From 47700ba68885c11fd7ba592dc6284ab46ff28671 Mon Sep 17 00:00:00 2001
From: Molier <oscar.swyns@hotmail.com>
Date: Fri, 29 Nov 2024 10:47:38 +0000
Subject: [PATCH] feat: enhance base load analysis with timeframe support and
 add test cases

---
 ...ndby_verbruik.ipynb => demo_baseLoad.ipynb |  507 ++++-
 openenergyid/baseload/main.py                 |   75 +-
 openenergyid/baseload/test.py                 |   58 +
 poetry.lock                                   | 1771 ++++++++---------
 4 files changed, 1381 insertions(+), 1030 deletions(-)
 rename demo_standby_verbruik.ipynb => demo_baseLoad.ipynb (95%)
 create mode 100644 openenergyid/baseload/test.py

diff --git a/demo_standby_verbruik.ipynb b/demo_baseLoad.ipynb
similarity index 95%
rename from demo_standby_verbruik.ipynb
rename to demo_baseLoad.ipynb
index 2e0db94..35d5c00 100644
--- a/demo_standby_verbruik.ipynb
+++ b/demo_baseLoad.ipynb
@@ -20,7 +20,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# First some speedtesting"
+    "# First some speedtests"
    ]
   },
   {
@@ -32,14 +32,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6.07 μs ± 115 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
+      "7.42 μs ± 671 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
      ]
     }
    ],
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -83,7 +83,7 @@
        "└───────────────────────────────┴───────┘"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -105,14 +105,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "34.3 ms ± 1.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "39.9 ms ± 5.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -142,27 +142,336 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Base Load: 90570.4W\n",
-      "Daily Usage: 2173.7 kWh\n",
-      "Base Percentage: 65.5%\n"
-     ]
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (12, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>total_usage</th><th>base_load_kwh</th><th>period_start</th><th>base_percentage</th><th>base_load_watts</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>datetime[μs]</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2023-12-01 00:00:00</td><td>181.125</td><td>4347.0</td><td>2023-12-31 23:00:00</td><td>2400.0</td><td>181125.0</td></tr><tr><td>2024-01-01 00:00:00</td><td>148349.250304</td><td>2074.8</td><td>2024-01-01 00:00:00</td><td>1.398591</td><td>86450.0</td></tr><tr><td>2024-02-01 00:00:00</td><td>128940.875354</td><td>1923.6</td><td>2024-02-01 00:00:00</td><td>1.491847</td><td>80150.0</td></tr><tr><td>2024-03-01 00:00:00</td><td>128139.375394</td><td>1898.4</td><td>2024-03-01 00:00:00</td><td>1.481512</td><td>79100.0</td></tr><tr><td>2024-04-01 00:00:00</td><td>116175.500248</td><td>1705.2</td><td>2024-04-01 00:00:00</td><td>1.467779</td><td>71050.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2024-07-01 00:00:00</td><td>113613.500188</td><td>1789.2</td><td>2024-07-01 00:00:00</td><td>1.574813</td><td>74550.0</td></tr><tr><td>2024-08-01 00:00:00</td><td>107086.875286</td><td>1680.0</td><td>2024-08-01 00:00:00</td><td>1.56882</td><td>70000.0</td></tr><tr><td>2024-09-01 00:00:00</td><td>114579.500248</td><td>1730.4</td><td>2024-09-01 00:00:00</td><td>1.510218</td><td>72100.0</td></tr><tr><td>2024-10-01 00:00:00</td><td>126714.875234</td><td>1814.4</td><td>2024-10-01 00:00:00</td><td>1.431876</td><td>75600.0</td></tr><tr><td>2024-11-01 00:00:00</td><td>13360.375028</td><td>2108.4</td><td>2024-11-01 00:00:00</td><td>15.780994</td><td>87850.0</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (12, 6)\n",
+       "┌────────────────┬───────────────┬───────────────┬────────────────┬────────────────┬───────────────┐\n",
+       "│ timestamp      ┆ total_usage   ┆ base_load_kwh ┆ period_start   ┆ base_percentag ┆ base_load_wat │\n",
+       "│ ---            ┆ ---           ┆ ---           ┆ ---            ┆ e              ┆ ts            │\n",
+       "│ datetime[μs]   ┆ f64           ┆ f64           ┆ datetime[μs]   ┆ ---            ┆ ---           │\n",
+       "│                ┆               ┆               ┆                ┆ f64            ┆ f64           │\n",
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+       "│ 00:00:00       ┆               ┆               ┆ 00:00:00       ┆                ┆               │\n",
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+       "│ 00:00:00       ┆               ┆               ┆ 00:00:00       ┆                ┆               │\n",
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+       "│ 00:00:00       ┆               ┆               ┆ 00:00:00       ┆                ┆               │\n",
+       "└────────────────┴───────────────┴───────────────┴────────────────┴────────────────┴───────────────┘"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
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+      ],
+      "text/plain": [
+       "shape: (310, 6)\n",
+       "┌─────────────────┬─────────────┬───────────────┬────────────────┬────────────────┬────────────────┐\n",
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+       "│ ---             ┆ ---         ┆ ---           ┆ ---            ┆ e              ┆ s              │\n",
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+       "│ 2024-11-03      ┆ 2585.625022 ┆ 2175.6        ┆ 2024-11-03     ┆ 84.142131      ┆ 90650.0        │\n",
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+       "│ 00:00:00        ┆             ┆               ┆ 00:00:00       ┆                ┆                │\n",
+       "└─────────────────┴─────────────┴───────────────┴────────────────┴────────────────┴────────────────┘"
+      ]
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+     "output_type": "display_data"
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+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
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+      ],
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+       "shape: (7_416, 6)\n",
+       "┌─────────────────┬─────────────┬───────────────┬────────────────┬────────────────┬────────────────┐\n",
+       "│ timestamp       ┆ total_usage ┆ base_load_kwh ┆ period_start   ┆ base_percentag ┆ base_load_watt │\n",
+       "│ ---             ┆ ---         ┆ ---           ┆ ---            ┆ e              ┆ s              │\n",
+       "│ datetime[μs]    ┆ f64         ┆ f64           ┆ datetime[μs]   ┆ ---            ┆ ---            │\n",
+       "│                 ┆             ┆               ┆                ┆ f64            ┆ f64            │\n",
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+       "│ 23:00:00        ┆             ┆               ┆ 23:00:00       ┆                ┆                │\n",
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+       "│ 03:00:00        ┆             ┆               ┆ 03:00:00       ┆                ┆                │\n",
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+       "│ 2024-11-04      ┆ 243.249996  ┆ 5837.999904   ┆ 2024-11-04     ┆ 2400.0         ┆ 243249.996     │\n",
+       "│ 18:00:00        ┆             ┆               ┆ 18:00:00       ┆                ┆                │\n",
