Radio chronologie 14C 40K/40Ar U/Pb Rb/Sr

chrono.md

Datation absolue avec chrono.py

Principe de la datation absolue ou radio-chronologie

Télécharger chrono.py

Télécharger au format zip depuis Github

Télécharger avec git:

git clone https://gist.github.com/e4d0315fbf3b2b67859aba84c8d9c352.git

Télécharger avec wget:

wget https://gist.github.com/YannBouyeron/e4d0315fbf3b2b67859aba84c8d9c352/archive/15f7064b79d8f640a0c5e223133bdfa4d406a139.zip
unzip 15f7064b79d8f640a0c5e223133bdfa4d406a139.zip

Le fichier chrono.py doit ensuite être placé dans votre repertoire de travail pour pouvoir être importé avec python.

Importer matplotlib.pyplot

Si vous êtes dans jupyter:

import matplotlib.pyplot as plt
%matplotlib inline

Si vous travaillez en console linux sans interface graphique:

#import matplotlib
#import os

#if "DISPLAY" not in os.environ:
    
    #matplotlib.use("Agg")

#import matplotlib.pyplot as plt

Modification de l´apparence des graphiques pour permettre l´export depuis Jupyter en html / markdown dark theme, tout en conservant les axes visibles.

plt.style.use('seaborn-white') # blanc

#plt.style.use('dark_background') # noir

Importer chrono.py

Attention le fichier chrono.py doit se trouver dans le même repertoire que celui à partir duquel vous avez lancé python.

from chrono import Chrono

c = Chrono()

Datation au ¹⁴C

On mesure une concentration en ¹⁴C de 3 dpm dans un échantillon.

On utlilise alors la méthode c14(dpm) pour retourner l'âge de l'échantillon et la courbe de désintégration du ¹⁴C avec la position de la mesure.

age = c.c14(3)

age

12467.041271439173

On peut aussi simplement afficher la courbe de désintégration du ¹⁴C:

c.c14()

L'argument facultatif xmax permet de restreindre l'axe du temps en fixant un t max pour la représentation graphique:

c.c14(3, xmax=20000)

12467.041271439173

Datation par K/Ar

On utilise la méthode kar(k, Ar) avec k la concentration en ⁴⁰K et Ar la concentration en ⁴⁰Ar mesurées sur l'objet à dater.

age = c.kar(0.008, 0.0002)

Attention, sur le graphique le temps est bien en années , ici en x 10⁹

age

385910162.54356444

L'argument facultatif ymax (rapport k/Ar max) permet d'ajuster la représentation graphique:

c.kar(0.008, 0.0002, ymax=0.05)

385910162.54356444

La datation par U/Pb

Fiche synthèse datation U/Pb

Premier exemple:

On commence par tracer la concordia sur l'intervalle de temps présumé avec la méthode concordia:

help(c.concordia)

Help on method concordia in module chrono:

concordia(ti=0, tf=1000000000, incre=10000000, dotlabel=True, labelincre=3) method of chrono.Chrono instance
    Arguments:
    
            ti: age présumé inférieur à réouverture
    
            tf: age présumé superieur à age roche
    
            incre: incrément de temps pour la construction de la concordia en années
    
            dotlabel: si True (defaut): indication age sur concordia
    
            labelincre: icrémentation des labels des points de la concordia pour ne pas surcharger trop
    
    Return: array des points de la concordia.

c.concordia(ti=0, tf=800000000.0, incre=100000000)

(array([0.        , 0.01562075, 0.0314855 , 0.04759808, 0.06396234,
        0.08058223, 0.09746174, 0.11460491]),
 array([0.        , 0.10349785, 0.21770751, 0.34373762, 0.48281158,
        0.63627939, 0.80563079, 0.9925097 ]))

On ajoute ensuite nos mesures effectuées sur les zircons de l'échantillon de la roche:

pb206U = [0.06231448, 0.06247915, 0.06264385, 0.06280857, 0.06297332, 0.06313809,
 0.06330289, 0.06346772, 0.06363257, 0.06379744, 0.06396234]

pb207U = [0.46827978, 0.46972653, 0.4711747,  0.4726243,  0.47407533, 0.47552779,
 0.47698168, 0.478437, 0.47989376, 0.48135195, 0.48281158]

c.concordia(ti=0, tf=800000000.0, incre=100000000, labelincre=2)
plt.plot(pb207U, pb206U, "*r", label="Zircon Z1 à Z11")
plt.legend()

<matplotlib.legend.Legend at 0x6d262250>

On observe que les mesures des rapports pb/u effectuées sur les zircons (ici en rouge) s'alignent sur la concordia. Il n'y a pas eu de réouverture du système. L'âge de la roche est l'âge du plus vieux zircon lisible sur le graphique, soit 400 Ma. Il n'est donc pas nécessaire d'afficher la discordia.

Deuxième exemple:

On dispose des mesures suivantes effectuées sur 3 zircons d'une roche:

pb207U = [5, 3, 1]

pb206U = [0.3, 0.2, 0.1]

On affiche la concordia et on place nos mesures sur le graphique:

c.concordia(ti=0.4*10**9, tf=2.2*10**9, incre=100000000)
plt.plot(pb207U, pb206U, "*", label="Minéraux")
plt.legend()

<matplotlib.legend.Legend at 0x6d271530>

On observe que les mesures effectuées sur les minéraux ne sont pas alignées sur la concordia. Il y'a eu une ré-ouverture du système. Il faut donc afficher la discordia avec la méthode upb :

help(c.upb)

Help on method upb in module chrono:

upb(p207, p206, ti=100000000, tf=2500000000.0, delta=100000, incre=100000000, dotlabel=True, labelincre=3) method of chrono.Chrono instance
    Arguments:
    
            p206: liste valeurs 206Pb/238U mesurées (Y)
    
            p207: liste valeurs 207Pb/235U mesurées (X)
    
            ti: age présumé inférieur à réouverture
    
            tf: age présumé superieur à age roche
    
            delta: precision du calcul des intercepts en années
    
            incre: incrément de temps pour la construction de la concordia en années
    
            dotlabel: si True (defaut): indication age sur concordia
    
            labelincre: incrémentation des labels des points de la concordia pour ne pas surcharger trop
    
    Show: graphique concordia discordia             
    Return: {coeff correlation discordia, intersup, interlow, a, b}

dico = c.upb(pb207U, pb206U, ti=0.4*10**9, tf=2.2*10**9, incre=100000000, delta=1000)

dico

{'R': 1.0,
 'sup': 2033986000.0,
 'low': 514235000.0,
 'a': 0.05000000000000001,
 'b': 0.049999999999999954}

La méthode retourne un dictionnaire avec l'âge de la ré-ouverture du système correspondant à l'intercept inférieur: low = 515093000 +/- delta années; l'âge de la roche correspondant à l'intercept supérieur: sup = 2033278000 +/- delta années. La précision des âges obtenus dépend du paramètre "delta" passé en argument de la méthode upb.

Attention un delta faible augmente la précision, mais augmente aussi le temps des calculs.

