From 802562753fff9d228140d55ef84e9893122790a7 Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Mon, 2 Oct 2023 08:06:51 +1100 Subject: [PATCH 01/32] Naive tides implementation --- .../notebooks/CHE_evolution_demo.ipynb | 1138 ++++++++++++ .../CHE_evolution_demo_ANSWERS.ipynb | 1618 +++++++++++++++++ src/BaseBinaryStar.cpp | 24 +- src/BaseBinaryStar.h | 146 +- src/BaseStar.cpp | 30 +- src/BaseStar.h | 19 +- src/HG.h | 18 +- src/HeHG.h | 6 +- src/NS.cpp | 212 ++- src/NS.h | 43 +- src/constants.h | 6 +- src/utils.cpp | 15 +- 12 files changed, 3096 insertions(+), 179 deletions(-) create mode 100644 online-docs/notebooks/CHE_evolution_demo.ipynb create mode 100644 online-docs/notebooks/CHE_evolution_demo_ANSWERS.ipynb diff --git a/online-docs/notebooks/CHE_evolution_demo.ipynb b/online-docs/notebooks/CHE_evolution_demo.ipynb new file mode 100644 index 000000000..a1ab18daa --- /dev/null +++ b/online-docs/notebooks/CHE_evolution_demo.ipynb @@ -0,0 +1,1138 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d69d65d0", + "metadata": {}, + "source": [ + "
\n", + "\n", + " \n", + "# COMPAS Special Tutorial: \"Formation channels of Gravitational Waves (GWs)\"\n", + "\n", + "### This is a tutorial that can be used for live teaching/demos, it should fill about ~1hr of class\n", + " \n", + "In this jupyter notebook we will walk through and re-create some of the figures from https://arxiv.org/pdf/2010.00002.pdf on **Chemically Homogeneous Evolution** by Jeff Riley. A PDF of this paper can be found in the directory under the name CHE_paper.pdf.
\n", + "\n", + "\n", + "\n", + "Notebook by Floor Broekgaarden, Jeff Riley and Ilya Mandel, originally created for the Saas Fee PhD School
\n", + "
\n", + "\n", + "The original data can be found on Zenodo https://zenodo.org/record/5595426
\n", + "For this tutorial we have downloaded COMPAS_Output.h5 from the auhtor's dataset. Note that this data is run with a slightly older version of COMPAS than the most recent COMPAS. \n", + " \n", + "___\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "480b8a96", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Throughout this notebook and in class we will use several acronyms and definitions listed below \n", + " \n", + " \n", + " \n", + "### Definitions: \n", + " \n", + " \n", + " - CHE: Chemically Homogeneous Evolution, \n", + " - GW: Gravitational Waves \n", + " - DCO: Double Compact Object \n", + " - BH: Black Hole\n", + " - NS: Neutron Star\n", + " - Primary: in this notebook always refers to the star that was most massive at the zero age main sequence (ZAMS)\n", + " - Secondary: in this notebook always refers to the star that was least massive at the zero age main sequence (ZAMS)\n", + " - ZAMS: Zero Age Main Sequence: this is in COMPAS where stars start their lives. \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ed54cf65", + "metadata": {}, + "outputs": [], + "source": [ + "# first we will import some of the packages that we will use \n", + "import h5py as h5\n", + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# we will use astropy for some useful constants and units \n", + "from astropy import units as u\n", + "from astropy import constants as const\n", + "from matplotlib.ticker import (FormatStrFormatter,\n", + " AutoMinorLocator)\n", + "from IPython.display import Image # to open images in Ipython \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "64224ff3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CommonEnvelopes', 'DoubleCompactObjects', 'Supernovae', 'SystemParameters']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# add path to where the COMPASOutput.h5 file is stored. \n", + "# For you the part '~/Downloads/' is probably different\n", + "path = '/Users/floorbroekgaarden/Downloads/COMPAS_Output.h5' # change this line! \n", + "\n", + "# the following line reads in the data \n", + "fdata = h5.File(path, 'r')\n", + "list(fdata.keys()) # print the different files within the hdf5 folder: \n", + "\n", + "\n", + "\n", + "# to close the file you will have to use fdata.close()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8c1a0e92", + "metadata": {}, + "source": [ + "
\n", + "\n", + "\n", + "\n", + "the files above 'DoubleCompactObjects', 'Supernovae', 'SystemParameters' store the properties of the simulated binaries at the stages of the 'commen enevelope' (in case there is one), the moment of double object formation, the moment of the supernova, and the initial conditions (at the zero-age main sequence).\n", + "\n", + "#### We can view what parameters are stored by again using the command .keys()\n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "af4c3be7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Coalescence_Time', 'Eccentricity@DCO', 'MT_Case_1', 'MT_Case_2', 'Mass_1', 'Mass_2', 'Merges_Hubble_Time', 'Recycled_NS_1', 'Recycled_NS_2', 'SEED', 'Separation@DCO', 'Stellar_Type_1', 'Stellar_Type_2', 'Time']\n", + "\n", + "['CE_Alpha', 'CH_on_MS_1', 'CH_on_MS_2', 'Eccentricity@ZAMS', 'Equilibrated', 'Equilibrated_At_Birth', 'Error', 'Experienced_RLOF_1', 'Experienced_RLOF_2', 'Experienced_SN_Type_1', 'Experienced_SN_Type_2', 'LBV_Multiplier', 'LBV_Phase_Flag_1', 'LBV_Phase_Flag_2', 'Mass@ZAMS_1', 'Mass@ZAMS_2', 'Merger', 'Merger_At_Birth', 'Metallicity@ZAMS_1', 'Metallicity@ZAMS_2', 'Omega@ZAMS_1', 'Omega@ZAMS_2', 'SEED', 'SN_Kick_Magnitude_Random_Number_1', 'SN_Kick_Magnitude_Random_Number_2', 'SN_Kick_Mean_Anomaly_1', 'SN_Kick_Mean_Anomaly_2', 'SN_Kick_Phi_1', 'SN_Kick_Phi_2', 'SN_Kick_Theta_1', 'SN_Kick_Theta_2', 'Separation@ZAMS', 'Sigma_Kick_CCSN_BH', 'Sigma_Kick_CCSN_NS', 'Sigma_Kick_ECSN', 'Sigma_Kick_USSN', 'Stellar_Type@ZAMS_1', 'Stellar_Type@ZAMS_2', 'Stellar_Type_1', 'Stellar_Type_2', 'Time', 'Unbound', 'WR_Multiplier']\n", + "\n", + "['Applied_Kick_Velocity_SN', 'Drawn_Kick_Velocity_SN', 'Eccentricity', 'Eccentricity \n", + "\n", + "#### The meaning of all parameters and files are described here https://compas.readthedocs.io/en/latest/pages/User%20guide/COMPAS%20output/standard-logfiles.html\n", + "\n", + "\n", + "Now that we have the data, we can do some data investigation. Here is an example of how to read the \"SEED\" parameter, which is a unique number for each binary that is run. \n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b83022e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 400047 400065 400101 ... 11599854 11599926 11599965]\n" + ] + } + ], + "source": [ + "SEED_DCO = fdata['DoubleCompactObjects'][\"SEED\"][...].squeeze()\n", + "print(SEED_DCO)" + ] + }, + { + "cell_type": "markdown", + "id": "30966640", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Question 1\n", + "#### - a: check and write down the number of rows (entries) of each of the dataset groups, 'DoubleCompactObjects', 'Supernovae', 'SystemParameters'.
\n", + " \n", + "#### - b: If the lengths of the rows are different why is this so? And does it make sense which group has the most/least rows?
\n", + "\n", + "*Hint*: you might want to look at table 1 in the paper CHE_paper.pdf and the descriptions at https://compas.readthedocs.io/en/latest/pages/User%20guide/COMPAS%20output/standard-logfiles.html\n", + " \n", + "#### - c: Why is the number of rows in 'DoubleCompactObjects' not the same as the total number of 'BBHs formed' in Table 1 from this paper?" + ] + }, + { + "cell_type": "markdown", + "id": "96e2bf75", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29489b28", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a01f693e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4cc16c7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5ffcb1f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97c05125", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d051687d", + "metadata": {}, + "source": [ + "
\n", + "\n", + " \n", + "## Example 1: plotting BH masses \n", + "___\n", + "below we show an example of how to obtain and plot the compact object masses in the dataset \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d142978f", + "metadata": {}, + "outputs": [], + "source": [ + "# this is just a little function that we will use to make the plot more beautiful (bigger ticks, labels)\n", + "# However, you do not have to use this (just uncommoment \"layoutAxes\" everywhere)\n", + "\n", + "def layoutAxes(ax, nameX='', nameY='', \\\n", + " labelSizeMajor = 10, fontsize = 25, second=False, labelpad=None, setMinor=True):\n", + " \"\"\"\n", + " Tiny code to do the layout for axes in matplotlib\n", + " \"\"\"\n", + " tickLengthMajor = 10\n", + " tickLengthMinor = 5\n", + " tickWidthMajor = 1.5\n", + " tickWidthMinor = 1.5\n", + " \n", + " #rc('axes', linewidth=2)\n", + " #label1 always refers to first axis not the twin \n", + " if not second:\n", + " for tick in ax.xaxis.get_major_ticks():\n", + " tick.label1.set_fontsize(fontsize)\n", + " #tick.label1.set_fontweight('bold')\n", + " for tick in ax.yaxis.get_major_ticks():\n", + " tick.label1.set_fontsize(fontsize)\n", + " #tick.label1.set_fontweight('bold')\n", + " if second:\n", + " for tick in ax.xaxis.get_major_ticks():\n", + " tick.label2.set_fontsize(fontsize)\n", + " #tick.label1.set_fontweight('bold')\n", + " for tick in ax.yaxis.get_major_ticks():\n", + " tick.label2.set_fontsize(fontsize)\n", + " #tick.label1.set_fontweight('bold')\n", + " for axis in ['top','bottom','left','right']:\n", + " ax.spines[axis].set_linewidth(1.2)\n", + " ax.tick_params(length=tickLengthMajor, width=tickWidthMajor, which='major')\n", + " ax.tick_params(length=tickLengthMinor, width=tickWidthMinor, which='minor')\n", + " ax.set_xlabel(nameX, fontsize=fontsize,labelpad=labelpad)#,fontweight='bold')\n", + " ax.set_ylabel(nameY, fontsize=fontsize,labelpad=labelpad)#, fontweight='bold') \n", + " \n", + " if setMinor==True:\n", + " # add minor ticks:\n", + " ax.xaxis.set_minor_locator(AutoMinorLocator())\n", + " ax.yaxis.set_minor_locator(AutoMinorLocator())\n", + "\n", + " return ax\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "869c90d1", + "metadata": {}, + "outputs": [], + "source": [ + "fDCO = fdata['DoubleCompactObjects']\n", + "\n", + "\n", + "M1 = fDCO['Mass_1'][...].squeeze() # mass in Msun of the compact object resulting from the *primary star*\n", + "M2 = fDCO['Mass_2'][...].squeeze() # mass in Msun of the compact object resulting from the *secondary star*\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ff02ea16", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", + "\n", + "plt.scatter(M1, M2)\n", + "plt.xlabel('Mass of compact object from primary star [Msun]', fontsize=20)\n", + "plt.ylabel('Mass of compact object from secondary star [Msun]', fontsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0db7f21e", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### Question 2: \n", + " \n", + " - a): can you explain some of the features in the plot above? E.g., where are the gaps, where are the most datapoints?\n", + " \n", + " \n", + " - b): Are there any BH+NS or NS+NS in the dataset? If so, plot them\n", + " \n", + " \n", + " - c): extra: how many BH+NS, vs. NS+NS vs. BH-BH systems are there? And what is the total? \n", + "\n", + "*Hint*: A NS in this COMPAS simulation is defined as a compact object with mass < 2.5 Msun \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "51727576", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42a75636", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48bd18ce", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1181d94", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a252ea2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc00bc37", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9f09ef6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d3421fd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "131d98f5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad78bc31", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c850bd60", + "metadata": {}, + "source": [ + "
\n", + " \n", + "### Question 3: \n", + " \n", + " \n", + " - a): Using the parameters in the 'DoubleCompactObjects' dataset and the example above, try to make a scatter plot of Total Mass (M1+M2) versus orbital Period of the BBH systems that merge within a Hubble time (13.7 Gyr) \n", + " \n", + " Plot the period on the y-axis and the total mass on the x-axis. Plot the period in days. \n", + " \n", + "*Hint: You might want to use Kerpler's III law to complete the function below *\n", + " \n", + " \n", + "*Hint:* you will have to select BH+BH systems, and only systems that merge within a Hubble time " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2a2e21da", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def separation_to_period_circular_case(separation=10*u.AU, M1=1*u.M_sun, M2=1*u.M_sun):\n", + " \"\"\"calculate Period from separation\n", + " separation is separation of the binary (needs to be given in astropy units)\n", + " M1 and M2 are masses of the binary\n", + " This is based on Kepler's law, using a circular orbit\n", + " \n", + " \"\"\"\n", + " G = const.G # [g cm s^2]\n", + " \n", + " ## use Kepler;s III law to calculate the period here \n", + " \n", + " \n", + " \n", + " ###\n", + " \n", + " return period\n" + ] + }, + { + "cell_type": "markdown", + "id": "30ad2751", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f3c8674", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecec29a5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62cc5edc", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b23ac61", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "201763f3", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Question 4: \n", + " \n", + " \n", + " - a): Why does the plot that you created look different compared to the figure 6 in https://arxiv.org/pdf/2010.00002.pdf? (you may ignore the metallicity axes) \n", + " \n", + " \n", + " \n", + " - b): There is a tail of systems at rather large orbital periods that are merging. How is this possible? \n", + "\n", + "*Hint 4b: plot the eccentricity as a color gradient on the marker using the \"c=\" option of plt.scatter. \n", + "How is eccentricity imparted to these systems?* " + ] + }, + { + "cell_type": "markdown", + "id": "c3322c83", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fba311f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "0e08bfed", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Selecting CHE binaries: \n", + " \n", + " \n", + " For binaries, Stellar_Type@ZAMS(1) and Stellar_Type@ZAMS(2) will tell you the initial stellar type of each star - type 16 is CH.\n", + "CH_on_MS(1) and CH_on_MS(2) are each true if the star remained as CH for the entire MS - they will be false if the star spun down and stopped being CH on the MS. So any star that was initially CH, and stayed CH on the entire MS is considered to be CHE. We can check which of our binary black holes is a \"CHE\" by using this information stored in the 'systemParameters' file, and matching it with the double compact object files using the randomSeed.\n", + "\n", + "Note that we also have to remove binaries that merged on the ZAMS as stars, since we are not interested in these\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "714118e5", + "metadata": {}, + "outputs": [], + "source": [ + "fsys = fdata['SystemParameters']\n", + "\n", + "CH_on_MS_1 = fsys['CH_on_MS_1'][...].squeeze() # mass in Msun of the compact object resulting from the primary\n", + "CH_on_MS_2 = fsys['CH_on_MS_2'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", + "Stellar_TypeZAMS_1 = fsys['Stellar_Type@ZAMS_1'][...].squeeze() # mass in Msun of the compact object resulting from the primary\n", + "Stellar_TypeZAMS_2 = fsys['Stellar_Type@ZAMS_2'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", + "\n", + "# binaries that merge at birth as stars\n", + "Merger_At_Birth = fsys['Merger_At_Birth'][...].squeeze()\n", + "\n", + "# SEED of the system Parameters (unique number corresponding to each binary)\n", + "SEED = fsys['SEED'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "99057a0b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13644 are CHE out of 12000000 systems run\n", + "[ 400378 400412 402049 ... 11589507 11589863 11594670]\n" + ] + } + ], + "source": [ + "\n", + "# the CHE systems are then selected by systems that are CHE on ZAMS (stellar type 16) AND remain CHE on the MS (main sequence)\n", + "# in addition we do not want systems that Merged at Birth \n", + "mask_CHE = (CH_on_MS_1==1) & (CH_on_MS_2==1) & (Stellar_TypeZAMS_1==16) & (Stellar_TypeZAMS_2==16) & (Merger_At_Birth==0)\n", + "\n", + "print(np.sum(mask_CHE), 'are CHE out of ', len(mask_CHE), 'systems run')\n", + "\n", + "\n", + "# let's find the seed of the CHE systems: \n", + "SEED_CHE = SEED[mask_CHE]\n", + "print(SEED_CHE)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c8919d0", + "metadata": {}, + "source": [ + "
\n", + " \n", + "We find 13644 total CHE binaries in our simulation, note that this is the same as the number quoted in the CHE paper under \"Both stars remained on the CH\" and \"Total\" in Table 1\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "f2115984", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Question 5: \n", + " \n", + " - a): Using the code above, recreate figure 6 in https://arxiv.org/pdf/2010.00002.pdf? (you may ignore the metallicity axes) \n", + " \n", + " - b): Explain what you see \n", + " \n", + "#### Hint: A useful line of code is: np.in1d(), below is an example of how it works" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6ba613a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False True]\n" + ] + } + ], + "source": [ + "# example of np.in1d() function\n", + "\n", + "A = [1,2,3]\n", + "B = [1,3,5,7,9]\n", + "\n", + "print(np.in1d(A, B))" + ] + }, + { + "cell_type": "markdown", + "id": "4405bc29", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2724ab2d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27f0c3e5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b5cb307", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bb15a45", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a25df87b", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Question 6: \n", + " \n", + " - a): Try to recreeat the figure 4 in https://arxiv.org/pdf/2010.00002.pdf? \n", + " \n", + " \n", + " - b): Explain what you see " + ] + }, + { + "cell_type": "markdown", + "id": "0175ec47", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7c16f01", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f745cda9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cca1041", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "544dc9c9", + "metadata": {}, + "source": [ + "
\n", + "\n", + " \n", + "For the last part of this excersize we will use the code 'FastCosmicIntegrator'. Our goal will be to calculate the merger rate for BBHs, so that we can compare to the analytical estimate we made earlier on. \n", + "\n", + "## Question 7: \n", + " \n", + " \n", + "Using the code below, plot the merger rate of CHE BBHs as a function of redshift. You should at the end of running this code create a plot with four panels that show the properties of the CHE binaries \n", + " \n", + " - a) what do the panels show? What are the differences between the panels? \n", + " - b): please write down the local (z=0) BBH merger rate, and compare this with your analytically calculated rate \n", + " - c): compare both rates with the other BBH rates as reported in Mandel & Broekgaarden et al. (2021) (Fig 3)\n", + "\n", + " \n", + " - d): Repeat the excersize above, and answer 7a & 7b above, but now for all BBHs (including non CHE). You can do this by changing dco_type to 'BBH'. What are the differences\n", + "\n", + " \n", + " \n", + " *Hint* we can do an approximate calculation by combining the Wolf Rayet factors. If you want to do the more expert version you can modify the code in ClassCOMPAS (setCOMPASDCOmask) and add the mask of a specific f_WR factor" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "681a2767", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.11/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/floorbroekgaarden/Projects/GitHub/COMPAS/utils/Tutorial/Tutorial_reproduce_CHE_paper_teaching_demo_example/FastCosmicIntegration.py': [Errno 2] No such file or directory\r\n" + ] + } + ], + "source": [ + "!python3 FastCosmicIntegration.py \\\n", + "--dco_type 'CHE_BBH' \\\n", + "--path '/Users/floorbroekgaarden/Downloads/COMPAS_Output.h5' \\\n", + "--maxz 15 \\\n", + "--dontAppend" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "285095f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CHE_evolution_demo.ipynb\r\n", + "CHE_evolution_demo_ANSWERS.ipynb\r\n", + "COMPAS_Documentation.pdf\r\n", + "Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png\r\n", + "SNR_Grid_IMRPhenomPv2_FD_all_noise.hdf5\r\n", + "\u001b[34m__pycache__\u001b[m\u001b[m\r\n", + "old_ClassCOMPAS.py\r\n", + "old_FastCosmicIntegration.py\r\n", + "old_selection_effects.py\r\n", + "old_totalMassEvolvedPerZ.py\r\n" + ] + } + ], + "source": [ + "!ls " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0b87ce45", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show the image in the notebook:\n", + "Image(filename='./Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png') \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ee442a0e", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 7\n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3597ac3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d85530d5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1b8225c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "6c657d0a", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Extra Question 8: \n", + " \n", + " \n", + "Play around with some of the parameters in the code FastCosmicIntegrater, do the rates go up or down? Is this expected?" + ] + }, + { + "cell_type": "markdown", + "id": "00e3011c", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 8\n", + " \n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c64aa01", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66ebbb5a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "921b84b0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "93c376a5", + "metadata": {}, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "## EXTRA using the CHE data: \n", + " \n", + "If there is time left, try to plot some other BBH or ZAMS properties of the BBH, (or NSBH or BNS), examples include chirp mass, mass ratio, individual masses. How do these compare with LIGOs observations (paper is attached to this directory)\n", + "\n", + " \n", + " \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a647b78", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c370cf85", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4a02ec4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "017418fc", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3e78c305", + "metadata": {}, + "source": [ + "
\n", + " \n", + " # Extra material:" + ] + }, + { + "cell_type": "markdown", + "id": "0b84006f", + "metadata": {}, + "source": [ + "[//]: ## (grip -b README.md)\n", + "\n", + "\n", + "\n", + "# Compact Object Mergers: Population Astrophysics & Statistics\n", + "\n", + "[![Documentation](https://img.shields.io/badge/Documentation-latest-orange.svg?style=flat)](https://github.com/TeamCOMPAS/COMPAS/blob/Documentation/COMPAS_Documentation.pdf)\n", + "\n", + "[//]: ## (Outline features)\n", + "COMPAS is a publicly available rapid binary population synthesis code (https://compas.science/) that is designed so that evolution prescriptions and model parameters are easily \n", + "adjustable. COMPAS draws properties for a binary star system from a set of initial distributions, and evolves it from zero-age main sequence to the end of its life as two compact \n", + "remnants. It has been used for inference from observations of gravitational-wave mergers, Galactic neutron stars, X-ray binaries, and luminous red novae.\n", + "\n", + "## Documentation\n", + "https://compas.science/docs\n", + "\n", + "## Contact\n", + "Please email your queries to compas-user@googlegroups.com. You are also welcome to join the [COMPAS User Google Group](https://groups.google.com/forum/#!members/compas-user) to engage in discussions with COMPAS users and developers.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "## Example of additional excersizes\n", + "\n", + "If you are interested, you can download the COMPAS code from Github and do any of the following excersizes: \n", + "\n", + "\n", + "1). Try to run any of the demos that are provided (I recommend the Chirp mass distribution demo, and/or the detailed evolution demo)\n", + "\n", + "2.) Try to use the code, and the data above, to plot a detailed evolution plot of a CHE BBH. \n", + "\n", + "3.) Run a larger COMPAS simulation with your own favorite settings, compare this to the data given in this demo. \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f54328e3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/online-docs/notebooks/CHE_evolution_demo_ANSWERS.ipynb b/online-docs/notebooks/CHE_evolution_demo_ANSWERS.ipynb new file mode 100644 index 000000000..e85097d7f --- /dev/null +++ b/online-docs/notebooks/CHE_evolution_demo_ANSWERS.ipynb @@ -0,0 +1,1618 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d69d65d0", + "metadata": {}, + "source": [ + "# COMPAS Special Tutorial: \"Formation channels of Gravitational Waves (GWs)\" -- ANSWERS\n", + "\n", + "### This is a tutorial that can be used for live teaching/demos, it should fill about ~1hr of class\n", + " \n", + "In this jupyter notebook we will walk through and re-create some of the figures from https://arxiv.org/pdf/2010.00002.pdf on **Chemically Homogeneous Evolution** by Jeff Riley. A PDF of this paper can be found in the directory under the name CHE_paper.pdf.
\n", + "\n", + "\n", + "\n", + "Notebook by Floor Broekgaarden, Jeff Riley and Ilya Mandel, originally created for the Saas Fee PhD School
\n", + "
\n", + "\n", + "The original data can be found on Zenodo https://zenodo.org/record/5595426
\n", + "For this tutorial we have downloaded COMPAS_Output.h5 from the auhtor's dataset. Note that this data is run with a slightly older version of COMPAS than the most recent COMPAS. \n", + " \n", + "___\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "480b8a96", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Throughout this notebook and in class we will use several acronyms and definitions listed below \n", + " \n", + " \n", + " \n", + "### Definitions: \n", + " \n", + " \n", + " - CHE: Chemically Homogeneous Evolution, \n", + " - GW: Gravitational Waves \n", + " - DCO: Double Compact Object \n", + " - BH: Black Hole\n", + " - NS: Neutron Star\n", + " - Primary: in this notebook always refers to the star that was most massive at the zero age main sequence (ZAMS)\n", + " - Secondary: in this notebook always refers to the star that was least massive at the zero age main sequence (ZAMS)\n", + " - ZAMS: Zero Age Main Sequence: this is in COMPAS where stars start their lives. \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ed54cf65", + "metadata": {}, + "outputs": [], + "source": [ + "# first we will import some of the packages that we will use \n", + "import h5py as h5\n", + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# we will use astropy for some useful constants and units \n", + "from astropy import units as u\n", + "from astropy import constants as const\n", + "from matplotlib.ticker import (FormatStrFormatter,\n", + " AutoMinorLocator)\n", + "from IPython.display import Image # to open images in Ipython \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "64224ff3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CommonEnvelopes', 'DoubleCompactObjects', 'Supernovae', 'SystemParameters']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# add path to where the COMPASOutput.h5 file is stored. \n", + "# For you the part '~/Downloads/' is probably different\n", + "path = '/Users/floorbroekgaarden/Downloads/COMPAS_Output.h5' # change this line! \n", + "\n", + "# the following line reads in the data \n", + "fdata = h5.File(path, 'r')\n", + "list(fdata.keys()) # print the different files within the hdf5 folder: \n", + "\n", + "\n", + "\n", + "# to close the file you will have to use fdata.close()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8c1a0e92", + "metadata": {}, + "source": [ + "
\n", + "\n", + "\n", + "\n", + "the files above 'DoubleCompactObjects', 'Supernovae', 'SystemParameters' store the properties of the simulated binaries at the stages of the 'commen enevelope' (in case there is one), the moment of double object formation, the moment of the supernova, and the initial conditions (at the zero-age main sequence).\n", + "\n", + "#### We can view what parameters are stored by again using the command .keys()\n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "af4c3be7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Coalescence_Time', 'Eccentricity@DCO', 'MT_Case_1', 'MT_Case_2', 'Mass_1', 'Mass_2', 'Merges_Hubble_Time', 'Recycled_NS_1', 'Recycled_NS_2', 'SEED', 'Separation@DCO', 'Stellar_Type_1', 'Stellar_Type_2', 'Time']\n", + "\n", + "['CE_Alpha', 'CH_on_MS_1', 'CH_on_MS_2', 'Eccentricity@ZAMS', 'Equilibrated', 'Equilibrated_At_Birth', 'Error', 'Experienced_RLOF_1', 'Experienced_RLOF_2', 'Experienced_SN_Type_1', 'Experienced_SN_Type_2', 'LBV_Multiplier', 'LBV_Phase_Flag_1', 'LBV_Phase_Flag_2', 'Mass@ZAMS_1', 'Mass@ZAMS_2', 'Merger', 'Merger_At_Birth', 'Metallicity@ZAMS_1', 'Metallicity@ZAMS_2', 'Omega@ZAMS_1', 'Omega@ZAMS_2', 'SEED', 'SN_Kick_Magnitude_Random_Number_1', 'SN_Kick_Magnitude_Random_Number_2', 'SN_Kick_Mean_Anomaly_1', 'SN_Kick_Mean_Anomaly_2', 'SN_Kick_Phi_1', 'SN_Kick_Phi_2', 'SN_Kick_Theta_1', 'SN_Kick_Theta_2', 'Separation@ZAMS', 'Sigma_Kick_CCSN_BH', 'Sigma_Kick_CCSN_NS', 'Sigma_Kick_ECSN', 'Sigma_Kick_USSN', 'Stellar_Type@ZAMS_1', 'Stellar_Type@ZAMS_2', 'Stellar_Type_1', 'Stellar_Type_2', 'Time', 'Unbound', 'WR_Multiplier']\n", + "\n", + "['Applied_Kick_Velocity_SN', 'Drawn_Kick_Velocity_SN', 'Eccentricity', 'Eccentricity \n", + "\n", + "#### The meaning of all parameters and files are described here https://compas.readthedocs.io/en/latest/pages/User%20guide/COMPAS%20output/standard-logfiles.html\n", + "\n", + "\n", + "Now that we have the data, we can do some data investigation. Here is an example of how to read the \"SEED\" parameter, which is a unique number for each binary that is run. \n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b83022e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 400047 400065 400101 ... 11599854 11599926 11599965]\n" + ] + } + ], + "source": [ + "SEED_DCO = fdata['DoubleCompactObjects'][\"SEED\"][...].squeeze()\n", + "print(SEED_DCO)" + ] + }, + { + "cell_type": "markdown", + "id": "30966640", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Question 1\n", + "#### - a: check and write down the number of rows (entries) of each of the dataset groups, 'DoubleCompactObjects', 'Supernovae', 'SystemParameters'.
\n", + " \n", + "#### - b: If the lengths of the rows are different why is this so? And does it make sense which group has the most/least rows?
\n", + "\n", + "*Hint*: you might want to look at table 1 in the paper CHE_paper.pdf and the descriptions at https://compas.readthedocs.io/en/latest/pages/User%20guide/COMPAS%20output/standard-logfiles.html\n", + " \n", + "#### - c: Why is the number of rows in 'DoubleCompactObjects' not the same as the total number of 'BBHs formed' in Table 1 from this paper?" + ] + }, + { + "cell_type": "markdown", + "id": "b4746cff", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Answer 1: \n", + " \n", + " \n", + "Below are a few methods shown to print the number of rows (but there are many ways to do this).
\n", + "We find that DoubleCompactObjects has 291253 rows, SystemParameters has 12000000 number of rows and the group Supernovae has 6213168 number of rows.
\n", + " \n", + " \n", + " - The systemparameters is easiest to understand: this file prints one line (row) for each binary that the author simulated in COMPAS. You can read in the paper or see in Table 1 (under \"Number of binaries evolved\" and then the \"total\" column) that this is exactly 12 million. Which is indeed the same as the number of rows in the dataset.
\n", + " \n", + " \n", + " \n", + " - Second we have the group 'DoubleCompactObjects', this is the dataset that prints the properties at the moment a double compact object binary (DCO) is formed. This is a rare occurance in our simulations (even if we only model massive stars). And so the number of rows/entries in this file is much much smaller and reduced to only 291253 (almost 300 000) which is the outcome of about ~2.4% of the all simulated systems. You can see that this number is close to, but slightly larger than the 261,741 \"Total\" BBHs formed in Table 1 of the paper. This is because the 'DoubleCompactObjects' file also contains NS+NS and BH+NS mergers on top of the BH+BH mergers, whereas the table only quotes the number of BH+BH mergers residing in the data.
\n", + "\n", + " \n", + " - Last, the 'Supernovae' file prints the properties of the binary whenever in the simulation a supernova occurs. \n", + "This is a tricky one to explain. There are 2 supernova that occur to form a DCO system, so you expect this file to always be larger than 2x the double compact object file. On the other hand, of the stars that do not form a DCO system, many either merge as stars, or disrupt during the first SN, in which case the simulation stops following the evolution of the binary and future SNe are not printed.
\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8936583a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "group DoubleCompactObjects has 291253 number of rows\n", + "group SystemParameters has 12000000 number of rows\n", + "group Supernovae has 6213168 number of rows\n" + ] + } + ], + "source": [ + "# Method 1, based on the example above\n", + "\n", + "\n", + "# all three groups contain the parameter \"SEED\", so we can use this to print the lengths\n", + "for group in ['DoubleCompactObjects', 'SystemParameters', 'Supernovae']:\n", + " SEED = fdata[group][\"SEED\"][...].squeeze()\n", + " print('group %s has %s number of rows'%(group, len(SEED)))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "759a127c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "group DoubleCompactObjects has 291253 number of rows\n", + "group SystemParameters has 12000000 number of rows\n", + "group Supernovae has 6213168 number of rows\n" + ] + } + ], + "source": [ + "# Method 2: Or even shorter: \n", + "\n", + "# all three groups contain the parameter \"SEED\", so we can use this to print the lengths\n", + "for group in ['DoubleCompactObjects', 'SystemParameters', 'Supernovae']:\n", + " print('group %s has %s number of rows'%(group, fdata[group][\"SEED\"].len()))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a1981b5c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "group DoubleCompactObjects has 291253 number of rows\n", + "group SystemParameters has 12000000 number of rows\n", + "group Supernovae has 6213168 number of rows\n" + ] + } + ], + "source": [ + "# Method 3, writing everything out: \n", + "\n", + "lenDCO = len(fdata['DoubleCompactObjects']['SEED'][...].squeeze())\n", + "print('group DoubleCompactObjects has %s number of rows'%lenDCO)\n", + "\n", + "lenSys = len(fdata['SystemParameters']['SEED'][...].squeeze())\n", + "print('group SystemParameters has %s number of rows'%lenSys)\n", + "\n", + "lenSNe = len(fdata['Supernovae']['SEED'][...].squeeze())\n", + "print('group Supernovae has %s number of rows'%lenSNe)" + ] + }, + { + "cell_type": "markdown", + "id": "d051687d", + "metadata": {}, + "source": [ + "
\n", + "\n", + " \n", + "## Example 1: plotting BH masses \n", + "___\n", + "below we show an example of how to obtain and plot the compact object masses in the dataset \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d142978f", + "metadata": {}, + "outputs": [], + "source": [ + "# this is just a little function that we will use to make the plot more beautiful (bigger ticks, labels)\n", + "# However, you do not have to use this (just uncommoment \"layoutAxes\" everywhere)\n", + "\n", + "def layoutAxes(ax, nameX='', nameY='', \\\n", + " labelSizeMajor = 10, fontsize = 25, second=False, labelpad=None, setMinor=True):\n", + " \"\"\"\n", + " Tiny code to do the layout for axes in matplotlib\n", + " \"\"\"\n", + " tickLengthMajor = 10\n", + " tickLengthMinor = 5\n", + " tickWidthMajor = 1.5\n", + " tickWidthMinor = 1.5\n", + " \n", + " #rc('axes', linewidth=2)\n", + " #label1 always refers to first axis not the twin \n", + " if not second:\n", + " for tick in ax.xaxis.get_major_ticks():\n", + " tick.label1.set_fontsize(fontsize)\n", + " #tick.label1.set_fontweight('bold')\n", + " for tick in ax.yaxis.get_major_ticks():\n", + " tick.label1.set_fontsize(fontsize)\n", + " #tick.label1.set_fontweight('bold')\n", + " if second:\n", + " for tick in ax.xaxis.get_major_ticks():\n", + " tick.label2.set_fontsize(fontsize)\n", + " #tick.label1.set_fontweight('bold')\n", + " for tick in ax.yaxis.get_major_ticks():\n", + " tick.label2.set_fontsize(fontsize)\n", + " #tick.label1.set_fontweight('bold')\n", + " for axis in ['top','bottom','left','right']:\n", + " ax.spines[axis].set_linewidth(1.2)\n", + " ax.tick_params(length=tickLengthMajor, width=tickWidthMajor, which='major')\n", + " ax.tick_params(length=tickLengthMinor, width=tickWidthMinor, which='minor')\n", + " ax.set_xlabel(nameX, fontsize=fontsize,labelpad=labelpad)#,fontweight='bold')\n", + " ax.set_ylabel(nameY, fontsize=fontsize,labelpad=labelpad)#, fontweight='bold') \n", + " \n", + " if setMinor==True:\n", + " # add minor ticks:\n", + " ax.xaxis.set_minor_locator(AutoMinorLocator())\n", + " ax.yaxis.set_minor_locator(AutoMinorLocator())\n", + "\n", + " return ax\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "869c90d1", + "metadata": {}, + "outputs": [], + "source": [ + "fDCO = fdata['DoubleCompactObjects']\n", + "\n", + "\n", + "M1 = fDCO['Mass_1'][...].squeeze() # mass in Msun of the compact object resulting from the *primary star*\n", + "M2 = fDCO['Mass_2'][...].squeeze() # mass in Msun of the compact object resulting from the *secondary star*\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ff02ea16", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", + "\n", + "plt.scatter(M1, M2)\n", + "plt.xlabel('Mass of compact object from primary star [Msun]', fontsize=20)\n", + "plt.ylabel('Mass of compact object from secondary star [Msun]', fontsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0db7f21e", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### Question 2: \n", + " \n", + " - a): can you explain some of the features in the plot above? E.g., where are the gaps, where are the most datapoints?\n", + " \n", + " \n", + " - b): Are there any BH+NS or NS+NS in the dataset? If so, plot them\n", + " \n", + " \n", + " - c): extra: how many BH+NS, vs. NS+NS vs. BH-BH systems are there? And what is the total? \n", + "\n", + "*Hint*: A NS in this COMPAS simulation is defined as a compact object with mass < 2.5 Msun \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "045858e9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e2efc0e0", + "metadata": {}, + "source": [ + "\n", + "
\n", + " \n", + "## Answer 2:\n", + " \n", + " \n", + "We first plot the NS-NS, NS-BH and BH-BH DCO systems in seperate plots below. \n", + " \n", + "For this we use the definition that a NS in COMPAS is always below < 2.5 Msun, and that this value thus indicates the boundary between NSs and BHs. This does not have to be true in nature (nature might actually have a slightly different value for this boundary, for which a lot of active research is being conducted). \n", + "\n", + "There are also many other ways to do this, including checking for the StellarType of the stars. " + ] + }, + { + "cell_type": "markdown", + "id": "535226c4", + "metadata": {}, + "source": [ + "## NS-NS\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c2f9ba12", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total number of NS+NS systems in the dataset is 11248\n" + ] + } + ], + "source": [ + "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", + "\n", + "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", + "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", + "\n", + "mask_NSNS = ((mask_M1isNS==1) & (mask_M2isNS==1)) \n", + "\n", + "plt.scatter(M1[mask_NSNS], M2[mask_NSNS], c='darkgreen', label='NS+NS')\n", + "\n", + "\n", + "layoutAxes(ax=ax, nameX='Mass of compact object from primary star [Msun]',\\\n", + " nameY='Mass of compact object from secondary star [Msun]')\n", + "\n", + "plt.legend(fontsize=20)\n", + "plt.show()\n", + "\n", + "print('The total number of NS+NS systems in the dataset is %s'%len(M1[mask_NSNS]))\n" + ] + }, + { + "cell_type": "markdown", + "id": "f2502840", + "metadata": {}, + "source": [ + "
\n", + "\n", + "\n", + "Features in the plot above: \n", + "\n", + " \n", + "In the plot above we see that the NS-NS systems formed in COMPAS have masses between 1.2-2.5 solar masses. This is the neutron star mass range that is expected from the NS equation of state. \n", + "\n", + "I don't expect that most students will know this: \n", + "There is a gap around 1.7 Msun. But this is an artifact from the SN remnant mass prescription used in COMPAS\n", + "There is a little bit of a pile up of NS systems around 1.25 solar masses. These systems come from electron capture supernovae and ultra-stripped supernovae. " + ] + }, + { + "cell_type": "markdown", + "id": "22dc2dcd", + "metadata": {}, + "source": [ + "## BH-NS " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8c48a3dd", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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rVy9ddtllio6OVnh4uNLS0rRv3z7FxcVpwYIFRRYSDx8+XOPGjTtvzNzcXM2aNUuzZs1S586d1b9/f3Xo0EH16tVTWFiYkpOTtWnTJn3yySeKj4939wsJCdFnn32msLAwn/zsFUbCPClulCSz6PsZhwve7/WF1HikI9EAAACquieeeEIzZsxQZmamnnrqKX333Xce961WrZr7eteuXeURz+cOHjwoqeAseZRdmddQGoah/Px8zZ07V3PnzrU1jGEY7t1hrTJNU2vXrtXatWs9an/XXXfpnXfeKXVr3w0bNmjDhg2ljtegQQN98MEHuuqqqzz6/krDlS9teFDnFZPSf94zpA0PSY1GSAGBvs0GAAAAxcTE6M9//rNee+01LV26VGvWrPH476znrrmcNm2ahg8frv79+5dX1HI3Z84cnTx5UpI0YsQIh9P4J6825Tl79qykokeC2MGOc1q6dOmiyZMna8mSJdq1a9cFM1arVk3Dhg3TpEmT1Lt37xLbxcTE6JZbbtHKlSvdc8ZLUr9+fY0fP14PPvigez52lXIyTspIvEADU8pIKGhXv6+vUgEAAFyYK7/g7yeZR6WwGCm6V6X+x+/HH39c06ZNU3p6up5++mmtWLHCo37NmjXTddddp4ULFyorK0sDBgxQ165ddc0116hHjx7q2rVrhfs78P79+4tkys/P1/Hjx7V48WJNnTpVktS2bVs98sgjpY51+PBhbd++vdR26enp3gf2M2UqKO0uIMvDRRddpFdffVWvvvqqzpw5oy1btmjfvn1KSkpSdna2IiIiVKtWLV166aXq0KGDQkNDSx2zfv36+vjjjyVJCQkJ2rFjhw4dOqTk5GTl5uaqevXqqlu3rjp27KhLL720ShxgWqLMo/a2AwAAKG8J8wpmWJ37j+LhF0mdp1TaZTr16tXT/fffr5deekkrV67UypUr1a9fP4/6zp49W9dee617E5t169YV2dCmZcuWGjx4sO666y516tSpXPKXReEursWJiorSM888o3vvvdejowP//ve/6+9//7uN6fyfxwXl7NmzyzNHuahZs6b69OmjPn362DZm48aN1bhxY9vGq3TCYuxtBwAAUJ6q8N4Pjz76qKZOnarU1FQ99dRTWrNmjUf96tatq7Vr1+q9997TO++8o40bNxb5fPfu3dq9e7fefPNN3XbbbXrnnXcUERFRHj+CZSkpKZoxY4bq1KmjiRMnOh3HL3lcUN55553lmQOVRXSvgn/Ryzis4tdRGgWfR/fydTIAAICiqvjeD3Xq1NFDDz2kf/7zn1q7dq2+++47DRkyxKO+wcHBGj9+vMaPH68jR44oLi5O69ev1y+//KKff/5Zubm5kqQPP/xQR44c0dKlSxUYWPT3cOzYsZozZ06p31V4HmVx+vTpo1WrVl2w/8qVK9W3b1/3vWmaOnv2rLZs2aLXX39dX331lf7f//t/+u233/Taa69dcKzZs2dr7NixpWbu27evVq9eXWq7yqBiHxIJ/xMQWDA9RJL0x6m//7nv/Hql/D9lAADgZ8qy90MlNXnyZPdUz2eeecarMRo2bKibbrpJ//rXv/TDDz/o2LFjeuKJJ9zn0a9YsUKffPKJXZEtMwxDUVFR6t27t+bNm6cxY8ZIkv79739r2bJlDqfzPxSUsF/jkQXTQ8IbFX0//KJKPW0EAAD4GfZ+UM2aNTV58mRJ0i+//KKFCxdaHrN27dr6n//5Hz322GPu94o7HeKFF17Qtm3bin2de5TJd999V2I7O5blPfroo+7rWbNmWR6vqvFql1f4Xkk7RblcLve//lQojUcWTA+pQrulAQAAP8PeD5Kkhx56SFOmTNGpU6f0zDPP6LrrrrNl3PHjx+vFF1+UJP3+++/nfd6oUSM1atTovPclKTIy0n3dsmVLxcbG2pKpOK1bt3Zfb9u2rdy+p7KqgJUIihMZGVnsKz4+3uloJQsILDgaJPaWgl8pJgEAQEVSuPfDect0ChlSeONKv/dD9erV3U/pNm7cqK+++sqWcRs2bOi+rsinIOTl5RV7Dc9QUAIAAKBqYu8Ht/vvv1/16tWTVLCWsqTjAstyjOD69evd1xdffLG1gOXo3Jyc5lB2HhWUnTp1UqdOnfTAAw+Udx63+++/X506dVLnzp199p0VWVpaWrGvcx/RAwAAoIzY+0GSFBERoccff1xSwbTPRYsWFdtu8eLFGj16tDZt2nTB8U6fPq1Jkya570eMGGFfWBtlZ2frqaeect8PHTrUwTT+yaM1lJs3b5ZhGGrQoEF553Hbu3ev+3uhEs/uqZDrJwEAAPwJez9Ikv785z/rlVde0dGjR5WUlFRsG5fLpblz52ru3Lnq0KGDhg4dqq5duyomJkbVqlXTiRMntGbNGk2fPl0nTpyQJHXu3NnRIwj379+vunXrFnnv7Nmz2rx5s6ZOnaodO3ZIki655BJNmDDBiYh+jU15AAAAgMK9H6qwsLAw/e1vf7vgrMRatWopIiJC6enp2rJli7Zs2XLBMQcNGqRPPvlEQUHOlR133313qW06dOig+fPnKywszAeJKhcKSgAAAACSCnZmffnll5WQkFDs51deeaVOnjypZcuWadWqVdqwYYP27NmjU6dOKT8/XzVq1FBsbKy6du2qm2++WX379vXtD+Ch8PBw1atXT506ddKoUaN04403Olr0+jPD9GBlbUBAgAzDUK1atdS+fXtf5NLWrVt1+vRpGYah/Px8n3ynP2rbtq0kuR/VAwAAVGRZWVnav3+/mjVrptDQUKfjABWSt/87caI2KFMZnpycrNWrV5dXFgAAAACAH/G4oCzLFsEAAAAAgMrPo4Jy9uzZ5Z0DAAAAAOBnPCoondzmFwAAAABQMXGIIQAAAADAKxSUAAAAAACvUFACAAAAALxCQQkAAAAA8AoFJQAAAADAKxSUAAAAAACvUFACAADA50zTdDoCUGH50/8+PDqHEvCKK186GSdlHpXCYqToXlJAoNOpAACAgwICCp5nuFwuh5MAFVd+fr6k//7vpSKjoET5SJgnbXhQykj873vhF0mdp0iNRzqXCwAAOCooKEgBAQHKyspSRESE03GACikjI0OBgYEKDg52OkqpKn7JC0lSenp6sa8K+a97CfOkuFFFi0lJyjhc8H7CPGdyAQAAxwUEBCg8PFxpaWlORwEqJNM0dfbsWVWvXl2GYTgdp1QUlH4iMjKy2Fd8fLzT0Ypy5Rc8mVRx877/896GhwraAQCAKqlGjRrKyMhQcnKy01GACsU0TR05ckS5ubmKiopyOo5HmPIKe52MO//JZBGmlJFQ0K5+X1+lAgAAFUhUVJQyMzN17NgxpaenKyoqSkFBQX7xNAawm2mays/PV0ZGhs6ePavc3FxddNFFCg8PdzqaRywXlM8995z7ety4cWrUqJHVIVGMkqaFdOnSxcdJSpF51N52AACgUqpfv76qVaumM2fOKDHxQv8YDVQNgYGBql69uqKiovymmJRsKCifffZZGYahWrVq6cknn7QjE4pR0qL1CrfzU1iMve0AAEClZBiGateurVq1aikvL8+9qyVQFQUEBCg4ONgvn9JbLigjIiKUkZGhVq1aKTCQIyGqvOheBbu5ZhxW8esojYLPo3v5OhkAAKiADMNQcHCwX+xmCeB8lh9vxcQUPGkKCmI5JlRwzmTnKf+5+eO/sPznvvPrnEcJAAAAVAKWC8rLL79cpmlqz549duRBZdB4pNTrCyn8D+tpwy8qeJ9zKAEAAIBKwXJBedttt0mSjh8/ruXLl1sOhEqi8Uhp+AFpwEqp58cFvw7fTzEJAAAAVCKWC8phw4apf//+Mk1T9957r06ePGlHLlQGAYEFR4PE3lLwK9NcAQAAgErFckFpGIY+/vhjderUSXv27FHXrl311VdfyeVy2ZEPAAAAAFBB2XYO5eDBg7Vnzx4dOnRIo0aNUnR0tHr06KHmzZurevXqHh9v8fTTT1uNBAAAAADwAcM0zeLOdvBYQEDAeeelFA7pzTkqnEFUNm3btpUk7dixw+EkAAAAAJzkRG1gy1kfJdWkZa1V/fEgTwAAAACoqiwXlHfeeacdOQAAAAAAfsZyQTl79mw7cgAAAAAA/IzlXV4BAAAAAFUTBSUAAAAAwCsUlAAAAAAAr1BQAgAAAAC8YsuxISXJyspSSkqKsrOzPe7TpEmTckwEAAAAALCLrQVlenq63n//fS1YsEDr169XcnJymfobhqG8vDw7I1Ua6enpxb7vcrkUEMCDZgAAAAC+Z1tB+e233+ruu+9WUlKS+z3TNO0avsqLjIws8bM2bdr4MAkAAAAAFLCloFy4cKGuv/56uVyu84pIwzAkFV9cXugzAAAAAEDFZnmuZHp6usaOHav8/HxJUo8ePbR8+XKlpaVpyJAh7mLR5XLp7Nmz2rVrl2bNmqXevXu7Pxs/fryysrLcY+B8aWlpxb5at27tdLTiufKl46ukA58U/OrizxYAAACobCw/oZw1a5ZOnz4twzDUvXt3rVy5UiEhIcW2jYyMVKtWrdSqVSuNHTtWc+fO1V133aUZM2Zo9+7dWr58OesBSxAREVHs+xXy9ythnrThQSkj8b/vhV8kdZ4iNR7pXC4AAAAAtrJcjSxdutR9/corr5RYTBbnxhtv1JdffinTNPXDDz/oH//4h9U4cFrCPCluVNFiUiq4jxtV8DkAAACASsFyQbl161ZJUt26ddWzZ88S25W0TnLIkCEaOXKkTNPUW2+9xS6v/syVX/BkUiWtiTWlDQ8x/RUAAACoJCwXlKdOnZJhGGrZsuV5nwUF/XdGbWZmZoljXH/99ZKk5ORkrVq1ymokOOVk3PlPJv8oI6GgHQAAAAC/Z7mgLHyiGBYWdt5n1atXd18fO3asxDGaNGnivj5w4IDVSHBKwnx72wEAAACo0CwXlLVq1ZIkpaamnvdZdHS0+3r37t0ljpGenu6+PvccS/gRV760/z3P2u6ewlpKAAAAoBKwXFC2aNFCpmkW+2Sxffv27uvvv/++xDFWrlzpvq5Ro4bVSHDCyTgpN8Xz9qylBAAAAPye5YKyU6dOkqQTJ06cN611wIABMgxDkjRz5kzt27fvvP7bt2/X22+/7b7v0KGD1UhwQubRsrVnLSUAAADg9ywXlAMGDHBfL1q0qMhnsbGxGjhwoEzT1NmzZ9WtWzc9//zzWrx4sRYvXqy///3vuuqqq5SWlibDMNSsWTNdccUVViPBCWExZe9T1iIUAAAAQIVimCWd5+GhrKws1a9fX6mpqbrqqqv0ww8/FPk8Pj5eXbp0UWZmpkzTdD+xLFT49QEBAVqwYIGGDh1qJU6V07ZtW0nSjh07nA3iype+ji19l9dzDVgp1e9bXokAAACAKsWJ2iCo9CYXFhoaqmnTpmnv3r0yDEOZmZlFdnxt3bq1Fi1apNGjR+vEiRPFnkcZFhamadOmUUz6s4BAqfMUKW6USj6HspAhhV8kRffyRTIAAAAA5cTyE0pPpaamaubMmVq2bJkOHTqk3NxcxcTEqE+fPpowYYJiYryYMomK84SyUMI8acODF3hS+Z8n1L2+kBqP9FksAAAAoLJzojbwWUGJ8lHhCkqpYPrryTgpcYF04EMp+5yjYMIbS51fp5gEAAAAbOaXU16B8wQEFqyNrN9X6vCS9PtUKXWvVL25dMm9UlA1pxMCAAAAsIHlgvLQoUOSCtZBRkdHezVGUlKSMjIyJElNmjSxGgkVxaG50rp7iz6hjH+1YK0lTygBAAAAv2f52JDY2Fg1a9ZMY8eO9XqM8ePHq1mzZrr44outxkFFsekxac3oosWkVLC2Mm5UwVpLAAAAAH7NckFZyOpSTNM0LY+BCuLgF9Kuf12ggSlteKhgrSUAAAAAv8UaSj+Rnp5e7Psul0sBAbb9u4B1rnxp/b2lt8tIKNi4h3MoAQAAAL9VIQrKvLw8SVJQUIWIUyFFRkaW+FmbNm18mKQUJ+Ok7JOetc08Wr5ZAAAAAJSrCvFoKyEhQZJUo0YNh5PAsrIUiWGcPQoAAAD4M8cfCa5atUpbt26VYRhq2bKl03EqrLS0tGLf79Kli4+TlMLTIjEkWoruVb5ZAAAAAJSrMhWUd999d4mfbdu27YKfn8s0TWVmZur333/X5s2b3e8PGDCgLHGqlIiIiGLfr1DrJyUp87hn7Tq/UXBeJQAAAAC/ZZhl2Fo1ICBAhmEUea+w+x/fLwvTNFWrVi1t375dMTFMgyyLtm3bSpJ27NjhcBIVHAUSd4NnbQesZEMeAAAAwEZO1AZlfrxVeLzHH4/5+OP7ZXldfvnlWrp0KcWkP3PlS79M8Lw9G/IAAAAAfq9MU16feeaZ8977xz/+IcMw1Lx5c916660ejRMQEKCIiAg1aNBAnTp1UuvWrcsSAxXR8VVSzinP27MhDwAAAOD3yjTltTiF02CHDBmiRYsW2ZULHqowU163PCXteN6ztuGNpeH7WUMJAAAA2MiJ2sDyLq+9e/eWYRhq3769HXlQFXR+nWISAAAAqAQsF5SrVq2yIQb8Xv2+nj2hbPeM1HhkuccBAAAAUP4q2JkT8Fv1+krV6ly4TbU6UrunfBIHAAAAQPmz/ISyrNauXaulS5fq+PHjql27tnr37q0hQ4ZYOnbkj3Jzc7Vx40b9+OOP2rp1q+Lj43Xw4EGdOXNGubm5qlGjhho3bqwuXbroxhtv1KBBg8p8nuO2bds0e/ZsLVu2TImJicrJyVGjRo3UpUsX3X777br66qtt+3n8QkCg1H36hY8N6T6dqa4AAABAJWJ5U56cnBxNnjxZLpdLoaGhevXVV4stDjMzMzVmzBh9/fXX533WtWtXffHFF7rooousRHF79NFH9corr3jc/vLLL9esWbPUsWPHUtvm5eXp6aef1ksvvSSXy1Viu+uuu06zZs1SdHS0xzm8UWE25SmUME9aP0nKPPzf98IukrpMYaorAAAAUI78clOepUuXaurUqTIMQ3fddVeJTxrvu+8+LViwoNjPfv31Vw0aNEgbNmxQeHi41Uj6Y40cERGh5s2bq1atWjIMQ8eOHdPu3bvdBeHmzZvVu3dvLVmyRFdeeeUFx544caJmzZrlvg8ODlabNm0UGRmp+Ph4nTpVcHTGwoULNWjQIK1du1YRERGWfya/0Xik1GiEdDKu4KzJsBgpuhdPJgEAAIBKyPIaypUrV7qvx4wZU2ybrVu3as6cOe5is27duhoxYoT69+/vnmq6e/du/c///I/VOJKksLAwXXfddZo+fbri4+OVlpamLVu2aNWqVVq5cqV27dqlY8eO6cknn1RgYEGhk5aWpjFjxig9Pb3EcadPn16kmBw+fLj279+vzZs3a82aNTp69KjeeOMNBQUV1OlbtmzRhAkTbPmZ/EpAYMEmPbG3FBSTJ+OkA58UnFXpync6HQAAAACbWJ7y2q9fP61evVrVqlVTWlqau5g61wMPPKC33nrLfbzIsmXLVKdOwQYu3377rUaMGCGXy6Xo6GgdOXLEXeT5wowZMzR+/Hj3/axZs3TXXXed1y4jI0PNmzfXsWPHJEl9+/bVsmXLis06c+ZM3XPPPZIkwzC0fv16derUqVzyV7gpr+dKmCdteFDKSPzve+EXSZ2Z/goAAADYzYnawPITyr1798owDLVo0aLYYlJSkamuL774oruYlKShQ4fqhhsKNnJJSkrSpk2brEYqk3vuuUfNmzd335d0DMqcOXPcxaRhGHr77bdLLHzHjRun7t27SyqYfvvSSy/ZG9ofJMyT4kYVLSYlKeNwwfsJ85zJBQAAAMA2lgvK06dPSyqYxlqcPXv2KDExUYZhqG7duho8ePB5ba699lr39bZt26xGKrNznx4WFo1/9OWXX7qv+/Tpo9atW19wzIkTJ7qvFy1apOzsbIsp/Ygrv+DJpIp7+P2f9zY8xPRXAAAAwM9ZLigLC6Xg4OBiP4+Li3Nf9+/fv9hNe2JjY93XhZva+FJeXp77ukaNGud9npaWph9++MF978mRINdcc02R/qtXr7aY0o+cjDv/yWQRppSRUNAOAAAAgN+yXFBGRkZKKrkQPLeQ6tOnT7Ftzp06mpOTYzVSmeTm5uqnn35y319xxRXntdm5c6dyc3Mv2OaPGjRoUKRQ3rJli7Wg/iSx+N18z5N5tHxzAAAAAChXlgvKiy66SKZpKj4+XllZWUU+y8/P1+LFi933ffv2LXaMwmmzklS9enWrkcrkySefdE9zrV27tsaOHXtem127dhW5P3fN5YWc2+6PY1RaCfOk3173rG1YTLlGAQAAAFC+LJ9D2a1bN+3YsUNZWVmaMWOG7r//fvdnn376qZKSkiRJDRs2LHHd4bnF1kUXXWQ10gXl5eXp5MmT+uWXXzR16lR9//33kqTQ0FB9/PHHql279nl9Dh486L4OCgpSTIxnhVCTJk3c1wcOHCi1/YWOLCmJy+VyH73iOPfaSQ+ENy44UgQAAACA37JcUI4ZM0azZ8+WJD366KNKSkpSz549tXXrVv3jH/+QVLAr6m233VbiGOdOOW3VqpXVSOepW7fuBddmDhw4UK+++qrat29f7Odnz551X1evXt3jAu7c9Zipqamlti+cPlxWbdq08aqf7UpdO3mOzq8XnFcJAAAAwG9ZLigHDBigwYMHa+nSpcrJydE///nP89pERUVp8uTJxfZPTU3V0qVL3e18XRxdddVVuv/++3XZZZeV2ObcJ4ehoaEejx0WFlbsGJWWp2siWz3EOZQAAABAJWC5oJSkzz77TNddd53Wrl173meRkZH67LPPFB0dXWzfjz76SFlZWTIMQ/369bMjznkGDBiglJQUSQW70h47dky7d++Wy+XSmjVrtGbNGnXr1k2fffZZkY10Cp27IU9JZ20W59y2nmw2lJaW5vHYhbp06VLmPuUmtJ5n7YKjyjcHAAAAAJ+wpaCMiorSDz/8oK+++kqLFi1SYmKiQkND1blzZ40bN04NGzYsse/cuXPVtGlTSdLo0aPtiHOezz777Lz3Tp8+rRkzZui5555Tenq6fv31V/Xp00fr1q1TvXpFC6Pw8HD39R83HrqQc9tGRESU2t6TNn9UYdZPSsUfO1mcPW9J7Z5iyisAAADg52wpKKWCdZIjR47UyJFlm8q4fPlyuyKUSe3atfXYY49p0KBB6tOnj1JTU3Xo0CE9/PDD+uCDD4q0PXdtY2ZmpsffkZGRUewYlVb2CQ/bJRWst6zft1zjAAAAAChfFejxljM6duyoJ554wn3/6aefFjnGRCrY1KdQWlqax1NTC48jkaQ6depYTOoHynIMCGdQAgAAAH6vyheUUtGptnl5eVq/fn2Rz/+48+yhQ4c8GjchIcF9XdKRKZVKdC8ppPi1sufhDEoAAADA71FQSmrcuHGR+8KzMwtdeumlRe43b95c6pi5ubnavn17iWNUSgGBUuc3S29nBEp1epZ/HgAAAADlioJScu8AW6hmzZpF7i+++GJddNFF7vs1a9aUOuaGDRuKrLfs3bu3tZD+IjW+9DZmvnTqx/LPAgAAAKBcUVBKiouLK3LfvHnz89oMHz7cfT137txSjwH56KOP3Ndt27YtdsxKJ2GetO0Zz9qyhhIAAADwe1W+oMzJydHzzz/vvm/evPl5ayYlaezYse7rpKQkTZs2rcQxExMTNWfOnGL7VlqufGn9JM/bs4YSAAAA8HuVrqD8/vvv9eijj+rw4cOltj169KiGDRumTZs2ud/761//Wmzbrl27FnlK+be//U1r1649r93Zs2c1ZswYpaamSpJiYmJ03333lfXH8D8n46TM0n/PJRVs3BPdq3zzAAAAACh3tp1DWVGkp6frlVde0auvvqqePXuqV69euuyyyxQdHa3w8HClpaVp3759iouL04IFC4qcFTl8+HCNGzeuxLGnTJmin376SSdPnlRaWpoGDBigcePGafDgwYqMjNTWrVv1xhtvaP/+/ZKkgIAATZs2TWFhYeX+czuuLFNYY28t2MAHAAAAgF+rdAVlIdM0tXbt2mKfIhbnrrvu0jvvvCPDMEpsExsbq/nz52vYsGE6ffq0srOzNXXqVE2dOvW8toGBgZoyZYqGDRvm9c/gV8oyhfWiEeWXAwAAAIDPVLopr126dNHkyZPVpk2bCxaHklStWjXdcMMNWr16tWbNmqVq1aqVOn7Pnj21detW3XDDDQoKKr4e79atm+Li4qrGVNdC0b2ksEalt6tWh+muAAAAQCVhmKZpOh2ivJw5c0ZbtmzRvn37lJSUpOzsbEVERKhWrVq69NJL1aFDB4WGhno9/smTJ/XDDz8oMTFROTk5atiwobp27aqWLVva+FNcWNu2bSVJO3bs8Nl3lmjTY9Kuf5XerteXUuOR5Z8HAAAAqEKcqA0qdUFZFVSYgjJhnhQ3SpIH/zmFN5aG72cdJQAAAGAjJ2oDy1NeSzuPEVWAK1/a8KA8KiYlKSOhYFdYAAAAAH7NckHZsGFD/eUvf9H27dvtyAN/dDJOykgsW58MD48YAQAAAFBhWS4oT58+rf/7v/9Thw4d1KNHD82cOVPp6el2ZIO/KMuRIYWyT9qfAwAAAIBP2bbLq2maWrdunSZMmKCYmBiNHz9eP//8s13DV3np6enFvlwul9PRynZkSKG0/fbnAAAAAOBTlgvKDz/8UP369ZNhGDJNU6ZpKi0tTbNmzdKVV16pyy67TFOmTNHp06ftyFtlRUZGFvuKj493OlrBMSDhF0m68DEtRRz8pGDtJQAAAAC/ZbmgHDNmjJYtW6bff/9df/vb39SoUcFZhIXF5c6dOzV58mQ1atRIt9xyi5YtW2Y5NCqYgECp8xR5vCmPVDDllY15AAAAAL9m+7EhLpdLS5Ys0cyZM7Vw4ULl5ub+98uMgidYTZs21bhx4zR27Fh3AYoLK2ldapcuXRQQEOD8sSGufOmLmlJemud9en4sxd5SbpEAAACAqsQvjw05b8CAAF177bX68ssvlZiYqJdeekmtW7eW9N+nlgcPHtTTTz+t2NhYDR06VPPnz1d+PtMfLyQiIqLYV0CA7X+E3jmxqmzFpOTd2ksAAAAAFUa5ViPR0dF69NFHtXPnTsXFxenOO+9URESEu7DMz8/XkiVLdMMNN+iiiy7SX//6V+3Zs6c8I6G8HF9VtvZB1QvWXgIAAADwWz57vHXllVdq9uzZOnr0qN555x1169bN/Zlpmjp+/Lj+9a9/qXXr1urbt6/mzp1bMXYwhWfOlnFzoNYPF6y9BAAAAOC3fD5fMjIyUp07d1bHjh0VGhoqqWBt5bm7xMbFxenmm29Wq1at9MUXX/g6IsrKlV+2J5RB1aV2fy+3OAAAAAB8w2cF5ZkzZ/Tmm2+qY8eO6tatm6ZPn67s7Gx3EVmrVi1dccUVRQrLvXv36qabbtKECRN8FRPeOBkn5SR53r77LJ5OAgAAAJVAuReUK1as0K233qqGDRvqwQcf1NatW90Fo2ma6tWrlz788EMdOXJEa9eu1d69e/XEE0+odu3akgqmw86cOVOzZ88u76jwVsbhsrUPrVs+OQAAAAD4VLkUlEeOHNELL7ygSy65RIMGDdKnn36qrKwsdxFZp04d/eUvf9GuXbu0evVqjRkzRtWqVZMkxcbG6oUXXtD+/ft18803SyooKqdPn14eUWGH7JNla595tHxyAAAAAPCpILsGys/P19dff62ZM2fqu+++c2+oc+4xl71799bEiRN1ww03uAvIklSvXl2zZs3S8uXLdfLkSW3fvt2uqLBbSHTZ2nNcCAAAAFApWC4of/vtN82cOVPvv/++Tp4seFJ1bhFZp04d3XnnnZowYYJatmxZprFDQ0N1+eWX6/vvv1dGRobVqCgv4Y08bxsSzXEhAAAAQCVhuaC89NJL3RvpnKtPnz6aMGGCR08jLyQ8PNxqRJS36F5S2EVSZmLpbfMzpcMLpMYjyz8XAAAAgHJl25RXqeBp5NixYzVhwgS1aNHCljH79++vmjVr2jIWyklAoNRlihR3Q+lt89IK2vX6kqISAAAA8HOG+cdHi2UUEBCgvn37asKECRo5cqSlp5Eou7Zt20qSduzY4XASSQnzpLVjJFd26W3DLpJGHOD4EAAAAMAmTtQGlp9Q7t69W5dccokdWeDvzHzPikmpYHrsyTipft9yjQQAAACg/FguKD/++GP39bhx49SoURk2aEHl4cqXfr6nbH04PgQAAADwa5YLymeffVaGYahWrVp68skn7cgEf3RilZR3tmx9OD4EAAAA8GsBVgeIiIiQJLVq1UqBgayHq7KOrShb+7CLOD4EAAAA8HOWC8qYmIKnTEFBtm4YC3+TfrBs7btMYUMeAAAAwM9ZrgIvv/xy/f7779qzZ48deVCC9PT0Yt93uVwKCLD87wI2cJWxeRnbAwAAAKhwLFcit912myTp+PHjWr58ueVAKF5kZGSxr/j4eKej/UcZ/1P6dVzBRj4AAAAA/JblgnLYsGHq37+/TNPUvffeq5MnT9qRC/4momnZ2ueelY6vKpcoAAAAAHzDckFpGIY+/vhjderUSXv27FHXrl311VdfycWURlulpaUV+2rdurXT0QoEBJe9z4lVtscAAAAA4DuW11A+99xzkqTBgwdrz549OnTokEaNGqXo6Gj16NFDzZs3V/Xq1T1e5/f0009bjVQpFe6m+0cVYv1kwjxp+z+cTgEAAADAxwzTNE0rAwQEBMgwjCLvFQ75x/c9kZ/PurqyaNu2rSRpx44dzgRw5Utfx0oZiWXv23+Z1GCA7ZEAAACAqsiJ2sCWsz5KqknLWqt6U4DCYSfjvCsmJal2T3uzAAAAAPApywXlnXfeaUcO+KvMo9733TdNav2QbVEAAAAA+JblgnL27Nl25IC/Covxvm/qXvtyAAAAAPC5CrCjC/xadC8prJF3fas3tzcLAAAAAJ+ioIQ1AYHSJRO863vJvfZmAQAAAOBTFJSwLtKLJ42BYQXFKAAAAAC/RUEJ67JPlr1PfmbBDrEAAAAA/JYtx4aUJCsrSykpKcrOzva4T5MmTcoxEcpFSLR3/azsEAsAAADAcbYWlOnp6Xr//fe1YMECrV+/XsnJyWXqbxiG8vLy7IwEXwht4F2/kHr25gAAAADgU7YVlN9++63uvvtuJSUlud8zTdOu4VGRGT7uBwAAAKBCsKWgXLhwoa6//nq5XK7zikjDKKgaiisuL/QZ/EjWCd/2AwAAAFAhWN6UJz09XWPHjlV+fr4kqUePHlq+fLnS0tI0ZMgQd7Hocrl09uxZ7dq1S7NmzVLv3r3dn40fP15ZWVnuMeBnwmJ82w8AAABAhWC5oJw1a5ZOnz4twzDUvXt3rVy5Uv369VN4ePh5bSMjI9WqVSuNHTtWq1at0meffaawsDDNmDFDQ4YMkcvlshoHTojuJQXXKFufanUL+gEAAADwW5YLyqVLl7qvX3nlFYWEhHjc98Ybb9SXX34p0zT1ww8/6B//+IfVOHBCQKBUf2DZ+rS8j3MoAQAAAD9nuaDcunWrJKlu3brq2bNnie1KWic5ZMgQjRw5UqZp6q233mKXV38V1aZs7Wu0Kp8cAAAAAHzGckF56tQpGYahli1bnvdZUNB/9/zJzMwscYzrr79ekpScnKxVq1ZZjVQppaenF/uqMNOEjTJu2Zq6p3xyAAAAAPAZywVl4RPFsLCw8z6rXr26+/rYsWMljtGkSRP39YEDB6xGqpQiIyOLfcXHxzsdTXLlS/FTytbn93cL+gEAAADwW5YLylq1akmSUlNTz/ssOjrafb179+4Sx0hPT3dfn3uOJfzE8VVS3tmy9clMlE7GlUscAAAAAL5huaBs0aKFTNMs9sli+/bt3dfff/99iWOsXLnSfV2jRhl3C60i0tLSin21bt3a6WjSiVXe9cs8amsMAAAAAL5luaDs1KmTJOnEiRPnTWsdMGCAjP+srZs5c6b27dt3Xv/t27fr7bffdt936NDBaqRKKSIiothXQIDlP0LrTC/XcXIOJQAAAODXLFcjAwYMcF8vWrSoyGexsbEaOHCgTNPU2bNn1a1bNz3//PNavHixFi9erL///e+66qqrlJaWJsMw1KxZM11xxRVWI8HXQup40SeacygBAAAAPxdUepMLGzRokKpXr67U1FS99957uvvuu4t8PmXKFHXp0kWZmZk6ffq0nnnmmSKfFx4nYhiGpkyZUjGeuKFsQuuXvU/srZxDCQAAAPg5ywVlaGiopk2bpr1798owDGVmZhbZ8bV169ZatGiRRo8erRMnThR7HmVYWJimTZumoUOHWo0DJ4Q1KHufhtfZnwMAAACAT1kuKCXp5ptvvuDnvXv31p49ezRz5kwtW7ZMhw4dUm5urmJiYtSnTx9NmDBBMTGsp/NbJ7zZrZUjQwAAAAB/Z5jFPTKE32jbtq0kaceOHc4EcOVL8+pJOafL1q/t36UO/yyfTAAAAEAV5ERtwIJFWHMyruzFpOT9zrAAAAAAKgwKSliTcdi7ftVq25sDAAAAgM9RUMKa7JPe9fNmIx8AAAAAFQoFJawJifauX3gje3MAAAAA8DmPdnm9+OKLyzuHpIKzKPfu3euT74JNvCkMwxpJ0b3szwIAAADApzwqKA8cOCDDMEptV9yGsX/sV1Ib0zQ9+g5UMNG9pOAaUu5Zz/vEjpECAssvEwAAAACf8HjKq2mapb6kguKw8FVcv0LntuPkEj8WECh1mVq2Prv+JSXMK588AAAAAHzGoyeUK1euLLVNYmKi/vKXv+jUqVMyTVOXXXaZrrnmGrVt21Z16tRRSEiIUlNTtX//fv3666/69ttvlZaWJsMwdPPNN2vixImWfxg4xJuNedY/KDUawZNKAAAAwI95VFD26dPngp9v2rRJDz30kE6fPq1LL71Ub7/9tnr1uvAaufT0dP3P//yPXnrpJX322WcKCQnR7NmzPU+OiuPsb2Xvk5lYcIZl/b62xwEAAADgG5Z3eU1JSdHIkSN16tQpderUST/99FOpxaQkRURE6IUXXtAHH3wg0zT1/vvv67XXXrMaB07IOOpdv0wv+wEAAACoECwXlO+9954OHjwowzA0a9YsVa9evUz9b7nlFl1//fUyTVMvvvii8vLyrEaCr5ku7/pVq2NvDgAAAAA+Zbmg/PzzzyVJLVq00GWXXebVGDfeeKMk6dSpU1qxYoXVSJVSenp6sS+Xy8tizk6uLO/6pWyzNwcAAAAAn/JoDeWF7Nu3T4ZhqFEj7w+qP7fv/v37rUaqlCIjI0v8rE2bNj5MUozanaTj35e9X9oB26MAAAAA8B3LTyjPnDkjSTpx4oTXY5zbNyUlxWok+FrmMe/6VW9ubw4AAAAAPmW5oIyJiZFpmtq5c6f27dvn1RgLFixwXzdo0MBqpEopLS2t2Ffr1q2dDebKlw5/7UVHQ7rkXtvjAAAAAPAdywXlVVdd5b6eMGGCcnNzy9R/2bJl+vjjj933V155pdVIlVJERESxr4AAy3+E1pyMk3KTy96vVkcpqJr9eQAAAAD4jOVqZPz48e7rlStXauDAgdq9e3ep/Vwul9566y0NHz5cLpdLhmGob9++at6caZB+xdujPzISCp5uAgAAAPBbljfl6dWrl8aPH693331XhmFozZo1ateunfr166chQ4aoXbt2qlOnjqpVq6bU1FQdOHBAv/76q+bNm6fDhw/LNE1JUnh4uN555x3LPxB8LCzGu37ZJwuebtbva2scAAAAAL5juaCUpKlTpyo1NVWffvqpDMNQXl6eli1bpmXLlpXYxzRNGYYhSapRo4YWLlyoFi1a2BEHvhTdSwqqIeWdLXtfb59uAgAAAKgQbFmAFxgYqI8//ljvvvuu6tQpOKzeNE3308fC63PvC38dOnSotmzZUmQtJvyMK8e7ft4+3QQAAABQIdi6o8u4ceOUkJCgDz/8UKNGjVLTpk3dxWOh0NBQXXHFFXrssce0bds2ffPNN2ratKmdMeBLx1dJriwvOhpSnZ52pwEAAADgQ7ZMeT1XSEiIxowZozFjxkiS8vLydObMGeXk5KhGjRqKjIy0+yvhpBOrvOxoSqd+ZA0lAAAA4MdsLyjP+4KgINWtW7e8vwb+iDWUAAAAgF9z+BDD8nPmzBl99dVXmjRpknr37q0GDRooJCREkZGRatKkiYYNG6bXX39dycmenaF44MABGYZR5teSJUvK+Sd1mJUnjKyhBAAAAPxauT+h9LX4+Hg9+uijWrp0qXJyzt8sJicnR+np6UpISNDChQv15JNP6oUXXtCDDz7o3nUWZVCvr1SttpRzuowdWUMJAAAA+LtKV1Bu375dCxcuLPJeYGCgLrnkEtWvX1/5+fnatWuXTp8uKIAyMjL0l7/8Rdu3b3efpemJIUOGeNQuOjq6bD+AvwkIlFo9KG17powdTSkpTmowoFxiAQAAACh/thaUe/fu1QcffKC1a9cqPj5eZ86cUUZGhsf9C8+wtENQUJCuu+46jR07Vv369VONGjXcn5mmqa+//lr33XefDh8+LEmaOXOmOnfurD//+c8ejV/pp7KWRUQz7/odX0VBCQAAAPgxWwrKvLw8TZ48WW+//bZcLpf7/T8eGeILwcHBuueee/TUU0+pSZMmxbYxDEMjRoxQp06d1K1bNx07dkyS9PTTT+uee+5RcHCwLyP7v/jXnE4AAAAAwAG2bMpz00036a233lJ+fr5M03S/nDBixAi9++67JRaT52rcuLH+8Y9/uO+TkpL0ww8/lGe8yicvRzqz2bu+da6yNQoAAAAA37L8hPKrr77SV1995V572LBhQ91zzz266qqr1KhRI4WHh1sOWZ6GDRumiRMnuu/j4+M1YADTMD22503v+6bukOTZWlQAAAAAFY/lgnLOnDnu6/79+2vBggWKiIiwOqzP1K5du8j92bNnHUrip05YeKKbtt++HAAAAAB8zvKU13Xr1kkq2En1/fff96tiUpIOHjxY5L5evXoOJfFT+Z5vunSeSC838wEAAABQIVh+Qnnq1CkZhqH27durYcOGdmTyqXnz5hW579Gjh0f97rjjDv300086cuSIJKlOnTpq06aN+vfvr7Fjx3pVmKanp5e5j8vlUkCALUthvVOzk3Tse+/6Rl1mbxYAAAAAPmW5oKxbt66OHj2qmjVr2hDHt1JSUjRlyhT3ffv27dW2bVuP+n7wwQdF7jMyMpSQkKDvvvtOzzzzjB555BE9++yzCgwM9DhPZGSkx23P1aZNG6/62SLMwjmbOUn25QAAAADgc5YfbbVq1UqmabrPc/QnDz/8sPvIEEl6/vnnPe4bExOjK664Qv3791fHjh0VGhrq/iwrK0vPP/+8hg4dqtzcXFszVzghFqYIh8XYlwMAAACAz1kuKG+77TZJ0u7du7Vv3z7LgXxl1qxZmjlzpvv+pptu0rBhw0psbxiGunXrpnfffVdHjhzRkSNH9OOPP2r58uXauHGjzpw5o48//lixsbHuPt99950mTZrkcaa0tLQyv1q3bu3Vz2+brBPe9QsMl6J72ZsFAAAAgE8ZpsUDI3NyctS9e3dt3bpVI0aMOG9NYkUUFxenQYMGKTs7W5LUrFkzbdq0SVFRUZbHTk5O1qBBg7RhwwZJBYXo5s2b1b59e8tjF6dwiu6OHTvKZfxSbX5S2vk/Ze8XECGNTpECPJ8SDAAAAKBkTtQGlp9QVqtWTV988YViYmK0YMEC3XHHHUpLS7MjW7nYsmWLhg0b5i4m69WrpyVLlthSTEpSrVq1NG/ePPcUWNM09eabFs5qrOgML/8TcqVLJ+PszQIAAADApyxvyvPDDwXnEL766qu6//779dFHH+nbb7/VzTffrCuuuEINGjRQtWrVPB6vd+/eViOV6LffftPgwYOVkpIiqaD4W7p0qVq2bGnr9zRp0kQ333yz3nvvPUnS9997uQuqP7AybTXD/9bdAgAAAPgvywVl3759ZRiG+940TSUnJ+udd97RO++8U6axDMNQXl6e1UjF2r9/vwYOHKgTJwrW/EVGRmrx4sXq0KFDuXxf37593QXlgQMHlJOTU6bC2m+Y+d73zT5pXw4AAAAAPmfbAYaFSzENw5BhGDJN06tXeUhMTNSAAQOUmJgoSQoLC9PChQvVvXv3cvk+qWAX2HOdOnWq3L7LUQc/9r5viIUjRwAAAAA4zvITyiZNmhR5QlnRHD9+XAMHDtT+/fslSSEhIZo/f7769OlTrt+bkZFR5D48PLxcv88xeRbWy4Y1sC8HAAAAAJ+zXFAeOHDAhhjl4/Tp0xo0aJB+++03SVJwcLA+//xzDR48uNy/e+fOne7rkJAQ2zb9qXDq9JQS53vXt3weSAMAAADwEdumvFY0Z8+e1ZAhQ7Rt2zZJUmBgoD766CMNHz683L/bNE199tln7vsrrrii3L/TMbUtrEHN9vIMSwAAAAAVQqUsKDMyMjR06FCtX79ekhQQEKDZs2frxhtv9Mn3v/XWW9q6dav7/k9/+pNPvtcR2RbWhobFlN4GAAAAQIVV6QrK7OxsjRgxQmvWrJFUsEnQ9OnTdfvtt3s95o4dOzRu3Dj31NmSmKapKVOm6KGHHnK/17BhQ02YMMHr767wvC0KA0KtHTkCAAAAwHGW11B6Ki8vT0FB5f91U6ZM0bJly9z3NWvW1Ny5czV37lyP+g8aNEgPP/xwkfdyc3M1a9YszZo1S507d1b//v3VoUMH1atXT2FhYUpOTtamTZv0ySefKD4+3t0vJCREn332mcLCwuz54Sqi6F5SQIjkyi5bv0sflQICyycTAAAAAJ8olwpv5cqVmj9/vn755Rf9/vvvSklJkcvlUlhYmKKjo9WpUyf16tVLt912m+rWrWvrd/9xd9Xk5GR99913Hvdv0ODCO49u2LBBGzZs8GicDz74QFdddZXH3+2XXPllLyZlSJc9Uy5xAAAAAPiOrVNe16xZo3bt2mngwIF68803tW7dOp0+fVr5+fkyTVMZGRk6ePCg5s+fr4cffliNGzfWQw89pMzMTDtj2C4mJka33HJLqcWmJNWvX19///vftW3bNg0cONAH6Ry2+w0vOpnSlidsjwIAAADAtwzTNG05vOG1117TY489JtM05cmQhmHINE0ZhqGWLVtq+fLlatiwoR1RylVCQoJ27NihQ4cOKTk5Wbm5uapevbrq1q2rjh076tJLL/XpuZxt27aVVLDO0xGrRkhHvvau7+hsKaiavXkAAACAKsqJ2sCWKa9z5szRI488IknuYqpVq1a67rrrdNlll6lu3bqqVq2aUlNTtXfvXv3yyy/69ttvlZWVJUn67bffNHjwYK1bt67Crzds3LixGjdu7HSMiiMz0fu+v02R2j5qXxYAAAAAPmW5oDx9+rQmT54sqaCYjI2N1VtvvaWrr776gv3OnDmjp556Sm+99ZYMw9CuXbv04osv6h//+IfVSPCl3HTv+x5eQEEJAAAA+DHLayhnz56t5ORkGYah1q1b65dffim1mJQKdl9944039Oabb7qnyb7xxhvKz8+3Ggm+4sqXMg46nQIAAACAQywXlIsWLXJfz5gxo8y7tt57770aNGiQJCklJUVr1661Ggm+cjJOcmV537/RMPuyAAAAAPA5ywXl7t27JUmxsbG64oorvBrjtttuO288FJWenl7sy+VyORcq86i1/rU62ZMDAAAAgCMsr6E8efKke+2kt5o2beq+PnXqlNVIlVJkZGSJn7Vp08aHSc4RFmOtf/YJe3IAAAAAcITlJ5TVq1eXJCUnJ3s9xpkzZ9zXERERViPBV6J7SUaw9/0zj9mXBQAAAIDPWS4oGzVqJNM0tX37dp08edKrMZYvX15kPJwvLS2t2Ffr1q2dCxUQKNW41Pv+OaftywIAAADA5ywXlAMGDJAk5efn67HHHitz/z179mjGjBmSpMDAQPXt29dqpEopIiKi2FdAgOU/Qmsuf8n7vobD2QEAAABYYvlv9LfddpsMw5Akvf/++7rvvvuUleXZzp/r16/XwIEDlZmZKcMwdM0116hWrVpWI8GX6ve30LevbTEAAAAA+J7lgrJjx4664447ZJqmJOmdd95Ry5Yt9fzzz2v9+vXKzs4u0j4xMVHz5s3TjTfeqB49eigxMVGSVK1aNb388stW48DXji8vvU1xjGCpXl9bowAAAADwLcMsrAQtyMrK0uDBg7VmzRoZhiHTNN1PLSUpLCxM1apVU1pamvLz893vF351YGCgPvvsM40cOdJqlCqnbdu2kqQdO3Y4E2D5IOn4srL3CwyTbkwtWIcJAAAAwDInagNbFrGFhobqu+++05///Gf3e6Zpul8ZGRk6c+aM8vLy3EVk4a9NmzbV0qVLKSb9VcYh7/rlZ0rHV9kaBQAAAIBv2bYrSlhYmN566y1t2rRJ48ePV7169UpsGxgYqO7du2vatGnauXOn+vXrZ1cM+Fp4E+/7nlhlWwwAAAAAvhdk94Dt27fXtGnTNG3aNB08eFC///67zpw5o+zsbEVFRSk6Olrt27dXaGio3V8NJ7R6yLsprwAAAAD8nu0F5bmaNm2qpk2bludXwGlBFv5hgF1eAQAAAL/GQYCwJuuEd/2CItnlFQAAAPBzFJSwJqTktbIXFBhibw4AAAAAPmfLlNfnnntOZ86ckWEY+uc//6nw8HCP+86bN09r1qyRJN12223q1KmTHZHgK1uf8q5f9inpZBzTXgEAAAA/Zrmg3LBhg5599lkZhqHhw4eXqZiUpNjYWI0aNUqGYSghIUFz5861Ggm+kpMpnfrJ+/6ZR+3LAgAAAMDnLE95nT9/vvt63LhxZe7fqVMndezYUaZpatGiRcrOzrYaCb6y+WFr/cNi7MkBAAAAwBGWC8rC6arBwcEaPHiwV2MMHTpUkpSVlaV169ZZjQRfOWXlzypQiu5lWxQAAAAAvme5oIyPj5dhGGrdurWqVavm1RgdO3YsMh78REgtC53zJVe+bVEAAAAA+J7lgjI5OVmSVLduXa/HiI6Odl+fPn3aaiT4SkuLU15/n2pPDgAAAACOsFxQFj6VzMjI8HqMzMxMqzHghAb9rPU/u9ueHAAAAAAcYXmX1+joaKWlpWnv3r1ej7Fnz54i4+F86enpxb7vcrkUEODQcaIn46z1Z5dXAAAAwK9ZrkTatWsnSUpKSnJv0FNW8+bNc1+3bt3aaqRKKTIystiXo2tOjy2z1j+0gT05AAAAADjCckE5ZMgQ9/Xjjz+u/PyybbSyaNEirVixQpJUs2ZN9ejRw2ok+Mrp9db612hhTw4AAAAAjrBcUN56662qWbOmJOnnn3/WzTffXOL0zD9asWKFbrnlFkmSYRiaMGGCDMOwGqlSSktLK/bl6BPdwHBr/aMusycHAAAAAEdYLiijoqL0zDPPyDRNSQXTV9u1a6cpU6YoISHhvPY5OTlatWqVbr31Vg0ePFipqamSpAYNGuivf/2r1TiVVkRERLEvx9ZPSlJYQ2v9c5LsyQEAAADAEZY35ZGkBx98UJs3b9acOXNkGIYOHjyoyZMna/LkyapTp46io6MVEhKilJQUJSYmKi8vT5LcRWhkZKS+/fZbRUVF2REHvhJS01r/sBhbYgAAAABwhi0FpSTNmjVLTZs21fPPP+8uFE3TVFJSkk6dOuVuV/hZoUsvvVSfffaZe3Mf+BOLT0drdbcnBgAAAABH2DZf0jAMPfvss9q8ebNuu+02hYWFuT8zTdP9KtSmTRtNnTpVGzdupJj0VyF1rPXfN82eHAAAAAAcYdsTykLt2rXT+++/r1mzZmnDhg3atWuXTp8+rezsbNWsWVMNGjRQjx49FBPDdEe/F1rfWv/UPaW3AQAAAFBh2V5QugcOClL37t3VvTvTGisty+dIsqMvAAAA4M8c3CIUfs8s25mj56nDPzYAAAAA/oyCEt47sdJa/4jG9uQAAAAA4IhymfK6Z88erVixQhs3blRSUpJSUlJkmqaWL19+XtucnBz3dbVq1cojDsrLqfUWOgdL0b1siwIAAADA92wtKDdu3KjHH39cK1asKPK+aZoyjOLXy/Xp00e//vqrDMPQr7/+qk6dOtkZCeUpN8P7vuGNpYBA+7IAAAAA8Dnbpry+9dZb6tmzp1asWFHkmJA/njv5R48++qi73Zw5c+yKA18IDve+b+ZhyWVxDSYAAAAAR9lSUH7wwQd64IEHlJubK9M0VadOHd1zzz16/fXXSz1jctiwYapZs6YkaeHChXbEga/U6eJ9XzNbOrHKtigAAAAAfM9yQXnq1Ck9+OCD7vvx48fr0KFDmj59uiZNmqRGjRpdsH9wcLCuvvpqmaapAwcO6NChQ1YjwVfq9rXW/9iK0tsAAAAAqLAsF5Tvvvuuzpw5I8MwdPPNN2vatGkKCwsr0xhduvz3Sdf27dutRoKvnN1mrX/afntyAAAAAHCE5YJy0aJFkiTDMPSvf/3LqzFatGjhvj548KDVSPCV43HW+meftCcHAAAAAEdYLij37NkjwzDUpk0bNWzY0KsxCtdQStLZs2etRoKvZB221j8owp4cAAAAABxhuaBMTk6WJNWvX9/rMfLy8tzXgYEcJeE3wi68PrZUnEMJAAAA+DXLBWVUVJQka08Wjxw54r6uU6eO1UiVUnp6erEvl8vlXKh6va31b/mAPTkAAAAAOMJyQdmoUSOZpqkdO3YUedJYFqtXr3Zft2zZ0mqkSikyMrLYV3x8vHOhLrnX+7412khB1ezLAgAAAMDnLBeU/fv3lyRlZmZq7ty5Ze6fnJysTz/9VJIUERGh7t27W40EX0n+xfu+jYbblwMAAACAIywXlDfddJP7+tFHH9WJEyfK1P/uu+9WWlqa+9iRoKAgq5EqpbS0tGJfrVu3di5UeoL3fUPr2pcDAAAAgCMsF5Rdu3bVyJEjZZqmjh49qp49e+rHH38std++fft0zTXX6Ouvv5YkhYSE6Mknn7Qap9KKiIgo9hUQYPmP0HtJP3nfN7SBfTkAAAAAOMKWx4HvvPOOtmzZon379mn//v3q1auXOnXqpP79+xc5V/K1117TiRMn9PPPP+vHH39Ufn6+TNOUYRh655131LRpUzviwFcyLBwbElzbvhwAAAAAHGGYpmnaMdD+/ft1/fXXa+vWrQUDG8YF2xd+bVBQkF599VU98AA7fnqjbdu2kqQdO3b4/suXD5aOf+9d3/CLpT/ttTcPAAAAUIU5URvYNl+yWbNm+vXXX/XUU0+pdu3aMk2zyEvSefc9e/bUqlWrKCb9Ve3O3vfN2Cfl5diXBQAAAIDP2faE8lyZmZlatGiR4uLitH37dp06dUrp6emKiopS/fr11aNHDw0ZMkRdu3a1+6urHEefUB5bLq0Y6H3/Tv+WWj9kWxwAAACgKnOiNiiXLVXDwsJ0ww036IYbbiiP4VFR1O1lrX8qU14BAAAAf+bgFqHwe0lx1vpXb25PDgAAAACOoKCE944ut9b/knvtyQEAAADAEeUy5fVCDh06pOXLl+v48eOqXbu2evfurdatW/s6BuyQvN77vkY1KSDQviwAAAAAfM5yQelyufTaa6/J5XIpMDBQkydPLvbIkPz8fD3yyCN66623lJ+fX+SzkSNHasaMGYqKirIaB75khHnf18yRTsZJ9fvaFgcAAACAb1kuKFetWqXHHntMhmFo1KhRJZ4/+eSTT2rKlCnFfjZv3jwdOXJEP/zwgwIDeWrlN8JjrPVPT7AnBwAAAABHWF5DuWzZMvf17bffXmybffv26bXXXpNhGDIMQ8HBwerQoYOaNy/YlMU0Tf388896/fXXrcaBL1Wrba1/0k/25AAAAADgCMsF5fr1BevoAgICNHBg8WcSTp8+XXl5eZKk2NhYbd++XZs2bdKePXv01ltvSSooKt944w2rceBLrjxr/TOP2JMDAAAAgCMsF5S///67JKl58+YKDQ0tts28efPc1y+99JJatGjhvv/zn/+sIUOGSJISEhK0bds2q5HgK0lrrfUPrm5PDgAAAACOsFxQJiUlyTAM1a9fv9jPExIS3EVn9erVNWLEiPPajBw50n29ZcsWq5HgKzkp1vrHFj9FGgAAAIB/sFxQZmZmSlKJTyfj4uIkSYZhqF+/fgoODj6vzSWXXOK+PnHihNVI8AuG1GCA0yEAAAAAWGC5oAwPD5ckJScnF/v56tWr3dd9+vQptk21atXc11lZWVYjwVcim1roXPxuwAAAAAD8h+WCMiYmRqZpKj4+/rzzJSVp8eLF7uu+ffsWO8a5xWhERITVSJVSenp6sS+Xy+VcqPxsC51dBedQAgAAAPBblgvKzp07SyooeD7//PMiny1atEiJiYmSpDp16ujyyy8vdozCNZZSQYGK80VGRhb7io+Pdy6UabGYzTxqTw4AAAAAjgiyOsCNN96oTz75RJJ07733Kjs7Wz179tTWrVv1wAMPSCpYPzl69OgSx/jll1/c1y1btrQaCb6Sb3F6chj/eAAAAAD4M8M0TdPKAKZpqnv37lq/fr0M4/x1caZpKjQ0VDt37lRsbOx5n2dnZ6tevXpKTU1VRESEUlJSFBBg+cFppZOenl7s+126dFFAQIB27Njh40SSvukgpW71rm9ghHRjihQQaG8mAAAAoIpq27atJPm0NrBcuRmGoS+//FLNmzeXaZrnvQIDAzV9+vRii0lJ+vLLL5WamirDMHTVVVdRTJYgIiKi2Jejv185x73vGxJNMQkAAAD4OctTXiWpcePG2rp1q6ZOnarFixcrMTFRoaGh6ty5s+6//3517NixxL4vv/yypIInmcOHD7cjDvxBxgEpL0cKqlZqUwAAAAAVk+Upr3CWE4+13b5sImUneN+/46vSpZPtywMAAABUYX455RVVWE6qtf7Hf7AnBwAAAABHUFDCe2aatf75GfbkAAAAAOAICkp4L8DiEtxanezJAQAAAMARFJTwXmhDa/1DatuTAwAAAIAjKm1BeebMGX311VeaNGmSevfurQYNGigkJESRkZFq0qSJhg0bptdff13Jyclejb9t2zZNnjxZ7du3V+3atRUZGalWrVrp1ltv1ZIlS2z+aSqomiXv3uuRM16eYQkAAACgQqh0u7zGx8fr0Ucf1dKlS5WTk1Nq+/DwcL3wwgt68MEHZRhGqe3z8vL09NNP66WXXpLL5Sqx3XXXXadZs2YpOjq6TPnLytFdXpf2kZIsbKzTcITUd75tcQAAAICqjF1ebbB9+3YtXLiwSDEZGBioVq1aqXfv3rryyitVu/Z/p1pmZGToL3/5i8aPHy9PauuJEyfqf//3f93FZHBwsDp06KArr7xSderUcbdbuHChBg0apPT0dBt/ugrmzDZr/cMtTpkFAAAA4KhKV1AWCgoK0p/+9CfNnz9fp0+fVnx8vFavXq01a9YoKSlJ8+fPV6NGjdztZ86cqXfeeeeCY06fPl2zZs1y3w8fPlz79+/X5s2btWbNGh09elRvvPGGgoIKNqvZsmWLJkyYUD4/YEXgyrbWv3Z3e3IAAAAAcESlKyiDg4N1zz33aO/evfrqq680YsQI1ahRo0gbwzA0YsQI/fTTT2rQoIH7/aefflq5ubnFjpuRkaFnnnnGfd+3b1/NmzevSFEaHBys+++/v0hh+sknn2jjxo12/XgVS3BNa/3zvFu/CgAAAKBiqHQF5YgRI/Tuu++qSZMmpbZt3Lix/vGPf7jvk5KS9MMPxa8JnDNnjo4dOyapoCB9++23FRgYWGzbcePGqXv3gqdvpmnqpZdeKuuP4R8aDbfWP6R815cCAAAAKF+VrqAsq2HDhhW5j4+PL7bdl19+6b7u06ePWrdufcFxJ06c6L5etGiRsrMtTg+tiBK+stY/vFHpbQAAAABUWFW+oDx3gx5JOnv27Hlt0tLSijy5vPrqq0sd95prrinSf/Xq1RZSVlC5p6z1j+5lTw4AAAAAjqjyBeXBgweL3NerV++8Njt37iyytvKKK64oddwGDRooNjbWfb9lyxbvQ1ZYlerEGQAAAABlVOULynnz5hW579Gjx3ltdu3aVeS+efPmHo19brs/jlEpGKHW+h9bak8OAAAAAI4IKq+B8/LylJKSooyMDI/OdyzkyWY6dklJSdGUKVPc9+3bt3cfBnquc59iBgUFKSYmxqPxz/1ZDhw4UGp7b86sdLlcCghw6N8FzOJ3xPXYzlelhteU3g4AAABAhWRrQblixQp98MEHWrt2rfbt21emQlIq2D01Ly/PzkgX9PDDD7t3bpWk559/vth2566rrF69uscF3LnHlaSmppbaPjIy0qNx/6hNmzZe9bPO4p/V2Ur41BYAAACoQmwpKJOTkzVmzBgtXfrfKYxlKSYNwyhz8WnVrFmzNHPmTPf9TTfddN6Or4XOfXIYGur5NM+wsLBix6g8giTleN89L8O2JAAAAAB8z3JBmZOTo379+mnbtm1eF4W+Libj4uJ07733uu+bNWumadOmldj+3A15goI8/y07t21OTumFV1pamsdjF+rSpUuZ+9gmuLq1nV4Dw+3LAgAAAMDnLBeUU6ZM0datW2UYhiSpb9++mjBhgrp3764GDRoUeUpXEWzZskXDhg1znwtZr149LVmyRFFRUSX2CQ//b+GTlZXl8Xed2zYiIqLU9p60+SPH1k9KUlANawVl1KX2ZQEAAADgc5YLys8//9x9/Ze//EWvvvqq1SHLzW+//abBgwcrJSVFklSrVi0tXbpULVu2vGC/c9c2ZmZmevx9GRn/ndLp7frICi3v/DM7y6T1o/bkAAAAAOAIy4+39uzZI0mqWbOmXnzxRcuBysv+/fs1cOBAnThxQlJBgbd48WJ16NCh1L5169Z1X6elpXk8NfXcDX/q1KlTxsR+wGVxU56GA+3JAQAAAMARlgvK7OxsGYahyy67TMHBwXZksl1iYqIGDBigxMRESQWb5SxcuFDdu3f3qH+rVq2K3B86dMijfgkJCe7r1q1be5jWj5hWCsoqfwQqAAAA4Pcs/62+8KxFXx73URbHjx/XwIEDtX//fklSSEiI5s+frz59+ng8xqWXFl3rt3nz5lL75Obmavv27SWOUTlY+QcEl3QyzrYkAAAAAHzPckHZr18/maapnTt3Kj8/345Mtjl9+rQGDRqk3377TZIUHByszz//XIMHDy7TOBdffLEuuugi9/2aNWtK7bNhw4Yi6y179+5dpu/0C6bFP+/Mo/bkAAAAAOAIywXlpEmTFBgYqJSUFM2ZM8eOTLY4e/ashgwZom3btkmSAgMD9dFHH2n48OFejXduv7lz55Z6DMhHH33kvm7btq2aN2/u1fdWbLmlN7mQsBh7YgAAAABwhOWCsk2bNnruuedkmqYeeughj57elbeMjAwNHTpU69evl1RwtMbs2bN14403ej3m2LFj3ddJSUkXPLcyMTGxSHF9bt9KxSz9bM0LquXZGlYAAAAAFZMtO6M88cQTeuGFF5SRkaH+/fvr/vvv18aNG+VyuewYvkyys7M1YsQId2FrGIamT5+u22+/3dK4Xbt2LfKU8m9/+5vWrl17XruzZ89qzJgxSk1NlSTFxMTovvvus/TdFZdhrfueN+2JAQAAAMARhmmapl2DLV26VMOHD1dubsFUyJCQENWuXVvVqlXzLIxhaO/evZYyvPzyy3r88cfd97Vq1VK3bt087j9o0CA9/PDDxX524MABdevWTSdPnpRU8PONGzdOgwcPVmRkpLZu3ao33njDvQFQQECA5s+fr2HDhln4iS6sbdu2kqQdO3aU23eU6OMgSRbWUda9Uhrs/BNtAAAAoDJwojYIsmugl19+WS+++KK7mDRNU1lZWTp61LONV0zTlGFYfOKlgumu50pOTtZ3333ncf8GDRqU+FlsbKy7QDx9+rSys7M1depUTZ069by2gYGBmjJlSrkWk44Lqi3lnXQ6BQAAAACH2DLl9YEHHtATTzyhM2fOSCooDguZpunRy1/07NlTW7du1Q033KCgoOLr8W7duikuLq4ST3X9jwDPnjyXqEZlPEoFAAAAqDosT3mdN2+eRo0aJcMwZJqmateurRtvvFHdunVTgwYNFB4eXqbxynI+pNNOnjypH374QYmJicrJyVHDhg3VtWtXtWzZ0mcZnJ3yGikp3fv+jW+Wen1iWxwAAACgKvPLKa9vv/22+3rAgAH64osvFBUVZXVYvxAdHa0bbrjB6RgOyrLWPTPBnhgAAAAAHGF5yuuGDRskScHBwfr444+rTDEJyfJ/PhFN7YkBAAAAwBGWC8rMzEwZhqHLL79c0dHRdmSCvzAsrqG8+E57cgAAAABwhOWCslGjRpJU5rWSqATMTAudA6X6A2yLAgAAAMD3LK+h7Ny5s/bt22f5/EhcWHp68ZvfuFwuBQTYslmvF1zed41oKgUE2hcFAAAAgM9ZrkTGjRsnSUpMTNTq1astB0LxIiMji33Fx8c7Hc1L/nNUDAAAAIDiWS4oBw8erNtuu02maWrixIlKSkqyIxcqu8yjkivf6RQAAAAALLBlruT06dN10003affu3erUqZO++OILuVwWpkPiPGlpacW+Wrdu7XQ077iypJNxTqcAAAAAYIHlNZR33323JCksLEy1a9dWYmKibrrpJkVFRalz586qX7++qlXzbDdQwzA0c+ZMq5EqpYiIiGLfd279pA0yjzqdAAAAAIAFhmmalhazBQQEyDCMIu8VDvnH9z2Rn880yLJo27atJGnHjh2+//KPy/7nW8SAlVL9vrZEAQAAAKo6J2oDy08opf8WkJ6+XxJvClD4sTo9nU4AAAAAwALLBeUzzzxjRw5URad+5AklAAAA4McoKOEc1lACAAAAfs2Pd3SB3wuLcToBAAAAAAsoKGFBqPddQ6Kl6F72RQEAAADgcxSU8J5hYUfeJjdLAYH2ZQEAAADgc7bs8lqS+Ph4/f7770pOTlZ2drZq1qyp6OhodezYUTVq1CjPr4YvmLne93Vl2ZcDAAAAgCNsLyjXrVunN998U19//bXOnj1bbBvDMNSuXTvdc889Gjt2rCIjI+2OgYouYZ7UfbrTKQAAAABYYNuU1/T0dI0fP149evTQhx9+qJSUFPc5lKZpFnm5XC5t27ZNDz74oC699FJ9//33dsWAv8g5JeXlOJ0CAAAAgAW2FJQZGRkaMmSIZs2a5S4aCwUGBqpu3bpq2LChqlev7n6/sM3hw4d17bXXau7cuXZEgT/Z84bTCQAAAABYYEtBOXHiRP3444/u+6ZNm+p///d/tW3bNmVmZurEiRNKTExUSkqKTpw4oW+++UajR4+WYRgyDEP5+fm6/fbbtWvXLjviwF+ciHM6AQAAAAALLBeU69at00cffSTDMCRJ9913n+Lj4/X444+rbdu2CgwsupNn3bp1NXToUH366af66aef1KhRIxmGoZycHD322GNW48Cf5KU7nQAAAACABZYLyo8++sh9fffdd+uNN95QSEiIR327du2qZcuWKSwsTJK0ZMkSnTp1ymok+ItaHZ1OAAAAAMACywXlihUrJElBQUF68cUXy9y/ZcuWmjBhgiTJ5XJp9erVViPBX+SlOp0AAAAAgAWWC8ojR47IMAxddtllqlOnjldj9O/f3319+PBhq5HgN2zbZBgAAACAAyyfQ5meXrAOrkaNGl6PcW7fjIwMq5EqpcLf5z9yuVwKCPDTwqxGC6cTAAAAALDAckFZt25dHT58WPv27fN6jP3797uvvX3KWdlFRkaW+FmbNm18mMRGze5xOgEAAAAACyw/2mrVqpUkKSEhQWvWrPFqjA8//NB93bp1a6uR4C/2z3A6AQAAAAALLBeUQ4cOdV/fc889SkpKKlP/qVOnavny5ZKkWrVqqWfPnlYjVUppaWnFvvy6AE/d63QCAAAAABZYLijvuusu9zTV3bt3q1u3bvr2229L7Xf69GlNmjRJDzzwgCTJMAxNmjTJf9cDlrOIiIhiX379+xUZ63QCAAAAABZYXkNZs2ZNvf7667r99ttlGIYOHDig4cOHq1mzZrr66qvdu79Wq1ZNqamp2r9/v3799Vd9//33ysnJkWmakqR27drpscces/wDwY/UaOt0AgAAAAAWWC4oJenWW2/VqVOn9PDDD8vlcsk0Te3bt09vv/12iX1M05RhGJIKNpX57rvvFBoaakcc+IsTq6VGVzudAgAAAICXbJsvOWnSJMXFxalDhw7u90zTdL+Kuw8LC9Mjjzyi9evXq0GDBnZFgb84vd7pBAAAAAAssOUJZaEePXpo48aN+vnnnzV//nz98ssv+v3333XmzBllZ2crKipK0dHR6tSpk3r16qWbb75ZUVFRdkaAP8nPdDoBAAAAAAtsLSgL9ejRQz169CiPoVGZhDd1OgEAAAAACywXlP3793dfz5o1S7GxsVaHRFVRq73TCQAAAABYYLmgXLVqlQzDUOPGjSkmUTY5Z5xOAAAAAMACy5vy1KpVS5LUrFkzy2FQxWQccjoBAAAAAAssF5QNGzaUJGVlZVkOg6rGcDoAAAAAAAssF5R9+vSRaZrauXOncnNz7ciEqiK8idMJAAAAAFhguaAcO3asJCktLU3vvfee1eFQlYTWcToBAAAAAAssF5RdunTRgw8+KNM09cgjj2jt2rV25EJVEFzb6QQAAAAALLBcUErSv/71L917771KTU1Vv3799OCDD2r79u12DI3KLHG+0wkAAAAAWGDrOZTVq1dXamqq3nzzTb355puKiopSs2bNVL16dQUElF67Goah5cuXW40Ef5F52OkEAAAAACyw7RzKQoXXpmnqzJkz2rx5s0fjmKZZZBxUAcFRTicAAAAAYIHlglIqKAa9+QxVXGRzpxMAAAAAsMByQTl79mw7cqAqCgx2OgEAAAAACywXlHfeeacdOVCK9PT0Yt93uVwerU+tkCJinU4AAAAAwAKPCspDhw5JksLCwhQdHV2ugVC8yMjIEj9r06aND5PYiNnQAAAAgF/zqKCMjY2VYRi6+uqr9e233xb57IcffpAk1a5dW+3atbM/ISqv9ANOJwAAAABgQZmmvBa3wU7fvn1lGIaGDBmiRYsW2RYMRaWlpRX7fpcuXXycxE48ogQAAAD8mS27vKL8RUREFPu+366flKSgGk4nAAAAAGCBR9VIeHi4JCkjI6Ncw6CKyUx0OgEAAAAACzwqKOvXry/TNLV9+3bl5eWVdyYAAAAAgB/waMpr165dtX//fiUnJ+uGG27Qww8/rMaNGyswMNDdJjMz070brBVNmjSxPAb8RERTpxMAAAAAsMAwi9tp5w+WLFmia6+9VoZhnPdZYffiPitzGMPgCWgZtW3bVpK0Y8cO33/5xxb/zPt9J8UMticLAAAAUMU5URt4NOX16quv1sSJE2Wa5nmvQsV95unr3P6oSgJLbwIAAACgwvJ4i9C3335bc+fO1TXXXKN69eopKKhgtqwdTyYpJKuo7BNOJwAAAABgQZnOnLjhhhv07bff6tixY8rJyZHL5ZJpmjIMQ1dffbVcLpelV35+fnn9nKiIwmKcTgAAAADAAj8+xBD+zZDq9HQ6BAAAAAALbCkombKKsjOlUz86HQIAAACABR4dG3IhLpfLjhyoijKPOp0AAAAAgAVMeYVzQuo5nQAAAACABRSUcBCbMAEAAAD+jIISzjm+2ukEAAAAACygoIRz0g86nQAAAACABRSUAAAAAACvUFDCORFNnU4AAAAAwALLx4bAN9LT04t93+VyKSDAT/9doH4fpxMAAAAAsICC0k9ERkaW+FmbNm18mMRGptMBAAAAAFjhp4+2UCkcX+l0AgAAAAAW8ITST6SlpRX7fpcuXXycxEanNzidAAAAAIAFFJR+IiIiotj3/Xb9pCQFhjmdAAAAAIAFlgvK999/X5LUqFEjDRgwwKsxVq1apUOHDkmS7rjjDquR4C/CGzqdAAAAAIAFlgvKsWPHyjAMDRkyxOuCcsqUKfr6669lGAYFZVVSu4fTCQAAAABYUGGmvJomW35WOblJTicAAAAAYIEfL8CD38uioAQAAAD8WYUoKLOysiRJoaGhDieBT2UkOJ0AAAAAgAUVoqDctWuXJKlWrVoOJ4FPmS6nEwAAAACwwNGCMjU1Vc8//7wOHTokwzDUrl07W8c/efKkFi9erOeee07Dhw9XTEyMDMNwv957770yjXduX09f77zzjq0/EwAAAABUFGXalOfiiy8u8bPVq1df8PNzmaapzMxMJSUlFdmMZ/jw4WWJU6Jjx46pR48eOnjwoC3joZzwhBIAAADwa2UqKA8cOCDDMM573zRNZWVllamA++Ouru3atdO4cePKEqdEZc3ijd69eyssLKzUdk2aNCnXHH4t+4TTCQAAAABYUOZjQ0o63sPbYz/q1aunMWPG6Nlnn1VISIhXY1xIdHS0OnfurC5duqhr164aMWKELePOmTNHsbGxtoxVZWWddDoBAAAAAAvKVFCuXLmyyL1pmurfv78Mw1DXrl310ksveTROQECAIiIi1KBBAzVs2LAsETxSu3ZtzZ07V127dlXTpk1tHx82qRbldAIAAAAAFpSpoOzTp0+x75umqdq1a5f4ua/VqFFDo0aNcjoGStPInqfFAAAAAJxR5imvf/TMM89Iki655BLLYVDFtHrQ6QQAAAAALLCtoATKxpACAp0OAQAAAMACR8+hRFVmSidWOR0CAAAAgAWWn1BKBTuepqSkSJLuuecehYeHe9x3+fLl2rFjhyTp2muv9Zups48++qh27typhIQE5ebmqk6dOmrRooX69OmjO++8U82aNXM6YsV3bIXUYIDTKQAAAAB4yXJBGR8fr7vuukuGYahPnz6aNGlSmfoHBgbqoYcekmEY2rRpk2bPnm01kk988cUXRe4PHz6sw4cPa9WqVXrhhRc0btw4/fvf//borMpC6enpZc7hcrkUEOCnD5ozEpxOAAAAAMACywXlvHnz3NcTJ04sc/++ffuqZcuW2r17t7766ivNmDFDgYEVf21ddHS0Lr74YkVGRiolJUXx8fFKS0uTJOXl5WnatGn69ddftXLlSkVFeXY8RmRkpFdZ2rRp41U/x4U3djoBAAAAAAssP9pavXp1wUABAbruuuu8GmPEiILjI1JTU7Vx40arkcpNmzZt9Prrr2vv3r06ceKEfv75Zy1btkzr1q1TcnKyFi5cqPbt27vbb9q0STfffLODiSu4+hXjmBkAAAAA3rH8hHLnzp0yDEMtW7ZURESEV2N06dKlyHhdu3a1GqtcFK71LE5QUJCGDh2qAQMGaNSoUfr2228lSUuWLNE333yjYcOGlTp+4RPOsjj3987/VPwn0QAAAABKZrmgTEpKkiQ1aNDA6zHq16/vvj558qTVSI4KDQ3VJ598ohYtWuj48eOSpDfeeMOjgtKbgtxv109KUuYRpxMAAAAAsMByNVJY0OTk5Hg9Rm5urvs6Pz/faiTHVa9eXX/+85/d93FxccrKynIwUQV1cq3TCQAAAABYYLmgjI6OlmmaOnDggNdjnNu3bt26ViNVCH379nVfZ2VlKSGBHU3Pk7zN6QQAAAAALLBcULZq1UqSdOTIEW3ZssWrMRYuXOi+vvjii61GqhBiYmKK3BdODcY58s46nQAAAACABZYLysGDB7uvn3zyyTL3//XXX/X1119LKlhDeNVVV1mNVCFkZGQUuQ8PD3coSQUWWr/0NgAAAAAqLMsF5ZgxYxQWFiZJWrx4sf7yl7/INE2P+u7atUt/+tOfZJqmDMPQmDFjFBwcbDVShbBz584i9/Xq1XMoSQVWq6PTCQAAAABYYLmgjImJ0UMPPeQuIv/v//5PPXr00IIFC5SXl1dsnwMHDujJJ59Uly5d3DuhVq9eXc8++6zVOBXGp59+6r6OjY09bwosJKXucToBAAAAAAssHxsiSc8995w2btyo7777ToZhaP369Ro5cqRCQkLUrl07RUdHKyQkRCkpKdqzZ48OHz4sSe4iNDg4WF988YWlo0cqkm+++UbffPON+/5Pf/qTc2Eqsryyn7sJAAAAoOKw5RDDwMBAzZs3T3fccYdM03QXillZWdqwYYOWLFmiBQsWaNWqVUpMTCwyJbZevXr69ttvNXDgQDuilIuUlBTdcMMN2rBhQ6ltP/nkE91yyy3u+/DwcD3++OPlGc9/uThKBQAAAPBnhunpgkcPLVq0SC+++KLWrFlzwXY1a9bUhAkT9PDDDys6OtrOCJKk8ePH64MPPjjv/ezsbPd1UFCQAgMDz2vzxzMjz5w5o1q1akmSWrdurSFDhujyyy9XTEyMIiIilJqaqm3btumLL77QunXr3P0Mw9Cnn36q0aNH2/Vjnadt27aSpB07dpTbd5ToY8Na/+b/T+r+tj1ZAAAAgCrOidrAlimv57r22mt17bXX6siRI4qLi9OuXbt0+vRpZWdnq2bNmmrQoIF69OihLl26lOsGPLm5uUWKx+Lk5eWVuM6zJPHx8YqPjy+1XfXq1TVt2rRyLSb9XlQrpxMAAAAAsMD2grJQw4YNddNNN5XX8D4VFhamu+66SytWrNDBgwcv2DYqKkp33nmnHn74YTVp0sRHCf3UJfc6nQAAAACABbZPea3sTpw4oa1bt+rQoUNKSkpSdna2IiMjVbt2bbVv317t27cvdhptefHrKa+jMqRqYfZkAQAAAKq4SjHltbKrV69ehd5AyK9smswaSgAAAMCPlUtBmZqaqh9//FEbN25UUlKSUlJSZJqmZs6cWR5fB391el3pbQAAAABUWLYWlAkJCXruuef08ccfF9kp1TRNGYZRbEE5cOBA7dy5U4ZhaOXKlWrZsqWdkVCRMdsaAAAA8Gu2nEMpSQsWLFCHDh00a9YsZWZmus+jLG2J5m233aZjx47p2LFjmjNnjl1x4A+CazqdAAAAAIAFthSUS5cu1ejRo91TW4ODgzVw4EA99NBDat68+QX7jh49WuHh4ZKk+fPn2xGnUkpPTy/25XK5nI7mveBIpxMAAAAAsMDylNeMjAzdddddys3NlWEYuuaaazR9+nQ1atRIkrRr1y7t3bu3xP7h4eEaNGiQFixYoPj4eB07dkwNGjSwGqvSiYwsufhq06aND5PYKJQ/ZwAAAMCfWX5COXPmTB09elSGYWjgwIH65ptv3MWkp7p37+6+3rZtm9VI8BchtZ1OAAAAAMACy08ov/nmG/f1G2+8oYCAsteorVu3dl/v27fPaqRKKS0trdj3u3Tp4uMkNjJsW8ILAAAAwAGWC8qdO3dKklq0aOH1Dq21atVyX6ekpFiNVClFREQU+743BXyFEVTd6QQAAAAALLBcjSQlJckwDF100UV25EFVcny50wkAAAAAWGC5oKxeveApU0ZGhtdjHD9+3H1dp04dq5HgL3KSnU4AAAAAwALLBWVMTIxM09SuXbtKPXOyJD/++KP7ulmzZlYjwV+EsMsrAAAA4M8sF5S9evWSJJ09e1aLFy8uc//MzEx9/PHHkqSQkBBdeeWVViPBX3AOJQAAAODXLBeUI0eOdF8/8sgjSk9PL1P/yZMnu9dhDhs2TCEhIVYjwV9kJDidAAAAAIAFlgvKAQMGqG/fvjJNU7/99psGDhyoAwcOlNovNTVVEyZM0PTp0yVJhmHoqaeeshoH/iQ/y+kEAAAAACywfGyIJM2YMUM9e/bUyZMn9euvv+rSSy/VsGHD1K9fP504ccLdbt68eTpx4oR+/vlnLViwQGfPnpVpmjIMQ//85z/Vrl07O+LAX4Q3dDoBAAAAAAsM09uddP5g48aNGj58uI4cOVIwsGFcsP25Xzt58mS98sordsSoctq2bStJ2rFjh++//OML/xmXquEIqe98W6IAAAAAVZ0TtYHlKa+FOnXqpK1bt+r2229XUFCQTNN0vwr98b2mTZvq008/pZisqsLY5RUAAADwZ7ZMeS1Uu3ZtzZkzR//zP/+jTz/9VHFxcdq+fbtOnTql9PR0RUVFqX79+urRo4eGDBmikSNHKjAw0M4I8CelPMUGAAAAULHZWlAWatSokR5++GE9/PDD5TE8Kovgmk4nAAAAAGCBbVNegTIz+M8PAAAA8Gf8jR7OqVbb6QQAAAAALKCghHPYlAcAAADwaxSUcE54I6cTAAAAALDAo015Lr74Yve1YRjau3dvsZ9ZZRiGQkJCFBUVpRYtWqh379668cYbFRUVZdt3oIIIriNF93I6BQAAAAALDPPcgyJLEBAQIMMwZJqmDMNQfn7+eZ+Vl1q1amn27NkaNmxYuX2HP0hPTy/2/S5duiggIMCnh5e6fWzhz73dM1L7Z22LAgAAAFR1bdu2lSSf1gYeHxtyobrTg5rUa6dPn9bo0aO1fv16929QVRQZGVniZ23atPFhEpucWud0AgAAAAAWeVRQrly50qvPvJGbm6vk5GRt375dM2fO1JEjR5STk6N///vfmjFjhq3fBQcdXSzl5UhB1ZxOAgAAAMBLHk15dcqBAwfUqlUr5eXlKTY2tsjazaqm0k15laRO/5ZaP2RLFAAAAKCqq9BTXp0QGxurjh076tdff9XRo0edjuOoiIiIYt8PCPDjjXpT9zidAAAAAIAFFb4aqVWrliQpOzvb4SSwX/lt5gQAAACg/FXoJ5SSNGfOHGVmZjodA+WhTnenEwAAAACwoNwKyqSkJK1fv1579uzRmTNnlJ2drRo1aqhevXrq1KmT2rVr59F0zXr16pVXRDgtorHTCQAAAABYYHtB+dFHH+mdd97Rjz/+eMF2NWvW1G233aYHH3xQF198sd0x4A/q9HQ6AQAAAAALbFtDuXfvXvXs2VN33HGHfvzxR/fZlKZpFvtKTk7Wm2++qcsuu0wvv/yyXTHgT06scjoBAAAAAAtseUK5fft2DRo0SCdOnNAfTyFp1qyZ6tSpo5CQEKWmpurAgQM6e/as+/PMzEw98cQTOnDggKZOnWpHHPhMsKRc77sf+EBqONi2NAAAAAB8y/ITyuzsbI0ePVrHjx93v9evXz99+eWXSk1N1d69e/Xrr78qLi5Omzdv1pkzZ7Rr1y49/fTTioqKkmEYMk1T06ZN05w5c6zGgU+5rHXPOVt6GwAAAAAVluWCcubMmYqPj5dhGAoKCtKMGTO0fPlyXX/99QoPDy+2T6tWrfTss8/qt99+U48ePSQVTI3961//KpfLYpECH8p3OgAAAAAAB1kuKD/77DP39T//+U/dfffdHveNjo7WwoUL1bBhQ0nSiRMntHr1aquR4C/CGzmdAAAAAIAFlgvK+Ph4SVJkZKQmTZpU5v61atXSvffee954qAJqtHQ6AQAAAAALLBeUqampMgxDbdu2VWhoqFdjdOnSpch4qCIunuh0AgAAAAAWWC4oC6erBgV5v2HsuX1jYmKsRoK/SP7F6QQAAAAALLBcUHbs2FGmaWrXrl3Kz/duk5YtW7YUGQ9VROZRpxMAAAAAsMByQVm4Cc/p06e9OvYjOztb77zzjgzDUKdOndSuXTurkeAvQus5nQAAAACABZYLymuuuUajR4+WaZp66KGHtGLFCo/75uTk6NZbb9WePXsUEhKiadOmWY0Df+Li2BEAAADAn1kuKCVp1qxZGjVqlNLS0jRkyBBNmDBBW7duLbF9enq6PvzwQ7Vr105fffWV6tSpoy+//FKdOnWyIw78xQmOiAEAAAD8mUc76XhytmRkZKTq1q2rpKQkzZw5UzNnzlT9+vXVrl071alTR9WqVVNqaqr279+vXbt2KTc3V6ZpyjAMdezYUXPnztUXX3yhmTNnWv6hKqP09PRi33e5XAoIsOXfBbwQLCnX++4ZCbYlAQAAAOB7hmmaZmmNAgICZBhGmQYuHLa4fhf6zNuNfSq7C/3+t2nTRjt27PBhmv/4oomUY6EobPM36fIX7MsDAAAAVGFt27aVJJ/WBh6f9eFB3Vnmfn/8rKxFKxyWe9pa/wb97ckBAAAAwBEeFZTPPPNMeedAKdLS0op9v0uXLj5Ocg4z01r/2j3tyQEAAADAERSUfiIiIqLY951bPylJLmvd906VLn3YnigAAAAAfM7JagRV3Yk4pxMAAAAAsICCEs7JTXU6AQAAAAALKCjhIDZhAgAAAPyZx7u8eiM+Pl6///67kpOTlZ2drZo1ayo6OlodO3ZUjRo1yvOr4Q+CI51OAAAAAMAC2wvKdevW6c0339TXX3+ts2fPFtvGMAy1a9dO99xzj8aOHavISAqLKqleL6cTAAAAALDAtimv6enpGj9+vHr06KEPP/xQKSkp7nMmTdMs8nK5XNq2bZsefPBBXXrppfr+++/tigG/YUgtHnA6BAAAAAALbCkoMzIyNGTIEM2aNctdNBYKDAxU3bp11bBhQ1WvXt39fmGbw4cP69prr9XcuXPtiAJ/EdFMCqrmdAoAAAAAFthSUE6cOFE//vij+75p06b63//9X23btk2ZmZk6ceKEEhMTlZKSohMnTuibb77R6NGjZRiGDMNQfn6+br/9du3atcuOOPAH6fulvBynUwAAAACwwHJBuW7dOn300UcyjIIdO++77z7Fx8fr8ccfV9u2bRUYGFikfd26dTV06FB9+umn+umnn9SoUSMZhqGcnBw99thjVuPAb5jSnjecDgEAAADAAssF5UcffeS+vvvuu/XGG28oJCTEo75du3bVsmXLFBYWJklasmSJTp06ZTUS/MXJNU4nAAAAAGCB5YJyxYoVkqSgoCC9+OKLZe7fsmVLTZgwQZLkcrm0evVqq5HgLwJCnU4AAAAAwALLBeWRI0dkGIYuu+wy1alTx6sx+vfv774+fPiw1UjwF8FRTicAAAAAYIHlgjI9PV2SVKNGDa/HOLdvRkaG1UjwF4Ztp9YAAAAAcIDlv9HXrVtXpmlq3759Xo+xf/9+97W3Tznhh845XgYAAACA/7FcULZq1UqSlJCQoDVrvNtk5cMPP3Rft27d2mok+ItqNZ1OAAAAAMACywXl0KFD3df33HOPkpKSytR/6tSpWr58uSSpVq1a6tmzp9VI8BcBQU4nAAAAAGCB5YLyrrvuck9T3b17t7p166Zvv/221H6nT5/WpEmT9MADD0iSDMPQpEmTFBDAuroqo35fpxMAAAAAsMDyI6KaNWvq9ddf1+233y7DMHTgwAENHz5czZo109VXX+3e/bVatWpKTU3V/v379euvv+r7779XTk6OzP+so2vXrp0ee+wxyz9QZVW4+dEfuVwuPy3CA6R6fZ0OAQAAAMACW+Yc3nrrrTp16pQefvhhuVwu9yY9b7/9dol9TNOUYRiSpDZt2ui7775TaCjnEpYkMjKyxM/atGnjwyR2cUmufCkg0OkgAAAAALxk26OtSZMmKS4uTh06dHC/Z5qm+1XcfVhYmB555BGtX79eDRo0sCsK/MXuN5xOAAAAAMACW3dF6dGjhzZu3Kiff/5Z8+fP1y+//KLff/9dZ86cUXZ2tqKiohQdHa1OnTqpV69euvnmmxUVxeH2nkhLSyv2/S5duvg4iY1Oxkl62OkUAAAAALxULtts9ujRQz169CiPoausiIiIYt/3z/WT/5FXfJEMAAAAwD/4cTUCvxdc2+kEAAAAACygoIRzsk46nQAAAACABRSUcE7GfqcTAAAAALCAghLOMfOdTgAAAADAAgpKOCf8YqcTAAAAALCAghLOcWU5nQAAAACABZW6oDx58qQWL16s5557TsOHD1dMTIwMw3C/3nvvPa/H3rdvn55++ml17txZ0dHRCgsLU/PmzXX99dfriy++UH4+0zlLlXHA6QQAAAAALCiXcyidduzYMfXo0UMHDx4sl/Fff/11/fWvf1V2dnaR9/ft26d9+/Zp/vz5uuKKK/TRRx+pWbNm5ZKhUsjLcDoBAAAAAAsq5RPKrKyscism//nPf+ovf/mLu5gMCAhQu3bt1Lt3b8XExLjb/fTTT+rdu7eOHTtWLjkqheBopxMAAAAAsKBSFpTnio6O1tVXX62///3vWrBggaWxvvvuOz3zzDPu+yuuuEK7du3Stm3btHr1aiUmJuqTTz5RZGSkJCkxMVGjRo2y9J2VWnCo0wkAAAAAWFApp7zWrl1bc+fOVdeuXdW0aVNbxjRNU48//rhM05QktWrVSsuWLVN4eLi7TUBAgG6++WbVrVtXgwYNkiStXbtWX331la6//npbclQsgZIsrBUNCLEtCQAAAADfq5RPKGvUqKFRo0bZVkxK0pIlS7Rlyxb3/ZQpU4oUk+caOHCgbrrpJvf9iy++aFuOisXixkP56fbEAAAAAOAIywXlDz/8oB9++EHbt2/3eoydO3e6x6movvzyS/d1s2bNNHjw4Au2nzhxovt63bp1SkxMLLdsfssMdDoBAAAAAAssF5R9+/ZVv3799Nhjj3k9xpNPPql+/fqpf//+VuOUm2+//dZ9PWTIEBmGccH2vXr1UkREhKSC6bKLFi0q13x+KZ9dXgEAwP9v766jo7jaMIA/s3GBOCRAIJBiwQoELwT34oWW4hTaUlfq1IUKlEJbrDgUt0JxLxR3ghMIGoLEfe/3x36Z7ibrs5vNhud3Tg6zu3fuvjt3d5h35s69ROTMik2XVyGEfH9icZOQkKAzWmuzZs1MruPq6opGjRrJj7W7y5YcfspWz2GXVyIiIiIiZ1ZsEsriLDY2VudxZGSkWetplytYR4ng6qZsfal4nkAgIiIiIiLzFItRXrOzswEA7u7uDo5Ev4JzWlasWNGs9bTLxcXFmSyflmb5FTu1Wg2VykHnBXJTlK2vzrJNHERERERE5BDFIqG8dOkSAMDf39+xgRiQnJys89jPz7yunqVLl5aXU1JMJ1/581daKioqyqr1lFOYEErF4utHRERERERWcniX13nz5uH8+fOQJAk1atRwdDh6Fbxy6OnpadZ6Xl5eBusgAComlEREREREzsyiI3pjo7AeOHDA7FFahRDIyMjA5cuXce/ePfn5Ll26WBJOkcnJydF57Opq3mbTLpffrdeY1NRUywIDEB0dbfE6xYa7v6MjICIiIiIiBSxKKHfs2KF3ugwhBB48eICdO3da9OZCCLm+ChUqYPTo0RatX1S8vb11HmdmZhZ6Tp/MzEx5OX8KEWPMKVOQw+6fBABIABQMrOOucJRYIiIiIiJyKIuzkfzpPQpO81HweXP+AMDFxQW9evXCrl27iu09lAXvbczIyDBrvfT0/+ZZtPb+yOJN4SitZdvZJgwiIiIiInIIi65Qzpo1S+exEAIjRoyAJEmoXbs23nzzTbPqUalU8PHxQWhoKOrUqYNSpUpZEkaRCw4O1nl869YtBAUFmVxPe+5Kc8o/cjyCTZchIiIiIqJiy6KEcujQoYWeGzFiBACgfPnyel8vCapXr67z+Nq1a6hdu7bJ9eLj4+Xl4jrgkEPdO+zoCIiIiIiISAHFw2wOGTIEkiShTp06toinWKpatSpcXV2Rm5sLADh27Bi6du1qcr2jR4/KyzVr1rRbfE7r/gFHR0BERERERAooTihnz55tgzCKN3d3dzRp0gT//PMPAGDPnj0m17l9+zYuXrwoP27VqpXd4nNaeQrnsSQiIiIiIody+DyUzqJnz57y8pYtW5CQkGC0/IIFC+Rlf39/xMTE2C02pyUUDupDREREREQOZZOEcvv27VizZg3WrFlj9gio+Y4ePSqve+fOHVuEYxfPPPMMPDw8AGjmpfzuu+8Mlk1NTcWkSZPkx88++6zZc1c+UnKSHR0BEREREREpoDihjI+PR4cOHdC7d2989tln8PLysmj9q1evolevXujduze++uorpeHYTYUKFfD888/Lj3/++WcsX768ULmcnBwMHz4c165dAwB4eXnhww8/LLI4nYva0QEQEREREZECihPKZcuWQa3WJAYvv/yyxev36tULFSpUgBACixcvVhqObNSoUfD09Cz0Z2kZbZ9++imqVq0KAMjLy0P//v0xePBgLF++HNu3b8fvv/+O6OhoLFu2TF7n+++/R1hYmM0+V4ni6u/oCIiIiIiISAHF/TC3b98uL/fq1cuqOnr37o1ffvkFiYmJOH78OOrVq6c0LOTk5CAry/igL7m5ufLIreYICAjAX3/9hfbt2yM+Ph5qtRrz58/H/Pnz9ZYfO3YsXnrpJYvifqT4Rjo6AiIiIiIiUkDxFcqTJ08CACIjIxEQEGBVHU2aNJGXT506pTQku6pWrRpOnDiBkSNHGuzeGxUVhbVr1+Lbb78t4uicTPpF02WIiIiIiKjYUnyFMiEhAZIkoVy5clbXob3u7du3lYYEQDOdib2mNPH398eMGTMwYcIEbNu2DfHx8UhLS0NYWBjq1KmD+vXr2+V9Sx7J0QEQEREREZECihPK/PsnbSU7O9um9dlTqVKldKYTIQt5V3J0BEREREREpIDiLq8hISEQQiA+Pt7qOrTXDQoKUhoSOQvP8o6OgIiIiIiIFFCcUFauXBkAEBcXh4sXrbsnbtOmTfJypUq8avXIuLvN0REQEREREZECihPKDh06yMtffvmlxetfuHABS5YsAQC4u7ujZcuWSkMiZ5Gb7ugIiIiIiIhIAcUJ5TPPPANXV82tmPPmzcPEiRPNXjchIQE9e/ZETk4OJElCr1694O3trTSkEiktLU3vn63vYS1SkuE5P4mIiIiIqPhTnFBGRkZixIgREEJACIG33noL/fr1w7Fjxwyuk5GRgenTp6NOnTo4d+4cAMDNzQ1ffPGF0nBKLF9fX71/Z8+edVxQ7gq7J7vx5AERERERkTNTPMorAEyYMAGHDh3CkSNHAAArV67EypUrERERgYYNGyIkJAQeHh5ISkrChQsXcOTIEWRkZEAIAQCQJAnTp0/HY489ZotwqKioM5Wtn5NsmziIiIiIiMghbJJQenl5YcOGDRgwYAC2b98OSZIghMCVK1cQFxdXqLwQApIkyetOmTIFgwcPtkUoJVZqaqre56Ojo4s4Ei25CcrWFxm2iYOIiIiIiBxCcZfXfMHBwdiyZQsmT56MiIgI+fn8rrDafwCgUqnQv39/HD58GMOGDbNVGCWWj4+P3j+VymZNaAXhwPcmIiIiIiJHs8kVynySJGHMmDF44YUX8O+//2L37t2IjY3F/fv3kZWVBX9/f4SGhqJp06Zo06YNQkNDbfn25Gwk3kNJREREROTMbJpQ5lOpVGjevDmaN29uj+qppHDxcHQERERERESkgCP7S9KjLjfF0REQEREREZECTCiJiIiIiIjIKkwoyXFU7o6OgIiIiIiIFLDLPZQAkJycjJs3byIpKQlZWVlmr9eqVSt7hUTFjt2+fkREREREVARsekR/9epV/Prrr1i9ejUuXrwoTxFiLkmSkJuba8uQyK5cAShoL5Fjs0iIiIiIiKjo2Syh/O233/D2228jMzMTACxOJskZqZWt7sour0REREREzswmCeWMGTPw0ksv6TwXHByM1NRUZGZmQpIkVKxYESkpKXj48CHUak0iIkkSAKBChQpQqXg756OH04YQERERETkzxVlcYmIi3njjDQCaBLFPnz64ePEiEhISEBMTI5e7cuUKEhMTkZKSgm3btmHw4MFyQlmrVi0cOnQIV65cURoOOZOcB46OgIiIiIiIFFCcUE6fPh1paWmQJAldunTB0qVLUaVKFYPlvby80Lp1a8yZMwf//PMPQkJCsHHjRrRp0wYZGRlKw6EipbRbM++hJCIiIiJyZooTym3btsnL3377rXzV0RxNmjTB+vXr4eLigtOnT+Odd95RGg4VKYUJpcQur0REREREzkxxQhkbGwsACAsLQ+3atQ2Wy8vL0/t8gwYN8Oyzz0IIgTlz5iA9PV1pSOQs3Mo6OgIiIiIiIlJAcUJ5//59SJKEyMjIQq+5ubnJy8a6s3bt2hUAkJ6ejh07digNiZyFxC6vRERERETOzGZDq7q7F54ColSpUvLyrVu3DK4bFhYmL8fHx9sqJCruVDadBpWIiIiIiIqY4oQyMDAQAJCcnFzoNe1E8cyZMwbrePDggd5lKuFUbqbLEBERERFRsaU4oaxevTqEELh06VKh1+rVqycvr1+/3mAdGzZskJcDAgKUhlQipaWl6f3Ln9PTKeXwflkiIiIiImemOKFs1KgRAM2Vxbi4OJ3XOnXqBBcXFwDAvHnzsH///kLrb9u2DTNmzJAfR0dHKw2pRPL19dX7d/bsWUeHZr3cNEdHQERERERECihOKNu3by8vr1u3Tue1MmXKoE+fPhBCIDMzEzExMRg5ciR+++03/Pbbbxg0aBA6d+6MnJwcSJKEOnXqoGHDhkpDImfhwi6vRERERETOTBJCKJpMMDc3F6Ghobh//z7q1auHo0eP6rx+8+ZNPP7447h37x6EEIXmqcx/ew8PD+zYsQNNmjRREk6JlZam/2pedHQ0VCoVTp8+XcQRAVho/pyjenmUAfresU0sRERERESPuFq1agFAkeYGiofZdHV1xfLly3H9+nUAmulBvLy85NfLlSuHHTt2oH///jhz5gz05a+hoaFYsGABk0kjfHx89D6vUtlsoN6ip1Z0LoOIiIiIiBzMJvM2xMTEGH09KioKJ06cwNq1a7F582Zcu3YNOTk5CAsLQ0xMDAYMGABPT09bhELOJNfw3KRERERERFT8FdlEgCqVCj179kTPnj2L6i2puBNMKImIiIiInJkT95ck58cur0REREREzowJJTlQkV0gJyIiIiIiO7D7EX1KSgoePnyIrKwslC5dGoGBgXB1ZSJBAFx43ywRERERkTOzeWaXlpaGP//8E6tWrcLBgwdx9+5d3Td0dUWtWrXwxBNPYNiwYWjQoIGtQyBn4VbK0REQEREREZECNu3y+vvvvyM8PByjR4/G+vXrkZCQACGEzl9OTg6OHz+OKVOmoFGjRujUqRMuXbpkyzDIWYR1dnQERERERESkgE0SypycHPTv3x8vvfQSHj58CAB655vMp51gbt68GfXr18fOnTttEQo5k3LdHR0BEREREREpYJMury+88AKWLVv2X6WurujcuTO6dOmCWrVqISgoCB4eHkhJScGVK1dw4MABLF26FJcvX4YkSUhNTUXXrl1x5MgRVK9e3RYhkTM4/TlQqZejoyAiIiIiIitJwtilRDPs2LEDbdu2hSRJEEKgTZs2mD59OqpUqWJ0PSEEZsyYgddffx2ZmZkQQiAmJgbbt29XEs4jp1atWgCA06dPF/2bL5SUre/iCwxIsU0sRERERESPOEfkBoq7vM6YMUNe7tChAzZu3GgymQQASZIwatQo/PXXX5AkTWKya9cuXLx4UWlI5CzUjg6AiIiIiIiUUJxQ7t27F4AmQZw6darFU4K0adMGgwYNkh//888/SkMiZ+Hi7ugIiIiIiIhIAcUJ5a1btyBJEqKiohAREWFVHT169JCX79y5ozQkchYubo6OgIiIiIiIFFCcUPr7+wMAQkJCrK5De10/Pz+lIZGz8Crv6AiIiIiIiEgBxQlllSpVIITA9evXra7jxo0bOvXRI6JCH0dHQERERERECihOKPv37w8AuHjxIk6cOGFVHUuWLAEABAUFoU2bNkpDImeR7ICRaYmIiIiIyGYUJ5TDhg1DxYoVAQDPPfccUlNTLVp/8eLFWLVqFSRJwtixYy0e1OdRkZaWpvdPrXbioVJT4xwdARERERERKaA4ofTz88OKFSsQEBCAw4cPo0WLFvLIr8akp6fjk08+kUd4HTRoEN566y2l4ZRYvr6+ev/Onj3r6NCsl5ng6AiIiIiIiEgBxZcDd+3aBQCYOHEi3nzzTZw8eRItW7ZEvXr10LlzZ9SuXRtBQUFwd3dHSkoKrly5ggMHDmDdunVISdFMaj9gwACMHDlSrsuQVq1aKQ2XipPcNEdHQERERERECkhCCKGkApVKBUmSdJ7Lr7Lg85aW0SZJEnJzc62M0vmlpelPvqKjo6FSqXD6tAPuR1xoXtsZJLkDz2TZJhYiIiIiokdcrVq1AKBIcwOb3bAohJCTw/x/jeWq2omkwpz2keDj46P3eZVKca9lxxGP7gkCIiIiIqKSQHFCWbFiRbOvMhLpkDwcHQERERERESmgOKGMi4uzQRj0SArv6+gIiIiIiIhIASfuL0lOr3Q1R0dAREREREQKMKEkx7m8wNEREBERERGRAkwoyXHSzzs6AiIiIiIiUoAJJTkQR/clIiIiInJmTCiJiIiIiIjIKjabhxIALl68iAULFuCff/5BbGwsHj58iPT0dLPXlyQJubmcm/CRoQp0dARERERERKSATRLK7OxsvPbaa5gxYwbUarX8vBDs0khGePo7OgIiIiIiIlJAcUIphEC3bt2wbds2JpBkGUlydARERERERKSA4oRyxowZ2Lp1K6T/JwdVq1bF8OHD0aRJE4SGhsLb21txkFRCqXkCgoiIiIjImSlOKOfOnSsvP/3005g7dy5cXW16ayaVVFmJjo6AiIiIiIgUUDzK65kzZwAA3t7emDZtGpNJMp8609EREBERERGRAooTyrS0NEiShMcffxy+vr62iIkeFRJnrSEiIiIicmaKj+jDwsI0FamYHJCFXP0dHQERERERESmgOAts2bIlhBA4e/asLeKhR4m7j6MjICIiIiIiBRQnlGPGjAEAJCYmYtWqVUqrIwPS0tL0/mnP++l0JHdHR0BERERERAooTiibNm2Kl156CUIIjBkzBhcuXLBFXFSAr6+v3j+nvjKcl+7oCIiIiIiISAGb3Pg4adIkjBo1Crdv30Z0dDR++OEH3Lt3zxZVU7HmonB1zlFKREREROTMJCGEzWaXX7x4MYYOHYqcnBxIkoQqVaqgTJkycHc3r2ujJEnYunWrrcIpUdLS0vQ+Hx0dDZVKhdOnTxdxRAAWegFQMPVHmbZAe7Y3EREREZEt1KpVCwCKNDew2aSRS5Yswccff4ycnBwIISCEwKVLl3Dp0iWz1hdCQJIkW4VT4vj46B/AxqlH1/UIcXQERERERESkgE0SyvHjx+P9998v9LwNL35SsaRwQKCsRNuEQUREREREDqE4ody+fTvee+89+bGbmxvatm2Lxo0bIzQ0FN7evE+u5MpWtrorpw0hIiIiInJmihPKiRMnAtDc/1i3bl0sX74cVapUUVotPQq8whwdARERERERKaA4ody3bx8Azb18K1asQOXKlRUHRY8INz9HR0BERERERAooHtElOTlZvjrJZPJRo3DakIzrtgmDiIiIiIgcQnFCGRoaCgDw9/dXWhU5HTdlq3PQJiIiIiIip6Y4oaxTpw6EELh27Zot4iGnonCaF6FwlFgiIiIiInIoxQnlkCFDAACXL1/G8ePHFQdUnO3YsQOSJFn8d/bsWUeHbicZylbPTLBNGERERERE5BCKE8p+/fqhQ4cOEELg+eefR3p6ui3iokdBFhNKIiIiIiJnpniUV0mS8Oeff6Jnz57Ys2cPWrZsialTpyI6OtoW8RVbnp6eiImJMausr6+vnaNxUu7+jo6AiIiIiIgUUJxQfv755wCAmJgYnDlzBkePHkWTJk1Qq1YtNGnSBGXLloW7u7vZ9X3yySdKQyoSZcuWxYYNGxwdhnMr193RERARERERkQKKE8pPP/0UkvTf4CySJEEIgdOnT+P06dMW1+csCSXZguIe10RERERE5ECKE0oAEHqmf9D3nCnaiSk5AwmAgqk/7u21WSRERERERFT0FCeUQ4cOtUUc5JQUziOZm2abMIiIiIiIyCEUJ5SzZs2yRRz0KAos2QM3ERERERGVdLyJjRynbBtHR0BERERERAowobTSw4cP0b9/f0RERMDLywulSpVC5cqV0atXL0yePBnJycmODrH44y2zREREREROzSaD8jyKkpKSsHTpUp3nUlNTERcXh9WrV+Ojjz7CF198gVdeecXsOtPSLL+nUK1WQ6Vy0vMCt7cBYR0dHQUREREREVnJrgllWloarly5ggcPHiArKwv+/v4ICQlBpUqV7Pm2RSYiIgLly5eHh4cHEhMTcebMGeTm5gLQJJyvvvoqjh07hpkzZ5pVn6+vr1VxREVFWbWew93aDNT/1tFREBERERGRlWyeUN68eRNTp07FqlWrcObMGajV6kJlAgIC8MQTT+C5555Dt27dnGa6EJVKhfbt22P48OHo3LkzAgMDdV5PTU3F/Pnz8fHHHyMxMREA8Mcff6BatWoYO3asI0Iu3nJTHR0BEREREREpIAlrJozUQ61W45tvvsEXX3yBnJwcALpzUUqSVOgxADz++OOYO3cuatWqZYswioX4+Hi0atUKcXFxAABvb29cvnwZZcuWNbqeNV1eo6OjoVKpcPr0aWtCVWahwhMBIW2ADttsEwsRERER0SMuP6cqytzAJjff5eXloV+/fvjkk0+QnZ0NQDeZNPRYCIGjR4+icePG2L17ty1CKRbCw8Px559/yo/T09PN6vbq4+Nj8Z/T3j8JAN4VHR0BEREREREpYJNs5N1338WqVavkx76+vnj++eexdu1axMXFITU1FdnZ2bh37x4OHDiAX375BU2aNAGguVKZkZGBJ598EtevX7dFOMVCkyZN0Lp1a/nx5s2bHRdMcXX/oKMjICIiIiIiBRQnlGfPnsUvv/wid2Ht0aMHLly4gN9++w3dunVDxYoV4e3tDVdXVwQEBCA6OhovvfQS9u3bh6VLl6JUqVKQJAkpKSl4//33FX+g4kQ7oTx//rzjAimuMu84OgIiIiIiIlJAcUI5d+5ceWTT7t27Y+XKlShTpoxZ6/bt2xcbNmyAi4sLhBBYunQpUlNLzkAtYWFh8nL+ID2kxcXH0REQEREREZECihPKTZs2AdB0XZ08ebLFI7Y2bdoUQ4cOBQDk5ORgx44dSkMqNtLT0+Vlb29vB0ZSTEkujo6AiIiIiIgUUJxQxsfHQ5Ik1KpVC+Hh4VbV0bVrV3n52rVrSkMqNs6cOSMvm3vV9pGSfc/RERARERERkQKKE8qkpCQAQHBwsNV1aK+bnJysNKRiISMjA2vWrJEfN2/e3IHRFFN5mY6OgIiIiIiIFFCcUAYGBkIIoWiE1hs3bujUVxJ8/PHHuHPnv0FnevXq5bhgiiuVq6MjICIiIiIiBRQnlFWqVAEAXLx4EadOnbKqjuXLlxeqr7jZtGkT3nrrLZOJc05ODt577z38+OOP8nMNGjRAjx497B2i81FxUB4iIiIiImem+BJR165dsXfvXgDAqFGjsG3bNnh5eZm9/urVq+WE0sfHB61atVIakl2kp6fjp59+wsSJE9GiRQvExMSgdu3aCA4Ohru7OxITE3HgwAEsWLAA8fHx8nqBgYFYuHChxYMVPRJKP+boCIiIiIiISAHFCeWwYcPwzTffID09HQcOHEBMTAxmzpyJOnXqGF0vLy8PP//8Mz744AMAmlFiR44cCXd3d6Uh2ZVarcbu3buxe/duk2WrVq2KxYsXo3r16kUQmRMq18XRERARERERkQKKE8py5crhs88+w9tvvw1JknDo0CHUr18fMTEx6NKlC+rUqYOgoCC4u7sjJSUFV65cwYEDB7B8+XLcvn0bQggAQPny5TFu3DjFH8heatSogV69emHnzp148OCB0bIREREYM2YMxowZAx8fdus06O4+R0dAREREREQKSCI/o1PorbfewoQJEyBJEoQQJrt45pcRQqBs2bLYvn07atSoYYtQ7O7SpUuIjY3F9evX8fDhQ+Tl5aF06dIoU6YMGjVqVKT3gdaqVQsAcPr06SJ7T9lChd14XUoBA0rGqL5ERERERI7miNzAZsNs/vjjj2jatClee+01nSuP+UljPu3HQgj069cPU6ZMQUhIiK1CsbvIyEhERkY6OowSwCbnMoiIiIiIyEEUj/Kq7amnnsLVq1excOFC9O/fH5UqVULBC6Cenp5o3rw5xo4di7Nnz2LJkiVOlUySDfk6xxVpIiIiIiLSz+YTAbq5ueHpp5/G008/DUAz+M7Dhw+RlZUFPz8/3lNI/3Hld4GIiIiIyJnZfWZ5FxcXBAUF2fttyBll33J0BEREREREpIDdE0qyjbS0NL3Pq9VqqFQ27blcdLzKOzoCIiIiIiJSgAmlk/D19TX4WlRUVBFGYkO+jzk6AiIiIiIiUkDxpS21Wo22bduiQYMGaNGiBdLT0y1a/9dff0WDBg3QoEEDLFu2TGk45Ewy2eWViIiIiMiZKU4oN2zYgB07duD48eNo0KABvL29LVq/d+/eOHXqFI4fP46JEycqDafESk1N1fvnLHN36pV+09EREBERERGRAooTynXr1snLgwcPtnj9sLAwtGvXDkII7N+/H/fv31caUonk4+Oj989p758EwB7XRERERETOTXE2cvDgQQCae/waN25sVR3t2rUDoOk+m18fPQIy4x0dARERERERKaA4obx06RIkSVLU9bJWrVo69dEjIjPR0REQEREREZECihPKlJQUAEDp0qWtrkN73aSkJKUhkdMQjg6AiIiIiIgUUJxQlipVCgDw8OFDq+vQXtfT01NhROQ88hwdABERERERKaA4oSxTpgyEEDh79iyys7OtquPYsWM69dGjws3RARARERERkQKKE8omTZoAANLT07F8+XKL11er1ViwYIH8uGHDhkpDImfhGeboCIiIiIiISAHFCWW3bt3k5bFjx+LOnTsWrf/tt9/i7NmzkCQJlStXdu55FckyDX50dARERERERKSA4oSyT58+qFq1KgDg+vXraNOmDY4fP25yvby8PIwbNw4ff/yx/NzYsWOVhkPO5OERR0dAREREREQKKJ5Z3sXFBZMnT0bXrl2hVqtx9uxZNGrUCF27dkXfvn3RsGFDhISEwMPDA0lJSbhw4QJ27dqFOXPm4Nq1axBCQJIkNG/eHCNHjrTFZyJnkXbV0REQEREREZECihNKAOjQoQN+/fVXvPjiiwCA3NxcrF27FmvXrjW4Tn4iCQA1a9bEmjVroFIpvmBKzkRwlFciIiIiImdmswxu1KhR2LRpE8qVKwdAkzAa+8s3bNgwHDhwAAEBAbYKhZyG5OgAiIiIiIhIAZteEmzbti0uX76MGTNmoH379vDx8dFbrmrVqhgzZgxOnTqFP/74A97e3rYMg5yFOsfRERARERERkQI26fKqzc3NDSNGjMCIESOQl5eH+Ph43L9/H1lZWfD390doaCivRpLGg2OOjoCIiIiIiBSweUKpzcXFBREREYiIiLDn25Czykt1dARERERERKQAR8Ehx/Eq5+gIiIiIiIhIASaU5Djlejs6AiIiIiIiUsCuXV7JdtLS0vQ+r1arnXe6laQTjo6AiIiIiIgUYELpJHx9fQ2+FhUVVYSR2ND9g46OgIiIiIiIFHDSS1tUIqizHR0BEREREREpwCuUTiI1Vf+IqNHR0UUciQ1lJzs6AiIiIiIiUoAJpZPw8fHR+7zT3j8J8AolEREREZGTc+JshJyei5ujIyAiIiIiIgWYUJLjuIU4OgIiIiIiIlKAXV7JgQSgzgPu7gYybgFeYUBIS0DlolssNxu48Atwdw/g6gtUGgio3IGsBMPrAP/VnX4DyLoLuAcB2fcAjxDAu7zueobiMCc+7fe7swNI2KF5XLY1UKa14fJERERERE7OrITy888/BwA89thjGDhwoF0DokdI5lXgz4JfQRXgXQXITQHyMoC8dAC5ukXi5uupTAVAAHABJBdA5BVeT986pesCealA2kU9r7sAyCvwnARI7oDIAaA2Xv3pL4286AZ4hQNhXYDEHUDWA0DkAipfIO++5r09ywLBzQCPYCD9miYpdvXRJLWPjQHu7wVubwPS4wHvcCC0rW4Cq87TJLe3t2nW96qgqcsrtHBCrU07ifYsA6jVwN1dmtcKJsmWJNyWMLdeY+XsFZulikscRERERHYgCSGEqUIqlQqSJKFTp05Yv369zmsjRowAANSpUwdvvPGGfaIkg2rVqgUAOH36dNG/+UKp6N+TjHPxBsrEaJLR+NVA7gPDZT1CgIaTAa8yQFo8cPcfTQKaevn/CbMB7kFAk2ma5cOvAenX/3vNuwLQ8GcgvE/hRCqoOXBvr+nEKn5F4XrdA4Fqr2jWybytSa5TrwBXFwJZibrlqr8GlI4Cjr5hfmzWJHnm1KHvs2jHQURERGRDjsgNFCeUxl4j+3NoQnn0QyD266J/XyrmJKDm28DVRbqJlHzl+P88goGIQUCFnv8lY/ErgN39oLnabCcVB2iS5wyt2Nz8gMrDgPBeha9yJuzQdGUGNFdny7YG4lcCh8ZoEtt8BRPW018BJ8fpCeD/J2JaLmNSSURERDZVbBNKV1dXCCHQsWNH/P333zqvMaF0LIcmlLnZwBKPon9fKnnc/ICIIcC1P3WTNEfIT3Td/IHYH4G8FN3XJTcjV3D/n0zHLQQybhh5E0mTgPa4wu6vREREZDOOyA3MGuW1VKlSAIDExEQTJemR4uoO1HzH0VFQSZCTpBl4ydHJJKDpQntuInDq08LJJGC8OzAEEPu9iWTy/+XS4zVdZomIiIicmFkJZWRkJIQQOH78OC5e1Dd4CT2y6o9nUklkrYxbjo6AiIiISBGzRnlt06YNjhw5gry8PDRs2BB9+/ZFeHg4XFz+66p18eJFeTRYJT755BPFdVARqz8eqPMlcPFXIPmC5grOpXkAMh0dGVHx5hXm6AiIiIiIFDHrHspr166hdu3aSEtLgxACkvTf6J75q2s/p0ReXsFpGsgYh95DaczKCmZ0+yN6hLkHAX3u8B5KIiIispliew9lxYoVsWLFCoSEhADQJJH5f/m0n7P2j0oSHiQTGZV9D7ix2tFREBERESliVpdXAGjfvj3i4uKwdu1aHD58GPfv30dOTg7mzJkDSZJQrlw5tG/f3p6xkjPJy3Z0BETFnAQcfh0o35NXKYmIiIqCLeahpkLM6vJqDKcNcaxi2+V1oTeADEdHQVT8tduumduSiIiI7Cd+BXD4Nd05srXnkC4him2XVyLLcUAeIrNwpFciIiL7il8B7O6nm0wCQPoNzfPxKxwTVwlhdpdXQ4YMGQJJklCnTh1bxEMlBu+JJTKLRxlHR0BERFRyqfM0Vyb1HpsKKLoFhV1oAdggoZw9e7YNwiBT0tLS9D6vVquhUhXHC80uADhiL5FJthkgm4iIiPS5u7vwlUkdAkiP15Sz5BaUR6QLrTkUJ5RUNHx9fQ2+FhUVVYSRmMk9EMi+6+goiIq/zARHR0BERFRymXtriSW3oOR3oS141TO/C23LZY9UUmmXhFIIgWPHjmH//v24ePEiHjx4gKysLPj7+yMkJAQNGjRAs2bNEBwcbI+3p+LAI5QJJZE5vMIcHQEREVHJZe7/s+aWs2cXWidl04QyJycHEydOxG+//YarV68aLevi4oIePXrgvffeQ3R0tC3DKJFSU1P1Pl9st132Q0dHQFT8uQdp7rcgIiIi+whpqemKmn4D+pNASfO6uf8f26sLrROz2c13sbGxaNCgAd577z3ExcVBCGH0Lzc3FytXrkTz5s3x8ccf2yqMEsvHx0fvX/G8fxKAmlOGEBEREZGDqVw09zUCKDxwwf8fN5xo/tVEe3ShdXI2uUJ58eJFtG3bFgkJuvcCBQUFoU6dOggODoa7uztSUlJw6dIlnDt3Dnl5mgFbcnNz8fXXXyM1NRUTJkywRThUHDwil/iJFMm+90idwSQiInKI8D6a+xr1DqIz0bL7HW3dhbYEsElCOXjwYNy5cweSJEEIgf79++ONN95AkyZN9JZPSkrCokWL8OWXX+LmzZsQQmDSpEno0KEDunbtaouQyNHcg4CsO46Ogqj4e4TOYBIRETlMeB/NfY1Kp/mwdRfaEkBxf8lVq1Zh//79kCQJHh4eWLp0Kf7880+DySQA+Pn54YUXXsDZs2fRrl07AJqBfD744AOl4VBxEVDX0REQOYdH6AwmERGRQ6lcNL2CIp7R/GtNjzpbd6EtARQnlMuXL5eXv/nmG/Tt29fsdX19fbFixQpUrFgRAHDy5ElcunRJaUhUHFR6xtEREP2fCijfw9FB6PeIncEkIiIqEfK70HqX133eu8IjN2UIYIOE8t9//wUAlC5dGi+++KLF65cqVQovvPBCofrIyd38y9EREAEu3kDzRUDMaqDmO46OprCGPz9SZzCJiIhKjPA+QI84oN12oPlCzb89rjxyySRgg3so8++djIqKgru7u1V1NGzYUKc+KgESdjo6AiIgLwPY+zTg4grUHw8ENQL+fQ7ITXZsXG6lgaazHsn/dIiIiEqM/C60jzjFVyjVarWmIgXTV2ivK4S+m1vJ6eRlOToCIsg3yx9+XTMRcYU+mmTO4XhVkoiIiEoGxQll2bJlIYRAbGysPBWIpU6ePCkvlylTRmlIVBy4+js6AqL/05pg+M4OIMPYZMRFJOcBsLsfEL/C0ZEQEREVps7T/J8Zt0jzr9rMY3x961lbFzkNxV1eGzZsiCtXruDBgweYP38+hg4datH6OTk5mDZtmvy4QYMGSkOi4sDVy9EREOnaNwLIuOHoKLQIzZXT8j15HyURERUf8SsKz9foEQxEDAIq9DQ81cbVZcChMUDW3f+ecw/S/Jt977/nvCoAkSMB8f/Eskzr/7qNak/pEdQcuLdXd4qPgmWsmfaDbE4SCvuYLliwAIMHDwagGZhn3bp1aNGihVnrqtVqDBs2DPPnz4ckSYiMjMT58+eVhPPIqVWrFgDg9OnTDo6kgK3tgTtbHR0FUfHXbjvvvyAiouIhfoWmB43e+RX/z7uCZlA57XEAjr4LxH5v/fu6+gIqD93EU3L5L+kE9Cen+mJ5xDkiN1Dc5fWZZ55BVFQUJElCcnIy2rVrh3feeQfXrl0zuE5eXh7WrVuHRo0aYcGCBfLzn332mdJwqLi4m+ToCIicQ+pVR0dARESk6Yp6+DUYTSYBIP2G7m0b15YqSyYBIDdVN1EEdJNJQPN6wTIFYyGHUHyFEgAOHz6Mdu3aISUlBUIISJIESZJQo0YN1KlTB0FBQXB3d0dKSgquXLmCo0eP4uHDh/L6Qgg888wzOsklmafYXqFcWHCiVyLSK6wb0IbT7BARkYPd2QFsbWNmYUlzdbD7RWB1eSAr0Z6RmRdLjyvs/grH5AaK76EENPdRrl+/Hv3798fNmzcBaLqzxsbGIjY2tlD5/KQzP5cdPnw4pk6daotQiIicy8Pjjo6AiIhIc1+i2f4/4N3FXx2cTGrFcnc3byFxEMVdXvM1b94cp0+fxuuvvw4/Pz8AmsRR31/+a82aNcNff/2FmTNnwtXVJrktERERERFZyivM8nVSLtk+DmtZlBCTLdk0i/Pz88NPP/2Er776Cjt37sT+/ftx8eJFPHz4EFlZWfDz80NISAgaNGiAli1bonr16rZ8eypOomcAh55zdBRExV9YF0dHQEREpBkx1buC5r5EU/dR5isVadeQLGJNQkw2YZN7KMlxiu09lADvoyQyR790wJ3T7BARUTFgziivAHTuofwrUneKkSLHeyi1OeUor1Q00tLS9P6p1WpHh2bYQJ6rIDKqfE8mk0REVHyE9wFaLtMkaAb9/4JBw4mAq7tm2g4YuYjg6mvDAI3EwmTSYXiF0klIkuEfalRUVPG8Qpnv/Ex2fyU7yD8fVoxPqhhTvicQs8rRURARERWmztMMcnN9NRC3AMi6+99r3uGaBE577sf4FZopR7SvVHqEAI2mABX6aOrKuAWkXADO/Qxk39d9P6vnodQTyyPOEVcomVA6CadOKPPl75zSb2h2TK4BwL19wMOTQPZDQOUOuHgA2WlA2gVA5AJwgyZhyHZs7A7lBSCjwHMu0NwCnaWnvL7XpP8/n/f/ZS/AuxyQkwyIHAAqQGQAajUAodmpu/sBbsFA6gVAnYb/zj5KgOQKuHoDQg2oXQF1EoAcrbfz0bwmx62C0cQv5EnANU/zH01eDuAeoIlPUmnOkpaKBErXAhL/0ZQv2xoo01qznLADuLUFuHcAyEwA3HwBtwAg5QqQkwi4+AC+kUBQAyAnFcj/Lbn5aep3K635DualAUEtgMC6QOZdIPM2kHEXuH8QyLyj+Twunpr/vIKaAulXgZSLmtc8QgC3UkCp6kDmTUCdq4kl/RaQceP/208AkidQaQAQ/SuvTBIRkXPIP37LuKW5TzGkpf6rgZaUS9ihmaYE0Px/nj86q/b6Qc2Be3t16ytYxtB7PMKYUJJBaWlpep+Pjo6GSqVyjoSSiIiIiIjsxmnnoST78/Hx0fu8SsXbYImIiIiIyDGYjRAREREREZFVmFASERERERGRVZhQEhERERERkVWYUBIREREREZFVmFASERERERGRVZhQEhERERERkVWYUBIREREREZFVmFAqsHfvXowePRpRUVEoXbo0SpcujaioKIwePRp79+51dHhERERERER2JQkhRFG+4T///INNmzbhzp07CAwMRKtWrdCpUydIklSUYSiSlpaGV199FX/88YfRciNGjMCkSZPg4+Njt1hq1aoFADh9+rTd3oOIiIiIiIo/R+QGihPK7OxsvPnmm1Cr1fD09MSPP/6oNznMyMjAwIEDsWbNmkKvNWrUCMuWLUOFChWUhFIk8vLy0LVrV2zatEl+zsvLC7Vq1YKrqyvOnDmD5ORk+bVOnTph3bp1cHFxsUs8TCiJiIiIiAhwTG6guMvrpk2b8Ouvv2Lq1KlITk42eKXxpZdewurVqyGEKPR34MABdOjQAenp6UrDsbuPP/5YJ5kcNWoUrl+/joMHD2Lfvn24efMmPvroI/n1jRs34pNPPnFEqERERERERHalOKHcvn27vDxw4EC9ZU6cOIE5c+bIyWZwcDB69uyJtm3bQqXShHD+/Hl8/fXXSsOxqxs3bmDChAny48GDB2PatGkIDAyUn/Px8cEXX3yhk1ROmDABN2/eLNJYiYiIiIiI7E1xQnnkyBEAgJubG1q1aqW3zPTp05Hfs7ZevXqIjY3FypUrsWXLFqxevRoqlQpCCEyfPh15eXlKQ7KbSZMmITMzEwDg7e2NiRMnGiz78ccfIzw8HICmu+/PP/9cFCESEREREREVGcUJ5aVLlyBJEqpWrQpXV1e9ZVavXi0vf/vttwgKCpIfd+vWDX379gUAJCYm4ujRo0pDspsVK1bIy/3799e5MlmQu7s7hg8fLj9euXKlXWMjIiIiIiIqaooTyvv37wPQdGPV58KFC7h+/TokSUJwcDA6duxYqEzXrl3l5ZMnTyoNyS7OnTuHixcvyo87d+5scp0uXbrIyxcuXMD58+ftEhsREREREZEjKE4os7KyAGi6vOqze/dueblt27Z6B+2JiIiQl+/du6c0JLs4fvy4zuNmzZqZXKdBgwZwd3c3WAcREREREZEz099H1QK+vr5ITk42mAju3LlTXo6JidFbRntKjezsbKUh2UVsbKy87O7uLt8faUx+uUuXLhWqQ5+0tDSL41Kr1fLARkREREREREVJcUJZoUIFnD59GmfPnkVmZiY8PT3l1/Ly8vD333/Lj1u3bq23jvxuswBQqlQppSHZxdWrV+XlChUqGJwepaCKFSvKCWVcXJzRsr6+vlbFFhUVZdV6RERERERESii+tNW4cWMAQGZmJmbMmKHz2p9//onExEQAQLly5VCjRg29dWhfuatQoYLSkOwiOTlZXvbz8zN7vdKlS8vLKSkpNo2JiIiIiIjIkRRfoRw4cCBmzZoFAHjnnXeQmJiI5s2b48SJE/jss88AAJIkYdCgQQbr2Ldvn7xcvXp1pSHZhXZ3VO2rsKZ4eXnprUOf1NRUi+OKjo62eB0iIiIiIiJbUJxQtmvXDh07dsSmTZuQnZ2NL774olAZPz8/vPnmm3rXT0lJwaZNm+RyxbX7Zk5OjrxsaHoUfbTLmro/1MfHx+K4eP8kERERERE5ik2ykcWLF6NFixYQQhT68/X1xeLFixESEqJ33QULFiAzMxOSJKFNmza2CMcuvL295eXMzEyz19Mua03CSEREREREVFwpvkIJaK4s7tq1CytXrsT69etx/fp1eHp6omHDhhg5ciTKlStncN2lS5eiUqVKAID+/fvbIhy70B4wJyMjw+z10tPT9dZBRERERETk7GySUAKa+yT79OmDPn36WLTe1q1bbRWCXQUHB8vLt27dMnu927dvy8tBQUE2jYmIiIiIiMiReAOembQHC7p3757OlUdj4uPj5WVDo9wSERERERE5IyaUZqpZs6bO42PHjplc58aNG7h7967BOoiIiIiIiJyZzbq8muvatWvYunUr7ty5g8DAQLRq1coprtw1btwYHh4eyMrKAgDs2bMHzZs3N7rO7t275WVPT095zk5bunbtGnJyclCrVi2b101ERERERM7j0qVLcHNzK9L3VJxQqtVq/PTTT1Cr1XBxccGbb74JSZIKlcvLy8Pbb7+NKVOmIC8vT+e1Pn36YMaMGfDz81Majt34+vqiXbt2WL9+PQDN6LTvvvuu0XUWLFggL7dr184uo7z6+PggLS0NZ86cAaDpVmuvqUTUajXOnj3L9ynG78X34fsU9Xvxffg+Rfk+RflefB++T1G+T1G+F9+nZL+Pm5tb0c8sIRTaunWrkCRJqFQq0b9/f4Plxo4dKyRJ0vunUqlE8+bNRW5urtJw7GrJkiUCgPy3Zs0ag2UPHz4sXFxc5LJLly61W1ypqany+6SmpvJ9itn7FOV78X34PkX9Xnwfvk9Rvk9Rvhffh+9TlO9TlO/F9+H72Jri1HrLli3y8uDBg/WWuXz5Mn766SdIkgRJkuDm5oZ69eohMjISACCEwL///ouJEycqDceu+vXrh3r16smPn3/+efkMgrZbt25h0KBB8pXYxx9/HH379i2yOImIiIiIiIqC4oTy0KFDmopUKrRv315vmWnTpiE3NxcAEBERgVOnTuHo0aO4cOECpkyZAkCTVP7yyy9Kw7ErSZIwY8YMeHl5AdAkjk2aNMF7772H9evXY9OmTfjyyy9Rv359xMbGAgC8vLwwffp0vd2AiYiIiIiInJnihPLixYsAgMjISHh6euots2LFCnn5u+++Q9WqVeXHL774Ijp16gRAM8XGyZMnlYZkV9HR0ViwYIGcVCYnJ+O7775Dt27d0KlTJ3z88ce4c+cOAE0yuWDBAkRHRzsyZCIiIiIiIrtQnFAmJiZCkiSULVtW7+vx8fFy0lmqVCn07NmzUJk+ffrIy8ePH1cakt317t0bhw8fRrt27fReeZQkCe3bt8eRI0fQu3dvB0RIRERERERkf4pHec3IyAAAg1cn86fOkCQJbdq00TuM7WOPPSYvJyQkKA2pSNSsWRNbtmxBfHw89u7dixs3bgAAypcvj+bNmyM8PNzBERIREREREdmX4oTS29sbqampePDggd7Xd+7cKS/HxMToLePu7i4vZ2ZmKg2pSIWHh2PAgAEOjcHHxwdCCIfGYEtF9XmKcruxjfg+Ra2kbbuS1kYlbbuVtPYBSt62K2ltVBK3G9uI7+OsFHd5DQsLgxACZ8+eLTS/JAD8/fff8nLr1q311qGdjBb5vClERERERERkFcUJZcOGDQEAaWlpWLJkic5r69evx/Xr1wEAQUFBePzxx/XWkX+PJaBJUImIiIiIiKj4U9zl9amnnsKiRYsAAGPGjEFWVhaaN2+OEydO4JVXXgGguX+yf//+BuvYv3+/vFytWjWlIREREREREVERUJxQ9uzZE9HR0Th06BCSk5MxcuTIQmU8PDzw9ttv610/KysL69evB6C5H7Nu3bpKQyIiIiIiIqIioLjLqyRJWL58OSIjIyGEKPTn4uKCadOmISIiQu/6y5cvR0pKCiRJwhNPPAGVSnFIREREREREVAQUX6EENCOdnjhxAr/++iv+/vtvXL9+HZ6enmjYsCFefvll1K9f3+C648ePBwAIIdCjRw9bhENERERERERFQBIc/5aIiIiIiIiswP6lREREREREZBUmlERERERERGQVJpRERERERERkFSaUREREREREZBWbjPKq7e+//8bq1atx8OBB3LhxA0lJScjOzjZrXUmSkJuba+uQiIiIiIiIyA5sdoXy6NGjqF27Nrp3747p06fj2LFjSEhIQFZWlt75KQ39UfGzd+9ejB49GlFRUShdujRKly6NqKgojB49Gnv37nV0eCXKw4cPsXLlSrz66qto1aoVQkND4eHhAV9fX1SsWBFPPvkkJk6ciAcPHlhV/8mTJ/Hmm2+ibt26CAwMhK+vL6pXr45nn30WGzZssPGnefTExcXB19cXkiTJf59++qlFdbCNbOvu3bv47bff0KlTJ0RGRsLb2xteXl4IDw9Hx44d8eWXX2Lv3r3Iy8szqz62j+0kJCTg+++/R+fOnVGhQgV4e3vD09MTYWFhaNOmDT755BNcuXLF4nrZRobdvXsXf//9Nz7//HP06NEDYWFhOvur2bNnW1335cuX8cknn6Bhw4YICQmBl5cXIiMj0bt3byxbtszs31hR1Vsc2bp9srOzsWPHDnz88cfo0qULKlWqBB8fH/l3FhMTgw8//BAXL160Oubbt2/ju+++Q7NmzRAWFgZPT09ERESgc+fOmDNnDjIyMqyuuziy52+ooNzcXNSrV0+n/tatW1tcT5G0kbCBf//9V3h7ewuVSiUkSdL5U6lURp/Xfk2lUtkiHLKR1NRUMWLECAHA6N+IESNEamqqo8N1arGxsaJ79+7C3d3d5PYGILy9vcWECROEWq02q/6cnBzx/vvvC5VKZbTe7t27i4SEBDt/2pKrY8eOhbbpuHHjzFqXbWRbeXl5YuLEicLX19es39TBgweN1sf2sa0JEyYILy8vk+3i4uIi3nnnHZGdnW2yTraRYbdu3RKVKlUyub1nzZplVf0TJkwQHh4eRutu1qyZuHz5crGot7ixdftkZGSI4cOHC39/f7P2f5Ikieeee04kJydbFPeiRYuEn5+f0bpr1Kghjhw5YsVWKV7s/RvS5+uvvy5Uf0xMjEV1FFUbKU4os7OzRcWKFeWkMDIyUvzxxx/i9OnTIiYmRk4U4+LixMmTJ8WGDRvEuHHjRJUqVeTXRo8eLa5cuSLi4uKUhkM2kpubW+jg2MvLS0RHR4umTZuK0qVL67zWqVMnkZub6+iwndbSpUv1HkhVr15dtGrVSrRo0UIEBgYWKjNy5EizksqCJwbc3NxEvXr1RIsWLURQUJDOa/Xq1eMJAivMmzdP747a3ISSbWQ7OTk5ok+fPoXaokqVKqJly5aiZcuWomrVqjqJh6mEku1jO2PHji3UNmFhYaJly5YiJiZG70Fbv379TO7r2EaGXblyxazEwpqD4c8//1ynDpVKJWrXri1atWolwsLCdF6rUKGCuHXrlkPrLY5s3T53797Vu37FihVF06ZNRevWrUVERESh1xs0aCAePnxo1nvMnTu30PrVqlXT+xsuXbq0OH36tIIt5Hj2/A3pc+HCBeHp6akooSzKNlKcUP7xxx9yYlizZk1x//59+bXOnTsbvPKYl5cnxo8fL1xdXYVKpRLPPPOM0lDIht5//32dL9qoUaPEvXv35NdTU1PFRx99pFPmgw8+cGDEzi0/oXR1dRW9evUSq1atEklJSTpl1Gq1WLVqlShfvrzOdv/111+N1j116lSd8j169BDXr1+XX8/Ozha//PKLcHV1lcsMHDjQLp+zpLp7964IDg4WAETNmjVFuXLlLEoo2Ua2NXDgQHk7ubq6irfeekvvCcukpCSxdOlS0a1bN6NnZ9k+trN79+5CBzfbt28vVO7gwYOiQYMGZh+osY2M0z4YDgkJEZ07dxYfffSRWL16taKD4Q0bNghJkuT1mzVrJs6dOye/npeXJxYtWqTTU6BFixYOq7e4snX7aCeUTZo0EdOmTdP5PeQ7cuSIaNGihc579O/f32T9J06c0LlyXL16dXHo0CGdMps2bRJly5aVy1SpUkVkZGSYFX9xZK/fkCFt2rQRAISnp6dOG5mbUBZ1GylOKPv16ycnjX///bfOa8YSynyzZ8+Wy/zyyy9KwyEbuH79us5ZkcGDBxssq51Uenl5iRs3bhRhpCXHqlWrxHPPPSeuXr1qsuy1a9dEaGiovN2Dg4MNdgdLS0vTKdu6dWuDV5JnzJghl5MkSRw+fFjRZ3qUDBo0SN52O3fu1DnzZyqhZBvZ1oIFC3T2SVu2bFFUH9vHtgYMGCBvIz8/P6P/Zzx8+FDntxQdHa23HNvItPyTJ/pOrFh7MKxWq0W9evV0DljT0tL0lt28ebPO+6xYsaLI6y3ObN0+iYmJol27dmLv3r0my2ZnZ8vJS/6fqe6PTz75pM4xyO3bt/WWO3XqlE5S89NPP5kVf3Fkj9+QIdr7qc8//1wMHTrU4oSyqNtIcUJZuXJlIUmSCAgIKPSaOQmlEEK0b99eSJIkypUrZ/Y9YWQ/7777rvzF8vb21rkyWVBWVpYIDw+Xy7/77rtFGOmjq+DZeEMHzb/++qvOwVNsbKzReps0aWLRWUoSYuPGjfI2Gz58uBBCWJRQso1sJyUlRYSEhMjbx9TVe3OwfWxL++r9yy+/bLL8999/r7P9s7KyCpVhGylj7cHw+vXrddbdsGGD0fLaJxMaN25c5PU6K3tc/Sro3LlzOu/z0UcfGSx7+vRpnbK///670bq1u7iHhoaKvLw8W4fvcLZso9u3b4uAgAABaO5tzMrKsjihdEQbKR7lNTExEZIkoUaNGoVec3FxkZeNjSD0zDPPANCMQrRnzx6lIZFCK1askJf79++PwMBAg2Xd3d0xfPhw+fHKlSvtGhtpPPnkkzqPz549q7fc8uXL5eWYmBi9v1Ntzz//vLy8fv16ZGVlKYiy5EtPT8cLL7wAAAgODsb3339vcR1sI9tZtGgR7t69CwCoVq2azrayFtvHtvLbBwBq165tsrx2GSEEEhMTC5VhGzmG9navXLkyOnbsaLS89nY/ePAgrl+/XqT1kmHVqlVDtWrV5MeGjikA3WNEX19fPPvss0brHj16tLx8+/Zt7Nu3T0GkJd8rr7wij+T/+++/w93d3eI6HNFGihPK/B2xr69vode0n9P+T6SgyMhIeVnJ0MWk3Llz53TaoHPnzibX6dKli7x84cIFnD9/3i6x0X8KJvnJycmFyqSmpmLXrl3yY0vbMjU1FTt37lQQZcn38ccfy9Ma/PDDDwgKCrJofbaRbc2YMUNeHjRoEFQqZf/FsX1sT/u4wJw5qrWTPUmS4Ofnp/M628hx1q1bJy936tQJkiQZLd+yZUv4+PgA0JwcWL9+fZHWS8ZpH1foO6bIp90+TzzxhN7jf21VqlRB9erV9a5PutauXYulS5cCAIYNG4aYmBir6nFEGylOKPN37mlpaYVe0z64unTpksE6cnJy5OWEhASlIZECx48f13ncrFkzk+s0aNBA5wxKwTrI9q5evarzuEyZMoXKnDlzRue3ZU5bhoaGIiIiQn7MtjTs8OHD+PnnnwEArVu3xtChQy2ug21kO0lJSTh48KD8uG3btorrZPvYXuPGjeVl7UTQEO1kr379+nLikI9t5BgJCQm4ffu2/Nic7e7q6opGjRrJj/Vtd3vVS6ZpH1foO6YANAn7yZMn5cfmtE/Bcmwf/VJSUjBmzBgAmvzJmh5PgOPaSHFCGRkZCSEEbty4Ueg17a4q27ZtM1iH9qVVLy8vpSGRArGxsfKyu7s7wsPDTa5TsJx2HWQf2t0ZAKBp06aFyhRsB+2eAMZol2Nb6pebm4vnnnsOeXl5cHd3x2+//WZVPWwj2zl48CCEEPLjOnXqAAD27t2LYcOGITIyEp6enggMDETdunXx+uuv49ixY0brZPvYXv4BE6DZjxk7Njh27BimTp0qP3777bcLlWEbOYa9tjvb0zH+/fdf3Lp1S36s75gCAK5du6ZzAYntYzvvv/++3F37+++/R3BwsFX1OKqNFCeU9erVAwDEx8fLfX7zaV+qnT59ut57H27evIlffvlFflyrVi2lIZEC2meoKlSoYLKrSb6KFSvKy3FxcbYOi7QkJSXJV8YAoG7dunp/N9pt6erqirCwMLPqZ1ua9uOPP8rJyNixY03es2UI28h2Tpw4IS/7+PjA09MTL7zwAlq0aIE5c+bg8uXLyMrKwoMHD3Dy5En8/PPPaNCgAUaNGmWw6yXbx/Z69OiBV155BQCgVqvRpUsXvP/++zh58iQyMjKQlZWFc+fO4auvvkLLli2Rnp4OAHj33Xfl8Ra0sY0co2AvGe3taYyp7W6vesm4zz77TF729PREnz599JazRftcvXpV5+QfaS6s5Z+YbtWqFYYNG2Z1XY5qI8UJpXa3oo0bN+q8FhUVJXdDuHv3Lpo1a4b58+cjNjYWsbGxmDFjBpo1ayYnmmXKlEHLli2VhkQKaPebL3ivijGlS5eWl1NSUmwaE+l66623dLoEffnll3rLabdlqVKlzL6fjG1p3KVLl+T/fKtWrYoPPvjA6rrYRrZz7949eblUqVIYMWKEfHXLxcUFjz/+ONq0aYOqVavK5YQQmDFjBrp164a8vLxCdbJ97GPSpEmYNGkSQkJCkJ2djW+//RZ169aFt7c3PD09UaNGDXz00UdITU1FjRo1MHfuXHz33Xd662IbOUbBe+zMPV4wtd3tVS8ZNn/+fGzYsEF+PGbMGIMnZmzRPmq1Wj5RRJrb/kaNGgW1Wg13d3f8/vvvZl/M0cdRbaQ4oezcuTM8PDwAaL6UBf3888/yDv7y5csYOnQoateujdq1a+P555/H9evXIUkSJEnCN998Y9VoRmQ72pfJPT09zV5Pu6uyvvtpyTb++OMPzJw5U348YMCAQiO+5mNb2sfzzz8vj1r966+/WrRtC2Ib2U5SUpK8fPv2bSxYsACAZhTx69ev4+jRo9i2bRvOnz+PY8eOITo6Wi6/ZcsWfP7554XqZPvYzyuvvIIVK1YYvbpftmxZjBkzBr179zZYhm3kGAW3mbnb3tR2t1e9pN/JkyflkcoB4LHHHtO5WlmQLdpHXz2Psm+++QanT58GoOmJUbNmTUX1OaqNFCeUpUuXxpdffokRI0agfPnyhaYHadq0KebPnw8PDw8IzbyXhf4A4PPPP1d0iZdsQ3twA1dXV7PX0y5rzsh9ZLndu3fr3H9UuXJlnfuLCmJb2t6sWbOwdetWAMCzzz6L9u3bK6qPbWQ7mZmZhZ4bOHAgFi5ciNDQUJ3n69Wrh23btiEqKkp+7scff8T9+/d1yrF97OPatWvo0KEDWrZsKU9PUKZMGbRo0QKtW7dGlSpVAAB37tzBq6++iipVqhgcuZNt5Bja2x0wf9ub2u72qpcKu3PnDnr16iUnDh4eHli4cKHREUFt0T4A2yjf2bNn8fXXXwPQ3MP44YcfKq7TUW2kOKEENF3wpk+fjqlTp+odVGfAgAGIjY3Fq6++iqioKPj6+sLDwwMREREYOnQoDh48aJONSMp5e3vLy/oO0AzRLltwFD5S7vjx43jyySflIfTLlCmDDRs2GO3KwLa0rYSEBHlQkICAAPz000+K62Qb2U7B7eDl5YVJkyYZLF+qVClMmDBBfpyWloYlS5bolGH72F5cXByaN2+OLVu2AABq1qyJrVu34s6dO9izZw+2b9+OS5cuITY2Ft26dQOguWWmZ8+e+PvvvwvVxzZyDO3tDpi/7U1td3vVS7qSkpLQuXNnXL58GYDmtoAFCxbojJarjy3aB2AbAZpbLkaPHi0f1/3222+Kejzlc1QbmX86T6FKlSph4sSJRfV2ZCXtM1MFrzYbo93X2tR8N2SZc+fOoWPHjnKXvoCAAGzatElnEmJ92Ja29eqrr8pXsL799luDw6pbgm1kOwW3Q5cuXUzOC9qhQweUKVNGnq5q165dOt2/2D62N2TIEHlU+GrVqmHfvn16T4zVqFEDa9euRf/+/bFs2TLk5uZi+PDhuHTpks6BDtvIMQpus4yMjEIHsvqY2u72qpf+k5aWhq5du8oDy0mShJkzZ6Jv374m19XXPuYoeD8e2wiYOnUqdu/eDUBza0aHDh1sUq+j2sgmVyip5NAeplh7CGlTtAeJsXRydzLsypUraN++vXzA6+vri7///lseXdkY7bZMTU1FamqqWe/Jtixs3759WLx4MQDNXE2jRo2ySb1sI9spOMR6gwYNTK4jSRLq168vP84/W6+vTraPcnv37pUPoADgu+++M9rLQpIk/PLLL/LYCnfu3MGff/6pU4Zt5BgFf2/mHi+Y2u72qpc0MjMz0bNnT+zdu1d+bvLkyWbPo2yL9ilVqhTc3NzMWq+kyszMxHvvvQcA8Pf31+kto5Sj2ogJJemoXr26vHzv3j2zR3mKj4+Xl62dQoF0Xb9+He3atZPnJfLy8sJff/2FJk2amLW+dlsCmvuWzMG2LOzOnTvy8r59+6BSqeTBxPT9aQ/b/dlnn+m8pj2kPdvIdgoOZGDuQaV2uYJTX7F9bCu/mysAuLm5oXPnzibXCQ0NRePGjeXHu3bt0nmdbeQY9trubE/7ycnJQb9+/eRxAABg/PjxOmMzmFKwZxTbxzqZmZlyr7OHDx8iNDTU6DHFnDlz5HV37typ89rs2bN16nZUGzGhJB0FD8pMTfwNADdu3MDdu3cN1kGWu3PnDtq3b48rV64A0Nwsv2rVKp25XU2xpi1zcnJw6tQpg3WQbbGNbKfgXKz596WYon3fSMH7V9g+tpXf1RUAQkJCzL5fKDw8XF7WPosOsI0cpWrVqjqDeJiz3QHg6NGj8rK+7W6veh91eXl5eOaZZ7Bu3Tr5uU8//RTvvPOORfX4+vrq/B7ZPsWPo9qICSXpaNy4sTwNDADs2bPH5DraXZg8PT11ziaT5e7fv48OHTrg3LlzADRn8pcsWYKOHTtaVE+VKlVQoUIF+bE5bXn48GGd/vatWrWy6D1LKg8PDwQFBZn9pz0XnpeXl85rLi4u8mtsI9sJDw+XRwcFIJ+MMUX7inHZsmV1XmP72Jb2/y3W3u9YcOA/tpFjuLu76/SWMWe73759GxcvXpQf69vu9qr3UaZWqzFs2DAsX75cfu7dd9/FuHHjrKpPe/ua0z45OTnYv3+/3vUfVSqVyqJjCu19p6urq85r+k7MOaKNzB6U591337W4cmuMHz++SN6H9PP19UW7du3kIdoXLFhgsu3z53sDgHbt2nH0LgWSk5PRqVMnnDx5EsB/I6/16NHDqvp69OiBX3/9FQCwdOlSTJw40ehcr9ptWatWLURGRlr1viVNly5dkJiYaHb5iIgIudvru+++i08//dRgWbaR7fTu3Rs//vgjAGDz5s0my9+5cwcnTpyQHzdt2rRQGbaP7ZQrV05efvDgAS5fvqxzEsCQw4cPy8vly5cv9DrbyDF69uyJf/75B4CmO3NCQoLRwcq0t7u/v7/BHjf2qvdR9eKLL+rME//qq6/iu+++s7q+nj17yts8NjYWR48e1bkXvaA1a9YgJSUFgCaRMjR39qOkdOnSFh1TDBs2TO722qJFC+zYscNoeYe0kTCTJElCpVLZ/Y8cb8mSJQKA/LdmzRqDZQ8fPixcXFzkskuXLi3CSEuWtLQ08cQTT8jbUqVSiblz5yqq88CBAzptOWnSJINl4+PjRalSpeSy33//vaL3fpRVqlRJ3o7jxo0zWpZtZDunTp0SKpVK3j6rV682Wv7tt9/W2faHDh0qVIbtYzt79uzR2ZavvvqqyXWWLl2qs87ixYsLlWEbKaO97WbNmmX2evHx8cLDw0Ne98033zRYNiUlRVSsWFEu+9JLLxV5vc7K2vYRQog33nhDZ/3Ro0cLtVqtKJ7U1FQREhIi19mnTx+DZXNzc0V0dLRctlu3boreu7hS0kbmGDp0qFx/TEyMyfKOaCOLE0pJkuz2x4SyeFCr1aJevXrylyssLEzExsYWKnfz5k1Rs2ZNudzjjz+ueEf1qMrMzBTt27eXt6UkSWLGjBk2qbtHjx5yvb6+vmLPnj2FyiQlJYmWLVvqtHl6erpN3v9RZElCKQTbyJaGDBkib6OQkBBx/PhxveUWLVqkczKsU6dOButk+9hGbm6uqFGjhs5+btq0aQbL7927VwQEBMjly5YtK1JTU/WWZRtZT8nB8Kuvviqv6+LiIpYtW1aoTHZ2tujXr59czsvLS9y8edMh9Toja9vn448/1ll32LBhNjtG++mnn3TqnjBhQqEyarVavP766zq/9yNHjtjk/Yub4pZQClH0bSQJIQTMkD+qIaCZU7JSpUrmrGax7du326VessyhQ4fQqlUr+T6T0qVL48UXX0SrVq3g6uqKAwcOYPLkyfLol15eXti1axeio6MdGbbTGj9+PMaOHSs/DggIsOhe1A4dOuCtt97S+1pcXBwaN24sD5zk4eGBkSNHomPHjvD19cWJEyfwyy+/yPecqVQqrFq1it1SFNDu8jpu3DijXV4BtpEtJSQkoGnTpjoDWj333HPo2LEjAgICEB8fj6VLl2LVqlXyOsHBwTh8+DAqVqyot062j+1s2rQJXbt2RV5envxcq1at8PTTT6NatWpwc3PDtWvXsG7dOixdulSn3Lx58zBo0CC99bKNTBs1ahTmzZtX6HntAaxcXV117vPOZ2hy9AcPHqBJkya4cOECAM12HThwIHr16oXAwECcO3cOv/32m07X8smTJ+Oll14yGqu96i3ObNk+Bw4c0LkXVaVSoV27djr39xtTtmxZnZFFC8rOzkbbtm3lrskA0L17dzz77LMIDQ1FXFwcZs6cqXP/3ttvv43vv//erPcvruzxGzKHdpfXmJgYk11eAQe0kdmZp1aXVxcXFxETEyNmzpwpkpOTrcpkqfhbsWKF8PLy0jnDoe/Py8tLrFixwtHhOrVx48aZ3M7G/oYOHWq0/n/++UcEBgaarMfFxUVMnjy5aD50CWbpFUoh2Ea2FBsbq9MNzthfuXLlxOHDh03WyfaxndmzZ+t0aTT15+rqKn788UeT9bKNjNO+ymHpnzHnzp0T4eHhZtUzduxYs+O1V73FlS3bZ/v27YqOKSpVqmQy3oSEBFGnTh2z6hs4cKDIy8uzw1YrWvb6DVnyvuZeoRSiaNvI7E/YvXt34ebmptM9VaVSCW9vbzFw4ECxceNGdncsgc6cOSPatWsnJEkq9OWTJEm0b99eb3dYsoy9E0ohhLh+/bro27evcHV11VtH48aNxd69e+3/YR8B1iSUQrCNbCkpKUm8+OKLwtfXV++29PT0FGPGjBG3b982u062j+2cPn1aDBgwQLi5uRncr6lUKtGjRw9x4MABs+tlGxlmz4PhBw8eiJEjRxo8CR0VFSXWrl1rccz2qrc4craEUgghMjIyxDvvvCP8/Pz01hMRESFmzpxp4y3lOM6WUApRdG1kdpdXALh79y7mz5+PefPm6cxrkt8VNjQ0FM8++ywGDx6MOnXqmFstOYH4+Hjs3btXnkesfPnyaN68uc5cN+Qc7t69i127duH69evIzs5GuXLl0KhRo0KT4ZLjsI1sJz09HTt37sTVq1dx//59+Pv747HHHkPLli0LTUFhLraP7aSmpuLgwYM4f/48Hjx4AADw8/NDZGQkGjduDH9/f6vqZRs5RkpKCrZt24b4+HikpaUhLCwMderUMTrCpCPrJdvIzMzEjh07EBcXhwcPHqBs2bKoWbMmmjZtKucI5Fj2biOLEkptp06dwuzZs7Fw4UJ5omHtgOrVq4ehQ4di4MCBCAkJURwoERERERERFS9WJ5T51Go1Nm3ahDlz5mD16tXyTaf5yaWLiws6duyIYcOGoUePHkbnhiIiIiIiIiLnoTih1JacnIzFixdj3rx5OqMG5SeXfn5+6N+/PwYPHowWLVrY6m2JiIiIiIjIAWyaUGq7cuUK5syZg/nz5+Py5cv/veH/k8sqVarIw0ETERERERGR87FbQqltz549mDNnDpYuXYrk5GTNG0uSzvxSRERERERE5FzMm+FUodDQUISGhsLPz4+jPREREREREZUQrvaq+OHDh/jzzz8xd+5c7N+/X34+/4JoYGCgvd6aiIiIiIiIioBNE8q8vDysX78ec+fOxV9//YXs7GwA/yWRbm5u6Nq1K4YMGYLu3bvb8q2JiIiIiIioiNkkoTx8+DDmzp2LP//8E4mJiQD+SyIBIDo6GkOHDsUzzzzDK5NEREREREQlhNUJ5Y0bNzB//nzMmzcPsbGxAHSTyAoVKmDQoEEYMmQIatSooTxSIiIiIiIiKlYsSijT09OxfPlyzJ07Fzt27IBarQbwXyLp4+ODPn36YMiQIWjbti0H4CEiIiIiIirBzB7ldejQoQgNDcWwYcOwbds25OXlQQgBSZLQtm1bzJkzB3fu3MGcOXPQrl07JpPklK5evYp33nkHDRs2hL+/P1xcXCBJEiRJwqeffuro8IiKjU8//VT+bQwbNsxm9e7YsUOuNyIiwmb1lnQbNmzAgAEDUKVKFfj4+MjbUJIkxMXFOTo8soO4uDiddiYq6WbPnq3zndf+e/zxxx0d3iPN7IRy3rx5SEtLgxACQghERUXhm2++QVxcHLZs2YLBgwfD29vbnrE61LBhwwp9eQcPHmxxPU8++WShet5++207REyW2rJlC2rXro0ffvgBR44cQVJSknwVnoiouHrxxRfRpUsXLFmyBFeuXEF6erqjQyIiKjb0JaKVKlXSuVXPHD/++GOheqKjo+0UtXOx+B7K/LPGUVFROHz4MA4fPmyzYCRJwuLFi21Wn72tXLkSqamp8PX1Nat8QkICNmzYYOeoyBoZGRl49tlnkZqaCgBwcXFBvXr1EBwcLJ/5feyxxxwZIpVQs2fPxvDhwwEAMTEx2LFjh2MDIpvYsWMH2rRpAwCoVKmS3a4SLl++HL///rv8OCAgALVr19Y5wevl5WWX9yYix/r000/x2WefAdD0JJw9e7ZjAypCAQEBaNy4sfw4MjLSovWvXbums582x5w5cyx6j0eJVYPyxMXF2fw/x/zus84kLS0Ny5cvx9ChQ80qv2DBAuTm5to5KrLG2rVrkZCQAADw9fXFoUOHUL16dQdHRURk3MyZM+Xlbt26YcWKFXB3d3dgRERE9le3bl3FF2nmzp1rdkJ59OhRnDx5UtH7lWRmd3kFIHd3tcefM9G+r2fu3Llmr5dfNv9SOxUf2lfa+/bty2SSyEFat24t/7/Ae/9M0953vf3220wmHyERERFOexxF5CilS5dGQEAAAE0PD3NvEdA+3uf9/YWZfYXS3Ktwj4KGDRvCy8sLsbGx2LFjB+Lj4xEeHm50nZMnT+LYsWMAgCeeeAIqlQpXr14tgmjJHPfv35eXTbUlEVFxwX0XEZH53Nzc8NRTT+H3339HSkoKVq5ciWeffdboOrm5uVi4cCEAwNvbG3379sWPP/5YFOE6DbMTylmzZtkzDqczePBgfPDBB1Cr1Zg/fz7ef/99o+W1+10PGTIE8+fPt3eIZIGcnBx52cXFxYGREBGZT/s2Cu67iIhMGzJkiHzv+dy5c00mlBs2bJBvi+rdu7fZY6c8Sizq8kr/GTRoEFQqzeabN2+e0bJ5eXlYsGABAMDT0xNPPfWURe+VlJSExYsX4/nnn0fTpk0REhICd3d3lCpVCpUrV0a/fv3wxx9/IDs72+w61Wo1Vq5ciYEDB6J69eooXbo0XF1d5To7dOiA999/H1u3bkVeXp7RurZs2YIRI0agdu3a8Pf3h6urK3x8fBAeHo7WrVvjzTffxNq1a5GVlWXR5zYlf7s+9dRT8lD5pUqVwmOPPYZBgwZh5cqVRrsBaY/cq53wf/bZZ4VG8bLVtAi5ublYsmQJhg4diho1aiAwMBBubm4IDAxEo0aNMGbMGKxbt87kNgeAGzdu4KuvvkKLFi0QFhYGDw8PlClTBg0bNsT777+P2NhYs2LS3g75U6Oo1WosXboU3bt3R6VKleDh4YGQkBD06NED27Zt01vPvn37MHjwYERERMDDwwOBgYF44oknMG3aNLNGy42IiJDjyB+Y5uHDh/j555/RvHlzhIaGwtPTE5UrV8azzz6L7du3m/X5AM0Jg82bN2Ps2LFo27YtypcvDy8vL3h5eaF8+fJo3749vv76a9y9e9fsOrVdvHgRX3zxBVq1aoXw8HB4enrC29sbERERePLJJ/HTTz/h2rVrOuvkb/f8AXkAYOfOnQaHRLfVYD0PHjzAhAkT0K5dO1SoUAGenp4ICgpCnTp18Nprr+HAgQOK6v/777/Rr18/REZGwsvLC2XKlEGrVq0wZcoUZGZmmlzf2mlDtmzZgjFjxqBOnToIDg6Gh4cHypcvjw4dOmDSpElIS0uz+LPcvHkTP/zwAzp27IhKlSrB29sbnp6eqFChAjp27Iivvvqq0O8sfzoV7ftyrl69arBdrRlEw9BUEZUrVzb6vdEe6bB169by8wcPHsRLL72E2rVrIzAwsNDr2mz1/TE07czGjRvRv39/REZGwtPTEwEBAWjfvj2WL1+ut57Tp0/j+eefR7Vq1eDp6Qk/Pz9ER0dj/PjxNv8/p3Xr1oXaLSMjAzNnzkTbtm3l7REeHo7evXtj1apVZtVraB+8evVqPPXUU6hatSp8fX0LTV9l7rQhxWUfD2i215o1a/Daa6+hZcuWCA0NhYeHB3x8fFCxYkV0794dkyZNkgfIs/W2u3Dhgs42M/f/SQBYuHChvF5gYKBZ+zNTrD1+yv8u5g/IA2guXBjaz+i7fcDRbeFIzZo1Q9WqVQEAW7duxa1bt4yWL3hRyBK2ON7Wt+8xxZypo/S1GaDZD+cfW3t6eiI4OBgtW7bExIkTDe9XBZll6NChAoAAIPr27SuEEKJt27bycwcOHDC47vr16+VyAwYMEEIIERMTIz/31ltvGVz3+++/Fx4eHnJZY3/h4eFi3759Jj/L1atXRaNGjcyqE4D47rvv9Nbz4MED0alTJ7PrefHFF03GZq5Dhw6JWrVqmXzPJk2aiPPnz+utQ7tNTf0NHTpUccybNm0S1apVM+v9YmJijNb1448/Ch8fH6N1uLq6ijfeeEPk5OQYrUt7O4wbN07cu3dPdOzY0Wjd3377rbx+Xl6eePnll42Wb9euncjIyDAaR6VKleTy27dvF4cPH9Z5Tt/f8OHDRVZWltF6t2/fLoKCgsza7j4+PmLatGlG69OWlpYmnn/+eeHq6mqybpVKJbZs2aJ3u5vzt337drPjMmT+/PlmbYtnn31WpKamGq1r3LhxOr+P1NRUMWDAAKP11qhRQ5w4ccJovdu3b5fLV6pUyeRnunjxomjTpo3JzxQWFiY2bNhg1nbKyckRH3zwgfDy8jKrbf744w+928Wcv1mzZpkVkzZrvzezZs3S2cdkZ2eL119/3ex9kD2/P+np6WLw4MFG6x0zZoxOHV9//bVwcXExWL5u3boiMTHR4u1riPb/27NmzRKXLl0SdevWNRpz165dxcOHD43WW3AffPv2bdGuXTu99Y0bN05e78qVKzqvmVu/o/bxCxcuFKVKlTLrexsYGCjWrFljsk2s2XatW7eWnzN2/FWQ9jHfK6+8YvZ6+ig9ftL+Lprzd+XKFZ31i0tbWKrgPsya9YKCgoQQQnz++efyc+PHjze47v379+Xj8HLlyom8vDyd/VfDhg0Nrmur4+2C+x5zGGv/fAXbLCUlRTz99NNGY6xevbqIj48vVJdVo7ySxtChQ+UzeXPnzkWjRo30ltM+s2Hpvahnz57VORsQHh6O8uXLw8fHB6mpqTh79iySkpIAAPHx8WjTpg327NmDhg0b6q0vPT0d7dq1w8WLF+XnfHx8UKNGDQQEBCAzMxN37tzBpUuX5DOO+s48qtVqPPnkk9izZ4/8nKenJ2rUqIGgoCDk5OQgMTERFy5ckLuT2mpOx71796JLly5ITk6WnwsICEDNmjWhVqtx5swZ+bX9+/ejZcuW2Lp1K2rVqqVTT506ddCpUycAmntcb968CUAz9HTBKULq1KmjKObp06fjxRdf1Dn75O3tjRo1asDf3x/Jyck4e/asfCbw4cOHBut666238NNPP+k899hjj6FChQpITEzE6dOnIYRAbm4uJkyYgMuXL2PZsmVwdTX9c8/NzUXPnj3ldq1SpQoqVqyIhw8f4sSJE3Ibvvfee6hUqRKefvppvPjii5g2bRoAIDg4GDVq1IBarcaxY8fkm923bt2K1157DVOnTjVre8XHx+P111+X7w/L/3x3797FmTNn5CvPs2bNQnJyMpYsWSL3GCjo+vXruHfvnvw4ICAAkZGRKF26NLKzs3Hp0iX57GRaWhpGjx6N7OxsvPTSS0ZjvHv3Lrp06VJo6qSIiAj5XrabN2/i0qVLADTf/5SUFLlc/vfvxo0bOHXqlByb9jDo2gIDA43GY8qkSZPw2muv6TwXHh6OKlWqIDk5GSdPnpS7Ty5YsACXL1/Gxo0bUapUKbPqHzZsGJYtWybHWrNmTeTm5uL06dPy9/rs2bNo37499uzZI58dVuLIkSPo0qWL3BUJ0Ay4ULNmTXh7e+P69eu4cOECAODWrVt48sknsXjxYvTu3dtgnenp6ejbt2+h0QPLlSuHiIgIuLu74/bt2zh//rz8e8jfBwOa72qnTp1w//59HDx4EIBm3xgTE6P3/cqXL2/x587fbwGaM8n5WrVqVWiKEGPfm9dffx2//vorAM3o1lFRUfD09MSVK1cKlbX392f48OHytGEVKlRAZGQk0tPTcezYMfn/kF9//RXh4eF477338M033+CDDz4AAPj5+SEqKgqurq44efKkvP88ceIEBg4cqLONbCU5ORkdOnTA5cuXAQAVK1ZE5cqVkZSUhJMnT8r7+vXr16Nbt27YvHmzWdO3ZGVloWvXrjhy5AgAoGzZsqhatSpyc3Nx7tw5xXE7ch9/6dIlnX1gmTJlEBERgVKlSiEjIwPnz59HYmIiAM29wb169cKqVavw5JNPmvXZzN12o0ePlq/cz507F9988w3c3NyM1n358mWdXjHPPfecWTHpY4vjp8aNG8PT0xMXL16U/48pV66cwWOVgt+94tIWjjR48GCMGzcOQgjMmzcP77zzjt5yixcvlo/DtXsnmmKr4+2ikpeXh759+2LTpk0AgLCwMDz22GPIy8vD8ePH5V4+586dQ/fu3XHo0CHd40qz0lzSe4UyNTVVvkoUHBwssrOzC6338OFD4enpKQCI0NBQkZubK4Qw/wrlqFGjRJcuXcT8+fP1nmnNy8sT69atE9WrV5frq1mzpsjLy9Nb3w8//CCXK126tJg3b57euNPS0sTq1atFv3799J65WbZsmVyPu7u7mDhxokhLSytULisrS2zevFkMHz5cvPbaawY/p7kePHggypcvL793qVKlxMyZM3U+Q0ZGhvjpp590ruxGRUWJzMxMg/UWPEtjS1u3bhUqlUquv3z58mLevHmFzubm5eWJffv2iTFjxoimTZvqrWvJkiU6Z4oaNWokjh07plMmLi5OdO/eXafc559/bjA+7c8eGBgoAIjo6Ghx+PBhnXIXL17UORsfGRkpFi1aJACIMmXKiGXLlul875KSknSuWKlUKnHu3DmDcWhfjQwODhYARIMGDcSRI0d0yl26dEm0b99e5/NNmTLFYL3z5s0TdevWFT///LO4dOmS3jLHjx8XXbp0kevz9PQUly9fNlhnXl6eztlqAGLQoEF6P19iYqKYMWOGePzxx8XKlSsLvW7tGVdL7Nu3T+dKTtWqVcWOHTt0yiQkJIgRI0bofKYRI0YYrFP7DG1+e+Vf4dX+PaalpYlvvvlG5ypukyZNDO6jzL1CmZiYqLMvqF69ulizZo28j80XGxur833x8/MTcXFxBusteOW4c+fOhX4LQgiRnJwsFi5cKGJiYsSECROs/hxKacdq6Cx0Pu3vWv7VCV9fX/Hbb78V2j9q/1bs/f3J3+889thjYuvWrTrlbt++rfNb8/PzExs2bBAqlUrv9y0zM7PQVdfNmzeb2oxm0f5/O/87X6VKFbFt2zadcrdu3RIDBw7UieGdd94xWK/2dy6/XSIjI8XGjRuFWq2Wy2VnZ4tr167Jj625QunIffyXX34pWrRoIaZNmyZu3Liht8zu3btFkyZNdLZzcnKyTbddZmamzpX2ZcuWGaw/34cffqjz/64Stjx+Knil31zFpS0sZcsrlELo/qYLHmvka9asmVzm1KlTQghh1hVKWx1vF4zTXlco838TUVFRhXpEpaeni9dee02nzhkzZui+n1lRkd6EUgih00Vn1apVhdabNm2a/Pqbb74pP29uQmmq21C+e/fuiSpVqsh1/vXXX3rLaf/HXPDLYEjBAzQhhM6Bw0cffWR1PZZ644035Pd1c3MTO3fuNFh26dKlOl9+7S48BdkroczKyhIVKlSQ665WrZrBnbe2lJQUvXWFhobKdTVo0MDg9yMvL0/06NFDZ1vp66IgROED6Hr16ul9fyE0Bxxubm46/xn6+vqK2NhYveWzs7NF1apV5fKffPKJwc9csHtrrVq1RFJSksF6tbs5+vn5GfyPztzfUF5enujTp49c59tvv22w7JQpU3Ri/eGHH0zWr1ar9cZSFAll/fr1dZKb27dvGyz70ksv6Xy2f//9V2+5gl07VSqV2LRpk8F6Z8yYoVN+7ty5esuZm4gNGTJE5wDP2IFOTk6OzgkDQ4nOunXrdGJ85ZVXdA6EDNH3eynuCSUA4eLiYnQfmq8ovj/ly5c3WO+DBw/kRCh/v+Pi4lIoqdWm3a1xyJAhJj+jOQp2MwwNDTW4XxVCiEGDBsllXV1dDZ6kKrgPDg8PF7du3TIZjzUJpSP38ebuizMyMkTTpk3lOidPnmz2ZzN327311lvyOl26dDFaNjc3V+fk1dSpU836HIbY8vjJ2oSyOLWFJWydUM6cOVN+/vXXXy+03vnz53WOufKZk1Da6nhbiKJJKAHNBakHDx4YrPPJJ5+Uy7Zs2VLnNQ7Ko5D2zbn65qTUfs7SG3kBzeVxcwQGBuLDDz+UH69evVpvuevXr8vLLVq0MKtufSMH2qoeS6Snp+uMNvzyyy+jVatWBsv369cP/fv3lx//9ttvZg12Y0vz58+Xt5WLiwsWLVqEcuXKmVxP3whiy5cvx+3btwFobraeOXOmwe+HSqXCtGnT5O5mOTk5Znc3nTp1qsERzCIjI9G2bVv5cXZ2Nj766CPUqFFDb3k3NzedQWf++ecfs2IANO1VunRpg/VOnz5d7qaUlJSEP//8U29Zc39DKpUK48ePlx8b+g3l5eXhhx9+kB/36tULb731lsn6JUkyOxZb2rdvH44ePSo/njRpEsqWLWuw/A8//KAzT+7kyZPNep9hw4ahQ4cOBl8fOXKkzkA1+SPsWeP69evyEO7u7u5YtGiR0a6Vrq6umDZtmvx9WbhwoU431XzffvutvBwdHY0JEyYYHewkn7OO+PfCCy8Y3YcCRff9+fHHHw3W6+/vrzOYXXZ2NkaNGmWwGzGg6daYz5L9jiXGjx+PChUqGHz9l19+kbsc5+bmYsaMGWbV+9NPPyE0NNQmMerjqH28ufs/T09PfPXVV/JjQ/tifczddtrfj40bN+oc0xS0YcMG3LhxA4DmMzzzzDNmx6OPI46fCipObeFITz31lNwdeNGiRTqjZgPKjuGLQztbaurUqfD39zf4+htvvCEvHzhwQGd7MaFUKH9kNwD466+/dOYEu3z5stxHvl69eqhXr55dY2nSpIm8nH/vTkGenp7y8okTJ6x+L1vVY4mdO3fK98ZIkoRXX33V5DraX/6rV6/Kc4EWlUWLFsnLXbt2RYMGDayuS3vEwJiYGDz++ONGy5ctWxYDBw7Uu74htWrV0vke6aN9j58kSRgxYoTR8tr1mTuiXr169dCyZUujZSIjI9G5c2f5sbkjKpqqMzg4GIBm5FZ997L++++/OveYffzxx4rf1560t0vlypXRo0cPo+U9PT3xwgsvyI/XrFlj1n0dr7zyiskyL7/8sry8d+9enXsfLfHnn3/K/5F1794dkZGRJtepUKGCnIBkZmZi7969Oq/fuHEDu3fvlh+///77Dv/P3d60D6oNKYrvj5+fH/r27Wu0TMF7i0eOHGm0vPZ+5/LlyxaNgm6O4OBgk4mFv7+/znQE5uyjQkJC0KtXL4XRGVZc9vGmaNd56NAhs9axZNtVq1ZN3h+o1WqjU+PNnDlTXu7fv7/Z9wUb4ojjJyXs3RaOVKpUKfme+jt37ujcby3+f28loDlxon08ZQ5na+caNWqYPO5q1qyZfA9pVlaWzrEQB+VRSKVSYdCgQfj222+RnZ2NP//8E2PGjAGg/OpkQadPn8bOnTtx6tQp3Lt3D6mpqTpX3DIyMuTl/LNpBTVs2FD+Yr/yyivw8fFB165dzToLX7CeNWvWANBMs1GuXDk8/fTTZg38Yq39+/fLyzVq1DBrSoGmTZsiKChIHpRl//79BgcssrXc3Fzs27dPfmzqgMkU7c/fpUsXs9bp3r27fGXyzJkzSElJMfqfoakDDQA6ZxyrVKmCkJAQs8sbG2xIm3aiaEyXLl2wdu1aAIZPomjL/w/j+PHjuHXrFlJSUnTmIAUgDyAjhMDNmzcLna3TTjoiIiIUnSQoCtrfG3O3a/fu3eW5dfMHjIqKijJYvmzZsiZPcACawWQkSZIHVTp06BC6du1qVkzatNtA+2qKKbVr18aWLVsA/Degj746PTw80L17d4vjciZ+fn5mDTZWFN+fhg0bmvy/Q3s/4uHhYfL7pl1eCIGkpCST+ypLtGvXzqz/77p06YJffvkFAOSB14xd0W7atKld/x8tLvv4K1euYOvWrThx4gTu3r2LlJSUQleH8j148ADp6enw9vY2Wqel22706NHYuXMnAM0Abx999FGhY6GEhAT89ddf8mMlg/Hkc8TxkzHFoS0caciQIXKPlzlz5qBbt24ANBcxrl69CkCz77N0/2Gr4+2i0qxZM5Nl8qeJyp9iTfv37hytXcwNGTJE7io1d+5cjBkzRufMhouLi8lJU435999/LZ4fTl93LkBzhWDu3LnIy8tDQkICunfvjgoVKqBLly6IiYlBy5YtUbFiRZP1jxw5Et9//z1SU1ORnp6OwYMH44033tCpp1q1ambHaw7tkbIsGXW1Tp068ohu2nXYW3x8vM7cd0oS2dzcXHnHBpj/+bXLqdVqXLlyBXXr1jVY3lhXtnza/5GUKVPGovL5IwKaUrt2bbPKaY/ce/fuXSQlJcHPz69QuRs3buDNN9/E8uXLLer2rO93pH0GvqhOTihhze+mRo0acHV1lQ8qLl68aDQhMLe9fHx8EBERIZ/VzB+B1VL5o+ICmqsH+ScVTNHeFvkjGObTbtfatWvD3d3dqticRf6claYUxffH0v1OYGCgyYPVgge85u57zGXNPkqtVuPSpUtGeyuZc7VdCUfv48+ePYvXXnsNmzdvNjpPdEFJSUkmkxhLt13fvn3x6quv4t69e3JS1b59e50yc+bMkU86RkVFoXnz5ha9hz6OOH7Spzi1hSO1b98eYWFhuHXrFtauXYuHDx/C399f0dyTgO2Ot4uKud2TDf3e2eXVBmrWrIno6GgAmrO558+fx549e+ThxDt27GjWTlyfxYsX44knnrB4snFD3XsaNGiAP/74Ax4eHvJz169fx/Tp0zFo0CBUqlQJVatWxdixY+WhqPUpX748li9frnPwnpiYiHnz5uG5555D9erVER4ejpdffhnHjx+3KHZDtM+EBAUFmb1efhdGQHN2rahod38GoOjseMGzvuZ+fu3PDpj+/JYeRNvroNvcz1ewnL6z42fOnEH9+vWxZMkSi++h1TeBr3a72vKKh71Y87txdXXVuTJr6ntjye9Ru6y5VzMK0p4G5ujRo9i4caNZf9r7tIInC5ytXZUyt9teUXx/imK/Y8kBszlsuY/SprQ7pSmO3Mfv2rULDRs2xKZNmyxuD4OTqWuxdNt5eHjoTOWm3bU13x9//CEvm+pmbS5HHD8VVNzawpG0L/pkZmZiyZIlSE9Px/LlywFopvMyd7oUbbY63i4qSverTChtRHunNHfuXEVzT+aLi4vDsGHD5IPgkJAQfPDBB9iyZQuuXLkid3kVmtF69c4dps+QIUNw5swZvPjii3r/U7x48SLGjx+PGjVqYOzYsQa7PnTs2BHnzp3Du+++q3egmevXr2PKlCmoX78+hg8frvgMsfZOzJIvvnZZc3aEtlLwvbR3KkrrMvfzFyxXlJ9fCXM/X8FtWvDz5eXloX///nL3DA8PD4wcORIrVqyQ53DNzs6Wf0NCCJ0BRfTRfg8lbVpUiuJ3Y0m92tvM2u+j9pV/axW8r8/Z2lUpc+dSc7b9blGx1T6qIHPbxdkkJyfjqaeeko8DSpUqhddeew3r1q3DhQsX5G6W2vtiS1mz7bTvI165cqXOiaU9e/bg7NmzADTtbYtbl/IV9fGTtuLaFo5U8Bh+xYoV8jydAwYMsPr/BFsdbzsDdnm1kWeeeQZvvvkmcnJyMHfuXPnst5+fH3r27GlVnRMnTkRmZiYAzX0Me/fuNXqlU3uSWlOqVKmCX3/9FZMnT8axY8ewY8cO7Ny5E9u3b5fryc3Nxfjx45GZmYmff/5Zbz1ly5bFd999h++++w5nzpyR69m2bZvcpUwIgdmzZ+P+/fsWjRBWkPbZPEs+q3ZZY6NX2VrB90pKSrL6/Qt24zT38xcsV5SfXwlrP1/BUWHXrl2L06dPA9DcVL9t2zaTXZZMvbf2NjTUtbw48fPzk6/o2et3Y229hkbxNUX7My1duhT9+vWzqh5tztauRaUovj/OyFb7qEfFH3/8IQ/CFRAQgP3796Nq1aoGy1vyXVOievXqaNWqFXbt2oWsrCzMnz9fHvBP+4plz549C/X4Uaooj5+0Fde2cKTatWujfv36OHr0KP755x+dngRKTyTY6njbEkU9owHAK5Q2ExQUJA8uER8fj+TkZACaIYm1R3qyxObNm+XlTz75xGS3WUMD8RijUqnQoEEDvPnmm1i9ejXu3r2LxYsX6wx4M2XKFMTFxZmsKyoqCmPGjMHixYtx+/ZtrF+/Xud+vTVr1ugMfGEp7W5o5l6NBaDTlaAou7IV7I9u7f1igGZagvyhrQHzP3/BbhTO0pXP3M+nM8KYq6s8RH8+7d/Qs88+azKZTE9PN9klTbtdlbRpUbHmd5M/MIO+OvQxt14hhM6+xJz7s/TR3hdaO1JsQc7WrkWlKL4/zsiafRRg/Xfe2Wnvi1977TWjCQxg3fGMtZ5//nl5OT+JTElJwdKlS+XnbTEYjzH2Pn7SVpzbwpG0E8f8E9FVq1Y1a7Aacyg53tbuEVFwIEF9rL2dRAkmlDak7yyGkjMb165dk5fz79E0RntEUWt5eHigf//+2LhxozxnW15eHrZu3WpRPS4uLujSpQu2bt2qc1Zv06ZNVsemPZrm0aNHzf5RnTt3Tm8d9hYYGKizo961a5ei+urXry8vm3tPrfYIjQEBAWaNjFscmPv5tMvVqlWrUDc0S39D+/fvNznFQdOmTeXlI0eOKO5+qd01yNb3eQG633lrvjeSJOl89/SJjY016yz2uXPn5JNtBWOzhHYb/Pvvv1bVYazOGzduyPfAW8ve7VpUiuL744ys2Uf5+/ujcuXK9gqpWHPE8Yy5+vbtK3dHPHHiBA4ePIhFixbJ+/ZKlSoVGqzHniw5frJmP1Oc28KRBg4cWGiwr8GDB9vt/Sw53ta+J9WcsUC0B64rKkwobah79+46V0gqV66MJ554wur6zEmY8uXl5cmjytpCtWrVdEblu3PnjlX1BAcH60zoam09AHTmx3n48CE2bNhgcp0///xTvvTv4uJiszNN5tKe6F17tDhraH/+5cuXm1XX/Pnz5eUnnnii2A5XXdC6devk6TsMUavVOmeQ9f3WLN3es2fPNlmmTZs28vyEGRkZOtvYGtoTTGtP/WMr2t+bzZs3FxrdVB/tz1S7dm2TXRZzcnKwcuVKk/X++eef8rK/v7/ZI2UW1KlTJ3l59erVNumiGh0drfM5p0+frqg+e7drUSmK748z2r9/v1k9d7S/8y1atHCafbCt2WNfbCseHh46J/9nzpyp0911xIgRDrkn0JzjJ2v2M8W5LRypTJkyOlMjSZJk14QynznH29qjwZozp6WtukdbggmlDbm7u+PevXvyTcyXL19W9J9HWFiYvPzPP/8YLfvTTz+Z1QXHkjPl2gf0BbsSWlKP9pWLgvVYokaNGjoJ4SeffGL0BuaUlBR88cUX8uMnn3yyyLsbvfTSS/J34Nq1a/j888+trmv48OHy8u3bt032s1++fLnOlQJbjVBXFFJSUuSpeAyZOXOmzlWkYcOGFSpjyW9o//79ZiWHYWFhOnOKfvTRR7h9+7bJ9QzR7mp5+fJlm1/Nevrpp+VhvrOzs/Hpp58aLX/w4EGdRN3c783nn38u3/Otz927d3W+s88++6zV85T16dNHHjwpOTkZb731llX1aHN3d9cZoGPixIlytydraLdrYmKi096XWVTfH2cjhMBHH31ktMzmzZvleQ4B/fuoR4Ul++KlS5cq7tFjKe3f/pw5c+QryyqVSuf/Xluw5fGT9n7G3GnRintbONLatWvlY3i1Wq2oV5etjrcB3R5qf//9t9GeUXFxcYpPiFqDCWUxFhMTIy9/8cUXBvuxz5kzR55E2pTHH38cCxYsMHrgBwC//fabzv13rVq10nm9Xbt2+O2333S6r+mzbt06eQ5IffVY6sMPP5SXjx07hmHDhumdIiU5ORl9tftVVgAADU1JREFU+vTBzZs3AWiuTpq7jWwpKipKZ/SwL7/8El999ZXRG6Zv3ryJKVOmFHq+evXqOoOPfPDBB1i1apXeOv7991+MGDFCflyvXj2nm6j9m2++waJFi/S+tn37drz22mvy43bt2untuqP9G1qyZInBrttHjhxBjx49THZ3zffpp5/K97QmJiaiTZs2OH/+vMHyQgisXLkShw8fLvRanTp15MTq3r17mDt3rlkxmMvf3x9jxoyRH0+ZMkXv9wsAzp8/jz59+sjboVy5cmYfTF26dAkDBw7Uu2958OABevXqJd/X4enpiddff92yD6LF1dUV3333nfx45syZeOGFF0yOhJiWlobZs2ejXbt2el9/66235JNOmZmZaN++vc5JGX22b9+OLVu2FHq+fPnycl1CCJsMtOAIRfX9cUYLFizA+PHj9b526tQpnfmna9asafUAfSWB9r548uTJOHnypN5yGzdudEjiXaNGDflqvPY+rGPHjggPD7fpe9ny+Em7S/qxY8ewfft2k+9f3NuipLDV8TYAdOvWTb6l5/79+3jnnXf01nXz5k306NHDZA8ve+Aor8XYK6+8grlz50IIgevXr+Pxxx/HK6+8gmbNmsHNzQ2XLl3CokWL5IPk5557DjNmzDBa54kTJzBo0CC8+OKL6Nq1K5o0aYLHHnsMAQEByM7OxsWLF7FixQps3LhRXqd3796FJqW+fPkyxowZgzfffBMdO3ZEs2bNULNmTQQGBiIvLw9xcXFYv349li9fLh9cREdH63RVs0a3bt0wfPhwzJo1C4DmP/TDhw9j9OjRqFOnDtRqNQ4fPozff/9d5z6Bd999F40bN1b03taaPHkyDhw4gDNnzgDQXNGaP38+nn32WTz++OPw9/dHcnIyTp8+ja1bt2Lr1q2oVasWXnrppUJ1TZkyBbt378adO3eQk5OD3r17o2/fvujbty/Kly+PxMRErF+/HnPmzJGv3np6emLu3LlyN01n0L9/fyxZsgQDBw7EkiVL0L9/f5QvXx53797F2rVrMW/ePPl75ePjg99++01vPQMGDMD777+P27dvIy8vD126dMFzzz2Hzp07IyAgALdu3cL69euxYMEC5ObmolOnTjhz5gzi4+ONxlezZk1MmTJFTtrPnj2L2rVro3///joHIDdv3sT+/fuxcuVKXL9+HStXrkTDhg116ipdujS6d+8unxwYNmwYvv76a0RGRurcE/rll19a3UX0888/x/r16+Xv4Msvv4yVK1di0KBBqFy5MpKTk7Ft2zZMmzZNTspUKhVmzpxp1qiU7dq1w9GjR7Fy5UrUrVsXL7zwAurVq4fc3FwcOnQIv/76q3xyB9D0Lnjssces+iz5BgwYgP3792PChAkAgKlTp2L58uUYOHAgmjdvLg/c8+DBA5w9exb79+/H5s2bkZ6ebnCAszJlymD+/Pno2rUrcnNzcfv2bTRr1gw9evRA9+7dERERATc3N9y5cweHDh3C6tWrcf78eUyYMEHvPVYDBw7ExIkTAQDjxo3DzJkzUbNmTZ2B2l599VW0bdtW0bawN3t/f5zRU089haVLl2Ls2LH4+++/MXToUFSuXBlJSUnYsmULpk+fLh9Iuri4YOrUqfJ9Uo+i0aNHY/z48cjIyEBKSgqaN2+OF198EW3atIGPjw+uXbuGlStXYsWKFQDMO56xteeff77QwDf2GIzHlsdPNWvWxOOPP45jx45BCIG2bduibt26CA8P1+kBMm3aNPkElzO0RUlgq+NtQDPw57BhwzBt2jQAmiT0woULGD58OMLDw/HgwQPs3LkT06dPR0pKCgYNGqT4dhyLCTLL0KFDBQABQPTt21dxfTExMXJ9b731lsFyX3zxhVzO2F///v3F5cuXdZ7Tx5y6tP+aNGki7t+/X6ieSpUqWVRP1apVxdWrVxVvNyGEyM7OFk8//bTZ7/3KK68ItVpttE7t9h03bpxN4tSWmJgomjdvbnbM9erVM1jXmTNnRPny5c2qp1SpUmL79u1GY7P0s8+aNUsuHxMTY7L8lStXTH4vhdD9Tm3fvl0MGjTI5Ofz9PQUW7duNfr+W7duFe7u7ibrioqKEgkJCYXiMGb27NnCzc3N7HZduXKlwW1UoUIFo+uaisWUmzdvitq1a5sVp5ubm1i0aJHR+saNGyeXHzp0qPjrr7+Eh4eHybpHjx5ttN7t27fLZStVqmTyc3355ZdCkiSL9kdly5Y1WufGjRtFqVKlzK5vwoQJeut5+PChyW0+a9Ysk5/RGO26rly5YrSspb9dbfb+/phi6fdCCMu2jTm0/9+eNWuW+Oijj0xuC5VKJebNm2e0Xmv//zF331pc9vFz584167fasmVLkZGRYVb72fL/7szMTBEYGCjXV6ZMGZGdna2oTn1sffx08OBB4e/vb7SOgtuvuLeFIdbuw7TXCwoKUhyH9v6rYcOGestY0saA4ePtfPfv3xc1a9Y0q83S09Pt1maGjpHY5bWY++ijjzBz5kyD9/6FhoZi4sSJWLx4sVn3a06ePBkdO3aU74kxJDw8HN999x12796NgICAQq9/88036NWrV6H5EQsKDg7Ge++9hyNHjujcVKyEm5sbFi5ciPnz5yMyMtJgudq1a2PNmjWYNGmSwwdCCAoKws6dO/H7778bHeVPpVKhWbNm+OCDDwyWqVmzJk6cOIFXX31V54Z8bW5ubnjmmWdw6tQptG7dWmn4DjFv3jyMHz9e72TAgGawkMOHD5u8utO2bVvs2LEDderU0fu6t7c3XnjhBRw4cMDi6Q2GDh2KU6dOoX///kYnOg8ODsaYMWPQpEkTva9HRETg+PHj+Oabb9CqVSuUKVPGoknkzREWFob9+/dj3Lhxen/TgOb716VLFxw5cgRPP/20RfV369YNe/bsMThya5kyZTBz5kxMnTrV7DrN+d1++OGHOHbsGPr162dy8un8yaO1u5Hp07FjR5w9exajRo0y+BsDNCPvDR48WJ4yqiA/Pz8cOHAAv/zyCzp06ICwsDCrp5FyNHt/f5zRF198gTlz5qBChQp6X69bty52796NQYMGFXFkxdPgwYOxevVqg/8HBgQE4MMPP8S2bdsc8jvx8PBA9erV5cdDhgyxy1VlWx8/RUdH49SpU/jwww/RtGlTBAYGmrw/vbi3RUlgq+PtfAEBAdixY4fBeZd9fX0xduxYbN26VWeauaIi/T+LpmIuMzMTu3fvxunTp5GRkYEyZcrgsccewxNPPGFVV8bc3FycOHEC58+fx61bt5CWlgZPT0+UKVMG9erVQ506dcwa1UytVuPMmTM4d+4crl+/jpSUFLi7uyMoKAh16tRB/fr17d7N5+TJkzhy5AgSEhIgSRLKli2Lpk2bmpxbyZHOnDmDw4cPIyEhAZmZmfDz80NkZCQaNWpk0eTJmZmZ2LVrFy5fvoz79++jdOnSqFixIlq3bu10Xc0iIiJw9epVAJr70vIT4ezsbGzfvh2XL19GUlISypYtixYtWqBatWoW1S+EwKFDh3Do0CE8ePAAAQEBCA8PR+vWreHr66s4/tTUVOzevRtXr17FvXv34O7ujtDQUNSuXRv16tVzyCiBhuTm5mLv3r04e/Ys7t27B29vb5QvXx4xMTE2mTPwxIkTOHr0KG7duoVSpUqhRo0aiImJMWsQns2bN6Njx44ANPcNnz171uz3zcjIwD///IMrV67g3r17ADRJXZUqVVCnTh2UK1fO4s+SlZWFPXv24PLly0hMTISLiwtCQkIQFRWFBg0aPJLdGO39/SmuWrduLQ+yM2vWLPn+MrVajV27duH8+fO4d+8eQkJC0LBhwxI5XYot5ObmYt++fTh+/DiSk5MRHByMiIgItG7d2uYn0ixx5coVREZGyoOpnD17VifBtLXicPxUXNvCkNmzZ8v3ZcfExJg8OVgc2Op4W1t8fDy2b9+OW7duwcvLC5UqVUK7du1scixjLSaURFQsGEoo6dGyZMkSDBgwAADQuHFjk4PiEBUVQwkllQwffvghvv76awCaHjCP0uimzsIZE8pHBQflISKiYkN7Qubi3MuAiEqO5ORk/P777/Jj7ZGNqXg6ceKEzryRkZGRBkegJvtjQklERMVCXFyczoTiSqcZIiIyJSMjAy+88ALu378PAKhSpYrB+9So+Hjw4IHOCKn16tVzYDTEhJKIiBzmzp07GDp0KB48eICjR48iJycHgGYAgqeeesrB0RFRSTRp0iRs27YN6enpOHbsGO7evSu/9vXXX5t1vzcR/Ye/GCIicpiMjAyds8wA4O7ujj/++MPoiHdERNY6cuQIVq9eXej5ESNGyPdwU/EzbNgw3rtcTDGhJCIih/Pw8EBYWBhat26NN9980+A0L0REtuTt7Y3atWtj1KhRGDlypKPDIXJKHOWViIiIiIiIrFJ8JkYjIiIiIiIip8KEkoiIiIiIiKzChJKIiIiIiIiswoSSiIiIiIiIrMKEkoiIiIiIiKzChJKIiIiIiIiswoSSiIiIiIiIrMKEkoiIiIiIiKzChJKIiIiIiIis8j/ooSMxhYrDcwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total number of BH+NS systems in the dataset is 19383\n" + ] + } + ], + "source": [ + "\n", + "\n", + "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", + "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", + "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", + "\n", + "\n", + "mask_NSBH = ((mask_M1isNS==1) & (mask_M2isNS==0)) | ((mask_M1isNS==0) & (mask_M2isNS==1)) \n", + "\n", + "\n", + "plt.scatter(M1[mask_NSBH], M2[mask_NSBH], c='orange', label='NS+BH')\n", + "\n", + "layoutAxes(ax=ax, nameX='Mass of compact object from primary star [Msun]',\\\n", + " nameY='Mass of compact object from secondary star [Msun]')\n", + "plt.legend(fontsize=20)\n", + "plt.show()\n", + "\n", + "print('The total number of BH+NS systems in the dataset is %s'%len(M1[mask_NSBH]))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "eeb6be29", + "metadata": {}, + "source": [ + "
\n", + "\n", + "\n", + "Features in the plot above: \n", + "\n", + " \n", + "In the plot above we see that the BH-NS systems formed in COMPAS have NS masses between 1.2-2.5 solar masses. This is the neutron star mass range that is expected from the NS equation of state. The BHs are typically between 2.5 and 40 solar masses (but some extreme cases exist, but do not trust these too much). \n", + "\n", + "The gap between 40 and ~120 solar masses where no BHs form is a result from the pair-instability SN remnant mass gap. Where it is predicted that the Helium cores that form the BHs with masses in this gap undergo pair-instabillity and completely explode, not leaving behind any remnant. " + ] + }, + { + "cell_type": "markdown", + "id": "df1ac034", + "metadata": {}, + "source": [ + "## BH-BH" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "59eebae2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SpD59+ii4nCTrzZs366OPPpIkhYaGqmfPngGuCABQWni6gI9UfmdFAbl59M7lzz//LMMwVKtWLV/XY9m7d6/1XKC4Bg50bjeyZo1z8Z64OOf/6ZfFXHX8+HFt27Ytz7WMjAzt2bNH77//vr7++mtJUlhYmF544YVAlOgTWVlZF/3cOTk5OnbsmJYuXarJkycrMzNTkvTkk09a73oCAFCQ8+el0aM9W8CnvM6KAi7Egj4o84KDpa5dA12F702dOlVTp04tsE1sbKxmz56tVq1a+akq3zt8+HChP49hGHr00Uf14osv+qkqAEBpFh8v/elPUlKSZ+3r1nUGy/I2Kwq4EOESKMNCQ0NVrVo1tWjRQjfccIPuu+8+Va1aNdBl+VxQUJCioqLUsGFDXXfddRoxYoSuuuqqQJcFACgFXFNhPRmxlKS//1167jnPRixzcsrHbCqUX4ZpFv6vTlBQkAzDUNWqVXXFFVf4oy5t2bJFycnJMgxDOTk5fnkm/tCiRQtJ0vbt24t0X0ZGhvbv368GDRooLCzMF6UBZQL/rgBAyZOT41y8x5N3LF1WrPBshlR8vHOfzNx9163rXICQEU+UdJ5mgyKNXJ46dUqrVq0qflUAAABACbVypefB0jCc4dCTBXzyGw09dMh5vTwuNIiyyeOtSEzT9PsHAAAA8If4eGnw4KLd48kCPjk5zhFLd/9p67o2dqyzHVDaeTRyOXPmTF/XAQAAAAREUd+zjI2V3n7bs9HGNWsKHg01TengQWe78rAAIco2j8Llvffe6+s6AAAAAL8raGTRndhYZ1gMDfWs/ZEj3m0HlGQeT4sFAAAAyprCRhZzMwzniKWnwVJyrgrrzXZASUa4BAAAQLnl6YhhTEzxFt7p1Mm58I9huP/eMKRLLvFsYSCgpCNcAgAAoNzydMTw44+Lt6JrcLBzuxHp4oDpOvdkYSCgNCBcAgAAoNzydGTRzmI7Awc6Rz3r1Ml7vW5dtiFB2VKkfS4BAACAssQ1sjho0MXfeXNkceBAacAA5zueR444R0w7dWLEEmUL4RIAAADlXrVq0smTF1+bNs17I4vBwWw3grKNcFnOpaWlub3ucDgUFFT8WdOmp+t5A+UU/44AgP+dPy9NmSLt3Ss1aiSNGSMtXJj/HpcXhk0ABSNclnORkZH5fte8efMi9+cKpA6Ho9g1AeVBTk6OJNn6SxwAgOfGjZPefFPK/Z8o48ZJ4eH573FpGNLYsc7prExfBQrHf9XAq0JCQhQUFKSMjIxAlwKUaOfOnVNwcLAqVKgQ6FIAoMy75Rbp9dfzBktJysmRUlPzv880pYMHne9JAigcI5flXGo+/4/atm3bYvUXFBSkSpUqKTU1VTExMXZKA8os0zR19uxZRUVFychveUIAgFd88om0YIG9PjzdCxMo7wiX5VxERITb63am6lWuXFmHDx/WqVOnVLVq1WL3A5RFpmnq8OHDysrKUnR0dKDLAYAyLSdHGjXKfj+e7oUJlHeES3hddHS00tPTdfToUaWlpSk6OlohISGM0KDcMk1TOTk5OnfunM6ePausrCzVrVtXlSpVCnRpAFCmrVkjnTlT/PsNw7kXZadO3qsJKMtsh8sXXnjBOh4xYoTqXLg7LMqlmjVrKjQ0VKdPn1ZiYmKgywFKhODgYEVFRSk6OppgCQB+YGc6qzf3uATKC8O0uR5+UFCQDMNQ1apVdezYMQXzb1+Z0KJFC0nS9u3bbfVjmqays7OtlTGB8iooKEgVKlRgBB8A/GjlSqlbt8LbGYZz6uvhw39cu+QSZ7D01h6XQGnmaTawPXIZERGhc+fOqWnTpgRLXMQwDFWoUIEVMQEAgN916uSc1lrYJKonnpDGj3dOoz1yxBk0r71WWr9e+vBD53mnToxgAoWxvRVJ3P/ecA4J4fVNAAAAlBzBwdLEiX9McXVnwADptdecbbt2le66S0pOlho1co56Dhni/LN+fSk+3l+VA6WT7XB51VVXyTRN7dmzxxv1AAAAAF4zcKA0f75zBDO3KlWkjz+WPv887/X4eGnQoItHOw8dcl4nYAL5sx0uhw0bJkk6duyYli1bZrsgAAAAwJsGDpQSEqQVK6S5c51/JiVJgwfnbZeTIz32mORuRRLXtbFjne0AXMx2uLz55pvVvXt3maapMWPG6MSJE96oCwAAAPBITo5z8Z4PP3T+6S785Z722rWr+/cn16wp+P1M05QOHnS2A3Ax2+HSMAzNnTtXbdq00Z49e9SuXTt99tlncjgc3qgPAAAAyFd8vPN9SG+8H+np1iV2tjgByjKv7XPZu3dv7dmzRwcOHNCgQYMUGxurjh07qlGjRoqKilJQkGc59plnnrFbEgAAAMoB1/uRF05jdb0fOX9+0bYS+d86lV5rB5Q3XtvnMjdXl8XZz439EEsGb+1zCQAA4As5Oc4RyvymsRqGcxGf/fs930LE1eehQ+7fuyxOn0BZ4Gk2sD0tVnKGydyf/K4X9gEAAAA88c9/ev/9SNfWJdLF25e4zidMKF6w9OS9UKC0sz0t9t577/VGHQAAAIBH4uOlZ5/1rG1R3490bV3y2GN5w2vdus5gWZRpti7x8e77mzixeP0BJZXtabEl2YkTJ7Rp0yZt3LjR+vPo0aPW9zNnztTw4cM97u/8+fNav369li1bpk2bNmnHjh1KSkpSTk6OqlatqiZNmuj666/Xfffdp8suu6xYNR89elSzZs3S559/roSEBJ06dUq1atVSs2bNdNddd2nw4MEKDw8vVt9FwbRYAABQEhU2HfZCK1Y4V4ctznPWrHGG07g4qVOn4o1Y5vdeqGsktKjvhQKB4Gk2KJPh8ujRo+rYsaN+//33Att5Gi4zMjI0ZswYffbZZzp9+nSh7Q3D0IgRI/TGG28oKirKw6qljz76SH/605905syZfNs0a9ZMc+fOVevWrT3utzgIlwAAoCRaudK5IqwnLrkksO9H+uK9UCAQ/PrOZUmTkZFRaLAsitTUVM2cOfOiYHnppZeqY8eO6tq1q+rXr29dN01T7777rrp27VpgUMztgw8+0F133ZWnfZMmTdSlSxfVq1fPurZr1y517dpVO3bssPUzAQAAlEZFmeZa3PcjvYV9M1HelMlwmVtsbKz69u2rv//971qwYIHt/jp06KBp06YpMTFRv//+uzZs2KAVK1Zo//792rx5s6677jqr7ebNmzVq1KhC+9y6datGjhxpnTdt2lSbNm3Sr7/+qpUrVyohIUFLlixRzZo1JUlnz57VzTffrIyMDNs/DwAAQGni6TYgzz8f+Omm7JuJ8qZMhstq1app3rx5SkhI0PHjx7V48WK9+OKL6t+/f7H6MwxDPXr00Pr16/Xdd99p5MiRqlOnzkXtWrdurRUrVqhbrrkan3zyiX766acC+3/66aeVmZkpSapevbpWrVqlq6++Ok+bXr16admyZapYsaIkad++fZo6dWqxfh4AAIDSqlMn51TSgna8q1tXevpp/9WUH/bNRHnj03CZkZGhY8eO6cCBAx5/vKFy5coaNGhQnumkdsTExGjp0qW65pprCm1boUIFvf3223muxcfH59t+x44d+vLLL63zl156yRqhvFCLFi00duxY6/yVV16Rw+EotCYAAICyorDtQgzD+X1JeIexsCBsGM73Qjt18m9dgK94NVympaVp6tSp6tu3r6pXr66IiAjVrl1bDRo08OjTsGFDb5YTME2aNFGTJk2s8127duXbNnfwjIyM1NChQwvsO/c026NHj2rDhg02KgUAACh9XNuFXDiRrG7dkrX6qi/3zQRKItv7XLp89dVXuv/++5WUlGRdK4ML0XqsWrVq1vHZs2fzbffVV19Zx9dff70iIyML7Ldhw4Zq2rSpfv31V+v+3O95AgAAlAcDB0oDBnhnuxBf8sW+mUBJ5ZVwuXDhQt16661yOBwXBUrjf38t4y5oFvRdaZd7tdoaNWq4bWOaprZu3WqdezLt1tXOFS5/+eUXG1UCAACUXsHBxdvD0t9KSxAG7LI9LTYtLU3Dhw9XTk6OJKljx45atmyZUlNT1adPHys4OhwOnT17Vjt37tSMGTPUuXNn67uRI0cqIyPD6qO0++6773Qk17JfHTt2dNvuwIEDSktLs84bNWrkUf+52+3cubOYVQIAAMBfXEH4rrucfxIsURbZHrmcMWOGkpOTZRiGOnTooBUrVlgrml4oMjJSTZs2VdOmTTV8+HDNmzdP9913n959913t3r1by5YtU1BQ6V/A9vnnn7eOw8LCNDCf+Q4X7sV56aWXetR/7na///67TNO0RoHdyR1gPeVwOMrE/xYAAAAA/MN2uFyyZIl1/Nprr+UbLN25/fbbVblyZfXr10+rV6/W888/nyeYlUazZ8/W119/bZ2PGTNGcfmsL33hu5jR0dEePaNy5crWscPh0Llz5xQREZFv+8Le48xP8+bNi3UfAAAAgPLH9tDUli1bJDn3Z7z22mvzbZffe5V9+vTRwIEDZZqmJk+erOzsbLslBczWrVv1pz/9yTq/7LLLCgzLF44ohoWFefSc8PDwAvsBAAAAAH+zHS5PnjwpwzDybL3hEhLyx8Boenp6vn3ceuutkqRTp05p5cqVdksKiGPHjumWW26xgl7FihU1d+7cAkcNs7Ky8pzn/n0V5MJ258+fL7B9ampqkT/NmjXzqBYAAAAAkLwwLdY10njhaJokRUVFWcdHjx7Ndx/L3O8QJiQk2C3J786cOaO+fftq3759kqTg4GDNmTNH7dq1K/C+SpUq5TnPyMjw6HkXtitoSqwn37vD+5YAAAAAisJ2gqhataokKSUl5aLvYmNjrePdu3fn20fuaZ2598ksDdLS0nTDDTfo559/luTcXmX69Om67bbbCr33wlHNgkZ3czt37lyB/QAAAACAv9kOl40bN5Zpmm5HHK+44grr+Ntvv823jxUrVljHuRerKekyMjI0YMAArV+/3rr2n//8R/fee69H91evXj3Pee7tSwpy9OhR6zgqKkoVKlTw6D4AAAAA8BXb4bJNmzaSpOPHj+cJPZLUo0cPa4uM6dOnW9NGc9u2bZumTp1qnV955ZV2S/KLrKwsDRo0SMuWLbOuvfLKKxozZozHfVz4nuqBAwc8uu/gwYPWMe9GAgAAACgJbIfLHj16WMeLFi3K8139+vXVs2dPmaaps2fPqn379nrppZe0ePFiLV68WH//+991/fXXKzU1VYZhqEGDBrrmmmvsluRzOTk5uuuuu/TVV19Z15577jk9+eSTReonMjJSl1xyiXXumlpbmJ9++sk6vvzyy4v0TAAAAADwBdsL+vTq1UtRUVFKSUnRe++9p/vvvz/P9xMnTlTbtm2Vnp6u5ORkPfvss3m+d21RYhiGJk6cWOIXknE4HBo+fLg+/fRT69pTTz110c/lqc6dO2vOnDmSpLVr1xbaPisrS99//32e+wEAAAAg0GwnubCwML3zzjt68cUX1bdv34sWpWnWrJkWLVpkLe5jmmaej+Rcafa9997TjTfeaLccn3vwwQc1e/Zs6/zRRx/V+PHji93fgAEDrOOdO3fmGZV054svvrAWTwoKCtLNN99c7GcDAAAAgLfYHrmUpDvvvLPA7zt37qw9e/Zo+vTpWrp0qQ4cOKCsrCzFxcWpS5cuGjVqlOLi4rxRik898cQTmjZtmnU+atQoTZgwwVafN9xwg2JjY3XixAlJ0ksvvZRnVDS3nJwcvfzyy9Z5v379VKNGDVvPBwAAAABv8Eq49ERUVJTGjh2rsWPH+uuRXvXMM8/ozTfftM6HDx+ut99+21qwqLgiIiL0f//3f3riiSckSfHx8ZowYcJFvyfTNDVu3Dht2rRJknMa8Ysvvmjr2QAAAADgLYbpmptaxowcOVIffPDBRdczMzOt45CQEAUHB1/UJiMjI8/5Dz/8oA4dOljnQUFB6tGjh8fvh9asWVOzZs3K9/vz58+re/fuWrdunXXtpptu0tChQ1WrVi0lJCRo+vTped7JHDdunF599VWPnl8cLVq0kCRt377dZ88AAAAAUPJ5mg1sj1y6ts8IDw+33qssqqSkJJ07d06SdOmll9otSZJz4ZvcQdKd7OxsZWdnF9qXqzYXh8NR4L6dF6pXr16B34eGhuqzzz5Tjx49tHXrVknSwoULtXDhQrfthwwZYus9TwAAAADwNtsL+tSvX18NGjTQ8OHDi93HyJEj1aBBAzVs2NBuOaVWbGysfvjhBz355JOKjo5226Z+/fqaPn265syZU+JX1QUAAABQvtieFhsUFCTDMNSnT5+L9rn01K233qoFCxbIMAzl5OTYKadMyMjI0MqVK5WQkKBTp06pZs2auvzyy9WxY0fb73h6immxAAAAACQ/TouF94WFhalv376BLgMAAAAAPFYi5la63nsMCSHrAgAAAEBpVCLC5cGDByVJlStXDnAlAAAAAIDiCHi4XLlypbZs2SLDMNSkSZNAlwMAAAAAKIYizUO9//778/1u69atBX6fm2maSk9P12+//aaff/7Zut6jR4+ilAMAAAAAKCGKtFqsa2XY3Fy321nF1DRNVa1aVdu2bVNcXFyx+4H3sFosAAAAAMnzbFDkabGmaeb55He9KJ+rrrpKS5YsIVgCAAAAQClVpGmxzz777EXXnn/+eRmGoUaNGmno0KEe9RMUFKSIiAjVqlVLbdq0UbNmzYpSBgAAAACghCnStFh3XFNl+/Tpo0WLFnmrLgQY02IBAAAASJ5nA9sbS3bu3FmGYeiKK66w2xUAAAAAoJSyHS5XrlzphTIQKGlpaW6vOxwOBQUFfKcaAAAAAKWE7XCJ0i0yMjLf75o3b+7HSgAAAACUZn4Pl+vWrdOSJUt07NgxVatWTZ07d1afPn1sbWUCAAAAAAgs2+Hy/PnzeuKJJ+RwOBQWFqbXX3/dbVBMT0/XkCFD9MUXX+S5Pn78eLVr107z589X3bp17ZaDIkpNTXV7vW3btn6uBAAAAEBpZjtcLlmyRFOmTJFhGLrvvvvyHYF86KGHtGDBArff/fDDD+rVq5d+/PFHVapUyW5JKIKIiAi313nfEgAAAEBR2E4QK1assI6HDBnits2WLVs0a9YsK3hWr15dAwYMUPfu3a0Qs3v3bv3rX/+yWw4AAAAAIABsh8vNmzdLkipUqKDOnTu7bfPf//5Xru00r7zySu3cuVOfffaZli5dqgULFigoKEimaeq///2vcnJy7JYEAAAAAPAz2+Fy7969MgxDjRs3VkiI+1m2uafDvvzyy4qJibHOb7zxRt12222SpKSkJP300092SwIAAAAA+JntcJmcnCzJOdXVnT179igxMVGGYah69erq3bv3RW1uuOEG63jr1q12SwIAAAAA+JntcJmZmSnJOS3WnTVr1ljH3bt3d7vgT/369a3jkydP2i0JAAAAAOBntsNlZGSkpPxD4apVq6zjLl26uG0THBxsHZ8/f95uSQAAAAAAP7MdLuvWrSvTNLVr1y5lZGTk+S4nJ0eLFy+2zrt27eq2D9fUWkmKioqyWxIAAAAAwM9sh8v27dtLkjIyMvTuu+/m+e6jjz5SUlKSJKl27dpq1qyZ2z527txpHdetW9duSQAAAAAAP3O/vGsRDBkyRDNnzpQkPfnkk0pKStK1116rLVu26Pnnn5ckGYahYcOG5dvHhg0brOOmTZvaLQkAAAAA4GeG6dqA0oa+fftqyZIlbhfrMU1TVapU0a+//qrY2NiLvk9JSVHNmjWVkZGhKlWq5Jkii8Bp0aKFJGn79u0BrgQAAABAIHmaDWxPi5Wkjz/+WNddd51M07zoExkZqY8//thtsJSkOXPmKCMjQ4ZhqFu3bt4oBwAAAADgZ7anxUpSdHS0Vq9erc8++0yLFi1SYmKiwsLCdPXVV2vEiBGqXbt2vvfOmzdP9erVkyQNHjzYG+UAAAAAAPzMK9NiUfYwLRYAAACA5OdpsQAAAACA8o1wCQAAAACwjXAJAAAAALCNcAkAAAAAsI1wCQAAAACwjXAJAAAAALCNcAkAAAAAsI1wCQAAAACwjXAJAAAAALCNcAkAAAAAsC0k0AUgsNLS0txedzgcCgri7x4AAAAAeIZwWc5FRkbm+13z5s39WAkAAACA0sz20NT58+e9UQcAAAAAoBSzHS5r166txx9/XNu2bfNGPfCz1NRUt59mzZoFujQAAAAApYjtcJmcnKy33npLV155pTp27Kjp06fn+x4fSp6IiAi3H963BAAAAFAUXksQpmlq48aNGjVqlOLi4jRy5Eh999133uoeAAAAAFCC2Q6Xs2fPVrdu3WQYhkzTlGmaSk1N1YwZM3TdddepVatWmjhxopKTk71RLwAAAACgBDJM0zS90dH+/fs1ffp0zZo1S4cOHfrjAYYhSQoNDdUtt9yiESNGqGfPnt54JHyoRYsWkqTt27cHuBIAAAAAgeRpNvBauHRxOBz6+uuvNX36dC1cuFBZWVl/POx/QbNevXoaMWKEhg8frjp16njz8fASwiUAAAAAKYDhMrcTJ07ovffe08yZM7Vr164/Hvq/kBkUFKTevXtr5MiRuvnmmxUcHOyrUlBEhEsAAAAAUgkJl7mtW7dO7777rubPn59nNVlX0KxRo4buvfdejRgxQo0bN/ZHSSgA4RIAAACA5Hk28Nt+E9ddd51mzpypI0eO6O2331b79u2t70zT1LFjx/Tqq6+qWbNm6tq1q+bNmyeHw+Gv8gAAAAAANvh9M8PIyEhdffXVat26tcLCwiQ5Ry9zrza7Zs0a3XnnnWratKnmz5/v7xIBAAAAAEXkt3B5+vRp/ec//1Hr1q3Vvn17TZs2TZmZmVagrFq1qq655po8IXPv3r264447NGrUKH+VCQAAAAAoBp+Hy+XLl2vo0KGqXbu2HnvsMW3ZssUKj6ZpqlOnTpo9e7YOHz6sdevWae/evfq///s/VatWTZJzyuz06dM1c+ZMX5cKAAAAACgmnyzoc/jwYc2cOVMzZ87U/v37JTlDoktMTIzuuecejRo1Sk2bNnXbR0pKikaPHq2PPvpIktShQwdt2LDB26UiHyzoAwAAAEDyPBuEeOuBOTk5+uKLLzR9+nR988031mI8uUNl586dNXr0aN12220KDQ0tsL+oqCjNmDFDy5Yt04kTJ7Rt2zZvlQoAAAAA8DLb4fLXX3/V9OnT9f777+vEiROSLh6lvPfeezVq1Cg1adKkSH2HhYXpqquu0rfffqtz587ZLRUAAAAA4CO2w+Xll19uLcKTW5cuXTRq1CiPRikLUqlSJbslAgAAAAB8zGvTYiXnKOXw4cM1atQoNW7c2Ct9du/eXVWqVPFKXwAAAAAA3/BKuHSNUg4cONDWKKU7jzzyiFf7AwAAAAB4n+1wuXv3bl122WXeqAUAAAAAUErZDpdz5861jkeMGKE6derY7RIAAAAAUMrY3ucyKChIhmGoatWqOnbsmIKDg71VGwKIfS4BAAAASJ5ngyC7D4qIiJAkNW3alGAJAAAAAOWU7XAZFxcnSQoJ8erCswAAAACAUsR2uLzqqqtkmqb27NnjjXoAAAAAAKWQ7XA5bNgwSdKxY8e0bNky2wXBv9LS0tx+HA5HoEsDAAAAUIrYDpc333yzunfvLtM0NWbMGJ04ccIbdcFPIiMj3X527doV6NIAAAAAlCK2w6VhGJo7d67atGmjPXv2qF27dvrss88Y+QIAAACAcsT2KjwvvPCCJKl3797as2ePDhw4oEGDBik2NlYdO3ZUo0aNFBUVpaAgz3LsM888Y7ckFEFqaqrb623btvVzJQAAAABKM6/tc5mbq8sLr3siJyfHTjnwEva5BAAAACB5ng28sn9Ifvm0qLm1OGEUAAAAABB4tsPlvffe6406AAAAAAClmO1wOXPmTG/UAQAAAAAoxWyvFgsAAAAAAOESAAAAAGAb4RIAAAAAYBvhEgAAAABgm1e2IslPRkaGzpw5o8zMTI/vufTSS31YEQAAAADAF7waLtPS0vT+++9rwYIF2rRpk06dOlWk+w3DUHZ2tjdLAgAAAAD4gdfC5VdffaX7779fSUlJ1jXTNL3VPQAAAACgBPNKuFy4cKFuvfVWORyOiwKlYRiS3AfNgr4DAAAAAJQethf0SUtL0/Dhw5WTkyNJ6tixo5YtW6bU1FT16dPHCo4Oh0Nnz57Vzp07NWPGDHXu3Nn6buTIkcrIyLD6AAAAAACULrbD5YwZM5ScnCzDMNShQwetWLFC3bp1U6VKlS5qGxkZqaZNm2r48OFauXKlPv74Y4WHh+vdd99Vnz595HA47JYDAAAAAAgA2+FyyZIl1vFrr72mihUrenzv7bffrk8//VSmaWr16tV6/vnn7ZZzkRMnTmjx4sV64YUX1L9/f8XFxckwDOvz3nvvFbvvffv26ZlnntHVV1+t2NhYhYeHq1GjRrr11ls1f/78Yo/E+qpfAAAAAPAVw7T5wmO9evV08OBBxcbG6tixY3m+69evn7755htrFVjXO5YXGjRokOLj41WtWjUdPXpUISH2XwU9evSoOnbsqN9//73AdjNnztTw4cOL3P+ECRP017/+tcBtVq655hrNmTNHDRo0CHi/RdWiRQtJ0vbt2332DAAAAAAln6fZwPbI5cmTJ2UYhpo0aXLRd7lDYnp6er593HrrrZKkU6dOaeXKlXZLkuTcY7OwYFlcL774oh5//HErAAYFBally5bq3Lmz4uLirHYbNmxQ586ddfTo0YD2CwAAAAC+ZjtcuvalDA8Pv+i7qKgo67igIHTppZdaxwkJCXZLukhsbKz69u2rv//971qwYIGtvr755hs9++yz1vk111yjnTt3auvWrVq1apUSExP14YcfKjIyUpKUmJioQYMGBaxfAAAAAPAH2/NPq1atquPHjyslJeWi72JjY63j3bt3q2HDhm77SEtLs45z75NpR7Vq1TRv3jy1a9dO9erV80qfpmnqL3/5i7XKbdOmTbV06dI8ixcFBQXpzjvvVPXq1dWrVy9J0rp16/TZZ59ZI7T+6hcAAAAA/MX2yGXjxo1lmqbbEccrrrjCOv7222/z7WPFihXWceXKle2WZPUzaNAgrwVLSfr666/1yy+/WOcTJ050uyquJPXs2VN33HGHdf7yyy/7vV8AAAAA8Bfb4bJNmzaSpOPHj1809bVHjx7WIj7Tp0/Xvn37Lrp/27Ztmjp1qnV+5ZVX2i3JZz799FPruEGDBurdu3eB7UePHm0db9y4UYmJiX7tFwAAAAD8xXa47NGjh3W8aNGiPN/Vr19fPXv2lGmaOnv2rNq3b6+XXnpJixcv1uLFi/X3v/9d119/vVJTU2UYhho0aKBrrrnGbkk+89VXX1nHffr0yXf1W5dOnTopIiJCknPq64W/H1/3CwAAAAD+Yjtc9urVS1FRUTJN0+2eka4pnoZhKDk5Wc8++6xuuukm3XTTTfr3v/+ts2fPSpIMw9DEiRMVFGS7JJ+4cGTWkxAcEhKidu3aWee5p776ul8AAAAA8CfbSS4sLEzvvPOOXnzxRfXt2/eiLUeaNWumRYsWWYv7mKaZ5yM5V5p97733dOONN9otx2d27tyZ57