{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Writing and Reading Time Series with TimeDB SDK\n",
    "\n",
    "This notebook demonstrates:\n",
    "1. Writing time series data without specifying series_id (auto-generated based on column name)\n",
    "2. Reading data back and plotting with series metadata in legend\n",
    "3. Writing multiple batches with the same series_id (extending a time series)\n",
    "4. Discovering series by labels (e.g., filtering by turbine, site, or measurement type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import timedb as td\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timezone, timedelta\n",
    "\n",
    "# Load environment variables (for database connection)\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Two Different Time Series (Auto-Generated Series)\n",
    "\n",
    "First, let's create the schema and write two different time series.\n",
    "Each column in the DataFrame becomes a separate series with auto-generated series_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete database schema\n",
    "td.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating database schema...\n",
      "✓ Schema created successfully\n"
     ]
    }
   ],
   "source": [
    "# Create database schema\n",
    "td.create()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data values inserted successfully.\n",
      "✓ Inserted first time series\n",
      "  Series name -> series_id mapping: {'wind_power': UUID('ae0ccfab-41a8-4ec4-90d5-9f88190dca1e')}\n",
      "  Time range: 2025-01-01 00:00:00+00:00 to 2025-01-01 23:00:00+00:00\n",
      "  Value range: 50.0 - 109.5 MW\n"
     ]
    }
   ],
   "source": [
    "# Create first time series: Wind power data (first 24 hours: 0-23)\n",
    "base_time = datetime(2025, 1, 1, 0, 0, tzinfo=timezone.utc)\n",
    "dates1 = [base_time + timedelta(hours=i) for i in range(24)]\n",
    "df1 = pd.DataFrame({\n",
    "    'valid_time': dates1,\n",
    "    'wind_power': [50.0 + i * 1.5 + (i % 6) * 5 for i in range(24)]  # MW, varying pattern\n",
    "})\n",
    "\n",
    "# Insert first time series (series_id will be auto-generated)\n",
    "result1 = td.insert_batch(df=df1)\n",
    "print(f\"✓ Inserted first time series\")\n",
    "print(f\"  Series name -> series_id mapping: {result1.series_ids}\")\n",
    "print(f\"  Time range: {dates1[0]} to {dates1[-1]}\")\n",
    "print(f\"  Value range: {df1['wind_power'].min():.1f} - {df1['wind_power'].max():.1f} MW\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data values inserted successfully.\n",
      "✓ Inserted second time series\n",
      "  Series name -> series_id mapping: {'solar_power': UUID('07a85687-17fc-445b-ad7b-a296b58aedcc')}\n",
      "  Time range: 2025-01-02 00:00:00+00:00 to 2025-01-02 23:00:00+00:00\n",
      "  Value range: 0.0 - 30.0 MW\n"
     ]
    }
   ],
   "source": [
    "# Create second time series: Solar power data (next 24 hours: 24-47)\n",
    "dates2 = [base_time + timedelta(hours=i) for i in range(24, 48)]  # Hours 24-47\n",
    "# Solar has day/night pattern - peak at noon (hour 12 and 36)\n",
    "df2 = pd.DataFrame({\n",
    "    'valid_time': dates2,\n",
    "    'solar_power': [max(0, 30.0 * (1 - abs((i % 24) - 12) / 12)) for i in range(24, 48)]  # MW, peaks at noon\n",
    "})\n",
    "\n",
    "# Insert second time series (series_id will be auto-generated)\n",
    "result2 = td.insert_batch(df=df2)\n",
    "print(f\"✓ Inserted second time series\")\n",
    "print(f\"  Series name -> series_id mapping: {result2.series_ids}\")\n",
    "print(f\"  Time range: {dates2[0]} to {dates2[-1]}\")\n",