La datation par Rb/Sr

On utilise la méthode rbsr()

help(c.rbsr)

Help on method rbsr in module chrono:

rbsr(rbsr, srsr) method of chrono.Chrono instance
    Arguments:
    
            rbsr: liste rapport 87Rb/86Sr
    
            srsr: liste rapport 87Sr/86Sr
    
    Show graphique isochrone
    
    Return equation droite reg, age (en annees)

rbsr = [0.288, 0.31, 0.996, 0.787, 0.898, 0.945, 0.901, 5.84, 3.36]

srsr = [0.7165, 0.7171, 0.7556, 0.7413, 0.7471, 0.7521, 0.7495, 1.0133, 0.8807]

y , a = c.rbsr(rbsr, srsr)

# fonction de la droite isochrone

print(y)

y = 0.05354693x + 0.7

# age 

print(a)

3673415678.371164

La méthode creatRbsr(age) permet de créer des couples de valeurs rbsr et srsr fictifs en fonction de l'âge désiré.

help(c.creatRbsr)

Help on method creatRbsr in module chrono:

creatRbsr(age, n=5, b=0.7, out='') method of chrono.Chrono instance
    Création de données RbSr en fonction de l’age.
    
    Arguments:
    
            age: age désiré
            
            n: nombre de couples SrSr, RbSr
            
            b: ordonnée à l’origine = Sr/Sr à t0
    
            out: path en .xlsx ou en .json pour sauvegarder les données crées
    
    Show graphique isochrone
    
    Return RbSr (liste), SrSr (liste)

rbsr, srsr = c.creatRbsr(5000000)

rbsr

[2.282978862971912,
 4.988633011174326,
 3.3254901343712717,
 2.091505108366786,
 4.1554292953511585]

srsr

[0.7001620972536554,
 0.7003542055179406,
 0.7002361181816367,
 0.7001485021344575,
 0.7002950459539775]

On peut vérifier l'âge en ré utilisant la méthode rbsr():

y , a = c.rbsr(rbsr, srsr)

a

4999999.999987198

chrono.py

#import os
#import matplotlib


#if 'DISPLAY' not in os.environ:

	#matplotlib.use('Agg')

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

class Chrono:

	def __init__(self):

		# constantes radioactives lambda

		self.l = {
			"u238": 1.55 * 10**-10,
			"u235": 9.8485 * 10**-10,
			"rb": 1.42 * 10**-11,
			"c14": 1.21 * 10**-4,
			"kar": 5.81 * 10**-11,
			"kca": 4.962 * 10**-10
		}

		# Périodes (demie-vie)

		self.T = {k: np.log(2) / self.l[k] for k in list(self.l.keys())}

	
	def interc_upper(self, p206, p207, tf=10**9, delta=100000):

		l = np.polyfit(p207, p206, 1)
		a = l[0]
		b = l[1]
		cc = np.corrcoef(p207, p206)
		r = cc[0, 1]

		while 0 == 0:

			cp206 = np.exp(self.l["u238"] * tf) - 1  # y = p206 / u238

			cp207 = np.exp(self.l["u235"] * tf) - 1  # x = p207 / u235

			if cp206 > a *cp207 + b:
				
				return tf

			else:
				
				tf -= delta
			


	def interc_lower(self, p206, p207, ti=100*10**6, delta=100000):

		l = np.polyfit(p207, p206, 1)
		a = l[0]
		b = l[1]
		cc = np.corrcoef(p207, p206)
		r = cc[0, 1]

		while 0 == 0:

			cp206 = np.exp(self.l["u238"] * ti) - 1  # y = p206 / u238

			cp207 = np.exp(self.l["u235"] * ti) - 1  # x = p207 / u235

			if cp206 > a *cp207 + b:
				
				return ti

			else:
				
				ti += delta


	def intercept(self, p206, p207, ti=100*10**6, tf=10**9, delta=100000):

		return self.interc_upper(p206, p207, tf=tf, delta=delta), self.interc_lower(p206, p207, ti=ti, delta=delta)


	def concordia(self, ti=0, tf=10**9, incre=10**7, dotlabel=True, labelincre=3):


		"""
		Arguments:

			ti: age présumé inférieur à réouverture

			tf: age présumé superieur à age roche

			incre: incrément de temps pour la construction de la concordia en années

			dotlabel: si True (defaut): indication age sur concordia

			labelincre: icrémentation des labels des points de la concordia pour ne pas surcharger trop

		Return: array des points de la concordia.
		"""


		dt = np.arange(ti, tf, incre)

		# creation de la liste des dotlabel

		dt_str = ["   " + str(round(i*10**-9, 3)) + " Ga" if tf >= 10**9 else "   " + str(round(i*10**-6, 3)) + " Ma" for i in dt]

		dt_str = [j if i%labelincre == 0 else "" for i, j in enumerate(dt_str)] 

		# calcul concordia sur dt

		p206 = np.exp(self.l["u238"] * dt) - 1  # y = p206 / u238

		p207 = np.exp(self.l["u235"] * dt) - 1  # x = p207 / u235

		# representation graphique concordia

		plt.close()

		plt.plot(p207, p206, "--*k", markersize=6, label="Concordia")

		plt.xlabel("207p / 235u", fontsize=8)

		plt.ylabel("206p / 238u", fontsize=8)

		if dotlabel == True:

			k = 0

			for xy in zip(p207, p206):

				plt.annotate(dt_str[k], xy=xy, textcoords='data', fontsize=6, verticalalignment='top')

				k += 1


		return p206, p207





	def discordia(self, p207, p206, ti=100*10**6, tf=2.5*10**9, delta=100000):

		l = np.polyfit(p207, p206, 1)
		a = l[0]
		b = l[1]
		cc = np.corrcoef(p207, p206)
		r = cc[0, 1]


		#calcule position intercept

		inter = self.intercept(p206, p207, ti=ti, tf=tf, delta=delta)

		p207.append(np.exp(self.l["u235"] * inter[1]) - 1)
		p207.append(np.exp(self.l["u235"] * inter[0]) - 1)

		p206.append(np.exp(self.l["u238"] * inter[1]) - 1)
		p206.append(np.exp(self.l["u238"] * inter[0]) - 1)


		plt.plot(p207, p206, '*b', label='', markersize=6)

		plt.plot(p207, [a * i + b for i in p207], '--b', markersize=3, label='Discordia')

		plt.legend(fontsize=6)

		
		return {"R": r, "sup": inter[0], "low": inter[1], "a": a, "b": b}


	def upb(self, p207, p206, ti=100*10**6, tf=2.5*10**9, delta=100000, incre=10**8, dotlabel=True, labelincre=3):

		"""
		Arguments:

			p206: liste valeurs 206Pb/238U mesurées (Y)

			p207: liste valeurs 207Pb/235U mesurées (X)

			ti: age présumé inférieur à réouverture

			tf: age présumé superieur à age roche

			delta: precision du calcul des intercepts en années

			incre: incrément de temps pour la construction de la concordia en années

			dotlabel: si True (defaut): indication age sur concordia

			labelincre: incrémentation des labels des points de la concordia pour ne pas surcharger trop

