{ "cells": [ { "cell_type": "markdown", "id": "e68b3d09", "metadata": {}, "source": [ "# Atlantic hurricanes: a shift in activity in the 1990s\n", "\n", "Atlantic hurricane activity is not stationary. The Atlantic Multidecadal\n", "Oscillation (AMO), a roughly 60–80-year swing in North Atlantic sea-surface\n", "temperature, modulates it in long phases: an active phase ~1944–1969, a quiet\n", "phase ~1970–1994, and a markedly more active phase since 1995. In a *Science*\n", "paper, Goldenberg, Landsea, Mestas-Nuñez & Gray (2001) argued that\n", "major-hurricane activity jumped after 1995 and would persist for decades. Here\n", "we ask whether that transition can be detected and dated from the count data\n", "alone, and how large it was, without telling the model when to look.\n", "\n", "This is the kind of question *bayesloop* is built for. We put a Poisson\n", "observation model on the annual storm counts, let the rate vary in time, and ask\n", "the data which kind of variation it prefers, using the marginal likelihood\n", "(model evidence) as an objective, complexity-penalised score. Along the way we\n", "exercise a focused slice of the toolbox: the `Poisson` observation model;\n", "`Study`, `HyperStudy` and `ChangepointStudy`; and the `Static`,\n", "`GaussianRandomWalk` and `ChangePoint` transition models. The analysis mirrors a\n", "stationary-rate analysis (for example global earthquakes), run with identical\n", "machinery, but here the evidence comes down firmly on the side of a changing\n", "rate." ] }, { "cell_type": "code", "execution_count": 1, "id": "5cd85705", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:54:14.734381Z", "iopub.status.busy": "2026-06-12T09:54:14.734310Z", "iopub.status.idle": "2026-06-12T09:54:16.143884Z", "shell.execute_reply": "2026-06-12T09:54:16.143328Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import io\n", "import urllib.request\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "\n", "import bayesloop as bl\n", "\n", "try:\n", " import seaborn as sns\n", " sns.set_style(\"whitegrid\")\n", "except ImportError:\n", " plt.style.use(\"default\")\n", "mpl.rcParams.update({\n", " \"figure.dpi\": 110, \"font.size\": 10.5, \"axes.titlesize\": 12,\n", " \"axes.titleweight\": \"bold\", \"axes.edgecolor\": \"#444444\", \"grid.color\": \"#d9d9d9\",\n", "})\n", "C = {\"data\": \"#8a8a8a\", \"accent\": \"#c1272d\", \"accent2\": \"#0b6e99\",\n", " \"green\": \"#2e7d32\", \"amber\": \"#e8a33d\", \"muted\": \"#8a8a8a\",\n", " \"band\": \"#c1272d\", \"band2\": \"#0b6e99\"}\n", "\n", "\n", "def posterior_band(S, name, lo=0.05, hi=0.95):\n", " \"\"\"Posterior mean and an equal-tailed credible band for a time-varying parameter.\"\"\"\n", " x, p = S.get_parameter_distributions(name, density=False)\n", " p = np.asarray(p, dtype=float)\n", " p /= p.sum(axis=1, keepdims=True)\n", " mean = (p * x[None, :]).sum(axis=1)\n", " cdf = np.cumsum(p, axis=1)\n", " low = np.array([np.interp(lo, cdf[t], x) for t in range(p.shape[0])])\n", " high = np.array([np.interp(hi, cdf[t], x) for t in range(p.shape[0])])\n", " return mean, low, high\n", "\n", "\n", "def prob_in(S, name, lo=None, hi=None):\n", " \"\"\"Posterior probability the parameter lies in (lo, hi] at each time step.\"\"\"\n", " x, p = S.get_parameter_distributions(name, density=False)\n", " p = np.asarray(p, dtype=float)\n", " p /= p.sum(axis=1, keepdims=True)\n", " mask = np.ones_like(x, dtype=bool)\n", " if lo is not None:\n", " mask &= x > lo\n", " if hi is not None:\n", " mask &= x <= hi\n", " return p[:, mask].sum(axis=1)" ] }, { "cell_type": "markdown", "id": "c23b5d9e", "metadata": {}, "source": [ "## The data\n", "\n", "We use NOAA's HURDAT2 Atlantic hurricane best-track database, the canonical\n", "record of every known Atlantic tropical cyclone since 1851, maintained by the\n", "National Hurricane Center. It is a single plain-text file with a simple structure:\n", "each storm starts with a *header line* (`AL{nn}{year}, NAME, n_records`), followed\n", "by one *data line* per six-hour fix giving the storm's status and maximum\n", "sustained wind in knots.