{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "bb2059d6",
      "metadata": {},
      "source": [
        "# Time Averaging of 1-min Data (floor / ceiling / center)\n",
        "\n",
        "This notebook loads one monthly archive (**§2**), runs the same **solpos → clear-sky → `qc_test` → `qc_mask`** pipeline as **`2.quality_control.ipynb`** in brief (**§3**), then shows averaging on the dataset via **`BSRNDataset.average`** (**§4**). The rest focuses on **`pretty_average`** and direct `DataFrame` use.\n",
        "\n",
        "**§5** explains how `pretty_average` differs from `pandas.resample` (windows, monthly label trim, coverage, `match_ceiling_labels`); **§5.1** uses **`ds.data()`** (QC-masked minute data after **§3**) for alignments; **§5.2** compares **`match_ceiling_labels`** for **center** only; **§5.3** tabulates **samples per 1 h interval** (floor / ceiling / center). **§6** applies 1 h averaging to the QIQ month. **§7–8** plot comparisons (plots at the end of the workflow).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "860b2a9a",
      "metadata": {},
      "source": [
        "## 1. Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "10cef9c2",
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import bsrn\n",
        "from bsrn.utils import pretty_average\n",
        "\n",
        "INPUT_FILE = \"/Volumes/Macintosh Research/Data/bsrn-qc/data/QIQ/qiq0824.dat.gz\"\n",
        "\n",
        "pd.options.display.max_columns = None\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4770a32",
      "metadata": {},
      "source": [
        "## 2. Load QIQ August data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "fc514719",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data for QIQ loaded successfully (2024-08).\n"
          ]
        },
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ghi</th>\n",
              "      <th>bni</th>\n",
              "      <th>dhi</th>\n",
              "      <th>lwd</th>\n",
              "      <th>temp</th>\n",
              "      <th>rh</th>\n",
              "      <th>pressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:00:00+00:00</th>\n",
              "      <td>105.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>421.0</td>\n",
              "      <td>25.2</td>\n",
              "      <td>70.0</td>\n",
              "      <td>981.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:01:00+00:00</th>\n",
              "      <td>108.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>422.0</td>\n",
              "      <td>25.2</td>\n",
              "      <td>71.0</td>\n",
              "      <td>981.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:02:00+00:00</th>\n",
              "      <td>112.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>421.0</td>\n",
              "      <td>25.2</td>\n",
              "      <td>70.0</td>\n",
              "      <td>981.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:03:00+00:00</th>\n",
              "      <td>113.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>421.0</td>\n",
              "      <td>25.2</td>\n",
              "      <td>70.0</td>\n",
              "      <td>981.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:04:00+00:00</th>\n",
              "      <td>116.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>421.0</td>\n",
              "      <td>25.2</td>\n",
              "      <td>71.0</td>\n",
              "      <td>981.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             ghi  bni    dhi    lwd  temp    rh  pressure\n",
              "2024-08-01 00:00:00+00:00  105.0  0.0  106.0  421.0  25.2  70.0     981.0\n",
              "2024-08-01 00:01:00+00:00  108.0  0.0  109.0  422.0  25.2  71.0     981.0\n",
              "2024-08-01 00:02:00+00:00  112.0  0.0  113.0  421.0  25.2  70.0     981.0\n",
              "2024-08-01 00:03:00+00:00  113.0  0.0  114.0  421.0  25.2  70.0     981.0\n",
              "2024-08-01 00:04:00+00:00  116.0  0.0  117.0  421.0  25.2  71.0     981.0"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "if os.path.exists(INPUT_FILE):\n",
        "    ds = bsrn.BSRNDataset.from_file(INPUT_FILE)\n",
        "    stn = ds.station_code\n",
        "    print(\n",
        "        f\"Data for {stn} loaded successfully ({ds.year}-{ds.month:02d}).\"\n",
        "    )\n",
        "    display(ds.data().head())\n",
        "else:\n",
        "    raise FileNotFoundError(\n",
        "        f\"File not found: {INPUT_FILE}. Current working directory: {os.getcwd()}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "sec3-prep-md",