		Show: graphique concordia discordia		
		Return: {coeff correlation discordia, intersup, interlow, a, b}
		"""

		self.concordia(ti=ti, tf=tf, incre=incre, dotlabel=True, labelincre=labelincre)

		r = self.discordia(p207, p206, ti=ti, tf=tf, delta=delta)


		return r

	def rbsr(self, rbsr, srsr):

		"""

		Arguments:
	
			rbsr: liste rapport 87Rb/86Sr
	
			srsr: liste rapport 87Sr/86Sr

		Show graphique isochrone
	
		Return equation droite reg, age (en annees)
		
		"""

		l = np.polyfit(rbsr, srsr, 1)
		a = l[0]
		b = l[1]
		cc = np.corrcoef(rbsr, srsr)
		r = cc[0, 1]

		f = 'y = ' + str(round(a, 8)) + 'x + ' + str(round(
			b, 2))  # equation droit reg lin y = ax + b

		t = (np.log(a + 1)) / self.l["rb"]  # age = ln(a+1) / lambda

		plt.close()

		plt.plot(rbsr, srsr, '^k', label='', markersize=6)
		plt.plot(rbsr, [a * i + b for i in rbsr], '--b', label='Isochrone RbSr')

		plt.xlabel("87Rb / 86Sr", fontsize=8)

		plt.ylabel("87Sr / 86Sr", fontsize=8)

		plt.legend(fontsize=6)


		return f, t



	def creatRbsr(self, age, n=5, b=0.7, out=""):

		"""

		Création de données RbSr en fonction de l’age.

		Arguments:

			age: age désiré
			
			n: nombre de couples SrSr, RbSr
			
			b: ordonnée à l’origine = Sr/Sr à t0

			out: path en .xlsx ou en .json pour sauvegarder les données crées

		Show graphique isochrone

		Return RbSr (liste), SrSr (liste)

		"""

		a = np.exp(self.l["rb"] * age) - 1

		Rbsr = np.random.uniform(0.00200, 5.00000, n)

		Srsr = [a * x + b for x in Rbsr]

		if out[len(out) - 5:] == '.xlsx':

			df = pd.DataFrame({"RbSr": Rbsr, "SrSr": Srsr})
			df.to_excel(out)

		elif out[len(out) - 5:] == '.json':

			df = pd.DataFrame({"RbSr": Rbsr, "SrSr": Srsr})
			df.to_json(out)

		plt.close()

		self.rbsr(Rbsr, Srsr)

		return Rbsr.tolist(), Srsr



	def c14(self, pt=0, p0=13.56, xmax=50000):

		"""Méthode 14C

		Arguments:

			p0: concentration initial P0 (defaut: 13.56 dpm)

			pt: concentration P à l’instant t (mesure)

			xmax: age max présumé pour la courbe

		Show graphique décroissance 

		Return age

		"""

		xmax = np.arange(0, xmax, 1)

		p = p0 * np.exp(-self.l["c14"] * xmax)

		plt.close()

		plt.plot(xmax, p, "--k", markersize=6, label="14C dpm")

		plt.xlabel("Temps (années)", fontsize=8)

		plt.ylabel("14C dpm", fontsize=8)


		if pt > 0:

			age = np.log(p0 / pt) / self.l["c14"]

			plt.plot([age], [pt], "*r", markersize=8)

			return age


		
		

	def kar(self, k, ar, ymax=0.2):

		""" Méthode K Ar:

		Arguments:
		
			k: quantité de 40K mesurée dans l’échantillon
		
			ar: quantité de 40Ar mesurée dans l’échantillon
		
			ymax: limite sup de la courbe de référence

		Show graphique Ar/k = f(t)

		Return age (années)

		"""


		# clalcul age echantillon

		lam = self.l["kar"] + self.l["kca"]

		age  = (1/lam) * np.log(1 + (ar/k) * (1 + self.l["kca"]/self.l["kar"]))


		# construction courbe reférence

		ark = np.arange(0.0, ymax, 0.01)

		t  = [(1/lam) * np.log(1 + i * (1 + self.l["kca"]/self.l["kar"])) for i in ark]

		plt.close()

		plt.plot(t, ark, "--k", markersize=6, label="Ar/K")
		plt.plot(age, ar/k, "*b", markersize=6, label="")

		plt.xlabel("Temps (années)", fontsize=8)
		plt.ylabel("40Ar/40K", fontsize=8)


