{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df71c286-78ab-43f8-8867-99ce2eac544a",
   "metadata": {},
   "source": [
    "# Unit Test for Calculate_MDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5c09c4fd-2809-4442-aa54-a060188d861b",
   "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 unittest\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import anndata as ad\n",
    "from unittest.mock import patch\n",
    "import os\n",
    "os.sys.path.append('../../../')\n",
    " \n",
    "from mesa import ecospatial as eco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa4d769c-53ea-4db7-9f53-e677adce6744",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TestCalculateMDI(unittest.TestCase):\n",
    "    def setUp(self):\n",
    "        # Set up test data\n",
    "        self.spatial_data = pd.DataFrame({\n",
    "            'x': 1000 * np.random.rand(5000),\n",
    "            'y': 1000 * np.random.rand(5000),\n",
    "            'library_key': ['sample_1'] * 2500 + ['sample_2'] * 2500,\n",
    "            'cluster_key': np.random.randint(0, 10, size=5000)\n",
    "        })\n",
    "        self.scales = [1., 2., 4., 8.]\n",
    "        self.library_key = 'library_key'\n",
    "        self.library_id = ['sample_1', 'sample_2']\n",
    "        self.spatial_key = ['x', 'y']\n",
    "        self.cluster_key = 'cluster_key'\n",
    "        self.expected_index = self.library_id\n",
    "        self.expected_columns = [scale for scale in self.scales] + ['Slope']\n",
    "        \n",
    "    def test_calculate_MDI_with_random_patch_false(self):\n",
    "            # Call the function under test with random_patch=False\n",
    "            result = eco.calculate_MDI(\n",
    "                spatial_data=self.spatial_data,\n",
    "                scales=self.scales,\n",
    "                library_key=self.library_key,\n",
    "                library_id=self.library_id,\n",
    "                spatial_key=self.spatial_key,\n",
    "                cluster_key=self.cluster_key,\n",
    "                random_patch=False,\n",
    "                plotfigs=False,\n",
    "                savefigs=False,\n",
    "                patch_kwargs={},  \n",
    "                other_kwargs={}\n",
    "            )\n",
    "            # Check that result is a DataFrame\n",
    "            self.assertIsInstance(result, pd.DataFrame)\n",
    "            \n",
    "            # Check that the DataFrame has the expected index and columns\n",
    "            self.assertListEqual(list(result.columns), self.expected_columns)\n",
    "            self.assertListEqual(list(result.index), self.expected_index)\n",
    "\n",
    "            # Verify that 'Slope' column exists and is numeric\n",
    "            self.assertIn('Slope', result.columns)\n",
    "            self.assertTrue(np.issubdtype(result['Slope'].dtype, np.number))\n",
    "\n",
    "    def test_calculate_MDI_with_random_patch_true(self):\n",
    "            # Call the function under test with random_patch=True\n",
    "            result_random = eco.calculate_MDI(\n",
    "                spatial_data=self.spatial_data,\n",
    "                scales=self.scales,\n",
    "                library_key=self.library_key,\n",
    "                library_id=self.library_id,\n",
    "                spatial_key=self.spatial_key,\n",
    "                cluster_key=self.cluster_key,\n",
    "                random_patch=True,\n",
    "                plotfigs=False,\n",
    "                savefigs=False,\n",
    "                patch_kwargs={},  \n",
    "                other_kwargs={}\n",
    "            )\n",
    "        \n",
    "            # Check the results as before\n",
    "            self.assertIsInstance(result_random, pd.DataFrame)\n",
    "            self.assertListEqual(list(result_random.columns), self.expected_columns)\n",
    "            self.assertListEqual(list(result_random.index), self.expected_index)\n",
    "\n",
    "            # Verify that 'Slope' column exists and is numeric\n",
    "            self.assertIn('Slope', result_random.columns)\n",
    "            self.assertTrue(np.issubdtype(result_random['Slope'].dtype, np.number))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6e429722-44eb-4b93-9f8e-a99f5d394f29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing region: sample_1 at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "sample_1 at scale 1.0 has 0 patches with zero diveristy\n",
      "sample_1 at scale 1.0 diversity is 3.320548430536853\n",
      "Processing region: sample_2 at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "sample_2 at scale 1.0 has 0 patches with zero diveristy\n",
      "sample_2 at scale 1.0 diversity is 3.318675527712075\n",
      "Processing region: sample_1 at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "sample_1 at scale 2.0 has 0 patches with zero diveristy\n",
      "sample_1 at scale 2.0 diversity is 3.3157615698139944\n",
      "Processing region: sample_2 at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "sample_2 at scale 2.0 has 0 patches with zero diveristy\n",
      "sample_2 at scale 2.0 diversity is 3.3098586262794134\n",
      "Processing region: sample_1 at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "sample_1 at scale 4.0 has 0 patches with zero diveristy\n",
      "sample_1 at scale 4.0 diversity is 3.2738724660974885\n",
      "Processing region: sample_2 at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "sample_2 at scale 4.0 has 0 patches with zero diveristy\n",
      "sample_2 at scale 4.0 diversity is 3.2712741280351403\n",
      "Processing region: sample_1 at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "sample_1 at scale 8.0 has 0 patches with zero diveristy\n",
      "sample_1 at scale 8.0 diversity is 3.1357805949294244\n",
      "Processing region: sample_2 at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "sample_2 at scale 8.0 has 0 patches with zero diveristy\n",
      "sample_2 at scale 8.0 diversity is 3.136249663683922\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5, 0.5571428571428572, 0.6714285714285715, 0.9]\n",
      "Processing region: sample_1 at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "sample_1 at scale 1.0 has 0 patches with zero diveristy\n",
      "sample_1 at scale 1.0 diversity is 3.319397130221288\n",
      "Processing region: sample_2 at scale 1.0\n",
      "0.000 per cent patches are empty\n",
      "sample_2 at scale 1.0 has 0 patches with zero diveristy\n",
      "sample_2 at scale 1.0 diversity is 3.3198048602087438\n",
      "Processing region: sample_1 at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "sample_1 at scale 2.0 has 0 patches with zero diveristy\n",
      "sample_1 at scale 2.0 diversity is 3.311824991920736\n",
      "Processing region: sample_2 at scale 2.0\n",
      "0.000 per cent patches are empty\n",
      "sample_2 at scale 2.0 has 0 patches with zero diveristy\n",
      "sample_2 at scale 2.0 diversity is 3.3127017488287462\n",
      "Processing region: sample_1 at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "sample_1 at scale 4.0 has 0 patches with zero diveristy\n",
      "sample_1 at scale 4.0 diversity is 3.2742115498481206\n",
      "Processing region: sample_2 at scale 4.0\n",
      "0.000 per cent patches are empty\n",
      "sample_2 at scale 4.0 has 0 patches with zero diveristy\n",
      "sample_2 at scale 4.0 diversity is 3.280909902805943\n",
      "Processing region: sample_1 at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "sample_1 at scale 8.0 has 0 patches with zero diveristy\n",
      "sample_1 at scale 8.0 diversity is 3.1625338119527555\n",
      "Processing region: sample_2 at scale 8.0\n",
      "0.000 per cent patches are empty\n",
      "sample_2 at scale 8.0 has 0 patches with zero diveristy\n",
      "sample_2 at scale 8.0 diversity is 3.153896959644078\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".\n",
      "----------------------------------------------------------------------\n",
      "Ran 2 tests in 1.135s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.main.TestProgram at 0x150b6c9d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run the tests in the notebook\n",
    "unittest.main(argv=['first-arg-is-ignored'], exit=False)"
   ]
  }
 ],
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