\n", "\n", "To get annual counts we stream the file once, track each storm's lifetime-maximum\n", "wind, and bin storms into the standard intensity categories:\n", "\n", "- named storms, peak wind ≥ 34 kt (tropical-storm strength or above),\n", "- hurricanes, peak wind ≥ 64 kt,\n", "- major hurricanes, peak wind ≥ 96 kt (Saffir–Simpson Category 3+).\n", "\n", "Major hurricanes are the cleanest AMO indicator: they are both the least affected\n", "by historical observational undercount and where the multidecadal signal is\n", "strongest." ] }, { "cell_type": "code", "execution_count": 2, "id": "ce41afdf", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:54:16.145243Z", "iopub.status.busy": "2026-06-12T09:54:16.145129Z", "iopub.status.idle": "2026-06-12T09:54:17.111041Z", "shell.execute_reply": "2026-06-12T09:54:17.110492Z" }, "lines_to_next_cell": 1 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HURDAT2 parsed: 1851-2023 (173 seasons); 2023 had 7 hurricanes, 3 major.\n" ] } ], "source": [ "UA = \"Mozilla/5.0 (bayesloop docs; research)\"\n", "HURDAT_URLS = [\n", " \"https://www.nhc.noaa.gov/data/hurdat/hurdat2-1851-2023-051124.txt\",\n", " \"https://www.nhc.noaa.gov/data/hurdat/hurdat2-1851-2024-040425.txt\",\n", " \"https://www.nhc.noaa.gov/data/hurdat/hurdat2-1851-2022-050423.txt\",\n", "]\n", "text = None\n", "for url in HURDAT_URLS:\n", " try:\n", " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", " text = urllib.request.urlopen(req, timeout=90).read().decode(\"utf-8\", \"replace\")\n", " break\n", " except Exception: # noqa: BLE001 — try the next mirror\n", " continue\n", "if text is None:\n", " raise RuntimeError(\"Could not download HURDAT2 from any mirror.\")\n", "\n", "\n", "def parse_hurdat2(text):\n", " \"\"\"Parse the raw HURDAT2 text into annual counts of named storms, hurricanes\n", " and major hurricanes (binned on each storm's lifetime-maximum wind in knots).\"\"\"\n", " rows, year, maxwind = [], None, -999\n", "\n", " def flush():\n", " if year is not None:\n", " rows.append((year, maxwind))\n", "\n", " for line in text.splitlines():\n", " line = line.strip()\n", " if not line:\n", " continue\n", " parts = [p.strip() for p in line.split(\",\")]\n", " if parts[0].startswith(\"AL\") and len(parts[0]) == 8:\n", " # storm header line: close out the previous storm and start a new one\n", " flush()\n", " year = int(parts[0][4:8])\n", " maxwind = -999\n", " else:\n", " # track fix: field index 6 is the max sustained wind in knots\n", " try:\n", " w = int(parts[6])\n", " except (ValueError, IndexError):\n", " w = -999\n", " if w > maxwind:\n", " maxwind = w\n", " flush()\n", "\n", " df = pd.DataFrame(rows, columns=[\"year\", \"maxwind\"])\n", " df = df[df[\"maxwind\"] >= 0]\n", " return df.groupby(\"year\").agg(\n", " named=(\"maxwind\", lambda w: int((w >= 34).sum())),\n", " hurricanes=(\"maxwind\", lambda w: int((w >= 64).sum())),\n", " major=(\"maxwind\", lambda w: int((w >= 96).sum())),\n", " ).reset_index()\n", "\n", "\n", "g = parse_hurdat2(text)\n", "\n", "# The basin-wide record before the routine aircraft-reconnaissance era (1944-)\n", "# undercounts storms that stayed at sea, so we analyse from 1944 onward and stop at\n", "# the last complete season in this file.\n", "g = g[g[\"year\"] <= 2023]\n", "print(f\"HURDAT2 parsed: {int(g['year'].min())}-{int(g['year'].max())} \"\n", " f\"({len(g)} seasons); 2023 had {int(g[g['year']==2023]['hurricanes'].iloc[0])} \"\n", " f\"hurricanes, {int(g[g['year']==2023]['major'].iloc[0])} major.