    "print(f\"  Value range: {df2['solar_power'].min():.1f} - {df2['solar_power'].max():.1f} MW\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Read 48 rows\n",
      "\n",
      "DataFrame shape: (48, 2)\n",
      "\n",
      "Columns (series): ['solar_power', 'wind_power']\n",
      "\n",
      "First few rows:\n",
      "name                       solar_power  wind_power\n",
      "valid_time                                        \n",
      "2025-01-01 00:00:00+00:00          nan        50.0\n",
      "2025-01-01 01:00:00+00:00          nan        56.5\n",
      "2025-01-01 02:00:00+00:00          nan        63.0\n",
      "2025-01-01 03:00:00+00:00          nan        69.5\n",
      "2025-01-01 04:00:00+00:00          nan        76.0\n",
      "2025-01-01 05:00:00+00:00          nan        82.5\n",
      "2025-01-01 06:00:00+00:00          nan        59.0\n",
      "2025-01-01 07:00:00+00:00          nan        65.5\n",
      "2025-01-01 08:00:00+00:00          nan        72.0\n",
      "2025-01-01 09:00:00+00:00          nan        78.5\n"
     ]
    }
   ],
   "source": [
    "# Read back all time series\n",
    "df_read = td.read()\n",
    "print(f\"✓ Read {len(df_read)} rows\")\n",
    "print(f\"\\nDataFrame shape: {df_read.shape}\")\n",
    "print(f\"\\nColumns (series): {df_read.columns.tolist()}\")\n",
    "print(f\"\\nFirst few rows:\")\n",
    "print(df_read.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✓ Plotted 2 different time series\n"
     ]
    }
   ],
   "source": [
    "# Plot the time series\n",
    "plt.figure(figsize=(12, 6))\n",
    "for series_id in df_read.columns:\n",
    "    # Get series metadata to use in legend\n",
    "    name = df_read[series_id].attrs.get('name', 'unknown')\n",
    "    unit = df_read[series_id].attrs.get('unit', 'dimensionless')\n",
    "    \n",
    "    # Extract numeric values (handle both plain floats and Pint quantities)\n",
    "    values = df_read[series_id]\n",
    "    try:\n",
    "        # Try to extract magnitudes if Pint quantities\n",
    "        plot_values = values.apply(lambda v: v.magnitude if hasattr(v, 'magnitude') and pd.notna(v) else (float('nan') if pd.isna(v) else v))\n",
    "    except:\n",
    "        # Fallback to raw values\n",
    "        plot_values = values\n",
    "    \n",
    "    plt.plot(df_read.index, plot_values, marker='o', label=f'{name} ({unit})', linewidth=2)\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Power (MW)')\n",
    "plt.title('Two Different Time Series')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\n✓ Plotted {len(df_read.columns)} different time series\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Explicit Series IDs (Consolidated Time Series)\n",
    "\n",
    "Now let's create a series with explicit metadata (name, unit, labels) and use the same series_id for multiple writes.\n",
    "This demonstrates how to extend a time series over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating database schema...\n",
      "✓ Schema created successfully\n"
     ]
    }
   ],
   "source": [
    "# Delete schema to start fresh\n",
    "td.delete()\n",
    "\n",
    "# Create schema again\n",
    "td.create()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created series with ID: 1b7b1f04-b466-41e2-800e-0af2a5375f33\n",
      "  name='wind_power', unit='MW'\n",
      "  labels={'turbine': 'T01', 'site': 'Site_A'}\n"
     ]
    }
   ],
   "source": [
    "# Create a series with explicit metadata\n",
    "import uuid\n",
    "\n",
    "# Create a series with name, unit, and labels\n",
