{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1885a594",
   "metadata": {},
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/niconoe/pyinaturalist/main?filepath=examples%2FData%2520Visualizations%2520-%2520Regional%2520Observation%2520Stats.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "separate-stylus",
   "metadata": {},
   "source": [
    "# Regional observation stats\n",
    "\n",
    "This example shows how to get some general statistics on all observations in a given region.\n",
    "See https://www.inaturalist.org/places to find place IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "thirty-banking",
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import sleep\n",
    "\n",
    "import altair as alt\n",
    "import pandas as pd\n",
    "from IPython.display import Image\n",
    "from pyinaturalist import (\n",
    "    get_observations,\n",
    "    get_observation_species_counts,\n",
    "    get_observation_observers,\n",
    "    get_observation_identifiers,\n",
    ")\n",
    "from pyinaturalist.constants import ICONIC_TAXA\n",
    "\n",
    "\n",
    "# Adjustable values\n",
    "PLACE_ID = 6\n",
    "PLACE_NAME = 'Alaska'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "touched-fusion",
   "metadata": {},
   "source": [
    "### General stats\n",
    "Total observations, unique taxa, identifiers, and observers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "indirect-authority",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total observations: 160888\n",
      "Total taxa observed: 5868\n",
      "Total identifiers: 5003\n",
      "Total observers: 5972\n"
     ]
    }
   ],
   "source": [
    "total_observations = get_observations(\n",
    "    place_id=PLACE_ID,\n",
    "    verifiable=True,\n",
    "    per_page=0,\n",
    ")['total_results']\n",
    "print(f'Total observations: {total_observations}')\n",
    "\n",
    "total_taxa = get_observation_species_counts(\n",
    "    place_id=PLACE_ID,\n",
    "    verifiable=True,\n",
    "    per_page=0,\n",
    ")['total_results']\n",
    "print(f'Total taxa observed: {total_taxa}')\n",
    "\n",
    "total_identifiers = get_observation_identifiers(place_id=PLACE_ID, per_page=0)['total_results']\n",
    "print(f'Total identifiers: {total_identifiers}')\n",
    "\n",
    "total_observers = get_observation_observers(place_id=PLACE_ID, per_page=0)['total_results']\n",
    "print(f'Total observers: {total_observers}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outside-airline",
   "metadata": {},
   "source": [
    "### Stats by iconic taxon\n",
    "Show a breakdown of observations and taxa observed for each of the iconic taxa (major species groups), using their corresponding icons on iNaturalist.\n",
    "Here are a couple helper functions to make this easier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "preceding-divide",
   "metadata": {},
   "outputs": [],
   "source": [
    "THROTTLING_DELAY = 1.0  # Time to wait in between subsequent requests\n",
    "TAXON_IMAGE_URL = 'https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/{taxon}-75px.png'\n",
    "iconic_taxa = list(ICONIC_TAXA.values())\n",
    "iconic_taxa.remove('Unknown')\n",
    "\n",
    "\n",
    "# Run one search for each iconic taxon\n",
    "def get_iconic_taxa_counts(function):\n",
    "    iconic_taxa_counts = {}\n",
    "    for taxon_name in iconic_taxa:\n",
    "        total_taxon_observations = function(\n",
    "            place_id=PLACE_ID,\n",
    "            iconic_taxa=taxon_name,\n",
    "            verifiable=True,\n",
    "            per_page=0,\n",
    "        )['total_results']\n",
    "\n",
    "        iconic_taxa_counts[taxon_name] = total_taxon_observations\n",
    "        print(f'Total results for {taxon_name}: {total_taxon_observations}')\n",
    "        if taxon_name != iconic_taxa[-1]:\n",
    "            sleep(THROTTLING_DELAY)\n",
    "    return iconic_taxa_counts\n",
    "\n",
    "\n",
    "def get_iconic_icon(taxon_name):\n",
    "    return TAXON_IMAGE_URL.format(taxon=taxon_name.lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medium-joining",
   "metadata": {},
   "source": [
    "#### Observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "interim-republic",
