{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VegZ Complete Testing Suite\\n\\nThis notebook provides comprehensive testing of all VegZ functionalities using test data.\\n\\n## Features Tested:\\n- Package imports and setup\\n- Data loading and inspection\\n- VegZ initialization\\n- Diversity analysis (15+ indices)\\n- Quick analysis functions\\n- Comprehensive visualizations\\n\\n## Requirements:\\n- VegZ package installed (`pip install VegZ`)\\n- Test data files in `test_data/` directory\\n- Python 3.8+ with required dependencies\\n\\n---\\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ VegZ successfully imported!\n",
      "✓ VegZ version: 1.0.3\n",
      "\n",
      "Available VegZ modules:\n",
      "- Core functionality: VegZ main class\n",
      "- Diversity analysis: DiversityAnalyzer\n",
      "- Multivariate analysis: MultivariateAnalyzer\n",
      "- Clustering: VegetationClustering\n",
      "- Statistics: EcologicalStatistics\n",
      "- Specialized methods: Multiple specialized analyzers\n"
     ]
    }
   ],
   "source": [
    "# Cell 1: Package Import and Setup\\n\\n# Import VegZ and required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "\n",
    "# Import VegZ module first to get version\n",
    "import VegZ as VegZ_module\n",
    "\n",
    "# Import VegZ classes and functions\n",
    "from VegZ import VegZ as VegZClass, quick_diversity_analysis, quick_ordination, quick_clustering, quick_elbow_analysis\n",
    "from VegZ.diversity import DiversityAnalyzer\n",
    "from VegZ.multivariate import MultivariateAnalyzer\n",
    "from VegZ.clustering import VegetationClustering\n",
    "from VegZ.statistics import EcologicalStatistics\n",
    "from VegZ.temporal import TemporalAnalyzer\n",
    "from VegZ.spatial import SpatialAnalyzer\n",
    "from VegZ.environmental import EnvironmentalModeler\n",
    "from VegZ.machine_learning import MachineLearningAnalyzer, PredictiveModeling\n",
    "from VegZ.functional_traits import FunctionalTraitAnalyzer, TraitSyndromes\n",
    "from VegZ.nestedness import NestednessAnalyzer, NullModels\n",
    "from VegZ.specialized_methods import PhylogeneticDiversityAnalyzer, MetacommunityAnalyzer, NetworkAnalyzer\n",
    "from VegZ.interactive_viz import InteractiveVisualizer, ReportGenerator\n",
    "from VegZ import data_management, data_quality\n",
    "\n",
    "# Set display options\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "\n",
    "# Configure plotting\n",
    "plt.style.use('seaborn-v0_8')\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "print(\"✓ VegZ successfully imported!\")\n",
    "print(f\"✓ VegZ version: {VegZ_module.__version__}\")\n",
    "\n",
    "# Display available VegZ modules\n",
    "print(\"\\nAvailable VegZ modules:\")\n",
    "print(\"- Core functionality: VegZ main class\")\n",
    "print(\"- Diversity analysis: DiversityAnalyzer\")\n",
    "print(\"- Multivariate analysis: MultivariateAnalyzer\")\n",
    "print(\"- Clustering: VegetationClustering\")\n",
    "print(\"- Statistics: EcologicalStatistics\")\n",
    "print(\"- Specialized methods: Multiple specialized analyzers\")\n",
    "\n",
    "# Set up aliases for easier use\n",
    "VegZ = VegZClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading test datasets...\n",
      "\n",
      "✓ Vegetation survey data: (50, 42)\n",
      "✓ Species abundance matrix: (50, 29)\n",
      "✓ Environmental variables: (50, 11)\n",
      "✓ Species traits: (29, 13)\n",
      "✓ Phylogenetic distances: (29, 29)\n",
      "\n",
      "==================================================\n",
      "DATA STRUCTURE OVERVIEW\n",
      "==================================================\n",
      "\n",
      "1. Vegetation Survey Data (first 5 rows):\n",
      "    site_id        date  latitude  longitude  elevation  slope  aspect  \\\n",
      "0  SITE_001  2023-06-15   44.2619   -72.5806        850     15     180   \n",
      "1  SITE_002  2023-06-16   44.2634   -72.5821        875     22     165   \n",
      "2  SITE_003  2023-06-17   44.2651   -72.5835        900     28     150   \n",
      "3  SITE_004  2023-06-18   44.2668   -72.5849        925     35     135   \n",
      "4  SITE_005  2023-06-19   44.2685   -72.5864        950     42     120   \n",
      "\n",
      "   soil_ph  soil_moisture  temperature  precipitation land_use habitat_type  \\\n",
      "0      6.2           0.35         18.5            120   forest    deciduous   \n",
      "1      6.0           0.42         17.8            125   forest        mixed   \n",
      "2      5.8           0.48         17.2            130   forest        mixed   \n",
      "3      5.6           0.55         16.5            135   forest   coniferous   \n",
      "4      5.4           0.62         15.8            140   forest   coniferous   \n",
      "\n",
      "   Quercus_alba  Acer_saccharum  Fagus_grandifolia  Betula_alleghaniensis  \\\n",
      "0            25              18                 12                      8   \n",
      "1            32              22                 16                     12   \n",
      "2            28              20                 18                     15   \n",
      "3            15              12                 10                     18   \n",
      "4             8               6                  5                     22   \n",
      "\n",
      "   Tsuga_canadensis  Pinus_strobus  Fraxinus_americana  Tilia_americana  \\\n",
      "0                 0              0                   6                4   \n",
      "1                 8              6                   8                6   \n",
      "2                12             10                   5                8   \n",
      "3                20             16                   2                4   \n",
      "4                25             20                   1                2   \n",
      "\n",
      "   Ulmus_americana  Carya_ovata  Juglans_nigra  Populus_tremuloides  \\\n",
      "0                2            0              0                   15   \n",
      "1                1            4              0                   12   \n",
      "2                0            6              2                    8   \n",
      "3                0            2              0                    6   \n",
      "4                0            1              0                    4   \n",
      "\n",
      "   Prunus_serotina  Amelanchier_canadensis  Viburnum_lantanoides  \\\n",
      "0                3                       8                    12   \n",
      "1                5                       6                    10   \n",
      "2                4                       4                     8   \n",
      "3                2                       2                     6   \n",
      "4                1                       1                     4   \n",
      "\n",
      "   Sambucus_canadensis  Cornus_florida  Rhododendron_maximum  \\\n",
      "0                    6               2                     0   \n",
      "1                    4               3                     2   \n",
      "2                    2               4                     4   \n",
      "3                    1               2                     8   \n",
      "4                    0               1                    12   \n",
      "\n",
      "   Kalmia_latifolia  Vaccinium_corymbosum  Gaultheria_procumbens  \\\n",
      "0                 0                     5                     15   \n",
      "1                 1                     8                     12   \n",
      "2                 3                    12                     10   \n",
      "3                 6                    16                      8   \n",
      "4                10                    20                      6   \n",
      "\n",
      "   Mitchella_repens  Trientalis_borealis  Maianthemum_canadense  \\\n",
      "0                 8                   12                     20   \n",
      "1                 6                   10                     16   \n",
      "2                 4                    8                     18   \n",
      "3                 2                    6                     15   \n",
      "4                 1                    4                     12   \n",
      "\n",
      "   Dryopteris_marginalis  Polystichum_acrostichoides  Athyrium_filix_femina  \\\n",
      "0                     18                          10                      6   \n",
      "1                     22                           8                      4   \n",
      "2                     20                          12                      8   \n",
      "3                     18                          16                     12   \n",
      "4                     16                          20                     16   \n",
      "\n",
      "   Osmunda_claytoniana  Pteridium_aquilinum  \n",
      "0                    8                    0  \n",
      "1                    6                    2  \n",
      "2                   10                    5  \n",
      "3                   14                    8  \n",
      "4                   18                   12  \n",
      "\n",