    "shared_series_id = td.create_series(\n",
    "    name='wind_power',\n",
    "    unit='MW',\n",
    "    labels={'turbine': 'T01', 'site': 'Site_A'},\n",
    "    description='Wind power output from turbine T01 at Site A'\n",
    ")\n",
    "print(f\"Created series with ID: {shared_series_id}\")\n",
    "print(f\"  name='wind_power', unit='MW'\")\n",
    "print(f\"  labels={{'turbine': 'T01', 'site': 'Site_A'}}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data values inserted successfully.\n",
      "✓ Inserted first batch with series_id: 1b7b1f04-b466-41e2-800e-0af2a5375f33\n",
      "  Time range: 2025-01-01 00:00:00+00:00 to 2025-01-01 23:00:00+00:00\n",
      "  Value range: 45.0 - 72.6 MW\n"
     ]
    }
   ],
   "source": [
    "# Insert first batch of data with explicit series_id\n",
    "df_batch1 = pd.DataFrame({\n",
    "    'valid_time': dates1,\n",
    "    'wind_power': [45.0 + i * 1.2 for i in range(24)]  # Gradual increase\n",
    "})\n",
    "\n",
    "result1 = td.insert_batch(df=df_batch1, series_ids={'wind_power': shared_series_id})\n",
    "print(f\"✓ Inserted first batch with series_id: {result1.series_ids['wind_power']}\")\n",
    "print(f\"  Time range: {dates1[0]} to {dates1[-1]}\")\n",
    "print(f\"  Value range: {df_batch1['wind_power'].min():.1f} - {df_batch1['wind_power'].max():.1f} MW\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data values inserted successfully.\n",
      "✓ Inserted second batch with series_id: 1b7b1f04-b466-41e2-800e-0af2a5375f33\n",
      "  Time range: 2025-01-02 00:00:00+00:00 to 2025-01-02 23:00:00+00:00\n",
      "  Value range: 52.0 - 70.4 MW\n",
      "  Same series_id: True\n",
      "  → The two batches are consolidated into one continuous time series!\n"
     ]
    }
   ],
   "source": [
    "# Prepare second batch of data for the same series\n",
    "df_batch2 = pd.DataFrame({\n",
    "    'valid_time': dates2,\n",
    "    'wind_power': [52.0 + i * 0.8 for i in range(24)]  # Different pattern\n",
    "})\n",
    "\n",
    "# Insert second batch with the SAME series_id\n",
    "result2 = td.insert_batch(df=df_batch2, series_ids={'wind_power': shared_series_id})\n",
    "print(f\"✓ Inserted second batch with series_id: {result2.series_ids['wind_power']}\")\n",
    "print(f\"  Time range: {dates2[0]} to {dates2[-1]}\")\n",
    "print(f\"  Value range: {df_batch2['wind_power'].min():.1f} - {df_batch2['wind_power'].max():.1f} MW\")\n",
    "print(f\"  Same series_id: {result1.series_ids['wind_power'] == result2.series_ids['wind_power']}\")\n",
    "print(f\"  → The two batches are consolidated into one continuous time series!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Read 48 rows\n",
      "\n",
      "DataFrame shape: (48, 1)\n",
      "\n",
      "Columns (series_ids): ['wind_power']\n",
      "\n",
      "First few rows:\n",
      "name                       wind_power\n",
      "valid_time                           \n",
      "2025-01-01 00:00:00+00:00        45.0\n",
      "2025-01-01 01:00:00+00:00        46.2\n",
      "2025-01-01 02:00:00+00:00        47.4\n",
      "2025-01-01 03:00:00+00:00        48.6\n",
      "2025-01-01 04:00:00+00:00        49.8\n",
      "2025-01-01 05:00:00+00:00        51.0\n",
      "2025-01-01 06:00:00+00:00        52.2\n",
      "2025-01-01 07:00:00+00:00        53.4\n",
      "2025-01-01 08:00:00+00:00        54.6\n",
      "2025-01-01 09:00:00+00:00        55.8\n",
      "\n",
      "Last few rows:\n",
      "name                       wind_power\n",
      "valid_time                           \n",
