n",
      "Processing region: reg035_B at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg035_B at scale 16.0 has 41 patches with zero diveristy\n",
      "reg035_B at scale 16.0 diversity is 1.2016286929363773\n",
      "Processing region: reg036_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg036_A at scale 16.0 has 4 patches with zero diveristy\n",
      "reg036_A at scale 16.0 diversity is 1.9680403838736258\n",
      "Processing region: reg036_B at scale 16.0\n",
      "3.516 per cent patches are empty\n",
      "reg036_B at scale 16.0 has 70 patches with zero diveristy\n",
      "reg036_B at scale 16.0 diversity is 1.1095966581191898\n",
      "Processing region: reg037_A at scale 16.0\n",
      "23.438 per cent patches are empty\n",
      "reg037_A at scale 16.0 has 36 patches with zero diveristy\n",
      "reg037_A at scale 16.0 diversity is 1.4047305990975751\n",
      "Processing region: reg037_B at scale 16.0\n",
      "27.734 per cent patches are empty\n",
      "reg037_B at scale 16.0 has 104 patches with zero diveristy\n",
      "reg037_B at scale 16.0 diversity is 0.5498515718539568\n",
      "Processing region: reg038_A at scale 16.0\n",
      "3.516 per cent patches are empty\n",
      "reg038_A at scale 16.0 has 5 patches with zero diveristy\n",
      "reg038_A at scale 16.0 diversity is 1.8435324598746055\n",
      "Processing region: reg038_B at scale 16.0\n",
      "12.891 per cent patches are empty\n",
      "reg038_B at scale 16.0 has 38 patches with zero diveristy\n",
      "reg038_B at scale 16.0 diversity is 1.3237002393813107\n",
      "Processing region: reg039_A at scale 16.0\n",
      "16.406 per cent patches are empty\n",
      "reg039_A at scale 16.0 has 10 patches with zero diveristy\n",
      "reg039_A at scale 16.0 diversity is 1.5830801490646864\n",
      "Processing region: reg039_B at scale 16.0\n",
      "26.172 per cent patches are empty\n",
      "reg039_B at scale 16.0 has 59 patches with zero diveristy\n",
      "reg039_B at scale 16.0 diversity is 0.9548139293737187\n",
      "Processing region: reg040_A at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg040_A at scale 16.0 has 17 patches with zero diveristy\n",
      "reg040_A at scale 16.0 diversity is 1.5511215316331814\n",
      "Processing region: reg040_B at scale 16.0\n",
      "30.859 per cent patches are empty\n",
      "reg040_B at scale 16.0 has 45 patches with zero diveristy\n",
      "reg040_B at scale 16.0 diversity is 1.0575943641473295\n",
      "Processing region: reg041_A at scale 16.0\n",
      "4.297 per cent patches are empty\n",
      "reg041_A at scale 16.0 has 31 patches with zero diveristy\n",
      "reg041_A at scale 16.0 diversity is 1.4565673183329213\n",
      "Processing region: reg041_B at scale 16.0\n",
      "14.844 per cent patches are empty\n",
      "reg041_B at scale 16.0 has 71 patches with zero diveristy\n",
      "reg041_B at scale 16.0 diversity is 1.0363120071917764\n",
      "Processing region: reg042_A at scale 16.0\n",
      "7.422 per cent patches are empty\n",
      "reg042_A at scale 16.0 has 66 patches with zero diveristy\n",
      "reg042_A at scale 16.0 diversity is 0.759286183040036\n",
      "Processing region: reg042_B at scale 16.0\n",
      "9.766 per cent patches are empty\n",
      "reg042_B at scale 16.0 has 78 patches with zero diveristy\n",
      "reg042_B at scale 16.0 diversity is 0.689848033437531\n",
      "Processing region: reg043_A at scale 16.0\n",
      "3.125 per cent patches are empty\n",
      "reg043_A at scale 16.0 has 45 patches with zero diveristy\n",
      "reg043_A at scale 16.0 diversity is 1.1180553298288896\n",
      "Processing region: reg043_B at scale 16.0\n",
      "35.938 per cent patches are empty\n",
      "reg043_B at scale 16.0 has 86 patches with zero diveristy\n",
      "reg043_B at scale 16.0 diversity is 0.5346895605617722\n",
      "Processing region: reg044_A at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg044_A at scale 16.0 has 52 patches with zero diveristy\n",
      "reg044_A at scale 16.0 diversity is 1.1342625563333575\n",
      "Processing region: reg044_B at scale 16.0\n",
      "48.438 per cent patches are empty\n",
      "reg044_B at scale 16.0 has 70 patches with zero diveristy\n",
      "reg044_B at scale 16.0 diversity is 0.535590639344215\n",
      "Processing region: reg045_A at scale 16.0\n",
      "8.203 per cent patches are empty\n",
      "reg045_A at scale 16.0 has 7 patches with zero diveristy\n",
      "reg045_A at scale 16.0 diversity is 1.5799282366123537\n",
      "Processing region: reg045_B at scale 16.0\n",
      "16.016 per cent patches are empty\n",
      "reg045_B at scale 16.0 has 46 patches with zero diveristy\n",
      "reg045_B at scale 16.0 diversity is 1.1684881061695667\n",
      "Processing region: reg046_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg046_A at scale 16.0 has 7 patches with zero diveristy\n",
      "reg046_A at scale 16.0 diversity is 1.969037799740305\n",
      "Processing region: reg046_B at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg046_B at scale 16.0 has 12 patches with zero diveristy\n",
      "reg046_B at scale 16.0 diversity is 1.8436947313787408\n",
      "Processing region: reg047_A at scale 16.0\n",
      "14.062 per cent patches are empty\n",
      "reg047_A at scale 16.0 has 27 patches with zero diveristy\n",
      "reg047_A at scale 16.0 diversity is 1.609495838803236\n",
      "Processing region: reg047_B at scale 16.0\n",
      "16.406 per cent patches are empty\n",
      "reg047_B at scale 16.0 has 95 patches with zero diveristy\n",
      "reg047_B at scale 16.0 diversity is 0.6530865052102662\n",
      "Processing region: reg048_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg048_A at scale 16.0 has 17 patches with zero diveristy\n",
      "reg048_A at scale 16.0 diversity is 1.5852563690364594\n",
      "Processing region: reg048_B at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg048_B at scale 16.0 has 41 patches with zero diveristy\n",
      "reg048_B at scale 16.0 diversity is 1.3406049350276892\n",
      "Processing region: reg049_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg049_A at scale 16.0 has 12 patches with zero diveristy\n",
      "reg049_A at scale 16.0 diversity is 1.6165216368651798\n",
      "Processing region: reg049_B at scale 16.0\n",
      "12.500 per cent patches are empty\n",
      "reg049_B at scale 16.0 has 38 patches with zero diveristy\n",
      "reg049_B at scale 16.0 diversity is 1.2533039766157301\n",
      "Processing region: reg050_A at scale 16.0\n",
      "13.281 per cent patches are empty\n",
      "reg050_A at scale 16.0 has 44 patches with zero diveristy\n",
      "reg050_A at scale 16.0 diversity is 1.045985333218258\n",
      "Processing region: reg050_B at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg050_B at scale 16.0 has 31 patches with zero diveristy\n",
      "reg050_B at scale 16.0 diversity is 1.274441178015937\n",
      "Processing region: reg051_A at scale 16.0\n",
      "15.625 per cent patches are empty\n",
      "reg051_A at scale 16.0 has 50 patches with zero diveristy\n",
      "reg051_A at scale 16.0 diversity is 1.3737017086628374\n",
      "Processing region: reg051_B at scale 16.0\n",
      "7.031 per cent patches are empty\n",
      "reg051_B at scale 16.0 has 30 patches with zero diveristy\n",
      "reg051_B at scale 16.0 diversity is 1.3149655951658545\n",
      "Processing region: reg052_A at scale 16.0\n",
      "3.906 per cent patches are empty\n",
      "reg052_A at scale 16.0 has 21 patches with zero diveristy\n",
      "reg052_A at scale 16.0 diversity is 1.5149924189868127\n",
      "Processing region: reg052_B at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg052_B at scale 16.0 has 40 patches with zero diveristy\n",
      "reg052_B at scale 16.0 diversity is 1.2074496480795733\n",
      "Processing region: reg053_A at scale 16.0\n",
      "5.859 per cent patches are empty\n",
      "reg053_A at scale 16.0 has 19 patches with zero diveristy\n",
      "reg053_A at scale 16.0 diversity is 1.828181943385345\n",
      "Processing region: reg053_B at scale 16.0\n",
      "70.703 per cent patches are empty\n",
      "reg053_B at scale 16.0 has 48 patches with zero diveristy\n",
      "reg053_B at scale 16.0 diversity is 0.42990472146248787\n",
      "Processing region: reg054_A at scale 16.0\n",
      "18.359 per cent patches are empty\n",
      "reg054_A at scale 16.0 has 29 patches with zero diveristy\n",
      "reg054_A at scale 16.0 diversity is 1.5096096613617158\n",
      "Processing region: reg054_B at scale 16.0\n",
      "59.766 per cent patches are empty\n",
      "reg054_B at scale 16.0 has 36 patches with zero diveristy\n",
      "reg054_B at scale 16.0 diversity is 0.7921187180915548\n",
      "Processing region: reg055_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg055_A at scale 16.0 has 8 patches with zero diveristy\n",
      "reg055_A at scale 16.0 diversity is 1.9212566918689848\n",
      "Processing region: reg055_B at scale 16.0\n",
      "69.531 per cent patches are empty\n",
      "reg055_B at scale 16.0 has 51 patches with zero diveristy\n",
      "reg055_B at scale 16.0 diversity is 0.40933933688926766\n",
      "Processing region: reg056_A at scale 16.0\n",
      "23.047 per cent patches are empty\n",
      "reg056_A at scale 16.0 has 44 patches with zero diveristy\n",
      "reg056_A at scale 16.0 diversity is 1.2747267956517867\n",
      "Processing region: reg056_B at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg056_B at scale 16.0 has 38 patches with zero diveristy\n",
      "reg056_B at scale 16.0 diversity is 1.4029016678673227\n",
      "Processing region: reg057_A at scale 16.0\n",
      "87.500 per cent patches are empty\n",
      "reg057_A at scale 16.0 has 15 patches with zero diveristy\n",
      "reg057_A at scale 16.0 diversity is 0.6693315263855854\n",
      "Processing region: reg057_B at scale 16.0\n",
      "31.250 per cent patches are empty\n",
      "reg057_B at scale 16.0 has 58 patches with zero diveristy\n",
      "reg057_B at scale 16.0 diversity is 1.0136105580975299\n",
      "Processing region: reg058_A at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg058_A at scale 16.0 has 93 patches with zero diveristy\n",
      "reg058_A at scale 16.0 diversity is 0.597051939321446\n",
      "Processing region: reg058_B at scale 16.0\n",
      "14.453 per cent patches are empty\n",
      "reg058_B at scale 16.0 has 139 patches with zero diveristy\n",
      "reg058_B at scale 16.0 diversity is 0.3408536314425973\n",
      "Processing region: reg059_A at scale 16.0\n",
      "11.328 per cent patches are empty\n",
      "reg059_A at scale 16.0 has 9 patches with zero diveristy\n",
      "reg059_A at scale 16.0 diversity is 2.0891563560721482\n",
      "Processing region: reg059_B at scale 16.0\n",
      "8.594 per cent patches are empty\n",
      "reg059_B at scale 16.0 has 12 patches with zero diveristy\n",
      "reg059_B at scale 16.0 diversity is 1.5843132976275534\n",
      "Processing region: reg060_A at scale 16.0\n",
      "7.031 per cent patches are empty\n",
      "reg060_A at scale 16.0 has 11 patches with zero diveristy\n",
      "reg060_A at scale 16.0 diversity is 1.9823834022017208\n",
      "Processing region: reg060_B at scale 16.0\n",
      "8.203 per cent patches are empty\n",
      "reg060_B at scale 16.0 has 5 patches with zero diveristy\n",
      "reg060_B at scale 16.0 diversity is 1.916517030735813\n",
      "Processing region: reg061_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg061_A at scale 16.0 has 36 patches with zero diveristy\n",
      "reg061_A at scale 16.0 diversity is 1.4336904201745997\n",
      "Processing region: reg061_B at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg061_B at scale 16.0 has 15 patches with zero diveristy\n",
      "reg061_B at scale 16.0 diversity is 1.6479345872567748\n",
      "Processing region: reg062_A at scale 16.0\n",
      "2.344 per cent patches are empty\n",
      "reg062_A at scale 16.0 has 12 patches with zero diveristy\n",
      "reg062_A at scale 16.0 diversity is 1.667230459467464\n",
      "Processing region: reg062_B at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg062_B at scale 16.0 has 26 patches with zero diveristy\n",
      "reg062_B at scale 16.0 diversity is 1.461775327443554\n",
      "Processing region: reg063_A at scale 16.0\n",
      "24.219 per cent patches are empty\n",
      "reg063_A at scale 16.0 has 41 patches with zero diveristy\n",
      "reg063_A at scale 16.0 diversity is 1.0847166528206407\n",
      "Processing region: reg063_B at scale 16.0\n",
      "9.766 per cent patches are empty\n",
      "reg063_B at scale 16.0 has 22 patches with zero diveristy\n",
      "reg063_B at scale 16.0 diversity is 1.3759567260882974\n",
      "Processing region: reg064_A at scale 16.0\n",
      "8.594 per cent patches are empty\n",
      "reg064_A at scale 16.0 has 11 patches with zero diveristy\n",
      "reg064_A at scale 16.0 diversity is 1.4355060178845582\n",
      "Processing region: reg064_B at scale 16.0\n",
      "6.250 per cent patches are empty\n",
      "reg064_B at scale 16.0 has 20 patches with zero diveristy\n",
      "reg064_B at scale 16.0 diversity is 1.6946818144977838\n",
      "Processing region: reg065_A at scale 16.0\n",
      "11.328 per cent patches are empty\n",
      "reg065_A at scale 16.0 has 67 patches with zero diveristy\n",
      "reg065_A at scale 16.0 diversity is 1.0101402887124225\n",
      "Processing region: reg065_B at scale 16.0\n",
      "23.438 per cent patches are empty\n",
      "reg065_B at scale 16.0 has 94 patches with zero diveristy\n",
      "reg065_B at scale 16.0 diversity is 0.6448394289785003\n",
      "Processing region: reg066_A at scale 16.0\n",
      "7.812 per cent patches are empty\n",
      "reg066_A at scale 16.0 has 23 patches with zero diveristy\n",
      "reg066_A at scale 16.0 diversity is 1.3853495554427253\n",
      "Processing region: reg066_B at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg066_B at scale 16.0 has 54 patches with zero diveristy\n",
      "reg066_B at scale 16.0 diversity is 1.0393069690487353\n",
      "Processing region: reg067_A at scale 16.0\n",
      "37.500 per cent patches are empty\n",
      "reg067_A at scale 16.0 has 47 patches with zero diveristy\n",
      "reg067_A at scale 16.0 diversity is 1.1133641212890972\n",
      "Processing region: reg067_B at scale 16.0\n",
      "84.375 per cent patches are empty\n",
      "reg067_B at scale 16.0 has 24 patches with zero diveristy\n",
      "reg067_B at scale 16.0 diversity is 0.48788885823487665\n",
      "Processing region: reg068_A at scale 16.0\n",
      "8.594 per cent patches are empty\n",
      "reg068_A at scale 16.0 has 32 patches with zero diveristy\n",
      "reg068_A at scale 16.0 diversity is 1.3039123926387821\n",
      "Processing region: reg068_B at scale 16.0\n",
      "7.031 per cent patches are empty\n",
      "reg068_B at scale 16.0 has 19 patches with zero diveristy\n",
      "reg068_B at scale 16.0 diversity is 1.7179625275097257\n",
      "Processing region: reg069_A at scale 16.0\n",
      "6.641 per cent patches are empty\n",
      "reg069_A at scale 16.0 has 27 patches with zero diveristy\n",
      "reg069_A at scale 16.0 diversity is 1.5653739676202172\n",
      "Processing region: reg069_B at scale 16.0\n",
      "14.453 per cent patches are empty\n",
      "reg069_B at scale 16.0 has 67 patches with zero diveristy\n",
      "reg069_B at scale 16.0 diversity is 1.0116912721249247\n",
      "Processing region: reg070_A at scale 16.0\n",
      "8.984 per cent patches are empty\n",
      "reg070_A at scale 16.0 has 42 patches with zero diveristy\n",
      "reg070_A at scale 16.0 diversity is 1.2684280512692527\n",
      "Processing region: reg070_B at scale 16.0\n",
      "10.547 per cent patches are empty\n",
      "reg070_B at scale 16.0 has 45 patches with zero diveristy\n",
      "reg070_B at scale 16.0 diversity is 1.0081353626718108\n",
      "Processing region: reg001_A at scale 32.0\n",
      "46.582 per cent patches are empty\n",
      "reg001_A at scale 32.0 has 302 patches with zero diveristy\n",
      "reg001_A at scale 32.0 diversity is 0.5557217232368197\n",
      "Processing region: reg001_B at scale 32.0\n",
      "75.293 per cent patches are empty\n",
      "reg001_B at scale 32.0 has 197 patches with zero diveristy\n",
      "reg001_B at scale 32.0 diversity is 0.23847465007070615\n",
      "Processing region: reg002_A at scale 32.0\n",
      "45.996 per cent patches are empty\n",
      "reg002_A at scale 32.0 has 245 patches with zero diveristy\n",
      "reg002_A at scale 32.0 diversity is 0.6994581913297628\n",
      "Processing region: reg002_B at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "reg002_B at scale 32.0 has 555 patches with zero diveristy\n",
      "reg002_B at scale 32.0 diversity is 0.31562102488293486\n",
      "Processing region: reg003_A at scale 32.0\n",
      "46.875 per cent patches are empty\n",
      "reg003_A at scale 32.0 has 233 patches with zero diveristy\n",
      "reg003_A at scale 32.0 diversity is 0.657804877104138\n",
      "Processing region: reg003_B at scale 32.0\n",
      "43.945 per cent patches are empty\n",
      "reg003_B at scale 32.0 has 312 patches with zero diveristy\n",
      "reg003_B at scale 32.0 diversity is 0.5146402418896122\n",
      "Processing region: reg004_A at scale 32.0\n",
      "35.254 per cent patches are empty\n",
      "reg004_A at scale 32.0 has 270 patches with zero diveristy\n",
      "reg004_A at scale 32.0 diversity is 0.7402479198378501\n",
      "Processing region: reg004_B at scale 32.0\n",
      "17.871 per cent patches are empty\n",
      "reg004_B at scale 32.0 has 396 patches with zero diveristy\n",
      "reg004_B at scale 32.0 diversity is 0.5949950790704666\n",
      "Processing region: reg005_A at scale 32.0\n",
      "18.359 per cent patches are empty\n",
      "reg005_A at scale 32.0 has 303 patches with zero diveristy\n",
      "reg005_A at scale 32.0 diversity is 0.7934137071575837\n",
      "Processing region: reg005_B at scale 32.0\n",
      "40.527 per cent patches are empty\n",
      "reg005_B at scale 32.0 has 235 patches with zero diveristy\n",
      "reg005_B at scale 32.0 diversity is 0.7466883213515009\n",
      "Processing region: reg006_A at scale 32.0\n",
      "37.402 per cent patches are empty\n",
      "reg006_A at scale 32.0 has 304 patches with zero diveristy\n",
      "reg006_A at scale 32.0 diversity is 0.6191400438725643\n",
      "Processing region: reg006_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "reg006_B at scale 32.0 has 306 patches with zero diveristy\n",
      "reg006_B at scale 32.0 diversity is 0.7358364002341685\n",
      "Processing region: reg007_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "reg007_A at scale 32.0 has 359 patches with zero diveristy\n",
      "reg007_A at scale 32.0 diversity is 0.7421368500862212\n",
      "Processing region: reg007_B at scale 32.0\n",
      "26.660 per cent patches are empty\n",
      "reg007_B at scale 32.0 has 255 patches with zero diveristy\n",
      "reg007_B at scale 32.0 diversity is 0.8171170853651124\n",
      "Processing region: reg008_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "reg008_A at scale 32.0 has 261 patches with zero diveristy\n",
      "reg008_A at scale 32.0 diversity is 0.5266790007306591\n",
      "Processing region: reg008_B at scale 32.0\n",
      "8.691 per cent patches are empty\n",
      "reg008_B at scale 32.0 has 367 patches with zero diveristy\n",
      "reg008_B at scale 32.0 diversity is 0.7249209437948831\n",
      "Processing region: reg009_A at scale 32.0\n",
      "12.793 per cent patches are empty\n",
      "reg009_A at scale 32.0 has 315 patches with zero diveristy\n",
      "reg009_A at scale 32.0 diversity is 0.8268542667139614\n",
      "Processing region: reg009_B at scale 32.0\n",
      "6.543 per cent patches are empty\n",
      "reg009_B at scale 32.0 has 133 patches with zero diveristy\n",
      "reg009_B at scale 32.0 diversity is 1.1522742349013497\n",
      "Processing region: reg010_A at scale 32.0\n",
      "25.391 per cent patches are empty\n",
      "reg010_A at scale 32.0 has 260 patches with zero diveristy\n",
      "reg010_A at scale 32.0 diversity is 0.8655377989146098\n",
      "Processing region: reg010_B at scale 32.0\n",
      "42.383 per cent patches are empty\n",
      "reg010_B at scale 32.0 has 318 patches with zero diveristy\n",
      "reg010_B at scale 32.0 diversity is 0.5597647637376048\n",
      "Processing region: reg011_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "reg011_A at scale 32.0 has 140 patches with zero diveristy\n",
      "reg011_A at scale 32.0 diversity is 1.0948781842246476\n",
      "Processing region: reg011_B at scale 32.0\n",
      "51.562 per cent patches are empty\n",
      "reg011_B at scale 32.0 has 294 patches with zero diveristy\n",
      "reg011_B at scale 32.0 diversity is 0.47114908705905273\n",
      "Processing region: reg012_A at scale 32.0\n",
      "32.910 per cent patches are empty\n",
      "reg012_A at scale 32.0 has 291 patches with zero diveristy\n",
      "reg012_A at scale 32.0 diversity is 0.715899417238251\n",
      "Processing region: reg012_B at scale 32.0\n",
      "77.051 per cent patches are empty\n",
      "reg012_B at scale 32.0 has 141 patches with zero diveristy\n",
      "reg012_B at scale 32.0 diversity is 0.46494303672351367\n",
      "Processing region: reg013_A at scale 32.0\n",
      "25.098 per cent patches are empty\n",
      "reg013_A at scale 32.0 has 399 patches with zero diveristy\n",
      "reg013_A at scale 32.0 diversity is 0.5717931888532037\n",
      "Processing region: reg013_B at scale 32.0\n",
      "19.922 per cent patches are empty\n",
      "reg013_B at scale 32.0 has 418 patches with zero diveristy\n",
      "reg013_B at scale 32.0 diversity is 0.5932263292627094\n",
      "Processing region: reg014_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "reg014_A at scale 32.0 has 305 patches with zero diveristy\n",
      "reg014_A at scale 32.0 diversity is 0.45247809666690697\n",
      "Processing region: reg014_B at scale 32.0\n",
      "21.191 per cent patches are empty\n",
      "reg014_B at scale 32.0 has 283 patches with zero diveristy\n",
      "reg014_B at scale 32.0 diversity is 0.7752757566055144\n",
      "Processing region: reg015_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "reg015_A at scale 32.0 has 313 patches with zero diveristy\n",
      "reg015_A at scale 32.0 diversity is 0.7413513744314911\n",
      "Processing region: reg015_B at scale 32.0\n",
      "31.055 per cent patches are empty\n",
      "reg015_B at scale 32.0 has 463 patches with zero diveristy\n",
      "reg015_B at scale 32.0 diversity is 0.36216571944242704\n",
      "Processing region: reg016_A at scale 32.0\n",
      "27.539 per cent patches are empty\n",
      "reg016_A at scale 32.0 has 326 patches with zero diveristy\n",
      "reg016_A at scale 32.0 diversity is 0.6797345134618556\n",
      "Processing region: reg016_B at scale 32.0\n",
      "17.773 per cent patches are empty\n",
      "reg016_B at scale 32.0 has 351 patches with zero diveristy\n",
      "reg016_B at scale 32.0 diversity is 0.6839141072927567\n",
      "Processing region: reg017_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "reg017_A at scale 32.0 has 315 patches with zero diveristy\n",
      "reg017_A at scale 32.0 diversity is 0.8149216127699066\n",
      "Processing region: reg017_B at scale 32.0\n",
      "18.066 per cent patches are empty\n",
      "reg017_B at scale 32.0 has 292 patches with zero diveristy\n",
      "reg017_B at scale 32.0 diversity is 0.8927257639894027\n",
      "Processing region: reg018_A at scale 32.0\n",
      "29.492 per cent patches are empty\n",
      "reg018_A at scale 32.0 has 283 patches with zero diveristy\n",
      "reg018_A at scale 32.0 diversity is 0.7870709176203579\n",
      "Processing region: reg018_B at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "reg018_B at scale 32.0 has 346 patches with zero diveristy\n",
      "reg018_B at scale 32.0 diversity is 0.6650753971585537\n",
      "Processing region: reg019_A at scale 32.0\n",
      "53.613 per cent patches are empty\n",
      "reg019_A at scale 32.0 has 164 patches with zero diveristy\n",
      "reg019_A at scale 32.0 diversity is 0.7941809439673526\n",
      "Processing region: reg019_B at scale 32.0\n",
      "92.090 per cent patches are empty\n",
      "reg019_B at scale 32.0 has 70 patches with zero diveristy\n",
      "reg019_B at scale 32.0 diversity is 0.1327763889155984\n",
      "Processing region: reg020_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "reg020_A at scale 32.0 has 294 patches with zero diveristy\n",
      "reg020_A at scale 32.0 diversity is 0.8202500829777284\n",
      "Processing region: reg020_B at scale 32.0\n",
      "19.434 per cent patches are empty\n",
      "reg020_B at scale 32.0 has 176 patches with zero diveristy\n",
      "reg020_B at scale 32.0 diversity is 1.0608140188257598\n",
      "Processing region: reg021_A at scale 32.0\n",
      "36.328 per cent patches are empty\n",
      "reg021_A at scale 32.0 has 164 patches with zero diveristy\n",
      "reg021_A at scale 32.0 diversity is 0.956050983418754\n",
      "Processing region: reg021_B at scale 32.0\n",
      "76.270 per cent patches are empty\n",
      "reg021_B at scale 32.0 has 195 patches with zero diveristy\n",
      "reg021_B at scale 32.0 diversity is 0.22551707626951734\n",
      "Processing region: reg022_A at scale 32.0\n",
      "31.934 per cent patches are empty\n",
      "reg022_A at scale 32.0 has 248 patches with zero diveristy\n",
      "reg022_A at scale 32.0 diversity is 0.8199605494643846\n",
      "Processing region: reg022_B at scale 32.0\n",
      "62.402 per cent patches are empty\n",
      "reg022_B at scale 32.0 has 247 patches with zero diveristy\n",
      "reg022_B at scale 32.0 diversity is 0.4188788755622188\n",
      "Processing region: reg023_A at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "reg023_A at scale 32.0 has 334 patches with zero diveristy\n",
      "reg023_A at scale 32.0 diversity is 0.34879789982334336\n",
      "Processing region: reg023_B at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "reg023_B at scale 32.0 has 260 patches with zero diveristy\n",
      "reg023_B at scale 32.0 diversity is 0.9160669109208432\n",
      "Processing region: reg024_A at scale 32.0\n",
      "46.191 per cent patches are empty\n",
      "reg024_A at scale 32.0 has 358 patches with zero diveristy\n",
      "reg024_A at scale 32.0 diversity is 0.364969293738499\n",
      "Processing region: reg024_B at scale 32.0\n",
      "15.625 per cent patches are empty\n",
      "reg024_B at scale 32.0 has 449 patches with zero diveristy\n",
      "reg024_B at scale 32.0 diversity is 0.5456811479117779\n",
      "Processing region: reg025_A at scale 32.0\n",
      "39.648 per cent patches are empty\n",
      "reg025_A at scale 32.0 has 323 patches with zero diveristy\n",
      "reg025_A at scale 32.0 diversity is 0.5890890340494715\n",
      "Processing region: reg025_B at scale 32.0\n",
      "31.348 per cent patches are empty\n",
      "reg025_B at scale 32.0 has 299 patches with zero diveristy\n",
      "reg025_B at scale 32.0 diversity is 0.7363894695016134\n",
      "Processing region: reg026_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "reg026_A at scale 32.0 has 263 patches with zero diveristy\n",
      "reg026_A at scale 32.0 diversity is 0.8776847062283261\n",
      "Processing region: reg026_B at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "reg026_B at scale 32.0 has 256 patches with zero diveristy\n",
      "reg026_B at scale 32.0 diversity is 0.9004768676847865\n",
      "Processing region: reg027_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "reg027_A at scale 32.0 has 303 patches with zero diveristy\n",
      "reg027_A at scale 32.0 diversity is 0.79723050865919\n",
      "Processing region: reg027_B at scale 32.0\n",
      "17.578 per cent patches are empty\n",
      "reg027_B at scale 32.0 has 394 patches with zero diveristy\n",
      "reg027_B at scale 32.0 diversity is 0.6004464067236152\n",
      "Processing region: reg028_A at scale 32.0\n",
      "16.016 per cent patches are empty\n",
      "reg028_A at scale 32.0 has 253 patches with zero diveristy\n",
      "reg028_A at scale 32.0 diversity is 0.9714370162005411\n",
      "Processing region: reg028_B at scale 32.0\n",
      "13.965 per cent patches are empty\n",
      "reg028_B at scale 32.0 has 342 patches with zero diveristy\n",
      "reg028_B at scale 32.0 diversity is 0.7582122104379305\n",
      "Processing region: reg029_A at scale 32.0\n",
      "15.039 per cent patches are empty\n",
      "reg029_A at scale 32.0 has 435 patches with zero diveristy\n",
      "reg029_A at scale 32.0 diversity is 0.6153299482473555\n",
      "Processing region: reg029_B at scale 32.0\n",
      "23.633 per cent patches are empty\n",
      "reg029_B at scale 32.0 has 379 patches with zero diveristy\n",
      "reg029_B at scale 32.0 diversity is 0.5455665967513191\n",
      "Processing region: reg030_A at scale 32.0\n",
      "17.969 per cent patches are empty\n",
      "reg030_A at scale 32.0 has 272 patches with zero diveristy\n",
      "reg030_A at scale 32.0 diversity is 0.8442162056036804\n",
      "Processing region: reg030_B at scale 32.0\n",
      "25.195 per cent patches are empty\n",