+       "│ 2024-11-04      ┆ 208.25      ┆ 4998.0        ┆ 2024-11-04     ┆ 2400.0         ┆ 208250.0       │\n",
+       "│ 19:00:00        ┆             ┆               ┆ 19:00:00       ┆                ┆                │\n",
+       "│ 2024-11-04      ┆ 199.5       ┆ 4788.0        ┆ 2024-11-04     ┆ 2400.0         ┆ 199500.0       │\n",
+       "│ 20:00:00        ┆             ┆               ┆ 20:00:00       ┆                ┆                │\n",
+       "│ 2024-11-04      ┆ 170.625     ┆ 4095.0        ┆ 2024-11-04     ┆ 2400.0         ┆ 170625.0       │\n",
+       "│ 21:00:00        ┆             ┆               ┆ 21:00:00       ┆                ┆                │\n",
+       "│ 2024-11-04      ┆ 124.250002  ┆ 2982.000048   ┆ 2024-11-04     ┆ 2400.0         ┆ 124250.002     │\n",
+       "│ 22:00:00        ┆             ┆               ┆ 22:00:00       ┆                ┆                │\n",
+       "└─────────────────┴─────────────┴───────────────┴────────────────┴────────────────┴────────────────┘"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "from openenergyid.baseload.main import main\n",
+    "from openenergyid.baseload.main import main, TimeFrame\n",
+    "\n",
+    "# Monthly analysis\n",
+    "monthly_metrics = main(\"data/PP/energy_use_big.ndjson\", TimeFrame.MONTHLY)\n",
+    "\n",
+    "# Daily analysis\n",
+    "daily_metrics = main(\"data/PP/energy_use_big.ndjson\", TimeFrame.DAILY)\n",
+    "\n",
+    "# Hourly analysis\n",
+    "hourly_metrics = main(\"data/PP/energy_use_big.ndjson\", TimeFrame.HOURLY)\n",
+    "# print the metrics\n",
+    "display(monthly_metrics)\n",
+    "display(daily_metrics)\n",
+    "display(hourly_metrics)\n",
     "\n",
-    "metrics = main(\"data/PP/energy_use_big.ndjson\")\n",
+    "\n",
+    "# metrics = main(\"data/PP/energy_use_big.ndjson\")\n",
     "# display(metrics)\n",
-    "print(f\"Base Load: {metrics.base_load_watts:.1f}W\")\n",
-    "print(f\"Daily Usage: {metrics.daily_usage_kwh:.1f} kWh\")\n",
-    "print(f\"Base Percentage: {metrics.base_percentage:.1f}%\")"
+    "# print(f\"Base Load: {metrics.base_load_watts:.1f}W\")\n",
+    "# print(f\"Daily Usage: {metrics.daily_usage_kwh:.1f} kWh\")\n",
+    "# print(f\"Base Percentage: {metrics.base_percentage:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
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\"base_percentage\": 2400.0, \"base_load_watts\": 260749.99599999998}, {\"timestamp\": \"2024-09-10T00:00:00\", \"total_usage\": 83.125, \"base_load_kwh\": 1995.0, \"period_start\": \"2024-09-10T00:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 83125.0}, {\"timestamp\": \"2024-05-05T05:00:00\", \"total_usage\": 107.625002, \"base_load_kwh\": 2583.000048, \"period_start\": \"2024-05-05T05:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 107625.002}, {\"timestamp\": \"2024-01-08T03:00:00\", \"total_usage\": 140.875, \"base_load_kwh\": 3381.0, \"period_start\": \"2024-01-08T03:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 140875.0}, {\"timestamp\": \"2024-10-13T05:00:00\", \"total_usage\": 90.125, \"base_load_kwh\": 2163.0, \"period_start\": \"2024-10-13T05:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 90125.0}, {\"timestamp\": \"2024-07-15T05:00:00\", \"total_usage\": 242.375004, \"base_load_kwh\": 5817.000096, \"period_start\": \"2024-07-15T05:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 242375.004}, {\"timestamp\": \"2024-01-25T04:00:00\", \"total_usage\": 126.00000399999999, \"base_load_kwh\": 3024.0000959999998, \"period_start\": \"2024-01-25T04:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 126000.00399999999}, {\"timestamp\": \"2024-05-18T08:00:00\", \"total_usage\": 109.37500399999999, \"base_load_kwh\": 2625.0000959999998, \"period_start\": \"2024-05-18T08:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 109375.00399999999}, {\"timestamp\": \"2024-09-15T01:00:00\", \"total_usage\": 84.0, \"base_load_kwh\": 2016.0, \"period_start\": \"2024-09-15T01:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 84000.0}, {\"timestamp\": \"2024-07-09T21:00:00\", \"total_usage\": 90.125, \"base_load_kwh\": 2163.0, \"period_start\": \"2024-07-09T21:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 90125.0}, {\"timestamp\": \"2024-09-13T19:00:00\", \"total_usage\": 100.625, \"base_load_kwh\": 2415.0, \"period_start\": \"2024-09-13T19:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 100625.0}, {\"timestamp\": \"2024-02-19T08:00:00\", \"total_usage\": 399.875, \"base_load_kwh\": 9597.0, \"period_start\": \"2024-02-19T08:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 399875.0}, {\"timestamp\": \"2024-03-13T09:00:00\", \"total_usage\": 331.625, \"base_load_kwh\": 7959.0, \"period_start\": \"2024-03-13T09:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 331625.0}, {\"timestamp\": \"2024-05-24T23:00:00\", \"total_usage\": 88.375, \"base_load_kwh\": 2121.0, \"period_start\": \"2024-05-24T23:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 88375.0}, {\"timestamp\": \"2024-09-11T05:00:00\", \"total_usage\": 256.375, \"base_load_kwh\": 6153.0, \"period_start\": \"2024-09-11T05:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 256375.0}, {\"timestamp\": \"2024-05-12T19:00:00\", \"total_usage\": 118.12500599999998, \"base_load_kwh\": 2835.000144, \"period_start\": \"2024-05-12T19:00:00\", \"base_percentage\": 2400.0000000000005, \"base_load_watts\": 118125.00600000001}, {\"timestamp\": \"2024-09-06T00:00:00\", \"total_usage\": 87.5, \"base_load_kwh\": 2100.0, \"period_start\": \"2024-09-06T00:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 87500.0}, {\"timestamp\": \"2024-09-10T23:00:00\", \"total_usage\": 97.125002, \"base_load_kwh\": 2331.000048, \"period_start\": \"2024-09-10T23:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 97125.002}, {\"timestamp\": \"2024-05-19T01:00:00\", \"total_usage\": 86.625, \"base_load_kwh\": 2079.0, \"period_start\": \"2024-05-19T01:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 86625.0}, {\"timestamp\": \"2024-04-20T03:00:00\", \"total_usage\": 88.375, \"base_load_kwh\": 2121.0, \"period_start\": \"2024-04-20T03:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 88375.0}, {\"timestamp\": \"2024-07-22T06:00:00\", \"total_usage\": 267.75, \"base_load_kwh\": 6426.0, \"period_start\": \"2024-07-22T06:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 267750.0}, {\"timestamp\": \"2024-01-09T11:00:00\", \"total_usage\": 341.25, \"base_load_kwh\": 8190.0, \"period_start\": \"2024-01-09T11:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 341250.0}, {\"timestamp\": \"2024-08-08T05:00:00\", \"total_usage\": 225.750004, \"base_load_kwh\": 5418.000096, \"period_start\": \"2024-08-08T05:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 225750.004}, {\"timestamp\": \"2024-08-10T10:00:00\", \"total_usage\": 100.625, \"base_load_kwh\": 2415.0, \"period_start\": \"2024-08-10T10:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 100625.0}, {\"timestamp\": \"2024-03-31T08:00:00\", \"total_usage\": 85.75, \"base_load_kwh\": 