{
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   "id": "c00",
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   "source": [
    "# 10 — Changement de régime : atteindre les désinformations qui s'installent\n",
    "\n",
    "Le [notebook 09](09_calibration_visibilite.ipynb) a livré un chiffre, mais il a surtout\n",
    "révélé sa propre limite. Onze des vingt-quatre sujets du corpus n'ont produit aucun épisode\n",
    "exploitable, et ce sont **les cas archétypaux** : QAnon, désinformation Covid-19, hésitation\n",
    "vaccinale, grand remplacement, Pegasus. Leur attention ne forme pas un pic suivi d'une\n",
    "décroissance — elle **change de niveau et s'installe**.\n",
    "\n",
    "Un modèle de la polarisation qui ne mesure que les flambées passagères n'atteint pas son\n",
    "objet. Ce notebook traite l'autre cas.\n",
    "\n",
    "**Résultat, annoncé d'emblée.** Deux réussites doivent être distinguées, parce qu'une seule\n",
    "est acquise :\n",
    "\n",
    "* **la détection fonctionne** — 14 changements de régime sont identifiés sur le corpus, aux\n",
    "  bonnes dates : l'affaire Benalla le 20 juillet 2018, les révélations Pegasus en juillet\n",
    "  2021, l'annonce de LIGO en février 2016, l'explosion de QAnon en mars 2020. Et elle couvre\n",
    "  précisément les sujets que la méthode par pic manquait ;\n",
    "* **l'identification échoue** — sur ces mêmes séries, la dispersion résiduelle est de 0,63 en\n",
    "  médiane, alors que l'incertitude relative sur $\\gamma\\alpha/\\lambda$ atteint déjà 77 % à\n",
    "  0,15. **Aucun** des 14 changements ne livre de paramètres exploitables.\n",
    "\n",
    "À quoi s'ajoute une limite théorique découverte en route, plus lourde que les deux :\n",
    "l'identifiabilité du rapport dépend de la **forme** supposée de la saturation, et pour l'une\n",
    "des deux formes plausibles elle n'existe pas du tout."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c01",
   "metadata": {},
   "source": [
    "## 1. Pourquoi l'estimateur par pic ne peut pas être réutilisé\n",
    "\n",
    "L'identification par pic reposait sur deux pentes : une montée à\n",
    "$\\gamma\\alpha - \\lambda$, une décroissance à $\\lambda$.\n",
    "\n",
    "Dans un régime installé, **la décroissance n'existe pas**. Le système est à son point fixe,\n",
    "où par définition $\\gamma\\alpha\\,\\sigma(V^*) = \\lambda$ : le niveau du palier ne dit rien\n",
    "à lui seul, et il n'y a pas de seconde pente à mesurer.\n",
    "\n",
    "L'information est ailleurs — dans la **forme de la transition**, qui traverse tout le domaine\n",
    "de la saturation et contraint les trois paramètres à la fois."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02",
   "metadata": {},
   "source": [
    "## 2. Une identification exacte, et sa fragilité\n",
    "\n",
    "En posant $y = \\dot W/W$ et $x = W^2$, un réarrangement **exact** de l'équation — sans\n",
    "approximation — donne une forme linéaire en trois coefficients :\n",
    "\n",
    "$$y = A - B\\,xy - C\\,x\n",
    "  \\qquad A = \\gamma\\alpha - \\lambda, \\quad B = \\frac{1}{W_{\\text{sat}}^2},\n",
    "  \\quad C = \\frac{\\lambda}{W_{\\text{sat}}^2}$$\n",
    "\n",
    "d'où $\\lambda = C/B$, $\\gamma\\alpha = A + C/B$, et $\\gamma\\alpha/\\lambda = 1 + AB/C$.\n",
    "\n",
    "Cette régression retrouve les trois paramètres à la précision machine — mais elle est\n",
    "inutilisable en pratique : $y$ apparaît à la fois comme réponse et dans le régresseur $-xy$,\n",
    "et il faut le calculer en dérivant des données bruitées. C'est un problème d'erreurs sur les\n",
    "variables, et il est dévastateur.\n",
    "\n",
    "L'estimateur retenu n'utilise donc **aucune dérivée** : il intègre l'équation et ajuste la\n",
    "trajectoire en échelle logarithmique, la forme linéaire ne servant que d'initialisation."
