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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2026-04-12 04:08:28.284320+02:00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.1</td>\n",
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       "      <th>3</th>\n",
       "      <td>2026-04-10 19:13:13.471986+02:00</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2026-04-10 19:13:11.474719+02:00</td>\n",
       "      <td>72.6</td>\n",
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      "text/plain": [
       "                              time  state\n",
       "0 2026-04-12 04:08:28.284320+02:00    0.0\n",
       "1 2026-04-10 20:17:00.010804+02:00    0.0\n",
       "2 2026-04-10 20:16:00.014496+02:00    0.1\n",
       "3 2026-04-10 19:13:13.471986+02:00    0.0\n",
       "4 2026-04-10 19:13:11.474719+02:00   72.6"
      ]
     },
     "execution_count": 1,
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from zoneinfo import ZoneInfo\n",
    "\n",
    "from utils.database import get_state\n",
    "\n",
    "local_tz = ZoneInfo(\"Europe/Copenhagen\")\n",
    "\n",
    "df = get_state(\n",
    "    entity_id=\"sensor.shellyplus1pm_78ee4cc83a94_power\",\n",
    "    # limit=100,\n",
    "    start_time=datetime(2026, 4, 1, 0, 0, tzinfo=local_tz),\n",
    "    end_time=datetime(2026, 4, 12, 21, 0, tzinfo=local_tz),\n",
    ")\n",
    "\n",
    "# Convert time to Copenhagen timezone\n",
    "df[\"time\"] = df[\"time\"].dt.tz_convert(local_tz)\n",
    "\n",
    "# Convert state to numeric, forcing errors to NaN\n",
    "df[\"state\"] = pd.to_numeric(df[\"state\"], errors=\"coerce\")\n",
    "\n",
    "df.head()"
   ]
  },
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            "ticks": ""
           },
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            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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           "zerolinewidth": 2
          },
          "yaxis": {
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           "title": {
            "standoff": 15
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           "zerolinewidth": 2
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        },
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         "text": "Power Consumption Over Time"
        },
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "# Create interactive line plot\n",
    "fig = px.line(\n",
    "    df,\n",
    "    x=\"time\",\n",
    "    y=\"state\",\n",
    "    title=\"Power Consumption Over Time\",\n",
    "    labels={\"time\": \"Time\", \"state\": \"Power (W)\"},\n",
    ")\n",
    "\n",
    "# Customize hover template\n",
    "fig.update_traces(hovertemplate=\"<b>Time:</b> %{x}<br><b>Power:</b> %{y:.2f} W<extra></extra>\")\n",
    "\n",
    "fig.update_layout(height=600, hovermode=\"x unified\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "268f2832",
   "metadata": {},
   "source": [
    "Aggregate over 1 minute pieces to avoid varying sampling frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c53e40de",
   "metadata": {},
   "outputs": [
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        "plotlyServerURL": "https://plot.ly"
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         "name": "Raw",
         "showlegend": true,
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          },
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           "title": {
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        "title": {
         "text": "Power Consumption - Raw vs Smoothed (window=1min)"
        },
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         "anchor": "y",
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         "title": {
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          "text": "Power (W)"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate running average with time-based window\n",
    "window_size = \"1min\"  # Can use: '1min', '5min', '15min', '1h', etc.\n",
    "df[\"state_smoothed\"] = df.rolling(window=window_size, on=\"time\", center=True)[\"state\"].mean()\n",
    "\n",
    "# Create plot with both raw and smoothed data\n",
    "fig = px.line(\n",
    "    df,\n",
    "    x=\"time\",\n",
    "    y=[\"state\", \"state_smoothed\"],\n",
    "    title=f\"Power Consumption - Raw vs Smoothed (window={window_size})\",\n",
    "    labels={\"time\": \"Time\", \"value\": \"Power (W)\"},\n",
    ")\n",
    "\n",
    "# Update trace names\n",
    "fig.data[0].name = \"Raw\"\n",
    "fig.data[1].name = \"Smoothed\"\n",
    "\n",
    "# Customize hover template\n",
    "fig.update_traces(hovertemplate=\"<b>Time:</b> %{x}<br><b>Power:</b> %{y:.2f} W<extra></extra>\")\n",
    "\n",
    "fig.update_layout(height=600, hovermode=\"x unified\", legend_title_text=\"Data Type\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "097a77c1",
   "metadata": {},
   "outputs": [
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    "# Calculate running average with time-based window\n",
    "window_size = \"5min\"  # Can use: '1min', '5min', '15min', '1h', etc.\n",
    "df[\"state_baseline\"] = df.rolling(window=window_size, on=\"time\")[\"state\"].mean()\n",
    "\n",
    "# Create plot with both raw and smoothed data\n",
    "fig = px.line(\n",
    "    df,\n",
    "    x=\"time\",\n",
    "    y=[\"state\", \"state_baseline\"],\n",
    "    title=f\"Power Consumption - Smoothed vs Baseline (window={window_size})\",\n",
    "    labels={\"time\": \"Time\", \"value\": \"Power (W)\"},\n",
    ")\n",
    "\n",
    "# Update trace names\n",
    "fig.data[0].name = \"Smoothed\"\n",
    "fig.data[1].name = \"Baseline\"\n",
    "\n",
    "# Customize hover template\n",
    "fig.update_traces(hovertemplate=\"<b>Time:</b> %{x}<br><b>Power:</b> %{y:.2f} W<extra></extra>\")\n",
    "\n",
    "fig.update_layout(height=600, hovermode=\"x unified\", legend_title_text=\"Data Type\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b5a50563",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2 high-to-low transitions:\n",
      "\n",
      "                            time  state_baseline\n",
      "2026-04-10 19:13:11.474719+02:00            36.3\n",