2058.0, \"period_start\": \"2024-03-31T08:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 85750.0}, {\"timestamp\": \"2024-07-16T10:00:00\", \"total_usage\": 262.5, \"base_load_kwh\": 6300.0, \"period_start\": \"2024-07-16T10:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 262500.0}, {\"timestamp\": \"2024-10-11T09:00:00\", \"total_usage\": 292.25, \"base_load_kwh\": 7014.0, \"period_start\": \"2024-10-11T09:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 292250.0}, {\"timestamp\": \"2024-11-04T20:00:00\", \"total_usage\": 199.5, \"base_load_kwh\": 4788.0, \"period_start\": \"2024-11-04T20:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 199500.0}, {\"timestamp\": \"2024-10-24T23:00:00\", \"total_usage\": 92.75, \"base_load_kwh\": 2226.0, \"period_start\": \"2024-10-24T23:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 92750.0}, {\"timestamp\": \"2024-04-10T06:00:00\", \"total_usage\": 264.25, \"base_load_kwh\": 6342.0, \"period_start\": \"2024-04-10T06:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 264250.0}, {\"timestamp\": \"2024-02-03T03:00:00\", \"total_usage\": 107.625002, \"base_load_kwh\": 2583.000048, \"period_start\": \"2024-02-03T03:00:00\", \"base_percentage\": 2400.0, \"base_load_watts\": 107625.002}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.VConcatChart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def create_monthly_chart(df):\n",
+    "    \"\"\"Create bar chart for monthly data\"\"\"\n",
+    "    bars = (\n",
+    "        alt.Chart(df)\n",
+    "        .mark_bar(opacity=0.7)\n",
+    "        .encode(\n",
+    "            x=alt.X(\"period_start:T\", timeUnit=\"month\", title=\"Month\"),\n",
+    "            y=alt.Y(\"total_usage:Q\", title=\"Total Usage (kWh)\"),\n",
+    "            tooltip=[\"period_start:T\", \"total_usage:Q\", \"base_percentage:Q\"],\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    base_line = (\n",
+    "        alt.Chart(df)\n",
+    "        .mark_bar(color=\"purple\", opacity=0.5)\n",
+    "        .encode(\n",
+    "            x=alt.X(\"period_start:T\", timeUnit=\"month\"),\n",
+    "            y=alt.Y(\"base_load_kwh:Q\"),\n",
+    "            tooltip=[\"base_load_kwh:Q\", \"base_percentage:Q\"],\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    return (bars + base_line).properties(\n",
+    "        width=800, height=200, title=\"Monthly Energy Usage and Base Load\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def create_daily_chart(df):\n",
+    "    \"\"\"Create line chart for daily data\"\"\"\n",
+    "    lines = (\n",
+    "        alt.Chart(df)\n",
+    "        .mark_line(opacity=0.7)\n",
+    "        .encode(\n",
+    "            x=alt.X(\"period_start:T\", title=\"Date\"),\n",
+    "            y=alt.Y(\"total_usage:Q\", title=\"Daily Usage (kWh)\"),\n",
+    "            tooltip=[\"period_start:T\", \"total_usage:Q\", \"base_percentage:Q\"],\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    base = (\n",
+    "        alt.Chart(df)\n",
+    "        .mark_line(color=\"purple\")\n",
+    "        .encode(\n",
+    "            x=\"period_start:T\",\n",
+    "            y=\"base_load_kwh:Q\",\n",
+    "            tooltip=[\"base_load_kwh:Q\", \"base_percentage:Q\"],\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    return (lines + base).properties(width=800, height=200, title=\"Daily Energy Usage Pattern\")\n",
+    "\n",
+    "\n",
+    "def create_hourly_chart(df):\n",
+    "    \"\"\"Create line chart for sampled hourly data\"\"\"\n",
+    "    # Sample data to prevent overcrowding\n",
+    "    sampled = df.sample(n=500, seed=42)\n",
+    "\n",
+    "    return (\n",
+    "        alt.Chart(sampled)\n",
+    "        .mark_line(opacity=0.7)\n",
+    "        .encode(\n",
+    "            x=alt.X(\"period_start:T\", title=\"Hour\"),\n",
+    "            y=alt.Y(\"total_usage:Q\", title=\"Hourly Usage (kWh)\"),\n",
+    "            tooltip=[\"period_start:T\", \"total_usage:Q\", \"base_percentage:Q\"],\n",
+    "        )\n",
+    "        .properties(width=800, height=200, title=\"Hourly Energy Usage (Sampled)\")\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# Combine all charts\n",
+    "combined = alt.vconcat(\n",
+    "    create_monthly_chart(monthly_metrics),\n",
+    "    create_daily_chart(daily_metrics),\n",
+    "    create_hourly_chart(hourly_metrics),\n",
+    ").resolve_scale(y=\"independent\")\n",
+    "\n",
+    "combined.display()"
    ]
   },
   {
@@ -174,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -260,14 +569,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_22265/3491075188.py:2: DeprecationWarning: `GroupBy.count` is deprecated. It has been renamed to `len`.\n",
+      "/tmp/ipykernel_13152/3491075188.py:2: DeprecationWarning: `GroupBy.count` is deprecated. It has been renamed to `len`.\n",
       "  value_counts = tf.group_by(\"total\").count().sort(\"total\")\n"
      ]
     },
@@ -276,23 +585,23 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac.vega-embed {\n",
+       "  #altair-viz-7010651b9b1b4cfc9096210826d236f2.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac.vega-embed details,\n",
-       "  #altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac.vega-embed details summary {\n",
+       "  #altair-viz-7010651b9b1b4cfc9096210826d236f2.vega-embed details,\n",
+       "  #altair-viz-7010651b9b1b4cfc9096210826d236f2.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac\"></div>\n",
+       "<div id=\"altair-viz-7010651b9b1b4cfc9096210826d236f2\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-c5d0a2dabcb9499abc7b36d0ce5f45ac\");\n",
+       "    if (outputDiv.id !== \"altair-viz-7010651b9b1b4cfc9096210826d236f2\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-7010651b9b1b4cfc9096210826d236f2\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -380,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -446,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -498,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -506,23 +815,23 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-c172578651154edf9497a5b652a8a3a6.vega-embed {\n",
+       "  #altair-viz-ed33389683094f468a5e820ee71cd03a.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-c172578651154edf9497a5b652a8a3a6.vega-embed details,\n",
-       "  #altair-viz-c172578651154edf9497a5b652a8a3a6.vega-embed details summary {\n",
+       "  #altair-viz-ed33389683094f468a5e820ee71cd03a.vega-embed details,\n",
+       "  #altair-viz-ed33389683094f468a5e820ee71cd03a.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-c172578651154edf9497a5b652a8a3a6\"></div>\n",
+       "<div id=\"altair-viz-ed33389683094f468a5e820ee71cd03a\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-c172578651154edf9497a5b652a8a3a6\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-c172578651154edf9497a5b652a8a3a6\");\n",
+       "    if (outputDiv.id !== \"altair-viz-ed33389683094f468a5e820ee71cd03a\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-ed33389683094f468a5e820ee71cd03a\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -615,7 +924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -623,23 +932,23 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-b8f5fa002c2a44e9870e7d1d16005480.vega-embed {\n",