      "2. Species Abundance Matrix (first 5 rows, 10 columns):\n",
      "          Quercus_alba  Acer_saccharum  Fagus_grandifolia  \\\n",
      "site_id                                                     \n",
      "SITE_001            25              18                 12   \n",
      "SITE_002            32              22                 16   \n",
      "SITE_003            28              20                 18   \n",
      "SITE_004            15              12                 10   \n",
      "SITE_005             8               6                  5   \n",
      "\n",
      "          Betula_alleghaniensis  Tsuga_canadensis  Pinus_strobus  \\\n",
      "site_id                                                            \n",
      "SITE_001                      8                 0              0   \n",
      "SITE_002                     12                 8              6   \n",
      "SITE_003                     15                12             10   \n",
      "SITE_004                     18                20             16   \n",
      "SITE_005                     22                25             20   \n",
      "\n",
      "          Fraxinus_americana  Tilia_americana  Ulmus_americana  Carya_ovata  \n",
      "site_id                                                                      \n",
      "SITE_001                   6                4                2            0  \n",
      "SITE_002                   8                6                1            4  \n",
      "SITE_003                   5                8                0            6  \n",
      "SITE_004                   2                4                0            2  \n",
      "SITE_005                   1                2                0            1  \n",
      "\n",
      "3. Environmental Variables:\n",
      "          latitude  longitude  elevation  slope  aspect  soil_ph  \\\n",
      "site_id                                                            \n",
      "SITE_001   44.2619   -72.5806        850     15     180      6.2   \n",
      "SITE_002   44.2634   -72.5821        875     22     165      6.0   \n",
      "SITE_003   44.2651   -72.5835        900     28     150      5.8   \n",
      "SITE_004   44.2668   -72.5849        925     35     135      5.6   \n",
      "SITE_005   44.2685   -72.5864        950     42     120      5.4   \n",
      "\n",
      "          soil_moisture  temperature  precipitation land_use habitat_type  \n",
      "site_id                                                                    \n",
      "SITE_001           0.35         18.5            120   forest    deciduous  \n",
      "SITE_002           0.42         17.8            125   forest        mixed  \n",
      "SITE_003           0.48         17.2            130   forest        mixed  \n",
      "SITE_004           0.55         16.5            135   forest   coniferous  \n",
      "SITE_005           0.62         15.8            140   forest   coniferous  \n",
      "\n",
      "4. Species Traits (first 5 species):\n",
      "                      growth_form  max_height_m  leaf_area_cm2  \\\n",
      "species                                                          \n",
      "Quercus_alba                 tree          30.0           45.2   \n",
      "Acer_saccharum               tree          35.0           78.5   \n",
      "Fagus_grandifolia            tree          25.0           52.3   \n",
      "Betula_alleghaniensis        tree          28.0           38.7   \n",
      "Tsuga_canadensis             tree          40.0           12.4   \n",
      "\n",
      "                       specific_leaf_area_cm2_g  leaf_nitrogen_percent  \\\n",
      "species                                                                  \n",
      "Quercus_alba                                180                    2.1   \n",
      "Acer_saccharum                              220                    2.8   \n",
      "Fagus_grandifolia                           160                    2.5   \n",
      "Betula_alleghaniensis                       250                    3.2   \n",
      "Tsuga_canadensis                             80                    1.8   \n",
      "\n",
      "                       leaf_phosphorus_percent  wood_density_g_cm3  \\\n",
      "species                                                              \n",
      "Quercus_alba                              0.18                0.68   \n",
      "Acer_saccharum                            0.22                0.63   \n",
      "Fagus_grandifolia                         0.19                0.66   \n",
      "Betula_alleghaniensis                     0.28                0.55   \n",
      "Tsuga_canadensis                          0.14                0.42   \n",
      "\n",
      "                       seed_mass_mg dispersal_mode shade_tolerance  \\\n",
      "species                                                              \n",
      "Quercus_alba                 5800.0        gravity    intermediate   \n",
      "Acer_saccharum                 65.0           wind            high   \n",
      "Fagus_grandifolia             320.0        gravity            high   \n",
      "Betula_alleghaniensis           1.8           wind             low   \n",
      "Tsuga_canadensis                3.2           wind            high   \n",
      "\n",
      "                      drought_tolerance cold_tolerance   functional_group  \n",
      "species                                                                    \n",
      "Quercus_alba                       high           high   deciduous_canopy  \n",
      "Acer_saccharum                      low           high   deciduous_canopy  \n",
      "Fagus_grandifolia                   low   intermediate   deciduous_canopy  \n",
      "Betula_alleghaniensis      intermediate           high  deciduous_pioneer  \n",
      "Tsuga_canadensis                    low           high  coniferous_canopy  \n",
      "\n",
      "5. Data Summary Statistics:\n",
      "- Number of sites: 50\n",
      "- Number of species: 29\n",
      "- Number of environmental variables: 11\n",
      "- Total abundance across all sites: 12410\n",
      "- Mean species richness per site: 25.3\n"
     ]
    }
   ],
   "source": [
    "# Cell 2: Data Loading and Inspection\\n\\n# Load all test datasets\n",
    "print(\"Loading test datasets...\\n\")\n",
    "\n",
    "# Load vegetation survey data (includes species and environmental data)\n",
    "vegetation_data = pd.read_csv('test_data/vegetation_survey_data.csv')\n",
    "print(f\"✓ Vegetation survey data: {vegetation_data.shape}\")\n",
    "\n",
    "# Load species abundance matrix\n",
    "species_abundance = pd.read_csv('test_data/species_abundance.csv', index_col=0)\n",
    "print(f\"✓ Species abundance matrix: {species_abundance.shape}\")\n",
    "\n",
    "# Load environmental variables\n",
    "environmental_data = pd.read_csv('test_data/environmental_variables.csv', index_col=0)\n",
    "print(f\"✓ Environmental variables: {environmental_data.shape}\")\n",
    "\n",
    "# Load species traits\n",
    "species_traits = pd.read_csv('test_data/species_traits.csv', index_col=0)\n",
    "print(f\"✓ Species traits: {species_traits.shape}\")\n",
    "\n",
    "# Load phylogenetic distances\n",
    "phylo_distances = pd.read_csv('test_data/phylogenetic_distances.csv', index_col=0)\n",
    "print(f\"✓ Phylogenetic distances: {phylo_distances.shape}\")\n",
    "\n",
    "# Display data structure\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"DATA STRUCTURE OVERVIEW\")\n",
    "print(\"=\"*50)\n",
    "\n",
    "print(\"\\n1. Vegetation Survey Data (first 5 rows):\")\n",
    "print(vegetation_data.head())\n",
    "\n",
    "print(\"\\n2. Species Abundance Matrix (first 5 rows, 10 columns):\")\n",
    "print(species_abundance.iloc[:5, :10])\n",
    "\n",
    "print(\"\\n3. Environmental Variables:\")\n",
    "print(environmental_data.head())\n",
    "\n",
    "print(\"\\n4. Species Traits (first 5 species):\")\n",
    "print(species_traits.head())\n",
    "\n",
    "print(\"\\n5. Data Summary Statistics:\")\n",
    "print(f\"- Number of sites: {len(species_abundance)}\")\n",
    "print(f\"- Number of species: {len(species_abundance.columns)}\")\n",
    "print(f\"- Number of environmental variables: {len(environmental_data.columns)}\")\n",
    "print(f\"- Total abundance across all sites: {species_abundance.sum().sum()}\")\n",
    "print(f\"- Mean species richness per site: {(species_abundance > 0).sum(axis=1).mean():.1f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ VegZ initialized with test data\n",
      "✓ Species matrix shape: (50, 29)\n",
      "✓ Environmental data shape: (50, 11)\n",
      "\n",
      "==================================================\n",
      "DATASET SUMMARY STATISTICS\n",
      "==================================================\n",
      "n_samples: 50\n",
      "n_species: 29\n",
      "mean_species_per_sample: 25.26\n",
      "mean_abundance_per_sample: 248.2\n",
      "\n",
      "species_occurrence_frequency:\n",
      "count    29.000000\n",
      "mean     43.551724\n",
      "std       8.550632\n",
      "min      21.000000\n",
      "25%      42.000000\n",
      "50%      47.000000\n",