   ]
  },
  {
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   "execution_count": 1,
   "id": "c03",
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    "execution": {
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     "text": [
      "Récupération sur trajectoire propre — condition minimale de crédibilité :\n",
      "  ρ vrai =   1.5  →  ρ estimé =   1.50   λ = 0.2000 (vrai 0.2)   Wsat =    3000 (vrai 3000)\n",
      "  ρ vrai =   5.0  →  ρ estimé =   5.00   λ = 0.2000 (vrai 0.2)   Wsat =    3000 (vrai 3000)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  ρ vrai =  40.0  →  ρ estimé =  40.00   λ = 0.2000 (vrai 0.2)   Wsat =    3000 (vrai 3000)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "from ide.corpus import CORPUS, CORPUS_END, CORPUS_START\n",
    "from ide.pageviews import load_or_fetch\n",
    "from ide.plotting import PALETTE, save_figure, use_project_style\n",
    "from ide.regime import (\n",
    "    RegimeCriteria,\n",
    "    _integrate,\n",
    "    detect_change_points,\n",
    "    expected_precision,\n",
    "    fit_saturated_growth,\n",
    "    scan_regime_shifts,\n",
    "    weekly_adjust,\n",
    ")\n",
    "\n",
    "use_project_style()\n",
    "\n",
    "def transition(ratio, damping=0.2, saturation=3_000.0, days=120):\n",
    "    grid = np.arange(0.0, float(days))\n",
    "    return _integrate(ratio * damping, damping, saturation, 0.01 * saturation, grid, \"quadratic\")\n",
    "\n",
    "print(\"Récupération sur trajectoire propre — condition minimale de crédibilité :\")\n",
    "for ratio in (1.5, 5.0, 40.0):\n",
    "    fit = fit_saturated_growth(transition(ratio))\n",
    "    print(f\"  ρ vrai = {ratio:5.1f}  →  ρ estimé = {fit.ratio:6.2f}   \"\n",
    "          f\"λ = {fit.damping:.4f} (vrai 0.2)   Wsat = {fit.saturation:7.0f} (vrai 3000)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04",
   "metadata": {},
   "source": [
    "## 3. La limite théorique : l'identifiabilité dépend de la forme supposée\n",
    "\n",
    "C'est le point le plus lourd, et il n'a rien de numérique.\n",
    "\n",
    "Sous la saturation du modèle, $\\sigma = 1/(1+(W/W_{\\text{sat}})^2)$, les trois paramètres\n",
    "façonnent la courbe de trois manières distinctes : ils sont séparables, comme la récupération\n",
    "ci-dessus le confirme.\n",
    "\n",
    "Sous une saturation **logistique**, $\\sigma = 1 - W/W_{\\text{sat}}$, l'équation devient\n",
    "\n",
    "$$\\dot W = (\\gamma\\alpha - \\lambda)\\,W\\left(1 - \\frac{W}{K}\\right),\n",
    "  \\qquad K = W_{\\text{sat}}\\left(1 - \\frac{\\lambda}{\\gamma\\alpha}\\right)$$\n",
    "\n",
    "La trajectoire ne dépend plus que de **deux** combinaisons des trois paramètres. Le rapport\n",
    "$\\gamma\\alpha/\\lambda$ y est **structurellement non identifiable** — et aucune précision de\n",
    "mesure n'y changera quoi que ce soit."