xRo0Ye3Ze73YV9+LJfAAAAAPAn26vFStKdd95Z4PedO3fWnj17NH36dC1dulQHDhxQVlaW4uLi1KVLF40aNSrPPo4l0YV7ZubePqUghW2z4qt+c8u9Gq+nHA5HiR1FBgAAAFDyeCVceiIqKkpjx47V2LFj/fVIr3JN33WJjo726L7cq9+6267FV/3m5tobs6iaN29erPsAAAAAlD8MTXnowtG/sLAwj+4LDw/Ptw9f9gsAAAAA/uS3kcvSLisrK895SIhnv7rc7c6fP++3fnNLTU31qM/c2rZtW+R7AAAAAJRfhEsPVapUKc95RkbGRdfcycjIsI5d24f4o9/cCvveHd63BAAAAFAUJAgPXfje4oWr4ubn3Llz+fbhy34BAAAAwJ88Grls2LChr+uQ5Nzrcu/evX55VlFVr149z/mRI0cUExNT6H2597B0195X/QIAAACAP3kULhMSEmQYRqHtXPtW5nbhffm1MU3To2cEStOmTfOcHzhwQC1btiz0voMHD1rHzZo181u/AAAAAOBPHk+LNU2z0I/kDIquj7v7XHK3cxc4S5rGjRvnWUTn559/9ui+n376yTq+/PLL/dYvAAAAAPiTRyOXK1asKLRNYmKiHn/8cZ08eVKmaapVq1bq16+fWrRooZiYGFWsWFEpKSnav3+/fvjhB3311VdKTU2VYRi68847NXr0aNs/jC+FhoaqQ4cOWrdunSRp7dq1hd5z9OhR/fbbb9Z5586d/dYvAAAAAPiTR+GyS5cuBX7/008/aezYsUpOTtbll1+uqVOnqlOnTgXek5aWpn/9618aP368Pv74Y1WsWFEzZ870vPIAGDBggBUCly5dquPHj6tGjRr5tp8zZ451XKVKlXx/j77qFwAAAAD8xfZqsWfOnNHAgQN18uRJtWnTRhs2bCg0WErO7TH++c9/6oMPPpBpmnr//ff1xhtv2C3Hp+666y5VrFhRknN/yvHjx+fbNjU1VW+99ZZ1PnTo0Hz3sPRVvwAAAADgL7bD5Xvvvafff/9dhmFoxowZioqKKtL9d911l2699VaZpqmXX35Z2dnZdkvymbp16+aZvjtx4kR9+umnF7XLysrSfffdpwMHDkiSwsPD9fTTT/u9XwAAAADwF8O0uZrOddddpw0bNqhJkybatWtXsfr46KOPNGTIEBmGocWLF6t37952SrKMHDlSH3zwwUXXMzMzreOQkBAFBwdf1CYjI8Ntn6dOnVKHDh20Z88eSVJQUJCGDBmiW265RdWqVdOvv/6qqVOnasuWLdY9//nPf/TQQw8VWKuv+i2uFi1aSJK2b9/uk/4BAAAAlA6eZgPb8yn37dsnwzBUp06dYveR+979+/fbLcmSlZWVJ0i6k52dXaTR0qpVq2rhwoXq2bOnDh48KIfDodmzZ2v27Nlu2//lL3/xKAD6ql8AAAAA8Afb02JPnz4tSTp+/Hix+8h975kzZ+yW5HNNmjTRli1bNGLECIWHh7tt07x5c3355Zd6+eWXA94vAAAAAPia7WmxDRs2VEJCggzD0J49e9SwYcMi93HPPfdo9uzZMgxDM2fO1D333GOnJL9KSUnR8uXLdfDgQaWlpSkuLk6tWrVS69atS2S/nmJaLAAAAADJj9Nir7/+eiUkJEiSRo0apcWLF6tChQoe37906VLNnTvXOr/uuuvsluRXUVFRGjBgQKnpFwAAAAB8wfa02JEjR1rHK1asUM+ePbV79+5C73M4HJo8ebL69+8vh8MhwzDUtWtXNWrUyG5JAAAAAAA/sz0tVpJGjx6t//73vzIMQ5IUHBysbt26qU+fPmrZsqViYmIUGhqqlJQUJSQk6IcfflB8fLwOHTok1+MjIiK0efNmNW7c2G458AKmxQIAAACQ/DgtVpKmTJmilJQUffTRRzIMQ9nZ2Vq6dKmWLl2a7z2maVphtHLlylq4cCHBEgAAAABKKdvTYiXnSOXcuXP13//+VzExMZKc4dE1Kuk6zn3u+vPGG2/UL7/8ouuvv94bpQAAAAAAAsAr4dJlxIgROnjwoGbPnq1BgwapXr16unDWbVhYmK655ho99dRT2rp1q7788kvVq1fPm2UAAAAAAPzMK+9cFiQ7O1unT5/W+fPnVblyZUVGRvrycfAS3rkEAAAAIPn5ncsCHxASourVq/v6MQAAAACAAPLqtFgAAAAAQPlEuAQAAAAA2Ea4BAAAAADY5tV3Lvfu3asPPvhA69at065du3T69GmdO3fO4/tde2TCf9LS0txedzgcCgri7x4AAAAAeMYr4TI7O1tPPPGEpk6dKofDYV338UK08IKCVu9t3ry5HysBAAAAUJp5JVzecccd+vzzzwmTAAAAAFBO2Q6Xn332mT777DMZhiFJql27th544AFdf/31qlOnjipVqmS7SPhOamqq2+tt27b1cyUAAAAASjPb4XLWrFnWcffu3bVgwQJFRETY7RZ+kt//VrxvCQAAAKAobCeIjRs3SpKCg4P1/vvvEywBAAAAoByyHS5PnjwpwzB0xRVXqHbt2t6oCQAAAABQytgOl9WrV5ckValSxW5XAAAAAIBSyna4bNq0qUzT1KFDh7xRDwAAAACgFLIdLocNGyZJ2r17t/bt22e7IAAAAABA6WM7XA4dOlRXXnmlJGncuHG2CwIAAAAAlD62w2VoaKjmz5+vuLg4LViwQPfcc0++eycCAAAAAMomwzRN004Hq1evliQdOXJEDz/8sJKTk1WlShXdeeeduuaaa1SrVi2FhoZ63F/nzp3tlAMvadGihSRp+/btAa4EAAAAQCB5mg1sh8ugoCAZhmGdu7rLfc1ThmEoOzvbTjnwEsIlAAAAAMnzbBDirQeapinDMKxQaTOzAgAAAABKEdvh8tJLLy3WKCUAAAAAoOywHS4TEhK8UAYAAAAAoDSzvVosAAAAAACESwAAAACAbYRLAAAAAIBtfguXbDECAAAAAGWX17YiyW3FihX6/PPP9f333+u3337TmTNn5HA4FB4ertjYWLVp00adOnXSsGHDVL16dV+UAAAAAADwI8P04oaUa9eu1Z/+9Cft3LnTuuaue9fWJaGhoRo9erT+/e9/Kzw83FtlwAs83SgVAAAAQNnmaTbw2rTYN954Q127dtXOnTtlmqb1ccd1PTMzU5MmTVKbNm10+PBhb5UCAAAAAPAzr0yLnTVrlsaNGyfpj1HJpk2b6qabblKrVq1UvXp1hYaGKiUlRXv37tX333+vr776ShkZGZKkX3/9Vb1799bGjRsZwQQAAACAUsh2uExOTtYTTzwhyRks69evr8mTJ6tv374F3nf69Gn94x//0OTJk2UYhnbu3KmXX35Zzz//vN2SAAAAAAB+Znta7MyZM3Xq1CkZhqFmzZrp+++/LzRYSlKVKlU0adIk/ec//7Gm0E6aNEk5OTl2SwIAAAAA+JntcLlo0SLr+N133y3y6q9jxoxRr169JElnzpzRunXr7JaEIkhLS3P7cTgcgS4NAAAAQCliO1zu3r1bklS/fn1dc801xepj2LBhF/UH/4iMjHT72bVrV6BLAwAAAFCK2A6XJ06csN61LK569epZxydPnrRbEgAAAADAz2wv6BMVFaXk5GSdOnWq2H2cPn3aOo6IiLBbEoogNTXV7fW2bdv6uRIAAAAApZntcFmnTh2dPHlS27Zt04kTJxQbG1vkPpYtW5anP/hPfmE+KMhrW6ACAAAAKAdsJ4gePXpIknJycvTUU08V+f49e/bo3XfflSQFBwera9eudksCAAAAAPiZ7XA5bNgwGYYhSXr//ff10EMPKSMjw6N7N23apJ49eyo9PV2GYahfv36qWrWq3ZIAAAAAAH5mO1y2bt1a99xzj0zTlCS9/fbbatKkiV566SVt2rRJmZmZedonJiYqPj5et99+uzp27KjExERJUmhoqF555RW75QAAAAAAAsAwXanQhoyMDPXu3Vtr166VYRgyTdMazZSk8PBwhYaGKjU1VTk5OdZ116ODg4P18ccfa+DAgXZLgZe0aNFCkrR9+/YAVwIAAAAgkDzNBl5ZtSUsLEzffPONHnzwQeuaaZrW59y5czp9+rSys7OtQOn6s169elqyZAnBEgAAAABKMa8tCRoeHq7Jkyfrp59+0siRI1WjRo182wYHB6tDhw565513tGPHDnXr1s1bZQAAAAAAAsAr02Lz8/vvv+u3337T6dOnlZmZqejoaMXGxuqKK65QWFiYrx4LL2BaLAAAAADJ82xge5/LgtSrV0/16tXz5SMAAAAAACWA16bFAgAAAADKL8IlAAAAAMA2r0yLfeGFF3T69GkZhqEXX3xRlSpV8vje+Ph4rV27VpI0bNgwtWnTxhslAQAAAAD8yHa4/PHHH/Xcc8/JMAz179+/SMFSkurXr69BgwbJMAwdPHhQ8+bNs1sSAAAAAMDPbE+L/fzzz63jESNGFPn+Nm3aqHXr1jJNU4sWLVJmZqbdkgAAAAAAfmY7XLqmtFaoUEG9e/cuVh833nijJCkjI0MbN260WxIAAAAAwM9sh8tdu3bJMAw1a9ZMoaGhxeqjdevWefoDAAAAAJQutsPlqVOnJEnVq1cvdh+xsbHWcXJyst2SAAAAAAB+ZjtcukYrz507V+w+0tPT7ZYBAAAAAAgg2+EyNjZWpmlq7969xe5jz549efoDAAAAAJQutsNly5YtJUlJSUnW4j5FFR8fbx03a9bMbkkAAAAAAD+zHS779OljHf/lL39RTk5Oke5ftGiRli9fLkmqUqWKOnbsaLckAAAAAICf2Q6XQ4cOVZUqVSRJ3333ne68806lpaV5dO/y5ct11113SZIMw9CoUaNkGIbdkgAAAAAAfmY7XEZHR+vZZ5+VaZqSnFNcW7ZsqYkTJ+rgwYMXtT9//rxWrlypoUOHqnfv3kpJSZEk1apVS3/961/tlgMAAAAACADDdKVCm+677z7NmjVLhmHINE1rBDImJkaxsbGqWLGizpw5o8TERGVnZ0uSFUgjIyO1evVqXXXVVd4oBV7QokULSdL27dsDXAkAAACAQPI0G4R464EzZsxQvXr19NJLL1mh0TRNJSUl6eTJk1a7C7Ps5Zdfro8//thaGAj+ld8UZofDoaAg2wPbAAAAAMoJr6UHwzD03HPP6eeff9awYcMUHh5ufWeapvVxad68uaZMmaLNmzcTLAMoMjLS7WfXrl2BLg0AAABAKeK1abEXys7O1o8//qidO3cqOTlZmZmZqlKlimrVqqWOHTsqLi7OF49FERW0gFLz5s2ZFgsAAACUc36fFntRxyEh6tChgzp06OCrR8ALUlNT3V5v27atnysBAAAAUJr5LFyidIiIiHB7nfctAQAAABQFCQIAAAAAYJtPRi737Nmj5cuXa/PmzUpKStKZM2dkmqaWLVt2Udvz589bx6Ghob4oBwAAAADgY14Nl5s3b9Zf/vIXLV++PM/13PteXqhLly764YcfZBiGfvjhB7Vp08abJQEAAAAA/MBr02InT56sa6+9VsuXL8+z9Uhhi9E++eSTVrtZs2Z5qxwAAAAAgB95JVx+8MEHeuSRR5SVlSXTNBUTE6MHHnhAEyZMKHQPy5tvvllVqlSRJC1cuNAb5QAAAAAA/Mx2uDx58qQee+wx63zkyJE6cOCApk2bpkcffVR16tQp8P4KFSqob9++Mk1TCQkJOnDggN2SAAAAAAB+Zjtc/ve//9Xp06dlGIbuvPNOvfPOOwoPDy9SH7n3VNy2bZvdkgAAAAAAfmY7XC5atEiSZBiGXn311WL10bhxY+v4999/t1sSAAAAAMDPbIfLPXv2yDAMNW/eXLVr1y5WH653LiXp7NmzdksCAAAAAPiZ7XB56tQpSVLNmjWL3Ud2drZ1HBwcbLckAAAAAICf2Q6X0dHRkuyNOB4+fNg6jomJsVsSAAAAAMDPbIfLOnXqyDRNbd++Pc8IZFGsWrXKOm7SpIndkgAAAAAAfmY7XHbv3l2SlJ6ernnz5hX5/lOnTumjjz6SJEVERKhDhw52SwIAAAAA+JntcHnHHXdYx08++aSOHz9epPvvv/9+paamWluZhISE2C3JL44fP65XX31Vffv2Vd26dVWpUiWFhYUpLi5O3bp10zPPPKP9+/cXud+tW7fqiSee0BVXXKFq1aopMjJSTZs21dChQ/X111/74CcBAAAAAPsM0zRNu50MGjRI8fHxMgxDDRo00Pvvv69rr71WktSvXz998803MgxDOTk51j379u3TQw89pCVLlsg0TYWFhWnnzp2qV6+e3XJ8bsKECfrb3/6m9PT0AtsFBwfriSee0D//+U9VqFChwLbZ2dl65plnNH78eDkcjnzb3XTTTZoxY4ZiY2OLVbunWrRoIUnavn27T58DAAAAoGTzNBt4JVwmJSXpmmuu0b59+6xrbdq0Uffu3fXll19q165d1j6Yx48f13fffaf169crJydHpmnKMAzNnDlT99xzj91SfO6vf/2rxo8fn+daXFycLrvsMgUFBSkhIeGivToHDRqkTz75RIZh5NvviBEjNGPGDOu8QoUKat68uSIjI7Vr1y6dPHnS+u7KK6/UunXrFBER4aWf6mKESwAAAACSn8OlJO3fv1+33nqrtmzZ4uy4gCAlSa7HhoSE6PXXX9cjjzzijTJ8au3aterUqZN13qRJE73zzjvq2rVrnnabNm3S6NGjtXnzZuvazJkzNXz4cLf9Tps2TaNHj7bO+/fvrylTpqhOnTqSpKysLL3zzjt6/PHHrUWThgwZojlz5njpJ7sY4RIAAACAFIBwKUnnz5/XP//5T02ePFnJycl5H2QYuvBR1157rV555RVrCm1Jd+edd+rjjz+W5NyCZceOHapdu7bbtmfOnNGVV15pjWK2bdtWGzduvKjduXPn1KhRIx09elSS1LVrVy1dutTtfp/Tp0/XAw88IMn5+9y0aZPatGnjlZ/tQoRLAAAAAFKAwqVLenq6Fi1apDVr1mjbtm06efKk0tLSFB0drZo1a6pjx47q06eP2rVr5+1H+1SdOnWsPTkffvhhTZo0qcD2r732mp588klJzjCYkZGh0NDQPG2mTp2qMWPGWG127NihZs2a5dtnx44d9f3330uSBg8ebIVdbyNcAgAAAJA8zwY+WZo1PDxct912m2677TZfdB8wJ06csI5btmxZaPvcbUzTVFJS0kUjnZ9++ql13KVLlwKDpSSNHj3aCpeLFi1SZmamKlas6FH9AAAAAOArtrciKU8iIyOt4/PnzxfaPjMz0zo2DEPR0dF5vk9NTdXq1aut8759+xbaZ79+/fLcv2rVqkLvAQAAAABfI1wWQfv27a3j3KEwP7mDX+vWrS9a3XXHjh3Kysqyzq+55ppC+6xVq5bq169vnf/yyy+F3gMAAAAAvuaTabEFOXDggJYtW6Zjx46pWrVq6ty5c6FTQUuKMWPG6JtvvpEkxcfHa/ny5erevbvbtj///LPeeecd63zcuHEXtdm5c2ee80aNGnlUR6NGjZSQkOC2D3fS0tI86jc3h8OhoCD+7gEAAACAZ2yHS4fDoTfeeEMOh0PBwcF64okn3G5DkpOTo3Hjxmny5MnKycnJ893AgQP17rvvXjRttKTp37+/HnnkEU2aNEkOh0P9+vXTE088oSFDhuTZ53L+/Pl6+eWXde7cOUnSU089pbvuuuui/nLvhxkSEqK4uDiP6rj00kutY1fILEju6bxF0bx582LdBwAAAKD8sR0uV65cqaeeekqGYWjQoEH57m/59NNPa+LEiW6/i4+P1+HDh7V69Wq3W3CUJG+99ZYaN26sF198USdOnNDLL7+sl19+2W3bZs2a6W9/+5vuvvtut9+fPXvWOo6KivJ4pLBy5crWcUpKShGqBwAAAADfsD3vcenSpdZxfiFq3759euONN2QYhgzDUIUKFXTllVda00BN09R3332nCRMm2C3HLx555BHFx8cXOJ23Zs2aGjNmjG699dZ82+SerhoWFubx88PDw932kZ/U1NQif0rLVGUAAAAAJYPtcLlp0yZnR0FB6tmzp9s206ZNU3Z2tiSpfv362rZtm3766Sft2bNHkydPluQMmIXtG1kSHDhwQL169VKnTp20a9cuSVKNGjV03XXXqWvXrmrYsKEk6dixY3r00UfVsGFDLVq0yG1fuRfzCQnxfBA5d1tPVq2NiIgo8of3LQEAAAAUhe0E8dtvv0lyLjKT3+hbfHy8dTx+/Hg1btzYOn/wwQfVp08fSdLBgwe1detWuyX5TEJCgq699lprtPbyyy+3Fidau3atVqxYob1792rnzp268cYbJTn3xhwwYIAWL158UX+VKlWyjjMyMjyuI3fbC1egBQAAAIBAsB0uk5KSZBiGatas6fb7gwcPWgE0KipKAwYMuKjNwIEDreOSvLXGPffco0OHDkmSmjRpog0bNrhdLbZZs2b68ssvNWjQIElSdna27rvvvoumsOZeaCc9Pd3jOlwLBV3YBwAAAAAEiu1w6QpF+Y1arlmzRpJkGIa6deumChUqXNTmsssus46PHz9utySfWL9+vfWzSM4R2IJWtzUMQ5MmTVJoaKgk5zTZjz76KE+b6tWrW8eudx09cfToUes4JibGo3sAAAAAwJdsh0vX1M5Tp065/X7VqlXWcZcuXdy2cQUwqWjTQ/0p98JFFSpUUN++fQu9p1atWmrfvr11vnr16jzfN23aNM/5gQMHPKrl4MGD1jEL7wAAAAAoCWyHy7i4OJmmqV27dl20f6WkPO8adu3a1W0fuYNpSX2H0DUdVpJiY2M9Xt31kksusY5zjzhKznc2c/v5558L7S8rK0vbtm3Ltw8AAAAACATb4fLqq6+W5NwS45NPPsnz3aJFi5SYmCjJOX3zqquuctuH651MyRlWS6KKFStax8V9PzL3FiKS1LBhQ9WtW9c6X7t2baH9/fjjj3me37lzZ49rAQAAAABfsR0ub7/9dut4zJgxeu+997R7927Nnz9fI0aMkOR8/3Dw4MH59vH9999bx02aNLFbkk/Url3bOj516pT27dvn0X0//vijdVynTp2Lvu/fv791PG/evEK3FpkzZ4513KJFC2uvUAAAAAAIJNvhcsCAAWrbtq1M09TZs2c1YsQIXX755brjjjusxXkqVqyocePGub0/MzPT2geyUqVKuuKKK+yW5BOdOnXKcz5x4sRC75k/f741ciu5f+d0+PDh1nFSUpLeeeedfPtLTEzUrFmz3N4LAAAAAIFkO1wahqFPP/1UjRo1kmmaF32Cg4M1bdo01a9f3+39n376qVJSUmQYhq6//noFBdkuySc6duyYZ/GcSZMm6b///W++7Tds2KBRo0ZZ5zVr1rT2vsytXbt2eUYv//a3v2ndunUXtTt79qyGDBmilJQUSc7pww899FCxfhYAAAAA8DbDNE3TGx2lp6drypQpWrx4sRITExUWFqarr75aDz/8sFq3bp3vfVdddZW2bNkiSZo8ebIefPBBb5TjE0uWLNENN9yQZ+Gizp07684771STJk1UoUIFHThwQF999ZXmzZuXp90HH3ygYcOGue03ISFB7du314kTJyQ5R3pHjBih3r17KzIyUlu2bNGkSZO0f/9+SVJQUJA+//xz3XzzzT77WVu0aCFJ2r59u8+eAQAAAKDk8zQbeC1clhezZs3S6NGjlZmZ6VH7kJAQjR8/Xk888USB7davX6+bb75ZycnJBbYLDg7WxIkTfT5qSbgEAAAAIHmeDUrmHNQS7N5779XmzZt1xx13qEKFCvm2CwoKUv/+/bV+/fpCg6UkXXvttdqyZYtuu+02hYSEuG3Tvn17rVmzhumwAAAAAEocRi5tSE1N1caNG7V7925rr87o6Gg1atRI7du3V5UqVYrV74kTJ7R69WolJibq/Pnzql27ttq1a+fXlXQZuQQAAAAgMS0WNhEuAQAAAEhMiwUAAAAA+BHhEgAAAABgG+ESAAAAAGAb4RIAAAAAYBvhEgAAAABgG+ESAAAAAGAb4RIAAAAAYBvhEgAAAABgG+ESAAAAAGBbSKALQGClpaW5ve5wOBQUxN89AAAAAPAM4bKci4yMzPe75s2b+7ESAAAAAKWZz8Jldna2zpw5o3Pnzsk0TY/vu/TSS31VEgAAAADAR7waLpcvX64PPvhA69at0759+4oUKiXJMAxlZ2d7syQUIjU11e31tm3b+rkSAAAAAKWZV8LlqVOnNGTIEC1ZssS6VpRgaRhGkYMovCMiIsLtdd63BAAAAFAUtsPl+fPn1a1bN23durXYAZFgCQAAAAClm+3hqYkTJ2rLli3WedeuXTV37lzt3btXaWlpcjgcHn9ycnLslgMAAAAACADbI5effPKJdfz444/r9ddft9slAAAAAKCUsT1yuWfPHklSlSpV9PLLL9suCAAAAABQ+tgOl5mZmTIMQ61atVKFChW8URMAAAAAoJSxHS5d+1KyhQgAAAAAlF+2w2W3bt1kmqZ27NjBgjwAAAAAUE7ZDpePPvqogoODdebMGc2aNcsbNQEAAAAAShnb4bJ58+Z64YUXZJqmxo4dq7Vr13qjLgAAAABAKWI7XErS//3f/+mf//ynzp07p+7du+vhhx/W5s2b5XA4vNE9AAAAAKCEM0zTNL3V2ZIlS9S/f39lZWVJkipWrKhq1aopNDTUs2IMQ3v37vVWObChRYsWkqTt27cHuBIAAAAAgeRpNgjx1gNfeeUVvfzyy1awNE1TGRkZOnLkiEf3m6YpwzC8VQ4AAAAAwI+8Ei4feeQRTZkyxQqIuQdDvTgwCgAAAAAooWyHy/j4eE2ePNkadaxatapuv/12tW/fXrVq1VKlSpVsFwkAAAAAKNlsh8upU6daxz169ND8+fMVHR1tt1sAAAAAQClie7XYH3/8UZJUoUIFzZ07l2AJAAAAAOWQ7XCZnp4uwzB01VVXKTY21hs1AQAAAABKGdvhsk6dOpLEu5UAAAAAUI7ZDpdXX321TNNkf0oAAAAAKMdsh8sRI0ZIkhITE7Vq1SrbBQEAAAAASh/b4bJ3794aNmyYTNPU6NGjlZSU5I26AAAAAACliO1wKUnTpk3THXfcod27d6tNmzaaP3++HA6HN7oGAAAAAJQCtve5vP/++yVJ4eHhqlatmhITE3XHHXcoOjpaV199tWrWrKnQ0FCP+jIMQ9OnT7dbEoogLS3N7XWHw6GgIK/83QMAAACAcsAwTdO000FQUJAMw8hzzdXlhdc9kZOTY6ccFFFB/xs1b95c27dv92M1AAAAAEqaFi1aSFKh2cD2yKX0R5j09Hp+ihNGAQAAAACBZztcPvvss96oAwGSmprq9nrbtm39XAkAAACA0oxwWc5FRES4vc77lgAAAACKggQBAAAAALCNcAkAAAAAsI1wCQAAAACwzSurxeZn165d+u2333Tq1CllZmaqSpUqio2NVevWrVW5cmVfPhoAAAAA4EdeD5cbN27Uf/7zH33xxRc6e/as2zaGYahly5Z64IEHNHz4cEVGRnq7DAAAAACAH3ltWmxaWppGjhypjh07avbs2Tpz5oy1z6Vpmnk+DodDW7du1WOPPabLL79c3377rbfKAAAAAAAEgFfC5blz59SnTx/NmDHDCpAuwcHBql69umrXrq2oqCjruqvNoUOHdMMNN2jevHneKAUAAAAAEABeCZejR4/W+vXrrfN69erp3//+t7Zu3ar09HQdP35ciYmJOnPmjI4fP64vv/xSgwcPlmEYMgxDOTk5uvvuu7Vz505vlAMAAAAA8DPDzD3MWAwbN25Uhw4dZBiGJGnMmDF67bXXVLFiRY/uve2223To0CGZpqkbb7xRX375pZ1y4CUtWrSQJG3fvj3AlQAAAAAIJE+zge2Ryzlz5ljH999/vyZNmuRRsJSkdu3aaenSpQoPD5ckff311zp58qTdkgAAAAAAfmY7XC5fvlySFBISopdffrnI9zdp0kSjRo2SJDkcDq1atcpuSQAAAAAAP7MdLg8fPizDMNSqVSvFxMQUq4/u3btbx4cOHbJbEgAAAADAz2yHy7S0NElS5cqVi91H7nvPnTtntyQAAAAAgJ/ZDpfVq1eXaZrat29fsfvYv3+/dVzc0U8AAAAAQODYDpdNmzaVJB08eFBr164tVh+zZ8+2jps1a2a3JAAAAACAn9kOlzfeeKN1/MADDygpKalI90+ZMkXLli2TJFWtWlXXXnut3ZIAAAAAAH5mO1zed9991lTW3bt3q3379vrqq68KvS85OVmPPvqoHnnkEUmSYRh69NFHFRRkuyQAAAAAgJ8ZpmmadjuZM2eO7r77bhmGIdM0ZRiGGjRooL59+1qryIaGhiolJUX79+/XDz/8oG+//Vbnz5+X6/GtWrXS999/r7CwMNs/FOzzdKNUAAAAAGWbp9kgxBsPGzp0qE6ePKk///nPcjgc1gI/U6dOzfceVwiVpObNm+ubb74hWAIAAABAKeW1OaiPPvqo1qxZoyuvvNK6Zpqm9XF3Hh4ernHjxmnTpk2qVauWt0oBAAAAAPiZV0YuXTp27KjNmzfru+++0+eff67vv/9ev/32m06fPq3MzExFR0crNjZWbdq0UadOnXTnnXcqOjramyUAAAAAAALAq+HSpWPHjurYsaMvugYAAAAAlEC2w2X37t2t4xkzZqh+/fp2uwQAAAAAlDK2w+XKlStlGIYuueQSgmUplJaW5va6w+FgWxgAAAAAHrMdLqtWrarTp0+rQYMG3qgHfhYZGZnvd82bN/djJQAAAABKM9tDU7Vr15YkZWRk2C4GAAAAAFA62R657NKli7Zv364dO3YoKytLFSpU8EZd8JPU1FS319u2bevnSgAAAACUZrZHLocPHy7JGVLee+89u93BzyIiItx+eN8SAAAAQFHYThBt27bVY489JtM0NW7cOK1bt84bdQEAAAAAShGvDE+9+uqrGjNmjFJSUtStWzc99thj2rZtmze6BgAAAACUAoZpmqadDnLvc/njjz8qJSVFhmFIkqKjo9WgQQNFRUV5NM3SMAwtW7bMTjnwkhYtWkiStm/fHuBKAAAAAASSp9nAa/tcuriOTdPU6dOn9fPPP3vUj2maefoBAAAAAJQetsOl5AyGxfkOAAAAAFA22A6XM2fO9EYdAAAAAIBSzHa4vPfee71RBwAAAACgFPMoXB44cECSFB4ertjYWJ8WBAAAAAAofTwKl/Xr15dhGOrbt6+++uqrPN+tXr1aklStWjW1bNnS+xUCAAAAAEq8Ik2Ldbc4T9euXWUYhvr06aNFixZ5rTAAAAAAQOlR+OaTAAAAAAAUwqNwWalSJUnSuXPnfFoMAAAAAKB08ihc1qxZU6Zpatu2bcrOzvZ1TQAAAACAUsajdy7btWun/fv369SpU7rtttv05z//WZdccomCg4OtNunp6daqsnZceumltvvwpxMnTmj+/Pn6/PPP9dtvv+nIkSMyTVPVq1fX5Zdfrs6dO6t79+7q0KFDnt9XfrZu3aqZM2dq6dKlSkxM1Pnz51WnTh21bdtWd999t/r27euHnwoAAAAAisYw3a3Sc4Gvv/5aN9xwgwzDuOg71+3uvityMYZRakZGHQ6HJk2apL///e9KTU0ttP3GjRvVtm3bfL/Pzs7WM888o/Hjx8vhcOTb7qabbtKMGTN8viVMixYtJEnbt2/36XMAAAAAlGyeZgOPRi779u2r0aNH65133sm3jQcZNV+GYdi639+ys7N1xx13KD4+Ps/1hg0bqk6dOpKko0ePau/evQUGxdxGjx6tGTNmWOcVKlRQ8+bNFRkZqV27dunkyZOSpIULF6pXr15at26dIiIivPQTAQAAAIA9Hq8WO3XqVM2bN0/9+vVTjRo1FBLizKXeGLEsTcFSku69914rWIaEhOjPf/6zEhIStHfvXq1evVqrV6/W7t27derUKc2bN0833nhjgVNip02blidY9u/fX/v379fPP/+stWvX6siRI5o0aZL1O//ll180atQo3/6QAAAAAFAEHk2LLUhQUFC52udy7ty5Gjp0qCQpPDxcX375pXr06FHs/s6dO6dGjRrp6NGjkpz7hi5dutRtGJ0+fboeeOABSc5Qv2nTJrVp06bYzy4I02IBAAAASJ5nA/a5LILU1FSNHTvWOn/99ddtBUtJmjVrlhUsDcPQ1KlT8x3lHDFihDp06CDJOdo7fvx4W88GAAAAAG/xSrgsbdNai+vDDz/UiRMnJElNmjTR6NGjbff56aefWsddunRRs2bNCmyf+5mLFi1SZmam7RoAAAAAwC6PFvQpiKcL1pQF7777rnU8bNgwBQXZy+apqalavXq1de7JNiP9+vXLc/+qVavUu3dvW3UAAAAAgF1Mi/XQmTNntHHjRuu8e/futvvcsWOHsrKyrPNrrrmm0Htq1aql+vXrW+e//PKL7ToAAAAAwC7CpYc2btyYZ/pvq1atJEnr16/X8OHD1ahRI4WFhalatWq64oorNHbsWP38888F9rlz5848540aNfKoltztLuwDAAAAAALB9rTY8mLLli3WcUREhMLCwvSnP/3por0/MzMzderUKW3dulVvvfWWRowYocmTJys0NPSiPn///XfrOCQkRHFxcR7Vcumll1rHCQkJhbZPS0vzqN/cHA6H7Wm/AAAAAMoPwqWHTp48aR1HRUXp/vvv15w5cyRJwcHBatWqlapWrarExETt2bNHknOho3fffVcJCQn6+uuvL1oF9uzZs3n69DTMVa5c2TpOSUkptH1kZKRH/V6oefPmxboPAAAAQPnD0JSHzpw5Yx0fPXrUCpZ33XWXEhMT9dNPP2n58uXavXu3fv75Z7Vt29Zqv3TpUr3wwgsX9Zl7RDEsLMzjWsLDw932AQAAAACBQrj0UEZGxkXXhgwZorlz56pWrVp5rl955ZVavnx5npG/119/XcnJyXna5V7MJyTE80Hk3G3Pnz9faPvU1NQifwrbEgUAAAAAciNceigiIiLPeXh4uN56661820dFRenNN9+0ztPS0vTJJ5/kaVOpUiXr2F14zU/uthfW5U5ERESRP7xvCQAAAKAoSBAeuvC9xX79+ikmJqbAe3r16qUaNWpY57n3tLywz/T0dI9rOXfuXL51AQAAAEAgEC49VL169Tznbdq0KfQewzDUunVr63zfvn359umajuqJo0ePWseFBVwAAAAA8AfCpYcuv/zyPOeehrrc7U6dOpXnu6ZNm+Y5P3DggEd9Hjx40Drm3UgAAAAAJQHh0kMtWrTIc56ZmenRfbnfj7xwRdgLA+vPP/9caH9ZWVnatm1bvn0AAAAAQCDYDpfvv/++3n//fS1btqzYfaxcudLqp6S65JJL1LBhQ+t8//79Ht2XkJBgHdesWTPPdw0bNlTdunWt87Vr1xba348//pjn/czOnTt7VAcAAAAA+JLtcDl8+HDdd999ev3114vdx8SJE3Xffffp/vvvt1uOT916663W8bffflto+2PHjmnLli3WeceOHS9q079/f+t43rx5hW4t4tpfU3KOpjZq1KjQOgAAAADA10rMtFjTNGWaZqDLKNB9991nbdGxY8cOffHFFwW2f+2115SdnW2dDxgw4KI2w4cPt46TkpL0zjvv5NtfYmKiZs2a5fZeAAAAAAikEhMuS4MWLVpo2LBh1vkDDzyQZ2Qyt48++ijPPpd9+vTR1VdffVG7du3a5Rm9/Nvf/qZ169Zd1O7s2bMaMmSIUlJSJElxcXF66KGHiv2zAAAAAIA3hQS6AOmPRW8uXPCmJHr11Ve1Zs0a7d+/XydOnFD79u31wAMPqHfv3qpataoOHjyoefPm6fPPP7fuqV69uqZNm5ZvnxMnTtSGDRt04sQJpaamqkePHhoxYoR69+6tyMhIbdmyRZMmTbLe8wwKCtI777yj8PBwX/+4AAAAAOARw7Q5FzUoKEiGYahPnz5atGhRsfqoX7++Dhw4oNq1aysxMdFOOX6xa9cu9enTx6OtQ2rXrq0vv/yy0H0x169fr5tvvlnJyckFtgsODtbEiRN9PmrpWh13+/btPn0OAAAAgJLN02wQ0GmxKSkpeumll3TgwAEZhqGWLVsGshyPNWvWTFu3btWDDz6oyMhIt23CwsI0ZswYbd68udBgKUnXXnuttmzZottuu00hIe4HlNu3b681a9YwHRYAAABAiVOkkcvcW3G4JCQkyDAMhYWFXbTVRn5M01R6erqSkpKshXwMw9CkSZM0ZswYz6svAc6dO6dVq1bp999/V3JysqpUqaLLLrtMnTp1Kva01RMnTmj16tVKTEzU+fPnVbt2bbVr105NmjTxcvX5Y+QSAAAAgOR5NihSuHRNgc3NdfuF1wtz4WNbtWqlH374QRUrVixSP/ANwiUAAAAAyYfTYl0jjRduHXLh9cI+LjVq1NDYsWO1Zs0agiUAAAAAlFJFWi12xYoVec5N01T37t1lGIbatWun8ePHe9RPUFCQIiIiVKtWLdWuXbsoJQAAAAAASqAihcsuXbq4vW6apqpVq5bv9wAAAACAss32PpfPPvusJOmyyy6zXQwAAAAAoHTyWrgEAAAAAJRfAd3nEgAAAABQNtgeuZSkWbNm6cyZM5KkBx54QJUqVfL43mXLlllL2t5www1MrwUAAACAUsh2uNy1a5fuu+8+GYahLl266NFHHy3S/cHBwRo7dqwMw9BPP/2kmTNn2i0JAAAAAOBntqfFxsfHW8ejR48u8v1du3ZVkyZNZJqmPvvsM+Xk5NgtCQAAAADgZ7bD5apVq5wdBQXppptuKlYfAwYMkCSlpKRo8+bNdktCEaSlpbn9OByOQJcGAAAAoBSxPS12x44dMgxDTZo0UURERLH6aNu2bZ7+2rVrZ7cseCgyMjLf75o3b+7HSgAAAACUZrZHLpOSkiRJtWrVKnYfNWvWtI5PnDhhtyQAAAAAgJ/ZHrkMCnLm0/Pnzxe7j6ysLOuYdy79KzU11e313KPJAAAAAFAY2+EyNjZWBw4cUEJCQrH7yH1v9erV7ZaEIshvKrPrLw0Af8jJkdaskY4ckeLipE6dpODgQFcFAACAorCdIJo2bSpJOnz4sH755Zdi9bFw4ULruGHDhnZLAlCKxMdL9etL3bpJQ4Y4/6xf33kdAAAApYftcNm7d2/r+Omnny7y/T/88IO++OILSc5RtOuvv95uSQBKifh4adAgKTEx7/VDh5zXCZgAAAClh+1wOWTIEIWHh0uSFi9erMcff1ymaXp0786dO3XLLbfINE0ZhqEhQ4aoQoUKdksCUArk5EiPPSa5+78L17WxY53tAAAAUPLZDpdxcXEaO3asFSjfeustdezYUQsWLFB2drbbexISEvT000+rbdu2OnbsmCQpKipKzz33nN1yAJQSa9ZcPGKZm2lKBw862wEAAKDks72gjyS98MIL2rx5s7755hsZhqFNmzZp4MCBqlixolq2bKnY2FhVrFhRZ86c0Z49e3To0CFJsgJphQoVNH/+fFvbmQAoXY4c8azdggVS164+LQUAAABe4JVwGRwcrPj4eD344IN6//33JUmGYSgjI0M//vhjnrauQGkYhiSpRo0amj17tnr27OmNUgCUEnFxnrWbM0d67TVWjwUAACjpvLbfRHh4uN577z0tXLhQ119/vRUiTdPM83GJjo7WU089pa1btxIsgXKoUycpNrbwdidOMDUWAACgNPDKyGVuN9xwg2644QYdPnxYa9as0c6dO5WcnKzMzExVqVJFtWrVUseOHdW2bVsW7wHKseBgaehQacKEwtt6OoUWAAAAgeP1cOlSu3Zt3XHHHb7qHkAplJPjHIU8csQ5LfammzwLl55OoQUAAEDg+CxcAkBu8fHOrUdyrxBbt64UEyOdPOn+HsNwtunUyT81AgAAoPgIlwB8Lj5eGjTo4j0tDx3645ph5P3+f2t+acIEFvMBAAAoDXwSLlNSUrR+/Xpt3rxZSUlJOnPmjEzT1PTp033xOAAlWE6Oc8TywmApOa8ZhlStmhQW5gybLnXrOoPlwIF+KxUAAAA2eDVcHjx4UC+88ILmzp2rjIwM67ppmjIMw2247Nmzp3bs2CHDMLRixQo1adLEmyUBCLA1a/JOhb2QaTqnxS5d6hyhdL2P2akTI5YAAAClide2IlmwYIGuvPJKzZgxQ+np6W63H3Fn2LBhOnr0qI4ePapZs2Z5qxwAJYSnK70uXCh17SrddZfzT0lauVL68EPnnzk5vqkPAAAA3uGVcLlkyRINHjzYmv5aoUIF9ezZU2PHjlWjRo0KvHfw4MGqVKmSJOnzzz/3RjkAShBPV3qdM+ePADlvnlSrltStmzRkiPPP+vWd724CAACgZLIdLs+dO6f77rtPWVlZkqR+/fpp3759WrJkid544w1ddtllBd5fqVIl9erVS6ZpateuXTp69KjdkgCUIJ06SZUrF97uxAnnFNqnnpIGD5aSkvJ+n5joXBSIgAkAAFAy2Q6X06dP15EjR2QYhnr27Kkvv/xSderUKVIfHTp0sI63bt1qtyQAJYzD4Vm7zz+XXn01/+9NUxo7limyAAAAJZHtcPnll19ax5MmTVJQUNG7bNasmXW8b98+uyUBKEHWrJFSUz1r+957hbc5eNDZJwAAAEoW2+Fyx44dkqTGjRsXe6XXqlWrWsdnzpyxWxKAEsTTBX0qV5Y8/dff0z4BAADgP7bDZVJSkgzDUN26db1RD4AyxtMFfVq39n6fAAAA8B/b4TIqKkqSc2Gf4jp27Jh1HBMTY7ckACVIp06SJ/9ar1rlWX+xsc4+AQAAULLYDpdxcXEyTVM7d+4sdE/L/Kxfv946btCggd2SAJRhkydLwcGBrgIAAAAXsh0uO/1vCOHs2bNavHhxke9PT0/X3LlzJUkVK1bUddddZ7ckACXImjXSyZPe6evJJ6Xbb/dOXwAAAPAu2+Fy4MCB1vG4ceOUlpZWpPufeOIJ673Nm2++WRUrVrRbEoogLS3N7cfh6d4RQCG8sfhObKw0b570yiv2+wIAAIBv2A6XPXr0UNeuXWWapn799Vf17NlTCQkJhd6XkpKiUaNGadq0aZIkwzD0j3/8w245KKLIyEi3n127dgW6NJQRNWoU776//12aO1dascIZUAcN8m5dAAAA8K4Qb3Ty7rvv6tprr9WJEyf0ww8/6PLLL9fNN9+sbt266fjx41a7+Ph4HT9+XN99950WLFigs2fPyjRNGYahF198US1btvRGOQDKgB49pK5dA10FAAAAPGWYxV2F5wKbN29W//79dfjwYWfHhlFg+9yPfeKJJ/Taa695owwUUX7TmNu2baugoCBt377dzxWhrJkzRxo2rGj3xMRIx465X7gnJ8f5HueRI84tSTp1YoEfAAAAX2rRooUkFZoNbE+LdWnTpo22bNmiu+++WyEhITJN0/q4XHitXr16+uijjwiWARQREeH2ExTktX80UM6dOOG9vuLjpfr1pW7dpCFDnH/Wr++8DgAAgMDyaoKoVq2aZs2apf379+vVV19V//791bBhQ1WuXFnBwcGKiYlR8+bNdf/99+vjjz/Wb7/9psGDB3uzBAAlTGxs0e85edI5OplbfLzzvcvExLzXDx1yXidgAgAABJbXpsWibPF06BsozMqVzhHGopo7V7rrLudxTo5zhPLCYOliGFLdutL+/UyRBQAA8Da/T4sFAHeSkooX+OLi/jhesyb/YClJpikdPHjxaCcAAAD8xyurxQKAO/PmSUWd+e4ahezU6Y9rnu6V6Y09NQEAAFA8jFwC8In58/+Y1uop1yLTEybkHe3MPYpZEE/bAQAAwPsIlwC8Lj5euv1257uSRVG3rjOUDhyY93pSUuH3XnJJ3tFOAAAA+JdH02IbNmxoHRuGob1797r9zi7DMFSxYkVFR0ercePG6ty5s26//XZFR0d77RkAfCsnR3rssaLd8/e/Sz16uN+zMidHevzxwvt4/XUW8wEAAAgkj8JlQkKCDMOQaZoyXPPWLvjO23744QfNmTNHf/3rXzVz5kzdfPPNXn8GAO8rbPGd/Bw54rz3woDpaX87dxb9mQAAAPAej6fFFrRjiWmaPvskJydr8ODBbIkBlBILFhT9npdekoYMcW5ZUr9+3j0rPV2kZ+LEok/DBQAAgPd4NHK5YsWKYn1XHFlZWTp16pS2bdum6dOn6/Dhwzp//rzefPNNvfvuu159FuBtOTnOkbYjR5yLy7ib5lmWxcc7F+Ox49AhadCgP9699HSRnuRk5+++a1d7zwcAAEDxGGZBQ5IBlpCQoKZNmyo7O1v169fP864nfMvTjVLxh/h457uGuadw1q3rHFG7cIGasignxznqWJwpsRdybUeyf7/zvEYNZ3gszNy5RV+hFgAAAAXzNBuU6NVi69evr9atW8s0TR1hAzuUYPHxztG2C4OVaxQu9zTPsqq471q6Y5rSwYPOPoODPV8giK1IAAAAAqdEh0tJqlq1qiQpMzMzwJUA7rlWR3U3B8B1bezYsv8+oC/+/sfV59NPSzEx+bczDLYiAQAACLQSHy5nzZql/fv3a9++fYEuBXCrsBG73KNwZZkvRg1dfQYHS9OmuW/jWqx6woTy9X4rAABASePRgj7FkZSUpE2bNmnPnj06ffq0MjMzVblyZdWoUUNt2rRRy5YtFRRUeLatUaOGr0oEvMLTEbuyPrO7Uyfn6OLJk/b7cr1zmXskcuBA6dNP3b/XOmFC+XivFQAAoCTzericM2eO3n77ba1fv77AdlWqVNGwYcP02GOPqWHDht4uA/AbT0fsyvr7gAsWeC9YSu5HIgcOlAYMKN8r8gIAAJRUXlstdu/evbr77rv1/fffS3LufWkYRoH7YxqGobCwMD377LN66qmnvFEGvITVYj3nWiX10CH3713mXvm0rIYgb64UW55W2AUAACgNPM0GXhm53LZtm3r16qXjx49fFCYbNGigmJgYVaxYUSkpKUpISNDZs2et79PT0/V///d/SkhI0JQpU7xRDuBXwcHOMDRoUP5tyvr7gN5cKfa996QePbzTFwAAAPzH9oI+mZmZGjx4sI4dO2Zd69atmz799FOlpKRo7969+uGHH7RmzRr9/PPPOn36tHbu3KlnnnlG0dHR1ujmO++8o1mzZtktBwiIgQOlceMuDpDBwc7rZX0Uzpvvk65cKX34ofPPsr7CLgAAQFlie1rslClT9PDDD8swDIWEhGjq1Km6//77Pbr3xIkTuuWWW7RhwwZJUs2aNXXo0CGPFvqBbzEttmhc+1xe+G+T6/3B+fPLdsBcuVLq1s37/TJFFgAAIPA8zQa2U9zHH39sHb/44oseB0tJio2N1cKFC1W7dm1J0vHjx7Vq1Sq7JQF+xT6XzkV16tb9I0x7y6FDztAeH+/dfgEAAOB9tsPlrl27JEmRkZF69NFHi3x/1apVNWbMmIv6A0oL9rn8471TybsB0zSdn5EjpWXLynZABwAAKO1sh8uUlBQZhqEWLVooLCysWH20bds2T3/wn7S0NLcfh8MR6NJKDfa5dBo40Dn9t04d7/ednCz17OlckZZRTAAAgJLJdrh0TWkNCSn+wrO5740r65sBljCRkZFuP4wge87Tf2T37PFtHSXBwIFSQoK0YoU0d660dKn07LPe659psgAAACWX7XDZunVrmaapnTt3KqeYc9Z++eWXPP0BpUlSkmdTQf/73/IxrTM4WOraVbrrLuef06d7r+/y8g4rAABAaWQ7XLoW8ElOTi7WViKZmZl6++23ZRiG2rRpo5YtW9otCUWQmprq9tOsWbNAl1YqxMdLgwe7X8znQomJZfu9S3e8uf+lS3l4hxUAAKA0sh0u+/Xrp8GDB8s0TY0dO1bLly/3+N7z589r6NCh2rNnjypWrKh33nnHbjkoooiICLcftoMpXEGrxOanrL93KTl/L669Kv/2N989pzz8LgEAAEoTrySIGTNmaNCgQUpNTVWfPn00atQobdmyJd/2aWlpmj17tlq2bKnPPvtMMTEx+vTTT9WmTRtvlAP4RXFG5cr6K8Xx8c5Fd7p1k4YMkf63ha1PlPXfJQAAQGljmGbh4y6e7l25cOFCJSUlyfjfC2g1a9ZUy5YtFRMTo9DQUKWkpGj//v3auXOnsrKyZJqmDMNQjx49VLduXRmGoenefEELxebpRqnl2YcfOgOUp8LDpZQU5zuJZVF8vHOxnaKM5BaHYTj31Ny/v+z+LgEAAEoST7OBR+EyKCjICoyecnXr7r6CvivuokDwLsJl4VaudI7QeapSJens2bIZiHJynCOW3n6/8kKu/8uYP9+5Mi0AAAB8z9Ns4PG0WNM0i/Qp6L78vgNKk06dnCNonjp3ruwuQmNn4Z6OHT1vW7cuwRIAAKCk8mhzyme9uVEdUEYEB0sTJ0q33eb5PYcO+a6eQCru4jpBQVKfPtJ33xXe9s03pUceKZsjvwAAAGUB4RKwYeBA6R//kF580bP2J074tp5AKe7iOg6H9PzzUkyMlJyc//ua1apJrVoVvz4AAAD4HvtNADbk5EjR0Z6337fPd7UEkmuKcBFfzZaU95787k9Olnr2dL7XGR9frBIBAADgY4RLoJhc226MG+f5PR984AykZY1rinBxmKZ08qT03HPOEcqCHDrkXJGWgAkAAFDyEC6BYnBtu1HURWxOny67i/oMHOgMiMV16pQzZBbENW127NiyGdIBAABKM4/euSyuXbt26bffftOpU6eUmZmpKlWqKDY2Vq1bt1blypV9+WjAZ3JypMceK/5+jsVd/KY0aNy4+PfOnu1ZO9OUDh50hvSuXYv/PAAAAHiX18Plxo0b9Z///EdffPGFzp4967aNYRhq2bKlHnjgAQ0fPlyRkZHeLgPwGTvbbkjFX/ymNCjOz2YYUvXqRV/sqCyHdAAAgNLIa9Ni09LSNHLkSHXs2FGzZ8/WmTNnrL0rL9zP0uFwaOvWrXrsscd0+eWX69tvv/VWGYDP2Qk1sbHOxW/KquIs7GOaUqNGRX9WWQ7pAAAApZFXwuW5c+fUp08fzZgxwwqQLsHBwapevbpq166tqKgo67qrzaFDh3TDDTdo3rx53igF8Dk7oebOO8v2Po25F/YpSsD0ZJ9LF8OQLrmkbId0AACA0sgr4XL06NFav369dV6vXj39+9//1tatW5Wenq7jx48rMTFRZ86c0fHjx/Xll19q8ODBMgxDhmEoJydHd999t3bu3OmNcgImISFBkZGR1s9lGIaeK+IKJ1u3btUTTzyhK664QtWqVVNkZKSaNm2qoUOH6uuvv/ZN4SgS1+hccTgc3q2lpMnJca74+sgjUq6/S5LkDITz5jn3tSwuV2CdMKFsh3QAAIDSyPY7lxs3btScOXNk/O+/+h566CG99tprqlixotv21atX14033qgbb7xRGzdu1G233aZDhw7p/Pnzeuqpp/Tll1/aLSlgRo8erbS0tGLdm52drWeeeUbjx4+X44IEsnv3bu3evVtz587VTTfdpBkzZig2NtYbJaMYFiyQ0tOLd29xFwEqDeLjnQsduXsfNTZWeuMN6dZbpccfL/4z6tZ1BsuBA4vfBwAAAHzD9sjlnDlzrOP7779fkyZNyjdYXqhdu3ZaunSpwsPDJUlff/21Tha2F0EJNXv2bC1ZsqTY948ePVr//ve/rWBZoUIFXXnllbruuusUExNjtVu4cKF69epV7BALe1xbkBT3H1M7q6mWZIVtzXLihDR4sPTPfxZvMaS+faUVK6T9+wmWAAAAJZXtcLl8+XJJUkhIiF5++eUi39+kSRONGjVKkuRwOLRq1Sq7JfldUlKSHv/fcMzll1+u2rVrF+n+adOmacaMGdZ5//79tX//fv38889au3atjhw5okmTJikkxDnQ/Msvv1i/M/iP3S1IgoKkMWO8W1NJ4OnvxTT/eB+zqP7yF+e2I0yFBQAAKLlsh8vDhw/LMAy1atUqzwhbUXTv3t06PnTokN2S/O7xxx9XUlKSJOntt99WhQoVPL733LlzevbZZ63zrl27Kj4+XnXq1LGuVahQQQ8//LDefvtt69qHH36ozZs3e6F6eMruFiShoWUzHBXl95KcXPT+Y2JYvAcAAKA0sB0uXdMzK1euXOw+ct977tw5uyX51ZIlSzT7f7u/33fffercuXOR7p81a5aOHj0qybn/59SpUxWcTwIZMWKEOnToIMm52u748eNtVI6isruvYkaGtHKlV0opUXy93+TJk873XAEAAFCy2Q6X1atXl2ma2rdvX7H72L9/v3Vc3NHPQDh37pz+9Kc/SXL+Hl599dUi9/Hpp59ax126dFGzZs0KbD969GjreNGiRcrMzCzyM1E83thX8X+zyMsUX+83aRjS2LHO6bcAAAAouWyHy6ZNm0qSDh48qLVr1xarD9fIn6RCw1VJ8o9//MMKxq+99lqRg3FqaqpWr15tnfft27fQe/r165fn/tL4jmpp1amTc4qmHQcOeKeWksS1NYsn+1oGBzu3KikK05QOHnROvwUAAEDJZTtc3njjjdbxAw88YL176KkpU6Zo2bJlkqSqVavq2muvtVuSX/z444+a+L/VSbp27ap77723yH3s2LFDWVlZ1vk111xT6D21atVS/fr1rfNffvmlyM9F4Fx6aaAr8L7gYM8X6snJKd57l5Lvp98CAADAHtv7XN53333617/+peTkZO3evVvt27fXpEmT8oROd5KTk/Xcc89p8uTJkpzvGz766KMKCrKdd30uOztbDzzwgHJychQaGqqpU6cWq5+dO3fmOW/UqJFH9zVq1EgJCQlu+yhPcnKco1lHjjinZl57rbR+vXTokHPri9hYqU4d58ha7tdYc3Kc7z663n/s2tWzlUjXrCn+FiQuudau8roLfx+un9t1/dAh53c//eQcQb30Uql1a6lmTefP5fp9deggvfWWNHu2dOqUFBYmVa8uZWU5FyWqVMl5npwshYdLDof022+++7lchgxxfgpiGP7bS7Qs71kKAABQHLbDZZUqVTRhwgTdfffdMgxDCQkJ6t+/vxo0aKC+fftaq8iGhoYqJSVF+/fv1w8//KBvv/1W58+fl/m//0Jr2bKlnnrqKds/kD+8/vrr+vnnnyVJf/nLX4o9lff333+3jkNCQhTn4ctrl+Ya/nKFzIIUZ09Mh8NRooN+fLxz+4vcq5TmFyzq1nWOrA0Y4Nxn8ZVXpNy/kpdekipXlqZPl2691X1Ak+yPnFWu7AyxvuDu91GnjnNfzQ0bpPxezZ0717P+9+61X6M/+DPw+TPIAgAAlAa2w6UkDR06VCdPntSf//xnORwOa4Gfgkb0TNOU8b+XtJo3b65vvvlGYWFh3ijHp/bu3avnn39ektS4cWP97W9/K3ZfZ8+etY6joqI8DnO5V9dNSUkptH1kZGTRi5Pzf5eSKD5eGjTo4v+wz+8/9BMTpdtukyIjpdRU923OnpVuv/3iNq5gOnCg/YVratXyzVYk+f0+Dh1yfuA7BEwAAIA/eG1o6tFHH9WaNWt05ZVXWtdM07Q+7s7Dw8M1btw4bdq0SbVq1fJWKT41evRopaenS3K+L2onEOceUSxKP+Hh4W77KA9ycpwjdMX5D/r8gmVBbQ4dcga3+Pg/Fq4prt27pQsH511TdD/80PlnUVdEtfP7gHd4spARAABAeeCVkUuXjh07avPmzfruu+/0+eef6/vvv9dvv/2m06dPKzMzU9HR0YqNjVWbNm3UqVMn3XnnnYqOjvZmCT41c+ZMa/GhoUOHqmfPnrb6y72YT0iI5/9T5G57/vz5QtunepKqLtC2bdsi3+MPa9bknfrpa67QNnasc1rtG29IgwcXv7833nBOww0NdT+VNfdIqSf8/fsAAAAA8uPVcOnSsWNHdezY0RddB8zx48c1btw4Sc5Vbd944w3bfVaqVMk6zsjI8Pi+3G0jIiIKbe9JmwuV1PctFywIzHNdW2HExtrrJydHmjLFuZhOflNZBw2S5s/3LGCygioAAABKipKZIEqgRx99VMn/20Ph5ZdfVo0aNWz3mftdSNdUW0+cO3fObR9lXXy8NGFC4J7vWm3Vrj178p/Kmnuk1JMpsnbfAwUAAAC8hXDpgQ0bNujjjz+W5NyLcuTIkV7pt3r16tZxamqqx9NXjx49ah3HxMR4pZaSLidHevTRwNawYYO0Y4f9fgyj4KmspvnHSGlhXO+B8t4fAAAAAo1w6YFjx45Zxxs2bFBQUJAMw8j3k3uLkeeffz7Pd7m3DmnatGme5xw4cMCjeg4ePGgdF3cblNIkJ0eaNCnwK59Onux8X9IOw3DuI+kJT0ZJg4Od72i6+gYAAAAChXAZQJdffnmec9femQXJysrStm3b8u2jrImPl+rXlx5/PNCVeIdpSjVretbW0ymvAwc639HMNRAOAAAA+J1PFvQpaypWrFik6aenTp2Sw+GQ5Nw2JPfCPcG5Njps2LCh6tatq8T/zZFcu3athgwZUmDfP/74Y573Mzt37uxxXaVNfvs3lnZbtzqnsh465P5nMwzn9506FdxPTo5z6uyRI84g+vrr0j33+KZmAAAAoDCMXHqgX79+SkpK8vhzySWXWPc+9dRT+X4nSf3797eO582bV+jWInPmzLGOW7RooUaNGnnppyxZyvL+jfv25T+V1XU+YYJzymt+XCO63bpJQ4Y4//zzn31RLQAAAOAZwmWADR8+3DpOSkrSO++8k2/bxMREzZo1y+29ZU1Z3r/xt9/+mMpap07e7+rWLXwbEteI7oW/nxMnvF8rAAAA4CnCZYC1a9cuz+jl3/72N61bt+6idmfPntWQIUOUkpIiSYqLi9NDDz3ktzr9rSzv37hkiTMgDhwoJSRIK1ZIc+c6/9y/XxowQFq5UvrwQ+efri1JcnKkZcukkSPL5oguAAAASjfeuSwBJk6cqA0bNujEiRNKTU1Vjx49NGLECPXu3VuRkZHasmWLJk2apP3790uSgoKC9M477yg8PDzAlftOWd+/8dFHnSEyOFjq2vWP6/HxzunAuUcl69aV7rrLGTbL6mguAAAASj/CZQlQv359ff7557r55puVnJyszMxMTZkyRVOmTLmobXBwsCZOnKibb745AJX6T6dOUrVqUnJyoCvxjUOHnFN/LwyW7hYwSkyUXn3Vr+UBAAAARca02BLi2muv1ZYtW3TbbbcpJMR95m/fvr3WrFlTpqfDugQHS716BboK38q9b2dZXsAIAAAA5QMjlz6QkJBQrPvq1Kmj+fPn68SJE1q9erUSExN1/vx51a5dW+3atVOTJk28W2gJFh8vffxxoKvwrdwL8JTlBYwAAABQPtgOl6tXr5YkVatWTS1btixWHzt27FBSUpKksr1vo6diY2N12223BbqMgHGN4pV1sbF/HJflBYwAAABQPtgOl127dpVhGOrTp48WLVpUrD6efvppffHFFzIMQ9nZ2XZLQilXXkbxcm9DUtYXMAIAAEDZV2LeuTRNUyYvnEHlYxTvkkucixa5dOrkXBUWAAAAKK1KTLgEXMrDKN6ECc5Fi1yCg53bjQAAAAClVYkIl+fPn5ckhYaGBrgSlASdOknVqwe6Ct+IiZE+/VQaODDv9Zwc5z6WBckdRgEAAICSpkSsFrt3715JUpUqVQJbCEqE4GBp2DDn6F5ZERMjPfqo9PTTzp8vJ8f5bumRI86R2pycwt8zzclx/l4MQ/rgA//UDQAAAHgq4OHygw8+0O7du2UYhpo1axboclBCDBhQdsLl3XdLM2f+MfIYH+9cDTd3mKxWzbO+Zs/2fn0AAACANxQpXHbv3j3f73744YcCv8/NNE2lp6dr3759OnnypHW9X79+RSkHZZhrgZtDh6TSvs7TBx9ItWpJr7ziDJaDBl38MyUnB6Y2AAAAwFsMswhLtAYFBckwjDzXXLdfeN0TpmnKMAyZpqlLLrlEv/zyC1NjS4gWLVpIkrZv3x6wGvILYqXVRx9J48aVj21Wypuy8s8oAACAO55mgyIv6OPaMuTCrUMuvO7JR5KCg4N1yy23aPXq1QRL5DFwoDR/ftnZomP0aIIlAAAAyq4iTYudOXNmnnPTNHX//ffLMAy1bNlSTzzxhEf9BAUFKSIiQrVq1VKrVq0UFRVVlDLgRWlpaW6vOxwOBQWViMWEy8yo0JkznrWrVo1psgAAACh9ijQt1h3XVNk+ffpo0aJF3qoLflLQdObmzZszLTYAvvlGCg2Vli2TXnop0NXAE+Xtn1EAAFC+eDot1vZqsffcc48Mw1CrVq3sdgVYcnKcK6qWx/9oDw6WunZ1Lmr03ntlY1EjAAAAlH22w+V7773nhTIQKKmpqW6vt23b1s+V5LVmTeDfTzSMwIS6o0ellSude2COHCk991zgagEAAAA8FfB9LhFYERERbq8H+n3LI0cC+nhJf4S5atWc+25e8Mqxz4wdKyUl/XEeEyNlZUlnz/rn+QAAAEBxeCVcrlixQikpKZKkXr16KTw83ON7f/rpJx08eFCS1KFDB9WsWdMbJaGUq1Ej0BX8ITnZGSyjoqT//WNuS2GjkLmDpev5jFoCAACgpLMdLg8ePKhevXrJNE1dddVV6t+/f5Hu//333zVw4EAZhqGHHnpIb731lt2SAJ/IZwZxkZnmxQGzoMBJsAQAAEBpYHvu4/z58+VwOCRJDz/8cJHvv+WWW1S3bl2ZpqmPP/7YbjkoIxYuDHQFF/NWyBs7VqpTJ++16tW90zcAAAAQKLbD5YoVK6zjW265pVh93HrrrZKkpKQk/fLLL3ZLQimXkyO9+26gq/CdAQOkhARpxQpp7lznn2++GeiqAAAAAHtsh8utW7dKkho1aqSqVasWq48OHTpYx9u2bbNbEkq5lSu9NwW1pAkJcW4x4tpu5K67nH9eOJIJAAAAlDa2w+Xx48dlGIZq165d7D5y33v06FG7JaGUW7ky0BX4Tna2NH/+xdc7dZLq1nW+ewkAAACURrbDpet9S285f/68V/sDSpr773dO/c0tOFh64w0W7wEAAEDpZXu12NjYWCUmJlrbiRRH7ntjYmLsloRSrmtX6aWXAl2F75w7Jz3zjPPn/Plnaf166eRJ57E7VatKp075sUAUSYC3hAUAACgxbIfLBg0aKDExUQkJCfrtt9902WWXFbmPJUuWWMf16tWzWxJKua5dpZgYZ+DyhcL2mfSHf/3L+fHEmTO+rQX2BPqfJQAAgJLC9t+59+rVyzp+qRjDTXv27NEnn3wiSQoNDVWnTp3sloRSLjhYmjbNd/3XrSt9+qn05JO+e4Y3eXnmObyMcAkAAPD/7d13eFPVGwfwb7pbWrqhhRYKFShlFNlDKHsPBQRlDxniQEHFjfpzgQqIArK3CMgSQfYWZG8oCBRo2aN0z/T8/oi5JG3GTZM2Sfv9PE+f3iTnnrw5SW7ue++556iYnVy+/PLLcHJSnQBdunQppk2bJnvd+/fvo0ePHsjOzoZCocDzzz8PDw8Pc0MiMui77wAvL+Cnn6wdCRERERFR8WF2chkeHo5hw4ZBCAEhBMaPH4/evXvjlL4LyACkp6dj7ty5qFWrFi5dugQAcHZ2xv/+9z9zw6FiQKkExo4tvPpfeglo3x7IyCi85yAiIiIiKmkUQpjfqSs9PR3NmzfHiRMnoNCYSyEsLAz16tVDYGAgXF1dkZiYiH///RcnTpxAeno61E+tUCiwaNEiDBw40NxQyEJq1KgBADh//nyRP/eePUCrVkX+tEQFxq6xREREVJzJzQ3MHtAHANzd3bFlyxb07dsXu3fvhkKhgBACsbGxuH79er7yQggpCXV3d8eMGTOYWJLkzh1rR2DY6NGAmxtgQg9wIiIiIqJiz2KD6AcEBGDHjh34+eefERYWJt2v7i6r+QcADg4O6NOnD44fP44hQ4ZYKgwqBoKDrR2BYT4+wO+/WzsKIiIiIiLbYpEzl2oKhQJjxozB6NGj8c8//2D//v24ePEiHj9+jMzMTPj4+CAoKAiNGzdGq1atEBQUZMmnp2KiaVPbmC5En/h41R8RERERET1l0eRSzcHBAU2bNkXTpk0Lo3oq5g4etN3EEgAqVLB2BGRLHCzW/4OIiIjIvnG3iGzOrVvWjkA/f3+gdWtrR0G2xJYPhBAREREVJSaXZHMePLB2BPrNmQM8fqzqtksEMLkkIiIiUmNySTYnMNDaEeQXEgKsWaNa7tuXCQURERERUV6Fcs0lACQlJeH27dtITExEZmam7PVatGhRWCGRnShf3toR5DdlCtCjBxAWxsSSiIiIiEgXiyaXN27cwMyZM7FhwwZcuXJFmnZELoVCgZycHEuGRHaoeXMgIAB4+NDakTw1fjzg58dRYik/dpEmIiIiUrFYcjlr1iy88847yMjIAACTE0siNUdHoGFDYPNma0fyVFwc8P771o6CbJGzs7UjICIiIrINFkku582bh9dee03rvoCAAKSkpCAjIwMKhQIVKlRAcnIynjx5gtzcXACqM5UAEBISAgeO528VqampOu/Pzc212nuiVAIHDljlqQ06dszaEZAtcnGxdgREREREtsHs7OHhw4d4++23AaiSxZ49e+LKlSu4f/8+oqOjpXKxsbF4+PAhkpOTsWvXLgwcOFBKLmvUqIFjx44hNjbW3HDIRJ6enjr/YmJirBbT/v1AUpLVnp7IJGlp1o6AiIiIyDaYnVzOnTsXqampUCgU6NSpE1avXo3KlSvrLe/u7o6WLVti8eLF+PvvvxEYGIitW7eiVatWSE9PNzccKgbu3LF2BETy/dcRg4iIiKjEMzu53LVrl7T87bffSmcj5WjUqBE2b94MR0dHnD9/Hu+++6654ZCJUlJSdP5FRERYLabgYKs9NZHJ3NysHQERERGRbTA7ubx48SIAIDg4GDVr1tRbTqlU6ry/bt266N+/P4QQWLx4MdLYx6xIlSpVSuefNa+Bbd4c8PKy2tMTmcTPz9oREBEREdkGszOIx48fQ6FQIDw8PN9jzhrDKBrq8tq5c2cAQFpaGvbs2WNuSGTnHB2BceOsHQWRPI6O1o6AiIiIyDZY7PSUi44hE700Tj/dMXAhXbBGP8i4uDhLhUR27JNPAE9Pa0dBZFxysrUjICIiIrINZieXfv/1CUvSMbynZtJ44cIFvXUkJCToXKaSy9ERGD3a2lEQGceRjYmIiIhUzE4uq1WrBiEErl69mu+xqKgoaXnz5s1669iyZYu07Ovra25IVAwolcCMGdaOgsg4jhZLREREpGJ2ctmgQQMAqjOO169f13qsQ4cOcPzvgqSlS5fi8OHD+dbftWsX5s2bJ92uX7++uSFRMbBrF8CZaYiIiIiI7IfZyWXbtm2l5U2bNmk9VqZMGfTs2RNCCGRkZCA6OhrDhw/HrFmzMGvWLAwYMAAdO3ZEdnY2FAoFatWqhXr16pkbEhUDixdbOwIiIiIiIjKFQgghzKkgJycHQUFBePz4MaKionDy5Emtx2/fvo06derg0aNHEELkmwdT/fSurq7Ys2cPGjVqZE44ZCE1atQAAJw/f94qz1+9OhATY5WnJjKZeVtRIiIiItsmNzdwMveJnJycsGbNGsTHxwNQTTni7u4uPV6uXDns2bMHffr0wYULF6Arlw0KCsLy5cuZWBIA1fWWN25YOwoiIiIiIjKF2cklAERHRxt8PDIyEmfOnMHGjRuxfft23Lx5E9nZ2QgODkZ0dDT69u0LNzc3S4RCxcD+/bzekoiIiIjI3lgkuZTDwcEBPXr0QI8ePYrqKclOGZgSlYiIiIiIbJTZA/oQWZrG9KhERERERGQnmFySzWna1NoREBERERGRqQq9W2xycjKePHmCzMxMlC5dGn5+fnByKrLeuGSHDh60dgRERERERGQqi2d5qamp+O2337B+/XocPXoUDx480H5CJyfUqFEDzz33HIYMGYK6detaOgSyc7zmkoiIiIjI/li0W+wvv/yC0NBQjBw5Eps3b8b9+/chhND6y87OxunTpzFjxgw0aNAAHTp0wNWrVy0ZBtk5XnNJRERERGR/LJJcZmdno0+fPnjttdfw5MkTANA5n6WaZrK5fft2PPvss9i7d68lQqFioHlzwNXV2lEQEREREZEpLNItdvTo0fj999+fVurkhI4dO6JTp06oUaMG/P394erqiuTkZMTGxuLIkSNYvXo1rl27BoVCgZSUFHTu3BknTpxAtWrVLBES2TFHR6BMGSAuztqREBERERGRXAph6BSjDHv27EHr1q2hUCgghECrVq0wd+5cVK5c2eB6QgjMmzcPb731FjIyMiCEQHR0NHbv3m1OOGQhNWrUAACcP3/eKs8fGQlcvGiVpyYymXlbUSIiIiLbJjc3MLtb7Lx586Tldu3aYevWrUYTSwBQKBQYMWIE/vzzTygUCgDAvn37cOXKFXNDomLg2jVrR0BERERERKYwO7k8+N+8EQqFArNnzzZ5mpFWrVphwIAB0u2///7b3JDIzqWnA5mZ1o6CiIiIiIhMYXZyeefOHSgUCkRGRiIsLKxAdXTv3l1avnfvnrkhkZ0bP97aERARERERkanMTi59fHwAAIGBgQWuQ3Ndb29vc0MiO3f0qLUjICIiIiIiU5mdXFauXBlCCMTHxxe4jlu3bmnVRyWbr6+1IyAiIiIiIlOZnVz26dMHAHDlyhWcOXOmQHWsWrUKAODv749WrVqZGxKZIDU1Vedfbm6u1WIaN85qT01kMjc3a0dAREREZBvMTi6HDBmCChUqAABeeeUVpKSkmLT+ypUrsX79eigUCkyYMMHkAYHIPJ6enjr/YmJirBZT69ZWe2oik2VnWzsCIiIiIttgdnLp7e2NtWvXwtfXF8ePH0ezZs2kEWQNSUtLw6effiqNFDtgwACM50guBGD/fmtHQCSfUmntCIiIiIhsg9mnCfft2wcAmDZtGsaNG4ezZ8+iefPmiIqKQseOHVGzZk34+/vDxcUFycnJiI2NxZEjR7Bp0yYkJycDAPr27Yvhw4dLdenTokULc8OlPPSdaa5fv34RR/LUrl1We2oiIiIiIioghRBCmFOBg4MDFAqF1n3qKvPeb2oZTQqFAjk5OQWMkkxVo0YNAMD58+eL/Ln79wd+/bXIn5aowMzbihIRERHZNrm5gcUucBRCSImi+r+hvFUzqTQzv6Viht0MiYiIiIjsj9nJZYUKFWSffSSS48EDa0dARERERESmMju5vH79ugXCIHoqPd3aERARERERkanMHi2WyNJcXa0dAZF87LhBREREpMLkkojIDLxknIiIiEiFySXZHF5zSURERERkf5hcks1hN0MiIiIiIvvD5JJsTs2a1o6AiIiIiIhMZdHk8sqVK/j888/Rvn17hIaGwsvLC46OjrL/nJwsNu0m2bGXX7Z2BEREREREZCqLZHNZWVkYO3Ys5s2bh9zcXOl+wZEuqAD+/NPaERARERERkanMTi6FEOjSpQt27drFZJIsYu9ea0dARERERESmMju5nDdvHnbu3AnFf6OwVKlSBUOHDkWjRo0QFBQEDw8Ps4OkkiUz09oREBERERGRqcxOLpcsWSItv/TSS1iyZEmxvnbyyZMn2L17N3bv3o1Tp07h8uXLSEhIgLOzM/z8/BAVFYU2bdpg8ODB8PX1Nbn+s2fPYuHChdixYwfi4+ORlZWF8uXLo379+hg4cCA6duxYCK/Ktvj4ADduWDsKInkcOCwaEREREQBAIczsy+rv74+EhASUKlUKd+7cgaenp6VisykxMTF49913sW3bNmRlZRkt7+Hhga+++gpjx46VzuoakpOTg08//RSTJk3Sum41r65du2LBggUIDAw0KX5T1ahRAwBw/vz5Qn0eXZo0Af75p8iflqhAHBwApdLaURAREREVHrm5gdnH3FNTU6FQKFCnTp1im1gCwLlz5/Dnn39qJZaOjo6oVq0aWrRogWbNmsHPz096LC0tDW+//TZGjBgh61rUUaNG4ZtvvpESS2dnZ0RFRaFZs2bw9/eXyv35559o164dUlNTLfjqbIuM3J3IZrDnPxEREZGK2cllcHCwqqIS0jfMyckJzz//PNavX4/Hjx8jJiYGe/fuxYEDB/Dw4UOsX78e5cuXl8rPnz8fv/zyi8E658yZgwULFki3u3fvjtjYWJw6dQoHDhzAnTt38NNPP0ndjU+fPo2RI0cWzgu0Af99pIjsQjE+pkZERERkErMzwubNm0MIgZiYGEvEY7OcnZ3xyiuv4OrVq1i3bh169OiB0qVLa5VRKBTo0aMHDh06hKCgIOn+Tz/9FNnZ2TrrTUtLw8SJE6XbLVu2xNq1a7USVGdnZ7z++utaSeqKFStw4sQJS708myKjFzGRzXj40NoREBEREdkGs5PLMWPGAIB01q646tGjB+bOnYsKFSoYLRsaGorPP/9cuv3w4UPs27dPZ9nFixfj7t27AFTJ6axZs+Do6Kiz7PDhw9GoUSMAqilgJk2aZOrLsAuc0YbsSU6OtSMgIiIisg1mJ5eNGzfGa6+9BiEExowZg3///dcScdm9bt26ad3Wd2Z3zZo10nJ0dDQiIiIM1jtq1ChpefPmzcgshvN2XLpk7QiIiIiIiMhUFrlQcvr06RgxYgTu3r2L+vXr4/vvv8ejR48sUbXd0hzcBwCSkpLylUlJSdE6oylnmpFOnTpprb93714zorRN6enWjoCIiIiIiExlkQkpFQoFZs+ejdatW2Pw4MGYMGEC3n//fVSuXBllypSBi4uL7Hp27txpiZCs7kaeiRrLlCmTr8yFCxe0rsVs0qSJ0XqDgoIQFhaG69evA1AN7tO+fXvzgrUxenoFExERERGRDbNIcgkAq1atwieffILs7GwIISCEwNWrV3H16lVZ6wshZM0HaS/Wrl2rdbtx48b5yly8eFHrdnh4uKy6w8PDpeQybx3FgZPFPpVEha8YbbaIiIiIzGKR3fjJkyfjgw8+yHe/nPkdi6PExET8+OOP0u3atWtLE49q0jy76eTkJE3rYozmoELqJNOQgsyJmZuba7XpZUrIrDZUTPDzSkRERKRidnK5e/duvP/++9JtZ2dntG7dGg0bNkRQUBA8SuAM4+PHj5dGgAWAL7/8Umc5zeswvby8ZCdzmlOgJCcnGy3vWcCJ+CIjIwu0nrlKlbLK0xIViI+PtSMgIiIisg1mJ5fTpk0DoLpesnbt2lizZg0qV65sbrV2a8GCBZg/f750u2/fvvlGjlXTPKPo5uYm+znc3d111lFccGed7Am7xRIRERGpmJ1cHjp0CADg4OCAtWvXolKlSmYHZa/2798vzfsJAJUqVcLs2bP1ltcczMfJhAsNNctmZWUZLZ+SkiK7brX69eubvI6lyHhJRDYjIcHaERARERHZBrOTy6SkJOmsZUlOLE+fPo1u3bpJ806WKVMGW7Zsgbe3t951NLsMZ2RkyH4uzbKlZPQhlVMmL2tdbwkAzs5We2oikymV1o6AiIiIyDaYnUEEBQUBAHxKcF/GS5cuoX379khMTAQA+Pr6Ytu2bahatarB9TSvhUw3YXLHtLQ0nXUUFzEx1o6AiIiIiIhMZXZyWatWLQghcPPmTUvEY3diY2PRtm1b3L9/H4Aq2fvrr78QFRVldN2AgABpOSUlRXb3Vc3Bgvz9/U2M2PY9emTtCIjk4zWXRERERCpmJ5eDBg0CAFy7dg2nT582OyB7Eh8fjzZt2iA+Ph6AaqCdP//8E40aNZK1frVq1bRuy03Q4+LipOWIiAiZ0doPdjMke8J5WYmIiIhUzE4ue/fujXbt2kEIgVGjRml12SzO7t27h7Zt2yI2NhYA4OrqivXr1yM6Olp2HdWrV9e6ferUKaPrZGdn49y5c3rrKA44byARERERkf0xezdeoVDgt99+w3PPPYcjR46gefPmOHbsmCVis1mPHz9Gu3btcOnSJQCquT1XrVqF9u3bm1RP5cqVERISIt0+cOCA0XWOHz+udX1mixYtTHpOe+DoaO0IiOTTGPSZiIiIqEQzu0PXF198AQCIjo7GhQsXcPLkSTRq1Ag1atRAo0aNULZsWbi4uMiu79NPPzU3pEKVlJSEDh064OzZswAAR0dHLF++HN27dy9Qfd27d8fMmTMBAKtXr8a0adMMttfy5cul5Ro1aiA8PLxAz2vL3Ny4w05EREREZG8UQghhTgUODg5Q5BnRQgiR7z65lDZ8wV1aWho6dOggnWF0cHDAokWLMHDgwALXefToUTRs2FC6PX36dLzxxhs6y8bHxyMyMhLJyckAgO+++w7vvPNOgZ/bkBo1agAAzp8/Xyj1G+LvDzx+XORPS1QgCgWQm2vtKIiIiIgKj9zcwCJXtwkhtP503Sfnz5ZlZmaiR48eUmKpUCgwZ84csxJLAGjQoIHWWc8PP/wQf//9d75ySUlJ6Nevn5RYBgcH47XXXjPruW2Vq6u1IyCSz4SOGURERETFmtndYgcPHmyJOGzejz/+iB07dki3fXx8sHr1aqxevVrW+u3atcP48eP11n3o0CE8ePAAKSkpaNOmDYYPH4727dvD09MTZ86cwU8//SQNHuTg4IDZs2fD3d3d/Bdmg4rpy6JiitcIExEREamYnVwuXLjQEnHYvLyj4CYkJGDr1q2y1w8KCtL7WFhYGNavX49u3brh8ePHyMzMxMyZM6VrMTU5Ojrixx9/RLdu3eQHb2eSkqwdAZF8OTnWjoCIiIjINnDSBxvRtGlTnDlzBr169YKTnonzGjZsiP379xfb7rBqnIqE7AkHnyIiIiJSMXtAH7K8Bw8eYN++fYiPj0dWVhbKlSuHBg0aoGrVqkUWgzUH9ClfHrh9u8iflqjAuBUlIiKi4kxubmB2t1iyvMDAQPTq1cvaYVhN6dJMLomIiIiI7E2hJpepqamIjY1FQkICMjMz4ePjg8DAQFSsWLEwn5bsnJ5ewUREREREZMMsvht/+/ZtzJ49G+vXr8eFCxeQq2MCOF9fXzz33HN45ZVX0KVLlwLPiUnFU2QkcO6ctaMgIiIiIiJTWGzolNzcXHz11VeoXLkyvvzyS5w7dw5KpVLn3JePHz/Gxo0b0aNHD9SvX98q1/WR7UpJsXYERERERERkKoskl0qlEr1798ann36KrKwsAEDecYJ03RZC4OTJk9IoqEQAcOmStSMgIiIiIiJTWSS5fO+997B+/XrptqenJ0aNGoWNGzfi+vXrSElJQVZWFh49eoQjR47gp59+QqNGjQAACoUC6enp6NatG+Lj4y0RDtm5+/etHQEREREREZnK7KlIYmJiULt2bSiVSgBAt27dMGfOHJQpU8boumvWrMHw4cORnJwMAOjXrx+WLl1qTjhkIdacisTHB0hMLPKnJSowTkVCRERExZnc3MDsM5dLlixBTk4OAKBr165Yt26drMQSAHr16oUtW7bA0dERQgisXr0aKbzgrsTz8LB2BEREREREZCqzk8tt27YBUHVv/fnnn00e+bVx48YYPHgwACA7Oxt79uwxNySyc5yKhOyJp6e1IyAiIiKyDWYnl3FxcVAoFKhRowZCQ0MLVEfnzp2l5Zs3b5obEtk5dokle+LoaO0IiIiIiGyD2cll4n+ZQEBAQIHr0Fw3KSnJ3JDIzvH6NbInaWnWjoCIiIjINpjdAdHPzw937941a6TXW7duadVHRSc1NVXn/bm5uXBwsNg0qCbx9gb+G+OJyOZlZ1s7AiIiIiLbYHb2ULlyZQDAlStXcO7cuQLVsWbNmnz1UdHw9PTU+RcTE2O1mP4beJiIiIiIiOyI2cml5vWSI0aMQHp6uknrb9iwQUouS5UqhRYtWpgbEtk5DuhDRERERGR/zE4uhwwZglKlSgEAjhw5gujoaJw9e9boekqlElOmTEHfvn0BqEabHT58OFxcXMwNiUyQkpKi8y8iIsJqMSUkWO2piYiIiIiogBRCmD98ypQpU/DOO+9AoVBACAEHBwdER0ejU6dOqFWrFvz9/eHi4oLk5GTExsbiyJEjWLNmDe7evQv104eEhODMmTPw8fExNxyyALkTpRYGX1/gyZMif1qiAuMgVERERFScyc0NLNIBcdy4cbh16xamTp0KhUKB3Nxc7Nmzx+CclUIIaU7MsmXLYtu2bUwsCQAQHMzkkoiIiKg4UiqB/fuBO3dU+3zNm3Nar+LEYsOB/vDDD1i5ciXKli0LQJU8qs9Kqpc1b6v/9+7dG2fOnLFqN0yyLRUqWDsCIiIiIrK0tWuBsDCgVSugXz/V/7Aw1f1UPFh0rokXX3wRN27cwK+//oo+ffqgYsWKyNvr1s3NDU2bNsWECRMQExODVatWITAw0JJhkJ27fNnaERARERGRJa1dC/TuDeSdvfDWLdX9TDCLB4tcc2mIUqnEkydPkJmZCW9vb2nwH7Jt1rzmskoV4MqVIn9aogLjNZdERET6KZWqM5R5E0s1hQIICQFiY9lFFrDNrsNycwOLnrnUxdHREf7+/ihXrhwTS5KlfHlrR0BERERElrJ/v/7EElAdpI2LU5Ur6ey963ChJ5dEpsrKsnYERERERGQpd+5YtlxxVRy6DjO5JJvDE9xkT/4b9JqIiIj0CA62bLniSKkExo7VfamN+r633lKVs2VmJ5e5ublo3bo16tati2bNmiEtLc2k9WfOnIm6deuibt26+P33380Nh4qBunWtHQGRfP7+1o6AiIjItjVvrrqmUt8BWYUCCA1VlSupikvXYbOTyy1btmDPnj04ffo06tatCw8PD5PWf+GFF3Du3DmcPn0a06ZNMzccKga4s072pFUra0dARERk2xwdgR9/VC3nTTDVt6dNs/6gNdZUXLoOm51cbtq0SVoeOHCgyesHBwejTZs2EELg8OHDePz4sbkhkZ07etTaERDJV6+etSMgIiKyfT17Ar//nn/gxpAQ1f09e1onLltRXLoOm51cHv0vE/D09ETDhg0LVEebNm0AqLrYHmVmUeLt3GntCIjke/LE2hEQERHZh549gevXgd27gV9/Vf2PjWViCRSfrsNO5lZw9epVKBQKREREFLgO9bwp6vqoZDPxsl0iq7p509oREBER2Q9HR6BlS2tHYXvUXYd791YlkpoD+9hT12Gzz1wmJycDAEqXLl3gOjTXTUxMNDcksnMcfZPsSW6utSMgIiKi4qA4dB02+8yll5cXnjx5gidm9A3TXNfNzc3ckMjOOZn9qSQqOg8eWDsCIiIiKi569gR69FCNCnvnjuoay+bNbf+MpZrZu/FlypRBQkICYmJikJWVBRcXF5PrOHXqlFZ9VLIxuSR7YuIA2UREREQG2XPXYbO7xTZq1AgAkJaWhjVr1pi8fm5uLpYvXy7drsehF0u81FRrR0Akn4PZW1EiIiKi4sHs3aIuXbpIyxMmTMC9e/dMWv/bb79FTEwMFAoFKlWqZNbAQFQ8KJXWjoBIvqAga0dAREREZBvMTi579uyJKlWqAADi4+PRqlUrnD592uh6SqUSEydOxCeffCLdN2HCBHPDoWKAA6SQPWE3biIiIiIVs3eLHB0d8fPPP6Nz587Izc1FTEwMGjRogM6dO6NXr16oV68eAgMD4erqisTERPz777/Yt28fFi9ejJs3b0IIAYVCgaZNm2L48OGWeE1EREXmvysDiIiIiEo8ixxzb9euHWbOnIlXX30VAJCTk4ONGzdi48aNetdRJ5UAUL16dfzxxx9w4MVLRGRnQkOtHQERERGRbbBYNjdixAhs27YN5cqVA6BKHg39qQ0ZMgRHjhyBr6+vpUIhO+fsbO0IiOTjmUsiIiIiFYueKmzdujWuXbuGefPmoW3btihVqpTOclWqVMGYMWNw7tw5LFiwAB4cy580uLtbOwIi+WbPtnYERERERLbB4kNRODs7Y9iwYRg2bBiUSiXi4uLw+PFjZGZmwsfHB0FBQTxLaUNS9cz7kZuba7VuypmZVnlaogK5etXaERARERHZhkId59DR0RFhYWEICwsrzKchM3h6eup9LDIysggjeYrJJdkTbt6IiIiIVDiCDhGRGax0DIaIiIjI5jC5LOFSUlJ0/kVERFg7NCK7cPCgtSMgIiIisg2c/ruE0zfoEqeFISIiIiIiUzCDICIyQ8uW1o6AiIiIyDbwzCURkRmaN7d2BEREJZNSCezfD9y5AwQHq7bHjo7WjoqoZOOZSyIiM+zZY+0IiIhKnrVrVaN1t2oF9Oun+h8WprqfiKyHySURkRmWLLF2BEREJcvatUDv3kB8vPb9t26p7meCSWQ9TC6JiMwQG2vtCIiISg6lEhg7FhAi/2Pq+956S1WOVO2wZw+wYoXqP9uFChuTSyIiM7i5WTsCIqKSY//+/GcsNQkBxMWpypV07DpM1sDkkojIDGXLWjsCIqKS484dy5azZ4bOSrLrMFmLrNFiv/jiCwDAM888g379+hVqQERE9iQszNoREBGVHMHBli1nr9auVXUP1kweQ0KAH38EevQw3HVYoVB1He7Ro3BG1+UoviWbrOTys88+g0KhQIcOHfIll8OGDQMA1KpVC2+//bblIyQismF+ftaOgIio5GjeXJVE3bqlO3lSKFSPF+dpotRnJfO+fvVZyc8+k9912NJzNRtKenv2tOxzkW0yu1vsokWLsHjxYmzfvt0S8RAR2ZXHj60dARFRyeHoqEpUAFUiqUl9e9q04numTM6ARur2McbSXYfZFZcAmcmlg4OqmND1SSayMF9fa0dAJJ+ho8NERGR5PXsCv/8OlC+vfX9IiOr+4nyGTM6ARnIPelqy6zBH8SU1Wd1ivby8kJSUhIcPHxZ2PETIzLR2BETyhYRYOwIiopKnZ0/VNYMl7do+uWcb/fyAhATdyR6gaqcHDywXlymj+Fq6Ky7ZFllnLsPDwyGEwOnTp3HlypXCjolKuLQ0a0dAJF9AgLUjICIqmRwdVYnKyy+r/hf3xBKQf7Zx7FjDjyuVQN++luuqylF8SU3WmctWrVrhxIkTUCqVqFevHnr16oXQ0FA4anyLr1y5Io0qa45PP/3U7DqIiIoKpyIhIqKiIndAo48+AiIjgZdeMtwV1VKjxnIUX1JTCBkXUt68eRM1a9ZEamoqhBBQaFxBrV5dkfeq6gJSsjO2TahRowYA4Pz580X+3Bb6KBEVid272cWHiIiKjnrgHEA7wVTvP6mvO92zB2jVynh9lvgdUypVU3MZS3pjY0vGGebiSG5uIKtbbIUKFbB27VoEBgYCUCWU6j81zfsK+kcEcEAfsh9ubsV7uHsiIrI9cgc0KsquqiV9FF96Sla3WABo27Ytrl+/jo0bN+L48eN4/PgxsrOzsXjxYigUCpQrVw5t27YtzFiphKhcGTh+3NpREBnnYPZkTkRERKaTM6BRUXdVVSe9uua5nDateI/iS0/J6hZriIODAxQKBTp06IDNmzdbKi6yMmt2i/X2BpKSivxpiQqE3WKJiMgWWaurqlJZ8kbxLQnk5gayz1wSFRWOFkv25NYta0dARESUn7qrau/eqkRS1/WZhdFVVT2KL5VMZieXgwYNgkKhQK1atSwRDxFycqwdAZF8lpwnjIiIbJc9npFjV1UqamYnl4sWLbJAGERE9um/cc6IiKgYW7tWd4L244+2n6DJuT6TyFLYLZaIyAx5R+sjIqLiRT31R97rFm/dUt2vOUKrrWJXVSoqhZJcCiFw6tQpHD58GFeuXEFCQgIyMzPh4+ODwMBA1K1bF02aNEFAQEBhPD0RUZHhVCRERMWXUqk6Y6lrQBwhVNcuvvWW6swgzwQSWTi5zM7OxrRp0zBr1izcuHHDYFlHR0d0794d77//PurXr2/JMMgEqampOu/Pzc2FA+dZIDKoVCnuTBARFSd5r6tUKrW7wuYlBBAXp1qHZwaJLJhcXrx4EX369MGFCxcgZ3aTnJwcrFu3Dn/88QcmTJiA//3vf5YKhUzg6emp97HIyMgijITI/hj4+hARkZ3RdV2ln5+8de/cKZyYiOyNRZLLK1euoHXr1rh//77W/f7+/qhVqxYCAgLg4uKC5ORkXL16FZcuXYJSqQSgSjK//vprpKSkYOrUqZYIh4ioSDx5ojqqzbOXRET2SX2mcsMG1eipeT1+LK+e4GCLhkVktyySXA4cOBD37t2DQqGAEAJ9+vTB22+/jUaNGuksn5iYiBUrVuDLL7/E7du3IYTA9OnT0a5dO3Tu3NkSIZFMKSkpOu9nV2Ui4zIzgT17gDZtrB0JERGZSteZSlMpFKpRY3n9PZGK2RfVrV+/HocPH4ZCoYCrqytWr16N3377TW9iCQDe3t4YPXo0YmJi0Oa/vTIhBD788ENzwyETlSpVSucfr7ckkmfXLmtHQEREplKPAGtuYgmozniyBwuRitkZxJo1a6Tlb775Br169ZK9rqenJ9auXYsKFSoAAM6ePYurV6+aGxIRUZG5edPaERARkSkMjQBrSN7rL0NCCj4NiVKp6vmyYoXq/39XixHZPbOTy3/++QcAULp0abz66qsmr+/l5YXRo0fnq49KLp40JXsSGmrtCIiIyBT79xfsjOWqVcDu3cCvv6r+x8YWLLFcuxYICwNatQL69VP9DwtT3U9k78y+5lJ9rWVkZCRcXFwKVEe9evW06qOSTd3NhMge+PtbOwIiIjKFqSO7qq+rbNnS/O6v6u64ec+a3rqlur+gZ0KJbIXZ54hyc3NVFZlxuklzXTnTmFDxxq4hZE+CgqwdARFRyVWQ7qWmjOxqyesqDXXHVd/31lvcDyL7ZnZyWbZsWQghcPHiRWl6EVOdPXtWWi5Tpoy5IRERFRkml0RE1lHQ7qXNm6vORMrpKWXOdZV5GeuOKwQQF6cqR2SvzE4u1V1aExISsGzZMpPXz87Oxpw5c6TbdevWNTckIiIiIirG9I32qu5eaijBdHQEfvxRtZw3wVTffust866r1EVud1xTu+0S2RKzk8sePXoAUHVnHTt2LP7++2/Z6+bm5mL48OGIiYmBQqFAeHg4atSoYW5IRERF5u5da0dARFSyWKJ7ac+eqjOS5ctr3x8SAqxZA0ydaplrLDXJ7Y5rSrddIltjdnL58ssvIzIyEgqFAklJSWjTpg3effdd3DQwPr9SqcSmTZvQoEEDLF++XLr/888/NzccIqIi9eCBtSMgIipZLNW9tGdP4Pp1y4wAK4ex7rgKhWoE8ubNC+f5iYqC2aPFOjg4YPHixWjTpg2Sk5ORlZWFKVOmYOrUqYiIiECtWrXg7+8PFxcXJCcnIzY2FidPnsSTJ0+06nnppZfw8ssvmxsOFQMODsB/40QR2bzAQGtHQERUsliye6mjo+oMZVFQd8ft3VuVSGqeebXkwEFE1mR2cgmorrvcvHkz+vTpg9u3bwNQdXm9ePEiLl68mK+8EAIKhUIaGXbo0KGYPXu2JUKhYsDRkckl2Y+8XaqIiKhw2XP3UnV33LFjtc++hoSoEktOQ0L2TiEsOPdHYmIiPv/8cyxatCjfmUldmjRpgo8++gidO3e2VAhkIeprX8+fP1/kz12uHC9mJ/sQEqLqUsWjzERERUepVI0Ke+uW7usu1fNSxsba7vZZqVR1271zR5UEN29uu7ESAfJzA4sml2rp6enYu3cvDh8+jCtXruDJkyfIzMyEt7c3AgMDUbduXTRv3hzVqlWz9FOThVgzuRwxApg3r8iflshka9bwKDMRkTWoR4sFdHcvtdT0IUSkYtXkkuyfNZPL9HTAw6PIn5ZINi8vYNEi7rgQEVnT2rX5u5eGhrJ7KVFhkJsbmD1aLFnewYMHMXLkSERGRqJ06dIoXbo0IiMjMXLkSBw8eNDa4RU6d3fgvxluqASSM6l1YT63QgGsWgV89pkqidTk5aW6PyGBOy5ERNZW1KO9EpFxPHNpQ1JTU/Hmm29iwYIFBssNGzYM06dPR6lSpQotFmueuVR7/nlgwwarPb3dcHJSXbuh+U0uVUp17UZSknY5b2/Vfzc3oEYNICcHOHQISE42/Bx5R7VzdFTVERgIvP++6myzvz/w6BFw9SqwZIn2c2tq1AiIiAD++EOVpKmVLg28/Tbw4YfAwYPA+vWqs4OJibrrCQkBhg9/Oo+ZgwMwY4YqBn1xA6q4o6NVjx0+rB1D3iPevCaGiIiIiN1i7Y5SqUTnzp2xbds26T53d3fUqFEDTk5OuHDhApI09tY7dOiATZs2wbGQ9nRtIbkEVEnL6NHA6tVAVpZqFNm8n9g6dYAGDVQJUlycKgk4dw64cQPIzladCfXzU5VJTQXOnFElPo6OQEAAUL060KWLKpGJiQHS0lTP4eCgSkRcXVUJWUCA6n71YEPlywNVq6oey8oC/v4buH9fFaOTkypx08XVVRVjWppq2dNTtX56OuDsrIpVPc9VVBRw964qCVIqVQmRj8/TodPVEzzrSoIAeYmRet1bt1RzNqqTxMBA1Ws0pa68dcbFqWIXAqhSBRgzBnBx0S5jqE7NMmXKqO67f19e+eBgoGlT1e09e1SPa7aZ3BiIiIiISjoml3bmww8/xDfffCPdHjFiBL799lv4+fkBUJ3V/Pbbb/Hll19qrfPVV18VSjy2klwSEREREZF1Mbm0I7du3cIzzzyDjIwMAMDAgQOxZMkSnWU/+eQTKcF0d3fHlStXUK5cOYvHxOSSiIiIiIgADuhjV6ZPny4llh4eHpg2bZresp988glCQ0MBqKZ8+fHHH4siRCIiIiIiIoOYXNqAtWvXSst9+vSRusLq4uLigqFDh0q3161bV6ixERERERERycHk0souXbqEK1euSLc7duxodJ1OnTpJy//++y8uX75cKLERERERERHJxeTSyk6fPq11u0mTJkbXqVu3LlzUQ27qqIOIiIiIiKioOVk7gJLu4sWL0rKLi4t0PaUh6nJXr17NV4cuqampJseVm5sLBwceeyAiIiIiInmYXFrZjRs3pOWQkBAoFApZ61WoUEFKLq9fv26wrKenZ4Fii4yMLNB6RERERERU8vDUlJUlJSVJy97e3rLXK126tLScnJxs0ZiIiIiIiIhMxTOXVqbZZdXNzU32eu7u7jrr0CUlJcXkuOrXr2/yOkREREREVHIVeXL5999/Y9u2bbh37x78/PzQokULdOjQQXZ30OImOztbWnZykv92aJbNysoyWLZUqVImx8XrLYmIiIiIyBRmJ5dZWVkYN24ccnNz4ebmhh9++EFnopieno5+/frhjz/+0Lp/0qRJaNCgAX7//XeEhISYG47d8fDwkJYzMjJkr6dZtiDJIxERERERkSWZfXpq27ZtmDlzJmbPno2kpCS9ZyBfe+01bNiwAUKIfH9HjhxBu3btkJaWZm44dkdzsJ309HTZ62m2VUEH7CEiIiIiIrIUs5PL3bt3S8v9+vXTWebMmTNYvHixlHgGBASgR48eaN26tdT98vLly/j666/NDcfuBAQESMt37tyRvd7du3elZX9/f4vGREREREREZCqzk8sTJ04AAJydndGiRQudZebOnQshBAAgKioKFy9exLp167Bjxw5s2LABDg4OEEJg7ty5UCqV5oZkV6pVqyYtP3r0SPbZ27i4OGk5IiLC4nERERERERGZwuzk8urVq1AoFKhSpYreAWk2bNggLX/77bdaZ9q6dOmCXr16AQAePnyIkydPmhuSXalevbrW7VOnThld59atW3jw4IHeOoiIiIiIiIqa2cnl48ePAWh379T077//Ij4+HgqFAgEBAWjfvn2+Mp07d5aWz549a25IdqVhw4ZwdXWVbh84cMDoOvv375eW3dzc0LBhw0KJjYiIiIiISC6zk8vMzEwAqm6xumgmQq1bt9Y54E9YWJi0/OjRI3NDsiuenp5o06aNdHv58uVG19Es06ZNG44WS0REREREVmd2cqkeqVRfUrh3715pOTo6WmcZR0dHadnYnI3F0ZAhQ6TlM2fOYOPGjXrLnjhxAn/99ZfOdYmIiIiIiKzF7OQyJCQEQgjExMTkm6dRqVRqJUItW7bUWYe6ay0AeHl5mRuS3enduzeioqKk26NGjUJMTEy+cnfu3MGAAQOkQY/q1KkjXa9KRERERERkTWYnl+rr/TIyMjBv3jytx3777Tc8fPgQAFCuXDm9o5pevHhRWg4JCTE3JLujUCgwb948uLu7A1AlkY0aNcL777+PzZs3Y9u2bfjyyy/x7LPPSm3l7u6OuXPn6p1XlIiIiIiIqCjpHt7VBP369cPChQsBAO+++y4ePnyIpk2b4syZM/j8888BqJKnAQMG6K3j0KFD0rLm1BwlSf369bF8+XL0798f6enpSEpKwqRJkzBp0qR8Zd3d3bF8+XLUr1/fCpESERERERHlpxDqCSjN0LFjR2zbtk3nWTQhBHx8fHDp0iUEBgbmezw5ORlly5ZFRkYGfHx8tLrIlkQXL17EG2+8gV27diHvW6NQKNCmTRv89NNPhT63ZY0aNQAA58+fL9TnISIiIiIi2yY3NzD7zCUArFy5El27dsXff/+d7zFPT0+sXLlSZ2IJqEY+zcjIgEKhQKtWrSwRjl2rXr06duzYgbi4OBw8eBC3bt0CAJQvXx5NmzZFaGiolSMkIiIiIiLKzyLJpbe3N/bt24d169Zh8+bNiI+Ph5ubG+rVq4fhw4ejXLlyetddvXo1KlasCADo06ePJcIpFkJDQ9G3b19rh0FERERERCSLRbrFUvHDbrFERERERATIzw3MHi2WiIiIiIiIiMklERERERERma3Iu8XevHkTO3fuxL179+Dn54cWLVoU+sinZDovLy9kZ2cjPDzc2qEQEREREZEVXb16Fc7OzkhOTjZYzuwBfXJzczFlyhTk5ubC0dER48aN0zkliVKpxDvvvIMZM2ZAqVRqPdazZ0/MmzcP3t7e5oZDFuLh4YH79+/jwoULiIiIgIODZU5y5+bmIiYmBgBYL+tlvYVUJ+tlvayX9RZlnayX9bLe4l+vs7MzSpUqZbwSYaadO3cKhUIhHBwcRJ8+ffSWmzBhglAoFDr/HBwcRNOmTUVOTo654ZCFpKSkCAACgEhJSWG9rJf1FlK99hQr62W9rNf+67WnWFkv62W99lev2Wnujh07pOWBAwfqLHPt2jVMmTIFCoUCCoUCzs7OiIqKkrpcCiHwzz//YNq0aeaGQ0RERERERFZgdnJ57NgxVUUODmjbtq3OMnPmzEFOTg4AICwsDOfOncPJkyfx77//YsaMGQBUCeZPP/1kbjhERERERERkBWYnl1euXAEAhIeHw83NTWeZtWvXSsuTJk1ClSpVpNuvvvoqOnToAACIi4vD2bNnzQ2JiIiIiIiIipjZyeXDhw+hUChQtmxZnY/HxcVJCaiXlxd69OiRr0zPnj2l5dOnT5sbEhERERERERUxs5PL9PR0ANB71nL//v0AAIVCgVatWsHZ2TlfmWeeeUZavn//vrkhERERERERUREzeyoSDw8PpKSkICEhQefje/fulZajo6N1lnFxcZGWMzIyzA2JLKBUqVIQRTsFqlkKK157q7ew2Fs72FP72lsb2FPbAvbXDmxf+6y3sBRGvGxbFXtrB7avfdZbWGy5Hcw+cxkcHAwhBGJiYvLNXwkAf/31l7TcsmVLnXVoJqay5k8hIiIiIiIim2J2clmvXj0AQGpqKlatWqX12ObNmxEfHw8A8Pf3R506dXTWob4mE1Alq0RERERERGRfzO4W++KLL2LFihUAgDFjxiAzMxNNmzbFmTNn8MYbbwBQXW/Zp08fvXUcPnxYWq5ataq5IREREREREVERMzu57NGjB+rXr49jx44hKSkJw4cPz1fG1dUV77zzjs71MzMzsXnzZgCq6zdr165tbkhERERERERUxMzuFqtQKLBmzRqEh4dDCJHvz9HREXPmzEFYWJjO9desWYPk5GQoFAo899xzcHAwOyQiIiIiIiIqYmafuQSA0NBQnDlzBjNnzsRff/2F+Ph4uLm5oV69enj99dfx7LPP6l138uTJAAAhBLp3726JcIiIiIiIiKiIKYQ9jbtLRERERERENol9UImIiIiIiMhsTC6JiIiIiIjIbEwuiYiIiIiIyGxMLomIiIiIiMhsFhktVtNff/2FDRs24OjRo7h16xYSExORlZUla12FQoGcnBxLh0RERERERESFzGJnLk+ePImaNWuia9eumDt3Lk6dOoX79+8jMzNT5/yX+v6oeDp48CBGjhyJyMhIlC5dGqVLl0ZkZCRGjhyJgwcPWjs8m/HkyROsW7cOb775Jlq0aIGgoCC4urrC09MTFSpUQLdu3TBt2jQkJCQUqP6zZ89i3LhxqF27Nvz8/ODp6Ylq1aqhf//+2LJli4VfjX25fv06PD09oVAopL/PPvvMpDrYvtoePHiAWbNmoUOHDggPD4eHhwfc3d0RGhqK9u3b48svv8TBgwehVCpl1cf2Be7fv4/vvvsOHTt2REhICDw8PODm5obg4GC0atUKn376KWJjY02ut7i27YMHD/DXX3/hiy++QPfu3REcHKz1HV+0aFGB67527Ro+/fRT1KtXD4GBgXB3d0d4eDheeOEF/P7777I/10VVb2GwdPtmZWVhz549+OSTT9CpUydUrFgRpUqVkj7j0dHR+Oijj3DlypUCx3z37l1MmjQJTZo0QXBwMNzc3BAWFoaOHTti8eLFSE9PL3DdllaYn9+8cnJyEBUVpVV/y5YtTa7HXtq3sNs2IyMDv//+O1566SVERkbCx8cHLi4uKFOmDJo2bYp33nkHmzdvRkpKiuw67aVtISzgn3/+ER4eHsLBwUEoFAqtPwcHB4P3az7m4OBgiXDIhqSkpIhhw4YJAAb/hg0bJlJSUqwdrtVcvHhRdO3aVbi4uBhtKwDCw8NDTJ06VeTm5sqqPzs7W3zwwQfCwcHBYL1du3YV9+/fL+RXa5vat2+frz0mTpwoa122rzalUimmTZsmPD09ZX2ejx49arA+tq/K1KlThbu7u9H2dHR0FO+++67IysoyWmdxbds7d+6IihUrGm2rhQsXFqj+qVOnCldXV4N1N2nSRFy7ds0m6rU0S7dvenq6GDp0qPDx8ZG1zVAoFOKVV14RSUlJJsW9YsUK4e3tbbDuiIgIceLEiQK0iuUU9udXl6+//jpf/dHR0SbVYQ/tWxRtu2XLFlGpUiVZn+XvvvtOVp320LZqZieXWVlZokKFClKCGB4eLhYsWCDOnz8voqOjpaTx+vXr4uzZs2LLli1i4sSJonLlytJjI0eOFLGxseL69euWeE1kI3JycvLtsLu7u4v69euLxo0bi9KlS2s91qFDB5GTk2PtsK1i9erVOncQq1WrJlq0aCGaNWsm/Pz88pUZPny4rAQzb4Lv7OwsoqKiRLNmzYS/v7/WY1FRUSUu0V+6dKnODbXc5JLt+1R2drbo2bNnvrasXLmyaN68uWjevLmoUqWKVjJjLLlk+woxYcKEfG0aHBwsmjdvLqKjo3XuLPXu3dvo9qG4tm1sbKysHbuC7EB+8cUXWnU4ODiImjVrihYtWojg4GCtx0JCQsSdO3esWm9hsHT7PnjwQOf6FSpUEI0bNxYtW7YUYWFh+R6vW7euePLkiaznWLJkSb71q1atqvP7U7p0aXH+/HkzWsg8hfn51eXff/8Vbm5u+eo3Jbm0l/Yt7Lb94Ycf8tUVFBQkGjVqJFq1aiVq166tdQBJTnJpL22rZnZyuWDBAilJrF69unj8+LH0WMeOHfWekVQqlWLy5MnCyclJODg4iJdfftncUMjGfPDBB1of+BEjRohHjx5Jj6ekpIiPP/5Yq8yHH35oxYitR51cOjk5ieeff16sX79eJCYmapXJzc0V69evF+XLl9dqs5kzZxqse/bs2Vrlu3fvLuLj46XHs7KyxE8//SScnJykMv369SuU12mLHjx4IAICAgQAUb16dVGuXDmpHeQkl2xfbf369ZNep5OTkxg/frzOA4eJiYli9erVokuXLgaPtLJ9hdi/f3++nYrdu3fnK3f06FFRt25d2TtIxbltNXcgAwMDRceOHcXHH38sNmzYYNYO5JYtW4RCoZDWb9Kkibh06ZL0uFKpFCtWrNA6a9+sWTOr1VtYLN2+msllo0aNxJw5c7Q+i2onTpwQzZo103qOPn36GK3/zJkzWjv01apVE8eOHdMqs23bNlG2bFmpTOXKlUV6erqs+C2tsD6/+rRq1UoAEG5ublrtKze5tKf2Lcy2zbtN7dq1a752EEK1bd21a5cYNWqUmDVrlsE67alt1cxOLnv37i0lkH/99ZfWY4aSS7VFixZJZX766SdzwyEbER8fr3UUbODAgXrLaiaY7u7u4tatW0UYqW1Yv369eOWVV8SNGzeMlr1586YICgqS2iwgIEBv97fU1FStsi1bttR7dnjevHlSOYVCIY4fP27Wa7IXAwYMkF733r17tY4CGksu2b7ali9frvVd3rFjh1n1sX1V+vbtK702b29vg9vIJ0+eaH2G69evr7NccW9b9cELXQc2CroDmZubK6KiorR28lJTU3WW3b59u9bzrF27tsjrLUyWbt+HDx+KNm3aiIMHDxotm5WVJSVD6j9jXQG7deum9Zt59+5dneXOnTuntSM/ZcoUWfFbWmF8fvXR/H5/8cUXYvDgwSYnl/bUvoXVtleuXNG6bOGrr76ySLz21LZqZieXlSpVEgqFQvj6+uZ7TE5yKYQQbdu2FQqFQpQrV072NWRk29577z3pA+7h4aF1xjKvzMxMERoaKpV/7733ijBS+5T36Ji+nfiZM2dq7RRevHjRYL2NGjUy6Wiwvdu6dav0eocOHSqEECYll2zfp5KTk0VgYKD0+oydUZeD7auieTb99ddfN1r+u+++02q3zMzMfGVKctsWdAdy8+bNWutu2bLFYHnNgwINGzYs8nqtxdLJjy6XLl3Sep6PP/5Yb9nz589rlf3ll18M1q3ZBT0oKEgolUpLh28WS7bv3bt3ha+vrwBU1+tlZmaanFwWp/Y1p207duxo8W2kvbat2aPFPnz4EAqFAhEREfkec3R0lJYNjWD08ssvA1CNgnTgwAFzQyIbsHbtWmm5T58+8PPz01vWxcUFQ4cOlW6vW7euUGMrDrp166Z1OyYmRme5NWvWSMvR0dE6v6eaRo0aJS1v3rwZmZmZZkRp29LS0jB69GgAQEBAAL777juT62D7PrVixQo8ePAAAFC1alWt11pQbF8VdbsCQM2aNY2W1ywjhMDDhw/zlWHbmk6zzSpVqoT27dsbLK/ZZkePHkV8fHyR1lucVa1aFVWrVpVu6/sNBLT3Rzw9PdG/f3+DdY8cOVJavnv3Lg4dOmRGpLbtjTfekEaf/+WXX+Di4mJyHWxf4PLly9Ko2g4ODgXan9DFXtvW7ORS/QPj6emZ7zHN+zR/HPMKDw+Xls0ZXppsw6VLl7Tex44dOxpdp1OnTtLyv//+i8uXLxdKbMVF3mQ9KSkpX5mUlBTs27dPum3q+5CSkoK9e/eaEaVt++STT6QpG77//nv4+/ubtD7bV9u8efOk5QEDBsDBwbyfF7bvU5q/pXLmjdZM/BQKBby9vbUeZ9sWzKZNm6TlDh06QKFQGCzfvHlzlCpVCoAqyd+8eXOR1lvcaf4O6voNVNNs3+eee07n/qqmypUro1q1ajrXL042btyI1atXAwCGDBmC6OjoAtXD9gXmz58vLbds2RIVKlSwSL322rZmJ5fqH63U1NR8j2nurF29elVvHdnZ2dLy/fv3zQ2JrOz06dNat5s0aWJ0nbp162odMctbB2m7ceOG1u0yZcrkK3PhwgWt75ac9yEoKAhhYWHS7eL6Phw/fhw//vgjANUPweDBg02ug+37VGJiIo4ePSrdbt26tdl1sn2fatiwobSsmRTqo5n4Pfvss1Iiosa2Nd39+/dx9+5d6bacNnNyckKDBg2k27rarLDqLQk0fwd1/QYCquT77Nmz0m057Zu3XHFs3+TkZIwZMwaAal+9oGfa2L4q27dvl5Yt8fsH2Hfbmp1choeHQwiBW7du5XtMs2vOrl279NahedrW3d3d3JDIyi5evCgtu7i4IDQ01Og6ectp1kH5aXaVAIDGjRvnK5O3DTV7CBiiWa44vg85OTl45ZVXoFQq4eLiglmzZhWoHrbvU0ePHoUQQrpdq1YtAMDBgwcxZMgQhIeHw83NDX5+fqhduzbeeustnDp1ymCdbN+n1DuBgOq7b+j39NSpU5g9e7Z0+5133slXhm1rusJqM74XBfPPP//gzp070m1dv4EAcPPmTa2TH2xflQ8++EDqTv3dd98hICCgQPWwfYGMjAytJLB27doAgNjYWLz//vuoWbMmvLy84OXlhWeeeQaDBg3Cn3/+abRee25bs5PLqKgoAEBcXJzUb1tN8xT73LlzdV73cfv2bfz000/S7Ro1apgbElmZ5tHEkJAQo1181DS7EVy/ft3SYRUbiYmJ0lk3QLUh0/W90XwfnJycEBwcLKv+4v4+/PDDD1JiM2HCBKPXmunD9n3qzJkz0nKpUqXg5uaG0aNHo1mzZli8eDGuXbuGzMxMJCQk4OzZs/jxxx9Rt25djBgxQm83T7bvU927d8cbb7wBAMjNzUWnTp3wwQcf4OzZs0hPT0dmZiYuXbqEr776Cs2bN0daWhoA4L333pPGNNDEtjVd3t4icru9GWuzwqq3uPv888+lZTc3N/Ts2VNnOUu0740bN7QOntm7Q4cOSQdVW7RogSFDhhS4LravqidITk6OdDs4OBizZ89GjRo1MGnSJJw/fx4pKSlISUnB1atXsXTpUnTr1g3NmjXTeWJOzZ7b1uzkUvP079atW7Uei4yMlLpuPHjwAE2aNMGyZctw8eJFXLx4EfPmzUOTJk2kpLNMmTJo3ry5uSGRlWle+5D3Wh9DSpcuLS0nJydbNKbiZPz48VrdqL788kud5TTfBy8vL9nXwBXn9+Hq1avSTkmVKlXw4YcfFrgutu9Tjx49kpa9vLwwbNgw6eyZo6Mj6tSpg1atWqFKlSpSOSEE5s2bhy5dukCpVOark+2rbfr06Zg+fToCAwORlZWFb7/9FrVr14aHhwfc3NwQERGBjz/+GCkpKYiIiMCSJUswadIknXWxbU2X95o+ub9txtqssOotzpYtWyYNngKozuzrO0BiifbNzc2VDtjYu+zsbIwYMQK5ublwcXHBL7/8IvsEgC5sX+3fPwBYuXIlRo8eLQ1kWqVKFbRq1Qp16tTRGuj04MGDaNSokd4E057b1uzksmPHjnB1dQWg+sLn9eOPP0o/XNeuXcPgwYNRs2ZN1KxZE6NGjUJ8fDwUCgUUCgW++eabAo1URbZF8zS+m5ub7PU0u0TruoaXgAULFmhdON63b998I8eq8X3Ib9SoUdIGf+bMmSa1S15s36cSExOl5bt372L58uUAVCOBx8fH4+TJk9i1axcuX76MU6dOoX79+lL5HTt24IsvvshXJ9s3vzfeeANr1641eLa9bNmyGDNmDF544QW9Zdi2psv7euW2m7E2K6x6i6uzZ89Ko3wDwDPPPKN1FjMvS7Svrnrs1TfffIPz588DUPVsqF69uln1sX21f/8A1QCBgGoskRMnTuDy5cvYtWsXTp48iVu3bmmN+Jr3tiZ7bluzk8vSpUvjyy+/xLBhw1C+fPl8U440btwYy5Ytg6urK4RqXs18fwDwxRdfmHVqnmyH5kARTk5OstfTLCtnRMSSZv/+/VrXXlWqVEnr2qq8+D5oW7hwIXbu3AkA6N+/P9q2bWtWfWzfpzIyMvLd169fP/z6668ICgrSuj8qKgq7du1CZGSkdN8PP/yAx48fa5Vj+2q7efMm2rVrh+bNm0vTLpQpUwbNmjVDy5YtUblyZQDAvXv38Oabb6Jy5cp6RxFl25pOs80A+e1mrM0Kq97i6N69e3j++eelnWVXV1f8+uuvBkfQtET7AsWjjWNiYvD1118DUF2X99FHH5ldJ9tX9+9ftWrVsHv3bjz77LNa95ctWxbLli3DwIEDpfv27t2br+cnYN9ta3ZyCai66c2dOxezZ8/WOSBP3759cfHiRbz55puIjIyEp6cnXF1dERYWhsGDB+Po0aMW+ZCTbfDw8JCWdX3p9NEsm3d0w5Lu9OnT6NatmzTFQJkyZbBlyxaD3ST4Pjx1//59aWATX19fTJkyxew62b5P5X0d7u7umD59ut7yXl5emDp1qnQ7NTUVq1at0irD9n3q+vXraNq0KXbs2AEAqF69Onbu3Il79+7hwIED2L17N65evYqLFy+iS5cuAFSXovTo0QN//fVXvvrYtqbTbDNAfrsZa7PCqre4SUxMRMeOHXHt2jUAqu72y5cv1xo1VxdLtC9g/20shMDIkSOlfYhZs2aZ1XNHje2rO/apU6dqdU/N68cff9Rqu4ULF+YrY89ta5HkUo6KFSti2rRpOHfuHJKSkpCeno5r165h4cKFqFu3blGFQUVA8yhi3jPZhmj2Czc2l09JcunSJbRv317qeuHr64tt27ZpTSCtC9+Hp958803pzNi3336rd9h6U7B9n8r7Ojp16mR03tB27dppvQ95p9hg+z41aNAg6bqcqlWr4tChQzqHu4+IiMDGjRvRu3dvAKqRkYcOHZqvWxTb1nR5X6/cdjPWZoVVb3GSmpqKzp07SwOxKRQKzJ8/H7169TK6riXaV1c99mb27NnYv38/ANXlCu3atbNIvWzf/LEHBAQYnTvY19dXOhAI6J5iyp7btsiSSyo5NIe01hwq3BjNQWpMndC+uIqNjUXbtm2l+V89PT3x119/SaM0G6L5PqhHKpOjuL0Phw4dwsqVKwGo5n8aMWKERepl+z6Vdxh7OQcMFQqFVpch9RkJXXWW5PY9ePCgtFMIAJMmTTLYY0GhUOCnn36Sxi+4d+8efvvtN60ybFvT5f2My/1tM9ZmhVVvcZGRkYEePXrg4MGD0n0///yz7LmJLdG+Xl5ecHZ2lrWeLcrIyMD7778PAPDx8dHqNWIutm/+NqhTp46sQZI0fyfv3LmT74yjPbctk0uyuGrVqknLjx49kj1SVVxcnLRc0OkhipP4+Hi0adNGmovK3d0df/75Jxo1aiRrfc33AVBdsyVHcXsf7t27Jy0fOnQIDg4O0iBiuv40h//+/PPPtR7THPKf7ftU3kEh5O7sapbLO5UV21dF3RUWAJydnY0eEQeAoKAgNGzYULqd96g429Z0hdVmfC/0y87ORu/evaVr5QFg8uTJWmMPGJO3h09JbN+MjAyp59OTJ08QFBRk8Ddw8eLF0rp79+7VemzRokVadbN9Vd9hzWSyIL9/QP7fQHtuWyaXZHF5dzSNTZYOqEbMevDggd46Spp79+6hbdu2iI2NBaAauGD9+vVac8caU5D3ITs7G+fOndNbBz3F9n0q7zyr6ut6jNE8Upv3+h+2r4rmMPWBgYGyr5MKDQ2VljWPZANs24KoUqWK1kAZctoMAE6ePCkt62qzwqrX3imVSrz88svYtGmTdN9nn32Gd99916R6PD09tb4LbF/LYvuqro2sVKmSdLsgv39A/t9Ae25bJpdkcQ0bNpSmpwGAAwcOGF1Hs9uXm5ub1lH3kubx48do164dLl26BEB1tmLVqlVo3769SfVUrlwZISEh0m0578Px48e1+vW3aNHCpOe0Ra6urvD395f9pznnn7u7u9ZjmnNUsX2fCg0NlUYrBSAdFDFG80xw2bJltR5j+6pobksLen1k3oH22Lamc3Fx0eo1IqfN7t69iytXrki3dbVZYdVrz3JzczFkyBCsWbNGuu+9997DxIkTC1SfZvvIad/s7GwcPnxY5/r2yMHBwaTfQM1tjpOTk9Zjug5ulfT2BaB14L8gv38uLi7w8fHJV8Ze21b2GOTvvfdeYcYhmTx5cpE8DxUeT09PtGnTRhoGf/ny5UY/P+p58QCgTZs2dj1ymDmSkpLQoUMHnD17FsDTEfG6d+9eoPq6d++OmTNnAgBWr16NadOmGZxLVvN9qFGjBsLDwwv0vLakU6dOePjwoezyYWFhUtfY9957D5999pnesmzfp1544QX88MMPAIDt27cbLX/v3j2cOXNGut24ceN8Zdi+QLly5aTlhIQEXLt2TSuR1+f48ePScvny5fM9zrY1XY8ePfD3338DUHVXvn//vsHBwTTbzMfHR2/Pk8Kq1169+uqrWvOmv/nmm5g0aVKB6+vRo4fUZhcvXsTJkyfzTRGh6Y8//kBycjIAVWKmby5pe1G6dGmTfgOHDBkidY1t1qwZ9uzZY7B8SW9fAOjZs6c04uu5c+dw584dBAcHG1xH83eyUaNGOq/TtNu2FTIpFArh4OBQ6H9UPKxatUoAkP7++OMPvWWPHz8uHB0dpbKrV68uwkhtR2pqqnjuueekdnBwcBBLliwxq84jR45ovQ/Tp0/XWzYuLk54eXlJZb/77juzntteVaxYUWqDiRMnGizL9n3q3LlzwsHBQXp9GzZsMFj+nXfe0Wq7Y8eO5SvD9hXiwIEDWm3w5ptvGl1n9erVWuusXLkyX5mS3Laar3vhwoWy14uLixOurq7SuuPGjdNbNjk5WVSoUEEq+9prrxV5vdZS0PYVQoi3335ba/2RI0eK3Nxcs+JJSUkRgYGBUp09e/bUWzYnJ0fUr19fKtulSxeznrswmNO+cgwePFiqPzo62mj54tS+BW3bzMxMERoaKq379ttvGyz/559/aj3X999/r7OcvbatycmlQqEotD8ml8VHbm6uiIqKkj7kwcHB4uLFi/nK3b59W1SvXl0qV6dOHbN/SOxRRkaGaNu2rdQOCoVCzJs3zyJ1d+/eXarX09NTHDhwIF+ZxMRE0bx5c633Ky0tzSLPb29MSS6FYPtqGjRokPQaAwMDxenTp3WWW7FihdYBpQ4dOuits6S3b05OjoiIiNDaNsyZM0dv+YMHDwpfX1+pfNmyZUVKSorOsiW1bc3ZOX/zzTeldR0dHcXvv/+er0xWVpbo3bu3VM7d3V3cvn3bKvVaQ0Hb95NPPtFad8iQIRbbH5gyZYpW3VOnTs1XJjc3V7z11lta37UTJ05Y5PktydaSSyGKT/ua07YLFiyQ1nVwcBDLli3TWe7s2bOibNmyUll/f3+RnJyst157bFuFEEJABvUIi4BqzsqKFSvKWc1ku3fvLpR6qegdO3YMLVq0kK7TKV26NF599VW0aNECTk5OOHLkCH7++WdpNE93d3fs27cP9evXt2bYVjF58mRMmDBBuu3r62vSdaft2rXD+PHjdT52/fp1NGzYUBowydXVFcOHD0f79u3h6emJM2fO4KeffpKuE3BwcMD69euLRVeVgtDsFjtx4kSD3WIBtq+m+/fvo3HjxloDUb3yyito3749fH19ERcXh9WrV2P9+vXSOgEBATh+/DgqVKigs062L7Bt2zZ07twZSqVSuq9FixZ46aWXULVqVTg7O+PmzZvYtGkTVq9erVVu6dKlGDBggM56i3vbjhgxAkuXLs13v+aAG05OTlrXUqvpm7A8ISEBjRo1wr///gtA1Sb9+vXD888/Dz8/P1y6dAmzZs3S6vL9888/47XXXjMYa2HVW5gs2b5HjhzRuvbUwcEBbdq00boG3pCyZctqjXKaV1ZWFlq3bi11PwaArl27on///ggKCsL169cxf/58reva3nnnHXz33Xeynr8wFMbnVw7NbrHR0dFGu8UC9te+hdG2ubm5eP7557Fx40bpvh49eqBPnz4IDQ1FQkICtm/fjnnz5kl1KBQKbNy4UWvOy7zsrW0BoEDdYh0dHUV0dLSYP3++SEpKKrzUl+ze2rVrhbu7u9ZRF11/7u7uYu3atdYO12omTpxotI0M/Q0ePNhg/X///bfw8/MzWo+jo6P4+eefi+ZF2yhTz1wKwfbVdPHiRa1ue4b+ypUrJ44fP260TravEIsWLdLqOmnsz8nJSfzwww9G6y3Obat5BsbUP0MuXbqk1QXO0N+ECRNkx1tY9RYWS7bv7t27zfoNrFixotF479+/L2rVqiWrvn79+gmlUlkIrSZfYX1+TXleuWcuhbCv9i2stk1NTRWtWrWSVY+Li4tYvHixrHjtqW2FMKFbbNeuXYWzs7NWF1YHBwfh4eEh+vXrJ7Zu3VoiuzOScRcuXBBt2rQRCoUi35dAoVCItm3b6uwyW5IUdnIphBDx8fGiV69ewsnJSWcdDRs2FAcPHiz8F2vjCpJcCsH21ZSYmCheffVV4enpqbMt3NzcxJgxY8Tdu3dl18n2FeL8+fOib9++wtnZWe+2wMHBQXTv3l0cOXJEdr3FtW0Lc+c8ISFBDB8+XO/B08jISLFx40aTYy6seguDvSWXQgiRnp4u3n33XeHt7a2znrCwMDF//nwLt1TB2FtyKYT9tG9htq1SqRRTpkwR5cuX17m+QqEQnTt3FqdOnTIpZntpWyFM6BYLAA8ePMCyZcuwdOlSrflW1N1lg4KC0L9/fwwcOBC1atWSWy2VEHFxcTh48KA0b1v58uXRtGlTrXl8qPA9ePAA+/btQ3x8PLKyslCuXDk0aNAg34S9VDBs36fS0tKwd+9e3LhxA48fP4aPjw+eeeYZNG/ePN/0GHKxfYGUlBQcPXoUly9flibe9vb2Rnh4OBo2bKhzSHs52LamS05Oxq5duxAXF4fU1FQEBwejVq1aBkd0tGa9pJKRkYE9e/bg+vXrSEhIQNmyZVG9enU0btxY56idZBq2r6qb7KFDh3Dp0iXcu3cPHh4eCAkJQYsWLRAYGFjgeu2hbU1KLjWdO3cOixYtwq+//ipN0Kz5oqKiojB48GD069fPrEYkIiIiIiIi21fg5FItNzcX27Ztw+LFi7Fhwwati1QB1Tx97du3x5AhQ9C9e3eD82gRERERERGRfTI7udSUlJSElStXYunSpVqjFqkTTW9vb/Tp0wcDBw5Es2bNLPW0REREREREZGUWTS41xcbGYvHixVi2bBmuXbv29An/SzQrV64sDblNRERERERE9q3QkktNBw4cwOLFi7F69WokJSWpnlih0JqLi4iIiIiIiOyXvNlpzRQUFISgoCB4e3vbzEhGREREREREZDlOhVXxkydP8Ntvv2HJkiU4fPiwdL/6RKmfn19hPTUREREREREVMYsml0qlEps3b8aSJUvw559/IisrC8DThNLZ2RmdO3fGoEGD0LVrV0s+NREREREREVmRRZLL48ePY8mSJfjtt9/w8OFDAE8TSgCoX78+Bg8ejJdffplnLImIiIiIiIqhAieXt27dwrJly7B06VJcvHgRgHZCGRISggEDBmDQoEGIiIgwP1IiIiIiIiKyWSYll2lpaVizZg2WLFmCPXv2IDc3F8DTpLJUqVLo2bMnBg0ahNatW3PwHiIiIiIiohJC9mixgwcPRlBQEIYMGYJdu3ZBqVRCCAGFQoHWrVtj8eLFuHfvHhYvXow2bdowsSTScOPGDbz77ruoV68efHx84OjoCIVCAYVCgc8++8za4RHZjM8++0z6bgwZMsRi9e7Zs0eqNywszGL1FndbtmxB3759UblyZZQqVUpqQ4VCgevXr1s7PCoE169f13qfiYq7RYsWaX3mNf/q1Klj7fDsjuzkcunSpUhNTYUQAkIIREZG4ptvvsH169exY8cODBw4EB4eHoUZa7E0ZMiQfB/kgQMHmlxPt27d8tXzzjvvFELEZKodO3agZs2a+P7773HixAkkJiZKZ/2JiGzVq6++ik6dOmHVqlWIjY1FWlqatUMiIrIZupLSihUral0mKMcPP/yQr5769esXUtSFz+RrLtVHfSMjI3H8+HEcP37cYsEoFAqsXLnSYvXZq3Xr1iElJQWenp6yyt+/fx9btmwp5KioINLT09G/f3+kpKQAABwdHREVFYWAgADpiPAzzzxjzRCpmFq0aBGGDh0KAIiOjsaePXusGxBZxJ49e9CqVSsAQMWKFQvt7OGaNWvwyy+/SLd9fX1Rs2ZNrYPI7u7uhfLcRGRdn332GT7//HMAqp6LixYtsm5ARcjX1xcNGzaUboeHh5u0/s2bN7W203IsXrzYpOewdQUa0Of69esW/0FTd7ElIDU1FWvWrMHgwYNllV++fDlycnIKOSoqiI0bN+L+/fsAAE9PTxw7dgzVqlWzclRERIbNnz9fWu7SpQvWrl0LFxcXK0ZERFT4ateubfYJmyVLlshOLk+ePImzZ8+a9Xy2Rna3WABSl9jC+CNoXQe0ZMkS2eupy6pPx5Pt0Dyz36tXLyaWRFbSsmVL6feG1woap7nteuedd5hYliBhYWHcPyMyUenSpeHr6wtA1fND7mUEmvv7xWU8ANlnLuWeRaOCq1evHtzd3XHx4kXs2bMHcXFxCA0NNbjO2bNncerUKQDAc889BwcHB9y4caMIoiU5Hj9+LC0bey+JiGwFt11ERPI5OzvjxRdfxC+//ILk5GSsW7cO/fv3N7hOTk4Ofv31VwCAh4cHevXqhR9++KEowi1UspPLhQsXFmYc9J+BAwfiww8/RG5uLpYtW4YPPvjAYHnNftqDBg3CsmXLCjtEMkF2dra07OjoaMVIiIjk07zUgtsuIiLjBg0aJF2rvmTJEqPJ5ZYtW6RLp1544QXZY63YOpO6xVLhGzBgABwcVG/L0qVLDZZVKpVYvnw5AMDNzQ0vvviiSc+VmJiIlStXYtSoUWjcuDECAwPh4uICLy8vVKpUCb1798aCBQuQlZUlu87c3FysW7cO/fr1Q7Vq1VC6dGk4OTlJdbZr1w4ffPABdu7cCaVSabCuHTt2YNiwYahZsyZ8fHzg5OSEUqVKITQ0FC1btsS4ceOwceNGZGZmmvS6jVG364svvigNv+/l5YVnnnkGAwYMwLp16wx2FdIcAVgz+f/888/zjQZmqakWcnJysGrVKgwePBgRERHw8/ODs7Mz/Pz80KBBA4wZMwabNm0y2uYAcOvWLXz11Vdo1qwZgoOD4erqijJlyqBevXr44IMPcPHiRVkxabaDerqV3NxcrF69Gl27dkXFihXh6uqKwMBAdO/eHbt27dJZz6FDhzBw4ECEhYXB1dUVfn5+eO655zBnzhxZo+6GhYVJcagHtXny5Al+/PFHNG3aFEFBQXBzc0OlSpXQv39/7N69W9brA1QHD7Zv344JEyagdevWKF++PNzd3eHu7o7y5cujbdu2+Prrr/HgwQPZdWq6cuUK/ve//6FFixYIDQ2Fm5sbPDw8EBYWhm7dumHKlCm4efOm1jrqdlcP5gMAe/fu1TvMuqUG+klISMDUqVPRpk0bhISEwM3NDf7+/qhVqxbGjh2LI0eOmFX/X3/9hd69eyM8PBzu7u4oU6YMWrRogRkzZiAjI8Po+gWdimTHjh0YM2YMatWqhYCAALi6uqJ8+fJo164dpk+fjtTUVJNfy+3bt/H999+jffv2qFixIjw8PODm5oaQkBC0b98eX331Vb7vmXqKFs3reG7cuKH3fS3IABz6pp+oVKmSwc+N5oiJLVu2lO4/evQoXnvtNdSsWRN+fn75Htdkqc+Pvqlstm7dij59+iA8PBxubm7w9fVF27ZtsWbNGp31nD9/HqNGjULVqlXh5uYGb29v1K9fH5MnT7b4b07Lli3zvW/p6emYP38+WrduLbVHaGgoXnjhBaxfv15Wvfq2wRs2bMCLL76IKlWqwNPTM9+UWHKnIrGVbTygaq8//vgDY8eORfPmzREUFARXV1eUKlUKFSpUQNeuXTF9+nRpcD1Lt92///6r1WZyfycB4Ndff5XW8/Pzk7U9M6ag+0/qz6J6MB9AdRJD33ZG1yUG1n4vrKlJkyaoUqUKAGDnzp24c+eOwfJ5TxCZwhL727q2PcbImo5KkFUNHjxYABAARK9evYQQQrRu3Vq678iRI3rX3bx5s1Sub9++QgghoqOjpfvGjx+vd93vvvtOuLq6SmUN/YWGhopDhw4ZfS03btwQDRo0kFUnADFp0iSd9SQkJIgOHTrIrufVV181Gptcx44dEzVq1DD6nI0aNRKXL1/WWYfme2rsb/DgwWbHvG3bNlG1alVZzxcdHW2wrh9++EGUKlXKYB1OTk7i7bffFtnZ2Qbr0myHiRMnikePHon27dsbrPvbb7+V1lcqleL11183WL5NmzYiPT3dYBwVK1aUyu/evVscP35c6z5df0OHDhWZmZkG6929e7fw9/eX1e6lSpUSc+bMMVifptTUVDFq1Cjh5ORktG4HBwexY8cOne0u52/37t2y49Jn2bJlstqif//+IiUlxWBdEydO1Pp+pKSkiL59+xqsNyIiQpw5c8Zgvbt375bKV6xY0ehrunLlimjVqpXR1xQcHCy2bNkiq52ys7PFhx9+KNzd3WW9NwsWLNDZLnL+Fi5cKCsmTQX93CxcuFBrG5OVlSXeeust2dugwvz8pKWliYEDBxqsd8yYMVp1fP3118LR0VFv+dq1a4uHDx+a3L76aP5uL1y4UFy9elXUrl3bYMydO3cWT548MVhv3m3w3bt3RZs2bXTWN3HiRGm92NhYrcfk1m+tbfyvv/4qvLy8ZH1u/fz8xB9//GH0PSlI27Vs2VK6z9D+V16a+3xvvPGG7PV0MXf/SfOzKOcvNjZWa31beS9MlXcbVpD1/P39hRBCfPHFF9J9kydP1rvu48ePpf3wcuXKCaVSqbX9qlevnt51LbW/nXfbI4eh91+tQKPFUuEaPHiwdIRvyZIlaNCggc5ymkc8TL0mNiYmRuuIVWhoKMqXL49SpUohJSUFMTExSExMBADExcWhVatWOHDgAOrVq6ezvrS0NLRp0wZXrlyR7itVqhQiIiLg6+uLjIwM3Lt3D1evXpWOROo6Ipmbm4tu3brhwIED0n1ubm6IiIiAv78/srOz8fDhQ/z7779Sl1NLzRl58OBBdOrUCUlJSdJ9vr6+qF69OnJzc3HhwgXpscOHD6N58+bYuXMnatSooVVPrVq10KFDBwCqa2Jv374NQDWcdd5pR2rVqmVWzHPnzsWrr76qdVTKw8MDERER8PHxQVJSEmJiYqQjhE+ePNFb1/jx4zFlyhSt+5555hmEhITg4cOHOH/+PIQQyMnJwdSpU3Ht2jX8/vvvcHIyvhnJyclBjx49pPe1cuXKqFChAp48eYIzZ85I7+H777+PihUr4qWXXsKrr76KOXPmAAACAgIQERGB3NxcnDp1SrpQfufOnRg7dixmz54tq73i4uLw1ltvSdeTqV/fgwcPcOHCBemM9MKFC5GUlIRVq1ZJPQnyio+Px6NHj6Tbvr6+CA8PR+nSpZGVlYWrV69KRy1TU1MxcuRIZGVl4bXXXjMY44MHD9CpU6d80zyFhYVJ177dvn0bV69eBaD6/CcnJ0vl1J+/W7du4dy5c1JsmkOra/Lz8zMYjzHTp0/H2LFjte4LDQ1F5cqVkZSUhLNnz0pdLJcvX45r165h69at8PLyklX/kCFD8Pvvv0uxVq9eHTk5OTh//rz0uY6JiUHbtm1x4MAB6aixOU6cOIFOnTpJ3ZUA1WAN1atXh4eHB+Lj4/Hvv/8CAO7cuYNu3bph5cqVeOGFF/TWmZaWhl69euUbhbBcuXIICwuDi4sL7t69i8uXL0vfB/U2GFB9Vjt06IDHjx/j6NGjAFTbxujoaJ3PV758eZNft3q7BajO9Km1aNEi37Qjhj43b731FmbOnAlANUp2ZGQk3NzcEBsbm69sYX9+hg4dKk1xFhISgvDwcKSlpeHUqVPSb8jMmTMRGhqK999/H9988w0+/PBDAIC3tzciIyPh5OSEs2fPStvPM2fOoF+/flptZClJSUlo164drl27BgCoUKECKlWqhMTERJw9e1ba1m/evBldunTB9u3bZU0Jk5mZic6dO+PEiRMAgLJly6JKlSrIycnBpUuXzI7bmtv4q1evam0Dy5Qpg7CwMHh5eSE9PR2XL1/Gw4cPAaiuJX7++eexfv16dOvWTdZrk9t2I0eOlM7oL1myBN988w2cnZ0N1n3t2jWt3jKvvPKKrJh0scT+U8OGDeHm5oYrV65IvzHlypXTu6+S97NnK++FNQ0cOBATJ06EEAJLly7Fu+++q7PcypUrpf1wzV6Lxlhqf7tQyUpTqdDoOnOZkpIinT0KCAgQWVlZ+dZ78uSJcHNzEwBEUFCQyMnJEULIP3M5YsQI0alTJ7Fs2TKdR2CVSqXYtGmTqFatmlRf9erVhVKp1Fnf999/L5UrXbq0WLp0qc64U1NTxYYNG0Tv3r11HtH5/fffpXpcXFzEtGnTRGpqar5ymZmZYvv27WLo0KFi7Nixel+nXAkJCaJ8+fLSc3t5eYn58+drvYb09HQxZcoUrTO+kZGRIiMjQ2+9eY+4WdLOnTuFg4ODVH/58uXF0qVL8x3lVSqV4tChQ2LMmDGicePGOutatWqV1tGoBg0aiFOnTmmVuX79uujatatWuS+++EJvfJqv3c/PTwAQ9evXF8ePH9cqd+XKFa2j9OHh4WLFihUCgChTpoz4/ffftT53iYmJWmeyHBwcxKVLl/TGoXmWMiAgQAAQdevWFSdOnNAqd/XqVdG2bVut1zdjxgy99S5dulTUrl1b/Pjjj+Lq1as6y5w+fVp06tRJqs/NzU1cu3ZNb51KpVLrKDYAMWDAAJ2v7+HDh2LevHmiTp06Yt26dfkeL+iRWFMcOnRI6wxPlSpVxJ49e7TK3L9/XwwbNkzrNQ0bNkxvnZpHbtXvl/rMr+b3MTU1VXzzzTdaZ3cbNWqkdxsl98zlw4cPtbYF1apVE3/88Ye0jVW7ePGi1ufF29tbXL9+XW+9ec8od+zYMd93QQghkpKSxK+//iqio6PF1KlTC/w6zKUZq76j02qanzX1WQtPT08xa9asfNtHze9KYX9+1NudZ555RuzcuVOr3N27d7W+a97e3mLLli3CwcFB5+ctIyMj39nY7du3G2tGWTR/t9Wf+cqVK4tdu3Zplbtz547o16+fVgzvvvuu3no1P3Pq9yU8PFxs3bpV5ObmSuWysrLEzZs3pdsFOXNpzW38l19+KZo1aybmzJkjbt26pbPM/v37RaNGjbTaOSkpyaJtl5GRoXUG/vfff9dbv9pHH32k9btrDkvuP+XtASCXrbwXprLkmUshtL/Tefc11Jo0aSKVOXfunBBCyDpzaan97bxxWvLMJZNLK9OVXAohtLrxrF+/Pt96c+bMkR4fN26cdL/c5NJY1yK1R48eicqVK0t1/vnnnzrLaf5Iz5s3T1bdeXfWhBBaOxEff/xxgesx1dtvvy09r7Ozs9i7d6/esqtXr9b6cml288mrsJLLzMxMERISItVdtWpVvRtyTcnJyTrrCgoKkuqqW7eu3s+HUqkU3bt312qruLg4nWXz7kxHRUXpfH4hVDsfzs7OWj+Mnp6e4uLFizrLZ2VliSpVqkjlP/30U72vOW8X2Bo1aojExES99Wp2hfT29tb7oyf3O6RUKkXPnj2lOt955x29ZWfMmKEV6/fff2+0/tzcXJ2xFEVy+eyzz2olOnfv3tVb9rXXXtN6bf/884/Ocnm7fzo4OIht27bprXfevHla5ZcsWaKznNykbNCgQVo7e4Z2erKzs7UOHuhLejZt2qQV4xtvvKG1U6SPru+LrSeXAISjo6PBbahaUXx+ypcvr7fehIQEKSlSb3ccHR3zJbiaNLs+Dho0yOhrlCNvV8SgoCC921UhhBgwYIBU1snJSe8Bq7zb4NDQUHHnzh2j8RQkubTmNl7utjg9PV00btxYqvPnn3+W/drktt348eOldTp16mSwbE5OjtaBrNmzZ8t6HfpYcv+poMmlLb0XprB0cjl//nzp/rfeeivfepcvX9ba51KTk1xaan9biMJLLjmgj43SvLBX15yXmveZehEwoDqFLoefnx8++ugj6faGDRt0louPj5eWmzVrJqtuXSMQWqoeU6SlpWmNhvz666+jRYsWesv37t0bffr0kW7PmjVL1kA5lrRs2TKprRwdHbFixQqUK1fO6Hq6RiJbs2YN7t69C0B1ofb8+fP1fj4cHBwwZ84cqUtadna27C6ps2fP1jsSWnh4OFq3bi3dzsrKwscff4yIiAid5Z2dnbUGrPn7779lxQCo3q/SpUvrrXfu3LlSV6bExET89ttvOsvK/Q45ODhg8uTJ0m193yGlUonvv/9euv38889j/PjxRutXKBSyY7GkQ4cO4eTJk9Lt6dOno2zZsnrLf//991rz8P7888+ynmfIkCFo166d3seHDx+uNciNeqS+goiPj5eGhXdxccGKFSsMdr90cnLCnDlzpM/Lr7/+qtWVVe3bb7+VluvXr4+pU6caHChFzV5HDhw9erTBbShQdJ+fH374QW+9Pj4+WgPhZWVlYcSIEXq7GgOqro9qpmx3TDF58mSEhIToffynn36SuiXn5ORg3rx5suqdMmUKgoKCLBKjLtbaxsvd/rm5ueGrr76SbuvbFusit+00Px9bt27V2qfJa8uWLbh16xYA1Wt4+eWXZcejizX2n/KypffCml588UWpy/CKFSu0Rt8GzNuHt4X32RgmlzZKPUIcAPz5559ac45du3ZN6lMfFRWFqKioQo2lUaNG0rL6Wp+83NzcpOUzZ84U+LksVY8p9u7dK11Lo1Ao8Oabbxpd5+2335aWb9y4Ic01WlRWrFghLXfu3Bl169YtcF2aIw9GR0ejTp06BsuXLVsW/fr107m+PjVq1ND6HOmieU2gQqHAsGHDDJbXrE/uyHxRUVFo3ry5wTLh4eHo2LGjdFvuyIzG6gwICACgGgFW17Wv//zzj9Y1aZ988onZz1uYNNulUqVK6N69u8Hybm5uGD16tHT7jz/+kHUdyBtvvGG0zOuvvy4tHzx4UOtaSVP89ttv0k5A165dER4ebnSdkJAQKRnJyMjAwYMHtR6/desW9u/fL93+4IMPiv3UHpo72PoUxefH29sbvXr1Mlgm77XIw4cPN1hec7tz7do1k0ZTlyMgIMBokuHj46M1xYGcbVRgYCCef/55M6PTz1a28cZo1nns2DFZ65jSdlWrVpW2B7m5uQan8Zs/f7603KdPH9nXEetjjf0ncxT2e2FNXl5e0jX49+7d07o+W/x3LSagOoiiuT8lhz28zxzQx0Y5ODhgwIAB+Pbbb5GVlYXffvsNY8aMAWD+Wcu8zp8/j7179+LcuXN49OgRUlJStM7EpaenS8vqo2x51atXT/qQv/HGGyhVqhQ6d+4s6+h83nr++OMPAKqpO8qVK4eXXnpJ1qAxBXX48GFpOSIiQtY0BY0bN4a/v780oMvhw4f1DnZkaTk5OTh06JB029jOkzGar79Tp06y1unatat0xvLChQtITk42+MNobKcDgNaRyMqVKyMwMFB2eUMDFWnSTBoN6dSpEzZu3AhA/wEVTeofj9OnT+POnTtITk7WmuMUgDT4jBACt2/fho+Pj9bjmglIWFiYWQcMioLm50Zuu3bt2lWau1c92FRkZKTe8mXLljV6sANQDUSjUCikAZmOHTuGzp07y4pJk+Z7oHmWxZiaNWtix44dAJ4OBqSrTldXV3Tt2tXkuOyJt7e3rIHKiuLzU69ePaO/HZrbEVdXV6OfN83yQggkJiYa3VaZok2bNrJ+7zp16oSffvoJAKRB2wyd6W7cuHGh/o7ayjY+NjYWO3fuxJkzZ/DgwQMkJyfnO2uklpCQgLS0NHh4eBis09S2GzlyJPbu3QtANTjcxx9/nG9f6P79+/jzzz+l2+YM5KNmjf0nQ2zhvbCmQYMGST1hFi9ejC5dugBQndC4ceMGANW2z9Tth6X2twuTfbxDJdSgQYOk7lRLlizBmDFjtI54ODo6Gp2g1ZB//vnH5PnndHX5AlRnDpYsWQKlUon79++ja9euCAkJQadOnRAdHY3mzZujQoUKRusfPnw4vvvuO6SkpCAtLQ0DBw7E22+/rVVP1apVZccrh+aIW6aM3lqrVi1pZDjNOgpbXFyc1tx65iS1OTk50kYOkP/6Ncvl5uYiNjYWtWvX1lveUHc3Nc0flTJlyphUXj2yoDE1a9aUVU5zBOAHDx4gMTER3t7e+crdunUL48aNw5o1a0zqGq3re6R5ZL6oDlSYoyDfm4iICDg5OUk7GFeuXDGYHMh9v0qVKoWwsDDpzK96JFdTqUfXBVRnFdQHGIzRbAv1SIhqmu9rzZo14eLiUqDY7IV6TkxjiuLzY+p2x8/Pz+iOa96dX7nbHrkKso3Kzc3F1atXDfZiknMW3hzW3sbHxMRg7Nix2L59u8F5qPNKTEw0mtCY2na9evXCm2++iUePHkkJVtu2bbXKLF68WDoAGRkZiaZNm5r0HLpYY/9JF1t6L6ypbdu2CA4Oxp07d7Bx40Y8efIEPj4+Zs1tCVhuf7swsVusDatevTrq168PQHWU9/Llyzhw4IA0RHn79u1lbdB1WblyJZ577jmTJzbX1wWobt26WLBgAVxdXaX74uPjMXfuXAwYMAAVK1ZElSpVMGHCBGl4a13Kly+PNWvWaO3IP3z4EEuXLsUrr7yCatWqITQ0FK+//jpOnz5tUuz6aB4R9ff3l72eupsjoDrqVlQ0u0gDMOuoed6jwXJfv+ZrB4y/flN3qAtrB1zu68tbTtdR8wsXLuDZZ5/FqlWrTL7mVtck7JrvqyXPhBSWgnxvnJyctM7YGvvcmPJ91Cwr9yxHXppTy5w8eRJbt26V9ae5Tct74MDe3ldzye3aVxSfn6LY7piy8yyHJbdRmsztcmmMNbfx+/btQ7169bBt2zaT3w9d2+K8TG07V1dXrenhNLu/qi1YsEBaNtYVWy5r7D/lZWvvhTVpngDKyMjAqlWrkJaWhjVr1gBQTREmdwoWTZba3y5MTC5tnOYGasmSJWbNbal2/fp1DBkyRNohDgwMxIcffogdO3YgNjZW6hYrVKMJ65ybTJdBgwbhwoULePXVV3X+QF65cgWTJ09GREQEJkyYoLd7RPv27XHp0iW89957OgepiY+Px4wZM/Dss89i6NChZh851tygmfKDp1lWzkbRUvI+l+YGxty65L7+vOWK8vWbQ+7ry9umeV+fUqlEnz598ODBA6n88OHDsXbtWmmO2KysLOk7JITQGoxEF83nMOc9LSpF8b0xpV7NNivo51GzR0BB5b0O0N7eV3PJnavN3ra7RcVS26i85L4v9iYpKQkvvviitB/g5eWFsWPHYtOmTfj333+lrpia22JTFaTtNK87XrdundZBpgMHDiAmJgaA6v22xOVNakW9/6TJVt8La8q7D7927VppHtC+ffsW+DfBUvvbhYXdYm3cyy+/jHHjxiE7OxtLliyRjop7e3ujR48eBapz2rRpyMjIAKC67uHgwYMGz4BqTohrTOXKlTFz5kz8/PPPOHXqFPbs2YO9e/di9+7dUj05OTmYPHkyMjIy8OOPP+qsp2zZspg0aRImTZqECxcuSPXs2rVL6nYmhMCiRYvw+PFjk0Yay0vzKJ8pr1WzbN7r5wpT3udKTEws8PPn7eop9/XnLVeUr98cBX19eUeX3bhxI86fPw9AdUH+rl27jHZrMvbcmm2or/u5LfH29pbO9BXW96ag9eobDdgYzde0evVq9O7du0D1aLK397WoFMXnxx5ZahtVUixYsEAawMvX1xeHDx9GlSpV9JY35bNmjmrVqqFFixbYt28fMjMzsWzZMmmwQM0zmT169MjXE8hcRbn/pMlW3wtrqlmzJp599lmcPHkSf//9t1YPA3MPKlhqf9sUcntp2dchgBLI399fGpgiLi4OSUlJAFTDHGuOGGWK7du3S8uffvqp0a61+gbxMcTBwQF169bFuHHjsGHDBjx48AArV67UGixnxowZuH79utG6IiMjMWbMGKxcuRJ3797F5s2bta7v++OPP7QGzTCVZlc1uWdpAWh1NyjK7m55h+Au6PVlgGqqA/Vw2YD815+3q4W9dPeT+/o0yzk5OUnD/qtpfof69+9vNLFMS0sz2m1N83015z0tKgX53qgHddBVhy5y6xVCaG1L5FzPpYvmtrCgI87mZW/va1Epis+PPSrINgoo+Gfe3mlui8eOHWswmQEKtj9TUKNGjZKW1QllcnIyVq9eLd1viYF8DCns/SdNtvxeWJNmEqk+KF2lShU0adLEIvWbs7+t2VMi7yCEusi95ITJpR3QdXTDnCMeN2/elJbV13QaojkyaUG5urqiT58+2Lp1qzQnnFKpxM6dO02qx9HREZ06dcLOnTu1jvZt27atwLFpjsp58uRJ2V+wS5cu6ayjsPn5+WlttPft22dWfc8++6y0LPcaXM2RHn19fWWNsGsL5L4+zXI1atTI11XN1O/Q4cOHjU6b0LhxY2n5xIkTZnfR1Ow+ZOnrwgDtz3xBPjcKhULrs6fLxYsXZR3dvnTpknTgLW9sptB8D/75558C1WGozlu3bknXzBdUYb+vRaUoPj/2qCDbKB8fH1SqVKmwQrJp1tifkatXr15Sl8UzZ87g6NGjWLFihbRtr1ixYr6BfgqTKftPBdnO2PJ7YU39+vXLN1DYwIEDC+35TNnf1ryGVc7YIZqD3hnC5NIOdO3aVevMSaVKlfDcc88VuD45yZOaUqmURqe1hKpVq2qN7nfv3r0C1RMQEKA1eWxB6wGgNe/hkydPsGXLFqPr/Pbbb1L3AEdHR4sdgZJLc1J5zVHnCkLz9a9Zs0ZWXcuWLZOWn3vuOZsaAtuQTZs2SVOC6JObm6t1ZFnXd83U9l60aJHRMq1atZLmP0xPT9dq44LQnMxaczohS9H83Gzfvj3fKKm6aL6mmjVrGu3WmJ2djXXr1hmt97fffpOWfXx8ZI+4mVeHDh2k5Q0bNlikG2v9+vW1XufcuXPNqq+w39eiUhSfH3t0+PBhWT16ND/zzZo1s5ttsKUVxrbYUlxdXbVOBMyfP1+rS+ywYcOscg2hnP2ngmxnbPm9sKYyZcpoTbekUCgKNblUk7O/rTmqrJw5M+V2oWZyaQdcXFzw6NEj6QLoa9eumfVDEhwcLC3//fffBstOmTJFVjcdU46ga+7c5+1uaEo9mmc08tZjioiICK3k8NNPPzV48XNycjL+97//Sbe7detW5F2SXnvtNekzcPPmTXzxxRcFrmvo0KHS8t27d432y1+zZo3WGQRLjXRXFJKTk6XpffSZP3++1tmlIUOG5Ctjynfo8OHD7JRbXQAADnJJREFUshLF4OBgrTlLP/74Y9y9e9foevpodse8du2axc9yvfTSS9Kw8VlZWfjss88Mlj969KhW0i73c/PFF19I14jr8uDBA63PbP/+/Qs8D1rPnj2lgZeSkpIwfvz4AtWjycXFRWtwj2nTpkldowpC8319+PCh3V7HWVSfH3sjhMDHH39ssMz27duleRQB3duoksKUbfHq1avN7uljKs3v/uLFi6Uzzg4ODlq/vZZgyf0nze2M3KnWbP29sKaNGzdK+/C5ublm9fay1P42oN1z7a+//jLYY+r69euyD44yuSyBoqOjpeX//e9/evu9L168WJqw2pg6depg+fLlBncCAWDWrFla1+u1aNFC6/E2bdpg1qxZWl3cdNm0aZM0x6Suekz10UcfScunTp3CkCFDdE67kpSUhJ49e+L27dsAVGct5baRJUVGRmqNQvbll1/iq6++Mnix9e3btzFjxox891erVk1r4JIPP/wQ69ev11nHP//8g2HDhkm3o6Ki7G5S+G+++QYrVqzQ+dju3bsxduxY6XabNm10du/R/A6tWrVKb/fuEydOoHv37ka7xKp99tln0jWwDx8+RKtWrXD58mW95YUQWLduHY4fP57vsVq1aklJ1qNHj7BkyRJZMcjl4+ODMWPGSLdnzJih8/MFAJcvX0bPnj2ldihXrpzsHaurV6+iX79+OrctCQkJeP7556XrQNzc3PDWW2+Z9kI0ODk5YdKkSdLt+fPnY/To0UZHVExNTcWiRYvQpk0bnY+PHz9eOgCVkZGBtm3bah2g0WX37t3YsWNHvvvLly8v1SWEsMggDdZQVJ8fe7R8+XJMnjxZ52Pnzp3Tmt+6evXqBR7crzjQ3Bb//PPPOHv2rM5yW7dutUoSHhERIZ2l19yGtW/fHqGhoRZ9LkvuP2l2Wz916hR2795t9Plt/b0oLiy1vw0AXbp0kS77efz4Md59912ddd2+fRvdu3c32vNLjaPFlkBvvPEGlixZAiEE4uPjUadOHbzxxhto0qQJnJ2dcfXqVaxYsULaYX7llVcwb948g3WeOXMGAwYMwKuvvorOnTujUaNGeOaZZ+Dr64usrCxcuXIFa9euxdatW6V1XnjhhXwTYF+7dg1jxozBuHHj0L59ezRp0gTVq1eHn58flEolrl+/js2bN2PNmjXSjkb9+vW1urMVRJcuXTB06FAsXLgQgOrH/fjx4xg5ciRq1aqF3NxcHD9+HL/88ovWdQXvvfceGjZsaNZzF9TPP/+MI0eO4MKFCwBUZ7qWLVuG/v37o06dOvDx8UFSUhLOnz+PnTt3YufOnahRowZee+21fHXNmDED+/fvx71795CdnY0XXngBvXr1Qq9evVC+fHk8fPgQmzdvxuLFi6Wzum5ubliyZInUldMe9OnTB6tWrUK/fv2watUq9OnTB+XLl8eDBw+wceNGLF26VPpclSpVCrNmzdJZT9++ffHBBx/g7t27UCqV6NSpE1555RV07NgRvr6+uHPnDjZv3ozly5cjJycHHTp0wIULFxAXF2cwvurVq2PGjBlSAh8TE4OaNWuiT58+Wjsjt2/fxuHDh7Fu3TrEx8dj3bp1qFevnlZdpUuXRteuXaUDBUOGDMHXX3+N8PBwrWtIv/zyywJ3I/3iiy+wefNm6TP4+uuvY926dRgwYAAqVaqEpKQk7Nq1C3PmzJESNAcHB8yfP1/W6JZt2rTByZMnsW7dOtSuXRujR49GVFQUcnJycOzYMcycOVM60AOoeh0888wzBXotan379sXhw4cxdepUAMDs2bOxZs0a9OvXD02bNpUG/UlISEBMTAwOHz6M7du3Iy0tTe/gaGXKlMGyZcvQuXNn5OTk4O7du2jSpAm6d++Orl27IiwsDM7Ozrh37x6OHTuGDRs24PLly5g6darOa7L69euHadOmAQAmTpyI+fPno3r16lqDvL355pto3bq1WW1R2Ar782OPXnzxRaxevRoTJkzAX3/9hcGDB6NSpUpITEzEjh07MHfuXGmn0tHREbNnz5auqyqJRo4cicmTJyM9PR3Jyclo2rQpXn31VbRq1QqlSpXCzZs3sW7dOqxduxaAvP0ZSxs1alS+QXMKYyAfS+4/Va9eHXXq1MGpU6cghEDr1q1Ru3ZthIaGavUMmTNnjnSwyx7ei+LAUvvbgGrQ0CFDhmDOnDkAVAnpv//+i6FDhyI0NBQJCQnYu3cv5s6di+TkZAwYMEDeJTuCrGrw4MECgAAgevXqZXZ90dHRUn3jx4/XW+5///ufVM7QX58+fcS1a9e07tNFTl2af40aNRKPHz/OV0/FihVNqqdKlSrixo0bZrebEEJkZWWJl156SfZzv/HGGyI3N9dgnZrv78SJEy0Sp6aHDx+Kpk2byo45KipKb10XLlwQ5cuXl1WPl5eX2L17t8HYTH3tCxculMpHR0cbLR8bG2v0cymE9mdq9+7dYsCAAUZfn5ubm9i5c6fB59+5c6dwcXExWldkZKS4f/9+vjgMWbRokXB2dpb9vq5bt05vG4WEhBhc11gsxty+fVvUrFlTVpzOzs5ixYoVBuubOHGiVH7w4MHizz//FK6urkbrHjlypMF6d+/eLZWtWLGi0df15ZdfCoVCYdL2qGzZsgbr3Lp1q/Dy8pJd39SpU3XW8+TJE6NtvnDhQqOv0RDNumJjYw2WNfW7q6mwPz/GmPq5EMK0tpFD83d74cKF4uOPPzbaFg4ODmLp0qUG6y3o74/cbautbOOXLFki67vavHlzkZ6eLuv9s+Rvd0ZGhvDz85PqK1OmjMjKyjKrTl0svf909OhR4ePjY7COvO1n6++FPgXdhmmu5+/vb3YcmtuvevXq6SxjynsM6N/fVnv8+LGoXr26rPcsLS1N1nvGbrEl1Mcff4z58+frvVYwKCgI06ZNw8qVK2Vd3/nzzz+jffv20jU0+oSGhmLSpEnYv38/fH198z3+zTff4Pnnn883/2JeAQEBeP/993HixAmtC5LN4ezsjF9//RXLli1DeHi43nI1a9bEH3/8genTp1t9EAV/f3/s3bsXv/zyi8HRAh0cHNCkSRN8+OGHestUr14dZ86cwZtvvql1Mb8mZ2dnvPzyyzh37hxatmxpbvhWsXTpUkyePFnnxMOAaqCR48ePGz3r07p1a+zZswe1atXS+biHhwdGjx6NI0eOmDxlwuDBg3Hu3Dn06dPH4KTqAQEBGDNmDBo1aqTz8bCwMJw+fRrffPMNWrRogTJlypg0Yb0cwcHBOHz4MCZOnKjzOw2oPn+dOnXCiRMn8NJLL5lUf5cuXXDgwAG9I8CWKVMG8+fPx+zZs2XXKed7+9FHH+HUqVPo3bu30Ymu1RNVa3Y106V9+/aIiYnBiBEj9H7HANUIfgMHDpSmocrL29sbR44cwU8//YR27dohODi4wFNTWVthf37s0f/+9z8sXrwYISEhOh+vXbs29u/fjwEDBhRxZLZp4MCB2LBhg97fQF9fX3z00UfYtWuXVb4nrq6uqFatmnR70KBBhXK22dL7T/Xr18e5c+fw0UcfoXHjxvDz8zN6PbutvxfFgaX2t9V8fX2xZ88evfM6e3p6YsKECdi5c6fW1HWGKP7LgqmEysjIwP79+3H+/Hmkp6ejTJkyeOaZZ/Dcc88VqLtjTk4Ozpw5g8uXL+POnTtITU2Fm5sbypQpg6ioKNSqVUvW6Gi5ubm4cOECLl26hPj4eCQnJ8PFxQX+/v6oVasWnn322ULvCnT27FmcOHEC9+/fh0KhQNmyZdG4cWOjczdZ04ULF3D8+HHcv38fGRkZ8Pb2Rnh4OBo0aGDSRM0ZGRnYt28frl27hsePH6N06dKoUKECWrZsaXfd0cLCwnDjxg0AquvY1ElxVlYWdu/ejWvXriExMRFly5ZFs2bNULVqVZPqF0Lg2LFjOHbsGBISEuDr64vQ0FC0bNkSnp6eZsefkpKC/fv348aNG3j06BFcXFwQFBSEmjVrIioqyiqjDeqTk5ODgwcPIiYmBo8ePYKHhwfKly+P6Ohoi8xJeObMGZw8eRJ37tyBl5cXIiIiEB0dLWsAn+3bt6N9+/YAVNcZx8TEyH7e9PR0/P3334iNjcWjR48AqBK8ypUro1atWihXrpzJryUzMxMHDhzAtWvX8PDhQzg6OiIwMBCRkZGoW7duiezqWNifH1vVsmVLaYCehQsXStej5ebmYt++fbh8+TIePXqEwMBA1KtXr1hOwWIJOTk5OHToEE6fPo2kpCQEBAQgLCwMLVu2tPhBNVPExsYiPDxcGoglJiZGK9m0NFvYf7LV90KfRYsWSddxR0dHGz1QaAsstb+tKS4uDrt378adO3fg7u6OihUrok2bNibvyzC5JKJiTV9ySSXLqlWr0LdvXwBAw4YNjQ6oQ1RU9CWXVDx89NFH+PrrrwGoesaUpFFS7YU9Jpe2jAP6EBFRsac5+bMt9z4gouIjKSkJv/zyi3Rbc4Rksk1nzpzRmpcyPDxc70jWpBuTSyIiKtauX7+uNXm5uVMXEREZk56ejtGjR+Px48cAgMqVK+u9ro1sR0JCgtZIq1FRUVaMxj4xuSQiomLn3r17GDx4MBISEnDy5ElkZ2cDUA1e8OKLL1o5OiIqjqZPn45du3YhLS0Np06dwoMHD6THvv76a1nXhxPZO37KiYio2ElPT9c6+gwALi4uWLBggcGR84iICurEiRPYsGFDvvuHDRsmXfNNtmfIkCG81tmCmFwSEVGx5erqiuDgYLRs2RLjxo3TO3UMEZEleXh4oGbNmhgxYgSGDx9u7XCIigxHiyUiIiIiIiKz2c4EaURERERERGS3mFwSERERERGR2ZhcEhERERERkdmYXBIREREREZHZmFwSERERERGR2ZhcEhERERERkdmYXBIREREREZHZmFwSERERERGR2ZhcEhERERERkdn+DxjofkopPU5kAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total number of BH+BH systems in the dataset is 260622\n" + ] + } + ], + "source": [ + "# now the BH-BH systems: \n", + "\n", + "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", + "\n", + "mask_M1isNS = (M1 < 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", + "mask_M2isNS = (M2 < 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", + "\n", + "\n", + "Stellar_Type_1 = fDCO['Stellar_Type_1'][...].squeeze()\n", + "Stellar_Type_2 = fDCO['Stellar_Type_2'][...].squeeze()\n", + "\n", + "\n", + "\n", + "mask_BHBH = ((mask_M1isNS==0) & (mask_M2isNS==0)) \n", + "\n", + "\n", + "plt.scatter(M1[mask_BHBH], M2[mask_BHBH], c='blue', label='BH+BH')\n", + "\n", + "layoutAxes(ax=ax, nameX='Mass of compact object from primary star [Msun]',\\\n", + " nameY='Mass of compact object from secondary star [Msun]')\n", + "plt.legend(fontsize=20)\n", + "plt.show()\n", + "\n", + "print('The total number of BH+BH systems in the dataset is %s'%len(M1[mask_BHBH]))\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1f1e2fc0", + "metadata": {}, + "source": [ + "
