hse_metplotter.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "hse_metplotter.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPFjxwOUJBKpFpcsq7uukuR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anipatel22/IronMetPlots/blob/main/hse_metplotter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This file is for plotting HSE compositons (normalized to Orgueil, a CI chondrite) of individual meteorites and/or meteroite groups. This must be used with excel file IronMeteoriteData.xlsx. See metplotter.ipynb for more details. This notebook uses the same format of code as that notebook."
      ],
      "metadata": {
        "id": "wW1B-snG9CIZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Packages"
      ],
      "metadata": {
        "id": "41h1s-qR73It"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uzYxnvml7xjI"
      },
      "outputs": [],
      "source": [
        "from typing import ValuesView\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Read + store data from excel"
      ],
      "metadata": {
        "id": "8kuiqIGy77bo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xls = pd.ExcelFile('IronMeteoriteData.xlsx')\n",
        "dfIAB = pd.read_excel(xls, 'IABnorm')\n",
        "dfIC = pd.read_excel(xls,'ICnorm')\n",
        "dfIIAB = pd.read_excel(xls, 'IIABnorm')\n",
        "dfIIC = pd.read_excel(xls, 'IICnorm')\n",
        "dfIID = pd.read_excel(xls, 'IIDnorm')\n",
        "dfIIF = pd.read_excel(xls, 'IIFnorm')\n",
        "dfIIIAB = pd.read_excel(xls, 'IIIABnorm')\n",
        "dfIIIF = pd.read_excel(xls,'IIIFnorm')\n",
        "dfIIIE = pd.read_excel(xls,'IIIEnorm')\n",
        "dfIVA = pd.read_excel(xls,'IVAnorm')\n",
        "dfIVB = pd.read_excel(xls, 'IVBnorm')\n",
        "dfUG = pd.read_excel(xls, 'UGnorm')\n",
        "\n",
        "# These are IVA meteorite compositions from McCoy et al. 2011 normalized to Orgueil\n",
        "elem = dfIVA['Elem']\n",
        "JT = dfIVA['Jamestown AVG']\n",
        "ME = dfIVA['Maria Elena AVG']\n",
        "Y = dfIVA['Yanhuitlan AVG']\n",
        "G = dfIVA['Gibeon AVG']\n",
        "C = dfIVA['Charlotte AVG']\n",
        "M = dfIVA['Muonionalusta']\n",
        "BL = dfIVA['Bushman Land AVG']\n",
        "S = dfIVA['Steinbach AVG']\n",
        "NW = dfIVA['New Westville  AVG']\n",
        "D = dfIVA['Duchesne AVG']\n",
        "DH = dfIVA['Duel Hill (1854) AVG']\n",
        "FC = dfIVA['Fuzzy Creek AVG']\n",
        "C2 = dfIVA['Chinautla AVG']\n",
        "E = dfIVA['EET 83230']\n",
        "\n",
        "# These are IVB meteorite compositions from Walker et al. 2008, Campbell and Humayun 2005,\n",
        "# and Honesto et al. 2007 normalized to Orgueil.\n",
        "mIVB1 = dfIVB['CoGH AVG']\n",
        "mIVB2 = dfIVB['Iquique']\n",
        "mIVB3 = dfIVB['Kokomo']\n",
        "mIVB4 = dfIVB['Tlacotepec AVG']\n",
        "mIVB5 = dfIVB['Hoba AVG']\n",
        "mIVB6 = dfIVB['Santa Clara']\n",
        "mIVB7 = dfIVB['Tinnie']\n",
        "mIVB8 = dfIVB['Temera']\n",
        "mIVB9 = dfIVB['Weaver Mtns AVG']\n",
        "mIVB10 = dfIVB['Tawallah Valley AVG']\n",
        "mIVB11 = dfIVB['Skookum AVG']\n",
        "mIVB12 = dfIVB['Warburton  Range AVG']\n",
        "mIVB13 = dfIVB['Cape of Good Hope ']\n",
        "mIVB14 = dfIVB['Tlacotepec']\n",
        "mIVB15 = dfIVB['Hoba']\n",
        "mIVB16 = dfIVB['Santa Clara2']\n",
        "mIVB17 = dfIVB['Skookum']\n",
        "mIVB18 = dfIVB['Tawallah Valley']\n",
        "mIVB19 = dfIVB['Warburton Range']\n",
        "mIVB20 = dfIVB['Weaver Mountains']\n",
        "mIVB21 = dfIVB['Iquique2']\n",
        "mIVB22 = dfIVB['Kokomo2']\n",
        "\n",
        "#IAB meteorite compositions from Worsham et al. 2016 normalized to Orgueil\n",
        "mIAB1 = dfIAB['Landes']\n",
        "mIAB2 = dfIAB['Seligman']\n",
        "mIAB3 = dfIAB['Yenberrie']\n",
        "mIAB4 = dfIAB['Ballinger AVG']\n",
        "mIAB5 = dfIAB['Cranbourne']\n",
        "mIAB6 = dfIAB['Campo del Cielo']\n",
        "mIAB7 = dfIAB['New Leipzig']\n",
        "mIAB8 = dfIAB['Magura']\n",
        "mIAB9 = dfIAB['Canyon Diablo']\n",
        "mIAB10 = dfIAB['Odessa']\n",
        "mIAB11 = dfIAB['Jenkins']\n",
        "mIAB12 = dfIAB['ALHA 77283']\n",
        "mIAB13 = dfIAB['Youndegin']\n",
        "mIAB14 = dfIAB['Smithville']\n",
        "mIAB15 = dfIAB['Seymour']\n",
        "mIAB16 = dfIAB['Bogou']\n",
        "mIAB17 = dfIAB['Burgavali']\n",
        "mIAB18 = dfIAB['Morasko']\n",
        "mIAB19 = dfIAB['Seeliisgen']\n",
        "mIAB20 = dfIAB['Sarepta AVG']\n",
        "mIAB21 = dfIAB['Hope']\n",
        "mIAB22 = dfIAB['Pine River']\n",
        "mIAB23 = dfIAB['Shrewsbury AVG']\n",
        "mIAB24 = dfIAB['Comanche']\n",
        "mIAB25 = dfIAB['Bahjoi']\n",
        "mIAB26 = dfIAB['Toluca']\n",
        "mIAB27 = dfIAB['Deport']\n",
        "mIAB28 = dfIAB['Balfour Downs']\n",
        "mIAB29 = dfIAB['Bischtiibe AVG']\n",
        "mIAB30 = dfIAB['Goose Lake']\n",
        "mIAB31 = dfIAB['Persimmon Creek AVG']\n",