		return age

jupyter_netbook_chrono.ipynb

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   "id": "3322b0af-06eb-42ff-9468-c9271f04cb5c",
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   "source": [
    "[Principe de la datation absolue ou radio-chronologie](https://github.com/YannBouyeron/SPET/blob/master/Geologie/Datation%20absolue.md)"
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    "### Télécharger chrono.py\n",
    "\n",
    "Télécharger au format zip depuis [Github](https://gist.github.com/YannBouyeron/e4d0315fbf3b2b67859aba84c8d9c352)\n",
    "\n",
    "Télécharger avec git:\n",
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    "\n",
    "    wget https://gist.github.com/YannBouyeron/e4d0315fbf3b2b67859aba84c8d9c352/archive/15f7064b79d8f640a0c5e223133bdfa4d406a139.zip\n",
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    "    \n",
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    "#import matplotlib\n",
    "#import os\n",
    "\n",
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    "    #matplotlib.use(\"Agg\")\n",
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    "### Importer chrono.py\n",
    "\n",
    "Attention le fichier chrono.py doit se trouver dans le même repertoire que celui à partir duquel vous avez lancé python."
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   "cell_type": "code",
   "execution_count": 4,
   "id": "14dd7a59-bbc6-452f-8347-fc0d0d3a07d6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from chrono import Chrono\n",
    "\n",
    "c = Chrono()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775bed83-ad89-4ede-9144-8520ae7593ca",
   "metadata": {},
   "source": [
    "### Datation au <sup>14</sup>C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab6afcc-14f1-44c1-ac14-cee08fda974a",
   "metadata": {},
   "source": [
    "On mesure une concentration en <sup>14</sup>C de 3 dpm dans un échantillon.\n",
    "\n",
    "On utlilise alors la méthode c14(dpm) pour retourner l'âge de l'échantillon et la courbe de désintégration du <sup>14</sup>C avec la position de la mesure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f50d25a7-7c8b-44f6-ae3c-66dd72591d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = c.c14(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e68fdc79-c5f5-4290-9e0b-df4f43f4cfdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12467.041271439173"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dbe4ae1-5486-43ab-a0f4-05224c310371",
   "metadata": {},
   "source": [
    "On peut aussi simplement afficher la courbe de désintégration du <sup>14</sup>C:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7dc5973c-0c8c-494a-9031-182b8726850b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.c14()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab55fc1f-f0c9-4af3-a424-ede7be8358b3",
   "metadata": {},
   "source": [
    "L'argument facultatif xmax permet de restreindre l'axe du temps en fixant un t max pour la représentation graphique:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a37a534-64a8-4d74-a19b-7f937c15a98d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12467.041271439173"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mJggKCoKFhQWSkpJgYmKijFykQBKJBEuWLEFeXh5q1qyJ3NxcaGpqQltbW+xoRKQAby36RYsWIT4+HikpKejbty+sra2VkYsUTCKRoGbNmigpKYGvry+Ki4uxb98+6Ovrix2NiOTsnXbOWltbo3fv3ix5NSSVSvHZZ5/h+PHj6N69O9LT08WORERyxqNwBH9/f0RHRyMxMRF2dna86DiRmim36LOysvDo0aMyyx49eoSsrCyFhyLlc3d3R0xMDB4/fgxfX19ecJxIjZRb9IGBgcjJySmzLDc3F7Nnz1Z4KBJH165dcebMmTLz2hNR1VfuwdinT5++Mq9N06ZNuUWv5iwsLGRfBwYGwtraGp999pmIiYiost64jz4vL6/M7X9v4ZP6evHiBWJjY9G/f38sX76cu3KIqrByt+jHjRuHUaNGwdPTE4aGhkhPT8fBgwcxbtw4ZeYjkejo6ODo0aMYOnQovvrqK9y6dQuhoaHQ0tISOxoRvadyi75r164wNzfHyZMncePGDRgZGWHVqlUwMDBQZj4Ska6uLnbv3o1WrVph8eLFSE1NRXR0NPffE1Uxb/zAVL169eDl5SW7HRERgUGDBik8FKkOqVSKRYsWoVWrVqhZsyZLnqgKKrfoBw4cWOYftSAIuH37Nn788Ue5z0NPqu+LL76Qfb1nzx40b94cnTp1EjEREb2rcovexcUFN27cgLe3N7p06QIAGDFiBDZt2qS0cKR6CgsLMWfOHDx48ADbt2+Hr6+v2JGI6C3KPevG398f8+bNw+3btzFlyhTExMQoMxepKC0tLZw6dQo2Njbo27cvvvnmG5SUlIgdi4je4I2nV2pra2PQoEFYtmwZnj59CnNzc2XlIhVmbGyMEydOYOjQoZg3bx769u2L4uJisWMRUTneOnslUHrBipd/osfHx3NyM0KNGjWwZcsW2NraIiMjAxoaGmJHIqJylFv05f05vmLFCmzZskVhgajqkEgkmDx5suz2+fPnkZOTA2dnZxFTEdG/lVv0tra2sLGxgSAIsrNvBEHAzZs3lRaOqpY5c+bgxIkTCAkJwcSJE3kqJpGKKLfoW7Zsie++++6VC1HwUoJUnv3798PPzw+TJ0/GhQsXsH79etSqVUvsWETVXrkHY9etWwcdHZ1Xlm/cuLFSA2ZnZ2PSpEno2bMnevXqhcuXL1fq+Uh16OvrY//+/Zg/fz527tyJLl268EImRCqg3C16IyOj1z9A852O35Zr4cKFcHBwwOrVq1FQUID8/PxKPR+pFqlUisDAQHTp0gU7duxAgwYNxI5EVO0p9QpTz58/R1xcHPr27Qug9PTN2rVrKzMCKYmLiwvCw8MhlUrx4MEDzJ49G4WFhWLHIqqWlFr0Dx48gIGBAQICAuDl5YXZs2cjNzdXmRFIBNHR0Vi0aBG6d++Ov/76S+w4RNWOUou+qKgIiYmJGDBgAKKjo6Grq4sNGzYoMwKJYOLEidi1axf++OMP2Nra4siRI2JHIqpWlFr0JiYmMDExgY2NDQCgZ8+eSExMVGYEEkn//v0RFxcHY2Nj9OrVC7t37xY7ElG1odSiNzQ0hImJCe7evQsAOHfuHFq2bKnMCCQiCwsLXLhwAYGBgejduzcA8MpVREqg1KIHSj9UM336dHh4eCApKQljxoxRdgQSka6uLubPn4/atWsjLy8PDg4O2Llzp9ixiNRa5c6VrAALCwvs379f2cOSCsrOzgYADBo0CEePHsWaNWugp6cncioi9aP0LXqil4yNjREbG4vAwECEh4ejY8eOuHDhgtixiNQOi55Epampifnz5+P48ePIy8vDxIkTud+eSM6UvuuG6HU++eQTxMfHIzMzExKJBJmZmUhLS+M1EIjkgFv0pDLq1q2L5s2bAwACAgJga2uL1atX8wpWRJXEoieV9M0338DJyQmTJ09Gjx49kJKSInYkoiqLRU8qqWHDhjh06BDWr1+Pc+fOwcrKCkePHhU7FlGVxKInlSWRSDBq1ChcuXIF3bp1g6WlJQB+yIrofbHoSeW1bNkSBw4cQKNGjVBSUgIPDw+sWbOG++6J3hGLnqqU58+fo7i4GJMmTYKjoyNu3LghdiQilceipyqldu3a+PHHH7F161YkJibCxsYGS5Ys4Vz3RG/AoqcqRyKRYOjQoUhMTISbmxvWrVvHoid6AxY9VVkmJibYt28f4uLiULNmTeTn5yMgIACPHz8WOxqRSmHRU5VnaGgIAPj111+xbNkymJubIzw8nGfnEP0Pi57UhouLCy5duoTWrVtj2LBh6N69O5KSksSORSQ6Fj2pFWtra5w+fRobNmxAfHw8r3dABBY9qSGpVIqRI0fi+vXr2LhxIwAgLS0N4eHhPPeeqiUWPaktIyMjtGnTBgCwadMmDBs2DF27dsVvv/0mcjIi5WLRU7UQEBCA8PBwJCcn46OPPsKwYcPw8OFDsWMRKQWLnqoFqVSKIUOG4ObNm5gxYwZ27dqFWbNmiR2LSCl44RGqVvT19bFkyRKMGDECNWvWBADEx8fj8uXLGDx4MDQ0NEROSCR/3KKnaqlVq1Zo1KgRAGDz5s0YNmwYOnbsiF9++UXkZETyx6Knai8kJAS7du1CdnY2evTogR49euDKlStixyKSGxY9VXtSqRT9+/dHUlISQkJCEBcXh71794odi0huRCn64uJieHl5YfTo0WIMT/RaOjo6mDJlCu7cuYMZM2YAAA4dOoTx48fj77//FjkdUcWJUvTbtm1Dy5YtxRia6K3q1asHPT09AMC1a9ewYcMGtGzZEtOnT0dGRobI6Yjen9KLPjU1FbGxsejbt6+yhyZ6bzNmzMCNGzfw+eefY8WKFWjevDlWrVoldiyi96L0ol+0aBG++uorSKU8PEBVQ4sWLbB161Zcu3YN7u7u0NfXBwDk5+cjKytL5HREb6fUtj1x4gQMDAxgZWWlzGGJ5MLc3By7d+/GF198AQDYuHEjzMzMEBQUxDnwSaUptegvXbqE48ePw8nJCVOnTsX58+cxffp0ZUYgkhsHBwc4Oztj/vz5MDMzw9dff43U1FSxYxG9QqlFP23aNJw6dQrHjx9HSEgIPvroIyxfvlyZEYjkpkOHDti3bx8SEhLg6emJ//znPxg4cKDYsYhewR3lRJVkaWmJiIgI3LhxAyEhIQCA9PR0jB07Fjdv3hQ5HZGIRd+lSxesX79erOGJ5K5Vq1bo0KEDAODcuXPYsmULzM3N4e3tjTNnzvDShiQabtETKYCnpyf+/PNPBAYG4tSpU7C3t4e9vT0KCgrEjkbVEIueSEGMjY0xb948JCcnIzQ0FJ07d4a2tjYA4PDhw8jNzRU5IVUXnKaYSMFq1aqF8ePHy27fu3cPHh4eMDAwwKhRozB27FiYmpqKmJDUHbfoiZSsWbNmOHXqFBwcHBAcHIzmzZujb9++uHfvntjRSE2x6ImUTCKRwN7eHlFRUbhz5w6mTZuGc+fOyebXuXPnDnJyckROSeqERU8kombNmiE4OBjJyckwNDQEAPj5+aFJkyaYNm0a7ty5I3JCUgcseiIV8M9LGAYHB8PFxQWrVq1Cq1at4OLigmPHjr3+gVlZgLd36f+JysGiJ1IxDg4O2LNnD/788098++23uHnzpuyDV8+ePcOtW7f+/84HDwLR0cAPP4iUlqoCFj2RimrcuDGCgoJw9+5djBgxAgAQERGBNm3awMnJCbt370bJpk2ld968WcSkpOp4eiWRitPQ0JDt2vH09MSICROgeeIEcOIEiv93H+HkSUgkktIb2trAixfihCWVxC16oiqkYcOG0Hz8GCXh4chs1w5F//sFINHRAT7+GL9/+SVS4+NFTkmqhkVPVNXUqQPpkCGoN3MmdHR1S5dpaCBv2DDYhYWhcbt26NWrF3bv3o28vDxxs5JKYNETVVWbNwO5uUCNGkBuLnR37cKVK1cwc+ZMXLt2DQMGDEDDhg0RHR0tdlISGYueqKrS0QHGjAHOny/9v7Y22rZti4ULF+L+/fs4duwYPD090a5dOwDAsWPHMGXKFFy4cIEzaVYzPBhLVFUdOfL/X3/3XZlvSaVSODs7w9nZWbbsypUrWLt2LVauXInmzZujf//+GDBgAKysrP7/QC6pJW7RE1UT06ZNQ1paGrZs2YLWrVtj6dKl6NOnj+z7GRkZIqYjReIWPVE1UrduXQwbNgzDhg1Deno67t69C4lEgqKiIlhYWMDY2Bje3t7w8fGBra0tt/TVBLfoiaopIyMjfPTRRwCAoqIizJ07F8bGxli8eDE6duyI5s2bIyoqSuSUJA8seiJCjRo1MGHCBBw/fhxpaWnYvHkz2rdvj/r16wMALl68iNGjR+PIkSN4wQ9jVTkseiIqo0GDBvD398cPP/wAR0dHAEBSUhIiIiLQq1cv1K9fHz4+Pti8eTPy8/NFTkvvgkVPRG/l5+eHjIwM/PDDD/Dz80NcXBymTJkCqbS0Qo4ePYrff/8dJSUlIiel12HRE9E70dXVhbu7O8LCwpCcnIyrV6/KroE7ZcoUfPjhh2jSpAlGjBiBqKgoZHHqZJXBoiei9yaRSNC0aVPZ7RMnTiA8PBwODg6IjIyEj48PxowZI/v+xYsXUVRUJEZUgpJPr3z48CG+/vprPHr0CFKpFJ999hmGDh2qzAhEpACGhoYYMmQIhgwZgsLCQpw9e7bMpRE7deqEunXrwtnZGa6urnB1dUWzZs3EDV2NKLXoNTQ0MHPmTFhaWuL58+fw9fWFnZ0dWrVqpcwYRKRAWlpa6Natm+y2sbExdu/ejaNHj+Lnn3/Gvn37AAD79++Ht7c3MjMzUVxcjAYNGogVWe0pddeNkZERLC0tAQB6enpo0aIF0tLSlBmBiJRMT08Pn3/+OTZt2oTk5GQkJSVh1apVcHBwAABs3boVhoaGsLGxweTJkxEVFYUnT56InFq9iPbJ2JSUFCQlJcHGxkasCESkZBKJBObm5jA3N5ctc3V1xYIFCxAbG4uNGzdi9erV0NLSwtOnT1GzZk3cvHkThoaGqFevnojJqzZRij4nJweTJk3CrFmzZPvxiKh6srS0hKWlJWbPno2CggJcuHAB169fR82aNQEAY8aMQWxsLGxsbGBnZwc7OzvY29vD1NRU5ORVh9LPuiksLMSkSZPg4eEBV1dXZQ9PRCpMW1sb9vb2smvkAsCCBQswd+5c1K9fH1u3bsXAgQPLfD8iIgKXLl3iWT1voNQtekEQMHv2bLRo0QL+/v7KHJqIqqiPP/4YH3/8MYDSOXni4+NlpZ6dnQ0/Pz8IggA9PT106dIFdnZ28PX1hbW1tZixVYpSi/7333/HgQMH0KZNG3h6egIApk6dWuYIPRFReTQ1NfHBBx/Ibuvr6+PevXs4c+YMzp49izNnzmDBggUwNDSEtbU1kpOTMWvWLHTu3BmdO3dGhw4dUKNGDRF/AnEoteg//PBD3LhxQ5lDEpEak0gkMDMzg5mZGQYOHAigdCv/peTkZBw/fhwREREASn9R2NjYYMOGDfjggw+Qn58PbW1t2VQO6orz0RORWqldu7bsa3t7e/z999/466+/cOHCBdl/L2fl3LhxI2bPng1bW1vY2trigw8+gK2tLdq1awcNDQ2xfgS5Y9ETkdpr3LgxvL294e3tXWZ5hw4d4Ofnh0uXLmHDhg3Iy8uDRCJBdnY29PT0EBUVhbS0NNja2qJ9+/ayM4GqGhY9EVVbDg4Osg9uFRcX4+bNm7h586bstO8dO3Zg//79AEqvw2tubo5PPvkE3/3vGr1ZWVmoXbu2yl+Ji0VPRITSKVosLCxgYWEhW7Z3714kJyfj8uXLuHTpEi5fvlzmU7vdunXDn3/+CSsrK7Rv3x7t27dHly5dyhwwVgUseiKicvzzYK+Xl9cr3x8/fjx+//13JCQkYOfOncjKysLAgQMREREBQRDw+eefw8zMDO3bt4eFhQXMzc2hr6+v9J+DRU9EVEEjR47EyJEjAZR+TiglJQWFhYUASmcAuHXrFg4ePFjm8ovz589HYGAgcnNzER4eLvsrwsjISGG7gFj0RERyIJFIykzLoKenh8uXL6OoqAi3b99GUlISkpKSYG9vDwC4fv06xo0bJ7t/vXr1cPv2bRgYGMg9G4vItTWXAAAME0lEQVSeiEiBNDU1ZRO5/fOsnw4dOshm87x+/Tru3LmjsInbWPRERCKQSqUwNTWFqampwuf9Uu+PgxEREYueiEjdseiJiNQci56ISM2x6ImI1ByLnohIzbHoiYjUnEqdR19cXAwASE1NFTkJEVHV8bIzX3bov6lU0WdkZAAABg0aJHISIqKqJyMjA2ZmZq8slwiCIIiQ57Xy8/ORkJAAQ0NDtbq6CxGRIhUXFyMjIwNWVlavvSauShU9ERHJHw/GEhGpObUp+lOnTqFHjx5wcXHBhg0bFD7ew4cP4efnh169esHNzQ3h4eEAgDVr1sDBwQGenp7w9PTEyZMnZY9Zv349XFxc0KNHD/z6668Ky+7k5AQPDw94enrCx8cHAPD06VP4+/vD1dUV/v7+yMrKAlA6h/aCBQvg4uICDw8PXLt2TfY8UVFRcHV1haurK6KioiqV6e7du7J14unpiQ8++ABbt24VZX0FBASga9eucHd3ly2T5/pJSEiAh4cHXFxcsGDBArzrH82vyxUcHIyePXvCw8MD48ePR3Z2NgAgJSUF1tbWsvUWFBT01vHL+xkrkkuer9uDBw/Qr18/uLq64ssvv0RBQUGFc3355ZeyTE5OTvD09FT6+iqvG0R9jwlqoKioSHB2dhaSk5OFFy9eCB4eHsKtW7cUOmZaWpqQkJAgCIIgPHv2THB1dRVu3bolrF69Wti0adMr979165bg4eEhvHjxQkhOThacnZ2FoqIihWTv3r278Pjx4zLLgoODhfXr1wuCIAjr168Xli5dKgiCIMTGxgrDhw8XSkpKhMuXLwt9+/YVBEEQMjMzBScnJyEzM1N4+vSp4OTkJDx9+rRSuV4qKioSPv74YyElJUWU9XXhwgUhISFBcHNzky2T5/rx9fUVLl26JJSUlAjDhw8XYmNjK5zr119/FQoLCwVBEISlS5fKcj148KDM/f6pvPHL+xkrkkuer9ukSZOEQ4cOCYIgCHPmzBEiIiIqnOufFi9eLKxZs0YQBOWur/K6Qcz3mFps0cfHx8PMzAympqbQ1taGm5sbYmJiFDqmkZERLC0tAZReYKBFixZIS0sr9/4xMTFwc3ODtrY2TE1NYWZmhvj4eKVlj4mJkV0KzcvLC8eOHSuzXCKRoEOHDsjOzkZ6ejpOnz4NOzs71K1bF3Xq1IGdnV2ZrbPKOHfuHExNTdG4ceM35lXU+urUqRPq1KnzynjyWD/p6el4/vw5bG1tIZFI4OXl9c75XpfL3t4empqlJ8d16NDhracev2n88n7GiuQqz/u+boIg4Pz58+jRowcAwNvbu1Lr6yVBEPDTTz+V2dp/HUWsr/K6Qcz3mFoUfVpaGkxMTGS3jY2N31i68paSkoKkpCTY2NgAACIiIuDh4YGAgADZn2flZVRU9uHDh8PHxwfff/89AODx48cwMjICUPpGfHmB43+Pb2JiotBcAHD48OEy/wBVYX3Ja/2Ud3952LdvHxwdHWW3U1JS4OXlhcGDB+PixYtvzPumn7Gi5PG6ZWZmonbt2rJfZvJaXxcvXkT9+vXRrFkz2TIx1tc/u0HM95haFL3wmv1Tirr24r/l5ORg0qRJmDVrFvT09DBgwAAcPXoUBw4cgJGREZYsWfLGjIrIvmvXLkRFRWHjxo2IiIhAXFxcufdVZi4AKCgowPHjx9GzZ08AUIn19Sbvm0NR+cLCwqChoYE+ffoAKC2KEydOIDo6GjNnzsS0adPw/Plzpa0fRb5u8sh76NChMhsTYqyvf3dDeZTxHlOLojcxMSnzJ21aWprsN6ciFRYWYtKkSfDw8JBdIaZBgwbQ0NCAVCpFv379cPXq1TdmVER2Y2NjAED9+vXh4uKC+Ph41K9fH+np6QBK/1x9eV3Kf4+fmpqqsFxA6QE5S0tLNGjQAIBqrC8Acls/5d2/MqKiohAbG4vly5fL/kFra2vLLjtnZWWFpk2b4t69e28cv7yfsSLk9brVq1cP2dnZKCoqeiVvRRUVFeHo0aPo3bu3bJmy19frukHM95haFH379u1x//59PHjwAAUFBTh8+DCcnJwUOqYgCJg9ezZatGgBf39/2fKXLyQAHDt2DK1btwZQeibM4cOHUVBQgAcPHuD+/fuwtraWe/bc3Fw8f/5c9vWZM2fQunVrODk5ITo6GgAQHR0NZ2dnWa7o6GgIgoA//vgD+vr6MDIygr29PU6fPo2srCxkZWXh9OnTsosaV8bhw4fh5uYmuy32+npJXuvHyMgItWrVwh9//AFBEMo8V0WcOnUKGzduRFhYGHR1dWXLnzx5Ivu4+8v1Y2pq+sbxy/sZK0Jer5tEIkGXLl3w888/Ayj9pVbZ1/Ps2bNo0aJFmd0bylxf5XWDqO+xdzqMXAXExsYKrq6ugrOzs7B27VqFjxcXFye0adNGcHd3F/r06SP06dNHiI2NFaZPny64u7sL7u7uwujRo4W0tDTZY9auXSs4OzsLrq6uZY6SyzN7cnKy4OHhIXh4eAi9e/eWPd+TJ0+EIUOGCC4uLsKQIUOEzMxMQRAEoaSkRJg7d67g7OwsuLu7C/Hx8bLnioyMFD799FPh008/Ffbu3VupXIIgCLm5uULnzp2F7Oxs2TIx1teUKVMEOzs7oV27doKDg4OwZ88eua6f+Ph4wc3NTXB2dha+/fZboaSkpMK5Pv30U8HR0VH2HpszZ44gCIJw5MgRoXfv3oKHh4fg5eUlxMTEvHX88n7GiuSS5+uWnJws+Pr6Cp9++qkwceJE4cWLFxXOJQiCMGPGDGHnzp1l7qvM9VVeN4j5HuMnY4mI1Jxa7LohIqLyseiJiNQci56ISM2x6ImI1ByLnohIzbHoiZQsLi4O9+/fFzsGVSMseqoynj17Bj8/P/j5+aFjx47w8/NDQECAwsfNyckpM61tZbx48QI7duyAqanpOz8mODhYqXM3kfrhefRUJQ0YMAC7du1Syli7du1Co0aN0K1bt0o/1+3btyGRSNCyZct3fkxSUhKOHTuGiRMnVnp8qp5U6uLgRO/r0aNHCAwMRE5ODtq0aYM5c+ZgxYoVSEtLw8OHD9G4cWM0bNgQp06dgpOTE8aOHYsVK1YgJSUFjx49QtOmTTF//nwcOXIEmzZtQs2aNTFy5Eg4ODjIxjh58iRWrFgBAFi7di3Onj2LgoICzJs3D+bm5hgwYAA6dOiA3377DYMHD4aPj89rl12+fBkhISEoLCxE//794eXl9dpl06dPR2pqKiQSCbZv3w4LCwssXbpUrFVMaoC7bqhKW7duHcaNG4ft27dDS0sL8fHxAABLS0uEh4cjJSUFlpaW2LNnT5n5xNu2bYvw8HAIgoCrV6/i559/RmhoKLZt2/bKnD7Pnj2TzTPj7++PHTt2YPHixdiyZYvsPl5eXti5cyf2799f7rI1a9Zg3bp1stlFi4uLX1mWm5uLJ0+eYMeOHbIrEwF456suEb0Ot+ipSrt79y6WLVsGoHRf+ocffggAskm2jIyM0Lp1a0gkEmhra8se165dOwCAhYUFkpOTMX78eKxevRqCIGDs2LFo2rTpa8fbv38/Dh8+DKlUCi0tLdnyNm3aQCKRlJku9t/LEhMTMWbMGABAZmYmnjx58sqynJwcuLm5Yfr06TA1NcXEiRMhlXJ7jCqHRU9VWvPmzdGvXz+Ym5tDEAQUFxfj6tWrZQr3dXN1X79+Hfb29rh+/Tr69euHJk2aYNGiRYiLi0N4eDjmzJkju6++vj7y8vKgq6uL77//HgcOHMDdu3cxf/78N47x72WWlpYIDQ2Frq4uCgsLoaWl9coyiUSCPn36wNfXFwEBAUhMTISVlVWZXypE74tFT1Xa2LFjERQUhOfPn0MqlWLRokXv9Lhbt25hyJAhaNKkCaytrREcHIz4+Hjk5eVh1qxZZe7r4OCAuLg4ODo6wtLSEoMGDULHjh3fO+vEiRMxevRoCIIAAwMDrFq16pVl3377LcaPH4/i4mLUrl0brVq1QmJiImxtbd97PKKXeNYNVTsrVqzAxx9/jC5durzT/Z8/f46lS5di3rx5Ck72esHBwRg2bJjsgjJE74tFT9XO+xY9UVXHoiciUnM8nE9EpOZY9EREao5FT0Sk5lj0RERqjkVPRKTm/g9MtxTmuZbSpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.c14(3, xmax=20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f0358e-b20c-470e-920f-49c0d46b38d5",
   "metadata": {},
   "source": [
    "### Datation par K/Ar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f191feb0-94a7-4397-b7cb-6e7412a34979",
   "metadata": {
    "tags": []
   },
   "source": [
    "On utilise la méthode kar(k, Ar) avec k la concentration en <sup>40</sup>K et Ar la concentration en <sup>40</sup>Ar mesurées sur l'objet à dater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "616b8764-0217-439c-9653-0f20ae3179f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = c.kar(0.008, 0.0002)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1d07d2-46da-45f1-bc5f-edf397967239",
   "metadata": {},
   "source": [
    "Attention, sur le graphique le temps est bien en années , ici en x 10<sup>9</sup>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8186fad0-06d0-4e64-877c-57ecb79cec4b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "385910162.54356444"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89d78d6a-68eb-4e7f-ae93-dc934196972e",
   "metadata": {},
   "source": [
    "L'argument facultatif ymax (rapport k/Ar max) permet d'ajuster la représentation graphique:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98683f3e-62dd-4594-9650-6b57f4e9a243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "385910162.54356444"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.kar(0.008, 0.0002, ymax=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07afc470-b675-4e09-8a0c-595c88abc6c3",
   "metadata": {},
   "source": [
    "### La datation par U/Pb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee5c281-cc0d-47fb-a24a-3ac53db97f15",
   "metadata": {},
   "source": [
    "[Fiche synthèse datation U/Pb](https://github.com/YannBouyeron/SPET/blob/master/Geologie/FS%20U:Pb.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757eb68f-3dd7-4ac0-9a38-0e937716029d",
   "metadata": {},
   "source": [
    "**Premier exemple:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80f8e627-ae88-45d3-95e9-20bb8418bc25",
   "metadata": {},
   "source": [
    "On commence par tracer la concordia sur l'intervalle de temps présumé avec la méthode concordia:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4caad0f7-1bb8-4bde-ab52-8c3b092de55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method concordia in module chrono:\n",
      "\n",
      "concordia(ti=0, tf=1000000000, incre=10000000, dotlabel=True, labelincre=3) method of chrono.Chrono instance\n",
      "    Arguments:\n",
      "    \n",
      "            ti: age présumé inférieur à réouverture\n",
      "    \n",
      "            tf: age présumé superieur à age roche\n",
      "    \n",
      "            incre: incrément de temps pour la construction de la concordia en années\n",
      "    \n",
      "            dotlabel: si True (defaut): indication age sur concordia\n",
      "    \n",
      "            labelincre: icrémentation des labels des points de la concordia pour ne pas surcharger trop\n",
      "    \n",
      "    Return: array des points de la concordia.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(c.concordia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5685bacd-a1bd-4018-a95f-86382e8a3b12",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.01562075, 0.0314855 , 0.04759808, 0.06396234,\n",
       "        0.08058223, 0.09746174, 0.11460491]),\n",
       " array([0.        , 0.10349785, 0.21770751, 0.34373762, 0.48281158,\n",
       "        0.63627939, 0.80563079, 0.9925097 ]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.concordia(ti=0, tf=800000000.0, incre=100000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98eeaaa-96a7-4afb-bd4a-32c9ec1e0027",
   "metadata": {},
   "source": [
    "On ajoute ensuite nos mesures effectuées sur les zircons de l'échantillon de la roche:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "407d2c0b-930e-4c05-933c-f310a0063cd3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pb206U = [0.06231448, 0.06247915, 0.06264385, 0.06280857, 0.06297332, 0.06313809,\n",
    " 0.06330289, 0.06346772, 0.06363257, 0.06379744, 0.06396234]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cb72aee1-2ef0-414b-8bcc-3586d04f2579",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pb207U = [0.46827978, 0.46972653, 0.4711747,  0.4726243,  0.47407533, 0.47552779,\n",
    " 0.47698168, 0.478437, 0.47989376, 0.48135195, 0.48281158]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48db053c-4198-46ec-8d4e-2918974c3f6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x6d262250>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.concordia(ti=0, tf=800000000.0, incre=100000000, labelincre=2)\n",
    "plt.plot(pb207U, pb206U, \"*r\", label=\"Zircon Z1 à Z11\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85cc3138-4b85-453e-8416-2825437bb165",
   "metadata": {
    "tags": []
   },
   "source": [
    "On observe que les mesures des rapports pb/u effectuées sur les zircons (ici en rouge) s'alignent sur la concordia. Il n'y a pas eu de réouverture du système. L'âge de la roche est l'âge du plus vieux zircon lisible sur le graphique, soit 400 Ma. Il n'est donc pas nécessaire d'afficher la discordia."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370e5bba-eec5-4761-8873-bde4dc4ef2ef",
   "metadata": {},
   "source": [
    "**Deuxième exemple:**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0317da63-4b97-4580-acb1-c86d21121053",
   "metadata": {},
   "source": [
    "On dispose des mesures suivantes effectuées sur 3 zircons d'une roche:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "10ed4624-83d8-4013-ad4c-a5bd2c05fdba",
   "metadata": {},
   "outputs": [],
   "source": [
    "pb207U = [5, 3, 1]\n",
    "\n",
    "pb206U = [0.3, 0.2, 0.1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47d40cb-7c09-4839-9325-ee1284183dc9",
   "metadata": {},
   "source": [
    "On affiche la concordia et on place nos mesures sur le graphique:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "396fa2a3-4775-44b6-9b3e-d70dc7a7ffc7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x6d271530>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.concordia(ti=0.4*10**9, tf=2.2*10**9, incre=100000000)\n",
    "plt.plot(pb207U, pb206U, \"*\", label=\"Minéraux\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8551cd34-3081-4669-bb17-47938e456c1e",
   "metadata": {},
   "source": [
    "On observe que les mesures effectuées sur les minéraux ne sont pas alignées sur la concordia. Il y'a eu une ré-ouverture du système. Il faut donc afficher la discordia avec la méthode upb :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6027a0d5-4536-4e37-80e7-b37a4a0505a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method upb in module chrono:\n",
      "\n",
      "upb(p207, p206, ti=100000000, tf=2500000000.0, delta=100000, incre=100000000, dotlabel=True, labelincre=3) method of chrono.Chrono instance\n",
      "    Arguments:\n",
      "    \n",
      "            p206: liste valeurs 206Pb/238U mesurées (Y)\n",
      "    \n",
      "            p207: liste valeurs 207Pb/235U mesurées (X)\n",
      "    \n",
      "            ti: age présumé inférieur à réouverture\n",
      "    \n",
      "            tf: age présumé superieur à age roche\n",
      "    \n",
      "            delta: precision du calcul des intercepts en années\n",
      "    \n",
      "            incre: incrément de temps pour la construction de la concordia en années\n",
      "    \n",
      "            dotlabel: si True (defaut): indication age sur concordia\n",
      "    \n",
      "            labelincre: incrémentation des labels des points de la concordia pour ne pas surcharger trop\n",
      "    \n",
      "    Show: graphique concordia discordia             \n",
      "    Return: {coeff correlation discordia, intersup, interlow, a, b}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(c.upb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2cefff5d-4479-403d-8d0f-03f6962d296c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dico = c.upb(pb207U, pb206U, ti=0.4*10**9, tf=2.2*10**9, incre=100000000, delta=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a2541164-d16f-4328-b331-e977d0c82d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R': 1.0,\n",
       " 'sup': 2033986000.0,\n",
       " 'low': 514235000.0,\n",
       " 'a': 0.05000000000000001,\n",
       " 'b': 0.049999999999999954}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dico"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c579193-9e30-45aa-ae4b-363ef5636687",
   "metadata": {},
   "source": [
    "La méthode retourne un dictionnaire avec l'âge de la ré-ouverture du système correspondant à l'intercept inférieur: low = 515093000 +/- delta années; l'âge de la roche correspondant à l'intercept supérieur: sup = 2033278000 +/- delta années. La précision des âges obtenus dépend du paramètre \"delta\" passé en argument de la méthode upb. \n",
    "\n",
    "Attention un delta faible augmente la précision, mais augmente aussi le temps des calculs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b546f3d-b7d8-4318-acff-dfa78d692bb0",
   "metadata": {},
   "source": [
    "### La datation par Rb/Sr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b2547e-22ab-4a4c-8226-46bb2f712707",
   "metadata": {
    "tags": []
   },
   "source": [
    "On utilise la méthode rbsr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ff1b1f17-f68b-47b6-aa5f-8b4f8778d7f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method rbsr in module chrono:\n",
      "\n",
      "rbsr(rbsr, srsr) method of chrono.Chrono instance\n",
      "    Arguments:\n",
      "    \n",
      "            rbsr: liste rapport 87Rb/86Sr\n",
      "    \n",
      "            srsr: liste rapport 87Sr/86Sr\n",
      "    \n",
      "    Show graphique isochrone\n",
      "    \n",
      "    Return equation droite reg, age (en annees)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(c.rbsr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3624dff0-5329-43fe-874b-196bcb5c3735",
   "metadata": {},
   "outputs": [],
   "source": [
    "rbsr = [0.288, 0.31, 0.996, 0.787, 0.898, 0.945, 0.901, 5.84, 3.36]\n",
    "\n",
    "srsr = [0.7165, 0.7171, 0.7556, 0.7413, 0.7471, 0.7521, 0.7495, 1.0133, 0.8807]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "566c02dc-aaf3-4728-a384-e3a508181d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y , a = c.rbsr(rbsr, srsr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b2e52a4-a515-406d-a743-4b8def5acc70",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0.05354693x + 0.7\n"
     ]
    }
   ],
   "source": [
    "# fonction de la droite isochrone\n",
    "\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c553516b-592a-4ce2-9c41-bd6c1617b400",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3673415678.371164\n"
     ]
    }
   ],
   "source": [
    "# age \n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497bdfa2-6545-475a-a6b3-439879687caa",
   "metadata": {
    "tags": []
   },
   "source": [
    "La méthode creatRbsr(age) permet de créer des couples de valeurs rbsr et srsr fictifs en fonction de l'âge désiré."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ae4f42bb-3075-4bee-aa9c-d18e971ea9c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method creatRbsr in module chrono:\n",
      "\n",
      "creatRbsr(age, n=5, b=0.7, out='') method of chrono.Chrono instance\n",
      "    Création de données RbSr en fonction de l’age.\n",
      "    \n",
      "    Arguments:\n",
      "    \n",
      "            age: age désiré\n",
      "            \n",
      "            n: nombre de couples SrSr, RbSr\n",
      "            \n",
      "            b: ordonnée à l’origine = Sr/Sr à t0\n",
      "    \n",
      "            out: path en .xlsx ou en .json pour sauvegarder les données crées\n",
      "    \n",
      "    Show graphique isochrone\n",
      "    \n",
      "    Return RbSr (liste), SrSr (liste)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(c.creatRbsr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c649a1fb-dc7c-4aa1-bdca-c78c4c4e27f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rbsr, srsr = c.creatRbsr(5000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e54b762f-c268-4382-9765-3dd68022d715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.282978862971912,\n",
       " 4.988633011174326,\n",
       " 3.3254901343712717,\n",
       " 2.091505108366786,\n",
       " 4.1554292953511585]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbsr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "546ff54a-0ad7-49c8-a62c-b44e68241382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7001620972536554,\n",
       " 0.7003542055179406,\n",
       " 0.7002361181816367,\n",
       " 0.7001485021344575,\n",
       " 0.7002950459539775]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srsr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1600e45-8da5-48b8-8942-aa57b0a4d581",
   "metadata": {},
   "source": [
    "On peut vérifier l'âge en ré utilisant la méthode rbsr():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e14bfc42-3e8b-486e-9c53-c2188b83dfd7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y , a = c.rbsr(rbsr, srsr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f0c202ce-b67c-4058-bfbb-3612323fb2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4999999.999987198"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.5"
  },
  "toc-autonumbering": false,
  "toc-showcode": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}