\n", + "\n", + "\n", + "Features in the plot above: \n", + "\n", + " \n", + "In the plot above we see that the BH-NS systems formed in COMPAS have NS masses between 1.2-2.5 solar masses. This is the neutron star mass range that is expected from the NS equation of state. The BHs are typically between 2.5 and 40 solar masses (but some extreme cases exist, but do not trust these too much). \n", + "\n", + "The gap between 40 and ~120 solar masses where no BHs form is a result from the pair-instability SN remnant mass gap. Where it is predicted that the Helium cores that form the BHs with masses in this gap undergo pair-instabillity and completely explode, not leaving behind any remnant. " + ] + }, + { + "cell_type": "markdown", + "id": "63e62e6f", + "metadata": {}, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "There is a couple of interesting things to note: \n", + " \n", + "First of all the number of DCO systems are:\n", + " BHBH: 260622\n", + " BHNS: 19383 \n", + " NSNS: 11248\n", + " \n", + "BHBH are most common in te simulation, but that is partly due to the systems that have been evolved in COMPAS and the low metallicities. \n", + " \n", + "There are systems that undergo \"mass ratio reversal\", these are systems where the mass of the compact object from the secondary star at ZAMS (initially least massive star) ends up forming the most massive BH in the BBH system (top left systems). These systems have \"reversed\" the mass ratio at some point in their evolution (during one of the mass transfer phases). \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42a75636", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c850bd60", + "metadata": {}, + "source": [ + "
\n", + " \n", + "### Question 3: \n", + " \n", + " \n", + " - a): Using the parameters in the 'DoubleCompactObjects' dataset and the example above, try to make a scatter plot of Total Mass (M1+M2) versus orbital Period of the BBH systems that merge within a Hubble time (13.7 Gyr) \n", + " \n", + " Plot the period on the y-axis and the total mass on the x-axis. Plot the period in days. \n", + " \n", + "*Hint: You might want to use Kerpler's III law to complete the function below *\n", + " \n", + " \n", + "*Hint:* you will have to select BH+BH systems, and only systems that merge within a Hubble time " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2a2e21da", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def separation_to_period_circular_case(separation=10*u.AU, M1=1*u.M_sun, M2=1*u.M_sun):\n", + " \"\"\"calculate Period from separation\n", + " separation is separation of the binary (needs to be given in astropy units)\n", + " M1 and M2 are masses of the binary\n", + " This is based on Kepler's law, using a circular orbit\n", + " \n", + " \"\"\"\n", + " G = const.G # [g cm s^2]\n", + " \n", + " ## use Kepler;s III law to calculate the period here \n", + " \n", + " \n", + " \n", + " ###\n", + " \n", + " return period\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2e26d37", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "63efe5ab", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Answer 3:\n", + " \n", + "See the function below \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "706f15cb", + "metadata": {}, + "outputs": [], + "source": [ + "def separation_to_period_circular_case(separation=10*u.AU, M1=1*u.M_sun, M2=1*u.M_sun):\n", + " \"\"\"calculate Period from separation\n", + " separation is separation of the binary (needs to be given in astropy units)\n", + " M1 and M2 are masses of the binary\n", + " This is based on Kepler's law, using a circular orbit\n", + " \n", + " \"\"\"\n", + " G = const.G # [g cm s^2]\n", + " \n", + "\n", + " mu = G*(M1+M2)\n", + " period = 2*np.pi * np.sqrt(separation**3/mu)\n", + " \n", + " return period\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7a70078d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", + "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", + "mask_BHBH = ((mask_M1isNS==0) & (mask_M2isNS==0)) # if true then the system is a BHBH\n", + "\n", + "separation = fDCO['Separation@DCO'][...].squeeze() # in AU \n", + "Period = separation_to_period_circular_case(separation*u.au, M1*u.M_sun, M2*u.M_sun)\n", + "# the merger time is called the \"coalescence time\"\n", + "coalescence_time = fDCO['Coalescence_Time'][...].squeeze() * u.Myr # Myr \n", + "t_Hubble = 13.7 *u.Gyr\n", + "mask_tHubble = (coalescence_time < t_Hubble)\n", + "\n", + "mask_systemsOfInterest = (mask_BHBH==1) & (mask_tHubble==1)\n", + "\n", + "\n", + "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", + "\n", + "plt.scatter((M1+M2)[mask_systemsOfInterest], Period[mask_systemsOfInterest].to(u.d))\n", + "\n", + "xlabel = 'Total BBH Mass [Msun]'\n", + "ylabel = 'Period [day]'\n", + "layoutAxes(ax=ax, nameX=xlabel,nameY=ylabel)\n", + "plt.yscale('log') \n", + "\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "201763f3", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Question 4: \n", + " \n", + " \n", + " - a): Why does the plot that you created look different compared to the figure 6 in https://arxiv.org/pdf/2010.00002.pdf? (you may ignore the metallicity axes) \n", + " \n", + " - b): There is a tail of systems at rather large orbital periods that are merging. How is this possible? \n", + "\n", + "*Hint 4b: plot the eccentricity as a color gradient on the marker using the \"c=\" option of plt.scatter. \n", + "How is eccentricity imparted to these systems?* \n" + ] + }, + { + "cell_type": "markdown", + "id": "069d602e", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Answer 4:\n", + " \n", + "Possible answers: \n", + "Figure 6 only shows the data for one of the submodels (f_WR=.2) and only shows the CHE systems, and the axes are in log. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d2424b2e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "ecc = fDCO['Eccentricity@DCO'][...].squeeze()\n", + "\n", + "\n", + "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", + "\n", + "plt.scatter((M1+M2)[mask_systemsOfInterest], Period[mask_systemsOfInterest].to(u.d), c=ecc[mask_systemsOfInterest])\n", + "\n", + "xlabel = 'Total BBH Mass [Msun]'\n", + "ylabel = 'Period [day]'\n", + "layoutAxes(ax=ax, nameX=xlabel,nameY=ylabel)\n", + "plt.yscale('log') \n", + "plt.colorbar(label='eccentrcity')\n", + "\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0e08bfed", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Selecting CHE binaries: \n", + " \n", + " \n", + " For binaries, Stellar_Type@ZAMS(1) and Stellar_Type@ZAMS(2) will tell you the initial stellar type of each star - type 16 is CH.\n", + "CH_on_MS(1) and CH_on_MS(2) are each true if the star remained as CH for the entire MS - they will be false if the star spun down and stopped being CH on the MS. So any star that was initially CH, and stayed CH on the entire MS is considered to be CHE. We can check which of our binary black holes is a \"CHE\" by using this information stored in the 'systemParameters' file, and matching it with the double compact object files using the randomSeed.\n", + "\n", + "Note that we also have to remove binaries that merged on the ZAMS as stars, since we are not interested in these\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "714118e5", + "metadata": {}, + "outputs": [], + "source": [ + "fsys = fdata['SystemParameters']\n", + "\n", + "CH_on_MS_1 = fsys['CH_on_MS_1'][...].squeeze() # mass in Msun of the compact object resulting from the primary\n", + "CH_on_MS_2 = fsys['CH_on_MS_2'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", + "Stellar_TypeZAMS_1 = fsys['Stellar_Type@ZAMS_1'][...].squeeze() # mass in Msun of the compact object resulting from the primary\n", + "Stellar_TypeZAMS_2 = fsys['Stellar_Type@ZAMS_2'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", + "\n", + "# binaries that merge at birth as stars\n", + "Merger_At_Birth = fsys['Merger_At_Birth'][...].squeeze()\n", + "\n", + "# SEED of the system Parameters (unique number corresponding to each binary)\n", + "SEED = fsys['SEED'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "99057a0b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13644 are CHE out of 12000000 systems run\n", + "[ 400378 400412 402049 ... 11589507 11589863 11594670]\n" + ] + } + ], + "source": [ + "\n", + "# the CHE systems are then selected by systems that are CHE on ZAMS (stellar type 16) AND remain CHE on the MS (main sequence)\n", + "# in addition we do not want systems that Merged at Birth \n", + "mask_CHE = (CH_on_MS_1==1) & (CH_on_MS_2==1) & (Stellar_TypeZAMS_1==16) & (Stellar_TypeZAMS_2==16) & (Merger_At_Birth==0)\n", + "\n", + "print(np.sum(mask_CHE), 'are CHE out of ', len(mask_CHE), 'systems run')\n", + "\n", + "\n", + "# let's find the seed of the CHE systems: \n", + "SEED_CHE = SEED[mask_CHE]\n", + "print(SEED_CHE)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c8919d0", + "metadata": {}, + "source": [ + "
\n", + " \n", + "We find 13644 total CHE binaries in our simulation, note that this is the same as the number quoted in the CHE paper under \"Both stars remained on the CH\" and \"Total\" in Table 1\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "f2115984", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Question 5: \n", + " \n", + " - a): Using the code above, recreate figure 6 in https://arxiv.org/pdf/2010.00002.pdf? (you may ignore the metallicity axes) \n", + " \n", + " - b): Explain what you see \n", + " \n", + "#### Hint: A useful line of code is: np.in1d(), below is an example of how it works" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6ba613a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False True]\n" + ] + } + ], + "source": [ + "# example of np.in1d() function\n", + "\n", + "A = [1,2,3]\n", + "B = [1,3,5,7,9]\n", + "\n", + "print(np.in1d(A, B))" + ] + }, + { + "cell_type": "markdown", + "id": "34b76779", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 5 " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "87e401a9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2320\n" + ] + } + ], + "source": [ + "\n", + "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", + "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", + "mask_BHBH = ((mask_M1isNS==0) & (mask_M2isNS==0)) # if true then the system is a BHBH\n", + "\n", + "separation = fDCO['Separation@DCO'][...].squeeze() # in AU \n", + "Period = separation_to_period_circular_case(separation*u.au, M1*u.M_sun, M2*u.M_sun)\n", + "# the merger time is called the \"coalescence time\"\n", + "coalescence_time = fDCO['Coalescence_Time'][...].squeeze() * u.Myr # Myr \n", + "t_Hubble = 13.7 *u.Gyr\n", + "mask_tHubble = (coalescence_time < t_Hubble)\n", + "\n", + "# this is the parameter that describes the Wolf Rayet factor f_WR that was used. \n", + "WR_Multiplier = fsys['WR_Multiplier'][...].squeeze()\n", + "\n", + "# mask BBHs that merge in a Hubble time \n", + "mask_systemsOfInterest = (mask_BHBH==1) & (mask_tHubble==1)\n", + "\n", + "# add the mask of systems that are CHE. Since the CHE mask is based on systemParameters we have \n", + "# to match the systems from systemParameters that we want to mask with the DCO systems, we can do this using the SEED\n", + "# a system in systemParameters will have the same SEED in DoubleCompactObjects, if it exists in both\n", + "mask_DCO_that_are_CHE = np.in1d(SEED_DCO, SEED_CHE) \n", + "mask_DCO_that_are_BBH_and_CHE = (mask_DCO_that_are_CHE ==1) & (mask_systemsOfInterest==1)\n", + "\n", + "# we can mask for the f_WR = 0.2 factor that is used in Figure 6 of the paper. \n", + "mask_fWR_02 = (WR_Multiplier==0.2)\n", + "SEED_fWR_02 = SEED[mask_fWR_02]\n", + "mask_DCO_that_are_fWR_02 = np.in1d(SEED_DCO, SEED_fWR_02)\n", + "\n", + "# combine all the masks above\n", + "mask_DCO_that_are_BBH_and_CHE_and_fWR_02 = (mask_DCO_that_are_CHE ==1) & (mask_systemsOfInterest==1) & (mask_DCO_that_are_fWR_02==1)\n", + "\n", + "\n", + "# plot the systems \n", + "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", + "\n", + "plt.scatter((M1+M2)[mask_DCO_that_are_BBH_and_CHE_and_fWR_02], Period[mask_DCO_that_are_BBH_and_CHE_and_fWR_02].to(u.d))\n", + "\n", + "xlabel = 'Total BBH Mass [Msun]'\n", + "ylabel = 'Period [day]'\n", + "layoutAxes(ax=ax, nameX=xlabel,nameY=ylabel)\n", + "# plt.yscale('log') \n", + "\n", + "\n", + "plt.show()\n", + "\n", + "\n", + "print(len((M1+M2)[mask_DCO_that_are_BBH_and_CHE_and_fWR_02]))\n" + ] + }, + { + "cell_type": "markdown", + "id": "b676d7ab", + "metadata": {}, + "source": [ + "
\n", + "\n", + "The plot above looks very similar to Figure 6, the only differences are that we did not add the Metallicity axis in this example. You can add this by using the property 'Metallicity@ZAMS_1' from the systemParameters. \n", + " \n", + "In addition, our Period range plotted above (the ylim) is slightly larger then the ylim range plotted in Figure 6\n", + " \n", + "Last, the total number of events 2320 is slightly different compared to Fig 6. This is a result from a small typo/error by the auhtor in the data version that is published versus the version that was used to run this specific figure" + ] + }, + { + "cell_type": "markdown", + "id": "a25df87b", + "metadata": {}, + "source": [ + "
\n", + " \n", + "## Question 6: \n", + " \n", + " - a): Try to recreeat the figure 4 in https://arxiv.org/pdf/2010.00002.pdf? \n", + " \n", + " \n", + " - b): Explain what you see " + ] + }, + { + "cell_type": "markdown", + "id": "816ebd41", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 6 \n", + " \n", + " \n", + "See the code below. The method is very similar to the Answer to question 5, but now we have to get the ZAMS properties through the systemParameters file. For which we have to use the np.in1d again to match the datapoints" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "fe11ee0f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2320\n" + ] + } + ], + "source": [ + "\n", + "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", + "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", + "mask_BHBH = ((mask_M1isNS==0) & (mask_M2isNS==0)) # if true then the system is a BHBH\n", + "\n", + "\n", + "\n", + "\n", + "M1ZAMS = fsys['Mass@ZAMS_1'][...].squeeze() # mass in Msun of the compact object resulting from the *primary star*\n", + "M2ZAMS = fsys['Mass@ZAMS_2'][...].squeeze() # mass in Msun of the compact object resulting from the *secondary star*\n", + "\n", + "\n", + "# the separation At ZAMS is given by \n", + "separationZAMS = fsys['Separation@ZAMS'][()]\n", + "PeriodZAMS = separation_to_period_circular_case(separationZAMS*u.au, M1ZAMS*u.M_sun, M2ZAMS*u.M_sun)\n", + "\n", + "# the merger time is called the \"coalescence time\"\n", + "coalescence_time = fDCO['Coalescence_Time'][...].squeeze() * u.Myr # Myr \n", + "t_Hubble = 13.7 *u.Gyr\n", + "mask_tHubble = (coalescence_time < t_Hubble)\n", + "\n", + "# this is the parameter that describes the Wolf Rayet factor f_WR that was used. \n", + "WR_Multiplier = fsys['WR_Multiplier'][...].squeeze()\n", + "\n", + "# mask BBHs that merge in a Hubble time \n", + "mask_systemsOfInterest = (mask_BHBH==1) & (mask_tHubble==1)\n", + "\n", + "# add the mask of systems that are CHE. Since the CHE mask is based on systemParameters we have \n", + "# to match the systems from systemParameters that we want to mask with the DCO systems, we can do this using the SEED\n", + "# a system in systemParameters will have the same SEED in DoubleCompactObjects, if it exists in both\n", + "mask_DCO_that_are_CHE = np.in1d(SEED_DCO, SEED_CHE) \n", + "mask_DCO_that_are_BBH_and_CHE = (mask_DCO_that_are_CHE ==1) & (mask_systemsOfInterest==1)\n", + "\n", + "# we can mask for the f_WR = 1 factor that is used in Figure 4 of the paper. \n", + "mask_fWR_1 = (WR_Multiplier==1)\n", + "SEED_fWR_1 = SEED[mask_fWR_1]\n", + "mask_DCO_that_are_fWR_1 = np.in1d(SEED_DCO, SEED_fWR_1)\n", + "\n", + "# combine all the masks above\n", + "mask_DCO_that_are_BBH_and_CHE_and_fWR_1 = (mask_DCO_that_are_CHE ==1) & (mask_systemsOfInterest==1) & (mask_DCO_that_are_fWR_1==1)\n", + "\n", + "mask_Fig4 = np.in1d(SEED, SEED_DCO[mask_DCO_that_are_BBH_and_CHE_and_fWR_1])\n", + "\n", + "\n", + "\n", + "\n", + "# plot the systems \n", + "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", + "\n", + "plt.scatter((M1ZAMS+M2ZAMS)[mask_Fig4], PeriodZAMS[mask_Fig4].to(u.d))\n", + "\n", + "xlabel = 'Total Mass at ZAMS [Msun]'\n", + "ylabel = 'Period at ZAMS [day]'\n", + "layoutAxes(ax=ax, nameX=xlabel,nameY=ylabel)\n", + "plt.xscale('log') \n", + "\n", + "\n", + "plt.show()\n", + "\n", + "\n", + "print(len((M1+M2)[mask_DCO_that_are_BBH_and_CHE_and_fWR_02]))\n" + ] + }, + { + "cell_type": "markdown", + "id": "544dc9c9", + "metadata": {}, + "source": [ + "
\n", + "\n", + " \n", + "For the last part of this excersize we will use the code 'FastCosmicIntegrator'. Our goal will be to calculate the merger rate for BBHs, so that we can compare to the analytical estimate we made earlier on. \n", + "\n", + "## Question 7: \n", + " \n", + " \n", + "Using the code below, plot the merger rate of CHE BBHs as a function of redshift. You should at the end of running this code create a plot with four panels that show the properties of the CHE binaries \n", + " \n", + " - a) what do the panels show? What are the differences between the panels? \n", + " - b): please write down the local (z=0) BBH merger rate, and compare this with your analytically calculated rate \n", + " - c): compare both rates with the other BBH rates as reported in Mandel & Broekgaarden et al. (2021) (Fig 3)\n", + "\n", + " \n", + " - d): Repeat the excersize above, and answer 7a & 7b above, but now for all BBHs (including non CHE). You can do this by changing dco_type to 'BBH'. What are the differences\n", + "\n", + " \n", + " \n", + " *Hint* we can do an approximate calculation by combining the Wolf Rayet factors. If you want to do the more expert version you can modify the code in ClassCOMPAS (setCOMPASDCOmask) and add the mask of a specific f_WR factor" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "681a2767", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.11/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/floorbroekgaarden/Projects/GitHub/COMPAS/utils/Tutorial/Tutorial_reproduce_CHE_paper_teaching_demo_example/FastCosmicIntegration.py': [Errno 2] No such file or directory\r\n" + ] + } + ], + "source": [ + "# change the line to point to your path \n", + "\n", + "!python3 FastCosmicIntegration.py \\\n", + "--dco_type 'CHE_BBH' \\\n", + "--path '/Users/floorbroekgaarden/Downloads/COMPAS_Output.h5' \\\n", + "--maxz 15 \\\n", + "--dontAppend" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "285095f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CHE_evolution_demo.ipynb\r\n", + "CHE_evolution_demo_ANSWERS.ipynb\r\n", + "COMPAS_Documentation.pdf\r\n", + "Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png\r\n", + "SNR_Grid_IMRPhenomPv2_FD_all_noise.hdf5\r\n", + "\u001b[34m__pycache__\u001b[m\u001b[m\r\n", + "old_ClassCOMPAS.py\r\n", + "old_FastCosmicIntegration.py\r\n", + "old_selection_effects.py\r\n", + "old_totalMassEvolvedPerZ.py\r\n" + ] + } + ], + "source": [ + "!ls " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "0b87ce45", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show the image in the notebook:\n", + "Image(filename='./Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png') \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "919656e9", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 7 \n", + " \n", + " \n", + "The rates for CHE around redshift 0 are about 20 Gpc^-3 yr^-1 for the mixture model (note that we have to select either of the Wolf Rayet factors if we want to get an absolute rate for one fixed model. )\n", + "This is indeed about the mean of the Rates quoted by Riley +2021, and corresponds to the CHE rates as shown in Fig 3 of Mandel & Broekgaarden\n", + " \n", + "The CHE rates are somewhat lower compared to the isolated binary evoluton rates, and many of the other channel rates. This is because the CHE channel is a rare channel: you need very specific intitial conditions (masses, metallicities) to form CHE stars. \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98ccf72f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a92fadd4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "15d8abef", + "metadata": {}, + "source": [ + "
\n", + "\n", + "## Extra Question 8: \n", + " \n", + " \n", + "Play around with some of the parameters in the code FastCosmicIntegrater, do the rates go up or down? Is this expected?" + ] + }, + { + "cell_type": "markdown", + "id": "8b34f7ba", + "metadata": {}, + "source": [ + "
\n", + "\n", + "# Answer 8\n", + " \n", + " \n", + "Doing this for 'BBH' should give you a rate of about 100 Gpc^-3 yr^-1 which is right on the ballpark of the observations\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2f7452f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.11/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/floorbroekgaarden/Projects/GitHub/COMPAS/utils/Tutorial/Tutorial_reproduce_CHE_paper_teaching_demo_example/FastCosmicIntegration.py': [Errno 2] No such file or directory\r\n" + ] + } + ], + "source": [ + "!python3 FastCosmicIntegration.py \\\n", + "--dco_type 'BBH' \\\n", + "--path '../../data/COMPAS_Output_reduced.h5' \\\n", + "--maxz 15 \\\n", + "--dontAppend" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a4c53c13", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show the image in the notebook:\n", + "Image(filename='./Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png') \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06928d4f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "93c376a5", + "metadata": {}, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "## EXTRA using the CHE data: \n", + " \n", + "If there is time left, try to plot some other BBH or ZAMS properties of the BBH, (or NSBH or BNS), examples include chirp mass, mass ratio, individual masses. How do these compare with LIGOs observations (paper is attached to this directory)\n", + "\n", + " \n", + " \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a647b78", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c370cf85", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4a02ec4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab7e8335", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3e78c305", + "metadata": {}, + "source": [ + "
\n", + " \n", + " # Extra material:" + ] + }, + { + "cell_type": "markdown", + "id": "0b84006f", + "metadata": {}, + "source": [ + "[//]: ## (grip -b README.md)\n", + "\n", + "\n", + "\n", + "# Compact Object Mergers: Population Astrophysics & Statistics\n", + "\n", + "[![Documentation](https://img.shields.io/badge/Documentation-latest-orange.svg?style=flat)](https://github.com/TeamCOMPAS/COMPAS/blob/Documentation/COMPAS_Documentation.pdf)\n", + "\n", + "[//]: ## (Outline features)\n", + "COMPAS is a publicly available rapid binary population synthesis code (https://compas.science/) that is designed so that evolution prescriptions and model parameters are easily \n", + "adjustable. COMPAS draws properties for a binary star system from a set of initial distributions, and evolves it from zero-age main sequence to the end of its life as two compact \n", + "remnants. It has been used for inference from observations of gravitational-wave mergers, Galactic neutron stars, X-ray binaries, and luminous red novae.\n", + "\n", + "## Documentation\n", + "https://compas.science/docs\n", + "\n", + "## Contact\n", + "Please email your queries to compas-user@googlegroups.com. You are also welcome to join the [COMPAS User Google Group](https://groups.google.com/forum/#!members/compas-user) to engage in discussions with COMPAS users and developers.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "## Example of additional excersizes\n", + "\n", + "If you are interested, you can download the COMPAS code from Github and do any of the following excersizes: \n", + "\n", + "\n", + "1). Try to run any of the demos that are provided (I recommend the Chirp mass distribution demo, and/or the detailed evolution demo)\n", + "\n", + "2.) Try to use the code, and the data above, to plot a detailed evolution plot of a CHE BBH. \n", + "\n", + "3.) Run a larger COMPAS simulation with your own favorite settings, compare this to the data given in this demo. \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f54328e3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/BaseBinaryStar.cpp b/src/BaseBinaryStar.cpp index 103f5ead3..c1da0c02a 100644 --- a/src/BaseBinaryStar.cpp +++ b/src/BaseBinaryStar.cpp @@ -2213,17 +2213,17 @@ void BaseBinaryStar::CalculateEnergyAndAngularMomentum() { if (m_Star1->IsOneOf({ STELLAR_TYPE::MASSLESS_REMNANT }) || m_Star2->IsOneOf({ STELLAR_TYPE::MASSLESS_REMNANT })) return; // Calculate orbital energy and angular momentum - m_OrbitalEnergyPrev = m_OrbitalEnergy; - m_OrbitalAngularMomentumPrev = m_OrbitalAngularMomentum; + m_OrbitalEnergyPrev = m_OrbitalEnergy; + m_OrbitalAngularMomentumPrev = m_OrbitalAngularMomentum; - double totalMass = m_Star1->Mass() + m_Star2->Mass(); - double reducedMass = (m_Star1->Mass() * m_Star2->Mass()) / totalMass; - m_OrbitalEnergy = CalculateOrbitalEnergy(reducedMass, totalMass, m_SemiMajorAxis); - m_OrbitalAngularMomentum = CalculateOrbitalAngularMomentum(reducedMass, totalMass, m_SemiMajorAxis); + double totalMass = m_Star1->Mass() + m_Star2->Mass(); + double reducedMass = (m_Star1->Mass() * m_Star2->Mass()) / totalMass; + m_OrbitalEnergy = CalculateOrbitalEnergy(reducedMass, totalMass, m_SemiMajorAxis); + m_OrbitalAngularMomentum = CalculateOrbitalAngularMomentum(reducedMass, totalMass, m_SemiMajorAxis); // Calculate total energy and angular momentum using regular conservation of energy, especially useful for checking tides and rotational effects - m_TotalEnergy = CalculateTotalEnergy(); - m_TotalAngularMomentum = CalculateAngularMomentum(); + m_TotalEnergy = CalculateTotalEnergy(); + m_TotalAngularMomentum = CalculateAngularMomentum(); } @@ -2320,7 +2320,7 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { EvaluateSupernovae(); // evaluate supernovae (both stars) - immediate event (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output if (HasOneOf({ STELLAR_TYPE::NEUTRON_STAR })) { - (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters + (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters } } else { @@ -2341,7 +2341,7 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { EvaluateSupernovae(); // evaluate supernovae (both stars) if mass changes are responsible for a supernova (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output if (HasOneOf({ STELLAR_TYPE::NEUTRON_STAR })) { - (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters + (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters } } @@ -2351,6 +2351,10 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { CalculateEnergyAndAngularMomentum(); // perform energy and angular momentum calculations + // naive tides calculations - if required + // circularise and synchronise the binary + + m_Star1->UpdateMagneticFieldAndSpin(m_CEDetails.CEEnow, m_Dt * MYR_TO_YEAR * SECONDS_IN_YEAR, EPSILON_PULSAR); // update pulsar parameters for star1 m_Star2->UpdateMagneticFieldAndSpin(m_CEDetails.CEEnow, m_Dt * MYR_TO_YEAR * SECONDS_IN_YEAR, EPSILON_PULSAR); // update pulsar parameters for star2 } diff --git a/src/BaseBinaryStar.h b/src/BaseBinaryStar.h index 8c6cfa304..6a06a434b 100644 --- a/src/BaseBinaryStar.h +++ b/src/BaseBinaryStar.h @@ -543,9 +543,93 @@ class BaseBinaryStar { //Functor for the boost root finder to determine how much mass needs to be lost from a donor without an envelope in order to fit inside the Roche lobe template - struct RadiusEqualsRocheLobeFunctor + struct RadiusEqualsRocheLobeFunctor { + RadiusEqualsRocheLobeFunctor(BaseBinaryStar *p_Binary, BinaryConstituentStar *p_Donor, BinaryConstituentStar *p_Accretor, ERROR *p_Error, double p_FractionAccreted) { + m_Binary = p_Binary; + m_Donor = p_Donor; + m_Accretor = p_Accretor; + m_Error = p_Error; + m_FractionAccreted = p_FractionAccreted; + } + T operator()(double const& dM) { + + if (dM >= m_Donor->Mass()) { // Can't remove more than the donor's mass + *m_Error = ERROR::TOO_MANY_RLOF_ITERATIONS; + return m_Donor->Radius(); + } + + double donorMass = m_Donor->Mass(); + double accretorMass = m_Accretor->Mass(); + + BinaryConstituentStar* donorCopy = new BinaryConstituentStar(*m_Donor); + double semiMajorAxis = m_Binary->CalculateMassTransferOrbit(donorCopy->Mass(), -dM , *m_Accretor, m_FractionAccreted); + double RLRadius = semiMajorAxis * (1 - m_Binary->Eccentricity()) * CalculateRocheLobeRadius_Static(donorMass - dM, accretorMass + (m_Binary->FractionAccreted() * dM)) * AU_TO_RSOL; + + (void)donorCopy->UpdateAttributes(-dM, -dM*donorCopy->Mass0()/donorCopy->Mass()); + + // Modify donor Mass0 and Age for MS (including HeMS) and HG stars + donorCopy->UpdateInitialMass(); // update initial mass (MS, HG & HeMS) + donorCopy->UpdateAgeAfterMassLoss(); // update age (MS, HG & HeMS) + + (void)donorCopy->AgeOneTimestep(0.0); // recalculate radius of star - don't age - just update values + + double thisRadiusAfterMassLoss = donorCopy->Radius(); + + delete donorCopy; donorCopy = nullptr; + + return (RLRadius - thisRadiusAfterMassLoss); + } + private: + BaseBinaryStar *m_Binary; + BinaryConstituentStar *m_Donor; + BinaryConstituentStar *m_Accretor; + ERROR *m_Error; + double m_FractionAccreted; + }; + + + //Root solver to determine how much mass needs to be lost from a donor without an envelope in order to fit inside the Roche lobe + double MassLossToFitInsideRocheLobe(BaseBinaryStar *p_Binary, BinaryConstituentStar *p_Donor, BinaryConstituentStar *p_Accretor, double p_FractionAccreted) { + + using namespace std; // Help ADL of std functions. + using namespace boost::math::tools; // For bracket_and_solve_root. + + double guess = ADAPTIVE_RLOF_FRACTION_DONOR_GUESS * p_Donor->Mass(); // Rough guess at solution + double factor = ADAPTIVE_RLOF_SEARCH_FACTOR; // Size of search steps + + const boost::uintmax_t maxit = ADAPTIVE_RLOF_MAX_ITERATIONS; // Limit to maximum iterations. + boost::uintmax_t it = maxit; // Initially our chosen max iterations, but updated with actual. + bool is_rising = true; // So if result with guess is too low, then try increasing guess. + int digits = std::numeric_limits::digits; // Maximum possible binary digits accuracy for type T. + + // Some fraction of digits is used to control how accurate to try to make the result. + int get_digits = digits - 5; // We have to have a non-zero interval at each step, so + + // maximum accuracy is digits - 1. But we also have to + // allow for inaccuracy in f(x), otherwise the last few + // iterations just thrash around. + eps_tolerance tol(get_digits); // Set the tolerance. + + std::pair root(0.0, 0.0); + try { + ERROR error = ERROR::NONE; + root = bracket_and_solve_root(RadiusEqualsRocheLobeFunctor(p_Binary, p_Donor, p_Accretor, &error, p_FractionAccreted), guess, factor, is_rising, tol, it); + if (error != ERROR::NONE) SHOW_WARN(error); + } + catch(exception& e) { + SHOW_ERROR(ERROR::TOO_MANY_RLOF_ITERATIONS, e.what()); // Catch generic boost root finding error + } + SHOW_WARN_IF(it>=maxit, ERROR::TOO_MANY_RLOF_ITERATIONS); + + return root.first + (root.second - root.first) / 2.0; // Midway between brackets is our result, if necessary we could return the result as an interval here. + } + + + //Functor for the boost root finder to determine rotational frequency + template + struct OmegaFunctor { - RadiusEqualsRocheLobeFunctor(BaseBinaryStar *p_Binary, BinaryConstituentStar *p_Donor, BinaryConstituentStar *p_Accretor, ERROR *p_Error, double p_FractionAccreted) + OmegaFunctor(BaseBinaryStar *p_Binary, BinaryConstituentStar *p_Donor, BinaryConstituentStar *p_Accretor, ERROR *p_Error, double p_FractionAccreted) { m_Binary = p_Binary; m_Donor = p_Donor; @@ -579,7 +663,25 @@ class BaseBinaryStar { delete donorCopy; donorCopy = nullptr; - return (RLRadius-thisRadiusAfterMassLoss); + return (RLRadius - thisRadiusAfterMassLoss); + + + + + + //A = I_1_final + I_2_final + + //one_e_init = 1.0 - e_init + + //B = -((I_1_init * omega_1_init) + (I_2_init * omega_2_init) + std::sqrt(G * M1_init * M1_init * M2_init * M2_init * a_init * one_e_init * one_e_init / (M1_init + M2_init))) + + //C = PPOW(G, 2.0 / 3.0) * (M1 * M2) / PPOW((M1 + M2), 1.0 / 3.0) + + //x = PPOW(omega_final, 1.0 / 3.0) + + //return Ax^4 + Bx + C + + } private: BaseBinaryStar *m_Binary; @@ -588,12 +690,14 @@ class BaseBinaryStar { ERROR *m_Error; double m_FractionAccreted; }; + - - //Root solver to determine how much mass needs to be lost from a donor without an envelope in order to fit inside the Roche lobe - double MassLossToFitInsideRocheLobe(BaseBinaryStar * p_Binary, BinaryConstituentStar * p_Donor, BinaryConstituentStar * p_Accretor, double p_FractionAccreted) - { - using namespace std; // Help ADL of std functions. + /* + //Root solver to determine rotational frequency + double Omega(BaseBinaryStar *p_Binary, BinaryConstituentStar *p_Donor, BinaryConstituentStar *p_Accretor, double p_FractionAccreted) { + + std::cout << "MassLossToFitInsideRocheLobe() @ start\n"; + //using namespace std; // Help ADL of std functions. using namespace boost::math::tools; // For bracket_and_solve_root. double guess = ADAPTIVE_RLOF_FRACTION_DONOR_GUESS * p_Donor->Mass(); // Rough guess at solution @@ -612,20 +716,40 @@ class BaseBinaryStar { // iterations just thrash around. eps_tolerance tol(get_digits); // Set the tolerance. - std::pair root; + + double I1init = m_Star1->CalculateMomentOfInertia(); + double I2init = m_Star2->CalculateMomentOfInertia(); + + double omega1init = m_Star1->Omega(); + double omega2init = m_Star2->Omega(); + + double eInit = m_Eccentricity; + + double m1Init = m_Star1->Mass(); + double m2Init = m_Star2->Mass(); + + M1 + M2 + + std::pair root(0.0, 0.0); try { ERROR error = ERROR::NONE; + std::cout << "MassLossToFitInsideRocheLobe() @ call bracket_and_solve_root()\n"; root = bracket_and_solve_root(RadiusEqualsRocheLobeFunctor(p_Binary, p_Donor, p_Accretor, &error, p_FractionAccreted), guess, factor, is_rising, tol, it); + std::cout << "MassLossToFitInsideRocheLobe() @ return from bracket_and_solve_root()\n"; if (error != ERROR::NONE) SHOW_WARN(error); } catch(exception& e) { SHOW_ERROR(ERROR::TOO_MANY_RLOF_ITERATIONS, e.what()); // Catch generic boost root finding error - m_Donor->Radius(); } SHOW_WARN_IF(it>=maxit, ERROR::TOO_MANY_RLOF_ITERATIONS); - return root.first + (root.second - root.first)/2; // Midway between brackets is our result, if necessary we could return the result as an interval here. + std::cout << "MassLossToFitInsideRocheLobe() @ return: " << root.first + (root.second - root.first) / 2.0 << " <--------------\n"; + return root.first + (root.second - root.first) / 2.0; // Midway between brackets is our result, if necessary we could return the result as an interval here. } + */ + + }; #endif // __BaseBinaryStar_h__ diff --git a/src/BaseStar.cpp b/src/BaseStar.cpp index 1c8489d01..a2fe91867 100755 --- a/src/BaseStar.cpp +++ b/src/BaseStar.cpp @@ -111,10 +111,13 @@ BaseStar::BaseStar(const unsigned long int p_RandomSeed, m_TZAMS = CalculateTemperatureOnPhase_Static(m_LZAMS, m_RZAMS); m_OmegaCHE = CalculateOmegaCHE(m_MZAMS, m_Metallicity); + m_OmegaZAMS = p_RotationalFrequency >= 0.0 // valid rotational frequency passed in? ? p_RotationalFrequency // yes - use it : CalculateZAMSAngularFrequency(m_MZAMS, m_RZAMS); // no - calculate it + m_MomentOfInertiaZAMS = CalculateMomentOfInertia(); + // Effective initial Zero Age Main Sequence parameters corresponding to Mass0 m_RZAMS0 = m_RZAMS; m_LZAMS0 = m_LZAMS; @@ -140,6 +143,8 @@ BaseStar::BaseStar(const unsigned long int p_RandomSeed, m_DominantMassLossRate = MASS_LOSS_TYPE::NONE; m_Omega = m_OmegaZAMS; + m_MomentOfInertia = m_MomentOfInertiaZAMS; + m_AngularMomentum = DEFAULT_INITIAL_DOUBLE_VALUE; m_MinimumLuminosityOnPhase = DEFAULT_INITIAL_DOUBLE_VALUE; m_LBVphaseFlag = false; @@ -2457,7 +2462,7 @@ double BaseStar::CalculateRotationalVelocity(double p_MZAMS) const { * Hurley et al. 2000, eq 108 * * - * double CalculateRotationalAngularFrequency(const double p_MZAMS, const double p_RZAMS) + * double CalculateZAMSAngularFrequency(const double p_MZAMS, const double p_RZAMS) * * @param [IN] p_MZAMS Zero age main sequence mass in Msol * @param [IN] p_RZAMS Zero age main sequence radius in Rsol @@ -3242,10 +3247,11 @@ void BaseStar::UpdateAttributesAndAgeOneTimestepPreamble(const double p_DeltaMas // record some current values before they are (possibly) changed by evolution if (p_DeltaTime > 0.0) { // don't use utils::Compare() here - m_StellarTypePrev = m_StellarType; - m_DtPrev = m_Dt; - m_MassPrev = m_Mass; - m_RadiusPrev = m_Radius; + m_StellarTypePrev = m_StellarType; + m_DtPrev = m_Dt; + m_MassPrev = m_Mass; + m_RadiusPrev = m_Radius; + m_OmegaPrev = m_Omega; } // the GBParams and Timescale calculations need to be done @@ -3438,21 +3444,21 @@ STELLAR_TYPE BaseStar::EvolveOnPhase() { if (ShouldEvolveOnPhase()) { // Evolve timestep on phase - m_Tau = CalculateTauOnPhase(); + m_Tau = CalculateTauOnPhase(); - m_COCoreMass = CalculateCOCoreMassOnPhase(); - m_CoreMass = CalculateCoreMassOnPhase(); - m_HeCoreMass = CalculateHeCoreMassOnPhase(); + m_COCoreMass = CalculateCOCoreMassOnPhase(); + m_CoreMass = CalculateCoreMassOnPhase(); + m_HeCoreMass = CalculateHeCoreMassOnPhase(); - m_Luminosity = CalculateLuminosityOnPhase(); + m_Luminosity = CalculateLuminosityOnPhase(); std::tie(m_Radius, stellarType) = CalculateRadiusAndStellarTypeOnPhase(); // Radius and possibly new stellar type - m_Mu = CalculatePerturbationMuOnPhase(); + m_Mu = CalculatePerturbationMuOnPhase(); PerturbLuminosityAndRadiusOnPhase(); - m_Temperature = CalculateTemperatureOnPhase(); + m_Temperature = CalculateTemperatureOnPhase(); STELLAR_TYPE thisStellarType = ResolveEnvelopeLoss(); // Resolve envelope loss if it occurs - possibly new stellar type if (thisStellarType != m_StellarType) { // thisStellarType overrides stellarType (from CalculateRadiusAndStellarTypeOnPhase()) diff --git a/src/BaseStar.h b/src/BaseStar.h index 45dfe0a8c..4311f087b 100644 --- a/src/BaseStar.h +++ b/src/BaseStar.h @@ -98,6 +98,7 @@ class BaseStar { std::string MassTransferDonorHistoryString() const; double Mdot() const { return m_Mdot; } double Metallicity() const { return m_Metallicity; } + double MomentOfInertia() const { return m_MomentOfInertia; } double MZAMS() const { return m_MZAMS; } double Omega() const { return m_Omega; } double OmegaCHE() const { return m_OmegaCHE; } @@ -195,7 +196,7 @@ class BaseStar { double CalculateOmegaCHE(const double p_MZAMS, const double p_Metallicity) const; - double CalculateRadialChange() const { return (utils::Compare(m_RadiusPrev,0)<=0)? 0 : std::abs(m_Radius - m_RadiusPrev) / m_RadiusPrev; } // Return fractional radial change (if previous radius is negative or zero, return 0 to avoid NaN + double CalculateRadialChange() const { return (utils::Compare(m_RadiusPrev,0)<=0)? 0 : std::abs(m_Radius - m_RadiusPrev) / m_RadiusPrev; } // Return fractional radial change (if previous radius is negative or zero, return 0 to avoid NaN double CalculateRadialExpansionTimescale() const { return CalculateRadialExpansionTimescale_Static(m_StellarType, m_StellarTypePrev, m_Radius, m_RadiusPrev, m_DtPrev); } // Use class member variables @@ -214,13 +215,13 @@ class BaseStar { double CalculateTimestep(); double CalculateZetaAdiabatic(); - virtual double CalculateZetaConstantsByEnvelope(ZETA_PRESCRIPTION p_ZetaPrescription) { return 0.0; } // Use inheritance hierarchy + virtual double CalculateZetaConstantsByEnvelope(ZETA_PRESCRIPTION p_ZetaPrescription) { return 0.0; } // Use inheritance hierarchy void ClearCurrentSNEvent() { m_SupernovaDetails.events.current = SN_EVENT::NONE; } // Clear supernova event/state for current timestep virtual ENVELOPE DetermineEnvelopeType() const { return ENVELOPE::REMNANT; } // Default is REMNANT - but should never be called - void HaltWinds() { m_Mdot = 0.0; } // Disable wind mass loss in current time step (e.g., if star is a donor or accretor in a RLOF episode) + void HaltWinds() { m_Mdot = 0.0; } // Disable wind mass loss in current time step (e.g., if star is a donor or accretor in a RLOF episode) virtual ACCRETION_REGIME DetermineAccretionRegime(const bool p_HeRich, const double p_DonorThermalMassLossRate) { return ACCRETION_REGIME::NONE; } // Placeholder, use inheritance for WDs @@ -242,12 +243,12 @@ class BaseStar { void SetStellarTypePrev(const STELLAR_TYPE p_StellarTypePrev) { m_StellarTypePrev = p_StellarTypePrev; } - bool ShouldEnvelopeBeExpelledByPulsations() const { return false; } // Default is that there is no envelope expulsion by pulsations + bool ShouldEnvelopeBeExpelledByPulsations() const { return false; } // Default is that there is no envelope expulsion by pulsations void StashSupernovaDetails(const STELLAR_TYPE p_StellarType, const SSE_SN_RECORD_TYPE p_RecordType = SSE_SN_RECORD_TYPE::DEFAULT) { LOGGING->StashSSESupernovaDetails(this, p_StellarType, p_RecordType); } - virtual void UpdateAgeAfterMassLoss() { } // Default is NO-OP + virtual void UpdateAgeAfterMassLoss() { } // Default is NO-OP STELLAR_TYPE UpdateAttributesAndAgeOneTimestep(const double p_DeltaMass, const double p_DeltaMass0, @@ -312,6 +313,8 @@ class BaseStar { double m_OmegaCHE; // Minimum angular frequency at which CHE will occur (calculated at ZAMS) double m_RZAMS; // ZAMS Radius double m_TZAMS; // ZAMS Temperature + double m_MomentOfInertiaZAMS; // ZAMS moment of inertia (Msol Rsol^2) + // Effective Zero Age Main Sequence double m_LZAMS0; // Effective ZAMS Luminosity @@ -334,7 +337,9 @@ class BaseStar { double m_Mdot; // Current mass loss rate (Msol per ?) MASS_LOSS_TYPE m_DominantMassLossRate; // Current dominant mass loss rate double m_Mu; // Current small envelope parameter mu - double m_Omega; // Current angular frequency (yr-1) + double m_Omega; // Current angular frequency (yr^-1) + double m_MomentOfInertia; // Current moment of inertia (Msol Rsol^2) + double m_AngularMomentum; // Current angular momentum double m_Radius; // Current radius (Rsol) double m_Tau; // Relative time double m_Temperature; // Current temperature (Tsol) @@ -343,7 +348,7 @@ class BaseStar { // Previous timestep variables double m_DtPrev; // Previous timestep double m_MassPrev; // Previous mass (Msol) - double m_OmegaPrev; // Previous angular frequency (yr-1) + double m_OmegaPrev; // Previous angular frequency (yr^-1) double m_RadiusPrev; // Previous radius (Rsol) STELLAR_TYPE m_StellarTypePrev; // Stellar type at previous timestep diff --git a/src/HG.h b/src/HG.h index 663a75792..53a92a201 100755 --- a/src/HG.h +++ b/src/HG.h @@ -127,20 +127,20 @@ class HG: virtual public BaseStar, public GiantBranch { { Mass0YieldsDesiredCoreMassFunctor(HG *p_Star, double p_DesiredCoreMass, ERROR *p_Error) { - m_Star = p_Star; - m_DesiredCoreMass = p_DesiredCoreMass; - m_Error = p_Error; + m_Star = p_Star; + m_DesiredCoreMass = p_DesiredCoreMass; + m_Error = p_Error; } T operator()(double const& guessMass0) { - HG * copy = new HG(*m_Star, false); + HG *copy = new HG(*m_Star, false); copy->UpdateAttributesAndAgeOneTimestep(0.0, guessMass0 - copy->Mass0(), 0.0, true); - double coreMassEstimate=copy->CalculateCoreMassOnPhase(guessMass0, copy->Age()); + double coreMassEstimate = copy->CalculateCoreMassOnPhase(guessMass0, copy->Age()); delete copy; copy = nullptr; return (coreMassEstimate - m_DesiredCoreMass); } private: - HG *m_Star; + HG *m_Star; double m_DesiredCoreMass; ERROR *m_Error; }; @@ -155,7 +155,7 @@ class HG: virtual public BaseStar, public GiantBranch { double guess = p_Star->Mass(); // Rough guess at solution double factor = ADAPTIVE_MASS0_SEARCH_FACTOR; // Size of search steps - const boost::uintmax_t maxit = ADAPTIVE_MASS0_MAX_ITERATIONS; // Limit to maximum iterations. + const boost::uintmax_t maxit = ADAPTIVE_MASS0_MAX_ITERATIONS; // Limit to maximum iterations. boost::uintmax_t it = maxit; // Initially our chosen max iterations, but updated with actual. bool is_rising = true; // So if result with guess is too low, then try increasing guess. int digits = std::numeric_limits::digits; // Maximum possible binary digits accuracy for type T. @@ -175,11 +175,11 @@ class HG: virtual public BaseStar, public GiantBranch { if (error != ERROR::NONE) SHOW_WARN(error); } catch(exception& e) { - SHOW_ERROR(ERROR::TOO_MANY_MASS0_ITERATIONS, e.what()); // Catch generic boost root finding error + SHOW_ERROR(ERROR::TOO_MANY_MASS0_ITERATIONS, e.what()); // Catch generic boost root finding error } SHOW_WARN_IF(it>=maxit, ERROR::TOO_MANY_MASS0_ITERATIONS); - return root.first + (root.second - root.first)/2; // Midway between brackets is our result, if necessary we could return the result as an interval here. + return root.first + (root.second - root.first) / 2.0; // Midway between brackets is our result, if necessary we could return the result as an interval here. } }; diff --git a/src/HeHG.h b/src/HeHG.h index 25fe27531..389e007a3 100755 --- a/src/HeHG.h +++ b/src/HeHG.h @@ -72,7 +72,7 @@ class HeHG: virtual public BaseStar, public HeMS { double CalculateHeCoreMassOnPhase() const { return m_Mass; } // NO-OP double CalculateLambdaNanjingStarTrack(const double p_Mass, const double p_Metallicity) const; - double CalculateLambdaNanjingEnhanced(const int p_MassInd, const int p_Zind) const { return CalculateLambdaNanjingStarTrack(0.0, 0.0); } // 0.0 are dummy values that are not used + double CalculateLambdaNanjingEnhanced(const int p_MassInd, const int p_Zind) const { return CalculateLambdaNanjingStarTrack(0.0, 0.0); } // 0.0 are dummy values that are not used double CalculateLuminosityOnPhase() const; double CalculateLuminosityAtPhaseEnd() const { return m_Luminosity; } // NO-OP @@ -104,7 +104,7 @@ class HeHG: virtual public BaseStar, public HeMS { void CalculateTimescales(const double p_Mass, DBL_VECTOR &p_Timescales); void CalculateTimescales() { CalculateTimescales(m_Mass0, m_Timescales); } // Use class member variables - double CalculateZetaConstantsByEnvelope(ZETA_PRESCRIPTION p_ZetaPrescription) { return GiantBranch::CalculateZetaConstantsByEnvelope(p_ZetaPrescription); } // Calculate Zetas as for other giant stars (HeMS stars were an exception) + double CalculateZetaConstantsByEnvelope(ZETA_PRESCRIPTION p_ZetaPrescription) { return GiantBranch::CalculateZetaConstantsByEnvelope(p_ZetaPrescription); } // Calculate Zetas as for other giant stars (HeMS stars were an exception) double ChooseTimestep(const double p_Time) const; @@ -122,7 +122,7 @@ class HeHG: virtual public BaseStar, public HeMS { void ResolveHeliumFlash() { } // NO-OP STELLAR_TYPE ResolveSkippedPhase() { return m_StellarType; } // NO-OP - bool ShouldEnvelopeBeExpelledByPulsations() const { return CHeB::ShouldEnvelopeBeExpelledByPulsations(); } // Envelope of convective star with luminosity to mass ratio beyond threshold should be expelled + bool ShouldEnvelopeBeExpelledByPulsations() const { return CHeB::ShouldEnvelopeBeExpelledByPulsations(); } // Envelope of convective star with luminosity to mass ratio beyond threshold should be expelled bool ShouldEvolveOnPhase() const; bool ShouldSkipPhase() const { return false; } // Never skip HeMS phase diff --git a/src/NS.cpp b/src/NS.cpp index 0a04aaf84..5da7182ff 100755 --- a/src/NS.cpp +++ b/src/NS.cpp @@ -17,7 +17,7 @@ */ double NS::CalculateLuminosityOnPhase_Static(const double p_Mass, const double p_Time) { double t = std::max(p_Time, 0.1); - return 0.02 * PPOW(p_Mass, 2.0/3.0) / (t * t); + return 0.02 * PPOW(p_Mass, 2.0 / 3.0) / (t * t); } @@ -199,11 +199,11 @@ double NS::CalculatePulsarBirthSpinPeriod_Static() { * according to selected distribution (by commandline option) * * - * double CalculatePulsarBirthMagneticField_Static() + * double CalculatePulsarBirthMagneticField() * * @return log10 of the birth magnetic field in G */ -double NS::CalculatePulsarBirthMagneticField_Static() { +double NS::CalculatePulsarBirthMagneticField() { double log10B; @@ -265,27 +265,29 @@ double NS::CalculatePulsarBirthMagneticField_Static() { /* * Calculate the moment of inertia for a Neutron Star using a model independent relation between - * the moment of inertia, mass and radius of a neutron star + * the moment of inertia, mass and radius of a neutron star - return MoI in CGS. + * + * Uses m_Mass and m_Radius to calculate moment of inertia. * * Raithel et al. 2016, eq 8 in https://arxiv.org/abs/1603.06594 * https://tap.arizona.edu/sites/tap.arizona.edu/files/Raithel_2015_manuscript.pdf * * - * double CalculateMomentOfInertia_Static(const double p_Mass, const double p_Radius) + * double CalculateMomentOfInertiaCGS() * - * @param [IN] p_Mass Mass in Msol - * @param [IN] p_Radius Radius in km * @return Moment of inertia in g cm^2 */ -double NS::CalculateMomentOfInertia_Static(const double p_Mass, const double p_Radius) { +double NS::CalculateMomentOfInertiaCGS() const { // pow() is slow - use multiplication - double m_r = p_Mass / p_Radius; + + double radius = m_Radius * RSOL_TO_KM; + double m_r = m_Mass / radius; double m_r_4 = m_r * m_r * m_r * m_r; - double r_km = p_Radius * KM_TO_CM; - double r_km_2 = r_km * r_km; + double r_cm = m_Radius * KM_TO_CM; + double r_cm_2 = r_cm * r_cm; - return 0.237 * p_Mass * MSOL_TO_G * r_km_2 * (1.0 + (4.2 * m_r) + 90.0 * m_r_4); + return 0.237 * m_Mass * MSOL_TO_G * r_cm_2 * (1.0 + (4.2 * m_r) + 90.0 * m_r_4); } @@ -297,7 +299,7 @@ double NS::CalculateMomentOfInertia_Static(const double p_Mass, const double p_R * This is changed to the form of calculating spindown with P and Pdot, then convert to OmegaDot and to be recorded in the output file. * Evolution of the inclination between pulsar magnetic and rotational axes will be considered in a future version. * - * double CalculateSpinDownRate_Static(const double p_Omega, const double p_MomentOfInteria, const double p_MagField, const double p_Radius) + * double CalculateSpinDownRate(const double p_Omega, const double p_MomentOfInteria, const double p_MagField, const double p_Radius) * * @param [IN] p_Omega Pulsar spin frequency. * @param [IN] p_MomentOfInteria Moment of Interia of the Neutron Star in kg m^2 @@ -305,44 +307,55 @@ double NS::CalculateMomentOfInertia_Static(const double p_Mass, const double p_R * @param [IN] p_Radius Radius of the Neutron Star in metres * @return Spin down rate (spin frequency derivative) of an isolated Neutron Star in s^(-2) */ -double NS::CalculateSpinDownRate_Static(const double p_Omega, const double p_MomentOfInteria, const double p_MagField, const double p_Radius) { +double NS::CalculateSpinDownRate(const double p_Omega, const double p_MomentOfInteria, const double p_MagField, const double p_Radius) const { // pow() is slow - use multiplication - double Period = 2 * M_PI / p_Omega ; //Convert frequency to period - double cgs_Radius = p_Radius * KM_TO_CM; // radius in cm - double radius_6 = cgs_Radius * cgs_Radius * cgs_Radius * cgs_Radius * cgs_Radius * cgs_Radius; - double cgs_MagField = p_MagField * TESLA_TO_GAUSS; //B field in G - double magField_2 = cgs_MagField * cgs_MagField; - constexpr double _8_PI_2 = 8.0 * M_PI * M_PI; - constexpr double _3_C_3 = 3.0 * (C * 100.0) * (C * 100.0) * (C * 100.0) ; - double pDotTop = _8_PI_2 * radius_6 * magField_2; - double pDotBottom = _3_C_3 * p_MomentOfInteria * Period ; - double pDot = pDotTop / pDotBottom ; //Period derivative - return( -1 * pDot * p_Omega / Period); //convert period derivative to frequency derivative, which is what is recorded in the output. - + double period = _2_PI / p_Omega; // convert frequency to period + double cgsRadius = p_Radius * KM_TO_CM; // radius in cm + double radius_6 = cgsRadius * cgsRadius * cgsRadius * cgsRadius * cgsRadius * cgsRadius; + double cgsMagField = p_MagField * TESLA_TO_GAUSS; // B field in G + double magField_2 = cgsMagField * cgsMagField; + constexpr double _8_PI_2 = 8.0 * PI_2; + constexpr double _3_C_3 = 3.0 * (C * 100.0) * (C * 100.0) * (C * 100.0); + double pDotTop = _8_PI_2 * radius_6 * magField_2; + double pDotBottom = _3_C_3 * p_MomentOfInteria * period; + double pDot = pDotTop / pDotBottom; // period derivative + + return(-pDot * p_Omega / period); // convert period derivative to frequency derivative, which is what is recorded in the output } /* * Calculates and sets pulsar parameters at birth of pulsar * + * Modifies the following class member variables: + * + * m_AngularMomentum + * m_MomentOfInertia_CGS + * m_PulsarDetails.birthPeriod + * m_PulsarDetails.birthSpinDownRate + * m_PulsarDetails.magneticField + * m_PulsarDetails.spinDownRate + * m_PulsarDetails.spinFrequency + * + * * void CalculateAndSetPulsarParameters() */ void NS::CalculateAndSetPulsarParameters() { - m_PulsarDetails.magneticField = PPOW(10.0, CalculatePulsarBirthMagneticField_Static()) * GAUSS_TO_TESLA ; // magnetic field in Gauss -> convert to Tesla - m_PulsarDetails.spinPeriod = CalculatePulsarBirthSpinPeriod_Static(); // spin period in ms + m_PulsarDetails.magneticField = PPOW(10.0, CalculatePulsarBirthMagneticField()) * GAUSS_TO_TESLA; // magnetic field in Gauss -> convert to Tesla + m_PulsarDetails.spinPeriod = CalculatePulsarBirthSpinPeriod_Static(); // spin period in ms m_PulsarDetails.spinFrequency = _2_PI / (m_PulsarDetails.spinPeriod * SECONDS_IN_MS); - m_PulsarDetails.birthPeriod = m_PulsarDetails.spinPeriod / 1000.0; //convert from ms to s + m_PulsarDetails.birthPeriod = m_PulsarDetails.spinPeriod / 1000.0; // convert from ms to s - m_MomentOfInertia = CalculateMomentOfInertia_Static(m_Mass, m_Radius * RSOL_TO_KM) ; // in CGS g cm^2 - // Note we convert neutronStarMomentOfInertia from CGS to SI here + m_MomentOfInertia_CGS = CalculateMomentOfInertiaCGS(); // in CGS g cm^2 + + // Note we convert neutronStarMomentOfInertia from CGS to SI here constexpr double factor = G_TO_KG * CM_TO_M * CM_TO_M; - m_PulsarDetails.spinDownRate = CalculateSpinDownRate_Static(m_PulsarDetails.spinFrequency, m_MomentOfInertia, m_PulsarDetails.magneticField, m_Radius * RSOL_TO_KM); + m_PulsarDetails.spinDownRate = CalculateSpinDownRate(m_PulsarDetails.spinFrequency, m_MomentOfInertia_CGS, m_PulsarDetails.magneticField, m_Radius * RSOL_TO_KM); m_PulsarDetails.birthSpinDownRate = m_PulsarDetails.spinDownRate; - m_AngularMomentum = _2_PI * m_MomentOfInertia / (m_PulsarDetails.spinPeriod * SECONDS_IN_MS) * factor; // in kg m^2 sec^-1 - + m_AngularMomentum = _2_PI * m_MomentOfInertia_CGS / (m_PulsarDetails.spinPeriod * SECONDS_IN_MS) * factor; // in kg m^2 sec^-1 } @@ -365,38 +378,42 @@ void NS::CalculateAndSetPulsarParameters() { */ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { - double NSRadius_IN_CM = m_Radius * RSOL_TO_KM * KM_TO_CM ; - double NSRadius_3 = NSRadius_IN_CM * NSRadius_IN_CM * NSRadius_IN_CM; - double NSRadius_6 = NSRadius_3 * NSRadius_3; - constexpr double _8_PI_2 = 8.0 * M_PI * M_PI; - constexpr double _3_C_3 = 3.0 * C * C * C * 1000000.0; + double NSradius_IN_CM = m_Radius * RSOL_TO_KM * KM_TO_CM; + double NSradius_3 = NSradius_IN_CM * NSradius_IN_CM * NSradius_IN_CM; + double NSradius_6 = NSradius_3 * NSradius_3; + constexpr double _8_PI_2 = 8.0 * PI_2; + constexpr double _3_C_3 = 3.0 * C * C * C * 1000000.0; - double initialMagField = m_PulsarDetails.magneticField; // (in T) - double initialMagField_G = initialMagField * TESLA_TO_GAUSS; - double initialSpinPeriod = 2.0 * M_PI / m_PulsarDetails.spinFrequency; - double magFieldLowerLimit = PPOW(10.0, OPTIONS->PulsarLog10MinimumMagneticField()) * GAUSS_TO_TESLA; - double magFieldLowerLimit_G = magFieldLowerLimit * TESLA_TO_GAUSS; - double momentOfInertia = m_MomentOfInertia; - double tau = OPTIONS->PulsarMagneticFieldDecayTimescale() * MYR_TO_YEAR * SECONDS_IN_YEAR; - - // calculate isolated decay of the magnetic field for a neutron star see Equation 6 in arXiv:0903.3538v2 - m_PulsarDetails.magneticField = magFieldLowerLimit + (initialMagField - magFieldLowerLimit) * exp(-p_Stepsize / tau); //Update pulsar magnetic field in SI. - // calculate the spin down rate for isolated neutron stars, see Equation 6 in arxiv:1912.02415. The rest of the calculations are carried out in cgs. - double constant_2 = (_8_PI_2 * NSRadius_6) / (_3_C_3 * momentOfInertia); - double term1 = magFieldLowerLimit_G * magFieldLowerLimit_G * p_Stepsize; - double term2 = tau * magFieldLowerLimit_G * ( m_PulsarDetails.magneticField * TESLA_TO_GAUSS - initialMagField_G); - double term3 = (tau / 2.0) * (TESLA_TO_GAUSS * TESLA_TO_GAUSS * (m_PulsarDetails.magneticField * m_PulsarDetails.magneticField) - (initialMagField_G * initialMagField_G)); - double PSquared = 2 * constant_2 * (term1 - term2 - term3) + (initialSpinPeriod * initialSpinPeriod); + double initialMagField = m_PulsarDetails.magneticField; // (in T) + double initialMagField_G = initialMagField * TESLA_TO_GAUSS; + double initialSpinPeriod = PI_2 / m_PulsarDetails.spinFrequency; + double magFieldLowerLimit = PPOW(10.0, OPTIONS->PulsarLog10MinimumMagneticField()) * GAUSS_TO_TESLA; + double magFieldLowerLimit_G = magFieldLowerLimit * TESLA_TO_GAUSS; + double tau = OPTIONS->PulsarMagneticFieldDecayTimescale() * MYR_TO_YEAR * SECONDS_IN_YEAR; + + // calculate isolated decay of the magnetic field for a neutron star + // see Equation 6 in arXiv:0903.3538v2 + m_PulsarDetails.magneticField = magFieldLowerLimit + (initialMagField - magFieldLowerLimit) * exp(-p_Stepsize / tau); // update pulsar magnetic field in SI. - double P_f = std::sqrt(PSquared); - m_PulsarDetails.spinFrequency = 2.0 * M_PI / P_f; // pulsar spin frequency + // calculate the spin down rate for isolated neutron stars + // see Equation 6 in arxiv:1912.02415 + // The rest of the calculations are carried out in cgs. + double constant2 = (_8_PI_2 * NSradius_6) / (_3_C_3 * m_MomentOfInertia_CGS); + double term1 = magFieldLowerLimit_G * magFieldLowerLimit_G * p_Stepsize; + double term2 = tau * magFieldLowerLimit_G * ( m_PulsarDetails.magneticField * TESLA_TO_GAUSS - initialMagField_G); + double term3 = (tau / 2.0) * (TESLA_TO_GAUSS * TESLA_TO_GAUSS * (m_PulsarDetails.magneticField * m_PulsarDetails.magneticField) - (initialMagField_G * initialMagField_G)); + double Psquared = 2 * constant2 * (term1 - term2 - term3) + (initialSpinPeriod * initialSpinPeriod); + + double P_f = std::sqrt(Psquared); + m_PulsarDetails.spinFrequency = _2_PI / P_f; // pulsar spin frequency - // calculate the spin down rate for isolated neutron stars, see Equation 4 in arXiv:0903.3538v2 (Our version is in cgs) - double pDotTop = constant_2 * TESLA_TO_GAUSS * TESLA_TO_GAUSS * m_PulsarDetails.magneticField * m_PulsarDetails.magneticField; - double pDot = pDotTop / P_f; - m_PulsarDetails.spinDownRate = -2.0 * M_PI * pDot / (P_f * P_f); + // calculate the spin down rate for isolated neutron stars + // see Equation 4 in arXiv:0903.3538v2 (Our version is in cgs) + double pDotTop = constant2 * TESLA_TO_GAUSS * TESLA_TO_GAUSS * m_PulsarDetails.magneticField * m_PulsarDetails.magneticField; + double pDot = pDotTop / P_f; + m_PulsarDetails.spinDownRate = -_2_PI * pDot / (P_f * P_f); - m_AngularMomentum = m_PulsarDetails.spinFrequency * momentOfInertia; // angular momentum of star + m_AngularMomentum = m_PulsarDetails.spinFrequency * m_MomentOfInertia_CGS; // angular momentum of star } @@ -426,51 +443,54 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { */ void NS::UpdateMagneticFieldAndSpin(const bool p_CommonEnvelope, const bool p_RecycledNS, const double p_Stepsize, const double p_MassGainPerTimeStep, const double p_Epsilon) { - constexpr double unitsMoI = G_TO_KG * CM_TO_M * CM_TO_M; - double initialMagField = m_PulsarDetails.magneticField; // (in T) + constexpr double unitsMoI = G_TO_KG * CM_TO_M * CM_TO_M; + + double initialMagField = m_PulsarDetails.magneticField; // (in T) double magFieldLowerLimit = PPOW(10.0, OPTIONS->PulsarLog10MinimumMagneticField()) * GAUSS_TO_TESLA; double kappa = OPTIONS->PulsarMagneticFieldDecayMassscale() * MSOL_TO_KG; if ((!p_RecycledNS && !p_CommonEnvelope) || (!p_RecycledNS && utils::Compare(p_MassGainPerTimeStep, 0.0) == 0 )) { - //These are the ''classical'' isolated pulsars + // these are the ''classical'' isolated pulsars SpinDownIsolatedPulsar(p_Stepsize); } - else if ((m_PulsarDetails.spinFrequency < 2.0 * M_PI * 1000.0) && (p_RecycledNS || p_CommonEnvelope) && utils::Compare(p_MassGainPerTimeStep, 0.0) > 0) { - //This part of the code does pulsar recycling through acretion - //Recycling happens for pulsar with spin period larger than 1 ms and in a binary system with mass transfer - //The pulsar being recycled is either in a common envolope, or should have started the recycling process in previous time steps. - double mass_kg = m_Mass * MSOL_TO_KG; //in kg - double r_m = m_Radius * RSOL_TO_KM * 1000.0; //in meters + else if (utils::Compare(m_PulsarDetails.spinFrequency, _2_PI * 1000.0) < 0 && (p_RecycledNS || p_CommonEnvelope) && utils::Compare(p_MassGainPerTimeStep, 0.0) > 0) { - double MOI_SI = m_MomentOfInertia * unitsMoI; - double angularMomentum_SI = m_AngularMomentum * unitsMoI; - - double newPulsarMagneticField = (initialMagField - magFieldLowerLimit) * exp(-1 * p_MassGainPerTimeStep / 1000.0 / kappa) + magFieldLowerLimit ; + // his part of the code does pulsar recycling through acretion + // recycling happens for pulsar with spin period larger than 1 ms and in a binary system with mass transfer + // the pulsar being recycled is either in a common envolope, or should have started the recycling process in previous time steps. + double mass_kg = m_Mass * MSOL_TO_KG; // in kg + double r_m = m_Radius * RSOL_TO_KM * 1000.0; // in metres - - //Calculate the Alfven radius for an accreting neutron star, see Equation 8 in arXiv:0903.3538v2 - double mDot = p_MassGainPerTimeStep / 1000.0 / p_Stepsize ; - double R_M_6 = r_m * r_m * r_m * r_m * r_m * r_m; - double B_4 = newPulsarMagneticField * newPulsarMagneticField * newPulsarMagneticField * newPulsarMagneticField; - double R_a_top = 8.0 * R_M_6 * R_M_6 * B_4; - double R_a_bot = mass_kg * mDot * mDot * G; - double alfvenRadius = PPOW(R_a_top / R_a_bot, 1.0/7.0); + double MoI_SI = m_MomentOfInertia_CGS * unitsMoI; + double angularMomentum_SI = m_AngularMomentum * unitsMoI; + + double newPulsarMagneticField = (initialMagField - magFieldLowerLimit) * exp(-p_MassGainPerTimeStep / 1000.0 / kappa) + magFieldLowerLimit; - // calculate the difference in the keplerian angular velocity and surface angular velocity of the neutron star in m - see Equation 2 in 1994MNRAS.269..455J - double keplerianVelocityAtAlfvenRadius = std::sqrt(2.0 * G * mass_kg / alfvenRadius); - double keplerianAngularVelocityAtAlfvenRadius = 4.0 * M_PI * keplerianVelocityAtAlfvenRadius / alfvenRadius; - double velocityDifference = keplerianAngularVelocityAtAlfvenRadius - m_PulsarDetails.spinFrequency; - - // calculate the change in angular momentum due to accretion, see Equation 12 in arXiv:0805.0059/ Equation 8 in arxiv:1912.02415 - double Jdot = p_Epsilon * velocityDifference * alfvenRadius * alfvenRadius * mDot ; + // calculate the Alfven radius for an accreting neutron star + // see Equation 8 in arXiv:0903.3538v2 + double mDot = p_MassGainPerTimeStep / 1000.0 / p_Stepsize; + double R_M_6 = r_m * r_m * r_m * r_m * r_m * r_m; + double B_4 = newPulsarMagneticField * newPulsarMagneticField * newPulsarMagneticField * newPulsarMagneticField; + double R_a_top = 8.0 * R_M_6 * R_M_6 * B_4; + double R_a_bot = mass_kg * mDot * mDot * G; + double alfvenRadius = PPOW(R_a_top / R_a_bot, 1.0 / 7.0); - angularMomentum_SI = angularMomentum_SI + Jdot * p_Stepsize ; + // calculate the difference in the keplerian angular velocity and surface angular velocity of the neutron star in m + // see Equation 2 in 1994MNRAS.269..455J + double keplerianVelocityAtAlfvenRadius = std::sqrt(2.0 * G * mass_kg / alfvenRadius); + double keplerianAngularVelocityAtAlfvenRadius = 4.0 * M_PI * keplerianVelocityAtAlfvenRadius / alfvenRadius; + double velocityDifference = keplerianAngularVelocityAtAlfvenRadius - m_PulsarDetails.spinFrequency; + + // calculate the change in angular momentum due to accretion + // see Equation 12 in arXiv:0805.0059/ Equation 8 in arxiv:1912.02415 + double Jdot = p_Epsilon * velocityDifference * alfvenRadius * alfvenRadius * mDot; + angularMomentum_SI = angularMomentum_SI + Jdot * p_Stepsize; - if (angularMomentum_SI / MOI_SI > 0) { - m_PulsarDetails.magneticField = newPulsarMagneticField ; - m_PulsarDetails.spinFrequency = angularMomentum_SI / MOI_SI; - m_PulsarDetails.spinDownRate = Jdot / MOI_SI; - m_AngularMomentum = angularMomentum_SI / unitsMoI; + if (utils::Compare(angularMomentum_SI / MoI_SI, 0.0) > 0) { + m_PulsarDetails.magneticField = newPulsarMagneticField; + m_PulsarDetails.spinFrequency = angularMomentum_SI / MoI_SI; + m_PulsarDetails.spinDownRate = Jdot / MoI_SI; + m_AngularMomentum = angularMomentum_SI / unitsMoI; } else { SpinDownIsolatedPulsar(p_Stepsize); diff --git a/src/NS.h b/src/NS.h index fbcdb18e5..b98b48c7b 100755 --- a/src/NS.h +++ b/src/NS.h @@ -43,6 +43,9 @@ class NS: virtual public BaseStar, public Remnants { protected: + double m_MomentOfInertia_CGS; // MoI in CGS - only required in NS class + + void Initialise() { m_StellarType = STELLAR_TYPE::NEUTRON_STAR; // Set stellar type CalculateTimescales(); // Initialise timescales @@ -60,38 +63,34 @@ class NS: virtual public BaseStar, public Remnants { CalculateAndSetPulsarParameters(); } - double m_MomentOfInertia; // in CGS g cm^2 - double m_AngularMomentum; // Current angular momentum in (Msol AU^2 yr-1) - + // member functions - alphabetically - void CalculateAndSetPulsarParameters(); + void CalculateAndSetPulsarParameters(); - double CalculateLuminosityOnPhase() const { return CalculateLuminosityOnPhase_Static(m_Mass, m_Age); } // Use class member variables + double CalculateLuminosityOnPhase() const { return CalculateLuminosityOnPhase_Static(m_Mass, m_Age); } // Use class member variables - double CalculateMassLossRate() { return 0.0; } // Ensure that NSs don't lose mass in winds + double CalculateMassLossRate() { return 0.0; } // Ensure that NSs don't lose mass in winds - static double CalculateMomentOfInertia_Static(const double p_Mass, const double p_Radius); + double CalculateMomentOfInertiaCGS() const; // MoI in CGS + double CalculateMomentOfInertia(const double p_RemnantRadius = 0.0) const { return CalculateMomentOfInertiaCGS() / MSOL_TO_G / RSOL_TO_CM / RSOL_TO_CM; } // MoI (default is solar units) - static double CalculatePulsarBirthMagneticField_Static(); + double CalculatePulsarBirthMagneticField(); - double CalculateRadiusOnPhase() const { return CalculateRadiusOnPhase_Static(m_Mass); } // Use class member variables - returns radius in Rsol + double CalculateRadiusOnPhase() const { return CalculateRadiusOnPhase_Static(m_Mass); } // Use class member variables - returns radius in Rsol - static double CalculateSpinDownRate_Static(const double p_Omega, - const double p_MomentOfInteria, - const double p_MagField, - const double p_Radius); + double CalculateSpinDownRate(const double p_Omega, const double p_MomentOfInteria, const double p_MagField, const double p_Radius) const; - double ChooseTimestep(const double p_Time) const; + double ChooseTimestep(const double p_Time) const; - STELLAR_TYPE EvolveToNextPhase() { return STELLAR_TYPE::BLACK_HOLE; } + STELLAR_TYPE EvolveToNextPhase() { return STELLAR_TYPE::BLACK_HOLE; } - bool ShouldEvolveOnPhase() const { return (m_Mass <= OPTIONS->MaximumNeutronStarMass()); } // Evolve as a neutron star unless mass > maximum neutron star mass (e.g. through accretion) - void SpinDownIsolatedPulsar(const double p_Stepsize); - void UpdateMagneticFieldAndSpin(const bool p_CommonEnvelope, - const bool p_RecycledNS, - const double p_Stepsize, - const double p_MassGainPerTimeStep, - const double p_Epsilon); + bool ShouldEvolveOnPhase() const { return (m_Mass <= OPTIONS->MaximumNeutronStarMass()); } // Evolve as a neutron star unless mass > maximum neutron star mass (e.g. through accretion) + void SpinDownIsolatedPulsar(const double p_Stepsize); + void UpdateMagneticFieldAndSpin(const bool p_CommonEnvelope, + const bool p_RecycledNS, + const double p_Stepsize, + const double p_MassGainPerTimeStep, + const double p_Epsilon); }; diff --git a/src/constants.h b/src/constants.h index 3b80565eb..6ab3755cd 100755 --- a/src/constants.h +++ b/src/constants.h @@ -232,6 +232,7 @@ constexpr double GAUSS_TO_TESLA = 1.0 / TESLA_TO_GAUSS; // constants constexpr double _2_PI = M_PI * 2; // 2PI +constexpr double PI_2 = M_PI * M_PI; // PI squared constexpr double SQRT_M_2_PI = 0.79788456080286536; // sqrt(2/PI) constexpr double DEGREE = M_PI / 180.0; // 1 degree in radians @@ -243,7 +244,7 @@ constexpr double HUBBLE_TIME = 1 / H0SI; constexpr double G = 6.67E-11; // Gravitational constant in m^3 kg^-1 s^-2 (more accurately known as G M_sol) constexpr double G_CGS = 6.6743E-8; // Gravitational constant in cm^3 g^-1 s^-2 -constexpr double G1 = 4.0 * M_PI * M_PI; // Gravitational constant in AU^3 Msol^-1 yr^-2 +constexpr double G1 = 4.0 * PI_2; // Gravitational constant in AU^3 Msol^-1 yr^-2 constexpr double G_SN = G * 1.0E-9 / KG_TO_MSOL; // Gravitational constant in km^3 Msol^-1 s^-2, for use in the ResolveSupernova() function constexpr double G_SOLAR_YEAR = 3.14E7; // Gravitational constant in Lsol Rsol yr Msol^-2 for calculating photon tiring limit @@ -1795,6 +1796,7 @@ const COMPASUnorderedMap PROPERTY_TYPE_LABEL = { MDOT, \ MEAN_ANOMALY, \ METALLICITY, \ + MOMENT_OF_INERTIA, \ MZAMS, \ NUCLEAR_TIMESCALE, \ NUCLEAR_TIMESCALE_POST_COMMON_ENVELOPE, \ @@ -1949,6 +1951,7 @@ const COMPASUnorderedMap STAR_PROPERTY_LABEL = { { STAR_PROPERTY::MDOT, "MDOT" }, { STAR_PROPERTY::MEAN_ANOMALY, "MEAN_ANOMALY" }, { STAR_PROPERTY::METALLICITY, "METALLICITY" }, + { STAR_PROPERTY::MOMENT_OF_INERTIA, "MOMENT_OF_INERTIA"}, { STAR_PROPERTY::MZAMS, "MZAMS" }, { STAR_PROPERTY::NUCLEAR_TIMESCALE, "NUCLEAR_TIMESCALE" }, { STAR_PROPERTY::NUCLEAR_TIMESCALE_POST_COMMON_ENVELOPE, "NUCLEAR_TIMESCALE_POST_COMMON_ENVELOPE" }, @@ -2835,6 +2838,7 @@ const std::map ANY_STAR_PROPERTY_DETAIL = { { ANY_STAR_PROPERTY::MASS_TRANSFER_DONOR_HISTORY, { TYPENAME::STRING, "MT_Donor_Hist", "-", 16, 1 }}, { ANY_STAR_PROPERTY::MDOT, { TYPENAME::DOUBLE, "Mdot", "Msol yr^-1", 14, 6 }}, { ANY_STAR_PROPERTY::METALLICITY, { TYPENAME::DOUBLE, "Metallicity@ZAMS", "-", 14, 6 }}, + { ANY_STAR_PROPERTY::MOMENT_OF_INERTIA, { TYPENAME::DOUBLE, "Moment_Of_Inertia", "Msol Rsol^2", 14, 6 }}, { ANY_STAR_PROPERTY::MZAMS, { TYPENAME::DOUBLE, "Mass@ZAMS", "Msol", 14, 6 }}, { ANY_STAR_PROPERTY::NUCLEAR_TIMESCALE, { TYPENAME::DOUBLE, "Tau_Nuclear", "Myr", 16, 8 }}, { ANY_STAR_PROPERTY::NUCLEAR_TIMESCALE_POST_COMMON_ENVELOPE, { TYPENAME::DOUBLE, "Tau_Nuclear>CE", "Myr", 16, 8 }}, diff --git a/src/utils.cpp b/src/utils.cpp index 05051061c..c6b59d04f 100644 --- a/src/utils.cpp +++ b/src/utils.cpp @@ -205,10 +205,9 @@ namespace utils { */ double ConvertPeriodInDaysToSemiMajorAxisInAU(const double p_Mass1, const double p_Mass2, const double p_Period) { - double a_cubed_SI_top = G * ((p_Mass1 * MSOL_TO_KG) + (p_Mass2 * MSOL_TO_KG)) * p_Period * p_Period * SECONDS_IN_DAY * SECONDS_IN_DAY; - double a_cubed_SI_bottom = 4.0 * M_PI * M_PI; - double a_cubed_SI = a_cubed_SI_top / a_cubed_SI_bottom; - double a_SI = std::cbrt(a_cubed_SI); + double a_cubed_SI_top = G * ((p_Mass1 * MSOL_TO_KG) + (p_Mass2 * MSOL_TO_KG)) * p_Period * p_Period * SECONDS_IN_DAY * SECONDS_IN_DAY; + double a_cubed_SI = a_cubed_SI_top / G1; + double a_SI = std::cbrt(a_cubed_SI); return a_SI / AU; } @@ -883,7 +882,7 @@ namespace utils { // Sampling function taken from binpop.f in NBODY6 do { - eccentricity = 0.23 * std::sqrt(-2.0 * log(RAND->Random())) * cos(2.0 * M_PI * RAND->Random()) + 0.38; + eccentricity = 0.23 * std::sqrt(-2.0 * log(RAND->Random())) * cos(_2_PI * RAND->Random()) + 0.38; } while (eccentricity < p_Min || eccentricity > p_Max); // JR: don't use utils::Compare() here break; @@ -892,7 +891,7 @@ namespace utils { // Sampling function taken from binpop.f in NBODY6 do { - eccentricity = 0.15 * std::sqrt(-2.0 * log(RAND->Random())) * cos(2.0 * M_PI * RAND->Random()) + 0.3; + eccentricity = 0.15 * std::sqrt(-2.0 * log(RAND->Random())) * cos(_2_PI * RAND->Random()) + 0.3; } while (eccentricity < p_Min or eccentricity > p_Max); // JR: don't use utils::Compare() here break; @@ -1100,7 +1099,7 @@ namespace utils { case MASS_RATIO_DISTRIBUTION::DUQUENNOYMAYOR1991: // mass ratio distribution from Duquennoy & Mayor (1991) (http://adsabs.harvard.edu/abs/1991A%26A...248..485D) do { // JR: todo: catch non-convergence? - q = 0.42 * std::sqrt(-2.0 * log(RAND->Random())) * cos(2.0 * M_PI * RAND->Random()) + 0.23; + q = 0.42 * std::sqrt(-2.0 * log(RAND->Random())) * cos(_2_PI * RAND->Random()) + 0.23; } while (q < p_Min || q > p_Max); // JR: don't use utils::Compare() here break; @@ -1238,7 +1237,7 @@ namespace utils { // Make sure that the drawn semi-major axis is in the range specified by the user do { // JR: todo: catch for non-convergence? - double periodInDays = PPOW(10.0, 2.3 * std::sqrt(-2.0 * log(RAND->Random())) * cos(2.0 * M_PI * RAND->Random()) + 4.8); + double periodInDays = PPOW(10.0, 2.3 * std::sqrt(-2.0 * log(RAND->Random())) * cos(_2_PI * RAND->Random()) + 4.8); semiMajorAxis = utils::ConvertPeriodInDaysToSemiMajorAxisInAU(p_Mass1, p_Mass2, periodInDays); // convert period in days to semi-major axis in AU } while (semiMajorAxis < p_AdistMin || semiMajorAxis > p_AdistMax); // JR: don't use utils::Compare() here break; From f1b3980e6e5c9daeff521a0ccedc586138b2708e Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Wed, 25 Oct 2023 15:35:31 +1100 Subject: [PATCH 02/32] Enhancement, a little cleanup: - Added naive tides implementation. Functionality enable with new optin . default is no tides. --- src/BH.h | 2 +- src/BaseBinaryStar.cpp | 854 ++++++++++++++++++++--------------------- src/BaseBinaryStar.h | 176 +++------ src/BaseStar.cpp | 12 +- src/BaseStar.h | 5 - src/COWD.h | 1 - src/MainSequence.cpp | 2 +- src/NS.cpp | 110 +++--- src/NS.h | 34 +- src/Options.cpp | 10 + src/Options.h | 7 + src/changelog.h | 4 +- src/constants.h | 29 +- src/outfile | 117 ++++++ src/utils.cpp | 45 ++- src/utils.h | 2 +- src/yaml.h | 3 + 17 files changed, 758 insertions(+), 655 deletions(-) create mode 100644 src/outfile diff --git a/src/BH.h b/src/BH.h index b597c966a..ea99dd1d5 100755 --- a/src/BH.h +++ b/src/BH.h @@ -56,7 +56,7 @@ class BH: virtual public BaseStar, public Remnants { double CalculateMassLossRate() { return 0.0; } // Ensure that BHs don't lose mass in winds - double CalculateMomentOfInertia(const double p_RemnantRadius = 0.0) const { return (2.0 / 5.0) * m_Mass * m_Radius * m_Radius; } + double CalculateMomentOfInertia(const double p_RemnantRadius = 0.0) const { return -(2.0 / 5.0) * m_Mass * m_Radius * m_Radius; } double CalculateMomentOfInertiaAU(const double p_RemnantRadius = 0.0) const { return CalculateMomentOfInertia(p_RemnantRadius * RSOL_TO_AU) * RSOL_TO_AU * RSOL_TO_AU; } double CalculateRadiusOnPhase() const { return CalculateRadiusOnPhase_Static(m_Mass); } // Use class member variables - returns radius in Rsol diff --git a/src/BaseBinaryStar.cpp b/src/BaseBinaryStar.cpp index c1da0c02a..525fed3ab 100644 --- a/src/BaseBinaryStar.cpp +++ b/src/BaseBinaryStar.cpp @@ -161,8 +161,8 @@ BaseBinaryStar::BaseBinaryStar(const unsigned long int p_Seed, const long int p_ double starToRocheLobeRadiusRatio1 = (m_Star1->Radius() * RSOL_TO_AU) / (m_SemiMajorAxis * (1.0 - m_Eccentricity) * CalculateRocheLobeRadius_Static(mass1, mass2)); double starToRocheLobeRadiusRatio2 = (m_Star2->Radius() * RSOL_TO_AU) / (m_SemiMajorAxis * (1.0 - m_Eccentricity) * CalculateRocheLobeRadius_Static(mass2, mass1)); - m_Flags.massesEquilibrated = false; // default - m_Flags.massesEquilibratedAtBirth = false; // default + m_Flags.massesEquilibrated = false; // default + m_Flags.massesEquilibratedAtBirth = false; // default rlof = utils::Compare(starToRocheLobeRadiusRatio1, 1.0) > 0 || utils::Compare(starToRocheLobeRadiusRatio2, 1.0) > 0; // either star overflowing Roche Lobe? @@ -272,9 +272,19 @@ void BaseBinaryStar::SetRemainingValues() { m_SemiMajorAxisAtDCOFormation = DEFAULT_INITIAL_DOUBLE_VALUE; m_EccentricityAtDCOFormation = DEFAULT_INITIAL_DOUBLE_VALUE; - // if CHE enabled, update rotational frequency for constituent stars - assume tidally locked + double gyrationRadius1 = m_Star1->CalculateGyrationRadius(); + double gyrationRadius2 = m_Star2->CalculateGyrationRadius(); + + m_TotalEnergy = CalculateTotalEnergy(m_SemiMajorAxis, m_Star1->Mass(), m_Star2->Mass(), m_Star1->RZAMS(), m_Star2->RZAMS(), m_Star1->Omega(), m_Star2->Omega(), gyrationRadius1, gyrationRadius2); + + m_TotalAngularMomentum = CalculateAngularMomentum(m_SemiMajorAxis, m_Eccentricity, m_Star1->Mass(), m_Star2->Mass(), m_Star1->RZAMS(), m_Star2->RZAMS(), m_Star1->Omega(), m_Star2->Omega(), gyrationRadius1, gyrationRadius2); + m_TotalAngularMomentumPrev = m_TotalAngularMomentum; + + m_Omega = 0.0; - if (OPTIONS->CHEMode() != CHE_MODE::NONE) { + if (OPTIONS->CHEMode() != CHE_MODE::NONE) { // CHE enabled? + + // CHE enabled, update rotational frequency for constituent stars - assume tidally locked m_Star1->SetOmega(OrbitalAngularVelocity()); m_Star2->SetOmega(OrbitalAngularVelocity()); @@ -312,209 +322,185 @@ void BaseBinaryStar::SetRemainingValues() { } } - double gyrationRadius1 = m_Star1->CalculateGyrationRadius(); - double gyrationRadius2 = m_Star2->CalculateGyrationRadius(); - - m_TotalEnergy = CalculateTotalEnergy(m_SemiMajorAxis, - m_Star1->Mass(), - m_Star2->Mass(), - m_Star1->RZAMS(), - m_Star2->RZAMS(), - m_Star1->Omega(), - m_Star2->Omega(), - gyrationRadius1, - gyrationRadius2); - - m_TotalAngularMomentum = CalculateAngularMomentum(m_SemiMajorAxis, - m_Eccentricity, - m_Star1->Mass(), - m_Star2->Mass(), - m_Star1->RZAMS(), - m_Star2->RZAMS(), - m_Star1->Omega(), - m_Star2->Omega(), - gyrationRadius1, - gyrationRadius2); + double totalMass = m_Star1->Mass() + m_Star2->Mass(); + double reducedMass = (m_Star1->Mass() * m_Star2->Mass()) / totalMass; + m_OrbitalEnergy = CalculateOrbitalEnergy(reducedMass, totalMass, m_SemiMajorAxis); + m_OrbitalEnergyPrev = m_OrbitalEnergy; - double totalMass = m_Star1->Mass() + m_Star2->Mass(); - double reducedMass = (m_Star1->Mass() * m_Star2->Mass()) / totalMass; - m_OrbitalEnergy = CalculateOrbitalEnergy(reducedMass, totalMass, m_SemiMajorAxis); - m_OrbitalEnergyPrev = m_OrbitalEnergy; + m_OrbitalAngularMomentum = CalculateOrbitalAngularMomentum(reducedMass, totalMass, m_SemiMajorAxis); + m_OrbitalAngularMomentumPrev = m_OrbitalAngularMomentum; - m_OrbitalAngularMomentum = CalculateOrbitalAngularMomentum(reducedMass, totalMass, m_SemiMajorAxis); - m_OrbitalAngularMomentumPrev = m_OrbitalAngularMomentum; + m_Time = DEFAULT_INITIAL_DOUBLE_VALUE; + m_Dt = DEFAULT_INITIAL_DOUBLE_VALUE; + m_TimePrev = DEFAULT_INITIAL_DOUBLE_VALUE; + m_DCOFormationTime = DEFAULT_INITIAL_DOUBLE_VALUE; - m_Time = DEFAULT_INITIAL_DOUBLE_VALUE; - m_Dt = DEFAULT_INITIAL_DOUBLE_VALUE; - m_TimePrev = DEFAULT_INITIAL_DOUBLE_VALUE; - m_DCOFormationTime = DEFAULT_INITIAL_DOUBLE_VALUE; + m_aMassLossDiff = DEFAULT_INITIAL_DOUBLE_VALUE; - m_aMassLossDiff = DEFAULT_INITIAL_DOUBLE_VALUE; + m_aMassTransferDiff = DEFAULT_INITIAL_DOUBLE_VALUE; - m_aMassTransferDiff = DEFAULT_INITIAL_DOUBLE_VALUE; + m_MassTransferTrackerHistory = MT_TRACKING::NO_MASS_TRANSFER; + m_MassTransfer = false; - m_MassTransferTrackerHistory = MT_TRACKING::NO_MASS_TRANSFER; - m_MassTransfer = false; + m_JLoss = OPTIONS->MassTransferJloss(); - m_JLoss = OPTIONS->MassTransferJloss(); - - m_FractionAccreted = OPTIONS->MassTransferFractionAccreted(); + m_FractionAccreted = OPTIONS->MassTransferFractionAccreted(); // Common Envelope - m_CEDetails.CEEcount = 0; - m_CEDetails.CEEnow = false; - m_CEDetails.doubleCoreCE = false; - m_CEDetails.optimisticCE = false; - m_CEDetails.postCEE.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; - m_CEDetails.postCEE.rocheLobe1to2 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_CEDetails.postCEE.rocheLobe2to1 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_CEDetails.postCEE.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; - m_CEDetails.preCEE.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; - m_CEDetails.preCEE.rocheLobe1to2 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_CEDetails.preCEE.rocheLobe2to1 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_CEDetails.preCEE.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; - - m_Flags.stellarMerger = false; - m_Flags.stellarMergerAtBirth = false; - - m_Mass1Final = DEFAULT_INITIAL_DOUBLE_VALUE; - m_Mass2Final = DEFAULT_INITIAL_DOUBLE_VALUE; - m_MassEnv1 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_MassEnv2 = DEFAULT_INITIAL_DOUBLE_VALUE; - - m_ZetaLobe = DEFAULT_INITIAL_DOUBLE_VALUE; - m_ZetaStar = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CEDetails.CEEcount = 0; + m_CEDetails.CEEnow = false; + m_CEDetails.doubleCoreCE = false; + m_CEDetails.optimisticCE = false; + m_CEDetails.postCEE.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CEDetails.postCEE.rocheLobe1to2 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CEDetails.postCEE.rocheLobe2to1 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CEDetails.postCEE.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CEDetails.preCEE.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CEDetails.preCEE.rocheLobe1to2 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CEDetails.preCEE.rocheLobe2to1 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CEDetails.preCEE.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; + + m_Flags.stellarMerger = false; + m_Flags.stellarMergerAtBirth = false; + + m_Mass1Final = DEFAULT_INITIAL_DOUBLE_VALUE; + m_Mass2Final = DEFAULT_INITIAL_DOUBLE_VALUE; + m_MassEnv1 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_MassEnv2 = DEFAULT_INITIAL_DOUBLE_VALUE; + + m_ZetaLobe = DEFAULT_INITIAL_DOUBLE_VALUE; + m_ZetaStar = DEFAULT_INITIAL_DOUBLE_VALUE; // Initialise other parameters to 0 - m_uK = DEFAULT_INITIAL_DOUBLE_VALUE; - m_CosIPrime = DEFAULT_INITIAL_DOUBLE_VALUE; - m_IPrime = DEFAULT_INITIAL_DOUBLE_VALUE; - m_TimeToCoalescence = DEFAULT_INITIAL_DOUBLE_VALUE; + m_uK = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CosIPrime = DEFAULT_INITIAL_DOUBLE_VALUE; + m_IPrime = DEFAULT_INITIAL_DOUBLE_VALUE; + m_TimeToCoalescence = DEFAULT_INITIAL_DOUBLE_VALUE; - m_SupernovaState = SN_STATE::NONE; + m_SupernovaState = SN_STATE::NONE; - m_Flags.mergesInHubbleTime = false; - m_Unbound = false; + m_Flags.mergesInHubbleTime = false; + m_Unbound = false; - m_SystemicVelocity = Vector3d(); - m_OrbitalAngularMomentumVector = Vector3d(); - m_ThetaE = DEFAULT_INITIAL_DOUBLE_VALUE; - m_PhiE = DEFAULT_INITIAL_DOUBLE_VALUE; - m_PsiE = DEFAULT_INITIAL_DOUBLE_VALUE; + m_SystemicVelocity = Vector3d(); + m_OrbitalAngularMomentumVector = Vector3d(); + m_ThetaE = DEFAULT_INITIAL_DOUBLE_VALUE; + m_PhiE = DEFAULT_INITIAL_DOUBLE_VALUE; + m_PsiE = DEFAULT_INITIAL_DOUBLE_VALUE; - m_SynchronizationTimescale = DEFAULT_INITIAL_DOUBLE_VALUE; - m_CircularizationTimescale = DEFAULT_INITIAL_DOUBLE_VALUE; + m_SynchronizationTimescale = DEFAULT_INITIAL_DOUBLE_VALUE; + m_CircularizationTimescale = DEFAULT_INITIAL_DOUBLE_VALUE; // RLOF details - m_RLOFDetails.experiencedRLOF = false; - m_RLOFDetails.immediateRLOFPostCEE = false; - m_RLOFDetails.isRLOF = false; - m_RLOFDetails.simultaneousRLOF = false; - m_RLOFDetails.stableRLOFPostCEE = false; + m_RLOFDetails.experiencedRLOF = false; + m_RLOFDetails.immediateRLOFPostCEE = false; + m_RLOFDetails.isRLOF = false; + m_RLOFDetails.simultaneousRLOF = false; + m_RLOFDetails.stableRLOFPostCEE = false; // RLOF details - properties 1 - m_RLOFDetails.props1.id = -1l; + m_RLOFDetails.props1.id = -1l; - m_RLOFDetails.props1.stellarType1 = STELLAR_TYPE::NONE; - m_RLOFDetails.props1.stellarType2 = STELLAR_TYPE::NONE; + m_RLOFDetails.props1.stellarType1 = STELLAR_TYPE::NONE; + m_RLOFDetails.props1.stellarType2 = STELLAR_TYPE::NONE; - m_RLOFDetails.props1.mass1 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props1.mass2 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props1.mass1 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props1.mass2 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props1.radius1 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props1.radius2 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props1.radius1 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props1.radius2 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props1.starToRocheLobeRadiusRatio1 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props1.starToRocheLobeRadiusRatio2 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props1.starToRocheLobeRadiusRatio1 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props1.starToRocheLobeRadiusRatio2 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props1.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props1.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props1.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props1.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props1.eventCounter = DEFAULT_INITIAL_ULONGINT_VALUE; + m_RLOFDetails.props1.eventCounter = DEFAULT_INITIAL_ULONGINT_VALUE; - m_RLOFDetails.props1.time = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props1.time = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props1.isRLOF1 = false; - m_RLOFDetails.props1.isRLOF2 = false; + m_RLOFDetails.props1.isRLOF1 = false; + m_RLOFDetails.props1.isRLOF2 = false; - m_RLOFDetails.props1.isCE = false; + m_RLOFDetails.props1.isCE = false; // RLOF details - properties 2 m_RLOFDetails.props2.id = -1l; - m_RLOFDetails.props2.stellarType1 = STELLAR_TYPE::NONE; - m_RLOFDetails.props2.stellarType2 = STELLAR_TYPE::NONE; + m_RLOFDetails.props2.stellarType1 = STELLAR_TYPE::NONE; + m_RLOFDetails.props2.stellarType2 = STELLAR_TYPE::NONE; - m_RLOFDetails.props2.mass1 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props2.mass2 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props2.mass1 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props2.mass2 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props2.radius1 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props2.radius2 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props2.radius1 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props2.radius2 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props2.starToRocheLobeRadiusRatio1 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props2.starToRocheLobeRadiusRatio2 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props2.starToRocheLobeRadiusRatio1 = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props2.starToRocheLobeRadiusRatio2 = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props2.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props2.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props2.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props2.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props2.eventCounter = DEFAULT_INITIAL_ULONGINT_VALUE; + m_RLOFDetails.props2.eventCounter = DEFAULT_INITIAL_ULONGINT_VALUE; - m_RLOFDetails.props2.time = DEFAULT_INITIAL_DOUBLE_VALUE; + m_RLOFDetails.props2.time = DEFAULT_INITIAL_DOUBLE_VALUE; - m_RLOFDetails.props2.isRLOF1 = false; - m_RLOFDetails.props2.isRLOF2 = false; + m_RLOFDetails.props2.isRLOF1 = false; + m_RLOFDetails.props2.isRLOF2 = false; - m_RLOFDetails.props2.isCE = false; + m_RLOFDetails.props2.isCE = false; // RLOF details - pre/post-MT props pointers - m_RLOFDetails.propsPostMT = &m_RLOFDetails.props1; - m_RLOFDetails.propsPreMT = &m_RLOFDetails.props2; + m_RLOFDetails.propsPostMT = &m_RLOFDetails.props1; + m_RLOFDetails.propsPreMT = &m_RLOFDetails.props2; // BeBinary details - properties 1 - m_BeBinaryDetails.props1.id = -1l; + m_BeBinaryDetails.props1.id = -1l; - m_BeBinaryDetails.props1.dt = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props1.totalTime = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props1.dt = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props1.totalTime = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props1.massNS = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props1.massNS = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props1.companionMass = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props1.companionLuminosity = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props1.companionTeff = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props1.companionRadius = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props1.companionMass = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props1.companionLuminosity = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props1.companionTeff = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props1.companionRadius = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props1.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props1.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props1.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props1.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; // BeBinary details - properties 2 - m_BeBinaryDetails.props2.id = -1l; + m_BeBinaryDetails.props2.id = -1l; - m_BeBinaryDetails.props2.dt = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props2.totalTime = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props2.dt = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props2.totalTime = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props2.massNS = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props2.massNS = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props2.companionMass = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props2.companionLuminosity = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props2.companionTeff = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props2.companionRadius = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props2.companionMass = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props2.companionLuminosity = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props2.companionTeff = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props2.companionRadius = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props2.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; - m_BeBinaryDetails.props2.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props2.semiMajorAxis = DEFAULT_INITIAL_DOUBLE_VALUE; + m_BeBinaryDetails.props2.eccentricity = DEFAULT_INITIAL_DOUBLE_VALUE; // BeBinary details - current/prev props pointers - m_BeBinaryDetails.currentProps = &m_BeBinaryDetails.props1; - m_BeBinaryDetails.previousProps = &m_BeBinaryDetails.props2; + m_BeBinaryDetails.currentProps = &m_BeBinaryDetails.props1; + m_BeBinaryDetails.previousProps = &m_BeBinaryDetails.props2; // pointers - m_Donor = nullptr; - m_Accretor = nullptr; + m_Donor = nullptr; + m_Accretor = nullptr; - m_Supernova = nullptr; - m_Companion = nullptr; + m_Supernova = nullptr; + m_Companion = nullptr; } @@ -833,7 +819,7 @@ bool BaseBinaryStar::IsHMXRBinary() const { if (m_Star1->StellarType() < STELLAR_TYPE::NEUTRON_STAR && utils::Compare(StarToRocheLobeRadiusRatio1(), MIN_HMXRB_STAR_TO_ROCHE_LOBE_RADIUS_RATIO) > 0) return true; if (m_Star2->StellarType() < STELLAR_TYPE::NEUTRON_STAR && utils::Compare(StarToRocheLobeRadiusRatio2(), MIN_HMXRB_STAR_TO_ROCHE_LOBE_RADIUS_RATIO) > 0) return true; } - return false; + return false; } @@ -858,7 +844,7 @@ bool BaseBinaryStar::PrintRLOFParameters(const RLOF_RECORD_TYPE p_RecordType) { if (m_Star1->IsRLOF() || m_Star2->IsRLOF()) { // print if either star is in RLOF m_RLOFDetails.propsPostMT->eventCounter += 1; // every time we print a MT event happened, increment counter - ok = LOGGING->LogRLOFParameters(this, p_RecordType); // yes - write to log file + ok = LOGGING->LogRLOFParameters(this, p_RecordType); // yes - write to log file } if (OPTIONS->HMXRBinaries()) { @@ -905,7 +891,7 @@ bool BaseBinaryStar::PrintBeBinary(const BE_BINARY_RECORD_TYPE p_RecordType) { */ void BaseBinaryStar::StashRLOFProperties(const MASS_TRANSFER_TIMING p_Which) { - if (!OPTIONS->RLOFPrinting()) return; // nothing to do + if (!OPTIONS->RLOFPrinting()) return; // nothing to do // set whether to update pre-MT or post-MT parameters depending on input argument RLOFPropertiesT* rlofPropertiesToReset; @@ -924,7 +910,7 @@ void BaseBinaryStar::StashRLOFProperties(const MASS_TRANSFER_TIMING p_Which) { rlofPropertiesToReset->stellarType1 = m_Star1->StellarType(); rlofPropertiesToReset->stellarType2 = m_Star2->StellarType(); rlofPropertiesToReset->eccentricity = m_Eccentricity; - rlofPropertiesToReset->semiMajorAxis = m_SemiMajorAxis * AU_TO_RSOL; // semi-major axis - change units to Rsol + rlofPropertiesToReset->semiMajorAxis = m_SemiMajorAxis * AU_TO_RSOL; // semi-major axis - change units to Rsol rlofPropertiesToReset->time = m_Time; rlofPropertiesToReset->timePrev = m_TimePrev; rlofPropertiesToReset->isRLOF1 = m_Star1->IsRLOF(); @@ -948,8 +934,7 @@ void BaseBinaryStar::StashBeBinaryProperties() { if (!OPTIONS->BeBinaries() || !IsBeBinary()) return; // nothing to do; // switch previous<->current (preserves existing current as (new) previous) - BeBinaryPropertiesT* tmp; - tmp = m_BeBinaryDetails.previousProps; // save pointer to existing previous props + BeBinaryPropertiesT* tmp = m_BeBinaryDetails.previousProps; // save pointer to existing previous props m_BeBinaryDetails.previousProps = m_BeBinaryDetails.currentProps; // existing current props become new previous props (values will be preserved) m_BeBinaryDetails.currentProps = tmp; // new current props points at existing previous (values will be replaced) @@ -1137,36 +1122,33 @@ void BaseBinaryStar::ResolveCoalescence() { * * Note: the systemic speed is only valid for intact binaries, and component speeds are only valid for disrupted binaries. * - * ///////////////////////////////// - * // Logic - * // - * // If (Unbound before SN): - * // - * // Must be 2nd SN, only need to update starSN component velocity (rotated into previous reference frame). - * // - * // Else: (Intact before SN) - * // - * // Evolve binary according to vector algebra to determine centerofmass velocity, h', e', a', and whether bound or unbound. - * // - * // Update binary systemic velocity (even if disrupted, just for consistency) - rotate into previous reference frame if needed. - * // - * // If now unbound: - * // - * // Set m_Unbound to True - should be the only place in the code this is done. - * // - * // Continue vector algebra to find v1inf and v2inf. - * // Add these values to previous component velocities (rotated if need be) which will be the systemic velocity if this is the 2nd SN. - * // - * // For unbound binary, new Euler Angles should be randomized (see vector3d.cpp). - * // - * // If still intact: - * // - * // Binary systemic velocity has already been set, so just set the component velocities to the same vector. - * // (this is to make it easier to add just a component velocity later). - * // - * // For intact binary, Euler Angles must be calculated according to the vector algebra (see vector3d.h). - * // - * ///////////////////////////////////////////////////////////////////////////// + * Logic: + * + * if (Unbound before SN): + * + * Must be 2nd SN, only need to update starSN component velocity (rotated into previous reference frame). + * + * else: (Intact before SN) + * + * Evolve binary according to vector algebra to determine centerofmass velocity, h', e', a', and whether bound or unbound. + * Update binary systemic velocity (even if disrupted, just for consistency) - rotate into previous reference frame if needed. + * + * if now unbound: + * + * Set m_Unbound to True - should be the only place in the code this is done. + * + * Continue vector algebra to find v1inf and v2inf. + * Add these values to previous component velocities (rotated if need be) which will be the systemic velocity if this is the 2nd SN. + * + * For unbound binary, new Euler Angles should be randomized (see vector3d.cpp). + * + * if still intact: + * + * Binary systemic velocity has already been set, so just set the component velocities to the same vector. + * (this is to make it easier to add just a component velocity later). + * + * For intact binary, Euler Angles must be calculated according to the vector algebra (see vector3d.h). + * * * * bool ResolveSupernova() @@ -1177,57 +1159,51 @@ bool BaseBinaryStar::ResolveSupernova() { if (!m_Supernova->IsSNevent()) { SHOW_WARN(ERROR::RESOLVE_SUPERNOVA_IMPROPERLY_CALLED); - return false; // not a supernova event - bail out + return false; // not a supernova event - bail out } // Set relevant preSN parameters - m_EccentricityPreSN = m_Eccentricity; + m_EccentricityPreSN = m_Eccentricity; m_SemiMajorAxisPreSN = m_SemiMajorAxis; - double totalMassPreSN = m_Supernova->SN_TotalMassAtCOFormation() + m_Companion->Mass(); // Total Mass preSN - double reducedMassPreSN = m_Supernova->SN_TotalMassAtCOFormation() * m_Companion->Mass() / totalMassPreSN; // Reduced Mass preSN - m_Supernova->SetOrbitalEnergyPreSN(CalculateOrbitalEnergy(reducedMassPreSN, totalMassPreSN, m_SemiMajorAxisPreSN)); // Orbital energy preSN + double totalMassPreSN = m_Supernova->SN_TotalMassAtCOFormation() + m_Companion->Mass(); // total Mass preSN + double reducedMassPreSN = m_Supernova->SN_TotalMassAtCOFormation() * m_Companion->Mass() / totalMassPreSN; // reduced Mass preSN + m_Supernova->SetOrbitalEnergyPreSN(CalculateOrbitalEnergy(reducedMassPreSN, totalMassPreSN, m_SemiMajorAxisPreSN)); // orbital energy preSN // Define the natal kick vector (see above for precise definitions of the angles) - double theta = m_Supernova->SN_Theta(); // Angle out of the binary plane - double phi = m_Supernova->SN_Phi(); // Angle in the binary plane - Vector3d natalKickVector = m_Supernova->SN_KickMagnitude() *Vector3d(cos(theta)*cos(phi), - cos(theta)*sin(phi), + double theta = m_Supernova->SN_Theta(); // angle out of the binary plane + double phi = m_Supernova->SN_Phi(); // angle in the binary plane + Vector3d natalKickVector = m_Supernova->SN_KickMagnitude() *Vector3d(cos(theta) * cos(phi), + cos(theta) * sin(phi), sin(theta)); // Check if the system is already unbound - if (IsUnbound()) { // Is system already unbound? + if (IsUnbound()) { // is system already unbound? - m_Supernova->UpdateComponentVelocity( natalKickVector.RotateVector(m_ThetaE, m_PhiE, m_PsiE)); // yes - only need to update the velocity of the star undergoing SN + m_Supernova->UpdateComponentVelocity( natalKickVector.RotateVector(m_ThetaE, m_PhiE, m_PsiE)); // yes - only need to update the velocity of the star undergoing SN // The quantities below are meaningless in this context, so they are set to nan to avoid misuse m_OrbitalVelocityPreSN = -nan(""); - m_uK = nan(""); // -- - Dimensionless kick magnitude - + m_uK = nan(""); // -- - Dimensionless kick magnitude } - else { // no - evaluate orbital changes and calculate velocities - - ////////////////////////////////////////////////////////////////////////////////////////////////// - // - // Evolve SN out of binary - // - ////////////////////////////////////////////////////////////////////////////////////////////////// - + else { // no - evaluate orbital changes and calculate velocities + // Evolve SN out of binary // Functions defined in vector3d.h - #define cross(x,y) linalg::cross(x,y) - #define dot(x,y) linalg::dot(x,y) - #define angleBetween(x,y) linalg::angleBetween(x,y) - #define mag Magnitude() - #define hat UnitVector() + // Defined here for convenience - undefined later + #define cross(x,y) linalg::cross(x,y) + #define dot(x,y) linalg::dot(x,y) + #define angleBetween(x,y) linalg::angleBetween(x,y) + #define mag Magnitude() + #define hat UnitVector() // Pre-SN parameters - double semiMajorAxisPrev_km = m_SemiMajorAxis * AU_TO_KM; // km - Semi-Major axis - double eccentricityPrev = m_Eccentricity; // -- - Eccentricity, written with a prev to distinguish from later use - double sqrt1MinusEccPrevSquared = std::sqrt(1 - eccentricityPrev * eccentricityPrev); // useful function of eccentricity + double semiMajorAxisPrev_km = m_SemiMajorAxis * AU_TO_KM; // km - Semi-Major axis + double eccentricityPrev = m_Eccentricity; // -- - Eccentricity, written with a prev to distinguish from later use + double sqrt1MinusEccPrevSquared = std::sqrt(1.0 - eccentricityPrev * eccentricityPrev); // useful function of eccentricity - double m1Prev = m_Supernova->SN_TotalMassAtCOFormation(); // Mo - SN star pre-SN mass - double m2Prev = m_Companion->Mass(); // Mo - CP star pre-SN mass - double totalMassPrev = m1Prev + m2Prev; // Mo - Total binary pre-SN mass + double m1Prev = m_Supernova->SN_TotalMassAtCOFormation(); // Mo - SN star pre-SN mass + double m2Prev = m_Companion->Mass(); // Mo - CP star pre-SN mass + double totalMassPrev = m1Prev + m2Prev; // Mo - Total binary pre-SN mass // Functions of eccentric anomaly m_Supernova->CalculateSNAnomalies(eccentricityPrev); @@ -1235,93 +1211,82 @@ bool BaseBinaryStar::ResolveSupernova() { double sinEccAnomaly = sin(m_Supernova->SN_EccentricAnomaly()); // Derived quantities - double omega = std::sqrt(G_SN*totalMassPrev / (semiMajorAxisPrev_km * semiMajorAxisPrev_km*semiMajorAxisPrev_km)); // rad/s - Keplerian orbital frequency + double aPrev = semiMajorAxisPrev_km; + double aPrev_2 = aPrev * aPrev; + double aPrev_3 = aPrev_2 * aPrev; - Vector3d separationVectorPrev = Vector3d( semiMajorAxisPrev_km * (cosEccAnomaly - eccentricityPrev), - semiMajorAxisPrev_km * (sinEccAnomaly) * sqrt1MinusEccPrevSquared, - 0.0 ); // km - Relative position vector, from m1Prev to m2Prev - double separationPrev = separationVectorPrev.mag; // km - Instantaneous Separation + double omega = std::sqrt(G_SN * totalMassPrev / aPrev_3); // rad/s - Keplerian orbital frequency - Vector3d relativeVelocityVectorPrev = Vector3d(-((semiMajorAxisPrev_km * semiMajorAxisPrev_km) * omega / separationPrev) * sinEccAnomaly, - ((semiMajorAxisPrev_km * semiMajorAxisPrev_km) * omega / separationPrev) * cosEccAnomaly * sqrt1MinusEccPrevSquared, - 0.0 ); // km/s - Relative velocity vector, in the m1Prev rest frame + Vector3d separationVectorPrev = Vector3d(aPrev * (cosEccAnomaly - eccentricityPrev), + aPrev * (sinEccAnomaly) * sqrt1MinusEccPrevSquared, + 0.0); // km - Relative position vector, from m1Prev to m2Prev + double separationPrev = separationVectorPrev.mag; // km - Instantaneous Separation + double fact1 = aPrev_2 * omega / separationPrev; - Vector3d orbitalAngularMomentumVectorPrev = cross(separationVectorPrev, relativeVelocityVectorPrev); // km^2 s^-1 - Specific orbital angular momentum vector + Vector3d relativeVelocityVectorPrev = Vector3d(-fact1 * sinEccAnomaly, + fact1 * cosEccAnomaly * sqrt1MinusEccPrevSquared, + 0.0); // km/s - Relative velocity vector, in the m1Prev rest frame - Vector3d eccentricityVectorPrev = cross(relativeVelocityVectorPrev, orbitalAngularMomentumVectorPrev) / (G_SN * totalMassPrev) - separationVectorPrev.hat; // -- - Laplace-Runge-Lenz vector (magnitude = eccentricity) + Vector3d orbitalAngularMomentumVectorPrev = cross(separationVectorPrev, relativeVelocityVectorPrev); // km^2 s^-1 - Specific orbital angular momentum vector - m_OrbitalVelocityPreSN = relativeVelocityVectorPrev.mag; // km/s - Set the Pre-SN orbital velocity and - m_uK = m_Supernova->SN_KickMagnitude() / m_OrbitalVelocityPreSN; // -- - Dimensionless kick magnitude + Vector3d eccentricityVectorPrev = cross(relativeVelocityVectorPrev, orbitalAngularMomentumVectorPrev) / (G_SN * totalMassPrev) - separationVectorPrev.hat; // -- - Laplace-Runge-Lenz vector (magnitude = eccentricity) + + m_OrbitalVelocityPreSN = relativeVelocityVectorPrev.mag; // km/s - Set the Pre-SN orbital velocity and + m_uK = m_Supernova->SN_KickMagnitude() / m_OrbitalVelocityPreSN; // -- - Dimensionless kick magnitude - ///////////////////////////////////////////////////////////////////////////////////////// // Note: In the following, // orbitalAngularMomentumVectorPrev defines the Z-axis, // eccentricityVectorPrev defines the X-axis, and // (orbitalAngularMomentumVectorPrev x eccentricityVectorPrev) defines the Y-axis - ///////////////////////////////////////////////////////////////////////////////////////// - - ///////////////////////////////////////////////////////////////////////////////////////// // Apply supernova natal kick and mass loss // // Note: the code allows for mass loss and kick in the companion // (due to ablation), though we currently do not apply these. - ///////////////////////////////////////////////////////////////////////////////////////// - Vector3d companionRecoilVector = Vector3d(0.0, 0.0, 0.0); // km/s - The recoil of the companion due to ablation - double m1 = m_Supernova->Mass(); // Mo - supernova star postSN mass - double m2 = m_Companion->Mass(); // Mo - companion star postSN mass - double totalMass = m1 + m2; // Mo - Total binary postSN mass - - double dm1 = (m1Prev - m1); // Mo - Mass difference of supernova star - double dm2 = (m2Prev - m2); // Mo - Mass difference of companion star + Vector3d companionRecoilVector = Vector3d(0.0, 0.0, 0.0); // km/s - The recoil of the companion due to ablation - Vector3d centerOfMassVelocity = (-m2Prev * dm1 / (totalMassPrev*totalMass) + m1Prev * dm2 / (totalMassPrev * totalMass)) * relativeVelocityVectorPrev - + (m1 / totalMass) * natalKickVector - + (m2 / totalMass) * companionRecoilVector; // km/s - PostSN center of mass velocity vector + double m1 = m_Supernova->Mass(); // Mo - supernova star postSN mass + double m2 = m_Companion->Mass(); // Mo - companion star postSN mass + double totalMass = m1 + m2; // Mo - Total binary postSN mass + double fact2 = totalMassPrev * totalMass; + double dm1 = (m1Prev - m1); // Mo - Mass difference of supernova star + double dm2 = (m2Prev - m2); // Mo - Mass difference of companion star - Vector3d relativeVelocityVector = relativeVelocityVectorPrev + (natalKickVector - companionRecoilVector); // km/s - PostSN relative velocity vector + Vector3d centerOfMassVelocity = (-m2Prev * dm1 / fact2 + m1Prev * dm2 / fact2) * relativeVelocityVectorPrev + + (m1 / totalMass) * natalKickVector + (m2 / totalMass) * companionRecoilVector; // km/s - PostSN center of mass velocity vector - Vector3d orbitalAngularMomentumVector = cross(separationVectorPrev, relativeVelocityVector); // km^2 s^-1 - PostSN specific orbital angular momentum vector - double orbitalAngularMomentum = orbitalAngularMomentumVector.mag; // km^2 s^-1 - PostSN specific orbital angular momentum - m_OrbitalAngularMomentumVector = orbitalAngularMomentumVector/orbitalAngularMomentum; // set unit vector here to make printing out the inclination vector easier + Vector3d relativeVelocityVector = relativeVelocityVectorPrev + (natalKickVector - companionRecoilVector); // km/s - PostSN relative velocity vector - Vector3d eccentricityVector = cross(relativeVelocityVector, orbitalAngularMomentumVector) / (G_SN * totalMass) - - separationVectorPrev / separationPrev; // PostSN Laplace-Runge-Lenz vector - m_Eccentricity = eccentricityVector.mag; // PostSN eccentricity - double eccSquared = m_Eccentricity * m_Eccentricity; // useful function of eccentricity + Vector3d orbitalAngularMomentumVector = cross(separationVectorPrev, relativeVelocityVector); // km^2 s^-1 - PostSN specific orbital angular momentum vector + double orbitalAngularMomentum = orbitalAngularMomentumVector.mag; // km^2 s^-1 - PostSN specific orbital angular momentum + m_OrbitalAngularMomentumVector = orbitalAngularMomentumVector / orbitalAngularMomentum; // set unit vector here to make printing out the inclination vector easier - double semiMajorAxis_km = (orbitalAngularMomentum*orbitalAngularMomentum) / (G_SN * totalMass * (1 - eccSquared)); // km - PostSN semi-major axis - m_SemiMajorAxis = semiMajorAxis_km * KM_TO_AU; // AU - PostSN semi-major axis + Vector3d eccentricityVector = cross(relativeVelocityVector, orbitalAngularMomentumVector) / + (G_SN * totalMass) - separationVectorPrev / separationPrev; // PostSN Laplace-Runge-Lenz vector + m_Eccentricity = eccentricityVector.mag; // PostSN eccentricity + double eccSquared = m_Eccentricity * m_Eccentricity; // useful function of eccentricity + double semiMajorAxis_km = (orbitalAngularMomentum * orbitalAngularMomentum) / (G_SN * totalMass * (1.0 - eccSquared)); // km - PostSN semi-major axis + m_SemiMajorAxis = semiMajorAxis_km * KM_TO_AU; // AU - PostSN semi-major axis - ///////////////////////////////////////////////////////////////////////////////////////// // Note: similar to above, // orbitalAngularMomentumVector defines the Z'-axis, // eccentricityVector defines the X'-axis, and // (orbitalAngularMomentumVector x eccentricityVector) defines the Y'-axis - ///////////////////////////////////////////////////////////////////////////////////////// - UpdateSystemicVelocity(centerOfMassVelocity.RotateVector(m_ThetaE, m_PhiE, m_PsiE)); // Update the system velocity with the new center of mass velocity + UpdateSystemicVelocity(centerOfMassVelocity.RotateVector(m_ThetaE, m_PhiE, m_PsiE)); // update the system velocity with the new center of mass velocity - - ///////////////////////////////////////////////////////////////////////////////////////// // Split off and evaluate depending on whether the binary is now bound or unbound - if (utils::Compare(m_Eccentricity, 1.0) >= 0) { - - //////////////////////////////////////// - // - // Binary has become unbound - // - //////////////////////////////////////// - + if (utils::Compare(m_Eccentricity, 1.0) >= 0) { // unbound? + // yes, unbound m_Unbound = true; // Calculate the asymptotic Center of Mass velocity - double relativeVelocityAtInfinity = (G_SN*totalMass/orbitalAngularMomentum) * std::sqrt(eccSquared - 1); + double relativeVelocityAtInfinity = (G_SN*totalMass/orbitalAngularMomentum) * std::sqrt(eccSquared - 1.0); Vector3d relativeVelocityVectorAtInfinity = relativeVelocityAtInfinity - * (-1 * (eccentricityVector.hat / m_Eccentricity) - + std::sqrt(1 - 1.0 / eccSquared) * cross(orbitalAngularMomentumVector.hat, eccentricityVector.hat)); + * (-1.0 * (eccentricityVector.hat / m_Eccentricity) + + std::sqrt(1.0 - 1.0 / eccSquared) * cross(orbitalAngularMomentumVector.hat, eccentricityVector.hat)); // Calculate the asymptotic velocities of Star1 (SN) and Star2 (CP) Vector3d component1VelocityVectorAtInfinity = (m2 / totalMass) * relativeVelocityVectorAtInfinity + centerOfMassVelocity; @@ -1332,101 +1297,84 @@ bool BaseBinaryStar::ResolveSupernova() { m_Companion->UpdateComponentVelocity(component2VelocityVectorAtInfinity.RotateVector(m_ThetaE, m_PhiE, m_PsiE)); // Set Euler Angles - m_ThetaE = angleBetween(orbitalAngularMomentumVectorPrev, orbitalAngularMomentumVector); // Angle between the angular momentum unit vectors, always well defined + m_ThetaE = angleBetween(orbitalAngularMomentumVectorPrev, orbitalAngularMomentumVector); // angle between the angular momentum unit vectors, always well defined m_PhiE = _2_PI * RAND->Random(); m_PsiE = _2_PI * RAND->Random(); } - else { - - //////////////////////////////////////// - // - // Binary is still bound - // - //////////////////////////////////////// + else { // no - binary still bound // Set the component velocites to the system velocity. System velocity was already correctly set above. m_Supernova->UpdateComponentVelocity(centerOfMassVelocity.RotateVector(m_ThetaE, m_PhiE, m_PsiE)); m_Companion->UpdateComponentVelocity(centerOfMassVelocity.RotateVector(m_ThetaE, m_PhiE, m_PsiE)); - //////////////////////////////////////////////////////////////////////////////////// // Calculate Euler angles - see RotateVector() in vector.cpp for details - - m_ThetaE = angleBetween(orbitalAngularMomentumVector, orbitalAngularMomentumVectorPrev); // Angle between the angular momentum unit vectors, always well defined + m_ThetaE = angleBetween(orbitalAngularMomentumVector, orbitalAngularMomentumVectorPrev); // angle between the angular momentum unit vectors, always well defined // If the new orbital A.M. is parallel or anti-parallel to the previous orbital A.M., // then the cross product is not well-defined, and we need to account for degeneracy between eccentricity vectors. // Also, if either eccentricity is 0.0, then the eccentricity vector is not well defined. - if ((utils::Compare(m_ThetaE, 0.0) == 0) && // Is orbitalAngularMomentumVectorPrev parallel to orbitalAngularMomentumVector ... - ((utils::Compare(eccentricityPrev, 0.0) > 0) && // ... - (utils::Compare(m_Eccentricity, 0.0) > 0))) { // ...and both eccentricityVectorPrev and eccentricityVector are well defined? + if ((utils::Compare(m_ThetaE, 0.0) == 0) && // is orbitalAngularMomentumVectorPrev parallel to orbitalAngularMomentumVector ... + ((utils::Compare(eccentricityPrev, 0.0) > 0) && (utils::Compare(m_Eccentricity, 0.0) > 0))) { // ...and both eccentricityVectorPrev and eccentricityVector are well defined? - double psiPlusPhi = angleBetween(eccentricityVector, eccentricityVectorPrev); // yes - then psi + phi is constant - m_PhiE = _2_PI * RAND->Random(); - m_PsiE = psiPlusPhi - m_PhiE; + double psiPlusPhi = angleBetween(eccentricityVector, eccentricityVectorPrev); // yes - then psi + phi is constant + m_PhiE = _2_PI * RAND->Random(); + m_PsiE = psiPlusPhi - m_PhiE; } - else if ((utils::Compare(m_ThetaE, M_PI) == 0) && // Is orbitalAngularMomentumVectorPrev anti-parallel to orbitalAngularMomentumVector ... - ((utils::Compare(eccentricityPrev, 0.0) > 0) && // ... - (utils::Compare(m_Eccentricity, 0.0) > 0))) { // ...and both eccentricityVectorPrev and eccentricityVector are well defined? - - // yes - then psi - phi is constant - double psiMinusPhi = angleBetween(eccentricityVector, eccentricityVectorPrev); - m_PhiE = _2_PI * RAND->Random(); - m_PsiE = psiMinusPhi + m_PhiE; + else if ((utils::Compare(m_ThetaE, M_PI) == 0) && // is orbitalAngularMomentumVectorPrev anti-parallel to orbitalAngularMomentumVector ... + ((utils::Compare(eccentricityPrev, 0.0) > 0) && (utils::Compare(m_Eccentricity, 0.0) > 0))) { // ...and both eccentricityVectorPrev and eccentricityVector are well defined? + + double psiMinusPhi = angleBetween(eccentricityVector, eccentricityVectorPrev); // yes - then psi - phi is constant + m_PhiE = _2_PI * RAND->Random(); + m_PsiE = psiMinusPhi + m_PhiE; } - else { // Neither - the cross product of the orbit normals is well-defined + else { // neither - the cross product of the orbit normals is well-defined - Vector3d orbitalPivotAxis = cross(orbitalAngularMomentumVectorPrev, orbitalAngularMomentumVector); // Cross product of the orbit normals + Vector3d orbitalPivotAxis = cross(orbitalAngularMomentumVectorPrev, orbitalAngularMomentumVector); // cross product of the orbit normals - if ( utils::Compare(eccentricityPrev, 0.0) == 0 ) { // Is eccentricityVectorPrev well-defined? - m_PhiE = _2_PI * RAND->Random(); // no - set phi random + if (utils::Compare(eccentricityPrev, 0.0) == 0 ) { // is eccentricityVectorPrev well-defined? + m_PhiE = _2_PI * RAND->Random(); // no - set phi random } - else { // yes - phi is +/- angle between eccentricityVectorPrev and orbitalPivotAxis + else { // yes - phi is +/- angle between eccentricityVectorPrev and orbitalPivotAxis - m_PhiE = utils::Compare( dot(eccentricityVectorPrev, orbitalAngularMomentumVector), 0.0) >= 0 ? // Are eccentricityVectorPrev and orbitalAngularMomentumVector in the same hemisphere? - angleBetween(eccentricityVectorPrev, orbitalPivotAxis): // yes - phi in [0,pi) - -angleBetween(eccentricityVectorPrev, orbitalPivotAxis); // no - phi in [-pi,0) + m_PhiE = utils::Compare( dot(eccentricityVectorPrev, orbitalAngularMomentumVector), 0.0) >= 0 // are eccentricityVectorPrev and orbitalAngularMomentumVector in the same hemisphere? + ? angleBetween(eccentricityVectorPrev, orbitalPivotAxis) // yes - phi in [0,pi) + : -angleBetween(eccentricityVectorPrev, orbitalPivotAxis); // no - phi in [-pi,0) } - if ( utils::Compare(m_Eccentricity, 0.0) == 0 ) { // Is eccentricityVector well-defined? - m_PsiE = _2_PI * RAND->Random(); // no - set psi random + if ( utils::Compare(m_Eccentricity, 0.0) == 0 ) { // is eccentricityVector well-defined? + m_PsiE = _2_PI * RAND->Random(); // no - set psi random } - else { // yes - psi is +/- angle between eccentricityVector and orbitalPivotAxis - - m_PsiE = utils::Compare( dot(eccentricityVector, orbitalAngularMomentumVectorPrev), 0.0) >= 0 ? // Are eccentricityVector and orbitalAngularMomentumVectorPrev in the same hemisphere? - angleBetween(eccentricityVector, orbitalPivotAxis): // yes - psi in [0,pi) - -angleBetween(eccentricityVector, orbitalPivotAxis); // no - psi in [-pi,0) + else { // yes - psi is +/- angle between eccentricityVector and orbitalPivotAxis + m_PsiE = utils::Compare( dot(eccentricityVector, orbitalAngularMomentumVectorPrev), 0.0) >= 0 // are eccentricityVector and orbitalAngularMomentumVectorPrev in the same hemisphere? + ? angleBetween(eccentricityVector, orbitalPivotAxis) // yes - psi in [0,pi) + : -angleBetween(eccentricityVector, orbitalPivotAxis); // no - psi in [-pi,0) } } // Note: There is some evidence for evolution of periapsis in mass transferring binaries (see e.g Dosopoulou & Kalogera 2016, 2018). - // This should be investigated in more depth, but until then, we assume that the periapsis *may* evolve, - // and accordingly randomize the angle of periapsis around the new orbital angular momentum, (i.e, Psi) - // - RTW 15/05/20 + // This should be investigated in more depth, but until then, we assume that the periapsis *may* evolve, and accordingly randomize + // the angle of periapsis around the new orbital angular momentum, (i.e, Psi) - RTW 15/05/20 m_PsiE = _2_PI * RAND->Random(); } - // Undefine the pre-processor commands - #undef cross - #undef dot - #undef angleBetween - #undef mag #undef hat + #undef mag + #undef angleBetween + #undef dot + #undef cross } - ////////////////////////// - // Do for all systems - // Set remaining post-SN values - double totalMass = m_Supernova->Mass() + m_Companion->Mass(); // Total Mass - double reducedMass = m_Supernova->Mass() * m_Companion->Mass() / totalMass; // Reduced Mass - m_Supernova->SetOrbitalEnergyPostSN(CalculateOrbitalEnergy(reducedMass, totalMass, m_SemiMajorAxis)); // Orbital energy + double totalMass = m_Supernova->Mass() + m_Companion->Mass(); // total Mass + double reducedMass = m_Supernova->Mass() * m_Companion->Mass() / totalMass; // reduced Mass + m_Supernova->SetOrbitalEnergyPostSN(CalculateOrbitalEnergy(reducedMass, totalMass, m_SemiMajorAxis)); // orbital energy - m_IPrime = m_ThetaE; // Inclination angle between preSN and postSN orbital planes + m_IPrime = m_ThetaE; // inclination angle between preSN and postSN orbital planes m_CosIPrime = cos(m_IPrime); - (void)PrintSupernovaDetails(); // Log record to supernovae logfile + (void)PrintSupernovaDetails(); // log record to supernovae logfile m_Supernova->ClearCurrentSNEvent(); return true; @@ -1501,16 +1449,16 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { double alphaCE = OPTIONS->CommonEnvelopeAlpha(); // CE efficiency parameter - double eccentricity = Eccentricity(); // current eccentricity (before CEE) - double semiMajorAxisRsol= SemiMajorAxisRsol(); // current semi-major axis in default units, Rsol (before CEE) - double periastronRsol = PeriastronRsol(); // periastron, Rsol (before CEE) - double rRLd1Rsol = periastronRsol * CalculateRocheLobeRadius_Static(m_Star1->Mass(), m_Star2->Mass()); // Roche-lobe radius at periastron in Rsol at the moment where CEE begins, seen by star1 - double rRLd2Rsol = periastronRsol * CalculateRocheLobeRadius_Static(m_Star2->Mass(), m_Star1->Mass()); // Roche-lobe radius at periastron in Rsol at the moment where CEE begins, seen by star2 + double eccentricity = Eccentricity(); // current eccentricity (before CEE) + double semiMajorAxisRsol = SemiMajorAxisRsol(); // current semi-major axis in default units, Rsol (before CEE) + double periastronRsol = PeriastronRsol(); // periastron, Rsol (before CEE) + double rRLd1Rsol = periastronRsol * CalculateRocheLobeRadius_Static(m_Star1->Mass(), m_Star2->Mass()); // Roche-lobe radius at periastron in Rsol at the moment where CEE begins, seen by star1 + double rRLd2Rsol = periastronRsol * CalculateRocheLobeRadius_Static(m_Star2->Mass(), m_Star1->Mass()); // Roche-lobe radius at periastron in Rsol at the moment where CEE begins, seen by star2 bool isDonorMS = false; // check for main sequence donor if (OPTIONS->AllowMainSequenceStarToSurviveCommonEnvelope()) { // allow main sequence stars to survive CEE? if (m_Star1->IsOneOf(ALL_MAIN_SEQUENCE)) { // yes - star1 MS_LTE_07, MS_GT_07 or NAKED_HELIUM_STAR_MS? - isDonorMS = isDonorMS || m_Star1->IsRLOF(); // yes - donor MS? + isDonorMS = isDonorMS || m_Star1->IsRLOF(); // yes - donor MS? m_Mass1Final = m_Star1->Mass(); // set mass m_MassEnv1 = 0.0; // no envelope } @@ -1520,7 +1468,7 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { } if (m_Star2->IsOneOf(ALL_MAIN_SEQUENCE)) { // star2 MS_LTE_07, MS_GT_07 or NAKED_HELIUM_STAR_MS? - isDonorMS = isDonorMS || m_Star2->IsRLOF(); // yes - donor MS? + isDonorMS = isDonorMS || m_Star2->IsRLOF(); // yes - donor MS? m_Mass2Final = m_Star2->Mass(); // yes - set mass m_MassEnv2 = 0.0; // no envelope } @@ -1563,16 +1511,16 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { // due to the CEE as described in Belczynsky et al. 2002, eq. (12) if( OPTIONS->CommonEnvelopeFormalism() == CE_FORMALISM::ENERGY ) { - double k1 = m_Star1->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (lambda1 * alphaCE)) * m_Star1->Mass() * m_MassEnv1 / m_Star1->Radius(); - double k2 = m_Star2->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (lambda2 * alphaCE)) * m_Star2->Mass() * m_MassEnv2 / m_Star2->Radius(); - double k3 = m_Star1->Mass() * m_Star2->Mass() / periastronRsol; //assumes immediate circularisation at periastron at start of CE - double k4 = (m_Mass1Final * m_Mass2Final); - double aFinalRsol = k4 / (k1 + k2 + k3); - m_SemiMajorAxis = aFinalRsol*RSOL_TO_AU; + double k1 = m_Star1->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (lambda1 * alphaCE)) * m_Star1->Mass() * m_MassEnv1 / m_Star1->Radius(); + double k2 = m_Star2->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (lambda2 * alphaCE)) * m_Star2->Mass() * m_MassEnv2 / m_Star2->Radius(); + double k3 = m_Star1->Mass() * m_Star2->Mass() / periastronRsol; //assumes immediate circularisation at periastron at start of CE + double k4 = (m_Mass1Final * m_Mass2Final); + double aFinalRsol = k4 / (k1 + k2 + k3); + m_SemiMajorAxis = aFinalRsol * RSOL_TO_AU; } - + // Two-stage common envelope, Hirai & Mandel (2022) - else if( OPTIONS->CommonEnvelopeFormalism() == CE_FORMALISM::TWO_STAGE ) { + else if ( OPTIONS->CommonEnvelopeFormalism() == CE_FORMALISM::TWO_STAGE ) { double convectiveEnvelopeMass1 = m_Star1->CalculateConvectiveEnvelopeMass(); double radiativeIntershellMass1 = m_MassEnv1 - convectiveEnvelopeMass1; double endOfFirstStageMass1 = m_Mass1Final + radiativeIntershellMass1; @@ -1581,35 +1529,34 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { double endOfFirstStageMass2 = m_Mass2Final + radiativeIntershellMass2; // Stage 1: convective envelope removal on a dynamical timescale; assumes lambda = 1.5, motivated by bottom panel of Figure 3 of Hirai & Mandel 2022, including internal energy - double k1 = m_Star1->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (1.5 * alphaCE)) * m_Star1->Mass() * convectiveEnvelopeMass1 / m_Star1->Radius(); - double k2 = m_Star2->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (1.5 * alphaCE)) * m_Star2->Mass() * convectiveEnvelopeMass2 / m_Star2->Radius(); - double k3 = m_Star1->Mass() * m_Star2->Mass() / periastronRsol; //assumes immediate circularisation at periastron at start of CE - double k4 = (endOfFirstStageMass1 * endOfFirstStageMass2); - double aFinalRsol = k4 / (k1 + k2 + k3); - m_SemiMajorAxis = aFinalRsol*RSOL_TO_AU; - + double k1 = m_Star1->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (1.5 * alphaCE)) * m_Star1->Mass() * convectiveEnvelopeMass1 / m_Star1->Radius(); + double k2 = m_Star2->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (1.5 * alphaCE)) * m_Star2->Mass() * convectiveEnvelopeMass2 / m_Star2->Radius(); + double k3 = m_Star1->Mass() * m_Star2->Mass() / periastronRsol; //assumes immediate circularisation at periastron at start of CE + double k4 = (endOfFirstStageMass1 * endOfFirstStageMass2); + double aFinalRsol = k4 / (k1 + k2 + k3); + m_SemiMajorAxis = aFinalRsol*RSOL_TO_AU; + // Stage 2: radiative envelope removal on a thermal timescale; assumed to be fully non-conservative if( utils::Compare(radiativeIntershellMass1, 0.0) > 0 ) { m_SemiMajorAxis = CalculateMassTransferOrbit(endOfFirstStageMass1, -radiativeIntershellMass1, *m_Star2, 0.0); } + if( utils::Compare(radiativeIntershellMass2, 0.0) > 0 ) { m_SemiMajorAxis = CalculateMassTransferOrbit(endOfFirstStageMass2, -radiativeIntershellMass2, *m_Star1, 0.0); } - } - - else { // Invalid CE formalism + } + else { // invalid CE formalism SHOW_WARN_STATIC(ERROR::UNKNOWN_CE_FORMALISM, // show warning "Orbital properties unchanged by CE", OBJECT_TYPE::BASE_BINARY_STAR, STELLAR_TYPE::BINARY_STAR); } - - double rRLdfin1 = m_SemiMajorAxis * CalculateRocheLobeRadius_Static(m_Mass1Final, m_Mass2Final); // Roche-lobe radius in AU after CEE, seen by star1 - double rRLdfin2 = m_SemiMajorAxis * CalculateRocheLobeRadius_Static(m_Mass2Final, m_Mass1Final); // Roche-lobe radius in AU after CEE, seen by star2 - double rRLdfin1Rsol = rRLdfin1 * AU_TO_RSOL; // Roche-lobe radius in Rsol after CEE, seen by star1 - double rRLdfin2Rsol = rRLdfin2 * AU_TO_RSOL; // Roche-lobe radius in Rsol after CEE, seen by star2 - m_Eccentricity = 0.0; // We assume that a common envelope event (CEE) circularises the binary + double rRLdfin1 = m_SemiMajorAxis * CalculateRocheLobeRadius_Static(m_Mass1Final, m_Mass2Final); // Roche-lobe radius in AU after CEE, seen by star1 + double rRLdfin2 = m_SemiMajorAxis * CalculateRocheLobeRadius_Static(m_Mass2Final, m_Mass1Final); // Roche-lobe radius in AU after CEE, seen by star2 + double rRLdfin1Rsol = rRLdfin1 * AU_TO_RSOL; // Roche-lobe radius in Rsol after CEE, seen by star1 + double rRLdfin2Rsol = rRLdfin2 * AU_TO_RSOL; // Roche-lobe radius in Rsol after CEE, seen by star2 + m_Eccentricity = 0.0; // we assume that a common envelope event (CEE) circularises the binary m_Star1->ResolveCommonEnvelopeAccretion(m_Mass1Final); // update star1's mass after CE accretion m_Star2->ResolveCommonEnvelopeAccretion(m_Mass2Final); // update star2's mass after CE accretion @@ -1664,9 +1611,9 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { m_Star2->SetPostCEEValues(); // squirrel away post CEE stellar values for star 2 SetPostCEEValues(m_SemiMajorAxis * AU_TO_RSOL, m_Eccentricity, rRLdfin1Rsol, rRLdfin2Rsol); // squirrel away post CEE binary values (checks for post-CE RLOF, so should be done at end) - if (m_RLOFDetails.immediateRLOFPostCEE == true && !OPTIONS->AllowImmediateRLOFpostCEToSurviveCommonEnvelope()) { // Is there immediate post-CE RLOF which is not allowed? + if (m_RLOFDetails.immediateRLOFPostCEE == true && !OPTIONS->AllowImmediateRLOFpostCEToSurviveCommonEnvelope()) { // is there immediate post-CE RLOF which is not allowed? m_MassTransferTrackerHistory = MT_TRACKING::MERGER; - m_Flags.stellarMerger = true; + m_Flags.stellarMerger = true; } (void)PrintCommonEnvelope(); // print (log) common envelope details @@ -1686,8 +1633,8 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { * @return Radius of Roche Lobe in units of the semi-major axis a */ double BaseBinaryStar::CalculateRocheLobeRadius_Static(const double p_MassPrimary, const double p_MassSecondary) { - double q = p_MassPrimary / p_MassSecondary; - double qCubeRoot = PPOW(q, 1.0 / 3.0); // cube roots are expensive, only compute once + double q = p_MassPrimary / p_MassSecondary; + double qCubeRoot = std::cbrt(q); // cube roots are expensive, only compute once return 0.49 / (0.6 + log(1.0 + qCubeRoot) / qCubeRoot / qCubeRoot); } @@ -1719,18 +1666,18 @@ double BaseBinaryStar::CalculateGammaAngularMomentumLoss(const double p_DonorMas case MT_ANGULAR_MOMENTUM_LOSS_PRESCRIPTION::JEANS : gamma = p_AccretorMass / p_DonorMass; break; // vicinity of the donor case MT_ANGULAR_MOMENTUM_LOSS_PRESCRIPTION::ISOTROPIC_RE_EMISSION: gamma = p_DonorMass / p_AccretorMass; break; // vicinity of the accretor case MT_ANGULAR_MOMENTUM_LOSS_PRESCRIPTION::CIRCUMBINARY_RING : gamma = (M_SQRT2 * (p_DonorMass + p_AccretorMass) * (p_DonorMass + p_AccretorMass)) / (p_DonorMass * p_AccretorMass); break; // Based on the assumption that a_ring ~= 2*a*, Vinciguerra+, 2020 - case MT_ANGULAR_MOMENTUM_LOSS_PRESCRIPTION::MACLEOD_LINEAR : { // Linear interpolation on separation between accretor and L2 point + case MT_ANGULAR_MOMENTUM_LOSS_PRESCRIPTION::MACLEOD_LINEAR : { // linear interpolation on separation between accretor and L2 point double q = p_AccretorMass / p_DonorMass; // interpolate in separation between a_acc and a_L2, both normalized to units of separation a - double aL2 = std::sqrt(M_SQRT2); // roughly, coincides with CIRCUMBINARY_RING def above - double aAcc = 1/(1+q); + double aL2 = std::sqrt(M_SQRT2); // roughly, coincides with CIRCUMBINARY_RING def above + double aAcc = 1.0 / (1.0 + q); double aGamma = aAcc + (aL2 - aAcc)*OPTIONS->MassTransferJlossMacLeodLinearFraction(); - gamma = aGamma*aGamma*(1+q)*(1+q)/q; + gamma = aGamma * aGamma * (1.0 + q) * (1.0 + q) / q; break; } case MT_ANGULAR_MOMENTUM_LOSS_PRESCRIPTION::ARBITRARY : gamma = OPTIONS->MassTransferJloss(); break; default: // unknown mass transfer angular momentum loss prescription - shouldn't happen - gamma = 1.0; // default value + gamma = 1.0; // default value m_Error = ERROR::UNKNOWN_MT_ANGULAR_MOMENTUM_LOSS_PRESCRIPTION; // set error value SHOW_WARN(m_Error); // warn that an error occurred } @@ -1761,30 +1708,28 @@ double BaseBinaryStar::CalculateMassTransferOrbit(const double p BinaryConstituentStar& p_Accretor, const double p_FractionAccreted) { - double semiMajorAxis = m_SemiMajorAxis; // new semi-major axis value - default is no change - double massA = p_Accretor.Mass(); // accretor mass - double massD = p_DonorMass; // donor mass - double massAtimesMassD = massA * massD; // accretor mass * donor mass - double massAplusMassD = massA + massD; // accretor mass + donor mass - double jOrb = (massAtimesMassD / massAplusMassD) * std::sqrt(semiMajorAxis * G1 * massAplusMassD); // orbital angular momentum - double jLoss; // specific angular momentum carried away by non-conservative mass transfer + double semiMajorAxis = m_SemiMajorAxis; // new semi-major axis value - default is no change + double massA = p_Accretor.Mass(); // accretor mass + double massD = p_DonorMass; // donor mass + double massAtimesMassD = massA * massD; // accretor mass * donor mass + double massAplusMassD = massA + massD; // accretor mass + donor mass + double jOrb = (massAtimesMassD / massAplusMassD) * std::sqrt(semiMajorAxis * G1 * massAplusMassD); // orbital angular momentum + double jLoss; // specific angular momentum carried away by non-conservative mass transfer - if (utils::Compare(p_DeltaMassDonor, 0.0) >= 0) { // no mass loss from donor, nothing to do here - return semiMajorAxis; - } - int numberIterations = fmax( floor (fabs(p_DeltaMassDonor/(MAXIMUM_MASS_TRANSFER_FRACTION_PER_STEP*massD))), 1); // number of iterations - - double dM = p_DeltaMassDonor / numberIterations; // mass change per time step + if (utils::Compare(p_DeltaMassDonor, 0.0) < 0) { // mass loss from donor? + // yes + int numberIterations = fmax( floor (fabs(p_DeltaMassDonor/(MAXIMUM_MASS_TRANSFER_FRACTION_PER_STEP * massD))), 1.0); // number of iterations + double dM = p_DeltaMassDonor / numberIterations; // mass change per time step - for(int i = 0; i < numberIterations ; i++) { - - jLoss = CalculateGammaAngularMomentumLoss(massD, massA); - jOrb = jOrb + ((jLoss * jOrb * (1.0 - p_FractionAccreted) / massAplusMassD) * dM); - semiMajorAxis = semiMajorAxis + (((-2.0 * dM / massD) * (1.0 - (p_FractionAccreted * (massD / massA)) - ((1.0 - p_FractionAccreted) * (jLoss + 0.5) * (massD / massAplusMassD)))) * semiMajorAxis); + for(int i = 0; i < numberIterations ; i++) { + jLoss = CalculateGammaAngularMomentumLoss(massD, massA); + jOrb = jOrb + ((jLoss * jOrb * (1.0 - p_FractionAccreted) / massAplusMassD) * dM); + semiMajorAxis = semiMajorAxis + (((-2.0 * dM / massD) * (1.0 - (p_FractionAccreted * (massD / massA)) - ((1.0 - p_FractionAccreted) * (jLoss + 0.5) * (massD / massAplusMassD)))) * semiMajorAxis); - massD = massD + dM; - massA = massA - (dM * p_FractionAccreted); - massAplusMassD = massA + massD; + massD = massD + dM; + massA = massA - (dM * p_FractionAccreted); + massAplusMassD = massA + massD; + } } return semiMajorAxis; @@ -1811,13 +1756,11 @@ double BaseBinaryStar::CalculateZetaRocheLobe(const double p_jLoss) const { double accretorMass = m_Accretor->Mass(); // accretor mass double beta = m_FractionAccreted; // fraction of mass accreted by accretor double gamma = p_jLoss; - - double q = donorMass / accretorMass; - - double q_1_3 = PPOW(q, 1.0 / 3.0); + double q = donorMass / accretorMass; + double cbrt_q = std::cbrt(q); double k1 = -2.0 * (1.0 - (beta * q) - (1.0 - beta) * (gamma + 0.5) * (q / (1.0 + q))); - double k2 = (2.0 / 3.0) - q_1_3 * (1.2 * q_1_3 + 1.0 / (1.0 + q_1_3)) / (3.0 * (0.6 * q_1_3 * q_1_3 + log(1.0 + q_1_3))); + double k2 = (2.0 / 3.0) - cbrt_q * (1.2 * cbrt_q + 1.0 / (1.0 + cbrt_q)) / (3.0 * (0.6 * cbrt_q * cbrt_q + log(1.0 + cbrt_q))); double k3 = 1.0 + (beta * q); return k1 + (k2 * k3); @@ -1903,8 +1846,8 @@ void BaseBinaryStar::CalculateMassTransfer(const double p_Dt) { // Calculate accretion fraction if stable // This passes the accretor's Roche lobe radius to m_Accretor->CalculateThermalMassAcceptanceRate() // just in case MT_THERMALLY_LIMITED_VARIATION::RADIUS_TO_ROCHELOBE is used; otherwise, the radius input is ignored - double accretorRLradius = CalculateRocheLobeRadius_Static(m_Accretor->Mass(), m_Donor->Mass()) * AU_TO_RSOL * m_SemiMajorAxis * (1.0 - m_Eccentricity); - bool donorIsHeRich = m_Donor->IsOneOf(He_RICH_TYPES); + double accretorRLradius = CalculateRocheLobeRadius_Static(m_Accretor->Mass(), m_Donor->Mass()) * AU_TO_RSOL * m_SemiMajorAxis * (1.0 - m_Eccentricity); + bool donorIsHeRich = m_Donor->IsOneOf(He_RICH_TYPES); std::tie(std::ignore, m_FractionAccreted) = m_Accretor->CalculateMassAcceptanceRate(m_Donor->CalculateThermalMassLossRate(), m_Accretor->CalculateThermalMassAcceptanceRate(accretorRLradius), donorIsHeRich); @@ -1934,7 +1877,7 @@ void BaseBinaryStar::CalculateMassTransfer(const double p_Dt) { isUnstable = true; if (!m_Donor->IsOneOf(GIANTS)) m_Flags.stellarMerger = true; } - else if (OPTIONS->QCritPrescription() != QCRIT_PRESCRIPTION::NONE) { // Determine stability based on critical mass ratios + else if (OPTIONS->QCritPrescription() != QCRIT_PRESCRIPTION::NONE) { // Determine stability based on critical mass ratios // NOTE: Critical mass ratio is defined as mAccretor/mDonor double qCrit = m_Donor->CalculateCriticalMassRatio(m_Accretor->IsDegenerate()); @@ -1961,11 +1904,11 @@ void BaseBinaryStar::CalculateMassTransfer(const double p_Dt) { : MT_TRACKING::STABLE_2_TO_1_SURV; double massDiffDonor; - double envMassDonor = m_Donor->Mass() - m_Donor->CoreMass(); + double envMassDonor = m_Donor->Mass() - m_Donor->CoreMass(); bool isEnvelopeRemoved = false; if (utils::Compare(m_Donor->CoreMass(), 0) > 0 && utils::Compare(envMassDonor, 0) > 0) { // donor has a core and an envelope - massDiffDonor = -envMassDonor; // set donor mass loss to (negative of) the envelope mass + massDiffDonor = -envMassDonor; // set donor mass loss to (negative of) the envelope mass isEnvelopeRemoved = true; } else{ // donor has no envelope @@ -1974,10 +1917,10 @@ void BaseBinaryStar::CalculateMassTransfer(const double p_Dt) { } double massGainAccretor = -massDiffDonor * m_FractionAccreted; // set accretor mass gain to mass loss * conservativeness - m_Donor->SetMassTransferDiffAndResolveWDShellChange(massDiffDonor); // set new mass of donor - m_Accretor->SetMassTransferDiffAndResolveWDShellChange(massGainAccretor); // set new mass of accretor + m_Donor->SetMassTransferDiffAndResolveWDShellChange(massDiffDonor); // set new mass of donor + m_Accretor->SetMassTransferDiffAndResolveWDShellChange(massGainAccretor); // set new mass of accretor - aFinal = CalculateMassTransferOrbit(m_Donor->Mass(), massDiffDonor, *m_Accretor, m_FractionAccreted); // calculate new orbit + aFinal = CalculateMassTransferOrbit(m_Donor->Mass(), massDiffDonor, *m_Accretor, m_FractionAccreted); // calculate new orbit m_aMassTransferDiff = aFinal - aInitial; // set change in orbit (semi-major axis) STELLAR_TYPE stellarTypeDonor = m_Donor->StellarType(); // donor stellar type before resolving envelope loss @@ -2070,7 +2013,7 @@ void BaseBinaryStar::InitialiseMassTransfer() { // If you don't do this, you end up modifying pre-MT pre-circularisation orbit // JR: todo: check that this is proper functionality, or just a kludge - if kludge, resolve it m_SemiMajorAxisPrev = m_SemiMajorAxis; - m_EccentricityPrev = m_Eccentricity; + m_EccentricityPrev = m_Eccentricity; } } } @@ -2179,6 +2122,7 @@ double BaseBinaryStar::CalculateAngularMomentum(const double p_SemiMajorAxis, const double p_Star2_SpinAngularVelocity, const double p_Star1_GyrationRadius, const double p_Star2_GyrationRadius) const { + double m1 = p_Star1Mass; double m2 = p_Star2Mass; @@ -2215,6 +2159,7 @@ void BaseBinaryStar::CalculateEnergyAndAngularMomentum() { // Calculate orbital energy and angular momentum m_OrbitalEnergyPrev = m_OrbitalEnergy; m_OrbitalAngularMomentumPrev = m_OrbitalAngularMomentum; + m_TotalAngularMomentumPrev = m_TotalAngularMomentum; double totalMass = m_Star1->Mass() + m_Star2->Mass(); double reducedMass = (m_Star1->Mass() * m_Star2->Mass()) / totalMass; @@ -2245,14 +2190,14 @@ void BaseBinaryStar::ResolveMassChanges() { STELLAR_TYPE stellarType2 = m_Star2->StellarTypePrev(); // star 2 stellar type before updating attributes // update mass of star1 according to mass loss and mass transfer, then update age accordingly - (void)m_Star1->UpdateAttributes(m_Star1->MassPrev() - m_Star1->Mass() + m_Star1->MassLossDiff() + m_Star1->MassTransferDiff(), 0.0); // update mass for star1 + (void)m_Star1->UpdateAttributes(m_Star1->MassPrev() - m_Star1->Mass() + m_Star1->MassLossDiff() + m_Star1->MassTransferDiff(), 0.0); // update mass for star1 m_Star1->UpdateInitialMass(); // update effective initial mass of star1 (MS, HG & HeMS) m_Star1->UpdateAgeAfterMassLoss(); // update age of star1 m_Star1->ApplyMassTransferRejuvenationFactor(); // apply age rejuvenation factor for star1 m_Star1->UpdateAttributes(0.0, 0.0, true); // rinse and repeat for star2 - (void)m_Star2->UpdateAttributes(m_Star2->MassPrev() - m_Star2->Mass() + m_Star2->MassLossDiff() + m_Star2->MassTransferDiff(), 0.0); // update mass for star2 + (void)m_Star2->UpdateAttributes(m_Star2->MassPrev() - m_Star2->Mass() + m_Star2->MassLossDiff() + m_Star2->MassTransferDiff(), 0.0); // update mass for star2 m_Star2->UpdateInitialMass(); // update effective initial mass of star 2 (MS, HG & HeMS) m_Star2->UpdateAgeAfterMassLoss(); // update age of star2 m_Star2->ApplyMassTransferRejuvenationFactor(); // apply age rejuvenation factor for star2 @@ -2263,7 +2208,7 @@ void BaseBinaryStar::ResolveMassChanges() { //Envelope ejection for convective envelope stars exceeding threshold luminosity to mass ratio: assume the entire envelope was lost on timescales long relative to the orbit if(m_Star1->EnvelopeJustExpelledByPulsations() || m_Star2->EnvelopeJustExpelledByPulsations()) { - m_SemiMajorAxis /= (2.0 - ((m_Star1->MassPrev() + m_Star2->MassPrev()) / (m_Star1->Mass() + m_Star2->Mass()))); // update separation in response to pulsational mass loss + m_SemiMajorAxis /= (2.0 - ((m_Star1->MassPrev() + m_Star2->MassPrev()) / (m_Star1->Mass() + m_Star2->Mass()))); // update separation in response to pulsational mass loss m_Star1->ResetEnvelopeExpulsationByPulsations(); m_Star2->ResetEnvelopeExpulsationByPulsations(); } @@ -2302,58 +2247,111 @@ void BaseBinaryStar::ResolveMassChanges() { */ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { - CalculateMassTransfer(p_Dt); // calculate mass transfer if necessary + CalculateMassTransfer(p_Dt); // calculate mass transfer if necessary - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MT); // print (log) detailed output + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MT); // print (log) detailed output - CalculateWindsMassLoss(); // calculate mass loss dues to winds + CalculateWindsMassLoss(); // calculate mass loss dues to winds - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_WINDS); // print (log) detailed output + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_WINDS); // print (log) detailed output - if ((m_CEDetails.CEEnow || StellarMerger()) && // CEE or merger? - !