From 4b21e81691ba65aaafde422cd073539a28982d0e Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Fri, 8 May 2026 11:07:00 -0400 Subject: [PATCH 01/13] add transfer example --- book/notebooks/Transfer.ipynb | 415 +++ .../B5-example-tr-checkpoint.in | 46 + .../B5-example-tr-checkpoint.template | 43 + .../.ipynb_checkpoints/frescox-checkpoint.in | 43 + .../Transfer_template/B5-example-tr.in | 46 + .../Transfer_template/B5-example-tr.template | 43 + book/notebooks/Transfer_template/benchmark.16 | 100 + .../notebooks/Transfer_template/benchmark.out | 433 +++ book/notebooks/Transfer_template/fort.13 | 7 + book/notebooks/Transfer_template/fort.16 | 1226 +++++++ book/notebooks/Transfer_template/fort.201 | 411 +++ book/notebooks/Transfer_template/fort.202 | 411 +++ book/notebooks/Transfer_template/fort.203 | 411 +++ book/notebooks/Transfer_template/fort.239 | 1 + book/notebooks/Transfer_template/fort.3 | 517 +++ book/notebooks/Transfer_template/fort.35 | 1 + book/notebooks/Transfer_template/fort.38 | 1 + book/notebooks/Transfer_template/fort.39 | 1 + book/notebooks/Transfer_template/fort.4 | 0 book/notebooks/Transfer_template/fort.40 | 1 + book/notebooks/Transfer_template/fort.46 | 2 + book/notebooks/Transfer_template/fort.48 | 3046 +++++++++++++++++ book/notebooks/Transfer_template/fort.55 | 0 book/notebooks/Transfer_template/fort.56 | 0 book/notebooks/Transfer_template/fort.59 | 0 book/notebooks/Transfer_template/fort.7 | 0 book/notebooks/Transfer_template/fort.75 | 1 + book/notebooks/Transfer_template/frescox.out | 1923 +++++++++++ 28 files changed, 9129 insertions(+) create mode 100644 book/notebooks/Transfer.ipynb create mode 100644 book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.in create mode 100644 book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.template create mode 100644 book/notebooks/Transfer_template/.ipynb_checkpoints/frescox-checkpoint.in create mode 100644 book/notebooks/Transfer_template/B5-example-tr.in create mode 100644 book/notebooks/Transfer_template/B5-example-tr.template create mode 100644 book/notebooks/Transfer_template/benchmark.16 create mode 100644 book/notebooks/Transfer_template/benchmark.out create mode 100644 book/notebooks/Transfer_template/fort.13 create mode 100644 book/notebooks/Transfer_template/fort.16 create mode 100644 book/notebooks/Transfer_template/fort.201 create mode 100644 book/notebooks/Transfer_template/fort.202 create mode 100644 book/notebooks/Transfer_template/fort.203 create mode 100644 book/notebooks/Transfer_template/fort.239 create mode 100644 book/notebooks/Transfer_template/fort.3 create mode 100644 book/notebooks/Transfer_template/fort.35 create mode 100644 book/notebooks/Transfer_template/fort.38 create mode 100644 book/notebooks/Transfer_template/fort.39 create mode 100644 book/notebooks/Transfer_template/fort.4 create mode 100644 book/notebooks/Transfer_template/fort.40 create mode 100644 book/notebooks/Transfer_template/fort.46 create mode 100644 book/notebooks/Transfer_template/fort.48 create mode 100644 book/notebooks/Transfer_template/fort.55 create mode 100644 book/notebooks/Transfer_template/fort.56 create mode 100644 book/notebooks/Transfer_template/fort.59 create mode 100644 book/notebooks/Transfer_template/fort.7 create mode 100644 book/notebooks/Transfer_template/fort.75 create mode 100644 book/notebooks/Transfer_template/frescox.out diff --git a/book/notebooks/Transfer.ipynb b/book/notebooks/Transfer.ipynb new file mode 100644 index 0000000..4f40a95 --- /dev/null +++ b/book/notebooks/Transfer.ipynb @@ -0,0 +1,415 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1a4a60d4-5bf5-4251-a6bf-f165d2df198c", + "metadata": {}, + "source": [ + "## $^{14}$N( $^{17}$F, $^{18}$Ne)$^{13}$C transfer reaction\n", + "\n", + "We will use the [transfer input file from the fresco website](https://www.fresco.org.uk/examples/B5-example-tr.in) as a template.\n", + "\n", + "In this reaction, a proton from $^{14}$N is transfered to $^{17}$F, to give $^{18}$Ne, leaving a residual $^{13}$C nucleus. From the [Fresco website](https://www.fresco.org.uk/guide/fresco-starting.pdf):\n", + "\n", + "> Transfer reactions are often used to extract structure information to input in astrophysical simulations. Here we\n", + "consider the 14N(17F,18Ne)13C transfer reaction at 10 MeV per nucleon. This reaction was measured with the\n", + "aim of extracting the asymptotic normalization coefficient of specific states in 18Ne which in turn provides a\n", + "significant part of the rate for 17F(p,γ). The proton capture reaction on 17F appears in the rp-process in novae\n", + "environments. The ratio of the proton capture rate and the decay rate of 17F is also very important for the\n", + "understanding of galactic 17O, 18O and 15N. \n", + "\n", + "\n", + "The interactions involved are:\n", + "- the entrance channel interaction between $^{14}$N and $^{17}$F,\n", + "- the exit channel interaction between $^{18}$Ne and $^{13}$C,\n", + "- the binding potential for the single $p$ in $^{17}$F,\n", + "- the binding potential for the single $p$ in $^{13}$C,\n", + "- the core-core potential between $^{17}$F and $^{13}$C,\n", + "\n", + "We will compare to experimental data from [EXFOR Entry C1137](https://www-nds.iaea.org/exfor/servlet/X4sGetSubent?reqx=2507&subID=121137003&plus=1), which was measured at Oak Ridge National Laboratory and first [reported in the literature in 2004](https://www.sciencedirect.com/science/article/pii/S0375947404010188?via%3Dihub)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f8419bb9-8a3b-4b3b-a9bb-e54ecb27ad76", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from pathlib import Path\n", + "import inspect\n", + "import pandas as pd\n", + "import json\n", + "\n", + "with open(\"MatplotlibEsthetics.json\", \"r\") as fptr:\n", + " esthetics = json.load(fptr)\n", + "\n", + "plt.style.use(esthetics[\"style\"])\n", + "FONTSIZE = esthetics[\"fontsize\"]\n", + "TICK_FONTSIZE = esthetics[\"tick_fontsize\"]\n", + "MARKERSIZE = esthetics[\"markersize\"]\n", + "LINEWIDTH = esthetics[\"linewidth\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "96ccd90d-74be-40d8-bac7-6f6ee86aa696", + "metadata": {}, + "outputs": [], + "source": [ + "import bfrescox" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fcbeb06a-3f7c-4d4f-b68d-4bf88c60c183", + "metadata": {}, + "outputs": [], + "source": [ + "EXAMPLE_DIR = Path(\"./Transfer_template/\")\n", + "TEMPLATE_FILE_PATH = Path(\"./Transfer_template/B5-example-tr.template\") \n", + "INPUT_FILE_PATH = Path(\"./Transfer_template/B5-example-tr.in\") " + ] + }, + { + "cell_type": "markdown", + "id": "fa04225c-606c-4b80-9081-6eb0789754be", + "metadata": {}, + "source": [ + "## Let's run the input file exactly as is from the website" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "63150057-47dd-4097-ad62-1fe8ebeb6600", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Frescox input file from https://www.fresco.org.uk/examples/B5-example-tr.out:\n", + "-----------------------------------\n", + "n14(f17,ne18)c13 @ 170 MeV; \n", + "NAMELIST\n", + " &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 \n", + "\t jtmin=0.0 jtmax=120 absend=-1.0\n", + "\t thmin=0.00 thmax=40.00 thinc=0.10\n", + "\t iter=1 nnu=36\n", + "\t chans=1 xstabl=1 \n", + "\t elab=170.0 /\n", + "\n", + " &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 /\n", + " &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 /\n", + "\n", + " &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 /\n", + " &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 /\n", + " &STATES jp=4. bandp=1 ep=3.376 cpot=2 copyt /\n", + "\n", + "\n", + " &partition /\n", + "\n", + " &POT kp=1 ap=17.000 at=14.000 rc=1.3 /\n", + " &POT kp=1 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 /\n", + "\n", + " &POT kp=2 ap=18.000 at=13.000 rc=1.3 /\n", + " &POT kp=2 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 /\n", + "\n", + " &POT kp=3 at=17 rc=1.2 /\n", + " &POT kp=3 type=1 p1=50.00 p2=1.2 p3=0.65 /\n", + " &POT kp=3 type=3 p1=6.00 p2=1.2 p3=0.65 /\n", + "\n", + " &POT kp=4 at=13 rc=1.2 /\n", + " &POT kp=4 type=1 p1=50.00 p2=1.2 p3=0.65 /\n", + " &POT kp=4 type=3 p1=6.00 p2=1.2 p3=0.65 /\n", + "\n", + " &POT kp=5 ap=17.000 at=14.000 rc=1.3 /\n", + " &POT kp=5 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 /\n", + " &pot /\n", + "\n", + " &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=3.922 isc=1 ipc=0 /\n", + " &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 /\n", + " &overlap /\n", + "\n", + " &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 /\n", + " &CFP in=1 ib=1 ia=1 kn=1 a=1.00 /\n", + " &CFP in=2 ib=1 ia=1 kn=2 a=1.00 /\n", + " &CFP /\n", + " &coupling /\n" + ] + } + ], + "source": [ + "with open(INPUT_FILE_PATH, \"r\") as temp:\n", + " input_file = temp.read()\n", + "\n", + "print(\"Frescox input file from https://www.fresco.org.uk/examples/B5-example-tr.out:\")\n", + "print(\"-----------------------------------\")\n", + "print(input_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5b269a97-36fc-42a7-8290-38032694e32a", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the frescox input file by filling in the user-defined template with parameters\n", + "cfg = bfrescox.Configuration.from_template(\n", + " INPUT_FILE_PATH,\n", + " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", + " {},\n", + " overwrite=True,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "88cece8b-d36b-45fc-943a-f8f2b4f1ba98", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 4.07 ms, sys: 901 μs, total: 4.97 ms\n", + "Wall time: 4.02 ms\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/home/beyerk/Projects/Bfrescox/book/notebooks/Transfer_template/frescox.in'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mbfrescox.run_simulation(cfg, EXAMPLE_DIR.joinpath(\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfrescox.out\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m), cwd=EXAMPLE_DIR, overwrite=True)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/IPython/core/interactiveshell.py:2565\u001b[39m, in \u001b[36mInteractiveShell.run_cell_magic\u001b[39m\u001b[34m(self, magic_name, line, cell)\u001b[39m\n\u001b[32m 2563\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.builtin_trap:\n\u001b[32m 2564\u001b[39m args = (magic_arg_s, cell)\n\u001b[32m-> \u001b[39m\u001b[32m2565\u001b[39m result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2567\u001b[39m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[32m 2568\u001b[39m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[32m 2569\u001b[39m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[32m 2570\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/IPython/core/magics/execution.py:1452\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m 1450\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupt_occured:\n\u001b[32m 1451\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exit_on_interrupt \u001b[38;5;129;01mand\u001b[39;00m captured_exception:\n\u001b[32m-> \u001b[39m\u001b[32m1452\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m captured_exception\n\u001b[32m 1453\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m 1454\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/IPython/core/magics/execution.py:1402\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m 1400\u001b[39m st = clock2()\n\u001b[32m 1401\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1402\u001b[39m out = \u001b[38;5;28;43meval\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1403\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 1404\u001b[39m captured_exception = e\n", + "\u001b[36mFile \u001b[39m\u001b[32m:1\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/bfrescox/run_simulation.py:80\u001b[39m, in \u001b[36mrun_simulation\u001b[39m\u001b[34m(configuration, filename, overwrite, external, cwd)\u001b[39m\n\u001b[32m 75\u001b[39m cwd = Path.cwd()\n\u001b[32m 77\u001b[39m \u001b[38;5;66;03m# This function assumes that all error checking of arguments will be handled\u001b[39;00m\n\u001b[32m 78\u001b[39m \u001b[38;5;66;03m# by this internal function. This includes the case of incorrectly\u001b[39;00m\n\u001b[32m 79\u001b[39m \u001b[38;5;66;03m# providing an MPI-based external installation.\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m80\u001b[39m \u001b[43m_run_frescox_simulation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 81\u001b[39m \u001b[43m \u001b[49m\u001b[43mfrescox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 82\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfiguration\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 83\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 84\u001b[39m \u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 85\u001b[39m \u001b[43m \u001b[49m\u001b[43mNO_MPI_PLEASE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 86\u001b[39m \u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 87\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/bfrescox/_run_frescox_simulation.py:121\u001b[39m, in \u001b[36m_run_frescox_simulation\u001b[39m\u001b[34m(frescox, config, filename, overwrite, mpi_setup, cwd)\u001b[39m\n\u001b[32m 119\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid working directory (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcwd\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 120\u001b[39m fname_in = Path(cwd).resolve().joinpath(\u001b[33m\"\u001b[39m\u001b[33mfrescox.in\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m121\u001b[39m \u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrite_to_nml\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname_in\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 123\u001b[39m \u001b[38;5;66;03m# ----- CHECK STATE OF FILES & WRITE INPUT\u001b[39;00m\n\u001b[32m 124\u001b[39m fname_out = Path(filename).resolve()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/bfrescox/Configuration.py:153\u001b[39m, in \u001b[36mConfiguration.write_to_nml\u001b[39m\u001b[34m(self, filename, overwrite)\u001b[39m\n\u001b[32m 149\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInput file (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfname_in\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m) already exists\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 151\u001b[39m \u001b[38;5;66;03m# ----- WRITE CONFIGURATION TO FILE\u001b[39;00m\n\u001b[32m 152\u001b[39m \u001b[38;5;66;03m# Trivial for NML\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m153\u001b[39m \u001b[43mshutil\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__nml\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname_in\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/bfrescox/lib/python3.14/shutil.py:489\u001b[39m, in \u001b[36mcopy\u001b[39m\u001b[34m(src, dst, follow_symlinks)\u001b[39m\n\u001b[32m 487\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m os.path.isdir(dst):\n\u001b[32m 488\u001b[39m dst = os.path.join(dst, os.path.basename(src))\n\u001b[32m--> \u001b[39m\u001b[32m489\u001b[39m \u001b[43mcopyfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_symlinks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_symlinks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 490\u001b[39m copymode(src, dst, follow_symlinks=follow_symlinks)\n\u001b[32m 491\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m dst\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/bfrescox/lib/python3.14/shutil.py:313\u001b[39m, in \u001b[36mcopyfile\u001b[39m\u001b[34m(src, dst, follow_symlinks)\u001b[39m\n\u001b[32m 311\u001b[39m os.symlink(os.readlink(src), dst)\n\u001b[32m 312\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m313\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrb\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m fsrc:\n\u001b[32m 314\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 315\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(dst, \u001b[33m'\u001b[39m\u001b[33mwb\u001b[39m\u001b[33m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m fdst:\n\u001b[32m 316\u001b[39m \u001b[38;5;66;03m# macOS\u001b[39;00m\n", + "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/home/beyerk/Projects/Bfrescox/book/notebooks/Transfer_template/frescox.in'" + ] + } + ], + "source": [ + "%%time\n", + "bfrescox.run_simulation(cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7204a4d5-2022-4867-b0b9-9801221e54d1", + "metadata": {}, + "outputs": [], + "source": [ + "results = bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", + "results.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83e95f1f-6872-419d-9cf0-d1b23ab58656", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(\n", + " results[\"channel_3\"][\"Theta_deg\"], \n", + " results[\"channel_3\"][\"sigma_mb_sr\"],\n", + " label=\"Frescox\"\n", + ")\n", + "\n", + "plt.xlim([0,20])\n", + "plt.xlabel(r\"$\\theta$ [deg]\")\n", + "plt.ylabel(r\"$d\\sigma/d\\Omega$ [mb/Sr]\")" + ] + }, + { + "cell_type": "markdown", + "id": "1876ee48-cb79-4871-9b01-345584117c3e", + "metadata": {}, + "source": [ + "## Now let's look at the template and see how to change the parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "453ffa1b-22b3-4273-8b6a-cc42dfa77903", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "with open(TEMPLATE_FILE_PATH, \"r\") as temp:\n", + " template_file = temp.read()\n", + "\n", + "print(\"Frescox Transfer Template:\")\n", + "print(\"-----------------------------------\")\n", + "print(template_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "552b4770-e4ba-4ff3-9ce8-2dacb87a7a0f", + "metadata": {}, + "outputs": [], + "source": [ + "base_params = {\n", + " \"rC_entrance\" : 1.3,\n", + " \"V_entrance\": 37.2,\n", + " \"r_entrance\": 1.2,\n", + " \"a_entrance\" : 0.6,\n", + " \"W_entrance\": 21.6,\n", + " \"rw_entrance\": 1.2,\n", + " \"aw_entrance\" : 0.69,\n", + " \"rC_exit\" : 1.3,\n", + " \"V_exit\": 37.2,\n", + " \"r_exit\": 1.2,\n", + " \"a_exit\" : 0.6,\n", + " \"W_exit\": 21.6,\n", + " \"rw_exit\": 1.2,\n", + " \"aw_exit\" : 0.69,\n", + " \"rC_p17F\": 1.2,\n", + " \"V_p17F\": 50, \n", + " \"r_p17F\": 1.2,\n", + " \"a_p17F\": 0.65,\n", + " \"Vso_p17F\": 6, \n", + " \"rso_p17F\": 1.2,\n", + " \"aso_p17F\": 0.65,\n", + " \"rC_p13C\": 1.2,\n", + " \"V_p13C\": 50, \n", + " \"r_p13C\": 1.2,\n", + " \"a_p13C\": 0.65,\n", + " \"Vso_p13C\": 6, \n", + " \"rso_p13C\": 1.2,\n", + " \"aso_p13C\": 0.65,\n", + " \"rC_17F_13C\": 1.3,\n", + " \"V_17F_13C\": 37.2,\n", + " \"r_17F_13C\": 1.2, \n", + " \"a_17F_13C\": 0.6, \n", + " \"W_17F_13C\": 21.6, \n", + " \"rw_17F_13C\": 1.2, \n", + " \"aw_17F_13C\": 0.69, \n", + " \"BE_p_17F\" : 3.922,\n", + " \"BE_p_13C\": 7.5506,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca7c257f-e078-48f6-bd94-05dd803219ce", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the frescox input file by filling in the user-defined template with parameters\n", + "cfg = bfrescox.Configuration.from_template(\n", + " TEMPLATE_FILE_PATH,\n", + " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", + " base_params,\n", + " overwrite=True,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3eea39f-07e2-45cb-92be-7281c5fd385b", + "metadata": {}, + "outputs": [], + "source": [ + "bfrescox.run_simulation(cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39b2d9b2-52a9-4413-a997-2f086d8396fe", + "metadata": {}, + "outputs": [], + "source": [ + "results = bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", + "display(results[\"channel_1\"].head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "504e3d7d-5ccb-4a18-a3b5-adc78d6b54c4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b94096cb-bb10-4282-954d-a53fe0dc4fdf", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(\n", + " results[\"channel_2\"][\"Theta_deg\"], \n", + " results[\"channel_2\"][\"sigma_mb_sr\"],\n", + " label=\"Fresco\"\n", + ")\n", + "plt.legend()\n", + "plt.xlim([0,20])\n", + "plt.xlabel(r\"$\\theta$ [deg]\")\n", + "plt.ylabel(r\"$d\\sigma/d\\Omega$ [mb/Sr]\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.14.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.in b/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.in new file mode 100644 index 0000000..1cdccdf --- /dev/null +++ b/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.in @@ -0,0 +1,46 @@ +n14(f17,ne18)c13 @ 170 MeV; +NAMELIST + &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 + jtmin=0.0 jtmax=120 absend=-1.0 + thmin=0.00 thmax=40.00 thinc=0.10 + iter=1 nnu=36 + chans=1 xstabl=1 + elab=170.0 / + + &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / + &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / + + &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 / + &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / + &STATES jp=4. bandp=1 ep=3.376 cpot=2 copyt / + + + &partition / + + &POT kp=1 ap=17.000 at=14.000 rc=1.3 / + &POT kp=1 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / + + &POT kp=2 ap=18.000 at=13.000 rc=1.3 / + &POT kp=2 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / + + &POT kp=3 at=17 rc=1.2 / + &POT kp=3 type=1 p1=50.00 p2=1.2 p3=0.65 / + &POT kp=3 type=3 p1=6.00 p2=1.2 p3=0.65 / + + &POT kp=4 at=13 rc=1.2 / + &POT kp=4 type=1 p1=50.00 p2=1.2 p3=0.65 / + &POT kp=4 type=3 p1=6.00 p2=1.2 p3=0.65 / + + &POT kp=5 ap=17.000 at=14.000 rc=1.3 / + &POT kp=5 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / + &pot / + + &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=3.922 isc=1 ipc=0 / + &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / + &overlap / + + &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / + &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / + &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / + &CFP / + &coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.template b/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.template new file mode 100644 index 0000000..e9cef3c --- /dev/null +++ b/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.template @@ -0,0 +1,43 @@ +n14(f17,ne18)c13 @ 170 MeV; +NAMELIST + &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 + jtmin=0.0 jtmax=120 absend=-1.0 + thmin=0.00 thmax=40.00 thinc=0.10 + iter=1 nnu=36 + chans=1 xstabl=1 + elab=170.0 / + + &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / + &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / + + &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / + &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / + &partition / + + &POT kp=1 ap=17.000 at=14.000 rc=@rC_entrance@ / + &POT kp=1 type=1 p1=@V_entrance@ p2=@r_entrance@ p3=@a_entrance@ p4=@W_entrance@ p5=@rw_entrance@ p6=@aw_entrance@ / + + &POT kp=2 ap=18.000 at=13.000 rc=@rC_exit@ / + &POT kp=2 type=1 p1=@V_exit@ p2=@r_exit@ p3=@a_exit@ p4=@W_exit@ p5=@rw_exit@ p6=@aw_exit@ / + + &POT kp=3 at=17 rc=@rC_p17F@ / + &POT kp=3 type=1 p1=@V_p17F@ p2=@r_p17F@ p3=@a_p17F@ / + &POT kp=3 type=3 p1=@Vso_p17F@ p2=@rso_p17F@ p3=@aso_p17F@ / + + &POT kp=4 at=13 rc=@rC_p13C@ / + &POT kp=4 type=1 p1=@V_p13C@ p2=@r_p13C@ p3=@a_p13C@ / + &POT kp=4 type=3 p1=@Vso_p13C@ p2=@rso_p13C@ p3=@aso_p13C@ / + + &POT kp=5 ap=17.000 at=14.000 rc=@rC_17F_13C@ / + &POT kp=5 type=1 p1=@V_17F_13C@ p2=@r_17F_13C@ p3=@a_17F_13C@ p4=@W_17F_13C@ p5=@rw_17F_13C@ p6=@aw_17F_13C@ / + &pot / + + &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=@BE_p_17F@ isc=1 ipc=0 / + &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=@BE_p_13C@ isc=1 ipc=0 / + &overlap / + + &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / + &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / + &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / + &CFP / + &coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/.ipynb_checkpoints/frescox-checkpoint.in b/book/notebooks/Transfer_template/.ipynb_checkpoints/frescox-checkpoint.in new file mode 100644 index 0000000..cbcdf9a --- /dev/null +++ b/book/notebooks/Transfer_template/.ipynb_checkpoints/frescox-checkpoint.in @@ -0,0 +1,43 @@ +n14(f17,ne18)c13 @ 170 MeV; +NAMELIST + &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 + jtmin=0.0 jtmax=120 absend=-1.0 + thmin=0.00 thmax=40.00 thinc=0.10 + iter=1 nnu=36 + chans=1 xstabl=1 + elab=170.0 / + + &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / + &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / + + &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / + &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / + &partition / + + &POT kp=1 ap=17.000 at=14.000 rc=1.300000000 / + &POT kp=1 type=1 p1=37.200000000 p2=1.200000000 p3=0.690000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / + + &POT kp=2 ap=18.000 at=13.000 rc=1.300000000 / + &POT kp=2 type=1 p1=37.200000000 p2=1.200000000 p3=0.690000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / + + &POT kp=3 at=17 rc=1.200000000 / + &POT kp=3 type=1 p1=50.000000000 p2=1.200000000 p3=0.650000000 / + &POT kp=3 type=3 p1=6.000000000 p2=1.200000000 p3=0.650000000 / + + &POT kp=4 at=13 rc=1.200000000 / + &POT kp=4 type=1 p1=50.000000000 p2=1.200000000 p3=0.650000000 / + &POT kp=4 type=3 p1=6.000000000 p2=1.200000000 p3=0.650000000 / + + &POT kp=5 ap=17.000 at=14.000 rc=1.300000000 / + &POT kp=5 type=1 p1=37.200000000 p2=1.200000000 p3=0.600000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / + &pot / + + &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=7.550600000 isc=1 ipc=0 / + &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=3.922000000 isc=1 ipc=0 / + &overlap / + + &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / + &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / + &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / + &CFP / + &coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/B5-example-tr.in b/book/notebooks/Transfer_template/B5-example-tr.in new file mode 100644 index 0000000..1cdccdf --- /dev/null +++ b/book/notebooks/Transfer_template/B5-example-tr.in @@ -0,0 +1,46 @@ +n14(f17,ne18)c13 @ 170 MeV; +NAMELIST + &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 + jtmin=0.0 jtmax=120 absend=-1.0 + thmin=0.00 thmax=40.00 thinc=0.10 + iter=1 nnu=36 + chans=1 xstabl=1 + elab=170.0 / + + &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / + &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / + + &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 / + &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / + &STATES jp=4. bandp=1 ep=3.376 cpot=2 copyt / + + + &partition / + + &POT kp=1 ap=17.000 at=14.000 rc=1.3 / + &POT kp=1 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / + + &POT kp=2 ap=18.000 at=13.000 rc=1.3 / + &POT kp=2 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / + + &POT kp=3 at=17 rc=1.2 / + &POT kp=3 type=1 p1=50.00 p2=1.2 p3=0.65 / + &POT kp=3 type=3 p1=6.00 p2=1.2 p3=0.65 / + + &POT kp=4 at=13 rc=1.2 / + &POT kp=4 type=1 p1=50.00 p2=1.2 p3=0.65 / + &POT kp=4 type=3 p1=6.00 p2=1.2 p3=0.65 / + + &POT kp=5 ap=17.000 at=14.000 rc=1.3 / + &POT kp=5 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / + &pot / + + &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=3.922 isc=1 ipc=0 / + &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / + &overlap / + + &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / + &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / + &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / + &CFP / + &coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/B5-example-tr.template b/book/notebooks/Transfer_template/B5-example-tr.template new file mode 100644 index 0000000..e9cef3c --- /dev/null +++ b/book/notebooks/Transfer_template/B5-example-tr.template @@ -0,0 +1,43 @@ +n14(f17,ne18)c13 @ 170 MeV; +NAMELIST + &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 + jtmin=0.0 jtmax=120 absend=-1.0 + thmin=0.00 thmax=40.00 thinc=0.10 + iter=1 nnu=36 + chans=1 xstabl=1 + elab=170.0 / + + &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / + &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / + + &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / + &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / + &partition / + + &POT kp=1 ap=17.000 at=14.000 rc=@rC_entrance@ / + &POT kp=1 type=1 p1=@V_entrance@ p2=@r_entrance@ p3=@a_entrance@ p4=@W_entrance@ p5=@rw_entrance@ p6=@aw_entrance@ / + + &POT kp=2 ap=18.000 at=13.000 rc=@rC_exit@ / + &POT kp=2 type=1 p1=@V_exit@ p2=@r_exit@ p3=@a_exit@ p4=@W_exit@ p5=@rw_exit@ p6=@aw_exit@ / + + &POT kp=3 at=17 rc=@rC_p17F@ / + &POT kp=3 type=1 p1=@V_p17F@ p2=@r_p17F@ p3=@a_p17F@ / + &POT kp=3 type=3 p1=@Vso_p17F@ p2=@rso_p17F@ p3=@aso_p17F@ / + + &POT kp=4 at=13 rc=@rC_p13C@ / + &POT kp=4 type=1 p1=@V_p13C@ p2=@r_p13C@ p3=@a_p13C@ / + &POT kp=4 type=3 p1=@Vso_p13C@ p2=@rso_p13C@ p3=@aso_p13C@ / + + &POT kp=5 ap=17.000 at=14.000 rc=@rC_17F_13C@ / + &POT kp=5 type=1 p1=@V_17F_13C@ p2=@r_17F_13C@ p3=@a_17F_13C@ p4=@W_17F_13C@ p5=@rw_17F_13C@ p6=@aw_17F_13C@ / + &pot / + + &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=@BE_p_17F@ isc=1 ipc=0 / + &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=@BE_p_13C@ isc=1 ipc=0 / + &overlap / + + &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / + &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / + &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / + &CFP / + &coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/benchmark.16 b/book/notebooks/Transfer_template/benchmark.16 new file mode 100644 index 0000000..d9c52ca --- /dev/null +++ b/book/notebooks/Transfer_template/benchmark.16 @@ -0,0 +1,100 @@ +# 41 angles, 1 tensor ranks for 0=projectile +@subtitle "n14(f17,ne18)c13 @ 170 MeV;" +@legend ON +@legend x1 0.2 +@legend y1 0.8 +@g0 type LOGY +@xaxis label "Scattering angle (degrees)" +@yaxis label "Cross section (mb/sr)" +@ s0 legend "Partition= 1 Excit= 1 near/far= 1" +# s0 legend "Lab energy = 170.0000" +@s0 linestyle 1 +# Theta sigma iT11 T20 T21 T22 Kyy for projectile + 0.1000E-01 1.000 + 1.000 1.021 + 2.000 0.9897 + 3.000 1.072 + 4.000 1.049 + 5.000 1.029 + 6.000 0.7693 + 7.000 0.5406 + 8.000 0.5171 + 9.000 0.4873 + 10.00 0.3257 + 11.00 0.1793 + 12.00 0.1751 + 13.00 0.2277 + 14.00 0.1983 + 15.00 0.9729E-01 + 16.00 0.4037E-01 + 17.00 0.7063E-01 + 18.00 0.1131 + 19.00 0.9389E-01 + 20.00 0.3463E-01 + 21.00 0.5847E-02 + 22.00 0.2852E-01 + 23.00 0.5783E-01 + 24.00 0.5123E-01 + 25.00 0.1895E-01 + 26.00 0.1716E-03 + 27.00 0.1043E-01 + 28.00 0.2882E-01 + 29.00 0.3034E-01 + 30.00 0.1515E-01 + 31.00 0.2305E-02 + 32.00 0.3898E-02 + 33.00 0.1345E-01 + 34.00 0.1760E-01 + 35.00 0.1228E-01 + 36.00 0.4631E-02 + 37.00 0.2501E-02 + 38.00 0.5968E-02 + 39.00 0.9339E-02 + 40.00 0.8594E-02 + & +@ s1 legend "Partition= 2 Excit= 1 near/far= 1" +# s0 legend "Lab energy = 170.0000" +@s1 linestyle 2 +# Theta sigma iT11 T20 T21 T22 Kyy for projectile + 0.000 0.2592 + 1.000 0.2840 + 2.000 0.5831 + 3.000 1.607 + 4.000 3.345 + 5.000 4.771 + 6.000 4.605 + 7.000 2.914 + 8.000 1.191 + 9.000 0.5963 + 10.00 0.8486 + 11.00 1.011 + 12.00 0.7148 + 13.00 0.3005 + 14.00 0.1378 + 15.00 0.2001 + 16.00 0.2515 + 17.00 0.1846 + 18.00 0.7945E-01 + 19.00 0.3612E-01 + 20.00 0.5534E-01 + 21.00 0.7524E-01 + 22.00 0.6123E-01 + 23.00 0.3111E-01 + 24.00 0.1512E-01 + 25.00 0.1872E-01 + 26.00 0.2598E-01 + 27.00 0.2412E-01 + 28.00 0.1526E-01 + 29.00 0.8432E-02 + 30.00 0.7638E-02 + 31.00 0.9616E-02 + 32.00 0.9844E-02 + 33.00 0.7477E-02 + 34.00 0.4758E-02 + 35.00 0.3575E-02 + 36.00 0.3737E-02 + 37.00 0.3928E-02 + 38.00 0.3403E-02 + 39.00 0.2468E-02 + 40.00 0.1770E-02 + & diff --git a/book/notebooks/Transfer_template/benchmark.out b/book/notebooks/Transfer_template/benchmark.out new file mode 100644 index 0000000..56824ed --- /dev/null +++ b/book/notebooks/Transfer_template/benchmark.out @@ -0,0 +1,433 @@ +Running on beyerk-linux + FRESCOX - version 7.2-20-ga7f491: Coupled Reaction Channels on gfortran + + Using NAMELIST input + + n14(f17,ne18)c13 @ 170 MeV; + + 0.030 40.000 0.200 0.100 5.000 0.000 0.000 0.000 0.000 0.000 + + Centre-of-mass Range is 1334 * 0.0300 fm., Maximum at 39.96 fm., Interpolating NL forms every 0.21 fm. + Non - locality width is 57 * 0.0900 fm., Maximum of 5.04 fm., Centred at 0.00 fm. + 2-Nucleon Separation of 0 * 0.2100 fm., Maximum of 0.00 fm., Minimum at 0.00 fm. + Maximum single particle bins of 39.9000 fm. + M,Mint = 1333 1331 + + + Range of total J is 0.0 <= J <= 120.0 (at least 0.0) and Absorbtion => -1.0000 mb. + Dry Run = F, CC set limits = 0 0, Relativistic kinematics = , Both/Far/Near Analyses = 1 + + Cross Sections (and up to T0 for 0=projectile) for Theta from 0.0 to 40.0 in steps of 1.0 degrees, DGAM=0, grace=T, Coordinates = 0 (Mads) + + Lower Radial Cutoff = maximum of -1.60*L*h & 0.0 fm., Lower Cutoff for Couplings = 0.0 fm. + + + Iterate Couplings between 0 and 1 times, to 0.000 % if sooner. + Block solved exactly = 0 chs., with Pade = 0 & Isocen = =0, NOSOL = F, CCREAL = F, initwf = 0 + Small channels are 0.00E+00 and small couplings are 1.00E-12 of unitarity + + NL quadrature with 36 Gaussian points, Calculate multipoles up to 130 from 0 + M-transfers for lp+lt greater than or equal to 6, Angular Integration Cutoff below 0.6944 % + + + Trace switches are : CHANS = 1, LISTCC = 0, TRENEG = 0, CDETR = 0, SMATS = 0, XSTABL = 1, NLPL = 0 + + WAVES = 0, LAMPL = 0, VEFF = 0, KFUS = 0, WDISK = 0, BPM = 0, MELFIL = 0 + + CDCC = 0, NFUS = 0, TCFILE = 0 + + Using unit mass = 1.000000 amu and 1/fine-structure constant = 137.03599 ( 1.000000 * true ) with hc = 197.32705 MeV.fm, + thus 2*amu/hbar^2 = 0.0478450 = 1/20.9008 and Coulomb constant = 0.1574855 so e^2 = 1.43996515, and nuclear magneton= 0.1261183 + + Now pre-scan input and save to file 3 + + + + *********** PARTITION NUMBER 1 ****************************************************************************************** + + PROJ=f17 MASS= 17.0000 Z= 9.0, # STATES= 1T, TARG=n14 MASS= 14.0000 Z= 7.0, Q-VALUE = 0.0000 MeV + + MIXPOT = 0: no couplings (default) + + 1: J= 2.5+ (B# 1), E= 0.0000, K= 0.5 Potl# 1 J= 1.0+ (B# 1), E= 0.0000, K= 0.0 + + + *********** PARTITION NUMBER 2 ****************************************************************************************** + + PROJ=ne18 MASS= 18.0000 Z= 10.0, # STATES= 1T, TARG=c13 MASS= 13.0000 Z= 6.0, Q-VALUE = 3.6286 MeV + + MIXPOT = 0: no couplings (default) + + 1: J= 0.0+ (B# 1), E= 0.0000, K= 0.0 Potl# 2 J= 0.5- (B#-1), E= 0.0000, K= 0.5 +0****** WARNING : COMPARED WITH PARTITION 1, THIS PARTITION HAS TOTAL MASS & CHARGE EXCESSES 0.00390 & 0.0000 + + + ************************************************************************************************************************************ + + The following POTENTIALS are defined : + + KP# TYPE IT SHAPE at V1 r1 a1 V2 r2 a2 A-in A-used + + + A#1 A#2 r0c ac h @ 1 + 1 0=Coulomb 0=CHARGE (WS) 1 14.000 17.000 1.3000 0.0000 0.0000 0.0000 0.000 123.612 0.03000 + + 1 1=Volume 0=Woods-Saxon 2 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 123.612 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 2 + 2 0=Coulomb 0=CHARGE (WS) 3 13.000 18.000 1.3000 0.0000 0.0000 0.0000 0.000 122.917 0.03000 + + 2 1=Volume 0=Woods-Saxon 4 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 122.917 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 0 + 3 0=Coulomb 0=CHARGE (WS) 5 17.000 0.000 1.2000 0.0000 0.0000 0.0000 0.000 17.000 0.03000 + + 3 1=Volume 0=Woods-Saxon 6 50.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 17.000 + + 3 3=Projtl S.O. 0=Woods-Saxon 7 6.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 17.000 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 0 + 4 0=Coulomb 0=CHARGE (WS) 8 13.000 0.000 1.2000 0.0000 0.0000 0.0000 0.000 13.000 0.03000 + + 4 1=Volume 0=Woods-Saxon 9 50.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 13.000 + + 4 3=Projtl S.O. 0=Woods-Saxon 10 6.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 13.000 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 0 + 5 0=Coulomb 0=CHARGE (WS) 11 13.000 17.000 1.3000 0.0000 0.0000 0.0000 0.000 119.286 0.03000 + + 5 1=Volume 0=Woods-Saxon 12 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 119.286 + + + ************************************************************************************************************************************ + + The following real SINGLE-PARTICLE FORM FACTORS are constructed: + + + No. P1 P2 IN KIND T N L S1 IA J/S IB BIND XFER BE SC PC FIL AFRAC Adjust to Z Mass K Norm rms D0 D ANC/Gsp + + 1: 1 2 1 0 1 2 0.5 1 2.5 0 3 0 3.9220 1 0 0 0.0000 VR 1.2622; 1. 1.000 0.4210 1.0000 3.321 -27.2 102.5 2.5472 + + NO ROOM FOR 1 MORE CHANNELS: INCREASE KN2 + + 2: 2 1 2 3 1 1 0.5 1 0.5 1 4 0 7.5506 1 0 0 0.0000 VR 1.1252; 1. 1.000 0.5792 1.0000 2.843 -129.3 80.1 4.8540 + + ************************************************************************************************************************************ + + ONE-way COUPLING # 1 for partitions -2 <- 1 of KIND 7, 0 -1 5 -1 -1 & P1,P2 = 0.0000 0.0000 : for J <= 120.5 & R < 39.9 fm. + + + data = 1 1 1 1 1.0000, so < [ne18 # 1] / [f17 # 1] * Eigen-form # 1> = 1.0000 BE = 3.922 1 + + data = 2 1 1 2 1.0000, so < [n14 # 1] / [c13 # 1] * Eigen-form # 2> = 1.0000 BE = 7.551 2 + KNT = 2, KNP = 1: NK,NG,loc= 1 1 1 13 T T F + KNT = 2, KNP = 1: NK,NG,loc= 1 1 1 13 + + So FINITE-RANGE TRANSFER : IP1 = 0 (POST ), IP2=-1 =COMPLEX , for 1 pairs of bound states summed into 1 pairs + The main coupling potential is that binding the transferred particle to the c13 core. + + Using between f17 & c13 the potential No. 5, and subtracting optical potential No. 2 + + A real*8 is 8 bytes + NLL,NLN,NLO = 191 191 57 +0File 9 needs RL = 21774 REAL*8 numbers, and NR = 132. i.e. 22.99 Megabytes + File 9 RECL = 173280 +0RECOMMENDED NON-LOCAL WIDTH IS GREATER THAN 0.90 FM., RECOMMENDED CENTRATION 0.00, after 0.14 secs. +0Relative non-local usages (rms) are + 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + 0.000 0.000 0.000 0.000 0.000 0.001 0.002 0.014 0.143 1.886 10.088 44.347 86.166 100.000 76.514 + 37.771 11.522 2.177 0.298 0.040 0.007 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 +0Binding Energies: (ne18 # 1 / f17 # 1)(BE = 3.922)-(n14 # 1 / c13 # 1)(BE = 7.551)- DISCREPANCY = 7.2572 + + Incoming partition 1 in excitation state # 1, Laboratory Energy given for partition 1 Nucleus 1 in Excitation pair 1 + +0Lab. ENERGY ranges : + + from 170.000 to 0.00000 in 0 intervals + from 0.00000 to 0.00000 in 0 intervals + from 0.00000 to 0.00000 in 0 intervals + + Largest real,imaginary parts of any form factor at R= 39.66 are 3.63E-02 1.36E-20 MeV + Finished all Couplings @ 0.148940012 + + Non-symmetric Hamiltonian +1*********************************************************************************************************************************** +************************************************************************************************************************************ + + INCOMING f17 ; LABORATORY f17 ENERGY = 170.00 MeV. + + *********************************************************************************************************************************** + *********************************************************************************************************************************** + + + NOTE: Coulomb excitation cut off below 2.984 deg by JTMAX, and below 1.694 deg by Rmax + + Allocate arrays for 4 channels, of which 3 need wfs. + + Total SPIN, PARITY = 0.5 +, 4 chs, 0 cc. Rmin & Coul turning = 0.0 1.2 + + + C Projectl Target # EX. (L Proj) J + Targ = Jtotal E-cm Re K Re Eta RM*K CH G-REL + 1 f17 n14 # 1: I 2 2.5 0.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.46689 0.18925 1 1 1 1 1.00000 + 2 f17 n14 # 1: I 2 2.5 1.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.46689 0.18925 1 1 1 1 1.00000 + 3 f17 n14 # 1: I 4 2.5 1.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.15871 -0.47817 1 1 1 1 1.00000 + 4 ne18 c13 # 1: 1 0.0 1.0 0.5 0.5 80.40279 5.38866 2.89523 215.3310 0.23289 -0.44631 2 1 2 1 1.00000 + Allocate arrays for 7 channels, of which 6 need wfs. + Allocate arrays for 9 channels, of which 8 need wfs. + Allocate arrays for 10 channels, of which 9 need wfs. + Reaction Xsec 117.5+/14 @ 1 = 0.016#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/16 @ 2 = 0.009#, Out: 0.000# 0.001# 0.000f + Reaction Xsec 117.5+/16 @ 3 = 0.009#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/16 @ 4 = 0.009#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/18 @ 5 = 0.005#, Out: 0.000# 0.001# 0.000f + Reaction Xsec 117.5+/18 @ 6 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/18 @ 7 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/20 @ 8 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/20 @ 9 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/15 @ 1 = 0.012#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/15 @ 2 = 0.012#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/17 @ 3 = 0.007#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/17 @ 4 = 0.007#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/17 @ 5 = 0.007#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/19 @ 6 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/19 @ 7 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/19 @ 8 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/21 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/16 @ 1 = 0.009#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/16 @ 2 = 0.009#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/18 @ 3 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/18 @ 4 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/18 @ 5 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/20 @ 6 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/20 @ 7 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/20 @ 8 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/22 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/15 @ 1 = 0.012#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/17 @ 2 = 0.007#, Out: 0.000# 0.001# 0.000f + Reaction Xsec 118.5-/17 @ 3 = 0.007#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/17 @ 4 = 0.007#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/19 @ 5 = 0.004#, Out: 0.000# 0.001# 0.000f + Reaction Xsec 118.5-/19 @ 6 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/19 @ 7 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/21 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/21 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/16 @ 1 = 0.009#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/18 @ 2 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/18 @ 3 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/18 @ 4 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/20 @ 5 = 0.003#, Out: 0.000# 0.001# 0.000f + Reaction Xsec 119.5+/20 @ 6 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/20 @ 7 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/22 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/22 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/17 @ 1 = 0.007#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/17 @ 2 = 0.007#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/19 @ 3 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/19 @ 4 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/19 @ 5 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/21 @ 6 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/21 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/21 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/23 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/18 @ 1 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/18 @ 2 = 0.005#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/20 @ 3 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/20 @ 4 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/20 @ 5 = 0.003#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/22 @ 6 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/22 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/22 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/24 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/17 @ 1 = 0.007#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/19 @ 2 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/19 @ 3 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/19 @ 4 = 0.004#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/21 @ 5 = 0.002#, Out: 0.000# 0.001# 0.000f + Reaction Xsec 120.5-/21 @ 6 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/21 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/23 @ 8 = 0.001#, Out: 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/23 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000f + Finished all CC sets @ 4.17416477 +0CUMULATIVE REACTION cross section = 1760.34720 = 26.73 = 820.4 +0CUMULATIVE ELASTIC cross section = 0.00000 +0CUMULATIVE outgoing cross sections in partition 1 : 0.00000 +0CUMULATIVE outgoing cross sections in partition 2 : 0.30977 +0Cumulative ABSORBTION by Imaginary Potentials = 1760.03742 = 26.72 = 820.2 + Fusion for specific f17 M-states : 1760.219778 1761.060821 1761.059661 1761.059661 + 1761.060821 1760.219778 +0CUMULATIVE OUTGOING cross section = 0.30977 + Strength functions * 10^4 for L=0-2 = 1.63476 1.64024 16.36406 (with r0= 1.35 fm). R' = 0.000 fm + To convert to S-factors (MeV.mb = keV.b), multiply by 2.7966E+10 + + + CROSS SECTIONS FOR OUTGOING f17 & n14 in state # 1 with spins & parities 2.5 + & 1.0 +; 0 + + 0.01 deg.: X-S = 1.504680E+16 mb/sr, ++ /R = 9.999977E-01 + 1.00 deg.: X-S = 1.536335E+08 mb/sr, ++ /R = 1.020984E+00 + 2.00 deg.: X-S = 9.309363E+06 mb/sr, ++ /R = 9.897070E-01 + 3.00 deg.: X-S = 1.991957E+06 mb/sr, ++ /R = 1.071819E+00 + 4.00 deg.: X-S = 6.167877E+05 mb/sr, ++ /R = 1.048523E+00 + 5.00 deg.: X-S = 2.480009E+05 mb/sr, ++ /R = 1.028814E+00 + 6.00 deg.: X-S = 8.947949E+04 mb/sr, ++ /R = 7.692891E-01 + 7.00 deg.: X-S = 3.396543E+04 mb/sr, ++ /R = 5.406344E-01 + 8.00 deg.: X-S = 1.905639E+04 mb/sr, ++ /R = 5.170637E-01 + 9.00 deg.: X-S = 1.122074E+04 mb/sr, ++ /R = 4.872592E-01 + 10.00 deg.: X-S = 4925.769863 mb/sr, ++ /R = 3.257044E-01 + 11.00 deg.: X-S = 1853.649547 mb/sr, ++ /R = 1.792605E-01 + 12.00 deg.: X-S = 1280.161315 mb/sr, ++ /R = 1.751332E-01 + 13.00 deg.: X-S = 1210.173751 mb/sr, ++ /R = 2.277446E-01 + 14.00 deg.: X-S = 784.455251 mb/sr, ++ /R = 1.982949E-01 + 15.00 deg.: X-S = 292.483558 mb/sr, ++ /R = 9.728762E-02 + 16.00 deg.: X-S = 93.912546 mb/sr, ++ /R = 4.037479E-02 + 17.00 deg.: X-S = 129.122488 mb/sr, ++ /R = 7.062780E-02 + 18.00 deg.: X-S = 164.779196 mb/sr, ++ /R = 1.130831E-01 + 19.00 deg.: X-S = 110.409648 mb/sr, ++ /R = 9.388784E-02 + 20.00 deg.: X-S = 33.232349 mb/sr, ++ /R = 3.462640E-02 + 21.00 deg.: X-S = 4.626397 mb/sr, ++ /R = 5.847100E-03 + 22.00 deg.: X-S = 18.774672 mb/sr, ++ /R = 2.851893E-02 + 23.00 deg.: X-S = 31.942779 mb/sr, ++ /R = 5.783082E-02 + 24.00 deg.: X-S = 23.925617 mb/sr, ++ /R = 5.123234E-02 + 25.00 deg.: X-S = 7.535930 mb/sr, ++ /R = 1.895175E-02 + 26.00 deg.: X-S = 0.058494 mb/sr, ++ /R = 1.716429E-04 + 27.00 deg.: X-S = 3.063338 mb/sr, ++ /R = 1.042561E-02 + 28.00 deg.: X-S = 7.341448 mb/sr, ++ /R = 2.881694E-02 + 29.00 deg.: X-S = 6.736465 mb/sr, ++ /R = 3.033864E-02 + 30.00 deg.: X-S = 2.946664 mb/sr, ++ /R = 1.515238E-02 + 31.00 deg.: X-S = 0.394413 mb/sr, ++ /R = 2.305217E-03 + 32.00 deg.: X-S = 0.589267 mb/sr, ++ /R = 3.897891E-03 + 33.00 deg.: X-S = 1.803496 mb/sr, ++ /R = 1.344770E-02 + 34.00 deg.: X-S = 2.101716 mb/sr, ++ /R = 1.759871E-02 + 35.00 deg.: X-S = 1.310177 mb/sr, ++ /R = 1.227616E-02 + 36.00 deg.: X-S = 0.443206 mb/sr, ++ /R = 4.631277E-03 + 37.00 deg.: X-S = 0.215315 mb/sr, ++ /R = 2.501186E-03 + 38.00 deg.: X-S = 0.463584 mb/sr, ++ /R = 5.968456E-03 + 39.00 deg.: X-S = 0.656412 mb/sr, ++ /R = 9.339481E-03 + 40.00 deg.: X-S = 0.548048 mb/sr, ++ /R = 8.593908E-03 + Integrated 2.8799E+11 mb, over [ 0.000, 40.000] at 170.0000 MeV + + CROSS SECTIONS FOR OUTGOING ne18 & c13 in state # 1 with spins & parities 0.0 + & 0.5 -; 0 + + 0.00 deg.: X-S = 0.259193 mb/sr, ++ REAC 3.0977E-01 mb + 1.00 deg.: X-S = 0.284023 mb/sr, + 2.00 deg.: X-S = 0.583055 mb/sr, + 3.00 deg.: X-S = 1.607115 mb/sr, + 4.00 deg.: X-S = 3.345344 mb/sr, + 5.00 deg.: X-S = 4.770862 mb/sr, + 6.00 deg.: X-S = 4.605237 mb/sr, + 7.00 deg.: X-S = 2.914194 mb/sr, + 8.00 deg.: X-S = 1.191222 mb/sr, + 9.00 deg.: X-S = 0.596343 mb/sr, + 10.00 deg.: X-S = 0.848641 mb/sr, + 11.00 deg.: X-S = 1.011236 mb/sr, + 12.00 deg.: X-S = 0.714818 mb/sr, + 13.00 deg.: X-S = 0.300542 mb/sr, + 14.00 deg.: X-S = 0.137752 mb/sr, + 15.00 deg.: X-S = 0.200101 mb/sr, + 16.00 deg.: X-S = 0.251509 mb/sr, + 17.00 deg.: X-S = 0.184583 mb/sr, + 18.00 deg.: X-S = 0.079453 mb/sr, + 19.00 deg.: X-S = 0.036124 mb/sr, + 20.00 deg.: X-S = 0.055340 mb/sr, + 21.00 deg.: X-S = 0.075241 mb/sr, + 22.00 deg.: X-S = 0.061230 mb/sr, + 23.00 deg.: X-S = 0.031108 mb/sr, + 24.00 deg.: X-S = 0.015123 mb/sr, + 25.00 deg.: X-S = 0.018722 mb/sr, + 26.00 deg.: X-S = 0.025985 mb/sr, + 27.00 deg.: X-S = 0.024124 mb/sr, + 28.00 deg.: X-S = 0.015255 mb/sr, + 29.00 deg.: X-S = 0.008432 mb/sr, + 30.00 deg.: X-S = 0.007638 mb/sr, + 31.00 deg.: X-S = 0.009616 mb/sr, + 32.00 deg.: X-S = 0.009844 mb/sr, + 33.00 deg.: X-S = 0.007477 mb/sr, + 34.00 deg.: X-S = 0.004758 mb/sr, + 35.00 deg.: X-S = 0.003575 mb/sr, + 36.00 deg.: X-S = 0.003737 mb/sr, + 37.00 deg.: X-S = 0.003928 mb/sr, + 38.00 deg.: X-S = 0.003403 mb/sr, + 39.00 deg.: X-S = 0.002468 mb/sr, + 40.00 deg.: X-S = 0.001770 mb/sr, + Integrated 3.0883E-01 mb, over [ 0.000, 40.000] at 170.0000 MeV + Finished all xsecs @ 4.30928183 + + The following files have been created: + 3:local copy of User input. 6:standard output. + 12:transfer kernels. 13:total cross sections/state. + 16:tables of cross sections. 35:Astrophysics S-factors / Ecm. + 39:cross sections for each Ecm. 40:all cross sectns. for each Elab. + 46:bs wave functions & ANC ratios. 56:Fusion for each Jtotal. + 75:S-factors for lab energies. 201:Separate cross sections. + 202:Separate cross sections. + + PARAMETERS : MAXQRN MLOC LMAX1 MCLIST MFNL MPWCOUP + ALLOWED : 5 12 140 5 100 0 + REQUIRED: 1 12 125 2 6 0 + + + ACCURACY ANALYSIS at 170.000 MeV : + + Elastic h*k = 0.159 so OK compared with 0.200 + + Real(S-el) > 0.01 first at J = 29.5 + Real(S-el) > 0.10 first at J = 30.5 + Real(S-el) > 0.50 first at J = 33.5 + Real(S-el) > 0.90 first at J = 39.5 + Real(S-el) > 0.99 first at J = 48.5 + R-turn = 23.29 fm at J = 120.5 + + Forward-angle excitation cut off below 2.971 deg by max JT = 120.5 + and below 1.694 deg by max R. + + + 1: Recommended RNL: non-local width > 0.90 cf: 5.04 fm: OK + Recommended CENTRE: centration ~ 0.00 cf: 0.00 fm + + Total CPU 0 time = 4.31 seconds diff --git a/book/notebooks/Transfer_template/fort.13 b/book/notebooks/Transfer_template/fort.13 new file mode 100644 index 0000000..370c167 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.13 @@ -0,0 +1,7 @@ + Integrated cross sections for all states + 1 1 2 1.7000E+02 3.9220 7.5506 + 1 1 + 2.5 1 0.0000 1.0 1 0.0000 1.7603E+03 3.2375E+03 1.4772E+03 + 2 2 + 0.0 1 0.0000 0.5 1 0.0000 2.6397E-01 + 4.0 1 3.3760 0.5 1 0.0000 0.0000E+00 diff --git a/book/notebooks/Transfer_template/fort.16 b/book/notebooks/Transfer_template/fort.16 new file mode 100644 index 0000000..26245d1 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.16 @@ -0,0 +1,1226 @@ +# 401 angles, 1 tensor ranks for 0=projectile +@subtitle "n14(f17,ne18)c13 @ 170 MeV;" +@legend ON +@legend x1 0.2 +@legend y1 0.8 +@g0 type LOGY +@xaxis label "Scattering angle (degrees)" +@yaxis label "Cross section (mb/sr)" +@ s0 legend "Partition= 1 Excit= 1 near/far= 1" +# s0 legend "Lab energy = 170.0000" +@s0 linestyle 1 +# Theta sigma iT11 T20 T21 T22 Kyy for projectile + 0.1000E-01 1.000 + 0.1000 1.000 + 0.2000 0.9991 + 0.3000 1.001 + 0.4000 1.002 + 0.5000 0.9963 + 0.6000 0.9919 + 0.7000 0.9962 + 0.8000 1.007 + 0.9000 1.017 + 1.000 1.021 + 1.100 1.016 + 1.200 1.004 + 1.300 0.9883 + 1.400 0.9721 + 1.500 0.9595 + 1.600 0.9528 + 1.700 0.9530 + 1.800 0.9601 + 1.900 0.9729 + 2.000 0.9897 + 2.100 1.008 + 2.200 1.027 + 2.300 1.045 + 2.400 1.059 + 2.500 1.070 + 2.600 1.077 + 2.700 1.080 + 2.800 1.080 + 2.900 1.077 + 3.000 1.072 + 3.100 1.066 + 3.200 1.059 + 3.300 1.053 + 3.400 1.048 + 3.500 1.044 + 3.600 1.042 + 3.700 1.041 + 3.800 1.042 + 3.900 1.045 + 4.000 1.049 + 4.100 1.053 + 4.200 1.057 + 4.300 1.060 + 4.400 1.062 + 4.500 1.063 + 4.600 1.062 + 4.700 1.058 + 4.800 1.051 + 4.900 1.042 + 5.000 1.029 + 5.100 1.013 + 5.200 0.9939 + 5.300 0.9721 + 5.400 0.9477 + 5.500 0.9211 + 5.600 0.8926 + 5.700 0.8627 + 5.800 0.8318 + 5.900 0.8005 + 6.000 0.7693 + 6.100 0.7385 + 6.200 0.7087 + 6.300 0.6802 + 6.400 0.6534 + 6.500 0.6286 + 6.600 0.6060 + 6.700 0.5859 + 6.800 0.5682 + 6.900 0.5531 + 7.000 0.5406 + 7.100 0.5306 + 7.200 0.5229 + 7.300 0.5174 + 7.400 0.5138 + 7.500 0.5119 + 7.600 0.5114 + 7.700 0.5120 + 7.800 0.5134 + 7.900 0.5152 + 8.000 0.5171 + 8.100 0.5188 + 8.200 0.5200 + 8.300 0.5205 + 8.400 0.5200 + 8.500 0.5183 + 8.600 0.5152 + 8.700 0.5106 + 8.800 0.5045 + 8.900 0.4967 + 9.000 0.4873 + 9.100 0.4762 + 9.200 0.4636 + 9.300 0.4496 + 9.400 0.4343 + 9.500 0.4178 + 9.600 0.4004 + 9.700 0.3823 + 9.800 0.3636 + 9.900 0.3447 + 10.00 0.3257 + 10.10 0.3069 + 10.20 0.2885 + 10.30 0.2708 + 10.40 0.2539 + 10.50 0.2380 + 10.60 0.2234 + 10.70 0.2101 + 10.80 0.1982 + 10.90 0.1879 + 11.00 0.1793 + 11.10 0.1722 + 11.20 0.1669 + 11.30 0.1631 + 11.40 0.1610 + 11.50 0.1603 + 11.60 0.1610 + 11.70 0.1630 + 11.80 0.1662 + 11.90 0.1702 + 12.00 0.1751 + 12.10 0.1806 + 12.20 0.1866 + 12.30 0.1927 + 12.40 0.1989 + 12.50 0.2050 + 12.60 0.2108 + 12.70 0.2161 + 12.80 0.2208 + 12.90 0.2247 + 13.00 0.2277 + 13.10 0.2298 + 13.20 0.2308 + 13.30 0.2307 + 13.40 0.2294 + 13.50 0.2270 + 13.60 0.2234 + 13.70 0.2186 + 13.80 0.2128 + 13.90 0.2060 + 14.00 0.1983 + 14.10 0.1897 + 14.20 0.1805 + 14.30 0.1707 + 14.40 0.1604 + 14.50 0.1498 + 14.60 0.1390 + 14.70 0.1283 + 14.80 0.1176 + 14.90 0.1073 + 15.00 0.9729E-01 + 15.10 0.8783E-01 + 15.20 0.7901E-01 + 15.30 0.7093E-01 + 15.40 0.6367E-01 + 15.50 0.5730E-01 + 15.60 0.5190E-01 + 15.70 0.4748E-01 + 15.80 0.4409E-01 + 15.90 0.4172E-01 + 16.00 0.4037E-01 + 16.10 0.4002E-01 + 16.20 0.4061E-01 + 16.30 0.4210E-01 + 16.40 0.4442E-01 + 16.50 0.4749E-01 + 16.60 0.5121E-01 + 16.70 0.5550E-01 + 16.80 0.6024E-01 + 16.90 0.6532E-01 + 17.00 0.7063E-01 + 17.10 0.7606E-01 + 17.20 0.8149E-01 + 17.30 0.8681E-01 + 17.40 0.9192E-01 + 17.50 0.9672E-01 + 17.60 0.1011 + 17.70 0.1050 + 17.80 0.1083 + 17.90 0.1111 + 18.00 0.1131 + 18.10 0.1144 + 18.20 0.1150 + 18.30 0.1148 + 18.40 0.1139 + 18.50 0.1122 + 18.60 0.1098 + 18.70 0.1067 + 18.80 0.1030 + 18.90 0.9870E-01 + 19.00 0.9389E-01 + 19.10 0.8862E-01 + 19.20 0.8299E-01 + 19.30 0.7707E-01 + 19.40 0.7094E-01 + 19.50 0.6469E-01 + 19.60 0.5841E-01 + 19.70 0.5218E-01 + 19.80 0.4609E-01 + 19.90 0.4021E-01 + 20.00 0.3463E-01 + 20.10 0.2940E-01 + 20.20 0.2459E-01 + 20.30 0.2025E-01 + 20.40 0.1644E-01 + 20.50 0.1318E-01 + 20.60 0.1050E-01 + 20.70 0.8423E-02 + 20.80 0.6959E-02 + 20.90 0.6103E-02 + 21.00 0.5847E-02 + 21.10 0.6171E-02 + 21.20 0.7049E-02 + 21.30 0.8444E-02 + 21.40 0.1031E-01 + 21.50 0.1261E-01 + 21.60 0.1529E-01 + 21.70 0.1828E-01 + 21.80 0.2152E-01 + 21.90 0.2496E-01 + 22.00 0.2852E-01 + 22.10 0.3214E-01 + 22.20 0.3576E-01 + 22.30 0.3932E-01 + 22.40 0.4275E-01 + 22.50 0.4599E-01 + 22.60 0.4900E-01 + 22.70 0.5173E-01 + 22.80 0.5414E-01 + 22.90 0.5618E-01 + 23.00 0.5783E-01 + 23.10 0.5907E-01 + 23.20 0.5988E-01 + 23.30 0.6025E-01 + 23.40 0.6018E-01 + 23.50 0.5968E-01 + 23.60 0.5875E-01 + 23.70 0.5742E-01 + 23.80 0.5570E-01 + 23.90 0.5363E-01 + 24.00 0.5123E-01 + 24.10 0.4855E-01 + 24.20 0.4563E-01 + 24.30 0.4251E-01 + 24.40 0.3923E-01 + 24.50 0.3585E-01 + 24.60 0.3240E-01 + 24.70 0.2895E-01 + 24.80 0.2552E-01 + 24.90 0.2218E-01 + 25.00 0.1895E-01 + 25.10 0.1589E-01 + 25.20 0.1302E-01 + 25.30 0.1038E-01 + 25.40 0.8001E-02 + 25.50 0.5903E-02 + 25.60 0.4109E-02 + 25.70 0.2631E-02 + 25.80 0.1480E-02 + 25.90 0.6599E-03 + 26.00 0.1716E-03 + 26.10 0.9916E-05 + 26.20 0.1652E-03 + 26.30 0.6238E-03 + 26.40 0.1368E-02 + 26.50 0.2376E-02 + 26.60 0.3623E-02 + 26.70 0.5083E-02 + 26.80 0.6724E-02 + 26.90 0.8516E-02 + 27.00 0.1043E-01 + 27.10 0.1242E-01 + 27.20 0.1446E-01 + 27.30 0.1652E-01 + 27.40 0.1857E-01 + 27.50 0.2056E-01 + 27.60 0.2248E-01 + 27.70 0.2428E-01 + 27.80 0.2596E-01 + 27.90 0.2748E-01 + 28.00 0.2882E-01 + 28.10 0.2996E-01 + 28.20 0.3090E-01 + 28.30 0.3162E-01 + 28.40 0.3211E-01 + 28.50 0.3237E-01 + 28.60 0.3241E-01 + 28.70 0.3221E-01 + 28.80 0.3179E-01 + 28.90 0.3117E-01 + 29.00 0.3034E-01 + 29.10 0.2933E-01 + 29.20 0.2814E-01 + 29.30 0.2681E-01 + 29.40 0.2535E-01 + 29.50 0.2379E-01 + 29.60 0.2214E-01 + 29.70 0.2042E-01 + 29.80 0.1867E-01 + 29.90 0.1691E-01 + 30.00 0.1515E-01 + 30.10 0.1343E-01 + 30.20 0.1176E-01 + 30.30 0.1016E-01 + 30.40 0.8649E-02 + 30.50 0.7249E-02 + 30.60 0.5972E-02 + 30.70 0.4829E-02 + 30.80 0.3832E-02 + 30.90 0.2989E-02 + 31.00 0.2305E-02 + 31.10 0.1784E-02 + 31.20 0.1428E-02 + 31.30 0.1234E-02 + 31.40 0.1199E-02 + 31.50 0.1318E-02 + 31.60 0.1583E-02 + 31.70 0.1985E-02 + 31.80 0.2514E-02 + 31.90 0.3156E-02 + 32.00 0.3898E-02 + 32.10 0.4726E-02 + 32.20 0.5624E-02 + 32.30 0.6577E-02 + 32.40 0.7569E-02 + 32.50 0.8583E-02 + 32.60 0.9603E-02 + 32.70 0.1061E-01 + 32.80 0.1160E-01 + 32.90 0.1255E-01 + 33.00 0.1345E-01 + 33.10 0.1428E-01 + 33.20 0.1504E-01 + 33.30 0.1572E-01 + 33.40 0.1630E-01 + 33.50 0.1678E-01 + 33.60 0.1716E-01 + 33.70 0.1744E-01 + 33.80 0.1760E-01 + 33.90 0.1765E-01 + 34.00 0.1760E-01 + 34.10 0.1744E-01 + 34.20 0.1718E-01 + 34.30 0.1682E-01 + 34.40 0.1637E-01 + 34.50 0.1584E-01 + 34.60 0.1524E-01 + 34.70 0.1457E-01 + 34.80 0.1385E-01 + 34.90 0.1308E-01 + 35.00 0.1228E-01 + 35.10 0.1145E-01 + 35.20 0.1061E-01 + 35.30 0.9772E-02 + 35.40 0.8940E-02 + 35.50 0.8126E-02 + 35.60 0.7339E-02 + 35.70 0.6589E-02 + 35.80 0.5883E-02 + 35.90 0.5228E-02 + 36.00 0.4631E-02 + 36.10 0.4097E-02 + 36.20 0.3631E-02 + 36.30 0.3235E-02 + 36.40 0.2911E-02 + 36.50 0.2662E-02 + 36.60 0.2488E-02 + 36.70 0.2386E-02 + 36.80 0.2357E-02 + 36.90 0.2396E-02 + 37.00 0.2501E-02 + 37.10 0.2668E-02 + 37.20 0.2891E-02 + 37.30 0.3165E-02 + 37.40 0.3484E-02 + 37.50 0.3842E-02 + 37.60 0.4231E-02 + 37.70 0.4646E-02 + 37.80 0.5078E-02 + 37.90 0.5521E-02 + 38.00 0.5968E-02 + 38.10 0.6413E-02 + 38.20 0.6847E-02 + 38.30 0.7267E-02 + 38.40 0.7664E-02 + 38.50 0.8035E-02 + 38.60 0.8374E-02 + 38.70 0.8678E-02 + 38.80 0.8941E-02 + 38.90 0.9163E-02 + 39.00 0.9339E-02 + 39.10 0.9470E-02 + 39.20 0.9552E-02 + 39.30 0.9587E-02 + 39.40 0.9575E-02 + 39.50 0.9516E-02 + 39.60 0.9412E-02 + 39.70 0.9265E-02 + 39.80 0.9077E-02 + 39.90 0.8853E-02 + 40.00 0.8594E-02 + & +@ s1 legend "Partition= 2 Excit= 1 near/far= 1" +# s0 legend "Lab energy = 170.0000" +@s1 linestyle 2 +# Theta sigma iT11 T20 T21 T22 Kyy for projectile + 0.000 3.123 + 0.1000 3.120 + 0.2000 3.112 + 0.3000 3.098 + 0.4000 3.079 + 0.5000 3.056 + 0.6000 3.030 + 0.7000 3.000 + 0.8000 2.969 + 0.9000 2.937 + 1.000 2.906 + 1.100 2.876 + 1.200 2.849 + 1.300 2.826 + 1.400 2.808 + 1.500 2.797 + 1.600 2.794 + 1.700 2.799 + 1.800 2.813 + 1.900 2.838 + 2.000 2.874 + 2.100 2.921 + 2.200 2.979 + 2.300 3.049 + 2.400 3.129 + 2.500 3.220 + 2.600 3.320 + 2.700 3.428 + 2.800 3.543 + 2.900 3.664 + 3.000 3.789 + 3.100 3.915 + 3.200 4.042 + 3.300 4.166 + 3.400 4.285 + 3.500 4.398 + 3.600 4.502 + 3.700 4.594 + 3.800 4.674 + 3.900 4.738 + 4.000 4.786 + 4.100 4.815 + 4.200 4.825 + 4.300 4.815 + 4.400 4.784 + 4.500 4.732 + 4.600 4.659 + 4.700 4.564 + 4.800 4.451 + 4.900 4.318 + 5.000 4.168 + 5.100 4.002 + 5.200 3.822 + 5.300 3.631 + 5.400 3.430 + 5.500 3.222 + 5.600 3.010 + 5.700 2.796 + 5.800 2.583 + 5.900 2.373 + 6.000 2.169 + 6.100 1.972 + 6.200 1.786 + 6.300 1.611 + 6.400 1.450 + 6.500 1.303 + 6.600 1.172 + 6.700 1.058 + 6.800 0.9608 + 6.900 0.8804 + 7.000 0.8171 + 7.100 0.7702 + 7.200 0.7392 + 7.300 0.7231 + 7.400 0.7208 + 7.500 0.7310 + 7.600 0.7523 + 7.700 0.7830 + 7.800 0.8215 + 7.900 0.8662 + 8.000 0.9153 + 8.100 0.9671 + 8.200 1.020 + 8.300 1.073 + 8.400 1.123 + 8.500 1.170 + 8.600 1.213 + 8.700 1.250 + 8.800 1.281 + 8.900 1.305 + 9.000 1.321 + 9.100 1.328 + 9.200 1.328 + 9.300 1.320 + 9.400 1.303 + 9.500 1.279 + 9.600 1.247 + 9.700 1.209 + 9.800 1.164 + 9.900 1.114 + 10.00 1.060 + 10.10 1.001 + 10.20 0.9402 + 10.30 0.8772 + 10.40 0.8132 + 10.50 0.7490 + 10.60 0.6855 + 10.70 0.6235 + 10.80 0.5637 + 10.90 0.5068 + 11.00 0.4533 + 11.10 0.4038 + 11.20 0.3587 + 11.30 0.3183 + 11.40 0.2827 + 11.50 0.2521 + 11.60 0.2266 + 11.70 0.2061 + 11.80 0.1905 + 11.90 0.1796 + 12.00 0.1730 + 12.10 0.1706 + 12.20 0.1719 + 12.30 0.1765 + 12.40 0.1839 + 12.50 0.1938 + 12.60 0.2056 + 12.70 0.2188 + 12.80 0.2331 + 12.90 0.2479 + 13.00 0.2628 + 13.10 0.2773 + 13.20 0.2912 + 13.30 0.3040 + 13.40 0.3155 + 13.50 0.3254 + 13.60 0.3335 + 13.70 0.3396 + 13.80 0.3436 + 13.90 0.3454 + 14.00 0.3450 + 14.10 0.3424 + 14.20 0.3377 + 14.30 0.3309 + 14.40 0.3221 + 14.50 0.3117 + 14.60 0.2996 + 14.70 0.2861 + 14.80 0.2714 + 14.90 0.2557 + 15.00 0.2393 + 15.10 0.2225 + 15.20 0.2053 + 15.30 0.1881 + 15.40 0.1712 + 15.50 0.1546 + 15.60 0.1386 + 15.70 0.1233 + 15.80 0.1090 + 15.90 0.9568E-01 + 16.00 0.8354E-01 + 16.10 0.7263E-01 + 16.20 0.6303E-01 + 16.30 0.5477E-01 + 16.40 0.4787E-01 + 16.50 0.4233E-01 + 16.60 0.3812E-01 + 16.70 0.3519E-01 + 16.80 0.3349E-01 + 16.90 0.3293E-01 + 17.00 0.3343E-01 + 17.10 0.3489E-01 + 17.20 0.3719E-01 + 17.30 0.4022E-01 + 17.40 0.4386E-01 + 17.50 0.4797E-01 + 17.60 0.5244E-01 + 17.70 0.5713E-01 + 17.80 0.6194E-01 + 17.90 0.6674E-01 + 18.00 0.7142E-01 + 18.10 0.7589E-01 + 18.20 0.8006E-01 + 18.30 0.8384E-01 + 18.40 0.8716E-01 + 18.50 0.8997E-01 + 18.60 0.9223E-01 + 18.70 0.9388E-01 + 18.80 0.9492E-01 + 18.90 0.9534E-01 + 19.00 0.9513E-01 + 19.10 0.9430E-01 + 19.20 0.9288E-01 + 19.30 0.9089E-01 + 19.40 0.8839E-01 + 19.50 0.8540E-01 + 19.60 0.8198E-01 + 19.70 0.7819E-01 + 19.80 0.7408E-01 + 19.90 0.6973E-01 + 20.00 0.6518E-01 + 20.10 0.6052E-01 + 20.20 0.5579E-01 + 20.30 0.5106E-01 + 20.40 0.4640E-01 + 20.50 0.4184E-01 + 20.60 0.3745E-01 + 20.70 0.3328E-01 + 20.80 0.2935E-01 + 20.90 0.2572E-01 + 21.00 0.2240E-01 + 21.10 0.1942E-01 + 21.20 0.1680E-01 + 21.30 0.1456E-01 + 21.40 0.1269E-01 + 21.50 0.1119E-01 + 21.60 0.1007E-01 + 21.70 0.9302E-02 + 21.80 0.8881E-02 + 21.90 0.8785E-02 + 22.00 0.8991E-02 + 22.10 0.9471E-02 + 22.20 0.1020E-01 + 22.30 0.1113E-01 + 22.40 0.1225E-01 + 22.50 0.1352E-01 + 22.60 0.1490E-01 + 22.70 0.1636E-01 + 22.80 0.1786E-01 + 22.90 0.1938E-01 + 23.00 0.2087E-01 + 23.10 0.2232E-01 + 23.20 0.2369E-01 + 23.30 0.2497E-01 + 23.40 0.2613E-01 + 23.50 0.2715E-01 + 23.60 0.2801E-01 + 23.70 0.2871E-01 + 23.80 0.2924E-01 + 23.90 0.2958E-01 + 24.00 0.2975E-01 + 24.10 0.2973E-01 + 24.20 0.2954E-01 + 24.30 0.2917E-01 + 24.40 0.2864E-01 + 24.50 0.2796E-01 + 24.60 0.2713E-01 + 24.70 0.2618E-01 + 24.80 0.2512E-01 + 24.90 0.2397E-01 + 25.00 0.2273E-01 + 25.10 0.2144E-01 + 25.20 0.2010E-01 + 25.30 0.1873E-01 + 25.40 0.1736E-01 + 25.50 0.1600E-01 + 25.60 0.1466E-01 + 25.70 0.1336E-01 + 25.80 0.1211E-01 + 25.90 0.1092E-01 + 26.00 0.9814E-02 + 26.10 0.8788E-02 + 26.20 0.7853E-02 + 26.30 0.7017E-02 + 26.40 0.6283E-02 + 26.50 0.5653E-02 + 26.60 0.5130E-02 + 26.70 0.4713E-02 + 26.80 0.4400E-02 + 26.90 0.4188E-02 + 27.00 0.4072E-02 + 27.10 0.4047E-02 + 27.20 0.4106E-02 + 27.30 0.4243E-02 + 27.40 0.4448E-02 + 27.50 0.4713E-02 + 27.60 0.5030E-02 + 27.70 0.5388E-02 + 27.80 0.5778E-02 + 27.90 0.6190E-02 + 28.00 0.6616E-02 + 28.10 0.7047E-02 + 28.20 0.7473E-02 + 28.30 0.7887E-02 + 28.40 0.8282E-02 + 28.50 0.8649E-02 + 28.60 0.8985E-02 + 28.70 0.9283E-02 + 28.80 0.9539E-02 + 28.90 0.9749E-02 + 29.00 0.9912E-02 + 29.10 0.1002E-01 + 29.20 0.1009E-01 + 29.30 0.1010E-01 + 29.40 0.1005E-01 + 29.50 0.9965E-02 + 29.60 0.9828E-02 + 29.70 0.9645E-02 + 29.80 0.9421E-02 + 29.90 0.9159E-02 + 30.00 0.8863E-02 + 30.10 0.8536E-02 + 30.20 0.8185E-02 + 30.30 0.7812E-02 + 30.40 0.7423E-02 + 30.50 0.7023E-02 + 30.60 0.6616E-02 + 30.70 0.6208E-02 + 30.80 0.5803E-02 + 30.90 0.5404E-02 + 31.00 0.5017E-02 + 31.10 0.4644E-02 + 31.20 0.4289E-02 + 31.30 0.3955E-02 + 31.40 0.3645E-02 + 31.50 0.3361E-02 + 31.60 0.3104E-02 + 31.70 0.2875E-02 + 31.80 0.2676E-02 + 31.90 0.2507E-02 + 32.00 0.2368E-02 + 32.10 0.2257E-02 + 32.20 0.2176E-02 + 32.30 0.2121E-02 + 32.40 0.2092E-02 + 32.50 0.2088E-02 + 32.60 0.2105E-02 + 32.70 0.2142E-02 + 32.80 0.2197E-02 + 32.90 0.2266E-02 + 33.00 0.2349E-02 + 33.10 0.2441E-02 + 33.20 0.2541E-02 + 33.30 0.2646E-02 + 33.40 0.2753E-02 + 33.50 0.2860E-02 + 33.60 0.2966E-02 + 33.70 0.3067E-02 + 33.80 0.3162E-02 + 33.90 0.3250E-02 + 34.00 0.3328E-02 + 34.10 0.3396E-02 + 34.20 0.3452E-02 + 34.30 0.3496E-02 + 34.40 0.3527E-02 + 34.50 0.3543E-02 + 34.60 0.3546E-02 + 34.70 0.3535E-02 + 34.80 0.3511E-02 + 34.90 0.3473E-02 + 35.00 0.3422E-02 + 35.10 0.3359E-02 + 35.20 0.3286E-02 + 35.30 0.3201E-02 + 35.40 0.3108E-02 + 35.50 0.3006E-02 + 35.60 0.2898E-02 + 35.70 0.2784E-02 + 35.80 0.2667E-02 + 35.90 0.2546E-02 + 36.00 0.2423E-02 + 36.10 0.2301E-02 + 36.20 0.2179E-02 + 36.30 0.2059E-02 + 36.40 0.1943E-02 + 36.50 0.1830E-02 + 36.60 0.1723E-02 + 36.70 0.1621E-02 + 36.80 0.1526E-02 + 36.90 0.1438E-02 + 37.00 0.1357E-02 + 37.10 0.1284E-02 + 37.20 0.1219E-02 + 37.30 0.1162E-02 + 37.40 0.1113E-02 + 37.50 0.1072E-02 + 37.60 0.1039E-02 + 37.70 0.1013E-02 + 37.80 0.9948E-03 + 37.90 0.9830E-03 + 38.00 0.9775E-03 + 38.10 0.9776E-03 + 38.20 0.9827E-03 + 38.30 0.9924E-03 + 38.40 0.1006E-02 + 38.50 0.1022E-02 + 38.60 0.1042E-02 + 38.70 0.1063E-02 + 38.80 0.1085E-02 + 38.90 0.1108E-02 + 39.00 0.1130E-02 + 39.10 0.1152E-02 + 39.20 0.1173E-02 + 39.30 0.1193E-02 + 39.40 0.1210E-02 + 39.50 0.1225E-02 + 39.60 0.1237E-02 + 39.70 0.1245E-02 + 39.80 0.1251E-02 + 39.90 0.1253E-02 + 40.00 0.1252E-02 + & +@ s2 legend "Partition= 2 Excit= 2 near/far= 1" +# s0 legend "Lab energy = 170.0000" +@s2 linestyle 3 +# Theta sigma iT11 T20 T21 T22 Kyy for projectile + 0.000 0.000 + 0.1000 0.000 + 0.2000 0.000 + 0.3000 0.000 + 0.4000 0.000 + 0.5000 0.000 + 0.6000 0.000 + 0.7000 0.000 + 0.8000 0.000 + 0.9000 0.000 + 1.000 0.000 + 1.100 0.000 + 1.200 0.000 + 1.300 0.000 + 1.400 0.000 + 1.500 0.000 + 1.600 0.000 + 1.700 0.000 + 1.800 0.000 + 1.900 0.000 + 2.000 0.000 + 2.100 0.000 + 2.200 0.000 + 2.300 0.000 + 2.400 0.000 + 2.500 0.000 + 2.600 0.000 + 2.700 0.000 + 2.800 0.000 + 2.900 0.000 + 3.000 0.000 + 3.100 0.000 + 3.200 0.000 + 3.300 0.000 + 3.400 0.000 + 3.500 0.000 + 3.600 0.000 + 3.700 0.000 + 3.800 0.000 + 3.900 0.000 + 4.000 0.000 + 4.100 0.000 + 4.200 0.000 + 4.300 0.000 + 4.400 0.000 + 4.500 0.000 + 4.600 0.000 + 4.700 0.000 + 4.800 0.000 + 4.900 0.000 + 5.000 0.000 + 5.100 0.000 + 5.200 0.000 + 5.300 0.000 + 5.400 0.000 + 5.500 0.000 + 5.600 0.000 + 5.700 0.000 + 5.800 0.000 + 5.900 0.000 + 6.000 0.000 + 6.100 0.000 + 6.200 0.000 + 6.300 0.000 + 6.400 0.000 + 6.500 0.000 + 6.600 0.000 + 6.700 0.000 + 6.800 0.000 + 6.900 0.000 + 7.000 0.000 + 7.100 0.000 + 7.200 0.000 + 7.300 0.000 + 7.400 0.000 + 7.500 0.000 + 7.600 0.000 + 7.700 0.000 + 7.800 0.000 + 7.900 0.000 + 8.000 0.000 + 8.100 0.000 + 8.200 0.000 + 8.300 0.000 + 8.400 0.000 + 8.500 0.000 + 8.600 0.000 + 8.700 0.000 + 8.800 0.000 + 8.900 0.000 + 9.000 0.000 + 9.100 0.000 + 9.200 0.000 + 9.300 0.000 + 9.400 0.000 + 9.500 0.000 + 9.600 0.000 + 9.700 0.000 + 9.800 0.000 + 9.900 0.000 + 10.00 0.000 + 10.10 0.000 + 10.20 0.000 + 10.30 0.000 + 10.40 0.000 + 10.50 0.000 + 10.60 0.000 + 10.70 0.000 + 10.80 0.000 + 10.90 0.000 + 11.00 0.000 + 11.10 0.000 + 11.20 0.000 + 11.30 0.000 + 11.40 0.000 + 11.50 0.000 + 11.60 0.000 + 11.70 0.000 + 11.80 0.000 + 11.90 0.000 + 12.00 0.000 + 12.10 0.000 + 12.20 0.000 + 12.30 0.000 + 12.40 0.000 + 12.50 0.000 + 12.60 0.000 + 12.70 0.000 + 12.80 0.000 + 12.90 0.000 + 13.00 0.000 + 13.10 0.000 + 13.20 0.000 + 13.30 0.000 + 13.40 0.000 + 13.50 0.000 + 13.60 0.000 + 13.70 0.000 + 13.80 0.000 + 13.90 0.000 + 14.00 0.000 + 14.10 0.000 + 14.20 0.000 + 14.30 0.000 + 14.40 0.000 + 14.50 0.000 + 14.60 0.000 + 14.70 0.000 + 14.80 0.000 + 14.90 0.000 + 15.00 0.000 + 15.10 0.000 + 15.20 0.000 + 15.30 0.000 + 15.40 0.000 + 15.50 0.000 + 15.60 0.000 + 15.70 0.000 + 15.80 0.000 + 15.90 0.000 + 16.00 0.000 + 16.10 0.000 + 16.20 0.000 + 16.30 0.000 + 16.40 0.000 + 16.50 0.000 + 16.60 0.000 + 16.70 0.000 + 16.80 0.000 + 16.90 0.000 + 17.00 0.000 + 17.10 0.000 + 17.20 0.000 + 17.30 0.000 + 17.40 0.000 + 17.50 0.000 + 17.60 0.000 + 17.70 0.000 + 17.80 0.000 + 17.90 0.000 + 18.00 0.000 + 18.10 0.000 + 18.20 0.000 + 18.30 0.000 + 18.40 0.000 + 18.50 0.000 + 18.60 0.000 + 18.70 0.000 + 18.80 0.000 + 18.90 0.000 + 19.00 0.000 + 19.10 0.000 + 19.20 0.000 + 19.30 0.000 + 19.40 0.000 + 19.50 0.000 + 19.60 0.000 + 19.70 0.000 + 19.80 0.000 + 19.90 0.000 + 20.00 0.000 + 20.10 0.000 + 20.20 0.000 + 20.30 0.000 + 20.40 0.000 + 20.50 0.000 + 20.60 0.000 + 20.70 0.000 + 20.80 0.000 + 20.90 0.000 + 21.00 0.000 + 21.10 0.000 + 21.20 0.000 + 21.30 0.000 + 21.40 0.000 + 21.50 0.000 + 21.60 0.000 + 21.70 0.000 + 21.80 0.000 + 21.90 0.000 + 22.00 0.000 + 22.10 0.000 + 22.20 0.000 + 22.30 0.000 + 22.40 0.000 + 22.50 0.000 + 22.60 0.000 + 22.70 0.000 + 22.80 0.000 + 22.90 0.000 + 23.00 0.000 + 23.10 0.000 + 23.20 0.000 + 23.30 0.000 + 23.40 0.000 + 23.50 0.000 + 23.60 0.000 + 23.70 0.000 + 23.80 0.000 + 23.90 0.000 + 24.00 0.000 + 24.10 0.000 + 24.20 0.000 + 24.30 0.000 + 24.40 0.000 + 24.50 0.000 + 24.60 0.000 + 24.70 0.000 + 24.80 0.000 + 24.90 0.000 + 25.00 0.000 + 25.10 0.000 + 25.20 0.000 + 25.30 0.000 + 25.40 0.000 + 25.50 0.000 + 25.60 0.000 + 25.70 0.000 + 25.80 0.000 + 25.90 0.000 + 26.00 0.000 + 26.10 0.000 + 26.20 0.000 + 26.30 0.000 + 26.40 0.000 + 26.50 0.000 + 26.60 0.000 + 26.70 0.000 + 26.80 0.000 + 26.90 0.000 + 27.00 0.000 + 27.10 0.000 + 27.20 0.000 + 27.30 0.000 + 27.40 0.000 + 27.50 0.000 + 27.60 0.000 + 27.70 0.000 + 27.80 0.000 + 27.90 0.000 + 28.00 0.000 + 28.10 0.000 + 28.20 0.000 + 28.30 0.000 + 28.40 0.000 + 28.50 0.000 + 28.60 0.000 + 28.70 0.000 + 28.80 0.000 + 28.90 0.000 + 29.00 0.000 + 29.10 0.000 + 29.20 0.000 + 29.30 0.000 + 29.40 0.000 + 29.50 0.000 + 29.60 0.000 + 29.70 0.000 + 29.80 0.000 + 29.90 0.000 + 30.00 0.000 + 30.10 0.000 + 30.20 0.000 + 30.30 0.000 + 30.40 0.000 + 30.50 0.000 + 30.60 0.000 + 30.70 0.000 + 30.80 0.000 + 30.90 0.000 + 31.00 0.000 + 31.10 0.000 + 31.20 0.000 + 31.30 0.000 + 31.40 0.000 + 31.50 0.000 + 31.60 0.000 + 31.70 0.000 + 31.80 0.000 + 31.90 0.000 + 32.00 0.000 + 32.10 0.000 + 32.20 0.000 + 32.30 0.000 + 32.40 0.000 + 32.50 0.000 + 32.60 0.000 + 32.70 0.000 + 32.80 0.000 + 32.90 0.000 + 33.00 0.000 + 33.10 0.000 + 33.20 0.000 + 33.30 0.000 + 33.40 0.000 + 33.50 0.000 + 33.60 0.000 + 33.70 0.000 + 33.80 0.000 + 33.90 0.000 + 34.00 0.000 + 34.10 0.000 + 34.20 0.000 + 34.30 0.000 + 34.40 0.000 + 34.50 0.000 + 34.60 0.000 + 34.70 0.000 + 34.80 0.000 + 34.90 0.000 + 35.00 0.000 + 35.10 0.000 + 35.20 0.000 + 35.30 0.000 + 35.40 0.000 + 35.50 0.000 + 35.60 0.000 + 35.70 0.000 + 35.80 0.000 + 35.90 0.000 + 36.00 0.000 + 36.10 0.000 + 36.20 0.000 + 36.30 0.000 + 36.40 0.000 + 36.50 0.000 + 36.60 0.000 + 36.70 0.000 + 36.80 0.000 + 36.90 0.000 + 37.00 0.000 + 37.10 0.000 + 37.20 0.000 + 37.30 0.000 + 37.40 0.000 + 37.50 0.000 + 37.60 0.000 + 37.70 0.000 + 37.80 0.000 + 37.90 0.000 + 38.00 0.000 + 38.10 0.000 + 38.20 0.000 + 38.30 0.000 + 38.40 0.000 + 38.50 0.000 + 38.60 0.000 + 38.70 0.000 + 38.80 0.000 + 38.90 0.000 + 39.00 0.000 + 39.10 0.000 + 39.20 0.000 + 39.30 0.000 + 39.40 0.000 + 39.50 0.000 + 39.60 0.000 + 39.70 0.000 + 39.80 0.000 + 39.90 0.000 + 40.00 0.000 + & diff --git a/book/notebooks/Transfer_template/fort.201 b/book/notebooks/Transfer_template/fort.201 new file mode 100644 index 0000000..816ef8c --- /dev/null +++ b/book/notebooks/Transfer_template/fort.201 @@ -0,0 +1,411 @@ +# 401 angles, 1 tensor ranks for 0=projectile +@subtitle "n14(f17,ne18)c13 @ 170 MeV;" +@legend ON +@legend x1 0.2 +@legend y1 0.8 +@g0 type LOGY +@ s0 legend "Lab energy = 170.0000" +# s0 legend "Partition= 1 Excit= 1 near/far= 1" +# Theta sigma iT11 T20 T21 T22 Kyy for projectile + 0.1000E-01 1.000 + 0.1000 1.000 + 0.2000 0.9991 + 0.3000 1.001 + 0.4000 1.002 + 0.5000 0.9963 + 0.6000 0.9919 + 0.7000 0.9962 + 0.8000 1.007 + 0.9000 1.017 + 1.000 1.021 + 1.100 1.016 + 1.200 1.004 + 1.300 0.9883 + 1.400 0.9721 + 1.500 0.9595 + 1.600 0.9528 + 1.700 0.9530 + 1.800 0.9601 + 1.900 0.9729 + 2.000 0.9897 + 2.100 1.008 + 2.200 1.027 + 2.300 1.045 + 2.400 1.059 + 2.500 1.070 + 2.600 1.077 + 2.700 1.080 + 2.800 1.080 + 2.900 1.077 + 3.000 1.072 + 3.100 1.066 + 3.200 1.059 + 3.300 1.053 + 3.400 1.048 + 3.500 1.044 + 3.600 1.042 + 3.700 1.041 + 3.800 1.042 + 3.900 1.045 + 4.000 1.049 + 4.100 1.053 + 4.200 1.057 + 4.300 1.060 + 4.400 1.062 + 4.500 1.063 + 4.600 1.062 + 4.700 1.058 + 4.800 1.051 + 4.900 1.042 + 5.000 1.029 + 5.100 1.013 + 5.200 0.9939 + 5.300 0.9721 + 5.400 0.9477 + 5.500 0.9211 + 5.600 0.8926 + 5.700 0.8627 + 5.800 0.8318 + 5.900 0.8005 + 6.000 0.7693 + 6.100 0.7385 + 6.200 0.7087 + 6.300 0.6802 + 6.400 0.6534 + 6.500 0.6286 + 6.600 0.6060 + 6.700 0.5859 + 6.800 0.5682 + 6.900 0.5531 + 7.000 0.5406 + 7.100 0.5306 + 7.200 0.5229 + 7.300 0.5174 + 7.400 0.5138 + 7.500 0.5119 + 7.600 0.5114 + 7.700 0.5120 + 7.800 0.5134 + 7.900 0.5152 + 8.000 0.5171 + 8.100 0.5188 + 8.200 0.5200 + 8.300 0.5205 + 8.400 0.5200 + 8.500 0.5183 + 8.600 0.5152 + 8.700 0.5106 + 8.800 0.5045 + 8.900 0.4967 + 9.000 0.4873 + 9.100 0.4762 + 9.200 0.4636 + 9.300 0.4496 + 9.400 0.4343 + 9.500 0.4178 + 9.600 0.4004 + 9.700 0.3823 + 9.800 0.3636 + 9.900 0.3447 + 10.00 0.3257 + 10.10 0.3069 + 10.20 0.2885 + 10.30 0.2708 + 10.40 0.2539 + 10.50 0.2380 + 10.60 0.2234 + 10.70 0.2101 + 10.80 0.1982 + 10.90 0.1879 + 11.00 0.1793 + 11.10 0.1722 + 11.20 0.1669 + 11.30 0.1631 + 11.40 0.1610 + 11.50 0.1603 + 11.60 0.1610 + 11.70 0.1630 + 11.80 0.1662 + 11.90 0.1702 + 12.00 0.1751 + 12.10 0.1806 + 12.20 0.1866 + 12.30 0.1927 + 12.40 0.1989 + 12.50 0.2050 + 12.60 0.2108 + 12.70 0.2161 + 12.80 0.2208 + 12.90 0.2247 + 13.00 0.2277 + 13.10 0.2298 + 13.20 0.2308 + 13.30 0.2307 + 13.40 0.2294 + 13.50 0.2270 + 13.60 0.2234 + 13.70 0.2186 + 13.80 0.2128 + 13.90 0.2060 + 14.00 0.1983 + 14.10 0.1897 + 14.20 0.1805 + 14.30 0.1707 + 14.40 0.1604 + 14.50 0.1498 + 14.60 0.1390 + 14.70 0.1283 + 14.80 0.1176 + 14.90 0.1073 + 15.00 0.9729E-01 + 15.10 0.8783E-01 + 15.20 0.7901E-01 + 15.30 0.7093E-01 + 15.40 0.6367E-01 + 15.50 0.5730E-01 + 15.60 0.5190E-01 + 15.70 0.4748E-01 + 15.80 0.4409E-01 + 15.90 0.4172E-01 + 16.00 0.4037E-01 + 16.10 0.4002E-01 + 16.20 0.4061E-01 + 16.30 0.4210E-01 + 16.40 0.4442E-01 + 16.50 0.4749E-01 + 16.60 0.5121E-01 + 16.70 0.5550E-01 + 16.80 0.6024E-01 + 16.90 0.6532E-01 + 17.00 0.7063E-01 + 17.10 0.7606E-01 + 17.20 0.8149E-01 + 17.30 0.8681E-01 + 17.40 0.9192E-01 + 17.50 0.9672E-01 + 17.60 0.1011 + 17.70 0.1050 + 17.80 0.1083 + 17.90 0.1111 + 18.00 0.1131 + 18.10 0.1144 + 18.20 0.1150 + 18.30 0.1148 + 18.40 0.1139 + 18.50 0.1122 + 18.60 0.1098 + 18.70 0.1067 + 18.80 0.1030 + 18.90 0.9870E-01 + 19.00 0.9389E-01 + 19.10 0.8862E-01 + 19.20 0.8299E-01 + 19.30 0.7707E-01 + 19.40 0.7094E-01 + 19.50 0.6469E-01 + 19.60 0.5841E-01 + 19.70 0.5218E-01 + 19.80 0.4609E-01 + 19.90 0.4021E-01 + 20.00 0.3463E-01 + 20.10 0.2940E-01 + 20.20 0.2459E-01 + 20.30 0.2025E-01 + 20.40 0.1644E-01 + 20.50 0.1318E-01 + 20.60 0.1050E-01 + 20.70 0.8423E-02 + 20.80 0.6959E-02 + 20.90 0.6103E-02 + 21.00 0.5847E-02 + 21.10 0.6171E-02 + 21.20 0.7049E-02 + 21.30 0.8444E-02 + 21.40 0.1031E-01 + 21.50 0.1261E-01 + 21.60 0.1529E-01 + 21.70 0.1828E-01 + 21.80 0.2152E-01 + 21.90 0.2496E-01 + 22.00 0.2852E-01 + 22.10 0.3214E-01 + 22.20 0.3576E-01 + 22.30 0.3932E-01 + 22.40 0.4275E-01 + 22.50 0.4599E-01 + 22.60 0.4900E-01 + 22.70 0.5173E-01 + 22.80 0.5414E-01 + 22.90 0.5618E-01 + 23.00 0.5783E-01 + 23.10 0.5907E-01 + 23.20 0.5988E-01 + 23.30 0.6025E-01 + 23.40 0.6018E-01 + 23.50 0.5968E-01 + 23.60 0.5875E-01 + 23.70 0.5742E-01 + 23.80 0.5570E-01 + 23.90 0.5363E-01 + 24.00 0.5123E-01 + 24.10 0.4855E-01 + 24.20 0.4563E-01 + 24.30 0.4251E-01 + 24.40 0.3923E-01 + 24.50 0.3585E-01 + 24.60 0.3240E-01 + 24.70 0.2895E-01 + 24.80 0.2552E-01 + 24.90 0.2218E-01 + 25.00 0.1895E-01 + 25.10 0.1589E-01 + 25.20 0.1302E-01 + 25.30 0.1038E-01 + 25.40 0.8001E-02 + 25.50 0.5903E-02 + 25.60 0.4109E-02 + 25.70 0.2631E-02 + 25.80 0.1480E-02 + 25.90 0.6599E-03 + 26.00 0.1716E-03 + 26.10 0.9916E-05 + 26.20 0.1652E-03 + 26.30 0.6238E-03 + 26.40 0.1368E-02 + 26.50 0.2376E-02 + 26.60 0.3623E-02 + 26.70 0.5083E-02 + 26.80 0.6724E-02 + 26.90 0.8516E-02 + 27.00 0.1043E-01 + 27.10 0.1242E-01 + 27.20 0.1446E-01 + 27.30 0.1652E-01 + 27.40 0.1857E-01 + 27.50 0.2056E-01 + 27.60 0.2248E-01 + 27.70 0.2428E-01 + 27.80 0.2596E-01 + 27.90 0.2748E-01 + 28.00 0.2882E-01 + 28.10 0.2996E-01 + 28.20 0.3090E-01 + 28.30 0.3162E-01 + 28.40 0.3211E-01 + 28.50 0.3237E-01 + 28.60 0.3241E-01 + 28.70 0.3221E-01 + 28.80 0.3179E-01 + 28.90 0.3117E-01 + 29.00 0.3034E-01 + 29.10 0.2933E-01 + 29.20 0.2814E-01 + 29.30 0.2681E-01 + 29.40 0.2535E-01 + 29.50 0.2379E-01 + 29.60 0.2214E-01 + 29.70 0.2042E-01 + 29.80 0.1867E-01 + 29.90 0.1691E-01 + 30.00 0.1515E-01 + 30.10 0.1343E-01 + 30.20 0.1176E-01 + 30.30 0.1016E-01 + 30.40 0.8649E-02 + 30.50 0.7249E-02 + 30.60 0.5972E-02 + 30.70 0.4829E-02 + 30.80 0.3832E-02 + 30.90 0.2989E-02 + 31.00 0.2305E-02 + 31.10 0.1784E-02 + 31.20 0.1428E-02 + 31.30 0.1234E-02 + 31.40 0.1199E-02 + 31.50 0.1318E-02 + 31.60 0.1583E-02 + 31.70 0.1985E-02 + 31.80 0.2514E-02 + 31.90 0.3156E-02 + 32.00 0.3898E-02 + 32.10 0.4726E-02 + 32.20 0.5624E-02 + 32.30 0.6577E-02 + 32.40 0.7569E-02 + 32.50 0.8583E-02 + 32.60 0.9603E-02 + 32.70 0.1061E-01 + 32.80 0.1160E-01 + 32.90 0.1255E-01 + 33.00 0.1345E-01 + 33.10 0.1428E-01 + 33.20 0.1504E-01 + 33.30 0.1572E-01 + 33.40 0.1630E-01 + 33.50 0.1678E-01 + 33.60 0.1716E-01 + 33.70 0.1744E-01 + 33.80 0.1760E-01 + 33.90 0.1765E-01 + 34.00 0.1760E-01 + 34.10 0.1744E-01 + 34.20 0.1718E-01 + 34.30 0.1682E-01 + 34.40 0.1637E-01 + 34.50 0.1584E-01 + 34.60 0.1524E-01 + 34.70 0.1457E-01 + 34.80 0.1385E-01 + 34.90 0.1308E-01 + 35.00 0.1228E-01 + 35.10 0.1145E-01 + 35.20 0.1061E-01 + 35.30 0.9772E-02 + 35.40 0.8940E-02 + 35.50 0.8126E-02 + 35.60 0.7339E-02 + 35.70 0.6589E-02 + 35.80 0.5883E-02 + 35.90 0.5228E-02 + 36.00 0.4631E-02 + 36.10 0.4097E-02 + 36.20 0.3631E-02 + 36.30 0.3235E-02 + 36.40 0.2911E-02 + 36.50 0.2662E-02 + 36.60 0.2488E-02 + 36.70 0.2386E-02 + 36.80 0.2357E-02 + 36.90 0.2396E-02 + 37.00 0.2501E-02 + 37.10 0.2668E-02 + 37.20 0.2891E-02 + 37.30 0.3165E-02 + 37.40 0.3484E-02 + 37.50 0.3842E-02 + 37.60 0.4231E-02 + 37.70 0.4646E-02 + 37.80 0.5078E-02 + 37.90 0.5521E-02 + 38.00 0.5968E-02 + 38.10 0.6413E-02 + 38.20 0.6847E-02 + 38.30 0.7267E-02 + 38.40 0.7664E-02 + 38.50 0.8035E-02 + 38.60 0.8374E-02 + 38.70 0.8678E-02 + 38.80 0.8941E-02 + 38.90 0.9163E-02 + 39.00 0.9339E-02 + 39.10 0.9470E-02 + 39.20 0.9552E-02 + 39.30 0.9587E-02 + 39.40 0.9575E-02 + 39.50 0.9516E-02 + 39.60 0.9412E-02 + 39.70 0.9265E-02 + 39.80 0.9077E-02 + 39.90 0.8853E-02 + 40.00 0.8594E-02 + & diff --git a/book/notebooks/Transfer_template/fort.202 b/book/notebooks/Transfer_template/fort.202 new file mode 100644 index 0000000..e24c329 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.202 @@ -0,0 +1,411 @@ +# 401 angles, 1 tensor ranks for 0=projectile +@subtitle "n14(f17,ne18)c13 @ 170 MeV;" +@legend ON +@legend x1 0.2 +@legend y1 0.8 +@g0 type LOGY +@ s0 legend "Lab energy = 170.0000" +# s0 legend "Partition= 2 Excit= 1 near/far= 1" +# Theta sigma iT11 T20 T21 T22 Kyy for projectile + 0.000 3.123 + 0.1000 3.120 + 0.2000 3.112 + 0.3000 3.098 + 0.4000 3.079 + 0.5000 3.056 + 0.6000 3.030 + 0.7000 3.000 + 0.8000 2.969 + 0.9000 2.937 + 1.000 2.906 + 1.100 2.876 + 1.200 2.849 + 1.300 2.826 + 1.400 2.808 + 1.500 2.797 + 1.600 2.794 + 1.700 2.799 + 1.800 2.813 + 1.900 2.838 + 2.000 2.874 + 2.100 2.921 + 2.200 2.979 + 2.300 3.049 + 2.400 3.129 + 2.500 3.220 + 2.600 3.320 + 2.700 3.428 + 2.800 3.543 + 2.900 3.664 + 3.000 3.789 + 3.100 3.915 + 3.200 4.042 + 3.300 4.166 + 3.400 4.285 + 3.500 4.398 + 3.600 4.502 + 3.700 4.594 + 3.800 4.674 + 3.900 4.738 + 4.000 4.786 + 4.100 4.815 + 4.200 4.825 + 4.300 4.815 + 4.400 4.784 + 4.500 4.732 + 4.600 4.659 + 4.700 4.564 + 4.800 4.451 + 4.900 4.318 + 5.000 4.168 + 5.100 4.002 + 5.200 3.822 + 5.300 3.631 + 5.400 3.430 + 5.500 3.222 + 5.600 3.010 + 5.700 2.796 + 5.800 2.583 + 5.900 2.373 + 6.000 2.169 + 6.100 1.972 + 6.200 1.786 + 6.300 1.611 + 6.400 1.450 + 6.500 1.303 + 6.600 1.172 + 6.700 1.058 + 6.800 0.9608 + 6.900 0.8804 + 7.000 0.8171 + 7.100 0.7702 + 7.200 0.7392 + 7.300 0.7231 + 7.400 0.7208 + 7.500 0.7310 + 7.600 0.7523 + 7.700 0.7830 + 7.800 0.8215 + 7.900 0.8662 + 8.000 0.9153 + 8.100 0.9671 + 8.200 1.020 + 8.300 1.073 + 8.400 1.123 + 8.500 1.170 + 8.600 1.213 + 8.700 1.250 + 8.800 1.281 + 8.900 1.305 + 9.000 1.321 + 9.100 1.328 + 9.200 1.328 + 9.300 1.320 + 9.400 1.303 + 9.500 1.279 + 9.600 1.247 + 9.700 1.209 + 9.800 1.164 + 9.900 1.114 + 10.00 1.060 + 10.10 1.001 + 10.20 0.9402 + 10.30 0.8772 + 10.40 0.8132 + 10.50 0.7490 + 10.60 0.6855 + 10.70 0.6235 + 10.80 0.5637 + 10.90 0.5068 + 11.00 0.4533 + 11.10 0.4038 + 11.20 0.3587 + 11.30 0.3183 + 11.40 0.2827 + 11.50 0.2521 + 11.60 0.2266 + 11.70 0.2061 + 11.80 0.1905 + 11.90 0.1796 + 12.00 0.1730 + 12.10 0.1706 + 12.20 0.1719 + 12.30 0.1765 + 12.40 0.1839 + 12.50 0.1938 + 12.60 0.2056 + 12.70 0.2188 + 12.80 0.2331 + 12.90 0.2479 + 13.00 0.2628 + 13.10 0.2773 + 13.20 0.2912 + 13.30 0.3040 + 13.40 0.3155 + 13.50 0.3254 + 13.60 0.3335 + 13.70 0.3396 + 13.80 0.3436 + 13.90 0.3454 + 14.00 0.3450 + 14.10 0.3424 + 14.20 0.3377 + 14.30 0.3309 + 14.40 0.3221 + 14.50 0.3117 + 14.60 0.2996 + 14.70 0.2861 + 14.80 0.2714 + 14.90 0.2557 + 15.00 0.2393 + 15.10 0.2225 + 15.20 0.2053 + 15.30 0.1881 + 15.40 0.1712 + 15.50 0.1546 + 15.60 0.1386 + 15.70 0.1233 + 15.80 0.1090 + 15.90 0.9568E-01 + 16.00 0.8354E-01 + 16.10 0.7263E-01 + 16.20 0.6303E-01 + 16.30 0.5477E-01 + 16.40 0.4787E-01 + 16.50 0.4233E-01 + 16.60 0.3812E-01 + 16.70 0.3519E-01 + 16.80 0.3349E-01 + 16.90 0.3293E-01 + 17.00 0.3343E-01 + 17.10 0.3489E-01 + 17.20 0.3719E-01 + 17.30 0.4022E-01 + 17.40 0.4386E-01 + 17.50 0.4797E-01 + 17.60 0.5244E-01 + 17.70 0.5713E-01 + 17.80 0.6194E-01 + 17.90 0.6674E-01 + 18.00 0.7142E-01 + 18.10 0.7589E-01 + 18.20 0.8006E-01 + 18.30 0.8384E-01 + 18.40 0.8716E-01 + 18.50 0.8997E-01 + 18.60 0.9223E-01 + 18.70 0.9388E-01 + 18.80 0.9492E-01 + 18.90 0.9534E-01 + 19.00 0.9513E-01 + 19.10 0.9430E-01 + 19.20 0.9288E-01 + 19.30 0.9089E-01 + 19.40 0.8839E-01 + 19.50 0.8540E-01 + 19.60 0.8198E-01 + 19.70 0.7819E-01 + 19.80 0.7408E-01 + 19.90 0.6973E-01 + 20.00 0.6518E-01 + 20.10 0.6052E-01 + 20.20 0.5579E-01 + 20.30 0.5106E-01 + 20.40 0.4640E-01 + 20.50 0.4184E-01 + 20.60 0.3745E-01 + 20.70 0.3328E-01 + 20.80 0.2935E-01 + 20.90 0.2572E-01 + 21.00 0.2240E-01 + 21.10 0.1942E-01 + 21.20 0.1680E-01 + 21.30 0.1456E-01 + 21.40 0.1269E-01 + 21.50 0.1119E-01 + 21.60 0.1007E-01 + 21.70 0.9302E-02 + 21.80 0.8881E-02 + 21.90 0.8785E-02 + 22.00 0.8991E-02 + 22.10 0.9471E-02 + 22.20 0.1020E-01 + 22.30 0.1113E-01 + 22.40 0.1225E-01 + 22.50 0.1352E-01 + 22.60 0.1490E-01 + 22.70 0.1636E-01 + 22.80 0.1786E-01 + 22.90 0.1938E-01 + 23.00 0.2087E-01 + 23.10 0.2232E-01 + 23.20 0.2369E-01 + 23.30 0.2497E-01 + 23.40 0.2613E-01 + 23.50 0.2715E-01 + 23.60 0.2801E-01 + 23.70 0.2871E-01 + 23.80 0.2924E-01 + 23.90 0.2958E-01 + 24.00 0.2975E-01 + 24.10 0.2973E-01 + 24.20 0.2954E-01 + 24.30 0.2917E-01 + 24.40 0.2864E-01 + 24.50 0.2796E-01 + 24.60 0.2713E-01 + 24.70 0.2618E-01 + 24.80 0.2512E-01 + 24.90 0.2397E-01 + 25.00 0.2273E-01 + 25.10 0.2144E-01 + 25.20 0.2010E-01 + 25.30 0.1873E-01 + 25.40 0.1736E-01 + 25.50 0.1600E-01 + 25.60 0.1466E-01 + 25.70 0.1336E-01 + 25.80 0.1211E-01 + 25.90 0.1092E-01 + 26.00 0.9814E-02 + 26.10 0.8788E-02 + 26.20 0.7853E-02 + 26.30 0.7017E-02 + 26.40 0.6283E-02 + 26.50 0.5653E-02 + 26.60 0.5130E-02 + 26.70 0.4713E-02 + 26.80 0.4400E-02 + 26.90 0.4188E-02 + 27.00 0.4072E-02 + 27.10 0.4047E-02 + 27.20 0.4106E-02 + 27.30 0.4243E-02 + 27.40 0.4448E-02 + 27.50 0.4713E-02 + 27.60 0.5030E-02 + 27.70 0.5388E-02 + 27.80 0.5778E-02 + 27.90 0.6190E-02 + 28.00 0.6616E-02 + 28.10 0.7047E-02 + 28.20 0.7473E-02 + 28.30 0.7887E-02 + 28.40 0.8282E-02 + 28.50 0.8649E-02 + 28.60 0.8985E-02 + 28.70 0.9283E-02 + 28.80 0.9539E-02 + 28.90 0.9749E-02 + 29.00 0.9912E-02 + 29.10 0.1002E-01 + 29.20 0.1009E-01 + 29.30 0.1010E-01 + 29.40 0.1005E-01 + 29.50 0.9965E-02 + 29.60 0.9828E-02 + 29.70 0.9645E-02 + 29.80 0.9421E-02 + 29.90 0.9159E-02 + 30.00 0.8863E-02 + 30.10 0.8536E-02 + 30.20 0.8185E-02 + 30.30 0.7812E-02 + 30.40 0.7423E-02 + 30.50 0.7023E-02 + 30.60 0.6616E-02 + 30.70 0.6208E-02 + 30.80 0.5803E-02 + 30.90 0.5404E-02 + 31.00 0.5017E-02 + 31.10 0.4644E-02 + 31.20 0.4289E-02 + 31.30 0.3955E-02 + 31.40 0.3645E-02 + 31.50 0.3361E-02 + 31.60 0.3104E-02 + 31.70 0.2875E-02 + 31.80 0.2676E-02 + 31.90 0.2507E-02 + 32.00 0.2368E-02 + 32.10 0.2257E-02 + 32.20 0.2176E-02 + 32.30 0.2121E-02 + 32.40 0.2092E-02 + 32.50 0.2088E-02 + 32.60 0.2105E-02 + 32.70 0.2142E-02 + 32.80 0.2197E-02 + 32.90 0.2266E-02 + 33.00 0.2349E-02 + 33.10 0.2441E-02 + 33.20 0.2541E-02 + 33.30 0.2646E-02 + 33.40 0.2753E-02 + 33.50 0.2860E-02 + 33.60 0.2966E-02 + 33.70 0.3067E-02 + 33.80 0.3162E-02 + 33.90 0.3250E-02 + 34.00 0.3328E-02 + 34.10 0.3396E-02 + 34.20 0.3452E-02 + 34.30 0.3496E-02 + 34.40 0.3527E-02 + 34.50 0.3543E-02 + 34.60 0.3546E-02 + 34.70 0.3535E-02 + 34.80 0.3511E-02 + 34.90 0.3473E-02 + 35.00 0.3422E-02 + 35.10 0.3359E-02 + 35.20 0.3286E-02 + 35.30 0.3201E-02 + 35.40 0.3108E-02 + 35.50 0.3006E-02 + 35.60 0.2898E-02 + 35.70 0.2784E-02 + 35.80 0.2667E-02 + 35.90 0.2546E-02 + 36.00 0.2423E-02 + 36.10 0.2301E-02 + 36.20 0.2179E-02 + 36.30 0.2059E-02 + 36.40 0.1943E-02 + 36.50 0.1830E-02 + 36.60 0.1723E-02 + 36.70 0.1621E-02 + 36.80 0.1526E-02 + 36.90 0.1438E-02 + 37.00 0.1357E-02 + 37.10 0.1284E-02 + 37.20 0.1219E-02 + 37.30 0.1162E-02 + 37.40 0.1113E-02 + 37.50 0.1072E-02 + 37.60 0.1039E-02 + 37.70 0.1013E-02 + 37.80 0.9948E-03 + 37.90 0.9830E-03 + 38.00 0.9775E-03 + 38.10 0.9776E-03 + 38.20 0.9827E-03 + 38.30 0.9924E-03 + 38.40 0.1006E-02 + 38.50 0.1022E-02 + 38.60 0.1042E-02 + 38.70 0.1063E-02 + 38.80 0.1085E-02 + 38.90 0.1108E-02 + 39.00 0.1130E-02 + 39.10 0.1152E-02 + 39.20 0.1173E-02 + 39.30 0.1193E-02 + 39.40 0.1210E-02 + 39.50 0.1225E-02 + 39.60 0.1237E-02 + 39.70 0.1245E-02 + 39.80 0.1251E-02 + 39.90 0.1253E-02 + 40.00 0.1252E-02 + & diff --git a/book/notebooks/Transfer_template/fort.203 b/book/notebooks/Transfer_template/fort.203 new file mode 100644 index 0000000..bf63fc3 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.203 @@ -0,0 +1,411 @@ +# 401 angles, 1 tensor ranks for 0=projectile +@subtitle "n14(f17,ne18)c13 @ 170 MeV;" +@legend ON +@legend x1 0.2 +@legend y1 0.8 +@g0 type LOGY +@ s0 legend "Lab energy = 170.0000" +# s0 legend "Partition= 2 Excit= 2 near/far= 1" +# Theta sigma iT11 T20 T21 T22 Kyy for projectile + 0.000 0.000 + 0.1000 0.000 + 0.2000 0.000 + 0.3000 0.000 + 0.4000 0.000 + 0.5000 0.000 + 0.6000 0.000 + 0.7000 0.000 + 0.8000 0.000 + 0.9000 0.000 + 1.000 0.000 + 1.100 0.000 + 1.200 0.000 + 1.300 0.000 + 1.400 0.000 + 1.500 0.000 + 1.600 0.000 + 1.700 0.000 + 1.800 0.000 + 1.900 0.000 + 2.000 0.000 + 2.100 0.000 + 2.200 0.000 + 2.300 0.000 + 2.400 0.000 + 2.500 0.000 + 2.600 0.000 + 2.700 0.000 + 2.800 0.000 + 2.900 0.000 + 3.000 0.000 + 3.100 0.000 + 3.200 0.000 + 3.300 0.000 + 3.400 0.000 + 3.500 0.000 + 3.600 0.000 + 3.700 0.000 + 3.800 0.000 + 3.900 0.000 + 4.000 0.000 + 4.100 0.000 + 4.200 0.000 + 4.300 0.000 + 4.400 0.000 + 4.500 0.000 + 4.600 0.000 + 4.700 0.000 + 4.800 0.000 + 4.900 0.000 + 5.000 0.000 + 5.100 0.000 + 5.200 0.000 + 5.300 0.000 + 5.400 0.000 + 5.500 0.000 + 5.600 0.000 + 5.700 0.000 + 5.800 0.000 + 5.900 0.000 + 6.000 0.000 + 6.100 0.000 + 6.200 0.000 + 6.300 0.000 + 6.400 0.000 + 6.500 0.000 + 6.600 0.000 + 6.700 0.000 + 6.800 0.000 + 6.900 0.000 + 7.000 0.000 + 7.100 0.000 + 7.200 0.000 + 7.300 0.000 + 7.400 0.000 + 7.500 0.000 + 7.600 0.000 + 7.700 0.000 + 7.800 0.000 + 7.900 0.000 + 8.000 0.000 + 8.100 0.000 + 8.200 0.000 + 8.300 0.000 + 8.400 0.000 + 8.500 0.000 + 8.600 0.000 + 8.700 0.000 + 8.800 0.000 + 8.900 0.000 + 9.000 0.000 + 9.100 0.000 + 9.200 0.000 + 9.300 0.000 + 9.400 0.000 + 9.500 0.000 + 9.600 0.000 + 9.700 0.000 + 9.800 0.000 + 9.900 0.000 + 10.00 0.000 + 10.10 0.000 + 10.20 0.000 + 10.30 0.000 + 10.40 0.000 + 10.50 0.000 + 10.60 0.000 + 10.70 0.000 + 10.80 0.000 + 10.90 0.000 + 11.00 0.000 + 11.10 0.000 + 11.20 0.000 + 11.30 0.000 + 11.40 0.000 + 11.50 0.000 + 11.60 0.000 + 11.70 0.000 + 11.80 0.000 + 11.90 0.000 + 12.00 0.000 + 12.10 0.000 + 12.20 0.000 + 12.30 0.000 + 12.40 0.000 + 12.50 0.000 + 12.60 0.000 + 12.70 0.000 + 12.80 0.000 + 12.90 0.000 + 13.00 0.000 + 13.10 0.000 + 13.20 0.000 + 13.30 0.000 + 13.40 0.000 + 13.50 0.000 + 13.60 0.000 + 13.70 0.000 + 13.80 0.000 + 13.90 0.000 + 14.00 0.000 + 14.10 0.000 + 14.20 0.000 + 14.30 0.000 + 14.40 0.000 + 14.50 0.000 + 14.60 0.000 + 14.70 0.000 + 14.80 0.000 + 14.90 0.000 + 15.00 0.000 + 15.10 0.000 + 15.20 0.000 + 15.30 0.000 + 15.40 0.000 + 15.50 0.000 + 15.60 0.000 + 15.70 0.000 + 15.80 0.000 + 15.90 0.000 + 16.00 0.000 + 16.10 0.000 + 16.20 0.000 + 16.30 0.000 + 16.40 0.000 + 16.50 0.000 + 16.60 0.000 + 16.70 0.000 + 16.80 0.000 + 16.90 0.000 + 17.00 0.000 + 17.10 0.000 + 17.20 0.000 + 17.30 0.000 + 17.40 0.000 + 17.50 0.000 + 17.60 0.000 + 17.70 0.000 + 17.80 0.000 + 17.90 0.000 + 18.00 0.000 + 18.10 0.000 + 18.20 0.000 + 18.30 0.000 + 18.40 0.000 + 18.50 0.000 + 18.60 0.000 + 18.70 0.000 + 18.80 0.000 + 18.90 0.000 + 19.00 0.000 + 19.10 0.000 + 19.20 0.000 + 19.30 0.000 + 19.40 0.000 + 19.50 0.000 + 19.60 0.000 + 19.70 0.000 + 19.80 0.000 + 19.90 0.000 + 20.00 0.000 + 20.10 0.000 + 20.20 0.000 + 20.30 0.000 + 20.40 0.000 + 20.50 0.000 + 20.60 0.000 + 20.70 0.000 + 20.80 0.000 + 20.90 0.000 + 21.00 0.000 + 21.10 0.000 + 21.20 0.000 + 21.30 0.000 + 21.40 0.000 + 21.50 0.000 + 21.60 0.000 + 21.70 0.000 + 21.80 0.000 + 21.90 0.000 + 22.00 0.000 + 22.10 0.000 + 22.20 0.000 + 22.30 0.000 + 22.40 0.000 + 22.50 0.000 + 22.60 0.000 + 22.70 0.000 + 22.80 0.000 + 22.90 0.000 + 23.00 0.000 + 23.10 0.000 + 23.20 0.000 + 23.30 0.000 + 23.40 0.000 + 23.50 0.000 + 23.60 0.000 + 23.70 0.000 + 23.80 0.000 + 23.90 0.000 + 24.00 0.000 + 24.10 0.000 + 24.20 0.000 + 24.30 0.000 + 24.40 0.000 + 24.50 0.000 + 24.60 0.000 + 24.70 0.000 + 24.80 0.000 + 24.90 0.000 + 25.00 0.000 + 25.10 0.000 + 25.20 0.000 + 25.30 0.000 + 25.40 0.000 + 25.50 0.000 + 25.60 0.000 + 25.70 0.000 + 25.80 0.000 + 25.90 0.000 + 26.00 0.000 + 26.10 0.000 + 26.20 0.000 + 26.30 0.000 + 26.40 0.000 + 26.50 0.000 + 26.60 0.000 + 26.70 0.000 + 26.80 0.000 + 26.90 0.000 + 27.00 0.000 + 27.10 0.000 + 27.20 0.000 + 27.30 0.000 + 27.40 0.000 + 27.50 0.000 + 27.60 0.000 + 27.70 0.000 + 27.80 0.000 + 27.90 0.000 + 28.00 0.000 + 28.10 0.000 + 28.20 0.000 + 28.30 0.000 + 28.40 0.000 + 28.50 0.000 + 28.60 0.000 + 28.70 0.000 + 28.80 0.000 + 28.90 0.000 + 29.00 0.000 + 29.10 0.000 + 29.20 0.000 + 29.30 0.000 + 29.40 0.000 + 29.50 0.000 + 29.60 0.000 + 29.70 0.000 + 29.80 0.000 + 29.90 0.000 + 30.00 0.000 + 30.10 0.000 + 30.20 0.000 + 30.30 0.000 + 30.40 0.000 + 30.50 0.000 + 30.60 0.000 + 30.70 0.000 + 30.80 0.000 + 30.90 0.000 + 31.00 0.000 + 31.10 0.000 + 31.20 0.000 + 31.30 0.000 + 31.40 0.000 + 31.50 0.000 + 31.60 0.000 + 31.70 0.000 + 31.80 0.000 + 31.90 0.000 + 32.00 0.000 + 32.10 0.000 + 32.20 0.000 + 32.30 0.000 + 32.40 0.000 + 32.50 0.000 + 32.60 0.000 + 32.70 0.000 + 32.80 0.000 + 32.90 0.000 + 33.00 0.000 + 33.10 0.000 + 33.20 0.000 + 33.30 0.000 + 33.40 0.000 + 33.50 0.000 + 33.60 0.000 + 33.70 0.000 + 33.80 0.000 + 33.90 0.000 + 34.00 0.000 + 34.10 0.000 + 34.20 0.000 + 34.30 0.000 + 34.40 0.000 + 34.50 0.000 + 34.60 0.000 + 34.70 0.000 + 34.80 0.000 + 34.90 0.000 + 35.00 0.000 + 35.10 0.000 + 35.20 0.000 + 35.30 0.000 + 35.40 0.000 + 35.50 0.000 + 35.60 0.000 + 35.70 0.000 + 35.80 0.000 + 35.90 0.000 + 36.00 0.000 + 36.10 0.000 + 36.20 0.000 + 36.30 0.000 + 36.40 0.000 + 36.50 0.000 + 36.60 0.000 + 36.70 0.000 + 36.80 0.000 + 36.90 0.000 + 37.00 0.000 + 37.10 0.000 + 37.20 0.000 + 37.30 0.000 + 37.40 0.000 + 37.50 0.000 + 37.60 0.000 + 37.70 0.000 + 37.80 0.000 + 37.90 0.000 + 38.00 0.000 + 38.10 0.000 + 38.20 0.000 + 38.30 0.000 + 38.40 0.000 + 38.50 0.000 + 38.60 0.000 + 38.70 0.000 + 38.80 0.000 + 38.90 0.000 + 39.00 0.000 + 39.10 0.000 + 39.20 0.000 + 39.30 0.000 + 39.40 0.000 + 39.50 0.000 + 39.60 0.000 + 39.70 0.000 + 39.80 0.000 + 39.90 0.000 + 40.00 0.000 + & diff --git a/book/notebooks/Transfer_template/fort.239 b/book/notebooks/Transfer_template/fort.239 new file mode 100644 index 0000000..715a435 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.239 @@ -0,0 +1 @@ +170.0000 1.760083E+03 1760.3 3237.5 0.61071-309 0.0000 0.26397 0.0000 diff --git a/book/notebooks/Transfer_template/fort.3 b/book/notebooks/Transfer_template/fort.3 new file mode 100644 index 0000000..bf411ce --- /dev/null +++ b/book/notebooks/Transfer_template/fort.3 @@ -0,0 +1,517 @@ +&PARTITION + NAMEP='f17 ', + MASSP= 17.0000000 , + ZP= 9.00000000 , + NEX=1 , + PWF=T, + NAMET='n14 ', + MASST= 14.0000000 , + ZT= 7.00000000 , + QVAL= 0.00000000 , + READSTATES=0 , + PRMAX= 0.0000000000000000 , + LPMAX=-1 , + MIXPOT=0 , + / +&STATES + JP= 2.50000000 , + COPYP=0 , + PTYP=1 , + BANDP=1 , + EP= 0.00000000 , + KKP= 0.00000000 , + TP= 0.00000000 , + CPOT=1 , + JT= 1.00000000 , + COPYT=0 , + PTYT=1 , + BANDT=1 , + ET= 0.00000000 , + KKT= 0.00000000 , + TT= 0.00000000 , + FEXCH=F, + IGNORE=F, + INFAM=0 , + OUTFAM=0 , + / +&PARTITION + NAMEP='ne18 ', + MASSP= 18.0000000 , + ZP= 10.0000000 , + NEX=2 , + PWF=T, + NAMET='c13 ', + MASST= 13.0000000 , + ZT= 6.00000000 , + QVAL= 3.62859988 , + READSTATES=0 , + PRMAX= 0.0000000000000000 , + LPMAX=-1 , + MIXPOT=0 , + / +&STATES + JP= 0.00000000 , + COPYP=0 , + PTYP=1 , + BANDP=1 , + EP= 0.00000000 , + KKP= 0.00000000 , + TP= 0.00000000 , + CPOT=2 , + JT= 0.500000000 , + COPYT=0 , + PTYT=1 , + BANDT=1 , + ET= 0.00000000 , + KKT= 0.00000000 , + TT= 0.00000000 , + FEXCH=F, + IGNORE=F, + INFAM=0 , + OUTFAM=0 , + / +&STATES + JP= 4.00000000 , + COPYP=0 , + PTYP=1 , + BANDP=1 , + EP= 3.37599993 , + KKP= 0.00000000 , + TP= 0.00000000 , + CPOT=2 , + JT= 0.00000000 , + COPYT=0 , + PTYT=0 , + BANDT=0 , + ET= 0.00000000 , + KKT= 0.00000000 , + TT= 0.00000000 , + FEXCH=F, + IGNORE=F, + INFAM=0 , + OUTFAM=0 , + / + &partition / +&POTL + KPI=1 , + TYPEI=0 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 14.0000000 , 17.0000000 , 1.29999995 , 9*0.00000000 , + + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=1 , + TYPEI=1 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 37.2000008 , 1.20000005 , 0.600000024 , 21.6000004 , + 1.20000005 , 0.689999998 , 6*0.00000000 , + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=2 , + TYPEI=0 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 13.0000000 , 18.0000000 , 1.29999995 , 9*0.00000000 , + + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=2 , + TYPEI=1 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 37.2000008 , 1.20000005 , 0.600000024 , 21.6000004 , + 1.20000005 , 0.689999998 , 6*0.00000000 , + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=3 , + TYPEI=0 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 17.0000000 , 0.00000000 , 1.20000005 , 9*0.00000000 , + + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=3 , + TYPEI=1 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 50.0000000 , 1.20000005 , 0.649999976 , 9*0.00000000 , + + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=3 , + TYPEI=3 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 6.00000000 , 1.20000005 , 0.649999976 , 9*0.00000000 , + + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=4 , + TYPEI=0 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 13.0000000 , 0.00000000 , 1.20000005 , 9*0.00000000 , + + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=4 , + TYPEI=1 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 50.0000000 , 1.20000005 , 0.649999976 , 9*0.00000000 , + + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=4 , + TYPEI=3 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 6.00000000 , 1.20000005 , 0.649999976 , 9*0.00000000 , + + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=5 , + TYPEI=0 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 14.0000000 , 17.0000000 , 1.29999995 , 9*0.00000000 , + + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / +&POTL + KPI=5 , + TYPEI=1 , + ITT=F, + NOSUB=F, + SHAPEI=0 , + P= 0.00000000 , 37.2000008 , 1.20000005 , 0.600000024 , 21.6000004 , + 1.20000005 , 0.689999998 , 6*0.00000000 , + LSHAPEI=0 , + JL=' ', + XLVARY= 0.0000000000000000 , + ALVARY= 0.0000000000000000 , + DATAFILE=' ', + / + &potl / +&OVERLAP + KN1=1 , + KN2=1 , + IC1=1 , + IC2=2 , + IN=1 , + KIND=0 , + CH1=' ', + NN=1 , + L=2 , + LMAX=0 , + SN= 0.500000000 , + IA=0 , + J= 2.50000000 , + IB=0 , + KBPOT=3 , + KRPOT=0 , + BE= 3.92199993 , + ISC=1 , + IPC=0 , + NFL=0 , + NAM=0 , + AMPL= 0.00000000 , + KEEP=F, + DM= 1.0000000000000000 , + NK=0 , + ER= 0.00000000 , + E= 0.0000000000000000 , + PPOWER= 0.0000000000000000 , + RSMIN= 0.0000000000000000 , + RSMAX= 39.899999999999999 , + NLAG=0 , + PHASE= 0.0000000000000000 , + AUTOWID= 0.00000000 , + / +&OVERLAP + KN1=2 , + KN2=2 , + IC1=2 , + IC2=1 , + IN=2 , + KIND=3 , + CH1=' ', + NN=1 , + L=1 , + LMAX=1 , + SN= 0.500000000 , + IA=1 , + J= 1.00000000 , + IB=1 , + KBPOT=4 , + KRPOT=0 , + BE= 7.55060005 , + ISC=1 , + IPC=0 , + NFL=0 , + NAM=0 , + AMPL= 0.00000000 , + KEEP=F, + DM= 1.0000000000000000 , + NK=0 , + ER= 0.00000000 , + E= 0.0000000000000000 , + PPOWER= 0.0000000000000000 , + RSMIN= 0.0000000000000000 , + RSMAX= 39.899999999999999 , + NLAG=0 , + PHASE= 0.0000000000000000 , + AUTOWID= 0.00000000 , + / + &overlap / +&COUPLING + ICTO=-2 , + ICFROM=1 , + KIND=7 , + IP1=0 , + IP2=-1 , + IP3=5 , + IP4=-1 , + IP5=-1 , + P1= 0.00000000 , + P2= 0.00000000 , + JMAX= 0.00000000 , + RMAX= 0.0000000000000000 , + KFRAG=0 , + KCORE=0 , + NFORMS=0 , + INFILE=4 , + / +&CFP + IN=1 , + IB=1 , + IA=1 , + KN=1 , + A= 1.00000000 , + KEEP=F, + / +&CFP + IN=2 , + IB=1 , + IA=1 , + KN=2 , + A= 1.00000000 , + KEEP=F, + / + &cfp / + &coupling / + 'End of couplings' + + + parameters: mxx mxp mxpex maxit maxcpl maxnnu + required: 2 2 3 0 1 36 + + parameters: maxn mint maxnln maxnlo msp lmax1 mpair + required: 1334 1331 191 57 2 140 1 + + parameters: mloc mclist maxqrn mpwcoup + required: 12 5 4 0 + + + parameters: mloc maxqrn maxqrn2 mclist nchbinmax + required: 12 4 1 5 1 + + +n14(f17,ne18)c13 @ 170 MeV; +NAMELIST +&FRESCO + HCM= 2.9999999999999999E-002, + RMATCH= 39.960000000000001 , + RINTP= 0.20999999999999999 , + HNL= 8.9999999999999997E-002, + RNL= 5.0400000000000000 , + CENTRE= 0.0000000000000000 , + HNN= 0.20999999999999999 , + RNN= 0.0000000000000000 , + RMIN= 0.0000000000000000 , + RSP= 39.899999999999999 , + RASYM= 0.0000000000000000 , + ACCRCY= 1.0000000474974513E-003, + SWITCH= 0.0000000000000000 , + AJSWTCH= 0.0000000000000000 , + SINJMAX= 0.0000000000000000 , + PLANE=0 , + JTMIN= 0.0000000000000000 , + JTMAX= 120.00000000000000 , + ABSEND= -1.0000000000000000 , + DRY=F, + RELA=' ', + NEARFA=1 , + JUMP= 6*0 , + JBORD= 7*0.00000000 , + PSET=0 , + JSET=0 , + JLEAST= 0.0000000000000000 , + KQMAX=0 , + PP=0 , + THMIN= 0.0000000000000000 , + THMAX= 40.000000000000000 , + THINC= 0.10000000000000001 , + KOORDS=0 , + CUTL= -1.6000000238418579 , + CUTR= 0.0000000000000000 , + CUTC= 0.0000000000000000 , + COMPLEXBINS=F, + IPS= 0.0000000000000000 , + IT0=0 , + ITER=1 , + FATAL=T, + IBLOCK=0 , + PADE=0 , + PSIREN=F, + ISO=' ', + NNU=36 , + MAXL=130 , + MINL=0 , + MTMIN=6 , + EPC= 0.69444441795349121 , + ERANGE= 0.0000000000000000 , + DK= 0.0000000000000000 , + NOSOL=F, + NRBASES=0 , + NRBMIN=0 , + PRALPHA=F, + PCON=0 , + RMATR= 39.960000000000001 , + BNDX= 2*0.0000000000000000 , + MEIGS=0 , + BUTTLE=0 , + WEAK= 1.0000000474974513E-003, + LLMAX=-1 , + EXPAND= 1.2000000476837158 , 1.0000000000000000 , 0.0000000000000000 , 4.0000000000000000 , 0.40000000596046448 , + 2.0000000000000000 , 5*1.0000000000000000 , + HKTARG= 0.20000000298023224 , + MPIHELP=1 , + EIGENS=0 , + NLAGCC=0 , + CHANS=1 , + LISTCC=0 , + TRENEG=0 , + CDETR=0 , + SMATS=0 , + XSTABL=1 , + NLPL=0 , + WAVES=0 , + CCREAL=F, + LAMPL=0 , + VEFF=0 , + KFUS=0 , + WDISK=0 , + BPM=0 , + MELFIL=0 , + CDCC=0 , + NFUS=0 , + NPARAMETERS=0 , + SMALLCHAN= 0.0000000000000000 , + SMALLCOUP= 9.9999999999999998E-013, + SUMFORM=0 , + MAXCOUP= 3*0 , + OMPFORM=0 , + INITWF=0 , + PLUTO= 10*0 , + INH=0 , + TMP='/tmp/ ', + MASFIL='/netopt/PHS1IT/lib/m88lrb ', + UNITMASS= 1.0000000000000000 , + FINEC= 137.03599000000000 , + PEL=1 , + EXL=1 , + LAB=1 , + LIN=1 , + LEX=1 , + ELAB= 170.00000000000000 , 4*0.0000000000000000 , + NLAB= 4*0 , + VSEARCH=0 , + ECHAN=0 , + ENODES=0 , + GSCOUPLONLY=F, + CCBINS=F, + SOCK= 10*1.0000000000000000 , + BTYPE=' ', + BOLDPLOT=F, + KRM=0 , + DIST0= -1.0000000000000000 , + TCFILE=0 , + DGAM=0 , + EOBS=F, + GRACE=T, + ELPMAX= 9.9999997764825821E-003, + RELREF=1 , + TCFILENAME='fort.5420 ', + NUMNODE=0 , + SUMFORM=0 , + / + 'cxwf=' F ' ccbins=' F ' sumccbins =' F ' sumkql =' F ' ccbins=' F ' sumform =' 0 diff --git a/book/notebooks/Transfer_template/fort.35 b/book/notebooks/Transfer_template/fort.35 new file mode 100644 index 0000000..98bf274 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.35 @@ -0,0 +1 @@ +76.77419 7.382128E+09 0.0000 1.6348 1.6402 16.364 0.39437E-04 3.4286 diff --git a/book/notebooks/Transfer_template/fort.38 b/book/notebooks/Transfer_template/fort.38 new file mode 100644 index 0000000..4a74963 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.38 @@ -0,0 +1 @@ + 3 1 1 diff --git a/book/notebooks/Transfer_template/fort.39 b/book/notebooks/Transfer_template/fort.39 new file mode 100644 index 0000000..b3fc8a1 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.39 @@ -0,0 +1 @@ +170.0000 1.760083E+03 1760.3 3237.5 0.26397 0.0000 0.26397 diff --git a/book/notebooks/Transfer_template/fort.4 b/book/notebooks/Transfer_template/fort.4 new file mode 100644 index 0000000..e69de29 diff --git a/book/notebooks/Transfer_template/fort.40 b/book/notebooks/Transfer_template/fort.40 new file mode 100644 index 0000000..0d39123 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.40 @@ -0,0 +1 @@ +170.0000 1.760083E+03 1760.3 0.0000 0.26397 0.0000 3237.5 1477.2 diff --git a/book/notebooks/Transfer_template/fort.46 b/book/notebooks/Transfer_template/fort.46 new file mode 100644 index 0000000..2df7208 --- /dev/null +++ b/book/notebooks/Transfer_template/fort.46 @@ -0,0 +1,2 @@ + 39.870 2.54722 0.130237E-07 0.511291E-08 + 39.870 5.75214 0.149787E-09 0.260401E-10 diff --git a/book/notebooks/Transfer_template/fort.48 b/book/notebooks/Transfer_template/fort.48 new file mode 100644 index 0000000..917ecab --- /dev/null +++ b/book/notebooks/Transfer_template/fort.48 @@ -0,0 +1,3046 @@ + kp,loop = 1 0 is type,so,p(:) = 0 F 0.000 14.00 17.00 1.300 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 1 -1 is type,so,p(:) = 1 F 0.000 37.20 1.200 0.6000 21.60 1.200 0.6900 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 2 0 is type,so,p(:) = 0 F 0.000 13.00 18.00 1.300 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 2 -1 is type,so,p(:) = 1 F 0.000 37.20 1.200 0.6000 21.60 1.200 0.6900 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 3 0 is type,so,p(:) = 0 F 0.000 17.00 0.000 1.200 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 3 -1 is type,so,p(:) = 1 F 0.000 50.00 1.200 0.6500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 3 -1 is type,so,p(:) = 3 T 0.000 6.000 1.200 0.6500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 4 0 is type,so,p(:) = 0 F 0.000 13.00 0.000 1.200 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 4 -1 is type,so,p(:) = 1 F 0.000 50.00 1.200 0.6500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 4 -1 is type,so,p(:) = 3 T 0.000 6.000 1.200 0.6500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 5 0 is type,so,p(:) = 0 F 0.000 14.00 17.00 1.300 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + kp,loop = 5 -1 is type,so,p(:) = 1 F 0.000 37.20 1.200 0.6000 21.60 1.200 0.6900 0.000 0.000 0.000 0.000 0.000 0.000 + mnpair increased by 0 to 0 + mclist = 5 from 0 3 0 , 1 + FRXX0: MINL,MAXL = 0 130 + MAXLAM0,J14T16,J40P = 0 F F + ipmax = 2 + MINL,MAXL,MDONE = 0 130 54 + JBORD: 0.50000000000000000 120.50000000000000 + JUMP: 1 + 740 M,N = 1333 1334 + Try JTOTAL = 0.50000000000000000 +Allocate CLIST( 6 6 5) in 0.000 GB + Allocate NCLIST in 8.63999958E-07 GB + Allocate SRC/PSI etc in 1.92095991E-04 GB + Allocate FIM(D) in 3.83999975E-07 GB 48.000000000000000 + Allocate 12 spaces in coupling list 6 + XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -1 + XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -2 + XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -3 + DONE incremented c by -3 to -3 0.157004997 + DONE now c -3 + Allocate 12 spaces in coupling list 6 + XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -4 + XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -5 + XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -6 + DONE incremented c by -6 to -9 0.165546000 + DONE now c -9 + Try JTOTAL = 1.5000000000000000 +Allocate CLIST( 11 11 5) in 0.000 GB + Allocate NCLIST in 2.90399998E-06 GB + Allocate SRC/PSI etc in 3.62847990E-04 GB + Allocate FIM(D) in 7.04000001E-07 GB 88.000000000000000 + Allocate 22 spaces in coupling list 11 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -1 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -2 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -3 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -4 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -5 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -6 + DONE incremented c by -6 to -15 0.179991007 + DONE now c -15 + Allocate 22 spaces in coupling list 11 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -7 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -8 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -9 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -10 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -11 + XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -12 + DONE incremented c by -12 to -27 0.188767999 + DONE now c -27 + Try JTOTAL = 2.5000000000000000 +Allocate CLIST( 15 15 5) in 0.000 GB + Allocate NCLIST in 5.39999974E-06 GB + Allocate SRC/PSI etc in 4.90912003E-04 GB + Allocate FIM(D) in 9.59999966E-07 GB 120.00000000000000 + Allocate 30 spaces in coupling list 15 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -1 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -2 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -3 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -4 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -5 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -6 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -7 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -8 + DONE incremented c by -8 to -35 0.203993008 + DONE now c -35 + Allocate 30 spaces in coupling list 15 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -9 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -10 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -11 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -12 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -13 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -14 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -15 + XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -16 + DONE incremented c by -16 to -51 0.220496997 + DONE now c -51 + Try JTOTAL = 3.5000000000000000 +Allocate CLIST( 18 18 5) in 0.000 GB + Allocate NCLIST in 7.77599962E-06 GB + Allocate SRC/PSI etc in 5.76287974E-04 GB + Allocate FIM(D) in 1.15199998E-06 GB 144.00000000000000 + Allocate 36 spaces in coupling list 18 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -1 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -2 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -3 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -4 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -5 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -6 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -7 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -8 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -60 0.237006009 + DONE now c -60 + Allocate 36 spaces in coupling list 18 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -10 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -11 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -12 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -13 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -14 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -15 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -16 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -17 + XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -78 0.253939003 + DONE now c -78 + Try JTOTAL = 4.5000000000000000 +Allocate CLIST( 19 19 5) in 0.000 GB + Allocate NCLIST in 8.66399932E-06 GB + Allocate SRC/PSI etc in 5.97632024E-04 GB + Allocate FIM(D) in 1.21599999E-06 GB 152.00000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -1 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -2 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -3 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -4 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -5 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -6 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -7 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -8 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -87 0.271463990 + DONE now c -87 + Allocate 38 spaces in coupling list 19 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -10 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -11 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -12 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -13 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -14 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -15 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -16 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -17 + XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -105 0.288421988 + DONE now c -105 + Try JTOTAL = 5.5000000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -1 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -2 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -3 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -4 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -5 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -6 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -7 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -8 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -114 0.306445986 + DONE now c -114 + Allocate 38 spaces in coupling list 19 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -10 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -11 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -12 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -13 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -14 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -15 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -16 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -17 + XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -132 0.326427996 + DONE now c -132 + Try JTOTAL = 6.5000000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -1 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -2 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -3 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -4 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -5 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -6 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -7 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -8 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -141 0.343539983 + DONE now c -141 + Allocate 38 spaces in coupling list 19 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -10 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -11 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -12 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -13 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -14 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -15 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -16 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -17 + XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -159 0.361419976 + DONE now c -159 + Try JTOTAL = 7.5000000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -1 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -2 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -3 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -4 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -5 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -6 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -7 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -8 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -168 0.378007978 + DONE now c -168 + Allocate 38 spaces in coupling list 19 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -10 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -11 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -12 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -13 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -14 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -15 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -16 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -17 + XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -186 0.395273000 + DONE now c -186 + Try JTOTAL = 8.5000000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -1 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -2 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -3 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -4 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -5 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -6 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -7 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -8 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -195 0.413942993 + DONE now c -195 + Allocate 38 spaces in coupling list 19 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -10 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -11 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -12 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -13 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -14 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -15 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -16 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -17 + XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -213 0.431522995 + DONE now c -213 + Try JTOTAL = 9.5000000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -1 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -2 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -3 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -4 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -5 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -6 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -7 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -8 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -222 0.448094994 + DONE now c -222 + Allocate 38 spaces in coupling list 19 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -10 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -11 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -12 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -13 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -14 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -15 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -16 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -17 + XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -240 0.465817988 + DONE now c -240 + Try JTOTAL = 10.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -1 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -2 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -3 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -4 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -5 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -6 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -7 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -8 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -249 0.483444989 + DONE now c -249 + Allocate 38 spaces in coupling list 19 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -10 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -11 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -12 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -13 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -14 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -15 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -16 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -17 + XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -267 0.501227975 + DONE now c -267 + Try JTOTAL = 11.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -1 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -2 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -3 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -4 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -5 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -6 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -7 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -8 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -276 0.519330978 + DONE now c -276 + Allocate 38 spaces in coupling list 19 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -10 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -11 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -12 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -13 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -14 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -15 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -16 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -17 + XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -294 0.537755013 + DONE now c -294 + Try JTOTAL = 12.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -1 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -2 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -3 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -4 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -5 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -6 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -7 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -8 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -303 0.555871010 + DONE now c -303 + Allocate 38 spaces in coupling list 19 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -10 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -11 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -12 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -13 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -14 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -15 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -16 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -17 + XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -321 0.573882997 + DONE now c -321 + Try JTOTAL = 13.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -1 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -2 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -3 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -4 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -5 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -6 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -7 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -8 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -330 0.591790974 + DONE now c -330 + Allocate 38 spaces in coupling list 19 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -10 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -11 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -12 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -13 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -14 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -15 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -16 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -17 + XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -348 0.609620988 + DONE now c -348 + Try JTOTAL = 14.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -1 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -2 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -3 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -4 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -5 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -6 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -7 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -8 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -357 0.627115965 + DONE now c -357 + Allocate 38 spaces in coupling list 19 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -10 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -11 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -12 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -13 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -14 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -15 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -16 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -17 + XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -375 0.644883990 + DONE now c -375 + Try JTOTAL = 15.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -1 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -2 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -3 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -4 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -5 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -6 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -7 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -8 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -384 0.662989974 + DONE now c -384 + Allocate 38 spaces in coupling list 19 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -10 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -11 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -12 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -13 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -14 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -15 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -16 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -17 + XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -402 0.680739999 + DONE now c -402 + Try JTOTAL = 16.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -1 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -2 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -3 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -4 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -5 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -6 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -7 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -8 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -411 0.698785961 + DONE now c -411 + Allocate 38 spaces in coupling list 19 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -10 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -11 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -12 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -13 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -14 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -15 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -16 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -17 + XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -429 0.716197968 + DONE now c -429 + Try JTOTAL = 17.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -1 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -2 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -3 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -4 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -5 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -6 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -7 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -8 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -438 0.734008014 + DONE now c -438 + Allocate 38 spaces in coupling list 19 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -10 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -11 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -12 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -13 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -14 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -15 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -16 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -17 + XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -456 0.751689970 + DONE now c -456 + Try JTOTAL = 18.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -1 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -2 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -3 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -4 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -5 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -6 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -7 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -8 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -465 0.769773006 + DONE now c -465 + Allocate 38 spaces in coupling list 19 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -10 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -11 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -12 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -13 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -14 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -15 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -16 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -17 + XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -483 0.787189007 + DONE now c -483 + Try JTOTAL = 19.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -1 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -2 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -3 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -4 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -5 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -6 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -7 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -8 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -492 0.804587960 + DONE now c -492 + Allocate 38 spaces in coupling list 19 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -10 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -11 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -12 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -13 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -14 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -15 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -16 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -17 + XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -510 0.822567999 + DONE now c -510 + Try JTOTAL = 20.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -1 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -2 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -3 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -4 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -5 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -6 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -7 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -8 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -519 0.840171993 + DONE now c -519 + Allocate 38 spaces in coupling list 19 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -10 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -11 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -12 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -13 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -14 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -15 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -16 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -17 + XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -537 0.857761979 + DONE now c -537 + Try JTOTAL = 21.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -1 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -2 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -3 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -4 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -5 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -6 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -7 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -8 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -546 0.875167012 + DONE now c -546 + Allocate 38 spaces in coupling list 19 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -10 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -11 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -12 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -13 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -14 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -15 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -16 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -17 + XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -564 0.892459989 + DONE now c -564 + Try JTOTAL = 22.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -1 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -2 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -3 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -4 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -5 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -6 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -7 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -8 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -573 0.910026968 + DONE now c -573 + Allocate 38 spaces in coupling list 19 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -10 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -11 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -12 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -13 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -14 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -15 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -16 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -17 + XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -591 0.927650988 + DONE now c -591 + Try JTOTAL = 23.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -1 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -2 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -3 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -4 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -5 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -6 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -7 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -8 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -600 0.945043981 + DONE now c -600 + Allocate 38 spaces in coupling list 19 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -10 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -11 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -12 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -13 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -14 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -15 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -16 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -17 + XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -618 0.962583005 + DONE now c -618 + Try JTOTAL = 24.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -1 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -2 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -3 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -4 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -5 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -6 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -7 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -8 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -627 0.980051994 + DONE now c -627 + Allocate 38 spaces in coupling list 19 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -10 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -11 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -12 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -13 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -14 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -15 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -16 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -17 + XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -645 0.997898936 + DONE now c -645 + Try JTOTAL = 25.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -1 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -2 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -3 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -4 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -5 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -6 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -7 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -8 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -654 1.01554704 + DONE now c -654 + Allocate 38 spaces in coupling list 19 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -10 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -11 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -12 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -13 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -14 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -15 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -16 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -17 + XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -672 1.03328001 + DONE now c -672 + Try JTOTAL = 26.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -1 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -2 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -3 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -4 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -5 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -6 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -7 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -8 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -681 1.05058396 + DONE now c -681 + Allocate 38 spaces in coupling list 19 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -10 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -11 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -12 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -13 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -14 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -15 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -16 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -17 + XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -699 1.06805301 + DONE now c -699 + Try JTOTAL = 27.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -1 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -2 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -3 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -4 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -5 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -6 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -7 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -8 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -708 1.08550799 + DONE now c -708 + Allocate 38 spaces in coupling list 19 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -10 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -11 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -12 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -13 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -14 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -15 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -16 + XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -17 + XS 3.46 > -1.0000 & JT 27.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -726 1.10348403 + DONE now c -726 + Try JTOTAL = 28.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -1 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -2 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -3 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -4 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -5 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -6 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -7 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -8 + XS 3.57 > -1.0000 & JT 28.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -735 1.12246799 + DONE now c -735 + Allocate 38 spaces in coupling list 19 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -10 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -11 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -12 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -13 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -14 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -15 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -16 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -17 + XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -753 1.14026201 + DONE now c -753 + Try JTOTAL = 29.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -1 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -2 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -3 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -4 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -5 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -6 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -7 + XS 3.69 > -1.0000 & JT 29.5 > 0.0 so DONES now = -8 + XS 3.69 > -1.0000 & JT 29.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -762 1.15736401 + DONE now c -762 + Allocate 38 spaces in coupling list 19 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -10 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -11 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -12 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -13 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -14 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -15 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -16 + XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -17 + XS 3.62 > -1.0000 & JT 29.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -780 1.17444599 + DONE now c -780 + Try JTOTAL = 30.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -1 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -2 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -3 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -4 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -5 + XS 3.82 > -1.0000 & JT 30.5 > 0.0 so DONES now = -6 + XS 3.82 > -1.0000 & JT 30.5 > 0.0 so DONES now = -7 + XS 3.82 > -1.0000 & JT 30.5 > 0.0 so DONES now = -8 + XS 3.54 > -1.0000 & JT 30.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -789 1.19149697 + DONE now c -789 + Allocate 38 spaces in coupling list 19 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -10 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -11 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -12 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -13 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -14 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -15 + XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -16 + XS 3.74 > -1.0000 & JT 30.5 > 0.0 so DONES now = -17 + XS 3.74 > -1.0000 & JT 30.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -807 1.20832801 + DONE now c -807 + Try JTOTAL = 31.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -1 + XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -2 + XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -3 + XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -4 + XS 3.94 > -1.0000 & JT 31.5 > 0.0 so DONES now = -5 + XS 3.94 > -1.0000 & JT 31.5 > 0.0 so DONES now = -6 + XS 3.94 > -1.0000 & JT 31.5 > 0.0 so DONES now = -7 + XS 3.66 > -1.0000 & JT 31.5 > 0.0 so DONES now = -8 + XS 3.66 > -1.0000 & JT 31.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -816 1.22464597 + DONE now c -816 + Allocate 38 spaces in coupling list 19 + XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -10 + XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -11 + XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -12 + XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -13 + XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -14 + XS 3.86 > -1.0000 & JT 31.5 > 0.0 so DONES now = -15 + XS 3.86 > -1.0000 & JT 31.5 > 0.0 so DONES now = -16 + XS 3.86 > -1.0000 & JT 31.5 > 0.0 so DONES now = -17 + XS 3.33 > -1.0000 & JT 31.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -834 1.24205196 + DONE now c -834 + Try JTOTAL = 32.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -1 + XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -2 + XS 4.06 > -1.0000 & JT 32.5 > 0.0 so DONES now = -3 + XS 4.06 > -1.0000 & JT 32.5 > 0.0 so DONES now = -4 + XS 4.06 > -1.0000 & JT 32.5 > 0.0 so DONES now = -5 + XS 3.77 > -1.0000 & JT 32.5 > 0.0 so DONES now = -6 + XS 3.77 > -1.0000 & JT 32.5 > 0.0 so DONES now = -7 + XS 3.77 > -1.0000 & JT 32.5 > 0.0 so DONES now = -8 + XS 2.99 > -1.0000 & JT 32.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -843 1.25828505 + DONE now c -843 + Allocate 38 spaces in coupling list 19 + XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -10 + XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -11 + XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -12 + XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -13 + XS 3.98 > -1.0000 & JT 32.5 > 0.0 so DONES now = -14 + XS 3.98 > -1.0000 & JT 32.5 > 0.0 so DONES now = -15 + XS 3.98 > -1.0000 & JT 32.5 > 0.0 so DONES now = -16 + XS 3.43 > -1.0000 & JT 32.5 > 0.0 so DONES now = -17 + XS 3.43 > -1.0000 & JT 32.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -861 1.27527905 + DONE now c -861 + Try JTOTAL = 33.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 4.21 > -1.0000 & JT 33.5 > 0.0 so DONES now = -1 + XS 4.19 > -1.0000 & JT 33.5 > 0.0 so DONES now = -2 + XS 4.19 > -1.0000 & JT 33.5 > 0.0 so DONES now = -3 + XS 4.19 > -1.0000 & JT 33.5 > 0.0 so DONES now = -4 + XS 3.89 > -1.0000 & JT 33.5 > 0.0 so DONES now = -5 + XS 3.89 > -1.0000 & JT 33.5 > 0.0 so DONES now = -6 + XS 3.89 > -1.0000 & JT 33.5 > 0.0 so DONES now = -7 + XS 3.08 > -1.0000 & JT 33.5 > 0.0 so DONES now = -8 + XS 3.08 > -1.0000 & JT 33.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -870 1.29287505 + DONE now c -870 + Allocate 38 spaces in coupling list 19 + XS 4.21 > -1.0000 & JT 33.5 > 0.0 so DONES now = -10 + XS 4.21 > -1.0000 & JT 33.5 > 0.0 so DONES now = -11 + XS 4.10 > -1.0000 & JT 33.5 > 0.0 so DONES now = -12 + XS 4.10 > -1.0000 & JT 33.5 > 0.0 so DONES now = -13 + XS 4.10 > -1.0000 & JT 33.5 > 0.0 so DONES now = -14 + XS 3.53 > -1.0000 & JT 33.5 > 0.0 so DONES now = -15 + XS 3.53 > -1.0000 & JT 33.5 > 0.0 so DONES now = -16 + XS 3.53 > -1.0000 & JT 33.5 > 0.0 so DONES now = -17 + XS 2.60 > -1.0000 & JT 33.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -888 1.31055701 + DONE now c -888 + Try JTOTAL = 34.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 4.31 > -1.0000 & JT 34.5 > 0.0 so DONES now = -1 + XS 4.31 > -1.0000 & JT 34.5 > 0.0 so DONES now = -2 + XS 4.00 > -1.0000 & JT 34.5 > 0.0 so DONES now = -3 + XS 4.00 > -1.0000 & JT 34.5 > 0.0 so DONES now = -4 + XS 4.00 > -1.0000 & JT 34.5 > 0.0 so DONES now = -5 + XS 3.17 > -1.0000 & JT 34.5 > 0.0 so DONES now = -6 + XS 3.17 > -1.0000 & JT 34.5 > 0.0 so DONES now = -7 + XS 3.17 > -1.0000 & JT 34.5 > 0.0 so DONES now = -8 + XS 2.20 > -1.0000 & JT 34.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -897 1.32783604 + DONE now c -897 + Allocate 38 spaces in coupling list 19 + XS 4.33 > -1.0000 & JT 34.5 > 0.0 so DONES now = -10 + XS 4.22 > -1.0000 & JT 34.5 > 0.0 so DONES now = -11 + XS 4.22 > -1.0000 & JT 34.5 > 0.0 so DONES now = -12 + XS 4.22 > -1.0000 & JT 34.5 > 0.0 so DONES now = -13 + XS 3.64 > -1.0000 & JT 34.5 > 0.0 so DONES now = -14 + XS 3.64 > -1.0000 & JT 34.5 > 0.0 so DONES now = -15 + XS 3.64 > -1.0000 & JT 34.5 > 0.0 so DONES now = -16 + XS 2.68 > -1.0000 & JT 34.5 > 0.0 so DONES now = -17 + XS 2.68 > -1.0000 & JT 34.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -915 1.34563398 + DONE now c -915 + Try JTOTAL = 35.