   "metadata": {
    "scrolled": true,
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total results for Animalia: 9427\n",
      "Total results for Aves: 28266\n",
      "Total results for Amphibia: 380\n",
      "Total results for Reptilia: 4\n",
      "Total results for Mammalia: 9825\n",
      "Total results for Actinopterygii: 2215\n",
      "Total results for Mollusca: 5810\n",
      "Total results for Arachnida: 1882\n",
      "Total results for Insecta: 17786\n",
      "Total results for Plantae: 63246\n",
      "Total results for Fungi: 20499\n",
      "Total results for Chromista: 951\n",
      "Total results for Protozoa: 479\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-42bc8a999cab44a4b9dcf7bb1c0b513f\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-42bc8a999cab44a4b9dcf7bb1c0b513f\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-42bc8a999cab44a4b9dcf7bb1c0b513f\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-ec7b64f503479131e91eb7ed862c19fd\"}, \"mark\": \"image\", \"encoding\": {\"url\": {\"type\": \"nominal\", \"field\": \"img\"}, \"x\": {\"type\": \"nominal\", \"field\": \"iconic taxon\", \"sort\": \"-y\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"observations\"}}, \"height\": 500, \"title\": \"Verifiable observations in Alaska by iconic taxon\", \"width\": 750, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-ec7b64f503479131e91eb7ed862c19fd\": [{\"iconic taxon\": \"Animalia\", \"observations\": 9427, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/animalia-75px.png\"}, {\"iconic taxon\": \"Aves\", \"observations\": 28266, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/aves-75px.png\"}, {\"iconic taxon\": \"Amphibia\", \"observations\": 380, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/amphibia-75px.png\"}, {\"iconic taxon\": \"Reptilia\", \"observations\": 4, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/reptilia-75px.png\"}, {\"iconic taxon\": \"Mammalia\", \"observations\": 9825, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/mammalia-75px.png\"}, {\"iconic taxon\": \"Actinopterygii\", \"observations\": 2215, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/actinopterygii-75px.png\"}, {\"iconic taxon\": \"Mollusca\", \"observations\": 5810, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/mollusca-75px.png\"}, {\"iconic taxon\": \"Arachnida\", \"observations\": 1882, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/arachnida-75px.png\"}, {\"iconic taxon\": \"Insecta\", \"observations\": 17786, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/insecta-75px.png\"}, {\"iconic taxon\": \"Plantae\", \"observations\": 63246, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/plantae-75px.png\"}, {\"iconic taxon\": \"Fungi\", \"observations\": 20499, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/fungi-75px.png\"}, {\"iconic taxon\": \"Chromista\", \"observations\": 951, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/chromista-75px.png\"}, {\"iconic taxon\": \"Protozoa\", \"observations\": 479, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/protozoa-75px.png\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_observations_by_iconic_taxon = get_iconic_taxa_counts(get_observations)\n",
    "\n",
    "# Create a chart, sorted by number of observations, using the appropriate iNaturalist icons\n",
    "observations_df = pd.DataFrame([\n",
    "    {'iconic taxon': k, 'observations': v, 'img': get_iconic_icon(k)}\n",
    "    for k, v in total_observations_by_iconic_taxon.items()\n",
    "])\n",
    "alt.Chart(\n",
    "    observations_df,\n",
    "    title=f'Verifiable observations in {PLACE_NAME} by iconic taxon',\n",
    "    width=750,\n",
    "    height=500,\n",
    ").mark_image().encode(\n",
    "    x=alt.X('iconic taxon:N', sort='-y'),\n",
    "    y='observations:Q',\n",
    "    url='img'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlikely-mongolia",
   "metadata": {},
   "source": [
    "#### Taxa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "hybrid-investor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total results for Animalia: 550\n",
      "Total results for Aves: 346\n",