\")" ] }, { "cell_type": "markdown", "id": "e4914494", "metadata": {}, "source": [ "## A time-varying rate over the aircraft era\n", "\n", "Our observation model is a `Poisson` whose rate is allowed to drift from year to\n", "year. We wrap it in a `HyperStudy` with a `GaussianRandomWalk` on the rate and\n", "let bayesloop marginalise over the unknown step size `sigma`, so the amount of\n", "smoothing is inferred rather than hand-tuned. We fit two series over the\n", "aircraft-reconnaissance era (1944–2023): all hurricanes, and major hurricanes." ] }, { "cell_type": "code", "execution_count": 3, "id": "18273f5f", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:54:17.112287Z", "iopub.status.busy": "2026-06-12T09:54:17.112198Z", "iopub.status.idle": "2026-06-12T09:54:17.679356Z", "shell.execute_reply": "2026-06-12T09:54:17.678877Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", " --> Hyper-study\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Gaussian random walk. Hyper-Parameter(s): ['sigma']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", " --> Hyper-study\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Gaussian random walk. Hyper-Parameter(s): ['sigma']\n" ] } ], "source": [ "def fit_rate(series_years, series_counts, ratemax):\n", " S = bl.HyperStudy()\n", " S.load_data(series_counts.astype(float), timestamps=series_years.astype(float))\n", " S.set(bl.om.Poisson(\"rate\", bl.oint(0, ratemax, 600)),\n", " bl.tm.GaussianRandomWalk(\"sigma\", bl.cint(0, 0.8, 40), target=\"rate\"))\n", " S.fit(silent=True)\n", " return S\n", "\n", "\n", "air = g[g[\"year\"] >= 1944]\n", "ay = air[\"year\"].values\n", "Smaj = fit_rate(ay, air[\"major\"].values, 9.0)\n", "maj_mean, maj_lo, maj_hi = posterior_band(Smaj, \"rate\")\n", "Shur = fit_rate(ay, air[\"hurricanes\"].values, 16.0)\n", "hur_mean, hur_lo, hur_hi = posterior_band(Shur, \"rate\")" ] }, { "cell_type": "markdown", "id": "988742fb", "metadata": {}, "source": [ "Plotting the inferred rate against the AMO phases shows the signal clearly:\n", "the major-hurricane rate sits high in the warm 1944–69 phase, sags through the\n", "cool 1970–94 phase, and climbs sharply after 1995. The all-hurricane series shows\n", "the same shape more weakly, as expected." ] }, { "cell_type": "code", "execution_count": 4, "id": "1b2a0c51", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:54:17.681235Z", "iopub.status.busy": "2026-06-12T09:54:17.681126Z", "iopub.status.idle": "2026-06-12T09:54:17.824825Z", "shell.execute_reply": "2026-06-12T09:54:17.824412Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phases = [(1944, 1969, \"AMO warm\\n(active)\"), (1970, 1994, \"AMO cool\\n(quiet)\"),\n", " (1995, 2023, \"AMO warm\\n(active)\")]\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(9, 5.4), sharex=True)\n", "for ax, counts, mean, lo, hi, lab, col in [\n", " (ax1, air[\"hurricanes\"].values, hur_mean, hur_lo, hur_hi, \"all hurricanes\", C[\"accent2\"]),\n", " (ax2, air[\"major\"].values, maj_mean, maj_lo, maj_hi, \"major hurricanes (Cat 3+)\", C[\"accent\"]),\n", "]:\n", " ax.bar(ay, counts, width=0.8, color=C[\"muted\"], alpha=0.45, label=f\"{lab} count\")\n", " ax.plot(ay, mean, color=col, lw=2.2, label=\"inferred rate\")\n", " ax.fill_between(ay, lo, hi, color=col, alpha=0.18, label=\"90% credible band\")\n", " for x0, x1, _ in phases:\n", " ax.axvspan(x0, x1, color=(\"#cc6677\" if x0 != 1970 else \"#4477aa\"), alpha=0.06)\n", " ax.axvline(1995, color=\"k\", ls=\"--\", lw=1, alpha=0.7)\n", " ax.set_ylabel(\"storms / year\")\n", " ax.legend(loc=\"upper left\", ncol=3, fontsize=8)\n", "for x0, x1, txt in phases:\n", " ax1.text((x0 + x1) / 2, ax1.get_ylim()[1] * 0.92, txt, ha=\"center\", va=\"top\", fontsize=7.5)\n", "ax1.set_title(\"Atlantic hurricane activity tracks the AMO; major hurricanes jump after 1995\")\n", "ax2.set_xlabel(\"year\")\n", "ax2.set_xlim(1943, 2024)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d3f4704c", "metadata": {}, "source": [ "## Constant, gradual, or a single break?\n", "\n", "The picture above assumes a smoothly drifting rate. We now test whether the data\n", "prefer that over the alternatives by pitting three hypotheses against each other.