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 4.43 > -1.0000 & JT 35.5 > 0.0 so DONES now = -1 + XS 4.11 > -1.0000 & JT 35.5 > 0.0 so DONES now = -2 + XS 4.11 > -1.0000 & JT 35.5 > 0.0 so DONES now = -3 + XS 4.11 > -1.0000 & JT 35.5 > 0.0 so DONES now = -4 + XS 3.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -5 + XS 3.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -6 + XS 3.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -7 + XS 2.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -8 + XS 2.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -924 1.36347699 + DONE now c -924 + Allocate 38 spaces in coupling list 19 + XS 4.34 > -1.0000 & JT 35.5 > 0.0 so DONES now = -10 + XS 4.34 > -1.0000 & JT 35.5 > 0.0 so DONES now = -11 + XS 3.74 > -1.0000 & JT 35.5 > 0.0 so DONES now = -12 + XS 3.74 > -1.0000 & JT 35.5 > 0.0 so DONES now = -13 + XS 3.74 > -1.0000 & JT 35.5 > 0.0 so DONES now = -14 + XS 2.75 > -1.0000 & JT 35.5 > 0.0 so DONES now = -15 + XS 2.75 > -1.0000 & JT 35.5 > 0.0 so DONES now = -16 + XS 2.75 > -1.0000 & JT 35.5 > 0.0 so DONES now = -17 + XS 1.83 > -1.0000 & JT 35.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -942 1.38071096 + DONE now c -942 + Try JTOTAL = 36.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 4.23 > -1.0000 & JT 36.5 > 0.0 so DONES now = -1 + XS 4.23 > -1.0000 & JT 36.5 > 0.0 so DONES now = -2 + XS 3.36 > -1.0000 & JT 36.5 > 0.0 so DONES now = -3 + XS 3.36 > -1.0000 & JT 36.5 > 0.0 so DONES now = -4 + XS 3.36 > -1.0000 & JT 36.5 > 0.0 so DONES now = -5 + XS 2.33 > -1.0000 & JT 36.5 > 0.0 so DONES now = -6 + XS 2.33 > -1.0000 & JT 36.5 > 0.0 so DONES now = -7 + XS 2.33 > -1.0000 & JT 36.5 > 0.0 so DONES now = -8 + XS 1.50 > -1.0000 & JT 36.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -951 1.39867198 + DONE now c -951 + Allocate 38 spaces in coupling list 19 + XS 4.46 > -1.0000 & JT 36.5 > 0.0 so DONES now = -10 + XS 3.85 > -1.0000 & JT 36.5 > 0.0 so DONES now = -11 + XS 3.85 > -1.0000 & JT 36.5 > 0.0 so DONES now = -12 + XS 3.85 > -1.0000 & JT 36.5 > 0.0 so DONES now = -13 + XS 2.83 > -1.0000 & JT 36.5 > 0.0 so DONES now = -14 + XS 2.83 > -1.0000 & JT 36.5 > 0.0 so DONES now = -15 + XS 2.83 > -1.0000 & JT 36.5 > 0.0 so DONES now = -16 + XS 1.88 > -1.0000 & JT 36.5 > 0.0 so DONES now = -17 + XS 1.88 > -1.0000 & JT 36.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -969 1.41588902 + DONE now c -969 + Try JTOTAL = 37.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 4.34 > -1.0000 & JT 37.5 > 0.0 so DONES now = -1 + XS 3.45 > -1.0000 & JT 37.5 > 0.0 so DONES now = -2 + XS 3.45 > -1.0000 & JT 37.5 > 0.0 so DONES now = -3 + XS 3.45 > -1.0000 & JT 37.5 > 0.0 so DONES now = -4 + XS 2.39 > -1.0000 & JT 37.5 > 0.0 so DONES now = -5 + XS 2.39 > -1.0000 & JT 37.5 > 0.0 so DONES now = -6 + XS 2.39 > -1.0000 & JT 37.5 > 0.0 so DONES now = -7 + XS 1.54 > -1.0000 & JT 37.5 > 0.0 so DONES now = -8 + XS 1.54 > -1.0000 & JT 37.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -978 1.43311596 + DONE now c -978 + Allocate 38 spaces in coupling list 19 + XS 3.95 > -1.0000 & JT 37.5 > 0.0 so DONES now = -10 + XS 3.95 > -1.0000 & JT 37.5 > 0.0 so DONES now = -11 + XS 2.91 > -1.0000 & JT 37.5 > 0.0 so DONES now = -12 + XS 2.91 > -1.0000 & JT 37.5 > 0.0 so DONES now = -13 + XS 2.91 > -1.0000 & JT 37.5 > 0.0 so DONES now = -14 + XS 1.93 > -1.0000 & JT 37.5 > 0.0 so DONES now = -15 + XS 1.93 > -1.0000 & JT 37.5 > 0.0 so DONES now = -16 + XS 1.93 > -1.0000 & JT 37.5 > 0.0 so DONES now = -17 + XS 1.22 > -1.0000 & JT 37.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -996 1.45026994 + DONE now c -996 + Try JTOTAL = 38.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.54 > -1.0000 & JT 38.5 > 0.0 so DONES now = -1 + XS 3.54 > -1.0000 & JT 38.5 > 0.0 so DONES now = -2 + XS 2.45 > -1.0000 & JT 38.5 > 0.0 so DONES now = -3 + XS 2.45 > -1.0000 & JT 38.5 > 0.0 so DONES now = -4 + XS 2.45 > -1.0000 & JT 38.5 > 0.0 so DONES now = -5 + XS 1.58 > -1.0000 & JT 38.5 > 0.0 so DONES now = -6 + XS 1.58 > -1.0000 & JT 38.5 > 0.0 so DONES now = -7 + XS 1.58 > -1.0000 & JT 38.5 > 0.0 so DONES now = -8 + XS 0.984 > -1.0000 & JT 38.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1005 1.46736097 + DONE now c -1005 + Allocate 38 spaces in coupling list 19 + XS 4.05 > -1.0000 & JT 38.5 > 0.0 so DONES now = -10 + XS 2.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -11 + XS 2.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -12 + XS 2.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -13 + XS 1.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -14 + XS 1.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -15 + XS 1.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -16 + XS 1.25 > -1.0000 & JT 38.5 > 0.0 so DONES now = -17 + XS 1.25 > -1.0000 & JT 38.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1023 1.48463595 + DONE now c -1023 + Try JTOTAL = 39.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 3.63 > -1.0000 & JT 39.5 > 0.0 so DONES now = -1 + XS 2.52 > -1.0000 & JT 39.5 > 0.0 so DONES now = -2 + XS 2.52 > -1.0000 & JT 39.5 > 0.0 so DONES now = -3 + XS 2.52 > -1.0000 & JT 39.5 > 0.0 so DONES now = -4 + XS 1.62 > -1.0000 & JT 39.5 > 0.0 so DONES now = -5 + XS 1.62 > -1.0000 & JT 39.5 > 0.0 so DONES now = -6 + XS 1.62 > -1.0000 & JT 39.5 > 0.0 so DONES now = -7 + XS 1.01 > -1.0000 & JT 39.5 > 0.0 so DONES now = -8 + XS 1.01 > -1.0000 & JT 39.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1032 1.50226200 + DONE now c -1032 + Allocate 38 spaces in coupling list 19 + XS 3.06 > -1.0000 & JT 39.5 > 0.0 so DONES now = -10 + XS 3.06 > -1.0000 & JT 39.5 > 0.0 so DONES now = -11 + XS 2.03 > -1.0000 & JT 39.5 > 0.0 so DONES now = -12 + XS 2.03 > -1.0000 & JT 39.5 > 0.0 so DONES now = -13 + XS 2.03 > -1.0000 & JT 39.5 > 0.0 so DONES now = -14 + XS 1.28 > -1.0000 & JT 39.5 > 0.0 so DONES now = -15 + XS 1.28 > -1.0000 & JT 39.5 > 0.0 so DONES now = -16 + XS 1.28 > -1.0000 & JT 39.5 > 0.0 so DONES now = -17 + XS 0.789 > -1.0000 & JT 39.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1050 1.52027500 + DONE now c -1050 + Try JTOTAL = 40.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.58 > -1.0000 & JT 40.5 > 0.0 so DONES now = -1 + XS 2.58 > -1.0000 & JT 40.5 > 0.0 so DONES now = -2 + XS 1.66 > -1.0000 & JT 40.5 > 0.0 so DONES now = -3 + XS 1.66 > -1.0000 & JT 40.5 > 0.0 so DONES now = -4 + XS 1.66 > -1.0000 & JT 40.5 > 0.0 so DONES now = -5 + XS 1.03 > -1.0000 & JT 40.5 > 0.0 so DONES now = -6 + XS 1.03 > -1.0000 & JT 40.5 > 0.0 so DONES now = -7 + XS 1.03 > -1.0000 & JT 40.5 > 0.0 so DONES now = -8 + XS 0.630 > -1.0000 & JT 40.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1059 1.53806901 + DONE now c -1059 + Allocate 38 spaces in coupling list 19 + XS 3.14 > -1.0000 & JT 40.5 > 0.0 so DONES now = -10 + XS 2.09 > -1.0000 & JT 40.5 > 0.0 so DONES now = -11 + XS 2.09 > -1.0000 & JT 40.5 > 0.0 so DONES now = -12 + XS 2.09 > -1.0000 & JT 40.5 > 0.0 so DONES now = -13 + XS 1.32 > -1.0000 & JT 40.5 > 0.0 so DONES now = -14 + XS 1.32 > -1.0000 & JT 40.5 > 0.0 so DONES now = -15 + XS 1.32 > -1.0000 & JT 40.5 > 0.0 so DONES now = -16 + XS 0.808 > -1.0000 & JT 40.5 > 0.0 so DONES now = -17 + XS 0.808 > -1.0000 & JT 40.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1077 1.55526590 + DONE now c -1077 + Try JTOTAL = 41.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 2.64 > -1.0000 & JT 41.5 > 0.0 so DONES now = -1 + XS 1.70 > -1.0000 & JT 41.5 > 0.0 so DONES now = -2 + XS 1.70 > -1.0000 & JT 41.5 > 0.0 so DONES now = -3 + XS 1.70 > -1.0000 & JT 41.5 > 0.0 so DONES now = -4 + XS 1.06 > -1.0000 & JT 41.5 > 0.0 so DONES now = -5 + XS 1.06 > -1.0000 & JT 41.5 > 0.0 so DONES now = -6 + XS 1.06 > -1.0000 & JT 41.5 > 0.0 so DONES now = -7 + XS 0.645 > -1.0000 & JT 41.5 > 0.0 so DONES now = -8 + XS 0.645 > -1.0000 & JT 41.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1086 1.57276392 + DONE now c -1086 + Allocate 38 spaces in coupling list 19 + XS 2.14 > -1.0000 & JT 41.5 > 0.0 so DONES now = -10 + XS 2.14 > -1.0000 & JT 41.5 > 0.0 so DONES now = -11 + XS 1.35 > -1.0000 & JT 41.5 > 0.0 so DONES now = -12 + XS 1.35 > -1.0000 & JT 41.5 > 0.0 so DONES now = -13 + XS 1.35 > -1.0000 & JT 41.5 > 0.0 so DONES now = -14 + XS 0.828 > -1.0000 & JT 41.5 > 0.0 so DONES now = -15 + XS 0.828 > -1.0000 & JT 41.5 > 0.0 so DONES now = -16 + XS 0.828 > -1.0000 & JT 41.5 > 0.0 so DONES now = -17 + XS 0.501 > -1.0000 & JT 41.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1104 1.59070206 + DONE now c -1104 + Try JTOTAL = 42.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.75 > -1.0000 & JT 42.5 > 0.0 so DONES now = -1 + XS 1.75 > -1.0000 & JT 42.5 > 0.0 so DONES now = -2 + XS 1.08 > -1.0000 & JT 42.5 > 0.0 so DONES now = -3 + XS 1.08 > -1.0000 & JT 42.5 > 0.0 so DONES now = -4 + XS 1.08 > -1.0000 & JT 42.5 > 0.0 so DONES now = -5 + XS 0.660 > -1.0000 & JT 42.5 > 0.0 so DONES now = -6 + XS 0.660 > -1.0000 & JT 42.5 > 0.0 so DONES now = -7 + XS 0.660 > -1.0000 & JT 42.5 > 0.0 so DONES now = -8 + XS 0.397 > -1.0000 & JT 42.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1113 1.60836899 + DONE now c -1113 + Allocate 38 spaces in coupling list 19 + XS 2.19 > -1.0000 & JT 42.5 > 0.0 so DONES now = -10 + XS 1.38 > -1.0000 & JT 42.5 > 0.0 so DONES now = -11 + XS 1.38 > -1.0000 & JT 42.5 > 0.0 so DONES now = -12 + XS 1.38 > -1.0000 & JT 42.5 > 0.0 so DONES now = -13 + XS 0.848 > -1.0000 & JT 42.5 > 0.0 so DONES now = -14 + XS 0.848 > -1.0000 & JT 42.5 > 0.0 so DONES now = -15 + XS 0.848 > -1.0000 & JT 42.5 > 0.0 so DONES now = -16 + XS 0.513 > -1.0000 & JT 42.5 > 0.0 so DONES now = -17 + XS 0.513 > -1.0000 & JT 42.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1131 1.62549090 + DONE now c -1131 + Try JTOTAL = 43.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.79 > -1.0000 & JT 43.5 > 0.0 so DONES now = -1 + XS 1.11 > -1.0000 & JT 43.5 > 0.0 so DONES now = -2 + XS 1.11 > -1.0000 & JT 43.5 > 0.0 so DONES now = -3 + XS 1.11 > -1.0000 & JT 43.5 > 0.0 so DONES now = -4 + XS 0.676 > -1.0000 & JT 43.5 > 0.0 so DONES now = -5 + XS 0.676 > -1.0000 & JT 43.5 > 0.0 so DONES now = -6 + XS 0.676 > -1.0000 & JT 43.5 > 0.0 so DONES now = -7 + XS 0.406 > -1.0000 & JT 43.5 > 0.0 so DONES now = -8 + XS 0.406 > -1.0000 & JT 43.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1140 1.64270401 + DONE now c -1140 + Allocate 38 spaces in coupling list 19 + XS 1.41 > -1.0000 & JT 43.5 > 0.0 so DONES now = -10 + XS 1.41 > -1.0000 & JT 43.5 > 0.0 so DONES now = -11 + XS 0.867 > -1.0000 & JT 43.5 > 0.0 so DONES now = -12 + XS 0.867 > -1.0000 & JT 43.5 > 0.0 so DONES now = -13 + XS 0.867 > -1.0000 & JT 43.5 > 0.0 so DONES now = -14 + XS 0.525 > -1.0000 & JT 43.5 > 0.0 so DONES now = -15 + XS 0.525 > -1.0000 & JT 43.5 > 0.0 so DONES now = -16 + XS 0.525 > -1.0000 & JT 43.5 > 0.0 so DONES now = -17 + XS 0.314 > -1.0000 & JT 43.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1158 1.65969706 + DONE now c -1158 + Try JTOTAL = 44.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.13 > -1.0000 & JT 44.5 > 0.0 so DONES now = -1 + XS 1.13 > -1.0000 & JT 44.5 > 0.0 so DONES now = -2 + XS 0.691 > -1.0000 & JT 44.5 > 0.0 so DONES now = -3 + XS 0.691 > -1.0000 & JT 44.5 > 0.0 so DONES now = -4 + XS 0.691 > -1.0000 & JT 44.5 > 0.0 so DONES now = -5 + XS 0.416 > -1.0000 & JT 44.5 > 0.0 so DONES now = -6 + XS 0.416 > -1.0000 & JT 44.5 > 0.0 so DONES now = -7 + XS 0.416 > -1.0000 & JT 44.5 > 0.0 so DONES now = -8 + XS 0.248 > -1.0000 & JT 44.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1167 1.67681003 + DONE now c -1167 + Allocate 38 spaces in coupling list 19 + XS 1.44 > -1.0000 & JT 44.5 > 0.0 so DONES now = -10 + XS 0.887 > -1.0000 & JT 44.5 > 0.0 so DONES now = -11 + XS 0.887 > -1.0000 & JT 44.5 > 0.0 so DONES now = -12 + XS 0.887 > -1.0000 & JT 44.5 > 0.0 so DONES now = -13 + XS 0.537 > -1.0000 & JT 44.5 > 0.0 so DONES now = -14 + XS 0.537 > -1.0000 & JT 44.5 > 0.0 so DONES now = -15 + XS 0.537 > -1.0000 & JT 44.5 > 0.0 so DONES now = -16 + XS 0.322 > -1.0000 & JT 44.5 > 0.0 so DONES now = -17 + XS 0.322 > -1.0000 & JT 44.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1185 1.69455194 + DONE now c -1185 + Try JTOTAL = 45.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 1.16 > -1.0000 & JT 45.5 > 0.0 so DONES now = -1 + XS 0.706 > -1.0000 & JT 45.5 > 0.0 so DONES now = -2 + XS 0.706 > -1.0000 & JT 45.5 > 0.0 so DONES now = -3 + XS 0.706 > -1.0000 & JT 45.5 > 0.0 so DONES now = -4 + XS 0.425 > -1.0000 & JT 45.5 > 0.0 so DONES now = -5 + XS 0.425 > -1.0000 & JT 45.5 > 0.0 so DONES now = -6 + XS 0.425 > -1.0000 & JT 45.5 > 0.0 so DONES now = -7 + XS 0.254 > -1.0000 & JT 45.5 > 0.0 so DONES now = -8 + XS 0.254 > -1.0000 & JT 45.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1194 1.71168602 + DONE now c -1194 + Allocate 38 spaces in coupling list 19 + XS 0.907 > -1.0000 & JT 45.5 > 0.0 so DONES now = -10 + XS 0.907 > -1.0000 & JT 45.5 > 0.0 so DONES now = -11 + XS 0.548 > -1.0000 & JT 45.5 > 0.0 so DONES now = -12 + XS 0.548 > -1.0000 & JT 45.5 > 0.0 so DONES now = -13 + XS 0.548 > -1.0000 & JT 45.5 > 0.0 so DONES now = -14 + XS 0.329 > -1.0000 & JT 45.5 > 0.0 so DONES now = -15 + XS 0.329 > -1.0000 & JT 45.5 > 0.0 so DONES now = -16 + XS 0.329 > -1.0000 & JT 45.5 > 0.0 so DONES now = -17 + XS 0.196 > -1.0000 & JT 45.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1212 1.72870994 + DONE now c -1212 + Try JTOTAL = 46.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.722 > -1.0000 & JT 46.5 > 0.0 so DONES now = -1 + XS 0.722 > -1.0000 & JT 46.5 > 0.0 so DONES now = -2 + XS 0.434 > -1.0000 & JT 46.5 > 0.0 so DONES now = -3 + XS 0.434 > -1.0000 & JT 46.5 > 0.0 so DONES now = -4 + XS 0.434 > -1.0000 & JT 46.5 > 0.0 so DONES now = -5 + XS 0.259 > -1.0000 & JT 46.5 > 0.0 so DONES now = -6 + XS 0.259 > -1.0000 & JT 46.5 > 0.0 so DONES now = -7 + XS 0.259 > -1.0000 & JT 46.5 > 0.0 so DONES now = -8 + XS 0.154 > -1.0000 & JT 46.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1221 1.74569798 + DONE now c -1221 + Allocate 38 spaces in coupling list 19 + XS 0.927 > -1.0000 & JT 46.5 > 0.0 so DONES now = -10 + XS 0.560 > -1.0000 & JT 46.5 > 0.0 so DONES now = -11 + XS 0.560 > -1.0000 & JT 46.5 > 0.0 so DONES now = -12 + XS 0.560 > -1.0000 & JT 46.5 > 0.0 so DONES now = -13 + XS 0.336 > -1.0000 & JT 46.5 > 0.0 so DONES now = -14 + XS 0.336 > -1.0000 & JT 46.5 > 0.0 so DONES now = -15 + XS 0.336 > -1.0000 & JT 46.5 > 0.0 so DONES now = -16 + XS 0.200 > -1.0000 & JT 46.5 > 0.0 so DONES now = -17 + XS 0.200 > -1.0000 & JT 46.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1239 1.76277995 + DONE now c -1239 + Try JTOTAL = 47.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.737 > -1.0000 & JT 47.5 > 0.0 so DONES now = -1 + XS 0.443 > -1.0000 & JT 47.5 > 0.0 so DONES now = -2 + XS 0.443 > -1.0000 & JT 47.5 > 0.0 so DONES now = -3 + XS 0.443 > -1.0000 & JT 47.5 > 0.0 so DONES now = -4 + XS 0.265 > -1.0000 & JT 47.5 > 0.0 so DONES now = -5 + XS 0.265 > -1.0000 & JT 47.5 > 0.0 so DONES now = -6 + XS 0.265 > -1.0000 & JT 47.5 > 0.0 so DONES now = -7 + XS 0.157 > -1.0000 & JT 47.5 > 0.0 so DONES now = -8 + XS 0.157 > -1.0000 & JT 47.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1248 1.77981591 + DONE now c -1248 + Allocate 38 spaces in coupling list 19 + XS 0.572 > -1.0000 & JT 47.5 > 0.0 so DONES now = -10 + XS 0.572 > -1.0000 & JT 47.5 > 0.0 so DONES now = -11 + XS 0.343 > -1.0000 & JT 47.5 > 0.0 so DONES now = -12 + XS 0.343 > -1.0000 & JT 47.5 > 0.0 so DONES now = -13 + XS 0.343 > -1.0000 & JT 47.5 > 0.0 so DONES now = -14 + XS 0.204 > -1.0000 & JT 47.5 > 0.0 so DONES now = -15 + XS 0.204 > -1.0000 & JT 47.5 > 0.0 so DONES now = -16 + XS 0.204 > -1.0000 & JT 47.5 > 0.0 so DONES now = -17 + XS 0.121 > -1.0000 & JT 47.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1266 1.79657900 + DONE now c -1266 + Try JTOTAL = 48.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.453 > -1.0000 & JT 48.5 > 0.0 so DONES now = -1 + XS 0.453 > -1.0000 & JT 48.5 > 0.0 so DONES now = -2 + XS 0.270 > -1.0000 & JT 48.5 > 0.0 so DONES now = -3 + XS 0.270 > -1.0000 & JT 48.5 > 0.0 so DONES now = -4 + XS 0.270 > -1.0000 & JT 48.5 > 0.0 so DONES now = -5 + XS 0.161 > -1.0000 & JT 48.5 > 0.0 so DONES now = -6 + XS 0.161 > -1.0000 & JT 48.5 > 0.0 so DONES now = -7 + XS 0.161 > -1.0000 & JT 48.5 > 0.0 so DONES now = -8 + XS 0.953E-01 > -1.0000 & JT 48.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1275 1.81361890 + DONE now c -1275 + Allocate 38 spaces in coupling list 19 + XS 0.584 > -1.0000 & JT 48.5 > 0.0 so DONES now = -10 + XS 0.350 > -1.0000 & JT 48.5 > 0.0 so DONES now = -11 + XS 0.350 > -1.0000 & JT 48.5 > 0.0 so DONES now = -12 + XS 0.350 > -1.0000 & JT 48.5 > 0.0 so DONES now = -13 + XS 0.209 > -1.0000 & JT 48.5 > 0.0 so DONES now = -14 + XS 0.209 > -1.0000 & JT 48.5 > 0.0 so DONES now = -15 + XS 0.209 > -1.0000 & JT 48.5 > 0.0 so DONES now = -16 + XS 0.124 > -1.0000 & JT 48.5 > 0.0 so DONES now = -17 + XS 0.124 > -1.0000 & JT 48.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1293 1.83069205 + DONE now c -1293 + Try JTOTAL = 49.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.462 > -1.0000 & JT 49.5 > 0.0 so DONES now = -1 + XS 0.276 > -1.0000 & JT 49.5 > 0.0 so DONES now = -2 + XS 0.276 > -1.0000 & JT 49.5 > 0.0 so DONES now = -3 + XS 0.276 > -1.0000 & JT 49.5 > 0.0 so DONES now = -4 + XS 0.164 > -1.0000 & JT 49.5 > 0.0 so DONES now = -5 + XS 0.164 > -1.0000 & JT 49.5 > 0.0 so DONES now = -6 + XS 0.164 > -1.0000 & JT 49.5 > 0.0 so DONES now = -7 + XS 0.972E-01 > -1.0000 & JT 49.5 > 0.0 so DONES now = -8 + XS 0.972E-01 > -1.0000 & JT 49.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1302 1.84722996 + DONE now c -1302 + Allocate 38 spaces in coupling list 19 + XS 0.357 > -1.0000 & JT 49.5 > 0.0 so DONES now = -10 + XS 0.357 > -1.0000 & JT 49.5 > 0.0 so DONES now = -11 + XS 0.213 > -1.0000 & JT 49.5 > 0.0 so DONES now = -12 + XS 0.213 > -1.0000 & JT 49.5 > 0.0 so DONES now = -13 + XS 0.213 > -1.0000 & JT 49.5 > 0.0 so DONES now = -14 + XS 0.126 > -1.0000 & JT 49.5 > 0.0 so DONES now = -15 + XS 0.126 > -1.0000 & JT 49.5 > 0.0 so DONES now = -16 + XS 0.126 > -1.0000 & JT 49.5 > 0.0 so DONES now = -17 + XS 0.748E-01 > -1.0000 & JT 49.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1320 1.86434400 + DONE now c -1320 + Try JTOTAL = 50.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.281 > -1.0000 & JT 50.5 > 0.0 so DONES now = -1 + XS 0.281 > -1.0000 & JT 50.5 > 0.0 so DONES now = -2 + XS 0.167 > -1.0000 & JT 50.5 > 0.0 so DONES now = -3 + XS 0.167 > -1.0000 & JT 50.5 > 0.0 so DONES now = -4 + XS 0.167 > -1.0000 & JT 50.5 > 0.0 so DONES now = -5 + XS 0.992E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -6 + XS 0.992E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -7 + XS 0.992E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -8 + XS 0.587E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1329 1.88104796 + DONE now c -1329 + Allocate 38 spaces in coupling list 19 + XS 0.364 > -1.0000 & JT 50.5 > 0.0 so DONES now = -10 + XS 0.217 > -1.0000 & JT 50.5 > 0.0 so DONES now = -11 + XS 0.217 > -1.0000 & JT 50.5 > 0.0 so DONES now = -12 + XS 0.217 > -1.0000 & JT 50.5 > 0.0 so DONES now = -13 + XS 0.129 > -1.0000 & JT 50.5 > 0.0 so DONES now = -14 + XS 0.129 > -1.0000 & JT 50.5 > 0.0 so DONES now = -15 + XS 0.129 > -1.0000 & JT 50.5 > 0.0 so DONES now = -16 + XS 0.763E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -17 + XS 0.763E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1347 1.89799404 + DONE now c -1347 + Try JTOTAL = 51.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.287 > -1.0000 & JT 51.5 > 0.0 so DONES now = -1 + XS 0.171 > -1.0000 & JT 51.5 > 0.0 so DONES now = -2 + XS 0.171 > -1.0000 & JT 51.5 > 0.0 so DONES now = -3 + XS 0.171 > -1.0000 & JT 51.5 > 0.0 so DONES now = -4 + XS 0.101 > -1.0000 & JT 51.5 > 0.0 so DONES now = -5 + XS 0.101 > -1.0000 & JT 51.5 > 0.0 so DONES now = -6 + XS 0.101 > -1.0000 & JT 51.5 > 0.0 so DONES now = -7 + XS 0.598E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -8 + XS 0.598E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1356 1.91500807 + DONE now c -1356 + Allocate 38 spaces in coupling list 19 + XS 0.221 > -1.0000 & JT 51.5 > 0.0 so DONES now = -10 + XS 0.221 > -1.0000 & JT 51.5 > 0.0 so DONES now = -11 + XS 0.131 > -1.0000 & JT 51.5 > 0.0 so DONES now = -12 + XS 0.131 > -1.0000 & JT 51.5 > 0.0 so DONES now = -13 + XS 0.131 > -1.0000 & JT 51.5 > 0.0 so DONES now = -14 + XS 0.778E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -15 + XS 0.778E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -16 + XS 0.778E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -17 + XS 0.460E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1374 1.93199599 + DONE now c -1374 + Try JTOTAL = 52.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.174 > -1.0000 & JT 52.5 > 0.0 so DONES now = -1 + XS 0.174 > -1.0000 & JT 52.5 > 0.0 so DONES now = -2 + XS 0.103 > -1.0000 & JT 52.5 > 0.0 so DONES now = -3 + XS 0.103 > -1.0000 & JT 52.5 > 0.0 so DONES now = -4 + XS 0.103 > -1.0000 & JT 52.5 > 0.0 so DONES now = -5 + XS 0.610E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -6 + XS 0.610E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -7 + XS 0.610E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -8 + XS 0.360E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1383 1.94902098 + DONE now c -1383 + Allocate 38 spaces in coupling list 19 + XS 0.226 > -1.0000 & JT 52.5 > 0.0 so DONES now = -10 + XS 0.134 > -1.0000 & JT 52.5 > 0.0 so DONES now = -11 + XS 0.134 > -1.0000 & JT 52.5 > 0.0 so DONES now = -12 + XS 0.134 > -1.0000 & JT 52.5 > 0.0 so DONES now = -13 + XS 0.793E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -14 + XS 0.793E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -15 + XS 0.793E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -16 + XS 0.469E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -17 + XS 0.469E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1401 1.96652007 + DONE now c -1401 + Try JTOTAL = 53.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.177 > -1.0000 & JT 53.5 > 0.0 so DONES now = -1 + XS 0.105 > -1.0000 & JT 53.5 > 0.0 so DONES now = -2 + XS 0.105 > -1.0000 & JT 53.5 > 0.0 so DONES now = -3 + XS 0.105 > -1.0000 & JT 53.5 > 0.0 so DONES now = -4 + XS 0.621E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -5 + XS 0.621E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -6 + XS 0.621E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -7 + XS 0.367E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -8 + XS 0.367E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1410 1.98339891 + DONE now c -1410 + Allocate 38 spaces in coupling list 19 + XS 0.136 > -1.0000 & JT 53.5 > 0.0 so DONES now = -10 + XS 0.136 > -1.0000 & JT 53.5 > 0.0 so DONES now = -11 + XS 0.808E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -12 + XS 0.808E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -13 + XS 0.808E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -14 + XS 0.477E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -15 + XS 0.477E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -16 + XS 0.477E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -17 + XS 0.282E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1428 2.00151181 + DONE now c -1428 + Try JTOTAL = 54.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.107 > -1.0000 & JT 54.5 > 0.0 so DONES now = -1 + XS 0.107 > -1.0000 & JT 54.5 > 0.0 so DONES now = -2 + XS 0.633E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -3 + XS 0.633E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -4 + XS 0.633E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -5 + XS 0.374E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -6 + XS 0.374E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -7 + XS 0.374E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -8 + XS 0.220E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1437 2.01906800 + DONE now c -1437 + Allocate 38 spaces in coupling list 19 + XS 0.139 > -1.0000 & JT 54.5 > 0.0 so DONES now = -10 + XS 0.823E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -11 + XS 0.823E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -12 + XS 0.823E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -13 + XS 0.486E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -14 + XS 0.486E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -15 + XS 0.486E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -16 + XS 0.287E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -17 + XS 0.287E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1455 2.03606701 + DONE now c -1455 + Try JTOTAL = 55.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.109 > -1.0000 & JT 55.5 > 0.0 so DONES now = -1 + XS 0.644E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -2 + XS 0.644E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -3 + XS 0.644E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -4 + XS 0.380E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -5 + XS 0.380E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -6 + XS 0.380E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -7 + XS 0.224E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -8 + XS 0.224E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1464 2.05322003 + DONE now c -1464 + Allocate 38 spaces in coupling list 19 + XS 0.838E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -10 + XS 0.838E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -11 + XS 0.495E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -12 + XS 0.495E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -13 + XS 0.495E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -14 + XS 0.292E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -15 + XS 0.292E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -16 + XS 0.292E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -17 + XS 0.172E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1482 2.07037997 + DONE now c -1482 + Try JTOTAL = 56.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.656E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -1 + XS 0.656E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -2 + XS 0.387E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -3 + XS 0.387E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -4 + XS 0.387E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -5 + XS 0.228E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -6 + XS 0.228E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -7 + XS 0.228E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -8 + XS 0.135E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1491 2.08754611 + DONE now c -1491 + Allocate 38 spaces in coupling list 19 + XS 0.853E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -10 + XS 0.504E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -11 + XS 0.504E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -12 + XS 0.504E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -13 + XS 0.297E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -14 + XS 0.297E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -15 + XS 0.297E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -16 + XS 0.175E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -17 + XS 0.175E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1509 2.10460901 + DONE now c -1509 + Try JTOTAL = 57.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.667E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -1 + XS 0.394E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -2 + XS 0.394E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -3 + XS 0.394E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -4 + XS 0.232E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -5 + XS 0.232E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -6 + XS 0.232E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -7 + XS 0.137E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -8 + XS 0.137E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1518 2.12192082 + DONE now c -1518 + Allocate 38 spaces in coupling list 19 + XS 0.513E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -10 + XS 0.513E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -11 + XS 0.303E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -12 + XS 0.303E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -13 + XS 0.303E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -14 + XS 0.179E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -15 + XS 0.179E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -16 + XS 0.179E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -17 + XS 0.105E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1536 2.14045095 + DONE now c -1536 + Try JTOTAL = 58.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.401E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -1 + XS 0.401E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -2 + XS 0.236E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -3 + XS 0.236E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -4 + XS 0.236E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -5 + XS 0.139E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -6 + XS 0.139E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -7 + XS 0.139E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -8 + XS 0.821E-02 > -1.0000 & JT 58.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1545 2.15812993 + DONE now c -1545 + Allocate 38 spaces in coupling list 19 + XS 0.522E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -10 + XS 0.308E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -11 + XS 0.308E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -12 + XS 0.308E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -13 + XS 0.182E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -14 + XS 0.182E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -15 + XS 0.182E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -16 + XS 0.107E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -17 + XS 0.107E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1563 2.17493296 + DONE now c -1563 + Try JTOTAL = 59.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.408E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -1 + XS 0.240E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -2 + XS 0.240E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -3 + XS 0.240E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -4 + XS 0.142E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -5 + XS 0.142E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -6 + XS 0.142E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -7 + XS 0.835E-02 > -1.0000 & JT 59.5 > 0.0 so DONES now = -8 + XS 0.835E-02 > -1.0000 & JT 59.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1572 2.19169116 + DONE now c -1572 + Allocate 38 spaces in coupling list 19 + XS 0.313E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -10 + XS 0.313E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -11 + XS 0.185E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -12 + XS 0.185E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -13 + XS 0.185E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -14 + XS 0.109E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -15 + XS 0.109E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -16 + XS 0.109E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -17 + XS 0.641E-02 > -1.0000 & JT 59.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1590 2.20863986 + DONE now c -1590 + Try JTOTAL = 60.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.245E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -1 + XS 0.245E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -2 + XS 0.144E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -3 + XS 0.144E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -4 + XS 0.144E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -5 + XS 0.849E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -6 + XS 0.849E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -7 + XS 0.849E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -8 + XS 0.500E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1599 2.22598600 + DONE now c -1599 + Allocate 38 spaces in coupling list 19 + XS 0.318E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -10 + XS 0.188E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -11 + XS 0.188E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -12 + XS 0.188E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -13 + XS 0.111E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -14 + XS 0.111E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -15 + XS 0.111E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -16 + XS 0.652E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -17 + XS 0.652E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1617 2.24298096 + DONE now c -1617 + Try JTOTAL = 61.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.249E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -1 + XS 0.147E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -2 + XS 0.147E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -3 + XS 0.147E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -4 + XS 0.863E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -5 + XS 0.863E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -6 + XS 0.863E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -7 + XS 0.508E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -8 + XS 0.508E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1626 2.25979304 + DONE now c -1626 + Allocate 38 spaces in coupling list 19 + XS 0.191E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -10 + XS 0.191E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -11 + XS 0.112E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -12 + XS 0.112E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -13 + XS 0.112E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -14 + XS 0.662E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -15 + XS 0.662E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -16 + XS 0.662E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -17 + XS 0.390E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1644 2.27643299 + DONE now c -1644 + Try JTOTAL = 62.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.149E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -1 + XS 0.149E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -2 + XS 0.877E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -3 + XS 0.877E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -4 + XS 0.877E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -5 + XS 0.516E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -6 + XS 0.516E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -7 + XS 0.516E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -8 + XS 0.304E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1653 2.29310393 + DONE now c -1653 + Allocate 38 spaces in coupling list 19 + XS 0.194E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -10 + XS 0.114E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -11 + XS 0.114E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -12 + XS 0.114E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -13 + XS 0.673E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -14 + XS 0.673E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -15 + XS 0.673E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -16 + XS 0.396E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -17 + XS 0.396E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1671 2.31052899 + DONE now c -1671 + Try JTOTAL = 63.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.151E-01 > -1.0000 & JT 63.5 > 0.0 so DONES now = -1 + XS 0.891E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -2 + XS 0.891E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -3 + XS 0.891E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -4 + XS 0.525E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -5 + XS 0.525E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -6 + XS 0.525E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -7 + XS 0.309E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -8 + XS 0.309E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1680 2.32722497 + DONE now c -1680 + Allocate 38 spaces in coupling list 19 + XS 0.116E-01 > -1.0000 & JT 63.5 > 0.0 so DONES now = -10 + XS 0.116E-01 > -1.0000 & JT 63.5 > 0.0 so DONES now = -11 + XS 0.684E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -12 + XS 0.684E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -13 + XS 0.684E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -14 + XS 0.403E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -15 + XS 0.403E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -16 + XS 0.403E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -17 + XS 0.237E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1698 2.34474993 + DONE now c -1698 + Try JTOTAL = 64.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.905E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -1 + XS 0.905E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -2 + XS 0.533E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -3 + XS 0.533E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -4 + XS 0.533E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -5 + XS 0.314E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -6 + XS 0.314E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -7 + XS 0.314E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -8 + XS 0.184E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1707 2.36247802 + DONE now c -1707 + Allocate 38 spaces in coupling list 19 + XS 0.118E-01 > -1.0000 & JT 64.5 > 0.0 so DONES now = -10 + XS 0.694E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -11 + XS 0.694E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -12 + XS 0.694E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -13 + XS 0.409E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -14 + XS 0.409E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -15 + XS 0.409E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -16 + XS 0.241E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -17 + XS 0.241E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1725 2.37914991 + DONE now c -1725 + Try JTOTAL = 65.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.919E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -1 + XS 0.541E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -2 + XS 0.541E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -3 + XS 0.541E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -4 + XS 0.318E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -5 + XS 0.318E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -6 + XS 0.318E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -7 + XS 0.187E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -8 + XS 0.187E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1734 2.39618802 + DONE now c -1734 + Allocate 38 spaces in coupling list 19 + XS 0.705E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -10 + XS 0.705E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -11 + XS 0.415E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -12 + XS 0.415E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -13 + XS 0.415E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -14 + XS 0.244E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -15 + XS 0.244E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -16 + XS 0.244E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -17 + XS 0.144E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1752 2.41458893 + DONE now c -1752 + Try JTOTAL = 66.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.549E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -1 + XS 0.549E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -2 + XS 0.323E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -3 + XS 0.323E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -4 + XS 0.323E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -5 + XS 0.190E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -6 + XS 0.190E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -7 + XS 0.190E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -8 + XS 0.112E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1761 2.43262601 + DONE now c -1761 + Allocate 38 spaces in coupling list 19 + XS 0.716E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -10 + XS 0.421E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -11 + XS 0.421E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -12 + XS 0.421E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -13 + XS 0.248E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -14 + XS 0.248E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -15 + XS 0.248E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -16 + XS 0.146E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -17 + XS 0.146E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1779 2.44936109 + DONE now c -1779 + Try JTOTAL = 67.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.557E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -1 + XS 0.328E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -2 + XS 0.328E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -3 + XS 0.328E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -4 + XS 0.193E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -5 + XS 0.193E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -6 + XS 0.193E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -7 + XS 0.114E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -8 + XS 0.114E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1788 2.46554804 + DONE now c -1788 + Allocate 38 spaces in coupling list 19 + XS 0.428E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -10 + XS 0.428E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -11 + XS 0.252E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -12 + XS 0.252E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -13 + XS 0.252E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -14 + XS 0.148E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -15 + XS 0.148E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -16 + XS 0.148E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -17 + XS 0.870E-03 > -1.0000 & JT 67.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1806 2.48162007 + DONE now c -1806 + Try JTOTAL = 68.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.333E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -1 + XS 0.333E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -2 + XS 0.196E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -3 + XS 0.196E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -4 + XS 0.196E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -5 + XS 0.115E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -6 + XS 0.115E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -7 + XS 0.115E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -8 + XS 0.677E-03 > -1.0000 & JT 68.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1815 2.49846101 + DONE now c -1815 + Allocate 38 spaces in coupling list 19 + XS 0.434E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -10 + XS 0.255E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -11 + XS 0.255E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -12 + XS 0.255E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -13 + XS 0.150E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -14 + XS 0.150E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -15 + XS 0.150E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -16 + XS 0.883E-03 > -1.0000 & JT 68.5 > 0.0 so DONES now = -17 + XS 0.883E-03 > -1.0000 & JT 68.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1833 2.51491094 + DONE now c -1833 + Try JTOTAL = 69.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.338E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -1 + XS 0.199E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -2 + XS 0.199E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -3 + XS 0.199E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -4 + XS 0.117E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -5 + XS 0.117E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -6 + XS 0.117E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -7 + XS 0.687E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -8 + XS 0.687E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1842 2.53127694 + DONE now c -1842 + Allocate 38 spaces in coupling list 19 + XS 0.259E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -10 + XS 0.259E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -11 + XS 0.152E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -12 + XS 0.152E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -13 + XS 0.152E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -14 + XS 0.896E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -15 + XS 0.896E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -16 + XS 0.896E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -17 + XS 0.527E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1860 2.54810405 + DONE now c -1860 + Try JTOTAL = 70.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.202E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -1 + XS 0.202E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -2 + XS 0.119E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -3 + XS 0.119E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -4 + XS 0.119E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -5 + XS 0.697E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -6 + XS 0.697E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -7 + XS 0.697E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -8 + XS 0.409E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1869 2.56430411 + DONE now c -1869 + Allocate 38 spaces in coupling list 19 + XS 0.263E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -10 + XS 0.155E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -11 + XS 0.155E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -12 + XS 0.155E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -13 + XS 0.909E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -14 + XS 0.909E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -15 + XS 0.909E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -16 + XS 0.534E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -17 + XS 0.534E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1887 2.58184004 + DONE now c -1887 + Try JTOTAL = 71.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.204E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -1 + XS 0.120E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -2 + XS 0.120E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -3 + XS 0.120E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -4 + XS 0.706E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -5 + XS 0.706E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -6 + XS 0.706E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -7 + XS 0.415E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -8 + XS 0.415E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1896 2.59904003 + DONE now c -1896 + Allocate 38 spaces in coupling list 19 + XS 0.157E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -10 + XS 0.157E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -11 + XS 0.921E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -12 + XS 0.921E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -13 + XS 0.921E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -14 + XS 0.542E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -15 + XS 0.542E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -16 + XS 0.542E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -17 + XS 0.318E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1914 2.61483002 + DONE now c -1914 + Try JTOTAL = 72.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.122E-02 > -1.0000 & JT 72.5 > 0.0 so DONES now = -1 + XS 0.122E-02 > -1.0000 & JT 72.5 > 0.0 so DONES now = -2 + XS 0.716E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -3 + XS 0.716E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -4 + XS 0.716E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -5 + XS 0.421E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -6 + XS 0.421E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -7 + XS 0.421E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -8 + XS 0.247E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1923 2.63018489 + DONE now c -1923 + Allocate 38 spaces in coupling list 19 + XS 0.159E-02 > -1.0000 & JT 72.5 > 0.0 so DONES now = -10 + XS 0.934E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -11 + XS 0.934E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -12 + XS 0.934E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -13 + XS 0.549E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -14 + XS 0.549E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -15 + XS 0.549E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -16 + XS 0.323E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -17 + XS 0.323E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1941 2.64546609 + DONE now c -1941 + Try JTOTAL = 73.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.124E-02 > -1.0000 & JT 73.5 > 0.0 so DONES now = -1 + XS 0.726E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -2 + XS 0.726E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -3 + XS 0.726E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -4 + XS 0.427E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -5 + XS 0.427E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -6 + XS 0.427E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -7 + XS 0.251E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -8 + XS 0.251E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1950 2.66094613 + DONE now c -1950 + Allocate 38 spaces in coupling list 19 + XS 0.947E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -10 + XS 0.947E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -11 + XS 0.557E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -12 + XS 0.557E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -13 + XS 0.557E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -14 + XS 0.327E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -15 + XS 0.327E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -16 + XS 0.327E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -17 + XS 0.192E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1968 2.67613912 + DONE now c -1968 + Try JTOTAL = 74.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.736E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -1 + XS 0.736E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -2 + XS 0.432E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -3 + XS 0.432E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -4 + XS 0.432E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -5 + XS 0.254E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -6 + XS 0.254E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -7 + XS 0.254E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -8 + XS 0.149E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -1977 2.69139886 + DONE now c -1977 + Allocate 38 spaces in coupling list 19 + XS 0.960E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -10 + XS 0.564E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -11 + XS 0.564E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -12 + XS 0.564E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -13 + XS 0.331E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -14 + XS 0.331E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -15 + XS 0.331E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -16 + XS 0.195E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -17 + XS 0.195E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -1995 2.70695090 + DONE now c -1995 + Try JTOTAL = 75.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.746E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -1 + XS 0.438E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -2 + XS 0.438E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -3 + XS 0.438E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -4 + XS 0.257E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -5 + XS 0.257E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -6 + XS 0.257E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -7 + XS 0.151E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -8 + XS 0.151E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2004 2.72231793 + DONE now c -2004 + Allocate 38 spaces in coupling list 19 + XS 0.572E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -10 + XS 0.572E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -11 + XS 0.336E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -12 + XS 0.336E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -13 + XS 0.336E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -14 + XS 0.197E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -15 + XS 0.197E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -16 + XS 0.197E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -17 + XS 0.116E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2022 2.73754907 + DONE now c -2022 + Try JTOTAL = 76.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.444E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -1 + XS 0.444E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -2 + XS 0.261E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -3 + XS 0.261E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -4 + XS 0.261E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -5 + XS 0.153E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -6 + XS 0.153E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -7 + XS 0.153E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -8 + XS 0.899E-04 > -1.0000 & JT 76.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2031 2.75273013 + DONE now c -2031 + Allocate 38 spaces in coupling list 19 + XS 0.579E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -10 + XS 0.340E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -11 + XS 0.340E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -12 + XS 0.340E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -13 + XS 0.200E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -14 + XS 0.200E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -15 + XS 0.200E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -16 + XS 0.117E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -17 + XS 0.117E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2049 2.76800609 + DONE now c -2049 + Try JTOTAL = 77.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.450E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -1 + XS 0.264E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -2 + XS 0.264E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -3 + XS 0.264E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -4 + XS 0.155E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -5 + XS 0.155E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -6 + XS 0.155E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -7 + XS 0.911E-04 > -1.0000 & JT 77.5 > 0.0 so DONES now = -8 + XS 0.911E-04 > -1.0000 & JT 77.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2058 2.78316402 + DONE now c -2058 + Allocate 38 spaces in coupling list 19 + XS 0.345E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -10 + XS 0.345E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -11 + XS 0.203E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -12 + XS 0.203E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -13 + XS 0.203E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -14 + XS 0.119E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -15 + XS 0.119E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -16 + XS 0.119E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -17 + XS 0.698E-04 > -1.0000 & JT 77.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2076 2.79825997 + DONE now c -2076 + Try JTOTAL = 78.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.268E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -1 + XS 0.268E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -2 + XS 0.157E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -3 + XS 0.157E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -4 + XS 0.157E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -5 + XS 0.923E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -6 + XS 0.923E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -7 + XS 0.923E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -8 + XS 0.542E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2085 2.81349492 + DONE now c -2085 + Allocate 38 spaces in coupling list 19 + XS 0.349E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -10 + XS 0.205E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -11 + XS 0.205E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -12 + XS 0.205E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -13 + XS 0.120E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -14 + XS 0.120E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -15 + XS 0.120E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -16 + XS 0.707E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -17 + XS 0.707E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2103 2.82871103 + DONE now c -2103 + Try JTOTAL = 79.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.271E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -1 + XS 0.159E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -2 + XS 0.159E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -3 + XS 0.159E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -4 + XS 0.935E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -5 + XS 0.935E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -6 + XS 0.935E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -7 + XS 0.549E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -8 + XS 0.549E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2112 2.84378290 + DONE now c -2112 + Allocate 38 spaces in coupling list 19 + XS 0.208E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -10 + XS 0.208E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -11 + XS 0.122E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -12 + XS 0.122E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -13 + XS 0.122E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -14 + XS 0.716E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -15 + XS 0.716E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -16 + XS 0.716E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -17 + XS 0.420E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2130 2.85903001 + DONE now c -2130 + Try JTOTAL = 80.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.161E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -1 + XS 0.161E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -2 + XS 0.946E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -3 + XS 0.946E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -4 + XS 0.946E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -5 + XS 0.555E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -6 + XS 0.555E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -7 + XS 0.555E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -8 + XS 0.326E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2139 2.87413287 + DONE now c -2139 + Allocate 38 spaces in coupling list 19 + XS 0.210E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -10 + XS 0.123E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -11 + XS 0.123E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -12 + XS 0.123E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -13 + XS 0.725E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -14 + XS 0.725E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -15 + XS 0.725E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -16 + XS 0.425E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -17 + XS 0.425E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2157 2.88918400 + DONE now c -2157 + Try JTOTAL = 81.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.163E-03 > -1.0000 & JT 81.5 > 0.0 so DONES now = -1 + XS 0.958E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -2 + XS 0.958E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -3 + XS 0.958E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -4 + XS 0.562E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -5 + XS 0.562E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -6 + XS 0.562E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -7 + XS 0.330E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -8 + XS 0.330E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2166 2.90421605 + DONE now c -2166 + Allocate 38 spaces in coupling list 19 + XS 0.125E-03 > -1.0000 & JT 81.5 > 0.0 so DONES now = -10 + XS 0.125E-03 > -1.0000 & JT 81.5 > 0.0 so DONES now = -11 + XS 0.734E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -12 + XS 0.734E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -13 + XS 0.734E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -14 + XS 0.431E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -15 + XS 0.431E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -16 + XS 0.431E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -17 + XS 0.253E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2184 2.91937709 + DONE now c -2184 + Try JTOTAL = 82.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.970E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -1 + XS 0.970E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -2 + XS 0.569E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -3 + XS 0.569E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -4 + XS 0.569E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -5 + XS 0.334E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -6 + XS 0.334E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -7 + XS 0.334E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -8 + XS 0.196E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2193 2.93439293 + DONE now c -2193 + Allocate 38 spaces in coupling list 19 + XS 0.127E-03 > -1.0000 & JT 82.5 > 0.0 so DONES now = -10 + XS 0.743E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -11 + XS 0.743E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -12 + XS 0.743E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -13 + XS 0.436E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -14 + XS 0.436E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -15 + XS 0.436E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -16 + XS 0.256E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -17 + XS 0.256E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2211 2.94937396 + DONE now c -2211 + Try JTOTAL = 83.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.981E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -1 + XS 0.576E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -2 + XS 0.576E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -3 + XS 0.576E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -4 + XS 0.338E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -5 + XS 0.338E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -6 + XS 0.338E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -7 + XS 0.198E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -8 + XS 0.198E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2220 2.96442103 + DONE now c -2220 + Allocate 38 spaces in coupling list 19 + XS 0.752E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -10 + XS 0.752E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -11 + XS 0.441E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -12 + XS 0.441E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -13 + XS 0.441E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -14 + XS 0.259E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -15 + XS 0.259E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -16 + XS 0.259E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -17 + XS 0.152E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2238 2.98023295 + DONE now c -2238 + Try JTOTAL = 84.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.583E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -1 + XS 0.583E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -2 + XS 0.342E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -3 + XS 0.342E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -4 + XS 0.342E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -5 + XS 0.201E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -6 + XS 0.201E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -7 + XS 0.201E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -8 + XS 0.118E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2247 2.99694800 + DONE now c -2247 + Allocate 38 spaces in coupling list 19 + XS 0.761E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -10 + XS 0.447E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -11 + XS 0.447E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -12 + XS 0.447E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -13 + XS 0.262E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -14 + XS 0.262E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -15 + XS 0.262E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -16 + XS 0.154E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -17 + XS 0.154E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2265 3.01399803 + DONE now c -2265 + Try JTOTAL = 85.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.590E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -1 + XS 0.346E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -2 + XS 0.346E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -3 + XS 0.346E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -4 + XS 0.203E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -5 + XS 0.203E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -6 + XS 0.203E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -7 + XS 0.119E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -8 + XS 0.119E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2274 3.03121996 + DONE now c -2274 + Allocate 38 spaces in coupling list 19 + XS 0.452E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -10 + XS 0.452E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -11 + XS 0.265E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -12 + XS 0.265E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -13 + XS 0.265E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -14 + XS 0.156E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -15 + XS 0.156E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -16 + XS 0.156E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -17 + XS 0.912E-05 > -1.0000 & JT 85.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2292 3.04884315 + DONE now c -2292 + Try JTOTAL = 86.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.350E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -1 + XS 0.350E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -2 + XS 0.205E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -3 + XS 0.205E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -4 + XS 0.205E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -5 + XS 0.120E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -6 + XS 0.120E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -7 + XS 0.120E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -8 + XS 0.707E-05 > -1.0000 & JT 86.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2301 3.06791806 + DONE now c -2301 + Allocate 38 spaces in coupling list 19 + XS 0.457E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -10 + XS 0.268E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -11 + XS 0.268E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -12 + XS 0.268E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -13 + XS 0.157E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -14 + XS 0.157E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -15 + XS 0.157E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -16 + XS 0.923E-05 > -1.0000 & JT 86.5 > 0.0 so DONES now = -17 + XS 0.923E-05 > -1.0000 & JT 86.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2319 3.08753204 + DONE now c -2319 + Try JTOTAL = 87.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.354E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -1 + XS 0.208E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -2 + XS 0.208E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -3 + XS 0.208E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -4 + XS 0.122E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -5 + XS 0.122E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -6 + XS 0.122E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -7 + XS 0.715E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -8 + XS 0.715E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2328 3.10498405 + DONE now c -2328 + Allocate 38 spaces in coupling list 19 + XS 0.271E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -10 + XS 0.271E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -11 + XS 0.159E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -12 + XS 0.159E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -13 + XS 0.159E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -14 + XS 0.933E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -15 + XS 0.933E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -16 + XS 0.933E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -17 + XS 0.547E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2346 3.12191296 + DONE now c -2346 + Try JTOTAL = 88.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.210E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -1 + XS 0.210E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -2 + XS 0.123E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -3 + XS 0.123E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -4 + XS 0.123E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -5 + XS 0.723E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -6 + XS 0.723E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -7 + XS 0.723E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -8 + XS 0.424E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2355 3.14091182 + DONE now c -2355 + Allocate 38 spaces in coupling list 19 + XS 0.274E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -10 + XS 0.161E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -11 + XS 0.161E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -12 + XS 0.161E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -13 + XS 0.944E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -14 + XS 0.944E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -15 + XS 0.944E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -16 + XS 0.553E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -17 + XS 0.553E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2373 3.16135502 + DONE now c -2373 + Try JTOTAL = 89.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.212E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -1 + XS 0.125E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -2 + XS 0.125E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -3 + XS 0.125E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -4 + XS 0.731E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -5 + XS 0.731E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -6 + XS 0.731E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -7 + XS 0.429E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -8 + XS 0.429E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2382 3.17812610 + DONE now c -2382 + Allocate 38 spaces in coupling list 19 + XS 0.163E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -10 + XS 0.163E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -11 + XS 0.954E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -12 + XS 0.954E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -13 + XS 0.954E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -14 + XS 0.560E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -15 + XS 0.560E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -16 + XS 0.560E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -17 + XS 0.328E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2400 3.19348788 + DONE now c -2400 + Try JTOTAL = 90.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.126E-04 > -1.0000 & JT 90.5 > 0.0 so DONES now = -1 + XS 0.126E-04 > -1.0000 & JT 90.5 > 0.0 so DONES now = -2 + XS 0.739E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -3 + XS 0.739E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -4 + XS 0.739E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -5 + XS 0.433E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -6 + XS 0.433E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -7 + XS 0.433E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -8 + XS 0.254E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2409 3.20937610 + DONE now c -2409 + Allocate 38 spaces in coupling list 19 + XS 0.165E-04 > -1.0000 & JT 90.5 > 0.0 so DONES now = -10 + XS 0.965E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -11 + XS 0.965E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -12 + XS 0.965E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -13 + XS 0.566E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -14 + XS 0.566E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -15 + XS 0.566E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -16 + XS 0.332E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -17 + XS 0.332E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2427 3.22444701 + DONE now c -2427 + Try JTOTAL = 91.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.127E-04 > -1.0000 & JT 91.5 > 0.0 so DONES now = -1 + XS 0.747E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -2 + XS 0.747E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -3 + XS 0.747E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -4 + XS 0.438E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -5 + XS 0.438E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -6 + XS 0.438E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -7 + XS 0.257E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -8 + XS 0.257E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2436 3.23933911 + DONE now c -2436 + Allocate 38 spaces in coupling list 19 + XS 0.976E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -10 + XS 0.976E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -11 + XS 0.572E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -12 + XS 0.572E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -13 + XS 0.572E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -14 + XS 0.335E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -15 + XS 0.335E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -16 + XS 0.335E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -17 + XS 0.197E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2454 3.25509191 + DONE now c -2454 + Try JTOTAL = 92.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.755E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -1 + XS 0.755E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -2 + XS 0.443E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -3 + XS 0.443E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -4 + XS 0.443E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -5 + XS 0.260E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -6 + XS 0.260E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -7 + XS 0.260E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -8 + XS 0.152E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2463 3.27168393 + DONE now c -2463 + Allocate 38 spaces in coupling list 19 + XS 0.986E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -10 + XS 0.578E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -11 + XS 0.578E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -12 + XS 0.578E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -13 + XS 0.339E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -14 + XS 0.339E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -15 + XS 0.339E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -16 + XS 0.199E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -17 + XS 0.199E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2481 3.28776503 + DONE now c -2481 + Try JTOTAL = 93.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.763E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -1 + XS 0.448E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -2 + XS 0.448E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -3 + XS 0.448E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -4 + XS 0.262E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -5 + XS 0.262E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -6 + XS 0.262E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -7 + XS 0.154E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -8 + XS 0.154E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2490 3.30399394 + DONE now c -2490 + Allocate 38 spaces in coupling list 19 + XS 0.585E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -10 + XS 0.585E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -11 + XS 0.343E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -12 + XS 0.343E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -13 + XS 0.343E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -14 + XS 0.201E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -15 + XS 0.201E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -16 + XS 0.201E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -17 + XS 0.118E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2508 3.32012701 + DONE now c -2508 + Try JTOTAL = 94.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.452E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -1 + XS 0.452E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -2 + XS 0.265E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -3 + XS 0.265E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -4 + XS 0.265E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -5 + XS 0.155E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -6 + XS 0.155E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -7 + XS 0.155E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -8 + XS 0.911E-06 > -1.0000 & JT 94.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2517 3.33644199 + DONE now c -2517 + Allocate 38 spaces in coupling list 19 + XS 0.591E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -10 + XS 0.346E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -11 + XS 0.346E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -12 + XS 0.346E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -13 + XS 0.203E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -14 + XS 0.203E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -15 + XS 0.203E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -16 + XS 0.119E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -17 + XS 0.119E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2535 3.35398889 + DONE now c -2535 + Try JTOTAL = 95.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.457E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -1 + XS 0.268E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -2 + XS 0.268E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -3 + XS 0.268E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -4 + XS 0.157E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -5 + XS 0.157E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -6 + XS 0.157E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -7 + XS 0.920E-06 > -1.0000 & JT 95.5 > 0.0 so DONES now = -8 + XS 0.920E-06 > -1.0000 & JT 95.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2544 3.37092495 + DONE now c -2544 + Allocate 38 spaces in coupling list 19 + XS 0.350E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -10 + XS 0.350E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -11 + XS 0.205E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -12 + XS 0.205E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -13 + XS 0.205E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -14 + XS 0.120E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -15 + XS 0.120E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -16 + XS 0.120E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -17 + XS 0.704E-06 > -1.0000 & JT 95.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2562 3.38735700 + DONE now c -2562 + Try JTOTAL = 96.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.271E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -1 + XS 0.271E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -2 + XS 0.159E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -3 + XS 0.159E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -4 + XS 0.159E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -5 + XS 0.930E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -6 + XS 0.930E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -7 + XS 0.930E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -8 + XS 0.545E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2571 3.40363407 + DONE now c -2571 + Allocate 38 spaces in coupling list 19 + XS 0.354E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -10 + XS 0.207E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -11 + XS 0.207E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -12 + XS 0.207E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -13 + XS 0.121E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -14 + XS 0.121E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -15 + XS 0.121E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -16 + XS 0.712E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -17 + XS 0.712E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2589 3.41980386 + DONE now c -2589 + Try JTOTAL = 97.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.274E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -1 + XS 0.160E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -2 + XS 0.160E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -3 + XS 0.160E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -4 + XS 0.939E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -5 + XS 0.939E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -6 + XS 0.939E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -7 + XS 0.550E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -8 + XS 0.550E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2598 3.43626404 + DONE now c -2598 + Allocate 38 spaces in coupling list 19 + XS 0.209E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -10 + XS 0.209E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -11 + XS 0.123E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -12 + XS 0.123E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -13 + XS 0.123E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -14 + XS 0.719E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -15 + XS 0.719E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -16 + XS 0.719E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -17 + XS 0.421E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2616 3.45237994 + DONE now c -2616 + Try JTOTAL = 98.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.162E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -1 + XS 0.162E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -2 + XS 0.949E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -3 + XS 0.949E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -4 + XS 0.949E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -5 + XS 0.556E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -6 + XS 0.556E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -7 + XS 0.556E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -8 + XS 0.326E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2625 3.46868587 + DONE now c -2625 + Allocate 38 spaces in coupling list 19 + XS 0.212E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -10 + XS 0.124E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -11 + XS 0.124E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -12 + XS 0.124E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -13 + XS 0.726E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -14 + XS 0.726E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -15 + XS 0.726E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -16 + XS 0.425E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -17 + XS 0.425E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2643 3.48524904 + DONE now c -2643 + Try JTOTAL = 99.500000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.164E-05 > -1.0000 & JT 99.5 > 0.0 so DONES now = -1 + XS 0.958E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -2 + XS 0.958E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -3 + XS 0.958E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -4 + XS 0.562E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -5 + XS 0.562E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -6 + XS 0.562E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -7 + XS 0.329E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -8 + XS 0.329E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2652 3.50168586 + DONE now c -2652 + Allocate 38 spaces in coupling list 19 + XS 0.125E-05 > -1.0000 & JT 99.5 > 0.0 so DONES now = -10 + XS 0.125E-05 > -1.0000 & JT 99.5 > 0.0 so DONES now = -11 + XS 0.734E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -12 + XS 0.734E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -13 + XS 0.734E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -14 + XS 0.430E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -15 + XS 0.430E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -16 + XS 0.430E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -17 + XS 0.252E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2670 3.51833105 + DONE now c -2670 + Try JTOTAL = 100.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.968E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -1 + XS 0.968E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -2 + XS 0.567E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -3 + XS 0.567E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -4 + XS 0.567E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -5 + XS 0.332E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -6 + XS 0.332E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -7 + XS 0.332E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -8 + XS 0.195E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2679 3.53480887 + DONE now c -2679 + Allocate 38 spaces in coupling list 19 + XS 0.126E-05 > -1.0000 & JT 100.5 > 0.0 so DONES now = -10 + XS 0.741E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -11 + XS 0.741E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -12 + XS 0.741E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -13 + XS 0.434E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -14 + XS 0.434E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -15 + XS 0.434E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -16 + XS 0.254E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -17 + XS 0.254E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2697 3.55087304 + DONE now c -2697 + Try JTOTAL = 101.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.978E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -1 + XS 0.573E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -2 + XS 0.573E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -3 + XS 0.573E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -4 + XS 0.335E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -5 + XS 0.335E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -6 + XS 0.335E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -7 + XS 0.196E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -8 + XS 0.196E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2706 3.56677508 + DONE now c -2706 + Allocate 38 spaces in coupling list 19 + XS 0.748E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -10 + XS 0.748E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -11 + XS 0.438E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -12 + XS 0.438E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -13 + XS 0.438E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -14 + XS 0.257E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -15 + XS 0.257E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -16 + XS 0.257E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -17 + XS 0.150E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2724 3.58220005 + DONE now c -2724 + Try JTOTAL = 102.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.578E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -1 + XS 0.578E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -2 + XS 0.339E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -3 + XS 0.339E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -4 + XS 0.339E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -5 + XS 0.198E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -6 + XS 0.198E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -7 + XS 0.198E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -8 + XS 0.116E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2733 3.59758902 + DONE now c -2733 + Allocate 38 spaces in coupling list 19 + XS 0.756E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -10 + XS 0.443E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -11 + XS 0.443E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -12 + XS 0.443E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -13 + XS 0.259E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -14 + XS 0.259E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -15 + XS 0.259E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -16 + XS 0.152E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -17 + XS 0.152E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2751 3.61303806 + DONE now c -2751 + Try JTOTAL = 103.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.584E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -1 + XS 0.342E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -2 + XS 0.342E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -3 + XS 0.342E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -4 + XS 0.200E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -5 + XS 0.200E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -6 + XS 0.200E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -7 + XS 0.117E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -8 + XS 0.117E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2760 3.62773800 + DONE now c -2760 + Allocate 38 spaces in coupling list 19 + XS 0.447E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -10 + XS 0.447E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -11 + XS 0.262E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -12 + XS 0.262E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -13 + XS 0.262E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -14 + XS 0.153E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -15 + XS 0.153E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -16 + XS 0.153E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -17 + XS 0.898E-07 > -1.0000 & JT 103.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2778 3.64243507 + DONE now c -2778 + Try JTOTAL = 104.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.345E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -1 + XS 0.345E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -2 + XS 0.202E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -3 + XS 0.202E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -4 + XS 0.202E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -5 + XS 0.118E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -6 + XS 0.118E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -7 + XS 0.118E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -8 + XS 0.693E-07 > -1.0000 & JT 104.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2787 3.65715194 + DONE now c -2787 + Allocate 38 spaces in coupling list 19 + XS 0.451E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -10 + XS 0.264E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -11 + XS 0.264E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -12 + XS 0.264E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -13 + XS 0.155E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -14 + XS 0.155E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -15 + XS 0.155E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -16 + XS 0.906E-07 > -1.0000 & JT 104.5 > 0.0 so DONES now = -17 + XS 0.906E-07 > -1.0000 & JT 104.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2805 3.67178893 + DONE now c -2805 + Try JTOTAL = 105.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.349E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -1 + XS 0.204E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -2 + XS 0.204E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -3 + XS 0.204E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -4 + XS 0.120E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -5 + XS 0.120E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -6 + XS 0.120E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -7 + XS 0.700E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -8 + XS 0.700E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2814 3.68646312 + DONE now c -2814 + Allocate 38 spaces in coupling list 19 + XS 0.267E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -10 + XS 0.267E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -11 + XS 0.156E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -12 + XS 0.156E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -13 + XS 0.156E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -14 + XS 0.915E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -15 + XS 0.915E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -16 + XS 0.915E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -17 + XS 0.536E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2832 3.70125198 + DONE now c -2832 + Try JTOTAL = 106.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.206E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -1 + XS 0.206E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -2 + XS 0.121E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -3 + XS 0.121E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -4 + XS 0.121E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -5 + XS 0.707E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -6 + XS 0.707E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -7 + XS 0.707E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -8 + XS 0.414E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2841 3.71663594 + DONE now c -2841 + Allocate 38 spaces in coupling list 19 + XS 0.269E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -10 + XS 0.158E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -11 + XS 0.158E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -12 + XS 0.158E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -13 + XS 0.923E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -14 + XS 0.923E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -15 + XS 0.923E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -16 + XS 0.541E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -17 + XS 0.541E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2859 3.73173213 + DONE now c -2859 + Try JTOTAL = 107.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.208E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -1 + XS 0.122E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -2 + XS 0.122E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -3 + XS 0.122E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -4 + XS 0.713E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -5 + XS 0.713E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -6 + XS 0.713E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -7 + XS 0.417E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -8 + XS 0.417E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2868 3.74660993 + DONE now c -2868 + Allocate 38 spaces in coupling list 19 + XS 0.159E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -10 + XS 0.159E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -11 + XS 0.932E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -12 + XS 0.932E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -13 + XS 0.932E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -14 + XS 0.546E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -15 + XS 0.546E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -16 + XS 0.546E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -17 + XS 0.319E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2886 3.76245999 + DONE now c -2886 + Try JTOTAL = 108.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.123E-06 > -1.0000 & JT 108.5 > 0.0 so DONES now = -1 + XS 0.123E-06 > -1.0000 & JT 108.5 > 0.0 so DONES now = -2 + XS 0.720E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -3 + XS 0.720E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -4 + XS 0.720E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -5 + XS 0.421E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -6 + XS 0.421E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -7 + XS 0.421E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -8 + XS 0.247E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2895 3.77726603 + DONE now c -2895 + Allocate 38 spaces in coupling list 19 + XS 0.161E-06 > -1.0000 & JT 108.5 > 0.0 so DONES now = -10 + XS 0.941E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -11 + XS 0.941E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -12 + XS 0.941E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -13 + XS 0.551E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -14 + XS 0.551E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -15 + XS 0.551E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -16 + XS 0.322E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -17 + XS 0.322E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2913 3.79361200 + DONE now c -2913 + Try JTOTAL = 109.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.124E-06 > -1.0000 & JT 109.5 > 0.0 so DONES now = -1 + XS 0.726E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -2 + XS 0.726E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -3 + XS 0.726E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -4 + XS 0.425E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -5 + XS 0.425E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -6 + XS 0.425E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -7 + XS 0.249E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -8 + XS 0.249E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2922 3.80976987 + DONE now c -2922 + Allocate 38 spaces in coupling list 19 + XS 0.949E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -10 + XS 0.949E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -11 + XS 0.556E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -12 + XS 0.556E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -13 + XS 0.556E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -14 + XS 0.325E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -15 + XS 0.325E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -16 + XS 0.325E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -17 + XS 0.190E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2940 3.82535386 + DONE now c -2940 + Try JTOTAL = 110.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.733E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -1 + XS 0.733E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -2 + XS 0.429E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -3 + XS 0.429E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -4 + XS 0.429E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -5 + XS 0.251E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -6 + XS 0.251E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -7 + XS 0.251E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -8 + XS 0.147E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2949 3.84111094 + DONE now c -2949 + Allocate 38 spaces in coupling list 19 + XS 0.958E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -10 + XS 0.561E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -11 + XS 0.561E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -12 + XS 0.561E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -13 + XS 0.328E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -14 + XS 0.328E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -15 + XS 0.328E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -16 + XS 0.192E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -17 + XS 0.192E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2967 3.85669804 + DONE now c -2967 + Try JTOTAL = 111.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.740E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -1 + XS 0.433E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -2 + XS 0.433E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -3 + XS 0.433E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -4 + XS 0.253E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -5 + XS 0.253E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -6 + XS 0.253E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -7 + XS 0.148E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -8 + XS 0.148E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -2976 3.87234092 + DONE now c -2976 + Allocate 38 spaces in coupling list 19 + XS 0.566E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -10 + XS 0.566E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -11 + XS 0.331E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -12 + XS 0.331E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -13 + XS 0.331E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -14 + XS 0.194E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -15 + XS 0.194E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -16 + XS 0.194E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -17 + XS 0.113E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -2994 3.88805985 + DONE now c -2994 + Try JTOTAL = 112.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.437E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -1 + XS 0.437E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -2 + XS 0.256E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -3 + XS 0.256E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -4 + XS 0.256E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -5 + XS 0.150E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -6 + XS 0.150E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -7 + XS 0.150E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -8 + XS 0.875E-08 > -1.0000 & JT 112.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -3003 3.90285397 + DONE now c -3003 + Allocate 38 spaces in coupling list 19 + XS 0.571E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -10 + XS 0.334E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -11 + XS 0.334E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -12 + XS 0.334E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -13 + XS 0.196E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -14 + XS 0.196E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -15 + XS 0.196E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -16 + XS 0.114E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -17 + XS 0.114E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -3021 3.91842389 + DONE now c -3021 + Try JTOTAL = 113.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.441E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -1 + XS 0.258E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -2 + XS 0.258E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -3 + XS 0.258E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -4 + XS 0.151E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -5 + XS 0.151E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -6 + XS 0.151E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -7 + XS 0.883E-08 > -1.0000 & JT 113.5 > 0.0 so DONES now = -8 + XS 0.883E-08 > -1.0000 & JT 113.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -3030 3.93405700 + DONE now c -3030 + Allocate 38 spaces in coupling list 19 + XS 0.337E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -10 + XS 0.337E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -11 + XS 0.197E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -12 + XS 0.197E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -13 + XS 0.197E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -14 + XS 0.115E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -15 + XS 0.115E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -16 + XS 0.115E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -17 + XS 0.676E-08 > -1.0000 & JT 113.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -3048 3.94923711 + DONE now c -3048 + Try JTOTAL = 114.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.260E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -1 + XS 0.260E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -2 + XS 0.152E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -3 + XS 0.152E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -4 + XS 0.152E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -5 + XS 0.891E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -6 + XS 0.891E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -7 + XS 0.891E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -8 + XS 0.521E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -3057 3.96404696 + DONE now c -3057 + Allocate 38 spaces in coupling list 19 + XS 0.340E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -10 + XS 0.199E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -11 + XS 0.199E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -12 + XS 0.199E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -13 + XS 0.116E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -14 + XS 0.116E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -15 + XS 0.116E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -16 + XS 0.681E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -17 + XS 0.681E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -3075 3.97855091 + DONE now c -3075 + Try JTOTAL = 115.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.262E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -1 + XS 0.154E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -2 + XS 0.154E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -3 + XS 0.154E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -4 + XS 0.899E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -5 + XS 0.899E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -6 + XS 0.899E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -7 + XS 0.526E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -8 + XS 0.526E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -3084 3.99356008 + DONE now c -3084 + Allocate 38 spaces in coupling list 19 + XS 0.201E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -10 + XS 0.201E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -11 + XS 0.117E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -12 + XS 0.117E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -13 + XS 0.117E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -14 + XS 0.687E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -15 + XS 0.687E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -16 + XS 0.687E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -17 + XS 0.402E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -3102 4.00859213 + DONE now c -3102 + Try JTOTAL = 116.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.155E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -1 + XS 0.155E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -2 + XS 0.906E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -3 + XS 0.906E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -4 + XS 0.906E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -5 + XS 0.530E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -6 + XS 0.530E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -7 + XS 0.530E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -8 + XS 0.310E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -3111 4.02322006 + DONE now c -3111 + Allocate 38 spaces in coupling list 19 + XS 0.202E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -10 + XS 0.118E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -11 + XS 0.118E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -12 + XS 0.118E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -13 + XS 0.693E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -14 + XS 0.693E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -15 + XS 0.693E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -16 + XS 0.406E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -17 + XS 0.406E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -3129 4.03844023 + DONE now c -3129 + Try JTOTAL = 117.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.156E-07 > -1.0000 & JT 117.5 > 0.0 so DONES now = -1 + XS 0.914E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -2 + XS 0.914E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -3 + XS 0.914E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -4 + XS 0.535E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -5 + XS 0.535E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -6 + XS 0.535E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -7 + XS 0.313E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -8 + XS 0.313E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -3138 4.05478621 + DONE now c -3138 + Allocate 38 spaces in coupling list 19 + XS 0.120E-07 > -1.0000 & JT 117.5 > 0.0 so DONES now = -10 + XS 0.120E-07 > -1.0000 & JT 117.5 > 0.0 so DONES now = -11 + XS 0.699E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -12 + XS 0.699E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -13 + XS 0.699E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -14 + XS 0.409E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -15 + XS 0.409E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -16 + XS 0.409E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -17 + XS 0.239E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -3156 4.07090712 + DONE now c -3156 + Try JTOTAL = 118.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.922E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -1 + XS 0.922E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -2 + XS 0.539E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -3 + XS 0.539E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -4 + XS 0.539E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -5 + XS 0.316E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -6 + XS 0.316E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -7 + XS 0.316E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -8 + XS 0.185E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -3165 4.08657026 + DONE now c -3165 + Allocate 38 spaces in coupling list 19 + XS 0.121E-07 > -1.0000 & JT 118.5 > 0.0 so DONES now = -10 + XS 0.705E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -11 + XS 0.705E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -12 + XS 0.705E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -13 + XS 0.413E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -14 + XS 0.413E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -15 + XS 0.413E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -16 + XS 0.241E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -17 + XS 0.241E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -3183 4.10243130 + DONE now c -3183 + Try JTOTAL = 119.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.930E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -1 + XS 0.544E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -2 + XS 0.544E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -3 + XS 0.544E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -4 + XS 0.318E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -5 + XS 0.318E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -6 + XS 0.318E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -7 + XS 0.186E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -8 + XS 0.186E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -3192 4.11774731 + DONE now c -3192 + Allocate 38 spaces in coupling list 19 + XS 0.711E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -10 + XS 0.711E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -11 + XS 0.416E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -12 + XS 0.416E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -13 + XS 0.416E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -14 + XS 0.243E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -15 + XS 0.243E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -16 + XS 0.243E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -17 + XS 0.142E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -3210 4.13360929 + DONE now c -3210 + Try JTOTAL = 120.50000000000000 + Allocate 38 spaces in coupling list 19 + XS 0.548E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -1 + XS 0.548E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -2 + XS 0.321E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -3 + XS 0.321E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -4 + XS 0.321E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -5 + XS 0.188E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -6 + XS 0.188E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -7 + XS 0.188E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -8 + XS 0.110E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -9 + DONE incremented c by -9 to -3219 4.14869595 + DONE now c -3219 + Allocate 38 spaces in coupling list 19 + XS 0.717E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -10 + XS 0.419E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -11 + XS 0.419E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -12 + XS 0.419E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -13 + XS 0.245E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -14 + XS 0.245E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -15 + XS 0.245E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -16 + XS 0.144E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -17 + XS 0.144E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -18 + DONE incremented c by -18 to -3237 4.16427135 + DONE now c -3237 diff --git a/book/notebooks/Transfer_template/fort.55 b/book/notebooks/Transfer_template/fort.55 new file mode 100644 index 0000000..e69de29 diff --git a/book/notebooks/Transfer_template/fort.56 b/book/notebooks/Transfer_template/fort.56 new file mode 100644 index 0000000..e69de29 diff --git a/book/notebooks/Transfer_template/fort.59 b/book/notebooks/Transfer_template/fort.59 new file mode 100644 index 0000000..e69de29 diff --git a/book/notebooks/Transfer_template/fort.7 b/book/notebooks/Transfer_template/fort.7 new file mode 100644 index 0000000..e69de29 diff --git a/book/notebooks/Transfer_template/fort.75 b/book/notebooks/Transfer_template/fort.75 new file mode 100644 index 0000000..368f49b --- /dev/null +++ b/book/notebooks/Transfer_template/fort.75 @@ -0,0 +1 @@ +170.0000 7.382128E+09 0.0000 1.6348 1.6402 16.364 0.39437E-04 3.4286 diff --git a/book/notebooks/Transfer_template/frescox.out b/book/notebooks/Transfer_template/frescox.out new file mode 100644 index 0000000..95fcbf2 --- /dev/null +++ b/book/notebooks/Transfer_template/frescox.out @@ -0,0 +1,1923 @@ +Running on beyerk-linux + FRESCOX - version 7.2-20-ga7f491: Coupled Reaction Channels on gfortran + + Using NAMELIST input + + n14(f17,ne18)c13 @ 170 MeV; + + 0.030 40.000 0.200 0.100 5.000 0.000 0.000 0.000 0.000 0.000 + + Centre-of-mass Range is 1334 * 0.0300 fm., Maximum at 39.96 fm., Interpolating NL forms every 0.21 fm. + Non - locality width is 57 * 0.0900 fm., Maximum of 5.04 fm., Centred at 0.00 fm. + 2-Nucleon Separation of 0 * 0.2100 fm., Maximum of 0.00 fm., Minimum at 0.00 fm. + Maximum single particle bins of 39.9000 fm. + M,Mint = 1333 1331 + + + Range of total J is 0.0 <= J <= 120.0 (at least 0.0) and Absorbtion => -1.0000 mb. + Dry Run = F, CC set limits = 0 0, Relativistic kinematics = , Both/Far/Near Analyses = 1 + + Cross Sections (and up to T0 for 0=projectile) for Theta from 0.0 to 40.0 in steps of 0.1 degrees, DGAM=0, grace=T, Coordinates = 0 (Mads) + + Lower Radial Cutoff = maximum of -1.60*L*h & 0.0 fm., Lower Cutoff for Couplings = 0.0 fm. + + + Iterate Couplings between 0 and 1 times, to 0.000 % if sooner. + Block solved exactly = 0 chs., with Pade = 0 & Isocen = =0, NOSOL = F, CCREAL = F, initwf = 0 + Small channels are 0.00E+00 and small couplings are 1.00E-12 of unitarity + + NL quadrature with 36 Gaussian points, Calculate multipoles up to 130 from 0 + M-transfers for lp+lt greater than or equal to 6, Angular Integration Cutoff below 0.6944 % + + + Trace switches are : CHANS = 1, LISTCC = 0, TRENEG = 0, CDETR = 0, SMATS = 0, XSTABL = 1, NLPL = 0 + + WAVES = 0, LAMPL = 0, VEFF = 0, KFUS = 0, WDISK = 0, BPM = 0, MELFIL = 0 + + CDCC = 0, NFUS = 0, TCFILE = 0 + + Using unit mass = 1.000000 amu and 1/fine-structure constant = 137.03599 ( 1.000000 * true ) with hc = 197.32705 MeV.fm, + thus 2*amu/hbar^2 = 0.0478450 = 1/20.9008 and Coulomb constant = 0.1574855 so e^2 = 1.43996515, and nuclear magneton= 0.1261183 + + Now pre-scan input and save to file 3 + + + + *********** PARTITION NUMBER 1 ****************************************************************************************** + + PROJ=f17 MASS= 17.0000 Z= 9.0, # STATES= 1T, TARG=n14 MASS= 14.0000 Z= 7.0, Q-VALUE = 0.0000 MeV + + MIXPOT = 0: no couplings (default) + + 1: J= 2.5+ (B# 1), E= 0.0000, K= 0.5 Potl# 1 J= 1.0+ (B# 1), E= 0.0000, K= 0.0 + + + *********** PARTITION NUMBER 2 ****************************************************************************************** + + PROJ=ne18 MASS= 18.0000 Z= 10.0, # STATES= 2T, TARG=c13 MASS= 13.0000 Z= 6.0, Q-VALUE = 3.6286 MeV + + MIXPOT = 0: no couplings (default) + + 1: J= 0.0+ (B# 1), E= 0.0000, K= 0.0 Potl# 2 J= 0.5+ (B# 1), E= 0.0000, K= 0.5 + + 2: J= 4.0+ (B# 1), E= 3.3760, K= 0.0 Potl# 2 J= 0.5+ (B# 1), E= 0.0000, K= 0.5 + (Just State 1) +0****** WARNING : COMPARED WITH PARTITION 1, THIS PARTITION HAS TOTAL MASS & CHARGE EXCESSES 0.00390 & 0.0000 + + + ************************************************************************************************************************************ + + The following POTENTIALS are defined : + + KP# TYPE IT SHAPE at V1 r1 a1 V2 r2 a2 A-in A-used + + + A#1 A#2 r0c ac h @ 1 + 1 0=Coulomb 0=CHARGE (WS) 1 14.000 17.000 1.3000 0.0000 0.0000 0.0000 0.000 123.612 0.03000 + + 1 1=Volume 0=Woods-Saxon 2 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 123.612 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 2 + 2 0=Coulomb 0=CHARGE (WS) 3 13.000 18.000 1.3000 0.0000 0.0000 0.0000 0.000 122.917 0.03000 + + 2 1=Volume 0=Woods-Saxon 4 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 122.917 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 0 + 3 0=Coulomb 0=CHARGE (WS) 5 17.000 0.000 1.2000 0.0000 0.0000 0.0000 0.000 17.000 0.03000 + + 3 1=Volume 0=Woods-Saxon 6 50.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 17.000 + + 3 3=Projtl S.O. 0=Woods-Saxon 7 6.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 17.000 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 0 + 4 0=Coulomb 0=CHARGE (WS) 8 13.000 0.000 1.2000 0.0000 0.0000 0.0000 0.000 13.000 0.03000 + + 4 1=Volume 0=Woods-Saxon 9 50.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 13.000 + + 4 3=Projtl S.O. 0=Woods-Saxon 10 6.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 13.000 + -------------------------------------------------------------------------------------------------------------------- + + A#1 A#2 r0c ac h @ 0 + 5 0=Coulomb 0=CHARGE (WS) 11 14.000 17.000 1.3000 0.0000 0.0000 0.0000 0.000 123.612 0.03000 + + 5 1=Volume 0=Woods-Saxon 12 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 123.612 + + + ************************************************************************************************************************************ + + The following real SINGLE-PARTICLE FORM FACTORS are constructed: + + + No. P1 P2 IN KIND T N L S1 IA J/S IB BIND XFER BE SC PC FIL AFRAC Adjust to Z Mass K Norm rms D0 D ANC/Gsp + + 1: 1 2 1 0 1 2 0.5 1 2.5 0 3 0 3.9220 1 0 0 0.0000 VR 1.2622; 1. 1.000 0.4210 1.0000 3.321 -27.2 102.5 2.5472 + + 2: 2 1 2 3 1 0 0.5 1 0.5 1 4 0 7.5506 1 0 0 0.0000 VR 0.5889; 1. 1.000 0.5792 1.0000 2.584 -177.9 -128.5 5.7521 + + ************************************************************************************************************************************ + + ONE-way COUPLING # 1 for partitions -2 <- 1 of KIND 7, 0 -1 5 -1 -1 & P1,P2 = 0.0000 0.0000 : for J <= 120.5 & R < 39.9 fm. + + + data = 1 1 1 1 1.0000, so < [ne18 # 1] / [f17 # 1] * Eigen-form # 1> = 1.0000 BE = 3.922 1 + + data = 2 1 1 2 1.0000, so < [n14 # 1] / [c13 # 1] * Eigen-form # 2> = 1.0000 BE = 7.551 2 + KNT = 2, KNP = 1: NK,NG,loc= 1 1 1 13 T T F + KNT = 2, KNP = 1: NK,NG,loc= 1 1 1 13 + + So FINITE-RANGE TRANSFER : IP1 = 0 (POST ), IP2=-1 =COMPLEX , for 1 pairs of bound states summed into 1 pairs + The main coupling potential is that binding the transferred particle to the c13 core. + + Using between f17 & c13 the potential No. 5, and subtracting optical potential No. 2 + + A real*8 is 8 bytes + NLL,NLN,NLO = 191 191 57 +0File 9 needs RL = 21774 REAL*8 numbers, and NR = 132. i.e. 22.99 Megabytes + File 9 RECL = 173280 +0RECOMMENDED NON-LOCAL WIDTH IS GREATER THAN 0.99 FM., RECOMMENDED CENTRATION 0.04, after 0.14 secs. +0Relative non-local usages (rms) are + 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + 0.000 0.000 0.000 0.001 0.003 0.009 0.020 0.057 0.354 3.452 14.972 51.537 90.794 100.000 76.670 + 40.484 14.102 3.168 0.505 0.072 0.011 0.002 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 + 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 +0Binding Energies: (ne18 # 1 / f17 # 1)(BE = 3.922)-(n14 # 1 / c13 # 1)(BE = 7.551)- DISCREPANCY = 7.2572 + + Incoming partition 1 in excitation state # 1, Laboratory Energy given for partition 1 Nucleus 1 in Excitation pair 1 + +0Lab. ENERGY ranges : + + from 170.000 to 0.00000 in 0 intervals + from 0.00000 to 0.00000 in 0 intervals + from 0.00000 to 0.00000 in 0 intervals + + Largest real,imaginary parts of any form factor at R= 39.66 are 3.63E-02 1.36E-20 MeV + Finished all Couplings @ 0.152808011 + + Non-symmetric Hamiltonian +1*********************************************************************************************************************************** +************************************************************************************************************************************ + + INCOMING f17 ; LABORATORY f17 ENERGY = 170.00 MeV. + + *********************************************************************************************************************************** + *********************************************************************************************************************************** + + + NOTE: Coulomb excitation cut off below 2.984 deg by JTMAX, and below 1.694 deg by Rmax + + Allocate arrays for 6 channels, of which 3 need wfs. + + Total SPIN, PARITY = 0.5 +, 6 chs, 0 cc. Rmin & Coul turning = 0.0 1.2 + + + C Projectl Target # EX. (L Proj) J + Targ = Jtotal E-cm Re K Re Eta RM*K CH G-REL + 1 f17 n14 # 1: I 2 2.5 0.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.46689 0.18925 1 1 1 1 1.00000 + 2 f17 n14 # 1: I 2 2.5 1.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.46689 0.18925 1 1 1 1 1.00000 + 3 f17 n14 # 1: I 4 2.5 1.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.15871 -0.47817 1 1 1 1 1.00000 + 4 ne18 c13 # 1: 0 0.0 0.0 0.5 0.5 80.40279 5.38866 2.89523 215.3310 0.36422 -0.34753 2 1 2 1 1.00000 + 5 ne18 c13 # 2: 4 4.0 0.0 0.5 0.5 77.02679 5.27432 2.95800 210.7618 -0.16050 -0.47737 2 2 2 1 1.00000 + 6 ne18 c13 # 2: 4 4.0 1.0 0.5 0.5 77.02679 5.27432 2.95800 210.7618 -0.16050 -0.47737 2 2 2 1 1.00000 + Allocate arrays for 11 channels, of which 6 need wfs. + Allocate arrays for 15 channels, of which 8 need wfs. + Allocate arrays for 18 channels, of which 9 need wfs. + Allocate arrays for 19 channels, of which 9 need wfs. + Reaction Xsec 117.5+/14 @ 1 = 0.016#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/16 @ 2 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/16 @ 3 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/16 @ 4 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/18 @ 5 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/18 @ 6 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/18 @ 7 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5+/20 @ 8 = 0.003#, Out: 0.000# 0.001# 0.000# 0.000f + Reaction Xsec 117.5+/20 @ 9 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/15 @ 1 = 0.012#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/15 @ 2 = 0.012#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/17 @ 3 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/17 @ 4 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/17 @ 5 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/19 @ 6 = 0.004#, Out: 0.000# 0.002# 0.000# 0.000f + Reaction Xsec 117.5-/19 @ 7 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/19 @ 8 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 117.5-/21 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/16 @ 1 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/16 @ 2 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/18 @ 3 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/18 @ 4 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/18 @ 5 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/20 @ 6 = 0.003#, Out: 0.000# 0.002# 0.000# 0.000f + Reaction Xsec 118.5+/20 @ 7 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/20 @ 8 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5+/22 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/15 @ 1 = 0.012#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/17 @ 2 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/17 @ 3 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/17 @ 4 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/19 @ 5 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/19 @ 6 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/19 @ 7 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 118.5-/21 @ 8 = 0.002#, Out: 0.000# 0.001# 0.000# 0.000f + Reaction Xsec 118.5-/21 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/16 @ 1 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/18 @ 2 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/18 @ 3 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/18 @ 4 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/20 @ 5 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/20 @ 6 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/20 @ 7 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5+/22 @ 8 = 0.002#, Out: 0.000# 0.001# 0.000# 0.000f + Reaction Xsec 119.5+/22 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/17 @ 1 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/17 @ 2 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/19 @ 3 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/19 @ 4 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/19 @ 5 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/21 @ 6 = 0.002#, Out: 0.000# 0.001# 0.000# 0.000f + Reaction Xsec 119.5-/21 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/21 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 119.5-/23 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/18 @ 1 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/18 @ 2 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/20 @ 3 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/20 @ 4 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/20 @ 5 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/22 @ 6 = 0.002#, Out: 0.000# 0.001# 0.000# 0.000f + Reaction Xsec 120.5+/22 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/22 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5+/24 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/17 @ 1 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/19 @ 2 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/19 @ 3 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/19 @ 4 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/21 @ 5 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/21 @ 6 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/21 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/23 @ 8 = 0.001#, Out: 0.000# 0.000# 0.000# 0.000f + Reaction Xsec 120.5-/23 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000# 0.000f + Finished all CC sets @ 4.16427708 +0CUMULATIVE REACTION cross section = 1760.34720 = 26.73 = 820.4 +0CUMULATIVE ELASTIC cross section = 0.00000 +0CUMULATIVE outgoing cross sections in partition 1 : 0.00000 +0CUMULATIVE outgoing cross sections in partition 2 : 0.26397 0.00000 +0Cumulative ABSORBTION by Imaginary Potentials = 1760.08323 = 26.73 = 820.2 + Fusion for specific f17 M-states : 1760.648139 1760.906181 1760.923348 1760.923348 + 1760.906181 1760.648139 +0CUMULATIVE OUTGOING cross section = 0.26397 + Strength functions * 10^4 for L=0-2 = 1.63476 1.64024 16.36406 (with r0= 1.35 fm). R' = 0.000 fm + To convert to S-factors (MeV.mb = keV.b), multiply by 2.7966E+10 + + + CROSS SECTIONS FOR OUTGOING f17 & n14 in state # 1 with spins & parities 2.5 + & 1.0 +; 0 + + 0.01 deg.: X-S = 1.504680E+16 mb/sr, ++ /R = 9.999977E-01 + 0.10 deg.: X-S = 1.504693E+12 mb/sr, ++ /R = 1.000006E+00 + 0.20 deg.: X-S = 9.395796E+10 mb/sr, ++ /R = 9.990968E-01 + 0.30 deg.: X-S = 1.860001E+10 mb/sr, ++ /R = 1.001270E+00 + 0.40 deg.: X-S = 5.892191E+09 mb/sr, ++ /R = 1.002463E+00 + 0.50 deg.: X-S = 2.398531E+09 mb/sr, ++ /R = 9.962646E-01 + 0.60 deg.: X-S = 1.151676E+09 mb/sr, ++ /R = 9.919328E-01 + 0.70 deg.: X-S = 6.243319E+08 mb/sr, ++ /R = 9.962121E-01 + 0.80 deg.: X-S = 3.698753E+08 mb/sr, ++ /R = 1.006830E+00 + 0.90 deg.: X-S = 2.332519E+08 mb/sr, ++ /R = 1.017026E+00 + 1.00 deg.: X-S = 1.536335E+08 mb/sr, ++ /R = 1.020984E+00 + 1.10 deg.: X-S = 1.044615E+08 mb/sr, ++ /R = 1.016378E+00 + 1.20 deg.: X-S = 7.288526E+07 mb/sr, ++ /R = 1.004356E+00 + 1.30 deg.: X-S = 5.206888E+07 mb/sr, ++ /R = 9.882556E-01 + 1.40 deg.: X-S = 3.807933E+07 mb/sr, ++ /R = 9.721049E-01 + 1.50 deg.: X-S = 2.852055E+07 mb/sr, ++ /R = 9.594630E-01 + 1.60 deg.: X-S = 2.187768E+07 mb/sr, ++ /R = 9.527516E-01 + 1.70 deg.: X-S = 1.717211E+07 mb/sr, ++ /R = 9.530390E-01 + 1.80 deg.: X-S = 1.376463E+07 mb/sr, ++ /R = 9.601475E-01 + 1.90 deg.: X-S = 1.123566E+07 mb/sr, ++ /R = 9.729451E-01 + 2.00 deg.: X-S = 9.309363E+06 mb/sr, ++ /R = 9.897070E-01 + 2.10 deg.: X-S = 7.804179E+06 mb/sr, ++ /R = 1.008468E+00 + 2.20 deg.: X-S = 6.600322E+06 mb/sr, ++ /R = 1.027316E+00 + 2.30 deg.: X-S = 5.618285E+06 mb/sr, ++ /R = 1.044608E+00 + 2.40 deg.: X-S = 4.804686E+06 mb/sr, ++ /R = 1.059102E+00 + 2.50 deg.: X-S = 4.122991E+06 mb/sr, ++ /R = 1.070014E+00 + 2.60 deg.: X-S = 3.547498E+06 mb/sr, ++ /R = 1.077014E+00 + 2.70 deg.: X-S = 3.059473E+06 mb/sr, ++ /R = 1.080179E+00 + 2.80 deg.: X-S = 2.644695E+06 mb/sr, ++ /R = 1.079915E+00 + 2.90 deg.: X-S = 2.291925E+06 mb/sr, ++ /R = 1.076866E+00 + 3.00 deg.: X-S = 1.991957E+06 mb/sr, ++ /R = 1.071819E+00 + 3.10 deg.: X-S = 1.737038E+06 mb/sr, ++ /R = 1.065612E+00 + 3.20 deg.: X-S = 1.520520E+06 mb/sr, ++ /R = 1.059061E+00 + 3.30 deg.: X-S = 1.336636E+06 mb/sr, ++ /R = 1.052892E+00 + 3.40 deg.: X-S = 1.180373E+06 mb/sr, ++ /R = 1.047695E+00 + 3.50 deg.: X-S = 1.047371E+06 mb/sr, ++ /R = 1.043897E+00 + 3.60 deg.: X-S = 9.338607E+05 mb/sr, ++ /R = 1.041745E+00 + 3.70 deg.: X-S = 8.365999E+05 mb/sr, ++ /R = 1.041305E+00 + 3.80 deg.: X-S = 7.528263E+05 mb/sr, ++ /R = 1.042475E+00 + 3.90 deg.: X-S = 6.802063E+05 mb/sr, ++ /R = 1.045006E+00 + 4.00 deg.: X-S = 6.167877E+05 mb/sr, ++ /R = 1.048523E+00 + 4.10 deg.: X-S = 5.609543E+05 mb/sr, ++ /R = 1.052561E+00 + 4.20 deg.: X-S = 5.113817E+05 mb/sr, ++ /R = 1.056594E+00 + 4.30 deg.: X-S = 4.669963E+05 mb/sr, ++ /R = 1.060070E+00 + 4.40 deg.: X-S = 4.269372E+05 mb/sr, ++ /R = 1.062436E+00 + 4.50 deg.: X-S = 3.905212E+05 mb/sr, ++ /R = 1.063171E+00 + 4.60 deg.: X-S = 3.572119E+05 mb/sr, ++ /R = 1.061807E+00 + 4.70 deg.: X-S = 3.265918E+05 mb/sr, ++ /R = 1.057948E+00 + 4.80 deg.: X-S = 2.983387E+05 mb/sr, ++ /R = 1.051287E+00 + 4.90 deg.: X-S = 2.722052E+05 mb/sr, ++ /R = 1.041613E+00 + 5.00 deg.: X-S = 2.480009E+05 mb/sr, ++ /R = 1.028814E+00 + 5.10 deg.: X-S = 2.255789E+05 mb/sr, ++ /R = 1.012886E+00 + 5.20 deg.: X-S = 2.048234E+05 mb/sr, ++ /R = 9.939204E-01 + 5.30 deg.: X-S = 1.856407E+05 mb/sr, ++ /R = 9.721028E-01 + 5.40 deg.: X-S = 1.679518E+05 mb/sr, ++ /R = 9.477016E-01 + 5.50 deg.: X-S = 1.516867E+05 mb/sr, ++ /R = 9.210565E-01 + 5.60 deg.: X-S = 1.367806E+05 mb/sr, ++ /R = 8.925651E-01 + 5.70 deg.: X-S = 1.231703E+05 mb/sr, ++ /R = 8.626681E-01 + 5.80 deg.: X-S = 1.107929E+05 mb/sr, ++ /R = 8.318349E-01 + 5.90 deg.: X-S = 9.958457E+04 mb/sr, ++ /R = 8.005481E-01 + 6.00 deg.: X-S = 8.947949E+04 mb/sr, ++ /R = 7.692891E-01 + 6.10 deg.: X-S = 8.041025E+04 mb/sr, ++ /R = 7.385249E-01 + 6.20 deg.: X-S = 7.230787E+04 mb/sr, ++ /R = 7.086954E-01 + 6.30 deg.: X-S = 6.510231E+04 mb/sr, ++ /R = 6.802027E-01 + 6.40 deg.: X-S = 5.872307E+04 mb/sr, ++ /R = 6.534018E-01 + 6.50 deg.: X-S = 5.309985E+04 mb/sr, ++ /R = 6.285936E-01 + 6.60 deg.: X-S = 4.816329E+04 mb/sr, ++ /R = 6.060190E-01 + 6.70 deg.: X-S = 4.384563E+04 mb/sr, ++ /R = 5.858556E-01 + 6.80 deg.: X-S = 4.008136E+04 mb/sr, ++ /R = 5.682159E-01 + 6.90 deg.: X-S = 3.680777E+04 mb/sr, ++ /R = 5.531474E-01 + 7.00 deg.: X-S = 3.396543E+04 mb/sr, ++ /R = 5.406344E-01 + 7.10 deg.: X-S = 3.149860E+04 mb/sr, ++ /R = 5.306009E-01 + 7.20 deg.: X-S = 2.935553E+04 mb/sr, ++ /R = 5.229157E-01 + 7.30 deg.: X-S = 2.748864E+04 mb/sr, ++ /R = 5.173977E-01 + 7.40 deg.: X-S = 2.585465E+04 mb/sr, ++ /R = 5.138223E-01 + 7.50 deg.: X-S = 2.441464E+04 mb/sr, ++ /R = 5.119291E-01 + 7.60 deg.: X-S = 2.313398E+04 mb/sr, ++ /R = 5.114297E-01 + 7.70 deg.: X-S = 2.198228E+04 mb/sr, ++ /R = 5.120154E-01 + 7.80 deg.: X-S = 2.093317E+04 mb/sr, ++ /R = 5.133654E-01 + 7.90 deg.: X-S = 1.996416E+04 mb/sr, ++ /R = 5.151551E-01 + 8.00 deg.: X-S = 1.905639E+04 mb/sr, ++ /R = 5.170637E-01 + 8.10 deg.: X-S = 1.819434E+04 mb/sr, ++ /R = 5.187814E-01 + 8.20 deg.: X-S = 1.736557E+04 mb/sr, ++ /R = 5.200159E-01 + 8.30 deg.: X-S = 1.656043E+04 mb/sr, ++ /R = 5.204989E-01 + 8.40 deg.: X-S = 1.577174E+04 mb/sr, ++ /R = 5.199909E-01 + 8.50 deg.: X-S = 1.499449E+04 mb/sr, ++ /R = 5.182856E-01 + 8.60 deg.: X-S = 1.422556E+04 mb/sr, ++ /R = 5.152134E-01 + 8.70 deg.: X-S = 1.346342E+04 mb/sr, ++ /R = 5.106439E-01 + 8.80 deg.: X-S = 1.270786E+04 mb/sr, ++ /R = 5.044872E-01 + 8.90 deg.: X-S = 1.195974E+04 mb/sr, ++ /R = 4.966949E-01 + 9.00 deg.: X-S = 1.122074E+04 mb/sr, ++ /R = 4.872592E-01 + 9.10 deg.: X-S = 1.049317E+04 mb/sr, ++ /R = 4.762125E-01 + 9.20 deg.: X-S = 9779.729900 mb/sr, ++ /R = 4.636246E-01 + 9.30 deg.: X-S = 9083.385365 mb/sr, ++ /R = 4.496007E-01 + 9.40 deg.: X-S = 8407.175674 mb/sr, ++ /R = 4.342779E-01 + 9.50 deg.: X-S = 7754.102552 mb/sr, ++ /R = 4.178212E-01 + 9.60 deg.: X-S = 7127.016486 mb/sr, ++ /R = 4.004194E-01 + 9.70 deg.: X-S = 6528.527248 mb/sr, ++ /R = 3.822804E-01 + 9.80 deg.: X-S = 5960.933413 mb/sr, ++ /R = 3.636264E-01 + 9.90 deg.: X-S = 5426.169578 mb/sr, ++ /R = 3.446891E-01 + 10.00 deg.: X-S = 4925.769863 mb/sr, ++ /R = 3.257044E-01 + 10.10 deg.: X-S = 4460.846172 mb/sr, ++ /R = 3.069078E-01 + 10.20 deg.: X-S = 4032.079642 mb/sr, ++ /R = 2.885294E-01 + 10.30 deg.: X-S = 3639.723699 mb/sr, ++ /R = 2.707899E-01 + 10.40 deg.: X-S = 3283.617115 mb/sr, ++ /R = 2.538957E-01 + 10.50 deg.: X-S = 2963.205518 mb/sr, ++ /R = 2.380358E-01 + 10.60 deg.: X-S = 2677.569853 mb/sr, ++ /R = 2.233783E-01 + 10.70 deg.: X-S = 2425.460340 mb/sr, ++ /R = 2.100676E-01 + 10.80 deg.: X-S = 2205.334590 mb/sr, ++ /R = 1.982220E-01 + 10.90 deg.: X-S = 2015.398612 mb/sr, ++ /R = 1.879323E-01 + 11.00 deg.: X-S = 1853.649547 mb/sr, ++ /R = 1.792605E-01 + 11.10 deg.: X-S = 1717.919095 mb/sr, ++ /R = 1.722392E-01 + 11.20 deg.: X-S = 1605.916681 mb/sr, ++ /R = 1.668719E-01 + 11.30 deg.: X-S = 1515.271554 mb/sr, ++ /R = 1.631334E-01 + 11.40 deg.: X-S = 1443.573098 mb/sr, ++ /R = 1.609706E-01 + 11.50 deg.: X-S = 1388.408769 mb/sr, ++ /R = 1.603049E-01 + 11.60 deg.: X-S = 1347.399166 mb/sr, ++ /R = 1.610331E-01 + 11.70 deg.: X-S = 1318.229843 mb/sr, ++ /R = 1.630310E-01 + 11.80 deg.: X-S = 1298.679604 mb/sr, ++ /R = 1.661551E-01 + 11.90 deg.: X-S = 1286.645050 mb/sr, ++ /R = 1.702464E-01 + 12.00 deg.: X-S = 1280.161315 mb/sr, ++ /R = 1.751332E-01 + 12.10 deg.: X-S = 1277.418920 mb/sr, ++ /R = 1.806344E-01 + 12.20 deg.: X-S = 1276.776815 mb/sr, ++ /R = 1.865633E-01 + 12.30 deg.: X-S = 1276.771698 mb/sr, ++ /R = 1.927310E-01 + 12.40 deg.: X-S = 1276.123777 mb/sr, ++ /R = 1.989495E-01 + 12.50 deg.: X-S = 1273.739164 mb/sr, ++ /R = 2.050354E-01 + 12.60 deg.: X-S = 1268.709155 mb/sr, ++ /R = 2.108129E-01 + 12.70 deg.: X-S = 1260.306664 mb/sr, ++ /R = 2.161167E-01 + 12.80 deg.: X-S = 1247.980098 mb/sr, ++ /R = 2.207946E-01 + 12.90 deg.: X-S = 1231.344999 mb/sr, ++ /R = 2.247102E-01 + 13.00 deg.: X-S = 1210.173751 mb/sr, ++ /R = 2.277446E-01 + 13.10 deg.: X-S = 1184.383698 mb/sr, ++ /R = 2.297984E-01 + 13.20 deg.: X-S = 1154.023984 mb/sr, ++ /R = 2.307926E-01 + 13.30 deg.: X-S = 1119.261424 mb/sr, ++ /R = 2.306700E-01 + 13.40 deg.: X-S = 1080.365732 mb/sr, ++ /R = 2.293950E-01 + 13.50 deg.: X-S = 1037.694363 mb/sr, ++ /R = 2.269547E-01 + 13.60 deg.: X-S = 991.677273 mb/sr, ++ /R = 2.233576E-01 + 13.70 deg.: X-S = 942.801824 mb/sr, ++ /R = 2.186338E-01 + 13.80 deg.: X-S = 891.598067 mb/sr, ++ /R = 2.128332E-01 + 13.90 deg.: X-S = 838.624611 mb/sr, ++ /R = 2.060248E-01 + 14.00 deg.: X-S = 784.455251 mb/sr, ++ /R = 1.982949E-01 + 14.10 deg.: X-S = 729.666498 mb/sr, ++ /R = 1.897449E-01 + 14.20 deg.: X-S = 674.826148 mb/sr, ++ /R = 1.804895E-01 + 14.30 deg.: X-S = 620.482981 mb/sr, ++ /R = 1.706545E-01 + 14.40 deg.: X-S = 567.157660 mb/sr, ++ /R = 1.603741E-01 + 14.50 deg.: X-S = 515.334877 mb/sr, ++ /R = 1.497884E-01 + 14.60 deg.: X-S = 465.456787 mb/sr, ++ /R = 1.390411E-01 + 14.70 deg.: X-S = 417.917698 mb/sr, ++ /R = 1.282767E-01 + 14.80 deg.: X-S = 373.060037 mb/sr, ++ /R = 1.176382E-01 + 14.90 deg.: X-S = 331.171530 mb/sr, ++ /R = 1.072643E-01 + 15.00 deg.: X-S = 292.483558 mb/sr, ++ /R = 9.728762E-02 + 15.10 deg.: X-S = 257.170606 mb/sr, ++ /R = 8.783220E-02 + 15.20 deg.: X-S = 225.350749 mb/sr, ++ /R = 7.901164E-02 + 15.30 deg.: X-S = 197.087069 mb/sr, ++ /R = 7.092744E-02 + 15.40 deg.: X-S = 172.389909 mb/sr, ++ /R = 6.366744E-02 + 15.50 deg.: X-S = 151.219870 mb/sr, ++ /R = 5.730468E-02 + 15.60 deg.: X-S = 133.491436 mb/sr, ++ /R = 5.189644E-02 + 15.70 deg.: X-S = 119.077122 mb/sr, ++ /R = 4.748360E-02 + 15.80 deg.: X-S = 107.812047 mb/sr, ++ /R = 4.409027E-02 + 15.90 deg.: X-S = 99.498804 mb/sr, ++ /R = 4.172377E-02 + 16.00 deg.: X-S = 93.912546 mb/sr, ++ /R = 4.037479E-02 + 16.10 deg.: X-S = 90.806183 mb/sr, ++ /R = 4.001794E-02 + 16.20 deg.: X-S = 89.915586 mb/sr, ++ /R = 4.061249E-02 + 16.30 deg.: X-S = 90.964729 mb/sr, ++ /R = 4.210331E-02 + 16.40 deg.: X-S = 93.670679 mb/sr, ++ /R = 4.442216E-02 + 16.50 deg.: X-S = 97.748369 mb/sr, ++ /R = 4.748902E-02 + 16.60 deg.: X-S = 102.915088 mb/sr, ++ /R = 5.121371E-02 + 16.70 deg.: X-S = 108.894639 mb/sr, ++ /R = 5.549754E-02 + 16.80 deg.: X-S = 115.421117 mb/sr, ++ /R = 6.023512E-02 + 16.90 deg.: X-S = 122.242283 mb/sr, ++ /R = 6.531625E-02 + 17.00 deg.: X-S = 129.122488 mb/sr, ++ /R = 7.062780E-02 + 17.10 deg.: X-S = 135.845152 mb/sr, ++ /R = 7.605563E-02 + 17.20 deg.: X-S = 142.214779 mb/sr, ++ /R = 8.148648E-02 + 17.30 deg.: X-S = 148.058505 mb/sr, ++ /R = 8.680978E-02 + 17.40 deg.: X-S = 153.227191 mb/sr, ++ /R = 9.191938E-02 + 17.50 deg.: X-S = 157.596075 mb/sr, ++ /R = 9.671521E-02 + 17.60 deg.: X-S = 161.064997 mb/sr, ++ /R = 1.011047E-01 + 17.70 deg.: X-S = 163.558226 mb/sr, ++ /R = 1.050043E-01 + 17.80 deg.: X-S = 165.023920 mb/sr, ++ /R = 1.083403E-01 + 17.90 deg.: X-S = 165.433244 mb/sr, ++ /R = 1.110502E-01 + 18.00 deg.: X-S = 164.779196 mb/sr, ++ /R = 1.130831E-01 + 18.10 deg.: X-S = 163.075162 mb/sr, ++ /R = 1.144004E-01 + 18.20 deg.: X-S = 160.353257 mb/sr, ++ /R = 1.149764E-01 + 18.30 deg.: X-S = 156.662480 mb/sr, ++ /R = 1.147979E-01 + 18.40 deg.: X-S = 152.066728 mb/sr, ++ /R = 1.138647E-01 + 18.50 deg.: X-S = 146.642717 mb/sr, ++ /R = 1.121888E-01 + 18.60 deg.: X-S = 140.477828 mb/sr, ++ /R = 1.097943E-01 + 18.70 deg.: X-S = 133.667945 mb/sr, ++ /R = 1.067165E-01 + 18.80 deg.: X-S = 126.315289 mb/sr, ++ /R = 1.030012E-01 + 18.90 deg.: X-S = 118.526312 mb/sr, ++ /R = 9.870375E-02 + 19.00 deg.: X-S = 110.409648 mb/sr, ++ /R = 9.388784E-02 + 19.10 deg.: X-S = 102.074182 mb/sr, ++ /R = 8.862437E-02 + 19.20 deg.: X-S = 93.627228 mb/sr, ++ /R = 8.299009E-02 + 19.30 deg.: X-S = 85.172862 mb/sr, ++ /R = 7.706632E-02 + 19.40 deg.: X-S = 76.810413 mb/sr, ++ /R = 7.093746E-02 + 19.50 deg.: X-S = 68.633122 mb/sr, ++ /R = 6.468966E-02 + 19.60 deg.: X-S = 60.726997 mb/sr, ++ /R = 5.840935E-02 + 19.70 deg.: X-S = 53.169841 mb/sr, ++ /R = 5.218188E-02 + 19.80 deg.: X-S = 46.030487 mb/sr, ++ /R = 4.609020E-02 + 19.90 deg.: X-S = 39.368213 mb/sr, ++ /R = 4.021356E-02 + 20.00 deg.: X-S = 33.232349 mb/sr, ++ /R = 3.462640E-02 + 20.10 deg.: X-S = 27.662072 mb/sr, ++ /R = 2.939725E-02 + 20.20 deg.: X-S = 22.686362 mb/sr, ++ /R = 2.458777E-02 + 20.30 deg.: X-S = 18.324134 mb/sr, ++ /R = 2.025195E-02 + 20.40 deg.: X-S = 14.584510 mb/sr, ++ /R = 1.643546E-02 + 20.50 deg.: X-S = 11.467234 mb/sr, ++ /R = 1.317507E-02 + 20.60 deg.: X-S = 8.963208 mb/sr, ++ /R = 1.049834E-02 + 20.70 deg.: X-S = 7.055133 mb/sr, ++ /R = 8.423323E-03 + 20.80 deg.: X-S = 5.718237 mb/sr, ++ /R = 6.958580E-03 + 20.90 deg.: X-S = 4.921076 mb/sr, ++ /R = 6.103209E-03 + 21.00 deg.: X-S = 4.626397 mb/sr, ++ /R = 5.847100E-03 + 21.10 deg.: X-S = 4.792027 mb/sr, ++ /R = 6.171298E-03 + 21.20 deg.: X-S = 5.371798 mb/sr, ++ /R = 7.048505E-03 + 21.30 deg.: X-S = 6.316465 mb/sr, ++ /R = 8.443694E-03 + 21.40 deg.: X-S = 7.574632 mb/sr, ++ /R = 1.031483E-02 + 21.50 deg.: X-S = 9.093640 mb/sr, ++ /R = 1.261370E-02 + 21.60 deg.: X-S = 10.820435 mb/sr, ++ /R = 1.528675E-02 + 21.70 deg.: X-S = 12.702384 mb/sr, ++ /R = 1.827612E-02 + 21.80 deg.: X-S = 14.688034 mb/sr, ++ /R = 2.152055E-02 + 21.90 deg.: X-S = 16.727816 mb/sr, ++ /R = 2.495645E-02 + 22.00 deg.: X-S = 18.774672 mb/sr, ++ /R = 2.851893E-02 + 22.10 deg.: X-S = 20.784611 mb/sr, ++ /R = 3.214279E-02 + 22.20 deg.: X-S = 22.717181 mb/sr, ++ /R = 3.576358E-02 + 22.30 deg.: X-S = 24.535865 mb/sr, ++ /R = 3.931852E-02 + 22.40 deg.: X-S = 26.208391 mb/sr, ++ /R = 4.274742E-02 + 22.50 deg.: X-S = 27.706961 mb/sr, ++ /R = 4.599358E-02 + 22.60 deg.: X-S = 29.008397 mb/sr, ++ /R = 4.900451E-02 + 22.70 deg.: X-S = 30.094210 mb/sr, ++ /R = 5.173266E-02 + 22.80 deg.: X-S = 30.950597 mb/sr, ++ /R = 5.413601E-02 + 22.90 deg.: X-S = 31.568363 mb/sr, ++ /R = 5.617858E-02 + 23.00 deg.: X-S = 31.942779 mb/sr, ++ /R = 5.783082E-02 + 23.10 deg.: X-S = 32.073389 mb/sr, ++ /R = 5.906989E-02 + 23.20 deg.: X-S = 31.963749 mb/sr, ++ /R = 5.987985E-02 + 23.30 deg.: X-S = 31.621140 mb/sr, ++ /R = 6.025172E-02 + 23.40 deg.: X-S = 31.056225 mb/sr, ++ /R = 6.018345E-02 + 23.50 deg.: X-S = 30.282687 mb/sr, ++ /R = 5.967977E-02 + 23.60 deg.: X-S = 29.316838 mb/sr, ++ /R = 5.875195E-02 + 23.70 deg.: X-S = 28.177211 mb/sr, ++ /R = 5.741745E-02 + 23.80 deg.: X-S = 26.884149 mb/sr, ++ /R = 5.569954E-02 + 23.90 deg.: X-S = 25.459383 mb/sr, ++ /R = 5.362675E-02 + 24.00 deg.: X-S = 23.925617 mb/sr, ++ /R = 5.123234E-02 + 24.10 deg.: X-S = 22.306127 mb/sr, ++ /R = 4.855367E-02 + 24.20 deg.: X-S = 20.624369 mb/sr, ++ /R = 4.563153E-02 + 24.30 deg.: X-S = 18.903612 mb/sr, ++ /R = 4.250946E-02 + 24.40 deg.: X-S = 17.166590 mb/sr, ++ /R = 3.923298E-02 + 24.50 deg.: X-S = 15.435189 mb/sr, ++ /R = 3.584892E-02 + 24.60 deg.: X-S = 13.730159 mb/sr, ++ /R = 3.240464E-02 + 24.70 deg.: X-S = 12.070863 mb/sr, ++ /R = 2.894732E-02 + 24.80 deg.: X-S = 10.475063 mb/sr, ++ /R = 2.552325E-02 + 24.90 deg.: X-S = 8.958734 mb/sr, ++ /R = 2.217721E-02 + 25.00 deg.: X-S = 7.535930 mb/sr, ++ /R = 1.895175E-02 + 25.10 deg.: X-S = 6.218668 mb/sr, ++ /R = 1.588671E-02 + 25.20 deg.: X-S = 5.016869 mb/sr, ++ /R = 1.301863E-02 + 25.30 deg.: X-S = 3.938316 mb/sr, ++ /R = 1.038033E-02 + 25.40 deg.: X-S = 2.988656 mb/sr, ++ /R = 8.000501E-03 + 25.50 deg.: X-S = 2.171434 mb/sr, ++ /R = 5.903385E-03 + 25.60 deg.: X-S = 1.488150 mb/sr, ++ /R = 4.108540E-03 + 25.70 deg.: X-S = 0.938352 mb/sr, ++ /R = 2.630665E-03 + 25.80 deg.: X-S = 0.519741 mb/sr, ++ /R = 1.479515E-03 + 25.90 deg.: X-S = 0.228311 mb/sr, ++ /R = 6.598790E-04 + 26.00 deg.: X-S = 0.058494 mb/sr, ++ /R = 1.716429E-04 + 26.10 deg.: X-S = 0.003329 mb/sr, ++ /R = 9.916137E-06 + 26.20 deg.: X-S = 0.054637 mb/sr, ++ /R = 1.652294E-04 + 26.30 deg.: X-S = 0.203208 mb/sr, ++ /R = 6.237927E-04 + 26.40 deg.: X-S = 0.438985 mb/sr, ++ /R = 1.367811E-03 + 26.50 deg.: X-S = 0.751263 mb/sr, ++ /R = 2.375848E-03 + 26.60 deg.: X-S = 1.128871 mb/sr, ++ /R = 3.623238E-03 + 26.70 deg.: X-S = 1.560368 mb/sr, ++ /R = 5.082530E-03 + 26.80 deg.: X-S = 2.034213 mb/sr, ++ /R = 6.723961E-03 + 26.90 deg.: X-S = 2.538945 mb/sr, ++ /R = 8.515949E-03 + 27.00 deg.: X-S = 3.063338 mb/sr, ++ /R = 1.042561E-02 + 27.10 deg.: X-S = 3.596553 mb/sr, ++ /R = 1.241925E-02 + 27.20 deg.: X-S = 4.128272 mb/sr, ++ /R = 1.446290E-02 + 27.30 deg.: X-S = 4.648816 mb/sr, ++ /R = 1.652281E-02 + 27.40 deg.: X-S = 5.149251 mb/sr, ++ /R = 1.856591E-02 + 27.50 deg.: X-S = 5.621475 mb/sr, ++ /R = 2.056029E-02 + 27.60 deg.: X-S = 6.058285 mb/sr, ++ /R = 2.247565E-02 + 27.70 deg.: X-S = 6.453435 mb/sr, ++ /R = 2.428365E-02 + 27.80 deg.: X-S = 6.801671 mb/sr, ++ /R = 2.595828E-02 + 27.90 deg.: X-S = 7.098751 mb/sr, ++ /R = 2.747619E-02 + 28.00 deg.: X-S = 7.341448 mb/sr, ++ /R = 2.881694E-02 + 28.10 deg.: X-S = 7.527544 mb/sr, ++ /R = 2.996321E-02 + 28.20 deg.: X-S = 7.655801 mb/sr, ++ /R = 3.090098E-02 + 28.30 deg.: X-S = 7.725927 mb/sr, ++ /R = 3.161960E-02 + 28.40 deg.: X-S = 7.738528 mb/sr, ++ /R = 3.211192E-02 + 28.50 deg.: X-S = 7.695045 mb/sr, ++ /R = 3.237421E-02 + 28.60 deg.: X-S = 7.597693 mb/sr, ++ /R = 3.240620E-02 + 28.70 deg.: X-S = 7.449380 mb/sr, ++ /R = 3.221091E-02 + 28.80 deg.: X-S = 7.253628 mb/sr, ++ /R = 3.179459E-02 + 28.90 deg.: X-S = 7.014492 mb/sr, ++ /R = 3.116648E-02 + 29.00 deg.: X-S = 6.736465 mb/sr, ++ /R = 3.033864E-02 + 29.10 deg.: X-S = 6.424395 mb/sr, ++ /R = 2.932564E-02 + 29.20 deg.: X-S = 6.083392 mb/sr, ++ /R = 2.814436E-02 + 29.30 deg.: X-S = 5.718739 mb/sr, ++ /R = 2.681362E-02 + 29.40 deg.: X-S = 5.335810 mb/sr, ++ /R = 2.535388E-02 + 29.50 deg.: X-S = 4.939983 mb/sr, ++ /R = 2.378690E-02 + 29.60 deg.: X-S = 4.536564 mb/sr, ++ /R = 2.213539E-02 + 29.70 deg.: X-S = 4.130714 mb/sr, ++ /R = 2.042269E-02 + 29.80 deg.: X-S = 3.727382 mb/sr, ++ /R = 1.867236E-02 + 29.90 deg.: X-S = 3.331248 mb/sr, ++ /R = 1.690790E-02 + 30.00 deg.: X-S = 2.946664 mb/sr, ++ /R = 1.515238E-02 + 30.10 deg.: X-S = 2.577614 mb/sr, ++ /R = 1.342815E-02 + 30.20 deg.: X-S = 2.227674 mb/sr, ++ /R = 1.175650E-02 + 30.30 deg.: X-S = 1.899984 mb/sr, ++ /R = 1.015746E-02 + 30.40 deg.: X-S = 1.597223 mb/sr, ++ /R = 8.649480E-03 + 30.50 deg.: X-S = 1.321599 mb/sr, ++ /R = 7.249270E-03 + 30.60 deg.: X-S = 1.074839 mb/sr, ++ /R = 5.971579E-03 + 30.70 deg.: X-S = 0.858192 mb/sr, ++ /R = 4.829055E-03 + 30.80 deg.: X-S = 0.672433 mb/sr, ++ /R = 3.832127E-03 + 30.90 deg.: X-S = 0.517881 mb/sr, ++ /R = 2.988923E-03 + 31.00 deg.: X-S = 0.394413 mb/sr, ++ /R = 2.305217E-03 + 31.10 deg.: X-S = 0.301494 mb/sr, ++ /R = 1.784419E-03 + 31.20 deg.: X-S = 0.238204 mb/sr, ++ /R = 1.427595E-03 + 31.30 deg.: X-S = 0.203267 mb/sr, ++ /R = 1.233515E-03 + 31.40 deg.: X-S = 0.195095 mb/sr, ++ /R = 1.198741E-03 + 31.50 deg.: X-S = 0.211819 mb/sr, ++ /R = 1.317739E-03 + 31.60 deg.: X-S = 0.251336 mb/sr, ++ /R = 1.583011E-03 + 31.70 deg.: X-S = 0.311343 mb/sr, ++ /R = 1.985262E-03 + 31.80 deg.: X-S = 0.389389 mb/sr, ++ /R = 2.513581E-03 + 31.90 deg.: X-S = 0.482908 mb/sr, ++ /R = 3.155636E-03 + 32.00 deg.: X-S = 0.589267 mb/sr, ++ /R = 3.897891E-03 + 32.10 deg.: X-S = 0.705800 mb/sr, ++ /R = 4.725826E-03 + 32.20 deg.: X-S = 0.829854 mb/sr, ++ /R = 5.624171E-03 + 32.30 deg.: X-S = 0.958819 mb/sr, ++ /R = 6.577135E-03 + 32.40 deg.: X-S = 1.090162 mb/sr, ++ /R = 7.568644E-03 + 32.50 deg.: X-S = 1.221465 mb/sr, ++ /R = 8.582571E-03 + 32.60 deg.: X-S = 1.350442 mb/sr, ++ /R = 9.602959E-03 + 32.70 deg.: X-S = 1.474973 mb/sr, ++ /R = 1.061424E-02 + 32.80 deg.: X-S = 1.593116 mb/sr, ++ /R = 1.160143E-02 + 32.90 deg.: X-S = 1.703133 mb/sr, ++ /R = 1.255033E-02 + 33.00 deg.: X-S = 1.803496 mb/sr, ++ /R = 1.344770E-02 + 33.10 deg.: X-S = 1.892902 mb/sr, ++ /R = 1.428139E-02 + 33.20 deg.: X-S = 1.970274 mb/sr, ++ /R = 1.504050E-02 + 33.30 deg.: X-S = 2.034769 mb/sr, ++ /R = 1.571549E-02 + 33.40 deg.: X-S = 2.085773 mb/sr, ++ /R = 1.629825E-02 + 33.50 deg.: X-S = 2.122902 mb/sr, ++ /R = 1.678219E-02 + 33.60 deg.: X-S = 2.145989 mb/sr, ++ /R = 1.716229E-02 + 33.70 deg.: X-S = 2.155078 mb/sr, ++ /R = 1.743509E-02 + 33.80 deg.: X-S = 2.150415 mb/sr, ++ /R = 1.759872E-02 + 33.90 deg.: X-S = 2.132428 mb/sr, ++ /R = 1.765286E-02 + 34.00 deg.: X-S = 2.101716 mb/sr, ++ /R = 1.759871E-02 + 34.10 deg.: X-S = 2.059028 mb/sr, ++ /R = 1.743893E-02 + 34.20 deg.: X-S = 2.005247 mb/sr, ++ /R = 1.717754E-02 + 34.30 deg.: X-S = 1.941372 mb/sr, ++ /R = 1.681984E-02 + 34.40 deg.: X-S = 1.868494 mb/sr, ++ /R = 1.637230E-02 + 34.50 deg.: X-S = 1.787780 mb/sr, ++ /R = 1.584243E-02 + 34.60 deg.: X-S = 1.700449 mb/sr, ++ /R = 1.523864E-02 + 34.70 deg.: X-S = 1.607757 mb/sr, ++ /R = 1.457010E-02 + 34.80 deg.: X-S = 1.510973 mb/sr, ++ /R = 1.384662E-02 + 34.90 deg.: X-S = 1.411364 mb/sr, ++ /R = 1.307845E-02 + 35.00 deg.: X-S = 1.310177 mb/sr, ++ /R = 1.227616E-02 + 35.10 deg.: X-S = 1.208621 mb/sr, ++ /R = 1.145047E-02 + 35.20 deg.: X-S = 1.107855 mb/sr, ++ /R = 1.061213E-02 + 35.30 deg.: X-S = 1.008973 mb/sr, ++ /R = 9.771712E-03 + 35.40 deg.: X-S = 0.912991 mb/sr, ++ /R = 8.939544E-03 + 35.50 deg.: X-S = 0.820842 mb/sr, ++ /R = 8.125526E-03 + 35.60 deg.: X-S = 0.733362 mb/sr, ++ /R = 7.339031E-03 + 35.70 deg.: X-S = 0.651284 mb/sr, ++ /R = 6.588789E-03 + 35.80 deg.: X-S = 0.575238 mb/sr, ++ /R = 5.882790E-03 + 35.90 deg.: X-S = 0.505743 mb/sr, ++ /R = 5.228197E-03 + 36.00 deg.: X-S = 0.443206 mb/sr, ++ /R = 4.631277E-03 + 36.10 deg.: X-S = 0.387926 mb/sr, ++ /R = 4.097343E-03 + 36.20 deg.: X-S = 0.340089 mb/sr, ++ /R = 3.630710E-03 + 36.30 deg.: X-S = 0.299778 mb/sr, ++ /R = 3.234671E-03 + 36.40 deg.: X-S = 0.266972 mb/sr, ++ /R = 2.911482E-03 + 36.50 deg.: X-S = 0.241555 mb/sr, ++ /R = 2.662363E-03 + 36.60 deg.: X-S = 0.223318 mb/sr, ++ /R = 2.487518E-03 + 36.70 deg.: X-S = 0.211972 mb/sr, ++ /R = 2.386156E-03 + 36.80 deg.: X-S = 0.207153 mb/sr, ++ /R = 2.356542E-03 + 36.90 deg.: X-S = 0.208430 mb/sr, ++ /R = 2.396041E-03 + 37.00 deg.: X-S = 0.215315 mb/sr, ++ /R = 2.501186E-03 + 37.10 deg.: X-S = 0.227274 mb/sr, ++ /R = 2.667749E-03 + 37.20 deg.: X-S = 0.243733 mb/sr, ++ /R = 2.890821E-03 + 37.30 deg.: X-S = 0.264092 mb/sr, ++ /R = 3.164899E-03 + 37.40 deg.: X-S = 0.287730 mb/sr, ++ /R = 3.483980E-03 + 37.50 deg.: X-S = 0.314019 mb/sr, ++ /R = 3.841651E-03 + 37.60 deg.: X-S = 0.342327 mb/sr, ++ /R = 4.231194E-03 + 37.70 deg.: X-S = 0.372032 mb/sr, ++ /R = 4.645681E-03 + 37.80 deg.: X-S = 0.402528 mb/sr, ++ /R = 5.078075E-03 + 37.90 deg.: X-S = 0.433230 mb/sr, ++ /R = 5.521323E-03 + 38.00 deg.: X-S = 0.463584 mb/sr, ++ /R = 5.968456E-03 + 38.10 deg.: X-S = 0.493071 mb/sr, ++ /R = 6.412674E-03 + 38.20 deg.: X-S = 0.521212 mb/sr, ++ /R = 6.847433E-03 + 38.30 deg.: X-S = 0.547572 mb/sr, ++ /R = 7.266525E-03 + 38.40 deg.: X-S = 0.571767 mb/sr, ++ /R = 7.664145E-03 + 38.50 deg.: X-S = 0.593461 mb/sr, ++ /R = 8.034962E-03 + 38.60 deg.: X-S = 0.612371 mb/sr, ++ /R = 8.374167E-03 + 38.70 deg.: X-S = 0.628270 mb/sr, ++ /R = 8.677524E-03 + 38.80 deg.: X-S = 0.640981 mb/sr, ++ /R = 8.941410E-03 + 38.90 deg.: X-S = 0.650385 mb/sr, ++ /R = 9.162839E-03 + 39.00 deg.: X-S = 0.656412 mb/sr, ++ /R = 9.339481E-03 + 39.10 deg.: X-S = 0.659043 mb/sr, ++ /R = 9.469675E-03 + 39.20 deg.: X-S = 0.658308 mb/sr, ++ /R = 9.552430E-03 + 39.30 deg.: X-S = 0.654282 mb/sr, ++ /R = 9.587410E-03 + 39.40 deg.: X-S = 0.647082 mb/sr, ++ /R = 9.574926E-03 + 39.50 deg.: X-S = 0.636863 mb/sr, ++ /R = 9.515904E-03 + 39.60 deg.: X-S = 0.623813 mb/sr, ++ /R = 9.411860E-03 + 39.70 deg.: X-S = 0.608153 mb/sr, ++ /R = 9.264853E-03 + 39.80 deg.: X-S = 0.590126 mb/sr, ++ /R = 9.077448E-03 + 39.90 deg.: X-S = 0.569997 mb/sr, ++ /R = 8.852661E-03 + 40.00 deg.: X-S = 0.548048 mb/sr, ++ /R = 8.593908E-03 + Integrated 2.8834E+10 mb, over [ 0.000, 40.000] at 170.0000 MeV + + CROSS SECTIONS FOR OUTGOING ne18 & c13 in state # 1 with spins & parities 0.0 + & 0.5 +; 0 + + 0.00 deg.: X-S = 3.122980 mb/sr, ++ REAC 2.6397E-01 mb + 0.10 deg.: X-S = 3.120152 mb/sr, + 0.20 deg.: X-S = 3.111752 mb/sr, + 0.30 deg.: X-S = 3.098031 mb/sr, + 0.40 deg.: X-S = 3.079400 mb/sr, + 0.50 deg.: X-S = 3.056429 mb/sr, + 0.60 deg.: X-S = 3.029832 mb/sr, + 0.70 deg.: X-S = 3.000455 mb/sr, + 0.80 deg.: X-S = 2.969265 mb/sr, + 0.90 deg.: X-S = 2.937327 mb/sr, + 1.00 deg.: X-S = 2.905790 mb/sr, + 1.10 deg.: X-S = 2.875860 mb/sr, + 1.20 deg.: X-S = 2.848777 mb/sr, + 1.30 deg.: X-S = 2.825793 mb/sr, + 1.40 deg.: X-S = 2.808142 mb/sr, + 1.50 deg.: X-S = 2.797010 mb/sr, + 1.60 deg.: X-S = 2.793514 mb/sr, + 1.70 deg.: X-S = 2.798666 mb/sr, + 1.80 deg.: X-S = 2.813353 mb/sr, + 1.90 deg.: X-S = 2.838304 mb/sr, + 2.00 deg.: X-S = 2.874072 mb/sr, + 2.10 deg.: X-S = 2.921009 mb/sr, + 2.20 deg.: X-S = 2.979249 mb/sr, + 2.30 deg.: X-S = 3.048692 mb/sr, + 2.40 deg.: X-S = 3.128997 mb/sr, + 2.50 deg.: X-S = 3.219572 mb/sr, + 2.60 deg.: X-S = 3.319578 mb/sr, + 2.70 deg.: X-S = 3.427931 mb/sr, + 2.80 deg.: X-S = 3.543312 mb/sr, + 2.90 deg.: X-S = 3.664188 mb/sr, + 3.00 deg.: X-S = 3.788825 mb/sr, + 3.10 deg.: X-S = 3.915323 mb/sr, + 3.20 deg.: X-S = 4.041641 mb/sr, + 3.30 deg.: X-S = 4.165637 mb/sr, + 3.40 deg.: X-S = 4.285104 mb/sr, + 3.50 deg.: X-S = 4.397810 mb/sr, + 3.60 deg.: X-S = 4.501546 mb/sr, + 3.70 deg.: X-S = 4.594165 mb/sr, + 3.80 deg.: X-S = 4.673633 mb/sr, + 3.90 deg.: X-S = 4.738063 mb/sr, + 4.00 deg.: X-S = 4.785761 mb/sr, + 4.10 deg.: X-S = 4.815266 mb/sr, + 4.20 deg.: X-S = 4.825376 mb/sr, + 4.30 deg.: X-S = 4.815183 mb/sr, + 4.40 deg.: X-S = 4.784091 mb/sr, + 4.50 deg.: X-S = 4.731839 mb/sr, + 4.60 deg.: X-S = 4.658501 mb/sr, + 4.70 deg.: X-S = 4.564498 mb/sr, + 4.80 deg.: X-S = 4.450589 mb/sr, + 4.90 deg.: X-S = 4.317857 mb/sr, + 5.00 deg.: X-S = 4.167696 mb/sr, + 5.10 deg.: X-S = 4.001781 mb/sr, + 5.20 deg.: X-S = 3.822041 mb/sr, + 5.30 deg.: X-S = 3.630618 mb/sr, + 5.40 deg.: X-S = 3.429830 mb/sr, + 5.50 deg.: X-S = 3.222125 mb/sr, + 5.60 deg.: X-S = 3.010037 mb/sr, + 5.70 deg.: X-S = 2.796133 mb/sr, + 5.80 deg.: X-S = 2.582970 mb/sr, + 5.90 deg.: X-S = 2.373047 mb/sr, + 6.00 deg.: X-S = 2.168757 mb/sr, + 6.10 deg.: X-S = 1.972347 mb/sr, + 6.20 deg.: X-S = 1.785880 mb/sr, + 6.30 deg.: X-S = 1.611199 mb/sr, + 6.40 deg.: X-S = 1.449903 mb/sr, + 6.50 deg.: X-S = 1.303317 mb/sr, + 6.60 deg.: X-S = 1.172484 mb/sr, + 6.70 deg.: X-S = 1.058148 mb/sr, + 6.80 deg.: X-S = 0.960751 mb/sr, + 6.90 deg.: X-S = 0.880441 mb/sr, + 7.00 deg.: X-S = 0.817071 mb/sr, + 7.10 deg.: X-S = 0.770219 mb/sr, + 7.20 deg.: X-S = 0.739206 mb/sr, + 7.30 deg.: X-S = 0.723116 mb/sr, + 7.40 deg.: X-S = 0.720823 mb/sr, + 7.50 deg.: X-S = 0.731023 mb/sr, + 7.60 deg.: X-S = 0.752265 mb/sr, + 7.70 deg.: X-S = 0.782981 mb/sr, + 7.80 deg.: X-S = 0.821525 mb/sr, + 7.90 deg.: X-S = 0.866202 mb/sr, + 8.00 deg.: X-S = 0.915303 mb/sr, + 8.10 deg.: X-S = 0.967139 mb/sr, + 8.20 deg.: X-S = 1.020068 mb/sr, + 8.30 deg.: X-S = 1.072522 mb/sr, + 8.40 deg.: X-S = 1.123036 mb/sr, + 8.50 deg.: X-S = 1.170263 mb/sr, + 8.60 deg.: X-S = 1.212999 mb/sr, + 8.70 deg.: X-S = 1.250194 mb/sr, + 8.80 deg.: X-S = 1.280964 mb/sr, + 8.90 deg.: X-S = 1.304597 mb/sr, + 9.00 deg.: X-S = 1.320561 mb/sr, + 9.10 deg.: X-S = 1.328499 mb/sr, + 9.20 deg.: X-S = 1.328231 mb/sr, + 9.30 deg.: X-S = 1.319744 mb/sr, + 9.40 deg.: X-S = 1.303186 mb/sr, + 9.50 deg.: X-S = 1.278855 mb/sr, + 9.60 deg.: X-S = 1.247184 mb/sr, + 9.70 deg.: X-S = 1.208724 mb/sr, + 9.80 deg.: X-S = 1.164134 mb/sr, + 9.90 deg.: X-S = 1.114156 mb/sr, + 10.00 deg.: X-S = 1.059600 mb/sr, + 10.10 deg.: X-S = 1.001328 mb/sr, + 10.20 deg.: X-S = 0.940229 mb/sr, + 10.30 deg.: X-S = 0.877208 mb/sr, + 10.40 deg.: X-S = 0.813163 mb/sr, + 10.50 deg.: X-S = 0.748973 mb/sr, + 10.60 deg.: X-S = 0.685478 mb/sr, + 10.70 deg.: X-S = 0.623473 mb/sr, + 10.80 deg.: X-S = 0.563687 mb/sr, + 10.90 deg.: X-S = 0.506782 mb/sr, + 11.00 deg.: X-S = 0.453336 mb/sr, + 11.10 deg.: X-S = 0.403845 mb/sr, + 11.20 deg.: X-S = 0.358713 mb/sr, + 11.30 deg.: X-S = 0.318251 mb/sr, + 11.40 deg.: X-S = 0.282678 mb/sr, + 11.50 deg.: X-S = 0.252120 mb/sr, + 11.60 deg.: X-S = 0.226615 mb/sr, + 11.70 deg.: X-S = 0.206116 mb/sr, + 11.80 deg.: X-S = 0.190495 mb/sr, + 11.90 deg.: X-S = 0.179553 mb/sr, + 12.00 deg.: X-S = 0.173023 mb/sr, + 12.10 deg.: X-S = 0.170583 mb/sr, + 12.20 deg.: X-S = 0.171863 mb/sr, + 12.30 deg.: X-S = 0.176452 mb/sr, + 12.40 deg.: X-S = 0.183911 mb/sr, + 12.50 deg.: X-S = 0.193778 mb/sr, + 12.60 deg.: X-S = 0.205580 mb/sr, + 12.70 deg.: X-S = 0.218842 mb/sr, + 12.80 deg.: X-S = 0.233093 mb/sr, + 12.90 deg.: X-S = 0.247877 mb/sr, + 13.00 deg.: X-S = 0.262755 mb/sr, + 13.10 deg.: X-S = 0.277317 mb/sr, + 13.20 deg.: X-S = 0.291184 mb/sr, + 13.30 deg.: X-S = 0.304012 mb/sr, + 13.40 deg.: X-S = 0.315501 mb/sr, + 13.50 deg.: X-S = 0.325390 mb/sr, + 13.60 deg.: X-S = 0.333466 mb/sr, + 13.70 deg.: X-S = 0.339561 mb/sr, + 13.80 deg.: X-S = 0.343553 mb/sr, + 13.90 deg.: X-S = 0.345368 mb/sr, + 14.00 deg.: X-S = 0.344975 mb/sr, + 14.10 deg.: X-S = 0.342386 mb/sr, + 14.20 deg.: X-S = 0.337653 mb/sr, + 14.30 deg.: X-S = 0.330867 mb/sr, + 14.40 deg.: X-S = 0.322150 mb/sr, + 14.50 deg.: X-S = 0.311654 mb/sr, + 14.60 deg.: X-S = 0.299557 mb/sr, + 14.70 deg.: X-S = 0.286056 mb/sr, + 14.80 deg.: X-S = 0.271367 mb/sr, + 14.90 deg.: X-S = 0.255715 mb/sr, + 15.00 deg.: X-S = 0.239332 mb/sr, + 15.10 deg.: X-S = 0.222456 mb/sr, + 15.20 deg.: X-S = 0.205318 mb/sr, + 15.30 deg.: X-S = 0.188147 mb/sr, + 15.40 deg.: X-S = 0.171163 mb/sr, + 15.50 deg.: X-S = 0.154572 mb/sr, + 15.60 deg.: X-S = 0.138565 mb/sr, + 15.70 deg.: X-S = 0.123316 mb/sr, + 15.80 deg.: X-S = 0.108977 mb/sr, + 15.90 deg.: X-S = 0.095681 mb/sr, + 16.00 deg.: X-S = 0.083538 mb/sr, + 16.10 deg.: X-S = 0.072634 mb/sr, + 16.20 deg.: X-S = 0.063033 mb/sr, + 16.30 deg.: X-S = 0.054774 mb/sr, + 16.40 deg.: X-S = 0.047875 mb/sr, + 16.50 deg.: X-S = 0.042331 mb/sr, + 16.60 deg.: X-S = 0.038117 mb/sr, + 16.70 deg.: X-S = 0.035190 mb/sr, + 16.80 deg.: X-S = 0.033487 mb/sr, + 16.90 deg.: X-S = 0.032932 mb/sr, + 17.00 deg.: X-S = 0.033435 mb/sr, + 17.10 deg.: X-S = 0.034893 mb/sr, + 17.20 deg.: X-S = 0.037195 mb/sr, + 17.30 deg.: X-S = 0.040224 mb/sr, + 17.40 deg.: X-S = 0.043857 mb/sr, + 17.50 deg.: X-S = 0.047969 mb/sr, + 17.60 deg.: X-S = 0.052435 mb/sr, + 17.70 deg.: X-S = 0.057131 mb/sr, + 17.80 deg.: X-S = 0.061936 mb/sr, + 17.90 deg.: X-S = 0.066736 mb/sr, + 18.00 deg.: X-S = 0.071421 mb/sr, + 18.10 deg.: X-S = 0.075893 mb/sr, + 18.20 deg.: X-S = 0.080059 mb/sr, + 18.30 deg.: X-S = 0.083839 mb/sr, + 18.40 deg.: X-S = 0.087164 mb/sr, + 18.50 deg.: X-S = 0.089975 mb/sr, + 18.60 deg.: X-S = 0.092226 mb/sr, + 18.70 deg.: X-S = 0.093884 mb/sr, + 18.80 deg.: X-S = 0.094925 mb/sr, + 18.90 deg.: X-S = 0.095339 mb/sr, + 19.00 deg.: X-S = 0.095127 mb/sr, + 19.10 deg.: X-S = 0.094300 mb/sr, + 19.20 deg.: X-S = 0.092879 mb/sr, + 19.30 deg.: X-S = 0.090895 mb/sr, + 19.40 deg.: X-S = 0.088385 mb/sr, + 19.50 deg.: X-S = 0.085396 mb/sr, + 19.60 deg.: X-S = 0.081977 mb/sr, + 19.70 deg.: X-S = 0.078186 mb/sr, + 19.80 deg.: X-S = 0.074082 mb/sr, + 19.90 deg.: X-S = 0.069727 mb/sr, + 20.00 deg.: X-S = 0.065185 mb/sr, + 20.10 deg.: X-S = 0.060518 mb/sr, + 20.20 deg.: X-S = 0.055791 mb/sr, + 20.30 deg.: X-S = 0.051064 mb/sr, + 20.40 deg.: X-S = 0.046397 mb/sr, + 20.50 deg.: X-S = 0.041843 mb/sr, + 20.60 deg.: X-S = 0.037455 mb/sr, + 20.70 deg.: X-S = 0.033278 mb/sr, + 20.80 deg.: X-S = 0.029353 mb/sr, + 20.90 deg.: X-S = 0.025717 mb/sr, + 21.00 deg.: X-S = 0.022399 mb/sr, + 21.10 deg.: X-S = 0.019422 mb/sr, + 21.20 deg.: X-S = 0.016804 mb/sr, + 21.30 deg.: X-S = 0.014557 mb/sr, + 21.40 deg.: X-S = 0.012686 mb/sr, + 21.50 deg.: X-S = 0.011191 mb/sr, + 21.60 deg.: X-S = 0.010067 mb/sr, + 21.70 deg.: X-S = 0.009302 mb/sr, + 21.80 deg.: X-S = 0.008881 mb/sr, + 21.90 deg.: X-S = 0.008785 mb/sr, + 22.00 deg.: X-S = 0.008991 mb/sr, + 22.10 deg.: X-S = 0.009471 mb/sr, + 22.20 deg.: X-S = 0.010196 mb/sr, + 22.30 deg.: X-S = 0.011135 mb/sr, + 22.40 deg.: X-S = 0.012254 mb/sr, + 22.50 deg.: X-S = 0.013521 mb/sr, + 22.60 deg.: X-S = 0.014900 mb/sr, + 22.70 deg.: X-S = 0.016358 mb/sr, + 22.80 deg.: X-S = 0.017860 mb/sr, + 22.90 deg.: X-S = 0.019375 mb/sr, + 23.00 deg.: X-S = 0.020872 mb/sr, + 23.10 deg.: X-S = 0.022320 mb/sr, + 23.20 deg.: X-S = 0.023695 mb/sr, + 23.30 deg.: X-S = 0.024971 mb/sr, + 23.40 deg.: X-S = 0.026127 mb/sr, + 23.50 deg.: X-S = 0.027146 mb/sr, + 23.60 deg.: X-S = 0.028011 mb/sr, + 23.70 deg.: X-S = 0.028711 mb/sr, + 23.80 deg.: X-S = 0.029238 mb/sr, + 23.90 deg.: X-S = 0.029584 mb/sr, + 24.00 deg.: X-S = 0.029749 mb/sr, + 24.10 deg.: X-S = 0.029733 mb/sr, + 24.20 deg.: X-S = 0.029538 mb/sr, + 24.30 deg.: X-S = 0.029172 mb/sr, + 24.40 deg.: X-S = 0.028642 mb/sr, + 24.50 deg.: X-S = 0.027958 mb/sr, + 24.60 deg.: X-S = 0.027135 mb/sr, + 24.70 deg.: X-S = 0.026184 mb/sr, + 24.80 deg.: X-S = 0.025122 mb/sr, + 24.90 deg.: X-S = 0.023966 mb/sr, + 25.00 deg.: X-S = 0.022731 mb/sr, + 25.10 deg.: X-S = 0.021436 mb/sr, + 25.20 deg.: X-S = 0.020098 mb/sr, + 25.30 deg.: X-S = 0.018734 mb/sr, + 25.40 deg.: X-S = 0.017363 mb/sr, + 25.50 deg.: X-S = 0.015999 mb/sr, + 25.60 deg.: X-S = 0.014659 mb/sr, + 25.70 deg.: X-S = 0.013358 mb/sr, + 25.80 deg.: X-S = 0.012109 mb/sr, + 25.90 deg.: X-S = 0.010924 mb/sr, + 26.00 deg.: X-S = 0.009814 mb/sr, + 26.10 deg.: X-S = 0.008788 mb/sr, + 26.20 deg.: X-S = 0.007853 mb/sr, + 26.30 deg.: X-S = 0.007017 mb/sr, + 26.40 deg.: X-S = 0.006283 mb/sr, + 26.50 deg.: X-S = 0.005653 mb/sr, + 26.60 deg.: X-S = 0.005130 mb/sr, + 26.70 deg.: X-S = 0.004713 mb/sr, + 26.80 deg.: X-S = 0.004400 mb/sr, + 26.90 deg.: X-S = 0.004188 mb/sr, + 27.00 deg.: X-S = 0.004072 mb/sr, + 27.10 deg.: X-S = 0.004047 mb/sr, + 27.20 deg.: X-S = 0.004106 mb/sr, + 27.30 deg.: X-S = 0.004243 mb/sr, + 27.40 deg.: X-S = 0.004448 mb/sr, + 27.50 deg.: X-S = 0.004713 mb/sr, + 27.60 deg.: X-S = 0.005030 mb/sr, + 27.70 deg.: X-S = 0.005388 mb/sr, + 27.80 deg.: X-S = 0.005778 mb/sr, + 27.90 deg.: X-S = 0.006190 mb/sr, + 28.00 deg.: X-S = 0.006616 mb/sr, + 28.10 deg.: X-S = 0.007047 mb/sr, + 28.20 deg.: X-S = 0.007473 mb/sr, + 28.30 deg.: X-S = 0.007887 mb/sr, + 28.40 deg.: X-S = 0.008282 mb/sr, + 28.50 deg.: X-S = 0.008649 mb/sr, + 28.60 deg.: X-S = 0.008985 mb/sr, + 28.70 deg.: X-S = 0.009283 mb/sr, + 28.80 deg.: X-S = 0.009539 mb/sr, + 28.90 deg.: X-S = 0.009749 mb/sr, + 29.00 deg.: X-S = 0.009912 mb/sr, + 29.10 deg.: X-S = 0.010024 mb/sr, + 29.20 deg.: X-S = 0.010085 mb/sr, + 29.30 deg.: X-S = 0.010095 mb/sr, + 29.40 deg.: X-S = 0.010055 mb/sr, + 29.50 deg.: X-S = 0.009965 mb/sr, + 29.60 deg.: X-S = 0.009828 mb/sr, + 29.70 deg.: X-S = 0.009645 mb/sr, + 29.80 deg.: X-S = 0.009421 mb/sr, + 29.90 deg.: X-S = 0.009159 mb/sr, + 30.00 deg.: X-S = 0.008863 mb/sr, + 30.10 deg.: X-S = 0.008536 mb/sr, + 30.20 deg.: X-S = 0.008185 mb/sr, + 30.30 deg.: X-S = 0.007812 mb/sr, + 30.40 deg.: X-S = 0.007423 mb/sr, + 30.50 deg.: X-S = 0.007023 mb/sr, + 30.60 deg.: X-S = 0.006616 mb/sr, + 30.70 deg.: X-S = 0.006208 mb/sr, + 30.80 deg.: X-S = 0.005803 mb/sr, + 30.90 deg.: X-S = 0.005404 mb/sr, + 31.00 deg.: X-S = 0.005017 mb/sr, + 31.10 deg.: X-S = 0.004644 mb/sr, + 31.20 deg.: X-S = 0.004289 mb/sr, + 31.30 deg.: X-S = 0.003955 mb/sr, + 31.40 deg.: X-S = 0.003645 mb/sr, + 31.50 deg.: X-S = 0.003361 mb/sr, + 31.60 deg.: X-S = 0.003104 mb/sr, + 31.70 deg.: X-S = 0.002875 mb/sr, + 31.80 deg.: X-S = 0.002676 mb/sr, + 31.90 deg.: X-S = 0.002507 mb/sr, + 32.00 deg.: X-S = 0.002368 mb/sr, + 32.10 deg.: X-S = 0.002257 mb/sr, + 32.20 deg.: X-S = 0.002176 mb/sr, + 32.30 deg.: X-S = 0.002121 mb/sr, + 32.40 deg.: X-S = 0.002092 mb/sr, + 32.50 deg.: X-S = 0.002088 mb/sr, + 32.60 deg.: X-S = 0.002105 mb/sr, + 32.70 deg.: X-S = 0.002142 mb/sr, + 32.80 deg.: X-S = 0.002197 mb/sr, + 32.90 deg.: X-S = 0.002266 mb/sr, + 33.00 deg.: X-S = 0.002349 mb/sr, + 33.10 deg.: X-S = 0.002441 mb/sr, + 33.20 deg.: X-S = 0.002541 mb/sr, + 33.30 deg.: X-S = 0.002646 mb/sr, + 33.40 deg.: X-S = 0.002753 mb/sr, + 33.50 deg.: X-S = 0.002860 mb/sr, + 33.60 deg.: X-S = 0.002966 mb/sr, + 33.70 deg.: X-S = 0.003067 mb/sr, + 33.80 deg.: X-S = 0.003162 mb/sr, + 33.90 deg.: X-S = 0.003250 mb/sr, + 34.00 deg.: X-S = 0.003328 mb/sr, + 34.10 deg.: X-S = 0.003396 mb/sr, + 34.20 deg.: X-S = 0.003452 mb/sr, + 34.30 deg.: X-S = 0.003496 mb/sr, + 34.40 deg.: X-S = 0.003527 mb/sr, + 34.50 deg.: X-S = 0.003543 mb/sr, + 34.60 deg.: X-S = 0.003546 mb/sr, + 34.70 deg.: X-S = 0.003535 mb/sr, + 34.80 deg.: X-S = 0.003511 mb/sr, + 34.90 deg.: X-S = 0.003473 mb/sr, + 35.00 deg.: X-S = 0.003422 mb/sr, + 35.10 deg.: X-S = 0.003359 mb/sr, + 35.20 deg.: X-S = 0.003286 mb/sr, + 35.30 deg.: X-S = 0.003201 mb/sr, + 35.40 deg.: X-S = 0.003108 mb/sr, + 35.50 deg.: X-S = 0.003006 mb/sr, + 35.60 deg.: X-S = 0.002898 mb/sr, + 35.70 deg.: X-S = 0.002784 mb/sr, + 35.80 deg.: X-S = 0.002667 mb/sr, + 35.90 deg.: X-S = 0.002546 mb/sr, + 36.00 deg.: X-S = 0.002423 mb/sr, + 36.10 deg.: X-S = 0.002301 mb/sr, + 36.20 deg.: X-S = 0.002179 mb/sr, + 36.30 deg.: X-S = 0.002059 mb/sr, + 36.40 deg.: X-S = 0.001943 mb/sr, + 36.50 deg.: X-S = 0.001830 mb/sr, + 36.60 deg.: X-S = 0.001723 mb/sr, + 36.70 deg.: X-S = 0.001621 mb/sr, + 36.80 deg.: X-S = 0.001526 mb/sr, + 36.90 deg.: X-S = 0.001438 mb/sr, + 37.00 deg.: X-S = 0.001357 mb/sr, + 37.10 deg.: X-S = 0.001284 mb/sr, + 37.20 deg.: X-S = 0.001219 mb/sr, + 37.30 deg.: X-S = 0.001162 mb/sr, + 37.40 deg.: X-S = 0.001113 mb/sr, + 37.50 deg.: X-S = 0.001072 mb/sr, + 37.60 deg.: X-S = 0.001039 mb/sr, + 37.70 deg.: X-S = 0.001013 mb/sr, + 37.80 deg.: X-S = 0.000995 mb/sr, + 37.90 deg.: X-S = 0.000983 mb/sr, + 38.00 deg.: X-S = 0.000977 mb/sr, + 38.10 deg.: X-S = 0.000978 mb/sr, + 38.20 deg.: X-S = 0.000983 mb/sr, + 38.30 deg.: X-S = 0.000992 mb/sr, + 38.40 deg.: X-S = 0.001006 mb/sr, + 38.50 deg.: X-S = 0.001022 mb/sr, + 38.60 deg.: X-S = 0.001042 mb/sr, + 38.70 deg.: X-S = 0.001063 mb/sr, + 38.80 deg.: X-S = 0.001085 mb/sr, + 38.90 deg.: X-S = 0.001108 mb/sr, + 39.00 deg.: X-S = 0.001130 mb/sr, + 39.10 deg.: X-S = 0.001152 mb/sr, + 39.20 deg.: X-S = 0.001173 mb/sr, + 39.30 deg.: X-S = 0.001193 mb/sr, + 39.40 deg.: X-S = 0.001210 mb/sr, + 39.50 deg.: X-S = 0.001225 mb/sr, + 39.60 deg.: X-S = 0.001237 mb/sr, + 39.70 deg.: X-S = 0.001245 mb/sr, + 39.80 deg.: X-S = 0.001251 mb/sr, + 39.90 deg.: X-S = 0.001253 mb/sr, + 40.00 deg.: X-S = 0.001252 mb/sr, + Integrated 2.6352E-01 mb, over [ 0.000, 40.000] at 170.0000 MeV + + CROSS SECTIONS FOR OUTGOING ne18 & c13 in state # 2 with spins & parities 4.0 + & 0.5 +; 0 + + 0.00 deg.: X-S = 0.000000E+00 mb/sr, ++ REAC 0.0000E+00 mb + 0.10 deg.: X-S = 0.000000E+00 mb/sr, + 0.20 deg.: X-S = 0.000000E+00 mb/sr, + 0.30 deg.: X-S = 0.000000E+00 mb/sr, + 0.40 deg.: X-S = 0.000000E+00 mb/sr, + 0.50 deg.: X-S = 0.000000E+00 mb/sr, + 0.60 deg.: X-S = 0.000000E+00 mb/sr, + 0.70 deg.: X-S = 0.000000E+00 mb/sr, + 0.80 deg.: X-S = 0.000000E+00 mb/sr, + 0.90 deg.: X-S = 0.000000E+00 mb/sr, + 1.00 deg.: X-S = 0.000000E+00 mb/sr, + 1.10 deg.: X-S = 0.000000E+00 mb/sr, + 1.20 deg.: X-S = 0.000000E+00 mb/sr, + 1.30 deg.: X-S = 0.000000E+00 mb/sr, + 1.40 deg.: X-S = 0.000000E+00 mb/sr, + 1.50 deg.: X-S = 0.000000E+00 mb/sr, + 1.60 deg.: X-S = 0.000000E+00 mb/sr, + 1.70 deg.: X-S = 0.000000E+00 mb/sr, + 1.80 deg.: X-S = 0.000000E+00 mb/sr, + 1.90 deg.: X-S = 0.000000E+00 mb/sr, + 2.00 deg.: X-S = 0.000000E+00 mb/sr, + 2.10 deg.: X-S = 0.000000E+00 mb/sr, + 2.20 deg.: X-S = 0.000000E+00 mb/sr, + 2.30 deg.: X-S = 0.000000E+00 mb/sr, + 2.40 deg.: X-S = 0.000000E+00 mb/sr, + 2.50 deg.: X-S = 0.000000E+00 mb/sr, + 2.60 deg.: X-S = 0.000000E+00 mb/sr, + 2.70 deg.: X-S = 0.000000E+00 mb/sr, + 2.80 deg.: X-S = 0.000000E+00 mb/sr, + 2.90 deg.: X-S = 0.000000E+00 mb/sr, + 3.00 deg.: X-S = 0.000000E+00 mb/sr, + 3.10 deg.: X-S = 0.000000E+00 mb/sr, + 3.20 deg.: X-S = 0.000000E+00 mb/sr, + 3.30 deg.: X-S = 0.000000E+00 mb/sr, + 3.40 deg.: X-S = 0.000000E+00 mb/sr, + 3.50 deg.: X-S = 0.000000E+00 mb/sr, + 3.60 deg.: X-S = 0.000000E+00 mb/sr, + 3.70 deg.: X-S = 0.000000E+00 mb/sr, + 3.80 deg.: X-S = 0.000000E+00 mb/sr, + 3.90 deg.: X-S = 0.000000E+00 mb/sr, + 4.00 deg.: X-S = 0.000000E+00 mb/sr, + 4.10 deg.: X-S = 0.000000E+00 mb/sr, + 4.20 deg.: X-S = 0.000000E+00 mb/sr, + 4.30 deg.: X-S = 0.000000E+00 mb/sr, + 4.40 deg.: X-S = 0.000000E+00 mb/sr, + 4.50 deg.: X-S = 0.000000E+00 mb/sr, + 4.60 deg.: X-S = 0.000000E+00 mb/sr, + 4.70 deg.: X-S = 0.000000E+00 mb/sr, + 4.80 deg.: X-S = 0.000000E+00 mb/sr, + 4.90 deg.: X-S = 0.000000E+00 mb/sr, + 5.00 deg.: X-S = 0.000000E+00 mb/sr, + 5.10 deg.: X-S = 0.000000E+00 mb/sr, + 5.20 deg.: X-S = 0.000000E+00 mb/sr, + 5.30 deg.: X-S = 0.000000E+00 mb/sr, + 5.40 deg.: X-S = 0.000000E+00 mb/sr, + 5.50 deg.: X-S = 0.000000E+00 mb/sr, + 5.60 deg.: X-S = 0.000000E+00 mb/sr, + 5.70 deg.: X-S = 0.000000E+00 mb/sr, + 5.80 deg.: X-S = 0.000000E+00 mb/sr, + 5.90 deg.: X-S = 0.000000E+00 mb/sr, + 6.00 deg.: X-S = 0.000000E+00 mb/sr, + 6.10 deg.: X-S = 0.000000E+00 mb/sr, + 6.20 deg.: X-S = 0.000000E+00 mb/sr, + 6.30 deg.: X-S = 0.000000E+00 mb/sr, + 6.40 deg.: X-S = 0.000000E+00 mb/sr, + 6.50 deg.: X-S = 0.000000E+00 mb/sr, + 6.60 deg.: X-S = 0.000000E+00 mb/sr, + 6.70 deg.: X-S = 0.000000E+00 mb/sr, + 6.80 deg.: X-S = 0.000000E+00 mb/sr, + 6.90 deg.: X-S = 0.000000E+00 mb/sr, + 7.00 deg.: X-S = 0.000000E+00 mb/sr, + 7.10 deg.: X-S = 0.000000E+00 mb/sr, + 7.20 deg.: X-S = 0.000000E+00 mb/sr, + 7.30 deg.: X-S = 0.000000E+00 mb/sr, + 7.40 deg.: X-S = 0.000000E+00 mb/sr, + 7.50 deg.: X-S = 0.000000E+00 mb/sr, + 7.60 deg.: X-S = 0.000000E+00 mb/sr, + 7.70 deg.: X-S = 0.000000E+00 mb/sr, + 7.80 deg.: X-S = 0.000000E+00 mb/sr, + 7.90 deg.: X-S = 0.000000E+00 mb/sr, + 8.00 deg.: X-S = 0.000000E+00 mb/sr, + 8.10 deg.: X-S = 0.000000E+00 mb/sr, + 8.20 deg.: X-S = 0.000000E+00 mb/sr, + 8.30 deg.: X-S = 0.000000E+00 mb/sr, + 8.40 deg.: X-S = 0.000000E+00 mb/sr, + 8.50 deg.: X-S = 0.000000E+00 mb/sr, + 8.60 deg.: X-S = 0.000000E+00 mb/sr, + 8.70 deg.: X-S = 0.000000E+00 mb/sr, + 8.80 deg.: X-S = 0.000000E+00 mb/sr, + 8.90 deg.: X-S = 0.000000E+00 mb/sr, + 9.00 deg.: X-S = 0.000000E+00 mb/sr, + 9.10 deg.: X-S = 0.000000E+00 mb/sr, + 9.20 deg.: X-S = 0.000000E+00 mb/sr, + 9.30 deg.: X-S = 0.000000E+00 mb/sr, + 9.40 deg.: X-S = 0.000000E+00 mb/sr, + 9.50 deg.: X-S = 0.000000E+00 mb/sr, + 9.60 deg.: X-S = 0.000000E+00 mb/sr, + 9.70 deg.: X-S = 0.000000E+00 mb/sr, + 9.80 deg.: X-S = 0.000000E+00 mb/sr, + 9.90 deg.: X-S = 0.000000E+00 mb/sr, + 10.00 deg.: X-S = 0.000000E+00 mb/sr, + 10.10 deg.: X-S = 0.000000E+00 mb/sr, + 10.20 deg.: X-S = 0.000000E+00 mb/sr, + 10.30 deg.: X-S = 0.000000E+00 mb/sr, + 10.40 deg.: X-S = 0.000000E+00 mb/sr, + 10.50 deg.: X-S = 0.000000E+00 mb/sr, + 10.60 deg.: X-S = 0.000000E+00 mb/sr, + 10.70 deg.: X-S = 0.000000E+00 mb/sr, + 10.80 deg.: X-S = 0.000000E+00 mb/sr, + 10.90 deg.: X-S = 0.000000E+00 mb/sr, + 11.00 deg.: X-S = 0.000000E+00 mb/sr, + 11.10 deg.: X-S = 0.000000E+00 mb/sr, + 11.20 deg.: X-S = 0.000000E+00 mb/sr, + 11.30 deg.: X-S = 0.000000E+00 mb/sr, + 11.40 deg.: X-S = 0.000000E+00 mb/sr, + 11.50 deg.: X-S = 0.000000E+00 mb/sr, + 11.60 deg.: X-S = 0.000000E+00 mb/sr, + 11.70 deg.: X-S = 0.000000E+00 mb/sr, + 11.80 deg.: X-S = 0.000000E+00 mb/sr, + 11.90 deg.: X-S = 0.000000E+00 mb/sr, + 12.00 deg.: X-S = 0.000000E+00 mb/sr, + 12.10 deg.: X-S = 0.000000E+00 mb/sr, + 12.20 deg.: X-S = 0.000000E+00 mb/sr, + 12.30 deg.: X-S = 0.000000E+00 mb/sr, + 12.40 deg.: X-S = 0.000000E+00 mb/sr, + 12.50 deg.: X-S = 0.000000E+00 mb/sr, + 12.60 deg.: X-S = 0.000000E+00 mb/sr, + 12.70 deg.: X-S = 0.000000E+00 mb/sr, + 12.80 deg.: X-S = 0.000000E+00 mb/sr, + 12.90 deg.: X-S = 0.000000E+00 mb/sr, + 13.00 deg.: X-S = 0.000000E+00 mb/sr, + 13.10 deg.: X-S = 0.000000E+00 mb/sr, + 13.20 deg.: X-S = 0.000000E+00 mb/sr, + 13.30 deg.: X-S = 0.000000E+00 mb/sr, + 13.40 deg.: X-S = 0.000000E+00 mb/sr, + 13.50 deg.: X-S = 0.000000E+00 mb/sr, + 13.60 deg.: X-S = 0.000000E+00 mb/sr, + 13.70 deg.: X-S = 0.000000E+00 mb/sr, + 13.80 deg.: X-S = 0.000000E+00 mb/sr, + 13.90 deg.: X-S = 0.000000E+00 mb/sr, + 14.00 deg.: X-S = 0.000000E+00 mb/sr, + 14.10 deg.: X-S = 0.000000E+00 mb/sr, + 14.20 deg.: X-S = 0.000000E+00 mb/sr, + 14.30 deg.: X-S = 0.000000E+00 mb/sr, + 14.40 deg.: X-S = 0.000000E+00 mb/sr, + 14.50 deg.: X-S = 0.000000E+00 mb/sr, + 14.60 deg.: X-S = 0.000000E+00 mb/sr, + 14.70 deg.: X-S = 0.000000E+00 mb/sr, + 14.80 deg.: X-S = 0.000000E+00 mb/sr, + 14.90 deg.: X-S = 0.000000E+00 mb/sr, + 15.00 deg.: X-S = 0.000000E+00 mb/sr, + 15.10 deg.: X-S = 0.000000E+00 mb/sr, + 15.20 deg.: X-S = 0.000000E+00 mb/sr, + 15.30 deg.: X-S = 0.000000E+00 mb/sr, + 15.40 deg.: X-S = 0.000000E+00 mb/sr, + 15.50 deg.: X-S = 0.000000E+00 mb/sr, + 15.60 deg.: X-S = 0.000000E+00 mb/sr, + 15.70 deg.: X-S = 0.000000E+00 mb/sr, + 15.80 deg.: X-S = 0.000000E+00 mb/sr, + 15.90 deg.: X-S = 0.000000E+00 mb/sr, + 16.00 deg.: X-S = 0.000000E+00 mb/sr, + 16.10 deg.: X-S = 0.000000E+00 mb/sr, + 16.20 deg.: X-S = 0.000000E+00 mb/sr, + 16.30 deg.: X-S = 0.000000E+00 mb/sr, + 16.40 deg.: X-S = 0.000000E+00 mb/sr, + 16.50 deg.: X-S = 0.000000E+00 mb/sr, + 16.60 deg.: X-S = 0.000000E+00 mb/sr, + 16.70 deg.: X-S = 0.000000E+00 mb/sr, + 16.80 deg.: X-S = 0.000000E+00 mb/sr, + 16.90 deg.: X-S = 0.000000E+00 mb/sr, + 17.00 deg.: X-S = 0.000000E+00 mb/sr, + 17.10 deg.: X-S = 0.000000E+00 mb/sr, + 17.20 deg.: X-S = 0.000000E+00 mb/sr, + 17.30 deg.: X-S = 0.000000E+00 mb/sr, + 17.40 deg.: X-S = 0.000000E+00 mb/sr, + 17.50 deg.: X-S = 0.000000E+00 mb/sr, + 17.60 deg.: X-S = 0.000000E+00 mb/sr, + 17.70 deg.: X-S = 0.000000E+00 mb/sr, + 17.80 deg.: X-S = 0.000000E+00 mb/sr, + 17.90 deg.: X-S = 0.000000E+00 mb/sr, + 18.00 deg.: X-S = 0.000000E+00 mb/sr, + 18.10 deg.: X-S = 0.000000E+00 mb/sr, + 18.20 deg.: X-S = 0.000000E+00 mb/sr, + 18.30 deg.: X-S = 0.000000E+00 mb/sr, + 18.40 deg.: X-S = 0.000000E+00 mb/sr, + 18.50 deg.: X-S = 0.000000E+00 mb/sr, + 18.60 deg.: X-S = 0.000000E+00 mb/sr, + 18.70 deg.: X-S = 0.000000E+00 mb/sr, + 18.80 deg.: X-S = 0.000000E+00 mb/sr, + 18.90 deg.: X-S = 0.000000E+00 mb/sr, + 19.00 deg.: X-S = 0.000000E+00 mb/sr, + 19.10 deg.: X-S = 0.000000E+00 mb/sr, + 19.20 deg.: X-S = 0.000000E+00 mb/sr, + 19.30 deg.: X-S = 0.000000E+00 mb/sr, + 19.40 deg.: X-S = 0.000000E+00 mb/sr, + 19.50 deg.: X-S = 0.000000E+00 mb/sr, + 19.60 deg.: X-S = 0.000000E+00 mb/sr, + 19.70 deg.: X-S = 0.000000E+00 mb/sr, + 19.80 deg.: X-S = 0.000000E+00 mb/sr, + 19.90 deg.: X-S = 0.000000E+00 mb/sr, + 20.00 deg.: X-S = 0.000000E+00 mb/sr, + 20.10 deg.: X-S = 0.000000E+00 mb/sr, + 20.20 deg.: X-S = 0.000000E+00 mb/sr, + 20.30 deg.: X-S = 0.000000E+00 mb/sr, + 20.40 deg.: X-S = 0.000000E+00 mb/sr, + 20.50 deg.: X-S = 0.000000E+00 mb/sr, + 20.60 deg.: X-S = 0.000000E+00 mb/sr, + 20.70 deg.: X-S = 0.000000E+00 mb/sr, + 20.80 deg.: X-S = 0.000000E+00 mb/sr, + 20.90 deg.: X-S = 0.000000E+00 mb/sr, + 21.00 deg.: X-S = 0.000000E+00 mb/sr, + 21.10 deg.: X-S = 0.000000E+00 mb/sr, + 21.20 deg.: X-S = 0.000000E+00 mb/sr, + 21.30 deg.: X-S = 0.000000E+00 mb/sr, + 21.40 deg.: X-S = 0.000000E+00 mb/sr, + 21.50 deg.: X-S = 0.000000E+00 mb/sr, + 21.60 deg.: X-S = 0.000000E+00 mb/sr, + 21.70 deg.: X-S = 0.000000E+00 mb/sr, + 21.80 deg.: X-S = 0.000000E+00 mb/sr, + 21.90 deg.: X-S = 0.000000E+00 mb/sr, + 22.00 deg.: X-S = 0.000000E+00 mb/sr, + 22.10 deg.: X-S = 0.000000E+00 mb/sr, + 22.20 deg.: X-S = 0.000000E+00 mb/sr, + 22.30 deg.: X-S = 0.000000E+00 mb/sr, + 22.40 deg.: X-S = 0.000000E+00 mb/sr, + 22.50 deg.: X-S = 0.000000E+00 mb/sr, + 22.60 deg.: X-S = 0.000000E+00 mb/sr, + 22.70 deg.: X-S = 0.000000E+00 mb/sr, + 22.80 deg.: X-S = 0.000000E+00 mb/sr, + 22.90 deg.: X-S = 0.000000E+00 mb/sr, + 23.00 deg.: X-S = 0.000000E+00 mb/sr, + 23.10 deg.: X-S = 0.000000E+00 mb/sr, + 23.20 deg.: X-S = 0.000000E+00 mb/sr, + 23.30 deg.: X-S = 0.000000E+00 mb/sr, + 23.40 deg.: X-S = 0.000000E+00 mb/sr, + 23.50 deg.: X-S = 0.000000E+00 mb/sr, + 23.60 deg.: X-S = 0.000000E+00 mb/sr, + 23.70 deg.: X-S = 0.000000E+00 mb/sr, + 23.80 deg.: X-S = 0.000000E+00 mb/sr, + 23.90 deg.: X-S = 0.000000E+00 mb/sr, + 24.00 deg.: X-S = 0.000000E+00 mb/sr, + 24.10 deg.: X-S = 0.000000E+00 mb/sr, + 24.20 deg.: X-S = 0.000000E+00 mb/sr, + 24.30 deg.: X-S = 0.000000E+00 mb/sr, + 24.40 deg.: X-S = 0.000000E+00 mb/sr, + 24.50 deg.: X-S = 0.000000E+00 mb/sr, + 24.60 deg.: X-S = 0.000000E+00 mb/sr, + 24.70 deg.: X-S = 0.000000E+00 mb/sr, + 24.80 deg.: X-S = 0.000000E+00 mb/sr, + 24.90 deg.: X-S = 0.000000E+00 mb/sr, + 25.00 deg.: X-S = 0.000000E+00 mb/sr, + 25.10 deg.: X-S = 0.000000E+00 mb/sr, + 25.20 deg.: X-S = 0.000000E+00 mb/sr, + 25.30 deg.: X-S = 0.000000E+00 mb/sr, + 25.40 deg.: X-S = 0.000000E+00 mb/sr, + 25.50 deg.: X-S = 0.000000E+00 mb/sr, + 25.60 deg.: X-S = 0.000000E+00 mb/sr, + 25.70 deg.: X-S = 0.000000E+00 mb/sr, + 25.80 deg.: X-S = 0.000000E+00 mb/sr, + 25.90 deg.: X-S = 0.000000E+00 mb/sr, + 26.00 deg.: X-S = 0.000000E+00 mb/sr, + 26.10 deg.: X-S = 0.000000E+00 mb/sr, + 26.20 deg.: X-S = 0.000000E+00 mb/sr, + 26.30 deg.: X-S = 0.000000E+00 mb/sr, + 26.40 deg.: X-S = 0.000000E+00 mb/sr, + 26.50 deg.: X-S = 0.000000E+00 mb/sr, + 26.60 deg.: X-S = 0.000000E+00 mb/sr, + 26.70 deg.: X-S = 0.000000E+00 mb/sr, + 26.80 deg.: X-S = 0.000000E+00 mb/sr, + 26.90 deg.: X-S = 0.000000E+00 mb/sr, + 27.00 deg.: X-S = 0.000000E+00 mb/sr, + 27.10 deg.: X-S = 0.000000E+00 mb/sr, + 27.20 deg.: X-S = 0.000000E+00 mb/sr, + 27.30 deg.: X-S = 0.000000E+00 mb/sr, + 27.40 deg.: X-S = 0.000000E+00 mb/sr, + 27.50 deg.: X-S = 0.000000E+00 mb/sr, + 27.60 deg.: X-S = 0.000000E+00 mb/sr, + 27.70 deg.: X-S = 0.000000E+00 mb/sr, + 27.80 deg.: X-S = 0.000000E+00 mb/sr, + 27.90 deg.: X-S = 0.000000E+00 mb/sr, + 28.00 deg.: X-S = 0.000000E+00 mb/sr, + 28.10 deg.: X-S = 0.000000E+00 mb/sr, + 28.20 deg.: X-S = 0.000000E+00 mb/sr, + 28.30 deg.: X-S = 0.000000E+00 mb/sr, + 28.40 deg.: X-S = 0.000000E+00 mb/sr, + 28.50 deg.: X-S = 0.000000E+00 mb/sr, + 28.60 deg.: X-S = 0.000000E+00 mb/sr, + 28.70 deg.: X-S = 0.000000E+00 mb/sr, + 28.80 deg.: X-S = 0.000000E+00 mb/sr, + 28.90 deg.: X-S = 0.000000E+00 mb/sr, + 29.00 deg.: X-S = 0.000000E+00 mb/sr, + 29.10 deg.: X-S = 0.000000E+00 mb/sr, + 29.20 deg.: X-S = 0.000000E+00 mb/sr, + 29.30 deg.: X-S = 0.000000E+00 mb/sr, + 29.40 deg.: X-S = 0.000000E+00 mb/sr, + 29.50 deg.: X-S = 0.000000E+00 mb/sr, + 29.60 deg.: X-S = 0.000000E+00 mb/sr, + 29.70 deg.: X-S = 0.000000E+00 mb/sr, + 29.80 deg.: X-S = 0.000000E+00 mb/sr, + 29.90 deg.: X-S = 0.000000E+00 mb/sr, + 30.00 deg.: X-S = 0.000000E+00 mb/sr, + 30.10 deg.: X-S = 0.000000E+00 mb/sr, + 30.20 deg.: X-S = 0.000000E+00 mb/sr, + 30.30 deg.: X-S = 0.000000E+00 mb/sr, + 30.40 deg.: X-S = 0.000000E+00 mb/sr, + 30.50 deg.: X-S = 0.000000E+00 mb/sr, + 30.60 deg.: X-S = 0.000000E+00 mb/sr, + 30.70 deg.: X-S = 0.000000E+00 mb/sr, + 30.80 deg.: X-S = 0.000000E+00 mb/sr, + 30.90 deg.: X-S = 0.000000E+00 mb/sr, + 31.00 deg.: X-S = 0.000000E+00 mb/sr, + 31.10 deg.: X-S = 0.000000E+00 mb/sr, + 31.20 deg.: X-S = 0.000000E+00 mb/sr, + 31.30 deg.: X-S = 0.000000E+00 mb/sr, + 31.40 deg.: X-S = 0.000000E+00 mb/sr, + 31.50 deg.: X-S = 0.000000E+00 mb/sr, + 31.60 deg.: X-S = 0.000000E+00 mb/sr, + 31.70 deg.: X-S = 0.000000E+00 mb/sr, + 31.80 deg.: X-S = 0.000000E+00 mb/sr, + 31.90 deg.: X-S = 0.000000E+00 mb/sr, + 32.00 deg.: X-S = 0.000000E+00 mb/sr, + 32.10 deg.: X-S = 0.000000E+00 mb/sr, + 32.20 deg.: X-S = 0.000000E+00 mb/sr, + 32.30 deg.: X-S = 0.000000E+00 mb/sr, + 32.40 deg.: X-S = 0.000000E+00 mb/sr, + 32.50 deg.: X-S = 0.000000E+00 mb/sr, + 32.60 deg.: X-S = 0.000000E+00 mb/sr, + 32.70 deg.: X-S = 0.000000E+00 mb/sr, + 32.80 deg.: X-S = 0.000000E+00 mb/sr, + 32.90 deg.: X-S = 0.000000E+00 mb/sr, + 33.00 deg.: X-S = 0.000000E+00 mb/sr, + 33.10 deg.: X-S = 0.000000E+00 mb/sr, + 33.20 deg.: X-S = 0.000000E+00 mb/sr, + 33.30 deg.: X-S = 0.000000E+00 mb/sr, + 33.40 deg.: X-S = 0.000000E+00 mb/sr, + 33.50 deg.: X-S = 0.000000E+00 mb/sr, + 33.60 deg.: X-S = 0.000000E+00 mb/sr, + 33.70 deg.: X-S = 0.000000E+00 mb/sr, + 33.80 deg.: X-S = 0.000000E+00 mb/sr, + 33.90 deg.: X-S = 0.000000E+00 mb/sr, + 34.00 deg.: X-S = 0.000000E+00 mb/sr, + 34.10 deg.: X-S = 0.000000E+00 mb/sr, + 34.20 deg.: X-S = 0.000000E+00 mb/sr, + 34.30 deg.: X-S = 0.000000E+00 mb/sr, + 34.40 deg.: X-S = 0.000000E+00 mb/sr, + 34.50 deg.: X-S = 0.000000E+00 mb/sr, + 34.60 deg.: X-S = 0.000000E+00 mb/sr, + 34.70 deg.: X-S = 0.000000E+00 mb/sr, + 34.80 deg.: X-S = 0.000000E+00 mb/sr, + 34.90 deg.: X-S = 0.000000E+00 mb/sr, + 35.00 deg.: X-S = 0.000000E+00 mb/sr, + 35.10 deg.: X-S = 0.000000E+00 mb/sr, + 35.20 deg.: X-S = 0.000000E+00 mb/sr, + 35.30 deg.: X-S = 0.000000E+00 mb/sr, + 35.40 deg.: X-S = 0.000000E+00 mb/sr, + 35.50 deg.: X-S = 0.000000E+00 mb/sr, + 35.60 deg.: X-S = 0.000000E+00 mb/sr, + 35.70 deg.: X-S = 0.000000E+00 mb/sr, + 35.80 deg.: X-S = 0.000000E+00 mb/sr, + 35.90 deg.: X-S = 0.000000E+00 mb/sr, + 36.00 deg.: X-S = 0.000000E+00 mb/sr, + 36.10 deg.: X-S = 0.000000E+00 mb/sr, + 36.20 deg.: X-S = 0.000000E+00 mb/sr, + 36.30 deg.: X-S = 0.000000E+00 mb/sr, + 36.40 deg.: X-S = 0.000000E+00 mb/sr, + 36.50 deg.: X-S = 0.000000E+00 mb/sr, + 36.60 deg.: X-S = 0.000000E+00 mb/sr, + 36.70 deg.: X-S = 0.000000E+00 mb/sr, + 36.80 deg.: X-S = 0.000000E+00 mb/sr, + 36.90 deg.: X-S = 0.000000E+00 mb/sr, + 37.00 deg.: X-S = 0.000000E+00 mb/sr, + 37.10 deg.: X-S = 0.000000E+00 mb/sr, + 37.20 deg.: X-S = 0.000000E+00 mb/sr, + 37.30 deg.: X-S = 0.000000E+00 mb/sr, + 37.40 deg.: X-S = 0.000000E+00 mb/sr, + 37.50 deg.: X-S = 0.000000E+00 mb/sr, + 37.60 deg.: X-S = 0.000000E+00 mb/sr, + 37.70 deg.: X-S = 0.000000E+00 mb/sr, + 37.80 deg.: X-S = 0.000000E+00 mb/sr, + 37.90 deg.: X-S = 0.000000E+00 mb/sr, + 38.00 deg.: X-S = 0.000000E+00 mb/sr, + 38.10 deg.: X-S = 0.000000E+00 mb/sr, + 38.20 deg.: X-S = 0.000000E+00 mb/sr, + 38.30 deg.: X-S = 0.000000E+00 mb/sr, + 38.40 deg.: X-S = 0.000000E+00 mb/sr, + 38.50 deg.: X-S = 0.000000E+00 mb/sr, + 38.60 deg.: X-S = 0.000000E+00 mb/sr, + 38.70 deg.: X-S = 0.000000E+00 mb/sr, + 38.80 deg.: X-S = 0.000000E+00 mb/sr, + 38.90 deg.: X-S = 0.000000E+00 mb/sr, + 39.00 deg.: X-S = 0.000000E+00 mb/sr, + 39.10 deg.: X-S = 0.000000E+00 mb/sr, + 39.20 deg.: X-S = 0.000000E+00 mb/sr, + 39.30 deg.: X-S = 0.000000E+00 mb/sr, + 39.40 deg.: X-S = 0.000000E+00 mb/sr, + 39.50 deg.: X-S = 0.000000E+00 mb/sr, + 39.60 deg.: X-S = 0.000000E+00 mb/sr, + 39.70 deg.: X-S = 0.000000E+00 mb/sr, + 39.80 deg.: X-S = 0.000000E+00 mb/sr, + 39.90 deg.: X-S = 0.000000E+00 mb/sr, + 40.00 deg.: X-S = 0.000000E+00 mb/sr, + Finished all xsecs @ 4.49308205 + + The following files have been created: + 3:local copy of User input. 6:standard output. + 12:transfer kernels. 13:total cross sections/state. + 16:tables of cross sections. 35:Astrophysics S-factors / Ecm. + 39:cross sections for each Ecm. 40:all cross sectns. for each Elab. + 46:bs wave functions & ANC ratios. 56:Fusion for each Jtotal. + 75:S-factors for lab energies. 201:Separate cross sections. + 202:Separate cross sections. 203:Separate cross sections. + + PARAMETERS : MAXQRN MLOC LMAX1 MCLIST MFNL MPWCOUP + ALLOWED : 4 12 140 5 361 0 + REQUIRED: 1 12 126 2 6 0 + + + ACCURACY ANALYSIS at 170.000 MeV : + + Elastic h*k = 0.159 so OK compared with 0.200 + + Real(S-el) > 0.01 first at J = 29.5 + Real(S-el) > 0.10 first at J = 30.5 + Real(S-el) > 0.50 first at J = 33.5 + Real(S-el) > 0.90 first at J = 39.5 + Real(S-el) > 0.99 first at J = 48.5 + R-turn = 23.29 fm at J = 120.5 + + Forward-angle excitation cut off below 2.971 deg by max JT = 120.5 + and below 1.694 deg by max R. + + + 1: Recommended RNL: non-local width > 0.99 cf: 5.04 fm: OK + Recommended CENTRE: centration ~ 0.04 cf: 0.00 fm + +Note: The following floating-point exceptions are signalling: IEEE_DENORMAL + Total CPU 0 time = 4.50 seconds From 849d076475476c9e721513dc7b7ac75f0ff18717 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Fri, 8 May 2026 15:08:24 -0400 Subject: [PATCH 02/13] first crack at transfer notebook --- bfrescox_pypkg/tox.ini | 1 + book/_toc.yml | 1 + book/notebooks/Transfer.ipynb | 883 ++++++++++++++++-- .../Transfer_template/B5-18neon_4+_tr.in | 34 + .../B5-18neon_4+_tr.template | 44 + .../Transfer_template/B5-example-tr.in | 9 +- .../Transfer_template/blackmon_etal.csv | 12 + book/notebooks/Transfer_template/frescox.in | 44 + 8 files changed, 936 insertions(+), 92 deletions(-) create mode 100644 book/notebooks/Transfer_template/B5-18neon_4+_tr.in create mode 100644 book/notebooks/Transfer_template/B5-18neon_4+_tr.template create mode 100644 book/notebooks/Transfer_template/blackmon_etal.csv create mode 100644 book/notebooks/Transfer_template/frescox.in diff --git a/bfrescox_pypkg/tox.ini b/bfrescox_pypkg/tox.ini index ede34e4..d0d74d4 100644 --- a/bfrescox_pypkg/tox.ini +++ b/bfrescox_pypkg/tox.ini @@ -76,6 +76,7 @@ deps = notebook jupyter-book<2.0.0 nbdime + tqdm commands = jupyter-book clean --all {env:BOOK_ROOT} jupyter-book build --all --warningiserror {env:BOOK_ROOT} diff --git a/book/_toc.yml b/book/_toc.yml index 621ce52..01f92e2 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -12,6 +12,7 @@ parts: - file: notebooks/Elastic_from_template_example.ipynb - file: notebooks/Inelastic_from_template_example.ipynb - file: notebooks/User_provided_template_example.ipynb + - file: notebooks/Transfer.ipynb - caption: Reference chapters: - file: bibliography diff --git a/book/notebooks/Transfer.ipynb b/book/notebooks/Transfer.ipynb index 4f40a95..5f4a93b 100644 --- a/book/notebooks/Transfer.ipynb +++ b/book/notebooks/Transfer.ipynb @@ -5,7 +5,7 @@ "id": "1a4a60d4-5bf5-4251-a6bf-f165d2df198c", "metadata": {}, "source": [ - "## $^{14}$N( $^{17}$F, $^{18}$Ne)$^{13}$C transfer reaction\n", + "# $^{14}$N( $^{17}$F, $^{18}$Ne)$^{13}$C transfer reaction\n", "\n", "We will use the [transfer input file from the fresco website](https://www.fresco.org.uk/examples/B5-example-tr.in) as a template.\n", "\n", @@ -45,14 +45,16 @@ "import pandas as pd\n", "import json\n", "\n", + "from tqdm import tqdm\n", + "\n", "with open(\"MatplotlibEsthetics.json\", \"r\") as fptr:\n", " esthetics = json.load(fptr)\n", "\n", "plt.style.use(esthetics[\"style\"])\n", - "FONTSIZE = esthetics[\"fontsize\"]\n", + "FONTSIZE = esthetics[\"fontsize\"]\n", "TICK_FONTSIZE = esthetics[\"tick_fontsize\"]\n", - "MARKERSIZE = esthetics[\"markersize\"]\n", - "LINEWIDTH = esthetics[\"linewidth\"]" + "MARKERSIZE = esthetics[\"markersize\"]\n", + "LINEWIDTH = esthetics[\"linewidth\"]" ] }, { @@ -73,8 +75,8 @@ "outputs": [], "source": [ "EXAMPLE_DIR = Path(\"./Transfer_template/\")\n", - "TEMPLATE_FILE_PATH = Path(\"./Transfer_template/B5-example-tr.template\") \n", - "INPUT_FILE_PATH = Path(\"./Transfer_template/B5-example-tr.in\") " + "TEMPLATE_FILE_PATH = Path(\"./Transfer_template/B5-example-tr.template\")\n", + "INPUT_FILE_PATH = Path(\"./Transfer_template/B5-example-tr.in\")" ] }, { @@ -111,15 +113,14 @@ "\t jtmin=0.0 jtmax=120 absend=-1.0\n", "\t thmin=0.00 thmax=40.00 thinc=0.10\n", "\t iter=1 nnu=36\n", - "\t chans=1 xstabl=1 \n", + "\t chans=3 xstabl=1 \n", "\t elab=170.0 /\n", "\n", " &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 /\n", " &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 /\n", "\n", " &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 /\n", - " &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 /\n", - " &STATES jp=4. bandp=1 ep=3.376 cpot=2 copyt /\n", + " &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=-1 et=0.0000 /\n", "\n", "\n", " &partition /\n", @@ -148,9 +149,10 @@ "\n", " &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 /\n", " &CFP in=1 ib=1 ia=1 kn=1 a=1.00 /\n", - " &CFP in=2 ib=1 ia=1 kn=2 a=1.00 /\n", + " &CFP in=1 ib=2 ia=1 kn=1 a=1.00 /\n", " &CFP /\n", - " &coupling /\n" + " &coupling /\n", + "\n" ] } ], @@ -165,23 +167,23 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "id": "5b269a97-36fc-42a7-8290-38032694e32a", "metadata": {}, "outputs": [], "source": [ "# Create the frescox input file by filling in the user-defined template with parameters\n", "cfg = bfrescox.Configuration.from_template(\n", - " INPUT_FILE_PATH,\n", - " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", - " {},\n", - " overwrite=True,\n", - " )" + " INPUT_FILE_PATH,\n", + " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", + " {},\n", + " overwrite=True,\n", + ")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "88cece8b-d36b-45fc-943a-f8f2b4f1ba98", "metadata": {}, "outputs": [ @@ -189,42 +191,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 4.07 ms, sys: 901 μs, total: 4.97 ms\n", - "Wall time: 4.02 ms\n" - ] - }, - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '/home/beyerk/Projects/Bfrescox/book/notebooks/Transfer_template/frescox.in'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mbfrescox.run_simulation(cfg, EXAMPLE_DIR.joinpath(\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfrescox.out\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m), cwd=EXAMPLE_DIR, overwrite=True)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/IPython/core/interactiveshell.py:2565\u001b[39m, in \u001b[36mInteractiveShell.run_cell_magic\u001b[39m\u001b[34m(self, magic_name, line, cell)\u001b[39m\n\u001b[32m 2563\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m.builtin_trap:\n\u001b[32m 2564\u001b[39m args = (magic_arg_s, cell)\n\u001b[32m-> \u001b[39m\u001b[32m2565\u001b[39m result = \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2567\u001b[39m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[32m 2568\u001b[39m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[32m 2569\u001b[39m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[32m 2570\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/IPython/core/magics/execution.py:1452\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m 1450\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m interrupt_occured:\n\u001b[32m 1451\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exit_on_interrupt \u001b[38;5;129;01mand\u001b[39;00m captured_exception:\n\u001b[32m-> \u001b[39m\u001b[32m1452\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m captured_exception\n\u001b[32m 1453\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m 1454\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/IPython/core/magics/execution.py:1402\u001b[39m, in \u001b[36mExecutionMagics.time\u001b[39m\u001b[34m(self, line, cell, local_ns)\u001b[39m\n\u001b[32m 1400\u001b[39m st = clock2()\n\u001b[32m 1401\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1402\u001b[39m out = \u001b[38;5;28;43meval\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mglob\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_ns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1403\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 1404\u001b[39m captured_exception = e\n", - "\u001b[36mFile \u001b[39m\u001b[32m:1\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/bfrescox/run_simulation.py:80\u001b[39m, in \u001b[36mrun_simulation\u001b[39m\u001b[34m(configuration, filename, overwrite, external, cwd)\u001b[39m\n\u001b[32m 75\u001b[39m cwd = Path.cwd()\n\u001b[32m 77\u001b[39m \u001b[38;5;66;03m# This function assumes that all error checking of arguments will be handled\u001b[39;00m\n\u001b[32m 78\u001b[39m \u001b[38;5;66;03m# by this internal function. This includes the case of incorrectly\u001b[39;00m\n\u001b[32m 79\u001b[39m \u001b[38;5;66;03m# providing an MPI-based external installation.\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m80\u001b[39m \u001b[43m_run_frescox_simulation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 81\u001b[39m \u001b[43m \u001b[49m\u001b[43mfrescox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 82\u001b[39m \u001b[43m \u001b[49m\u001b[43mconfiguration\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 83\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 84\u001b[39m \u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 85\u001b[39m \u001b[43m \u001b[49m\u001b[43mNO_MPI_PLEASE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 86\u001b[39m \u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 87\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/bfrescox/_run_frescox_simulation.py:121\u001b[39m, in \u001b[36m_run_frescox_simulation\u001b[39m\u001b[34m(frescox, config, filename, overwrite, mpi_setup, cwd)\u001b[39m\n\u001b[32m 119\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid working directory (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcwd\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 120\u001b[39m fname_in = Path(cwd).resolve().joinpath(\u001b[33m\"\u001b[39m\u001b[33mfrescox.in\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m121\u001b[39m \u001b[43mconfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrite_to_nml\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname_in\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 123\u001b[39m \u001b[38;5;66;03m# ----- CHECK STATE OF FILES & WRITE INPUT\u001b[39;00m\n\u001b[32m 124\u001b[39m fname_out = Path(filename).resolve()\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Projects/Bfrescox/bfrescox_pypkg/.tox/book/lib/python3.14/site-packages/bfrescox/Configuration.py:153\u001b[39m, in \u001b[36mConfiguration.write_to_nml\u001b[39m\u001b[34m(self, filename, overwrite)\u001b[39m\n\u001b[32m 149\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInput file (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfname_in\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m) already exists\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 151\u001b[39m \u001b[38;5;66;03m# ----- WRITE CONFIGURATION TO FILE\u001b[39;00m\n\u001b[32m 152\u001b[39m \u001b[38;5;66;03m# Trivial for NML\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m153\u001b[39m \u001b[43mshutil\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__nml\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname_in\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/bfrescox/lib/python3.14/shutil.py:489\u001b[39m, in \u001b[36mcopy\u001b[39m\u001b[34m(src, dst, follow_symlinks)\u001b[39m\n\u001b[32m 487\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m os.path.isdir(dst):\n\u001b[32m 488\u001b[39m dst = os.path.join(dst, os.path.basename(src))\n\u001b[32m--> \u001b[39m\u001b[32m489\u001b[39m \u001b[43mcopyfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_symlinks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_symlinks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 490\u001b[39m copymode(src, dst, follow_symlinks=follow_symlinks)\n\u001b[32m 491\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m dst\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/bfrescox/lib/python3.14/shutil.py:313\u001b[39m, in \u001b[36mcopyfile\u001b[39m\u001b[34m(src, dst, follow_symlinks)\u001b[39m\n\u001b[32m 311\u001b[39m os.symlink(os.readlink(src), dst)\n\u001b[32m 312\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m313\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrb\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m fsrc:\n\u001b[32m 314\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 315\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(dst, \u001b[33m'\u001b[39m\u001b[33mwb\u001b[39m\u001b[33m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m fdst:\n\u001b[32m 316\u001b[39m \u001b[38;5;66;03m# macOS\u001b[39;00m\n", - "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/home/beyerk/Projects/Bfrescox/book/notebooks/Transfer_template/frescox.in'" + "CPU times: user 1.26 ms, sys: 179 μs, total: 1.44 ms\n", + "Wall time: 6.36 ms\n" ] } ], "source": [ "%%time\n", - "bfrescox.run_simulation(cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True)" + "bfrescox.run_simulation(\n", + " cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "7204a4d5-2022-4867-b0b9-9801221e54d1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['channel_1', 'channel_2'])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "results = bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", "results.keys()" @@ -232,18 +227,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "83e95f1f-6872-419d-9cf0-d1b23ab58656", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$d\\\\sigma/d\\\\Omega$ [mb/Sr]')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAG4CAYAAABPb0OmAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAZVhJREFUeJzt3Xl4VOXd//H3PdlIAkmAEJKQBZIQwr6J1h1xrVAVrRtd8FGsLdpFH9v+Kmq1ipYqVvvUtrZiFYsVSqGigpUqWhQVFJCdEMIOIQmQAAlkm/v3xyRDImu2ObN8XtfFlcyZM2e+d06WD+fc53uMtdYiIiIiIgC4nC5ARERExJ8oHImIiIg0onAkIiIi0ojCkYiIiEgjCkciIiIijSgciYiIiDSicCQiIiLSiMKRiIiISCMKRyIiIiKNhDtdgJMOHDhAbW2t02X4VLdu3SgpKXG6DJ/TuEOLxh1aNO7QER4eTufOndv/fdr9HfxYbW0tNTU1TpfhM8YYwDPuULprjMatcYcCjVvjlraj02oiIiIijSgciYiIiDSicCQiIiLSiMKRiIiISCMKRyIiIiKNKByJiIiINKJwJCIiItKIwpGIiIhIIwpHIiIiIo0oHImIiIg04ne3D3G73cyaNYvFixdTVlZGly5duPjii7nhhhu87dKttcyaNYv33nuPiooK8vLymDBhAikpKQ5XLyIiIoHO744c/etf/2LhwoXccccd/Pa3v+Vb3/oW8+bNY8GCBd513njjDRYsWMCdd97JE088QVRUFJMnT6a6utrBykVERCQY+N2Ro/z8fM466yyGDRsGQFJSEh999BEFBQWA56jR/Pnzuf766xkxYgQA99xzD3feeSfLli3j/PPPP26bNTU1TW4wa4whOjoaY4z3aFQoaBhrKI0ZPON1H6kMyXE3/hgqNG6NOxSE+rjbm9+Fo9zcXN577z12795NamoqW7duZePGjXz3u98FoLi4mLKyMgYNGuR9TUxMDDk5OeTn558wHM2dO5fZs2d7H/fq1YspU6aQmJjY/gPyQ8nJyU6X4FOH5s5g17Rn6XjNLSTc/mNMuN9927erUNvfDTTu0KJxS1vyu78S1113HUeOHOHee+/F5XLhdru55ZZbuPDCCwEoKysDID4+vsnr4uPjvc991dixYxkzZoz3cUPyLC0tbXJEKdgZY0hOTqaoqAhrrdPl+EztgjlgLYff+DsVG9fiuutnmE7xp39hgAvV/a1xa9yhIFTHHRER4ZMDG34Xjj755BM++ugjfvSjH5Gens7WrVt5+eWX6dy5MyNHjmzRNiMiIoiIiDhuubU2pL6pGoTSuG3pXti1DVxhEBGJ3bCKusfuxTXxAUxmttPl+UQo7e/GNO7QonGHBl+N1e8mZP/tb3/j2muv5fzzzycjI4OLLrqI0aNH869//QuAhIQEAMrLy5u8rry83PucSAO7ahkAUf0GEzbpaUhKhf0luKf8HPenHzhbnIiI+CW/C0dVVVW4XE3Lcrlc3rSYlJREQkICq1ev9j5fWVlJQUEBubm5Pq1V/F9DOOpw9gWY1Axck56GgWdBTTV22jO4Z07D1tU5XKWIiPgTvzutNnz4cObMmUNiYiJpaWls3bqVt956i0suuQTwnGe9+uqrmTNnDikpKSQlJfH666/TuXNn79VrIgD26BHY6AnR0WdfRAVgYjriumcS9o2/Y+fPwv7nDezOLbi+9zNMpzhnCxYREb/gd+Ho9ttvZ+bMmbz44ouUl5fTpUsXLr/8cr75zW9617n22mupqqrihRdeoLKykry8PB544AEiIyMdrFz8zvovobYWuqUQnpYJRUUAGFcYZuy3sRlZuP/6LGxYhXvyfZ55SBlZztYsIiKO87twFB0dzW233cZtt9120nWMMdx8883cfPPNvitMAo79cikAZvCIE/bGMMPPw5XcA/fzk6GkCPeUn2G++0Nc51zs61JFRMSP+N2cI5G2YN1u7OrPATCDTn661fTIxDXpGRgwDKqrsS9Oxf2PlzQPSUQkhCkcSXDathkOlkGHaExu/1OuamI74vrhQ5ive07d2nf/hfu5R7CHD/qgUBER8TcKRxKUGq5So/9QTPjxPa6+yrjCcF3/XVx3/Qwio2D9l7gfvw+7Y0s7VyoiIv5G4UiCkl1VP9/oFKfUTsScdQGuXzwF3ZJhXzHuX/8U99L/tkeJIiLipxSOJOjYA/tgeyEYgxkwvNmvN2k9cU2aCv2GeuYh/eVp3LP/inVrHpKISChQOJKgY1fXn1LrlYuJS2jRNkxsJ1w/fhhz1Q2ebf57Lu7nHsVWHGqjKkVExF8pHEnQsatOf5XamTCuMFw3jMd8r34e0rqVnnlIOzUPSUQkmCkcSVCx1VWwfiXg6W/UFlwjLsD1i99AYnco3Yv7yZ/hXvZRm2xbRET8j8KRBJcNq6C6GrokQo+ebbZZk9arfh7SEKiuwv75N7j/+YrmIYmIBCGFIwkqDZfwm0En7ordGqZjHK4f/RJz5VjPe73zT9y/+5XmIYmIBBmFIwka1tpG843Obpf3MGFhuL75P5g774fISFi7Avfk/8Xu3Nou7yciIr6ncCTBY+dWOFDqmTydN7Bd38p19kW4fv4b6JrkuS/br3+G/VzzkEREgoHCkQSNhhvN0ncwJiKy3d/PZGThevAZ6DsYqo7i/vNTupJNRCQIKBxJ0Gg838hXTMc4XD9+BPoMBGuxa1f47L1FRKR9KBxJULAHD8DWTQCYQWf59L1NWBhmoKcTt928wafvLSIibU/hSIKCXb0crIXMHExCV5+/v8nK83xSuBFrrc/fX0RE2o7CkQSFYzea9e1RI6/MbAgLg/IDULrXmRpERKRNKBxJwLM1NbB2JeDb+UaNmcgoyMj21FO40ZEaRESkbSgcSeDbtAaqjkB8Z29AcYLJ6uP5RPOOREQCmsKRBLzGN5o1Lge/pbM984505EhEJLApHElAs9Z6+xs5Nt+onndS9o5CbNVRR2sREZGWUziSwLZnh2cCdHgE5A12tpYuiZDQBdxu2FrgbC0iItJiCkcS0BoaP5I3ENMh2tFajDGQ1XBqTfOOREQClcKRBDQnumKfimmYd6RJ2SIiAUvhSAKWrTgEBZ4Q4m/hSM0gRUQCl8KRBCy7+guwbuiRiema5HQ5HhnZEB4Oh8qhpMjpakREpAUUjiRw+dkpNQATEdGoGaROrYmIBCKFIwlItrYWu3Y54F/hCBpd0q95RyIiAUnhSALT5g1QWQEd4yAr1+lqmjA5mpQtIhLIFI4kIHlvNDtwOMYV5nA1X9Fw5GjnNuzRI87WIiIizaZwJAHJ3y7hb8x07uppCGndsHWT0+WIiEgzhTtdwFfdfffdlJSUHLf8iiuuYMKECVRXVzN9+nSWLFlCTU0NgwcPZsKECSQkJPi+WHGE3bsbinZBWBj0G+p0OSdksvKw+z/Cbt6AyRvkdDkiItIMfheOnnzySdxut/fx9u3befzxxzn33HMBeOWVV1i+fDn33XcfMTExTJs2jalTp/LYY485VbL4mF1d3xU7dwAmJtbZYk4muw98/pFuQisiEoD8LhzFxcU1efyvf/2L7t27069fPyorK3n//ff58Y9/zIABAwCYOHEi9957L/n5+eTmnnhibk1NDTU1Nd7Hxhiio6Mxxnhu+RAiGsYa8GNe9TkArkEjzmgsTozbld2XOoD6y/md+JoHzf5uJo1b4w4FoT7u9uZ34aix2tpaFi9ezOjRozHGUFhYSF1dHQMHDvSu06NHDxITE08ZjubOncvs2bO9j3v16sWUKVNITExs9zH4o+TkZKdLaDF3xWF25a8BIOmy0USkpJzxa305bpuYyM6ISDh8iG62hojUTJ+991cF8v5uDY07tGjc0pb8OhwtXbqUiooKRo4cCUBZWRnh4eHExjY9lRIfH09ZWdlJtzN27FjGjBnjfdyQPEtLS5scUQp2xhiSk5MpKioK2FtbuD//COrqILkHpSYc9uw57WscG3dmNhSsp/iTxbjOj/Td+9YLhv3dEhq3xh0KQnXcERERPjmw4dfhaNGiRQwZMoQuXbq0ajsRERFEREQct9xaG1LfVA0Cedz2y/pL+AeNaPYYfD1uk5WHLViP3bwBe94on73vVwXy/m4NjTu0aNyhwVdj9dtL+UtKSli1ahWXXnqpd1lCQgK1tbVUVFQ0Wbe8vFxXq4UA667z3E8NMIPOdria0zPZfQDdRkREJND4bThatGgR8fHxDBs2zLssKyuLsLAwVq9e7V22e/duSktLTzrfSIJIYT4cPggxsZCd53Q1p9fQDHLXNuyRSmdrERGRM+aXp9XcbjcffPABF198MWFhx7ofx8TEMGrUKKZPn07Hjh2JiYnhpZdeIjc3V+EoBHgbP/Yfhgn3y2/dJkxCF+iaBPuKYUs+9BvidEkiInIG/PIvzOrVqyktLeWSSy457rnx48djjGHq1KnU1tZ6m0BK8GsIR/hhV+yTMdl52H3F2MINGIUjEZGA4JfhaPDgwcyaNeuEz0VGRjJhwgQFohBj9xXDrm1gXJiBw50u58xl5cHS/2I3qxmkiEig8Ns5RyKNeY8a5eRhYjs5W0wzNEzKpnAjtlHndxER8V8KRxIQ/PlGs6eU1gsiI6HyMOzd7XQ1IiJyBhSOxO/Zo0dgwyog8MKRCQ+HzBwA7Ob1DlcjIiJnQuFI/N+GL6G2FhK7Q0q609U0m8nu6/lEN6EVEQkICkfi92z9jWbN4LMD8iaL3maQm9UMUkQkECgciV+zbnej+UZnOVxNCzU0g9yzA1tZcep1RUTEcQpH4t+2b4byAxAVDb0HOF1Ni5i4BOiWDNZ6mkGKiIhfUzgSv+a9hL//EMwJbh4cKExWw6k1TcoWEfF3Ckfi17zzjQLgRrOnVH8vODWDFBHxfwpH4rds2T7YVgDGYAYOO/0L/JhpuFHulnw1gxQR8XMKR+K3Go4a0bM3Jq6zs8W0Vo+eENUBjlTAnp1OVyMiIqegcCR+K2C7Yp+ACQuDnr0BsIW6pF9ExJ8pHIlfstVVsP5LIDjCERyblI36HYmI+DWFI/FPG9dAdRV0ToT0Xk5X0yaMd1K2wpGIiD9TOBK/ZFctBTyNHwOxK/YJNTSDLNqJrTjkbC0iInJSCkfid6y1QTXfqIHpFAdJqZ4HhWoGKSLirxSOxP/s2gr7SyEyEvIGOV1Nm/LeZ02TskVE/JbCkfgd+2V9V+y+QzCRUc4W09ayNO9IRMTfKRyJ3wn4G82egrcZZGE+1l3nbDEiInJCCkfiV+zBMu/NWc3A4Jlv5NUjw3MT3aojsHu709WIiMgJKByJX7FrvvDcvT4jC9O5q9PltDnjCoOsXED3WRMR8VcKR+JXjp1SC/AbzZ6CmkGKiPg3hSPxG7a2BtauAILrEv6v8jaDLNSRIxERf6RwJP4jfy0cPQJxCZCZ7XQ17afhyNHeXdhDB52tRUREjqNwJH7De0pt4FkYV/B+a5rYTpDcw/NAR49ERPxO8P4FkoASrF2xT+bYqTXNOxIR8TcKR+IfinZBSRGEh0O/IU5X0/7UDFJExG8pHIlfaLjRLH0GYjpEO1uMD3ibQW7dhK1TM0gREX+icCR+IZROqQGQkg7RMVB1FHZtc7oaERFpROFIHGcrDkHBeiB0wpFxuaBXQzNInVoTEfEnCkfiOLtmObjd0CMTk9jd6XJ8xmQ13GdN4UhExJ+EO13Aiezfv5+//e1vrFy5kqqqKpKTk5k4cSLZ2Z7eN9ZaZs2axXvvvUdFRQV5eXlMmDCBlJQUhyuXFgniG82eisnOw6IjRyIi/sbvwtHhw4d56KGH6N+/Pw888ABxcXHs2bOH2NhY7zpvvPEGCxYs4O677yYpKYmZM2cyefJknnnmGSIjIx2sXprL1tV57qdG6JxS86q/xxolRdiDZZi4BEfLERERD78LR2+88QZdu3Zl4sSJ3mVJSUnez621zJ8/n+uvv54RIzx/TO+55x7uvPNOli1bxvnnn3/cNmtqaqipqfE+NsYQHR2NMQZjTDuOxr80jNWvxly4ASorILYTJjuvXWrzy3HjaQbpTk2H3TtgSz5myDltu30/HXd707g17lAQ6uNub34Xjj7//HMGDx7MM888w7p16+jSpQtXXHEFl112GQDFxcWUlZUxaNAg72tiYmLIyckhPz//hOFo7ty5zJ492/u4V69eTJkyhcTExPYfkB9KTk52ugSv8kVvchCIOes8uvZIa9f38qdxN9g/YBgVu3cQu3cnCSnXtct7+OO4fUHjDi0at7QlvwtHxcXFLFy4kNGjRzN27Fg2b97MX//6V8LDwxk5ciRlZWUAxMfHN3ldfHy897mvGjt2LGPGjPE+bkiepaWlTY4oBTtjDMnJyRQVFWGtdbocAOq+9JxSO9qjJ3v27GmX9/DHcTdwp2QAcOjLZRxp4/H787jbk8atcYeCUB13RESETw5s+F04crvdZGdnM27cOMBzlGf79u0sXLiQkSNHtmibERERREREHLfcWhtS31QN/GXc1lrslnzPg5657V6Tv4y7iUbNIN01NZjwtv+R9Mtx+4DGHVo07tDgq7H63aX8nTt3Ji2t6emVtLQ0SktLAUhISACgvLy8yTrl5eXe5yRAFO+BikMQHgHpPZ2uxhnde0BMLFRXw66tTlcjIiL4YTjq06cPu3fvbrJs9+7ddOvWDfBMzk5ISGD16tXe5ysrKykoKCA3N9entUrr2C31d6TPzMaEH39kLxQYlwuy+gC6pF9ExF/4XTgaPXo0mzZtYs6cORQVFfHRRx/x3nvvceWVVwKe86xXX301c+bM4fPPP2f79u38/ve/p3Pnzt6r1yRAFHrCkekV2qHW2wxy80ZnCxEREcAP5xzl5ORw//3389prr/HPf/6TpKQkxo8fz4UXXuhd59prr6WqqooXXniByspK8vLyeOCBB9TjKMDYwvr5RvVHTkKVye7jaQapTtkiIn7B78IRwPDhwxk+fPhJnzfGcPPNN3PzzTf7sCppS7a6CnZuAXTkiF59wBgo3YstP4CJ7+x0RSIiIc3vTqtJiNheCHV10Ckeuiadfv0gZqJjINVzST+adyQi4jiFI3GE9xL+rD4h1+H1REz9Jf06tSYi4jyFI3FGfTgK+VNqDeonZVtNyhYRcZzCkTjCNlypFuKTsRuY7Pqvw7YCbG3odG0XEfFHCkfic/bgAdhX7JmE3LO30+X4h+49ILYT1FTDji1OVyMiEtIUjsT3Gi7hT0n3TEYWz7wrNYMUEfELCkfic1bNH0+oYVJ2Q3NMERFxhsKR+FzjK9XkGKMjRyIifkHhSHzKuutg6yYATJaOHDXRKxeMC/aXYMv2OV2NiEjIUjgS39qzE44egagOxxofCgCmQzT0yPQ80CX9IiKOUTgSn2qYb0TP3hhXmLPF+KGGS/rt5vUOVyIiEroUjsS31Pzx1LL7Ao1CpIiI+JzCkfiUrlQ7tSbNIGvUDFJExAkKR+Iz9ugR2L3D80CTsU+sWwp0jIPaWti+2elqRERCksKR+M62ArBu6JKISejqdDV+yRgD3pvQ6tSaiIgTFI7EZ7x/7HVK7ZSO9TvSpGwREScoHInP2Prbhuhms6dm6idl63J+ERFnKByJT1hrYUvDZGyFo1PqmQMuF5Ttw+4vcboaEZGQo3AkvrG/FMoPeP7oZ2Q7XY1fM1EdIK0XAFZHj0REfE7hSHyj/qgRab0wUVHO1hIAvJf0F+o+ayIivhbenJUPHz7cqjeLiYnB5VIeC0UNN5vV/dTOUFYeLJqvm9CKiDigWeHojjvuaNWbPfTQQwwYMKBV25DApCvVmsdk52EBthdia6oxEZFOlyQiEjKaFY4ARowYQWZmZrNeU1VVxZtvvtnct5IgYWtrYZunoaGuVDtDid0hLgEOlnn6Q+X0c7oiEZGQ0exw9LWvfY0LLrigWa85dOiQwlEo27UNaqohJhaSUp2uJiAYYzyn1lZ+it28EaNwJCLiM82aADR+/HiysrKa/SYdOnRg/PjxpKbqD2Mo8p5S65mL0ZyzM9YwKdtqUraIiE8168jR1Vdf3aI3iYiIaPFrJQg09DfSKbVmMVn18442b8Ba6zmaJCIi7a7F/42vqqri5z//Oe+++25b1iNBSFeqtVDPHAgL8/SH2lfsdDUiIiGjxeEoKiqK4uJi/W9WTslWHIaiXZ4HPRWOmsNERkG65zS2LukXEfGdVk0AGTJkCF9++WVb1SLBqP6oEUkpmE5xztYSgEx2nueTQnXKFhHxlVaFoxtuuIE9e/bwf//3f2zYsIH9+/dz+PDh4/5J6PKeUlN/o5apn6elI0ciIr7T7Ev5G/vf//1fAHbu3MlHH3100vVmzpzZmreRAHas+aMmY7eEtxnkzi3Y6irPqTYREWlXrQpHN9xwQ5vPOZo1axazZ89usiw1NZVnn30WgOrqaqZPn86SJUuoqalh8ODBTJgwgYSEhDatQ1rPWgtbGyZjKxy1SJduEN8FyvfD1gLI7e90RSIiQa9V4eimm25qqzqaSE9P56GHHvI+bnw/tldeeYXly5dz3333ERMTw7Rp05g6dSqPPfZYu9QirVCyBw4fgvAISO/pdDUByRgD2X1g+SfYzRswCkciIu2uVeHoq3bt2sUnn3xCWVkZqampjBw5kpiYmGZvx+VynfBIUGVlJe+//z4//vGPvfdomzhxIvfeey/5+fnk5p54XktNTQ01NTXex8YYoqOjMcaE1NV2DWP11ZhtYf1k7IwsXA7eG8zX425rruy+uJd/AoUbmjWGQB93S2ncGncoCPVxt7dmh6N33nmHBQsW8NhjjxEXd+zqo88//5zf/va31NbWepctWLCAyZMnN1nvTBQVFXHXXXcRERFBbm4u48aNIzExkcLCQurq6hg4cKB33R49epCYmHjKcDR37twmp+p69erFlClTSExMbFZdwSI5Odkn73Ng704OAx0HDqNzSopP3vNUfDXutlZ19vkU/+MlzNZNJCcnN/uXQ6COu7U07tCicUtbanY4+vzzz+nevXuTwFNXV8cLL7yAy+XiBz/4AdnZ2SxfvpzXX3+dOXPmcNttt53x9nv37s3EiRNJTU3lwIEDzJ49m4cffpipU6dSVlZGeHg4sbGxTV4THx9PWVnZSbc5duxYxowZ433c8MeltLS0yRGlYGeMITk5maKiIs98oHZWu2YFAJXd0zi6Z0+7v9/J+Hrcbc12TICwcNxl+9mzeiWm25n9Mgz0cbeUxq1xh4JQHXdERIRPDmw0Oxzt3LmTSy+9tMmytWvXcvDgQcaOHcvIkSMBz7yhbdu2sWLFimaFo6FDh3o/z8zM9IalTz75hMjIlp2aiYiIICIi4rjl1tqQ+qZq4Itx25pq2LHF86Bnb7/4Ogfs/g6PgIws2JKPu2A9rsTuzXp5wI67lTTu0KJxhwZfjbXZfY4OHTpE165dmyxbvXo1AGeffXaT5X369KG0tLQV5UFsbCypqakUFRWRkJBAbW0tFRUVTdYpLy/X1Wr+Znsh1NVCp3ho5h9zOZ63GaT6HYmItLtmh6OEhITjTmFt2LCBqKgoMjMzmywPDw8nPLx1c76PHj3qDUZZWVmEhYV5wxjA7t27KS0tPel8I3GGrb/ZLFl9Qm7CYHtoCEe2UOFIRKS9NTu5ZGVl8eGHH/L1r3+d6OhoduzYQUFBAWeddRZhYWFN1t21a9dxR5lOZ/r06Zx11lkkJiZy4MABZs2ahcvl4oILLiAmJoZRo0Yxffp0OnbsSExMDC+99BK5ubkKR/6mUJ2x21RW/ZGjnVuxR49gOkQ7W4+ISBBrdji68cYb+cUvfsGPfvQj0tPTKSwsBDyTnr9q2bJl9O/fvL4s+/fv57nnnuPQoUPExcWRl5fX5Iq38ePHY4xh6tSp1NbWeptAin9p6Iyt5o9tw3RJhC6JsL8Utm6CvEFOlyQiErSaHY4yMjJ4+OGHmTNnDsXFxfTu3ZtvfOMbZGVlNVlv7dq1REZGcu655zZr+z/5yU9O+XxkZCQTJkxQIPJj9uAB2FcMxkDP3k6XEzRMdl/s/sWeZpAKRyIi7aZFE4L69OnDL37xi1Ou079/f6ZOndqioiTANTR/TE7DRDe/CaicRHYeLFuMLVjvdCUiIkGt2ROy582bx65du9qjFgkSdovup9YeTE5fzyeFG7But7PFiIgEsWYfOZo3bx4zZsygW7duDBs2jGHDhtG/f/8T9hGS0NQQjsjSZOw21aMnREZBZQUU7YTUDKcrEhEJSs0OR3/5y1/YtGkTK1euZPny5fz73/8mMjKS/v37M3z4cIYOHRqyt+UQsO46aDhy1EtHjtqSCQ+HXrmwcTW2YD1G4UhEpF00OxwZY7yXzt90002UlZWxfPlyVq5cyYwZM3jxxRdJS0tj2LBhDB8+nNzcXFyuZp+9k0C1ZxccPQJRHXRkox2Y7L7Yjas9zSAvutLpckREglLrOjTiaQo5atQoRo0aRV1dHevXr2fFihV88cUXzJs3j5iYGAYPHszo0aPp3VtXLgU7b/PHzBzMV/peSeuZnDwsYNUpW0Sk3bQ6HDUWFhbGgAEDGDBgAN/5zncoLi5m+fLlrFixgvXr1yschYKG/kZq/tg+Gia5792FPVSO6RTvbD0iIkGoTcPRVyUlJXHVVVdx1VVXtefbiB/RlWrty8R2gpR02LPDc2ptyDlOlyQiEnTaJBxt376dFStWUFJSAkC3bt0YOnQoGRmacxJK7NEjsGu754GuVGs3Jqcvds8OTzNIhSMRkTbXqnBUU1PDn//8Z/773/8CeG8waq3ltdde48ILL+T73/9+q28+KwFiWwFYN3ROxCQ075560gzZebD4XTWDFBFpJ61KLTNmzOC///0vV1xxBV//+tfp3r07xhiKioqYP38+CxcupGPHjtx2221tVK74M1uo/ka+YLL7YgG2bsLW1mDC1WNMRKQtteoa+8WLF3PhhRdyxx13kJqaSlhYGC6Xi9TUVCZMmMAFF1zA4sWL26pW8XMNV6qpv1E7654KHeOgtga2FzpdjYhI0GlVOKqtrSU39+RHCfr06UNdXV1r3kIChLXWe081XanWvowxnlNroFNrIiLtoFXhaPDgwaxcufKkz69cuZJBg3T38JBwoBTK94PLBZk5TlcT9Ey25z5r6nckItL2mhWODh8+3OTfLbfcQklJCU8//TSrV6+mpKSEkpISVq1axVNPPUVJSQm33HJLe9Uu/qThfmppPTFRUc7WEgJM/ZEjNq/3HLUTEZE206wJ2XfccccJl2/fvp1ly5ad8Ln77ruP119/vfmVSUCxav7oWz1zICwcyg9A6V7olux0RSIiQaNZ4eiGG27wXq4v0tixK9U0GdsXTGQUZGTBlnxPvyOFIxGRNtOscHTTTTe1Vx0SwGxtLWwvAHSlmi+Z7L6ejuSbN8DXRjpdjohI0GjVhGwRAHZtg+pqiI71XGYuPmFy6idl64o1EZE21erW1Rs2bOD999+nuLiYioqK4yaHGmN46qmnWvs24sca+hvRKxfjUt72mez6o3S7tmGPVGKiY5ytR0QkSLQqHL311lu8+uqrREZGkpqaSseOHduqLgkkDf2N1Bnbp0xCV+iaBPuKPVcL9hvidEkiIkGhVeFo3rx55OXl8fOf/5yYGP2vNVQd64ytcORrJqcvdl8xtmA9RuFIRKRNtOocSFVVFRdccIGCUQizFYehaJfngSZj+56aQYqItLlWhaP+/fuzffv2tqpFAtHWTZ6P3ZIxneKcrSUEeZtBFm7AunWrHhGRttCqcHT77bezZs0a5s2bx+HDh9uqJgkgx5o/6qiRI9IyISoajh6B3fqPiohIW2jVnKPExEQuu+wyXn31VWbMmEFkZCSuE1yt9Morr7TmbcSP2S1q/ugk4wqDrFxY/yW2YAMmrZfTJYmIBLxWhaOZM2cyZ84cunTpQnZ2tuYehRhrLTRMxtaVao4x2X2x67+Ezeth5NedLkdEJOC1KhwtXLiQYcOG8dOf/vSER4wkyJXsgcOHIDwcdMTCMSanLxZNyhYRaSutSjS1tbUMGzZMwShEee+nlpGNiYhwtphQ1isXjIGSImz5AaerEREJeK1KNcOGDWP9et26IGTVzzdSfyNnmZhYSM3wPNisn0cRkdZqVTi68cYb2bVrFy+++CKFhYUcPHiQw4cPH/dPglPDlWooHDnOe581nVoTEWm1Vs05+slPfgLA1q1bWbhw4UnXmzlzZovf41//+hevvfYaV199NbfddhsA1dXVTJ8+nSVLllBTU8PgwYOZMGECCQkJLX4faR5bUw07tgBgdKWa87L7wofvKByJiLSBVoWjG264AWNMW9VynIKCAhYuXEhmZmaT5a+88grLly/nvvvuIyYmhmnTpjF16lQee+yxdqtFvmJ7IdTVQqd4SOzudDUhr2FSNtsKsDXVmIhIp0sSEQlYrQpHN910U1vVcZyjR4/yf//3f9x1113MmTPHu7yyspL333+fH//4xwwYMACAiRMncu+995Kfn09urk7x+ELD/dTolduuAVnOUGJ3iEuAg2WwrQBy+jldkYhIwGpVOGpPL774IkOHDmXQoEFNwlFhYSF1dXUMHDjQu6xHjx4kJiaeNBzV1NRQU1PjfWyMITo6GmNMSP1hbxhrm4x5i+e2Ia6sPn7/NWzTcfspYww2uy92xSeweSOmd/+QGPeJaNwadygI9XG3t2aFo/vvv59x48YxbNiwZr1JZWUlDz/8MN///vfJyck57foff/wxW7Zs4cknnzzuubKyMsLDw4mNjW2yPD4+nrKyshNub+7cucyePdv7uFevXkyZMoXExMRmjSNYJCcnt3obu7dtpg7oeta5dEhJaX1RPtAW4/ZnB4edQ/mKT4jaWUhio30S7OM+GY07tGjc0paaFY527NhBZWVls9+krq6OHTt2cPTo0dOuW1payssvv8yDDz5IZGTbzJsYO3YsY8aM8T5uSJ6lpaVNjigFO2MMycnJFBUVebpbt5A9WEbd3l1gDPvjumL27GnDKtteW43b39mkHgAcWbuS3bt343K5QmLcXxUq+/urNG6NOxRERET45MBGs0+rvfLKK7z++uvNek1zdlxhYSHl5eX8/Oc/9y5zu92sX7+ed955h0mTJlFbW0tFRUWTo0fl5eUnvVotIiKCiBM0KbTWhtQ3VYPWjtt7CX9yGkTHBMzXMNj3t03PgvAIOFSOLd6N7e4JS8E+7pPRuEOLxh0afDXWZoWjiy++uFVv1rlz59OuM3DgQJ5++ukmy/74xz+SmprKtddeS2JiImFhYaxevZqvfe1rAOzevZvS0lJNxvaRhs7Yup+afzEREdAzBwrWYwvWQ304EhGR5mlWOJo4cWJ71eEVHR1NRkZGk2VRUVF06tTJu3zUqFFMnz6djh07EhMTw0svvURubq7CkY8cu1JN/Y38jcnO8wSjzRvg/MucLkdEJCD57dVqpzJ+/HiMMUydOpXa2lpvE0hpf9bthq2eK9XU/NH/mOy+WOaqGaSISCsERDh65JFHmjyOjIxkwoQJCkROKNoJRyohMurY/bzEf2TneT7u3o6t1K17RERaolX3VpPQ452M3TMHExbmbDFyHBOXAEkpYO2xfSUiIs2icCTNs6V+MrbmG/ktU3/0yBasd7gSEZHApHAkzaIr1QJATl/PR4UjEZEWaddwVF1dTWlpaXu+hfiQPXoEdm3zPNCRI79lsj3hyG7Jx9bVOlyNiEjgafGE7EOHDrFgwQK++OILioqKcLvddOnShYEDB3LFFVeQkZHBZ599xu9//3tmzpzZljWLU7ZtBuuGzomYzl2drkZOJiUdomPhSAU1WwsgJt7pikREAkqLwtHGjRt5+umnOXjwIDExMaSlpVFXV0dJSQkLFy7kvffe4/rrr9c9X4LMsf5GOqXmz4zLBdl9YM1yqtZ9CWdd5HRJIiIBpdnhaN++ffz6178mJiaGn/3sZwwfPrzJ8xs2bOCNN95g9uzZCkdBpuHqJ8038n8mOw+7ZjnV61cpHImINFOzw9HcuXMBePTRR09487e8vDzy8vJ4++23mT59eusrFP+hK9UChqcZJFSt+xLjdDEiIgGm2eFoxYoVjBo16rR3xR09ejSVlZWsW7euxcWJ/7D7S6FsP7hckJnjdDlyOr1ywbioKykibH8paI6YiMgZa3Y4KisrIy0t7YzWvfHGG5tdkPiphvlGPTIxUVHO1iKnZTpEQ3pP2F6ILdyAGX6+0yWJiASMZl/KHxMTw6FDh85o3VWrVjF79uxmFyX+51h/I51SCxQmpx+gZpAiIs3V7HDUp08fPv7449Out23bNp566in+8Y9/tKgw8S/HrlRTOAoU6pQtItIyzQ5H11xzDVu3buWPf/wjNTU1J1zno48+4pe//CW1tWpAFwxsbS1sKwB0pVogMQ2dsncUYquqnC1GRCSANHvOUW5uLv/zP//Dyy+/zKpVqzj33HNJTU2ltraW4uJili1bRnFxMdnZ2Zx33nm8+uqr7VG3+NLubVBd7Wks2L2H09XImerSjbCuSdTtK4atm6DPAKcrEhEJCC1qAnnVVVeRkZHBrFmzmD9/PtZa73NJSUl85zvf4etf/zpLlixps0LFOQ3zjejV29NgUAKCMYaIvoM48tF/sJvXYxSORETOSItvH9KvXz8eeeQRKisr2bt3L7W1tXTu3LnJJf59+vThBz/4QZsUKg5qaP6oztgBJ8objjY4XYqISMBoVjh67bXXyMjIICMjgx49ehAWFkZMTAy9evU64fpJSUkkJSW1SaHiHLtFV6oFqsh+gz2fbN6Adbt15E9E5Aw0KxwtWLCA6upqAMLCwkhJSSEjI4P09HRvaFIYCi628jAU7fQ80JGjgBOZ1QciI6HiEOzdDSln1qNMRCSUNSscvfrqqxQXF7Nz50527NjBjh072LRpU5O5RR06dGgSltLT0+nfv3+bFy4+smWT52O3ZEwn3d090JjwcOjZG/LXeuYdKRyJiJxWs+ccNZwqGzZsGAUFBXz55Zdcfvnl9OvXD7fbTUFBAYsXL2bTpk3e18ycObNNixbfaehvpPupBS6T0w+bvxYK1sMFlztdjoiI32vxhGyAadOmce6553L77bd7l11wwQXceuut/POf/2TRokXcdtttra1RHOS9Uk39jQKWyc7DgiZli4icoVbNzty+fTuZmZnHLY+KimLcuHH06dOHL774ojVvIQ6y1nrvqaYr1QJXQ6dsinZiDx90thgRkQDQqnDUo0cPVq5cedLnhwwZwqpVq1rzFuKkkj1w+BCEh0N6ltPVSAuZjnGQXD/XaPNGZ4sREQkArQpH1113HUuXLmXmzJneq9ga27p1q24hEsDsmuWeT3rlYiIinC1GWsV7n7XNus+aiMjptGrO0Xnnncf+/fuZMWMGCxcu5KKLLiIry3OEYc2aNXzwwQcMHz68TQoV37NfLgPADD7b4Uqk1bLz4GM1gxQROROtCkcAY8aMYcCAAcyZM4eFCxc2OYI0aNAgvve977X2LcQB9mgl5K8GwAxSOAp0JqcfFmBrPra21nOJv4iInFCzf0POmzeP4cOH06PHsRuQ9uzZk/vuu4/a2lqKioqorq4mMTGRuLi4Ni1WfGjdSqithaQUSNbNZgNe91SI7eRpBrljC/Tq7XRFIiJ+q0XhaMaMGXTr1o1hw4YxbNgw+vfvT0REBOHh4aSlqclcMPCeUhs0AmOMw9VIaxmXC7L6wOrPPc0gFY5ERE6q2eHoL3/5C5s2bWLlypUsX76cf//730RGRtK/f39vWGp881kJPNZdh139OeAJRxIcTE5fz34tWA+XXeN0OSIifqvZ4cgYQ25uLrm5udx0002UlZWxfPlyVq5cyWuvvca0adNIS0tj2LBhDB8+nNzcXFy62WVg2bIJDpVDdCz01q1fgoXJ7lvfDHI91lodERQROYlWz8pMSEhg1KhRjBo1irq6OtavX8+KFSv44osvmDdvHjExMQwePJjRo0fTu7cO5QcC++VSAMyAYZq4G0x69oawMCjbD/tLoWs3pysSEfFLbfqXLywsjAEDBjBgwAC+853vUFxczPLly1mxYgXr168/o3D07rvv8u6771JSUgJAWloa3/zmNxk6dCgA1dXVTJ8+nSVLllBTU8PgwYOZMGECCQkJbTmUkGZXeeYboVNqQcVERXmaeW7dhC1Yh+l6sdMliYj4pVad7/rhD3/I7NmzT/p8UlISV111Fb/4xS+45pozm+PQpUsXxo0bx69//WuefPJJBgwYwG9+8xt27NgBwCuvvMIXX3zBfffdx6OPPsqBAweYOnVqa4Yhjdh9xbBrGxgXZqB6VAUb761E1O9IROSkWhWOiouLKSwspLCwkI8++ojly5ezd+/eVhV01llnMWzYMFJSUkhNTeXWW2+lQ4cObNq0icrKSt5//33Gjx/PgAEDyMrKYuLEiWzcuJH8/PxWva94NJxSIycPE9vJ2WKkzZmcvoA6ZYuInEqrT6t98cUXx91cNj09nbFjx3L++ee3attut5tPPvmEqqoqcnNzKSwspK6ujoEDB3rX6dGjB4mJieTn55Obe+Kbo9bU1FBTU+N9bIwhOjoaY0xITUptGOspx1x/Ss01+Jyg+dqc0biD0AnHne0JR+zYClVHMR2ifV9YO9P+1rhDQaiPu721OhyFh4czduxY+vXrR21tLYWFhXz66af87ne/Y+3atS3qkL19+3YmTZpETU0NHTp04P777yctLY2tW7cSHh5ObGxsk/Xj4+MpKys76fbmzp3b5PRfr169mDJlSsi2HEhOTj7hcndlBbs2rgEg6bLRRKSk+LKsdneycQe7JuNOSWF3t2TqSorocnAfHXoF77wy7e/QonFLW2p1OBo9ejTf/OY3vY8HDRrEddddx1tvvcWrr75Kbm4uI0eObNY2U1NTeeqpp6isrOTTTz/l+eef59FHH21xjWPHjmXMmDHexw3Js7S0tMkRpWBnjCE5OZmioiKstcc97/7iY6itgaQUSsIiMXv2OFBl2zvduIPVycbt7pULJUXs++wjXEnB17RV+1vjDgWhOu6IiAifHNhoVTiKjIykS5cuJ3xuzJgxbN68mXfeeafZ4Sg8PNybhrOysti8eTPz58/nvPPOo7a2loqKiiZHj8rLy095tVpERAQRJ7irvLU2pL6pGpxs3HZl/SX89fdSC7avjfZ3vew8WPpf3JvXY4L466H9HVo07tDgq7G2akJ2amoqK1asOOnzffv2ZefOna15C8Az96impoasrCzCwsJYvXq197ndu3dTWlp60vlGcmasuw67xjN3zAwO3lMt4mkGCcDmjVi329liRET8UKvC0eWXX87KlSt56aWXqK6uPu75DRs2EBUV1axtvvbaa6xbt47i4mK2b9/ufXzhhRcSExPDqFGjmD59OmvWrKGwsJA//OEP3o7d0gqNu2Ln9HO6GmlPaT0hqgMcqYA9O5yuRkTE77TqtNpll13Gzp07WbBgAR9//DFDhw4lPT2d8PBwVq9ezYoVK5p9Sq28vJznn3+eAwcOEBMTQ2ZmJpMmTWLQoEEAjB8/HmMMU6dOpba21tsEUlpHXbFDhwkLg165sGGV5ya0PTKdLklExK+0+q/gbbfdxjnnnMPbb7/NsmXLWLx4sfe5ESNGMH78+GZt7wc/+MEpn4+MjGTChAkKRG1MXbFDi8nOw25YBQUb4KKrnC5HRMSvNCscvfbaa2RkZJCRkUGPHj0ICwsDPHOL+vbti9vtpri4mKNHj5KYmEjHjh3bpWhpW7Z0r6crtktdsUOFyTl2E1oREWmqWeFowYIF3rlFYWFhpKSkkJGRQXp6OhkZGWRmZqrnQgCyX9YfNcrpq67YoSKrj+dj8R7swTJMXIKj5YiI+JNmhaNXX32V4uJidu7cyY4dO9ixYwebNm1iyZIl3nU6dOjgDUsNwal///5tXri0nYZTag2X8EvwMzEdITUDdm+Hwg0w5GtOlyQi4jeaPecoKSmJpKQkhg0bRkFBAV9++SWXX345/fr1w+12U1BQwOLFi9m0aZP3NTNnzmzToqXt2KOVkO9pjaBL+EOLyemL3b0dW7Aeo3AkIuLVqgnZ06ZN49xzz+X222/3Lrvgggu49dZb+ec//8miRYu47bbbWlujtKe1K6G2FpJSMcnB1y1ZTiE7D/77b+zmDU5XIiLiV1rV52j79u1kZh5/GXBUVBTjxo2jT58+x92UVvyL9xJ+XaUWcrzNILcWYEPoNjoiIqfTqnDUo0cPVq5cedLnhwwZwqpVq1rzFtKOrLsOu/pzQKfUQlJSCnSK99xPb/tmp6sREfEbrQpH1113HUuXLmXmzJkn7JC9detWamtrW/MW0p4K8+HwQXXFDlHGGM+pNXRJv4hIY62ac3Teeeexf/9+ZsyYwcKFC7nooovIysoCYM2aNXzwwQcMH66+Of7KrlJX7FBncvpiV36meUciIo20+i/imDFjGDBgAHPmzGHhwoVNjiANGjSI733ve619C2kndpXnlBqDdQl/qDLZeViAgvVYaz1Hk0REQlybHC7o2bMn9913H7W1tRQVFVFdXU1iYiJxcXFtsXlpB026Yg8Y5nQ54pTMHAgPh4NlULoXuqmJq4hIm55LCQ8PJy1Nl4MHAnXFFgATEekJSJs3eG5Cq3AkItK6CdkSuLzzjdQVO+SZ+knZFGhStogIKByFJHukEjauAXQJvxwLR5qULSLioXAUguzaFVCnrthSr6EZ5K5tnuAsIhLiFI5CkPeUmo4aCWDiO3smYlsLhRudLkdExHEKRyHG1tV5L+E3uoRf6hk1gxQR8VI4CjHVG9d4umLHxB47nSJS/71gNSlbREThKNQcWboYANNfXbHlGJNTf8VaYT7WXedsMSIiDlM4CjFHlv7X84lOqUljqRkQHQNVR2DXdqerERFxlMJRCLElRdRuK6zviq173skxxhUGvfoAOrUmIqJwFELsqoau2P0wsR2dLUb8jrcZpCZli0iIUzgKIfZLzyX8Lp1SkxMwOfWTsteuwFZWOFyNiIhzFI5ChD1SifV2xVY4khPI7Q/JPeDwQey815yuRkTEMQpHoWKdpyt2eGoGJrmH09WIHzLhEbhuvQsA+/7b2B1bHK5IRMQZCkchouGUWodzLnS4EvFnpt8QzFkXgHXjnvFHrNvtdEkiIj6ncBQCrLsOu/oLAKLPVjiSUzM33g5RHWDzBuwni5wuR0TE5xSOQkHhRm9X7Kh+Q5yuRvyc6ZKI+catANh/voytOOxwRSIivqVwFAIaLuE3A4arK7acEXPpNyAlHQ6VY//1N6fLERHxKYWjEGC/rA9HukpNzpAJD8f1re8DYD9cgN1W4HBFIiK+o3AU5GxJEezerq7Y0mymz0DM2ReDtbhn/EmTs0UkZPjdOZa5c+eydOlSdu3aRWRkJLm5uXz7298mNTXVu051dTXTp09nyZIl1NTUMHjwYCZMmEBCQoJzhfspdcWW1jA3/g921VLYko/9aCHmoiudLklEpN353ZGjdevWceWVVzJ58mQefPBB6urqePzxxzl69Kh3nVdeeYUvvviC++67j0cffZQDBw4wdepUB6v2Xw2X8JvBIxyuRAKRSeiCuXYcAHbOdOzhgw5XJCLS/vwuHE2aNImRI0eSnp5Oz549ufvuuyktLaWwsBCAyspK3n//fcaPH8+AAQPIyspi4sSJbNy4kfz8fIer9y/2SCXkrwXADNJ8I2kZc8kY6JEJFYewc6Y7XY6ISLvzu9NqX1VZWQlAx46eU0KFhYXU1dUxcOBA7zo9evQgMTGR/Px8cnNzj9tGTU0NNTU13sfGGKKjozHGYIxp5xE4x65bCXW10L0HrpQ071iDecwnonG3btwmPBzz7R9QN+X/YT9aCBdegcnq0xYltgvtb407FIT6uNubX4cjt9vNyy+/TJ8+fcjIyACgrKyM8PBwYmNjm6wbHx9PWVnZCbczd+5cZs+e7X3cq1cvpkyZQmJiYrvV7g/2bVpNJdDpvEtISEnxLk9OTnauKAdp3K2QksK+z8dQ+d5buGa+SPffvoIJC2v9dtuR9ndo0bilLfl1OJo2bRo7duzgV7/6Vau2M3bsWMaMGeN93JA8S0tLmxxRCibWXUfdZ4sBqMzpx5E9ezDGkJycTFFREdZahyv0HY27bcZtr74JliyiZvMGds98GdclV7dBlW1P+1vjDgWhOu6IiAifHNjw23A0bdo0li9fzqOPPkrXrl29yxMSEqitraWioqLJ0aPy8vKTXq0WERFBRETEccuttUH7TWU3b/B2xbZZedBonME87lPRuFspLgEz9tvY117APXc6DDsXE5fQ+u22E+3v0KJxhwZfjdXvJmRba5k2bRpLly7l4YcfJikpqcnzWVlZhIWFsXr1au+y3bt3U1paesL5RqHK2/hRXbGlDZmLr4KMLKiswM55xelyRETahd+Fo2nTprF48WJ+/OMfEx0dTVlZGWVlZVRXVwMQExPDqFGjmD59OmvWrKGwsJA//OEP5ObmKhw10nAJP4N0Cb+0HeMKwzWuvnP2x+9hC9Y5XJGISNvzu0MK7777LgCPPPJIk+UTJ05k5MiRAIwfPx5jDFOnTqW2ttbbBFI8bEkR7NmhrtjSLkx2HuaCy7EfLcQ94wVcDz7j95OzRUSaw+/C0axZs067TmRkJBMmTFAgOglvV+ze/dUVW9qFuf672OWfwM4t2A/me25UKyISJPzutJq0nrcrtk6pSTsxneIx138XAPvGDGz5AYcrEhFpOwpHQaZJV+zB6oot7cdceDlk5sCRSuzsvzpdjohIm1E4CjZrl3u7YpvuqadfX6SFjCsM17d+AMZgP/0Am7/G6ZJERNqEwlGQ8V7CrxvNig+YXr0xF14JgHvGn7C1tQ5XJCLSegpHQcS667BrPgd0o1nxHXP9d6BjJ9i9Hfv+W06XIyLSagpHwaRwIxw+BDEdIaev09VIiDCxnTDXjwfAzvs79sA+hysSEWkdhaMg0qQrtvrOiA+Z8y+DrD5QdQT7j5ecLkdEpFUUjoKItyu25huJjxmXC9e3vg/GhV22GLv+S6dLEhFpMYWjING0K/Ywp8uREGQysjEjvw6A+7UXsLU1zhYkItJCftchW1rGe9Sod39MjLpiizPMdd/Cfv4RFO3ELpyH+foNTpfUara2Bor3QNEubNHOYx8jInDdeDumZ2+nSxSRNqZwFCQabhmirtjiJBPTEfPN/8H+9VnsW69jz7kI06Wb02WdlrUWDh+EPTuxe3d5wl2R5yOle8HtPuHr3FP+H+bbE3Gdf6mPKxaR9qRwFAQ8XbE9DfjUFVucZs69BLv4XShYh3vmNMJ+8P+cLsnL1tZCSRHs3Ynds8vzsWgXFO2CikMnf2GHaE9j1ZQ0z8fkHrg/WQSrlmFffg73tk2Ym+7AhEf4bjAi0m4UjoLB2uVQVwfJ6ootzjPG4PrWXbgfuxeWL8GuWe7zeXB1h8qxBetx79nR5HQYpUWen5UTFw5dunl+jpLTmnwkvgvGmCaru4ad5zk69ubr2EXzsTu24vr+zzHxnX0wQhFpTwpHQcB7Cb8aP4qfMGm9MKPGYP8zD/ff/4zrkf/DRLTfURVrLezcil3xKXblZ+zeUXjylaM61B/9qQ8+yWmY5B6QlIqJijrj9zQuF+aacdiMbNwv/dZzpOzxe3F9//9hsvPaYFQi4hSFowDXpCu2LuEXP2KuGYddthiKd2PfnYsZfVObbt/W1UHBeuzKT7ErPoV9xU1X6JLoCT7de0BK/cfkNOjc9bijQK1hhpyD64Gncf/hSdizA/dTD2DGfQ/XRVe12XuIiG8pHAW6zY26YmerK7b4DxMdg7nxduyLU7HzZ2HPuRiT2L1V27RVVbB+BXbFZ9hVSz3f+w0iIqH/UFxDv0by5d+guPKI54iSD5jkNFwPPIX7r8/B8k+wr/4B99YCzK13tesRMxFpHwpHAa7hEn51xRZ/ZM6+yDM5e+Nq3DNfJOzuSc3ehj18ELtqGXbFZ7BuOVRXH3sythNm0AjM0K9BvyGYqA4YYwiLT4DKI203kDNgOsTg+v7/wy6Yjf3X37CL38Xu3Oo5zdYl0ae1iEjrKBwFuIZL+NUVW/yRMQbXuLtw/+rHsPIz7KplZ9Ruwpbuxa78DLvyM9i0tuml9F2TMEPO8QSinH5+9Z8CYwzm6huxGVm4/zIVtuTXz0P6OSZ3gNPlicgZUjgKYN6u2GFh6ootfsukZmAuuwb777meydl5gzCRTSc+N51Q/Sns2NJ0I+m9PIFoyNc8n7fhnKH2YAYMx/XgM7j/8ATs3Ir7mYcwN96BGTXa72sXEYWjgObtip3TT12xxa+ZMbdgP/svlO7FvvNPz2TtU02oNi7I7V8fiM5p9VwlJ5huybj+32+wr/yf535zr/8Ztm6C70w8LhyKiH9ROApg3q7Yavwofs50iMZ18x24X/gNdsE/cZfs9Vxl2XhCdWQk9BuGGXoOZuAITKc45wpuIyaqA9x5P/Tsjf3ny9hPF2F3b8P1g18EZOATCRUKRwHK7twKG1cDuoRfAsTw86HvYFj/JfbTRZ5lHTthBp2NGXoO9B3arD5DgcIYg7niOs88pBd+A9sLcU++D9f3fobpO9jp8kTkBBSOApB1u3H/7Q+eSapDvoZJUlds8X/GGFzfvQf363/BdEv2zB/K6etXE6rbk8kbhOvB3+L+45OwrQD3b3+JuWE85orrNA9JxM8oHAUg+99/w+YNEBWN69bvOV2OyBkzid0Ju+dBp8twjOnaDdfPnsT+7Y/YT97Hzv4rbCuA8T/0nIITEb/gcroAaR5bth87ZzoAZuy31T9FJMCYyCjM//wYM+4uCAvDLluM+8mfYov3OF2aiNRTOAowduaLcKQCMnMwl1ztdDki0gLGGFyXjMb1v5MhLgF2bcM9+T7s6i+cLk1EUDgKKHb159jPPwKXC9d378a4QmOuhkiwMr374Xrwt5DVByorcP/fr3C/PQvbuOmliPicwlGAsFVHcc/4EwDmsmswGdkOVyQibcF07orr/icwF10F1mL/9Tfcf/w19kil06WJhCyFowBh3/y7p0lel26Yb9zqdDki0oZMRASu70zEfPceCA+HlZ/ifuJ+bNFOp0sTCUkKRwHAbi/ELnwDANe472M6RDtckYi0B9eFV+D66ZOQ0BWKduJ+4qfYrZucLksk5Cgc+TnrrjvW02j4eWr4KBLkTFYfXA89A9l5cKQC928fxm4rcLoskZDil32O1q1bx7x589iyZQsHDhzg/vvv5+yzj90iw1rLrFmzeO+996ioqCAvL48JEyaQkpLiYNXtw374DmzJh+gYXLfc6XQ5IuIDJq4zrp88gvu5R6FgPe5nHsJ132OYzBynSxMJCX555KiqqoqePXtyxx13nPD5N954gwULFnDnnXfyxBNPEBUVxeTJk6murvZxpe3LHtjXqKfRdzEJXR2uSER8xXSIwfXjX0JOX8+VbM88jN222emyREKCX4ajoUOHcssttzQ5WtTAWsv8+fO5/vrrGTFiBJmZmdxzzz0cOHCAZcuWOVBt+3G//hc4egR65WIuvtLpckTEx7wBKTsPKg/jfuYhBSQRH/DL02qnUlxcTFlZGYMGDfIui4mJIScnh/z8fM4///zjXlNTU0NNTY33sTGG6Ohozw0h/fSeRu6VS2H5EnC5CPvuPZiw1u+qhrH665jbi8atcQcyEx2L+cmj1D37S9i8AfczDxF2/+PHtfMItnGfKY07NMfd3gIuHJWVlQEQHx/fZHl8fLz3ua+aO3cus2fP9j7u1asXU6ZMITHRP2+94T5SSdHMPwPQaey3STj7vDbdfnJycptuL1Bo3KEl2Mbt/vWfKHnoh1RvWI397S9JnPwHIrP7HLdesI37TGnc0pYCLhy1xNixYxkzZoz3cUPyLC0tbXJEyV/UzXwRW7IXEpOoHPUNjuxpm3suGWNITk6mqKgIa22bbDMQaNwad7CwEyfBs7/EXbiRvb/4PmH3T8ak9wKCe9ynonGH1rgjIiJ8cmAj4MJRQkICAOXl5XTu3Nm7vLy8nJ49e57wNREREURERBy33Frrd99Udttm7H/eBMA17gcQGdXmNfrjuH1B4w4tQTnu6BhcP34E97O/hC351E2dhOu+x70BCYJ03GdA4w4NvhqrX07IPpWkpCQSEhJYvXq1d1llZSUFBQXk5uY6WFnrWXcd7lefB+vGjLgQM3C40yWJiJ8xMbG4fvIo9MqFw4dwP/MgducWp8sSCSp+GY6OHj3K1q1b2bp1K+CZhL1161ZKS0sxxnD11VczZ84cPv/8c7Zv387vf/97OnfuzIgRgd0g0b7/NmwrgOhYzM0TnC5HRPyUJyA9Aj17ewLS1IewO7c6XZZI0PDL02qbN2/m0Ucf9T6ePt3T6+fiiy/m7rvv5tprr6WqqooXXniByspK8vLyeOCBB4iMjHSq5Faz+0uw/5oBgLlhPCa+82leISKhzMR0xHXvo7ifeRi2FVD39CSqp/wZomKdLk0k4BkbSicrv6KkpMRvJmTXPT8ZVn4G2Xm4fvZrjKvtD+oZY0hJSWHPnj0hdY5a49a4g5mtOIz7t56A5IpLwPzv45Ca4XRZPhNq+7tBqI47IiKCbt26tfv7+OVptVBjV3zqCUZhYbi+c3e7BCMRCU4mtiOue38FmTm4D5ZR9/Qk7K7tTpclEtD0V9hh9kgl7tdeAMBceT2mR6bDFYlIoDGxHQm77zEisvPgUDnuqZOwuxWQRFpK4chh9o0ZULYPuiVjRt/kdDkiEqBMbEe6TX4eMrI8AelpBSSRllI4cpDdsgn7/lsAuL79A0xklMMViUggC+sUT9h9j0N6r/ojSA9i9+xwuiyRgKNw5BBbV4f71d+DtZhzLsb0G+p0SSISBEzHTrjue8wTkA6WeY4gKSCJNIvCkUPse2/Cji0Q0xFz0x1OlyMiQcR0jPMEpLTGAWmn02WJBAyFIwfYfcWeuUaA+eZtmLgEZwsSkaBzLCD19ASkqZOwRQpIImdC4cjHrLW4Z/wJqqugdz/M+Zc5XZKIBCnTKQ7XfY9Dj0woP4D76QcVkETOgMKRry1fAqs/h7Bw9TQSkXZnOsXh+t+GgLS/PiDtcrosEb+mv8w+ZCsrcP/9LwCYr9+ASUl3uCIRCQWmU3zTgDR1EnbvbqfLEvFbCkc+ZOe+CuX7ISkVc/WNTpcjIiHEdIr3zEFKzYCy/biffkABSeQkFI58xG7egP1wAVDf0ygicG+SKyKBycQleI4gpaTXB6RJOsUmcgIKRz5ga2txv/q8p6fRuZdg+g52uiQRCVEmLgHX/Q0BaR/uR36I+6XfYrdvdro0Eb+hcOQD9j9vwK5t0LET5kb1NBIRZ5m4zp6AlNsf6mqxnyzC/di91D31C+yKT7HuOqdLFHFUuNMFBDtbUoR98+8AmG/ejukU53BFIiKegBT20yexhRux772J/eJjyF+LO38tJHbHXDoGc/7lmOgYp0sV8TmFo3ZkrcX92p+guhr6DMScN8rpkkREmjBZfTBZfbA33Ib9YD72v/+G0r3YmdOwb7yGOf8yzKgxmKQUp0sV8RmFo3ZkP/8I1iyH8HDPJGxjnC5JROSETJdEzPXfxY6+GfvpIux/5kHRTs9RpfffgsFn47rsGsgdoN9lEvQUjtqJrTiMfb2hp9GNmOQ0hysSETk9ExWFufgq7IVXwLqVuN+b5/lP3srPcK/8DNJ7YS67BjPiIkxEhNPlirQLhaM2Zq2F/DW433wdDpZBcg/M17/pdFkiIs1iXC4YMIywAcOwe3Z4jiB98j7s2IL963PY2S9jRl6NGXkVJq6z0+W2G+t2Q+VhqKuDuAQdNQsRCkdtxFZVYT/7wHP4edc2z8KGW4Tof1ciEsBMSjrm2xOxY7+D/e+7nt9zZfuwb/4du+AfmLMv9hxNSu/ldKmnZaur4NBBOHwQDpVjDzd87vloD5c3eczhQ2DdnhdHRUP3VEz3VEjuAd17YJJ7eJZ10MT1YKJw1Eq2dK9nEuPihZ7/XQBERnn6GV36Dd0iRESChonthPn6DdjLr8UuX+KZl7QlH7vkPeyS96DPQM+8pEFnYVxh7V6PrauDIxXU1FZhtxRgD9aHnUPlnlBzuOHxsTBEdVXL3swYqDoC2zc36QllGz6J7+I5U9C9PizVhycSu2PC2v9rIW1L4agFrLWwcTXu996CL5ce+19FYnfMJaM9V3fEdnS2SBGRdmLCwzFnXwRnX+Tp/t/QCmDjatwbV0O3ZM9/Ds+/9IyOqNiaGqg4BBWHPR8rD2EbPm/00TZZ5zAcqQSgqLkDCAuHTnHQ0fPPdIqHjp2gY3z98nhMx07QKd67Dlgo2Qt7d3puu1K0C7t3FxTt8oSu8v1Qvh+7cbVnTI3fq1tyk8BkuvfwHHnqFK/TdH5K4agZPKfOFmHff/vYqTOAvoNxXfoNGDjcJ/9bEhHxFyY7D5Odh91/G3ZRfSuAkiLs63/BvjEDc96lnqMq9YHGfiXwUHGo5UdzGmqI7YiN7eQJMZ3qg02ToBN3LAx1iocO0S0LJSlpkJLGV19pKw/D3t2eW7EU7YK99cGpeLenlUvRTs+Vf1/Wr9/wwujY+qNNqZ7QlJYJ2f3UD88PKBydAVu61/ND/9FXTp2dN8pzpCg1w9kCRUQcZrp0w9wwHjvmZuwni7DvzfMcXXnvzTPcgAtiO0JsJ+9HExPb5DGxHTGxnSCm4XEnTGxHUtPS2LNnj+eovgNMTEfolYvpldtkuXW74cC+Ex9t2l8CRyo8pyW35HvWb3hhchqmdz/I6YvJ6ec5EqcjTD6lcHQS1lrYsAr3+283PXXWLbn+1Nmlnh8IERHxMlEdMCO/jr3oSli7ArvsvycOPg2PG4JOh2jPFXLNfT8/Dg3G5YKu3aBrN0y/oU2es9VVUFLUJDDZrZtgzw7PUaainbD4XU9giu+CyekLvft5wlJaT0y4/ny3J311v8JWHcV+Wn/V2e7tx57oNwTXqG/AwGE6dSYichrG5fJMNRg43OlS/JKJjIIemdAjs8lpOnv4IGzegN20DluwDrYWeOYyffExfPGxJyxFRWOz+1A+9Bzcyemeo1ZRHRwaSXBSOKpnS4qwHyzAfvQuVFZ4FkZ1wJw7CjNqtK46ExGRdmc6xsHgszGDzwbqjzBtLcAWrMNuWgebN8CRCuy6lRxct9LzorAwyMjGNJyGy+mLiUtwbAzBIOTDkV3/Je73G646qz/j2y3ZE4jO06kzERFxjomMgtz+mNz+AFh3neesRsF6OuzcQuWqL+BAqXfukl34hueF3XscOxXXux90S/HrU5D+JqTDUd2ffo370w+PLeg3FNelY2DA8Bad+xYREWlPxhUGab0w6Vl0TUmhes8e3KV7PUeVCtZhC9Z7rqZuuGLu4/94TsXFJUBOP0yPTOiSiOmSCF26QedumKgoh0flf0I6HFFSBFHRmPMuwVwyBpOi+5+JiEhgMV2TMF2T4GsjATztEjZvqD8Vtx625ntuZ7V8CXb5Es86jTcQ2wnqw5LpXB+auiR6Pu/azTMhPMQmgIfWaL/CdeX1uPoM8lwuKiIiEgRMbCcYNAIzaAQAtqa6ft7SeijZgz1QCvtKPKfjjh6p7zl1yHPfvEbb8X5uDMR3bhSe6oNUl0To3A26Jnr6SbXwjIu1FtxuqKv13MOutvbY53U19R9rPcujY6Bbt1Z9fc5EQIejd955hzfffJOysjIyMzO5/fbbycnJOePXm7MvwtTUtGOFIiIizjIRkcfmHn2FrazwhKT9Jdj9pbC//vP6ZRwo9YSSsv1Qth/LxmOvbbyh8HDonAidu3paN3jDTX2oafJ5o8cNn59hj6q67D4wdEYrvyKnF7DhaMmSJUyfPp0777yT3r178/bbbzN58mSeffZZ4uPjnS5PRETE75mYWIiJPa6lQAPrdsPh8mOhaX+pJzDtawhQpVB+wBN6Soo8/9qkMJcncIWFeW7B0vB558S22f5pBGw4euutt7j00ku55JJLALjzzjtZvnw5ixYt4rrrrnO2OBERkSBgXC6I6+z517P3iQNUba3n3nL7S7Fl+zyvawg1YV8NOPWPT/Z5WASEhZ30FF1YREQ7jvaYgAxHtbW1FBYWNglBLpeLgQMHkp+ff9z6NTU11DQ6fWaMITrac2+dULq0sWGsoTRm0Lg17tCgcWvcTjEREZDY3fOvvd/LR+MNyHB08OBB3G43CQkJTZYnJCSwe/fu49afO3cus2fP9j7u1asXU6ZMITHRN4fn/E1ycrLTJThC4w4tGndo0bilLQVkOGqusWPHMmbMGO/jhuRZWlra5IhSsDPGkJycTFFRkWM3aHSCxq1xhwKNW+MOBRERET45sBGQ4SguLg6Xy0VZWVmT5WVlZccdTQLPFzPiBOcprbUh9U3VQOMOLRp3aNG4Q0uojdtXYw3INtDh4eFkZWWxZs0a7zK3282aNWvIzc11sDIREREJdAF55AhgzJgxPP/882RlZZGTk8P8+fOpqqpi5MiRTpcmIiIiASxgw9F5553HwYMHmTVrFmVlZfTs2ZMHHnjghKfVRERERM5UwIYjgKuuuoqrrrrK6TJEREQkiATknCMRERGR9qJwJCIiItKIwpGIiIhIIwpHIiIiIo0oHImIiIg0onAkIiIi0ojCkYiIiEgjCkciIiIijSgciYiIiDQS0B2yWys8PDSHr3GHFo07tGjcoSXUxu2r8RprrfXJO/mRmpoaIiIinC5DREREWqC9/46H5Gm1mpoannvuOY4cOeJ0KT515MgRfv7zn2vcIULj1rhDgcYdeuN+7rnnqKmpadf3CclwBPDxxx8TagfNrLVs2bJF4w4RGrfGHQo07tAb98cff9zu7xOy4UhERETkRBSORERERBoJyXAUERHBN7/5zZCblK1xa9yhQOPWuEOBxt2+4w7Jq9VERERETiYkjxyJiIiInIzCkYiIiEgjCkciIiIijSgciYiIiDQStDdleeedd3jzzTcpKysjMzOT22+/nZycnJOu/8knnzBz5kxKSkpITk7mW9/6FsOGDfNhxa0zd+5cli5dyq5du4iMjCQ3N5dvf/vbpKamnvQ1H3zwAX/4wx+aLIuIiGDGjBntXW6bmjVrFrNnz26yLDU1lWefffakrwn0/Q1w9913U1JSctzyK664ggkTJhy3PFD397p165g3bx5btmzhwIED3H///Zx99tne5621zJo1i/fee4+Kigry8vKYMGECKSkpp9xuc39H+Nqpxl1bW8vrr7/OihUrKC4uJiYmhoEDBzJu3Di6dOly0m225GfF1063v59//nk+/PDDJq8ZPHgwkyZNOuV2A3l/A9x0000nfN23v/1trrnmmhM+5+/7+0z+blVXVzN9+nSWLFlCTU0NgwcPZsKECSQkJJx0uy39ndBYUIajJUuWMH36dO6880569+7N22+/zeTJk3n22WeJj48/bv2NGzfy3HPPMW7cOIYNG8ZHH33EU089xZQpU8jIyHBgBM23bt06rrzySrKzs6mrq+Pvf/87jz/+OM888wwdOnQ46euio6N57rnnfFhp+0hPT+ehhx7yPna5Tn5QNBj2N8CTTz6J2+32Pt6+fTuPP/4455577klfE4j7u6qqip49ezJq1Ciefvrp455/4403WLBgAXfffTdJSUnMnDmTyZMn88wzzxAZGXnCbTb3d4QTTjXu6upqtmzZwg033EDPnj05fPgwL7/8Mr/5zW/49a9/fcrtNudnxQmn298AQ4YMYeLEid7Hp7sZaaDvb4A///nPTR6vWLGCP/3pT5xzzjmn3K4/7+8z+bv1yiuvsHz5cu677z5iYmKYNm0aU6dO5bHHHjvpdlvyO+Gr/Oer1IbeeustLr30Ui655BLS0tK48847iYyMZNGiRSdcf/78+QwZMoRrrrmGtLQ0brnlFrKysnjnnXd8XHnLTZo0iZEjR5Kenk7Pnj25++67KS0tpbCw8JSvM8aQkJDQ5F8gcrlcTcYQFxd30nWDYX8DxMXFNRnz8uXL6d69O/369TvpawJxfw8dOpRbbrmlyf+iG1hrmT9/Ptdffz0jRowgMzOTe+65hwMHDrBs2bKTbrO5vyOccKpxx8TE8NBDD3HeeeeRmppKbm4ut99+O4WFhZSWlp5yu835WXHCqcbdIDw8vMkYOnbseMptBvr+Bo77uV22bBn9+/ene/fup9yuP+/v0/3dqqys5P3332f8+PEMGDCArKwsJk6cyMaNG8nPzz/hNlv6O+Grgu7IUW1tLYWFhVx33XXeZS6Xi4EDB570i5mfn8+YMWOaLBs8eHCzvpD+prKyEuC0vzSOHj3KxIkTsdbSq1cvbr31VtLT031RYpsqKirirrvuIiIigtzcXMaNG0diYuIJ1w3G/V1bW8vixYsZPXo0xpiTrhcs+7tBcXExZWVlDBo0yLssJiaGnJwc8vPzOf/88497TUt+RwSCyspKjDHExMSccr3m/Kz4q3Xr1jFhwgRiY2MZMGAAt9xyC506dTrhusG4v8vKylixYgV33333adcNpP391b9bhYWF1NXVMXDgQO86PXr0IDExkfz8fHJzc4/bRkt+J5xI0IWjgwcP4na7j/sfcUJCArt37z7ha8rKyo47tBofH09ZWVk7Vdm+3G43L7/8Mn369DnlaaLU1FR+8IMfkJmZSWVlJfPmzePBBx/kmWeeoWvXrj6suHV69+7NxIkTSU1N5cCBA8yePZuHH36YqVOnEh0dfdz6wba/AZYuXUpFRQUjR4486TrBsr8ba9hnzdmfLfkd4e+qq6uZMWMG559//inDUXN/VvzRkCFDOOecc0hKSqKoqIi///3vPPHEE0yePPmEp4yCcX9/+OGHdOjQ4ZRH1yCw9veJ/m6VlZURHh5ObGxsk3VP9fPdkt8JJxJ04Uhg2rRp7Nixg1/96lenXC83N7dJ8s7NzeXee+9l4cKF3HLLLe1dZpsZOnSo9/PMzEzvL4RPPvmEUaNGOViZ7yxatIghQ4accjJusOxvaaq2tpbf/va3ACeciN9YMPysNP6ff0ZGBpmZmfzwhz9k7dq1TY4wBLNFixZx4YUXnnb+TCDt7zP9u+UrQTfnKC4uDpfLdVxCLCsrO+n8ioSEBMrLy5ssKy8vD4j5GF81bdo0li9fzi9/+ctmHw0IDw+nV69eFBUVtVN1vhEbG0tqaupJxxFM+xugpKSEVatWcemllzbrdcGwvxv2WXP2Z0t+R/irhmBUWlrKgw8+eNpTal91up+VQNC9e3c6dep00jEE0/4GWL9+Pbt3725RuPHX/X2yv1sJCQnU1tZSUVHRZP1T/Xy35HfCiQRdOAoPDycrK4s1a9Z4l7ndbtasWXPC85Pg+R/06tWrmyxbtWoVvXv3btda25K1lmnTprF06VIefvhhkpKSmr0Nt9vN9u3b6dy5cztU6DtHjx6lqKjopD8IwbC/G1u0aBHx8fHNbkUQDPs7KSmJhISEJvuzsrKSgoKCk/68t+R3hD9qCEZFRUU89NBDJ51zcyqn+1kJBPv27ePw4cMn/T4Olv3d4P333ycrK4uePXs2+7X+tr9P93crKyuLsLCwJj/fu3fvprS09KT7riW/E04kKE+rjRkzhueff56srCxycnKYP38+VVVV3vkYv//97+nSpQvjxo0D4Oqrr+aRRx7hzTffZNiwYXz88cds3ryZ733vew6OonmmTZvGRx99xM9+9jOio6O9/0uKiYnxHnr96rhnz55N7969SU5OpqKignnz5lFSUtLsIxBOmz59OmeddRaJiYkcOHCAWbNm4XK5uOCCC4Dg3N8N3G43H3zwARdffDFhYWFNnguW/d3wC71BcXExW7dupWPHjiQmJnL11VczZ84cUlJSSEpK4vXXX6dz586MGDHC+5pf/epXnH322Vx11VXA6X9H+INTjTshIYFnnnmGLVu28POf/xy32+39me/YsaP30vavjvt0Pyv+4FTj7tixI//4xz8455xzSEhIYO/evfztb38jOTmZwYMHe18TbPu7YQJ1ZWUln376Kd/5zndOuI1A29+n+7sVExPDqFGjmD59Oh07diQmJoaXXnrpuCkCP/nJTxg3bhxnn302xpgz+p1wOkEZjs477zwOHjzIrFmzKCsro2fPnjzwwAPetFxaWtrkip4+ffrwox/9iNdff52///3vpKSk8NOf/jSget68++67ADzyyCNNlk+cONH7C+Cr4z58+DAvvPACZWVlxMbGkpWVxeOPP05aWpqvym4T+/fv57nnnuPQoUPExcWRl5fH5MmTvZesBuP+brB69WpKS0u55JJLjnsuWPb35s2befTRR72Pp0+fDsDFF1/M3XffzbXXXktVVRUvvPAClZWV5OXl8cADDzSZj7F3714OHjzofXy63xH+4FTjvvHGG/n8888B+NnPftbkdb/85S/p378/cPy4T/ez4g9ONe4777yT7du38+GHH1JRUUGXLl0YNGgQN998MxEREd7XBNv+brgqbcmSJVhrTxpuAm1/n8nfrfHjx2OMYerUqdTW1nqbQDa2e/du75VuwBn9TjgdY621LRuWiIiISPAJujlHIiIiIq2hcCQiIiLSiMKRiIiISCMKRyIiIiKNKByJiIiINKJwJCIiItKIwpGIiIhIIwpHIiIiIo0oHImIiIg0onAkIiIi0khQ3ltNRPxTXV0db7zxBu+99x7l5eVkZ2dz1113kZqaetLXPP/883z44YcApKenM3Xq1FO+x6xZs5g9ezazZs1q09q/6u233+aVV17xPn7xxRf95p5VItI6Ckci4hNut5unn36a/Px8Ro8eTWRkJHPnzmXKlCk888wzhIWFnfS1nTp1Yvz48cTGxvqw4lMbMmQInTp1YunSpSxdutTpckSkDSkciYhPzJs3jzVr1vDEE0+Qnp4OQEJCAr/73e9Yu3YtgwYNOulrO3TowEUXXeSrUs9Ijx496NGjB0VFRQpHIkFGc45EpN1VVlYyd+5crr76am8wAsjNzQVg27ZtTpUmInIcHTkSkXa3ePFijh49ymWXXdZkeXi451fQkSNHWrTdDRs28Morr7B9+3a6dOnCNddcc9J19+/fz+uvv86KFSuoqKggOTmZMWPGMGrUqCbrrV27lldffZUdO3Z4t3ngwAGfzGMSEf+gcCQi7W7p0qWkpaURFRXFwYMHvctLS0sBz2mz5tq+fTuPP/44cXFx3HjjjdTV1TFr1iwSEhKOW7esrIxJkyYBcOWVVxIXF8fKlSv505/+xJEjRxg9ejQAW7Zs4YknniAhIYEbb7wRt9vN7NmzNdFaJMQoHIlIu3K73eTn51NVVcWECRNOuE5SUlKztztz5kystfzqV78iMTERgHPOOYf777//uHVff/1174TwTp06AXDFFVfw7LPP8o9//IPLL7+cyMhIZs2ahcvl4rHHHqNLly4AnHfeedx7773Nrk9EApfCkYi0q6KiIqqqqrjmmmuOm3S9aNEiPv74YzIyMpq1TbfbzZdffsmIESO8wQggLS2NwYMHs2LFCu8yay2fffYZ5557LtbaJkeuhgwZwpIlSygsLCQ3N5fVq1dz9tlne4MRQHJyMkOGDOGLL75o7tBFJEApHIlIuyopKQGgf//+x4WjN954g/j4+FP2OTqRgwcPUl1dTUpKynHPpaamNglHBw8epKKigv/85z/85z//Oen2ysvLqa6uJjk5+bjnT7RMRIKXwpGItKuqqioAoqKimiyvrKxk/fr1XHLJJe36/tZaAC688EIuvvjiE66TmZmJ2+1u1zpEJHAoHIlIu2qYbH306NEmyz/44ANqa2u54oormr3NuLg4IiMj2bNnz3HP7d69+7h1o6Ojcbvdp+yl5Ha7iYiIoKio6LjnTrRMRIKX+hyJSLvKzMzEGMPatWu9y/bt28c///lPLrroIjIzM5u9TZfLxeDBg1m2bJn3ijeAnTt38uWXXx637jnnnMNnn33G9u3bj9tWwxwkl8vFwIEDWbZsGfv37/c+X1RUxMqVK5tdo4gELh05EpF2FR8fz4gRI5g/fz5RUVHExMTw9ttv06VLF26//fYWb/emm25i5cqVPPzww1xxxRW43W4WLFhAenr6cU0lx40bx9q1a5k0aRKXXnopaWlpHD58mMLCQlavXs1f//pX7zYffPBBHnroIe8233nnHdLT09m6dWtrvgwiEkAUjkSk3X3/+9/nT3/6E2+++SYdOnTg3HPP5dZbbyU6OrrF28zMzGTSpElMnz6dWbNm0bVrV2666SYOHDhwXDhKSEjgiSeeYPbs2Xz22Wf8+9//plOnTqSnp/Otb33Lu15WVhYPPPAAr776KjNnzqRr167cfPPN7Ny5k127drW4VhEJLMY2zFYUEfFDzz//PGvWrGHKlCmEhYU5cvPZ3/zmN+zcuZPf/e533mXV1dUcPXqUefPmMW/ePF588UU1ixQJEjpyJCJ+b9++fUyYMIH09HSmTp3aru9VXV1NZGSk9/GePXtYsWLFcVe6LVy4kFdeeaVdaxERZygciYhfu/baa7nwwguBlt1mpLnuueceRo4cSVJSEqWlpbz77ruEh4dz7bXXNlnvnHPOaXIT3ZiYmHavTUR8Q6fVREQa+cMf/sDatWspKysjPDyc3Nxcbr31VrKyspwuTUR8ROFIREREpBH1ORIRERFpROFIREREpBGFIxEREZFGFI5EREREGlE4EhEREWlE4UhERESkEYUjERERkUYUjkREREQaUTgSERERaUThSERERKSR/w9Ez704RV8D5gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(\n", - " results[\"channel_3\"][\"Theta_deg\"], \n", - " results[\"channel_3\"][\"sigma_mb_sr\"],\n", - " label=\"Frescox\"\n", + " results[\"channel_2\"][\"Theta_deg\"],\n", + " results[\"channel_2\"][\"sigma_mb_sr\"],\n", + " label=\"Frescox\",\n", ")\n", "\n", - "plt.xlim([0,20])\n", + "plt.xlim([0, 20])\n", "plt.xlabel(r\"$\\theta$ [deg]\")\n", "plt.ylabel(r\"$d\\sigma/d\\Omega$ [mb/Sr]\")" ] @@ -258,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "453ffa1b-22b3-4273-8b6a-cc42dfa77903", "metadata": { "editable": true, @@ -269,7 +285,59 @@ "remove-input" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Frescox Transfer Template:\n", + "-----------------------------------\n", + "n14(f17,ne18)c13 @ 170 MeV; \n", + "NAMELIST\n", + " &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 \n", + "\t jtmin=0.0 jtmax=120 absend=-1.0\n", + "\t thmin=0.00 thmax=40.00 thinc=0.10\n", + "\t iter=1 nnu=36\n", + "\t chans=1 xstabl=1 \n", + "\t elab=170.0 /\n", + "\n", + " &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 /\n", + " &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 /\n", + "\n", + " &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 /\n", + " &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 /\n", + " &partition /\n", + "\n", + " &POT kp=1 ap=17.000 at=14.000 rc=@rC_entrance@ /\n", + " &POT kp=1 type=1 p1=@V_entrance@ p2=@r_entrance@ p3=@a_entrance@ p4=@W_entrance@ p5=@rw_entrance@ p6=@aw_entrance@ /\n", + "\n", + " &POT kp=2 ap=18.000 at=13.000 rc=@rC_exit@ /\n", + " &POT kp=2 type=1 p1=@V_exit@ p2=@r_exit@ p3=@a_exit@ p4=@W_exit@ p5=@rw_exit@ p6=@aw_exit@ /\n", + "\n", + " &POT kp=3 at=17 rc=@rC_p17F@ /\n", + " &POT kp=3 type=1 p1=@V_p17F@ p2=@r_p17F@ p3=@a_p17F@ /\n", + " &POT kp=3 type=3 p1=@Vso_p17F@ p2=@rso_p17F@ p3=@aso_p17F@ /\n", + "\n", + " &POT kp=4 at=13 rc=@rC_p13C@ /\n", + " &POT kp=4 type=1 p1=@V_p13C@ p2=@r_p13C@ p3=@a_p13C@ /\n", + " &POT kp=4 type=3 p1=@Vso_p13C@ p2=@rso_p13C@ p3=@aso_p13C@ /\n", + "\n", + " &POT kp=5 ap=17.000 at=14.000 rc=@rC_17F_13C@ /\n", + " &POT kp=5 type=1 p1=@V_17F_13C@ p2=@r_17F_13C@ p3=@a_17F_13C@ p4=@W_17F_13C@ p5=@rw_17F_13C@ p6=@aw_17F_13C@ /\n", + " &pot /\n", + "\n", + " &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=@BE_p_17F@ isc=1 ipc=0 /\n", + " &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=@BE_p_13C@ isc=1 ipc=0 /\n", + " &overlap /\n", + "\n", + " &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 /\n", + " &CFP in=1 ib=1 ia=1 kn=1 a=1.00 /\n", + " &CFP in=2 ib=1 ia=1 kn=2 a=1.00 /\n", + " &CFP /\n", + " &coupling /\n" + ] + } + ], "source": [ "with open(TEMPLATE_FILE_PATH, \"r\") as temp:\n", " template_file = temp.read()\n", @@ -281,84 +349,154 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "552b4770-e4ba-4ff3-9ce8-2dacb87a7a0f", "metadata": {}, "outputs": [], "source": [ "base_params = {\n", - " \"rC_entrance\" : 1.3,\n", + " \"rC_entrance\": 1.3,\n", " \"V_entrance\": 37.2,\n", " \"r_entrance\": 1.2,\n", - " \"a_entrance\" : 0.6,\n", + " \"a_entrance\": 0.6,\n", " \"W_entrance\": 21.6,\n", " \"rw_entrance\": 1.2,\n", - " \"aw_entrance\" : 0.69,\n", - " \"rC_exit\" : 1.3,\n", + " \"aw_entrance\": 0.69,\n", + " \"rC_exit\": 1.3,\n", " \"V_exit\": 37.2,\n", " \"r_exit\": 1.2,\n", - " \"a_exit\" : 0.6,\n", + " \"a_exit\": 0.6,\n", " \"W_exit\": 21.6,\n", " \"rw_exit\": 1.2,\n", - " \"aw_exit\" : 0.69,\n", + " \"aw_exit\": 0.69,\n", " \"rC_p17F\": 1.2,\n", - " \"V_p17F\": 50, \n", + " \"V_p17F\": 50,\n", " \"r_p17F\": 1.2,\n", - " \"a_p17F\": 0.65,\n", - " \"Vso_p17F\": 6, \n", + " \"a_p17F\": 0.65,\n", + " \"Vso_p17F\": 6,\n", " \"rso_p17F\": 1.2,\n", - " \"aso_p17F\": 0.65,\n", + " \"aso_p17F\": 0.65,\n", " \"rC_p13C\": 1.2,\n", - " \"V_p13C\": 50, \n", + " \"V_p13C\": 50,\n", " \"r_p13C\": 1.2,\n", - " \"a_p13C\": 0.65,\n", - " \"Vso_p13C\": 6, \n", + " \"a_p13C\": 0.65,\n", + " \"Vso_p13C\": 6,\n", " \"rso_p13C\": 1.2,\n", - " \"aso_p13C\": 0.65,\n", + " \"aso_p13C\": 0.65,\n", " \"rC_17F_13C\": 1.3,\n", " \"V_17F_13C\": 37.2,\n", - " \"r_17F_13C\": 1.2, \n", - " \"a_17F_13C\": 0.6, \n", - " \"W_17F_13C\": 21.6, \n", - " \"rw_17F_13C\": 1.2, \n", - " \"aw_17F_13C\": 0.69, \n", - " \"BE_p_17F\" : 3.922,\n", + " \"r_17F_13C\": 1.2,\n", + " \"a_17F_13C\": 0.6,\n", + " \"W_17F_13C\": 21.6,\n", + " \"rw_17F_13C\": 1.2,\n", + " \"aw_17F_13C\": 0.69,\n", + " \"BE_p_17F\": 3.922,\n", " \"BE_p_13C\": 7.5506,\n", "}" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "ca7c257f-e078-48f6-bd94-05dd803219ce", "metadata": {}, "outputs": [], "source": [ "# Create the frescox input file by filling in the user-defined template with parameters\n", "cfg = bfrescox.Configuration.from_template(\n", - " TEMPLATE_FILE_PATH,\n", - " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", - " base_params,\n", - " overwrite=True,\n", - " )" + " TEMPLATE_FILE_PATH,\n", + " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", + " base_params,\n", + " overwrite=True,\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "c3eea39f-07e2-45cb-92be-7281c5fd385b", "metadata": {}, "outputs": [], "source": [ - "bfrescox.run_simulation(cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True)" + "bfrescox.run_simulation(\n", + " cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "39b2d9b2-52a9-4413-a997-2f086d8396fe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Theta_degsigma_mb_sr
00.011.0000
10.101.0000
20.200.9991
30.301.0010
40.401.0020
\n", + "
" + ], + "text/plain": [ + " Theta_deg sigma_mb_sr\n", + "0 0.01 1.0000\n", + "1 0.10 1.0000\n", + "2 0.20 0.9991\n", + "3 0.30 1.0010\n", + "4 0.40 1.0020" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "results = bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", "display(results[\"channel_1\"].head())" @@ -374,21 +512,592 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "b94096cb-bb10-4282-954d-a53fe0dc4fdf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$d\\\\sigma/d\\\\Omega$ [mb/Sr]')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAG5CAYAAACObz5RAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAXldJREFUeJzt3Xd4VGX+/vH3M2kkQAgQQkiFAAHpRVCRJthh7bLKrmtD/apbXVd/irrqqrv2sutWseDqAiIoKhZULIgK0nuHQEJIAgQIgbR5fn8MiUYSYJKZnCn367pykZmcOefz5CSZm3OeYqy1FhEREZEw4HK6ABEREZGmouAjIiIiYUPBR0RERMKGgo+IiIiEDQUfERERCRsKPiIiIhI2FHxEREQkbCj4iIiISNhQ8BEREZGwoeAjIiIiYSPS6QJ+bNq0aUyfPr3WcykpKTzzzDNe72vv3r1UVlb6qLLg0K5dOwoLC50uo8mp3eFF7Q4vanf4iIyMpHXr1v49hl/33kDp6ence++9NY9droZdmKqsrKSiosJXZQU8YwzgaXc4LcGmdqvd4UDtVrvFNwIy+LhcLhISEpwuQ0REREJMQAaf/Px8brrpJqKiosjOzmb8+PEkJibWu31FRUWtKzvGGGJjYzHG1KTmcFDd1nBqM6jdand4ULvV7nDQFO01NsCuoS1ZsoTDhw+TkpLC3r17mT59Onv27OHJJ58kNja2ztf8uF9Qp06dePTRR5uqZBEREQkSARd8fuzgwYPccsstXH311YwaNarObeq74lNUVBR2fXySk5PJz88Pq3vCarfaHQ7UbrU7HERFRR3zDo8vBOStrh9q3rw5KSkp5Ofn17tNVFQUUVFRRz1vrQ2rH5hqand4UbvDi9rtW5WVlZSWlvp8v75QWlpKeXm502X4XFxcHJGRdcePpvjZDvjgc/jwYfLz8xk2bJjTpYiISAiprKzk4MGDtGzZssGjh/0pKioq5O5auN1uDhw4QPPmzesNP/4WcMFn8uTJnHzyySQmJrJ3716mTZuGy+Vi6NChTpcmIiIhpLS0NGBDT6hyuVy0bNmSkpIS4uPjHakh4ILPnj17ePbZZzlw4ADx8fF0796dhx9+2LFvkIiIhC6Fnqbn9Pc84ILPb3/7W6dLEBERkRClqCsiIiJhQ8FHREREwoaCj4iISBD57W9/S2pq6lEfW7Zscbq0oBBwfXwk9NnDpRAZhYk8eu4lERE5vjPOOIOnnnqq1nNt27at9bi8vJzo6OimLCsoKPhIk7DFu7Gff4hdtRi2boDoGOjeB9NnEGbIaIxD8zmIiASj6OhokpKSaj132WWX0a1bNyIiIpgxYwbdu3dn+vTprF27loceeohvv/2WuLg4hg8fzgMPPECbNm0AePfdd3n66afZunUrzZo1o1evXrz00kvExcUBMGXKFP71r3+xdetWEhISOP/883n44YcByM3N5Z577mHevHm4XC5GjhzJQw89RLt27Zr2G+IFvduI39kNq3H/489wYN/3T5YdhmULsMsWYBfNx/V/d2Ji45wrUkTCnrUWysucOXh0jE8W6HzjjTf4xS9+wVtvvQXAvn37GDduHFdeeSX3338/hw8f5uGHH+amm27ijTfeYNeuXdx6661MnDiR8847j5KSEr799tuaGZRfeeUVHnzwQe666y7OOOMMDhw4wMKFCwHPZITXXnstzZs3580336SyspKJEydy880311o/M9Ao+Ihfub/8CPvaP6GqEtI6Ys68AHNSPyjZj13xHXb2G7B6Ce7H/h+uX92HaePfNVpEROpVXob7l+McObTrb9MgptkJb//xxx/TtWvXmsdnnHEG4Fmk+5577ql5/plnnqFXr17cddddNc89+eSTDBo0iE2bNlFaWkplZSXnn38+aWlpAJx00kk12z733HPceOONTJgwoea5fv36ATBv3jzWrl3L119/TWpqKgDPPvssZ5xxBkuXLq3ZLtAo+Ijf2MVfYyf/DQAz8HTMtb/BVP9it0nEZGRhe/bH/dc/wY6tuJ++D9c9T2NiYhysWkQk8A0ZMoQ///nPNY/j4uK49dZb6dOnT63tVq9ezfz582uFpGrbtm1jxIgRDB06lNGjRzNixAhGjBjBmDFjSEhIoKioiPz8/HpXTtiwYQMpKSk1oQcgOzubVq1asWHDBgUfCS+2aBfuV54DwJwxBnPljXVexjUdu+K663Hcf7kT8ndgZ07GXHFDU5crIgLRMZ4rLw4d2xtxcXF06tTpqOdjY2NrPS4tLeWss87i7rvvPmrb9u3bExERwZQpU/juu+/4/PPPeemll3j00Ud59913a/oAhRoNZxefs5WVuP/9OJQehE7ZmHHXH/PetUlsj+vqX3le+8k72LXLm6pUEZEaxhhMTDNnPnzQv6cuvXr1Yt26daSnp9OpU6daH9Wdl40xDBo0iNtvv50PP/yQqKgo3n//fVq0aEF6ejrz5s2rc99du3YlLy+P3NzcmufWr1/Pvn37yM7O9kt7fEHBR3zOvjcVtqyHuOa4bvzDCY3YMr0HYoafA4D7pWexh0r9XaaISMi75pprKC4u5pZbbmHp0qVs3bqVzz77jN/97ndUVVWxePFinnvuOZYtW0Zubi6zZ89mz549NbfGbrvtNv79738zadIkNm/ezIoVK3jxxRcBGDZsGN27d+dXv/oVK1asYMmSJfzmN7/htNNOo2/fvk42+5h0q0t8yu4uxH44EwDz81sxie1P+LXm8uuwq5dC0S7sx7MwP7nCT1WKiISH5ORk3nrrLR555BHGjx9PWVkZaWlpjBw5smal9G+//ZYXXniBkpISUlNTue+++xg1ahQA48aNo6ysjP/85z/86U9/ok2bNowZMwbwXCl66aWXuOeee7jkkktqDWcPZMZWj1kLQYWFhVRUVDhdRpMxxtChQwd27tyJU6fVPelp7DdzIbsXrtsf9vryrXvhl9h/Pw5xLXD95YUTGuIeCO12gtqtdocDf7Z7//79xMfH+3SfvhQVFRWy72H1fe+joqL8PgeQbnWJz9htGz2hB3Bdfm2D7lmbgUMgORVKS7Cfve/rEkVEJMwp+IhPWGtxv/ESAObUkZiORw+dPBHGFYE573LPPue8hS077LMaRUREFHzEN9avgnUrPGtwXXRVo3ZlThkB7ZLhwD7sFx/6qEAREREFH/ER95y3ADCnj8a0bdz9WRMRgTnvMuDI8Ha3u7HliYiIAAo+4gM2PxeWe9ZuMWde4JN9mlNGQGxz2F0A61f6ZJ8iIiIKPtJo9pN3wFroMwiTnOaTfZroGMwgzzTpdv4nPtmniMiPuXVFuck5/T1X8JFGsQcPYOd/DIDrrAt9um8zZLTnGIvmYw9rQkMR8a24uDgOHDjg+BtxOHG73Rw4cKBm1mgnaAJDaRT7xYdQXg7pnaBbb9/uPKsbtE+FXbnYRfMxp5/p2/2LSFiLjIykefPmlJSUOF1KnaKjoykvL3e6DJ9r3rw5kScwo7+/KPhIg1m3G/vlRwCY0T/x+VozxhjMkFHYma96bncp+IiIj0VGRgbkJIbhOmFlU9CtLmm49SuhMB+axWJOHuqXQ5hTzwBjYP0qbMFOvxxDRETCh4KPNJidNwcAM3g4JqaZX45h2iRC9z6e4y2e75djiIhI+FDwkQaxpSXYxV8DYIae5ddjmQGneY655Bu/HkdEREKfgo80iP32C6goh9RMaODyFCfK9D3F88nmddjiPX49loiIhDYFH2mQmttcQ8/0eafmHzOt20KnbM9xl37r12OJiEhoU/ARr9ncHMjZBBGRmFPOaJJjmv663SUiIo2n4CNeswu/8HzSawCmZdMMAzX9j9zuWrccWxqYc26IiEjgU/ARr1hrsQvnAWAGDWuy45rkNOiQDlVV2BWLmuy4IiISWhR8xDs5m6EgD6KjMX0HN+mhTf9TPZ/odpeIiDSQgo94xS78EgDTexCmWWyTHtv0GeSpYc0yrLuqSY8tIiKhQcFHTpi1Fvtd09/mqtGxK8Q2h9IS2Lap6Y8vIiJBT8FHTtzmdbC7AGJioffAJj+8iYiA7p6FUO3qpU1+fBERCX4KPnLC7HdfAWD6DcZExzhSgzmpn6eWNcscOb6IiAQ3BR85IdZa7JIjS1QMPN2xOkyPfp5PNq3Blh12rA4REQlOCj5yYrZv8dzmio6GHv2dqyOpA7RpB5WVsGGVc3WIiEhQUvCRE1IzY3LPAZgYZ25zARhjaq76qJ+PiIh4S8FHTkjNba5+pzpcCVAdfNTPR0REvKTgI8dlC3ZC7jZwuTB9BzldDqZ7H88nO7Zi9+91thgREQkqCj5yXHbpkdtc3Xpjmrd0thjAtGwF6Z0AsOvUz0dERE6cgo8cV3X/HtPvFIcr+Z7p2tPziTo4i4iIFxR85Jjs/mLYtBYItODTAwC7YbXDlYiISDBR8JFjsisWgbWQ0RnTpp3T5Xyv+opP7lZsaYmztYiISNBQ8JFjsssXAN8vEBooTKvWnjl9rMUeuSIlIiJyPAo+Ui9bUQGrlgIExGiuHzNddLtLRES8o+Aj9Vu/EsoOQas2kNHZ6WqOVtPPRx2cRUTkxCj4SL3ssurbXCdjXIH3o1IzsmvLemx5mbPFiIhIUAi8dzMJCNZa7PKFQOD176mR1AHiE6CykvINa5yuRkREgoCCj9QtL8ezKGlUNJzUz+lq6mSMgSP9fMpWLXG4GhERCQYKPlKn6qs9dO/j6KKkx1M9n0/ZkU7YIiIix6LgI3WyKxcBYHqf7HAlx2Y6dwegfP1KrLUOVyMiIoFOwUeOYksPwkZPnxnTa4DD1RxHWieIjMS9fx8U5jtdjYiIBDgFHzna2mXgdkP7VEy7ZKerOSYTFVUz1N5uWedwNSIiEugUfOQoduViIAiu9hxhOmUDYDevd7gSEREJdAo+Uou1NviCT9aR4LNFwUdERI5NwUdqy8uBvUWeYezZvZyu5oSYTt08n+Rs8iyzISIiUg8FH6ml+moP3XphogN3GHst7ZJxHZnIkB1bnK5GREQCmIKP1FIzjL3XQIcrOXHGGKKzPctXqJ+PiIgcS8AHn7feeotx48bx8ssvO11KyLOHD8GRlc5Nz+Do31MtutuR23Ia2SUiIscQ0MFn48aNzJkzh8zMTKdLCQ9rl0NVJbRLhvYpTlfjlergow7OIiJyLJFOF1Cfw4cP89e//pWbbrqJGTNmHHPbiooKKn7QqdUYQ2xsLMYYz3pOYaK6rQ1ts3tV9WiugbgCcDX2+nhudXmWrqBgJxw8gGkR72xRTaCx5ztYqd1qdzgI93b7U8AGnxdeeIH+/fvTp0+f4wafmTNnMn369JrHnTp14tFHHyUxMdHfZQak5GTvJx201rJzzTKqgLbDzyS2QwffF+ZnkSkZVObl0Hr/HmK7dnO6nCbTkPMdCtTu8KJ2i68EZPD56quv2LJlC3/+859PaPuLL76YsWPH1jyuToxFRUW1rgSFOmMMycnJ5Ofne71uld25napdeRAZyd6kNIp37vRTlb5X3e6qtI6Ql8OepQtxpXR0uiy/a8z5DmZqt9odDsK13VFRUX6/aBFwwaeoqIiXX36Ze+65h+jo6BN6TVRUFFFRUUc9b60Nqx+Yag1pt/vIaC669oTomKD8vpnMztgFX+DeuhEThPU3lH7Ow4vaHV7Crd1N0daACz6bN29m37593HnnnTXPud1u1qxZwwcffMDrr78eVP1PgoVd8X3/nqB1ZM0ucjY5W4eIiASsgAs+vXv35oknnqj13D/+8Q9SUlK48MILFXr8wJaVwfqVQPAsU1EXk3kk+BTtwh48gGne0tmCREQk4ARc8ImNjSUjI6PWczExMbRs2fKo58VH1q+Aygpo0w46pDtdTYOZuBaeofiF+bBtE/To53RJIiISYHT5RGotShrsQyfNkdtdVre7RESkDgF3xacu999/v9MlhLRgXKaiXpldYNFXnis+IiIiP6IrPmHOFuR5Jv2LiIDufZwup9FMZhYAdttGhysREZFApOAT5mpWY+/SAxMb52wxvlA9sqswH1ta4mwtIiIScBR8wlxN/54gW5S0PqZFPLRN8jzI2exsMSIiEnAUfMKYrSiHdcsBML1DI/gAcGRYu1U/HxER+REFn3C2YRWUl0NCG0jt6HQ1PlM9sgv18xERkR9R8AljNbM19wz+Yew/VDOkfcdWZwsREZGAo+ATxr4fxh5Ct7kA0jt5/s3P9cxKLSIicoSCT5iyhfmQvwNcLjipn9Pl+Far1tCyFVg35G1zuhoREQkgCj5hqmYYe+fumOYtnC3Gx4wxNVd97PYtDlcjIiKBRMEnTNkV3wFgeg9yuBL/MNW3uxR8RETkBxR8wpAtL/vBMPYQWKaiLulHZnDerrl8RETkewo+4Wj9Ss8w9taJkJrpdDV+YdKOXPHZsQ3rdjtbjIiIBAwFnzBkVxwZzdV7YEgNY68lORUio6DsEBTlO12NiIgECAWfMGOt/UH/nhC9zQWYiIjvr2apn4+IiByh4BNuduVCYT5EREL3vk5X41cmo7qfj4KPiIh4KPiEmerbXGT3xDSLdbYYf0vrCCj4iIjI9xR8wsz3t7lOdrgS/zNHRnbpVpeIiFRT8Akj9vAhWL8KCO3+PTWOXPFhbxG2ZL+jpYiISGBQ8Akna5dBVSW0S4b2qU5X43cmNg4S23se5GrpChERUfAJK98PYz85dIex/9iRkV12h4KPiIgo+IQNzzD27+fvCRcmtaPnk9ytTpYhIiIBQsEnXORug71FEB0N2b2crqbppGYAYPNyHC5EREQCgYJPmKgZxt6tDyY6xtlimtD3V3y2Ya11tBYREXGegk+YsCsWAuExjL2W9imeyRoPH4I9hU5XIyIiDlPwCQO2ZD9sXAuEV/8eABMZ6Vm3C0AdnEVEwp6CTxiwy78D64a0jpjq4d1hpPp2l81T8BERCXcKPmHALvsWANPvFIcrcciRDs664iMiIgo+Ic6Wl8HKxQCYfqc6XI0zaq74aEi7iEjYU/AJdWuWQ3kZtEmEI6uVh53qKz75udjKSmdrERERRyn4hLia21x9B4fPbM0/1jYJmsV6luvYled0NSIi4iAFnxBm3VXYpWHevwc8ga966Qp1cBYRCWsKPqFs83o4sA9i48JrtuY6mBR1cBYREQWfkGaXfA0cWZQ0MsrhahymIe0iIoKCT8iy1mIXzQfADBzicDXOM9UdnHMVfEREwpmCT6jathF2F0BMM+gVXrM116l6za7CfOzhQ46WIiIizlHwCVH2u6+AI7e5wmhR0vqYlvHQqrXnwc7tzhYjIiKOUfAJQdZa7GLd5jrKkQ7OdsdWZ+sQERHHKPiEopxNUJgP0dEQbquxH0P1DM7k5Thah4iIOEfBJwS5j3RqptfJmJhmzhYTSI50cLbq4CwiErYUfEKMtRb73TwAzMmnO1xNYKm54qPgIyISthR8Qkz5+lVQsBOiYzC6zVVbSjoYA/uLsQf2OV2NiIg4QMEnxJTOfR/wrMRumsU6XE1gMTHNILG954Gu+oiIhCUFnxBiq6oo/XIOAObUEQ5XE6CqZ3BW8BERCUsKPiHErlmKu3gPtIiHk/o5XU5A0gzOIiLhTcEnhNhvPgPADBqGiYx0tphApSs+IiJhTcEnRNiyw9gl3wDgOnWks8UEsO+v+ORg3W5nixERkSbn1WWBkpKSRh0sLi4Ol0tZyx/skq+h7DARyamQ1c3pcgJX+1SIjISyQ561zNolO12RiIg0Ia+Cz/XXX9+og91777306tWrUfuQutl5HwPQfPQYSo3BWutwRYHJRERAchrs2Ap52xV8RETCjNcdQQYNGkRmZqZXrykrK+Odd97x9lByguyuPFi3Aoyh+VkXUFqp0HMsJiUDu2MrNi8H03eQ0+WIiEgT8jr4nHrqqQwdOtSr1xw4cEDBx4/sV0eGsPccQGS7ZNi50+GKAtyRxUq1ZpeISPjxqsPN1VdfTVZWltcHadasGVdffTUpKSlev1aOzVZWYud/CoAZdrbD1QQHU71Ku4KPiEjY8eqKz/nnn9+gg0RFRTX4tXIcK76DfXuhZStM38FOVxMcqkd27dyOdVdhXBHO1iMiIk2mwUOsysrKuPPOO/noo498WY9P2YVfYJd+g9231+lS/Mb9pef7b4aM1tw9JyqxPURFQ0U5FO1yuhoREWlCDX6njImJoaCgAGOML+vxKfcHM3BvWud5kNge0/9UzLBzMB3SnC3MR2x+rueKD7rN5Q3jioAOaZCz2dPPJ0m3YEVEwkWjJtXp168fy5Yt81UtPmd69IPUTM+K3EW7sHPexn3fLVQ9fjd2/Sqny2s0+8mRDuN9B2Pa683bGybFMzLR5qqfj4hIOGlU8Ln00kvZuXMnf/3rX1m7di179uyhpKTkqA+nuC69hoj7/4rrmddx3Xo39B0MLhesX4n78buoeu5B7M4djtXXGPbgAex8z9w9rrMudLiaIKSRXSIiYalRnUJ+//vfA7Bjxw7mzZtX73ZTp05tzGEazcQ1h36nEtHvVOyeIuzsadgvP4IV3+FesxQz5qeYcy8Nqj4y9vMPoLwcMrIgW5NCesukZGDRyC4RkXDTqHf6Sy+9NKD7+NTFtEnE/PwW7FkX4Z7yH1i5CPv2a9hF83Hd8Puaoc6BzFZWYD99DwBz1oVBdw4CQkq659/8HdiqKs+MziIiEvIaFXzGjRvnqzpqfPTRR3z00UcUFhYCkJaWxmWXXUb//v19ehzTPgXXr+/Dfvs5dup/YMcW3A/fhrniRszQswI6TNhvPoN9e6BVG8zJ3k0mKUe0TYKYZlB2GAp2ejo7i4hIyPPpvZ3c3Fy+/vpriouLSUlJYeTIkcTFxXm1jzZt2jB+/Hg6dOiAtZbPP/+cxx57jMcee4z09HRflosxBnPqSOxJfXG/+DSsXoqd/DfYuAauugUTGeXT4/mCrazAvuu5dWjOviggawwGxuWCDumwdYOnn4+Cj4hIWPC6c/MHH3zAb37zG/bv31/r+e+++4477riDN954gzlz5vDKK69w5513HrXd8Zx88skMGDCADh06kJKSwpVXXkmzZs3YsGGDt6WeMNOqNa7f3I+59GpwubDzP8H9zP3Yg851zK6Pnf+JZ1XxVq0xI89zupygphmcRUTCj9dXfL777jvat29PfHx8zXNVVVX861//wuVycfPNN9O5c2cWL17MlClTmDFjBtdcc02DinO73Xz99deUlZWRnZ1d73YVFRVUVFTUPDbGEBsb67mic4K3rExEBJx3Ge70Trj/+SisW4H7L38g4td/xCR1aFD9vmYrKrDvTQPAdf7luGKa1fp6dVsD+TadPzS03SbV08GZvG1B+T3T+Va7w4HaHZ7t9ievg8+OHTsYPXp0redWrVrF/v37ufjiixk5ciQA6enpbNu2jSVLlngdfHJycpg4cSIVFRU0a9aM22+/nbS0+m9FzJw5k+nTp9c87tSpE48++iiJiYleHReADmMp79qdovt/Q1V+LvYvd9D23ieI6dHP+3352IF3p1G8p4iItkl0GHc1Jjqmzu2Sk5ObuLLA4G27D/XqT9EbLxFRsJMOHQIj3DaEznd4UbvDS7i225+8Dj4HDhygbdu2tZ5bsWIFAIMH114rqlu3bnz77bdeF5WSksLjjz9OaWkp33zzDc8//zwPPPBAveHn4osvZuzYsTWPqxNjUVFRrStBJyymOdz5GPz1T7i3baTgrptxXfc7XIOHeb8vH7ElB6j67788n597Kfm79xy1jTGG5ORk8vPzsdY2dYmOaWi7bWwLACpzt5G3PSfo+kvpfKvd4UDtDq92R0VFNeyihRe8Dj4JCQkUFxfXem7t2rXExMSQmZlZe+eRkUQ2YG6cyMjImpSblZXFpk2bmD17NjfeeGOd20dFRREVdfSblrW24T8wrVrj+sMjuF94CpZ+g/vfj2H3Fjk2fNw94xU4sM/TIXfomcdsV6PaHcS8bbdNaAuxcXCo1LP8R2rm8V8UgHS+w4vaHV7Crd1N0VavOzdnZWXx+eefc+jQIQC2b9/Oxo0b6du3LxE/mgslNzf3qKtDDeF2uxt25aaRTEwzXDffiRn9EwDsGy9ip76AdVc1aR1201rsFx8C4Pr5zUF3ZSJQGWNqZnBWB2cRkfDg9eWYyy+/nLvuuotf//rXpKens3nzZsBzu+nHFi5cSM+ePb3a/+uvv06/fv1ITEzk8OHDzJs3j9WrVzNx4kRvS/UJ44qAn06ANonYN17CfvIOdu9uXNf/rt4+Nr5kq6pw//cfnlqGjMZolmafMikZ2E1rtXSFiEiY8Dr4ZGRkcN999zFjxgwKCgro2rUrP/nJT8jKyqq13apVq4iOjua0007zav/79u3j+eefZ+/evcTFxZGZmcnEiRPp06ePt6X6jDEGc/bFuBPaYl96BhbPx71/L65bJ2JaxB/39Y1hZ/0PdmyBuBaYy67x67HC0pEZnHXFR0QkPDRoAsNu3bpx1113HXObnj178uSTT3q975tvvrkhJTUJ1+Dh2FatcT//CGxcg/vPd+D61b2Y5FS/HM+uWISd7Rm+bn5+M6ZlK78cJ5xVr9mFVmkXEQkLXvfxmTVrFrm5uf6oJSiYbr1x3fmoZ8mDgjzcf74du2aZz49j9xTifvEpzzFHno9rkHMjykJaypEOzQU7sRXlztYiIiJ+16Dgc9ttt/HLX/6SF198kaVLlzrS8dhJJjUD192PQ1Y3KD2I++k/4n5/Otbt9sn+7cEDuP/2EJQcgMwumHHX+2S/UodWrSGuBVg35IdvoBcRCRde3+r6z3/+w4YNG1i6dCmLFy/mww8/JDo6mp49ezJw4ED69+/v9zH4gcDEt8Z1+8PYV5/Hfj0XO2MydsNqXNf8GhOf0OD92oMluJ+6D7ZvgZatcN10B6aOofriGzUjuzauxuZuw6R3crokERHxI6+DjzGG7OxssrOzGTduHMXFxSxevJilS5fy2muv8cILL5CWlsaAAQMYOHAg2dnZuFxeX1gKCiYqGq79LXTtif3fv2HFd7jvuxXz0wmYU0d6Pd+P3VOE+++PQM4mT+i5/WFMO83a6W8mJQO7cbVGdomIhIFGr86ekJDAqFGjGDVqFFVVVaxZs4YlS5awaNEiZs2aRVxcHH379mXMmDF07drVFzUHFGMMZtjZ2E5dcU96BnZswb74NPbrT3Fd+DNM5+4ntB/3wi+x//07lB6EFvG4fv9QzSKa4mepmstHRCRcNDr4/FBERAS9evWiV69eXHXVVRQUFLB48WKWLFnCmjVrQjL4VDNpnXBNfBL74Qzsu1NhzTLca5ZBrwGY00Zheg3ExDWv9RpbUYFd8rVncsJ1nmU/6NgV14TfY9qnONCK8FQzskvBR0Qk5Pk0+PxYUlIS5557Lueee64/DxMwTGQkZsw47ODh2PemYb/+FFYuxq5cjI2IhORUaNkKoqJhdwEU5kP1SCKXC3P+5ZgxP8U0YJkPaYTqK2tFu7BlZZgY/09MKSIizvDJO2xOTg5LliyhsLAQgHbt2tG/f38yMsLzVo1pl4y55tfY8y/DfjkHu/RbyN8BuduO3rh1Iub0MzFDz8S0TWr6YsXTGb1lK89aaPnbIbOL0yWJiIifNCr4VFRU8O9//5svvvgC+H5VdGstr7/+OsOGDeP//u//GrRQaSgwSSmYS6+GS6/GFuyEwnzsgX1QfhjTtj20S4bE9pgQ7fwdVFIyYN0KbG4ORsFHRCRkNSqRvPbaa3zxxRecffbZnHfeebRv3x5jDPn5+cyePZs5c+bQokULrrnmGh+VG7xMUgdI6kDTr+suJ8KkpGPXrYC8Oq7KiYhIyGjUpYYvv/ySYcOGcf3115OSkkJERAQul4uUlBQmTJjA0KFD+fLLL31Vq4j/1KzSvt3hQkRExJ8aFXwqKyvJzs6u9+vdunWjqqqqMYcQaRKmeukKjewSEQlpjQo+ffv2ZenSpfV+fenSpY6uqi5ywo7M5cPuAuzhUmdrERERv/Eq+JSUlNT6uOKKKygsLOSJJ55gxYoVFBYWUlhYyPLly3n88ccpLCzkiiuu8FftIj5jmrf0rNsFoNtdIiIhy6vOzddfX/dimTk5OSxcuLDOr912221MmTLF+8pEmlpKBuzbi83LwWR1c7oaERHxA6+Cz6WXXur1+lMiwcKkZGDXLFM/HxGREOZV8Bk3bpy/6hBxXvXIrlwFHxGRUKWZ80SOqFkUVld8RERCVqOnVF67di2ffvopBQUFHDx4EGttra8bY3j88ccbexgR/6sOPsW7saUlmLgWztYjIiI+16jg8+677/Lqq68SHR1NSkoKLVrojUKCl4lrDq0TYW+RZ2RXl5OcLklERHysUcFn1qxZdO/enTvvvJO4uDhf1STinJR02FuEzduGUfAREQk5jerjU1ZWxtChQxV6JGR8389Hc/mIiISiRgWfnj17kpOjjqASQlI9S1dYdXAWEQlJjQo+1113HStXrmTWrFmUlJT4qiYRx9Rc8cnVKu0iIqGoUX18EhMTOfPMM3n11Vd57bXXiI6OxuU6Oku98sorjTmMSNPpkO75d38xtmQ/pkW8s/WIiIhPNSr4TJ06lRkzZtCmTRs6d+6svj4S9EyzWGibBLsLPPP5ZPdyuiQREfGhRgWfOXPmMGDAAP7whz/UeaVHJCilZHhWac/NwSj4iIiElEallcrKSgYMGKDQIyFFMziLiISuRiWWAQMGsGbNGl/VIhIYqtfsUvAREQk5jQo+l19+Obm5ubzwwgts3ryZ/fv3U1JSctSHSDAxR4a0k7ftqCVYREQkuDWqj89vf/tbALZu3cqcOXPq3W7q1KmNOYxI00pOA2Og5AAcKIb41k5XJCIiPtKo4HPppZdijPFVLSIBwcTEQGJ7KMyH3BwFHxGRENKo4DNu3Dhf1SESWFIyoDAfm7cdc1Jfp6sREREf0XAskTr8sJ+PiIiEDq+Cz+23387ixYu9PkhpaSm33347Gzdu9Pq1Io7QyC4RkZDkVfDZvn07paWlXh+kqqqK7du3c/jwYa9fK+KEH87lo5FdIiKhw+s+Pq+88gpTpkzx6jV645Cgk5wKxgWlB2HfHkho63RFIiLiA14FnxEjRjTqYK1ba3SMBAcTFQ3tO0B+rmdkl4KPiEhI8Cr43HLLLf6qQyTwpGRAfi42LwfTs7/T1YiIiA9oVJdIPbRml4hI6FHwEalPimdIu83VkHYRkVCh4CNSj5orPju3q4O+iEiIUPARqU/7DhARAYcPwZ4ip6sREREfUPARqYeJjIKkFM8D9fMREQkJfg0+5eXlFBXpf8oSvKqXrrBaukJEJCQ0eJHSAwcO8P7777No0SLy8/Nxu920adOG3r17c/bZZ5ORkcG3337L3/72N6ZOnerLmkWaTnU/n1xd8RERCQUNCj7r1q3jiSeeYP/+/cTFxZGWlkZVVRWFhYXMmTOHTz75hEsuuYTk5GRf1yvSpExKBhat2SUiEiq8Dj67d+/mL3/5C3Fxcdxxxx0MHDiw1tfXrl3L22+/zfTp0xV8JPj9cGSX241xqVuciEgw8zr4zJw5E4AHHniAxMTEo77evXt3unfvznvvvcfkyZMbX6GIk5I6QGQklJfB7gJopzAvIhLMvA4+S5YsYdSoUXWGnh8aM2YMpaWlrF69usHFiTjNRERAchrs2OoZ2aXgIyIS1LwOPsXFxaSlpZ3QtpdffrnXBYkEGpOSgd2xFZu7DdN3sNPliIhII3jdYSEuLo4DBw6c0LbLly9n+vTpXhclElCODGlHS1eIiAQ9r4NPt27d+Oqrr4673bZt23j88cd54403GlSYSKAwaR0BsDu2OlqHiIg0ntfB54ILLmDr1q384x//oKKios5t5s2bxx//+EcqKysbXaCI49I6ef7N34Gt52deRESCg9d9fLKzs7n22mt5+eWXWb58OaeddhopKSlUVlZSUFDAwoULKSgooHPnzgwZMoRXX33VH3WLNJ3WbSGuBZSWwM4cyOjsdEUiItJADZrA8NxzzyUjI4Np06Yxe/bsWitXJyUlcdVVV3Heeecxf/58nxUq4hRjDKR1hPUrsTu2YhR8RESCVoOXrOjRowf3338/paWl7Nq1i8rKSlq3bl1rmHu3bt24+eabfVKoiJNMeifs+pWeYe0iIhK0vAo+r7/+OhkZGWRkZJCamkpERARxcXF06tSpzu2TkpJISkrySaEijqperFTBR0QkqHkVfN5//33Ky8sBiIiIoEOHDmRkZJCenl4TiBR0JBSZtE5YgB1bsdZ6bn+JiEjQ8Sr4vPrqqxQUFLBjxw62b9/O9u3b2bBhQ62+PM2aNasVhNLT0+nZs+cJH2PmzJksWLCA3NxcoqOjyc7O5uc//zkpKSnelCriWykZYFxwYB/sL4ZWrZ2uSEREGsDrPj7Vt68GDBjAxo0bWbZsGWeddRY9evTA7XazceNGvvzySzZs2FDzmqlTp57w/levXs0555xD586dqaqq4n//+x8PPfQQTz31FM2aNfO2XBGfMDEx0L4D5Od6+vko+IiIBKUGd24GmDRpEqeddhrXXXddzXNDhw7lyiuv5M0332Tu3Llcc801Xu1z4sSJtR7feuutTJgwgc2bN9OjR4/GlCvSKCa1IzY/1zOyq2d/p8sREZEGaFTwycnJ4cwzzzzq+ZiYGMaPH09eXh6LFi3i9NNPb/AxSktLAWjRokW921RUVNSaTNEYQ2xsLMaYsOqLUd3WcGozNF27TXon7KKvYMeWgPge63yr3eFA7Q7PdvtTo4JPamoqS5cuZfTo0XV+vV+/fkyZMqXB+3e73bz88st069aNjIyMerebOXNmrTXBOnXqxKOPPnrcFeRDVXJyeK4g7u92H+ozgKK3/kvkrlySO3Tw67G8ofMdXtTu8BKu7fanRgWfiy66iGeffZapU6dy8cUXEx0dXevrW7dubdSyFZMmTWL79u08+OCDx9zu4osvZuzYsTWPqxNjUVFRvctqhCJjDMnJyeTn59eaVDLUNVW7bfNWAFTkbCFvew4mMspvxzoROt9qdzhQu8Or3VFRUX6/aNGo4DNkyBD27NnDa6+9xpw5cxg+fDhZWVkArFy5ks8++4yBAwc2aN+TJk1i8eLFPPDAA7Rt2/aY20ZFRREVdfSbkLU2rH5gqqndftp/68SapSts7raAWbpC5zu8qN3hJdza3RRtbVTwARg7diy9evVixowZzJkzp2aeH4A+ffpw4403erU/ay0vvvgiCxYs4P7779e8QBIwjDGQ3gnWrcDmbNbSFSIiQcjr4DNr1iwGDhxIampqzXMdO3bktttuo7Kykvz8fMrLy0lMTCQ+Pt7rgiZNmsS8efO44447iI2Npbi4GIC4uLijbqWJNDWTkYVdtwJyNjtdioiINECDgs9rr71Gu3btGDBgAAMGDKBnz55ERUURGRlJWlpaowr66KOPALj//vtrPX/LLbcwcuTIRu1bpNEyPLdy7XYFHxGRYOR18PnPf/7Dhg0bWLp0KYsXL+bDDz8kOjqanj171gShxnRMmjZtWoNfK+JvJr2zZ+mK7VuxbjfG5XK6JBER8YLXwccYQ3Z2NtnZ2YwbN47i4mIWL17M0qVLef3115k0aRJpaWkMGDCAgQMHkp2djUtvDhIqklMhKhrKDkHBTs9jEREJGo3u3JyQkMCoUaMYNWoUVVVVrFmzhiVLlrBo0SJmzZpFXFwcffv2ZcyYMXTt2tUXNYs4xkREQFpH2LIeu30LRsFHRCSoNDr4/FBERAS9evWiV69eXHXVVRQUFLB48WKWLFnCmjVrFHwkJJj0LOyW9bB9Ewwa6nQ5IiLihUYFn1/96leMGDGCyy67rM6vJyUlce6553Luuec25jAigaW6g7NGdomIBJ1GBZ+CggI2b97M5s2bycvLIy4ujtTUVNq3b++r+kQCjsnI8nRwztmMtTbs1tIREQlmjb7VtWjRIhYtWlTrufT0dC6++OJGLU4qErBSM8HlggP7YN8eSDj2zOIiIhI4Gh18IiMjufjii+nRoweVlZVs3ryZb775hueee45Vq1Z5PXOzSKAz0TGQnAZ5OZ6JDBV8RESCRqODz5gxY2r18enTpw8XXXQR7777Lq+++irZ2dmaeFBCjsnIwublYHM2YfoMcrocERE5QY2aYCc6Opo2bdrU+bWxY8cyZMgQPvjgg8YcQiQwZXrW6bLbNjlciIiIeKNRwSclJYUlS5bU+/WTTjqJHTt2NOYQIgHJZB6ZmmHrRmcLERERrzQq+Jx11lksXbqUF198sdaq7NXWrl1LTExMYw4hEpgyssC4oHg3tniP09WIiMgJalQfnzPPPJMdO3bw/vvv89VXX9G/f3/S09OJjIxkxYoVLFmyRP17JCSZmGaQkg6522DbRkgY7HRJIiJyAhrdufmaa67hlFNO4b333mPhwoV8+eWXNV8bNGgQV199dWMPIRKQTGYXbO427NaNmL4KPiIiwcCr4PP666+TkZFBRkYGqampREREAJ6+PCeddBJut5uCggIOHz5MYmIiLVq08EvRIgGhYxeY/wl2m/r5iIgEC6+Cz/vvv1/TlyciIoIOHTqQkZFBeno6GRkZZGZmkpyc7JdCRQKNyezimcF56wbN4CwiEiS8Cj6vvvoqBQUF7Nixg+3bt7N9+3Y2bNjA/Pnza7Zp1qxZTRCqDkU9e/b0eeEijkvrCBERnhmc9xZBm3ZOVyQiIsfhdR+fpKQkkpKSGDBgABs3bmTZsmWcddZZ9OjRA7fbzcaNG/nyyy/ZsGFDzWumTp3q06JFAoGJjoGUDNi+xTOsXcFHRCTgNapz86RJkzjttNO47rrrap4bOnQoV155JW+++SZz587lmmuuaWyNIgHLdOyK3b4Fu20jZsBpTpcjIiLH0ah5fHJycsjMzDzq+ZiYGMaPH0+3bt2OWsBUJKRkdgHAaiJDEZGg0Kjgk5qaytKlS+v9er9+/Vi+fHljDiES0ExHT/Bh20astc4WIyIix9Wo4HPRRRexYMECpk6dWufMzVu3bqWysrIxhxAJbKmZEBUNBw/ArjynqxERkeNoVB+fIUOGsGfPHl577TXmzJnD8OHDycrKAmDlypV89tlnDBw40CeFigQiExnlWbB04xrs5nWY5FSnSxIRkWNo9MzNY8eOpVevXsyYMYM5c+bUuvLTp08fbrzxxsYeQiSgmazu2I1rYPNaGDLK6XJEROQYGh18ADp27Mhtt91GZWUl+fn5lJeXk5iYSHx8vC92LxLQTFY3LGA3rXO6FBEROQ6fBJ+anUVGkpaW5stdigS+zt08/+Zuwx4+hGkW62w9IiJSr0Z1bhYRMAltPZMXWjds3XD8F4iIiGMUfER8wGR5rvrYzbrdJSISyBR8RHyhs4KPiEgwUPAR8QGT1d3zyaa1mshQRCSAKfiI+EJ6FkRGQsl+KMx3uhoREamHgo+ID5ioqO/X7dq81uFqRESkPgo+Ij5iOh+53bVhjbOFiIhIvRR8RHzEdO0JgN2wyuFKRESkPgo+Ir7StYfn353bsfuLHS1FRETqpuAj4iOmeUvPau0AG1Y7W4yIiNRJwUfEh0y2bneJiAQyBR8RX+raCwC7fqXDhYiISF0UfER8yFT389mxFVta4mwxIiJyFJ+uzi4S7kxCG0hKgYI82LgG+gxyuqQmZSsqIHcrNmcT7NoJHJnFuk07z3D/tE6YSP3ZERHn6C+QiI+Z7J7Ygjzs+lWYMAk+dk8h9pN3sV9+CIdK694GoFksZvg5mLMu8oREEZEmpuAj4mtde8K8OWHRz8eWl2Fn/hc7912oqvI82aIlZHTBpKRDRCRYN3bnDti0FkpLsB+9hf30Xcyon2AuHI+JjnG2ESISVhR8RHzMdOvlubqxbSP2UCkmNs7pkvzCblqL+6VnYVeu54luvXGdfRH0GohxHd190LrdsGox7vemeRZz/WgmdtkCXNf+5vtZr0VE/EzBR8THTNskSOoABTth3XLod6rTJfmce94c7H//7rnKk9AG1y9+iel98jFfY1wu6H0yrl4DYcV3uCc/D7tycT/6/zA/+z9cI85toupFJJxpVJeIH5ge/QGwq5c6W4iPWWtxv/0a9pW/QlUV5uShuO7/23FDzw8ZYzB9BuF64G+YwcM9t8L++3fcs17HWuu/4kVEUPAR8QvTox8AdtVSR+vwJWst9vV/Yt+dCoAZMw5z4x8wzVs0aH+meQvMhN9jxl7h2f87U7Cv/0vhR0T8SsFHxB+69QaXCwrysEW7nK6m0ay12GmTsJ+9D8ZgfvFLXBf9HGNMo/ZrjMF14XjMz28B48J+Nhs7Y7KPqhYROZqCj4gfmLjmkNUNCI3bXfat17AfzwLwhJ5hZ/t0/64R52KuusVzrA/exP3+dJ/uX0SkmoKPiJ+Yk/p5Pgny4OP+8iPs7GkAmPE34Rp6ll+O4xp2NubyawGwMybj/uYzvxxHRMKbgo+In5ieRzo4r1mGdVc5XE3D2PUrsa/9EwDzkytxnTHGr8dznX0x5pxLPMd+9W/Y7Vv8ejwRCT8KPiL+0rErxDaH0hLYttnparxmi3bh/sdfoKoSc/JQzE+uaJLjmkuugp79obwc998fwR480CTHFZHwoOAj4icmIgK69wbArljocDXesRXlntBTsh8yu2Cu+U2jOzKfKOOKwHXD7ZDYHop24Z70tEZ6iYjPKPiI+JHpewoAdum3DlfiHTvtRcjZBC1a4rrlLkxM0y4rYZq3xHXzXRAZBSu+w342u0mPLyKhS8FHxI9Mn0FgXLB9S9AMa3cv+KImaLiuvw3Tpp0jdZiMLMxl1wBg33gJm5vjSB0iEloUfET8yLSMh649gOC46mML87GTnwfAnD8O02ugo/WYUWOh1wCoKMf9whPYigpH6xGR4KfgI+Jnpn9w3O6ybjful5+FskPQpQfmgiudLskzweE1v4GWrWDHVuy7U5wuSUSCnIKPiJ9V9/Nh/SpsyX5nizkG+/EsWL8KYprhuu63ns7ZAcC0ao3rZzcDnskN7baNDlckIsFMwUfEz0y7ZEjr5FmMc3lgju6yeTnYma8CYMZd76k5gJiBQzADTwe3m6qXntUtLxFpMAUfkSZQc7tryTcOV3I0W1mJe9LTUFkBvQZifLwcha+Y8TdBi3jYsZX9b7zsdDkiEqQUfESagOl/mueTlYuxpSXOFvMjdvY0z9D1uBa4rv5lk83X4y0Tn4C54gYA9k97CVuY73BFIhKMAjL4rF69mr/85S/cdNNNjBs3jgULFjhdkkjjpHWElAyorMAumu90NTXslg3Y946sw/XzmzEJbR2u6NjM4OGYk/p6RnlNfcHpckQkCAVk8CkrK6Njx45cf/31Tpci4hPGGMxpZwBgv/7U4Wo8bHkZ7hefBrcbM2gYrkHDnC7puIwxuK68ESIisEu/xa74zumSRCTIRDpdQF369+9P//79T3j7iooKKn7Q2dEYQ2xsrOfNJkAv2/tDdVvDqc0QPO12nXoGVTMmw4bVULSr0R2IG9tu98xXIX8HtGqD62f/F/Dfv2omNZO4C67kwMz/4p7yHyJO6oeJinK6LL8Llp9zX1O7w7Pd/hSQwcdbM2fOZPr06TWPO3XqxKOPPkpiYqKDVTknOTmwRuQ0lYBvd4cOFPQdRNnSBTRftYhWV07wyW4b0u7Dy76j8ONZACT+7j5iu3bzSS1NxT1+Agc/ex93wU5afPMJ8eOudbqkJhPwP+d+onaLr4RE8Ln44osZO3ZszePqxFhUVFTrSlCoM8aQnJxMfn5+WC3qGEztdg88HZYuYP9Hb3NwxPmN+t9NQ9ttD5VS9eR9nn0MP4fitM4U79zZ4DqaWnW7zWXXwH+eZN//XqCk50DHltZoKsH0c+5Land4tTsqKsrvFy1CIvhERUURVcelbmttWP3AVFO7A1i/UyGmGRTsxG5cDV16NHqX3rbb/fq/YHcBJLbHXH5t4H/P6jN4BMx9Hzauxj3tRVw33eF0RU0iKH7O/UDtDg9N0daA7NwsEqpMs1jMyacDYD99r8mPb7+b5+lcbVy4rv0tpllck9fgK8YYXONvAuPytGvNMqdLEpEgoOAj0sTM6AsAsIu+wu4pbLLj2j1FuF/9u6eG8y7FZPdssmP7i0nvhBl5HgDuqS9gq6qcLUhEAl5ABp/Dhw+zdetWtm7dCkBBQQFbt26lqKjI2cJEfMCkd4JuvcHtbrKrPjULkJaWQGYXzE+cX4DUV8yFP4PmLSF3G/bLj5wuR0QCXEAGn02bNnHHHXdwxx2ee/aTJ0/mjjvuYOrUqQ5XJuIbrrMuAsB+8SH28CG/H89+/DasWQbRMbgm3IaJDInufQCY5i1qgpyd9Tq29KDDFYlIIAvIv349e/Zk2rRpTpch4j+9B0L7VNiVi53/CWbU2OO/poHs9i21FyBNTvPbsZxiRpyL/Ww25O/Azp6GuSx8hreLiHcC8oqPSKgzLhdm9E8AsB+9ha0o98txbHkZ7heehMpK6DsYM/wcvxzHaSYyEte46wCwn7yDLQie4fki0rQUfEQcYoaMhoS2sLsA+/E7Pt+/tRb7v39DXg60bIXrF4G7AKlP9BoIPfpDZSXuN192uhoRCVAKPiIOMTExmEt+AXhWSLf7i326f/vFh9h5c8AYXNffholP8On+A40xxnPVx7hg8dfYdSudLklEApCCj4iDzCkjILMLHD6EnfW6z/ZrN631XO0BzMVXYXqe+Np3wcykZmKGnw2Ae9okrNvtcEUiEmgUfEQcZFyu7/umfPERdtumRu/TFuThfv5hqKqEgUMw517a6H0GE3PhzyA2DnI2Yb+e63Q5IhJgFHxEHGaye2EGng7WjfvfjzVqOLbduxv3U/fBgX2Q3gnXNb8J7X49dTAtW2HG/BQAO3Nyk0wXICLBQ8FHJACYn98MbdpBwU7ck//aoPVq7IF9uJ/5o2cdrqQOuH57P6ZZrB+qDXxm1Fholwz79mI/eNPpckQkgCj4iAQA0yLes8hmRCQsmo/9aKZXr7f5O3D/+Q+eEVwJbXD97kFMfGs/VRv4TFQUrsuuAY5MF7C76ZYGEZHApuAjEiBMVjfM5Z6J9+z0l3HPeOWEOue61yzD/ec7oDAfEtvj+v1DmMT2/i438PU/DbJ7QUU5dsZkp6sRkQCh4CMSQMyosd8vv/D+m9j/PIEt3l3ntnbfXnY/eR/uJ+/xrMGV1Q3XXY+H5MzMDWGMwfXT68EY7ILPsZvWOl2SiASAgFyyQiRcGWMwF1yJOzEJO/lv2O/mYZd84xn23q0XJq459sB+7MrFsGoxpWWHwRjMsLMxP52AiY5xugkBxWR0xgwZhf3qE9zTJuH6f4+FXWdvEalNwUckALmGjMYmJuOe+SpsXI2d/wnM/4Qfd3mO6toD9+XXQ6eujtQZDMxFV2G/+wo2r8Mu+MITIkUkbCn4iAQok92TiDv/4pmM8KuPsXsKofQgRERiTuqLq/fJtD9tGPm7djVoFFi4MAltMOddhn3rv9gZr2D7nYqJ0ZUxkXCl4CMS4Ezn7pjO3Y9+3hiMS930ToQ560LsFx/CnkLsnLcwY3/qdEki4hD91RSRkGeif7Au2gdv1tthXERCn4KPiIQFM3g4ZHWDssPYt/7rdDki4hAFHxEJC57h7RMAsPM/9cm6aCISfBR8RCRsmKxumMEjwFrc015Qp3CRMKTgIyJhxVzyC4iKhvWrYMnXTpcjIk1MwUdEwopp2w5zzsUAuKe/jK2ocLgiEWlKCj4iEnbMOZdAqzZQmI/99B2nyxGRJqTgIyJhxzSLxVx8FQD2vWnY/cXOFiQiTUbBR0TCkjntDMjoDIdKtXq7SBhR8BGRsGRcLlxX3gDgWRJk/UqHKxKRpqDgIyJhy3TpgRl+DgDuV/+ujs4iYUDBR0TCmrnkamjZCvJ3YD+c4XQ5IuJnCj4iEtZM8xaY6hmd35uKzctxuCIR8ScFHxEJe2bwcOg1ECorcb/8HLaqyumSRMRPFHxEJOwZY3BddSvENoct67Efv+10SSLiJwo+IiKAaZOI+en1ANi3XsPu3O5wRSLiDwo+IiJHmCGjj9zyqsD9wpMa5SUSghR8RESOMMbguvqX0KIl5GzGvvVfp0sSER9T8BER+QGT0BbX1b8GwH40E7tqicMViYgvKfiIiPyI6XcKZuT5ALhffBpbvNvZgkTEZxR8RETqYC6/FlIzYX8x7n8+iq1Ufx+RUKDgIyJSBxMdg+uWuzxD3DetxU6d5HRJIuIDCj4iIvUwSSm4JtwGgP1sNu4vPnS4ImfZykrswQPYvbuxxbux+/diyw5jrXW6NJETFul0ASIigcz0GYS5YDx21uvY1/6BbZ2I6T3Q6bL8xlZWwo4t2O1bIHcbNn8H7CmCvUVw+FDdL4qM8qx31jYJ0649dEjHpGdBRhYmPqFJ6xc5HgUfEZHjMGN/CoU7sV/Pxf2vR3H94RFMZheny/IJay3k5WCXLfCMYNu6HsrLj/0i15GbBW6359/KCk8w2luE3bjas9/qbVMzMSf1w/ToB9k9MTHN/NEMkROm4CMichzGGPjFL7HFe2DNMtzPPoDr9w9jUjOcLq1BbFUVrFvhCTvLF0LRrtobxLWAzM6Y1I6Qko5pmwRtEqFFPDSLxURGefZjLZQdgpIDsL8Yu7sACnZ6rhRt3wy78jyf527zLAMSGQldemAGD8cMHIKJa9H0jZewp+AjInICTGQUrpvvwv3ERMjZhPvJiUEXfuyeQuy8Odh5H3uu0FSLjIKT+mL6nIzp1hvap2Jcx+8CaoyBZnGej8T2mKxutY93YD927XJYsxS7einsLoC1y7Frl2Nf/yf0GYzr1JHQayAmKsq3jRWph4KPiMgJMrFxuG57EPdT90LOZk/4+e0DmIwsp0url62qghXfeTpmr1wM9sjtqRYtMf1OxfQdBCf188stKNMyHjNoKAwa6rk6VLATu+Rr7DefQe42WDwf9+L50LwlZuhZmDPO91xdEvEjBR8RES+Y5i1x3fan78PPY3fhuukPmN4nO11aLXZ3wZGrO3OgeM/3X+jWGzP8HEz/05r0KosxBtqnYM69FM69FLtjC/abz7Dffg7Fe7AfzsB+9Bb0HYxr9FhPncY0WX0SPhR8RES8ZJq3xPX7h3D/4y+wdjnuvz6EGXcdZvRPHH2ztpWV31/dWbUYqoeZt4jHnD4aM/RsTHKqY/X9kEnrhLmsE/aSX8CKRbg/eQfWLIOl3+Be+o2nU/Q5l+AaPNzpUiXEKPiIiDSAiWuB6zd/xP7379ivPsFOfQG7djmuq3+NaRnfpLXYol3YL+dgv/oY9v3g6k73Ppjh53qW4AjQPjTGFQF9BxPRdzA2Lwc79z3s13M9naJffJqqt1/jwLhrsL0HQ1S00+VKCFDwERFpIBMZBVf/GtI6Yd98GZYtwP3Arz1XfwYN8+vVH1tRASsWeq7urF76/dWdlq0wQ0Zjhp2NaZ/it+P7g0nJwPzsZuzFV2E//wA7523YXUDxPx7ztGv0Tzz9gDQaTBrB2BCecrOwsJCKivBZX8cYQ4cOHdi5c2dYzaSqdqvdgcBu34L7P0/Azu2eJ7qchOuyazGdu/tk/8YYkpOSyPviY+y3n2MXfQWlB7/f4KS+uIafA/1OqRluHuxseRnM/wTz8SyqduV5noyNw4w4D3PWBZj41s4W6EeB+nPub1FRUbRr186vx1DwCSHh+ouidqvdgcKWl2E/mol9/00oL/M8mdUNM2qs53ZTA0ZO2Ypy2LAau+I7XEu+pmp34fdfTGiDOfUMz9WdpA4+akVgMcaQ3K4debOm4X5/OuTleL4QGeUZCXbOxZjE9s4W6QeB/HPuT00RfHSrS0TER0x0DGbsFdghZ3qWuPj2M9i8Drt5HbZ6rpxuvTHpnSAl3dPpuHoyQLcbDh2Eol3YgnzI2YTdsh62rKuZSbkKIK45ZuDpmMHDPTMhuyIca29TMZGRuE47AwYP93Tenv2G5/v62WzsFx9gBo/AnHcpJiV45lQS5yj4iIj4mGmTiLnm19hLrsJ+9r6ns27RLljxHXbFd9T6/3t0jKd/TsUxlolo1RrTsz9tR49hb0onzwzIYci4XJ7h7n0GwfqVuGdPh9VLsN/MxX4zF/qdiuv8yzCdsp0uVQJYeP72iIg0ARPf2rPA6U+uhLzt2OULsds2wPatULjTE3iqb4lVi0/wzIKcmgmdsj1v4qmZuFwuYjt0oDjMbn3UxRgD3XoT0a03dusG3O+/CUu+/n4ofLfeuM66CHoPPKEZqCW8KPiIiPiZMQZSM2otb+G5tVUKpSWeRT+joj3rYEXHOFhp8DEduxJx8//D7tyB/eBNz+3FdStwr1sBSSmYM3+COW0Uplms06VKgFDwERFxgHG5oHkLz4c0mumQhrn2N9gLxmPnvov94iMoyMO+/i/sW//1dIQeejamQ5rTpYrDFHxERCRkmLbtMJddix17BXb+J9hP3vGsEfbRW54lMbqc5AlBJw/1y/pkEvgUfEREJOSYZrGYUWOxI8/3LInx5Yew4jvYuAa7cQ12yn88o+MGDvGMtguRuY+OxVoLZYfgwH4oOQAl+7El+6Fkv+e2a3lZrQ/rruL7nvieT4wrAqKiPLdmo6IhMgpi445cvWyJOfIvcUf+jY0LuDXXFHxERCRkeUaCDSKi7yBs8W7s/E89C7cW5mO/+tizzEdsHKbPIMyAIdBzACYmOPtZ2YoK2FMIu3dhiwpgd4FneoTdBbC7EEr2QWVl447h7dcjIqBVG2jd1jPvVELbI5+3xbRuC22ToHXbJp2WQcFHRETCgkloizn/cux5l8H6Vdjv5mGXfAP79nhmw/72c89UAZ2yPfMtZfeCzt0DpsO5rayEvUWeMFO0q3awKSrwrNN2IiP+oqKhZTy0aOWZS6pFPMTGeqZWiI7xfD065gfTJhiovmhT5YbKcs/0CxUVnjmmDpdiDx6AgyVQ/W9piefKUVWVJ4zt8Uy8+cPqaj6PiIA27SCxPRG9BsA1t/rmG1YPBR8REQkrnuHwvTDdemGvvBE2r8Uu+hq75GtPmNiwGrthNZapnjf/9CzP9AKpGZjUjp6V4+MTfFqTtdazBMneQthThN1TRHHFYapythy5YlMAe/eAdR97R9Exnqsoie0xiUnQ9si/bZI8UyW0iG+yK1q2ohwO7IPiPbB3N7Z4N+zdDcW7scV7PCFuT6HnKlRhvucqXPnh8A0+H3zwAe+88w7FxcVkZmZy3XXX0aVLF6fLEhGREGJcLujSA9OlB3bcdVC4E7tuJaxbgV23wvOmvWW9ZxZtfnCVolms5xZOQhvMkX+JjTtyteRI35eoKHBbqKz4/qOiwhNwDu7HlhzwXCEp2e8JNWWHatV2oK6Co6KPBJskz1IdbX8QbhLbe4JNgPSpMVHRnis5bTxLUNRVlXW7Pd/jonxs0a46t/G1gAw+8+fPZ/Lkydxwww107dqV9957j4cffphnnnmGVq1aOV2eiIiEIGOMZ+6fpBQYdrbnKkzhTsjZjM3dht2xDfK2ea5OHD4Eh3NhV+5x+714pUU8tEnEtGlH87RMSmPisG0SMW2ToF17aJkQMMHGF4zLBW0SPW3O7oUryv+dzAMy+Lz77ruMHj2aM844A4AbbriBxYsXM3fuXC666CJnixMRkbBQHYRISsGcPLTmeVtW5rlNs2+P55bNvj2eqxZlhz19XirKsZUVnn4wxngWVK2+AlQzCqoltIzHNI+HFi2PdABOrLkNZYyhdYcOHNZM3T4XcMGnsrKSzZs31wo4LpeL3r17s379+jpfU1FRUWsVdmMMsbGxGGNCKhkfT3Vbw6nNoHar3eFB7Q6cdptmzaBDmufDX8cIwHY3haZob8AFn/379+N2u0lISKj1fEJCAnl5eXW+ZubMmUyfPr3mcadOnXj00UdJTEz0Z6kBKzk52ekSHKF2hxe1O7yo3eIrARd8GuLiiy9m7NixNY+rE2NRUVGtK0GhzhhDcnIy+fn5YXVpVO1Wu8OB2q12h4OoqCi/X7QIuOATHx+Py+WiuLi41vPFxcVHXQWqFhUVRVQdHaKstWH1A1NN7Q4vand4UbvDS7i1uyna6vL7EbwUGRlJVlYWK1eurHnO7XazcuVKsrOzHaxMREREgl3AXfEBGDt2LM8//zxZWVl06dKF2bNnU1ZWxsiRI50uTURERIJYQAafIUOGsH//fqZNm0ZxcTEdO3bk7rvvrvdWl4iIiMiJCMjgA3Duuedy7rnnOl2GiIiIhJCA6+MjIiIi4i8KPiIiIhI2FHxEREQkbCj4iIiISNhQ8BEREZGwoeAjIiIiYUPBR0RERMKGgo+IiIiEDQUfERERCRsBO3OzL0RGhnTz6qV2hxe1O7yo3eEl3NrdFO01NgTXu6+oqCAqKsrpMkRERKQB/Pk+HpK3uioqKnj22Wc5dOiQ06U0qUOHDnHnnXeq3WFC7Va7w4HaHX7tfvbZZ6moqPDbMUIy+AB89dVXhODFrGOy1rJlyxa1O0yo3Wp3OFC7w6/dX331lV+PEbLBR0REROTHFHxEREQkbIRk8ImKiuKyyy4Luw7OarfaHQ7UbrU7HKjd/mt3SI7qEhEREalLSF7xEREREamLgo+IiIiEDQUfERERCRsKPiIiIhI2gnYRkA8++IB33nmH4uJiMjMzue666+jSpUu923/99ddMnTqVwsJCkpOT+dnPfsaAAQOasOLGmTlzJgsWLCA3N5fo6Giys7P5+c9/TkpKSr2v+eyzz/j73/9e67moqChee+01f5frM9OmTWP69Om1nktJSeGZZ56p9zXBfq4Bbr31VgoLC496/uyzz2bChAlHPR+s53r16tXMmjWLLVu2sHfvXm6//XYGDx5c83VrLdOmTeOTTz7h4MGDdO/enQkTJtChQ4dj7tfbvw9N7VjtrqysZMqUKSxZsoSCggLi4uLo3bs348ePp02bNvXusyG/K03teOf7+eef5/PPP6/1mr59+zJx4sRj7jeYzzfAuHHj6nzdz3/+cy644II6vxbo5/tE3rPKy8uZPHky8+fPp6Kigr59+zJhwgQSEhLq3W9D/yb8UFAGn/nz5zN58mRuuOEGunbtynvvvcfDDz/MM888Q6tWrY7aft26dTz77LOMHz+eAQMGMG/ePB5//HEeffRRMjIyHGiB91avXs0555xD586dqaqq4n//+x8PPfQQTz31FM2aNav3dbGxsTz77LNNWKnvpaenc++999Y8drnqv1AZCuca4M9//jNut7vmcU5ODg899BCnnXZava8JxnNdVlZGx44dGTVqFE888cRRX3/77bd5//33ufXWW0lKSmLq1Kk8/PDDPPXUU0RHR9e5T2//PjjhWO0uLy9ny5YtXHrppXTs2JGSkhJefvllHnvsMf7yl78cc7/e/K444XjnG6Bfv37ccsstNY+Pt2hlsJ9vgH//+9+1Hi9ZsoR//vOfnHLKKcfcbyCf7xN5z3rllVdYvHgxt912G3FxcUyaNIknn3ySP/3pT/XutyF/E34scL5LXnj33XcZPXo0Z5xxBmlpadxwww1ER0czd+7cOrefPXs2/fr144ILLiAtLY0rrriCrKwsPvjggyauvOEmTpzIyJEjSU9Pp2PHjtx6660UFRWxefPmY77OGENCQkKtj2Djcrlq1R8fH1/vtqFwrgHi4+NrtXnx4sW0b9+eHj161PuaYDzX/fv354orrqj1v99q1lpmz57NJZdcwqBBg8jMzOSXv/wle/fuZeHChfXu09u/D044Vrvj4uK49957GTJkCCkpKWRnZ3PdddexefNmioqKjrlfb35XnHCsdleLjIys1YYWLVocc5/Bfr6Bo35vFy5cSM+ePWnfvv0x9xvI5/t471mlpaV8+umnXH311fTq1YusrCxuueUW1q1bx/r16+vcZ0P/JvxY0F3xqaysZPPmzVx00UU1z7lcLnr37l3vN2v9+vWMHTu21nN9+/b16hsVaEpLSwGO+0fh8OHD3HLLLVhr6dSpE1deeSXp6elNUaLP5Ofnc9NNNxEVFUV2djbjx48nMTGxzm1D8VxXVlby5ZdfMmbMGIwx9W4XCuf6hwoKCiguLqZPnz41z8XFxdGlSxfWr1/P6aefftRrGvL3IRiUlpZijCEuLu6Y23nzuxKoVq9ezYQJE2jevDm9evXiiiuuoGXLlnVuG4rnu7i4mCVLlnDrrbced9tgOt8/fs/avHkzVVVV9O7du2ab1NRUEhMTWb9+PdnZ2UftoyF/E+oSdMFn//79uN3uo/43m5CQQF5eXp2vKS4uPuqSZ6tWrSguLvZTlf7ldrt5+eWX6dat2zFv36SkpHDzzTeTmZlJaWkps2bN4p577uGpp56ibdu2TVhxw3Xt2pVbbrmFlJQU9u7dy/Tp07nvvvt48skniY2NPWr7UDvXAAsWLODgwYOMHDmy3m1C4Vz/WPU58+Z8NuTvQ6ArLy/ntdde4/TTTz9m8PH2dyUQ9evXj1NOOYWkpCTy8/P53//+xyOPPMLDDz9c522cUDzfn3/+Oc2aNTvmVTEIrvNd13tWcXExkZGRNG/evNa2x/r9bsjfhLoEXfARmDRpEtu3b+fBBx885nbZ2dm1UnN2dja/+93vmDNnDldccYW/y/SJ/v3713yemZlZ88v+9ddfM2rUKAcrazpz586lX79+x+zYGgrnWo5WWVnJ008/DVBnp/YfCoXflR/+jz0jI4PMzEx+9atfsWrVqlpXBkLZ3LlzGTZs2HH7qwTT+T7R96ymEnR9fOLj43G5XEelu+Li4nr7NCQkJLBv375az+3bty8o+kD82KRJk1i8eDF//OMfvf6ffGRkJJ06dSI/P99P1flf8+bNSUlJqbcNoXSuAQoLC1m+fDmjR4/26nWhcK6rz5k357Mhfx8CVXXoKSoq4p577jnuba4fO97vSjBo3749LVu2rLcNoXS+AdasWUNeXl6Dgkugnu/63rMSEhKorKzk4MGDtbY/1u93Q/4m1CXogk9kZCRZWVmsXLmy5jm3283KlSvrvCcInv/9rlixotZzy5cvp2vXrn6t1ZestUyaNIkFCxZw3333kZSU5PU+3G43OTk5tG7d2g8VNo3Dhw+Tn59f7w95KJzrH5o7dy6tWrXyejh+KJzrpKQkEhISap3P0tJSNm7cWO/vekP+PgSi6tCTn5/PvffeW28fl2M53u9KMNi9ezclJSX1/hyHyvmu9umnn5KVlUXHjh29fm2gne/jvWdlZWURERFR6/c7Ly+PoqKies9dQ/4m1CUob3WNHTuW559/nqysLLp06cLs2bMpKyur6QPxt7/9jTZt2jB+/HgAzj//fO6//37eeecdBgwYwFdffcWmTZu48cYbHWyFdyZNmsS8efO44447iI2NrfkfTlxcXM0l0R+3e/r06XTt2pXk5GQOHjzIrFmzKCws9PrqgZMmT57MySefTGJiInv37mXatGm4XC6GDh0KhOa5ruZ2u/nss88YMWIEERERtb4WKue6+o91tYKCArZu3UqLFi1ITEzk/PPPZ8aMGXTo0IGkpCSmTJlC69atGTRoUM1rHnzwQQYPHsy5554LHP/vQyA4VrsTEhJ46qmn2LJlC3feeSdut7vm971FixY1w7t/3O7j/a4EgmO1u0WLFrzxxhuccsopJCQksGvXLv773/+SnJxM3759a14Taue7ujNyaWkp33zzDVdddVWd+wi2832896y4uDhGjRrF5MmTadGiBXFxcbz44otH3bb/7W9/y/jx4xk8eDDGmBP6m3A8QRl8hgwZwv79+5k2bRrFxcV07NiRu+++uybpFhUV1Rr90q1bN379618zZcoU/ve//9GhQwf+8Ic/BNW8Lh999BEA999/f63nb7nllppf8B+3u6SkhH/9618UFxfTvHlzsrKyeOihh0hLS2uqshttz549PPvssxw4cID4+Hi6d+/Oww8/XDNsMxTPdbUVK1ZQVFTEGWeccdTXQuVcb9q0iQceeKDm8eTJkwEYMWIEt956KxdeeCFlZWX861//orS0lO7du3P33XfX6v+wa9cu9u/fX/P4eH8fAsGx2n355Zfz3XffAXDHHXfUet0f//hHevbsCRzd7uP9rgSCY7X7hhtuICcnh88//5yDBw/Spk0b+vTpw09/+lOioqJqXhNq57t69Nb8+fOx1tYbXILtfJ/Ie9bVV1+NMYYnn3ySysrKmgkMfygvL69mRBhwQn8TjsdYa23DmiUiIiISXIKuj4+IiIhIQyn4iIiISNhQ8BEREZGwoeAjIiIiYUPBR0RERMKGgo+IiIiEDQUfERERCRsKPiIiIhI2FHxEREQkbCj4iIiISNgIyrW6RCQwVVVV8fbbb/PJJ5+wb98+OnfuzE033URKSkq9r3n++ef5/PPPAUhPT+fJJ5885jGmTZvG9OnTmTZtmk9r/7H33nuPV155pebxCy+8EDDrIIlIwyn4iIhPuN1unnjiCdavX8+YMWOIjo5m5syZPProozz11FNHrTD/Qy1btuTqq6+mefPmTVjxsfXr14+WLVuyYMECFixY4HQ5IuIjCj4i4hOzZs1i5cqVPPLII6SnpwOQkJDAc889x6pVq+jTp0+9r23WrBnDhw9vqlJPSGpqKqmpqeTn5yv4iIQQ9fERkUYrLS1l5syZnH/++TWhByA7OxuAbdu2OVWaiEgtuuIjIo325ZdfcvjwYc4888xaz0dGev7EHDp0qEH7Xbt2La+88go5OTm0adOGCy64oN5t9+zZw5QpU1iyZAkHDx4kOTmZsWPHMmrUqFrbrVq1ildffZXt27fX7HPv3r1N0m9IRJyn4CMijbZgwQLS0tKIiYlh//79Nc8XFRUBnltZ3srJyeGhhx4iPj6eyy+/nKqqKqZNm0ZCQsJR2xYXFzNx4kQAzjnnHOLj41m6dCn//Oc/OXToEGPGjAFgy5YtPPLIIyQkJHD55ZfjdruZPn26Oi2LhBEFHxFpFLfbzfr16ykrK2PChAl1bpOUlOT1fqdOnYq1lgcffJDExEQATjnlFG6//fajtp0yZUpN5+qWLVsCcPbZZ/PMM8/wxhtvcNZZZxEdHc20adNwuVz86U9/ok2bNgAMGTKE3/3ud17XJyLBScFHRBolPz+fsrIyLrjggqM6MM+dO5evvvqKjIwMr/bpdrtZtmwZgwYNqgk9AGlpafTt25clS5bUPGet5dtvv+W0007DWlvrilO/fv2YP38+mzdvJjs7mxUrVjB48OCa0AOQnJxMv379WLRokbdNF5EgpOAjIo1SWFgIQM+ePY8KPm+//TatWrU65jw+ddm/fz/l5eV06NDhqK+lpKTUCj779+/n4MGDfPzxx3z88cf17m/fvn2Ul5eTnJx81Nfrek5EQpOCj4g0SllZGQAxMTG1ni8tLWXNmjWcccYZfj2+tRaAYcOGMWLEiDq3yczMxO12+7UOEQkOCj4i0ijVHZcPHz5c6/nPPvuMyspKzj77bK/3GR8fT3R0NDt37jzqa3l5eUdtGxsbi9vtPuZcQW63m6ioKPLz84/6Wl3PiUho0jw+ItIomZmZGGNYtWpVzXO7d+/mzTffZPjw4WRmZnq9T5fLRd++fVm4cGHNyDCAHTt2sGzZsqO2PeWUU/j222/Jyck5al/VfX5cLhe9e/dm4cKF7Nmzp+br+fn5LF261OsaRSQ46YqPiDRKq1atGDRoELNnzyYmJoa4uDjee+892rRpw3XXXdfg/Y4bN46lS5dy3333cfbZZ+N2u3n//fdJT08/akLE8ePHs2rVKiZOnMjo0aNJS0ujpKSEzZs3s2LFCl566aWafd5zzz3ce++9Nfv84IMPSE9PZ+vWrY35NohIkFDwEZFG+7//+z/++c9/8s4779CsWTNOO+00rrzySmJjYxu8z8zMTCZOnMjkyZOZNm0abdu2Zdy4cezdu/eo4JOQkMAjjzzC9OnT+fbbb/nwww9p2bIl6enp/OxnP6vZLisri7vvvptXX32VqVOn0rZtW37605+yY8cOcnNzG1yriAQPY6t7BoqIOOD5559n5cqVPProo0RERDiyUOljjz3Gjh07eO6552qeKy8v5/Dhw8yaNYtZs2ZpdXaREKErPiLiuN27dzNhwgTS09N58skn/Xqs8vJyoqOjax7v3LmTJUuWHDUibM6cObzyyit+rUVEmp6Cj4g46sILL2TYsGFAw5a28NYvf/lLRo4cSVJSEkVFRXz00UdERkZy4YUX1trulFNOqbXgalxcnN9rExH/060uEQkrf//731m1ahXFxcVERkaSnZ3NlVdeSVZWltOliUgTUPARERGRsKF5fERERCRsKPiIiIhI2FDwERERkbCh4CMiIiJhQ8FHREREwoaCj4iIiIQNBR8REREJGwo+IiIiEjYUfERERCRsKPiIiIhI2Pj/zMPMRjz4BzoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(\n", - " results[\"channel_2\"][\"Theta_deg\"], \n", + " results[\"channel_2\"][\"Theta_deg\"],\n", " results[\"channel_2\"][\"sigma_mb_sr\"],\n", - " label=\"Fresco\"\n", + " label=\"Fresco\",\n", ")\n", "plt.legend()\n", - "plt.xlim([0,20])\n", + "plt.xlim([0, 20])\n", "plt.xlabel(r\"$\\theta$ [deg]\")\n", "plt.ylabel(r\"$d\\sigma/d\\Omega$ [mb/Sr]\")" ] + }, + { + "cell_type": "markdown", + "id": "666f8a25-c148-4cc6-9e67-a6340ae35d97", + "metadata": {}, + "source": [ + "## Testing optical model parameter dependence\n", + "\n", + "We can now run an ensemble of calculations with different parameters, and compare how they change the cross section. There are many different parameters in this calculation, so let's focus on just the p + $^{17}F$ interaction, but you can try perturbing other parameters yourself!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d3da923f-820f-4a5c-90f3-dad3962db92b", + "metadata": {}, + "outputs": [], + "source": [ + "params_to_perturb = {\n", + " \"rC_p17F\": [1.0, 1.2, 1.4],\n", + " \"V_p17F\": [40, 50, 60],\n", + " \"r_p17F\": [1.0, 1.2, 1.4],\n", + " \"a_p17F\": [0.5, 0.65, 0.8],\n", + " \"Vso_p17F\": [4, 8, 12],\n", + " \"rso_p17F\": [1.0, 1.2, 1.4],\n", + " \"aso_p17F\": [0.5, 0.65, 0.8],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "27b6fef3-e94d-4520-b5e2-496e980daac2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████| 7/7 [01:25<00:00, 12.16s/it]\n" + ] + } + ], + "source": [ + "results = {}\n", + "for param_name, values in tqdm(params_to_perturb.items()):\n", + " results[param_name] = []\n", + " for val in values:\n", + " params = base_params.copy()\n", + " params[param_name] = val\n", + " cfg = bfrescox.Configuration.from_template(\n", + " TEMPLATE_FILE_PATH,\n", + " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", + " params,\n", + " overwrite=True,\n", + " )\n", + " bfrescox.run_simulation(\n", + " cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True\n", + " )\n", + " results[param_name].append(\n", + " bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3f881db8-1bbe-4f99-8fff-4a494adcfba6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 46.17it/s]\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAIwCAYAAABp1sinAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAuhxJREFUeJzs3Xl8VOXZ+P/PmTNLMkkm+05WIAHCmoCyKZsgCipUBKW1WKu21tZW2z5VfGz199SvS1trXR6ruD1SUCiKAgKKCgqyiISwkwUICUtIAmTfZjm/PyKpkRnIZJKZLNf79coL5sw5577mygSuuXMviqZpGkIIIYQQQvQwOl8HIIQQQgghRGeQQlcIIYQQQvRIUugKIYQQQogeSQpdIYQQQgjRI0mhK4QQQggheiQpdIUQQgghRI8kha4QQgghhOiRpNAVQgghhBA9khS6QgghhBCiR5JCVwghhBBC9EhS6AohhBBCiB5JCl0hRK/35ZdfcuONNxIfH4+iKLz11luXPP/JJ59EURR++ctfeifAS2hL7MnJySiKctHXjBkzvB+wEEJ4kRS6Qoher6amhsGDB/OPf/wDf3//S567fft2Xn31VYYOHeql6C6tLbHv3LmT06dPt3xlZ2ejKApz5871crRCCOFdUugKIXq966+/nv/3//4fc+bMQadz/c9iZWUlP/zhD3njjTcIDQ3ttHgWLFhAVFQUtbW1lz23LbFHRkYSExPT8rV27VosFkuXL3QLCwtRFIU77rij1fFdu3ahKAqvvfaabwITQnQbUugKIUQb3XPPPcyZM4dJkya5POfCsICkpCQaGhqcnnNhKIHNZrvouZ07d7J48WIeeughAgICOiz2CzRN4/XXX+dHP/qR0x5gZ0Mcvvt1uWEd3pCVlcWsWbN49NFHqamp8XU4QoguTO/rAIQQojtYtGgRBQUF/Otf/2rT+UVFRTz33HM89NBDbrXzyCOPYLFYuPfee9sT5mVt2LCBY8eOcffdd1/yvD/96U9Ojw8fPrwTonLfww8/zJVXXsnzzz/PwoULfR2OEKKLkkJXCCEuIzc3l4ULF7JlyxYMBsNlzw8NDUVRFJ566inuuusuIiIi2tROXl4en376KXfddddlxwq316JFixg1ahTDhg275HmPPfZYp7TfUa644goGDBjAK6+8wkMPPXTJISdCiN5L/mUQQojL2LZtG+Xl5WRkZKDX69Hr9XzxxRf87//+L3q9nsbGxlbnm81mHn30USorK3n88cfb3M4bb7yBpmnMmzevo18CAKWlpXz44YeX7c3tbDt27GDOnDnExMRgNBpJSEjgZz/7GadOnXLrPrfeeitFRUVs2LChkyIVQnR3UugKIcRlzJo1i3379pGTk9PyNXLkSG699VZycnIwGo0XXXPffffRt29fXnnlFfLz89vUzqeffoqqqowePbqjXwIAb731FiaTidtuu61T7t8Wb7zxBuPGjWPdunVMmjSJ3/zmN4wcOZLXXnuNkSNHUlRU1OZ7jRs3DkAKXSGESzJ0QQjR69XU1FBQUACAw+GgqKiInJwcwsLCSExMJCQkhJCQkFbXBAQEEBYWxuDBg53e02Aw8NRTT3HLLbfwhz/8gffff/+SMdTW1pKTk8PAgQPdmoR2udgv0DSN1157jVtvvZXAwMDL3tfZ0IXk5OSLVkBwR15eHj//+c9JTk7miy++ID4+vuW5zz77jGnTpvHrX/+alStXtul+o0aNAprXEhZCCKc0IYTo5TZu3KgBF30tWLDA5TUTJkzQ7rvvvouOA1p8fHzL4zFjxmiAtnnz5pZjSUlJGqBZrdaWY7m5uRqgTZ06tVNi//zzzzVA27FjxyXv5+xeF74mTJjgVmzf95vf/EYDtDVr1jh9ftasWZqqqlpVVZWmaZp27Nixy34f/Pz8tOjoaI/iEkL0XNKjK4To9SZOnIimaW5ds2nTpjad97e//Y2xY8fyu9/9ju3bt7s87+zZswBur8/b1tgnTZrk1mt0Nx9tsW3bNgC++OILdu7cedHzpaWl2O128vLyyMrKatM9w8LCOHPmTIfGKYToOaTQFUKITjRmzBjmzJnDihUrWLZsmcuJZhdWWXC19m5PcKGY/8tf/nLJ89xZG7e+vr7TVqgQQnR/MhlNCCE62ZNPPonBYODhhx+mqanJ6TlRUVHAf4rBnig4OBho3mFO0zSXXxMmTGjT/RwOBxUVFS25E0KI75NCVwghOlm/fv34xS9+wbFjx3jhhRecnhMbG0tkZCS5ublejs57LqwmsXnz5g65X25uLpqmdZlNLIQQXY8UukII4QV//OMfCQkJ4YknnnD6q3lFUbj66qspLy9vWUWhq7vjjjvc2hb4l7/8JQaDgQceeIC8vLyLnm9qanKrCL4w5vlSWzILIXo3KXSFEMILwsLCWLhwIefPn3c5POHmm28G4OOPP/ZmaO3mcDgA0OvbNt1jwIABvPHGGxQWFpKRkcENN9zAb3/7W+6//35mzZpFbGysW5tZfPLJJ6iqyk033dSu+IUQPZ+idcbUWiGE6KUURSE+Pp4TJ05c9FxjYyMDBgygsLAQAKvV2qpIbGpqIiEhgeTkZHbs2OGtkFtRFAVo26oLI0aM4MiRIxw/ftyt1SL27dvH3/72NzZu3EhJSQkBAQHExcUxbtw45s2bx+TJkwEoLCwkJSWFBQsWXNRrXFlZSUxMDNdeey0ffPBBm9sWQvQuUugKIUQX8uSTT7Jw4UKys7MZMWKEr8NxqaKigvDwcH7729/yzDPPeL39F154gfvvv5/Nmzczfvx4r7cvhOgepNAVQogupKGhgfT0dIYOHcrq1at9HY5Lq1ev5pZbbqGwsJCYmBivtl1fX0/fvn0ZO3YsK1as8GrbQojuRdbRFUKILsTPz4/FixezceNGamtr3doO2JtuuOEGn635W1hYyD333OPRdsRCiN5BenSFEEIIIUSPJKsuCCGEEEKIHkkKXSGEEEII0SNJoSuEEEIIIXokKXSFEEIIIUSPJIWuEEIIIYTokWR5MSfOnz+PzWbzSluRkZGUlZV5pa3uRnLjnOTFNcmNc5IX1yQ3zkleXJPcOOfNvOj1+jbvxiiFrhM2mw2r1drp7VzYatNms7Vpu83eRHLjnOTFNcmNc5IX1yQ3zkleXJPcONeV8yJDF4QQQgghRI8kha4QQgghhOiRpNAVQgghhBA9khS6QgghhBCiR5LJaEIIIYToFRobG2lsbPToHvX19TQ1NXVQRD1HR+dFURQCAwNbJrq1lxS6opUzJ8v5amspdVYLCgoK9fRPdJA5pi+qqvo6PCGEEKJdamtrURSFoKAgj4ong8HglZWZupuOzktTUxM1NTUEBQV5dB8ZuiBaZG87wubNCgZHHMFqIBY1gCA1gpKTUbzzbjHFR0t8HaIQQgjRLjabDbPZ7HEPofAOo9HYIUuVSaErAMjeWsDx46GYFJVqRwM600kCLaeptpdh1zSC9SHs+NrA5g2HfR2qEEII4TYpcHsnGbogOFdWSf7xYMw6HZX2SmbNiCAwOKbl+by9Rezcr8eimqk4F8MHKw5yw+z0dg1lOHH0DPv2lXO+1ogDE6Bh0NUzOiuYpP6xHfiqhBBCCNHbSY+uYN2Gcsw6A7WOJm64NozA4IBWz6cNTeTm2eHUOEoBUO1xLFt2lNqqujbd3263s31TLv9aWkT210ZsDfEEqZEEqxaC1WDMSgw5u/xYsewQdru9w1+fEEIIIXonKXR7ueytBVjUcDRNY0BKNcHhzgd9+/mbmDu3L3b1JJqmEaRGsuqjanZtLXB5b7vdzo4vcnln2WnKzkQTrFpQFIUqey31lGAwn0IxnaTSXoVOUTARy7vLCmWQvxBCCCE6hBS6vZjNZuNQoRmAGsdZho/ue8nzVVVl1pwMomNLadBsBOpMnCqO4F9Lj7F9Yy41lbUANNQ2smn9Id5ZdprSkmiC1UDsmkat4wxDhtfxw/nxzJ03gOk3DGLmrAx+ND8Rvf8pHJqGRQ1nxYpj0rMrhBBCtMHevXu59957yczMJDU1lXHjxvGrX/2K3NzcTmlv+/btLFiwgMzMTOLj41m/fn2br33rrbe48sorSU1NZebMmezevbtTYvwuKXR7se2bCrCoZqyag8njQ9p83ZUT0pk8WU+1vRxN0whWQykrjWbjeisr3j3Lx6vrqK6MbSlwaxyljBpt5dbb0klOj3N6z+tuHIQluARN0wjURbH2Q5n0JoQQQrhis9lYsmQJM2fOJCgoiEWLFvHll1/y1FNPUVNTw8qVKzul3bq6OgYNGsQTTzzh1nUffvghjz/+OA8++CDr169n0KBB/PCHP6S8vLxT4rxAJqP1YoWlZoJVsFJGTGK6W9eGR4Uwf34IuXuK2HWgEVUJwawzYFKaJ6jVazagnLFXhNInNa1N95x03UA+WHEQ1R5HU2MMx/JO4Bcka/cKIYToeJqmQZP7m0doDjuap0PsjCa3VoEoLi5m9OjRvPzyy7z55pvk5ORw//338+yzz/LnP/+ZBQsWtJzbp08frrrqKs6fP+9ZjC5MnjyZyZMnu33dokWLmD9/PvPmzQPgqaee4rPPPuPdd9/ll7/8ZUeH2UIK3V4qf/8JglULDk1j9Ij2L8acPiyR9GHN43HPn6mi7EwlIWEBRMaHotdHuH2/mbPSWLb8DBY1gPdWH+eH81PbHZsQQgjhUlMjjl/Odfsyz/ZVa6Z7cTmY/Np8/sGDBwH45z//yUMPPURCQgI/+9nPGD16dKsi97tCQ0OdHn/++ed54YUXLtnepk2biI+Pb3N8l9PU1MTevXtbFbQ6nY7x48eza9euDmvHGSl0u6HaqjpydhYzJDMOS2j7itRv9tURqAuk2l5JUnqyxzGpqkpEXCgRcc5/sNpKr9czLL2Jo/lmgnSh7N91jIxMz+MTQgghuqsDBw5gNpt55ZVXSEhIID8/nwMHDvDqq6+6fa/bb7+dG2644ZLnREdHtzdUp86dO4fdbicionUHWGRkJEeOHOnQtr5PCt1u5pM1B6msicJPiWbd+gbiI08x/hr3hh2cK63ET2l+s6UldL39ugdnpbAn9wgWNZzdhyEj09cRCSGE6HGMpuaeVTd1yFa3RpNbpx88eJBp06aRkJAAwL59+wAYOnSo202Hhoa67O3tibpsoXvw4EFWrVrFsWPHOH/+PL/73e+44oorLnnNgQMHePvttykuLiY8PJybb76ZiRMneidgL9i+KZf6mlj8FAWHpmHWGThbHsXB7EIGudHr+eWXJ9ErcVTb67l+3KVXWvCVK4aYOHRAI1gNcfv1CSGEEJejKIpbwwdarjMYUHTenT9y4MAB7rvvvpbH9fX1AAQEBLi6xCVfDF0ICwtDVdWLJp6VlZURGRnZYe0402UL3cbGRpKTk5k8eTJ//etfL3t+aWkpTz31FFOnTuVXv/oV+/fv55///CchISEMHz688wPuZCcLSykuCcdPUaiylzPz2mhWf3yOYDWY3YdN9B3UhMnPeNn7WK1WGqwRBOggNOAcqto1dyNLG5rAzn1HCVJDyTlsZZD06gohhOiFqqurKS4uZvDgwS3H0tObf5O7Y8cOrrvuuouuqa+vx9/f3+n9fDF0wWg0MnToULZs2cL06dMBcDgcbNmyhZ/85Ccd2tb3ddlCd8SIEYwYMaLN53/yySdERUXx4x//GGiedXj48GE++ugjl4Wu1Wpt9esHRVFa3hje2BP7q88OU3jmCJqmoSp2QMOuBWLS+dGkWUlLrGHU+P40NjSxcauVYDWAans9s2+MJyDIzNSrmtj8lR2L6s8na/O58ebBl2/z8yME6GJo1OxMmZLaZff+VhSFrAw/8g6Dvy6cc6WVhEeH+Dosn7vw/eqq3zdfktw4J3lxTXLjnOSlazl48CCqqjJgwICWYyNHjmTChAksXLiQ2tpasrKycDgc7Nmzh7fffptnnnmGtDTnKx55OnShtraWY8eOtTwuKipi//79REZGthTIb775JuvWrWP58v8MDbn77rt54IEHGDp0KCNGjGDRokXU19e3rMLgiqfvwy5b6LorPz+fIUOGtDo2bNgw3nrrLZfXrFy5khUrVrQ8TklJ4emnn+70bvQLzlfnEqQLc/qcSVE5WWziyLv52OwGgtVgmjQH100Opl9a83CD2NhY9uz9gsaaSOqbogkwB2IJvvTktJNni7GooKjn6Jc25JLn+lrktEh2HcwhSOfP1m1nuOfegb4OqcuIiYnxdQhdluTGOcmLa5Ib53paXurr6zEYDB1yr466T1vk5ubSr18/goJa//++ePFiXnnlFf75z39y/PhxTCYTKSkpTJs2jYEDB6LTdc5WCQcOHGD27Nktjx9//HEA5s2b1zIkoqKigqKiolZ5mjNnDpWVlfztb3+jtLSUwYMH8+677xIX53x9fWjuCY6N9ew3z4qmaZpHd/CCuXPnXnaM7q9//WsmTpzYKvnZ2dk89dRT/Otf/8JovPjX+q56dMvKyrDZbB37IpzYt/MohSet2Kw2muw6HA6FiGA7MdH+7Mm1Y1H/MzvRoWkEh5Uw6dpBre7RUN/IBysrMesMYDh5yV7dA9mFHMkLxqFpjLzSSp/Ujv3VREdSFIWYmBhef/lTbA1x1DgamTcvElXt3evqXshLSUnz5hriPyQ3zkleXJPcONdT81JZWYnFYvH4Ph0yGa0H6oy8VFVVERwcfNFxvV7f5k7JHtOj2x4Gg8HlpzJv/HAPGZXKtBtjOX369EXtDc6Cr7/MI++EGbCRNRAGjhh40XkmPyOB/qU4GuOpb4qmprqWgECz0/Z2H7JhUaHGcZ74lNRu8Q/YuAmJfLa+iUCdib07jzH8yq45ec7bNE3rFt8/X5DcOCd5cU1y45zkRXQFnr4He8wWwCEhIVRWVrY6VllZib+/v9Pe3O7giqvT+NH8PvxofjIDRyS7PG/Ktf2oc1jxV/R89skxp+cU5p4i8NthEsPSu8+4q+DQIOoczbu7HDpq93E0QgghhOhOekyh279//5Z15S7Yu3evy8HYPYmfv4lA/1IAGpqiqa2pu+icrbtq0SkKlfZKBmeleDtEj6REN/8qxKCE01DfEXvSCCGEEKI36LKFbkNDA4WFhRQWFgLNy4cVFha2rMG2dOlSXnzxxZbzp02bRmlpKf/61784efIkH3/8Mdu2bWPGjBm+CN/rvtur+9FHRa2e2/fNMQJ0zeN9ByR2vQ0iLmfk+L7UazZMiso3XznvsRZCCCGE+L4uO0b3yJEjLTP5AN5++20AJkyYwH333cf58+dbLTwcFRXFQw89xP/93/+xdu1awsPD+fnPf94j1tBtCz9/E9Gh5VRXxmLSojm0u5CBI5JpqG9kb54Ji6pQZT/HDeP7+zpUtxlNBuzaOVCiKCrt3ZPRhBBCCNF2XbbQzcjIaLX+2vd9d4eQ717zzDPPdGZYXdrE6QP515JCgvUh7M8NoujEQcqqAwlWLTRpdqaM93y2qa/0jYczp8GoC6OxoW2bYwghhBCid+uyQxdE+0ydGEi1vR4/RaWpLo5g1YJVcxAfXU5MYsTlb9BFDb8yhQbNjklRyd4mwxeEEEIIcXlS6PYw0fER3DjDQo2jlCp7LZX2Svr3q2T0pHRfh+YRo8mA1XEOgMLT3WfVCCGEEEL4TpcduiDaLzA4gNtu63mrTSRE2ag4CzolFLvd3us3jxBCCCHEpUmPrug2Mq9Iwq5pmHUGCvaf9HU4QgghhOjipNAV3UaAxUyNowqAg/k1Po5GCCGE8L29e/dy7733kpmZSWpqKuPGjeNXv/oVubm5ndLe9u3bWbBgAZmZmcTHx7N+/frLXvPCCy9w/fXXk5aWxtChQ7nzzjspKCjolPi+Twpd0a0E+9UCUNsU5ONIhBBCCN+x2WwsWbKEmTNnEhQUxKJFi/jyyy956qmnqKmpYeXKlZ3Sbl1dHYMGDeKJJ55o8zUXiuPVq1fzzjvvYLVamT9/PnV1F29w1dFkjK7oVoZkhLAvB4J0gVSUVxES0X2XTBNCCOE7mqbRaNfcvs6OA6vN4VHbJlVBUdo+sbq4uJjRo0fz8ssv8+abb5KTk8P999/Ps88+y5///GcWLFjQcm6fPn246qqrOH/+vEcxujJ58mQmT57s1jVLlixp9fi5555j6NCh7N27l9GjR3dkeBeRQld0K8npcWzLPkOgzsTunSeZdJ0UukIIIdzXaNeYtyzPJ20vm5eGn77the7BgwcB+Oc//8lDDz1EQkICP/vZzxg9enSrIve7QkNDnR5//vnneeGFFy7Z3qZNm4iPj29zfO6qqmoehhgSEtJpbVzQoYXuwYMHefXVV6murmbMmDH85Cc/QVVVioqKyM7OZteuXfzP//xPRzYpeiGFCiCa0+cNvg5FCCGE6HQHDhzAbDbzyiuvkJCQQH5+PgcOHODVV191+1633347N9xwwyXPiY6Obm+ol+VwOPjTn/7EqFGjGDBgQKe1c0GHFrqvv/46V155Jenp6Xz44Ye8+eab5OfnU1JSQkZGBuPHj+/I5kQvlRwDZaWg14Vgs9nQ6+UXE0IIIdxjUhWWzXN/KU6D3oDVZvW4bXccPHiQadOmkZCQAMC+ffsAGDp0qNtth4aGuuzt9YaFCxeSm5vbaWOIv69DK4SysjJuu+02AJKTk7n33nv54Q9/yPXXXy/FiOgww69IZu3qWvwVPYdyihkyMsXXIQkhhOhmFEVxa/jABQaDDtXLc/kPHDjAfffd1/K4vr4egICAALfv5cuhC4888giffvop77//PnFxcR1+f2c6tPr87sDqsLAwTCYTN954Y0c2IQR+ASbq7KcJ1oeQf6yeISN9HZEQQgjROaqrqykuLmbw4MEtx9LTm3c73bFjB9ddd91F19TX1+Pv7+/0fr4YuqBpGv/93//N+vXr+fe//01iYmKH3v9SOrTQbWho4He/+x3JycmkpqaiKMolky1EewX714E1hDqrLDMmhBCi5zp48CCqqrYazzpy5EgmTJjAwoULqa2tJSsrC4fDwZ49e3j77bd55plnSEtzPizD06ELtbW1HDt2rOVxUVER+/fvJzIysqVAfvPNN1m3bh3Lly8HmocrfPDBB7zxxhsEBgZSWloKQFBQUKfXiB1a6L744oscP36cwsJCDh06RHBwMHfccQdhYWEkJCSQkJDA7bff3pFNil4qvV8guYcgQBdAXW095gD5MCWEEKLnOXjwIH379sXPz6/V8TfeeINFixbx8ssvc/z4cUwmEykpKVxzzTX069ev0+LZs2cPt9xyS8vjxx9/HIB58+bx7LPPAnDu3DmOHz/ecs7bb78NwJw5c1rd69lnn2XevHmdFiuAomma+4vIuaGhoYGioiIKCws5fvw4d999d2c21yHKysqwWj0baN4WiqIQGxvL6dOn6eRvQ7dzudzY7Xbe+/d5/BU90bGlXHG1+xMKuiN5z7gmuXFO8uKa5Ma5npqXqqoqLBbPl6Q0GAxeqRG6m87Ii6vvmcFgIDIysk336PQZYn5+fqSlpbnsQheiPVRVxeqoxF8N5/hpO1f4OiAhhBBCdDkdNm2wqalJPuEIrwoLaACg0S6bRgghhBDiYu3u0T1w4AA7d+4kNzeXEydO0NTUBIDJZCI+Pp709HRGjRpFRkZGhwUrxHcNGhTG3mwI0pmpOl+NJVQmpgkhhBDiP9wqdG02G59++ilr1qyhrKyMwMBAUlJSuOqqqwgMDETTNGprayktLWXz5s2sW7eOiIgIbrjhBq655hpZS1d0qKT+sWz7ppQAnZG9u04y/prO32FFCCGEEN2HW5Xn/fffj81mY8KECYwZM4bU1NRLnn/06FG2bdvGypUrWb16NS+99JJHwQrxfQ6tEojkZLn7i34LIYQQomdzq9CdPXs2EydOxGAwtOn81NRUUlNTmTdvHhs3bmxXgEJcSlSwlfoasDtknK4QQgghWnNrMtrUqVPbXORC8wS18vJy9Ho9U6dOdTs4IS5n8JAoAIJUf8pPnfdxNEIIIYToSto9aLa6upp169axa9cuSkpKcDgchIWFMWTIEKZNm0ZiYiI7duzgxRdfZNmyZR0ZsxAtYhIjqP6qhCCdH/v2lDAprv27vQghhBCiZ2lXoZubm8tf//pXqqqqMJvN9OnTB7vdTllZGRs2bOCzzz7jBz/4ATExMR0drxAX0VEF+FFyXvV1KEIIIYToQtwudM+ePctTTz2F2Wzmv/7rv8jKymr1/OHDh/nwww9ZsWKFx4Xu+vXrWb16NRUVFSQlJXHnnXdeclu7jz76iE8++YTy8nIsFgtXXnkl8+fPx2g0ehSH6Npiw+1UnQeUYF+HIoQQQoguxO0NI1auXAk07238/SIXYMCAAfzhD3/gxz/+MSUlJe0ObOvWrbz99tvMmTOHp59+mqSkJJ544gkqKyudnr9lyxaWLl3KLbfcwt///nd+/vOfs23bNt555512xyC6hyEj4nBoGoE6EyeOnvF1OEIIIYTX7N27l3vvvZfMzExSU1MZN24cv/rVr8jNze2U9rZv386CBQvIzMwkPj6e9evXu32PF198kfj4eP74xz92QoStuV3o7t69m8mTJxMREXHJ82bMmMGcOXMYNGhQuwJbs2YNU6ZMYdKkSfTp04e7774bo9HocvWG3Nxc0tPTGT9+PFFRUQwbNoxx48ZRUFDQrvZF9xEWGUyNox6AAwfO+jgaIYQQovPZbDaWLFnCzJkzCQoKYtGiRXz55Zc89dRT1NTUtHRMdrS6ujoGDRrEE0880a7rc3Jy+Ne//sXAgQM7ODLn3B66UFFRQZ8+fdp07i233OJ2QND8zTt69CizZs1qOabT6RgyZAh5eXlOr0lPT2fz5s0UFBTQr18/zpw5w+7du7nqqqtctmO1WlttW6woCv7+/i1/72wX2vBGW92Nu7kx6KoAM+XVph6dT3nPuCa5cU7y4prkxrnekhdN07Db3b9OUTRsNs2jtlXVvfwWFxczevRoXn75Zd58801ycnK4//77efbZZ/nzn//MggULWs7t06cPV111FefPd85KRJMnT2by5Mntura2tpZf/vKXPPPMMzz//PNtusbT96Hbha7ZbKa6urpN5+7du5e8vDzmzJnjVhtVVVU4HA5CQkJaHQ8JCeHUqVNOrxk/fjxVVVU8+uijANjtdqZOncoPfvADl+2sXLmSFStWtDxOSUnh6aefJjIy0q14PSWT9lxra27SU4ooKgS9YiEqKgpV7dkT0+Q945rkxjnJi2uSG+d6Wl7q6+tbLZFqs2msWV7uk1huvDUCvb7tBdyFYQivvPIKCxcuJDExkbvuuouxY8dy1113Ob0mKirK6fHnnnuO55577pLtbdmypc2dmqqqtuT1ckvQ/vd//zfTpk1jypQpvPDCC62udcZoNBIbG9umOFxxu9BNT0/nq6++4sYbb7zkecePH+cvf/kLTU1Nbhe67XHgwAFWrlzJXXfdRf/+/SkpKeHNN99kxYoVLtufPXs2M2fObHl84VNDWVkZNput02NWFIWYmBhKSkrQNM8+HfY07uam/8Bwjh1rxKwzsHXjLvplJHghSu+T94xrkhvnJC+uSW6c66l5aWpqavVbXE97ZT1htVrRtLYXunv37sVsNvPPf/6ThIQE8vPz2b9/P6+++mqr19QW8+fP5/rrr7/kOeHh4W2+r91ux2q1YjAYLnnNhx9+yN69e/noo4++ff1ay7WuNDU1cfr06YuO6/X6NndKul3o3njjjTz66KO8/PLL3HXXXU4r8S1btvDaa6+1u1i0WCzodDoqKipaHa+oqLiol/eCZcuWcfXVVzNlyhQAEhMTaWho4NVXX+UHP/gBOt3Fw5ENBoPLTxLe/OHWNK1H/WPSkdqaG7PFTI3jHMFqIIdyK+k7qG2fRLsrec+4JrlxTvLimuTGuZ6eF1WF6252f7WeyxV0bW3bHQcPHmTatGkkJDR34uzbtw+AoUOHut12aGgooaHeXXP+5MmT/PGPf+Sdd97Bz8/PrWs9fQ+6XeimpaXxk5/8hLfeeou9e/cyZswY4uLisNlslJaWsnPnTkpLS+nbty9jx45l8eLF7gel15Oamsr+/fu54oorAHA4HOzfv5/p06c7vaaxsfGicRzOilvRc/nrq0ALpKLe7OtQhBBCdHGKoqBvx24Cer3iVm9sRzhw4AD33Xdfy+P6+uYJ2AEBAW7f6/nnn+eFF1645DmbNm0iPj7e7Xu7sm/fPsrLy1vVcHa7ne3bt/PWW29x7NixThty2K4NI6ZPn05iYiLLly9n7dq1rartqKgobr/9dq677jq2bt3a7sBmzpzJSy+9RGpqKv369WPt2rU0NjYyceJEoHlpirCwMObPnw9AVlYWH330ESkpKS1DF5YtW0ZWVpYUvL1ESh8jJ4vBT2fBZrOhb8+/YEIIIUQXUl1dTXFxMYMHD245lp6eDsCOHTu47rrrLrqmvr6+ZXL9991+++3ccMMNl2wzOjrag4gvNn78eD777LNWxx588EH69u3Lfffd16nzatpdCQwaNIjHHnuMuro6zpw5g81mIzQ0tNWyY+np6dx7773tuv/YsWOpqqpi+fLlVFRUkJyczMKFC1uGLpSXl7fqwb355ptRFIV3332Xc+fOYbFYyMrK4rbbbmvvSxTdTEZmIoVFNZgUlcN7ihmcleLrkIQQQgiPHDx4EFVVGTBgQMuxkSNHMmHCBBYuXEhtbS1ZWVk4HA727NnD22+/zTPPPENaWprT+3k6dKG2tpZjx461PC4qKmL//v1ERka2FMhvvvkm69atY/ny5QAEBga2ih+aFzcIDQ296HhHc6vQXbp0KYmJiSQmJhIfH4+qqpjNZlJSnBcUUVFRLmf9tcX06dNdDlV47LHHWj1WVZVbbrml3Uuaie7P5GekzlFNsBpMwbF6Bl+8n4kQQgjRrRw8eJC+ffteNLb1jTfeYNGiRbz88sscP34ck8lESkoK11xzzSV3kfXUnj17WtVajz/+OADz5s3j2WefBeDcuXMcP36802Jwh6K5Mcr39ttvp6mpCWguLGNjY0lMTCQhIaGlAPaksO0qysrKPB5o3haKohAbG8vp06d79ID/9mhvbj5YcQDVHk+lvZIfzU/qxAh9Q94zrklunJO8uCa5ca6n5qWqqgqLxeLxfTpiMlpP1Bl5cfU9MxgMnbPqwuLFiyktLeXEiRMUFxdTXFxMfn5+q7G4fn5+rQrfhIQEMjIy3GlGiHZLSw3gSD6YdUE01Dfi52/ydUhCCCGE8BG3x+heGI6QmZlJQUEBe/bsYerUqQwaNAiHw0FBQQGbN28mPz+/5Zply5Z1aNBCuJI2tA8H8irxU1QO7C4ma2zn/fpGCCGEEF2bR9PSX3/9dcaMGcOdd97Zcmz8+PHcdtttvPfee2zcuJE77rjD0xiFaDO9Xk+jvRI/fRjHTliRYbpCCCFE7+XRultFRUUkJV08DtJkMjF//nzS09PZtWuXJ00I4bbQgOb1BRtsQT6ORAghhBC+5FGhGx8fT05Ojsvnhw8fzt69ez1pQgi3DRwQAkCgLoDaqjrfBiOEEEIIn/Go0J01axZff/01y5Yta1mN4bsKCwvbvQ2wEO2V1D+GOocVVVHY802xr8MRQgjRRTgcDl+HINqoo1b88GiM7tixYzl37hxLlixhw4YNXH311aSmpgKwf/9+Nm3aRFaWjJIU3qWqKnatEojgRGnPWRpHCCFE+5nNZqqrqwkKCpIdU7uBuro6TCbPV07yeI/UmTNnMnjwYN5//302bNjQqmd36NCh3HPPPZ42IYTbIixNNNaC1eH5molCCCG6P71eT0BAADU1NR7dx2g0Ov0tdm/XkXnRNA29Xu+bQnfVqlVkZWURHx/fciw5OZkHH3wQm81GSUkJTU1NREREdMjCzEK0x9ChUezcBhbVzJmT5UTHR1z+IiGEED2aXq/3qDbpqZtpeKor56Vdhe6SJUuIjIwkMzOTzMxMMjIyMBgM6PV6+vTp0xlxCuGWmMQIqracxqL6k5NdyrVS6AohhBC9jtuF7qJFi8jPzycnJ4fs7Gw+/vhjjEYjGRkZLYVvRIQUFcL3jGoF4E95td/lThVCCCFED+R2oasoCmlpaaSlpTF37lwqKirIzs4mJyeHpUuX8vrrr9OnTx8yMzPJysoiLS1NBn0Ln+ibYOBkMZh0wTQ1WjGaDL4OSQghhBBe5PFktJCQECZPnszkyZOx2+0cOnSI3bt3s2vXLlatWoXZbGbYsGHMmDGD/v37d0TMQrTJkFFJHC2qwqSo7Nt1XLYDFkIIIXoZjwvd71JVlcGDBzN48GBuv/12SktLyc7OZvfu3Rw6dEgKXeFVBoOBRkclJjWMI8U22Q5YCCGE6GU8KnR/9atfMWHCBObMmeP0+aioKKZPn8706dM9aUaIdou0NNBYCzZ7sK9DEUIIIYSXeTR4trS0lKNHj3L06FG2bNlCdnY2Z86c6ajYhPDY8MwYNE0jSPXn9PFSX4cjhBBCCC/yeOjCrl272LVrV6tjCQkJzJ49m3Hjxnl6eyE8EhUXRrXjFBbVTM6ecmKTonwdkhBCCCG8xONCV6/XM3v2bAYNGoTNZuPo0aNs376d559/ngMHDsjOaMLnTPpK0MycrfH3dShCCCGE8CKPC90ZM2a0GqM7dOhQZs2axZo1a1i8eDFpaWlMnDjR02aEaLcBff04VgABumBqq+oIsJh9HZIQQgghvMCjMbpGo5GwsDCnz82cOZOxY8eyfv16T5oQwmMDhydS62hCr+j4emuhr8MRQgghhJd4VOjGxcWxe/dul88PHDiQEydOeNKEEB5TVRVFOQfAqXMmH0cjhBBCCG/xqNCdOnUqOTk5vPHGGzQ1NV30/OHDhzGZpLAQvtc/sXmUjp8ulIbaRh9HI4QQQghv8GiM7jXXXMOJEydYt24dX331FSNGjCAhIQG9Xs++ffvYvXu3jM8VXcLQK5IpOF6Bv07Pjq+OMmHaQF+HJIQQQohO5vFktDvuuIMrr7ySjz76iJ07d7J58+aW50aNGsWCBQvafe/169ezevVqKioqSEpK4s4776RfP9fbuNbW1vLOO+/w9ddfU1NTQ2RkJAsWLCAzM7PdMYieQa/XoylngWhOlMlvGYQQQojewK1Cd+nSpSQmJpKYmEh8fDyqqgLNY3EHDhyIw+GgtLSUhoYGIiIiCAwMbHdgW7du5e233+buu++mf//+fPTRRzzxxBM899xzBAdfvMuVzWbjz3/+MxaLhQcffJCwsDDKy8sxm2WGvWg2qJ+RwiNgVkOoOl+NJTTI1yEJIYQQohO5VeiuW7euZSyuqqrExsaSmJhIQkICiYmJJCUlERMT0yGBrVmzhilTpjBp0iQA7r77brKzs9m4cSOzZs266PzPP/+cmpoa/ud//ge9vvllRUXJ5gDiPwaNSGR/fjmBOhNbtxQz/YZBvg5JCCGEEJ3IrUJ38eLFlJaWcuLECYqLiykuLiY/P5+tW7e2nOPn59dS+F4ogjMyMtwK6sLGE98taHU6HUOGDCEvL8/pNbt27aJ///68/vrrfPPNN1gsFsaNG8esWbPQ6ZzPubNarVit1pbHiqLg7+/f8vfOdqENb7TV3XRGbvR6PUb9OXDEUl5t6ZZ5l/eMa5Ib5yQvrklunJO8uCa5ca4r58XtMbpRUVFERUWRmZlJQUEBe/bsYerUqQwaNAiHw0FBQQGbN28mPz+/5Zply5a51UZVVRUOh4OQkJBWx0NCQjh16pTTa86cOUNZWRnjx4/n4YcfpqSkhNdeew273c4tt9zi9JqVK1eyYsWKlscpKSk8/fTTREZGuhWvpzqqF7wn6ujcXH+tjU/WVhGsBnKmuIrhV6R36P29Rd4zrklunJO8uCa5cU7y4prkxrmumBePJqO9/vrrjBkzhjvvvLPl2Pjx47ntttt477332LhxI3fccYenMbaJpmlYLBZ+9rOfodPpSE1N5dy5c6xatcploTt79mxmzpzZ8vjCJ5GysjJsNlunx6woCjExMZSUlKBpWqe31510Vm7MwXqq7ZUE60PYuOUk0QmWDru3N8h7xjXJjXOSF9ckN85JXlyT3Djn7bzo9fo2d0p6VOgWFRVxzTXXXHTcZDIxf/58Tp06xa5duxg3bpxb97VYLOh0OioqKlodr6iouKiX94KQkBD0en2rYQrx8fFUVFRgs9laxu1+l8FgwGAwOL2fN9/AmqbJD4wLnZGbpKgGKs6BSiQ1lbXdcktgec+4JrlxTvLimuTGOcmLa5Ib57piXjzaMCI+Pp6cnByXzw8fPpy9e/e6fV+9Xk9qair79+9vOeZwONi/fz9paWlOr0lPT6ekpASHw9Fy7PTp04SGhjotckXvNXpiX2odTZgUlY2fHfN1OEIIIYToJB4VurNmzeLrr79m2bJlTndGKywsbPcQgJkzZ/LZZ5+xadMmTpw4wWuvvUZjY2PLBhQvvvgiS5cubTl/2rRp1NTU8NZbb3Hq1Cmys7NZuXIl1157bbvaFz2XwWAgwFQOQE1DZKsJiUIIIYToOTzq6hw7diznzp1jyZIlbNiwgauvvprU1FQA9u/fz6ZNm8jKymr3vauqqli+fDkVFRUkJyezcOHClqEL5eXlrWb3RURE8Mgjj/B///d//P73vycsLIzrrrvO6VJkQkyYnMiG9Y0E6Ix8uSGfKdfLUmNCCCFET+Px7/RnzpzJ4MGDef/999mwYUOrnt2hQ4dyzz33tPve06dPZ/r06U6fe+yxxy46lpaWxhNPPNHu9kTvYQkJRFOKgVhKK8NparRiNDkfry2EEEKI7qlDBq8mJyfz4IMPYrPZKCkpoampiYiICCyW7jWjXfQuU6/pw4YNTQTqTHy6Po/rb3JvvWchhBBCdG0dOktLr9fTp0+fjrylEJ0mODwIvf4A2OOprY+hvKSCiJgQX4clhBBCiA7i0WQ0Ibq76TP6UeNowE9R+eTzcl+HI4QQQogO5Fah+8ADD/DFF1+4tZKC1Wpl48aNPPDAA24HJ0Rn8/M3kZ5ci6ZpBKkRfLr2oK9DEkIIIUQHcWvowsSJE3n77bd56623yMrKYujQoaSkpBAVFYXJZAKgoaGB0tJSjh49yt69e9m1axd6vZ4bb7yxU16AEJ7KHNOXguOH8SeG6qoY9u86xuCsFF+HJYQQQggPuVXo3nTTTUybNo3PP/+cTZs2sXnz5pbnVFUFwG63txxLSEhg7ty5TJo0CbO5++0+JXqP2Tf3Zdm/TxOsWjicb8FhP8rQK1J9HZYQQgghPOD2ZDR/f39mzJjBjBkzKC0tJS8vj5MnT1JdXQ1AUFAQ8fHxpKWlERUV1eEBC9EZDAYD108NZt2GGixqAEeOhlB44gDXXt8fk5/R6TV2u50TR0vJyzvP2SoVm+aPTjGiQ4dDs+OggSC/OkZfGU10fISXX5EQQgghPFp1ISoqSopZ0WOERQZzw3U6Vq07R7AaDNZ4PlhZCcpZwi0O/IwqDU12KqoVGuxmjLoA/BV/wJ8Ap6Pd/cEayvbNGrWOfKZMCCM6PtzLr0oIIYTovdxedWHVqlWcPHmyM2IRwucsoUHcNq8PivEkDZods86AWYmhvjqO82ejqa+Ow0QswWow/ooeh6ZRZa+j1nEGvd9JwiPPEJdQTmh4CVbdKarstegUhSA1ks2bYcunh339EoUQQohew+0e3VWrVrFkyRIiIyPJzMwkMzOTjIwMDAbZVUr0DKqqMnN2BnW19WzZeIzyKiMO/FBQ0bCj0IDFr4n4GCPpQ+MICAy95P12fVVA7vEAglR/zp+N4cP3DnLTzbLlsBBCCNHZ3C50Fy1aRH5+Pjk5OWRnZ/Pxxx9jNBrJyMggKyuLESNGEBEh4xFF92cO8GfaTM8L0qxx/cgY3sjKVccJ1EWhs8WxcsVBZs+RYlcIIYToTG4XuoqikJaWRlpaGnPnzqWiooLs7GxycnJYsmQJr732Gn369CEzM5OsrCzS0tLQ6WRfCtG7+QWYmDu3Lyvfy8OkxaK3x/Hp2oNcc70Uu0IIIURn8XgL4JCQECZPnszkyZOx2+0cOnSI3bt3s2vXLlatWoXZbGbYsGHMmDGD/v37d0TMQnRLqqoyZ+5Alr17GLMSQ01VLPu+OcaQkbJmrxBCCNEZPC50v0tVVQYPHszgwYO5/fbbKS0tJTs7m927d3Po0CEpdIUAfnBzX5YtP0mwPoT9+WaS+tVgCQn0dVhCCCFEj9NhhW5TUxOKorSalBYVFcX06dOZPn16RzUjRLdnMBi4bmoIn31mJVBnYs3a08yfLx8ChRBCiI7W7kL3wIED7Ny5k9zcXE6cOEFTUxMAJpOJ+Ph40tPTGTVqFBkZGR0WrBA9RXhUCMnx+Zw5HUmQGsnOLXmMGp/m67CEEEKIHsWtQtdms/Hpp5+yZs0aysrKCAwMJCUlhauuuorAwEA0TaO2tpbS0lI2b97MunXriIiI4IYbbuCaa65Br+/QkRJCdGtXXN2fpUvzCVIjKSgOZkhtI34BJl+HJYQQQvQYblWe999/PzabjQkTJjBmzBhSU1Mvef7Ro0fZtm0bK1euZPXq1bz00kseBStETzN9alTLEIb1644wS5YcE0IIITqMW4Xu7NmzmThxYps3h0hNTSU1NZV58+axcePGdgUoRE8WFhlMoPkgtvo47LYYSk+dIyouzNdhCSGEED2CWwvcTp06tV07oOn1eqZOner2dUL0Btdc158qez1GRcfnX5T7OhwhhBCix+jUnRyampooL5f/uIW4FIPBQN/YKgD8lEhOFpb6OCIhhBCiZ2j37LDq6mrWrVvHrl27KCkpweFwEBYWxpAhQ5g2bRqJiYns2LGDF198kWXLlnVkzEL0OKOu7sc7y04TrAayeVsFtyZH+TokIYQQottrV6Gbm5vLX//6V6qqqjCbzfTp0we73U5ZWRkbNmzgs88+4wc/+AExMTEdHa8QPZKqqvSLq6XsTGBLr268FLtCCCGER9wudM+ePctTTz2F2Wzmv/7rv8jKymr1/OHDh/nwww9ZsWKFFLpCuGHUVf/p1f1yWwW3SaErhBBCeMTtQnflypUAPP7440RERFz0/IABAxgwYAAfffQRb7/9tucRCtFLqKpK//haSksC8VciOXH0DH1So30dlhBCCNFtuT0Zbffu3UyePNlpkftdM2bMYM6cOQwa5Nm6oOvXr+e+++7jhz/8IQsXLqSgoKBN13311VfMnTuXZ555xqP2hfCmkeP7UWmvQVUUvvq6wtfhCCGEEN2a2z26FRUV9OnTp03n3nLLLW4H9F1bt27l7bff5u6776Z///589NFHPPHEEzz33HMEBwe7vK60tJTFixczcOBAj9oXwttUVSUpqpqKs4HoiaTybDXB4UG+DksIIYToltwudM1mM9XV1W06d+/eveTl5TFnzhy3AwNYs2YNU6ZMYdKkSQDcfffdZGdns3HjRmbNmuX0GofDwQsvvMDcuXM5dOgQtbW1Lu9vtVqxWq0tjxVFwd/fv+Xvne1CG95oq7vpzbkZOymN5cvLCdSZ2LSpmFlzMlqe6815uRzJjXOSF9ckN85JXlyT3DjXlfPidqGbnp7OV199xY033njJ844fP85f/vIXmpqa2lXo2mw2jh492qqg1el0DBkyhLy8PJfXrVixAovFwuTJkzl06NAl21i5ciUrVqxoeZySksLTTz9NZGSk2/F6QibtudZbcxMZfJT66kgamiIJCwvHZDK2er635qUtJDfOSV5ck9w4J3lxTXLjXFfMi9uF7o033sijjz7Kyy+/zF133eV0p7QtW7bw2muvYbPZ2h1YVVUVDoeDkJCQVsdDQkI4deqU02sOHz7M559/3uZxubNnz2bmzJktjy98EikrK/Mo9rZSFIWYmBhKSkrQNK3T2+tOentuxl7dh4/W1GHWGVi++EuumdHcq9vb83IpkhvnJC+uSW6ck7y4Jrlxztt50ev1be6UdLvQTUtL4yc/+QlvvfUWe/fuZcyYMcTFxWGz2SgtLWXnzp2UlpbSt29fxo4dy+LFi91+Ae1RX1/PCy+8wM9+9jMsFkubrjEYDC63NPbmG1jTNPmBcaG35sYc6I+iFAKxlFSEYLPZUFW15fnempe2kNw4J3lxTXLjnOTFNcmNc10xL+3aMGL69OkkJiayfPly1q5d2+pFRUVFcfvtt3PdddexdevWdgdmsVjQ6XRUVFS0Ol5RUXFRLy/AmTNnKCsr4+mnn245diGuW2+9leeee65LdqkL4cpV46PYtlnDoprJ2X6MrHH9fB2SEEII0a24VeguXbqUxMREEhMTSU9P57HHHqOuro4zZ85gs9kIDQ1ttexYeno69957b/sC0+tJTU1l//79XHHFFUDzRLP9+/czffr0i86Pi4vjr3/9a6tj7777Lg0NDdxxxx2XXQ5NiK4mOj6cOkcBQWoEhwt1ZI3zdURCCCFE9+JWobtu3TqampqA5mWQYmNjSUxMJCEhgcTExIuW/IqKiiIqqv27O82cOZOXXnqJ1NRU+vXrx9q1a2lsbGTixIkAvPjii4SFhTF//nyMRiOJiYmtrg8ICAC46LgQ3cXwAXqO5EOgGsqJo2dI6Cu/lRBCCCHayq1Cd/HixZSWlnLixAmKi4spLi4mPz+/1RAFPz+/lsL3QhGckZFxibu6NnbsWKqqqli+fDkVFRUkJyezcOHClqEL5eXlXXIpCyE6yqDMZLIPnyBYDWTbznNS6AohhBBucHuM7oVe2szMTAoKCtizZw9Tp05l0KBBOBwOCgoK2Lx5M/n5+S3XLFu2rN0BTp8+3elQBYDHHnvsktfed9997W5XiK4iPqyKmspA0KKoq633dThCCCFEt9GuyWgXvP7664wZM4Y777yz5dj48eO57bbbeO+999i4cSN33HGHpzEK0auNm9Sfle9XYNYZ+OLTo/Ttl+rrkIQQQohuQefJxUVFRSQlJV103GQyMX/+fNLT09m1a5cnTQjR6xlNBoz6MgDO1oRit9t9HJEQQgjRPXhU6MbHx5OTk+Py+eHDh7N3715PmhBCAFddFYdNc2BR/fni4xxfhyOEEEJ0Cx4VurNmzeLrr79m2bJlLasxfFdhYaFXdhgToqeLiAmh3nEWgOyDDT6ORgghhOgePBqjO3bsWM6dO8eSJUvYsGEDV199NampzeMH9+/fz6ZNm8jKyuqQQIXo7UYMNFCQBwG6EE4cPUN8SvuX7hNCCCF6A48KXWhe63bw4MG8//77bNiwoVXP7tChQ7nnnns8bUIIAQwckcyuQ8UEq0F89fU55kqhK4QQQlySx4UuQHJyMg8++CA2m42SkhKampqIiIjAYrF0xO2FEN+KD6uhpjIInRZJbU0dAYFmX4ckhBBCdFluj9FdtWoVJ0+edPqcXq+nT58+pKamSpErRCcYP7k/dQ4rJkXly8+P+TocIYQQoktzu0d31apVLFmyhMjISDIzM8nMzCQjIwODwdAZ8QkhvsNoMmD2Ow9NUZyrCcNut6Oqqq/DEkIIIboktwvdRYsWkZ+fT05ODtnZ2Xz88ccYjUYyMjLIyspixIgRREREdEasQgjgppsyeG/5GSyqPznbj5E1rp+vQxJCCCG6JLcLXUVRSEtLIy0tjblz51JRUUF2djY5OTksWbKE1157jT59+pCZmUlWVhZpaWnodB6tYiaE+I6YPpHUawcJUiI5VKgja5yvIxJCCCG6Jo8no4WEhDB58mQmT56M3W7n0KFD7N69m127drFq1SrMZjPDhg1jxowZ9O/fvyNiFqLXyxxoJD8XgtRQio+WkJAa4+uQhBBCiC7Ho67WX/3qV6xYsaLlsaqqDB48mNtvv51nn32WF154gXnz5lFfX8+hQ4c8DlYI0WzgiGQq7dXoFIVtOyt8HY4QQgjRJXnUo1taWsrRo0c5evQop06dwmw2Ex8fT3R0NABRUVFMnz6d6dOnd0iwQoj/6BNWQ7UsNSaEEEK45PHQhV27drFr165WxxISEpg9ezbjxsngQSE6y9hJ/Vj5fgVmnYEvPy/kuhsH+TokIYQQokvxuNDV6/XMnj2bQYMGYbPZOHr0KNu3b+f555/nwIEDsjOaEJ3EaDJgNJSBPY6K2nBsNht6fYfsASOEEEL0CB7/rzhjxgzmzJnT8njo0KHMmjWLNWvWsHjxYtLS0pg4caKnzQghnJg8KYFPNzQRqDPx5YY8Jl8nvbpCCCHEBR5NRjMajYSFhTl9bubMmYwdO5b169d70oQQ4hKCw4PQlFIASs6HYrfbfRyREEII0XV4VOjGxcWxe/dul88PHDiQEydOeNKEEOIyJk2Kxao5CFL9+eqzPF+HI4QQQnQZHhW6U6dOJScnhzfeeIOmpqaLnj98+DAmk8mTJoQQlxEeFYLt217d4vJg6dUVQgghvuXRGN1rrrmGEydOsG7dOr766itGjBhBQkICer2effv2sXv3bhmfK4QXTBgfydbNDiyqmW0bCxh/TbqvQxJCCCF8zuPJaHfccQdXXnklH330ETt37mTz5s0tz40aNYoFCxZ42oQQ4jKi48Np4jB6YigqC6ap0YrRZPBpTMVHSziw/xyVdSo2R3MsqmIlOMDG8GGRxCZF+TQ+IYQQPV+HrEU0cOBABg4ciMPhoLS0lIaGBiIiIggMDOyI2wsh2uCayTFs/NxOkM6PT9blMXNWhtdjsFqtfLEhn5IKC8FqIBCHWQHU/5xjq4dvtkPlV0X0i6tn9ETpfRZCCNE53Cp0ly5dSmJiIomJicTHx6OqaqvndTodMTExHRqgEKJtwqNCMBkPgjWO+oYYzpVVEhYZ7LX2t3yaS3FZCIG6OIJVcGgaNY46VKUGf2PzuOH6JhW7FoRFNROsWig7Y+FfSwqZNM5MfLL08AohhOhYbhW669ata5l0pqoqsbGxJCYmkpCQ0FIAR0V17H9W69evZ/Xq1VRUVJCUlMSdd95Jv379nJ776aef8uWXX1JcXAxAamoqt912m8vzhehprp3Rn/feP0ugzo8Nn5Uw79bOL3Srzlezel0pFjWaQB00anbQlTLmynDik+OdXnM89xRbs2swK5EE60PYtt1OdP5hrpo6oNPjFUII0Xu4VeguXryY0tJSTpw4QXFxMcXFxeTn57N169aWc/z8/FoVvgkJCWRktO9XqFu3buXtt9/m7rvvpn///nz00Uc88cQTPPfccwQHX/wf+MGDBxk3bhzp6ekYDAY+/PBD/vznP/Pss8+6XO9XiJ7EaDKQFFXB2fIY/Igib18xaUMSOq29/buOsT8vAIsajkPTaOAM06fGExw+8JLXJaXHkZQOh3OOs+ugEYvqz/mz0axccZAbZ6df9NsiIYQQoj3cHqMbFRVFVFQUmZmZFBQUsGfPHqZOncqgQYNwOBwUFBSwefNm8vPzW65ZtmxZu4Jbs2YNU6ZMYdKkSQDcfffdZGdns3HjRmbNmnXR+ffff3+rxz//+c/ZsWMH+/btY8KECe2KQYjuZuyUAfxr6XGC1WB27lNJTuv4iWl2u521qw5jb4wjQKdQ57DSL6mSrLHu9cgOGJ5EcnojKz84TqAuCr09jhX/LmDOLf2k2BVCCOExjyajvf7664wZM4Y777yz5dj48eO57bbbeO+999i4cSN33HFHu+5ts9k4evRoq4JWp9MxZMgQ8vLatih+Y2MjNpvN5aQ4q9WK1WpteawoCv7+/i1/72wX2vBGW92N5Ma5tuZl0hh/tu9oXm5szap8bp7bcRPTzpVV8tEnZwlW41EVqLRVcN01IUTE9G/X/fzNfsyb14/VKw+js8VhVqL59/IC5s7r71axK+8Z5yQvrklunJO8uCa5ca4r58WjQreoqIhrrrnmouMmk4n58+dz6tQpdu3axbhx49y+d1VVFQ6Hg5CQkFbHQ0JCOHXqVJvusWTJEsLCwhgyZIjT51euXMmKFStaHqekpPD0008TGRnpdryekAl8rklunLtcXmJjY8nN/ZLayghUeyz7vznB1BtGedzul5/k8M0+CFZDsWsaRv8yfnvPVR3S+3rvr/rw9mtfUF8dSYAumo8+LOBn9010+z7ynnFO8uKa5MY5yYtrkhvnumJePCp04+PjycnJYcqUKU6fHz58OO+++64nTbTbBx98wFdffcVjjz2G0Wh0es7s2bOZOXNmy+MLn0TKysqw2WydHqOiKMTExFBSUoKmaZ3eXnciuXHOnbxMnJbKO+8ew6KGczDfD/PnO0kd2Kdd7dpsNlavzEWxxRKgU6h1NDGoXy3DrkintLS0Xfd0ZuqMND5YcQCdLQ6aonjzlU+ZfmPbeqPlPeOc5MU1yY1zkhfXJDfOeTsver2+zZ2SHhW6s2bN4h//+AfLli1j9uzZFxWUhYWF7S4YLRYLOp2OioqKVscrKiou6uX9vlWrVvHBBx/w6KOPkpSU5PI8g8GAweB87KI338CapskPjAuSG+fakhedTsesmbF8uKaSINWfHbsNGIwl9EmNdqutk4WlbNxaT7AaBwpU2s4x89oIQiKiOuV7c9PNg1j2bi5mJZqG2li2fn6YMZPavtauvGeck7y4JrlxTvLimuTGua6YF48K3bFjx3Lu3DmWLFnChg0buPrqq0lNTQVg//79bNq0iaysrPYFpteTmprK/v37ueKKKwBwOBzs37+f6dOnu7zuww8/5P333+eRRx6hb9++7WpbiJ4iwGJm8vg6Nn3VRIDOyFc7rAyvLiJ9WOJlr7Xb7Xy85jD19bEEq8HYNQ2D3ylumzOg0yeK3TynL8uWH8eihnPyTASFuadITo/r1DaFEEL0PB7vjDZz5kwGDx7M+++/z4YNG1rW2QUYOnQo99xzj0f3fumll0hNTaVfv36sXbuWxsZGJk6cCMCLL75IWFgY8+fPB5qHKyxfvpz777+fqKiolt5gPz8//Pz82h2HEN1ZTGIEY5tK+GpnI4E6EwcPqRw+coDp1/fH5HfxsB673c43WwrIPxlAsBqPUYEqex1Zg6wMGO6d3db0ej0339SH9z4sx6IGsHWXQmRsHQEWs1faF0II0TN0yBbAycnJPPjgg9hsNkpKSmhqaiIiIgKLxeLRfceOHUtVVRXLly+noqKC5ORkFi5c2DJ0oby8vNUMvw0bNmCz2Xj22Wdb3WfOnDnMnTvXo1iE6M4S+8VgDqxg/efVBKtBYI3ng5WVKLqzxIRBYICemlobpec0rI5QAnXRBKtg0xwo+hLm/qC/y2E+ncUvwMSE0Xq2fW0jSPXngzWnuXVesiw7JoQQos0UrasNpugCysrKWi071lkURSE2NpbTp093uTEtvia5cc7TvNhsNtavyaW+IRo/xfXnXJvmoEEr56org90e09vRdm7J49SJSHSKgmY4yY0/cN6rLO8Z5yQvrklunJO8uCa5cc7beTEYDG2ejKZz58YPPPAAX3zxhVsTzKxWKxs3buSBBx5wpykhRCfQ6/XMnJXB9TPNGM2nqLKfpcpeT62jiSp7PVX2c+j9TjLpGj233Zbm8yIXYNT4NDA2LynoaIojZ8cRH0ckhBCiu3Br6MLEiRN5++23eeutt8jKymLo0KGkpKQQFRWFyWQCoKGhgdLSUo4ePcrevXvZtWsXer2eG2+8sVNegBDCfQGBZq69YZCvw2izmTcN4J1lRQSroeQetZCQUkF4VIivwxJCCNHFuVXo3nTTTUybNo3PP/+cTZs2sXnz5pbnLoybs9vtLccSEhKYO3cukyZNwmyWSSRCiPZRVZWZ08JZv6GBAJ2RdZ9WcuvcQPT6DplmIIQQoody+38Jf39/ZsyYwYwZMygtLSUvL4+TJ09SXV0NQFBQEPHx8aSlpREVFdXhAQsheqeQCAtD0s6Sn28gWA1m9Qd5zJ7TfXqlhRBCeJ9H3SFRUVFSzAohvCYjK4XjxQexN8ahs8Wy66sCssb183VYQgghuii3JqMJIYSvXXtDOlX2s+gUhSNFIZSeOufrkIQQQnRRUugKIboVVVW54fpoahyN+Ov0fLKxxivLAXYku91O6alz5O0t4tDuQo4ePkltTZ2vwxJCiB5HZnIIIbodS0ggWRnnOHDQQLDewocrC7hlXtcer1tw4AR7DlRRbwvCXxeAUdEB/9lU58CeJmodNdi1KmJCmhh9dTIBgTKJVwghPCGFrhCiW0obmsix4wdpqovD6Ihh26ZcfnBbrK/DasVms/HV5/kUlwcRrAZiJBDjtxu7aZpGEw40NPTo0Cs6AnRGIIK6ali3ph4HRYweGUJivxifvg4hhOiupNAVQnRb194wiKVL8wlSIyk+HcaxvBP4Bfl+i2C73c7Gj3MpqwwnUBdLsAoOTaPaUUWIfw19kwNISYvFL8DUcv75M1XkHS6luFQDLRyzzgDEsPsbja925jF1QiQRcaG+fWFCCNHNSKErhOjWbroxng9XVRKk+rNi1Wl+MCscf7Ofz+LZ+/VR9haYCFbjCNRBk2bHrpRx1ZgwYpOSnF6jqioRcaEthazVamXbxgKKygMJVoMIVKL44ksHesNBrr+xPwaDwZsvSQghui2ZjCaE6NYCAs1cOcJOk+bAogbw/gen3NqmvKPUVNby7ju5FB4NJVgNwKY5aFJOMXW6ibnzBhCb1PalGA0GA1dPG8iP5icQHVtGtb0eo6JDZ4tj2b9LOXr4ZCe+EiGE6Dmk0BVCdHt9B/ahT2w5dk3Doobx/ooCr7a/c3Mea9bWEqCLRlEUquxnuWK0jZvnDsISEujRva+4uj+33BKBajpJk+YgWA1gT44/a1YeaLUTpRBCiItJoSuE6BFGT0wnJOwsAP5KDKveP9DpbVZV1PDOO3mUnIoiQGekzmElPKKEH87vS3xyx22mYzAYuH5WBleMaqLSXo1e0aE1xfPuu8epOl/dYe0IIURPI4WuEKLHmH/H1TQppwFQrPF8sKLzit0dX+Sydl0dgbrmgrbaXsb0aX6MnTKg09pM6BvDrXNjcehP4dA0LPowPlpfx+Gc453WphBCdGdS6AohepTZc9KppwQA1R7Pe8sPduj9K89Ws3RpPqUl0S29uJFRZ5g/vz/B4UEd2pYzer2em24eRELSOeocVgJ1Jg4dDuLj1R37OoUQoieQVReEED2KqqrMnTeA95YfxKjFYdTieOedPGbPSsLP3+TRvbd8epiTZeEEqZEA1DhKmTE9FktoekeE7pbMMX1JTDrP+o1VBOstNNXFsXRpPrNu6oM5wN/r8XSEc2WVHMk9Q0WljYZGaLQpAOh1GnoVggN1xMQGkpAS2bI0mxBCXIoUukKIHunmuYP4YMUBVHs8gboo3ltZzugRGn0H9nH7Xrl7ith5AILVGMw6qHU0kRpfwQ1Xp3VC5G0XERfKvLmBfLiyAKMjhiA1kpUfVjA+6zxJ6XE+ja0tTh8vZc+ecs7W+KFXgr5dO/g/Y5tbSlk7aHaoONf8dXB/HTWOs+h11cSGa2RdmUiARXaRE0JcTApdIUSPNWtOBl9uOEzZ2SgsagB79zjI3nuIqVPiCYmwXPb6nO1HOHAUAnVhBKsKdk2jkTPMvD6BwOCOm2zmCYPBwJy5A9n62WFOlUViUf35Zred40WHuXpq540Xbq/SU+fYseM0VfUhWNQAIA7Ld/b4qHNYsWoNgA0dzcvEaejQNBWdYsJPZ8Kg6LCoZsBM5Tn4ZG0DtY4zxITUM3ZCSrft0RZCdDwpdIUQPdrVUwdQfKSEL3Y0Eaxa0BPL55/aaHDkkxhlp//ASKLjwwGoraqj4FAJR4sbqGsKwaKGtxRhlfbzjM00kjqg6xWPAGOnDKD4aAlfbm/AogZQeS6G5csOM3t2P1+HBkDOjiMcOKIQqAtFp8RjUZu3Qa521GNUK0mK1dNvYBRhkZGXvI/dbufE0VJyc89TXmVAVUIw6wwEq6HUV4eydnUdNq2YYQOMDByR7J0XJ4TosqTQFUL0eAl9Y7gt2c7mDXmcPh9KoM4PgxrJ+bPw9Rawa+fR0NArOiAMPWBRwa5p1DrOMqSfjqFXpPr6ZVxWQmoMN8c28sEHhQToovEnhmUrSrjhmiZCogO8Hk9To5XNn+VTUhHc6kNDlb2OQL/zXDkympjEOKDtwyxUVSWpfyxJ/WOB5sL34O4iDh9pwOGIwKwzYFKiKMiD7EOnCAs8z9WTUwgIlKENQvRGUugKIXoFVVWZOH0gdrudnZsLOHZKQVFCMCsGVEUBmic+NWh2Gh01BPvVMubKaKLju0aPaFv5+Zu49bZ0Plt7kMqqGILVQD77vAGTXyHTb0hHVdXL38RD50or+eLLkzRZIzHr4lp9aBierpKRlYI7xe2lqKrKkJEpDBnZvHXyzi+PcPSMiUBdCBbVjK3ezLo19dgp5soRQSR3g7HLztTV1lNSfI6yMzWcr7RT1wh2h4LdoUP79r2roKHqHBgNDvyNCoH+OkLD/EjsGyVjmEWvJYWuEKJXUVWV0RPTGf3t49qqOs6XV2O32gkKCyA4LARVDfdpjB1hyvWDKMw9xZZdDoLVQByNcbyz7DTD060MzkrplDbz9haxa38DfroI9Eocel3zBwdFKWX82EhiEjv3Q4PBYGDslAGMBc6cPMtXW89gtUV+O8ktmr27NbbsOk5yVAOjJ/bFYDB0ajztVXykhCP55ymvhAabPwZdwLevIeDbLzB8++V0kVArNFqhsRbOlkNBXhN1jlqsWgOqUk+w2UpSHzP9MuIx+Rm99rqE8AUpdIUQvVqAxdxje7uS0+NI6Gfn4zVHaKyPIFgN5Gi+xt7cfK66MoiEvjEet2G1Wtm2sYCi8kCCVQuBavMkv2p7PaGB55gxJRVzwECP23FXdHw4P7gl/D/xlQUQrLcQrAZz/mwwK/59Dj9DOT/4gR46v5PbJbvdTlF+CYfzKjlXY0SvBGPW+QGxmADTd/6XtmkOGjQrdkcjitKEqjjQ6Rzo0NAAjeYeXoemR8OADj1GnRGTon5bKBuAIOwNcLQA8vNrqXWUo6OGkAArKUmB9M2Iw2iU4lf0HIqmaZqvg7iU9evXs3r1aioqKkhKSuLOO++kXz/XvQLbtm1j2bJllJWVERMTww9/+EMyMzPdarOsrAyr1epp6JelKAqxsbGcPn2aLv5t8DrJjXOSF9ckN85dyMvXm7P5YkcdFjUMAIemUeM4z6AUjaFXJLs1pMFut3Mop4gD+Y0oWgT+On3LPasdFaT1sZI1rq9Xhkm4o+DgCb7ZU4tBicSoNHeF2jQH9Y5zpMbayRybitHUub28drudY4dPk5dfyfk6PwyKBX9d6zYdmkatowGoJdCvkbgoI8n9IgmNDGpXTstLKig6dpay8kYqa3U0OQLw1wW05OC7rJqDOkcdfvp6woNs9EsLJS45sst9L+12O+UlFZScqOTcuUaq6jQarCp2h4oDFdCjQ49OUVEVHToUFJRv/wQHGg5AQ8OhObDjwKHZ0LChYEfBjl61429wEOivEBxsIDzcTGR8KP3S+sq/M9/j7X9/DQYDkZeZuHpBly50t27dyosvvsjdd99N//79+eijj9i+fTvPPfccwcHBF52fm5vLn/70J+bPn09mZiZbtmzhww8/5OmnnyYxMbHN7Uqh63uSG+ckL65Jbpz7fl52bS3gUKGJYPU/u7jVOaw4tPPEhNqJiwsgoV9kqyW6zpZWUHzsLCVnGjlbY8SgBLcqzho1O3bKGJ0V3DJJrCurqazly43HqagLJUj9z+ts0GxYHeeJj7AxYlQfLKGe73RntVrJ33eSI4V1VDb4Y9IF4ae0/mWqXdOocdRhVKvpE6kwODMeS0igx21fyoWe5CNHKzlbpcPqCMCsC8DgpPht0Gw02mvwN9QRGaqjT0IQCX2jOnXox4UxyaUlNZyvslHboKPJpseBH6pixF8xfju23vuaNAdNmhW71oSCFb3Oiklvx+wHlkCVsDB/wmMshIS374NJR7NardRVNVBXU09tbRP1dVYa6q00NNppbNKwWjWabAo2h4KmwYV/PjUU0Gj+bYGmoCgaigI6NBQd6BQNnQI6nYaqKPj5GckaFe2VHSJ7TKG7cOFC+vbty09/+lMAHA4H9957L9dddx2zZs266Py///3vNDY28tBDD7Uce+SRR0hKSuKee+5pc7tS6Pqe5MY5yYtrkhvnXOXlYHYhOYdt+OvCvl1t4j80TcNG87kKXPQ8XCjOKugT3sDo8andcqcyh8NB/r4z7DpQg1EXhkn5T1HS3ONdj06pITTARlSEkdh4C5HxYej1F4/6q62p48yJ85SX1nLmrJXqeiMaAZh1fhflz6Y5qHXU4adWkxCjY/CIhC4xfMZqtXL04GmOHa+hot6I3R5AoM4PnZOC0q5p1GmNOLR6VMWKSW8j4NtCz+Snx99Pj9lswORvbH4/2e047Bo2m526Wiu11U3UNdhpaNSob1JotBlwYESHEYPOiJ9y+QJR0zQaNDtWrRG0RlSdFYNqx2jQ8DMq+JtUAgL0+Pnp0RtU9HoFvVFFQcFutWO1O7BbHTRZ7dTXWamrs9PQpNFoBatNwepQsWsGFIyoigG/lomrbWPXNBo1GzbN+m1PcXNvsV5nR686MKig6jR0qoJeBb1OQdUDmoKmaTg00BzNvc52B9hsGja7gs3+7URETYdDU3BoOjRU+PZLh66lJ1uPzunPb2cZPKyWlAHxnd6OO4Vulx2ja7PZOHr0aKuCVqfTMWTIEPLy8pxek5eXx8yZM1sdGzZsGDt37nR6vtVqbVXQKoqCv79/y98724U2vNFWdyO5cU7y4prkxjlXecnISiEjC2qr68jeUURxqYJDC8RP54dR0WGg9fn1DhtNWgNGtYbEaIWhIxMJCOr6S65dil6vZ9J1WQwcUUJDfSNfbzlKcamCQggBOmPLphTWejhZ3Pzl0KqxoWHHgaZp6BQdepRvi4n/TBYL+k6d1jwcoAazoYbkeCMZwxPxC+h6Ex6NRiMDhicxcIRCTEwMJSUl1FTVkrv/FMWnGqluMKFgxl9nQq/oCFL8AL/mix3QVAflda7u7rr31wgYndS1LWOStUZ0SiMmvY1AP43QYANRMYHEJoZ7vK23O+x2O1Xna2mq1ThWeIaqaiu19dBo02FzGNAwoipGDIoek6KiKgpmpWXaYGuO5i87zV/udK3pv/3iP4vFtInj2w+wds3+7VAN+7cDOOzfDtdwoKCBcmEdj+Ye3OamNDS+7fFFQdMUNHTfPtMciIKOgKDALvdvcJctdKuqqnA4HISEhLQ6HhISwqlTp5xeU1FRcdGQhuDgYCoqKpyev3LlSlasWNHyOCUlhaeffrrNnxI6SkyM5xNCeirJjXOSF9ckN865zEss9Evr2/LQbrdTUlxG5fkaFOXbHuGESILDLr+TXHd1ITfJKUktxw7lFLB372lKzjlosvmjV0z4KXp0ioIRBdBdVGRcKMwcWgN+hkaiw/WkpUUyaFj/Th/72xliYmIgpvX7A5rXRz5y6DgF+WWUnWuktkGhydbc+6lTDOjQtepNdGgXJss1/2nDgV2z4dDsgA2dYsPPYCfIrBAaYiQm2kKf5Eii4sK7xK/+W/l2B/Hhoy+9cUxtVS0njp+h5FQl587XU1dvo77R0dJbbHeoOLRv30ctFeuFv1+gfedPDXA0F6OK49vhAhqqTkOvgtEAJqMOP6OKn58es7+BQIsfgYEmgoIDCAoNICDQ3PXy6QVdttD1htmzZ7fqAb7wKaSsrAybzdbp7SvKfz41y69aW5PcOCd5cU1y41x78qIzQWjMfzaYqGuspe50bWeF6DOXyk1IdABXT2098bmp0UrZ6fPU1zbS0GDDbnPg56fH6GfAEmImJCLEaSFx9lx5p76OjtaW90xIdAAj27AJid1ub2dxZae0tLQd13Uud36eLBH+WCK6wnbUGrV1NdTW1XRaC97+91ev13f/oQsWiwWdTndRb2xFRcVFvbwXhISEUFlZ2epYZWWly/MNBoPLwfTe/I9S0zT5j9kFyY1zkhfXJDfOSV5ca2tuDEY9cUmX/s+1J+W4I94zOp2uR+XkAvl5cq4r5sV7I5TdpNfrSU1NZf/+/S3HHA4H+/fvJy0tzek1aWlp7Nu3r9WxvXv30r9//06NVQghhBBCdD1dttAFmDlzJp999hmbNm3ixIkTvPbaazQ2NjJx4kQAXnzxRZYuXdpy/vXXX8+ePXtYvXo1J0+eZPny5Rw5coTp06f76BUIIYQQQghf6bJDFwDGjh1LVVUVy5cvp6KiguTkZBYuXNgyFKG8vLzV7L709HTuv/9+3n33Xd555x1iY2P5/e9/79YaukIIIYQQomfo0oUuwPTp0132yD722GMXHRszZgxjxozp5KiEEEIIIURX16WHLgghhBBCCNFeUugKIYQQQogeqcsPXfAFZ9s79qT2uhPJjXOSF9ckN85JXlyT3DgneXFNcuOct/LiTjuK1tUWPBNCCCGEEKIDyNAFH6qvr+cPf/gD9fX1vg6ly5HcOCd5cU1y45zkxTXJjXOSF9ckN8515bxIoetDmqZx7NixLreLSFcguXFO8uKa5MY5yYtrkhvnJC+uSW6c68p5kUJXCCGEEEL0SFLoCiGEEEKIHkkKXR8yGAzMmTMHg8Hg61C6HMmNc5IX1yQ3zkleXJPcOCd5cU1y41xXzousuiCEEEIIIXok6dEVQgghhBA9khS6QgghhBCiR5JCVwghhBBC9EhS6AohhBBCiB5JCl0hhBBCCNEjSaErhBBCCCF6JCl0hRBCCCFEjySFrhBCCCGE6JGk0BVCCCGEED2SFLpCCCGEEKJHkkJXCNFlfPnll9x4443Ex8ejKApvvfXWJc9/8sknURSFX/7yl94J8BLaEntycjKKolz0NWPGjC4Xq91u59FHHyUlJQU/Pz9SUlL47//+b2w2m1djdUdbXtdLL73E0KFDsVgsWCwWxowZw0cffeT9YIUQXiGFrhCiy6ipqWHw4MH84x//wN/f/5Lnbt++nVdffZWhQ4d6KbpLa0vsO3fu5PTp0y1f2dnZKIrC3Llzu1ysTz/9NC+99BLPP/88hw8f5h//+AcvvfQSTz75pFdjdUdbXlefPn14+umnyc7O5ptvvmHy5MnMmjWLvXv3ejlaIYRXaEII0QUFBARob775ptPnKioqtNTUVO3zzz/XJkyYoN13332dEsOPf/xjLTIyUqupqXHrukvF/l1//vOfteDgYK2urq6dEXrOVawzZszQfvzjH7c69uMf/1ibMWOGlyK72DfffKMB2qJFiy57blu/B5qmaaGhodo///lPD6MTQnRF0qMrhOh27rnnHubMmcOkSZNcnnNhWEBSUhINDQ1Oz7kwlMDZr+N37tzJ4sWLeeihhwgICOiw2C/QNI3XX3+dH/3oR057H50Ncfju1+WGdXhq/PjxbNy4kcOHDwNw8OBBPv/8c66//nqXsbY3122VlZXFrFmzePTRR6mpqWn3fS6w2+28++671NTUMHbsWKfnHD58mF/96lcMHjyY4OBgjEYjcXFxzJgxg9dff53GxkaP4xBCdB69rwMQQgh3LFq0iIKCAv71r3+16fyioiKee+45HnroIbfaeeSRR7BYLNx7773tCfOyNmzYwLFjx7j77rsved6f/vQnp8eHDx/eCVH9xx/+8Aeqq6sZNGgQqqpis9l45JFH+MUvfuHymvbm2h0PP/wwV155Jc8//zwLFy5s1z327dvHmDFjaGhoIDAwkJUrVzJkyJCLzvv//r//j8cffxyHw8GYMWNYsGABgYGBnDlzhk2bNnHXXXfx8ssv880333j6soQQnUQKXSFEt5Gbm8vChQvZsmULBoPhsueHhoaiKApPPfUUd911FxEREW1qJy8vj08//ZS77rrrsmOF22vRokWMGjWKYcOGXfK8xx57rFPav5xly5bx9ttvs3TpUjIyMsjJyeHXv/41KSkp/PSnP73o/Pbm2l1XXHEFAwYM4JVXXuGhhx5Cp3P/F5Pp6enk5ORQWVnJihUrWLBgAZs2bWLw4MEt5/y///f/+NOf/kRCQgL//ve/ufLKKy+6z5o1a/jb3/7m0esRQnQuGboghOg2tm3bRnl5ORkZGej1evR6PV988QX/+7//i16vv+jXyGazmUcffZTKykoef/zxNrfzxhtvoGka8+bN6+iXAEBpaSkffvjhZXtzfen3v/89v/vd77j11lsZMmQIt99+Ow8++KDLyWjtzfWOHTuYM2cOMTExGI1GEhIS+NnPfsapU6dcXnPrrbdSVFTEhg0b3H5dAEajkX79+pGVlcWTTz7J8OHD+fvf/97yfGFhIY899hgGg4G1a9c6LXIBZs6cyfr169sVgxDCO6TQFUJ0G7NmzWLfvn3k5OS0fI0cOZJbb72VnJwcjEbjRdfcd9999O3bl1deeYX8/Pw2tfPpp5+iqiqjR4/u6JcAwFtvvYXJZOK2227rlPt3hLq6OlRVbXVMVVUcDofLa9zN9RtvvMG4ceNYt24dkyZN4je/+Q0jR47ktddeY+TIkRQVFTm9bty4cQDtLnS/z+FwtPqQ9Oabb2K1Wrn55ptb9fI6YzKZOiQGIUTnkKELQoguo6amhoKCAqC5+CgqKiInJ4ewsDASExMJCQkhJCSk1TUBAQGEhYW5LEgMBgNPPfUUt9xyC3/4wx94//33LxlDbW0tOTk5DBw40K1JaJeL/QJN03jttde49dZbCQwMvOx9nQ1dSE5O5o477mhzbO2J9YYbbuCpp54iJSWFjIwMdu/ezbPPPsuPf/xjl/d1J9d5eXn8/Oc/Jzk5mS+++IL4+PiW5z777DOmTZvGr3/9a1auXHnRtaNGjQKa181193U99NBDzJgxg4SEBKqrq1m6dCmbNm1qtZbuli1bAJgyZYrrJAohugcfr/oghBAtNm7cqAEXfS1YsMDlNa6WFwO0+Pj4lsdjxozRAG3z5s0tx5KSkjRAs1qtLcdyc3M1QJs6dWqnxP75559rgLZjx45L3s/ZvS58TZgwwa3Y2hNrVVWV9utf/1pLTEzU/Pz8tJSUFO3hhx/W6uvrncbqbq5/85vfaIC2Zs0apzHOmjVLU1VVq6qqcvq8n5+fFh0d7fbrWrBggZaYmKgZjUYtMjJSmzJlirZ+/fpW9xk4cKAGaOvWrXOeQCFEt6FomqZ1aiUthBA+oCgK8fHxnDhxAmge3zt27FiuvPJKtm/fDjT3jB4/fhyr1Yper2913ty5c1m2bJlP44fmHuCurj25Hj16NDt27OD3v/89ZrP5ontu2LCBrVu38s0335CVlXXR8/Hx8Zw5c6ZTdmobNGgQhw4dYt26dUyfPr3D7y+E8B4ZuiCE6BXGjBnDnDlzWLFiBcuWLXM50ezCKguu1oMVl9eWXJ89exaAv/zlL5e8l6v1cuvr6zttRYzY2FgOHTrEyZMnO+X+QgjvkcloQohe48knn8RgMPDwww/T1NTk9JyoqCjgP4WYaJ/L5To4OBiAyspKNE1z+TVhwoSLrnU4HFRUVLR8rzra+PHjgeaxwkKI7k0KXSFEr9GvXz9+8YtfcOzYMV544QWn58TGxhIZGUlubq6Xo+tZLpfrCytabN682e175+bmomlap22a8ZOf/ASDwcB7773HwYMHL3mu7IwmRNcmha4Qolf54x//SEhICE888YTTX4srisLVV19NeXl5ywz+ru6OO+7wyrbA7rpUrn/5y19iMBh44IEHyMvLu+japqYml0XwhXG/l9oC2hPJyck89thjNDU1MWPGDJc7n61fv57rrruuU2IQQnQMGaMrhOhVwsLCWLhwIf/1X//l8pybb76Z9957j48//ph+/fp5Mbr2ubC27YUJdV3FpXI9YMAA3njjDe68804yMjKYPn06aWlpWK1WioqK2Lx5M5GRkRw+fPiiaz/55BNUVeWmm27qtNgXLlyIzWbj8ccfZ9SoUYwdO5aRI0e2bAH85Zdfkp+fz8iRIzstBiGE52TVBSFEj/T9lQC+q7GxkQEDBlBYWAjQatUFaO5NTEhIIDk5mR07dngr5FbcWXVhxIgRHDlyhOPHjxMaGtrZoV3Ek1zv27ePv/3tb2zcuJGSkhICAgKIi4tj3LhxzJs3j8mTJ7e6X2VlJTExMVx77bV88MEHnfmyADh06BD/+7//y8aNGykqKqKhoYHw8HCGDx/OnDlz+NGPfiSbRgjRhUmhK4QQTjz55JMsXLiQ7OxsRowY4etwXKqoqCA8PJzf/va3PPPMM74Op9O98MIL3H///WzevLll0pgQQrgiha4QQjjR0NBAeno6Q4cOZfXq1b4Ox6XVq1dzyy23UFhYSExMjK/D6VT19fX07duXsWPHsmLFCl+HI4ToBrrWgC4hhOgi/Pz8WLx4MRs3bqS2ttat7YC96YYbbug1a/4WFhZyzz33eLT9sRCid5EeXSGEEEII0SPJ8mJCCCGEEKJHkkJXCCGEEEL0SFLoCiGEEEKIHkkKXSGEEEII0SNJoSuEEEIIIXqkHrW82PLlyy9aWzEuLo7nnnvOrfucP38em83WgZG5FhkZSVlZmVfa6m4kN85JXlyT3DgneXFNcuOc5MU1yY1z3syLXq9v8y6QParQBUhISODRRx9teazTud9pbbPZsFqtHRmWUxe2+LTZbG3a5rM3kdw4J3lxTXLjnOTFNcmNc5IX1yQ3znXlvPS4Qlen0xESEtKmc61Wa6uCVlEU/P39W/7e2S604Y22uhvJjXOSF9ckN85JXlyT3DgneXFNcuNcV85Lj9owYvny5axevRqz2YzBYCAtLY358+cTERHh8vzvDnVISUnh6aef9la4QgghhBCiE/WoQnf37t00NDQQFxfH+fPnWbFiBefOneNvf/tbS0/td7nq0S0rK/PKGF1FUYiJiaGkpKTLdfX7muTGOcmLa5Ib5yQvrklunJO8uCa5cc7bedHr9URGRrbt3E6OxatGjBjR8vekpCT69+/PL37xC7Zt28bkyZMvOt9gMGAwGJzey5tvYE3T5AfGBcmNc5IX1yQ3zkleXJPcOCd5cU1y41xXzEuPXl4sICCAuLg4SkpKfB2KEEIIIYTwsh7Vo/t9DQ0NlJSUcNVVV/k6lF5NKz+DtvUztKO5KFGxKKOuRuk/yNdhCSGEEKKH61GF7ttvv83IkSOJiIjg/PnzLF++HJ1Ox/jx430dWq+lHdiN45VnoL625bG2cS3KLXeimzbLt8EJIYQQokfrUYXuuXPn+Mc//kF1dTUWi4UBAwbwxBNPYLFYfB1ar6Tl7cfx/OMcDEpk3eAfkWdJJLapgvH5m5jy7zdAVdFNucHXYQohhBCih+pRhe5vfvMbX4cgvqVVVeBY9FdWx43j//rOxKEoBNh17Fcj2TvgFg6FpPDz9xZjGjAMJT7R1+EKIYQQogfqUYWu6Dq0dxfxuV8Kb/a7gaFKACOMARjsOjSdRoGtgS0xo/CzNXL3W/9At/CvXXKRaSGEEEJ0bz161QXhG9qRwxw/kMdr/WczRRfCFWoQBnvzW01xKPTX+TNTDWNTn6vIqQT2Z/s2YCGEEEL0SFLoig6laRr2FW/xcvrNDDeEk6LzQ6eDIVn+XHdzMGMmBmA0KYQrBibqgvnf9Dk0rl3R5dbdE0IIIUT3J4Wu6Fh5B9hWqdIYks5gXQAAWWMDSO5nQq9XiIg2MHZyIDoV4nUmov3j2VQbAEcO+ThwIYQQQvQ0UuiKDtW04UOWps5gjK55pYu+A0zExLfefS7IopIxrHlL5ixdIKuTpmLd/KnXYxVCCCFEzyaFrugwWskJtp5uJMqcQICi4mdWSB/s5/TcpH5GgkJ0GBUdMeZ4thZWoDXUeTliIYQQQvRkUuiKDuPYtJ61iZMY9u2QhUHD/FFV56spKIrCgMHNvbqDFDNfRI5E27nFa7EKIYQQoueTQld0CM1m5cCBI5iD+mFQdAQG64hLMFzymug4PWaLglHRYQvLoHTn116KVgghhBC9gRS6omPs2cnHYSPI0JkB6D/Q77Jr4yqKQr+05qENaboANtVZ0KorOz1UIYQQQvQOUuiKDlGx9UvKo0bir6gY/Lhsb+4FcYlG0EGIomd/7Gi0vTs7OVIhhBBC9BZS6AqPadVVfHFeJU0NBKBfmh86Xdt2OjMYFGL6NG/QF2Luw8k9+zstTiGEEEL0LlLoCo9p2Vv5OnY8UYoR0EhIMbp1fUpfEwBJih/bz+vQGhs6IUohhBBC9DZS6AqPFWfvISgwEYDIWAMmP/feVuERetBr+Ck6cqNGQd6BzghTCCGEEL2MFLrCI1rFObbWmumrNC8VltLP5PY9FJ1CdFzzmF59YCJnDxzs0BiFEEII0TtJoSs8ou39mtzYMfgrOtBDZIy+XfdJSmoukJMVP3aflJUXhBBCCOE5KXSFR07sP0yAOQ6A2D6GNk9C+76IaD2aohGgqOQak9Eqz3dkmEIIIYTohaTQFe2mWa1sqzCQrDSvhZuU7N4ktO9SVYWAsOa3Y2XIAOwHcjoiRCGEEEL0YlLoivbL28/h6Cvw+3bYQnhk+4YtXJCU0Dx8IUIfRGHu0Y6IUAghhBC9mBS6ot1O7t1PgDke8GzYwgUx305Ii1aM5JzXPI5PCCGEEL2bFLqi3baebiLp22ELyR4MW7ggIFCHpnegKgrFfv2wnz/r8T2FEEII0XtJoSvaRSs5Sb4l49thC5rHwxYAFEUhJLr5PlZLEvUyTlcIIYQQHpBCV7RLxd7dmAISAIjpY0DxcNjCBSl9msfphqsB5O473CH3FEIIIUTvJIWuaJfsgjMkXhi2kOT+JhGuREQ1j9MNR8/uk7Uddl8hhBBC9D5S6Aq3aQ11HHTE46+oaErHDFu4wN+sw6G3o1MUjhGH1tjQYfcWQgghRO8iha5wm/3gXmpC0gEIjNChUztm2MIFlojmwrkuKBmt+FiH3lsIIYQQvYcUusJtufvzidEHAtA32a/D758U3zwUItQQSNkRKXSFEEII0T5S6Aq3aJpG9lmVCKV5LO2FtW87UnRM8z0jMHD4ZGWH318IIYQQvYMUusI9xcc4FTwIAJ1Zw+TX8W8hc4AOu2JHVRSONgR1+P2FEEII0Tv02EL3gw8+YO7cubz11lu+DqVHKd+Tg9k/BoCExI5bbeG7FEXB2DwyggpjLFp9Xae0I4QQQoiereOmy19CTU2NR9ebzWZ0urbX5AUFBWzYsIGkpCSP2hUX21V4nvjI5l3QEhM83w3NlahoE6erbeAfja2wAMPAoZ3WlhBCCCF6Jq8Uuj/96U89uv7RRx9l8ODBbTq3oaGBF154gZ/97Ge8//77lzzXarVitVpbHiuKgr+/f8vfO9uFNrzRVkfQaqrIV1NIVnQ4VAchYfpOiz0xzo/TBTWE60wUHjlB2qBhndJOd9Pd3jPeJLlxTvLimuTGOcmLa5Ib57pyXrxS6AKMGjXK7R7WxsZGVq9e7dY1r732GiNGjGDo0KGXLXRXrlzJihUrWh6npKTw9NNPExkZ6VabnoqJifFqe+1V8VkODcH9AIhLCiQuLq7T2goJtrHjyzyCUTl6zsqE2NhOa6s76i7vGV+Q3DgneXFNcuOc5MU1yY1zXTEvXit0R48ezfjx4926prq62q1C96uvvuLYsWM8+eSTbTp/9uzZzJw5s+XxhU8iZWVl2Gw2t2JtD0VRiImJoaSkBE3TOr09T2Vv202cuTlffWLh9OnTndaWoihoqh3FrlJcZ+7UtrqT7vae8SbJjXOSF9ckN85JXlyT3Djn7bzo9fo2d0p6pdBdsGABqampbl/n5+fHggUL2tRzWF5ezltvvcV///d/YzS2beyowWDAYHC+PJY338CapnX5HxjNYWdPdQDhAXo0NCKjDZ0ec2CEidozNmpMsTiqKlCCgju1ve6kO7xnfEVy45zkxTXJjXOSF9ckN851xbx4pdC9/vrr23WdwWBo87VHjx6lsrKSP/zhDy3HHA4Hhw4dYv369SxdutStCW3ie47mURacQThgsCjoDZ0/DichwcLhM+dQ/cKwHSvAMDSr09sUQgghRM/htaELFzQ2NvLHP/6RKVOmMG3atA6775AhQ/jrX//a6tjLL79MXFwcN910kxS5HirZsxeLaSIAKcmds6zY9/VPDuLwN+eIUEwUHS2mrxS6QgghhHCD1wtdk8lEaWlph8/M8/f3JzEx8aK2goKCLjou3PdNcR0xMc1DQhI6cVmx74qM9kdDI1BRKShtoq9XWhVCCCFET+GTbs7hw4ezZ88eXzQt2kE7f5Zj/v3RKQqawUFAoOqVdg1GHXa1eVLgaXuoV9oUQgghRM/hk0L35ptv5vTp07zwwgscPnyYc+fOUVNTc9GXpx577DHuuOMOzwPu5Rr27MIelAxAZJzzyXudxT+4uaiu9YtFq632attCCCGE6N68PnQB4Le//S0AJ06cYMuWLS7PW7ZsmbdCEpewL7eI+ODm8bH9kv282nZ0jIkT56zo/cJxFBeiDhji1faFEEII0X35pNC9+eabu+TuGeJimtXKvsYIohQVh+IgPNK7b5mkOBMnDloJVYycLDxJohS6QgghhGgjnxS6c+fO9UWzoh20vP2cDxlEFOAfqkOnevcDSkiIHk3T8FdUjpxpRKYVCiGEEKKtfFLoft/JkyfZtm0bFRUVxMXFMXHiRMxms6/DEsDJvQcIM14LQF8vD1sAUPUKNp0Vg2bkZGOg19sXQgghRPfltUJ3/fr1rFu3jv/5n//BYrG0HP/mm2/4+9//3mrL3XXr1vHEE0+0Ok94n6Zp7CqxtUxAi+/jnWXFvs8UAI4aqNKHo9ntKKp3Vn0QQgghRPfmtVUXvvnmG6Kjo1sVr3a7nVdeeQWdTse9997LX//6V+bPn095eTnvv/++t0ITrpSc4HjgIAA0Pwd+/r7ZdCM82h8Ahykc7cwpn8QghBBCiO7Ha5XLiRMn6N+/f6tjBw4coKqqihkzZjBx4kQSEhK46aabGDNmDLt37/ZWaMKFmt3foAtMACDOR725AImxzTuxBav+lBYW+SwOIYQQQnQvXit0q6urCQ8Pb3Vs3759AFxxxRWtjqenp1NeXu6t0IQLuwrKiVOai0xfjM+9ICyseYSNBZXCE+d9FocQQgghuhevFbohISFUVFS0Onb48GFMJhNJSUmtjuv1evT6LjFPrtfSaqo4oEvCqOhw6ByEhPluXKyfvw4bNnSKQnG1dzesEEIIIUT35bVCNzU1lS+++IL6+noAiouLKSgoYNiwYajfm1x08uTJi3p/hXdZ9+6iNjQdgOAovc/XPdYZmycrnteCfRqHEEIIIboPr3Wb3nLLLTz88MPcf//9JCQkcPToUQBmz5590bk7d+4kIyPDW6EJJw4eOEpsUPPmDP2STT6OBoLCjNSWgNUYjlZbjRIQ5OuQhBBCCNHFea1HNzExkT/+8Y+kpqZy/vx5+vfvz8MPP0xqamqr8w4cOIDRaGTMmDHeCk18j2azsqs2iBBFj4ZGdKzvhwvExjavvGAyWLAWHfdxNEIIIYToDrw6EDY9PZ2HH374kudkZGTwt7/9zUsRCWe03AOUhw0lCjAGKxiMvllW7LviowwU0EioYqC48CR9Bw72dUhCCCGE6OK8VsGsWrWKkydPeqs54YHjOfuIMIYB0DfFd6stfFegRcWhOTAqOgrLrL4ORwghhBDdgNd6dFetWsWSJUuIjIwkMzOTzMxMMjIyMBh8/2tx8R+a3c7OEo2oPkY0NBISfbd+7nfpdAo21YrRYeJMk2wFLIQQQojL81qhu2jRIvLz88nJySE7O5uPP/4Yo9FIRkYGWVlZjBgxgoiICG+FI1zJ28/xsEwGALoAfLYbmjMms4ZWAzW6UDRN8/lKEEIIIYTo2rxW6CqKQlpaGmlpacydO5eKigqys7PJyclhyZIlvPbaa/Tp04fMzEyysrJIS0tDp+s6RVZvcXxnNsH+M4CuM2zhgrBof87WaGimMDhfDmGRvg5JCCGEEF2Yz3ZlCAkJYfLkyUyePBm73c6hQ4fYvXs3u3btYtWqVZjNZoYNG8aMGTMu2jpYdA7NZmVLmUpMfPNwkqQusKzYdyXE+nH2SD0Bqj81xccJkkJXCCGEEJfQJbYfU1WVwYMHM3jwYG6//XZKS0vJzs5m9+7dHDp0SApdL9EO7qEoPIshioIuAMwBXatHPSrcANRjQeVY8VmGDvN1REIIIYToyrpEoft9UVFRTJ8+nenTp/s6lF6l4Js9hPnPBCC1i/XmApj8dFg1GwZFT/F5haG+DkgIIYQQXZpPC92ioiJ2795NWVkZAJGRkYwYMYLExERfhtUradYmtpw3EWtuXmUhMalrrLZwEb0V7HrKrAG+jkQIIYQQXZxPCl2r1cqrr77Kl19+CdAye17TNJYuXcpVV13Fz3/+c/T6Ltnh3CM59nzD6chRRCoK+kAICFJ9HZJT5kAFayXU60LQHA4UmbAohBBCCBd8UkkuWbKEL7/8kmnTpnHdddcRHR2NoiiUlJSwdu1aNmzYQGBgIHfccYcvwuuVDnydQ2zYLQD079e1Vlv4rsgYM6cqHeiMIWhnS1EiY3wdkhBCCCG6KJ90h23evJmrrrqKn/70p8TFxaGqKjqdjri4OO666y7Gjx/P5s2bfRFar6SdK+NLazyRiqF5k4jkLjpsAUiI9QfAojNxtuiEj6MRQgghRFfmk0LXZrORlpbm8vn09HTsdrsXI/INLW8/NWtX4Nj1FdrJ42gOh0/iqPlqEzURIwAIjFAxmbrucIDQ0OYhFYGKyrETFb4NRgghhBBdmk+GLgwbNoycnBymTZvm9PmcnByGDu35c+qLv95FZfZOgq21RDacx2A2o1xxNcqUG1Ci47wSg2azsenQGfomNm+rO2hA1x22AGAw6rBixYCBk5VdtyAXQgghhO95pdCtqalp9fjWW2/l73//O3/961+59tpriYlpHmd5+vRpPv74Y8rKynjggQe8EZpPfRwwiE8zR9OAA73DSv+qYq4+vJurNv8G89iJKD+4HSUgqFNjcHzzFXsjr2KoogODRnSsoVPb6wiK3go2A+dsgb4ORQghhBBdmFcK3Z/+9KdOjxcVFbFz506nzz344IO8++67nRmWz4UGpPLDxuZeyUrNxpmwcFaFDuCdlGnMLfiUaY/eh+Env0YZktUp7WuaRvZXu4iO/xEAKf1MKDqlU9rqSIEWHQ3noEkfLCsvCCGEEMIlrxS6N998c8sSYp3pk08+4ZNPPmlZl7dPnz7MmTOHESNGdHrb7ZEW5k/huUYAghU9wYqeNMyc8w/ik/Rb+er8IX656EVip01Huf6Wji/oDu3hM/MIMhQjGhr9+nftYQsXRMUGUnTOht4QjKP8DGpUrK9DEkIIIUQX5JVCd+7cud5ohrCwMObPn09sbCyapvHFF1/wzDPP8Mwzz5CQkOCVGNwxdKSZqTNSOX78FJXnbJSctHLieBNhNgNT1VCOhmXy8MgEfrRjJVOOP4Xup79B8TN3SNuappG/dj3+8XcAENlHj59/9+gZ7RNjouiAjWDFQOnxo8RKoSuEEEIIJ7pHZdNGI0eOJDMzk9jYWOLi4rjtttvw8/MjPz/f16G5pNMp+PnpiIo1MHSkmWtusNBvoAlFgVSdHzONcawaeDtvVYdje/ohtLNlHdPwvm943384yUrzVr8ZGR1TQHtDcIgeTdPwV1SOnazydThCCCGE6KJ8tvXY4cOH+fzzzyktLaW2thZN01o9rygKf/nLX9p9f4fDwbZt22hsbHS5lJnVasVqtbZq09/fv+Xvne1CG99ty2RSGTTMTFyCkd3ba6EKrlVD+Sbxev5SGsZvnnoI830PoaS4Xp7tcjSrlYOr12FKvRudohAcpRIc2rV2oXOWmwsMBgWrYsWIkVNVeq98r7qKS+Wlt5PcOCd5cU1y45zkxTXJjXNdOS+K9v0K0wvWrFnD4sWLMRqNxMXFYTY7703805/+5Pa9i4qKeOSRR7Barfj5+XH//feTmZnp9Nzly5ezYsWKlscpKSk8/fTTbrfZWWw2B9u/PMOBPecBKHDUc6piPw/lLaXv/X/APG5yu+57/t03+P3hAEZaBqMoCjf/MJWIqO4xPveCV17OhgY/SuuP8ujCmb4ORwghhBBdkE8K3XvuuYfY2Fj+8Ic/uCxy28tms1FeXk5dXR3bt2/ns88+4/HHH6dPnz4XneuqR7esrAybzdahcTmjKAoxMTGUlJRc1KP9XYUFjezdVQcanNGa2F13jN/ufY2U6dehTP+BW5+gtOMFrH7zPU6mLSBeZyI8Ts+4qzt3CbP2uFxuPvushNoyEycay7n39lQUneqDKL2vre+Z3khy45zkxTXJjXOSF9ckN855Oy96vZ7IyMi2ndvJsTjV2NjI+PHjO7zIheYXf2Fd3tTUVI4cOcLatWu55557LjrXYDBgMDhfN9abb2BN0y7ZXlJfI+ZAhZ1f1RJtNTLe3I+/ZD7ATz9/g8wzJ1F++HMU/eXXv9Vqqznx+j/Z2u8exupMoGgMG+HfpX9YXeUmOi6Qo2VWDIYgrGdOY4iJ90F0vnO590xvJrlxTvLimuTGOcmLa5Ib57piXnwyGS0jI4OioiKvtOVwOFr12nZXkdEGrp4ahH+gQqCiMtUYw+Jh97L2aA325x5Dqzp/yeu12hrqn/v/eCH+RjIN4QCkD/YnILB79oT2iWmeRBeqGDhVeNLH0QghhBCiK/JJoXvnnXeyf/9+Vq1addGuaZ5YunQpBw8e5P9v796jo6rv/f8/9549M8nkHkLIPSFAELmDQEGRO1KhKq33tr9ai+2ptl2nXR67fqXtUls9x7b69bSHtr/vEY9CvYBUKsqloqIH1AJyk5vcQiBcQggkEMhtZvb+/RGSGkm4Z3aYvB4sVjJ779n7Pe98krznk8/+fMrLy9m/f3/z49GjR1+xa7gpPsHDjZMS6JLuwWuYTLS6sLr3N/g/Zn+qH/0J9oolOK0U9c6+3dQ+9f/yVOKN9E7sTYxhEpdk0vMavwuv4spISPBgOw4+w2TfodNuhyMiIiIdkCtDF9LS0pg4cSJz587lpZdewufzYbayGMKLL754Uec9ceIEs2bNorKykkAgQH5+PjNnzmTAgAFXKnTX+XwmXxoTz9YNtZTsbmC4J4FdmWP4cXIPvv7OW9yw+DWsQcMguwCC9TjbN1G2u4Tf97mH3JSBpBs+TAu+NDoO8ypYBa0tpscgZNTjI4bDpzrWjBEiIiLSMbhSIcybN4/XX3+d1NRUevToccXG6n7/+9+/Iufp6EzToP/QAAmJHjZvqKWXGUu3QHcW9r2P+acPMqJ0M9k7PiFoWmxP6s3m4XdyvTeNTMOHYcLw6+MIxF2dQxY+z+sLQQOctDvezXQiIiLiPlcK3eXLlzNkyBD+7d/+rdWeXLkwBb38xCeabFhdA7UWkz0pVCYkUBJfwKdOCAPIMHzcasTgNUxMD4wYHUdat/PfuHY1SEqxOHkEQp4knHAYw3P1F+8iIiJy5bhS6IZCIYYMGaIi9wpI6+Zl3JcT2bWtjr2760kJWaQY8Wcdl5LmYfCIwFV781lrMnMSOXmkgRhvHA1lh/Bnd7xlnkVERMQ9rhS6Q4YMYfv27UyaNMmNy0cdy2vQZ2AsPfv4KTsU4mhZkNrTNoYBcQkesvN9dOnq6ZArllyOzHQfO2ggGS8HSvbSQ4WuiIiIfI4rhe4dd9zBs88+y3PPPcf48eNJS0trtXc3Pv7snklpm9dnklvgI7fA53YoEREf7yHs2FiGSUlZLT3cDkhEREQ6FFcK3X/9138FoKSkhOXLl7d53Lx58yIUkVyNDNMgbNTjIZby052juBcREZEL50qh+7WvfS3q/owu7vDFhKEOqm31/ouIiEhLrhS6d955pxuXlSiUkuql8hDYVhJOKIRhaU5dERERaaRpD+Sqlp2bCECsFaD28CGXoxEREZGOJCKF7sMPP8z69esv+nk1NTU8/PDD7N69ux2ikmjQrWvjMsbJWJSWqNAVERGRf4pIoVtaWkpNTc1FPy8cDlNaWkpdXV07RCXRIDZgEHbCmIZByZF6t8MRERGRDiRiAxpffPFFXn311Yt6juM47RSNRAvDMAib9XicAEdP+90OR0RERDqQiBS6Y8aMuaznp6SkXKFIJBrFxIaxa6CGBLdDERERkQ4kIoXugw8+GInLSCeV2jWGin2AlYQTbMDwak5dERER0awLEgVy8xpnXoj3xHBqf6nL0YiIiEhHoUJXrnrpXbwAJBgWe0uOuByNiIiIdBQqdOWq5/ObNDhBAA5UhF2ORkRERDoKFboSHTyNU4tV1MW4HIiIiIh0FCp0JSoE4ho/1hmaeUFEREQaqdCVqNA1IwCAx0rEqb34xUlEREQk+rhe6DY0NBAMBt0OQ65y+bmNPbmJZgyVJftdjkZEREQ6goitjNZk69atrF27lh07dnDgwAEaGhoA8Pv9ZGdn07t3b4YNG0bfvn0jHZpcxVJTLBzHIcYw2bvvGKl93I5IRERE3BaRQjcUCvHOO+/w1ltvcfToUeLj4+nevTujR48mPj4ex3E4ffo05eXlrFy5kqVLl5KWlsZXvvIVJk6ciGVFvB6Xq4zHMmigAT9+Dh53GOp2QCIiIuK6iFSQP/rRjwiFQowZM4aRI0dSWFh4zuOLi4v5+OOPWbhwIW+++SazZs2KRJhylTOsBgj7qQwG3A5FREREOoCIFLrTp09n7NixeL3eCzq+sLCQwsJC7rrrLlasWNHO0Um0SEgwqK+CBs28ICIiIkToZrRJkyZdcJH7eZZlMWnSpHaISKJRenZjgev1JmKfOO5yNCIiIuI212ddaE1DQwMVFRVuhyFXmYKcxsl0kwwf5Xs084KIiEhnF/G7vKqrq1m6dCnr1q2jrKwM27ZJTU2lf//+TJ48mby8PFavXs1//dd/MW/evEiHJ1expEQPYcfGMkx27z9JxhC3IxIRERE3RbTQ3bFjB7/73e84efIkgUCAnJwcwuEwR48eZfny5bz77rt89atfJSMjI5JhSZQwTIOgUYeHAIdPaqYOERGRzi5i1cCxY8f4j//4DwKBAI888ghDh7acAOqzzz7jjTfeYMGCBZdc6C5cuJA1a9Zw8OBBfD4fRUVFfOMb3yArK+tKvAS5CvhjQlAHJ51Et0MRERERl0Ws0F24cCEAjz32GGlpaWftv+aaa7jmmmtYvHgxc+bMuaRrbNu2jZtuuokePXoQDod55ZVX+PWvf80zzzxDTEzMZcUvV4cu6TEc3w9YyTjBBgyvz+2QRERExCURK3Q3bNjA+PHjWy1yP2/q1KnU1NSwbdu2i77GzJkzWzx+6KGHmDFjBsXFxVx77bUXfT65+hR0T+L4/loSPLFU79tHYs9ebockIiIiLolYoVtVVUVOTs4FHXvHHXdckWvW1NQAEB8f3+r+YDBIMBhsfmwYBrGxsc2ft7ema0TiWlebS81NZlc/jlNDrOFhT/FRhvQqao/wXKM20zblpnXKS9uUm9YpL21TblrXkfMSsUI3EAhQXV19Qcd++umn7Ny5k9tvv/2Sr2fbNi+88AK9e/cmLy+v1WMWLlzIggULmh93796dp556iq5du17ydS+Fbr5r26Xkpt44Qgx+Dp+wyMzMbIeo3Kc20zblpnXKS9uUm9YpL21TblrXEfMSsUK3d+/efPjhh9xyyy3nPG7fvn389re/paGh4bIK3dmzZ1NaWsrjjz/e5jHTp09n2rRpzY+b3okcPXqUUCh0yde+UIZhkJGRQVlZGY7jtPv1riaXkxuPVQ8hP+U1Pg4fPtxOEbpDbaZtyk3rlJe2KTetU17apty0LtJ5sSzrgjslI1bo3nLLLfziF7/gT3/6EzNmzGh1pbRVq1bx3HPPXXaROXv2bNavX89jjz1Gly5d2jzO6/W2uWJbJBuw4zj6hmnDpeQmKcVDzVEIe5KwbbtD/inlcqnNtE25aZ3y0jblpnXKS9uUm9Z1xLxErNAtKiri29/+Ni+88AKffvopI0eOJCsri1AoRHl5OWvXrqW8vJwePXowatQo5s6de9HXcByH559/njVr1vDoo4+Snp7eDq9EOrrcgmR2HA0SYyUQLC/D1y06hy+IiIjIuUV0Vv0pU6aQl5fH/PnzWbJkSYuqPz09nW9+85t8+ctf5qOPPrqk88+ePZtVq1bxyCOPEBsbS1VVFdA4Ptjn0zRTnUV+Vgw7CJJoWOzbXUIvFboiIiKdUkQK3Zdffpm8vDzy8vLo3bs3jz76KDU1NRw5coRQKERKSkqLacd69+7N97///Yu+zttvvw3Ao48+2mL7gw8+yNixYy/nJchVxB/jod4J4je8FB+uRxOMiYiIdE4RKXSXLl1KQ0MDAB6Ph8zMTPLy8sjNzSUvL4+kpKQWx6enp1/SsIP58+dfkXjl6ueYdeB4qajRQiEiIiKdVUQK3blz51JeXs6BAwcoLS2ltLSUXbt2tRiiEBMT01z4NhXBffv2jUR4EoXi4x1C1VCHlgIWERHprCI2Rrepl3bIkCHs3r2bTZs2MWnSJK699lps22b37t2sXLmSXbt2NT9n3rx5kQpPokxmbiKl28DrTSR84jiepFS3QxIREZEIi+jNaE1mz57NyJEjuf/++5u33XDDDdxzzz389a9/ZcWKFdx3331uhCZRomf3BEq3VZNk+CjdVkzBSBW6IiIinY3pxkX3799Pfn7+Wdv9fj/33nsvvXv3Zt26dS5EJtEiLs6kwQliGgY7SmvdDkdERERc4Eqhm52dzcaNG9vcP2jQID799NPIBSRRxzAMHE8dAOW1AZejERERETe4UujedtttrFmzhnnz5jXPxvB5JSUlEVmCV6Jbckrjimj1npQOt1KLiIiItD9XxuiOGjWK48eP89JLL7F8+XJuvPFGCgsLAdiyZQvvv/8+Q4cOdSM0iSIFhSlsPxYkzkqg/tBBYrJz3A5JREREIsiVQhdg2rRp9OvXj9dff53ly5e36NkdMGAA3/3ud90KTaJEfk4s29Y0EG9Y7Nh+gIEqdEVERDoV1wpdgIKCAn7yk58QCoUoKyujoaGBtLQ0EhM196lcPq/PpI56Yolhb7nDQLcDEhERkYiK2BjdRYsWcfDgwVb3WZZFTk4OhYWFKnLlivL66gGoDCa4HImIiIhEWsR6dBctWsRLL71E165dGTJkCEOGDKFv3754vd5IhSCdUHo3P8cPAN4UnGAQQ+1NRESk04hYofvf//3f7Nq1i40bN7J+/Xr+/ve/4/P56Nu3L0OHDmXw4MGkpaVFKhzpJHr2TmHNgVqSzFgq9+wl9Zoit0MSERGRCIlYoWsYBkVFRRQVFXHnnXdSVVXF+vXr2bhxIy+99BLPPfccOTk5DBkyhKFDh1JUVIRpujL7mUSR9FQfIecUXsPD9l3HuP4atyMSERGRSHHtZrTk5GTGjx/P+PHjCYfDbN++nQ0bNrBu3ToWLVpEIBBg4MCBTJ06lV69erkVplzlDNMgaNRiEc+BE67eeykiIiIR5spv/h/+8IeMGTOG22+/HQCPx0O/fv3o168f3/zmNykvL2f9+vVs2LCB7du3q9CVyxIXF8Y+DafsZLdDERERkQhypdAtLy+nuLiY4uJiDh06RCAQIDs7m27dugGQnp7OlClTmDJlihvhSZTJL0xi72aI8SbTcLQcX9d0t0MSERGRCHDtb7nr1q1j3bp1Lbbl5uYyffp0rr/+epeikmhU1CORPZ+eINH0sn3TZwycqEJXRESkM3Ct0LUsi+nTp3PttdcSCoUoLi7mH//4B7///e/ZunWrVkaTK8bnN6mnjlhi2X3E6BALRziOA0cO4ezbDcEGjEA8FPbGSE51OzQREZGo4VqhO3Xq1OYxutC47O9tt93GW2+9xdy5cykqKmLs2LFuhSdRJjbQALWxnAgnuxqHY9sEP36flf/YymZfd04l5IHhxQzVkPreUsbFVlD05SkYhb1djVNERCQauFLo+nw+UlNb77maNm0ae/bsYdmyZSp05YrJ7Z5A6Tbwe1MJHT+Gldol4jE4xyv45C/zWJo0ju7ZA+hhfOHbL74H79r1LP77Fr7RdT1db7sDw9JMESIiIpfKlYlqs7Ky2LBhQ5v7+/Tpw4EDByIYkUS7PkXJOI5Doull2/pdEb9+uGQ3c+auYFP6HQyOySLZsHAMB3+SQXxXE08AHBxyTT+9Uocyt/561v7f53HqaiMeq4iISLRwpdCdNGkSGzdu5Pnnn6ehoeGs/Z999hl+v9+FyCRa+f0mdTQWjZ+VGRG9dnD3Z/xxaQmBbhPoZviwDYde/f3cPD2ZyVOSGDc+kZu/ksyEqYmkZnsAKLIS2ZRyK+/996s49XURjVdERCRauPJ30YkTJ3LgwAGWLl3Khx9+yODBg8nNzcWyLDZv3syGDRs0bEGuuPjEEOFqqHbScBwHw2j/gjdUWsz/985hclKGYhkGZsBm4oRkYgNnv8eMi/dw/Q0JHDrYwJpVp8gw/ZR2ncb//s98bnzg6xgeT7vHKyIiEk1cGwB43333MWLECBYvXszatWtZuXJl875hw4bxrW99y63QJEoV9enC9jVBEr3JVJfsJbF7YbtezzlRyZw3PyM7dSSWYeBPdhg/IQXLOneBnZXtY9zkRFYsP0EXvOxOuomU1/7GgLu/1q7xioiIRBtX73Tp06cPffr0wbZtysvLqaurIy0tjfj4eDfDkihVmB9g8+rjxBgeNmw8zJh2LHQdO8ySl94mIXUSlmHgTbSZMDEFj+fCepGTUizGTEzig+UnSDf9rAwNJ+PDVXS7YXS7xSwiIhJtIjJG9+WXX2bVqlXs37+fcDh8dhCmSUZGBgUFBSpypd2YpkHIUwNAycm4dr1W8VtLqUgZR4xhYvtCTJx04UVuk5RUi+E3JmA7DnlWAvN3xBMuP9xOEYuIiESfiPToLl26tPmmM4/HQ2ZmJnl5eeTm5pKXl0deXh7p6VqtStpfVo6P4/sBXyahqkqs5JQrfo36kt0sqSqiwO8jSJgpk88/XKEtWZk+8q9toHR7iNxAPov++r98v19HWPJCRESk44tIoTt37lzKy8s5cOAApaWllJaWsmvXLj766KPmY2JiYloUvrm5ufTt2zcS4UknMnhQGsv3nSTV9PPp6u0MuWnkFT2/01DPq3/fS0HyMAAGDg8QiLu8m8gG9o/jQGkF5ikv1cnXs3HBQjJu1DLZIiIi5xOxMbrp6emkp6czZMgQdu/ezaZNm5g0aRLXXnsttm2ze/duVq5cya5d/5zjdN68eRd1jW3btrFo0SL27t1LZWUlDz/8MMOHD7/SL0WuYoFYD/VGDbHEsf2IjyFX+Pyf/HUpMUmN42hj08L06B572ec0DIPJE7vw5t+OkWB4eW1vGt+7thxvl66XfW4REZFo5so8urNnz2bkyJHMmDGDUaNGccMNN3Dffffxxz/+kVtvvZXExER+9KMfXfR56+vrKSgo4Dvf+U47RC3RouuZUTINVibh09VX7LxVmzay3h5KwPAQNIOMG9P66n+Xwuc3GTi8cfx6rq8rf3tjA47jXLHzi4iIRCNXCt39+/eTn59/1na/38+9995L7969Wbdu3UWfd/Dgwdx9993qxZVzGjK0sdJN8wTY8L+br8g57VPVvPpJA1lW481jo8cl47nEcblt6dk9FiuhHtMwqI0dRNnq1Vf0/CIiItHGlenFsrOz2bhxIxMmTGh1/6BBg3j11VfbPY5gMEgwGGx+bBgGsbGxzZ+3t6ZrROJaV5v2zE1yko86o5wYJ46t5XEMuwLXePu1D+gW1zhuNqOnSXpX32WfszUTJqTz1t+Ok2L6eGubnweG1GP4Y9rlWlcbfT+1Tnlpm3LTOuWlbcpN6zpyXlwpdG+77Tb+8z//k3nz5jF9+nR8vpZFQUlJCaFQqN3jWLhwIQsWLGh+3L17d5566im6do3s2MeMjIyIXu9q0l65ubbvaYq3NOD1ZxFoaCCplb8wXKjPlizjSMwwUg0DAiFu+0r/dv1mHzDCYfvqk6QFCtj0zlq+POPOdrvW1UjfT61TXtqm3LROeWmbctO6jpgXVwrdUaNGcfz4cV566SWWL1/OjTfeSGFh4+T9W7Zs4f3332fo0KHtHsf06dOZNm1a8+Om4uTo0aMRKbQNwyAjI4OysjKNt/yC9s5N796x7Nxc13hz118/Zuo9l9YDG6oo5y+bvOTG+Ak6YaZMSKWsrOwKR/tPhmEwemQ2mz4pxxeO4ePjuVy7eTO+tLR2u+bVQt9PrVNe2qbctE55aZty07pI58WyrAvulHRtZbRp06bRr18/Xn/9dZYvX948zy7AgAED+O53v9vuMXi9Xrxeb6v7ItmAHcfRN0wb2is3lmVgxp6GugQOhfIJnT6FJ3Bxi0g4dpjXFm4kN6FxKrFrh8YQCJjt/rU0DIMbxqSy+t3TZHuTWLR4I1/7f8a26zWvJvp+ap3y0jblpnXKS9uUm9Z1xLy4ugRwQUEBP/nJTwiFQpSVldHQ0EBaWhqJiYluhiWdxJdGpbPmvVrSrUTWvLuekV+5uOV1173+NsQ33vjoTarnml7J7RBl6zLS/XgTKgidCnDK24djO3bSpXdRxK4vIiJyNXBl1oUvsiyLnJwcCgsLL6vIrauro6SkhJKSEgDKy8spKSmhoqLiCkUq0aRbVz/15ikMw2BzZQZ27ekLfu7R9RvY1DCIOMNDg9HAxImRX9lv7PhuhJwwqaafNz6q6nDvokVERNwWkUL3xz/+MR988MFFjXsNBoOsWLGCH//4xxf8nD179vDII4/wyCOPADBnzhweeeSRi154QjqPIcMalwDu5kvjf99ac0HPCVYcZcHmGNI9sYScMOMmXfoSv5cjNtZDRm4YgPjYHuxatTbiMYiIiHRkERm6MHbsWObMmcMLL7zA0KFDGTBgAN27dyc9PR2/3w809saWl5dTXFzMp59+yrp167Asi1tuueWCr9O3b1/mz5/fXi9DolBhQRyb1h/ECsZR3NCLYfv2EXeOGRjs06d46W/byUkYAEDRQB+pKa2P846EEV/qyusLyok1/Ly3N5GeI+oxfX7X4hEREelIIlLo3nrrrUyePJn33nuP999/n5UrVzbv83g8AITD4eZtubm53HnnnYwbN45AIBCJEKUTGzM+nf9ddoquVjyvvLuL++9Nx4w5e+le+3Q1817+B8kJ1wEQ06Wevn26RTrcFkyPwYDBsezYYJPp68bKxasZM/1GV2MSERHpKCJ2M1psbCxTp05l6tSplJeXs3PnTg4ePEh1deMSrAkJCWRnZ1NUVER6euTHO0rnlZrspWtemOOlFmmBnsybu5K77vkSZvw/x4s3HNjH3KUlpCZch8cwILaeCeM7RjstKkpk66cHsMLx7KzpzpcqK/GnpLgdloiIiOtcmXUhPT1dxax0KKNGduHNo0fw1MUQmzCM/ztvB6NTDtMlOcD2/dVs9fQnP3Fg48Ex9dw8NR3T7DgrwIwel86Hy0+TYSXw5tKt3H7v9W6HJCIi4roOMeuCiNsMw+Dmqd0w4urxGAbZ8b3Y3nAD75QPojpmLPm+xgUZYtOCTP1KOh5PxylyAdK6+PAnNM4aUWv0omLPPpcjEhERcZ8KXZEzLMtg6tR0uvU0CRLCb5gkGRaGYRCyGhh0QywTJ3TtUD25nzduYhZBJ0Sy6WPRh8fcDkdERMR1ri4YIdLRGIbB8KGJXDfIofpkmIYGh4REDzGxHf89od9vkp0bpPyARYKvgM8+3sQ1Iwe6HZaIiIhrOv5vbxEXmB6DpBSLrt28V0WR22TYyAzqnVpiDJMPdsdiX8Tc1SIiItHm6vkNLiLnZZoGg4c0To2W4e3KB0u0iISIiHReKnRFokyPomRCZjWmYVByKp/aCo3XFRGRzkmFrkgUGjspnZBjk+YJ8NaS3W6HIyIi4goVuiJRKCXZT3xa43Rj+HpSvGaLuwGJiIi4QIWuSJQaPy6bGqeWGMPDeztiCdXXux2SiIhIRKnQFYlSHo/JsGGxOI5DN18XFr++3u2QREREIkqFrkgUK+yRjBk4CUCDUcT+9VtdjkhERCRyVOiKRLmbbs7ltFPXOIRhWzz11dVuhyQiIhIRKnRFopzXMhk+MkDYcejiTeK1N3bjOI7bYYmIiLQ7FboinUBhfiJJaY1DGOKsAt5f/InLEYmIiLQ/y+0ARCQyxk7I4/XXSvGRSPmp7uz5ZDs9ruvjWjyO42CfqOTYgTIOHDrJ6TqbsO0QtiHeb9IlxUt6ehIJ+fkYXp9rcYqIyNVLha5IJ2EYBjffks2ihUcJmDF8vDOF+OR9dOuZH7EYao+Us/GTPZQc91JDKj4rgQQjE4+R1eK4miCUn4JN+21OrqnACFWSbFUzuFcseYP7Ylj60SUiIuen3xYinUhsjIcx4xNYuaKWJE8My9aE+bLnAOndc9rtmvWVVaxe+RnFVQn4fd2IN/oQ74P4zx1jOw4hwjg4gIMHD5bhwWeYpHkC4AkAsGGPw3s7DxNwjjCybyy5g67FMIx2i11ERK5uKnRFOpmMbnEMGVrPxnVhUj1xLPu4hnG1e8i9tscVu4bjOJRu3M6HW+oIWzkkmdfQxd+4L+zYNBg1JCba5PdMITsjQEKciWG2LFhDQYfjJ4Ls3nWcI2X1BOtjiDX8pFkJQALrdji8s2UPBQnHGD2xL76EhCsWv4iIRAcVuiKdUI9eqdhOBZ+uD5PiCbBqk5dehzZy3cRBl3Xe+hMn+Oj9HZScTCXZm0m8r7F4DTk2IU813XvEMqh/GpY39bznsrwG6Wk+0tMymrcdOVrHxvXlnKj0EWvE0NWXxun6NF5ZVE2SZzv33DUC1MErIiJnqNAV6aR6FaXh91fxj48aiDd9HKjIZ9dfNjFtcg4J6V0u+DyO41C8fjtrtgdxPJkkmEWknbl3rMauJb1biJHXZxPjP39xez7dusZw0015jdfce5KN6ysxQ4mkeGKBXjz/ajmEDzDuS6l065F32dcTEZGrmwpdkU4sLz+ZpOR6li07QsBIJN6bz1vvBvGykVFDksgsKmh1DKwdbGD3ht1s3lNPdTiVFCuTeG/jcUHHxrZOMGhwCj0KM9plDK1hGPQoTKJHYRInq4OsfL+U2tOJxBkWWAWsWmtT+/FWbhjgp2BAzyt+fRERuTqo0BXp5JKS/Nx5Zy4r39/HkSNxxJteoIC1GxxOfHII7Gq8ZhADh6BjETbi8JsB4s0sfCZ0OTMb92n7NJkZIUaMyrkivbcXKjHBy9SvFBIKO3yyupx9+yBg+vF5s9m4zeGDT3dyXWGIa0f20Y1rIiKdjApdEcEwDG4cV0BtfZhVK/ZxvDJAwIwhxYoD4lp9ju041Dg1JMXXMWBwOjk52ZEN+gu8lslXbx/MoUOHWPPxAfaUGMSbcaR609mz3+GT4hJ6Z1QzbEwfPF6vq7GKiEhkqNAVkWaxfg+TphQCcPjwaXZuL+d4VZhQyMBxDDwem8R4g8zseLr36EJcIMXliM9mGAbDR+Yw7EsOW7ccYfOWeuLNJFK9KRw9lsIrr1UQZ5UzemQGabnd3A5XRETakQpdEWlVZmYcmZnd3Q7jkhmGQb/+GfTrD3v3VLDmk5P4nRSSPLHg5LPyQ5tTod3kdall2PUFBBLdmZ7McRyqj1Zy+EAlR4/Vcuq0TTAMoTCEwiZhTMBpnkzCwMEyHbyWg8+CGL9BQryXLl1i6ZqVQmxigoZoiIicoUJXRKJe9x5pdO+RxomqOj5cuZ+TpxKJM2NI9qZx8iQsXRKkJryHlNgaigrj6NEvD88VWn0tWFfHkdJjHCmr5nhVkOpag7qgRRg/huHHZ/qINSyg5UwXFmCZ5zhxCBpC0FAHJ0/AwYPgbApTxzGCdhDbacCkHr8VJCXhM+JibLp2jSUzv4trRb2ISKRFZaG7bNky3nzzTaqqqsjPz+f++++nZ0/deS3S2SUlx3DzV4pwHIctGw+ybUcdJsnEGBY+qwtOsAs7dsCmz05QF67BY9QQ5w8SH2OQlOAhEOfD77Ow/B6CdWHq6kPU14eorw9zqibM6TqoD3kI2T4w/FiGjxjDi8f451hnL+Bt5SdvyLGpd4LYThAIYxg2hhHmn+toOI39ug7YjonjeAAPhuHBg4XfsDANg1gsYj0WENt87prqxv9Hj8LWrSHqqKDBrsdx6rHMBmKtEAlxkJbqJz0jka45qVjW1TOO2XEc9WKLSKuirtD96KOPmDNnDg888AC9evVi8eLFPPHEEzz77LMkJSW5HZ6IdACGYdB/cA79B0M4HGbrxoPsKq6lLhhHnBlHjOEh5swKbISg5lTj//Np7oX9Qk+s7TjUOyFCTgNQj88KEeu3SU700qVrgMzcFBIT/ZdVrNlhm+OVtVSUnaTyWA0nT4U4XQP1IQsHHyY+YgwfnhbF8JkbDe3GQnh/NezfB+F/nKLWCRJ26jGox2OEsDw2XtPB54UYn4Hfb2JZJh7TwOMx8VgGnjNVeTjsEArZhMM2obBD2HYIhxv/B0MO4TCE7cbhGbZjELbBcQzCjonjGDg0fmxMpAlG40cDE8MwMDExMPAYjZ95MM4sHt34dqDVj46NjYPj2DjYjS8aG4O9QAgDB9OwMY0zH00HjwkeD1img+UxGv97TXzWmY8+C5/Pg9fnwR/jxef3YnpMTNPA9Bh4TBPDMjENs3m7x/I0f83C4TBO2CYcdnDsMGHbwQ7b2LaNHXawww5h28YJO9i23fi53fi1dgDHdrDtxkK/+b/d9DktP9qNmWh8DIbRuLaKYTauSmgaYJiNX0PTNCkvPUl19YnGfBsGpmViGmA2He8xMc3G12SYBh6P0Xisp3Ff4zk9jfs9BobReLxIpEVdofvWW28xYcIExo0bB8ADDzzA+vXrWbFiBbfddluLY4PBIMFgsPmxYRjExsY2f97emq6hnoizKTetU17adqm5sSyLgdflM/C6xse1tUH2bD/MwUOnqTplEgx7AR+W4cU0TDxnyqwwDjY24TOFk+2EMGjA8oTwe8MkBExSU/10y06ma0YS3s8VOO3BY3no2jWerl3jW2w3DIOMjAzKysoIh8NUHjtF2YEqjlbUUl0dpqbBQyjsxcGH1/ATY3jwGAbxhg/wAWeGOTjAmbHDp+rgAur+88d85r8XzlRdV+CkbZ3kYs99phZ2QhCk8f+FaSq5AcJtH3VBvdAGjRlyQxiIP+9RjT7/mj+31XGat37xY9NnX3yWc459595z+S78rEfb68SX9IT2ycalKOf64Ta5PTPOf2gERVWhGwqFKC4ublHQmqZJ//792blz51nHL1y4kAULFjQ/7t69O0899RRdu3aNRLjNMjI6VqPoSJSb1ikvbbsSuSksjL5V1Zrykp0N/Qa0fVxdXZD9ew5Tuq+CoxU1nKgOUx+CkG1i2y2HTBjN/wAae/QMGnuwHVr2qTpOUzFkN340bAwcjKYeVAM8poNpgscEywOWx8BrGViWic9r4vWZ+H0WPp+FP8aLP8ZLTIyXmFgfju0QDtmEwmHC4cbe5HDwzOe2Q7AhRF1dkIaGEPX1Ng1Bm2DQJhi2CYUgZHOmp9nAdgxs28BpjBCcpm76xp7Jxtd8pqe2sZ8ZD409nxfqYt6QNRWMLXuqz+zD+UKh88VS8vMP2tjXGFDze43PfUVb2cYFv86m9tDG3gs6R1ToRC81Pt5HZmam22G0EFWF7smTJ7Ftm+Tk5Bbbk5OTOXTo0FnHT58+nWnTpjU/bvrBc/ToUUKhULvG2nS9pp6Wxl8C0kS5aZ3y0jblpnWXkpeEVC/XpnasX1bn1zTU4cJ/rV3pNhMMhrBt58xwhDC27ZwZhnBmKIJtN+6zncY/+xsGhmXiMc3P/cnfbBwK4jmzzYz8n/wvJC+O4xAOn3lttnNmqEUYx3H++dhuHDJhO3bj0AnbwXYaH2PTuN0BJ2w3vv0J281FvW2DY9v/LMgv9MtzEV/Hxjcx5z7+i3sNA+LjEzh1qrr5qec8w8W2q3Mcf6kt9OJCMM485yKvZkBCXAIxibEcPnz44p57CSzLuuBOyagqdC+W1+vF28bE8ZH8Rdk0tkrOpty0Tnlpm3LTOuWlbVcqN1Y7DU9x6+t2vrw0FeSdiWEYZGZmcvjwYX0/fU5HzktUtdDExERM06SqqqrF9qqqqrN6eUVEREQkukVVoWtZFoWFhWzZsqV5m23bbNmyhaKiIhcjExEREZFIi7qhC9OmTWPWrFkUFhbSs2dPlixZQn19PWPHjnU7NBERERGJoKgrdEeNGsXJkyeZP38+VVVVFBQU8LOf/UxDF0REREQ6magrdAGmTJnClClT3A5DRERERFwUVWN0RURERESaRGWP7uWyrMimJdLXu5ooN61TXtqm3LROeWmbctM65aVtyk3rIpWXi7mO4XS0Cc9ERERERK4ADV1wUW1tLT/96U+pra11O5QOR7lpnfLSNuWmdcpL25Sb1ikvbVNuWteR86JC10WO47B3794Ot4pIR6DctE55aZty0zrlpW3KTeuUl7YpN63ryHlRoSsiIiIiUUmFroiIiIhEJRW6LvJ6vdx+++14vV63Q+lwlJvWKS9tU25ap7y0TblpnfLSNuWmdR05L5p1QURERESiknp0RURERCQqqdAVERERkaikQldEREREopIKXRERERGJSlqs2UXLli3jzTffpKqqivz8fO6//3569uzpdliumj9/PgsWLGixLSsri2effdadgFyybds2Fi1axN69e6msrOThhx9m+PDhzfsdx2H+/Pm8++67nD59mmuuuYYZM2aQmZnpYtSRcb7czJo1iw8++KDFcwYOHMjMmTMjHWpELVy4kDVr1nDw4EF8Ph9FRUV84xvfICsrq/mYhoYG5syZw0cffUQwGGTgwIHMmDGD5ORk9wJvZxeSl0cffZRt27a1eN7EiRP57ne/G+lwI+btt9/m7bff5ujRowDk5ORw++23M3jwYKBztpUm58tNZ2wvrfnb3/7Gyy+/zM0338x9990HdMx2o0LXJR999BFz5szhgQceoFevXixevJgnnniCZ599lqSkJLfDc1Vubi6/+MUvmh+bZuf7w0N9fT0FBQWMHz+e3/3ud2ftf+ONN1i6dCkPPfQQ6enpzJs3jyeeeIJnnnkGn8/nQsSRc77cAAwaNIgHH3yw+bFlRf+Pum3btnHTTTfRo0cPwuEwr7zyCr/+9a955plniImJAeDFF19k/fr1/OQnPyEQCDB79myefvppfvWrX7kcffu5kLwATJgwgbvuuqv5cbR/H6WmpnLvvfeSmZmJ4zh88MEH/OY3v+E3v/kNubm5nbKtNDlfbqDztZcv2r17N8uXLyc/P7/F9o7YbjpfBdFBvPXWW0yYMIFx48aRk5PDAw88gM/nY8WKFW6H5jrTNElOTm7+n5iY6HZIETd48GDuvvvuFj2VTRzHYcmSJXz1q19l2LBh5Ofn84Mf/IDKykrWrl3rQrSRda7cNLEsq0Ubio+Pj2CE7pg5cyZjx44lNzeXgoICHnroISoqKiguLgagpqaG9957j29961v069ePwsJCHnzwQXbs2MHOnTtdjr79nC8vTfx+f4s2EwgEXIo4Mq677jqGDBlCZmYmWVlZ3HPPPcTExLBr165O21aanCs3TTpbe/m8uro6/vCHP/C9732PuLi45u0dtd1EfzdHBxQKhSguLua2225r3maaJv379+8UP0TOp6ysjO9973t4vV6Kioq49957SUtLczusDqO8vJyqqioGDBjQvC0QCNCzZ0927tzJ9ddf72J0HcO2bduYMWMGcXFx9OvXj7vvvpuEhAS3w4qompoagOYiv7i4mHA4TP/+/ZuPyc7OJi0tjZ07d1JUVORKnJH2xbw0WblyJStXriQ5OZmhQ4fyta99Db/f70aIEWfbNh9//DH19fUUFRWprXzOF3PTpDO3l+eee47BgwczYMAAXn/99ebtHbXdqNB1wcmTJ7Ft+6wxK8nJyRw6dMidoDqIXr168eCDD5KVlUVlZSULFizgl7/8JU8//TSxsbFuh9chVFVVAZw1xCUpKal5X2c2aNAgRowYQXp6OmVlZbzyyis8+eSTPPHEE51mGIxt27zwwgv07t2bvLw8oLHdWJbVogcGOle7aS0vADfccANpaWmkpqayb98+XnrpJQ4dOsTDDz/sYrTtb//+/cycOZNgMEhMTAwPP/wwOTk5lJSUdPq20lZuoPO2F4APP/yQvXv38u///u9n7euoP2NU6EqH0jTYHyA/P7+58P34448ZP368i5HJ1eLzPdp5eXnk5+fzwx/+kK1bt7boaYhms2fPprS0lMcff9ztUDqUtvIyceLE5s/z8vJISUnh8ccfp6ysjIyMjEiHGTFZWVn89re/paamhn/84x/MmjWLxx57zO2wOoS2cpOTk9Np20tFRQUvvPACP//5z6+qMckqdF2QmJiIaZpnvcOpqqrqFHe0Xoy4uDiysrIoKytzO5QOo6mNnDhxgpSUlObtJ06coKCgwJ2gOrBu3bqRkJBAWVlZpyh0Z8+ezfr163nsscfo0qVL8/bk5GRCoRCnT59u0eNy4sSJTvFzp628tKZp9ptoL1wsy2p+fYWFhezZs4clS5YwatSoTt1WoO3ctDazQmdpL8XFxZw4cYKf/vSnzdts22b79u0sW7aMmTNndsh2o0LXBZZlUVhYyJYtW5pvqLFtmy1btjBlyhSXo+tY6urqKCsrY/To0W6H0mGkp6eTnJzM5s2bmwvbmpoadu/ezeTJk90NrgM6duwYp06davGmIBo5jsPzzz/PmjVrePTRR0lPT2+xv7CwEI/Hw+bNm/nSl74EwKFDh6ioqIjqMZfny0trSkpKAKK+zXyRbdsEg8FO21bOpSk3reks7aV///5nzXTzpz/9iaysLG699VbS0tI6ZLtRoeuSadOmMWvWLAoLC+nZsydLliyhvr6esWPHuh2aq+bMmcN1111HWloalZWVzJ8/H9M0ueGGG9wOLaKaCvwm5eXllJSUEB8fT1paGjfffDOvv/46mZmZpKen8+qrr5KSksKwYcNcjDoyzpWb+Ph4XnvtNUaMGEFycjJHjhzhL3/5CxkZGQwcONDFqNvf7NmzWbVqFY888gixsbHNfzEKBAL4fD4CgQDjx49nzpw5xMfHEwgEeP755ykqKorq4uV8eSkrK2PVqlUMGTKE+Ph49u/fz4svvkifPn3Omjopmrz88ssMGjSItLQ06urqWLVqFdu2bWPmzJmdtq00OVduOmt7AYiNjW0xth0aZ59ISEho3t4R243hOI7j2tU7uWXLlrFo0SKqqqooKCjg29/+Nr169XI7LFc9++yzbN++nerqahITE7nmmmu4++67o/rPQa3ZunVrq2PlxowZw0MPPdS8YMQ777xDTU0N11xzDd/5zndaTIIfrc6VmwceeIDf/va37N27l9OnT5OamsqAAQO46667ov5PrnfeeWer2x988MHmN9BNk7l/+OGHhEKhDjGZe3s7X14qKir4wx/+QGlpKfX19XTp0oXhw4fz1a9+NaqnjPrTn/7Eli1bqKysJBAIkJ+fz6233to8m0tnbCtNzpWbztpe2vLoo49SUFBw1oIRHandqNAVERERkajUOebaEREREZFOR4WuiIiIiEQlFboiIiIiEpVU6IqIiIhIVFKhKyIiIiJRSYWuiIiIiEQlFboiIiIiEpVU6IqIiIhIVFKhKyIiIiJRSYWuiIiIiEQly+0AREQ6q3A4zBtvvMG7777LiRMn6NGjB9/73vfIyspq8zmzZs3igw8+ACA3N5enn376nNeYP38+CxYsYP78+Vc09i9avHgxL774YvPj5557jsTExHa9pojI+ajQFRFxgW3b/O53v2Pnzp1MnToVn8/HwoULeeqpp3jmmWfweDxtPjchIYFvfetbxMXFRTDicxs0aBAJCQmsWbOGNWvWuB2OiAigQldExBWLFi1iy5YtPPnkk+Tm5gKQnJzM73//e7Zu3cqAAQPafG5MTAw33nhjpEK9INnZ2WRnZ1NWVqZCV0Q6DI3RFRGJsJqaGhYuXMjNN9/cXOQCFBUVAbBv3z63QhMRiSrq0RURibCVK1dSV1fHxIkTW2y3rMYfybW1tZd03s8++4wXX3yR/fv3k5qayi233NLmscePH+fVV19lw4YNnD59moyMDKZNm8b48eNbHLd161bmzp1LaWlp8zkrKysjMu5XRORyqdAVEYmwNWvWkJOTg9/v5+TJk83bKyoqgMahCRdr//79/PrXvyYxMZE77riDcDjM/PnzSU5OPuvYqqoqZs6cCcBNN91EYmIiGzdu5M9//jO1tbVMnToVgL179/Lkk0+SnJzMHXfcgW3bLFiwQDeZichVQ4WuiEgE2bbNzp07qa+vZ8aMGa0ek56eftHnnTdvHo7j8Pjjj5OWlgbAiBEjePjhh8869tVXX22+GS4hIQGAyZMn8+yzz/Laa68xadIkfD4f8+fPxzRNfvWrX5GamgrAqFGj+PGPf3zR8YmIuEGFrohIBJWVlVFfX88tt9xy1g1nK1as4MMPPyQvL++izmnbNps2bWLYsGHNRS5ATk4OAwcOZMOGDc3bHMdh9erVjBw5EsdxWvQoDxo0iI8++oji4mKKiorYvHkzw4cPby5yATIyMhg0aBDr1q272JcuIhJxKnRFRCLo6NGjAPTt2/esQveNN94gKSnpnPPotubkyZM0NDSQmZl51r6srKwWhe7Jkyc5ffo077zzDu+8806b5ztx4gQNDQ1kZGSctb+1bSIiHZEKXRGRCKqvrwfA7/e32F5TU8P27dsZN25cu17fcRwARo8ezZgxY1o9Jj8/H9u22zUOEZFIUKErIhJBTTea1dXVtdj+/vvvEwqFmDx58kWfMzExEZ/Px+HDh8/ad+jQobOOjY2Nxbbtc87Va9s2Xq+XsrKys/a1tk1EpCPSPLoiIhGUn5+PYRhs3bq1eduxY8f461//yo033kh+fv5Fn9M0TQYOHMjatWubZ24AOHDgAJs2bTrr2BEjRrB69Wr2799/1rmaxuyapkn//v1Zu3Ytx48fb95fVlbGxo0bLzpGERE3qEdXRCSCkpKSGDZsGEuWLMHv9xMIBFi8eDGpqancf//9l3zeO++8k40bN/LLX/6SyZMnY9s2S5cuJTc396wFKO699162bt3KzJkzmTBhAjk5OZw6dYri4mI2b97M//zP/zSf8+c//zm/+MUvms+5bNkycnNzKSkpuZw0iIhEhApdEZEI+5d/+Rf+/Oc/8+abbxITE8PIkSO55557iI2NveRz5ufnM3PmTObMmcP8+fPp0qULd955J5WVlWcVusnJyTz55JMsWLCA1atX8/e//52EhARyc3P5+te/3nxcYWEhP/vZz5g7dy7z5s2jS5cu3HXXXRw4cICDBw9ecqwiIpFiOE13JoiISIc3a9YstmzZwlNPPYXH4yEuLi7iMfzmN7/hwIED/P73v2/e1tDQQF1dHYsWLWLRokU899xzWlhCRFynHl0RkavMsWPHmDFjBrm5uTz99NPteq2GhgZ8Pl/z48OHD7Nhw4azZmxYvnw5L774YrvGIiJysVToiohcRW699VZGjx4NXNpSwRfrBz/4AWPHjiU9PZ2KigrefvttLMvi1ltvbXHciBEjyM3NbX4cCATaPTYRkfPR0AUREWnTH//4R7Zu3UpVVRWWZVFUVMQ999xDYWGh26GJiJyXCl0RERERiUqaR1dEREREopIKXRERERGJSip0RURERCQqqdAVERERkaikQldEREREopIKXRERERGJSip0RURERCQqqdAVERERkaikQldEREREopIKXRERERGJSv8/tE5YuxqGkXkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAIwCAYAAABp1sinAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAtx1JREFUeJzs3Xl4lNXZ+PHvM8/MJJkkk4XsISEJkATCHhZZREBRFFRQBMRal6qt1dpq66viz1bf97Uu1WpFu7mUVwSFosgmKCoICCJbZM9CCGELSQjZt1me3x+RaJqZkMkkM1nuz3XNJXmWc+65M+Cdk/Oco2iapiGEEEIIIUQ3o/N2AEIIIYQQQnQEKXSFEEIIIUS3JIWuEEIIIYTolqTQFUIIIYQQ3ZIUukIIIYQQoluSQlcIIYQQQnRLUugKIYQQQohuSQpdIYQQQgjRLUmhK4QQQgghuiUpdIUQQgghRLckha4QQgghhOiWpNAVQvR4W7Zs4YYbbiA2NhZFUVi0aFGL1z/33HMoisKDDz7omQBb0JrYExISUBSl2Wv69OmeD1gIITxICl0hRI9XWVnJoEGD+Mtf/oKfn1+L137zzTf885//ZMiQIR6KrmWtiX3Xrl2cPXu28bV3714URWHOnDkejlYIITxLCl0hRI933XXX8cc//pHZs2ej0zn/Z7GsrIzbbruNd955h5CQkA6L54477iAiIoKqqqpLXtua2MPDw4mKimp8ffLJJ5jN5k5f6Obl5aEoCnfeeWeT43v27EFRFN566y3vBCaE6DKk0BVCiFa67777mD17NpMnT3Z6zcVpAX369KG2ttbhNRenElit1mbndu3axeLFi3n88cfx9/dvt9gv0jSNt99+m5/85CcOR4AdTXH48etS0zo8IT09nZkzZ/LUU09RWVnp7XCEEJ2Y3tsBCCFEV/Dmm2+Sk5PDe++916rr8/PzefXVV3n88cdd6ufJJ5/EbDZz//33tyXMS9q4cSPHjx/n3nvvbfG6P/zhDw6PDxs2rAOict0TTzzBmDFjeO2111iwYIG3wxFCdFJS6AohxCVkZmayYMECtm3bhsFguOT1ISEhKIrC888/zz333ENYWFir+snKyuLzzz/nnnvuueRc4bZ68803GTVqFEOHDm3xuqeffrpD+m8vo0ePJjU1lX/84x88/vjjLU45EUL0XPIvgxBCXMKOHTsoLi4mLS0NvV6PXq/nq6++4q9//St6vZ66urom15tMJp566inKysp45plnWt3PO++8g6ZpzJ07t73fAgCFhYWsWrXqkqO5HW3nzp3Mnj2bqKgojEYjcXFx/PznP+fMmTMutTNv3jzy8/PZuHFjB0UqhOjqpNAVQohLmDlzJgcOHCAjI6PxNXLkSObNm0dGRgZGo7HZPQ888AB9+/blH//4B9nZ2a3q5/PPP0dVVS677LL2fgsALFq0CB8fH2699dYOab813nnnHcaPH8/69euZPHkyv/nNbxg5ciRvvfUWI0eOJD8/v9VtjR8/HkAKXSGEUzJ1QQjR41VWVpKTkwOA3W4nPz+fjIwMQkNDiY+PJzg4mODg4Cb3+Pv7ExoayqBBgxy2aTAYeP7557nlllt47LHH+Oijj1qMoaqqioyMDAYMGODSQ2iXiv0iTdN46623mDdvHgEBAZds19HUhYSEhGYrILgiKyuLX/ziFyQkJPDVV18RGxvbeO6LL77g6quv5te//jUrV65sVXujRo0CGtYSFkIIhzQhhOjhNm3apAHNXnfccYfTe6644grtgQceaHYc0GJjYxu/Hjt2rAZoW7dubTzWp08fDdAsFkvjsczMTA3Qpk6d2iGxf/nllxqg7dy5s8X2HLV18XXFFVe4FNt/+s1vfqMB2tq1ax2enzlzpqaqqlZeXq5pmqYdP378kt8HX19fLTIy0q24hBDdl4zoCiF6vEmTJqFpmkv3bN68uVXXvfzyy4wbN47f/e53fPPNN06vO3/+PIDL6/O2NvbJkye79B5dzUdr7NixA4CvvvqKXbt2NTtfWFiIzWYjKyuL9PT0VrUZGhrKuXPn2jVOIUT3IYWuEEJ0oLFjxzJ79mxWrFjBsmXLnD5odnGVBWdr73YHF4v5P/3pTy1e58rauDU1NR22QoUQouuTh9GEEKKDPffccxgMBp544gnq6+sdXhMREQH8UAx2R0FBQUDDDnOapjl9XXHFFa1qz263U1pa2pg7IYT4T1LoCiFEB+vXrx+//OUvOX78OAsXLnR4TXR0NOHh4WRmZno4Os+5uJrE1q1b26W9zMxMNE3rNJtYCCE6Hyl0hRDCA37/+98THBzMs88+6/BX84qiMHHiRIqLixtXUejs7rzzTpe2BX7wwQcxGAw8/PDDZGVlNTtfX1/vUhF8cc5zS1syCyF6Nil0hRDCA0JDQ1mwYAEXLlxwOj3h5ptvBuDTTz/1ZGhtZrfbAdDrW/e4R2pqKu+88w55eXmkpaVx/fXX89vf/paHHnqImTNnEh0d7dJmFp999hmqqnLjjTe2KX4hRPenaB3xaK0QQvRQiqIQGxvLqVOnmp2rq6sjNTWVvLw8ACwWS5Misb6+nri4OBISEti5c6enQm5CURSgdasuDB8+nGPHjnHixAmXVos4cOAAL7/8Mps2baKgoAB/f39iYmIYP348c+fOZcqUKQDk5eWRmJjIHXfc0WzUuKysjKioKK655ho+/vjjVvcthOhZpNAVQohO5LnnnmPBggXs3buX4cOHezscp0pLS+nVqxe//e1vefHFFz3e/8KFC3nooYfYunUrEyZM8Hj/QoiuQQpdIYToRGpra0lJSWHIkCGsWbPG2+E4tWbNGm655Rby8vKIioryaN81NTX07duXcePGsWLFCo/2LYToWmQdXSGE6ER8fX1ZvHgxmzZtoqqqyqXtgD3p+uuv99qav3l5edx3331ubUcshOgZZERXCCGEEEJ0S7LqghBCCCGE6Jak0BVCCCGEEN2SFLpCCCGEEKJbkkJXCCGEEEJ0S1LoCiGEEEKIbkmWF3PgwoULWK1Wj/QVHh5OUVGRR/rqaiQ3jklenJPcOCZ5cU5y45jkxTnJjWOezIter2/1boxS6DpgtVqxWCwd3s/FrTatVmurttvsSSQ3jklenJPcOCZ5cU5y45jkxTnJjWOdOS8ydUEIIYQQQnRLUugKIYQQQohuSQpdIYQQQgjRLUmhK4QQQgghuiV5GE0IIYQQ3ZamaVRWVrbbQ1I1NTXU19e3S1vdSXvnRVEUAgICGh90ayspdEUT504X8/X2QqotZhQUFGroH29nxNi+qKrq7fCEEEIIl1RWVuLj44PRaGyX9gwGg0dWZupq2jsv9fX1VFZWEhgY6FY7MnVBNNq74xhbtyoY7DEEqQGYVX8C1TAKTkfw/gcnOZlb4O0QhRBCCJdomtZuRa7wHKPR2C6j8FLoCgD2bs/hxIkQfBSVCnstOp/TBJjPUmErwqZpBOmD2fmtga0bj3o7VCGEEEKIVpGpC4KSojKyTwRh0ukos5Uxc3oYAUFRjeez9uez66Aes2qitCSKj1cc5vpZKW2aynAq9xwHDhRzocqIHR9Aw6Cr4bL0IPr0j27HdyWEEEKInk5GdAXrNxZj0hmostdz/TWhBAT5NzmfPCSem2f1otJeCIBqi2HZslyqyqtb1b7NZuObzZm8tzSfvd8asdbGEqiGE6SaCVKDMClRZOzxZcWyI9hstnZ/f0IIIYTomaTQ7eH2bs/BrPZC0zRSEysI6uV40revnw9z5vTFpp5G0zQC1XBWr6tgz/Ycp23bbDZ2fpXJ+8vOUnQukiDVjKIolNuqqKEAg+kMis9pymzl6BQFH6L5YFmeTPIXQgghRLuQQrcHs1qtHMkzAVBpP8+wy/q2eL2qqsycnUZkdCG1mpUAnQ9nTobx3tLjfLMpk8qyKgBqq+rYvOEI7y87S2FBJEFqADZNo8p+jsHDqrltfixz5qYy7fqBzJiZxk/mx6P3O4Nd0zCrvVix4riM7AohhOjRJk+ezEsvveTw3MKFC0lLS6OkpKTD+n/99deJjY3l97//fbNzixYtYsyYMSQlJTFjxgz27dvXqjbbep87pNDtwb7ZnINZNWHR7EyZENzq+8ZckcKUKXoqbMVomkaQGkJRYSSbNlhY8cF5Pl1TTUVZdGOBW2kvZNRlFubdmkJCSozDNq+9YSDmoAI0TSNAF8Enq+ShNyGEED1XamoqmZmZzY6fO3eOhQsX8uijjxIaGtohfWdkZPDee+8xYMCAZudWrVrFM888wyOPPMKGDRsYOHAgt912G0VFRS226ey+4uLiDnkPF0mh24PlFTaM5looIio+zKV7e0UEM39+P1IGVFBhK6La3jDdwEdR0SkKNZqVGq2AkaPrufXWZGITIi7Z5uRrB2DXnwWgvi6K41mnXHxHQgghhHOapqHV1Xrn5eJSWQMGDODo0eaDPs8//zzx8fHcfvvt7ZWWJqqqqnjwwQd58cUXCQ4Obnb+zTffZP78+cydO5fk5GSef/55/Pz8eP/991ts19l9H3zwQYe8j4tk1YUeKvvgKYJUM3ZN47LhbV+MOWVoPClDG+bjXjhXTtG5MoJD/QmPDUGvd614BpgxM5lly89hVv35cM0Jbpuf1ObYhBBCiCbq67A/OMetJuraeJ/u9eXg49vq61NTUzlx4gS1tbX4+jbct3//flasWMGyZcscrnz02muvsXDhwhbb3bx5M7GxsU7PL1iwgCuvvJKJEyfy2muvNTlXX1/P/v37efDBB394XzodEyZMYPfu3U7bbOm+PXv2tBivu6TQ7YKqyqvJ2HWSwSNiMIe0rUjdfaCaAF0AFbYy+qQkuB2TqqqExYQQFhPiVjt6vZ6hKfXkZpsI1IVwcM9x0ka4H58QQgjRlQwcOBCbzUZOTg6DBg0C4A9/+APXXXcd48aNc3jP7bffzvXXX99iu5GRkU7PrVq1ioMHD7Ju3TqH50tKSrDZbISFNR3ICg8PJzc312m7Ld137NixFuN1lxS6Xcxnaw9TVhmBrxLJ+g21xIafYcJVKS61UVJYhq/S8GFLjut8+3UPSk/ku8xjmNVe7DsKaSO8HZEQQohuwejTMLLqhjZvdWv0ceny3r17YzabyczMZNCgQaxatYr9+/ezZcsWp/eEhIQQEtK2AafTp0/z+9//nvfff79xBLk76LSF7uHDh1m9ejXHjx/nwoUL/O53v2P06NEt3nPo0CHeffddTp48Sa9evbj55puZNGmSZwL2gG82Z1JTGY2vomDXNEw6A+eLIzi8N4+BLox6btlyGr0SQ4WthuvGt7zSgreMHuzDkUMaQWqwy+9PCCGEcERRFJemDzhsw2BA0bm+YVJbpKSkkJmZSW1tLX/84x954IEHWpx24M7UhQMHDlBcXMy0adMaj9lsNr755hsWLVrE8ePHCQ0NRVXVZg+QFRUVERHh/Fmclu4LDw9vMV53ddpCt66ujoSEBKZMmeJ0eY0fKyws5Pnnn2fq1Kn86le/4uDBg/z9738nODiYYcOGdXzAHex0XiEnC3rhqyiU24qZcU0kaz4tIUgNYt9RH/oOrMfH99J7eVssFmotYfjrIMS/BFXtnLuRJQ+JY9eBXALVEDKOWhgoo7pCCCF6mIsPpP39738H4P7772/xenemLkyYMIEvvviiybFHHnmEvn378sADD6CqKqqqMmTIELZt29ZYENvtdrZt28Y999zjtE+j0ej0vrvuuqvFeN3VaQvd4cOHM3z48FZf/9lnnxEREcFPf/pToGHI/+jRo6xbt85poWuxWJr8+kFRFPz8/Br/3NG+/uIoeeeOoWkaqmIDNGxaAD46X+o1C8nxlYya0J+62no2bbcQpPpTYath1g2x+AeamHp5PVu/tmFW/fjsk2xuuHnQpfv88hj+uijqNBtXXpnkkffZFoqikJ7mS9ZR8NP1oqSwjF6Rwd4Oy+sufr866/fNmyQ3jklenJPcOCZ56TxSU1NZt24dO3bs4JVXXmmsUZxxZ+pCQEAAqampTY6ZTCZCQkKaHL/33nt5+OGHGTJkCMOHD+fNN9+kpqaGefPmNV7zr3/9i/Xr17N8+fJL3jd37twW43L3c9hpC11XZWdnM3jw4CbHhg4dyqJFi5zes3LlSlasWNH4dWJiIi+88EKHD6NfdKEik0Cd4zXwfBSV0yd9OPZBNlabgSA1iHrNzrVTguiX3DDdIDo6mu/2f0VdZTg19ZH4mwIwB7X8cNrp8ycxq6CoJfRLHtzitd4WfnU4ew5nEKjzY/uOc9x3f/P1/HqqqKgob4fQaUluHJO8OCe5cay75KWmpgaDwdCubbZ3e84MHjyY8+fPM2HCBGbNmuWRPn9MURRUVW3yfmfPnk1ZWRkvv/wyhYWFDBo0iA8++KDJ1IXS0lLy8/NbdV9MjOP19aFhJDg62r3fPCuaqwu7ecGcOXMuOUf317/+NZMmTWryQdi7dy/PP/887733HkZj81/rOxvRLSoqwmq1tu+bcODArlzyTluwWqzU23TY7QphQTaiIv34LtOGWf3h6US7phEUWsDkawY2aaO2po6PV5Zh0hnAcLrFUd1De/M4lhWEXdMYOcZC7yTnT156m6IoREVF8fbfPsdaG0OlvY65c8MdLqfSk1zMS0FBgctrMnZ3khvHJC/OSW4c6255KSsrw2w2t1t7bX4YrZvriLyUl5cTFBTU7Lher2/1oGS3GdFtC4PB4PSnMk/85R48Komrb4jm7NmzzfoblA7fbski65QJsJI+AAYMH9DsOh9fIwF+hdjrYqmpj6Syogr/AJPD/vYdsWJWodJ+gdjEpC7xD9j4K+L5YkM9ATof9u86zrAxnfPhOU/TNK1LfP+8QXLjmOTFOcmNY5IX0Rm4+xnsNjujBQcHU1ZW1uRYWVkZfn5+Dkdzu4LRE5P5yfze/GR+AgOGJzi97spr+lFtt+Cn6Pnis+MOr8nLPEPA99MkhqZ0nXlXQSGBVNsvAHAk1+blaIQQQgjRlXSbQrd///4cOHCgybH9+/eTnJzspYg8x9fPhwC/QgBq6yOpqqxuds32PVXoFIUyWxmD0hM9HaJbEiMbfhViUHpRW9PWPWmEEEII0dN02kK3traWvLw88vLygIblw/Ly8hrXYFu6dCmvv/564/VXX301hYWFvPfee5w+fZpPP/2UHTt2MH36dG+E73E/HtVdty6/ybkDu4/jr2uY75sa3/k2iLiUkRP6UqNZ8VFUdn/teMRaCCGEEOI/ddo5useOHeOZZ55p/Prdd98F4IorruCBBx7gwoULTRYejoiI4PHHH+f//u//+OSTT+jVqxe/+MUvusUauq3h6+dDZEgxFWXR+GiRHNmXx4DhCdTW1LE/ywezqlBuK+H6Cf29HarLjD4GbFoJKBHkF/bsh9GEEEII0XqdttBNS0trsv7af3rggQcc3vPiiy92ZFid2qRpA3hvSR5B+mAOZgaSf+owRRUBBKlm6jUbV05ov6dOPa1vLJw7C0ZdKHW1rdscQwghhBA9W6eduiDaZuqkACpsNfgqKvXVMQSpZiyandjIYqLiwy7dQCc1bEwitZoNH0Vl7w6ZviCEEEKIS5NCt5uJjA3jhulmKu2FlNuqKLOV0b9fGZdNTvF2aG4x+hiw2EsAyDvbdVaNEEIIIYT3dNqpC6LtAoL8ufXW7rfaRFyEldLzoFNCsNlsPX7zCCGEEEK0TEZ0RZcxYnQfbJqGSWcg5+Bpb4cjhBBCiE5OCl3RZfibTVTaywE4nF3p5WiEEEKIjjN58mReeuklh+cWLlxIWloaJSUl7drnyy+/TGxsbJPXxIkTm123aNEixowZQ1JSEjNmzGDfvn2tar+t97lDCl3RpQT5VgFQVR/o5UiEEEKIjpOamkpmZmaz4+fOnWPhwoU8+uijhIaGtnu/KSkp7Nu3r/H18ccfNzm/atUqnnnmGR555BE2bNjAwIEDue222ygqKmqxXWf3/Xip2I4gha7oUganBQMQqAugtLjcu8EIIYToUjRNo9Zqd+9ladt9mqa5FOuAAQM4evRos+PPP/888fHx3H777e2VliZUVSUiIqLx9Z/F9Jtvvsn8+fOZO3cuycnJPP/88/j5+fH++++32K6z+z744IMOeR8XycNooktJSIlhx95zBOh82LfrNJOv7bprAwshhPCsOpvG3GVZXul72dxkfPWtXzUoNTWVEydOUFtbi6+vLwD79+9nxYoVLFu2zOED2a+99hoLFy5ssd3NmzcTGxvr9Pzx48cZMWIEPj4+pKen88QTTzReX19fz/79+3nwwQcbr9fpdEyYMIHdu3c7bbOl+/bs2dNivO5q10L38OHD/POf/6SiooKxY8dy1113oaoq+fn57N27lz179vA///M/7dml6IEUSoFIzl4weDsUIYQQokMMHDgQm81GTk4OgwYNAuAPf/gD1113HePGjXN4z+23387111/fYruRkZFOzw0fPpxXXnmFvn37UlhYyJ///GdmzZrFl19+SUBAACUlJdhsNsLCmq7LHx4eTm5urtN2W7rv2LFjLcbrrnYtdN9++23GjBlDSkoKq1at4l//+hfZ2dkUFBSQlpbGhAkT2rM70UMlREFRIeh1wVitVvR6+cWEEEKIS/NRFZbNdW/5TYPegMVqaVPfrujduzdms5nMzEwGDRrEqlWr2L9/P1u2bHF6T0hICCEhIS7HdtGUKVMa/zxw4ECGDx/OmDFjWLNmDbfeemub2/Wmdq0QioqKGhORkJDA/fffz2233cZ1110nxYhoN8NGJ/DJmir8FD1HMk4yeGSit0MSQgjRBSiK4tL0AUcMBh2qhx5xSklJITMzk9raWv74xz/ywAMPtDjtoD2mLvxYUFAQSUlJ5OXlARAaGoqqqs0eICsqKiIiIsJpOy3dFx4e3qpY2qpdq09F+eHDExoaio+PDzfccEN7diEEvv4+VNvOEqQPJvt4DYNHejsiIYQQov1dfCDt73//OwD3339/i9e7O3XhP1VVVXHixAluvvlmAIxGI0OGDGHbtm1MmzYNALvdzrZt27jnnnucttPSfXfddVer42mLdi10a2tr+d3vfkdCQgJJSUkoikJNTQ1+fn7t2Y0QBPlVgyWYaossMyaEEKJ7Sk1NZd26dezYsYNXXnnlkvWUu1MX/vu//5upU6fSu3dvCgoKePnll9HpdMycObPxmnvvvZeHH36YIUOGMHz4cN58801qamqYN29e4zX/+te/WL9+PcuXL7/kfXPnzm1zvK3RroXu66+/zokTJ8jLy+PIkSMEBQVx5513EhoaSlxcHHFxcR22HIboWVL6BZB5BPx1/lRX1WDylx+mhBBCdC8DBw7k/PnzjBs3jhkzZnR4f2fPnuWBBx7gwoULhIaGMnr0aNasWUOvXr0ar7nxxhspKSnhpZdeoqioiLS0NN577z0iIiKwWBrmLpeUlHDixIkmbTu7r6OnLiiaqwu7uai2tpb8/Hzy8vI4ceIE9957b0d21y6Kiooav1kdSVEUoqOjOXv2rMvr63V3l8qNzWbjw39fwE/RExldyOiJ7j1c0FXIZ8Y5yY1jkhfnJDeOdbe8lJeXYza331KUBoPBIzVCV9MReXH2vTMYDK0ukDv8CTFfX1+Sk5NJTu4ZhYjwDFVVsdjL8FN7ceKsjdHeDkgIIYQQnU67PTZYX18vP+EIjwr1rwWgziabRgghhBCiuTaP6B46dIhdu3aRmZnJqVOnqK+vB8DHx4fY2FhSUlIYNWoUaWlp7RasED82cGAo+/dCoM5E+YUKzCHyYJoQQgghfuBSoWu1Wvn8889Zu3YtRUVFBAQEkJiYyOWXX05AQACaplFVVUVhYSFbt25l/fr1hIWFcf3113PVVVfJWrqiXfXpH82O3YX464zs33OaCVelejskIYQQQnQiLlWeDz30EFarlSuuuIKxY8eSlJTU4vW5ubns2LGDlStXsmbNGt544w23ghXiP9m1MiCc08XuLQAuhBBCiO7HpUJ31qxZTJo0CYPB0Krrk5KSSEpKYu7cuWzatKlNAQrRkoggCzWVYLPLPF0hhBBCNOXSw2hTp05tdZELDQ+oFRcXo9frmTp1qsvBCXEpgwY3bDkYqPpRfOaCl6MRQgghRGfS5kmzFRUVrF+/nj179lBQUIDdbic0NJTBgwdz9dVXEx8fz86dO3n99ddZtmxZe8YsRKOo+DAqvi4gUOfLge8KmBzT9h1hhBBCCNG9tKnQzczM5KWXXqK8vByTyUTv3r2x2WwUFRWxceNGvvjiC2666SaioqLaO14hmtFRDvhScEH1dihCCCGE6ERcLnTPnz/P888/j8lk4r/+679IT09vcv7o0aOsWrWKFStWuF3obtiwgTVr1lBaWkqfPn24++676devn9Pr161bx2effUZxcTFms5kxY8Ywf/58jEajW3GIzi26l43yC4AS5O1QhBBCCNGJuLxhxMqVKwF45plnmhW5AKmpqTz22GP89Kc/paCgoM2Bbd++nXfffZfZs2fzwgsv0KdPH5599lnKysocXr9t2zaWLl3KLbfcwiuvvMIvfvELduzYwfvvv9/mGETXMHh4DHZNI0Dnw6ncc94ORwghhHDb5MmTeemllxyeW7hwIWlpaZSUlLR7v2fPnuVXv/oVaWlp9O3blyuvvJLvvvuuyTWLFi1izJgxJCUlMWPGDPbt29eqttt6nztcLnT37dvHlClTCAsLa/G66dOnM3v2bAYOHNimwNauXcuVV17J5MmT6d27N/feey9Go9Hp6g2ZmZmkpKQwYcIEIiIiGDp0KOPHjycnJ6dN/YuuIzQ8iEp7DQCHDp33cjRCCCGE+1JTU8nMzGx2/Ny5cyxcuJBHH32U0NDQdu2ztLSUmTNnotfree+999i0aRO///3vCQr64Temq1at4plnnuGRRx5hw4YNDBw4kNtuu42ioqIW23Z2X3Fxcbu+h//k8tSF0tJSevfu3aprb7nlFpcDgoaNKXJzc5k5c2bjMZ1Ox+DBg8nKynJ4T0pKClu3biUnJ4d+/fpx7tw59u3bx+WXX+60H4vF0mTbYkVR8PPza/xzR7vYhyf66mpczY1BVw6YKK7w6db5lM+Mc5IbxyQvzkluHOvOedE0DZvNvTYURcNq1Vy+T1Vdy+mAAQP497//3ez4888/T3x8PLfffrvLMVzKX//6V2JiYnjllVcaj8XHxze55s0332T+/PnMnTu3MZ4vvviC999/n/vvv99p287u++CDD3jwwQed3ufu59DlQtdkMlFRUdGqa/fv309WVhazZ892qY/y8nLsdjvBwcFNjgcHB3PmzBmH90yYMIHy8nKeeuopAGw2G1OnTuWmm25y2s/KlStZsWJF49eJiYm88MILhIeHuxSvu+ShPedam5uUxHzy80CvmImIiEBVu/eDafKZcU5y45jkxTnJjWPdJS81NTWNS6NarRprl3fsCKIzN8wLQ69vfdE2aNAgXnrpJWw2G76+vgB89913rFixgg8//LDx2I+9+uqrvPrqqy22u23bNqcDlhs3bmTy5MmN0z+joqK46667Govq+vp69u/fz69//esmy81OnDiR3bt3O12CtqX79u3b5/Q+o9FIdHR0i+/nUlwudFNSUvj666+54YYbWrzuxIkT/OlPf6K+vt7lQrctDh06xMqVK7nnnnvo378/BQUF/Otf/2LFihVO+581axYzZsxo/PriTw1FRUVYrdYOj1lRFKKioigoKEDTXP/psDtzNTf9B/Ti+PE6TDoD2zftoV9anAei9Dz5zDgnuXFM8uKc5Max7paX+vr6xt/etmUktr1YLBY0rfWFbnJyMjabjSNHjjBo0CAAnnzySa677jpGjx7d5DfSF82fP5/rrruuxXZ79erl8F5oqN0WLVrEvffey4MPPkhGRgZPPvkkOp2OOXPmcO7cOWw2GyEhIU3a6NWrFzk5OU7bbem+7Oxsp/fV19dz9uzZZsf1en2rByVdLnRvuOEGnnrqKf72t79xzz33OKzCt23bxltvvdXmYtFsNqPT6SgtLW1yvLS0tNko70XLli1j4sSJXHnllUDDUHttbS3//Oc/uemmm9Dpmk9HNhgMTn+K8ORfbk3TusU/Jh2htbkxmU1U2ksIUgM4kllG34Gtm17TVclnxjnJjWOSF+ckN451x7yoKlx7s3sr9BgMBqeF2aX6dkXv3r0xm81kZmYyaNAgVq1axf79+9myZYvTe0JCQggJaft68na7nSFDhvDEE08ADaPKmZmZLF68mDlz5rS5XXe4+xl0udBNTk7mrrvuYtGiRezfv5+xY8cSExOD1WqlsLCQXbt2UVhYSN++fRk3bhyLFy92PSi9nqSkJA4ePMjo0aOBhuQfPHiQadOmObynrq6u2TwOR8Wt6L789OWgBVBaY/J2KEIIITohRVHQt3mrrAZ6veLSyKw7UlJSyMzMpLa2lj/+8Y888MADxMbGOr3+tddeY+HChS22uXnzZqdtREREkJyc3ORYv379+OSTTwAIDQ1FVdVmD5AVFRURERHhtM+W7uvo6aJt+nZPmzaN+Ph4li9fzieffNKk2o6IiOD222/n2muvZfv27W0ObMaMGbzxxhskJSU1Jrmuro5JkyYB8PrrrxMaGsr8+fMBSE9PZ926dSQmJjZOXVi2bBnp6elS8PYQib2NnD4JvjozVqsVvbv/mgkhhBBeNGDAAI4ePcrf//53gBYf9gK4/fbbuf7661u8JjIy0um5UaNGcezYsSbHcnNzGwtjo9HIkCFD2LZtW+PAo91uZ9u2bdxzzz1O223pvrvuuqvFeN3V5kpg4MCBPP3001RXV3Pu3DmsVishISFNlh1LSUm55DfFmXHjxlFeXs7y5cspLS0lISGBBQsWNE5dKC4ubjKCe/PNN6MoCh988AElJSWYzWbS09O59dZb2/oWRReTNiKevPxKfBSVo9+dZFB6ordDEkIIIdosNTWVdevWsWPHDl555ZXGlaGccXfqwr333suNN97Ia6+9xvXXX09GRgZLlizhxRdfbHLNww8/zJAhQxg+fDhvvvkmNTU1zJs3r/Gaf/3rX6xfv57ly5df8r6LqzB0FJcK3aVLlxIfH098fDyxsbGoqorJZCIx0XFBERER0eJQ9qVMmzbN6VSFp59+usnXqqpyyy23tHlJM9H1+fgaqbZXEKQGkXO8hkHN9zMRQgghuoyBAwdy/vx5xo0b1+Th+Y4ybNgw3nrrLZ5//nleffVV4uLieOaZZ5qsYHXjjTdSUlLCSy+9RFFREWlpabz33ntEREQ0zl0uKSnhxIkTTdp2dl9HT11QNBdm+d5+++3U19cDDYVldHQ08fHxxMXFNRbA7hS2nUVRUVGbJpq7SlEUoqOjOXv2bLeb8O+utubm4xWHUG2xlNnK+Mn8Ph0YoXfIZ8Y5yY1jkhfnJDeOdbe8lJeXYzab2629tj6M1t11RF6cfe8MBkPHrLqwePFiCgsLOXXqFCdPnuTkyZNkZ2c3mYvr6+vbpPCNi4sjLS3NlW6EaLPkJH+OZYNJF0htTR2+fj7eDkkIIYQQXuLyHN2L0xFGjBhBTk4O3333HVOnTmXgwIHY7XZycnLYunUr2dnZjfcsW7asXYMWwpnkIb05lFWGr6JyaN9J0sf183ZIQgghhPAStx5Lf/vttxk7dix3331347EJEyZw66238uGHH7Jp0ybuvPNOd2MUotX0ej11tjJ89aEcP2VBpukKIYQQPZdb627l5+fTp0/zeZA+Pj7Mnz+flJQU9uzZ404XQrgsxL8GgFproJcjEUIIIYQ3uVXoxsbGkpGR4fT8sGHD2L9/vztdCOGyAanBAATo/Kkqr/ZuMEIIIYTwGrcK3ZkzZ/Ltt9+ybNmyxtUYfiwvL6/N2wAL0VZ9+kdRbbegKgrf7T7p7XCEEEJ4WXdYPaKnaa/vmVtzdMeNG0dJSQlLlixh48aNTJw4kaSkJAAOHjzI5s2bSU+XWZLCs1RVxaaVAWGcKpR/3IQQoifz8fGhpqYGk0m2h+9Kqqur8fFxf+Ukt/dInTFjBoMGDeKjjz5i48aNTUZ2hwwZwn333eduF0K4LMxcT10VWOztt3aiEEKIrsfHx4eqqirKysqa7KjaVkaj0eFvsXu69syLpmno9XrvFLqrV68mPT29cd9jgISEBB555BGsVisFBQXU19cTFhbWrgs0C+GKIUMi2LUDzKqJc6eLiYwNu/RNQgghuiV/f/92aae7babRXjpzXtpU6C5ZsoTw8HBGjBjBiBEjSEtLw2AwoNfr6d27d0fEKYRLouLDKN92FrPqR8beQq6RQlcIIYTocVwudN98802ys7PJyMhg7969fPrppxiNRtLS0hoL37AwKSqE9xnVUsCP4gpfb4cihBBCCC9wudBVFIXk5GSSk5OZM2cOpaWl7N27l4yMDJYuXcrbb79N7969GTFiBOnp6SQnJ6PTubW4gxBt0jfOwOmT4KMLor7OgtHH4O2QhBBCCOFBbj+MFhwczJQpU5gyZQo2m40jR46wb98+9uzZw+rVqzGZTAwdOpTp06fTv3//9ohZiFYZPKoPufnl+CgqB/ackO2AhRBCiB7G7UL3x1RVZdCgQQwaNIjbb7+dwsJC9u7dy759+zhy5IgUusKjDAYDdfYyfNRQjp20ynbAQgghRA/jVqH7q1/9iiuuuILZs2c7PB8REcG0adOYNm2aO90I0Wbh5lrqqsBqC/J2KEIIIYTwMLcmzxYWFpKbm0tubi7btm1j7969nDt3rr1iE8Jtw0ZEoWkagaofZ08UejscIYQQQniQ21MX9uzZw549e5oci4uLY9asWYwfP97d5oVwS0RMKBX2M5hVExnfFRPdJ8LbIQkhhBDCQ9wudPV6PbNmzWLgwIFYrVZyc3P55ptveO211zh06JDsjCa8zkdfBpqJ85V+3g5FCCGEEB7kdqE7ffr0JnN0hwwZwsyZM1m7di2LFy8mOTmZSZMmuduNEG2W2teX4zngrwuiqrwaf7Psdy6EEEL0BG7N0TUajYSGhjo8N2PGDMaNG8eGDRvc6UIItw0YFk+VvR69ouPb7XneDkcIIYQQHuJWoRsTE8O+ffucnh8wYACnTp1ypwsh3KaqKopSAsCZEh8vRyOEEEIIT3Gr0J06dSoZGRm888471NfXNzt/9OhRfHyksBDe1z++YZaOry6E2qo6L0cjhBBCCE9wa47uVVddxalTp1i/fj1ff/01w4cPJy4uDr1ez4EDB9i3b5/MzxWdwpDRCeScKMVPp2fn17lccfUAb4ckhBBCiA7m9sNod955J2PGjGHdunXs2rWLrVu3Np4bNWoUd9xxR5vb3rBhA2vWrKG0tJQ+ffpw991306+f821cq6qqeP/99/n222+prKwkPDycO+64gxEjRrQ5BtE96PV6NOU8EMmpIvktgxBCCNETuFToLl26lPj4eOLj44mNjUVVVaBhLu6AAQOw2+0UFhZSW1tLWFgYAQEBbQ5s+/btvPvuu9x7773079+fdevW8eyzz/Lqq68SFNR8lyur1cr//u//YjabeeSRRwgNDaW4uBiTSZ6wFw0G9jOSdwxMajDlFyowhwR6OyQhhBBCdCCXCt3169c3zsVVVZXo6Gji4+OJi4sjPj6ePn36EBUV1S6BrV27liuvvJLJkycDcO+997J37142bdrEzJkzm13/5ZdfUllZyf/8z/+g1ze8rYgI2RxA/GDg8HgOZhcToPNh+7aTTLt+oLdDEkIIIUQHcqnQXbx4MYWFhZw6dYqTJ09y8uRJsrOz2b59e+M1vr6+jYXvxSI4LS3NpaAubjzx44JWp9MxePBgsrKyHN6zZ88e+vfvz9tvv83u3bsxm82MHz+emTNnotM5fubOYrFgsVgav1YUBT8/v8Y/d7SLfXiir66mI3Kj1+sx6kvAHk1xhblL5l0+M85JbhyTvDgnuXFM8uKc5MaxzpwXl+foRkREEBERwYgRI8jJyeG7775j6tSpDBw4ELvdTk5ODlu3biU7O7vxnmXLlrnUR3l5OXa7neDg4CbHg4ODOXPmjMN7zp07R1FRERMmTOCJJ56goKCAt956C5vNxi233OLwnpUrV7JixYrGrxMTE3nhhRcIDw93KV53tdcoeHfU3rm57horn31STpAawLmT5QwbndKu7XuKfGack9w4JnlxTnLjmOTFOcmNY50xL249jPb2228zduxY7r777sZjEyZM4NZbb+XDDz9k06ZN3Hnnne7G2CqapmE2m/n5z3+OTqcjKSmJkpISVq9e7bTQnTVrFjNmzGj8+uJPIkVFRVit1g6PWVEUoqKiKCgoQNO0Du+vK+mo3JiC9FTYygjSB7Np22ki48zt1rYnyGfGOcmNY5IX5yQ3jklenJPcOObpvOj1+lYPSrpV6Obn53PVVVc1O+7j48P8+fM5c+YMe/bsYfz48S61azab0el0lJaWNjleWlrabJT3ouDgYPR6fZNpCrGxsZSWlmK1Whvn7f6YwWDAYDA4bM+TH2BN0+QvjBMdkZs+EbWUloBKOJVlVV1yS2D5zDgnuXFM8uKc5MYxyYtzkhvHOmNe3NowIjY2loyMDKfnhw0bxv79+11uV6/Xk5SUxMGDBxuP2e12Dh48SHJyssN7UlJSKCgowG63Nx47e/YsISEhDotc0XNdNqkvVfZ6fBSVTV8c93Y4QgghhOggbhW6M2fO5Ntvv2XZsmUOd0bLy8tr8xSAGTNm8MUXX7B582ZOnTrFW2+9RV1dXeMGFK+//jpLly5tvP7qq6+msrKSRYsWcebMGfbu3cvKlSu55ppr2tS/6L4MBgP+PsUAVNaGN3kgUQghhBDdh1tDnePGjaOkpIQlS5awceNGJk6cSFJSEgAHDx5k8+bNpKent7nt8vJyli9fTmlpKQkJCSxYsKBx6kJxcXGTp/vCwsJ48skn+b//+z8effRRQkNDufbaax0uRSbEFVPi2bihDn+dkS0bs7nyOllqTAghhOhu3P6d/owZMxg0aBAfffQRGzdubDKyO2TIEO677742tz1t2jSmTZvm8NzTTz/d7FhycjLPPvtsm/sTPYc5OABNOQlEU1jWi/o6C0Yfx/O1hRBCCNE1tcvk1YSEBB555BGsVisFBQXU19cTFhaG2dy1nmgXPcvUq3qzcWM9ATofPt+QxXU3urbesxBCCCE6t3Z9Skuv19O7d+/2bFKIDhPUKxC9/hDYYqmqiaK4oJSwqGBvhyWEEEKIduLWw2hCdHXTpvej0l6Lr6Ly2ZfF3g5HCCGEEO3IpUL34Ycf5quvvnJpJQWLxcKmTZt4+OGHXQ5OiI7m6+dDSkIVmqYRqIbx+SeHvR2SEEIIIdqJS1MXJk2axLvvvsuiRYtIT09nyJAhJCYmEhERgY+PDwC1tbUUFhaSm5vL/v372bNnD3q9nhtuuKFD3oAQ7hoxti85J47iRxQV5VEc3HOcQemJ3g5LCCGEEG5yqdC98cYbufrqq/nyyy/ZvHkzW7dubTynqioANput8VhcXBxz5sxh8uTJmExdb/cp0XPMurkvy/59liDVzNFsM3ZbLkNGJ3k7LCGEEEK4weWH0fz8/Jg+fTrTp0+nsLCQrKwsTp8+TUVFBQCBgYHExsaSnJxMREREuwcsREcwGAxcNzWI9RsrMav+HMsNJu/UIa65rj8+vkaH99hsNk7lFpKVdYHz5SpWzQ+dYkSHDrtmw04tgb7VXDYmksjYMA+/IyGEEEK4tepCRESEFLOi2wgND+L6a3WsXl9CkBoEllg+XlkGynl6me34GlVq622UVijU2kwYdf74KX6AH/4OZ7v7gSWEb7ZqVNmzufKKUCJje3n4XQkhhBA9l8urLqxevZrTp093RCxCeJ05JJBb5/ZGMZ6mVrNh0hkwKVHUVMRw4XwkNRUx+BBNkBqEn6LHrmmU26qpsp9D73uaXuHniIkrJqRXARbdGcptVegUhUA1nK1bYdvnR739FoUQQogew+UR3dWrV7NkyRLCw8MZMWIEI0aMIC0tDYNBdpUS3YOqqsyYlUZ1VQ3bNh2nuNyIHV8UVDRsKNRi9q0nNspIypAY/ANCWmxvz9c5ZJ7wJ1D148L5KFZ9eJgbb5Yth4UQQoiO5nKh++abb5KdnU1GRgZ79+7l008/xWg0kpaWRnp6OsOHDycsTOYjiq7P5O/H1TPcL0jTx/cjbVgdK1efIEAXgc4aw8oVh5k1W4pdIYQQoiO5XOgqikJycjLJycnMmTOH0tJS9u7dS0ZGBkuWLOGtt96id+/ejBgxgvT0dJKTk9HpZF8K0bP5+vswZ05fVn6YhY8Wjd4Ww+efHOaq66TYFUIIITqK21sABwcHM2XKFKZMmYLNZuPIkSPs27ePPXv2sHr1akwmE0OHDmX69On079+/PWIWoktSVZXZcwaw7IOjmJQoKsujObD7OINHypq9QgghREdwu9D9MVVVGTRoEIMGDeL222+nsLCQvXv3sm/fPo4cOSKFrhDATTf3Zdny0wTpgzmYbaJPv0rMwQHeDksIIYTodtqt0K2vr0dRlCYPpUVERDBt2jSmTZvWXt0I0eUZDAaunRrMF19YCND5sPaTs8yfLz8ECiGEEO2tzYXuoUOH2LVrF5mZmZw6dYr6+noAfHx8iI2NJSUlhVGjRpGWltZuwQrRXfSKCCYhNptzZ8MJVMPZtS2LUROSvR2WEEII0a24VOharVY+//xz1q5dS1FREQEBASQmJnL55ZcTEBCApmlUVVVRWFjI1q1bWb9+PWFhYVx//fVcddVV6PXtOlNCiC5t9MT+LF2aTaAaTs7JIAZX1eHr7+PtsIQQQohuw6XK86GHHsJqtXLFFVcwduxYkpKSWrw+NzeXHTt2sHLlStasWcMbb7zhVrBCdDfTpkY0TmHYsP4YM2XJMSGEEKLduFTozpo1i0mTJrV6c4ikpCSSkpKYO3cumzZtalOAQnRnoeFBBJgOY62JwWaNovBMCRExod4OSwghhOgWXFrgdurUqW3aAU2v1zN16lSX7xOiJ7jq2v6U22owKjq+/KrY2+EIIYQQ3UaH7uRQX19PcbH8j1uIlhgMBvpGlwPgq4RzOq/QyxEJIYQQ3UObnw6rqKhg/fr17Nmzh4KCAux2O6GhoQwePJirr76a+Ph4du7cyeuvv86yZcvaM2Yhup1RE/vx/rKzBKkBbN1RyryECG+HJIQQQnR5bSp0MzMzeemllygvL8dkMtG7d29sNhtFRUVs3LiRL774gptuuomoqKj2jleIbklVVfrFVFF0LqBxVDdWil0hhBDCLS4XuufPn+f555/HZDLxX//1X6Snpzc5f/ToUVatWsWKFSuk0BXCBaMu/2FUd8uOUm6VQlcIIYRwi8uF7sqVKwF45plnCAsLa3Y+NTWV1NRU1q1bx7vvvut+hEL0EKqq0j+2isKCAPyUcE7lnqN3UqS3wxJCCCG6LJcfRtu3bx9TpkxxWOT+2PTp05k9ezYDB7q3LuiGDRt44IEHuO2221iwYAE5OTmtuu/rr79mzpw5vPjii271L4QnjZzQjzJbJaqi8PW3pd4ORwghhOjSXB7RLS0tpXfv3q269pZbbnE5oB/bvn077777Lvfeey/9+/dn3bp1PPvss7z66qsEBQU5va+wsJDFixczYMAAt/oXwtNUVaVPRAWl5wPQE07Z+QqCegV6OywhhBCiS3K50DWZTFRUVLTq2v3795OVlcXs2bNdDgxg7dq1XHnllUyePBmAe++9l71797Jp0yZmzpzp8B673c7ChQuZM2cOR44coaqqymn7FosFi8XS+LWiKPj5+TX+uaNd7MMTfXU1PTk34yYns3x5MQE6HzZvPsnM2WmN53pyXi5FcuOY5MU5yY1jkhfnJDeOdea8uFzopqSk8PXXX3PDDTe0eN2JEyf405/+RH19fZsKXavVSm5ubpOCVqfTMXjwYLKyspzet2LFCsxmM1OmTOHIkSMt9rFy5UpWrFjR+HViYiIvvPAC4eHhLsfrDnloz7mempvwoFxqKsKprQ8nNLQXPj7GJud7al5aQ3LjmOTFOcmNY5IX5yQ3jnXGvLhc6N5www089dRT/O1vf+Oee+5xuFPatm3beOutt7BarW0OrLy8HLvdTnBwcJPjwcHBnDlzxuE9R48e5csvv2z1vNxZs2YxY8aMxq8v/iRSVFTkVuytpSgKUVFRFBQUoGlah/fXlfT03Iyb2Jt1a6sx6QwsX7yFq6Y3jOr29Ly0RHLjmOTFOcmNY5IX5yQ3jnk6L3q9vtWDki4XusnJydx1110sWrSI/fv3M3bsWGJiYrBarRQWFrJr1y4KCwvp27cv48aNY/HixS6/gbaoqalh4cKF/PznP8dsNrfqHoPB4HRLY09+gDVNk78wTvTU3JgC/FCUPCCagtJgrFYrqqo2nu+peWkNyY1jkhfnJDeOSV6ck9w41hnz0qYNI6ZNm0Z8fDzLly/nk08+afKmIiIiuP3227n22mvZvn17mwMzm83odDpKS0ubHC8tLW02ygtw7tw5ioqKeOGFFxqPXYxr3rx5vPrqq51ySF0IZy6fEMGOrRpm1UTGN8dJH9/P2yEJIYQQXYpLhe7SpUuJj48nPj6elJQUnn76aaqrqzl37hxWq5WQkJAmy46lpKRw//33ty0wvZ6kpCQOHjzI6NGjgYYHzQ4ePMi0adOaXR8TE8NLL73U5NgHH3xAbW0td9555yWXQxOis4mM7UW1PYdANYyjeTrSx3s7IiGEEKJrcanQXb9+PfX19UDDMkjR0dHEx8cTFxdHfHx8syW/IiIiiIho++5OM2bM4I033iApKYl+/frxySefUFdXx6RJkwB4/fXXCQ0NZf78+RiNRuLj45vc7+/vD9DsuBBdxbBUPceyIUAN4VTuOeL6ym8lhBBCiNZyqdBdvHgxhYWFnDp1ipMnT3Ly5Emys7ObTFHw9fVtLHwvFsFpaWkttOrcuHHjKC8vZ/ny5ZSWlpKQkMCCBQsapy4UFxd3yqUshGgvA0cksPfoKYLUAHbsKpFCVwghhHCBy3N0L47SjhgxgpycHL777jumTp3KwIEDsdvt5OTksHXrVrKzsxvvWbZsWZsDnDZtmsOpCgBPP/10i/c+8MADbe5XiM4iNrScyrIA0CKorqrxdjhCCCFEl9Gmh9Euevvttxk7dix3331347EJEyZw66238uGHH7Jp0ybuvPNOd2MUokcbP7k/Kz8qxaQz8NXnufTtl+TtkIQQQoguQefOzfn5+fTp06fZcR8fH+bPn09KSgp79uxxpwshejyjjwGjvgiA85Uh2Gw2L0ckhBBCdA1uFbqxsbFkZGQ4PT9s2DD279/vThdCCODyy2OwanbMqh9ffZrh7XCEEEKILsGtQnfmzJl8++23LFu2rHE1hh/Ly8vzyA5jQnR3YVHB1NjPA7D3cK2XoxFCCCG6Brfm6I4bN46SkhKWLFnCxo0bmThxIklJDfMHDx48yObNm0lPT2+XQIXo6YYPMJCTBf66YE7lniM2se1L9wkhhBA9gVuFLjSsdTto0CA++ugjNm7c2GRkd8iQIdx3333udiGEAAYMT2DPkZMEqYF8/W0Jc6TQFUIIIVrkdqELkJCQwCOPPILVaqWgoID6+nrCwsIwm83t0bwQ4nuxoZVUlgWi08KpqqzGP8Dk7ZCEEEKITsvlObqrV6/m9OnTDs/p9Xp69+5NUlKSFLlCdIAJU/pTbbfgo6hs+fK4t8MRQgghOjWXR3RXr17NkiVLCA8PZ8SIEYwYMYK0tDQMBkNHxCeE+BGjjwGT7wWoj6CkMhSbzYaqqt4OSwghhOiUXC5033zzTbKzs8nIyGDv3r18+umnGI1G0tLSSE9PZ/jw4YSFhXVErEII4MYb0/hw+TnMqh8Z3xwnfXw/b4ckhBBCdEouF7qKopCcnExycjJz5syhtLSUvXv3kpGRwZIlS3jrrbfo3bs3I0aMID09neTkZHQ6t1YxE0L8SFTvcGq0wwQq4RzJ05E+3tsRCSGEEJ2T2w+jBQcHM2XKFKZMmYLNZuPIkSPs27ePPXv2sHr1akwmE0OHDmX69On079+/PWIWoscbMcBIdiYEqiGczC0gLinK2yEJIYQQnY5bQ62/+tWvWLFiRePXqqoyaNAgbr/9dv785z+zcOFC5s6dS01NDUeOHHE7WCFEgwHDEyizVaBTFHbsKvV2OEIIIUSn5NaIbmFhIbm5ueTm5nLmzBlMJhOxsbFERkYCEBERwbRp05g2bVq7BCuE+EHv0EoqZKkxIYQQwim3py7s2bOHPXv2NDkWFxfHrFmzGD9eJg8K0VHGTe7Hyo9KMekMbPkyj2tvGOjtkIQQQohOxe1CV6/XM2vWLAYOHIjVaiU3N5dvvvmG1157jUOHDsnOaEJ0EKOPAaOhCGwxlFb1wmq1ote3yx4wQgghRLfg9v8Vp0+fzuzZsxu/HjJkCDNnzmTt2rUsXryY5ORkJk2a5G43QggHpkyO4/ON9QTofNiyMYsp18qorhBCCHGRWw+jGY1GQkNDHZ6bMWMG48aNY8OGDe50IYRoQVCvQDSlEICCCyHYbDYvRySEEEJ0Hm4VujExMezbt8/p+QEDBnDq1Cl3uhBCXMLkydFYNDuBqh9ff5Hl7XCEEEKITsOtQnfq1KlkZGTwzjvvUF9f3+z80aNH8fHxcacLIcQl9IoIxvr9qO7J4iAZ1RVCCCG+59Yc3auuuopTp06xfv16vv76a4YPH05cXBx6vZ4DBw6wb98+mZ8rhAdcMSGc7VvtmFUTOzblMOGqFG+HJIQQQnid2w+j3XnnnYwZM4Z169axa9cutm7d2nhu1KhR3HHHHe52IYS4hMjYXtRzFD1R5BcFUV9nwehj8GpMJ3MLOHSwhLJqFau9IRZVsRDkb2XY0HCi+0R4NT4hhBDdX7usRTRgwAAGDBiA3W6nsLCQ2tpawsLCCAgIaI/mhRCtcNWUKDZ9aSNQ58tn67OYMTPN4zFYLBa+2phNQamZIDUAiMGkAOoP11hrYPc3UPZ1Pv1iarhskow+CyGE6BguFbpLly4lPj6e+Ph4YmNjUVW1yXmdTkdUVFS7BiiEaJ1eEcH4GA+DJYaa2ihKisoIDQ/yWP/bPs/kZFEwAboYglSwaxqV9mpUpRI/Y8O84Zp6FZsWiFk1EaSaKTpn5r0leUwebyI2QUZ4hRBCtC+XCt3169c3PnSmqirR0dHEx8cTFxfXWABHRLTv/6w2bNjAmjVrKC0tpU+fPtx9993069fP4bWff/45W7Zs4eTJkwAkJSVx6623Or1eiO7mmun9+fCj8wTofNn4RQFz53V8oVt+oYI16wsxq5EE6KBOs4GukLFjehGbEOvwnhOZZ9i+txKTEk6QPpgd39iIzD7K5VNTOzxeIYQQPYdLhe7ixYspLCzk1KlTnDx5kpMnT5Kdnc327dsbr/H19W1S+MbFxZGW1rZfoW7fvp13332Xe++9l/79+7Nu3TqeffZZXn31VYKCmv8P/PDhw4wfP56UlBQMBgOrVq3if//3f/nzn//sdL1fIboTo4+BPhGlnC+OwpcIsg6cJHlwXIf1d3DPcQ5m+WNWe2HXNGo5x7SpsQT1GtDifX1SYuiTAkczTrDnsBGz6seF85GsXHGYG2alNPttkRBCCNEWLs/RjYiIICIighEjRpCTk8N3333H1KlTGThwIHa7nZycHLZu3Up2dnbjPcuWLWtTcGvXruXKK69k8uTJANx7773s3buXTZs2MXPmzGbXP/TQQ02+/sUvfsHOnTs5cOAAV1xxRZtiEKKrGXdlKu8tPUGQGsSuAyoJye3/YJrNZuOT1Uex1cXgr1Ootlvo16eM9HGujcimDutDQkodKz8+QYAuAr0thhX/zmH2Lf2k2BVCCOE2tx5Ge/vttxk7dix3331347EJEyZw66238uGHH7Jp0ybuvPPONrVttVrJzc1tUtDqdDoGDx5MVlbrFsWvq6vDarU6fSjOYrFgsVgav1YUBT8/v8Y/d7SLfXiir65GcuNYa/Myeawf3+xsWG5s7epsbp7Tfg+mlRSVse6z8wSpsagKlFlLufaqYMKi+repPT+TL3Pn9mPNyqPorDGYlEj+vTyHOXP7u1TsymfGMcmLc5IbxyQvzkluHOvMeXGr0M3Pz+eqq65qdtzHx4f58+dz5swZ9uzZw/jx411uu7y8HLvdTnBwcJPjwcHBnDlzplVtLFmyhNDQUAYPHuzw/MqVK1mxYkXj14mJibzwwguEh4e7HK875AE+5yQ3jl0qL9HR0WRmbqGqLAzVFs3B3aeYev0ot/vd8lkGuw9AkBqCTdMw+hXx2/sub5fR1/t/1Zt33/qKmopw/HWRrFuVw88fmORyO/KZcUzy4pzkxjHJi3OSG8c6Y17cKnRjY2PJyMjgyiuvdHh+2LBhfPDBB+500WYff/wxX3/9NU8//TRGo9HhNbNmzWLGjBmNX1/8SaSoqAir1drhMSqKQlRUFAUFBWia1uH9dSWSG8dcycukq5N4/4PjmNVeHM72xfTlLpIG9G5Tv1arlTUrM1Gs0fjrFKrs9QzsV8XQ0SkUFha2qU1Hpk5P5uMVh9BZY6A+gn/943Om3dC60Wj5zDgmeXFOcuOY5MU5yY1jns6LXq9v9aCkW4XuzJkz+ctf/sKyZcuYNWtWs4IyLy+vzQWj2WxGp9NRWlra5HhpaWmzUd7/tHr1aj7++GOeeuop+vTp4/Q6g8GAweB47qInP8CapslfGCckN461Ji86nY6ZM6JZtbaMQNWPnfsMGIwF9E6KdKmv03mFbNpeQ5AaAwqUWUuYcU0YwWERHfK9ufHmgSz7IBOTEkltVTTbvzzK2MmtX2tXPjOOSV6ck9w4JnlxTnLjWGfMi1uF7rhx4ygpKWHJkiVs3LiRiRMnkpSUBMDBgwfZvHkz6enpbQtMrycpKYmDBw8yevRoAOx2OwcPHmTatGlO71u1ahUfffQRTz75JH379m1T30J0F/5mE1MmVLP563r8dUa+3mlhWEU+KUPjL3mvzWbj07VHqamJJkgNwqZpGHzPcOvs1A5/UOzm2X1ZtvwEZrUXp8+FkZd5hoSUmA7tUwghRPfj9s5oM2bMYNCgQXz00Uds3LixcZ1dgCFDhnDfffe51fYbb7xBUlIS/fr145NPPqGuro5JkyYB8PrrrxMaGsr8+fOBhukKy5cv56GHHiIiIqJxNNjX1xdfX982xyFEVxYVH8a4+gK+3lVHgM6Hw0dUjh47xLTr+uPj23xaj81mY/e2HLJP+xOkxmJUoNxWTfpAC6nDPLPbml6v5+Ybe/PhqmLMqj/b9yiER1fjbzZ5pH8hhBDdQ7tsAZyQkMAjjzyC1WqloKCA+vp6wsLCMJvNbrU7btw4ysvLWb58OaWlpSQkJLBgwYLGqQvFxcVNnvDbuHEjVquVP//5z03amT17NnPmzHErFiG6svh+UZgCStnwZQVBaiBYYvl4ZRmK7jxRoRDgr6eyykphiYbFHkKALpIgFayaHUVfwJyb+jud5tNRfP19uOIyPTu+tRKo+vHx2rPMm5sgy44JIYRoNUXrbJMpOoGioqImy451FEVRiI6O5uzZs51uTou3SW4cczcvVquVDWszqamNxFdx/nOuVbNTqxVz+Zggl+f0trdd27I4cyocnaKgGU5zw02OR5XlM+OY5MU5yY1jkhfnJDeOeTovBoOh1Q+j6Vxp+OGHH+arr75y6QEzi8XCpk2bePjhh13pSgjRAfR6PTNmpnHdDBNG0xnKbecpt9VQZa+n3FZDua0Eve9pJl+l59Zbk71e5AKMmpAMxoYlBe31MWTsPObliIQQQnQVLk1dmDRpEu+++y6LFi0iPT2dIUOGkJiYSEREBD4+PgDU1tZSWFhIbm4u+/fvZ8+ePej1em644YYOeQNCCNf5B5i45vqB3g6j1WbcmMr7y/IJUkPIzDUTl1hKr4hgb4clhBCik3Op0L3xxhu5+uqr+fLLL9m8eTNbt25tPHdx3pzNZms8FhcXx5w5c5g8eTImkzxEIoRoG1VVmXF1LzZsrMVfZ2T952XMmxOAXt8ujxkIIYToplz+v4Sfnx/Tp09n+vTpFBYWkpWVxenTp6moqAAgMDCQ2NhYkpOTiYiIaPeAhRA9U3CYmcHJ58nONhCkBrHm4yxmze46o9JCCCE8z63hkIiICClmhRAek5aeyImTh7HVxaCzRrPn6xzSx/fzdlhCCCE6KZceRhNCCG+75voUym3n0SkKx/KDKTxT4u2QhBBCdFJS6AohuhRVVbn+ukgq7XX46fR8tqnSI8sBtiebzUbhmRKy9udzZF8euUdPU1VZ7e2whBCi25EnOYQQXY45OID0tBIOHTYQpDezamUOt8zt3PN1cw6d4rtD5dRYA/HT+WNUdMAPm+oc+q6eKnslNq2cqOB6LpuYgH+APMQrhBDukEJXCNElJQ+J5/iJw9RXx2C0R7FjcyY33Rrt7bCasFqtfP1lNieLAwlSAzASgPH7jd00TaMeOxoaenToFR3+OiMQRnUFrF9bg518LhsZTHy/KK++DyGE6Kqk0BVCdFnXXD+QpUuzCVTDOXk2lONZp/AN9P4WwTabjU2fZlJU1osAXTRBKtg1jQp7OcF+lfRN8CcxORpff5/G6y+cKyfraCEnCzXQemHSGYAo9u3W+HpXFlOvCCcsJsS7b0wIIboYKXSFEF3ajTfEsmp1GYGqHytWn+Wmmb3wM/l6LZ793+ayP8eHIDWGAB3UazZsShGXjw0luk8fh/eoqkpYTEhjIWuxWNixKYf84gCC1EAClAi+2mJHbzjMdTf0x2AwePItCSFElyUPowkhujT/ABNjhtuo1+yYVX8++viMS9uUt5fKsio+eD+TvNwQglR/rJqdeuUMU6f5MGduKtF9Wr8Uo8FgYOLVA/jJ/Dgio4uosNVgVHTorDEs+3chuUdPd+A7EUKI7kMKXSFEl9d3QG96Rxdj0zTMaigfrcjxaP+7tmax9pMq/HWRKIpCue08oy+zcvOcgZiDA9xqe/TE/txySxiqz2nqNTtBqj/fZfixduWhJjtRCiGEaE4KXSFEt3DZpBSCQ88D4KdEsfqjQx3eZ3lpJe+/n0XBmQj8dUaq7RZ6hRVw2/y+xCa032Y6BoOB62amMXpUPWW2CvSKDq0+lg8+OEH5hYp260cIIbobKXSFEN3G/DsnUq+cBUCxxPLxio4rdnd+lckn66sJ0DUUtBW2IqZd7cu4K1M7rM+4vlHMmxONXX8Gu6Zh1oeybkM1RzNOdFifQgjRlUmhK4ToVmbNTqGGAgBUWywfLj/cru2Xna9g6dJsCgsiG0dxwyPOMX9+f4J6BbZrX47o9XpuvHkgcX1KqLZbCND5cORoIJ+uad/3KYQQ3YGsuiCE6FZUVWXO3FQ+XH4YoxaDUYvh/fezmDWzD75+Pm61ve3zo5wu6kWgGg5Apb2Q6dOiMYektEfoLhkxti/xfS6wYVM5QXoz9dUxLF2azcwbe2Py9/N4PO2hpKiMY5nnKC2zUlsHdVYFAL1OQ69CUICOqOgA4hLDG5dmE0KIlkihK4Tolm6eM5CPVxxCtcUSoIvgw5XFXDZco++A3i63lfldPrsOQZAahUkHVfZ6kmJLuX5icgdE3nphMSHMnRPAqpU5GO1RBKrhrFxVyoT0C/RJifFqbK1x9kQh331XzPlKX/RK4PdrB/8wt7mxlLWBZoPSkobX4YPVVNrPo9dVEN1LI31MPP5m2UVOCNGcFLpCiG5r5uw0tmw8StH5CMyqP/u/s7N3/xGmXhlLcJj5kvdnfHOMQ7kQoAslSFWwaRp1nGPGdXEEBLXfw2buMBgMzJ4zgO1fHOVMUThm1Y/d+2ycyD/KxKkdN1+4rQrPlLBz51nKa4Ixq/5ADOYf7fFRbbdg0WoBKzoalonT0KFpKjrFB1+dDwZFh1k1ASbKSuCzT2qpsp8jKriGcVckdtkRbSFE+5NCVwjRrU2cmsrJYwV8tbOeINWMnmi+/NxKrT2b+Agb/QeEExnbC4Cq8mpyjhSQe7KW6vpgzGqvxiKszHaBcSOMJKV2vuIRYNyVqZzMLWDLN7WYVX/KSqJYvuwos2b183ZoAGTsPMahYwoBuhB0SixmtWEb5Ap7DUa1jD7RevoNiCA0PLzFdmw2G6dyC8nMvEBxuQFVCcakMxCkhlBTEcIna6qxaicZmmpkwPAEz7w5IUSnJYWuEKLbi+sbxa0JNrZuzOLshRACdL4Y1HAunIdvt4FNu4CGhl7RAaHoAbMKNk2jyn6ewf10DBmd5O23cUlxSVHcHF3Hxx/n4a+LxI8olq0o4Pqr6gmO9Pd4PPV1FrZ+kU1BaVCTHxrKbdUE+F5gzMhIouJjgNZPs1BVlT79o+nTPxpoKHwP78vn6LFa7PYwTDoDPkoEOVmw98gZQgMuMHFKIv4BMrVBiJ5ICl0hRI+gqiqTpg3AZrOxa2sOx88oKEowJsWAqihAw4NPtZqNOnslQb5VjB0TSWRs5xgRbS1fPx/m3ZrCF58cpqw8iiA1gC++rMXHN49p16egquqlG3FTSWEZX205Tb0lHJMupskPDcNSVNLSE3GluG2JqqoMHpnI4JENWyfv2nKM3HM+BOiCMasmrDUm1q+twcZJxgwPJKELzF12pLqqhoKTJRSdq+RCmY3qOrDZFWx2Hdr3n10FDVVnx2iw42dUCPDTERLqS3zfCJnDLHosKXSFED2KqqpcNimFy77/uqq8mgvFFdgsNgJD/QkKDUZVe3k1xvZw5XUDycs8w7Y9doLUAOx1Mby/7CzDUiwMSk/skD6z9uez52Atvrow9EoMel3DDw6KUsiEceFExXfsDw0Gg4FxV6YyDjh3+jxfbz+HxRr+/UNukezfp7FtzwkSImq5bFJfDAZDh8bTViePFXAs+wLFZVBr9cOg8//+Pfh//wLD9y+Hi4RaoM4CdVVwvhhysuqptldh0WpRlRqCTBb69DbRLy0WH1+jx96XEN4gha4QokfzN5u67WhXQkoMcf1sfLr2GHU1YQSpAeRma+zPzObyMYHE9Y1yuw+LxcKOTTnkFwcQpJoJUBse8quw1RASUML0K5Mw+Q9wux9XRcb24qZbev0QX5E/QXozQWoQF84HseLfJfgairnpJj10/CC3UzabjfzsAo5mlVFSaUSvBGHS+QLR+AA+P/q/tFWzU6tZsNnrUJR6VMWOTmdHh4YGaDSM8No1PRoGdOgx6oz4KOr3hbIBCMRWC7k5kJ1dRZW9GB2VBPtbSOwTQN+0GIxGKX5F96FomqZ5O4iWbNiwgTVr1lBaWkqfPn24++676dfP+ajAjh07WLZsGUVFRURFRXHbbbcxYsQIl/osKirCYrG4G/olKYpCdHQ0Z8+epZN/GzxOcuOY5MU5yY1jF/Py7da9fLWzGrMaCoBd06i0X2BgosaQ0QkuTWmw2WwcycjnUHYdihaGn07f2GaFvZTk3hbSx/f1yDQJV+QcPsXu76owKOEYlYahUKtmp8ZeQlK0jRHjkjD6dOwor81m4/jRs2Rll3Gh2heDYsZP17RPu6ZRZa8FqgjwrSMmwkhCv3BCwgPblNPiglLyj5+nqLiOsiod9XZ//HT+jTn4MYtmp9peja++hl6BVvolhxCTEN7pvpc2m43iglIKTpVRUlJHebVGrUXFZlexowJ6dOjRKSqqokOHgoLy/X/BjoYd0NCwa3Zs2LFrVjSsKNhQsKFXbfgZ7AT4KQQFGejVy0R4bAj9kvvKvzP/wdP//hoMBsIv8eDqRZ260N2+fTuvv/469957L/3792fdunV88803vPrqqwQFBTW7PjMzkz/84Q/Mnz+fESNGsG3bNlatWsULL7xAfHx8q/uVQtf7JDeOSV6ck9w49p952bM9hyN5PgSpP+ziVm23YNcuEBViIybGn7h+4U2W6DpfWMrJ4+cpOFfH+UojBiWoSXFWp9mwUcRl6UGND4l1ZpVlVWzZdILS6hAC1R/eZ61mxWK/QGyYleGjemMOcX+nO4vFQvaB0xzLq6as1g8fXSC+StNfpto0jUp7NUa1gt7hCoNGxGIODnC775ZcHEk+llvG+XIdFrs/Jp0/BgfFb61mpc5WiZ+hmvAQHb3jAonrG9GhUz8uzkkuLKjkQrmVqlod9VY9dnxRFSN+ivH7ufWeV6/Zqdcs2LR6FCzodRZ89DZMvmAOUAkN9aNXlJngXm37waS9WSwWqstrqa6soaqqnppqC7U1FmrrbNTVa1gsGvVWBatdQdPg4j+fGgpoNPy2QFNQFA1FAR0aig50ioZOAZ1OQ1UUfH2NpI+K9MgOkd2m0F2wYAF9+/blZz/7GQB2u53777+fa6+9lpkzZza7/pVXXqGuro7HH3+88diTTz5Jnz59uO+++1rdrxS63ie5cUzy4pzkxjFneTm8N4+Mo1b8dKHfrzbxA03TsNJwrQLNzsPF4qyU3r1quWxCUpfcqcxut5N94Bx7DlVi1IXio/xQlDSMeNegUyoJ8bcSEWYkOtZMeGwoen3zWX9VldWcO3WB4sIqzp23UFFjRMMfk863Wf6smp0qezW+agVxUToGDY/rFNNnLBYLuYfPcvxEJaU1Rmw2fwJ0vugcFJQ2TaNaq8Ou1aAqFnz0Vvy/L/R8fPX4+eoxmQz4+BkbPk82G3abhtVqo7rKQlVFPdW1NmrrNGrqFeqsBuwY0WHEoDPiq1y6QNQ0jVrNhkWrA60OVWfBoNowGjR8jQp+Pir+/np8ffXoDSp6vYLeqKKgYLPYsNjs2Cx26i02aqotVFfbqK3XqLOAxapgsavYNAMKRlTFgG/jg6utY9M06jQrVs3y/Uhxw2ixXmdDr9oxqKDqNHSqgl4FvU5B1QOagqZp2DXQ7A2jzjY7WK0aVpuC1fb9g4iaDrumYNd0aKjw/UuHrnEkW4/O4d/fjjJoaBWJqbEd3o8rhW6nnaNrtVrJzc1tUtDqdDoGDx5MVlaWw3uysrKYMWNGk2NDhw5l165dDq+3WCxNClpFUfDz82v8c0e72Icn+upqJDeOSV6ck9w45iwvaemJpKVDVUU1e3fmc7JQwa4F4KvzxajoMND0+hq7lXqtFqNaSXykwpCR8fgHdv4l11qi1+uZfG06A4YXUFtTx7fbcjlZqKAQjL/O2LgphaUGTp9seNm1Cqxo2LCjaRo6RYce5fti4oeHxQJ/VKc1TAeoxGSoJCHWSNqweHz9O98Dj0ajkdRhfRgwXCEqKoqCggIqy6vIPHiGk2fqqKj1QcGEn84HvaIjUPEFfBtutkN9NRRXO2vd+eivETA6qGsb5yRrdeiUOnz0VgJ8NUKCDEREBRAd38vtbb1dYbPZKL9QRX2VxvG8c5RXWKiqgTqrDqvdgIYRVTFiUPT4KCqqomBSGh8bbMre8LLR8HJlaE3//YsfFotpFfv3P8DaNNv3UzVs30/gsH0/XcOOggbKxXU8GkZwG7rS0Ph+xBcFTVPQ0H1/piEQBR3+gQGd7t/gTlvolpeXY7fbCQ4ObnI8ODiYM2fOOLyntLS02ZSGoKAgSktLHV6/cuVKVqxY0fh1YmIiL7zwQqt/SmgvUVHuPxDSXUluHJO8OCe5ccxpXqKhX3Lfxi9tNhsFJ4sou1CJonw/IhwXTlDopXeS66ou5iYhsU/jsSMZOezff5aCEjv1Vj/0ig++ih6domBEAXTNioyLhZldq8XXUEdkLz3JyeEMHNq/w+f+doSoqCiIavr5gIb1kY8dOUFOdhFFJXVU1SrUWxtGP3WKAR26JqOJdu3iw3IN/7Vix6ZZsWs2wIpOseJrsBFoUggJNhIVaaZ3QjgRMb06xa/+m/h+B/Fhl7W8cUxVeRWnTpyj4EwZJRdqqK6xUlNnbxwtttlV7Nr3n6PGivXiny/SfvRfDbA3FKOK/fvpAhqqTkOvgtEAPkYdvkYVX189Jj8DAWZfAgJ8CAzyJzDEH/8AU+fLpwd02kLXE2bNmtVkBPjiTyFFRUVYrdYO719RfvipWX7V2pTkxjHJi3OSG8fakhedD4RE/bDBRHVdFdVnqzoqRK9pKTfBkf5MnNr0wef6OgtFZy9QU1VHba0Vm9WOr68eo68Bc7CJ4LBgh4XE+ZLiDn0f7a01n5ngSH9GtmITEpvN1sbiykZhYWEb7utYrvx9Mof5YQ7rDNtRa1RVV1JVXdlhPXj631+9Xt/1py6YzWZ0Ol2z0djS0tJmo7wXBQcHU1ZW1uRYWVmZ0+sNBoPTyfSe/B+lpmnyP2YnJDeOSV6ck9w4JnlxrrW5MRj1xPRp+X+u3SnH7fGZ0el03SonF8nfJ8c6Y148N0PZRXq9nqSkJA4ePNh4zG63c/DgQZKTkx3ek5yczIEDB5oc279/P/379+/QWIUQQgghROfTaQtdgBkzZvDFF1+wefNmTp06xVtvvUVdXR2TJk0C4PXXX2fp0qWN11933XV89913rFmzhtOnT7N8+XKOHTvGtGnTvPQOhBBCCCGEt3TaqQsA48aNo7y8nOXLl1NaWkpCQgILFixonIpQXFzc5Om+lJQUHnroIT744APef/99oqOjefTRR11aQ1cIIYQQQnQPnbrQBZg2bZrTEdmnn3662bGxY8cyduzYDo5KCCGEEEJ0dp166oIQQgghhBBtJYWuEEIIIYToljr91AVvcLS9Y3fqryuR3DgmeXFOcuOY5MU5yY1jkhfnJDeOeSovrvSjaJ1twTMhhBBCCCHagUxd8KKamhoee+wxampqvB1KpyO5cUzy4pzkxjHJi3OSG8ckL85JbhzrzHmRQteLNE3j+PHjnW4Xkc5AcuOY5MU5yY1jkhfnJDeOSV6ck9w41pnzIoWuEEIIIYTolqTQFUIIIYQQ3ZIUul5kMBiYPXs2BoPB26F0OpIbxyQvzkluHJO8OCe5cUzy4pzkxrHOnBdZdUEIIYQQQnRLMqIrhBBCCCG6JSl0hRBCCCFEtySFrhBCCCGE6Jak0BVCCCGEEN2SFLpCCCGEEKJbkkJXCCGEEEJ0S1LoCiGEEEKIbkkKXSGEEEII0S1JoSuEEEIIIbolKXSFEEIIIUS3JIWuEKLT2LJlCzfccAOxsbEoisKiRYtavP65555DURQefPBBzwTYgtbEnpCQgKIozV7Tp0/vdLHabDaeeuopEhMT8fX1JTExkf/3//4fVqvVo7G6ojXv64033mDIkCGYzWbMZjNjx45l3bp1ng9WCOERUugKITqNyspKBg0axF/+8hf8/PxavPabb77hn//8J0OGDPFQdC1rTey7du3i7Nmzja+9e/eiKApz5szpdLG+8MILvPHGG7z22mscPXqUv/zlL7zxxhs899xzHo3VFa15X7179+aFF15g79697N69mylTpjBz5kz279/v4WiFEB6hCSFEJ+Tv76/961//cniutLRUS0pK0r788kvtiiuu0B544IEOieGnP/2pFh4erlVWVrp0X0ux/9j//u//akFBQVp1dXUbI3Sfs1inT5+u/fSnP21y7Kc//ak2ffp0D0XW3O7duzVAe/PNNy95bWu/B5qmaSEhIdrf//53N6MTQnRGMqIrhOhy7rvvPmbPns3kyZOdXnNxWkCfPn2ora11eM3FqQSOfh2/a9cuFi9ezOOPP46/v3+7xX6Rpmm8/fbb/OQnP3E4+uhoisOPX5ea1uGuCRMmsGnTJo4ePQrA4cOH+fLLL7nuuuucxtrWXLdWeno6M2fO5KmnnqKysrLN7Vxks9n44IMPqKysZNy4cQ6vOXr0KL/61a8YNGgQQUFBGI1GYmJimD59Om+//TZ1dXVuxyGE6Dh6bwcghBCuePPNN8nJyeG9995r1fX5+fm8+uqrPP744y718+STT2I2m7n//vvbEuYlbdy4kePHj3Pvvfe2eN0f/vAHh8eHDRvWAVH94LHHHqOiooKBAweiqipWq5Unn3ySX/7yl07vaWuuXfHEE08wZswYXnvtNRYsWNCmNg4cOMDYsWOpra0lICCAlStXMnjw4GbX/fd//zfPPPMMdrudsWPHcscddxAQEMC5c+fYvHkz99xzD3/729/YvXu3u29LCNFBpNAVQnQZmZmZLFiwgG3btmEwGC55fUhICIqi8Pzzz3PPPfcQFhbWqn6ysrL4/PPPueeeey45V7it3nzzTUaNGsXQoUNbvO7pp5/ukP4vZdmyZbz77rssXbqUtLQ0MjIy+PWvf01iYiI/+9nPml3f1ly7avTo0aSmpvKPf/yDxx9/HJ3O9V9MpqSkkJGRQVlZGStWrOCOO+5g8+bNDBo0qPGaP/7xj/zhD38gLi6Of//734wZM6ZZO2vXruXll1926/0IITqWTF0QQnQZO3bsoLi4mLS0NPR6PXq9nq+++oq//vWv6PX6Zr9GNplMPPXUU5SVlfHMM8+0up933nkHTdOYO3due78FAAoLC1m1atUlR3O96dFHH+V3v/sd8+bNY/Dgwdx+++088sgjTh9Ga2uud+7cyezZs4mKisJoNBIXF8fPf/5zzpw54/SeefPmkZ+fz8aNG11+XwBGo5F+/fqRnp7Oc889x7Bhw3jllVcaz+fl5fH0009jMBj45JNPHBa5ADNmzGDDhg1tikEI4RlS6AohuoyZM2dy4MABMjIyGl8jR45k3rx5ZGRkYDQam93zwAMP0LdvX/7xj3+QnZ3dqn4+//xzVFXlsssua++3AMCiRYvw8fHh1ltv7ZD220N1dTWqqjY5pqoqdrvd6T2u5vqdd95h/PjxrF+/nsmTJ/Ob3/yGkSNH8tZbbzFy5Ejy8/Md3jd+/HiANhe6/8lutzf5Ielf//oXFouFm2++uckoryM+Pj7tEoMQomPI1AUhRKdRWVlJTk4O0FB85Ofnk5GRQWhoKPHx8QQHBxMcHNzkHn9/f0JDQ50WJAaDgeeff55bbrmFxx57jI8++qjFGKqqqsjIyGDAgAEuPYR2qdgv0jSNt956i3nz5hEQEHDJdh1NXUhISODOO+9sdWxtifX666/n+eefJzExkbS0NPbt28ef//xnfvrTnzpt15VcZ2Vl8Ytf/IKEhAS++uorYmNjG8998cUXXH311fz6179m5cqVze4dNWoU0LBurqvv6/HHH2f69OnExcVRUVHB0qVL2bx5c5O1dLdt2wbAlVde6TyJQoiuwcurPgghRKNNmzZpQLPXHXfc4fQeZ8uLAVpsbGzj12PHjtUAbevWrY3H+vTpowGaxWJpPJaZmakB2tSpUzsk9i+//FIDtJ07d7bYnqO2Lr6uuOIKl2JrS6zl5eXar3/9ay0+Pl7z9fXVEhMTtSeeeEKrqalxGKuruf7Nb36jAdratWsdxjhz5kxNVVWtvLzc4XlfX18tMjLS5fd1xx13aPHx8ZrRaNTCw8O1K6+8UtuwYUOTdgYMGKAB2vr16x0nUAjRZSiapmkdWkkLIYQXKIpCbGwsp06dAhrm944bN44xY8bwzTffAA0joydOnMBisaDX65tcN2fOHJYtW+bV+KFhBLiza0uuL7vsMnbu3Mmjjz6KyWRq1ubGjRvZvn07u3fvJj09vdn52NhYzp071yE7tQ0cOJAjR46wfv16pk2b1u7tCyE8R6YuCCF6hLFjxzJ79mxWrFjBsmXLnD5odnGVBWfrwYpLa02uz58/D8Cf/vSnFttytl5uTU1Nh62IER0dzZEjRzh9+nSHtC+E8Bx5GE0I0WM899xzGAwGnnjiCerr6x1eExERAfxQiIm2uVSug4KCACgrK0PTNKevK664otm9drud0tLSxu9Ve5swYQLQMFdYCNG1SaErhOgx+vXrxy9/+UuOHz/OwoULHV4THR1NeHg4mZmZHo6ue7lUri+uaLF161aX287MzETTtA7bNOOuu+7CYDDw4Ycfcvjw4RavlZ3RhOjcpNAVQvQov//97wkODubZZ591+GtxRVGYOHEixcXFjU/wd3Z33nmnR7YFdlVLuX7wwQcxGAw8/PDDZGVlNbu3vr7eaRF8cd5vS1tAuyMhIYGnn36a+vp6pk+f7nTnsw0bNnDttdd2SAxCiPYhc3SFED1KaGgoCxYs4L/+67+cXnPzzTfz4Ycf8umnn9KvXz8PRtc2F9e2vfhAXWfRUq5TU1N55513uPvuu0lLS2PatGkkJydjsVjIz89n69athIeHc/To0Wb3fvbZZ6iqyo033thhsS9YsACr1cozzzzDqFGjGDduHCNHjmzcAnjLli1kZ2czcuTIDotBCOE+WXVBCNEt/edKAD9WV1dHamoqeXl5AE1WXYCG0cS4uDgSEhLYuXOnp0JuwpVVF4YPH86xY8c4ceIEISEhHR1aM+7k+sCBA7z88sts2rSJgoIC/P39iYmJYfz48cydO5cpU6Y0aa+srIyoqCiuueYaPv744458WwAcOXKEv/71r2zatIn8/Hxqa2vp1asXw4YNY/bs2fzkJz+RTSOE6MSk0BVCCAeee+45FixYwN69exk+fLi3w3GqtLSUXr168dvf/pYXX3zR2+F0uIULF/LQQw+xdevWxofGhBDCGSl0hRDCgdraWlJSUhgyZAhr1qzxdjhOrVmzhltuuYW8vDyioqK8HU6HqqmpoW/fvowbN44VK1Z4OxwhRBfQuSZ0CSFEJ+Hr68vixYvZtGkTVVVVLm0H7EnXX399j1nzNy8vj/vuu8+t7Y+FED2LjOgKIYQQQohuSZYXE0IIIYQQ3ZIUukIIIYQQoluSQlcIIYQQQnRL3ephtOXLlzd7EjcmJoZXX33VOwEJIYQQQgiv6VaFLkBcXBxPPfVU49c6nQxaCyGEEEL0RN2u0NXpdAQHB7fqWovFgsViafxaURT8/Py4cOECVqu1gyL8gaIohIWFUVxc3Krdj3oSyY1jkhfnJDeOSV6ck9w4JnlxTnLjmKfzotfrW70LZLdaXmz58uWsWbMGk8mEwWAgOTmZ+fPnExYW5vT6H091SExM5IUXXvBUuEIIIYQQogN1q0J337591NbWEhMTw4ULF1ixYgUlJSW8/PLL+Pn5Nbve2YhuUVGRx0Z0o6KiKCgokJ8M/4PkxjHJi3OSG8ckL85JbhyTvDgnuXHM03nR6/WEh4e37toOjsWjfrwffZ8+fejfvz+//OUv2bFjB1OmTGl2vcFgwGAwOGzLkx9gTdPkL4wTkhvHJC/OSW4ck7w4J7lxTPLinOTGsc6Yl279pJa/vz8xMTEUFBR4OxQhhBBCCOFh3brQra2tpaCgoNUPpwkhhBBCiO6jW01dePfddxk5ciRhYWFcuHCB5cuXo9PpmDBhgrdDE0IIIYQQHtatCt2SkhL+8pe/UFFRgdlsJjU1lWeffRaz2ezt0Hq0E9ln2bWvlFqrP6pSS3IfHenj+nk7LCGEEEJ0c92q0P3Nb37j7RDEf9j2eSbnisPxUaLxUQHMnDkJ+SsOc8OsFFRV9XaIQgghhOimuvUcXeFd327J4nxxBEZFR7mtGs1wmnJbCQB6WwxrVh71coRCCCGE6M661Yiu6DzOnT5P3plQ/BSFclsJN8+KxdcvBoCP/n0Ygz0GrDEczThB6rA+Xo5WCCGEEN2RjOiKDvH55jL8FD0V9lpmzojC18+n8dyNN6VQZitDVRT2HDZgs9m8GKkQQgghuispdEW727vjGGZ9KJqmMTS5Bn+zqcl5VVW5crwJi2bHrJrY/mW2lyIVQgghRHcmha5oVzabjSPHfQGotJ8nLT3R4XXRfcKxUAhAfpFZRnWFEEII0e6k0BXtas/XuZhVf6yancnjW17WbdLlYVi/H9Xd8/UxD0UohBBCiJ5CCl3RrrJOGgCo1YqJ7hPR4rWRsWHU2osByD4ly4wJIYQQon1JoSvaTc6hUwTpgwEYNdTU8sXfG9C3ocA16UIoKy7vqNCEEEII0QNJoSvaze79VQCU2croN7B3q+4ZPDKBCnstekXH9m0nOzI8IYQQQvQwUuiKdlFbVYdeCQMgIaKm1fepqoqfoWETiZLqoA6JTQghhBA9kxS6ol1s33IMH0Wlxm5hzBV9Xbp3xLBQAAJ0/hSdLumI8IQQQgjRA0mhK9rFmQv+ACi6YgwGg0v39ukXRbmtBp2isHtPQUeEJ4QQQogeSApd4bbiMxcI1DUsJZY+tOUlxZzx0V8A4HxF6x5iE0IIIYS4FCl0hdt27TqDTlEot9WQlBrbpjbS+jeMCAeoQZSXVrZneEIIIYTooaTQFW47XxUAgJ/hQpvbSB7Smyp7PaqisH/3qfYKTQghhBA9mBS6wi0lhWWN0xaGpLVt2gI0rL5g18oAOFWktEtsQgghhOjZpNAVbvl252l0ikKFvbbVa+c6ExlsAcCOLDMmhBBCCPdJoSvcUlzRMLfWqLZ92sJFQ0fEoGkagTpfzp4ocrs9IYQQQvRsUuiKNistLifg4rSFVH+32wuLCqbC3rDZxP79hW63J4QQQoieTQpd0WbffnMKVVGotNeRPCS+Xdo06MoBKC43tkt7QgghhOi5pNAVbVZY7geAXtd+u5nFNOwijKIEtlubQgghhOiZpNAVbVJbU4dJ1/DQWEqiT7u1O3BoNJqmEaDzIT/ndLu1K4QQQoieRwpd0Sb7d53AoOio1awMHNE+0xYAQsODqLTXAvDtzuPt1q4QQggheh4pdEWbHD9jB6DeXoper2/XthWlAoBThbZ2bVcIIYQQPYsUuqJNbFowAJHB9e3edniQFQCrPaDd2xZCCCFEzyGFrnDZydwCAnW+2DWN4SNj2r39AQMbnkjzV3ypLKtq9/aFEEII0TNIoStcdmD/eQAq7NX0ighu9/ZjEyKosVvQKQpH9ssDaUIIIYRoGyl0hctKqhs2h/DTl3VYH/VaJQCnCiwd1ocQQgghurduW+h+/PHHzJkzh0WLFnk7lG6ltqoO0/e7oSUn+XVYP/7Ghh3SKus7rg8hhBBCdG/dstDNyclh48aN9OnTx9uhdDsZu/IalxUbMCyuw/rpHdmwkoNBkQfShBBCCNE23a7Qra2tZeHChfz85z/H39/f2+F0OycKNKBjlhX7sdQhsdg1DZPOwNkThR3WjxBCCCG6r46rVH6ksrLSrftNJhM6Xetq8rfeeovhw4czZMgQPvrooxavtVgsWCw/zAFVFAU/P7/GP3e0i314oq/2YteCQYGo4PoOjTsoJJAq7QyBii+ZR0uISYjssL66kq74mfEUyY1jkhfnJDeOSV6ck9w41pnz4pFC92c/+5lb9z/11FMMGjToktd9/fXXHD9+nOeee65V7a5cuZIVK1Y0fp2YmMgLL7xAeHh4m2Nti6ioKI/211ZZB/MI+H5ZsWuuHUZEdFiH9qeQDfhSUqEjOjq6Q/vqarrKZ8YbJDeOSV6ck9w4JnlxTnLjWGfMi0cKXYBRo0a5PGe2rq6ONWvWtOra4uJiFi1axP/7f/8Po9HYqntmzZrFjBkzGr+++JNIUVERVqvVpVjbQlEUoqKiKCgoQNO0Du/PXV9tyQFiqLBXY9NZOHv2bIf1pSgKIQE2LNVQXe/ToX11JV3tM+NJkhvHJC/OSW4ck7w4J7lxzNN50ev1rR6U9Fihe9lllzFhwgSX7qmoqGh1oZubm0tZWRmPPfZY4zG73c6RI0fYsGEDS5cubTb9wWAwYDAYHLbnyQ+wpmld4i/MhSoTQXowGcrQtNgO7y8xPpCso2BU/LtEfjypq3xmvEFy45jkxTnJjWOSF+ckN451xrx4pNC94447SEpKcvk+X19f7rjjDmJiLr371uDBg3nppZeaHPvb3/5GTEwMN954Y6vn+ArHaqvqMKkdv6zYjw0b1Y/MI6fw0+k5d/o8kbG9PNKvEEIIIboHjxS61113XZvuMxgMrb7Xz8+P+Pj4Jsd8fHwIDAxsdly4LuPbPAxKJDWalQHDPJPPkLAgqrRcAhQfjh0tkkJXCCGEEC7x+DBnXV0djz32GJ999pmnuxZuyCto+K/VXoqqqh7r165VAVBQYvdYn0IIIYToHjw2R/ciHx8fCgsLPbIExdNPP93hffQUGsEARIXUe7Rfk7EWbFBd7+vRfoUQQgjR9Xll4uqwYcP47rvvvNG1aIMTmWcI0Plg1zSGjez4h9B+LKpXw89iep3skCaEEEII13il0L355ps5e/YsCxcu5OjRo5SUlFBZWdnsJTqHA4dLAaiwVxEaHuTRvpNTG5YP8dcZKTtf4dG+hRBCCNG1eXzqAsBvf/tbAE6dOsW2bducXrds2TJPhSRaUFrjT5AKJkO5x/sOiwml2l6ISWcg+0gBIycEejwGIYQQQnRNXil0b7755k65TZxorrqqBn9dQ3GZ2tfklRgs9irQBXOm0LPzg4UQQgjRtXml0J0zZ443uhVtkLEzH70SSY3dSsrQOK/E4KuvAYKpqPXxSv9CCCGE6Jq8Uuj+p9OnT7Njxw5KS0uJiYlh0qRJmEzeGT0UTeWfA38dWLULqGqYV2IID4HyC6Dg75X+hRBCCNE1eazQ3bBhA+vXr+d//ud/MJvNjcd3797NK6+8gtVqbTy2fv16nn322SbXCc+z2WxACADRIdaWL+5ASX1DyNgN/jpfaqvq8PWXkV0hhBBCXJrHVl3YvXs3kZGRTYpXm83GP/7xD3Q6Hffffz8vvfQS8+fPp7i4mI8++shToQknco+cxV9nxKZppF/W22txxCSEU6fZ0CkK2UfOeC0OIYQQQnQtHit0T506Rf/+/ZscO3ToEOXl5UyfPp1JkyYRFxfHjTfeyNixY9m3b5+nQhNOHDzasMpCpa0Cc4j3VjtQVZVaezUAp8/WeC0OIYQQQnQtHit0Kyoq6NWrV5NjBw4cAGD06NFNjqekpFBcXOyp0IQTVfUNo+9BJu+vX6sqDQVuWZXnth8WQgghRNfmsUI3ODiY0tLSJseOHj2Kj48Pffr0aXJcr9ej13eK5+R6rOKCUgJ1DQ9/DR3c6xJXd7wgPwsAFruflyMRQgghRFfhsUI3KSmJr776ipqahpG5kydPkpOTw9ChQ1HVpqN0p0+fbjb6Kzxrz64z6BSFClsN8f2ivB0OUREND6AZFSl0hRBCCNE6Hhs2veWWW3jiiSd46KGHiIuLIzc3F4BZs2Y1u3bXrl2kpaV5KjThQFGFH0EqGNRSINrb4dA3JZLTJ2346QyUFJYRGuHZrYiFEEII0fV4bEQ3Pj6e3//+9yQlJXHhwgX69+/PE088QVJSUpPrDh06hNFoZOzYsZ4KTfyH2po6TLqGQrJ/gtHL0TQI6hVItb1h+kJuVqGXoxFCCCFEV+DRibApKSk88cQTLV6TlpbGyy+/7KGIhCP7vsnDoERSo1kZlN7n0jd4iEWrBoIoKLJ4OxQhhBBCdAEeG9FdvXo1p0+f9lR3wg15BQoAVntpp3oo0Kg2zO+uqO08MQkhhBCi8/JYxbB69WqWLFlCeHg4I0aMYMSIEaSlpWEwGDwVgmiF+joLeqXhQcA+kTYvR9NUsL+duiqwabI9tBBCCCEuzWOF7ptvvkl2djYZGRns3buXTz/9FKPRSFpaGunp6QwfPpywsDBPhSOc2Ls9F18lkjrNxsjxid4Op4m4WBM5WeCn88NmszVbrUMIIYQQ4sc8VugqikJycjLJycnMmTOH0tJS9u7dS0ZGBkuWLOGtt96id+/ejBgxgvT0dJKTk9HpPDazQnwv96xKoAr19hJ8fDvXEm+JydFkZVZjVFTOniimd1Kkt0MSQgghRCfmtcmOwcHBTJkyhSlTpmCz2Thy5Aj79u1jz549rF69GpPJxNChQ5k+fXqzrYNFx6itqcNHFwJAv96al6Npztffh2rtAgGKLyeOl0qhK4QQQogWdYqnelRVZdCgQQwaNIjbb7+dwsJC9u7dy759+zhy5IgUuh6y6+tcjEo0NZqV4WM717SFi+z2atD5Unihc80fFkIIIUTn0ykK3f8UERHBtGnTmDZtmrdD6VHyC42YVbBr5zEYOud8aV9DLWhQXd851vcVQgghROfl1UI3Pz+fffv2UVRUBEB4eDjDhw8nPj7em2H1SFWV1fh9P20huU/nfcgrLFih/AKAv7dDEUIIIUQn55VC12Kx8M9//pMtW7YADQ+qAWiaxtKlS7n88sv5xS9+0anWcO3uvt16AoMSTbXdwpDRCd4Ox6n4eDMHL4C/zheLxSLL0wkhhBDCKa9UkkuWLGHLli1cffXVXHvttURGRqIoCgUFBXzyySds3LiRgIAA7rzzTm+E1yOdvmAiSAVFdx69Ptzb4TgV1y+SjIxy9IqOE9nn6Dewt7dDEkIIIUQn5ZX1u7Zu3crll1/Oz372M2JiYlBVFZ1OR0xMDPfccw8TJkxg69at3gitRzp5rACzzgzAyKGBXo6mZXq9nmp7LQCn8iu8HI0QQgghOjOvjOharVaSk5Odnk9JSWHPnj0ejMg7dn6VRUlZJgbVSlSkH8lDenvlV/E791zAR4mmzFZBUmqcx/t3XTVg4nx551sCTQghhBCdh1cK3aFDh5KRkcHVV1/t8HxGRgZDhgzxcFSed+y0RoAuAguQWwFHs8up184zaqjJY7+Sr62pw24LBx1EBnWNEVKTsR5sUGv19XYoQgghhOjEPDJ1obKysslr3rx5FBUV8dJLL3HgwAGKioooKipi//79/OlPf6KoqIh58+Z5IjSvCjZZKbeXUmarxKLZMSoqAboIDu33Z+nSbEqLyzs8hi1fHMNPp6dGs3L5lL4d3l97CA9u+NgqisnLkQghhBCiM/PIiO7PfvYzh8fz8/PZtWuXw3OPPPIIH3zwgUv9fPbZZ3z22WeNy5X17t2b2bNnM3z4cNcC9pDrZqYRHR3N2bNnqa+vZ8/XueSc8SVIDSJQDefTjXUkxmYxeqLzaR7usNlsFJWHYFZBrxbi69c51879T/EJwVw4DybFh7raenx8ZU1dIYQQQjTnkUL35ptvblxCrCOFhoYyf/58oqOj0TSNr776ihdffJEXX3yRuLjOPffUYDBw2aQULgN2bc3i2Klg/HVGzp2NYNkHR7np5r7tPn9351c5mNVIrJqdK66Iade2O1JsYji7d5dhUHScyCogeYisuyyEEEKI5jxS6M6ZM8cT3TBy5MgmX99666189tlnZGdnOyx0LRYLFoul8WtFUfDz82v8c0e72Md/9jV6YgqppZWsWXeGQDUckxLFsn+f5qYZEfgHtc9GCTabjeMFgZhVqKOIsMjUdmm3vTjLDTSsvFBjr8Gg+nPqdBUpQzv+e9VZtJSXnk5y45jkxTnJjWOSF+ckN4515rx02x0Z7HY7O3bsoK6uzukKDytXrmTFihWNXycmJvLCCy8QHu7ZdWSjoqKaHYuOhpQB/Vn+3jaKC0MIUoNZubaUubNMxPd1f/R19b93YFaDsGp2Zt+YQnR0tNttdgRHuQHQKTmAP2VVaqeNvSM5y4uQ3DgjeXFOcuOY5MU5yY1jnTEviqZpXlmj6ejRo3z55ZcUFhZSVVXFf4ahKAp/+tOfXG43Pz+fJ598EovFgq+vLw899BAjRoxweK2zEd2ioiKsVqvLfbtKURSioqIoKCho9v5/bM/X2eTmh+CjqFTZ6xk5uJ7+g9s+FaO2qo6PVl0gQOdDtXaOebd2rtFcuHRuPl5xCJ01hjLbBW6/LckLEXpHaz8zPZHkxjHJi3OSG8ckL85JbhzzdF70en2rByW9MqK7du1aFi9ejNFoJCYmhoCAgHZrOyYmhj/96U9UV1fzzTff8MYbb/DMM8/Qu3fz5boMBoPTea+e/ABrmtZifyPG9SMo5BS7Moz464xkHFQpKcli9MT+bepv7docAnQx1GhWrrkqulP/ZXWWm/AQlfNFoCqmTh1/R7nUZ6Ynk9w4JnlxTnLjmOTFOcmNY50xL14pdFevXk1qaiqPPfYYJlP7LhGl1+sbh86TkpI4duwYn3zyCffdd1+79uNpfQf0JjCohI2bqzCr/pw5E8anaw5zzfUDXWpn/7e5qPZoUCDUXEhwmGv3dxZ9kkI4XwQmxUhtVR2+/j7eDkkIIYQQnYxXtgCuq6tjwoQJ7V7kOmK325tMT+jKImJCmXlDCOXWElRFob46huXLjrb6/Z0vLOXwsQBURaHMWsrka1I6OOKOExXXi3rNjk5ROJ5V4O1whBBCCNEJeaXQTUtLIz8/v93bXbp0KYcPH6awsJD8/PzGry+//PJ278tb/ANMzJ0bTw0NxZ0fUSz7dwGn8wpbvK+kqIwNn1fjrzNSZa/nuqkhqKrqiZA7hKqq1NhrADh9psrL0QghhBCiM/JKoXv33Xdz8OBBVq9eTWVlZbu1W1ZWxhtvvMFvfvMb/vu//5tjx47x5JNPdrvthPV6PXPmpuLjfwarZidIDeSbb/Ss+ugQ1VU1za4/vDePDZ/XYFZN1Gk2hqbWEBoR5IXI25eOagAuVHa+5UyEEEII4X1emaMbFhbGVVddxeLFi1myZAlGoxGdrnnN/X//938utXv//fe3V4hdwtUzBpJ14CS7DqiYVRNYYlm3pgqL/RQmYz02TUeNxYRZF0SgTqFWs5KWXMmA4QneDr1dBPjWgwXqrH7eDkUIIYQQnZBXCt1ly5bx0UcfERoaSt++fT0yV7e7Sh4cR2Kqhc/WZVFZE4FJZ8BXDQcbqIDx+9kJZbYLXD0piIiYBG+G264iQvUUnQO9Tj4/QgghhGjOK4Xuxo0bGTFiBI8++qjDkVzhGoPBwPSZadTW1LF3+3FOFilY7QbAjp+hnqGDzPQdkOjtMNtdYv9eFJ0Dk2KgqrIa/wApeIUQQgjxA68UularlREjRkiR2858/XwYd2Xn2/yho4RFhVCnleKjqBzPPMeg9O5XzAshhBCi7bxSaY4YMYIjR454o2vRjaiqSu33Ky+cOdv8ITwhhBD/v707j466vvc//vx+v5NJMlkmCSFkTwgQ9l2k4IYIiEIFrHvbU2u1vVfbntsejj2ntP2prd5rWz3e9tr2nCu9itcFSkVRgYoWvSAqlEX2NQTCEkIge8gy8/3+/hiSEkmQJZlvmLwenDnJfNf3vPNJeM9nPt/PV6Rnc6XQvfPOOzly5AgvvPACRUVFVFdXU1tbe85D5MtYRmjmhcpafTogIiIibbkydOHf/u3fACguLmblypUdbrdw4cIwRSRXqoSYAE4TNNmaeUFERETacqXQ/drXvoZhaO5TuXx9Uj2UHgWPoUJXRERE2nKl0L3rrrvcOK1EoL79elN6FOJML9WVtSQmxbsdkoiIiHQTGtgoV7TUzGQanAAAB3af/zbIIiIi0rOEpdCdO3cuGzduvOj96uvrmTt3Lvv27euCqCRSNJ6ZeeHY8QaXIxEREZHuJCyFbklJCfX19Re9XzAYpKSkhIYGFTDSMY8RKnSr6vUBhYiIiPxT2MbovvTSS7z++usXtY/jOF0UjUSSxNhmgo3QrJkXRERE5CxhKXRvuOGGy9o/OTm5kyKRSNQnLZqjJRBl6BbAIiIi8k9hKXQffvjhcJxGeqi+A3pztMTBZ0ZRdbIGf68Et0MSERGRbkCDGuWKl9Lbz2k7NPPC/j2aeUFERERCVOhKRGiyQxc7lpbpwkUREREJUaErEcFjhmZeqK535R4oIiIi0g2p0JWI4PeFhi4EHM28ICIiIiEqdCUiZPSJAcBrauYFERERCVGhKxGhYGAaALGGh5Nlle4GIyIiIt2C64VuU1MTzc3NbochV7jE5ATq7VA7OrDnhMvRiIiISHcQ9it3tm/fzvr169m9ezeHDx+mqakJgOjoaLKyshg4cCDjxo1j6NCh4Q5NrnDNTj3gp7S8ye1QREREpBsIS6EbCAR4//33eeeddzhx4gTx8fH07duX6667jvj4eBzHoa6ujrKyMlavXs3y5ctJTU3lq1/9KlOmTMHj0ZX08uWizNOAn5rTUW6HIiIiIt1AWCrIH/7whwQCAW644QYmTJhAQUHBebcvKirik08+YcmSJbz99ts8//zz4QhTrnD+OJvmeghq5gUREREhTIXunDlzmDRpElFRF9bTVlBQQEFBAXfffTerVq3q4ugkUmT2ieHgAYjWzAsiIiJCmC5Gmzp16gUXuWfzeDxMnTq1CyKSSFQwsA+O4xBjWBw/Uu52OCIiIuIy12ddaE9TUxPl5SpU5OLE++Ooc0IXou3ffdLlaERERMRtYb/Kq6amhuXLl7NhwwZKS0uxbZuUlBSGDx/OtGnTyM3N5bPPPuO//uu/WLhwYbjDkyucbdeBGc3xU0G3QxERERGXhbXQ3b17N7/97W+prq7G5/ORnZ1NMBjkxIkTrFy5kg8++IDbb7+d9PT0Szr+kiVLWLduHUeOHMHr9VJYWMg3vvENMjMzO/mVSHcV620AG+qbot0ORURERFwWtkL35MmT/Md//Ac+n49HH32UsWPHtlm/a9cu3nrrLRYvXnzJhe6OHTu4+eab6devH8FgkNdee41f/epXPPvss8TExHTGy5BuLi3ZpOIkGEac26GIiIiIy8JW6C5ZsgSAxx9/nNTU1HPWDxo0iEGDBvHuu++yYMGCSzrHvHnz2jx/5JFHePDBBykqKmLIkCGXdEy5svTtn0LFSYgzoqmrrScuXjMwiIiI9FRhK3Q3bdrE5MmT2y1yzzZjxgzq6+vZsWPHZZ+zvr4egPj4+HbXNzc3t7n9sGEYxMbGtn7f1VrOEY5zXWkuNTcZuak0fFpBjGGxf+dxRl59/jmbrzRqMx1TbtqnvHRMuWmf8tIx5aZ93TkvYSt0Kysryc7OvqBt77zzzss+n23bvPjiiwwcOJDc3Nx2t1myZAmLFy9ufd63b1+efvppevfufdnnvxiXOlSjJ7iU3DTaR4ixEigrD5CRkdEFUblPbaZjyk37lJeOKTftU146pty0rzvmJWyFrs/no6am5oK23bJlC3v27OGOO+645PPNnz+fkpISnnjiiQ63mTNnDjNnzmx93vJO5MSJEwQCgUs+94UyDIP09HRKS0txHKfLz3cluZzcRJl1QAInKh2OHTvWNQG6RG2mY8pN+5SXjik37VNeOqbctC/cefF4PBfcKRm2QnfgwIF8/PHH3Hbbbefd7uDBg/zmN7+hqanpkgvd+fPns3HjRh5//HF69erV4XZRUVEd3sginA3YcRz9wnTgUnKTHG/TWAdBxxexeVWb6Zhy0z7lpWPKTfuUl44pN+3rjnkJ2w0jbrvtNoqLi/njH//YZlzs2dasWcP/+3//75J7Ux3HYf78+axbt45f/OIXpKWlXU7IcoXKyQrNuBBr+ggGNZ+uiIhITxW2Ht3CwkK+/e1v8+KLL7JlyxYmTJhAZmYmgUCAsrIy1q9fT1lZGf369WPixIm8/PLLF32O+fPns2bNGh599FFiY2OprKwEQsMmvF5vJ78i6a4KBmewe3cdXsPk8L7j5A3UPMoiIiI9UVhvGDF9+nRyc3NZtGgRy5Yta9O9nZaWxje/+U1uueUW1q5de0nHf++99wB47LHH2ix/+OGHmTRp0qWGLVeY6BgvdfZJEq1YDhyoUqErIiLSQ4Wl0H311VfJzc0lNzeXgQMH8thjj1FfX8/x48cJBAIkJye3mXZs4MCB/Ou//utFn2fRokWdGbZcwQzqgFjKq9yORERERNwSlkJ3+fLlNDU1AWBZFhkZGeTm5pKTk0Nubi5+v7/N9mlpaRpfK5clLqYRmqEhGOt2KCIiIuKSsBS6L7/8MmVlZRw+fJiSkhJKSkrYu3dvmyEKMTExrYVvSxE8dOjQcIQnESizdxSlRyFKtwIWERHpscI2Rrell3bMmDHs27ePzz//nKlTpzJkyBBs22bfvn2sXr2avXv3tu6zcOHCcIUnEWbgsAyOHQngM6MoPVROeu7578gnIiIikSesF6O1mD9/PhMmTOCBBx5oXXbttddy77338te//pVVq1Zx//33uxGaRIjE5ARqnVISjBj27FahKyIi0hOFbR7dsx06dIi8vLxzlkdHR3PfffcxcOBANmzY4EJkElGcWgCOn+pek1eLiIhIeLhS6GZlZbF58+YO148aNYotW7aELyCJSIm+RgAagxqnKyIi0hO5UujOnj2bdevWsXDhwtbZGM5WXFx8yXdHE2mRn+MDINaMU3sSERHpgVwZoztx4kROnTrFK6+8wsqVK7n++uspKCgAYNu2bXz44YeMHTvWjdAkggwYmsXevbV4DYviPaX0H5LtdkgiIiISRq4UugAzZ85k2LBhvPHGG6xcubJNz+6IESP47ne/61ZoEiFCd0g7jd+KY//+avoPcTsiERERCSfXCl2A/Px8fvzjHxMIBCgtLaWpqYnU1FQSExPdDEsiiMeoBeI4VetqUxcREREXhG2M7tKlSzly5Ei76zweD9nZ2RQUFKjIlU6V6g8CYDvxLkciIiIi4Ra2bq6lS5fyyiuv0Lt3b8aMGcOYMWMYOnQoUVFR4QpBeqD+/ZLYuhnizFga6hqJiYt2OyQREREJk7AVuv/93//N3r172bx5Mxs3buRvf/sbXq+XoUOHMnbsWEaPHk1qqib1l86V078P6zdVEmNY7Np2mFHj+7kdkoiIiIRJ2ApdwzAoLCyksLCQu+66i8rKSjZu3MjmzZt55ZVXeOGFF8jOzmbMmDGMHTuWwsJCTNOV2c8kgliWRaNdS4zlp+RII6PcDkhERETCxrUrdJKSkpg8eTKTJ08mGAyyc+dONm3axIYNG1i6dCk+n4+RI0cyY8YMBgwY4FaYEgFiPPXg+Klu0LAFERGRnsSVQvcHP/gBN9xwA3fccQcQ6nUbNmwYw4YN45vf/CZlZWVs3LiRTZs2sXPnThW6clmyepuUl4FlJLgdioiIiISRK4VuWVkZRUVFFBUVcfToUXw+H1lZWfTp0weAtLQ0pk+fzvTp090ITyLM0NFZrFrRTJzppWR/KTn90t0OSURERMLAtaELGzZsYMOGDW2W5eTkMGfOHK655hqXopJIlJgUT619lETLx86dp1ToioiI9BCuFboej4c5c+YwZMgQAoEARUVFfPrpp/zud79j+/btujOadCqPWQP4KK/uHtPZBQIB9m49zOGSOgI2xMaaDBrcm/RczTwiIiLSWVwrdGfMmNE6RhdCt/2dPXs277zzDi+//DKFhYVMmjTJrfAkwmT0cqg6BYbh7g1JmpubWfnuXqpO9yLeTAKSAKhphPWfQNWagwzvF2Tk+AI3wxQREYkIrszf5fV6SUlJaXfdzJkzmThxIitWrAhzVBLJho3MwHEc4s1oSg+VuxLDoX2lLPzLcYKNmcSb0TQ7NpXBGiqDldTYDTiOg9/yU3wgmYWv76LhdKMrcYqIiEQKVwrdzMxMNm3a1OH6wYMHc/jw4TBGJJEuJc1Pjd0AwJYtZWE//5Z1RXz2Dw9+K55mxyZoHWHKdC/fvC+Hb96Xz333pjN0ZB3VwZOYhoHPSGfxG8eprqgJe6wiIiKRwpVCd+rUqWzevJk///nPNDU1nbN+165dREdrzlPpXFFWFQAnqsPbtjZ/up99RUnEGB5qgqcZO7qB2XcMJTEpvs12/QZn8/X7+pHgP0azY+P3JPL28mpqq+rCGq+IiEikcGWM7pQpUzh8+DDLly/n448/ZvTo0eTk5ODxeNi6dSubNm3S+FzpdPkZJsePQZTpJxgMYllWl59z56aD7C9OwmuYVAVr+OrNSfh7nX8+30nTB/P5Z0XsPeAn0YrjzXdPcsccDzGxevMnIiJyMVy7GO3+++9n/PjxvPvuu6xfv57Vq1e3rhs3bhzf+ta33ApNItSIsXksf7uOWMPDni2HGTw6r0vPV3b0FFt2+fCZFlXBOmbPSCHeH3dB+44cX4Dj7KeoOBm/lciSNw9y772FXRqviIhIpHGt0IXQWNzBgwdj2zZlZWU0NDSQmppKfHz8l+8scpFi4qKps0vxW352769j8OiuO1cgEOC9D+vwWwnU2U3cMiXugovcFqO+0o/qml1UnUon3kzjvXd2cPNXh3ZRxCIiIpEnLGN0X331VdasWcOhQ4cIBoPnBmGapKenk5+fryJXupQ/JjTetbapa6cZe+fNPfitBJodmzHDGumVlnRJx7l+6iAaOAZATW06JfuOdWKUIiIikS0sPbrLly9vvejMsiwyMjLIzc0lJyeH3NxccnNzSUtLC0co0sONGpnC5n9AghnH8SMn6ZPVq9PPsXNTMQQywICYmFIKhw+5rOPNmt2fv/y1jEQrjg8/a2bMhEDnBCoiIhLhwlLovvzyy5SVlXH48GFKSkooKSlh7969rF27tnWbmJiYNoVvTk4OQ4fqY1rpXDn90vm/z46RaMWyccNxbunkQre+7jSbdsWQYBpUBSq5946Bl31Mb3QU40c5bN1i47cSWDB/DbfcdvnHFRERiXRhG6OblpZGWloaY8aMYd++fXz++edMnTqVIUOGYNs2+/btY/Xq1ezdu7d1n4ULF17UOXbs2MHSpUs5cOAAFRUVzJ07l6uvvrqzX4pc4WI8FeDEcrLG1+nHfvvtQySYfWhwAkydlNBpMzv0H5LNzl07oDmT03Wp7N9xmILBWZ1ybBERkUjlyjy68+fPZ8KECTz44INMnDiRa6+9lvvvv58//OEPzJo1i8TERH74wx9e9HEbGxvJz8/nO9/5ThdELZFiaGHoorB4y8+psqpOO+5nH+3GZ/QBIL1XeacPi7jltkKqgjV4DJO1m4LtjncXERGRf3Kl0D106BB5eedO7RQdHc19993HwIED2bBhw0Ufd/To0dxzzz3qxZXzGjQqjxq7Acsw+HRt59yB71RZFcVHQ7e1rgme4LqpgzrluGfzeDxcM8Yk6Dj4rUTWfLCn088hIiISSVyZXiwrK4vNmzdz0003tbt+1KhRvP76610eR3NzM83Nza3PDcMgNja29fuu1nKOcJzrStPVufFFnYJgJqfqkzrlHMvfP0milUKd3cRXZ2R2Wdz9huSwadsurGAax072or72NHEJnT8E40qk36f2KS8dU27ap7x0TLlpX3fOiyuF7uzZs/nP//xPFi5cyJw5c/B6vW3WFxcXEwh0/ZXlS5YsYfHixa3P+/bty9NPP03v3r27/NxnS09PD+v5riRdlZtbpgdZ8U4VfiuOitI6hozuf8nHWrhgNYlWL2zH4arhMHDwgE6M9Fzf/IaPP//PAeJML39bUcIjP5zUpee70uj3qX3KS8eUm/YpLx1TbtrXHfPiSqE7ceJETp06xSuvvMLKlSu5/vrrKSgoAGDbtm18+OGHjB07tsvjmDNnDjNnzmx93vJO5MSJE2EptA3DID09ndLSUhzH6fLzXUm6OjcxCRY1djV+y8/fVpWQnH5xN3NoUVJUSll5Ml4Dmo1SBgwfwrFjXTfXbUte0pLLqa3KwAj05tMPN5A3MLPLznml0O9T+5SXjik37VNeOqbctC/cefF4PBfcKenandFmzpzJsGHDeOONN1i5cmXrPLsAI0aM4Lvf/W6XxxAVFUVUVFS768LZgB3H0S9MB7oyN/lpDVSc9BNFbyrLq/H3Srio/Zubm/nwkyb8VjTVwXruuL1f2H6ON0wbyKuvH8FvJbB6/WlyC9V+Wuj3qX3KS8eUm/YpLx1TbtrXHfPi6i2A8/Pz+fGPf0wgEKC0tJSmpiZSU1NJTOzau1aJAHxlUj/+8peTxJvRfPhhCbO+dnE3dnjzjX34rQyaHZvxo2yiY7xfvlMnsSyLsYOD7NntkOhJYcPH+xh7zaUPvxAREYlErsy68EUej4fs7GwKCgouq8htaGiguLiY4uJiAMrKyiguLqa8vLyTIpVI4vF48MeG2kZTcxrVFTUXvO/aD3YR7YTGIiUklNJ/SHaXxHg+g0fnU2eH4t910BeW4TYiIiJXkrAUuj/60Y/46KOPLuo/4ubmZlatWsWPfvSjC95n//79PProozz66KMALFiwgEcfffSibzwhPcfkm/tTZzcRY3j423sXNtXY4aLjHDnRG8MwqA2WMXXG5d3i93LcdEMyzY5NouXjg+WabkxERORsYRm6MGnSJBYsWMCLL77I2LFjGTFiBH379iUtLY3o6Ggg1BtbVlZGUVERW7ZsYcOGDXg8Hm677bYLPs/QoUNZtGhRV70MiUAxsdGkJ5+kpioDj9OHPVsOUTgit8PtK8ur+ehTm0TLojp4mjmzcsIY7bn6ZKViWDvAzqSqtje1VXXE+y/twjoREZFIE5ZCd9asWUybNo2///3vfPjhh6xevbp1XcstUs++y1NOTg533XUXN954Iz6f5giVrnXd1EJee/0Ifk8iG7ZHk5nXfrF46kQVy1bW4LfiaXACXDPWwRcX60LEbd18a1+WLq0hzvSyYsUh7rh7sNshiYiIdAthuxgtNjaWGTNmMGPGDMrKytizZw9HjhyhpiY0LjIhIYGsrCwKCwtJS0sLV1giWJbF1Ek+PlodIN6M5s13K7hlSjO90pJat9mz5RAbtnvxW/E0OTaDB9SQP7Cve0GfxRcXS1rSAeqqM7GcPhwuOk52QR+3wxIREXGdK7MupKWlqZiVbqVPVir98/Zz4GAyfiueD/4ewDR24ot2qKj3Em/2It40OG0HGFpYw9Cx3aPIbXHDtIG8tvAYfiuejz6t5esqdEVERLrHrAsi3cGYCf0YOqiGOruJWMNDNBkEGzNJtFIxDYOqQCXXTLC7XZELoV7pEf1Dc1EnWr3Y/Nl+lyMSERFxnwpdkbMMGpXH7Nl+vL6jVAdPURWsos4+TkbWCb7x9Xyy8rvvJxEjri6gOhiabmzb/pg2495FRER6IldvGCHSHcXERnPzV92bMuxyTJqYyLpPbfxWHB/+bTc33Xplvg4REZHOoB5dkQiSlZ9G0DwOQHlVb+rrTrsckYiIiHtU6IpEmFum51FvN+Mzo1ix7IDb4YiIiLhGha5IhIlL9JGccCL0JJhO6SHdAltERHomFboiEWjy9EKqg/VEGSZ/X1PpdjgiIiKuUKErEoE8Hg9D+p7GcRwSrFQ+XbXb7ZBERETCToWuSIQaPaEfdU5oCENxaTK1VXUuRyQiIhJeKnRFItiM6RnU283EmV7eWXbY7XBERETCSoWuSARLTE4gq/cpAGKNNDZ9ojumiYhIz6FCVyTCXTtlINXBk5iGwe7iBE6VVbkdkoiISFio0BXpAW6Zmkqd3USc6WXZygrdHlhERHoEFboiPUBKbz9DB9QRdBz8niTeeVOzMIiISOTzuB2AiITH8Kv6UnxoBzRnQnMG/1izl6uuHeBaPMFgkLLDpzhSUsmJU000NRvYDjgOeKMcEn0mySkxFAzqQ1y8z7U4RUTkyqVCV6QHuXXWQF5//SCJnhSKD6eQuLWEwuE5YTt/SVEpW7acpLI+Fq+ZSIwRBfQGQh8vtXzE5DRCVSNUVUDRvkZq7Uoso5a8dIcxE/oSHeMNW8wiInLlUqEr0oNYlsXsWem8ubSCRCuOzdtiMa3D9B+S3WXnLD9awSefHqOqwY/figOySLRC62zH4bTTTMBpwKAZcDAAGw8GXrxmNDGGRaLlA3yUl8E7b9bQYFdQmGMzZmI/LMvqsthFROTKpkJXpIeJi/dx86RG3vuogQQzho1bYmg4fYBhY/t22jmCwSCbPili9yGTODMFy8jEb4HjOFTb9cR4qsnLtBg6OgdfXPJ5j3O4qIy9eysoq4wiykwmxvDgtVIpPQqLFpXj855k0qQcklITOy1+ERGJDCp0RXqg1Mxkbrr+JB/8X6jY3bPXz/HjO7jp1iGXddyTZZV8vOYIdY29iDd7t/bcVgdPEx9dwVVjU8nKzwKyLuh4lmWRNyCDvAEZADQ3N7Nl3UF2H3SIMXsRb0ZDIJP3VzYTZCezZgaJSVAPr4iIhKjQFemh+mT1YvqUKpatrMZvJVBfk8krr+7npmv9pOemXvBxgsEgGz/Zz55D1pne2yziTQg4NqftUwzrbzLi6gIg47JjjoqKYuw1/Rl7DVRX1LD6o0NUn04NFbyks/ydKurtk4wbEcOAYV03HENERK4MKnRFerCU3n7uvD2WpW/tJ9pJJ9Hqxdq1Nk0f72bkoGgKR+S0Owa24XQjW9YfouiIDSQTZ6a19t7WBE+TEHuKG6/LISm1f5fFnpicwIzZQwkEAqz9+14OlSfgt+JJsFLZuc3hsy0HGN7PYeT4gi6LQUREujcVuiI9XHSMlzvvHszmT/ezvSiGRCuOKKMP+/bA1t0VNNl1mDQBoYvELMOHz/BiGr2JOzNNQrNj02CfZEg/k+FX5WNZl997e6E8Hg/XTxuMYRjs2VLKZ1tP47eS8FvJHCqGrftL6J9Zz7jr+uvCNRGRHkaFrogAMOor/Rg+Lsi6/9vH/mPRxJmJxBoeYi1/u9s3OEGa7AoyU5oY+5VcEpPcm5O3xQ03j6ZwxDF2bT7IP7Y3E2+m4LcSOHE8gdcXHiczuYprJvfHGx3ldqgiIhIGKnRFpJVlWUy4cSATgLrqenZtPUrpiUYamw0cBzweh95+i9y+yWTkpWJZvdwOuV0DR+YycCQc3HuMtf+oIcZIJdHyUVvt4803KjHNcsaPSyanIN3tUEVEpAup0BWRdsUl+hh7TdeNsQ2Hlhkbjh85yUdrSjGc3sSaUUAGG9c5fPTpQdL9dYz7Sg7+XgmuxBgMBikvreT4kSpOnmqgrsEhEDAIBA2CjontGK3bGjgAWKaNx3LwehyivQbxPouUXj5S0xPxp8RriIaIyBkqdEUk4vXJ6sVdd/eioa6RNR/up7QqEb8Vj9/yc7rWz6qVAWrtgyRE19E/P5aBI3PweDrnz2N93WlKS05x4ngtp6oC1DWYNAU82ERjGdHEGFF4DAtIad3HOvPAOPNoTxDsIJxuhNM1cOI47N7hEHCqaHQCBJwmDBqJsppJit+DL9omNTWGzLwUEpPdKepFRMItIgvdFStW8Pbbb1NZWUleXh4PPPAA/ftf2T1TInL5YuKimTIjNFfwvu2H2bitlqCdTLwZjd/yQ8BP0T7Yvbea0/ZpDOqJ9TYTFwP+BAtfnBdvlEVUtEVzY5CGhgCNjUEamwLU1TvUN0JjwEPQicIgGq/pJcbwAHFnHhANRH+hw9VxHBoI0mw34tCMQRDTCGIaNpYR6sV1Wrdt6ek1cfAAFpYRhdeIwmtYeAwTj+EFvEA8AI21oUfFSdi7O0iTc5JGuwmbJiyjkZioAAk+SEnykpaeQJ+cFKKiNI5ZRK58EVforl27lgULFvDQQw8xYMAA3n33XZ588kmee+45/P72L6oRkZ6n/9Bs+g8NDR3Yu/UwO/fVUdcUh89MIMowibLOFKdBaKyDsrovP2YUEGWeuzzg2DQ4zQSdRkwaifY0Ex8Lyf4o0vrEkZ7bi5jY6Mt+TXW19Zw4UsnJ8noqqpqorYfTzR5sJ/rMLZW9RBsWXsPCa8UCsaEdg6Fe4SM1cKQE7HW1Z27NHOoVtowAHtMmyhMaLhHjNYn2GniiTEzTwLRMPJaBZYW6nwMBh2DQDj0CDoEg2LZNMAjNQYdAAII2BGwI2ga2Y2DbBg4GjmPiYOI4JhgGYGFgYGBiGCYmJqZhYGJiYWAZBrbzzzcDDl94YwDY2NiOg42N49g4hB5gY3DgzFcbw7AxDQfDcLAMG9MEjwmWCZYFXo+Bx2MQ5TGI8lpERZl4vRbeGA9er4eYGC+mpyUnBqZpnnl+5mEZrZ8UBINBHMchGLBxgg5BJ4gTdLBtm0DAxnEcnIBD0Dmz3raxbRvHBidoh16X44S2s8FxwLFtbNsJfX9mnX0mEXbQweHMtpz5sMAE0zAwTAPLNDAMIxS7aVJ2uJqa6moMI7SN6TExjNDP2DBMDMvAMkwMT2g/yzRDxzND60LH+ufrDu2rITUSfhFX6L7zzjvcdNNN3HjjjQA89NBDbNy4kVWrVjF79uw22zY3N9Pc3Nz63DAMYmNjW7/vai3nCMe5rjTKTfuUl45dam48Hg+DR+czeHToeVNjMwd2HeXQ4TpO1Rg0B6Nw8GIZ3jNFVqjACuJgO/aZIiqIQwCTJjxWAJ/XJj7OoHdqDOlZyST3TgrLf/LxCXHED4rj7Js5G4ZBeno6paWlOI5DVUUNpSUVnDhRT1V1kLpGg6agF4doPIaXWCMK0zCI+0KvMAABCASgtgFqOyFeD2f9J9TOG4QLZbb5mbf38zc7HgLSEQcIhh5nvpyZZO9Cdmwps4Ptb+E4F9hOQ4W+O4K0fArx5VqSBR295hZnvykJfT37Tcq5y/jCd13h4o9+ootPcHE7dG12LkYZ11xtk9O/e13kG1GFbiAQoKioqE1Ba5omw4cPZ8+ePedsv2TJEhYvXtz6vG/fvjz99NP07t07HOG2Sk/vXo2iO1Fu2qe8dKwzcpOXn9sJkXQvLXnJyMhg0Hnu9NzU2EzJgVJKDpygrLyOiuogjQEI2Ca2beI4FuAJ9eqdGUTc+u9M8eY4Z3oPz/yDUO9iS/9qy6Ol99Q0HEzTxjLAY4V6Tz0egyjLCPWaRpl4oyxioi280VHExEYRG+slJtZLlNcDjkPQdrCDNnbQwXaCoe9tCASDNDUEaGhoprEpQFNjkMamIM0Bm+ZmO9TD3OyEepiDEHTAPtPL7DgmjmPgYBKqxA0407Mc6mP+5xsf80zv8oW42DdjX9ZjTWuWaV3a5nvn3KWtsWCEOs7P/DT/+bXlJ8s5z+GLby4u3Ln79YA37T3gJbaIj/eSkRG+edQvREQVutXV1di2TVJSUpvlSUlJHD169Jzt58yZw8yZM1uft/zxOXHiBIFAoEtjbTnf2T0t8k/KTfuUl44pN+27lLz4/B4GjspgYBfH1nkMQiOKL673s7PbTCAQCBXatk0wEMSxQ0MOggEbx3ZwgnZoCELQwfSYWIaB6bEwTSM0FOA8Qx3C6ULzEgwGQ6/Ncc4MrwiC7RBsfR4aJ2G3DKk4M/TCdkI5cOzQEIzQ0A0nlKMzQzHss55frEv9UV7IboYB8fEJ1NbWtA4R6bqzfWGPS31dl7DfRe9jQEJcAjGJsRw7duziT3iRPB7PBXdKRlShe7GioqI6vOAinP9RtoylknMpN+1TXjqm3LRPeelYZ+XGsiw6e4SKmz+zL8uLaZqY3ssYc3IFMgyDjIwMjh07pt+ns3TnvERUC01MTMQ0TSorK9ssr6ysPKeXV0REREQiW0QVuh6Ph4KCArZt29a6zLZttm3bRmFhoYuRiYiIiEi4RdzQhZkzZ/L8889TUFBA//79WbZsGY2NjUyaNMnt0EREREQkjCKu0J04cSLV1dUsWrSIyspK8vPz+elPf6qhCyIiIiI9TMQVugDTp09n+vTpbochIiIiIi6KqDG6IiIiIiItIrJH93KFe95CN+ZJvFIoN+1TXjqm3LRPeemYctM+5aVjyk37wpWXizmP4XS3Cc9ERERERDqBhi646PTp0/zkJz/h9OnTbofS7Sg37VNeOqbctE956Zhy0z7lpWPKTfu6c15U6LrIcRwOHDjQ7e4i0h0oN+1TXjqm3LRPeemYctM+5aVjyk37unNeVOiKiIiISERSoSsiIiIiEUmFrouioqK44447iIqKcjuUbke5aZ/y0jHlpn3KS8eUm/YpLx1TbtrXnfOiWRdEREREJCKpR1dEREREIpIKXRERERGJSCp0RURERCQiqdAVERERkYikmzW7aMWKFbz99ttUVlaSl5fHAw88QP/+/d0Oy1WLFi1i8eLFbZZlZmby3HPPuROQS3bs2MHSpUs5cOAAFRUVzJ07l6uvvrp1veM4LFq0iA8++IC6ujoGDRrEgw8+SEZGhotRh8eX5eb555/no48+arPPyJEjmTdvXrhDDaslS5awbt06jhw5gtfrpbCwkG984xtkZma2btPU1MSCBQtYu3Ytzc3NjBw5kgcffJCkpCT3Au9iF5KXxx57jB07drTZb8qUKXz3u98Nd7hh89577/Hee+9x4sQJALKzs7njjjsYPXo00DPbSosvy01PbC/tefPNN3n11Ve59dZbuf/++4Hu2W5U6Lpk7dq1LFiwgIceeogBAwbw7rvv8uSTT/Lcc8/h9/vdDs9VOTk5/PznP299bpo974OHxsZG8vPzmTx5Mr/97W/PWf/WW2+xfPlyHnnkEdLS0li4cCFPPvkkzz77LF6v14WIw+fLcgMwatQoHn744dbnHk/k/6nbsWMHN998M/369SMYDPLaa6/xq1/9imeffZaYmBgAXnrpJTZu3MiPf/xjfD4f8+fP55lnnuGXv/yly9F3nQvJC8BNN93E3Xff3fo80n+PUlJSuO+++8jIyMBxHD766CN+/etf8+tf/5qcnJwe2VZafFluoOe1ly/at28fK1euJC8vr83y7thuel4F0U2888473HTTTdx4441kZ2fz0EMP4fV6WbVqlduhuc40TZKSklofiYmJbocUdqNHj+aee+5p01PZwnEcli1bxu233864cePIy8vj+9//PhUVFaxfv96FaMPrfLlp4fF42rSh+Pj4MEbojnnz5jFp0iRycnLIz8/nkUceoby8nKKiIgDq6+v5+9//zre+9S2GDRtGQUEBDz/8MLt372bPnj0uR991viwvLaKjo9u0GZ/P51LE4XHVVVcxZswYMjIyyMzM5N577yUmJoa9e/f22LbS4ny5adHT2svZGhoa+P3vf8/3vvc94uLiWpd313YT+d0c3VAgEKCoqIjZs2e3LjNNk+HDh/eIPyJfprS0lO9973tERUVRWFjIfffdR2pqqtthdRtlZWVUVlYyYsSI1mU+n4/+/fuzZ88errnmGhej6x527NjBgw8+SFxcHMOGDeOee+4hISHB7bDCqr6+HqC1yC8qKiIYDDJ8+PDWbbKyskhNTWXPnj0UFha6Eme4fTEvLVavXs3q1atJSkpi7NixfO1rXyM6OtqNEMPOtm0++eQTGhsbKSwsVFs5yxdz06Int5cXXniB0aNHM2LECN54443W5d213ajQdUF1dTW2bZ8zZiUpKYmjR4+6E1Q3MWDAAB5++GEyMzOpqKhg8eLF/OIXv+CZZ54hNjbW7fC6hcrKSoBzhrj4/f7WdT3ZqFGjGD9+PGlpaZSWlvLaa6/x1FNP8eSTT/aYYTC2bfPiiy8ycOBAcnNzgVC78Xg8bXpgoGe1m/byAnDttdeSmppKSkoKBw8e5JVXXuHo0aPMnTvXxWi73qFDh5g3bx7Nzc3ExMQwd+5csrOzKS4u7vFtpaPcQM9tLwAff/wxBw4c4N///d/PWddd/8ao0JVupWWwP0BeXl5r4fvJJ58wefJkFyOTK8XZPdq5ubnk5eXxgx/8gO3bt7fpaYhk8+fPp6SkhCeeeMLtULqVjvIyZcqU1u9zc3NJTk7miSeeoLS0lPT09HCHGTaZmZn85je/ob6+nk8//ZTnn3+exx9/3O2wuoWOcpOdnd1j20t5eTkvvvgiP/vZz66oMckqdF2QmJiIaZrnvMOprKzsEVe0Xoy4uDgyMzMpLS11O5Ruo6WNVFVVkZyc3Lq8qqqK/Px8d4Lqxvr06UNCQgKlpaU9otCdP38+Gzdu5PHHH6dXr16ty5OSkggEAtTV1bXpcamqquoRf3c6ykt7Wma/ifTCxePxtL6+goIC9u/fz7Jly5g4cWKPbivQcW7am1mhp7SXoqIiqqqq+MlPftK6zLZtdu7cyYoVK5g3b163bDcqdF3g8XgoKChg27ZtrRfU2LbNtm3bmD59usvRdS8NDQ2UlpZy3XXXuR1Kt5GWlkZSUhJbt25tLWzr6+vZt28f06ZNcze4bujkyZPU1ta2eVMQiRzH4c9//jPr1q3jscceIy0trc36goICLMti69atfOUrXwHg6NGjlJeXR/SYyy/LS3uKi4sBIr7NfJFt2zQ3N/fYtnI+LblpT09pL8OHDz9npps//vGPZGZmMmvWLFJTU7tlu1Gh65KZM2fy/PPPU1BQQP/+/Vm2bBmNjY1MmjTJ7dBctWDBAq666ipSU1OpqKhg0aJFmKbJtdde63ZoYdVS4LcoKyujuLiY+Ph4UlNTufXWW3njjTfIyMggLS2N119/neTkZMaNG+di1OFxvtzEx8fzl7/8hfHjx5OUlMTx48f53//9X9LT0xk5cqSLUXe9+fPns2bNGh599FFiY2NbPzHy+Xx4vV58Ph+TJ09mwYIFxMfH4/P5+POf/0xhYWFEFy9flpfS0lLWrFnDmDFjiI+P59ChQ7z00ksMHjz4nKmTIsmrr77KqFGjSE1NpaGhgTVr1rBjxw7mzZvXY9tKi/Plpqe2F4DY2Ng2Y9shNPtEQkJC6/Lu2G4Mx3Ec187ew61YsYKlS5dSWVlJfn4+3/72txkwYIDbYbnqueeeY+fOndTU1JCYmMigQYO45557IvrjoPZs37693bFyN9xwA4888kjrDSPef/996uvrGTRoEN/5znfaTIIfqc6Xm4ceeojf/OY3HDhwgLq6OlJSUhgxYgR33313xH/ketddd7W7/OGHH259A90ymfvHH39MIBDoFpO5d7Uvy0t5eTm///3vKSkpobGxkV69enH11Vdz++23R/SUUX/84x/Ztm0bFRUV+Hw+8vLymDVrVutsLj2xrbQ4X256anvpyGOPPUZ+fv45N4zoTu1Gha6IiIiIRKSeMdeOiIiIiPQ4KnRFREREJCKp0BURERGRiKRCV0REREQikgpdEREREYlIKnRFREREJCKp0BURERGRiKRCV0REREQikgpdEREREYlIKnRFREREJCJ53A5ARKSnCgaDvPXWW3zwwQdUVVXRr18/vve975GZmdnhPs8//zwfffQRADk5OTzzzDPnPceiRYtYvHgxixYt6tTYv+jdd9/lpZdean3+wgsvkJiY2KXnFBH5Mip0RURcYNs2v/3tb9mzZw8zZszA6/WyZMkSnn76aZ599lksy+pw34SEBL71rW8RFxcXxojPb9SoUSQkJLBu3TrWrVvndjgiIoAKXRERVyxdupRt27bx1FNPkZOTA0BSUhK/+93v2L59OyNGjOhw35iYGK6//vpwhXpBsrKyyMrKorS0VIWuiHQbGqMrIhJm9fX1LFmyhFtvvbW1yAUoLCwE4ODBg26FJiISUdSjKyISZqtXr6ahoYEpU6a0We7xhP4knz59+pKOu2vXLl566SUOHTpESkoKt912W4fbnjp1itdff51NmzZRV1dHeno6M2fOZPLkyW222759Oy+//DIlJSWtx6yoqAjLuF8RkculQldEJMzWrVtHdnY20dHRVFdXty4vLy8HQkMTLtahQ4f41a9+RWJiInfeeSfBYJBFixaRlJR0zraVlZXMmzcPgJtvvpnExEQ2b97Mn/70J06fPs2MGTMAOHDgAE899RRJSUnceeed2LbN4sWLdZGZiFwxVOiKiISRbdvs2bOHxsZGHnzwwXa3SUtLu+jjLly4EMdxeOKJJ0hNTQVg/PjxzJ0795xtX3/99daL4RISEgCYNm0azz33HH/5y1+YOnUqXq+XRYsWYZomv/zlL0lJSQFg4sSJ/OhHP7ro+ERE3KBCV0QkjEpLS2lsbOS2224754KzVatW8fHHH5Obm3tRx7Rtm88//5xx48a1FrkA2dnZjBw5kk2bNrUucxyHzz77jAkTJuA4Tpse5VGjRrF27VqKioooLCxk69atXH311a1FLkB6ejqjRo1iw4YNF/vSRUTCToWuiEgYnThxAoChQ4eeU+i+9dZb+P3+886j257q6mqamprIyMg4Z11mZmabQre6upq6ujref/993n///Q6PV1VVRVNTE+np6eesb2+ZiEh3pEJXRCSMGhsbAYiOjm6zvL6+np07d3LjjTd26fkdxwHguuuu44Ybbmh3m7y8PGzb7tI4RETCQYWuiEgYtVxo1tDQ0Gb5hx9+SCAQYNq0aRd9zMTERLxeL8eOHTtn3dGjR8/ZNjY2Ftu2zztXr23bREVFUVpaes669paJiHRHmkdXRCSM8vLyMAyD7du3ty47efIkf/3rX7n++uvJy8u76GOapsnIkSNZv35968wNAIcPH+bzzz8/Z9vx48fz2WefcejQoXOO1TJm1zRNhg8fzvr16zl16lTr+tLSUjZv3nzRMYqIuEE9uiIiYeT3+xk3bhzLli0jOjoan8/Hu+++S0pKCg888MAlH/euu+5i8+bN/OIXv2DatGnYts3y5cvJyck55wYU9913H9u3b2fevHncdNNNZGdnU1tbS1FREVu3buV//ud/Wo/5s5/9jJ///Oetx1yxYgU5OTkUFxdfThpERMJCha6ISJj9y7/8C3/60594++23iYmJYcKECdx7773ExsZe8jHz8vKYN28eCxYsYNGiRfTq1Yu77rqLioqKcwrdpKQknnrqKRYvXsxnn33G3/72NxISEsjJyeHrX/9663YFBQX89Kc/5eWXX2bhwoX06tWLu+++m8OHD3PkyJFLjlVEJFwMp+XKBBER6faef/55tm3bxtNPP41lWcTFxYU9hl//+tccPnyY3/3ud63LmpqaaGhoYOnSpSxdupQXXnhBN5YQEdepR1dE5Apz8uRJHnzwQXJycnjmmWe69FxNTU14vd7W58eOHWPTpk3nzNiwcuVKXnrppS6NRUTkYqnQFRG5gsyaNYvrrrsOuLRbBV+s73//+0yaNIm0tDTKy8t577338Hg8zJo1q81248ePJycnp/W5z+fr8thERL6Mhi6IiEiH/vCHP7B9+3YqKyvxeDwUFhZy7733UlBQ4HZoIiJfSoWuiIiIiEQkzaMrIiIiIhFJha6IiIiIRCQVuiIiIiISkVToioiIiEhEUqErIiIiIhFJha6IiIiIRCQVuiIiIiISkVToioiIiEhEUqErIiIiIhFJha6IiIiIRKT/D01f5NipllXnAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAIwCAYAAABp1sinAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAvapJREFUeJzs3Xl8VOXZ+P/PmTMz2Sf7TlYgAcIeEFlUFkFkR5DNWi1Vq9VatfVRaX3UtlS02lq3fltw+bmg8KAooKCooCCKbJGdJISQBBKSELIvs53fH5FomhnIOpPler9e84I5c865r7kY4Mo996JomqYhhBBCCCFEN6NzdwBCCCGEEEJ0BCl0hRBCCCFEtySFrhBCCCGE6Jak0BVCCCGEEN2SFLpCCCGEEKJbkkJXCCGEEEJ0S1LoCiGEEEKIbkkKXSGEEEII0S1JoSuEEEIIIbolKXSFEEIIIUS3JIWuEEIIIYTolqTQFUL0eF999RWzZs0iOjoaRVF4/fXXL3n+k08+iaIo3HPPPa4J8BKaE3t8fDyKojR5TJ8+3fUBCyGEC0mhK4To8SorKxk4cCD//Oc/8fLyuuS53377Lf/5z38YPHiwi6K7tObEvmfPHvLz8xse+/fvR1EUFixY4OJohRDCtaTQFUL0eNOmTeOvf/0r8+fPR6dz/s9iWVkZN910E6+++iqBgYEdFs8tt9xCWFgYVVVVlz23ObGHhoYSERHR8Pj4448xmUydvtDNzs5GURRuvfXWRsf37duHoiisWrXKPYEJIboMKXSFEKKZ7rjjDubPn8+ECROcnnNxWEBcXBy1tbUOz7k4lMBqtTZ5bc+ePbz55ps8/PDD+Pj4tFvsF2maxiuvvMLPfvYzhz3AjoY4/PRxuWEdrpCamsqcOXN49NFHqaysdHc4QohOTO/uAIQQoitYuXIlmZmZvPXWW806Pycnh+eee46HH364Re384Q9/wGQycdddd7UmzMvaunUrp06d4vbbb7/keY899pjD40OHDu2AqFrukUceYdSoUTz//PMsW7bM3eEIITopKXSFEOIyTpw4wbJly9i5cycGg+Gy5wcGBqIoCitWrOC2224jJCSkWe2kp6fz2Wefcdttt112rHBrrVy5kpEjRzJkyJBLnvf44493SPvt5YorrqBfv378+9//5uGHH77kkBMhRM8l/zIIIcRlfPPNNxQXF5OSkoJer0ev1/Pll1/y8ssvo9frqaura3S+t7c3jz76KGVlZTzxxBPNbufVV19F0zQWLlzY3m8BgMLCQj788MPL9uZ2tN27dzN//nwiIiIwGo3ExMTwq1/9irNnz7boPosWLSInJ4etW7d2UKRCiK5OCl0hhLiMOXPmcOjQIdLS0hoeI0aMYNGiRaSlpWE0Gptcc/fdd9O7d2/+/e9/k5GR0ax2PvvsM1RV5corr2zvtwDA66+/joeHB4sXL+6Q+zfHq6++ytixY9m8eTMTJkzgvvvuY8SIEaxatYoRI0aQk5PT7HuNHTsWQApdIYRTMnRBCNHjVVZWkpmZCYDdbicnJ4e0tDSCgoKIjY0lICCAgICARtf4+PgQFBTEwIEDHd7TYDCwYsUKbrzxRh566CHef//9S8ZQVVVFWloa/fv3b9EktMvFfpGmaaxatYpFixbh6+t72fs6GroQHx/fZAWElkhPT+fOO+8kPj6eL7/8kujo6IbXPv/8c6ZMmcJvf/tb1q9f36z7jRw5EqhfS1gIIRzShBCih9u2bZsGNHnccsstTq+55pprtLvvvrvJcUCLjo5ueD569GgN0Hbs2NFwLC4uTgM0i8XScOzEiRMaoE2ePLlDYv/iiy80QNu9e/cl7+foXhcf11xzTYti+2/33XefBmibNm1y+PqcOXM0VVW18vJyTdM07dSpU5f9c/D09NTCw8PbFJcQovuSHl0hRI83fvx4NE1r0TXbt29v1nnPPvssY8aM4fe//z3ffvut0/POnz8P0OL1eZsb+4QJE1r0Hluaj+b45ptvAPjyyy/Zs2dPk9cLCwux2Wykp6eTmprarHsGBQVx7ty5do1TCNF9SKErhBAdaPTo0cyfP59169axZs0apxPNLq6y4Gzt3e7gYjH/t7/97ZLntWRt3Jqamg5boUII0fXJZDQhhOhgTz75JAaDgUceeQSz2ezwnLCwMODHYrA78vf3B+p3mNM0zenjmmuuadb97HY7paWlDbkTQoj/JoWuEEJ0sD59+vDrX/+aU6dO8cILLzg8JzIyktDQUE6cOOHi6Fzn4moSO3bsaJf7nThxAk3TOs0mFkKIzkcKXSGEcIH//d//JSAggOXLlzv8al5RFK6++mqKi4sbVlHo7G699dYWbQt8zz33YDAYuP/++0lPT2/yutlsblERfHHM86W2ZBZC9GxS6AohhAsEBQWxbNkyLly44HR4wrx58wD45JNPXBlaq9ntdgD0+uZN9+jXrx+vvvoq2dnZpKSkMHPmTH73u99x7733MmfOHCIjI1u0mcWnn36KqqrMnj27VfELIbo/ReuIqbVCCNFDKYpCdHQ0eXl5TV6rq6ujX79+ZGdnA2CxWBoViWazmZiYGOLj49m9e7erQm5EURSgeasuDBs2jJMnT3L69OkWrRZx6NAhnn32WbZt20ZBQQE+Pj5ERUUxduxYFi5cyMSJEwHIzs4mISGBW265pUmvcVlZGREREVx33XV88MEHzW5bCNGzSKErhBCdyJNPPsmyZcvYv38/w4YNc3c4TpWWlhIcHMzvfvc7nn76aZe3/8ILL3DvvfeyY8cOxo0b5/L2hRBdgxS6QgjRidTW1pKcnMzgwYPZuHGju8NxauPGjdx4441kZ2cTERHh0rZramro3bs3Y8aMYd26dS5tWwjRtcg6ukII0Yl4enry5ptvsm3bNqqqqlq0HbArzZw5021r/mZnZ3PHHXe0aTtiIUTPID26QgghhBCiW5JVF4QQQgghRLckha4QQgghhOiWpNAVQgghhBDdkhS6QgghhBCiW5JCVwghhBBCdEuyvJgDFy5cwGq1uqSt0NBQioqKXNJWVyO5cUzy4pzkxjHJi3OSG8ckL85JbhxzZV70en2zd2OUQtcBq9WKxWLp8HYubrVptVqbtd1mTyK5cUzy4pzkxjHJi3OSG8ckL85JbhzrzHmRoQtCCCGEEKJbkkJXCCGEEEJ0S1LoCiGEEEKIbkkKXSGEEEII0S3JZDQhhBBCdAt1dXXU1dV1aBs1NTWYzeYObaMrau+8KIqCr69vw0S31pJCVzRy7kwxX+8qpNpiQkFBoYa+sXaGj+6NqqruDk8IIYRwqKqqCkVR8PPza3NxdCkGg8ElKzN1Ne2dF7PZTGVlJX5+fm26jwxdEA32f3OSHTsUDPYo/FVfTKoPfmoIBWfCeOfdXHKzCtwdohBCCOGQ1WrF29u7Q4tc4TpGo7FdliqTQlcAsH9XJqdPB+KhqFTYa9F5nMHXlE+FrQibpuGvD2D3dwZ2bD3u7lCFEEKIJqTAFY7I0AVBSVEZGaf98dbpKLOVMWd6CL7+EQ2vpx/MYc9hPSbVm9KSCD5Yd5SZc5NbNZQhL+schw4Vc6HKiB0PQMOgq+HKVH/i+ka247sSQgghRE8nPbqCzVuL8dYZqLKbmXldEL7+Po1eTxocy7y5wVTaCwFQbVGsWZNFVXl1s+5vs9n4dvsJ3lqdw/7vjFhro/FTQ/FXTfir/ngrEaTt82TdmmPYbLZ2f39CCCGE6Jmk0O3h9u/KxKQGo2ka/RIq8A92POjb08uDBQt6Y1PPoGkafmooGz6qYN+uTKf3ttls7P7yBO+syafoXDj+qglFUSi3VVFDAQbvsygeZyizlaNTFDyI5N012TLIXwghhBDtQoYu9GBWq5Vj2d6YVKi0n2folX0ueb6qqsyZn8LuL0+Qkx+Mr86Ds7keHFt9ij6RZgYO74Wvvw+1VXV8uyOLvBI//NVw/FWwaRq1WiFXDvcjPjm6yb03bziKuToSkxrMunWnWLjw0rEIIYQQwj1ef/11/vWvf1FUVMSAAQP485//zBVXXOH0/GeffZa///3vjY717t2br776qqNDlUK3J/t2eyYmNQKLZmfiuIBmXzfqmmT6FJbyyWfF+OqC8VcDKSqEbVss1GnnMaBDp0Q2FLg1WhFXjw4gOj7Z6T2vnzWAbZuPUVEWga8ujI8/PM4dd/dqh3cphBBCiPby4Ycf8sQTT7BixQqGDRvGqlWruOmmm9i1axcBAQFOr0tOTubdd99teK7Xu6YElaELPVh2oTcAFoqIiA1p0bXBYQEsWdKH5P4VVNiKqLbXDzfwUFR0ikKNZqVGK2DEFWYWL04iOj7ssveccH1/7Pp8AMx1EZxKz2vhOxJCCCFA0zS0ulr3PFqxJNYzzzzDpEmT6NOnD0OGDOHhhx9uGMZnt9t5/vnnGTt2LImJiQwZMoT77ruv4drjx49z8803k5yczNChQ3niiSc6dEOLlStXsmTJEhYuXEhSUhIrVqzAy8uLd95555LXqapKWFhYwyMoKKjDYvwp6dHtoTIO5+GvmrBrGlcOa/1izMlDYkkeUj8e98K5corOlREQ5ENodCB6fcuKZ4AZc5JYs/YcJtWH9zae5qYlia2OTQghRA9lrsN+z4IOufXl9l3TvbgWPDybfT9N09A0jRUrVhAZGUl6ejr33Xcf/fv355ZbbuGFF15g48aNPP3008TFxZGfn8/JkycBOHz4MPPmzWPp0qX86U9/Ij8/n3vuuQeTycT999/vsL3nn3+eF1544ZIxbd++nejopsMMzWYzBw8e5J577vnx/ep0jBs3jr17917ynqdOnWL48OF4eHiQmprKI4884rCN9iaFbhdUVV5N2p5cBg2PwhTYuiJ176FqfHW+VNjKiEuOb3NMqqoSEhVISFRgm+6j1+sZkmwmK8MbP10gh/edImV42+MTQgghOiNFUXjwwQcbnvfq1YurrrqqoZj98ssvufbaaxk7dmzD6yNHjgTgwQcfZN68eTz00EMAJCQksGDBAj7//HOnhe7NN9/MzJkzLxlTeHi4w+MlJSXYbDZCQhp3ZIWGhpKVleX0fsOGDeMf//gHvXv3prCwkL///e/MnTuXL774Al9f30vG0lZS6HYxn246SlllGJ5KOJu31BIdepZx1zof++pISWEZnkr9hzQppvPt1z0wNYHvT5zEpAZz4DikDHd3REIIIboUo0d9z2oHuOxWt0aPFt0vLy+Pl19+mW+//ZaCggIsFgt1dXUsW7YMgClTprB8+XIOHjzIjBkzmDZtGgEBAWRmZnLw4MEmvbNGo5G6Ouf9zoGBgQQGtq1TqqUmTpzY8PsBAwYwbNgwRo0axcaNG1m8eHGHtt1pC92jR4+yYcMGTp06xYULF/j9739/yRl9AEeOHOGNN94gNzeX4OBg5s2bx/jx410TsAt8u/0ENZWReCoKdk3DW2fgfHEYR/dnM6AFvZ5ffXUGvRJFha2GaWN7d1zAbXDFIA+OHdHwVwNa/P6EEEL0bIqitGj4QIvubTCg6Fq+YZIj58+fZ9q0aYwdO5bHHnuMiIgIbDYb06ZNY8CAAQDceeedTJ48mU8++YSVK1eyfPlyNm/ezIkTJzAYDCQmNh7il56eTr9+/Zy22ZahC0FBQaiqSnFxcaPjRUVFhIVdfi7ORf7+/iQmJpKdnd3sa1qr0xa6dXV1xMfHM3HiRJ555pnLnl9YWMiKFSuYPHkyv/nNbzh8+DD/7//9PwICAhg6dGjHB9zBzmQXklsQjKeiUG4rZsZ14Wz8pAR/1Z8Dxz3oPcCMh6fxsvexWCzUWkLw0UGgTwmq2jl3I0saHMOeQ1n4qYGkHbcwQHp1hRBCdDNbt27FZrPx8ssvN2xh/Nprr2GxWEhJSWk4r3fv3vz6179m6dKl9OvXj/T0dHx9fbHZbFgsFjw86nuRc3Jy2LJlC6+99prTNtsydMFoNDJ48GB27tzJ1KlTgfrJcjt37uS2225r9vuuqqri9OnTzJs3r9nXtFanLXSHDRvGsGHDmn3+p59+SlhYGD//+c+B+jEsx48f56OPPnJa6FoslkZfPyiKgpeXV8PvO9rXnx8n+9xJNE1DVWyAhk3zxUPniVmzkBRbychxfamrNbNtlwV/1YcKWw1zZ0Xj4+fN5KvM7Pjahkn14tOPM5g1b+Dl2/ziJD66COo0G5MmJXbavcEVRSE1xZP04+ClC6aksIzg8AB3h+V2F/+8OuufmztJbhyTvDgnuXFM8uI6gYGBVFZW8umnn9K3b1+2bt3Kiy++SEREBMHBwbz88suEhoYydOhQFEXhrbfeIjAwkBEjRqDT6TCZTPz1r39l6dKl5Obm8sc//pFZs2YxYcKES7bZlqELt99+O/fffz+DBw9m2LBhrFy5kpqaGhYtWtRwzmuvvcbmzZtZu7Z++Mif/vQnJk+eTK9evSgoKODZZ59Fp9MxZ86cy7bX1s9hpy10WyojI4NBgwY1OjZkyBBef/11p9esX7+edevWNTxPSEjgqaeeIjQ0tKPCbORCxQn8dI6X1/BQVM7kenDy3QysNgP+qj9mzc71E/3pk1Q/3CAyMpLvD35JXWUoNeZwfLx9MflfenLamfO5mFRQ1BL6JA265LnuFjollH1H0/DTebHrm3PccVd/d4fUaURERLg7hE5LcuOY5MU5yY1jXS0vNTU1GAwGl7TVXu1MmzaNJUuWcO+99+Lp6cn8+fOZPXs2eXl5DWOBX3jhBc6cOYOPjw8jR47k/fffb6hT3njjDf74xz/y1ltvER4ezs0338zdd9+NqrbP0ApH5s+fT1lZGc8++yyFhYUMHDiQd999t9HQhdLSUnJychrydO7cOe6++24uXLhAcHAwo0aNYvPmzZf9jBmNRiIj2/bNs6K1ZsE3F1uwYMFlx+j+9re/Zfz48cydO7fh2P79+1mxYgVvvfUWRmPTr/Wd9egWFRVhtVrb9004cGhPFtlnLFgtVsw2HXa7Qoi/jYhwL74/YcOk/jir0a5p+AcVMOG6AY3uUVtTxwfry/DWGcBw5pK9ukf2Z3My3R+7pjFilIVeiY6/mugMFEUhIiKCV/71GdbaKCrtdSxcGNqhf3m7got5KSgoaNVajd2Z5MYxyYtzkhvHumpeysrKMJlMHd7OZSej9VAdkZfy8nL8/f2bHNfr9c3ulOw2PbqtYTAYnP5U5oq/3INGJjJlViT5+flN2huYCt99lU56njdgJbU/9B/Wv8l5Hp5GfL0KsddFU2MOp7KiCh9fb4ftHThm/WG73wtEJyR2iX/Axl4Ty+dbzPjqPDi45xRDR3XOyXOudnHdRdGU5MYxyYtzkhvHJC+iM2jrZ7Db7IwWEBBAWVlZo2NlZWV4eXk57M3tCq64OomfLenFz5bE039YvNPzJl3Xh2q7BS9Fz+efnnJ4TvaJs/j+MExiSHLXGXflH+hHtf0CAMeybG6ORgghhBBdSbcpdPv27cuhQ4caHTt48CBJSUluish1PL088PUqBKDWHE5VZXWTc3btq0KnKJTZyhiYmuDqENskIbz+qxCDEkxtzeX2pBFCCCGEqNdpC93a2lqys7Mb1lgrLCwkOzu7Ye221atX8+KLLzacP2XKFAoLC3nrrbc4c+YMn3zyCd988w3Tp093R/gu99Ne3Y8+ymn02qG9p/DR1Y/37Rfb+TaIuJwR43pTo1nxUFT2fu24x1oIIYQQ4r912jG6J0+e5Iknnmh4/sYbbwBwzTXXNMzc++mCxWFhYTz88MP8f//f/8fHH39McHAwd955Z7dYQ7c5PL08CA8spqIsEg8tnGMHsuk/LJ7amjoOpntgUhXKbSXMHNfX3aG2mNHDgE0rASWMnMKePRlNCCGEEM3XaQvdlJSUhvXXHLn77rsdXvP00093ZFid2vip/Xnr7Wz89QEcPuFHTt5Riip88VdNmDUbk8Z1/GzUjtI7Gs7lg1EXRF1t8zbHEEIIIUTP1mmHLojWmTzelwpbDZ6Kirk6Cn/VhEWzEx1eTERsyOVv0EkNHZVArWbDQ1HZ/40MXxBCCCHE5Umh282ER4cwa7qJSnsh5bYqymxl9O1TxpUTkt0dWpsYPQxY7CUAZOd3nVUjhBBCCOE+nXbogmg9X38fFi/ufqtNxIRZKT0POiUQm83W4zePEEIIIcSlSY+u6DKGXxGHTdPw1hnIPHzG3eEIIYQQopOTQld0GT4mbyrt5QAczah0czRCCCFEz/T6668zatQoEhMTmTFjBgcOHHB67qhRo4iOjm7yWLZsmUtilaELokvx96wCiz9VZj93hyKEEEL0OB9++CFPPPEEK1asYNiwYaxatYqbbrqJXbt2ERAQ0OT8jz/+GJvtx51Njx8/zuLFi5kxY4ZL4pUeXdGlDEoJAMBP50tpcbl7gxFCCNEpaZpGrdXeMQ/LpV/XNK3F8T7zzDNMmjSJPn36MGTIEB5++GEslvpdQe12O88//zxjx44lMTGRIUOGcN999zVce/z4cW6++WaSk5MZOnQoTzzxBGZzx20OtXLlSpYsWcLChQtJSkpixYoVeHl58c477zg8Pzg4mLCwsIbHZ599Rnx8PKNHj+6wGH9KenRFlxKfHMU3+8/hq/PgwJ4zTLi+664NLIQQomPU2TQWrkl3S9trFibhqW/+6kCapqFpGitWrCAyMpL09HTuu+8++vfvzy233MILL7zAxo0befrpp4mLiyM/P5+TJ08CcPjwYebNm8fSpUv505/+RH5+Pvfccw8mk4n777/fYXvPP/88L7zwwiVj2r59O9HR0U2Om81mDh48yD333NNwTKfTMW7cOPbu3XvZ92o2m3n//fe54447UBTXrKDUroXu0aNH+c9//kNFRQWjR4/mF7/4BaqqkpOTw/79+9m3bx9//vOf27NJ0QMplALh5F8wuDsUIYQQok0UReHBBx9seN6rVy+uuuqqhmL2yy+/5Nprr2Xs2LENr48cORKABx98kHnz5vHQQw8BkJCQwIIFC/j888+dFro333wzM2fOvGRM4eHhDo+XlJRgs9kICWm8Ln9oaChZWVmXfa9btmyhvLycBQsWXPbc9tKuhe4rr7zCqFGjSE5O5sMPP+S1114jIyODgoICUlJSGDduXHs2J3qo+AgoKgS9LgCr1YpeL19MCCGE+JGHqrBmYccss2nQG7BYLZdsuyXy8vJ4+eWX+fbbbykoKMBisVBXV9cwWWvKlCksX76cgwcPMmPGDKZNm0ZAQACZmZkcPHiwSe+s0Wikrq7OaXuBgYEEBga2KMb28u677zJhwgQiIiJc1ma7VghFRUUsXrwYgPj4eO666y5uuukmpk2bJsWIaDdDr4jn441VeCl6jqXlMmhEgrtDEkII0YkoitKi4QMtYTDoUNtpitP58+eZNm0aY8eO5bHHHiMiIgKbzca0adMYMGAAAHfeeSeTJ0/mk08+YeXKlSxfvpzNmzdz4sQJDAYDiYmJje6Znp5Ov379nLbZlqELQUFBqKpKcXFxo+NFRUWEhYVd8p55eXns2LGDVatWXfK89tau1edPx1sEBQXh4eHBrFmz2rMJIfD08aDalo+/PoCMUzUMGuHuiIQQQoiW27p1KzabjZdffrmhhnrttdewWCykpKQ0nNe7d29+/etfs3TpUvr160d6ejq+vr7YbDYsFgseHh4A5OTksGXLFl577TWnbbZl6ILRaGTw4MHs3LmTqVOnAvWT5Xbu3Mltt912yXuuWbOGkJAQJk2adMnz2lu7Frq1tbX8/ve/Jz4+nsTERBRFoaamBi8vr/ZsRgj8varBEkC1RZYZE0II0TUFBgZSWVnJp59+St++fdm6dSsvvvgiERERBAcH8/LLLxMaGsrQoUNRFIW33nqLwMBARowYgU6nw2Qy8de//pWlS5eSm5vLH//4R2bNmsWECRMu2WZbhi7cfvvt3H///QwePJhhw4axcuVKampqWLRoUcM5r732Gps3b2bt2rVAfTG8Zs0abrzxRpd/w9+urb344oucPn2a7Oxsjh07hr+/P7feeitBQUHExMQQExPDzTff3J5Nih4quY8vJ46Bj86H6qoavH3khykhhBBdy+TJk1m0aBH33nsvnp6e3HDDDcycOZO8vDwA6urqeP755zl79ize3t6MHDmStWvXNqxX++qrr/LYY4/x1ltvER4ezpIlS7jrrrs6NObZs2dTUlLCM888Q1FRESkpKbz11luEhYU1LIlWUlLC6dOnG67ZsWMHZ86cYeHChR0amyOK1poF31qgtraWnJwcsrOzOX36NLfffntHNtcuioqKGv6wOpKiKERGRpKfn9+qdfe6s8vlxmaz8d7/XcBL0RMeWcgVV3fMpIPORj4zzkluHJO8OCe5cayr5qW8vByTqeOXnDQYDC6pEbqajsiLsz9Tg8FAaGhos+7R4f3Hnp6eJCUlkZTUMwoR4RqqqmKxl+GlBnM638YV7g5ICCGEEJ1Ou+2MZjab5Scc4VJBPrUA1Nlk0wghhBBCNNXqHt0jR46wZ88eTpw4QV5eXsN2cx4eHkRHR5OcnMzIkSMbzRoUoj0NGBDEwf3gp/Om/EIFpkCZmCaEEEKIH7Wo0LVarXz22Wds2rSJoqIifH19SUhI4KqrrsLX1xdN06iqqqKwsJAdO3awefNmQkJCmDlzJtdee62spSvaVVzfSL7ZW4iPzsjBfWcYd63zdQOFEEII0fO0qPK89957sVqtXHPNNYwePbrJIsX/LSsri2+++Yb169ezceNGXnrppTYFK8R/s2tlQChnil2zZ7YQQgghuo4WFbpz585l/PjxGAyGZp2fmJhIYmIiCxcuZNu2ba0KUIhLCfO3UFMJNruM0xVCCCFEYy2ajDZ58uRmF7lQP0GtuLgYvV7P5MmTWxycEJczcFD9loN+qhfFZy+4ORohhBBCdCatHjRbUVHB5s2b2bdvHwUFBdjtdoKCghg0aBBTpkwhNjaW3bt38+KLL7JmzZr2jFmIBhGxIVR8XYCfzpND3xcwIar1u70IIYQQontpVaF74sQJnnnmGcrLy/H29qZXr17YbDaKiorYunUrn3/+OTfccAMRERHtHa8QTegoBzwpuKC6OxQhhBBCdCItLnTPnz/PihUr8Pb25n/+539ITU1t9Prx48f58MMPWbduXZsL3S1btrBx40ZKS0uJi4tj6dKl9OnTx+n5H330EZ9++inFxcWYTCZGjRrFkiVLMBqNbYpDdG6RwTbKLwCKv7tDEUIIIUQn0uINI9avXw/AE0880aTIBejXrx8PPfQQP//5zykoKGh1YLt27eKNN95g/vz5PPXUU8TFxbF8+XLKysocnr9z505Wr17NjTfeyD/+8Q/uvPNOvvnmG955551WxyC6hkHDorBrGr46D/Kyzrk7HCGEEKJbe/311xk1ahSJiYnMmDGDAwcOXPJ8m83G008/zZVXXknv3r0ZM2YM//jHP1yyxXSLC90DBw4wceJEQkJCLnne9OnTmT9/PgMGDGhVYJs2bWLSpElMmDCBXr16cfvtt2M0Gp2u3nDixAmSk5MZN24cYWFhDBkyhLFjx5KZmdmq9kXXERTqT6W9BoAjR867ORohhBCi+/rwww954okneOCBB9iyZQsDBgzgpptuoqioyOk1L730Em+88QZ/+ctf2L59O8uWLeNf//oXr776aofH2+KhC6WlpfTq1atZ5954440tDgjqN6bIyspizpw5Dcd0Oh2DBg0iPT3d4TXJycns2LGDzMxM+vTpw7lz5zhw4ABXXXWV03YsFkujbYsVRcHLy6vh9x3tYhuuaKuraWluDLpywJviCo9unU/5zDgnuXFM8uKc5Max7pAXTdOw2Trm3oqiYbU674lU1Zbn7plnnmHz5s2cPn0aHx8frr/+ev785z9jMBiw2+0NE/vz8/Px8/NjwoQJPPfcc0D9kNHly5fz3Xff4eXlxdy5c3nkkUc6bNjmypUrWbJkCQsXLgRgxYoVfP7557zzzjvcddddDq/Zu3cv1113Hddeey0AMTExfPjhh6SlpV22vbZ+Dltc6Hp7e1NRUdGscw8ePEh6ejrz589vURvl5eXY7XYCAgIaHQ8ICODs2bMOrxk3bhzl5eU8+uijQH03+eTJk7nhhhuctrN+/XrWrVvX8DwhIYGnnnqK0NDQFsXbVjJpz7nm5iY5IYecbNArJsLCwlDV7j0xTT4zzkluHJO8OCe5cayr5aWmpqZhCVSrVWPT2mK3xDFrUQh6ffOLM03TUBSFZ555hsjISNLT0/nNb37DwIED+cUvfsE//vEPNmzYwN///nfi4uLIz88nMzMTg8HAoUOHmD17Nrfffjt//etfyc/P58477yQwMJDf/e53Dtt77rnnGopkZ3bu3OmwU9NsNnPw4EF++9vfNlpu9uqrr2bv3r1Ol6AdNWoUb775Jjk5OfTu3ZvDhw/z3Xff8ac//emSy9YajUYiIyMvGevltLjQTU5O5uuvv2bWrFmXPO/06dP87W9/w2w2t7jQbY0jR46wfv16brvtNvr27UtBQQGvvfYa69atc9r+3LlzmTFjRsPziz81FBUVYbVaOzxmRVGIiIigoKDAJeNUupKW5qZv/2BOnarDW2dg17Z99EmJcUGUriefGeckN45JXpyT3DjWVfNiNpsbvqW9VI9rR7NYLGhay3ohf1qURkREMG7cONLT07FYLHzxxRdce+21jBo1quH1YcOGYbFYuP/++5k3bx6///3vgfqe0gULFvDpp59y7733OmxryZIlTJs27ZLxBAcHN/rG+6Jz585hs9kIDAxs9HpwcDCZmZkOrwG46667KCsrY8yYMaiqis1m46GHHmL27NlOr4H6P9P8/Pwmx/V6fbM7JVtc6M6aNYtHH32Uf/3rX9x2220OK/GdO3eyatWqVheLJpMJnU5HaWlpo+OlpaVNenkvWrNmDVdffTWTJk0CIDY2ltraWv7zn/9www03oNM1HY5sMBic/iThyr/cmqZ1qX9MXKm5ufE2eVNpL8Ff9eXYiTJ6D2je8JquSj4zzkluHJO8OCe5cawr50VV4fp5HbMSj8FguGRx1tIvFPPy8nj55Zf59ttvKSgowGKxUFdXx7JlywCYMmUKy5cv5+DBg8yYMYNp06YREBBAZmYmBw8e5IUXXmh0P6PRSF1dndP2AgMDCQx07ZrzGzdu5P333+ell14iKSmJI0eO8NhjjxEeHs6CBQsueW1bP4MtLnSTkpL4xS9+weuvv87BgwcZPXo0UVFRWK1WCgsL2bNnD4WFhQ2z6t58882WB6XXk5iYyOHDh7niiisAsNvtHD58mKlTpzq8pq6ursk4DkfFrei+vPTloPlSWuPt7lCEEEK4kaIo6Fu9Jdal6fVKi3tsnTl//jzTpk1j7NixPPbYY0RERGCz2Zg2bVrDZP4777yTyZMn88knn7By5UqWL1/O5s2bOXHiBAaDgcTExEb3TE9Pp1+/fk7bfP7555sUx/9t+/btREdHNzkeFBSEqqoUFzceFlJUVERYWJjT+/35z3/mnnvuYfbs2QD079+fvLw8XnzxxcsWum3Vqo/B1KlTiY2NZe3atXz88ceNqu2wsDBuvvlmrr/+enbt2tXqwGbMmMFLL71EYmIiffr04eOPP6auro7x48cD8OKLLxIUFMSSJUsASE1N5aOPPiIhIaFh6MKaNWtITU2VgreHSOhl5EwueOpMWK1W9B31r5wQQgjRDrZu3YrNZuPll19u6Kx77bXXsFgspKSkNJzXu3dvfv3rX7N06VL69etHeno6vr6+2Gw2LBYLHh4eAOTk5LBlyxZee+01p23efPPNzJw585JxhYeHOzxuNBoZPHgwO3fubOh4tNvt7Ny5k9tuu83p/Wpqapp0Rqqqit1uv2Qc7aHVlcCAAQN4/PHHqa6u5ty5c1itVgIDAxstO5acnOx0Bt7ljBkzhvLyctauXUtpaSnx8fEsW7asYehCcXFxo6TNmzcPRVF49913KSkpwWQykZqayuLFi1v7FkUXkzI8luycSjwUlePf5zIwNcHdIQkhhBBOBQYGUllZyaeffkrfvn3ZunUrL774IhEREQQHB/Pyyy8TGhrK0KFDURSFt956i8DAQEaMGIFOp8NkMvHXv/6VpUuXkpubyx//+EdmzZrFhAkTLtlmW4Yu3H777dx///0MHjyYYcOGsXLlSmpqali0aFHDOa+99hqbN29m7dq1AEyePJnnn3+e6OhokpOTOXz4MP/5z38aXdNRWlTorl69mtjYWGJjY4mOjkZVVby9vUlIcFxQhIWFXbIr+3KmTp3qdKjC448/3ui5qqrceOONrV7STHR9Hp5Gqu0V+Kv+ZJ6qYWDT/UyEEEKITmPy5MksWrSIe++9F09PT2644QZmzpxJXl4eUD8s8/nnn+fs2bN4e3szcuRI1q5d29Dp9+qrr/LYY4/x1ltvER4ezpIlS1rdwdhcs2fPpqSkhGeeeYaioiJSUlJ46623CAsLaxi7XFJSwunTpxuu+ctf/sLTTz/NsmXLOH/+POHh4fzsZz/j/vvv79BYARStBaN8b775ZsxmM1BfWEZGRhIbG0tMTExDAdyWwrazKCoquuRA8/aiKAqRkZHk5+d32QH/HaW1uflg3RFUWzRltjJ+tiSuAyN0D/nMOCe5cUzy4pzkxrGumpfy8nJMJlOHt3O5yWg9VUfkxdmfqcFg6JhVF958800KCwvJy8sjNzeX3NxcMjIyGo3F9fT0bFT4xsTENBpnIkRHSkr04WQGeOv8qK2pw9PLw90hCSGEEMJNWjxG9+JwhOHDh5OZmcn333/P5MmTGTBgAHa7nczMTHbs2EFGRkbDNWvWrGnXoIVwJmlwL46kl+GpqBw5kEvqmD7uDkkIIYQQbtKmaemvvPIKo0ePZunSpQ3Hxo0bx+LFi3nvvffYtm0bt956a1tjFKLZ9Ho9dbYyPPVBnMqzIMN0hRBCiJ6rTetu5eTkEBfXdBykh4cHS5YsITk5mX379rWlCSFaLNCnBoBaq5+bIxFCCCGEO7Wp0I2OjiYtLc3p60OHDuXgwYNtaUKIFuvfLwAAX50PVeXV7g1GCCGEEG7TpkJ3zpw5fPfdd6xZs6ZhNYafys7ObvU2wEK0VlzfCKrtFlRF4fu9ue4ORwghhIu4YgMC4RrtteJHm8bojhkzhpKSEt5++222bt3K1Vdf3bAV3eHDh9m+fTupqTJKUriWqqrYtDIghLzCrrM0jhBCiNbz9vamoqICPz8/2RG1G6iurm7Y8a0t2rxH6owZMxg4cCDvv/8+W7dubdSzO3jwYO644462NiFEi4WYzNRVgcXe8WsqCiGEcD+9Xo+Pjw+VlZUd2o7RaHT4LXZP15550TQNvV7vnkJ3w4YNpKamEh0d3XAsPj6eBx54AKvVSkFBAWazmZCQEJcs3CyEI4MHh7HnGzCp3pw7U0x4dMjlLxJCCNGl6fX6Dq09uupmGh2tM+elVYXu22+/TWhoKMOHD2f48OGkpKRgMBjQ6/X06tWrI+IUokUiYkMo35mPSfUibX8h10mhK4QQQvQ4LS50V65cSUZGBmlpaezfv59PPvkEo9FISkpKQ+EbEiJFhXA/o1oKeFFc4enuUIQQQgjhBi0udBVFISkpiaSkJBYsWEBpaSn79+8nLS2N1atX88orr9CrVy+GDx9OamoqSUlJMihcuEXvGANncsFD54+5zoLRw+DukIQQQgjhQm2ejBYQEMDEiROZOHEiNpuNY8eOceDAAfbt28eGDRvw9vZmyJAhTJ8+nb59+7ZHzEI0y6CRcWTllOOhqBzad1q2AxZCCCF6mDYXuj+lqioDBw5k4MCB3HzzzRQWFrJ//34OHDjAsWPHpNAVLmUwGKizl+GhBnEy1yrbAQshhBA9TJsK3d/85jdcc801zJ8/3+HrYWFhTJ06lalTp7alGSFaLdRUS10VWG3+7g5FCCGEEC7WpsGzhYWFZGVlkZWVxc6dO9m/fz/nzp1rr9iEaLOhwyPQNA0/1Yv804XuDkcIIYQQLtTmoQv79u1j3759jY7FxMQwd+5cxo4d29bbC9EmYVFBVNjPYlK9Sfu+mMi4MHeHJIQQQggXaXOhq9frmTt3LgMGDMBqtZKVlcW3337L888/z5EjR2RnNOF2Hvoy0Lw5X+nl7lCEEEII4UJtLnSnT5/eaIzu4MGDmTNnDps2beLNN98kKSmJ8ePHt7UZIVqtX29PTmWCj86fqvJqfEze7g5JCCGEEC7QpjG6RqORoKAgh6/NmDGDMWPGsGXLlrY0IUSb9R8aS5XdjF7R8d2ubHeHI4QQQggXaVOhGxUVxYEDB5y+3r9/f/Ly8trShBBtpqoqilICwNkSDzdHI4QQQghXaVOhO3nyZNLS0nj11Vcxm81NXj9+/DgeHlJYCPfrG1s/SsdTF0htVZ2boxFCCCGEK7RpjO61115LXl4emzdv5uuvv2bYsGHExMSg1+s5dOgQBw4ckPG5olMYfEU8madL8dLp2f11FtdM6e/ukIQQQgjRwdo8Ge3WW29l1KhRfPTRR+zZs4cdO3Y0vDZy5EhuueWWVt97y5YtbNy4kdLSUuLi4li6dCl9+jjfxrWqqop33nmH7777jsrKSkJDQ7nlllsYPnx4q2MQ3YNer0dTzgPh5BXJtwxCCCFET9CiQnf16tXExsYSGxtLdHQ0qqoC9WNx+/fvj91up7CwkNraWkJCQvD19W11YLt27eKNN97g9ttvp2/fvnz00UcsX76c5557Dn//prtcWa1W/vKXv2AymXjggQcICgqiuLgYb2+ZYS/qDehjJPskeKsBlF+owBTo5+6QhBBCCNGBWlTobt68uWEsrqqqREZGEhsbS0xMDLGxscTFxREREdEugW3atIlJkyYxYcIEAG6//Xb279/Ptm3bmDNnTpPzv/jiCyorK/nzn/+MXl//tsLCZHMA8aMBw2I5nFGMr86DXTtzmTpzgLtDEkIIIUQHalGh++abb1JYWEheXh65ubnk5uaSkZHBrl27Gs7x9PRsKHwvFsEpKSktCurixhM/LWh1Oh2DBg0iPT3d4TX79u2jb9++vPLKK+zduxeTycTYsWOZM2cOOp3jOXcWiwWLxdLwXFEUvLy8Gn7f0S624Yq2upqOyI1er8eoLwF7JMUVpi6Zd/nMOCe5cUzy4pzkxjHJi3OSG8c6c15aPEY3LCyMsLAwhg8fTmZmJt9//z2TJ09mwIAB2O12MjMz2bFjBxkZGQ3XrFmzpkVtlJeXY7fbCQgIaHQ8ICCAs2fPOrzm3LlzFBUVMW7cOB555BEKCgpYtWoVNpuNG2+80eE169evZ926dQ3PExISeOqppwgNDW1RvG3VXr3g3VF752badVY+/bgcf9WXc7nlDL0iuV3v7yrymXFOcuOY5MU5yY1jkhfnJDeOdca8tGky2iuvvMLo0aNZunRpw7Fx48axePFi3nvvPbZt28att97a1hibRdM0TCYTv/rVr9DpdCQmJlJSUsKGDRucFrpz585lxowZDc8v/iRSVFSE1Wrt8JgVRSEiIoKCggI0Tevw9rqSjsqNt7+eClsZ/voAtu08Q3iMqd3u7QrymXFOcuOY5MU5yY1jkhfnJDeOuTover2+2Z2SbSp0c3JyuPbaa5sc9/DwYMmSJZw9e5Z9+/YxduzYFt3XZDKh0+koLS1tdLy0tLRJL+9FAQEB6PX6RsMUoqOjKS0txWq1Nozb/SmDwYDBYHB4P1d+gDVNk78wTnREbuLCaiktAZVQKsuquuSWwPKZcU5y45jkxTnJjWOSF+ckN451xry0acOI6Oho0tLSnL4+dOhQDh482OL76vV6EhMTOXz4cMMxu93O4cOHSUpKcnhNcnIyBQUF2O32hmP5+fkEBgY6LHJFz3Xl+N5U2c14KCrbPj/l7nCEEEII0UHaVOjOmTOH7777jjVr1jjcGS07O7vVQwBmzJjB559/zvbt28nLy2PVqlXU1dU1bEDx4osvsnr16obzp0yZQmVlJa+//jpnz55l//79rF+/nuuuu65V7Yvuy2Aw4ONRDEBlbWijCYlCCCGE6D7a1NU5ZswYSkpKePvtt9m6dStXX301iYmJABw+fJjt27eTmpra6nuXl5ezdu1aSktLiY+PZ9myZQ1DF4qLixvN7gsJCeEPf/gD/9//9//x4IMPEhQUxPXXX+9wKTIhrpkYy9YtdfjojHy1NYNJ02SpMSGEEKK7afN3+jNmzGDgwIG8//77bN26tVHP7uDBg7njjjtafe+pU6cydepUh689/vjjTY4lJSWxfPnyVrcneg5TgC+akgtEUlgWjLnOgtHD8XhtIYQQQnRN7TJ4NT4+ngceeACr1UpBQQFms5mQkBBMpq41o130LJOv7cXWrWZ8dR58tiWdabNbtt6zEEIIITq3dp2lpdfr6dWrV3veUogO4x/sh15/BGzRVNVEUFxQSkhEgLvDEkIIIUQ7adNkNCG6uqnT+1Bpr8VTUfn0i2J3hyOEEEKIdtSiQvf+++/nyy+/bNFKChaLhW3btnH//fe3ODghOpqnlwfJ8VVomoafGsJnHx91d0hCCCGEaCctGrowfvx43njjDV5//XVSU1MZPHgwCQkJhIWF4eHhAUBtbS2FhYVkZWVx8OBB9u3bh16vZ9asWR3yBoRoq+Gje5N5+jheRFBRHsHhfacYmJrg7rCEEEII0UYtKnRnz57NlClT+OKLL9i+fTs7duxoeE1VVQBsNlvDsZiYGBYsWMCECRPw9u56u0+JnmPuvN6s+b98/FUTxzNM2G1ZDL4i0d1hCSGEEKINWjwZzcvLi+nTpzN9+nQKCwtJT0/nzJkzVFRUAODn50d0dDRJSUmEhYW1e8BCdASDwcC0yf5s3lqJSfXhZFYA2XlHuG5aXzw8jQ6vsdls5GUVkp5+gfPlKlbNC51iRIcOu2bDTi1+ntVcOSqc8OgQF78jIYQQQrRp1YWwsDApZkW3ERTqz8zrdWzYXIK/6g+WaD5YXwbKeYJNdjyNKrVmG6UVCrU2b4w6H7wUL8ALH4ej3b3AEsi3OzSq7BlMuiaI8OhgF78rIYQQoudq8aoLGzZs4MyZMx0RixBuZwr0Y/HCXijGM9RqNrx1BryVCGoqorhwPpyaiig8iMRf9cdL0WPXNMpt1VTZz6H3PENw6DmiYooJDC7AojtLua0KnaLgp4ayYwfs/Oy4u9+iEEII0WO0uEd3w4YNvP3224SGhjJ8+HCGDx9OSkoKBoPsKiW6B1VVmTE3heqqGnZuO0VxuRE7niioaNhQqMXkaSY6wkjy4Ch8fAMveb99X2dy4rQPfqoXF85H8OF7R5k9T7YcFkIIITpaiwvdlStXkpGRQVpaGvv37+eTTz7BaDSSkpJCamoqw4YNIyRExiOKrs/bx4spM9pekKaO7UPK0DrWbziNry4MnTWK9euOMne+FLtCCCFER2pxoasoCklJSSQlJbFgwQJKS0vZv38/aWlpvP3226xatYpevXoxfPhwUlNTSUpKQqeTfSlEz+bp48GCBb1Z/146HlokelsUn318lGunSbErhBBCdJQ2bwEcEBDAxIkTmThxIjabjWPHjnHgwAH27dvHhg0b8Pb2ZsiQIUyfPp2+ffu2R8xCdEmqqjJ/QX/WvHscbyWCyvJIDu09xaARsmavEEII0RHaXOj+lKqqDBw4kIEDB3LzzTdTWFjI/v37OXDgAMeOHZNCVwjghnm9WbP2DP76AA5neBPXpxJTgK+7wxJCCCG6nXYrdM1mM4qiNJqUFhYWxtSpU5k6dWp7NSNEl2cwGLh+cgCff27BV+fBpo/zWbJEfggUQggh2lurC90jR46wZ88eTpw4QV5eHmazGQAPDw+io6NJTk5m5MiRpKSktFuwQnQXwWEBxEdncC4/FD81lD070xk5LsndYQkhhBDdSosKXavVymeffcamTZsoKirC19eXhIQErrrqKnx9fdE0jaqqKgoLC9mxYwebN28mJCSEmTNncu2116LXt+tICSG6tCuu7svq1Rn4qaFk5vozqKoOTx8Pd4clhBBCdBstqjzvvfderFYr11xzDaNHjyYxMfGS52dlZfHNN9+wfv16Nm7cyEsvvdSmYIXobqZODmsYwrBl80nmyJJjQgghRLtpUaE7d+5cxo8f3+zNIRITE0lMTGThwoVs27atVQEK0Z0Fhfrj630Ua00UNmsEhWdLCIsKcndYQgghRLfQogVuJ0+e3Kod0PR6PZMnT27xdUL0BNde35dyWw1GRccXXxa7OxwhhBCi2+jQnRzMZjPFxfIftxCXYjAY6B1ZDoCnEsqZ7EI3RySEEEJ0D62eHVZRUcHmzZvZt28fBQUF2O12goKCGDRoEFOmTCE2Npbdu3fz4osvsmbNmvaMWYhuZ+TVfXhnTT7+qi87villUXyYu0MSQgghurxWFbonTpzgmWeeoby8HG9vb3r16oXNZqOoqIitW7fy+eefc8MNNxAREdHe8QrRLamqSp+oKorO+Tb06kZLsSuEEEK0SYsL3fPnz7NixQq8vb35n//5H1JTUxu9fvz4cT788EPWrVsnha4QLTDyqh97db/6ppTFUugKIYQQbdLiQnf9+vUAPPHEE4SEhDR5vV+/fvTr14+PPvqIN954o+0RCtFDqKpK3+gqCgt88VJCycs6R6/EcHeHJYQQQnRZLZ6MduDAASZOnOiwyP2p6dOnM3/+fAYMaNu6oFu2bOHuu+/mpptuYtmyZWRmZjbruq+//poFCxbw9NNPt6l9IVxpxLg+lNkqURWFr78rdXc4QgghRJfW4h7d0tJSevXq1axzb7zxxhYH9FO7du3ijTfe4Pbbb6dv37589NFHLF++nOeeew5/f3+n1xUWFvLmm2/Sv3//NrUvhKupqkpcWAWl533RE0rZ+Qr8g/3cHZYQQgjRJbW40PX29qaioqJZ5x48eJD09HTmz5/f4sAANm3axKRJk5gwYQIAt99+O/v372fbtm3MmTPH4TV2u50XXniBBQsWcOzYMaqqqpze32KxYLFYGp4rioKXl1fD7zvaxTZc0VZX05NzM2ZCEmvXFuOr82D79lzmzE9peK0n5+VyJDeOSV6ck9w4JnlxTnLjWGfOS4sL3eTkZL7++mtmzZp1yfNOnz7N3/72N8xmc6sKXavVSlZWVqOCVqfTMWjQINLT051et27dOkwmExMnTuTYsWOXbGP9+vWsW7eu4XlCQgJPPfUUoaGhLY63LWTSnnM9NTeh/lnUVIRSaw4lKCgYDw9jo9d7al6aQ3LjmOTFOcmNY5IX5yQ3jnXGvLS40J01axaPPvoo//rXv7jtttsc7pS2c+dOVq1ahdVqbXVg5eXl2O12AgICGh0PCAjg7NmzDq85fvw4X3zxRbPH5c6dO5cZM2Y0PL/4k0hRUVGbYm8uRVGIiIigoKAATdM6vL2upKfnZszVvfhoUzXeOgNr3/yKa6fX9+r29LxciuTGMcmLc5IbxyQvzkluHHN1XvR6fbM7JVtc6CYlJfGLX/yC119/nYMHDzJ69GiioqKwWq0UFhayZ88eCgsL6d27N2PGjOHNN99s8RtojZqaGl544QV+9atfYTKZmnWNwWBwuqWxKz/AmqbJXxgnempuvH29UJRsIJKC0gCsViuqqja83lPz0hySG8ckL85JbhyTvDgnuXGsM+alVRtGTJ06ldjYWNauXcvHH3/c6E2FhYVx8803c/3117Nr165WB2YymdDpdJSWljY6Xlpa2qSXF+DcuXMUFRXx1FNPNRy7GNeiRYt47rnnOmWXuhDOXDUujG92aJhUb9K+PUXq2D7uDkkIIYToUlpU6K5evZrY2FhiY2NJTk7m8ccfp7q6mnPnzmG1WgkMDGy07FhycjJ33XVX6wLT60lMTOTw4cNcccUVQP1Es8OHDzN16tQm50dFRfHMM880Ovbuu+9SW1vLrbfeetnl0ITobMKjg6m2Z+KnhnA8W0fqWHdHJIQQQnQtLSp0N2/ejNlsBuqXQYqMjCQ2NpaYmBhiY2ObLPkVFhZGWFjrd3eaMWMGL730EomJifTp04ePP/6Yuro6xo8fD8CLL75IUFAQS5YswWg0Ehsb2+h6Hx8fgCbHhegqhvbTczIDfNVA8rLOEdNbvpUQQgghmqtFhe6bb75JYWEheXl55ObmkpubS0ZGRqMhCp6eng2F78UiOCUl5RJ3dW7MmDGUl5ezdu1aSktLiY+PZ9myZQ1DF4qLizvlUhZCtJcBw+PZfzwPf9WXb/aUSKErhBBCtECLx+he7KUdPnw4mZmZfP/990yePJkBAwZgt9vJzMxkx44dZGRkNFyzZs2aVgc4depUh0MVAB5//PFLXnv33Xe3ul0hOovooHIqy3xBC6O6qsbd4QghhBBdRqsmo130yiuvMHr0aJYuXdpwbNy4cSxevJj33nuPbdu2ceutt7Y1RiF6tLET+rL+/VK8dQa+/CyL3n0S3R2SEEII0SXo2nJxTk4OcXFxTY57eHiwZMkSkpOT2bdvX1uaEKLHM3oYMOqLADhfGYjNZnNzREIIIUTX0KZCNzo6mrS0NKevDx06lIMHD7alCSEEcNVVUVg1OybViy8/SXN3OEIIIUSX0KZCd86cOXz33XesWbOmYTWGn8rOznbJDmNCdHchEQHU2M8DsP9orZujEUIIIbqGNo3RHTNmDCUlJbz99tts3bqVq6++msTE+vGDhw8fZvv27aSmprZLoEL0dMP6G8hMBx9dAHlZ54hOaP3SfUIIIURP0KZCF+rXuh04cCDvv/8+W7dubdSzO3jwYO644462NiGEAPoPi2ffsVz8VT++/q6EBVLoCiGEEJfU5kIXID4+ngceeACr1UpBQQFms5mQkBBMJlN73F4I8YPooEoqy/zQaaFUVVbj4+vt7pCEEEKITqvFY3Q3bNjAmTNnHL6m1+vp1asXiYmJUuQK0QHGTexLtd2Ch6Ly1Ren3B2OEEII0am1uEd3w4YNvP3224SGhjJ8+HCGDx9OSkoKBoOhI+ITQvyE0cOAt+cFMIdRUhmEzWZDVVV3hyWEEEJ0Si0udFeuXElGRgZpaWns37+fTz75BKPRSEpKCqmpqQwbNoyQkJCOiFUIAcyencJ7a89hUr1I+/YUqWP7uDskIYQQolNqcaGrKApJSUkkJSWxYMECSktL2b9/P2lpabz99tusWrWKXr16MXz4cFJTU0lKSkKna9MqZkKIn4joFUqNdhQ/JZRj2TpSx7o7IiGEEKJzavNktICAACZOnMjEiROx2WwcO3aMAwcOsG/fPjZs2IC3tzdDhgxh+vTp9O3btz1iFqLHG97fSMYJ8FMDyc0qICYxwt0hCSGEEJ1Om7paf/Ob37Bu3bqG56qqMnDgQG6++Wb+/ve/88ILL7Bw4UJqamo4duxYm4MVQtTrPyyeMlsFOkXhmz2l7g5HCCGE6JTa1KNbWFhIVlYWWVlZnD17Fm9vb6KjowkPDwcgLCyMqVOnMnXq1HYJVgjxo15BlVTIUmNCCCGEU20eurBv3z727dvX6FhMTAxz585l7FgZPChERxkzoQ/r3y/FW2fgqy+yuX7WAHeHJIQQQnQqbS509Xo9c+fOZcCAAVitVrKysvj22295/vnnOXLkiOyMJkQHMXoYMBqKwBZFaVUwVqsVvb5d9oARQgghuoU2/684ffp05s+f3/B88ODBzJkzh02bNvHmm2+SlJTE+PHj29qMEMKBiRNi+GyrGV+dB19tTWfi9dKrK4QQQlzUpsloRqORoKAgh6/NmDGDMWPGsGXLlrY0IYS4BP9gPzSlEICCC4HYbDY3RySEEEJ0Hm0qdKOiojhw4IDT1/v3709eXl5bmhBCXMaECZFYNDt+qhdff57u7nCEEEKITqNNhe7kyZNJS0vj1VdfxWw2N3n9+PHjeHh4tKUJIcRlBIcFYP2hVze32F96dYUQQogftGmM7rXXXkteXh6bN2/m66+/ZtiwYcTExKDX6zl06BAHDhyQ8blCuMA140LZtcOOSfXmm22ZjLs22d0hCSGEEG7X5slot956K6NGjeKjjz5iz5497Nixo+G1kSNHcsstt7S1CSHEZYRHB2PmOHoiyCnyx1xnwehhcGtMuVkFHDlcQlm1itVeH4uqWPD3sTJ0SCiRcWFujU8IIUT31y5rEfXv35/+/ftjt9spLCyktraWkJAQfH192+P2QohmuHZiBNu+sOGn8+TTzenMmJPi8hgsFgtfbs2goNSEv+oLROGtAOqP51hrYO+3UPZ1Dn2iarhyvPQ+CyGE6BgtKnRXr15NbGwssbGxREdHo6pqo9d1Oh0RERHtGqAQonmCwwLwMB4FSxQ1tRGUFJURFOrvsvZ3fnaC3KIAfHVR+Ktg1zQq7dWoSiVexvpxwzVmFZvmh0n1xl81UXTOxFtvZzNhrDfR8dLDK4QQon21qNDdvHlzw6QzVVWJjIwkNjaWmJiYhgI4LKx9/7PasmULGzdupLS0lLi4OJYuXUqfPn0cnvvZZ5/x1VdfkZubC0BiYiKLFy92er4Q3c110/vy3vvn8dV5svXzAhYu6vhCt/xCBRs3F2JSw/HVQZ1mA10ho0cFEx0f7fCa0yfOsmt/Jd5KKP76AL751kZ4xnGumtyvw+MVQgjRc7So0H3zzTcpLCwkLy+P3NxccnNzycjIYNeuXQ3neHp6Nip8Y2JiSElp3Veou3bt4o033uD222+nb9++fPTRRyxfvpznnnsOf/+m/4EfPXqUsWPHkpycjMFg4MMPP+Qvf/kLf//7352u9ytEd2L0MBAXVsr54gg8CSP9UC5Jg2I6rL3D+05xON0HkxqMXdOo5RxTJ0fjH9z/ktfFJUcRlwzH006z76gRk+rFhfPhrF93lFlzk5t8WySEEEK0RovH6IaFhREWFsbw4cPJzMzk+++/Z/LkyQwYMAC73U5mZiY7duwgIyOj4Zo1a9a0KrhNmzYxadIkJkyYAMDtt9/O/v372bZtG3PmzGly/r333tvo+Z133snu3bs5dOgQ11xzTatiEKKrGTOpH2+tPo2/6s+eQyrxSe0/Mc1ms/HxhuPY6qLw0SlU2y30iSsjdUzLemT7DY0jPrmO9R+cxlcXht4Wxbr/y2T+jX2k2BVCCNFmbZqM9sorrzB69GiWLl3acGzcuHEsXryY9957j23btnHrrbe26t5Wq5WsrKxGBa1Op2PQoEGkpzdvUfy6ujqsVqvTSXEWiwWLxdLwXFEUvLy8Gn7f0S624Yq2uhrJjWPNzcuE0V58u7t+ubFNGzKYt6D9JqaVFJXx0afn8VejURUos5Zy/bUBhET0bdX9vLw9WbiwDxvXH0dnjcJbCef/1mayYGHfFhW78plxTPLinOTGMcmLc5IbxzpzXtpU6Obk5HDttdc2Oe7h4cGSJUs4e/Ys+/btY+zYsS2+d3l5OXa7nYCAgEbHAwICOHv2bLPu8fbbbxMUFMSgQYMcvr5+/XrWrVvX8DwhIYGnnnqK0NDQFsfbFjKBzznJjWOXy0tkZCQnTnxFVVkIqi2Sw3vzmDxzZJvb/erTNPYeAn81EJumYfQq4nd3XNUuva93/aYXb6z6kpqKUHx04Xz0YSa/unt8i+8jnxnHJC/OSW4ck7w4J7lxrDPmpU2FbnR0NGlpaUyaNMnh60OHDuXdd99tSxOt9sEHH/D111/z+OOPYzQaHZ4zd+5cZsyY0fD84k8iRUVFWK3WDo9RURQiIiIoKChA07QOb68rkdw41pK8jJ+SyDvvnsKkBnM0wxPvL/aQ2L9Xq9q1Wq1sXH8CxRqJj06hym5mQJ8qhlyRTGFhYavu6cjk6Ul8sO4IOmsUmMN47d+fMXVW83qj5TPjmOTFOcmNY5IX5yQ3jrk6L3q9vtmdkm0qdOfMmcM///lP1qxZw9y5c5sUlNnZ2a0uGE0mEzqdjtLS0kbHS0tLm/Ty/rcNGzbwwQcf8OijjxIXF+f0PIPBgMHgeOyiKz/AmqbJXxgnJDeONScvOp2OOTMi+XBTGX6qF7sPGDAYC+iVGN6its5kF7JtVw3+ahQoUGYtYcZ1IQSEhHXIn83seQNY8+4JvJVwaqsi2fXFcUZPaP5au/KZcUzy4pzkxjHJi3OSG8c6Y17aVOiOGTOGkpIS3n77bbZu3crVV19NYmIiAIcPH2b79u2kpqa2LjC9nsTERA4fPswVV1wBgN1u5/Dhw0ydOtXpdR9++CHvv/8+f/jDH+jdu3er2haiu/AxeTNxXDXbvzbjozPy9W4LQytySB4Se9lrbTYbn2w6Tk1NJP6qPzZNw+B5lsXz+3X4RLF583uzZu1pTGowZ86FkH3iLPHJUR3aphBCiO6nzTujzZgxg4EDB/L++++zdevWhnV2AQYPHswdd9zRpnu/9NJLJCYm0qdPHz7++GPq6uoYP348AC+++CJBQUEsWbIEqB+usHbtWu69917CwsIaeoM9PT3x9PRsdRxCdGURsSGMMRfw9Z46fHUeHD2mcvzkEaZO64uHZ9NhPTabjb07M8k444O/Go1RgXJbNakDLPQb6prd1vR6PfNm9+K9D4sxqT7s2qcQGlmNj8nbJe0LIYToHtplC+D4+HgeeOABrFYrBQUFmM1mQkJCMJlMbbrvmDFjKC8vZ+3atZSWlhIfH8+yZcsahi4UFxc3muG3detWrFYrf//73xvdZ/78+SxYsKBNsQjRlcX2icDbt5QtX1Tgr/qBJZoP1peh6M4TEQS+Pnoqq6wUlmhY7IH46sLxV8Gq2VH0BSy4oa/TYT4dxdPHg2uu1PPNd1b8VC8+2JTPooXxsuyYEEKIZlO0zjaYohMoKipqtOxYR1EUhcjISPLz8zvdmBZ3k9w41ta8WK1Wtmw6QU1tOJ6K859zrZqdWq2Yq0b5t3hMb3vbszOds3mh6BQFzXCGWTc47lWWz4xjkhfnJDeOSV6ck9w45uq8GAyGZk9G07Xkxvfffz9ffvlliyaYWSwWtm3bxv3339+SpoQQHUCv1zNjTgrTZnhj9D5Lue085bYaquxmym01lNtK0HueYcK1ehYvTnJ7kQswclwSGOuXFLSbo0jbfdLNEQkhhOgqWjR0Yfz48bzxxhu8/vrrpKamMnjwYBISEggLC8PDwwOA2tpaCgsLycrK4uDBg+zbtw+9Xs+sWbM65A0IIVrOx9eb62YOcHcYzTZjdj/eWZODvxrIiSwTMQmlBIcFuDssIYQQnVyLCt3Zs2czZcoUvvjiC7Zv386OHTsaXrs4bs5mszUci4mJYcGCBUyYMAFvb5lEIoRoHVVVmTElmC1ba/HRGdn8WRmLFvii17fLNAMhhBDdVIv/l/Dy8mL69OlMnz6dwsJC0tPTOXPmDBUVFQD4+fkRHR1NUlISYWFh7R6wEKJnCggxMSjpPBkZBvxVfzZ+kM7c+V2nV1oIIYTrtak7JCwsTIpZIYTLpKQmcDr3KLa6KHTWSPZ9nUnq2D7uDksIIUQn1aLJaEII4W7XzUym3HYenaJwMieAwrMl7g5JCCFEJyWFrhCiS1FVlZnTwqm01+Gl0/PptkqXLAfYnmw2G4VnS0g/mMOxA9lkHT9DVWW1u8MSQohuR2ZyCCG6HFOAL6kpJRw5asBfb+LD9ZncuLBzj9fNPJLH90fKqbH64aXzwajogB831TnyvZkqeyU2rZyIADNXXh2Pj69M4hVCiLaQQlcI0SUlDY7l1OmjmKujMNoj+Gb7CW5YHOnusBqxWq18/UUGucV++Ku+GPHF+MPGbpqmYcaOhoYeHXpFh4/OCIRQXQGbN9VgJ4crRwQQ2yfCre9DCCG6Kil0hRBd1nUzB7B6dQZ+aii5+UGcSs/D08/9WwTbbDa2fXKCorJgfHWR+Ktg1zQq7OUEeFXSO96HhKRIPH08Gs6/cK6c9OOF5BZqoAXjrTMAERzYq/H1nnQmXxNKSFSge9+YEEJ0MVLoCiG6tNmzovlwQxl+qhfrNuRzw5xgvLw93RbPwe+yOJjpgb8aha8OzJoNm1LEVaODiIyLc3iNqqqERAU2FLIWi4VvtmWSU+yLv+qHrxLGl1/Z0RuOMm1WXwwGgyvfkhBCdFkyGU0I0aX5+HozapgNs2bHpPrw/gdnW7RNeXupLKvi3XdOkJ0ViL/qg1WzY1bOMnmqBwsW9iMyrvlLMRoMBq6e0p+fLYkhPLKIClsNRkWHzhrFmv8rJOv4mQ58J0II0X1IoSuE6PJ69+9Fr8hibJqGSQ3i/XWZLm1/z450Nn1chY8uHEVRKLed54orrcxbMABTgG+b7n3F1X258cYQVI8zmDU7/qoP36d5sWn9kUY7UQohhGhKCl0hRLdw5fhkAoLOA+ClRLDh/SMd3mZ5aSXvvJNOwdkwfHRGqu0WgkMKuGlJb6Lj228zHYPBwLQ5KVwx0kyZrQK9okMzR/Puu6cpv1DRbu0IIUR3I4WuEKLbWHLr1ZiVfAAUSzQfrOu4Ynf3lyf4eHM1vrr6grbCVsTUKZ6MmdSvw9qM6R3BogWR2PVnsWsaJn0QH22p5nja6Q5rUwghujIpdIUQ3crc+cnUUACAaovmvbVH2/X+ZecrWL06g8KC8IZe3NCwcyxZ0hf/YL92bcsRvV7P7HkDiIkrodpuwVfnwbHjfnyysX3fpxBCdAey6oIQoltRVZUFC/vx3tqjGLUojFoU77yTztw5cXh6ebTp3js/O86ZomD81FAAKu2FTJ8aiSkwuT1Cb5Hho3sTG3eBLdvK8debMFdHsXp1BnNm98Lbx8vl8bSHkqIyTp44R2mZldo6qLMqAOh1GnoV/H11RET6EpMQ2rA0mxBCXIoUukKIbmneggF8sO4Iqi0aX10Y760v5sphGr3792rxvU58n8OeI+CvRuCtgyq7mcToUmZendQBkTdfSFQgCxf48uH6TIz2CPzUUNZ/WMq41AvEJUe5NbbmyD9dyPffF3O+0hO94vfD2sE/jm1uKGVtoNmgtKT+cfRwNZX28+h1FUQGa6SOisXHJLvICSGakkJXCNFtzZmfwldbj1N0PgyT6sPB7+3sP3iMyZOiCQgxXfb6tG9PciQLfHVB+KsKNk2jjnPMmBaDr3/7TTZrC4PBwPwF/dn1+XHOFoViUr3Ye8DG6ZzjXD2548YLt1bh2RJ2786nvCYAk+oDRGH6yR4f1XYLFq0WsKKjfpk4DR2apqJTPPDUeWBQdJhUb8CbshL49ONaquzniAioYcw1CV22R1sI0f6k0BVCdGtXT+5H7skCvtxtxl81oSeSLz6zUmvPIDbMRt/+oYRHBwNQVV5N5rECsnJrqTYHYFKDG4qwMtsFxgw3ktiv8xWPAGMm9SM3q4Cvvq3FpPpQVhLB2jXHmTu3j7tDAyBt90mOnFTw1QWiU6IxqfXbIFfYazCqZcRF6unTP4yg0NBL3sdms5GXVciJExcoLjegKgF46wz4q4HUVATy8cZqrFouQ/oZ6T8s3jVvTgjRaUmhK4To9mJ6R7A43saOrenkXwjEV+eJQQ3lwnn4bifYtAtoaOgVHRCEHjCpYNM0quznGdRHx+ArEt39Ni4rJjGCeZF1fPBBNj66cLyIYM26AmZeayYg3Mfl8ZjrLOz4PIOCUv9GPzSU26rx9bzAqBHhRMRGAc0fZqGqKnF9I4nrGwnUF75HD+Rw/GQtdnsI3joDHkoYmemw/9hZgnwvcPXEBHx8ZWiDED2RFLpCiB5BVVXGT+2PzWZjz45MTp1VUJQAvBUDqqIA9ROfajUbdfZK/D2rGD0qnPDoztEj2lyeXh4sWpzM5x8fpaw8An/Vl8+/qMXDM5upM5NRVfXyN2mjksIyvvzqDGZLKN66qEY/NAxNVklJTaAlxe2lqKrKoBEJDBpRv3Xynq9OknXOA19dACbVG2uNN5s31WAjl1HD/IjvAmOXHamuqqEgt4Sic5VcKLNRXQc2u4LNrkP74bOroKHq7BgNdryMCr5eOgKDPIntHSZjmEWPJYWuEKJHUVWVK8cnc+UPz6vKq7lQXIHNYsMvyAf/oABUNditMbaHSdMGkH3iLDv32fFXfbHXRfHOmnyGJlsYmJrQIW2mH8xh3+FaPHUh6JUo9Lr6HxwUpZBxY0KJiO3YHxoMBgNjJvVjDHDuzHm+3nUOizX0h0lu4Rw8oLFz32niw2q5cnxvDAZDh8bTWrknCziZcYHiMqi1emHQ+fzwHnx+eIDhh4fDRUItUGeBuio4XwyZ6Waq7VVYtFpUpQZ/bwtxvbzpkxKNh6fRZe9LCHeQQlcI0aP5mLy7bW9XfHIUMX1sfLLpJHU1IfirvmRlaBw8kcFVo/yI6R3R5jYsFgvfbMskp9gXf9WEr1o/ya/CVkOgbwnTJyXi7dO/ze20VHh0MDfcGPxjfEU++OtN+Kv+XDjvz7r/K8HTUMwNN+ih4zu5nbLZbORkFHA8vYySSiN6xR9vnScQiQfg8ZP/pa2anVrNgs1eh6KYURU7Op0dHRoaoFHfw2vX9GgY0KHHqDPioag/FMoGwA9bLWRlQkZGFVX2YnRUEuBjISHOl94pURiNUvyK7kPRNE1zdxCXsmXLFjZu3EhpaSlxcXEsXbqUPn2c9wp88803rFmzhqKiIiIiIrjpppsYPnx4i9osKirCYrG0NfTLUhSFyMhI8vPz6eR/DC4nuXFM8uKc5Maxi3n5bsd+vtxdjUkNAsCuaVTaLzAgQWPwFfEtGtJgs9k4lpbDkYw6FC0EL52+4Z4V9lKSellIHdvbJcMkWiLzaB57v6/CoIRiVOq7Qq2anRp7CYmRNoaPScTo0bG9vDabjVPH80nPKONCtScGxYSXrnGbdk2jyl4LVOHrWUdUmJH4PqEEhvq1KqfFBaXknDpPUXEdZVU6zHYfvHQ+DTn4KYtmp9pejae+hmA/K32SAomKD+10f5Y2m43iglIK8sooKamjvFqj1qJis6vYUQE9OvToFBVV0aFDQUH54Vewo2EHNDTsmh0bduyaFQ0rCjYUbOhVG14GO75eCv7+BoKDvQmNDqRPUm/5d+a/uPrfX4PBQOhlJq5e1KkL3V27dvHiiy9y++2307dvXz766CO+/fZbnnvuOfz9/Zucf+LECR577DGWLFnC8OHD2blzJx9++CFPPfUUsbGxzW5XCl33k9w4JnlxTnLj2H/nZd+uTI5le+Cv/riLW7Xdgl27QESgjagoH2L6hDZaout8YSm5p85TcK6O85VGDIp/o+KsTrNho4grU/0bJol1ZpVlVXy17TSl1YH4qT++z1rNisV+gegQK8NG9sIU2Pad7iwWCxmHznAyu5qyWi88dH54Ko2/TLVpGpX2aoxqBb1CFQYOj8YU4Nvmti/lYk/yyawyzpfrsNh98Nb5YHBQ/NZqVupslXgZqgkN1NErxo+Y3mEdOvTj4pjkwoJKLpRbqarVYbbqseOJqhjxUow/jK13PbNmx6xZsGlmFCzodRY89Da8PcHkqxIU5EVwhImA4Nb9YNLeLBYL1eW1VFfWUFVlpqbaQm2Nhdo6G3VmDYtFw2xVsNoVNA0u/vOpoYBG/bcFmoKiaCgK6NBQdKBTNHQK6HQaqqLg6WkkdWS4S3aI7DaF7rJly+jduze//OUvAbDb7dx1111cf/31zJkzp8n5//jHP6irq+Phhx9uOPaHP/yBuLg47rjjjma3K4Wu+0luHJO8OCe5ccxZXo7uzybtuBUvXdAPq038SNM0rNSfq0CT1+FicVZKr+BarhyX2CV3KrPb7WQcOse+I5UYdUF4KD8WJfU93jXolEoCfayEhRiJjDYRGh2EXt901F9VZTXn8i5QXFjFufMWKmqMaPjgrfNskj+rZqfKXo2nWkFMhI6Bw2I6xfAZi8VC1tF8Tp2upLTGiM3mg6/OE52DgtKmaVRrddi1GlTFgofeis8PhZ6Hpx4vTz3e3gY8vIz1nyebDbtNw2q1UV1loarCTHWtjdo6jRqzQp3VgB0jOowYdEY8lcsXiJqmUavZsGh1oNWh6iwYVBtGg4anUcHLQ8XHR4+npx69QUWvV9AbVRQUbBYbFpsdm8WO2WKjptpCdbWNWrNGnQUsVgWLXcWmGVAwoioGPBsmrjaPTdOo06xYNcsPPcX1vcV6nQ29aseggqrT0KkKehX0OgVVD2gKmqZh10Cz1/c62+xgtWpYbQpW2w8TETUddk3BrunQUOGHhw5dQ0+2Hp3Dv78dZeCQKhL6RXd4Oy0pdDvtGF2r1UpWVlajglan0zFo0CDS09MdXpOens6MGTMaHRsyZAh79uxxeL7FYmlU0CqKgpeXV8PvO9rFNlzRVlcjuXFM8uKc5MYxZ3lJSU0gJRWqKqrZvzuH3EIFu+aLp84To6LDQOPza+xWzFotRrWS2HCFwSNi8fHr/EuuXYper2fC9an0H1ZAbU0d3+3MIrdQQSEAH52xYVMKSw2cya1/2LUKrGjYsKNpGjpFhx7lh2Lix8lifj+p0+qHA1TibagkPtpIytBYPH0634RHo9FIv6Fx9B+mEBERQUFBAZXlVZw4fJbcs3VU1Hqg4I2XzgO9osNP8QQ86y+2g7kaiqud3d15768RMDqoaxvGJGt16JQ6PPRWfD01Av0NhEX4Ehkb3OZtvVvCZrNRfqEKc5XGqexzlFdYqKqBOqsOq92AhhFVMWJQ9HgoKqqi4K00TBtszF7/sFH/aEnXmv6HBz8uFtMs9h9+gLVpth+Gath+GMBh+2G4hh0FDZSL63jU9+DWN6Wh8UOPLwqapqCh++GV+kAUdPj4+Xa6f4M7baFbXl6O3W4nICCg0fGAgADOnj3r8JrS0tImQxr8/f0pLS11eP769etZt25dw/OEhASeeuqpZv+U0F4iIto+IaS7ktw4JnlxTnLjmNO8REKfpN4NT202GwW5RZRdqERRfugRjgnFP+jyO8l1VRdzE58Q13DsWFomBw/mU1Bix2z1Qq944Kno0SkKRhRA16TIuFiY2bVaPA11hAfrSUoKZcCQvh0+9rcjREREQETjzwfUr4988thpMjOKKCqpo6pWwWyt7/3UKQZ06Br1Jtq1i5Pl6n+1YsemWbFrNsCKTrHiabDh560QGGAkItxEr/hQwqKCO8VX/438sIP40CsvvXFMVXkVeafPUXC2jJILNVTXWKmpszf0FtvsKnbth89RQ8V68fcXaT/5VQPs9cWoYv9huICGqtPQq2A0gIdRh6dRxdNTj7eXAV+TJ76+Hvj5++AX6IOPr3fny6cLdNpC1xXmzp3bqAf44k8hRUVFWK3WDm9fUX78qVm+am1McuOY5MU5yY1jrcmLzgMCI37cYKK6rorq/KqOCtFtLpWbgHAfrp7ceOKzuc5CUf4FaqrqqK21YrPa8fTUY/Q0YArwJiAkwGEhcb6kuEPfR3trzmcmINyHEc3YhMRms7WyuLJRWFjYius6Vkv+PplCvDCFdIbtqDWqqiupqq7ssBZc/e+vXq/v+kMXTCYTOp2uSW9saWlpk17eiwICAigrK2t0rKyszOn5BoPB6WB6V/5HqWma/MfshOTGMcmLc5IbxyQvzjU3Nwajnqi4S//n2p1y3B6fGZ1O161ycpH8fXKsM+bFdSOUW0iv15OYmMjhw4cbjtntdg4fPkxSUpLDa5KSkjh06FCjYwcPHqRv374dGqsQQgghhOh8Om2hCzBjxgw+//xztm/fTl5eHqtWraKuro7x48cD8OKLL7J69eqG86dNm8b333/Pxo0bOXPmDGvXruXkyZNMnTrVTe9ACCGEEEK4S6cdugAwZswYysvLWbt2LaWlpcTHx7Ns2bKGoQjFxcWNZvclJydz77338u677/LOO+8QGRnJgw8+2KI1dIUQQgghRPfQqQtdgKlTpzrtkX388cebHBs9ejSjR4/u4KiEEEIIIURn16mHLgghhBBCCNFaUugKIYQQQohuqdMPXXAHR9s7dqf2uhLJjWOSF+ckN45JXpyT3DgmeXFOcuOYq/LSknYUrbMteCaEEEIIIUQ7kKELblRTU8NDDz1ETU2Nu0PpdCQ3jklenJPcOCZ5cU5y45jkxTnJjWOdOS9S6LqRpmmcOnWq0+0i0hlIbhyTvDgnuXFM8uKc5MYxyYtzkhvHOnNepNAVQgghhBDdkhS6QgghhBCiW5JC140MBgPz58/HYDC4O5ROR3LjmOTFOcmNY5IX5yQ3jklenJPcONaZ8yKrLgghhBBCiG5JenSFEEIIIUS3JIWuEEIIIYTolqTQFUIIIYQQ3ZIUukIIIYQQoluSQlcIIYQQQnRLUugKIYQQQohuSQpdIYQQQgjRLUmhK4QQQgghuiUpdIUQQgghRLckha4QQgghhOiWpNAVQnQaX331FbNmzSI6OhpFUXj99dcvef6TTz6Joijcc889rgnwEpoTe3x8PIqiNHlMnz6908Vqs9l49NFHSUhIwNPTk4SEBP74xz9itVpdGmtLNOd9vfTSSwwePBiTyYTJZGL06NF89NFHrg9WCOESUugKITqNyspKBg4cyD//+U+8vLwuee63337Lf/7zHwYPHuyi6C6tObHv2bOH/Pz8hsf+/ftRFIUFCxZ0ulifeuopXnrpJZ5//nmOHz/OP//5T1566SWefPJJl8baEs15X7169eKpp55i//797N27l4kTJzJnzhwOHjzo4miFEC6hCSFEJ+Tj46O99tprDl8rLS3VEhMTtS+++EK75pprtLvvvrtDYvj5z3+uhYaGapWVlS267lKx/9Rf/vIXzd/fX6uurm5lhG3nLNbp06drP//5zxsd+/nPf65Nnz7dRZE1tXfvXg3QVq5cedlzm/tnoGmaFhgYqP2///f/2hidEKIzkh5dIUSXc8cddzB//nwmTJjg9JyLwwLi4uKora11eM7FoQSOvo7fs2cPb775Jg8//DA+Pj7tFvtFmqbxyiuv8LOf/cxh76OjIQ4/fVxuWEdbjRs3jm3btnH8+HEAjh49yhdffMG0adOcxtraXDdXamoqc+bM4dFHH6WysrLV97nIZrPx7rvvUllZyZgxYxyec/z4cX7zm98wcOBA/P39MRqNREVFMX36dF555RXq6uraHIcQouPo3R2AEEK0xMqVK8nMzOStt95q1vk5OTk899xzPPzwwy1q5w9/+AMmk4m77rqrNWFe1tatWzl16hS33377Jc977LHHHB4fOnRoB0T1o4ceeoiKigoGDBiAqqpYrVb+8Ic/8Otf/9rpNa3NdUs88sgjjBo1iueff55ly5a16h6HDh1i9OjR1NbW4uvry/r16xk0aFCT8/70pz/xxBNPYLfbGT16NLfccgu+vr6cO3eO7du3c9ttt/Gvf/2LvXv3tvVtCSE6iBS6Qogu48SJEyxbtoydO3diMBgue35gYCCKorBixQpuu+02QkJCmtVOeno6n332Gbfddttlxwq31sqVKxk5ciRDhgy55HmPP/54h7R/OWvWrOGNN95g9erVpKSkkJaWxm9/+1sSEhL45S9/2eT81ua6pa644gr69evHv//9bx5++GF0upZ/MZmcnExaWhplZWWsW7eOW265he3btzNw4MCGc/7617/y2GOPERMTw//93/8xatSoJvfZtGkTzz77bJvejxCiY8nQBSFEl/HNN99QXFxMSkoKer0evV7Pl19+ycsvv4xer2/yNbK3tzePPvooZWVlPPHEE81u59VXX0XTNBYuXNjebwGAwsJCPvzww8v25rrTgw8+yO9//3sWLVrEoEGDuPnmm3nggQecTkZrba53797N/PnziYiIwGg0EhMTw69+9SvOnj3r9JpFixaRk5PD1q1bW/y+AIxGI3369CE1NZUnn3ySoUOH8o9//KPh9ezsbB5//HEMBgMff/yxwyIXYMaMGWzZsqVVMQghXEMKXSFElzFnzhwOHTpEWlpaw2PEiBEsWrSItLQ0jEZjk2vuvvtuevfuzb///W8yMjKa1c5nn32GqqpceeWV7f0WAHj99dfx8PBg8eLFHXL/9lBdXY2qqo2OqaqK3W53ek1Lc/3qq68yduxYNm/ezIQJE7jvvvsYMWIEq1atYsSIEeTk5Di8buzYsQCtLnT/m91ub/RD0muvvYbFYmHevHmNenkd8fDwaJcYhBAdQ4YuCCE6jcrKSjIzM4H64iMnJ4e0tDSCgoKIjY0lICCAgICARtf4+PgQFBTktCAxGAysWLGCG2+8kYceeoj333//kjFUVVWRlpZG//79WzQJ7XKxX6RpGqtWrWLRokX4+vpe9r6Ohi7Ex8dz6623Nju21sQ6c+ZMVqxYQUJCAikpKRw4cIC///3v/PznP3d635bkOj09nTvvvJP4+Hi+/PJLoqOjG177/PPPmTJlCr/97W9Zv359k2tHjhwJ1K+b29L39fDDDzN9+nRiYmKoqKhg9erVbN++vdFaujt37gRg0qRJzpMohOga3LzqgxBCNNi2bZsGNHnccsstTq9xtrwYoEVHRzc8Hz16tAZoO3bsaDgWFxenAZrFYmk4duLECQ3QJk+e3CGxf/HFFxqg7d69+5L3c3Svi49rrrmmRbG1Jtby8nLtt7/9rRYbG6t5enpqCQkJ2iOPPKLV1NQ4jLWlub7vvvs0QNu0aZPDGOfMmaOpqqqVl5c7fN3T01MLDw9v8fu65ZZbtNjYWM1oNGqhoaHapEmTtC1btjS6T//+/TVA27x5s+MECiG6DEXTNK1DK2khhHADRVGIjo4mLy8PqB/fO2bMGEaNGsW3334L1PeMnj59GovFgl6vb3TeggULWLNmjVvjh/oe4M6uNbm+8sor2b17Nw8++CDe3t5N7rl161Z27drF3r17SU1NbfJ6dHQ0586d65Cd2gYMGMCxY8fYvHkzU6dObff7CyFcR4YuCCF6hNGjRzN//nzWrVvHmjVrnE40u7jKgrP1YMXlNSfX58+fB+Bvf/vbJe/lbL3cmpqaDlsRIzIykmPHjnHmzJkOub8QwnVkMpoQosd48sknMRgMPPLII5jNZofnhIWFAT8WYqJ1Lpdrf39/AMrKytA0zenjmmuuaXKt3W6ntLS04c+qvY0bNw6oHysshOjapNAVQvQYffr04de//jWnTp3ihRdecHhOZGQkoaGhnDhxwsXRdS+Xy/XFFS127NjR4nufOHECTdM6bNOMX/ziFxgMBt577z2OHj16yXNlZzQhOjcpdIUQPcr//u//EhAQwPLlyx1+La4oCldffTXFxcUNM/g7u1tvvdUl2wK31KVyfc8992AwGLj//vtJT09vcq3ZbHZaBF8c93upLaDbIj4+nscffxyz2cz06dOd7ny2ZcsWrr/++g6JQQjRPmSMrhCiRwkKCmLZsmX8z//8j9Nz5s2bx3vvvccnn3xCnz59XBhd61xc2/bihLrO4lK57tevH6+++ipLly4lJSWFqVOnkpSUhMViIScnhx07dhAaGsrx48ebXPvpp5+iqiqzZ8/usNiXLVuG1WrliSeeYOTIkYwZM4YRI0Y0bAH81VdfkZGRwYgRIzosBiFE28mqC0KIbum/VwL4qbq6Ovr160d2djZAo1UXoL43MSYmhvj4eHbv3u2qkBtpyaoLw4YN4+TJk5w+fZrAwMCODq2JtuT60KFDPPvss2zbto2CggJ8fHyIiopi7NixLFy4kIkTJza6X1lZGREREVx33XV88MEHHfm2ADh27Bgvv/wy27ZtIycnh9raWoKDgxk6dCjz58/nZz/7mWwaIUQnJoWuEEI48OSTT7Js2TL279/PsGHD3B2OU6WlpQQHB/O73/2Op59+2t3hdLgXXniBe++9lx07djRMGhNCCGek0BVCCAdqa2tJTk5m8ODBbNy40d3hOLVx40ZuvPFGsrOziYiIcHc4HaqmpobevXszZswY1q1b5+5whBBdQOca0CWEEJ2Ep6cnb775Jtu2baOqqqpF2wG70syZM3vMmr/Z2dnccccdbdr+WAjRs0iPrhBCCCGE6JZkeTEhhBBCCNEtSaErhBBCCCG6JSl0hRBCCCFEtySFrhBCCCGE6Ja61aoLa9eubbLkTFRUFM8995x7AhJCCCGEEG7TrQpdgJiYGB599NGG5zpdyzutL1y4gNVqbc+wnAoNDaWoqMglbXU1khvHJC/OSW4ck7w4J7lxTPLinOTGMVfmRa/XN3sXyG5X6Op0OgICAtp0D6vVisViaZ+ALuHiFp9Wq7VZ23z2JJIbxyQvzkluHJO8OCe5cUzy4pzkxrHOnJduV+gWFBTwq1/9CoPBQFJSEkuWLCEkJMThuRaLpVFBqygKXl5eDb/vaBfbcEVbXY3kxjHJi3OSG8ckL85JbhyTvDgnuXGsM+elW20YceDAAWpra4mKiuLChQusW7eOkpISnn322YYC9qf+e0xvQkICTz31lCtDFkIIIYQQHaRbFbr/raqqil//+tfccsstTJw4scnrznp0i4qKXDJGV1EUIiIiKCgo6HRd/e4muXFM8uKc5MYxyYtzkhvHJC/OSW4cc3Ve9Ho9oaGhzTu3g2NxKx8fH6KioigoKHD4usFgwGAwOHzNlR9gTdPkL4wTkhvHJC/OSW4ck7w4J7lxTPLinOTGsc6Yl269jm5tbS0FBQVtnpwmhBBCCCG6nm7Vo/vGG28wYsQIQkJCuHDhAmvXrkWn0zFu3Dh3h9ajVVfZyD1l5sJ5Gz6+OqJijQSHdquPnhBCCCE6oW5VbZSUlPDPf/6TiooKTCYT/fr1Y/ny5ZhMJneH1mMVFVjYu6sK6w9DoYuA7EwzA4Z60jvZ062xCSGEEKJ761aF7n333efuEMRPnC+ysvurKjQNijQLGfYawhUDvXVeHE2rRadTSOjr4e4whRBCCNFNdesxusJ96mrt7Pm6vsg9Za/lc/NZ/Ap3c6Qmm722CgCOpNVQUW5zc6RCCCGE6K66VY+u6DwO7q/BUqdRqlk5VX6CZw69QlhdKXU6A/8YsJjcsKuIwYN931ZxzWS/TrnItBBCCCG6NunRFe3uQrGVglwLmqaxu66Ah46+Rvjseej+uRqve5Zx/+mNZFZmYtHsVFywU1jQ8WsWCyGEEKLnkUJXtCtN00jbXw1AulbDouOrCfvZbegmz0bx9kUZlIrX7/7EnelrOG6vP+/Q99Wdbt09IYQQQnR9UuiKdnW+0ErlBTtWTaPqwiFGD4xFN7Lx8m5KdBx9rp+CV+F3WDWNmjKNkiIZqyuEEEKI9iWFrmhXR47UAJBur+am/M/QzbvF4XnKhOnMqTnISa2+V/fYiRqXxSiEEEKInkEKXdFuKsptlBfZ0TQN9XwaiVMmo3h6OTxXURTir5uCWnIUgPP5VqwWGb4ghBBCiPYjha5oN+nHawHI0eqYWrEfZeykS18w9EpGV31PqWZFpynknja7IEohhBBC9BRS6Ip2YbNpnPmhUC2vOk3/caNQdOolr1F0OlJHDiDXcgGAExkyfEEIIYQQ7UcKXdEu8s+YUewKlZqNkQVfoRs9vlnX6UddTXThdwCYyzXMdfYOjFIIIYQQPYkUuqJdHPth2EK2rZxxg3qhGIzNuk7x9mVcQBXnNQsKCnl5MnxBCCGEEO1DCl3RZnV1dmou1PfEBp/bg+e4a1t0fcKVIyk21w9fSD9Z2+7xCSGEEKJnkkJXtFlujhkFhWLNwjUe+SjBoS26XkkeROiFwwDUXbBjs8rqC0IIIYRoOyl0RZtl/NALW2Quoe+oES2+XtHrSTWVUanZ0KHj3DlLe4cohBBCiB5ICl3RJuY6O5ay+h7YiOL9KENGtuo+/Yf0p8hWCUBGtgxfEEIIIUTbSaEr2iQvt37YwnnNwpUB1c2ehPbfdCnD8KjIBuBCgUxIE0IIIUTbSaEr2iS9YdjCBfoMG9jq+ygeHiQoZwFQrXpqa2WZMSGEEEK0jRS6otUsZjvm0vphC+HnD6AMHNam+w2LN1Gs1Y/PlWXGhBBCCNFWUuiKVrs4bKFEs3CFf2Wrhy1cFDJwEOU/LDN28nR1e4QohBBCiB5MCl3RaheHLRRaSklqw7CFBlExBFRlA1B53tr2+wkhhBCiR5NCV7SKzaZR+8MmEWHnD7Z52AKAoigkepUDYLAbqKmWYlcIIYQQrSeFrmiV4kIrOnRUazaGm8rbPGzhopTewZT8ME43K7uiXe4phBBCiJ5JCl3RKumnagAosFXTf0i/drtvYL/+VFrqe3WPnChpt/sKIYQQoueRQle0yvn8+lURjBVZqClD2+/GIeF4V+cBUHymqv3uK4QQQogeRwpd0WLVVXZUqx67ptFHy0Hx9Gq3eyuKQoyxvidXsajYrFq73VsIIYQQPYsUuqLFsnPqACjEQmpiYLvfP6WXL9WaDR0KRcWWdr+/EEIIIXoGKXRFi53Krh+fW11TSOCQtq+28N/C+ydRaqtv42RuTbvfXwghhBA9gxS6okVsNg1r/VwxwqoyUCKi270NJSoGpbYQgOJ8KXSFEEII0TrdttD94IMPWLBgAa+//rq7Q+lWLi4rVqXZGBpc1yFtKDodQVoxANbqbvsRFUIIIUQH65ZVRGZmJlu3biUuLs7doXQ7x7Pqe1gLbVX0HZTcYe0k+tWiaRoGDNTV2jusHSGEEEJ0X3pXNFJZWdmm6729vdHpmleT19bW8sILL/CrX/2K999/v03tiqYu5JtR0eNZfhI1eWyHtZOcGMLhk1YCMZCXb6Z3gmeHtSWEEEKI7sklhe4vf/nLNl3/6KOPMnDgwGadu2rVKoYNG8bgwYMvW+haLBYslh9n9SuKgpeXV8PvO9rFNlzRVnuoqrSh2uqXFUtSz6IzenRYW759k6k+XkagMYjsMzX0SWy/Jcy6sq72mXElyY1jkhfnJDeOSV6ck9w41pnz4pJCF2DkyJEtHkpQV1fHxo0bm33+119/zalTp3jyySebdf769etZt25dw/OEhASeeuopQkNDWxRnW0VERLi0vdb6Zvc5AAo1M7NSexMZGdlhbWkREXj833owBlF1wdqhbXVFXeUz4w6SG8ckL85JbhyTvDgnuXGsM+bFZYXulVdeybhx41p0TUVFRbML3eLiYl5//XX++Mc/YjQam3XN3LlzmTFjRsPziz+JFBUVYbVaWxRrayiKQkREBAUFBWha598Y4fChYkClpuYcSmIf8vPzO6wtRVEIM1ZiByzVdGhbXUlX+8y4kuTGMcmLc5IbxyQvzkluHHN1XvR6fbM7JV1S6N5yyy0kJia2+DpPT09uueUWoqKiLntuVlYWZWVlPPTQQw3H7HY7x44dY8uWLaxevbrJOF+DwYDBYHB4P1d+gDVN6/R/YWw2DWuFgg6IqMmEwMEdHnNShMrxUjBioLbWhodHt5w72Spd4TPjLpIbxyQvzkluHJO8OCe5cawz5sUlhe60adNadZ3BYGj2tYMGDeKZZ55pdOxf//oXUVFRzJ49u9mT2YRjhQWWhmXFhofZXNLmwAEJfPe1FZOiJ/+cmfhYmZAmhBBCiOZzefVXV1fHQw89xKefftqu9/Xy8iI2NrbRw8PDAz8/P2JjY9u1rZ6oYVkxayW9Bw9wSZsB/QdQZa1fseN0XpVL2hRCCCFE9+HyQtfDw4PCwsJOOTNPOFd6rn51Cu/Kk+j69HNJm6opANVcAsCFwlqXtCmEEEKI7sNlk9F+aujQoXz//fdMnjy5Q9t5/PHHO/T+PUVVpQ39D8uKJRsKUVTVZW0HqhUAWOvc8lEVQgghRBfmloGr8+bNIz8/nxdeeIHjx49TUlJCZWVlk4foHE5m1/emntPMDO3n2qVDYoPqd0Uzakasls41wF0IIYQQnZtbusl+97vfAZCXl8fOnTudnrdmzRpXhSQu4XR2DaDHXJOP7+BhLm07qXcYmQdt+CgqhefNREV03CYVQgghhOhe3FLozps3T8bodhE2q4a9Sle/rFhdNorfUJe2b0qIp+JACT56P7JPV0mhK4QQQohmc0uhu2DBAnc0K1qh4CfLil3hhg1PFG9fFPNJ0PtRfK7G9QEIIYQQosvqFDN8zpw5wzfffENpaSlRUVGMHz8eb29vd4clgBNZ1QAUWSuIvzLFLTGYlFIgFnON6ybBCSGEEKLrc1mhu2XLFjZv3syf//xnTCZTw/G9e/fyj3/8o9GWu5s3b2b58uWNzhOup2kaZees6NHjXZGFEjveLXH08q3jvA0Mmgd2m4ZOlWEvQgghhLg8l626sHfvXsLDwxsVrzabjX//+9/odDruuusunnnmGZYsWUJxcTHvv/++q0ITTlSU2dDb9dg0jf7eRShu2l2uT7QvtZodnaJQUmq9/AVCCCGEELiw0M3Ly6Nv376Njh05coTy8nKmT5/O+PHjiYmJYfbs2YwePZoDBw64KjThxIms+mXFCrRahg3o5bY4QhJjKbfXAZB9RjaOEEIIIUTzuKzQraioIDg4uNGxQ4cOAXDFFVc0Op6cnExxcbGrQhNOnMmpn/xlr8rFc5BrlxX7KSU0Es1SCkBRvqyvLIQQQojmcVmhGxAQQGlpaaNjx48fx8PDg7i4uEbH9Xo9en2nmCfXY9XV2tH9sBtZvO00iqf7JgcqOh2etjIAqirsbotDCCGEEF2LywrdxMREvvzyS2pq6nsJc3NzyczMZMiQIaj/taXsmTNnmvT+Ctc6dboOBYXzmoUrert/UmCIR/3qD4rV6OZIhBBCCNFVuKzb9MYbb+SRRx7h3nvvJSYmhqysLADmzp3b5Nw9e/aQkuKepaxEvZNZ1YBKRW0hwcOHuzscYoJ1ZJWBUTFgMdsxGN0zMU4IIYQQXYfLqoXY2Fj+93//l8TERC5cuEDfvn155JFHSExMbHTekSNHMBqNjB492lWhif9is2lYyuuX8AqrOYkSFOrmiCAhMYJKzQZAQZHFzdEIIYQQoitw6UDY5ORkHnnkkUuek5KSwrPPPuuiiIQjBQUWVHRUazZSwzvHmFiv2Diq9hTjq/cjJ7ecmGj3F99CCCGE6Nxc1qO7YcMGzpw546rmRBscy6gCoNBaTmLqQDdHU0/x9EKx1E9IKymUJcaEEEIIcXku69HdsGEDb7/9NqGhoQwfPpzhw4eTkpKCwWBwVQiiGex2jcpCOyoqvhUZKLGT3R1SAx+lAoBa2QpYCCGEEM3gskJ35cqVZGRkkJaWxv79+/nkk08wGo2kpKSQmprKsGHDCAkJcVU4wolzBRZUTaVWs5NqOo+idJ7tdiN8LVRYQdU80DStU8UmhBBCiM7HZYWuoigkJSWRlJTEggULKC0tZf/+/aSlpfH222+zatUqevXqxfDhw0lNTSUpKQmdm7ac7ckOH69fxuustZwbRnSOYQsXxUd58f1pDYOiUlNtx9tHenaFEEII4ZzbdmUICAhg4sSJTJw4EZvNxrFjxzhw4AD79u1jw4YNeHt7M2TIEKZPn95k62DRMew2jaoiGyoqAWVH0CVNdXdIjfSKj2LHaSsBGMgpqKVfbx93hySEEEKITqxTbD+mqioDBw5k4MCB3HzzzRQWFrJ//34OHDjAsWPHpNB1kTNnzaioVGs2RgVXoOg6V4+pGh5BnSUHjIHk55ZLoSuEEEKIS+oUhe5/CwsLY+rUqUyd2rl6FLu7I8erAB0FllJuHDvM3eE0oehU9LYyIJDSC1Z3hyOEEEKITs6thW5OTg4HDhygqKgIgNDQUIYNG0ZsbKw7w+qRbDaN2hJQgZDyoygJM9wdkkMmXSUAVnOn/BlNCCGEEJ2IW6oFi8XCf/7zH7766iuAhtnzmqaxevVqrrrqKu688070eilmXCUnpw4VHZWajdHhdZ12RYNIk5XiOjBoHmh2DUXXOeMUQgghhPu5pZJ8++23+eqrr5gyZQrXX3894eHhKIpCQUEBH3/8MVu3bsXX15dbb73VHeH1SEePVgEqhXXFJFw10t3hONW7lx8FmRp6RUdZuZWAAFmHWQghhBCOuWX9rh07dnDVVVfxy1/+kqioKFRVRafTERUVxW233ca4cePYsWOHO0Lrkaqr7Ngq6z8K0eUHUKLj3ByRcyHxMZRpZgBOn61xczRCCCGE6MzcUuharVaSkpKcvp6cnIzNZnNhRO5xvsjKsUMXyM8zU1lhQ9M0t8Rx6GgVCgr59jom9vF2SwzNpQSHYrWUA3DubKWboxFCCCFEZ+aWoQtDhgwhLS2NKVOmOHw9LS2NwYMHuzgq19tzuALzuYqG5wZPhdg4I3F9jPj4umZpL7tNIz+7flkxa3k6gTPGu6Td1lIUBaOtHAiloqz7/zAkhBBCiNZzSY9uZWVlo8eiRYsoKirimWee4dChQxQVFVFUVMTBgwf529/+RlFREYsWLXJFaG5VUFxAjr2WIs2CTdOw1GqcPFHHFx9XcHBfNRazvcNjOHWqDtWuUqXZuEJ/CsXP1OFttlWgoX73NptFxucKIYQQwjmX9Oj+8pe/dHg8JyeHPXv2OHztgQce4N133+3IsNxuUPV+TuQUUWb0ZbdvL6r9+9BP50svnQenM82cyTUz4kofQiM6pqDTNI0jh6tQUDlTd44F44Z3SDvtLTLATn4VeOCB3a6hk5UXhBBCCOGASwrdefPmuWS5qk8//ZRPP/20YV3eXr16MX/+fIYN63ybHwAMnzCaCTUVlJ4+hZZ7lPPfr+cT/4FsjZ3MSEMwAXV6vvmykn6DvOjb36Pdc/j/t3fn4VnVd/7/n+fcW3JnDyEkIQsJEJB9ERRERQQ3KIhVVNpOrYPtVKe9pr0ce12lna922s6orZfTGdpev4qjMFqhVEYUVEDRYVGgbLIKNCxhCUkg+3Jv5/z+CKQiiSwm9wl3Xg+v+yLn3Oecz/t+59zxfX/uz/mcUydDGAEXYdtmUPVGjOKHOvT4naUoN4Uj+yy8hklVdZge6erZFRERkQtFpdCdNWtWNJohPT2d2bNnk52djW3bfPjhhzzzzDM888wz5OXlRSWGy2HkF5GQnU3tyZPYtk1GOMTszeu4/Z3/j9/1uAl/zzEMMP18urOZM2fCXHt9Am53xxS7tm3zly0NgEFJuJrHxhZ12blzPy+tsIC6PQF6uOI4fLyRHukpTockIiIiXVBM3ZHh2muvPW/5wQcfZOXKlRw4cKDNQjcUChEKhVqXDcMgPj6+9efOdq6N1n89Xhg/iZ5jb+QnK5bw7l/+m/X9H+BadzoVx8N8uKqOCbckERf/5YdWHz8Wwm40CNkWhac+IO7Bv+tShe7nc3OelDQi4f3giqPiZD3GsNToBuegL8xLN6fctE15aZ9y0zblpX3KTdu6cl4cK3T37dvH+++/T3l5OQ0NDRdMrWUYBs8+++wVH9+yLD766CMCgUC7U5ktXbqUJUuWtC4XFhby9NNP07Nnzytu90pkZWVduPI7P+TrN2xj4PO/4reFX2NMfD7Uwgcr6/jKPQVkZMZfcXvhsMVb//spACWhan586yAyumCPN7STGyDO3gJk0tRgkJ2dHd2guoD28iLKTXuUl/YpN21TXtqn3LStK+bFsB2YvPWtt95i4cKFeL1ecnJy8Pvbnrv1//2//3fZxz569Chz584lFAoRFxfH97//fUaNavsiq/Z6dCsqKgiHw5fd9uUyDIOsrCzKysranUPXrj5DxR9+w3PJt3BN8iDSDDeYMPaGBLJ6e6+o3e3bGjj6aZAGO0LCsSV85QdzMLrY7ZYvlpu3Xvk/LNdQmuwA9z/Y9d5YneVSzpnuSrlpm/LSPuWmbcpL+5SbtkU7L263+5I7JR2pbpYtW8bAgQP50Y9+1G6Re6VycnJ49tlnaWxs5OOPP2bevHk89dRT5ObmXrCtx+PB42n7QqZonsC2bbffXkoaGf/0Y37y8u/4j/JqGnpeRy4+Nq6tZ9DwePoOuLyL1KpPhznyaQADg5KGw/zzjdeAy9Vl37Dt5aZ3mk1pLfjwEglbmK6u93VJZ/rCc6abU27apry0T7lpm/LSPuWmbV0xL47cGS0QCDBhwoQOL3KhpcrPysqiqKiI2bNn06dPH1asWNHh7UST4fbgf/h7/HNBA+bRt9hrNWJgsHdHM9s3N2FZl3ZSBYMW69bWY2BwyGpiZsNa3GNv7OToO0dRfjpB28I0DCqrQhffQURERLodRwrdwYMHc/To0ai0ZVnWecMTrlaGYeCZ8SCP3FjINfv/h48jNdi2zbFDQdatqSfQ/MU3lwgFLT58rw47ALV2mPQjbzFw1n1dcuD4pUgqyKfOCgBw5FiDw9GIiIhIV+RIofvwww+za9culi1bRn19fYcd99VXX2XPnj2Ul5dz9OjR1uUbb7w6ey3bYo6fxLQH7mTGvhf4IFRByLaoqYywankthw8GsCIX9u7WVIVZs7KO5lqbJtviyJnt3D++ACOrtwOvoGMYiclYoRoAKk6q0BUREZELOTJGNyMjg8mTJ7Nw4UJeeeUVvF4vpnlhzf3yyy9f1nFramqYN28eVVVV+P1+CgoKmDt3LsOGDeuo0LsEY8BQxnw3lV6//y9+n3s3xUn9yAh72Lmlib27muid5yUpyUUkYlNRHqaiLISBQaMdYU/tHh5PPIDnpu86/TK+NJ9dB2TRUN+1xgOJiIhI1+BIobto0SJef/110tPT6du3b4eN1f3ud6/+4u1SGdl55D/xU3760n+x+NAOPi64g2FmEv6AiyMHg+dvi0GJ1UTt6a087t1P4te+71DUHauHr5kQYEeubPYJERERiW2OFLqrVq1i1KhR/PM//3ObPblyaYyEROIf/RF/t/FDSt94nj/3vJ7SzNFkuvz4cWFhU2OHqWkqY8rR1dw+JAvznu9juFxOh94hcnsYHKqCOLxEIjaubjbzgoiIiHwxRwrdcDjMqFGjVOR2AMMwMK6fSP7I6/mntStp2vgH9tVEOONLxm1F6N18msJ+ubgf+ipGYds3zrhaFfbJYO8ZizjD5FRliJxe6tkVERGRv3Gk0B01ahR79+5lypQpTjQfkwxfHMbk6SRMns6ohno4fQpME3pmY/jinA6vU/jz86n/SxVxrgSOltaS0yvD6ZBERESkC3GkS/W+++7j+PHjvPDCC5SUlFBbW0t9ff0FD7kyRkIiRn5fjNzCmC1yAYw4P3aoFoDKU00ORyMiIiJdjSM9uv/0T/8EwOHDh1m1alW72y1atChKEcnVyk8dkE1jo8bnioiIyPkcKXS/+tWvXrU3KpCupUdcgGbA0MwLIiIi8jmOFLqzZs1yolmJQXk9XRyoAB8ewiELt0cXOIqIiEgLVQVyVetTmEmTHcEwDE5WBC++g4iIiHQbUSl0H3/8cbZu3XrZ+zU2NvL4449z8ODBTohKYoGvdx6NkZYL0Y6W1jocjYiIiHQlUSl0S0tLaWxsvOz9IpEIpaWlNDc3d0JUEgsMrw873FLgni7XeSIiIiJ/E7Uxui+//DKvvfbaZe1j23YnRSOxxG+0TEXX1KSROCIiIvI3USl0b7755i+1f1paWgdFIrGoZ3yQBgtMy+d0KCIiItKFRKXQffTRR6PRjHRT+Vle9p6AOMNDKGjh8apnV0RERDTrgsSA/KJsGu0IAKWnAg5HIyIiIl2FCl256nmycmgMt1zsePxItbPBiIiISJehQleueobLhWG1zLxw5rTm0hUREZEWKnQlJiQYLT26zc0uhyMRERGRrkKFrsSEnokhAFy2Zl4QERGRFip0JSYU5sQD4DM8BAKWw9GIiIhIV+B4oRsMBgmFQk6HIVe53v1602CHATh64vLvwiciIiKxJ2p3Rjtn9+7dbN68mU8//ZRjx44RDLZcPOTz+ejduzcDBgxgzJgxDB48ONqhyVXMld6T5nApCZ5kjh+pon9hotMhiYiIiMOiUuiGw2FWr17NW2+9RUVFBYmJiRQWFnLjjTeSmJiIbds0NDRQXl7O2rVrefvtt8nIyOArX/kKkydPxu2Oej0uVxnDMDCsOiCZqqqw0+GIiIhIFxCVCvL73/8+4XCYm2++mXHjxlFUVPSF25eUlPDRRx+xdOlS3nzzTebNmxeNMOUql+RqGbIQCHgcjkRERES6gqgUujNnzmTixIl4PJdWgBQVFVFUVMT999/PmjVrOjk6iRWZKTZVDeAmzulQREREpAuIysVoU6ZMueQi97PcbjdTpkzphIgkFhXmJQDgM9w0NUUcjkZERESc5visC20JBoNUVlY6HYZcZbL65lNrtczg8dfDdQ5HIyIiIk6L+lVedXV1vP3222zZsoWysjIsyyI9PZ2hQ4dy2223kZ+fz8aNG/mv//ovFi1aFO3w5CrmSkwiGKkAM50TR2sYck2q0yGJiIiIg6Ja6H766af86le/ora2Fr/fT25uLpFIhIqKClatWsV7773HPffcQ1ZWVjTDkhjiteqAdGpqnY5EREREnBa1Qvf06dP8+7//O36/nyeeeILRo0ef9/y+fft44403WLJkyRUXukuXLmXTpk0cP34cr9dLcXExX//618nJyemIlyBXgfT4ZsIWWGHdClhERKS7i1qhu3TpUgCeeuopMjIyLnh+4MCBDBw4kOXLl7NgwYIramPPnj3cfvvt9O3bl0gkwh//+Ed+/vOf89xzzxEXpyvxu4OCTA9/LYM4w0c4bOF2d8lh6CIiIhIFUSt0t23bxqRJk9oscj9r6tSpNDY2smfPnstuY+7cuectP/bYY8yZM4eSkhIGDRp02ceTq09hcTa7T1rEGSbHy5ooyE1wOiQRERFxSNQK3erqanJzcy9p2/vuu69D2mxsbLmBQGJi27eDDYVChEKh1mXDMIiPj2/9ubOdayMabV1trjQ38dnZNESOEedO5Mhfq+iTF1u3AtY50z7lpm3KS/uUm7YpL+1TbtrWlfMStULX7/dTV3dpUz598skn7N+/n3vvvfeK27Msi5deeokBAwaQn5/f5jZLly5lyZIlrcuFhYU8/fTT9OzZ84rbvRK6+K59V5Ibl70XSKSmxiY7O7vjg+oCdM60T7lpm/LSPuWmbcpL+5SbtnXFvESt0B0wYADr169n+vTpX7jdkSNHePbZZwkGg1+q0J0/fz6lpaX87Gc/a3ebmTNnMm3atNblc59EKioqCIfDV9z2pTIMg6ysLMrKyrBtu9Pbu5p8mdwkuuoBaGg0OHnyZGeE5xidM+1TbtqmvLRPuWmb8tI+5aZt0c6L2+2+5E7JqBW606dP56c//Sm/+93vmDNnTpt3Slu3bh0vvPDCly4y58+fz9atW3nqqafo0aNHu9t5PJ5279gWzRPYtm29YdpxJbnJSTMorwEv8ViW1SW/SvmydM60T7lpm/LSPuWmbcpL+5SbtnXFvESt0C0uLuZb3/oWL730Ep988gnjxo0jJyeHcDhMeXk5mzdvpry8nL59+zJ+/HgWLlx42W3Yts2LL77Ipk2bePLJJ8nMzOyEVyJdXb++6ZzcYuMxXJypDtMj7fJvPy0iIiJXv6jeMOKOO+4gPz+fxYsXs2LFivOq/szMTL7xjW9w5513smHDhis6/vz581m3bh1PPPEE8fHxVFdXAy3jg71eb0e8BLkKpBfmUbf5NKmuOEoOnqbHmK43ZkhEREQ6X1QK3VdffZX8/Hzy8/MZMGAATz75JI2NjZw6dYpwOExaWtp5044NGDCA7373u5fdzsqVKwF48sknz1v/6KOPMnHixC/zEuQqYrg9RCJ14IrjVFmj0+GIiIiIQ6JS6L799tsEg0EAXC4X2dnZ5Ofnk5eXR35+PikpKedtn5mZeUXDDhYvXtwh8crVL96sA3rS0OhyOhQRERFxSFQK3YULF1JeXs6xY8coLS2ltLSUAwcOnDdEIS4urrXwPVcEDx48OBrhSQzqmRihoRlMO97pUERERMQhURuje66XdtSoURw8eJAdO3YwZcoUBg0ahGVZHDx4kLVr13LgwIHWfRYtWhSt8CTG9M/3s+1TmzjDS31DhMQE9eyKiIh0N1G9GO2c+fPnM27cOB5++OHWdRMmTODBBx/kz3/+M2vWrOGhhx5yIjSJEb0HFvLhvnpSDC/7959m1EjNwCEiItLdmE40evToUQoKCi5Y7/P5mD17NgMGDGDLli0ORCaxwoz3Ew7XAHC8tMHhaERERMQJjhS6vXv3Zvv27e0+P2LECD755JPoBSQxKcGsBaCh0ZEvLkRERMRhjhS6d999N5s2bWLRokWtszF81uHDh6NyC16Jbb3TLAA8+LvcnVpERESk8znS1TV+/HjOnDnDK6+8wqpVq7jpppsoKioCYNeuXXzwwQeMHj3aidAkhlwzIIMPNtt4DRflpwP0yohzOiQRERGJIse+0502bRpDhgzh9ddfZ9WqVef17A4bNoxvf/vbToUmMSK1MJ/ajadId/k5sK+SXhNynQ5JREREosjRwYt9+vThhz/8IeFwmLKyMoLBIBkZGSQnJzsZlsQIw3RBpAZcfspPXThERkRERGJb1MboLlu2jOPHj7f5nNvtJjc3l6KiIhW50qFSvfUABIMatiAiItLdRK1Hd9myZbzyyiv07NmTUaNGMWrUKAYPHozH44lWCNINFWa5OVIGcUY8kbCFy+3I9ZciIiLigKgVun/4wx84cOAA27dvZ+vWrbz77rt4vV4GDx7M6NGjGTlyJBkZGdEKR7qJ4sF5fHoyQpzh4nBpHX0LU5wOSURERKIkaoWuYRgUFxdTXFzMrFmzqK6uZuvWrWzfvp1XXnmFF154gdzcXEaNGsXo0aMpLi7GNNX7Jl9OXEYGDZGjxLmTOXSgSoWuiIhIN+LYxWipqalMmjSJSZMmEYlE2Lt3L9u2bWPLli0sW7YMv9/P8OHDmTp1Kv3793cqTIkBHrsaSOZMtebSFRER6U4cKXS/973vcfPNN3PvvfcC4HK5GDJkCEOGDOEb3/gG5eXlbN26lW3btrF3714VuvKl9PQHCISASILToYiIiEgUOVLolpeXU1JSQklJCSdOnMDv99O7d2969eoFQGZmJnfccQd33HGHE+FJjBnSP5nNu23iTS9nqoKkp3mdDklERESiwLGhC1u2bGHLli3nrcvLy2PmzJnccMMNDkUlsSj7mr7U7KwgzRXP3t3l3KAbR4iIiHQLjhW6brebmTNnMmjQIMLhMCUlJXz88cf85je/Yffu3bozmnQYw+2GyBlw9eZkWcjpcACwbZuGOoszZ8KEQjZxcSYZmW58Pl2AKSIi0lEcK3SnTp3aOkYXWm77e/fdd/PWW2+xcOFCiouLmThxolPhSYzpGd9AOAKRkN/ROGzb5tP9zezb3Ygr5Dr/OWzcyTZjr00io6fmlxYREfmyHOk+8nq9pKent/nctGnTGD9+PO+8806Uo5JYNqhfErZt4zd91NQ606tbXxfhrTdPc2B7AFfIRdi2OWUHOWo1c8YOYWAQqTXZ8F496zbUYFmaJUJEROTLcKTQzcnJYdu2be0+f80113Ds2LEoRiSxLm9QX6qtAAA7d5RFvf2yU0FWraiGJjch26Kk8ShpZ5YxPWE9D2VsYSKraDr1LkfCdRiGQVWpzfLllYRDKnZFRESulCNDF6ZMmcIf/vAHXnzxRb7+9a/j9Z5/Ffy+ffvw+XxOhCYxyvR6IVIJrlxOnoxEte2Tp4Js/KAeNy5OW0EyKlfxvRnjMPO/2bpNApBvWdRu2sAfP7HolTwUd6OHt96sYNr0nrjdRlRjFhERiQWOFLqTJ0/m2LFjvP3226xfv56RI0eSl5eH2+1m586dbNu2TeNzpcP1Tm6ksRkMKxHbtjGMzi8eT1eF2bCmDq/hoizSyPWsZ8ijD2C4XBdsa5gmKddP4NsDz/Cn19YRSb0BX8jL8rcq+MpXemK6VOyKiIhcDscuRnvooYe47rrrWL58OZs3b2bt2rWtz40ZM4ZvfvObX7C3yOUbNbQXazZZ+Aw3pccbyM9N7NT2mpoirFlZhc/wcMYKcHPiDoqn33fR/czUdGY9NJk/L3iPUPJ4PAEvq9+r4LbbMjs1XhERkVjjWKELLWNxr7nmGizLory8nObmZjIyMkhM7NwCRLqn1MJ8ajeU0sOTzJ6dFZ1a6FqWzTsrKvHho84OM9K7heLpd13y/kZcPF/9+i0sfGUjacmjCFR52fZJDaOGp3ZazCIiIrEmKhejvfrqq6xbt46jR48SiVw4PtI0TbKysujTp4+KXOk0hmEQb1QCUFPTuXdHW7fhNO6wj6Bt0atpPUPvufOyj2H4E5g9cyjHm0oBOLQnTG1duKNDFRERiVlR6dF9++23CQaDALhcLrKzs8nPzycvL4/8/Hzy8/PJzNTXstL5huSblJyAePzU1odITuz4+WqPlwWoOubCNKC2fi/3/N0dVzwe2J3Rk1nXlrPskyZ6uOJZ+W45xf11ZzcREZFLEZVCd+HChZSXl3Ps2DFKS0spLS3lwIEDbNiwoXWbuLi48wrfvLw8Bg8eHI3wpBu5ZuxAtvy5mjRXHNv+UsbNE/M69PjhsM1HH9bgM7ycCNXw9ck5GN4vN4NI+pDB9N2zikrrWrzheN5d+VeGD0vooIhFRERiV9TG6GZmZpKZmcmoUaM4ePAgO3bsYMqUKQwaNAjLsjh48CBr167lwIEDrfssWrTostrYs2cPy5Yt49ChQ1RVVfH4448zduzYjn4pchUzfXG4IqfAVUDZqY6fo3b16lP4iKPBjjAh/SAJfW7tkONOuGciLy/YTo+E/vx1dzO98zz0SNXd00RERL6IIzeMmD9/PuPGjWPOnDmMHz+eCRMm8NBDD/Hb3/6WGTNmkJyczPe///3LPm4gEKBPnz78/d//fSdELbGif2bLndF8dhKNTR03p+6nB+oJ1cQBYDZsY9Adt3TYsQ23h3tvyaAy0ojbMFmzurzDji0iIhKrHCl0jx49SkFBwQXrfT4fs2fPZsCAAWzZsuWyjzty5EgeeOAB9eLKFxo1fgDVVgCXYbDp4xMdcsympgi7tjQDcDxwintmjsQwO/btlVhYyCD/fizbxhdJ4JNdVR16fBERkVjjyPRivXv3Zvv27dx6a9tf644YMYLXXnut0+MIhUKEQqHWZcMwiI+Pb/25s51rIxptXW06Mzee5GRc4YPgLaTslPml27Btm5XvluM14qmxQkwrrsXTY2AHRXu+66ffyIsL95ER15tPd4UYfI2N2+3I59UuR++ntikv7VNu2qa8tE+5aVtXzosjhe7dd9/Nf/zHf7Bo0SJmzpx5wS2ADx8+TDjc+dMoLV26lCVLlrQuFxYW8vTTT9OzZ89Ob/uzsrKyotre1aSzcnPLiBQ27bZJMBIIWwnk9U6+4mO9/8FhzEA8lm2TY+5i7D1f68BIL/TATSdZtjGM3/Cydm0ls2cP79T2rjZ6P7VNeWmfctM25aV9yk3bumJeHCl0x48fz5kzZ3jllVdYtWoVN910E0VFRQDs2rWLDz74gNGjR3d6HDNnzmTatGmty+c+iVRUVESl0DYMg6ysLMrKyrDtjr8w6mrW2bnJHtyHMzuOkeFJ5q03dnH3zMIrOk5VdYi9W+twGy4qmo4wffY4Tp482cHR/o1hGPS9YSyudUvAO4jqMhc7dx8hI71z5wW+Guj91DblpX3KTduUl/YpN22Ldl7cbvcld0o6dme0adOmMWTIEF5//XVWrVrVOs8uwLBhw/j2t7/d6TF4PB48nravXI/mCWzbtt4w7ei03JgmvbzHidjJRJqTaGqOEOe7vCEAlmWzemUFPiOeykgT9473Y/jiovK7nH7XAF59p4Ee7gTWvHeSe+/N7/Q2rxZ6P7VNeWmfctM25aV9yk3bumJeHL0FcJ8+ffjhD39IOBymrKyMYDBIRkYGyclX/jWyyKW6ZVIxf14VJMX0su7/jjF5yuUVi6vfK8NnxRO0LQbF7yF1QMdMJXYpfBkZ9EtYy5nmwfgiyRw4WEP/filRa19ERORq0CWuYnG73eTm5lJUVPSlitzm5mYOHz7M4cOHASgvL+fw4cNUVlZ2UKQSS+IzeuCNHAGg+rSf5oB1yfvu3ltN4EzLhYuBxt2MnTGxM0L8QuOnjqUiWAHA1r80dLlP0SIiIk6LSqH7gx/8gA8//PCyxr2GQiHWrFnDD37wg0ve569//StPPPEETzzxBAALFizgiSeeuOwbT0j3MWVCL2qtID7Dzfurj13SPlXVIT7d0TL/7slAGbPuGYJhujozzDaZHi839m0gZFv4DT8bPy6LegwiIiJdWVSGLkycOJEFCxbw0ksvMXr0aIYNG0ZhYSGZmZn4fC23R21ubqa8vJySkhI++eQTtmzZgtvtZvr06ZfczuDBg1m8eHFnvQyJQWl98kncsB4YTLAukRNlzeRkxbW7fVNzhNXvniHO8HEm0sQ9o2zcqWnRC/hz+o8fwcYF20mNK6T0iJtRoy283i7xRY2IiIjjolLozpgxg9tuu43333+fDz74gLVr17Y+53K19IRFIn+7Q1VeXh6zZs3illtuwe/3RyNE6cbuuGswryyvo6c7ifUf1DDjHm+bxWJTU4Tlb1YQRxyNdoShyZ+SMWRi9AP+DMMwmDohnXc3h0g0PLy3upQ777rwZiwiIiLdUdQuRouPj2fq1KlMnTqV8vJy9u/fz/Hjx6mrqwMgKSmJ3r17U1xcTGZmZrTCEsGXmsqNufvZcjKeBMPHsjdOMXVaJvHxfxuOUFYeYN2aGnzEEbAtetlbGDl1ioNR/0163wLiN34MroE01yZxpipAeprP6bBEREQc58isC5mZmSpmpUvpf/NYDv/xfWoYgc+KZ8UbVaT0gqQUH6eONUCjD5/hpd4OkxX5Czc/eFuXugPMnVMH8tryBtJdCby/+iT33tfH6ZBEREQcp8F8ImdNvv9mMo2/UGuF8BpumsrdlB+IYDTFYRgGJ0I1DE7cwcTZt2OYXeut40tJpSixtOVnK5W/Hqx2NiAREZEuoGv931rEQYbp4qb7b2NSYQnVdTs5FjzD8XAdx5uOQdNGvnWrm2HTbu1SPbmfdcOdo6kMtkylt1nTjYmIiDh7wwiRrij7+uv4xvVgN9RDYz2kDsDwDHE6rIsyPR5u6NfI7iMWCUYCmz4+wXXjejsdloiIiGPUoyvSDiMhEaNnFkY7t4nuiorHDaMh1DIf8JHDHoKhS78JhoiISKxRoSsSY+6ckEajHSbe9PLeyiNOhyMiIuIYFboiMaZHYR4+DgPQVJdCVVXA2YBEREQcokJXJAbdOXUA1ZEmPIbJ6tUnnA5HRETEESp0RWKQLymJwpTjAMRZaezYftLhiERERKJPha5IjLrhzlGcDpYBsG+vi+ZA5CJ7iIiIxBYVuiIxyjBNpk5IpMEK4ze9vL38sNMhiYiIRJUKXZEY1rMwl3TfYQDMYDr79pQ7G5CIiEgUqdAViXGTp4/gdLAS0zDYsQPq6oJOhyQiIhIVKnRFYpzpdnPneC/1Vgi/6WXF8pO6PbCIiHQLKnRFuoFeffMpyjhGxLbxGymsfKfE6ZBEREQ6ndvpAEQkOq6bMpIlr27D5SqkqSadnTtOMnR4tmPx2LZNfYPF8WM1nKpoItAYwopYWJjE+QxSkj30yE4lNysOt1ufyUVE5PKp0BXpRmbcM4hXlxwn3ZPO/r1ekpLO0KcoPWrtn64KsGNrGZWVNqadiM9w0/LFUsJ52zXUQUMlnCgJst0O0Gw34fU107dfIkMGZ+ByqfAVEZGLU6Er0o144nzMmJzEm+83kurys3lTCNNVRX5BWqe12RgIs359KeWnXCQaSZhGCvEABli2TQMRgpFGbKvlIjnDtrBNLy7TR7zpJc5wkWD4IeTn6F44uKeKiFnL4EEJDBrcE8MwOi12ERG5uqnQFelmUrN6cuu1pazZ0kyyGcfGDWECTeX0H5jZoe3sO3CabdurcUdSiTPSSD7bCXsm3IDbOk1OSjODi5LI6NcHMy7jgv1tyyJYWcGB/Sc4dLyZ08Fk/J4M4g0X2GmU7IZdO0+RktrETRPzSPB7OjR+ERG5+qnQFemGcorzmGgf4YOtkGzGsXO7xalTh5lwc58vddz6xhDr15ZSeSaOZNNPIj3AgGYrBNYphvU16T+6P6an90WPZZgmvsxeDMnsxZCz65rOVLN5/QEOVaeQ5MnAb8YRqo3j7TdqcHmquOfe+C8Vv4iIxBYVuiLdVO8BBdzmO8Hb6+tIcydRVZbKokWHuHVSJhk9Ey5+gLNs22bXznJ272vEa6XgMdJJNluGJdSHq+iTUs3Yif3xJA25+MEuIj49lZu+MoabgPKSY6zbWE4zuSSZXghn8PofjxM0znDd2FQKCnt86fZEROTqpkJXpBvr2SeHe9NqWfrmUZLcvfEbafzfewEsVxkjR6ZS1De9zTGwgWCEvZ+UcfBwM3YoiTjTRwI+MKDeCuC1yxg/PJHswX07LfbMolzuKcolUF/PB6sOUN7Ui1SXn3h6sGOjzYaPDzFyiJeBQy/eeywiIrFJha5IN+dPSWb21wazafUn7K/oSarLD1YP9myBrX85TdhuxDTDGNhEbBe27SPBiMM0EvCRACYEbYuGUCX9M+q5c+JA3PG9oha/LzGR22eOxLYi7PjoEDsOu0l3p5JopHFgD2zdeZSBfcKMHFeoC9dERLoZFboigmEYXDdlOKOam1m/eg9HqtNIdqcQZ7jBSP7MhmcfQL0dJhSuope/jgnX9yYtp9iR2M8xXW7uuu8mRpw4wScb9rH9kIcUVzpJrmSOl8K+wyfI71nH9bf0w+3Wnz4Rke5Af+1FpJUnLo6J00YBUH3iFHt3lXGq2iYQNgEDt2mRHh8hv7efgqGFuH0XzpbgNMMwGDZ+IMPGw8GdR/l4ZzMJZk+SXAlUnUng9T9V4fZVMe76TLJzUp0OV0REOpEKXRFpU2pOL8blRG8IQmfoNzSffkPh+OFy1n58Bhc9iTc9EMpk0//Z1FtHyO4RYMz4PJKSnJmxwbIsqs40cupYNZWVjTQ0WYTCEA4bRCwDyz7bhW7YgIGBjcu0cbstvG6DOK9BYqKH9B5+MnNSSUzyaYiGiMhZKnRFJOb17pPJA30yaaiqZe2aEiqaepDqTiLZlUJDNby3vJkGq5zk+Eb6FSXSf0gObperQ9puDoQoO1pFxak6ztSEaGiCQMiNZXtxGT58hge3YQKJZx8to0M8Zx+0VbNaQBDCQahvhPpqKDsGe3Y0E7YbCdhhInYQgyAed4i0pIP44y0yevrJzk93rKgXEYm2mCx033nnHd58802qq6spKCjg4Ycfpl+/fk6HJSIOS0hL5o57RmDbNp9uLWHH/gAWPUg0fSS7UiCYwsF9sHdvDU1WE6bRSLw3TGI8JCd58Cd48PrceDwugsEwgaYwgUCEYDBCfWOExmaDQNhFxPJg4MVjelvGOeMFWqY78wCeNu5g3GSHCVoBbDuIQQQDC5cRwTDs1m3ss1VvxDaxbRc2bsCFy/TgNTz4DBduw8RteM+2CdjQVNvyOH0KPt0VIGg3ErCC2ARxGQHivBGS/JCe5qNXdjKZvVM1jllEYkLM/SXbsGEDCxYs4JFHHqF///4sX76cX/ziFzz//POkpKQ4HZ6IdAGGYTBwdF8GjoZIOMy+7YfZVxKgMZSI35WE1zDxuBKABAhDU13L42LcgNsAPtcZHLItAnaIsBXApBmvO0SizyI1yUVmpp/s3HT8aV9uvLNlWdSfqaWirIbTp5uorg1T3wTNYS8WPgy8+EwvPsOF13DhdcUDZ3t2w9BY2/I4dgQsu45mO0TYDgIBXGYYj2nhdtl4PRDnNfD5TNwuE9Nl4HIZuNwuznWCR8I24bBFJGIRCVtEIhCxbCIRm1AEIhGbSATCloFlQcQysO2WYRo2JrbdMibcxsTAhLP/GoaJiYlpGGfXmLgMA8tu+TBgAzaf/bnlJwsby7axsLBsC1rWADaGcQhsCwML07AwDbvlYdq4TFqGibgMXC7wuMDtNvF4DDweE4/Xjddr4vG68MV58Hk9mG4T0zRa8mK6MNwGpmliukxcLhPX2STZtk3EsrDCFlakJS4rbGNZLXmzLRsrYhGxbeyIdXa9jW2dfdgtr8m2bWwLbLvlmFbExubsertlPmtssCy7dRvbBsNoeR+YpoFhgsswWpdNl0n5sTrqamswTKMl366W510uA8MwMVwGLsPEcJ9db5oYJphmy3MtxzLPvvZz+3bMtyQilyPmCt233nqLW2+9lVtuuQWARx55hK1bt7JmzRruvvvu87YNhUKEQqHWZcMwiI+Pb/25s51rQ+PpLqTctE15ad+V5sbt8TBkTH+GjGlZbm4McGjfcUqPN1BV7yIY8YLhxTS8uDAxDRMT42zxZJ0tniLYdrhlqIArSLzHItFvkJHuIysnhfScNNzuzr1FscvlIqVnGik90/js91eGYZCVlUVZWRmWZVFTWcPJY1VUVjZRXWfRGDAJWV5sfLgNL/GGB9Mw8Lf2CrcMp8AGwhAOQ30T1HdEzGcfrUM0rvC0Ns/7nbdzkMs9dks9jA2Ezj6aL2nHv5XYEGl7C9u+xPP0XFLa+Aqg04WBS71xjM3fXmvbr/mcz34oafn38x9Mzl/H537qDJd/9IpObuDydujc7FyOcm4Ya5HXL8vpQM4TU4VuOBympKTkvILWNE2GDh3K/v37L9h+6dKlLFmypHW5sLCQp59+mp49e0Yj3FZZWV3rpOhKlJu2KS/t64jcFPbt8+UD6WLO5SUnJ4drhrW/XaA5SGnJCUqPnKa8ooHqugiBkEHYMrHODpkAd0uv3tlCrPW/s8WbbZ/tVfzMf9h2ay8qtPSgGobd0pNqfqYX1QC3i7M9qAYed0vvqdd9tufUa+KL8xB39uGL8+L1eoic6wENR1p6ki2LSCTS0rscsggGwzQHwwQDYQJBi2AwQihsEQp/ppfZMohYELENLOtc7/K5QtM8+1rN1tdufuaDjwsD1yV+yLrcD2Of77H+W0FI6092G8utP9sXrm2NBYOWcIyznwWM1s8ERutv+Pxl+PyHi0t34X7d4EN7N3iJ5yQmesnOznY6jPPEVKFbW1uLZVmkpqaetz41NZUTJ05csP3MmTOZNm1a6/K5Pz4VFRWEw+FOjfVce+d6Wmy763wm6wqUm7YpL+1Tbtp2JXlJSPMxMC2HgZ0cW0e6kv7yjj5nwuHQ2eEHNpFIBDtinR2y0TIUwT5bgFsRG9PdMuzCODukwXCd+/q/5St+8+ywkHNf/0fTpeal5YOE1fLBJmITsSJg2WeHW7QMzWgZNmG1DJ2wLGyLlm9CImeXoWVb+29DMlq2+dvy5brSX+Wl7GYYkJiYRH19XetQkM5r7XN7XOnruoL9LnsfA5ISkohLjufkyZOX3+Blcrvdl9wpGVOF7uXyeDx4PG3/eYzm/yht+8rezN2BctM25aV9yk3blJf2dVRuXC43nTEM1anf28XyYpompteJYRXOMQyD7OxsTp48qffTZ3TlvMTUGZqcnIxpmlRXV5+3vrq6+oJeXhERERGJbTFV6LrdboqKiti1a1frOsuy2LVrF8XFzt6eVERERESiK+aGLkybNo158+ZRVFREv379WLFiBYFAgIkTJzodmoiIiIhEUcwVuuPHj6e2tpbFixdTXV1Nnz59+PGPf6yhCyIiIiLdTMwVugB33HEHd9xxh9NhiIiIiIiDYmqMroiIiIjIOTHZo/tlRfse77qnfPuUm7YpL+1TbtqmvLRPuWmb8tI+5aZt0crL5bRj2F1twjMRERERkQ6goQsOampq4kc/+hFNTU1Oh9LlKDdtU17ap9y0TXlpn3LTNuWlfcpN27pyXlToOsi2bQ4dOtTl7iLSFSg3bVNe2qfctE15aZ9y0zblpX3KTdu6cl5U6IqIiIhITFKhKyIiIiIxSYWugzweD/feey8ej8fpULoc5aZtykv7lJu2KS/tU27apry0T7lpW1fOi2ZdEBEREZGYpB5dEREREYlJKnRFREREJCap0BURERGRmKRCV0RERERikm7W7KB33nmHN998k+rqagoKCnj44Yfp16+f02E5avHixSxZsuS8dTk5OTz//PPOBOSQPXv2sGzZMg4dOkRVVRWPP/44Y8eObX3etm0WL17Me++9R0NDAwMHDmTOnDlkZ2c7GHV0XCw38+bN48MPPzxvn+HDhzN37txohxpVS5cuZdOmTRw/fhyv10txcTFf//rXycnJad0mGAyyYMECNmzYQCgUYvjw4cyZM4fU1FTnAu9kl5KXJ598kj179py33+TJk/n2t78d7XCjZuXKlaxcuZKKigoAcnNzuffeexk5ciTQPc+Vcy6Wm+54vrTlf//3f3n11Ve56667eOihh4Cued6o0HXIhg0bWLBgAY888gj9+/dn+fLl/OIXv+D5558nJSXF6fAclZeXx09/+tPWZdPsfl88BAIB+vTpw6RJk/jVr351wfNvvPEGb7/9No899hiZmZksWrSIX/ziFzz33HN4vV4HIo6ei+UGYMSIETz66KOty2537P+p27NnD7fffjt9+/YlEonwxz/+kZ///Oc899xzxMXFAfDyyy+zdetWfvjDH+L3+5k/fz6//vWv+dd//VeHo+88l5IXgFtvvZX777+/dTnW30fp6enMnj2b7OxsbNvmww8/5JlnnuGZZ54hLy+vW54r51wsN9D9zpfPO3jwIKtWraKgoOC89V3xvOl+FUQX8dZbb3Hrrbdyyy23kJubyyOPPILX62XNmjVOh+Y40zRJTU1tfSQnJzsdUtSNHDmSBx544LyeynNs22bFihXcc889jBkzhoKCAv7xH/+RqqoqNm/e7EC00fVFuTnH7Xafdw4lJiZGMUJnzJ07l4kTJ5KXl0efPn147LHHqKyspKSkBIDGxkbef/99vvnNbzJkyBCKiop49NFH+fTTT9m/f7/D0Xeei+XlHJ/Pd9454/f7HYo4Oq699lpGjRpFdnY2OTk5PPjgg8TFxXHgwIFue66c80W5Oae7nS+f1dzczH/+53/yne98h4SEhNb1XfW8if1uji4oHA5TUlLC3Xff3brONE2GDh3aLf6IXExZWRnf+c538Hg8FBcXM3v2bDIyMpwOq8soLy+nurqaYcOGta7z+/3069eP/fv3c8MNNzgYXdewZ88e5syZQ0JCAkOGDOGBBx4gKSnJ6bCiqrGxEaC1yC8pKSESiTB06NDWbXr37k1GRgb79++nuLjYkTij7fN5OWft2rWsXbuW1NRURo8ezVe/+lV8Pp8TIUadZVl89NFHBAIBiouLda58xudzc053Pl9eeOEFRo4cybBhw3j99ddb13fV80aFrgNqa2uxLOuCMSupqamcOHHCmaC6iP79+/Poo4+Sk5NDVVUVS5Ys4V/+5V/49a9/TXx8vNPhdQnV1dUAFwxxSUlJaX2uOxsxYgTXXXcdmZmZlJWV8cc//pFf/vKX/OIXv+g2w2Asy+Kll15iwIAB5OfnAy3njdvtPq8HBrrXedNWXgAmTJhARkYG6enpHDlyhFdeeYUTJ07w+OOPOxht5zt69Chz584lFAoRFxfH448/Tm5uLocPH+7250p7uYHue74ArF+/nkOHDvFv//ZvFzzXVf/GqNCVLuXcYH+AgoKC1sL3o48+YtKkSQ5GJleLz/Zo5+fnU1BQwPe+9z127959Xk9DLJs/fz6lpaX87Gc/czqULqW9vEyePLn15/z8fNLS0vjZz35GWVkZWVlZ0Q4zanJycnj22WdpbGzk448/Zt68eTz11FNOh9UltJeb3Nzcbnu+VFZW8tJLL/GTn/zkqhqTrELXAcnJyZimecEnnOrq6m5xRevlSEhIICcnh7KyMqdD6TLOnSM1NTWkpaW1rq+pqaFPnz7OBNWF9erVi6SkJMrKyrpFoTt//ny2bt3KU089RY8ePVrXp6amEg6HaWhoOK/Hpaamplv83WkvL205N/tNrBcubre79fUVFRXx17/+lRUrVjB+/Phufa5A+7lpa2aF7nK+lJSUUFNTw49+9KPWdZZlsXfvXt555x3mzp3bJc8bFboOcLvdFBUVsWvXrtYLaizLYteuXdxxxx0OR9e1NDc3U1ZWxo033uh0KF1GZmYmqamp7Ny5s7WwbWxs5ODBg9x2223OBtcFnT59mvr6+vM+FMQi27Z58cUX2bRpE08++SSZmZnnPV9UVITL5WLnzp1cf/31AJw4cYLKysqYHnN5sby05fDhwwAxf858nmVZhEKhbnuufJFzuWlLdzlfhg4desFMN7/73e/IyclhxowZZGRkdMnzRoWuQ6ZNm8a8efMoKiqiX79+rFixgkAgwMSJE50OzVELFizg2muvJSMjg6qqKhYvXoxpmkyYMMHp0KLqXIF/Tnl5OYcPHyYxMZGMjAzuuusuXn/9dbKzs8nMzOS1114jLS2NMWPGOBh1dHxRbhITE/nTn/7EddddR2pqKqdOneJ//ud/yMrKYvjw4Q5G3fnmz5/PunXreOKJJ4iPj2/9xsjv9+P1evH7/UyaNIkFCxaQmJiI3+/nxRdfpLi4OKaLl4vlpaysjHXr1jFq1CgSExM5evQoL7/8Mtdcc80FUyfFkldffZURI0aQkZFBc3Mz69atY8+ePcydO7fbnivnfFFuuuv5AhAfH3/e2HZomX0iKSmpdX1XPG8M27Ztx1rv5t555x2WLVtGdXU1ffr04Vvf+hb9+/d3OixHPf/88+zdu5e6ujqSk5MZOHAgDzzwQEx/HdSW3bt3tzlW7uabb+axxx5rvWHE6tWraWxsZODAgfz93//9eZPgx6ovys0jjzzCs88+y6FDh2hoaCA9PZ1hw4Zx//33x/xXrrNmzWpz/aOPPtr6AfrcZO7r168nHA53icncO9vF8lJZWcl//ud/UlpaSiAQoEePHowdO5Z77rknpqeM+t3vfseuXbuoqqrC7/dTUFDAjBkzWmdz6Y7nyjlflJvuer6058knn6RPnz4X3DCiK503KnRFREREJCZ1j7l2RERERKTbUaErIiIiIjFJha6IiIiIxCQVuiIiIiISk1ToioiIiEhMUqErIiIiIjFJha6IiIiIxCQVuiIiIiISk1ToioiIiEhMUqErIiIiIjHJ7XQAIiLdVSQS4Y033uC9996jpqaGvn378p3vfIecnJx295k3bx4ffvghAHl5efz617/+wjYWL17MkiVLWLx4cYfG/nnLly/n5Zdfbl1+4YUXSE5O7tQ2RUQuRoWuiIgDLMviV7/6Ffv372fq1Kl4vV6WLl3K008/zXPPPYfL5Wp336SkJL75zW+SkJAQxYi/2IgRI0hKSmLTpk1s2rTJ6XBERAAVuiIijli2bBm7du3il7/8JXl5eQCkpqbym9/8ht27dzNs2LB2942Li+Omm26KVqiXpHfv3vTu3ZuysjIVuiLSZWiMrohIlDU2NrJ06VLuuuuu1iIXoLi4GIAjR444FZqISExRj66ISJStXbuW5uZmJk+efN56t7vlT3JTU9MVHXffvn28/PLLHD16lPT0dKZPn97utmfOnOG1115j27ZtNDQ0kJWVxbRp05g0adJ52+3evZuFCxdSWlraesyqqqqojPsVEfmyVOiKiETZpk2byM3NxefzUVtb27q+srISaBmacLmOHj3Kz3/+c5KTk7nvvvuIRCIsXryY1NTUC7atrq5m7ty5ANx+++0kJyezfft2fv/739PU1MTUqVMBOHToEL/85S9JTU3lvvvuw7IslixZoovMROSqoUJXRCSKLMti//79BAIB5syZ0+Y2mZmZl33cRYsWYds2P/vZz8jIyADguuuu4/HHH79g29dee631YrikpCQAbrvtNp5//nn+9Kc/MWXKFLxeL4sXL8Y0Tf71X/+V9PR0AMaPH88PfvCDy45PRMQJKnRFRKKorKyMQCDA9OnTL7jgbM2aNaxfv578/PzLOqZlWezYsYMxY8a0FrkAubm5DB8+nG3btrWus22bjRs3Mm7cOGzbPq9HecSIEWzYsIGSkhKKi4vZuXMnY8eObS1yAbKyshgxYgRbtmy53JcuIhJ1KnRFRKKooqICgMGDB19Q6L7xxhukpKR84Ty6bamtrSUYDJKdnX3Bczk5OecVurW1tTQ0NLB69WpWr17d7vFqamoIBoNkZWVd8Hxb60REuiIVuiIiURQIBADw+XznrW9sbGTv3r3ccsstndq+bdsA3Hjjjdx8881tblNQUIBlWZ0ah4hINKjQFRGJonMXmjU3N5+3/oMPPiAcDnPbbbdd9jGTk5Pxer2cPHnygudOnDhxwbbx8fFYlvWFc/ValoXH46GsrOyC59paJyLSFWkeXRGRKCooKMAwDHbv3t267vTp0/z5z3/mpptuoqCg4LKPaZomw4cPZ/Pmza0zNwAcO3aMHTt2XLDtddddx8aNGzl69OgFxzo3Ztc0TYYOHcrmzZs5c+ZM6/NlZWVs3779smMUEXGCenRFRKIoJSWFMWPGsGLFCnw+H36/n+XLl5Oens7DDz98xcedNWsW27dv51/+5V+47bbbsCyLt99+m7y8vAtuQDF79mx2797N3LlzufXWW8nNzaW+vp6SkhJ27tzJf//3f7ce8yc/+Qk//elPW4/5zjvvkJeXx+HDh79MGkREokKFrohIlP3DP/wDv//973nzzTeJi4tj3LhxPPjgg8THx1/xMQsKCpg7dy4LFixg8eLF9OjRg1mzZlFVVXVBoZuamsovf/lLlixZwsaNG3n33XdJSkoiLy+Pr33ta63bFRUV8eMf/5iFCxeyaNEievTowf3338+xY8c4fvz4FccqIhIthn3uygQREeny5s2bx65du3j66adxuVwkJCREPYZnnnmGY8eO8Zvf/KZ1XTAYpLm5mWXLlrFs2TJeeOEF3VhCRBynHl0RkavM6dOnmTNnDnl5efz617/u1LaCwSBer7d1+eTJk2zbtu2CGRtWrVrFyy+/3KmxiIhcLhW6IiJXkRkzZnDjjTcCV3ar4Mv1j//4j0ycOJHMzEwqKytZuXIlbrebGTNmnLfdddddR15eXuuy3+/v9NhERC5GQxdERKRdv/3tb9m9ezfV1dW43W6Ki4t58MEHKSoqcjo0EZGLUqErIiIiIjFJ8+iKiIiISExSoSsiIiIiMUmFroiIiIjEJBW6IiIiIhKTVOiKiIiISExSoSsiIiIiMUmFroiIiIjEJBW6IiIiIhKTVOiKiIiISExSoSsiIiIiMen/B1qqxOdy+wTFAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for param_name, values in tqdm(params_to_perturb.items()):\n", + " fig, axes = plt.subplots(2, 1, sharex=True, figsize=(8, 6))\n", + " disp_name = param_name.split(\"_\")[0]\n", + " axes[0].set_title(\"$^{14}$N( $^{17}$F, el)\")\n", + " axes[1].set_title(\"$^{14}$N( $^{17}$F, $^{18}$Ne)$^{13}$C\")\n", + " for val, result in zip(params_to_perturb[param_name], results[param_name]):\n", + " axes[0].plot(\n", + " result[\"channel_1\"][\"Theta_deg\"],\n", + " result[\"channel_1\"][\"sigma_mb_sr\"],\n", + " label=f\"${disp_name}$ = {val:1.1f}\",\n", + " )\n", + " axes[1].plot(\n", + " result[\"channel_2\"][\"Theta_deg\"],\n", + " result[\"channel_2\"][\"sigma_mb_sr\"],\n", + " label=f\"${disp_name}$ = {val:1.1f}\",\n", + " )\n", + "\n", + " axes[0].legend()\n", + " axes[1].set_xlabel(r\"$\\theta$ [deg]\")\n", + " axes[0].set_ylabel(r\"$(d\\sigma/d\\Omega) / (d\\sigma/d\\Omega)_R$ \")\n", + " axes[1].set_ylabel(r\"$(d\\sigma/d\\Omega)$ [mb/Sr]\")" + ] + }, + { + "cell_type": "markdown", + "id": "91ce53e6-e144-4068-84ca-b7e1d8f64eb4", + "metadata": {}, + "source": [ + "Naturally, the p + $^{17}$F potential has no effect on the elastic scattering (at least in the DWBA used here). On the other hand, the range and diffuseness of the real, central potential have a huge impact on the transfer cross section." + ] + }, + { + "cell_type": "markdown", + "id": "ed7487c8-3f7f-4d6e-a6e3-4312e1a7e12b", + "metadata": {}, + "source": [ + "# Transfer to $^{18}$Ne(3.376 MeV, $4^+$) state\n", + "\n", + "In [this paper](http://dx.doi.org/10.1016/j.nuclphysa.2004.09.054), measurement of $^{14}$N( $^{17}$F, $^{18}$Ne)$^{13}$C with transfer of the $d_{5/2}$ proton to the $^{18}$Ne(3.376 MeV, $4^+$) state is reported, with the results available on [EXFOR here](https://www-nds.iaea.org/exfor/servlet/X4sGetSubent?reqx=2507&subID=121137003&plus=1). " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "733ec307-e7af-4261-8de0-523f0d5c3beb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "experimental_data = pd.read_csv(\n", + " EXAMPLE_DIR / \"blackmon_etal.csv\",\n", + " sep=r\"\\s+\",\n", + " skiprows=2,\n", + " names=[\"Theta_deg\", \"sigma_mb_sr\", \"err_sigma_mb_sr\"],\n", + ")\n", + "plt.errorbar(\n", + " experimental_data[\"Theta_deg\"],\n", + " experimental_data[\"sigma_mb_sr\"],\n", + " experimental_data[\"err_sigma_mb_sr\"],\n", + " linestyle=\"none\",\n", + " color=\"k\",\n", + " marker=\"o\",\n", + " label=\"Blackmon et al., 2004\",\n", + ")\n", + "plt.xlabel(r\"$\\theta$ [deg]\")\n", + "plt.ylabel(r\"$(d\\sigma/d\\Omega)$ [mb/Sr]\")\n", + "plt.title(\"$^{14}$N( $^{17}$F, $^{18}$Ne(3.376 MeV, $4^+$))$^{13}$C\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "aaa59b5f-bd4e-4f90-a30f-7da8b35225b8", + "metadata": {}, + "source": [ + "## Frescox input for $^{14}$N( $^{17}$F, $^{18}$Ne(3.376 MeV, $4^+$))$^{13}$C\n", + "\n", + "=\n", + "To modify our frescox template for this particular reaction, we will change a few lines:\n", + "\n", + "1. specify the 4+ state at 3.376 MeV in the $^{18}$Ne mass partition, rather than the ground state\n", + "```fortran\n", + "&PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 /\n", + "&STATES jp=4. bandp=1 ep=3.376 cpot=2 jt=0.5 bandt=-1 et=0.0000 /\n", + "```\n", + "\n", + "2. Change the $^{18}$Ne proton-binding overlap from the ground-state separation energy to the 4+ state separation energy:\n", + "\n", + "\\begin{equation}\n", + " B_p(^{18}\\text{Ne}, E_x = 3.376) \\approx S_p(^{18}\\text{Ne}) - E_x = 3.922 - 3.376 = 0.546 \\text{ MeV}\n", + "\\end{equation}\n", + "\n", + "```fortran\n", + "&Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=0.546 isc=1 ipc=0 /\n", + "```\n", + "\n", + "We've written this file for you; let's test it out!" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9dc72259-0235-4dfb-8852-1a6587ade5ca", + "metadata": {}, + "outputs": [], + "source": [ + "NE_EX3_INPUT_FILE_PATH = Path(\"./Transfer_template/B5-18neon_4+_tr.in\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ce1b970e-e27e-4d00-942d-c6eef880b663", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the frescox input file by filling in the user-defined template with parameters\n", + "cfg = bfrescox.Configuration.from_template(\n", + " NE_EX3_INPUT_FILE_PATH,\n", + " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", + " {},\n", + " overwrite=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6f0aacdb-57e6-451e-b88f-b4b1909c0cdc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.66 ms, sys: 246 μs, total: 2.9 ms\n", + "Wall time: 27.4 s\n" + ] + } + ], + "source": [ + "%%time\n", + "bfrescox.run_simulation(\n", + " cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9a74f95a-b649-47a3-9b1c-ed637810af5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['channel_1', 'channel_2'])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", + "results.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "207ddbca-7fc0-420c-87b0-d837ed358091", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$d\\\\sigma/d\\\\Omega$ [mb/Sr]')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAG4CAYAAABPb0OmAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAaoxJREFUeJzt3Xl4VOXd//H3PVkgAUKAELJBIAlh31UUN8S1QFW0btQWH0Vt0dbqY+uvKu7aUsVqW/tUK61itUIpVFSwUkWLooACshMg7CEkARIgAbLM/ftjyJCYBMgyc2b5vK6LK5kzZ+Z8b06WT865z/cYa61FRERERABwOV2AiIiISCBROBIRERGpQeFIREREpAaFIxEREZEaFI5EREREalA4EhEREalB4UhERESkBoUjERERkRoUjkRERERqiHS6ACcdOHCAyspKp8vwq86dO1NYWOh0GX6ncYcXjTu8aNzhIzIykg4dOvh+Oz7fQgCrrKykoqLC6TL8xhgDeMYdTneN0bg17nCgcWvc0nJ0Wk1ERESkBoUjERERkRoUjkRERERqUDgSERERqUHhSERERKQGhSMRERGRGhSORERERGpQOBIRERGpQeFIREREpAaFIxEREZEaAu72IW63m5kzZ7Jo0SKKi4vp2LEjF154Iddee623Xbq1lpkzZ/LRRx9RWlpK7969mThxIsnJyQ5XLyIiIsEu4I4c/etf/2LBggXcdttt/Pa3v+X73/8+c+fOZf78+d513nnnHebPn8/tt9/OM888Q6tWrXj66acpLy93sHIREREJBQF35CgnJ4czzjiDoUOHApCYmMhnn33G5s2bAc9Ro3nz5nHNNddw5plnAnD33Xdz++23s2zZMs4999w671lRUVHrBrPGGGJiYjDGeI9GhYPqsYbTmMEzXveRsrAcd82P4ULj1rjDQbiP29cCLhxlZ2fz0UcfkZeXR0pKCtu2bWPjxo388Ic/BKCgoIDi4mIGDhzofU1sbCxZWVnk5OTUG47mzJnDrFmzvI979OjBlClTSEhI8P2AAlBSUpLTJfjVoTlvsnvaC7S98kbib70HExlwX/Y+FW77u5rGHV40bmlJAfdb4uqrr+bIkSPce++9uFwu3G43N954I+effz4AxcXFALRv377W69q3b+997tvGjRvH2LFjvY+rk2dRUVGtI0qhzhhDUlIS+fn5WGudLsdvKufPBms5/M7fKd24Ftedv8C0a3/qFwa5cN3fGrfGHQ7CddxRUVF+ObARcOHoiy++4LPPPuOnP/0pXbt2Zdu2bbz22mt06NCBkSNHNuk9o6KiiIqKqrPcWhtWX1TVwmnctmgv7N4OrgiIisZuWEXVk/fimvQgJj3T6fL8Ipz2d00ad3jRuMODv8YacBOy//a3v3HVVVdx7rnn0q1bNy644ALGjBnDv/71LwDi4+MBKCkpqfW6kpIS73Mi1eyqZQC06juIiIeeg8QU2F+Ie8oDuL/8xNniREQkIAVcODp27BguV+2yXC6XNy0mJiYSHx/P6tWrvc+XlZWxefNmsrOz/VqrBL7qcNT6rPMwKd1wPfQcDDgDKsqx057HPWMatqrK4SpFRCSQBNxptWHDhjF79mwSEhJIS0tj27ZtvPfee1x00UWA5zzr6NGjmT17NsnJySQmJvL222/ToUMH79VrIgD26BHY6AnRMWddQClgYtviuvsh7Dt/x86bif3PO9hdW3Hd8QtMuzhnCxYRkYAQcOHo1ltvZcaMGbz66quUlJTQsWNHLr30Ur73ve9517nqqqs4duwYL7/8MmVlZfTu3ZsHH3yQ6OhoByuXgLP+G6ishM7JRKalQ34+AMYVgRl3M7ZbBu6/vgAbVuF++j7PPKRuGc7WLCIijgu4cBQTE8Mtt9zCLbfc0uA6xhhuuOEGbrjhBv8VJkHHfrMUADPozHp7Y5hhI3AlpeJ+6WkozMc95ReYH/4E1/AL/V2qiIgEkICbcyTSEqzbjV39FQBmYMOnW01qOq6Hnof+Q6G8HPvqVNz/+IvmIYmIhDGFIwlN27fAwWJoHYPJ7nfSVU2btrh+MhnzHc+pW/vhv3C/+Bj28EE/FCoiIoFG4UhCUvVVavQbgoms2+Pq24wrAtc1P8R15y8guhWs/wb3U/dhd271caUiIhJoFI4kJNlVx+cbneSUWn3MGefh+uWz0DkJ9hXg/vXPcS/9ry9KFBGRAKVwJCHHHtgHO3LBGEz/YY1+vUnrjuuhqdB3iGce0p+fwz3rr1i35iGJiIQDhSMJOXb18VNqPbIxcfFNeg/Tph2uex7BXHGt5z3/PQf3i49jSw+1UJUiIhKoFI4k5NhVp75K7XQYVwSuaydg7jg+D2ndSs88pF2ahyQiEsoUjiSk2PJjsH4l4Olv1BJcZ56H65e/gYQuULQX969+gXvZZy3y3iIiEngUjiS0bFgF5eXQMQFSu7fY25q0HsfnIQ2G8mPYV36D+5+vax6SiEgIUjiSkFJ9Cb8ZWH9X7OYwbeNw/fRRzOXjPNv64J+4f/eE5iGJiIQYhSMJGdbaGvONzvLJNkxEBK7v/Q/m9vshOhrWrsD99P9id23zyfZERMT/FI4kdOzaBgeKPJOnew/w6aZcZ12A64HfQKdEz33Zfv0L7FeahyQiEgoUjiRkVN9olj6DMFHRPt+e6ZaB6+Hnoc8gOHYU9yvP6ko2EZEQoHAkIaPmfCN/MW3jcN3zGPQaANZi167w27ZFRMQ3FI4kJNiDB2DbJgDMwDP8um0TEYEZ4OnEbbds8Ou2RUSk5SkcSUiwq5eDtZCehYnv5Pftm4zenk9yN2Kt9fv2RUSk5SgcSUg4caNZ/x418krPhIgIKDkARXudqUFERFqEwpEEPVtRAWtXAv6db1STiW4F3TI99eRudKQGERFpGQpHEvw2rYFjR6B9B29AcYLJ6OX5RPOORESCmsKRBL2aN5o1Lge/pDM984505EhEJLgpHElQs9Z6+xs5Nt/oOO+k7J252GNHHa1FRESaTuFIgtuenZ4J0JFR0HuQs7V0TID4juB2w7bNztYiIiJNpnAkQa268SO9B2BaxzhaizEGMqpPrWnekYhIsFI4kqDmRFfskzHV8440KVtEJGgpHEnQsqWHYLMnhARaOFIzSBGR4KVwJEHLrv4arBtS0zGdEp0ux6NbJkRGwqESKMx3uhoREWkChSMJXgF2Sg3AREXVaAapU2siIsFI4UiCkq2sxK5dDgRWOIIal/Rr3pGISFBSOJLgtGUDlJVC2zjIyHa6mlpMliZli4gEM4UjCUreG80OGIZxRThczbdUHznatR179IiztYiISKMpHElQCrRL+GsyHTp5GkJaN2zb5HQ5IiLSSJFOF/Btd911F4WFhXWWX3bZZUycOJHy8nKmT5/O4sWLqaioYNCgQUycOJH4+Hj/FyuOsHvzIH83RERA3yFOl1Mvk9Ebu/8z7JYNmN4DnS5HREQaIeDC0a9+9Svcbrf38Y4dO3jqqac455xzAHj99ddZvnw59913H7GxsUybNo2pU6fy5JNPOlWy+JldfbwrdnZ/TGwbZ4tpSGYv+Ooz3YRWRCQIBVw4iouLq/X4X//6F126dKFv376UlZXx8ccfc88999C/f38AJk2axL333ktOTg7Z2fVPzK2oqKCiosL72BhDTEwMxhjPLR/CRPVYg37Mq74CwDXwzNMaixPjdmX2oQrg+OX8Tvyfh8z+biSNW+MOB+E+bl8LuHBUU2VlJYsWLWLMmDEYY8jNzaWqqooBAwZ410lNTSUhIeGk4WjOnDnMmjXL+7hHjx5MmTKFhIQEn48hECUlJTldQpO5Sw+zO2cNAImXjCEqOfm0X+vPcduEBHZFRcPhQ3S2FUSlpPtt298WzPu7OTTu8KJxS0sK6HC0dOlSSktLGTlyJADFxcVERkbSpk3tUynt27enuLi4wfcZN24cY8eO9T6uTp5FRUW1jiiFOmMMSUlJ5OfnB+2tLdxffQZVVZCUSpGJhD17Tvkax8adngmb11PwxSJc50b7b7vHhcL+bgqNW+MOB+E67qioKL8c2AjocLRw4UIGDx5Mx44dm/U+UVFRREVF1VlurQ2rL6pqwTxu+83xS/gHntnoMfh73CajN3bzeuyWDdgRo/y23W8L5v3dHBp3eNG4w4O/xhqwl/IXFhayatUqLr74Yu+y+Ph4KisrKS0trbVuSUmJrlYLA9Zd5bmfGmAGnuVwNadmMnsBuo2IiEiwCdhwtHDhQtq3b8/QoUO9yzIyMoiIiGD16tXeZXl5eRQVFTU430hCSG4OHD4IsW0gs7fT1ZxadTPI3duxR8qcrUVERE5bQJ5Wc7vdfPLJJ1x44YVERJzofhwbG8uoUaOYPn06bdu2JTY2lr/85S9kZ2crHIUBb+PHfkMxkQH5pVuLie8InRJhXwFszYG+g50uSURETkNA/oZZvXo1RUVFXHTRRXWemzBhAsYYpk6dSmVlpbcJpIS+6nBEAHbFbojJ7I3dV4DN3YBROBIRCQoBGY4GDRrEzJkz630uOjqaiRMnKhCFGbuvAHZvB+PCDBjmdDmnL6M3LP0vdouaQYqIBIuAnXMkUpP3qFFWb0ybds4W0wjVk7LJ3Yit0fldREQCl8KRBIVAvtHsSaX1gOhoKDsMe/OcrkZERE6DwpEEPHv0CGxYBQRfODKRkZCeBYDdst7hakRE5HQoHEng2/ANVFZCQhdI7up0NY1mMvt4PtFNaEVEgoLCkQQ8e/xGs2bQWUF5k0VvM8gtagYpIhIMFI4koFm3u8Z8ozMcrqaJqptB7tmJLSs9+boiIuI4hSMJbDu2QMkBaBUDPfs7XU2TmLh46JwE1nqaQYqISEBTOJKA5r2Ev99gTD03Dw4WJqP61JomZYuIBDqFIwlo3vlGQXCj2ZM6fi84NYMUEQl8CkcSsGzxPti+GYzBDBh66hcEMFN9o9ytOWoGKSIS4BSOJGBVHzWie09MXAdni2mu1O7QqjUcKYU9u5yuRkRETkLhSAJW0HbFroeJiIDuPQGwubqkX0QkkCkcSUCy5cdg/TdAaIQjODEpG/U7EhEJaApHEpg2roHyY9AhAbr2cLqaFmG8k7IVjkREApnCkQQku2op4Gn8GIxdsetV3Qwyfxe29JCztYiISIMUjiTgWGtDar5RNdMuDhJTPA9y1QxSRCRQKRxJ4Nm9DfYXQXQ09B7odDUtynufNU3KFhEJWApHEnDsN8e7YvcZjIlu5WwxLS1D845ERAKdwpEEnKC/0exJeJtB5uZg3VXOFiMiIvVSOJKAYg8We2/OagaEznwjr9RunpvoHjsCeTucrkZEROqhcCQBxa752nP3+m4ZmA6dnC6nxRlXBGRkA7rPmohIoFI4koBy4pRakN9o9iTUDFJEJLApHEnAsJUVsHYFEFqX8H+btxlkro4ciYgEIoUjCRw5a+HoEYiLh/RMp6vxneojR3t3Yw8ddLYWERGpQ+FIAob3lNqAMzCu0P3SNG3aQVKq54GOHomIBJzQ/Q0kQSVUu2I35MSpNc07EhEJNApHEhjyd0NhPkRGQt/BTlfje2oGKSISsBSOJCBU32iWXgMwrWOcLcYPvM0gt23CVqkZpIhIIFE4koAQTqfUAEjuCjGxcOwo7N7udDUiIlKDwpE4zpYegs3rgfAJR8blgh7VzSB1ak1EJJAoHInj7Jrl4HZDajomoYvT5fiNyai+z5rCkYhIIIl0uoD67N+/n7/97W+sXLmSY8eOkZSUxKRJk8jM9PS+sdYyc+ZMPvroI0pLS+nduzcTJ04kOTnZ4cqlSUL4RrMnYzJ7Y9GRIxGRQBNw4ejw4cNMnjyZfv368eCDDxIXF8eePXto06aNd5133nmH+fPnc9ddd5GYmMiMGTN4+umnef7554mOjnawemksW1XluZ8a4XNKzev4PdYozMceLMbExTtajoiIeARcOHrnnXfo1KkTkyZN8i5LTEz0fm6tZd68eVxzzTWceabnl+ndd9/N7bffzrJlyzj33HPrvGdFRQUVFRXex8YYYmJiMMZgjPHhaAJL9VgDasy5G6CsFNq0w2T29kltATluPM0g3SldIW8nbM3BDB7esu8foOP2NY1b4w4H4T5uXwu4cPTVV18xaNAgnn/+edatW0fHjh257LLLuOSSSwAoKCiguLiYgQMHel8TGxtLVlYWOTk59YajOXPmMGvWLO/jHj16MGXKFBISEnw/oACUlJTkdAleJQvf5SAQe8YIOqWm+XRbgTTuavv7D6U0bydt9u4iPvlqn2wjEMftDxp3eNG4pSUFXDgqKChgwYIFjBkzhnHjxrFlyxb++te/EhkZyciRIykuLgagffv2tV7Xvn1773PfNm7cOMaOHet9XJ08i4qKah1RCnXGGJKSksjPz8da63Q5AFR94zmldjS1O3v27PHJNgJx3NXcyd0AOPTNMo608PgDedy+pHFr3OEgXMcdFRXllwMbAReO3G43mZmZjB8/HvAc5dmxYwcLFixg5MiRTXrPqKgooqKi6iy31obVF1W1QBm3tRa7NcfzoHu2z2sKlHHXUqMZpLuiAhPZ8t+SATluP9C4w4vGHR78NdaAu5S/Q4cOpKXVPr2SlpZGUVERAPHx8QCUlJTUWqekpMT7nASJgj1Qeggio6Brd6ercUaXVIhtA+XlsHub09WIiAgBGI569epFXl5erWV5eXl07twZ8EzOjo+PZ/Xq1d7ny8rK2Lx5M9nZ2X6tVZrHbj1+R/r0TExk3SN74cC4XJDRC9Al/SIigSLgwtGYMWPYtGkTs2fPJj8/n88++4yPPvqIyy+/HPCcZx09ejSzZ8/mq6++YseOHfzhD3+gQ4cO3qvXJEjkesKR6RHeodbbDHLLRmcLERERIADnHGVlZXH//ffz1ltv8c9//pPExEQmTJjA+eef713nqquu4tixY7z88suUlZXRu3dvHnzwQfU4CjI29/h8o+NHTsKVyezlaQapTtkiIgEh4MIRwLBhwxg2bFiDzxtjuOGGG7jhhhv8WJW0JFt+DHZtBXTkiB69wBgo2ostOYBp38HpikREwlrAnVaTMLEjF6qqoF176JR46vVDmImJhRTPJf1o3pGIiOMUjsQR3kv4M3qFXYfX+pjjl/Tr1JqIiPMUjsQZx8NR2J9Sq3Z8UrbVpGwREccpHIkjbPWVamE+GbuayTz+/7B9M7YyfLq2i4gEIoUj8Tt78ADsK/BMQu7e0+lyAkOXVGjTDirKYedWp6sREQlrCkfif9WX8Cd39UxGFs+8KzWDFBEJCApH4ndWzR/rVT0pu7o5poiIOEPhSPyu5pVqcoLRkSMRkYCgcCR+Zd1VsG0TACZDR45q6ZENxgX7C7HF+5yuRkQkbCkciX/t2QVHj0Cr1icaHwoApnUMpKZ7HuiSfhERxygciV9Vzzeie0+MK8LZYgJQ9SX9dst6hysREQlfCkfiX2r+eHKZfYAaIVJERPxO4Uj8SleqnVytZpAVagYpIuIEhSPxG3v0COTt9DzQZOz6dU6GtnFQWQk7tjhdjYhIWFI4Ev/ZvhmsGzomYOI7OV1NQDLGgPcmtDq1JiLiBIUj8RvvL3udUjupE/2ONClbRMQJCkfiN/b4bUN0s9mTM8cnZetyfhERZygciV9Ya2Fr9WRshaOT6p4FLhcU78PuL3S6GhGRsKNwJP6xvwhKDnh+6XfLdLqagGZatYa0HgBYHT0SEfE7hSPxj+NHjUjrgWnVytlagoD3kv5c3WdNRMTfIhuz8uHDh5u1sdjYWFwu5bFwVH2zWd1P7TRl9IaF83QTWhERBzQqHN12223N2tjkyZPp379/s95DgpOuVGsck9kbC7AjF1tRjomKdrokEZGw0ahwBHDmmWeSnp7eqNccO3aMd999t7GbkhBhKythu6ehoa5UO00JXSAuHg4We/pDZfV1uiIRkbDR6HB09tlnc9555zXqNYcOHVI4Cme7t0NFOcS2gcQUp6sJCsYYz6m1lV9it2zEKByJiPhNoyYATZgwgYyMjEZvpHXr1kyYMIGUFP1iDEfeU2rdszGac3baqidlW03KFhHxq0YdORo9enSTNhIVFdXk10oIqO5vpFNqjWIyjs872rIBa63naJKIiPhck/+MP3bsGA888AAffvhhS9YjIUhXqjVR9yyIiPD0h9pX4HQ1IiJho8nhqFWrVhQUFOivWTkpW3oY8nd7HnRXOGoME90KunpOY+uSfhER/2nWBJDBgwfzzTfftFQtEoqOHzUiMRnTLs7ZWoKQyezt+SRXnbJFRPylWeHo2muvZc+ePfz+979nw4YN7N+/n8OHD9f5J+HLe0pN/Y2a5vg8LR05EhHxn0Zfyl/T//7v/wKwa9cuPvvsswbXmzFjRnM2I0HsRPNHTcZuCm8zyF1bseXHPKfaRETEp5oVjq699toWn3M0c+ZMZs2aVWtZSkoKL7zwAgDl5eVMnz6dxYsXU1FRwaBBg5g4cSLx8fEtWoc0n7UWtlVPxlY4apKOnaF9RyjZD9s2Q3Y/pysSEQl5zQpH119/fUvVUUvXrl2ZPHmy93HN+7G9/vrrLF++nPvuu4/Y2FimTZvG1KlTefLJJ31SizRD4R44fAgio6Brd6erCUrGGMjsBcu/wG7ZgFE4EhHxuWaFo2/bvXs3X3zxBcXFxaSkpDBy5EhiY2Mb/T4ul6veI0FlZWV8/PHH3HPPPd57tE2aNIl7772XnJwcsrPrn9dSUVFBRUWF97ExhpiYGIwxYXW1XfVY/TVmm3t8Mna3DFwO3hvM3+Nuaa7MPriXfwG5Gxo1hmAfd1Np3Bp3OAj3cftao8PRBx98wPz583nyySeJiztx9dFXX33Fb3/7WyorK73L5s+fz9NPP11rvdORn5/PnXfeSVRUFNnZ2YwfP56EhARyc3OpqqpiwIAB3nVTU1NJSEg4aTiaM2dOrVN1PXr0YMqUKSQkJDSqrlCRlJTkl+0c2LuLw0DbAUPpkJzsl22ejL/G3dKOnXUuBf/4C2bbJpKSkhr9wyFYx91cGnd40bilJTU6HH311Vd06dKlVuCpqqri5ZdfxuVy8eMf/5jMzEyWL1/O22+/zezZs7nllltO+/179uzJpEmTSElJ4cCBA8yaNYtHHnmEqVOnUlxcTGRkJG3atKn1mvbt21NcXNzge44bN46xY8d6H1f/cikqKqp1RCnUGWNISkoiPz/fMx/IxyrXrACgrEsaR/fs8fn2GuLvcbc02zYeIiJxF+9nz+qVmM6n98Mw2MfdVBq3xh0OwnXcUVFRfjmw0ehwtGvXLi6++OJay9auXcvBgwcZN24cI0eOBDzzhrZv386KFSsaFY6GDBni/Tw9Pd0blr744guio5t2aiYqKoqoqKg6y621YfVFVc0f47YV5bBzq+dB954B8f8ctPs7Mgq6ZcDWHNyb1+NK6NKolwftuJtJ4w4vGnd48NdYG93n6NChQ3Tq1KnWstWrVwNw1lln1Vreq1cvioqKmlEetGnThpSUFPLz84mPj6eyspLS0tJa65SUlOhqtUCzIxeqKqFde2jkL3Opy9sMUv2ORER8rtHhKD4+vs4prA0bNtCqVSvS09NrLY+MjCQysnlzvo8ePeoNRhkZGURERHjDGEBeXh5FRUUNzjcSZ9jjN5slo1fYTRj0hepwZHMVjkREfK3RySUjI4NPP/2U73znO8TExLBz5042b97MGWecQURERK11d+/eXeco06lMnz6dM844g4SEBA4cOMDMmTNxuVycd955xMbGMmrUKKZPn07btm2JjY3lL3/5C9nZ2QpHgSZXnbFbVMbxI0e7tmGPHsG0jnG2HhGRENbocHTdddfxy1/+kp/+9Kd07dqV3NxcwDPp+duWLVtGv36N68uyf/9+XnzxRQ4dOkRcXBy9e/eudcXbhAkTMMYwdepUKisrvU0gJbBUd8ZW88eWYTomQMcE2F8E2zZB74FOlyQiErIaHY66devGI488wuzZsykoKKBnz55897vfJSMjo9Z6a9euJTo6mnPOOadR7/+zn/3spM9HR0czceJEBaIAZg8egH0FYAx07+l0OSHDZPbB7l/kaQapcCQi4jNNmhDUq1cvfvnLX550nX79+jF16tQmFSVBrrr5Y1IaJqbxTUClAZm9Ydki7Ob1TlciIhLSGj0he+7cuezevdsXtUiIsFt1PzVfMFl9PJ/kbsC63c4WIyISwhp95Gju3Lm8+eabdO7cmaFDhzJ06FD69etXbx8hCU/V4YgMTcZuUandIboVlJVC/i5I6eZ0RSIiIanR4ejPf/4zmzZtYuXKlSxfvpx///vfREdH069fP4YNG8aQIUPC9rYcAtZdBdVHjnroyFFLMpGR0CMbNq7Gbl6PUTgSEfGJRocjY4z30vnrr7+e4uJili9fzsqVK3nzzTd59dVXSUtLY+jQoQwbNozs7GxcrkafvZNgtWc3HD0CrVrryIYPmMw+2I2rPc0gL7jc6XJEREJS8zo04mkKOWrUKEaNGkVVVRXr169nxYoVfP3118ydO5fY2FgGDRrEmDFj6NlTVy6FOm/zx/QszLf6XknzmazeWMCqU7aIiM80OxzVFBERQf/+/enfvz8/+MEPKCgoYPny5axYsYL169crHIWD6v5Gav7oG9WT3Pfuxh4qwbRr72w9IiIhqEXD0bclJiZyxRVXcMUVV/hyMxJAdKWab5k27SC5K+zZ6Tm1Nni40yWJiIScFglHO3bsYMWKFRQWFgLQuXNnhgwZQrdumnMSTuzRI7B7h+eBrlTzGZPVB7tnp6cZpMKRiEiLa1Y4qqio4JVXXuG///0vgPcGo9Za3nrrLc4//3x+9KMfNfvmsxIktm8G64YOCZj4xt1TTxohszcs+lDNIEVEfKRZqeXNN9/kv//9L5dddhnf+c536NKlC8YY8vPzmTdvHgsWLKBt27bccsstLVSuBDKbq/5G/mAy+2ABtm3CVlZgItVjTESkJTXrGvtFixZx/vnnc9ttt5GSkkJERAQul4uUlBQmTpzIeeedx6JFi1qqVglw1Veqqb+Rj3VJgbZxUFkBO3KdrkZEJOQ0KxxVVlaSnd3wUYJevXpRVVXVnE1IkLDWeu+ppivVfMsY4zm1Bjq1JiLiA80KR4MGDWLlypUNPr9y5UoGDtTdw8PCgSIo2Q8uF6RnOV1NyDOZnvusqd+RiEjLa1Q4Onz4cK1/N954I4WFhTz33HOsXr2awsJCCgsLWbVqFc8++yyFhYXceOONvqpdAkn1/dTSumNatXK2ljBgjh85Yst6z1E7ERFpMY2akH3bbbfVu3zHjh0sW7as3ufuu+8+3n777cZXJkHFqvmjf3XPgohIKDkARXuhc5LTFYmIhIxGhaNrr73We7m+SE0nrlTTZGx/MNGtoFsGbM3x9DtSOBIRaTGNCkfXX3+9r+qQIGYrK2HHZkBXqvmTyezj6Ui+ZQOcPdLpckREQkazJmSLALB7O5SXQ0wbz2Xm4hcm6/ikbF2xJiLSoprdunrDhg18/PHHFBQUUFpaWmdyqDGGZ599trmbkQBW3d+IHtkYl/K232QeP0q3ezv2SBkmJtbZekREQkSzwtF7773HG2+8QXR0NCkpKbRt27al6pJgUt3fSJ2x/crEd4JOibCvwHO1YN/BTpckIhISmhWO5s6dS+/evXnggQeIjdVfreHqRGdshSN/M1l9sPsKsJvXYxSORERaRLPOgRw7dozzzjtPwSiM2dLDkL/b80CTsf1PzSBFRFpcs8JRv3792LFjR0vVIsFo2ybPx85JmHZxztYShrzNIHM3YN26VY+ISEtoVji69dZbWbNmDXPnzuXw4cMtVZMEkRPNH3XUyBFp6dAqBo4egTz9oSIi0hKaNecoISGBSy65hDfeeIM333yT6OhoXPVcrfT66683ZzMSwOxWNX90knFFQEY2rP8Gu3kDJq2H0yWJiAS9ZoWjGTNmMHv2bDp27EhmZqbmHoUZay1UT8bWlWqOMZl9sOu/gS3rYeR3nC5HRCToNSscLViwgKFDh/Lzn/+83iNGEuIK98DhQxAZCTpi4RiT1QeLJmWLiLSUZiWayspKhg4dqmAUprz3U+uWiYmKcraYcNYjG4yBwnxsyQGnqxERCXrNSjVDhw5l/XrduiBsHZ9vpP5GzjKxbSClm+fBFn0/iog0V7PC0XXXXcfu3bt59dVXyc3N5eDBgxw+fLjOPwlN1VeqoXDkOO991nRqTUSk2Zo15+hnP/sZANu2bWPBggUNrjdjxowmb+Nf//oXb731FqNHj+aWW24BoLy8nOnTp7N48WIqKioYNGgQEydOJD4+vsnbkcaxFeWwcysARleqOS+zD3z6gcKRiEgLaFY4uvbaazHGtFQtdWzevJkFCxaQnp5ea/nrr7/O8uXLue+++4iNjWXatGlMnTqVJ5980me1yLfsyIWqSmjXHhK6OF1N2KuelM32zdiKckxUtNMliYgErWaFo+uvv76l6qjj6NGj/P73v+fOO+9k9uzZ3uVlZWV8/PHH3HPPPfTv3x+ASZMmce+995KTk0N2tk7x+EP1/dToke3TgCynKaELxMXDwWLYvhmy+jpdkYhI0GpWOPKlV199lSFDhjBw4MBa4Sg3N5eqqioGDBjgXZaamkpCQkKD4aiiooKKigrvY2MMMTExGGPC6hd79VhbZMxbPbcNcWX0Cvj/wxYdd4AyxmAz+2BXfAFbNmJ69guLcddH49a4w0G4j9vXGhWO7r//fsaPH8/QoUMbtZGysjIeeeQRfvSjH5GVlXXK9T///HO2bt3Kr371qzrPFRcXExkZSZs2bWotb9++PcXFxfW+35w5c5g1a5b3cY8ePZgyZQoJCQmNGkeoSEpKavZ75G3fQhXQ6YxzaJ2c3Pyi/KAlxh3IDg4dTsmKL2i1K5eEGvsk1MfdEI07vGjc0pIaFY527txJWVlZozdSVVXFzp07OXr06CnXLSoq4rXXXuPhhx8mOrpl5k2MGzeOsWPHeh9XJ8+ioqJaR5RCnTGGpKQk8vPzPd2tm8geLKZq724whv1xnTB79rRglS2vpcYd6GxiKgBH1q4kLy8Pl8sVFuP+tnDZ39+mcWvc4SAqKsovBzYafVrt9ddf5+23327Uaxqz43JzcykpKeGBBx7wLnO73axfv54PPviAhx56iMrKSkpLS2sdPSopKWnwarWoqCii6mlSaK0Nqy+qas0dt/cS/qQ0iIkNmv/DUN/ftmsGREbBoRJsQR62iycshfq4G6JxhxeNOzz4a6yNCkcXXnhhszbWoUOHU64zYMAAnnvuuVrL/u///o+UlBSuuuoqEhISiIiIYPXq1Zx99tkA5OXlUVRUpMnYflLdGVv3UwssJioKumfB5vXYzevheDgSEZHGaVQ4mjRpkq/q8IqJiaFbt261lrVq1Yp27dp5l48aNYrp06fTtm1bYmNj+ctf/kJ2drbCkZ+cuFJN/Y0Cjcns7QlGWzbAuZc4XY6ISFAK2KvVTmbChAkYY5g6dSqVlZXeJpDie9bthm2eK9XU/DHwmMw+WOaoGaSISDMERTh67LHHaj2Ojo5m4sSJCkROyN8FR8ogutWJ+3lJ4Mjs7fmYtwNbplv3iIg0RbPurSbhxzsZu3sWJiLC2WKkDhMXD4nJYO2JfSUiIo2icCSNs/X4ZGzNNwpY5vjRI7t5vcOViIgEJ4UjaRRdqRYEsvp4PiociYg0iU/DUXl5OUVFRb7chPiRPXoEdm/3PNCRo4BlMj3hyG7NwVZVOlyNiEjwafKE7EOHDjF//ny+/vpr8vPzcbvddOzYkQEDBnDZZZfRrVs3lixZwh/+8AdmzJjRkjWLU7ZvAeuGDgmYDp2crkYaktwVYtrAkVIqtm2G2PZOVyQiElSaFI42btzIc889x8GDB4mNjSUtLY2qqioKCwtZsGABH330Eddcc43u+RJiTvQ30im1QGZcLsjsBWuWc2zdN3DGBU6XJCISVBodjvbt28evf/1rYmNj+cUvfsGwYcNqPb9hwwbeeecdZs2apXAUYqqvftJ8o8BnMntj1yynfP0qhSMRkUZqdDiaM2cOAI8//ni9N3/r3bs3vXv35v3332f69OnNr1ACh65UCxqeZpBwbN03GKeLEREJMo0ORytWrGDUqFGnvCvumDFjKCsrY926dU0uTgKH3V8ExfvB5YL0LKfLkVPpkQ3GRVVhPhH7i0BzxERETlujw1FxcTFpaWmnte51113X6IIkQFXPN0pNx7Rq5WwtckqmdQx07Q47crG5GzDDznW6JBGRoNHoS/ljY2M5dOjQaa27atUqZs2a1eiiJPCc6G+kU2rBwmT1BdQMUkSksRodjnr16sXnn39+yvW2b9/Os88+yz/+8Y8mFSaB5cSVagpHwUKdskVEmqbR4ejKK69k27Zt/N///R8VFRX1rvPZZ5/x6KOPUlmpBnShwFZWwvbNgK5UCyamulP2zlzssWPOFiMiEkQaPecoOzub//mf/+G1115j1apVnHPOOaSkpFBZWUlBQQHLli2joKCAzMxMRowYwRtvvOGLusWf8rZDebmnsWCXVKerkdPVsTMRnRKp2lcA2zZBr/5OVyQiEhSa1ATyiiuuoFu3bsycOZN58+ZhrfU+l5iYyA9+8AO+853vsHjx4hYrVJxTPd+IHj09DQYlKBhjiOozkCOf/Qe7ZT1G4UhE5LQ0+fYhffv25bHHHqOsrIy9e/dSWVlJhw4dal3i36tXL3784x+3SKHioOrmj+qMHXRaecPRBqdLEREJGo0KR2+99RbdunWjW7dupKamEhERQWxsLD169Kh3/cTERBITE1ukUHGO3aor1YJVdN9Bnk+2bMC63TryJyJyGhoVjubPn095eTkAERERJCcn061bN7p27eoNTQpDocWWHYb8XZ4HOnIUdKIzekF0NJQegr15kHx6PcpERMJZo8LRG2+8QUFBAbt27WLnzp3s3LmTTZs21Zpb1Lp161phqWvXrvTr16/FCxc/2brJ87FzEqad7u4ebExkJHTvCTlrPfOOFI5ERE6p0XOOqk+VDR06lM2bN/PNN99w6aWX0rdvX9xuN5s3b2bRokVs2rTJ+5oZM2a0aNHiP9X9jXQ/teBlsvpic9bC5vVw3qVOlyMiEvCaPCEbYNq0aZxzzjnceuut3mXnnXceN910E//85z9ZuHAht9xyS3NrFAd5r1RTf6OgZTJ7Y0GTskVETlOzZmfu2LGD9PT0OstbtWrF+PHj6dWrF19//XVzNiEOstZ676mmK9WCV3WnbPJ3YQ8fdLYYEZEg0KxwlJqaysqVKxt8fvDgwaxatao5mxAnFe6Bw4cgMhK6ZjhdjTSRaRsHScfnGm3Z6GwxIiJBoFnh6Oqrr2bp0qXMmDHDexVbTdu2bdMtRIKYXbPc80mPbExUlLPFSLN477O2RfdZExE5lWbNORoxYgT79+/nzTffZMGCBVxwwQVkZHiOMKxZs4ZPPvmEYcOGtUih4n/2m2UAmEFnOVyJNFtmb/hczSBFRE5Hs8IRwNixY+nfvz+zZ89mwYIFtY4gDRw4kDvuuKO5mxAH2KNlkLMaADNQ4SjYmay+WIBtOdjKSs8l/iIiUq9G/4ScO3cuw4YNIzX1xA1Iu3fvzn333UdlZSX5+fmUl5eTkJBAXFxcixYrfrRuJVRWQmIyJOlms0GvSwq0aedpBrlzK/To6XRFIiIBq0nh6M0336Rz584MHTqUoUOH0q9fP6KiooiMjCQtTU3mQoH3lNrAMzHGOFyNNJdxuSCjF6z+ytMMUuFIRKRBjQ5Hf/7zn9m0aRMrV65k+fLl/Pvf/yY6Opp+/fp5w1LNm89K8LHuKuzqrwBPOJLQYLL6ePbr5vVwyZVOlyMiErAaHY6MMWRnZ5Odnc31119PcXExy5cvZ+XKlbz11ltMmzaNtLQ0hg4dyrBhw8jOzsalm10Gl62b4FAJxLSBnrr1S6gwmX2ON4Ncj7VWRwRFRBrQ7FmZ8fHxjBo1ilGjRlFVVcX69etZsWIFX3/9NXPnziU2NpZBgwYxZswYevbUofxgYL9ZCoDpP1QTd0NJ954QEQHF+2F/EXTq7HRFIiIBqUV/80VERNC/f3/69+/PD37wAwoKCli+fDkrVqxg/fr1pxWOPvzwQz788EMKCwsBSEtL43vf+x5DhgwBoLy8nOnTp7N48WIqKioYNGgQEydOJD4+viWHEtbsKs98I3RKLaSYVq08zTy3bcJuXofpdKHTJYmIBKRmne/6yU9+wqxZsxp8PjExkSuuuIJf/vKXXHnl6c1x6NixI+PHj+fXv/41v/rVr+jfvz+/+c1v2LlzJwCvv/46X3/9Nffddx+PP/44Bw4cYOrUqc0ZhtRg9xXA7u1gXJgB6lEVary3ElG/IxGRBjUrHBUUFJCbm0tubi6fffYZy5cvZ+/evc0q6IwzzmDo0KEkJyeTkpLCTTfdROvWrdm0aRNlZWV8/PHHTJgwgf79+5ORkcGkSZPYuHEjOTk5zdqueFSfUiOrN6ZNO2eLkRZnsvoA6pQtInIyzT6t9vXXX9e5uWzXrl0ZN24c5557brPe2+1288UXX3Ds2DGys7PJzc2lqqqKAQMGeNdJTU0lISGBnJwcsrPrvzlqRUUFFRUV3sfGGGJiYjDGhNWk1OqxnnTMx0+puQYND5n/m9Madwiqd9yZnnDEzm1w7CimdYz/C/Mx7W+NOxyE+7h9rdnhKDIyknHjxtG3b18qKyvJzc3lyy+/5He/+x1r165tUofsHTt28NBDD1FRUUHr1q25//77SUtLY9u2bURGRtKmTZta67dv357i4uIG32/OnDm1Tv/16NGDKVOmhG3LgaSkpHqXu8tK2b1xDQCJl4whKjnZn2X5XEPjDnW1xp2cTF7nJKoK8+l4cB+te4TuvDLt7/CicUtLanY4GjNmDN/73ve8jwcOHMjVV1/Ne++9xxtvvEF2djYjR45s1HumpKTw7LPPUlZWxpdffslLL73E448/3uQax40bx9ixY72Pq5NnUVFRrSNKoc4YQ1JSEvn5+Vhr6zzv/vpzqKyAxGQKI6Ixe/Y4UGXLO9W4Q1VD43b3yIbCfPYt+QxXYug1bdX+1rjDQbiOOyoqyi8HNpoVjqKjo+nYsWO9z40dO5YtW7bwwQcfNDocRUZGetNwRkYGW7ZsYd68eYwYMYLKykpKS0trHT0qKSk56dVqUVFRRNVzV3lrbVh9UVVraNx25fFL+I/fSy3U/m+0v4/L7A1L/4t7y3pMCP9/aH+HF407PPhrrM2akJ2SksKKFSsafL5Pnz7s2rWrOZsAPHOPKioqyMjIICIigtWrV3ufy8vLo6ioqMH5RnJ6rLsKu8Yzd8wMCt1TLeJpBgnAlo1Yt9vZYkREAlCzwtGll17KypUr+ctf/kJ5eXmd5zds2ECrVq0a9Z5vvfUW69ato6CggB07dngfn3/++cTGxjJq1CimT5/OmjVryM3N5Y9//KO3Y7c0Q82u2Fl9na5GfCmtO7RqDUdKYc9Op6sREQk4zTqtdskll7Br1y7mz5/P559/zpAhQ+jatSuRkZGsXr2aFStWNPqUWklJCS+99BIHDhwgNjaW9PR0HnroIQYOHAjAhAkTMMYwdepUKisrvU0gpXnUFTt8mIgI6JENG1Z5bkKbmu50SSIiAaXZvwVvueUWhg8fzvvvv8+yZctYtGiR97kzzzyTCRMmNOr9fvzjH5/0+ejoaCZOnKhA1MLUFTu8mMze2A2rYPMGuOAKp8sREQkojQpHb731Ft26daNbt26kpqYSEREBeOYW9enTB7fbTUFBAUePHiUhIYG2bdv6pGhpWbZor6crtktdscOFyTpxE1oREamtUeFo/vz53rlFERERJCcn061bN7p27Uq3bt1IT09Xz4UgZL85ftQoq4+6YoeLjF6ejwV7sAeLMXHxjpYjIhJIGhWO3njjDQoKCti1axc7d+5k586dbNq0icWLF3vXad26tTcsVQenfv36tXjh0nKqT6lVX8Ivoc/EtoWUbpC3A3I3wOCznS5JRCRgNHrOUWJiIomJiQwdOpTNmzfzzTffcOmll9K3b1/cbjebN29m0aJFbNq0yfuaGTNmtGjR0nLs0TLI8bRG0CX84cVk9cHm7cBuXo9ROBIR8WrWhOxp06ZxzjnncOutt3qXnXfeedx0003885//ZOHChdxyyy3NrVF8ae1KqKyExBRMUuh1S5aTyOwN//03dssGpysREQkozepztGPHDtLT614G3KpVK8aPH0+vXr3q3JRWAov3En5dpRZ2vM0gt23GhtFtdERETqVZ4Sg1NZWVK1c2+PzgwYNZtWpVczYhPmTdVdjVXwE6pRaWEpOhXXvP/fR2bHG6GhGRgNGscHT11VezdOlSZsyYUW+H7G3btlFZWdmcTYgv5ebA4YPqih2mjDGeU2vokn4RkZqaNedoxIgR7N+/nzfffJMFCxZwwQUXkJGRAcCaNWv45JNPGDZMfXMClV2lrtjhzmT1wa5conlHIiI1NPs34tixY+nfvz+zZ89mwYIFtY4gDRw4kDvuuKO5mxAfsas8p9QYpEv4w5XJ7I0F2Lwea63naJKISJhrkcMF3bt357777qOyspL8/HzKy8tJSEggLi6uJd5efKBWV+z+Q50uR5ySngWRkXCwGIr2Qmc1cRURadFzKZGRkaSl6XLwYKCu2AJgoqI9AWnLBs9NaBWORESaNyFbgpd3vpG6Yoc9c3xSNps1KVtEBBSOwpI9UgYb1wC6hF9OhCNNyhYR8VA4CkN27QqoUldsOa66GeTu7Z7gLCIS5hSOwpD3lJqOGglg2nfwTMS2FnI3Ol2OiIjjFI7CjK2q8l7Cb3QJvxxn1AxSRMRL4SjMlG9c4+mKHdvmxOkUkeNfC1aTskVEFI7CzZGliwAw/dQVW04wWcevWMvNwbqrnC1GRMRhCkdh5sjS/3o+0Sk1qSmlG8TEwrEjsHuH09WIiDhK4SiM2MJ8KrfnHu+KrXveyQnGFQE9egE6tSYionAURuyq6q7YfTFt2jpbjAQcbzNITcoWkTCncBRG7DeeS/hdOqUm9TBZxydlr12BLSt1uBoREecoHIUJe6QM6+2KrXAk9cjuB0mpcPggdu5bTlcjIuIYhaNwsc7TFTsypRsmKdXpaiQAmcgoXDfdCYD9+H3szq0OVyQi4gyFozBRfUqt9fDzHa5EApnpOxhzxnlg3bjf/D+s2+10SSIifqdwFAasuwq7+msAYs5SOJKTM9fdCq1aw5YN2C8WOl2OiIjfKRyFg9yN3q7YrfoOdroaCXCmYwLmuzcBYP/5Grb0sMMViYj4l8JRGKi+hN/0H6au2HJazMXfheSucKgE+6+/OV2OiIhfKRyFAfvN8XCkq9TkNJnISFzf/xEA9tP52O2bHa5IRMR/FI5CnC3Mh7wd6ootjWZ6DcCcdSFYi/vNP2lytoiEjYA7xzJnzhyWLl3K7t27iY6OJjs7m5tvvpmUlBTvOuXl5UyfPp3FixdTUVHBoEGDmDhxIvHx8c4VHqDUFVuaw1z3P9hVS2FrDvazBZgLLne6JBERnwu4I0fr1q3j8ssv5+mnn+bhhx+mqqqKp556iqNHj3rXef311/n666+57777ePzxxzlw4ABTp051sOrAVX0Jvxl0psOVSDAy8R0xV40HwM6ejj180OGKRER8L+DC0UMPPcTIkSPp2rUr3bt356677qKoqIjc3FwAysrK+Pjjj5kwYQL9+/cnIyODSZMmsXHjRnJychyuPrDYI2WQsxYAM1DzjaRpzEVjITUdSg9hZ093uhwREZ8LuNNq31ZWVgZA27aeU0K5ublUVVUxYMAA7zqpqakkJCSQk5NDdnZ2nfeoqKigoqLC+9gYQ0xMDMYYjDE+HoFz7LqVUFUJXVJxJad5xxrKY66Pxt28cZvISMzNP6Zqyv/DfrYAzr8Mk9GrJUr0Ce1vjTschPu4fS2gw5Hb7ea1116jV69edOvWDYDi4mIiIyNp06ZNrXXbt29PcXFxve8zZ84cZs2a5X3co0cPpkyZQkJCgs9qDwT7Nq2mDGg34iLik5O9y5OSkpwrykEadzMkJ7Pvq7GUffQerhmv0uW3r2MiIpr/vj6k/R1eNG5pSQEdjqZNm8bOnTt54oknmvU+48aNY+zYsd7H1cmzqKio1hGlUGLdVVQtWQRAWVZfjuzZgzGGpKQk8vPzsdY6XKH/aNwtM247+npYvJCKLRvIm/EarotGt0CVLU/7W+MOB+E67qioKL8c2AjYcDRt2jSWL1/O448/TqdOnbzL4+PjqayspLS0tNbRo5KSkgavVouKiiIqKqrOcmttyH5R2S0bvF2xbUZvqDHOUB73yWjczRQXjxl3M/atl3HPmQ5Dz8HExTf/fX1E+zu8aNzhwV9jDbgJ2dZapk2bxtKlS3nkkUdITEys9XxGRgYRERGsXr3auywvL4+ioqJ65xuFK2/jR3XFlhZkLrwCumVAWSl29utOlyMi4hMBF46mTZvGokWLuOeee4iJiaG4uJji4mLKy8sBiI2NZdSoUUyfPp01a9aQm5vLH//4R7KzsxWOaqi+hJ+BuoRfWo5xReAaf7xz9ucfYTevc7giEZGWF3CHFD788EMAHnvssVrLJ02axMiRIwGYMGECxhimTp1KZWWltwmkeNjCfNizU12xxSdMZm/MeZdiP1uA+82XcT38fMBPzhYRaYyAC0czZ8485TrR0dFMnDhRgagB3q7YPfupK7b4hLnmh9jlX8CurdhP5nluVCsiEiIC7rSaNJ+3K7ZOqYmPmHbtMdf8EAD7zpvYkgMOVyQi0nIUjkJMra7Yg9QVW3zHnH8ppGfBkTLsrL86XY6ISItROAo1a5d7u2KbLimnXl+kiYwrAtf3fwzGYL/8BJuzxumSRERahMJRiPFewq8bzYofmB49MedfDoD7zT9hKysdrkhEpPkUjkKIdVdh13wF6Eaz4j/mmh9A23aQtwP78XtOlyMi0mwKR6EkdyMcPgSxbSGrj9PVSJgwbdphrpkAgJ37d+yBfQ5XJCLSPApHIaRWV2z1nZFvKSsrIzU1ldTUVMrKylr0vc25l0BGLzh2BPuPv7Toe4uI+JvCUQjxdsXWfCPxM+Ny4fr+j8C4sMsWYdd/43RJIiJNpnAUImp3xR7qdDkShky3TMzI7wDgfutlbGWFswWJiDRRwHXIlqbxHjXq2Q8Tq67Y4gxz9fexX30G+buwC+ZivnOt0yU1m62sgII9kL8bm7/rxMeoKFzX3Yrp3tPpEkWkhSkchYjqW4aoK7Y4ycS2xXzvf7B/fQH73tvY4RdgOnZ2uqxTstbC4YOwZxd2725PuMv3fKRoL7jd9b7OPeX/YW6ehOvci/1csYj4ksJRCPB0xfY04FNXbHGaOeci7KIPYfM63DOmEfHj/+d0SV62shIK82HvLuye3Z6P+bshfzeUHmr4ha1jPI1Vk9M8H5NScX+xEFYtw772Iu7tmzDX34aJjPLfYETEZxSOQsHa5VBVBUnqii3OM8bg+v6duJ+8F5Yvxq5Z7vd5cFWHSrCb1+Pes7PW6TCK8j3fK/UXDh07e76PktJqfaR9R4wxtVZ3DR3hOTr27tvYhfOwO7fh+tEDmPYd/DBCEfElhaMQ4L2EX40fJUCYtB6YUWOx/5mL+++v4Hrs95go3x1VsdbCrm3YFV9iVy4hb2duwyu3an386M/x4JOUhklKhcQUTKtWp71N43JhrhyP7ZaJ+y+/9Rwpe+peXD/6f5jM3i0wKhFxisJRkKvVFVuX8MtJVNU4YrJkyRIuuOACInzYD8tcOR67bBEU5GE/nIMZc32Lvr+tqoLN67Erv8Su+BL2FdReoWOCJ/h0SYXk4x+T0qBDpzpHgZrDDB6O68HncP/xV7BnJ+5nH8SMvwPXBVe02DZExL8UjoLdlhpdsTPVFVvqN2/ePCZPnux9fPPNN5OcnMwTTzzB6NGjfbJNExOLue5W7KtTsfNmYodfiEno0qz3tMeOwfoV2BVLsKuWer72q0VFQ78huIacTdKl36Wg7IjniJIfmKQ0XA8+i/uvL8LyL7Bv/BH3ts2Ym+706REzEfENhaMgV30Jv7piS0PmzZvHHXfcUSco5Ofnc8cdd/DKK6/4LiCddYFncvbG1bhnvErEXQ81+j3s4YPYVcuwK5bAuuVQXn7iyTbtMAPPxAw5G/oOxrRqjTGGiPbxUHak5QZyGkzrWFw/+n/Y+bOw//obdtGH2F3bPKfZOib4tRYRaR6FoyBXfQm/umJLfaqqqpg8eXK9R1CstRhjePTRR7n88st9corNGINr/J24n7gHVi7Brlp2Wu0mbNFe7Mol2JVLYNPa2pfSd0rEDB7uCURZfQPqjwJjDGb0ddhuGbj/PBW25hyfh/QAJru/0+WJyGlSOApi3q7YERHqii31WrRoEXv27GnweWsteXl5LFmyhBEjRvikBpPSDXPJldh/z/FMzu49EBNde+Jz7QnVX8LOrbXfpGsPTyAafLbn8xacM+QLpv8wXA8/j/uPz8Cubbifn4y57jbMqDEBX7uIKBwFNW9X7Ky+6oot9TpZMKqpoKDg1Cs1gxl7I3bJf6FoL/aDf3oma59sQrVxQXa/44FoeLPnKjnBdE7C9f9+g3399577zb39CmzbBD+YVCccikhgUTgKYt6u2Gr8KA1ITk4+rfUSExN9WodpHYPrhttwv/wb7Px/4i7c67nKsuaE6uho6DsUM2Q4ZsCZmHZxPq3JH0yr1nD7/dC9J/afr2G/XIjN247rx78MysAnEi4UjoKU3bUNNq4GdAm/NOz8888nOTmZ/Pz8eucdGWNITk5m+PDhvi9m2LnQZxCs/wb75ULPsrbtMAPPwgwZDn2GNKrPULAwxmAuu9ozD+nl38COXNxP34frjl9g+gxyujwRqYfL6QKk8azbjftvf/RMUh18NiZRXbGlfhERETz55JMAdea6VD9+/PHHfdrvqOb2XD+8GwadhbnkSlz3P4Pruem4/ucezOCzQzIY1WR6D8T18G8hPQsOH8L920dx/3uO39oNiMjpUzgKQva//4YtG6BVDK6b7nC6HAlwo0eP5pVXXqFLl9qncZKTk316GX99TEIXIu5+GNcNEzG9+gfUlWb+YDp1xvWLX2HOGQXWjZ31V+yfn8MeO+p0aSJSg8JRkLHF+7GzpwNgxt2s/ilyWkaPHs0nn3ziffy3v/2NL7/80q/BSDxMdCvM/9yDGX8nRERgly3C/aufYwtOb/K8iPiewlGQsTNehSOlkJ6FuUi/2OT01Tx1Nnz4cL+cSpP6GWNwXTQG1/8+DXHxsHs77qfvw67+2unSRASFo6BiV3+F/eozcLlw/fAujEu/3ESCmenZ1zMPKaMXlJXi/v0TuN+fia3Z9FJE/E7hKEjYY0dxv/knAMwlV2K6ZTpckYi0BNOhE677n8FccAVYi/3X33D/36+xR8qcLk0kbCkcBQn77t89TfI6dsZ89yanyxGRFmSionD9YBLmh3dDZCSs/BL3M/dj83c5XZpIWFI4CgJ2Ry52wTsAuMb/CNM6xuGKRMQXXOdfhuvnv4L4TpC/C/czP8du2+R0WSJhR+EowFl31YmeRsNGqOGjSIgzGb1wTX4eMnvDkVLcv30Eu32z02WJhJWA7JC9bt065s6dy9atWzlw4AD3338/Z5114hYZ1lpmzpzJRx99RGlpKb1792bixImnfauEYGI//QC25kBMLK4bb3e6HBHxAxPXAdfPHsP94uOweT3u5yfjuu9JTHqW06WJhIWAPHJ07Ngxunfvzm233Vbv8++88w7z58/n9ttv55lnnqFVq1Y8/fTTlJeX+7lS37IH9tXoafRDTHwnhyuSYBYbG8vu3bvZvXs3sbGxTpcjp2Bax+K651HI6uO5ku35R7DbtzhdlkhYCMhwNGTIEG688cZaR4uqWWuZN28e11xzDWeeeSbp6encfffdHDhwgGXLljlQre+43/4zHD0CPbIxF17udDki4mfegJTZG8oO435+sgKSiB8E5Gm1kykoKKC4uJiBAwd6l8XGxpKVlUVOTg7nnntunddUVFRQUVHhfWyMISYmxnNDyG/dbypQuFcuheWLweUi4od3YyKav6uqxxqoY/YVjVvjDmYmpg3mZ49T9cKjsGUD7ucnE3H/U3XaeYTauE+Xxh2e4/a1oAtHxcXFALRv377W8vbt23uf+7Y5c+Ywa9Ys7+MePXowZcoUEhIC89Yb7iNl5M94BYB2424m/qwRLfr+SUlJLfp+wULj9q/S0lLatm0LwOHDh2nTpo1ft109B9Hf2/YV96//ROHkn1C+YTX2t4+S8PQfic7sVWc9fZ2Hl3Adt68FXThqinHjxjF27Fjv4+rkWVRUVOuIUqComvEqtnAvJCRSNuq7HNnTMvdcMsaQlJREfn5+WN0JXON2ZtxlZSeaGObn5/ttnpMxhnbt2vl122VlZWRleSZLb9682Wfbs5MeghcexZ27kb2//BER9z+N6doDcH5/O0XjDq9xR0VF+eXARtCFo/j4eABKSkro0KGDd3lJSQndu3ev9zVRUVFERUXVWW6tDbgvKrt9C/Y/7wLgGv9jiG7V4jUG4rj9QeP2/3adqqGqqsr7+ZdffskFF1zg03vJVVZW+md7MbG47nkM9wuPwtYcqqY+hOu+p7wBCfR1Hm7Cbdz+GmtATsg+mcTEROLj41m9erV3WVlZGZs3byY7O9vByprPuqtwv/ESWDfmzPMxA4Y5XZJIk9UMKEuWLKn12JfmzZtH3759vY9vvvlmhg8fzrx583y2vZEjR/pteya2Da6fPQ49suHwIdzPP4zdtdUn2xIJVwEZjo4ePcq2bdvYtm0b4JmEvW3bNoqKijDGMHr0aGbPns1XX33Fjh07+MMf/kCHDh0488zgbpBoP34ftm+GmDaYGyY6XY5Ik/k7MNTc7u23387u3btrLc/Pz+eOO+5o8e3PmzePO+64g/z8fL9sr5onID0G3Xt6AtLUydhd23yyLZFwZGwAHo9bu3Ytjz/+eJ3lF154IXfddZe3CeR//vMfysrK6N27N7fddhspKSmN2k5hYWHAzDmy+wtxP3I3HDuCuXkSrguvaPFtGGNITk5mz549YXUYVuP277irA8O3t1k91++VV15h9OjRLb7dqqoqhg8fzp4G5uhV/398+eWXLXLKy9/bq48tO4z7+Uc8f1S1jaPLlFfY16qNvs7DQLiOOyoqis6dO/t8OwEZjvwlkMJR1UtPw8olkNkb1y9+jXG1/EG9cP1m0rj9N24nA8PixYu57rrrTrneP/7xD0aMaP4VoP7eXkNs6WHcv/UEJFdcPOZ/n4KUbj7bXqDR93d4jdtf4SggT6uFG7viS08wiojA9YO7fBKMRPxhyZIlDQYj8EymzMvLY8mSJS2+7YKCghZdL9C21xDTpi2ue5+A9CzcB4upeu4h7O4dPt2mSKjTb2GH2SNluN96GQBz+TWY1HSHKxJpOicDQ2JiYouuF2jbOxnTpi0R9z1JVGZvOFSCe+pD2DwFJJGmUjhymH3nTSjeB52TMGOud7ockWZxMjAMHz6c5OTkBjvoGmNISUlh+PDhQbm9UzFt2tL56ZegW4YnID2ngCTSVApHDrJbN2E/fg8A180/xkS3crgikeZxMjBERETwxBNPeLfz7e0CPP744y0218nf2/u2srIyUlNTSU1N9TbcjGjXnoj7noKuPY4fQXoYu2enT7YvEsoUjhxiq6pwv/EHsBYz/EJM3yFOlyTSbE4HhtGjR/PnP/+5zpWrycnJPrlKbvTo0bzyyit06dLFL9urqaE+UqZtO1z3PekJSAeLPUeQFJBEGkXhyCH2o3dh51aIbYu5/janyxFpMU4Ghurtr1u3zvv4b3/7G19++aXPtjt69Gg++eQTv20P6u8jddZZZzF79mwATNs4T0BKqxmQdvmsHpFQo3DkALuvwDPXCDDfuwUTF+9sQSItzInAUFPNI1PDhw/36a1DANq1a8fu3bvZvXs3F110kU+3d7LGk9/73ve8jSdPBKTunoA09SFsvgKSyOlQOPIzay3uN/8E5cegZ1/MuZc4XZKIT/g7oISDqqoqHnnkkXr72lQve+SRR06cYmsXh+u+pyA1HUoO4H7uYQUkkdOgcORvyxfD6q8gIlI9jUR8pE2bNuTl5bF7925iY2OdLqfFNKWPlGkXh+t/qwPS/uMBaXeD7yEiCkd+ZctKcf/9zwCY71yLSe7qcEUivhMbG+s91RRKAcVJTe0jZdq1rx2Qpj6E3ZvnixJFQoLCkR/ZOW9AyX5ITMGMPvVtB0REampOHynTrr1nDlJKNyjej/u5BxWQRBqgcOQndssG7KfzgeM9jaKiHa5IRIJNc/tImbh4zxGk5K7HA9JDOsUmUg+FIz+wlZW433jJ09PonIswfQY5XZKIBKHT6SP1xBNPnHTyu4mLx3V/dUDah/uxn+D+y2+xO7b4rnCRIKNw5Af2P+/A7u3Qth3mOvU0EpGmO1kfqVmzZp1WuwQT18ETkLL7QVUl9ouFuJ+8l6pnf4ld8SXWXXXK9xAJZZFOFxDqbGE+9t2/A2C+dyumXZzDFYlIsBs9ejSXX345S5YsoaCggMTERM4++2zS0tJOejVbTSauAxE//xU2dyP2o3exX38OOWtx56yFhC6Yi8dizr0UE6PJ9BJ+FI58yFqL+60/QXk59BqAGTHK6ZJEJEREREQwYsQI7+OG5iGdisnohcnohb32Fuwn87D//TcU7cXOmIZ95y3MuZdgRo3FJCa3VOkiAU/hyIfsV5/BmuUQGemZhN3EH14iIr5mOiZgrvkhdswN2C8XYv8zF/J3eY4qffweDDoL1yVXQnZ//SyTkKdw5CO29DD27eqeRtdhktIcrkhE5NRMq1aYC6/Ann8ZrFuJ+6O5nj/yVi7BvXIJdO2BueRKzJkXYKKinC5XxCcUjlqYtRZy1uB+9204WAxJqZjvfM/pskREGsW4XNB/KBH9h2L37PQcQfriY9i5FfvXF7GzXsOMHI0ZeQUmroNfaqqqqqo1z2r48OFERvr215h1u6HsMFRVQVy8jpqFCYWjFmKPHcMu+cRz+Hn3ds/C6luE6K8rEQliJrkr5uZJ2HE/wP73Q8/PueJ9VM59i6VvTKMgNZMuIy/j7O9e7bN76M2bN4/JkyfXuuFucnIyTz75JLfddvpXAdvyY3DoIBw+CIdKsIerP/d8tIdLaj3m8CGwbs+LW8VAlxRMlxRISoUuqZikVM+y1pq4HkoUjprJFu31TGJctMDz1wVAdCtPP6OLv6tbhIhIyDBt2mG+cy320quY99ILPPrSn9hTegRYD/94j+QH/h+P//RuRv/4boyr5ULSvHnzuOOOO+rccDc/P5/bb7+ddlUVnNc7C3vweNg5VOIJNYerH58IQ5Qfa1oRxsCxI7BjS62eUN6K2nf0nCnocjwsHQ9PJHTB6KbLQUfhqAmstbBxNe6P3oNvlp74qyKhC+aiMZ6rO9q0dbZIEREfmf/hh9z57At1w8rhMu585jf8afF/GH3rHZhzLz6tIyq2ogJKD0HpYc/HskPY459XHTrII5N/VWdb4PlZbICf3fNTFl80gIjTPeUVEQnt4qCt559p1x7atoO27Y8vb49p2w7atfeuAxYK98LeXZ7bruTvxu7dDfm7PaGrZD+U7MduXO2prea2OifVCkymS6rnyFO79jpNF6AUjhrBc+psIfbj90+cOgPoMwjXxd+FAcNa9K8lEZFAU1VVxSOPPFJ/WAEM8NiXq7ks5hUi3nkTM+Jiz1GV0kNQdhhbMwRVfzzJ0Zwl+w6xp+RQg89bYM/RCpa6ozmnZ4YncNQJOnEnwlC79tA65rRCSX1znCKS0/j2K23ZYdib57kVS/5u2Hs8OBXkeVq55O/yXPn3zYmaAYhpc/xoU4onNKWlQ2Zf9cMLAApHp8EW7cUunIf97FunzkaM8hwpSunmbIEiIn6yZMmSkzaa9IYVWnPOkTLsR++e3hsbF7RpC23aeT+a2DYU5GyDJTmnfHnRlT8g4uqrT29bp2HevHk88sgjtcaanJzME088UacLuYltCz2yMT2yay23bjcc2Ff/0ab9hXCkFLbmYLd6xucNTUlpuDN7s7TCRUF0GxKzsjn77LN9Np9L6lI4aoC1Fjaswv3x+7VPnXVOOn7q7GLPN4SISBgpKCg4rfUKR9+Eq2c6dtl/6w8+1Y9jj39sHeO5Qu5bkhYvhjdnn3J7376dSnOcbI7THXfcwSuvvHJ6t2lxuaBTZ+jUGdN3SK3nbPkxKMyvFZjstk2wZyfzV67msbfmsedohXf95HZtefyW7zP6pu9DWneMj6/SC3f63/0We+wo9svjV53l7TjxRN/BuEZ9FwYM1akzEQlbiYmJp7Vel6QkzIBhmAHDmrW94cOHk5ycTH5+fr2n8owxpKWlMXz48GZtp9pJTxtaizGGRx99lMsvv7xZR3JMdCtITYfU9Fqn6d6fPYsf/eQevr31/EOHufP3L/OnzxfwnfQUbGYvSoYMx53U1XPUqlXrJtcidSkcHWcL87GfzMd+9iGUlXoWtmqNOWcUZtQYXXUmIsLphZXk5OQWCysRERE88cQT3HHHHRhjam2zet7QCy+8QERERL31NNYpTxtaS15eHkuWLKl1+5aWUFVVxaPP/LpOMIIa87nW7+ayLvFErFvJwXUrPU9GREC3TExWH0xWX8jqg4mLb/S268yvCuPTeGEfjuz6b3B/XH3V2fEvyc5JnkA0QqfORERqOp2w8vjjj7foL9bRo0fzyiuvNDgH6JprrjntG+6eyumeNjzd9RrjtOZzHTnGsu/ewoj2MbTetZWyVV/DgSLv3CW74B3Pyl1SMVl9oGdfTM++0Dm5wUnojZlfFS7COhxV/enXuL/89MSCvkNwXTwW+g+r99y3iIicPKw8/vjjPvmFOnr0aC6//HKfd8g+3dOGp7teY5z2fC4bgeuiMXRKTqZ8zx7cRXuxm9bB5nXYzes9V1NXXzH3+X88R6Li4iGrLyY1HTomYDomQMfOzFvyNXfedVez51eFmrAORxTmQ6sYzIiLMBeNxSTr/mciIqejobDiy1MxERERLX4q69v8fdqwpqYGM9MpEdMpEc4eCeBpl7BlA3bzOuym9bAtx3M7q+WLscsXe9YBqqzl0YWrT9pD6tHJD3PZxRcT2apVM0YWfMI6HLkuvwZXr4GY2DZOlyIiEnT8EVb8zYnThtVaKpiZNu1g4JmYgWcCYCvKYdtmz1Glwj3YA0Wwr5ClOZtrXRH3bRbIy9/Ll+Ov4JyMdOjYGdMhAY4fdTIdE6BDZ+iU4Okn1cQzLtZacLuhqtJzD7vKyhOfV1Uc/1jpWR4TC507N2k7jRHU4eiDDz7g3Xffpbi4mPT0dG699VaysrJO+/XmrAswFQ1/YYiISPhx4rQh+C6YmajoE3OPaij6179g0V2nfH3B0Qoo3g/F+7Fs9C6vFd8iI6FDAnTo5Gnd4A03x0NNrc9rPK7+/DQn01dl9oIhb57Wus0RtOFo8eLFTJ8+ndtvv52ePXvy/vvv8/TTT/PCCy/Qvn17p8sTEZEg5sRpw+rt+iuYnXZbhnsn48rKgP2F2P1Fngng+wo9R6D2F0HJAU/oKcz3/GsJxuUJXBERnluwVH/eIaFl3v8UgjYcvffee1x88cVcdNFFANx+++0sX76chQsXcnULdkkVEZHw5NRpQ38Fs9M9jXf2RZd4bp7bvWedW6cA2MpKz73l9hdhi/d5XlsdaiK+HXCOP27o84goiIho8BRdRFRUS/4XNCgow1FlZSW5ubm1QpDL5WLAgAHk5NRtM19RUUFFjdNnxhhiYjz31gmnm/5VjzWcxgwat8YdHjTu0Bp3ZGQk5557boPPt8S4IyMjefLJJ7n99tsbPI33xBNPnPKKQBMVBQldPP98zF/7OSjD0cGDB3G73cTHx9daHh8fT15eXp3158yZw6xZs7yPe/TowZQpU0hI8M/huUCTlJTkdAmO0LjDi8YdXjTuprntttvo0KED99xzD7t27fIuT0tL44UXXuCaa65pbolBKSjDUWONGzeOsWPHeh9XJ8+ioqJaR5RCnTGGpKSkBg+hhiqNW+MOBxq3xt1U55xzDl988QVLlixh7969dOnSxXsar6Waa7aUqKgovxzYCMpwFBcXh8vlori4uNby4uLiOkeTwPOfGVXPeUprbVh9M1XTuMOLxh1eNO7w0lLjdrlcnHPOOXXeO9D4q6agbAMdGRlJRkYGa9as8S5zu92sWbOG7OxsBysTERGRYBeUR44Axo4dy0svvURGRgZZWVnMmzePY8eOMXLkSKdLExERkSAWtOFoxIgRHDx4kJkzZ1JcXEz37t158MEH6z2tJiIiInK6gjYcAVxxxRVcccUVTpchIiIiISQo5xyJiIiI+IrCkYiIiEgNCkciIiIiNSgciYiIiNSgcCQiIiJSg8KRiIiISA0KRyIiIiI1KByJiIiI1KBwJCIiIlJDUHfIbq7IyPAcvsYdXjTu8KJxh5dwG7e/xmustdYvWwogFRUVREVFOV2GiIiINIGvf4+H5Wm1iooKXnzxRY4cOeJ0KX515MgRHnjgAY07TGjcGnc40LjDb9wvvvgiFRUVPt1OWIYjgM8//5xwO2hmrWXr1q0ad5jQuDXucKBxh9+4P//8c59vJ2zDkYiIiEh9FI5EREREagjLcBQVFcX3vve9sJuUrXFr3OFA49a4w4HG7dtxh+XVaiIiIiINCcsjRyIiIiINUTgSERERqUHhSERERKQGhSMRERGRGkL2piwffPAB7777LsXFxaSnp3PrrbeSlZXV4PpffPEFM2bMoLCwkKSkJL7//e8zdOhQP1bcPHPmzGHp0qXs3r2b6OhosrOzufnmm0lJSWnwNZ988gl//OMfay2LiorizTff9HW5LWrmzJnMmjWr1rKUlBReeOGFBl8T7Psb4K677qKwsLDO8ssuu4yJEyfWWR6s+3vdunXMnTuXrVu3cuDAAe6//37OOuss7/PWWmbOnMlHH31EaWkpvXv3ZuLEiSQnJ5/0fRv7M8LfTjbuyspK3n77bVasWEFBQQGxsbEMGDCA8ePH07FjxwbfsynfK/52qv390ksv8emnn9Z6zaBBg3jooYdO+r7BvL8Brr/++npfd/PNN3PllVfW+1yg7+/T+b1VXl7O9OnTWbx4MRUVFQwaNIiJEycSHx/f4Ps29WdCTSEZjhYvXsz06dO5/fbb6dmzJ++//z5PP/00L7zwAu3bt6+z/saNG3nxxRcZP348Q4cO5bPPPuPZZ59lypQpdOvWzYERNN66deu4/PLLyczMpKqqir///e889dRTPP/887Ru3brB18XExPDiiy/6sVLf6Nq1K5MnT/Y+drkaPigaCvsb4Fe/+hVut9v7eMeOHTz11FOcc845Db4mGPf3sWPH6N69O6NGjeK5556r8/w777zD/Pnzueuuu0hMTGTGjBk8/fTTPP/880RHR9f7no39GeGEk427vLycrVu3cu2119K9e3cOHz7Ma6+9xm9+8xt+/etfn/R9G/O94oRT7W+AwYMHM2nSJO/jU92MNNj3N8Arr7xS6/GKFSv405/+xPDhw0/6voG8v0/n99brr7/O8uXLue+++4iNjWXatGlMnTqVJ598ssH3bcrPhG8LnP+lFvTee+9x8cUXc9FFF5GWlsbtt99OdHQ0CxcurHf9efPmMXjwYK688krS0tK48cYbycjI4IMPPvBz5U330EMPMXLkSLp27Ur37t256667KCoqIjc396SvM8YQHx9f618wcrlctcYQFxfX4LqhsL8B4uLiao15+fLldOnShb59+zb4mmDc30OGDOHGG2+s9Vd0NWst8+bN45prruHMM88kPT2du+++mwMHDrBs2bIG37OxPyOccLJxx8bGMnnyZEaMGEFKSgrZ2dnceuut5ObmUlRUdNL3bcz3ihNONu5qkZGRtcbQtm3bk75nsO9voM737bJly+jXrx9dunQ56fsG8v4+1e+tsrIyPv74YyZMmED//v3JyMhg0qRJbNy4kZycnHrfs6k/E74t5I4cVVZWkpuby9VXX+1d5nK5GDBgQIP/mTk5OYwdO7bWskGDBjXqPzLQlJWVAZzyh8bRo0eZNGkS1lp69OjBTTfdRNeuXf1RYovKz8/nzjvvJCoqiuzsbMaPH09CQkK964bi/q6srGTRokWMGTMGY0yD64XK/q5WUFBAcXExAwcO9C6LjY0lKyuLnJwczj333DqvacrPiGBQVlaGMYbY2NiTrteY75VAtW7dOiZOnEibNm3o378/N954I+3atat33VDc38XFxaxYsYK77rrrlOsG0/7+9u+t3NxcqqqqGDBggHed1NRUEhISyMnJITs7u857NOVnQn1CLhwdPHgQt9td5y/i+Ph48vLy6n1NcXFxnUOr7du3p7i42EdV+pbb7ea1116jV69eJz1NlJKSwo9//GPS09MpKytj7ty5PPzwwzz//PN06tTJjxU3T8+ePZk0aRIpKSkcOHCAWbNm8cgjjzB16lRiYmLqrB9q+xtg6dKllJaWMnLkyAbXCZX9XVP1PmvM/mzKz4hAV15ezptvvsm555570nDU2O+VQDR48GCGDx9OYmIi+fn5/P3vf+eZZ57h6aefrveUUSju708//ZTWrVuf9OgaBNf+ru/3VnFxMZGRkbRp06bWuif7/m7Kz4T6hFw4Epg2bRo7d+7kiSeeOOl62dnZtZJ3dnY29957LwsWLODGG2/0dZktZsiQId7P09PTvT8QvvjiC0aNGuVgZf6zcOFCBg8efNLJuKGyv6W2yspKfvvb3wLUOxG/plD4Xqn5l3+3bt1IT0/nJz/5CWvXrq11hCGULVy4kPPPP/+U82eCaX+f7u8tfwm5OUdxcXG4XK46CbG4uLjB+RXx8fGUlJTUWlZSUhIU8zG+bdq0aSxfvpxHH3200UcDIiMj6dGjB/n5+T6qzj/atGlDSkpKg+MIpf0NUFhYyKpVq7j44osb9bpQ2N/V+6wx+7MpPyMCVXUwKioq4uGHHz7lKbVvO9X3SjDo0qUL7dq1a3AMobS/AdavX09eXl6Twk2g7u+Gfm/Fx8dTWVlJaWlprfVP9v3dlJ8J9Qm5cBQZGUlGRgZr1qzxLnO73axZs6be85Pg+Qt69erVtZatWrWKnj17+rTWlmStZdq0aSxdupRHHnmExMTERr+H2+1mx44ddOjQwQcV+s/Ro0fJz89v8BshFPZ3TQsXLqR9+/aNbkUQCvs7MTGR+Pj4WvuzrKyMzZs3N/j93pSfEYGoOhjl5+czefLkBufcnMypvleCwb59+zh8+HCDX8ehsr+rffzxx2RkZNC9e/dGvzbQ9vepfm9lZGQQERFR6/s7Ly+PoqKiBvddU34m1CckT6uNHTuWl156iYyMDLKyspg3bx7Hjh3zzsf4wx/+QMeOHRk/fjwAo0eP5rHHHuPdd99l6NChfP7552zZsoU77rjDwVE0zrRp0/jss8/4xS9+QUxMjPevpNjYWO+h12+Pe9asWfTs2ZOkpCRKS0uZO3cuhYWFjT4C4bTp06dzxhlnkJCQwIEDB5g5cyYul4vzzjsPCM39Xc3tdvPJJ59w4YUXEhERUeu5UNnf1T/QqxUUFLBt2zbatm1LQkICo0ePZvbs2SQnJ5OYmMjbb79Nhw4dOPPMM72veeKJJzjrrLO44oorgFP/jAgEJxt3fHw8zz//PFu3buWBBx7A7XZ7v+fbtm3rvbT92+M+1fdKIDjZuNu2bcs//vEPhg8fTnx8PHv37uVvf/sbSUlJDBo0yPuaUNvf1ROoy8rK+PLLL/nBD35Q73sE2/4+1e+t2NhYRo0axfTp02nbti2xsbH85S9/qTNF4Gc/+xnjx4/nrLPOwhhzWj8TTiUkw9GIESM4ePAgM2fOpLi4mO7du/Pggw9603JRUVGtK3p69erFT3/6U95++23+/ve/k5yczM9//vOg6nnz4YcfAvDYY4/VWj5p0iTvD4Bvj/vw4cO8/PLLFBcX06ZNGzIyMnjqqadIS0vzV9ktYv/+/bz44oscOnSIuLg4evfuzdNPP+29ZDUU93e11atXU1RUxEUXXVTnuVDZ31u2bOHxxx/3Pp4+fToAF154IXfddRdXXXUVx44d4+WXX6asrIzevXvz4IMP1pqPsXfvXg4ePOh9fKqfEYHgZOO+7rrr+OqrrwD4xS9+Uet1jz76KP369QPqjvtU3yuB4GTjvv3229mxYweffvoppaWldOzYkYEDB3LDDTcQFRXlfU2o7e/qq9IWL16MtbbBcBNs+/t0fm9NmDABYwxTp06lsrLS2wSypry8PO+VbsBp/Uw4FWOttU0bloiIiEjoCbk5RyIiIiLNoXAkIiIiUoPCkYiIiEgNCkciIiIiNSgciYiIiNSgcCQiIiJSg8KRiIiISA0KRyIiIiI1KByJiIiI1KBwJCIiIlJDSN5bTUQCU1VVFe+88w4fffQRJSUlZGZmcuedd5KSktLga1566SU+/fRTALp27crUqVNPuo2ZM2cya9YsZs6c2aK1f9v777/P66+/7n386quvBsw9q0SkeRSORMQv3G43zz33HDk5OYwZM4bo6GjmzJnDlClTeP7554mIiGjwte3atWPChAm0adPGjxWf3ODBg2nXrh1Lly5l6dKlTpcjIi1I4UhE/GLu3LmsWbOGZ555hq5duwIQHx/P7373O9auXcvAgQMbfG3r1q254IIL/FXqaUlNTSU1NZX8/HyFI5EQozlHIuJzZWVlzJkzh9GjR3uDEUB2djYA27dvd6o0EZE6dORIRHxu0aJFHD16lEsuuaTW8shIz4+gI0eONOl9N2zYwOuvv86OHTvo2LEjV155ZYPr7t+/n7fffpsVK1ZQWlpKUlISY8eOZdSoUbXWW7t2LW+88QY7d+70vueBAwf8Mo9JRAKDwpGI+NzSpUtJS0ujVatWHDx40Lu8qKgI8Jw2a6wdO3bw1FNPERcXx3XXXUdVVRUzZ84kPj6+zrrFxcU89NBDAFx++eXExcWxcuVK/vSnP3HkyBHGjBkDwNatW3nmmWeIj4/nuuuuw+12M2vWLE20FgkzCkci4lNut5ucnByOHTvGxIkT610nMTGx0e87Y8YMrLU88cQTJCQkADB8+HDuv//+Ouu+/fbb3gnh7dq1A+Cyyy7jhRde4B//+AeXXnop0dHRzJw5E5fLxZNPPknHjh0BGDFiBPfee2+j6xOR4KVwJCI+lZ+fz7Fjx7jyyivrTLpeuHAhn3/+Od26dWvUe7rdbr755hvOPPNMbzACSEtLY9CgQaxYscK7zFrLkiVLOOecc7DW1jpyNXjwYBYvXkxubi7Z2dmsXr2as846yxuMAJKSkhg8eDBff/11Y4cuIkFK4UhEfKqwsBCAfv361QlH77zzDu3btz9pn6P6HDx4kPLycpKTk+s8l5KSUiscHTx4kNLSUv7zn//wn//8p8H3Kykpoby8nKSkpDrP17dMREKXwpGI+NSxY8cAaNWqVa3lZWVlrF+/nosuusin27fWAnD++edz4YUX1rtOeno6brfbp3WISPBQOBIRn6qebH306NFayz/55BMqKyu57LLLGv2ecXFxREdHs2fPnjrP5eXl1Vk3JiYGt9t90l5KbrebqKgo8vPz6zxX3zIRCV3qcyQiPpWeno4xhrVr13qX7du3j3/+859ccMEFpKenN/o9XS4XgwYNYtmyZd4r3gB27drFN998U2fd4cOHs2TJEnbs2FHnvarnILlcLgYMGMCyZcvYv3+/9/n8/HxWrlzZ6BpFJHjpyJGI+FT79u0588wzmTdvHq1atSI2Npb333+fjh07cuuttzb5fa+//npWrlzJI488wmWXXYbb7Wb+/Pl07dq1TlPJ8ePHs3btWh566CEuvvhi0tLSOHz4MLm5uaxevZq//vWv3vd8+OGHmTx5svc9P/jgA7p27cq2bdua898gIkFE4UhEfO5HP/oRf/rTn3j33Xdp3bo155xzDjfddBMxMTFNfs/09HQeeughpk+fzsyZM+nUqRPXX389Bw4cqBOO4uPjeeaZZ5g1axZLlizh3//+N+3ataNr1658//vf966XkZHBgw8+yBtvvMGMGTPo1KkTN9xwA7t27WL37t1NrlVEgoux1bMVRUQC0EsvvcSaNWuYMmUKERERjtx89je/+Q27du3id7/7nXdZeXk5R48eZe7cucydO5dXX31VzSJFQoSOHIlIwNu3bx8TJ06ka9euTJ061afbKi8vJzo62vt4z549rFixos6VbgsWLOD111/3aS0i4gyFIxEJaFdddRXnn38+0LTbjDTW3XffzciRI0lMTKSoqIgPP/yQyMhIrrrqqlrrDR8+vNZNdGNjY31em4j4h06riYjU8Mc//pG1a9dSXFxMZGQk2dnZ3HTTTWRkZDhdmoj4icKRiIiISA3qcyQiIiJSg8KRiIiISA0KRyIiIiI1KByJiIiI1KBwJCIiIlKDwpGIiIhIDQpHIiIiIjUoHImIiIjUoHAkIiIiUoPCkYiIiEgN/x9hH7yuO9aMWgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " results[\"channel_2\"][\"Theta_deg\"],\n", + " results[\"channel_2\"][\"sigma_mb_sr\"],\n", + " label=\"Frescox\",\n", + ")\n", + "plt.errorbar(\n", + " experimental_data[\"Theta_deg\"],\n", + " experimental_data[\"sigma_mb_sr\"],\n", + " experimental_data[\"err_sigma_mb_sr\"],\n", + " linestyle=\"none\",\n", + " color=\"k\",\n", + " marker=\"o\",\n", + " label=\"Blackmon et al., 2004\",\n", + ")\n", + "\n", + "\n", + "plt.xlim([0, 20])\n", + "plt.xlabel(r\"$\\theta$ [deg]\")\n", + "plt.ylabel(r\"$d\\sigma/d\\Omega$ [mb/Sr]\")" + ] + }, + { + "cell_type": "markdown", + "id": "fef8e734-b834-49b4-a5b4-dd99b3d325a8", + "metadata": {}, + "source": [ + "Note that the theory prediction, which assumes the spectroscopic factor of the $d_{5/2}$ proton in $^{18}$Ne(3.376 MeV, $4^+$) is 1, is only slightly higher than the experimental value. This implies the actual spectroscopic factor is indeed close to 1, implying the $d_{5/2}$ proton state is sharply peaked at 3.376 MeV, rather than being fragmented by many-body correlations." + ] + }, + { + "cell_type": "markdown", + "id": "1a929eb9-da42-4ec3-8c93-862fe2061b26", + "metadata": {}, + "source": [ + "## Parameter sweep for the $^{14}$N( $^{17}$F, $^{18}$Ne(3.376 MeV, $4^+$))$^{13}$C reaction\n", + "\n", + "We've also made a template with the same parameters as the previous one, but for this reaction. We will use it as before to do a parameter sweep of the p + $^{17}$F interaction parameters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "e6fd8aef-dfed-4dd4-ac2c-73e1c422a53d", + "metadata": {}, + "outputs": [], + "source": [ + "NE_EX3_TEMPLATE_FILE_PATH = Path(\"./Transfer_template/B5-18neon_4+_tr.template\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3ad067d4-4f28-40a4-9a47-caa0a071c94b", + "metadata": {}, + "outputs": [], + "source": [ + "# this template has the same params except for the binding energy ones, which we fix\n", + "del base_params[\"BE_p_13C\"]\n", + "del base_params[\"BE_p_17F\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "fcb16f29-989e-43b3-bf2d-eb7555aadb12", + "metadata": {}, + "outputs": [], + "source": [ + "params_to_perturb = {\n", + " \"r_p17F\": [1.0, 1.2, 1.4],\n", + " \"a_p17F\": [0.5, 0.65, 0.8],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "6af31346-87b4-4d24-ae64-53ab2c837748", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████| 2/2 [02:44<00:00, 82.26s/it]\n" + ] + } + ], + "source": [ + "results = {}\n", + "for param_name, values in tqdm(params_to_perturb.items()):\n", + " results[param_name] = []\n", + " for val in values:\n", + " params = base_params.copy()\n", + " params[param_name] = val\n", + " cfg = bfrescox.Configuration.from_template(\n", + " NE_EX3_TEMPLATE_FILE_PATH,\n", + " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", + " params,\n", + " overwrite=True,\n", + " )\n", + " bfrescox.run_simulation(\n", + " cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True\n", + " )\n", + " results[param_name].append(\n", + " bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "be3d0c9b-f12f-48c5-8c98-e050edc7440e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 59.59it/s]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for param_name, values in tqdm(params_to_perturb.items()):\n", + " fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + " disp_name = param_name.split(\"_\")[0]\n", + " ax.set_title(\"$^{14}$N( $^{17}$F, $^{18}$Ne(3.376 MeV, $4^+$))$^{13}$C\")\n", + " for val, result in zip(params_to_perturb[param_name], results[param_name]):\n", + " ax.plot(\n", + " result[\"channel_2\"][\"Theta_deg\"],\n", + " result[\"channel_2\"][\"sigma_mb_sr\"],\n", + " label=f\"${disp_name}$ = {val:1.1f}\",\n", + " )\n", + " plt.errorbar(\n", + " experimental_data[\"Theta_deg\"],\n", + " experimental_data[\"sigma_mb_sr\"],\n", + " experimental_data[\"err_sigma_mb_sr\"],\n", + " linestyle=\"none\",\n", + " color=\"k\",\n", + " marker=\"o\",\n", + " label=\"Blackmon et al., 2004\",\n", + " )\n", + "\n", + " ax.legend()\n", + " ax.set_xlabel(r\"$\\theta$ [deg]\")\n", + " ax.set_ylabel(r\"$(d\\sigma/d\\Omega)$ [mb/Sr]\")" + ] + }, + { + "cell_type": "markdown", + "id": "7f6dd39f-fad4-44c4-b6f1-97cedf3aa204", + "metadata": {}, + "source": [ + "Note that the optical model parameters significantly effect the prediction! If one were using a phenomenological potential to extract spectroscopic factors, one should be careful about the uncertainty due to the optical potential." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf7f3572-989e-4866-b8ce-724a86ae7d5f", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/book/notebooks/Transfer_template/B5-18neon_4+_tr.in b/book/notebooks/Transfer_template/B5-18neon_4+_tr.in new file mode 100644 index 0000000..f700e3d --- /dev/null +++ b/book/notebooks/Transfer_template/B5-18neon_4+_tr.in @@ -0,0 +1,34 @@ +n14(f17,ne18_3376_4+)c13 @ 170 MeV; +NAMELIST +&FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 +jtmin=0.0 jtmax=120 absend=-1.0 +thmin=0.00 thmax=40.00 thinc=1.00 +iter=1 nnu=36 +chans=1 xstabl=1 +elab=170.0 / +&PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / +&STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / +&PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / +&STATES jp=4. bandp=1 ep=3.376 cpot=2 jt=0.5 bandt=-1 et=0.0000 / +&partition / +&POT kp=1 ap=17.000 at=14.000 rc=1.3 / +&POT kp=1 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / +&POT kp=2 ap=18.000 at=13.000 rc=1.3 / +&POT kp=2 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / +&POT kp=3 at=17 rc=1.2 / +&POT kp=3 type=1 p1=50.00 p2=1.2 p3=0.65 / +&POT kp=3 type=3 p1=6.00 p2=1.2 p3=0.65 / +&POT kp=4 at=13 rc=1.2 / +&POT kp=4 type=1 p1=50.00 p2=1.2 p3=0.65 / +&POT kp=4 type=3 p1=6.00 p2=1.2 p3=0.65 / +&POT kp=5 ap=17.000 at=13.000 rc=1.3 / +&POT kp=5 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / +&pot / +&Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=0.546 isc=1 ipc=0 / +&Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / +&overlap / +&Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / +&CFP in=1 ib=1 ia=1 kn=1 a=1.00 / +&CFP in=2 ib=1 ia=1 kn=2 a=1.00 / +&CFP / +&coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/B5-18neon_4+_tr.template b/book/notebooks/Transfer_template/B5-18neon_4+_tr.template new file mode 100644 index 0000000..e52ccf6 --- /dev/null +++ b/book/notebooks/Transfer_template/B5-18neon_4+_tr.template @@ -0,0 +1,44 @@ +n14(f17,ne18_3376_4+)c13 @ 170 MeV; +NAMELIST + +&FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 +jtmin=0.0 jtmax=120 absend=-1.0 +thmin=0.00 thmax=40.00 thinc=1.00 +iter=1 nnu=36 +chans=1 xstabl=1 +elab=170.0 / + +&PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / +&STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / +&PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / +&STATES jp=4. bandp=1 ep=3.376 cpot=2 jt=0.5 bandt=-1 et=0.0000 / +&partition / + +&POT kp=1 ap=17.000 at=14.000 rc=@rC_entrance@ / +&POT kp=1 type=1 p1=@V_entrance@ p2=@r_entrance@ p3=@a_entrance@ p4=@W_entrance@ p5=@rw_entrance@ p6=@aw_entrance@ / + +&POT kp=2 ap=18.000 at=13.000 rc=@rC_exit@ / +&POT kp=2 type=1 p1=@V_exit@ p2=@r_exit@ p3=@a_exit@ p4=@W_exit@ p5=@rw_exit@ p6=@aw_exit@ / + +&POT kp=3 at=17 rc=@rC_p17F@ / +&POT kp=3 type=1 p1=@V_p17F@ p2=@r_p17F@ p3=@a_p17F@ / +&POT kp=3 type=3 p1=@Vso_p17F@ p2=@rso_p17F@ p3=@aso_p17F@ / + +&POT kp=4 at=13 rc=@rC_p13C@ / +&POT kp=4 type=1 p1=@V_p13C@ p2=@r_p13C@ p3=@a_p13C@ / +&POT kp=4 type=3 p1=@Vso_p13C@ p2=@rso_p13C@ p3=@aso_p13C@ / + +&POT kp=5 ap=17.000 at=14.000 rc=@rC_17F_13C@ / +&POT kp=5 type=1 p1=@V_17F_13C@ p2=@r_17F_13C@ p3=@a_17F_13C@ p4=@W_17F_13C@ p5=@rw_17F_13C@ p6=@aw_17F_13C@ / +&pot / + +&Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=0.546 isc=1 ipc=0 / +&Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / +&overlap / + +&Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / +&CFP in=1 ib=1 ia=1 kn=1 a=1.00 / +&CFP in=2 ib=1 ia=1 kn=2 a=1.00 / +&CFP / + +&coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/B5-example-tr.in b/book/notebooks/Transfer_template/B5-example-tr.in index 1cdccdf..f29278c 100644 --- a/book/notebooks/Transfer_template/B5-example-tr.in +++ b/book/notebooks/Transfer_template/B5-example-tr.in @@ -4,15 +4,14 @@ NAMELIST jtmin=0.0 jtmax=120 absend=-1.0 thmin=0.00 thmax=40.00 thinc=0.10 iter=1 nnu=36 - chans=1 xstabl=1 + chans=3 xstabl=1 elab=170.0 / &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 / - &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / - &STATES jp=4. bandp=1 ep=3.376 cpot=2 copyt / + &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=-1 et=0.0000 / &partition / @@ -41,6 +40,6 @@ NAMELIST &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / - &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / + &CFP in=1 ib=2 ia=1 kn=1 a=1.00 / &CFP / - &coupling / \ No newline at end of file + &coupling / diff --git a/book/notebooks/Transfer_template/blackmon_etal.csv b/book/notebooks/Transfer_template/blackmon_etal.csv new file mode 100644 index 0000000..967bc89 --- /dev/null +++ b/book/notebooks/Transfer_template/blackmon_etal.csv @@ -0,0 +1,12 @@ +ANG-CM DATA-CM DATA-ERR +ADEG MB/SR MB/SR +7.665e+00 1.387e+01 3.083e+00 +8.825e+00 8.095e+00 1.799e+00 +1.007e+01 8.268e+00 1.731e+00 +1.118e+01 8.356e+00 1.434e+00 +1.238e+01 6.282e+00 1.315e+00 +1.368e+01 2.533e+00 8.359e-01 +1.483e+01 2.785e+00 9.193e-01 +1.595e+01 1.904e+00 7.942e-01 +1.719e+01 1.642e+00 6.850e-01 +1.831e+01 1.010e+00 5.978e-01 \ No newline at end of file diff --git a/book/notebooks/Transfer_template/frescox.in b/book/notebooks/Transfer_template/frescox.in new file mode 100644 index 0000000..e5ecbde --- /dev/null +++ b/book/notebooks/Transfer_template/frescox.in @@ -0,0 +1,44 @@ +n14(f17,ne18_3376_4+)c13 @ 170 MeV; +NAMELIST + +&FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 +jtmin=0.0 jtmax=120 absend=-1.0 +thmin=0.00 thmax=40.00 thinc=1.00 +iter=1 nnu=36 +chans=1 xstabl=1 +elab=170.0 / + +&PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / +&STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / +&PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / +&STATES jp=4. bandp=1 ep=3.376 cpot=2 jt=0.5 bandt=-1 et=0.0000 / +&partition / + +&POT kp=1 ap=17.000 at=14.000 rc=1.300000000 / +&POT kp=1 type=1 p1=37.200000000 p2=1.200000000 p3=0.600000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / + +&POT kp=2 ap=18.000 at=13.000 rc=1.300000000 / +&POT kp=2 type=1 p1=37.200000000 p2=1.200000000 p3=0.600000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / + +&POT kp=3 at=17 rc=1.200000000 / +&POT kp=3 type=1 p1=50.000000000 p2=1.200000000 p3=0.800000000 / +&POT kp=3 type=3 p1=6.000000000 p2=1.200000000 p3=0.650000000 / + +&POT kp=4 at=13 rc=1.200000000 / +&POT kp=4 type=1 p1=50.000000000 p2=1.200000000 p3=0.650000000 / +&POT kp=4 type=3 p1=6.000000000 p2=1.200000000 p3=0.650000000 / + +&POT kp=5 ap=17.000 at=14.000 rc=1.300000000 / +&POT kp=5 type=1 p1=37.200000000 p2=1.200000000 p3=0.600000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / +&pot / + +&Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=0.546 isc=1 ipc=0 / +&Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / +&overlap / + +&Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / +&CFP in=1 ib=1 ia=1 kn=1 a=1.00 / +&CFP in=2 ib=1 ia=1 kn=2 a=1.00 / +&CFP / + +&coupling / \ No newline at end of file From 5de7d7aab44e8e996cc23de335c37a40c4385f58 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Tue, 12 May 2026 10:42:19 -0400 Subject: [PATCH 03/13] add ignores --- .gitignore | 8 + book/notebooks/Ozge_template/frescox.in | 21 - .../B5-example-tr-checkpoint.in | 46 - .../B5-example-tr-checkpoint.template | 43 - .../.ipynb_checkpoints/frescox-checkpoint.in | 43 - .../Transfer_template/B5-18neon_4+_tr.in | 34 - .../Transfer_template/B5-example-tr.in | 45 - book/notebooks/Transfer_template/fort.13 | 7 - book/notebooks/Transfer_template/fort.16 | 1226 ------- book/notebooks/Transfer_template/fort.201 | 411 --- book/notebooks/Transfer_template/fort.202 | 411 --- book/notebooks/Transfer_template/fort.203 | 411 --- book/notebooks/Transfer_template/fort.239 | 1 - book/notebooks/Transfer_template/fort.3 | 517 --- book/notebooks/Transfer_template/fort.35 | 1 - book/notebooks/Transfer_template/fort.38 | 1 - book/notebooks/Transfer_template/fort.39 | 1 - book/notebooks/Transfer_template/fort.4 | 0 book/notebooks/Transfer_template/fort.40 | 1 - book/notebooks/Transfer_template/fort.46 | 2 - book/notebooks/Transfer_template/fort.48 | 3046 ----------------- book/notebooks/Transfer_template/fort.55 | 0 book/notebooks/Transfer_template/fort.56 | 0 book/notebooks/Transfer_template/fort.59 | 0 book/notebooks/Transfer_template/fort.7 | 0 book/notebooks/Transfer_template/fort.75 | 1 - book/notebooks/Transfer_template/frescox.in | 44 - book/notebooks/Transfer_template/frescox.out | 1923 ----------- 28 files changed, 8 insertions(+), 8236 deletions(-) delete mode 100644 book/notebooks/Ozge_template/frescox.in delete mode 100644 book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.in delete mode 100644 book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.template delete mode 100644 book/notebooks/Transfer_template/.ipynb_checkpoints/frescox-checkpoint.in delete mode 100644 book/notebooks/Transfer_template/B5-18neon_4+_tr.in delete mode 100644 book/notebooks/Transfer_template/B5-example-tr.in delete mode 100644 book/notebooks/Transfer_template/fort.13 delete mode 100644 book/notebooks/Transfer_template/fort.16 delete mode 100644 book/notebooks/Transfer_template/fort.201 delete mode 100644 book/notebooks/Transfer_template/fort.202 delete mode 100644 book/notebooks/Transfer_template/fort.203 delete mode 100644 book/notebooks/Transfer_template/fort.239 delete mode 100644 book/notebooks/Transfer_template/fort.3 delete mode 100644 book/notebooks/Transfer_template/fort.35 delete mode 100644 book/notebooks/Transfer_template/fort.38 delete mode 100644 book/notebooks/Transfer_template/fort.39 delete mode 100644 book/notebooks/Transfer_template/fort.4 delete mode 100644 book/notebooks/Transfer_template/fort.40 delete mode 100644 book/notebooks/Transfer_template/fort.46 delete mode 100644 book/notebooks/Transfer_template/fort.48 delete mode 100644 book/notebooks/Transfer_template/fort.55 delete mode 100644 book/notebooks/Transfer_template/fort.56 delete mode 100644 book/notebooks/Transfer_template/fort.59 delete mode 100644 book/notebooks/Transfer_template/fort.7 delete mode 100644 book/notebooks/Transfer_template/fort.75 delete mode 100644 book/notebooks/Transfer_template/frescox.in delete mode 100644 book/notebooks/Transfer_template/frescox.out diff --git a/.gitignore b/.gitignore index 6b9cc0a..49ea367 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +*.ipynb_checkpoints + **/__pycache__ **/.DS_Store @@ -10,10 +12,16 @@ book/_build/ book/notebooks/.ipynb_checkpoints book/notebooks/12C_4He_inelastic_example/fort.* book/notebooks/12C_4He_inelastic_example/frescox.out +book/notebooks/12C_4He_inelastic_example/frescox.in book/notebooks/78Ni_p_elastic_example/fort* book/notebooks/78Ni_p_elastic_example/frescox.out +book/notebooks/78Ni_p_elastic_example/frescox.in book/notebooks/Ozge_template/fort.* book/notebooks/Ozge_template/frescox.out +book/notebooks/Ozge_template/frescox.in +book/notebooks/Transfer_template/fort.* +book/notebooks/Transfer_template/frescox.out +book/notebooks/Transfer_template/frescox.in bfrescox_pypkg/meson/subprojects/.wraplock diff --git a/book/notebooks/Ozge_template/frescox.in b/book/notebooks/Ozge_template/frescox.in deleted file mode 100644 index 6646b80..0000000 --- a/book/notebooks/Ozge_template/frescox.in +++ /dev/null @@ -1,21 +0,0 @@ -48Ca scattering -NAMELIST - &FRESCO hcm=0.01 rmatch=40 rintp=0.20 hnl=0.100000000 rnl=5.000000000 centre=-0.25 - jtmin=0.0 jtmax=35 absend=-1.0 - thmin=0.00 thmax=180.00 thinc=1.0 - iter=0 iblock=2 nnu=36 - chans=1 treneg=1 xstabl=1 - elab(1)= 7.97 / - - &PARTITION namep='n' massp=1.0 zp=0 - namet='48Ca' masst= 48. zt=20 nex=1 / - &STATES jp=0.5 bandp=1 ep=0.0 cpot=1 jt= 0 bandt= 1 et=0.0000 / - &partition / - - &pot kp= 1 type= 0 p(1:3)= 1.000 48. 0.00 / - &pot kp= 1 type= 1 p(1:6)= 49.284900000 0.907039000 0.679841000 1.0 1.33 0.62 / - &pot kp= 1 type= 2 p(1:6)= 0.0 0.0 0.0 3.394386000 1.094115000 0.276300000 / - &pot kp= 1 type= 3 p(1:6)= 6.6 1.33 0.62 0.0 1.36 0.63 / - &pot / - &overlap / - &COUPLING / diff --git a/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.in b/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.in deleted file mode 100644 index 1cdccdf..0000000 --- a/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.in +++ /dev/null @@ -1,46 +0,0 @@ -n14(f17,ne18)c13 @ 170 MeV; -NAMELIST - &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 - jtmin=0.0 jtmax=120 absend=-1.0 - thmin=0.00 thmax=40.00 thinc=0.10 - iter=1 nnu=36 - chans=1 xstabl=1 - elab=170.0 / - - &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / - &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / - - &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 / - &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / - &STATES jp=4. bandp=1 ep=3.376 cpot=2 copyt / - - - &partition / - - &POT kp=1 ap=17.000 at=14.000 rc=1.3 / - &POT kp=1 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / - - &POT kp=2 ap=18.000 at=13.000 rc=1.3 / - &POT kp=2 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / - - &POT kp=3 at=17 rc=1.2 / - &POT kp=3 type=1 p1=50.00 p2=1.2 p3=0.65 / - &POT kp=3 type=3 p1=6.00 p2=1.2 p3=0.65 / - - &POT kp=4 at=13 rc=1.2 / - &POT kp=4 type=1 p1=50.00 p2=1.2 p3=0.65 / - &POT kp=4 type=3 p1=6.00 p2=1.2 p3=0.65 / - - &POT kp=5 ap=17.000 at=14.000 rc=1.3 / - &POT kp=5 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / - &pot / - - &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=3.922 isc=1 ipc=0 / - &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / - &overlap / - - &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / - &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / - &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / - &CFP / - &coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.template b/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.template deleted file mode 100644 index e9cef3c..0000000 --- a/book/notebooks/Transfer_template/.ipynb_checkpoints/B5-example-tr-checkpoint.template +++ /dev/null @@ -1,43 +0,0 @@ -n14(f17,ne18)c13 @ 170 MeV; -NAMELIST - &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 - jtmin=0.0 jtmax=120 absend=-1.0 - thmin=0.00 thmax=40.00 thinc=0.10 - iter=1 nnu=36 - chans=1 xstabl=1 - elab=170.0 / - - &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / - &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / - - &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / - &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / - &partition / - - &POT kp=1 ap=17.000 at=14.000 rc=@rC_entrance@ / - &POT kp=1 type=1 p1=@V_entrance@ p2=@r_entrance@ p3=@a_entrance@ p4=@W_entrance@ p5=@rw_entrance@ p6=@aw_entrance@ / - - &POT kp=2 ap=18.000 at=13.000 rc=@rC_exit@ / - &POT kp=2 type=1 p1=@V_exit@ p2=@r_exit@ p3=@a_exit@ p4=@W_exit@ p5=@rw_exit@ p6=@aw_exit@ / - - &POT kp=3 at=17 rc=@rC_p17F@ / - &POT kp=3 type=1 p1=@V_p17F@ p2=@r_p17F@ p3=@a_p17F@ / - &POT kp=3 type=3 p1=@Vso_p17F@ p2=@rso_p17F@ p3=@aso_p17F@ / - - &POT kp=4 at=13 rc=@rC_p13C@ / - &POT kp=4 type=1 p1=@V_p13C@ p2=@r_p13C@ p3=@a_p13C@ / - &POT kp=4 type=3 p1=@Vso_p13C@ p2=@rso_p13C@ p3=@aso_p13C@ / - - &POT kp=5 ap=17.000 at=14.000 rc=@rC_17F_13C@ / - &POT kp=5 type=1 p1=@V_17F_13C@ p2=@r_17F_13C@ p3=@a_17F_13C@ p4=@W_17F_13C@ p5=@rw_17F_13C@ p6=@aw_17F_13C@ / - &pot / - - &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=@BE_p_17F@ isc=1 ipc=0 / - &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=@BE_p_13C@ isc=1 ipc=0 / - &overlap / - - &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / - &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / - &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / - &CFP / - &coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/.ipynb_checkpoints/frescox-checkpoint.in b/book/notebooks/Transfer_template/.ipynb_checkpoints/frescox-checkpoint.in deleted file mode 100644 index cbcdf9a..0000000 --- a/book/notebooks/Transfer_template/.ipynb_checkpoints/frescox-checkpoint.in +++ /dev/null @@ -1,43 +0,0 @@ -n14(f17,ne18)c13 @ 170 MeV; -NAMELIST - &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 - jtmin=0.0 jtmax=120 absend=-1.0 - thmin=0.00 thmax=40.00 thinc=0.10 - iter=1 nnu=36 - chans=1 xstabl=1 - elab=170.0 / - - &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / - &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / - - &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / - &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / - &partition / - - &POT kp=1 ap=17.000 at=14.000 rc=1.300000000 / - &POT kp=1 type=1 p1=37.200000000 p2=1.200000000 p3=0.690000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / - - &POT kp=2 ap=18.000 at=13.000 rc=1.300000000 / - &POT kp=2 type=1 p1=37.200000000 p2=1.200000000 p3=0.690000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / - - &POT kp=3 at=17 rc=1.200000000 / - &POT kp=3 type=1 p1=50.000000000 p2=1.200000000 p3=0.650000000 / - &POT kp=3 type=3 p1=6.000000000 p2=1.200000000 p3=0.650000000 / - - &POT kp=4 at=13 rc=1.200000000 / - &POT kp=4 type=1 p1=50.000000000 p2=1.200000000 p3=0.650000000 / - &POT kp=4 type=3 p1=6.000000000 p2=1.200000000 p3=0.650000000 / - - &POT kp=5 ap=17.000 at=14.000 rc=1.300000000 / - &POT kp=5 type=1 p1=37.200000000 p2=1.200000000 p3=0.600000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / - &pot / - - &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=7.550600000 isc=1 ipc=0 / - &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=3.922000000 isc=1 ipc=0 / - &overlap / - - &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / - &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / - &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / - &CFP / - &coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/B5-18neon_4+_tr.in b/book/notebooks/Transfer_template/B5-18neon_4+_tr.in deleted file mode 100644 index f700e3d..0000000 --- a/book/notebooks/Transfer_template/B5-18neon_4+_tr.in +++ /dev/null @@ -1,34 +0,0 @@ -n14(f17,ne18_3376_4+)c13 @ 170 MeV; -NAMELIST -&FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 -jtmin=0.0 jtmax=120 absend=-1.0 -thmin=0.00 thmax=40.00 thinc=1.00 -iter=1 nnu=36 -chans=1 xstabl=1 -elab=170.0 / -&PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / -&STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / -&PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / -&STATES jp=4. bandp=1 ep=3.376 cpot=2 jt=0.5 bandt=-1 et=0.0000 / -&partition / -&POT kp=1 ap=17.000 at=14.000 rc=1.3 / -&POT kp=1 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / -&POT kp=2 ap=18.000 at=13.000 rc=1.3 / -&POT kp=2 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / -&POT kp=3 at=17 rc=1.2 / -&POT kp=3 type=1 p1=50.00 p2=1.2 p3=0.65 / -&POT kp=3 type=3 p1=6.00 p2=1.2 p3=0.65 / -&POT kp=4 at=13 rc=1.2 / -&POT kp=4 type=1 p1=50.00 p2=1.2 p3=0.65 / -&POT kp=4 type=3 p1=6.00 p2=1.2 p3=0.65 / -&POT kp=5 ap=17.000 at=13.000 rc=1.3 / -&POT kp=5 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / -&pot / -&Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=0.546 isc=1 ipc=0 / -&Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / -&overlap / -&Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / -&CFP in=1 ib=1 ia=1 kn=1 a=1.00 / -&CFP in=2 ib=1 ia=1 kn=2 a=1.00 / -&CFP / -&coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/B5-example-tr.in b/book/notebooks/Transfer_template/B5-example-tr.in deleted file mode 100644 index f29278c..0000000 --- a/book/notebooks/Transfer_template/B5-example-tr.in +++ /dev/null @@ -1,45 +0,0 @@ -n14(f17,ne18)c13 @ 170 MeV; -NAMELIST - &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 - jtmin=0.0 jtmax=120 absend=-1.0 - thmin=0.00 thmax=40.00 thinc=0.10 - iter=1 nnu=36 - chans=3 xstabl=1 - elab=170.0 / - - &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / - &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / - - &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 / - &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=-1 et=0.0000 / - - - &partition / - - &POT kp=1 ap=17.000 at=14.000 rc=1.3 / - &POT kp=1 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / - - &POT kp=2 ap=18.000 at=13.000 rc=1.3 / - &POT kp=2 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / - - &POT kp=3 at=17 rc=1.2 / - &POT kp=3 type=1 p1=50.00 p2=1.2 p3=0.65 / - &POT kp=3 type=3 p1=6.00 p2=1.2 p3=0.65 / - - &POT kp=4 at=13 rc=1.2 / - &POT kp=4 type=1 p1=50.00 p2=1.2 p3=0.65 / - &POT kp=4 type=3 p1=6.00 p2=1.2 p3=0.65 / - - &POT kp=5 ap=17.000 at=14.000 rc=1.3 / - &POT kp=5 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / - &pot / - - &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=3.922 isc=1 ipc=0 / - &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / - &overlap / - - &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / - &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / - &CFP in=1 ib=2 ia=1 kn=1 a=1.00 / - &CFP / - &coupling / diff --git a/book/notebooks/Transfer_template/fort.13 b/book/notebooks/Transfer_template/fort.13 deleted file mode 100644 index 370c167..0000000 --- a/book/notebooks/Transfer_template/fort.13 +++ /dev/null @@ -1,7 +0,0 @@ - Integrated cross sections for all states - 1 1 2 1.7000E+02 3.9220 7.5506 - 1 1 - 2.5 1 0.0000 1.0 1 0.0000 1.7603E+03 3.2375E+03 1.4772E+03 - 2 2 - 0.0 1 0.0000 0.5 1 0.0000 2.6397E-01 - 4.0 1 3.3760 0.5 1 0.0000 0.0000E+00 diff --git a/book/notebooks/Transfer_template/fort.16 b/book/notebooks/Transfer_template/fort.16 deleted file mode 100644 index 26245d1..0000000 --- a/book/notebooks/Transfer_template/fort.16 +++ /dev/null @@ -1,1226 +0,0 @@ -# 401 angles, 1 tensor ranks for 0=projectile -@subtitle "n14(f17,ne18)c13 @ 170 MeV;" -@legend ON -@legend x1 0.2 -@legend y1 0.8 -@g0 type LOGY -@xaxis label "Scattering angle (degrees)" -@yaxis label "Cross section (mb/sr)" -@ s0 legend "Partition= 1 Excit= 1 near/far= 1" -# s0 legend "Lab energy = 170.0000" -@s0 linestyle 1 -# Theta sigma iT11 T20 T21 T22 Kyy for projectile - 0.1000E-01 1.000 - 0.1000 1.000 - 0.2000 0.9991 - 0.3000 1.001 - 0.4000 1.002 - 0.5000 0.9963 - 0.6000 0.9919 - 0.7000 0.9962 - 0.8000 1.007 - 0.9000 1.017 - 1.000 1.021 - 1.100 1.016 - 1.200 1.004 - 1.300 0.9883 - 1.400 0.9721 - 1.500 0.9595 - 1.600 0.9528 - 1.700 0.9530 - 1.800 0.9601 - 1.900 0.9729 - 2.000 0.9897 - 2.100 1.008 - 2.200 1.027 - 2.300 1.045 - 2.400 1.059 - 2.500 1.070 - 2.600 1.077 - 2.700 1.080 - 2.800 1.080 - 2.900 1.077 - 3.000 1.072 - 3.100 1.066 - 3.200 1.059 - 3.300 1.053 - 3.400 1.048 - 3.500 1.044 - 3.600 1.042 - 3.700 1.041 - 3.800 1.042 - 3.900 1.045 - 4.000 1.049 - 4.100 1.053 - 4.200 1.057 - 4.300 1.060 - 4.400 1.062 - 4.500 1.063 - 4.600 1.062 - 4.700 1.058 - 4.800 1.051 - 4.900 1.042 - 5.000 1.029 - 5.100 1.013 - 5.200 0.9939 - 5.300 0.9721 - 5.400 0.9477 - 5.500 0.9211 - 5.600 0.8926 - 5.700 0.8627 - 5.800 0.8318 - 5.900 0.8005 - 6.000 0.7693 - 6.100 0.7385 - 6.200 0.7087 - 6.300 0.6802 - 6.400 0.6534 - 6.500 0.6286 - 6.600 0.6060 - 6.700 0.5859 - 6.800 0.5682 - 6.900 0.5531 - 7.000 0.5406 - 7.100 0.5306 - 7.200 0.5229 - 7.300 0.5174 - 7.400 0.5138 - 7.500 0.5119 - 7.600 0.5114 - 7.700 0.5120 - 7.800 0.5134 - 7.900 0.5152 - 8.000 0.5171 - 8.100 0.5188 - 8.200 0.5200 - 8.300 0.5205 - 8.400 0.5200 - 8.500 0.5183 - 8.600 0.5152 - 8.700 0.5106 - 8.800 0.5045 - 8.900 0.4967 - 9.000 0.4873 - 9.100 0.4762 - 9.200 0.4636 - 9.300 0.4496 - 9.400 0.4343 - 9.500 0.4178 - 9.600 0.4004 - 9.700 0.3823 - 9.800 0.3636 - 9.900 0.3447 - 10.00 0.3257 - 10.10 0.3069 - 10.20 0.2885 - 10.30 0.2708 - 10.40 0.2539 - 10.50 0.2380 - 10.60 0.2234 - 10.70 0.2101 - 10.80 0.1982 - 10.90 0.1879 - 11.00 0.1793 - 11.10 0.1722 - 11.20 0.1669 - 11.30 0.1631 - 11.40 0.1610 - 11.50 0.1603 - 11.60 0.1610 - 11.70 0.1630 - 11.80 0.1662 - 11.90 0.1702 - 12.00 0.1751 - 12.10 0.1806 - 12.20 0.1866 - 12.30 0.1927 - 12.40 0.1989 - 12.50 0.2050 - 12.60 0.2108 - 12.70 0.2161 - 12.80 0.2208 - 12.90 0.2247 - 13.00 0.2277 - 13.10 0.2298 - 13.20 0.2308 - 13.30 0.2307 - 13.40 0.2294 - 13.50 0.2270 - 13.60 0.2234 - 13.70 0.2186 - 13.80 0.2128 - 13.90 0.2060 - 14.00 0.1983 - 14.10 0.1897 - 14.20 0.1805 - 14.30 0.1707 - 14.40 0.1604 - 14.50 0.1498 - 14.60 0.1390 - 14.70 0.1283 - 14.80 0.1176 - 14.90 0.1073 - 15.00 0.9729E-01 - 15.10 0.8783E-01 - 15.20 0.7901E-01 - 15.30 0.7093E-01 - 15.40 0.6367E-01 - 15.50 0.5730E-01 - 15.60 0.5190E-01 - 15.70 0.4748E-01 - 15.80 0.4409E-01 - 15.90 0.4172E-01 - 16.00 0.4037E-01 - 16.10 0.4002E-01 - 16.20 0.4061E-01 - 16.30 0.4210E-01 - 16.40 0.4442E-01 - 16.50 0.4749E-01 - 16.60 0.5121E-01 - 16.70 0.5550E-01 - 16.80 0.6024E-01 - 16.90 0.6532E-01 - 17.00 0.7063E-01 - 17.10 0.7606E-01 - 17.20 0.8149E-01 - 17.30 0.8681E-01 - 17.40 0.9192E-01 - 17.50 0.9672E-01 - 17.60 0.1011 - 17.70 0.1050 - 17.80 0.1083 - 17.90 0.1111 - 18.00 0.1131 - 18.10 0.1144 - 18.20 0.1150 - 18.30 0.1148 - 18.40 0.1139 - 18.50 0.1122 - 18.60 0.1098 - 18.70 0.1067 - 18.80 0.1030 - 18.90 0.9870E-01 - 19.00 0.9389E-01 - 19.10 0.8862E-01 - 19.20 0.8299E-01 - 19.30 0.7707E-01 - 19.40 0.7094E-01 - 19.50 0.6469E-01 - 19.60 0.5841E-01 - 19.70 0.5218E-01 - 19.80 0.4609E-01 - 19.90 0.4021E-01 - 20.00 0.3463E-01 - 20.10 0.2940E-01 - 20.20 0.2459E-01 - 20.30 0.2025E-01 - 20.40 0.1644E-01 - 20.50 0.1318E-01 - 20.60 0.1050E-01 - 20.70 0.8423E-02 - 20.80 0.6959E-02 - 20.90 0.6103E-02 - 21.00 0.5847E-02 - 21.10 0.6171E-02 - 21.20 0.7049E-02 - 21.30 0.8444E-02 - 21.40 0.1031E-01 - 21.50 0.1261E-01 - 21.60 0.1529E-01 - 21.70 0.1828E-01 - 21.80 0.2152E-01 - 21.90 0.2496E-01 - 22.00 0.2852E-01 - 22.10 0.3214E-01 - 22.20 0.3576E-01 - 22.30 0.3932E-01 - 22.40 0.4275E-01 - 22.50 0.4599E-01 - 22.60 0.4900E-01 - 22.70 0.5173E-01 - 22.80 0.5414E-01 - 22.90 0.5618E-01 - 23.00 0.5783E-01 - 23.10 0.5907E-01 - 23.20 0.5988E-01 - 23.30 0.6025E-01 - 23.40 0.6018E-01 - 23.50 0.5968E-01 - 23.60 0.5875E-01 - 23.70 0.5742E-01 - 23.80 0.5570E-01 - 23.90 0.5363E-01 - 24.00 0.5123E-01 - 24.10 0.4855E-01 - 24.20 0.4563E-01 - 24.30 0.4251E-01 - 24.40 0.3923E-01 - 24.50 0.3585E-01 - 24.60 0.3240E-01 - 24.70 0.2895E-01 - 24.80 0.2552E-01 - 24.90 0.2218E-01 - 25.00 0.1895E-01 - 25.10 0.1589E-01 - 25.20 0.1302E-01 - 25.30 0.1038E-01 - 25.40 0.8001E-02 - 25.50 0.5903E-02 - 25.60 0.4109E-02 - 25.70 0.2631E-02 - 25.80 0.1480E-02 - 25.90 0.6599E-03 - 26.00 0.1716E-03 - 26.10 0.9916E-05 - 26.20 0.1652E-03 - 26.30 0.6238E-03 - 26.40 0.1368E-02 - 26.50 0.2376E-02 - 26.60 0.3623E-02 - 26.70 0.5083E-02 - 26.80 0.6724E-02 - 26.90 0.8516E-02 - 27.00 0.1043E-01 - 27.10 0.1242E-01 - 27.20 0.1446E-01 - 27.30 0.1652E-01 - 27.40 0.1857E-01 - 27.50 0.2056E-01 - 27.60 0.2248E-01 - 27.70 0.2428E-01 - 27.80 0.2596E-01 - 27.90 0.2748E-01 - 28.00 0.2882E-01 - 28.10 0.2996E-01 - 28.20 0.3090E-01 - 28.30 0.3162E-01 - 28.40 0.3211E-01 - 28.50 0.3237E-01 - 28.60 0.3241E-01 - 28.70 0.3221E-01 - 28.80 0.3179E-01 - 28.90 0.3117E-01 - 29.00 0.3034E-01 - 29.10 0.2933E-01 - 29.20 0.2814E-01 - 29.30 0.2681E-01 - 29.40 0.2535E-01 - 29.50 0.2379E-01 - 29.60 0.2214E-01 - 29.70 0.2042E-01 - 29.80 0.1867E-01 - 29.90 0.1691E-01 - 30.00 0.1515E-01 - 30.10 0.1343E-01 - 30.20 0.1176E-01 - 30.30 0.1016E-01 - 30.40 0.8649E-02 - 30.50 0.7249E-02 - 30.60 0.5972E-02 - 30.70 0.4829E-02 - 30.80 0.3832E-02 - 30.90 0.2989E-02 - 31.00 0.2305E-02 - 31.10 0.1784E-02 - 31.20 0.1428E-02 - 31.30 0.1234E-02 - 31.40 0.1199E-02 - 31.50 0.1318E-02 - 31.60 0.1583E-02 - 31.70 0.1985E-02 - 31.80 0.2514E-02 - 31.90 0.3156E-02 - 32.00 0.3898E-02 - 32.10 0.4726E-02 - 32.20 0.5624E-02 - 32.30 0.6577E-02 - 32.40 0.7569E-02 - 32.50 0.8583E-02 - 32.60 0.9603E-02 - 32.70 0.1061E-01 - 32.80 0.1160E-01 - 32.90 0.1255E-01 - 33.00 0.1345E-01 - 33.10 0.1428E-01 - 33.20 0.1504E-01 - 33.30 0.1572E-01 - 33.40 0.1630E-01 - 33.50 0.1678E-01 - 33.60 0.1716E-01 - 33.70 0.1744E-01 - 33.80 0.1760E-01 - 33.90 0.1765E-01 - 34.00 0.1760E-01 - 34.10 0.1744E-01 - 34.20 0.1718E-01 - 34.30 0.1682E-01 - 34.40 0.1637E-01 - 34.50 0.1584E-01 - 34.60 0.1524E-01 - 34.70 0.1457E-01 - 34.80 0.1385E-01 - 34.90 0.1308E-01 - 35.00 0.1228E-01 - 35.10 0.1145E-01 - 35.20 0.1061E-01 - 35.30 0.9772E-02 - 35.40 0.8940E-02 - 35.50 0.8126E-02 - 35.60 0.7339E-02 - 35.70 0.6589E-02 - 35.80 0.5883E-02 - 35.90 0.5228E-02 - 36.00 0.4631E-02 - 36.10 0.4097E-02 - 36.20 0.3631E-02 - 36.30 0.3235E-02 - 36.40 0.2911E-02 - 36.50 0.2662E-02 - 36.60 0.2488E-02 - 36.70 0.2386E-02 - 36.80 0.2357E-02 - 36.90 0.2396E-02 - 37.00 0.2501E-02 - 37.10 0.2668E-02 - 37.20 0.2891E-02 - 37.30 0.3165E-02 - 37.40 0.3484E-02 - 37.50 0.3842E-02 - 37.60 0.4231E-02 - 37.70 0.4646E-02 - 37.80 0.5078E-02 - 37.90 0.5521E-02 - 38.00 0.5968E-02 - 38.10 0.6413E-02 - 38.20 0.6847E-02 - 38.30 0.7267E-02 - 38.40 0.7664E-02 - 38.50 0.8035E-02 - 38.60 0.8374E-02 - 38.70 0.8678E-02 - 38.80 0.8941E-02 - 38.90 0.9163E-02 - 39.00 0.9339E-02 - 39.10 0.9470E-02 - 39.20 0.9552E-02 - 39.30 0.9587E-02 - 39.40 0.9575E-02 - 39.50 0.9516E-02 - 39.60 0.9412E-02 - 39.70 0.9265E-02 - 39.80 0.9077E-02 - 39.90 0.8853E-02 - 40.00 0.8594E-02 - & -@ s1 legend "Partition= 2 Excit= 1 near/far= 1" -# s0 legend "Lab energy = 170.0000" -@s1 linestyle 2 -# Theta sigma iT11 T20 T21 T22 Kyy for projectile - 0.000 3.123 - 0.1000 3.120 - 0.2000 3.112 - 0.3000 3.098 - 0.4000 3.079 - 0.5000 3.056 - 0.6000 3.030 - 0.7000 3.000 - 0.8000 2.969 - 0.9000 2.937 - 1.000 2.906 - 1.100 2.876 - 1.200 2.849 - 1.300 2.826 - 1.400 2.808 - 1.500 2.797 - 1.600 2.794 - 1.700 2.799 - 1.800 2.813 - 1.900 2.838 - 2.000 2.874 - 2.100 2.921 - 2.200 2.979 - 2.300 3.049 - 2.400 3.129 - 2.500 3.220 - 2.600 3.320 - 2.700 3.428 - 2.800 3.543 - 2.900 3.664 - 3.000 3.789 - 3.100 3.915 - 3.200 4.042 - 3.300 4.166 - 3.400 4.285 - 3.500 4.398 - 3.600 4.502 - 3.700 4.594 - 3.800 4.674 - 3.900 4.738 - 4.000 4.786 - 4.100 4.815 - 4.200 4.825 - 4.300 4.815 - 4.400 4.784 - 4.500 4.732 - 4.600 4.659 - 4.700 4.564 - 4.800 4.451 - 4.900 4.318 - 5.000 4.168 - 5.100 4.002 - 5.200 3.822 - 5.300 3.631 - 5.400 3.430 - 5.500 3.222 - 5.600 3.010 - 5.700 2.796 - 5.800 2.583 - 5.900 2.373 - 6.000 2.169 - 6.100 1.972 - 6.200 1.786 - 6.300 1.611 - 6.400 1.450 - 6.500 1.303 - 6.600 1.172 - 6.700 1.058 - 6.800 0.9608 - 6.900 0.8804 - 7.000 0.8171 - 7.100 0.7702 - 7.200 0.7392 - 7.300 0.7231 - 7.400 0.7208 - 7.500 0.7310 - 7.600 0.7523 - 7.700 0.7830 - 7.800 0.8215 - 7.900 0.8662 - 8.000 0.9153 - 8.100 0.9671 - 8.200 1.020 - 8.300 1.073 - 8.400 1.123 - 8.500 1.170 - 8.600 1.213 - 8.700 1.250 - 8.800 1.281 - 8.900 1.305 - 9.000 1.321 - 9.100 1.328 - 9.200 1.328 - 9.300 1.320 - 9.400 1.303 - 9.500 1.279 - 9.600 1.247 - 9.700 1.209 - 9.800 1.164 - 9.900 1.114 - 10.00 1.060 - 10.10 1.001 - 10.20 0.9402 - 10.30 0.8772 - 10.40 0.8132 - 10.50 0.7490 - 10.60 0.6855 - 10.70 0.6235 - 10.80 0.5637 - 10.90 0.5068 - 11.00 0.4533 - 11.10 0.4038 - 11.20 0.3587 - 11.30 0.3183 - 11.40 0.2827 - 11.50 0.2521 - 11.60 0.2266 - 11.70 0.2061 - 11.80 0.1905 - 11.90 0.1796 - 12.00 0.1730 - 12.10 0.1706 - 12.20 0.1719 - 12.30 0.1765 - 12.40 0.1839 - 12.50 0.1938 - 12.60 0.2056 - 12.70 0.2188 - 12.80 0.2331 - 12.90 0.2479 - 13.00 0.2628 - 13.10 0.2773 - 13.20 0.2912 - 13.30 0.3040 - 13.40 0.3155 - 13.50 0.3254 - 13.60 0.3335 - 13.70 0.3396 - 13.80 0.3436 - 13.90 0.3454 - 14.00 0.3450 - 14.10 0.3424 - 14.20 0.3377 - 14.30 0.3309 - 14.40 0.3221 - 14.50 0.3117 - 14.60 0.2996 - 14.70 0.2861 - 14.80 0.2714 - 14.90 0.2557 - 15.00 0.2393 - 15.10 0.2225 - 15.20 0.2053 - 15.30 0.1881 - 15.40 0.1712 - 15.50 0.1546 - 15.60 0.1386 - 15.70 0.1233 - 15.80 0.1090 - 15.90 0.9568E-01 - 16.00 0.8354E-01 - 16.10 0.7263E-01 - 16.20 0.6303E-01 - 16.30 0.5477E-01 - 16.40 0.4787E-01 - 16.50 0.4233E-01 - 16.60 0.3812E-01 - 16.70 0.3519E-01 - 16.80 0.3349E-01 - 16.90 0.3293E-01 - 17.00 0.3343E-01 - 17.10 0.3489E-01 - 17.20 0.3719E-01 - 17.30 0.4022E-01 - 17.40 0.4386E-01 - 17.50 0.4797E-01 - 17.60 0.5244E-01 - 17.70 0.5713E-01 - 17.80 0.6194E-01 - 17.90 0.6674E-01 - 18.00 0.7142E-01 - 18.10 0.7589E-01 - 18.20 0.8006E-01 - 18.30 0.8384E-01 - 18.40 0.8716E-01 - 18.50 0.8997E-01 - 18.60 0.9223E-01 - 18.70 0.9388E-01 - 18.80 0.9492E-01 - 18.90 0.9534E-01 - 19.00 0.9513E-01 - 19.10 0.9430E-01 - 19.20 0.9288E-01 - 19.30 0.9089E-01 - 19.40 0.8839E-01 - 19.50 0.8540E-01 - 19.60 0.8198E-01 - 19.70 0.7819E-01 - 19.80 0.7408E-01 - 19.90 0.6973E-01 - 20.00 0.6518E-01 - 20.10 0.6052E-01 - 20.20 0.5579E-01 - 20.30 0.5106E-01 - 20.40 0.4640E-01 - 20.50 0.4184E-01 - 20.60 0.3745E-01 - 20.70 0.3328E-01 - 20.80 0.2935E-01 - 20.90 0.2572E-01 - 21.00 0.2240E-01 - 21.10 0.1942E-01 - 21.20 0.1680E-01 - 21.30 0.1456E-01 - 21.40 0.1269E-01 - 21.50 0.1119E-01 - 21.60 0.1007E-01 - 21.70 0.9302E-02 - 21.80 0.8881E-02 - 21.90 0.8785E-02 - 22.00 0.8991E-02 - 22.10 0.9471E-02 - 22.20 0.1020E-01 - 22.30 0.1113E-01 - 22.40 0.1225E-01 - 22.50 0.1352E-01 - 22.60 0.1490E-01 - 22.70 0.1636E-01 - 22.80 0.1786E-01 - 22.90 0.1938E-01 - 23.00 0.2087E-01 - 23.10 0.2232E-01 - 23.20 0.2369E-01 - 23.30 0.2497E-01 - 23.40 0.2613E-01 - 23.50 0.2715E-01 - 23.60 0.2801E-01 - 23.70 0.2871E-01 - 23.80 0.2924E-01 - 23.90 0.2958E-01 - 24.00 0.2975E-01 - 24.10 0.2973E-01 - 24.20 0.2954E-01 - 24.30 0.2917E-01 - 24.40 0.2864E-01 - 24.50 0.2796E-01 - 24.60 0.2713E-01 - 24.70 0.2618E-01 - 24.80 0.2512E-01 - 24.90 0.2397E-01 - 25.00 0.2273E-01 - 25.10 0.2144E-01 - 25.20 0.2010E-01 - 25.30 0.1873E-01 - 25.40 0.1736E-01 - 25.50 0.1600E-01 - 25.60 0.1466E-01 - 25.70 0.1336E-01 - 25.80 0.1211E-01 - 25.90 0.1092E-01 - 26.00 0.9814E-02 - 26.10 0.8788E-02 - 26.20 0.7853E-02 - 26.30 0.7017E-02 - 26.40 0.6283E-02 - 26.50 0.5653E-02 - 26.60 0.5130E-02 - 26.70 0.4713E-02 - 26.80 0.4400E-02 - 26.90 0.4188E-02 - 27.00 0.4072E-02 - 27.10 0.4047E-02 - 27.20 0.4106E-02 - 27.30 0.4243E-02 - 27.40 0.4448E-02 - 27.50 0.4713E-02 - 27.60 0.5030E-02 - 27.70 0.5388E-02 - 27.80 0.5778E-02 - 27.90 0.6190E-02 - 28.00 0.6616E-02 - 28.10 0.7047E-02 - 28.20 0.7473E-02 - 28.30 0.7887E-02 - 28.40 0.8282E-02 - 28.50 0.8649E-02 - 28.60 0.8985E-02 - 28.70 0.9283E-02 - 28.80 0.9539E-02 - 28.90 0.9749E-02 - 29.00 0.9912E-02 - 29.10 0.1002E-01 - 29.20 0.1009E-01 - 29.30 0.1010E-01 - 29.40 0.1005E-01 - 29.50 0.9965E-02 - 29.60 0.9828E-02 - 29.70 0.9645E-02 - 29.80 0.9421E-02 - 29.90 0.9159E-02 - 30.00 0.8863E-02 - 30.10 0.8536E-02 - 30.20 0.8185E-02 - 30.30 0.7812E-02 - 30.40 0.7423E-02 - 30.50 0.7023E-02 - 30.60 0.6616E-02 - 30.70 0.6208E-02 - 30.80 0.5803E-02 - 30.90 0.5404E-02 - 31.00 0.5017E-02 - 31.10 0.4644E-02 - 31.20 0.4289E-02 - 31.30 0.3955E-02 - 31.40 0.3645E-02 - 31.50 0.3361E-02 - 31.60 0.3104E-02 - 31.70 0.2875E-02 - 31.80 0.2676E-02 - 31.90 0.2507E-02 - 32.00 0.2368E-02 - 32.10 0.2257E-02 - 32.20 0.2176E-02 - 32.30 0.2121E-02 - 32.40 0.2092E-02 - 32.50 0.2088E-02 - 32.60 0.2105E-02 - 32.70 0.2142E-02 - 32.80 0.2197E-02 - 32.90 0.2266E-02 - 33.00 0.2349E-02 - 33.10 0.2441E-02 - 33.20 0.2541E-02 - 33.30 0.2646E-02 - 33.40 0.2753E-02 - 33.50 0.2860E-02 - 33.60 0.2966E-02 - 33.70 0.3067E-02 - 33.80 0.3162E-02 - 33.90 0.3250E-02 - 34.00 0.3328E-02 - 34.10 0.3396E-02 - 34.20 0.3452E-02 - 34.30 0.3496E-02 - 34.40 0.3527E-02 - 34.50 0.3543E-02 - 34.60 0.3546E-02 - 34.70 0.3535E-02 - 34.80 0.3511E-02 - 34.90 0.3473E-02 - 35.00 0.3422E-02 - 35.10 0.3359E-02 - 35.20 0.3286E-02 - 35.30 0.3201E-02 - 35.40 0.3108E-02 - 35.50 0.3006E-02 - 35.60 0.2898E-02 - 35.70 0.2784E-02 - 35.80 0.2667E-02 - 35.90 0.2546E-02 - 36.00 0.2423E-02 - 36.10 0.2301E-02 - 36.20 0.2179E-02 - 36.30 0.2059E-02 - 36.40 0.1943E-02 - 36.50 0.1830E-02 - 36.60 0.1723E-02 - 36.70 0.1621E-02 - 36.80 0.1526E-02 - 36.90 0.1438E-02 - 37.00 0.1357E-02 - 37.10 0.1284E-02 - 37.20 0.1219E-02 - 37.30 0.1162E-02 - 37.40 0.1113E-02 - 37.50 0.1072E-02 - 37.60 0.1039E-02 - 37.70 0.1013E-02 - 37.80 0.9948E-03 - 37.90 0.9830E-03 - 38.00 0.9775E-03 - 38.10 0.9776E-03 - 38.20 0.9827E-03 - 38.30 0.9924E-03 - 38.40 0.1006E-02 - 38.50 0.1022E-02 - 38.60 0.1042E-02 - 38.70 0.1063E-02 - 38.80 0.1085E-02 - 38.90 0.1108E-02 - 39.00 0.1130E-02 - 39.10 0.1152E-02 - 39.20 0.1173E-02 - 39.30 0.1193E-02 - 39.40 0.1210E-02 - 39.50 0.1225E-02 - 39.60 0.1237E-02 - 39.70 0.1245E-02 - 39.80 0.1251E-02 - 39.90 0.1253E-02 - 40.00 0.1252E-02 - & -@ s2 legend "Partition= 2 Excit= 2 near/far= 1" -# s0 legend "Lab energy = 170.0000" -@s2 linestyle 3 -# Theta sigma iT11 T20 T21 T22 Kyy for projectile - 0.000 0.000 - 0.1000 0.000 - 0.2000 0.000 - 0.3000 0.000 - 0.4000 0.000 - 0.5000 0.000 - 0.6000 0.000 - 0.7000 0.000 - 0.8000 0.000 - 0.9000 0.000 - 1.000 0.000 - 1.100 0.000 - 1.200 0.000 - 1.300 0.000 - 1.400 0.000 - 1.500 0.000 - 1.600 0.000 - 1.700 0.000 - 1.800 0.000 - 1.900 0.000 - 2.000 0.000 - 2.100 0.000 - 2.200 0.000 - 2.300 0.000 - 2.400 0.000 - 2.500 0.000 - 2.600 0.000 - 2.700 0.000 - 2.800 0.000 - 2.900 0.000 - 3.000 0.000 - 3.100 0.000 - 3.200 0.000 - 3.300 0.000 - 3.400 0.000 - 3.500 0.000 - 3.600 0.000 - 3.700 0.000 - 3.800 0.000 - 3.900 0.000 - 4.000 0.000 - 4.100 0.000 - 4.200 0.000 - 4.300 0.000 - 4.400 0.000 - 4.500 0.000 - 4.600 0.000 - 4.700 0.000 - 4.800 0.000 - 4.900 0.000 - 5.000 0.000 - 5.100 0.000 - 5.200 0.000 - 5.300 0.000 - 5.400 0.000 - 5.500 0.000 - 5.600 0.000 - 5.700 0.000 - 5.800 0.000 - 5.900 0.000 - 6.000 0.000 - 6.100 0.000 - 6.200 0.000 - 6.300 0.000 - 6.400 0.000 - 6.500 0.000 - 6.600 0.000 - 6.700 0.000 - 6.800 0.000 - 6.900 0.000 - 7.000 0.000 - 7.100 0.000 - 7.200 0.000 - 7.300 0.000 - 7.400 0.000 - 7.500 0.000 - 7.600 0.000 - 7.700 0.000 - 7.800 0.000 - 7.900 0.000 - 8.000 0.000 - 8.100 0.000 - 8.200 0.000 - 8.300 0.000 - 8.400 0.000 - 8.500 0.000 - 8.600 0.000 - 8.700 0.000 - 8.800 0.000 - 8.900 0.000 - 9.000 0.000 - 9.100 0.000 - 9.200 0.000 - 9.300 0.000 - 9.400 0.000 - 9.500 0.000 - 9.600 0.000 - 9.700 0.000 - 9.800 0.000 - 9.900 0.000 - 10.00 0.000 - 10.10 0.000 - 10.20 0.000 - 10.30 0.000 - 10.40 0.000 - 10.50 0.000 - 10.60 0.000 - 10.70 0.000 - 10.80 0.000 - 10.90 0.000 - 11.00 0.000 - 11.10 0.000 - 11.20 0.000 - 11.30 0.000 - 11.40 0.000 - 11.50 0.000 - 11.60 0.000 - 11.70 0.000 - 11.80 0.000 - 11.90 0.000 - 12.00 0.000 - 12.10 0.000 - 12.20 0.000 - 12.30 0.000 - 12.40 0.000 - 12.50 0.000 - 12.60 0.000 - 12.70 0.000 - 12.80 0.000 - 12.90 0.000 - 13.00 0.000 - 13.10 0.000 - 13.20 0.000 - 13.30 0.000 - 13.40 0.000 - 13.50 0.000 - 13.60 0.000 - 13.70 0.000 - 13.80 0.000 - 13.90 0.000 - 14.00 0.000 - 14.10 0.000 - 14.20 0.000 - 14.30 0.000 - 14.40 0.000 - 14.50 0.000 - 14.60 0.000 - 14.70 0.000 - 14.80 0.000 - 14.90 0.000 - 15.00 0.000 - 15.10 0.000 - 15.20 0.000 - 15.30 0.000 - 15.40 0.000 - 15.50 0.000 - 15.60 0.000 - 15.70 0.000 - 15.80 0.000 - 15.90 0.000 - 16.00 0.000 - 16.10 0.000 - 16.20 0.000 - 16.30 0.000 - 16.40 0.000 - 16.50 0.000 - 16.60 0.000 - 16.70 0.000 - 16.80 0.000 - 16.90 0.000 - 17.00 0.000 - 17.10 0.000 - 17.20 0.000 - 17.30 0.000 - 17.40 0.000 - 17.50 0.000 - 17.60 0.000 - 17.70 0.000 - 17.80 0.000 - 17.90 0.000 - 18.00 0.000 - 18.10 0.000 - 18.20 0.000 - 18.30 0.000 - 18.40 0.000 - 18.50 0.000 - 18.60 0.000 - 18.70 0.000 - 18.80 0.000 - 18.90 0.000 - 19.00 0.000 - 19.10 0.000 - 19.20 0.000 - 19.30 0.000 - 19.40 0.000 - 19.50 0.000 - 19.60 0.000 - 19.70 0.000 - 19.80 0.000 - 19.90 0.000 - 20.00 0.000 - 20.10 0.000 - 20.20 0.000 - 20.30 0.000 - 20.40 0.000 - 20.50 0.000 - 20.60 0.000 - 20.70 0.000 - 20.80 0.000 - 20.90 0.000 - 21.00 0.000 - 21.10 0.000 - 21.20 0.000 - 21.30 0.000 - 21.40 0.000 - 21.50 0.000 - 21.60 0.000 - 21.70 0.000 - 21.80 0.000 - 21.90 0.000 - 22.00 0.000 - 22.10 0.000 - 22.20 0.000 - 22.30 0.000 - 22.40 0.000 - 22.50 0.000 - 22.60 0.000 - 22.70 0.000 - 22.80 0.000 - 22.90 0.000 - 23.00 0.000 - 23.10 0.000 - 23.20 0.000 - 23.30 0.000 - 23.40 0.000 - 23.50 0.000 - 23.60 0.000 - 23.70 0.000 - 23.80 0.000 - 23.90 0.000 - 24.00 0.000 - 24.10 0.000 - 24.20 0.000 - 24.30 0.000 - 24.40 0.000 - 24.50 0.000 - 24.60 0.000 - 24.70 0.000 - 24.80 0.000 - 24.90 0.000 - 25.00 0.000 - 25.10 0.000 - 25.20 0.000 - 25.30 0.000 - 25.40 0.000 - 25.50 0.000 - 25.60 0.000 - 25.70 0.000 - 25.80 0.000 - 25.90 0.000 - 26.00 0.000 - 26.10 0.000 - 26.20 0.000 - 26.30 0.000 - 26.40 0.000 - 26.50 0.000 - 26.60 0.000 - 26.70 0.000 - 26.80 0.000 - 26.90 0.000 - 27.00 0.000 - 27.10 0.000 - 27.20 0.000 - 27.30 0.000 - 27.40 0.000 - 27.50 0.000 - 27.60 0.000 - 27.70 0.000 - 27.80 0.000 - 27.90 0.000 - 28.00 0.000 - 28.10 0.000 - 28.20 0.000 - 28.30 0.000 - 28.40 0.000 - 28.50 0.000 - 28.60 0.000 - 28.70 0.000 - 28.80 0.000 - 28.90 0.000 - 29.00 0.000 - 29.10 0.000 - 29.20 0.000 - 29.30 0.000 - 29.40 0.000 - 29.50 0.000 - 29.60 0.000 - 29.70 0.000 - 29.80 0.000 - 29.90 0.000 - 30.00 0.000 - 30.10 0.000 - 30.20 0.000 - 30.30 0.000 - 30.40 0.000 - 30.50 0.000 - 30.60 0.000 - 30.70 0.000 - 30.80 0.000 - 30.90 0.000 - 31.00 0.000 - 31.10 0.000 - 31.20 0.000 - 31.30 0.000 - 31.40 0.000 - 31.50 0.000 - 31.60 0.000 - 31.70 0.000 - 31.80 0.000 - 31.90 0.000 - 32.00 0.000 - 32.10 0.000 - 32.20 0.000 - 32.30 0.000 - 32.40 0.000 - 32.50 0.000 - 32.60 0.000 - 32.70 0.000 - 32.80 0.000 - 32.90 0.000 - 33.00 0.000 - 33.10 0.000 - 33.20 0.000 - 33.30 0.000 - 33.40 0.000 - 33.50 0.000 - 33.60 0.000 - 33.70 0.000 - 33.80 0.000 - 33.90 0.000 - 34.00 0.000 - 34.10 0.000 - 34.20 0.000 - 34.30 0.000 - 34.40 0.000 - 34.50 0.000 - 34.60 0.000 - 34.70 0.000 - 34.80 0.000 - 34.90 0.000 - 35.00 0.000 - 35.10 0.000 - 35.20 0.000 - 35.30 0.000 - 35.40 0.000 - 35.50 0.000 - 35.60 0.000 - 35.70 0.000 - 35.80 0.000 - 35.90 0.000 - 36.00 0.000 - 36.10 0.000 - 36.20 0.000 - 36.30 0.000 - 36.40 0.000 - 36.50 0.000 - 36.60 0.000 - 36.70 0.000 - 36.80 0.000 - 36.90 0.000 - 37.00 0.000 - 37.10 0.000 - 37.20 0.000 - 37.30 0.000 - 37.40 0.000 - 37.50 0.000 - 37.60 0.000 - 37.70 0.000 - 37.80 0.000 - 37.90 0.000 - 38.00 0.000 - 38.10 0.000 - 38.20 0.000 - 38.30 0.000 - 38.40 0.000 - 38.50 0.000 - 38.60 0.000 - 38.70 0.000 - 38.80 0.000 - 38.90 0.000 - 39.00 0.000 - 39.10 0.000 - 39.20 0.000 - 39.30 0.000 - 39.40 0.000 - 39.50 0.000 - 39.60 0.000 - 39.70 0.000 - 39.80 0.000 - 39.90 0.000 - 40.00 0.000 - & diff --git a/book/notebooks/Transfer_template/fort.201 b/book/notebooks/Transfer_template/fort.201 deleted file mode 100644 index 816ef8c..0000000 --- a/book/notebooks/Transfer_template/fort.201 +++ /dev/null @@ -1,411 +0,0 @@ -# 401 angles, 1 tensor ranks for 0=projectile -@subtitle "n14(f17,ne18)c13 @ 170 MeV;" -@legend ON -@legend x1 0.2 -@legend y1 0.8 -@g0 type LOGY -@ s0 legend "Lab energy = 170.0000" -# s0 legend "Partition= 1 Excit= 1 near/far= 1" -# Theta sigma iT11 T20 T21 T22 Kyy for projectile - 0.1000E-01 1.000 - 0.1000 1.000 - 0.2000 0.9991 - 0.3000 1.001 - 0.4000 1.002 - 0.5000 0.9963 - 0.6000 0.9919 - 0.7000 0.9962 - 0.8000 1.007 - 0.9000 1.017 - 1.000 1.021 - 1.100 1.016 - 1.200 1.004 - 1.300 0.9883 - 1.400 0.9721 - 1.500 0.9595 - 1.600 0.9528 - 1.700 0.9530 - 1.800 0.9601 - 1.900 0.9729 - 2.000 0.9897 - 2.100 1.008 - 2.200 1.027 - 2.300 1.045 - 2.400 1.059 - 2.500 1.070 - 2.600 1.077 - 2.700 1.080 - 2.800 1.080 - 2.900 1.077 - 3.000 1.072 - 3.100 1.066 - 3.200 1.059 - 3.300 1.053 - 3.400 1.048 - 3.500 1.044 - 3.600 1.042 - 3.700 1.041 - 3.800 1.042 - 3.900 1.045 - 4.000 1.049 - 4.100 1.053 - 4.200 1.057 - 4.300 1.060 - 4.400 1.062 - 4.500 1.063 - 4.600 1.062 - 4.700 1.058 - 4.800 1.051 - 4.900 1.042 - 5.000 1.029 - 5.100 1.013 - 5.200 0.9939 - 5.300 0.9721 - 5.400 0.9477 - 5.500 0.9211 - 5.600 0.8926 - 5.700 0.8627 - 5.800 0.8318 - 5.900 0.8005 - 6.000 0.7693 - 6.100 0.7385 - 6.200 0.7087 - 6.300 0.6802 - 6.400 0.6534 - 6.500 0.6286 - 6.600 0.6060 - 6.700 0.5859 - 6.800 0.5682 - 6.900 0.5531 - 7.000 0.5406 - 7.100 0.5306 - 7.200 0.5229 - 7.300 0.5174 - 7.400 0.5138 - 7.500 0.5119 - 7.600 0.5114 - 7.700 0.5120 - 7.800 0.5134 - 7.900 0.5152 - 8.000 0.5171 - 8.100 0.5188 - 8.200 0.5200 - 8.300 0.5205 - 8.400 0.5200 - 8.500 0.5183 - 8.600 0.5152 - 8.700 0.5106 - 8.800 0.5045 - 8.900 0.4967 - 9.000 0.4873 - 9.100 0.4762 - 9.200 0.4636 - 9.300 0.4496 - 9.400 0.4343 - 9.500 0.4178 - 9.600 0.4004 - 9.700 0.3823 - 9.800 0.3636 - 9.900 0.3447 - 10.00 0.3257 - 10.10 0.3069 - 10.20 0.2885 - 10.30 0.2708 - 10.40 0.2539 - 10.50 0.2380 - 10.60 0.2234 - 10.70 0.2101 - 10.80 0.1982 - 10.90 0.1879 - 11.00 0.1793 - 11.10 0.1722 - 11.20 0.1669 - 11.30 0.1631 - 11.40 0.1610 - 11.50 0.1603 - 11.60 0.1610 - 11.70 0.1630 - 11.80 0.1662 - 11.90 0.1702 - 12.00 0.1751 - 12.10 0.1806 - 12.20 0.1866 - 12.30 0.1927 - 12.40 0.1989 - 12.50 0.2050 - 12.60 0.2108 - 12.70 0.2161 - 12.80 0.2208 - 12.90 0.2247 - 13.00 0.2277 - 13.10 0.2298 - 13.20 0.2308 - 13.30 0.2307 - 13.40 0.2294 - 13.50 0.2270 - 13.60 0.2234 - 13.70 0.2186 - 13.80 0.2128 - 13.90 0.2060 - 14.00 0.1983 - 14.10 0.1897 - 14.20 0.1805 - 14.30 0.1707 - 14.40 0.1604 - 14.50 0.1498 - 14.60 0.1390 - 14.70 0.1283 - 14.80 0.1176 - 14.90 0.1073 - 15.00 0.9729E-01 - 15.10 0.8783E-01 - 15.20 0.7901E-01 - 15.30 0.7093E-01 - 15.40 0.6367E-01 - 15.50 0.5730E-01 - 15.60 0.5190E-01 - 15.70 0.4748E-01 - 15.80 0.4409E-01 - 15.90 0.4172E-01 - 16.00 0.4037E-01 - 16.10 0.4002E-01 - 16.20 0.4061E-01 - 16.30 0.4210E-01 - 16.40 0.4442E-01 - 16.50 0.4749E-01 - 16.60 0.5121E-01 - 16.70 0.5550E-01 - 16.80 0.6024E-01 - 16.90 0.6532E-01 - 17.00 0.7063E-01 - 17.10 0.7606E-01 - 17.20 0.8149E-01 - 17.30 0.8681E-01 - 17.40 0.9192E-01 - 17.50 0.9672E-01 - 17.60 0.1011 - 17.70 0.1050 - 17.80 0.1083 - 17.90 0.1111 - 18.00 0.1131 - 18.10 0.1144 - 18.20 0.1150 - 18.30 0.1148 - 18.40 0.1139 - 18.50 0.1122 - 18.60 0.1098 - 18.70 0.1067 - 18.80 0.1030 - 18.90 0.9870E-01 - 19.00 0.9389E-01 - 19.10 0.8862E-01 - 19.20 0.8299E-01 - 19.30 0.7707E-01 - 19.40 0.7094E-01 - 19.50 0.6469E-01 - 19.60 0.5841E-01 - 19.70 0.5218E-01 - 19.80 0.4609E-01 - 19.90 0.4021E-01 - 20.00 0.3463E-01 - 20.10 0.2940E-01 - 20.20 0.2459E-01 - 20.30 0.2025E-01 - 20.40 0.1644E-01 - 20.50 0.1318E-01 - 20.60 0.1050E-01 - 20.70 0.8423E-02 - 20.80 0.6959E-02 - 20.90 0.6103E-02 - 21.00 0.5847E-02 - 21.10 0.6171E-02 - 21.20 0.7049E-02 - 21.30 0.8444E-02 - 21.40 0.1031E-01 - 21.50 0.1261E-01 - 21.60 0.1529E-01 - 21.70 0.1828E-01 - 21.80 0.2152E-01 - 21.90 0.2496E-01 - 22.00 0.2852E-01 - 22.10 0.3214E-01 - 22.20 0.3576E-01 - 22.30 0.3932E-01 - 22.40 0.4275E-01 - 22.50 0.4599E-01 - 22.60 0.4900E-01 - 22.70 0.5173E-01 - 22.80 0.5414E-01 - 22.90 0.5618E-01 - 23.00 0.5783E-01 - 23.10 0.5907E-01 - 23.20 0.5988E-01 - 23.30 0.6025E-01 - 23.40 0.6018E-01 - 23.50 0.5968E-01 - 23.60 0.5875E-01 - 23.70 0.5742E-01 - 23.80 0.5570E-01 - 23.90 0.5363E-01 - 24.00 0.5123E-01 - 24.10 0.4855E-01 - 24.20 0.4563E-01 - 24.30 0.4251E-01 - 24.40 0.3923E-01 - 24.50 0.3585E-01 - 24.60 0.3240E-01 - 24.70 0.2895E-01 - 24.80 0.2552E-01 - 24.90 0.2218E-01 - 25.00 0.1895E-01 - 25.10 0.1589E-01 - 25.20 0.1302E-01 - 25.30 0.1038E-01 - 25.40 0.8001E-02 - 25.50 0.5903E-02 - 25.60 0.4109E-02 - 25.70 0.2631E-02 - 25.80 0.1480E-02 - 25.90 0.6599E-03 - 26.00 0.1716E-03 - 26.10 0.9916E-05 - 26.20 0.1652E-03 - 26.30 0.6238E-03 - 26.40 0.1368E-02 - 26.50 0.2376E-02 - 26.60 0.3623E-02 - 26.70 0.5083E-02 - 26.80 0.6724E-02 - 26.90 0.8516E-02 - 27.00 0.1043E-01 - 27.10 0.1242E-01 - 27.20 0.1446E-01 - 27.30 0.1652E-01 - 27.40 0.1857E-01 - 27.50 0.2056E-01 - 27.60 0.2248E-01 - 27.70 0.2428E-01 - 27.80 0.2596E-01 - 27.90 0.2748E-01 - 28.00 0.2882E-01 - 28.10 0.2996E-01 - 28.20 0.3090E-01 - 28.30 0.3162E-01 - 28.40 0.3211E-01 - 28.50 0.3237E-01 - 28.60 0.3241E-01 - 28.70 0.3221E-01 - 28.80 0.3179E-01 - 28.90 0.3117E-01 - 29.00 0.3034E-01 - 29.10 0.2933E-01 - 29.20 0.2814E-01 - 29.30 0.2681E-01 - 29.40 0.2535E-01 - 29.50 0.2379E-01 - 29.60 0.2214E-01 - 29.70 0.2042E-01 - 29.80 0.1867E-01 - 29.90 0.1691E-01 - 30.00 0.1515E-01 - 30.10 0.1343E-01 - 30.20 0.1176E-01 - 30.30 0.1016E-01 - 30.40 0.8649E-02 - 30.50 0.7249E-02 - 30.60 0.5972E-02 - 30.70 0.4829E-02 - 30.80 0.3832E-02 - 30.90 0.2989E-02 - 31.00 0.2305E-02 - 31.10 0.1784E-02 - 31.20 0.1428E-02 - 31.30 0.1234E-02 - 31.40 0.1199E-02 - 31.50 0.1318E-02 - 31.60 0.1583E-02 - 31.70 0.1985E-02 - 31.80 0.2514E-02 - 31.90 0.3156E-02 - 32.00 0.3898E-02 - 32.10 0.4726E-02 - 32.20 0.5624E-02 - 32.30 0.6577E-02 - 32.40 0.7569E-02 - 32.50 0.8583E-02 - 32.60 0.9603E-02 - 32.70 0.1061E-01 - 32.80 0.1160E-01 - 32.90 0.1255E-01 - 33.00 0.1345E-01 - 33.10 0.1428E-01 - 33.20 0.1504E-01 - 33.30 0.1572E-01 - 33.40 0.1630E-01 - 33.50 0.1678E-01 - 33.60 0.1716E-01 - 33.70 0.1744E-01 - 33.80 0.1760E-01 - 33.90 0.1765E-01 - 34.00 0.1760E-01 - 34.10 0.1744E-01 - 34.20 0.1718E-01 - 34.30 0.1682E-01 - 34.40 0.1637E-01 - 34.50 0.1584E-01 - 34.60 0.1524E-01 - 34.70 0.1457E-01 - 34.80 0.1385E-01 - 34.90 0.1308E-01 - 35.00 0.1228E-01 - 35.10 0.1145E-01 - 35.20 0.1061E-01 - 35.30 0.9772E-02 - 35.40 0.8940E-02 - 35.50 0.8126E-02 - 35.60 0.7339E-02 - 35.70 0.6589E-02 - 35.80 0.5883E-02 - 35.90 0.5228E-02 - 36.00 0.4631E-02 - 36.10 0.4097E-02 - 36.20 0.3631E-02 - 36.30 0.3235E-02 - 36.40 0.2911E-02 - 36.50 0.2662E-02 - 36.60 0.2488E-02 - 36.70 0.2386E-02 - 36.80 0.2357E-02 - 36.90 0.2396E-02 - 37.00 0.2501E-02 - 37.10 0.2668E-02 - 37.20 0.2891E-02 - 37.30 0.3165E-02 - 37.40 0.3484E-02 - 37.50 0.3842E-02 - 37.60 0.4231E-02 - 37.70 0.4646E-02 - 37.80 0.5078E-02 - 37.90 0.5521E-02 - 38.00 0.5968E-02 - 38.10 0.6413E-02 - 38.20 0.6847E-02 - 38.30 0.7267E-02 - 38.40 0.7664E-02 - 38.50 0.8035E-02 - 38.60 0.8374E-02 - 38.70 0.8678E-02 - 38.80 0.8941E-02 - 38.90 0.9163E-02 - 39.00 0.9339E-02 - 39.10 0.9470E-02 - 39.20 0.9552E-02 - 39.30 0.9587E-02 - 39.40 0.9575E-02 - 39.50 0.9516E-02 - 39.60 0.9412E-02 - 39.70 0.9265E-02 - 39.80 0.9077E-02 - 39.90 0.8853E-02 - 40.00 0.8594E-02 - & diff --git a/book/notebooks/Transfer_template/fort.202 b/book/notebooks/Transfer_template/fort.202 deleted file mode 100644 index e24c329..0000000 --- a/book/notebooks/Transfer_template/fort.202 +++ /dev/null @@ -1,411 +0,0 @@ -# 401 angles, 1 tensor ranks for 0=projectile -@subtitle "n14(f17,ne18)c13 @ 170 MeV;" -@legend ON -@legend x1 0.2 -@legend y1 0.8 -@g0 type LOGY -@ s0 legend "Lab energy = 170.0000" -# s0 legend "Partition= 2 Excit= 1 near/far= 1" -# Theta sigma iT11 T20 T21 T22 Kyy for projectile - 0.000 3.123 - 0.1000 3.120 - 0.2000 3.112 - 0.3000 3.098 - 0.4000 3.079 - 0.5000 3.056 - 0.6000 3.030 - 0.7000 3.000 - 0.8000 2.969 - 0.9000 2.937 - 1.000 2.906 - 1.100 2.876 - 1.200 2.849 - 1.300 2.826 - 1.400 2.808 - 1.500 2.797 - 1.600 2.794 - 1.700 2.799 - 1.800 2.813 - 1.900 2.838 - 2.000 2.874 - 2.100 2.921 - 2.200 2.979 - 2.300 3.049 - 2.400 3.129 - 2.500 3.220 - 2.600 3.320 - 2.700 3.428 - 2.800 3.543 - 2.900 3.664 - 3.000 3.789 - 3.100 3.915 - 3.200 4.042 - 3.300 4.166 - 3.400 4.285 - 3.500 4.398 - 3.600 4.502 - 3.700 4.594 - 3.800 4.674 - 3.900 4.738 - 4.000 4.786 - 4.100 4.815 - 4.200 4.825 - 4.300 4.815 - 4.400 4.784 - 4.500 4.732 - 4.600 4.659 - 4.700 4.564 - 4.800 4.451 - 4.900 4.318 - 5.000 4.168 - 5.100 4.002 - 5.200 3.822 - 5.300 3.631 - 5.400 3.430 - 5.500 3.222 - 5.600 3.010 - 5.700 2.796 - 5.800 2.583 - 5.900 2.373 - 6.000 2.169 - 6.100 1.972 - 6.200 1.786 - 6.300 1.611 - 6.400 1.450 - 6.500 1.303 - 6.600 1.172 - 6.700 1.058 - 6.800 0.9608 - 6.900 0.8804 - 7.000 0.8171 - 7.100 0.7702 - 7.200 0.7392 - 7.300 0.7231 - 7.400 0.7208 - 7.500 0.7310 - 7.600 0.7523 - 7.700 0.7830 - 7.800 0.8215 - 7.900 0.8662 - 8.000 0.9153 - 8.100 0.9671 - 8.200 1.020 - 8.300 1.073 - 8.400 1.123 - 8.500 1.170 - 8.600 1.213 - 8.700 1.250 - 8.800 1.281 - 8.900 1.305 - 9.000 1.321 - 9.100 1.328 - 9.200 1.328 - 9.300 1.320 - 9.400 1.303 - 9.500 1.279 - 9.600 1.247 - 9.700 1.209 - 9.800 1.164 - 9.900 1.114 - 10.00 1.060 - 10.10 1.001 - 10.20 0.9402 - 10.30 0.8772 - 10.40 0.8132 - 10.50 0.7490 - 10.60 0.6855 - 10.70 0.6235 - 10.80 0.5637 - 10.90 0.5068 - 11.00 0.4533 - 11.10 0.4038 - 11.20 0.3587 - 11.30 0.3183 - 11.40 0.2827 - 11.50 0.2521 - 11.60 0.2266 - 11.70 0.2061 - 11.80 0.1905 - 11.90 0.1796 - 12.00 0.1730 - 12.10 0.1706 - 12.20 0.1719 - 12.30 0.1765 - 12.40 0.1839 - 12.50 0.1938 - 12.60 0.2056 - 12.70 0.2188 - 12.80 0.2331 - 12.90 0.2479 - 13.00 0.2628 - 13.10 0.2773 - 13.20 0.2912 - 13.30 0.3040 - 13.40 0.3155 - 13.50 0.3254 - 13.60 0.3335 - 13.70 0.3396 - 13.80 0.3436 - 13.90 0.3454 - 14.00 0.3450 - 14.10 0.3424 - 14.20 0.3377 - 14.30 0.3309 - 14.40 0.3221 - 14.50 0.3117 - 14.60 0.2996 - 14.70 0.2861 - 14.80 0.2714 - 14.90 0.2557 - 15.00 0.2393 - 15.10 0.2225 - 15.20 0.2053 - 15.30 0.1881 - 15.40 0.1712 - 15.50 0.1546 - 15.60 0.1386 - 15.70 0.1233 - 15.80 0.1090 - 15.90 0.9568E-01 - 16.00 0.8354E-01 - 16.10 0.7263E-01 - 16.20 0.6303E-01 - 16.30 0.5477E-01 - 16.40 0.4787E-01 - 16.50 0.4233E-01 - 16.60 0.3812E-01 - 16.70 0.3519E-01 - 16.80 0.3349E-01 - 16.90 0.3293E-01 - 17.00 0.3343E-01 - 17.10 0.3489E-01 - 17.20 0.3719E-01 - 17.30 0.4022E-01 - 17.40 0.4386E-01 - 17.50 0.4797E-01 - 17.60 0.5244E-01 - 17.70 0.5713E-01 - 17.80 0.6194E-01 - 17.90 0.6674E-01 - 18.00 0.7142E-01 - 18.10 0.7589E-01 - 18.20 0.8006E-01 - 18.30 0.8384E-01 - 18.40 0.8716E-01 - 18.50 0.8997E-01 - 18.60 0.9223E-01 - 18.70 0.9388E-01 - 18.80 0.9492E-01 - 18.90 0.9534E-01 - 19.00 0.9513E-01 - 19.10 0.9430E-01 - 19.20 0.9288E-01 - 19.30 0.9089E-01 - 19.40 0.8839E-01 - 19.50 0.8540E-01 - 19.60 0.8198E-01 - 19.70 0.7819E-01 - 19.80 0.7408E-01 - 19.90 0.6973E-01 - 20.00 0.6518E-01 - 20.10 0.6052E-01 - 20.20 0.5579E-01 - 20.30 0.5106E-01 - 20.40 0.4640E-01 - 20.50 0.4184E-01 - 20.60 0.3745E-01 - 20.70 0.3328E-01 - 20.80 0.2935E-01 - 20.90 0.2572E-01 - 21.00 0.2240E-01 - 21.10 0.1942E-01 - 21.20 0.1680E-01 - 21.30 0.1456E-01 - 21.40 0.1269E-01 - 21.50 0.1119E-01 - 21.60 0.1007E-01 - 21.70 0.9302E-02 - 21.80 0.8881E-02 - 21.90 0.8785E-02 - 22.00 0.8991E-02 - 22.10 0.9471E-02 - 22.20 0.1020E-01 - 22.30 0.1113E-01 - 22.40 0.1225E-01 - 22.50 0.1352E-01 - 22.60 0.1490E-01 - 22.70 0.1636E-01 - 22.80 0.1786E-01 - 22.90 0.1938E-01 - 23.00 0.2087E-01 - 23.10 0.2232E-01 - 23.20 0.2369E-01 - 23.30 0.2497E-01 - 23.40 0.2613E-01 - 23.50 0.2715E-01 - 23.60 0.2801E-01 - 23.70 0.2871E-01 - 23.80 0.2924E-01 - 23.90 0.2958E-01 - 24.00 0.2975E-01 - 24.10 0.2973E-01 - 24.20 0.2954E-01 - 24.30 0.2917E-01 - 24.40 0.2864E-01 - 24.50 0.2796E-01 - 24.60 0.2713E-01 - 24.70 0.2618E-01 - 24.80 0.2512E-01 - 24.90 0.2397E-01 - 25.00 0.2273E-01 - 25.10 0.2144E-01 - 25.20 0.2010E-01 - 25.30 0.1873E-01 - 25.40 0.1736E-01 - 25.50 0.1600E-01 - 25.60 0.1466E-01 - 25.70 0.1336E-01 - 25.80 0.1211E-01 - 25.90 0.1092E-01 - 26.00 0.9814E-02 - 26.10 0.8788E-02 - 26.20 0.7853E-02 - 26.30 0.7017E-02 - 26.40 0.6283E-02 - 26.50 0.5653E-02 - 26.60 0.5130E-02 - 26.70 0.4713E-02 - 26.80 0.4400E-02 - 26.90 0.4188E-02 - 27.00 0.4072E-02 - 27.10 0.4047E-02 - 27.20 0.4106E-02 - 27.30 0.4243E-02 - 27.40 0.4448E-02 - 27.50 0.4713E-02 - 27.60 0.5030E-02 - 27.70 0.5388E-02 - 27.80 0.5778E-02 - 27.90 0.6190E-02 - 28.00 0.6616E-02 - 28.10 0.7047E-02 - 28.20 0.7473E-02 - 28.30 0.7887E-02 - 28.40 0.8282E-02 - 28.50 0.8649E-02 - 28.60 0.8985E-02 - 28.70 0.9283E-02 - 28.80 0.9539E-02 - 28.90 0.9749E-02 - 29.00 0.9912E-02 - 29.10 0.1002E-01 - 29.20 0.1009E-01 - 29.30 0.1010E-01 - 29.40 0.1005E-01 - 29.50 0.9965E-02 - 29.60 0.9828E-02 - 29.70 0.9645E-02 - 29.80 0.9421E-02 - 29.90 0.9159E-02 - 30.00 0.8863E-02 - 30.10 0.8536E-02 - 30.20 0.8185E-02 - 30.30 0.7812E-02 - 30.40 0.7423E-02 - 30.50 0.7023E-02 - 30.60 0.6616E-02 - 30.70 0.6208E-02 - 30.80 0.5803E-02 - 30.90 0.5404E-02 - 31.00 0.5017E-02 - 31.10 0.4644E-02 - 31.20 0.4289E-02 - 31.30 0.3955E-02 - 31.40 0.3645E-02 - 31.50 0.3361E-02 - 31.60 0.3104E-02 - 31.70 0.2875E-02 - 31.80 0.2676E-02 - 31.90 0.2507E-02 - 32.00 0.2368E-02 - 32.10 0.2257E-02 - 32.20 0.2176E-02 - 32.30 0.2121E-02 - 32.40 0.2092E-02 - 32.50 0.2088E-02 - 32.60 0.2105E-02 - 32.70 0.2142E-02 - 32.80 0.2197E-02 - 32.90 0.2266E-02 - 33.00 0.2349E-02 - 33.10 0.2441E-02 - 33.20 0.2541E-02 - 33.30 0.2646E-02 - 33.40 0.2753E-02 - 33.50 0.2860E-02 - 33.60 0.2966E-02 - 33.70 0.3067E-02 - 33.80 0.3162E-02 - 33.90 0.3250E-02 - 34.00 0.3328E-02 - 34.10 0.3396E-02 - 34.20 0.3452E-02 - 34.30 0.3496E-02 - 34.40 0.3527E-02 - 34.50 0.3543E-02 - 34.60 0.3546E-02 - 34.70 0.3535E-02 - 34.80 0.3511E-02 - 34.90 0.3473E-02 - 35.00 0.3422E-02 - 35.10 0.3359E-02 - 35.20 0.3286E-02 - 35.30 0.3201E-02 - 35.40 0.3108E-02 - 35.50 0.3006E-02 - 35.60 0.2898E-02 - 35.70 0.2784E-02 - 35.80 0.2667E-02 - 35.90 0.2546E-02 - 36.00 0.2423E-02 - 36.10 0.2301E-02 - 36.20 0.2179E-02 - 36.30 0.2059E-02 - 36.40 0.1943E-02 - 36.50 0.1830E-02 - 36.60 0.1723E-02 - 36.70 0.1621E-02 - 36.80 0.1526E-02 - 36.90 0.1438E-02 - 37.00 0.1357E-02 - 37.10 0.1284E-02 - 37.20 0.1219E-02 - 37.30 0.1162E-02 - 37.40 0.1113E-02 - 37.50 0.1072E-02 - 37.60 0.1039E-02 - 37.70 0.1013E-02 - 37.80 0.9948E-03 - 37.90 0.9830E-03 - 38.00 0.9775E-03 - 38.10 0.9776E-03 - 38.20 0.9827E-03 - 38.30 0.9924E-03 - 38.40 0.1006E-02 - 38.50 0.1022E-02 - 38.60 0.1042E-02 - 38.70 0.1063E-02 - 38.80 0.1085E-02 - 38.90 0.1108E-02 - 39.00 0.1130E-02 - 39.10 0.1152E-02 - 39.20 0.1173E-02 - 39.30 0.1193E-02 - 39.40 0.1210E-02 - 39.50 0.1225E-02 - 39.60 0.1237E-02 - 39.70 0.1245E-02 - 39.80 0.1251E-02 - 39.90 0.1253E-02 - 40.00 0.1252E-02 - & diff --git a/book/notebooks/Transfer_template/fort.203 b/book/notebooks/Transfer_template/fort.203 deleted file mode 100644 index bf63fc3..0000000 --- a/book/notebooks/Transfer_template/fort.203 +++ /dev/null @@ -1,411 +0,0 @@ -# 401 angles, 1 tensor ranks for 0=projectile -@subtitle "n14(f17,ne18)c13 @ 170 MeV;" -@legend ON -@legend x1 0.2 -@legend y1 0.8 -@g0 type LOGY -@ s0 legend "Lab energy = 170.0000" -# s0 legend "Partition= 2 Excit= 2 near/far= 1" -# Theta sigma iT11 T20 T21 T22 Kyy for projectile - 0.000 0.000 - 0.1000 0.000 - 0.2000 0.000 - 0.3000 0.000 - 0.4000 0.000 - 0.5000 0.000 - 0.6000 0.000 - 0.7000 0.000 - 0.8000 0.000 - 0.9000 0.000 - 1.000 0.000 - 1.100 0.000 - 1.200 0.000 - 1.300 0.000 - 1.400 0.000 - 1.500 0.000 - 1.600 0.000 - 1.700 0.000 - 1.800 0.000 - 1.900 0.000 - 2.000 0.000 - 2.100 0.000 - 2.200 0.000 - 2.300 0.000 - 2.400 0.000 - 2.500 0.000 - 2.600 0.000 - 2.700 0.000 - 2.800 0.000 - 2.900 0.000 - 3.000 0.000 - 3.100 0.000 - 3.200 0.000 - 3.300 0.000 - 3.400 0.000 - 3.500 0.000 - 3.600 0.000 - 3.700 0.000 - 3.800 0.000 - 3.900 0.000 - 4.000 0.000 - 4.100 0.000 - 4.200 0.000 - 4.300 0.000 - 4.400 0.000 - 4.500 0.000 - 4.600 0.000 - 4.700 0.000 - 4.800 0.000 - 4.900 0.000 - 5.000 0.000 - 5.100 0.000 - 5.200 0.000 - 5.300 0.000 - 5.400 0.000 - 5.500 0.000 - 5.600 0.000 - 5.700 0.000 - 5.800 0.000 - 5.900 0.000 - 6.000 0.000 - 6.100 0.000 - 6.200 0.000 - 6.300 0.000 - 6.400 0.000 - 6.500 0.000 - 6.600 0.000 - 6.700 0.000 - 6.800 0.000 - 6.900 0.000 - 7.000 0.000 - 7.100 0.000 - 7.200 0.000 - 7.300 0.000 - 7.400 0.000 - 7.500 0.000 - 7.600 0.000 - 7.700 0.000 - 7.800 0.000 - 7.900 0.000 - 8.000 0.000 - 8.100 0.000 - 8.200 0.000 - 8.300 0.000 - 8.400 0.000 - 8.500 0.000 - 8.600 0.000 - 8.700 0.000 - 8.800 0.000 - 8.900 0.000 - 9.000 0.000 - 9.100 0.000 - 9.200 0.000 - 9.300 0.000 - 9.400 0.000 - 9.500 0.000 - 9.600 0.000 - 9.700 0.000 - 9.800 0.000 - 9.900 0.000 - 10.00 0.000 - 10.10 0.000 - 10.20 0.000 - 10.30 0.000 - 10.40 0.000 - 10.50 0.000 - 10.60 0.000 - 10.70 0.000 - 10.80 0.000 - 10.90 0.000 - 11.00 0.000 - 11.10 0.000 - 11.20 0.000 - 11.30 0.000 - 11.40 0.000 - 11.50 0.000 - 11.60 0.000 - 11.70 0.000 - 11.80 0.000 - 11.90 0.000 - 12.00 0.000 - 12.10 0.000 - 12.20 0.000 - 12.30 0.000 - 12.40 0.000 - 12.50 0.000 - 12.60 0.000 - 12.70 0.000 - 12.80 0.000 - 12.90 0.000 - 13.00 0.000 - 13.10 0.000 - 13.20 0.000 - 13.30 0.000 - 13.40 0.000 - 13.50 0.000 - 13.60 0.000 - 13.70 0.000 - 13.80 0.000 - 13.90 0.000 - 14.00 0.000 - 14.10 0.000 - 14.20 0.000 - 14.30 0.000 - 14.40 0.000 - 14.50 0.000 - 14.60 0.000 - 14.70 0.000 - 14.80 0.000 - 14.90 0.000 - 15.00 0.000 - 15.10 0.000 - 15.20 0.000 - 15.30 0.000 - 15.40 0.000 - 15.50 0.000 - 15.60 0.000 - 15.70 0.000 - 15.80 0.000 - 15.90 0.000 - 16.00 0.000 - 16.10 0.000 - 16.20 0.000 - 16.30 0.000 - 16.40 0.000 - 16.50 0.000 - 16.60 0.000 - 16.70 0.000 - 16.80 0.000 - 16.90 0.000 - 17.00 0.000 - 17.10 0.000 - 17.20 0.000 - 17.30 0.000 - 17.40 0.000 - 17.50 0.000 - 17.60 0.000 - 17.70 0.000 - 17.80 0.000 - 17.90 0.000 - 18.00 0.000 - 18.10 0.000 - 18.20 0.000 - 18.30 0.000 - 18.40 0.000 - 18.50 0.000 - 18.60 0.000 - 18.70 0.000 - 18.80 0.000 - 18.90 0.000 - 19.00 0.000 - 19.10 0.000 - 19.20 0.000 - 19.30 0.000 - 19.40 0.000 - 19.50 0.000 - 19.60 0.000 - 19.70 0.000 - 19.80 0.000 - 19.90 0.000 - 20.00 0.000 - 20.10 0.000 - 20.20 0.000 - 20.30 0.000 - 20.40 0.000 - 20.50 0.000 - 20.60 0.000 - 20.70 0.000 - 20.80 0.000 - 20.90 0.000 - 21.00 0.000 - 21.10 0.000 - 21.20 0.000 - 21.30 0.000 - 21.40 0.000 - 21.50 0.000 - 21.60 0.000 - 21.70 0.000 - 21.80 0.000 - 21.90 0.000 - 22.00 0.000 - 22.10 0.000 - 22.20 0.000 - 22.30 0.000 - 22.40 0.000 - 22.50 0.000 - 22.60 0.000 - 22.70 0.000 - 22.80 0.000 - 22.90 0.000 - 23.00 0.000 - 23.10 0.000 - 23.20 0.000 - 23.30 0.000 - 23.40 0.000 - 23.50 0.000 - 23.60 0.000 - 23.70 0.000 - 23.80 0.000 - 23.90 0.000 - 24.00 0.000 - 24.10 0.000 - 24.20 0.000 - 24.30 0.000 - 24.40 0.000 - 24.50 0.000 - 24.60 0.000 - 24.70 0.000 - 24.80 0.000 - 24.90 0.000 - 25.00 0.000 - 25.10 0.000 - 25.20 0.000 - 25.30 0.000 - 25.40 0.000 - 25.50 0.000 - 25.60 0.000 - 25.70 0.000 - 25.80 0.000 - 25.90 0.000 - 26.00 0.000 - 26.10 0.000 - 26.20 0.000 - 26.30 0.000 - 26.40 0.000 - 26.50 0.000 - 26.60 0.000 - 26.70 0.000 - 26.80 0.000 - 26.90 0.000 - 27.00 0.000 - 27.10 0.000 - 27.20 0.000 - 27.30 0.000 - 27.40 0.000 - 27.50 0.000 - 27.60 0.000 - 27.70 0.000 - 27.80 0.000 - 27.90 0.000 - 28.00 0.000 - 28.10 0.000 - 28.20 0.000 - 28.30 0.000 - 28.40 0.000 - 28.50 0.000 - 28.60 0.000 - 28.70 0.000 - 28.80 0.000 - 28.90 0.000 - 29.00 0.000 - 29.10 0.000 - 29.20 0.000 - 29.30 0.000 - 29.40 0.000 - 29.50 0.000 - 29.60 0.000 - 29.70 0.000 - 29.80 0.000 - 29.90 0.000 - 30.00 0.000 - 30.10 0.000 - 30.20 0.000 - 30.30 0.000 - 30.40 0.000 - 30.50 0.000 - 30.60 0.000 - 30.70 0.000 - 30.80 0.000 - 30.90 0.000 - 31.00 0.000 - 31.10 0.000 - 31.20 0.000 - 31.30 0.000 - 31.40 0.000 - 31.50 0.000 - 31.60 0.000 - 31.70 0.000 - 31.80 0.000 - 31.90 0.000 - 32.00 0.000 - 32.10 0.000 - 32.20 0.000 - 32.30 0.000 - 32.40 0.000 - 32.50 0.000 - 32.60 0.000 - 32.70 0.000 - 32.80 0.000 - 32.90 0.000 - 33.00 0.000 - 33.10 0.000 - 33.20 0.000 - 33.30 0.000 - 33.40 0.000 - 33.50 0.000 - 33.60 0.000 - 33.70 0.000 - 33.80 0.000 - 33.90 0.000 - 34.00 0.000 - 34.10 0.000 - 34.20 0.000 - 34.30 0.000 - 34.40 0.000 - 34.50 0.000 - 34.60 0.000 - 34.70 0.000 - 34.80 0.000 - 34.90 0.000 - 35.00 0.000 - 35.10 0.000 - 35.20 0.000 - 35.30 0.000 - 35.40 0.000 - 35.50 0.000 - 35.60 0.000 - 35.70 0.000 - 35.80 0.000 - 35.90 0.000 - 36.00 0.000 - 36.10 0.000 - 36.20 0.000 - 36.30 0.000 - 36.40 0.000 - 36.50 0.000 - 36.60 0.000 - 36.70 0.000 - 36.80 0.000 - 36.90 0.000 - 37.00 0.000 - 37.10 0.000 - 37.20 0.000 - 37.30 0.000 - 37.40 0.000 - 37.50 0.000 - 37.60 0.000 - 37.70 0.000 - 37.80 0.000 - 37.90 0.000 - 38.00 0.000 - 38.10 0.000 - 38.20 0.000 - 38.30 0.000 - 38.40 0.000 - 38.50 0.000 - 38.60 0.000 - 38.70 0.000 - 38.80 0.000 - 38.90 0.000 - 39.00 0.000 - 39.10 0.000 - 39.20 0.000 - 39.30 0.000 - 39.40 0.000 - 39.50 0.000 - 39.60 0.000 - 39.70 0.000 - 39.80 0.000 - 39.90 0.000 - 40.00 0.000 - & diff --git a/book/notebooks/Transfer_template/fort.239 b/book/notebooks/Transfer_template/fort.239 deleted file mode 100644 index 715a435..0000000 --- a/book/notebooks/Transfer_template/fort.239 +++ /dev/null @@ -1 +0,0 @@ -170.0000 1.760083E+03 1760.3 3237.5 0.61071-309 0.0000 0.26397 0.0000 diff --git a/book/notebooks/Transfer_template/fort.3 b/book/notebooks/Transfer_template/fort.3 deleted file mode 100644 index bf411ce..0000000 --- a/book/notebooks/Transfer_template/fort.3 +++ /dev/null @@ -1,517 +0,0 @@ -&PARTITION - NAMEP='f17 ', - MASSP= 17.0000000 , - ZP= 9.00000000 , - NEX=1 , - PWF=T, - NAMET='n14 ', - MASST= 14.0000000 , - ZT= 7.00000000 , - QVAL= 0.00000000 , - READSTATES=0 , - PRMAX= 0.0000000000000000 , - LPMAX=-1 , - MIXPOT=0 , - / -&STATES - JP= 2.50000000 , - COPYP=0 , - PTYP=1 , - BANDP=1 , - EP= 0.00000000 , - KKP= 0.00000000 , - TP= 0.00000000 , - CPOT=1 , - JT= 1.00000000 , - COPYT=0 , - PTYT=1 , - BANDT=1 , - ET= 0.00000000 , - KKT= 0.00000000 , - TT= 0.00000000 , - FEXCH=F, - IGNORE=F, - INFAM=0 , - OUTFAM=0 , - / -&PARTITION - NAMEP='ne18 ', - MASSP= 18.0000000 , - ZP= 10.0000000 , - NEX=2 , - PWF=T, - NAMET='c13 ', - MASST= 13.0000000 , - ZT= 6.00000000 , - QVAL= 3.62859988 , - READSTATES=0 , - PRMAX= 0.0000000000000000 , - LPMAX=-1 , - MIXPOT=0 , - / -&STATES - JP= 0.00000000 , - COPYP=0 , - PTYP=1 , - BANDP=1 , - EP= 0.00000000 , - KKP= 0.00000000 , - TP= 0.00000000 , - CPOT=2 , - JT= 0.500000000 , - COPYT=0 , - PTYT=1 , - BANDT=1 , - ET= 0.00000000 , - KKT= 0.00000000 , - TT= 0.00000000 , - FEXCH=F, - IGNORE=F, - INFAM=0 , - OUTFAM=0 , - / -&STATES - JP= 4.00000000 , - COPYP=0 , - PTYP=1 , - BANDP=1 , - EP= 3.37599993 , - KKP= 0.00000000 , - TP= 0.00000000 , - CPOT=2 , - JT= 0.00000000 , - COPYT=0 , - PTYT=0 , - BANDT=0 , - ET= 0.00000000 , - KKT= 0.00000000 , - TT= 0.00000000 , - FEXCH=F, - IGNORE=F, - INFAM=0 , - OUTFAM=0 , - / - &partition / -&POTL - KPI=1 , - TYPEI=0 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 14.0000000 , 17.0000000 , 1.29999995 , 9*0.00000000 , - - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=1 , - TYPEI=1 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 37.2000008 , 1.20000005 , 0.600000024 , 21.6000004 , - 1.20000005 , 0.689999998 , 6*0.00000000 , - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=2 , - TYPEI=0 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 13.0000000 , 18.0000000 , 1.29999995 , 9*0.00000000 , - - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=2 , - TYPEI=1 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 37.2000008 , 1.20000005 , 0.600000024 , 21.6000004 , - 1.20000005 , 0.689999998 , 6*0.00000000 , - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=3 , - TYPEI=0 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 17.0000000 , 0.00000000 , 1.20000005 , 9*0.00000000 , - - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=3 , - TYPEI=1 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 50.0000000 , 1.20000005 , 0.649999976 , 9*0.00000000 , - - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=3 , - TYPEI=3 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 6.00000000 , 1.20000005 , 0.649999976 , 9*0.00000000 , - - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=4 , - TYPEI=0 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 13.0000000 , 0.00000000 , 1.20000005 , 9*0.00000000 , - - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=4 , - TYPEI=1 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 50.0000000 , 1.20000005 , 0.649999976 , 9*0.00000000 , - - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=4 , - TYPEI=3 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 6.00000000 , 1.20000005 , 0.649999976 , 9*0.00000000 , - - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=5 , - TYPEI=0 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 14.0000000 , 17.0000000 , 1.29999995 , 9*0.00000000 , - - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / -&POTL - KPI=5 , - TYPEI=1 , - ITT=F, - NOSUB=F, - SHAPEI=0 , - P= 0.00000000 , 37.2000008 , 1.20000005 , 0.600000024 , 21.6000004 , - 1.20000005 , 0.689999998 , 6*0.00000000 , - LSHAPEI=0 , - JL=' ', - XLVARY= 0.0000000000000000 , - ALVARY= 0.0000000000000000 , - DATAFILE=' ', - / - &potl / -&OVERLAP - KN1=1 , - KN2=1 , - IC1=1 , - IC2=2 , - IN=1 , - KIND=0 , - CH1=' ', - NN=1 , - L=2 , - LMAX=0 , - SN= 0.500000000 , - IA=0 , - J= 2.50000000 , - IB=0 , - KBPOT=3 , - KRPOT=0 , - BE= 3.92199993 , - ISC=1 , - IPC=0 , - NFL=0 , - NAM=0 , - AMPL= 0.00000000 , - KEEP=F, - DM= 1.0000000000000000 , - NK=0 , - ER= 0.00000000 , - E= 0.0000000000000000 , - PPOWER= 0.0000000000000000 , - RSMIN= 0.0000000000000000 , - RSMAX= 39.899999999999999 , - NLAG=0 , - PHASE= 0.0000000000000000 , - AUTOWID= 0.00000000 , - / -&OVERLAP - KN1=2 , - KN2=2 , - IC1=2 , - IC2=1 , - IN=2 , - KIND=3 , - CH1=' ', - NN=1 , - L=1 , - LMAX=1 , - SN= 0.500000000 , - IA=1 , - J= 1.00000000 , - IB=1 , - KBPOT=4 , - KRPOT=0 , - BE= 7.55060005 , - ISC=1 , - IPC=0 , - NFL=0 , - NAM=0 , - AMPL= 0.00000000 , - KEEP=F, - DM= 1.0000000000000000 , - NK=0 , - ER= 0.00000000 , - E= 0.0000000000000000 , - PPOWER= 0.0000000000000000 , - RSMIN= 0.0000000000000000 , - RSMAX= 39.899999999999999 , - NLAG=0 , - PHASE= 0.0000000000000000 , - AUTOWID= 0.00000000 , - / - &overlap / -&COUPLING - ICTO=-2 , - ICFROM=1 , - KIND=7 , - IP1=0 , - IP2=-1 , - IP3=5 , - IP4=-1 , - IP5=-1 , - P1= 0.00000000 , - P2= 0.00000000 , - JMAX= 0.00000000 , - RMAX= 0.0000000000000000 , - KFRAG=0 , - KCORE=0 , - NFORMS=0 , - INFILE=4 , - / -&CFP - IN=1 , - IB=1 , - IA=1 , - KN=1 , - A= 1.00000000 , - KEEP=F, - / -&CFP - IN=2 , - IB=1 , - IA=1 , - KN=2 , - A= 1.00000000 , - KEEP=F, - / - &cfp / - &coupling / - 'End of couplings' - - - parameters: mxx mxp mxpex maxit maxcpl maxnnu - required: 2 2 3 0 1 36 - - parameters: maxn mint maxnln maxnlo msp lmax1 mpair - required: 1334 1331 191 57 2 140 1 - - parameters: mloc mclist maxqrn mpwcoup - required: 12 5 4 0 - - - parameters: mloc maxqrn maxqrn2 mclist nchbinmax - required: 12 4 1 5 1 - - -n14(f17,ne18)c13 @ 170 MeV; -NAMELIST -&FRESCO - HCM= 2.9999999999999999E-002, - RMATCH= 39.960000000000001 , - RINTP= 0.20999999999999999 , - HNL= 8.9999999999999997E-002, - RNL= 5.0400000000000000 , - CENTRE= 0.0000000000000000 , - HNN= 0.20999999999999999 , - RNN= 0.0000000000000000 , - RMIN= 0.0000000000000000 , - RSP= 39.899999999999999 , - RASYM= 0.0000000000000000 , - ACCRCY= 1.0000000474974513E-003, - SWITCH= 0.0000000000000000 , - AJSWTCH= 0.0000000000000000 , - SINJMAX= 0.0000000000000000 , - PLANE=0 , - JTMIN= 0.0000000000000000 , - JTMAX= 120.00000000000000 , - ABSEND= -1.0000000000000000 , - DRY=F, - RELA=' ', - NEARFA=1 , - JUMP= 6*0 , - JBORD= 7*0.00000000 , - PSET=0 , - JSET=0 , - JLEAST= 0.0000000000000000 , - KQMAX=0 , - PP=0 , - THMIN= 0.0000000000000000 , - THMAX= 40.000000000000000 , - THINC= 0.10000000000000001 , - KOORDS=0 , - CUTL= -1.6000000238418579 , - CUTR= 0.0000000000000000 , - CUTC= 0.0000000000000000 , - COMPLEXBINS=F, - IPS= 0.0000000000000000 , - IT0=0 , - ITER=1 , - FATAL=T, - IBLOCK=0 , - PADE=0 , - PSIREN=F, - ISO=' ', - NNU=36 , - MAXL=130 , - MINL=0 , - MTMIN=6 , - EPC= 0.69444441795349121 , - ERANGE= 0.0000000000000000 , - DK= 0.0000000000000000 , - NOSOL=F, - NRBASES=0 , - NRBMIN=0 , - PRALPHA=F, - PCON=0 , - RMATR= 39.960000000000001 , - BNDX= 2*0.0000000000000000 , - MEIGS=0 , - BUTTLE=0 , - WEAK= 1.0000000474974513E-003, - LLMAX=-1 , - EXPAND= 1.2000000476837158 , 1.0000000000000000 , 0.0000000000000000 , 4.0000000000000000 , 0.40000000596046448 , - 2.0000000000000000 , 5*1.0000000000000000 , - HKTARG= 0.20000000298023224 , - MPIHELP=1 , - EIGENS=0 , - NLAGCC=0 , - CHANS=1 , - LISTCC=0 , - TRENEG=0 , - CDETR=0 , - SMATS=0 , - XSTABL=1 , - NLPL=0 , - WAVES=0 , - CCREAL=F, - LAMPL=0 , - VEFF=0 , - KFUS=0 , - WDISK=0 , - BPM=0 , - MELFIL=0 , - CDCC=0 , - NFUS=0 , - NPARAMETERS=0 , - SMALLCHAN= 0.0000000000000000 , - SMALLCOUP= 9.9999999999999998E-013, - SUMFORM=0 , - MAXCOUP= 3*0 , - OMPFORM=0 , - INITWF=0 , - PLUTO= 10*0 , - INH=0 , - TMP='/tmp/ ', - MASFIL='/netopt/PHS1IT/lib/m88lrb ', - UNITMASS= 1.0000000000000000 , - FINEC= 137.03599000000000 , - PEL=1 , - EXL=1 , - LAB=1 , - LIN=1 , - LEX=1 , - ELAB= 170.00000000000000 , 4*0.0000000000000000 , - NLAB= 4*0 , - VSEARCH=0 , - ECHAN=0 , - ENODES=0 , - GSCOUPLONLY=F, - CCBINS=F, - SOCK= 10*1.0000000000000000 , - BTYPE=' ', - BOLDPLOT=F, - KRM=0 , - DIST0= -1.0000000000000000 , - TCFILE=0 , - DGAM=0 , - EOBS=F, - GRACE=T, - ELPMAX= 9.9999997764825821E-003, - RELREF=1 , - TCFILENAME='fort.5420 ', - NUMNODE=0 , - SUMFORM=0 , - / - 'cxwf=' F ' ccbins=' F ' sumccbins =' F ' sumkql =' F ' ccbins=' F ' sumform =' 0 diff --git a/book/notebooks/Transfer_template/fort.35 b/book/notebooks/Transfer_template/fort.35 deleted file mode 100644 index 98bf274..0000000 --- a/book/notebooks/Transfer_template/fort.35 +++ /dev/null @@ -1 +0,0 @@ -76.77419 7.382128E+09 0.0000 1.6348 1.6402 16.364 0.39437E-04 3.4286 diff --git a/book/notebooks/Transfer_template/fort.38 b/book/notebooks/Transfer_template/fort.38 deleted file mode 100644 index 4a74963..0000000 --- a/book/notebooks/Transfer_template/fort.38 +++ /dev/null @@ -1 +0,0 @@ - 3 1 1 diff --git a/book/notebooks/Transfer_template/fort.39 b/book/notebooks/Transfer_template/fort.39 deleted file mode 100644 index b3fc8a1..0000000 --- a/book/notebooks/Transfer_template/fort.39 +++ /dev/null @@ -1 +0,0 @@ -170.0000 1.760083E+03 1760.3 3237.5 0.26397 0.0000 0.26397 diff --git a/book/notebooks/Transfer_template/fort.4 b/book/notebooks/Transfer_template/fort.4 deleted file mode 100644 index e69de29..0000000 diff --git a/book/notebooks/Transfer_template/fort.40 b/book/notebooks/Transfer_template/fort.40 deleted file mode 100644 index 0d39123..0000000 --- a/book/notebooks/Transfer_template/fort.40 +++ /dev/null @@ -1 +0,0 @@ -170.0000 1.760083E+03 1760.3 0.0000 0.26397 0.0000 3237.5 1477.2 diff --git a/book/notebooks/Transfer_template/fort.46 b/book/notebooks/Transfer_template/fort.46 deleted file mode 100644 index 2df7208..0000000 --- a/book/notebooks/Transfer_template/fort.46 +++ /dev/null @@ -1,2 +0,0 @@ - 39.870 2.54722 0.130237E-07 0.511291E-08 - 39.870 5.75214 0.149787E-09 0.260401E-10 diff --git a/book/notebooks/Transfer_template/fort.48 b/book/notebooks/Transfer_template/fort.48 deleted file mode 100644 index 917ecab..0000000 --- a/book/notebooks/Transfer_template/fort.48 +++ /dev/null @@ -1,3046 +0,0 @@ - kp,loop = 1 0 is type,so,p(:) = 0 F 0.000 14.00 17.00 1.300 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 1 -1 is type,so,p(:) = 1 F 0.000 37.20 1.200 0.6000 21.60 1.200 0.6900 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 2 0 is type,so,p(:) = 0 F 0.000 13.00 18.00 1.300 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 2 -1 is type,so,p(:) = 1 F 0.000 37.20 1.200 0.6000 21.60 1.200 0.6900 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 3 0 is type,so,p(:) = 0 F 0.000 17.00 0.000 1.200 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 3 -1 is type,so,p(:) = 1 F 0.000 50.00 1.200 0.6500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 3 -1 is type,so,p(:) = 3 T 0.000 6.000 1.200 0.6500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 4 0 is type,so,p(:) = 0 F 0.000 13.00 0.000 1.200 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 4 -1 is type,so,p(:) = 1 F 0.000 50.00 1.200 0.6500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 4 -1 is type,so,p(:) = 3 T 0.000 6.000 1.200 0.6500 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 5 0 is type,so,p(:) = 0 F 0.000 14.00 17.00 1.300 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - kp,loop = 5 -1 is type,so,p(:) = 1 F 0.000 37.20 1.200 0.6000 21.60 1.200 0.6900 0.000 0.000 0.000 0.000 0.000 0.000 - mnpair increased by 0 to 0 - mclist = 5 from 0 3 0 , 1 - FRXX0: MINL,MAXL = 0 130 - MAXLAM0,J14T16,J40P = 0 F F - ipmax = 2 - MINL,MAXL,MDONE = 0 130 54 - JBORD: 0.50000000000000000 120.50000000000000 - JUMP: 1 - 740 M,N = 1333 1334 - Try JTOTAL = 0.50000000000000000 -Allocate CLIST( 6 6 5) in 0.000 GB - Allocate NCLIST in 8.63999958E-07 GB - Allocate SRC/PSI etc in 1.92095991E-04 GB - Allocate FIM(D) in 3.83999975E-07 GB 48.000000000000000 - Allocate 12 spaces in coupling list 6 - XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -1 - XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -2 - XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -3 - DONE incremented c by -3 to -3 0.157004997 - DONE now c -3 - Allocate 12 spaces in coupling list 6 - XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -4 - XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -5 - XS 0.124 > -1.0000 & JT 0.5 > 0.0 so DONES now = -6 - DONE incremented c by -6 to -9 0.165546000 - DONE now c -9 - Try JTOTAL = 1.5000000000000000 -Allocate CLIST( 11 11 5) in 0.000 GB - Allocate NCLIST in 2.90399998E-06 GB - Allocate SRC/PSI etc in 3.62847990E-04 GB - Allocate FIM(D) in 7.04000001E-07 GB 88.000000000000000 - Allocate 22 spaces in coupling list 11 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -1 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -2 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -3 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -4 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -5 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -6 - DONE incremented c by -6 to -15 0.179991007 - DONE now c -15 - Allocate 22 spaces in coupling list 11 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -7 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -8 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -9 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -10 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -11 - XS 0.248 > -1.0000 & JT 1.5 > 0.0 so DONES now = -12 - DONE incremented c by -12 to -27 0.188767999 - DONE now c -27 - Try JTOTAL = 2.5000000000000000 -Allocate CLIST( 15 15 5) in 0.000 GB - Allocate NCLIST in 5.39999974E-06 GB - Allocate SRC/PSI etc in 4.90912003E-04 GB - Allocate FIM(D) in 9.59999966E-07 GB 120.00000000000000 - Allocate 30 spaces in coupling list 15 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -1 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -2 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -3 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -4 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -5 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -6 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -7 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -8 - DONE incremented c by -8 to -35 0.203993008 - DONE now c -35 - Allocate 30 spaces in coupling list 15 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -9 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -10 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -11 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -12 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -13 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -14 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -15 - XS 0.371 > -1.0000 & JT 2.5 > 0.0 so DONES now = -16 - DONE incremented c by -16 to -51 0.220496997 - DONE now c -51 - Try JTOTAL = 3.5000000000000000 -Allocate CLIST( 18 18 5) in 0.000 GB - Allocate NCLIST in 7.77599962E-06 GB - Allocate SRC/PSI etc in 5.76287974E-04 GB - Allocate FIM(D) in 1.15199998E-06 GB 144.00000000000000 - Allocate 36 spaces in coupling list 18 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -1 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -2 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -3 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -4 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -5 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -6 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -7 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -8 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -60 0.237006009 - DONE now c -60 - Allocate 36 spaces in coupling list 18 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -10 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -11 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -12 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -13 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -14 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -15 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -16 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -17 - XS 0.495 > -1.0000 & JT 3.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -78 0.253939003 - DONE now c -78 - Try JTOTAL = 4.5000000000000000 -Allocate CLIST( 19 19 5) in 0.000 GB - Allocate NCLIST in 8.66399932E-06 GB - Allocate SRC/PSI etc in 5.97632024E-04 GB - Allocate FIM(D) in 1.21599999E-06 GB 152.00000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -1 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -2 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -3 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -4 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -5 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -6 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -7 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -8 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -87 0.271463990 - DONE now c -87 - Allocate 38 spaces in coupling list 19 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -10 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -11 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -12 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -13 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -14 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -15 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -16 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -17 - XS 0.619 > -1.0000 & JT 4.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -105 0.288421988 - DONE now c -105 - Try JTOTAL = 5.5000000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -1 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -2 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -3 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -4 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -5 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -6 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -7 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -8 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -114 0.306445986 - DONE now c -114 - Allocate 38 spaces in coupling list 19 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -10 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -11 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -12 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -13 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -14 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -15 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -16 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -17 - XS 0.743 > -1.0000 & JT 5.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -132 0.326427996 - DONE now c -132 - Try JTOTAL = 6.5000000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -1 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -2 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -3 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -4 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -5 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -6 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -7 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -8 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -141 0.343539983 - DONE now c -141 - Allocate 38 spaces in coupling list 19 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -10 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -11 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -12 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -13 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -14 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -15 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -16 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -17 - XS 0.866 > -1.0000 & JT 6.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -159 0.361419976 - DONE now c -159 - Try JTOTAL = 7.5000000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -1 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -2 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -3 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -4 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -5 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -6 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -7 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -8 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -168 0.378007978 - DONE now c -168 - Allocate 38 spaces in coupling list 19 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -10 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -11 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -12 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -13 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -14 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -15 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -16 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -17 - XS 0.990 > -1.0000 & JT 7.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -186 0.395273000 - DONE now c -186 - Try JTOTAL = 8.5000000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -1 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -2 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -3 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -4 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -5 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -6 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -7 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -8 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -195 0.413942993 - DONE now c -195 - Allocate 38 spaces in coupling list 19 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -10 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -11 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -12 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -13 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -14 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -15 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -16 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -17 - XS 1.11 > -1.0000 & JT 8.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -213 0.431522995 - DONE now c -213 - Try JTOTAL = 9.5000000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -1 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -2 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -3 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -4 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -5 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -6 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -7 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -8 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -222 0.448094994 - DONE now c -222 - Allocate 38 spaces in coupling list 19 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -10 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -11 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -12 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -13 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -14 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -15 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -16 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -17 - XS 1.24 > -1.0000 & JT 9.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -240 0.465817988 - DONE now c -240 - Try JTOTAL = 10.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -1 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -2 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -3 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -4 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -5 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -6 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -7 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -8 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -249 0.483444989 - DONE now c -249 - Allocate 38 spaces in coupling list 19 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -10 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -11 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -12 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -13 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -14 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -15 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -16 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -17 - XS 1.36 > -1.0000 & JT 10.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -267 0.501227975 - DONE now c -267 - Try JTOTAL = 11.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -1 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -2 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -3 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -4 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -5 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -6 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -7 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -8 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -276 0.519330978 - DONE now c -276 - Allocate 38 spaces in coupling list 19 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -10 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -11 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -12 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -13 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -14 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -15 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -16 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -17 - XS 1.49 > -1.0000 & JT 11.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -294 0.537755013 - DONE now c -294 - Try JTOTAL = 12.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -1 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -2 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -3 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -4 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -5 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -6 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -7 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -8 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -303 0.555871010 - DONE now c -303 - Allocate 38 spaces in coupling list 19 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -10 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -11 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -12 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -13 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -14 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -15 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -16 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -17 - XS 1.61 > -1.0000 & JT 12.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -321 0.573882997 - DONE now c -321 - Try JTOTAL = 13.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -1 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -2 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -3 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -4 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -5 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -6 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -7 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -8 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -330 0.591790974 - DONE now c -330 - Allocate 38 spaces in coupling list 19 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -10 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -11 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -12 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -13 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -14 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -15 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -16 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -17 - XS 1.73 > -1.0000 & JT 13.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -348 0.609620988 - DONE now c -348 - Try JTOTAL = 14.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -1 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -2 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -3 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -4 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -5 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -6 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -7 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -8 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -357 0.627115965 - DONE now c -357 - Allocate 38 spaces in coupling list 19 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -10 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -11 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -12 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -13 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -14 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -15 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -16 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -17 - XS 1.86 > -1.0000 & JT 14.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -375 0.644883990 - DONE now c -375 - Try JTOTAL = 15.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -1 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -2 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -3 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -4 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -5 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -6 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -7 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -8 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -384 0.662989974 - DONE now c -384 - Allocate 38 spaces in coupling list 19 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -10 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -11 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -12 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -13 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -14 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -15 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -16 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -17 - XS 1.98 > -1.0000 & JT 15.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -402 0.680739999 - DONE now c -402 - Try JTOTAL = 16.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -1 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -2 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -3 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -4 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -5 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -6 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -7 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -8 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -411 0.698785961 - DONE now c -411 - Allocate 38 spaces in coupling list 19 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -10 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -11 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -12 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -13 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -14 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -15 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -16 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -17 - XS 2.10 > -1.0000 & JT 16.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -429 0.716197968 - DONE now c -429 - Try JTOTAL = 17.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -1 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -2 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -3 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -4 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -5 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -6 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -7 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -8 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -438 0.734008014 - DONE now c -438 - Allocate 38 spaces in coupling list 19 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -10 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -11 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -12 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -13 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -14 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -15 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -16 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -17 - XS 2.23 > -1.0000 & JT 17.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -456 0.751689970 - DONE now c -456 - Try JTOTAL = 18.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -1 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -2 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -3 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -4 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -5 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -6 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -7 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -8 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -465 0.769773006 - DONE now c -465 - Allocate 38 spaces in coupling list 19 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -10 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -11 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -12 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -13 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -14 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -15 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -16 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -17 - XS 2.35 > -1.0000 & JT 18.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -483 0.787189007 - DONE now c -483 - Try JTOTAL = 19.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -1 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -2 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -3 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -4 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -5 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -6 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -7 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -8 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -492 0.804587960 - DONE now c -492 - Allocate 38 spaces in coupling list 19 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -10 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -11 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -12 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -13 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -14 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -15 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -16 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -17 - XS 2.48 > -1.0000 & JT 19.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -510 0.822567999 - DONE now c -510 - Try JTOTAL = 20.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -1 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -2 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -3 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -4 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -5 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -6 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -7 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -8 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -519 0.840171993 - DONE now c -519 - Allocate 38 spaces in coupling list 19 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -10 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -11 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -12 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -13 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -14 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -15 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -16 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -17 - XS 2.60 > -1.0000 & JT 20.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -537 0.857761979 - DONE now c -537 - Try JTOTAL = 21.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -1 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -2 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -3 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -4 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -5 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -6 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -7 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -8 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -546 0.875167012 - DONE now c -546 - Allocate 38 spaces in coupling list 19 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -10 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -11 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -12 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -13 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -14 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -15 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -16 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -17 - XS 2.72 > -1.0000 & JT 21.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -564 0.892459989 - DONE now c -564 - Try JTOTAL = 22.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -1 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -2 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -3 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -4 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -5 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -6 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -7 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -8 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -573 0.910026968 - DONE now c -573 - Allocate 38 spaces in coupling list 19 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -10 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -11 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -12 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -13 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -14 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -15 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -16 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -17 - XS 2.85 > -1.0000 & JT 22.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -591 0.927650988 - DONE now c -591 - Try JTOTAL = 23.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -1 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -2 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -3 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -4 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -5 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -6 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -7 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -8 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -600 0.945043981 - DONE now c -600 - Allocate 38 spaces in coupling list 19 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -10 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -11 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -12 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -13 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -14 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -15 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -16 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -17 - XS 2.97 > -1.0000 & JT 23.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -618 0.962583005 - DONE now c -618 - Try JTOTAL = 24.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -1 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -2 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -3 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -4 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -5 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -6 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -7 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -8 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -627 0.980051994 - DONE now c -627 - Allocate 38 spaces in coupling list 19 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -10 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -11 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -12 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -13 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -14 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -15 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -16 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -17 - XS 3.09 > -1.0000 & JT 24.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -645 0.997898936 - DONE now c -645 - Try JTOTAL = 25.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -1 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -2 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -3 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -4 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -5 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -6 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -7 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -8 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -654 1.01554704 - DONE now c -654 - Allocate 38 spaces in coupling list 19 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -10 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -11 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -12 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -13 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -14 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -15 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -16 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -17 - XS 3.22 > -1.0000 & JT 25.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -672 1.03328001 - DONE now c -672 - Try JTOTAL = 26.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -1 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -2 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -3 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -4 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -5 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -6 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -7 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -8 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -681 1.05058396 - DONE now c -681 - Allocate 38 spaces in coupling list 19 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -10 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -11 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -12 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -13 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -14 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -15 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -16 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -17 - XS 3.34 > -1.0000 & JT 26.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -699 1.06805301 - DONE now c -699 - Try JTOTAL = 27.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -1 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -2 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -3 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -4 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -5 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -6 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -7 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -8 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -708 1.08550799 - DONE now c -708 - Allocate 38 spaces in coupling list 19 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -10 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -11 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -12 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -13 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -14 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -15 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -16 - XS 3.47 > -1.0000 & JT 27.5 > 0.0 so DONES now = -17 - XS 3.46 > -1.0000 & JT 27.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -726 1.10348403 - DONE now c -726 - Try JTOTAL = 28.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -1 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -2 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -3 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -4 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -5 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -6 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -7 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -8 - XS 3.57 > -1.0000 & JT 28.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -735 1.12246799 - DONE now c -735 - Allocate 38 spaces in coupling list 19 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -10 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -11 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -12 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -13 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -14 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -15 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -16 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -17 - XS 3.59 > -1.0000 & JT 28.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -753 1.14026201 - DONE now c -753 - Try JTOTAL = 29.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -1 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -2 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -3 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -4 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -5 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -6 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -7 - XS 3.69 > -1.0000 & JT 29.5 > 0.0 so DONES now = -8 - XS 3.69 > -1.0000 & JT 29.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -762 1.15736401 - DONE now c -762 - Allocate 38 spaces in coupling list 19 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -10 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -11 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -12 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -13 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -14 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -15 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -16 - XS 3.71 > -1.0000 & JT 29.5 > 0.0 so DONES now = -17 - XS 3.62 > -1.0000 & JT 29.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -780 1.17444599 - DONE now c -780 - Try JTOTAL = 30.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -1 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -2 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -3 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -4 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -5 - XS 3.82 > -1.0000 & JT 30.5 > 0.0 so DONES now = -6 - XS 3.82 > -1.0000 & JT 30.5 > 0.0 so DONES now = -7 - XS 3.82 > -1.0000 & JT 30.5 > 0.0 so DONES now = -8 - XS 3.54 > -1.0000 & JT 30.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -789 1.19149697 - DONE now c -789 - Allocate 38 spaces in coupling list 19 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -10 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -11 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -12 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -13 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -14 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -15 - XS 3.84 > -1.0000 & JT 30.5 > 0.0 so DONES now = -16 - XS 3.74 > -1.0000 & JT 30.5 > 0.0 so DONES now = -17 - XS 3.74 > -1.0000 & JT 30.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -807 1.20832801 - DONE now c -807 - Try JTOTAL = 31.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -1 - XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -2 - XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -3 - XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -4 - XS 3.94 > -1.0000 & JT 31.5 > 0.0 so DONES now = -5 - XS 3.94 > -1.0000 & JT 31.5 > 0.0 so DONES now = -6 - XS 3.94 > -1.0000 & JT 31.5 > 0.0 so DONES now = -7 - XS 3.66 > -1.0000 & JT 31.5 > 0.0 so DONES now = -8 - XS 3.66 > -1.0000 & JT 31.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -816 1.22464597 - DONE now c -816 - Allocate 38 spaces in coupling list 19 - XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -10 - XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -11 - XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -12 - XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -13 - XS 3.96 > -1.0000 & JT 31.5 > 0.0 so DONES now = -14 - XS 3.86 > -1.0000 & JT 31.5 > 0.0 so DONES now = -15 - XS 3.86 > -1.0000 & JT 31.5 > 0.0 so DONES now = -16 - XS 3.86 > -1.0000 & JT 31.5 > 0.0 so DONES now = -17 - XS 3.33 > -1.0000 & JT 31.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -834 1.24205196 - DONE now c -834 - Try JTOTAL = 32.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -1 - XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -2 - XS 4.06 > -1.0000 & JT 32.5 > 0.0 so DONES now = -3 - XS 4.06 > -1.0000 & JT 32.5 > 0.0 so DONES now = -4 - XS 4.06 > -1.0000 & JT 32.5 > 0.0 so DONES now = -5 - XS 3.77 > -1.0000 & JT 32.5 > 0.0 so DONES now = -6 - XS 3.77 > -1.0000 & JT 32.5 > 0.0 so DONES now = -7 - XS 3.77 > -1.0000 & JT 32.5 > 0.0 so DONES now = -8 - XS 2.99 > -1.0000 & JT 32.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -843 1.25828505 - DONE now c -843 - Allocate 38 spaces in coupling list 19 - XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -10 - XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -11 - XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -12 - XS 4.08 > -1.0000 & JT 32.5 > 0.0 so DONES now = -13 - XS 3.98 > -1.0000 & JT 32.5 > 0.0 so DONES now = -14 - XS 3.98 > -1.0000 & JT 32.5 > 0.0 so DONES now = -15 - XS 3.98 > -1.0000 & JT 32.5 > 0.0 so DONES now = -16 - XS 3.43 > -1.0000 & JT 32.5 > 0.0 so DONES now = -17 - XS 3.43 > -1.0000 & JT 32.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -861 1.27527905 - DONE now c -861 - Try JTOTAL = 33.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 4.21 > -1.0000 & JT 33.5 > 0.0 so DONES now = -1 - XS 4.19 > -1.0000 & JT 33.5 > 0.0 so DONES now = -2 - XS 4.19 > -1.0000 & JT 33.5 > 0.0 so DONES now = -3 - XS 4.19 > -1.0000 & JT 33.5 > 0.0 so DONES now = -4 - XS 3.89 > -1.0000 & JT 33.5 > 0.0 so DONES now = -5 - XS 3.89 > -1.0000 & JT 33.5 > 0.0 so DONES now = -6 - XS 3.89 > -1.0000 & JT 33.5 > 0.0 so DONES now = -7 - XS 3.08 > -1.0000 & JT 33.5 > 0.0 so DONES now = -8 - XS 3.08 > -1.0000 & JT 33.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -870 1.29287505 - DONE now c -870 - Allocate 38 spaces in coupling list 19 - XS 4.21 > -1.0000 & JT 33.5 > 0.0 so DONES now = -10 - XS 4.21 > -1.0000 & JT 33.5 > 0.0 so DONES now = -11 - XS 4.10 > -1.0000 & JT 33.5 > 0.0 so DONES now = -12 - XS 4.10 > -1.0000 & JT 33.5 > 0.0 so DONES now = -13 - XS 4.10 > -1.0000 & JT 33.5 > 0.0 so DONES now = -14 - XS 3.53 > -1.0000 & JT 33.5 > 0.0 so DONES now = -15 - XS 3.53 > -1.0000 & JT 33.5 > 0.0 so DONES now = -16 - XS 3.53 > -1.0000 & JT 33.5 > 0.0 so DONES now = -17 - XS 2.60 > -1.0000 & JT 33.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -888 1.31055701 - DONE now c -888 - Try JTOTAL = 34.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 4.31 > -1.0000 & JT 34.5 > 0.0 so DONES now = -1 - XS 4.31 > -1.0000 & JT 34.5 > 0.0 so DONES now = -2 - XS 4.00 > -1.0000 & JT 34.5 > 0.0 so DONES now = -3 - XS 4.00 > -1.0000 & JT 34.5 > 0.0 so DONES now = -4 - XS 4.00 > -1.0000 & JT 34.5 > 0.0 so DONES now = -5 - XS 3.17 > -1.0000 & JT 34.5 > 0.0 so DONES now = -6 - XS 3.17 > -1.0000 & JT 34.5 > 0.0 so DONES now = -7 - XS 3.17 > -1.0000 & JT 34.5 > 0.0 so DONES now = -8 - XS 2.20 > -1.0000 & JT 34.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -897 1.32783604 - DONE now c -897 - Allocate 38 spaces in coupling list 19 - XS 4.33 > -1.0000 & JT 34.5 > 0.0 so DONES now = -10 - XS 4.22 > -1.0000 & JT 34.5 > 0.0 so DONES now = -11 - XS 4.22 > -1.0000 & JT 34.5 > 0.0 so DONES now = -12 - XS 4.22 > -1.0000 & JT 34.5 > 0.0 so DONES now = -13 - XS 3.64 > -1.0000 & JT 34.5 > 0.0 so DONES now = -14 - XS 3.64 > -1.0000 & JT 34.5 > 0.0 so DONES now = -15 - XS 3.64 > -1.0000 & JT 34.5 > 0.0 so DONES now = -16 - XS 2.68 > -1.0000 & JT 34.5 > 0.0 so DONES now = -17 - XS 2.68 > -1.0000 & JT 34.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -915 1.34563398 - DONE now c -915 - Try JTOTAL = 35.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 4.43 > -1.0000 & JT 35.5 > 0.0 so DONES now = -1 - XS 4.11 > -1.0000 & JT 35.5 > 0.0 so DONES now = -2 - XS 4.11 > -1.0000 & JT 35.5 > 0.0 so DONES now = -3 - XS 4.11 > -1.0000 & JT 35.5 > 0.0 so DONES now = -4 - XS 3.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -5 - XS 3.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -6 - XS 3.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -7 - XS 2.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -8 - XS 2.27 > -1.0000 & JT 35.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -924 1.36347699 - DONE now c -924 - Allocate 38 spaces in coupling list 19 - XS 4.34 > -1.0000 & JT 35.5 > 0.0 so DONES now = -10 - XS 4.34 > -1.0000 & JT 35.5 > 0.0 so DONES now = -11 - XS 3.74 > -1.0000 & JT 35.5 > 0.0 so DONES now = -12 - XS 3.74 > -1.0000 & JT 35.5 > 0.0 so DONES now = -13 - XS 3.74 > -1.0000 & JT 35.5 > 0.0 so DONES now = -14 - XS 2.75 > -1.0000 & JT 35.5 > 0.0 so DONES now = -15 - XS 2.75 > -1.0000 & JT 35.5 > 0.0 so DONES now = -16 - XS 2.75 > -1.0000 & JT 35.5 > 0.0 so DONES now = -17 - XS 1.83 > -1.0000 & JT 35.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -942 1.38071096 - DONE now c -942 - Try JTOTAL = 36.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 4.23 > -1.0000 & JT 36.5 > 0.0 so DONES now = -1 - XS 4.23 > -1.0000 & JT 36.5 > 0.0 so DONES now = -2 - XS 3.36 > -1.0000 & JT 36.5 > 0.0 so DONES now = -3 - XS 3.36 > -1.0000 & JT 36.5 > 0.0 so DONES now = -4 - XS 3.36 > -1.0000 & JT 36.5 > 0.0 so DONES now = -5 - XS 2.33 > -1.0000 & JT 36.5 > 0.0 so DONES now = -6 - XS 2.33 > -1.0000 & JT 36.5 > 0.0 so DONES now = -7 - XS 2.33 > -1.0000 & JT 36.5 > 0.0 so DONES now = -8 - XS 1.50 > -1.0000 & JT 36.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -951 1.39867198 - DONE now c -951 - Allocate 38 spaces in coupling list 19 - XS 4.46 > -1.0000 & JT 36.5 > 0.0 so DONES now = -10 - XS 3.85 > -1.0000 & JT 36.5 > 0.0 so DONES now = -11 - XS 3.85 > -1.0000 & JT 36.5 > 0.0 so DONES now = -12 - XS 3.85 > -1.0000 & JT 36.5 > 0.0 so DONES now = -13 - XS 2.83 > -1.0000 & JT 36.5 > 0.0 so DONES now = -14 - XS 2.83 > -1.0000 & JT 36.5 > 0.0 so DONES now = -15 - XS 2.83 > -1.0000 & JT 36.5 > 0.0 so DONES now = -16 - XS 1.88 > -1.0000 & JT 36.5 > 0.0 so DONES now = -17 - XS 1.88 > -1.0000 & JT 36.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -969 1.41588902 - DONE now c -969 - Try JTOTAL = 37.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 4.34 > -1.0000 & JT 37.5 > 0.0 so DONES now = -1 - XS 3.45 > -1.0000 & JT 37.5 > 0.0 so DONES now = -2 - XS 3.45 > -1.0000 & JT 37.5 > 0.0 so DONES now = -3 - XS 3.45 > -1.0000 & JT 37.5 > 0.0 so DONES now = -4 - XS 2.39 > -1.0000 & JT 37.5 > 0.0 so DONES now = -5 - XS 2.39 > -1.0000 & JT 37.5 > 0.0 so DONES now = -6 - XS 2.39 > -1.0000 & JT 37.5 > 0.0 so DONES now = -7 - XS 1.54 > -1.0000 & JT 37.5 > 0.0 so DONES now = -8 - XS 1.54 > -1.0000 & JT 37.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -978 1.43311596 - DONE now c -978 - Allocate 38 spaces in coupling list 19 - XS 3.95 > -1.0000 & JT 37.5 > 0.0 so DONES now = -10 - XS 3.95 > -1.0000 & JT 37.5 > 0.0 so DONES now = -11 - XS 2.91 > -1.0000 & JT 37.5 > 0.0 so DONES now = -12 - XS 2.91 > -1.0000 & JT 37.5 > 0.0 so DONES now = -13 - XS 2.91 > -1.0000 & JT 37.5 > 0.0 so DONES now = -14 - XS 1.93 > -1.0000 & JT 37.5 > 0.0 so DONES now = -15 - XS 1.93 > -1.0000 & JT 37.5 > 0.0 so DONES now = -16 - XS 1.93 > -1.0000 & JT 37.5 > 0.0 so DONES now = -17 - XS 1.22 > -1.0000 & JT 37.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -996 1.45026994 - DONE now c -996 - Try JTOTAL = 38.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.54 > -1.0000 & JT 38.5 > 0.0 so DONES now = -1 - XS 3.54 > -1.0000 & JT 38.5 > 0.0 so DONES now = -2 - XS 2.45 > -1.0000 & JT 38.5 > 0.0 so DONES now = -3 - XS 2.45 > -1.0000 & JT 38.5 > 0.0 so DONES now = -4 - XS 2.45 > -1.0000 & JT 38.5 > 0.0 so DONES now = -5 - XS 1.58 > -1.0000 & JT 38.5 > 0.0 so DONES now = -6 - XS 1.58 > -1.0000 & JT 38.5 > 0.0 so DONES now = -7 - XS 1.58 > -1.0000 & JT 38.5 > 0.0 so DONES now = -8 - XS 0.984 > -1.0000 & JT 38.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1005 1.46736097 - DONE now c -1005 - Allocate 38 spaces in coupling list 19 - XS 4.05 > -1.0000 & JT 38.5 > 0.0 so DONES now = -10 - XS 2.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -11 - XS 2.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -12 - XS 2.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -13 - XS 1.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -14 - XS 1.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -15 - XS 1.98 > -1.0000 & JT 38.5 > 0.0 so DONES now = -16 - XS 1.25 > -1.0000 & JT 38.5 > 0.0 so DONES now = -17 - XS 1.25 > -1.0000 & JT 38.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1023 1.48463595 - DONE now c -1023 - Try JTOTAL = 39.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 3.63 > -1.0000 & JT 39.5 > 0.0 so DONES now = -1 - XS 2.52 > -1.0000 & JT 39.5 > 0.0 so DONES now = -2 - XS 2.52 > -1.0000 & JT 39.5 > 0.0 so DONES now = -3 - XS 2.52 > -1.0000 & JT 39.5 > 0.0 so DONES now = -4 - XS 1.62 > -1.0000 & JT 39.5 > 0.0 so DONES now = -5 - XS 1.62 > -1.0000 & JT 39.5 > 0.0 so DONES now = -6 - XS 1.62 > -1.0000 & JT 39.5 > 0.0 so DONES now = -7 - XS 1.01 > -1.0000 & JT 39.5 > 0.0 so DONES now = -8 - XS 1.01 > -1.0000 & JT 39.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1032 1.50226200 - DONE now c -1032 - Allocate 38 spaces in coupling list 19 - XS 3.06 > -1.0000 & JT 39.5 > 0.0 so DONES now = -10 - XS 3.06 > -1.0000 & JT 39.5 > 0.0 so DONES now = -11 - XS 2.03 > -1.0000 & JT 39.5 > 0.0 so DONES now = -12 - XS 2.03 > -1.0000 & JT 39.5 > 0.0 so DONES now = -13 - XS 2.03 > -1.0000 & JT 39.5 > 0.0 so DONES now = -14 - XS 1.28 > -1.0000 & JT 39.5 > 0.0 so DONES now = -15 - XS 1.28 > -1.0000 & JT 39.5 > 0.0 so DONES now = -16 - XS 1.28 > -1.0000 & JT 39.5 > 0.0 so DONES now = -17 - XS 0.789 > -1.0000 & JT 39.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1050 1.52027500 - DONE now c -1050 - Try JTOTAL = 40.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.58 > -1.0000 & JT 40.5 > 0.0 so DONES now = -1 - XS 2.58 > -1.0000 & JT 40.5 > 0.0 so DONES now = -2 - XS 1.66 > -1.0000 & JT 40.5 > 0.0 so DONES now = -3 - XS 1.66 > -1.0000 & JT 40.5 > 0.0 so DONES now = -4 - XS 1.66 > -1.0000 & JT 40.5 > 0.0 so DONES now = -5 - XS 1.03 > -1.0000 & JT 40.5 > 0.0 so DONES now = -6 - XS 1.03 > -1.0000 & JT 40.5 > 0.0 so DONES now = -7 - XS 1.03 > -1.0000 & JT 40.5 > 0.0 so DONES now = -8 - XS 0.630 > -1.0000 & JT 40.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1059 1.53806901 - DONE now c -1059 - Allocate 38 spaces in coupling list 19 - XS 3.14 > -1.0000 & JT 40.5 > 0.0 so DONES now = -10 - XS 2.09 > -1.0000 & JT 40.5 > 0.0 so DONES now = -11 - XS 2.09 > -1.0000 & JT 40.5 > 0.0 so DONES now = -12 - XS 2.09 > -1.0000 & JT 40.5 > 0.0 so DONES now = -13 - XS 1.32 > -1.0000 & JT 40.5 > 0.0 so DONES now = -14 - XS 1.32 > -1.0000 & JT 40.5 > 0.0 so DONES now = -15 - XS 1.32 > -1.0000 & JT 40.5 > 0.0 so DONES now = -16 - XS 0.808 > -1.0000 & JT 40.5 > 0.0 so DONES now = -17 - XS 0.808 > -1.0000 & JT 40.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1077 1.55526590 - DONE now c -1077 - Try JTOTAL = 41.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 2.64 > -1.0000 & JT 41.5 > 0.0 so DONES now = -1 - XS 1.70 > -1.0000 & JT 41.5 > 0.0 so DONES now = -2 - XS 1.70 > -1.0000 & JT 41.5 > 0.0 so DONES now = -3 - XS 1.70 > -1.0000 & JT 41.5 > 0.0 so DONES now = -4 - XS 1.06 > -1.0000 & JT 41.5 > 0.0 so DONES now = -5 - XS 1.06 > -1.0000 & JT 41.5 > 0.0 so DONES now = -6 - XS 1.06 > -1.0000 & JT 41.5 > 0.0 so DONES now = -7 - XS 0.645 > -1.0000 & JT 41.5 > 0.0 so DONES now = -8 - XS 0.645 > -1.0000 & JT 41.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1086 1.57276392 - DONE now c -1086 - Allocate 38 spaces in coupling list 19 - XS 2.14 > -1.0000 & JT 41.5 > 0.0 so DONES now = -10 - XS 2.14 > -1.0000 & JT 41.5 > 0.0 so DONES now = -11 - XS 1.35 > -1.0000 & JT 41.5 > 0.0 so DONES now = -12 - XS 1.35 > -1.0000 & JT 41.5 > 0.0 so DONES now = -13 - XS 1.35 > -1.0000 & JT 41.5 > 0.0 so DONES now = -14 - XS 0.828 > -1.0000 & JT 41.5 > 0.0 so DONES now = -15 - XS 0.828 > -1.0000 & JT 41.5 > 0.0 so DONES now = -16 - XS 0.828 > -1.0000 & JT 41.5 > 0.0 so DONES now = -17 - XS 0.501 > -1.0000 & JT 41.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1104 1.59070206 - DONE now c -1104 - Try JTOTAL = 42.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.75 > -1.0000 & JT 42.5 > 0.0 so DONES now = -1 - XS 1.75 > -1.0000 & JT 42.5 > 0.0 so DONES now = -2 - XS 1.08 > -1.0000 & JT 42.5 > 0.0 so DONES now = -3 - XS 1.08 > -1.0000 & JT 42.5 > 0.0 so DONES now = -4 - XS 1.08 > -1.0000 & JT 42.5 > 0.0 so DONES now = -5 - XS 0.660 > -1.0000 & JT 42.5 > 0.0 so DONES now = -6 - XS 0.660 > -1.0000 & JT 42.5 > 0.0 so DONES now = -7 - XS 0.660 > -1.0000 & JT 42.5 > 0.0 so DONES now = -8 - XS 0.397 > -1.0000 & JT 42.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1113 1.60836899 - DONE now c -1113 - Allocate 38 spaces in coupling list 19 - XS 2.19 > -1.0000 & JT 42.5 > 0.0 so DONES now = -10 - XS 1.38 > -1.0000 & JT 42.5 > 0.0 so DONES now = -11 - XS 1.38 > -1.0000 & JT 42.5 > 0.0 so DONES now = -12 - XS 1.38 > -1.0000 & JT 42.5 > 0.0 so DONES now = -13 - XS 0.848 > -1.0000 & JT 42.5 > 0.0 so DONES now = -14 - XS 0.848 > -1.0000 & JT 42.5 > 0.0 so DONES now = -15 - XS 0.848 > -1.0000 & JT 42.5 > 0.0 so DONES now = -16 - XS 0.513 > -1.0000 & JT 42.5 > 0.0 so DONES now = -17 - XS 0.513 > -1.0000 & JT 42.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1131 1.62549090 - DONE now c -1131 - Try JTOTAL = 43.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.79 > -1.0000 & JT 43.5 > 0.0 so DONES now = -1 - XS 1.11 > -1.0000 & JT 43.5 > 0.0 so DONES now = -2 - XS 1.11 > -1.0000 & JT 43.5 > 0.0 so DONES now = -3 - XS 1.11 > -1.0000 & JT 43.5 > 0.0 so DONES now = -4 - XS 0.676 > -1.0000 & JT 43.5 > 0.0 so DONES now = -5 - XS 0.676 > -1.0000 & JT 43.5 > 0.0 so DONES now = -6 - XS 0.676 > -1.0000 & JT 43.5 > 0.0 so DONES now = -7 - XS 0.406 > -1.0000 & JT 43.5 > 0.0 so DONES now = -8 - XS 0.406 > -1.0000 & JT 43.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1140 1.64270401 - DONE now c -1140 - Allocate 38 spaces in coupling list 19 - XS 1.41 > -1.0000 & JT 43.5 > 0.0 so DONES now = -10 - XS 1.41 > -1.0000 & JT 43.5 > 0.0 so DONES now = -11 - XS 0.867 > -1.0000 & JT 43.5 > 0.0 so DONES now = -12 - XS 0.867 > -1.0000 & JT 43.5 > 0.0 so DONES now = -13 - XS 0.867 > -1.0000 & JT 43.5 > 0.0 so DONES now = -14 - XS 0.525 > -1.0000 & JT 43.5 > 0.0 so DONES now = -15 - XS 0.525 > -1.0000 & JT 43.5 > 0.0 so DONES now = -16 - XS 0.525 > -1.0000 & JT 43.5 > 0.0 so DONES now = -17 - XS 0.314 > -1.0000 & JT 43.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1158 1.65969706 - DONE now c -1158 - Try JTOTAL = 44.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.13 > -1.0000 & JT 44.5 > 0.0 so DONES now = -1 - XS 1.13 > -1.0000 & JT 44.5 > 0.0 so DONES now = -2 - XS 0.691 > -1.0000 & JT 44.5 > 0.0 so DONES now = -3 - XS 0.691 > -1.0000 & JT 44.5 > 0.0 so DONES now = -4 - XS 0.691 > -1.0000 & JT 44.5 > 0.0 so DONES now = -5 - XS 0.416 > -1.0000 & JT 44.5 > 0.0 so DONES now = -6 - XS 0.416 > -1.0000 & JT 44.5 > 0.0 so DONES now = -7 - XS 0.416 > -1.0000 & JT 44.5 > 0.0 so DONES now = -8 - XS 0.248 > -1.0000 & JT 44.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1167 1.67681003 - DONE now c -1167 - Allocate 38 spaces in coupling list 19 - XS 1.44 > -1.0000 & JT 44.5 > 0.0 so DONES now = -10 - XS 0.887 > -1.0000 & JT 44.5 > 0.0 so DONES now = -11 - XS 0.887 > -1.0000 & JT 44.5 > 0.0 so DONES now = -12 - XS 0.887 > -1.0000 & JT 44.5 > 0.0 so DONES now = -13 - XS 0.537 > -1.0000 & JT 44.5 > 0.0 so DONES now = -14 - XS 0.537 > -1.0000 & JT 44.5 > 0.0 so DONES now = -15 - XS 0.537 > -1.0000 & JT 44.5 > 0.0 so DONES now = -16 - XS 0.322 > -1.0000 & JT 44.5 > 0.0 so DONES now = -17 - XS 0.322 > -1.0000 & JT 44.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1185 1.69455194 - DONE now c -1185 - Try JTOTAL = 45.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 1.16 > -1.0000 & JT 45.5 > 0.0 so DONES now = -1 - XS 0.706 > -1.0000 & JT 45.5 > 0.0 so DONES now = -2 - XS 0.706 > -1.0000 & JT 45.5 > 0.0 so DONES now = -3 - XS 0.706 > -1.0000 & JT 45.5 > 0.0 so DONES now = -4 - XS 0.425 > -1.0000 & JT 45.5 > 0.0 so DONES now = -5 - XS 0.425 > -1.0000 & JT 45.5 > 0.0 so DONES now = -6 - XS 0.425 > -1.0000 & JT 45.5 > 0.0 so DONES now = -7 - XS 0.254 > -1.0000 & JT 45.5 > 0.0 so DONES now = -8 - XS 0.254 > -1.0000 & JT 45.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1194 1.71168602 - DONE now c -1194 - Allocate 38 spaces in coupling list 19 - XS 0.907 > -1.0000 & JT 45.5 > 0.0 so DONES now = -10 - XS 0.907 > -1.0000 & JT 45.5 > 0.0 so DONES now = -11 - XS 0.548 > -1.0000 & JT 45.5 > 0.0 so DONES now = -12 - XS 0.548 > -1.0000 & JT 45.5 > 0.0 so DONES now = -13 - XS 0.548 > -1.0000 & JT 45.5 > 0.0 so DONES now = -14 - XS 0.329 > -1.0000 & JT 45.5 > 0.0 so DONES now = -15 - XS 0.329 > -1.0000 & JT 45.5 > 0.0 so DONES now = -16 - XS 0.329 > -1.0000 & JT 45.5 > 0.0 so DONES now = -17 - XS 0.196 > -1.0000 & JT 45.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1212 1.72870994 - DONE now c -1212 - Try JTOTAL = 46.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.722 > -1.0000 & JT 46.5 > 0.0 so DONES now = -1 - XS 0.722 > -1.0000 & JT 46.5 > 0.0 so DONES now = -2 - XS 0.434 > -1.0000 & JT 46.5 > 0.0 so DONES now = -3 - XS 0.434 > -1.0000 & JT 46.5 > 0.0 so DONES now = -4 - XS 0.434 > -1.0000 & JT 46.5 > 0.0 so DONES now = -5 - XS 0.259 > -1.0000 & JT 46.5 > 0.0 so DONES now = -6 - XS 0.259 > -1.0000 & JT 46.5 > 0.0 so DONES now = -7 - XS 0.259 > -1.0000 & JT 46.5 > 0.0 so DONES now = -8 - XS 0.154 > -1.0000 & JT 46.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1221 1.74569798 - DONE now c -1221 - Allocate 38 spaces in coupling list 19 - XS 0.927 > -1.0000 & JT 46.5 > 0.0 so DONES now = -10 - XS 0.560 > -1.0000 & JT 46.5 > 0.0 so DONES now = -11 - XS 0.560 > -1.0000 & JT 46.5 > 0.0 so DONES now = -12 - XS 0.560 > -1.0000 & JT 46.5 > 0.0 so DONES now = -13 - XS 0.336 > -1.0000 & JT 46.5 > 0.0 so DONES now = -14 - XS 0.336 > -1.0000 & JT 46.5 > 0.0 so DONES now = -15 - XS 0.336 > -1.0000 & JT 46.5 > 0.0 so DONES now = -16 - XS 0.200 > -1.0000 & JT 46.5 > 0.0 so DONES now = -17 - XS 0.200 > -1.0000 & JT 46.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1239 1.76277995 - DONE now c -1239 - Try JTOTAL = 47.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.737 > -1.0000 & JT 47.5 > 0.0 so DONES now = -1 - XS 0.443 > -1.0000 & JT 47.5 > 0.0 so DONES now = -2 - XS 0.443 > -1.0000 & JT 47.5 > 0.0 so DONES now = -3 - XS 0.443 > -1.0000 & JT 47.5 > 0.0 so DONES now = -4 - XS 0.265 > -1.0000 & JT 47.5 > 0.0 so DONES now = -5 - XS 0.265 > -1.0000 & JT 47.5 > 0.0 so DONES now = -6 - XS 0.265 > -1.0000 & JT 47.5 > 0.0 so DONES now = -7 - XS 0.157 > -1.0000 & JT 47.5 > 0.0 so DONES now = -8 - XS 0.157 > -1.0000 & JT 47.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1248 1.77981591 - DONE now c -1248 - Allocate 38 spaces in coupling list 19 - XS 0.572 > -1.0000 & JT 47.5 > 0.0 so DONES now = -10 - XS 0.572 > -1.0000 & JT 47.5 > 0.0 so DONES now = -11 - XS 0.343 > -1.0000 & JT 47.5 > 0.0 so DONES now = -12 - XS 0.343 > -1.0000 & JT 47.5 > 0.0 so DONES now = -13 - XS 0.343 > -1.0000 & JT 47.5 > 0.0 so DONES now = -14 - XS 0.204 > -1.0000 & JT 47.5 > 0.0 so DONES now = -15 - XS 0.204 > -1.0000 & JT 47.5 > 0.0 so DONES now = -16 - XS 0.204 > -1.0000 & JT 47.5 > 0.0 so DONES now = -17 - XS 0.121 > -1.0000 & JT 47.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1266 1.79657900 - DONE now c -1266 - Try JTOTAL = 48.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.453 > -1.0000 & JT 48.5 > 0.0 so DONES now = -1 - XS 0.453 > -1.0000 & JT 48.5 > 0.0 so DONES now = -2 - XS 0.270 > -1.0000 & JT 48.5 > 0.0 so DONES now = -3 - XS 0.270 > -1.0000 & JT 48.5 > 0.0 so DONES now = -4 - XS 0.270 > -1.0000 & JT 48.5 > 0.0 so DONES now = -5 - XS 0.161 > -1.0000 & JT 48.5 > 0.0 so DONES now = -6 - XS 0.161 > -1.0000 & JT 48.5 > 0.0 so DONES now = -7 - XS 0.161 > -1.0000 & JT 48.5 > 0.0 so DONES now = -8 - XS 0.953E-01 > -1.0000 & JT 48.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1275 1.81361890 - DONE now c -1275 - Allocate 38 spaces in coupling list 19 - XS 0.584 > -1.0000 & JT 48.5 > 0.0 so DONES now = -10 - XS 0.350 > -1.0000 & JT 48.5 > 0.0 so DONES now = -11 - XS 0.350 > -1.0000 & JT 48.5 > 0.0 so DONES now = -12 - XS 0.350 > -1.0000 & JT 48.5 > 0.0 so DONES now = -13 - XS 0.209 > -1.0000 & JT 48.5 > 0.0 so DONES now = -14 - XS 0.209 > -1.0000 & JT 48.5 > 0.0 so DONES now = -15 - XS 0.209 > -1.0000 & JT 48.5 > 0.0 so DONES now = -16 - XS 0.124 > -1.0000 & JT 48.5 > 0.0 so DONES now = -17 - XS 0.124 > -1.0000 & JT 48.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1293 1.83069205 - DONE now c -1293 - Try JTOTAL = 49.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.462 > -1.0000 & JT 49.5 > 0.0 so DONES now = -1 - XS 0.276 > -1.0000 & JT 49.5 > 0.0 so DONES now = -2 - XS 0.276 > -1.0000 & JT 49.5 > 0.0 so DONES now = -3 - XS 0.276 > -1.0000 & JT 49.5 > 0.0 so DONES now = -4 - XS 0.164 > -1.0000 & JT 49.5 > 0.0 so DONES now = -5 - XS 0.164 > -1.0000 & JT 49.5 > 0.0 so DONES now = -6 - XS 0.164 > -1.0000 & JT 49.5 > 0.0 so DONES now = -7 - XS 0.972E-01 > -1.0000 & JT 49.5 > 0.0 so DONES now = -8 - XS 0.972E-01 > -1.0000 & JT 49.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1302 1.84722996 - DONE now c -1302 - Allocate 38 spaces in coupling list 19 - XS 0.357 > -1.0000 & JT 49.5 > 0.0 so DONES now = -10 - XS 0.357 > -1.0000 & JT 49.5 > 0.0 so DONES now = -11 - XS 0.213 > -1.0000 & JT 49.5 > 0.0 so DONES now = -12 - XS 0.213 > -1.0000 & JT 49.5 > 0.0 so DONES now = -13 - XS 0.213 > -1.0000 & JT 49.5 > 0.0 so DONES now = -14 - XS 0.126 > -1.0000 & JT 49.5 > 0.0 so DONES now = -15 - XS 0.126 > -1.0000 & JT 49.5 > 0.0 so DONES now = -16 - XS 0.126 > -1.0000 & JT 49.5 > 0.0 so DONES now = -17 - XS 0.748E-01 > -1.0000 & JT 49.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1320 1.86434400 - DONE now c -1320 - Try JTOTAL = 50.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.281 > -1.0000 & JT 50.5 > 0.0 so DONES now = -1 - XS 0.281 > -1.0000 & JT 50.5 > 0.0 so DONES now = -2 - XS 0.167 > -1.0000 & JT 50.5 > 0.0 so DONES now = -3 - XS 0.167 > -1.0000 & JT 50.5 > 0.0 so DONES now = -4 - XS 0.167 > -1.0000 & JT 50.5 > 0.0 so DONES now = -5 - XS 0.992E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -6 - XS 0.992E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -7 - XS 0.992E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -8 - XS 0.587E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1329 1.88104796 - DONE now c -1329 - Allocate 38 spaces in coupling list 19 - XS 0.364 > -1.0000 & JT 50.5 > 0.0 so DONES now = -10 - XS 0.217 > -1.0000 & JT 50.5 > 0.0 so DONES now = -11 - XS 0.217 > -1.0000 & JT 50.5 > 0.0 so DONES now = -12 - XS 0.217 > -1.0000 & JT 50.5 > 0.0 so DONES now = -13 - XS 0.129 > -1.0000 & JT 50.5 > 0.0 so DONES now = -14 - XS 0.129 > -1.0000 & JT 50.5 > 0.0 so DONES now = -15 - XS 0.129 > -1.0000 & JT 50.5 > 0.0 so DONES now = -16 - XS 0.763E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -17 - XS 0.763E-01 > -1.0000 & JT 50.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1347 1.89799404 - DONE now c -1347 - Try JTOTAL = 51.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.287 > -1.0000 & JT 51.5 > 0.0 so DONES now = -1 - XS 0.171 > -1.0000 & JT 51.5 > 0.0 so DONES now = -2 - XS 0.171 > -1.0000 & JT 51.5 > 0.0 so DONES now = -3 - XS 0.171 > -1.0000 & JT 51.5 > 0.0 so DONES now = -4 - XS 0.101 > -1.0000 & JT 51.5 > 0.0 so DONES now = -5 - XS 0.101 > -1.0000 & JT 51.5 > 0.0 so DONES now = -6 - XS 0.101 > -1.0000 & JT 51.5 > 0.0 so DONES now = -7 - XS 0.598E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -8 - XS 0.598E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1356 1.91500807 - DONE now c -1356 - Allocate 38 spaces in coupling list 19 - XS 0.221 > -1.0000 & JT 51.5 > 0.0 so DONES now = -10 - XS 0.221 > -1.0000 & JT 51.5 > 0.0 so DONES now = -11 - XS 0.131 > -1.0000 & JT 51.5 > 0.0 so DONES now = -12 - XS 0.131 > -1.0000 & JT 51.5 > 0.0 so DONES now = -13 - XS 0.131 > -1.0000 & JT 51.5 > 0.0 so DONES now = -14 - XS 0.778E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -15 - XS 0.778E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -16 - XS 0.778E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -17 - XS 0.460E-01 > -1.0000 & JT 51.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1374 1.93199599 - DONE now c -1374 - Try JTOTAL = 52.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.174 > -1.0000 & JT 52.5 > 0.0 so DONES now = -1 - XS 0.174 > -1.0000 & JT 52.5 > 0.0 so DONES now = -2 - XS 0.103 > -1.0000 & JT 52.5 > 0.0 so DONES now = -3 - XS 0.103 > -1.0000 & JT 52.5 > 0.0 so DONES now = -4 - XS 0.103 > -1.0000 & JT 52.5 > 0.0 so DONES now = -5 - XS 0.610E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -6 - XS 0.610E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -7 - XS 0.610E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -8 - XS 0.360E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1383 1.94902098 - DONE now c -1383 - Allocate 38 spaces in coupling list 19 - XS 0.226 > -1.0000 & JT 52.5 > 0.0 so DONES now = -10 - XS 0.134 > -1.0000 & JT 52.5 > 0.0 so DONES now = -11 - XS 0.134 > -1.0000 & JT 52.5 > 0.0 so DONES now = -12 - XS 0.134 > -1.0000 & JT 52.5 > 0.0 so DONES now = -13 - XS 0.793E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -14 - XS 0.793E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -15 - XS 0.793E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -16 - XS 0.469E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -17 - XS 0.469E-01 > -1.0000 & JT 52.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1401 1.96652007 - DONE now c -1401 - Try JTOTAL = 53.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.177 > -1.0000 & JT 53.5 > 0.0 so DONES now = -1 - XS 0.105 > -1.0000 & JT 53.5 > 0.0 so DONES now = -2 - XS 0.105 > -1.0000 & JT 53.5 > 0.0 so DONES now = -3 - XS 0.105 > -1.0000 & JT 53.5 > 0.0 so DONES now = -4 - XS 0.621E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -5 - XS 0.621E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -6 - XS 0.621E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -7 - XS 0.367E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -8 - XS 0.367E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1410 1.98339891 - DONE now c -1410 - Allocate 38 spaces in coupling list 19 - XS 0.136 > -1.0000 & JT 53.5 > 0.0 so DONES now = -10 - XS 0.136 > -1.0000 & JT 53.5 > 0.0 so DONES now = -11 - XS 0.808E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -12 - XS 0.808E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -13 - XS 0.808E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -14 - XS 0.477E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -15 - XS 0.477E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -16 - XS 0.477E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -17 - XS 0.282E-01 > -1.0000 & JT 53.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1428 2.00151181 - DONE now c -1428 - Try JTOTAL = 54.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.107 > -1.0000 & JT 54.5 > 0.0 so DONES now = -1 - XS 0.107 > -1.0000 & JT 54.5 > 0.0 so DONES now = -2 - XS 0.633E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -3 - XS 0.633E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -4 - XS 0.633E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -5 - XS 0.374E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -6 - XS 0.374E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -7 - XS 0.374E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -8 - XS 0.220E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1437 2.01906800 - DONE now c -1437 - Allocate 38 spaces in coupling list 19 - XS 0.139 > -1.0000 & JT 54.5 > 0.0 so DONES now = -10 - XS 0.823E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -11 - XS 0.823E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -12 - XS 0.823E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -13 - XS 0.486E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -14 - XS 0.486E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -15 - XS 0.486E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -16 - XS 0.287E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -17 - XS 0.287E-01 > -1.0000 & JT 54.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1455 2.03606701 - DONE now c -1455 - Try JTOTAL = 55.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.109 > -1.0000 & JT 55.5 > 0.0 so DONES now = -1 - XS 0.644E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -2 - XS 0.644E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -3 - XS 0.644E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -4 - XS 0.380E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -5 - XS 0.380E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -6 - XS 0.380E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -7 - XS 0.224E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -8 - XS 0.224E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1464 2.05322003 - DONE now c -1464 - Allocate 38 spaces in coupling list 19 - XS 0.838E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -10 - XS 0.838E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -11 - XS 0.495E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -12 - XS 0.495E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -13 - XS 0.495E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -14 - XS 0.292E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -15 - XS 0.292E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -16 - XS 0.292E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -17 - XS 0.172E-01 > -1.0000 & JT 55.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1482 2.07037997 - DONE now c -1482 - Try JTOTAL = 56.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.656E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -1 - XS 0.656E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -2 - XS 0.387E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -3 - XS 0.387E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -4 - XS 0.387E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -5 - XS 0.228E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -6 - XS 0.228E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -7 - XS 0.228E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -8 - XS 0.135E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1491 2.08754611 - DONE now c -1491 - Allocate 38 spaces in coupling list 19 - XS 0.853E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -10 - XS 0.504E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -11 - XS 0.504E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -12 - XS 0.504E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -13 - XS 0.297E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -14 - XS 0.297E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -15 - XS 0.297E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -16 - XS 0.175E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -17 - XS 0.175E-01 > -1.0000 & JT 56.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1509 2.10460901 - DONE now c -1509 - Try JTOTAL = 57.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.667E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -1 - XS 0.394E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -2 - XS 0.394E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -3 - XS 0.394E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -4 - XS 0.232E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -5 - XS 0.232E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -6 - XS 0.232E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -7 - XS 0.137E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -8 - XS 0.137E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1518 2.12192082 - DONE now c -1518 - Allocate 38 spaces in coupling list 19 - XS 0.513E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -10 - XS 0.513E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -11 - XS 0.303E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -12 - XS 0.303E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -13 - XS 0.303E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -14 - XS 0.179E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -15 - XS 0.179E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -16 - XS 0.179E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -17 - XS 0.105E-01 > -1.0000 & JT 57.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1536 2.14045095 - DONE now c -1536 - Try JTOTAL = 58.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.401E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -1 - XS 0.401E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -2 - XS 0.236E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -3 - XS 0.236E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -4 - XS 0.236E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -5 - XS 0.139E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -6 - XS 0.139E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -7 - XS 0.139E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -8 - XS 0.821E-02 > -1.0000 & JT 58.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1545 2.15812993 - DONE now c -1545 - Allocate 38 spaces in coupling list 19 - XS 0.522E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -10 - XS 0.308E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -11 - XS 0.308E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -12 - XS 0.308E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -13 - XS 0.182E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -14 - XS 0.182E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -15 - XS 0.182E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -16 - XS 0.107E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -17 - XS 0.107E-01 > -1.0000 & JT 58.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1563 2.17493296 - DONE now c -1563 - Try JTOTAL = 59.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.408E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -1 - XS 0.240E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -2 - XS 0.240E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -3 - XS 0.240E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -4 - XS 0.142E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -5 - XS 0.142E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -6 - XS 0.142E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -7 - XS 0.835E-02 > -1.0000 & JT 59.5 > 0.0 so DONES now = -8 - XS 0.835E-02 > -1.0000 & JT 59.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1572 2.19169116 - DONE now c -1572 - Allocate 38 spaces in coupling list 19 - XS 0.313E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -10 - XS 0.313E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -11 - XS 0.185E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -12 - XS 0.185E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -13 - XS 0.185E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -14 - XS 0.109E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -15 - XS 0.109E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -16 - XS 0.109E-01 > -1.0000 & JT 59.5 > 0.0 so DONES now = -17 - XS 0.641E-02 > -1.0000 & JT 59.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1590 2.20863986 - DONE now c -1590 - Try JTOTAL = 60.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.245E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -1 - XS 0.245E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -2 - XS 0.144E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -3 - XS 0.144E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -4 - XS 0.144E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -5 - XS 0.849E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -6 - XS 0.849E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -7 - XS 0.849E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -8 - XS 0.500E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1599 2.22598600 - DONE now c -1599 - Allocate 38 spaces in coupling list 19 - XS 0.318E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -10 - XS 0.188E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -11 - XS 0.188E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -12 - XS 0.188E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -13 - XS 0.111E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -14 - XS 0.111E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -15 - XS 0.111E-01 > -1.0000 & JT 60.5 > 0.0 so DONES now = -16 - XS 0.652E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -17 - XS 0.652E-02 > -1.0000 & JT 60.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1617 2.24298096 - DONE now c -1617 - Try JTOTAL = 61.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.249E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -1 - XS 0.147E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -2 - XS 0.147E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -3 - XS 0.147E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -4 - XS 0.863E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -5 - XS 0.863E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -6 - XS 0.863E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -7 - XS 0.508E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -8 - XS 0.508E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1626 2.25979304 - DONE now c -1626 - Allocate 38 spaces in coupling list 19 - XS 0.191E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -10 - XS 0.191E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -11 - XS 0.112E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -12 - XS 0.112E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -13 - XS 0.112E-01 > -1.0000 & JT 61.5 > 0.0 so DONES now = -14 - XS 0.662E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -15 - XS 0.662E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -16 - XS 0.662E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -17 - XS 0.390E-02 > -1.0000 & JT 61.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1644 2.27643299 - DONE now c -1644 - Try JTOTAL = 62.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.149E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -1 - XS 0.149E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -2 - XS 0.877E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -3 - XS 0.877E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -4 - XS 0.877E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -5 - XS 0.516E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -6 - XS 0.516E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -7 - XS 0.516E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -8 - XS 0.304E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1653 2.29310393 - DONE now c -1653 - Allocate 38 spaces in coupling list 19 - XS 0.194E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -10 - XS 0.114E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -11 - XS 0.114E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -12 - XS 0.114E-01 > -1.0000 & JT 62.5 > 0.0 so DONES now = -13 - XS 0.673E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -14 - XS 0.673E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -15 - XS 0.673E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -16 - XS 0.396E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -17 - XS 0.396E-02 > -1.0000 & JT 62.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1671 2.31052899 - DONE now c -1671 - Try JTOTAL = 63.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.151E-01 > -1.0000 & JT 63.5 > 0.0 so DONES now = -1 - XS 0.891E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -2 - XS 0.891E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -3 - XS 0.891E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -4 - XS 0.525E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -5 - XS 0.525E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -6 - XS 0.525E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -7 - XS 0.309E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -8 - XS 0.309E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1680 2.32722497 - DONE now c -1680 - Allocate 38 spaces in coupling list 19 - XS 0.116E-01 > -1.0000 & JT 63.5 > 0.0 so DONES now = -10 - XS 0.116E-01 > -1.0000 & JT 63.5 > 0.0 so DONES now = -11 - XS 0.684E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -12 - XS 0.684E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -13 - XS 0.684E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -14 - XS 0.403E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -15 - XS 0.403E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -16 - XS 0.403E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -17 - XS 0.237E-02 > -1.0000 & JT 63.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1698 2.34474993 - DONE now c -1698 - Try JTOTAL = 64.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.905E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -1 - XS 0.905E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -2 - XS 0.533E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -3 - XS 0.533E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -4 - XS 0.533E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -5 - XS 0.314E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -6 - XS 0.314E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -7 - XS 0.314E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -8 - XS 0.184E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1707 2.36247802 - DONE now c -1707 - Allocate 38 spaces in coupling list 19 - XS 0.118E-01 > -1.0000 & JT 64.5 > 0.0 so DONES now = -10 - XS 0.694E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -11 - XS 0.694E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -12 - XS 0.694E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -13 - XS 0.409E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -14 - XS 0.409E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -15 - XS 0.409E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -16 - XS 0.241E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -17 - XS 0.241E-02 > -1.0000 & JT 64.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1725 2.37914991 - DONE now c -1725 - Try JTOTAL = 65.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.919E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -1 - XS 0.541E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -2 - XS 0.541E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -3 - XS 0.541E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -4 - XS 0.318E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -5 - XS 0.318E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -6 - XS 0.318E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -7 - XS 0.187E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -8 - XS 0.187E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1734 2.39618802 - DONE now c -1734 - Allocate 38 spaces in coupling list 19 - XS 0.705E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -10 - XS 0.705E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -11 - XS 0.415E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -12 - XS 0.415E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -13 - XS 0.415E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -14 - XS 0.244E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -15 - XS 0.244E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -16 - XS 0.244E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -17 - XS 0.144E-02 > -1.0000 & JT 65.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1752 2.41458893 - DONE now c -1752 - Try JTOTAL = 66.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.549E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -1 - XS 0.549E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -2 - XS 0.323E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -3 - XS 0.323E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -4 - XS 0.323E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -5 - XS 0.190E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -6 - XS 0.190E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -7 - XS 0.190E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -8 - XS 0.112E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1761 2.43262601 - DONE now c -1761 - Allocate 38 spaces in coupling list 19 - XS 0.716E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -10 - XS 0.421E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -11 - XS 0.421E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -12 - XS 0.421E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -13 - XS 0.248E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -14 - XS 0.248E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -15 - XS 0.248E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -16 - XS 0.146E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -17 - XS 0.146E-02 > -1.0000 & JT 66.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1779 2.44936109 - DONE now c -1779 - Try JTOTAL = 67.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.557E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -1 - XS 0.328E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -2 - XS 0.328E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -3 - XS 0.328E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -4 - XS 0.193E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -5 - XS 0.193E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -6 - XS 0.193E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -7 - XS 0.114E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -8 - XS 0.114E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1788 2.46554804 - DONE now c -1788 - Allocate 38 spaces in coupling list 19 - XS 0.428E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -10 - XS 0.428E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -11 - XS 0.252E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -12 - XS 0.252E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -13 - XS 0.252E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -14 - XS 0.148E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -15 - XS 0.148E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -16 - XS 0.148E-02 > -1.0000 & JT 67.5 > 0.0 so DONES now = -17 - XS 0.870E-03 > -1.0000 & JT 67.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1806 2.48162007 - DONE now c -1806 - Try JTOTAL = 68.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.333E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -1 - XS 0.333E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -2 - XS 0.196E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -3 - XS 0.196E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -4 - XS 0.196E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -5 - XS 0.115E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -6 - XS 0.115E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -7 - XS 0.115E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -8 - XS 0.677E-03 > -1.0000 & JT 68.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1815 2.49846101 - DONE now c -1815 - Allocate 38 spaces in coupling list 19 - XS 0.434E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -10 - XS 0.255E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -11 - XS 0.255E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -12 - XS 0.255E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -13 - XS 0.150E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -14 - XS 0.150E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -15 - XS 0.150E-02 > -1.0000 & JT 68.5 > 0.0 so DONES now = -16 - XS 0.883E-03 > -1.0000 & JT 68.5 > 0.0 so DONES now = -17 - XS 0.883E-03 > -1.0000 & JT 68.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1833 2.51491094 - DONE now c -1833 - Try JTOTAL = 69.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.338E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -1 - XS 0.199E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -2 - XS 0.199E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -3 - XS 0.199E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -4 - XS 0.117E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -5 - XS 0.117E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -6 - XS 0.117E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -7 - XS 0.687E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -8 - XS 0.687E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1842 2.53127694 - DONE now c -1842 - Allocate 38 spaces in coupling list 19 - XS 0.259E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -10 - XS 0.259E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -11 - XS 0.152E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -12 - XS 0.152E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -13 - XS 0.152E-02 > -1.0000 & JT 69.5 > 0.0 so DONES now = -14 - XS 0.896E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -15 - XS 0.896E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -16 - XS 0.896E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -17 - XS 0.527E-03 > -1.0000 & JT 69.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1860 2.54810405 - DONE now c -1860 - Try JTOTAL = 70.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.202E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -1 - XS 0.202E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -2 - XS 0.119E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -3 - XS 0.119E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -4 - XS 0.119E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -5 - XS 0.697E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -6 - XS 0.697E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -7 - XS 0.697E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -8 - XS 0.409E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1869 2.56430411 - DONE now c -1869 - Allocate 38 spaces in coupling list 19 - XS 0.263E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -10 - XS 0.155E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -11 - XS 0.155E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -12 - XS 0.155E-02 > -1.0000 & JT 70.5 > 0.0 so DONES now = -13 - XS 0.909E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -14 - XS 0.909E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -15 - XS 0.909E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -16 - XS 0.534E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -17 - XS 0.534E-03 > -1.0000 & JT 70.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1887 2.58184004 - DONE now c -1887 - Try JTOTAL = 71.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.204E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -1 - XS 0.120E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -2 - XS 0.120E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -3 - XS 0.120E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -4 - XS 0.706E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -5 - XS 0.706E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -6 - XS 0.706E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -7 - XS 0.415E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -8 - XS 0.415E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1896 2.59904003 - DONE now c -1896 - Allocate 38 spaces in coupling list 19 - XS 0.157E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -10 - XS 0.157E-02 > -1.0000 & JT 71.5 > 0.0 so DONES now = -11 - XS 0.921E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -12 - XS 0.921E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -13 - XS 0.921E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -14 - XS 0.542E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -15 - XS 0.542E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -16 - XS 0.542E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -17 - XS 0.318E-03 > -1.0000 & JT 71.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1914 2.61483002 - DONE now c -1914 - Try JTOTAL = 72.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.122E-02 > -1.0000 & JT 72.5 > 0.0 so DONES now = -1 - XS 0.122E-02 > -1.0000 & JT 72.5 > 0.0 so DONES now = -2 - XS 0.716E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -3 - XS 0.716E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -4 - XS 0.716E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -5 - XS 0.421E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -6 - XS 0.421E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -7 - XS 0.421E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -8 - XS 0.247E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1923 2.63018489 - DONE now c -1923 - Allocate 38 spaces in coupling list 19 - XS 0.159E-02 > -1.0000 & JT 72.5 > 0.0 so DONES now = -10 - XS 0.934E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -11 - XS 0.934E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -12 - XS 0.934E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -13 - XS 0.549E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -14 - XS 0.549E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -15 - XS 0.549E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -16 - XS 0.323E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -17 - XS 0.323E-03 > -1.0000 & JT 72.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1941 2.64546609 - DONE now c -1941 - Try JTOTAL = 73.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.124E-02 > -1.0000 & JT 73.5 > 0.0 so DONES now = -1 - XS 0.726E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -2 - XS 0.726E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -3 - XS 0.726E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -4 - XS 0.427E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -5 - XS 0.427E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -6 - XS 0.427E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -7 - XS 0.251E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -8 - XS 0.251E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1950 2.66094613 - DONE now c -1950 - Allocate 38 spaces in coupling list 19 - XS 0.947E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -10 - XS 0.947E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -11 - XS 0.557E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -12 - XS 0.557E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -13 - XS 0.557E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -14 - XS 0.327E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -15 - XS 0.327E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -16 - XS 0.327E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -17 - XS 0.192E-03 > -1.0000 & JT 73.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1968 2.67613912 - DONE now c -1968 - Try JTOTAL = 74.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.736E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -1 - XS 0.736E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -2 - XS 0.432E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -3 - XS 0.432E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -4 - XS 0.432E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -5 - XS 0.254E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -6 - XS 0.254E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -7 - XS 0.254E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -8 - XS 0.149E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -1977 2.69139886 - DONE now c -1977 - Allocate 38 spaces in coupling list 19 - XS 0.960E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -10 - XS 0.564E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -11 - XS 0.564E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -12 - XS 0.564E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -13 - XS 0.331E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -14 - XS 0.331E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -15 - XS 0.331E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -16 - XS 0.195E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -17 - XS 0.195E-03 > -1.0000 & JT 74.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -1995 2.70695090 - DONE now c -1995 - Try JTOTAL = 75.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.746E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -1 - XS 0.438E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -2 - XS 0.438E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -3 - XS 0.438E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -4 - XS 0.257E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -5 - XS 0.257E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -6 - XS 0.257E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -7 - XS 0.151E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -8 - XS 0.151E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2004 2.72231793 - DONE now c -2004 - Allocate 38 spaces in coupling list 19 - XS 0.572E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -10 - XS 0.572E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -11 - XS 0.336E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -12 - XS 0.336E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -13 - XS 0.336E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -14 - XS 0.197E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -15 - XS 0.197E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -16 - XS 0.197E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -17 - XS 0.116E-03 > -1.0000 & JT 75.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2022 2.73754907 - DONE now c -2022 - Try JTOTAL = 76.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.444E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -1 - XS 0.444E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -2 - XS 0.261E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -3 - XS 0.261E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -4 - XS 0.261E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -5 - XS 0.153E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -6 - XS 0.153E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -7 - XS 0.153E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -8 - XS 0.899E-04 > -1.0000 & JT 76.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2031 2.75273013 - DONE now c -2031 - Allocate 38 spaces in coupling list 19 - XS 0.579E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -10 - XS 0.340E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -11 - XS 0.340E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -12 - XS 0.340E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -13 - XS 0.200E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -14 - XS 0.200E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -15 - XS 0.200E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -16 - XS 0.117E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -17 - XS 0.117E-03 > -1.0000 & JT 76.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2049 2.76800609 - DONE now c -2049 - Try JTOTAL = 77.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.450E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -1 - XS 0.264E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -2 - XS 0.264E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -3 - XS 0.264E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -4 - XS 0.155E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -5 - XS 0.155E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -6 - XS 0.155E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -7 - XS 0.911E-04 > -1.0000 & JT 77.5 > 0.0 so DONES now = -8 - XS 0.911E-04 > -1.0000 & JT 77.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2058 2.78316402 - DONE now c -2058 - Allocate 38 spaces in coupling list 19 - XS 0.345E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -10 - XS 0.345E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -11 - XS 0.203E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -12 - XS 0.203E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -13 - XS 0.203E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -14 - XS 0.119E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -15 - XS 0.119E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -16 - XS 0.119E-03 > -1.0000 & JT 77.5 > 0.0 so DONES now = -17 - XS 0.698E-04 > -1.0000 & JT 77.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2076 2.79825997 - DONE now c -2076 - Try JTOTAL = 78.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.268E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -1 - XS 0.268E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -2 - XS 0.157E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -3 - XS 0.157E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -4 - XS 0.157E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -5 - XS 0.923E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -6 - XS 0.923E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -7 - XS 0.923E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -8 - XS 0.542E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2085 2.81349492 - DONE now c -2085 - Allocate 38 spaces in coupling list 19 - XS 0.349E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -10 - XS 0.205E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -11 - XS 0.205E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -12 - XS 0.205E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -13 - XS 0.120E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -14 - XS 0.120E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -15 - XS 0.120E-03 > -1.0000 & JT 78.5 > 0.0 so DONES now = -16 - XS 0.707E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -17 - XS 0.707E-04 > -1.0000 & JT 78.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2103 2.82871103 - DONE now c -2103 - Try JTOTAL = 79.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.271E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -1 - XS 0.159E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -2 - XS 0.159E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -3 - XS 0.159E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -4 - XS 0.935E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -5 - XS 0.935E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -6 - XS 0.935E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -7 - XS 0.549E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -8 - XS 0.549E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2112 2.84378290 - DONE now c -2112 - Allocate 38 spaces in coupling list 19 - XS 0.208E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -10 - XS 0.208E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -11 - XS 0.122E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -12 - XS 0.122E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -13 - XS 0.122E-03 > -1.0000 & JT 79.5 > 0.0 so DONES now = -14 - XS 0.716E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -15 - XS 0.716E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -16 - XS 0.716E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -17 - XS 0.420E-04 > -1.0000 & JT 79.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2130 2.85903001 - DONE now c -2130 - Try JTOTAL = 80.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.161E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -1 - XS 0.161E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -2 - XS 0.946E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -3 - XS 0.946E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -4 - XS 0.946E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -5 - XS 0.555E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -6 - XS 0.555E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -7 - XS 0.555E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -8 - XS 0.326E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2139 2.87413287 - DONE now c -2139 - Allocate 38 spaces in coupling list 19 - XS 0.210E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -10 - XS 0.123E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -11 - XS 0.123E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -12 - XS 0.123E-03 > -1.0000 & JT 80.5 > 0.0 so DONES now = -13 - XS 0.725E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -14 - XS 0.725E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -15 - XS 0.725E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -16 - XS 0.425E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -17 - XS 0.425E-04 > -1.0000 & JT 80.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2157 2.88918400 - DONE now c -2157 - Try JTOTAL = 81.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.163E-03 > -1.0000 & JT 81.5 > 0.0 so DONES now = -1 - XS 0.958E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -2 - XS 0.958E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -3 - XS 0.958E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -4 - XS 0.562E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -5 - XS 0.562E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -6 - XS 0.562E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -7 - XS 0.330E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -8 - XS 0.330E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2166 2.90421605 - DONE now c -2166 - Allocate 38 spaces in coupling list 19 - XS 0.125E-03 > -1.0000 & JT 81.5 > 0.0 so DONES now = -10 - XS 0.125E-03 > -1.0000 & JT 81.5 > 0.0 so DONES now = -11 - XS 0.734E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -12 - XS 0.734E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -13 - XS 0.734E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -14 - XS 0.431E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -15 - XS 0.431E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -16 - XS 0.431E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -17 - XS 0.253E-04 > -1.0000 & JT 81.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2184 2.91937709 - DONE now c -2184 - Try JTOTAL = 82.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.970E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -1 - XS 0.970E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -2 - XS 0.569E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -3 - XS 0.569E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -4 - XS 0.569E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -5 - XS 0.334E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -6 - XS 0.334E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -7 - XS 0.334E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -8 - XS 0.196E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2193 2.93439293 - DONE now c -2193 - Allocate 38 spaces in coupling list 19 - XS 0.127E-03 > -1.0000 & JT 82.5 > 0.0 so DONES now = -10 - XS 0.743E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -11 - XS 0.743E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -12 - XS 0.743E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -13 - XS 0.436E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -14 - XS 0.436E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -15 - XS 0.436E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -16 - XS 0.256E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -17 - XS 0.256E-04 > -1.0000 & JT 82.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2211 2.94937396 - DONE now c -2211 - Try JTOTAL = 83.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.981E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -1 - XS 0.576E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -2 - XS 0.576E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -3 - XS 0.576E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -4 - XS 0.338E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -5 - XS 0.338E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -6 - XS 0.338E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -7 - XS 0.198E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -8 - XS 0.198E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2220 2.96442103 - DONE now c -2220 - Allocate 38 spaces in coupling list 19 - XS 0.752E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -10 - XS 0.752E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -11 - XS 0.441E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -12 - XS 0.441E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -13 - XS 0.441E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -14 - XS 0.259E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -15 - XS 0.259E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -16 - XS 0.259E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -17 - XS 0.152E-04 > -1.0000 & JT 83.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2238 2.98023295 - DONE now c -2238 - Try JTOTAL = 84.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.583E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -1 - XS 0.583E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -2 - XS 0.342E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -3 - XS 0.342E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -4 - XS 0.342E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -5 - XS 0.201E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -6 - XS 0.201E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -7 - XS 0.201E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -8 - XS 0.118E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2247 2.99694800 - DONE now c -2247 - Allocate 38 spaces in coupling list 19 - XS 0.761E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -10 - XS 0.447E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -11 - XS 0.447E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -12 - XS 0.447E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -13 - XS 0.262E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -14 - XS 0.262E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -15 - XS 0.262E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -16 - XS 0.154E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -17 - XS 0.154E-04 > -1.0000 & JT 84.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2265 3.01399803 - DONE now c -2265 - Try JTOTAL = 85.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.590E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -1 - XS 0.346E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -2 - XS 0.346E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -3 - XS 0.346E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -4 - XS 0.203E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -5 - XS 0.203E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -6 - XS 0.203E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -7 - XS 0.119E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -8 - XS 0.119E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2274 3.03121996 - DONE now c -2274 - Allocate 38 spaces in coupling list 19 - XS 0.452E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -10 - XS 0.452E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -11 - XS 0.265E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -12 - XS 0.265E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -13 - XS 0.265E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -14 - XS 0.156E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -15 - XS 0.156E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -16 - XS 0.156E-04 > -1.0000 & JT 85.5 > 0.0 so DONES now = -17 - XS 0.912E-05 > -1.0000 & JT 85.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2292 3.04884315 - DONE now c -2292 - Try JTOTAL = 86.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.350E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -1 - XS 0.350E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -2 - XS 0.205E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -3 - XS 0.205E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -4 - XS 0.205E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -5 - XS 0.120E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -6 - XS 0.120E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -7 - XS 0.120E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -8 - XS 0.707E-05 > -1.0000 & JT 86.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2301 3.06791806 - DONE now c -2301 - Allocate 38 spaces in coupling list 19 - XS 0.457E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -10 - XS 0.268E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -11 - XS 0.268E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -12 - XS 0.268E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -13 - XS 0.157E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -14 - XS 0.157E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -15 - XS 0.157E-04 > -1.0000 & JT 86.5 > 0.0 so DONES now = -16 - XS 0.923E-05 > -1.0000 & JT 86.5 > 0.0 so DONES now = -17 - XS 0.923E-05 > -1.0000 & JT 86.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2319 3.08753204 - DONE now c -2319 - Try JTOTAL = 87.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.354E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -1 - XS 0.208E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -2 - XS 0.208E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -3 - XS 0.208E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -4 - XS 0.122E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -5 - XS 0.122E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -6 - XS 0.122E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -7 - XS 0.715E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -8 - XS 0.715E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2328 3.10498405 - DONE now c -2328 - Allocate 38 spaces in coupling list 19 - XS 0.271E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -10 - XS 0.271E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -11 - XS 0.159E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -12 - XS 0.159E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -13 - XS 0.159E-04 > -1.0000 & JT 87.5 > 0.0 so DONES now = -14 - XS 0.933E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -15 - XS 0.933E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -16 - XS 0.933E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -17 - XS 0.547E-05 > -1.0000 & JT 87.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2346 3.12191296 - DONE now c -2346 - Try JTOTAL = 88.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.210E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -1 - XS 0.210E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -2 - XS 0.123E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -3 - XS 0.123E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -4 - XS 0.123E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -5 - XS 0.723E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -6 - XS 0.723E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -7 - XS 0.723E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -8 - XS 0.424E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2355 3.14091182 - DONE now c -2355 - Allocate 38 spaces in coupling list 19 - XS 0.274E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -10 - XS 0.161E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -11 - XS 0.161E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -12 - XS 0.161E-04 > -1.0000 & JT 88.5 > 0.0 so DONES now = -13 - XS 0.944E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -14 - XS 0.944E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -15 - XS 0.944E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -16 - XS 0.553E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -17 - XS 0.553E-05 > -1.0000 & JT 88.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2373 3.16135502 - DONE now c -2373 - Try JTOTAL = 89.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.212E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -1 - XS 0.125E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -2 - XS 0.125E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -3 - XS 0.125E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -4 - XS 0.731E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -5 - XS 0.731E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -6 - XS 0.731E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -7 - XS 0.429E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -8 - XS 0.429E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2382 3.17812610 - DONE now c -2382 - Allocate 38 spaces in coupling list 19 - XS 0.163E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -10 - XS 0.163E-04 > -1.0000 & JT 89.5 > 0.0 so DONES now = -11 - XS 0.954E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -12 - XS 0.954E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -13 - XS 0.954E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -14 - XS 0.560E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -15 - XS 0.560E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -16 - XS 0.560E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -17 - XS 0.328E-05 > -1.0000 & JT 89.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2400 3.19348788 - DONE now c -2400 - Try JTOTAL = 90.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.126E-04 > -1.0000 & JT 90.5 > 0.0 so DONES now = -1 - XS 0.126E-04 > -1.0000 & JT 90.5 > 0.0 so DONES now = -2 - XS 0.739E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -3 - XS 0.739E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -4 - XS 0.739E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -5 - XS 0.433E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -6 - XS 0.433E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -7 - XS 0.433E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -8 - XS 0.254E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2409 3.20937610 - DONE now c -2409 - Allocate 38 spaces in coupling list 19 - XS 0.165E-04 > -1.0000 & JT 90.5 > 0.0 so DONES now = -10 - XS 0.965E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -11 - XS 0.965E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -12 - XS 0.965E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -13 - XS 0.566E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -14 - XS 0.566E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -15 - XS 0.566E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -16 - XS 0.332E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -17 - XS 0.332E-05 > -1.0000 & JT 90.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2427 3.22444701 - DONE now c -2427 - Try JTOTAL = 91.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.127E-04 > -1.0000 & JT 91.5 > 0.0 so DONES now = -1 - XS 0.747E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -2 - XS 0.747E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -3 - XS 0.747E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -4 - XS 0.438E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -5 - XS 0.438E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -6 - XS 0.438E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -7 - XS 0.257E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -8 - XS 0.257E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2436 3.23933911 - DONE now c -2436 - Allocate 38 spaces in coupling list 19 - XS 0.976E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -10 - XS 0.976E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -11 - XS 0.572E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -12 - XS 0.572E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -13 - XS 0.572E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -14 - XS 0.335E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -15 - XS 0.335E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -16 - XS 0.335E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -17 - XS 0.197E-05 > -1.0000 & JT 91.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2454 3.25509191 - DONE now c -2454 - Try JTOTAL = 92.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.755E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -1 - XS 0.755E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -2 - XS 0.443E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -3 - XS 0.443E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -4 - XS 0.443E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -5 - XS 0.260E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -6 - XS 0.260E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -7 - XS 0.260E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -8 - XS 0.152E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2463 3.27168393 - DONE now c -2463 - Allocate 38 spaces in coupling list 19 - XS 0.986E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -10 - XS 0.578E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -11 - XS 0.578E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -12 - XS 0.578E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -13 - XS 0.339E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -14 - XS 0.339E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -15 - XS 0.339E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -16 - XS 0.199E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -17 - XS 0.199E-05 > -1.0000 & JT 92.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2481 3.28776503 - DONE now c -2481 - Try JTOTAL = 93.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.763E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -1 - XS 0.448E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -2 - XS 0.448E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -3 - XS 0.448E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -4 - XS 0.262E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -5 - XS 0.262E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -6 - XS 0.262E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -7 - XS 0.154E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -8 - XS 0.154E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2490 3.30399394 - DONE now c -2490 - Allocate 38 spaces in coupling list 19 - XS 0.585E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -10 - XS 0.585E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -11 - XS 0.343E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -12 - XS 0.343E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -13 - XS 0.343E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -14 - XS 0.201E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -15 - XS 0.201E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -16 - XS 0.201E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -17 - XS 0.118E-05 > -1.0000 & JT 93.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2508 3.32012701 - DONE now c -2508 - Try JTOTAL = 94.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.452E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -1 - XS 0.452E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -2 - XS 0.265E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -3 - XS 0.265E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -4 - XS 0.265E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -5 - XS 0.155E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -6 - XS 0.155E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -7 - XS 0.155E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -8 - XS 0.911E-06 > -1.0000 & JT 94.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2517 3.33644199 - DONE now c -2517 - Allocate 38 spaces in coupling list 19 - XS 0.591E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -10 - XS 0.346E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -11 - XS 0.346E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -12 - XS 0.346E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -13 - XS 0.203E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -14 - XS 0.203E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -15 - XS 0.203E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -16 - XS 0.119E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -17 - XS 0.119E-05 > -1.0000 & JT 94.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2535 3.35398889 - DONE now c -2535 - Try JTOTAL = 95.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.457E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -1 - XS 0.268E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -2 - XS 0.268E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -3 - XS 0.268E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -4 - XS 0.157E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -5 - XS 0.157E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -6 - XS 0.157E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -7 - XS 0.920E-06 > -1.0000 & JT 95.5 > 0.0 so DONES now = -8 - XS 0.920E-06 > -1.0000 & JT 95.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2544 3.37092495 - DONE now c -2544 - Allocate 38 spaces in coupling list 19 - XS 0.350E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -10 - XS 0.350E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -11 - XS 0.205E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -12 - XS 0.205E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -13 - XS 0.205E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -14 - XS 0.120E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -15 - XS 0.120E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -16 - XS 0.120E-05 > -1.0000 & JT 95.5 > 0.0 so DONES now = -17 - XS 0.704E-06 > -1.0000 & JT 95.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2562 3.38735700 - DONE now c -2562 - Try JTOTAL = 96.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.271E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -1 - XS 0.271E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -2 - XS 0.159E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -3 - XS 0.159E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -4 - XS 0.159E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -5 - XS 0.930E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -6 - XS 0.930E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -7 - XS 0.930E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -8 - XS 0.545E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2571 3.40363407 - DONE now c -2571 - Allocate 38 spaces in coupling list 19 - XS 0.354E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -10 - XS 0.207E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -11 - XS 0.207E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -12 - XS 0.207E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -13 - XS 0.121E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -14 - XS 0.121E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -15 - XS 0.121E-05 > -1.0000 & JT 96.5 > 0.0 so DONES now = -16 - XS 0.712E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -17 - XS 0.712E-06 > -1.0000 & JT 96.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2589 3.41980386 - DONE now c -2589 - Try JTOTAL = 97.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.274E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -1 - XS 0.160E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -2 - XS 0.160E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -3 - XS 0.160E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -4 - XS 0.939E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -5 - XS 0.939E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -6 - XS 0.939E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -7 - XS 0.550E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -8 - XS 0.550E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2598 3.43626404 - DONE now c -2598 - Allocate 38 spaces in coupling list 19 - XS 0.209E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -10 - XS 0.209E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -11 - XS 0.123E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -12 - XS 0.123E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -13 - XS 0.123E-05 > -1.0000 & JT 97.5 > 0.0 so DONES now = -14 - XS 0.719E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -15 - XS 0.719E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -16 - XS 0.719E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -17 - XS 0.421E-06 > -1.0000 & JT 97.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2616 3.45237994 - DONE now c -2616 - Try JTOTAL = 98.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.162E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -1 - XS 0.162E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -2 - XS 0.949E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -3 - XS 0.949E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -4 - XS 0.949E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -5 - XS 0.556E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -6 - XS 0.556E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -7 - XS 0.556E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -8 - XS 0.326E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2625 3.46868587 - DONE now c -2625 - Allocate 38 spaces in coupling list 19 - XS 0.212E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -10 - XS 0.124E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -11 - XS 0.124E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -12 - XS 0.124E-05 > -1.0000 & JT 98.5 > 0.0 so DONES now = -13 - XS 0.726E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -14 - XS 0.726E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -15 - XS 0.726E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -16 - XS 0.425E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -17 - XS 0.425E-06 > -1.0000 & JT 98.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2643 3.48524904 - DONE now c -2643 - Try JTOTAL = 99.500000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.164E-05 > -1.0000 & JT 99.5 > 0.0 so DONES now = -1 - XS 0.958E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -2 - XS 0.958E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -3 - XS 0.958E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -4 - XS 0.562E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -5 - XS 0.562E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -6 - XS 0.562E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -7 - XS 0.329E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -8 - XS 0.329E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2652 3.50168586 - DONE now c -2652 - Allocate 38 spaces in coupling list 19 - XS 0.125E-05 > -1.0000 & JT 99.5 > 0.0 so DONES now = -10 - XS 0.125E-05 > -1.0000 & JT 99.5 > 0.0 so DONES now = -11 - XS 0.734E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -12 - XS 0.734E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -13 - XS 0.734E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -14 - XS 0.430E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -15 - XS 0.430E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -16 - XS 0.430E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -17 - XS 0.252E-06 > -1.0000 & JT 99.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2670 3.51833105 - DONE now c -2670 - Try JTOTAL = 100.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.968E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -1 - XS 0.968E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -2 - XS 0.567E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -3 - XS 0.567E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -4 - XS 0.567E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -5 - XS 0.332E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -6 - XS 0.332E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -7 - XS 0.332E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -8 - XS 0.195E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2679 3.53480887 - DONE now c -2679 - Allocate 38 spaces in coupling list 19 - XS 0.126E-05 > -1.0000 & JT 100.5 > 0.0 so DONES now = -10 - XS 0.741E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -11 - XS 0.741E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -12 - XS 0.741E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -13 - XS 0.434E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -14 - XS 0.434E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -15 - XS 0.434E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -16 - XS 0.254E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -17 - XS 0.254E-06 > -1.0000 & JT 100.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2697 3.55087304 - DONE now c -2697 - Try JTOTAL = 101.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.978E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -1 - XS 0.573E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -2 - XS 0.573E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -3 - XS 0.573E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -4 - XS 0.335E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -5 - XS 0.335E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -6 - XS 0.335E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -7 - XS 0.196E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -8 - XS 0.196E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2706 3.56677508 - DONE now c -2706 - Allocate 38 spaces in coupling list 19 - XS 0.748E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -10 - XS 0.748E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -11 - XS 0.438E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -12 - XS 0.438E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -13 - XS 0.438E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -14 - XS 0.257E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -15 - XS 0.257E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -16 - XS 0.257E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -17 - XS 0.150E-06 > -1.0000 & JT 101.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2724 3.58220005 - DONE now c -2724 - Try JTOTAL = 102.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.578E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -1 - XS 0.578E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -2 - XS 0.339E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -3 - XS 0.339E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -4 - XS 0.339E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -5 - XS 0.198E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -6 - XS 0.198E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -7 - XS 0.198E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -8 - XS 0.116E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2733 3.59758902 - DONE now c -2733 - Allocate 38 spaces in coupling list 19 - XS 0.756E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -10 - XS 0.443E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -11 - XS 0.443E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -12 - XS 0.443E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -13 - XS 0.259E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -14 - XS 0.259E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -15 - XS 0.259E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -16 - XS 0.152E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -17 - XS 0.152E-06 > -1.0000 & JT 102.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2751 3.61303806 - DONE now c -2751 - Try JTOTAL = 103.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.584E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -1 - XS 0.342E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -2 - XS 0.342E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -3 - XS 0.342E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -4 - XS 0.200E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -5 - XS 0.200E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -6 - XS 0.200E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -7 - XS 0.117E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -8 - XS 0.117E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2760 3.62773800 - DONE now c -2760 - Allocate 38 spaces in coupling list 19 - XS 0.447E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -10 - XS 0.447E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -11 - XS 0.262E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -12 - XS 0.262E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -13 - XS 0.262E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -14 - XS 0.153E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -15 - XS 0.153E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -16 - XS 0.153E-06 > -1.0000 & JT 103.5 > 0.0 so DONES now = -17 - XS 0.898E-07 > -1.0000 & JT 103.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2778 3.64243507 - DONE now c -2778 - Try JTOTAL = 104.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.345E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -1 - XS 0.345E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -2 - XS 0.202E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -3 - XS 0.202E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -4 - XS 0.202E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -5 - XS 0.118E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -6 - XS 0.118E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -7 - XS 0.118E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -8 - XS 0.693E-07 > -1.0000 & JT 104.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2787 3.65715194 - DONE now c -2787 - Allocate 38 spaces in coupling list 19 - XS 0.451E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -10 - XS 0.264E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -11 - XS 0.264E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -12 - XS 0.264E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -13 - XS 0.155E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -14 - XS 0.155E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -15 - XS 0.155E-06 > -1.0000 & JT 104.5 > 0.0 so DONES now = -16 - XS 0.906E-07 > -1.0000 & JT 104.5 > 0.0 so DONES now = -17 - XS 0.906E-07 > -1.0000 & JT 104.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2805 3.67178893 - DONE now c -2805 - Try JTOTAL = 105.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.349E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -1 - XS 0.204E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -2 - XS 0.204E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -3 - XS 0.204E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -4 - XS 0.120E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -5 - XS 0.120E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -6 - XS 0.120E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -7 - XS 0.700E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -8 - XS 0.700E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2814 3.68646312 - DONE now c -2814 - Allocate 38 spaces in coupling list 19 - XS 0.267E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -10 - XS 0.267E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -11 - XS 0.156E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -12 - XS 0.156E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -13 - XS 0.156E-06 > -1.0000 & JT 105.5 > 0.0 so DONES now = -14 - XS 0.915E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -15 - XS 0.915E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -16 - XS 0.915E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -17 - XS 0.536E-07 > -1.0000 & JT 105.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2832 3.70125198 - DONE now c -2832 - Try JTOTAL = 106.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.206E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -1 - XS 0.206E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -2 - XS 0.121E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -3 - XS 0.121E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -4 - XS 0.121E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -5 - XS 0.707E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -6 - XS 0.707E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -7 - XS 0.707E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -8 - XS 0.414E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2841 3.71663594 - DONE now c -2841 - Allocate 38 spaces in coupling list 19 - XS 0.269E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -10 - XS 0.158E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -11 - XS 0.158E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -12 - XS 0.158E-06 > -1.0000 & JT 106.5 > 0.0 so DONES now = -13 - XS 0.923E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -14 - XS 0.923E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -15 - XS 0.923E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -16 - XS 0.541E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -17 - XS 0.541E-07 > -1.0000 & JT 106.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2859 3.73173213 - DONE now c -2859 - Try JTOTAL = 107.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.208E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -1 - XS 0.122E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -2 - XS 0.122E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -3 - XS 0.122E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -4 - XS 0.713E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -5 - XS 0.713E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -6 - XS 0.713E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -7 - XS 0.417E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -8 - XS 0.417E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2868 3.74660993 - DONE now c -2868 - Allocate 38 spaces in coupling list 19 - XS 0.159E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -10 - XS 0.159E-06 > -1.0000 & JT 107.5 > 0.0 so DONES now = -11 - XS 0.932E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -12 - XS 0.932E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -13 - XS 0.932E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -14 - XS 0.546E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -15 - XS 0.546E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -16 - XS 0.546E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -17 - XS 0.319E-07 > -1.0000 & JT 107.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2886 3.76245999 - DONE now c -2886 - Try JTOTAL = 108.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.123E-06 > -1.0000 & JT 108.5 > 0.0 so DONES now = -1 - XS 0.123E-06 > -1.0000 & JT 108.5 > 0.0 so DONES now = -2 - XS 0.720E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -3 - XS 0.720E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -4 - XS 0.720E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -5 - XS 0.421E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -6 - XS 0.421E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -7 - XS 0.421E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -8 - XS 0.247E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2895 3.77726603 - DONE now c -2895 - Allocate 38 spaces in coupling list 19 - XS 0.161E-06 > -1.0000 & JT 108.5 > 0.0 so DONES now = -10 - XS 0.941E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -11 - XS 0.941E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -12 - XS 0.941E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -13 - XS 0.551E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -14 - XS 0.551E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -15 - XS 0.551E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -16 - XS 0.322E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -17 - XS 0.322E-07 > -1.0000 & JT 108.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2913 3.79361200 - DONE now c -2913 - Try JTOTAL = 109.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.124E-06 > -1.0000 & JT 109.5 > 0.0 so DONES now = -1 - XS 0.726E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -2 - XS 0.726E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -3 - XS 0.726E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -4 - XS 0.425E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -5 - XS 0.425E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -6 - XS 0.425E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -7 - XS 0.249E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -8 - XS 0.249E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2922 3.80976987 - DONE now c -2922 - Allocate 38 spaces in coupling list 19 - XS 0.949E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -10 - XS 0.949E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -11 - XS 0.556E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -12 - XS 0.556E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -13 - XS 0.556E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -14 - XS 0.325E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -15 - XS 0.325E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -16 - XS 0.325E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -17 - XS 0.190E-07 > -1.0000 & JT 109.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2940 3.82535386 - DONE now c -2940 - Try JTOTAL = 110.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.733E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -1 - XS 0.733E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -2 - XS 0.429E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -3 - XS 0.429E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -4 - XS 0.429E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -5 - XS 0.251E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -6 - XS 0.251E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -7 - XS 0.251E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -8 - XS 0.147E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2949 3.84111094 - DONE now c -2949 - Allocate 38 spaces in coupling list 19 - XS 0.958E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -10 - XS 0.561E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -11 - XS 0.561E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -12 - XS 0.561E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -13 - XS 0.328E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -14 - XS 0.328E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -15 - XS 0.328E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -16 - XS 0.192E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -17 - XS 0.192E-07 > -1.0000 & JT 110.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2967 3.85669804 - DONE now c -2967 - Try JTOTAL = 111.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.740E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -1 - XS 0.433E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -2 - XS 0.433E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -3 - XS 0.433E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -4 - XS 0.253E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -5 - XS 0.253E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -6 - XS 0.253E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -7 - XS 0.148E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -8 - XS 0.148E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -2976 3.87234092 - DONE now c -2976 - Allocate 38 spaces in coupling list 19 - XS 0.566E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -10 - XS 0.566E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -11 - XS 0.331E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -12 - XS 0.331E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -13 - XS 0.331E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -14 - XS 0.194E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -15 - XS 0.194E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -16 - XS 0.194E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -17 - XS 0.113E-07 > -1.0000 & JT 111.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -2994 3.88805985 - DONE now c -2994 - Try JTOTAL = 112.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.437E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -1 - XS 0.437E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -2 - XS 0.256E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -3 - XS 0.256E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -4 - XS 0.256E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -5 - XS 0.150E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -6 - XS 0.150E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -7 - XS 0.150E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -8 - XS 0.875E-08 > -1.0000 & JT 112.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -3003 3.90285397 - DONE now c -3003 - Allocate 38 spaces in coupling list 19 - XS 0.571E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -10 - XS 0.334E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -11 - XS 0.334E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -12 - XS 0.334E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -13 - XS 0.196E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -14 - XS 0.196E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -15 - XS 0.196E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -16 - XS 0.114E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -17 - XS 0.114E-07 > -1.0000 & JT 112.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -3021 3.91842389 - DONE now c -3021 - Try JTOTAL = 113.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.441E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -1 - XS 0.258E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -2 - XS 0.258E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -3 - XS 0.258E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -4 - XS 0.151E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -5 - XS 0.151E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -6 - XS 0.151E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -7 - XS 0.883E-08 > -1.0000 & JT 113.5 > 0.0 so DONES now = -8 - XS 0.883E-08 > -1.0000 & JT 113.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -3030 3.93405700 - DONE now c -3030 - Allocate 38 spaces in coupling list 19 - XS 0.337E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -10 - XS 0.337E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -11 - XS 0.197E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -12 - XS 0.197E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -13 - XS 0.197E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -14 - XS 0.115E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -15 - XS 0.115E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -16 - XS 0.115E-07 > -1.0000 & JT 113.5 > 0.0 so DONES now = -17 - XS 0.676E-08 > -1.0000 & JT 113.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -3048 3.94923711 - DONE now c -3048 - Try JTOTAL = 114.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.260E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -1 - XS 0.260E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -2 - XS 0.152E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -3 - XS 0.152E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -4 - XS 0.152E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -5 - XS 0.891E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -6 - XS 0.891E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -7 - XS 0.891E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -8 - XS 0.521E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -3057 3.96404696 - DONE now c -3057 - Allocate 38 spaces in coupling list 19 - XS 0.340E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -10 - XS 0.199E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -11 - XS 0.199E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -12 - XS 0.199E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -13 - XS 0.116E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -14 - XS 0.116E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -15 - XS 0.116E-07 > -1.0000 & JT 114.5 > 0.0 so DONES now = -16 - XS 0.681E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -17 - XS 0.681E-08 > -1.0000 & JT 114.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -3075 3.97855091 - DONE now c -3075 - Try JTOTAL = 115.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.262E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -1 - XS 0.154E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -2 - XS 0.154E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -3 - XS 0.154E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -4 - XS 0.899E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -5 - XS 0.899E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -6 - XS 0.899E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -7 - XS 0.526E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -8 - XS 0.526E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -3084 3.99356008 - DONE now c -3084 - Allocate 38 spaces in coupling list 19 - XS 0.201E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -10 - XS 0.201E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -11 - XS 0.117E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -12 - XS 0.117E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -13 - XS 0.117E-07 > -1.0000 & JT 115.5 > 0.0 so DONES now = -14 - XS 0.687E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -15 - XS 0.687E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -16 - XS 0.687E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -17 - XS 0.402E-08 > -1.0000 & JT 115.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -3102 4.00859213 - DONE now c -3102 - Try JTOTAL = 116.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.155E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -1 - XS 0.155E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -2 - XS 0.906E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -3 - XS 0.906E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -4 - XS 0.906E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -5 - XS 0.530E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -6 - XS 0.530E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -7 - XS 0.530E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -8 - XS 0.310E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -3111 4.02322006 - DONE now c -3111 - Allocate 38 spaces in coupling list 19 - XS 0.202E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -10 - XS 0.118E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -11 - XS 0.118E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -12 - XS 0.118E-07 > -1.0000 & JT 116.5 > 0.0 so DONES now = -13 - XS 0.693E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -14 - XS 0.693E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -15 - XS 0.693E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -16 - XS 0.406E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -17 - XS 0.406E-08 > -1.0000 & JT 116.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -3129 4.03844023 - DONE now c -3129 - Try JTOTAL = 117.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.156E-07 > -1.0000 & JT 117.5 > 0.0 so DONES now = -1 - XS 0.914E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -2 - XS 0.914E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -3 - XS 0.914E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -4 - XS 0.535E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -5 - XS 0.535E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -6 - XS 0.535E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -7 - XS 0.313E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -8 - XS 0.313E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -3138 4.05478621 - DONE now c -3138 - Allocate 38 spaces in coupling list 19 - XS 0.120E-07 > -1.0000 & JT 117.5 > 0.0 so DONES now = -10 - XS 0.120E-07 > -1.0000 & JT 117.5 > 0.0 so DONES now = -11 - XS 0.699E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -12 - XS 0.699E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -13 - XS 0.699E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -14 - XS 0.409E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -15 - XS 0.409E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -16 - XS 0.409E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -17 - XS 0.239E-08 > -1.0000 & JT 117.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -3156 4.07090712 - DONE now c -3156 - Try JTOTAL = 118.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.922E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -1 - XS 0.922E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -2 - XS 0.539E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -3 - XS 0.539E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -4 - XS 0.539E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -5 - XS 0.316E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -6 - XS 0.316E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -7 - XS 0.316E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -8 - XS 0.185E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -3165 4.08657026 - DONE now c -3165 - Allocate 38 spaces in coupling list 19 - XS 0.121E-07 > -1.0000 & JT 118.5 > 0.0 so DONES now = -10 - XS 0.705E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -11 - XS 0.705E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -12 - XS 0.705E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -13 - XS 0.413E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -14 - XS 0.413E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -15 - XS 0.413E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -16 - XS 0.241E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -17 - XS 0.241E-08 > -1.0000 & JT 118.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -3183 4.10243130 - DONE now c -3183 - Try JTOTAL = 119.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.930E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -1 - XS 0.544E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -2 - XS 0.544E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -3 - XS 0.544E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -4 - XS 0.318E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -5 - XS 0.318E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -6 - XS 0.318E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -7 - XS 0.186E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -8 - XS 0.186E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -3192 4.11774731 - DONE now c -3192 - Allocate 38 spaces in coupling list 19 - XS 0.711E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -10 - XS 0.711E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -11 - XS 0.416E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -12 - XS 0.416E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -13 - XS 0.416E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -14 - XS 0.243E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -15 - XS 0.243E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -16 - XS 0.243E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -17 - XS 0.142E-08 > -1.0000 & JT 119.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -3210 4.13360929 - DONE now c -3210 - Try JTOTAL = 120.50000000000000 - Allocate 38 spaces in coupling list 19 - XS 0.548E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -1 - XS 0.548E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -2 - XS 0.321E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -3 - XS 0.321E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -4 - XS 0.321E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -5 - XS 0.188E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -6 - XS 0.188E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -7 - XS 0.188E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -8 - XS 0.110E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -9 - DONE incremented c by -9 to -3219 4.14869595 - DONE now c -3219 - Allocate 38 spaces in coupling list 19 - XS 0.717E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -10 - XS 0.419E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -11 - XS 0.419E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -12 - XS 0.419E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -13 - XS 0.245E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -14 - XS 0.245E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -15 - XS 0.245E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -16 - XS 0.144E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -17 - XS 0.144E-08 > -1.0000 & JT 120.5 > 0.0 so DONES now = -18 - DONE incremented c by -18 to -3237 4.16427135 - DONE now c -3237 diff --git a/book/notebooks/Transfer_template/fort.55 b/book/notebooks/Transfer_template/fort.55 deleted file mode 100644 index e69de29..0000000 diff --git a/book/notebooks/Transfer_template/fort.56 b/book/notebooks/Transfer_template/fort.56 deleted file mode 100644 index e69de29..0000000 diff --git a/book/notebooks/Transfer_template/fort.59 b/book/notebooks/Transfer_template/fort.59 deleted file mode 100644 index e69de29..0000000 diff --git a/book/notebooks/Transfer_template/fort.7 b/book/notebooks/Transfer_template/fort.7 deleted file mode 100644 index e69de29..0000000 diff --git a/book/notebooks/Transfer_template/fort.75 b/book/notebooks/Transfer_template/fort.75 deleted file mode 100644 index 368f49b..0000000 --- a/book/notebooks/Transfer_template/fort.75 +++ /dev/null @@ -1 +0,0 @@ -170.0000 7.382128E+09 0.0000 1.6348 1.6402 16.364 0.39437E-04 3.4286 diff --git a/book/notebooks/Transfer_template/frescox.in b/book/notebooks/Transfer_template/frescox.in deleted file mode 100644 index e5ecbde..0000000 --- a/book/notebooks/Transfer_template/frescox.in +++ /dev/null @@ -1,44 +0,0 @@ -n14(f17,ne18_3376_4+)c13 @ 170 MeV; -NAMELIST - -&FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 -jtmin=0.0 jtmax=120 absend=-1.0 -thmin=0.00 thmax=40.00 thinc=1.00 -iter=1 nnu=36 -chans=1 xstabl=1 -elab=170.0 / - -&PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / -&STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / -&PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / -&STATES jp=4. bandp=1 ep=3.376 cpot=2 jt=0.5 bandt=-1 et=0.0000 / -&partition / - -&POT kp=1 ap=17.000 at=14.000 rc=1.300000000 / -&POT kp=1 type=1 p1=37.200000000 p2=1.200000000 p3=0.600000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / - -&POT kp=2 ap=18.000 at=13.000 rc=1.300000000 / -&POT kp=2 type=1 p1=37.200000000 p2=1.200000000 p3=0.600000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / - -&POT kp=3 at=17 rc=1.200000000 / -&POT kp=3 type=1 p1=50.000000000 p2=1.200000000 p3=0.800000000 / -&POT kp=3 type=3 p1=6.000000000 p2=1.200000000 p3=0.650000000 / - -&POT kp=4 at=13 rc=1.200000000 / -&POT kp=4 type=1 p1=50.000000000 p2=1.200000000 p3=0.650000000 / -&POT kp=4 type=3 p1=6.000000000 p2=1.200000000 p3=0.650000000 / - -&POT kp=5 ap=17.000 at=14.000 rc=1.300000000 / -&POT kp=5 type=1 p1=37.200000000 p2=1.200000000 p3=0.600000000 p4=21.600000000 p5=1.200000000 p6=0.690000000 / -&pot / - -&Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=0.546 isc=1 ipc=0 / -&Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / -&overlap / - -&Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / -&CFP in=1 ib=1 ia=1 kn=1 a=1.00 / -&CFP in=2 ib=1 ia=1 kn=2 a=1.00 / -&CFP / - -&coupling / \ No newline at end of file diff --git a/book/notebooks/Transfer_template/frescox.out b/book/notebooks/Transfer_template/frescox.out deleted file mode 100644 index 95fcbf2..0000000 --- a/book/notebooks/Transfer_template/frescox.out +++ /dev/null @@ -1,1923 +0,0 @@ -Running on beyerk-linux - FRESCOX - version 7.2-20-ga7f491: Coupled Reaction Channels on gfortran - - Using NAMELIST input - - n14(f17,ne18)c13 @ 170 MeV; - - 0.030 40.000 0.200 0.100 5.000 0.000 0.000 0.000 0.000 0.000 - - Centre-of-mass Range is 1334 * 0.0300 fm., Maximum at 39.96 fm., Interpolating NL forms every 0.21 fm. - Non - locality width is 57 * 0.0900 fm., Maximum of 5.04 fm., Centred at 0.00 fm. - 2-Nucleon Separation of 0 * 0.2100 fm., Maximum of 0.00 fm., Minimum at 0.00 fm. - Maximum single particle bins of 39.9000 fm. - M,Mint = 1333 1331 - - - Range of total J is 0.0 <= J <= 120.0 (at least 0.0) and Absorbtion => -1.0000 mb. - Dry Run = F, CC set limits = 0 0, Relativistic kinematics = , Both/Far/Near Analyses = 1 - - Cross Sections (and up to T0 for 0=projectile) for Theta from 0.0 to 40.0 in steps of 0.1 degrees, DGAM=0, grace=T, Coordinates = 0 (Mads) - - Lower Radial Cutoff = maximum of -1.60*L*h & 0.0 fm., Lower Cutoff for Couplings = 0.0 fm. - - - Iterate Couplings between 0 and 1 times, to 0.000 % if sooner. - Block solved exactly = 0 chs., with Pade = 0 & Isocen = =0, NOSOL = F, CCREAL = F, initwf = 0 - Small channels are 0.00E+00 and small couplings are 1.00E-12 of unitarity - - NL quadrature with 36 Gaussian points, Calculate multipoles up to 130 from 0 - M-transfers for lp+lt greater than or equal to 6, Angular Integration Cutoff below 0.6944 % - - - Trace switches are : CHANS = 1, LISTCC = 0, TRENEG = 0, CDETR = 0, SMATS = 0, XSTABL = 1, NLPL = 0 - - WAVES = 0, LAMPL = 0, VEFF = 0, KFUS = 0, WDISK = 0, BPM = 0, MELFIL = 0 - - CDCC = 0, NFUS = 0, TCFILE = 0 - - Using unit mass = 1.000000 amu and 1/fine-structure constant = 137.03599 ( 1.000000 * true ) with hc = 197.32705 MeV.fm, - thus 2*amu/hbar^2 = 0.0478450 = 1/20.9008 and Coulomb constant = 0.1574855 so e^2 = 1.43996515, and nuclear magneton= 0.1261183 - - Now pre-scan input and save to file 3 - - - - *********** PARTITION NUMBER 1 ****************************************************************************************** - - PROJ=f17 MASS= 17.0000 Z= 9.0, # STATES= 1T, TARG=n14 MASS= 14.0000 Z= 7.0, Q-VALUE = 0.0000 MeV - - MIXPOT = 0: no couplings (default) - - 1: J= 2.5+ (B# 1), E= 0.0000, K= 0.5 Potl# 1 J= 1.0+ (B# 1), E= 0.0000, K= 0.0 - - - *********** PARTITION NUMBER 2 ****************************************************************************************** - - PROJ=ne18 MASS= 18.0000 Z= 10.0, # STATES= 2T, TARG=c13 MASS= 13.0000 Z= 6.0, Q-VALUE = 3.6286 MeV - - MIXPOT = 0: no couplings (default) - - 1: J= 0.0+ (B# 1), E= 0.0000, K= 0.0 Potl# 2 J= 0.5+ (B# 1), E= 0.0000, K= 0.5 - - 2: J= 4.0+ (B# 1), E= 3.3760, K= 0.0 Potl# 2 J= 0.5+ (B# 1), E= 0.0000, K= 0.5 - (Just State 1) -0****** WARNING : COMPARED WITH PARTITION 1, THIS PARTITION HAS TOTAL MASS & CHARGE EXCESSES 0.00390 & 0.0000 - - - ************************************************************************************************************************************ - - The following POTENTIALS are defined : - - KP# TYPE IT SHAPE at V1 r1 a1 V2 r2 a2 A-in A-used - - - A#1 A#2 r0c ac h @ 1 - 1 0=Coulomb 0=CHARGE (WS) 1 14.000 17.000 1.3000 0.0000 0.0000 0.0000 0.000 123.612 0.03000 - - 1 1=Volume 0=Woods-Saxon 2 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 123.612 - -------------------------------------------------------------------------------------------------------------------- - - A#1 A#2 r0c ac h @ 2 - 2 0=Coulomb 0=CHARGE (WS) 3 13.000 18.000 1.3000 0.0000 0.0000 0.0000 0.000 122.917 0.03000 - - 2 1=Volume 0=Woods-Saxon 4 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 122.917 - -------------------------------------------------------------------------------------------------------------------- - - A#1 A#2 r0c ac h @ 0 - 3 0=Coulomb 0=CHARGE (WS) 5 17.000 0.000 1.2000 0.0000 0.0000 0.0000 0.000 17.000 0.03000 - - 3 1=Volume 0=Woods-Saxon 6 50.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 17.000 - - 3 3=Projtl S.O. 0=Woods-Saxon 7 6.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 17.000 - -------------------------------------------------------------------------------------------------------------------- - - A#1 A#2 r0c ac h @ 0 - 4 0=Coulomb 0=CHARGE (WS) 8 13.000 0.000 1.2000 0.0000 0.0000 0.0000 0.000 13.000 0.03000 - - 4 1=Volume 0=Woods-Saxon 9 50.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 13.000 - - 4 3=Projtl S.O. 0=Woods-Saxon 10 6.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 13.000 - -------------------------------------------------------------------------------------------------------------------- - - A#1 A#2 r0c ac h @ 0 - 5 0=Coulomb 0=CHARGE (WS) 11 14.000 17.000 1.3000 0.0000 0.0000 0.0000 0.000 123.612 0.03000 - - 5 1=Volume 0=Woods-Saxon 12 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 123.612 - - - ************************************************************************************************************************************ - - The following real SINGLE-PARTICLE FORM FACTORS are constructed: - - - No. P1 P2 IN KIND T N L S1 IA J/S IB BIND XFER BE SC PC FIL AFRAC Adjust to Z Mass K Norm rms D0 D ANC/Gsp - - 1: 1 2 1 0 1 2 0.5 1 2.5 0 3 0 3.9220 1 0 0 0.0000 VR 1.2622; 1. 1.000 0.4210 1.0000 3.321 -27.2 102.5 2.5472 - - 2: 2 1 2 3 1 0 0.5 1 0.5 1 4 0 7.5506 1 0 0 0.0000 VR 0.5889; 1. 1.000 0.5792 1.0000 2.584 -177.9 -128.5 5.7521 - - ************************************************************************************************************************************ - - ONE-way COUPLING # 1 for partitions -2 <- 1 of KIND 7, 0 -1 5 -1 -1 & P1,P2 = 0.0000 0.0000 : for J <= 120.5 & R < 39.9 fm. - - - data = 1 1 1 1 1.0000, so < [ne18 # 1] / [f17 # 1] * Eigen-form # 1> = 1.0000 BE = 3.922 1 - - data = 2 1 1 2 1.0000, so < [n14 # 1] / [c13 # 1] * Eigen-form # 2> = 1.0000 BE = 7.551 2 - KNT = 2, KNP = 1: NK,NG,loc= 1 1 1 13 T T F - KNT = 2, KNP = 1: NK,NG,loc= 1 1 1 13 - - So FINITE-RANGE TRANSFER : IP1 = 0 (POST ), IP2=-1 =COMPLEX , for 1 pairs of bound states summed into 1 pairs - The main coupling potential is that binding the transferred particle to the c13 core. - - Using between f17 & c13 the potential No. 5, and subtracting optical potential No. 2 - - A real*8 is 8 bytes - NLL,NLN,NLO = 191 191 57 -0File 9 needs RL = 21774 REAL*8 numbers, and NR = 132. i.e. 22.99 Megabytes - File 9 RECL = 173280 -0RECOMMENDED NON-LOCAL WIDTH IS GREATER THAN 0.99 FM., RECOMMENDED CENTRATION 0.04, after 0.14 secs. -0Relative non-local usages (rms) are - 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - 0.000 0.000 0.000 0.001 0.003 0.009 0.020 0.057 0.354 3.452 14.972 51.537 90.794 100.000 76.670 - 40.484 14.102 3.168 0.505 0.072 0.011 0.002 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0Binding Energies: (ne18 # 1 / f17 # 1)(BE = 3.922)-(n14 # 1 / c13 # 1)(BE = 7.551)- DISCREPANCY = 7.2572 - - Incoming partition 1 in excitation state # 1, Laboratory Energy given for partition 1 Nucleus 1 in Excitation pair 1 - -0Lab. ENERGY ranges : - - from 170.000 to 0.00000 in 0 intervals - from 0.00000 to 0.00000 in 0 intervals - from 0.00000 to 0.00000 in 0 intervals - - Largest real,imaginary parts of any form factor at R= 39.66 are 3.63E-02 1.36E-20 MeV - Finished all Couplings @ 0.152808011 - - Non-symmetric Hamiltonian -1*********************************************************************************************************************************** -************************************************************************************************************************************ - - INCOMING f17 ; LABORATORY f17 ENERGY = 170.00 MeV. - - *********************************************************************************************************************************** - *********************************************************************************************************************************** - - - NOTE: Coulomb excitation cut off below 2.984 deg by JTMAX, and below 1.694 deg by Rmax - - Allocate arrays for 6 channels, of which 3 need wfs. - - Total SPIN, PARITY = 0.5 +, 6 chs, 0 cc. Rmin & Coul turning = 0.0 1.2 - - - C Projectl Target # EX. (L Proj) J + Targ = Jtotal E-cm Re K Re Eta RM*K CH G-REL - 1 f17 n14 # 1: I 2 2.5 0.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.46689 0.18925 1 1 1 1 1.00000 - 2 f17 n14 # 1: I 2 2.5 1.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.46689 0.18925 1 1 1 1 1.00000 - 3 f17 n14 # 1: I 4 2.5 1.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.15871 -0.47817 1 1 1 1 1.00000 - 4 ne18 c13 # 1: 0 0.0 0.0 0.5 0.5 80.40279 5.38866 2.89523 215.3310 0.36422 -0.34753 2 1 2 1 1.00000 - 5 ne18 c13 # 2: 4 4.0 0.0 0.5 0.5 77.02679 5.27432 2.95800 210.7618 -0.16050 -0.47737 2 2 2 1 1.00000 - 6 ne18 c13 # 2: 4 4.0 1.0 0.5 0.5 77.02679 5.27432 2.95800 210.7618 -0.16050 -0.47737 2 2 2 1 1.00000 - Allocate arrays for 11 channels, of which 6 need wfs. - Allocate arrays for 15 channels, of which 8 need wfs. - Allocate arrays for 18 channels, of which 9 need wfs. - Allocate arrays for 19 channels, of which 9 need wfs. - Reaction Xsec 117.5+/14 @ 1 = 0.016#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/16 @ 2 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/16 @ 3 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/16 @ 4 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/18 @ 5 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/18 @ 6 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/18 @ 7 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/20 @ 8 = 0.003#, Out: 0.000# 0.001# 0.000# 0.000f - Reaction Xsec 117.5+/20 @ 9 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/15 @ 1 = 0.012#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/15 @ 2 = 0.012#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/17 @ 3 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/17 @ 4 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/17 @ 5 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/19 @ 6 = 0.004#, Out: 0.000# 0.002# 0.000# 0.000f - Reaction Xsec 117.5-/19 @ 7 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/19 @ 8 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/21 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/16 @ 1 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/16 @ 2 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/18 @ 3 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/18 @ 4 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/18 @ 5 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/20 @ 6 = 0.003#, Out: 0.000# 0.002# 0.000# 0.000f - Reaction Xsec 118.5+/20 @ 7 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/20 @ 8 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/22 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/15 @ 1 = 0.012#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/17 @ 2 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/17 @ 3 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/17 @ 4 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/19 @ 5 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/19 @ 6 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/19 @ 7 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/21 @ 8 = 0.002#, Out: 0.000# 0.001# 0.000# 0.000f - Reaction Xsec 118.5-/21 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/16 @ 1 = 0.009#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/18 @ 2 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/18 @ 3 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/18 @ 4 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/20 @ 5 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/20 @ 6 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/20 @ 7 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/22 @ 8 = 0.002#, Out: 0.000# 0.001# 0.000# 0.000f - Reaction Xsec 119.5+/22 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/17 @ 1 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/17 @ 2 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/19 @ 3 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/19 @ 4 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/19 @ 5 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/21 @ 6 = 0.002#, Out: 0.000# 0.001# 0.000# 0.000f - Reaction Xsec 119.5-/21 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/21 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/23 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/18 @ 1 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/18 @ 2 = 0.005#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/20 @ 3 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/20 @ 4 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/20 @ 5 = 0.003#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/22 @ 6 = 0.002#, Out: 0.000# 0.001# 0.000# 0.000f - Reaction Xsec 120.5+/22 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/22 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/24 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/17 @ 1 = 0.007#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/19 @ 2 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/19 @ 3 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/19 @ 4 = 0.004#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/21 @ 5 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/21 @ 6 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/21 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/23 @ 8 = 0.001#, Out: 0.000# 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/23 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000# 0.000f - Finished all CC sets @ 4.16427708 -0CUMULATIVE REACTION cross section = 1760.34720 = 26.73 = 820.4 -0CUMULATIVE ELASTIC cross section = 0.00000 -0CUMULATIVE outgoing cross sections in partition 1 : 0.00000 -0CUMULATIVE outgoing cross sections in partition 2 : 0.26397 0.00000 -0Cumulative ABSORBTION by Imaginary Potentials = 1760.08323 = 26.73 = 820.2 - Fusion for specific f17 M-states : 1760.648139 1760.906181 1760.923348 1760.923348 - 1760.906181 1760.648139 -0CUMULATIVE OUTGOING cross section = 0.26397 - Strength functions * 10^4 for L=0-2 = 1.63476 1.64024 16.36406 (with r0= 1.35 fm). R' = 0.000 fm - To convert to S-factors (MeV.mb = keV.b), multiply by 2.7966E+10 - - - CROSS SECTIONS FOR OUTGOING f17 & n14 in state # 1 with spins & parities 2.5 + & 1.0 +; 0 - - 0.01 deg.: X-S = 1.504680E+16 mb/sr, -+ /R = 9.999977E-01 - 0.10 deg.: X-S = 1.504693E+12 mb/sr, -+ /R = 1.000006E+00 - 0.20 deg.: X-S = 9.395796E+10 mb/sr, -+ /R = 9.990968E-01 - 0.30 deg.: X-S = 1.860001E+10 mb/sr, -+ /R = 1.001270E+00 - 0.40 deg.: X-S = 5.892191E+09 mb/sr, -+ /R = 1.002463E+00 - 0.50 deg.: X-S = 2.398531E+09 mb/sr, -+ /R = 9.962646E-01 - 0.60 deg.: X-S = 1.151676E+09 mb/sr, -+ /R = 9.919328E-01 - 0.70 deg.: X-S = 6.243319E+08 mb/sr, -+ /R = 9.962121E-01 - 0.80 deg.: X-S = 3.698753E+08 mb/sr, -+ /R = 1.006830E+00 - 0.90 deg.: X-S = 2.332519E+08 mb/sr, -+ /R = 1.017026E+00 - 1.00 deg.: X-S = 1.536335E+08 mb/sr, -+ /R = 1.020984E+00 - 1.10 deg.: X-S = 1.044615E+08 mb/sr, -+ /R = 1.016378E+00 - 1.20 deg.: X-S = 7.288526E+07 mb/sr, -+ /R = 1.004356E+00 - 1.30 deg.: X-S = 5.206888E+07 mb/sr, -+ /R = 9.882556E-01 - 1.40 deg.: X-S = 3.807933E+07 mb/sr, -+ /R = 9.721049E-01 - 1.50 deg.: X-S = 2.852055E+07 mb/sr, -+ /R = 9.594630E-01 - 1.60 deg.: X-S = 2.187768E+07 mb/sr, -+ /R = 9.527516E-01 - 1.70 deg.: X-S = 1.717211E+07 mb/sr, -+ /R = 9.530390E-01 - 1.80 deg.: X-S = 1.376463E+07 mb/sr, -+ /R = 9.601475E-01 - 1.90 deg.: X-S = 1.123566E+07 mb/sr, -+ /R = 9.729451E-01 - 2.00 deg.: X-S = 9.309363E+06 mb/sr, -+ /R = 9.897070E-01 - 2.10 deg.: X-S = 7.804179E+06 mb/sr, -+ /R = 1.008468E+00 - 2.20 deg.: X-S = 6.600322E+06 mb/sr, -+ /R = 1.027316E+00 - 2.30 deg.: X-S = 5.618285E+06 mb/sr, -+ /R = 1.044608E+00 - 2.40 deg.: X-S = 4.804686E+06 mb/sr, -+ /R = 1.059102E+00 - 2.50 deg.: X-S = 4.122991E+06 mb/sr, -+ /R = 1.070014E+00 - 2.60 deg.: X-S = 3.547498E+06 mb/sr, -+ /R = 1.077014E+00 - 2.70 deg.: X-S = 3.059473E+06 mb/sr, -+ /R = 1.080179E+00 - 2.80 deg.: X-S = 2.644695E+06 mb/sr, -+ /R = 1.079915E+00 - 2.90 deg.: X-S = 2.291925E+06 mb/sr, -+ /R = 1.076866E+00 - 3.00 deg.: X-S = 1.991957E+06 mb/sr, -+ /R = 1.071819E+00 - 3.10 deg.: X-S = 1.737038E+06 mb/sr, -+ /R = 1.065612E+00 - 3.20 deg.: X-S = 1.520520E+06 mb/sr, -+ /R = 1.059061E+00 - 3.30 deg.: X-S = 1.336636E+06 mb/sr, -+ /R = 1.052892E+00 - 3.40 deg.: X-S = 1.180373E+06 mb/sr, -+ /R = 1.047695E+00 - 3.50 deg.: X-S = 1.047371E+06 mb/sr, -+ /R = 1.043897E+00 - 3.60 deg.: X-S = 9.338607E+05 mb/sr, -+ /R = 1.041745E+00 - 3.70 deg.: X-S = 8.365999E+05 mb/sr, -+ /R = 1.041305E+00 - 3.80 deg.: X-S = 7.528263E+05 mb/sr, -+ /R = 1.042475E+00 - 3.90 deg.: X-S = 6.802063E+05 mb/sr, -+ /R = 1.045006E+00 - 4.00 deg.: X-S = 6.167877E+05 mb/sr, -+ /R = 1.048523E+00 - 4.10 deg.: X-S = 5.609543E+05 mb/sr, -+ /R = 1.052561E+00 - 4.20 deg.: X-S = 5.113817E+05 mb/sr, -+ /R = 1.056594E+00 - 4.30 deg.: X-S = 4.669963E+05 mb/sr, -+ /R = 1.060070E+00 - 4.40 deg.: X-S = 4.269372E+05 mb/sr, -+ /R = 1.062436E+00 - 4.50 deg.: X-S = 3.905212E+05 mb/sr, -+ /R = 1.063171E+00 - 4.60 deg.: X-S = 3.572119E+05 mb/sr, -+ /R = 1.061807E+00 - 4.70 deg.: X-S = 3.265918E+05 mb/sr, -+ /R = 1.057948E+00 - 4.80 deg.: X-S = 2.983387E+05 mb/sr, -+ /R = 1.051287E+00 - 4.90 deg.: X-S = 2.722052E+05 mb/sr, -+ /R = 1.041613E+00 - 5.00 deg.: X-S = 2.480009E+05 mb/sr, -+ /R = 1.028814E+00 - 5.10 deg.: X-S = 2.255789E+05 mb/sr, -+ /R = 1.012886E+00 - 5.20 deg.: X-S = 2.048234E+05 mb/sr, -+ /R = 9.939204E-01 - 5.30 deg.: X-S = 1.856407E+05 mb/sr, -+ /R = 9.721028E-01 - 5.40 deg.: X-S = 1.679518E+05 mb/sr, -+ /R = 9.477016E-01 - 5.50 deg.: X-S = 1.516867E+05 mb/sr, -+ /R = 9.210565E-01 - 5.60 deg.: X-S = 1.367806E+05 mb/sr, -+ /R = 8.925651E-01 - 5.70 deg.: X-S = 1.231703E+05 mb/sr, -+ /R = 8.626681E-01 - 5.80 deg.: X-S = 1.107929E+05 mb/sr, -+ /R = 8.318349E-01 - 5.90 deg.: X-S = 9.958457E+04 mb/sr, -+ /R = 8.005481E-01 - 6.00 deg.: X-S = 8.947949E+04 mb/sr, -+ /R = 7.692891E-01 - 6.10 deg.: X-S = 8.041025E+04 mb/sr, -+ /R = 7.385249E-01 - 6.20 deg.: X-S = 7.230787E+04 mb/sr, -+ /R = 7.086954E-01 - 6.30 deg.: X-S = 6.510231E+04 mb/sr, -+ /R = 6.802027E-01 - 6.40 deg.: X-S = 5.872307E+04 mb/sr, -+ /R = 6.534018E-01 - 6.50 deg.: X-S = 5.309985E+04 mb/sr, -+ /R = 6.285936E-01 - 6.60 deg.: X-S = 4.816329E+04 mb/sr, -+ /R = 6.060190E-01 - 6.70 deg.: X-S = 4.384563E+04 mb/sr, -+ /R = 5.858556E-01 - 6.80 deg.: X-S = 4.008136E+04 mb/sr, -+ /R = 5.682159E-01 - 6.90 deg.: X-S = 3.680777E+04 mb/sr, -+ /R = 5.531474E-01 - 7.00 deg.: X-S = 3.396543E+04 mb/sr, -+ /R = 5.406344E-01 - 7.10 deg.: X-S = 3.149860E+04 mb/sr, -+ /R = 5.306009E-01 - 7.20 deg.: X-S = 2.935553E+04 mb/sr, -+ /R = 5.229157E-01 - 7.30 deg.: X-S = 2.748864E+04 mb/sr, -+ /R = 5.173977E-01 - 7.40 deg.: X-S = 2.585465E+04 mb/sr, -+ /R = 5.138223E-01 - 7.50 deg.: X-S = 2.441464E+04 mb/sr, -+ /R = 5.119291E-01 - 7.60 deg.: X-S = 2.313398E+04 mb/sr, -+ /R = 5.114297E-01 - 7.70 deg.: X-S = 2.198228E+04 mb/sr, -+ /R = 5.120154E-01 - 7.80 deg.: X-S = 2.093317E+04 mb/sr, -+ /R = 5.133654E-01 - 7.90 deg.: X-S = 1.996416E+04 mb/sr, -+ /R = 5.151551E-01 - 8.00 deg.: X-S = 1.905639E+04 mb/sr, -+ /R = 5.170637E-01 - 8.10 deg.: X-S = 1.819434E+04 mb/sr, -+ /R = 5.187814E-01 - 8.20 deg.: X-S = 1.736557E+04 mb/sr, -+ /R = 5.200159E-01 - 8.30 deg.: X-S = 1.656043E+04 mb/sr, -+ /R = 5.204989E-01 - 8.40 deg.: X-S = 1.577174E+04 mb/sr, -+ /R = 5.199909E-01 - 8.50 deg.: X-S = 1.499449E+04 mb/sr, -+ /R = 5.182856E-01 - 8.60 deg.: X-S = 1.422556E+04 mb/sr, -+ /R = 5.152134E-01 - 8.70 deg.: X-S = 1.346342E+04 mb/sr, -+ /R = 5.106439E-01 - 8.80 deg.: X-S = 1.270786E+04 mb/sr, -+ /R = 5.044872E-01 - 8.90 deg.: X-S = 1.195974E+04 mb/sr, -+ /R = 4.966949E-01 - 9.00 deg.: X-S = 1.122074E+04 mb/sr, -+ /R = 4.872592E-01 - 9.10 deg.: X-S = 1.049317E+04 mb/sr, -+ /R = 4.762125E-01 - 9.20 deg.: X-S = 9779.729900 mb/sr, -+ /R = 4.636246E-01 - 9.30 deg.: X-S = 9083.385365 mb/sr, -+ /R = 4.496007E-01 - 9.40 deg.: X-S = 8407.175674 mb/sr, -+ /R = 4.342779E-01 - 9.50 deg.: X-S = 7754.102552 mb/sr, -+ /R = 4.178212E-01 - 9.60 deg.: X-S = 7127.016486 mb/sr, -+ /R = 4.004194E-01 - 9.70 deg.: X-S = 6528.527248 mb/sr, -+ /R = 3.822804E-01 - 9.80 deg.: X-S = 5960.933413 mb/sr, -+ /R = 3.636264E-01 - 9.90 deg.: X-S = 5426.169578 mb/sr, -+ /R = 3.446891E-01 - 10.00 deg.: X-S = 4925.769863 mb/sr, -+ /R = 3.257044E-01 - 10.10 deg.: X-S = 4460.846172 mb/sr, -+ /R = 3.069078E-01 - 10.20 deg.: X-S = 4032.079642 mb/sr, -+ /R = 2.885294E-01 - 10.30 deg.: X-S = 3639.723699 mb/sr, -+ /R = 2.707899E-01 - 10.40 deg.: X-S = 3283.617115 mb/sr, -+ /R = 2.538957E-01 - 10.50 deg.: X-S = 2963.205518 mb/sr, -+ /R = 2.380358E-01 - 10.60 deg.: X-S = 2677.569853 mb/sr, -+ /R = 2.233783E-01 - 10.70 deg.: X-S = 2425.460340 mb/sr, -+ /R = 2.100676E-01 - 10.80 deg.: X-S = 2205.334590 mb/sr, -+ /R = 1.982220E-01 - 10.90 deg.: X-S = 2015.398612 mb/sr, -+ /R = 1.879323E-01 - 11.00 deg.: X-S = 1853.649547 mb/sr, -+ /R = 1.792605E-01 - 11.10 deg.: X-S = 1717.919095 mb/sr, -+ /R = 1.722392E-01 - 11.20 deg.: X-S = 1605.916681 mb/sr, -+ /R = 1.668719E-01 - 11.30 deg.: X-S = 1515.271554 mb/sr, -+ /R = 1.631334E-01 - 11.40 deg.: X-S = 1443.573098 mb/sr, -+ /R = 1.609706E-01 - 11.50 deg.: X-S = 1388.408769 mb/sr, -+ /R = 1.603049E-01 - 11.60 deg.: X-S = 1347.399166 mb/sr, -+ /R = 1.610331E-01 - 11.70 deg.: X-S = 1318.229843 mb/sr, -+ /R = 1.630310E-01 - 11.80 deg.: X-S = 1298.679604 mb/sr, -+ /R = 1.661551E-01 - 11.90 deg.: X-S = 1286.645050 mb/sr, -+ /R = 1.702464E-01 - 12.00 deg.: X-S = 1280.161315 mb/sr, -+ /R = 1.751332E-01 - 12.10 deg.: X-S = 1277.418920 mb/sr, -+ /R = 1.806344E-01 - 12.20 deg.: X-S = 1276.776815 mb/sr, -+ /R = 1.865633E-01 - 12.30 deg.: X-S = 1276.771698 mb/sr, -+ /R = 1.927310E-01 - 12.40 deg.: X-S = 1276.123777 mb/sr, -+ /R = 1.989495E-01 - 12.50 deg.: X-S = 1273.739164 mb/sr, -+ /R = 2.050354E-01 - 12.60 deg.: X-S = 1268.709155 mb/sr, -+ /R = 2.108129E-01 - 12.70 deg.: X-S = 1260.306664 mb/sr, -+ /R = 2.161167E-01 - 12.80 deg.: X-S = 1247.980098 mb/sr, -+ /R = 2.207946E-01 - 12.90 deg.: X-S = 1231.344999 mb/sr, -+ /R = 2.247102E-01 - 13.00 deg.: X-S = 1210.173751 mb/sr, -+ /R = 2.277446E-01 - 13.10 deg.: X-S = 1184.383698 mb/sr, -+ /R = 2.297984E-01 - 13.20 deg.: X-S = 1154.023984 mb/sr, -+ /R = 2.307926E-01 - 13.30 deg.: X-S = 1119.261424 mb/sr, -+ /R = 2.306700E-01 - 13.40 deg.: X-S = 1080.365732 mb/sr, -+ /R = 2.293950E-01 - 13.50 deg.: X-S = 1037.694363 mb/sr, -+ /R = 2.269547E-01 - 13.60 deg.: X-S = 991.677273 mb/sr, -+ /R = 2.233576E-01 - 13.70 deg.: X-S = 942.801824 mb/sr, -+ /R = 2.186338E-01 - 13.80 deg.: X-S = 891.598067 mb/sr, -+ /R = 2.128332E-01 - 13.90 deg.: X-S = 838.624611 mb/sr, -+ /R = 2.060248E-01 - 14.00 deg.: X-S = 784.455251 mb/sr, -+ /R = 1.982949E-01 - 14.10 deg.: X-S = 729.666498 mb/sr, -+ /R = 1.897449E-01 - 14.20 deg.: X-S = 674.826148 mb/sr, -+ /R = 1.804895E-01 - 14.30 deg.: X-S = 620.482981 mb/sr, -+ /R = 1.706545E-01 - 14.40 deg.: X-S = 567.157660 mb/sr, -+ /R = 1.603741E-01 - 14.50 deg.: X-S = 515.334877 mb/sr, -+ /R = 1.497884E-01 - 14.60 deg.: X-S = 465.456787 mb/sr, -+ /R = 1.390411E-01 - 14.70 deg.: X-S = 417.917698 mb/sr, -+ /R = 1.282767E-01 - 14.80 deg.: X-S = 373.060037 mb/sr, -+ /R = 1.176382E-01 - 14.90 deg.: X-S = 331.171530 mb/sr, -+ /R = 1.072643E-01 - 15.00 deg.: X-S = 292.483558 mb/sr, -+ /R = 9.728762E-02 - 15.10 deg.: X-S = 257.170606 mb/sr, -+ /R = 8.783220E-02 - 15.20 deg.: X-S = 225.350749 mb/sr, -+ /R = 7.901164E-02 - 15.30 deg.: X-S = 197.087069 mb/sr, -+ /R = 7.092744E-02 - 15.40 deg.: X-S = 172.389909 mb/sr, -+ /R = 6.366744E-02 - 15.50 deg.: X-S = 151.219870 mb/sr, -+ /R = 5.730468E-02 - 15.60 deg.: X-S = 133.491436 mb/sr, -+ /R = 5.189644E-02 - 15.70 deg.: X-S = 119.077122 mb/sr, -+ /R = 4.748360E-02 - 15.80 deg.: X-S = 107.812047 mb/sr, -+ /R = 4.409027E-02 - 15.90 deg.: X-S = 99.498804 mb/sr, -+ /R = 4.172377E-02 - 16.00 deg.: X-S = 93.912546 mb/sr, -+ /R = 4.037479E-02 - 16.10 deg.: X-S = 90.806183 mb/sr, -+ /R = 4.001794E-02 - 16.20 deg.: X-S = 89.915586 mb/sr, -+ /R = 4.061249E-02 - 16.30 deg.: X-S = 90.964729 mb/sr, -+ /R = 4.210331E-02 - 16.40 deg.: X-S = 93.670679 mb/sr, -+ /R = 4.442216E-02 - 16.50 deg.: X-S = 97.748369 mb/sr, -+ /R = 4.748902E-02 - 16.60 deg.: X-S = 102.915088 mb/sr, -+ /R = 5.121371E-02 - 16.70 deg.: X-S = 108.894639 mb/sr, -+ /R = 5.549754E-02 - 16.80 deg.: X-S = 115.421117 mb/sr, -+ /R = 6.023512E-02 - 16.90 deg.: X-S = 122.242283 mb/sr, -+ /R = 6.531625E-02 - 17.00 deg.: X-S = 129.122488 mb/sr, -+ /R = 7.062780E-02 - 17.10 deg.: X-S = 135.845152 mb/sr, -+ /R = 7.605563E-02 - 17.20 deg.: X-S = 142.214779 mb/sr, -+ /R = 8.148648E-02 - 17.30 deg.: X-S = 148.058505 mb/sr, -+ /R = 8.680978E-02 - 17.40 deg.: X-S = 153.227191 mb/sr, -+ /R = 9.191938E-02 - 17.50 deg.: X-S = 157.596075 mb/sr, -+ /R = 9.671521E-02 - 17.60 deg.: X-S = 161.064997 mb/sr, -+ /R = 1.011047E-01 - 17.70 deg.: X-S = 163.558226 mb/sr, -+ /R = 1.050043E-01 - 17.80 deg.: X-S = 165.023920 mb/sr, -+ /R = 1.083403E-01 - 17.90 deg.: X-S = 165.433244 mb/sr, -+ /R = 1.110502E-01 - 18.00 deg.: X-S = 164.779196 mb/sr, -+ /R = 1.130831E-01 - 18.10 deg.: X-S = 163.075162 mb/sr, -+ /R = 1.144004E-01 - 18.20 deg.: X-S = 160.353257 mb/sr, -+ /R = 1.149764E-01 - 18.30 deg.: X-S = 156.662480 mb/sr, -+ /R = 1.147979E-01 - 18.40 deg.: X-S = 152.066728 mb/sr, -+ /R = 1.138647E-01 - 18.50 deg.: X-S = 146.642717 mb/sr, -+ /R = 1.121888E-01 - 18.60 deg.: X-S = 140.477828 mb/sr, -+ /R = 1.097943E-01 - 18.70 deg.: X-S = 133.667945 mb/sr, -+ /R = 1.067165E-01 - 18.80 deg.: X-S = 126.315289 mb/sr, -+ /R = 1.030012E-01 - 18.90 deg.: X-S = 118.526312 mb/sr, -+ /R = 9.870375E-02 - 19.00 deg.: X-S = 110.409648 mb/sr, -+ /R = 9.388784E-02 - 19.10 deg.: X-S = 102.074182 mb/sr, -+ /R = 8.862437E-02 - 19.20 deg.: X-S = 93.627228 mb/sr, -+ /R = 8.299009E-02 - 19.30 deg.: X-S = 85.172862 mb/sr, -+ /R = 7.706632E-02 - 19.40 deg.: X-S = 76.810413 mb/sr, -+ /R = 7.093746E-02 - 19.50 deg.: X-S = 68.633122 mb/sr, -+ /R = 6.468966E-02 - 19.60 deg.: X-S = 60.726997 mb/sr, -+ /R = 5.840935E-02 - 19.70 deg.: X-S = 53.169841 mb/sr, -+ /R = 5.218188E-02 - 19.80 deg.: X-S = 46.030487 mb/sr, -+ /R = 4.609020E-02 - 19.90 deg.: X-S = 39.368213 mb/sr, -+ /R = 4.021356E-02 - 20.00 deg.: X-S = 33.232349 mb/sr, -+ /R = 3.462640E-02 - 20.10 deg.: X-S = 27.662072 mb/sr, -+ /R = 2.939725E-02 - 20.20 deg.: X-S = 22.686362 mb/sr, -+ /R = 2.458777E-02 - 20.30 deg.: X-S = 18.324134 mb/sr, -+ /R = 2.025195E-02 - 20.40 deg.: X-S = 14.584510 mb/sr, -+ /R = 1.643546E-02 - 20.50 deg.: X-S = 11.467234 mb/sr, -+ /R = 1.317507E-02 - 20.60 deg.: X-S = 8.963208 mb/sr, -+ /R = 1.049834E-02 - 20.70 deg.: X-S = 7.055133 mb/sr, -+ /R = 8.423323E-03 - 20.80 deg.: X-S = 5.718237 mb/sr, -+ /R = 6.958580E-03 - 20.90 deg.: X-S = 4.921076 mb/sr, -+ /R = 6.103209E-03 - 21.00 deg.: X-S = 4.626397 mb/sr, -+ /R = 5.847100E-03 - 21.10 deg.: X-S = 4.792027 mb/sr, -+ /R = 6.171298E-03 - 21.20 deg.: X-S = 5.371798 mb/sr, -+ /R = 7.048505E-03 - 21.30 deg.: X-S = 6.316465 mb/sr, -+ /R = 8.443694E-03 - 21.40 deg.: X-S = 7.574632 mb/sr, -+ /R = 1.031483E-02 - 21.50 deg.: X-S = 9.093640 mb/sr, -+ /R = 1.261370E-02 - 21.60 deg.: X-S = 10.820435 mb/sr, -+ /R = 1.528675E-02 - 21.70 deg.: X-S = 12.702384 mb/sr, -+ /R = 1.827612E-02 - 21.80 deg.: X-S = 14.688034 mb/sr, -+ /R = 2.152055E-02 - 21.90 deg.: X-S = 16.727816 mb/sr, -+ /R = 2.495645E-02 - 22.00 deg.: X-S = 18.774672 mb/sr, -+ /R = 2.851893E-02 - 22.10 deg.: X-S = 20.784611 mb/sr, -+ /R = 3.214279E-02 - 22.20 deg.: X-S = 22.717181 mb/sr, -+ /R = 3.576358E-02 - 22.30 deg.: X-S = 24.535865 mb/sr, -+ /R = 3.931852E-02 - 22.40 deg.: X-S = 26.208391 mb/sr, -+ /R = 4.274742E-02 - 22.50 deg.: X-S = 27.706961 mb/sr, -+ /R = 4.599358E-02 - 22.60 deg.: X-S = 29.008397 mb/sr, -+ /R = 4.900451E-02 - 22.70 deg.: X-S = 30.094210 mb/sr, -+ /R = 5.173266E-02 - 22.80 deg.: X-S = 30.950597 mb/sr, -+ /R = 5.413601E-02 - 22.90 deg.: X-S = 31.568363 mb/sr, -+ /R = 5.617858E-02 - 23.00 deg.: X-S = 31.942779 mb/sr, -+ /R = 5.783082E-02 - 23.10 deg.: X-S = 32.073389 mb/sr, -+ /R = 5.906989E-02 - 23.20 deg.: X-S = 31.963749 mb/sr, -+ /R = 5.987985E-02 - 23.30 deg.: X-S = 31.621140 mb/sr, -+ /R = 6.025172E-02 - 23.40 deg.: X-S = 31.056225 mb/sr, -+ /R = 6.018345E-02 - 23.50 deg.: X-S = 30.282687 mb/sr, -+ /R = 5.967977E-02 - 23.60 deg.: X-S = 29.316838 mb/sr, -+ /R = 5.875195E-02 - 23.70 deg.: X-S = 28.177211 mb/sr, -+ /R = 5.741745E-02 - 23.80 deg.: X-S = 26.884149 mb/sr, -+ /R = 5.569954E-02 - 23.90 deg.: X-S = 25.459383 mb/sr, -+ /R = 5.362675E-02 - 24.00 deg.: X-S = 23.925617 mb/sr, -+ /R = 5.123234E-02 - 24.10 deg.: X-S = 22.306127 mb/sr, -+ /R = 4.855367E-02 - 24.20 deg.: X-S = 20.624369 mb/sr, -+ /R = 4.563153E-02 - 24.30 deg.: X-S = 18.903612 mb/sr, -+ /R = 4.250946E-02 - 24.40 deg.: X-S = 17.166590 mb/sr, -+ /R = 3.923298E-02 - 24.50 deg.: X-S = 15.435189 mb/sr, -+ /R = 3.584892E-02 - 24.60 deg.: X-S = 13.730159 mb/sr, -+ /R = 3.240464E-02 - 24.70 deg.: X-S = 12.070863 mb/sr, -+ /R = 2.894732E-02 - 24.80 deg.: X-S = 10.475063 mb/sr, -+ /R = 2.552325E-02 - 24.90 deg.: X-S = 8.958734 mb/sr, -+ /R = 2.217721E-02 - 25.00 deg.: X-S = 7.535930 mb/sr, -+ /R = 1.895175E-02 - 25.10 deg.: X-S = 6.218668 mb/sr, -+ /R = 1.588671E-02 - 25.20 deg.: X-S = 5.016869 mb/sr, -+ /R = 1.301863E-02 - 25.30 deg.: X-S = 3.938316 mb/sr, -+ /R = 1.038033E-02 - 25.40 deg.: X-S = 2.988656 mb/sr, -+ /R = 8.000501E-03 - 25.50 deg.: X-S = 2.171434 mb/sr, -+ /R = 5.903385E-03 - 25.60 deg.: X-S = 1.488150 mb/sr, -+ /R = 4.108540E-03 - 25.70 deg.: X-S = 0.938352 mb/sr, -+ /R = 2.630665E-03 - 25.80 deg.: X-S = 0.519741 mb/sr, -+ /R = 1.479515E-03 - 25.90 deg.: X-S = 0.228311 mb/sr, -+ /R = 6.598790E-04 - 26.00 deg.: X-S = 0.058494 mb/sr, -+ /R = 1.716429E-04 - 26.10 deg.: X-S = 0.003329 mb/sr, -+ /R = 9.916137E-06 - 26.20 deg.: X-S = 0.054637 mb/sr, -+ /R = 1.652294E-04 - 26.30 deg.: X-S = 0.203208 mb/sr, -+ /R = 6.237927E-04 - 26.40 deg.: X-S = 0.438985 mb/sr, -+ /R = 1.367811E-03 - 26.50 deg.: X-S = 0.751263 mb/sr, -+ /R = 2.375848E-03 - 26.60 deg.: X-S = 1.128871 mb/sr, -+ /R = 3.623238E-03 - 26.70 deg.: X-S = 1.560368 mb/sr, -+ /R = 5.082530E-03 - 26.80 deg.: X-S = 2.034213 mb/sr, -+ /R = 6.723961E-03 - 26.90 deg.: X-S = 2.538945 mb/sr, -+ /R = 8.515949E-03 - 27.00 deg.: X-S = 3.063338 mb/sr, -+ /R = 1.042561E-02 - 27.10 deg.: X-S = 3.596553 mb/sr, -+ /R = 1.241925E-02 - 27.20 deg.: X-S = 4.128272 mb/sr, -+ /R = 1.446290E-02 - 27.30 deg.: X-S = 4.648816 mb/sr, -+ /R = 1.652281E-02 - 27.40 deg.: X-S = 5.149251 mb/sr, -+ /R = 1.856591E-02 - 27.50 deg.: X-S = 5.621475 mb/sr, -+ /R = 2.056029E-02 - 27.60 deg.: X-S = 6.058285 mb/sr, -+ /R = 2.247565E-02 - 27.70 deg.: X-S = 6.453435 mb/sr, -+ /R = 2.428365E-02 - 27.80 deg.: X-S = 6.801671 mb/sr, -+ /R = 2.595828E-02 - 27.90 deg.: X-S = 7.098751 mb/sr, -+ /R = 2.747619E-02 - 28.00 deg.: X-S = 7.341448 mb/sr, -+ /R = 2.881694E-02 - 28.10 deg.: X-S = 7.527544 mb/sr, -+ /R = 2.996321E-02 - 28.20 deg.: X-S = 7.655801 mb/sr, -+ /R = 3.090098E-02 - 28.30 deg.: X-S = 7.725927 mb/sr, -+ /R = 3.161960E-02 - 28.40 deg.: X-S = 7.738528 mb/sr, -+ /R = 3.211192E-02 - 28.50 deg.: X-S = 7.695045 mb/sr, -+ /R = 3.237421E-02 - 28.60 deg.: X-S = 7.597693 mb/sr, -+ /R = 3.240620E-02 - 28.70 deg.: X-S = 7.449380 mb/sr, -+ /R = 3.221091E-02 - 28.80 deg.: X-S = 7.253628 mb/sr, -+ /R = 3.179459E-02 - 28.90 deg.: X-S = 7.014492 mb/sr, -+ /R = 3.116648E-02 - 29.00 deg.: X-S = 6.736465 mb/sr, -+ /R = 3.033864E-02 - 29.10 deg.: X-S = 6.424395 mb/sr, -+ /R = 2.932564E-02 - 29.20 deg.: X-S = 6.083392 mb/sr, -+ /R = 2.814436E-02 - 29.30 deg.: X-S = 5.718739 mb/sr, -+ /R = 2.681362E-02 - 29.40 deg.: X-S = 5.335810 mb/sr, -+ /R = 2.535388E-02 - 29.50 deg.: X-S = 4.939983 mb/sr, -+ /R = 2.378690E-02 - 29.60 deg.: X-S = 4.536564 mb/sr, -+ /R = 2.213539E-02 - 29.70 deg.: X-S = 4.130714 mb/sr, -+ /R = 2.042269E-02 - 29.80 deg.: X-S = 3.727382 mb/sr, -+ /R = 1.867236E-02 - 29.90 deg.: X-S = 3.331248 mb/sr, -+ /R = 1.690790E-02 - 30.00 deg.: X-S = 2.946664 mb/sr, -+ /R = 1.515238E-02 - 30.10 deg.: X-S = 2.577614 mb/sr, -+ /R = 1.342815E-02 - 30.20 deg.: X-S = 2.227674 mb/sr, -+ /R = 1.175650E-02 - 30.30 deg.: X-S = 1.899984 mb/sr, -+ /R = 1.015746E-02 - 30.40 deg.: X-S = 1.597223 mb/sr, -+ /R = 8.649480E-03 - 30.50 deg.: X-S = 1.321599 mb/sr, -+ /R = 7.249270E-03 - 30.60 deg.: X-S = 1.074839 mb/sr, -+ /R = 5.971579E-03 - 30.70 deg.: X-S = 0.858192 mb/sr, -+ /R = 4.829055E-03 - 30.80 deg.: X-S = 0.672433 mb/sr, -+ /R = 3.832127E-03 - 30.90 deg.: X-S = 0.517881 mb/sr, -+ /R = 2.988923E-03 - 31.00 deg.: X-S = 0.394413 mb/sr, -+ /R = 2.305217E-03 - 31.10 deg.: X-S = 0.301494 mb/sr, -+ /R = 1.784419E-03 - 31.20 deg.: X-S = 0.238204 mb/sr, -+ /R = 1.427595E-03 - 31.30 deg.: X-S = 0.203267 mb/sr, -+ /R = 1.233515E-03 - 31.40 deg.: X-S = 0.195095 mb/sr, -+ /R = 1.198741E-03 - 31.50 deg.: X-S = 0.211819 mb/sr, -+ /R = 1.317739E-03 - 31.60 deg.: X-S = 0.251336 mb/sr, -+ /R = 1.583011E-03 - 31.70 deg.: X-S = 0.311343 mb/sr, -+ /R = 1.985262E-03 - 31.80 deg.: X-S = 0.389389 mb/sr, -+ /R = 2.513581E-03 - 31.90 deg.: X-S = 0.482908 mb/sr, -+ /R = 3.155636E-03 - 32.00 deg.: X-S = 0.589267 mb/sr, -+ /R = 3.897891E-03 - 32.10 deg.: X-S = 0.705800 mb/sr, -+ /R = 4.725826E-03 - 32.20 deg.: X-S = 0.829854 mb/sr, -+ /R = 5.624171E-03 - 32.30 deg.: X-S = 0.958819 mb/sr, -+ /R = 6.577135E-03 - 32.40 deg.: X-S = 1.090162 mb/sr, -+ /R = 7.568644E-03 - 32.50 deg.: X-S = 1.221465 mb/sr, -+ /R = 8.582571E-03 - 32.60 deg.: X-S = 1.350442 mb/sr, -+ /R = 9.602959E-03 - 32.70 deg.: X-S = 1.474973 mb/sr, -+ /R = 1.061424E-02 - 32.80 deg.: X-S = 1.593116 mb/sr, -+ /R = 1.160143E-02 - 32.90 deg.: X-S = 1.703133 mb/sr, -+ /R = 1.255033E-02 - 33.00 deg.: X-S = 1.803496 mb/sr, -+ /R = 1.344770E-02 - 33.10 deg.: X-S = 1.892902 mb/sr, -+ /R = 1.428139E-02 - 33.20 deg.: X-S = 1.970274 mb/sr, -+ /R = 1.504050E-02 - 33.30 deg.: X-S = 2.034769 mb/sr, -+ /R = 1.571549E-02 - 33.40 deg.: X-S = 2.085773 mb/sr, -+ /R = 1.629825E-02 - 33.50 deg.: X-S = 2.122902 mb/sr, -+ /R = 1.678219E-02 - 33.60 deg.: X-S = 2.145989 mb/sr, -+ /R = 1.716229E-02 - 33.70 deg.: X-S = 2.155078 mb/sr, -+ /R = 1.743509E-02 - 33.80 deg.: X-S = 2.150415 mb/sr, -+ /R = 1.759872E-02 - 33.90 deg.: X-S = 2.132428 mb/sr, -+ /R = 1.765286E-02 - 34.00 deg.: X-S = 2.101716 mb/sr, -+ /R = 1.759871E-02 - 34.10 deg.: X-S = 2.059028 mb/sr, -+ /R = 1.743893E-02 - 34.20 deg.: X-S = 2.005247 mb/sr, -+ /R = 1.717754E-02 - 34.30 deg.: X-S = 1.941372 mb/sr, -+ /R = 1.681984E-02 - 34.40 deg.: X-S = 1.868494 mb/sr, -+ /R = 1.637230E-02 - 34.50 deg.: X-S = 1.787780 mb/sr, -+ /R = 1.584243E-02 - 34.60 deg.: X-S = 1.700449 mb/sr, -+ /R = 1.523864E-02 - 34.70 deg.: X-S = 1.607757 mb/sr, -+ /R = 1.457010E-02 - 34.80 deg.: X-S = 1.510973 mb/sr, -+ /R = 1.384662E-02 - 34.90 deg.: X-S = 1.411364 mb/sr, -+ /R = 1.307845E-02 - 35.00 deg.: X-S = 1.310177 mb/sr, -+ /R = 1.227616E-02 - 35.10 deg.: X-S = 1.208621 mb/sr, -+ /R = 1.145047E-02 - 35.20 deg.: X-S = 1.107855 mb/sr, -+ /R = 1.061213E-02 - 35.30 deg.: X-S = 1.008973 mb/sr, -+ /R = 9.771712E-03 - 35.40 deg.: X-S = 0.912991 mb/sr, -+ /R = 8.939544E-03 - 35.50 deg.: X-S = 0.820842 mb/sr, -+ /R = 8.125526E-03 - 35.60 deg.: X-S = 0.733362 mb/sr, -+ /R = 7.339031E-03 - 35.70 deg.: X-S = 0.651284 mb/sr, -+ /R = 6.588789E-03 - 35.80 deg.: X-S = 0.575238 mb/sr, -+ /R = 5.882790E-03 - 35.90 deg.: X-S = 0.505743 mb/sr, -+ /R = 5.228197E-03 - 36.00 deg.: X-S = 0.443206 mb/sr, -+ /R = 4.631277E-03 - 36.10 deg.: X-S = 0.387926 mb/sr, -+ /R = 4.097343E-03 - 36.20 deg.: X-S = 0.340089 mb/sr, -+ /R = 3.630710E-03 - 36.30 deg.: X-S = 0.299778 mb/sr, -+ /R = 3.234671E-03 - 36.40 deg.: X-S = 0.266972 mb/sr, -+ /R = 2.911482E-03 - 36.50 deg.: X-S = 0.241555 mb/sr, -+ /R = 2.662363E-03 - 36.60 deg.: X-S = 0.223318 mb/sr, -+ /R = 2.487518E-03 - 36.70 deg.: X-S = 0.211972 mb/sr, -+ /R = 2.386156E-03 - 36.80 deg.: X-S = 0.207153 mb/sr, -+ /R = 2.356542E-03 - 36.90 deg.: X-S = 0.208430 mb/sr, -+ /R = 2.396041E-03 - 37.00 deg.: X-S = 0.215315 mb/sr, -+ /R = 2.501186E-03 - 37.10 deg.: X-S = 0.227274 mb/sr, -+ /R = 2.667749E-03 - 37.20 deg.: X-S = 0.243733 mb/sr, -+ /R = 2.890821E-03 - 37.30 deg.: X-S = 0.264092 mb/sr, -+ /R = 3.164899E-03 - 37.40 deg.: X-S = 0.287730 mb/sr, -+ /R = 3.483980E-03 - 37.50 deg.: X-S = 0.314019 mb/sr, -+ /R = 3.841651E-03 - 37.60 deg.: X-S = 0.342327 mb/sr, -+ /R = 4.231194E-03 - 37.70 deg.: X-S = 0.372032 mb/sr, -+ /R = 4.645681E-03 - 37.80 deg.: X-S = 0.402528 mb/sr, -+ /R = 5.078075E-03 - 37.90 deg.: X-S = 0.433230 mb/sr, -+ /R = 5.521323E-03 - 38.00 deg.: X-S = 0.463584 mb/sr, -+ /R = 5.968456E-03 - 38.10 deg.: X-S = 0.493071 mb/sr, -+ /R = 6.412674E-03 - 38.20 deg.: X-S = 0.521212 mb/sr, -+ /R = 6.847433E-03 - 38.30 deg.: X-S = 0.547572 mb/sr, -+ /R = 7.266525E-03 - 38.40 deg.: X-S = 0.571767 mb/sr, -+ /R = 7.664145E-03 - 38.50 deg.: X-S = 0.593461 mb/sr, -+ /R = 8.034962E-03 - 38.60 deg.: X-S = 0.612371 mb/sr, -+ /R = 8.374167E-03 - 38.70 deg.: X-S = 0.628270 mb/sr, -+ /R = 8.677524E-03 - 38.80 deg.: X-S = 0.640981 mb/sr, -+ /R = 8.941410E-03 - 38.90 deg.: X-S = 0.650385 mb/sr, -+ /R = 9.162839E-03 - 39.00 deg.: X-S = 0.656412 mb/sr, -+ /R = 9.339481E-03 - 39.10 deg.: X-S = 0.659043 mb/sr, -+ /R = 9.469675E-03 - 39.20 deg.: X-S = 0.658308 mb/sr, -+ /R = 9.552430E-03 - 39.30 deg.: X-S = 0.654282 mb/sr, -+ /R = 9.587410E-03 - 39.40 deg.: X-S = 0.647082 mb/sr, -+ /R = 9.574926E-03 - 39.50 deg.: X-S = 0.636863 mb/sr, -+ /R = 9.515904E-03 - 39.60 deg.: X-S = 0.623813 mb/sr, -+ /R = 9.411860E-03 - 39.70 deg.: X-S = 0.608153 mb/sr, -+ /R = 9.264853E-03 - 39.80 deg.: X-S = 0.590126 mb/sr, -+ /R = 9.077448E-03 - 39.90 deg.: X-S = 0.569997 mb/sr, -+ /R = 8.852661E-03 - 40.00 deg.: X-S = 0.548048 mb/sr, -+ /R = 8.593908E-03 - Integrated 2.8834E+10 mb, over [ 0.000, 40.000] at 170.0000 MeV - - CROSS SECTIONS FOR OUTGOING ne18 & c13 in state # 1 with spins & parities 0.0 + & 0.5 +; 0 - - 0.00 deg.: X-S = 3.122980 mb/sr, -+ REAC 2.6397E-01 mb - 0.10 deg.: X-S = 3.120152 mb/sr, - 0.20 deg.: X-S = 3.111752 mb/sr, - 0.30 deg.: X-S = 3.098031 mb/sr, - 0.40 deg.: X-S = 3.079400 mb/sr, - 0.50 deg.: X-S = 3.056429 mb/sr, - 0.60 deg.: X-S = 3.029832 mb/sr, - 0.70 deg.: X-S = 3.000455 mb/sr, - 0.80 deg.: X-S = 2.969265 mb/sr, - 0.90 deg.: X-S = 2.937327 mb/sr, - 1.00 deg.: X-S = 2.905790 mb/sr, - 1.10 deg.: X-S = 2.875860 mb/sr, - 1.20 deg.: X-S = 2.848777 mb/sr, - 1.30 deg.: X-S = 2.825793 mb/sr, - 1.40 deg.: X-S = 2.808142 mb/sr, - 1.50 deg.: X-S = 2.797010 mb/sr, - 1.60 deg.: X-S = 2.793514 mb/sr, - 1.70 deg.: X-S = 2.798666 mb/sr, - 1.80 deg.: X-S = 2.813353 mb/sr, - 1.90 deg.: X-S = 2.838304 mb/sr, - 2.00 deg.: X-S = 2.874072 mb/sr, - 2.10 deg.: X-S = 2.921009 mb/sr, - 2.20 deg.: X-S = 2.979249 mb/sr, - 2.30 deg.: X-S = 3.048692 mb/sr, - 2.40 deg.: X-S = 3.128997 mb/sr, - 2.50 deg.: X-S = 3.219572 mb/sr, - 2.60 deg.: X-S = 3.319578 mb/sr, - 2.70 deg.: X-S = 3.427931 mb/sr, - 2.80 deg.: X-S = 3.543312 mb/sr, - 2.90 deg.: X-S = 3.664188 mb/sr, - 3.00 deg.: X-S = 3.788825 mb/sr, - 3.10 deg.: X-S = 3.915323 mb/sr, - 3.20 deg.: X-S = 4.041641 mb/sr, - 3.30 deg.: X-S = 4.165637 mb/sr, - 3.40 deg.: X-S = 4.285104 mb/sr, - 3.50 deg.: X-S = 4.397810 mb/sr, - 3.60 deg.: X-S = 4.501546 mb/sr, - 3.70 deg.: X-S = 4.594165 mb/sr, - 3.80 deg.: X-S = 4.673633 mb/sr, - 3.90 deg.: X-S = 4.738063 mb/sr, - 4.00 deg.: X-S = 4.785761 mb/sr, - 4.10 deg.: X-S = 4.815266 mb/sr, - 4.20 deg.: X-S = 4.825376 mb/sr, - 4.30 deg.: X-S = 4.815183 mb/sr, - 4.40 deg.: X-S = 4.784091 mb/sr, - 4.50 deg.: X-S = 4.731839 mb/sr, - 4.60 deg.: X-S = 4.658501 mb/sr, - 4.70 deg.: X-S = 4.564498 mb/sr, - 4.80 deg.: X-S = 4.450589 mb/sr, - 4.90 deg.: X-S = 4.317857 mb/sr, - 5.00 deg.: X-S = 4.167696 mb/sr, - 5.10 deg.: X-S = 4.001781 mb/sr, - 5.20 deg.: X-S = 3.822041 mb/sr, - 5.30 deg.: X-S = 3.630618 mb/sr, - 5.40 deg.: X-S = 3.429830 mb/sr, - 5.50 deg.: X-S = 3.222125 mb/sr, - 5.60 deg.: X-S = 3.010037 mb/sr, - 5.70 deg.: X-S = 2.796133 mb/sr, - 5.80 deg.: X-S = 2.582970 mb/sr, - 5.90 deg.: X-S = 2.373047 mb/sr, - 6.00 deg.: X-S = 2.168757 mb/sr, - 6.10 deg.: X-S = 1.972347 mb/sr, - 6.20 deg.: X-S = 1.785880 mb/sr, - 6.30 deg.: X-S = 1.611199 mb/sr, - 6.40 deg.: X-S = 1.449903 mb/sr, - 6.50 deg.: X-S = 1.303317 mb/sr, - 6.60 deg.: X-S = 1.172484 mb/sr, - 6.70 deg.: X-S = 1.058148 mb/sr, - 6.80 deg.: X-S = 0.960751 mb/sr, - 6.90 deg.: X-S = 0.880441 mb/sr, - 7.00 deg.: X-S = 0.817071 mb/sr, - 7.10 deg.: X-S = 0.770219 mb/sr, - 7.20 deg.: X-S = 0.739206 mb/sr, - 7.30 deg.: X-S = 0.723116 mb/sr, - 7.40 deg.: X-S = 0.720823 mb/sr, - 7.50 deg.: X-S = 0.731023 mb/sr, - 7.60 deg.: X-S = 0.752265 mb/sr, - 7.70 deg.: X-S = 0.782981 mb/sr, - 7.80 deg.: X-S = 0.821525 mb/sr, - 7.90 deg.: X-S = 0.866202 mb/sr, - 8.00 deg.: X-S = 0.915303 mb/sr, - 8.10 deg.: X-S = 0.967139 mb/sr, - 8.20 deg.: X-S = 1.020068 mb/sr, - 8.30 deg.: X-S = 1.072522 mb/sr, - 8.40 deg.: X-S = 1.123036 mb/sr, - 8.50 deg.: X-S = 1.170263 mb/sr, - 8.60 deg.: X-S = 1.212999 mb/sr, - 8.70 deg.: X-S = 1.250194 mb/sr, - 8.80 deg.: X-S = 1.280964 mb/sr, - 8.90 deg.: X-S = 1.304597 mb/sr, - 9.00 deg.: X-S = 1.320561 mb/sr, - 9.10 deg.: X-S = 1.328499 mb/sr, - 9.20 deg.: X-S = 1.328231 mb/sr, - 9.30 deg.: X-S = 1.319744 mb/sr, - 9.40 deg.: X-S = 1.303186 mb/sr, - 9.50 deg.: X-S = 1.278855 mb/sr, - 9.60 deg.: X-S = 1.247184 mb/sr, - 9.70 deg.: X-S = 1.208724 mb/sr, - 9.80 deg.: X-S = 1.164134 mb/sr, - 9.90 deg.: X-S = 1.114156 mb/sr, - 10.00 deg.: X-S = 1.059600 mb/sr, - 10.10 deg.: X-S = 1.001328 mb/sr, - 10.20 deg.: X-S = 0.940229 mb/sr, - 10.30 deg.: X-S = 0.877208 mb/sr, - 10.40 deg.: X-S = 0.813163 mb/sr, - 10.50 deg.: X-S = 0.748973 mb/sr, - 10.60 deg.: X-S = 0.685478 mb/sr, - 10.70 deg.: X-S = 0.623473 mb/sr, - 10.80 deg.: X-S = 0.563687 mb/sr, - 10.90 deg.: X-S = 0.506782 mb/sr, - 11.00 deg.: X-S = 0.453336 mb/sr, - 11.10 deg.: X-S = 0.403845 mb/sr, - 11.20 deg.: X-S = 0.358713 mb/sr, - 11.30 deg.: X-S = 0.318251 mb/sr, - 11.40 deg.: X-S = 0.282678 mb/sr, - 11.50 deg.: X-S = 0.252120 mb/sr, - 11.60 deg.: X-S = 0.226615 mb/sr, - 11.70 deg.: X-S = 0.206116 mb/sr, - 11.80 deg.: X-S = 0.190495 mb/sr, - 11.90 deg.: X-S = 0.179553 mb/sr, - 12.00 deg.: X-S = 0.173023 mb/sr, - 12.10 deg.: X-S = 0.170583 mb/sr, - 12.20 deg.: X-S = 0.171863 mb/sr, - 12.30 deg.: X-S = 0.176452 mb/sr, - 12.40 deg.: X-S = 0.183911 mb/sr, - 12.50 deg.: X-S = 0.193778 mb/sr, - 12.60 deg.: X-S = 0.205580 mb/sr, - 12.70 deg.: X-S = 0.218842 mb/sr, - 12.80 deg.: X-S = 0.233093 mb/sr, - 12.90 deg.: X-S = 0.247877 mb/sr, - 13.00 deg.: X-S = 0.262755 mb/sr, - 13.10 deg.: X-S = 0.277317 mb/sr, - 13.20 deg.: X-S = 0.291184 mb/sr, - 13.30 deg.: X-S = 0.304012 mb/sr, - 13.40 deg.: X-S = 0.315501 mb/sr, - 13.50 deg.: X-S = 0.325390 mb/sr, - 13.60 deg.: X-S = 0.333466 mb/sr, - 13.70 deg.: X-S = 0.339561 mb/sr, - 13.80 deg.: X-S = 0.343553 mb/sr, - 13.90 deg.: X-S = 0.345368 mb/sr, - 14.00 deg.: X-S = 0.344975 mb/sr, - 14.10 deg.: X-S = 0.342386 mb/sr, - 14.20 deg.: X-S = 0.337653 mb/sr, - 14.30 deg.: X-S = 0.330867 mb/sr, - 14.40 deg.: X-S = 0.322150 mb/sr, - 14.50 deg.: X-S = 0.311654 mb/sr, - 14.60 deg.: X-S = 0.299557 mb/sr, - 14.70 deg.: X-S = 0.286056 mb/sr, - 14.80 deg.: X-S = 0.271367 mb/sr, - 14.90 deg.: X-S = 0.255715 mb/sr, - 15.00 deg.: X-S = 0.239332 mb/sr, - 15.10 deg.: X-S = 0.222456 mb/sr, - 15.20 deg.: X-S = 0.205318 mb/sr, - 15.30 deg.: X-S = 0.188147 mb/sr, - 15.40 deg.: X-S = 0.171163 mb/sr, - 15.50 deg.: X-S = 0.154572 mb/sr, - 15.60 deg.: X-S = 0.138565 mb/sr, - 15.70 deg.: X-S = 0.123316 mb/sr, - 15.80 deg.: X-S = 0.108977 mb/sr, - 15.90 deg.: X-S = 0.095681 mb/sr, - 16.00 deg.: X-S = 0.083538 mb/sr, - 16.10 deg.: X-S = 0.072634 mb/sr, - 16.20 deg.: X-S = 0.063033 mb/sr, - 16.30 deg.: X-S = 0.054774 mb/sr, - 16.40 deg.: X-S = 0.047875 mb/sr, - 16.50 deg.: X-S = 0.042331 mb/sr, - 16.60 deg.: X-S = 0.038117 mb/sr, - 16.70 deg.: X-S = 0.035190 mb/sr, - 16.80 deg.: X-S = 0.033487 mb/sr, - 16.90 deg.: X-S = 0.032932 mb/sr, - 17.00 deg.: X-S = 0.033435 mb/sr, - 17.10 deg.: X-S = 0.034893 mb/sr, - 17.20 deg.: X-S = 0.037195 mb/sr, - 17.30 deg.: X-S = 0.040224 mb/sr, - 17.40 deg.: X-S = 0.043857 mb/sr, - 17.50 deg.: X-S = 0.047969 mb/sr, - 17.60 deg.: X-S = 0.052435 mb/sr, - 17.70 deg.: X-S = 0.057131 mb/sr, - 17.80 deg.: X-S = 0.061936 mb/sr, - 17.90 deg.: X-S = 0.066736 mb/sr, - 18.00 deg.: X-S = 0.071421 mb/sr, - 18.10 deg.: X-S = 0.075893 mb/sr, - 18.20 deg.: X-S = 0.080059 mb/sr, - 18.30 deg.: X-S = 0.083839 mb/sr, - 18.40 deg.: X-S = 0.087164 mb/sr, - 18.50 deg.: X-S = 0.089975 mb/sr, - 18.60 deg.: X-S = 0.092226 mb/sr, - 18.70 deg.: X-S = 0.093884 mb/sr, - 18.80 deg.: X-S = 0.094925 mb/sr, - 18.90 deg.: X-S = 0.095339 mb/sr, - 19.00 deg.: X-S = 0.095127 mb/sr, - 19.10 deg.: X-S = 0.094300 mb/sr, - 19.20 deg.: X-S = 0.092879 mb/sr, - 19.30 deg.: X-S = 0.090895 mb/sr, - 19.40 deg.: X-S = 0.088385 mb/sr, - 19.50 deg.: X-S = 0.085396 mb/sr, - 19.60 deg.: X-S = 0.081977 mb/sr, - 19.70 deg.: X-S = 0.078186 mb/sr, - 19.80 deg.: X-S = 0.074082 mb/sr, - 19.90 deg.: X-S = 0.069727 mb/sr, - 20.00 deg.: X-S = 0.065185 mb/sr, - 20.10 deg.: X-S = 0.060518 mb/sr, - 20.20 deg.: X-S = 0.055791 mb/sr, - 20.30 deg.: X-S = 0.051064 mb/sr, - 20.40 deg.: X-S = 0.046397 mb/sr, - 20.50 deg.: X-S = 0.041843 mb/sr, - 20.60 deg.: X-S = 0.037455 mb/sr, - 20.70 deg.: X-S = 0.033278 mb/sr, - 20.80 deg.: X-S = 0.029353 mb/sr, - 20.90 deg.: X-S = 0.025717 mb/sr, - 21.00 deg.: X-S = 0.022399 mb/sr, - 21.10 deg.: X-S = 0.019422 mb/sr, - 21.20 deg.: X-S = 0.016804 mb/sr, - 21.30 deg.: X-S = 0.014557 mb/sr, - 21.40 deg.: X-S = 0.012686 mb/sr, - 21.50 deg.: X-S = 0.011191 mb/sr, - 21.60 deg.: X-S = 0.010067 mb/sr, - 21.70 deg.: X-S = 0.009302 mb/sr, - 21.80 deg.: X-S = 0.008881 mb/sr, - 21.90 deg.: X-S = 0.008785 mb/sr, - 22.00 deg.: X-S = 0.008991 mb/sr, - 22.10 deg.: X-S = 0.009471 mb/sr, - 22.20 deg.: X-S = 0.010196 mb/sr, - 22.30 deg.: X-S = 0.011135 mb/sr, - 22.40 deg.: X-S = 0.012254 mb/sr, - 22.50 deg.: X-S = 0.013521 mb/sr, - 22.60 deg.: X-S = 0.014900 mb/sr, - 22.70 deg.: X-S = 0.016358 mb/sr, - 22.80 deg.: X-S = 0.017860 mb/sr, - 22.90 deg.: X-S = 0.019375 mb/sr, - 23.00 deg.: X-S = 0.020872 mb/sr, - 23.10 deg.: X-S = 0.022320 mb/sr, - 23.20 deg.: X-S = 0.023695 mb/sr, - 23.30 deg.: X-S = 0.024971 mb/sr, - 23.40 deg.: X-S = 0.026127 mb/sr, - 23.50 deg.: X-S = 0.027146 mb/sr, - 23.60 deg.: X-S = 0.028011 mb/sr, - 23.70 deg.: X-S = 0.028711 mb/sr, - 23.80 deg.: X-S = 0.029238 mb/sr, - 23.90 deg.: X-S = 0.029584 mb/sr, - 24.00 deg.: X-S = 0.029749 mb/sr, - 24.10 deg.: X-S = 0.029733 mb/sr, - 24.20 deg.: X-S = 0.029538 mb/sr, - 24.30 deg.: X-S = 0.029172 mb/sr, - 24.40 deg.: X-S = 0.028642 mb/sr, - 24.50 deg.: X-S = 0.027958 mb/sr, - 24.60 deg.: X-S = 0.027135 mb/sr, - 24.70 deg.: X-S = 0.026184 mb/sr, - 24.80 deg.: X-S = 0.025122 mb/sr, - 24.90 deg.: X-S = 0.023966 mb/sr, - 25.00 deg.: X-S = 0.022731 mb/sr, - 25.10 deg.: X-S = 0.021436 mb/sr, - 25.20 deg.: X-S = 0.020098 mb/sr, - 25.30 deg.: X-S = 0.018734 mb/sr, - 25.40 deg.: X-S = 0.017363 mb/sr, - 25.50 deg.: X-S = 0.015999 mb/sr, - 25.60 deg.: X-S = 0.014659 mb/sr, - 25.70 deg.: X-S = 0.013358 mb/sr, - 25.80 deg.: X-S = 0.012109 mb/sr, - 25.90 deg.: X-S = 0.010924 mb/sr, - 26.00 deg.: X-S = 0.009814 mb/sr, - 26.10 deg.: X-S = 0.008788 mb/sr, - 26.20 deg.: X-S = 0.007853 mb/sr, - 26.30 deg.: X-S = 0.007017 mb/sr, - 26.40 deg.: X-S = 0.006283 mb/sr, - 26.50 deg.: X-S = 0.005653 mb/sr, - 26.60 deg.: X-S = 0.005130 mb/sr, - 26.70 deg.: X-S = 0.004713 mb/sr, - 26.80 deg.: X-S = 0.004400 mb/sr, - 26.90 deg.: X-S = 0.004188 mb/sr, - 27.00 deg.: X-S = 0.004072 mb/sr, - 27.10 deg.: X-S = 0.004047 mb/sr, - 27.20 deg.: X-S = 0.004106 mb/sr, - 27.30 deg.: X-S = 0.004243 mb/sr, - 27.40 deg.: X-S = 0.004448 mb/sr, - 27.50 deg.: X-S = 0.004713 mb/sr, - 27.60 deg.: X-S = 0.005030 mb/sr, - 27.70 deg.: X-S = 0.005388 mb/sr, - 27.80 deg.: X-S = 0.005778 mb/sr, - 27.90 deg.: X-S = 0.006190 mb/sr, - 28.00 deg.: X-S = 0.006616 mb/sr, - 28.10 deg.: X-S = 0.007047 mb/sr, - 28.20 deg.: X-S = 0.007473 mb/sr, - 28.30 deg.: X-S = 0.007887 mb/sr, - 28.40 deg.: X-S = 0.008282 mb/sr, - 28.50 deg.: X-S = 0.008649 mb/sr, - 28.60 deg.: X-S = 0.008985 mb/sr, - 28.70 deg.: X-S = 0.009283 mb/sr, - 28.80 deg.: X-S = 0.009539 mb/sr, - 28.90 deg.: X-S = 0.009749 mb/sr, - 29.00 deg.: X-S = 0.009912 mb/sr, - 29.10 deg.: X-S = 0.010024 mb/sr, - 29.20 deg.: X-S = 0.010085 mb/sr, - 29.30 deg.: X-S = 0.010095 mb/sr, - 29.40 deg.: X-S = 0.010055 mb/sr, - 29.50 deg.: X-S = 0.009965 mb/sr, - 29.60 deg.: X-S = 0.009828 mb/sr, - 29.70 deg.: X-S = 0.009645 mb/sr, - 29.80 deg.: X-S = 0.009421 mb/sr, - 29.90 deg.: X-S = 0.009159 mb/sr, - 30.00 deg.: X-S = 0.008863 mb/sr, - 30.10 deg.: X-S = 0.008536 mb/sr, - 30.20 deg.: X-S = 0.008185 mb/sr, - 30.30 deg.: X-S = 0.007812 mb/sr, - 30.40 deg.: X-S = 0.007423 mb/sr, - 30.50 deg.: X-S = 0.007023 mb/sr, - 30.60 deg.: X-S = 0.006616 mb/sr, - 30.70 deg.: X-S = 0.006208 mb/sr, - 30.80 deg.: X-S = 0.005803 mb/sr, - 30.90 deg.: X-S = 0.005404 mb/sr, - 31.00 deg.: X-S = 0.005017 mb/sr, - 31.10 deg.: X-S = 0.004644 mb/sr, - 31.20 deg.: X-S = 0.004289 mb/sr, - 31.30 deg.: X-S = 0.003955 mb/sr, - 31.40 deg.: X-S = 0.003645 mb/sr, - 31.50 deg.: X-S = 0.003361 mb/sr, - 31.60 deg.: X-S = 0.003104 mb/sr, - 31.70 deg.: X-S = 0.002875 mb/sr, - 31.80 deg.: X-S = 0.002676 mb/sr, - 31.90 deg.: X-S = 0.002507 mb/sr, - 32.00 deg.: X-S = 0.002368 mb/sr, - 32.10 deg.: X-S = 0.002257 mb/sr, - 32.20 deg.: X-S = 0.002176 mb/sr, - 32.30 deg.: X-S = 0.002121 mb/sr, - 32.40 deg.: X-S = 0.002092 mb/sr, - 32.50 deg.: X-S = 0.002088 mb/sr, - 32.60 deg.: X-S = 0.002105 mb/sr, - 32.70 deg.: X-S = 0.002142 mb/sr, - 32.80 deg.: X-S = 0.002197 mb/sr, - 32.90 deg.: X-S = 0.002266 mb/sr, - 33.00 deg.: X-S = 0.002349 mb/sr, - 33.10 deg.: X-S = 0.002441 mb/sr, - 33.20 deg.: X-S = 0.002541 mb/sr, - 33.30 deg.: X-S = 0.002646 mb/sr, - 33.40 deg.: X-S = 0.002753 mb/sr, - 33.50 deg.: X-S = 0.002860 mb/sr, - 33.60 deg.: X-S = 0.002966 mb/sr, - 33.70 deg.: X-S = 0.003067 mb/sr, - 33.80 deg.: X-S = 0.003162 mb/sr, - 33.90 deg.: X-S = 0.003250 mb/sr, - 34.00 deg.: X-S = 0.003328 mb/sr, - 34.10 deg.: X-S = 0.003396 mb/sr, - 34.20 deg.: X-S = 0.003452 mb/sr, - 34.30 deg.: X-S = 0.003496 mb/sr, - 34.40 deg.: X-S = 0.003527 mb/sr, - 34.50 deg.: X-S = 0.003543 mb/sr, - 34.60 deg.: X-S = 0.003546 mb/sr, - 34.70 deg.: X-S = 0.003535 mb/sr, - 34.80 deg.: X-S = 0.003511 mb/sr, - 34.90 deg.: X-S = 0.003473 mb/sr, - 35.00 deg.: X-S = 0.003422 mb/sr, - 35.10 deg.: X-S = 0.003359 mb/sr, - 35.20 deg.: X-S = 0.003286 mb/sr, - 35.30 deg.: X-S = 0.003201 mb/sr, - 35.40 deg.: X-S = 0.003108 mb/sr, - 35.50 deg.: X-S = 0.003006 mb/sr, - 35.60 deg.: X-S = 0.002898 mb/sr, - 35.70 deg.: X-S = 0.002784 mb/sr, - 35.80 deg.: X-S = 0.002667 mb/sr, - 35.90 deg.: X-S = 0.002546 mb/sr, - 36.00 deg.: X-S = 0.002423 mb/sr, - 36.10 deg.: X-S = 0.002301 mb/sr, - 36.20 deg.: X-S = 0.002179 mb/sr, - 36.30 deg.: X-S = 0.002059 mb/sr, - 36.40 deg.: X-S = 0.001943 mb/sr, - 36.50 deg.: X-S = 0.001830 mb/sr, - 36.60 deg.: X-S = 0.001723 mb/sr, - 36.70 deg.: X-S = 0.001621 mb/sr, - 36.80 deg.: X-S = 0.001526 mb/sr, - 36.90 deg.: X-S = 0.001438 mb/sr, - 37.00 deg.: X-S = 0.001357 mb/sr, - 37.10 deg.: X-S = 0.001284 mb/sr, - 37.20 deg.: X-S = 0.001219 mb/sr, - 37.30 deg.: X-S = 0.001162 mb/sr, - 37.40 deg.: X-S = 0.001113 mb/sr, - 37.50 deg.: X-S = 0.001072 mb/sr, - 37.60 deg.: X-S = 0.001039 mb/sr, - 37.70 deg.: X-S = 0.001013 mb/sr, - 37.80 deg.: X-S = 0.000995 mb/sr, - 37.90 deg.: X-S = 0.000983 mb/sr, - 38.00 deg.: X-S = 0.000977 mb/sr, - 38.10 deg.: X-S = 0.000978 mb/sr, - 38.20 deg.: X-S = 0.000983 mb/sr, - 38.30 deg.: X-S = 0.000992 mb/sr, - 38.40 deg.: X-S = 0.001006 mb/sr, - 38.50 deg.: X-S = 0.001022 mb/sr, - 38.60 deg.: X-S = 0.001042 mb/sr, - 38.70 deg.: X-S = 0.001063 mb/sr, - 38.80 deg.: X-S = 0.001085 mb/sr, - 38.90 deg.: X-S = 0.001108 mb/sr, - 39.00 deg.: X-S = 0.001130 mb/sr, - 39.10 deg.: X-S = 0.001152 mb/sr, - 39.20 deg.: X-S = 0.001173 mb/sr, - 39.30 deg.: X-S = 0.001193 mb/sr, - 39.40 deg.: X-S = 0.001210 mb/sr, - 39.50 deg.: X-S = 0.001225 mb/sr, - 39.60 deg.: X-S = 0.001237 mb/sr, - 39.70 deg.: X-S = 0.001245 mb/sr, - 39.80 deg.: X-S = 0.001251 mb/sr, - 39.90 deg.: X-S = 0.001253 mb/sr, - 40.00 deg.: X-S = 0.001252 mb/sr, - Integrated 2.6352E-01 mb, over [ 0.000, 40.000] at 170.0000 MeV - - CROSS SECTIONS FOR OUTGOING ne18 & c13 in state # 2 with spins & parities 4.0 + & 0.5 +; 0 - - 0.00 deg.: X-S = 0.000000E+00 mb/sr, -+ REAC 0.0000E+00 mb - 0.10 deg.: X-S = 0.000000E+00 mb/sr, - 0.20 deg.: X-S = 0.000000E+00 mb/sr, - 0.30 deg.: X-S = 0.000000E+00 mb/sr, - 0.40 deg.: X-S = 0.000000E+00 mb/sr, - 0.50 deg.: X-S = 0.000000E+00 mb/sr, - 0.60 deg.: X-S = 0.000000E+00 mb/sr, - 0.70 deg.: X-S = 0.000000E+00 mb/sr, - 0.80 deg.: X-S = 0.000000E+00 mb/sr, - 0.90 deg.: X-S = 0.000000E+00 mb/sr, - 1.00 deg.: X-S = 0.000000E+00 mb/sr, - 1.10 deg.: X-S = 0.000000E+00 mb/sr, - 1.20 deg.: X-S = 0.000000E+00 mb/sr, - 1.30 deg.: X-S = 0.000000E+00 mb/sr, - 1.40 deg.: X-S = 0.000000E+00 mb/sr, - 1.50 deg.: X-S = 0.000000E+00 mb/sr, - 1.60 deg.: X-S = 0.000000E+00 mb/sr, - 1.70 deg.: X-S = 0.000000E+00 mb/sr, - 1.80 deg.: X-S = 0.000000E+00 mb/sr, - 1.90 deg.: X-S = 0.000000E+00 mb/sr, - 2.00 deg.: X-S = 0.000000E+00 mb/sr, - 2.10 deg.: X-S = 0.000000E+00 mb/sr, - 2.20 deg.: X-S = 0.000000E+00 mb/sr, - 2.30 deg.: X-S = 0.000000E+00 mb/sr, - 2.40 deg.: X-S = 0.000000E+00 mb/sr, - 2.50 deg.: X-S = 0.000000E+00 mb/sr, - 2.60 deg.: X-S = 0.000000E+00 mb/sr, - 2.70 deg.: X-S = 0.000000E+00 mb/sr, - 2.80 deg.: X-S = 0.000000E+00 mb/sr, - 2.90 deg.: X-S = 0.000000E+00 mb/sr, - 3.00 deg.: X-S = 0.000000E+00 mb/sr, - 3.10 deg.: X-S = 0.000000E+00 mb/sr, - 3.20 deg.: X-S = 0.000000E+00 mb/sr, - 3.30 deg.: X-S = 0.000000E+00 mb/sr, - 3.40 deg.: X-S = 0.000000E+00 mb/sr, - 3.50 deg.: X-S = 0.000000E+00 mb/sr, - 3.60 deg.: X-S = 0.000000E+00 mb/sr, - 3.70 deg.: X-S = 0.000000E+00 mb/sr, - 3.80 deg.: X-S = 0.000000E+00 mb/sr, - 3.90 deg.: X-S = 0.000000E+00 mb/sr, - 4.00 deg.: X-S = 0.000000E+00 mb/sr, - 4.10 deg.: X-S = 0.000000E+00 mb/sr, - 4.20 deg.: X-S = 0.000000E+00 mb/sr, - 4.30 deg.: X-S = 0.000000E+00 mb/sr, - 4.40 deg.: X-S = 0.000000E+00 mb/sr, - 4.50 deg.: X-S = 0.000000E+00 mb/sr, - 4.60 deg.: X-S = 0.000000E+00 mb/sr, - 4.70 deg.: X-S = 0.000000E+00 mb/sr, - 4.80 deg.: X-S = 0.000000E+00 mb/sr, - 4.90 deg.: X-S = 0.000000E+00 mb/sr, - 5.00 deg.: X-S = 0.000000E+00 mb/sr, - 5.10 deg.: X-S = 0.000000E+00 mb/sr, - 5.20 deg.: X-S = 0.000000E+00 mb/sr, - 5.30 deg.: X-S = 0.000000E+00 mb/sr, - 5.40 deg.: X-S = 0.000000E+00 mb/sr, - 5.50 deg.: X-S = 0.000000E+00 mb/sr, - 5.60 deg.: X-S = 0.000000E+00 mb/sr, - 5.70 deg.: X-S = 0.000000E+00 mb/sr, - 5.80 deg.: X-S = 0.000000E+00 mb/sr, - 5.90 deg.: X-S = 0.000000E+00 mb/sr, - 6.00 deg.: X-S = 0.000000E+00 mb/sr, - 6.10 deg.: X-S = 0.000000E+00 mb/sr, - 6.20 deg.: X-S = 0.000000E+00 mb/sr, - 6.30 deg.: X-S = 0.000000E+00 mb/sr, - 6.40 deg.: X-S = 0.000000E+00 mb/sr, - 6.50 deg.: X-S = 0.000000E+00 mb/sr, - 6.60 deg.: X-S = 0.000000E+00 mb/sr, - 6.70 deg.: X-S = 0.000000E+00 mb/sr, - 6.80 deg.: X-S = 0.000000E+00 mb/sr, - 6.90 deg.: X-S = 0.000000E+00 mb/sr, - 7.00 deg.: X-S = 0.000000E+00 mb/sr, - 7.10 deg.: X-S = 0.000000E+00 mb/sr, - 7.20 deg.: X-S = 0.000000E+00 mb/sr, - 7.30 deg.: X-S = 0.000000E+00 mb/sr, - 7.40 deg.: X-S = 0.000000E+00 mb/sr, - 7.50 deg.: X-S = 0.000000E+00 mb/sr, - 7.60 deg.: X-S = 0.000000E+00 mb/sr, - 7.70 deg.: X-S = 0.000000E+00 mb/sr, - 7.80 deg.: X-S = 0.000000E+00 mb/sr, - 7.90 deg.: X-S = 0.000000E+00 mb/sr, - 8.00 deg.: X-S = 0.000000E+00 mb/sr, - 8.10 deg.: X-S = 0.000000E+00 mb/sr, - 8.20 deg.: X-S = 0.000000E+00 mb/sr, - 8.30 deg.: X-S = 0.000000E+00 mb/sr, - 8.40 deg.: X-S = 0.000000E+00 mb/sr, - 8.50 deg.: X-S = 0.000000E+00 mb/sr, - 8.60 deg.: X-S = 0.000000E+00 mb/sr, - 8.70 deg.: X-S = 0.000000E+00 mb/sr, - 8.80 deg.: X-S = 0.000000E+00 mb/sr, - 8.90 deg.: X-S = 0.000000E+00 mb/sr, - 9.00 deg.: X-S = 0.000000E+00 mb/sr, - 9.10 deg.: X-S = 0.000000E+00 mb/sr, - 9.20 deg.: X-S = 0.000000E+00 mb/sr, - 9.30 deg.: X-S = 0.000000E+00 mb/sr, - 9.40 deg.: X-S = 0.000000E+00 mb/sr, - 9.50 deg.: X-S = 0.000000E+00 mb/sr, - 9.60 deg.: X-S = 0.000000E+00 mb/sr, - 9.70 deg.: X-S = 0.000000E+00 mb/sr, - 9.80 deg.: X-S = 0.000000E+00 mb/sr, - 9.90 deg.: X-S = 0.000000E+00 mb/sr, - 10.00 deg.: X-S = 0.000000E+00 mb/sr, - 10.10 deg.: X-S = 0.000000E+00 mb/sr, - 10.20 deg.: X-S = 0.000000E+00 mb/sr, - 10.30 deg.: X-S = 0.000000E+00 mb/sr, - 10.40 deg.: X-S = 0.000000E+00 mb/sr, - 10.50 deg.: X-S = 0.000000E+00 mb/sr, - 10.60 deg.: X-S = 0.000000E+00 mb/sr, - 10.70 deg.: X-S = 0.000000E+00 mb/sr, - 10.80 deg.: X-S = 0.000000E+00 mb/sr, - 10.90 deg.: X-S = 0.000000E+00 mb/sr, - 11.00 deg.: X-S = 0.000000E+00 mb/sr, - 11.10 deg.: X-S = 0.000000E+00 mb/sr, - 11.20 deg.: X-S = 0.000000E+00 mb/sr, - 11.30 deg.: X-S = 0.000000E+00 mb/sr, - 11.40 deg.: X-S = 0.000000E+00 mb/sr, - 11.50 deg.: X-S = 0.000000E+00 mb/sr, - 11.60 deg.: X-S = 0.000000E+00 mb/sr, - 11.70 deg.: X-S = 0.000000E+00 mb/sr, - 11.80 deg.: X-S = 0.000000E+00 mb/sr, - 11.90 deg.: X-S = 0.000000E+00 mb/sr, - 12.00 deg.: X-S = 0.000000E+00 mb/sr, - 12.10 deg.: X-S = 0.000000E+00 mb/sr, - 12.20 deg.: X-S = 0.000000E+00 mb/sr, - 12.30 deg.: X-S = 0.000000E+00 mb/sr, - 12.40 deg.: X-S = 0.000000E+00 mb/sr, - 12.50 deg.: X-S = 0.000000E+00 mb/sr, - 12.60 deg.: X-S = 0.000000E+00 mb/sr, - 12.70 deg.: X-S = 0.000000E+00 mb/sr, - 12.80 deg.: X-S = 0.000000E+00 mb/sr, - 12.90 deg.: X-S = 0.000000E+00 mb/sr, - 13.00 deg.: X-S = 0.000000E+00 mb/sr, - 13.10 deg.: X-S = 0.000000E+00 mb/sr, - 13.20 deg.: X-S = 0.000000E+00 mb/sr, - 13.30 deg.: X-S = 0.000000E+00 mb/sr, - 13.40 deg.: X-S = 0.000000E+00 mb/sr, - 13.50 deg.: X-S = 0.000000E+00 mb/sr, - 13.60 deg.: X-S = 0.000000E+00 mb/sr, - 13.70 deg.: X-S = 0.000000E+00 mb/sr, - 13.80 deg.: X-S = 0.000000E+00 mb/sr, - 13.90 deg.: X-S = 0.000000E+00 mb/sr, - 14.00 deg.: X-S = 0.000000E+00 mb/sr, - 14.10 deg.: X-S = 0.000000E+00 mb/sr, - 14.20 deg.: X-S = 0.000000E+00 mb/sr, - 14.30 deg.: X-S = 0.000000E+00 mb/sr, - 14.40 deg.: X-S = 0.000000E+00 mb/sr, - 14.50 deg.: X-S = 0.000000E+00 mb/sr, - 14.60 deg.: X-S = 0.000000E+00 mb/sr, - 14.70 deg.: X-S = 0.000000E+00 mb/sr, - 14.80 deg.: X-S = 0.000000E+00 mb/sr, - 14.90 deg.: X-S = 0.000000E+00 mb/sr, - 15.00 deg.: X-S = 0.000000E+00 mb/sr, - 15.10 deg.: X-S = 0.000000E+00 mb/sr, - 15.20 deg.: X-S = 0.000000E+00 mb/sr, - 15.30 deg.: X-S = 0.000000E+00 mb/sr, - 15.40 deg.: X-S = 0.000000E+00 mb/sr, - 15.50 deg.: X-S = 0.000000E+00 mb/sr, - 15.60 deg.: X-S = 0.000000E+00 mb/sr, - 15.70 deg.: X-S = 0.000000E+00 mb/sr, - 15.80 deg.: X-S = 0.000000E+00 mb/sr, - 15.90 deg.: X-S = 0.000000E+00 mb/sr, - 16.00 deg.: X-S = 0.000000E+00 mb/sr, - 16.10 deg.: X-S = 0.000000E+00 mb/sr, - 16.20 deg.: X-S = 0.000000E+00 mb/sr, - 16.30 deg.: X-S = 0.000000E+00 mb/sr, - 16.40 deg.: X-S = 0.000000E+00 mb/sr, - 16.50 deg.: X-S = 0.000000E+00 mb/sr, - 16.60 deg.: X-S = 0.000000E+00 mb/sr, - 16.70 deg.: X-S = 0.000000E+00 mb/sr, - 16.80 deg.: X-S = 0.000000E+00 mb/sr, - 16.90 deg.: X-S = 0.000000E+00 mb/sr, - 17.00 deg.: X-S = 0.000000E+00 mb/sr, - 17.10 deg.: X-S = 0.000000E+00 mb/sr, - 17.20 deg.: X-S = 0.000000E+00 mb/sr, - 17.30 deg.: X-S = 0.000000E+00 mb/sr, - 17.40 deg.: X-S = 0.000000E+00 mb/sr, - 17.50 deg.: X-S = 0.000000E+00 mb/sr, - 17.60 deg.: X-S = 0.000000E+00 mb/sr, - 17.70 deg.: X-S = 0.000000E+00 mb/sr, - 17.80 deg.: X-S = 0.000000E+00 mb/sr, - 17.90 deg.: X-S = 0.000000E+00 mb/sr, - 18.00 deg.: X-S = 0.000000E+00 mb/sr, - 18.10 deg.: X-S = 0.000000E+00 mb/sr, - 18.20 deg.: X-S = 0.000000E+00 mb/sr, - 18.30 deg.: X-S = 0.000000E+00 mb/sr, - 18.40 deg.: X-S = 0.000000E+00 mb/sr, - 18.50 deg.: X-S = 0.000000E+00 mb/sr, - 18.60 deg.: X-S = 0.000000E+00 mb/sr, - 18.70 deg.: X-S = 0.000000E+00 mb/sr, - 18.80 deg.: X-S = 0.000000E+00 mb/sr, - 18.90 deg.: X-S = 0.000000E+00 mb/sr, - 19.00 deg.: X-S = 0.000000E+00 mb/sr, - 19.10 deg.: X-S = 0.000000E+00 mb/sr, - 19.20 deg.: X-S = 0.000000E+00 mb/sr, - 19.30 deg.: X-S = 0.000000E+00 mb/sr, - 19.40 deg.: X-S = 0.000000E+00 mb/sr, - 19.50 deg.: X-S = 0.000000E+00 mb/sr, - 19.60 deg.: X-S = 0.000000E+00 mb/sr, - 19.70 deg.: X-S = 0.000000E+00 mb/sr, - 19.80 deg.: X-S = 0.000000E+00 mb/sr, - 19.90 deg.: X-S = 0.000000E+00 mb/sr, - 20.00 deg.: X-S = 0.000000E+00 mb/sr, - 20.10 deg.: X-S = 0.000000E+00 mb/sr, - 20.20 deg.: X-S = 0.000000E+00 mb/sr, - 20.30 deg.: X-S = 0.000000E+00 mb/sr, - 20.40 deg.: X-S = 0.000000E+00 mb/sr, - 20.50 deg.: X-S = 0.000000E+00 mb/sr, - 20.60 deg.: X-S = 0.000000E+00 mb/sr, - 20.70 deg.: X-S = 0.000000E+00 mb/sr, - 20.80 deg.: X-S = 0.000000E+00 mb/sr, - 20.90 deg.: X-S = 0.000000E+00 mb/sr, - 21.00 deg.: X-S = 0.000000E+00 mb/sr, - 21.10 deg.: X-S = 0.000000E+00 mb/sr, - 21.20 deg.: X-S = 0.000000E+00 mb/sr, - 21.30 deg.: X-S = 0.000000E+00 mb/sr, - 21.40 deg.: X-S = 0.000000E+00 mb/sr, - 21.50 deg.: X-S = 0.000000E+00 mb/sr, - 21.60 deg.: X-S = 0.000000E+00 mb/sr, - 21.70 deg.: X-S = 0.000000E+00 mb/sr, - 21.80 deg.: X-S = 0.000000E+00 mb/sr, - 21.90 deg.: X-S = 0.000000E+00 mb/sr, - 22.00 deg.: X-S = 0.000000E+00 mb/sr, - 22.10 deg.: X-S = 0.000000E+00 mb/sr, - 22.20 deg.: X-S = 0.000000E+00 mb/sr, - 22.30 deg.: X-S = 0.000000E+00 mb/sr, - 22.40 deg.: X-S = 0.000000E+00 mb/sr, - 22.50 deg.: X-S = 0.000000E+00 mb/sr, - 22.60 deg.: X-S = 0.000000E+00 mb/sr, - 22.70 deg.: X-S = 0.000000E+00 mb/sr, - 22.80 deg.: X-S = 0.000000E+00 mb/sr, - 22.90 deg.: X-S = 0.000000E+00 mb/sr, - 23.00 deg.: X-S = 0.000000E+00 mb/sr, - 23.10 deg.: X-S = 0.000000E+00 mb/sr, - 23.20 deg.: X-S = 0.000000E+00 mb/sr, - 23.30 deg.: X-S = 0.000000E+00 mb/sr, - 23.40 deg.: X-S = 0.000000E+00 mb/sr, - 23.50 deg.: X-S = 0.000000E+00 mb/sr, - 23.60 deg.: X-S = 0.000000E+00 mb/sr, - 23.70 deg.: X-S = 0.000000E+00 mb/sr, - 23.80 deg.: X-S = 0.000000E+00 mb/sr, - 23.90 deg.: X-S = 0.000000E+00 mb/sr, - 24.00 deg.: X-S = 0.000000E+00 mb/sr, - 24.10 deg.: X-S = 0.000000E+00 mb/sr, - 24.20 deg.: X-S = 0.000000E+00 mb/sr, - 24.30 deg.: X-S = 0.000000E+00 mb/sr, - 24.40 deg.: X-S = 0.000000E+00 mb/sr, - 24.50 deg.: X-S = 0.000000E+00 mb/sr, - 24.60 deg.: X-S = 0.000000E+00 mb/sr, - 24.70 deg.: X-S = 0.000000E+00 mb/sr, - 24.80 deg.: X-S = 0.000000E+00 mb/sr, - 24.90 deg.: X-S = 0.000000E+00 mb/sr, - 25.00 deg.: X-S = 0.000000E+00 mb/sr, - 25.10 deg.: X-S = 0.000000E+00 mb/sr, - 25.20 deg.: X-S = 0.000000E+00 mb/sr, - 25.30 deg.: X-S = 0.000000E+00 mb/sr, - 25.40 deg.: X-S = 0.000000E+00 mb/sr, - 25.50 deg.: X-S = 0.000000E+00 mb/sr, - 25.60 deg.: X-S = 0.000000E+00 mb/sr, - 25.70 deg.: X-S = 0.000000E+00 mb/sr, - 25.80 deg.: X-S = 0.000000E+00 mb/sr, - 25.90 deg.: X-S = 0.000000E+00 mb/sr, - 26.00 deg.: X-S = 0.000000E+00 mb/sr, - 26.10 deg.: X-S = 0.000000E+00 mb/sr, - 26.20 deg.: X-S = 0.000000E+00 mb/sr, - 26.30 deg.: X-S = 0.000000E+00 mb/sr, - 26.40 deg.: X-S = 0.000000E+00 mb/sr, - 26.50 deg.: X-S = 0.000000E+00 mb/sr, - 26.60 deg.: X-S = 0.000000E+00 mb/sr, - 26.70 deg.: X-S = 0.000000E+00 mb/sr, - 26.80 deg.: X-S = 0.000000E+00 mb/sr, - 26.90 deg.: X-S = 0.000000E+00 mb/sr, - 27.00 deg.: X-S = 0.000000E+00 mb/sr, - 27.10 deg.: X-S = 0.000000E+00 mb/sr, - 27.20 deg.: X-S = 0.000000E+00 mb/sr, - 27.30 deg.: X-S = 0.000000E+00 mb/sr, - 27.40 deg.: X-S = 0.000000E+00 mb/sr, - 27.50 deg.: X-S = 0.000000E+00 mb/sr, - 27.60 deg.: X-S = 0.000000E+00 mb/sr, - 27.70 deg.: X-S = 0.000000E+00 mb/sr, - 27.80 deg.: X-S = 0.000000E+00 mb/sr, - 27.90 deg.: X-S = 0.000000E+00 mb/sr, - 28.00 deg.: X-S = 0.000000E+00 mb/sr, - 28.10 deg.: X-S = 0.000000E+00 mb/sr, - 28.20 deg.: X-S = 0.000000E+00 mb/sr, - 28.30 deg.: X-S = 0.000000E+00 mb/sr, - 28.40 deg.: X-S = 0.000000E+00 mb/sr, - 28.50 deg.: X-S = 0.000000E+00 mb/sr, - 28.60 deg.: X-S = 0.000000E+00 mb/sr, - 28.70 deg.: X-S = 0.000000E+00 mb/sr, - 28.80 deg.: X-S = 0.000000E+00 mb/sr, - 28.90 deg.: X-S = 0.000000E+00 mb/sr, - 29.00 deg.: X-S = 0.000000E+00 mb/sr, - 29.10 deg.: X-S = 0.000000E+00 mb/sr, - 29.20 deg.: X-S = 0.000000E+00 mb/sr, - 29.30 deg.: X-S = 0.000000E+00 mb/sr, - 29.40 deg.: X-S = 0.000000E+00 mb/sr, - 29.50 deg.: X-S = 0.000000E+00 mb/sr, - 29.60 deg.: X-S = 0.000000E+00 mb/sr, - 29.70 deg.: X-S = 0.000000E+00 mb/sr, - 29.80 deg.: X-S = 0.000000E+00 mb/sr, - 29.90 deg.: X-S = 0.000000E+00 mb/sr, - 30.00 deg.: X-S = 0.000000E+00 mb/sr, - 30.10 deg.: X-S = 0.000000E+00 mb/sr, - 30.20 deg.: X-S = 0.000000E+00 mb/sr, - 30.30 deg.: X-S = 0.000000E+00 mb/sr, - 30.40 deg.: X-S = 0.000000E+00 mb/sr, - 30.50 deg.: X-S = 0.000000E+00 mb/sr, - 30.60 deg.: X-S = 0.000000E+00 mb/sr, - 30.70 deg.: X-S = 0.000000E+00 mb/sr, - 30.80 deg.: X-S = 0.000000E+00 mb/sr, - 30.90 deg.: X-S = 0.000000E+00 mb/sr, - 31.00 deg.: X-S = 0.000000E+00 mb/sr, - 31.10 deg.: X-S = 0.000000E+00 mb/sr, - 31.20 deg.: X-S = 0.000000E+00 mb/sr, - 31.30 deg.: X-S = 0.000000E+00 mb/sr, - 31.40 deg.: X-S = 0.000000E+00 mb/sr, - 31.50 deg.: X-S = 0.000000E+00 mb/sr, - 31.60 deg.: X-S = 0.000000E+00 mb/sr, - 31.70 deg.: X-S = 0.000000E+00 mb/sr, - 31.80 deg.: X-S = 0.000000E+00 mb/sr, - 31.90 deg.: X-S = 0.000000E+00 mb/sr, - 32.00 deg.: X-S = 0.000000E+00 mb/sr, - 32.10 deg.: X-S = 0.000000E+00 mb/sr, - 32.20 deg.: X-S = 0.000000E+00 mb/sr, - 32.30 deg.: X-S = 0.000000E+00 mb/sr, - 32.40 deg.: X-S = 0.000000E+00 mb/sr, - 32.50 deg.: X-S = 0.000000E+00 mb/sr, - 32.60 deg.: X-S = 0.000000E+00 mb/sr, - 32.70 deg.: X-S = 0.000000E+00 mb/sr, - 32.80 deg.: X-S = 0.000000E+00 mb/sr, - 32.90 deg.: X-S = 0.000000E+00 mb/sr, - 33.00 deg.: X-S = 0.000000E+00 mb/sr, - 33.10 deg.: X-S = 0.000000E+00 mb/sr, - 33.20 deg.: X-S = 0.000000E+00 mb/sr, - 33.30 deg.: X-S = 0.000000E+00 mb/sr, - 33.40 deg.: X-S = 0.000000E+00 mb/sr, - 33.50 deg.: X-S = 0.000000E+00 mb/sr, - 33.60 deg.: X-S = 0.000000E+00 mb/sr, - 33.70 deg.: X-S = 0.000000E+00 mb/sr, - 33.80 deg.: X-S = 0.000000E+00 mb/sr, - 33.90 deg.: X-S = 0.000000E+00 mb/sr, - 34.00 deg.: X-S = 0.000000E+00 mb/sr, - 34.10 deg.: X-S = 0.000000E+00 mb/sr, - 34.20 deg.: X-S = 0.000000E+00 mb/sr, - 34.30 deg.: X-S = 0.000000E+00 mb/sr, - 34.40 deg.: X-S = 0.000000E+00 mb/sr, - 34.50 deg.: X-S = 0.000000E+00 mb/sr, - 34.60 deg.: X-S = 0.000000E+00 mb/sr, - 34.70 deg.: X-S = 0.000000E+00 mb/sr, - 34.80 deg.: X-S = 0.000000E+00 mb/sr, - 34.90 deg.: X-S = 0.000000E+00 mb/sr, - 35.00 deg.: X-S = 0.000000E+00 mb/sr, - 35.10 deg.: X-S = 0.000000E+00 mb/sr, - 35.20 deg.: X-S = 0.000000E+00 mb/sr, - 35.30 deg.: X-S = 0.000000E+00 mb/sr, - 35.40 deg.: X-S = 0.000000E+00 mb/sr, - 35.50 deg.: X-S = 0.000000E+00 mb/sr, - 35.60 deg.: X-S = 0.000000E+00 mb/sr, - 35.70 deg.: X-S = 0.000000E+00 mb/sr, - 35.80 deg.: X-S = 0.000000E+00 mb/sr, - 35.90 deg.: X-S = 0.000000E+00 mb/sr, - 36.00 deg.: X-S = 0.000000E+00 mb/sr, - 36.10 deg.: X-S = 0.000000E+00 mb/sr, - 36.20 deg.: X-S = 0.000000E+00 mb/sr, - 36.30 deg.: X-S = 0.000000E+00 mb/sr, - 36.40 deg.: X-S = 0.000000E+00 mb/sr, - 36.50 deg.: X-S = 0.000000E+00 mb/sr, - 36.60 deg.: X-S = 0.000000E+00 mb/sr, - 36.70 deg.: X-S = 0.000000E+00 mb/sr, - 36.80 deg.: X-S = 0.000000E+00 mb/sr, - 36.90 deg.: X-S = 0.000000E+00 mb/sr, - 37.00 deg.: X-S = 0.000000E+00 mb/sr, - 37.10 deg.: X-S = 0.000000E+00 mb/sr, - 37.20 deg.: X-S = 0.000000E+00 mb/sr, - 37.30 deg.: X-S = 0.000000E+00 mb/sr, - 37.40 deg.: X-S = 0.000000E+00 mb/sr, - 37.50 deg.: X-S = 0.000000E+00 mb/sr, - 37.60 deg.: X-S = 0.000000E+00 mb/sr, - 37.70 deg.: X-S = 0.000000E+00 mb/sr, - 37.80 deg.: X-S = 0.000000E+00 mb/sr, - 37.90 deg.: X-S = 0.000000E+00 mb/sr, - 38.00 deg.: X-S = 0.000000E+00 mb/sr, - 38.10 deg.: X-S = 0.000000E+00 mb/sr, - 38.20 deg.: X-S = 0.000000E+00 mb/sr, - 38.30 deg.: X-S = 0.000000E+00 mb/sr, - 38.40 deg.: X-S = 0.000000E+00 mb/sr, - 38.50 deg.: X-S = 0.000000E+00 mb/sr, - 38.60 deg.: X-S = 0.000000E+00 mb/sr, - 38.70 deg.: X-S = 0.000000E+00 mb/sr, - 38.80 deg.: X-S = 0.000000E+00 mb/sr, - 38.90 deg.: X-S = 0.000000E+00 mb/sr, - 39.00 deg.: X-S = 0.000000E+00 mb/sr, - 39.10 deg.: X-S = 0.000000E+00 mb/sr, - 39.20 deg.: X-S = 0.000000E+00 mb/sr, - 39.30 deg.: X-S = 0.000000E+00 mb/sr, - 39.40 deg.: X-S = 0.000000E+00 mb/sr, - 39.50 deg.: X-S = 0.000000E+00 mb/sr, - 39.60 deg.: X-S = 0.000000E+00 mb/sr, - 39.70 deg.: X-S = 0.000000E+00 mb/sr, - 39.80 deg.: X-S = 0.000000E+00 mb/sr, - 39.90 deg.: X-S = 0.000000E+00 mb/sr, - 40.00 deg.: X-S = 0.000000E+00 mb/sr, - Finished all xsecs @ 4.49308205 - - The following files have been created: - 3:local copy of User input. 6:standard output. - 12:transfer kernels. 13:total cross sections/state. - 16:tables of cross sections. 35:Astrophysics S-factors / Ecm. - 39:cross sections for each Ecm. 40:all cross sectns. for each Elab. - 46:bs wave functions & ANC ratios. 56:Fusion for each Jtotal. - 75:S-factors for lab energies. 201:Separate cross sections. - 202:Separate cross sections. 203:Separate cross sections. - - PARAMETERS : MAXQRN MLOC LMAX1 MCLIST MFNL MPWCOUP - ALLOWED : 4 12 140 5 361 0 - REQUIRED: 1 12 126 2 6 0 - - - ACCURACY ANALYSIS at 170.000 MeV : - - Elastic h*k = 0.159 so OK compared with 0.200 - - Real(S-el) > 0.01 first at J = 29.5 - Real(S-el) > 0.10 first at J = 30.5 - Real(S-el) > 0.50 first at J = 33.5 - Real(S-el) > 0.90 first at J = 39.5 - Real(S-el) > 0.99 first at J = 48.5 - R-turn = 23.29 fm at J = 120.5 - - Forward-angle excitation cut off below 2.971 deg by max JT = 120.5 - and below 1.694 deg by max R. - - - 1: Recommended RNL: non-local width > 0.99 cf: 5.04 fm: OK - Recommended CENTRE: centration ~ 0.04 cf: 0.00 fm - -Note: The following floating-point exceptions are signalling: IEEE_DENORMAL - Total CPU 0 time = 4.50 seconds From e40f58234d72b5c488e2a087fc78804c77bb6d95 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Tue, 12 May 2026 10:45:36 -0400 Subject: [PATCH 04/13] add back standard input files, remove unused benchmarks --- .../Transfer_template/B5-19neon_4+_tr.in | 34 ++ .../Transfer_template/B5-example-tr.in | 45 ++ book/notebooks/Transfer_template/benchmark.16 | 100 ---- .../notebooks/Transfer_template/benchmark.out | 433 ------------------ 4 files changed, 79 insertions(+), 533 deletions(-) create mode 100644 book/notebooks/Transfer_template/B5-19neon_4+_tr.in create mode 100644 book/notebooks/Transfer_template/B5-example-tr.in delete mode 100644 book/notebooks/Transfer_template/benchmark.16 delete mode 100644 book/notebooks/Transfer_template/benchmark.out diff --git a/book/notebooks/Transfer_template/B5-19neon_4+_tr.in b/book/notebooks/Transfer_template/B5-19neon_4+_tr.in new file mode 100644 index 0000000..bea7cb3 --- /dev/null +++ b/book/notebooks/Transfer_template/B5-19neon_4+_tr.in @@ -0,0 +1,34 @@ +n14(f17,ne18_3376_4+)c13 @ 170 MeV; +NAMELIST +&FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 +jtmin=0.0 jtmax=120 absend=-1.0 +thmin=0.00 thmax=40.00 thinc=1.00 +iter=1 nnu=36 +chans=1 xstabl=1 +elab=170.0 / +&PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / +&STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / +&PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / +&STATES jp=4. bandp=1 ep=3.376 cpot=2 jt=0.5 bandt=-1 et=0.0000 / +&partition / +&POT kp=1 ap=17.000 at=14.000 rc=1.3 / +&POT kp=1 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / +&POT kp=2 ap=18.000 at=13.000 rc=1.3 / +&POT kp=2 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / +&POT kp=3 at=17 rc=1.2 / +&POT kp=3 type=1 p1=50.00 p2=1.2 p3=0.65 / +&POT kp=3 type=3 p1=6.00 p2=1.2 p3=0.65 / +&POT kp=4 at=13 rc=1.2 / +&POT kp=4 type=1 p1=50.00 p2=1.2 p3=0.65 / +&POT kp=4 type=3 p1=6.00 p2=1.2 p3=0.65 / +&POT kp=5 ap=17.000 at=13.000 rc=1.3 / +&POT kp=5 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / +&pot / +&Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=0.546 isc=1 ipc=0 / +&Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / +&overlap / +&Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / +&CFP in=1 ib=1 ia=1 kn=1 a=1.00 / +&CFP in=2 ib=1 ia=1 kn=2 a=1.00 / +&CFP / +&coupling / diff --git a/book/notebooks/Transfer_template/B5-example-tr.in b/book/notebooks/Transfer_template/B5-example-tr.in new file mode 100644 index 0000000..f29278c --- /dev/null +++ b/book/notebooks/Transfer_template/B5-example-tr.in @@ -0,0 +1,45 @@ +n14(f17,ne18)c13 @ 170 MeV; +NAMELIST + &FRESCO hcm=0.03 rmatch=40 rintp=0.20 hnl=0.1 rnl=5.00 centre=0.0 + jtmin=0.0 jtmax=120 absend=-1.0 + thmin=0.00 thmax=40.00 thinc=0.10 + iter=1 nnu=36 + chans=3 xstabl=1 + elab=170.0 / + + &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / + &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / + + &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 / + &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=-1 et=0.0000 / + + + &partition / + + &POT kp=1 ap=17.000 at=14.000 rc=1.3 / + &POT kp=1 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / + + &POT kp=2 ap=18.000 at=13.000 rc=1.3 / + &POT kp=2 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / + + &POT kp=3 at=17 rc=1.2 / + &POT kp=3 type=1 p1=50.00 p2=1.2 p3=0.65 / + &POT kp=3 type=3 p1=6.00 p2=1.2 p3=0.65 / + + &POT kp=4 at=13 rc=1.2 / + &POT kp=4 type=1 p1=50.00 p2=1.2 p3=0.65 / + &POT kp=4 type=3 p1=6.00 p2=1.2 p3=0.65 / + + &POT kp=5 ap=17.000 at=14.000 rc=1.3 / + &POT kp=5 type=1 p1=37.2 p2=1.2 p3=0.6 p4=21.6 p5=1.2 p6=0.69 / + &pot / + + &Overlap kn1=1 ic1=1 ic2=2 in=1 kind=0 nn=1 l=2 sn=0.5 j=2.5 kbpot=3 be=3.922 isc=1 ipc=0 / + &Overlap kn1=2 ic1=2 ic2=1 in=2 kind=3 nn=1 l=1 sn=0.5 ia=1 ib=1 j=1.0 kbpot=4 be=7.5506 isc=1 ipc=0 / + &overlap / + + &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / + &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / + &CFP in=1 ib=2 ia=1 kn=1 a=1.00 / + &CFP / + &coupling / diff --git a/book/notebooks/Transfer_template/benchmark.16 b/book/notebooks/Transfer_template/benchmark.16 deleted file mode 100644 index d9c52ca..0000000 --- a/book/notebooks/Transfer_template/benchmark.16 +++ /dev/null @@ -1,100 +0,0 @@ -# 41 angles, 1 tensor ranks for 0=projectile -@subtitle "n14(f17,ne18)c13 @ 170 MeV;" -@legend ON -@legend x1 0.2 -@legend y1 0.8 -@g0 type LOGY -@xaxis label "Scattering angle (degrees)" -@yaxis label "Cross section (mb/sr)" -@ s0 legend "Partition= 1 Excit= 1 near/far= 1" -# s0 legend "Lab energy = 170.0000" -@s0 linestyle 1 -# Theta sigma iT11 T20 T21 T22 Kyy for projectile - 0.1000E-01 1.000 - 1.000 1.021 - 2.000 0.9897 - 3.000 1.072 - 4.000 1.049 - 5.000 1.029 - 6.000 0.7693 - 7.000 0.5406 - 8.000 0.5171 - 9.000 0.4873 - 10.00 0.3257 - 11.00 0.1793 - 12.00 0.1751 - 13.00 0.2277 - 14.00 0.1983 - 15.00 0.9729E-01 - 16.00 0.4037E-01 - 17.00 0.7063E-01 - 18.00 0.1131 - 19.00 0.9389E-01 - 20.00 0.3463E-01 - 21.00 0.5847E-02 - 22.00 0.2852E-01 - 23.00 0.5783E-01 - 24.00 0.5123E-01 - 25.00 0.1895E-01 - 26.00 0.1716E-03 - 27.00 0.1043E-01 - 28.00 0.2882E-01 - 29.00 0.3034E-01 - 30.00 0.1515E-01 - 31.00 0.2305E-02 - 32.00 0.3898E-02 - 33.00 0.1345E-01 - 34.00 0.1760E-01 - 35.00 0.1228E-01 - 36.00 0.4631E-02 - 37.00 0.2501E-02 - 38.00 0.5968E-02 - 39.00 0.9339E-02 - 40.00 0.8594E-02 - & -@ s1 legend "Partition= 2 Excit= 1 near/far= 1" -# s0 legend "Lab energy = 170.0000" -@s1 linestyle 2 -# Theta sigma iT11 T20 T21 T22 Kyy for projectile - 0.000 0.2592 - 1.000 0.2840 - 2.000 0.5831 - 3.000 1.607 - 4.000 3.345 - 5.000 4.771 - 6.000 4.605 - 7.000 2.914 - 8.000 1.191 - 9.000 0.5963 - 10.00 0.8486 - 11.00 1.011 - 12.00 0.7148 - 13.00 0.3005 - 14.00 0.1378 - 15.00 0.2001 - 16.00 0.2515 - 17.00 0.1846 - 18.00 0.7945E-01 - 19.00 0.3612E-01 - 20.00 0.5534E-01 - 21.00 0.7524E-01 - 22.00 0.6123E-01 - 23.00 0.3111E-01 - 24.00 0.1512E-01 - 25.00 0.1872E-01 - 26.00 0.2598E-01 - 27.00 0.2412E-01 - 28.00 0.1526E-01 - 29.00 0.8432E-02 - 30.00 0.7638E-02 - 31.00 0.9616E-02 - 32.00 0.9844E-02 - 33.00 0.7477E-02 - 34.00 0.4758E-02 - 35.00 0.3575E-02 - 36.00 0.3737E-02 - 37.00 0.3928E-02 - 38.00 0.3403E-02 - 39.00 0.2468E-02 - 40.00 0.1770E-02 - & diff --git a/book/notebooks/Transfer_template/benchmark.out b/book/notebooks/Transfer_template/benchmark.out deleted file mode 100644 index 56824ed..0000000 --- a/book/notebooks/Transfer_template/benchmark.out +++ /dev/null @@ -1,433 +0,0 @@ -Running on beyerk-linux - FRESCOX - version 7.2-20-ga7f491: Coupled Reaction Channels on gfortran - - Using NAMELIST input - - n14(f17,ne18)c13 @ 170 MeV; - - 0.030 40.000 0.200 0.100 5.000 0.000 0.000 0.000 0.000 0.000 - - Centre-of-mass Range is 1334 * 0.0300 fm., Maximum at 39.96 fm., Interpolating NL forms every 0.21 fm. - Non - locality width is 57 * 0.0900 fm., Maximum of 5.04 fm., Centred at 0.00 fm. - 2-Nucleon Separation of 0 * 0.2100 fm., Maximum of 0.00 fm., Minimum at 0.00 fm. - Maximum single particle bins of 39.9000 fm. - M,Mint = 1333 1331 - - - Range of total J is 0.0 <= J <= 120.0 (at least 0.0) and Absorbtion => -1.0000 mb. - Dry Run = F, CC set limits = 0 0, Relativistic kinematics = , Both/Far/Near Analyses = 1 - - Cross Sections (and up to T0 for 0=projectile) for Theta from 0.0 to 40.0 in steps of 1.0 degrees, DGAM=0, grace=T, Coordinates = 0 (Mads) - - Lower Radial Cutoff = maximum of -1.60*L*h & 0.0 fm., Lower Cutoff for Couplings = 0.0 fm. - - - Iterate Couplings between 0 and 1 times, to 0.000 % if sooner. - Block solved exactly = 0 chs., with Pade = 0 & Isocen = =0, NOSOL = F, CCREAL = F, initwf = 0 - Small channels are 0.00E+00 and small couplings are 1.00E-12 of unitarity - - NL quadrature with 36 Gaussian points, Calculate multipoles up to 130 from 0 - M-transfers for lp+lt greater than or equal to 6, Angular Integration Cutoff below 0.6944 % - - - Trace switches are : CHANS = 1, LISTCC = 0, TRENEG = 0, CDETR = 0, SMATS = 0, XSTABL = 1, NLPL = 0 - - WAVES = 0, LAMPL = 0, VEFF = 0, KFUS = 0, WDISK = 0, BPM = 0, MELFIL = 0 - - CDCC = 0, NFUS = 0, TCFILE = 0 - - Using unit mass = 1.000000 amu and 1/fine-structure constant = 137.03599 ( 1.000000 * true ) with hc = 197.32705 MeV.fm, - thus 2*amu/hbar^2 = 0.0478450 = 1/20.9008 and Coulomb constant = 0.1574855 so e^2 = 1.43996515, and nuclear magneton= 0.1261183 - - Now pre-scan input and save to file 3 - - - - *********** PARTITION NUMBER 1 ****************************************************************************************** - - PROJ=f17 MASS= 17.0000 Z= 9.0, # STATES= 1T, TARG=n14 MASS= 14.0000 Z= 7.0, Q-VALUE = 0.0000 MeV - - MIXPOT = 0: no couplings (default) - - 1: J= 2.5+ (B# 1), E= 0.0000, K= 0.5 Potl# 1 J= 1.0+ (B# 1), E= 0.0000, K= 0.0 - - - *********** PARTITION NUMBER 2 ****************************************************************************************** - - PROJ=ne18 MASS= 18.0000 Z= 10.0, # STATES= 1T, TARG=c13 MASS= 13.0000 Z= 6.0, Q-VALUE = 3.6286 MeV - - MIXPOT = 0: no couplings (default) - - 1: J= 0.0+ (B# 1), E= 0.0000, K= 0.0 Potl# 2 J= 0.5- (B#-1), E= 0.0000, K= 0.5 -0****** WARNING : COMPARED WITH PARTITION 1, THIS PARTITION HAS TOTAL MASS & CHARGE EXCESSES 0.00390 & 0.0000 - - - ************************************************************************************************************************************ - - The following POTENTIALS are defined : - - KP# TYPE IT SHAPE at V1 r1 a1 V2 r2 a2 A-in A-used - - - A#1 A#2 r0c ac h @ 1 - 1 0=Coulomb 0=CHARGE (WS) 1 14.000 17.000 1.3000 0.0000 0.0000 0.0000 0.000 123.612 0.03000 - - 1 1=Volume 0=Woods-Saxon 2 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 123.612 - -------------------------------------------------------------------------------------------------------------------- - - A#1 A#2 r0c ac h @ 2 - 2 0=Coulomb 0=CHARGE (WS) 3 13.000 18.000 1.3000 0.0000 0.0000 0.0000 0.000 122.917 0.03000 - - 2 1=Volume 0=Woods-Saxon 4 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 122.917 - -------------------------------------------------------------------------------------------------------------------- - - A#1 A#2 r0c ac h @ 0 - 3 0=Coulomb 0=CHARGE (WS) 5 17.000 0.000 1.2000 0.0000 0.0000 0.0000 0.000 17.000 0.03000 - - 3 1=Volume 0=Woods-Saxon 6 50.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 17.000 - - 3 3=Projtl S.O. 0=Woods-Saxon 7 6.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 17.000 - -------------------------------------------------------------------------------------------------------------------- - - A#1 A#2 r0c ac h @ 0 - 4 0=Coulomb 0=CHARGE (WS) 8 13.000 0.000 1.2000 0.0000 0.0000 0.0000 0.000 13.000 0.03000 - - 4 1=Volume 0=Woods-Saxon 9 50.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 13.000 - - 4 3=Projtl S.O. 0=Woods-Saxon 10 6.0000 1.2000 0.6500 0.0000 0.0000 0.0000 0.000 13.000 - -------------------------------------------------------------------------------------------------------------------- - - A#1 A#2 r0c ac h @ 0 - 5 0=Coulomb 0=CHARGE (WS) 11 13.000 17.000 1.3000 0.0000 0.0000 0.0000 0.000 119.286 0.03000 - - 5 1=Volume 0=Woods-Saxon 12 37.2000 1.2000 0.6000 21.6000 1.2000 0.6900 0.000 119.286 - - - ************************************************************************************************************************************ - - The following real SINGLE-PARTICLE FORM FACTORS are constructed: - - - No. P1 P2 IN KIND T N L S1 IA J/S IB BIND XFER BE SC PC FIL AFRAC Adjust to Z Mass K Norm rms D0 D ANC/Gsp - - 1: 1 2 1 0 1 2 0.5 1 2.5 0 3 0 3.9220 1 0 0 0.0000 VR 1.2622; 1. 1.000 0.4210 1.0000 3.321 -27.2 102.5 2.5472 - - NO ROOM FOR 1 MORE CHANNELS: INCREASE KN2 - - 2: 2 1 2 3 1 1 0.5 1 0.5 1 4 0 7.5506 1 0 0 0.0000 VR 1.1252; 1. 1.000 0.5792 1.0000 2.843 -129.3 80.1 4.8540 - - ************************************************************************************************************************************ - - ONE-way COUPLING # 1 for partitions -2 <- 1 of KIND 7, 0 -1 5 -1 -1 & P1,P2 = 0.0000 0.0000 : for J <= 120.5 & R < 39.9 fm. - - - data = 1 1 1 1 1.0000, so < [ne18 # 1] / [f17 # 1] * Eigen-form # 1> = 1.0000 BE = 3.922 1 - - data = 2 1 1 2 1.0000, so < [n14 # 1] / [c13 # 1] * Eigen-form # 2> = 1.0000 BE = 7.551 2 - KNT = 2, KNP = 1: NK,NG,loc= 1 1 1 13 T T F - KNT = 2, KNP = 1: NK,NG,loc= 1 1 1 13 - - So FINITE-RANGE TRANSFER : IP1 = 0 (POST ), IP2=-1 =COMPLEX , for 1 pairs of bound states summed into 1 pairs - The main coupling potential is that binding the transferred particle to the c13 core. - - Using between f17 & c13 the potential No. 5, and subtracting optical potential No. 2 - - A real*8 is 8 bytes - NLL,NLN,NLO = 191 191 57 -0File 9 needs RL = 21774 REAL*8 numbers, and NR = 132. i.e. 22.99 Megabytes - File 9 RECL = 173280 -0RECOMMENDED NON-LOCAL WIDTH IS GREATER THAN 0.90 FM., RECOMMENDED CENTRATION 0.00, after 0.14 secs. -0Relative non-local usages (rms) are - 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - 0.000 0.000 0.000 0.000 0.000 0.001 0.002 0.014 0.143 1.886 10.088 44.347 86.166 100.000 76.514 - 37.771 11.522 2.177 0.298 0.040 0.007 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 - 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0Binding Energies: (ne18 # 1 / f17 # 1)(BE = 3.922)-(n14 # 1 / c13 # 1)(BE = 7.551)- DISCREPANCY = 7.2572 - - Incoming partition 1 in excitation state # 1, Laboratory Energy given for partition 1 Nucleus 1 in Excitation pair 1 - -0Lab. ENERGY ranges : - - from 170.000 to 0.00000 in 0 intervals - from 0.00000 to 0.00000 in 0 intervals - from 0.00000 to 0.00000 in 0 intervals - - Largest real,imaginary parts of any form factor at R= 39.66 are 3.63E-02 1.36E-20 MeV - Finished all Couplings @ 0.148940012 - - Non-symmetric Hamiltonian -1*********************************************************************************************************************************** -************************************************************************************************************************************ - - INCOMING f17 ; LABORATORY f17 ENERGY = 170.00 MeV. - - *********************************************************************************************************************************** - *********************************************************************************************************************************** - - - NOTE: Coulomb excitation cut off below 2.984 deg by JTMAX, and below 1.694 deg by Rmax - - Allocate arrays for 4 channels, of which 3 need wfs. - - Total SPIN, PARITY = 0.5 +, 4 chs, 0 cc. Rmin & Coul turning = 0.0 1.2 - - - C Projectl Target # EX. (L Proj) J + Targ = Jtotal E-cm Re K Re Eta RM*K CH G-REL - 1 f17 n14 # 1: I 2 2.5 0.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.46689 0.18925 1 1 1 1 1.00000 - 2 f17 n14 # 1: I 2 2.5 1.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.46689 0.18925 1 1 1 1 1.00000 - 3 f17 n14 # 1: I 4 2.5 1.5 1.0 0.5 76.77419 5.31048 3.13748 212.2068 0.15871 -0.47817 1 1 1 1 1.00000 - 4 ne18 c13 # 1: 1 0.0 1.0 0.5 0.5 80.40279 5.38866 2.89523 215.3310 0.23289 -0.44631 2 1 2 1 1.00000 - Allocate arrays for 7 channels, of which 6 need wfs. - Allocate arrays for 9 channels, of which 8 need wfs. - Allocate arrays for 10 channels, of which 9 need wfs. - Reaction Xsec 117.5+/14 @ 1 = 0.016#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/16 @ 2 = 0.009#, Out: 0.000# 0.001# 0.000f - Reaction Xsec 117.5+/16 @ 3 = 0.009#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/16 @ 4 = 0.009#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/18 @ 5 = 0.005#, Out: 0.000# 0.001# 0.000f - Reaction Xsec 117.5+/18 @ 6 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/18 @ 7 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/20 @ 8 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5+/20 @ 9 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/15 @ 1 = 0.012#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/15 @ 2 = 0.012#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/17 @ 3 = 0.007#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/17 @ 4 = 0.007#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/17 @ 5 = 0.007#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/19 @ 6 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/19 @ 7 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/19 @ 8 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 117.5-/21 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/16 @ 1 = 0.009#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/16 @ 2 = 0.009#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/18 @ 3 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/18 @ 4 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/18 @ 5 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/20 @ 6 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/20 @ 7 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/20 @ 8 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5+/22 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/15 @ 1 = 0.012#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/17 @ 2 = 0.007#, Out: 0.000# 0.001# 0.000f - Reaction Xsec 118.5-/17 @ 3 = 0.007#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/17 @ 4 = 0.007#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/19 @ 5 = 0.004#, Out: 0.000# 0.001# 0.000f - Reaction Xsec 118.5-/19 @ 6 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/19 @ 7 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/21 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 118.5-/21 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/16 @ 1 = 0.009#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/18 @ 2 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/18 @ 3 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/18 @ 4 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/20 @ 5 = 0.003#, Out: 0.000# 0.001# 0.000f - Reaction Xsec 119.5+/20 @ 6 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/20 @ 7 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/22 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5+/22 @ 9 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/17 @ 1 = 0.007#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/17 @ 2 = 0.007#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/19 @ 3 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/19 @ 4 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/19 @ 5 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/21 @ 6 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/21 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/21 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 119.5-/23 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/18 @ 1 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/18 @ 2 = 0.005#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/20 @ 3 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/20 @ 4 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/20 @ 5 = 0.003#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/22 @ 6 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/22 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/22 @ 8 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5+/24 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/17 @ 1 = 0.007#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/19 @ 2 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/19 @ 3 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/19 @ 4 = 0.004#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/21 @ 5 = 0.002#, Out: 0.000# 0.001# 0.000f - Reaction Xsec 120.5-/21 @ 6 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/21 @ 7 = 0.002#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/23 @ 8 = 0.001#, Out: 0.000# 0.000# 0.000f - Reaction Xsec 120.5-/23 @ 9 = 0.001#, Out: 0.000# 0.000# 0.000f - Finished all CC sets @ 4.17416477 -0CUMULATIVE REACTION cross section = 1760.34720 = 26.73 = 820.4 -0CUMULATIVE ELASTIC cross section = 0.00000 -0CUMULATIVE outgoing cross sections in partition 1 : 0.00000 -0CUMULATIVE outgoing cross sections in partition 2 : 0.30977 -0Cumulative ABSORBTION by Imaginary Potentials = 1760.03742 = 26.72 = 820.2 - Fusion for specific f17 M-states : 1760.219778 1761.060821 1761.059661 1761.059661 - 1761.060821 1760.219778 -0CUMULATIVE OUTGOING cross section = 0.30977 - Strength functions * 10^4 for L=0-2 = 1.63476 1.64024 16.36406 (with r0= 1.35 fm). R' = 0.000 fm - To convert to S-factors (MeV.mb = keV.b), multiply by 2.7966E+10 - - - CROSS SECTIONS FOR OUTGOING f17 & n14 in state # 1 with spins & parities 2.5 + & 1.0 +; 0 - - 0.01 deg.: X-S = 1.504680E+16 mb/sr, -+ /R = 9.999977E-01 - 1.00 deg.: X-S = 1.536335E+08 mb/sr, -+ /R = 1.020984E+00 - 2.00 deg.: X-S = 9.309363E+06 mb/sr, -+ /R = 9.897070E-01 - 3.00 deg.: X-S = 1.991957E+06 mb/sr, -+ /R = 1.071819E+00 - 4.00 deg.: X-S = 6.167877E+05 mb/sr, -+ /R = 1.048523E+00 - 5.00 deg.: X-S = 2.480009E+05 mb/sr, -+ /R = 1.028814E+00 - 6.00 deg.: X-S = 8.947949E+04 mb/sr, -+ /R = 7.692891E-01 - 7.00 deg.: X-S = 3.396543E+04 mb/sr, -+ /R = 5.406344E-01 - 8.00 deg.: X-S = 1.905639E+04 mb/sr, -+ /R = 5.170637E-01 - 9.00 deg.: X-S = 1.122074E+04 mb/sr, -+ /R = 4.872592E-01 - 10.00 deg.: X-S = 4925.769863 mb/sr, -+ /R = 3.257044E-01 - 11.00 deg.: X-S = 1853.649547 mb/sr, -+ /R = 1.792605E-01 - 12.00 deg.: X-S = 1280.161315 mb/sr, -+ /R = 1.751332E-01 - 13.00 deg.: X-S = 1210.173751 mb/sr, -+ /R = 2.277446E-01 - 14.00 deg.: X-S = 784.455251 mb/sr, -+ /R = 1.982949E-01 - 15.00 deg.: X-S = 292.483558 mb/sr, -+ /R = 9.728762E-02 - 16.00 deg.: X-S = 93.912546 mb/sr, -+ /R = 4.037479E-02 - 17.00 deg.: X-S = 129.122488 mb/sr, -+ /R = 7.062780E-02 - 18.00 deg.: X-S = 164.779196 mb/sr, -+ /R = 1.130831E-01 - 19.00 deg.: X-S = 110.409648 mb/sr, -+ /R = 9.388784E-02 - 20.00 deg.: X-S = 33.232349 mb/sr, -+ /R = 3.462640E-02 - 21.00 deg.: X-S = 4.626397 mb/sr, -+ /R = 5.847100E-03 - 22.00 deg.: X-S = 18.774672 mb/sr, -+ /R = 2.851893E-02 - 23.00 deg.: X-S = 31.942779 mb/sr, -+ /R = 5.783082E-02 - 24.00 deg.: X-S = 23.925617 mb/sr, -+ /R = 5.123234E-02 - 25.00 deg.: X-S = 7.535930 mb/sr, -+ /R = 1.895175E-02 - 26.00 deg.: X-S = 0.058494 mb/sr, -+ /R = 1.716429E-04 - 27.00 deg.: X-S = 3.063338 mb/sr, -+ /R = 1.042561E-02 - 28.00 deg.: X-S = 7.341448 mb/sr, -+ /R = 2.881694E-02 - 29.00 deg.: X-S = 6.736465 mb/sr, -+ /R = 3.033864E-02 - 30.00 deg.: X-S = 2.946664 mb/sr, -+ /R = 1.515238E-02 - 31.00 deg.: X-S = 0.394413 mb/sr, -+ /R = 2.305217E-03 - 32.00 deg.: X-S = 0.589267 mb/sr, -+ /R = 3.897891E-03 - 33.00 deg.: X-S = 1.803496 mb/sr, -+ /R = 1.344770E-02 - 34.00 deg.: X-S = 2.101716 mb/sr, -+ /R = 1.759871E-02 - 35.00 deg.: X-S = 1.310177 mb/sr, -+ /R = 1.227616E-02 - 36.00 deg.: X-S = 0.443206 mb/sr, -+ /R = 4.631277E-03 - 37.00 deg.: X-S = 0.215315 mb/sr, -+ /R = 2.501186E-03 - 38.00 deg.: X-S = 0.463584 mb/sr, -+ /R = 5.968456E-03 - 39.00 deg.: X-S = 0.656412 mb/sr, -+ /R = 9.339481E-03 - 40.00 deg.: X-S = 0.548048 mb/sr, -+ /R = 8.593908E-03 - Integrated 2.8799E+11 mb, over [ 0.000, 40.000] at 170.0000 MeV - - CROSS SECTIONS FOR OUTGOING ne18 & c13 in state # 1 with spins & parities 0.0 + & 0.5 -; 0 - - 0.00 deg.: X-S = 0.259193 mb/sr, -+ REAC 3.0977E-01 mb - 1.00 deg.: X-S = 0.284023 mb/sr, - 2.00 deg.: X-S = 0.583055 mb/sr, - 3.00 deg.: X-S = 1.607115 mb/sr, - 4.00 deg.: X-S = 3.345344 mb/sr, - 5.00 deg.: X-S = 4.770862 mb/sr, - 6.00 deg.: X-S = 4.605237 mb/sr, - 7.00 deg.: X-S = 2.914194 mb/sr, - 8.00 deg.: X-S = 1.191222 mb/sr, - 9.00 deg.: X-S = 0.596343 mb/sr, - 10.00 deg.: X-S = 0.848641 mb/sr, - 11.00 deg.: X-S = 1.011236 mb/sr, - 12.00 deg.: X-S = 0.714818 mb/sr, - 13.00 deg.: X-S = 0.300542 mb/sr, - 14.00 deg.: X-S = 0.137752 mb/sr, - 15.00 deg.: X-S = 0.200101 mb/sr, - 16.00 deg.: X-S = 0.251509 mb/sr, - 17.00 deg.: X-S = 0.184583 mb/sr, - 18.00 deg.: X-S = 0.079453 mb/sr, - 19.00 deg.: X-S = 0.036124 mb/sr, - 20.00 deg.: X-S = 0.055340 mb/sr, - 21.00 deg.: X-S = 0.075241 mb/sr, - 22.00 deg.: X-S = 0.061230 mb/sr, - 23.00 deg.: X-S = 0.031108 mb/sr, - 24.00 deg.: X-S = 0.015123 mb/sr, - 25.00 deg.: X-S = 0.018722 mb/sr, - 26.00 deg.: X-S = 0.025985 mb/sr, - 27.00 deg.: X-S = 0.024124 mb/sr, - 28.00 deg.: X-S = 0.015255 mb/sr, - 29.00 deg.: X-S = 0.008432 mb/sr, - 30.00 deg.: X-S = 0.007638 mb/sr, - 31.00 deg.: X-S = 0.009616 mb/sr, - 32.00 deg.: X-S = 0.009844 mb/sr, - 33.00 deg.: X-S = 0.007477 mb/sr, - 34.00 deg.: X-S = 0.004758 mb/sr, - 35.00 deg.: X-S = 0.003575 mb/sr, - 36.00 deg.: X-S = 0.003737 mb/sr, - 37.00 deg.: X-S = 0.003928 mb/sr, - 38.00 deg.: X-S = 0.003403 mb/sr, - 39.00 deg.: X-S = 0.002468 mb/sr, - 40.00 deg.: X-S = 0.001770 mb/sr, - Integrated 3.0883E-01 mb, over [ 0.000, 40.000] at 170.0000 MeV - Finished all xsecs @ 4.30928183 - - The following files have been created: - 3:local copy of User input. 6:standard output. - 12:transfer kernels. 13:total cross sections/state. - 16:tables of cross sections. 35:Astrophysics S-factors / Ecm. - 39:cross sections for each Ecm. 40:all cross sectns. for each Elab. - 46:bs wave functions & ANC ratios. 56:Fusion for each Jtotal. - 75:S-factors for lab energies. 201:Separate cross sections. - 202:Separate cross sections. - - PARAMETERS : MAXQRN MLOC LMAX1 MCLIST MFNL MPWCOUP - ALLOWED : 5 12 140 5 100 0 - REQUIRED: 1 12 125 2 6 0 - - - ACCURACY ANALYSIS at 170.000 MeV : - - Elastic h*k = 0.159 so OK compared with 0.200 - - Real(S-el) > 0.01 first at J = 29.5 - Real(S-el) > 0.10 first at J = 30.5 - Real(S-el) > 0.50 first at J = 33.5 - Real(S-el) > 0.90 first at J = 39.5 - Real(S-el) > 0.99 first at J = 48.5 - R-turn = 23.29 fm at J = 120.5 - - Forward-angle excitation cut off below 2.971 deg by max JT = 120.5 - and below 1.694 deg by max R. - - - 1: Recommended RNL: non-local width > 0.90 cf: 5.04 fm: OK - Recommended CENTRE: centration ~ 0.00 cf: 0.00 fm - - Total CPU 0 time = 4.31 seconds From a60a8dd77d1cf0d42937e6ecbff82d2acd969eb9 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Tue, 12 May 2026 15:47:37 -0400 Subject: [PATCH 05/13] working transfer example --- book/notebooks/Transfer.ipynb | 107 +++++++++--------- ...{B5-19neon_4+_tr.in => B5-18neon_4+_tr.in} | 0 .../Transfer_template/B5-example-tr.in | 12 +- 3 files changed, 57 insertions(+), 62 deletions(-) rename book/notebooks/Transfer_template/{B5-19neon_4+_tr.in => B5-18neon_4+_tr.in} (100%) diff --git a/book/notebooks/Transfer.ipynb b/book/notebooks/Transfer.ipynb index 5f4a93b..7f6916b 100644 --- a/book/notebooks/Transfer.ipynb +++ b/book/notebooks/Transfer.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "id": "f8419bb9-8a3b-4b3b-a9bb-e54ecb27ad76", "metadata": {}, "outputs": [], @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "id": "96ccd90d-74be-40d8-bac7-6f6ee86aa696", "metadata": {}, "outputs": [], @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "id": "fcbeb06a-3f7c-4d4f-b68d-4bf88c60c183", "metadata": {}, "outputs": [], @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "id": "63150057-47dd-4097-ad62-1fe8ebeb6600", "metadata": { "editable": true, @@ -105,7 +105,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Frescox input file from https://www.fresco.org.uk/examples/B5-example-tr.out:\n", + "Frescox input file from https://www.fresco.org.uk/examples/B5-example-tr.in:\n", "-----------------------------------\n", "n14(f17,ne18)c13 @ 170 MeV; \n", "NAMELIST\n", @@ -113,16 +113,14 @@ "\t jtmin=0.0 jtmax=120 absend=-1.0\n", "\t thmin=0.00 thmax=40.00 thinc=0.10\n", "\t iter=1 nnu=36\n", - "\t chans=3 xstabl=1 \n", + "\t chans=1 xstabl=1 \n", "\t elab=170.0 /\n", "\n", " &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 /\n", " &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 /\n", "\n", - " &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 /\n", - " &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=-1 et=0.0000 /\n", - "\n", - "\n", + " &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 /\n", + " &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 /\n", " &partition /\n", "\n", " &POT kp=1 ap=17.000 at=14.000 rc=1.3 /\n", @@ -149,10 +147,9 @@ "\n", " &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 /\n", " &CFP in=1 ib=1 ia=1 kn=1 a=1.00 /\n", - " &CFP in=1 ib=2 ia=1 kn=1 a=1.00 /\n", + " &CFP in=2 ib=1 ia=1 kn=2 a=1.00 /\n", " &CFP /\n", - " &coupling /\n", - "\n" + " &coupling /\n" ] } ], @@ -160,14 +157,14 @@ "with open(INPUT_FILE_PATH, \"r\") as temp:\n", " input_file = temp.read()\n", "\n", - "print(\"Frescox input file from https://www.fresco.org.uk/examples/B5-example-tr.out:\")\n", + "print(\"Frescox input file from https://www.fresco.org.uk/examples/B5-example-tr.in:\")\n", "print(\"-----------------------------------\")\n", "print(input_file)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "id": "5b269a97-36fc-42a7-8290-38032694e32a", "metadata": {}, "outputs": [], @@ -183,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "88cece8b-d36b-45fc-943a-f8f2b4f1ba98", "metadata": {}, "outputs": [ @@ -191,8 +188,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1.26 ms, sys: 179 μs, total: 1.44 ms\n", - "Wall time: 6.36 ms\n" + "CPU times: user 1.61 ms, sys: 1.82 ms, total: 3.42 ms\n", + "Wall time: 4.51 s\n" ] } ], @@ -205,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "id": "7204a4d5-2022-4867-b0b9-9801221e54d1", "metadata": {}, "outputs": [ @@ -215,7 +212,7 @@ "dict_keys(['channel_1', 'channel_2'])" ] }, - "execution_count": 7, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -227,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "id": "83e95f1f-6872-419d-9cf0-d1b23ab58656", "metadata": {}, "outputs": [ @@ -237,13 +234,13 @@ "Text(0, 0.5, '$d\\\\sigma/d\\\\Omega$ [mb/Sr]')" ] }, - "execution_count": 8, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -274,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "id": "453ffa1b-22b3-4273-8b6a-cc42dfa77903", "metadata": { "editable": true, @@ -349,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "id": "552b4770-e4ba-4ff3-9ce8-2dacb87a7a0f", "metadata": {}, "outputs": [], @@ -397,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "id": "ca7c257f-e078-48f6-bd94-05dd803219ce", "metadata": {}, "outputs": [], @@ -413,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 20, "id": "c3eea39f-07e2-45cb-92be-7281c5fd385b", "metadata": {}, "outputs": [], @@ -425,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 21, "id": "39b2d9b2-52a9-4413-a997-2f086d8396fe", "metadata": {}, "outputs": [ @@ -512,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "id": "b94096cb-bb10-4282-954d-a53fe0dc4fdf", "metadata": {}, "outputs": [ @@ -522,7 +519,7 @@ "Text(0, 0.5, '$d\\\\sigma/d\\\\Omega$ [mb/Sr]')" ] }, - "execution_count": 14, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, @@ -561,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 23, "id": "d3da923f-820f-4a5c-90f3-dad3962db92b", "metadata": {}, "outputs": [], @@ -579,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "id": "27b6fef3-e94d-4520-b5e2-496e980daac2", "metadata": {}, "outputs": [ @@ -587,7 +584,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|████████████████████████████████████████████████████████████████| 7/7 [01:25<00:00, 12.16s/it]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [01:36<00:00, 13.72s/it]\n" ] } ], @@ -614,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "id": "3f881db8-1bbe-4f99-8fff-4a494adcfba6", "metadata": {}, "outputs": [ @@ -622,7 +619,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 46.17it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 45.03it/s]\n" ] }, { @@ -740,17 +737,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 26, "id": "733ec307-e7af-4261-8de0-523f0d5c3beb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, @@ -818,7 +815,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "id": "9dc72259-0235-4dfb-8852-1a6587ade5ca", "metadata": {}, "outputs": [], @@ -828,7 +825,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 28, "id": "ce1b970e-e27e-4d00-942d-c6eef880b663", "metadata": {}, "outputs": [], @@ -844,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 29, "id": "6f0aacdb-57e6-451e-b88f-b4b1909c0cdc", "metadata": {}, "outputs": [ @@ -852,8 +849,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 2.66 ms, sys: 246 μs, total: 2.9 ms\n", - "Wall time: 27.4 s\n" + "CPU times: user 1.34 ms, sys: 1.94 ms, total: 3.28 ms\n", + "Wall time: 30.3 s\n" ] } ], @@ -866,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 30, "id": "9a74f95a-b649-47a3-9b1c-ed637810af5c", "metadata": {}, "outputs": [ @@ -876,7 +873,7 @@ "dict_keys(['channel_1', 'channel_2'])" ] }, - "execution_count": 22, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -888,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 31, "id": "207ddbca-7fc0-420c-87b0-d837ed358091", "metadata": {}, "outputs": [ @@ -898,7 +895,7 @@ "Text(0, 0.5, '$d\\\\sigma/d\\\\Omega$ [mb/Sr]')" ] }, - "execution_count": 23, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, @@ -955,7 +952,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 32, "id": "e6fd8aef-dfed-4dd4-ac2c-73e1c422a53d", "metadata": {}, "outputs": [], @@ -965,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 33, "id": "3ad067d4-4f28-40a4-9a47-caa0a071c94b", "metadata": {}, "outputs": [], @@ -977,7 +974,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 34, "id": "fcb16f29-989e-43b3-bf2d-eb7555aadb12", "metadata": {}, "outputs": [], @@ -990,7 +987,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "id": "6af31346-87b4-4d24-ae64-53ab2c837748", "metadata": {}, "outputs": [ @@ -998,7 +995,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|████████████████████████████████████████████████████████████████| 2/2 [02:44<00:00, 82.26s/it]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [02:57<00:00, 88.98s/it]\n" ] } ], @@ -1025,7 +1022,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 36, "id": "be3d0c9b-f12f-48c5-8c98-e050edc7440e", "metadata": {}, "outputs": [ @@ -1033,7 +1030,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 59.59it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 88.04it/s]\n" ] }, { @@ -1094,7 +1091,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bf7f3572-989e-4866-b8ce-724a86ae7d5f", + "id": "376eb19b-c2bb-4c7d-9e18-585e54df23ee", "metadata": {}, "outputs": [], "source": [] diff --git a/book/notebooks/Transfer_template/B5-19neon_4+_tr.in b/book/notebooks/Transfer_template/B5-18neon_4+_tr.in similarity index 100% rename from book/notebooks/Transfer_template/B5-19neon_4+_tr.in rename to book/notebooks/Transfer_template/B5-18neon_4+_tr.in diff --git a/book/notebooks/Transfer_template/B5-example-tr.in b/book/notebooks/Transfer_template/B5-example-tr.in index f29278c..8e8ca53 100644 --- a/book/notebooks/Transfer_template/B5-example-tr.in +++ b/book/notebooks/Transfer_template/B5-example-tr.in @@ -4,16 +4,14 @@ NAMELIST jtmin=0.0 jtmax=120 absend=-1.0 thmin=0.00 thmax=40.00 thinc=0.10 iter=1 nnu=36 - chans=3 xstabl=1 + chans=1 xstabl=1 elab=170.0 / &PARTITION namep='f17' massp=17. zp=9 namet='n14' masst=14. zt=7 nex=1 / &STATES jp=2.5 bandp=1 ep=0.0 cpot=1 jt=1.0 bandt=1 et=0.0000 / - &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=2 / - &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=-1 et=0.0000 / - - + &PARTITION namep='ne18' massp=18. zp=10 namet='c13' masst=13. zt=6 qval=3.6286 nex=1 / + &STATES jp=0. bandp=1 ep=0.0 cpot=2 jt=0.5 bandt=1 et=0.0000 / &partition / &POT kp=1 ap=17.000 at=14.000 rc=1.3 / @@ -40,6 +38,6 @@ NAMELIST &Coupling icto=-2 icfrom=1 kind=7 ip1=0 ip2=-1 ip3=5 / &CFP in=1 ib=1 ia=1 kn=1 a=1.00 / - &CFP in=1 ib=2 ia=1 kn=1 a=1.00 / + &CFP in=2 ib=1 ia=1 kn=2 a=1.00 / &CFP / - &coupling / + &coupling / \ No newline at end of file From 22a6ca2faedbccec61c1b5ea60e8f383089320d8 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Tue, 12 May 2026 17:32:52 -0400 Subject: [PATCH 06/13] add initial files for breakup calculation --- .gitignore | 3 + book/notebooks/Breakup.ipynb | 371 ++++++++ .../B3-example-br-long-spdf.in | 891 ++++++++++++++++++ .../Breakup_template/B3-example-br-long.in | 150 +++ 4 files changed, 1415 insertions(+) create mode 100644 book/notebooks/Breakup.ipynb create mode 100644 book/notebooks/Breakup_template/B3-example-br-long-spdf.in create mode 100644 book/notebooks/Breakup_template/B3-example-br-long.in diff --git a/.gitignore b/.gitignore index 49ea367..514e45d 100644 --- a/.gitignore +++ b/.gitignore @@ -22,6 +22,9 @@ book/notebooks/Ozge_template/frescox.in book/notebooks/Transfer_template/fort.* book/notebooks/Transfer_template/frescox.out book/notebooks/Transfer_template/frescox.in +book/notebooks/Breakup_template/fort.* +book/notebooks/Breakup_template/frescox.out +book/notebooks/Breakup_template/frescox.in bfrescox_pypkg/meson/subprojects/.wraplock diff --git a/book/notebooks/Breakup.ipynb b/book/notebooks/Breakup.ipynb new file mode 100644 index 0000000..fe750ab --- /dev/null +++ b/book/notebooks/Breakup.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0bfe8974-b46a-4ac1-af8c-38f44e806286", + "metadata": {}, + "source": [ + "# Breakup CDCC in $^{8}$B+$^{208}$Pb at 83 MeV/u\n", + "\n", + "We will adapt [this example from the Frescox documentation](https://www.fresco.org.uk/examples/B3-example-br-long.in).\n", + "\n", + "From the documentation:\n", + "\n", + "> Breakup calculations can be modeled as single-particle excitation into the continuum. In this example we show a typical CDCC calculation. It calculates the breakup of 8B into p + 7Be, under the field of 208Pb at intermediate energies. [...] The breakup of 8B has been measured many times with the aim of extracting the proton capture rate on 7Be\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2a4dcc2f-3332-4f1f-ab05-2426c57282be", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from pathlib import Path\n", + "import inspect\n", + "import pandas as pd\n", + "import json\n", + "\n", + "from tqdm import tqdm\n", + "\n", + "with open(\"MatplotlibEsthetics.json\", \"r\") as fptr:\n", + " esthetics = json.load(fptr)\n", + "\n", + "plt.style.use(esthetics[\"style\"])\n", + "FONTSIZE = esthetics[\"fontsize\"]\n", + "TICK_FONTSIZE = esthetics[\"tick_fontsize\"]\n", + "MARKERSIZE = esthetics[\"markersize\"]\n", + "LINEWIDTH = esthetics[\"linewidth\"]" + ] + }, + { + "cell_type": "markdown", + "id": "e360a9fa-48b8-483a-91a2-ae4a93fa347b", + "metadata": {}, + "source": [ + "## BfrescoxPro\n", + "\n", + "CDCC calculations are expensive. For this reason, we will use {{{Bfrescoxpro}}}, to enable parallelization with MPI. For more information, [see the docs](https://bfrescox.readthedocs.io/en/latest/advanced_users.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e5398285-c4a1-4936-809f-9f91d832c035", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'bfrescoxpro'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m bfrescoxpro\n\u001b[32m 2\u001b[39m bfrescoxpro.information()\n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'bfrescoxpro'" + ] + } + ], + "source": [ + "import bfrescoxpro\n", + "bfrescoxpro.information()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "27d1b892-4c2a-471e-8b63-e269b2e193c3", + "metadata": {}, + "outputs": [], + "source": [ + "EXAMPLE_DIR = Path(\"./Breakup_template//\")\n", + "TEMPLATE_FILE_PATH = Path(EXAMPLE_DIR / \"B3-example-br.template\")\n", + "INPUT_FILE_PATH = Path(EXAMPLE_DIR / \"B3-example-br-long.in\")" + ] + }, + { + "cell_type": "markdown", + "id": "82ec2162-30a7-4d9f-abc0-b2caa416a2e0", + "metadata": {}, + "source": [ + "## Let's run the input file exactly as is from the website" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d764cf83-f48c-4d1a-bfde-b9da97718733", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Frescox input file from https://www.fresco.org.uk/examples/B5-example-br.in:\n", + "-----------------------------------\n", + "CDCC 8B+208Pb ; nuclear and coulomb s-waves \n", + "NAMELIST\n", + " &Fresco hcm= 0.01 rmatch= -60.000 rintp= 0.15 rsp= 0.0\n", + " rasym= 1000.00 accrcy= 0.0010000 \n", + " jtmin= 0.0 jtmax= 9000.0 absend= -50.0000 \n", + " jump = 1 10 50 200 \n", + " jbord= 0.0 200.0 300.0 1000.0 9000.0 \n", + " thmin= 0.00 thmax= 20.00 thinc= 0.05 cutr=-20.00\n", + " ips= 0.0000 it0= 0 iter= 0 iblock= 21 nnu= 24\n", + " smallchan= 1.00E-12 smallcoup= 1.00E-12\n", + " chans= 1 smats= 2 xstabl= 1 cdcc= 1\n", + " elab= 656.0000 pel=1 exl=1 lab=1 lin=1 lex=1 /\n", + "\n", + " &Partition namep='8B ' massp= 8.0000 zp= 5 nex= 21 pwf=T \n", + " namet='208Pb ' masst=208.0000 zt= 82 qval= 0.1370/ \n", + " &States jp= 1.5 ptyp=-1 ep= 0.0000 cpot= 1 \n", + " jt= 0.0 ptyt= 1 et= 0.0000/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.1583 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.2180 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.3260 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.4830 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.6889 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.9438 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 1.2478 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 1.6007 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 2.0027 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 2.4536 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 2.9536 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 3.5025 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 4.1005 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 4.7474 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 5.4434 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 6.1884 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 6.9824 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 7.8253 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 8.7173 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 9.6583 cpot= 1 copyt= 1/ \n", + " &Partition namep='7Be ' massp= 7.0000 zp= 4 nex= -1 pwf=T \n", + " namet='208Pb +p' masst=209.0000 zt= 83 qval= 0.0000/ \n", + " &States jp= 0.0 ptyp= 1 ep= 0.0000 cpot= 2 \n", + " jt= 0.0 ptyt= 1 et= 0.0000/ \n", + " &Partition / ! END OF DEFINING PARTITIONS \n", + " \n", + " &Pot kp= 1 type= 0 shape= 0 \n", + " p(1:3)= 1.0000 0.0000 2.6500 / \n", + " &Pot kp= 2 type= 0 shape= 0 \n", + " p(1:3)= 208.0000 0.0000 1.3000 / \n", + " &Pot kp= 2 type= 1 shape= 0 \n", + " p(1:7)= 114.2000 1.2860 0.8530 9.4400 1.7390 0.8090 0.0000 / \n", + " &Pot kp= 3 type= 0 shape= 0 \n", + " p(1:3)= 208.0000 0.0000 1.3000 / \n", + " &Pot kp= 3 type= 1 shape= 0 \n", + " p(1:7)= 34.8190 1.1700 0.7500 15.3400 1.3200 0.6010 0.0000 / \n", + " &Pot kp= 4 type= 0 shape= 0 \n", + " p(1:3)= 1.0000 0.0000 2.3910 / \n", + " &Pot kp= 4 type= 1 shape= 0 \n", + " p(1:3)= 44.6750 2.3910 0.4800 / \n", + " &Pot kp= 4 type= 3 shape= 0 \n", + " p(1:3)= 4.8980 2.3910 0.4800 / \n", + " &Pot / ! END OF DEFINING POTENTIALS \n", + " \n", + " &Overlap kn1= 1 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 1 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= 0.1370 isc= 1 ipc=0 / \n", + " &Overlap kn1= 2 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.0182 isc=12 ipc=2 nk= 20 er= -0.0344 / \n", + " &Overlap kn1= 3 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.0771 isc=12 ipc=2 nk= 20 er= -0.0834 / \n", + " &Overlap kn1= 4 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.1850 isc=12 ipc=2 nk= 20 er= -0.1324 / \n", + " &Overlap kn1= 5 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.3419 isc=12 ipc=2 nk= 20 er= -0.1814 / \n", + " &Overlap kn1= 6 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.5479 isc=12 ipc=2 nk= 20 er= -0.2304 / \n", + " &Overlap kn1= 7 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.8028 isc=12 ipc=2 nk= 20 er= -0.2794 / \n", + " &Overlap kn1= 8 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -1.1067 isc=12 ipc=2 nk= 20 er= -0.3284 / \n", + " &Overlap kn1= 9 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -1.4596 isc=12 ipc=2 nk= 20 er= -0.3774 / \n", + " &Overlap kn1= 10 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -1.8616 isc=12 ipc=2 nk= 20 er= -0.4264 / \n", + " &Overlap kn1= 11 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -2.3125 isc=12 ipc=2 nk= 20 er= -0.4754 / \n", + " &Overlap kn1= 12 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -2.8125 isc=12 ipc=2 nk= 20 er= -0.5245 / \n", + " &Overlap kn1= 13 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -3.3614 isc=12 ipc=2 nk= 20 er= -0.5735 / \n", + " &Overlap kn1= 14 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -3.9594 isc=12 ipc=2 nk= 20 er= -0.6225 / \n", + " &Overlap kn1= 15 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -4.6064 isc=12 ipc=2 nk= 20 er= -0.6715 / \n", + " &Overlap kn1= 16 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -5.3023 isc=12 ipc=2 nk= 20 er= -0.7205 / \n", + " &Overlap kn1= 17 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -6.0473 isc=12 ipc=2 nk= 20 er= -0.7695 / \n", + " &Overlap kn1= 18 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -6.8413 isc=12 ipc=2 nk= 20 er= -0.8185 / \n", + " &Overlap kn1= 19 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -7.6843 isc=12 ipc=2 nk= 20 er= -0.8675 / \n", + " &Overlap kn1= 20 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -8.5763 isc=12 ipc=2 nk= 20 er= -0.9165 / \n", + " &Overlap kn1= 21 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -9.5173 isc=12 ipc=2 nk= 20 er= -0.9655 / \n", + " &Overlap / ! END OF DEFINING OVERLAPS \n", + " \n", + " &Coupling icto= 1 icfrom= 2 kind=3 ip1= 2 ip2= 0 ip3= 0 \n", + " p1= 3.0000 p2= 2.0000 / \n", + " &Cfp in= 1 ib= 1 ia= 1 kn= 1 a= 1.0000 / \n", + " &Cfp in= 1 ib= 2 ia= 1 kn= 2 a= 1.0000 / \n", + " &Cfp in= 1 ib= 3 ia= 1 kn= 3 a= 1.0000 / \n", + " &Cfp in= 1 ib= 4 ia= 1 kn= 4 a= 1.0000 / \n", + " &Cfp in= 1 ib= 5 ia= 1 kn= 5 a= 1.0000 / \n", + " &Cfp in= 1 ib= 6 ia= 1 kn= 6 a= 1.0000 / \n", + " &Cfp in= 1 ib= 7 ia= 1 kn= 7 a= 1.0000 / \n", + " &Cfp in= 1 ib= 8 ia= 1 kn= 8 a= 1.0000 / \n", + " &Cfp in= 1 ib= 9 ia= 1 kn= 9 a= 1.0000 / \n", + " &Cfp in= 1 ib= 10 ia= 1 kn= 10 a= 1.0000 / \n", + " &Cfp in= 1 ib= 11 ia= 1 kn= 11 a= 1.0000 / \n", + " &Cfp in= 1 ib= 12 ia= 1 kn= 12 a= 1.0000 / \n", + " &Cfp in= 1 ib= 13 ia= 1 kn= 13 a= 1.0000 / \n", + " &Cfp in= 1 ib= 14 ia= 1 kn= 14 a= 1.0000 / \n", + " &Cfp in= 1 ib= 15 ia= 1 kn= 15 a= 1.0000 / \n", + " &Cfp in= 1 ib= 16 ia= 1 kn= 16 a= 1.0000 / \n", + " &Cfp in= 1 ib= 17 ia= 1 kn= 17 a= 1.0000 / \n", + " &Cfp in= 1 ib= 18 ia= 1 kn= 18 a= 1.0000 / \n", + " &Cfp in= 1 ib= 19 ia= 1 kn= 19 a= 1.0000 / \n", + " &Cfp in= 1 ib= 20 ia= 1 kn= 20 a= 1.0000 / \n", + " &Cfp in= 1 ib= 21 ia= 1 kn= 21 a= 1.0000 / \n", + " ! ******* END OF CDCC INPUTS ******* \n" + ] + } + ], + "source": [ + "with open(INPUT_FILE_PATH, \"r\") as temp:\n", + " input_file = temp.read()\n", + "\n", + "print(\"Frescox input file from https://www.fresco.org.uk/examples/B5-example-br.in:\")\n", + "print(\"-----------------------------------\")\n", + "print(input_file)" + ] + }, + { + "cell_type": "markdown", + "id": "97ee0550-df30-4217-b444-3de2c1932b3c", + "metadata": {}, + "source": [ + "## TODO use MPI" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "eb6111f5-8563-48a7-918f-8a639bf440ea", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the frescox input file by filling in the user-defined template with parameters\n", + "cfg = bfrescox.Configuration.from_template(\n", + " INPUT_FILE_PATH,\n", + " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", + " {},\n", + " overwrite=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d1f7b9cb-3a59-4395-b81e-f8c0bf9b0303", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 6.15 ms, sys: 1.02 ms, total: 7.17 ms\n", + "Wall time: 1min 50s\n" + ] + } + ], + "source": [ + "%%time\n", + "bfrescox.run_simulation(\n", + " cfg, EXAMPLE_DIR.joinpath(\"frescox.out\"), cwd=EXAMPLE_DIR, overwrite=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "13a46916-041f-4533-b20f-c9dbce9c24ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['channel_1', 'channel_2', 'channel_3', 'channel_4', 'channel_5', 'channel_6', 'channel_7', 'channel_8', 'channel_9', 'channel_10', 'channel_11', 'channel_12', 'channel_13', 'channel_14', 'channel_15', 'channel_16', 'channel_17', 'channel_18', 'channel_19', 'channel_20', 'channel_21'])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", + "results.keys()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.14.