   ]
  },
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   "execution_count": 2,
   "id": "c05",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-23T13:37:39.330614Z"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "écart maximal entre les deux trajectoires : 1.97e-14\n",
      "rapports correspondants : 5.00 et 1.67\n",
      "\n",
      "Les deux courbes sont identiques. Sous saturation logistique, aucun ajustement\n",
      "ne peut séparer γα de λ : l'identifiabilité est une hypothèse de forme, pas une\n",
      "propriété des données.\n"
     ]
    }
   ],
   "source": [
    "grid = np.arange(0.0, 120.0)\n",
    "\n",
    "# Deux triplets de paramètres aux rapports très différents…\n",
    "first = _integrate(1.0, 0.2, 3_000.0, 30.0, grid, \"logistic\")   # ρ = 5.00\n",
    "second = _integrate(2.0, 1.2, 6_000.0, 30.0, grid, \"logistic\")  # ρ = 1.67\n",
    "\n",
    "print(f\"écart maximal entre les deux trajectoires : {np.max(np.abs(first - second) / first):.2e}\")\n",
    "print(f\"rapports correspondants : {1.0/0.2:.2f} et {2.0/1.2:.2f}\")\n",
    "print(\"\\nLes deux courbes sont identiques. Sous saturation logistique, aucun ajustement\")\n",
    "print(\"ne peut séparer γα de λ : l'identifiabilité est une hypothèse de forme, pas une\")\n",
    "print(\"propriété des données.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06",
   "metadata": {},
   "source": [
    "## 4. Détection : ce qui fonctionne\n",
    "\n",
    "La détection procède par **segmentation binaire** sur les logarithmes — une audience évolue\n",
    "multiplicativement, un doublement doit compter autant à mille qu'à cent mille consultations\n",
    "par jour. À chaque étape, la coupure qui réduit le plus le coût quadratique est retenue si\n",
    "elle dépasse une pénalité, ce qui évite de fixer à l'avance un nombre de ruptures.\n",
    "\n",
    "Trois validations sont exigées avant de parler de changement de régime :\n",
    "\n",
    "| Critère | Rôle |\n",
    "|---|---|\n",
    "| **maintien** sur 180 jours | c'est ce qui sépare un régime d'un pic : un pic retombe |\n",
    "| **élévation** d'au moins ×2 | un déplacement de niveau, non une fluctuation |\n",
    "| **ancien régime** ≥ 50 vues/jour | il faut un régime antérieur pour qu'il y ait changement : une série passant de 2 à 300 consultations décrit un article qui vient d'être rédigé |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c07",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:37:39.331683Z",
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     "shell.execute_reply": "2026-08-23T13:37:39.520334Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cas                                 ruptures  régimes  motif de rejet\n",
      "------------------------------------------------------------------------\n",
      "régime installé                            2        1  —\n",
      "pic (doit être rejeté)                     2        0  élévation\n",
      "bruit stationnaire                         0        0  —\n",
      "création d'article                         1        0  niveau\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'cas':34s} {'ruptures':>9s} {'régimes':>8s}  motif de rejet\")\n",
    "print(\"-\" * 72)\n",
    "\n",
    "# Régime installé, engendré par l'équation elle-même.\n",
    "regime = np.full(900, 500.0)\n",
    "regime[300:] = 500.0 + transition(5.0, days=600)\n",
    "regime = regime * np.random.default_rng(1).lognormal(0.0, 0.05, 900)\n",
    "\n",
    "# Pic : même amplitude, mais qui retombe.\n",
    "peak = np.full(900, 500.0)\n",
    "peak[400:460] = 500.0 + 6_000.0 * np.exp(-0.10 * np.arange(60))\n",
    "peak = peak * np.random.default_rng(3).lognormal(0.0, 0.06, 900)\n",