      "2025-01-02 14:00:00+00:00        63.2\n",
      "2025-01-02 15:00:00+00:00        64.0\n",
      "2025-01-02 16:00:00+00:00        64.8\n",
      "2025-01-02 17:00:00+00:00        65.6\n",
      "2025-01-02 18:00:00+00:00        66.4\n",
      "2025-01-02 19:00:00+00:00        67.2\n",
      "2025-01-02 20:00:00+00:00        68.0\n",
      "2025-01-02 21:00:00+00:00        68.8\n",
      "2025-01-02 22:00:00+00:00        69.6\n",
      "2025-01-02 23:00:00+00:00        70.4\n"
     ]
    }
   ],
   "source": [
    "# Read back the consolidated time series\n",
    "df_read = td.read()\n",
    "print(f\"✓ Read {len(df_read)} rows\")\n",
    "print(f\"\\nDataFrame shape: {df_read.shape}\")\n",
    "print(f\"\\nColumns (series_ids): {df_read.columns.tolist()}\")\n",
    "print(f\"\\nFirst few rows:\")\n",
    "print(df_read.head(10))\n",
    "print(f\"\\nLast few rows:\")\n",
    "print(df_read.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✓ Plotted 1 time series\n",
      "  Both batches used the same series_id → one consolidated time series spanning 48 hours\n"
     ]
    }
   ],
   "source": [
    "# Plot the consolidated time series\n",
    "plt.figure(figsize=(12, 6))\n",
    "for series_id in df_read.columns:\n",
    "    # Get series metadata from attrs\n",
    "    name = df_read[series_id].attrs.get('name', 'unknown')\n",
    "    unit = df_read[series_id].attrs.get('unit', 'dimensionless')\n",
    "    labels = df_read[series_id].attrs.get('labels', {})\n",
    "    \n",
    "    # Build a descriptive label\n",
    "    label_str = ', '.join([f\"{k}={v}\" for k, v in labels.items()])\n",
    "    legend_label = f'{name} ({unit}) [{label_str}]' if label_str else f'{name} ({unit})'\n",
    "    \n",
    "    # Extract numeric values (handle both plain floats and Pint quantities)\n",
    "    values = df_read[series_id]\n",
    "    try:\n",
    "        # Try to extract magnitudes if Pint quantities\n",
    "        plot_values = values.apply(lambda v: v.magnitude if hasattr(v, 'magnitude') and pd.notna(v) else (float('nan') if pd.isna(v) else v))\n",
    "    except:\n",
    "        # Fallback to raw values\n",
    "        plot_values = values\n",
    "    \n",
    "    plt.plot(df_read.index, plot_values, marker='o', label=legend_label, linewidth=2)\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Consolidated Time Series (Same Series ID)')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\n✓ Plotted {len(df_read.columns)} time series\")\n",
    "print(f\"  Both batches used the same series_id → one consolidated time series spanning 48 hours\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Discovering Series with get_mapping()\n",
    "\n",
    "The new `get_mapping()` function allows you to discover series based on their metadata (name, unit, labels).\n",
    "Let's create multiple series with different labels to demonstrate various filtering capabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating database schema...\n",
      "✓ Schema created successfully\n",
      "✓ Created 6 series with various labels\n",
      "  Wind power series: 2\n",
      "  Solar power series: 2\n",
      "  Temperature series: 2\n"
     ]
    }
   ],
   "source": [
    "# First, let's create a fresh database with multiple series\n",
    "td.delete()\n",
    "td.create()\n",
    "\n",
    "# Create multiple series with different labels\n",
    "series_configs = [\n",
    "    # Wind turbines at different sites\n",
    "    {'name': 'wind_power', 'unit': 'MW', 'labels': {'turbine': 'T01', 'site': 'Site_A', 'type': 'wind'}},\n",