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/notebooks/Breakup_template/B3-example-br-long-spdf.in b/book/notebooks/Breakup_template/B3-example-br-long-spdf.in new file mode 100644 index 0000000..907eb5e --- /dev/null +++ b/book/notebooks/Breakup_template/B3-example-br-long-spdf.in @@ -0,0 +1,891 @@ +CDCC 8B+208Pb at 83 MeV A to s,p,d,f; 1+20+4*10+2*5 chs 0-10 MeV, C q=0,1,2 rbin +NAMELIST + &Fresco hcm= 0.010 rmatch= -60.000 rintp= 0.05 rsp= 0.0 + rasym= 1000.00 accrcy= 0.0010000 switch= 0.00 ajswtch= 0. sinjmax= 0. + jtmin= 0.0 jtmax= 10000.0 absend= -50.0000 pset= 0 + jump = 4 10 50 200 0 0 + jbord= 0.0 200.0 300.0 1000.0 10000.0 0.0 + thmin= 0.00 thmax= 20.00 thinc= 0.05 cutl= 0.00 cutr=-20.00 cutc= 0.00 + ips= 0.0000 it0= 0 iter= 0 iblock=142 pade=0 nnu= 24 + smallchan= 1.00E-12 smallcoup= 1.00E-12 + chans= 1 listcc= 0 treneg= 0 cdetr= 0 smats= 2 xstabl= 1 + waves= 0 lampl= 0 veff= 0 kfus= 0 wdisk= 0 nlpl= 0 melfil= 0 cdcc= 1 + elab= 664.0000 pel=1 exl=1 lab=1 lin=1 lex=1 / + + &Partition namep='8B ' massp= 8.0000 zp= 5 nex=141 pwf=T + namet='208Pb ' masst=208.0000 zt= 82 qval= 0.1370/ + &States jp= 1.5 ptyp=-1 ep= 0.0000 cpot= 1 + jt= 0.0 ptyt= 1 et= 0.0000/ + &States jp= 0.5 ptyp= 1 ep= 0.2305 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.3711 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.5195 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.6688 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.8184 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.9681 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.1180 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.2678 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.4177 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.5677 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.7176 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.8675 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.0175 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.1675 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.3174 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.4674 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.6174 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.7674 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.9173 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 3.0673 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 0.1996 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 0.3628 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 0.5145 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 0.6652 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 0.8156 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 0.9659 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 1.1160 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 1.2662 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 1.4163 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 1.5663 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 1.7164 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 1.8665 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 2.0165 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 2.1665 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 2.3166 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 2.4666 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 2.6166 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 2.7666 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 2.9167 cpot= 1 + copyt= 1/ + &States jp= 0.5 ptyp=-1 ep= 3.0667 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 0.1996 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 0.3628 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 0.5145 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 0.6652 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 0.8156 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 0.9659 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 1.1160 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 1.2662 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 1.4163 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 1.5663 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 1.7164 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 1.8665 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 2.0165 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 2.1665 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 2.3166 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 2.4666 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 2.6166 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 2.7666 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 2.9167 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp=-1 ep= 3.0667 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 0.1996 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 0.3628 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 0.5145 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 0.6652 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 0.8156 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 0.9659 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 1.1160 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 1.2662 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 1.4163 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 1.5663 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 1.7164 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 1.8665 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 2.0165 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 2.1665 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 2.3166 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 2.4666 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 2.6166 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 2.7666 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 2.9167 cpot= 1 + copyt= 1/ + &States jp= 1.5 ptyp= 1 ep= 3.0667 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 0.1996 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 0.3628 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 0.5145 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 0.6652 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 0.8156 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 0.9659 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 1.1160 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 1.2662 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 1.4163 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 1.5663 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 1.7164 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 1.8665 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 2.0165 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 2.1665 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 2.3166 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 2.4666 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 2.6166 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 2.7666 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 2.9167 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp= 1 ep= 3.0667 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 0.1996 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 0.3628 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 0.5145 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 0.6652 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 0.8156 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 0.9659 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 1.1160 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 1.2662 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 1.4163 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 1.5663 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 1.7164 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 1.8665 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 2.0165 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 2.1665 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 2.3166 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 2.4666 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 2.6166 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 2.7666 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 2.9167 cpot= 1 + copyt= 1/ + &States jp= 2.5 ptyp=-1 ep= 3.0667 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 0.1996 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 0.3628 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 0.5145 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 0.6652 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 0.8156 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 0.9659 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 1.1160 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 1.2662 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 1.4163 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 1.5663 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 1.7164 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 1.8665 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 2.0165 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 2.1665 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 2.3166 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 2.4666 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 2.6166 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 2.7666 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 2.9167 cpot= 1 + copyt= 1/ + &States jp= 3.5 ptyp=-1 ep= 3.0667 cpot= 1 + copyt= 1/ + &Partition namep='7Be ' massp= 7.0000 zp= 4 nex= -1 pwf=T + namet='208Pb +p' masst=209.0000 zt= 83 qval= 0.0000/ + &States jp= 0.0 ptyp= 1 ep= 0.0000 cpot= 2 + jt= 0.0 ptyt= 1 et= 0.0000/ + &Partition / ! END OF DEFINING PARTITIONS + + &Pot kp= 1 type= 0 shape= 0 nosub=F itt=F + p(1:3)= 1.0000 0.0000 2.6500 / + &Pot kp= 2 type= 0 shape= 0 nosub=F itt=F + p(1:3)= 208.0000 0.0000 1.3000 / + &Pot kp= 2 type= 1 shape= 0 nosub=F itt=F + p(1:7)= 114.2000 1.2860 0.8530 9.4400 1.7390 0.8090 0.0000 / + &Pot kp= 3 type= 0 shape= 0 nosub=F itt=F + p(1:3)= 208.0000 0.0000 1.3000 / + &Pot kp= 3 type= 1 shape= 0 nosub=F itt=F + p(1:7)= 34.8190 1.1700 0.7500 15.3400 1.3200 0.6010 0.0000 / + &Pot kp= 4 type= 0 shape= 0 nosub=F itt=F + p(1:3)= 1.0000 0.0000 2.3910 / + &Pot kp= 4 type= 1 shape= 0 nosub=F itt=F + p(1:3)= 44.6500 2.3910 0.5200 / + &Pot kp= 4 type= 3 shape= 0 nosub=F itt=F + p(1:3)= 4.8980 2.3910 0.5200 / + &Pot / ! END OF DEFINING POTENTIALS + + &Overlap kn1= 1 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 1 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= 0.1370 isc= 1 ipc=0 / + &Overlap kn1= 2 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 3 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.2300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 4 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.3800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 5 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.5300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 6 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.6800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 7 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.8300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 8 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.9800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 9 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.1300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 10 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.2800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 11 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.4300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 12 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.5800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 13 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.7300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 14 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.8800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 15 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.0300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 16 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.1800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 17 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.3300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 18 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.4800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 19 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.6300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 20 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.7800 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 21 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.9300 isc=12 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 22 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 23 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 24 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 25 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 26 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 27 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 28 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 29 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 30 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 31 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 32 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 33 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 34 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 35 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 36 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 37 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 38 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 39 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 40 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 41 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 42 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 43 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 44 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 45 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 46 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 47 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 48 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 49 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 50 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 51 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 52 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 53 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 54 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 55 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 56 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 57 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 58 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 59 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 60 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 61 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 62 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 63 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 64 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 65 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 66 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 67 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 68 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 69 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 70 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 71 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 72 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 73 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 74 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 75 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 76 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 77 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 78 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 79 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 80 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 81 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 82 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 83 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 84 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 85 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 86 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 87 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 88 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 89 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 90 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 91 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 92 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 93 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 94 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 95 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 96 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 97 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 98 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 99 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 100 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 101 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 102 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 103 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 104 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 105 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 106 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 107 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 108 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 109 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 110 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 111 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 112 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 113 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 114 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 115 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 116 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 117 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 118 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 119 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 120 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 121 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 122 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 123 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 124 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 125 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 126 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 127 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 128 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 129 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 130 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 131 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 132 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 133 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 134 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 135 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 136 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 137 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 138 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 139 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 140 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap kn1= 141 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / + &Overlap / ! END OF DEFINING OVERLAPS + + &Coupling icto= 1 icfrom= 2 kind=3 ip1= 2 ip2= 0 ip3= 0 + p1= 3.0000 p2= 2.0000 / + &Cfp in= 1 ib= 1 ia= 1 kn= 1 a= 1.0000 / + &Cfp in= 1 ib= 2 ia= 1 kn= 2 a= 1.0000 / + &Cfp in= 1 ib= 3 ia= 1 kn= 3 a= 1.0000 / + &Cfp in= 1 ib= 4 ia= 1 kn= 4 a= 1.0000 / + &Cfp in= 1 ib= 5 ia= 1 kn= 5 a= 1.0000 / + &Cfp in= 1 ib= 6 ia= 1 kn= 6 a= 1.0000 / + &Cfp in= 1 ib= 7 ia= 1 kn= 7 a= 1.0000 / + &Cfp in= 1 ib= 8 ia= 1 kn= 8 a= 1.0000 / + &Cfp in= 1 ib= 9 ia= 1 kn= 9 a= 1.0000 / + &Cfp in= 1 ib= 10 ia= 1 kn= 10 a= 1.0000 / + &Cfp in= 1 ib= 11 ia= 1 kn= 11 a= 1.0000 / + &Cfp in= 1 ib= 12 ia= 1 kn= 12 a= 1.0000 / + &Cfp in= 1 ib= 13 ia= 1 kn= 13 a= 1.0000 / + &Cfp in= 1 ib= 14 ia= 1 kn= 14 a= 1.0000 / + &Cfp in= 1 ib= 15 ia= 1 kn= 15 a= 1.0000 / + &Cfp in= 1 ib= 16 ia= 1 kn= 16 a= 1.0000 / + &Cfp in= 1 ib= 17 ia= 1 kn= 17 a= 1.0000 / + &Cfp in= 1 ib= 18 ia= 1 kn= 18 a= 1.0000 / + &Cfp in= 1 ib= 19 ia= 1 kn= 19 a= 1.0000 / + &Cfp in= 1 ib= 20 ia= 1 kn= 20 a= 1.0000 / + &Cfp in= 1 ib= 21 ia= 1 kn= 21 a= 1.0000 / + &Cfp in= 1 ib= 22 ia= 1 kn= 22 a= 1.0000 / + &Cfp in= 1 ib= 23 ia= 1 kn= 23 a= 1.0000 / + &Cfp in= 1 ib= 24 ia= 1 kn= 24 a= 1.0000 / + &Cfp in= 1 ib= 25 ia= 1 kn= 25 a= 1.0000 / + &Cfp in= 1 ib= 26 ia= 1 kn= 26 a= 1.0000 / + &Cfp in= 1 ib= 27 ia= 1 kn= 27 a= 1.0000 / + &Cfp in= 1 ib= 28 ia= 1 kn= 28 a= 1.0000 / + &Cfp in= 1 ib= 29 ia= 1 kn= 29 a= 1.0000 / + &Cfp in= 1 ib= 30 ia= 1 kn= 30 a= 1.0000 / + &Cfp in= 1 ib= 31 ia= 1 kn= 31 a= 1.0000 / + &Cfp in= 1 ib= 32 ia= 1 kn= 32 a= 1.0000 / + &Cfp in= 1 ib= 33 ia= 1 kn= 33 a= 1.0000 / + &Cfp in= 1 ib= 34 ia= 1 kn= 34 a= 1.0000 / + &Cfp in= 1 ib= 35 ia= 1 kn= 35 a= 1.0000 / + &Cfp in= 1 ib= 36 ia= 1 kn= 36 a= 1.0000 / + &Cfp in= 1 ib= 37 ia= 1 kn= 37 a= 1.0000 / + &Cfp in= 1 ib= 38 ia= 1 kn= 38 a= 1.0000 / + &Cfp in= 1 ib= 39 ia= 1 kn= 39 a= 1.0000 / + &Cfp in= 1 ib= 40 ia= 1 kn= 40 a= 1.0000 / + &Cfp in= 1 ib= 41 ia= 1 kn= 41 a= 1.0000 / + &Cfp in= 1 ib= 42 ia= 1 kn= 42 a= 1.0000 / + &Cfp in= 1 ib= 43 ia= 1 kn= 43 a= 1.0000 / + &Cfp in= 1 ib= 44 ia= 1 kn= 44 a= 1.0000 / + &Cfp in= 1 ib= 45 ia= 1 kn= 45 a= 1.0000 / + &Cfp in= 1 ib= 46 ia= 1 kn= 46 a= 1.0000 / + &Cfp in= 1 ib= 47 ia= 1 kn= 47 a= 1.0000 / + &Cfp in= 1 ib= 48 ia= 1 kn= 48 a= 1.0000 / + &Cfp in= 1 ib= 49 ia= 1 kn= 49 a= 1.0000 / + &Cfp in= 1 ib= 50 ia= 1 kn= 50 a= 1.0000 / + &Cfp in= 1 ib= 51 ia= 1 kn= 51 a= 1.0000 / + &Cfp in= 1 ib= 52 ia= 1 kn= 52 a= 1.0000 / + &Cfp in= 1 ib= 53 ia= 1 kn= 53 a= 1.0000 / + &Cfp in= 1 ib= 54 ia= 1 kn= 54 a= 1.0000 / + &Cfp in= 1 ib= 55 ia= 1 kn= 55 a= 1.0000 / + &Cfp in= 1 ib= 56 ia= 1 kn= 56 a= 1.0000 / + &Cfp in= 1 ib= 57 ia= 1 kn= 57 a= 1.0000 / + &Cfp in= 1 ib= 58 ia= 1 kn= 58 a= 1.0000 / + &Cfp in= 1 ib= 59 ia= 1 kn= 59 a= 1.0000 / + &Cfp in= 1 ib= 60 ia= 1 kn= 60 a= 1.0000 / + &Cfp in= 1 ib= 61 ia= 1 kn= 61 a= 1.0000 / + &Cfp in= 1 ib= 62 ia= 1 kn= 62 a= 1.0000 / + &Cfp in= 1 ib= 63 ia= 1 kn= 63 a= 1.0000 / + &Cfp in= 1 ib= 64 ia= 1 kn= 64 a= 1.0000 / + &Cfp in= 1 ib= 65 ia= 1 kn= 65 a= 1.0000 / + &Cfp in= 1 ib= 66 ia= 1 kn= 66 a= 1.0000 / + &Cfp in= 1 ib= 67 ia= 1 kn= 67 a= 1.0000 / + &Cfp in= 1 ib= 68 ia= 1 kn= 68 a= 1.0000 / + &Cfp in= 1 ib= 69 ia= 1 kn= 69 a= 1.0000 / + &Cfp in= 1 ib= 70 ia= 1 kn= 70 a= 1.0000 / + &Cfp in= 1 ib= 71 ia= 1 kn= 71 a= 1.0000 / + &Cfp in= 1 ib= 72 ia= 1 kn= 72 a= 1.0000 / + &Cfp in= 1 ib= 73 ia= 1 kn= 73 a= 1.0000 / + &Cfp in= 1 ib= 74 ia= 1 kn= 74 a= 1.0000 / + &Cfp in= 1 ib= 75 ia= 1 kn= 75 a= 1.0000 / + &Cfp in= 1 ib= 76 ia= 1 kn= 76 a= 1.0000 / + &Cfp in= 1 ib= 77 ia= 1 kn= 77 a= 1.0000 / + &Cfp in= 1 ib= 78 ia= 1 kn= 78 a= 1.0000 / + &Cfp in= 1 ib= 79 ia= 1 kn= 79 a= 1.0000 / + &Cfp in= 1 ib= 80 ia= 1 kn= 80 a= 1.0000 / + &Cfp in= 1 ib= 81 ia= 1 kn= 81 a= 1.0000 / + &Cfp in= 1 ib= 82 ia= 1 kn= 82 a= 1.0000 / + &Cfp in= 1 ib= 83 ia= 1 kn= 83 a= 1.0000 / + &Cfp in= 1 ib= 84 ia= 1 kn= 84 a= 1.0000 / + &Cfp in= 1 ib= 85 ia= 1 kn= 85 a= 1.0000 / + &Cfp in= 1 ib= 86 ia= 1 kn= 86 a= 1.0000 / + &Cfp in= 1 ib= 87 ia= 1 kn= 87 a= 1.0000 / + &Cfp in= 1 ib= 88 ia= 1 kn= 88 a= 1.0000 / + &Cfp in= 1 ib= 89 ia= 1 kn= 89 a= 1.0000 / + &Cfp in= 1 ib= 90 ia= 1 kn= 90 a= 1.0000 / + &Cfp in= 1 ib= 91 ia= 1 kn= 91 a= 1.0000 / + &Cfp in= 1 ib= 92 ia= 1 kn= 92 a= 1.0000 / + &Cfp in= 1 ib= 93 ia= 1 kn= 93 a= 1.0000 / + &Cfp in= 1 ib= 94 ia= 1 kn= 94 a= 1.0000 / + &Cfp in= 1 ib= 95 ia= 1 kn= 95 a= 1.0000 / + &Cfp in= 1 ib= 96 ia= 1 kn= 96 a= 1.0000 / + &Cfp in= 1 ib= 97 ia= 1 kn= 97 a= 1.0000 / + &Cfp in= 1 ib= 98 ia= 1 kn= 98 a= 1.0000 / + &Cfp in= 1 ib= 99 ia= 1 kn= 99 a= 1.0000 / + &Cfp in= 1 ib=100 ia= 1 kn=100 a= 1.0000 / + &Cfp in= 1 ib=101 ia= 1 kn=101 a= 1.0000 / + &Cfp in= 1 ib=102 ia= 1 kn=102 a= 1.0000 / + &Cfp in= 1 ib=103 ia= 1 kn=103 a= 1.0000 / + &Cfp in= 1 ib=104 ia= 1 kn=104 a= 1.0000 / + &Cfp in= 1 ib=105 ia= 1 kn=105 a= 1.0000 / + &Cfp in= 1 ib=106 ia= 1 kn=106 a= 1.0000 / + &Cfp in= 1 ib=107 ia= 1 kn=107 a= 1.0000 / + &Cfp in= 1 ib=108 ia= 1 kn=108 a= 1.0000 / + &Cfp in= 1 ib=109 ia= 1 kn=109 a= 1.0000 / + &Cfp in= 1 ib=110 ia= 1 kn=110 a= 1.0000 / + &Cfp in= 1 ib=111 ia= 1 kn=111 a= 1.0000 / + &Cfp in= 1 ib=112 ia= 1 kn=112 a= 1.0000 / + &Cfp in= 1 ib=113 ia= 1 kn=113 a= 1.0000 / + &Cfp in= 1 ib=114 ia= 1 kn=114 a= 1.0000 / + &Cfp in= 1 ib=115 ia= 1 kn=115 a= 1.0000 / + &Cfp in= 1 ib=116 ia= 1 kn=116 a= 1.0000 / + &Cfp in= 1 ib=117 ia= 1 kn=117 a= 1.0000 / + &Cfp in= 1 ib=118 ia= 1 kn=118 a= 1.0000 / + &Cfp in= 1 ib=119 ia= 1 kn=119 a= 1.0000 / + &Cfp in= 1 ib=120 ia= 1 kn=120 a= 1.0000 / + &Cfp in= 1 ib=121 ia= 1 kn=121 a= 1.0000 / + &Cfp in= 1 ib=122 ia= 1 kn=122 a= 1.0000 / + &Cfp in= 1 ib=123 ia= 1 kn=123 a= 1.0000 / + &Cfp in= 1 ib=124 ia= 1 kn=124 a= 1.0000 / + &Cfp in= 1 ib=125 ia= 1 kn=125 a= 1.0000 / + &Cfp in= 1 ib=126 ia= 1 kn=126 a= 1.0000 / + &Cfp in= 1 ib=127 ia= 1 kn=127 a= 1.0000 / + &Cfp in= 1 ib=128 ia= 1 kn=128 a= 1.0000 / + &Cfp in= 1 ib=129 ia= 1 kn=129 a= 1.0000 / + &Cfp in= 1 ib=130 ia= 1 kn=130 a= 1.0000 / + &Cfp in= 1 ib=131 ia= 1 kn=131 a= 1.0000 / + &Cfp in= 1 ib=132 ia= 1 kn=132 a= 1.0000 / + &Cfp in= 1 ib=133 ia= 1 kn=133 a= 1.0000 / + &Cfp in= 1 ib=134 ia= 1 kn=134 a= 1.0000 / + &Cfp in= 1 ib=135 ia= 1 kn=135 a= 1.0000 / + &Cfp in= 1 ib=136 ia= 1 kn=136 a= 1.0000 / + &Cfp in= 1 ib=137 ia= 1 kn=137 a= 1.0000 / + &Cfp in= 1 ib=138 ia= 1 kn=138 a= 1.0000 / + &Cfp in= 1 ib=139 ia= 1 kn=139 a= 1.0000 / + &Cfp in= 1 ib=140 ia= 1 kn=140 a= 1.0000 / + &Cfp in=-1 ib=141 ia= 1 kn=141 a= 1.0000 / + &Coupling icto= 0 icfrom= 1 kind=1 ip1= 1 ip2= 1 ip3= 1 / diff --git a/book/notebooks/Breakup_template/B3-example-br-long.in b/book/notebooks/Breakup_template/B3-example-br-long.in new file mode 100644 index 0000000..c7bd21e --- /dev/null +++ b/book/notebooks/Breakup_template/B3-example-br-long.in @@ -0,0 +1,150 @@ +CDCC 8B+208Pb ; nuclear and coulomb s-waves +NAMELIST + &Fresco hcm= 0.01 rmatch= -60.000 rintp= 0.15 rsp= 0.0 + rasym= 1000.00 accrcy= 0.0010000 + jtmin= 0.0 jtmax= 9000.0 absend= -50.0000 + jump = 1 10 50 200 + jbord= 0.0 200.0 300.0 1000.0 9000.0 + thmin= 0.00 thmax= 20.00 thinc= 0.05 cutr=-20.00 + ips= 0.0000 it0= 0 iter= 0 iblock= 21 nnu= 24 + smallchan= 1.00E-12 smallcoup= 1.00E-12 + chans= 1 smats= 2 xstabl= 1 cdcc= 1 + elab= 656.0000 pel=1 exl=1 lab=1 lin=1 lex=1 / + + &Partition namep='8B ' massp= 8.0000 zp= 5 nex= 21 pwf=T + namet='208Pb ' masst=208.0000 zt= 82 qval= 0.1370/ + &States jp= 1.5 ptyp=-1 ep= 0.0000 cpot= 1 + jt= 0.0 ptyt= 1 et= 0.0000/ + &States jp= 0.5 ptyp= 1 ep= 0.1583 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.2180 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.3260 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.4830 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.6889 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.9438 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.2478 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.6007 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.0027 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.4536 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.9536 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 3.5025 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 4.1005 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 4.7474 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 5.4434 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 6.1884 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 6.9824 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 7.8253 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 8.7173 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 9.6583 cpot= 1 copyt= 1/ + &Partition namep='7Be ' massp= 7.0000 zp= 4 nex= -1 pwf=T + namet='208Pb +p' masst=209.0000 zt= 83 qval= 0.0000/ + &States jp= 0.0 ptyp= 1 ep= 0.0000 cpot= 2 + jt= 0.0 ptyt= 1 et= 0.0000/ + &Partition / ! END OF DEFINING PARTITIONS + + &Pot kp= 1 type= 0 shape= 0 + p(1:3)= 1.0000 0.0000 2.6500 / + &Pot kp= 2 type= 0 shape= 0 + p(1:3)= 208.0000 0.0000 1.3000 / + &Pot kp= 2 type= 1 shape= 0 + p(1:7)= 114.2000 1.2860 0.8530 9.4400 1.7390 0.8090 0.0000 / + &Pot kp= 3 type= 0 shape= 0 + p(1:3)= 208.0000 0.0000 1.3000 / + &Pot kp= 3 type= 1 shape= 0 + p(1:7)= 34.8190 1.1700 0.7500 15.3400 1.3200 0.6010 0.0000 / + &Pot kp= 4 type= 0 shape= 0 + p(1:3)= 1.0000 0.0000 2.3910 / + &Pot kp= 4 type= 1 shape= 0 + p(1:3)= 44.6750 2.3910 0.4800 / + &Pot kp= 4 type= 3 shape= 0 + p(1:3)= 4.8980 2.3910 0.4800 / + &Pot / ! END OF DEFINING POTENTIALS + + &Overlap kn1= 1 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 1 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= 0.1370 isc= 1 ipc=0 / + &Overlap kn1= 2 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0182 isc=12 ipc=2 nk= 20 er= -0.0344 / + &Overlap kn1= 3 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0771 isc=12 ipc=2 nk= 20 er= -0.0834 / + &Overlap kn1= 4 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.1850 isc=12 ipc=2 nk= 20 er= -0.1324 / + &Overlap kn1= 5 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.3419 isc=12 ipc=2 nk= 20 er= -0.1814 / + &Overlap kn1= 6 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.5479 isc=12 ipc=2 nk= 20 er= -0.2304 / + &Overlap kn1= 7 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.8028 isc=12 ipc=2 nk= 20 er= -0.2794 / + &Overlap kn1= 8 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.1067 isc=12 ipc=2 nk= 20 er= -0.3284 / + &Overlap kn1= 9 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.4596 isc=12 ipc=2 nk= 20 er= -0.3774 / + &Overlap kn1= 10 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.8616 isc=12 ipc=2 nk= 20 er= -0.4264 / + &Overlap kn1= 11 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.3125 isc=12 ipc=2 nk= 20 er= -0.4754 / + &Overlap kn1= 12 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.8125 isc=12 ipc=2 nk= 20 er= -0.5245 / + &Overlap kn1= 13 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -3.3614 isc=12 ipc=2 nk= 20 er= -0.5735 / + &Overlap kn1= 14 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -3.9594 isc=12 ipc=2 nk= 20 er= -0.6225 / + &Overlap kn1= 15 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -4.6064 isc=12 ipc=2 nk= 20 er= -0.6715 / + &Overlap kn1= 16 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -5.3023 isc=12 ipc=2 nk= 20 er= -0.7205 / + &Overlap kn1= 17 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -6.0473 isc=12 ipc=2 nk= 20 er= -0.7695 / + &Overlap kn1= 18 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -6.8413 isc=12 ipc=2 nk= 20 er= -0.8185 / + &Overlap kn1= 19 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -7.6843 isc=12 ipc=2 nk= 20 er= -0.8675 / + &Overlap kn1= 20 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -8.5763 isc=12 ipc=2 nk= 20 er= -0.9165 / + &Overlap kn1= 21 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -9.5173 isc=12 ipc=2 nk= 20 er= -0.9655 / + &Overlap / ! END OF DEFINING OVERLAPS + + &Coupling icto= 1 icfrom= 2 kind=3 ip1= 2 ip2= 0 ip3= 0 + p1= 3.0000 p2= 2.0000 / + &Cfp in= 1 ib= 1 ia= 1 kn= 1 a= 1.0000 / + &Cfp in= 1 ib= 2 ia= 1 kn= 2 a= 1.0000 / + &Cfp in= 1 ib= 3 ia= 1 kn= 3 a= 1.0000 / + &Cfp in= 1 ib= 4 ia= 1 kn= 4 a= 1.0000 / + &Cfp in= 1 ib= 5 ia= 1 kn= 5 a= 1.0000 / + &Cfp in= 1 ib= 6 ia= 1 kn= 6 a= 1.0000 / + &Cfp in= 1 ib= 7 ia= 1 kn= 7 a= 1.0000 / + &Cfp in= 1 ib= 8 ia= 1 kn= 8 a= 1.0000 / + &Cfp in= 1 ib= 9 ia= 1 kn= 9 a= 1.0000 / + &Cfp in= 1 ib= 10 ia= 1 kn= 10 a= 1.0000 / + &Cfp in= 1 ib= 11 ia= 1 kn= 11 a= 1.0000 / + &Cfp in= 1 ib= 12 ia= 1 kn= 12 a= 1.0000 / + &Cfp in= 1 ib= 13 ia= 1 kn= 13 a= 1.0000 / + &Cfp in= 1 ib= 14 ia= 1 kn= 14 a= 1.0000 / + &Cfp in= 1 ib= 15 ia= 1 kn= 15 a= 1.0000 / + &Cfp in= 1 ib= 16 ia= 1 kn= 16 a= 1.0000 / + &Cfp in= 1 ib= 17 ia= 1 kn= 17 a= 1.0000 / + &Cfp in= 1 ib= 18 ia= 1 kn= 18 a= 1.0000 / + &Cfp in= 1 ib= 19 ia= 1 kn= 19 a= 1.0000 / + &Cfp in= 1 ib= 20 ia= 1 kn= 20 a= 1.0000 / + &Cfp in= 1 ib= 21 ia= 1 kn= 21 a= 1.0000 / + ! ******* END OF CDCC INPUTS ******* \ No newline at end of file From 9291ab7a57a45d12912b696ce9b3d12346c42d76 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Wed, 13 May 2026 17:19:12 -0400 Subject: [PATCH 07/13] add full breakup notebook --- book/notebooks/Breakup.ipynb | 293 +++++++++++++++++++++++++++++++---- 1 file changed, 265 insertions(+), 28 deletions(-) diff --git a/book/notebooks/Breakup.ipynb b/book/notebooks/Breakup.ipynb index fe750ab..09e885e 100644 --- a/book/notebooks/Breakup.ipynb +++ b/book/notebooks/Breakup.ipynb @@ -7,12 +7,13 @@ "source": [ "# Breakup CDCC in $^{8}$B+$^{208}$Pb at 83 MeV/u\n", "\n", - "We will adapt [this example from the Frescox documentation](https://www.fresco.org.uk/examples/B3-example-br-long.in).\n", + "We will adapt [this example from the Frescox documentation](https://www.fresco.org.uk/examples/B3-example-br-long.in), which only models $s$-wave breakup. \n", "\n", "From the documentation:\n", "\n", "> Breakup calculations can be modeled as single-particle excitation into the continuum. In this example we show a typical CDCC calculation. It calculates the breakup of 8B into p + 7Be, under the field of 208Pb at intermediate energies. [...] The breakup of 8B has been measured many times with the aim of extracting the proton capture rate on 7Be\n", - "\n" + "\n", + "Note, that even when neglecting couplings with $l >0$, the CDCC caclulation will already take a minute or so to run! For production use, expensive calculations like this would be better performed using [{{{bfrescoxpro}}}](https://bfrescox.readthedocs.io/en/latest/advanced_users.html), which allows MPI and OpenMP parallelization." ] }, { @@ -22,15 +23,16 @@ "metadata": {}, "outputs": [], "source": [ + "import inspect\n", + "import json\n", "import os\n", "import shutil\n", + "from pathlib import Path\n", + "\n", + "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "from pathlib import Path\n", - "import inspect\n", "import pandas as pd\n", - "import json\n", - "\n", "from tqdm import tqdm\n", "\n", "with open(\"MatplotlibEsthetics.json\", \"r\") as fptr:\n", @@ -60,25 +62,29 @@ "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'bfrescoxpro'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m bfrescoxpro\n\u001b[32m 2\u001b[39m bfrescoxpro.information()\n", - "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'bfrescoxpro'" - ] + "data": { + "text/plain": [ + "{'supports_mpi': False,\n", + " 'supports_openmp': False,\n", + " 'supports_lapack': False,\n", + " 'supports_corex': False,\n", + " 'frescox_exe': PosixPath('/home/beyerk/Projects/Bfrescox/bfrescox_pypkg/src/bfrescox/bin/frescox')}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "import bfrescoxpro\n", - "bfrescoxpro.information()" + "import bfrescox\n", + "\n", + "bfrescox.information()" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "27d1b892-4c2a-471e-8b63-e269b2e193c3", "metadata": {}, "outputs": [], @@ -98,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "d764cf83-f48c-4d1a-bfde-b9da97718733", "metadata": { "editable": true, @@ -279,16 +285,41 @@ ] }, { - "cell_type": "markdown", - "id": "97ee0550-df30-4217-b444-3de2c1932b3c", + "cell_type": "code", + "execution_count": 5, + "id": "8c04875e-41aa-41be-a69d-928ca512f604", "metadata": {}, + "outputs": [], "source": [ - "## TODO use MPI" + "energy_bins = np.array(\n", + " [\n", + " 0.1583,\n", + " 0.2180,\n", + " 0.3260,\n", + " 0.4830,\n", + " 0.6889,\n", + " 0.9438,\n", + " 1.2478,\n", + " 1.6007,\n", + " 2.0027,\n", + " 2.4536,\n", + " 2.9536,\n", + " 3.5025,\n", + " 4.1005,\n", + " 4.7474,\n", + " 5.4434,\n", + " 6.1884,\n", + " 6.9824,\n", + " 7.8253,\n", + " 8.7173,\n", + " 9.6583,\n", + " ]\n", + ")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "eb6111f5-8563-48a7-918f-8a639bf440ea", "metadata": {}, "outputs": [], @@ -304,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "d1f7b9cb-3a59-4395-b81e-f8c0bf9b0303", "metadata": {}, "outputs": [ @@ -312,8 +343,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 6.15 ms, sys: 1.02 ms, total: 7.17 ms\n", - "Wall time: 1min 50s\n" + "CPU times: user 3.55 ms, sys: 2.26 ms, total: 5.81 ms\n", + "Wall time: 1min 46s\n" ] } ], @@ -326,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "id": "13a46916-041f-4533-b20f-c9dbce9c24ea", "metadata": {}, "outputs": [ @@ -336,7 +367,7 @@ "dict_keys(['channel_1', 'channel_2', 'channel_3', 'channel_4', 'channel_5', 'channel_6', 'channel_7', 'channel_8', 'channel_9', 'channel_10', 'channel_11', 'channel_12', 'channel_13', 'channel_14', 'channel_15', 'channel_16', 'channel_17', 'channel_18', 'channel_19', 'channel_20', 'channel_21'])" ] }, - "execution_count": 13, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -345,6 +376,212 @@ "results = bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", "results.keys()" ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8fec8ed3-d9ff-41d8-986c-8573cc56ffa7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAHQCAYAAAChlGDDAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAgiRJREFUeJzt3XdYVFf6B/DvHYYqZURAelNBxUKxxN6NUWMsibEk0TUxm8RN2Wyym7L+NMVs3PS6cTcmllgwJMausccWK6hgQUUERKTIgPQy5/fHOBNHhjLMwDDD9/M8PMzccu575s4ML+eee44khBAgIiIiagVk5g6AiIiIqLkw8SEiIqJWg4kPERERtRpMfIiIiKjVYOJDRERErQYTHyIiImo1mPgQERFRq8HEh4iIiFoNJj5ERETUajDxsQLBwcEIDg7WWbZs2TJIkoRly5bV2P7XX39F//79oVAoIEkSJk6cqF134sQJjBo1Ch4eHpAkCZGRkU0auznV9RoZYuHChZAkCfv27TNJXPfat28fJEnCwoULm6R8Q6WmpkKSJMyePdvcobQqkiRh6NCh5g6DWrChQ4dCkiRzh9HiMfFpZVJTU/HQQw/h6tWrmDNnDhYsWIBp06YBAAoLCzFu3DgcO3YM06ZNw4IFC/DMM8+YOeLGa2kJgyVp7X9k60tmZ8+eDUmSkJqa2qxxUevG951pyM0dADWNSZMm4b777oOPj4/O8l27dqGsrAwfffQRZsyYobPu2LFjyM7OxqJFi/DGG280Z7hmUdtrRHXz8/PD+fPn4ebmZu5QWpXz58/DycnJ3GFQC7ZixQqUlJSYO4wWj4mPlXJzc9P7hykzMxMA4Ovra9A6a1Tba0R1s7W1RefOnc0dRqvD15zqExgYaO4QLIMgi6BSqcQXX3whunbtKuzt7YWvr6+YN2+eUCqVIigoSAQFBels//333wsA4vvvvxdCCLF3714BQO+PZtva1mkUFxeL9957T/Ts2VM4OTmJNm3aiPvuu0+sXr26Rrya4y1YsEAcPXpUjB07VrRt21YAEFevXtVut3r1ajF06FDh5uYm7O3tRefOncU777wjysrKapQJQAwZMkTk5OSIuXPnCm9vb2FnZye6du0qvvvuO51tZ82aVWud9u7dq/c10tizZ4+YO3eu6NKli3BxcREODg4iIiJCLFy4UJSWltaIa8GCBTrlNkRjX8u7nThxQrzwwguiR48eom3btsLe3l507NhRvPzyy+LWrVs1yikvLxefffaZiIqKEgqFQjg6OoqgoCAxYcIEsXPnTp3XRN+P5vhXr14VAMSsWbP01uv9998XMTExwtnZWbRp00Z07txZPP/88yIrK6ve10WlUolly5aJfv36CQ8PD2Fvby/8/f3F6NGjxdq1a2tsn56eLp5//nnRsWNH4eDgINq2bSt69+4t3n77bZ3tDDmnQUFBtb4GQoha1937GczLyxOvvfaa6Ny5s3BwcBCurq5i+PDhYseOHTXqcfd7cdu2bWLIkCHC1dVVe0zNcYcMGaKz393vvR9//FH07t1bODo6irZt24pHH31UZGRk6H2djx07JkaNGiWcnZ2Fi4uLGDFihDh8+HCj38sNOeeaz+SVK1fE559/Lrp37y4cHBx06pScnCwef/xx4evrK2xtbYWPj494/PHHRXJyco3jFhYWirfffltEREQIFxcX4ezsLEJDQ8XUqVPFiRMndLbdsGGDGD58uPY7w8fHRwwePFh89dVXDapjQz47dzt//ryYNWuW8Pf3F7a2tsLLy0tMnz5dXLhwodGvYUPed0OGDNF5z2hUV1eL//znP6JXr16iTZs2wsnJSfTq1Ut8/fXXorq6usb2hnzXWiK2+FiIl156CZ9//jl8fHzw9NNPw9bWFhs2bMDRo0dRUVEBOzu7OvcPDg7GggULsG/fPuzfvx+zZs3SdoiOjIzEggULkJCQgA0bNuChhx7SdmrW/FYqlRg+fDji4+MRHR2NOXPmQKVSYceOHZgxYwaSkpLw7rvv1jjukSNH8K9//QsDBw7EnDlzkJubq411zpw5+P777+Hv748pU6ZAoVDg999/x/z587F7927s3LkTcrnuW1SpVGLAgAGws7PDww8/jPLycvz444+YM2cOZDIZZs2aBQDaDtvLly/HkCFDdPqr3NsR/F6LFy/GhQsX0L9/f4wbNw5lZWU4dOgQFi5ciH379mHXrl2wsbGps4y6NPa1vNf//vc/rF+/HkOGDMHIkSOhUqlw8uRJfPzxx9i2bRuOHj0KFxcX7fazZ8/GmjVr0K1bNzzxxBNwdHREZmYmDh48iO3bt2PkyJHa98Jbb72FoKAgnQ7M9fX5yc/Px7Bhw3D69GmEh4djzpw5sLOzw5UrV/D9999j8uTJaN++fZ1lvPnmm/jXv/6FkJAQTJ06FW5ubrhx4waOHz+OH3/8EY8++qh22xMnTuD+++/HrVu3MHjwYEyePBklJSU4d+4cFi5ciPnz52u3NeScvvTSS/jll19qfE40FixYgF9++QWnT5/Giy++CIVCAQDa3wBw7do1DB06FKmpqRg0aBDGjBmD4uJibN68GWPGjMGSJUswd+7cGvWPi4vD9u3b8cADD+CZZ57BtWvX6ny9NL7++mts3LgREyZMwJAhQ3D06FHExsbi9OnTSEhIgL29vXbb3377DaNHj0Z1dTUmT56MDh064OzZsxg2bBiGDx/eoONpNOacv/jiizhw4ADGjRuHsWPHal/348ePY+TIkbh9+zYmTJiArl274sKFC/jhhx+wYcMG7Nq1C7179wYACCEwZswYHD58GP369cNTTz0FuVyOjIwM7N27F4MGDUJMTAwA4L///S/+/Oc/w9vbGw8++CA8PDyQnZ2NM2fO4Pvvv8dzzz1Xbz0b8tnR2L59OyZPnozKyko8+OCD6NixIzIyMvDzzz9jy5Yt2Lt3L6Kjow1+DRvyvqvN448/jtWrVyMgIABPPfUUJEnC+vXr8dxzz+HgwYNYtWpVjX0a+l1rkcydeVH9Dh06JACIDh06iLy8PO3y0tJScd999+n9b7O21oy6/qOrbR8h/vhvbfHixTrLS0tLxf333y8kSRLx8fHa5Xe3MH3zzTe1HmvSpEmipKREb4yffvqpznJNeU8++aSoqqrSLk9KShI2NjaiS5cuOtvX1lJSX32vXLkiVCpVje3/+c9/CgA1Wh4M/S+5sa/lvfVITU3VeR00vv32WwFAvP/++9plSqVSSJIkYmJi9O6Tm5ur8xx6Whc0amvxmT59ugAgnnnmmRr/Rd6+fVsolUq95d3N3d1d+Pn5ieLi4hrrcnJytI/Ly8tFcHCwACBWrVpVY9v09HSd56Y+p5pzeHfr5d2GDBkiJEkSa9as0Vmen58vevbsKRwcHHRaQzTvRUmSxLZt2/SWqe+caOJ0cXERZ86c0VmnOR+xsbHaZdXV1aJjx44CgNi6davO9v/5z39qtIrWx5BzrnnNfH19RUpKis62KpVKdO7cWQAQP/zwg866tWvXCgAiPDxce4wzZ84IAGLixIk1YqqurtZp8YyOjhZ2dnbi5s2bNba9+z1VG0M+O7du3RIKhUK0a9dOJCUl6Wx39uxZ0aZNGxEVFaWzvDGvYV3vu3v/rK9evVoAEFFRUeL27dva5UVFRSImJkbvZ8jQ71pLw8THAjz11FMCgN4mRs0fxaZMfHJzc4WNjY3o1auX3vgSEhIEAPHqq6/WiCsyMlLvPpGRkUIul4v8/Pwa66qqqkS7du1E7969dZYDEE5OTqKgoKDGPoMHDxYAdD7YjU18apOXlycAiD/96U86yw1JfIx5LWurx71UKpVwdXUVw4YN0y4rKCgQAET//v31JgD3MjTxuXnzppDJZMLHx0cUFRU1KE593N3dRXBwsN5LnXeLi4sTAMSECRMafSwhGn9O6/oDpDmHDz/8sN59f/nlFwFA5zKL5r2o7w+5Rl2Jz5tvvllj+z179ggA4m9/+5t22YEDBwQAnfeGRnV1tQgLC2vwe9nQc655ze79h0YIIQ4ePCgAiH79+undd+DAgQKA2L9/vxDij8Rn+vTp9R43OjpaODk56b382xCGfHY+/fRTAUB8+eWXete/9NJLAoA2KWrsa2hI4jNy5EgBQO8l1l27dul9Pxj6XWtpeKnLApw6dQoAMGTIkBrrBg4caNRll4Y4fvw4qqura701vLKyEoD6rpN79enTp8aykpISnD59Gh4eHvj000/1HtPe3l5veZ06dYKrq2uN5QEBAQDUzcbOzs51VadexcXF+Oyzz7B+/XokJyfj9u3bEEJo11+/fr3RZRvzWurbdsmSJVi7di3OnTuHgoICqFQqvXG6urriwQcfxKZNmxAZGYkpU6Zg0KBB6Nu3r0nuFDp+/DhUKhUGDx6MNm3aNLqcmTNn4osvvkDXrl0xdepUDBkyBP369avRCf33338HADzwwAMNKrcpz+m9jhw5AgAoKCjQe45zcnIANPzz0hC9evWqsezuz4RGfHw8APX3xr1kMhn69++P5OTkBh2zsedcXx0133G1XWobPnw4Dh48iPj4eAwePBhdu3ZFZGQk1qxZg2vXruGhhx7CwIED0atXrxqX/WfOnIm//e1v6Nq1K6ZNm4YhQ4ZgwIAB8PT0bFC8hnx2NOf+9OnTes+95rU9f/48unbtarLPTV1OnToFmUym91L1kCFDYGNjo31f3K05vmvNhYmPBSgoKAAAvf0j5HI5PDw8mvT4eXl5ANRfdMePH691u6KiohrLvL29ayzLz8+HEAI5OTl46623DIqltuvZmr5A1dXVBpV3r8rKSgwfPhzHjh1Dt27d8Oijj8LT0xO2trYAgLfeegvl5eWNLt+Y1/Jejz76KNavX4/Q0FA89NBD8Pb21vbl+PTTT2vEGRsbi8WLF2P16tVYsGABAMDBwQEPP/wwPvzww3r739RFqVQCUN/qboxPPvkEoaGh+P777/H+++/j/fffh1wux9ixY/HRRx+hY8eOBh+vqc/pvTTneOfOndi5c2et2zX089IQ+j4X+j4TdX2X1LVcn8aec3111MRV29ASmuWaY9rY2GDPnj14++23ERcXh3/84x8AABcXF8yaNQv/+te/tH+UX375ZXh4eODrr7/G559/jk8//RSSJGHIkCH44IMP9CaN92roZ0dz7v/3v//VWZ7m3Jvqc1OXgoICuLu76+0Hqvn7kZ2dXWNdU3/XmhMTHwug+W/35s2bCA0N1VlXVVWF3Nxc+Pv7N/nx//rXv+Ljjz82aF99o4hqyouKitL+p9dSbNiwAceOHcPs2bPx/fff66y7ceOGwYnavYx5Le924sQJrF+/HiNHjsS2bdt0OoGrVCr8+9//rrGPo6MjFi5ciIULFyI9PR2//fYbli1bhh9++AGpqak4cOBAo+PRfEka23JiY2ODl156CS+99BKys7Nx8OBBrF27Fj/++COSkpKQlJQEe3t7g47X1Of0Xppz/Nlnn+GFF14waN+mHnVX8x/8zZs39a6vbbk+jT3ndX0nZGVl6d3nxo0bOtsBQNu2bfHJJ5/gk08+weXLl7F//34sWbIEX375JZRKJVauXKnd9oknnsATTzwBpVKJw4cPY/369fjuu+9w//3348KFC/W2/jT0s6OJ7/Tp0+jRo0e9r4WpPjd1cXNzw61bt1BZWalN9jU0fz/0texYM47cbAE0dwDs37+/xrqDBw82eebdp08fyGQyo/4w3s3Z2RkRERFISkrCrVu3TFKmPppLgIa8PpcvXwYATJ48ucY6fa+/oUz1WmrinDBhQo07344dO4bS0tI69w8ICMDMmTOxY8cOdOzYEQcPHtT+twqoL3sY8rpp6vXbb7+huLjYgJrUzsvLC5MnT8a6deswfPhwXLlyBYmJiQCA++67DwCwbdu2estpzDmt771T13pNbKb6vJhSVFQUAPX3xr1UKhUOHz7c4LJMec41cdU2UvbevXsBQOduqLt17NgRTz75JPbv3w9nZ2ds2LBB73YKhQJjx47F//73P8yePRu3bt3Cb7/9ZlCsdX12DD33hr6GjflOi4qKgkql0lvP3377DdXV1bW+rtaKiY8F0NxSvGjRIp1EoaysDK+//nqTH9/LywszZ87EiRMn8M477+j90F25cgVXr15tcJkvv/wyKioqMGfOHG1z793y8/ONbg1q164dACAtLa3B+2huXb73CzglJUXbnG4MU72WtcWZnZ2NefPm1dg+JycHZ8+erbG8uLgYRUVFkMvlOk3h7dq1Q3p6egNqpObp6Ylp06bhxo0beOWVV3T6GgHqpn3N5YzalJeX49ChQzWWV1ZWat/3mj4VDz74IIKDg7Fx40asWbOmxj4ZGRnax405p/W9d+pa36tXLwwaNAg///wzvvvuO737nz17Vu/lhaY2YMAAdOjQAXv37q2RNP73v/9tcP8ewDTn/O64wsPDcfDgQcTFxemsi4uLw4EDBxAWFqbtm3T16lWkpKTUKCc/Px/l5eVwdHTULtu7d69Ofy4NzetfXx83Qz47f/rTn6BQKPDWW2/h2LFjNfZRqVQ670NDX8PGfKfNmTMHAPD666/rjOpcUlKC1157DQDw5JNPNrg8a8BLXRZgwIABeP755/HFF1+gW7duePjhh7Xj+LRt27ZZplz48ssvcenSJfzf//0fVq5ciYEDB6J9+/bIzMzE+fPncfz4caxZswYhISENKm/OnDk4efIkvv76a3To0AH3338/AgMDcevWLVy9ehW//fYb/vSnP+Gbb75pdMzh4eHw8/PD2rVrYWtri6CgIEiShMcffxxBQUF699GMu/Hxxx/j7NmziIqKQlpaGjZv3oxx48YZ9IVTG1O8lr1798aAAQPw888/o3///hg4cCBu3ryJbdu2ITw8vMbo29evX0dUVBS6d++OHj16ICAgAIWFhdi8eTOysrLwwgsv6Iz5M2LECKxduxYPPvggoqOjYWtri8GDB2Pw4MF11isxMRHffPMN9u3bh/vvvx92dna4evUqduzYgY0bN9Y5FlBpaSkGDhyIjh07IiYmBkFBQSgrK8POnTtx/vx5TJgwAV26dAEA2NnZ4ccff8To0aMxY8YMLFmyBPfddx/Kyspw/vx57N69G1VVVQAad06HDRsGmUyG119/HYmJiWjbti0A4J///Kf29fnggw8wd+5cTJkyBS4uLlAoFPjLX/4CAFi9ejWGDx+OJ598Ep9//jn69u0LhUKBjIwMnDlzBomJiThy5Ai8vLxqfT2agkwmw7fffosxY8ZgwoQJmDJlCjp06IAzZ85g586deOCBB7Bt2zbIZA37n9jYc64hSRKWL1+OUaNG4dFHH8VDDz2Ezp074+LFi/jll1/g4uKCFStWaOM6ffo0Jk+ejN69e6NLly7w9fVFTk4ONmzYgMrKSp2EdtKkSXB2dsZ9992H4OBgCCFw4MABHD9+HDExMTpj8OhjyGenXbt2iIuL006HM2LECERERECSJKSnp+PIkSPIy8tDWVlZo17D+t53+syYMQMbNmzAunXrEBERgYkTJ0KSJPzyyy+4evUqHn30UcycObPec2RVzHpPGTWYZuTmzp07a0cefe655xo8crNGY8fxEUI9dsoXX3wh+vXrJ1xdXYWdnZ0ICAgQw4cPF5988onOeBYNvQV706ZNYty4ccLT01PY2tqK9u3bi969e4s333xTnD9/Xmdb1HGLdW23eR47dkwMHz5cuLq6CkmSdOpeW33T0tLEjBkzhK+vr3BwcBBdu3YVixcvFpWVlXXeUmzIaLemeC3z8vLEs88+K4KCgoS9vb0IDQ0Vr7/+uiguLq7xnsjPzxdvvfWWGDZsmPD19RV2dnbC29tbDBkyRKxevbrGbbo3b94U06dPF15eXkImk+kcv66Rm4uKisS7774runfvLhwdHYWzs7Po0qWLePHFF/WOo3K3iooKsXjxYjFmzBgREBAg7O3thYeHh+jbt6/4z3/+I8rLy2vsc+3aNfHss8+K4OBgYWtrK9zd3UWfPn3EokWLdLYz9JwKIcTKlSu1Y+7grpGbNT766CPt5xF6hpQoLCwUixYtEtHR0aJNmzbCwcFBBAcHi7Fjx4olS5bo3L7ckKEVDH3v1XWefv/9dzFy5Ejh7OwsnJ2dtSM3z5s3TwDQGUeqPg095/Xdii2EEBcuXBCPPfaY8Pb2FnK5XHh7e4uZM2fWGPE4PT1dvP7666J///6iffv2ws7OTvj5+YkxY8boHZ9o4sSJIiQkRDuqdWRkpFi8eLEoLCyst36GfnaEUL/28+bNEx07dhT29vbCxcVFhIeHi8cee0ysX7++0a+hEHW/7+oaufmrr74SMTExwtHRUTg6Ooro6Gjx5Zdf1jlysz4NOY8tnSSEnjZAIiJqdQYMGICjR4+ioKCgyW6vJjI39vEhImpFSkpK9ParW7ZsGQ4fPozRo0cz6SGrxhYfIqJW5MKFC4iKisKoUaPQsWNHVFVVIT4+HgcPHoRCocDhw4e1famIrBETHyKiViQ/Px+vvvoq9u/fj6ysLJSXl8Pb2xsjR47Em2++iQ4dOpg7RKImxcSHiIiIWg328SEiIqJWg4kPERERtRpMfIiIiKjVYOJDRERErQanrNAjPz9fO9y9KXl6eiInJ8fk5bYUrJ/ls/Y6Wnv9AOuvI+tn+ZqijnK5XDu1TL3bmvTIVqKqqgqVlZUmLVOSJG3Z1ngjHetn+ay9jtZeP8D668j6Wb6WUEde6iIiIqJWg4kPERERtRpMfIiIiKjVYOJDRERErQYTHyIiImo1mPgQERFRq8HEh4iIiFoNJj5ERETUajDxISIiolaDiQ8RERG1Gkx8iIiIqNVg4kNEREStBhMfahaiqhKiCWa8JyIiMgRnZ6cmJaqrIX5eAbF3CxAQAtmr/4Ik59uOiIjMgy0+1KTEgV8hfl0PVFYAKRch9m01d0hERNSKMfGhJiNU1eqkBwDaeamXbVwDUV5uxqiIiKg1Y+JDTefMCSAnC2jjAtmCz9XJT2kxkHTK3JEREVErxcSHmow4ewIAIN03FJKjE6Sofurl8b+bMywiImrFWmwv03PnzmHjxo24evUq8vPz8corr6BPnz61bn/06FH8+uuvSE1NRVVVFfz9/fHII48gMjKy+YImHeLCGQCA1CVS/TvqPohdGyDOHIOoqmInZyIianYttsWnvLwcwcHBePLJJxu0/fnz59GjRw+8/vrreP/99xEREYHFixfj6tWrTRwp6SPycoDsG4BMBoRFqBd27Aw4uwIlxUDqJfMGSERErVKL/Zc7KioKUVFRDd5+9uzZOs9nzJiBEydO4OTJkwgJCTFxdFQfcVHd2oOgjpAcnQAAkswGCAkDzp6ASLsCqWMXM0ZIREStUYtNfIylUqlQWloKZ2fnWreprKxEZWWl9rkkSXB0dNQ+NiVNeaYut6WoUb/L59XPw7vr1FkK6qDu+5OWYlGvhbWfP8D662jt9QOsv46sn+VrCXW02sRn06ZNKCsrQ79+/WrdZv369YiLi9M+DwkJweLFi+Hp6dlkcXl7ezdZ2S2Bpn5ZmWmoBOAe3QdOPj7a9SWRvZG3ORbyzDR437XcUlj7+QOsv47WXj/A+uvI+lk+c9bRKhOfgwcPIi4uDq+++irc3Nxq3W7SpEkYP3689rkmA83JyUGViadXkCQJ3t7eyMrKghDCpGW3BHfXT1VZgerUywAApYs7Cm7c0G4nXN0BAJVpV5CZdg2SrZ1Z4jWUtZ8/wPrraO31A6y/jqyf5WuqOsrl8gY3Wlhd4nPo0CF88803ePnll9GjR486t7W1tYWtra3edU31phNCWO0bGrhTv4xrQHUV4OQM0c4LuKu+QtFO3cG5qBAiPRUI6WS+YBvB2s8fYP11tPb6AdZfR9bP8pmzji32rq7GOHjwIL7++mu8+OKLiI6ONnc4rZZIu6J+ENShxnVcSZKAAHVnc5F5rblDIyKiVq7FJj5lZWVITU1FamoqACA7OxupqanIzc0FAKxevRpffvmldvuDBw/iq6++whNPPIFOnTpBqVRCqVSipKTEHOG3bncSHykwVO9qydtP/SDrenNFREREBKAFX+q6cuUK3nrrLe3zFStWAACGDBmCefPmIT8/X5sEAcCuXbtQXV2NpUuXYunSpdrlmu2p+Yj0O2MnBehPfNDeX70dEx8iImpmLTbxiYiIwLp162pdf28ys3DhwiaOiBpCCAFkpgEAJL8gvdtI3n4QAHCTiQ8RETWvFnupiyzUrRygtASwsQE0l7TupVmefQOiurr5YiMiolaPiQ+ZlLh+p8Nyez9Icv13zKGtB2Bnp77zK+9m8wVHREStHhMfMq2MVAC1X+YCAEkmA7x81U9u8HIXERE1HyY+ZFLiurp/D+pIfABAaq++3CXYz4eIiJoREx8yKXE9FUDdLT4AAK8701XkZDVtQERERHdh4kMmI6qqgBsZ6icNTHxE9o26tyMiIjIhJj5kMlWZaeoOy/aOQDuvOreVPDUtPkx8iIio+TDxIZOpvDMxKfwC1R2Y6+J5Z2beWzm8pZ2IiJoNEx8yGU3iU2//HgBQuANyW6C6Wj32DxERUTNg4kMmU3ntzuSkDUh8JJnsj1YfXu4iIqJmwsSHTKbCkBYfgB2ciYio2Rk0V1dRUZFRB3NycoKsvr4fZJFEWSmqNZOONjDxkTy91XN28ZZ2IiJqJgYlPk8++aRRB5s/fz66detmVBnUQmWmq3+7KiC5uDVsHw/1pS6Rm91EQREREekyeHb23r17IyiogZcy7igvL8emTZsMPRRZkD8GLgxu8D6Sh5e6xSePiQ8RETUPgxOf++67DwMHDjRon9u3bzPxsXLayUn9Ahu+k0d79e9cTlRKRETNw6AON7NmzUJoaKjBB3FwcMCsWbPg6+tr8L5kIe4kPpJ/cMP3aeep/l18G6K0xPQxERER3cOgFp+xY8c26iC2traN3pcsg8i4k/g09I4uAJKDE+DsAhTdBvJuAv4hTRUeERERAN7OTiYgCvKB20pAkgBfAy51AUA7zeUu9vMhIqKmZ3Afn7ulpqYiIyNDp89PQkIC1q9fj8rKSgwcOJAtPa3BnfF75AEhgL0DhBAN39fDC7h2GSIvG1IThUdERKRhVIvPDz/8gMOHD2ufZ2dn48MPP0R2tvq/9+XLl2PXrl3GRUgtnki9BACw69TV4H2lduzgTEREzceoxOfatWvo3Lmz9vn+/fshk8mwePFivPfee7jvvvuwc+dOo4Oklk2b+IQZnvho7uziWD5ERNQcjEp8SkpK4OLion0eHx+PHj16wNXVFQDQo0cPZGVxVF5rJoQAjGnx8fBSP8hjiw8RETU9oxIfhUKB69fV0xTk5+cjJSUFPXr00K4vKyuDJLHnhlXLywaKCgEbG9iFdDJ8/3Z3Eh+2+BARUTMwqnNz7969sW3bNlRUVODy5cuwtbVFnz59tOuvXbuG9u3bGx0ktVziygX1g4AQSHb2hheg6eNTWgxRUgTJydl0wREREd3DqBafadOmoW/fvjhw4AAKCgrw3HPPQaFQAFBfBvv99991WoDICl05DwCQOjaifw8Ayd4e0MztxVYfIiJqYka1+Dg4OOCFF16odd0333wDOzs7Yw5BLZy4rEl8ujS+EI/2wO0C9Z1dgYaPDE5ERNRQTTaAoUwmg5OTE+Ryo3IrasFEWQmgGbHZiMRH0tzZxclKiYioiXEAQ2q8KxcBoQLaeUFStGt8OZoOzkx8iIioiXEAQ2o0cT4BACCFdTOuoDuJj+AghkRE1MQ4gCE1mkhKUD+IiDKqHM2lLo7eTERETY0DGFKjiIJ8IOMqIEmQukYaV5jHH5e6DJrni4iIyEAcwJAaRSSeUj8I7ABJczt6Y2n6+JSVAsW3jSuLiIioDhzAkBpFHN0HAJB69Da6LMnWDnBzBwpuqTs4O7saXSYREZE+HMCwFVDFfovqF6ah+vW5ENmZRpcn8nKAC2cAAFK/YUaXB+CPy10cxJCIiJoQBzC0ciInC2L3JkAIoLQEYs8WSNPmGlfm73vV5YV3h+TpbZI4pXZeEFcuQOTeBC+OEhFRUzHJAIaVlZVITk7G8ePHUVhYqC6YAxi2COK3HeokRfP86D6IqsrGlycExOHdAACp/3Cj49PS3NnFWdqJiKgJGZ34bN26FU8//TTmz5+PDz/8EGlpaQCAwsJCPPnkk9izZ4/RQVLjCFU1xCH1OEqyZ15T96Mpug2cOdH4Qq+cB7JvAPYOkKL7myhS3DWWDy91ERFR0zEq8dm7dy+WL1+OyMhIPPvsszrrXF1dERERoTPAITWztBT1HFiOTkBkX0gx6kRFJCc2ukhxWJ3ISjEDIDk4miRMAJC0fXzY4kNERE3HqMRn8+bN6NWrF1588UXExMTUWB8aGor09HRjDkFGEHc6ICOsGyQbGyCoo3p5ekrjyisvhzh+AAAg9R9hkhi17rrUJVQq05ZNRER0h1GJT1ZWFqKiah+119nZGUVFRcYcgoygSXykzuo766TAEPWK9KuNGihQxB9Rj7Xj0R7o1NVkcQIA3L0AGxugogJQ3jJt2URERHcYlfg4OTlpOzPrk5GRob29nZqXqKoELp0D8EfiA29/QC4HSksadUlJHLlzmav/CEgyk/SL15LkcqDdnVYfE9xyT0REpI9Rf72ioqKwe/duFBcX11iXnp6O3bt3670ERs3g6iWgohxwcQN8AwEAktxW+xjpVw0qThQV/jF2T98hJg1Vq72v+lg3mfgQEVHTMHoAQ5VKhb/97W9Yu3YtAGDfvn34/PPP8dprr8HNzQ0PP/ywSQIlw2gvc4V312mdkQJC1esNTXzOHAdUKsA/GJKXj+kCvYvU3k/94Ob1JimfiIjIqEF23N3d8f7772PNmjXau7cOHDgABwcHDBgwADNnztROWGqoc+fOYePGjbh69Sry8/Pxyiuv6EyHoU9SUhJWrFiB9PR0tGvXDlOmTMHQoUMbdfyWRiQnQrU5FrKRExo0TYS2Y3Pne0bO9g9Sr89MM+z4p44AAKSo+wzazyDt1QmVyL7RdMcgIqJWzejRBd3c3PDMM8/gmWeeQWFhIVQqFVxdXSEzsg9IeXk5goODMXz4cHz44Yf1bp+dnY33338fo0aNwvPPP4/ExER88803UCgUiIyMNCqW5iRu5apvQff2h2Rvr16WdR2qrxYBJcVQJSdC9sxrkCL71l5GeTmQcgHAXf177pDa+0MABrWqiIpy4HyCev+ofgbVxxBSez+DYyMiIjKESYdVbmzrjj5RUVF13jF2r19//RVeXl544oknAAD+/v64cOECtmzZ0qITH5F2BeLIPkjjHwVyb0K16G+AUAGBoZD98xNIkgTVT8uBkmL1eDylJVCt+gayiCj15J76XEoEqqqAth7AvZelvDWXkzIhVNWQZDb1B3npnPpuq7YegH+wUfWtk5e6jw9ysiCqq9W34BMREZmQQYlPbm5uow7i4eHRqP0McenSJXTv3l1nWc+ePbFs2bImP3Z9REkRRMpFlF53h6gWEC5uENt+gii4BVy+oJ6VvLwUsLVTJz2AevDB66kQ7p5AonqkZdnL70D1n38Bt3Ih9m+DNPIh/cc7fQwAIHWPgSTdM/NVO09AbgtUVQJ5OUAD5toSiafU5XWLrlmeKbVtp34NKivUs7Q3UV8iIiJqvQxKfObNm9eog8TGxjZqP0MolUq4ubnpLHNzc0NpaSkqKir0TpZaWVmJyso/5q2SJAmOjo7axyaTdR3Vn72FutJGcXDnH3NqyWSASgVx6ggkTx91641vAGQhYcD4aVCt+BJiUyzQdygkV4VuOUL8kfhE3lejHpKNHCovHyAzDcjObFBHZZFUf+KjWW7M6ybZ2Khju35NHdudu7xaAlPUr6Wz9jpae/0A668j62f5WkIdDUp87p2WwtKtX78ecXFx2uchISFYvHgxPD09TXqcivJi3OrQGRAqVN/KheqeAfokpzYQJeohASR7Byj+/AryP38XNmeOwaatB8oBuA57AG4+PhAPP4abB35F5dVk2MV9h3Z/fw+Sra22rPLkJGTn50FycITvsNGQ7OxrxJMb3AGlmWlwLbkNF5+6E5/qvBxk3kgHZDL4DL0fMmeXOrf39jZutvbcoFCUXr8G19KiemMzB2PrZwmsvY7WXj/A+uvI+lk+c9bRoMSnJd8hpVAoUFBQoLOsoKAAjo6Oelt7AGDSpEkYP3689rkmA83JyUFVVZXpgrNvA+mND+Ht7Y2srCxIZerLWuJcPERyEmSDRqP6+0+B5CRgwEgUdogA5LaoupaCqmspgEyG4m69UXJDfbeTmP5n4F+vovTwXmS8MBOyBx6GFN0fko0Nqpd/rT5m917IytM/AnK1Qn3psSD5HIpu1H0HlerknbnW/IJw83YRcFv/SNySJGnr15hRobWxubVTx3bpQr2xNSdT1a8ls/Y6Wnv9AOuvI+tn+ZqqjnK5vMGNFibr3FxWVlbr9BTN0cenU6dOiI+P11l25swZhIWF1bqPra0tbO9qLblbU73phBDAnVYYKSIaUkQ0AED2t0VAVoa6g69cDmnsIxAbV6u3GzAS8Gj/R0whnSCb9wZUSz8B0lKgWvJvwN0DUpeeEAlHAZkM0oPTaq+DZqDArOv11lOkXFTHEBLeoNdECGHca3fn0pu4WX9s5mB0/SyAtdfR2usHWH8dWT/LZ846GpX4VFRUIC4uDnv27MHt27dr3a4xfXzKysqQlZWlfZ6dnY3U1FQ4OzvDw8MDq1evxq1bt/CXv/wFADB69Gjs2LEDP/zwA4YNG4bExEQcOXIEr732muEVMwNJJvtjVGUA0gNTIM6eAHJuQBr3aM3te/aB7J2vIfZtg9i/Td3h+dBu9bqRD0HyCaj9WJrbxrPqv21cXFUnPgitPYE0pT9uaefozUREZHpGJT7ffvst9u/fj969e6NLly5o06aNqeLClStX8NZbb2mfr1ixAgAwZMgQzJs3D/n5+Tp3mXl5eeG1117D8uXLsXXrVrRr1w7PPPNMi76VvS6S3Bayv78PqKr19tMBAMmtLaSHZkCMfRji2AEg5SKkLj2A6P51F+7tr/6tzIMoK4Xk4Kh3M1FdDaReVh8rNLzRdTHInUEMcSsHorKi9lv2iYiIGsGoxOfYsWMYMWIEnn76aVPFoxUREYF169bVul7fHWYRERH497//bfJYzEWSy9GQUyTZ2kEaMAIYMKJh5bZxVs/hdbtA3bIS1EH/htevqef7cmwDaKaTaGouCu14Rci+AfgFNc9xiYioVTBqeGVJkhASEmKqWKg53UlkRFZGrZuIq8nqByGdTD4be20kSdK2SBk6rQYREVF9jPpr1qtXL5w9e9ZUsVAzkrwbMCGotmNz8/Tv0ZA0o0NnXGvW4xIRkfUzKvGZMmUKbt68iSVLliAlJQWFhYUoKiqq8UMtkCbxqaODs6bFp9n692j4BauPfz21eY9LRERWz6g+Pi+++CIAIDU1FXv27Kl1u+YYuZkMo7l7StTS4iNKioAb6eonzd7iE6S+s+s6W3yIiMi0jEp8pkyZYtVDa1u1uycrFaLmeUy9pP7t6Q3JRXcqkCan6dCcexOirASSg1PzHp+IiKyWUYnP1KlTTRUHNTcPb8DGBigvA/LzAHfdQSbvHriwuUnOroCbu3ry1utpQIfOzR4DERFZJ5PdqlNWVoaMjAxkZGSgrKzMVMVSE5HkcnXyA+jt4CxS7tzR1dz9ezT81a0+Ij3FPMcnIiKrZPSUFZcvX8aqVatw4cIFqFQqAIBMJkPnzp3x2GOPoUOHWsaIIfPz9gNuXofIug6pS0/tYiEEcGfEZqmZRmy+lxTUCSIpXjuAIhERkSkYlfhcunQJCxcuhFwux/Dhw+Hnp+43cv36dRw6dAgLFizAwoUL0bFjR5MES6b1x9QV94zlk5MFFN0G5LZAgHnGaZKCO6o7X19j4kNERKZjVOKzdu1auLu745133oFCodBZ98gjj2D+/PlYs2YN5s+fb8xhqKl4awYx1L3Upenfg8BQSHL9k7g2ueBO6t+ZaRDl5ZDs9U/bQUREZAij+vhcunQJo0aNqpH0AIBCocDIkSNx6dIlYw5BTUhqX8sghpfPqdeHmrFTscIdcGsLqFRAxlXzxUFERFbF6Ckrqqura12vUql4u3tLppnBPS8boviPgSbFxUQAgBQeYY6o1MeWJCBIfYlUO3UGERGRkYxKfMLDw7Fjxw7k5OTUWJebm4tff/0VnTvzVuSWSnJxBbx81U+unAcAiMJ8dZ8fSQI6mS/xAf4YMVpcOmfWOIiIyHoY1cdn+vTpWLBgAV566SX06dMHPj4+AIDMzEycOHECNjY2mD59ukkCpaYhdeoKkZ0JcfkcpB69geQk9Qq/YEhtXMwbW3g3defrS0n6B1kkIiIykFGJT0hICN577z2sWbMGJ06cQEVFBQDAzs4OkZGRmDZtGvz9/U0SKDWRTl2BQ7sgLt1p8UmKB6BOOswuuBNgZwfcLlBPn+EbaO6IiIjIwhk9jo+/vz9effVVqFQqFBYWAgBcXV0hk5lsbERqQlLHrupWldRkiNsFECcOqpdH3WfWuACo7ygL7QxcOANxMRESEx8iIjKSybITmUwGhUIBhULBpMeSePkAnt5AVRVUH7wBlJWqn4e1gBYfAJImjuRE8wZCRERWwaAWn9zcXACAh4eHzvP6aLanlkeSJEjjH4X4/jPtbOxS/xEtpj+NFKbu5yOSE9nPh4iIjGZQ4jNv3jwAwKpVqyCXy7XP6xMbG2t4ZNRspPuGQuzeBKSlAD37QBo5wdwh/SE0TD2CdKFSPd6QN/uMERFR4xmU+Dz77LMAABsbG53nZNkkmQ1kf1sEFCoh3RnNuaWQbO3UyU9yEkRyIiQmPkREZASDEp+hQ4fW+Zwsl+TUBnBqY+4w9JLCukEkJwEXE4HBY8wdDhERWTD2QqYWT+rcAwAgzp+GUKnMHA0REVkyo25nT01NRUZGBgYOHKhdlpCQgPXr16OyshIDBw7E2LFjjQ6SWrnQzoCdvXo8n+vXzDZjPBERWT6jWnx++OEHHD58WPs8OzsbH374IbKzswEAy5cvx65du4yLkFo9ydZWe3u9OJdg3mCIiMiiGZX4XLt2TWcurv3790Mmk2Hx4sV47733cN9992Hnzp1GB0kkRUQCYOJDRETGMSrxKSkpgYvLH/M5xcfHo0ePHnB1dQUA9OjRA1lZWcZFSARA6hKlfnApCaKywrzBEBGRxTIq8VEoFLh+/ToAID8/HykpKejRo4d2fVlZGQecI9PwDQAU7kBlBcDZ2omIqJGM6tzcu3dvbNu2DRUVFbh8+TJsbW3Rp08f7fpr166hffv2RgdJJEkSpC6REEf2QJxLgNQ10twhERGRBTKqxWfatGno27cvDhw4gIKCAjz33HNQKBQA1JfBfv/9d50WICKj3El2xLl488ZBREQWy6gWHwcHB7zwwgu1rvvmm29gZ2dnzCGItKSuPdUzyWekQhTfhtTGpb5diIiIdBiV+NytrKwMRUVFNZaXlJRwklIyCcm1LeAToJ5MNTkJiLrP3CEREZGFMSrxqaioQFxcHPbs2YPbt2/Xuh0nKSVTkcIiIG6kq+ftYuJDREQGMirx+fbbb7F//3707t0bXbp0QZs2LXOuJ7IiYd2A/dshkhPNHQkREVkgoxKfY8eOYcSIEXj66adNFQ9RnaSwbup+PulXIUqKIDk5mzskIiKyIEbd1SVJEkJCOG8SNR9J4Q54egNCAFcvmTscIiKyMEYlPr169cLZs2dNFQtRg0ih4QAAkXLRzJEQEZGlMSrxmTJlCm7evIklS5YgJSUFhYWFKCoqqvFDZFJMfIiIqJGM6uPz4osvAgBSU1OxZ8+eWrfjXV1kSlJouLqfT8pFCCE4LQoRETWYUYnPlClT+EeHmp9/MCC3BUqKgJuZgLefuSMiIiILYVTiM3XqVFPFQdRgktwWCAxVt/hcuwyJiQ8RETWQUX18NCorK5GcnIzjx4+jsLDQFEUS1UkKDFU/SL9q3kCIiMiiGD1lxdatW/Hjjz+ipKQEADB//nx069YNhYWF+Otf/4qZM2di+PDhRgdKpCNAPYyCSE8xcyBERGRJjGrx2bt3L5YvX47IyEg8++yzOutcXV0RERGBw4cPGxUgkT5SQAf1g7QUCCHMGwwREVkMoxKfzZs3o1evXnjxxRcRExNTY31oaCjS09ONOQSRfv5BgEwGFBUC+XnmjoaIiCyEUZe6srKy8MADD9S63tnZ2ahxfLZv345NmzZBqVQiKCgIc+bMQceOHWvdfsuWLfj111+Rm5sLV1dX9O3bFzNmzICdnV2jY6CWSbK1U8/Ufv2aup+Pu4e5QyIiIgtgVIuPk5NTnZ2ZMzIyoFAoGlX24cOHsWLFCjz88MNYvHgxgoKCsGjRIhQUFOjd/uDBg1i9ejUeeeQRfPLJJ3jmmWdw5MgRrFmzplHHp5ZP8gsGAIjMa+YNhIiILIZRiU9UVBR2796N4uLiGuvS09Oxe/duvZfAGmLz5s0YMWIEhg0bBn9/f8ydOxd2dnbYu3ev3u0vXryI8PBwDBw4EF5eXujZsycGDBiAy5cvN+r4ZAF8A9S/M9PMGwcREVkMoy51TZs2DW+++Sb+9re/aROcffv2Yc+ePTh69Cjatm2Lhx9+2OByq6qqkJKSgokTJ2qXyWQydO/eHcnJyXr3CQ8Px4EDB3D58mV07NgRN2/eRHx8PAYNGlTrcSorK1FZWal9LkkSHB0dtY9NSVOetQ74aI76SX6BEABEZnqTH9fazx9g/XW09voB1l9H1s/ytYQ6GpX4uLu74/3338eaNWu0d28dOHAADg4OGDBgAGbOnAlXV1eDyy0sLIRKpapxmUyhUCAzM1PvPgMHDkRhYSHmz58PAKiursaoUaMwefLkWo+zfv16xMXFaZ+HhIRg8eLF8PT0NDjmhvL29m6ysluC5qxfpSoGWQCkmxnwbt8ekswkw1LVydrPH2D9dbT2+gHWX0fWz/KZs45Gj+Pj5uaGZ555Bs8884w2YXF1dYWsGf4I3S0pKQnr16/HU089hU6dOiErKwvff/894uLiam11mjRpEsaPH699rslAc3JyUFVVZdL4JEmCt7c3srKyrPL2a3PUTwgbQG4LUV6OG4mnIXk23QfJ2s8fYP11tPb6AdZfR9bP8jVVHeVyeYMbLYxOfO7WmNad2sqRyWRQKpU6y5VKZa2dpWNjYzF48GCMGDECABAYGIiysjL897//xeTJk/UmYra2trC1tdVbXlO96YQQVvuGBpq5fjKZep6ujFSI69cAj/ZNfkhrP3+A9dfR2usHWH8dWT/LZ846Nm+zTAPJ5XKEhoYiMTFRu0ylUiExMRFhYWF69ykvL69xzbC5W52o+Um+gQDU/XyIiIjqY3SLz4ULF7Bnzx5kZ2ejuLi4RgYnSRI++OADg8sdP348vvrqK4SGhqJjx47YunUrysvLMXToUADAl19+CXd3d8yYMQMAEBMTgy1btiAkJER7qSs2NhYxMTFMgKyZt7/6983r5o2DiIgsglGJz+bNm7Fy5UrY2dnB19cXzs7OpooL/fv3R2FhIdatWwelUong4GC88cYb2ktdubm5Oi08U6ZMgSRJWLt2LW7dugVXV1fExMRg+vTpJouJWiAvHwCAuKm/0zsREdHdjEp8Nm7ciM6dO+Mf//gHnJycTBWT1pgxYzBmzBi96xYuXKjz3MbGBo888ggeeeQRk8dBLZfk7QcBsMWHiIgaxKhrQOXl5Rg4cGCTJD1EDeLlq/59uwCipPHToxARUetgVOITERGBtDSOmkvmIzk6AW5t1U9u3jBvMERE1OIZlfjMmTMHiYmJ2Lhxo1GTkRIZpb261UfwchcREdXDqD4+Hh4eGDlyJFauXIlVq1bBzs5O7x1Uy5cvN+YwRHWS2vtBJCcB7OBMRET1MCrxiY2Nxc8//wx3d3d06NCBfX3IPO7c2YVsXuoiIqK6GZX47Ny5E9HR0Xj11Vc5Vg6ZjeTRXj1Zad5Nc4dCREQtnFHZSlVVFaKjo5n0kHlppqrIZeJDRER1MypjiY6Oxvnz500VC1HjaBKfgnyIinLzxkJERC2aUYnPI488guvXr+Pbb79FSkoKCgsLUVRUVOOHqEm1cQEcHNWP87LNGwsREbVoRvXxeemllwAAqamp2LlzZ63bxcbGGnMYojpJkqRu9clIBXKyAJ8Ac4dEREQtlFGJj2Z+LCKzu5P4iNyb4DuSiIhqY1TiM3XqVFPFQWQUzZ1d7OBMRER1MSjxyc3NBaAeuPDu5/XRbE/UZO50cBZMfIiIqA4GJT7z5s0DAKxatQpyuVz7vD7s40NNjS0+RETUEAYlPs8++ywAwMbGRuc5kdlpx/LhXV1ERFQ7gxKfoUOH1vmcyGzaeal/lxZDFBdBauNs3niIiKhF4pDLZBUkB0fAxU39hJe7iIioFkx8yHpw6goiIqqHQZe6Hn300UYdhJ2bqTlIHu0hriZzLB8iIqqVQYmPvgELjx07hoyMDPTs2RO+vr4AgOvXr+PMmTMICAhA7969TRctUV087vTzYYsPERHVwqDE594BC3ft2oXCwkJ89NFH2qRHIyMjA2+//Tbatm1rfJREDcGxfIiIqB5G9fHZuHEj7r///hpJDwD4+/tjzJgx2LhxozGHIGowiX18iIioHkYlPnl5eZDLa280srGxQV5enjGHIGq4dncSn1vZEEKYNxYiImqRjEp8AgMDsWPHDty6davGury8PPz6668IDAw05hBEDed+Z2qUigqgqNC8sRARUYtk1CSls2bNwqJFi/Diiy+id+/e8Pb2BgBkZWXh+PHjEELg+eefN0mgRPWRbO0At7ZAQT6Ql/3HuD5ERER3GJX4dO7cGYsWLUJsbCyOHz+OiooKAICdnR169uyJqVOnssWHmpe7553EJwcI7mTuaIiIqIUxKvEB1Je7Xn31VahUKhQWqi8vuLq6Qibj2IjU/KR2XuqxfPKyOZYPERHVYHTioyGTyaBQKExVHFHjtPNU/76VY944iIioRTKoWeaVV17BqVOnDD5ISUkJXnnlFVy+fNngfYkMcmeyUpHHxIeIiGoyKPFJT09HSUmJwQeprq5Geno6ysrKDN6XyBCSu6bFJ9u8gRARUYtk8KWu5cuXY+3atQbtwzFVqNloLnWxxYeIiPQwKPEZMmSIUQfj9BXU5NzvzNdVfBuirBSSg6N54yEiohbFoMTnueeea6o4iExCcmoDOLYBSovVrT5+HE6BiIj+wHvOyfq0Yz8fIiLSj4kPWR/tnV1MfIiISBcTH7I6f9zZxQ7ORESki4kPWZ87LT68s4uIiO7FxIesjnSnjw8vdRER0b2Y+JD1cedYPkREpB8TH7I+mktdBbcgqirNGwsREbUoJpukFADOnTuH//73v7h9+zb69euHP/3pT7CxsUFaWhpOnTqFkydP4p133jHlIYlqcnED5LZAVSWQnwd4eps7IiIiaiFMmvgsXboUffv2RXh4ODZs2IDvv/8ely5dQlZWFiIiIjBw4EBTHo5IL0kmU1/uys4E8rKZ+BARkZZJE5+cnBxMnz4dABAcHIxnn30WM2fOxNixYyGXm/RQRHVrp058RF4OJHPHQkRELYZJsxFJ+uNPjLu7O+zt7TFhwgRTHoKoQaR2XhAAx/IhIiIdJk18ysrK8MorryA4OBihoaGQJAmlpaVwdGzcRJHbt2/Hpk2boFQqERQUhDlz5qBjx461bl9cXIw1a9bg2LFjKCoqgqenJ2bNmoXo6OjGVokslXaWdt7STkREfzBp4vPll1/i2rVrSE1Nxfnz5+Hm5obZs2fD3d0dAQEBCAgIwOOPP96gsg4fPowVK1Zg7ty56NSpE7Zs2YJFixbh008/hZubW43tq6qq8O6778LV1RUvv/wy3N3dkZubCycnJ1NWkSzFnVnaBVt8iIjoLiZNfDw9PeHp6YlevXppl5WVlSEtLQ2pqam4du1ag8vavHkzRowYgWHDhgEA5s6di1OnTmHv3r2YOHFije337NmDoqIivPPOO9r+RF5eXsZViCyW9lIXW3yIiOguTd7j2MHBAWFhYQgLC2vwPlVVVUhJSdFJcGQyGbp3747k5GS9+5w8eRKdOnXC0qVLceLECbi6umLAgAGYOHEiZDL9wxVVVlaisvKPcV4kSdJelru7v5IpaMozdbktRYurX7u75usSQn2nlxFaXP2agLXX0drrB1h/HVk/y9cS6miyxKeiogKSJMHW1tbosgoLC6FSqaBQKHSWKxQKZGZm6t3n5s2byMnJwcCBA/H6668jKysL3377Laqrq/HII4/o3Wf9+vWIi4vTPg8JCcHixYvh6elpdB1q4+1t3bdWt5T6CU9PZMhkQFUV2jvaw8bdwyTltpT6NSVrr6O11w+w/jqyfpbPnHVsdOKTlJSE48eP4+LFi8jIyEBFRQUAwN7eHn5+fggPD0fv3r0RERFhsmDrIoSAq6sr/vznP0MmkyE0NBS3bt3Cxo0ba018Jk2ahPHjx2ufazLQnJwcVFVVmTQ+SZLg7e2NrKwsCCFMWnZL0CLr5+YO5Ofi5rmzkDp0NqqoFlk/E7P2Olp7/QDrryPrZ/maqo5yubzBjRYGJT5VVVXYtWsXNm/ejJycHDg7OyMkJASDBg2Cs7MzhBAoLi5GdnY2Dhw4gG3btsHDwwMPPvggRo4c2eCxfFxdXSGTyaBUKnWWK5XKGq1AGgqFAnK5XOeylp+fH5RKJaqqqvQe29bWttYWqqZ60wkhrPYNDbSw+rXzBPJzocrLhiw03CRFtqj6NRFrr6O11w+w/jqyfpbPnHU0KPF54YUXUFVVhSFDhqBfv34IDQ2tc/uUlBQcOXIE69evx6ZNm/DVV181LCi5HKGhoUhMTESfPn0AACqVComJiRgzZozefcLDw3Ho0CGoVCpt8nPjxg20bduWgye2UpK7FwTOcywfIiLSMigjmDRpEoYOHdrgfjyhoaEIDQ3Fo48+ir179xoU2Pjx4/HVV18hNDQUHTt2xNatW1FeXo6hQ4cCUN867+7ujhkzZgAARo8ejR07dmDZsmUYM2YMsrKysH79ejzwwAMGHZesCMfyISKiexiU+IwaNcqgwisqKlBYWAgPDw+D9+3fvz8KCwuxbt06KJVKBAcH44033tBe6srNzdXpFe7h4YE333wTy5cvx6uvvgp3d3c88MADem99p1biziztIo8tPkREpNboa0C3b9/Gtm3bcPLkSWRlZUGlUsHd3R3du3fH6NGjERgYiKNHj+LLL79EbGxso44xZsyYWi9tLVy4sMaysLAwLFq0qFHHIusjtfPkWD5ERKSjUYnPxYsX8eGHH6KwsBBOTk7w9/dHdXU1cnJysHPnTuzevRuTJ09uFbfkUQt2p8WHfXyIiEjD4MQnLy8P77//PpycnPD3v/8dMTExOusvXLiADRs2IC4ujokPmZf7nT4+pSUQJUWQnJzNGw8REZmdwYnP+vXrAQBvvfUWPDxqDgrXuXNndO7cGVu2bMGKFSuMj5CokSR7B8DZBSi6DeTlAEx8iIhaPYMTn/j4eAwfPlxv0nO3cePGoaSkBOfOnWt0cERGc/e6k/hkAwEh5o6GiIjMzODER6lUwt/fv0Hb1jZiMlGzaecJpF2BuJUD6539hoiIGsrgmRudnJxw+/btBm175swZnbmwiJqbpOngzFvaiYgIjUh8NCMk1+fatWv44IMP8OOPPzYqMCKT4CCGRER0F4MTnwkTJiA1NRX/+c9/UFlZqXebgwcPYsGCBSaf6JPIUJL7nUEMeUs7ERGhEX18wsLC8Kc//QnLli3DmTNn0K9fP/j6+qKqqgrZ2dk4fvw4srOz0aFDB/Tv3x8rV65siriJGkZ7qYstPkRE1MgBDMeMGYPAwECsW7cOW7du1Zlh1cvLC48//jgeeOABHD582GSBEjWK5lJXoRKisgKSrZ154yEiIrNq9JQVXbt2xcKFC1FSUoKbN2+iqqoKbdu21bnNPTw8HM8++6xJAiVqlDYugJ09UFGu7uDs7WfuiIiIyIwMSnxWr16NwMBABAYGws/PDzY2NnByckJIiP7xUby8vODl5WWSQIkaQ5Ik9eWuG+nArWwmPkRErZxBic+2bdtQUVEBALCxsYGPjw8CAwMREBCgTYiY6FCL084TuJEOkcexfIiIWjuDEp+VK1ciOzsbGRkZSE9PR3p6Oi5duqTTl8fBwUEnEQoICEBERITJAydqKKmdl3qW9lx2cCYiau0M7uOjuXwVHR2Ny5cv4/Tp0xg1ahS6du0KlUqFy5cv48CBA7h06ZJ2n9jYWJMGTWQQTx/175wb5o2DiIjMrtGdmwFg6dKl6NevH+bMmaNdNnDgQEyfPh0//fQT9u7di9mzZxsbI5FRJC8fCAAim4kPEVFrZ/AAhndLS0tDUFBQjeX29vaYMWMGwsPDcfLkSWMOQWQ8rzstPtk3dIZeICKi1seoxMfPzw8JCQm1ro+MjMSZM2eMOQSR8Ty91b9Li4Hihs0zR0RE1smoxGfixIk4duwYYmNjtXd73S01NZXTVpDZSXb2QNs740vdzDRvMEREZFZG9fHp378/bt26hVWrVmHnzp0YPHgwQkNDAQCJiYnYt28fYmJiTBIokVG8fID8XIicG5A6dDZ3NEREZCZGJT4AMH78eHTr1g0///wzdu7cqdPy06NHDzz99NPGHoLIaJKXD8TFswA7OBMRtWpGJz4AEBwcjJdffhlVVVXIyspCRUUFPDw84OrqaoriiYyn6eDMS11ERK2awX18Nm7ciOvXr+tdJ5fL4e/vj9DQUCY91KJId6aqEFkZZo6EiIjMyeAWn40bN2LVqlXw9PREdHQ0oqOjERERAVtb26aIj8g0vAPUv7MyIFQqSDKj+vUTEZGFMjjx+d///odLly4hISEBp06dwo4dO2BnZ4eIiAjExMQgKipKZ4Z2ohbB0xuQy4GKCuBWDuDR3twRERGRGRic+EiShLCwMISFhWHq1KlQKpU4deoUEhISsGrVKnz77bfw9/dHdHQ0YmJiEBYWBhn/uyYzk2xsAC9fIDMNyMpg4kNE1EoZ3blZoVBg+PDhGD58OKqrq3H+/HmcOnUKJ0+exMaNG+Hk5ISePXti3Lhx6NSpkyliJmocb38gMw3iRgakbhxmgYioNTLJXV0AUFhYCFdXV3Tr1g3dunXDE088gezsbJw6dQrx8fE4f/48Ex8yK8nHXz1L+410c4dCRERmYrLE5y9/+Qvs7e0RGBiIgIAA7c/QoUMxZswYUx2GqPF81B2cBRMfIqJWy2SJz4oVK5CdnY20tDSkp6cjKSkJ27dvR2ZmJhQKhTYRmjlzpqkOSWQQyS9I3eJz/RqEEJAkydwhERFRMzNZ4gMAXl5e8PLyQq9evbTLVCoVMjMzkZ6ejvR0/qdNZuTtr76zq7QEyL35x+SlRETUapg08dFQqVTYs2cPUlJS0KZNG8ycORP+/v7o169fUxyOqEEkuVx9uSv9KnA9lYkPEVEr1CT3mS9ZsgSHDh1CSEgIfv31VwDA6dOnsXnz5qY4HFGDSf7BAACRnmrWOIiIyDyaJPE5ceIE/vGPf2DUqFHaMXxCQ0O1SRCR2fiHAABExlUzB0JERObQJImPs7MzysrKdJa5uLigoKCgKQ5H1GBSgDrxQToTHyKi1qhJEp9Ro0bhiy++0El0MjMz4eTk1BSHI2q4wA7q3zlZELcLzRsLERE1uybp3Dxu3DgUFRXhhRdeQGVlJZYsWYKEhAQ88MADTXE4ogaT2jgD3n5A1nUgNRno3qv+nYiIyGo0SeIjSRKmTZuGMWPG4MyZMygqKsKgQYPQtWvXpjgckUGkkHCIrOsQKRchMfEhImpVjEp8nn/+eQwZMgQPP/yw3vUKhQKDBw825hBEphcaDhzZA5Fy0dyREBFRMzMq8cnOzkZKSgpSUlK0fXj8/PzQvj1nvqaWSwoNU4/gfPUShEoFSdYkXd2IiKgFMvpS18mTJ3Hy5EmdZQEBAZg0aRIGDBhgbPFEpucXDDg4AqXF6ru7gjqYOyIiImomRic+crkckyZNQteuXVFVVYWUlBT8/vvv+Pzzz5GUlISnn37aFHESmYxkYwOEdQPOHIe4cAYSEx8iolbD6MRn3LhxOn18evTogYkTJ2Lz5s1YuXIlwsLCMHToUGMPQ2RSUuceEGeOQ1w4Ddw/ydzhEBFRMzGqc4OdnR3c3d31rhs/fjz69++P7du3G3MIbN++HfPmzcPMmTPxxhtv4PLlyw3a79ChQ5g6dSr+/e9/G3V8sk5Slx7qB8lJEFWV5g2GiIiajVGJj6+vL+Lj42td36VLF2RkZDS6/MOHD2PFihV4+OGHsXjxYgQFBWHRokX1jgCdnZ2NlStXokuXLo0+Nlk53yDAxQ2oKAeSk8wdDRERNROjEp9Ro0YhISEB3333HSoqKmqsv3DhAuzt7Rtd/ubNmzFixAgMGzYM/v7+mDt3Luzs7LB3795a91GpVPjiiy8wdepUeHl5NfrYZN0kmQxSzz4AABH/u5mjISKi5mJUH5+RI0ciIyMD27Ztw6FDhxAVFYWAgADI5XKcPXsW8fHxje7fo+koPXHiRO0ymUyG7t27Izk5udb94uLi4OrqiuHDh+P8+fONOja1DlLUfRAHd0Ik/A4x/Wne1k5E1AoY3bl59uzZ6Nu3L7Zs2YLjx4/jwIED2nW9e/fGrFmzGlVuYWEhVCoVFAqFznKFQoHMzEy9+1y4cAF79uxpcL+eyspKVFb+0b9DkiQ4OjpqH5uSpjxTl9tSWGT9ukaqb2tX3oJ0NRlSx9ovjVpk/Qxk7XW09voB1l9H1s/ytYQ6mmTKii5duqBLly5QqVTIzs5GWVkZPDw84OzsbIriG6S0tBRffPEF/vznP8PV1bVB+6xfvx5xcXHa5yEhIVi8eDE8PT2bKkx4e3s3WdktgaXVL6//MJTs2QqH+CNwHzS83u0trX6NYe11tPb6AdZfR9bP8pmzjgYlPqtXr0ZgYCACAwPh5+cHGxsbnfUymcxklXF1dYVMJoNSqdRZrlQqa7QCAcDNmzeRk5ODxYsXa5cJIQAA06ZNw6efflojtkmTJmH8+PHa55oMNCcnB1VVVSapx91le3t7IysrSxuXNbHU+qmiBwJ7tqJ4/3aUTZgByd5B73aWWj9DWHsdrb1+gPXXkfWzfE1VR7lc3uBGC4MSn23btmk7MdvY2MDHxweBgYEICAjQJkSm6lAsl8sRGhqKxMRE9Omj7oSqUqmQmJiIMWPG1Nje19cXH374oc6ytWvXoqysDLNnz4aHh0eNfWxtbWFra6v3+E31phNCWO0bGrDA+oVFAJ7eQE4WVL/vhWxwzffW3Syufo1g7XW09voB1l9H1s/ymbOOBiU+K1euRHZ2NjIyMpCeno709HRcunQJhw8f1m7j4OCgkwgFBAQgIiKiUcGNHz8eX331FUJDQ9GxY0ds3boV5eXl2g7TX375Jdzd3TFjxgzY2dkhMDBQZ/82bdoAQI3lRBqSJEEaNg5i3VKI7T9DDBilHtmZiIisksF9fLy8vODl5YXo6GhcvnwZp0+fxqhRo9C1a1eoVCpcvnwZBw4cwKVLl7T7xMbGNiq4/v37o7CwEOvWrYNSqURwcDDeeOMN7aWu3Nxcq+4ERs1DGnw/xNZ1QE4WxImDkPoOMXdIRETURIzq3Lx06VL069cPc+bM0S4bOHAgpk+fjp9++gl79+7F7NmzjQpwzJgxei9tAcDChQvr3HfevHlGHZtaB8neAdLIhyB++QFiyzqI3oN4azsRkZUy6ts9LS0NQUFBNZbb29tjxowZCA8PrzFzO1FLJA0bBzi2AW6kAwkc0JCIyFoZlfj4+fkhISGh1vWRkZE4c+aMMYcgahaSUxt18gNAtSkWQqUyc0RERNQUjEp8Jk6ciGPHjiE2NlbvlBWpqakmvy2cqKlIoyaoBzTMuApwGgsiIqtkVB+f/v3749atW1i1ahV27tyJwYMHIzQ0FACQmJiIffv2ISYmxiSBEjU1ydkV0sgJEJtjodq4GrKovpBkvMOLiMiaGD1y8/jx49GtWzf8/PPP2Llzp07LT48ePfD0008bewiiZiONeghiz2YgMw3ixCFIfQabOyQiIjIhk0xZERwcjJdffhlVVVXIyspCRUUFPDw8Gjx1BFFLITk5Qxo1EWLDKohNayF6DeQdXkREVsSk3+hyuRz+/v4IDQ1l0kMWSxrxoPoOr6wM4CzvSiQisiYGJT5//etfsX//foM6LFdWVmLv3r3461//anBwROYgOTpBGjwaAKDa+Yt5gyEiIpMy6FLX0KFDsWLFCixbtgwxMTHo0aMHQkJC4OXlBXt7ewBAWVkZsrOzkZKSgjNnzuDkyZOQy+WYMGFCk1SAqClIwx+E2LURuHgW4noaJP+a41UREZHlMSjxeeihhzB69Gjs2bMH+/btw4EDB7TrNDO1V1dXa5cFBARg6tSpGDZsGJycnEwUMlHTk9w9gB69gfjfIQ7+Ckyba+6QiIjIBAzu3Ozo6Ihx48Zh3LhxyM7ORnJyMq5fv47bt28DAFxcXODn54ewsDCTzdROZA6yQaOhiv8d4sheiCmzzB0OERGZgFF3dWkmLCWyShFRQFsPID8X4uxJIJCXu4iILJ3Bd3Vt3LgR169fb4pYiFoUSWYDqfdAAIA4fqCerYmIyBIY3OKzceNGrFq1Cp6enoiOjkZ0dDQiIiJga2vbFPERmZXUaxDEr79AnD4GVVmpucMhIiIjGZz4/O9//8OlS5eQkJCAU6dOYceOHbCzs0NERARiYmIQFRUFDw+PpoiVqPkFdwQ8vYGcLJSdOAR0iDB3REREZASDEx9JkhAWFoawsDBMnToVSqUSp06dQkJCAlatWoVvv/0W/v7+iI6ORkxMDMLCwiDjyLdkoSRJghTZF2LnBpQeO8DEh4jIwhk9ZYVCocDw4cMxfPhwVFdX4/z584iPj8fJkyexceNGODk5oWfPnhg3bhw6depkipiJmpXUsw/Ezg0oO34I0qNzAYmJPBGRpTLJXF0aNjY26NatG7p164bHH38cGRkZSEpKwqlTp3D+/HkmPmSZOnQBnNpAVaiEzZWLQMcu5o6IiIgaqdGJz+3bt7Ft2zacPHkSWVlZUKlUcHd3R/fu3TF69GgEBgbi6tWr+O677xAbG2vKmImalSSXQ+oWA3HsN6iSTkHGxIeIyGI1KvG5ePEiPvzwQxQWFsLJyQn+/v6orq5GTk4Odu7cid27d2Py5Mnw9vY2dbxEZiF1jYQ49hvE+TPAQzPNHQ4RETWSwYlPXl4e3n//fTg5OeHvf/87YmJidNZfuHABGzZsQFxcHBMfshpSl57qB6nJEGUlkBw4BQsRkSUyOPFZv349AOCtt97Se9t6586d0blzZ2zZsgUrVqwwPkKiFkBq5wUbbz9UZ10HkpPU83gREZHFMTjxiY+Px/Dhw+sdq2fcuHEoKSnBuXPnGh0cUUvi0LMPirPWQ5w/A4mJDxGRRTI48VEqlfD392/Qto888ojBARG1VA6RvVG8Yz3EhTPmDoWIiBrJ4AFJnJyctDOx1+fMmTOIi4szOCiilsi+Ry/1g4yrELcLzBsMERE1isGJT3h4OA4dOlTvdteuXcMHH3yAH3/8sVGBEbU0Ngp3wC8YACAunDVvMERE1CgGJz4TJkxAamoq/vOf/6CyslLvNgcPHsSCBQtQVVVldIBELYnUpYf6wYXT5g2EiIgaxeA+PmFhYfjTn/6EZcuW4cyZM+jXrx98fX1RVVWF7OxsHD9+HNnZ2ejQoQP69++PlStXNkXcRGYhhXeH2LURIjnJ3KEQEVEjNGoAwzFjxiAwMBDr1q3D1q1bIYTQrvPy8sLjjz+OBx54AIcPHzZZoEQtgdSpq/pBVgbE7QJILm7mDYiIiAzS6CkrunbtioULF6KkpAQ3b95EVVUV2rZtq3Obe3h4OJ599lmTBErUEkjOroBPAHAjHbh0DojuZ+6QiIjIAAYlPqtXr0ZgYCACAwPh5+cHGxsbODk5ISQkRO/2Xl5e8PLyMkmgRC2F1CkC4kY6xKVzkJj4EBFZFIMSn23btqGiogKAeiZ2Hx8fBAYGIiAgQJsQMdEhq9epK/DbdohL7OdDRGRpDEp8Vq5ciezsbGRkZCA9PR3p6em4dOmSTl8eBwcHnUQoICAAERERJg+cyFykThEQAJCeAlFWCsnB0dwhERFRAxncx0dz+So6OhqXL1/G6dOnMWrUKHTt2hUqlQqXL1/GgQMHcOnSJe0+sbGxJg2ayJykdp6AuydwKwdIuQB0jTJ3SERE1ECN7twMAEuXLkW/fv0wZ84c7bKBAwdi+vTp+Omnn7B3717Mnj3b2BiJWhwpLALi933qfj5MfIiILIbBAxjeLS0tDUFBQTWW29vbY8aMGQgPD8fJkyeNOQRRy3TntnZxiZPwEhFZEqMSHz8/PyQkJNS6PjIyEmfOcEJHsj5Spzv91lIuQlTpH8GciIhaHqMSn4kTJ+LYsWOIjY3V3u11t9TUVE5bQdbJ2x9wdgUqK4BrV8wdDRERNZBRfXz69++PW7duYdWqVdi5cycGDx6M0NBQAEBiYiL27duHmJgYkwRK1JJIkgR07Aok/A5xKQlSh87mDomIiBrAqMQHAMaPH49u3brh559/xs6dO3Vafnr06IGnn37a2EMQtUhSp64QCb+r+/mMmWLucIiIqAGMTnwAIDg4GC+//DKqqqqQlZWFiooKeHh4wNXV1RTFE7VI2vF8Lp+HUKkgyYy6ckxERM3A4G/qjRs34vr163rXyeVy+Pv7IzQ0lEkPWb/AUMDeASgpAjLTzB0NERE1gMEtPhs3bsSqVavg6emJ6OhoREdHIyIiAra2tk0RH1GLJdnYAKHhwPnT6vF8/IPNHRIREdXD4MTnf//7Hy5duoSEhAScOnUKO3bsgJ2dHSIiIhATE4OoqCidGdqJrJnUKQLi/GngUhIwbKy5wyEionoYnPhIkoSwsDCEhYVh6tSpUCqVOHXqFBISErBq1Sp8++238Pf3R3R0NGJiYhAWFgaZEX0ftm/fjk2bNkGpVCIoKAhz5sxBx44d9W67a9cu/Pbbb0hPTwcAhIaGYvr06bVuT2QsqVNXCKgHMhRCqO/2IiKiFsvozs0KhQLDhw/H8OHDUV1djfPnz+PUqVM4efIkNm7cCCcnJ/Ts2RPjxo1Dp06dDCr78OHDWLFiBebOnYtOnTphy5YtWLRoET799FO4ubnV2P7cuXMYMGAAwsPDYWtriw0bNuDdd9/Fxx9/DHd3d2OrSlRTSDhgIweUeUDuTcDT29wRERFRHUx2G0phYSFsbGzQrVs3PPHEE/j444/xxRdf4NFHH0VpaSnOnz9vcJmbN2/GiBEjMGzYMPj7+2Pu3Lmws7PD3r179W7/wgsv4P7770dwcDD8/PzwzDPPQAiBs2fPGls9Ir0ke3sgqAMATl9BRGQJTHI7OwD85S9/gb29PQIDAxEQEKD9GTp0KMaMGWNweVVVVUhJScHEiRO1y2QyGbp3747k5OQGlVFeXo6qqio4OzvrXV9ZWYnKyj+mG5AkCY6OjtrHpqQpz1ovhbTm+kmdIiBSLgKXz0EaMKK5QzOZ1nwOrYW115H1s3wtoY4mS3xWrFiB7OxspKWlIT09HUlJSdi+fTsyMzOhUCi0idDMmTMbVF5hYSFUKhUUCoXOcoVCgczMzAaVsWrVKri7u6N79+56169fvx5xcXHa5yEhIVi8eDE8PT0bVH5jeHtb96WQ1li/0r4DkbvjZ9hcvQgfHx8zRGVarfEcWhtrryPrZ/nMWUeTJT4A4OXlBS8vL/Tq1Uu7TKVSITMzE+np6dpOx83hl19+waFDh7Bw4ULY2dnp3WbSpEkYP3689rkmA83JyTH5HGOSJMHb2xtZWVkQQpi07JagNddPuKs/wFUZ15B58TwkV4UZIjReaz6H1sLa68j6Wb6mqqNcLm9wo4VJEx8NlUqFPXv2ICUlBW3atMHMmTPh7++Pfv36NbgMV1dXyGQyKJVKneVKpbJGK9C9Nm7ciF9++QXz589HUFBQrdvZ2trWOv5QU73phBBW+4YGWmn92jgDfkHA9WsQF88CvQaaJzgTaZXn0MpYex1ZP8tnzjo2yRj7S5YswaFDhxASEoJff/0VAHD69Gls3ry5wWXI5XKEhoYiMTFRu0ylUiExMRFhYWG17rdhwwb89NNPeOONN9ChQ4fGV4LIAFKXSACAOJdg1jiIiKhuTZL4nDhxAv/4xz8watQo7Rg+oaGh2iSoocaPH4/du3dj3759yMjIwLfffovy8nIMHToUAPDll19i9erV2u1/+eUXxMbG4tlnn4WXlxeUSiWUSiXKyspMVjcifaSIKACASDxl9f+pERFZsia51OXs7IyysjI4ODhol7m4uKCgoMCgcvr374/CwkKsW7cOSqUSwcHBeOONN7SXunJzc3V6hu/cuRNVVVX4+OOPdcp5+OGHMXXq1MZXiKg+YRGArR2QnwtkpgN+geaOiIiI9GiSxGfUqFH44osv8MILL2iXZWZmwsnJyeCyxowZU+vt8AsXLtR5/tVXXxlcPpEpSHb2QHg3IPEUxNnjkJj4EBG1SE2S+IwbNw5FRUV44YUXUFlZiSVLliAhIQEPPPBAUxyOqEWQevRRX+pKOAqMmWLucIiISI8mSXwkScK0adMwZswYnDlzBkVFRRg0aBC6du3aFIcjahGkyL4Qq78BrlyAUN6CpOA0KURELY1RnZuff/55nQEA76VQKDB48GCMHTuWSQ9ZPaltOyBEfcehSPjdzNEQEZE+RiU+2dnZSElJQUpKCg4ePIhTp07h5s2bpoqNyOJIMf0BAOLofjNHQkRE+hh9qevkyZM4efKkzrKAgABMmjQJAwYMMLZ4Iosi9R0C8dMK4PJ5iKzrkLz9zB0SERHdxejERy6XY9KkSejatat2YtHff/8dn3/+OZKSkvD000+bIk4iiyAp2gHdooGzJyAO74I0eZa5QyIiorsYnfiMGzcODz/8sPZ5jx49MHHiRGzevBkrV65EWFiYdsBBotZANnAUVGdPQOzfATH2EUgOhg/jQERETcOoPj52dnZwd9d/58r48ePRv39/bN++3ZhDEFmeyD5Aez+gpAjitx3mjoaIiO5iVOLj6+uL+Pj4Wtd36dIFGRkZxhyCyOJIMhtIYyYDAMS2nyCKCs0cERERaRiV+IwaNQoJCQn47rvvUFFRUWP9hQsXYG9vb8whiCySdN8w9YztRYUQcd+bOxwiIrrDqD4+I0eOREZGBrZt24ZDhw4hKioKAQEBkMvlOHv2LOLj49m/h1olSS6HbOazUP37NYhDu6EK6ADZiPHmDouIqNUzunPz7Nmz0bdvX2zZsgXHjx/HgQMHtOt69+6NWbN4Vwu1TlKnrpAmPQ6xfiXE2v9ClXYF0ogHAd9ASPImGTSdiIjqYdC37+rVqxEYGIjAwED4+fnBxsYGgLovT5cuXaBSqZCdnY2ysjJ4eHjA2dm5SYImshTSAw8DFeUQW3+EOLwb4vDuu1bKAJkEuHtC6twD0rBxkAJCzBcsEVErYFDis23bNm1fHhsbG/j4+CAwMBABAQEIDAxEUFAQvL29myRQIkskSRKkiY9BdIuGascvwPkEoLxMvVKogGoAOVkQOVkQB3dBeuBhSA/NgCQzqvsdERHVwqDEZ+XKlcjOzkZGRgbS09ORnp6OS5cu4fDhw9ptHBwctImQJimKiIgweeBElkTq2BU2HbtCCAEU3wZU1YBKANXVwI10qA78Cpw6DLF1HaDMA2b9BZLMxtxhExFZHYM7Gnh5ecHLywvR0dG4fPkyTp8+jVGjRqFr165QqVS4fPkyDhw4gEuXLmn3iY2NNWnQRJZKkiTA2VV3YTtP2HSLhurIXohln6kvhynaQZr0mHmCJCKyYkb1sFy6dCn69euHOXPmaJcNHDgQ06dPx08//YS9e/di9uzZxsZI1CrI+g2DSgiI7z+F2LoOomMXSN1jzB0WEZFVMaojQVpaGoKCgmost7e3x4wZMxAeHl5jAlMiqp2s/3BIw8YCAFQrv4IoLTFzRERE1sWoxMfPzw8JCQm1ro+MjMSZM2eMOQRRqyNN+RPg6Q3k50JsWGXucIiIrIpRic/EiRNx7NgxxMbG6h25OTU1FVVVVcYcgqjVkeztIZv5LABA7NsGkXvTzBEREVkPo/r49O/fH7du3cKqVauwc+dODB48GKGhoQCAxMRE7Nu3DzEx7KNAZCgpIgro0hM4fxpiw2pIT/7V3CEREVkFo4ePHT9+PLp164aff/4ZO3fu1Gn56dGjB55++mljD0HUKskmPwHVor9BHN0Hcf9ESP4c3JCIyFgmGTc/ODgYL7/8MqqqqpCVlYWKigp4eHjA1dW1/p2JSC8puBOkmAEQJw9Btf4H2Dw/39whERFZPJMODyuXy+Hv74/Q0FAmPUQmIE18TD21xZnjENcumzscIiKLx3HxiVowydsPUt/BAADVprVmjoaIyPIx8SFq4aSxUwFJAk4fg0i7Yu5wiIgsGhMfohZO8vGH1HsQAEC1ZZ2ZoyEismxMfIgsgLbV59QRiIxUc4dDRGSxmPgQWQDJLxBSdH8AgGCrDxFRozHxIbIQ0vipAABx8hBEZpqZoyEiskxMfIgshOQfAkTdBwgBseVHc4dDRGSRmPgQWRDZ+GkAAHH8AMSNDDNHQ0RkeZj4EFkQKTAUiOwLCBVUP35n7nCIiCwOEx8iCyObMguwsQHOnoA4fdzg/YUQENk3IM7FQ1w8C1F8uwmiJCJqmUwyVxcRNR/J2x/SyAkQO9ZD9cNXkHX8AlIbl3r3E5UVEHs2Q+zfDuRk3VWgBHSLgc2D0wEfnyaMnIjI/NjiQ2SBpAdnAN5+gPIWVN99CqGqrnVbIYR6otP5z0HELVMnPXI54BsIeLQHhADOnkD1v16B8vsv6iyLiMjSscWHyAJJ9vaQPfkyVItfU09guvJr4LHnINnY6Gwn0lKgiv0WSE5UL1C0gzRhOqQ+gyHZO6i3uZkJsXktxO/7cDtuOaTkJEjPvAbJ1q65q0VE1OSY+BBZKCm4E2RP/hWq/34AcXAnxI10yB54RN0SdPM6xOE9EKcOq1t0bO0g3T8Z0pjJ2oRHW057X0hPvgzRsw/Ess8gzpyA+Po9yJ57g8kPEVkdJj5EFkzqNRAyGzlUSz8GrlyA6st3am7TexCkKbMhtfOssyxZ70FoGxyKnAUvAomnoPr6X3eSH9umCp+IqNmxjw+RhZOi7oPs7a8hDRsLeHoD9o6Alw+kIWMgW/AZZE+/Wm/So+HQoxdkLywA7OyBxJMQSz9mnx8isips8SGyApK7B6QZz5ikLFnn7sC8N6H6/G2Ik4eAVc7q/kOSZJLyRVkpUFEOOLZhaxIRNTsmPkRUg9Q1ErK5f4NqyQcQv+0AnF0hTXq8UWUJVTWQlABxbD/EhbOAMu+PlV4+kCKiIA26H1JAiImiJyKqHRMfItJLihkA6bEiiJVfQWz9ESp7B8jGPtLg/YUQQMJRqNavBG6k698o+4Z6MMW9W4EOnSF7aCakLj1NVAMiopqY+BBRrWSD74equAji5+UQ61dCVXwb0pRZkGQ2de4nLp6F6qflwNVk9QKnNpD6DYcU2RcIDAUc2wC3C4CryRC/74OIP6LunP3xfKB7L8imzIbkF2iyeoiyUuBWDsrzsiDy8yHktoBHe0htnE12DCKyDC0+8dm+fTs2bdoEpVKJoKAgzJkzBx07dqx1+yNHjiA2NhY5OTnw9vbGzJkzER0d3YwRE1kX2QNToJJJEHHLIH79BeLaFcie+AskL91RnoUQwPkEqHasB84lqBfa2UMaOQHS/ZMgOd2TZLgqgJ59IPXsA1GQD7EtDmLfVuDsCagST0EaOFI95pCincExi8pK4OIZiNPHIc6eAPKyAQDZ927o7AqEhEEK7wYprDsQGFpjLCQisi4tOvE5fPgwVqxYgblz56JTp07YsmULFi1ahE8//RRubm41tr948SI+++wzzJgxA9HR0Th48CA++OADLF68GIGBpvvvkai1kd0/GSp3T4hlnwEXz0I1/1kg8j5IYd0ABwf1JauEo0BmmnoHGxt1v51xUyEp3OstX3JrC2naXIihY6H6eTkQ/zvEgV8hju6DNGwcpCEPQPL0rrMMUaiEOHsC4vQxdeJVXqa7gVMbyNu2Q1VlpXpdQT5QVKie8+zsCQgAcHQCwrtD6tJTfcmtvR8kWcNufhUV5cCtHCA3G+JWNpCbDeRlQ+RlA/m5gAAgkwE2csDBEXBrC8lVAbi5A23bQXL3ANp6AO4egJOz0Z3JhaoaqKhQdySvrACqqgBbW/Ude7b2gK1tg+tGZE0kIYQwdxC1eeONN9ChQwc8+eSTAACVSoVnn30WDzzwACZOnFhj+08++QTl5eV47bXXtMvefPNNBAUF4emnn27wcXNyclBZWWl0/HeTJAk+Pj64ceMGWvBL3misn+VrSB1FdiZUq5cASfH6C7F3gDRgpLqVp55EpS7i0jmofloGXLmgCQ7oEgmpezQkv2CgjTNQUgxRkA+kXoa4lASkXVEP1qihcIfUozekHn2ATl0ga+OiUz9RVgpkZUBcOgdx8SyQnASUFusG4uAIBIRA8vQBFO3UzyWok4iiQqAgX53Y5GUDhcpG17cGOzugrSfg6qYensDeHpKdvbp+1dXqpKayUp3QVJQD5eV3EpxyyCoroSorBaoa8B1mawfY2wN2DoC9gzopslc/luwd1OtsNP8fS+q6S5L6MQSgEoBQqePS/tx5rhLqbYQAVCrte0qSydQJoEwGSHc91v7Y6F8uySDZ2MDFzQ23i4ohZNKd5Ta6cUlQ7y9pYr6TQEqS7jaQAJmkZxvZnc3qKu/ufe9ars1V7yrvbtJdx9Ju88c+kiShnYcH8vJy8cc7uY6ydJbfU57e5ffsU2u8DSnLkPL+eCzJZGjv64fs8kqTfpfa2trC07Nhw3a02BafqqoqpKSk6CQ4MpkM3bt3R3Jyst59kpOTMX78eJ1lPXv2xPHj+mewrqys1ElwJEmCo6Oj9rEpacozdbktBetn+RpSR6m9H2R/fRsi7QpUp44A19OAynL1VBgdu0KKvq/mJa3GxBIWAem1f0OcOQ6xexPEuQTgXLx6Rvm6dgzsAKlnH8h69gGCOujU5d76SY5OQEiY+mf0RHUykZYCcS4B4vxpiMvngbJS4NI5iEvnGha4vYO671A7T6Bde0geXkA7L0htPdQJhKoaqK6CKC1RtzgV5EMU3ALy8yBu5apbhm4XqFtqbl5X/9zR0D8RKn0Lbe0AGxt1slRd9cfyygr1D27X2KWp0ntjyhUACkwVSD3HMZcal2OtUG7nHpBefc9sx2+xiU9hYSFUKhUUCoXOcoVCgczMTL37KJXKGpfA3NzcoFQq9W6/fv16xMXFaZ+HhIRg8eLFDc4aG8Pbu/H/BVsC1s/yNaiOPj5A34FNH4zvQ8CYh1B5PQ2lR/aiPCkBVZlpUBUXQdbGBTYKd8gDgmHfLRr23aIh9/Cqt8g66+fnD/QbDAAQVVWozEhFZcpFVOfcRHVeDlTlZYAQkORyyFwVsHFrCxsvH8i9fGDT3gcyFzfjL1FVlKM6LwdVuTehUuZDlJVCVV4KUV6mbo2QywGZDJKtHSR7B8jutM7U+mNnp3NJS1RXQZSXQ1SUQ5SXQZSXQVVWClFaClFeqj5emfq3KCuD0CRK4q4WHAF1i4ckU9f3TkuJdpmmNUQmU6+TyaBtBVBVq1uAVKq7Hqt/o/qux3cvV1Wrt6++6/Hd5Wham6Db0iS0y6EuB3div7Otel/cVS+hs43QtmbV3EaoRM3XBHe2075ef/wWuGube9bV3Ed/WaKe9TXLu/t495ZhTFl3lVNX7LWUJdnaor0Zv0tbbOLTHCZNmqTTQqT5wsrJyUFVVVVtuzWKJEnw9vZGVlaWVV4qYf0sX4uuo8wWGDBa/YM/hpyvvvNTDgCV1cCNG7UW0aj62bcBukQDXRqwbXGp+sckbAAPX/WPAXTqWFIKlNQXjwTYOqp/XBofbXNp0e9RE6ivfqZobzZ3m7UkSfBqgnMol8st/1KXq6srZDJZjdYapVJZoxVIQ6FQoKBAtyG0oKCg1u1tbW1hW8vIsU31oRKa/0KsFOtn+ay9jtZeP8D668j6WT5z1rHFdumXy+UIDQ1FYmKidplKpUJiYiLCwsL07hMWFoazZ8/qLDtz5gw6derUpLESERGRZWixiQ8AjB8/Hrt378a+ffuQkZGBb7/9FuXl5Rg6dCgA4Msvv8Tq1au1248dOxanT5/Gpk2bcP36daxbtw5XrlzBmDFjzFQDIiIiakla7KUuAOjfvz8KCwuxbt06KJVKBAcH44033tBeusrNzdXpSBgeHo4XXngBa9euxZo1a+Dj44NXX32VY/gQERERgBae+ADAmDFjam2xWbhwYY1l/fr1Q79+/Zo4KiIiIrJELfpSFxEREZEpMfEhIiKiVoOJDxEREbUaTHyIiIio1WDiQ0RERK0GEx8iIiJqNZj4EBERUavBxIeIiIhaDSY+RERE1Gq0+JGbzUEub7qXpSnLbglYP8tn7XW09voB1l9H1s/ymbqOhpQnCXPNC09ERETUzHipq5mUlpbiH//4B0pLS80dSpNg/SyftdfR2usHWH8dWT/L1xLqyMSnmQghcPXqVVhrAxvrZ/msvY7WXj/A+uvI+lm+llBHJj5ERETUajDxISIiolaDiU8zsbW1xcMPPwxbW1tzh9IkWD/LZ+11tPb6AdZfR9bP8rWEOvKuLiIiImo12OJDRERErQYTHyIiImo1mPgQERFRq8HEh4iIiFoN658QpBlt374dmzZtglKpRFBQEObMmYOOHTvWuv2RI0cQGxuLnJwceHt7Y+bMmYiOjm7GiBtm/fr1OHbsGK5fvw47OzuEhYXhscceg6+vb6377Nu3D19//bXOMltbW6xataqpw22UdevWIS4uTmeZr68vPv3001r3sZTzBwDz5s1DTk5OjeWjR4/GU089VWN5Sz9/586dw8aNG3H16lXk5+fjlVdeQZ8+fbTrhRBYt24ddu/ejeLiYnTu3BlPPfUUfHx86izX0M9wU6qrjlVVVVi7di3i4+ORnZ0NJycndO/eHTNmzIC7u3utZTbmfd5U6juHX331Ffbv36+zT8+ePfHmm2/WWa6lnEMAmDp1qt79HnvsMUyYMEHvupZyDhvyd6GiogIrVqzA4cOHUVlZiZ49e+Kpp56CQqGotdzGfnYNwcTHRA4fPowVK1Zg7ty56NSpE7Zs2YJFixbh008/hZubW43tL168iM8++wwzZsxAdHQ0Dh48iA8++ACLFy9GYGCgGWpQu3PnzuH+++9Hhw4dUF1djTVr1uDdd9/Fxx9/DAcHh1r3c3R0xGeffdaMkRonICAA8+fP1z6XyWpvELWk8wcA//rXv6BSqbTP09LS8O6776Jfv3617tOSz195eTmCg4MxfPhwfPjhhzXWb9iwAdu2bcO8efPg5eWF2NhYLFq0CB9//DHs7Oz0lmnoZ7ip1VXHiooKXL16FVOmTEFwcDCKioqwbNky/Pvf/8b7779fZ7mGvM+bUn3nEAAiIyPx3HPPaZ/XNxGlJZ1DAPjvf/+r8zw+Ph7ffPMN+vbtW2e5LeEcNuTvwvLly3Hq1Cm8/PLLcHJywtKlS/HRRx/hnXfeqbXcxnx2DcVLXSayefNmjBgxAsOGDYO/vz/mzp0LOzs77N27V+/2W7duRWRkJCZMmAB/f39MmzYNoaGh2L59ezNHXr8333wTQ4cORUBAAIKDgzFv3jzk5uYiJSWlzv0kSYJCodD5aclkMplOrK6urrVua0nnDwBcXV116nbq1Cm0b98eXbt2rXWflnz+oqKiMG3aNJ3/njWEENi6dSsmT56M3r17IygoCH/5y1+Qn5+P48eP11qmoZ/hplZXHZ2cnDB//nz0798fvr6+CAsLw5w5c5CSkoLc3Nw6yzXkfd6U6qqfhlwu14nV2dm5zjIt6RwCqPH5On78OCIiItC+ffs6y20J57C+vwslJSXYs2cPZs2ahW7duiE0NBTPPfccLl68iOTkZL1lNvazayi2+JhAVVUVUlJSMHHiRO0ymUyG7t2713qCk5OTMX78eJ1lPXv2NOnJbSolJSUAUO+XUFlZGZ577jkIIRASEoLp06cjICCgOUJslKysLPz5z3+Gra0twsLCMGPGDHh4eOjd1pLPX1VVFQ4cOIBx48ZBkqRat7O086eRnZ0NpVKJHj16aJc5OTmhY8eOSE5OxoABA2rs05jPcEtTUlICSZLg5ORU53aGvM/N7dy5c3jqqafQpk0bdOvWDdOmTYOLi4vebS39HCqVSsTHx2PevHn1btsSz+G9fxdSUlJQXV2N7t27a7fx8/ODh4cHkpOTERYWVqOMxnx2G4OJjwkUFhZCpVLV+I9YoVAgMzNT7z5KpbJG06ubmxuUSmUTRWkaKpUKy5YtQ3h4eJ2XdHx9ffHss88iKCgIJSUl2LhxI/75z3/i448/Rrt27Zox4obp1KkTnnvuOfj6+iI/Px9xcXH4v//7P3z00UdwdHSssb2lnj8AOHbsGIqLizF06NBat7G083c3zTkw5Pw05jPcklRUVGDVqlUYMGBAnYmPoe9zc4qMjETfvn3h5eWFrKwsrFmzBu+99x4WLVqk99KOpZ/D/fv3w8HBoc4WMKBlnkN9fxeUSiXkcjnatGmjs21dn8PGfHYbg4kPGWTp0qVIT0/H22+/Xed2YWFhOhl9WFgY/vrXv2Lnzp2YNm1aU4dpsKioKO3joKAg7ZfLkSNHMHz4cDNGZnp79+5FZGRknZ1gLe38tWZVVVX45JNPAEBvR/W7WdL7/O7/7gMDAxEUFITnn38eSUlJOq0I1mLv3r0YNGhQvf1YWuI5bOjfhZaCfXxMwNXVFTKZrEZGqlQqa+0XoVAoUFBQoLOsoKCgRfWjuNfSpUtx6tQpLFiwwOD/+uVyOUJCQpCVldVE0ZlWmzZt4OvrW2u8lnj+ACAnJwdnzpzBiBEjDNrPks6f5hwYcn4a8xluCTRJT25uLv75z3/We5nrXvW9z1uS9u3bw8XFpdZYLfUcAsD58+eRmZnZqMTF3Oewtr8LCoUCVVVVKC4u1tm+rs9hYz67jcHExwTkcjlCQ0ORmJioXaZSqZCYmKj3Oiag/g/67NmzOsvOnDmDTp06NWmsjSGEwNKlS3Hs2DH83//9H7y8vAwuQ6VSIS0tDW3btm2CCE2vrKwMWVlZtX7YLOn83W3v3r1wc3Mz+LZ7Szp/Xl5eUCgUOuenpKQEly9frvXz2JjPsLlpkp6srCzMnz+/1r4vdanvfd6S5OXloaioqNb3oCWeQ409e/YgNDQUwcHBBu9rrnNY39+F0NBQ2NjY6HwOMzMzkZubW+v5aMxntzF4qctExo8fj6+++gqhoaHo2LEjtm7divLycm0/ii+//BLu7u6YMWMGAGDs2LFYuHAhNm3ahOjoaBw6dAhXrlzB008/bcZa6Ld06VIcPHgQf//73+Ho6Kj9j8rJyUnbLHtv/eLi4tCpUyd4e3ujuLgYGzduRE5OjsEtDc1lxYoV6NWrFzw8PJCfn49169ZBJpNh4MCBACz7/GmoVCrs27cPQ4YMgY2Njc46Szt/mi97jezsbKSmpsLZ2RkeHh4YO3Ysfv75Z/j4+MDLywtr165F27Zt0bt3b+0+b7/9Nvr06YMxY8YAqP8z3NzqqqNCocDHH3+Mq1ev4h//+AdUKpX2c+ns7Ky97fveOtb3Pm8p9XN2dsaPP/6Ivn37QqFQ4ObNm/jhhx/g7e2Nnj17avex5HOo6YxcUlKC33//HY8//rjeMlrqOazv74KTkxOGDx+OFStWwNnZGU5OTvjuu+9qXEZ/6aWXMGPGDPTp0weSJDXos2ssJj4m0r9/fxQWFmLdunVQKpUIDg7GG2+8oc3Cc3Nzde6gCQ8PxwsvvIC1a9dizZo18PHxwauvvtoix4D59ddfAQALFy7UWf7cc89pv1DurV9RURGWLFkCpVKJNm3aIDQ0FO+++y78/f2bK2yD3Lp1C5999hlu374NV1dXdO7cGYsWLdLeJmrJ50/j7NmzyM3NxbBhw2qss7Tzd+XKFbz11lva5ytWrAAADBkyBPPmzcNDDz2E8vJyLFmyBCUlJejcuTPeeOMNnf4TN2/eRGFhofZ5fZ/h5lZXHR955BGcOHECAPD3v/9dZ78FCxYgIiICQM061vc+b0511W/u3LlIS0vD/v37UVxcDHd3d/To0QOPPvoobG1ttftY8jnU3L11+PBhCCFqTVxa6jlsyN+FWbNmQZIkfPTRR6iqqtIOYHi3zMxM7R1hABr02TWWJIQQJiuNiIiIqAVjHx8iIiJqNZj4EBERUavBxIeIiIhaDSY+RERE1Gow8SEiIqJWg4kPERERtRpMfIiIiKjVYOJDRERErQYTHyIiImo1OGUFEZlddXU1NmzYgN27d6OgoAAdOnTAn//8Z/j6+ta6z1dffYX9+/cDAAICAvDRRx/VeYx169YhLi4O69atM2ns99qyZQuWL1+uff7tt9+aZUoIItKPiQ8RmZVKpcKHH36I5ORkjBs3DnZ2dli/fj0WL16Mjz/+uMaEqndzcXHBrFmz0KZNm2aMuG6RkZFwcXHBsWPHcOzYMXOHQ0T3YOJDRGa1ceNGJCYm4r333kNAQAAAQKFQ4PPPP0dSUhJ69OhR674ODg4YPHhwc4XaIH5+fvDz80NWVhYTH6IWiH18iMhsSkpKsH79eowdO1ab9ABAWFgYAODatWvmCo2IrBRbfIjIbA4cOICysjKMHDlSZ7lcrv5qKi0tbVS5Fy5cwPLly5GWlgZ3d3dMmDCh1m1v3bqFtWvXIj4+HsXFxfD29sb48eMxfPhwne2SkpKwcuVKpKena8vMz89vln5DRGQ6THyIyGyOHTsGf39/2Nvbo7CwULs8NzcXgPpSlqHS0tLw7rvvwtXVFY888giqq6uxbt06KBSKGtsqlUq8+eabAID7778frq6uSEhIwDfffIPS0lKMGzcOAHD16lW89957UCgUeOSRR6BSqRAXF8dOy0QWiIkPEZmFSqVCcnIyysvL8dRTT+ndxsvLy+ByY2NjIYTA22+/DQ8PDwBA37598corr9TYdu3atdrO1S4uLgCA0aNH49NPP8WPP/6IUaNGwc7ODuvWrYNMJsM777wDd3d3AED//v3x17/+1eD4iMi8mPgQkVlkZWWhvLwcEyZMqNGBee/evTh06BACAwMNKlOlUuH06dPo3bu3NukBAH9/f/Ts2RPx8fHaZUIIHD16FP369YMQQqfFKTIyEocPH0ZKSgrCwsJw9uxZ9OnTR5v0AIC3tzciIyNx8uRJQ6tORGbExIeIzCInJwcAEBERUSPx2bBhA9zc3Oocx0efwsJCVFRUwMfHp8Y6X19fncSnsLAQxcXF2LVrF3bt2lVreQUFBaioqIC3t3eN9fqWEVHLxsSHiMyivLwcAGBvb6+zvKSkBOfPn8ewYcOa9PhCCADAoEGDMGTIEL3bBAUFQaVSNWkcRNS8mPgQkVloOi6XlZXpLN+3bx+qqqowevRog8t0dXWFnZ0dbty4UWNdZmZmjW0dHR2hUqnqHCtIpVLB1tYWWVlZNdbpW0ZELRvH8SEiswgKCoIkSUhKStIuy8vLw08//YTBgwcjKCjI4DJlMhl69uyJ48ePa+8MA4CMjAycPn26xrZ9+/bF0aNHkZaWVqMsTZ8fmUyG7t274/jx47h165Z2fVZWFhISEgyOkYjMiy0+RGQWbm5u6N27N7Zu3Qp7e3s4OTlhy5YtcHd3x5w5cxpd7tSpU5GQkID/+7//w+jRo6FSqbBt2zYEBATUGBBxxowZSEpKwptvvokRI0bA398fRUVFSElJwdmzZ/H9999ry/znP/+J+fPna8vcvn07AgICkJqaaszLQETNjIkPEZnNM888g2+++QabNm2Cg4MD+vXrh+nTp8PR0bHRZQYFBeHNN9/EihUrsG7dOrRr1w5Tp05Ffn5+jcRHoVDgvffeQ1xcHI4ePYodO3bAxcUFAQEBmDlzpna70NBQvPHGG1i5ciViY2PRrl07PProo8jIyMD169cbHSsRNT9JaHr4ERFZkK+++gqJiYlYvHgxbGxszDJR6b///W9kZGTg888/1y6rqKhAWVkZNm7ciI0bN3J2dqIWhi0+RGSx8vLy8NRTTyEgIAAfffRRkx6roqICdnZ22uc3btxAfHx8jTvCdu7cieXLlzdpLETUeEx8iMgiPfTQQxg0aBCAxk1tYai//OUvGDp0KLy8vJCbm4tff/0VcrkcDz30kM52ffv21Zlw1cnJqcljI6KG46UuIqIG+Prrr5GUlASlUgm5XI6wsDBMnz4doaGh5g6NiAzAxIeIiIhaDY7jQ0RERK0GEx8iIiJqNZj4EBERUavBxIeIiIhaDSY+RERE1Gow8SEiIqJWg4kPERERtRpMfIiIiKjVYOJDRERErQYTHyIiImo1mPgQERFRq/H/JRxBfylxA2cAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "plt.plot(results[\"channel_1\"][\"Theta_deg\"], results[\"channel_1\"][\"sigma_mb_sr\"])\n", + "\n", + "ax.set_xlabel(r\"$\\theta$ [deg]\")\n", + "ax.set_ylabel(r\"$d\\sigma_{el}/d\\Omega / (d\\sigma_{el}/d\\Omega)_R$ [dimensionless]\")\n", + "ax.set_title(r\"differential elastic scattering cross section\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cfae24fc-8439-450f-a48d-52c876710d73", + "metadata": {}, + "outputs": [], + "source": [ + "# get breakup channels (elastic is always channel 1)\n", + "br_channels = dict([(k, results[k]) for k in results.keys() if k != \"channel_1\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "61d6d0ce-f05e-4a40-9f3e-339504a2a6bc", + "metadata": {}, + "outputs": [], + "source": [ + "dsigma_dOmega = np.zeros((len(br_channels), len(results[\"channel_1\"][\"Theta_deg\"])))\n", + "for i, key in enumerate(br_channels):\n", + " theta = np.array(np.deg2rad(results[key][\"Theta_deg\"]))\n", + " dsigma_dOmega[i, :] = results[key][\"sigma_mb_sr\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "b6a57560-6e54-4a7d-867a-44acb410eea7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "# Choose colormap and normalization\n", + "cmap = plt.get_cmap(\"viridis\")\n", + "norm = mpl.colors.Normalize(vmin=energy_bins.min(), vmax=energy_bins.max())\n", + "\n", + "for i in range(dsigma_dOmega.shape[0]):\n", + " plt.semilogy(\n", + " np.rad2deg(theta), dsigma_dOmega[i, :], color=cmap(norm(energy_bins[i]))\n", + " )\n", + "\n", + "sm = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)\n", + "cbar = fig.colorbar(sm, ax=ax)\n", + "cbar.set_label(\"E [MeV]\")\n", + "\n", + "ax.set_xlabel(r\"$\\theta$ [deg]\")\n", + "ax.set_ylabel(r\"$d\\sigma_{Br}/d\\Omega / dE$ [mb/Sr/MeV]\")\n", + "ax.set_title(\"double differential breakup cross section\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5a4d0d60-0a83-47a7-bcb3-da28f212176f", + "metadata": {}, + "source": [ + "Integrating over solid angle, we get the differential breakup cross section with respect to energy:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "c41068f9-f927-4e23-abc5-8c812dc20d3a", + "metadata": {}, + "outputs": [], + "source": [ + "x = theta[None, :]\n", + "dsigma_br_dE = (\n", + " 2\n", + " * np.pi\n", + " * np.trapezoid(dsigma_dOmega * np.sin(x), x, axis=1)[1:]\n", + " / np.diff(energy_bins)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "f13fe1cd-0901-452a-b004-c8947fa7f5af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 10.0)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.set_xlabel(r\"rel. energy [MeV]\")\n", + "ax.set_ylabel(r\"$d\\sigma_{Br}/dE$ [mb/MeV]\")\n", + "ax.set_title(\"differential breakup cross section\")\n", + "\n", + "ax.plot(energy_bins[1:], dsigma_br_dE)\n", + "ax.set_xlim([0, 10])" + ] + }, + { + "cell_type": "markdown", + "id": "0f3b68c1-399c-4541-ab5a-f988394e8a95", + "metadata": {}, + "source": [ + "Integrating over all energies, we get the breakup differential cross section with respect to angle:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9c7d705d-e3fe-4c8f-ae0e-45a93daa591f", + "metadata": {}, + "outputs": [], + "source": [ + "dsigma_br_dOmega = np.sum(dsigma_dOmega, axis=0) / 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "070d8c96-6956-48d4-b923-206fdc39eaa1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 5.0)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.set_xlabel(r\"$\\theta$ [deg]\")\n", + "ax.set_ylabel(r\"$d\\sigma_{Br}/d\\Omega$ [b/Sr]\")\n", + "ax.set_title(\"differential breakup cross section\")\n", + "\n", + "ax.plot(np.rad2deg(theta), dsigma_br_dOmega)\n", + "ax.set_xlim([0, 5])" + ] } ], "metadata": { From ec5a657be580d76c8644e58201bcd27a32a75f23 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Wed, 20 May 2026 16:00:46 -0400 Subject: [PATCH 08/13] add some text --- book/notebooks/Transfer.ipynb | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/book/notebooks/Transfer.ipynb b/book/notebooks/Transfer.ipynb index 7f6916b..5f3de09 100644 --- a/book/notebooks/Transfer.ipynb +++ b/book/notebooks/Transfer.ipynb @@ -266,7 +266,9 @@ "id": "1876ee48-cb79-4871-9b01-345584117c3e", "metadata": {}, "source": [ - "## Now let's look at the template and see how to change the parameters" + "## Now let's look at the template and see how to change the parameters\n", + "\n", + "The template is the same as the input file we just ran, but we've replaced the values of the interaction parameters with keys like `@rC_entrance@`:" ] }, { @@ -344,6 +346,14 @@ "print(template_file)" ] }, + { + "cell_type": "markdown", + "id": "39434136-2af6-407a-a71c-472b1e8de5bd", + "metadata": {}, + "source": [ + "Here are the parameters from the example:" + ] + }, { "cell_type": "code", "execution_count": 18, @@ -500,12 +510,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "504e3d7d-5ccb-4a18-a3b5-adc78d6b54c4", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "And we get the same thing:" + ] }, { "cell_type": "code", From e3ed848bab5fcf9c43ad03dfeb3c3cc64aa5811c Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Wed, 20 May 2026 16:01:21 -0400 Subject: [PATCH 09/13] update breakup example --- book/notebooks/Breakup.ipynb | 568 +++++++++++++++++- .../B3-example-br-long.template | 150 +++++ 2 files changed, 686 insertions(+), 32 deletions(-) create mode 100644 book/notebooks/Breakup_template/B3-example-br-long.template diff --git a/book/notebooks/Breakup.ipynb b/book/notebooks/Breakup.ipynb index 09e885e..9a2030e 100644 --- a/book/notebooks/Breakup.ipynb +++ b/book/notebooks/Breakup.ipynb @@ -5,7 +5,7 @@ "id": "0bfe8974-b46a-4ac1-af8c-38f44e806286", "metadata": {}, "source": [ - "# Breakup CDCC in $^{8}$B+$^{208}$Pb at 83 MeV/u\n", + "# Breakup Continuum Discretized Coupled Channels for $^{8}$B+$^{208}$Pb at 83 MeV/u\n", "\n", "We will adapt [this example from the Frescox documentation](https://www.fresco.org.uk/examples/B3-example-br-long.in), which only models $s$-wave breakup. \n", "\n", @@ -38,7 +38,7 @@ "with open(\"MatplotlibEsthetics.json\", \"r\") as fptr:\n", " esthetics = json.load(fptr)\n", "\n", - "plt.style.use(esthetics[\"style\"])\n", + "# plt.style.use(esthetics[\"style\"])\n", "FONTSIZE = esthetics[\"fontsize\"]\n", "TICK_FONTSIZE = esthetics[\"tick_fontsize\"]\n", "MARKERSIZE = esthetics[\"markersize\"]\n", @@ -90,7 +90,7 @@ "outputs": [], "source": [ "EXAMPLE_DIR = Path(\"./Breakup_template//\")\n", - "TEMPLATE_FILE_PATH = Path(EXAMPLE_DIR / \"B3-example-br.template\")\n", + "TEMPLATE_FILE_PATH = Path(EXAMPLE_DIR / \"B3-example-br-long.template\")\n", "INPUT_FILE_PATH = Path(EXAMPLE_DIR / \"B3-example-br-long.in\")" ] }, @@ -291,6 +291,8 @@ "metadata": {}, "outputs": [], "source": [ + "# from the input file\n", + "# Frescox uses\n", "energy_bins = np.array(\n", " [\n", " 0.1583,\n", @@ -343,8 +345,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 3.55 ms, sys: 2.26 ms, total: 5.81 ms\n", - "Wall time: 1min 46s\n" + "CPU times: user 7 ms, sys: 2.85 ms, total: 9.86 ms\n", + "Wall time: 1min 53s\n" ] } ], @@ -385,7 +387,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -407,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "id": "cfae24fc-8439-450f-a48d-52c876710d73", "metadata": {}, "outputs": [], @@ -418,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 11, "id": "61d6d0ce-f05e-4a40-9f3e-339504a2a6bc", "metadata": {}, "outputs": [], @@ -431,15 +433,15 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "id": "b6a57560-6e54-4a7d-867a-44acb410eea7", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -447,23 +449,25 @@ } ], "source": [ - "fig, ax = plt.subplots()\n", - "# Choose colormap and normalization\n", - "cmap = plt.get_cmap(\"viridis\")\n", - "norm = mpl.colors.Normalize(vmin=energy_bins.min(), vmax=energy_bins.max())\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(projection=\"3d\")\n", "\n", "for i in range(dsigma_dOmega.shape[0]):\n", - " plt.semilogy(\n", - " np.rad2deg(theta), dsigma_dOmega[i, :], color=cmap(norm(energy_bins[i]))\n", - " )\n", + " x = np.rad2deg(theta)\n", + " y = np.ones_like(x) * energy_bins[i]\n", + " z = dsigma_dOmega[i, :]\n", + " ax.plot(x, y, z, color=\"k\")\n", "\n", - "sm = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)\n", - "cbar = fig.colorbar(sm, ax=ax)\n", - "cbar.set_label(\"E [MeV]\")\n", + "# sm = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)\n", + "# cbar = fig.colorbar(sm, ax=ax)\n", + "# cbar.set_label(\"E [MeV]\")\n", "\n", + "ax.set_ylabel(r\"rel. energy [MeV]\")\n", "ax.set_xlabel(r\"$\\theta$ [deg]\")\n", - "ax.set_ylabel(r\"$d\\sigma_{Br}/d\\Omega / dE$ [mb/Sr/MeV]\")\n", + "ax.set_zlabel(r\"$d\\sigma_{Br}/d\\Omega / dE$ [mb/Sr/MeV]\")\n", + "# ax.set_zscale(\"log\")\n", "ax.set_title(\"double differential breakup cross section\")\n", + "plt.tight_layout()\n", "plt.show()" ] }, @@ -477,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 13, "id": "c41068f9-f927-4e23-abc5-8c812dc20d3a", "metadata": {}, "outputs": [], @@ -493,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 14, "id": "f13fe1cd-0901-452a-b004-c8947fa7f5af", "metadata": {}, "outputs": [ @@ -503,13 +507,13 @@ "(0.0, 10.0)" ] }, - "execution_count": 32, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -538,17 +542,17 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 15, "id": "9c7d705d-e3fe-4c8f-ae0e-45a93daa591f", "metadata": {}, "outputs": [], "source": [ - "dsigma_br_dOmega = np.sum(dsigma_dOmega, axis=0) / 1000" + "dsigma_br_dOmega_b_per_Sr = np.sum(dsigma_dOmega, axis=0) / 1000" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 16, "id": "070d8c96-6956-48d4-b923-206fdc39eaa1", "metadata": {}, "outputs": [ @@ -558,13 +562,13 @@ "(0.0, 5.0)" ] }, - "execution_count": 36, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -579,9 +583,509 @@ "ax.set_ylabel(r\"$d\\sigma_{Br}/d\\Omega$ [b/Sr]\")\n", "ax.set_title(\"differential breakup cross section\")\n", "\n", - "ax.plot(np.rad2deg(theta), dsigma_br_dOmega)\n", + "ax.plot(np.rad2deg(theta), dsigma_br_dOmega_b_per_Sr)\n", "ax.set_xlim([0, 5])" ] + }, + { + "cell_type": "markdown", + "id": "41168e10-641d-4b8d-b678-ee501894d417", + "metadata": {}, + "source": [ + "## Using the template for breakup\n", + "\n", + "The template is the same as the input file, but we've replace the parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0e2f98a6-8cd4-48c1-b11c-5a0400b1ba4f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------\n", + "CDCC 8B+208Pb ; nuclear and coulomb s-waves \n", + "NAMELIST\n", + " &Fresco hcm= 0.01 rmatch= -60.000 rintp= 0.15 rsp= 0.0\n", + " rasym= 1000.00 accrcy= 0.0010000 \n", + " jtmin= 0.0 jtmax= 9000.0 absend= -50.0000 \n", + " jump = 1 10 50 200 \n", + " jbord= 0.0 200.0 300.0 1000.0 9000.0 \n", + " thmin= 0.00 thmax= 20.00 thinc= 0.05 cutr=-20.00\n", + " ips= 0.0000 it0= 0 iter= 0 iblock= 21 nnu= 24\n", + " smallchan= 1.00E-12 smallcoup= 1.00E-12\n", + " chans= 1 smats= 2 xstabl= 1 cdcc= 1\n", + " elab= 656.0000 pel=1 exl=1 lab=1 lin=1 lex=1 /\n", + "\n", + " &Partition namep='8B ' massp= 8.0000 zp= 5 nex= 21 pwf=T \n", + " namet='208Pb ' masst=208.0000 zt= 82 qval= 0.1370/ \n", + " &States jp= 1.5 ptyp=-1 ep= 0.0000 cpot= 1 \n", + " jt= 0.0 ptyt= 1 et= 0.0000/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.1583 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.2180 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.3260 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.4830 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.6889 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 0.9438 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 1.2478 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 1.6007 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 2.0027 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 2.4536 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 2.9536 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 3.5025 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 4.1005 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 4.7474 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 5.4434 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 6.1884 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 6.9824 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 7.8253 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 8.7173 cpot= 1 copyt= 1/ \n", + " &States jp= 0.5 ptyp= 1 ep= 9.6583 cpot= 1 copyt= 1/ \n", + " &Partition namep='7Be ' massp= 7.0000 zp= 4 nex= -1 pwf=T \n", + " namet='208Pb +p' masst=209.0000 zt= 83 qval= 0.0000/ \n", + " &States jp= 0.0 ptyp= 1 ep= 0.0000 cpot= 2 \n", + " jt= 0.0 ptyt= 1 et= 0.0000/ \n", + " &Partition / ! END OF DEFINING PARTITIONS \n", + " \n", + " &Pot kp= 1 type= 0 shape= 0 \n", + " p(1:3)= 1.0000 0.0000 @rC_8B_208Pb@ / \n", + " &Pot kp= 2 type= 0 shape= 0 \n", + " p(1:3)= 208.0000 0.0000 @rC_7Be_208Pb@ / \n", + " &Pot kp= 2 type= 1 shape= 0 \n", + " p(1:7)= @V_7Be_208Pb@ @rV_7Be_208Pb@ @aV_7Be_208Pb@ @W_7Be_208Pb@ @rW_7Be_208Pb@ @aW_7Be_208Pb@ 0.0000 / \n", + " &Pot kp= 3 type= 0 shape= 0 \n", + " p(1:3)= 208.0000 0.0000 @rC_p_208Pb@ / \n", + " &Pot kp= 3 type= 1 shape= 0 \n", + " p(1:7)= @V_p_208Pb@ @rV_p_208Pb@ @aV_p_208Pb@ @W_p_208Pb@ @rW_p_208Pb@ @aW_p_208Pb@ 0.0000 / \n", + " &Pot kp= 4 type= 0 shape= 0 \n", + " p(1:3)= 1.0000 0.0000 @rC_p_7Be@ / \n", + " &Pot kp= 4 type= 1 shape= 0 \n", + " p(1:3)= @V_p_7Be@ @rV_p_7Be@ @aV_p_7Be@ / \n", + " &Pot kp= 4 type= 3 shape= 0 \n", + " p(1:3)= @Vso_p_7Be@ @rso_p_7Be@ @aso_p_7Be@ / \n", + " &Pot / ! END OF DEFINING POTENTIALS \n", + " \n", + " &Overlap kn1= 1 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 1 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= 0.1370 isc= 1 ipc=0 / \n", + " &Overlap kn1= 2 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.0182 isc=12 ipc=2 nk= 20 er= -0.0344 / \n", + " &Overlap kn1= 3 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.0771 isc=12 ipc=2 nk= 20 er= -0.0834 / \n", + " &Overlap kn1= 4 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.1850 isc=12 ipc=2 nk= 20 er= -0.1324 / \n", + " &Overlap kn1= 5 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.3419 isc=12 ipc=2 nk= 20 er= -0.1814 / \n", + " &Overlap kn1= 6 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.5479 isc=12 ipc=2 nk= 20 er= -0.2304 / \n", + " &Overlap kn1= 7 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -0.8028 isc=12 ipc=2 nk= 20 er= -0.2794 / \n", + " &Overlap kn1= 8 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -1.1067 isc=12 ipc=2 nk= 20 er= -0.3284 / \n", + " &Overlap kn1= 9 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -1.4596 isc=12 ipc=2 nk= 20 er= -0.3774 / \n", + " &Overlap kn1= 10 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -1.8616 isc=12 ipc=2 nk= 20 er= -0.4264 / \n", + " &Overlap kn1= 11 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -2.3125 isc=12 ipc=2 nk= 20 er= -0.4754 / \n", + " &Overlap kn1= 12 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -2.8125 isc=12 ipc=2 nk= 20 er= -0.5245 / \n", + " &Overlap kn1= 13 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -3.3614 isc=12 ipc=2 nk= 20 er= -0.5735 / \n", + " &Overlap kn1= 14 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -3.9594 isc=12 ipc=2 nk= 20 er= -0.6225 / \n", + " &Overlap kn1= 15 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -4.6064 isc=12 ipc=2 nk= 20 er= -0.6715 / \n", + " &Overlap kn1= 16 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -5.3023 isc=12 ipc=2 nk= 20 er= -0.7205 / \n", + " &Overlap kn1= 17 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -6.0473 isc=12 ipc=2 nk= 20 er= -0.7695 / \n", + " &Overlap kn1= 18 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -6.8413 isc=12 ipc=2 nk= 20 er= -0.8185 / \n", + " &Overlap kn1= 19 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -7.6843 isc=12 ipc=2 nk= 20 er= -0.8675 / \n", + " &Overlap kn1= 20 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -8.5763 isc=12 ipc=2 nk= 20 er= -0.9165 / \n", + " &Overlap kn1= 21 kn2= 0 ic1=1 ic2=2 in= 1 \n", + " kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 \n", + " kbpot= 4 be= -9.5173 isc=12 ipc=2 nk= 20 er= -0.9655 / \n", + " &Overlap / ! END OF DEFINING OVERLAPS \n", + " \n", + " &Coupling icto= 1 icfrom= 2 kind=3 ip1= 2 ip2= 0 ip3= 0 \n", + " p1= 3.0000 p2= 2.0000 / \n", + " &Cfp in= 1 ib= 1 ia= 1 kn= 1 a= 1.0000 / \n", + " &Cfp in= 1 ib= 2 ia= 1 kn= 2 a= 1.0000 / \n", + " &Cfp in= 1 ib= 3 ia= 1 kn= 3 a= 1.0000 / \n", + " &Cfp in= 1 ib= 4 ia= 1 kn= 4 a= 1.0000 / \n", + " &Cfp in= 1 ib= 5 ia= 1 kn= 5 a= 1.0000 / \n", + " &Cfp in= 1 ib= 6 ia= 1 kn= 6 a= 1.0000 / \n", + " &Cfp in= 1 ib= 7 ia= 1 kn= 7 a= 1.0000 / \n", + " &Cfp in= 1 ib= 8 ia= 1 kn= 8 a= 1.0000 / \n", + " &Cfp in= 1 ib= 9 ia= 1 kn= 9 a= 1.0000 / \n", + " &Cfp in= 1 ib= 10 ia= 1 kn= 10 a= 1.0000 / \n", + " &Cfp in= 1 ib= 11 ia= 1 kn= 11 a= 1.0000 / \n", + " &Cfp in= 1 ib= 12 ia= 1 kn= 12 a= 1.0000 / \n", + " &Cfp in= 1 ib= 13 ia= 1 kn= 13 a= 1.0000 / \n", + " &Cfp in= 1 ib= 14 ia= 1 kn= 14 a= 1.0000 / \n", + " &Cfp in= 1 ib= 15 ia= 1 kn= 15 a= 1.0000 / \n", + " &Cfp in= 1 ib= 16 ia= 1 kn= 16 a= 1.0000 / \n", + " &Cfp in= 1 ib= 17 ia= 1 kn= 17 a= 1.0000 / \n", + " &Cfp in= 1 ib= 18 ia= 1 kn= 18 a= 1.0000 / \n", + " &Cfp in= 1 ib= 19 ia= 1 kn= 19 a= 1.0000 / \n", + " &Cfp in= 1 ib= 20 ia= 1 kn= 20 a= 1.0000 / \n", + " &Cfp in= 1 ib= 21 ia= 1 kn= 21 a= 1.0000 / \n", + " ! ******* END OF CDCC INPUTS ******* \n" + ] + } + ], + "source": [ + "with open(TEMPLATE_FILE_PATH, \"r\") as temp:\n", + " template_file = temp.read()\n", + "\n", + "print(\"-----------------------------------\")\n", + "print(template_file)" + ] + }, + { + "cell_type": "markdown", + "id": "a91aa869-6320-48b2-9567-841ff26c3baf", + "metadata": {}, + "source": [ + "Because we have a few different interactions, it is nice to break up the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "94ac262c-9e8a-4106-a7af-450021af0c65", + "metadata": {}, + "outputs": [], + "source": [ + "params_by_interaction = {\n", + " \"8B_208Pb\": {\n", + " \"rC\": 2.65,\n", + " },\n", + " \"7Be_208Pb\": {\n", + " \"rC\": 1.3,\n", + " \"V\": 114.2,\n", + " \"rV\": 1.286,\n", + " \"aV\": 0.853,\n", + " \"W\": 9.44,\n", + " \"rW\": 1.739,\n", + " \"aW\": 0.809,\n", + " },\n", + " \"p_208Pb\": {\n", + " \"rC\": 1.3,\n", + " \"V\": 34.819,\n", + " \"rV\": 1.17,\n", + " \"aV\": 0.75,\n", + " \"W\": 15.430,\n", + " \"rW\": 1.32,\n", + " \"aW\": 0.601,\n", + " },\n", + " \"p_7Be\": {\n", + " \"rC\": 2.391,\n", + " \"V\": 44.675,\n", + " \"rV\": 2.391,\n", + " \"aV\": 0.48,\n", + " \"Vso\": 4.898,\n", + " \"rso\": 2.391,\n", + " \"aso\": 0.48,\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "7c06ed73-459c-4a96-b6cd-4b0ab6a8f9ea", + "metadata": {}, + "source": [ + "And we can write some handy functions to flatten parameter dicts in this form into the form expected by {{{brescox}}}, and vice versa:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5cce0abe-72f4-40a4-915f-5f218fdd644d", + "metadata": {}, + "outputs": [], + "source": [ + "def make_all_params(params_by_interaction: dict) -> dict:\n", + " all_params = {}\n", + " for suffix, param_set in params_by_interaction.items():\n", + " for key, val in param_set.items():\n", + " new_key = f\"{key}_{suffix}\"\n", + " assert \"@\" not in key and \"@\" not in suffix\n", + " assert new_key not in all_params\n", + " all_params[new_key] = val\n", + " return all_params\n", + "\n", + "\n", + "def make_params_by_interaction(all_params: dict) -> dict:\n", + " params_by_interaction = {}\n", + " for key, val in all_params.items():\n", + " split_key = key.split(\"_\")\n", + " new_key = split_key[0]\n", + " suffix = key[len(new_key) + 1 :]\n", + " if suffix not in params_by_interaction:\n", + " params_by_interaction[suffix] = {new_key: val}\n", + " else:\n", + " assert new_key not in params_by_interaction[suffix]\n", + " params_by_interaction[suffix][new_key] = val\n", + " return params_by_interaction" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "124f3fe3-83f8-4e1e-967a-dd0049713393", + "metadata": {}, + "outputs": [], + "source": [ + "assert params_by_interaction == make_params_by_interaction(\n", + " make_all_params(params_by_interaction)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "656d8069-477d-472f-b90f-886fc6fade7e", + "metadata": {}, + "source": [ + "The form expected by {{{bfrescox}}}, with parameter names corresponding to our template:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "486f4507-11c1-4ee3-a65f-587e3f89d056", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'rC_8B_208Pb': 2.65,\n", + " 'rC_7Be_208Pb': 1.3,\n", + " 'V_7Be_208Pb': 114.2,\n", + " 'rV_7Be_208Pb': 1.286,\n", + " 'aV_7Be_208Pb': 0.853,\n", + " 'W_7Be_208Pb': 9.44,\n", + " 'rW_7Be_208Pb': 1.739,\n", + " 'aW_7Be_208Pb': 0.809,\n", + " 'rC_p_208Pb': 1.3,\n", + " 'V_p_208Pb': 34.819,\n", + " 'rV_p_208Pb': 1.17,\n", + " 'aV_p_208Pb': 0.75,\n", + " 'W_p_208Pb': 15.43,\n", + " 'rW_p_208Pb': 1.32,\n", + " 'aW_p_208Pb': 0.601,\n", + " 'rC_p_7Be': 2.391,\n", + " 'V_p_7Be': 44.675,\n", + " 'rV_p_7Be': 2.391,\n", + " 'aV_p_7Be': 0.48,\n", + " 'Vso_p_7Be': 4.898,\n", + " 'rso_p_7Be': 2.391,\n", + " 'aso_p_7Be': 0.48}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "make_all_params(params_by_interaction)" + ] + }, + { + "cell_type": "markdown", + "id": "e74e9e66-b219-42f7-ae96-2b19ab86865d", + "metadata": {}, + "source": [ + "### Test the effect of individual parameters on the breakup cross sections\n", + "\n", + "This will take some time to run!" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "bf1403fa-9c31-44b0-aa94-cf145287f1c7", + "metadata": {}, + "outputs": [], + "source": [ + "params_to_perturb = {\n", + " \"p_7Be\": {\n", + " \"rV\": [1.3,1.9,2.5,3.1],\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "dad13f25-6f86-42d2-9157-832f35f25ae9", + "metadata": {}, + "outputs": [], + "source": [ + "def post_process(result):\n", + " theta_deg, elastic_ratio_rutherford = (\n", + " result[\"channel_1\"][\"Theta_deg\"],\n", + " result[\"channel_1\"][\"sigma_mb_sr\"],\n", + " )\n", + " br_channels = dict([(k, result[k]) for k in result.keys() if k != \"channel_1\"])\n", + " dsigma_dOmega = np.zeros((len(br_channels), len(results[\"channel_1\"][\"Theta_deg\"])))\n", + " for i, key in enumerate(br_channels):\n", + " theta = np.array(np.deg2rad(result[key][\"Theta_deg\"]))\n", + " dsigma_dOmega[i, :] = result[key][\"sigma_mb_sr\"]\n", + "\n", + " # angle integrated\n", + " x = theta[None, :]\n", + " dsigma_br_dE_mb_per_MeV = (\n", + " 2\n", + " * np.pi\n", + " * np.trapezoid(dsigma_dOmega * np.sin(x), x, axis=1)[1:]\n", + " / np.diff(energy_bins)\n", + " )\n", + " # energy integrated\n", + " dsigma_br_dOmega_mb_per_Sr = np.sum(dsigma_dOmega, axis=0)\n", + " return {\n", + " \"theta_deg\" : theta_deg,\n", + " \"elastic_ratio_rutherford\": elastic_ratio_rutherford,\n", + " \"dsigma_br_dE_mb_per_MeV\": dsigma_br_dE_mb_per_MeV,\n", + " \"dsigma_br_dOmega_mb_per_Sr\" : dsigma_br_dOmega_mb_per_Sr,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b2e9d398-9118-4012-9e58-4ff176e92467", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 104 ms, sys: 5.52 ms, total: 109 ms\n", + "Wall time: 7min 37s\n" + ] + } + ], + "source": [ + "%%time\n", + "perturbed_results = {}\n", + "for interaction, perturb_list in params_to_perturb.items():\n", + " perturbed_results[interaction] = {}\n", + " for key, vals in perturb_list.items():\n", + " perturbed_results[interaction][key] = []\n", + " for val in vals:\n", + "\n", + " params = params_by_interaction.copy()\n", + " params[interaction][key] = val\n", + " cfg = bfrescox.Configuration.from_template(\n", + " TEMPLATE_FILE_PATH,\n", + " EXAMPLE_DIR.joinpath(\"frescox.in\"),\n", + " make_all_params(params),\n", + " overwrite=True,\n", + " )\n", + " bfrescox.run_simulation(\n", + " cfg,\n", + " EXAMPLE_DIR.joinpath(\"frescox.out\"),\n", + " cwd=EXAMPLE_DIR,\n", + " overwrite=True,\n", + " )\n", + " perturbed_results[interaction][key].append(\n", + " post_process(\n", + " bfrescox.parse_fort16(EXAMPLE_DIR.joinpath(\"fort.16\"))\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "6e8434be-730a-404c-ba70-265c32f51cb2", + "metadata": {}, + "source": [ + "## Plot parameter sensitivities for breakup and elastic observables" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "5900ef42-fc44-485f-89ea-c84c628eddc8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for interaction, perturb_list in params_to_perturb.items():\n", + " for key, vals in perturb_list.items():\n", + " fig, axes = plt.subplots(3, 1, figsize=(8, 9))\n", + " for i in range(len(vals)):\n", + " result = perturbed_results[interaction][key][i]\n", + " \n", + " axes[0].semilogy(result[\"theta_deg\"], result[\"elastic_ratio_rutherford\"], label=f\"{key} = {vals[i]:1.2f}\")\n", + " axes[0].set_ylabel(r\"$(d\\sigma/d\\Omega)/(d\\sigma/d\\Omega)_R$\")\n", + " axes[0].set_title(\"elastic cross section\")\n", + " axes[1].set_xlabel(r\"$\\theta$ [deg]\")\n", + "\n", + " axes[1].plot(result[\"theta_deg\"], result[\"dsigma_br_dOmega_mb_per_Sr\"]/1000, label=f\"{key} = {vals[i]:1.2f}\")\n", + " axes[1].set_ylabel(r\"$d\\sigma_{Br}/d\\Omega$ [b/Sr]\")\n", + " axes[1].set_title(\"breakup cross section\")\n", + " axes[1].set_xlabel(r\"$\\theta$ [deg]\")\n", + " axes[1].set_xlim([0,10])\n", + "\n", + " axes[2].set_xlabel(r\"rel. energy [MeV]\")\n", + " axes[2].set_ylabel(r\"$d\\sigma_{Br}/dE$ [mb/MeV]\")\n", + " axes[2].set_title(\"differential breakup cross section\")\n", + " \n", + " axes[2].plot(energy_bins[1:], result[\"dsigma_br_dE_mb_per_MeV\"], label=f\"{key} = {vals[i]:1.2f}\")\n", + " axes[2].set_xlim([0, 10])\n", + " axes[2].legend()\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1eed2e1-a289-45df-b609-c642a2f0055f", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/book/notebooks/Breakup_template/B3-example-br-long.template b/book/notebooks/Breakup_template/B3-example-br-long.template new file mode 100644 index 0000000..551e3e0 --- /dev/null +++ b/book/notebooks/Breakup_template/B3-example-br-long.template @@ -0,0 +1,150 @@ +CDCC 8B+208Pb ; nuclear and coulomb s-waves +NAMELIST + &Fresco hcm= 0.01 rmatch= -60.000 rintp= 0.15 rsp= 0.0 + rasym= 1000.00 accrcy= 0.0010000 + jtmin= 0.0 jtmax= 9000.0 absend= -50.0000 + jump = 1 10 50 200 + jbord= 0.0 200.0 300.0 1000.0 9000.0 + thmin= 0.00 thmax= 20.00 thinc= 0.05 cutr=-20.00 + ips= 0.0000 it0= 0 iter= 0 iblock= 21 nnu= 24 + smallchan= 1.00E-12 smallcoup= 1.00E-12 + chans= 1 smats= 2 xstabl= 1 cdcc= 1 + elab= 656.0000 pel=1 exl=1 lab=1 lin=1 lex=1 / + + &Partition namep='8B ' massp= 8.0000 zp= 5 nex= 21 pwf=T + namet='208Pb ' masst=208.0000 zt= 82 qval= 0.1370/ + &States jp= 1.5 ptyp=-1 ep= 0.0000 cpot= 1 + jt= 0.0 ptyt= 1 et= 0.0000/ + &States jp= 0.5 ptyp= 1 ep= 0.1583 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.2180 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.3260 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.4830 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.6889 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 0.9438 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.2478 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 1.6007 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.0027 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.4536 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 2.9536 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 3.5025 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 4.1005 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 4.7474 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 5.4434 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 6.1884 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 6.9824 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 7.8253 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 8.7173 cpot= 1 copyt= 1/ + &States jp= 0.5 ptyp= 1 ep= 9.6583 cpot= 1 copyt= 1/ + &Partition namep='7Be ' massp= 7.0000 zp= 4 nex= -1 pwf=T + namet='208Pb +p' masst=209.0000 zt= 83 qval= 0.0000/ + &States jp= 0.0 ptyp= 1 ep= 0.0000 cpot= 2 + jt= 0.0 ptyt= 1 et= 0.0000/ + &Partition / ! END OF DEFINING PARTITIONS + + &Pot kp= 1 type= 0 shape= 0 + p(1:3)= 1.0000 0.0000 @rC_8B_208Pb@ / + &Pot kp= 2 type= 0 shape= 0 + p(1:3)= 208.0000 0.0000 @rC_7Be_208Pb@ / + &Pot kp= 2 type= 1 shape= 0 + p(1:7)= @V_7Be_208Pb@ @rV_7Be_208Pb@ @aV_7Be_208Pb@ @W_7Be_208Pb@ @rW_7Be_208Pb@ @aW_7Be_208Pb@ 0.0000 / + &Pot kp= 3 type= 0 shape= 0 + p(1:3)= 208.0000 0.0000 @rC_p_208Pb@ / + &Pot kp= 3 type= 1 shape= 0 + p(1:7)= @V_p_208Pb@ @rV_p_208Pb@ @aV_p_208Pb@ @W_p_208Pb@ @rW_p_208Pb@ @aW_p_208Pb@ 0.0000 / + &Pot kp= 4 type= 0 shape= 0 + p(1:3)= 1.0000 0.0000 @rC_p_7Be@ / + &Pot kp= 4 type= 1 shape= 0 + p(1:3)= @V_p_7Be@ @rV_p_7Be@ @aV_p_7Be@ / + &Pot kp= 4 type= 3 shape= 0 + p(1:3)= @Vso_p_7Be@ @rso_p_7Be@ @aso_p_7Be@ / + &Pot / ! END OF DEFINING POTENTIALS + + &Overlap kn1= 1 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 1 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 + kbpot= 4 be= 0.1370 isc= 1 ipc=0 / + &Overlap kn1= 2 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0182 isc=12 ipc=2 nk= 20 er= -0.0344 / + &Overlap kn1= 3 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.0771 isc=12 ipc=2 nk= 20 er= -0.0834 / + &Overlap kn1= 4 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.1850 isc=12 ipc=2 nk= 20 er= -0.1324 / + &Overlap kn1= 5 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.3419 isc=12 ipc=2 nk= 20 er= -0.1814 / + &Overlap kn1= 6 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.5479 isc=12 ipc=2 nk= 20 er= -0.2304 / + &Overlap kn1= 7 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -0.8028 isc=12 ipc=2 nk= 20 er= -0.2794 / + &Overlap kn1= 8 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.1067 isc=12 ipc=2 nk= 20 er= -0.3284 / + &Overlap kn1= 9 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.4596 isc=12 ipc=2 nk= 20 er= -0.3774 / + &Overlap kn1= 10 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -1.8616 isc=12 ipc=2 nk= 20 er= -0.4264 / + &Overlap kn1= 11 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.3125 isc=12 ipc=2 nk= 20 er= -0.4754 / + &Overlap kn1= 12 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -2.8125 isc=12 ipc=2 nk= 20 er= -0.5245 / + &Overlap kn1= 13 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -3.3614 isc=12 ipc=2 nk= 20 er= -0.5735 / + &Overlap kn1= 14 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -3.9594 isc=12 ipc=2 nk= 20 er= -0.6225 / + &Overlap kn1= 15 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -4.6064 isc=12 ipc=2 nk= 20 er= -0.6715 / + &Overlap kn1= 16 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -5.3023 isc=12 ipc=2 nk= 20 er= -0.7205 / + &Overlap kn1= 17 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -6.0473 isc=12 ipc=2 nk= 20 er= -0.7695 / + &Overlap kn1= 18 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -6.8413 isc=12 ipc=2 nk= 20 er= -0.8185 / + &Overlap kn1= 19 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -7.6843 isc=12 ipc=2 nk= 20 er= -0.8675 / + &Overlap kn1= 20 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -8.5763 isc=12 ipc=2 nk= 20 er= -0.9165 / + &Overlap kn1= 21 kn2= 0 ic1=1 ic2=2 in= 1 + kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 + kbpot= 4 be= -9.5173 isc=12 ipc=2 nk= 20 er= -0.9655 / + &Overlap / ! END OF DEFINING OVERLAPS + + &Coupling icto= 1 icfrom= 2 kind=3 ip1= 2 ip2= 0 ip3= 0 + p1= 3.0000 p2= 2.0000 / + &Cfp in= 1 ib= 1 ia= 1 kn= 1 a= 1.0000 / + &Cfp in= 1 ib= 2 ia= 1 kn= 2 a= 1.0000 / + &Cfp in= 1 ib= 3 ia= 1 kn= 3 a= 1.0000 / + &Cfp in= 1 ib= 4 ia= 1 kn= 4 a= 1.0000 / + &Cfp in= 1 ib= 5 ia= 1 kn= 5 a= 1.0000 / + &Cfp in= 1 ib= 6 ia= 1 kn= 6 a= 1.0000 / + &Cfp in= 1 ib= 7 ia= 1 kn= 7 a= 1.0000 / + &Cfp in= 1 ib= 8 ia= 1 kn= 8 a= 1.0000 / + &Cfp in= 1 ib= 9 ia= 1 kn= 9 a= 1.0000 / + &Cfp in= 1 ib= 10 ia= 1 kn= 10 a= 1.0000 / + &Cfp in= 1 ib= 11 ia= 1 kn= 11 a= 1.0000 / + &Cfp in= 1 ib= 12 ia= 1 kn= 12 a= 1.0000 / + &Cfp in= 1 ib= 13 ia= 1 kn= 13 a= 1.0000 / + &Cfp in= 1 ib= 14 ia= 1 kn= 14 a= 1.0000 / + &Cfp in= 1 ib= 15 ia= 1 kn= 15 a= 1.0000 / + &Cfp in= 1 ib= 16 ia= 1 kn= 16 a= 1.0000 / + &Cfp in= 1 ib= 17 ia= 1 kn= 17 a= 1.0000 / + &Cfp in= 1 ib= 18 ia= 1 kn= 18 a= 1.0000 / + &Cfp in= 1 ib= 19 ia= 1 kn= 19 a= 1.0000 / + &Cfp in= 1 ib= 20 ia= 1 kn= 20 a= 1.0000 / + &Cfp in= 1 ib= 21 ia= 1 kn= 21 a= 1.0000 / + ! ******* END OF CDCC INPUTS ******* \ No newline at end of file From 53386d56772932ca08da2bfc50446d0ff7a60ed9 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Wed, 20 May 2026 17:42:19 -0400 Subject: [PATCH 10/13] update nb --- book/notebooks/Breakup.ipynb | 18 +- .../B3-example-br-long-spdf.in | 891 ------------------ 2 files changed, 9 insertions(+), 900 deletions(-) delete mode 100644 book/notebooks/Breakup_template/B3-example-br-long-spdf.in diff --git a/book/notebooks/Breakup.ipynb b/book/notebooks/Breakup.ipynb index 9a2030e..5b0e9af 100644 --- a/book/notebooks/Breakup.ipynb +++ b/book/notebooks/Breakup.ipynb @@ -13,7 +13,7 @@ "\n", "> Breakup calculations can be modeled as single-particle excitation into the continuum. In this example we show a typical CDCC calculation. It calculates the breakup of 8B into p + 7Be, under the field of 208Pb at intermediate energies. [...] The breakup of 8B has been measured many times with the aim of extracting the proton capture rate on 7Be\n", "\n", - "Note, that even when neglecting couplings with $l >0$, the CDCC caclulation will already take a minute or so to run! For production use, expensive calculations like this would be better performed using [{{{bfrescoxpro}}}](https://bfrescox.readthedocs.io/en/latest/advanced_users.html), which allows MPI and OpenMP parallelization." + "Note, that even when neglecting couplings with $l >0$, the CDCC caclulation will already take a minute or so to run! For production use, expensive calculations like this would be better performed using [{{bfrescoxpro}}](https://bfrescox.readthedocs.io/en/latest/advanced_users.html), which allows MPI and OpenMP parallelization." ] }, { @@ -52,7 +52,7 @@ "source": [ "## BfrescoxPro\n", "\n", - "CDCC calculations are expensive. For this reason, we will use {{{Bfrescoxpro}}}, to enable parallelization with MPI. For more information, [see the docs](https://bfrescox.readthedocs.io/en/latest/advanced_users.html)." + "CDCC calculations are expensive. In practice, one would want to enable MPI parallelization for this sort of calculation. [See the docs for how to do this.](https://bfrescox.readthedocs.io/en/latest/advanced_users.html)." ] }, { @@ -345,8 +345,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 7 ms, sys: 2.85 ms, total: 9.86 ms\n", - "Wall time: 1min 53s\n" + "CPU times: user 5.03 ms, sys: 997 μs, total: 6.03 ms\n", + "Wall time: 1min 55s\n" ] } ], @@ -433,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 12, "id": "b6a57560-6e54-4a7d-867a-44acb410eea7", "metadata": {}, "outputs": [ @@ -823,7 +823,7 @@ "id": "7c06ed73-459c-4a96-b6cd-4b0ab6a8f9ea", "metadata": {}, "source": [ - "And we can write some handy functions to flatten parameter dicts in this form into the form expected by {{{brescox}}}, and vice versa:" + "And we can write some handy functions to flatten parameter dicts in this form into the form expected by {{brescox}}, and vice versa:" ] }, { @@ -875,7 +875,7 @@ "id": "656d8069-477d-472f-b90f-886fc6fade7e", "metadata": {}, "source": [ - "The form expected by {{{bfrescox}}}, with parameter names corresponding to our template:" + "The form expected by {{bfrescox}}, with parameter names corresponding to our template:" ] }, { @@ -990,8 +990,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 104 ms, sys: 5.52 ms, total: 109 ms\n", - "Wall time: 7min 37s\n" + "CPU times: user 86.5 ms, sys: 3.87 ms, total: 90.4 ms\n", + "Wall time: 7min 40s\n" ] } ], diff --git a/book/notebooks/Breakup_template/B3-example-br-long-spdf.in b/book/notebooks/Breakup_template/B3-example-br-long-spdf.in deleted file mode 100644 index 907eb5e..0000000 --- a/book/notebooks/Breakup_template/B3-example-br-long-spdf.in +++ /dev/null @@ -1,891 +0,0 @@ -CDCC 8B+208Pb at 83 MeV A to s,p,d,f; 1+20+4*10+2*5 chs 0-10 MeV, C q=0,1,2 rbin -NAMELIST - &Fresco hcm= 0.010 rmatch= -60.000 rintp= 0.05 rsp= 0.0 - rasym= 1000.00 accrcy= 0.0010000 switch= 0.00 ajswtch= 0. sinjmax= 0. - jtmin= 0.0 jtmax= 10000.0 absend= -50.0000 pset= 0 - jump = 4 10 50 200 0 0 - jbord= 0.0 200.0 300.0 1000.0 10000.0 0.0 - thmin= 0.00 thmax= 20.00 thinc= 0.05 cutl= 0.00 cutr=-20.00 cutc= 0.00 - ips= 0.0000 it0= 0 iter= 0 iblock=142 pade=0 nnu= 24 - smallchan= 1.00E-12 smallcoup= 1.00E-12 - chans= 1 listcc= 0 treneg= 0 cdetr= 0 smats= 2 xstabl= 1 - waves= 0 lampl= 0 veff= 0 kfus= 0 wdisk= 0 nlpl= 0 melfil= 0 cdcc= 1 - elab= 664.0000 pel=1 exl=1 lab=1 lin=1 lex=1 / - - &Partition namep='8B ' massp= 8.0000 zp= 5 nex=141 pwf=T - namet='208Pb ' masst=208.0000 zt= 82 qval= 0.1370/ - &States jp= 1.5 ptyp=-1 ep= 0.0000 cpot= 1 - jt= 0.0 ptyt= 1 et= 0.0000/ - &States jp= 0.5 ptyp= 1 ep= 0.2305 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 0.3711 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 0.5195 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 0.6688 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 0.8184 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 0.9681 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 1.1180 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 1.2678 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 1.4177 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 1.5677 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 1.7176 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 1.8675 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 2.0175 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 2.1675 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 2.3174 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 2.4674 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 2.6174 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 2.7674 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 2.9173 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp= 1 ep= 3.0673 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 0.1996 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 0.3628 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 0.5145 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 0.6652 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 0.8156 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 0.9659 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 1.1160 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 1.2662 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 1.4163 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 1.5663 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 1.7164 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 1.8665 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 2.0165 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 2.1665 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 2.3166 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 2.4666 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 2.6166 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 2.7666 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 2.9167 cpot= 1 - copyt= 1/ - &States jp= 0.5 ptyp=-1 ep= 3.0667 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 0.1996 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 0.3628 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 0.5145 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 0.6652 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 0.8156 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 0.9659 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 1.1160 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 1.2662 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 1.4163 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 1.5663 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 1.7164 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 1.8665 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 2.0165 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 2.1665 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 2.3166 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 2.4666 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 2.6166 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 2.7666 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 2.9167 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp=-1 ep= 3.0667 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 0.1996 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 0.3628 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 0.5145 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 0.6652 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 0.8156 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 0.9659 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 1.1160 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 1.2662 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 1.4163 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 1.5663 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 1.7164 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 1.8665 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 2.0165 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 2.1665 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 2.3166 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 2.4666 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 2.6166 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 2.7666 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 2.9167 cpot= 1 - copyt= 1/ - &States jp= 1.5 ptyp= 1 ep= 3.0667 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 0.1996 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 0.3628 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 0.5145 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 0.6652 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 0.8156 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 0.9659 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 1.1160 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 1.2662 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 1.4163 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 1.5663 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 1.7164 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 1.8665 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 2.0165 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 2.1665 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 2.3166 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 2.4666 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 2.6166 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 2.7666 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 2.9167 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp= 1 ep= 3.0667 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 0.1996 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 0.3628 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 0.5145 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 0.6652 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 0.8156 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 0.9659 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 1.1160 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 1.2662 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 1.4163 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 1.5663 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 1.7164 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 1.8665 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 2.0165 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 2.1665 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 2.3166 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 2.4666 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 2.6166 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 2.7666 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 2.9167 cpot= 1 - copyt= 1/ - &States jp= 2.5 ptyp=-1 ep= 3.0667 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 0.1996 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 0.3628 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 0.5145 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 0.6652 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 0.8156 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 0.9659 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 1.1160 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 1.2662 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 1.4163 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 1.5663 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 1.7164 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 1.8665 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 2.0165 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 2.1665 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 2.3166 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 2.4666 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 2.6166 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 2.7666 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 2.9167 cpot= 1 - copyt= 1/ - &States jp= 3.5 ptyp=-1 ep= 3.0667 cpot= 1 - copyt= 1/ - &Partition namep='7Be ' massp= 7.0000 zp= 4 nex= -1 pwf=T - namet='208Pb +p' masst=209.0000 zt= 83 qval= 0.0000/ - &States jp= 0.0 ptyp= 1 ep= 0.0000 cpot= 2 - jt= 0.0 ptyt= 1 et= 0.0000/ - &Partition / ! END OF DEFINING PARTITIONS - - &Pot kp= 1 type= 0 shape= 0 nosub=F itt=F - p(1:3)= 1.0000 0.0000 2.6500 / - &Pot kp= 2 type= 0 shape= 0 nosub=F itt=F - p(1:3)= 208.0000 0.0000 1.3000 / - &Pot kp= 2 type= 1 shape= 0 nosub=F itt=F - p(1:7)= 114.2000 1.2860 0.8530 9.4400 1.7390 0.8090 0.0000 / - &Pot kp= 3 type= 0 shape= 0 nosub=F itt=F - p(1:3)= 208.0000 0.0000 1.3000 / - &Pot kp= 3 type= 1 shape= 0 nosub=F itt=F - p(1:7)= 34.8190 1.1700 0.7500 15.3400 1.3200 0.6010 0.0000 / - &Pot kp= 4 type= 0 shape= 0 nosub=F itt=F - p(1:3)= 1.0000 0.0000 2.3910 / - &Pot kp= 4 type= 1 shape= 0 nosub=F itt=F - p(1:3)= 44.6500 2.3910 0.5200 / - &Pot kp= 4 type= 3 shape= 0 nosub=F itt=F - p(1:3)= 4.8980 2.3910 0.5200 / - &Pot / ! END OF DEFINING POTENTIALS - - &Overlap kn1= 1 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 1 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= 0.1370 isc= 1 ipc=0 / - &Overlap kn1= 2 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.0800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 3 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.2300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 4 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.3800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 5 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.5300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 6 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.6800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 7 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.8300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 8 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.9800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 9 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.1300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 10 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.2800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 11 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.4300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 12 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.5800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 13 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.7300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 14 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.8800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 15 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.0300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 16 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.1800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 17 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.3300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 18 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.4800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 19 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.6300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 20 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.7800 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 21 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=0 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.9300 isc=12 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 22 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 23 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 24 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 25 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 26 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 27 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 28 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 29 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 30 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 31 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 32 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 33 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 34 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 35 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 36 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 37 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 38 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 39 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 40 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 41 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 0.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 42 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 43 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 44 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 45 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 46 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 47 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 48 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 49 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 50 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 51 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 52 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 53 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 54 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 55 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 56 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 57 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 58 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 59 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 60 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 61 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=1 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 62 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 63 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 64 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 65 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 66 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 67 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 68 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 69 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 70 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 71 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 72 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 73 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 74 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 75 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 76 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 77 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 78 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 79 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 80 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 81 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 1.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 82 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 83 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 84 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 85 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 86 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 87 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 88 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 89 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 90 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 91 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 92 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 93 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 94 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 95 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 96 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 97 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 98 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 99 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 100 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 101 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=2 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 102 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 103 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 104 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 105 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 106 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 107 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 108 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 109 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 110 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 111 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 112 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 113 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 114 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 115 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 116 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 117 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 118 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 119 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 120 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 121 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 2.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 122 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.0800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 123 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.2300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 124 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.3800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 125 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.5300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 126 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.6800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 127 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.8300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 128 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -0.9800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 129 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.1300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 130 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.2800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 131 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.4300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 132 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.5800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 133 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.7300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 134 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -1.8800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 135 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.0300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 136 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.1800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 137 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.3300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 138 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.4800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 139 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.6300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 140 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.7800 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap kn1= 141 kn2= 0 ic1=1 ic2=2 in= 1 - kind=0 nn= 0 l=3 lmax=0 sn=0.5 j= 3.5 nam=1 ampl= 1.0000 - kbpot= 4 be= -2.9300 isc= 2 ipc=2 nk= 50 er= -0.1500 / - &Overlap / ! END OF DEFINING OVERLAPS - - &Coupling icto= 1 icfrom= 2 kind=3 ip1= 2 ip2= 0 ip3= 0 - p1= 3.0000 p2= 2.0000 / - &Cfp in= 1 ib= 1 ia= 1 kn= 1 a= 1.0000 / - &Cfp in= 1 ib= 2 ia= 1 kn= 2 a= 1.0000 / - &Cfp in= 1 ib= 3 ia= 1 kn= 3 a= 1.0000 / - &Cfp in= 1 ib= 4 ia= 1 kn= 4 a= 1.0000 / - &Cfp in= 1 ib= 5 ia= 1 kn= 5 a= 1.0000 / - &Cfp in= 1 ib= 6 ia= 1 kn= 6 a= 1.0000 / - &Cfp in= 1 ib= 7 ia= 1 kn= 7 a= 1.0000 / - &Cfp in= 1 ib= 8 ia= 1 kn= 8 a= 1.0000 / - &Cfp in= 1 ib= 9 ia= 1 kn= 9 a= 1.0000 / - &Cfp in= 1 ib= 10 ia= 1 kn= 10 a= 1.0000 / - &Cfp in= 1 ib= 11 ia= 1 kn= 11 a= 1.0000 / - &Cfp in= 1 ib= 12 ia= 1 kn= 12 a= 1.0000 / - &Cfp in= 1 ib= 13 ia= 1 kn= 13 a= 1.0000 / - &Cfp in= 1 ib= 14 ia= 1 kn= 14 a= 1.0000 / - &Cfp in= 1 ib= 15 ia= 1 kn= 15 a= 1.0000 / - &Cfp in= 1 ib= 16 ia= 1 kn= 16 a= 1.0000 / - &Cfp in= 1 ib= 17 ia= 1 kn= 17 a= 1.0000 / - &Cfp in= 1 ib= 18 ia= 1 kn= 18 a= 1.0000 / - &Cfp in= 1 ib= 19 ia= 1 kn= 19 a= 1.0000 / - &Cfp in= 1 ib= 20 ia= 1 kn= 20 a= 1.0000 / - &Cfp in= 1 ib= 21 ia= 1 kn= 21 a= 1.0000 / - &Cfp in= 1 ib= 22 ia= 1 kn= 22 a= 1.0000 / - &Cfp in= 1 ib= 23 ia= 1 kn= 23 a= 1.0000 / - &Cfp in= 1 ib= 24 ia= 1 kn= 24 a= 1.0000 / - &Cfp in= 1 ib= 25 ia= 1 kn= 25 a= 1.0000 / - &Cfp in= 1 ib= 26 ia= 1 kn= 26 a= 1.0000 / - &Cfp in= 1 ib= 27 ia= 1 kn= 27 a= 1.0000 / - &Cfp in= 1 ib= 28 ia= 1 kn= 28 a= 1.0000 / - &Cfp in= 1 ib= 29 ia= 1 kn= 29 a= 1.0000 / - &Cfp in= 1 ib= 30 ia= 1 kn= 30 a= 1.0000 / - &Cfp in= 1 ib= 31 ia= 1 kn= 31 a= 1.0000 / - &Cfp in= 1 ib= 32 ia= 1 kn= 32 a= 1.0000 / - &Cfp in= 1 ib= 33 ia= 1 kn= 33 a= 1.0000 / - &Cfp in= 1 ib= 34 ia= 1 kn= 34 a= 1.0000 / - &Cfp in= 1 ib= 35 ia= 1 kn= 35 a= 1.0000 / - &Cfp in= 1 ib= 36 ia= 1 kn= 36 a= 1.0000 / - &Cfp in= 1 ib= 37 ia= 1 kn= 37 a= 1.0000 / - &Cfp in= 1 ib= 38 ia= 1 kn= 38 a= 1.0000 / - &Cfp in= 1 ib= 39 ia= 1 kn= 39 a= 1.0000 / - &Cfp in= 1 ib= 40 ia= 1 kn= 40 a= 1.0000 / - &Cfp in= 1 ib= 41 ia= 1 kn= 41 a= 1.0000 / - &Cfp in= 1 ib= 42 ia= 1 kn= 42 a= 1.0000 / - &Cfp in= 1 ib= 43 ia= 1 kn= 43 a= 1.0000 / - &Cfp in= 1 ib= 44 ia= 1 kn= 44 a= 1.0000 / - &Cfp in= 1 ib= 45 ia= 1 kn= 45 a= 1.0000 / - &Cfp in= 1 ib= 46 ia= 1 kn= 46 a= 1.0000 / - &Cfp in= 1 ib= 47 ia= 1 kn= 47 a= 1.0000 / - &Cfp in= 1 ib= 48 ia= 1 kn= 48 a= 1.0000 / - &Cfp in= 1 ib= 49 ia= 1 kn= 49 a= 1.0000 / - &Cfp in= 1 ib= 50 ia= 1 kn= 50 a= 1.0000 / - &Cfp in= 1 ib= 51 ia= 1 kn= 51 a= 1.0000 / - &Cfp in= 1 ib= 52 ia= 1 kn= 52 a= 1.0000 / - &Cfp in= 1 ib= 53 ia= 1 kn= 53 a= 1.0000 / - &Cfp in= 1 ib= 54 ia= 1 kn= 54 a= 1.0000 / - &Cfp in= 1 ib= 55 ia= 1 kn= 55 a= 1.0000 / - &Cfp in= 1 ib= 56 ia= 1 kn= 56 a= 1.0000 / - &Cfp in= 1 ib= 57 ia= 1 kn= 57 a= 1.0000 / - &Cfp in= 1 ib= 58 ia= 1 kn= 58 a= 1.0000 / - &Cfp in= 1 ib= 59 ia= 1 kn= 59 a= 1.0000 / - &Cfp in= 1 ib= 60 ia= 1 kn= 60 a= 1.0000 / - &Cfp in= 1 ib= 61 ia= 1 kn= 61 a= 1.0000 / - &Cfp in= 1 ib= 62 ia= 1 kn= 62 a= 1.0000 / - &Cfp in= 1 ib= 63 ia= 1 kn= 63 a= 1.0000 / - &Cfp in= 1 ib= 64 ia= 1 kn= 64 a= 1.0000 / - &Cfp in= 1 ib= 65 ia= 1 kn= 65 a= 1.0000 / - &Cfp in= 1 ib= 66 ia= 1 kn= 66 a= 1.0000 / - &Cfp in= 1 ib= 67 ia= 1 kn= 67 a= 1.0000 / - &Cfp in= 1 ib= 68 ia= 1 kn= 68 a= 1.0000 / - &Cfp in= 1 ib= 69 ia= 1 kn= 69 a= 1.0000 / - &Cfp in= 1 ib= 70 ia= 1 kn= 70 a= 1.0000 / - &Cfp in= 1 ib= 71 ia= 1 kn= 71 a= 1.0000 / - &Cfp in= 1 ib= 72 ia= 1 kn= 72 a= 1.0000 / - &Cfp in= 1 ib= 73 ia= 1 kn= 73 a= 1.0000 / - &Cfp in= 1 ib= 74 ia= 1 kn= 74 a= 1.0000 / - &Cfp in= 1 ib= 75 ia= 1 kn= 75 a= 1.0000 / - &Cfp in= 1 ib= 76 ia= 1 kn= 76 a= 1.0000 / - &Cfp in= 1 ib= 77 ia= 1 kn= 77 a= 1.0000 / - &Cfp in= 1 ib= 78 ia= 1 kn= 78 a= 1.0000 / - &Cfp in= 1 ib= 79 ia= 1 kn= 79 a= 1.0000 / - &Cfp in= 1 ib= 80 ia= 1 kn= 80 a= 1.0000 / - &Cfp in= 1 ib= 81 ia= 1 kn= 81 a= 1.0000 / - &Cfp in= 1 ib= 82 ia= 1 kn= 82 a= 1.0000 / - &Cfp in= 1 ib= 83 ia= 1 kn= 83 a= 1.0000 / - &Cfp in= 1 ib= 84 ia= 1 kn= 84 a= 1.0000 / - &Cfp in= 1 ib= 85 ia= 1 kn= 85 a= 1.0000 / - &Cfp in= 1 ib= 86 ia= 1 kn= 86 a= 1.0000 / - &Cfp in= 1 ib= 87 ia= 1 kn= 87 a= 1.0000 / - &Cfp in= 1 ib= 88 ia= 1 kn= 88 a= 1.0000 / - &Cfp in= 1 ib= 89 ia= 1 kn= 89 a= 1.0000 / - &Cfp in= 1 ib= 90 ia= 1 kn= 90 a= 1.0000 / - &Cfp in= 1 ib= 91 ia= 1 kn= 91 a= 1.0000 / - &Cfp in= 1 ib= 92 ia= 1 kn= 92 a= 1.0000 / - &Cfp in= 1 ib= 93 ia= 1 kn= 93 a= 1.0000 / - &Cfp in= 1 ib= 94 ia= 1 kn= 94 a= 1.0000 / - &Cfp in= 1 ib= 95 ia= 1 kn= 95 a= 1.0000 / - &Cfp in= 1 ib= 96 ia= 1 kn= 96 a= 1.0000 / - &Cfp in= 1 ib= 97 ia= 1 kn= 97 a= 1.0000 / - &Cfp in= 1 ib= 98 ia= 1 kn= 98 a= 1.0000 / - &Cfp in= 1 ib= 99 ia= 1 kn= 99 a= 1.0000 / - &Cfp in= 1 ib=100 ia= 1 kn=100 a= 1.0000 / - &Cfp in= 1 ib=101 ia= 1 kn=101 a= 1.0000 / - &Cfp in= 1 ib=102 ia= 1 kn=102 a= 1.0000 / - &Cfp in= 1 ib=103 ia= 1 kn=103 a= 1.0000 / - &Cfp in= 1 ib=104 ia= 1 kn=104 a= 1.0000 / - &Cfp in= 1 ib=105 ia= 1 kn=105 a= 1.0000 / - &Cfp in= 1 ib=106 ia= 1 kn=106 a= 1.0000 / - &Cfp in= 1 ib=107 ia= 1 kn=107 a= 1.0000 / - &Cfp in= 1 ib=108 ia= 1 kn=108 a= 1.0000 / - &Cfp in= 1 ib=109 ia= 1 kn=109 a= 1.0000 / - &Cfp in= 1 ib=110 ia= 1 kn=110 a= 1.0000 / - &Cfp in= 1 ib=111 ia= 1 kn=111 a= 1.0000 / - &Cfp in= 1 ib=112 ia= 1 kn=112 a= 1.0000 / - &Cfp in= 1 ib=113 ia= 1 kn=113 a= 1.0000 / - &Cfp in= 1 ib=114 ia= 1 kn=114 a= 1.0000 / - &Cfp in= 1 ib=115 ia= 1 kn=115 a= 1.0000 / - &Cfp in= 1 ib=116 ia= 1 kn=116 a= 1.0000 / - &Cfp in= 1 ib=117 ia= 1 kn=117 a= 1.0000 / - &Cfp in= 1 ib=118 ia= 1 kn=118 a= 1.0000 / - &Cfp in= 1 ib=119 ia= 1 kn=119 a= 1.0000 / - &Cfp in= 1 ib=120 ia= 1 kn=120 a= 1.0000 / - &Cfp in= 1 ib=121 ia= 1 kn=121 a= 1.0000 / - &Cfp in= 1 ib=122 ia= 1 kn=122 a= 1.0000 / - &Cfp in= 1 ib=123 ia= 1 kn=123 a= 1.0000 / - &Cfp in= 1 ib=124 ia= 1 kn=124 a= 1.0000 / - &Cfp in= 1 ib=125 ia= 1 kn=125 a= 1.0000 / - &Cfp in= 1 ib=126 ia= 1 kn=126 a= 1.0000 / - &Cfp in= 1 ib=127 ia= 1 kn=127 a= 1.0000 / - &Cfp in= 1 ib=128 ia= 1 kn=128 a= 1.0000 / - &Cfp in= 1 ib=129 ia= 1 kn=129 a= 1.0000 / - &Cfp in= 1 ib=130 ia= 1 kn=130 a= 1.0000 / - &Cfp in= 1 ib=131 ia= 1 kn=131 a= 1.0000 / - &Cfp in= 1 ib=132 ia= 1 kn=132 a= 1.0000 / - &Cfp in= 1 ib=133 ia= 1 kn=133 a= 1.0000 / - &Cfp in= 1 ib=134 ia= 1 kn=134 a= 1.0000 / - &Cfp in= 1 ib=135 ia= 1 kn=135 a= 1.0000 / - &Cfp in= 1 ib=136 ia= 1 kn=136 a= 1.0000 / - &Cfp in= 1 ib=137 ia= 1 kn=137 a= 1.0000 / - &Cfp in= 1 ib=138 ia= 1 kn=138 a= 1.0000 / - &Cfp in= 1 ib=139 ia= 1 kn=139 a= 1.0000 / - &Cfp in= 1 ib=140 ia= 1 kn=140 a= 1.0000 / - &Cfp in=-1 ib=141 ia= 1 kn=141 a= 1.0000 / - &Coupling icto= 0 icfrom= 1 kind=1 ip1= 1 ip2= 1 ip3= 1 / From 2d04f1566b6659fd34475f512c72f5d6c973ed98 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Wed, 27 May 2026 15:47:16 -0400 Subject: [PATCH 11/13] update jb --- book/_toc.yml | 1 + book/notebooks/Breakup.ipynb | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/book/_toc.yml b/book/_toc.yml index 01f92e2..57ae071 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -13,6 +13,7 @@ parts: - file: notebooks/Inelastic_from_template_example.ipynb - file: notebooks/User_provided_template_example.ipynb - file: notebooks/Transfer.ipynb + - file: notebooks/Breakup.ipynb - caption: Reference chapters: - file: bibliography diff --git a/book/notebooks/Breakup.ipynb b/book/notebooks/Breakup.ipynb index 5b0e9af..e6bf3e0 100644 --- a/book/notebooks/Breakup.ipynb +++ b/book/notebooks/Breakup.ipynb @@ -823,7 +823,7 @@ "id": "7c06ed73-459c-4a96-b6cd-4b0ab6a8f9ea", "metadata": {}, "source": [ - "And we can write some handy functions to flatten parameter dicts in this form into the form expected by {{brescox}}, and vice versa:" + "And we can write some handy functions to flatten parameter dicts in this form into the form expected by {{bfrescox}}, and vice versa:" ] }, { From 648cfec1aad686b50dd47a15ae4284e5d2b54d30 Mon Sep 17 00:00:00 2001 From: Kyle Andrew Beyer Date: Fri, 29 May 2026 23:06:16 -0400 Subject: [PATCH 12/13] formatting --- common/_fill_in_template.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/common/_fill_in_template.py b/common/_fill_in_template.py index 5411b95..f64b6bc 100644 --- a/common/_fill_in_template.py +++ b/common/_fill_in_template.py @@ -30,7 +30,9 @@ def parse_valid_keys(template_path: Union[str, PathLike]): for line in input_nml: matches = np.array(re.findall(r"@(\w+)@", line)) if matches is not None and len(matches) > 0: - to_replace.update([match.lstrip("@").rstrip("@") for match in matches]) + to_replace.update( + [match.lstrip("@").rstrip("@") for match in matches] + ) return to_replace @@ -92,8 +94,10 @@ def fill_in_template_file( raise ValueError( f"Keys in template file {template_path} do not " "match keys in `parameters`:\n" - f" Keys that are in template but not in parameters: {valid_keys - set(parameters.keys())}\n" - f" Keys that are in parameters but not in template: {set(parameters.keys()) - valid_keys}" + f" Keys that are in template but not in parameters:" + f" {valid_keys - set(parameters.keys())}\n" + f" Keys that are in parameters but not in template: " + f"{set(parameters.keys()) - valid_keys}" ) if not isinstance(output_path, (str, PathLike)): From 78bb9098d080612f994eb90288759f88b9b28516 Mon Sep 17 00:00:00 2001 From: Kyle Beyer Date: Wed, 3 Jun 2026 17:36:05 -0400 Subject: [PATCH 13/13] update book text --- book/notebooks/Breakup.ipynb | 33 +++------------------------------ 1 file changed, 3 insertions(+), 30 deletions(-) diff --git a/book/notebooks/Breakup.ipynb b/book/notebooks/Breakup.ipynb index e6bf3e0..e21ff53 100644 --- a/book/notebooks/Breakup.ipynb +++ b/book/notebooks/Breakup.ipynb @@ -45,41 +45,14 @@ "LINEWIDTH = esthetics[\"linewidth\"]" ] }, - { - "cell_type": "markdown", - "id": "e360a9fa-48b8-483a-91a2-ae4a93fa347b", - "metadata": {}, - "source": [ - "## BfrescoxPro\n", - "\n", - "CDCC calculations are expensive. In practice, one would want to enable MPI parallelization for this sort of calculation. [See the docs for how to do this.](https://bfrescox.readthedocs.io/en/latest/advanced_users.html)." - ] - }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "e5398285-c4a1-4936-809f-9f91d832c035", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'supports_mpi': False,\n", - " 'supports_openmp': False,\n", - " 'supports_lapack': False,\n", - " 'supports_corex': False,\n", - " 'frescox_exe': PosixPath('/home/beyerk/Projects/Bfrescox/bfrescox_pypkg/src/bfrescox/bin/frescox')}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "import bfrescox\n", - "\n", - "bfrescox.information()" + "import bfrescox" ] }, {