Added the category composition pie chart

reports/README.md

@@ -8,6 +8,7 @@ The following reports are currently available:
 | --- | --- |
 | abundance_vs_frequency_scatter.ipynb | Abundance vs. frequency scatter plot for each species in a category at a location, indicating rarity at that location |
 | annual_category_location_heatmap.ipynb | Heatmap of number of sightings of each species in a category at a location during a specified year |
+| category_composition_chart.ipynb | Pie chart showing the species composition of a category at a location |
 | category_life_list.ipynb | Life list for the species in a category, including total sightings and location count |
 | location_richness_map.ipynb | Interactive map of species richness (number of unique species sighted) by location |
 | year_on_year_species_location_trend.ipynb | Year-on-year sightings trend for a species, optionally limited to one location |

reports/annual_category_location_heatmap.ipynb

@@ -117,7 +117,7 @@
     "locations_list = \"-\".join(locations)\n",
     "clean_locations = re.sub(\"[^0-9a-zA-Z ]+\", \"\", locations_list).replace(\" \", \"-\")\n",
     "export_file_path = export_folder_path / f\"{year}-{category}-{clean_locations}-Heatmap.xlsx\"\n",
-    "heatmap_data.to_excel(export_file_path.absolute(), sheet_name=\"Sightings\", index=False)\n",
+    "heatmap_data.to_excel(export_file_path.absolute(), sheet_name=\"Sightings\", index=True)\n",
     "\n",
     "# Print the heatmap data\n",
     "with pd.option_context('display.max_rows', None,\n",
@@ -176,7 +176,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.13.2"
   },
   "vscode": {
    "interpreter": {