    "    {'name': 'wind_power', 'unit': 'MW', 'labels': {'turbine': 'T02', 'site': 'Site_A', 'type': 'wind'}},\n",
    "    {'name': 'wind_power', 'unit': 'MW', 'labels': {'turbine': 'T01', 'site': 'Site_B', 'type': 'wind'}},\n",
    "    {'name': 'wind_power', 'unit': 'MW', 'labels': {'turbine': 'T02', 'site': 'Site_B', 'type': 'wind'}},\n",
    "    \n",
    "    # Solar installations\n",
    "    {'name': 'solar_power', 'unit': 'MW', 'labels': {'panel_id': 'P01', 'site': 'Site_A', 'type': 'solar'}},\n",
    "    {'name': 'solar_power', 'unit': 'MW', 'labels': {'panel_id': 'P02', 'site': 'Site_B', 'type': 'solar'}},\n",
    "    \n",
    "    # Temperature sensors\n",
    "    {'name': 'temperature', 'unit': 'degC', 'labels': {'sensor': 'S01', 'site': 'Site_A'}},\n",
    "    {'name': 'temperature', 'unit': 'degC', 'labels': {'sensor': 'S02', 'site': 'Site_B'}},\n",
    "]\n",
    "\n",
    "# Create all series and store their IDs\n",
    "series_ids = {}\n",
    "for config in series_configs:\n",
    "    series_id = td.create_series(\n",
    "        name=config['name'],\n",
    "        unit=config['unit'],\n",
    "        labels=config['labels'],\n",
    "        description=f\"{config['name']} at {config['labels'].get('site', 'unknown')}\"\n",
    "    )\n",
    "    key = f\"{config['name']}_{list(config['labels'].values())[0]}\"\n",
    "    series_ids[key] = series_id\n",
    "\n",
    "print(f\"✓ Created {len(series_ids)} series with various labels\")\n",
    "print(f\"  Wind power series: {sum(1 for k in series_ids if 'wind_power' in k)}\")\n",
    "print(f\"  Solar power series: {sum(1 for k in series_ids if 'solar_power' in k)}\")\n",
    "print(f\"  Temperature series: {sum(1 for k in series_ids if 'temperature' in k)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. All series in database: 8\n",
      "   {'type': 'solar', 'panel_id': 'P02', 'site': 'Site_B'} -> bf88bdeb-ea0e-4492-b389-06080fa3c57b\n",
      "   {'type': 'solar', 'panel_id': 'P01', 'site': 'Site_A'} -> eafad25c-bfdf-4457-b1c7-035620722be4\n",
      "   {'sensor': 'S01', 'site': 'Site_A'} -> 016152e8-075c-48c3-ab3e-279811acee6a\n",
      "   {'site': 'Site_B', 'sensor': 'S02'} -> 97f6ae5f-2081-4e95-924b-acdb2726f7eb\n",
      "   {'type': 'wind', 'turbine': 'T02', 'site': 'Site_B'} -> 1dd7b87d-1da1-45a8-bc7e-4d344e53811a\n",
      "   {'type': 'wind', 'site': 'Site_A', 'turbine': 'T01'} -> 71008a23-4b0e-41ea-bd43-801abb74996a\n",
      "   {'type': 'wind', 'site': 'Site_A', 'turbine': 'T02'} -> 89496bd2-7a71-4cc7-aacc-3a05de6f78a6\n",
      "   {'type': 'wind', 'turbine': 'T01', 'site': 'Site_B'} -> ea912f83-84d0-4e8b-8c52-b59a979ae40f\n"
     ]
    }
   ],
   "source": [
    "# Example 1: Get ALL series in the database\n",
    "all_series = td.get_mapping()\n",
    "print(f\"1. All series in database: {len(all_series)}\")\n",
    "for label_set, series_id in all_series.items():\n",
    "    labels = dict(label_set)\n",
    "    print(f\"   {labels} -> {series_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2. All wind_power series: 4\n",
      "   Turbine T02 at Site_B -> 1dd7b87d-1da1-45a8-bc7e-4d344e53811a\n",
      "   Turbine T01 at Site_A -> 71008a23-4b0e-41ea-bd43-801abb74996a\n",
      "   Turbine T02 at Site_A -> 89496bd2-7a71-4cc7-aacc-3a05de6f78a6\n",
      "   Turbine T01 at Site_B -> ea912f83-84d0-4e8b-8c52-b59a979ae40f\n"
     ]
    }
   ],
   "source": [
    "# Example 2: Filter by name (all wind power series)\n",
    "wind_series = td.get_mapping(name='wind_power')\n",
    "print(f\"\\n2. All wind_power series: {len(wind_series)}\")\n",