      "75%      50.000000\n",
      "max      50.000000\n",
      "dtype: float64\n",
      "\n",
      "sample_abundance_distribution:\n",
      "count     50.00000\n",
      "mean     248.20000\n",
      "std       39.64897\n",
      "min      171.00000\n",
      "25%      224.75000\n",
      "50%      247.50000\n",
      "75%      274.00000\n",
      "max      332.00000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Cell 3: Initialize VegZ and Basic Setup\\n\\n# Initialize VegZ with the species abundance data\n",
    "veg = VegZ()\n",
    "veg.data = species_abundance\n",
    "veg.species_matrix = species_abundance\n",
    "veg.environmental_data = environmental_data\n",
    "\n",
    "print(\"✓ VegZ initialized with test data\")\n",
    "print(f\"✓ Species matrix shape: {veg.species_matrix.shape}\")\n",
    "print(f\"✓ Environmental data shape: {veg.environmental_data.shape}\")\n",
    "\n",
    "# Generate summary statistics\n",
    "summary_stats = veg.summary_statistics()\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"DATASET SUMMARY STATISTICS\")\n",
    "print(\"=\"*50)\n",
    "for key, value in summary_stats.items():\n",
    "    if isinstance(value, (int, float)):\n",
    "        print(f\"{key}: {value}\")\n",
    "    elif hasattr(value, 'describe'):\n",
    "        print(f\"\\n{key}:\")\n",
    "        print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "COMPREHENSIVE DIVERSITY ANALYSIS\n",
      "============================================================\n",
      "1. Basic Diversity Indices (VegZ Core):\n",
      "           shannon   simpson  richness  evenness\n",
      "site_id                                         \n",
      "SITE_001  2.911125  0.938125        22  0.941794\n",
      "SITE_002  3.060434  0.942303        28  0.918441\n",
      "SITE_003  3.127717  0.948597        28  0.938633\n",
      "SITE_004  3.061307  0.947106        27  0.928841\n",
      "SITE_005  2.909975  0.936655        26  0.893152\n",
      "\n",
      "Diversity indices shape: (50, 4)\n",
      "\n",
      "2. All Available Diversity Indices (15 indices):\n",
      "           shannon   simpson  simpson_inv  richness  evenness  fisher_alpha  \\\n",
      "site_id                                                                       \n",
      "SITE_001  2.911125  0.061875    16.161521        22  0.941794      6.055646   \n",
      "SITE_002  3.060434  0.057697    17.331878        28  0.918441      8.059829   \n",
      "SITE_003  3.127717  0.051403    19.453954        28  0.938633      7.872871   \n",
      "SITE_004  3.061307  0.052894    18.905795        27  0.928841      7.723236   \n",
      "SITE_005  2.909975  0.063345    15.786448        26  0.893152      7.320015   \n",
      "\n",
      "          berger_parker  mcintosh  brillouin  menhinick  margalef      chao1  \\\n",
      "site_id                                                                        \n",
      "SITE_001       0.112108  0.805171   2.731514   1.473229  3.883731  22.000000   \n",
      "SITE_002       0.126984  0.810878   2.866156   1.763834  4.882963  29.000000   \n",
      "SITE_003       0.104478  0.823585   2.937790   1.710372  4.829201  28.000000   \n",
      "SITE_004       0.080972  0.822337   2.869694   1.717969  4.719217  27.083333   \n",
      "SITE_005       0.100806  0.799055   2.733635   1.651002  4.534383  44.000000   \n",
      "\n",
      "                ace  jack1  jack2  \n",
      "site_id                            \n",
      "SITE_001  22.000000     22   21.0  \n",
      "SITE_002  28.377358     30   28.0  \n",
      "SITE_003  28.000000     28   27.0  \n",
      "SITE_004  27.216216     28   24.5  \n",
      "SITE_005  27.836735     32   28.5  \n",
      "Available diversity indices: ['shannon', 'simpson', 'simpson_inv', 'richness', 'evenness', 'fisher_alpha', 'berger_parker', 'mcintosh', 'brillouin', 'menhinick', 'margalef', 'chao1', 'ace', 'jack1', 'jack2']\n",
      "\n",
      "5. Rarefaction Analysis:\n",
      "Rarefaction data shape: (2091, 3)\n",
      "Sample of rarefaction data:\n",
      "  sample_id  sample_size  expected_species\n",
      "0  SITE_001            1          1.000000\n",
      "1  SITE_001            7          5.915429\n",
      "2  SITE_001           13          9.433620\n",
      "3  SITE_001           19         12.008812\n",
      "4  SITE_001           25         13.934988\n",
      "5  SITE_001           31         15.405197\n",
      "6  SITE_001           37         16.548317\n",
      "7  SITE_001           43         17.451951\n",
      "8  SITE_001           49         18.176791\n",
      "9  SITE_001           55         18.765712\n"
     ]
    }
   ],
   "source": [
    "# Cell 4: Comprehensive Diversity Analysis\\n\\n# Test all diversity indices using VegZ core\n",
    "print(\"=\"*60)\n",
    "print(\"COMPREHENSIVE DIVERSITY ANALYSIS\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Calculate diversity using core VegZ functionality\n",
    "diversity_indices = veg.calculate_diversity([\n",
    "    'shannon', 'simpson', 'richness', 'evenness'\n",
    "])\n",
    "\n",
    "print(\"1. Basic Diversity Indices (VegZ Core):\")\n",
    "print(diversity_indices.head())\n",
    "print(f\"\\nDiversity indices shape: {diversity_indices.shape}\")\n",
    "\n",
    "# Use specialized DiversityAnalyzer for comprehensive analysis\n",
    "diversity_analyzer = DiversityAnalyzer()\n",
    "\n",
    "# Calculate all available diversity indices\n",
    "try:\n",
    "    all_diversity = diversity_analyzer.calculate_all_indices(species_abundance)\n",
    "    print(f\"\\n2. All Available Diversity Indices ({len(all_diversity.columns)} indices):\")\n",
    "    print(all_diversity.head())\n",
    "    print(f\"Available diversity indices: {diversity_analyzer.available_indices}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ All diversity indices calculation: {e}\")\n",
    "\n",
    "# Rarefaction curves\n",
    "try:\n",
    "    print(\"\\n5. Rarefaction Analysis:\")\n",
    "    rarefaction_data = veg.rarefaction_curve()\n",
    "    print(f\"Rarefaction data shape: {rarefaction_data.shape}\")\n",
    "    print(\"Sample of rarefaction data:\")\n",
    "    print(rarefaction_data.head(10))\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Rarefaction analysis: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "QUICK ANALYSIS FUNCTIONS TESTING\n",
      "============================================================\n",
      "1. Quick Diversity Analysis\n",
      "✓ Quick diversity analysis completed\n",
      "Shape: (50, 3)\n",
      "First 5 rows:\n",
      "           shannon   simpson  richness\n",
      "site_id                               \n",
      "SITE_001  2.911125  0.938125        22\n",
      "SITE_002  3.060434  0.942303        28\n",
      "SITE_003  3.127717  0.948597        28\n",
      "SITE_004  3.061307  0.947106        27\n",
      "SITE_005  2.909975  0.936655        26\n",
      "\n",
      "2. Quick Ordination Analysis\n",
      "✓ Quick PCA completed\n",
      "Explained variance ratio: [0.65586253 0.27589007 0.03733352]\n",
      "PCA scores shape: (50, 29)\n",
      "\n",
      "3. Quick Clustering Analysis\n",
      "✓ Quick K-means completed\n",
      "Inertia: 1.14\n",
      "Cluster distribution:\n",
      "cluster\n",
      "0    11\n",
      "1    11\n",
      "2    18\n",
      "3    10\n",
      "Name: count, dtype: int64\n",
      "\n",
      "4. Quick Elbow Analysis\n",
      "✓ Quick elbow analysis completed\n",
      "Recommended optimal k: 2\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 214000 characters]",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 5: Quick Functions Testing\\n\\n# Test quick analysis functions for immediate results\n",
    "print(\"=\"*60)\n",
    "print(\"QUICK ANALYSIS FUNCTIONS TESTING\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 1. Quick Diversity Analysis\n",
    "print(\"1. Quick Diversity Analysis\")\n",
    "try:\n",
    "    quick_div = quick_diversity_analysis(species_abundance)\n",
    "    print(\"✓ Quick diversity analysis completed\")\n",
    "    print(f\"Shape: {quick_div.shape}\")\n",
    "    print(\"First 5 rows:\")\n",
    "    print(quick_div.head())\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Quick diversity analysis: {e}\")\n",
    "\n",
    "# 2. Quick Ordination\n",
    "print(\"\\n2. Quick Ordination Analysis\")\n",
    "try:\n",
    "    quick_pca = quick_ordination(species_abundance, method='pca')\n",
    "    print(\"✓ Quick PCA completed\")\n",
    "    print(f\"Explained variance ratio: {quick_pca['explained_variance_ratio'][:3]}\")\n",
    "    print(f\"PCA scores shape: {quick_pca['scores'].shape}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Quick PCA: {e}\")\n",
    "\n",
    "# 3. Quick Clustering\n",
    "print(\"\\n3. Quick Clustering Analysis\")\n",
    "try:\n",
    "    quick_kmeans = quick_clustering(species_abundance, n_clusters=4, method='kmeans')\n",
    "    print(\"✓ Quick K-means completed\")\n",
    "    print(f\"Inertia: {quick_kmeans['inertia']:.2f}\")\n",
    "    print(\"Cluster distribution:\")\n",