        "mIAB32 = dfIAB['Mungindi']\n",
        "mIAB33 = dfIAB['Edmonton (KY) AVG']\n",
        "mIAB34 = dfIAB['Maltahohe']\n",
        "mIAB35 = dfIAB['Anoka AVG']\n",
        "mIAB36 = dfIAB['Carlton']\n",
        "mIAB37 = dfIAB['Lamesa AVG']\n",
        "mIAB38 = dfIAB['Tazewell']\n",
        "mIAB39 = dfIAB['Dayton AVG']\n",
        "mIAB40 = dfIAB['Freda']\n",
        "mIAB41 = dfIAB['Quarat al Hanish']\n",
        "mIAB42 = dfIAB['Chebankol']\n",
        "mIAB43 = dfIAB['Sombrerete']\n",
        "mIAB44 = dfIAB['Kofa']\n",
        "mIAB45 = dfIAB['ALHA 80104']\n",
        "mIAB46 = dfIAB['Mount Magnet']\n",
        "mIAB47 = dfIAB['TIL 91725']\n",
        "mIAB48 = dfIAB['Caddo County']\n",
        "mIAB49 = dfIAB['Zagora']\n",
        "mIAB50 = dfIAB['Mertzon']\n",
        "mIAB51 = dfIAB['EET 84300 AVG']\n",
        "mIAB52 = dfIAB['Woodbine']\n",
        "mIAB53 = dfIAB['Pitts AVG']\n",
        "mIAB54 = dfIAB['Mundrabilla']\n",
        "mIAB55 = dfIAB['Duplicate']\n",
        "mIAB56 = dfIAB['Waterville AVG']\n",
        "mIAB57 = dfIAB['Georgetown']\n",
        "mIAB58 = dfIAB['Osseo']\n",
        "mIAB59 = dfIAB['Sardis AVG']\n",
        "\n",
        "# IC comps from Tornabene 2020 normalized to Orgueil\n",
        "mIC1 = dfIC['Arispe']\n",
        "mIC2 = dfIC['Union County']\n",
        "mIC3 = dfIC['Mount Dooling']\n",
        "mIC4 = dfIC['Bendego']\n",
        "mIC5 = dfIC['Chihuahua City']\n",
        "mIC6 = dfIC['NWA 2743']\n",
        "mIC7 = dfIC['St. Francois County']\n",
        "mIC8 = dfIC['Santa Rosa']\n",
        "mIC9 = dfIC['Nocoleche (anomalous)']\n",
        "mIC10 = dfIC['Winburg (anomalous)']\n",
        "\n",
        "# IIAB comps from Hilton 2020 normalized to Orgueil\n",
        "mIIAB1 = dfIIAB['USNM1']\n",
        "mIIAB2 = dfIIAB['USNM2']\n",
        "mIIAB3 = dfIIAB['USNM3']\n",
        "mIIAB4 = dfIIAB['USNM4']\n",
        "mIIAB5 = dfIIAB['USNM5']\n",
        "\n",
        "# IIC comps from Tornabene 2020 normalized to Orgueil\n",
        "mIIC1 = dfIIC['Darinskoe']\n",
        "mIIC2 = dfIIC['Perryville']\n",
        "mIIC3 = dfIIC['Cratheu´s (1950)']\n",
        "mIIC4 = dfIIC['Kumerina']\n",
        "mIIC5 = dfIIC['Ballinoo']\n",
        "mIIC6 = dfIIC['Salt River']\n",
        "mIIC7 = dfIIC['Unter Mässing']\n",
        "mIIC8 = dfIIC['Wiley']\n",
        "mIIC9 = dfIIC['Cratheu´s (Field Museum)']\n",
        "mIIC10 = dfIIC['Para`  de Minas']\n",
        "\n",
        "# IID comps from Hilton 2020 normalized to Orgueil\n",
        "mIID1 = dfIID['UCLA']\n",
        "mIID2 = dfIID['USNM 2467']\n",
        "mIID3 = dfIID['USNM 1071']\n",
        "mIID4 = dfIID['USNM 2397']\n",
        "mIID5 = dfIID['USNM']\n",
        "mIID6 = dfIID['USNM 777']\n",
        "mIID7 = dfIID['USNM 309']\n",
        "mIID8 = dfIID['USNM 838']\n",
        "mIID9 = dfIID['USNM 3008']\n",
        "mIID10 = dfIID['USNM 3016']\n",
        "mIID11 = dfIID['USNM 3533']\n",
        "mIID12 = dfIID['USNM 788']\n",
        "\n",
        "# IIF comps from Hilton 2020 (IIF paper) normalized to Orgueil\n",
        "mIIF1 = dfIIF['Dorofeevka']\n",
        "DelRio = dfIIF['Del Rio']\n",
        "mIIF3 = dfIIF['Monahans (1983)']\n",
        "mIIF4 = dfIIF['Purmela']\n",
        "mIIF5 = dfIIF['Repeev Khutor']\n",
        "mIIF6 = dfIIF['Corowa']\n",
        "\n",
        "# IIIAB comps from Hilton 2020 normalized to Orgueil\n",
        "mIIIAB1 = dfIIIAB['m1']\n",
        "mIIIAB2 = dfIIIAB['m2']\n",
        "mIIIAB3 = dfIIIAB['m3']\n",
        "mIIIAB4 = dfIIIAB['m4']\n",
        "mIIIAB5 = dfIIIAB['m5']\n",
        "\n",
        "# IIIE comps from normalized to Orgueil\n",
        "mIIIE1 = dfIIIE['Armanty']\n",
        "mIIIE2 = dfIIIE['Ulasitai']\n",
        "\n",
        "# IIIF comps from Zhang et al. 2022 normalized to Orgueil\n",
        "mIIIF1 = dfIIIF['Cerro del Inca']\n",
        "mIIIF2 = dfIIIF['Moonbi']\n",
        "mIIIF3 = dfIIIF['St. Genevieve County']\n",
        "mIIIF4 = dfIIIF['Clark County']\n",
        "mIIIF5 = dfIIIF['Nelson County']\n",
        "mIIIF6 = dfIIIF['Oakley (iron)']\n",
        "mIIIF7 = dfIIIF['Klamath Falls']\n",
        "mIIIF8 = dfIIIF['Fitzwater Pass']\n",
        "\n",
        "\n",
        "# Ungrouped comps normalized to Orgueil\n",
        "mUG1 = dfUG[\"Babb's Mill\"]\n",
        "mUG2 = dfUG[\"South Byron\"]\n",
        "mUG3 = dfUG[\"ILD 83500\"]\n",
        "mUG4 = dfUG[\"unknown\"]\n",
        "mUG5 = dfUG[\"ALH84233\"]\n",
        "mUG6 = dfUG[\"EET87516\"]\n",
        "mUG7 = dfUG[\"LEW85369\"]\n",
        "mUG8 = dfUG[\"LEW86211\"]\n",
        "mUG9 = dfUG[\"Oglat Sidi Ali, Morroco\"]\n",
        "mUG10 = dfUG[\"Chinga\"]\n",
        "mUG11 = dfUG[\"Tishomingo\"]\n",
        "mUG12 = dfUG[\"Willow Grove\"]\n",
        "mUG13 = dfUG[\"Nedagolla\"]"
      ],
      "metadata": {
        "id": "pzc3lHBb8A9L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Individual Meteorite Data"
      ],
      "metadata": {
        "id": "qrE6LkLQ8G8M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sirjan HSE data"
      ],
      "metadata": {
        "id": "6SYLdxW78VUm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Re = 0.189\n",
        "Os = 1.423\n",
        "Ir = 2.019\n",
        "Ru = 5.223\n",
        "Pt = 7.055\n",
        "Pd = 4.079"
      ],
      "metadata": {
        "id": "Us8AWn-K8U0d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normalizing Sirjan to Orgueil"
      ],
      "metadata": {
        "id": "exVjTI-d8ZC-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Re = Re/0.04\n",
        "Os = Os/0.495\n",
        "Ir = Ir/0.469\n",
        "Ru = Ru/0.69\n",
        "Pt = Pt/0.925\n",
        "Pd = Pd/0.56\n",
        "\n",
        "sirj = [Re, Os, Ir, Ru, Pt, Pd]"
      ],
      "metadata": {
        "id": "CtoCtPb28c0D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inputting list of Nedagolla HSEs (already normalozed to Orgueil)"
      ],
      "metadata": {
        "id": "gNWex5WO8f_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nedag = [10.6, 11.7717, 9.5394, 8.402898551, 8.596756757, 3.130357143]"
      ],
      "metadata": {
        "id": "s--rh_S88fPP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting"
      ],
      "metadata": {
        "id": "mp9uKIJ_8omt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(231)\n",
        "ax2 = fig.add_subplot(232)\n",
        "ax3 = fig.add_subplot(233)\n",
        "ax4 = fig.add_subplot(234)\n",
        "ax5 = fig.add_subplot(235)\n",
        "ax6 = fig.add_subplot(236)\n",
        "\n",
        "ax1.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax1.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax1.set_title('IVA')\n",
        "ax1.set_ylabel('HSE/Orgueil')\n",
        "\n",
        "ax2.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax2.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax2.set_title('IVB')\n",
        "\n",
        "ax3.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax3.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax3.set_title('IAB')\n",
        "\n",
        "ax4.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax4.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax4.set_title('IIAB')\n",
        "ax4.set_ylabel('HSE/Orgueil')\n",
        "\n",
        "ax5.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax5.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax5.set_title('IIIAB')\n",
        "\n",
        "ax6.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax6.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax6.set_title('UG')\n",
        "\n",
        "\n",
        "\n",
        "ax1.plot(elem, JT, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, ME, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, Y, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, G, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, C, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, M, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, BL, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, S, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, NW, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, D, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, DH, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, FC, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, C2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, E, color = 'red', marker = '.', lw = 0.5)\n",
        "ax1.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax1.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "ax2.plot(elem, mIVB1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB6, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB7, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB8, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB9, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB10, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB11, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB12, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB13, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB14, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB15, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB16, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB17, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB18, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB19, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB20, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB21, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, mIVB22, color = 'red', marker = '.', lw = 0.5)\n",
        "ax2.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax2.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "ax3.plot(elem, mIAB1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB6, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB7, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB8, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB9, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB10, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB11, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB12, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB13, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB14, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB15, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB16, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB17, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB18, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB19, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB20, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB21, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB22, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB23, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB24, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB25, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB26, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB27, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB28, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB29, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB30, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB31, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB32, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB33, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB34, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB35, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB36, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB37, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB38, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB39, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB40, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB41, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB42, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB43, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB44, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB45, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB46, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB47, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB48, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB49, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB50, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB51, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB52, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB53, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB54, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB55, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB56, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB57, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB58, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, mIAB59, color = 'red', marker = '.', lw = 0.5)\n",
        "ax3.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax3.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "ax4.plot(elem, mIIAB1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax4.plot(elem, mIIAB2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax4.plot(elem, mIIAB3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax4.plot(elem, mIIAB4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax4.plot(elem, mIIAB5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax4.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax4.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "ax5.plot(elem, mIIIAB1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax5.plot(elem, mIIIAB2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax5.plot(elem, mIIIAB3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax5.plot(elem, mIIIAB4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax5.plot(elem, mIIIAB5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax5.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax5.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "\n",
        "ax6.plot(elem, mUG1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG6, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG7, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG8, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG9, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG10, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG11, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG12, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, mUG13, color = 'red', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax6.plot(elem, nedag, color = 'green', marker = '.', lw = 0.5)\n",
        "ax5.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "uzj6YPWE8rEI",
        "outputId": "5c2b07c0-2e8e-4e6f-fac1-4a7b63721db0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax11 = fig.add_subplot(231)\n",
        "ax12 = fig.add_subplot(232)\n",
        "ax13 = fig.add_subplot(233)\n",
        "ax14 = fig.add_subplot(234)\n",
        "ax15 = fig.add_subplot(235)\n",
        "ax16 = fig.add_subplot(236)\n",
        "\n",
        "ax11.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax11.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax11.set_title('IIC')\n",
        "ax11.set_ylabel('HSE/Orgueil')\n",
        "\n",
        "ax12.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax12.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax12.set_title('IID')\n",
        "\n",
        "ax13.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax13.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax13.set_title('IIF')\n",
        "\n",
        "ax14.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax14.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax14.set_title('IIIF')\n",
        "ax14.set_ylabel('HSE/Orgueil')\n",
        "\n",
        "ax15.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax15.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax15.set_title('IC')\n",
        "\n",
        "ax16.tick_params(axis=\"y\",direction=\"in\")\n",
        "ax16.tick_params(axis=\"x\",direction=\"in\")\n",
        "ax16.set_title('IIIE')\n",
        "\n",
        "\n",
        "ax11.plot(elem, mIIC1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, mIIC2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, mIIC3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, mIIC4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, mIIC5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, mIIC6, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, mIIC7, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, mIIC8, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, mIIC9, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, mIIC10, color = 'red', marker = '.', lw = 0.5)\n",
        "ax11.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax11.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "ax12.plot(elem, mIID1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID6, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID7, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID8, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID9, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID10, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID11, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, mIID12, color = 'red', marker = '.', lw = 0.5)\n",
        "ax12.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax12.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "ax13.plot(elem, mIIF1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax13.plot(elem, DelRio, color = 'purple', marker = '.', lw = 0.5)\n",
        "ax13.plot(elem, mIIF3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax13.plot(elem, mIIF4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax13.plot(elem, mIIF5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax13.plot(elem, mIIF6, color = 'red', marker = '.', lw = 0.5)\n",
        "ax13.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax13.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "ax14.plot(elem, mIIIF1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax14.plot(elem, mIIIF2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax14.plot(elem, mIIIF3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax14.plot(elem, mIIIF4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax14.plot(elem, mIIIF5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax14.plot(elem, mIIIF6, color = 'red', marker = '.', lw = 0.5)\n",
        "ax14.plot(elem, mIIIF7, color = 'red', marker = '.', lw = 0.5)\n",
        "ax14.plot(elem, mIIIF8, color = 'red', marker = '.', lw = 0.5)\n",
        "ax14.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax14.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "ax15.plot(elem, mIC1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, mIC2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, mIC3, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, mIC4, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, mIC5, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, mIC6, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, mIC7, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, mIC8, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, mIC9, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, mIC10, color = 'red', marker = '.', lw = 0.5)\n",
        "ax15.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax15.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "ax16.plot(elem, mIIIE1, color = 'red', marker = '.', lw = 0.5)\n",
        "ax16.plot(elem, mIIIE2, color = 'red', marker = '.', lw = 0.5)\n",
        "ax16.plot(elem, sirj, color = 'blue', marker = '.', lw = 0.5)\n",
        "ax16.axhline(y = 0, color = 'black', lw = 0.5)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "8hnr9j_j87JU",
        "outputId": "672f8eb5-cf71-4fde-b2ac-7917ac6494e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}