+       "  #altair-viz-56fe596321914a39860afc3bdbbf2f85.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-b8f5fa002c2a44e9870e7d1d16005480.vega-embed details,\n",
-       "  #altair-viz-b8f5fa002c2a44e9870e7d1d16005480.vega-embed details summary {\n",
+       "  #altair-viz-56fe596321914a39860afc3bdbbf2f85.vega-embed details,\n",
+       "  #altair-viz-56fe596321914a39860afc3bdbbf2f85.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-b8f5fa002c2a44e9870e7d1d16005480\"></div>\n",
+       "<div id=\"altair-viz-56fe596321914a39860afc3bdbbf2f85\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-b8f5fa002c2a44e9870e7d1d16005480\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-b8f5fa002c2a44e9870e7d1d16005480\");\n",
+       "    if (outputDiv.id !== \"altair-viz-56fe596321914a39860afc3bdbbf2f85\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-56fe596321914a39860afc3bdbbf2f85\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -733,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -741,23 +1050,23 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-f7e4ab97eadc466992389715fa030d9f.vega-embed {\n",
+       "  #altair-viz-16c3697ad90146a38b1e7ef902c4bebe.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-f7e4ab97eadc466992389715fa030d9f.vega-embed details,\n",
-       "  #altair-viz-f7e4ab97eadc466992389715fa030d9f.vega-embed details summary {\n",
+       "  #altair-viz-16c3697ad90146a38b1e7ef902c4bebe.vega-embed details,\n",
+       "  #altair-viz-16c3697ad90146a38b1e7ef902c4bebe.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-f7e4ab97eadc466992389715fa030d9f\"></div>\n",
+       "<div id=\"altair-viz-16c3697ad90146a38b1e7ef902c4bebe\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-f7e4ab97eadc466992389715fa030d9f\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-f7e4ab97eadc466992389715fa030d9f\");\n",
+       "    if (outputDiv.id !== \"altair-viz-16c3697ad90146a38b1e7ef902c4bebe\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-16c3697ad90146a38b1e7ef902c4bebe\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -810,7 +1119,7 @@
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -862,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -930,7 +1239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -938,23 +1247,23 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-4aae4e9853174cdba6e19415e752c215.vega-embed {\n",
+       "  #altair-viz-93c628b3b3f6481bb74cd82606f5f5d1.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-4aae4e9853174cdba6e19415e752c215.vega-embed details,\n",
-       "  #altair-viz-4aae4e9853174cdba6e19415e752c215.vega-embed details summary {\n",
+       "  #altair-viz-93c628b3b3f6481bb74cd82606f5f5d1.vega-embed details,\n",
+       "  #altair-viz-93c628b3b3f6481bb74cd82606f5f5d1.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-4aae4e9853174cdba6e19415e752c215\"></div>\n",
+       "<div id=\"altair-viz-93c628b3b3f6481bb74cd82606f5f5d1\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-4aae4e9853174cdba6e19415e752c215\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-4aae4e9853174cdba6e19415e752c215\");\n",
+       "    if (outputDiv.id !== \"altair-viz-93c628b3b3f6481bb74cd82606f5f5d1\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-93c628b3b3f6481bb74cd82606f5f5d1\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -1000,7 +1309,7 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v5.json\", \"data\": [{\"name\": \"source_0\", \"values\": [{\"timestamp\": \"2024-01-11T07:15:00.000\", \"total\": 119.874992}, {\"timestamp\": \"2024-01-11T07:45:00.000\", \"total\": 119.000008}, {\"timestamp\": \"2024-01-11T08:30:00.000\", \"total\": 117.25}, {\"timestamp\": \"2024-11-04T09:00:00.000\", \"total\": 117.25}, {\"timestamp\": \"2024-01-11T08:15:00.000\", \"total\": 116.375}, {\"timestamp\": \"2024-01-11T08:00:00.000\", \"total\": 115.5}, {\"timestamp\": \"2024-02-19T08:00:00.000\", \"total\": 113.749992}, {\"timestamp\": \"2024-04-17T04:15:00.000\", \"total\": 113.749992}, {\"timestamp\": \"2024-01-11T07:30:00.000\", \"total\": 112.000008}, {\"timestamp\": \"2024-04-17T05:15:00.000\", \"total\": 112.000008}, {\"timestamp\": \"2024-01-09T07:30:00.000\", \"total\": 111.125}, {\"timestamp\": \"2024-01-11T07:00:00.000\", \"total\": 110.25}, {\"timestamp\": \"2024-04-17T04:00:00.000\", \"total\": 110.25}, 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-       "  #altair-viz-46fd846d77f24fc4806debfeb686feb6.vega-embed {\n",
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        "    width: 100%;\n",
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-       "  #altair-viz-46fd846d77f24fc4806debfeb686feb6.vega-embed details summary {\n",
+       "  #altair-viz-534365273167482cb69a01313d03de94.vega-embed details,\n",
+       "  #altair-viz-534365273167482cb69a01313d03de94.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-46fd846d77f24fc4806debfeb686feb6\"></div>\n",
+       "<div id=\"altair-viz-534365273167482cb69a01313d03de94\"></div>\n",
        "<script type=\"text/javascript\">\n",
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        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
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        "    const paths = {\n",
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        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
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@@ -1246,7 +1555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1254,23 +1563,23 @@
       "text/html": [
        "\n",
        "<style>\n",
-       "  #altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16.vega-embed {\n",
+       "  #altair-viz-118c09fceea44485b6490766b12df098.vega-embed {\n",
        "    width: 100%;\n",
        "    display: flex;\n",
        "  }\n",
        "\n",
-       "  #altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16.vega-embed details,\n",
-       "  #altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16.vega-embed details summary {\n",
+       "  #altair-viz-118c09fceea44485b6490766b12df098.vega-embed details,\n",
+       "  #altair-viz-118c09fceea44485b6490766b12df098.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16\"></div>\n",
+       "<div id=\"altair-viz-118c09fceea44485b6490766b12df098\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-61aad07bbaf9410cb010fd0e7c5c8b16\");\n",
+       "    if (outputDiv.id !== \"altair-viz-118c09fceea44485b6490766b12df098\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-118c09fceea44485b6490766b12df098\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -1316,14 +1625,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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+       "  #altair-viz-c98c3a0eb34d414a87ba1c1bbc8afb8e.vega-embed details summary {\n",
        "    position: relative;\n",
        "  }\n",
        "</style>\n",
-       "<div id=\"altair-viz-3c17b36a1ea342ecb3578a1375c97ee2\"></div>\n",