    "    print(quick_kmeans['cluster_labels'].value_counts().sort_index())\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Quick K-means: {e}\")\n",
    "\n",
    "# 4. Quick Elbow Analysis\n",
    "print(\"\\n4. Quick Elbow Analysis\")\n",
    "try:\n",
    "    quick_elbow = quick_elbow_analysis(species_abundance, max_k=8, plot_results=True)\n",
    "    print(\"✓ Quick elbow analysis completed\")\n",
    "    if 'recommendations' in quick_elbow:\n",
    "        consensus_k = quick_elbow['recommendations']['consensus']\n",
    "        print(f\"Recommended optimal k: {consensus_k}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Quick elbow analysis: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "COMPREHENSIVE VISUALIZATION TESTING\n",
      "============================================================\n",
      "1. Diversity Visualizations\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 22404 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 21996 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Diversity distribution plots created\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 44628 characters]",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Environmental relationship plots created\n",
      "\n",
      "✓ All visualization testing completed\n"
     ]
    }
   ],
   "source": [
    "# Cell 6: Visualization Testing\\n\\n# Test visualization capabilities\n",
    "print(\"=\"*60)\n",
    "print(\"COMPREHENSIVE VISUALIZATION TESTING\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Set up plot parameters\n",
    "plt.rcParams['figure.figsize'] = (12, 8)\n",
    "plt.rcParams['font.size'] = 10\n",
    "\n",
    "# 1. Diversity Visualizations\n",
    "print(\"1. Diversity Visualizations\")\n",
    "\n",
    "# Plot diversity indices\n",
    "try:\n",
    "    fig1 = veg.plot_diversity(diversity_indices, 'shannon')\n",
    "    plt.title('Shannon Diversity Distribution')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    fig2 = veg.plot_diversity(diversity_indices, 'richness')\n",
    "    plt.title('Species Richness Distribution')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    print(\"✓ Diversity distribution plots created\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Diversity plots: {e}\")\n",
    "\n",
    "# 2. Environmental Relationship Plots\n",
    "try:\n",
    "    # Select key environmental variables\n",
    "    env_vars = ['elevation', 'soil_ph', 'temperature', 'precipitation']\n",
    "    available_env_vars = [var for var in env_vars if var in environmental_data.columns]\n",
    "\n",
    "    if len(available_env_vars) >= 2:\n",
    "        fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "        for i, env_var in enumerate(available_env_vars[:2]):\n",
    "            axes[i].scatter(environmental_data[env_var], diversity_indices['shannon'], alpha=0.7, s=50)\n",
    "            axes[i].set_xlabel(env_var.replace('_', ' ').title())\n",
    "            axes[i].set_ylabel('Shannon Diversity')\n",
    "            axes[i].set_title(f'Diversity vs {env_var.replace(\"_\", \" \").title()}')\n",
    "            axes[i].grid(True, alpha=0.3)\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        print(\"✓ Environmental relationship plots created\")\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Environmental relationship plots: {e}\")\n",
    "\n",
    "print(\"\\n✓ All visualization testing completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "MULTIVARIATE ANALYSIS TESTING\n",
      "============================================================\n",
      "1. Principal Component Analysis (PCA)\n",
      "✓ PCA analysis completed\n",
      "Explained variance ratio (first 3 components): [0.65586253 0.27589007 0.03733352]\n",
      "Cumulative variance explained: [0.65586253 0.93175259 0.96908611]\n",
      "PCA scores shape: (50, 29)\n",
      "\n",
      "2. Non-metric Multidimensional Scaling (NMDS)\n",
      "⚠ NMDS analysis: VegZ.nmds_analysis() got an unexpected keyword argument 'k'\n",
      "\n",
      "3. Correspondence Analysis (CA)\n",
      "✓ CA analysis completed\n",
      "Eigenvalues (first 3): [4247.58782056 1910.53075723  278.57550428]\n",
      "Site scores shape: (50, 29)\n",
      "Species scores shape: (29, 29)\n",
      "\n",
      "4. Alternative Ordination Methods\n",
      "✓ Quick PCA completed - Variance explained: [0.65586253 0.27589007]\n",
      "✓ Quick NMDS completed - Stress: 0.1704534395626517\n",
      "\n",
      "5. Distance Matrix Analysis\n",
      "✓ Braycurtis distance matrix calculated: (50, 50)\n",
      "⚠ jaccard distance: 'DataFrame' object has no attribute 'dtype'\n",
      "✓ Euclidean distance matrix calculated: (50, 50)\n",
      "Distance matrices calculated: 2\n",
      "\n",
      "✓ All multivariate analyses completed\n"
     ]
    }
   ],
   "source": [
    "# Cell 7: Multivariate Analysis Testing\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"MULTIVARIATE ANALYSIS TESTING\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Initialize multivariate analyzer\n",
    "multivar = MultivariateAnalyzer()\n",
    "\n",
    "# 1. Principal Component Analysis (PCA)\n",
    "print(\"1. Principal Component Analysis (PCA)\")\n",
    "try:\n",
    "    # Use VegZ core PCA functionality\n",
    "    pca_results = veg.pca_analysis(transform='hellinger')\n",
    "    print(\"✓ PCA analysis completed\")\n",
    "    print(f\"Explained variance ratio (first 3 components): {pca_results['explained_variance_ratio'][:3]}\")\n",
    "    print(f\"Cumulative variance explained: {pca_results['explained_variance_ratio'][:3].cumsum()}\")\n",
    "    print(f\"PCA scores shape: {pca_results['scores'].shape}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ PCA analysis: {e}\")\n",
    "\n",
    "# 2. Non-metric Multidimensional Scaling (NMDS)\n",
    "print(\"\\n2. Non-metric Multidimensional Scaling (NMDS)\")\n",
    "try:\n",
    "    # Use VegZ core NMDS functionality\n",
    "    nmds_results = veg.nmds_analysis(distance_metric='bray_curtis', k=2)\n",
    "    print(\"✓ NMDS analysis completed\")\n",
    "    print(f\"Final stress: {nmds_results['stress']:.4f}\")\n",
    "    print(f\"Convergence achieved: {nmds_results['converged']}\")\n",
    "    print(f\"Number of iterations: {nmds_results['n_iter']}\")\n",
    "    print(f\"NMDS scores shape: {nmds_results['scores'].shape}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ NMDS analysis: {e}\")\n",
    "\n",
    "# 3. Correspondence Analysis (CA)\n",
    "print(\"\\n3. Correspondence Analysis (CA)\")\n",
    "try:\n",
    "    ca_results = multivar.correspondence_analysis(species_abundance)\n",
    "    print(\"✓ CA analysis completed\")\n",
    "    print(f\"Eigenvalues (first 3): {ca_results['eigenvalues'][:3]}\")\n",
    "    print(f\"Site scores shape: {ca_results['site_scores'].shape}\")\n",
    "    print(f\"Species scores shape: {ca_results['species_scores'].shape}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ CA analysis: {e}\")\n",
    "\n",
    "# 4. Alternative multivariate methods using VegZ core\n",
    "print(\"\\n4. Alternative Ordination Methods\")\n",
    "try:\n",
    "    # Try different distance-based ordination\n",
    "    ordination_methods = ['pca', 'nmds']\n",
    "    for method in ordination_methods:\n",
    "        try:\n",
    "            if method == 'pca':\n",
    "                result = quick_ordination(species_abundance, method='pca')\n",
    "                print(f\"✓ Quick {method.upper()} completed - Variance explained: {result['explained_variance_ratio'][:2]}\")\n",
    "            elif method == 'nmds':\n",
    "                result = quick_ordination(species_abundance, method='nmds')\n",
    "                print(f\"✓ Quick {method.upper()} completed - Stress: {result.get('stress', 'N/A')}\")\n",
    "        except Exception as e:\n",
    "            print(f\"⚠ Quick {method}: {e}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Alternative ordination methods: {e}\")\n",
    "\n",
    "# 5. Distance matrix analysis\n",
    "print(\"\\n5. Distance Matrix Analysis\")\n",
    "try:\n",
    "    # Calculate different ecological distances\n",
    "    from sklearn.metrics import pairwise_distances\n",
    "    \n",
    "    distance_metrics = ['braycurtis', 'jaccard', 'euclidean']\n",
    "    distance_results = {}\n",
    "    \n",
    "    for metric in distance_metrics:\n",
    "        try:\n",
    "            if metric in ['braycurtis', 'jaccard']:\n",
    "                # For ecological data, use relative abundances\n",
    "                data_rel = species_abundance.div(species_abundance.sum(axis=1), axis=0).fillna(0)\n",
    "                distances = pairwise_distances(data_rel, metric=metric)\n",
    "            else:\n",
    "                distances = pairwise_distances(species_abundance, metric=metric)\n",
    "            \n",
    "            distance_results[metric] = distances\n",
    "            print(f\"✓ {metric.title()} distance matrix calculated: {distances.shape}\")\n",
    "        except Exception as e:\n",
    "            print(f\"⚠ {metric} distance: {e}\")\n",
    "            \n",
    "    print(f\"Distance matrices calculated: {len(distance_results)}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Distance matrix analysis: {e}\")\n",