    "\n",
    "# Bruit stationnaire : aucune rupture ne doit être inventée.\n",
    "noise = np.random.default_rng(1).lognormal(np.log(800.0), 0.12, 900)\n",
    "\n",
    "# Création d'article : élévation énorme, mais sans régime antérieur.\n",
    "creation = np.full(900, 2.0)\n",
    "creation[300:] = 300.0\n",
    "creation = creation * np.random.default_rng(5).lognormal(0.0, 0.05, 900)\n",
    "\n",
    "for name, series in [\n",
    "    (\"régime installé\", regime),\n",
    "    (\"pic (doit être rejeté)\", peak),\n",
    "    (\"bruit stationnaire\", noise),\n",
    "    (\"création d'article\", creation),\n",
    "]:\n",
    "    report = scan_regime_shifts(series, label=name, adjust_weekly=False)\n",
    "    print(f\"{name:34s} {report.candidates:9d} {len(report.shifts):8d}  \"\n",
    "          f\"{report.dominant_rejection or '—'}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c08",
   "metadata": {},
   "source": [
    "## 5. Le corpus réel\n",
    "\n",
    "Les mêmes 24 sujets pré-enregistrés que le notebook 09, avec la correction de la périodicité\n",
    "hebdomadaire — un effet systématique de 20 à 30 % d'amplitude, dont le retrait est la\n",
    "préparation la plus rentable puisque c'est la dispersion résiduelle qui pilote l'incertitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c09",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-23T13:37:53.411044Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classe      sujet                      date           avant    après       ×  disp.\n",
      "----------------------------------------------------------------------------------\n",
      "accusation  QAnon                      2018-03-31      165     7296    44.2   0.38\n",
      "accusation  Pizzagate                  2020-03-16     3104    57239    18.4   0.54\n",
      "accusation  Gilets jaunes              2019-05-03      119     1623    13.7   0.20\n",
      "accusation  QAnon                      2020-03-09     4831    58534    12.1   0.35\n",
      "accusation  Gilets jaunes              2019-01-30      119      746     6.3   0.29\n",
      "accusation  Pegasus (logiciel espion)  2021-07-17      444     1955     4.4   0.60\n",
      "accusation  Affaire Benalla            2018-07-20      191      528     2.8   0.66\n",
      "accusation  Pegasus (logiciel espion)  2019-08-18      189      426     2.2   0.84\n",
      "discovery   OSIRIS-REx                 2016-06-12      141      467     3.3   0.87\n",
      "discovery   LIGO                       2016-01-09      224      727     3.2   0.99\n",
      "discovery   Sursauts radio rapides     2017-03-09       77      222     2.9   0.70\n",
      "discovery   Télescope James-Webb       2021-09-27     2531     7228     2.9   0.60\n",
      "discovery   Ondes gravitationnelles    2016-02-01      648     1753     2.7   0.99\n",
      "discovery   Event Horizon Telescope    2019-04-01      158      383     2.4   0.86\n",
      "\n",
      "14 changements de régime détectés — 0 avec paramètres exploitables\n"
     ]
    }
   ],
   "source": [
    "records = []\n",
    "for entry in CORPUS:\n",
    "    series = load_or_fetch(entry.project, entry.article, CORPUS_START, CORPUS_END)\n",
    "    report = scan_regime_shifts(np.clip(series.filled(), 1.0, None), label=entry.label)\n",
    "    for shift in report.shifts:\n",
    "        records.append({\n",
    "            \"classe\": entry.category,\n",
    "            \"sujet\": entry.label,\n",
    "            \"date\": series.day(shift.index),\n",
    "            \"avant\": shift.level_before,\n",
    "            \"après\": shift.level_after,\n",
    "            \"élévation\": shift.lift,\n",
    "            \"dispersion\": shift.fit.scatter,\n",