+       "<div id=\"altair-viz-c98c3a0eb34d414a87ba1c1bbc8afb8e\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-3c17b36a1ea342ecb3578a1375c97ee2\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-3c17b36a1ea342ecb3578a1375c97ee2\");\n",
+       "    if (outputDiv.id !== \"altair-viz-c98c3a0eb34d414a87ba1c1bbc8afb8e\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-c98c3a0eb34d414a87ba1c1bbc8afb8e\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
@@ -1510,7 +1819,7 @@
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        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
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\"max_power_usage_per_day\": 8232.0}, {\"timestamp\": 1726603200000, \"total_daily_usage\": 4366.250002, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 9156.0}, {\"timestamp\": 1726689600000, \"total_daily_usage\": 4520.250014, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8064.0}, {\"timestamp\": 1726776000000, \"total_daily_usage\": 4292.75001, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8484.0}, {\"timestamp\": 1726862400000, \"total_daily_usage\": 2805.249985999999, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 4116.0}, {\"timestamp\": 1726948800000, \"total_daily_usage\": 2592.625006, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 3276.0}, {\"timestamp\": 1727035200000, \"total_daily_usage\": 4653.250006, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8820.0}, {\"timestamp\": 1727121600000, \"total_daily_usage\": 4691.7499960000005, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8316.0}, {\"timestamp\": 1727208000000, \"total_daily_usage\": 4514.999991999999, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 9072.0}, {\"timestamp\": 1727294400000, \"total_daily_usage\": 4638.375015999999, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8400.0}, {\"timestamp\": 1727380800000, \"total_daily_usage\": 4527.250013999999, \"lowest_recorded\": 22.75, \"min_power_usage_per_day\": 2184.0, \"max_power_usage_per_day\": 8316.0}, {\"timestamp\": 1727467200000, \"total_daily_usage\": 2450.8750179999997, \"lowest_recorded\": 20.125, \"min_power_usage_per_day\": 1932.0, \"max_power_usage_per_day\": 3612.0}, {\"timestamp\": 1727553600000, \"total_daily_usage\": 2293.37501, \"lowest_recorded\": 18.375, \"min_power_usage_per_day\": 1764.0, \"max_power_usage_per_day\": 3108.0}, {\"timestamp\": 1727640000000, \"total_daily_usage\": 4546.500002, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8568.0}, {\"timestamp\": 1727726400000, \"total_daily_usage\": 4729.375008, \"lowest_recorded\": 18.375, \"min_power_usage_per_day\": 1764.0, \"max_power_usage_per_day\": 9576.0}, {\"timestamp\": 1727812800000, \"total_daily_usage\": 4851.8749960000005, \"lowest_recorded\": 21.0, \"min_power_usage_per_day\": 2016.0, \"max_power_usage_per_day\": 8904.0}, {\"timestamp\": 1727899200000, \"total_daily_usage\": 4652.374999999999, \"lowest_recorded\": 21.0, \"min_power_usage_per_day\": 2016.0, \"max_power_usage_per_day\": 8400.0}, {\"timestamp\": 1727985600000, \"total_daily_usage\": 4482.624994, \"lowest_recorded\": 21.0, \"min_power_usage_per_day\": 2016.0, \"max_power_usage_per_day\": 8148.0}, {\"timestamp\": 1728072000000, \"total_daily_usage\": 2587.3750020000007, \"lowest_recorded\": 21.0, \"min_power_usage_per_day\": 2016.0, \"max_power_usage_per_day\": 3696.0}, {\"timestamp\": 1728158400000, \"total_daily_usage\": 2291.625006, \"lowest_recorded\": 18.375, \"min_power_usage_per_day\": 1764.0, \"max_power_usage_per_day\": 3108.0}, {\"timestamp\": 1728244800000, \"total_daily_usage\": 4494.875008, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 9240.0}, {\"timestamp\": 1728331200000, \"total_daily_usage\": 4795.0000199999995, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8400.0}, {\"timestamp\": 1728417600000, \"total_daily_usage\": 4885.125005999999, \"lowest_recorded\": 19.25, \"min_power_usage_per_day\": 1848.0, \"max_power_usage_per_day\": 8568.0}, {\"timestamp\": 1728504000000, \"total_daily_usage\": 4732.875002, \"lowest_recorded\": 21.875, \"min_power_usage_per_day\": 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\"total\"]}]}, {\"name\": \"source_0_x_domain_total\", \"source\": \"source_0\", \"transform\": [{\"type\": \"formula\", \"expr\": \"+datum['total']\", \"as\": \"total\"}, {\"type\": \"aggregate\", \"groupby\": [], \"fields\": [\"total\", \"total\"], \"ops\": [\"min\", \"max\"], \"as\": [\"min\", \"max\"]}]}], \"marks\": [{\"type\": \"area\", \"name\": \"marks\", \"from\": {\"data\": \"source_0\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"y2\": {\"field\": \"density_start\", \"scale\": \"y\"}, \"tooltip\": {\"signal\": \"{\\\"total\\\": format(datum[\\\"total\\\"], \\\"\\\"), \\\"density\\\": format(datum[\\\"density\\\"], \\\"\\\")}\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"total\\\"]) && isFinite(+datum[\\\"total\\\"]) && isValid(datum[\\\"density\\\"]) && isFinite(+datum[\\\"density\\\"])\"}, \"orient\": {\"value\": \"vertical\"}, \"y\": {\"field\": \"density_end\", \"scale\": \"y\"}, \"fill\": {\"value\": \"#4c78a8\"}, \"x\": {\"field\": \"total\", \"scale\": \"x\"}}}, \"style\": [\"area\"]}], \"scales\": [{\"name\": \"x\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_x_domain_total\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_x_domain_total\\\")[0] || {}).max\"}], \"range\": [0, {\"signal\": \"width\"}], \"zero\": false, \"nice\": true}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": {\"data\": \"source_0\", \"fields\": [\"density_start\", \"density_end\"]}, \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true}], \"axes\": [{\"scale\": \"x\", \"minExtent\": 0, \"orient\": \"bottom\", \"zindex\": 0, \"grid\": true, \"labels\": false, \"aria\": false, \"maxExtent\": 0, \"ticks\": false, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"domain\": false, \"gridScale\": \"y\"}, {\"scale\": \"y\", \"grid\": true, \"aria\": false, \"gridScale\": \"x\", \"domain\": false, \"orient\": \"left\", \"maxExtent\": 0, \"ticks\": false, \"labels\": false, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"minExtent\": 0}, {\"scale\": \"x\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"zindex\": 0, \"labelFlush\": true, \"grid\": false, \"orient\": \"bottom\", \"labelOverlap\": true, \"title\": \"total\"}, {\"scale\": \"y\", \"grid\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"title\": \"density\", \"orient\": \"left\", \"zindex\": 0, \"labelOverlap\": true}], \"title\": {\"text\": \"Kernel Density Estimate of Total Energy Usage\", \"frame\": \"group\"}, \"padding\": 5, \"width\": 800, \"background\": \"white\", \"style\": \"cell\", \"height\": 400}, {\"mode\": \"vega\"});\n",
        "</script>"
       ],
       "text/plain": [
@@ -1547,7 +1856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
diff --git a/openenergyid/baseload/main.py b/openenergyid/baseload/main.py
index b02f6bf..fb2dcf1 100644
--- a/openenergyid/baseload/main.py
+++ b/openenergyid/baseload/main.py
@@ -2,6 +2,7 @@
 This module provides functionality for loading, validating, and analyzing energy usage data.
 