    "\n",
    "print(\"\\n✓ All multivariate analyses completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "ADVANCED CLUSTERING ANALYSIS\n",
      "============================================================\n",
      "1. Comprehensive Elbow Analysis (5 algorithms)\n",
      "⚠ Elbow analysis: 'VegetationClustering' object has no attribute 'elbow_analysis'\n",
      "\n",
      "2. Hierarchical Clustering Analysis\n",
      "⚠ Hierarchical clustering: VegetationClustering.hierarchical_clustering() got an unexpected keyword argument 'distance_metric'\n",
      "\n",
      "3. TWINSPAN Classification\n",
      "✓ TWINSPAN analysis completed\n",
      "Final classification: 1.0    50\n",
      "Name: count, dtype: int64\n",
      "⚠ TWINSPAN analysis: 'division_history'\n",
      "\n",
      "4. Fuzzy C-means Clustering\n",
      "⚠ Fuzzy C-means: 'VegetationClustering' object has no attribute 'fuzzy_cmeans'\n",
      "\n",
      "✓ All advanced clustering analyses completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\VegZ\\clustering.py:182: PerformanceWarning:\n",
      "\n",
      "DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\numpy\\lib\\_function_base_impl.py:3045: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in divide\n",
      "\n",
      "C:\\Python 3.13.5\\Lib\\site-packages\\numpy\\lib\\_function_base_impl.py:3046: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in divide\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Cell 8: Advanced Clustering Analysis\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"ADVANCED CLUSTERING ANALYSIS\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Initialize clustering analyzer\n",
    "clustering = VegetationClustering()\n",
    "\n",
    "# 1. Comprehensive Elbow Analysis\n",
    "print(\"1. Comprehensive Elbow Analysis (5 algorithms)\")\n",
    "try:\n",
    "    elbow_results = clustering.elbow_analysis(\n",
    "        species_abundance,\n",
    "        k_range=range(2, 12),\n",
    "        methods=['knee_locator', 'derivative', 'variance_explained', 'jump_method', 'l_method'],\n",
    "        plot_results=True\n",
    "    )\n",
    "    print(\"✓ Comprehensive elbow analysis completed\")\n",
    "    print(f\"Individual recommendations: {elbow_results['individual_recommendations']}\")\n",
    "    print(f\"Consensus recommendation: {elbow_results['consensus_recommendation']}\")\n",
    "    print(f\"Confidence scores: {elbow_results['confidence_scores']}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Elbow analysis: {e}\")\n",
    "\n",
    "# 2. Hierarchical Clustering with different linkage methods\n",
    "print(\"\\n2. Hierarchical Clustering Analysis\")\n",
    "try:\n",
    "    hierarchical_results = clustering.hierarchical_clustering(\n",
    "        species_abundance, \n",
    "        method='ward',\n",
    "        distance_metric='euclidean',\n",
    "        n_clusters=4\n",
    "    )\n",
    "    print(\"✓ Hierarchical clustering completed\")\n",
    "    print(f\"Cluster distribution: {pd.Series(hierarchical_results['cluster_labels']).value_counts().sort_index()}\")\n",
    "    print(f\"Cophenetic correlation: {hierarchical_results['cophenetic_correlation']:.4f}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Hierarchical clustering: {e}\")\n",
    "\n",
    "# 3. TWINSPAN Analysis (Two-Way Indicator Species Analysis)\n",
    "print(\"\\n3. TWINSPAN Classification\")\n",
    "try:\n",
    "    twinspan_results = clustering.twinspan(\n",
    "        species_abundance,\n",
    "        cut_levels=[0, 2, 5, 10, 20],\n",
    "        max_divisions=4,\n",
    "        min_group_size=3\n",
    "    )\n",
    "    print(\"✓ TWINSPAN analysis completed\")\n",
    "    print(f\"Final classification: {pd.Series(twinspan_results['site_classification']).value_counts()}\")\n",
    "    print(f\"Number of divisions performed: {len(twinspan_results['division_history'])}\")\n",
    "    if 'indicator_species' in twinspan_results:\n",
    "        print(f\"Indicator species found: {len(twinspan_results['indicator_species'])} species\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ TWINSPAN analysis: {e}\")\n",
    "\n",
    "# 4. Fuzzy C-means clustering\n",
    "print(\"\\n4. Fuzzy C-means Clustering\")\n",
    "try:\n",
    "    fuzzy_results = clustering.fuzzy_cmeans(species_abundance, n_clusters=4)\n",
    "    print(\"✓ Fuzzy C-means completed\")\n",
    "    print(f\"Final objective function value: {fuzzy_results['objective_function']:.4f}\")\n",
    "    print(f\"Membership matrix shape: {fuzzy_results['membership_matrix'].shape}\")\n",
    "    print(f\"Average membership strength: {fuzzy_results['membership_matrix'].max(axis=1).mean():.3f}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Fuzzy C-means: {e}\")\n",
    "\n",
    "print(\"\\n✓ All advanced clustering analyses completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 9: Statistical Analysis and Hypothesis Testing\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"STATISTICAL ANALYSIS AND HYPOTHESIS TESTING\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Initialize statistical analyzer\n",
    "stats_analyzer = EcologicalStatistics()\n",
    "\n",
    "# Create some cluster labels for testing\n",
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters=4, random_state=42)\n",
    "cluster_labels = kmeans.fit_predict(species_abundance)\n",
    "\n",
    "# 1. PERMANOVA (Permutational Multivariate Analysis of Variance)\n",
    "print(\"1. PERMANOVA Analysis\")\n",
    "try:\n",
    "    permanova_results = stats_analyzer.permanova(\n",
    "        species_abundance, \n",
    "        cluster_labels, \n",
    "        distance_metric='bray_curtis',\n",
    "        n_permutations=999\n",
    "    )\n",
    "    print(\"✓ PERMANOVA analysis completed\")\n",
    "    print(f\"F-statistic: {permanova_results['F_statistic']:.4f}\")\n",
    "    print(f\"p-value: {permanova_results['p_value']:.4f}\")\n",
    "    print(f\"R-squared: {permanova_results['R_squared']:.4f}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ PERMANOVA analysis: {e}\")\n",
    "\n",
    "# 2. ANOSIM (Analysis of Similarities)\n",
    "print(\"\\n2. ANOSIM Analysis\")\n",
    "try:\n",
    "    anosim_results = stats_analyzer.anosim(\n",
    "        species_abundance, \n",
    "        cluster_labels, \n",
    "        distance_metric='bray_curtis',\n",
    "        n_permutations=999\n",
    "    )\n",
    "    print(\"✓ ANOSIM analysis completed\")\n",
    "    print(f\"R-statistic: {anosim_results['R_statistic']:.4f}\")\n",
    "    print(f\"p-value: {anosim_results['p_value']:.4f}\")\n",
    "    if anosim_results['R_statistic'] > 0.5:\n",
    "        print(\"Strong group separation detected\")\n",
    "    elif anosim_results['R_statistic'] > 0.25:\n",
    "        print(\"Moderate group separation detected\")\n",
    "    else:\n",
    "        print(\"Weak group separation detected\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ ANOSIM analysis: {e}\")\n",
    "\n",
    "# 3. Mantel Test\n",
    "print(\"\\n3. Mantel Test\")\n",
    "try:\n",
    "    # Create environmental distance matrix\n",
    "    env_subset = environmental_data.iloc[:, :3].fillna(environmental_data.mean())\n",
    "    mantel_results = stats_analyzer.mantel_test(\n",
    "        species_abundance, \n",
    "        env_subset,\n",
    "        species_metric='bray_curtis',\n",
    "        env_metric='euclidean',\n",
    "        n_permutations=999\n",
    "    )\n",
    "    print(\"✓ Mantel test completed\")\n",
    "    print(f\"Mantel r: {mantel_results['mantel_r']:.4f}\")\n",
    "    print(f\"p-value: {mantel_results['p_value']:.4f}\")\n",
    "    print(f\"Significance: {'Significant' if mantel_results['p_value'] < 0.05 else 'Not significant'}\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Mantel test: {e}\")\n",
    "\n",
    "# 4. Indicator Species Analysis\n",
    "print(\"\\n4. Indicator Species Analysis\")\n",
    "try:\n",
    "    indicator_results = stats_analyzer.indicator_species_analysis(\n",
    "        species_abundance, \n",
    "        cluster_labels,\n",
    "        significance_threshold=0.05\n",
    "    )\n",
    "    print(\"✓ Indicator species analysis completed\")\n",
    "    significant_indicators = sum(1 for p in indicator_results['p_values'].values() if p < 0.05)\n",
    "    print(f\"Significant indicator species found: {significant_indicators}\")\n",
    "    \n",
    "    # Show top 5 indicator species\n",
    "    sorted_indicators = sorted(indicator_results['indicator_values'].items(), \n",
    "                             key=lambda x: x[1], reverse=True)[:5]\n",
    "    print(\"Top 5 indicator species:\")\n",
    "    for species, value in sorted_indicators:\n",
    "        p_val = indicator_results['p_values'][species]\n",
    "        print(f\"  {species}: IV = {value:.3f}, p = {p_val:.3f}\")\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Indicator species analysis: {e}\")\n",
    "\n",
    "print(\"\\n✓ All statistical analyses completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 10: Environmental Modeling and Species Response Curves\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"ENVIRONMENTAL MODELING AND SPECIES RESPONSE\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Initialize environmental modeler\n",
    "env_modeler = EnvironmentalModeler()\n",
    "\n",
    "# Select top 5 most abundant species for modeling\n",
    "species_totals = species_abundance.sum()\n",
    "top_species = species_totals.nlargest(5).index.tolist()\n",
    "print(f\"Modeling response curves for top 5 species: {top_species}\")\n",
    "\n",
    "# 1. Gaussian Response Curves\n",
    "print(\"\\n1. Gaussian Species Response Curves\")\n",
    "try:\n",
    "    # Use a continuous environmental variable\n",
    "    env_var = environmental_data.iloc[:, 0].fillna(environmental_data.iloc[:, 0].mean())\n",
    "    \n",
    "    gaussian_results = {}\n",
    "    for species in top_species[:3]:  # Test with 3 species\n",
    "        species_data = species_abundance[species]\n",
    "        try:\n",
    "            response = env_modeler.gaussian_response_curve(\n",
    "                species_data, \n",
    "                env_var,\n",
    "                return_params=True\n",
    "            )\n",
    "            gaussian_results[species] = response\n",
    "            print(f\"✓ {species}: optimum = {response['optimum']:.2f}, tolerance = {response['tolerance']:.2f}\")\n",
    "        except Exception as e:\n",
    "            print(f\"⚠ {species} gaussian response: {e}\")\n",
    "            \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Gaussian response modeling: {e}\")\n",
    "\n",
    "# 2. GAM (Generalized Additive Models)\n",
    "print(\"\\n2. Generalized Additive Models (GAM)\")\n",
    "try:\n",
    "    # Select environmental predictors\n",
    "    env_predictors = environmental_data.iloc[:, :4].fillna(environmental_data.mean())\n",
    "    \n",
    "    gam_results = {}\n",
    "    for species in top_species[:2]:  # Test with 2 species\n",
    "        species_data = species_abundance[species]\n",
    "        try:\n",
    "            gam_result = env_modeler.gam_model(\n",
    "                species_data,\n",
    "                env_predictors,\n",
    "                smoother_type='spline'\n",
    "            )\n",
    "            gam_results[species] = gam_result\n",
    "            print(f\"✓ {species}: R² = {gam_result['r_squared']:.3f}, AIC = {gam_result['aic']:.1f}\")\n",
    "        except Exception as e:\n",
    "            print(f\"⚠ {species} GAM: {e}\")\n",
    "            \n",
    "except Exception as e:\n",
    "    print(f\"⚠ GAM modeling: {e}\")\n",
    "\n",
    "# 3. Environmental Niche Analysis\n",
    "print(\"\\n3. Environmental Niche Analysis\")\n",
    "try:\n",
    "    niche_results = env_modeler.niche_analysis(\n",
    "        species_abundance.iloc[:, :10],  # Use first 10 species\n",
    "        environmental_data.iloc[:, :3].fillna(environmental_data.mean()),\n",
    "        method='hypervolume'\n",
    "    )\n",
    "    print(\"✓ Environmental niche analysis completed\")\n",
    "    print(f\"Niche volumes calculated for {len(niche_results['niche_volumes'])} species\")\n",
    "    print(f\"Mean niche volume: {np.mean(list(niche_results['niche_volumes'].values())):.4f}\")\n",
    "    \n",
    "    # Show species with largest and smallest niches\n",
    "    sorted_niches = sorted(niche_results['niche_volumes'].items(), key=lambda x: x[1], reverse=True)\n",
    "    print(f\"Largest niche: {sorted_niches[0][0]} (volume: {sorted_niches[0][1]:.4f})\")\n",
    "    print(f\"Smallest niche: {sorted_niches[-1][0]} (volume: {sorted_niches[-1][1]:.4f})\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Niche analysis: {e}\")\n",
    "\n",
    "# 4. Environmental Gradient Analysis\n",
    "print(\"\\n4. Environmental Gradient Analysis\")\n",
    "try:\n",
    "    # Create artificial gradient based on first environmental variable\n",
    "    env_gradient = environmental_data.iloc[:, 0].fillna(environmental_data.iloc[:, 0].mean())\n",
    "    \n",
    "    gradient_results = env_modeler.gradient_analysis(\n",
    "        species_abundance.iloc[:, :8],  # Use first 8 species\n",
    "        env_gradient,\n",
    "        n_segments=5\n",
    "    )\n",
    "    print(\"✓ Environmental gradient analysis completed\")\n",
    "    print(f\"Gradient segments: {len(gradient_results['segment_boundaries'])} segments\")\n",
    "    print(f\"Species turnover along gradient: {gradient_results['species_turnover']:.3f}\")\n",
    "    \n",
    "    # Show species distribution along gradient\n",
    "    if 'segment_composition' in gradient_results:\n",
    "        print(\"Species composition along gradient:\")\n",
    "        for i, composition in enumerate(gradient_results['segment_composition']):\n",
    "            dominant_species = max(composition.items(), key=lambda x: x[1])\n",
    "            print(f\"  Segment {i+1}: {dominant_species[0]} ({dominant_species[1]:.1f}%)\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Gradient analysis: {e}\")\n",
    "\n",
    "print(\"\\n✓ All environmental modeling completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 11: Specialized Methods and Advanced Analysis\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"SPECIALIZED METHODS AND ADVANCED ANALYSIS\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 1. Functional Trait Analysis\n",
    "print(\"1. Functional Trait Analysis\")\n",
    "try:\n",
    "    trait_analyzer = FunctionalTraitAnalyzer()\n",
    "    \n",
    "    # Calculate functional diversity indices\n",
    "    functional_diversity = trait_analyzer.functional_diversity(\n",
    "        species_abundance.iloc[:, :15],  # Use first 15 species\n",
    "        species_traits.iloc[:15, :5].fillna(species_traits.mean())  # Use first 5 traits\n",
    "    )\n",
    "    print(\"✓ Functional diversity analysis completed\")\n",
    "    print(f\"Mean Functional Richness (FRic): {functional_diversity['FRic'].mean():.3f}\")\n",
    "    print(f\"Mean Functional Evenness (FEve): {functional_diversity['FEve'].mean():.3f}\")\n",
    "    print(f\"Mean Functional Divergence (FDiv): {functional_diversity['FDiv'].mean():.3f}\")\n",
    "    \n",
    "    # Community-weighted means\n",
    "    cwm_results = trait_analyzer.community_weighted_means(\n",
    "        species_abundance.iloc[:, :15],\n",
    "        species_traits.iloc[:15, :3].fillna(species_traits.mean())\n",
    "    )\n",
    "    print(f\"Community-weighted means shape: {cwm_results.shape}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Functional trait analysis: {e}\")\n",
    "\n",
    "# 2. Phylogenetic Diversity\n",
    "print(\"\\n2. Phylogenetic Diversity Analysis\")\n",
    "try:\n",
    "    phylo_analyzer = PhylogeneticDiversityAnalyzer()\n",
    "    \n",
    "    # Use phylogenetic distances matrix\n",
    "    phylo_div = phylo_analyzer.calculate_phylogenetic_diversity(\n",
    "        species_abundance.iloc[:, :15],\n",
    "        phylo_distances.iloc[:15, :15]\n",
    "    )\n",
    "    print(\"✓ Phylogenetic diversity analysis completed\")\n",
    "    print(f\"Mean Faith's PD: {phylo_div['faiths_pd'].mean():.3f}\")\n",
    "    print(f\"Mean Phylogenetic Endemism: {phylo_div['phylo_endemism'].mean():.3f}\")\n",
    "    \n",
    "    # Phylogenetic structure tests\n",
    "    phylo_structure = phylo_analyzer.phylogenetic_structure(\n",
    "        species_abundance.iloc[:10, :15],  # Use first 10 sites\n",
    "        phylo_distances.iloc[:15, :15],\n",
    "        n_randomizations=99\n",
    "    )\n",
    "    print(f\"Sites with phylogenetic clustering: {sum(phylo_structure['nri_p_values'] < 0.05)}\")\n",
    "    print(f\"Sites with phylogenetic overdispersion: {sum(phylo_structure['nri_p_values'] > 0.95)}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Phylogenetic diversity analysis: {e}\")\n",
    "\n",
    "# 3. Nestedness Analysis\n",
    "print(\"\\n3. Nestedness Analysis\")\n",
    "try:\n",
    "    nestedness_analyzer = NestednessAnalyzer()\n",
    "    \n",
    "    # Calculate NODF (Nestedness based on Overlap and Decreasing Fill)\n",
    "    nestedness_results = nestedness_analyzer.calculate_nodf(\n",
    "        (species_abundance > 0).astype(int)  # Convert to presence-absence\n",
    "    )\n",
    "    print(\"✓ Nestedness analysis completed\")\n",
    "    print(f\"NODF value: {nestedness_results['nodf']:.3f}\")\n",
    "    print(f\"Site nestedness: {nestedness_results['site_nodf']:.3f}\")\n",
    "    print(f\"Species nestedness: {nestedness_results['species_nodf']:.3f}\")\n",
    "    \n",
    "    # Null model test\n",
    "    null_test = nestedness_analyzer.null_model_test(\n",
    "        (species_abundance > 0).astype(int),\n",
    "        null_model='fixed_marginals',\n",
    "        n_randomizations=99\n",
    "    )\n",
    "    print(f\"Significance (p-value): {null_test['p_value']:.3f}\")\n",
    "    print(f\"Standardized effect size: {null_test['ses']:.3f}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Nestedness analysis: {e}\")\n",
    "\n",
    "# 4. Network Analysis\n",
    "print(\"\\n4. Co-occurrence Network Analysis\")\n",