(OPTIONS->CHEMode() != CHE_MODE::NONE && HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS}))) { // yes - avoid CEE if CH+CH + if ((m_CEDetails.CEEnow || StellarMerger()) && // CEE or merger? + !(OPTIONS->CHEMode() != CHE_MODE::NONE && HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS}))) { // yes - avoid CEE if CH+CH - ResolveCommonEnvelopeEvent(); // resolve CEE - immediate event - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_CEE); // print (log) detailed output + ResolveCommonEnvelopeEvent(); // resolve CEE - immediate event + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_CEE); // print (log) detailed output } else if (m_Star1->IsSNevent() || m_Star2->IsSNevent()) { - EvaluateSupernovae(); // evaluate supernovae (both stars) - immediate event - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output + EvaluateSupernovae(); // evaluate supernovae (both stars) - immediate event + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output if (HasOneOf({ STELLAR_TYPE::NEUTRON_STAR })) { - (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters + (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters } } else { - ResolveMassChanges(); // apply mass loss and mass transfer as necessary - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MASS_RESOLUTION); // print (log) detailed output + ResolveMassChanges(); // apply mass loss and mass transfer as necessary + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MASS_RESOLUTION); // print (log) detailed output - if (HasStarsTouching()) { // if stars emerged from mass transfer as touching, it's a merger + if (HasStarsTouching()) { // if stars emerged from mass transfer as touching, it's a merger m_Flags.stellarMerger = true; // Set Roche lobe flags for both stars so that they show correct RLOF status - m_Star1->SetRocheLobeFlags(m_CEDetails.CEEnow, m_SemiMajorAxis, m_Eccentricity); // set Roche lobe flags for star1 - m_Star2->SetRocheLobeFlags(m_CEDetails.CEEnow, m_SemiMajorAxis, m_Eccentricity); // set Roche lobe flags for star2 - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MASS_RESOLUTION_MERGER); // print (log) detailed output + m_Star1->SetRocheLobeFlags(m_CEDetails.CEEnow, m_SemiMajorAxis, m_Eccentricity); // set Roche lobe flags for star1 + m_Star2->SetRocheLobeFlags(m_CEDetails.CEEnow, m_SemiMajorAxis, m_Eccentricity); // set Roche lobe flags for star2 + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MASS_RESOLUTION_MERGER); // print (log) detailed output } } if ((m_Star1->IsSNevent() || m_Star2->IsSNevent())) { - EvaluateSupernovae(); // evaluate supernovae (both stars) if mass changes are responsible for a supernova - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output + EvaluateSupernovae(); // evaluate supernovae (both stars) if mass changes are responsible for a supernova + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output if (HasOneOf({ STELLAR_TYPE::NEUTRON_STAR })) { - (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters + (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters } } - // assign new values to "previous" values, for following timestep - m_EccentricityPrev = m_Eccentricity; - m_SemiMajorAxisPrev = m_SemiMajorAxis; - - CalculateEnergyAndAngularMomentum(); // perform energy and angular momentum calculations + CalculateEnergyAndAngularMomentum(); // perform energy and angular momentum calculations + + if (OPTIONS->EnableTides() && !m_Unbound) { + + /* + std::cout << "\nTime = " << m_Time << "\n"; + std::cout << "Total angular momentum before = " << m_TotalAngularMomentum << "\n"; + std::cout << "Semi-major axis before = " << m_SemiMajorAxis << "\n"; + std::cout << "Eccentricity before = " << m_Eccentricity << "\n"; + std::cout << "Omega (star1) before = " << m_Star1->Omega() << "\n"; + std::cout << "Omega (star2) before = " << m_Star2->Omega() << "\n"; + std::cout << "Omega (binary) before = " << m_Omega << "\n"; + std::cout << "MoI (star1) before = " << m_Star1->CalculateMomentOfInertia() << "\n"; + std::cout << "MoI (star2) before = " << m_Star2->CalculateMomentOfInertia() << "\n"; + std::cout << "Mass (star1) before = " << m_Star1->Mass() << "\n"; + std::cout << "Mass (star2) before = " << m_Star2->Mass() << "\n"; + std::cout << "Radius (star1) before = " << m_Star1->Radius() << "\n"; + std::cout << "Radius (star2) before = " << m_Star2->Radius() << "\n"; + std::cout << "Gyration radius (star1) before = " << m_Star1->CalculateGyrationRadius() << "\n"; + std::cout << "Gyration radius (star2) before = " << m_Star2->CalculateGyrationRadius() << "\n"; + */ + + // find omega assuming synchronisation + // rough guess is (m_Star1->Omega() + m_Star2->Omega()) / 2.0 + m_Omega = OmegaAfterSynchronisation(m_Star1->Mass(), + m_Star2->Mass(), + m_Star1->CalculateMomentOfInertiaAU(), + m_Star2->CalculateMomentOfInertiaAU(), + m_TotalAngularMomentum, + (m_Star1->Omega() + m_Star2->Omega()) / 2.0); + + if (m_Omega > 0.0) { // root found? + // yes + m_Star1->SetOmega(m_Omega); // synchronise star 1 + m_Star2->SetOmega(m_Omega); // synchronise star 2 + + m_SemiMajorAxis = std::cbrt(G1) * std::cbrt((m_Star1->Mass() + m_Star2->Mass())) / PPOW(m_Omega, 2.0 / 3.0); // re-calculate semi-major axis + m_Eccentricity = 0.0; // circularise + m_TotalAngularMomentum = CalculateAngularMomentum(); // re-calculate total angular momentum + + // assign new values to "previous" values, for following timestep + m_EccentricityPrev = m_Eccentricity; + m_SemiMajorAxisPrev = m_SemiMajorAxis; + } - // naive tides calculations - if required - // circularise and synchronise the binary - + /* + std::cout << "Total angular momentum after = " << m_TotalAngularMomentum << "\n"; + std::cout << "Semi-major axis after = " << m_SemiMajorAxis << "\n"; + std::cout << "Eccentricity after = " << m_Eccentricity << "\n"; + std::cout << "Omega (star1) after = " << m_Star1->Omega() << "\n"; + std::cout << "Omega (star2) after = " << m_Star2->Omega() << "\n"; + std::cout << "Omega (binary) after = " << m_Omega << "\n"; + std::cout << "MoI (star1) after = " << m_Star1->CalculateMomentOfInertia() << "\n"; + std::cout << "MoI (star2) after = " << m_Star2->CalculateMomentOfInertia() << "\n"; + std::cout << "Mass (star1) after = " << m_Star1->Mass() << "\n"; + std::cout << "Mass (star2) after = " << m_Star2->Mass() << "\n"; + std::cout << "Radius (star1) after = " << m_Star1->Radius() << "\n"; + std::cout << "Radius (star2) after = " << m_Star2->Radius() << "\n"; + std::cout << "Gyration radius (star1) after = " << m_Star1->CalculateGyrationRadius() << "\n"; + std::cout << "Gyration radius (star2) after = " << m_Star2->CalculateGyrationRadius() << "\n"; + */ + } m_Star1->UpdateMagneticFieldAndSpin(m_CEDetails.CEEnow, m_Dt * MYR_TO_YEAR * SECONDS_IN_YEAR, EPSILON_PULSAR); // update pulsar parameters for star1 m_Star2->UpdateMagneticFieldAndSpin(m_CEDetails.CEEnow, m_Dt * MYR_TO_YEAR * SECONDS_IN_YEAR, EPSILON_PULSAR); // update pulsar parameters for star2 diff --git a/src/BaseBinaryStar.h b/src/BaseBinaryStar.h index 6a06a434b..c0318033b 100644 --- a/src/BaseBinaryStar.h +++ b/src/BaseBinaryStar.h @@ -83,6 +83,7 @@ class BaseBinaryStar { m_MassTransferTrackerHistory = p_Star.m_MassTransferTrackerHistory; + m_Omega = p_Star.m_Omega; m_OrbitalVelocityPreSN = p_Star.m_OrbitalVelocityPreSN; m_RLOFDetails = p_Star.m_RLOFDetails; @@ -109,6 +110,7 @@ class BaseBinaryStar { m_TimePrev = p_Star.m_TimePrev; m_TimeToCoalescence = p_Star.m_TimeToCoalescence; + m_TotalAngularMomentumPrev = p_Star.m_TotalAngularMomentumPrev; m_TotalAngularMomentum = p_Star.m_TotalAngularMomentum; m_TotalEnergy = p_Star.m_TotalEnergy; @@ -216,10 +218,11 @@ class BaseBinaryStar { bool MassesEquilibratedAtBirth() const { return m_Flags.massesEquilibratedAtBirth; } MT_TRACKING MassTransferTrackerHistory() const { return m_MassTransferTrackerHistory; } bool MergesInHubbleTime() const { return m_Flags.mergesInHubbleTime; } + double Omega() const { return m_Omega; } bool OptimisticCommonEnvelope() const { return m_CEDetails.optimisticCE; } double OrbitalAngularVelocity() const { return std::sqrt(G1 * (m_Star1->Mass() + m_Star2->Mass()) / (m_SemiMajorAxis * m_SemiMajorAxis * m_SemiMajorAxis)); } // rads/year double OrbitalVelocityPreSN() const { return m_OrbitalVelocityPreSN; } - double Periastron() const { return m_SemiMajorAxis * (1.0-m_Eccentricity); } + double Periastron() const { return m_SemiMajorAxis * (1.0 - m_Eccentricity); } double PeriastronRsol() const { return Periastron() * AU_TO_RSOL; } double Radius1PostCEE() const { return m_Star1->RadiusPostCEE(); } double Radius2PostCEE() const { return m_Star2->RadiusPostCEE(); } @@ -348,6 +351,7 @@ class BaseBinaryStar { MT_TRACKING m_MassTransferTrackerHistory; + double m_Omega; // Orbital frequency double m_OrbitalVelocityPreSN; BinaryRLOFDetailsT m_RLOFDetails; // RLOF details @@ -374,6 +378,7 @@ class BaseBinaryStar { double m_DCOFormationTime; // Time of DCO formation double m_TotalAngularMomentum; + double m_TotalAngularMomentumPrev; double m_TotalEnergy; @@ -563,9 +568,9 @@ class BaseBinaryStar { BinaryConstituentStar* donorCopy = new BinaryConstituentStar(*m_Donor); double semiMajorAxis = m_Binary->CalculateMassTransferOrbit(donorCopy->Mass(), -dM , *m_Accretor, m_FractionAccreted); - double RLRadius = semiMajorAxis * (1 - m_Binary->Eccentricity()) * CalculateRocheLobeRadius_Static(donorMass - dM, accretorMass + (m_Binary->FractionAccreted() * dM)) * AU_TO_RSOL; + double RLRadius = semiMajorAxis * (1.0 - m_Binary->Eccentricity()) * CalculateRocheLobeRadius_Static(donorMass - dM, accretorMass + (m_Binary->FractionAccreted() * dM)) * AU_TO_RSOL; - (void)donorCopy->UpdateAttributes(-dM, -dM*donorCopy->Mass0()/donorCopy->Mass()); + (void)donorCopy->UpdateAttributes(-dM, -dM * donorCopy->Mass0() / donorCopy->Mass()); // Modify donor Mass0 and Age for MS (including HeMS) and HG stars donorCopy->UpdateInitialMass(); // update initial mass (MS, HG & HeMS) @@ -619,137 +624,72 @@ class BaseBinaryStar { catch(exception& e) { SHOW_ERROR(ERROR::TOO_MANY_RLOF_ITERATIONS, e.what()); // Catch generic boost root finding error } - SHOW_WARN_IF(it>=maxit, ERROR::TOO_MANY_RLOF_ITERATIONS); + SHOW_WARN_IF(it >= maxit, ERROR::TOO_MANY_RLOF_ITERATIONS); return root.first + (root.second - root.first) / 2.0; // Midway between brackets is our result, if necessary we could return the result as an interval here. } - //Functor for the boost root finder to determine rotational frequency - template - struct OmegaFunctor - { - OmegaFunctor(BaseBinaryStar *p_Binary, BinaryConstituentStar *p_Donor, BinaryConstituentStar *p_Accretor, ERROR *p_Error, double p_FractionAccreted) - { - m_Binary = p_Binary; - m_Donor = p_Donor; - m_Accretor = p_Accretor; - m_Error = p_Error; - m_FractionAccreted = p_FractionAccreted; - } - T operator()(double const& dM) - { - if (dM >= m_Donor->Mass()) { // Can't remove more than the donor's mass - *m_Error = ERROR::TOO_MANY_RLOF_ITERATIONS; - return m_Donor->Radius(); - } - - double donorMass = m_Donor->Mass(); - double accretorMass = m_Accretor->Mass(); - - BinaryConstituentStar* donorCopy = new BinaryConstituentStar(*m_Donor); - double semiMajorAxis = m_Binary->CalculateMassTransferOrbit(donorCopy->Mass(), -dM , *m_Accretor, m_FractionAccreted); - double RLRadius = semiMajorAxis * (1 - m_Binary->Eccentricity()) * CalculateRocheLobeRadius_Static(donorMass - dM, accretorMass + (m_Binary->FractionAccreted() * dM)) * AU_TO_RSOL; - - (void)donorCopy->UpdateAttributes(-dM, -dM*donorCopy->Mass0()/donorCopy->Mass()); - - // Modify donor Mass0 and Age for MS (including HeMS) and HG stars - donorCopy->UpdateInitialMass(); // update initial mass (MS, HG & HeMS) - donorCopy->UpdateAgeAfterMassLoss(); // update age (MS, HG & HeMS) - - (void)donorCopy->AgeOneTimestep(0.0); // recalculate radius of star - don't age - just update values - - double thisRadiusAfterMassLoss = donorCopy->Radius(); - - delete donorCopy; donorCopy = nullptr; - - return (RLRadius - thisRadiusAfterMassLoss); - - - - - - //A = I_1_final + I_2_final - - //one_e_init = 1.0 - e_init - - //B = -((I_1_init * omega_1_init) + (I_2_init * omega_2_init) + std::sqrt(G * M1_init * M1_init * M2_init * M2_init * a_init * one_e_init * one_e_init / (M1_init + M2_init))) - - //C = PPOW(G, 2.0 / 3.0) * (M1 * M2) / PPOW((M1 + M2), 1.0 / 3.0) - - //x = PPOW(omega_final, 1.0 / 3.0) - - //return Ax^4 + Bx + C - - - } - private: - BaseBinaryStar *m_Binary; - BinaryConstituentStar *m_Donor; - BinaryConstituentStar *m_Accretor; - ERROR *m_Error; - double m_FractionAccreted; - }; - - /* - //Root solver to determine rotational frequency - double Omega(BaseBinaryStar *p_Binary, BinaryConstituentStar *p_Donor, BinaryConstituentStar *p_Accretor, double p_FractionAccreted) { - - std::cout << "MassLossToFitInsideRocheLobe() @ start\n"; - //using namespace std; // Help ADL of std functions. - using namespace boost::math::tools; // For bracket_and_solve_root. + * Root solver to determine rotational frequency after synchronisation for tides + * + * Uses boost::math::tools::bracket_and_solve_root() + * + * + * double OmegaAfterSynchronisation(const double p_M1, const double p_M2, const double p_I1, const double p_I2, const double p_Omega) + * + * @param [IN] p_M1 Mass of star 1 + * @param [IN] p_M2 Mass of star 2 + * @param [IN] p_I1 Moment of intertia of star 1 + * @param [IN] p_I2 Moment of intertia of star 1 + * @param [IN] p_Ltot Total angular momentum for binary + * @param [IN] p_Guess Initial guess for value of root + * @return Root found: will be -1.0 if no real root found + */ + double OmegaAfterSynchronisation(const double p_M1, const double p_M2, const double p_I1, const double p_I2, const double p_Ltot, const double p_Guess) { - double guess = ADAPTIVE_RLOF_FRACTION_DONOR_GUESS * p_Donor->Mass(); // Rough guess at solution - double factor = ADAPTIVE_RLOF_SEARCH_FACTOR; // Size of search steps + const boost::uintmax_t maxit = TIDES_OMEGA_MAX_ITERATIONS; // maximum iterations + boost::uintmax_t it = maxit; // initially max iterations, but updated with actual count + int digits = std::numeric_limits::digits; // maximum possible binary digits accuracy + int get_digits = digits - 5; // we have to have a non-zero interval at each step + + // maximum accuracy is digits - 1. But we also have to allow for inaccuracy + // in the functor, otherwise the last few iterations just thrash around. + boost::math::tools::eps_tolerance tol(get_digits); // tolerance - const boost::uintmax_t maxit = ADAPTIVE_RLOF_MAX_ITERATIONS; // Limit to maximum iterations. - boost::uintmax_t it = maxit; // Initially our chosen max iterations, but updated with actual. - bool is_rising = true; // So if result with guess is too low, then try increasing guess. - int digits = std::numeric_limits::digits; // Maximum possible binary digits accuracy for type T. + // define functor + // function: ax + bx^(1/3) + c = 0 + double a = p_I1 + p_I2; + double b = PPOW(G1, 2.0 / 3.0) * p_M1 * p_M2 / std::cbrt(p_M1 + p_M2); + double c = -p_Ltot; - // Some fraction of digits is used to control how accurate to try to make the result. - int get_digits = digits - 5; // We have to have a non-zero interval at each step, so + auto func = [this, a, b, c](double x) -> double { return (a * x) + (b / std::cbrt(x)) + c; }; // functor - // maximum accuracy is digits - 1. But we also have to - // allow for inaccuracy in f(x), otherwise the last few - // iterations just thrash around. - eps_tolerance tol(get_digits); // Set the tolerance. - - - double I1init = m_Star1->CalculateMomentOfInertia(); - double I2init = m_Star2->CalculateMomentOfInertia(); - - double omega1init = m_Star1->Omega(); - double omega2init = m_Star2->Omega(); - - double eInit = m_Eccentricity; + // find root + double factor = TIDES_OMEGA_SEARCH_FACTOR; // size of search steps + bool is_rising = func(p_Guess) > func(p_Guess * factor) ? false : true; // so bracket_and_solve_root() knows whether to increase or decrease guess per iteration - double m1Init = m_Star1->Mass(); - double m2Init = m_Star2->Mass(); - - M1 - M2 - - std::pair root(0.0, 0.0); + std::pair root(-1.0, -1.0); // initialise root try { - ERROR error = ERROR::NONE; - std::cout << "MassLossToFitInsideRocheLobe() @ call bracket_and_solve_root()\n"; - root = bracket_and_solve_root(RadiusEqualsRocheLobeFunctor(p_Binary, p_Donor, p_Accretor, &error, p_FractionAccreted), guess, factor, is_rising, tol, it); - std::cout << "MassLossToFitInsideRocheLobe() @ return from bracket_and_solve_root()\n"; - if (error != ERROR::NONE) SHOW_WARN(error); + root = boost::math::tools::bracket_and_solve_root(func, p_Guess, factor, is_rising, tol, it); // iterate to find root } - catch(exception& e) { - SHOW_ERROR(ERROR::TOO_MANY_RLOF_ITERATIONS, e.what()); // Catch generic boost root finding error + catch(std::exception& e) { // catch generic boost root finding error + root.first = -1.0; // set error return + root.second = -1.0; + if (it < maxit) { // too many iterations? + SHOW_ERROR(ERROR::ROOT_FINDER_FAILED, e.what()); // no - some other error - show it + } } - SHOW_WARN_IF(it>=maxit, ERROR::TOO_MANY_RLOF_ITERATIONS); - - std::cout << "MassLossToFitInsideRocheLobe() @ return: " << root.first + (root.second - root.first) / 2.0 << " <--------------\n"; - return root.first + (root.second - root.first) / 2.0; // Midway between brackets is our result, if necessary we could return the result as an interval here. - } - */ + if (it >= maxit) { // too many iterations? + root.first = -1.0; // yes - set error return + root.second = -1.0; + SHOW_WARN(ERROR::TOO_MANY_OMEGA_ITERATIONS); // show warning + } + return root.first + (root.second - root.first) / 2.0; // midway between brackets (could return brackets...) + } + }; #endif // __BaseBinaryStar_h__ diff --git a/src/BaseStar.cpp b/src/BaseStar.cpp index a2fe91867..616d05c8a 100755 --- a/src/BaseStar.cpp +++ b/src/BaseStar.cpp @@ -116,8 +116,6 @@ BaseStar::BaseStar(const unsigned long int p_RandomSeed, ? p_RotationalFrequency // yes - use it : CalculateZAMSAngularFrequency(m_MZAMS, m_RZAMS); // no - calculate it - m_MomentOfInertiaZAMS = CalculateMomentOfInertia(); - // Effective initial Zero Age Main Sequence parameters corresponding to Mass0 m_RZAMS0 = m_RZAMS; m_LZAMS0 = m_LZAMS; @@ -143,7 +141,6 @@ BaseStar::BaseStar(const unsigned long int p_RandomSeed, m_DominantMassLossRate = MASS_LOSS_TYPE::NONE; m_Omega = m_OmegaZAMS; - m_MomentOfInertia = m_MomentOfInertiaZAMS; m_AngularMomentum = DEFAULT_INITIAL_DOUBLE_VALUE; m_MinimumLuminosityOnPhase = DEFAULT_INITIAL_DOUBLE_VALUE; @@ -156,7 +153,7 @@ BaseStar::BaseStar(const unsigned long int p_RandomSeed, m_RadiusPrev = m_RZAMS; m_DtPrev = DEFAULT_INITIAL_DOUBLE_VALUE; m_OmegaPrev = m_OmegaZAMS; - + // Lambdas m_Lambdas.dewi = DEFAULT_INITIAL_DOUBLE_VALUE; m_Lambdas.fixed = DEFAULT_INITIAL_DOUBLE_VALUE; @@ -2449,7 +2446,7 @@ double BaseStar::CalculateRotationalVelocity(double p_MZAMS) const { break; default: // unknown rorational velocity prescription - SHOW_WARN(ERROR::UNKNOWN_VROT_PRESCRIPTION, "Using default vRot = 0.0"); // show warning + SHOW_WARN(ERROR::UNKNOWN_VROT_PRESCRIPTION, "Using default vRot = 0.0"); // show warning } return vRot; } @@ -2470,7 +2467,7 @@ double BaseStar::CalculateRotationalVelocity(double p_MZAMS) const { */ double BaseStar::CalculateZAMSAngularFrequency(const double p_MZAMS, const double p_RZAMS) const { double vRot = CalculateRotationalVelocity(p_MZAMS); - return utils::Compare(vRot, 0.0) == 0 ? 0.0 : 45.35 * vRot / p_RZAMS; // Hurley et al. 2000, eq 108 JR: todo: added check for vRot = 0 + return utils::Compare(vRot, 0.0) == 0 ? 0.0 : 45.35 * vRot / p_RZAMS; // Hurley et al. 2000, eq 108 } @@ -2521,7 +2518,7 @@ double BaseStar::CalculateOmegaCHE(const double p_MZAMS, const double p_Metallic } // calculate omegaCHE(M, Z) - return (1.0 / ((0.09 * log(p_Metallicity / 0.004)) + 1.0) * omegaZ004) * SECONDS_IN_YEAR; // in rads/yr + return (1.0 / ((0.09 * log(p_Metallicity / 0.004)) + 1.0) * omegaZ004) * SECONDS_IN_YEAR; // in rads/yr #undef massCutoffs } @@ -3251,7 +3248,6 @@ void BaseStar::UpdateAttributesAndAgeOneTimestepPreamble(const double p_DeltaMas m_DtPrev = m_Dt; m_MassPrev = m_Mass; m_RadiusPrev = m_Radius; - m_OmegaPrev = m_Omega; } // the GBParams and Timescale calculations need to be done diff --git a/src/BaseStar.h b/src/BaseStar.h index 4311f087b..a9304cee1 100644 --- a/src/BaseStar.h +++ b/src/BaseStar.h @@ -98,7 +98,6 @@ class BaseStar { std::string MassTransferDonorHistoryString() const; double Mdot() const { return m_Mdot; } double Metallicity() const { return m_Metallicity; } - double MomentOfInertia() const { return m_MomentOfInertia; } double MZAMS() const { return m_MZAMS; } double Omega() const { return m_Omega; } double OmegaCHE() const { return m_OmegaCHE; } @@ -313,7 +312,6 @@ class BaseStar { double m_OmegaCHE; // Minimum angular frequency at which CHE will occur (calculated at ZAMS) double m_RZAMS; // ZAMS Radius double m_TZAMS; // ZAMS Temperature - double m_MomentOfInertiaZAMS; // ZAMS moment of inertia (Msol Rsol^2) // Effective Zero Age Main Sequence @@ -338,7 +336,6 @@ class BaseStar { MASS_LOSS_TYPE m_DominantMassLossRate; // Current dominant mass loss rate double m_Mu; // Current small envelope parameter mu double m_Omega; // Current angular frequency (yr^-1) - double m_MomentOfInertia; // Current moment of inertia (Msol Rsol^2) double m_AngularMomentum; // Current angular momentum double m_Radius; // Current radius (Rsol) double m_Tau; // Relative time @@ -422,8 +419,6 @@ class BaseStar { DBL_VECTOR &p_RConstants, DBL_VECTOR &p_GammaConstants); - virtual void CalculateAndSetPulsarParameters() { } // NO-OP for most stellar types - double CalculateBindingEnergy(const double p_CoreMass, const double p_EnvMass, const double p_Radius, const double p_Lambda) const; void CalculateBnCoefficients(DBL_VECTOR &p_BnCoefficients); diff --git a/src/COWD.h b/src/COWD.h index dcebf257d..c0f799d7c 100755 --- a/src/COWD.h +++ b/src/COWD.h @@ -26,7 +26,6 @@ class COWD: virtual public BaseStar, public WhiteDwarfs { // member functions - void CalculateAngularMomentum() const { } // NO-OP static double CalculateLuminosityOnPhase_Static(const double p_Mass, const double p_Time, diff --git a/src/MainSequence.cpp b/src/MainSequence.cpp index abdfa7218..4d5a04a10 100644 --- a/src/MainSequence.cpp +++ b/src/MainSequence.cpp @@ -598,7 +598,7 @@ double MainSequence::CalculateGyrationRadius() const { if ((utils::Compare(log10M, 0.2) > 0)) CUpper = -1.5; // log10(M) > 0.2 else if ((utils::Compare(log10M, 0.0) > 0)) CUpper = -2.5 + (5.0 * log10M); // 0.2 <= log10(M) > 0.0 (de Mink doesn't include '=' - we assume it to be here (and for log10(M) <= 0.0)) - double k0 = cLower+ std::min(0.21, std::max(0.09 - (0.27 * log10M), 0.037 + (0.033 * log10M))); // gyration radius squared for ZAMS stars + double k0 = cLower + std::min(0.21, std::max(0.09 - (0.27 * log10M), 0.037 + (0.033 * log10M)));// gyration radius squared for ZAMS stars double radiusRatio = m_Radius / m_RZAMS; diff --git a/src/NS.cpp b/src/NS.cpp index 5da7182ff..0b45843d3 100755 --- a/src/NS.cpp +++ b/src/NS.cpp @@ -1,5 +1,4 @@ #include "Rand.h" - #include "NS.h" @@ -8,6 +7,8 @@ * * Hurley et al. 2000, eq 93 * + * Called (indirectly) from GiantBranch, so must be static. + * * * double CalculateLuminosityOnPhase_Static(const double p_Mass, const double p_Time) * @@ -25,10 +26,8 @@ double NS::CalculateLuminosityOnPhase_Static(const double p_Mass, const double p * Choose timestep for Pulsar Evolution * * Pulsars evolve very fast when they are first born, and evolve slower as they age. - * Hence, timestep is chosen to be small when pulsar is young, - * and is slowly increased as the pulsar ages. - * - * Can change it to a choice that suits your simulation. + * Hence, timestep is chosen to be small when pulsar is young, and is slowly increased + * as the pulsar ages. * * double ChooseTimestep(const double p_Time) * @@ -36,7 +35,8 @@ double NS::CalculateLuminosityOnPhase_Static(const double p_Mass, const double p * @return Suggested timestep (dt) */ double NS::ChooseTimestep(const double p_Time) const { - double result; + double result = 500.0; // default value + if (p_Time < 0.01) { result = 0.001; } @@ -50,13 +50,11 @@ double NS::ChooseTimestep(const double p_Time) const { result = 1.0; } else if (p_Time < 500.0) { - double slope = log10(500.0) / (log10(500.0) - 1.0); + double slope = 1.58859191006; // 1.58859191006 = log10(500.0) / (log10(500.0) - 1.0) double log10_step = slope * (log10(p_Time) - 1.0); result = PPOW(10.0, log10_step); } - else { - result = 500.0; - } + return result; } @@ -74,13 +72,13 @@ double NS::CalculateRadiusOnPhaseInKM_Static(const double p_Mass) { double radius; - switch (OPTIONS->NeutronStarEquationOfState()) { // which equation-of-state? + switch (OPTIONS->NeutronStarEquationOfState()) { // which equation-of-state? - case NS_EOS::SSE: // SSE + case NS_EOS::SSE: // SSE radius = 10.0; break; - case NS_EOS::ARP3: { // ARP3 + case NS_EOS::ARP3: { // ARP3 // We don't extrapolate so masses outside table just set to extreme values @@ -105,13 +103,14 @@ double NS::CalculateRadiusOnPhaseInKM_Static(const double p_Mass) { } } break; - default: // unknown equation-of-state - SHOW_WARN_STATIC(ERROR::UNKNOWN_NS_EOS, // show warning + default: // unknown equation-of-state + SHOW_WARN_STATIC(ERROR::UNKNOWN_NS_EOS, // show warning "Using default NS radius = 10.0", OBJECT_TYPE::BASE_STAR, STELLAR_TYPE::NEUTRON_STAR); radius = 10.0; } + return radius; } @@ -119,16 +118,18 @@ double NS::CalculateRadiusOnPhaseInKM_Static(const double p_Mass) { /* * Calculate core collapse Supernova parameters * - * + * Called from GiantBranch, so must be static. + * + * * DBL_DBL_DBL CalculateCoreCollapseSNParams_Static(const double p_Mass) * * @param [IN] p_Mass Mass in Msol * @return Tuple containing Luminosity, Radius and Temperature of Neutron Star */ DBL_DBL_DBL NS::CalculateCoreCollapseSNParams_Static(const double p_Mass) { - double luminosity = CalculateLuminosityOnPhase_Static(p_Mass, 0.0); // Luminosity of Neutron Star as it cools - double radius = CalculateRadiusOnPhase_Static(p_Mass); // Radius of Neutron Star in Rsol - double temperature = BaseStar::CalculateTemperatureOnPhase_Static(luminosity, radius); // Temperature of NS + double luminosity = CalculateLuminosityOnPhase_Static(p_Mass, 0.0); // Luminosity of Neutron Star as it cools + double radius = CalculateRadiusOnPhase_Static(p_Mass); // Radius of Neutron Star in Rsol + double temperature = BaseStar::CalculateTemperatureOnPhase_Static(luminosity, radius); // Temperature of NS return std::make_tuple(luminosity, radius, temperature); } @@ -138,30 +139,30 @@ DBL_DBL_DBL NS::CalculateCoreCollapseSNParams_Static(const double p_Mass) { * Calculate the spin period of a Pulsar at birth according to selected distribution (by commandline option) * * - * double CalculatePulsarBirthSpinPeriod_Static() + * double CalculatePulsarBirthSpinPeriod() * * @return Birth spin period of Pulsar in ms */ -double NS::CalculatePulsarBirthSpinPeriod_Static() { +double NS::CalculatePulsarBirthSpinPeriod() { double pSpin; - switch (OPTIONS->PulsarBirthSpinPeriodDistribution()) { // which distribution? + switch (OPTIONS->PulsarBirthSpinPeriodDistribution()) { // which distribution? - case PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION::ZERO: // ZERO + case PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION::ZERO: // ZERO pSpin = 0.0; break; - case PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION::FIXED: // FIXED constant value as used in default model in Oslowski et al 2011 https://arxiv.org/abs/0903.3538 - SHOW_WARN_STATIC(ERROR::UNSUPPORTED_PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION, // show warning + case PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION::FIXED: // FIXED constant value as used in default model in Oslowski et al 2011 https://arxiv.org/abs/0903.3538 + SHOW_WARN_STATIC(ERROR::UNSUPPORTED_PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION, // show warning "Using spin = 0.0", OBJECT_TYPE::BASE_STAR, STELLAR_TYPE::NEUTRON_STAR); pSpin = 0.0; break; - case PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION::UNIFORM: { // UNIFORM distribution between minimum and maximum value as in Oslowski et al 2011 https://arxiv.org/abs/0903.3538 (default Pmin = and Pmax = ) - // and also Kiel et al 2008 https://arxiv.org/abs/0805.0059 (default Pmin = 10 ms and Pmax 100 ms, section 3.4) + case PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION::UNIFORM: { // UNIFORM distribution between minimum and maximum value as in Oslowski et al 2011 https://arxiv.org/abs/0903.3538 (default Pmin = and Pmax = ) + // and also Kiel et al 2008 https://arxiv.org/abs/0805.0059 (default Pmin = 10 ms and Pmax 100 ms, section 3.4) double maximum = OPTIONS->PulsarBirthSpinPeriodDistributionMax(); double minimum = OPTIONS->PulsarBirthSpinPeriodDistributionMin(); @@ -169,7 +170,7 @@ double NS::CalculatePulsarBirthSpinPeriod_Static() { pSpin = minimum + (RAND->Random() * (maximum - minimum)); } break; - case PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION::NORMAL: { // NORMAL distribution from Faucher-Giguere and Kaspi 2006 https://arxiv.org/abs/astro-ph/0512585 + case PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION::NORMAL: { // NORMAL distribution from Faucher-Giguere and Kaspi 2006 https://arxiv.org/abs/astro-ph/0512585 // Values hard-coded for now, can make them options if necessary // pulsarBirthSpinPeriodDistributionFaucherGiguereKaspi2006Mean = 300.0; @@ -182,8 +183,8 @@ double NS::CalculatePulsarBirthSpinPeriod_Static() { } break; - default: // unknown distribution - SHOW_WARN_STATIC(ERROR::UNKNOWN_PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION, // show warning + default: // unknown distribution + SHOW_WARN_STATIC(ERROR::UNKNOWN_PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION, // show warning "Using spin = 0.0", OBJECT_TYPE::BASE_STAR, STELLAR_TYPE::NEUTRON_STAR); @@ -207,21 +208,21 @@ double NS::CalculatePulsarBirthMagneticField() { double log10B; - switch (OPTIONS->PulsarBirthMagneticFieldDistribution()) { // which distribution? + switch (OPTIONS->PulsarBirthMagneticFieldDistribution()) { // which distribution? - case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::ZERO: // ZERO + case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::ZERO: // ZERO log10B = 0.0; break; - case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::FIXED: // FIXED - set to a fixed constant value - SHOW_WARN_STATIC(ERROR::UNSUPPORTED_PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION, // show warning + case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::FIXED: // FIXED - set to a fixed constant value + SHOW_WARN_STATIC(ERROR::UNSUPPORTED_PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION, // show warning "Using 0.0", OBJECT_TYPE::BASE_STAR, STELLAR_TYPE::NEUTRON_STAR); log10B = 0.0; break; - case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::FLATINLOG: { // FLAT IN LOG distribution from Oslowski et al 2011 https://arxiv.org/abs/0903.3538 (log10B0min = , log10B0max = ) + case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::FLATINLOG: { // FLAT IN LOG distribution from Oslowski et al 2011 https://arxiv.org/abs/0903.3538 (log10B0min = , log10B0max = ) double maximum = OPTIONS->PulsarBirthMagneticFieldDistributionMax(); double minimum = OPTIONS->PulsarBirthMagneticFieldDistributionMin(); @@ -230,7 +231,7 @@ double NS::CalculatePulsarBirthMagneticField() { } break; - case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::UNIFORM: { // UNIFORM flat distribution used in Kiel et al 2008 https://arxiv.org/abs/0805.0059 (log10B0min = 11, log10B0max = 13.5 see section 3.4 and Table 1.) + case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::UNIFORM: { // UNIFORM flat distribution used in Kiel et al 2008 https://arxiv.org/abs/0805.0059 (log10B0min = 11, log10B0max = 13.5 see section 3.4 and Table 1.) double maximum = PPOW(10.0, OPTIONS->PulsarBirthMagneticFieldDistributionMax()); @@ -239,7 +240,7 @@ double NS::CalculatePulsarBirthMagneticField() { log10B = log10(minimum + (RAND->Random() * (maximum - minimum))); } break; - case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::LOGNORMAL: { // LOG NORMAL distribution from Faucher-Giguere and Kaspi 2006 https://arxiv.org/abs/astro-ph/0512585 + case PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION::LOGNORMAL: { // LOG NORMAL distribution from Faucher-Giguere and Kaspi 2006 https://arxiv.org/abs/astro-ph/0512585 // Values hard-coded for now, can make them options if necessary // pulsarBirthMagneticFieldDistributionFaucherGiguereKaspi2006Mean = 12.65 @@ -251,8 +252,8 @@ double NS::CalculatePulsarBirthMagneticField() { log10B = RAND->RandomGaussian(sigma) + mean; } break; - default: // unknown distribution - SHOW_WARN_STATIC(ERROR::UNKNOWN_PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION, // show warning + default: // unknown distribution + SHOW_WARN_STATIC(ERROR::UNKNOWN_PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION, // show warning "Using 0.0", OBJECT_TYPE::BASE_STAR, STELLAR_TYPE::NEUTRON_STAR); @@ -311,18 +312,18 @@ double NS::CalculateSpinDownRate(const double p_Omega, const double p_MomentOfIn // pow() is slow - use multiplication - double period = _2_PI / p_Omega; // convert frequency to period - double cgsRadius = p_Radius * KM_TO_CM; // radius in cm + double period = _2_PI / p_Omega; // convert frequency to period + double cgsRadius = p_Radius * KM_TO_CM; // radius in cm double radius_6 = cgsRadius * cgsRadius * cgsRadius * cgsRadius * cgsRadius * cgsRadius; - double cgsMagField = p_MagField * TESLA_TO_GAUSS; // B field in G + double cgsMagField = p_MagField * TESLA_TO_GAUSS; // B field in G double magField_2 = cgsMagField * cgsMagField; constexpr double _8_PI_2 = 8.0 * PI_2; constexpr double _3_C_3 = 3.0 * (C * 100.0) * (C * 100.0) * (C * 100.0); double pDotTop = _8_PI_2 * radius_6 * magField_2; double pDotBottom = _3_C_3 * p_MomentOfInteria * period; - double pDot = pDotTop / pDotBottom; // period derivative + double pDot = pDotTop / pDotBottom; // period derivative - return(-pDot * p_Omega / period); // convert period derivative to frequency derivative, which is what is recorded in the output + return(-pDot * p_Omega / period); // convert period derivative to frequency derivative, which is what is recorded in the output } @@ -345,7 +346,7 @@ double NS::CalculateSpinDownRate(const double p_Omega, const double p_MomentOfIn void NS::CalculateAndSetPulsarParameters() { m_PulsarDetails.magneticField = PPOW(10.0, CalculatePulsarBirthMagneticField()) * GAUSS_TO_TESLA; // magnetic field in Gauss -> convert to Tesla - m_PulsarDetails.spinPeriod = CalculatePulsarBirthSpinPeriod_Static(); // spin period in ms + m_PulsarDetails.spinPeriod = CalculatePulsarBirthSpinPeriod(); // spin period in ms m_PulsarDetails.spinFrequency = _2_PI / (m_PulsarDetails.spinPeriod * SECONDS_IN_MS); m_PulsarDetails.birthPeriod = m_PulsarDetails.spinPeriod / 1000.0; // convert from ms to s @@ -384,7 +385,7 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { constexpr double _8_PI_2 = 8.0 * PI_2; constexpr double _3_C_3 = 3.0 * C * C * C * 1000000.0; - double initialMagField = m_PulsarDetails.magneticField; // (in T) + double initialMagField = m_PulsarDetails.magneticField; // (in T) double initialMagField_G = initialMagField * TESLA_TO_GAUSS; double initialSpinPeriod = PI_2 / m_PulsarDetails.spinFrequency; double magFieldLowerLimit = PPOW(10.0, OPTIONS->PulsarLog10MinimumMagneticField()) * GAUSS_TO_TESLA; @@ -393,7 +394,7 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { // calculate isolated decay of the magnetic field for a neutron star // see Equation 6 in arXiv:0903.3538v2 - m_PulsarDetails.magneticField = magFieldLowerLimit + (initialMagField - magFieldLowerLimit) * exp(-p_Stepsize / tau); // update pulsar magnetic field in SI. + m_PulsarDetails.magneticField = magFieldLowerLimit + (initialMagField - magFieldLowerLimit) * exp(-p_Stepsize / tau); // update pulsar magnetic field in SI. // calculate the spin down rate for isolated neutron stars // see Equation 6 in arxiv:1912.02415 @@ -405,7 +406,7 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { double Psquared = 2 * constant2 * (term1 - term2 - term3) + (initialSpinPeriod * initialSpinPeriod); double P_f = std::sqrt(Psquared); - m_PulsarDetails.spinFrequency = _2_PI / P_f; // pulsar spin frequency + m_PulsarDetails.spinFrequency = _2_PI / P_f; // pulsar spin frequency // calculate the spin down rate for isolated neutron stars // see Equation 4 in arXiv:0903.3538v2 (Our version is in cgs) @@ -413,7 +414,7 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { double pDot = pDotTop / P_f; m_PulsarDetails.spinDownRate = -_2_PI * pDot / (P_f * P_f); - m_AngularMomentum = m_PulsarDetails.spinFrequency * m_MomentOfInertia_CGS; // angular momentum of star + m_AngularMomentum = m_PulsarDetails.spinFrequency * m_MomentOfInertia_CGS; // angular momentum of star } @@ -443,9 +444,9 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { */ void NS::UpdateMagneticFieldAndSpin(const bool p_CommonEnvelope, const bool p_RecycledNS, const double p_Stepsize, const double p_MassGainPerTimeStep, const double p_Epsilon) { - constexpr double unitsMoI = G_TO_KG * CM_TO_M * CM_TO_M; + constexpr double unitsMoI = G_TO_KG * CM_TO_M * CM_TO_M; // converts CGS -> SI - double initialMagField = m_PulsarDetails.magneticField; // (in T) + double initialMagField = m_PulsarDetails.magneticField; // (in T) double magFieldLowerLimit = PPOW(10.0, OPTIONS->PulsarLog10MinimumMagneticField()) * GAUSS_TO_TESLA; double kappa = OPTIONS->PulsarMagneticFieldDecayMassscale() * MSOL_TO_KG; @@ -453,13 +454,14 @@ void NS::UpdateMagneticFieldAndSpin(const bool p_CommonEnvelope, const bool p_Re // these are the ''classical'' isolated pulsars SpinDownIsolatedPulsar(p_Stepsize); } - else if (utils::Compare(m_PulsarDetails.spinFrequency, _2_PI * 1000.0) < 0 && (p_RecycledNS || p_CommonEnvelope) && utils::Compare(p_MassGainPerTimeStep, 0.0) > 0) { + else if (utils::Compare(m_PulsarDetails.spinFrequency, _2_PI * 1000.0) < 0 && + (p_RecycledNS || p_CommonEnvelope) && utils::Compare(p_MassGainPerTimeStep, 0.0) > 0) { // his part of the code does pulsar recycling through acretion // recycling happens for pulsar with spin period larger than 1 ms and in a binary system with mass transfer // the pulsar being recycled is either in a common envolope, or should have started the recycling process in previous time steps. - double mass_kg = m_Mass * MSOL_TO_KG; // in kg - double r_m = m_Radius * RSOL_TO_KM * 1000.0; // in metres + double mass_kg = m_Mass * MSOL_TO_KG; // in kg + double r_m = m_Radius * RSOL_TO_KM * 1000.0; // in metres double MoI_SI = m_MomentOfInertia_CGS * unitsMoI; double angularMomentum_SI = m_AngularMomentum * unitsMoI; diff --git a/src/NS.h b/src/NS.h index b98b48c7b..ca46ca4f6 100755 --- a/src/NS.h +++ b/src/NS.h @@ -33,7 +33,7 @@ class NS: virtual public BaseStar, public Remnants { static double CalculateLuminosityOnPhase_Static(const double p_Mass, const double p_Time); - static double CalculatePulsarBirthSpinPeriod_Static(); + static double CalculatePulsarBirthSpinPeriod(); static double CalculateRadiusOnPhaseInKM_Static(const double p_Mass); // Radius on phase in km static double CalculateRadiusOnPhase_Static(const double p_Mass) { return CalculateRadiusOnPhaseInKM_Static(p_Mass) * KM_TO_RSOL; } // Radius on phase in Rsol @@ -42,49 +42,49 @@ class NS: virtual public BaseStar, public Remnants { protected: - - double m_MomentOfInertia_CGS; // MoI in CGS - only required in NS class - void Initialise() { m_StellarType = STELLAR_TYPE::NEUTRON_STAR; // Set stellar type CalculateTimescales(); // Initialise timescales //Set internal properties to zero to avoid meaningless values - m_Age = 0.0; - m_COCoreMass = 0.0; - m_HeCoreMass = 0.0; - m_CoreMass = 0.0; - m_Mass0 = 0.0; + m_Age = 0.0; + m_COCoreMass = 0.0; + m_HeCoreMass = 0.0; + m_CoreMass = 0.0; + m_Mass0 = 0.0; - m_Radius = NS::CalculateRadiusOnPhase_Static(m_Mass); // Set the NS radius, in Rsol - m_Luminosity = NS::CalculateLuminosityOnPhase_Static(m_Mass, m_Age); // Set the NS luminosity + m_Radius = CalculateRadiusOnPhase_Static(m_Mass); // Set the NS radius, in Rsol + m_Luminosity = CalculateLuminosityOnPhase_Static(m_Mass, m_Age); // Set the NS luminosity CalculateAndSetPulsarParameters(); } + double m_MomentOfInertia_CGS; // MoI in CGS - only required in NS class + // member functions - alphabetically void CalculateAndSetPulsarParameters(); - double CalculateLuminosityOnPhase() const { return CalculateLuminosityOnPhase_Static(m_Mass, m_Age); } // Use class member variables + double CalculateLuminosityOnPhase() const { return CalculateLuminosityOnPhase_Static(m_Mass, m_Age); } // Use class member variables - double CalculateMassLossRate() { return 0.0; } // Ensure that NSs don't lose mass in winds + double CalculateMassLossRate() { return 0.0; } // Ensure that NSs don't lose mass in winds double CalculateMomentOfInertiaCGS() const; // MoI in CGS - double CalculateMomentOfInertia(const double p_RemnantRadius = 0.0) const { return CalculateMomentOfInertiaCGS() / MSOL_TO_G / RSOL_TO_CM / RSOL_TO_CM; } // MoI (default is solar units) + double CalculateMomentOfInertia(const double p_RemnantRadius = 0.0) const { return CalculateMomentOfInertiaCGS() / MSOL_TO_G / RSOL_TO_CM / RSOL_TO_CM; } // MoI (default is solar units) + double CalculateMomentOfInertiaAU(const double p_RemnantRadius = 0.0) const { return CalculateMomentOfInertia() * RSOL_TO_AU * RSOL_TO_AU; } double CalculatePulsarBirthMagneticField(); - double CalculateRadiusOnPhase() const { return CalculateRadiusOnPhase_Static(m_Mass); } // Use class member variables - returns radius in Rsol + double CalculateRadiusOnPhase() const { return CalculateRadiusOnPhase_Static(m_Mass); } // Use class member variables - returns radius in Rsol double CalculateSpinDownRate(const double p_Omega, const double p_MomentOfInteria, const double p_MagField, const double p_Radius) const; double ChooseTimestep(const double p_Time) const; - STELLAR_TYPE EvolveToNextPhase() { return STELLAR_TYPE::BLACK_HOLE; } + STELLAR_TYPE EvolveToNextPhase() { return STELLAR_TYPE::BLACK_HOLE; } - bool ShouldEvolveOnPhase() const { return (m_Mass <= OPTIONS->MaximumNeutronStarMass()); } // Evolve as a neutron star unless mass > maximum neutron star mass (e.g. through accretion) + bool ShouldEvolveOnPhase() const { return (m_Mass <= OPTIONS->MaximumNeutronStarMass()); } // Evolve as a neutron star unless mass > maximum neutron star mass (e.g. through accretion) void SpinDownIsolatedPulsar(const double p_Stepsize); void UpdateMagneticFieldAndSpin(const bool p_CommonEnvelope, const bool p_RecycledNS, diff --git a/src/Options.cpp b/src/Options.cpp index 5ddabfad5..c32f27991 100644 --- a/src/Options.cpp +++ b/src/Options.cpp @@ -474,6 +474,10 @@ void Options::OptionValues::Initialise() { m_CommonEnvelopeRecombinationEnergyDensity = 1.5E13; + // Tides + m_EnableTides = false; // default is no tides + + // Zetas m_StellarZetaPrescription.type = ZETA_PRESCRIPTION::SOBERMAN; m_StellarZetaPrescription.typeString = ZETA_PRESCRIPTION_LABEL.at(m_StellarZetaPrescription.type); @@ -751,6 +755,12 @@ bool Options::AddOptions(OptionValues *p_Options, po::options_description *p_Opt ("Print detailed output to file (default = " + std::string(p_Options->m_DetailedOutput ? "TRUE" : "FALSE") + ")").c_str() ) + ( + "enable-tides", + po::value(&p_Options->m_EnableTides)->default_value(p_Options->m_EnableTides)->implicit_value(true), + ("Enable tides (default = " + std::string(p_Options->m_EnableTides ? "TRUE" : "FALSE") + ")").c_str() + ) + ( "enable-warnings", po::value(&p_Options->m_EnableWarnings)->default_value(p_Options->m_EnableWarnings)->implicit_value(true), diff --git a/src/Options.h b/src/Options.h index d3157df48..37e2b84a7 100755 --- a/src/Options.h +++ b/src/Options.h @@ -333,6 +333,7 @@ class Options { "eccentricity-distribution", "eccentricity-max", "eccentricity-min", + "enable-tides", "evolve-double-white-dwarfs", "evolve-pulsars", "evolve-unbound-systems", @@ -437,6 +438,7 @@ class Options { "detailed-output", "eccentricity-distribution", + "enable-tides", "enable-warnings", "envelope-state-prescription", "errors-to-file", @@ -896,6 +898,10 @@ class Options { double m_CommonEnvelopeRecombinationEnergyDensity; // Factor using to calculate the binding energy depending on the mass of the envelope. (default = 1.5x10^13 erg/g) + // Tides + bool m_EnableTides; // Whether to enable tides (default = False) + + // Zetas ENUM_OPT m_StellarZetaPrescription; // Prescription to use for calculating stellar zetas (default = SOBERMAN) @@ -1206,6 +1212,7 @@ class Options { bool DebugToFile() const { return m_CmdLine.optionValues.m_DebugToFile; } bool DetailedOutput() const { return m_CmdLine.optionValues.m_DetailedOutput; } + bool EnableTides() const { return OPT_VALUE("enable-tides", m_EnableTides, true); } bool EnableWarnings() const { return m_CmdLine.optionValues.m_EnableWarnings; } bool ErrorsToFile() const { return m_CmdLine.optionValues.m_ErrorsToFile; } double Eccentricity() const { return OPT_VALUE("eccentricity", m_Eccentricity, true); } diff --git a/src/changelog.h b/src/changelog.h index 0a77a1b38..78cfff942 100644 --- a/src/changelog.h +++ b/src/changelog.h @@ -1057,8 +1057,10 @@ // - Fixed a few typos, a little code cleanup. // 02.39.01 LC - Sep 01, 2023 - Defect repair: // - Fix for issue #945 - made HeSD SN types a sub-class of SNIA types. +// 02.40.00 JR - Oct 25, 2023 - Enhancement, a little cleanup: +// - Added naive tides implementation. Functionality enable with new optin `--enable-tides`. default is no tides. -const std::string VERSION_STRING = "02.39.01"; +const std::string VERSION_STRING = "02.40.00"; # endif // __changelog_h__ diff --git a/src/constants.h b/src/constants.h index 6ab3755cd..9d6e79599 100755 --- a/src/constants.h +++ b/src/constants.h @@ -177,10 +177,10 @@ extern OBJECT_ID globalObjectId; // // I've added _2_PI and SQRT_M_2_PI below -#undef COMPARE_WITH_TOLERANCE // define/undef this to compare floats with/without tolerance (see FLOAT_TOLERANCE_ABSOLUTE, FLOAT_TOLERANCE_RELATIVE and Compare() function) +#undef COMPARE_GLOBAL_TOLERANCE // define/undef this to compare floats with/without tolerance (see FLOAT_TOLERANCE_ABSOLUTE, FLOAT_TOLERANCE_RELATIVE and Compare() function) -constexpr double FLOAT_TOLERANCE_ABSOLUTE = 0.0000005; // Absolute tolerance for floating-point comparisons if COMPARE_WITH_TOLERANCE is defined -constexpr double FLOAT_TOLERANCE_RELATIVE = 0.0000005; // Relative tolerance for floating-point comparisons if COMPARE_WITH_TOLERANCE is defined +constexpr double FLOAT_TOLERANCE_ABSOLUTE = 0.0000005; // Absolute tolerance for floating-point comparisons if COMPARE_GLOBAL_TOLERANCE is defined +constexpr double FLOAT_TOLERANCE_RELATIVE = 0.0000005; // Relative tolerance for floating-point comparisons if COMPARE_GLOBAL_TOLERANCE is defined // initialisation constants @@ -317,16 +317,19 @@ constexpr double EPSILON_PULSAR = 1.0; constexpr double MIN_HMXRB_STAR_TO_ROCHE_LOBE_RADIUS_RATIO = 0.8; // Minimum value of stellar radius | Roche Lobe radius for visible HMXRBs -constexpr double ADAPTIVE_RLOF_FRACTION_DONOR_GUESS = 0.001; // Fraction of donor mass to use as guess in MassLossToFitInsideRocheLobe() -constexpr int ADAPTIVE_RLOF_MAX_ITERATIONS = 50; // Maximum number of iterations in MassLossToFitInsideRocheLobe() -constexpr double ADAPTIVE_RLOF_SEARCH_FACTOR = 2.0; // Search factor in MassLossToFitInsideRocheLobe() -constexpr int ADAPTIVE_MASS0_MAX_ITERATIONS = 50; // Maximum number of iterations in Mass0ToMatchDesiredCoreMass() -constexpr double ADAPTIVE_MASS0_SEARCH_FACTOR = 2.0; // Search factor in Mass0ToMatchDesiredCoreMass() +constexpr double ADAPTIVE_RLOF_FRACTION_DONOR_GUESS = 0.001; // Fraction of donor mass to use as guess in BaseBinaryStar::MassLossToFitInsideRocheLobe() +constexpr int ADAPTIVE_RLOF_MAX_ITERATIONS = 50; // Maximum number of iterations in BaseBinaryStar::MassLossToFitInsideRocheLobe() +constexpr double ADAPTIVE_RLOF_SEARCH_FACTOR = 2.0; // Search factor in BaseBinaryStar::MassLossToFitInsideRocheLobe() +constexpr int ADAPTIVE_MASS0_MAX_ITERATIONS = 50; // Maximum number of iterations in HG::Mass0ToMatchDesiredCoreMass() +constexpr double ADAPTIVE_MASS0_SEARCH_FACTOR = 2.0; // Search factor in HG::Mass0ToMatchDesiredCoreMass() constexpr double FARMER_PPISN_UPP_LIM_LIN_REGIME = 38.0; // Maximum CO core mass to result in the linear remnant mass regime of the FARMER PPISN prescription constexpr double FARMER_PPISN_UPP_LIM_QUAD_REGIME = 60.0; // Maximum CO core mass to result in the quadratic remnant mass regime of the FARMER PPISN prescription constexpr double FARMER_PPISN_UPP_LIM_INSTABILLITY = 140.0; // Maximum CO core mass to result in PI (upper edge of PISN gap) from FARMER PPISN prescription constexpr double STARTRACK_PPISN_HE_CORE_MASS = 45.0; // Helium core mass remaining following PPISN as assumed in StarTrack (Belczynski et al. 2017 https://arxiv.org/abs/1607.03116) +constexpr int TIDES_OMEGA_MAX_ITERATIONS = 1000; // Maximum number of iterations in BaseBinaryStar::OmegaAfterCircularisation() +constexpr double TIDES_OMEGA_SEARCH_FACTOR = 1.1; // Search factor in BaseBinaryStar::OmegaAfterCircularisation() + // logging constants @@ -591,11 +594,13 @@ enum class ERROR: int { RADIUS_NOT_POSITIVE, // radius is <= 0.0 - invalid RADIUS_NOT_POSITIVE_ONCE, // radius is <= 0.0 - invalid RESOLVE_SUPERNOVA_IMPROPERLY_CALLED, // ResolveSupernova() called, but m_Supernova->IsSNevent() is false + ROOT_FINDER_FAILED, // root finder threw an exception STELLAR_EVOLUTION_STOPPED, // evolution of current star stopped STELLAR_SIMULATION_STOPPED, // stellar simulation stopped SUGGEST_HELP, // suggest using --help TIMESTEP_BELOW_MINIMUM, // timestep too small - below minimum TOO_MANY_MASS0_ITERATIONS, // too many iterations in MASS0 root finder + TOO_MANY_OMEGA_ITERATIONS, // too many iterations in OMEGA root finder TOO_MANY_RLOF_ITERATIONS, // too many iterations in RLOF root finder UNEXPECTED_END_OF_FILE, // unexpected end of file UNEXPECTED_LOG_FILE_TYPE, // unexpected log file type @@ -731,11 +736,13 @@ const COMPASUnorderedMap> ERROR_CATA { ERROR::RADIUS_NOT_POSITIVE, { ERROR_SCOPE::ALWAYS, "Radius <= 0.0" }}, { ERROR::RADIUS_NOT_POSITIVE_ONCE, { ERROR_SCOPE::FIRST_IN_FUNCTION, "Radius <= 0.0" }}, { ERROR::RESOLVE_SUPERNOVA_IMPROPERLY_CALLED, { ERROR_SCOPE::ALWAYS, "ResolveSupernova() called, but m_Supernova->IsSNevent() is false" }}, + { ERROR::ROOT_FINDER_FAILED, { ERROR_SCOPE::ALWAYS, "Exception encountered in root finder" }}, { ERROR::STELLAR_EVOLUTION_STOPPED, { ERROR_SCOPE::ALWAYS, "Evolution of current star stopped" }}, { ERROR::STELLAR_SIMULATION_STOPPED, { ERROR_SCOPE::ALWAYS, "Stellar simulation stopped" }}, { ERROR::SUGGEST_HELP, { ERROR_SCOPE::ALWAYS, "Use option '-h' (or '--help') to see (descriptions of) available options" }}, { ERROR::TIMESTEP_BELOW_MINIMUM, { ERROR_SCOPE::ALWAYS, "Timestep below minimum - timestep taken" }}, { ERROR::TOO_MANY_MASS0_ITERATIONS, { ERROR_SCOPE::ALWAYS, "Reached maximum number of iterations when looking for effective initial mass Mass_0 to match desired stellar core of HG star following case A mass transfer" }}, + { ERROR::TOO_MANY_OMEGA_ITERATIONS, { ERROR_SCOPE::ALWAYS, "Reached maximum number of iterations when looking for omega when circularising and synchronising for tides" }}, { ERROR::TOO_MANY_RLOF_ITERATIONS, { ERROR_SCOPE::ALWAYS, "Reached maximum number of iterations when fitting star inside Roche Lobe in RLOF" }}, { ERROR::UNEXPECTED_END_OF_FILE, { ERROR_SCOPE::ALWAYS, "Unexpected end of file" }}, { ERROR::UNEXPECTED_LOG_FILE_TYPE, { ERROR_SCOPE::ALWAYS, "Unexpected log file type" }}, @@ -2764,7 +2771,7 @@ typedef std::tuple PROPERTY_DETAIL // STAR_PROPERTIES_LABEL const std::map ANY_STAR_PROPERTY_DETAIL = { { ANY_STAR_PROPERTY::AGE, { TYPENAME::DOUBLE, "Age", "Myr", 16, 8 }}, - { ANY_STAR_PROPERTY::ANGULAR_MOMENTUM, { TYPENAME::DOUBLE, "Ang_Momentum", "Msol*AU^2*yr^-1", 14, 6 }}, + { ANY_STAR_PROPERTY::ANGULAR_MOMENTUM, { TYPENAME::DOUBLE, "Ang_Momentum", "Msol AU^2 yr^-1", 14, 6 }}, { ANY_STAR_PROPERTY::BINDING_ENERGY_AT_COMMON_ENVELOPE, { TYPENAME::DOUBLE, "Binding_Energy@CE", "erg", 14, 6 }}, { ANY_STAR_PROPERTY::BINDING_ENERGY_FIXED, { TYPENAME::DOUBLE, "BE_Fixed", "erg", 14, 6 }}, { ANY_STAR_PROPERTY::BINDING_ENERGY_NANJING, { TYPENAME::DOUBLE, "BE_Nanjing", "erg", 14, 6 }}, @@ -3010,8 +3017,8 @@ const std::map BINARY_PROPERTY_DETAIL = { { BINARY_PROPERTY::SYSTEMIC_SPEED, { TYPENAME::DOUBLE, "SystemicSpeed", "kms^-1", 14, 6 }}, { BINARY_PROPERTY::TIME, { TYPENAME::DOUBLE, "Time", "Myr", 16, 8 }}, { BINARY_PROPERTY::TIME_TO_COALESCENCE, { TYPENAME::DOUBLE, "Coalescence_Time", "Myr", 16, 8 }}, - { BINARY_PROPERTY::TOTAL_ANGULAR_MOMENTUM, { TYPENAME::DOUBLE, "Ang_Momentum_Total", "Msol*AU^2*yr^-1", 14, 6 }}, - { BINARY_PROPERTY::TOTAL_ENERGY, { TYPENAME::DOUBLE, "Energy_Total", "Msol*AU^2*yr^-2", 14, 6 }}, + { BINARY_PROPERTY::TOTAL_ANGULAR_MOMENTUM, { TYPENAME::DOUBLE, "Ang_Momentum_Total", "Msol AU^2 yr^-1", 14, 6 }}, + { BINARY_PROPERTY::TOTAL_ENERGY, { TYPENAME::DOUBLE, "Energy_Total", "Msol AU^2 yr^-2", 14, 6 }}, { BINARY_PROPERTY::UNBOUND, { TYPENAME::BOOL, "Unbound", "State", 0, 0 }}, { BINARY_PROPERTY::ZETA_LOBE, { TYPENAME::DOUBLE, "Zeta_Lobe", "-", 14, 6 }}, { BINARY_PROPERTY::ZETA_STAR, { TYPENAME::DOUBLE, "Zeta_Star", "-", 14, 6 }} diff --git a/src/outfile b/src/outfile new file mode 100644 index 000000000..0729592b1 --- /dev/null +++ b/src/outfile @@ -0,0 +1,117 @@ + +COMPAS v02.40.00 +Compact Object Mergers: Population Astrophysics and Statistics +by Team COMPAS (http://compas.science/index.html) +A binary star simulator + +Start generating binaries at Wed Oct 25 15:33:51 2023 + +0: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +1: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +2: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +3: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +4: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Neutron_Star) +5: Stars touching: (Main_Sequence_>_0.7 -> Massless_Remnant) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +6: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +7: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +8: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +9: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +10: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +11: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) +12: Stars merged: (Main_Sequence_>_0.7 -> Naked_Helium_Star_MS) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +13: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) +14: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +15: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +16: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +17: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +18: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +19: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +20: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) +21: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +22: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +23: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +24: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) +25: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +26: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +27: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Neutron_Star) +28: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +29: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +30: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) +31: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +32: Allowed time exceeded: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +33: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) +34: Allowed time exceeded: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) +35: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +36: Allowed time exceeded: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) +37: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) +38: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +39: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +40: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) +41: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +42: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) +43: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Neutron_Star) +44: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +45: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) +46: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +47: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) +48: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) +49: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +50: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) +51: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +52: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) +53: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) +54: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +55: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Black_Hole) +56: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +57: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) +58: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +59: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +60: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +61: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +62: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +63: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +64: Allowed time exceeded: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) +65: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +66: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) +67: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +68: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) +69: Allowed time exceeded: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +70: Stars merged: (Main_Sequence_>_0.7 -> Naked_Helium_Star_MS) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +71: Stars merged: (Main_Sequence_>_0.7 -> Naked_Helium_Star_MS) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +72: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) +73: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +74: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) +75: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +76: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) +77: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Neutron_Star) +78: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) +79: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +80: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +81: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +82: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +83: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +84: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +85: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +86: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +87: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) +88: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +89: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +90: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +91: Stars merged: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Thermally_Pulsing_Asymptotic_Giant_Branch) +92: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +93: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +94: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +95: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) +96: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) +97: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +98: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) +99: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) + +Generated 100 of 100 binaries requested + +Simulation completed + +End generating binaries at Wed Oct 25 15:33:53 2023 + +Clock time = 2.12871 CPU seconds +Wall time = 0000:00:02 (hhhh:mm:ss) diff --git a/src/utils.cpp b/src/utils.cpp index c6b59d04f..326ab4a47 100644 --- a/src/utils.cpp +++ b/src/utils.cpp @@ -168,26 +168,53 @@ namespace utils { /* * Compare floating-point numbers with tolerance * - * Absolute and relative tolerance can be different - see constants.h - * Set relative tolerance = 0.0 to always use absolute - * Set absolute tolerance = 0.0 to always use relative - * Set both to zero for no tolerance - or #undef COMPARE_WITH_TOLERANCE for performance - * + * For comparisons using the global tolerance values (FLOAT_TOLERANCE_ABSOLUTE, FLOAT_TOLERANCE_RELATIVE): + * - Absolute and relative tolerance can be different - see constants.h + * - Set relative tolerance = 0.0 to always use absolute + * - Set absolute tolerance = 0.0 to always use relative + * - Set both to zero for no tolerance - or #undef COMPARE_GLOBAL_TOLERANCE for performance + * + * If p_Tolerance is > 0.0 it will be used in preference to the global tolerance values + * If p_Tolerance is > 0.0, then p_Absolute determines if p_tolerance should be treated as an absolute + * torelace (p_Absolute = true), or a relative tolerance (p_Absolete = false). + * * * int Compare(const double p_X, const double p_Y) * * @param [IN] p_X Floating-point value to be compared * @param [IN] p_Y Floating-point value to be compared + * @param [IN] p_Tolerance Floating-point tolerance value - if > 0.0 supersedes global tolerance + * @param [IN] p_Absolute Boolean indicatin whether p_Tolerance should be treated as absolute tolerance (true) or relative tolerance (false) * @return Integer indicating result of comparison: * -1 indicates p_X is less than p_Y * 0 indicates equality * 1 indicates p_X is greater than p_Y */ - int Compare(const double p_X, const double p_Y) { - #ifdef COMPARE_WITH_TOLERANCE - return (std::abs(p_X - p_Y) <= std::max(FLOAT_TOLERANCE_ABSOLUTE, FLOAT_TOLERANCE_RELATIVE * std::max(std::abs(p_X), fabs(p_Y)))) ? 0 : (p_X < p_Y ? -1 : 1); + int Compare(const double p_X, const double p_Y, const double p_Tolerance, const bool p_Absolute) { + #ifdef COMPARE_GLOBAL_TOLERANCE + if (p_Tolerance > 0.0) { // use tolerance passed? + if (p_Absolute) { // yes - absolute tolerance? + return (std::abs(p_X - p_Y) <= p_Tolerance) ? 0 : (p_X < p_Y ? -1 : 1); // yes + } + else { // no - relative tolerance + return (std::abs(p_X - p_Y) <= p_Tolerance * std::max(std::abs(p_X), fabs(p_Y))) ? 0 : (p_X < p_Y ? -1 : 1); + } + } + else { // use global tolerance + return (std::abs(p_X - p_Y) <= std::max(FLOAT_TOLERANCE_ABSOLUTE, FLOAT_TOLERANCE_RELATIVE * std::max(std::abs(p_X), fabs(p_Y)))) ? 0 : (p_X < p_Y ? -1 : 1); + } #else - return (p_X == p_Y) ? 0 : (p_X < p_Y ? -1 : 1); + if (p_Tolerance > 0.0) { // use tolerance passed? + if (p_Absolute) { // yes - absolute tolerance? + return (std::abs(p_X - p_Y) <= p_Tolerance) ? 0 : (p_X < p_Y ? -1 : 1); // yes + } + else { // no - relative tolerance + return (std::abs(p_X - p_Y) <= p_Tolerance * std::max(std::abs(p_X), fabs(p_Y))) ? 0 : (p_X < p_Y ? -1 : 1); + } + } + else { // no tolerance + return (p_X == p_Y) ? 0 : (p_X < p_Y ? -1 : 1); + } #endif } diff --git a/src/utils.h b/src/utils.h index 591b04b2b..459e78e26 100755 --- a/src/utils.h +++ b/src/utils.h @@ -21,7 +21,7 @@ namespace utils { std::string CentreJustify(const std::string p_Str, std::size_t p_Width); - int Compare(const double p_X, const double p_Y); + int Compare(const double p_X, const double p_Y, const double p_Tolerance = -1.0, const bool p_Absolute = true); double ConvertPeriodInDaysToSemiMajorAxisInAU(const double p_Mass1, const double p_Mass2, const double p_Period); diff --git a/src/yaml.h b/src/yaml.h index 3ec9e417d..1f12b9464 100644 --- a/src/yaml.h +++ b/src/yaml.h @@ -113,6 +113,9 @@ namespace yaml { " --common-envelope-lambda-nanjing-use-rejuvenated-mass", " --revised-energy-formalism-nandez-ivanova", "", + " ### TIDES", + " --enable-tides", + "", " ### SUPERNOVAE, KICKS AND REMNANTS", " --allow-non-stripped-ECSN", " --pair-instability-supernovae", From 6c1072f167f0b7696fd15bc1e4219a5e83cfdbb6 Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Wed, 25 Oct 2023 15:36:24 +1100 Subject: [PATCH 03/32] Enhancement, a little cleanup: Added naive tides implementation. Functionality enable with new optiin '--enable-tides'. default is no tides. --- src/changelog.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/changelog.h b/src/changelog.h index 78cfff942..f0db15744 100644 --- a/src/changelog.h +++ b/src/changelog.h @@ -1058,7 +1058,7 @@ // 02.39.01 LC - Sep 01, 2023 - Defect repair: // - Fix for issue #945 - made HeSD SN types a sub-class of SNIA types. // 02.40.00 JR - Oct 25, 2023 - Enhancement, a little cleanup: -// - Added naive tides implementation. Functionality enable with new optin `--enable-tides`. default is no tides. +// - Added naive tides implementation. Functionality enable with new option `--enable-tides`. default is no tides. const std::string VERSION_STRING = "02.40.00"; From e286196ff8bd88b06dba1477fbeb520df03008da Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Wed, 25 Oct 2023 15:50:47 +1100 Subject: [PATCH 04/32] Remove spurious files --- .../notebooks/CHE_evolution_demo.ipynb | 1138 ------------ .../CHE_evolution_demo_ANSWERS.ipynb | 1618 ----------------- 2 files changed, 2756 deletions(-) delete mode 100644 online-docs/notebooks/CHE_evolution_demo.ipynb delete mode 100644 online-docs/notebooks/CHE_evolution_demo_ANSWERS.ipynb diff --git a/online-docs/notebooks/CHE_evolution_demo.ipynb b/online-docs/notebooks/CHE_evolution_demo.ipynb deleted file mode 100644 index a1ab18daa..000000000 --- a/online-docs/notebooks/CHE_evolution_demo.ipynb +++ /dev/null @@ -1,1138 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d69d65d0", - "metadata": {}, - "source": [ - "
\n", - "\n", - " \n", - "# COMPAS Special Tutorial: \"Formation channels of Gravitational Waves (GWs)\"\n", - "\n", - "### This is a tutorial that can be used for live teaching/demos, it should fill about ~1hr of class\n", - " \n", - "In this jupyter notebook we will walk through and re-create some of the figures from https://arxiv.org/pdf/2010.00002.pdf on **Chemically Homogeneous Evolution** by Jeff Riley. A PDF of this paper can be found in the directory under the name CHE_paper.pdf.
\n", - "\n", - "\n", - "\n", - "Notebook by Floor Broekgaarden, Jeff Riley and Ilya Mandel, originally created for the Saas Fee PhD School
\n", - "
\n", - "\n", - "The original data can be found on Zenodo https://zenodo.org/record/5595426
\n", - "For this tutorial we have downloaded COMPAS_Output.h5 from the auhtor's dataset. Note that this data is run with a slightly older version of COMPAS than the most recent COMPAS. \n", - " \n", - "___\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "480b8a96", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Throughout this notebook and in class we will use several acronyms and definitions listed below \n", - " \n", - " \n", - " \n", - "### Definitions: \n", - " \n", - " \n", - " - CHE: Chemically Homogeneous Evolution, \n", - " - GW: Gravitational Waves \n", - " - DCO: Double Compact Object \n", - " - BH: Black Hole\n", - " - NS: Neutron Star\n", - " - Primary: in this notebook always refers to the star that was most massive at the zero age main sequence (ZAMS)\n", - " - Secondary: in this notebook always refers to the star that was least massive at the zero age main sequence (ZAMS)\n", - " - ZAMS: Zero Age Main Sequence: this is in COMPAS where stars start their lives. \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ed54cf65", - "metadata": {}, - "outputs": [], - "source": [ - "# first we will import some of the packages that we will use \n", - "import h5py as h5\n", - "import numpy as np\n", - "import os\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# we will use astropy for some useful constants and units \n", - "from astropy import units as u\n", - "from astropy import constants as const\n", - "from matplotlib.ticker import (FormatStrFormatter,\n", - " AutoMinorLocator)\n", - "from IPython.display import Image # to open images in Ipython \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "64224ff3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['CommonEnvelopes', 'DoubleCompactObjects', 'Supernovae', 'SystemParameters']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# add path to where the COMPASOutput.h5 file is stored. \n", - "# For you the part '~/Downloads/' is probably different\n", - "path = '/Users/floorbroekgaarden/Downloads/COMPAS_Output.h5' # change this line! \n", - "\n", - "# the following line reads in the data \n", - "fdata = h5.File(path, 'r')\n", - "list(fdata.keys()) # print the different files within the hdf5 folder: \n", - "\n", - "\n", - "\n", - "# to close the file you will have to use fdata.close()\n" - ] - }, - { - "cell_type": "markdown", - "id": "8c1a0e92", - "metadata": {}, - "source": [ - "
\n", - "\n", - "\n", - "\n", - "the files above 'DoubleCompactObjects', 'Supernovae', 'SystemParameters' store the properties of the simulated binaries at the stages of the 'commen enevelope' (in case there is one), the moment of double object formation, the moment of the supernova, and the initial conditions (at the zero-age main sequence).\n", - "\n", - "#### We can view what parameters are stored by again using the command .keys()\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "af4c3be7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Coalescence_Time', 'Eccentricity@DCO', 'MT_Case_1', 'MT_Case_2', 'Mass_1', 'Mass_2', 'Merges_Hubble_Time', 'Recycled_NS_1', 'Recycled_NS_2', 'SEED', 'Separation@DCO', 'Stellar_Type_1', 'Stellar_Type_2', 'Time']\n", - "\n", - "['CE_Alpha', 'CH_on_MS_1', 'CH_on_MS_2', 'Eccentricity@ZAMS', 'Equilibrated', 'Equilibrated_At_Birth', 'Error', 'Experienced_RLOF_1', 'Experienced_RLOF_2', 'Experienced_SN_Type_1', 'Experienced_SN_Type_2', 'LBV_Multiplier', 'LBV_Phase_Flag_1', 'LBV_Phase_Flag_2', 'Mass@ZAMS_1', 'Mass@ZAMS_2', 'Merger', 'Merger_At_Birth', 'Metallicity@ZAMS_1', 'Metallicity@ZAMS_2', 'Omega@ZAMS_1', 'Omega@ZAMS_2', 'SEED', 'SN_Kick_Magnitude_Random_Number_1', 'SN_Kick_Magnitude_Random_Number_2', 'SN_Kick_Mean_Anomaly_1', 'SN_Kick_Mean_Anomaly_2', 'SN_Kick_Phi_1', 'SN_Kick_Phi_2', 'SN_Kick_Theta_1', 'SN_Kick_Theta_2', 'Separation@ZAMS', 'Sigma_Kick_CCSN_BH', 'Sigma_Kick_CCSN_NS', 'Sigma_Kick_ECSN', 'Sigma_Kick_USSN', 'Stellar_Type@ZAMS_1', 'Stellar_Type@ZAMS_2', 'Stellar_Type_1', 'Stellar_Type_2', 'Time', 'Unbound', 'WR_Multiplier']\n", - "\n", - "['Applied_Kick_Velocity_SN', 'Drawn_Kick_Velocity_SN', 'Eccentricity', 'Eccentricity \n", - "\n", - "#### The meaning of all parameters and files are described here https://compas.readthedocs.io/en/latest/pages/User%20guide/COMPAS%20output/standard-logfiles.html\n", - "\n", - "\n", - "Now that we have the data, we can do some data investigation. Here is an example of how to read the \"SEED\" parameter, which is a unique number for each binary that is run. \n", - " \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b83022e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 400047 400065 400101 ... 11599854 11599926 11599965]\n" - ] - } - ], - "source": [ - "SEED_DCO = fdata['DoubleCompactObjects'][\"SEED\"][...].squeeze()\n", - "print(SEED_DCO)" - ] - }, - { - "cell_type": "markdown", - "id": "30966640", - "metadata": {}, - "source": [ - "
\n", - "\n", - "## Question 1\n", - "#### - a: check and write down the number of rows (entries) of each of the dataset groups, 'DoubleCompactObjects', 'Supernovae', 'SystemParameters'.
\n", - " \n", - "#### - b: If the lengths of the rows are different why is this so? And does it make sense which group has the most/least rows?
\n", - "\n", - "*Hint*: you might want to look at table 1 in the paper CHE_paper.pdf and the descriptions at https://compas.readthedocs.io/en/latest/pages/User%20guide/COMPAS%20output/standard-logfiles.html\n", - " \n", - "#### - c: Why is the number of rows in 'DoubleCompactObjects' not the same as the total number of 'BBHs formed' in Table 1 from this paper?" - ] - }, - { - "cell_type": "markdown", - "id": "96e2bf75", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29489b28", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a01f693e", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4cc16c7", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5ffcb1f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97c05125", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "d051687d", - "metadata": {}, - "source": [ - "
\n", - "\n", - " \n", - "## Example 1: plotting BH masses \n", - "___\n", - "below we show an example of how to obtain and plot the compact object masses in the dataset \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d142978f", - "metadata": {}, - "outputs": [], - "source": [ - "# this is just a little function that we will use to make the plot more beautiful (bigger ticks, labels)\n", - "# However, you do not have to use this (just uncommoment \"layoutAxes\" everywhere)\n", - "\n", - "def layoutAxes(ax, nameX='', nameY='', \\\n", - " labelSizeMajor = 10, fontsize = 25, second=False, labelpad=None, setMinor=True):\n", - " \"\"\"\n", - " Tiny code to do the layout for axes in matplotlib\n", - " \"\"\"\n", - " tickLengthMajor = 10\n", - " tickLengthMinor = 5\n", - " tickWidthMajor = 1.5\n", - " tickWidthMinor = 1.5\n", - " \n", - " #rc('axes', linewidth=2)\n", - " #label1 always refers to first axis not the twin \n", - " if not second:\n", - " for tick in ax.xaxis.get_major_ticks():\n", - " tick.label1.set_fontsize(fontsize)\n", - " #tick.label1.set_fontweight('bold')\n", - " for tick in ax.yaxis.get_major_ticks():\n", - " tick.label1.set_fontsize(fontsize)\n", - " #tick.label1.set_fontweight('bold')\n", - " if second:\n", - " for tick in ax.xaxis.get_major_ticks():\n", - " tick.label2.set_fontsize(fontsize)\n", - " #tick.label1.set_fontweight('bold')\n", - " for tick in ax.yaxis.get_major_ticks():\n", - " tick.label2.set_fontsize(fontsize)\n", - " #tick.label1.set_fontweight('bold')\n", - " for axis in ['top','bottom','left','right']:\n", - " ax.spines[axis].set_linewidth(1.2)\n", - " ax.tick_params(length=tickLengthMajor, width=tickWidthMajor, which='major')\n", - " ax.tick_params(length=tickLengthMinor, width=tickWidthMinor, which='minor')\n", - " ax.set_xlabel(nameX, fontsize=fontsize,labelpad=labelpad)#,fontweight='bold')\n", - " ax.set_ylabel(nameY, fontsize=fontsize,labelpad=labelpad)#, fontweight='bold') \n", - " \n", - " if setMinor==True:\n", - " # add minor ticks:\n", - " ax.xaxis.set_minor_locator(AutoMinorLocator())\n", - " ax.yaxis.set_minor_locator(AutoMinorLocator())\n", - "\n", - " return ax\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "869c90d1", - "metadata": {}, - "outputs": [], - "source": [ - "fDCO = fdata['DoubleCompactObjects']\n", - "\n", - "\n", - "M1 = fDCO['Mass_1'][...].squeeze() # mass in Msun of the compact object resulting from the *primary star*\n", - "M2 = fDCO['Mass_2'][...].squeeze() # mass in Msun of the compact object resulting from the *secondary star*\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ff02ea16", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", - "\n", - "plt.scatter(M1, M2)\n", - "plt.xlabel('Mass of compact object from primary star [Msun]', fontsize=20)\n", - "plt.ylabel('Mass of compact object from secondary star [Msun]', fontsize=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0db7f21e", - "metadata": {}, - "source": [ - "
\n", - "\n", - "### Question 2: \n", - " \n", - " - a): can you explain some of the features in the plot above? E.g., where are the gaps, where are the most datapoints?\n", - " \n", - " \n", - " - b): Are there any BH+NS or NS+NS in the dataset? If so, plot them\n", - " \n", - " \n", - " - c): extra: how many BH+NS, vs. NS+NS vs. BH-BH systems are there? And what is the total? \n", - "\n", - "*Hint*: A NS in this COMPAS simulation is defined as a compact object with mass < 2.5 Msun \n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "51727576", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42a75636", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48bd18ce", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1181d94", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a252ea2", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc00bc37", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9f09ef6", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d3421fd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "131d98f5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad78bc31", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "c850bd60", - "metadata": {}, - "source": [ - "
\n", - " \n", - "### Question 3: \n", - " \n", - " \n", - " - a): Using the parameters in the 'DoubleCompactObjects' dataset and the example above, try to make a scatter plot of Total Mass (M1+M2) versus orbital Period of the BBH systems that merge within a Hubble time (13.7 Gyr) \n", - " \n", - " Plot the period on the y-axis and the total mass on the x-axis. Plot the period in days. \n", - " \n", - "*Hint: You might want to use Kerpler's III law to complete the function below *\n", - " \n", - " \n", - "*Hint:* you will have to select BH+BH systems, and only systems that merge within a Hubble time " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2a2e21da", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def separation_to_period_circular_case(separation=10*u.AU, M1=1*u.M_sun, M2=1*u.M_sun):\n", - " \"\"\"calculate Period from separation\n", - " separation is separation of the binary (needs to be given in astropy units)\n", - " M1 and M2 are masses of the binary\n", - " This is based on Kepler's law, using a circular orbit\n", - " \n", - " \"\"\"\n", - " G = const.G # [g cm s^2]\n", - " \n", - " ## use Kepler;s III law to calculate the period here \n", - " \n", - " \n", - " \n", - " ###\n", - " \n", - " return period\n" - ] - }, - { - "cell_type": "markdown", - "id": "30ad2751", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f3c8674", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ecec29a5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62cc5edc", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b23ac61", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "201763f3", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Question 4: \n", - " \n", - " \n", - " - a): Why does the plot that you created look different compared to the figure 6 in https://arxiv.org/pdf/2010.00002.pdf? (you may ignore the metallicity axes) \n", - " \n", - " \n", - " \n", - " - b): There is a tail of systems at rather large orbital periods that are merging. How is this possible? \n", - "\n", - "*Hint 4b: plot the eccentricity as a color gradient on the marker using the \"c=\" option of plt.scatter. \n", - "How is eccentricity imparted to these systems?* " - ] - }, - { - "cell_type": "markdown", - "id": "c3322c83", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fba311f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "0e08bfed", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Selecting CHE binaries: \n", - " \n", - " \n", - " For binaries, Stellar_Type@ZAMS(1) and Stellar_Type@ZAMS(2) will tell you the initial stellar type of each star - type 16 is CH.\n", - "CH_on_MS(1) and CH_on_MS(2) are each true if the star remained as CH for the entire MS - they will be false if the star spun down and stopped being CH on the MS. So any star that was initially CH, and stayed CH on the entire MS is considered to be CHE. We can check which of our binary black holes is a \"CHE\" by using this information stored in the 'systemParameters' file, and matching it with the double compact object files using the randomSeed.\n", - "\n", - "Note that we also have to remove binaries that merged on the ZAMS as stars, since we are not interested in these\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "714118e5", - "metadata": {}, - "outputs": [], - "source": [ - "fsys = fdata['SystemParameters']\n", - "\n", - "CH_on_MS_1 = fsys['CH_on_MS_1'][...].squeeze() # mass in Msun of the compact object resulting from the primary\n", - "CH_on_MS_2 = fsys['CH_on_MS_2'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", - "Stellar_TypeZAMS_1 = fsys['Stellar_Type@ZAMS_1'][...].squeeze() # mass in Msun of the compact object resulting from the primary\n", - "Stellar_TypeZAMS_2 = fsys['Stellar_Type@ZAMS_2'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", - "\n", - "# binaries that merge at birth as stars\n", - "Merger_At_Birth = fsys['Merger_At_Birth'][...].squeeze()\n", - "\n", - "# SEED of the system Parameters (unique number corresponding to each binary)\n", - "SEED = fsys['SEED'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "99057a0b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "13644 are CHE out of 12000000 systems run\n", - "[ 400378 400412 402049 ... 11589507 11589863 11594670]\n" - ] - } - ], - "source": [ - "\n", - "# the CHE systems are then selected by systems that are CHE on ZAMS (stellar type 16) AND remain CHE on the MS (main sequence)\n", - "# in addition we do not want systems that Merged at Birth \n", - "mask_CHE = (CH_on_MS_1==1) & (CH_on_MS_2==1) & (Stellar_TypeZAMS_1==16) & (Stellar_TypeZAMS_2==16) & (Merger_At_Birth==0)\n", - "\n", - "print(np.sum(mask_CHE), 'are CHE out of ', len(mask_CHE), 'systems run')\n", - "\n", - "\n", - "# let's find the seed of the CHE systems: \n", - "SEED_CHE = SEED[mask_CHE]\n", - "print(SEED_CHE)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "3c8919d0", - "metadata": {}, - "source": [ - "
\n", - " \n", - "We find 13644 total CHE binaries in our simulation, note that this is the same as the number quoted in the CHE paper under \"Both stars remained on the CH\" and \"Total\" in Table 1\n", - " \n" - ] - }, - { - "cell_type": "markdown", - "id": "f2115984", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Question 5: \n", - " \n", - " - a): Using the code above, recreate figure 6 in https://arxiv.org/pdf/2010.00002.pdf? (you may ignore the metallicity axes) \n", - " \n", - " - b): Explain what you see \n", - " \n", - "#### Hint: A useful line of code is: np.in1d(), below is an example of how it works" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6ba613a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ True False True]\n" - ] - } - ], - "source": [ - "# example of np.in1d() function\n", - "\n", - "A = [1,2,3]\n", - "B = [1,3,5,7,9]\n", - "\n", - "print(np.in1d(A, B))" - ] - }, - { - "cell_type": "markdown", - "id": "4405bc29", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2724ab2d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27f0c3e5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b5cb307", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bb15a45", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "a25df87b", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Question 6: \n", - " \n", - " - a): Try to recreeat the figure 4 in https://arxiv.org/pdf/2010.00002.pdf? \n", - " \n", - " \n", - " - b): Explain what you see " - ] - }, - { - "cell_type": "markdown", - "id": "0175ec47", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 6" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7c16f01", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f745cda9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7cca1041", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "544dc9c9", - "metadata": {}, - "source": [ - "
\n", - "\n", - " \n", - "For the last part of this excersize we will use the code 'FastCosmicIntegrator'. Our goal will be to calculate the merger rate for BBHs, so that we can compare to the analytical estimate we made earlier on. \n", - "\n", - "## Question 7: \n", - " \n", - " \n", - "Using the code below, plot the merger rate of CHE BBHs as a function of redshift. You should at the end of running this code create a plot with four panels that show the properties of the CHE binaries \n", - " \n", - " - a) what do the panels show? What are the differences between the panels? \n", - " - b): please write down the local (z=0) BBH merger rate, and compare this with your analytically calculated rate \n", - " - c): compare both rates with the other BBH rates as reported in Mandel & Broekgaarden et al. (2021) (Fig 3)\n", - "\n", - " \n", - " - d): Repeat the excersize above, and answer 7a & 7b above, but now for all BBHs (including non CHE). You can do this by changing dco_type to 'BBH'. What are the differences\n", - "\n", - " \n", - " \n", - " *Hint* we can do an approximate calculation by combining the Wolf Rayet factors. If you want to do the more expert version you can modify the code in ClassCOMPAS (setCOMPASDCOmask) and add the mask of a specific f_WR factor" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "681a2767", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.11/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/floorbroekgaarden/Projects/GitHub/COMPAS/utils/Tutorial/Tutorial_reproduce_CHE_paper_teaching_demo_example/FastCosmicIntegration.py': [Errno 2] No such file or directory\r\n" - ] - } - ], - "source": [ - "!python3 FastCosmicIntegration.py \\\n", - "--dco_type 'CHE_BBH' \\\n", - "--path '/Users/floorbroekgaarden/Downloads/COMPAS_Output.h5' \\\n", - "--maxz 15 \\\n", - "--dontAppend" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "285095f6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHE_evolution_demo.ipynb\r\n", - "CHE_evolution_demo_ANSWERS.ipynb\r\n", - "COMPAS_Documentation.pdf\r\n", - "Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png\r\n", - "SNR_Grid_IMRPhenomPv2_FD_all_noise.hdf5\r\n", - "\u001b[34m__pycache__\u001b[m\u001b[m\r\n", - "old_ClassCOMPAS.py\r\n", - "old_FastCosmicIntegration.py\r\n", - "old_selection_effects.py\r\n", - "old_totalMassEvolvedPerZ.py\r\n" - ] - } - ], - "source": [ - "!ls " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0b87ce45", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show the image in the notebook:\n", - "Image(filename='./Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png') \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "ee442a0e", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 7\n", - " \n", - " \n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3597ac3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d85530d5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1b8225c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "6c657d0a", - "metadata": {}, - "source": [ - "
\n", - "\n", - "## Extra Question 8: \n", - " \n", - " \n", - "Play around with some of the parameters in the code FastCosmicIntegrater, do the rates go up or down? Is this expected?" - ] - }, - { - "cell_type": "markdown", - "id": "00e3011c", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 8\n", - " \n", - " \n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c64aa01", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66ebbb5a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "921b84b0", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "93c376a5", - "metadata": {}, - "source": [ - "
\n", - "\n", - " \n", - " \n", - "\n", - "## EXTRA using the CHE data: \n", - " \n", - "If there is time left, try to plot some other BBH or ZAMS properties of the BBH, (or NSBH or BNS), examples include chirp mass, mass ratio, individual masses. How do these compare with LIGOs observations (paper is attached to this directory)\n", - "\n", - " \n", - " \n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a647b78", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c370cf85", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4a02ec4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "017418fc", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "3e78c305", - "metadata": {}, - "source": [ - "
\n", - " \n", - " # Extra material:" - ] - }, - { - "cell_type": "markdown", - "id": "0b84006f", - "metadata": {}, - "source": [ - "[//]: ## (grip -b README.md)\n", - "\n", - "\n", - "\n", - "# Compact Object Mergers: Population Astrophysics & Statistics\n", - "\n", - "[![Documentation](https://img.shields.io/badge/Documentation-latest-orange.svg?style=flat)](https://github.com/TeamCOMPAS/COMPAS/blob/Documentation/COMPAS_Documentation.pdf)\n", - "\n", - "[//]: ## (Outline features)\n", - "COMPAS is a publicly available rapid binary population synthesis code (https://compas.science/) that is designed so that evolution prescriptions and model parameters are easily \n", - "adjustable. COMPAS draws properties for a binary star system from a set of initial distributions, and evolves it from zero-age main sequence to the end of its life as two compact \n", - "remnants. It has been used for inference from observations of gravitational-wave mergers, Galactic neutron stars, X-ray binaries, and luminous red novae.\n", - "\n", - "## Documentation\n", - "https://compas.science/docs\n", - "\n", - "## Contact\n", - "Please email your queries to compas-user@googlegroups.com. You are also welcome to join the [COMPAS User Google Group](https://groups.google.com/forum/#!members/compas-user) to engage in discussions with COMPAS users and developers.\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "## Example of additional excersizes\n", - "\n", - "If you are interested, you can download the COMPAS code from Github and do any of the following excersizes: \n", - "\n", - "\n", - "1). Try to run any of the demos that are provided (I recommend the Chirp mass distribution demo, and/or the detailed evolution demo)\n", - "\n", - "2.) Try to use the code, and the data above, to plot a detailed evolution plot of a CHE BBH. \n", - "\n", - "3.) Run a larger COMPAS simulation with your own favorite settings, compare this to the data given in this demo. \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f54328e3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/online-docs/notebooks/CHE_evolution_demo_ANSWERS.ipynb b/online-docs/notebooks/CHE_evolution_demo_ANSWERS.ipynb deleted file mode 100644 index e85097d7f..000000000 --- a/online-docs/notebooks/CHE_evolution_demo_ANSWERS.ipynb +++ /dev/null @@ -1,1618 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d69d65d0", - "metadata": {}, - "source": [ - "# COMPAS Special Tutorial: \"Formation channels of Gravitational Waves (GWs)\" -- ANSWERS\n", - "\n", - "### This is a tutorial that can be used for live teaching/demos, it should fill about ~1hr of class\n", - " \n", - "In this jupyter notebook we will walk through and re-create some of the figures from https://arxiv.org/pdf/2010.00002.pdf on **Chemically Homogeneous Evolution** by Jeff Riley. A PDF of this paper can be found in the directory under the name CHE_paper.pdf.
\n", - "\n", - "\n", - "\n", - "Notebook by Floor Broekgaarden, Jeff Riley and Ilya Mandel, originally created for the Saas Fee PhD School
\n", - "
\n", - "\n", - "The original data can be found on Zenodo https://zenodo.org/record/5595426
\n", - "For this tutorial we have downloaded COMPAS_Output.h5 from the auhtor's dataset. Note that this data is run with a slightly older version of COMPAS than the most recent COMPAS. \n", - " \n", - "___\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "480b8a96", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Throughout this notebook and in class we will use several acronyms and definitions listed below \n", - " \n", - " \n", - " \n", - "### Definitions: \n", - " \n", - " \n", - " - CHE: Chemically Homogeneous Evolution, \n", - " - GW: Gravitational Waves \n", - " - DCO: Double Compact Object \n", - " - BH: Black Hole\n", - " - NS: Neutron Star\n", - " - Primary: in this notebook always refers to the star that was most massive at the zero age main sequence (ZAMS)\n", - " - Secondary: in this notebook always refers to the star that was least massive at the zero age main sequence (ZAMS)\n", - " - ZAMS: Zero Age Main Sequence: this is in COMPAS where stars start their lives. \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ed54cf65", - "metadata": {}, - "outputs": [], - "source": [ - "# first we will import some of the packages that we will use \n", - "import h5py as h5\n", - "import numpy as np\n", - "import os\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# we will use astropy for some useful constants and units \n", - "from astropy import units as u\n", - "from astropy import constants as const\n", - "from matplotlib.ticker import (FormatStrFormatter,\n", - " AutoMinorLocator)\n", - "from IPython.display import Image # to open images in Ipython \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "64224ff3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['CommonEnvelopes', 'DoubleCompactObjects', 'Supernovae', 'SystemParameters']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# add path to where the COMPASOutput.h5 file is stored. \n", - "# For you the part '~/Downloads/' is probably different\n", - "path = '/Users/floorbroekgaarden/Downloads/COMPAS_Output.h5' # change this line! \n", - "\n", - "# the following line reads in the data \n", - "fdata = h5.File(path, 'r')\n", - "list(fdata.keys()) # print the different files within the hdf5 folder: \n", - "\n", - "\n", - "\n", - "# to close the file you will have to use fdata.close()\n" - ] - }, - { - "cell_type": "markdown", - "id": "8c1a0e92", - "metadata": {}, - "source": [ - "
\n", - "\n", - "\n", - "\n", - "the files above 'DoubleCompactObjects', 'Supernovae', 'SystemParameters' store the properties of the simulated binaries at the stages of the 'commen enevelope' (in case there is one), the moment of double object formation, the moment of the supernova, and the initial conditions (at the zero-age main sequence).\n", - "\n", - "#### We can view what parameters are stored by again using the command .keys()\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "af4c3be7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Coalescence_Time', 'Eccentricity@DCO', 'MT_Case_1', 'MT_Case_2', 'Mass_1', 'Mass_2', 'Merges_Hubble_Time', 'Recycled_NS_1', 'Recycled_NS_2', 'SEED', 'Separation@DCO', 'Stellar_Type_1', 'Stellar_Type_2', 'Time']\n", - "\n", - "['CE_Alpha', 'CH_on_MS_1', 'CH_on_MS_2', 'Eccentricity@ZAMS', 'Equilibrated', 'Equilibrated_At_Birth', 'Error', 'Experienced_RLOF_1', 'Experienced_RLOF_2', 'Experienced_SN_Type_1', 'Experienced_SN_Type_2', 'LBV_Multiplier', 'LBV_Phase_Flag_1', 'LBV_Phase_Flag_2', 'Mass@ZAMS_1', 'Mass@ZAMS_2', 'Merger', 'Merger_At_Birth', 'Metallicity@ZAMS_1', 'Metallicity@ZAMS_2', 'Omega@ZAMS_1', 'Omega@ZAMS_2', 'SEED', 'SN_Kick_Magnitude_Random_Number_1', 'SN_Kick_Magnitude_Random_Number_2', 'SN_Kick_Mean_Anomaly_1', 'SN_Kick_Mean_Anomaly_2', 'SN_Kick_Phi_1', 'SN_Kick_Phi_2', 'SN_Kick_Theta_1', 'SN_Kick_Theta_2', 'Separation@ZAMS', 'Sigma_Kick_CCSN_BH', 'Sigma_Kick_CCSN_NS', 'Sigma_Kick_ECSN', 'Sigma_Kick_USSN', 'Stellar_Type@ZAMS_1', 'Stellar_Type@ZAMS_2', 'Stellar_Type_1', 'Stellar_Type_2', 'Time', 'Unbound', 'WR_Multiplier']\n", - "\n", - "['Applied_Kick_Velocity_SN', 'Drawn_Kick_Velocity_SN', 'Eccentricity', 'Eccentricity \n", - "\n", - "#### The meaning of all parameters and files are described here https://compas.readthedocs.io/en/latest/pages/User%20guide/COMPAS%20output/standard-logfiles.html\n", - "\n", - "\n", - "Now that we have the data, we can do some data investigation. Here is an example of how to read the \"SEED\" parameter, which is a unique number for each binary that is run. \n", - " \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b83022e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 400047 400065 400101 ... 11599854 11599926 11599965]\n" - ] - } - ], - "source": [ - "SEED_DCO = fdata['DoubleCompactObjects'][\"SEED\"][...].squeeze()\n", - "print(SEED_DCO)" - ] - }, - { - "cell_type": "markdown", - "id": "30966640", - "metadata": {}, - "source": [ - "
\n", - "\n", - "## Question 1\n", - "#### - a: check and write down the number of rows (entries) of each of the dataset groups, 'DoubleCompactObjects', 'Supernovae', 'SystemParameters'.
\n", - " \n", - "#### - b: If the lengths of the rows are different why is this so? And does it make sense which group has the most/least rows?
\n", - "\n", - "*Hint*: you might want to look at table 1 in the paper CHE_paper.pdf and the descriptions at https://compas.readthedocs.io/en/latest/pages/User%20guide/COMPAS%20output/standard-logfiles.html\n", - " \n", - "#### - c: Why is the number of rows in 'DoubleCompactObjects' not the same as the total number of 'BBHs formed' in Table 1 from this paper?" - ] - }, - { - "cell_type": "markdown", - "id": "b4746cff", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Answer 1: \n", - " \n", - " \n", - "Below are a few methods shown to print the number of rows (but there are many ways to do this).
\n", - "We find that DoubleCompactObjects has 291253 rows, SystemParameters has 12000000 number of rows and the group Supernovae has 6213168 number of rows.
\n", - " \n", - " \n", - " - The systemparameters is easiest to understand: this file prints one line (row) for each binary that the author simulated in COMPAS. You can read in the paper or see in Table 1 (under \"Number of binaries evolved\" and then the \"total\" column) that this is exactly 12 million. Which is indeed the same as the number of rows in the dataset.
\n", - " \n", - " \n", - " \n", - " - Second we have the group 'DoubleCompactObjects', this is the dataset that prints the properties at the moment a double compact object binary (DCO) is formed. This is a rare occurance in our simulations (even if we only model massive stars). And so the number of rows/entries in this file is much much smaller and reduced to only 291253 (almost 300 000) which is the outcome of about ~2.4% of the all simulated systems. You can see that this number is close to, but slightly larger than the 261,741 \"Total\" BBHs formed in Table 1 of the paper. This is because the 'DoubleCompactObjects' file also contains NS+NS and BH+NS mergers on top of the BH+BH mergers, whereas the table only quotes the number of BH+BH mergers residing in the data.
\n", - "\n", - " \n", - " - Last, the 'Supernovae' file prints the properties of the binary whenever in the simulation a supernova occurs. \n", - "This is a tricky one to explain. There are 2 supernova that occur to form a DCO system, so you expect this file to always be larger than 2x the double compact object file. On the other hand, of the stars that do not form a DCO system, many either merge as stars, or disrupt during the first SN, in which case the simulation stops following the evolution of the binary and future SNe are not printed.
\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8936583a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "group DoubleCompactObjects has 291253 number of rows\n", - "group SystemParameters has 12000000 number of rows\n", - "group Supernovae has 6213168 number of rows\n" - ] - } - ], - "source": [ - "# Method 1, based on the example above\n", - "\n", - "\n", - "# all three groups contain the parameter \"SEED\", so we can use this to print the lengths\n", - "for group in ['DoubleCompactObjects', 'SystemParameters', 'Supernovae']:\n", - " SEED = fdata[group][\"SEED\"][...].squeeze()\n", - " print('group %s has %s number of rows'%(group, len(SEED)))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "759a127c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "group DoubleCompactObjects has 291253 number of rows\n", - "group SystemParameters has 12000000 number of rows\n", - "group Supernovae has 6213168 number of rows\n" - ] - } - ], - "source": [ - "# Method 2: Or even shorter: \n", - "\n", - "# all three groups contain the parameter \"SEED\", so we can use this to print the lengths\n", - "for group in ['DoubleCompactObjects', 'SystemParameters', 'Supernovae']:\n", - " print('group %s has %s number of rows'%(group, fdata[group][\"SEED\"].len()))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a1981b5c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "group DoubleCompactObjects has 291253 number of rows\n", - "group SystemParameters has 12000000 number of rows\n", - "group Supernovae has 6213168 number of rows\n" - ] - } - ], - "source": [ - "# Method 3, writing everything out: \n", - "\n", - "lenDCO = len(fdata['DoubleCompactObjects']['SEED'][...].squeeze())\n", - "print('group DoubleCompactObjects has %s number of rows'%lenDCO)\n", - "\n", - "lenSys = len(fdata['SystemParameters']['SEED'][...].squeeze())\n", - "print('group SystemParameters has %s number of rows'%lenSys)\n", - "\n", - "lenSNe = len(fdata['Supernovae']['SEED'][...].squeeze())\n", - "print('group Supernovae has %s number of rows'%lenSNe)" - ] - }, - { - "cell_type": "markdown", - "id": "d051687d", - "metadata": {}, - "source": [ - "
\n", - "\n", - " \n", - "## Example 1: plotting BH masses \n", - "___\n", - "below we show an example of how to obtain and plot the compact object masses in the dataset \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d142978f", - "metadata": {}, - "outputs": [], - "source": [ - "# this is just a little function that we will use to make the plot more beautiful (bigger ticks, labels)\n", - "# However, you do not have to use this (just uncommoment \"layoutAxes\" everywhere)\n", - "\n", - "def layoutAxes(ax, nameX='', nameY='', \\\n", - " labelSizeMajor = 10, fontsize = 25, second=False, labelpad=None, setMinor=True):\n", - " \"\"\"\n", - " Tiny code to do the layout for axes in matplotlib\n", - " \"\"\"\n", - " tickLengthMajor = 10\n", - " tickLengthMinor = 5\n", - " tickWidthMajor = 1.5\n", - " tickWidthMinor = 1.5\n", - " \n", - " #rc('axes', linewidth=2)\n", - " #label1 always refers to first axis not the twin \n", - " if not second:\n", - " for tick in ax.xaxis.get_major_ticks():\n", - " tick.label1.set_fontsize(fontsize)\n", - " #tick.label1.set_fontweight('bold')\n", - " for tick in ax.yaxis.get_major_ticks():\n", - " tick.label1.set_fontsize(fontsize)\n", - " #tick.label1.set_fontweight('bold')\n", - " if second:\n", - " for tick in ax.xaxis.get_major_ticks():\n", - " tick.label2.set_fontsize(fontsize)\n", - " #tick.label1.set_fontweight('bold')\n", - " for tick in ax.yaxis.get_major_ticks():\n", - " tick.label2.set_fontsize(fontsize)\n", - " #tick.label1.set_fontweight('bold')\n", - " for axis in ['top','bottom','left','right']:\n", - " ax.spines[axis].set_linewidth(1.2)\n", - " ax.tick_params(length=tickLengthMajor, width=tickWidthMajor, which='major')\n", - " ax.tick_params(length=tickLengthMinor, width=tickWidthMinor, which='minor')\n", - " ax.set_xlabel(nameX, fontsize=fontsize,labelpad=labelpad)#,fontweight='bold')\n", - " ax.set_ylabel(nameY, fontsize=fontsize,labelpad=labelpad)#, fontweight='bold') \n", - " \n", - " if setMinor==True:\n", - " # add minor ticks:\n", - " ax.xaxis.set_minor_locator(AutoMinorLocator())\n", - " ax.yaxis.set_minor_locator(AutoMinorLocator())\n", - "\n", - " return ax\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "869c90d1", - "metadata": {}, - "outputs": [], - "source": [ - "fDCO = fdata['DoubleCompactObjects']\n", - "\n", - "\n", - "M1 = fDCO['Mass_1'][...].squeeze() # mass in Msun of the compact object resulting from the *primary star*\n", - "M2 = fDCO['Mass_2'][...].squeeze() # mass in Msun of the compact object resulting from the *secondary star*\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "ff02ea16", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", - "\n", - "plt.scatter(M1, M2)\n", - "plt.xlabel('Mass of compact object from primary star [Msun]', fontsize=20)\n", - "plt.ylabel('Mass of compact object from secondary star [Msun]', fontsize=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0db7f21e", - "metadata": {}, - "source": [ - "
\n", - "\n", - "### Question 2: \n", - " \n", - " - a): can you explain some of the features in the plot above? E.g., where are the gaps, where are the most datapoints?\n", - " \n", - " \n", - " - b): Are there any BH+NS or NS+NS in the dataset? If so, plot them\n", - " \n", - " \n", - " - c): extra: how many BH+NS, vs. NS+NS vs. BH-BH systems are there? And what is the total? \n", - "\n", - "*Hint*: A NS in this COMPAS simulation is defined as a compact object with mass < 2.5 Msun \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "045858e9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "e2efc0e0", - "metadata": {}, - "source": [ - "\n", - "
\n", - " \n", - "## Answer 2:\n", - " \n", - " \n", - "We first plot the NS-NS, NS-BH and BH-BH DCO systems in seperate plots below. \n", - " \n", - "For this we use the definition that a NS in COMPAS is always below < 2.5 Msun, and that this value thus indicates the boundary between NSs and BHs. This does not have to be true in nature (nature might actually have a slightly different value for this boundary, for which a lot of active research is being conducted). \n", - "\n", - "There are also many other ways to do this, including checking for the StellarType of the stars. " - ] - }, - { - "cell_type": "markdown", - "id": "535226c4", - "metadata": {}, - "source": [ - "## NS-NS\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c2f9ba12", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The total number of NS+NS systems in the dataset is 11248\n" - ] - } - ], - "source": [ - "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", - "\n", - "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", - "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", - "\n", - "mask_NSNS = ((mask_M1isNS==1) & (mask_M2isNS==1)) \n", - "\n", - "plt.scatter(M1[mask_NSNS], M2[mask_NSNS], c='darkgreen', label='NS+NS')\n", - "\n", - "\n", - "layoutAxes(ax=ax, nameX='Mass of compact object from primary star [Msun]',\\\n", - " nameY='Mass of compact object from secondary star [Msun]')\n", - "\n", - "plt.legend(fontsize=20)\n", - "plt.show()\n", - "\n", - "print('The total number of NS+NS systems in the dataset is %s'%len(M1[mask_NSNS]))\n" - ] - }, - { - "cell_type": "markdown", - "id": "f2502840", - "metadata": {}, - "source": [ - "
\n", - "\n", - "\n", - "Features in the plot above: \n", - "\n", - " \n", - "In the plot above we see that the NS-NS systems formed in COMPAS have masses between 1.2-2.5 solar masses. This is the neutron star mass range that is expected from the NS equation of state. \n", - "\n", - "I don't expect that most students will know this: \n", - "There is a gap around 1.7 Msun. But this is an artifact from the SN remnant mass prescription used in COMPAS\n", - "There is a little bit of a pile up of NS systems around 1.25 solar masses. These systems come from electron capture supernovae and ultra-stripped supernovae. " - ] - }, - { - "cell_type": "markdown", - "id": "22dc2dcd", - "metadata": {}, - "source": [ - "## BH-NS " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8c48a3dd", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The total number of BH+NS systems in the dataset is 19383\n" - ] - } - ], - "source": [ - "\n", - "\n", - "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", - "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", - "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", - "\n", - "\n", - "mask_NSBH = ((mask_M1isNS==1) & (mask_M2isNS==0)) | ((mask_M1isNS==0) & (mask_M2isNS==1)) \n", - "\n", - "\n", - "plt.scatter(M1[mask_NSBH], M2[mask_NSBH], c='orange', label='NS+BH')\n", - "\n", - "layoutAxes(ax=ax, nameX='Mass of compact object from primary star [Msun]',\\\n", - " nameY='Mass of compact object from secondary star [Msun]')\n", - "plt.legend(fontsize=20)\n", - "plt.show()\n", - "\n", - "print('The total number of BH+NS systems in the dataset is %s'%len(M1[mask_NSBH]))\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "eeb6be29", - "metadata": {}, - "source": [ - "
\n", - "\n", - "\n", - "Features in the plot above: \n", - "\n", - " \n", - "In the plot above we see that the BH-NS systems formed in COMPAS have NS masses between 1.2-2.5 solar masses. This is the neutron star mass range that is expected from the NS equation of state. The BHs are typically between 2.5 and 40 solar masses (but some extreme cases exist, but do not trust these too much). \n", - "\n", - "The gap between 40 and ~120 solar masses where no BHs form is a result from the pair-instability SN remnant mass gap. Where it is predicted that the Helium cores that form the BHs with masses in this gap undergo pair-instabillity and completely explode, not leaving behind any remnant. " - ] - }, - { - "cell_type": "markdown", - "id": "df1ac034", - "metadata": {}, - "source": [ - "## BH-BH" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "59eebae2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The total number of BH+BH systems in the dataset is 260622\n" - ] - } - ], - "source": [ - "# now the BH-BH systems: \n", - "\n", - "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", - "\n", - "mask_M1isNS = (M1 < 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", - "mask_M2isNS = (M2 < 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", - "\n", - "\n", - "Stellar_Type_1 = fDCO['Stellar_Type_1'][...].squeeze()\n", - "Stellar_Type_2 = fDCO['Stellar_Type_2'][...].squeeze()\n", - "\n", - "\n", - "\n", - "mask_BHBH = ((mask_M1isNS==0) & (mask_M2isNS==0)) \n", - "\n", - "\n", - "plt.scatter(M1[mask_BHBH], M2[mask_BHBH], c='blue', label='BH+BH')\n", - "\n", - "layoutAxes(ax=ax, nameX='Mass of compact object from primary star [Msun]',\\\n", - " nameY='Mass of compact object from secondary star [Msun]')\n", - "plt.legend(fontsize=20)\n", - "plt.show()\n", - "\n", - "print('The total number of BH+BH systems in the dataset is %s'%len(M1[mask_BHBH]))\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "1f1e2fc0", - "metadata": {}, - "source": [ - "
\n", - "\n", - "\n", - "Features in the plot above: \n", - "\n", - " \n", - "In the plot above we see that the BH-NS systems formed in COMPAS have NS masses between 1.2-2.5 solar masses. This is the neutron star mass range that is expected from the NS equation of state. The BHs are typically between 2.5 and 40 solar masses (but some extreme cases exist, but do not trust these too much). \n", - "\n", - "The gap between 40 and ~120 solar masses where no BHs form is a result from the pair-instability SN remnant mass gap. Where it is predicted that the Helium cores that form the BHs with masses in this gap undergo pair-instabillity and completely explode, not leaving behind any remnant. " - ] - }, - { - "cell_type": "markdown", - "id": "63e62e6f", - "metadata": {}, - "source": [ - "
\n", - "\n", - " \n", - " \n", - "There is a couple of interesting things to note: \n", - " \n", - "First of all the number of DCO systems are:\n", - " BHBH: 260622\n", - " BHNS: 19383 \n", - " NSNS: 11248\n", - " \n", - "BHBH are most common in te simulation, but that is partly due to the systems that have been evolved in COMPAS and the low metallicities. \n", - " \n", - "There are systems that undergo \"mass ratio reversal\", these are systems where the mass of the compact object from the secondary star at ZAMS (initially least massive star) ends up forming the most massive BH in the BBH system (top left systems). These systems have \"reversed\" the mass ratio at some point in their evolution (during one of the mass transfer phases). \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42a75636", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "c850bd60", - "metadata": {}, - "source": [ - "
\n", - " \n", - "### Question 3: \n", - " \n", - " \n", - " - a): Using the parameters in the 'DoubleCompactObjects' dataset and the example above, try to make a scatter plot of Total Mass (M1+M2) versus orbital Period of the BBH systems that merge within a Hubble time (13.7 Gyr) \n", - " \n", - " Plot the period on the y-axis and the total mass on the x-axis. Plot the period in days. \n", - " \n", - "*Hint: You might want to use Kerpler's III law to complete the function below *\n", - " \n", - " \n", - "*Hint:* you will have to select BH+BH systems, and only systems that merge within a Hubble time " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "2a2e21da", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def separation_to_period_circular_case(separation=10*u.AU, M1=1*u.M_sun, M2=1*u.M_sun):\n", - " \"\"\"calculate Period from separation\n", - " separation is separation of the binary (needs to be given in astropy units)\n", - " M1 and M2 are masses of the binary\n", - " This is based on Kepler's law, using a circular orbit\n", - " \n", - " \"\"\"\n", - " G = const.G # [g cm s^2]\n", - " \n", - " ## use Kepler;s III law to calculate the period here \n", - " \n", - " \n", - " \n", - " ###\n", - " \n", - " return period\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2e26d37", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "63efe5ab", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Answer 3:\n", - " \n", - "See the function below \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "706f15cb", - "metadata": {}, - "outputs": [], - "source": [ - "def separation_to_period_circular_case(separation=10*u.AU, M1=1*u.M_sun, M2=1*u.M_sun):\n", - " \"\"\"calculate Period from separation\n", - " separation is separation of the binary (needs to be given in astropy units)\n", - " M1 and M2 are masses of the binary\n", - " This is based on Kepler's law, using a circular orbit\n", - " \n", - " \"\"\"\n", - " G = const.G # [g cm s^2]\n", - " \n", - "\n", - " mu = G*(M1+M2)\n", - " period = 2*np.pi * np.sqrt(separation**3/mu)\n", - " \n", - " return period\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "7a70078d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", - "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", - "mask_BHBH = ((mask_M1isNS==0) & (mask_M2isNS==0)) # if true then the system is a BHBH\n", - "\n", - "separation = fDCO['Separation@DCO'][...].squeeze() # in AU \n", - "Period = separation_to_period_circular_case(separation*u.au, M1*u.M_sun, M2*u.M_sun)\n", - "# the merger time is called the \"coalescence time\"\n", - "coalescence_time = fDCO['Coalescence_Time'][...].squeeze() * u.Myr # Myr \n", - "t_Hubble = 13.7 *u.Gyr\n", - "mask_tHubble = (coalescence_time < t_Hubble)\n", - "\n", - "mask_systemsOfInterest = (mask_BHBH==1) & (mask_tHubble==1)\n", - "\n", - "\n", - "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", - "\n", - "plt.scatter((M1+M2)[mask_systemsOfInterest], Period[mask_systemsOfInterest].to(u.d))\n", - "\n", - "xlabel = 'Total BBH Mass [Msun]'\n", - "ylabel = 'Period [day]'\n", - "layoutAxes(ax=ax, nameX=xlabel,nameY=ylabel)\n", - "plt.yscale('log') \n", - "\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "201763f3", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Question 4: \n", - " \n", - " \n", - " - a): Why does the plot that you created look different compared to the figure 6 in https://arxiv.org/pdf/2010.00002.pdf? (you may ignore the metallicity axes) \n", - " \n", - " - b): There is a tail of systems at rather large orbital periods that are merging. How is this possible? \n", - "\n", - "*Hint 4b: plot the eccentricity as a color gradient on the marker using the \"c=\" option of plt.scatter. \n", - "How is eccentricity imparted to these systems?* \n" - ] - }, - { - "cell_type": "markdown", - "id": "069d602e", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Answer 4:\n", - " \n", - "Possible answers: \n", - "Figure 6 only shows the data for one of the submodels (f_WR=.2) and only shows the CHE systems, and the axes are in log. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d2424b2e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "ecc = fDCO['Eccentricity@DCO'][...].squeeze()\n", - "\n", - "\n", - "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", - "\n", - "plt.scatter((M1+M2)[mask_systemsOfInterest], Period[mask_systemsOfInterest].to(u.d), c=ecc[mask_systemsOfInterest])\n", - "\n", - "xlabel = 'Total BBH Mass [Msun]'\n", - "ylabel = 'Period [day]'\n", - "layoutAxes(ax=ax, nameX=xlabel,nameY=ylabel)\n", - "plt.yscale('log') \n", - "plt.colorbar(label='eccentrcity')\n", - "\n", - "plt.show()\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "0e08bfed", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Selecting CHE binaries: \n", - " \n", - " \n", - " For binaries, Stellar_Type@ZAMS(1) and Stellar_Type@ZAMS(2) will tell you the initial stellar type of each star - type 16 is CH.\n", - "CH_on_MS(1) and CH_on_MS(2) are each true if the star remained as CH for the entire MS - they will be false if the star spun down and stopped being CH on the MS. So any star that was initially CH, and stayed CH on the entire MS is considered to be CHE. We can check which of our binary black holes is a \"CHE\" by using this information stored in the 'systemParameters' file, and matching it with the double compact object files using the randomSeed.\n", - "\n", - "Note that we also have to remove binaries that merged on the ZAMS as stars, since we are not interested in these\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "714118e5", - "metadata": {}, - "outputs": [], - "source": [ - "fsys = fdata['SystemParameters']\n", - "\n", - "CH_on_MS_1 = fsys['CH_on_MS_1'][...].squeeze() # mass in Msun of the compact object resulting from the primary\n", - "CH_on_MS_2 = fsys['CH_on_MS_2'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", - "Stellar_TypeZAMS_1 = fsys['Stellar_Type@ZAMS_1'][...].squeeze() # mass in Msun of the compact object resulting from the primary\n", - "Stellar_TypeZAMS_2 = fsys['Stellar_Type@ZAMS_2'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", - "\n", - "# binaries that merge at birth as stars\n", - "Merger_At_Birth = fsys['Merger_At_Birth'][...].squeeze()\n", - "\n", - "# SEED of the system Parameters (unique number corresponding to each binary)\n", - "SEED = fsys['SEED'][...].squeeze() # mass in Msun of the compact object resulting from the secondary\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "99057a0b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "13644 are CHE out of 12000000 systems run\n", - "[ 400378 400412 402049 ... 11589507 11589863 11594670]\n" - ] - } - ], - "source": [ - "\n", - "# the CHE systems are then selected by systems that are CHE on ZAMS (stellar type 16) AND remain CHE on the MS (main sequence)\n", - "# in addition we do not want systems that Merged at Birth \n", - "mask_CHE = (CH_on_MS_1==1) & (CH_on_MS_2==1) & (Stellar_TypeZAMS_1==16) & (Stellar_TypeZAMS_2==16) & (Merger_At_Birth==0)\n", - "\n", - "print(np.sum(mask_CHE), 'are CHE out of ', len(mask_CHE), 'systems run')\n", - "\n", - "\n", - "# let's find the seed of the CHE systems: \n", - "SEED_CHE = SEED[mask_CHE]\n", - "print(SEED_CHE)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "3c8919d0", - "metadata": {}, - "source": [ - "
\n", - " \n", - "We find 13644 total CHE binaries in our simulation, note that this is the same as the number quoted in the CHE paper under \"Both stars remained on the CH\" and \"Total\" in Table 1\n", - " \n" - ] - }, - { - "cell_type": "markdown", - "id": "f2115984", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Question 5: \n", - " \n", - " - a): Using the code above, recreate figure 6 in https://arxiv.org/pdf/2010.00002.pdf? (you may ignore the metallicity axes) \n", - " \n", - " - b): Explain what you see \n", - " \n", - "#### Hint: A useful line of code is: np.in1d(), below is an example of how it works" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "6ba613a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ True False True]\n" - ] - } - ], - "source": [ - "# example of np.in1d() function\n", - "\n", - "A = [1,2,3]\n", - "B = [1,3,5,7,9]\n", - "\n", - "print(np.in1d(A, B))" - ] - }, - { - "cell_type": "markdown", - "id": "34b76779", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 5 " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "87e401a9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2320\n" - ] - } - ], - "source": [ - "\n", - "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", - "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", - "mask_BHBH = ((mask_M1isNS==0) & (mask_M2isNS==0)) # if true then the system is a BHBH\n", - "\n", - "separation = fDCO['Separation@DCO'][...].squeeze() # in AU \n", - "Period = separation_to_period_circular_case(separation*u.au, M1*u.M_sun, M2*u.M_sun)\n", - "# the merger time is called the \"coalescence time\"\n", - "coalescence_time = fDCO['Coalescence_Time'][...].squeeze() * u.Myr # Myr \n", - "t_Hubble = 13.7 *u.Gyr\n", - "mask_tHubble = (coalescence_time < t_Hubble)\n", - "\n", - "# this is the parameter that describes the Wolf Rayet factor f_WR that was used. \n", - "WR_Multiplier = fsys['WR_Multiplier'][...].squeeze()\n", - "\n", - "# mask BBHs that merge in a Hubble time \n", - "mask_systemsOfInterest = (mask_BHBH==1) & (mask_tHubble==1)\n", - "\n", - "# add the mask of systems that are CHE. Since the CHE mask is based on systemParameters we have \n", - "# to match the systems from systemParameters that we want to mask with the DCO systems, we can do this using the SEED\n", - "# a system in systemParameters will have the same SEED in DoubleCompactObjects, if it exists in both\n", - "mask_DCO_that_are_CHE = np.in1d(SEED_DCO, SEED_CHE) \n", - "mask_DCO_that_are_BBH_and_CHE = (mask_DCO_that_are_CHE ==1) & (mask_systemsOfInterest==1)\n", - "\n", - "# we can mask for the f_WR = 0.2 factor that is used in Figure 6 of the paper. \n", - "mask_fWR_02 = (WR_Multiplier==0.2)\n", - "SEED_fWR_02 = SEED[mask_fWR_02]\n", - "mask_DCO_that_are_fWR_02 = np.in1d(SEED_DCO, SEED_fWR_02)\n", - "\n", - "# combine all the masks above\n", - "mask_DCO_that_are_BBH_and_CHE_and_fWR_02 = (mask_DCO_that_are_CHE ==1) & (mask_systemsOfInterest==1) & (mask_DCO_that_are_fWR_02==1)\n", - "\n", - "\n", - "# plot the systems \n", - "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", - "\n", - "plt.scatter((M1+M2)[mask_DCO_that_are_BBH_and_CHE_and_fWR_02], Period[mask_DCO_that_are_BBH_and_CHE_and_fWR_02].to(u.d))\n", - "\n", - "xlabel = 'Total BBH Mass [Msun]'\n", - "ylabel = 'Period [day]'\n", - "layoutAxes(ax=ax, nameX=xlabel,nameY=ylabel)\n", - "# plt.yscale('log') \n", - "\n", - "\n", - "plt.show()\n", - "\n", - "\n", - "print(len((M1+M2)[mask_DCO_that_are_BBH_and_CHE_and_fWR_02]))\n" - ] - }, - { - "cell_type": "markdown", - "id": "b676d7ab", - "metadata": {}, - "source": [ - "
\n", - "\n", - "The plot above looks very similar to Figure 6, the only differences are that we did not add the Metallicity axis in this example. You can add this by using the property 'Metallicity@ZAMS_1' from the systemParameters. \n", - " \n", - "In addition, our Period range plotted above (the ylim) is slightly larger then the ylim range plotted in Figure 6\n", - " \n", - "Last, the total number of events 2320 is slightly different compared to Fig 6. This is a result from a small typo/error by the auhtor in the data version that is published versus the version that was used to run this specific figure" - ] - }, - { - "cell_type": "markdown", - "id": "a25df87b", - "metadata": {}, - "source": [ - "