 Classes:
+    TimeFrame: An enumeration of different timeframes for data aggregation.
     BaseLoadMetrics: A NamedTuple container for base load analysis metrics.
     EnergySchema: A pandera DataFrameModel for validating energy usage data.
 
@@ -9,27 +10,33 @@
     load_data(path: str) -> pl.LazyFrame:
         Loads and validates energy usage data from an NDJSON file.
 
-    calculate_base_load(lf: pl.LazyFrame) -> BaseLoadMetrics:
-        Calculates base load metrics from energy usage data.
+    calculate_base_load(lf: pl.LazyFrame, timeframe: TimeFrame = TimeFrame.DAILY) -> pl.DataFrame:
+        Calculates base load metrics from energy usage data aggregated by the specified timeframe.
 
-    main(file_path: str) -> BaseLoadMetrics:
-        Processes energy data and returns base load metrics.
-
-    test_energy_validation():
-        Tests various data validation scenarios using pytest.
+    main(file_path: str, timeframe: TimeFrame) -> pl.DataFrame:
+        Processes energy data and returns base load metrics for the specified timeframe.
 """
 
+from enum import Enum
 from typing import NamedTuple
 import polars as pl
 import pandera.polars as pa
 ## VERY important to use pandera.polars instead of pandera to avoid pandas errors
 