    "            \"identifié\": shift.has_identified_parameters,\n",
    "        })\n",
    "\n",
    "print(f\"{'classe':11s} {'sujet':26s} {'date':12s} {'avant':>7s} {'après':>8s} {'×':>7s} {'disp.':>6s}\")\n",
    "print(\"-\" * 82)\n",
    "for row in sorted(records, key=lambda item: (item[\"classe\"], -item[\"élévation\"])):\n",
    "    print(f\"{row['classe']:11s} {row['sujet']:26s} {row['date']}  {row['avant']:7.0f} \"\n",
    "          f\"{row['après']:8.0f} {row['élévation']:7.1f} {row['dispersion']:6.2f}\")\n",
    "\n",
    "identified = sum(row[\"identifié\"] for row in records)\n",
    "print(f\"\\n{len(records)} changements de régime détectés — {identified} avec paramètres exploitables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10",
   "metadata": {},
   "source": [
    "### Les dates sont les bonnes\n",
    "\n",
    "C'est la validation la plus parlante de la détection : sans qu'aucune date ne lui ait été\n",
    "fournie, elle retrouve des événements datables.\n",
    "\n",
    "| Sujet | Date détectée | Événement |\n",
    "|---|---|---|\n",
    "| Affaire Benalla | 20 juillet 2018 | révélation de l'affaire |\n",
    "| QAnon | 31 mars 2018 | émergence du mouvement |\n",
    "| QAnon | 9 mars 2020 | bascule pandémique |\n",
    "| Pegasus | 17 juillet 2021 | révélations du *Pegasus Project* |\n",
    "| LIGO | 9 janvier 2016 | fuites précédant l'annonce |\n",
    "| Ondes gravitationnelles | 1ᵉʳ février 2016 | annonce de la détection |\n",
    "| Event Horizon Telescope | 1ᵉʳ avril 2019 | première image d'un trou noir |\n",
    "| Télescope James-Webb | 27 septembre 2021 | approche du lancement |\n",
    "\n",
    "Et surtout : **QAnon, la désinformation Covid-19 et l'hésitation vaccinale apparaissent\n",
    "ici**, alors qu'ils étaient invisibles à la détection par pic. L'angle mort du notebook 09\n",
    "est couvert — pour la détection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11",
   "metadata": {},
   "source": [
    "## 6. L'identification échoue, et il faut le dire\n",
    "\n",
    "Aucun des 14 changements ne livre de paramètres exploitables. La raison est mesurable : la\n",
    "dispersion résiduelle médiane est de 0,63, alors que l'incertitude relative sur\n",
    "$\\gamma\\alpha/\\lambda$ atteint déjà 77 % à une dispersion de 0,15.\n",
    "\n",
    "Les séries d'attention réelles ne sont pas seulement bruitées : elles portent des bosses\n",
    "médiatiques successives superposées à la tendance, que le modèle à trois paramètres ne peut\n",
    "pas absorber. L'ajustement est donc **refusé** plutôt que rapporté avec une barre d'erreur\n",
    "illusoire.\n",
    "\n",
    "Ce refus est un choix explicite. Une version antérieure de ce module tolérait une dispersion\n",
    "de 0,30, et produisait des valeurs comme $\\lambda = 4{,}4$ par jour — une mémoire collective\n",
    "de cinq heures — ou $\\rho = 139$. Ces chiffres avaient l'apparence de résultats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c12",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-23T13:38:01.282147Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  dispersion 0.03  →  ρ médian =  5.27 (vrai 5.00), IQR relatif = 0.07  (n = 8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  dispersion 0.05  →  ρ médian =  5.47 (vrai 5.00), IQR relatif = 0.12  (n = 8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  dispersion 0.10  →  ρ médian =  5.76 (vrai 5.00), IQR relatif = 0.25  (n = 8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  dispersion 0.15  →  ρ médian =  6.04 (vrai 5.00), IQR relatif = 0.43  (n = 8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  dispersion 0.25  →  ρ médian =  5.15 (vrai 5.00), IQR relatif = 1.18  (n = 8)\n",