      "metadata": {},
      "source": [
        "## 3. Solar geometry, clear-sky (REST2), and QC\n",
        "\n",
        "We use the **same pipeline** as **`2.quality_control.ipynb`**: ``BSRNDataset.solpos``, ``BSRNDataset.clear_sky`` (here ``model='rest2'`` with MERRA-2 via Hugging Face), then ``BSRNDataset.qc_test`` followed immediately by ``BSRNDataset.qc_mask`` (failed irradiance → NaN, standard ``flag*`` columns dropped). Read **`2.quality_control`** for details; we do not tabulate flags here. **`ds.data()`** after **§3** is the **QC-masked** minute frame for **`pretty_average`** from **§5** onward; **§4** calls **`average`** on **`ds.model_copy(deep=True)`** so **`ds`** stays at minute resolution.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "sec3-prep-code",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fetching MERRA-2 from Hugging Face: qiq/qiq0824_merra2.parquet\n"
          ]
        },
        {
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ghi</th>\n",
              "      <th>ghi_clear</th>\n",
              "      <th>zenith</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:00:00+00:00</th>\n",
              "      <td>105.0</td>\n",
              "      <td>507.439814</td>\n",
              "      <td>54.881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:01:00+00:00</th>\n",
              "      <td>108.0</td>\n",
              "      <td>509.994881</td>\n",
              "      <td>54.717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:02:00+00:00</th>\n",
              "      <td>112.0</td>\n",
              "      <td>512.545982</td>\n",
              "      <td>54.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:03:00+00:00</th>\n",
              "      <td>113.0</td>\n",
              "      <td>515.077560</td>\n",
              "      <td>54.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 00:04:00+00:00</th>\n",
              "      <td>116.0</td>\n",
              "      <td>517.620641</td>\n",
              "      <td>54.226</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             ghi   ghi_clear  zenith\n",
              "2024-08-01 00:00:00+00:00  105.0  507.439814  54.881\n",
              "2024-08-01 00:01:00+00:00  108.0  509.994881  54.717\n",
              "2024-08-01 00:02:00+00:00  112.0  512.545982  54.553\n",
              "2024-08-01 00:03:00+00:00  113.0  515.077560  54.390\n",
              "2024-08-01 00:04:00+00:00  116.0  517.620641  54.226"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Same pipeline as 2.quality_control.ipynb\n",
        "ds.solpos()\n",
        "ds.clear_sky(model=\"rest2\")\n",
        "ds.qc_test()\n",
        "ds.qc_mask()\n",
        "\n",
        "display(ds.data()[[\"ghi\", \"ghi_clear\", \"zenith\"]].head())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "18bd606c",
      "metadata": {},
      "source": [
        "## 4. Pipeline averaging: `BSRNDataset.average`\n",
        "\n",
        "On a **`BSRNDataset`**, call **`average(freq, ...)`** on the **dataset** (not on the bare `DataFrame` from **`data()`**). It delegates to **`pretty_average`** on the cached frame, then **replaces** that cache with the coarser-index result. **§5+** still need minute-level **`ds`**, so we **`model_copy(deep=True)`** (Pydantic) and call **`average`** on the copy—same columns as **`ds.data()`** after **§3** (including **QC-masked** irradiance).\n",
        "\n",
        "When you are done with minute data, you may call **`average`** on **`ds`** itself instead of a copy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "c13e6b2b",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ghi</th>\n",
              "      <th>ghi_clear</th>\n",
              "      <th>zenith</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-08-01 01:00:00+00:00</th>\n",
              "      <td>203.385965</td>\n",
              "      <td>581.242271</td>\n",
              "      <td>49.990333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 02:00:00+00:00</th>\n",
              "      <td>380.630435</td>\n",
              "      <td>706.014627</td>\n",
              "      <td>41.060483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 03:00:00+00:00</th>\n",
              "      <td>303.216667</td>\n",
              "      <td>791.170072</td>\n",
              "      <td>33.993883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 04:00:00+00:00</th>\n",
              "      <td>230.416667</td>\n",
              "      <td>826.488629</td>\n",
              "      <td>30.319967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-08-01 05:00:00+00:00</th>\n",