 
+class TimeFrame(Enum):
+    HOURLY = "1h"
+    DAILY = "1d"
+    WEEKLY = "1w"
+    MONTHLY = "1mo"
+    YEARLY = "1y"
+
+
 class BaseLoadMetrics(NamedTuple):
     """Container for base load analysis metrics"""
 
-    base_load_watts: float  # Average base load in watts
-    daily_usage_kwh: float  # Average daily usage in kWh
+    base_load_watts: float  # Base load in watts
+    usage_kwh: float  # Usage in kWh for the period
     base_percentage: float  # Base load as percentage of total
 
 
@@ -67,48 +74,36 @@ def load_data(path: str) -> pl.LazyFrame:
     return pl.LazyFrame(validated_df)
 
 
-def calculate_base_load(lf: pl.LazyFrame) -> BaseLoadMetrics:
-    """
-    Calculate base load metrics from energy usage data.
-
-    Takes lowest 10 totals per day to determine base load.
-    Returns watts, kwh, and percentage metrics.
-    """
-    metrics_df = (
+def calculate_base_load(lf: pl.LazyFrame, timeframe: TimeFrame = TimeFrame.DAILY) -> pl.DataFrame:
+    """Calculate base load metrics aggregated by specified timeframe"""
+    return (
         lf.filter(pl.col("total") >= 0)
         .sort("timestamp")
-        .group_by_dynamic("timestamp", every="1d")
+        .group_by_dynamic("timestamp", every=timeframe.value)
         .agg(
             [
-                pl.col("total").sum().alias("total_daily_usage"),
-                (pl.col("total").sort().head(10).mean() * 4 * 24).alias("base_load_daily_kwh"),
+                pl.col("total").sum().alias("total_usage"),
+                (pl.col("total").sort().head(10).mean() * 4 * 24).alias("base_load_kwh"),
+                pl.col("timestamp").first().alias("period_start"),
             ]
         )
         .with_columns(
             [
-                (pl.col("base_load_daily_kwh") / pl.col("total_daily_usage") * 100).alias(
-                    "base_percentage"
-                )
+                (pl.col("base_load_kwh") / pl.col("total_usage") * 100).alias("base_percentage"),
+                (pl.col("base_load_kwh") * 1000 / 24).alias("base_load_watts"),
             ]
         )
-        .select(
-            [
-                pl.col("base_load_daily_kwh").mean().alias("avg_daily_kwh"),
-                (pl.col("base_load_daily_kwh") * 1000 / 24).mean().alias("avg_watts"),
-                pl.col("base_percentage").mean().alias("avg_percentage"),
-            ]
-        )
-        .collect()  # TODO add validation for input data: correct format, not null, etc.
+        .sort("period_start")
+        .collect()
     )
 
-    return BaseLoadMetrics(
-        base_load_watts=metrics_df[0, "avg_watts"],
-        daily_usage_kwh=metrics_df[0, "avg_daily_kwh"],
-        base_percentage=metrics_df[0, "avg_percentage"],
-    )
+
+def main(file_path: str, timeframe: TimeFrame) -> pl.DataFrame:
+    """Process energy data and return base load metrics for specified timeframe"""
+    return calculate_base_load(load_data(file_path), timeframe)
 
 
-def main(file_path: str) -> BaseLoadMetrics:
-    """Process energy data and return base load metrics"""
-    lf = load_data(file_path)
-    return calculate_base_load(lf)
+# Example usage:
+if __name__ == "__main__":
+    results = main("data/energy_use.ndjson", TimeFrame.MONTHLY)
+    print(results)
diff --git a/openenergyid/baseload/test.py b/openenergyid/baseload/test.py
new file mode 100644
index 0000000..cb2a317
--- /dev/null
+++ b/openenergyid/baseload/test.py
@@ -0,0 +1,58 @@
+"""Test the TimeFrame feature of the calculate_base_load function."""
+
+from datetime import datetime
+import polars as pl
+from openenergyid.baseload.main import TimeFrame, calculate_base_load
+
+
+def test_timeframe_feature():
+    """
+    Test the calculate_base_load function with different timeframes.
+
+    This function creates a sample LazyFrame with test data and tests the
+    calculate_base_load function with DAILY and HOURLY timeframes. It verifies
+    the shape of the resulting DataFrame and checks for the presence of the
+    'base_load_watts' column.
+
+    Test Cases:
+    - DAILY timeframe: Expects 2 rows in the result.
+    - HOURLY timeframe: Expects 6 rows in the result.
+
+    Raises:
+        AssertionError: If the shape of the resulting DataFrame does not match
+                        the expected number of rows or if the 'base_load_watts'
+                        column is not present in the result.
+    """
+    # Create a sample LazyFrame with test data
+    data = {
+        "timestamp": [
+            datetime(2023, 1, 1, 0, 0),
+            datetime(2023, 1, 1, 1, 0),
+            datetime(2023, 1, 1, 2, 0),
+            datetime(2023, 1, 2, 0, 0),
+            datetime(2023, 1, 2, 1, 0),
+            datetime(2023, 1, 2, 2, 0),
+        ],
+        "total": [10, 20, 30, 40, 50, 60],
+    }
+    lf = pl.LazyFrame(data)
+
+    # Test with DAILY timeframe
+    result_daily = calculate_base_load(lf, TimeFrame.DAILY)
+    print("Daily Timeframe Result:")
+    print(result_daily)
+    assert result_daily.shape[0] == 2, "Expected 2 rows for DAILY timeframe"
+    assert "base_load_watts" in result_daily.columns, "Expected 'base_load_watts' column in result"
+
+    # Test with HOURLY timeframe
+    result_hourly = calculate_base_load(lf, TimeFrame.HOURLY)
+    print("\nHourly Timeframe Result:")
+    print(result_hourly)
+    assert result_hourly.shape[0] == 6, "Expected 6 rows for HOURLY timeframe"
+    assert "base_load_watts" in result_hourly.columns, "Expected 'base_load_watts' column in result"
+
+    print("\nAll tests passed!")
+
+
+if __name__ == "__main__":
+    test_timeframe_feature()
diff --git a/poetry.lock b/poetry.lock
index ae24010..fe83275 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,27 +1,28 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "altair"
-version = "5.4.1"
+version = "5.5.0"
 description = "Vega-Altair: A declarative statistical visualization library for Python."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "altair-5.4.1-py3-none-any.whl", hash = "sha256:0fb130b8297a569d08991fb6fe763582e7569f8a04643bbd9212436e3be04aef"},
-    {file = "altair-5.4.1.tar.gz", hash = "sha256:0ce8c2e66546cb327e5f2d7572ec0e7c6feece816203215613962f0ec1d76a82"},
+    {file = "altair-5.5.0-py3-none-any.whl", hash = "sha256:91a310b926508d560fe0148d02a194f38b824122641ef528113d029fcd129f8c"},
+    {file = "altair-5.5.0.tar.gz", hash = "sha256:d960ebe6178c56de3855a68c47b516be38640b73fb3b5111c2a9ca90546dd73d"},
 ]
 