      "\n",
      "Dispersion médiane observée sur les séries réelles : 0.63\n",
      "Incertitude relative correspondante : 135% ou davantage\n"
     ]
    }
   ],
   "source": [
    "table = {}\n",
    "for scatter in (0.03, 0.05, 0.10, 0.15, 0.25):\n",
    "    ratios = []\n",
    "    for seed in range(8):\n",
    "        series = np.full(900, 500.0)\n",
    "        series[300:] = 500.0 + transition(5.0, days=600)\n",
    "        series = series * np.random.default_rng(seed).lognormal(0.0, scatter, 900)\n",
    "        report = scan_regime_shifts(series, adjust_weekly=False)\n",
    "        ratios.extend(shift.ratio for shift in report.shifts)\n",
    "    values = np.array(ratios)\n",
    "    spread = (np.percentile(values, 75) - np.percentile(values, 25)) / np.median(values)\n",
    "    table[scatter] = (np.median(values), spread, len(values))\n",
    "    print(f\"  dispersion {scatter:.2f}  →  ρ médian = {np.median(values):5.2f} (vrai 5.00), \"\n",
    "          f\"IQR relatif = {spread:4.2f}  (n = {len(values)})\")\n",
    "\n",
    "observed = np.median([row[\"dispersion\"] for row in records])\n",
    "print(f\"\\nDispersion médiane observée sur les séries réelles : {observed:.2f}\")\n",
    "print(f\"Incertitude relative correspondante : {expected_precision(min(observed, 0.25)):.0%} ou davantage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:38:01.283561Z",
     "iopub.status.busy": "2026-08-23T13:38:01.283487Z",
     "iopub.status.idle": "2026-08-23T13:38:03.172439Z",
     "shell.execute_reply": "2026-08-23T13:38:03.171854Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1380x936 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(2, 2, figsize=(11.5, 7.8))\n",
    "example, forms, lifts, precision = axes.ravel()\n",
    "\n",
    "# (a) Un changement de régime réel, avec sa date détectée.\n",
    "target = \"QAnon\"\n",
    "entry = next(item for item in CORPUS if item.label == target)\n",
    "series = load_or_fetch(entry.project, entry.article, CORPUS_START, CORPUS_END)\n",
    "values = weekly_adjust(np.clip(series.filled(), 1.0, None))\n",
    "report = scan_regime_shifts(np.clip(series.filled(), 1.0, None), label=target)\n",
    "\n",
    "example.semilogy(np.arange(values.size), values, color=PALETTE[\"neutral\"], linewidth=0.7)\n",
    "for shift in report.shifts:\n",
    "    example.axvline(shift.index, color=PALETTE[\"field\"], linestyle=\"--\", linewidth=1.3)\n",
    "    example.annotate(f\"{series.day(shift.index)}\\n×{shift.lift:.0f}\",\n",
    "                     xy=(shift.index, values.max() * 0.4), fontsize=7.5,\n",
    "                     color=PALETTE[\"field\"], ha=\"center\")\n",
    "    example.hlines(shift.level_before, shift.index - 250, shift.index,\n",
    "                   color=PALETTE[\"order\"], linewidth=2.0)\n",
    "    example.hlines(shift.level_after, shift.index, shift.index + 250,\n",
    "                   color=PALETTE[\"disorder\"], linewidth=2.0)\n",
    "example.set_xlabel(\"jours depuis juillet 2015\")\n",
    "example.set_ylabel(\"consultations / jour\")\n",
    "example.set_title(f\"{target} — deux changements de régime détectés\", fontsize=10)\n",
    "\n",
    "# (b) La non-identifiabilité sous saturation logistique.\n",
    "forms.plot(grid, first, color=PALETTE[\"order\"], linewidth=2.6,\n",
    "           label=r\"$\\gamma\\alpha=1{,}0$  $\\lambda=0{,}2$  →  $\\rho = 5{,}0$\")\n",
    "forms.plot(grid, second, color=PALETTE[\"disorder\"], linewidth=1.2, linestyle=\"--\",\n",
    "           label=r\"$\\gamma\\alpha=2{,}0$  $\\lambda=1{,}2$  →  $\\rho = 1{,}7$\")\n",
    "forms.set_xlabel(\"jours\")\n",
    "forms.set_ylabel(\"excédent $W(t)$\")\n",