    "try:\n",
    "    network_analyzer = NetworkAnalyzer()\n",
    "    \n",
    "    # Build co-occurrence network\n",
    "    network_results = network_analyzer.build_cooccurrence_network(\n",
    "        species_abundance.iloc[:, :20],  # Use first 20 species\n",
    "        correlation_threshold=0.3,\n",
    "        significance_level=0.05\n",
    "    )\n",
    "    print(\"✓ Co-occurrence network analysis completed\")\n",
    "    print(f\"Number of nodes: {network_results['n_nodes']}\")\n",
    "    print(f\"Number of edges: {network_results['n_edges']}\")\n",
    "    print(f\"Network density: {network_results['density']:.3f}\")\n",
    "    \n",
    "    # Network properties\n",
    "    if network_results['n_edges'] > 0:\n",
    "        network_props = network_analyzer.network_properties(network_results['network'])\n",
    "        print(f\"Modularity: {network_props['modularity']:.3f}\")\n",
    "        print(f\"Average clustering coefficient: {network_props['avg_clustering']:.3f}\")\n",
    "        print(f\"Number of modules: {len(set(network_props['modules'].values()))}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Network analysis: {e}\")\n",
    "\n",
    "print(\"\\n✓ All specialized analyses completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 12: Machine Learning and Predictive Modeling\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"MACHINE LEARNING AND PREDICTIVE MODELING\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Initialize machine learning analyzer\n",
    "ml_analyzer = MachineLearningAnalyzer()\n",
    "\n",
    "# 1. Species Distribution Modeling (SDM)\n",
    "print(\"1. Species Distribution Modeling\")\n",
    "try:\n",
    "    # Select most abundant species for modeling\n",
    "    target_species = species_abundance.sum().idxmax()\n",
    "    species_data = (species_abundance[target_species] > 0).astype(int)  # Presence-absence\n",
    "    \n",
    "    # Use environmental predictors\n",
    "    env_predictors = environmental_data.iloc[:, :5].fillna(environmental_data.mean())\n",
    "    \n",
    "    sdm_results = ml_analyzer.species_distribution_model(\n",
    "        species_data,\n",
    "        env_predictors,\n",
    "        model_type='random_forest',\n",
    "        test_size=0.3,\n",
    "        cv_folds=5\n",
    "    )\n",
    "    print(f\"✓ SDM for {target_species} completed\")\n",
    "    print(f\"Cross-validation AUC: {sdm_results['cv_auc']:.3f} (±{sdm_results['cv_auc_std']:.3f})\")\n",
    "    print(f\"Test AUC: {sdm_results['test_auc']:.3f}\")\n",
    "    print(f\"Feature importance (top 3):\")\n",
    "    \n",
    "    # Show top 3 most important features\n",
    "    importance_sorted = sorted(sdm_results['feature_importance'].items(), \n",
    "                              key=lambda x: x[1], reverse=True)\n",
    "    for feature, importance in importance_sorted[:3]:\n",
    "        print(f\"  {feature}: {importance:.3f}\")\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Species distribution modeling: {e}\")\n",
    "\n",
    "# 2. Community Classification\n",
    "print(\"\\n2. Vegetation Community Classification\")\n",
    "try:\n",
    "    # Use hierarchical clusters as target\n",
    "    from sklearn.cluster import AgglomerativeClustering\n",
    "    target_clusters = AgglomerativeClustering(n_clusters=4).fit_predict(species_abundance)\n",
    "    \n",
    "    classification_results = ml_analyzer.community_classification(\n",
    "        species_abundance,\n",
    "        target_clusters,\n",
    "        classifier_type='gradient_boosting',\n",
    "        test_size=0.3,\n",
    "        cv_folds=5\n",
    "    )\n",
    "    print(\"✓ Community classification completed\")\n",
    "    print(f\"Cross-validation accuracy: {classification_results['cv_accuracy']:.3f} (±{classification_results['cv_accuracy_std']:.3f})\")\n",
    "    print(f\"Test accuracy: {classification_results['test_accuracy']:.3f}\")\n",
    "    print(f\"F1-score: {classification_results['test_f1_score']:.3f}\")\n",
    "    \n",
    "    # Show species importance\n",
    "    if 'feature_importance' in classification_results:\n",
    "        species_importance = sorted(classification_results['feature_importance'].items(),\n",
    "                                   key=lambda x: x[1], reverse=True)\n",
    "        print(\"Most important species (top 5):\")\n",
    "        for species, importance in species_importance[:5]:\n",
    "            print(f\"  {species}: {importance:.3f}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Community classification: {e}\")\n",
    "\n",
    "# 3. Abundance Prediction\n",
    "print(\"\\n3. Species Abundance Prediction\")\n",
    "try:\n",
    "    # Predict abundance of most variable species\n",
    "    species_cv = species_abundance.std() / species_abundance.mean()\n",
    "    target_species = species_cv.idxmax()\n",
    "    target_abundance = species_abundance[target_species]\n",
    "    \n",
    "    # Use other species as predictors\n",
    "    predictor_species = species_abundance.drop(columns=[target_species]).iloc[:, :10]  # Use first 10 species\n",
    "    \n",
    "    abundance_results = ml_analyzer.abundance_prediction(\n",
    "        target_abundance,\n",
    "        predictor_species,\n",
    "        model_type='random_forest',\n",
    "        test_size=0.3,\n",
    "        cv_folds=5\n",
    "    )\n",
    "    print(f\"✓ Abundance prediction for {target_species} completed\")\n",
    "    print(f\"Cross-validation R²: {abundance_results['cv_r2']:.3f} (±{abundance_results['cv_r2_std']:.3f})\")\n",
    "    print(f\"Test R²: {abundance_results['test_r2']:.3f}\")\n",
    "    print(f\"Test RMSE: {abundance_results['test_rmse']:.2f}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Abundance prediction: {e}\")\n",
    "\n",
    "# 4. Ensemble Modeling\n",
    "print(\"\\n4. Ensemble Model Performance\")\n",
    "try:\n",
    "    # Create ensemble for species presence prediction\n",
    "    target_species = species_abundance.sum().nlargest(3).index[1]  # Second most abundant\n",
    "    presence_data = (species_abundance[target_species] > 0).astype(int)\n",
    "    \n",
    "    ensemble_results = ml_analyzer.ensemble_modeling(\n",
    "        presence_data,\n",
    "        environmental_data.iloc[:, :6].fillna(environmental_data.mean()),\n",
    "        models=['random_forest', 'gradient_boosting', 'svm'],\n",
    "        test_size=0.3,\n",
    "        cv_folds=5\n",
    "    )\n",
    "    print(f\"✓ Ensemble modeling for {target_species} completed\")\n",
    "    print(\"Individual model performance:\")\n",
    "    for model_name, performance in ensemble_results['individual_performance'].items():\n",
    "        print(f\"  {model_name}: AUC = {performance['auc']:.3f}\")\n",
    "    \n",
    "    print(f\"Ensemble AUC: {ensemble_results['ensemble_auc']:.3f}\")\n",
    "    print(f\"Best single model: {ensemble_results['best_model']}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Ensemble modeling: {e}\")\n",
    "\n",
    "print(\"\\n✓ All machine learning analyses completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 13: Data Quality and Validation Testing\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"DATA QUALITY AND VALIDATION TESTING\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# Test data quality functions\n",
    "print(\"1. Data Quality Assessment\")\n",
    "\n",
    "# 1. Spatial validation\n",
    "print(\"\\n1.1 Spatial Coordinate Validation\")\n",
    "try:\n",
    "    # Create test coordinate data\n",
    "    test_coords = pd.DataFrame({\n",
    "        'latitude': [45.5, 46.2, 200.0, -91.0, 47.1],  # Include invalid coordinates\n",
    "        'longitude': [-72.5, -71.8, -73.2, -72.1, 181.0],\n",
    "        'site_id': ['SITE_001', 'SITE_002', 'SITE_003', 'SITE_004', 'SITE_005']\n",
    "    })\n",
    "    \n",
    "    coord_validation = data_quality.validate_coordinates(test_coords)\n",
    "    print(\"✓ Coordinate validation completed\")\n",
    "    print(f\"Valid coordinates: {coord_validation['valid_coords'].sum()}/{len(test_coords)}\")\n",
    "    print(f\"Invalid latitude count: {coord_validation['invalid_lat'].sum()}\")\n",
    "    print(f\"Invalid longitude count: {coord_validation['invalid_lon'].sum()}\")\n",
    "    \n",
    "    # Geographic outlier detection\n",
    "    outliers = data_quality.detect_geographic_outliers(\n",
    "        test_coords[coord_validation['valid_coords']], \n",
    "        country='US'\n",
    "    )\n",
    "    print(f\"Geographic outliers detected: {outliers['outliers'].sum()}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Spatial validation: {e}\")\n",
    "\n",
    "# 2. Temporal validation\n",
    "print(\"\\n1.2 Temporal Data Validation\")\n",
    "try:\n",
    "    # Create test temporal data\n",
    "    test_dates = pd.DataFrame({\n",
    "        'date': ['2023-05-15', '2025-12-32', '1800-01-01', '2024-02-29', 'invalid-date'],\n",
    "        'site_id': ['SITE_001', 'SITE_002', 'SITE_003', 'SITE_004', 'SITE_005']\n",
    "    })\n",
    "    \n",
    "    temporal_validation = data_quality.validate_temporal_data(test_dates)\n",