reports/category_composition_chart.ipynb

@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Category Composition Pie Chart\n",
+    "\n",
+    "This notebook generates and exports a pie chart showing the composition of a category at a location. To use it, update the location, category and required export format in the first code cell, below, before running the notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Location to report on\n",
+    "location = \"\"\n",
+    "\n",
+    "# Category to report on\n",
+    "category = \"\"\n",
+    "\n",
+    "# Export format for the trend chart:\n",
+    "# PNG     - export as PNG image\n",
+    "# PDF     - export as PDF file\n",
+    "# <blank> - do not export\n",
+    "export_format = \"PNG\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import sqlparse\n",
+    "\n",
+    "# Read the query file\n",
+    "query_file_path = Path(\"sql\") / \"category_composition.sql\"\n",
+    "with open(query_file_path.absolute(), \"r\") as f:\n",
+    "    query = f.read().replace(\"\\n\", \" \")\n",
+    "\n",
+    "# Replace the location and year placeholders\n",
+    "query = query.replace(\"$LOCATION\", location) \\\n",
+    "             .replace(\"$CATEGORY\", category)\n",
+    "\n",
+    "# Show a pretty-printed form of the query\n",
+    "print(sqlparse.format(query, reindent=True, keyword_case='upper'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import sqlite3\n",
+    "import os\n",
+    "\n",
+    "# Connect to the database, execute the query and read the results into a dataframe\n",
+    "database_path = os.environ[\"NATURE_RECORDER_DB\"]\n",
+    "connection = sqlite3.connect(database_path)\n",
+    "df = pd.read_sql_query(query, connection, parse_dates=[\"Date\"])\n",
+    "\n",
+    "# Check there is some data\n",
+    "if not df.shape[0]:\n",
+    "    message = f\"No data found for category '{category}' at location '{location}'\"\n",
+    "    raise ValueError(message)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import re\n",
+    "\n",
+    "# Group by species and sum number of individuals\n",
+    "species_counts = df.groupby(\"Species\")[\"Count\"].sum()\n",
+    "\n",
+    "# Sort values (optional, for nicer plots)\n",
+    "species_counts = species_counts.sort_values(ascending=False)\n",
+    "\n",
+    "# Create the folder to hold exported reports\n",
+    "export_folder_path = Path(\"exported\")\n",
+    "export_folder_path.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Export the data to Excel\n",
+    "clean_location = re.sub(\"[^0-9a-zA-Z ]+\", \"\", location).replace(\" \", \"-\")\n",
+    "clean_category = re.sub(\"[^0-9a-zA-Z ]+\", \"\", category).replace(\" \", \"-\")\n",
+    "export_file_name = f\"{clean_category}-{clean_location}-Category-Composition\"\n",
+    "export_file_path = export_folder_path / f\"{export_file_name}.xlsx\"\n",
+    "species_counts.to_excel(export_file_path.absolute(), sheet_name=\"Category Composition\", index=True)\n",
+    "\n",
+    "# Print the scatter plot data\n",
+    "with pd.option_context('display.max_rows', None,\n",
+    "                       'display.max_columns', None,\n",
+    "                       'display.precision', 3,\n",
+    "                       ):\n",
+    "    display(species_counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Prepare data\n",
+    "labels = species_counts.index\n",
+    "sizes = species_counts.values\n",
+    "percentages = sizes / sizes.sum() * 100\n",
+    "legend_labels = [f\"{label} ({pct:.1f}%)\" for label, pct in zip(labels, percentages)]\n",
+    "\n",
+    "# Create figure with extra vertical space for legend\n",
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "\n",
+    "wedges, _ = ax.pie(sizes, startangle=90, counterclock=False)\n",
+    "\n",
+    "# Equal aspect ratio\n",
+    "ax.set_aspect('equal')\n",
+    "ax.set_title(f\"Composition of '{category}' Category by Species\")\n",
+    "\n",
+    "# Remove tight_layout, manually control layout instead\n",
+    "fig.subplots_adjust(bottom=0.3)  # Increase this if needed\n",
+    "\n",
+    "# Place legend fully outside the plot\n",
+    "ax.legend(wedges, legend_labels,\n",
+    "            title=\"Species\",\n",
+    "            loc='upper center',\n",
+    "            bbox_to_anchor=(0.5, -0.005),\n",
+    "            ncol=3,\n",
+    "            frameon=False)\n",
+    "\n",
+    "\n",
+    "# Export to PNG\n",
+    "if export_format.casefold() == \"png\":\n",
+    "    export_file_path = export_folder_path / f\"{export_file_name}.png\"\n",
+    "    plt.savefig(export_file_path.absolute(), format=\"png\", dpi=300, bbox_inches=\"tight\")\n",
+    "\n",
+    "# Export to PDF\n",
+    "if export_format.casefold() == \"pdf\":\n",
+    "    export_file_path = export_folder_path / f\"{export_file_name}.pdf\"\n",
+    "    plt.savefig(export_file_path.absolute(), format=\"pdf\", bbox_inches=\"tight\")\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.13.2"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "f085c86085609b1ab2f295d8cd5b519618e19fd591a6919f4ec2f9290a6745f6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

reports/category_life_list.ipynb

@@ -115,7 +115,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -133,7 +133,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "7a792fcb311f9eb9f3c1b942a8c87ada8484712b89b670347c16a1088e0a1f69"
+    "hash": "f085c86085609b1ab2f295d8cd5b519618e19fd591a6919f4ec2f9290a6745f6"
    }
   }
  },

reports/location_richness_map.ipynb

@@ -170,7 +170,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -189,7 +189,7 @@
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "7a792fcb311f9eb9f3c1b942a8c87ada8484712b89b670347c16a1088e0a1f69"
+    "hash": "f085c86085609b1ab2f295d8cd5b519618e19fd591a6919f4ec2f9290a6745f6"
    }
   }
  },

reports/sql/category_composition.sql

@@ -0,0 +1,7 @@
+SELECT l.Name AS 'Location', sp.Name AS 'Species', c.Name AS 'Category', DATE( s.Date ) AS 'Date', IFNULL( s.Number, 1 ) AS 'Count'
+FROM SIGHTINGS s
+INNER JOIN SPECIES sp ON sp.Id = s.SpeciesId
+INNER JOIN CATEGORIES c ON c.Id = sp.CategoryId
+INNER JOIN LOCATIONS l ON l.Id = s.LocationId
+WHERE l.Name = '$LOCATION'
+AND c.Name = "$CATEGORY";

reports/year_on_year_species_location_trend.ipynb

@@ -188,7 +188,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.13.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f085c86085609b1ab2f295d8cd5b519618e19fd591a6919f4ec2f9290a6745f6"
+   }
   }
  },
  "nbformat": 4,