 [package.dependencies]
 jinja2 = "*"
 jsonschema = ">=3.0"
-narwhals = ">=1.5.2"
+narwhals = ">=1.14.2"
 packaging = "*"
-typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
+typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.14\""}
 
 [package.extras]
-all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "numpy", "pandas (>=0.25.3)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.6.0)"]
-dev = ["geopandas", "hatch", "ibis-framework[polars]", "ipython[kernel]", "mistune", "mypy", "pandas (>=0.25.3)", "pandas-stubs", "polars (>=0.20.3)", "pytest", "pytest-cov", "pytest-xdist[psutil] (>=3.5,<4.0)", "ruff (>=0.6.0)", "types-jsonschema", "types-setuptools"]
+all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "numpy", "pandas (>=1.1.3)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.7.0)"]
+dev = ["duckdb (>=1.0)", "geopandas", "hatch (>=1.13.0)", "ipython[kernel]", "mistune", "mypy", "pandas (>=1.1.3)", "pandas-stubs", "polars (>=0.20.3)", "pyarrow-stubs", "pytest", "pytest-cov", "pytest-xdist[psutil] (>=3.5,<4.0)", "ruff (>=0.6.0)", "types-jsonschema", "types-setuptools"]
 doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
+save = ["vl-convert-python (>=1.7.0)"]
 
 [[package]]
 name = "annotated-types"
@@ -36,13 +37,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.6.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
-    {file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
+    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
+    {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
 ]
 
 [package.dependencies]
@@ -53,7 +54,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -124,6 +125,88 @@ cffi = ">=1.0.1"
 dev = ["cogapp", "pre-commit", "pytest", "wheel"]
 tests = ["pytest"]
 
+[[package]]
+name = "arro3-core"
+version = "0.4.2"
+description = "Core library for representing Arrow data in Python."
+optional = false
+python-versions = ">=3.8"
+files = [
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 version = "1.3.0"
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-version = "6.1.0"
+version = "6.2.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
-python-versions = ">=3.8"
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 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.3)"]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
 
 [[package]]
 name = "certifi"
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 [package.dependencies]
@@ -580,33 +651,37 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
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 [[package]]
@@ -648,13 +723,13 @@ requests = "*"
 
 [[package]]
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+version = "0.6.16"
 description = "A python API wrapper for ENTSO-E"
 optional = false
 python-versions = "*"
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 [package.dependencies]
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 [[package]]
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 [package.extras]
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 ]
 
 [package.extras]
@@ -800,13 +877,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.6"
+version = "1.0.7"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -821,13 +898,13 @@ trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
-version = "0.27.2"
+version = "0.28.0"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -835,7 +912,6 @@ anyio = "*"
 certifi = "*"
 httpcore = "==1.*"
 idna = "*"
-sniffio = "*"
 
 [package.extras]
 brotli = ["brotli", "brotlicffi"]
@@ -904,13 +980,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.28.0"
+version = "8.29.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -977,22 +1053,22 @@ arrow = ">=0.15.0"
 
 [[package]]
 name = "jedi"
-version = "0.19.1"
+version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
-parso = ">=0.8.3,<0.9.0"
+parso = ">=0.8.4,<0.9.0"
 
 [package.extras]
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-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
@@ -1013,15 +1089,18 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "json5"
-version = "0.9.25"
+version = "0.10.0"
 description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.8.0"
 files = [
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 ]
 
+[package.extras]
+dev = ["build (==1.2.2.post1)", "coverage (==7.5.3)", "mypy (==1.13.0)", "pip (==24.3.1)", "pylint (==3.2.3)", "ruff (==0.7.3)", "twine (==5.1.1)", "uv (==0.5.1)"]
+
 [[package]]
 name = "jsonpointer"
 version = "3.0.0"
@@ -1257,13 +1336,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.2.5"
+version = "4.3.1"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1283,9 +1362,9 @@ tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"]
-docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
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+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.6.9)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<8.1.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.4.1)", "ipython (==8.16.1)", "ipywidgets (==8.1.5)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.2.post3)", "matplotlib (==3.9.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.3)", "scipy (==1.14.1)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
 upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"]
 
@@ -1461,72 +1540,72 @@ files = [
 
 [[package]]
 name = "markupsafe"
-version = "3.0.1"
+version = "3.0.2"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.9"
 files = [
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 name = "ptyprocess"
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 [[package]]
@@ -2899,114 +2913,101 @@ files = [
 
 [[package]]
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 optional = false
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 [[package]]
@@ -3098,23 +3099,23 @@ win32 = ["pywin32"]
 
 [[package]]
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+version = "75.6.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
-python-versions = ">=3.8"
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 [[package]]
 name = "six"
@@ -3129,13 +3130,13 @@ files = [
 
 [[package]]
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-version = "2.2.0"
+version = "2.2.2"
 description = "A web-based viewer for Python profiler output"
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 [package.dependencies]
@@ -3271,13 +3272,13 @@ typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "tinycss2"
-version = "1.3.0"
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 description = "A tiny CSS parser"
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -3289,33 +3290,63 @@ test = ["pytest", "ruff"]
 
 [[package]]
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 description = "A lil' TOML parser"
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 python-versions = ">=3.8"
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 [[package]]
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+version = "4.4.1"
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-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 version = "0.5.1"
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