\n", "Every model shares the identical Poisson observation model and the same data\n", "window, so their log₁₀ marginal likelihoods (`S.log10_evidence`) are directly\n", "comparable.\n", "\n", "| Model | Transition |\n", "|---|---|\n", "| Constant rate | `tm.Static()` |\n", "| Gradually varying | `tm.GaussianRandomWalk` (HyperStudy over the step size) |\n", "| Single change-point | `ChangepointStudy` + `tm.ChangePoint(\"t_change\", \"all\")` |\n", "\n", "We run the comparison on the homogeneous satellite era (1966–2023), where the\n", "observing system is uniform and the evidence test is cleanest. The\n", "`ChangepointStudy` scans every possible break year and returns a full posterior\n", "over the date of the transition." ] }, { "cell_type": "code", "execution_count": 5, "id": "acc1bcab", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:54:17.825937Z", "iopub.status.busy": "2026-06-12T09:54:17.825833Z", "iopub.status.idle": "2026-06-12T09:54:18.151597Z", "shell.execute_reply": "2026-06-12T09:54:18.151232Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Static/constant parameter values. Hyper-Parameter(s): []\n", "+ Created new study.\n", " --> Hyper-study\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Gaussian random walk. Hyper-Parameter(s): ['sigma']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", " --> Hyper-study\n", " --> Change-point analysis\n", "+ Successfully imported array.\n", "+ Observation model: Poisson. Parameter(s): ['rate']\n", "+ Transition model: Change-point. Hyper-Parameter(s): ['t_change']\n", "+ Detected 1 change-point(s) in transition model: ['t_change']\n", "+ Set hyper-prior(s): ['uniform']\n", "Major-hurricane model evidence (satellite era 1966-2023, log10):\n", " Constant rate -49.28\n", " Gradually varying -47.45\n", " Single change-point -48.56\n", " best: Gradually varying\n", "Change-point dated to 1994 (90% credible 1992-1997)\n", "Major-hurricane rate (bayesloop posterior): quiet 1.88/yr -> active 3.28/yr (1.74x); raw-count cross-check 1.55 -> 3.55 (2.29x)\n" ] } ], "source": [ "sat = g[g[\"year\"] >= 1966]\n", "sy = sat[\"year\"].values.astype(float)\n", "maj = sat[\"major\"].values.astype(float)\n", "\n", "\n", "def pois():\n", " return bl.om.Poisson(\"rate\", bl.oint(0, 9, 600))\n", "\n", "\n", "S0 = bl.Study(); S0.load_data(maj, timestamps=sy); S0.set(pois(), bl.tm.Static()); S0.fit(silent=True)\n", "S2 = bl.HyperStudy(); S2.load_data(maj, timestamps=sy)\n", "S2.set(pois(), bl.tm.GaussianRandomWalk(\"sigma\", bl.cint(0, 0.8, 40), target=\"rate\")); S2.fit(silent=True)\n", "Scp = bl.ChangepointStudy(); Scp.load_data(maj, timestamps=sy)\n", "Scp.set(pois(), bl.tm.ChangePoint(\"t_change\", \"all\")); Scp.fit(silent=True)\n", "cp_x, cp_p = Scp.get_hyper_parameter_distribution(\"t_change\")\n", "cp_mode = float(cp_x[np.argmax(cp_p)])\n", "order = np.argsort(cp_p)[::-1]; cum = np.cumsum(cp_p[order])\n", "cred = np.sort(cp_x[order[:np.searchsorted(cum, 0.90) + 1]])\n", "\n", "evid = {\n", " \"Constant rate\": float(S0.log10_evidence),\n", " \"Gradually varying\": float(S2.log10_evidence),\n", " \"Single change-point\": float(Scp.log10_evidence),\n", "}\n", "\n", "# Quantify the jump TWO ways, both on the satellite-era window the evidence/change-point use.\n", "# (a) From the bayesloop posterior rate of the gradually-varying model (S2): mean inferred\n", "# rate in the quiet (pre-break) vs active (post-break) AMO phases, split at 1995.\n", "# (b) As a model-independent cross-check, the raw annual-count means over the same phases.