              "      <td>237.150000</td>\n",
              "      <td>817.447416</td>\n",
              "      <td>31.301850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  ghi   ghi_clear     zenith\n",
              "2024-08-01 01:00:00+00:00  203.385965  581.242271  49.990333\n",
              "2024-08-01 02:00:00+00:00  380.630435  706.014627  41.060483\n",
              "2024-08-01 03:00:00+00:00  303.216667  791.170072  33.993883\n",
              "2024-08-01 04:00:00+00:00  230.416667  826.488629  30.319967\n",
              "2024-08-01 05:00:00+00:00  237.150000  817.447416  31.301850"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from IPython.display import display\n",
        "\n",
        "ds_h = ds.model_copy(deep=True)\n",
        "ds_h.average(\"1h\", alignment=\"ceiling\")\n",
        "display(ds_h.data()[[\"ghi\", \"ghi_clear\", \"zenith\"]].head())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2565572f",
      "metadata": {},
      "source": [
        "## 5. What `pretty_average` does\n",
        "\n",
        "`bsrn.utils.averaging.pretty_average` implements **explicit labeled windows** — it is **NOT**\n",
        "`pandas.DataFrame.resample`. For each output label time `L` (bin length `Δ` from `freq`), data rows are selected with a rule that depends on **`alignment`**.\n",
        "\n",
        "### Windows\n",
        "\n",
        "| Alignment | Interval | Role of `L` |\n",
        "| :--- | :--- | :--- |\n",
        "| **floor** | $[L, L+\\Delta)$ | Label at **left** edge |\n",
        "| **ceiling** | $(L-\\Delta, L]$ | Label at **right** edge |\n",
        "| **center** | $[L-\\Delta/2+\\mathrm{res},\\, L+\\Delta/2]$ | Label at **center**; `res` = native step (1 or 3 min inferred for LR0100 if omitted) |\n",
        "\n",
        "### Monthly label trim\n",
        "\n",
        "The regular grid from `floor(min)` … `ceil(max)` at `freq` is **trimmed** so edge bins do not straddle month boundaries (`lo` / `hi` from the data index). **floor** drops labels at the high edge; **ceiling** drops at the low edge. For **center**, choose the same trim as **ceiling** or **floor** via **`match_ceiling_labels`** (default `True`).\n",
        "\n",
        "### Coverage\n",
        "\n",
        "A numeric aggregate is emitted only if enough rows are “valid” (numeric columns: at least one finite value in that row).\n",
        "\n",
        "- **floor / ceiling:** valid count must be **>** half of the nominal step count $\\approx \\Delta/\\mathrm{res}$.\n",
        "- **center:** valid count must be **>** half of **rows in the window** (not the nominal count).\n",
        "\n",
        "Otherwise the row is still emitted but values are **NaN**.\n",
        "\n",
        "### Inputs\n",
        "\n",
        "- **`DatetimeIndex`** is required.\n",
        "\n",
        "**Below:** **§5.1** uses **`ds.data()`** for floor/ceiling/center; **§5.2** compares **`match_ceiling_labels`** (center only); **§5.3** counts **samples per 1 h** window. **§6** applies `pretty_average` for the workflow plots.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b4212af9",
      "metadata": {},
      "source": [
        "### 5.1 Case A — full month, different label times\n",
        "\n",
        "**All rows** in **`ds.data()`** after **§3** (one station-month from the archive). `freq='1h'`: **floor** / **ceiling** / **center** differ in where the hour label sits; counts can differ slightly at month edges.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c695d23f",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Case A — 1 h labels (UTC) on the loaded month:\n",
            "  native rows in window: 44640\n",
            "  floor  : n= 744  first=2024-08-01 00:00:00+00:00  last=2024-08-31 23:00:00+00:00\n",
            "  ceiling: n= 744  first=2024-08-01 01:00:00+00:00  last=2024-09-01 00:00:00+00:00\n",
            "  center : n= 744  first=2024-08-01 01:00:00+00:00  last=2024-09-01 00:00:00+00:00\n"
          ]
        }
      ],
      "source": [
        "# ds.data() after §3: one station-month (see INPUT_FILE)\n",
        "df_a = ds.data()[[\"ghi\", \"zenith\"]].copy()\n",
        "freq = \"1h\"\n",
        "\n",
        "f_a = pretty_average(df_a, freq, alignment=\"floor\")\n",
        "c_a = pretty_average(df_a, freq, alignment=\"ceiling\")\n",
        "z_a = pretty_average(df_a, freq, alignment=\"center\")\n",
        "\n",
        "print(\"Case A — 1 h labels (UTC) on the loaded month:\")\n",
        "print(f\"  native rows in window: {len(df_a)}\")\n",
        "for name, s in [(\"floor\", f_a), (\"ceiling\", c_a), (\"center\", z_a)]:\n",
        "    print(f\"  {name:7s}: n={len(s):4d}  first={s.index[0]}  last={s.index[-1]}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e2b23a57",
      "metadata": {},
      "source": [
        "### 5.2 Case B — `match_ceiling_labels` usage\n",
        "\n",
        "For **`alignment='center'`** only: **`match_ceiling_labels`** chooses whether monthly label trimming follows **ceiling** (default) or **floor**. Compare the two **`center`** outputs (row counts and first/last labels).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "01de6323",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Case B — center only: match_ceiling_labels True vs False\n",
            "  n_row: 744 vs 744\n",
            "  first label: 2024-08-01 01:00:00+00:00 vs 2024-08-01 00:00:00+00:00\n",
            "  last  label: 2024-09-01 00:00:00+00:00 vs 2024-08-31 23:00:00+00:00\n"
          ]
        }
      ],
      "source": [
        "freq = \"1h\"\n",
        "# Same input columns as §5.1; Case B only compares center + match_ceiling_labels\n",
        "df_b = ds.data()[[\"ghi\", \"zenith\"]].copy()\n",
        "\n",
        "z_hi = pretty_average(df_b, freq, alignment=\"center\", match_ceiling_labels=True)\n",
        "z_lo = pretty_average(df_b, freq, alignment=\"center\", match_ceiling_labels=False)\n",
        "\n",
        "print(\"Case B — center only: match_ceiling_labels True vs False\")\n",
        "print(f\"  n_row: {len(z_hi)} vs {len(z_lo)}\")\n",
        "print(\"  first label:\", z_hi.index[0], \"vs\", z_lo.index[0])\n",
        "print(\"  last  label:\", z_hi.index[-1], \"vs\", z_lo.index[-1])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "48f6d28d",
      "metadata": {},
      "source": [
        "### 5.3 Case C — samples per 1 h interval\n",
        "\n",
        "For **`freq='1h'`**, each output row is the mean over a **time window**; the table counts **how many native rows** fall in that window (**`n_samples`**) for **floor**, **ceiling**, and **center** (same logic as `pretty_average`). Labels and counts **differ** across alignments. Preview tables use **`display()`** so every row renders in the notebook (plain **`print`** on wide tables can be truncated).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "3e70f659",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Case C — n_samples per 1 h interval (native rows in each window)\n",
            "\n",
            "floor — head 4:\n"
          ]
        },
        {
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label_utc</th>\n",
              "      <th>n_samples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024-08-01 00:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-08-01 01:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-08-01 02:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-08-01 03:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  label_utc  n_samples\n",
              "0 2024-08-01 00:00:00+00:00         60\n",
              "1 2024-08-01 01:00:00+00:00         60\n",
              "2 2024-08-01 02:00:00+00:00         60\n",
              "3 2024-08-01 03:00:00+00:00         60"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "floor — tail 4:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label_utc</th>\n",
              "      <th>n_samples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>2024-08-31 20:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>2024-08-31 21:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>742</th>\n",
              "      <td>2024-08-31 22:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>743</th>\n",
              "      <td>2024-08-31 23:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    label_utc  n_samples\n",
              "740 2024-08-31 20:00:00+00:00         60\n",
              "741 2024-08-31 21:00:00+00:00         60\n",
              "742 2024-08-31 22:00:00+00:00         60\n",
              "743 2024-08-31 23:00:00+00:00         60"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "ceiling — head 4:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label_utc</th>\n",
              "      <th>n_samples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024-08-01 01:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-08-01 02:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-08-01 03:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-08-01 04:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  label_utc  n_samples\n",
              "0 2024-08-01 01:00:00+00:00         60\n",
              "1 2024-08-01 02:00:00+00:00         60\n",
              "2 2024-08-01 03:00:00+00:00         60\n",
              "3 2024-08-01 04:00:00+00:00         60"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ceiling — tail 4:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label_utc</th>\n",
              "      <th>n_samples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>2024-08-31 21:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>2024-08-31 22:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>742</th>\n",
              "      <td>2024-08-31 23:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>743</th>\n",
              "      <td>2024-09-01 00:00:00+00:00</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    label_utc  n_samples\n",
              "740 2024-08-31 21:00:00+00:00         60\n",
              "741 2024-08-31 22:00:00+00:00         60\n",
              "742 2024-08-31 23:00:00+00:00         60\n",
              "743 2024-09-01 00:00:00+00:00         59"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "center (match_ceiling_labels=True) — head 4:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label_utc</th>\n",
              "      <th>n_samples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024-08-01 01:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-08-01 02:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-08-01 03:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-08-01 04:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  label_utc  n_samples\n",
              "0 2024-08-01 01:00:00+00:00         60\n",
              "1 2024-08-01 02:00:00+00:00         60\n",
              "2 2024-08-01 03:00:00+00:00         60\n",
              "3 2024-08-01 04:00:00+00:00         60"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "center — tail 4:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label_utc</th>\n",
              "      <th>n_samples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>2024-08-31 21:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>2024-08-31 22:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>742</th>\n",
              "      <td>2024-08-31 23:00:00+00:00</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>743</th>\n",
              "      <td>2024-09-01 00:00:00+00:00</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    label_utc  n_samples\n",
              "740 2024-08-31 21:00:00+00:00         60\n",
              "741 2024-08-31 22:00:00+00:00         60\n",
              "742 2024-08-31 23:00:00+00:00         60\n",
              "743 2024-09-01 00:00:00+00:00         29"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Summary n_samples (all intervals):\n",
            "  floor  : count=744, min=60, max=60, mean=60.0\n",
            "  ceiling: count=744, min=59, max=60, mean=60.0\n",
            "  center : count=744, min=29, max=60, mean=60.0\n"
          ]
        }
      ],
      "source": [
        "import bsrn.utils.averaging as av\n",
        "from IPython.display import display\n",
        "\n",
        "def hourly_sample_counts(df, freq, alignment, match_ceiling_labels=True):\n",
        "    \"\"\"Native rows per 1 h label (same windows as pretty_average).\"\"\"\n",
        "    delta = av._period_delta(freq)\n",
        "    res = av._archive_timestep_1_or_3(df.index)\n",
        "    labels = av._label_grid(df.index, freq)\n",
        "    labels = av._trim_labels_for_alignment(\n",
        "        labels, df.index, freq, alignment, match_ceiling_labels=match_ceiling_labels,\n",
        "    )\n",
        "    idx = df.index\n",
        "    rows = []\n",
        "    for L in labels:\n",
        "        m = av._window_mask(idx, L, delta, alignment, res)\n",
        "        rows.append((L, int(m.sum())))\n",
        "    return pd.DataFrame(rows, columns=[\"label_utc\", \"n_samples\"])\n",
        "\n",
        "\n",
        "freq = \"1h\"\n",
        "dfc = ds.data()[[\"ghi\", \"zenith\"]].copy()\n",
        "\n",
        "floor_ct = hourly_sample_counts(dfc, freq, \"floor\")\n",
        "ceil_ct = hourly_sample_counts(dfc, freq, \"ceiling\")\n",
        "cent_ct = hourly_sample_counts(dfc, freq, \"center\", match_ceiling_labels=True)\n",
        "\n",
        "print(\"Case C — n_samples per 1 h interval (native rows in each window)\")\n",
        "# display() renders full HTML tables (avoids stdout truncation in long cells).\n",
        "# display() renders full HTML tables (avoids stdout truncation in long cells).\n",
        "print(\"\\nfloor — head 4:\")\n",
        "display(floor_ct.head(4))\n",
        "print(\"floor — tail 4:\")\n",