      "Total results for Amphibia: 7\n",
      "Total results for Reptilia: 2\n",
      "Total results for Mammalia: 80\n",
      "Total results for Actinopterygii: 143\n",
      "Total results for Mollusca: 295\n",
      "Total results for Arachnida: 131\n",
      "Total results for Insecta: 1196\n",
      "Total results for Plantae: 1791\n",
      "Total results for Fungi: 1195\n",
      "Total results for Chromista: 96\n",
      "Total results for Protozoa: 26\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-ab7e6aad620949649ec1c01aeea6bea4\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-ab7e6aad620949649ec1c01aeea6bea4\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-ab7e6aad620949649ec1c01aeea6bea4\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-8b8c0ae60a628974b6b10136c4df2e0e\"}, \"mark\": \"image\", \"encoding\": {\"url\": {\"type\": \"nominal\", \"field\": \"img\"}, \"x\": {\"type\": \"nominal\", \"field\": \"iconic taxon\", \"sort\": \"-y\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"unique taxa\"}}, \"height\": 500, \"title\": \"Unique taxa observed in Alaska by iconic taxon\", \"width\": 750, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-8b8c0ae60a628974b6b10136c4df2e0e\": [{\"iconic taxon\": \"Animalia\", \"unique taxa\": 550, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/animalia-75px.png\"}, {\"iconic taxon\": \"Aves\", \"unique taxa\": 346, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/aves-75px.png\"}, {\"iconic taxon\": \"Amphibia\", \"unique taxa\": 7, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/amphibia-75px.png\"}, {\"iconic taxon\": \"Reptilia\", \"unique taxa\": 2, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/reptilia-75px.png\"}, {\"iconic taxon\": \"Mammalia\", \"unique taxa\": 80, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/mammalia-75px.png\"}, {\"iconic taxon\": \"Actinopterygii\", \"unique taxa\": 143, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/actinopterygii-75px.png\"}, {\"iconic taxon\": \"Mollusca\", \"unique taxa\": 295, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/mollusca-75px.png\"}, {\"iconic taxon\": \"Arachnida\", \"unique taxa\": 131, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/arachnida-75px.png\"}, {\"iconic taxon\": \"Insecta\", \"unique taxa\": 1196, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/insecta-75px.png\"}, {\"iconic taxon\": \"Plantae\", \"unique taxa\": 1791, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/plantae-75px.png\"}, {\"iconic taxon\": \"Fungi\", \"unique taxa\": 1195, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/fungi-75px.png\"}, {\"iconic taxon\": \"Chromista\", \"unique taxa\": 96, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/chromista-75px.png\"}, {\"iconic taxon\": \"Protozoa\", \"unique taxa\": 26, \"img\": \"https://raw.githubusercontent.com/inaturalist/inaturalist/main/app/assets/images/iconic_taxa/protozoa-75px.png\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_taxa_by_iconic_taxon = get_iconic_taxa_counts(get_observation_species_counts)\n",
    "\n",
    "# Create a chart, sorted by number of observations, using the appropriate iNaturalist icons\n",
    "taxa_df = pd.DataFrame([\n",
    "    {'iconic taxon': k, 'unique taxa': v, 'img': get_iconic_icon(k)}\n",
    "    for k, v in total_taxa_by_iconic_taxon.items()\n",
    "])\n",
    "alt.Chart(\n",
    "    taxa_df,\n",
    "    title=f'Unique taxa observed in {PLACE_NAME} by iconic taxon',\n",
    "    width=750,\n",
    "    height=500,\n",
    ").mark_image().encode(\n",
    "    x=alt.X('iconic taxon:N', sort='-y'),\n",
    "    y='unique taxa:Q',\n",
    "    url='img'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "historical-friendship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Including the rendered image so the chart will display outside Jupyter, e.g. on GitHub's notebook viewer\n",
    "Image('images/total_observations_by_iconic_taxon.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "architectural-temperature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/total_taxa_by_iconic_taxon.png')"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}