\n", "r2_mean, _, _ = posterior_band(S2, \"rate\")\n", "pre_inf = float(r2_mean[(sy >= 1966) & (sy <= 1994)].mean())\n", "post_inf = float(r2_mean[(sy >= 1995) & (sy <= 2023)].mean())\n", "pre = float(sat[(sat[\"year\"] >= 1966) & (sat[\"year\"] <= 1994)][\"major\"].mean())\n", "post = float(sat[(sat[\"year\"] >= 1995) & (sat[\"year\"] <= 2023)][\"major\"].mean())\n", "\n", "best = max(evid, key=evid.get)\n", "print(\"Major-hurricane model evidence (satellite era 1966-2023, log10):\")\n", "for k, v in evid.items():\n", " print(f\" {k:22s} {v:8.2f}\")\n", "print(f\" best: {best}\")\n", "print(f\"Change-point dated to {cp_mode:.0f} (90% credible {cred.min():.0f}-{cred.max():.0f})\")\n", "print(f\"Major-hurricane rate (bayesloop posterior): quiet {pre_inf:.2f}/yr -> active {post_inf:.2f}/yr \"\n", " f\"({post_inf/pre_inf:.2f}x); raw-count cross-check {pre:.2f} -> {post:.2f} ({post/pre:.2f}x)\")" ] }, { "cell_type": "markdown", "id": "1d0d6fd1", "metadata": {}, "source": [ "The evidence favours a changing rate. A time-varying rate is about\n", "10¹·⁸ ≈ 60× more probable than a constant one, the mirror image of what the same\n", "tooling returns for a stationary process. The gradual random walk even edges out\n", "the single abrupt break, consistent with AMO transitions being fairly rapid but\n", "not instantaneous.\n", "\n", "The `ChangepointStudy` also dates the break, to 1994, with a 90% credible\n", "interval of 1992–1997, squarely on the 1995 transition identified by Goldenberg\n", "et al. The magnitude is close to a doubling: the smoothed posterior rate rises\n", "from ~1.9/yr to ~3.3/yr (×1.74), while the raw counts go from ~1.6/yr to ~3.6/yr\n", "(×2.29), in line with the \"~2.5×\" quoted in the *Science* paper. The smooth model\n", "is the more conservative of the two because the random walk spreads the rise\n", "across the transition years rather than snapping at a single break; both are\n", "consistent readings of the same shift." ] }, { "cell_type": "code", "execution_count": 6, "id": "e217cc59", "metadata": { "execution": { "iopub.execute_input": "2026-06-12T09:54:18.152852Z", "iopub.status.busy": "2026-06-12T09:54:18.152768Z", "iopub.status.idle": "2026-06-12T09:54:18.355911Z", "shell.execute_reply": "2026-06-12T09:54:18.355456Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (axa, axb) = plt.subplots(1, 2, figsize=(9.2, 3.4))\n", "axa.bar(cp_x, cp_p, width=0.9, color=C[\"green\"], alpha=0.85)\n", "axa.axvline(1995, color=C[\"accent\"], ls=\"--\", lw=1.2, label=\"1995\")\n", "axa.set_xlim(1966, 2010)\n", "axa.set_xlabel(\"year of structural break\")\n", "axa.set_ylabel(\"posterior probability\")\n", "axa.set_title(f\"Major-hurricane change-point: {cp_mode:.0f}\")\n", "axa.legend()\n", "labels = list(evid.keys())\n", "vals = np.array([evid[k] for k in labels]) - min(evid.values())\n", "axb.barh(labels, vals, color=[C[\"muted\"], C[\"accent2\"], C[\"green\"]])\n", "axb.set_xlabel(r\"log$_{10}$ evidence vs worst\")\n", "axb.set_title(\"A changing rate is strongly favoured\")\n", "for i, v in enumerate(vals):\n", " axb.text(v, i, f\" {v:.1f}\", va=\"center\", fontsize=8)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d6fe4482", "metadata": {}, "source": [ "## What this example showed\n", "\n", "On a single public dataset we used *bayesloop* to:\n", "\n", "- answer a climatological question with model evidence: constant versus changing\n", " rate is settled quantitatively, and a time-varying rate wins by ~60×, the\n", " opposite of a stationary process analysed the same way;\n", "- date a regime shift with a credible interval: the `ChangepointStudy` locates\n", " the AMO transition at 1994 (1992–1997) without being told when to look,\n", " independently confirming Goldenberg et al. (2001);\n", "- quantify the jump: close to a doubling in the major-hurricane rate (×1.74 in\n", " the smoothed posterior, ×2.29 in raw counts), with the dependence on smoothing\n", " made explicit;\n", "- exercise the core toolbox: the `Poisson` observation model with\n", " `Study`/`HyperStudy`/`ChangepointStudy` and the `Static`, `GaussianRandomWalk`\n", " and `ChangePoint` transition models, showing that restricting to the\n", " reliably-observed era is part of the analysis rather than an afterthought." ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all", "text_representation": { "extension": ".py", "format_name": "percent" } }, "kernelspec": { "display_name": "Python 3", "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }