{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of results of multi-zone MC simulations of reaction rate uncertainties for NOVA models (Python 3)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab ipympl \n", "\n", "import os \n", "import sys\n", "import h5py\n", "\n", "from scipy import stats\n", "from scipy.stats import norm\n", "from scipy.stats import lognorm\n", "import matplotlib as mpl\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "import pandas as pd\n", "\n", "from nugridpy import nugridse as nuse\n", "from nugridpy import utils\n", "from nugridpy import ppn\n", "from nugridpy import utils" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "mixing_case = 'MLT'" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# a color-blind color set \n", "CB_color = ['#377eb8', '#ff7f00', '#4daf4a',\n", " '#f781bf', '#a65628', '#984ea3',\n", " '#999999', '#e41a1c', '#dede00']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_everything_you_need(mixing_case):\n", " # the initial abundances for NOVA models are the Asplund et al. (2009) solar abundances \n", " upper = \"/user/niagara_scratch_fherwig/wendi.user/jissa/multizone/\"\n", " mppnp_test_dir=upper+mixing_case+\"_MC_results/\"\n", " \n", " sol_ab=upper+\"../mixing_results/iniab2.0E-02GN93.ppn\"\n", " \n", " init_ab=sol_ab\n", " \n", " utils.solar(sol_ab,1.)\n", " sol_abu=utils.solar_elem_abund\n", " n_sol=len(sol_abu)\n", "\n", " # read in initial elemental abundances in the path init_ab\n", " utils.solar(init_ab,1.)\n", " init_abu=utils.solar_elem_abund\n", " n_init=len(init_abu)\n", " print (\"\\nn_init =\",n_init)\n", "\n", " # read in solar abundances in the path sol_ab\n", " utils.solar(sol_ab,1.)\n", " sol_abu=utils.solar_elem_abund\n", " n_sol=len(sol_abu)\n", " print (\"\\nn_sol =\",n_sol)\n", " \n", " # initial and solar abundances do not include Tc and Pm\n", " # here we include them with the abundances 1e-99\n", " \n", " n_el = n_sol + 2 # n_sol + the unstable Tc (Z=43) and Pm (Z=61)\n", " \n", " el_name=[\" \" for x in range(n_el)]\n", " z_el=np.linspace(0,0,n_el)\n", " \n", " el_name[0]='n'\n", " for i in range(n_el):\n", " z_el[i]=float(i) # Z=i in mppnp surf data output\n", " if (i>0):\n", " el_name[i]=utils.get_el_from_z(i)\n", " \n", " # el_abu_init = np.linspace(1e-99,1e-99,n_el)\n", " el_abu_sol = np.linspace(1e-99,1e-99,n_el)\n", " \n", " for i in range(n_el): \n", " for k in range(n_sol):\n", " z_sol=k+1\n", " if float(z_sol)==z_el[i] and z_sol != 43 and z_sol != 61: \n", " # el_abu_init[i] = init_abu[k]\n", " el_abu_sol[i] = sol_abu[k]\n", " \n", " # print (\"X_init =\",el_abu_init[1],\", X_init(Tc) =\", el_abu_init[43],\", X_init(Pm) =\", el_abu_init[61])\n", " print (\"X_sol =\",el_abu_sol[1],\", X_sol(Tc) =\", el_abu_sol[43],\", X_sol(Pm) =\", el_abu_sol[61])\n", "\n", " work_dir = mppnp_test_dir\n", "\n", " model = 11200\n", " \n", " mc = 0 \n", " file_name = mixing_case+\"_MC_\"+str(mc)+\"_OC\"\n", " suffix = \".0010001.surf.h5\"\n", " h5_file = work_dir+file_name+suffix\n", " \n", " print(\"Zero variation case:\", h5_file)\n", " with h5py.File(h5_file, 'r') as file:\n", " dset = file[\"/cycle\"+str(model).zfill(10)+\"/SE_DATASET\"]\n", " el_abu_0 = dset['elem_massf_decay'][0]\n", " iso_abu_0 = dset['iso_massf_decay'][0]\n", " \n", " n_el = len(el_abu_0)\n", " n_iso = len(iso_abu_0)\n", " \n", " print (\"Number of Elements: \", n_el, \"\\nNumber of Isotopes:\", n_iso)\n", " \n", " mc_runs = 1000 # the total number of MC runs\n", " el_abu = np.zeros((mc_runs,n_el),dtype=float) # this 2d array stores surface abundances from all mc_runs\n", " iso_abu = np.zeros((mc_runs,n_iso),dtype=float) # this 2d array stores surface abundances from all mc_runs\n", " \n", " print('Reading all MC runs')\n", " for mc in range(mc_runs):\n", " \n", " if mc % 100 == 0: print('Reading in run', mc)\n", " \n", " mc1 = mc+1\n", " file_name = mixing_case+\"_MC_\"+str(mc1)+\"_OC\"\n", " h5_file = work_dir+file_name+suffix\n", " # print (file_name+suffix)\n", " \n", " try:\n", " with h5py.File(h5_file, 'r') as file:\n", " dset = file[\"/cycle\"+str(model).zfill(10)+\"/SE_DATASET\"]\n", " el_abu[mc,:] = dset['elem_massf_decay'][0]\n", " iso_abu[mc,:] = dset['iso_massf_decay'][0]\n", " except:\n", " print(f\"Missing run {mc1}\")\n", " \n", " \n", " el_name=[\" \" for x in range(n_el)]\n", " z_el=np.linspace(0,0,n_el)\n", " el_name[0]='n'\n", " for i in range(n_el):\n", " z_el[i]=float(i) # Z=i in mppnp surf data output\n", " if (i>0):\n", " el_name[i]=utils.get_el_from_z(i)\n", "\n", " iso_z=np.linspace(0,0,n_iso)\n", " iso_a=np.linspace(0,0,n_iso)\n", " iso_name=[\" \" for x in range(n_iso)]\n", " \n", " mc = 0\n", " mc1 = mc+1 # this is important !!!\n", " file_name = mixing_case+\"_MC_\"+str(mc1)+\"_OC\"\n", " \n", " # Basically, my version of mppnp kills all the header information for some reason.... so run this to get that properly\n", " \n", " h5_file = upper+\"../mixing_results/\"+mixing_case+\"_RUNS/hif7.95E+03/my_test_hif.0010001.surf.h5\"\n", " file = h5py.File(h5_file, 'r')\n", " dseta = file[\"A\"]\n", " dsetz = file[\"Z\"]\n", " iso_a[:] = dseta[:]\n", " iso_z[:] = dsetz[:]\n", " file.close()\n", " \n", " iso_name[0] = 'n'\n", " iso_name[1] = 'H'\n", " \n", " isomers = ['ALm', 'KRm', 'CDm', 'LUm', 'TAm']\n", " start = n_iso - len(isomers)\n", " for isomer in isomers: \n", " i = isomers.index(isomer)\n", " iso_name[start+i] = isomer\n", " \n", " for i in range(2,n_iso-len(isomers)):\n", " iz = int(iso_z[i])\n", " iso_name[i] = utils.get_el_from_z(int(iso_z[i]))\n", " \n", " iso_full_name = []\n", " for i in range(n_iso):\n", " iso_full_name.append(iso_name[i]+\"-\"+str(int(iso_a[i])))\n", "\n", " # Read the mult_rtypes file and parse the data\n", " file_mult = open(mppnp_test_dir + \"reaction_factors_mult_rtypes.txt\",\"r\")\n", " \n", " data = file_mult.readlines()\n", " \n", " mult = [x.split() for x in data]\n", " # print(mult)\n", " n_mc = 1000 # int(mult[0][0]) # number of combinations of mult factors = mc_runs\n", " n_fac = int(mult[0][1]) # number of reactions with varied rates\n", " \n", " print (\"n_mc =\", n_mc, \"must be equal to mc_runs =\", n_mc,\n", " \"\\nn_fac =\", n_fac, \"must be equal to the number of varied reaction rates\")\n", " \n", " mc_fac = np.zeros((n_mc, n_fac), dtype=float)\n", " \n", " nn = 5 # maximum number of array elements per line in the data\n", " kk = n_fac // nn\n", " mm = n_fac % nn\n", " \n", " nl = n_mc \n", " # nl = 2\n", " \n", " print(nn, kk, mm, nl)\n", " \n", " k1 = 0\n", " for i in range(nl):\n", " for k in range(kk):\n", " k1 += 1\n", " for n in range(nn):\n", " m = k * nn + n\n", " mc_fac[i, m] = float(mult[k1][n].replace('D', 'E'))\n", " if mm > 0:\n", " k1 += 1\n", " for n in range(mm):\n", " m = kk * nn + n\n", " mc_fac[i, m] = float(mult[k1][n].replace('D', 'E'))\n", " \n", " print(\"k1 =\", k1, \"must be equal to\", (kk + (1 if mm > 0 else 0)) * n_mc)\n", "\n", " print(\"These should be equal\")\n", " # check that these factors are the same\n", " # the first number in the first raw of the list (the file reaction_factors_mult.txt)\n", " print (mult[1][0], mc_fac[0,0])\n", " # the last number in the last raw of the list\n", " print (mult[k1][mm-1], mc_fac[249,n_fac-1]) # the first number in the last raw of the list\n", "\n", " file_mult = open(mppnp_test_dir + \"reaction_factors_rtypes.txt\",\"r\")\n", " \n", " data = file_mult.readlines()\n", " fac = [x.split() for x in data]\n", " name = []\n", " mass = []\n", " rtypes = []\n", " varmax = []\n", " print(fac[-1])\n", " for k in range(len(fac)):\n", " if len(fac[k]) == 4:\n", " name.append(fac[k][0])\n", " mass.append(fac[k][1])\n", " rtypes.append(fac[k][2])\n", " varmax.append(float(fac[k][3]))\n", " if len(fac[k]) == 3:\n", " name.append(fac[k][0][0:2])\n", " mass.append(fac[k][0][2:])\n", " rtypes.append(fac[k][1])\n", " varmax.append(float(fac[k][2]))\n", " \n", " # print the first and last raws of the file reaction_factors.txt\n", " k = 0\n", " print (k, name[k], mass[k], rtypes[k], varmax[k])\n", " k = len(name)-1\n", " print (k, name[k], mass[k], rtypes[k], varmax[k])\n", "\n", " return (mc_runs, el_name, z_el, n_el, el_abu_0, el_abu, el_abu_sol,\n", " n_fac, n_iso, iso_name, iso_a, iso_abu, iso_abu_0,\n", " mc_fac, name, mass, rtypes, varmax)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:\n", "This initial abundance file uses an element name that does\n", "not contain the mass number in the 3rd to 5th position.\n", "It is assumed that this is the proton and we will change\n", "the name to 'h 1' to be consistent with the notation used in\n", "iniab.dat files\n", "WARNING:\n", "This initial abundance file uses an element name that does\n", "not contain the mass number in the 3rd to 5th position.\n", "It is assumed that this is the proton and we will change\n", "the name to 'h 1' to be consistent with the notation used in\n", "iniab.dat files\n", "\n", "n_init = 83\n", "WARNING:\n", "This initial abundance file uses an element name that does\n", "not contain the mass number in the 3rd to 5th position.\n", "It is assumed that this is the proton and we will change\n", "the name to 'h 1' to be consistent with the notation used in\n", "iniab.dat files\n", "\n", "n_sol = 83\n", "X_sol = 0.706457139998516 , X_sol(Tc) = 1e-99 , X_sol(Pm) = 1e-99\n", "Zero variation case: /user/niagara_scratch_fherwig/wendi.user/jissa/multizone/MLT_MC_results/MLT_MC_0_OC.0010001.surf.h5\n", "Number of Elements: 85 \n", "Number of Isotopes: 1638\n", "Reading all MC runs\n", "Reading in run 0\n", "Reading in run 100\n", "Reading in run 200\n", "Reading in run 300\n", "Reading in run 400\n", "Reading in run 500\n", "Reading in run 600\n", "Reading in run 700\n", "Reading in run 800\n", "Reading in run 900\n", "n_mc = 1000 must be equal to mc_runs = 1000 \n", "n_fac = 1986 must be equal to the number of varied reaction rates\n", "5 397 1 1000\n", "k1 = 398000 must be equal to 398000\n", "These should be equal\n", "5.76199D-01 0.576199\n", "7.04132D+00 0.170795\n", "['PO210', '(g,a)', '10.00']\n", "0 SE 68 (g,n) 10.0\n", "1985 PO 210 (g,a) 10.0\n" ] } ], "source": [ "mc_runs, el_name, z_el, n_el, el_abu_0, el_abu, el_abu_sol, n_fac, n_iso, iso_name, iso_a, iso_abu, iso_abu_0, mc_fac, name, mass, rtypes, varmax = get_everything_you_need(mixing_case)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def get_sol_abu(isotopes): \n", "\n", " def get_sol(sol_ab=\"/user/niagara_scratch_fherwig/wendi.user/jissa/mixing_results/iniab2.0E-02GN93.ppn\"):\n", " '''\n", " grab the solar abundances and turn it into a dataframe\n", " '''\n", " \n", " f = open(sol_ab, 'r')\n", " \n", " sol_iso_z=[]\n", " sol_iso=[]\n", " sol_iso_name = []\n", " sol_iso_a = []\n", " sol_iso_abu=[]\n", " \n", " for line in f:\n", " n = len(line.split())\n", " if n == 3:\n", " sol_iso = line.split()[1]\n", " if sol_iso == 'PROT':\n", " sol_iso_name.append('h')\n", " sol_iso_a.append(1)\n", " sol_iso_z.append(int(line.split()[0]))\n", " sol_iso_abu.append(float(line.split()[2]))\n", " else:\n", " sol_iso_name.append(sol_iso[0:2])\n", " sol_iso_a.append(int(sol_iso[2:5]))\n", " sol_iso_z.append(int(line.split()[0]))\n", " sol_iso_abu.append(float(line.split()[2]))\n", " if n == 4:\n", " sol_iso_z.append(int(line.split()[0]))\n", " sol_iso_name.append(line.split()[1])\n", " sol_iso_a.append(int(line.split()[2]))\n", " sol_iso_abu.append(float(line.split()[3]))\n", " \n", " f.close()\n", " \n", " df_solar = pd.DataFrame()\n", " df_solar['Z'] = sol_iso_z\n", " df_solar['Element'] = sol_iso_name\n", " df_solar['sol_iso_a'] = sol_iso_a\n", " df_solar['sol_iso_abu'] = sol_iso_abu\n", " \n", " return df_solar\n", "\n", " df_solar = get_sol()\n", " \n", " if type(isotopes) == str:\n", " \n", " ele, num = isotopes.split('-')\n", " \n", " elemask = df_solar.Element == ele.lower()\n", " \n", " nummask = df_solar.sol_iso_a == int(num)\n", " \n", " mask = elemask & nummask\n", " \n", " abu = df_solar[mask].sol_iso_abu.to_numpy()[0]\n", " \n", " return abu\n", " \n", " else:\n", " res = []\n", " for iso in isotopes:\n", " ele, num = iso.split('-')\n", " \n", " elemask = df_solar.Element == ele.lower()\n", "\n", " nummask = df_solar.sol_iso_a == int(num)\n", "\n", " mask = elemask & nummask\n", " \n", " abu = df_solar[mask].sol_iso_abu.to_numpy()[0]\n", " res.append(abu)\n", " \n", " return np.array(res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List of template isotopic abundances" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "198f1145918f45a8956a9f8ec2b35c2f", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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