        "display(floor_ct.tail(4))\n",
        "print(\"\\nceiling — head 4:\")\n",
        "display(ceil_ct.head(4))\n",
        "print(\"ceiling — tail 4:\")\n",
        "display(ceil_ct.tail(4))\n",
        "print(\"\\ncenter (match_ceiling_labels=True) — head 4:\")\n",
        "display(cent_ct.head(4))\n",
        "print(\"center — tail 4:\")\n",
        "display(cent_ct.tail(4))\n",
        "\n",
        "print(\"\\nSummary n_samples (all intervals):\")\n",
        "for name, tab in [(\"floor\", floor_ct), (\"ceiling\", ceil_ct), (\"center\", cent_ct)]:\n",
        "    s = tab[\"n_samples\"]\n",
        "    print(f\"  {name:7s}: count={len(s)}, min={s.min()}, max={s.max()}, mean={s.mean():.1f}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e7e0c161",
      "metadata": {},
      "source": [
        "## 6. Apply to QIQ (1 h)\n",
        "\n",
        "Same `freq` and three alignments on the **QC-masked** August frame (**§3**). Row counts often differ slightly between alignments.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "fdc0d127",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row counts — floor: 744 ceiling: 744 center: 744\n"
          ]
        }
      ],
      "source": [
        "FREQ = \"1h\"\n",
        "\n",
        "# Irradiance + zenith (required by pretty_average)\n",
        "cols_avg = [c for c in (\"ghi\", \"ghi_clear\", \"zenith\") if c in ds.data().columns]\n",
        "df_in = ds.data()[cols_avg].copy()\n",
        "\n",
        "avg_floor = pretty_average(df_in, FREQ, alignment=\"floor\")\n",
        "avg_ceil = pretty_average(df_in, FREQ, alignment=\"ceiling\")\n",
        "avg_center = pretty_average(df_in, FREQ, alignment=\"center\")\n",
        "\n",
        "print(\"Row counts — floor:\", len(avg_floor), \"ceiling:\", len(avg_ceil), \"center:\", len(avg_center))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dab2ac61",
      "metadata": {},
      "source": [
        "## 7. Plots: compare GHI means\n",
        "\n",
        "Run **§6** first so `avg_floor`, `avg_ceil`, and `avg_center` exist.\n",
        "\n",
        "Overlay 1-hour mean **GHI** for the three alignments on a short slice (2024-08-08 UTC).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "88771d8f",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "day = \"2024-08-08\"\n",
        "sub = slice(f\"{day} 00:00\", f\"{day} 23:59\")\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 4), dpi=120)\n",
        "\n",
        "if \"ghi\" in ds.data().columns:\n",
        "    ax.plot(\n",
        "        ds.data().loc[sub].index,\n",
        "        ds.data().loc[sub, \"ghi\"],\n",
        "        color=\"0.85\",\n",
        "        linewidth=0.8,\n",
        "        label=\"GHI (1 min native)\",\n",
        "    )\n",
        "\n",
        "ax.plot(avg_floor.loc[sub].index, avg_floor.loc[sub, \"ghi\"], marker=\"o\", markersize=3, label=\"floor\")\n",
        "ax.plot(avg_ceil.loc[sub].index, avg_ceil.loc[sub, \"ghi\"], marker=\"s\", markersize=3, label=\"ceiling\")\n",
        "ax.plot(avg_center.loc[sub].index, avg_center.loc[sub, \"ghi\"], marker=\"^\", markersize=3, label=\"center\")\n",
        "\n",
        "ax.set_ylabel(\"GHI [W/m²]\")\n",
        "ax.set_xlabel(\"Time (UTC)\")\n",
        "ax.set_title(f\"{stn} — {day} — 1 h mean GHI vs alignment\")\n",
        "ax.legend(loc=\"upper left\", fontsize=8)\n",
        "fig.autofmt_xdate()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24d52551",
      "metadata": {},
      "source": [
        "## 8. Optional: compare clear-sky GHI\n",
        "\n",
        "Same overlay for **`ghi_clear`** (REST2).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "e643cbd1",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "if \"ghi_clear\" in avg_floor.columns:\n",
        "    fig, ax = plt.subplots(figsize=(10, 4), dpi=120)\n",
        "    ax.plot(avg_floor.loc[sub].index, avg_floor.loc[sub, \"ghi_clear\"], marker=\"o\", markersize=3, label=\"floor\")\n",
        "    ax.plot(avg_ceil.loc[sub].index, avg_ceil.loc[sub, \"ghi_clear\"], marker=\"s\", markersize=3, label=\"ceiling\")\n",
        "    ax.plot(avg_center.loc[sub].index, avg_center.loc[sub, \"ghi_clear\"], marker=\"^\", markersize=3, label=\"center\")\n",
        "    ax.set_ylabel(\"GHI clear [W/m²]\")\n",
        "    ax.set_xlabel(\"Time (UTC)\")\n",
        "    ax.set_title(f\"{stn} — {day} — REST2 ghi_clear (1 h)\")\n",
        "    ax.legend(loc=\"upper left\", fontsize=8)\n",
        "    fig.autofmt_xdate()\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.13.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}