    "    print(\"✓ Temporal validation completed\")\n",
    "    print(f\"Valid dates: {temporal_validation['valid_dates'].sum()}/{len(test_dates)}\")\n",
    "    print(f\"Invalid dates: {temporal_validation['invalid_dates'].sum()}\")\n",
    "    print(f\"Future dates: {temporal_validation['future_dates'].sum()}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Temporal validation: {e}\")\n",
    "\n",
    "# 2. Data Integrity Checks\n",
    "print(\"\\n2. Species Data Integrity\")\n",
    "try:\n",
    "    integrity_results = data_quality.check_species_data_integrity(species_abundance)\n",
    "    print(\"✓ Species data integrity check completed\")\n",
    "    print(f\"Empty sites: {integrity_results['empty_sites']}\")\n",
    "    print(f\"Empty species: {integrity_results['empty_species']}\")\n",
    "    print(f\"Negative values: {integrity_results['negative_values']}\")\n",
    "    print(f\"Missing values: {integrity_results['missing_values']}\")\n",
    "    print(f\"Data completeness: {integrity_results['completeness_ratio']:.1%}\")\n",
    "    \n",
    "    # Duplicate detection\n",
    "    duplicates = data_quality.detect_duplicate_records(species_abundance)\n",
    "    print(f\"Potential duplicate sites: {len(duplicates['duplicate_pairs'])}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Data integrity check: {e}\")\n",
    "\n",
    "# 3. Statistical outlier detection\n",
    "print(\"\\n3. Statistical Outlier Detection\")\n",
    "try:\n",
    "    # Detect abundance outliers\n",
    "    abundance_outliers = data_quality.detect_abundance_outliers(\n",
    "        species_abundance, \n",
    "        method='iqr',\n",
    "        threshold=3.0\n",
    "    )\n",
    "    print(\"✓ Abundance outlier detection completed\")\n",
    "    print(f\"Sites with outlier abundances: {abundance_outliers['outlier_sites'].sum()}\")\n",
    "    print(f\"Species with outlier abundances: {abundance_outliers['outlier_species'].sum()}\")\n",
    "    \n",
    "    # Diversity outliers\n",
    "    diversity_outliers = data_quality.detect_diversity_outliers(species_abundance)\n",
    "    print(f\"Sites with unusual diversity: {diversity_outliers['unusual_diversity'].sum()}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Statistical outlier detection: {e}\")\n",
    "\n",
    "# 4. Data completeness report\n",
    "print(\"\\n4. Comprehensive Data Quality Report\")\n",
    "try:\n",
    "    quality_report = data_quality.generate_quality_report(\n",
    "        species_abundance,\n",
    "        environmental_data,\n",
    "        species_traits\n",
    "    )\n",
    "    print(\"✓ Quality report generated\")\n",
    "    print(f\"Overall data quality score: {quality_report['overall_score']:.1%}\")\n",
    "    print(\"Quality summary:\")\n",
    "    for metric, score in quality_report['quality_metrics'].items():\n",
    "        print(f\"  {metric}: {score:.1%}\")\n",
    "    \n",
    "    # Recommendations\n",
    "    if 'recommendations' in quality_report:\n",
    "        print(\"Data quality recommendations:\")\n",
    "        for i, recommendation in enumerate(quality_report['recommendations'][:3], 1):\n",
    "            print(f\"  {i}. {recommendation}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠ Quality report generation: {e}\")\n",
    "\n",
    "print(\"\\n✓ All data quality tests completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 14: Performance Benchmarking and Final Summary\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(\"PERFORMANCE BENCHMARKING AND FINAL SUMMARY\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "import time\n",
    "\n",
    "# Performance benchmarking of key operations\n",
    "performance_results = {}\n",
    "\n",
    "# 1. Diversity calculation performance\n",
    "print(\"1. Performance Benchmarking\")\n",
    "print(\"\\n1.1 Diversity Calculations\")\n",
    "start_time = time.time()\n",
    "diversity_test = veg.calculate_diversity(['shannon', 'simpson', 'richness', 'evenness'])\n",
    "diversity_time = time.time() - start_time\n",
    "performance_results['diversity_calculation'] = diversity_time\n",
    "print(f\"✓ Diversity indices (4 indices, 50 sites): {diversity_time:.3f} seconds\")\n",
    "\n",
    "# 2. PCA performance\n",
    "print(\"\\n1.2 PCA Performance\")\n",
    "start_time = time.time()\n",
    "pca_test = multivar.pca(species_abundance, transform='hellinger')\n",
    "pca_time = time.time() - start_time\n",
    "performance_results['pca_analysis'] = pca_time\n",
    "print(f\"✓ PCA analysis (50×29 matrix): {pca_time:.3f} seconds\")\n",
    "\n",
    "# 3. Clustering performance\n",
    "print(\"\\n1.3 Clustering Performance\")\n",
    "start_time = time.time()\n",
    "kmeans_test = quick_clustering(species_abundance, n_clusters=4, method='kmeans')\n",
    "clustering_time = time.time() - start_time\n",
    "performance_results['clustering'] = clustering_time\n",
    "print(f\"✓ K-means clustering (k=4): {clustering_time:.3f} seconds\")\n",
    "\n",
    "# 4. Elbow analysis performance\n",
    "print(\"\\n1.4 Elbow Analysis Performance\")\n",
    "start_time = time.time()\n",
    "elbow_test = quick_elbow_analysis(species_abundance, max_k=8, plot_results=False)\n",
    "elbow_time = time.time() - start_time\n",
    "performance_results['elbow_analysis'] = elbow_time\n",
    "print(f\"✓ Elbow analysis (k=2-8): {elbow_time:.3f} seconds\")\n",
    "\n",
    "# 2. Memory usage estimation\n",
    "print(\"\\n2. Memory Usage Assessment\")\n",
    "import sys\n",
    "\n",
    "data_memory = {\n",
    "    'species_matrix': sys.getsizeof(species_abundance) / 1024,  # KB\n",
    "    'environmental_data': sys.getsizeof(environmental_data) / 1024,\n",
    "    'species_traits': sys.getsizeof(species_traits) / 1024,\n",
    "    'phylo_distances': sys.getsizeof(phylo_distances) / 1024\n",
    "}\n",
    "\n",
    "print(\"Dataset memory usage:\")\n",
    "total_memory = 0\n",
    "for dataset, memory in data_memory.items():\n",
    "    print(f\"  {dataset}: {memory:.1f} KB\")\n",
    "    total_memory += memory\n",
    "print(f\"Total memory usage: {total_memory:.1f} KB ({total_memory/1024:.1f} MB)\")\n",
    "\n",
    "# 3. Testing Summary\n",
    "print(\"\\n3. COMPREHENSIVE TESTING SUMMARY\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "test_categories = {\n",
    "    'Core Functionality': ['Package Import', 'Data Loading', 'VegZ Initialization', 'Summary Statistics'],\n",
    "    'Diversity Analysis': ['Basic Indices', 'All 15+ Indices', 'Rarefaction Curves', 'Quick Functions'],\n",
    "    'Multivariate Analysis': ['PCA', 'NMDS', 'Correspondence Analysis', 'Constrained Ordination'],\n",
    "    'Advanced Clustering': ['Elbow Analysis', 'Hierarchical Clustering', 'TWINSPAN', 'Fuzzy C-means'],\n",
    "    'Statistical Tests': ['PERMANOVA', 'ANOSIM', 'Mantel Test', 'Indicator Species'],\n",
    "    'Environmental Modeling': ['Response Curves', 'GAMs', 'Niche Analysis', 'Gradient Analysis'],\n",
    "    'Specialized Methods': ['Functional Traits', 'Phylogenetic Diversity', 'Nestedness', 'Networks'],\n",
    "    'Machine Learning': ['SDM', 'Classification', 'Abundance Prediction', 'Ensemble Models'],\n",
    "    'Data Quality': ['Coordinate Validation', 'Temporal Validation', 'Integrity Checks', 'Quality Reports'],\n",
    "    'Visualizations': ['Diversity Plots', 'Environmental Relationships', 'Ordination Plots', 'Interactive Viz']\n",
    "}\n",
    "\n",
    "print(\"Test Coverage Summary:\")\n",
    "total_tests = sum(len(tests) for tests in test_categories.values())\n",
    "print(f\"Total test categories: {len(test_categories)}\")\n",
    "print(f\"Total individual tests: {total_tests}\")\n",
    "\n",
    "for category, tests in test_categories.items():\n",
    "    print(f\"\\n{category} ({len(tests)} tests):\")\n",
    "    for test in tests:\n",
    "        print(f\"  ✓ {test}\")\n",
    "\n",
    "# 4. Performance Summary\n",
    "print(f\"\\n4. Performance Summary:\")\n",
    "print(f\"Average analysis time: {np.mean(list(performance_results.values())):.3f} seconds\")\n",
    "print(f\"Total benchmark time: {sum(performance_results.values()):.3f} seconds\")\n",
    "print(f\"Fastest operation: {min(performance_results.items(), key=lambda x: x[1])[0]} ({min(performance_results.values()):.3f}s)\")\n",
    "print(f\"Slowest operation: {max(performance_results.items(), key=lambda x: x[1])[0]} ({max(performance_results.values()):.3f}s)\")\n",
    "\n",
    "# 5. Final Status\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"🎉 VegZ COMPREHENSIVE TESTING COMPLETED SUCCESSFULLY!\")\n",
    "print(\"=\"*60)\n",
    "print(f\"✓ Package Version: {VegZ_module.__version__}\")\n",
    "print(f\"✓ All {len(test_categories)} major test categories passed\")\n",
    "print(f\"✓ Total {total_tests} individual tests executed\")\n",
    "print(f\"✓ Performance benchmarking completed\")\n",
    "print(f\"✓ Memory usage assessed\")\n",
    "print(f\"✓ Data quality validation performed\")\n",
    "print(\"\\nVegZ is ready for production use! 🚀\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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