    "forms.set_title(\"Saturation logistique : $\\\\rho$ non identifiable\", fontsize=10)\n",
    "forms.legend(fontsize=7.5, loc=\"lower right\")\n",
    "\n",
    "# (c) Élévation du palier, par registre émotionnel.\n",
    "rng = np.random.default_rng(0)\n",
    "for position, (category, colour, name) in enumerate(\n",
    "    [(\"accusation\", PALETTE[\"field\"], \"accusation\"), (\"discovery\", PALETTE[\"remedy\"], \"découverte\")]\n",
    "):\n",
    "    values_class = np.array([r[\"élévation\"] for r in records if r[\"classe\"] == category])\n",
    "    jitter = position + rng.uniform(-0.10, 0.10, size=values_class.size)\n",
    "    lifts.semilogy(jitter, values_class, \"o\", color=colour, markersize=6, alpha=0.85)\n",
    "    lifts.hlines(np.median(values_class), position - 0.28, position + 0.28,\n",
    "                 color=colour, linewidth=2.4)\n",
    "    lifts.text(position, 1.35, f\"n = {values_class.size}\", ha=\"center\", fontsize=8, color=colour)\n",
    "\n",
    "accusation = np.array([r[\"élévation\"] for r in records if r[\"classe\"] == \"accusation\"])\n",
    "discovery = np.array([r[\"élévation\"] for r in records if r[\"classe\"] == \"discovery\"])\n",
    "p_value = stats.mannwhitneyu(accusation, discovery, alternative=\"two-sided\").pvalue\n",
    "lifts.set_xticks([0, 1])\n",
    "lifts.set_xticklabels([\"accusation\", \"découverte\"])\n",
    "lifts.set_xlim(-0.5, 1.5)\n",
    "lifts.set_ylabel(\"élévation du palier (×)\")\n",
    "lifts.set_title(f\"Persistance : ×{np.median(accusation):.1f} contre ×{np.median(discovery):.1f}\"\n",
    "                f\"  (p = {p_value:.2f})\", fontsize=10)\n",
    "\n",
    "# (d) Incertitude en fonction de la dispersion, et position des séries réelles.\n",
    "scatters = np.array(sorted(table))\n",
    "spreads = np.array([table[s][1] for s in scatters])\n",
    "precision.plot(scatters, spreads, \"o-\", color=PALETTE[\"order\"], markersize=5,\n",
    "               label=\"récupération synthétique\")\n",
    "precision.axhline(1.0, color=PALETTE[\"neutral\"], linestyle=\":\", linewidth=1.0)\n",
    "precision.axvline(0.15, color=PALETTE[\"remedy\"], linestyle=\"--\", linewidth=1.3)\n",
    "precision.text(0.155, 0.1, \"seuil d'acceptation\", fontsize=7.5, color=PALETTE[\"remedy\"],\n",
    "               rotation=90, va=\"bottom\")\n",
    "precision.axvline(observed, color=PALETTE[\"disorder\"], linestyle=\"--\", linewidth=1.3)\n",
    "precision.text(observed - 0.01, 0.1, \"séries réelles\", fontsize=7.5,\n",
    "               color=PALETTE[\"disorder\"], rotation=90, va=\"bottom\", ha=\"right\")\n",
    "precision.set_xlabel(\"dispersion résiduelle\")\n",
    "precision.set_ylabel(\"incertitude relative sur $\\\\rho$\")\n",
    "precision.set_title(\"Les données réelles sont hors de portée\", fontsize=10)\n",
    "precision.legend(fontsize=8, loc=\"upper left\")\n",
    "\n",
    "save_figure(figure, \"fig10_regimes.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c14",
   "metadata": {},
   "source": [
    "## 7. Un écart entre registres, pour la première fois du projet\n",
    "\n",
    "Le notebook 09 ne trouvait aucune différence entre contenus d'accusation et annonces\n",
    "scientifiques : le rapport d'amplification était indiscernable entre les deux, et\n",
    "l'estimation ponctuelle allait même dans le sens contraire à la prédiction.\n",
    "\n",
    "La **persistance** dit autre chose. Les changements de régime du registre « accusation »\n",
    "élèvent le niveau d'attention d'un facteur médian **×9,2**, contre **×2,9** pour les\n",
    "annonces de découverte — un rapport de trois, avec $p = 0{,}08$.\n",
    "\n",
    "Ce n'est pas concluant : quatorze observations, un seuil de signification non franchi, et une\n",
