{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7371b5ed",
   "metadata": {},
   "source": [
    "# Accessing ROI Pixel Arrays\n",
    "\n",
    "A typical `craterpy` workflow defines regions of interest (ROIs) around craters and computes zonal statistics over a raster with `get_stats`. Sometimes you want the *raw* pixel values inside each ROI so you can run your own analysis (custom statistics, histograms, masking, etc.). `get_arrays` returns those underlying values as masked arrays.\n",
    "\n",
    "Here we use the sample data files `moon_craters_km.csv` crater list and `moon.tif` image from `craterpy.sample_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17b2981a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T16:19:12.379897Z",
     "iopub.status.busy": "2026-06-16T16:19:12.379795Z",
     "iopub.status.idle": "2026-06-16T16:19:13.456544Z",
     "shell.execute_reply": "2026-06-16T16:19:13.456178Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cjtu/projects/craterpy/craterpy/helper.py:19: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CraterDatabase of length 786 with attributes lat, lon, rad, center."
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from craterpy import CraterDatabase, sample_data\n",
    "\n",
    "craters = sample_data[\"moon_craters_km.csv\"]\n",
    "moon_tif = sample_data[\"moon.tif\"]\n",
    "\n",
    "cdb = CraterDatabase(craters, \"moon\", units=\"km\")\n",
    "cdb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc29cc56",
   "metadata": {},
   "source": [
    "Add a `rim` annulus region (1 to 1.5 crater radii past the rim), then compute zonal statistics over the raster as usual with [`get_stats`](https://craterpy.readthedocs.io/latest/api.html#craterpy.classes.CraterDatabase.get_stats)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e0d181d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T16:19:13.458141Z",
     "iopub.status.busy": "2026-06-16T16:19:13.457915Z",
     "iopub.status.idle": "2026-06-16T16:19:17.686037Z",
     "shell.execute_reply": "2026-06-16T16:19:17.685706Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "879bc16e45e941e99418ed93c2a4c187",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating polygons:   0%|          | 0/786 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2d5a3251f544f8b9a92ffb3342cc8f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Computing Zonal Stats:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diameter (km)</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>mean_rim</th>\n",
       "      <th>count_rim</th>\n",
       "      <th>std_rim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1145.53</td>\n",
       "      <td>34.72</td>\n",
       "      <td>-14.91</td>\n",
       "      <td>63.856143</td>\n",
       "      <td>2857</td>\n",
       "      <td>16.150758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>996.84</td>\n",
       "      <td>-47.77</td>\n",
       "      <td>91.99</td>\n",
       "      <td>83.909259</td>\n",
       "      <td>2700</td>\n",
       "      <td>29.274694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>875.75</td>\n",
       "      <td>8.35</td>\n",
       "      <td>30.83</td>\n",
       "      <td>60.931741</td>\n",
       "      <td>1465</td>\n",
       "      <td>16.523376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>840.35</td>\n",
       "      <td>-7.83</td>\n",
       "      <td>53.67</td>\n",
       "      <td>68.728070</td>\n",
       "      <td>1368</td>\n",
       "      <td>15.506291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>714.50</td>\n",
       "      <td>-20.59</td>\n",
       "      <td>-17.29</td>\n",
       "      <td>64.739488</td>\n",
       "      <td>1094</td>\n",
       "      <td>19.162319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Diameter (km) Latitude Longitude   mean_rim  count_rim    std_rim\n",
       "0       1145.53    34.72    -14.91  63.856143       2857  16.150758\n",
       "1        996.84   -47.77     91.99  83.909259       2700  29.274694\n",
       "2        875.75     8.35     30.83  60.931741       1465  16.523376\n",
       "3        840.35    -7.83     53.67  68.728070       1368  15.506291\n",
       "4        714.50   -20.59    -17.29  64.739488       1094  19.162319"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdb.add_annuli(\"rim\", 1, 1.5)\n",
    "stats = cdb.get_stats(moon_tif, \"rim\")\n",
    "stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fa0ae3",
   "metadata": {},
   "source": [
    "## Accessing the underlying arrays\n",
    "\n",
    "[`get_arrays`](https://craterpy.readthedocs.io/latest/api.html#craterpy.classes.CraterDatabase.get_arrays) takes the same arguments as [`get_stats`](https://craterpy.readthedocs.io/latest/api.html#craterpy.classes.CraterDatabase.get_stats), but each cell holds the `numpy.ma.MaskedArray` of pixel values clipped to that crater's ROI instead of a single statistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53ae93ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T16:19:17.687281Z",
     "iopub.status.busy": "2026-06-16T16:19:17.687154Z",
     "iopub.status.idle": "2026-06-16T16:19:19.827150Z",
     "shell.execute_reply": "2026-06-16T16:19:19.826731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8791626aed4741afbc4e9808c98998d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting Arrays:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ma.MaskedArray'> (119, 146)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "masked_array(\n",
       "  data=[[--, --, --, ..., --, --, --],\n",
       "        [--, --, --, ..., --, --, --],\n",
       "        [--, --, --, ..., --, --, --],\n",
       "        ...,\n",
       "        [--, --, --, ..., --, --, --],\n",
       "        [--, --, --, ..., --, --, --],\n",
       "        [--, --, --, ..., --, --, --]],\n",
       "  mask=[[ True,  True,  True, ...,  True,  True,  True],\n",
       "        [ True,  True,  True, ...,  True,  True,  True],\n",
       "        [ True,  True,  True, ...,  True,  True,  True],\n",
       "        ...,\n",
       "        [ True,  True,  True, ...,  True,  True,  True],\n",
       "        [ True,  True,  True, ...,  True,  True,  True],\n",
       "        [ True,  True,  True, ...,  True,  True,  True]],\n",
       "  fill_value=np.uint64(999999),\n",
       "  dtype=uint8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrays = cdb.get_arrays(moon_tif, \"rim\")\n",
    "roi = arrays[\"rim\"].iloc[0]\n",
    "print(type(roi), roi.shape)\n",
    "roi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b5d884",
   "metadata": {},
   "source": [
    "Now you can do anything you like with the pixels. For example, compute a custom statistic (here the 90th percentile of valid pixels) for every crater, or plot the value distribution of one ROI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65665d86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T16:19:19.828415Z",
     "iopub.status.busy": "2026-06-16T16:19:19.828289Z",
     "iopub.status.idle": "2026-06-16T16:19:19.855406Z",
     "shell.execute_reply": "2026-06-16T16:19:19.854927Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     84.0\n",
       "1    120.0\n",
       "2     82.0\n",
       "3     88.0\n",
       "4     91.0\n",
       "Name: rim, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Custom per-crater statistic from the raw arrays\n",
    "p90 = arrays[\"rim\"].apply(lambda a: np.percentile(a.compressed(), 90))\n",
    "p90.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "febf767e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T16:19:19.856684Z",
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     "shell.execute_reply": "2026-06-16T16:19:19.946915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x70162bf8ef90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for i in range(5):\n",
    "    roi = arrays[\"rim\"].iloc[i]\n",
    "    plt.hist(roi.compressed(), bins=30, histtype=\"stepfilled\", alpha=0.7, label=f\"Crater {i+1}\")\n",
    "    plt.xlabel(\"Pixel value\")\n",
    "    plt.ylabel(\"Count\")\n",
    "plt.title(f\"Crater rim pixel histograms\");\n",
    "plt.legend()\n"
   ]
  },
  {
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   "execution_count": null,
   "id": "61e3661d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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