    "for label_set, series_id in wind_series.items():\n",
    "    labels = dict(label_set)\n",
    "    print(f\"   Turbine {labels['turbine']} at {labels['site']} -> {series_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3. All series at Site_A: 4\n",
      "   {'type': 'solar', 'panel_id': 'P01', 'site': 'Site_A'} -> eafad25c-bfdf-4457-b1c7-035620722be4\n",
      "   {'sensor': 'S01', 'site': 'Site_A'} -> 016152e8-075c-48c3-ab3e-279811acee6a\n",
      "   {'type': 'wind', 'site': 'Site_A', 'turbine': 'T01'} -> 71008a23-4b0e-41ea-bd43-801abb74996a\n",
      "   {'type': 'wind', 'site': 'Site_A', 'turbine': 'T02'} -> 89496bd2-7a71-4cc7-aacc-3a05de6f78a6\n"
     ]
    }
   ],
   "source": [
    "# Example 3: Filter by single label (all series at Site_A)\n",
    "site_a_series = td.get_mapping(label_filter={'site': 'Site_A'})\n",
    "print(f\"\\n3. All series at Site_A: {len(site_a_series)}\")\n",
    "for label_set, series_id in site_a_series.items():\n",
    "    labels = dict(label_set)\n",
    "    print(f\"   {labels} -> {series_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "4. All series with type='wind': 4\n",
      "   T02 at Site_B -> 1dd7b87d-1da1-45a8-bc7e-4d344e53811a\n",
      "   T01 at Site_A -> 71008a23-4b0e-41ea-bd43-801abb74996a\n",
      "   T02 at Site_A -> 89496bd2-7a71-4cc7-aacc-3a05de6f78a6\n",
      "   T01 at Site_B -> ea912f83-84d0-4e8b-8c52-b59a979ae40f\n"
     ]
    }
   ],
   "source": [
    "# Example 4: Filter by label value (all wind type series)\n",
    "wind_type_series = td.get_mapping(label_filter={'type': 'wind'})\n",
    "print(f\"\\n4. All series with type='wind': {len(wind_type_series)}\")\n",
    "for label_set, series_id in wind_type_series.items():\n",
    "    labels = dict(label_set)\n",
    "    print(f\"   {labels['turbine']} at {labels['site']} -> {series_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5. Series matching turbine='T01' AND site='Site_A': 1\n",
      "   Full labels: {'type': 'wind', 'site': 'Site_A', 'turbine': 'T01'}\n",
      "   Series ID: 71008a23-4b0e-41ea-bd43-801abb74996a\n"
     ]
    }
   ],
   "source": [
    "# Example 5: Filter by multiple labels (turbine T01 at Site_A)\n",
    "specific_turbine = td.get_mapping(label_filter={'turbine': 'T01', 'site': 'Site_A'})\n",
    "print(f\"\\n5. Series matching turbine='T01' AND site='Site_A': {len(specific_turbine)}\")\n",
    "for label_set, series_id in specific_turbine.items():\n",
    "    labels = dict(label_set)\n",
    "    print(f\"   Full labels: {labels}\")\n",
    "    print(f\"   Series ID: {series_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "6. Exact match (wind_power in MW, turbine T01 at Site_A): 1\n",
      "   Full labels: {'type': 'wind', 'site': 'Site_A', 'turbine': 'T01'}\n",
      "   Series ID: 71008a23-4b0e-41ea-bd43-801abb74996a\n",
      "   ✓ This uniquely identifies one specific series\n"
     ]
    }
   ],
   "source": [
    "# Example 6: Combine name + unit + labels for exact match\n",
    "exact_match = td.get_mapping(\n",
    "    name='wind_power',\n",
    "    unit='MW',\n",
    "    label_filter={'turbine': 'T01', 'site': 'Site_A', 'type': 'wind'}\n",
    ")\n",
    "print(f\"\\n6. Exact match (wind_power in MW, turbine T01 at Site_A): {len(exact_match)}\")\n",
    "for label_set, series_id in exact_match.items():\n",
    "    labels = dict(label_set)\n",
    "    print(f\"   Full labels: {labels}\")\n",
    "    print(f\"   Series ID: {series_id}\")\n",
    "    print(f\"   ✓ This uniquely identifies one specific series\")"
   ]
  }
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