    "réserve supplémentaire — les changements du registre « découverte » se situent tous juste\n",
    "au-dessus du seuil de détection (×2,4 à ×3,3), ce qui suggère que certains sont des pics à\n",
    "décroissance lente plutôt que de véritables régimes.\n",
    "\n",
    "Mais la direction est celle que le modèle prédit, l'écart est net, et il porte sur une\n",
    "grandeur que le projet n'avait pas encore mesurée. **Ce n'est pas le taux d'amplification\n",
    "qui distingue les registres émotionnels, c'est la durée pendant laquelle l'attention\n",
    "reste captée.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c15",
   "metadata": {},
   "source": [
    "## 8. Ce que le notebook établit\n",
    "\n",
    "**La détection de changement de régime fonctionne, et couvre l'angle mort.** Quatorze\n",
    "changements sur le corpus, aux bonnes dates, dans les sujets même que la méthode par pic\n",
    "manquait. Le détecteur ne produit aucun faux positif sur bruit stationnaire, rejette les\n",
    "pics, et rejette les créations d'articles.\n",
    "\n",
    "**L'identification, elle, échoue sur données réelles.** Zéro des quatorze changements livre\n",
    "des paramètres exploitables : la dispersion résiduelle médiane est de 0,63 là où\n",
    "l'incertitude devient prohibitive dès 0,15. Ce n'est pas un défaut de l'implémentation — la\n",
    "récupération est exacte sur trajectoire propre, y compris pour $\\rho = 40$ — mais une\n",
    "inadéquation entre le modèle à trois paramètres et le bruit réel des séries d'attention.\n",
    "\n",
    "**Et une limite théorique domine les deux.** L'identifiabilité de $\\gamma\\alpha/\\lambda$\n",
    "sur un changement de régime dépend de la forme supposée de la saturation. Sous saturation\n",
    "logistique, elle n'existe pas : deux triplets de rapports 5,0 et 1,7 produisent la même\n",
    "courbe. Le rapport n'est donc pas une grandeur que les données déterminent — c'est une\n",
    "grandeur que le choix de modèle détermine.\n",
    "\n",
    "**Ce que cela déplace.** Le mémorandum recommandait de plafonner $\\gamma\\alpha/\\lambda$.\n",
    "Cette recommandation supposait le rapport mesurable ; il ne l'est ni sur les régimes\n",
    "installés, ni indépendamment d'une hypothèse de forme. En revanche, deux grandeurs se\n",
    "mesurent bien et distinguent les registres : **la date du basculement et l'élévation du\n",
    "palier**. Un cadre réglementaire adossé à celles-ci serait opposable, là où un seuil sur\n",
    "$\\rho$ ne l'est pas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16",
   "metadata": {},
   "source": [
    "## Pistes ouvertes\n",
    "\n",
    "1. **Réduire le modèle plutôt que d'améliorer l'ajustement.** Deux paramètres identifiables\n",
    "   sans hypothèse de forme — le taux de croissance initial et le niveau du palier — valent\n",
    "   mieux que trois dont un n'est pas déterminé par les données.\n",
    "2. **Un observable moins bruité.** La dispersion des consultations quotidiennes est\n",
    "   irréductible ; des séries agrégées par semaine, ou une mesure directe d'exposition plutôt\n",
    "   que de consultation, changeraient l'ordre de grandeur.\n",
    "3. **Étendre le corpus à la persistance.** L'écart ×9,2 contre ×2,9 mérite d'être testé sur\n",
    "   plusieurs centaines de sujets pré-enregistrés : c'est le seul résultat du projet qui\n",
    "   distingue les registres émotionnels, et il est actuellement à $p = 0{,}08$.\n",
    "4. **Vérifier que les régimes « découverte » sont bien des régimes.** Tous se situent juste\n",
    "   au-dessus du seuil : allonger la durée de maintien exigée trancherait."
   ]
  }
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