      "reg030_B at scale 32.0 has 436 patches with zero diveristy\n",
      "reg030_B at scale 32.0 diversity is 0.46990064731306624\n",
      "Processing region: reg031_A at scale 32.0\n",
      "11.426 per cent patches are empty\n",
      "reg031_A at scale 32.0 has 297 patches with zero diveristy\n",
      "reg031_A at scale 32.0 diversity is 0.8563650465552216\n",
      "Processing region: reg031_B at scale 32.0\n",
      "17.480 per cent patches are empty\n",
      "reg031_B at scale 32.0 has 301 patches with zero diveristy\n",
      "reg031_B at scale 32.0 diversity is 0.7979802932943109\n",
      "Processing region: reg032_A at scale 32.0\n",
      "11.230 per cent patches are empty\n",
      "reg032_A at scale 32.0 has 323 patches with zero diveristy\n",
      "reg032_A at scale 32.0 diversity is 0.7804075674761368\n",
      "Processing region: reg032_B at scale 32.0\n",
      "33.984 per cent patches are empty\n",
      "reg032_B at scale 32.0 has 272 patches with zero diveristy\n",
      "reg032_B at scale 32.0 diversity is 0.7500143014454015\n",
      "Processing region: reg033_A at scale 32.0\n",
      "26.367 per cent patches are empty\n",
      "reg033_A at scale 32.0 has 198 patches with zero diveristy\n",
      "reg033_A at scale 32.0 diversity is 0.9048044337514133\n",
      "Processing region: reg033_B at scale 32.0\n",
      "12.012 per cent patches are empty\n",
      "reg033_B at scale 32.0 has 394 patches with zero diveristy\n",
      "reg033_B at scale 32.0 diversity is 0.6896540868642623\n",
      "Processing region: reg034_A at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "reg034_A at scale 32.0 has 265 patches with zero diveristy\n",
      "reg034_A at scale 32.0 diversity is 0.8186291275482388\n",
      "Processing region: reg034_B at scale 32.0\n",
      "25.586 per cent patches are empty\n",
      "reg034_B at scale 32.0 has 373 patches with zero diveristy\n",
      "reg034_B at scale 32.0 diversity is 0.5952104538602976\n",
      "Processing region: reg035_A at scale 32.0\n",
      "21.777 per cent patches are empty\n",
      "reg035_A at scale 32.0 has 282 patches with zero diveristy\n",
      "reg035_A at scale 32.0 diversity is 0.8409628638917658\n",
      "Processing region: reg035_B at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "reg035_B at scale 32.0 has 324 patches with zero diveristy\n",
      "reg035_B at scale 32.0 diversity is 0.670885352693505\n",
      "Processing region: reg036_A at scale 32.0\n",
      "13.184 per cent patches are empty\n",
      "reg036_A at scale 32.0 has 184 patches with zero diveristy\n",
      "reg036_A at scale 32.0 diversity is 1.0632868714800234\n",
      "Processing region: reg036_B at scale 32.0\n",
      "10.742 per cent patches are empty\n",
      "reg036_B at scale 32.0 has 472 patches with zero diveristy\n",
      "reg036_B at scale 32.0 diversity is 0.5829003993727727\n",
      "Processing region: reg037_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "reg037_A at scale 32.0 has 236 patches with zero diveristy\n",
      "reg037_A at scale 32.0 diversity is 0.7665670072009133\n",
      "Processing region: reg037_B at scale 32.0\n",
      "70.020 per cent patches are empty\n",
      "reg037_B at scale 32.0 has 255 patches with zero diveristy\n",
      "reg037_B at scale 32.0 diversity is 0.188536565468344\n",
      "Processing region: reg038_A at scale 32.0\n",
      "11.719 per cent patches are empty\n",
      "reg038_A at scale 32.0 has 290 patches with zero diveristy\n",
      "reg038_A at scale 32.0 diversity is 0.8611117573150999\n",
      "Processing region: reg038_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "reg038_B at scale 32.0 has 334 patches with zero diveristy\n",
      "reg038_B at scale 32.0 diversity is 0.6321475317707992\n",
      "Processing region: reg039_A at scale 32.0\n",
      "30.273 per cent patches are empty\n",
      "reg039_A at scale 32.0 has 203 patches with zero diveristy\n",
      "reg039_A at scale 32.0 diversity is 0.9109905007254127\n",
      "Processing region: reg039_B at scale 32.0\n",
      "56.543 per cent patches are empty\n",
      "reg039_B at scale 32.0 has 304 patches with zero diveristy\n",
      "reg039_B at scale 32.0 diversity is 0.3608682315400152\n",
      "Processing region: reg040_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "reg040_A at scale 32.0 has 338 patches with zero diveristy\n",
      "reg040_A at scale 32.0 diversity is 0.6970079056544474\n",
      "Processing region: reg040_B at scale 32.0\n",
      "57.520 per cent patches are empty\n",
      "reg040_B at scale 32.0 has 244 patches with zero diveristy\n",
      "reg040_B at scale 32.0 diversity is 0.4863994130681796\n",
      "Processing region: reg041_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "reg041_A at scale 32.0 has 415 patches with zero diveristy\n",
      "reg041_A at scale 32.0 diversity is 0.6103192240635986\n",
      "Processing region: reg041_B at scale 32.0\n",
      "29.688 per cent patches are empty\n",
      "reg041_B at scale 32.0 has 453 patches with zero diveristy\n",
      "reg041_B at scale 32.0 diversity is 0.44274065377569166\n",
      "Processing region: reg042_A at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "reg042_A at scale 32.0 has 525 patches with zero diveristy\n",
      "reg042_A at scale 32.0 diversity is 0.3449762230060837\n",
      "Processing region: reg042_B at scale 32.0\n",
      "24.902 per cent patches are empty\n",
      "reg042_B at scale 32.0 has 534 patches with zero diveristy\n",
      "reg042_B at scale 32.0 diversity is 0.3104026185490923\n",
      "Processing region: reg043_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "reg043_A at scale 32.0 has 459 patches with zero diveristy\n",
      "reg043_A at scale 32.0 diversity is 0.4603516593074785\n",
      "Processing region: reg043_B at scale 32.0\n",
      "64.844 per cent patches are empty\n",
      "reg043_B at scale 32.0 has 298 patches with zero diveristy\n",
      "reg043_B at scale 32.0 diversity is 0.17661800225951405\n",
      "Processing region: reg044_A at scale 32.0\n",
      "43.164 per cent patches are empty\n",
      "reg044_A at scale 32.0 has 361 patches with zero diveristy\n",
      "reg044_A at scale 32.0 diversity is 0.4211034838504594\n",
      "Processing region: reg044_B at scale 32.0\n",
      "71.973 per cent patches are empty\n",
      "reg044_B at scale 32.0 has 239 patches with zero diveristy\n",
      "reg044_B at scale 32.0 diversity is 0.1811331518997645\n",
      "Processing region: reg045_A at scale 32.0\n",
      "13.672 per cent patches are empty\n",
      "reg045_A at scale 32.0 has 359 patches with zero diveristy\n",
      "reg045_A at scale 32.0 diversity is 0.7102307125802958\n",
      "Processing region: reg045_B at scale 32.0\n",
      "43.750 per cent patches are empty\n",
      "reg045_B at scale 32.0 has 370 patches with zero diveristy\n",
      "reg045_B at scale 32.0 diversity is 0.40808583092063516\n",
      "Processing region: reg046_A at scale 32.0\n",
      "15.723 per cent patches are empty\n",
      "reg046_A at scale 32.0 has 307 patches with zero diveristy\n",
      "reg046_A at scale 32.0 diversity is 0.824653080439295\n",
      "Processing region: reg046_B at scale 32.0\n",
      "15.234 per cent patches are empty\n",
      "reg046_B at scale 32.0 has 300 patches with zero diveristy\n",
      "reg046_B at scale 32.0 diversity is 0.8110346447308838\n",
      "Processing region: reg047_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "reg047_A at scale 32.0 has 256 patches with zero diveristy\n",
      "reg047_A at scale 32.0 diversity is 0.8265704496875337\n",
      "Processing region: reg047_B at scale 32.0\n",
      "45.117 per cent patches are empty\n",
      "reg047_B at scale 32.0 has 447 patches with zero diveristy\n",
      "reg047_B at scale 32.0 diversity is 0.217459352739393\n",
      "Processing region: reg048_A at scale 32.0\n",
      "24.512 per cent patches are empty\n",
      "reg048_A at scale 32.0 has 356 patches with zero diveristy\n",
      "reg048_A at scale 32.0 diversity is 0.632285241336113\n",
      "Processing region: reg048_B at scale 32.0\n",
      "33.203 per cent patches are empty\n",
      "reg048_B at scale 32.0 has 309 patches with zero diveristy\n",
      "reg048_B at scale 32.0 diversity is 0.6458676068956488\n",
      "Processing region: reg049_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "reg049_A at scale 32.0 has 329 patches with zero diveristy\n",
      "reg049_A at scale 32.0 diversity is 0.7307917431322573\n",
      "Processing region: reg049_B at scale 32.0\n",
      "38.086 per cent patches are empty\n",
      "reg049_B at scale 32.0 has 382 patches with zero diveristy\n",
      "reg049_B at scale 32.0 diversity is 0.44560107536559873\n",
      "Processing region: reg050_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "reg050_A at scale 32.0 has 353 patches with zero diveristy\n",
      "reg050_A at scale 32.0 diversity is 0.42630701809588917\n",
      "Processing region: reg050_B at scale 32.0\n",
      "25.000 per cent patches are empty\n",
      "reg050_B at scale 32.0 has 430 patches with zero diveristy\n",
      "reg050_B at scale 32.0 diversity is 0.49653871022115786\n",
      "Processing region: reg051_A at scale 32.0\n",
      "33.496 per cent patches are empty\n",
      "reg051_A at scale 32.0 has 329 patches with zero diveristy\n",
      "reg051_A at scale 32.0 diversity is 0.6293450103290034\n",
      "Processing region: reg051_B at scale 32.0\n",
      "23.047 per cent patches are empty\n",
      "reg051_B at scale 32.0 has 413 patches with zero diveristy\n",
      "reg051_B at scale 32.0 diversity is 0.5381406981089925\n",
      "Processing region: reg052_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "reg052_A at scale 32.0 has 404 patches with zero diveristy\n",
      "reg052_A at scale 32.0 diversity is 0.5983554029508932\n",
      "Processing region: reg052_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "reg052_B at scale 32.0 has 439 patches with zero diveristy\n",
      "reg052_B at scale 32.0 diversity is 0.5123369607711975\n",
      "Processing region: reg053_A at scale 32.0\n",
      "24.023 per cent patches are empty\n",
      "reg053_A at scale 32.0 has 270 patches with zero diveristy\n",
      "reg053_A at scale 32.0 diversity is 0.8474392211965895\n",
      "Processing region: reg053_B at scale 32.0\n",
      "88.477 per cent patches are empty\n",
      "reg053_B at scale 32.0 has 103 patches with zero diveristy\n",
      "reg053_B at scale 32.0 diversity is 0.13559322033898305\n",
      "Processing region: reg054_A at scale 32.0\n",
      "35.547 per cent patches are empty\n",
      "reg054_A at scale 32.0 has 263 patches with zero diveristy\n",
      "reg054_A at scale 32.0 diversity is 0.7302801732780377\n",
      "Processing region: reg054_B at scale 32.0\n",
      "78.320 per cent patches are empty\n",
      "reg054_B at scale 32.0 has 176 patches with zero diveristy\n",
      "reg054_B at scale 32.0 diversity is 0.21515613741662346\n",
      "Processing region: reg055_A at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "reg055_A at scale 32.0 has 150 patches with zero diveristy\n",
      "reg055_A at scale 32.0 diversity is 1.1737463544368825\n",
      "Processing region: reg055_B at scale 32.0\n",
      "89.062 per cent patches are empty\n",
      "reg055_B at scale 32.0 has 98 patches with zero diveristy\n",
      "reg055_B at scale 32.0 diversity is 0.1399391369305175\n",
      "Processing region: reg056_A at scale 32.0\n",
      "56.641 per cent patches are empty\n",
      "reg056_A at scale 32.0 has 251 patches with zero diveristy\n",
      "reg056_A at scale 32.0 diversity is 0.5368372306790336\n",
      "Processing region: reg056_B at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "reg056_B at scale 32.0 has 424 patches with zero diveristy\n",
      "reg056_B at scale 32.0 diversity is 0.4703016023428499\n",
      "Processing region: reg057_A at scale 32.0\n",
      "94.434 per cent patches are empty\n",
      "reg057_A at scale 32.0 has 43 patches with zero diveristy\n",
      "reg057_A at scale 32.0 diversity is 0.2720441520973911\n",
      "Processing region: reg057_B at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "reg057_B at scale 32.0 has 287 patches with zero diveristy\n",
      "reg057_B at scale 32.0 diversity is 0.4616586255070822\n",
      "Processing region: reg058_A at scale 32.0\n",
      "20.703 per cent patches are empty\n",
      "reg058_A at scale 32.0 has 616 patches with zero diveristy\n",
      "reg058_A at scale 32.0 diversity is 0.24497604843820361\n",
      "Processing region: reg058_B at scale 32.0\n",
      "36.719 per cent patches are empty\n",
      "reg058_B at scale 32.0 has 571 patches with zero diveristy\n",
      "reg058_B at scale 32.0 diversity is 0.1166579173232915\n",
      "Processing region: reg059_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "reg059_A at scale 32.0 has 156 patches with zero diveristy\n",
      "reg059_A at scale 32.0 diversity is 1.1330184879161005\n",
      "Processing region: reg059_B at scale 32.0\n",
      "21.875 per cent patches are empty\n",
      "reg059_B at scale 32.0 has 358 patches with zero diveristy\n",
      "reg059_B at scale 32.0 diversity is 0.6637857631949273\n",
      "Processing region: reg060_A at scale 32.0\n",
      "15.820 per cent patches are empty\n",
      "reg060_A at scale 32.0 has 220 patches with zero diveristy\n",
      "reg060_A at scale 32.0 diversity is 0.9501429530272408\n",
      "Processing region: reg060_B at scale 32.0\n",
      "14.355 per cent patches are empty\n",
      "reg060_B at scale 32.0 has 254 patches with zero diveristy\n",
      "reg060_B at scale 32.0 diversity is 0.862701243108512\n",
      "Processing region: reg061_A at scale 32.0\n",
      "25.488 per cent patches are empty\n",
      "reg061_A at scale 32.0 has 340 patches with zero diveristy\n",
      "reg061_A at scale 32.0 diversity is 0.6630208302428718\n",
      "Processing region: reg061_B at scale 32.0\n",
      "22.656 per cent patches are empty\n",
      "reg061_B at scale 32.0 has 354 patches with zero diveristy\n",
      "reg061_B at scale 32.0 diversity is 0.6594595749329318\n",
      "Processing region: reg062_A at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "reg062_A at scale 32.0 has 328 patches with zero diveristy\n",
      "reg062_A at scale 32.0 diversity is 0.7760791929900223\n",
      "Processing region: reg062_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "reg062_B at scale 32.0 has 365 patches with zero diveristy\n",
      "reg062_B at scale 32.0 diversity is 0.5599294374161436\n",
      "Processing region: reg063_A at scale 32.0\n",
      "50.879 per cent patches are empty\n",
      "reg063_A at scale 32.0 has 299 patches with zero diveristy\n",
      "reg063_A at scale 32.0 diversity is 0.4443646795632483\n",
      "Processing region: reg063_B at scale 32.0\n",
      "40.234 per cent patches are empty\n",
      "reg063_B at scale 32.0 has 424 patches with zero diveristy\n",
      "reg063_B at scale 32.0 diversity is 0.3359688445866314\n",
      "Processing region: reg064_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "reg064_A at scale 32.0 has 358 patches with zero diveristy\n",
      "reg064_A at scale 32.0 diversity is 0.637025867355701\n",
      "Processing region: reg064_B at scale 32.0\n",
      "19.141 per cent patches are empty\n",
      "reg064_B at scale 32.0 has 341 patches with zero diveristy\n",
      "reg064_B at scale 32.0 diversity is 0.7251007262551754\n",
      "Processing region: reg065_A at scale 32.0\n",
      "27.930 per cent patches are empty\n",
      "reg065_A at scale 32.0 has 419 patches with zero diveristy\n",
      "reg065_A at scale 32.0 diversity is 0.5237081980471305\n",
      "Processing region: reg065_B at scale 32.0\n",
      "66.895 per cent patches are empty\n",
      "reg065_B at scale 32.0 has 286 patches with zero diveristy\n",
      "reg065_B at scale 32.0 diversity is 0.16007275075660887\n",
      "Processing region: reg066_A at scale 32.0\n",
      "23.242 per cent patches are empty\n",
      "reg066_A at scale 32.0 has 362 patches with zero diveristy\n",
      "reg066_A at scale 32.0 diversity is 0.6228292576468444\n",
      "Processing region: reg066_B at scale 32.0\n",
      "39.941 per cent patches are empty\n",
      "reg066_B at scale 32.0 has 439 patches with zero diveristy\n",
      "reg066_B at scale 32.0 diversity is 0.3194654362038013\n",
      "Processing region: reg067_A at scale 32.0\n",
      "66.992 per cent patches are empty\n",
      "reg067_A at scale 32.0 has 185 patches with zero diveristy\n",
      "reg067_A at scale 32.0 diversity is 0.5395960404181045\n",
      "Processing region: reg067_B at scale 32.0\n",
      "93.945 per cent patches are empty\n",
      "reg067_B at scale 32.0 has 46 patches with zero diveristy\n",
      "reg067_B at scale 32.0 diversity is 0.2622281586603083\n",
      "Processing region: reg068_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "reg068_A at scale 32.0 has 402 patches with zero diveristy\n",
      "reg068_A at scale 32.0 diversity is 0.43936214997273054\n",
      "Processing region: reg068_B at scale 32.0\n",
      "27.148 per cent patches are empty\n",
      "reg068_B at scale 32.0 has 366 patches with zero diveristy\n",
      "reg068_B at scale 32.0 diversity is 0.6211276185185179\n",
      "Processing region: reg069_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "reg069_A at scale 32.0 has 371 patches with zero diveristy\n",
      "reg069_A at scale 32.0 diversity is 0.5914787152155453\n",
      "Processing region: reg069_B at scale 32.0\n",
      "49.121 per cent patches are empty\n",
      "reg069_B at scale 32.0 has 350 patches with zero diveristy\n",
      "reg069_B at scale 32.0 diversity is 0.36949704212580514\n",
      "Processing region: reg070_A at scale 32.0\n",
      "26.270 per cent patches are empty\n",
      "reg070_A at scale 32.0 has 399 patches with zero diveristy\n",
      "reg070_A at scale 32.0 diversity is 0.5562779234292821\n",
      "Processing region: reg070_B at scale 32.0\n",
      "41.699 per cent patches are empty\n",
      "reg070_B at scale 32.0 has 430 patches with zero diveristy\n",
      "reg070_B at scale 32.0 diversity is 0.29723632585509063\n"
     ]
    }
   ],
   "source": [
    "# Define the sequence of scales\n",
    "scales = [1., 2., 4., 8., 16., 32.]\n",
    "\n",
    "mdi_results = eco.calculate_MDI(spatial_data=protein,\n",
    "                               scales=scales,\n",
    "                               library_key='File Name',\n",
    "                               library_id=library_ids,\n",
    "                               spatial_key=['X:X','Y:Y'],\n",
    "                               cluster_key='ClusterName',\n",
    "                               selecting_scale=False,\n",
    "                               random_patch=False,\n",
    "                               plotfigs=False,\n",
    "                               savefigs=False,\n",
    "                               patch_kwargs={'random_seed': None, 'min_points':2},\n",
    "                               other_kwargs={'metric': 'Shannon Diversity'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "badddbfd-5623-4861-b1df-a0ff7e6f3168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>32.0</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Patients</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>reg001_A</th>\n",
       "      <td>3.316416</td>\n",
       "      <td>3.00672</td>\n",
       "      <td>2.691226</td>\n",
       "      <td>2.184427</td>\n",
       "      <td>1.278679</td>\n",
       "      <td>0.555722</td>\n",
       "      <td>0.556983</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg001_B</th>\n",
       "      <td>3.354147</td>\n",
       "      <td>3.078202</td>\n",
       "      <td>2.508512</td>\n",
       "      <td>1.397887</td>\n",
       "      <td>0.624072</td>\n",
       "      <td>0.238475</td>\n",
       "      <td>0.687182</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg002_A</th>\n",
       "      <td>3.151849</td>\n",
       "      <td>2.907866</td>\n",
       "      <td>2.576381</td>\n",
       "      <td>2.168661</td>\n",
       "      <td>1.497099</td>\n",
       "      <td>0.699458</td>\n",
       "      <td>0.482914</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg002_B</th>\n",
       "      <td>1.119223</td>\n",
       "      <td>1.084668</td>\n",
       "      <td>1.009291</td>\n",
       "      <td>0.858962</td>\n",
       "      <td>0.620206</td>\n",
       "      <td>0.315621</td>\n",
       "      <td>0.158906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg003_A</th>\n",
       "      <td>3.040349</td>\n",
       "      <td>2.789846</td>\n",
       "      <td>2.402995</td>\n",
       "      <td>1.966016</td>\n",
       "      <td>1.441875</td>\n",
       "      <td>0.657805</td>\n",
       "      <td>0.468389</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg068_B</th>\n",
       "      <td>3.656726</td>\n",
       "      <td>3.37063</td>\n",
       "      <td>3.051267</td>\n",
       "      <td>2.541294</td>\n",
       "      <td>1.717963</td>\n",
       "      <td>0.621128</td>\n",
       "      <td>0.589885</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg069_A</th>\n",
       "      <td>3.205998</td>\n",
       "      <td>3.053596</td>\n",
       "      <td>2.835891</td>\n",
       "      <td>2.338134</td>\n",
       "      <td>1.565374</td>\n",
       "      <td>0.591479</td>\n",
       "      <td>0.515286</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg069_B</th>\n",
       "      <td>3.553295</td>\n",
       "      <td>3.034952</td>\n",
       "      <td>2.560528</td>\n",
       "      <td>1.931253</td>\n",
       "      <td>1.011691</td>\n",
       "      <td>0.369497</td>\n",
       "      <td>0.646230</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg070_A</th>\n",
       "      <td>2.541924</td>\n",
       "      <td>2.31133</td>\n",
       "      <td>2.142624</td>\n",
       "      <td>1.737921</td>\n",
       "      <td>1.268428</td>\n",
       "      <td>0.556278</td>\n",
       "      <td>0.384618</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg070_B</th>\n",
       "      <td>2.682543</td>\n",
       "      <td>2.219082</td>\n",
       "      <td>2.058367</td>\n",
       "      <td>1.713258</td>\n",
       "      <td>1.008135</td>\n",
       "      <td>0.297236</td>\n",
       "      <td>0.454414</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1.0       2.0       4.0       8.0      16.0      32.0  \\\n",
       "reg001_A  3.316416   3.00672  2.691226  2.184427  1.278679  0.555722   \n",
       "reg001_B  3.354147  3.078202  2.508512  1.397887  0.624072  0.238475   \n",
       "reg002_A  3.151849  2.907866  2.576381  2.168661  1.497099  0.699458   \n",
       "reg002_B  1.119223  1.084668  1.009291  0.858962  0.620206  0.315621   \n",
       "reg003_A  3.040349  2.789846  2.402995  1.966016  1.441875  0.657805   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "reg068_B  3.656726   3.37063  3.051267  2.541294  1.717963  0.621128   \n",
       "reg069_A  3.205998  3.053596  2.835891  2.338134  1.565374  0.591479   \n",
       "reg069_B  3.553295  3.034952  2.560528  1.931253  1.011691  0.369497   \n",
       "reg070_A  2.541924   2.31133  2.142624  1.737921  1.268428  0.556278   \n",
       "reg070_B  2.682543  2.219082  2.058367  1.713258  1.008135  0.297236   \n",
       "\n",
       "             Slope  Patients  Condition  \n",
       "reg001_A  0.556983         1          1  \n",
       "reg001_B  0.687182         1          1  \n",
       "reg002_A  0.482914         1          1  \n",
       "reg002_B  0.158906         1          1  \n",
       "reg003_A  0.468389         2          2  \n",
       "...            ...       ...        ...  \n",
       "reg068_B  0.589885        34          1  \n",
       "reg069_A  0.515286        35          1  \n",
       "reg069_B  0.646230        35          1  \n",
       "reg070_A  0.384618        35          1  \n",
       "reg070_B  0.454414        35          1  \n",
       "\n",
       "[140 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdi_results['Patients'] = mdi_results.index.map(region_to_patient)\n",
    "mdi_results['Condition'] = mdi_results.index.map(region_to_condition)\n",
    "mdi_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94686ccf-6c14-4001-9db3-157444be5575",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Calculate GDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "68490636-1db3-47fe-9555-9f15eeebc418",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: reg001_A at scale 32.0\n",
      "46.582 per cent patches are empty\n",
      "Processing region: reg001_B at scale 32.0\n",
      "75.293 per cent patches are empty\n",
      "Processing region: reg002_A at scale 32.0\n",
      "45.996 per cent patches are empty\n",
      "Processing region: reg002_B at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "Processing region: reg003_A at scale 32.0\n",
      "46.875 per cent patches are empty\n",
      "Processing region: reg003_B at scale 32.0\n",
      "43.945 per cent patches are empty\n",
      "Processing region: reg004_A at scale 32.0\n",
      "35.254 per cent patches are empty\n",
      "Processing region: reg004_B at scale 32.0\n",
      "17.871 per cent patches are empty\n",
      "Processing region: reg005_A at scale 32.0\n",
      "18.359 per cent patches are empty\n",
      "Processing region: reg005_B at scale 32.0\n",
      "40.527 per cent patches are empty\n",
      "Processing region: reg006_A at scale 32.0\n",
      "37.402 per cent patches are empty\n",
      "Processing region: reg006_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "Processing region: reg007_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Processing region: reg007_B at scale 32.0\n",
      "26.660 per cent patches are empty\n",
      "Processing region: reg008_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "Processing region: reg008_B at scale 32.0\n",
      "8.691 per cent patches are empty\n",
      "Processing region: reg009_A at scale 32.0\n",
      "12.793 per cent patches are empty\n",
      "Processing region: reg009_B at scale 32.0\n",
      "6.543 per cent patches are empty\n",
      "Processing region: reg010_A at scale 32.0\n",
      "25.391 per cent patches are empty\n",
      "Processing region: reg010_B at scale 32.0\n",
      "42.383 per cent patches are empty\n",
      "Processing region: reg011_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Processing region: reg011_B at scale 32.0\n",
      "51.562 per cent patches are empty\n",
      "Processing region: reg012_A at scale 32.0\n",
      "32.910 per cent patches are empty\n",
      "Processing region: reg012_B at scale 32.0\n",
      "77.051 per cent patches are empty\n",
      "Processing region: reg013_A at scale 32.0\n",
      "25.098 per cent patches are empty\n",
      "Processing region: reg013_B at scale 32.0\n",
      "19.922 per cent patches are empty\n",
      "Processing region: reg014_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "Processing region: reg014_B at scale 32.0\n",
      "21.191 per cent patches are empty\n",
      "Processing region: reg015_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Processing region: reg015_B at scale 32.0\n",
      "31.055 per cent patches are empty\n",
      "Processing region: reg016_A at scale 32.0\n",
      "27.539 per cent patches are empty\n",
      "Processing region: reg016_B at scale 32.0\n",
      "17.773 per cent patches are empty\n",
      "Processing region: reg017_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Processing region: reg017_B at scale 32.0\n",
      "18.066 per cent patches are empty\n",
      "Processing region: reg018_A at scale 32.0\n",
      "29.492 per cent patches are empty\n",
      "Processing region: reg018_B at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "Processing region: reg019_A at scale 32.0\n",
      "53.613 per cent patches are empty\n",
      "Processing region: reg019_B at scale 32.0\n",
      "92.090 per cent patches are empty\n",
      "Processing region: reg020_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "Processing region: reg020_B at scale 32.0\n",
      "19.434 per cent patches are empty\n",
      "Processing region: reg021_A at scale 32.0\n",
      "36.328 per cent patches are empty\n",
      "Processing region: reg021_B at scale 32.0\n",
      "76.270 per cent patches are empty\n",
      "Processing region: reg022_A at scale 32.0\n",
      "31.934 per cent patches are empty\n",
      "Processing region: reg022_B at scale 32.0\n",
      "62.402 per cent patches are empty\n",
      "Processing region: reg023_A at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "Processing region: reg023_B at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Processing region: reg024_A at scale 32.0\n",
      "46.191 per cent patches are empty\n",
      "Processing region: reg024_B at scale 32.0\n",
      "15.625 per cent patches are empty\n",
      "Processing region: reg025_A at scale 32.0\n",
      "39.648 per cent patches are empty\n",
      "Processing region: reg025_B at scale 32.0\n",
      "31.348 per cent patches are empty\n",
      "Processing region: reg026_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Processing region: reg026_B at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "Processing region: reg027_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "Processing region: reg027_B at scale 32.0\n",
      "17.578 per cent patches are empty\n",
      "Processing region: reg028_A at scale 32.0\n",
      "16.016 per cent patches are empty\n",
      "Processing region: reg028_B at scale 32.0\n",
      "13.965 per cent patches are empty\n",
      "Processing region: reg029_A at scale 32.0\n",
      "15.039 per cent patches are empty\n",
      "Processing region: reg029_B at scale 32.0\n",
      "23.633 per cent patches are empty\n",
      "Processing region: reg030_A at scale 32.0\n",
      "17.969 per cent patches are empty\n",
      "Processing region: reg030_B at scale 32.0\n",
      "25.195 per cent patches are empty\n",
      "Processing region: reg031_A at scale 32.0\n",
      "11.426 per cent patches are empty\n",
      "Processing region: reg031_B at scale 32.0\n",
      "17.480 per cent patches are empty\n",
      "Processing region: reg032_A at scale 32.0\n",
      "11.230 per cent patches are empty\n",
      "Processing region: reg032_B at scale 32.0\n",
      "33.984 per cent patches are empty\n",
      "Processing region: reg033_A at scale 32.0\n",
      "26.367 per cent patches are empty\n",
      "Processing region: reg033_B at scale 32.0\n",
      "12.012 per cent patches are empty\n",
      "Processing region: reg034_A at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "Processing region: reg034_B at scale 32.0\n",
      "25.586 per cent patches are empty\n",
      "Processing region: reg035_A at scale 32.0\n",
      "21.777 per cent patches are empty\n",
      "Processing region: reg035_B at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "Processing region: reg036_A at scale 32.0\n",
      "13.184 per cent patches are empty\n",
      "Processing region: reg036_B at scale 32.0\n",
      "10.742 per cent patches are empty\n",
      "Processing region: reg037_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "Processing region: reg037_B at scale 32.0\n",
      "70.020 per cent patches are empty\n",
      "Processing region: reg038_A at scale 32.0\n",
      "11.719 per cent patches are empty\n",
      "Processing region: reg038_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "Processing region: reg039_A at scale 32.0\n",
      "30.273 per cent patches are empty\n",
      "Processing region: reg039_B at scale 32.0\n",
      "56.543 per cent patches are empty\n",
      "Processing region: reg040_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "Processing region: reg040_B at scale 32.0\n",
      "57.520 per cent patches are empty\n",
      "Processing region: reg041_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "Processing region: reg041_B at scale 32.0\n",
      "29.688 per cent patches are empty\n",
      "Processing region: reg042_A at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "Processing region: reg042_B at scale 32.0\n",
      "24.902 per cent patches are empty\n",
      "Processing region: reg043_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "Processing region: reg043_B at scale 32.0\n",
      "64.844 per cent patches are empty\n",
      "Processing region: reg044_A at scale 32.0\n",
      "43.164 per cent patches are empty\n",
      "Processing region: reg044_B at scale 32.0\n",
      "71.973 per cent patches are empty\n",
      "Processing region: reg045_A at scale 32.0\n",
      "13.672 per cent patches are empty\n",
      "Processing region: reg045_B at scale 32.0\n",
      "43.750 per cent patches are empty\n",
      "Processing region: reg046_A at scale 32.0\n",
      "15.723 per cent patches are empty\n",
      "Processing region: reg046_B at scale 32.0\n",
      "15.234 per cent patches are empty\n",
      "Processing region: reg047_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "Processing region: reg047_B at scale 32.0\n",
      "45.117 per cent patches are empty\n",
      "Processing region: reg048_A at scale 32.0\n",
      "24.512 per cent patches are empty\n",
      "Processing region: reg048_B at scale 32.0\n",
      "33.203 per cent patches are empty\n",
      "Processing region: reg049_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "Processing region: reg049_B at scale 32.0\n",
      "38.086 per cent patches are empty\n",
      "Processing region: reg050_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "Processing region: reg050_B at scale 32.0\n",
      "25.000 per cent patches are empty\n",
      "Processing region: reg051_A at scale 32.0\n",
      "33.496 per cent patches are empty\n",
      "Processing region: reg051_B at scale 32.0\n",
      "23.047 per cent patches are empty\n",
      "Processing region: reg052_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "Processing region: reg052_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "Processing region: reg053_A at scale 32.0\n",
      "24.023 per cent patches are empty\n",
      "Processing region: reg053_B at scale 32.0\n",
      "88.477 per cent patches are empty\n",
      "Processing region: reg054_A at scale 32.0\n",
      "35.547 per cent patches are empty\n",
      "Processing region: reg054_B at scale 32.0\n",
      "78.320 per cent patches are empty\n",
      "Processing region: reg055_A at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "Processing region: reg055_B at scale 32.0\n",
      "89.062 per cent patches are empty\n",
      "Processing region: reg056_A at scale 32.0\n",
      "56.641 per cent patches are empty\n",
      "Processing region: reg056_B at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Processing region: reg057_A at scale 32.0\n",
      "94.434 per cent patches are empty\n",
      "Processing region: reg057_B at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "Processing region: reg058_A at scale 32.0\n",
      "20.703 per cent patches are empty\n",
      "Processing region: reg058_B at scale 32.0\n",
      "36.719 per cent patches are empty\n",
      "Processing region: reg059_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Processing region: reg059_B at scale 32.0\n",
      "21.875 per cent patches are empty\n",
      "Processing region: reg060_A at scale 32.0\n",
      "15.820 per cent patches are empty\n",
      "Processing region: reg060_B at scale 32.0\n",
      "14.355 per cent patches are empty\n",
      "Processing region: reg061_A at scale 32.0\n",
      "25.488 per cent patches are empty\n",
      "Processing region: reg061_B at scale 32.0\n",
      "22.656 per cent patches are empty\n",
      "Processing region: reg062_A at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "Processing region: reg062_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "Processing region: reg063_A at scale 32.0\n",
      "50.879 per cent patches are empty\n",
      "Processing region: reg063_B at scale 32.0\n",
      "40.234 per cent patches are empty\n",
      "Processing region: reg064_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "Processing region: reg064_B at scale 32.0\n",
      "19.141 per cent patches are empty\n",
      "Processing region: reg065_A at scale 32.0\n",
      "27.930 per cent patches are empty\n",
      "Processing region: reg065_B at scale 32.0\n",
      "66.895 per cent patches are empty\n",
      "Processing region: reg066_A at scale 32.0\n",
      "23.242 per cent patches are empty\n",
      "Processing region: reg066_B at scale 32.0\n",
      "39.941 per cent patches are empty\n",
      "Processing region: reg067_A at scale 32.0\n",
      "66.992 per cent patches are empty\n",
      "Processing region: reg067_B at scale 32.0\n",
      "93.945 per cent patches are empty\n",
      "Processing region: reg068_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "Processing region: reg068_B at scale 32.0\n",
      "27.148 per cent patches are empty\n",
      "Processing region: reg069_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Processing region: reg069_B at scale 32.0\n",
      "49.121 per cent patches are empty\n",
      "Processing region: reg070_A at scale 32.0\n",
      "26.270 per cent patches are empty\n",
      "Processing region: reg070_B at scale 32.0\n",
      "41.699 per cent patches are empty\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>reg001_A</th>\n",
       "      <td>0.408648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg001_B</th>\n",
       "      <td>0.077049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg002_A</th>\n",
       "      <td>0.429751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg002_B</th>\n",
       "      <td>0.133040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg003_A</th>\n",
       "      <td>0.304637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg068_B</th>\n",
       "      <td>0.245274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg069_A</th>\n",
       "      <td>0.200747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg069_B</th>\n",
       "      <td>0.247712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg070_A</th>\n",
       "      <td>0.265592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg070_B</th>\n",
       "      <td>0.083936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               GDI\n",
       "reg001_A  0.408648\n",
       "reg001_B  0.077049\n",
       "reg002_A  0.429751\n",
       "reg002_B  0.133040\n",
       "reg003_A  0.304637\n",
       "...            ...\n",
       "reg068_B  0.245274\n",
       "reg069_A  0.200747\n",
       "reg069_B  0.247712\n",
       "reg070_A  0.265592\n",
       "reg070_B  0.083936\n",
       "\n",
       "[140 rows x 1 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdi_results = eco.calculate_GDI(spatial_data=protein, \n",
    "                                scale=32.0, \n",
    "                                library_key='File Name',\n",
    "                                library_id=library_ids, \n",
    "                                spatial_key=['X:X','Y:Y'],\n",
    "                                cluster_key='ClusterName',\n",
    "                                hotspot=True,\n",
    "                                restricted=False,\n",
    "                                metric='Shannon Diversity')\n",
    "gdi_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a22a87b7-3a28-4f08-ac76-ac9dd0597716",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Calculate DPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "513d4a86-c02c-4f61-8a52-6ca8e796ec0d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: reg001_A at scale 32.0\n",
      "46.582 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg001_A contains 92 diversity hotspots\n",
      "2 islands identified\n",
      "Processing region: reg001_B at scale 32.0\n",
      "75.293 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg001_B contains 5 diversity hotspots\n",
      "4 islands identified\n",
      "Processing region: reg002_A at scale 32.0\n",
      "45.996 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg002_A contains 115 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg002_B at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg002_B contains 19 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg003_A at scale 32.0\n",
      "46.875 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg003_A contains 53 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg003_B at scale 32.0\n",
      "43.945 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg003_B contains 60 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg004_A at scale 32.0\n",
      "35.254 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg004_A contains 31 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg004_B at scale 32.0\n",
      "17.871 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg004_B contains 22 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg005_A at scale 32.0\n",
      "18.359 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg005_A contains 78 diversity hotspots\n",
      "15 islands identified\n",
      "Processing region: reg005_B at scale 32.0\n",
      "40.527 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg005_B contains 80 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg006_A at scale 32.0\n",
      "37.402 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg006_A contains 38 diversity hotspots\n",
      "6 islands identified\n",
      "Processing region: reg006_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg006_B contains 61 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg007_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg007_A contains 136 diversity hotspots\n",
      "13 islands identified\n",
      "Processing region: reg007_B at scale 32.0\n",
      "26.660 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg007_B contains 94 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg008_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg008_A contains 60 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg008_B at scale 32.0\n",
      "8.691 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg008_B contains 74 diversity hotspots\n",
      "18 islands identified\n",
      "Processing region: reg009_A at scale 32.0\n",
      "12.793 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg009_A contains 57 diversity hotspots\n",
      "14 islands identified\n",
      "Processing region: reg009_B at scale 32.0\n",
      "6.543 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg009_B contains 14 diversity hotspots\n",
      "9 islands identified\n",
      "Processing region: reg010_A at scale 32.0\n",
      "25.391 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg010_A contains 109 diversity hotspots\n",
      "14 islands identified\n",
      "Processing region: reg010_B at scale 32.0\n",
      "42.383 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg010_B contains 57 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg011_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg011_A contains 134 diversity hotspots\n",
      "17 islands identified\n",
      "Processing region: reg011_B at scale 32.0\n",
      "51.562 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg011_B contains 44 diversity hotspots\n",
      "5 islands identified\n",
      "Processing region: reg012_A at scale 32.0\n",
      "32.910 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg012_A contains 85 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg012_B at scale 32.0\n",
      "77.051 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg012_B contains 47 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg013_A at scale 32.0\n",
      "25.098 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg013_A contains 54 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg013_B at scale 32.0\n",
      "19.922 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg013_B contains 44 diversity hotspots\n",
      "21 islands identified\n",
      "Processing region: reg014_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg014_A contains 43 diversity hotspots\n",
      "4 islands identified\n",
      "Processing region: reg014_B at scale 32.0\n",
      "21.191 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg014_B contains 34 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg015_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg015_A contains 30 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg015_B at scale 32.0\n",
      "31.055 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg015_B contains 27 diversity hotspots\n",
      "6 islands identified\n",
      "Processing region: reg016_A at scale 32.0\n",
      "27.539 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg016_A contains 54 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg016_B at scale 32.0\n",
      "17.773 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg016_B contains 37 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg017_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg017_A contains 91 diversity hotspots\n",
      "15 islands identified\n",
      "Processing region: reg017_B at scale 32.0\n",
      "18.066 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg017_B contains 120 diversity hotspots\n",
      "9 islands identified\n",
      "Processing region: reg018_A at scale 32.0\n",
      "29.492 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg018_A contains 144 diversity hotspots\n",
      "5 islands identified\n",
      "Processing region: reg018_B at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg018_B contains 53 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg019_A at scale 32.0\n",
      "53.613 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg019_A contains 126 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg019_B at scale 32.0\n",
      "92.090 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg019_B contains 2 diversity hotspots\n",
      "2 islands identified\n",
      "Processing region: reg020_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg020_A contains 70 diversity hotspots\n",
      "14 islands identified\n",
      "Processing region: reg020_B at scale 32.0\n",
      "19.434 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg020_B contains 59 diversity hotspots\n",
      "19 islands identified\n",
      "Processing region: reg021_A at scale 32.0\n",
      "36.328 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg021_A contains 131 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg021_B at scale 32.0\n",
      "76.270 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg021_B contains 7 diversity hotspots\n",
      "4 islands identified\n",
      "Processing region: reg022_A at scale 32.0\n",
      "31.934 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg022_A contains 149 diversity hotspots\n",
      "3 islands identified\n",
      "Processing region: reg022_B at scale 32.0\n",
      "62.402 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg022_B contains 33 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg023_A at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg023_A contains 21 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg023_B at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg023_B contains 116 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg024_A at scale 32.0\n",
      "46.191 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg024_A contains 14 diversity hotspots\n",
      "5 islands identified\n",
      "Processing region: reg024_B at scale 32.0\n",
      "15.625 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg024_B contains 7 diversity hotspots\n",
      "5 islands identified\n",
      "Processing region: reg025_A at scale 32.0\n",
      "39.648 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg025_A contains 107 diversity hotspots\n",
      "4 islands identified\n",
      "Processing region: reg025_B at scale 32.0\n",
      "31.348 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg025_B contains 129 diversity hotspots\n",
      "9 islands identified\n",
      "Processing region: reg026_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg026_A contains 59 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg026_B at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg026_B contains 30 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg027_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg027_A contains 79 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg027_B at scale 32.0\n",
      "17.578 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg027_B contains 15 diversity hotspots\n",
      "3 islands identified\n",
      "Processing region: reg028_A at scale 32.0\n",
      "16.016 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg028_A contains 77 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg028_B at scale 32.0\n",
      "13.965 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg028_B contains 30 diversity hotspots\n",
      "5 islands identified\n",
      "Processing region: reg029_A at scale 32.0\n",
      "15.039 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg029_A contains 75 diversity hotspots\n",
      "14 islands identified\n",
      "Processing region: reg029_B at scale 32.0\n",
      "23.633 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg029_B contains 16 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg030_A at scale 32.0\n",
      "17.969 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg030_A contains 34 diversity hotspots\n",
      "16 islands identified\n",
      "Processing region: reg030_B at scale 32.0\n",
      "25.195 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg030_B contains 82 diversity hotspots\n",
      "9 islands identified\n",
      "Processing region: reg031_A at scale 32.0\n",
      "11.426 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg031_A contains 55 diversity hotspots\n",
      "17 islands identified\n",
      "Processing region: reg031_B at scale 32.0\n",
      "17.480 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg031_B contains 40 diversity hotspots\n",
      "18 islands identified\n",
      "Processing region: reg032_A at scale 32.0\n",
      "11.230 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg032_A contains 63 diversity hotspots\n",
      "16 islands identified\n",
      "Processing region: reg032_B at scale 32.0\n",
      "33.984 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg032_B contains 62 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg033_A at scale 32.0\n",
      "26.367 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg033_A contains 101 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg033_B at scale 32.0\n",
      "12.012 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg033_B contains 97 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg034_A at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg034_A contains 44 diversity hotspots\n",
      "16 islands identified\n",
      "Processing region: reg034_B at scale 32.0\n",
      "25.586 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg034_B contains 46 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg035_A at scale 32.0\n",
      "21.777 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg035_A contains 104 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg035_B at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg035_B contains 67 diversity hotspots\n",
      "15 islands identified\n",
      "Processing region: reg036_A at scale 32.0\n",
      "13.184 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg036_A contains 34 diversity hotspots\n",
      "16 islands identified\n",
      "Processing region: reg036_B at scale 32.0\n",
      "10.742 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg036_B contains 62 diversity hotspots\n",
      "17 islands identified\n",
      "Processing region: reg037_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg037_A contains 123 diversity hotspots\n",
      "9 islands identified\n",
      "Processing region: reg037_B at scale 32.0\n",
      "70.020 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg037_B contains 8 diversity hotspots\n",
      "3 islands identified\n",
      "Processing region: reg038_A at scale 32.0\n",
      "11.719 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg038_A contains 104 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg038_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg038_B contains 65 diversity hotspots\n",
      "13 islands identified\n",
      "Processing region: reg039_A at scale 32.0\n",
      "30.273 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg039_A contains 150 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg039_B at scale 32.0\n",
      "56.543 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg039_B contains 14 diversity hotspots\n",
      "5 islands identified\n",
      "Processing region: reg040_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg040_A contains 53 diversity hotspots\n",
      "13 islands identified\n",
      "Processing region: reg040_B at scale 32.0\n",
      "57.520 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg040_B contains 27 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg041_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg041_A contains 82 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg041_B at scale 32.0\n",
      "29.688 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg041_B contains 61 diversity hotspots\n",
      "16 islands identified\n",
      "Processing region: reg042_A at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg042_A contains 39 diversity hotspots\n",
      "6 islands identified\n",
      "Processing region: reg042_B at scale 32.0\n",
      "24.902 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg042_B contains 33 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg043_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg043_A contains 22 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg043_B at scale 32.0\n",
      "64.844 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg043_B contains 8 diversity hotspots\n",
      "3 islands identified\n",
      "Processing region: reg044_A at scale 32.0\n",
      "43.164 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg044_A contains 21 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg044_B at scale 32.0\n",
      "71.973 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg044_B contains 7 diversity hotspots\n",
      "3 islands identified\n",
      "Processing region: reg045_A at scale 32.0\n",
      "13.672 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg045_A contains 21 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg045_B at scale 32.0\n",
      "43.750 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg045_B contains 12 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg046_A at scale 32.0\n",
      "15.723 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg046_A contains 72 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg046_B at scale 32.0\n",
      "15.234 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg046_B contains 51 diversity hotspots\n",
      "16 islands identified\n",
      "Processing region: reg047_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg047_A contains 174 diversity hotspots\n",
      "4 islands identified\n",
      "Processing region: reg047_B at scale 32.0\n",
      "45.117 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg047_B contains 5 diversity hotspots\n",
      "4 islands identified\n",
      "Processing region: reg048_A at scale 32.0\n",
      "24.512 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg048_A contains 31 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg048_B at scale 32.0\n",
      "33.203 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg048_B contains 70 diversity hotspots\n",
      "13 islands identified\n",
      "Processing region: reg049_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg049_A contains 26 diversity hotspots\n",
      "13 islands identified\n",
      "Processing region: reg049_B at scale 32.0\n",
      "38.086 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg049_B contains 29 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg050_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg050_A contains 38 diversity hotspots\n",
      "9 islands identified\n",
      "Processing region: reg050_B at scale 32.0\n",
      "25.000 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg050_B contains 21 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg051_A at scale 32.0\n",
      "33.496 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg051_A contains 69 diversity hotspots\n",
      "16 islands identified\n",
      "Processing region: reg051_B at scale 32.0\n",
      "23.047 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg051_B contains 54 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg052_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg052_A contains 54 diversity hotspots\n",
      "9 islands identified\n",
      "Processing region: reg052_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg052_B contains 56 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg053_A at scale 32.0\n",
      "24.023 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg053_A contains 64 diversity hotspots\n",
      "19 islands identified\n",
      "Processing region: reg053_B at scale 32.0\n",
      "88.477 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg053_B contains 1 diversity hotspots\n",
      "1 islands identified\n",
      "Processing region: reg054_A at scale 32.0\n",
      "35.547 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg054_A contains 81 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg054_B at scale 32.0\n",
      "78.320 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg054_B contains 5 diversity hotspots\n",
      "2 islands identified\n",
      "Processing region: reg055_A at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg055_A contains 77 diversity hotspots\n",
      "24 islands identified\n",
      "Processing region: reg055_B at scale 32.0\n",
      "89.062 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg055_B contains 0 diversity hotspots\n",
      "0 islands identified\n",
      "Processing region: reg056_A at scale 32.0\n",
      "56.641 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg056_A contains 41 diversity hotspots\n",
      "9 islands identified\n",
      "Processing region: reg056_B at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg056_B contains 30 diversity hotspots\n",
      "9 islands identified\n",
      "Processing region: reg057_A at scale 32.0\n",
      "94.434 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg057_A contains 4 diversity hotspots\n",
      "2 islands identified\n",
      "Processing region: reg057_B at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg057_B contains 56 diversity hotspots\n",
      "16 islands identified\n",
      "Processing region: reg058_A at scale 32.0\n",
      "20.703 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg058_A contains 13 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg058_B at scale 32.0\n",
      "36.719 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg058_B contains 7 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg059_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg059_A contains 59 diversity hotspots\n",
      "16 islands identified\n",
      "Processing region: reg059_B at scale 32.0\n",
      "21.875 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg059_B contains 57 diversity hotspots\n",
      "15 islands identified\n",
      "Processing region: reg060_A at scale 32.0\n",
      "15.820 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg060_A contains 32 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg060_B at scale 32.0\n",
      "14.355 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg060_B contains 20 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg061_A at scale 32.0\n",
      "25.488 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg061_A contains 95 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg061_B at scale 32.0\n",
      "22.656 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg061_B contains 31 diversity hotspots\n",
      "12 islands identified\n",
      "Processing region: reg062_A at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg062_A contains 61 diversity hotspots\n",
      "6 islands identified\n",
      "Processing region: reg062_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg062_B contains 15 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg063_A at scale 32.0\n",
      "50.879 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg063_A contains 22 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg063_B at scale 32.0\n",
      "40.234 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg063_B contains 2 diversity hotspots\n",
      "2 islands identified\n",
      "Processing region: reg064_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg064_A contains 15 diversity hotspots\n",
      "6 islands identified\n",
      "Processing region: reg064_B at scale 32.0\n",
      "19.141 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg064_B contains 34 diversity hotspots\n",
      "17 islands identified\n",
      "Processing region: reg065_A at scale 32.0\n",
      "27.930 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg065_A contains 110 diversity hotspots\n",
      "5 islands identified\n",
      "Processing region: reg065_B at scale 32.0\n",
      "66.895 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg065_B contains 0 diversity hotspots\n",
      "0 islands identified\n",
      "Processing region: reg066_A at scale 32.0\n",
      "23.242 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg066_A contains 44 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg066_B at scale 32.0\n",
      "39.941 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg066_B contains 17 diversity hotspots\n",
      "6 islands identified\n",
      "Processing region: reg067_A at scale 32.0\n",
      "66.992 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg067_A contains 38 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg067_B at scale 32.0\n",
      "93.945 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg067_B contains 6 diversity hotspots\n",
      "3 islands identified\n",
      "Processing region: reg068_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg068_A contains 13 diversity hotspots\n",
      "8 islands identified\n",
      "Processing region: reg068_B at scale 32.0\n",
      "27.148 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg068_B contains 75 diversity hotspots\n",
      "11 islands identified\n",
      "Processing region: reg069_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg069_A contains 47 diversity hotspots\n",
      "7 islands identified\n",
      "Processing region: reg069_B at scale 32.0\n",
      "49.121 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg069_B contains 46 diversity hotspots\n",
      "10 islands identified\n",
      "Processing region: reg070_A at scale 32.0\n",
      "26.270 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg070_A contains 61 diversity hotspots\n",
      "14 islands identified\n",
      "Processing region: reg070_B at scale 32.0\n",
      "41.699 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg070_B contains 16 diversity hotspots\n",
      "3 islands identified\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DPI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>reg001_A</th>\n",
       "      <td>65.053824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg001_B</th>\n",
       "      <td>2.878179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg002_A</th>\n",
       "      <td>71.058135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg002_B</th>\n",
       "      <td>2.891396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg003_A</th>\n",
       "      <td>25.809464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg068_B</th>\n",
       "      <td>45.784271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg069_A</th>\n",
       "      <td>31.319700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg069_B</th>\n",
       "      <td>23.723016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg070_A</th>\n",
       "      <td>27.607388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg070_B</th>\n",
       "      <td>4.899916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                DPI\n",
       "reg001_A  65.053824\n",
       "reg001_B   2.878179\n",
       "reg002_A  71.058135\n",
       "reg002_B   2.891396\n",
       "reg003_A  25.809464\n",
       "...             ...\n",
       "reg068_B  45.784271\n",
       "reg069_A  31.319700\n",
       "reg069_B  23.723016\n",
       "reg070_A  27.607388\n",
       "reg070_B   4.899916\n",
       "\n",
       "[140 rows x 1 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate DPI for hotspots\n",
    "dpi_results = eco.calculate_DPI(spatial_data=protein, \n",
    "                                scale=32.0, \n",
    "                                library_key='File Name',\n",
    "                                library_id=library_ids, \n",
    "                                spatial_key=['X:X','Y:Y'],\n",
    "                                cluster_key='ClusterName',\n",
    "                                hotspot=True,\n",
    "                                metric='Shannon Diversity')\n",
    "dpi_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64a1ddc-f3fe-4eb2-ab72-68bc7095be2e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Calculate Global Cell Frequency & Cell Co-Occurrence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d35b2ec2-586e-4829-a8df-229041b11703",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: reg001_A at scale 32.0\n",
      "46.582 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg001_B at scale 32.0\n",
      "75.293 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg002_A at scale 32.0\n",
      "45.996 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg002_B at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg003_A at scale 32.0\n",
      "46.875 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg003_B at scale 32.0\n",
      "43.945 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg004_A at scale 32.0\n",
      "35.254 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg004_B at scale 32.0\n",
      "17.871 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg005_A at scale 32.0\n",
      "18.359 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg005_B at scale 32.0\n",
      "40.527 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg006_A at scale 32.0\n",
      "37.402 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg006_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg007_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg007_B at scale 32.0\n",
      "26.660 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg008_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg008_B at scale 32.0\n",
      "8.691 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg009_A at scale 32.0\n",
      "12.793 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg009_B at scale 32.0\n",
      "6.543 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg010_A at scale 32.0\n",
      "25.391 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg010_B at scale 32.0\n",
      "42.383 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg011_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg011_B at scale 32.0\n",
      "51.562 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg012_A at scale 32.0\n",
      "32.910 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg012_B at scale 32.0\n",
      "77.051 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg013_A at scale 32.0\n",
      "25.098 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg013_B at scale 32.0\n",
      "19.922 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg014_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg014_B at scale 32.0\n",
      "21.191 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg015_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg015_B at scale 32.0\n",
      "31.055 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg016_A at scale 32.0\n",
      "27.539 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg016_B at scale 32.0\n",
      "17.773 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg017_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg017_B at scale 32.0\n",
      "18.066 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg018_A at scale 32.0\n",
      "29.492 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg018_B at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg019_A at scale 32.0\n",
      "53.613 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg019_B at scale 32.0\n",
      "92.090 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg020_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg020_B at scale 32.0\n",
      "19.434 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg021_A at scale 32.0\n",
      "36.328 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg021_B at scale 32.0\n",
      "76.270 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg022_A at scale 32.0\n",
      "31.934 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg022_B at scale 32.0\n",
      "62.402 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg023_A at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg023_B at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg024_A at scale 32.0\n",
      "46.191 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg024_B at scale 32.0\n",
      "15.625 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg025_A at scale 32.0\n",
      "39.648 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg025_B at scale 32.0\n",
      "31.348 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg026_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg026_B at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg027_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg027_B at scale 32.0\n",
      "17.578 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg028_A at scale 32.0\n",
      "16.016 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg028_B at scale 32.0\n",
      "13.965 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg029_A at scale 32.0\n",
      "15.039 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg029_B at scale 32.0\n",
      "23.633 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg030_A at scale 32.0\n",
      "17.969 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg030_B at scale 32.0\n",
      "25.195 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg031_A at scale 32.0\n",
      "11.426 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg031_B at scale 32.0\n",
      "17.480 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg032_A at scale 32.0\n",
      "11.230 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg032_B at scale 32.0\n",
      "33.984 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg033_A at scale 32.0\n",
      "26.367 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg033_B at scale 32.0\n",
      "12.012 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg034_A at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg034_B at scale 32.0\n",
      "25.586 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg035_A at scale 32.0\n",
      "21.777 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg035_B at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg036_A at scale 32.0\n",
      "13.184 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg036_B at scale 32.0\n",
      "10.742 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg037_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg037_B at scale 32.0\n",
      "70.020 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg038_A at scale 32.0\n",
      "11.719 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg038_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg039_A at scale 32.0\n",
      "30.273 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg039_B at scale 32.0\n",
      "56.543 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg040_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg040_B at scale 32.0\n",
      "57.520 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg041_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg041_B at scale 32.0\n",
      "29.688 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg042_A at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg042_B at scale 32.0\n",
      "24.902 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg043_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg043_B at scale 32.0\n",
      "64.844 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg044_A at scale 32.0\n",
      "43.164 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg044_B at scale 32.0\n",
      "71.973 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg045_A at scale 32.0\n",
      "13.672 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg045_B at scale 32.0\n",
      "43.750 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg046_A at scale 32.0\n",
      "15.723 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg046_B at scale 32.0\n",
      "15.234 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg047_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg047_B at scale 32.0\n",
      "45.117 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg048_A at scale 32.0\n",
      "24.512 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg048_B at scale 32.0\n",
      "33.203 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg049_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg049_B at scale 32.0\n",
      "38.086 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg050_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg050_B at scale 32.0\n",
      "25.000 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg051_A at scale 32.0\n",
      "33.496 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg051_B at scale 32.0\n",
      "23.047 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg052_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg052_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg053_A at scale 32.0\n",
      "24.023 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg053_B at scale 32.0\n",
      "88.477 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg054_A at scale 32.0\n",
      "35.547 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg054_B at scale 32.0\n",
      "78.320 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg055_A at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg055_B at scale 32.0\n",
      "89.062 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg056_A at scale 32.0\n",
      "56.641 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg056_B at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg057_A at scale 32.0\n",
      "94.434 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg057_B at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg058_A at scale 32.0\n",
      "20.703 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg058_B at scale 32.0\n",
      "36.719 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg059_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg059_B at scale 32.0\n",
      "21.875 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg060_A at scale 32.0\n",
      "15.820 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg060_B at scale 32.0\n",
      "14.355 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg061_A at scale 32.0\n",
      "25.488 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg061_B at scale 32.0\n",
      "22.656 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg062_A at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg062_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg063_A at scale 32.0\n",
      "50.879 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg063_B at scale 32.0\n",
      "40.234 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg064_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg064_B at scale 32.0\n",
      "19.141 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg065_A at scale 32.0\n",
      "27.930 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg065_B at scale 32.0\n",
      "66.895 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg066_A at scale 32.0\n",
      "23.242 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg066_B at scale 32.0\n",
      "39.941 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg067_A at scale 32.0\n",
      "66.992 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg067_B at scale 32.0\n",
      "93.945 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg068_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg068_B at scale 32.0\n",
      "27.148 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg069_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg069_B at scale 32.0\n",
      "49.121 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg070_A at scale 32.0\n",
      "26.270 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n",
      "Processing region: reg070_B at scale 32.0\n",
      "41.699 per cent patches are empty\n",
      "Using MoranI\n",
      "Considering whole tissue\n"
     ]
    }
   ],
   "source": [
    "global_cellfreq_df, global_co_occurrence_df = eco.spot_cellfreq(spatial_data=protein, \n",
    "                                                                scale=32.0, \n",
    "                                                                library_key='File Name',\n",
    "                                                                library_id=library_ids, \n",
    "                                                                spatial_key=['X:X','Y:Y'],\n",
    "                                                                cluster_key='ClusterName',\n",
    "                                                                spots='global',\n",
    "                                                                p_value=0.05,\n",
    "                                                                top=None,\n",
    "                                                                selected_comb=None,\n",
    "                                                                restricted=False,\n",
    "                                                                metric='Shannon Diversity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54ad6bae-97d9-4bdb-b653-f4f140f1cd05",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_cellfreq_df['Patients'] = global_cellfreq_df.index.map(region_to_patient)\n",
    "global_cellfreq_df['Condition'] = global_cellfreq_df.index.map(region_to_condition)\n",
    "\n",
    "global_co_occurrence_subcols = global_co_occurrence_df.loc[:,global_co_occurrence_df.mean()>0.01].columns.tolist()\n",
    "global_co_occurrence_df['Condition'] = global_co_occurrence_df.index.map(region_to_condition)\n",
    "global_co_occurrence_df['Patients'] = global_co_occurrence_df.index.map(region_to_patient)\n",
    "global_co_occurrence_subcols.extend([('Condition',''),('Patients','')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b2de03c-f28e-4e1d-add9-c6d4a4e6b67d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute averages by every patients\n",
    "global_cellfreq_df_avg = global_cellfreq_df.groupby(by=\"Patients\").mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "315b2bc9-2d30-4048-9844-9e0593824df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patients</th>\n",
       "      <th>Condition</th>\n",
       "      <th>ClusterName</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CLR</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.028638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>DII</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.020368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>DII</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.014561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>DII</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.002585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>DII</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.019890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>31</td>\n",
       "      <td>DII</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.073614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>32</td>\n",
       "      <td>CLR</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.011795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>33</td>\n",
       "      <td>CLR</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.041157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>34</td>\n",
       "      <td>CLR</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.071935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>35</td>\n",
       "      <td>CLR</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.050397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>980 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Patients Condition  ClusterName  Frequency\n",
       "0           1       CLR      B cells   0.028638\n",
       "1           2       DII      B cells   0.020368\n",
       "2           3       DII      B cells   0.014561\n",
       "3           4       DII      B cells   0.002585\n",
       "4           5       DII      B cells   0.019890\n",
       "..        ...       ...          ...        ...\n",
       "975        31       DII  vasculature   0.073614\n",
       "976        32       CLR  vasculature   0.011795\n",
       "977        33       CLR  vasculature   0.041157\n",
       "978        34       CLR  vasculature   0.071935\n",
       "979        35       CLR  vasculature   0.050397\n",
       "\n",
       "[980 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melt the dataframe for easier plotting and statistical analysis\n",
    "global_cellfreq_df_melt = global_cellfreq_df_avg.melt(id_vars=['Patients', 'Condition'])\n",
    "global_cellfreq_df_melt.columns = ['Patients', 'Condition', 'ClusterName', 'Frequency']\n",
    "global_cellfreq_df_melt['Condition'] = global_cellfreq_df_melt['Condition'].map(condition_num2str)\n",
    "global_cellfreq_df_melt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c0d21d4-1826-4307-be57-25bd1d749d92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B cells has p value of 0.003800830542975529\n",
      "CD11b+ monocytes has p value of 0.2641241005069516\n",
      "CD11b+CD68+ macrophages has p value of 0.10925741826128363\n",
      "CD11c+ DCs has p value of 0.7985564929462324\n",
      "CD163+ macrophages has p value of 0.2374412634569714\n",
      "CD3+ T cells has p value of 0.4237759212663951\n",
      "CD4+ T cells has p value of 0.11901416710873343\n",
      "CD4+ T cells CD45RO+ has p value of 0.7051704980689707\n",
      "CD4+ T cells GATA3+ has p value of 0.2679919848799888\n",
      "CD68+ macrophages has p value of 0.037611781498744286\n",
      "CD68+ macrophages GzmB+ has p value of 0.2893131441638549\n",
      "CD68+CD163+ macrophages has p value of 0.15990783277966897\n",
      "CD8+ T cells has p value of 0.19121534903649534\n",
      "NK cells has p value of 0.3059893418832312\n",
      "Tregs has p value of 0.028286994353345356\n",
      "adipocytes has p value of 0.03776143473828342\n",
      "granulocytes has p value of 0.03104681328507294\n",
      "immune cells has p value of 0.05700202991081446\n",
      "immune cells / vasculature has p value of 0.22678875447912733\n",
      "lymphatics has p value of 0.15303765759051852\n",
      "nerves has p value of 0.9158943619300999\n",
      "plasma cells has p value of 0.5127041366540996\n",
      "smooth muscle has p value of 0.8024660304551038\n",
      "stroma has p value of 0.4103830261215622\n",
      "tumor cells has p value of 0.5872076724557693\n",
      "tumor cells / immune cells has p value of 0.5572321565124586\n",
      "undefined has p value of 0.7706649629952046\n",
      "vasculature has p value of 0.1790266971489435\n",
      "------------------------------------------\n",
      "p-values after correction:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/mesa/lib/python3.11/site-packages/seaborn/categorical.py:3370: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B cells has p value = 0.106\n",
      "CD11b+ monocytes has p value = 0.469\n",
      "CD11b+CD68+ macrophages has p value = 0.417\n",
      "CD11c+ DCs has p value = 0.832\n",
      "CD163+ macrophages has p value = 0.469\n",
      "CD3+ T cells has p value = 0.593\n",
      "CD4+ T cells has p value = 0.417\n",
      "CD4+ T cells CD45RO+ has p value = 0.823\n",
      "CD4+ T cells GATA3+ has p value = 0.469\n",
      "CD68+ macrophages has p value = 0.211\n",
      "CD68+ macrophages GzmB+ has p value = 0.476\n",
      "CD68+CD163+ macrophages has p value = 0.446\n",
      "CD8+ T cells has p value = 0.446\n",
      "NK cells has p value = 0.476\n",
      "Tregs has p value = 0.211\n",
      "adipocytes has p value = 0.211\n",
      "granulocytes has p value = 0.211\n",
      "immune cells has p value = 0.266\n",
      "immune cells / vasculature has p value = 0.469\n",
      "lymphatics has p value = 0.446\n",
      "nerves has p value = 0.916\n",
      "plasma cells has p value = 0.684\n",
      "smooth muscle has p value = 0.832\n",
      "stroma has p value = 0.593\n",
      "tumor cells has p value = 0.715\n",
      "tumor cells / immune cells has p value = 0.709\n",
      "undefined has p value = 0.832\n",
      "vasculature has p value = 0.446\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform t-tests\n",
    "selected_cell_types = sorted(protein['ClusterName'].unique())\n",
    "selected_p_values = []\n",
    "for ct in selected_cell_types:\n",
    "    group1 = global_cellfreq_df_melt[(global_cellfreq_df_melt['ClusterName'] == ct) & (global_cellfreq_df_melt['Condition'] == 'CLR')]['Frequency']\n",
    "    group2 = global_cellfreq_df_melt[(global_cellfreq_df_melt['ClusterName'] == ct) & (global_cellfreq_df_melt['Condition'] == 'DII')]['Frequency']\n",
    "    t_stat, p_value = stats.ttest_ind(group1, group2, equal_var=False)\n",
    "    print(f\"{ct} has p value of {p_value}\")\n",
    "    selected_p_values.append(p_value)\n",
    "\n",
    "pvals_corrected = stats.false_discovery_control(selected_p_values, method='bh')\n",
    "print('-'*42)\n",
    "print(f\"p-values after correction:\")\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(30,10))\n",
    "sns.boxplot(data=global_cellfreq_df_melt, x='ClusterName', y='Frequency', hue='Condition', palette='muted', boxprops=dict(alpha=.3), ax=ax, dodge=True, order=selected_cell_types)\n",
    "sns.swarmplot(data=global_cellfreq_df_melt, x='ClusterName', y='Frequency', hue='Condition', palette='dark:black', size=2.0, dodge=True, order=selected_cell_types, ax=ax, edgecolor='auto', linewidth=0.5)\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:2], labels[:2], title=\"Groups\", handletextpad=1, columnspacing=1, bbox_to_anchor=(1, 1), ncol=3, frameon=True)\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "p_vals_corrected_dict = {}\n",
    "yrange = ax.get_ylim()[1] - ax.get_ylim()[0]\n",
    "for i, ct in enumerate(selected_cell_types):\n",
    "    ax.text(i, yrange, f\"p = {pvals_corrected[i]:.3f}\", ha='center', fontsize=12, rotation=0)\n",
    "    print(f\"{ct} has p value = {pvals_corrected[i]:.3f}\", flush=True)\n",
    "    p_vals_corrected_dict[ct] = pvals_corrected[i]\n",
    "    \n",
    "for i in range(len(selected_cell_types) - 1):\n",
    "    ax.axvline(i + 0.55, color='grey', linestyle='--', linewidth=0.5)\n",
    "    \n",
    "ax.set_ylabel(\"Frequency\", fontsize=14)\n",
    "ax.set_xlabel('') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e777dd-c4dc-4826-be4a-72dc0b642838",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Calculate Cell Frequency and Cell Co-Occurrence in hot/coldspots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e29f0020-0601-4698-b77b-c303657877a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: reg001_A at scale 32.0\n",
      "46.582 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg001_A contains 104 diversity hotspots\n",
      "Processing region: reg001_B at scale 32.0\n",
      "75.293 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg001_B contains 11 diversity hotspots\n",
      "Processing region: reg002_A at scale 32.0\n",
      "45.996 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg002_A contains 167 diversity hotspots\n",
      "Processing region: reg002_B at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg002_B contains 48 diversity hotspots\n",
      "Processing region: reg003_A at scale 32.0\n",
      "46.875 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg003_A contains 123 diversity hotspots\n",
      "Processing region: reg003_B at scale 32.0\n",
      "43.945 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg003_B contains 93 diversity hotspots\n",
      "Processing region: reg004_A at scale 32.0\n",
      "35.254 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg004_A contains 84 diversity hotspots\n",
      "Processing region: reg004_B at scale 32.0\n",
      "17.871 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg004_B contains 75 diversity hotspots\n",
      "Processing region: reg005_A at scale 32.0\n",
      "18.359 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg005_A contains 153 diversity hotspots\n",
      "Processing region: reg005_B at scale 32.0\n",
      "40.527 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg005_B contains 151 diversity hotspots\n",
      "Processing region: reg006_A at scale 32.0\n",
      "37.402 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg006_A contains 76 diversity hotspots\n",
      "Processing region: reg006_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg006_B contains 119 diversity hotspots\n",
      "Processing region: reg007_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg007_A contains 226 diversity hotspots\n",
      "Processing region: reg007_B at scale 32.0\n",
      "26.660 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg007_B contains 187 diversity hotspots\n",
      "Processing region: reg008_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg008_A contains 107 diversity hotspots\n",
      "Processing region: reg008_B at scale 32.0\n",
      "8.691 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg008_B contains 164 diversity hotspots\n",
      "Processing region: reg009_A at scale 32.0\n",
      "12.793 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg009_A contains 139 diversity hotspots\n",
      "Processing region: reg009_B at scale 32.0\n",
      "6.543 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg009_B contains 62 diversity hotspots\n",
      "Processing region: reg010_A at scale 32.0\n",
      "25.391 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg010_A contains 184 diversity hotspots\n",
      "Processing region: reg010_B at scale 32.0\n",
      "42.383 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg010_B contains 85 diversity hotspots\n",
      "Processing region: reg011_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg011_A contains 268 diversity hotspots\n",
      "Processing region: reg011_B at scale 32.0\n",
      "51.562 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg011_B contains 66 diversity hotspots\n",
      "Processing region: reg012_A at scale 32.0\n",
      "32.910 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg012_A contains 162 diversity hotspots\n",
      "Processing region: reg012_B at scale 32.0\n",
      "77.051 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg012_B contains 64 diversity hotspots\n",
      "Processing region: reg013_A at scale 32.0\n",
      "25.098 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg013_A contains 125 diversity hotspots\n",
      "Processing region: reg013_B at scale 32.0\n",
      "19.922 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg013_B contains 107 diversity hotspots\n",
      "Processing region: reg014_A at scale 32.0\n",
      "49.609 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg014_A contains 66 diversity hotspots\n",
      "Processing region: reg014_B at scale 32.0\n",
      "21.191 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg014_B contains 110 diversity hotspots\n",
      "Processing region: reg015_A at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg015_A contains 104 diversity hotspots\n",
      "Processing region: reg015_B at scale 32.0\n",
      "31.055 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg015_B contains 59 diversity hotspots\n",
      "Processing region: reg016_A at scale 32.0\n",
      "27.539 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg016_A contains 116 diversity hotspots\n",
      "Processing region: reg016_B at scale 32.0\n",
      "17.773 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg016_B contains 85 diversity hotspots\n",
      "Processing region: reg017_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg017_A contains 190 diversity hotspots\n",
      "Processing region: reg017_B at scale 32.0\n",
      "18.066 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg017_B contains 190 diversity hotspots\n",
      "Processing region: reg018_A at scale 32.0\n",
      "29.492 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg018_A contains 216 diversity hotspots\n",
      "Processing region: reg018_B at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg018_B contains 87 diversity hotspots\n",
      "Processing region: reg019_A at scale 32.0\n",
      "53.613 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg019_A contains 191 diversity hotspots\n",
      "Processing region: reg019_B at scale 32.0\n",
      "92.090 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg019_B contains 2 diversity hotspots\n",
      "Processing region: reg020_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg020_A contains 165 diversity hotspots\n",
      "Processing region: reg020_B at scale 32.0\n",
      "19.434 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg020_B contains 180 diversity hotspots\n",
      "Processing region: reg021_A at scale 32.0\n",
      "36.328 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg021_A contains 207 diversity hotspots\n",
      "Processing region: reg021_B at scale 32.0\n",
      "76.270 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg021_B contains 13 diversity hotspots\n",
      "Processing region: reg022_A at scale 32.0\n",
      "31.934 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg022_A contains 220 diversity hotspots\n",
      "Processing region: reg022_B at scale 32.0\n",
      "62.402 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg022_B contains 59 diversity hotspots\n",
      "Processing region: reg023_A at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg023_A contains 42 diversity hotspots\n",
      "Processing region: reg023_B at scale 32.0\n",
      "17.188 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg023_B contains 192 diversity hotspots\n",
      "Processing region: reg024_A at scale 32.0\n",
      "46.191 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg024_A contains 30 diversity hotspots\n",
      "Processing region: reg024_B at scale 32.0\n",
      "15.625 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg024_B contains 48 diversity hotspots\n",
      "Processing region: reg025_A at scale 32.0\n",
      "39.648 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg025_A contains 131 diversity hotspots\n",
      "Processing region: reg025_B at scale 32.0\n",
      "31.348 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg025_B contains 204 diversity hotspots\n",
      "Processing region: reg026_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg026_A contains 116 diversity hotspots\n",
      "Processing region: reg026_B at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg026_B contains 115 diversity hotspots\n",
      "Processing region: reg027_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg027_A contains 158 diversity hotspots\n",
      "Processing region: reg027_B at scale 32.0\n",
      "17.578 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg027_B contains 58 diversity hotspots\n",
      "Processing region: reg028_A at scale 32.0\n",
      "16.016 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg028_A contains 179 diversity hotspots\n",
      "Processing region: reg028_B at scale 32.0\n",
      "13.965 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg028_B contains 76 diversity hotspots\n",
      "Processing region: reg029_A at scale 32.0\n",
      "15.039 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg029_A contains 162 diversity hotspots\n",
      "Processing region: reg029_B at scale 32.0\n",
      "23.633 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg029_B contains 79 diversity hotspots\n",
      "Processing region: reg030_A at scale 32.0\n",
      "17.969 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg030_A contains 102 diversity hotspots\n",
      "Processing region: reg030_B at scale 32.0\n",
      "25.195 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg030_B contains 135 diversity hotspots\n",
      "Processing region: reg031_A at scale 32.0\n",
      "11.426 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg031_A contains 138 diversity hotspots\n",
      "Processing region: reg031_B at scale 32.0\n",
      "17.480 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg031_B contains 110 diversity hotspots\n",
      "Processing region: reg032_A at scale 32.0\n",
      "11.230 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg032_A contains 136 diversity hotspots\n",
      "Processing region: reg032_B at scale 32.0\n",
      "33.984 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg032_B contains 142 diversity hotspots\n",
      "Processing region: reg033_A at scale 32.0\n",
      "26.367 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg033_A contains 204 diversity hotspots\n",
      "Processing region: reg033_B at scale 32.0\n",
      "12.012 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg033_B contains 147 diversity hotspots\n",
      "Processing region: reg034_A at scale 32.0\n",
      "19.238 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg034_A contains 93 diversity hotspots\n",
      "Processing region: reg034_B at scale 32.0\n",
      "25.586 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg034_B contains 105 diversity hotspots\n",
      "Processing region: reg035_A at scale 32.0\n",
      "21.777 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg035_A contains 182 diversity hotspots\n",
      "Processing region: reg035_B at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg035_B contains 151 diversity hotspots\n",
      "Processing region: reg036_A at scale 32.0\n",
      "13.184 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg036_A contains 142 diversity hotspots\n",
      "Processing region: reg036_B at scale 32.0\n",
      "10.742 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg036_B contains 170 diversity hotspots\n",
      "Processing region: reg037_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg037_A contains 190 diversity hotspots\n",
      "Processing region: reg037_B at scale 32.0\n",
      "70.020 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg037_B contains 13 diversity hotspots\n",
      "Processing region: reg038_A at scale 32.0\n",
      "11.719 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg038_A contains 158 diversity hotspots\n",
      "Processing region: reg038_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg038_B contains 128 diversity hotspots\n",
      "Processing region: reg039_A at scale 32.0\n",
      "30.273 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg039_A contains 233 diversity hotspots\n",
      "Processing region: reg039_B at scale 32.0\n",
      "56.543 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg039_B contains 36 diversity hotspots\n",
      "Processing region: reg040_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg040_A contains 133 diversity hotspots\n",
      "Processing region: reg040_B at scale 32.0\n",
      "57.520 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg040_B contains 52 diversity hotspots\n",
      "Processing region: reg041_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg041_A contains 121 diversity hotspots\n",
      "Processing region: reg041_B at scale 32.0\n",
      "29.688 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg041_B contains 119 diversity hotspots\n",
      "Processing region: reg042_A at scale 32.0\n",
      "23.438 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg042_A contains 73 diversity hotspots\n",
      "Processing region: reg042_B at scale 32.0\n",
      "24.902 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg042_B contains 65 diversity hotspots\n",
      "Processing region: reg043_A at scale 32.0\n",
      "23.340 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg043_A contains 57 diversity hotspots\n",
      "Processing region: reg043_B at scale 32.0\n",
      "64.844 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg043_B contains 16 diversity hotspots\n",
      "Processing region: reg044_A at scale 32.0\n",
      "43.164 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg044_A contains 52 diversity hotspots\n",
      "Processing region: reg044_B at scale 32.0\n",
      "71.973 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg044_B contains 11 diversity hotspots\n",
      "Processing region: reg045_A at scale 32.0\n",
      "13.672 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg045_A contains 54 diversity hotspots\n",
      "Processing region: reg045_B at scale 32.0\n",
      "43.750 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg045_B contains 44 diversity hotspots\n",
      "Processing region: reg046_A at scale 32.0\n",
      "15.723 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg046_A contains 133 diversity hotspots\n",
      "Processing region: reg046_B at scale 32.0\n",
      "15.234 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg046_B contains 111 diversity hotspots\n",
      "Processing region: reg047_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg047_A contains 221 diversity hotspots\n",
      "Processing region: reg047_B at scale 32.0\n",
      "45.117 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg047_B contains 20 diversity hotspots\n",
      "Processing region: reg048_A at scale 32.0\n",
      "24.512 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg048_A contains 77 diversity hotspots\n",
      "Processing region: reg048_B at scale 32.0\n",
      "33.203 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg048_B contains 120 diversity hotspots\n",
      "Processing region: reg049_A at scale 32.0\n",
      "14.648 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg049_A contains 92 diversity hotspots\n",
      "Processing region: reg049_B at scale 32.0\n",
      "38.086 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg049_B contains 67 diversity hotspots\n",
      "Processing region: reg050_A at scale 32.0\n",
      "42.871 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg050_A contains 65 diversity hotspots\n",
      "Processing region: reg050_B at scale 32.0\n",
      "25.000 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg050_B contains 49 diversity hotspots\n",
      "Processing region: reg051_A at scale 32.0\n",
      "33.496 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg051_A contains 142 diversity hotspots\n",
      "Processing region: reg051_B at scale 32.0\n",
      "23.047 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg051_B contains 100 diversity hotspots\n",
      "Processing region: reg052_A at scale 32.0\n",
      "18.848 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg052_A contains 113 diversity hotspots\n",
      "Processing region: reg052_B at scale 32.0\n",
      "23.828 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg052_B contains 104 diversity hotspots\n",
      "Processing region: reg053_A at scale 32.0\n",
      "24.023 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg053_A contains 156 diversity hotspots\n",
      "Processing region: reg053_B at scale 32.0\n",
      "88.477 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg053_B contains 3 diversity hotspots\n",
      "Processing region: reg054_A at scale 32.0\n",
      "35.547 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg054_A contains 156 diversity hotspots\n",
      "Processing region: reg054_B at scale 32.0\n",
      "78.320 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg054_B contains 6 diversity hotspots\n",
      "Processing region: reg055_A at scale 32.0\n",
      "15.527 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg055_A contains 220 diversity hotspots\n",
      "Processing region: reg055_B at scale 32.0\n",
      "89.062 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg055_B contains 2 diversity hotspots\n",
      "Processing region: reg056_A at scale 32.0\n",
      "56.641 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg056_A contains 63 diversity hotspots\n",
      "Processing region: reg056_B at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg056_B contains 56 diversity hotspots\n",
      "Processing region: reg057_A at scale 32.0\n",
      "94.434 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg057_A contains 5 diversity hotspots\n",
      "Processing region: reg057_B at scale 32.0\n",
      "51.953 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg057_B contains 97 diversity hotspots\n",
      "Processing region: reg058_A at scale 32.0\n",
      "20.703 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg058_A contains 38 diversity hotspots\n",
      "Processing region: reg058_B at scale 32.0\n",
      "36.719 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg058_B contains 14 diversity hotspots\n",
      "Processing region: reg059_A at scale 32.0\n",
      "17.285 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg059_A contains 177 diversity hotspots\n",
      "Processing region: reg059_B at scale 32.0\n",
      "21.875 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg059_B contains 121 diversity hotspots\n",
      "Processing region: reg060_A at scale 32.0\n",
      "15.820 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg060_A contains 112 diversity hotspots\n",
      "Processing region: reg060_B at scale 32.0\n",
      "14.355 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg060_B contains 70 diversity hotspots\n",
      "Processing region: reg061_A at scale 32.0\n",
      "25.488 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg061_A contains 142 diversity hotspots\n",
      "Processing region: reg061_B at scale 32.0\n",
      "22.656 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg061_B contains 80 diversity hotspots\n",
      "Processing region: reg062_A at scale 32.0\n",
      "12.500 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg062_A contains 108 diversity hotspots\n",
      "Processing region: reg062_B at scale 32.0\n",
      "30.469 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg062_B contains 61 diversity hotspots\n",
      "Processing region: reg063_A at scale 32.0\n",
      "50.879 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg063_A contains 49 diversity hotspots\n",
      "Processing region: reg063_B at scale 32.0\n",
      "40.234 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg063_B contains 11 diversity hotspots\n",
      "Processing region: reg064_A at scale 32.0\n",
      "20.312 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg064_A contains 74 diversity hotspots\n",
      "Processing region: reg064_B at scale 32.0\n",
      "19.141 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg064_B contains 128 diversity hotspots\n",
      "Processing region: reg065_A at scale 32.0\n",
      "27.930 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg065_A contains 140 diversity hotspots\n",
      "Processing region: reg065_B at scale 32.0\n",
      "66.895 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg065_B contains 2 diversity hotspots\n",
      "Processing region: reg066_A at scale 32.0\n",
      "23.242 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg066_A contains 90 diversity hotspots\n",
      "Processing region: reg066_B at scale 32.0\n",
      "39.941 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg066_B contains 40 diversity hotspots\n",
      "Processing region: reg067_A at scale 32.0\n",
      "66.992 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg067_A contains 70 diversity hotspots\n",
      "Processing region: reg067_B at scale 32.0\n",
      "93.945 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg067_B contains 7 diversity hotspots\n",
      "Processing region: reg068_A at scale 32.0\n",
      "33.887 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg068_A contains 35 diversity hotspots\n",
      "Processing region: reg068_B at scale 32.0\n",
      "27.148 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg068_B contains 136 diversity hotspots\n",
      "Processing region: reg069_A at scale 32.0\n",
      "30.371 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg069_A contains 80 diversity hotspots\n",
      "Processing region: reg069_B at scale 32.0\n",
      "49.121 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg069_B contains 72 diversity hotspots\n",
      "Processing region: reg070_A at scale 32.0\n",
      "26.270 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg070_A contains 117 diversity hotspots\n",
      "Processing region: reg070_B at scale 32.0\n",
      "41.699 per cent patches are empty\n",
      "Using MoranI\n",
      "Region reg070_B contains 31 diversity hotspots\n"
     ]
    }
   ],
   "source": [
    "spot_cellfreq_df, spot_co_occurrence_df = eco.spot_cellfreq(spatial_data=protein, \n",
    "                                                            scale=32.0, \n",
    "                                                            library_key='File Name',\n",
    "                                                            library_id=library_ids, \n",
    "                                                            spatial_key=['X:X','Y:Y'],\n",
    "                                                            cluster_key='ClusterName',\n",
    "                                                            spots='hot',\n",
    "                                                            p_value=0.05,\n",
    "                                                            top=None,\n",
    "                                                            selected_comb=None,\n",
    "                                                            restricted=False,\n",
    "                                                            metric='Shannon Diversity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a43b879-3baf-459d-99f6-b844652d2857",
   "metadata": {},
   "outputs": [],
   "source": [
    "spot_cellfreq_df['Patients'] = spot_cellfreq_df.index.map(region_to_patient)\n",
    "spot_cellfreq_df['Condition'] = spot_cellfreq_df.index.map(region_to_condition)\n",
    "\n",
    "spot_co_occurrence_subcols = spot_co_occurrence_df.loc[:,spot_co_occurrence_df.mean()>0.01].columns.tolist()\n",
    "spot_co_occurrence_df['Condition'] = spot_co_occurrence_df.index.map(region_to_condition)\n",
    "spot_co_occurrence_df['Patients'] = spot_co_occurrence_df.index.map(region_to_patient)\n",
    "spot_co_occurrence_subcols.extend([('Condition',''),('Patients','')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c474e106-e935-4976-986a-30a2be61d7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "305"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spot_co_occurrence_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "980cd21c-7f94-4534-9929-a2ec3273912b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spot_co_occurrence_subcols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ef26636-6b70-4b75-ae7b-d5d173537b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spot_co_occurrence_subcols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d918fde-d9df-46fb-8fcd-73cf114fd5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute averages by every patients\n",
    "spot_cellfreq_df_avg = spot_cellfreq_df.groupby(by=\"Patients\").mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "12dd5fb4-8c31-4e8e-8d42-4cbd1e188173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patients</th>\n",
       "      <th>Condition</th>\n",
       "      <th>ClusterName</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CLR</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.064946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>DII</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.026271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>DII</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.021472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>DII</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.005076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>DII</td>\n",
       "      <td>B cells</td>\n",
       "      <td>0.032739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>31</td>\n",
       "      <td>DII</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.073528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>32</td>\n",
       "      <td>CLR</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.025834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>33</td>\n",
       "      <td>CLR</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.049112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>34</td>\n",
       "      <td>CLR</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.078962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>35</td>\n",
       "      <td>CLR</td>\n",
       "      <td>vasculature</td>\n",
       "      <td>0.071994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>980 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Patients Condition  ClusterName  Frequency\n",
       "0           1       CLR      B cells   0.064946\n",
       "1           2       DII      B cells   0.026271\n",
       "2           3       DII      B cells   0.021472\n",
       "3           4       DII      B cells   0.005076\n",
       "4           5       DII      B cells   0.032739\n",
       "..        ...       ...          ...        ...\n",
       "975        31       DII  vasculature   0.073528\n",
       "976        32       CLR  vasculature   0.025834\n",
       "977        33       CLR  vasculature   0.049112\n",
       "978        34       CLR  vasculature   0.078962\n",
       "979        35       CLR  vasculature   0.071994\n",
       "\n",
       "[980 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melt the dataframe for easier plotting and statistical analysis\n",
    "spot_cellfreq_df_melt = spot_cellfreq_df_avg.melt(id_vars=['Patients', 'Condition'])\n",
    "spot_cellfreq_df_melt.columns = ['Patients', 'Condition', 'ClusterName', 'Frequency']\n",
    "spot_cellfreq_df_melt['Condition'] = spot_cellfreq_df_melt['Condition'].map(condition_num2str)\n",
    "spot_cellfreq_df_melt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c8cab981-e8b7-45ae-b76a-af9a83c64328",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B cells has p value of 0.0014559325364744697\n",
      "CD11b+ monocytes has p value of 0.3136521910026323\n",
      "CD11b+CD68+ macrophages has p value of 0.08547151779873288\n",
      "CD11c+ DCs has p value of 0.8812799240247237\n",
      "CD163+ macrophages has p value of 0.10317064232335753\n",
      "CD3+ T cells has p value of 0.4364757788576631\n",
      "CD4+ T cells has p value of 0.08664898983355415\n",
      "CD4+ T cells CD45RO+ has p value of 0.6143836480682933\n",
      "CD4+ T cells GATA3+ has p value of 0.1986195723341697\n",
      "CD68+ macrophages has p value of 0.10691852501193803\n",
      "CD68+ macrophages GzmB+ has p value of 0.9152253048171965\n",
      "CD68+CD163+ macrophages has p value of 0.014926989928892282\n",
      "CD8+ T cells has p value of 0.10897466856576973\n",
      "NK cells has p value of 0.5045064620514788\n",
      "Tregs has p value of 0.0014366798780783448\n",
      "adipocytes has p value of 0.06189470353929312\n",
      "granulocytes has p value of 0.018585102531082573\n",
      "immune cells has p value of 0.07016880865384516\n",
      "immune cells / vasculature has p value of 0.11538101303037446\n",
      "lymphatics has p value of 0.17816431455675683\n",
      "nerves has p value of 0.42460752180793715\n",
      "plasma cells has p value of 0.6550870077521457\n",
      "smooth muscle has p value of 0.7241295343085075\n",
      "stroma has p value of 0.7661369664615391\n",
      "tumor cells has p value of 0.7070979944360491\n",
      "tumor cells / immune cells has p value of 0.7331463482451819\n",
      "undefined has p value of 0.22523108806578754\n",
      "vasculature has p value of 0.006739959365868687\n",
      "------------------------------------------\n",
      "p-values after correction:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/mesa/lib/python3.11/site-packages/seaborn/categorical.py:3370: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B cells has p value = 0.020\n",
      "CD11b+ monocytes has p value = 0.517\n",
      "CD11b+CD68+ macrophages has p value = 0.249\n",
      "CD11c+ DCs has p value = 0.914\n",
      "CD163+ macrophages has p value = 0.249\n",
      "CD3+ T cells has p value = 0.643\n",
      "CD4+ T cells has p value = 0.249\n",
      "CD4+ T cells CD45RO+ has p value = 0.819\n",
      "CD4+ T cells GATA3+ has p value = 0.371\n",
      "CD68+ macrophages has p value = 0.249\n",
      "CD68+ macrophages GzmB+ has p value = 0.915\n",
      "CD68+CD163+ macrophages has p value = 0.104\n",
      "CD8+ T cells has p value = 0.249\n",
      "NK cells has p value = 0.706\n",
      "Tregs has p value = 0.020\n",
      "adipocytes has p value = 0.249\n",
      "granulocytes has p value = 0.104\n",
      "immune cells has p value = 0.249\n",
      "immune cells / vasculature has p value = 0.249\n",
      "lymphatics has p value = 0.356\n",
      "nerves has p value = 0.643\n",
      "plasma cells has p value = 0.821\n",
      "smooth muscle has p value = 0.821\n",
      "stroma has p value = 0.825\n",
      "tumor cells has p value = 0.821\n",
      "tumor cells / immune cells has p value = 0.821\n",
      "undefined has p value = 0.394\n",
      "vasculature has p value = 0.063\n"
     ]
    },
    {
     "data": {
      "image/png": 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4uCW6e0/Pzda25Gf5V7/61TjllFPi+uuvj2OOOSb+9Kc/xbXXXhsnnXRSi/3e7e/Mrb1ljx49mv8hJqUUy5Yti6uvvjpmzZoVV155ZZu3efTRR+OWW26Jq6++OioqKlp87/zzz48JEyZESUlJu4+hs7p7S5/jrXWH10VPPPFERETccccd8eUvfzmWLl0au+22W3z3u9+NI488st3bdVR3bxkRMXr06HjhhRdi1KhRMXv27Db3eeaZZ+KEE05osa20tDTGjh3b4r59jelz/O225Lx8u4svvjiWLl0aDz74YIvtHTkvn3zyydh9993jV7/6VZx33nnx/PPPx7hx42LGjBnx0Y9+tEPH35Tu3tJrRa35HM++ZYQ/e97J89KWm6O7t+zo75BPPvlkVFZWxumnnx533nlnvPnmm/GBD3wgLr744thll12a99maf44X03W37t4yomPXiTrymihi6762EWHLd+oOLb224Xn5TrbMvmVHXxMNGzas1TXO9evXx6JFi1osRNuaf+/pyLWNQw89NJ5++un45Cc/Geecc05cddVV8corr8T73//++MlPftJ8fbgr/i1SylyG7zbYre2www5p0KBB6Y033mjedtttt6WISHfddRd6rKVLl6aISFdeeWWr740cObLVx1lttPEjVx544IEW29evX58iIs2YMaPN2y1fvjztuOOOadSoUWnlypUppbfeKvWdb+GbUkrf+ta3Us+ePTf3IbVQDC0bGxtTv3790oABA9Jll12Wfv/736cf/OAHacCAAemggw5q8bbFbxfv8pGqP/zhD1NpaWl69tlnO/V43qkYWrblj3/8Y+rdu3c69thjm7etX78+7bPPPumLX/xiSimlq6++utXHEdx///0pItLHP/7xtGDBgvTKK6+kiy++OPXt2zdFRFq0aFGnH1+xtDzwwAPT4MGD06233preeOONNHfu3LTvvvum3r17p09/+tNt3qa98/JLX/pS2muvvdL69evTwoULsbeELpaW7+R52bmWN9xwQ4qItN1226VTTz01zZo1K11++eVp4MCB6YADDmj++7IjLd9u3LhxKSJSnz59sMdaDD3b4rnZ+ZZr165Np5xySoqI5q8pU6akdevWtXubtv7OtOW/XH/99c0tP/ShDzW/dnynY489No0ZMyatX79+k/c3adIk7OMdiq3lRj7Hs39d9L3vfS9FRDrqqKPS/fffn+688850xBFHpNLS0i3+eIdiafl2Gz/a4p0ft9irV6/0rW99q9X+Bx54YDr88MOb/+xrzJZ8jm95y4aGhjRixIj0iU98otX3OnJevuc970nbbrttGjVqVLr22mvT/fffn4499thUUlLS4mMDO6MYWnqtqDWf4xx/9nA8Lzm25BT6d8gpU6akiEhf//rX08MPP5x+8YtfpHHjxqVhw4alJUuWpJT8OV4s192KoWVKnbtO1NZronfaGq9t2LKl7tDSaxuel+9kS06hXxN961vfShGRLrjggrR8+fL00ksvpU996lOpT58+qaqqqnm/rfn3no5c21i2bFmKiLTtttumAw88MN11113plltuSRMmTEiDBw9O//jHP1rdbyH+LVLqDvyo30045phjWryt7Yc//OHo2bNn81uHvlNKKTZs2NDuV1NTU5u327i9rZXvKaU2V1W/2+0ios3bLV26ND7wgQ/E8uXL49Zbb43+/fs339fmHn9zdPeWKaW4++67489//nN87nOfi0MOOSS+9rWvxeWXXx6PPPJIhz+O6J0uvfTS+PCHP9xqNfqW6O4t3+kPf/hDfOhDH4odd9wx/vd//7d5+4wZM+L111+PCy64oN3bHn744XH99dfHrFmzYscdd4yhQ4fGXXfdFWeddVZERJSXl3dohvYUQ8tbbrklDj744Jg6dWoMHDgwPvCBD8Rpp50Ww4cP36zHP3v27LjiiivimmuuafVuDYRiaPl2npedb7lu3bqIiNhnn33iqquuisMOOyw++9nPxuWXXx5/+tOf4oEHHoiIjrV8u8suuyzuv//+mDp1anz4wx+O66+/vkO3ezfdvec7eW5uWcv/9//+X9x0001x4YUXxuzZs+PHP/5xPP7443H88cdHSqnDj9WW/zJx4sT4/e9/H1dccUXMnTs33v/+98fatWtb7LN48eL47W9/G6effnpBfsZsSjG1jPA5HtE9XhedeOKJcc8998Rvf/vbOPzww+Poo4+Ou+66K3bZZZf47//+7w7fT3uKoWVHpJTe9b59jdmSz3Gm5U033RTLli2Lb3zjG23ex7vd97p16+Lll1+OO+64I0466aQ4/PDD4+abb45dd901zjvvvHc9/rvp7i29VtSaz/HWsm7ZHn/2/Os+PS9t+XbF1HJTv0NecMEF8cgjj8T3v//9OPjgg+OTn/xk3HfffVFXV9f8Mb5b+8/xYrru1t1bRmz+daL2XhMVmi05W1NLr214Xr6TLVvrrq+Jpk+fHtXV1XHOOefE8OHDY9y4cTFgwIA49thjm183bu2/93Tk2sbG100REffee28cddRRMXXq1Ljnnnti1apV8ZOf/KTV7Qr1b5FS1lz4twnbbbddiz+XlpbGkCFD4vXXX29z/9///vfRs2fPdr8+/elPt3m7gQMHRkREfX19q++tWrWqxV+6HbndypUrIyJa3e6pp56KAw44IJYsWRL33ntv7Lfffi3ua3OPvzm6e8uysrKYPHlyjB8/vsX2o446KiIi/u///m+Tj68t//d//xfPP/98fOITn9js225Kd2/5dr/5zW/i8MMPjx122CF+97vfNX+kxRNPPBHf/e5344orrojevXu3eEHQ2NgYjY2NzffxiU98IpYtWxbPPfdc/OMf/4hZs2bF+vXro7S0dIvPzWJoOWLEiLj99tvj9ddfj3nz5sU///nPOOWUU2Lp0qUd/oiQVatWxSmnnBLV1dWx6667xoYNG5obNzU1xYYNGzp0P5tSDC038rx8S2dbbvzo86OPPrrF9g9+8IMR8dZHsmxOy40OP/zwOPzww+OXv/xlHHTQQfHtb3+7zeNvru7e8+08N9/S2ZaPPvpo3HfffXHxxRfHN77xjZg0aVJ86Utfiuuuuy7uuOOOuPvuuzfj0W7dLd9u3Lhxccghh8R//ud/xi9/+ct46qmn4pZbbmmxz6233holJSXx8Y9//F3vj1ZMLX2OvyXr10URETvssEMceeSRLS4s9ezZM4444oj429/+1uH7aU8xtOyIysrKTd63rzFb8jn+FqLlzTffHBMmTGj+eJy3e7fzMuKt16vbbrtt7L333s3fLysri3/7t3+LJ5988l2P/266e0uvFbXmc7w1f/a0r5hael6+xZatddffId/73vfGgQce2GL/qqqqeM973tN8/X1r/zleTNfdunvLzb1OtKnXRIVmS87W1HJrv7bhedmaLVvrrq+JevToERdccEHU19fHvHnzYvny5XHppZc2X+P0956OXdvY+Lpp8uTJLRafjh49Ot7znve0+dqxUP8WKWWta9+So8i8+uqrLf7c2NgYr7zySgwfPrzN/ffZZ594/PHH272/oUOHtrm9vLw8Ro4cGS+88EKL7StWrIj6+vrYdddd27zdjjvuGGVlZa1ut/HPb7/dgw8+GMcee2xUVlbGww8/HLvttluL2+yyyy5x3333RVNTU4sXiS+88EK7x98c3b3lkiVL4p577okjjzwyRo0a1bx9zZo1mzzeptx1113Rr1+/5sWDlO7ecqPvf//7UV1dHYccckjccccdLX5w33HHHbFu3br4t3/7t1a3GzduXEyaNClmz54dzzzzTDz++ONx0kknxU477dS8z1//+tfYc889o6ysbJMzvJtiaPmb3/wmdt1119hjjz2aX+T85S9/icbGxhYXwDblL3/5S7z44ovxne98J77zne+0+N6pp54ap5566ma9E1ZbiqFlhOfl23W25cbH3NDQ0GL7+vXrIyKib9++HW754IMPxtq1a+NDH/pQi3323XffuPTSS9t9TJuju/fcyHPzXzrb8qWXXoqIaPWPCJMmTYqIiHnz5rW6cN6erb3l8uXLY+bMmXHkkUe2mGnjRZzFixe32P+uu+6KQw45JEaMGNHujIXS3Vtu5HP8X7J+XRQRcffdd0dDQ0NMnTq1xfY1a9bEkCFDOnw/7SmGlh2xyy67tLrvpqamWLhwYUydOtXXmG/jc/xftrTl+vXr4/7774/q6uo2v/9u52XEW69XFy9e3Oqdw9avXx99+/bd5PE7oru39FpRaz7HW/NnT/uKpaXn5b/YsrXu+Dvk+vXr45e//GWMHz8+DjjggBa3X7NmTfNMW/vP8WK67tbdW27OdaJ3e01UaLbkbE0tt/ZrG56Xrdmyte74mijirUV0a9eujSlTpjTf14YNG+Jvf/tbnHLKKf7eEx27tlFZWRnDhw9v9bopouVrx674t0gpc536gOCtwA477JC233771NDQ0LztlltuSRGRHnroIfx4p5xySho9enRau3Zt87bLLrsslZWVpUWLFrV7u0MPPTQdcMABqampqXnbmWeemQYOHJhWr16dUkpp7ty5qV+/fmnChAltfpZ5Sin9/ve/TxGR7rnnnuZty5cvT+Xl5em73/3uFj22Ymi5YMGCFBHpnHPOabH94osvTqWlpWn+/Plt3i4i0rnnntvm94455ph00EEHde5BtKMYWqaU0k9/+tMUEeljH/tYi1k3WrJkSXr88cdbfJ177rkpItJvf/vb5t433XRTioj0zDPPNN923rx5qUePHuk73/nOFj22Ymk5YcKE9PGPf7zFtn//939PAwcOTHV1dW3e5p3nZX19favev/3tb5v3e/zxx7fosRVLS8/L1jrTsqmpKY0ZMya9//3vb7H9qquuShGR5s6d2+GWn/rUp9KQIUNSfX198/2sX78+7bnnnmn//fff4sdXDD1T8txsS2dazp07N0VE+tnPftZi+7333psiIt1www1t3q6tn+Vbe8uNr4tmzJjRYvuNN97Y6vViU1NTqqysTGeffXaH5pk0aVKaNGnS5j+QNhRDy5R8jrcly9dFKb3186eioiK99tprzdtWrVqVRo4cmT7xiU904hH9S7G0fLuHHnqozfm+/e1vp/Ly8rR8+fLmbffcc0+KiPToo4/6GvP/53O8tS05L//617+miEizZs1q8/vvdl6mlNLVV1+dIiLdd999zfs0NDSkqqqq9OEPf3hLHlrRtPRaUWs+xzn+7OF4XnJsySnk75CjR49OBx98cIt9/vrXv6bS0tJ05ZVXppT8OV4s192KoWVHrxN15DXRO21t1zZs2basW27t1zY8L9tmS04hXxN94QtfSNtvv31at25d8z4/+9nPUkSkhx9+2N97/n8dubYxbdq0VFFRkVasWNG8z/z581NZWVn6n//5n5RS4f8tUuoOXPjXjh122CGVlJSkKVOmpLvvvjv99Kc/TYMGDUpTpkwpyPGeeeaZ1KdPn3TooYemO++8M1100UWpd+/e6fOf/3zzPmvXrk2PPfZYWrx4cfO23/3ud6mkpCQdd9xx6Z577klnn312KikpSRdeeGHzPnvttVfq2bNnuummm9Jjjz3W4uuFF15o3m/y5Mlp0KBB6corr0y33npr2mOPPdLIkSNbvGjsjGJp+alPfSr16tUrnX/++WnWrFlp+vTpqVevXumLX/xiu8fa1MK/MWPGpNNOOw17XCkVR8uXX3459e3bN+2www7pD3/4Q6tz7u0Xb99u40WdhQsXNm97880304477pj22muvdNddd6Vf//rXadSoUWnnnXdu8cO5M4qhZUpvvdArKSlJ5513XnrwwQfTaaedliIiXX755e0ea1Pn5UYLFy5MEZGuvvrqLX1oRdHS87JtnT0vb7rpplRSUpI+9rGPpfvvvz/9+Mc/Tv37908f/ehH2z1WWy2feuqp1K9fv3TQQQel3/72t+mOO+5Ihx12WOrZs2eaPXv2Fj++Yujpudm2zp6bH/3oR1N5eXm64IIL0kMPPZR+8pOfpKFDh6a99967xS/Rb9fW35m2TOmkk05KvXv3ThdccEH63e9+l2pqatKAAQPSlClTWvyi/eKLL6aISL/61a86NA99sae7t/Q53rasXxc99dRTqW/fvumAAw5Id9xxR7rlllvS/vvvnyorK1v8ftQZxdLy7dpbfLFixYo0dOjQtOeee6Zbb701XXnllWnQoEHpyCOPbHceX2P6HE9py87La665JkVEWrp0aZv33ZHzct26dWmfffZJQ4cOTVdeeWWaOXNm+uAHP5h69eqV/vrXv27RYyuWll4ras3nOMefPRzPS44tOYX8HfJ///d/U0SkadOmpfvvvz9dccUVaZtttknvfe970/r161NK/hxPqTiuuxVLy45cJ+roa6K329qubaRky7Zk3dJrG56XbbElp5CviZ566qnUq1evdOKJJ6ZZs2al//mf/0k9e/Zs9R88v93W9ntPSh27trFgwYJUWVmZ9tprr3TbbbelG2+8MY0bNy6NGTOm+XV4of8tUuoOXPjXjh122CGdeOKJ6fOf/3zq379/GjFiRPrqV7/avHq4EB5++OE0ceLE1Lt37zRy5Mj0zW9+s/kX3pT+9Rf6O//h6tZbb02777576tWrVxo7dmz6wQ9+0Py9jSvL2/uaNm1a876vvfZaOvnkk9PAgQNTRUVFOvLII9t9p7vNUSwt16xZk77zne+knXbaKfXu3TtVVVWl733ve2nDhg3tHmdTC6z69u2bqqurqYeUUiqOlhsv4LT31d4LkrYuTqSU0vPPP5+OOuqoVFlZmUaMGJFOPvnk9PLLL2/x4yqGlhv98Ic/TDvuuGPq169f2muvvd51cUUWC/+6e0vPy/Z19ry8884703777Zd69+6dtt122/T1r3+9xX+t807ttfzrX/+apkyZkgYPHpzKy8vT4Ycfnh577DHksRVDT8/N9nXm3GxoaEjnnHNOGjNmTOrVq1caN25c+sY3vpFWrlzZ7nHa+ztza2+5du3adP7556edd9459e7dO40ZMyadffbZrZ7nc+bMSRGRZs6c2aFZ6Is93b2lz/H2Zf266PHHH09HHHFEGjx4cOrfv3/60Ic+lJ566qktflzF1HKj9hZfpPTWhbHDDjss9e3bNw0fPjyddtppm/zHa19j+hzfqLPnZU1NTYqItGbNmnbvuyPn5WuvvZY++9nPpuHDh6e+ffumAw88MD388MNb/LiKqaXXilrzOc7xZw/H85JjS04hf4f89a9/nfbee+/Ur1+/NGzYsHTaaaelV199tcU+/hzv/tfdiqXlu10n2pzXRG+3tV3bSMmW7cm65dZ+bcPzsm225BTyNdH999+f9tlnn9S3b980duzYNH369HbfvODtx9pafu/ZaFPXNjaaN29eOvroo1P//v1TZWVlOu6441r9R2iF/LdIqTsoSWkLPwA8p8aMGROTJ0+Oa665JutRip4tObbk2JJjS44tWfbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2FLKn9KsB5AkSZIkSZIkSZIkSZIkSR3nwj9JkiRJkiRJkiRJkiRJkoqIH/UrSZIkSZIkSZIkSZIkSVIR8R3/JEmSJEmSJEmSJEmSJEkqIi78kyRJkiRJkiRJkiRJkiSpiLjwT5IkSZIkSZIkSZIkSZKkIuLCP0mSJEmSJEmSJEmSJEmSiogL/yRJkiRJkiRJkiRJkiRJKiIu/JMkSZIkSZIkSZIkSZIkqYi48E+SJEmSJEmSJEmSJEmSpCLiwj9JkiRJkiRJkiRJkiRJkoqIC/8kSZIkSZIkSZIkSZIkSSoiLvyTJEmSJEmSJEmSJEmSJKmIuPBPkiRJkiRJkiRJkiRJkqQi4sI/SZIkSZIkSZIkSZIkSZKKiAv/JEmSJEmSJEmSJEmSJEkqIi78kyRJkiRJkiRJkiRJkiSpiLjwT5IkSZIkSZIkSZIkSZKkIuLCP0mSJEmSJEmSJEmSJEmSiogL/yRJkiRJkiRJkiRJkiRJKiIu/JMkSZIkSZIkSZIkSZIkqYi48E+SJEmSJEmSJEmSJEmSpCLiwj9JkiRJkiRJkiRJkiRJkoqIC/8kSZIkSZIkSZIkSZIkSSoiLvyTJEmSJEmSJEmSJEmSJKmIuPBPkiRJkiRJkiRJkiRJkqQi4sI/SZIkSZIkSZIkSZIkSZKKiAv/JEmSJEmSJEmSJEmSJEkqIi78kyRJkiRJkiRJkiRJkiSpiLjwT5IkSZIkSZIkSZIkSZKkIuLCP0mSJEmSJEmSJEmSJEmSiogL/yRJkiRJkiRJkiRJkiRJKiIu/JMkSZIkSZIkSZIkSZIkqYj0yHqArDQ1NcXSpUtjwIABUVJSkvU4kiRJkiRJkiRJkiRJkjKWUoqVK1fGdtttF6Wlvqeauq+tduHf0qVLY/vtt896DEmSJEmSJEmSJEmSJEndzOLFi2PUqFFZjyG1a6td+DdgwICIeOtJWlFRkfE0+bNs2bIYMWJE1mPkgi05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tmTZk2NLji05tuTYkmNLji05tuyc+vr62H777ZvXFknd1Va78G/jx/tWVFS48K8Ann766dhpp52yHiMXbMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJcueHFtybMmxJceWHFtybMmx5ZbZuLZI6q78IGoVxMKFC7MeITdsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybGllG8u/FNB9OzZM+sRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFtK+VaSUkpZD5GF+vr6qKysjLq6Oj/qV5IkSZIkSZIkSZIkSZJrilQ0fMc/FcTNN9+c9Qi5YUuOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLVn25NiSY0uOLTm25NiSY0uOLQunqakp1q5d65df+FdTU1OHz8MeBTzHtRVbv3591iPkhi05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tmTZk2NLji05tuTYkmNLji05tiyMdevWxcKFCzdrgZbUUaWlpTF27Njo1avXu+7rwj8VxNixY7MeITdsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybElL6UUL7/8cpSVlcX2228fpaV+2Ko4TU1NsXTp0nj55Zdj9OjRUVJSssn9XfinghgzZkzWI+SGLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm2ZNmTY0uOLTm25NiSY0uOLTm25G3YsCFWr14d2223XfTr1y/rcZRDw4YNi6VLl8aGDRuiZ8+em9zXZacqiIceeijrEXLDlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbsuzJsSXHlhxbcmzJsSXHlhxb8hobGyMiOvQxrFJnbDy3Np5rm+LCP0mSJEmSJEmSJEmSJEnqoHf7CFapszbn3HLhnwri4IMPznqE3LAlx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ZSvrnwTwXxz3/+M+sRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFuqsxYtWhSf+9znYuzYsVFeXh6DBw+OD37wg/HAAw9kPZrexoV/Kojnn38+6xFyw5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW7LsybElx5YcW3JsybElx5YcW6oz/v73v8cee+wRDQ0NMXPmzKivr48XXnghPvnJT8axxx4bM2fOzHpE/f9c+KeC8LPMObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FKd8ZnPfCaOOOKI+PnPfx7jx4+PsrKyGDx4cHzyk5+Myy67LBoaGuKaa66JfffdN4444ogYOHBg/OpXv4o1a9bEmWeeGdtvv30MGjQoJk+eHI8//njz/ZaUlMTs2bOb/3zNNdfEmDFjIiJi9uzZMWrUqPjv//7vGDJkSAwZMiROP/30WLduXUREzJs3Lw455JAYNGhQ7LDDDjFt2rRYuXJlV2bplkpSSinrIbJQX18flZWVUVdXFxUVFVmPI0mSJEmSJEmSJEmSJCljm1pTtHbt2li4cGGMHTs2+vTpk9GEhfOPf/wjtt9++/jd734XH/jAB9rd75prrolTTjklrrnmmvj4xz8eTU1N8bnPfS6eeOKJuOWWW2L06NFx+eWXxznnnBN///vfY/To0VFSUhIPPfRQTJ48ufk+pk+fHi+++GLMnj07Dj300PjYxz4WV111Vfzzn/+MI488Mk488cQ477zzYtKkSXHYYYfFOeecE6+++mp84AMfiJNPPjnOOOOMLirTdTbnHPMd/1QQt99+e9Yj5IYtObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObbU5vrHP/4RERGjRo1q3va73/0uBg4cGAMHDowBAwbELrvsEhERvXr1ik996lPRu3fvKCkpiV//+tfxve99L8aNGxe9evWKr3zlKzF+/Pj41a9+1aFjl5SUxGWXXRYDBgyInXbaKc4888z4xS9+ERERffv2jZkzZ8bNN98cpaWl8eSTT+Zy0d/mcuGfCmLNmjVZj5AbtuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmVPji05tuTYkmNLji05tuTYUptr2223jYiIJUuWNG877LDD4o033og33ngjLrnkkmhoaIiIiG222SZKS99aevb666/HunXroqqqqsX9jR07Nl588cUOHXvw4MExZMiQ5j+PHj06Xn755YiIuOGGG+KAAw6Is846K4YNGxaTJ0+OefPmdfpx5oUL/1QQ22+/fdYj5IYtObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObbU5tphhx1iv/32i6uuuupd9y0pKWn+3yNGjIg+ffrEggULWuyzYMGC5sWEpaWlsW7duubvvfLKKy32feONN2L16tXNf164cGGMHj06mpqa4oknnojp06fH888/Hy+++GKMGDEiTj755M48xFxx4Z8KYvz48VmPkBu25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSZU+OLTm25NiSY0uOLTm25NhSnfHzn/88Zs6cGaeddlo899xzkVKKlStXxi9+8Ys499xzY7vttmt1m9LS0vj0pz8dZ511Vrzwwguxbt26+NGPfhTz5s2LE088MSIi3vOe98TNN98cGzZsiAULFsT//u//triPxsbG+PrXvx5r166NZ599Nr7//e/Hf/zHf0RpaWl86UtfirPPPjvWrl0bw4YNiz59+sTQoUO7pEd35sI/FcQDDzyQ9Qi5YUuOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLVn25NiSY0uOLTm25NiSY0uOLdUZu+22W8ybNy/69esXRx99dFRWVsaoUaPiiiuuiOrq6njooYfavN33v//9mDJlShx22GExZMiQuOGGG+K+++6LnXfeOSIiLrvsspg7d24MHjw4TjjhhDj11FNb3cegQYNi7NixMXny5Jg2bVp84xvfiIiIm266KZ555pnYdtttY8SIEfHGG2/EFVdcUbgIRaJH1gNIkiRJkiRJkiRJkiRJkrqHbbfdNn74wx/GD3/4wza/f/LJJ7f6qN1+/frFhRdeGBdeeGGbtznkkEPiL3/5S4ttX//611v8ecaMGTFjxoxWtx0/fnzMmjWr4w9gK+E7/qkg3v/+92c9Qm7YkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLlj05tuTYkmNLji05tuTYkmNLKd9c+KeCeO2117IeITdsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybGllG8u/FNBzJ8/P+sRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFuqWEyePDlSSlmPUXRc+CdJkiRJkiRJkiRJkiRJUhEpSVvpcsn6+vqorKyMurq6qKioyHqc3GlsbIyysrKsx8gFW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybInx5YcW3JsybElx5YcW3Js2TmbWlO0du3aWLhwYYwdOzb69OmT0YTKs805x3zHPxXEPffck/UIuWFLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji1Z9uTYkmNLji05tuTYkmNLji2lfHPhnwpi1apVWY+QG7bk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKtx5ZD6B82m677bIeITdsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybFl12loaIgNGzZ02fF69OgRvXv37rLjdbWu7FnMLUtSSinrIbKwqc/j1pZ74403YuDAgVmPkQu25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSZU+OLTm25NiSY0uOLTm25Niycza1pmjt2rWxcOHCGDt2bPTp0yci3lqk9pe5/xdvrlnfZTOW9+0Z++69Z9EuWNuUhoaGeOqJv8SGNW92yfF69C2P3ffat9u0bOsca48f9auCmDlzZtYj5IYtObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObbsGhs2bIg316yPXv1HRPng0QX/6tV/RLy5Zv0WvyPec889F9OmTYtRo0bFgAEDYscdd4xvfvObzR8RPWbMmLjmmmvavO2LL74YJSUlUV5eHv3794/+/ftHv379YtiwYXHCCSfEa6+91um5NmzYEBvWvBkjK/tE1fABBf0aWdknNqx5s9Mtx4wZE3369GluUF5eHu9973vjqquuat7n5JNPjpNPPrnFbdrrurn8qF9JkiRJkhRvvPFGTJ06NZYvXx4/+clPYvLkyVmPJEmSJEmSJElFo2ev3tGnT98uOda6Lbz9o48+Gocffnh8/etfjyeeeCKGDRsWzz33XJx22mlx+OGHxyOPPNKh+5k3b16MGTOmxZ8//OEPx1e+8pX4xS9+sUUz9u7dM/q9yzveMdZu0a1/+tOfNi/sW7duXdx1111xyimnxKuvvhrV1dXAfO1z4Z8KYv/99896hNywJceWHFtybMmxJceWHFtybMmxJacYWzY0NMSSJUva/N55550Xs2fPjpRSnHLKKfG73/2u3fsZOXIk+rEHxdiyu7Ilx5YcW3JsybElx5YcW7LsybElx5YcW3JsybElx5YcW6o9p512WkybNi2+/e1vN2/beeed44YbbojPfOYzUVtb26n7nTBhQnzkIx+Je++9lxq1qPTq1SumTp0ar776anz5y1+Oz33ucwU9ngv/VBCrV6/OeoTcsCXHlhxbcmzJsSXHlhxbcmzJsSWnGFsuWbKk3f/C8Omnn46SkpJIKcXrr7++yf8SsaamJqqqqrC5irFld2VLji05tuTYkmNLji05tmTZk2NLji05tuTYkmNLji05tlRbFixYEPPmzYvLL7+81fdGjBgRt99+e6fuN6UUc+fOjZtuuilOOOGELZyyuB199NFx2mmnxaOPPlrQ47jwTwXx97//PXbfffesx8gFW3JsybElx5YcW3JsybElx5YcW3KKseXIkSOjpqamze9t2LAhzj333Hj44Yfjhz/8Yeyzzz6bvB9SMbbsrmzJsSXHlhxbcmzJsSXHlix7cmzJsSXHlhxbcmzJsSXHlmrLihUrIiJim2222eL72mOPPaK0tDTWrVsXDQ0Nse+++8ZXv/rV+OIXv7jF913Mhg4dGhERr776akGP48I/SZIkSZK2Er17997kO/XNmDEjqqurY5999kHf0U+SJEmSJEmS1D1su+22ERHx8ssvx0477dTq+8uWLYsRI0Z06L7+9re/xZgxY+KVV16JL37xi/Hkk0/G8ccfHz16bN1L0pYvXx4REcOHDy/ocUoLeu/aak2dOjXrEXLDlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbsuzJsSXHlhxbcmzJsSXHlhxbqi077LBD7L777nHDDTe0+t7y5ctjhx12iF//+tebdZ9Dhw6NX/ziFzF8+PA44ogjYuXKldS4RenOO++M/v37xwEHHFDQ47jwTwXxu9/9LusRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFuqPZdcckn8/Oc/j+985zvx6quvRkopnnzyyTjmmGNi7733juOOOy4iIl5//fX4xz/+0eKroaGhzfvs2bNn/PrXv45//vOf8eUvf7krH0630dDQEDfccEP813/9V8yYMSMGDBhQ0ONt3e+rqIKpq6vLeoTcsCXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlix7cmzJsSXHlhxbcmzJsSXHll1r/bq2F8R1x+NMmjQpHn744ZgxY0bsuuuu8eabb8Y222wTxx9/fJx11lnRs2fPiIg444wz4owzzmhx25kzZ8b48ePbvN+RI0fGFVdcEccdd1x86EMfiuOPP77TMzY0rO/0bbvyGJ/97Gfji1/8YkRE9O3bN8aPHx8/+9nP4mMf+9gW3/e7ceGfCqLQn1G9NbElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5Zdo0ePHlHet2e8uWpZrOuiY5b37Rk9emzZsq/99tsvbr/99na//+KLL27y9imlNrd/9KMfbfd7HdGjR4/o0bc8ltS9GRFrO30/HT5e3/JOt3y3RhER11xzzWbfpqNK0paULmL19fVRWVkZdXV1UVFRkfU4ubNy5cqCv13l1sKWHFtybMmxJceWHFtybMmxJceWnDy2rK2tjerq6qipqYmqqqouO24eW2bFlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJsSXHlhxbcmzZOZtaU7R27dpYuHBhjB07Nvr06dO8vaGhITZs2NBlM/bo0SN69+7dZcfral3Zs7u1bO8ca0tpF82krcxdd92V9Qi5YUuOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLVn25NiSY0uOLTm25NiSY0uOLbtO7969o7y8vMu+utNCtULoyp7F3NKFf5IkSZIkSZIkSZIkSZIkFREX/qkg9t5776xHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJsSXHlhxbcmwp5ZsL/1QQTU1NWY+QG7bk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKNxf+qSCefPLJrEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybFk4KaWsR1BObc655cI/SZIkSZIkSZIkSZIkSXoXZWVlERGxbt26jCdRXm08tzaea5tSkrbSJaj19fVRWVkZdXV1UVFRkfU4ubN69ero169f1mPkgi05tuTYkmNLji05tuTYkmNLji05eWxZW1sb1dXVUVNTE1VVVV123Dy2zIotObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2LJzNrWmKKUUixYtivXr18d2220XpaW+55o4TU1NsXTp0ujZs2eMHj06SkpKNrl/jy6aS1uZRx55JI444oisx8gFW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybInx5YcW3JsybElx5YcW3JsySspKYltt902Fi5cGC+99FLW4yiHSktLO7ToL8KFfyqQV199NesRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFsWRq9evWKnnXby435VEL169erwO0m68E8FMXjw4KxHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJsSXHlhxbcmxZOKWlpdGnT5+sx9BWriSllLIeIgub+jxubbk1a9ZE3759sx4jF2zJsSXHlhxbcmzJsSXHlhxbcmzJyWPL2traqK6ujpqamqiqquqy4+axZVZsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ad45oiFYuOvS+gtJluv/32rEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/JEmSJEmSJEmSJEmSJEkqIi78U0HsscceWY+QG7bk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKNxf+qSB69uyZ9Qi5YUuOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLVn25NiSY0uOLTm25NiSY0uOLaV8c+GfCuKvf/1r1iPkhi05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tmTZk2NLji05tuTYkmNLji05tpTyzYV/kiRJkiRJkiRJkiRJkiQVkZKUUsp6iCzU19dHZWVl1NXVRUVFRdbj5E59fb1dIbbk2JJjS44tObbk2JJjS44tObbk5LFlbW1tVFdXR01NTVRVVXXZcfPYMiu25NiSY0uOLTm25NiSY0uWPTm25NiSY0uOLTm25NiSY8vOcU2RioXv+KeC+Mtf/pL1CLlhS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tWfbk2JJjS44tObbk2JJjS44tpXzrVgv/li9fHscee2wMHDgwhg4dGqeffnps2LBhk7f5+9//Hv369YvZs2d3zZDqkGXLlmU9Qm7YkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLlj05tuTYkmNLji05tuTYkmNLKd+61cK/E044Ifr37x9Lly6NP//5zzFr1qy4+OKL291/9erVceKJJ8aaNWu6cEp1hG91yrElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ZSvpWklFLWQ0REvPDCC7HTTjvFkiVLYrvttouIiBtuuCHOPPPMeOmll9q8zcknnxyjR4+O8847Lx566KGYPHlyh4/n53EX1vr166Nnz55Zj5ELtuTYkmNLji05tuTYkmNLji05tuTksWVtbW1UV1dHTU1NVFVVddlx89gyK7bk2JJjS44tObbk2JJjS5Y9Obbk2JJjS44tObbk2JJjy85xTZGKRbd5x7958+bF4MGDmxf9RUTsuuuusWjRonjjjTda7X/dddfFCy+8EOeee26H7r+hoSHq6+tbfKlwbr755qxHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJsSXHlhxbcmwp5Vu3Wfi3cuXKKC8vb7GtX79+ERGxatWqFtvnz58f3/rWt+JXv/pVlJWVdej+v/e970VlZWXz1/bbb88MLkmSJEmSJEmSJEmSJElSF+o2C//Ky8tj9erVLbZt/POAAQOat61duzZOOOGE+OEPfxijR4/u8P3/13/9V9TV1TV/LV68mBlcbZowYULWI+SGLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm2ZNmTY0uOLTm25NiSY0uOLTm2lPKt2yz822233eLVV1+NZcuWNW97+umnY9SoUVFZWdm87fHHH4/nnnsuTj311Bg4cGAMHDgwIiKOPvro+PznP9/u/ffu3TsqKipafKlw+vfvn/UIuWFLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji1Z9uTYkmNLji05tuTYkmNLji2lfOs2C/922mmnOOigg+L000+PlStXxsKFC+O8886LU089tcV+Bx98cKxZsybeeOON5q+IiLvuuisuu+yyDCZXW+bMmZP1CLlhS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tWfbk2JJjS44tObbk2JJjS44tpXzrNgv/IiJuvvnm2LBhQ4wdOzYmTpwYH/zgB+Occ86JiLdWIf/yl7/MeEJJkiRJkiRJkiRJkiRJkrJVklJKWQ+Rhfr6+qisrIy6ujo/9rcAXnvttRg8eHDWY+SCLTm25NiSY0uOLTm25NiSY0uOLTl5bFlbWxvV1dVRU1MTVVVVXXbcPLbMii05tuTYkmNLji05tuTYkmVPji05tuTYkmNLji05tuTYsnNcU6Ri0a3e8U/58fe//z3rEXLDlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbsuzJsSXHlhxbcmzJsSXHlhxbSvnmwj8VxJIlS7IeITdsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybGllG8u/FNBlJeXZz1CbtiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uWPTm25NiSY0uOLTm25NiSY0sp30pSSinrIbLg53EXVlNTU5SWuq6UYEuOLTm25NiSY0uOLTm25NiSY0tOHlvW1tZGdXV11NTURFVVVZcdN48ts2JLji05tuTYkmNLji05tmTZk2NLji05tuTYkmNLji05tuwc1xSpWPjsVkHccMMNWY+QG7bk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKNxf+SZIkSZIkSZIkSZIkSZJURFz4p4LYZZddsh4hN2zJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXLnhxbcmzJsSXHlhxbcmzJsaWUby78U0EMHTo06xFyw5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW7LsybElx5YcW3JsybElx5YcW0r55sI/FcQf//jHrEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/JEmSJEmSJEmSJEmSJEkqIiUppZT1EFmor6+PysrKqKuri4qKiqzHyZ0VK1bEsGHDsh4jF2zJsSXHlhxbcmzJsSXHlhxbcmzJyWPL2traqK6ujpqamqiqquqy4+axZVZsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ad45oiFQvf8U8F8dxzz2U9Qm7YkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLlj05tuTYkmNLji05tuTYkmNLKd9c+KeCWLRoUdYj5IYtObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObaU8s2FfyqI3r17Zz1CbtiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uWPTm25NiSY0uOLTm25NiSY0sp30pSSinrIbLg53FLkiRJktRSbW1tVFdXR01NTVRVVWU9jiRJkiRJkiR1OdcUqVj4jn8qiBtuuCHrEXLDlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbsuzJsSXHlhxbcmzJsSXHlhxbSvnmwj8VRFNTU9Yj5IYtObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObaU8s2FfyqIHXfcMesRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFtK+ebCPxXEyJEjsx4hN2zJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXLnhxbcmzJsSXHlhxbcmzJsaWUby78U0E8/PDDWY+QG7bk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKNxf+SZIkSZIkSZIkSZIkSZJURFz4p4KYPHly1iPkhi05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tmTZk2NLji05tuTYkmNLji05tpTyzYV/KohFixZlPUJu2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS5Y9Obbk2JJjS44tObbk2JJjSynfXPingqitrc16hNywJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWLHtybMmxJceWHFtybMmxJceWUr658E8F0aNHj6xHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJsSXHlhxbcmwp5VtJSillPUQW6uvro7KyMurq6qKioiLrcSRJkiRJylxtbW1UV1dHTU1NVFVVZT2OJEmSJEmSJHU51xSpWPiOfyqIW265JesRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFtK+ebCPxXEunXrsh4hN2zJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXLnhxbcmzJsSXHlhxbcmzJsaWUby78U0HssMMOWY+QG7bk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKNxf+qSB23HHHrEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/VBAPPvhg1iPkhi05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tmTZk2NLji05tuTYkmNLji05tpTyzYV/kiRJkiRJkiRJkiRJkiQVERf+qSAOOuigrEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/VBDLly/PeoTcsCXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlix7cmzJsSXHlhxbcmzJsSXHllK+ufBPBfHcc89lPUJu2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS5Y9Obbk2JJjS44tObbk2JJjSynfXPgnSZIkSZIkSZIkSZIkSVIRKUkppayHyEJ9fX1UVlZGXV1dVFRUZD2OJEmSJEmZq62tjerq6qipqYmqqqqsx5EkSZIkSZKkLueaIhUL3/FPBXHHHXdkPUJu2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS5Y9Obbk2JJjS44tObbk2JJjSynfXPingli9enXWI+SGLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm2ZNmTY0uOLTm25NiSY0uOLTm2lPLNhX8qiFGjRmU9Qm7YkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLlj05tuTYkmNLji05tuTYkmNLKd9c+KeC2HXXXbMeITdsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybGllG8u/FNB3H///VmPkBu25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSZU+OLTm25NiSY0uOLTm25NhSyjcX/kmSJEmSJEmSJEmSJEmSVERc+KeCeN/73pf1CLlhS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tWfbk2JJjS44tObbk2JJjS44tpXxz4Z8Koq6uLusRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFtK+ebCPxXE008/nfUIuWFLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji1Z9uTYkmNLji05tuTYkmNLji2lfHPhnyRJkiRJkiRJkiRJkiRJRaQkpZSyHiIL9fX1UVlZGXV1dVFRUZH1OLmzYcOG6NGjR9Zj5IItObbk2JJjS44tObbk2JJjS44tOXlsWVtbG9XV1VFTUxNVVVVddtw8tsyKLTm25NiSY0uOLTm25NiSZU+OLTm25NiSY0uOLTm25Niyc1xTpGLhO/6pIO69996sR8gNW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybInx5YcW3JsybElx5YcW3JsKeWbC/9UECtXrsx6hNywJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWLHtybMmxJceWHFtybMmxJceWUr658E8Fsc0222Q9Qm7YkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLlj05tuTYkmNLji05tuTYkmNLKd9KUkop6yGy4OdxF1ZdXV1UVlZmPUYu2JJjS44tObbk2JJjS44tObbk2JKTx5a1tbVRXV0dNTU1UVVV1WXHzWPLrNiSY0uOLTm25NiSY0uOLVn25NiSY0uOLTm25NiSY0uOLTvHNUUqFr7jnwrinnvuyXqE3LAlx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ZSvrnwT5IkSZIkSZIkSZIkSZKkIuLCPxXEfvvtl/UIuWFLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji1Z9uTYkmNLji05tuTYkmNLji2lfHPhnwpi7dq1WY+QG7bk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKNxf+qSCeeuqprEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/JEmSJEmSJEmSJEmSJEkqIiUppZT1EFmor6+PysrKqKuri4qKiqzHyZ21a9dGnz59sh4jF2zJsSXHlhxbcmzJsSXHlhxbcmzJyWPL2traqK6ujpqamqiqquqy4+axZVZsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ad45oiFQvf8U8FMXv27KxHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJsSXHlhxbcmwp5ZsL/1QQr7/+etYj5IYtObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObaU8s2FfyqIoUOHZj1CbtiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uWPTm25NiSY0uOLTm25NiSY0sp30pSSinrIbLg53EX1qpVq6J///5Zj5ELtuTYkmNLji05tuTYkmNLji05tuTksWVtbW1UV1dHTU1NVFVVddlx89gyK7bk2JJjS44tObbk2JJjS5Y9Obbk2JJjS44tObbk2JJjy85xTZGKhe/4p4K48847sx4hN2zJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXLnhxbcmzJsSXHlhxbcmzJsaWUby78kyRJkiRJkiRJkiRJkiSpiLjwTwWx1157ZT1CbtiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uWPTm25NiSY0uOLTm25NiSY0sp31z4J0mSJEmSJEmSJEmSJElSEXHhnwriiSeeyHqE3LAlx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ZSvrnwT5IkSZIkSZIkSZIkSZKkIlKSUkpZD5GF+vr6qKysjLq6uqioqMh6nNxZtWpV9O/fP+sxcsGWHFtybMmxJceWHFtybMmxJceWnDy2rK2tjerq6qipqYmqqqouO24eW2bFlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJsSXHlhxbcmzZOa4pUrHwHf9UEI899ljWI+SGLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25OStZWNjY8ybNy8eeeSRmDdvXjQ2NnbZsfPWMku25NiSZU+OLTm25NiSY0uOLTm25NhSyrceWQ+gfHrllVeyHiE3bMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJSdPLefMmRPXXnttrFixonnbsGHDYtq0aTFx4sSCHz9PLbNmS44tWfbk2JJjS44tObbk2JJjS44tpXzzHf9UEIMGDcp6hNywJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWnLy0nDNnTlx00UUxevTomDFjRlx33XUxY8aMGD16dFx00UUxZ86cgs+Ql5bdgS05tmTZk2NLji05tuTYkmNLji05tpTyrSSllLIeIgt+HndhrV27Nvr06ZP1GLlgS44tObbk2JJjS44tObbk2JJjS04eW9bW1kZ1dXXU1NREVVVVlx03jy2zYkuOLTm25OShZWNjY3zpS1+K0aNHx5lnnhm33XZbPPDAA/HVr341dtppp7jwwgtj8eLF8eMf/zjKysoKNkceWnYXtuTYkmVPji05tuTYkmNLji05tuTYsnNcU6Ri4Tv+qSBuu+22rEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMnJQ8v58+fHihUrYurUqTFnzpw47bTT4mc/+1lMmTIlmpqa4iMf+UgsX7485s+fX9A58tCyu7Alx5Yse3JsybElx5YcW3JsybElx5ZSvrnwT5IkSZIkSZLUJV5//fWIiNh+++3jxRdfjJUrV0ZERENDQzQ0NMTo0aNb7CdJkiRJkqS2ufBPBbH77rtnPUJu2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS04eWg4aNCgiIhYvXhxTp06NQw45JMaOHRuf+cxnory8PBYtWtRiv0LJQ8vuwpYcW7LsybElx5YcW3JsybElx5YcW0r51iPrAZRPfkY8x5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3Ly0HL8+PExbNiwuPXWW+PMM8+MWbNmNX+vqakpbrvtthg+fHiMHz++oHPkoWV3YUuOLVn25NiSY0uOLTm25NiSY0uOLaV88x3/VBCPP/541iPkhi05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTkoWVZWVlMmzYt5s6dGxdeeGE8++yzsWbNmnj22WfjwgsvjLlz58ZJJ50UZWVlBZ0jDy27C1tybMmyJ8eWHFtybMmxJceWHFtybCnlm+/4J0mSJEmSJEnqMhMnToyvfe1rce2118bZZ5/dvH348OHxta99LSZOnJjhdJIkSZIkScWhJKWUsh4iC/X19VFZWRl1dXVRUVGR9Ti5U1dXF5WVlVmPkQu25NiSY0uOLTm25NiSY0uOLTm25OSxZW1tbVRXV0dNTU1UVVV12XHz2DIrtuTYkmNLTt5aNjY2xvz58+P111+PQYMGxfjx4wv+Tn8b5a1llmzJsSXLnhxbcmzJsSXHlhxbcmzJsWXnuKZIxcKP+lVBzJ07N+sRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFty8tayrKwsJkyYEAcddFBMmDChyxb9ReSvZZZsybEly54cW3JsybElx5YcW3JsybGllG8u/FNB/POf/8x6hNywJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWLHtybMmxJceWHFtybMmxJceWUr658E8FMWDAgKxHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJsSXHlhxbcmwp5VtJSillPUQW/DzuwtqwYUP06NEj6zFywZYcW3JsybElx5YcW3JsybElx5acPLasra2N6urqqKmpiaqqqi47bh5bZsWWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybNk5rilSsfAd/1QQN910U9Yj5IYtObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObaU8s2Ff5IkSZIkSZIkSZIkSZIkFREX/qkgdt1116xHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJsSXHlhxbcmwp5ZsL/1QQlZWVWY+QG7bk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKNxf+qSAee+yxrEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/JEmSJEmSJEmSJEmSJEkqIiUppZT1EFmor6+PysrKqKuri4qKiqzHyZ1XX301hgwZkvUYuWBLji05tuTYkmNLji05tuTYkmNLTh5b1tbWRnV1ddTU1ERVVVWXHTePLbNiS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObbsHNcUqVj4jn8qiKeffjrrEXLDlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbsuzJsSXHlhxbcmzJsSXHlhxbSvnmwj8VxD/+8Y+sR8gNW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybInx5YcW3JsybElx5YcW3JsKeVbj6wHUD7169cv6xFyw5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW7LsybElJ28tGxsbY/78+fH666/HoEGDYvz48VFWVtYlx85byyzZkmNLji05tpTyrSSllLIeIgt+HrckSZIkSS3V1tZGdXV11NTURFVVVdbjSJIkSZK6qTlz5sS1114bK1asaN42bNiwmDZtWkycODHDySRJ2nKuKVKx8KN+VRC//vWvsx4hN2zJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXLnhxbcvLScs6cOXHRRRfF6NGjY8aMGXHdddfFjBkzYvTo0XHRRRfFnDlzCj5DXlp2B7bk2JJjS44tpXxz4Z8kSZIkSZIkSZIk6V01NjbGtddeG3vvvXccf/zxcccdd8Tzzz8fO++8c5x55pmx9957x3XXXReNjY1ZjypJkpR7LvxTQey8885Zj5AbtuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmVPji05eWg5f/78WLFiRUyZMiU+8IEPxJlnnhlTpkyJl156KUpLS+MjH/lILF++PObPn1/QOfLQsruwJceWHFtybCnlmwv/VBDDhw/PeoTcsCXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlix7cmzJyUPL119/PSIievXqFWvXro2IiFWrVsULL7wQERGjR49usV+h5KFld2FLji05tuTYUso3F/6pIB555JGsR8gNW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybInx5acPLQcNGhQRET06NEjDjzwwNhuu+1izz33jIMPPjgiIhYtWtRiv0LJQ8vuwpYcW3JsybGllG89sh5AkiRJkiRJkiRJktT9jR8/PoYNGxa33XZb3HzzzRERUVr61nvNNDU1xW233RbDhw+P8ePHZzmmJEnSVqEkpZSyHiIL9fX1UVlZGXV1dVFRUZH1OLmzbNmyGDFiRNZj5IItObbk2JJjS44tObbk2JJjS44tOXlsWVtbG9XV1VFTUxNVVVVddtw8tsyKLTm25NiSY0uOLTm25NiSZU+OLTl5aTlnzpy46KKLYu+9946PfOQjMXr06Fi0aFHcdtttMXfu3Pja174WEydOLOgMeWnZHdiSY0uOLTm27BzXFKlY+FG/KogFCxZkPUJu2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS5Y9Obbk5KXlxIkT42tf+1osWrQozj777DjppJPi7LPPjsWLF3fJor+I/LTsDmzJsSXHlhxbSvnmwj8VxEsvvZT1CLlhS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tWfbk2JKTp5YTJ06MSy65JKZPnx5f+cpXYvr06fHjH/+4Sxb9ReSrZdZsybElx5YcW0r51iPrAZRPvXr1ynqE3LAlx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5Yse3Jsyclby7KyspgwYUImx85byyzZkmNLji05tpTyrSSllLIeIgt+HrckSZIkSS3V1tZGdXV11NTURFVVVdbjSJIkSZIkSVKXc02RioUf9auCuOmmm7IeITdsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybGllG8u/FNBbNiwIesRcsOWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFtK+ebCPxWEHwnFsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlix7cmzJsSXHlhyyZUopVq9ejd1fsfG85NiSY0sp31z4p4IYPXp01iPkhi05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tmTZk2NLji05tuRQLVevXh0TJ06M3XbbLT784Q9HSgm532LiecmxJceWUr658E8FMXv27KxHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJ2ZyWDQ0NUVtb2+bXDTfcEC+//HL07Nkz/va3v8UjjzzS7r61tbXR0NBQuAeVEc9Lji05tpTyrUfWA0iSJEmSJEmSJEmSpO5tyZIlUV1d3eb33nzzzaivr4/GxsZYvXp1XHzxxVFWVtbufdXU1PgRpJIkbSEX/qkgDjnkkKxHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJ2ZyWI0eOjJqamna//9hjj8UPf/jDOOuss2LPPfd81/vKG89Lji05tpTyzY/6VUEsWbIk6xFyw5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW7LsybElx5YcW3I2p2Xv3r2jqqqq3a/3ve99MWbMmNhzzz03uV9VVVX07t27gI8qG56XHFtybCnlmwv/VBALFizIeoTcsCXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlix7cmzJsSXHlhxbcmzJsSXHllK+ufBPBVFa6qlFsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlix7cmzJsSXHlhxbcmzJsSXHllK+laSUUtZDZKG+vj4qKyujrq4uKioqsh5HkiRJkqTM1dbWRnV1ddTU1ERVVVXW40iSJEmSpCLidQVJeeGaIhULl/aqIG699dasR8gNW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybInx5YcW3JsybElx5YcW3JsKeWbC/9UEA0NDVmPkBu25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSZU+OLTm25NiSY0uOLTm25NhSyjcX/qkgRo8enfUIuWFLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji1Z9uTYkmNLji05tuTYkmNLji2lfHPhnwpi5513znqE3LAlx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ZSvrnwTwUxa9asrEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/JEmSJEmSJEmSJEmSJEkqIi78U0EceOCBWY+QG7bk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKNxf+qSBeeeWVrEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/VBDPPvts1iPkhi05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tmTZk2NLji05tuTYkmNLji05tpTyzYV/kiRJkiRJkiRJkiRJkiQVkZKUUsp6iCzU19dHZWVl1NXVRUVFRdbj5E5TU1OUlrqulGBLji05tuTYkmNLji05tuTYkmNLTh5b1tbWRnV1ddTU1ERVVVWXHTePLbNiS44tObbk2JJjS44tObZk2ZNjS44tObbkkC2zuq7QXXhecmzJsWXnuKZIxcJntwrirrvuynqE3LAlx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ZSvrnwTwXx5ptvZj1CbtiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uWPTm25NiSY0uOLTm25NiSY0sp31z4p4IYOXJk1iPkhi05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tmTZk2NLji05tuTYkmNLji05tpTyzYV/Kojddtst6xFyw5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW7LsybElx5YcW3JsybElx5YcW0r55sI/FcR9992X9Qi5YUuOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLVn25NiSY0uOLTm25NiSY0uOLaV8c+GfJEmSJEmSJEmSJEmSJElFxIV/KoiJEydmPUJu2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS5Y9Obbk2JJjS44tObbk2JJjSynfXPingli1alXWI+SGLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm2ZNmTY0uOLTm25NiSY0uOLTm2lPLNhX8qiHnz5mU9Qm7YkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLlj05tuTYkmNLji05tuTYkmNLKd9c+CdJkiRJkiRJkiRJkiRJUhEpSSmlrIfIQn19fVRWVkZdXV1UVFRkPU7urF+/Pnr27Jn1GLlgS44tObbk2JJjS44tObbk2JJjS04eW9bW1kZ1dXXU1NREVVVVlx03jy2zYkuOLTm25NiSY0uOLTm2ZNmTY0uOLTm25JAts7qu0F14XnJsybFl57imSMXCd/xTQdx///1Zj5AbtuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmVPji05tuTYkmNLji05tuTYUso3F/6pIOrr67MeITdsybElx5YcW3JsybElx5YcW3Jsycljy9deey1WrVrV5cfNY8us2JJjS44tObbk2JJjS44tWfbk2JJjS44tObbk2JJjS44tpXxz4Z8KYsSIEVmPkBu25NiSY0uOLTm25NiSY0uOLTm25OSt5aOPPhpHHXVUPPTQQ3Huued26bHz1jJLtuTYkmNLji05tuTYkmNLlj05tuTYkmNLji05tuTYkmNLKd9KUkop6yGy4OdxF1Z9fb1dIbbk2JJjS44tObbk2JJjS44tObbk5K3lKaecEtdcc01ERIwePTpeeumlLjt23lpmyZYcW3JsybElx5YcW3JsybInx5YcW3JsySFb1tbWRnV1ddTU1ERVVRVyn8XE85JjS44tO8c1RSoWvuOfCuLuu+/OeoTcsCXHlhxbcmzJsSXHlhxbcmzJsSUnby0//vGPx5AhQ6Jnz56xxx57dOmx89YyS7bk2JJjS44tObbk2JJjS5Y9Obbk2JJjS44tObbk2JJjSynfXPgnSZIkSVLGGhsbY968efHII4/EvHnzorGxsctnmDJlStx4441x4IEHxsUXX9zlx5ckSZIkSZIkSR3XI+sBlE/77LNP1iPkhi05tuTYkmNLji05tuTYkmNLji05VMs5c+bEtddeGytWrGjeNmzYsJg2bVpMnDgROUZHjRkzJoYOHRqlpV373wl6XnJsybElx5YcW3JsybElx5Yse3JsybElx5YcW3JsybElx5ZSvvmOfyqI9evXZz1CbtiSY0uOLTm25NiSY0uOLTm25NiSQ7ScM2dOXHTRRTF69OiYMWNGXHfddTFjxowYPXp0XHTRRTFnzhxg0u7P85JjS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObaU8s2FfyqIv/3tb1mPkBu25NiSY0uOLTm25NiSY0uOLTm25Gxpy8bGxrj22mtj7733jjPOOCP++Mc/xu233x7jxo2LM888M/bee++47rrrMvnY367mecmxJceWHFtybMmxJceWHFuy7MmxJceWHFtybMmxJceWHFtK+ebCP0mSJEmSMjB//vxYsWJFTJ06NU477bT47//+76iuro5vfetbUVpaGh/5yEdi+fLlMX/+/KxHlSRJkiRJkiRJ3UxJSillPUQW6uvro7KyMurq6qKioiLrcXJnzZo10bdv36zHyAVbcmzJsSXHlhxbcmzJsSXHlhxbcra05SOPPBI/+tGP4rrrrouDDjoo6uvrI6UUo0ePjgcffDDWrFkTJ510UnzlK1+Jgw46CJy8fbW1tVFdXR01NTVRVVXVJceM8Lwk2ZJjS44tObbk2JJjS44tWfbk2JJjS44tOWTLrK4rdBeelxxbcmzZOa4pUrHwHf9UEA8//HDWI+SGLTm25NiSY0uOLTm25NiSY0uOLTlb2nLQoEEREbF48eKYPn16bNiwIZqamuL888+PiIhFixa12C/PPC85tuTYkmNLji05tuTYkmNLlj05tuTYkmNLji05tuTYkmNLKd96ZD2A8um1117LeoTcsCXHlhxbcmzJsSXHlhxbcmzJsSVnS1uOHz8+hg0bFrfeemuceeaZcdRRR0VJSUmUlJREU1NT3HbbbTF8+PAYP348NHH35XnJsSXHlhxbcmzJsSXHlhxbsuzJsSXHlhxbcmzJsSXHlhxbSvnmO/6pIIYMGZL1CLlhS44tObbk2JJjS44tObbk2JJjS86WtiwrK4tp06bF3Llz48ILL4znn38+1q5dG88++2xceOGFMXfu3DjppJOirKwMmrj78rzk2JJjS44tObbk2JJjS44tWfbk2JJjS44tObbk2JJjS44tpXwrSSmlrIfIgp/HXVirV6+Ofv36ZT1GLtiSY0uOLTm25NiSY0uOLTm25NiSQ7WcM2dOXHvttbFixYrmbcOHD4+TTjopJk6cuMX3vzlqa2ujuro6ampqoqqqqsuO63nJsSXHlhxbcmzJsSXHlhxbsuzJsSXHlhxbcsiWWV1X6C48Lzm25Niyc1xTpGLhO/6pIO64446sR8gNW3JsybElx5YcW3JsybElx5YcW3KolhMnToxLLrkkpk+fHl/5yldi+vTp8eMf/7jLF/1lyfOSY0uOLTm25NiSY0uOLTm2ZNmTY0uOLTm25NiSY0uOLTm2lPKtR9YDSJIkSZK0tSsrK4sJEyZkPYYkSZIkSZIkSSoS3eod/5YvXx7HHntsDBw4MIYOHRqnn356bNiwodV+TU1NMX369Nh+++2jf//+sfvuu8eNN96YwcRqz3vf+96sR8gNW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybInx5YcW3JsybElx5YcW3JsKeVbt1r4d8IJJ0T//v1j6dKl8ec//zlmzZoVF198cav9Lr300rjuuuti9uzZsWrVqvje974XJ554YixYsCCDqdWW0tJudWoVNVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlW7d5hr/wwgsxe/bsuPDCC6Nfv35RVVUV55xzTvzkJz9pte8XvvCFeOqpp2LHHXeMhoaGWLFiRZSXl0e/fv0ymFxtmTt3btYj5IYtObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObZk2ZNjS44tObbk2JJjS44tObaU8q1H1gNsNG/evBg8eHBst912zdt23XXXWLRoUbzxxhsxcODA5u2lpaVRXl4e999/fxx55JGRUoqLL744tt1223bvv6GhIRoaGpr/XF9fX5DHIUmSJEmSJEmSJEmSJElSIZWklFLWQ0REXH/99XHWWWfFokWLmrctWLAgxo0bF4sXL45Ro0a1uk1DQ0OUlZXF73//+zj22GPjqquuihNOOKHN+58+fXp8+9vfbrW9rq4uKioquAeiiIhYuXJlDBgwIOsxcsGWHFtybMmxJceWHFtybMmxJceWnDy2rK2tjerq6qipqYmqqqouO24eW2bFlhxbcmzJsSXHlhxbcmzJsifHlhxbcmzJIVtmdV2hu/C85NiSY8vOqa+vj8rKStcUqdvrNh/1W15eHqtXr26xbeOf2/tLqHfv3tGjR4847LDD4lOf+lT86le/avf+/+u//ivq6uqavxYvXswNr1b+/Oc/Zz1CbtiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uWPTm25NiSY0uOLTm25NiSY0sp37rNR/3utttu8eqrr8ayZctixIgRERHx9NNPx6hRo6KysrLFvl/72tciIuKiiy5q3tbQ0BCDBw9u9/579+4dvXv3LsDkasvy5cuzHiE3bMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJcueHFtybMmxJceWHFtybMmxpZRv3eYd/3baaac46KCD4vTTT4+VK1fGwoUL47zzzotTTz211b6HHHJI/PSnP42HH344mpqa4s4774zf/OY38Z//+Z8ZTK62vHOxpjrPlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbsuzJsSXHlhxbcmzJsSXHlhxbSvlWklJKWQ+x0bJly+KLX/xiPPTQQ1FaWhonnXRS1NTURFlZWfTv3z9+9rOfxSc+8YmIiPj5z38eF1xwQSxbtix23nnnOP/882PKlCkdPpafx11YDQ0NvsMixJYcW3JsybElx5YcW3JsybElx5acPLasra2N6urqqKmpiaqqqi47bh5bZsWWHFtybMmxJceWHFtybMmyJ8eWHFtybMkhW2Z1XaG78Lzk2JJjy85xTZGKRbd5x7+IiBEjRsRNN90Ur7zySixfvjx+8IMfRFlZWURErFq1qnnRX0TEpz/96Xjuueeirq4uHn/88c1a9KfCu/XWW7MeITdsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybGllG/dauGfJEmSJEmSJEmSJEmSJEnaNBf+qSB22223rEfIDVtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/VBD9+vXLeoTcsCXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlhxbcmzJsSXHlix7cmzJsSXHlhxbcmzJsSXHllK+ufBPBfHnP/856xFyw5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW3JsybElx5YcW7LsybElx5YcW3JsybElx5YcW0r55sI/SZIkSZIkSZIkSZIkSZKKSElKKWU9RBbq6+ujsrIy6urqoqKiIutxcueNN96IgQMHZj1GLtiSY0uOLTm25NiSY0uOLTm25NiSk8eWtbW1UV1dHTU1NVFVVdVlx81jy6zYkmNLji05tuTYkmNLji1Z9uTYkmNLji05ZMusrit0F56XHFtybNk5rilSsfAd/1QQ//d//5f1CLlhS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tObbk2JJjS44tWfbk2JJjS44tObbk2JJjS44tpXxz4Z8KYunSpVmPkBu25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSZU+OLTm25NiSY0uOLTm25NhSyjcX/qkg+vfvn/UIuWFLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji1Z9uTYkmNLji05tuTYkmNLji2lfCtJKaWsh8iCn8ddWI2NjVFWVpb1GLlgS44tObbk2JJjS44tObbk2JJjS04eW9bW1kZ1dXXU1NREVVVVlx03jy2zYkuOLTm25NiSY0uOLTm2ZNmTY0uOLTm25JAts7qu0F14XnJsybFl57imSMXCd/xTQdx4441Zj5AbtuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmVPji05tuTYkmNLji05tuTYUso3F/5JkiRJkqRIKcXdd98dzz77bKxatSrrcSRJkiRJkiRJ0ia48E8FMX78+KxHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcvLW8sorr4yzzjornnrqqTj11FO79Nh5a5klW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybGllG8u/FNBDB48OOsRcsOWHFtybMmxJceWHFtybMmxJceWnLy1fPzxx5vf6W/ZsmVdeuy8tcySLTm25NiSY0uOLTm25NiSZU+OLTm25NiSY0uOLTm25NhSyjcX/qkgHn300axHyA1bcmzJsSXHlhxbcmzJsSXHlhxbcvLW8lvf+laMHTs2+vTpE5/5zGe69Nh5a5klW3JsybElx5YcW3JsybEly54cW3JsybElx5YcW3JsybGllG8u/JMkSZIkSTFmzJiYNWtWHH300XHiiSdmPY4kSZIkSZIkSdqEkpRSynqILNTX10dlZWXU1dVFRUVF1uPkziuvvBJDhw7NeoxcsCXHlhxbcmzJsSXHlhxbcmzJsSUnjy1ra2ujuro6ampqoqqqqsuOm8eWWbElx5YcW3JsybElx5YcW7LsybElx5YcW3LIllldV+guPC85tuTYsnNcU6Ri4Tv+qSDmz5+f9Qi5YUuOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLTm25NiSY0uOLVn25NiSY0uOLTm25NiSY0uOLaV8c+GfCmLx4sVZj5AbtuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmNLji05tuTYkmVPji05tuTYkmNLji05tuTYUso3F/6pIPr27Zv1CLlhS44tObbk2JJjS44tObbk2JJjS04eW6aUoqmpqcuPm8eWWbElx5YcW3JsybElx5YcW7LsybElx5YcW3JsybElx5YcW0r5VpJSSlkPkQU/j1uSJEmSpH95/vnn4wMf+EC88sor8bWvfS3OP//8rEeSJEmSJElFpLa2Nqqrq6OmpiaqqqqyHkeSOs01RSoWvuOfCuI3v/lN1iPkhi05tuTYkmNLji05tuTYkmNLji05eWt5wQUXxD/+8Y9Yu3ZtXHfddV167Ly1zJItObbk2JJjS44tObbk2JJlT44tObbk2JJjS44tObbk2FLKNxf+qSC20jeSLAhbcmzJsSXHlhxbcmzJsSXHlhxbcvLW8pBDDokBAwZESUlJjBo1qkuPnbeWWbIlx5YcW3JsybElx5YcW7LsybElx5YcW3JsybElx5YcW0r51iPrAZRPO+20U9Yj5IYtObbk2JJjS44tObbk2JJjS44tOXlrOW3atEgpxSWXXBJXXXVVlx47by2zZEuOLTm25NiSY0uOLTm2ZNmTY0uOLTm25NiSY0uOLTm2lPLNd/xTQWyzzTZZj5AbtuTYkmNLji05tuTYkmNLji05tuTkseUhhxwSVVVV0adPny49bh5bZsWWHFtybMmxJceWHFtybMmyJ8eWHFtybMmxJceWHFtybCnlmwv/VBB/+MMfsh4hN2zJsSXHlhxbcmzJsSXHlhxbcmzJyVvLpqamuPLKK2Pu3LnxyiuvdOmx89YyS7bk2JJjS44tObbk2JJjS5Y9Obbk2JJjS44tObbk2JJjSynfXPgnSZIkSZLif/7nf+JHP/pR1NbWxn/8x39kPY4kSZIkSZIkSdoEF/6pIA499NCsR8gNW3JsybElx5YcW3JsybElx5YcW3Ly1nLhwoWxZs2aiIioq6vr0mPnrWWWbMmxJceWHFtybMmxJceWLHtybMmxJceWHFtybMmxJceWUr658E8F8eKLL2Y9Qm7YkmNLji05tuTYkmNLji05tuTYkpO3lueee27svvvuUV5eHmeddVaXHjtvLbNkS44tObbk2JJjS44tObZk2ZN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",
      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform t-tests\n",
    "selected_cell_types = sorted(protein['ClusterName'].unique())\n",
    "selected_p_values = []\n",
    "for ct in selected_cell_types:\n",
    "    group1 = spot_cellfreq_df_melt[(spot_cellfreq_df_melt['ClusterName'] == ct) & (spot_cellfreq_df_melt['Condition'] == 'CLR')]['Frequency']\n",
    "    group2 = spot_cellfreq_df_melt[(spot_cellfreq_df_melt['ClusterName'] == ct) & (spot_cellfreq_df_melt['Condition'] == 'DII')]['Frequency']\n",
    "    t_stat, p_value = stats.ttest_ind(group1, group2, equal_var=False)\n",
    "    print(f\"{ct} has p value of {p_value}\")\n",
    "    selected_p_values.append(p_value)\n",
    "\n",
    "pvals_corrected = stats.false_discovery_control(selected_p_values, method='bh')\n",
    "print('-'*42)\n",
    "print(f\"p-values after correction:\")\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(30,10))\n",
    "sns.boxplot(data=spot_cellfreq_df_melt, x='ClusterName', y='Frequency', hue='Condition', palette='muted', boxprops=dict(alpha=.3), ax=ax, dodge=True, order=selected_cell_types)\n",
    "sns.swarmplot(data=spot_cellfreq_df_melt, x='ClusterName', y='Frequency', hue='Condition', palette='dark:black', size=2.0, dodge=True, order=selected_cell_types, ax=ax, edgecolor='auto', linewidth=0.5)\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:2], labels[:2], title=\"Groups\", handletextpad=1, columnspacing=1, bbox_to_anchor=(1, 1), ncol=3, frameon=True)\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "p_vals_corrected_dict = {}\n",
    "yrange = ax.get_ylim()[1] - ax.get_ylim()[0]\n",
    "for i, ct in enumerate(selected_cell_types):\n",
    "    ax.text(i, yrange, f\"p = {pvals_corrected[i]:.3f}\", ha='center', fontsize=12, rotation=0)\n",
    "    print(f\"{ct} has p value = {pvals_corrected[i]:.3f}\", flush=True)\n",
    "    p_vals_corrected_dict[ct] = pvals_corrected[i]\n",
    "    \n",
    "for i in range(len(selected_cell_types) - 1):\n",
    "    ax.axvline(i + 0.55, color='grey', linestyle='--', linewidth=0.5)\n",
    "    \n",
    "ax.set_ylabel(\"Frequency\", fontsize=14)\n",
    "ax.set_xlabel('') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b15bc9-a95a-49ca-a47f-2bdba4ee82aa",
   "metadata": {},
   "source": [
    "### Process Cell Co-Occurrence Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "af31cdf4-88a5-4788-8a47-e82fd29721cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    }
   ],
   "source": [
    "union_cols = set(global_co_occurrence_subcols).union(set(spot_co_occurrence_subcols))\n",
    "print(len(union_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3a603a93-0a30-47f4-be3c-5813a9659f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make them have the same set of columns\n",
    "global_co_occurrence_df = global_co_occurrence_df.reindex(columns=union_cols).fillna(0)\n",
    "spot_co_occurrence_df = spot_co_occurrence_df.reindex(columns=union_cols).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8d009539-d233-4c70-934f-01a6510c493b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patients</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Cell Combination</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>smooth muscle&amp;undefined</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>smooth muscle&amp;undefined</td>\n",
       "      <td>0.003953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>smooth muscle&amp;undefined</td>\n",
       "      <td>0.007233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>smooth muscle&amp;undefined</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>smooth muscle&amp;undefined</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8675</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>CD8+ T cells&amp;Tregs</td>\n",
       "      <td>0.005362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8676</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>CD8+ T cells&amp;Tregs</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8677</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>CD8+ T cells&amp;Tregs</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8678</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>CD8+ T cells&amp;Tregs</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8679</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>CD8+ T cells&amp;Tregs</td>\n",
       "      <td>0.003350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8680 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Patients  Condition         Cell Combination  Frequency\n",
       "0            1          1  smooth muscle&undefined   0.000000\n",
       "1            1          1  smooth muscle&undefined   0.003953\n",
       "2            1          1  smooth muscle&undefined   0.007233\n",
       "3            1          1  smooth muscle&undefined   0.000000\n",
       "4            2          2  smooth muscle&undefined   0.000000\n",
       "...        ...        ...                      ...        ...\n",
       "8675        34          1       CD8+ T cells&Tregs   0.005362\n",
       "8676        35          1       CD8+ T cells&Tregs   0.000000\n",
       "8677        35          1       CD8+ T cells&Tregs   0.000000\n",
       "8678        35          1       CD8+ T cells&Tregs   0.000000\n",
       "8679        35          1       CD8+ T cells&Tregs   0.003350\n",
       "\n",
       "[8680 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Global Cell Co-Occurrence \n",
    "# Multi-index to single-index column\n",
    "new_columns = []\n",
    "for col in global_co_occurrence_df.columns:\n",
    "    if isinstance(col, tuple):  # This checks if the column is a MultiIndex\n",
    "        # Join only if the column name is not 'Mouse' or 'Condition'\n",
    "        if \"Patients\" not in col and \"Condition\" not in col:\n",
    "            new_columns.append('&'.join(map(str, col)).strip())\n",
    "        else:\n",
    "            # If 'Mouse' or 'Condition' is in the column, it is not joined with '&'\n",
    "            new_columns.append(col[0])\n",
    "    else:\n",
    "        new_columns.append(col)\n",
    "\n",
    "global_co_occurrence_df_single = global_co_occurrence_df.copy()\n",
    "global_co_occurrence_df_single.columns = new_columns\n",
    "global_co_occurrence_df_single = global_co_occurrence_df_single[[col for col in global_co_occurrence_df_single.columns]]\n",
    "\n",
    "# Melt the DataFrame\n",
    "global_co_occurrence_melted = global_co_occurrence_df_single.melt(id_vars=['Patients', 'Condition'], var_name='Cell Combination', value_name='Frequency')\n",
    "global_co_occurrence_melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4d74c63f-6769-4459-be25-d17493e45eb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patients</th>\n",
       "      <th>Cell Combination</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;CD4+ T cells</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.003656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;CD4+ T cells CD45RO+</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.016910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;CD68+CD163+ macrophages</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.008217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;CD8+ T cells</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.012792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;granulocytes</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.010500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>35</td>\n",
       "      <td>stroma&amp;tumor cells</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.034694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2166</th>\n",
       "      <td>35</td>\n",
       "      <td>stroma&amp;undefined</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.012952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2167</th>\n",
       "      <td>35</td>\n",
       "      <td>stroma&amp;vasculature</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.013089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2168</th>\n",
       "      <td>35</td>\n",
       "      <td>tumor cells&amp;undefined</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.018471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2169</th>\n",
       "      <td>35</td>\n",
       "      <td>tumor cells&amp;vasculature</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.010777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2170 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Patients                 Cell Combination Condition  Frequency\n",
       "0            1             B cells&CD4+ T cells       CLR   0.003656\n",
       "1            1     B cells&CD4+ T cells CD45RO+       CLR   0.016910\n",
       "2            1  B cells&CD68+CD163+ macrophages       CLR   0.008217\n",
       "3            1             B cells&CD8+ T cells       CLR   0.012792\n",
       "4            1             B cells&granulocytes       CLR   0.010500\n",
       "...        ...                              ...       ...        ...\n",
       "2165        35               stroma&tumor cells       CLR   0.034694\n",
       "2166        35                 stroma&undefined       CLR   0.012952\n",
       "2167        35               stroma&vasculature       CLR   0.013089\n",
       "2168        35            tumor cells&undefined       CLR   0.018471\n",
       "2169        35          tumor cells&vasculature       CLR   0.010777\n",
       "\n",
       "[2170 rows x 4 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_co_occurrence_melted_avg = global_co_occurrence_melted.groupby(by=['Patients', 'Cell Combination', 'Condition'])['Frequency'].mean().reset_index()\n",
    "global_co_occurrence_melted_avg['Condition'] = global_co_occurrence_melted_avg['Condition'].map(condition_num2str)\n",
    "global_co_occurrence_melted_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9c7496a1-3b11-4409-8164-13735ce8e65d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value before correction:\n",
      "B cells&CD4+ T cells has p value = 0.059\n",
      "B cells&CD4+ T cells CD45RO+ has p value = 0.002\n",
      "B cells&CD68+CD163+ macrophages has p value = 0.099\n",
      "B cells&CD8+ T cells has p value = 0.011\n",
      "B cells&granulocytes has p value = 0.246\n",
      "B cells&plasma cells has p value = 0.215\n",
      "B cells&smooth muscle has p value = 0.310\n",
      "B cells&stroma has p value = 0.197\n",
      "B cells&tumor cells has p value = 0.032\n",
      "B cells&vasculature has p value = 0.045\n",
      "CD4+ T cells CD45RO+&CD68+CD163+ macrophages has p value = 0.175\n",
      "CD4+ T cells CD45RO+&CD8+ T cells has p value = 0.794\n",
      "CD4+ T cells CD45RO+&granulocytes has p value = 0.211\n",
      "CD4+ T cells CD45RO+&plasma cells has p value = 0.828\n",
      "CD4+ T cells CD45RO+&smooth muscle has p value = 0.120\n",
      "CD4+ T cells CD45RO+&stroma has p value = 0.198\n",
      "CD4+ T cells CD45RO+&tumor cells has p value = 0.582\n",
      "CD4+ T cells CD45RO+&undefined has p value = 0.987\n",
      "CD4+ T cells CD45RO+&vasculature has p value = 0.979\n",
      "CD4+ T cells&CD4+ T cells CD45RO+ has p value = 0.034\n",
      "CD4+ T cells&CD8+ T cells has p value = 0.053\n",
      "CD68+ macrophages&CD68+CD163+ macrophages has p value = 0.013\n",
      "CD68+ macrophages&granulocytes has p value = 0.095\n",
      "CD68+CD163+ macrophages&CD8+ T cells has p value = 0.024\n",
      "CD68+CD163+ macrophages&Tregs has p value = 0.008\n",
      "CD68+CD163+ macrophages&granulocytes has p value = 0.012\n",
      "CD68+CD163+ macrophages&immune cells / vasculature has p value = 0.497\n",
      "CD68+CD163+ macrophages&plasma cells has p value = 0.606\n",
      "CD68+CD163+ macrophages&smooth muscle has p value = 0.069\n",
      "CD68+CD163+ macrophages&stroma has p value = 0.094\n",
      "CD68+CD163+ macrophages&tumor cells has p value = 0.010\n",
      "CD68+CD163+ macrophages&undefined has p value = 0.171\n",
      "CD68+CD163+ macrophages&vasculature has p value = 0.346\n",
      "CD8+ T cells&Tregs has p value = 0.003\n",
      "CD8+ T cells&granulocytes has p value = 0.025\n",
      "CD8+ T cells&plasma cells has p value = 0.766\n",
      "CD8+ T cells&smooth muscle has p value = 0.158\n",
      "CD8+ T cells&stroma has p value = 0.266\n",
      "CD8+ T cells&tumor cells has p value = 0.003\n",
      "CD8+ T cells&undefined has p value = 0.220\n",
      "CD8+ T cells&vasculature has p value = 0.912\n",
      "Tregs&granulocytes has p value = 0.043\n",
      "Tregs&stroma has p value = 0.081\n",
      "granulocytes&plasma cells has p value = 0.225\n",
      "granulocytes&smooth muscle has p value = 0.151\n",
      "granulocytes&stroma has p value = 0.123\n",
      "granulocytes&tumor cells has p value = 0.012\n",
      "granulocytes&undefined has p value = 0.943\n",
      "granulocytes&vasculature has p value = 0.235\n",
      "plasma cells&smooth muscle has p value = 0.580\n",
      "plasma cells&stroma has p value = 0.730\n",
      "plasma cells&tumor cells has p value = 0.894\n",
      "plasma cells&vasculature has p value = 0.771\n",
      "smooth muscle&stroma has p value = 0.728\n",
      "smooth muscle&tumor cells has p value = 0.333\n",
      "smooth muscle&undefined has p value = 0.971\n",
      "smooth muscle&vasculature has p value = 0.668\n",
      "stroma&tumor cells has p value = 0.046\n",
      "stroma&undefined has p value = 0.418\n",
      "stroma&vasculature has p value = 0.459\n",
      "tumor cells&undefined has p value = 0.197\n",
      "tumor cells&vasculature has p value = 0.436\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7g/phdhh_ld3dlbnrst0t60bwzr0000gn/T/ipykernel_91782/4205923182.py:22: FutureWarning: Use \"auto\" to set automatic grayscale colors. From v0.14.0, \"gray\" will default to matplotlib's definition.\n",
      "  sns.swarmplot(data=df_filtered, x='Cell Combination', y='Frequency', hue='Condition', palette='dark:black', size=1.0, dodge=True,order=selected_cell_types, ax=ax, edgecolor='gray', linewidth=0.5)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "p-values after correction:\n",
      "B cells&CD4+ T cells has p value = 0.202\n",
      "B cells&CD4+ T cells CD45RO+ has p value = 0.065\n",
      "B cells&CD68+CD163+ macrophages has p value = 0.267\n",
      "B cells&CD8+ T cells has p value = 0.092\n",
      "B cells&granulocytes has p value = 0.401\n",
      "B cells&plasma cells has p value = 0.387\n",
      "B cells&smooth muscle has p value = 0.480\n",
      "B cells&stroma has p value = 0.383\n",
      "B cells&tumor cells has p value = 0.161\n",
      "B cells&vasculature has p value = 0.177\n",
      "CD4+ T cells CD45RO+&CD68+CD163+ macrophages has p value = 0.374\n",
      "CD4+ T cells CD45RO+&CD8+ T cells has p value = 0.895\n",
      "CD4+ T cells CD45RO+&granulocytes has p value = 0.387\n",
      "CD4+ T cells CD45RO+&plasma cells has p value = 0.917\n",
      "CD4+ T cells CD45RO+&smooth muscle has p value = 0.305\n",
      "CD4+ T cells CD45RO+&stroma has p value = 0.383\n",
      "CD4+ T cells CD45RO+&tumor cells has p value = 0.752\n",
      "CD4+ T cells CD45RO+&undefined has p value = 0.987\n",
      "CD4+ T cells CD45RO+&vasculature has p value = 0.987\n",
      "CD4+ T cells&CD4+ T cells CD45RO+ has p value = 0.161\n",
      "CD4+ T cells&CD8+ T cells has p value = 0.194\n",
      "CD68+ macrophages&CD68+CD163+ macrophages has p value = 0.092\n",
      "CD68+ macrophages&granulocytes has p value = 0.267\n",
      "CD68+CD163+ macrophages&CD8+ T cells has p value = 0.143\n",
      "CD68+CD163+ macrophages&Tregs has p value = 0.092\n",
      "CD68+CD163+ macrophages&granulocytes has p value = 0.092\n",
      "CD68+CD163+ macrophages&immune cells / vasculature has p value = 0.670\n",
      "CD68+CD163+ macrophages&plasma cells has p value = 0.767\n",
      "CD68+CD163+ macrophages&smooth muscle has p value = 0.224\n",
      "CD68+CD163+ macrophages&stroma has p value = 0.267\n",
      "CD68+CD163+ macrophages&tumor cells has p value = 0.092\n",
      "CD68+CD163+ macrophages&undefined has p value = 0.374\n",
      "CD68+CD163+ macrophages&vasculature has p value = 0.510\n",
      "CD8+ T cells&Tregs has p value = 0.065\n",
      "CD8+ T cells&granulocytes has p value = 0.143\n",
      "CD8+ T cells&plasma cells has p value = 0.885\n",
      "CD8+ T cells&smooth muscle has p value = 0.362\n",
      "CD8+ T cells&stroma has p value = 0.424\n",
      "CD8+ T cells&tumor cells has p value = 0.065\n",
      "CD8+ T cells&undefined has p value = 0.387\n",
      "CD8+ T cells&vasculature has p value = 0.975\n",
      "Tregs&granulocytes has p value = 0.177\n",
      "Tregs&stroma has p value = 0.250\n",
      "granulocytes&plasma cells has p value = 0.387\n",
      "granulocytes&smooth muscle has p value = 0.360\n",
      "granulocytes&stroma has p value = 0.305\n",
      "granulocytes&tumor cells has p value = 0.092\n",
      "granulocytes&undefined has p value = 0.987\n",
      "granulocytes&vasculature has p value = 0.394\n",
      "plasma cells&smooth muscle has p value = 0.752\n",
      "plasma cells&stroma has p value = 0.871\n",
      "plasma cells&tumor cells has p value = 0.973\n",
      "plasma cells&vasculature has p value = 0.885\n",
      "smooth muscle&stroma has p value = 0.871\n",
      "smooth muscle&tumor cells has p value = 0.503\n",
      "smooth muscle&undefined has p value = 0.987\n",
      "smooth muscle&vasculature has p value = 0.828\n",
      "stroma&tumor cells has p value = 0.177\n",
      "stroma&undefined has p value = 0.603\n",
      "stroma&vasculature has p value = 0.633\n",
      "tumor cells&undefined has p value = 0.383\n",
      "tumor cells&vasculature has p value = 0.615\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Global Cell Co-Occurrence \n",
    "selected_cell_types = sorted(global_co_occurrence_melted_avg['Cell Combination'].unique())\n",
    "selected_p_values = []\n",
    "\n",
    "# Perform t-tests\n",
    "print(f\"p-value before correction:\")\n",
    "for ct in selected_cell_types:\n",
    "    subset = global_co_occurrence_melted_avg[global_co_occurrence_melted_avg['Cell Combination'] == ct]\n",
    "    group1 = subset[subset['Condition'] == 'CLR']['Frequency']\n",
    "    group2 = subset[subset['Condition'] == 'DII']['Frequency']\n",
    "\n",
    "    t_stat, p_value = stats.ttest_ind(group1, group2, equal_var=False)\n",
    "    print(f\"{ct} has p value = {p_value:.3f}\")\n",
    "    selected_p_values.append(p_value)\n",
    "\n",
    "# Filter the dataframe based on selected Cell Combinations\n",
    "df_filtered = global_co_occurrence_melted_avg[global_co_occurrence_melted_avg['Cell Combination'].isin(selected_cell_types)]\n",
    "\n",
    "# Plot the filtered data\n",
    "fig, ax = plt.subplots(figsize=(45,10))\n",
    "sns.boxplot(data=df_filtered, x='Cell Combination', y='Frequency', hue='Condition', palette='muted', boxprops=dict(alpha=.3), ax=ax, dodge=True,order=selected_cell_types)\n",
    "sns.swarmplot(data=df_filtered, x='Cell Combination', y='Frequency', hue='Condition', palette='dark:black', size=1.0, dodge=True,order=selected_cell_types, ax=ax, edgecolor='gray', linewidth=0.5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:2], labels[:2], title=\"Groups\", handletextpad=1, columnspacing=1, bbox_to_anchor=(1, 1), ncol=3, frameon=True)\n",
    "\n",
    "pvals_corrected = stats.false_discovery_control(selected_p_values, method='bh')\n",
    "\n",
    "print('-'*42)\n",
    "print(f\"p-values after correction:\")\n",
    "\n",
    "p_vals_corrected_dict = {}\n",
    "yrange = ax.get_ylim()[1] - ax.get_ylim()[0]\n",
    "for i, ct in enumerate(selected_cell_types):\n",
    "    ax.text(i, yrange, f\"p = {pvals_corrected[i]:.3f}\", ha='center', fontsize=8, rotation=0)\n",
    "    print(f\"{ct} has p value = {pvals_corrected[i]:.3f}\", flush=True)\n",
    "    p_vals_corrected_dict[ct] = pvals_corrected[i]\n",
    "    \n",
    "for i in range(len(selected_cell_types) - 1):\n",
    "    ax.axvline(i + 0.55, color='grey', linestyle='--', linewidth=0.5)\n",
    "    \n",
    "ax.set_ylabel(\"Frequency\", fontsize=14)\n",
    "ax.set_xlabel('')   \n",
    "plt.xticks(rotation=90)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "04938f08-fdca-49d5-9423-dee4d45e83ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spot Cell Co-Occurrence\n",
    "# Multi-index to single-index column\n",
    "new_columns = []\n",
    "for col in spot_co_occurrence_df.columns:\n",
    "    if isinstance(col, tuple):  # This checks if the column is a MultiIndex\n",
    "        # Join only if the column name is not 'Mouse' or 'Condition'\n",
    "        if \"Patients\" not in col and \"Condition\" not in col:\n",
    "            new_columns.append('&'.join(map(str, col)).strip())\n",
    "        else:\n",
    "            # If 'Mouse' or 'Condition' is in the column, it is not joined with '&'\n",
    "            new_columns.append(col[0])\n",
    "    else:\n",
    "        new_columns.append(col)\n",
    "\n",
    "spot_co_occurrence_df_single = spot_co_occurrence_df.copy()\n",
    "spot_co_occurrence_df_single.columns = new_columns\n",
    "spot_co_occurrence_df_single = spot_co_occurrence_df_single[[col for col in spot_co_occurrence_df_single.columns]]\n",
    "\n",
    "# Melt the DataFrame\n",
    "spot_co_occurrence_melted = spot_co_occurrence_df_single.melt(id_vars=['Patients', 'Condition'], var_name='Cell Combination', value_name='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "896aa3bc-60c5-42cf-99a6-d49f83a6f952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patients</th>\n",
       "      <th>Cell Combination</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;CD4+ T cells</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.019231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;CD4+ T cells CD45RO+</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.081731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;CD68+CD163+ macrophages</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.030343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;CD8+ T cells</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.066401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>B cells&amp;granulocytes</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.057587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>35</td>\n",
       "      <td>stroma&amp;tumor cells</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.047035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2166</th>\n",
       "      <td>35</td>\n",
       "      <td>stroma&amp;undefined</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.013996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2167</th>\n",
       "      <td>35</td>\n",
       "      <td>stroma&amp;vasculature</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.054859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2168</th>\n",
       "      <td>35</td>\n",
       "      <td>tumor cells&amp;undefined</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.009375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2169</th>\n",
       "      <td>35</td>\n",
       "      <td>tumor cells&amp;vasculature</td>\n",
       "      <td>CLR</td>\n",
       "      <td>0.041797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2170 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Patients                 Cell Combination Condition  Frequency\n",
       "0            1             B cells&CD4+ T cells       CLR   0.019231\n",
       "1            1     B cells&CD4+ T cells CD45RO+       CLR   0.081731\n",
       "2            1  B cells&CD68+CD163+ macrophages       CLR   0.030343\n",
       "3            1             B cells&CD8+ T cells       CLR   0.066401\n",
       "4            1             B cells&granulocytes       CLR   0.057587\n",
       "...        ...                              ...       ...        ...\n",
       "2165        35               stroma&tumor cells       CLR   0.047035\n",
       "2166        35                 stroma&undefined       CLR   0.013996\n",
       "2167        35               stroma&vasculature       CLR   0.054859\n",
       "2168        35            tumor cells&undefined       CLR   0.009375\n",
       "2169        35          tumor cells&vasculature       CLR   0.041797\n",
       "\n",
       "[2170 rows x 4 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spot_co_occurrence_melted_avg = spot_co_occurrence_melted.groupby(by=['Patients', 'Cell Combination', 'Condition'])['Frequency'].mean().reset_index()\n",
    "spot_co_occurrence_melted_avg['Condition'] = spot_co_occurrence_melted_avg['Condition'].map(condition_num2str)\n",
    "spot_co_occurrence_melted_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7d10dd7f-1aa0-4042-baf6-6ddb26753352",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value before correction: \n",
      "B cells&CD4+ T cells has p value = 0.045\n",
      "B cells&CD4+ T cells CD45RO+ has p value = 0.001\n",
      "B cells&CD68+CD163+ macrophages has p value = 0.073\n",
      "B cells&CD8+ T cells has p value = 0.005\n",
      "B cells&granulocytes has p value = 0.130\n",
      "B cells&plasma cells has p value = 0.165\n",
      "B cells&smooth muscle has p value = 0.260\n",
      "B cells&stroma has p value = 0.114\n",
      "B cells&tumor cells has p value = 0.027\n",
      "B cells&vasculature has p value = 0.032\n",
      "CD4+ T cells CD45RO+&CD68+CD163+ macrophages has p value = 0.237\n",
      "CD4+ T cells CD45RO+&CD8+ T cells has p value = 0.893\n",
      "CD4+ T cells CD45RO+&granulocytes has p value = 0.181\n",
      "CD4+ T cells CD45RO+&plasma cells has p value = 0.585\n",
      "CD4+ T cells CD45RO+&smooth muscle has p value = 0.085\n",
      "CD4+ T cells CD45RO+&stroma has p value = 0.245\n",
      "CD4+ T cells CD45RO+&tumor cells has p value = 0.732\n",
      "CD4+ T cells CD45RO+&undefined has p value = 0.928\n",
      "CD4+ T cells CD45RO+&vasculature has p value = 0.524\n",
      "CD4+ T cells&CD4+ T cells CD45RO+ has p value = 0.021\n",
      "CD4+ T cells&CD8+ T cells has p value = 0.036\n",
      "CD68+ macrophages&CD68+CD163+ macrophages has p value = 0.016\n",
      "CD68+ macrophages&granulocytes has p value = 0.199\n",
      "CD68+CD163+ macrophages&CD8+ T cells has p value = 0.011\n",
      "CD68+CD163+ macrophages&Tregs has p value = 0.000\n",
      "CD68+CD163+ macrophages&granulocytes has p value = 0.003\n",
      "CD68+CD163+ macrophages&immune cells / vasculature has p value = 0.352\n",
      "CD68+CD163+ macrophages&plasma cells has p value = 0.637\n",
      "CD68+CD163+ macrophages&smooth muscle has p value = 0.073\n",
      "CD68+CD163+ macrophages&stroma has p value = 0.160\n",
      "CD68+CD163+ macrophages&tumor cells has p value = 0.504\n",
      "CD68+CD163+ macrophages&undefined has p value = 0.403\n",
      "CD68+CD163+ macrophages&vasculature has p value = 0.505\n",
      "CD8+ T cells&Tregs has p value = 0.000\n",
      "CD8+ T cells&granulocytes has p value = 0.025\n",
      "CD8+ T cells&plasma cells has p value = 0.533\n",
      "CD8+ T cells&smooth muscle has p value = 0.062\n",
      "CD8+ T cells&stroma has p value = 0.474\n",
      "CD8+ T cells&tumor cells has p value = 0.005\n",
      "CD8+ T cells&undefined has p value = 0.273\n",
      "CD8+ T cells&vasculature has p value = 0.806\n",
      "Tregs&granulocytes has p value = 0.028\n",
      "Tregs&stroma has p value = 0.101\n",
      "granulocytes&plasma cells has p value = 0.137\n",
      "granulocytes&smooth muscle has p value = 0.115\n",
      "granulocytes&stroma has p value = 0.170\n",
      "granulocytes&tumor cells has p value = 0.012\n",
      "granulocytes&undefined has p value = 0.276\n",
      "granulocytes&vasculature has p value = 0.544\n",
      "plasma cells&smooth muscle has p value = 0.455\n",
      "plasma cells&stroma has p value = 0.984\n",
      "plasma cells&tumor cells has p value = 0.705\n",
      "plasma cells&vasculature has p value = 0.401\n",
      "smooth muscle&stroma has p value = 0.730\n",
      "smooth muscle&tumor cells has p value = 0.946\n",
      "smooth muscle&undefined has p value = 0.333\n",
      "smooth muscle&vasculature has p value = 0.336\n",
      "stroma&tumor cells has p value = 0.737\n",
      "stroma&undefined has p value = 0.542\n",
      "stroma&vasculature has p value = 0.338\n",
      "tumor cells&undefined has p value = 0.120\n",
      "tumor cells&vasculature has p value = 0.078\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7g/phdhh_ld3dlbnrst0t60bwzr0000gn/T/ipykernel_91782/2632425307.py:22: FutureWarning: Use \"auto\" to set automatic grayscale colors. From v0.14.0, \"gray\" will default to matplotlib's definition.\n",
      "  sns.swarmplot(data=df_filtered, x='Cell Combination', y='Frequency', hue='Condition', palette='dark:black', size=2.0, dodge=True,order=selected_cell_types, ax=ax, edgecolor='gray', linewidth=0.5)\n",
      "/opt/miniconda3/envs/mesa/lib/python3.11/site-packages/seaborn/categorical.py:3370: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "p-values after correction:\n",
      "B cells&CD4+ T cells in hot spots has p value = 0.175\n",
      "B cells&CD4+ T cells CD45RO+ in hot spots has p value = 0.018\n",
      "B cells&CD4+ T cells CD45RO+ in whole tissue has p value = 0.065\n",
      "******************************************\n",
      "B cells&CD68+CD163+ macrophages in hot spots has p value = 0.239\n",
      "B cells&CD8+ T cells in hot spots has p value = 0.057\n",
      "B cells&granulocytes in hot spots has p value = 0.309\n",
      "B cells&plasma cells in hot spots has p value = 0.351\n",
      "B cells&smooth muscle in hot spots has p value = 0.460\n",
      "B cells&stroma in hot spots has p value = 0.297\n",
      "B cells&tumor cells in hot spots has p value = 0.136\n",
      "B cells&vasculature in hot spots has p value = 0.141\n",
      "CD4+ T cells CD45RO+&CD68+CD163+ macrophages in hot spots has p value = 0.446\n",
      "CD4+ T cells CD45RO+&CD8+ T cells in hot spots has p value = 0.938\n",
      "CD4+ T cells CD45RO+&granulocytes in hot spots has p value = 0.363\n",
      "CD4+ T cells CD45RO+&plasma cells in hot spots has p value = 0.698\n",
      "CD4+ T cells CD45RO+&smooth muscle in hot spots has p value = 0.251\n",
      "CD4+ T cells CD45RO+&stroma in hot spots has p value = 0.447\n",
      "CD4+ T cells CD45RO+&tumor cells in hot spots has p value = 0.801\n",
      "CD4+ T cells CD45RO+&undefined in hot spots has p value = 0.959\n",
      "CD4+ T cells CD45RO+&vasculature in hot spots has p value = 0.661\n",
      "CD4+ T cells&CD4+ T cells CD45RO+ in hot spots has p value = 0.129\n",
      "CD4+ T cells&CD8+ T cells in hot spots has p value = 0.147\n",
      "CD68+ macrophages&CD68+CD163+ macrophages in hot spots has p value = 0.109\n",
      "CD68+ macrophages&granulocytes in hot spots has p value = 0.385\n",
      "CD68+CD163+ macrophages&CD8+ T cells in hot spots has p value = 0.093\n",
      "CD68+CD163+ macrophages&Tregs in hot spots has p value = 0.008\n",
      "CD68+CD163+ macrophages&Tregs in whole tissue has p value = 0.092\n",
      "******************************************\n",
      "CD68+CD163+ macrophages&granulocytes in hot spots has p value = 0.040\n",
      "CD68+CD163+ macrophages&granulocytes in whole tissue has p value = 0.092\n",
      "******************************************\n",
      "CD68+CD163+ macrophages&immune cells / vasculature in hot spots has p value = 0.533\n",
      "CD68+CD163+ macrophages&plasma cells in hot spots has p value = 0.745\n",
      "CD68+CD163+ macrophages&smooth muscle in hot spots has p value = 0.239\n",
      "CD68+CD163+ macrophages&stroma in hot spots has p value = 0.351\n",
      "CD68+CD163+ macrophages&tumor cells in hot spots has p value = 0.661\n",
      "CD68+CD163+ macrophages&undefined in hot spots has p value = 0.581\n",
      "CD68+CD163+ macrophages&vasculature in hot spots has p value = 0.661\n",
      "CD8+ T cells&Tregs in hot spots has p value = 0.007\n",
      "CD8+ T cells&Tregs in whole tissue has p value = 0.065\n",
      "******************************************\n",
      "CD8+ T cells&granulocytes in hot spots has p value = 0.136\n",
      "CD8+ T cells&plasma cells in hot spots has p value = 0.661\n",
      "CD8+ T cells&smooth muscle in hot spots has p value = 0.228\n",
      "CD8+ T cells&stroma in hot spots has p value = 0.652\n",
      "CD8+ T cells&tumor cells in hot spots has p value = 0.057\n",
      "CD8+ T cells&undefined in hot spots has p value = 0.463\n",
      "CD8+ T cells&vasculature in hot spots has p value = 0.861\n",
      "Tregs&granulocytes in hot spots has p value = 0.136\n",
      "Tregs&stroma in hot spots has p value = 0.284\n",
      "granulocytes&plasma cells in hot spots has p value = 0.314\n",
      "granulocytes&smooth muscle in hot spots has p value = 0.297\n",
      "granulocytes&stroma in hot spots has p value = 0.351\n",
      "granulocytes&tumor cells in hot spots has p value = 0.093\n",
      "granulocytes&undefined in hot spots has p value = 0.463\n",
      "granulocytes&vasculature in hot spots has p value = 0.661\n",
      "plasma cells&smooth muscle in hot spots has p value = 0.641\n",
      "plasma cells&stroma in hot spots has p value = 0.984\n",
      "plasma cells&tumor cells in hot spots has p value = 0.801\n",
      "plasma cells&vasculature in hot spots has p value = 0.581\n",
      "smooth muscle&stroma in hot spots has p value = 0.801\n",
      "smooth muscle&tumor cells in hot spots has p value = 0.962\n",
      "smooth muscle&undefined in hot spots has p value = 0.523\n",
      "smooth muscle&vasculature in hot spots has p value = 0.523\n",
      "stroma&tumor cells in hot spots has p value = 0.801\n",
      "stroma&undefined in hot spots has p value = 0.661\n",
      "stroma&vasculature in hot spots has p value = 0.523\n",
      "tumor cells&undefined in hot spots has p value = 0.298\n",
      "tumor cells&vasculature in hot spots has p value = 0.242\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Spot Cell Co-Occurrence    \n",
    "selected_cell_types = sorted(spot_co_occurrence_melted_avg['Cell Combination'].unique())\n",
    "selected_p_values = []\n",
    "\n",
    "# Perform t-tests\n",
    "print(f\"p-value before correction: \")\n",
    "for ct in selected_cell_types: # df_melted['CellType'].unique():\n",
    "    subset = spot_co_occurrence_melted_avg[spot_co_occurrence_melted_avg['Cell Combination'] == ct]\n",
    "    group1 = subset[subset['Condition'] == 'CLR']['Frequency']\n",
    "    group2 = subset[subset['Condition'] == 'DII']['Frequency']\n",
    "\n",
    "    t_stat, p_value = stats.ttest_ind(group1, group2, equal_var=False)\n",
    "    print(f\"{ct} has p value = {p_value:.3f}\")\n",
    "    selected_p_values.append(p_value)\n",
    "\n",
    "# Filter the dataframe based on selected CellTypes\n",
    "df_filtered = spot_co_occurrence_melted_avg[spot_co_occurrence_melted_avg['Cell Combination'].isin(selected_cell_types)]\n",
    "\n",
    "# Plot the filtered data\n",
    "fig, ax = plt.subplots(figsize=(42,10))\n",
    "sns.boxplot(data=df_filtered, x='Cell Combination', y='Frequency', hue='Condition', palette='muted', boxprops=dict(alpha=.3), ax=ax, dodge=True,order=selected_cell_types)\n",
    "sns.swarmplot(data=df_filtered, x='Cell Combination', y='Frequency', hue='Condition', palette='dark:black', size=2.0, dodge=True,order=selected_cell_types, ax=ax, edgecolor='gray', linewidth=0.5)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[:2], labels[:2], title=\"Groups\", handletextpad=1, columnspacing=1, bbox_to_anchor=(1, 1), ncol=3, frameon=True)\n",
    "\n",
    "hot_pvals_corrected = stats.false_discovery_control(selected_p_values, method='bh')\n",
    "\n",
    "print('-'*42)\n",
    "print(f\"p-values after correction:\")\n",
    "\n",
    "highlighted_comb = []\n",
    "yrange = ax.get_ylim()[1] - ax.get_ylim()[0]\n",
    "for i, ct in enumerate(selected_cell_types):\n",
    "    ax.text(i, yrange, f\"p = {pvals_corrected[i]:.3f}\", ha='center', fontsize=8, rotation=0)\n",
    "    print(f\"{ct} in hot spots has p value = {hot_pvals_corrected[i]:.3f}\", flush=True)\n",
    "    if hot_pvals_corrected[i] < 0.05 and p_vals_corrected_dict[ct] >= 0.05:\n",
    "        highlighted_comb.append(tuple(map(str.strip, ct.split('&'))))\n",
    "        print(f\"{ct} in whole tissue has p value = {p_vals_corrected_dict[ct]:.3f}\", flush=True)\n",
    "        print('*'*42)\n",
    "    \n",
    "for i in range(len(selected_cell_types) - 1):\n",
    "    ax.axvline(i + 0.55, color='grey', linestyle='--', linewidth=0.5)\n",
    "    \n",
    "ax.set_ylabel(\"Frequency\", fontsize=14)\n",
    "ax.set_xlabel('')   \n",
    "plt.xticks(rotation=90)  \n",
    "plt.yticks(rotation=90)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571f31e7-ac38-47a3-b75b-85e813f045c4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Cell Co-Occurrence Circle Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7919b0f0-65d7-4482-9f1f-e45044dce938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">B cells</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"4\" halign=\"left\">plasma cells</th>\n",
       "      <th colspan=\"3\" halign=\"left\">smooth muscle</th>\n",
       "      <th colspan=\"2\" halign=\"left\">stroma</th>\n",
       "      <th>tumor cells</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>CD4+ T cells</th>\n",
       "      <th>CD4+ T cells CD45RO+</th>\n",
       "      <th>CD68+CD163+ macrophages</th>\n",
       "      <th>CD8+ T cells</th>\n",
       "      <th>granulocytes</th>\n",
       "      <th>plasma cells</th>\n",
       "      <th>smooth muscle</th>\n",
       "      <th>stroma</th>\n",
       "      <th>tumor cells</th>\n",
       "      <th>vasculature</th>\n",
       "      <th>...</th>\n",
       "      <th>smooth muscle</th>\n",
       "      <th>stroma</th>\n",
       "      <th>tumor cells</th>\n",
       "      <th>vasculature</th>\n",
       "      <th>stroma</th>\n",
       "      <th>tumor cells</th>\n",
       "      <th>vasculature</th>\n",
       "      <th>tumor cells</th>\n",
       "      <th>vasculature</th>\n",
       "      <th>vasculature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLR</th>\n",
       "      <td>0.012412</td>\n",
       "      <td>0.034873</td>\n",
       "      <td>0.018433</td>\n",
       "      <td>0.026145</td>\n",
       "      <td>0.008671</td>\n",
       "      <td>0.006010</td>\n",
       "      <td>0.002917</td>\n",
       "      <td>0.010934</td>\n",
       "      <td>0.011779</td>\n",
       "      <td>0.007876</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008861</td>\n",
       "      <td>0.010709</td>\n",
       "      <td>0.007356</td>\n",
       "      <td>0.005696</td>\n",
       "      <td>0.023346</td>\n",
       "      <td>0.012941</td>\n",
       "      <td>0.016308</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>0.015639</td>\n",
       "      <td>0.009926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DII</th>\n",
       "      <td>0.000752</td>\n",
       "      <td>0.007611</td>\n",
       "      <td>0.010686</td>\n",
       "      <td>0.006457</td>\n",
       "      <td>0.004830</td>\n",
       "      <td>0.003341</td>\n",
       "      <td>0.007947</td>\n",
       "      <td>0.006856</td>\n",
       "      <td>0.004796</td>\n",
       "      <td>0.003272</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006270</td>\n",
       "      <td>0.012660</td>\n",
       "      <td>0.007738</td>\n",
       "      <td>0.006562</td>\n",
       "      <td>0.025861</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.018927</td>\n",
       "      <td>0.029355</td>\n",
       "      <td>0.019093</td>\n",
       "      <td>0.011639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               B cells                                               \\\n",
       "          CD4+ T cells CD4+ T cells CD45RO+ CD68+CD163+ macrophages   \n",
       "Condition                                                             \n",
       "CLR           0.012412             0.034873                0.018433   \n",
       "DII           0.000752             0.007611                0.010686   \n",
       "\n",
       "                                                                          \\\n",
       "          CD8+ T cells granulocytes plasma cells smooth muscle    stroma   \n",
       "Condition                                                                  \n",
       "CLR           0.026145     0.008671     0.006010      0.002917  0.010934   \n",
       "DII           0.006457     0.004830     0.003341      0.007947  0.006856   \n",
       "\n",
       "                                   ...  plasma cells                        \\\n",
       "          tumor cells vasculature  ... smooth muscle    stroma tumor cells   \n",
       "Condition                          ...                                       \n",
       "CLR          0.011779    0.007876  ...      0.008861  0.010709    0.007356   \n",
       "DII          0.004796    0.003272  ...      0.006270  0.012660    0.007738   \n",
       "\n",
       "                      smooth muscle                              stroma  \\\n",
       "          vasculature        stroma tumor cells vasculature tumor cells   \n",
       "Condition                                                                 \n",
       "CLR          0.005696      0.023346    0.012941    0.016308    0.020004   \n",
       "DII          0.006562      0.025861    0.019929    0.018927    0.029355   \n",
       "\n",
       "                      tumor cells  \n",
       "          vasculature vasculature  \n",
       "Condition                          \n",
       "CLR          0.015639    0.009926  \n",
       "DII          0.019093    0.011639  \n",
       "\n",
       "[2 rows x 55 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circoplot_df1 = global_co_occurrence_df.sort_index(axis=1, level=[0,1]).drop(columns=['Patients'])\n",
    "circoplot_df1['Condition'] = circoplot_df1['Condition'].map(condition_num2str)\n",
    "circoplot_df1 = circoplot_df1[[col for col in circoplot_df1.columns if 'undefined' not in col]]\n",
    "# Group by 'Condition' and calculate the mean of the other columns\n",
    "circoplot_df1 = circoplot_df1.groupby('Condition').mean().reset_index()\n",
    "circoplot_df1 = circoplot_df1.set_index('Condition')\n",
    "circoplot_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b325f0c0-d646-480f-825c-69371966730e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ClusterName</th>\n",
       "      <th>B cells</th>\n",
       "      <th>CD11b+ monocytes</th>\n",
       "      <th>CD11b+CD68+ macrophages</th>\n",
       "      <th>CD11c+ DCs</th>\n",
       "      <th>CD163+ macrophages</th>\n",
       "      <th>CD3+ T cells</th>\n",
       "      <th>CD4+ T cells</th>\n",
       "      <th>CD4+ T cells CD45RO+</th>\n",
       "      <th>CD4+ T cells GATA3+</th>\n",
       "      <th>CD68+ macrophages</th>\n",
       "      <th>...</th>\n",
       "      <th>immune cells / vasculature</th>\n",
       "      <th>lymphatics</th>\n",
       "      <th>nerves</th>\n",
       "      <th>plasma cells</th>\n",
       "      <th>smooth muscle</th>\n",
       "      <th>stroma</th>\n",
       "      <th>tumor cells</th>\n",
       "      <th>tumor cells / immune cells</th>\n",
       "      <th>vasculature</th>\n",
       "      <th>Patients</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CLR</th>\n",
       "      <td>0.069471</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.001505</td>\n",
       "      <td>0.001385</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.012074</td>\n",
       "      <td>0.072276</td>\n",
       "      <td>0.000508</td>\n",
       "      <td>0.004358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016573</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.002579</td>\n",
       "      <td>0.038024</td>\n",
       "      <td>0.104830</td>\n",
       "      <td>0.091609</td>\n",
       "      <td>0.166579</td>\n",
       "      <td>0.003786</td>\n",
       "      <td>0.054342</td>\n",
       "      <td>20.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DII</th>\n",
       "      <td>0.016478</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>0.007303</td>\n",
       "      <td>0.001686</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>0.003185</td>\n",
       "      <td>0.065721</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.011092</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007733</td>\n",
       "      <td>0.001711</td>\n",
       "      <td>0.002704</td>\n",
       "      <td>0.029154</td>\n",
       "      <td>0.112189</td>\n",
       "      <td>0.078978</td>\n",
       "      <td>0.189360</td>\n",
       "      <td>0.007580</td>\n",
       "      <td>0.042994</td>\n",
       "      <td>15.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ClusterName   B cells  CD11b+ monocytes  CD11b+CD68+ macrophages  CD11c+ DCs  \\\n",
       "Condition                                                                      \n",
       "CLR          0.069471          0.000281                 0.001505    0.001385   \n",
       "DII          0.016478          0.004673                 0.007303    0.001686   \n",
       "\n",
       "ClusterName  CD163+ macrophages  CD3+ T cells  CD4+ T cells  \\\n",
       "Condition                                                     \n",
       "CLR                    0.000363      0.000272      0.012074   \n",
       "DII                    0.000082      0.000911      0.003185   \n",
       "\n",
       "ClusterName  CD4+ T cells CD45RO+  CD4+ T cells GATA3+  CD68+ macrophages  \\\n",
       "Condition                                                                   \n",
       "CLR                      0.072276             0.000508           0.004358   \n",
       "DII                      0.065721             0.000022           0.011092   \n",
       "\n",
       "ClusterName  ...  immune cells / vasculature  lymphatics    nerves  \\\n",
       "Condition    ...                                                     \n",
       "CLR          ...                    0.016573    0.000356  0.002579   \n",
       "DII          ...                    0.007733    0.001711  0.002704   \n",
       "\n",
       "ClusterName  plasma cells  smooth muscle    stroma  tumor cells  \\\n",
       "Condition                                                         \n",
       "CLR              0.038024       0.104830  0.091609     0.166579   \n",
       "DII              0.029154       0.112189  0.078978     0.189360   \n",
       "\n",
       "ClusterName  tumor cells / immune cells  vasculature   Patients  \n",
       "Condition                                                        \n",
       "CLR                            0.003786     0.054342  20.294118  \n",
       "DII                            0.007580     0.042994  15.833333  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circoplot_df2 = global_cellfreq_df.drop(columns=['undefined'])\n",
    "circoplot_df2['Condition'] = circoplot_df2['Condition'].map(condition_num2str)\n",
    "# Group by 'Condition' and calculate the mean of the other columns\n",
    "circoplot_df2 = circoplot_df2.groupby('Condition').mean().reset_index()\n",
    "circoplot_df2 = circoplot_df2.set_index('Condition')\n",
    "circoplot_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "97e904a0-ebc1-491e-9e46-4e7cfbf35625",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patient_group = 'CLR'\n",
    "eco.create_circos_plot(circoplot_df1.loc[[patient_group]], \n",
    "                       cell_type_colors_hex=None,\n",
    "                       cell_abundance=circoplot_df2.loc[[patient_group]],\n",
    "                       threshold=0.01, \n",
    "                       edge_weights_scaler=50,\n",
    "                       highlighted_edges=highlighted_comb,\n",
    "                       node_weights_scaler=5000,\n",
    "                       figure_size=(8,8),\n",
    "                       save_path=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da0df8c7-bbeb-4252-a45f-d24a0cf756b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3.11 (mesa)",
   "language": "python",
   "name": "python311_mesa"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}