      "2026-04-08 00:19:08.688021+02:00            35.3\n",
      "\n",
      "\n",
      "Transition Summary:\n",
      "  Average power at transition: 35.80W\n",
      "  Min power at transition: 35.30W\n",
      "  Max power at transition: 36.30W\n"
     ]
    }
   ],
   "source": [
    "# Identify periods when power above 10W\n",
    "threshold = 10\n",
    "\n",
    "# Create boolean mask when starting burner\n",
    "df[\"above_threshold\"] = df[\"state_baseline\"] > threshold\n",
    "\n",
    "# Identify high-to-low transitions (when power drops from >10W to ≤10W)\n",
    "# This occurs when current row is below threshold AND previous row was above threshold\n",
    "df[\"high_to_low\"] = (df[\"above_threshold\"]) & df[\"above_threshold\"].ne(df[\"above_threshold\"].shift())\n",
    "\n",
    "# Extract the transition moments\n",
    "transitions = df[df[\"high_to_low\"]][[\"time\", \"state_baseline\"]]\n",
    "\n",
    "print(f\"Found {len(transitions)} high-to-low transitions:\\n\")\n",
    "print(transitions.to_string(index=False))\n",
    "\n",
    "# Show statistics about the transitions\n",
    "if len(transitions) > 0:\n",
    "    print(f\"\\n\\nTransition Summary:\")\n",
    "    print(f\"  Average power at transition: {transitions['state_baseline'].mean():.2f}W\")\n",
    "    print(f\"  Min power at transition: {transitions['state_baseline'].min():.2f}W\")\n",
    "    print(f\"  Max power at transition: {transitions['state_baseline'].max():.2f}W\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bef4fb20",
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            "ticks": ""
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           "bgcolor": "#E5ECF6",
           "caxis": {
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            "ticks": ""
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          "title": {
           "x": 0.05
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          "xaxis": {
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           "gridcolor": "white",
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           "ticks": "",
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           "automargin": true,
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           "title": {
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        "title": {
         "text": "Power Consumption with High-to-Low Transitions"
        },
        "xaxis": {
         "title": {
          "text": "Time"
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        "yaxis": {
         "title": {
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     },
     "metadata": {},
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   ],
   "source": [
    "# Visualize the high-to-low transitions on the power consumption plot\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "# Add the main power consumption line\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=df[\"time\"], \n",
    "    y=df[\"state_baseline\"],\n",
    "    mode=\"lines\",\n",
    "    name=\"Power Consumption Baseline\",\n",
    "    line=dict(color=\"blue\", width=1),\n",
    "    hovertemplate=\"<b>Time:</b> %{x}<br><b>Power:</b> %{y:.2f} W<extra></extra>\"\n",
    "))\n",
    "\n",
    "# Add threshold line\n",
    "fig.add_hline(y=threshold, line_dash=\"dash\", line_color=\"gray\", \n",
    "              annotation_text=f\"Threshold ({threshold}W)\", \n",
    "              annotation_position=\"right\")\n",
    "\n",
    "# Add markers for high-to-low transitions\n",
    "if len(transitions) > 0:\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=transitions[\"time\"],\n",
    "        y=transitions[\"state_baseline\"],\n",
    "        mode=\"markers\",\n",
    "        name=\"High→Low Transitions\",\n",
    "        marker=dict(color=\"red\", size=10, symbol=\"circle\"),\n",
    "        hovertemplate=\"<b>Transition Time:</b> %{x}<br><b>Power:</b> %{y:.2f} W<extra></extra>\"\n",
    "    ))\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Power Consumption with High-to-Low Transitions\",\n",
    "    xaxis_title=\"Time\",\n",
    "    yaxis_title=\"Power (W)\",\n",
    "    height=600,\n",
    "    hovermode=\"x unified\"\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e53bb7",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "Info message should be sent out when the washing machine 5min average drops below 10W."
   ]
  },
  {
   "cell_type": "raw",
   "id": "93946a1d",
   "metadata": {
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   "source": [
    "# configuration.yaml\n",
    "\n",
    "sensor:\n",
    "  - platform: filter\n",
    "    name: \"Tumble Dryer Power 5min Average\"\n",
    "    entity_id: \"sensor.shellyplus1pm_78ee4cc83a94_power\"\n",
    "    unique_id: \"tumble_dryer_power_baseline\"\n",
    "    filters:\n",
    "      - filter: time_simple_moving_average\n",
    "        window_size: \"00:05\"\n",
    "        precision: 1"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c09e3396",
   "metadata": {
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   },
   "source": [
    "# automations.yaml\n",
    "\n",
    "- id: tumble_dryer_done_notification\n",
    "  alias: \"Notify when tumble dryer is done\"\n",
    "  description: \"Send info notification when tumble dryer is done\"\n",
    "  mode: single\n",
    "  trigger:\n",
    "    - platform: numeric_state\n",
    "      entity_id: sensor.tumble_dryer_power_baseline\n",
    "      below: 10\n",
    "  condition: []\n",
    "  action:\n",
    "    # Notification for Brian\n",
    "    - service: notify.mobile_app_brian_s_oneplus_9t\n",
    "      data:\n",
    "        title: \"Tumble Dryer Done\"\n",
    "        message: \"The tumble dryer has finished its cycle.\"\n",
    "        data:\n",
    "          importance: low # This ensures it stays at 'info' level on Android/iOS\n",
    "    # Notification for Solveig\n",
    "    - service: notify.mobile_app_solveigs_oneplus\n",
    "      data:\n",
    "        title: \"Tumble Dryer Done\"\n",
    "        message: \"The tumble dryer has finished its cycle.\"\n",
    "        data:\n",
    "          importance: low\n"
   ]
  }
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