\n", - " \n", - "## Question 6: \n", - " \n", - " - a): Try to recreeat the figure 4 in https://arxiv.org/pdf/2010.00002.pdf? \n", - " \n", - " \n", - " - b): Explain what you see " - ] - }, - { - "cell_type": "markdown", - "id": "816ebd41", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 6 \n", - " \n", - " \n", - "See the code below. The method is very similar to the Answer to question 5, but now we have to get the ZAMS properties through the systemParameters file. For which we have to use the np.in1d again to match the datapoints" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "fe11ee0f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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i/PbNZa6tMTOpt9dRQvNex/GZTkPnRy4vLq2qVbxvCc1BfGstLyaiwGOGkoiIiAImxiFhZM+OmDqwK0b27Ngqg0kgcHsd5fJi4Fw5say1lxcTUXAwoCQiIiIKsEDudWR5MRGFEkteiYiIiAIs0HsdWV5MRKHCgJKIiCiKNbncDDLCQDD2OsrlxUREwcSAkoiIKErZPaKCzPMepSKBo1SIKHpwDyUREVEUCsSICrKGex2JKBoxQ0lERBQFvEtbO7WLx2Mb9tk+ooKs415HIoo2DCiJiIginFJpqxbvERXccxd83OtIRNGEASUREVEEk0tblbKResyMqCAiIvLGPZREREQRqsnlxoKNBaaCScD8iAoiIiIZM5REREQRandRhXCZqzc7RlQQEREBzFASERFFLDMlqxxRQUREdmKGkoiIKEKZKVl1cg4lERHZiAElERFRhBqekYb05ASUVtUq7qOUS1v/On0AyqrrOKLCJO+RLDyHRES+GFASERFFqBiHhEcnZ2LOqlxIgE9Q6V3aOqp3pxCsLvyJBIpKI1nSmeUlIvKQ3G632eZwFGH69esHANi3b1+IV0JERHZi0GOcyDlTG8kih5xLZw7m+SWiiGVXbMCAMgpVV1crfn/o0KFwOBwMKImIohDLMsWJBIrjM50YvWibahdduZz4swfGes4zXwMiiiR2BZQseY1C7dq1U/1ZZmZmEFdCRETBEuOQMLJnx1AvI+xpze50ozlQfOTdfOz9b5XmSBY3gJKqWuwuqsDInh2ZJSaiVotjQ4iIiKjV0Jvd6QZQXl2Pf35UKHR/35+u9WQ8/e+3tKoWc1blIju/xMqSiYjCGjOUUejMmTOK3x86dGiQV0JERBRezMzu1NKpXTx+9+bXmhnPBRsLMD7TyfJXIopKzFBGoaSkJMU/DgdfbiIiat3MzO5UIqG5pBVuCJXGvrKjCE0utq0goujDCIOIiIhaDXl2p5VcofdIlrLqOqHbLNy0H6MXbWP5KxFFHQaURERE1GrIszsBmA4qnckJnpEhRjKe3FNJRNGIeyiJiIioVZmQlY6lMwe36MqqZ+6VPTGq13k+40DkjGdpVa3iPkpvodxTyZEmRBQoDCiJiIio1ZmQlY7xmU7sLqpA6alaLHx/HyqqGxSPlWdOzht/cYsgTM54zlmVCwkQCiq9x40EA0eaEFEgseSViIiIwkaTy42cwnKszzuGnMLygDaykWd3ThvUFU9O6w8JLctgvfdLqmX0xmc6cd+43khuGyv82HZ3m1XDkSZEFGjMUBIREVFYCGUmTa0M1qnz+EprFmFXt1ktTS43Fmws4EgTIgooBpREREQUcnImzT/4kTNpchOcQPIugxXZa6i2Zi1y+ezwjDRb1qxld1GF0EiTYJbfElH0YUBJREREIRVOmTS5DFaP1prViJTP2km0rDZY5bdEFJ24h5KIiCiCBXPPYaAYyaSFC701K/EeNxIMomW1wSi/JaLoxQwlERFRAAVyXIPdew5DNVoiEjNpomuZe2Uv9O7cLiSjOkRGmnRMisOQ7qlBWxMRRR8GlERERAESyCYzdu85DGVwGomZNNG1jOrVKWT7E0VGmpRX1+OKp7dzhAgRmcaSVyIiogAI5LgGvT2HQPOeQ9HyV7vXmp1fgtGLtmHGsp24d10eZizbidGLtqnej5xJU8vdSWgOboPRyEZUpKxZ7l7rTFYPgDlChIisYEBJRERkM7sDPn927jkMVnBaUlWLu1blYuHGfS32esqZNMDcHMhQiKQ1T8hKx8f3X4m0JOU5mXa8J4mo9WJASUREZLNAN5mxc89hsIJT2fIdxYoZS7VMWrAb2RgRSWv+6kglKqobVH8ejo2PiCgycA8lERGRzQLdZMbOPYfBDE69Ke31NDoHUoTaXk67GhAFYs2BEImNj4goMjCgJCIislmgm8zode+U0JwlE9m/F4rgFFCfLyk6B1KEWqOhKQPSseHrEtsaENm55kCJxMZHRBQZWPJKRERks0A3bLFz/57ZtSrNvzQajASyzFJrL+cLnxQFpFlSOIuUJkJEFHkYUBIREdksGA1b7Nq/Z2atal1cK6vrNYMWNXaXWYrs5fQX7Y1pIqmJEBFFFsntdkff/zVJUb9+/QAA+/btC/FKiIhah0DOoZTZtRdQdK1q8y/lR7xzTAZe/KQIgPLcQyVrZ4+wtWQ0p7AcM5btNH17u9cTToLxniSiyGBXbMA9lERERAESjIYtdu3fE1mr3ogRCcCGr0vw3M2DsHDTft0GPUb2ehphNeMZzY1pIqWJEBFFDgaUREREARQJDVtkemsVHTGSmhSPzx4Yi91FFdhSUIqXdxRDgm/GMpBlllYby0R7Y5pIek8SUfhjQElERERCjIyekIOWkT07YnhGWosyy7SkOEwd2AXJbePQ5HLbGlTqdcFVYyRjalepMRFRpIvqgPKHH37Al19+iS+++MLz39LSUs/PV6xYgdtuuy0gj+1yubB79258+OGH2L17N/Lz8/H999+jrq4OqampyMjIwGWXXYZZs2Zh4MCBAVkDERGRncyOnvAus9xaUIp3846hvLoeL+8oxss7im3fwyc3oJmzKrdFZlSNkYwp9yESEZ0TlQFlaWkpRowYgSNHjoTk8X/zm99g7dq1PsGrt++//x7ff/89du3ahcWLF2Pq1Kl44YUX0Llz5yCvlIiISJyV+ZcxDglVZ5uDSP/byiM7jHSn1SN3wRWdQ+kUDAjVmhIF4jkQEUWCqAwoa2trQxZMAsCLL76I6upqn+85nU5069YNSUlJOHbsGL799lvPz9avX4+8vDx8+umnuPDCC4O9XCIiIiFamT+9DJ9IQ58FGwswPtNpW+moVgOa30/oa7hkNRTPgYgo3EVlQOntvPPOw5AhQzB06FAMGzYMU6dODdpj9+vXD7/61a9w7bXXolevXj4/O3ToEH73u99h/fr1AIAjR47gxhtvRE5ODiSJ/wgREVF4Usv86WX4RBv67C6qsLVhjFoDGjONaUL1HIiIwllUBpRpaWl48803MWzYMHTv3j3ojz9s2DD8/ve/xzXXXKN6TK9evfDee+/hF7/4BVatWgUA2LVrF9577z1MmzYtWEslIiIyzMzoCSMNfcJVNDwHIiK7RWVA2aFDB0yfPj1kj799+3bhY//v//4P7777rqdE9p133mFASUREYc9ohk+0oU+ndvFmlxRwZpsSERFFM0eoF9DapaamYtSoUZ6vDxw4EMLVEBFRKDW53MgpLMf6vGPIKSxHk8vI0IvwJjf00dvU8ds38pCdXxKUNRml9xwkNDf9ERk7QkQULRhQhoG0tHP/8Jw6dSqEKyEiolDJzi/B6EXbMGPZTty7Lg8zlu3E6EXbwja4Mkpu6ANAM6g8caoOc1blhuXz1noORsaOEBFFEwaUYcC7I+35558fwpUQEVEoyKMo/Bu+yKMowjG4MkNu6NO5g3pJqJyTXbCxICwztPJzcCb7PgdncgJHhhBRqxSVeygjybFjx7Br1y7P1yNGjAjhaoiIKNha2yiKCVnpaB8fi1uW71I9Jty7pZppSkREFK0YUIbY448/DpfL5fl6xowZQrfzn3MpwuVyweFgUpqIKJy0xlEUZdV1QseFc7dUM2NHiIiiEQPKEPrkk0/w4osver6+/vrrMXjwYKHbtmvXztRjZmZmmrodEREFRmscRcFuqURE0YPpqhA5fvw4fv7zn3uykx07dsT//d//hXhVREQUbK0xuGK3VCKi6MEMZQhUV1fjuuuuQ2lpKQBAkiQsX74cXbt2Fb6PM2fOGH7coUOHGr4NEREFlhxclVbVKu6jlNDc8CWagiu5W+qcVbmQAJ/nzW6pRESRhRnKIKuvr8f111+PL774wvO9xYsXY+rUqYbuJykpyfAf7p8kIgo/rXUUBbulEhFFB2Yog6ipqQkzZszAv//9b8/3FixYgHvvvTeEqyIiolCTg6sFGwt8GvQ4kxPw6OTMqA2u2C2ViCjyMaAMEpfLhdtvvx3vvPOO53v3338//vjHP4ZwVUREFC5aa3DFbqlERJGNAWWQzJkzB6+99prn67vvvht/+ctfQrgiIiIKNwyuiIgo0nBTXRDcd999PuNBbr/9dixZsiSEKyIiIiIiIrKOAWWAPfLII3j22Wc9X8+YMQMvvfQSJCm6S5iIiIiIiCj6seQ1gJ588kk8+eSTnq+vv/56rFy5kt1WiYgoYjS53K1uXycREYljQBkgzz77LB555BHP15MmTcLatWvRpg1PORERRYbs/JIWnWfTo7zzLBERGcNUmQGSJHn+3HbbbarHLV++HPPmzfN8/dOf/hRvv/024uLigrBKIiIi67LzSzBnVa5PMAkApVW1mLMqF9n5JSFaGRERhZOoTZfNnj3bp6uq2jF33XVXi+/X1tYqHC2mpKQEd955J9xut8/9TZ06Vfg+srOzTT8+ERGRVU0uNxZsLIBb4WduABKABRsLMD7TyfJXIqJWLmoDyoaGBtTV1Wke09jYiMbGRlsft66uDi6Xy+d7n3zyia2PQUREFEi7iypaZCa9uQGUVNVid1EFx5wQEbVyURtQEhERkTnfnxar1BE9LpKwCRERkTFRG1C+8soreOWVV2y9T+8yVjU9evQQOo6IiChcnd8+wdbjIgWbEBERGcemPERERORjeEYa0pMToJaXk9AcaA3PSAvmsgLKjiZETS43cgrLsT7vGHIKy9Hk4gfMRBT9ojZDSURERObEOCQ8OjkTc1blQgJ8mvPIQeajkzOjphTUjiZEzG4SUWvFDCUREVEEC1RWbEJWOpbOHAxnsm9ZqzM5AUtnDo6qIMlIEyIl4ThihdlSIgoWZiiJiIgiVKCzYhOy0jE+0xn1TWqsNCEKxxErzJYSUTAxQ0lERBSBgpUVi3FIGNmzI6YO7IqRPTtGXTAJWGtCZDW7abdAvi+Y9SQiJcxQEhERRZhwzIrpCedxHHITotKqWsVzKqG51FepCVE4jVgJ5PuCWU8iUsMMJRERUYQJt6yYnuz8EoxetA0zlu3EvevyMGPZToxetC0kewuVyE2IALTobKvXhCicRqwE6n0RjntEiSh8MKAkIiKKMOGUFdMTKcGI2SZE4TRiJRDvC72sJ9Cc9WT5K1HrxZJXIiKiCBNOWTEtkVaaa6YJUTiNWAnE+8JI1nNkz47C90tE0YMZSiIiIi+R0HgkEFmxQDzvSCvNBcw1IQqXESuBeF9EUjaciEKDGUoiIqIfRUrjEbuzYoF63q0pGAmHESuByJZGSjaciEKHGUoiIiJEzl4/mV1ZsUA+79YWjITDiBW7s6XhtEeUiMITM5RERNTq2bnXL5jjMbyzYqVVZ1FRXY+0dvFIbhuHJpdbaK2PbQjcHkcr4zjIPDuzpeG0R5SIwpNQQDl27NhAr0OVJEn48MMPQ/b4REQU/exqPBKKktkYh4Sqs/X4y7/+Y/hxl2w7iNJTgWu4wmAkdORsqR3krKf/e9sZhuXgRBR8QgHlRx99BEkK/v/s3W53SB6XiIgihx0ZQTv2+smlo/6ZOLl0NFDNWcw+bnZ+CRZvPSj0GFb2ODIYiQ7hsEeUiMKToZJXtzv8Ot0REVHrZVdG0Opev1CNxzD7uPLtRFnd48hgJDrYmfUkouhhKKDs378/fvOb3wRqLT7+9re/IT8/PyiPRUREkcfOjKDVvX6hmtVn9nH1bufNroYrMQ4JwzPSPEHl7qIKBpVERFHAUEDZtWtX3HrrrYFai49169YxoCQiIkV2ZwSt7vUL1XgMs49rZB127XGMlJEsRERkDMeGEBFRxDGSmRNlZdxCqMZjmH1c0dvNG9fHlmAv0kaymNHkciOnsBzr845hx8Ey7DhUhvV5x5BTWI4mF7cMEVH0Es5Qcv8kERGFi0BlBM3u9Qv0eAy1xkNmH1fvdgDg7BCPuWN7mVqv/9pDsb80mJSyr97SkxMwf1ImUpPiuIeUiKKOUEC5ePFiAECPHj0CuRYfd911FyZMmBC0x4sm1dXVit93uVxwOJiUJqLIF8iMoJnGI4Ecj6FXKmrmcUXW+9iUfrYEPKHaXxosant5vZVU1eLuNbk+32O5LxFFC8nN1GPU0Rq1kpmZiX379gVxNURE9mtyuTF60TbdzNxnD4wNahbI7n2CasGK/IzkUlyzj2v0dmZGtKzPO4Z71+XpPVU8e9NATB3YVfe4cCK/D0UbHHnzfw2JiIKtX79+AGA5NjDUlIeIiCgcBDIjaIWd4zGMlIqafVwjtzMbtIZqf2kwGOmW6y9ayn2JiBhQRqEzZ84ofn/o0KFBXgkRUeDITXT8gxxniEsJ7ZrVZ7RUVOtxtTKL3rdTO87KiJZA7y8NJatdeyO93JeICGBAGZWSkpIUv8/9k0QUbezMCIYbuxoPiWYW1Y6bP6kvFm7ab7qpTrhmk+1gV1bV7nEyRETBZDnC6N+/P5599llUVIi3ZiciIrJKHtPw/t7jAIBrL+3iydRFAztKRUXHdWgdd/eaPZZHtFgZyRLO5Oyr1XdcJJb7EhHJLGco9+3bh9/85jd48MEHcd111+GOO+7AuHHj7FgbERGRIrub34Qjq6Wionswx17SWfM4UXpZtmjMJmtlX0VEcrkvEZHMthrIuro6vPHGG7j66qtx0UUX4YknnsDx48ftunsiIiIA4lm3SCcHKwBaZMBESkVF92C+llNsurGMN5Esm7xfc+rArlGTTVbLvuqJ9HJfIiKZ5bEhTz75JFasWIHCwsJzd/rj2AqHw4Grr74as2fPxrXXXouYmBhrqyVL7GoNTEQUKnpjGuSMz8f3X4mvjlRGRSbMbDZWdFzHrJHdsTLniOn1BWpEi5kRJaHkvd5OSfGABJSdqcP57RNQWV2HhZv2R3VGnYgij12xgW1zKD/66CO89NJLeOedd1Bbe+5/mHJw2blzZ9x6662444470KtXLzsekgxiQElEkS6nsBwzlu3UPS4tKQ4V1fWeryP94t1McCV6ruSmOyLUmurYvQ8yGkuaRV7DSAuiiSiyhV1AKauqqsKqVavw8ssvY8+ePb4P9mNwefnll2P27Nm44YYbkJDAjejBwoCSiCKdaNbNX2scIi9nc/X2YH58/5W44untusfNn5SJhZsCH+SpjSiJ9tcwGoNoIgpvYRtQesvLy8OyZcuwdu1anDx58tyD/hhYJicn4+abb8Ydd9yBQYMGBWoZ9CMGlEQU6USzbkoCVZoZzuTgDNDOLIoeF8gMWpPLjZ2Hy3HP6lycPNugeEy0voatNYgmotCKiIBSVldXhzfffBMvv/wyPv74Y3g/pBxcDhw4ELNnz8bNN9+MDh06BHpJrRIDSiKKdHpZNxFrZ4+I6CHyRoM6q3Mog5EhU3psLZH+GnoT3RccbUE0EYVeRAWU3g4fPoyXXnoJK1eubNEFVpIktG3bFjfeeCPmzJmD4cOHB3NpUY8BJRFFA7VsmqhnbxqIqQO72ruoIDEb9IkGocHaw+f9OMVlNXhm67eGXstIfg39iWbdoymIJqLwYFdsYHkOpVEXXXQR7r33XiQlJeGJJ55AXV2d52dutxs1NTVYuXIlVq5ciTFjxmDRokUMLImIyEMe0+AfWKUlxaKiWrlU0ps83iLSGqColUXK41K0yiLlcR16RI+zwmg2UonIiJJIoTe/0+hxRETBFrSA0uVyYfPmzVi+fDk2b96MxsZGz8/cbjfatGmDzp0749ixY56S2I8//hijRo3CggUL8PDDDwdrqUREFOYmZKVjfKbTJyAc0j1VqLnM8Iy0iGuA0uRyY8HGAsXn5Ubzc1uwsQDjM51BDYrNlN8qBcWivF/DaCEaHEdTEE1E0SXgAeXhw4exfPlyvPrqqygpaR427V1l26NHD/zqV7/CL3/5SzidTnzyySd44YUX8NZbb6GhoQFNTU2YP38++vXrh6lTpwZ6uUREFCGUsmmPTs7EnFW5quMtHp2ciS0FpaYzfaGyu6hCM6PnBlBSVYvdRRVBK4s0GpRrBcVGPDo5M6wzyUYNz0hDenKC0AchREThyBGIO62rq8Pq1asxduxY9O7dG0899RRKSkrgdrs92chp06bhgw8+QGFhIR5++GE4nU4AwJgxY7B69WocOHAAV1xxBYDmAPTZZ58NxFKJiCiKyOWwzmTfbI4zOQFLZw7G+EynZqYPaM70NbmC2l5AV7iVRcqZRv8gVw7Ks/NLWtxGLygWcd+4PmEX7FsV45Dw6ORMAOc++JB5fxASTUE0EUUXWzOUe/bswfLly7FmzRpUVVUBaJmNnD17Nn75y1+ic+fOmveVkZGBd999FxkZGaiqqkJubq6dSyUioiilVA4rl2LmFJaHXaZPRDiVRZotv7Uj2O3RKdHyfYQjtX3BzjAvw46kPchEFDiWA8qqqiqsXr0ay5cvR15eHgDfILJNmzaYMmUK7rzzTvz0pz81dN8pKSkYPHgwtm/fjtOnT1tdKhERtRJqzWXCLdMnanhGGlISY3GyRns+YzDKIs2W39oR7IbjPkK7AiutD0ICxezaI20PMhEFluWAMj093dOp1TuQzMjI8OyN1MtGauFMSiIisks4ZfqM2FJQqhpMAs1B3E3DuuH9vccDHoiYDcrlvYJmyl7DdR+h3YFVMLrsysyu3Uq3YSKKTpYDytraWkiS5NkbOXXqVNx5550YP368HetDVlYWTp48act9ERFR6xaJDVDkElMtEoDFW7/1fB3IbJHZoFzeK3jXKmNbWMJ1H2EkB1Zm1x6u3YaJKLRsacqTkZGBJ598Et999x3efPNN24JJAFi4cCG2b9+O7du323afRETUOkViAxSRZjZqgYFScxyr5KBcS7pKUD4hKx3zxvU29HhyQ6VwCs70AisgPJs7AdbWbqTcmYhaD8sB5b///W8cOnQIDz74oKXSViIiomDQ6wQbToELYG4/ZyCDmhiHhCkDtM/RlAHpqkH5nJ/0QlpSnOptJQAdk+Kw+GcDsHb2CHz2wNiwe00iObCysvZI3YNMRIFlueR13LhxdqyDiIgoaELRAMUss/s5A9WxtsnlxoavtTOfG74uwe8n9G1xPuV9exXV9Yq3k49+YlpWwIJIpUY0AAy9FyI5sLKy9kjdg0xEgWXr2BAiIqJIEcwGKFbo7fvUY3dQI1KCqxTIqu3b8xboMRlKjWhSEmMBwKfpkd4e1EgOrKysPRL3IBNR4Nmyh5KIiIgCQ2vfpwi7gxozGS6tfXuytKRYfHz/lQENJuesym0RDJ+saWjRQVdvD6ocWKm9HhLU95GGmpW1R+IeZCIKvIBkKF0uF/bt24djx46hqqrKM1ZExKxZswKxJCIiooilNvjeIQFqWyQDlS0yk+ESyWpWVDfgqyOVilljq7MeRQJab3odS+XAas6qXEjwbYqkFFjZNavSDkbX7k/tvRjo7DIRhS9bA8qdO3di8eLF2Lx5M2pqagzfXpIkBpREREQKlPZ9VlbX4Z41ewAYDwzMMlP2aGXfnh2zHkUCWn96e1BFAyu7Z1XawWpQGEl7kIko8GwLKB944AH87W9/g9vthtut/RmgPLeSiIgoHIVTRsmb0r7PpQ6pRWCQmhSLx6cGprGNmQyX2X17ds16tLKPVOu2WoFVk8uN//vwIJ798GCL24XDrEqrQWGk7EEmosCzJaBcuHAhnn76ac/Xbdu2Rb9+/VBYWIjKykpIkoQxY8bg9OnTOHbsGE6cOAGgObAEgOHDhyMhIfw2rhMRUesTjhklLROy0uFyAX9Yn+/pnlpR3YCFm/bD4ZACsmajGS4zWU29eYlaJan+rOwj1butUmCVnV+CB9/ei5NnGxVvY3T9gcKgkIjsYLkpz3fffYfHH38cQHOAeN999+H777/H7t27MXz4cM9x27dvx5dffomSkhIcPnwY8+fPR2JiIgCgtrYWy5Ytw/bt260uh4iIyDS1xi16TVpCKTu/BPesyW0xiiPQa56QlY7PHhiLtbNH4NmbBmrOjDTTzMXOWY96jWiUmG2sk51fgrtW5aoGk7JwnlVJRGSE5YBy2bJlaGho8Ox//Pvf/46kpCTN2/To0QMLFizA3r170bNnT+zduxdXXXUVKisrrS6HiIjIFL2MGNCcUWpS64ITAqFes5zhmjqwK0b27KiZaZOzms5k34yfMzmhRelnk8uNHYfKhNYgUs5qtFOu2T2oTS43HtuwT/h4IDxnVRIRGWG55PWjjz7y/P2Pf/yjodtmZGTgX//6F/r374///ve/mDt3LlavXm11SURERIYZyYiJlAkGYx+m3WsONJF9e0olx1pEy1nVynRTE2Phhu8cSrMdS3cXVaD0lHhne0B9/eG6j5eIyJ/lgPLQoUMAmrOOGRkZqsc1NjaiTZuWD5eRkYHbb78dzz33HN5++20899xzSElJsbosIiJqxcxcjFvpROovWPsw7VxzsGjt21NrwqPEzFgUtYAWgC3Bm9HzrFZSG2n7eImodbMcUFZUVECSJHTr1q3Fz+Li4jx/r6mpQYcOHRTvY9y4cXjuuefQ0NCAjz/+GFOnTrW6LCIiaqXMXoyb7USq9Ph2dCa1Yy1GjwslI7MirYxFUQto7cjgGj3PSusP5vuHiMgOlvdQylnHmJiYFj/zDiCPHTumeh+dOnUSOo7EVFdXK/5xuVyhXhoRUUBZaaqj17hFpElLsPc02rHmcGFkVqTSvstwMDwjDc4O8brHOSTgnzcParH+UO+JJSIyw3JAKQeDJ0+ebPGzCy64wPP3b775RvU+5DEiAHD69GmrS2r12rVrp/jnwIEDoV4aEVHAWL0YN9OJ1J+dnUlF2LHmcCFaLjr3yp6q3WRDLcYh4bEp/XSPWzJjMCZe2qXF94P9/iEisoPlgPKSSy6B2+327KX0NmjQIM/f33vvPdX7eOeddzx/985WEhERibLjYtxIJ1IlodjTaHXN4UK0XHRUr/PCOkCekJWO52cORkpibIufpSbG4vmZgzHxUuXXJBL3xBIRWd5DOWLECPz73//G6dOnceDAAVxyySWen1199dWIj49HfX093nzzTdx4442YNm2az+1XrlyJtWvXer6+7LLLrC6p1Ttz5ozi94cOHRrklRARKTPTNEfvNnZdjIt0IlUTqj2NVtYcLuTy3dKqWsUss5kmPKEivx47C8uRc7gMQPO+zREXaY9WiaY9sUTUelgOKMePH48//elPAIANGzb4BJTJycm49dZb8eKLL8LlcmH69On4yU9+4glsduzYgZycHLjdbkiShNGjR6Nv375Wl9Tqqc0BdTgsJ6SJiCwz0zRH5DZ2XoxrdSLVEsqgyOya9QRrfIVcvjtnVS4kwOf8RVr5LtD8fEb17oRRvcUrr6IpqCai1kNyu92Wdna73W5ceOGFOH78OHr27ImDBw/6/PzkyZMYPnw4Dh06BElS/kfA7XYjJSUFO3fuRJ8+fawshzT069e8r2PfPmNDl4mI7KLWwVL+10GpRFP0Nk0uN0Yv2qZ7Mf7ZA2MDGpTI6wWUg6JQlqEaDQ5DMb6itY/MCOf3DxFFF7tiA8sBJQD85z//QVlZGYDmssr4eN8OZ6Wlpbj99tvxr3/9S/H2AwYMwOrVq5GZmWl1KaSBASURhZIc8Kntc1QK+IzeJlwuxsMlKPIOIIvLarB291GUnhJbk5ngPxDrjsTyXavC5f1DRNEtrAJKUV9//TW2bNmCo0ePoqGhAenp6bjiiitwxRVXBGsJrRoDSiIKpZzCcsxYtlP3uLWzR3hKN83cJlwuxkMdFCmdB39qwaFoIP/x/VfiqyOVrTbwC6RQv3+IKPrZFRtY3kNpxIABAzBgwIBgPiQREYUJM01zzNwmXBrUWN3TaCWgUMsu+nOjOThcsLEA4zOdnvsX7Zg74s9bUVHd4Pk+s2j2CdSeWCIiuwU1oCQiotbLTNOcTu30h8R7H+cfhF17aZeIzOpYybJqzeNU4j1ORQ5gRAN572ASAEqrajFnVS73+RERtSIMKImIKChMdbA0EBWFS6mrVWrZRdFgTS+7qMY7iDQ7lkIt40lERNGLcySIiChobhp2oWowCbQcC1FWXSd0vx8eOIE5q3JbBFJyEJadX2JyxcGllV2Uv7dgYwGaXOqRttmh995BpBz8mwkHvTOeRjS53MgpLMf6vGPIKSzXfI7UjOeMiMKBUIby6NGjgV6HR7du3YL2WEREFBx6DWKcKplE0UzZe3nHVYMwOzJm3qW0ndrFA+7mYNfu/Zmiexe9y1P9Gc0uKmWGtWZCijIS2EZLdjmYeM6IKFwIBZQ9evRQnSFpJ0mS0NjYGPDHISKi4NFrEDNvXB/MHdtLMSgTKZNNTYpFRXW96uOLBGF669cKhu28iDfThMif3jnzppYZBpqbGy2dObjFc++YFIdyjfMtEw1srZb4Kon2DqmBOGdERGYZ2kMZxAkjREQUBfQaxEgA1n1xFHPH9lL8uVamTA4Ppg3siuU7inXXYqYUVKRbaomNF/FmGhcpBU+i2UW1zLBMqWNu+ek6/O/re6BWXam4F1aFSInvw+9+g7MNLjg7iAWG0Z650ztn3MNKRMEmHFCKBpNyJlPkeCPHEhFR5LGjhFMtUyYHQ8lt44QCSqOloEa6pbphz0W80cZFWsGT4jnrEI8Zw7uhR6ck4cyd9/iK7PwS/L91e3TPiX/GUy1jKNJAqKK6AfNez/N5bmqBYbRm7rzPX9npOsu/U0REdhIKKF0ul+4xmzdvxm233YaysjKkpaXhlltuwTXXXIN+/fqhY8eOiI+Px+nTp1FUVITdu3dj3bp1+PjjjyFJEm666Sa88MILaN++veUnRERE4cOOEk5Ae7Zkk8ttvHusAKPdUu24iBfJyMrBmkjw9NkDY20r/RQJsB0SsGTGIJ+gTSnoTUuKw+NTs9AgcH3hTSswjNbMnV7JtRqzzZmIiIyypcvrmjVrMGXKFJSXl2PmzJkoLCzEs88+iwkTJuDCCy9EYmIiYmJikJKSgkGDBuHXv/41tm/fjn/961/o1KkTXn/9dVx++eWoqamxYzlERBQmzJRwqpEzZVMHdsXInh09QYEchAFo0ZVUa4+gHjMX5HZcxMsZWWey7zlxJid4AinRbrAAFM+ZGSIBtssNpCadmx0qB73+t6uorsfda3KxpeCEoTVodbo1kg2PFGrnT4TZ0S9EREZZDiiLioowe/ZsuN1u3HzzzVi5ciWSk5OFbjt+/Hh8+OGHiI+PxzfffIP77rvP6nKIiCiM6I2fkNBcxmg0e+hPJAgzyswFuV0X8ROy0vHZA2OxdvYIPHvTQKydPQKfPTDW8zxCETwZzTY3udx4bIN2RvP9vSVIbtvG0HgStedmVzY8XBgpufZm1+8UEZEoQ015lLzwwgs4e/Ys2rRpg7///e+Gb5+VlYXZs2fjH//4B1atWoW//OUvSElJsbosIiIKA0ZKOK3SKos1Y3hGGlLaxuLk2Qah49OSYm29iPfeu+gvFMGT0Wzzkm0HUXpK//GbXG5PSaqR4Mn/udmZDbfCrg6zRkuuAft/p4iIRFjOUH7wwQcAgP79++O8884zdR/jxo0DANTV1WH79u1Wl0RERGEkENlDNWplsWbv6/ZRPYSPf3xqVtAu4kMRPBnJNmfnl2Dx1oNC93umrgnzxvVu8f7Q4//cgpUN15KdX4LRi7ZhxrKduHddHmYs24nRi7YhO7/E8H2Z+TDAmZyA524ehOS2cVifdww5heUtSoOJiOxmOUP53XffQZIk4TJXJd63/e6776wuiYiIwoxeU51wnRk4d2xvrPi8GCdrtLOUsy/PwMRLuwRpVca7wdpBNNsMnNu/KapHpyRPA6HSqrNYuGk/KqvrDT03O7LhVt6LdneYFf0wYP6kvujUPh7nt09AZXU9Fm6K3pEpRBSeLAeUDQ3N/8gePnzY9H0UFha2uD8iIooMohfhSiWc4T4zMMYh4anr+2vOopx9eQYemZQZ9HUFq5TYm94IlwlZ6cgpLDdcqnl++wSf90fbuBhTz01kfWqsvBcD0WFW9EOD20ZleLr+3rMm+kamEFH4k9wWh0D269cP+/fvhyRJ2Lp1K6688krD9/GTn/wEn3zyCSRJwrp163DjjTdaWRKp6NevHwBg3759IV4JEUULoxfh3sFncVm1YlmkfLkdThfAyqMvYvH41KygZiZF1hWMgFzrQ4T1ecdw77o84ftKT07AZw+MbRFoWQ3wjGQa1bKLou/FnMJyzFi2U3NNALB29ghDY2XkdQHKgbV319/Ri7apBvJy8Kl0nomo9bIrNrCcobzmmmuwf/9+uN1u3HHHHfj4449x4YUXCt/+8ccfxyeffAIAiI2N9eynJCKi8Ga0xE90np5dMwPtLKW1u+GPXfTWFahyYq2GQUb2bUrQzjaaPeda6/NnR3YxUE2SRDOuRrr+WpmTSkSkxHJAeffdd2Pp0qWora1FcXExhg0bhieeeAK/+MUvEBcXp3q7b7/9Fo888gjeeecdAIAkSZg1axZSU1OtLomIiALM6EW4WvCpxuoFcCAyd0aClGBSW5eRc2Bn4CmXaup9cCDyegTjnNsRjAWySZJIYB1tI1OIKLJYDigvuugi/O1vf8Pdd98NSZLw/fff484778Rvf/tbjBkzBllZWejYsSPi4uJw+vRpFBcXY/fu3fjmm28AAHLFbe/evfHXv/7V6nKIiCgIjFyED89IMzVPDzB3AWx3c5RIZOQc2B18xzgkTBmQjhc+KVI9Zt643pg7tnfAM6ki7AjGAt0kSS+wDpeRKUTUOlkOKAHgrrvuAgDcd999qK+vBwCcOnUKmzZtwqZNmxRv43a7IUnN/1gMHjwYmzZtQocOHexYDhERBZiRi3Az8/RkRi+AA9EcJdIYOQdbCkptD76z80vwokYw+esxGbh3XB+f40PZmEn0PVZcVqP6s1A1SZLpBbQA4JCAyuq6gDw+EbVuludQyu666y7k5eVh4sSJnkDR7Xar/gGAzp074+mnn8auXbvQuXNnu5ZCREQBZiQjYibLaHZmoJHMabQSPQc7D5drBp5Ac+BpZI6hVjALNL+uG74u8dynnEn1X68c0JqZ36i1tpzC8hbzGYdnpMHZIV739uu+OKp5LoI5b9WfHNBqcbmBe9bssfWcEhEBNmUoZZdccgnef/99/Pe//8WGDRvwxRdf4NChQ6isrER9fT06dOiA888/H4MGDcLll1+O8ePHIyYmxs4lEBFREBgp8TMavFnJ6HAvmfhz0xvvYWYfq12l0HZnk5WyoM4O8ZgxvBt6dErC6F6d8FbuMc37EDkXoWzeNCErHc/dPBhz1+ZC6zOAaM/QE1Hw2RpQyi644ALcfffdgbhrIiIKA0ZK/ETK8byJzAxUw71k4s/tv5XqJZzejATfoseWVp3FKzuKbAtotfZgqu4nPVWnOLZGi8jzC2XzptSkOM1gkt1eiSgQAhJQEhFR9BMdaSASfN43rg96dEq0rcOoleYooWwQYwfR/XTv5R0Xuj8jwbfosQs37UdFdb3QsUpBnP8807W7j6L01Ln9gfIezPGZTtMNoZSE+wcRRgL6nMLyiH2PE1F4YUBJRESmiZb4iQafVlltjhLqBjF20DoHMtFtkQ4JGNJdbJxXk8sNl9uNlLaxOHm2QfNY0WASaBnEicwzlfdg3jeut+mGUP7M7OkNNrMBfaS9x4kovEhuuUMORb1+/foBAPbt2xfilRBRaxWs7J+ZwFCtNFJenVZjlXDMaiqdA4ckHkzK1s4eoVseKRLkGSVnkz97YKxu+ara7ZMFgltRvx6TgYcmaje+CbUmlxujF20TLi+XibzHiSj62BUbMKBsRRhQElFrYiTIky/E1QIipeBGFs5ZTe9zUHa6Dgs37Td8H8/eNBBTB3ZV/blokJeWFIuKamPB3R2jemBcptOTGdR6jQItXeX1l4XLhwry6wEoZ6fVaL3HiSg62RUbCJW8Dh48GAAwatQo/OMf/7D0gKLmzp2Lzz//HJIk4auvvgrKYxIRUfQw0hzFSHdS7/tUbfhiYY6jnbzPwbt7tLuYqtEqo9QbEwIAKYmxeG7GYHx/pg7zXs8Tekw5k7p8RzGW7yhGenICbhp2oalgMjEuBjX1TYZv50+rmU04faigVl6eFB+D6jr188CGPURkltAcyry8PHz99dcoLCwM9Ho8CgsLkZeXh7y8vKA9JhERtU5mxo1oBVNm5zgGSnZ+Cf600fgn0O3i22juG9QLxAHgZE0DHA4Jzg7iDW38T1lpVa3hjqwyO4JJmdL7JJizNEVNyErHZw+MxbxxvZHSNhYANINJb9E8UoeIAkMooKTIUl1drfjH5XKFemlERGHJzLgRI1nNUMrOL8Fdq3JRWWN8L+GZukZsKShV/bmRQFzuPqtWTCmhOTOpxGxIbnflpv/7JJw/VNhSUIpnth40vIc03DvZElH4YUAZhdq1a6f458CBA6FeGhFRWBIJdvy7fJrJaopocrmRU1iO9XnHkFNYbioYke/j3dz/4ndv7jV8e29aAZGRQFzuPgugxXmWu9HaHXfZeX8dk+JaZGuNfqhgx2srQqQU2Z/Se5yISIShsSG7d+/G2LFjA7UWH3v3WvsHkIiISJSZcSNmspp67NiLZ3fHVa19dUbnfmqNj7kmy4mXdxTbsmYjfjGiOzZ9c1y3YdDCqVktmtUY+VAhmPssRUqRvYmM1CEiUmMooKysrMTHH38cqLWQTc6cOaP4/aFDhwZ5JUREkcPorEyjwZQeOxr8GBmrYUTpKeXgxEwgrja7dHdRRUgCyon90zGqV0fN8/brMRmYeGnLcy/6YUFxWQ2e2fpt0Jo3Gc2K2z0PlohaF+GAktNFIkdSUpLi9x0OVjgTkT28RyR0SooHJKDsTF3YzGD0JzrSQS3YUTrWTDCltT6tvXgSmktPx2c6NcdWGC1zFLXw/X1oG+tQDDiMBuKAcgdevQDdjLSkWFRWN+gG/DEOSfE5pCXF4vGpWZh4aRfF+xf5UKFzh3is3X3U0mtrlGigO/fKXhjVq1NY/s4SUeQQCihXrFgR6HUQEVGE0CupDJcZjLLs/BI8tqHAJ8vm7JCAx6aIBztqzARTSsyOLTFyH1ZUVDdoZtKMBOJq5AD9rh9nKFqVnpyA+ZP64p41e4QCfjPPQeRDhRnDu2l2qA3EuA7R7Pm88X0YSBKRZUIB5a233hrodRARUQQQKakMlxmMwLkOp/5KT9XirlW5eN6GNdoRTNnR4MfsuIckrzmNeplBrUyakUBczfhMJ1ISY3HSREdabxLgCeiXOiThgN/Mc9D7UKGuUazDup3jOuzMnhMR6TG0h5KIiFov0ZJKrTI+0dJTu9b74DvfaB7z0Dvf2FJqaDWYsqPBj9lxD3/72QAAwMPv5qOiul71uGAMvt9dVGE5mPTPkNsR8OvxfwzvMvCy03VC92H3uA67sudERHoYUBIRkRAjJZVKwUcwu1wCwM7D5brBSWVNA3YeLseoXp1sf3wj5BJFrfOrN9LB6B5E/3N/tr4J8974Wvd2gRx8b+W+U9rG4rmbB2NEz44tgkU7sqd65MfIzi/B79762ue1dEjqI0yMNm8yIhjBNBERA0oiIhKi1ulTixwg2NHB1KicwnLh48wElHZmW2McEqYMSMcLnxSpHjNlQLql/XxuAPPG9UaPTkmK63UmtxVaayAH31u576du6I8RPTuGNHhSe59rBZNAYMtPgxFME1HrxoCSiIh0ZeeXYOH7+wzf7vz2CbZ0MDVHtFeo8Z6idmdbm1xubPi6RPOYDV+X4PcT+mqeIytljiIZzpTEWLhcbjS53LqvlZmA20yn19TEWPz5+v4AgNGLtgUtA+5PpCTcP1PJ8lMiigYMKImISJOZ2YbeZXx2dDA1Y+RFnbBke6HQcUYEItsqUk4seo7MljlqZThlJ2sacMvyXbqBmtmAW6SZzL1X9UajywWgOfM24qKO2FJQGvQMuD+R19DlBuZP6otO7eNZfkpEUYODCYmISJWZ2Yb+ZXxbC0qFbqe0f67J5UZOYTnW5x1DTmE5mlRqB5WOG9GzI5Lban9umpIYixEGgli9bCvQnG1VW6caO7q8epPLHK/9cX7i+3uPa54/mZzhdCZrl57KgVp2fsusqhxw+wdXWrcRWYMzOQFLZw7GfeP74HdXX4LfXX2xp1TZyGsi+p4ySvS16dQ+HlMHdsVIhb2eRESRiBlKIiJSZWa2oXcZX5PLjXfzjgndzn//nGiWS+24KQPSIUnaF+xPXd/f0EW9kWyrnJ3VyhLKZaEHT5wRenwjewzNZgnlDOfOw+W4Z3UuTp5t2dhIrVTZrvJmI1lWI69J1dl620uV5TWGqpsrEVGoMaAkIiJVolmXe37SE32c7Vtc+O8uqkBFtf4YiI5JcT5dLkXLStWOK6mq1WxwAzRnJ40SPR9bCkrxmzfyNAMXpYBPjdFOoFbLcmMcEhySpBhMypRKle0sbxZtJmPkNVmxo9i2slil1y9U3VyJiEKJJa9ERKRKNJsyuvd5PmV8clnhBzrljbKpA7sIZ7mA5ixXfaPLcDmut5M1DUIlmPKacgrLcfDEaaH7fnlHsWbJp1pZqBKjnUDtKss1U4Zrd+muCNH36Ht5x20rVVZ7/ULZzTUSBKrcmIhCixlKIiJSpdd1UynrYiTzJhuf6fT8XTTL9VpOy6DNDL0STDPPR4lc8vnYhn0AJOFA2GgnULuyhKKBmvdxZm5jlch7NDUpFhXV9ar3YSRzym6u5gR7Di0RBQ8DSiIiUiXSddM762KmI2y6X0Aqmr06UlFj4FGU6QUSZp6P3uOVnhLbazf3yp4Y1es8Twmx6BgOu7KEZj5MMHMbq0Teo9MGdsXyHcW69yVy7tjN1bhQzKElouBhySsREWnS67opN9/ZcbAMD779jXDwJf34x78MUDR71T0tUfCR9Kl1mLVSUmtV787tPSXE2fklGL1oG2Ys24l71+VhxrKdGL1om2K5rl1ZQjlQA84FZjK1Ek75NmrnzK1wG8B6KaTee3ScVwZci8i5YzdXYwLVGZmIwgczlEREpEur66bZklC1MkDRLNcvRvbAS58VqR5nhFIgYabDrZ3kNRnN7tiZJZQDNf/X184STrtKIbXeo00ut23nJBRlvZEsVHNoiSh4GFASEZEQpa6bZkpCZ43sjmuy0lXLAEXLbOPaOFSPE6UVSNjZOMafaDdQM2M4jJYp6zEywkNerxr/9dpdCqnWGdbOcxKKst5IFopGTUQUXEEpeT1z5gxWrFiBX/3qV5g0aRKuu+463H333XjzzTdx9uzZYCyBiIhsZrYk9JqsdN0yQJEyW63j0pMT8OsxGZqjQfwDCf+yy05J8QafmfL9K9HrBnrTsG54f+9xvLKjSDi74030/ImSAzW9Ek4j2ahgl0KOz3TivnF9kNzW9z1h9JyYKQVuzZjRJYp+hjKUBw8exOnTzS3TnU4nunTponubNWvW4H//939RWVnZ4mcvvPAC0tPTsXTpUkyePNnIUoiIyALRBi9ajJaEGs3ciGbGvI8rPVWLijN1SEuKw9GKszhZoz5HMSUxFn++vr9nnmWLss4OCUhJjEVVTYOhfaEAcOeYDKzPK0HpKe3z45+pTEmMhRvA4q3fCj5iM6XsjpHMogiR94yRbFQwSyGVXt+UtrG4fVQPzB3b2/A5CUYpcLRgRpco+gkHlG63G5dddhkqKpo/Bd20aZNuQPn888/jnnvugdvd/L8QSfL9H7bb7cbx48dx/fXXY+3atZg+fbrR9RMRkUF27VkzUqJmNnMjOtw+xiGh6mw9/pJ9QDjIjW/jwPhMp2rZ5YlT5y6Alcok3WgOAL2DVu+AYkzv83HL8l2aa/DuBlpcVo3FWw8Krd3fwRNnkFNY3iLIEz1/ekTfM0ayUcEqhVR7favONuCZrQdxsbO9qQDQ7oA9Wtldgk1E4Uc4oPziiy9QXl4OAOjTpw8mTJigefy3336Le++9F263G5Ikwe12w+12o3379khKSsIPP/yApqYmSJKEpqYmzJ49G6NHj4bTKdaJjYiIjLNzz5qRErVAZ27M7OUsPVWHnYXlunsUUxJjEd/G4TPuQ34+WgFFWbXYeJBO7eNx7aVdMHrRNgOr97Vk+yEs2X4oIHP9jLxnjGSj/Mt01VgphTSzB9UIuwL2aMeMLlF0Ew4oP//8cwDNWcabbrpJ9/j58+ejoaHBk5Xs378/li5dissuuwwAUFVVhaVLl+KPf/wjmpqacOrUKfz1r3/FX//6VzPPg4iIdNh1cS2XPpaeqkVaUiwqqjXKStvG4rlbBmPERYEbnWBlvEfO4TLdssvKmgas/tX/wCFJioGjWkAhGgh1Soq3raOs3XP9jL5njGSjhmektcjw+ktJjLVUCrnzcDk7jIYJZnSJopdwQPnll196/j5t2jTNY8vLy/Hee+95MpNdunTBRx99hNTUVM8xycnJePDBB9GxY0f8+te/BtC835IBJRFRYNixZ010RIh8ifjUDf0xqlcnkysWYy0YE7uYLTtTh6kDuxq6Z71sney3b36NSf3tqc6xI+vmzcx7xs5slJXVZ+eX4MG3vxE61u4Oo3bsUY5GzOgSRSfhgPLbb5sbBCQmJuLSSy/VPPZf//oXGhqaP3GUJAkPPPCATzDpbfbs2fjHP/6B/Px8nDhxAt9++y369OkjuixdP/zwA7788kt88cUXnv+WlpZ6fr5ixQrcdttttj2emtLSUrz66qt47733UFxcjMrKSjidTlxyySWYMWMGfvazn6Ft27YBXwcRtV6iF81qjWSMlJUGs5TNTDAgl16O7NkRS7Yf0j3eTNmlVrbO24lTtVi+o1joPudP6ovKmnos2V6oeowc5C3e8h+M6nWepWDG7D7H8ZlOtI+PRc7hMgDNQYR/lnp3UYVmdhJozg6byR4aLYG2s8OoXXuUiYgihXBAefToUUiShN69e7doruPvk08+8fxdkiTcfPPNmsffcMMNyM/PBwDs3bvXloCytLQUI0aMwJEjRyzfl1Xr1q3DXXfdhaqqKp/vHzlyBEeOHMG//vUvPPXUU1izZg0GDRoUolUSUbQTvWhe+P4+tI11+Fz86pWVSgDSkuLwh0l94UxuG9SMjNFgwLv0csRFHQPagVLO1j22YZ/PHkxv8uM6JMDtVg485XXcNioD7+89LvTYS7YXYsn2QkvBTHFZjdBx3q+BUkD1du5/W6whUE15jJRA291h1O65mkREkUB4DuWpU6cAAGlp+v/TzcnJ8fy9f//+6NhR+5PFAQMGeP5+4sQJ0SVpqq2tDYtg8rXXXsOMGTN8gsk+ffrgiiuuQPfu3T3fO3DgAH7yk5+goEB9IDQRkRVyCaZemFdR3YA5q3KRnV/i+Z5I6WN5dT2cyW11Z0zaTfR5ybznDgZjpuCErHT87WcDdY9zuc+VrGqtw2gALQczm/ce95mzqTffscnlxtrdR3Xv39kh3hOQyQGV/3tFXoP3eypQ8wmNlkAbeX39Z5V6n0O9/aZu2DtXk4goXAhnKJuamgAAjY2NmsfV1NSgoKDAk8UcPny47n17B5zynEs7nXfeeRgyZAiGDh2KYcOGYerUqbY/hpJvvvkGs2fP9nx98cUXY/Xq1RgyZIjne1u2bMEvfvELnDhxAqdOncLkyZOxb98+JCRwwC8R2cu7BFOPfPEr78ML1ogHM/QawbgBzBvXGz06JSnuZwtGB8qyM2IdX385qgc+yC/VXIfo3kyZfMzctXt8Zl7qZS7lxkt6ZgzvhhiHZLiBT6DmE4q+B1MSY/HUj3NIReiVsooEsiVVtViy7SDuHWff1h4iolATDiiTk5NRXl6O48e1S22++uorzzgQABg2bJjufcvBKtByVqVZaWlpePPNNzFs2DCfTGAwPfLII6ira76I6NSpEz7++GN07tzZ55jx48fjww8/xJAhQ1BXV4fDhw9j6dKlmDdvXiiWTERRbkJWOp67eTDuWZsLt0404t1sJVDZJLtYDQoD3YFS9LyMz3TikUmZmusQ3Zvpzz8xpleGKRqY9eiUBMB4A59AzScUPdf3/KQnxmeKNUMSKWWta3QJ3ddiC7MviYjCkXDJ64UXXgi3243Dhw+jrKxM9biPPvoIAOD+8UpFHhOiRZ5vCQAdOnQQXZKmDh06YPr06SELJgsKCrBx40bP148//niLYFLWr18/3HfffZ6v//KXv8DlEvuHiYjIqNSkON1gUiYHFXplpRKaszV27UUzqsnlRnLbOPx+wiWYP6kvFv9sANbOHoHPHhgrfOEud6CcOrCr7WW7Rs6fyDrkANqZbD6Al98CamWYRj9EMJPFVnse3mXJRomWQD+x+QBGL9rmU4arRC/zCjSfw07t4oXXaEfpq1b5LRFRMAlnKIcNG4Y9e/bA7XbjpZdewoMPPqh43Nq1az3jQs477zxkZmbq3vf+/fs9f7/wwgtFlxTW3nnnHc/f27Vrh1tuuUXz+DvvvBOLFi0C0NxQKCcnB6NGjQroGomodTJSlioHC6LZJADIKSwP6rgErVLEcBnVEIhsnHdWdcehMqFutf60RsUYLUk1m8W2OztsJIMr0ixHNPMKd/P7TmT/ZklVLV7ZUYTbRmWYep7sJEtE4UQ4Q3n99dd7/v7444/j888/b3HM3//+dxw4cABAc+nqjTfeKHTf3k18Lr74YtElhbVNmzZ5/j569Gi0a9dO8/iLLrrI57l7356IyE6iF/4dk+J8Mo562SQAGL1oG2Ys24l71+VhxrKdQhkgK4w0gQm1QGTj5GzmvPF9DDUm8qf0IYPRhkVWsth2ZYflrF1dowv3jeuDzh203+t6WVpA/AOYsuo6z/kSsXDTflO/H5H0niei1kE4Q/nTn/4Uffv2xYEDB1BTU4Mrr7wSP//5zzFy5EicOXMGH330EbKzsz3ZyZiYGMyZM0f3fk+fPu0pk01JSUHv3r1NP5lw4Xa78c0354Ypjxw5Uuh2I0eOxH/+8x8AwNdffx2QtRERyRf+epmUhVOzWlzYq2WTthSUmh6XYHYIvNEmMOEgUHs1ze6rlKl9yGBkb2qg9kSKUsraOTvEY/rgrngr95jq7bSytICxzOvInh0xb1wfLN76rdBtjI4TicT3PBFFP+GAEgBefvlljBkzBo2NjWhoaMDq1auxevVqz8/dbjckSYIkSbjjjjuEyl1ff/11nD17FpIkYfTo0cafQRg6evQoqqurPV/37NlT6Hbex3mXASvxvn9RLpcLDodwUpqIopT3hb9a4PHrMRmYeKnyBa6cTZJZuci1UrpntAlMuPA/f3ZRC/70eI/9ULtf0SA4GB1zlag1zTlxqk4zmPSmlokU6aqbmhjrOYdzx/bC2t1HVOeOejMaBEbqe56IopuhgPJ//ud/8NZbb2HGjBmoqVEedux2u3HVVVfh2Wef1b0/t9uNZ5991tPZdeLEiUaWE7b8519269ZN6Hbexx05csQToCvRK6FVIxLkE1H0U7vwT0uKxeNTszDx0i6Kt1PKJpq9yLU6BD6cR5mEiu++yh+wZHuh7m3ksR9ajATBge6Y60+kaY4ItUyk/AHMXRrjdiprGrCloNQz2/SxKf0843n01mAkCOR7nojCkaGAEgAmT56MAwcO4IknnsD69etRWloKoHnP5IABAzB79mz8+te/RkxMjO59vfHGG9i3bx8AwOFwYNq0aUaXE5ZOnTrl83VycrLQ7bw73LpcLtTU1CApKcnWtRERyYxe+KtlE6/JEhu94H2Ra0fpXriPMgkVOfgzOvYjEGsIBpH5j1pE5l2Oz3QiJTEWJ2saVO/D+/1qJlss8nrxPU9E4chwQAkAF1xwAZYuXYqlS5fizJkzOH36NNLS0hAfL94yGwB+9rOfYfr06QCaA9JoKcf0L0dNSBD7H3vbtm1b3I9aQHnmzBnD6xo6dKjh2xBRdBO98NfKJr68o1josbwvcu0o3TPahdQMs/s7w0Gogo9gnzMj2Tizezt3F1WoBpOA8vtV/sDmlR1FWLhJexsLIPY6BOM9T0RklKmA0lu7du1Ml19KkiSUyYw0DQ2+/+i0aSN2mv2Pq6+vVz3WTOYyWgJ2IgoukWyiJAFaY/BSvPaYAfaU7gW6CYzR/Z2igVSwAq5QBB+hGGchGhDPG9cH6744ampvp9n3a4xDwm2jMvDSZ0W2vA6hbnxERKTEckBJLSUmJvp8XVsr9g+R/3EsdyWicCCSTXTrbBQ76bXHDLAvexaoJjBG93eKBlLBDLiCHXxY3RNrlmjgPHdsL8wd28tUMN+pnVgFltJxdr8OoWp8RESkhgFlAPhnbM+ePSt0O/9GR2Yzv0REZillz0SzM4lxMaipb1L8mf8eMzuzZ3Y3gTG6v1M0kApFwBWs4COU4yyMBmym9naKdvf58Tj/36PxmU5bX4dgNz4iItLCgDIAOnXq5PN1SYnYkGG5wREAtG/fHrGxsbaui4hIi1r27KZhYp2q1YJJoOUeM7uzNnY2gTGyv3N4RhoeXZ+vG0iNvaRzyAKuYAQfRvfE2l32G+jAuaxafwSIfJxWFvqzB8ba9ryD2fiIiEiLcEC5efPmQK7DIxpGh/Tp08fn66NHjwrd7rvvvvP8/ZJLLrF1TUREWrSyZ89s/RYpibGoqmlQzSYma3TA9Oad7QzX0j0j++XuXbcHJ06r73eXA6nXcopDOj8w0MGHkXOmFnDNn5SJ1KQ408GWkcDZaEArWqJdXFaDZ7Z+G/SyXyKiUBIOKK+99lrVmYh2kSQJjY2NAX2MYGjXrh0uvPBCT4CYl5cndLs9e/Z4/t63b99ALI2IqAWhpjtef1fKJt5+WQYWb/1W97H8L8zDsXSvuKxa/yAAWwpO4P29YhUonxz8Qei4SJ0fKBpwbSk4gU17S1q810qqanH3Gt85j2b2looEzmb2sYqUaHfuEI+1u4+GJAtNRBRKhtt+ut1unz92sfv+Qm3MmDGev3/22We6xzc0NGDXrl2KtyciCiSRcsXKmgbMG9cbzmTfwMGZnIClMwej9/n6TcTSVfZEykHA1IFdPeWwodLkcmPtbv2qks7t47D5G7FgEgByj54UOk4vMGtyuZFTWI71eceQU1iOJq3WuiaYvX854NLzvkIwqUbO6mXni59nPXIm3v/9rvdYcok2cO5DFJn89Yzh3VB6SiwLTUQUTQzvoZQkySfwi6Yg0E5Tp07F6tWrAQD79+/Hnj17MGjQINXjN2zYgNOnTwNoHu8xefLkoKyTiEg0K9ajU5LiHjAAGL1om+7t508K/3EGu4sqUHpKf7/cxc4OOHGwTOg+05JiUVGtXw7cMSlOswlRoDvEWrn/GIeEKQPS8cInRZbXIbM7q2e1cZBeiXZdo0toHZGahSYiUmOqKU9GRgZ+8YtfYNasWbjooovsXlNUmDhxIs477zz88ENzmdPjjz+Ot99+W/HYpqYmPPXUU56vr7nmGpx//vlBWScRkZERHkolhTmF5ZoZTllqUpyp9QWT6MW+kY9Spw3siuU7inWPmzqwi2rQpNch9rmbB1vaf2i1A22Ty431eceFH0+UnXtLjTYOUqJVop1TWC60DtHfNyKiSCFc8pqamuopSy0uLsbChQvRu3dvXH755Vi2bBmqqqoCuc6wIUmS589tt92melxSUhIeeughz9fvvPMOnnnmmRbHud1u/O53v8OXX37puf+FCxfavWwiIlVyuaJa+CFBvVwVAEqrxEYjRUJmRvRiv0fHRP2DAEwf3BXjMp1Cx45XOU4vs+YGMHdtLmYs24l71+VhxrKdGL1om3CpqN79A82ZO63y1yXbDgllds2y471jpHGQFrUSbau/R6EQ6BJqImodhAPK0tJSvP3225gyZQratGnjCS4///xz3HXXXUhPT8fPf/5zbNq0CS6XWNlHoM2ePRsJCQkt/hg9xqx77rkHo0aN8nw9b948TJ48GevWrcNHH32EV155BWPGjPEJNH/7299qlsYSEdlNa38Y0BxUzJ/UVzHjlZ1fgoWb9gs9TiRkZkSDgocnZkIvAeiQgCevv1T3PuVjK6uVu8XqZdYAwD8OKKmqxV2rcrFZoGmQkcydkuz8EqGGTFbY8d4xkok3Q2SfpZFROIGWnV+C0Yu2mf4ggohIJhxQxsbGYtq0aXjvvfdw/PhxPPPMMxgyZIgnsKytrcVbb72FKVOmoEuXLvjtb38r3N00UBoaGlBXV9fij7fGxkbdY8yKi4vDu+++i/79+3u+9/7772PGjBm48sorcfvtt/s07Ln55puxaNEiWx6biEhUk8uN5LZxuH1UD9Wy1IWb9re40JTLJCtUAiFvaUmxKD1VGzZZELXMjGhQ0DYuBrMvz9B8jNmXZyCujcPnPtW43MA9a5SbwljJzs1dm4vNe7VLUUXvf8ehH1qcLzm7GSh2ZvXsziAqvYfkfZZqzauCNTJEL/NotjkREZESyW2xq05BQQFeffVVrF69GsePn/tHSx4x0q9fP9x666245ZZb4HSKlf3Y5bbbbsOrr75q6rZqp8V7dMqtt96KV155Rfe+amtr8cc//hEvvviiYmlwjx49MH/+fPzyl780tVZR/fr1AwDs27cvoI9DROHNewZfcVk11u4+qluuKP+fT74obnK5MXrRNqG9k/7sbCRjhkjzGdEGNX/eXIAXPy2C/z8ZiXEx+PvPBvgcu3nvccxdu6dFNlEmoTnw+OyBsT5ZrJzCcsxYttPCMwae1whmzNy/fC6S28ZZXpsa//ecHeRAClAefyP6WHrvD605l0ZnYIqS73dLQSneyzvu80GP/9q0fnfV3odEFH3sig0sB5Qyt9uNrVu34tVXX8V7772Hmpqa5gf4MQBzOBwYN24cbr31Vlx33XW2lZVGktraWnz00UcoLi5GZWUlOnfujL59+2LEiBEBn/EJMKAkIuULYVHeF5q7iypMBxKBCBREqTWfUVqTyIV/dn4J7lqVC39K9ycauK2dPcKnKYwcAKjNQBSRrhEgmLl/z/zRUT3wskDDIRFpSXGaQZBdQZjVbrlG3kN2P7bWmrR+r73XJvohgP/7kIiiT9gFlN7OnDmDN954A6+99ho++eQTT7ZPDprat2+P6dOn46WXXrL7oUkDA0qi1k3tQtiotbNH4PvTtbh3XZ7p+whFFkQkq6oVeBm9P//nuD7vmNA5e/amgZg6sKvP99Qya0ZoBQhm7l9CcxBYLlDynBDrQF2DS/G+5fP08f1X4qsjlS2CxkAEYWYDVCvZPSuBqBbR32t5bb+/+mLMe+Nr3ftVeh8SUXSxKzYQ3kNpRLt27fDLX/4S27dvx+HDh7FgwQL06tXLs9/y1KlTWLFiRSAemoio1fHfL1Xf6Gqxf0qrk6dR8kW4FaEY8i7S3KakqhZLth205f78n6OVpjDy3rzkxFih+1CitVdSbe+fFjeA8up6pCbGajYcSk9OwN9vHABAe19qXBtHi+6pgdrrp9apVY38O7Z4y7emGhjZ0UlXbV2iv9fy2kT2PAOR0USLiMKDqTmURnTv3h3z58/HZZddhnnz5mHfvn2q+xOJiMgYpeyNQ/Lt+unsEI/RvTqZKnNVUna6DtdkpbcoUTQjmKNERB9r8daDuNjZXjdbZHQMhdwURqu0NDUxVrUpzPhMJx7bYP5TZL0AYUJWOsZe0hmv5RTjSEUNTp9twLsCsyXrGpszjxKU9yXKmcSlDqnFe9WpkWnUC8IkNAdh4zOdAc1ymykT939v2DEDU4nIhyT+0trFa74P5UxmOI03IaLwFtCA8sCBA1i5ciVWr16N//73v4F8KCKiVket1M0/yVF6qg5v5R6z7XEXbtqPlz4rwg2Du2LZp0WW7iuYWZDismrhY0UCFaMZR7nbq9KeS1llTQO2FJQqBli7iypMz3oU6V5qdn9tTX0TACA5MRYnaxo833cmJ+CmYd1Q92PGfHymE+MzncKlpoEKwowwWybu/96wawam1eMBwNmhOYifsypX80MANuQhIlG2B5QVFRVYu3YtVq5ciS+//NLzfTkred555+Gmm27CrbfeavdDExG1GnaWsJpRWlWLlz4twri+52Hr/h8M314C0LlDPFxuN9bnHbPcaEVvT1zzrESxUlZALFDRyzgqZXrGZzqR4hd4+d9GLZi1ks2dMiBds8voloJSS/trJQAJbRxY/av/QdmZOk/3YO/5lEb3PQYqCBNl5ndMLbsXqBmYRo73XluMQ8LSmYMNZYyJiNTYElA2NjZi48aNWLlyJT744AM0NDT/QykHkXFxcZg8eTJmzZqFa665Bm3aBLzSlogoqpkpdbOTXHK47/hp/GpUBl7aIZ6plLMitY0u3PLSLs/3zTZaERnh8OA73xi6TwDYWlCqGVDKGUcjmZ7dRRWqwSSgnXWzks3d8HUJfj+hL7YUlLYMIjrEo7ZRuWGOKDeaM+EOSUJ8Gwee2Xqwxf3J+x5Fm88EKggTZfR3TCu7Z+bDBxEiZdQyt9/aJmSlG8oYExGpsdSUZ9euXbjnnnvgdDoxffp0bNiwAfX19Z7mOyNGjMA///lPlJaW4s0338TkyZMZTBIR2SCYew/VyMHPVZmd8c+bByMtKU7odolxMQDQIrAy02hFpGnLzsJyzSBOzbt5x3SbpBgdZG8l6yYHD2Yu9+VmQ4rn6lSdqfOjpPRUrW3NZ/SerwSxUl6zjP6Oqb3mwLkPHwDtxkRGgzmt+xW9vZHmRIHk31zMaIMiIgodw9Hd0aNH8dprr+G1117DwYPN5UPeTXa6d++OmTNnYtasWejdu7d9KyUiIo9w6sD4/elaTB3YFVdnObHzcDnuWZ2Lk2fVA5SzDU2K3zfaaEW0acv1g8yNPqiobhDan2ck02Ml6+adETVjxY7igJdI7zj4g237Hs1kgO0k+lrNvbInRvU6Tze7J3/4YHeZqXy/j23Yp7nHNlhNjMwI1HxOIgoO4YByxYoVWLlyJT799FNPBlImz5WcNWsWrrjiioAslIiIzjFS6qalbawDZxtcltbi3XTGIUmawSTQsmmQNyMBh2jTluMnz2rejxbRLJWc6fHnv19xSPdUS6WPE7LSceeYDLz4SZHh113vddGTmtgGlTWNmsds3f+90H2JntdABWEiRMtU542/WDhAC1SZ6YSsdLRPiPUpIfcXjCZGZqg1PjJaIk1EoSMcUN5xxx2QJMkTSMbExOCqq67CrFmzMG3aNLRt2zZgiyQiIl9a2RsR8sXwwxP74v+t3WNqDUrBj12luCL3I/pYXVLN//tkJROslnWZMiAdL35SZCrrlp1fghc+MdZZV0LLDqxGb+9MTsDPh16AZz48pHmsaNBq5LxqBWF6zZisCFSGVO3DB6vKzoh1AA6HcnlZuIyGISJrDJe8SpKEHj16YMaMGejSpQtOnjyJFStW2Lagu+++27b7IiKKZmrZG/85lP785wPmHzupG6SIXlDbVYqrdT9yEHHwxGmh+7rsok5YveuooYDK6iw+razLi58U4c4xGdjwdYmhrJt88W2E/MrcflmGT8dVo7d/dHIm6hrFMtkpbWNRdbbB1uYzSkFYIMok/QPU8ZnOiOmGGuomRmaEw2gYIrLOVIec4uJi/PnPf7Z7LQAYUBIRGaGUvRnSPRVfHanE96drUVxWg7W7j6L0lPrF8EMTMzHgghT8YX0+KqrPBV3yxTkA3Qtq+UK89FQt0pJife7Hn0MC3G71rKpDAiqrlbMtRmYlysHLiJ4d8dT1/TXnP/rfDjC/P08k67Lh6xJ8fP+VntdJJLtmprOv/DqNz3Ri3RdHDZdId+4Qj8em9MOErHTkFJYL3eb2UT3wzNaDAd33GIgySa0A9bMHxtqWCQ1UVjVQnWQDKdSjYYjIHqZbrnrvobSLJLGcgYjIKKXsjffXc8f20r2AnXhpF1ydla56nFLJIQDkFJZja0Ep3s07phlEAucCitmXN+8BVONyA/es2YOlDsknKDAyZN4/eJmQlY7nZw7GYxsKfILr1MRYuOHbcdY7CMspLDd84S+adfmiqAKjencSeDbNjF5Uz5/UF7eNyvCs2VxDn3PPVzRgmTu2Ny52ttf8EMJKUBWIMslg7eMLZPOZUDcxMiMSs6pE1JLkFowMe/ToEZSAr6jI2N4QEtevXz8AwL59+0K8EiKKdEYyhTLvC+fNe0swd22uammuHJx89sBYz1650Yu2CT+e2kW6UiADoMX3lOY1il74r887hnvX5emuMaVtLJ66ob9wIJFTWI4Zy3YKHZvud+7k51dcVo1XPi9GpWD5r/yvvhxQyYEXoJxhnjeuD+aO7aW5v9FqUCV6HtbOHiFUJqn33vJ/L5qlFrT6n2OrIqljqnzu9T6ksHruiUiZXbGBcIayuLjY0gMREVF0EM0USgDSkuLwh0l94Uxu65OFSk2KM9TtVbTcc+6VvTCqVyfVjJdaQxTv71nNVolmU06ebTCU/RLt7CvhXCZKKbjo3D4e7eLb4EyddsdWoGXGT23frmzx1m+x7oujnuBFad+j1Uyg3WWSwdjHV9/owsPv5gel+UygOskGQiRmVYmoJUeoF0BERMElDxB/N/e/WP7pYby7R3yQuFa5oT83gPLqejiT23qGpsuP/UF+idBa5aBANDjo3bmd4QHt3gPVdxwsw2Mb1MspgeYLf61zJQd+oivQuz9ZjEPC/El9Nc99amJsi2yif7B04nSdUDAp8w6omlxuJLeNw+8nXILpg5Xne8rB4ea9x30G1dc3ujRLVYFz50JryL3dZZKB3seXnV+CEX/eiorqetVjvM+xHeQPTqYO7Gr49yHY5A8pnMm+r5czOYEjQ4gihOk9lEREZD8ze8uM3EarVFWkLM5MYxj5QtxMmawcFARqr5XRNallq/xfg/mT+uKeNfrjWLzvb3hGGnYWliPncBmA5oBgxEXngoHs/BIs3LRf8X5S2sbi9lE9MHdsb0/gLhr4i9pSUIrfvJGne67kx5y7do9PFlqvWZN8LpZsO4h1X3ynWrIpupdzSPdUoT2wgdzHZ2TfL2B/85lAjlWxUyRlVYmopbAJKF0uF7KzszFx4sRQL4WIKCTM7H0ychu9i9sSgbJDMxe857dPMHxh7d+R0mwHS60LaqNr8uZ9HpReg7SkOPzq8gy8+eV/hWYzbi0oxZzVX/k0B1qy/RBSEmPx1PX9AUBzrU9O64+Jl557zcwE/npe3lFs6Hj/pKte0ybZ4q0HW3zPvyRWr0xyyoB0XPH0dqHfi0B1RzUT1NvZfCaS9lICgZvPSUSBF/KS1/z8fNx///244IILMGXKlFAvh4goJNTKE8+VD5a0KAHUu012fsm58tY9x/Dwu98IXdwqlWDK9yM6+xFovhBP/zFTZOTCWg4KbhrWDe//WDYJwDPCxD9nobbXKju/BKMXbcOMZTtx77o8zFi2E8Oe2Io/bdyHHYfK8NiGfaYzeMVlNZ7HUHoNKqrrsezTIozuJXaBvHxHseKczJM1DbhrVS4efEf9tZMALNzk+5rZnekKdaLIvyRWq0zyzjHNXYS1fi+8yfv4APH3lggjQb38u2LXSA+R/zcQEdlFuMurncrKyrBmzRq8+uqryMvLA9A8hkSSJDQ1NQV7OVGnurpa8ftDhw6Fw+Fgl1eiMNPkcmPI41sUAwqZQ/LN+Dg7xKO20aV6GwlAcmIsEtrE+IzJEOXdIdNMqars+ZmDkdw2Trg7KaA8ykPOrLhcbtV5mWZHjJiRnpyAj++/skUWTEly2zY4dbYxYGuReb9mRjrCavHPAIYD7+fpn4Ee0j1V8zXR6hpqd0ZPtNuvvC679gsGq2stEUW+oHd5taqxsREbN27Eq6++ig8++ACNjc0NAeRAkuzTrl071Z9lZmYGcSVEJGLJtoOawSTQsnyw9FSd5vHnAjKxMkN/3vsezQZmKYmxGJ/pxPt7jwsdP2tkd3RMilMtebxrVS5SEmN9zlVaUhzmT/K94A/E/kF/JVW1eC2nWCjIbvzxxQt0cOadlRTtCKvHmZyAiVlOLDdY7hpI3s/Tv0wyp7DcdMdWu/fxiZavdkyKwxPTsmwrQw1G11oiIm8BDyi//PJLvPrqq1i3bh0qKpq7l/knRZ1OJ2644QbceOONgV4OEVFYaXK5sSKMLtZl57dPsByYnaxpwO6iCuEL66v7OfG7N79W/Jm8Bv/Au7K6HvesycVSx7nsTiD2Dyr55OAPQsdV1zVh3rg+WLv7iO4HAVZ4n2e5I+zdAo2BZBKAzh3i8befDUTZmTpPQLVkW8sAP5S03k+ipb5yl2H/gNHOfXwiQX1aUixyHroKcW3s24EU6K61RET+AhJQlpSU4LXXXsOrr76KAwcOAGgZRHbt2tUTRI4aNSoQy2i1zpw5o/j9oUOHBnklRKRnd1GFUNOWYPFuQGJHYPb96Vpce2kXpCcnaN6Xs0M84Ibhx1Oa4ResC+WPvy0TPrZHp0T87WcDcctLu2xfh1LTGK2OsGr3AQCPTemHUb06eb7f5HJj7e6jhtbjX55tZA2Sxm1FmuOIfnixMucIVuYcCWiTGpEZi09O629rMAkEtmstEZES2/4vVldXh3Xr1uGaa65Bt27d8NBDD+HAgQNwu90+Za2SJGHHjh347rvv8MwzzzCYDICkpCTFPw5HyHswEZGfcMwSyA1I7Fjb+e0TEOOQMGWA9gX76dpGfHjghKnH8J/hF8wLZdFqyPPbJ6DsjPnsZEpibHPA5fd9paYxag1ZZLMvz0C64My/3UUVQlnV6YO74tmbBmLt7BFYMmOw5lr9/+799ezLM4SfpxKjM0AD3aQmFDMW9c6B3Q2AiIgsZyh37NiBV199FW+++SZOnToFwDcb2bZtW0yZMgXr169HbW3zP24jR460+rBERFEhnLIE/tkaO9ZWWV2PJpcbr3/5X83jquubDI+l8CcHwHbtHxQhkolL98r4mvXzoRdgULfUFk1jnH6vmV6ZsgTg/b0l+Pj+K/HVkUrNvYJNLjd2HBLLwl7e5zxMHdjV8/VSx2DVtQLQfB4iz1ONVlZQiVKG227BnrEokhk107WWiEiNqYDyyJEjWLlyJVauXInDhw8D8A0iHQ4HfvKTn2DmzJm44YYb0L59e6SmpnoCSiIialZZXW+pRFCpk6vcAbaqpkF1rl5aUhwevuYSnDzbgLR28XB2aHmRO6R7KtrFx+BMnfnu2ws3FaB9QhvdpkMys+cCOBcAGw0qrLrqkvPw4QHl/ZQSzl28Wwl0X/ykCEtnpuKzB8YqBiZyt9Mdh8qEGrJ8daRSc6+g0c6+/h8+6AVRWj+zGoDJWUHR9QejSU2wZyyqnQPRwJyIyAjhgLK6uhpvvvkmXn31VXz66aeeANI7kOzfvz9mzpyJm2++GV27dlW7KyIiQvNF+z1rxDqoqmUanrq+v+LF95aCUs0MhV5Xyez8Ejz4zjeWgkmg+UL97Vzt7KQ3OZg0Eggq7a0zGlRY8avLe+KGwRfojjOxGujKWTT/wMTMWBetcmYjnX29z73/CI/hGWmqQZRegGU1APMOSj/IL8HKnCO6t/n+dK3ic4jUTF6wM6NE1HoJB5SdO3fG2bNnAfgGkV27dsWMGTMwc+ZMXHrppfavkIgoCol0UHVIwJIZg+BwSLqZBqURCGYzFNn5JbhrVa7uc0iKi0F1vX7AWVPfqHuMtztG9cDm/FKfdcvjQoyU8HlfUG8tKMW7ecd8Aj69TC7Q/Bq43crBn3cwFeOQcHVWuu7Fu/y6PLZhn6GOr2pZNLNjXdTKmY109vU+91sKSm2d4WgH76BUJKAsLqtpMb8x1M/BqmBnRomodRIOKGtqaiBJEtxuNzp06IBp06Zh5syZGDt2LOdIEhEZJNJB1eUGUpPiMbJnR1OZBjMZCjmgECESTALAsB4d8e+C74WOBYAuKW3x+wmXoOJMHdKS4uBMbuvJuhoNkOUL6pE9O+LhSZmGM7mzL8/Ai58U2boXbUJWOtonxJrq+OqdWTQz1kWvU6qRzr7eeyKVglq54Y1/85lgZwH1So3l0vFntn4r/ByIiOgcw3soJUnCpZdeiiuvvBIjRoxgMElEZILRWXEimQa1C3UjGQqjo0JS2sai6qz6Xk1ncgJuvawHnvvokPA+Su9xF3KGKMYhWS7hUzoXIplckSYxSmWn/tkt79fn4Anl8U56istqPH83+lqJBMGi78u5V/bEvPEXAwBGL9qm+PorNbwROU9202tSI38t+hyIiMiXoYBSHv+xY8cO7NixA3fffTemTJmCW265BRMmTEBMTEyg1klEFFXsmBXnHaAUl1Vj7e6jPmWUZi7UjY4KuX1UDzyz9aBmBi+ujQNPXpeFu9fsMXTfQMsMUSBK+PQCVb2fq5Wdeq8daNnZ1Ixntn6Li53tMD7TKdyBVSZS7iz6vhzV6zzEOCTkFJYLNQHaXVSBqrP1hjKZdtL64OCmYRdi8daDQs+B5aNERC0JB5Tbt2/HK6+8grfffhtnzjR/slpTU4PXX38dr7/+Ojp16oSf//znuPnmmzFixIiALZiIKBqIlOFplSaKNGIxc6FuZFRIenIC5o7tjYud7VUzeOMznXh267emR4IEMkNkpPRSKZBtcrmx83A5Hnz7G83s1kPvfINKweysHjeAB9/ea2gP5twre2JUr/OEsrlG35eiH0CUVp3FX/71n5BmAdU+GHh/73Gh24fjzFgionAgHFBeccUVuOKKK/Dcc8/h7bffxquvvort27d7GvT88MMPeO655/Dcc8/hoosuwsyZM3HLLbegV69eAVs8EVGksjIrTrQRi5kLddHRFt7jMNQu1LcUlGLI41uES121nofdGSKrpZeinVXdgG3BpOzk2UbgrH6jIzn4mzf+YkNlwVqdaN0A5k/q67k/0Q8gKqrrhTKZi7f8ByMv6gRIQNmZOtv3WCp9MGBHtQARUWvmMHqDxMRE/OIXv8DWrVtRXFyMhQsXok+fPgCaS2LdbjcOHz6MP/3pT7j44osxYsQI/OMf/0BTk7XW80RE0UYuw3Mm+16oOpMTVLOKRhuxeAdjIuSAAjgX2PpLTYxtsT75Qn3qwK4Y2bOjp9mN1WDSm10ZIjkg9w9w5Ixudn6JqduHEytNg8ZnOnHfuN5Ibhur+POFm/Z7zpH8AYTaI0hoDtTT2sULPfaS7YW4Zfku3PLSLty7Lg8zlu3E6EXbdF8TK0Sfg1q1ABFRaye5vWeAWJCTk4NXXnkFb775Jk6ePHnuAbya9sh7ME+fPo3ExEQ7HpYM6NevHwBg3759IV4JEXkzUnqZU1iOGct2Gn6MZ28aiKkDxecDK2XgUtrG4vZRPTB3bG/dTrH+4xfssHb2CMsZSr21yVm9zx4Yq/gcA/Xc5l7ZE4CEJdsP2XJ/ZhvdiGRe5bMif6ggB9iAcqZ96czB+E/pac19ilr8H08m/96Unqpt0RHYaBAt8hzY5ZWIoo1dsYHhLq9qRo4ciZEjR+L//u//sH79erz66qv497//7clMSpLkGTvSqVMn/PSnP8X06dMxZcoUdOjQwa5lEBFFHCONZsxm6YyW61npqGq0+6gevf2kgHhQrrc2vfJau5+bbFSv82zLwModWM0GVUZLqfU65QIwHUwqPZ5at1iZmWBau2lPN9Q1upBTWB7wESeRLNjjYIgofNgWUMri4+Pxs5/9DD/72c9QWlqK1157DStXrvREvpIkoba2Fhs3bsTGjRsRFxeHq666CtOnT8d1112HlJQUu5dERBQ1zOzjSkmMFS7Xs+OiMBDNS7RKN43shzQ6rkX0+2Z5B8uiZcl65A6sRpgtpd55uByjenVS/QACaB4rYpVIt1hZicmusf7PQe6cvHjrt55jAj3iJFKFYhwMEYUPw3sojXA6nbj//vvxzTff4Msvv8Q999yDtLTmf2DkStu6ujp88MEHuOOOO+B0OgO5HCKiiKe330uJ6LHZ+SUYvWgbZizbaWn/mp3NS1Lattyv6c3ofkirDViKy6qFbq/E/3Xw3+c4PCMNaUlxpu8faN7famavn9nM6z2rz51j/320MQ7J9oxuadVZocDXjeaMZpPL2K4e+TnEt3Hgma0HW3TSFd1n25pY3ZNMRJEvoAGlt8GDB+Mf//gHjh8/jnfeeQdTp05FmzbNCVK5mU9Dg72d8IiIIkmTy42cwnKszzuGnMJyxYthkaY5/iprGnSzX3ZeFDYHRsoNXYy67bLuqsGkVlZN/t6CjQWo/7FccX3eMbhcbjg7mGvAkp1fYrp0c9643rrNl2IcEh6fmmXq/mWVNQ3YUlBq+HZmM68nzzZovj/szujqdYv1ZqQZlTfR95XRYDUa8VwRERCAklc9sbGxuO6663DdddehrKwMq1evxsqVK7Fnj/GB10RE0cJIyZjafi8tShf2nqYmVWfxp/fVLwqNjh6JcUiYNrArlpucPelt3Rff4f9d1cfSfsgRf96KiupzH1imtG2jOmMROJcx9C7/7dQuHo9tMN+04M4xPTF3bG/dcuKJl6bj1//NwAufFJl6HLPzHIvLakw9nkztMe3KVsulwaLdYmVmAlqj+2xb895Bq3uSiSg6BD2g9NapUyfce++9uPfee/HNN99g5cqVoVwOEVFIqDVDKamqxV2rcvHPmwdj4qUtg8rxmU68sqMICzft130M/wt70TmKgLmLwnGZTlsCytJTdaqPKxoseAeTwI9zHBU4vQJ4I+dHxJpdR3DH5RcJnb+HJmZiwAUp+MP6/BZr12PmtWpyubF291FDjyP6mHKJtpXz6B3oJ7c1VhJsJqA1ss+2te8dtLonmYiiQ9BKXvX0798fTz/9dKiXQUQUMEolrSLNUOauzcXmvcdbfD/GIeG2URmGZ+iZnaNo5KJQZLZfSqJYWaza49o9aH7+pL4+YzDs3Pt3pMJYBnDipV2w86FxmD+pL2aO6IZfjOiGv9844MfxIvqMvFZLth1E6Snrz1XpMWMcEqYMsBZYeZcGG9lDbHZ2pOj7qrisptXvHbS6J5mIokNIM5SyDz/8EMeOHQMAzJo1K8SrISKyn1om46Zh3XQDF5cbuHvNHjzvkFpkPeQ9lXNW5UKC8gw97w6pRrt5ejNyUSiyrtsvy/DpoGn0ceXgorSq1tTz8bdw036My3SaPj9auqcZm72s9H7Zuv973DSsm9DtRV8rK/tC/Sk1LMrOL8GLGuW74zPPR/6xUz7PMy0pDrNGdEfGeUktSki931daJGh3Btai976SAHTuEI+1u4/aViYeqUTOld7IHyKKfMIZypiYGMTExKB79+7YtWuXrYv461//ittvvx233367rfdLRBQOtBreiARUsoff/Qbv7mnZsEfeU6nX9AUw383TTLZHb11zx/YynF31ZqZBkZaSqlo8/M5e2+dMOiTgFyN76B4nZ7D/tHEf7lJ5vzyz9VskxsWo3ofeOfN/vAUbC3SPE7V460GfrJzehxcSgPxjp/Dx/Vdi7ewRePamgVg7ewS+eGQc7hvfx6dbrLfxmU7cN643UtoqZ7jTFd73Rmi9r+SvZwzvppnV9S4DjmYi58psYE9EkUM4QymP+fjuu+9wxRVX4K9//Svmzp1r20Lcbjckif/DIaLoItIFUVRFdQPmvZ4HoOU+LbU5gP4Xcmb3Mpm9KFRa15DuqfjqSCXe33scNw27UDFDJj/S/El9NZ+TWoOijklxKK+uN7zet3KPGb6NntmXZyCujfbntyJ7NuX3S019k+YxSk2FlM6d3SM9AODBt79B+4RYjLioo3DDlq+OVArv91Q6T8ltYzG+7/kY1asTnMltbWmKo/a+kvfZ1jW6hO6nNewd1DtXrWEvKVFrZ6jkVQ746uvrce+992LHjh146aWXkJSUFJDFERFFukBctAPn9mn5j53QuzA3s5dp3rjePheFRrtaeq8rO78EVzy93eecSBLg9ouukxNj8fOhF2Dhpv26DU/UgtYrnt5uWzmslpTEWJysUW+eM6hbqubt1ZoymV3L+EynULMYI8HOfVf1xn9OnMYH+dojSU6ebcAtL+1CenICJmaJzZYWXYfaeTp1tgFv5x7DuMzOtnYS1fqQJqewXOg+WsveQdEPtIgoOhneQylnEt1uN9544w18/fXXePvtt9G3b99ArI+IKKIFKkOhtE9LJNAzuu8wPTkBc8f29nxtpaulWkDgH0wCwMmaBsXRGUqBNKAcTKvt4bQqJTEWz80YjLLqOnRKisdv3/wagHJAqbeXzsqeViUnaxqwZNshPLP12xb36X/uRIOday9Nx33j+2B93jHdgNL7sUS7/IqsQy/TH6g9i2of0nDvYEsiH2gRUXQy3OU1KysL/fr183x94MABDB8+HGvXrrV1YUREka7J5UbZ6bqA3b/3Pq3s/BKMXrQNM5btxL3r8jBj2U6MXrStRadJ0X2HElo2NtHaC6rX1dKuwElkWLq8F7Gu0YX7xvVG5w7GZhfqkQCM6NkRUwd2hcMhWdpLF4gM9oodRZol1o+8m493c/8Ll9sNZ4d43f2nm/aWIDu/xFC2TX4sh6T+PjOy39PIvMNg0Ps9cqO5XJsZOiJqDQwHlBdccAF27dqFmTNnerKV1dXVmDlzJubOnYuGBmMzs4iIopEc4InMiLRqa0GpYqAnz7HcvNc30FNrluPNv6GPyF5QrSDPzsBJK3jYvPc4hj2xxRNYN+/PlDBvXB88e9NATB/c1fLjV9Y0eB7b6hy+rQViGT8jTp5V/3fYDaC8uh7z3vgat7y0C7WNLqEgf8HGAgzpnop0jfeMEpf7XAbRm9GGLaLnORDnU43e79HCTftbxegQIiJTcyjbtm2LlStXYunSpYiLi/OUwC5duhSjR4/Gd999Z/c6iYgiRiDmGGp5N++Y4TmWE7LS8dkDYz3dNVff8T9Y/av/weKfD8T8SX3x+6svRnLbOE+AaDVDFIjSX//7/PPmAty9Zg8qqn0DqtJTzd1R49s4sGj6ADhtyFjKj21lDl92folwWSig38lWAlQ7n6qp0tj7KfNunmNmpuQvR/UQ6kCsRfQ8v5t3TPVDjUCYkJWO+ZMyFX8mmrn3n01LRBRpLM2h/PWvf42hQ4fixhtvRHFxMQDgiy++wKBBg7Bq1SpMmDDBjjUSEUUMO/fE3f2Tnlj3xXeorK5X3aeVJtDNVJ5j+U8AqUnxPnssvfc8ZeeX4C/ZBxT3R1rtahmI5iTe97l5b4ninkuZG+f22D02pZ9njqH/fEzR101+bLN76cyM7HAmJ2DKgHTPXEfF2Z6jehiaK2nkfVp6qhYbvjaecbuqb2c8MinTUsOW4RlpSEuKQ4XOe72iujl7HKy9fE0uNxZuUn4d9fZ2au1HZoMbIookpjKU3oYMGYLc3FxMmjTJUwJbUVGBa6+9Fo8++qgdayQiihiipZ3tE/RnCf72pxfjyWlZnu/5HwMAUwd2EV7b3LV7VPdY6u2PVBpar0QtcJQDLzsuif333jW53PjD+nzd28kZVK35mP+8eZDuOlMTYz2PbXYOn5ES4DtG9cDa2SPw2QNj8dDETJ3Znr1tO8/+Ks7Umcq6//aNPGwpKMXIH/edKs2W1BPjkHCd4Hs9mKM6zGbutX7f7lqViyGPb9HdD01EFC4sB5QAkJKSgo0bN+KJJ56Aw+GAJElwuVx4/PHHcfXVV6O8XKy9NhFRpBO9mP3ZkAs9jW+8+QchWsHP0pmDMT5TbDQD0Jyp9CYHi5v3lmjuj3QDWLv7KJwd1AMVvQYros2A/H8uEqTtPFyum7mSya+Pf8mvHLBNvLQLHp2cqZm5q6xpwL/ySzylislt4/DczYMMlXWKvk9+OaoH5k/u5xOEqa19Qla6ofNsRMekOKQlxZm67YlTdbqlnyJE3+vBHNVhZg+tyH5k/zE0IuWzREShYqnk1d9DDz2EESNG4Oabb8aJEycAAFu3bsXgwYPx+uuvY8SIEXY+HBFR2BG9mB2X6cSwjDShYeBaM96aXG6kJyeYyhzJJXnz1+frls2WnqrDvHF98MzWb1uUhoo2WFEbgO6QfINd+RwA0D0/2fklePDtbwSebTPv10dpzEGTy432CbGIa+NAvUaZ7z1r9/iMO0lPTsD8SZlITYoTKlMUfZ+oBVFaIxrUzrMVUwd2gTO5ranb2jXWQ85yqz2nUIzqMLOH1kyDqkCORiEissrWgBIArrzySuTm5uLnP/85PvvsM0iShO+++w5XXHEFnn76afzv//6v3Q9JRBQ2jOypi3FIwnul1AIIOSN11497Ao2Su36KKDtTh/vG9cHa3Ud9RmX4B3la8zCVguMh3VPx1ZFKxeO1zo/aXEs1aUmxPsGG/zorq+uxcJNYEOY/O7O0qhb3rMnFfeP6oEenRN3bVwqcc9GRGkrk8/yPDw/imQ/F91SqGZ/pNDzD1Jt36afZ/Y3ye11t/ysg3jXWLmb20JotybXjHBIRBYLtASUApKenY/v27XjggQfw97//HZIkoaGhAfPmzcOOHTuwfPlytGvXLhAPTUQUUt4XvSKZPKvDwJtcbiS3jcPto3rglc+LWwQ6dnpt5xEAgLNDPOaN640enZLQKSkekJqDzZzCclRW12Hhpv2KjUYmZKUrBpta1M6PmeZHj0/N8glG7czgyetYvPVbz/fSFbLNgHYjF29Kcwy1gnV//8ovwf9tsx5Mpnt9AOId0JlRWnUWOYXlppvNqGVflTL7wWD09x2wXpIbzD2iREQiAhJQAkBMTAz++te/YtSoUfjlL3+JU6dOwe1246233sI333yDt99+G3379g3Uw7dq1dXKzTNcLhccDlu2zRKRBisXvUYCBruDIlEnTtXhma0H8avLM/B27jHd/Yvy/q87x2Rgw9clPutNSWwec+G9Z0wtEPNmtGzw12MyMPHSLmhyubFk2yGfwC9Q5Oftv49SdO2pSb7jTbS6gvqfq+z8Ety9Zo/FZ9DMOyCakJWO524ehLlr97TYkyti4ab9Pu8Xkdfan1YJuDcjv0tWGP19t5LpBYK7R5SISETAAkrZtGnT0L9/f0yfPh179+4FABw4cADDhw/H888/j1tuuSXQS2h1tLK/mZnK87KIyF6iF73ejAYMRso91YiOHvEmP+ayT9XHdCgdrzTWw7/5CKAeiPkec1bosdvFt8FfbrgUEy9NR3Z+CR7bsA+lp+qEbmuV2r43M41c1F5vpXNlZiSJkpTEWDx1ff8Wr0FqUrypYBJAiw8fRF5rtcBQK7Nv5HfJDkZ+37WymlpCsUeUiEhEUNJVvXr1ws6dO3Hbbbd5RotUV1dj1qxZmDNnDurqgvOPOxFRMMkXvSKjEvTGdnh3d7Rz1qUbwMKpWUhPDp+sh/y8FmwsUBz0np1fgoWb9gvd1wszh3iCyTmrcoMWTMqUxkYYbeQi0hXU+1wZzd76d29NSYzFvHF98NUfxlvqUCtC5LUevWiboREaRn6X7GTk912te7OcsTcygoaIKNQCnqGUJSQk4OWXX8aoUaPw//7f/0NdXR3cbjdefPHFYC2h1Thz5ozi94cOHRrklRCRCL2AwT/LZaZLpJqUxFhcneWEwwFbMp52UWtAIpqZlbM5I3p2tDUAN0uewwgYb+RiZNbhyJ4dDQV86ckJ+Pj+K1WbIikxWnKZFBeD6vom1Z/L639lRxFuG5Wh23RJK6tp9HcplNSymlsKSsNmjygRkYigBZSyO+64A0OGDMH06dNx+PBhAIA7kF0kWqGkpCTF73P/JFHoaO3nCmTAoOdkTQN2F1UEZNSEHUTn9ymRszk5heUhf04v7yjG8Iw0n1mRoo1cjJbIGgn4Hp2cibg2Dt3GUN7v305J8ejcPh4nTotle7WCSW8LN+3HS58V4dHJmRif6TQVGBr9XQo1pdJdM+XyREShFPSAEgAGDhyI3Nxc3HrrrVi/fj0kSWJQSURRS2k/V1pSHK4b2AXjM50+Izi0mAkYjNyv94VsadXZFg1UQsHM/L60pFg8Oe3c3r9w6IrpHwAZaeRitERWpOmLQwKWzFDft+gtmM2f5OzjfeP6mAoMzexPDUdWuz8TEQWTcEDZrVs3SJKEzp072/LAHTp0wLvvvounn34ajzzyCBobG225XyKiYFLLPMrf31pQiuU7ilvcrqK6Hi/vKMbLO4qRlhQr9FhGAgYjvAMW7wvZA6WnFBvpBEtKYqyp+X3zr+1nKiATJQGQJBhqTKMUAIlmooyWyIo0fVkyYxAmXqrfbVjt/RsocvZxxedi7zv/94TR4JuIiKwTDiiLi4sDsoD7778fU6ZMQWlpaUDun4goUNQ6SU4ZkN5iPIaWiuqWnU69aQUMVqW0jYXL7UaTy+0TyDS53NjwdWCal4i6/bIMnzWJBgHODr7H2RmAywGa2aIa/wBIJBNlZtahWgZUpNNpqMbRyNxQ7v6rxP89YTT4JiIi60JS8urv4osvxsUXXxzqZRARCVNrGFJSVWt7Vs8N5YDhzjEZlh/r5NkG3PLSLjg7JOCxKecCDTsb/5iRkhiLuWN7+XzPbLBgdkyDkrSkOEwZ2AUrTGbtRIJipay3mdmmahlQAMgpLFfMqgdrRqeIlLaxqDrbYOtr7QZw07BugVkwEVErFRYBJRFRJAl219CUxFiMz3S2WMP6PPUMovTj7eLbOIRGZZSeqsVdq3Lx/I+dMwO5x6xdfBvcOKQrVnx+RPWYp67v36L0M8YhYf6kTNy9Rjkz6wYwZUC6T8mxHDSNz3Ta0nSovLoe6/OOGb6daGZMb36i0WYt/hlQraz6+rzjQR+rouX2UT3wzNaDwllZmV6DqcVbv8W6L46yayoRkU0YUBIRGRTs7J3cidU7MFiy7aBmMx83gMqaBjwysS8y0zvg+zN1KDtdh79v+Q/ONrhUb/fQO99gfKYzoHvMztQ14oLURPzz5kFYuGm/cElm8/zJAs37fvHHjK1/yXFaUiwen5qFzx4Y62k6tONQGd7KNR4c6pUo+xOdISg6JsNss5ZgZtWtkIPvuWN742Jne1MjNOTge8m2g1i89WCLn2uNHiEiImMYUBIRGRSKDpHej5mdX6J4kazkic37PUFaZpcOmsEk0ByE7jxcjhEXdbS18Y+/hZua1zV/UiZSk+J0M26i8yfdgGJwVFHdgLvX7MHsyysxpHsa/vKv/wTtQwGRACjQ8xPDYRanEr3so2hWVqlMGADWffGd4uOG20xKIqJIxoCSiMigUHSIlB9TDgyMKK1qLmcdcEEHoeM/P1QGhyRhYpZTscOn1X2I3uu6Z01zlmjqwK6qx9kZDC37tBjLPi224Z7EzJ/UF7eNytANWAI9PzHUe2KVzBvXB+u+OKqbfdRrXKRWxnvTsAsjaiYlEVGkYkBJRGTQkO6pSEuKC8qMRv+9d2YCAzkQ+/q/p4SOf+XzYjz3UaHna4ffiIzkxFjhLpwi69LLEoVjMKRHft1Egkkg8PMTw2nu4rmS1l6YO7aXoT2h/rTKhEWz+OF0boiIIpEj1AsgIook2fkluOLp7aaDyX5d2reYO5mS2Py1/2W00t67YFz8Vtc3+Xwtj8j45ageWH3H/yChTYytjydnidRE6gW/3p5Jb4Gen9gpKd7U7fRck+XUP8iL/3tazj5OHdgVI3t2NBRM6pUJi+JMSiIia5ihJCISJLqPT8sfJvXD8Iy0FlmZLQWlQs1HQnHxK+83+yC/FFf17azZDEh27aXp+OpIpXBmcUtBqWrZYbCf89wre6Ln+e2x8P19hhvwAM0Z3SUzjDV7qazW766abmV+YgC2CEoAco9UIDUxFpWCGWut/aRK+yC1AkyrmWvOpCQisgcDSiIiASL7+LT2FnpfvCrtCRNtPqI3izFQ5P1mOYXlQsePz+yMZ28ahFd2FGHhpv26x6/PO45HJiln9OTnHKyy11G9zsPInh3RNtaBOauaR5QYOdcuN5CaFCd8fJPLLXSO5qucHxHb9p8wdTstbgAnTutn6ueN640enZJ0my5pjUtRYiRzbXT0CBERiWPJKxERmi/qcwrLsT7vGHIKy9Hk8g0hRLIh8i1ESleViJT/yYPb7ZacIPr5olhoJWcVL3F2QFKcfolseXW9atlroJ6zPwm+WUB5nqEz2XiGVC/Y8X6/vbKjSChYNhKk+j/WuyZmZ9ohJTEWFzvba76n5cy//zmQR3tk5yvPWxXNXM8b16fFa+hMTuDIECIimzBDSUStnkh2RDQb8stRPfBBfqnhuXlGTMhKx51jMrDs0yK4FOI7I11Ypw3sgp8N6waX241bXtqle/zIizrh7dxjqhlSORNbWV2H0Yu2Gcoqap3jCVnp+OfNgzB37R7F52yVWtAvZ45FM60yrWBH6f0mwuxe0t1FFUKluwltHGgb50BlTaOpx1FSVdOgOe/RyrgUvWy9nc1/AsloqS8RUbhhQElErZroMHnRbMj4TCcemZQZ0AvE7PwSvPhJkXrQaCCi7JLaFt+frkWndvFwdojHiVN1mhfnI3p2xKOTMzFnVa5qGeGUAem4Z80ewyW5eud44qVdsAQS7l6Ta/CeW0rx61SrFfTHOCSkGcgOau11tLIP12xjnS0FpULHxTgk7Hp4PL46Utn8nkiKx2/f/BonTpkvr9YLCq2MS5Ez11rvRe8PCMJxNIiZUl8tDE6JKBQsB5QrV64EAHTt2hVXXXWVqfv46KOPcPToUQDArFmzrC6JiEiIkeyIaDZEbY+kyFpELgRF9nK6DVz9P7f93HiQlMRY1ecG+A6bXzpzsGITofmTMrFwk7GZkUaao0y8NB3/xGDMXZtrOlM5+/IMPHhNX8XzrfY6GOnqq1babHmepom4IDu/BC8rzBJVUl3fhK+OVPq8dx+bohywGaEVFFodl6L1XrSzKiAQRD/MMnJ/dganRESiLAeUt912GyRJwtVXX206oHz22WexYcMGSJLEgJKIgsZodsRINsQIIxeCRjpb+s+P1KM2W1Lp4lytiZDRzptmzl1qUpylstdlnxZhSPfUFudW6XVwdojHjOHdUHZGLKC8/bIeqhfvVruSlp3x7QSr9yGEHMAa4R+4qQVsZigFhXaMSxFtaBVOrJT6KrE7OCUiMiJsSl7dRj5SJyKygdHsSCCyIUYvBI3so7Nrr2FNvfKeOqVMrNF9fmbOnR1zKf0v1lVfh1N1WLz1oPD9/rSf+lxGq+v2Dqr0PoRocrmFm/2oPYbMP2D7/lQdntgsvp9U676HdE/V/eDDITUfp8VMVYARdpeSWin1VVqbncEpEZFRYRNQEhEFm5nsiJ3ZEDMXgkZnMibGxaCmvsnw2rxVnW3EXaty8bxAlsPI+tKSYvHx/Vciro1Yw3H5ov7giTPCj6HG+2Ldcinqj/TmRFqZp+l933ofQtw5JgPr80qE5oXK5LJjl8uN9XnHWryvvQO2JpcbS7YfRNVZseY9WiXNXx2p1P3gw+UGviiqwKjenYSfj50CUUpqtdTXm53BKRGRGWERUNbWNv+PMCEh+AO7iaj1MrIv0ptd2RAzF4KV1XWqxyuxGkx6E8lyGJkZWVHd0GLPnhqznVG1yBfrVktRRct2rcwQle9b70MIAHjhkyKD995827MNTbhl+blOv2pBU4xDwvi+TryV+1/D6/cnGljdsyYXT93QP+hlm4EqJbWj1FdmZ3BKRGRGWMyh3L+/uXQmNVW7pIWIyE7e8w3Nzo60wsiFYJPLjR0Hy/Dwu/kBWYuIkqpavLKjSHVWJ2B8ZqT3OVCbBao2p9Aq+WLd6oV25w7xQoGF1vtNy7xxvT33bTX4VSLPCfXfQ6s1BzIxXn+2KNCcIZfPjdLrKxpYnTzboDmTMhBEgvcFGwsUfw/0yB8uqL0P/GeiarEzOCUiMiOkGcrTp0/j2WefxdGjRyFJErKyskK5HCJqhULZJVL0Aq+4rNrwTMdA8Z7FKDes6dEpCee3T8CQ7qn46kgl6hpdmD64K97KPaZ7f/I52Lz3OP6wPt9nXmJ6cgLmT+qLhZv2C2f00pMTMOjCZHyQf0L1Nv6ZZ6sX2k9PH4A2MQ7FUlF/8vvtsQ0FQiWp6ckJmDu2t+frQGSZ4tpIUGpiq1R2LZcdnzqrP9cSAK7JcmJCVrpq2ej8SX0NZW2DuRcwkKWkRkeeaDFbaUFEZBdDAeVFF12k+rOPP/5Y8+fe3G43zp49i7KyMp9mPFOmTDGyHCIiW9i1L9Jo4w6REsjUxFhDTWGCyb9hjX9zFa1RE94XuX/eXKBYpllSVYu71+wRWsvQ7qnontYWnx0qw+b8E6rHKV2sWylFBYD/t3YPTp71DYT1P4zQfiTvdQJATmE5vj9di7LTxkqe9XRMikO5xkgUOWjaebgcXxZXYMWOYp/nqmfHoTJs3ntccS5paVUt7lmzB3eOyRAq0w32XsBAl5La9WGWncEpEZEZkttAe1WHwwFJ8v0fknxz/+/r8X/Y/v37Y/fu3YiPNze4mfT169cPALBv374Qr4Qo+pht3LF5bwnuXpOr+nMr8//ClfyvxdKZg+FyQfP5203tNZHLagHr59v7+ak9jt5jyOsE0OJ9ZXQcjNYabx/VQ2hOpZXmTmlJsT6ZZ/91dO4Qj9pGl+rYGn/P3jQQUwd2NbUWI3IKyzFj2U7d49bOHmEpwLWrgyznUBKRUXbFBoZLXtXiT7NjP84//3zcfPPNeOyxxxhMElFEMtu4Izu/BAs3ac8JjIZg0j8AkjMw4zOdGPbE1qCsIaVtLJ67ZTBGXNRR8WLdznmLah16RbrJJsU5cO9VfXBe+3j8p/S0YnbajnEw8muQ3DZOKKC00txJLZgEms9V6SljWddg7QUMVimpXU2+InEeJxFFB0MB5fbt232+drvdGDt2LCRJwrBhw7Bo0SKh+3E4HEhKSoLT6USXLl2MLIGIKKyIjP545N18nK1vgjO5recCTzRTFWwjLkrDgZLThsoa9bjcwPxJfdGpfbzPRW5OYTkqNMot7XTybAMckqR5ce1/QV5cVoMVO4pMnQu5PHPxlm8xqlcnDM9IE2qoU13vwpMfHBB6DP9APT05AVVnGzSDv8S4GCybNdQTWDe53JbKfYNNtFGNHSKxlDTQ8ziJiJQYCiivuOIKxe+73W6kpaWp/pyIKFqJNO4or67HvDe+BiA3IsnEwk3W5x4GQnFZNXY/Mg5fHan07NnzbsRjVqf28Zg6sKtPed+3padtWLG4ZZ8W6l5s+1+Qzx3bC0u2HWoRWKYkxgqVaC7ZfghLth+Cs0MCBnVLNr94BUqB+l+y92vuR/z7zwZgVK9z8xy1giarJABpOns0jQp2ABfKpl1ERJHCcpfXRx99FADQq1cvy4shIgoVs/uYjDbkKK2qDeqeQaNKT9X5zIZscrnx0mdFljNY57dPCMgsSSO2HfgBm/cex8RLxStjYhwS7h3XG3PH9vJ5f9Q3NOHWV74Qvp/SU7X4IN/+5y0H6kBzCfWLGsHkr8dkKAZAakGTaNCsRP7NWTg1Cws3FWiWjXbuEA9AwolT6u8xhwQsmTEoJAEcS0mJiLTZFlASEUUqK80sjO7nCsespD/vINmODJZDAsrO1OF/17bs9Blsf1ifj6uz0n32NYoECt6Zy+z8Ejz0zt6grluN/P7T258pAdjwdQl+P6Gv6h5S/6DJ5XLjluW7TK3LO4PncMDT8EjJY1Oam0JovceWzBiMiZeGLhvIUlIiInUhnUNJRBRqZhvqyKyOnLAqpW2srfsdgZZBspzB+s0bX5tqzuJyA49u2BfyYBJobhAjj50w80FCuOx99W8IY8fMRP+gSd5fKZpRfmRiX5zfIb5FYD4hKx13jsnAsk+LfPZ8OiRg9uXnsqZKWdJI7lJqV/dWIqJwx4CSiFotkYY6eoPUA7kHTcQt/9MNJ07V4oN9paiuM9+JE9DuWrnnaKXFTp/Bab4jorTqrKkPEkS6tAaT937CQMxMjHFImD+pr/As0Oe2H8JTN/RvEbDKpbj+583tBl78pAiDuqViQlZ6VJWWcoQHEbUmAQkoT5w4gd27d+PYsWOoqqpCXZ14S/A//vGPgVhSq1JdXa34fZfLBYfDEeTVEIUvO7I6gG8GxuQEJdOe+6hQ6DgJQHJiLAAo7ovT6lpZ3+jCsk/1B89HirIz9Xh5R8sAB9D+IEGkS2swpCTG4qnr+/sEJqKl10ZKtJvH2og3ZDp5tqFFMG70Q5toKC21WvVARBRpbA0oX3/9dfz973/Hl19+afo+GFBa165dO9WfZWZmBnElROHNrqyOWgYm3Dx1fX+Mz3RiybaDWLGj2KdUVqtr5cPv7LU0+zC+jQN1jS7zd6AgLSkW1w/sipcEZij6O1lTb+qDhC0FpSZWar/nZgzGqN6dfL5n98xEK6W93gGi6Ic2Ow+XwyFJEZ+ZtKPqgYgo0tgSUDY2NmLmzJl48803ATSPEdEiSZLicfL3iYiCwY6sTqDLICUJlrOeaUmxeHLauYzWveP6YO7Y3kKlhdn5JXgr95ilx7c7mASAqQO6YlO+uQDvcNkZoeN2HPrBc36GdE/Fe3nHTT2eXeSgcIRCBk+v9NoNYP4k9ZEb8n6/0qqzKDtThyXbC029p/2DcdEPbe5ZnevzAUeklofaVfVARBRJbAko7733Xrzxxhuer7t164bhw4fj888/x/HjxyFJEmbNmoXTp0/j2LFjyMvLQ11dnSeAnDhxIjp16qR292TQmTPKF0tDhw4N8kqIwltldX2L4fDeRLI6gS6DtKOEdv61/VpcmIuUFsrBcjha8XmxqdulJsbig/wTQscu2X6unDgtKRYV1fY2P1Jyw6AueHtPy8BVqyRZpjb+Q7ZwUwEcDrR4LwRinIscSIp+aOPfWCpSy0MDsZeViCjcWd5QV1BQgOeff775zhwOPPPMMyguLsYbb7yB/v37e45bsWIF3nrrLeTk5KCyshLLly9Hly5d4Ha7kZeXhzvvvBMrVqywuhwCkJSUpPiH+yeJzsnOL8E9a3J1SznVLuCbXG7kFJbjg/ySAK3QPue3i0dOYTnW5x1DTmE56htdPl83qZyEcNkz6E1Cc3dQs8zOVQx0MCmhOSv3lxsH4vmZg5Ge7BuIOZMThIKrCVnpmD+pr+LP5CAt2+s9K5e22v06y4GkXIpr9CWT35ELNhaovj/DUSD2shIRhTvLGcoVK1bA7XZDkiTMmzcP//u//6t7m4SEBNx+++2YNm0aJk6ciJ07d+Laa6/Fnj170K1bN6tLIiJS1eRyY2dhOR58+xvNkj6tQeqByOgE0q9XfYkzXh1g/bOyauWFoc6i+Jduyl9bydqGY2giP69rspo7nI7PdJrudtrkcqs20vHfw4cf/273OUlNjPVk9a10QY7E8lC797ISEUUCyymrTz75BEDz/sff/va3hm6bkpKC9evXIy0tDSdPnsScOXOsLoeISFV2fglGL9qGW5bv0p3d6HIDqUnxivcRiIxOIJ3xGyfin/ApUchcAaHPoqQmxfl8nZIYi8S4mBCtJoB+jBNf3lGMGct2YvSibdhSUIqRPTti6sCuGNmzo3ADFyN7+AKVgfYPpORSXKdf1jXlx67DeoLxwYZccaCXtdcjB9AAWmRlRcqWRdm1XiIiO1jOUB45cgSSJKF3795wOp0+P/NustPQ0IDY2Jb/eJx33nmYPXs2Fi1ahH//+98oKSlBenrk7Jcgoshgpmul94Vsk8uNnYf1M5uRyo2W3Sf1si2BNn9SXziT2+L707UoLqvG4q0HQ7CKwEiKj/HMDfXPuKrtH/Q0zjlVi4ozdUhLioMzua1P9jIc9vCdrGlokVVUmjHpcrlxy/JduvcX6A82zMyMlF8LpQyy2l5WrU7KgV4vEVEgWQ4oKysrAQBdunRp8bP4+HOf7p85cwapqamK9zF69GgsWrQILpcLn3/+OW644QaryyKiVkrpQq/J5cbD7xoPBOUL2UCWuMrzIRPaxKD01Ln712oWFCj+5YVWyhXt4Exui5E9O6LJ5cboRduC/OiBVe2XNfamNF5C6z2Y0jYWt4/KwNyxvQK+hy8lMVZoH6pSwOrfCKrJ5db9wCItKRZDuitfO9jBzMxIkYBOKYC2YxQKZ1wSUTiyHFDGxcWhsbFRcVRIhw4dPH8/duyYakCZnJzscxwRkRlKF3opibFobHLjTF2j8P1473OyMo9Pvq/kxFhU/XgR7r8nEDg3H9L74rOyug53r9lj8lHNK6066/O1WrYlkAGv/z6zcGwOFGjepalVZ+s134MnzzZg8dZvseLzIjx5XRbSkxN0z1dldT2uznIKHQs0B633XNkLlTX1+OdHhbrHiwSsIh9YVFQ34Iqntwck+2ZkZiTQ/D7cUlCKlxVmnyoFdCKdlAO1Xs64JKJgshxQdu7cGUVFRZ5MpbcePXp4/p6bm4usrCzF+/juu+88fz979qziMUREWtQCP6NdPeXLsPmTMi2XuHoHjAB0S+D8szeJcXtRU6+eyQqEiur6Ft9TyrYM6Z6Kr45UYmtBKd7NO6baBTUxzoGz9S7D51DeZ9bkcmPHoTITzyQ6lJ6qxV+yDwidv5M1Dbh7zR7MvrwHln1arHnsw+99g6uznJ6ATu/+6xtdeGKzcrMfb/4fBmiVhgL6o06AwGXfRPebLtl2COu+OKp7bKADOs64JKJwZTmgzMzMxOHDh3Hw4EFPt1fZ4MGDPX9//fXXMWvWLMX7WL16tefv3D9JREZpfXJvlDM5AVMGpGPhJuslrv4Bo5ESuCXbDgU9mASAtHYtGxEBytmWkT07YmTPjnh4Umbz3r6qsyg7U4+TZ+sh/XibZz88ZHgNyT82a4m0brqBUHGmzvDzf+ur/+oec7KmATsLyz0B3WMb9qH0VJ3q8TUN+u9F/6Yzonv9JmSlY+wlnTHizx8qfqDhHay1j49FWXWdLSWkovtIF2/9Vui4QAd04bA/lohIieWActSoUXj//fdx9uxZ5OXlYdCgQZ6f/fSnP0X79u1x5swZZGdnY/HixZg3b57n5263G48//jg2b97s+d6YMWOsLomIWhk7SiJT2sbiuVsGo6qmHves2WM4OJUva+8b1wc9OiUqXvCKlsA1udxYsaPI4Apajtkww9nB+N66GIeEqrP1+Mu//tOiLNaMkzUNuGtVrqHbpCTG4taR3fHKjmJU1YqXNweCs0M8ahtdqKppsJTddiYnIM2v062Iyhqx559zuAyjenfyekRrvD9AMbrX76sjlYrBpEwO1ryb+FhtRBOoZj+BCug445KIwpXlgPKnP/0pHnroIQDAe++95xNQtm3bFvfccw+eeuopSJKE3/3ud3juuecwZMgQAMCuXbs85a6SJGHy5Mk+ZbJERCKsXsBJAJ66oT9GXNQRoxdtMxUE2NXBEWgOkPXGmvibPrgrnrz+Unx1pBKlp2qx4+APeCvX2J70dIH5eP4ljEO6p2LpR4WKWZxgNBXyLivec7RSM5hsFx/TYoSKyP1LgvtFU9rG4rmbB2NEz47YUlBqupmRd6Yvua3xgNLII1ndIwwAc6/siVG9zvN8gGJmr5+Z32GrpbCB6mIcqICOMy6JKFxZDigHDRqEXr164dChQ3jppZcwf/58tGlz7m7/+Mc/Yvv27di1axckSUJRURGKilp+8n7hhRfihRdesLocImqFrFzApSXF4slp/TEhKx05heWmMp1JcTGYPykT4zOdyCkst9zV0ejFdWJcDKYNuuBcpjD7gKHnITofT6mEMRTdX73JgbzL5cYLn2hndc0Ek260HOuh5qkb+nsyfmp7A1MTY1FZ06B53rw/nBDphGrW/2Sk4fdv77V8v707t8fwjDTPBw1lp7XLdJVKQ838Dlvdt6jVFMjsBwGBDOj01gvYM+OSiMgoywEl0JxprKmpAeA7exIAEhISsGXLFvzud7/D8uXL0dTU8h/0KVOm4Pnnn0fnzp3tWA4RtTJmMw0dk+KQ89BViGvjAGA+01ld34S71+S2GKlgtCRPzv4dPHHG0OPX1DfhluW7dEc6XJPVGRd37oB1X3znM6JEJLuqlskKZTB5z5U98ZvxFwMAhj2xxfb7dyYn4Josp2JXT393jOrR4vypjY7YUlDaItBMS4rFtIFdMS7T6fNBhBxEiJYASwA6/1hyq/VeSE2MhUOSbNmfWlxWjdGLthm+L+/fN7O/w1b3LWrNjLxp2IXCs0+DFdAFesYlEZEZtgSUqampqiNBAKBdu3Z4/vnn8eSTT2Lbtm04evQoGhoakJ6ejjFjxrDMlYgsMTovUb7ce2JalieYBKyXqvlfwBspybOjAY1eR9sP8k/gg/wTcHaIx7xxvdGjU5JqJtW7tLVTu3g8tmFfSINHJWt3H0X/rslIbhun2mXWjJTEWDw3o7l0dXdRhVBAOe7H0RL+lPbNBmpGoXzrx6b0AwDNIPTP1/dHWbV6Ex4jRIMuf96/b1Znnlope1d7PQA0f/giEOQGM6AL1PuHiMgsya00QJKiUr9+zRcZ+/btC/FKiIzRGz0gUwrK2sW3gcvt9umYmp6cgPmTMpGaFNfiAnL0om22lhfKZXCfPTBW9YLPjn1sRtcEwBPo+p/fyup6W7rcBoME4JejemC5QNAnen/eHwA0udya7wmR11eE2nu8yeXGqKe2+WSU1fhnxP+8uQDLPi3y2f/pkIDZl2fgoYmZyCksx4xlO02v2Sytc7Z5bwn+sD5fs0GPkrWzRwSks6r8uwkoB7l3jOrRIqtMRBQp7IoNbMlQEhEFiujoAcD3k3ul+YjJbdtgfN/OaJfQpsVFq7NDAh6bkmkpS6JEryTPzpEnRtYk7z1zuRAxwaOad/OMNR9Sk5oYiz9f39/nfRWMfWta7/H/lJ4WCibnT+qL20ZleNaRnV+CFz8palmi7AZe+KQIDU1uXHVJZzg7JODEKfv3Z6rROmfZ+SVYuKnA5/cyNTEWLrcbp842hqQRjVqJqdUOs0RE0YQZylaEGUqKNGqZO/8Mm+jtRDw/s3l+rt3zD+de2QvzxvdpcREdqixRtElLijOc1fI2+VIn/vazQfjqSKViJtzIBxt6vLORxWXVpktGvf1/9s48Por6/v+v2WRzLcnmEnY5EyEIIdyIYBBLCBVBQbFVEG09ihe0Sm3Fqih+sVVq+wNbPFGwFoHWExClJQRFQhAkBAiBAjEBgQTIQRI2J9n9/RFmMzuZ4zPHbg7ez8eDdpPMfOazszPr5zXv9/v1fm3mMEwf1sM7PmtNI193GyhzpThbCBZPT8GUIb7nTOle90i85n8G5L8HzIQ1S4IgCKIjQRFKgiA6NXpaD6jtx8IfPj2I75+bBLcbeGyNtl6ISizfdhyf5JxqJUCoCbk53DasO1OtoxyZR85j/J8zUVLVUlcoFIysdWtqwsOMWlkphPWIWvqyVl6uu7WrGDoZITIsGNWX27mUXU6ntljgk1asdq/bI6wICw7SbCZlFqw9ZAmCIK5ESFASBNEuUVsUy6WSallMS1FR04idx0uxeFO+7jHkkDLpoSbkzcTarIaMdSYlO2APt+qO9rkamuBq8HUhF39eaqJCqv5PKEr9VSsr7h+q5SEFL9jCgi345wOj8eu1+zT3QFWjWtQbVHxeWe71CzWN+PDBEbBYOIoSEgRBtDMs6psQBEEEHtZF8bnqOjS5PcguKMP63NPIOn7e8LE/yTmlq4+jGryQeHFjPpouO6Xw7RKu9GXxs1OSEWsL0bwfhxZBNS8tCY4o8wS61Oclx8tf5uOxNTmt0m6LL4unLw8U+61WVlyPqPUhhQdASVU9jp6t1iQmOZnXLMcDWs4r671e6qrH2L5xmD6sB8b2jSMxSRAE0U4gQUkQRLuEdVHM98CbtWIXHl+Xi+XbCgwfu6bhkvpGAH7Sv7mJvd6+eUCL6YseoiOsuvZrj1yoadBVA+kBMCXF4T2fi6YlmyrOxZ+XFF8eOIO3txcqjvHUJ/v9Ynw0P71/q5RPvQ8pTpTXaNreYQ/DW/eMwFv3jIDD7nu/xtqUr03heWW91+Ntod4HR9kFZaoinyAIgggMlPJKEES7RK3ROYdmQWWGoYmYaxPi8N/8c6rbHThdqfsYwqgM7yT5zGcHNaV9hluD8Pf7h+PX68xPUwwkTnsYTlXUat7PwgFuD/BeVhHeyyryppfqOZdqyEXRmtwePLc+T3X/i/VNqttoxREVinlp/Vr9Xm9PR1aPvrkT+mJcv6t8Uk7F9aUlVXWY/69c1bHOVdfhliHdVe91e4QVT36036eGkpxWCYIg2gcUoSQIol0ijNyJIy38z/6IT3QJDcIvr09QjPBwaHarNCJYxFGZySlO7PpDOrqEsj/nK66sw9Fz2tIUhXDtJGNw4dSBWL+frfXHwqkD8UBqAgBAHKDia/MAYOEtg8ycomwUbXdhuanClQXu8r9F0wbJpn3yDynEkUM5LBzwz10nmbb9ZO8pVNY2+Bybry/l01FZU49Lq+vxxYEzmHltL289pxBeEF+oaWzVPoX/vDfnFTMdiyAIgvAPJCgJgmi3yC2KHfYwPJHe3y+ulHeN6oWQYIuqmJ0+rLvuY4hNVHgyj5zFxXq2dFserWmKQtpD06gnJvZDjC2USZTF2UJw79gEfJVXIvl3YW1e18hQU+YnrNGUoi1ceh32MKZWGZNTnPjm9xOYalO1ZI+erapXFXIsabcWDli86TAeX5eLpRnHEB1hhV2Uxt0tKlQ2tVtLjStBEAThP66IlNedO3fi/fffx44dO3Dq1CkAQM+ePTFu3Djcd999uP766/127KqqKqxevRr/+c9/sH//fpSWlqKxsRF2ux39+vXD9ddfj/vuuw8pKSl+mwNBdGTk2jV8ceAM0/7zJvRDUrcu+PZoKT7OOaW6fXqyw3vch8YnYsW3hT7Ci+OAOTck4ifXdNPdpmLmtb0l2028uFG7s2yf2AjN+/Cpou2BdXt+RFUdm4iePqw79p6oYHL/haelx6IRPABuvlyjKeUqGmiX3lhbCBZOZU/z3HuiwlB/TimU2vbwsKTdiq9BvoXJ/PT+SIiPQNfIMLg9Hsx+9zvFuUi5PRMEQRCBo1NHKF0uFx588EGkpqZixYoVOHz4MKqrq1FdXY3Dhw9jxYoVSE1NxYMPPgiXy2X68deuXYvExETMnTsXGzZswIkTJ+ByudDQ0IDz588jOzsbf/3rXzF48GDcf//9qK6uNn0OBNEZEKfTBVk45oV8ar94TB/WA3+aMRhqppAWDhjZJwZAc7/Ad7YXtlr0uj3AO9sLUeFqQHS4PlOchPjWIlBruxM+cnbvWPX0XEdUKD781XV4beYwLJw6sN2ISaA52sUqzCclO5gjguL0SD3w53RlVhFmrdiFcUsyfaJyTW4P3G6P7utADxWuBjy2JgevZRxlMqfxVwSVxaxILsNA7j70XP63KqsQ8V1CMToxFqUX66U3FkH9XAmCINqOThuhbGpqwowZM/Df//7X+7vw8HAMGjQIwcHByM/PR1VVFQBg5cqVOH36NDZt2oSgoCBTjv/WW2/h0Ucf9fldXFwcBgwYgJCQEJw6dQrHjrWYibz//vs4duwYMjIyEBZGfekIQg0W0x6HIFVx74kKVSHl9jRvNzoxVrXFw+JN+fjl9Ql4bat2UyBeDDe5Pd7I67GzF5n359fjL9ya7E3PlYoE8dstmjYIqf2aHWnX57LVKgYKPtrFqURN+bRTJQEj5LnP81DbaMwIRzyd4so6PLI6Bw+mJiAqPARrd580JFx/mtyVyfxJak5CMyolcxp/R1C/uiyw5XpCijMMSqvrsXjTYcUxL9Q2Yva738FpD8PMa3szzYP6uRIEQbQdnTZCuXDhQh8xOWfOHJw6dQp79uxBdnY2zpw5g+eee8779//85z94/vnnTTl2QUEBnnjiCe/PDocDn332Gc6fP48dO3YgMzMTR48exf/+9z9MmjTJu11WVhZeeeUVU+ZAEJ0dFtMeYX8+LX0tWRqtF1fWYXRirKbWHcJ6vM15xaJ2J8eZxxHX0CnVmopr7drjwtuDFjEpF2nlW4OM7BPD1BLDqJhU4r2sIizNOGpITEZHWHFtgnRdplaUzGkqXPWqkXkjfJB9QjJ6K0SYYRCvoba1pLIOyzKOIjrCqhiBV6pxJQiCIPwP52H1Ce9AnD59Gv369UNdXfN/7O+991588MEHktsuXLgQL730EoDmCObx48fRvbt+sw0AePrpp7FkyRIAQHBwML7//nsMHTpUctuGhgaMGzcOe/bsAQB069YNZ86cgcVivtYfNKjZ9fDQoUOmj00QbUGT24PlmcexKqvQx+nUaQ/DwqkDEWML9dZdut0ezH5PvhaLZ+2cMThXXYfH1+WqbvvazGEIDbbgkcvOokrw0cP56Umoqm3EezrqL6MjrHh91giMkWnqLox48rWmUrWa45ZkykZ2zUZL24ob+8fj4Okqn5o/cb2n0x6GaUOdeGd7YUDmr5cuoUG4WN+k+v7NqmflI/I7FqR5P/PNecV4dHVOwD5nAIpmQU1uD97PKlSNUIrHjY6woqKmUTYCz2JQRBAEQbTGLG3AJCi3b99u6CBaGD9+vOExFixYgD//+c8AgIiICPz444+IjZV+etnQ0IB+/frhxx9/BAA89dRTXjGol9TUVOzcuRMAcMstt2Djxo2K23/88cf4+c9/7v356NGjSEpKMjQHKUhQEp2FZiF5DKuyinyEZHS4FfenJiCpayQWb8r3iTI6osJQd6kJlTWNiimyOxakYXdhOWat2KU6j7VzxmBs3zhszivGixt9jycWCnwkU49JjNkLZ15oAP5pvSJEjzFOrM2K4b2isfXI+VZ/48/F1CFOfHGg/beLMMMYSAvzJvRFar+rMLJPDG58dZtipN3CAQ+OS8S73xYCMH4tSIlaHql7RAvz05Owbs+PPvtTH0qCIAhjmKUNmGoof/KTn4ALQMMyjuNw6ZI2y3wpPv30U+/rO++8U1ZMAkBISAjuv/9+/N///R8A4LPPPjMsKM+fb1kEsbi3irc5f/68XwQlQXQGNucV4+lPD0ou0itrG31qy4ScrWqJyMlFOmZe2xtfHDiDH867FCNH4vpMKSfakX1isPdEBc5V16GotAbLMo7qXrA7NCycWaKUfIqskQU+CxwAj8eDmMsRJlbKXY2SYhJoqbnccbzUlDmKibWFmOaKygEItwbh7w8Mx6/X7tPdL1QLy7cVYPm2AsTarKqtWNweIG1AN4zsE2PKtSDnuGpGpDQh3uZ92KN0bRMEQRCBR5MpT0fIjv3f//6H48dbapEmT56sus/NN9/sFZTHjh3D0aNH0b9/f91z6NKli/d1Q4P6wqS+3tfFLiYmRvexCaIzo7YwVfqG4oVIdIQVocEWlFS13HfREVZ4ACzNOMo8F2F9JtBSJyZkbN84NLk9SH0lU/dieuHUgbgvNZFp4SwVBZKL4ohFcMG5i/hbJnsdJwseAJW1xh8SSo1rdtSPP7svTU9pFd3WCy+wjp6tDoiYFMLS1xNorhmePqwH3G7gufV5pohpYb0y3w7H6Oqha2SY5D1GEARBtD1MgrJ3795MEcqysjJv+w1efFqtVtjtdoSGhqK6utrrrArAO2aPHj1Mc1fdv3+/z89jx45V3WfEiBEICQnxir/9+/cbEpSjR4/Gvn37ALClC3/zzTfe1/Hx8bjmmmt0H5sgOitmLEw9ACpqGvHhr66DheMuRw9dslFNKSwcsHyWdOqpODo4sk8Mnvn0gCHzlvjIUGYxKSW2ecOWJwS9/fjIDr9A5+sqr2TsEVa8MmMwJqc4sf9UBd7eXmja2EVlNaaNZTZdI8OwOa8Yc9eYV2spNH7S2g5HjDgbgCAIgmh/MAnKoqIi1W3efPNNPPnkk/B4POjfvz8efvhh3Hzzzejfv7+PwUxFRQV2796NtWvXYs2aNWhqakKfPn2wZs0a9OrVS/cb4Tl8uKXYPyQkhGlMfruCgoJWY+jh4YcfxooVK+B2u/H999/jH//4B375y19Kbnvy5Em8/PLL3p9/+9vf+sWQhyA6OkYXpkJKL9bjliHdseuHMrywXlvdgNsDFFfWYn3uaR9xJhUd5DjAaGIHiyurkthuaTPREn0VRy3NPLcdlXBrECYlO7z9R83k89xTpo5nBrxQ42stzRCTUuJPS39IuVR0cTYAQRAE0b4wpQ/lX//6Vzz11FMAgOeeew4vvPCCbMQxJiYGN910E2666SY89dRTuO2227Bz507ccMMN2Lt3L+LijKWznDhxwvu6Z8+ezLWfvXv39gpKFgGtxPDhw/HnP/8Zv//97+HxePDAAw9g165deOCBB5CcnOztQ/nFF1/gpZdewrlzzX3I7r77bvz+979nOgYfCdaC2+0msUp0WMxsXF5U6kLqK5m6I4dCl0ol11EjYlJLZEarIOSjlrzJDzWFb05N3fVDmSnpmWKq6/zXwkQPQqG290SFpmvHFhoEl4R7rZz4Y21TMz+9P9btOelrpEWmOwRBEB0Cw4Ly4MGDePrppwE0R9f4WkQWkpOTkZmZiSFDhuDHH3/Eww8/jI8//tjQfIQptXa7nXm/qKgo7+vq6mpDcwCAJ598Er169cJTTz2FEydO4K233sJbb70luW3v3r3xxBNPYP78+czjC+s0tZCcnKxrP4Joa8zqn9glNFhTiqsaxZV1pqZH8njQenEuZ7ijVRDy9aQvbszHpGRHu+xN2RZkF5SZFqnV0i5FD7ywY6FLaDAu1rfUsgqF2vrc05qO66pvknQslhN/oxNj4bSHybap4R+czEvrh3lp/bDrhzJkF5QB8GDs1fEYQzWTBEEQ7R7DgvLtt99GU1MTwsLCsHDhQs379+zZE48++ihefvllbNiwAcXFxXA69T+NFEbuwsLYF0nh4eGSYxjhzjvvxMCBA/Hwww8jOztbcpvIyEg89NBDuPfee005JkF0VtQWpqw0NrlNm5M/iY6wYlKyw/uzkuGOHkEodOQcnRgLR1SYoVpPMWb1VxRjRhqxHE0mTjjWFoIyk9xipWAVk83bXsJ8iRpaQN+DmsrLQlI4Ju9qLE4FD7JweOHWZDy6Okc1qim+xpdvK6DWIARBEB0Aw/mP27ZtA8dxGDx4sE+UTwvjxo0DADQ1NWHHjh2G5tPY2PLENDiYXS8Lt2VxZlWjvLwcs2bNwtChQ71i0m63Y+zYsZgwYQIGDBgAjuNQXV2N5557DomJiVi1ahXz+BcvXtT8b8CAAYbfF0G0FfzCFGhZiOqh/lLHEJQXahqxu7AcQIvhjjh6xqeuVrjq4bSH6Tov56rrsCW/BHWXjKdlzpvQD6/NHIaFUwf6RUwCzWLSFmqOiRsPh+Yx3/ymwPBY0eFWfPir6/Dc1IHGJ2Yi6/acxC1DumNs3zifqDf/oEbLtcN/tPyYlbUNuPHVbZi1YhceX5eLWSt2YdySTGzOa+4Tyrepcdh9xavDHuZNu1a7xvmxCIIgiPaH4QjlqVPNZgPCCJ9WhPvy4+klIiLC+7qujv1pu3Bbm81maA4VFRW48cYbkZeXB6DZxfZvf/sbbrvtNp8axlOnTmHhwoV4//33cfHiRTzwwAO4dOkS5syZo3oMPXOk+kmiozMp2YEn0pOwKqvIpw1DdIQV941NwLo9J3G2ql42gtklNAgXNUR22pqSylpkHS/F058clDXc4dBc07lwajLmrmkdBVKjqNSFZRnHTEnPTO0Xj7F94zSnUWrF7AilB9oifkpcqG2EhePgsOv/b6LZyPWHBKAYQWQZc3nmcckeq+I6XalerXwUU81USpieTeY8BEEQ7Q/DCoPjOHg8Hhw9yt6/TYzQVZXVREcOYW1hbW0t8341NS227nrrE3kef/xxr5i86qqrsHPnTsyYMaOVoOvZsydWrVqFJ5980mffH3/80dDxCaI90uT2ILugDOtzTyO7oExzeuHmvGKMW5KJpRnHvGIyOtyK+en9sfe5SXhiUn8smjZIcUF8Q1K8gXcQeBZvOozZ736n2MOQX9jnF1fhifT+6BbFnsIYawvBmu9OmCImoyOsXgMhf9dk1jSY91CgS2iQoYi3FOeq6zA6MRaxthCTRzaGXK2tXASRhVVZrc2ogBZh+uLGfO+9zrepmT6sh0+kVM1USiiICYIgiPaHYUGZmJgIADh79iw+/fRTzfs3NTVhxYoVrcbTS3x8y4KxuJg9RaakpMT72ojT7I8//ogPP/zQ+/MzzzyD3r17K+6zePFidO3aFUCzCH7nnXd0H58g2iO8GJRLiWPZXyodrrK2EcsyjmJLfonMnr7076YvLb+t0NJkfvm241iacRQej4c5JbTc1YCz1ebU+d1/faJXIOhJo2wrLtY3MQnqyLBg3DGiB9OYXSPDEGTh8NL0FM3zcdrD8MbdI7zGN2aiJPQnpzixY0Ea1s4Z401bZoHlYYeaEGQ1lQqEG7HRB18EQRBXIoYF5bRp0wAAHo8HjzzyCHJzc5n39Xg8mDdvnnef8PBwpKenG5rPNddc431dVlbmE3lUQhgVNFJruG3bNrjdLTVa/PlRIjw8HD/96U+9P2/fvl338QmivcFaGyW3kGPpsfjixnw0XHLjxY35inN5d8cPRt9Ou+dsdb1p6ZushFst6B0X4f3czKp3bU/UNVzCJznKqbwcmgUhH6mdMsSJh8ezPSTlLv974dZkTBnixN7nJmF+ehKiw40LS/G85BBGEO9LTVR8KMABzHNTE4KsEW1/R76NPvgiCIK4UjEsKB977DHExsaC4ziUlpYiNTUVCxcuxJkzZ2T38Xg8+O9//4uxY8d6o3Ecx+E3v/mN4frFgQN9n6qyCNzTp0/j/PnzsmNo4fRp3wVHr169mPYTbieMlhJER4ZVDH55QH4hx5oO98/sItWWD4EWWnqI8UNkyt/UNrox/1++n5uRNMr2SCODl5MHwLShTp86vz9MScYbdw9HrM33cxWXAgoNaoBmcfd4en/sXTgJa+eMwbwJ/XTP3QNg4dRkxfpD8QMdAN6HAnJjpvZjy+aJ7xKq+He1iDarIDYCmQIRBEHox7ApT7du3fDuu+/izjvvRFNTE2pra/GnP/0JL7/8MpKTk5GSkoK4uDiEhISguroaRUVFyMnJQUVFhc84Y8aMwaJFi4xOB6NHj0ZoaCjq6+sBADt27MD111+vuM+3337rfR0WFobRo0frPn5oqO9/OGtra2G1qi8QhZFUIwZHBNGeYBWDj63JafU3fiGXNuAqpmOdKGfLRpCDAxAeEmRqfZ4W5k3oC2tQEFZmmd/TMpAUV9bhkdU5eOuyOOKNWLKOl2L5tuNtPT2/8872QgzvHePT5mLKkO64KcXpY0jDt9kQG9SI4aOGrC1dImSu4cWb8mGxQLL9hlJLmofGJ8r2Wd10kPHhp0rWqJbWIv6ATIEIgiCMYYrt52233YZPP/0UMTEx3t+53W4cOnQI//rXv/DGG29g2bJleO+997B161aUl5fD4/F4/916663473//i5AQ4wYGXbp0wcSJE70/C+sZ5RBuM3HiRENR0u7du/v8/P333zPtt3fvXu/rHj3Y6nQIor1jpObJc/nf1iPn1TYFAPSJjVDfSOV4FoOmYHrgoy/JTjuWZRxFpUJNWkfi6U8PetNfx/aNQ1I3Y2ZnHQmhEQ2P2JAmJNgiaVAjxea8Yiz+4hDTsefccLXk7+UibWqRuX99b8x5HQBKXfWq27C0FvEXZApEEARhDNP6SNxyyy04cuQIHnvsMURGRgKAj2gU/uMZOXIkPvroI6xfv95wqquQ++67z/v6wIED2Lhxo+y2OTk5+OqrryT31cMNN9zg8/Nrr72mus+ePXt8+m/eeOONhuZAEO0Ff9c8AS2C7N6xCYaNYC7WXzJrWkzwc104dSAWb5KOkHRULtQ04vF1+7w/++taWDh1IF6bOQw/YzTMEcNfP3NuSGyVhqrnWjJbfPCCr9yl/qDBERWKf+2RdgmXcl1Vi8x50Pw5GoX/7NUMb8TGQGvnjMGOBWmmiEmlY7cnUyCCIIiOiOGUVyHx8fFYvnw5Xn31VWzbtg179uzB8ePHUVFRgYaGBkRFRaFr164YPnw4brjhBkPmN0r87Gc/w9ChQ7F//34AwMMPP4ykpKRWxysuLsY999yDpqbm9KBhw4bhjjvukBzz66+/xoQJE7w/r1q1SlJ89ujRA5MmTcKWLVsAABs2bMDzzz+PF198UbIlypEjR/Dzn//c+3NYWBjuvvtubW+YINopfG2UWm2jETxoTocLCbbo6qfXljgupxXaw0NMP0cxEVaEBHGmubjq4YsDxZiScgZThnQ3/Vrg0Hz+7kttNr15YT1bBE88BtB8/UxOceK3k67Bn77MR1FZDRLiImALCcZb2/UZORkRH01uD3YXlqOkqg6Lvzikei3z72PW6N5YmnFMdjtxP0q1yJxR+M9odGKsYlqtUDDykVw5+HOjliosROrYjqgwzBrdGwnxESitVo+gAoF5QEYQBNERMVVQ8oSHh2PKlCmYMmWKP4ZXheM4vPvuuxg/fjxqa2tRXFyM6667Do8++ijGjx+P4OBg7N69G8uXL8fZs2e9c16xYoXhPpgA8Je//AVjx4711kUuXrwYGzduxC9+8QukpKQgLCwMxcXF2LJlC1avXo26upb/yD333HPo2bOn4TkQRHsgyMJh2lCnbA2WGTyQmuBdkE5KduCJ9CSsyipSbGcgRazNyhQFkt43hLnFx7wJfZHULdJnMbw+V9k9VA93juqJpyYPxPLM5nYibcVz6/NwU4rTp07OqNgX19VlF5Rp/ryBFkE/OcXZSnR8e8yYQZJe8SElftSIDAvGz0f2RGMTg3MQWsSumRE3pdrHLfklkp87n1bLmtLKKkrF+0geu6rO576wcIBchxChMCYIgiBa4xdB2R4YNWoUPvzwQ8yePRu1tbWoqqrCkiVLsGTJklbbhoeH48MPP8SoUaNMOfaQIUPwySefYObMmaisrATQ7Dar5jg7f/58PPvss6bMgSDaA5vzivGOH8Uk0Cwi+WOJF5v2sGA0eQBX/SVVEfPS9BQs3nQYJZV1mgXPbcO6Y2VWEdO2qf2uahWB8UfkY8P+Yjw1eSAeT0/CNY4umkWKWZS7Gr0RMb5OzuhcHCIRwSqMOACPT0xC4lU2H0EvJzoqdKR7GhEfcvNQo6ruEt5jvP6AluvN6HXHv9eFU5OxeJMoAnj5M5qU7MC4JZmGDW9khaGCKFVK6RWjJCYB/5oCEQRBdHQ6raAEgNtvvx179+7Fr3/9a2RmZvrUbwLNkcyJEyfi73//u+npt5MnT8bBgwexePFirFmzBi6XS3bbn/zkJ3jmmWcwadIkU+dAEG2JlsWcFGppq+J0OqnFZlVdi5CUGy86wopXZgzG5BQnLBZOV8qsPdyKN+4egXlrc3RFOfh0UD1iVo7iyjq8n1WI+1ITMSnZgchQK7J/KIUHgD3UikPFlVi/PzCtEHjB1+T2wB4egqcmD0D5xXoUlrmwetdJpjFuG9YdN17TFeUX6xFrC4E9PMRr+sMqjDwAXtt6DK/fPQIA8MWBM4jvEooX1ueZct6NiA+j9wsL4mtQ7brj0Hx/VNQ0KkYgJ6c4cVOKQzIVNbugjNnwRi7VVa8Lq56UXnGkUvzwgiAIgmgN5xGrLBM5cuSIt4ayvr4e0dHRuOqqqzB8+HBERUX567CS/Pjjj9i5c6e3T2SPHj1w/fXXM/eJNEJdXR1ycnKQn5+P8vJyXLp0CXa7HX369MHo0aPRtWtXv88BAAYNGgQAOHRIe60RQWglu6AMs1bs0r2/0x6GaUOd3gin1GL2zXtGeCMgSgvH6AgrwoKDfFovREdYcf/1iZiX1s9nEbo5rxiLNhxCSRVbXRXPW/eMgNvtwWNr9sluc//1CfjpIIdk3dfmvGI8srp1+xSjRF9O2xSaqyil9/mDDx+8Dt+fqMCqrEKf1NQuoUG4yNgb9OYUB3J/vCCZ7shfA6yC3Iz33yU0GMFBnM95dV6O1sXYQjTV+AHG7xc1hPeMUBzxD2MA+XsMgOZUU571uafx+Lpc1fm9NnMYpg+TNlZiPTdr54zxEaWsxxazcOpAxEeGavr8CIIgOiJmaQPTI5R79uzB8uXLsWHDBlRVVUluw3EcUlJS8Ktf/Qr33XcfunTxv518r169cNddd/n9OFKEhYXh+uuvV+2HSRCdCaP1WQunDsSUId0xvHdMa0MNwWJWLQICXHYcndgHY66OV13ou91AQ5M2tcFHSHYsSMNb93CyKZ2rdhZh1c4iRIdbcX9qazHrD6RcOgMpJgFgzj/3oKahdX0fq5gEgK/yWvc8FKY78vWZLBh9/3G2EGT/YSKCLJxPVK7CVd8q9TPWFoKXpqdgyhBl4eVvB1G5SJtcGrJ4e76fqFahzBo9VtpOrwur3pTe+MhQWXFLEARBtMY0QelyufDEE09g5cqVAOBNL+U4rlWqqcfjwcGDB/H4449jyZIlWLlyJaV7EkQnw0h9Fgdg8abDuCnFickpTsXFLOti87WtxzHQGaW4UHz5y3xdBkLCtL3JKU7VSOWF2kYszTiKt7cX4OHxV+PRn/TDixvzNR+3oyAlJrXAcYBULo0w3XHHgjS8ec8IPP3JQV0GPVooczVg74kKjO0b542Ibc4rxtw1+1pFSMtdDXhsTQ4ePpWIP0xJlh3TXw6iD6QmYFKydFScR+0eA9TdV+VgSatVqznVK0r1ppKTmytBEIQ2TOlDWVNTg5tuugkrV65s1WsyKCgI8fHx6N69u7c/JdAiOE+fPo0pU6bgo48+MmMqBEG0E/jFnBm9/IRN4UcnxmJ3Ybm3n1x8l1DmcaUazvN8eeCMYTfac9V1aHJ7sHjTYabtaxqasDTjGEYs3tImhjkdBaXCDLGY5+sj/U1GfkvElKX+8e3thfjywBnZv6vdLxyaI6NL7xyKhVMHMs/zq7wSH3Eo149ReI+N7RtnWuScd/fl34MQ1ppTlnPjlBClSsfWMg5BEAShjCmC8uGHH8bOnTu9P/fp0wcvv/wyDh48iNraWpw7dw6nTp1CZWUlzp07h40bN+LOO+8Ex3HgOA5NTU249957cfgw2yKMIIj2j9bFnBTi6OPmvGKMW5KJWSt24fF1uZi1Yhce+3Av83hyDeeb3B48+3mezlm2UFpdj6Vb/qdZHF6sv2T42J2RWJsV91/fh2lb/loZ0zdO94MMLXyWexpNbg+a3B68n1XI9Jk/tz5P9oEGi/D64+0puH1ET9yXmohYWwjTPIXXvNT9M25JJjbntZgzyQlOI/BptQ67b+TPYQ9jahliRJTKHVsMubkSBEHox7Apz549e3Ddddd5+zc+9thj+Mtf/oLQUPWowZ49e3DHHXfg9OnT8Hg8mDp1KjZu3GhkOoQCZMpD+AulZuN6+urxCE029LZUEMObfwjn/O3RUnycc8rQuIE2urlSYO3xKXWtANrcerUyP70/1u05qenaFs5T6r7Zkl8iaYCzcOpAxNhCvdv+51Ax3t95gumYr80chtBgi+T9Y5b5DgtK3xMs6OlDKXXsolIX1u4+6WO8Zeb7JAiC6CiYpQ0MC8onnngCf/vb38BxHB544AGsWLFC0/5Hjx7FiBEjUFNTg6CgIJSUlCAuTnudBqEOCUrCH7As8sSLuTXfncTZankXVb6uaseCNARZODS5PapOrqysnTMGlbUNbdaX0V/ERFhxoabRrwJKCqc9DLWNTahsg2MDra8VHiMPMvwJ/0BD6b4R1zNWuBokzX5YhDYAfPir6/C7j/bLngthexCpvwGt3WH1YlRUGt3f7HEIgiA6Mu1GUA4ZMgR5eXmwWq04c+aMLjH429/+FsuWLQPHcfjoo48wY8YMI1MiZCBBSZiNXNRQbRHa5PZgeeYxLM041upvUvua2VJhzg2JePfbwjYRP/5k4oCrsPXI+YAdLzrcitdnj8CYq+OwJb8kIBFBMSzX2ZP/zsXnufK1i4GGf6DBet8o3WMs59ppD8NffjYUs9/7Tvec5US7VoxEGAmCIAjzMUsbGK6hPHPmDDiOw+DBg3VHFtPS0ryv+T6RBEG0b9SajQPyJjhBFg6Pp/fHG3cPR6zN6vM3qboqM1sqvLejfYvJVjVijOv3QIpJoNmp1sJxCLJwsnVqrHNnRXytdIsKxRPpSai/5Jas9wuycOgZE2HuJAzgtIdhZJ8YLNpwiOm+abjkxjOf5Sluq8YLtyaj1KWtp6rUseTqj1nhhbE4Ssq3fhHWcZqFP+pBCYIgiNYYbhvicrkAAFFRUbrHEO5bU1NjdEoEQQSA3YXliumEwkWoVLuBzXnFWLzpMMpdLWl2sbYQLJzanPKXXVDmTUfT4uSqht41ZURIEGoa2Psm6sWDlsbqpdX1TI6xrNEqFqLDrcxtN3hX292F5ai/5MZffj4U8AClrnpvquZja9h6Q7Kw8JZBcESFXU6drsHa3Sd9otxS0a6xfeOwfNtx0+agFw7N4u7Nrwt8avfE8PfN8sxj+Ed2kc/9oel4HPD6rBHeXq1moPfBjtrDJw7As5/lobahCQ57uCnppxQNJQiCCByGBWV8fDxOnz6NH374QfcYhYUtVv1UP0kQHQPWxWXW8fOtFohyaXy8AIm+XA/I44gKRXSEtc3q9B6fmIRrukXqFkeP/aQvzlXVY8P+M2hoUu/JGB8ZiluGdMfSLUeZxjfznLx+9wgcKaliErJFpa5Wta38op1/iPCWZQSe+SyPud5PCUdUGMb2jcPmvGIsyzja6n3z0S5hhHvM1XEIt1pQ26itFyYH4KHxifjX96d8rkU98OcEAJZmsH2mUungWvB4gJjLTrB82w2j9aR6+zOyPHwqczVg/r/3AzAu/OS+X6SuD4IgCMI4hlNer7nmGgDAjz/+iB07dugaY/Xq1d7XAwYMMDolgiACAOvicvm2Ap/WBCypsuIF/Nmq+jYxnAGaxezcCf3wf1/ka96X72v35E+vwV/uHIpV913LtN+3R0uR+srWgEbW+LmO6RuH+1ITVfv+xURYsTTjmGoK4+QUJ3b9YSLCrMb+c8P3B9Saar0lvwQWHdGu9OSuGN47BpUGxeT89P745vcTEBlqxdOfHDQ0llb4hz5BFg4LpybrHsdof0atkU0jabBGUvEJgiAIfRgWlFOnTvW+/tWvfoXS0lJN+7/xxhvYunUrACAmJgbXX3+90SkRBBEA1JqNCxEuENWiFVLwaXERIUF6puqFQ3N7Dy08f0sy3vz6OEqqtEd3PPDta8f3SFTj45xTimmRPHyzezMQzpWl75/ccly8aG9ye/Dm18dRpzFCKD4mPzctqdZ8pMpVrz1VOSP/HBZtkBYmrHAA3t9ZiPF/zsTs975jTiU2C+FDnxiD14mR/oxaI5tGhJ+W64MgCIIwB8OC8v777/emqR49ehSjR4/Gpk2bVPcrLy/Hb37zG/z6178GAHAch9/85jewWAxPiSCIAKAkOsQIF4h6hBk/hpEaRn6OEwd21bTfsXMXdacfzk9P8kmt488ZB/Vzpga//+LpKYgOtypuy4Jwrk1uD+zhIXggNQExEqZJj09MUkwDbakDPI7UVzINpW9GR1h9UhQz8kuY9iuprJWNVLHgAXRfq8IxKmoamR4OmIlURFFv/aOFA16/e7ihFFH+4ZMW9Ao/1vdpptEXQRDElY7hGsro6GgsW7YM9957LziOQ1FREaZNm4bExERMnjzZ6/4aEhKC6upqFBYWYvfu3diyZQsaGhrAdy1JSUnBU089ZfgNEQQROHh3T5Z+f/wCMeuYMTfS6HArKmvV01/FRjUOeximDXXine2FcrtIsiqrSOMMW0iIt7X6nZZzpoRDUGd27Fy14Zo7fq5SZiaxthDcNqw7JiU7UOGqxzOf5TGNyVovqMTrs0YgNSneO7f3GD+PcldDu+tBycKYq2Ox6wf90TP+QYM4ohhv02ds5fYAMTr35eFTbvXUIGsVfqzRUL31oARBEERrDAtKAJg9ezbKysrw5JNPwu12w+Px4IcffsCbb74pu4/H4wF32VM+OTkZ//nPfxAWRl/wBNHRmJzixKRkB5ZuOcpU87fpoLH2APenJmIZg1DxoLl+LSE+Al0jm9s13PjqNs0RKyNpinKLVv6cvZ9VyGR+I2behL6YP+kar2CYl5aEt7f/YCiC2zUyTNEsaVVWEaxBHN7ZHri2K3G2EIy5bO7D18apwaG5nUh5jXEToEDjtIeh8LzL0BgOOUMbAyFxJVHHu/zyjsxyDq16U261Cj8+GlpSWSd5nfI9NfXWgxIEQRCtMS2/9De/+Q2+/fZbDB061Ps7j8fj/Sf1c3h4OH73u9/h+++/h8PhMGsqBEEEmCALh9R+8UzbanXbFOK0h2FeWj88ND6RaX38/s5C3DKkO8b2jcPeExWaIlYcmtMt9eKIClVctAZZOMRH6ov8jL26+VzzPfZ2/VAGa5B+xeCICsXIPjGqZiYrvg1sD8/pw7p7xQlr7a0HQN0lN17fVmDo2ByaXWVNbqWpyMg+MThbrT89duHUgdixIE0yPbX0ov5xS6vrJWsZN+cVY9ySTMxasQuPr8vFrBW7fAy4hGiNNOo1AmKp/zVSD0oQBEG0xpQIJc+YMWOQk5ODXbt24fPPP8d3332H48eP48KFC6ivr4fdbsdVV12FESNG4IYbbsDMmTNht9vNnAJBEG0ES2QAMNbi4oVbk7Elv4Q5SlZR04hdP5QhtV+8pgUtP9f7r0/Unba5aNog1UWr3rS7uWtyAK61G65eZo3ujb9tbe3YKsSD5lYUgWRScsuDRi2fnxnn5aHxiRjeOwaPrs4xtc+nEjuOaTO1E+K0h+G+1ETZa66oVH+P58WbDuPdHYU+kU+trTm0XOtGhZ9cWrls9JYgCIIwhKmCkmfMmDEYM2aMP4YmCKKdwkcGpBbgZizIH0xNwKRkB8YtydQ0VnZBs6DUsqDlF56Tkh1Yt+ekrEiWIjrCildmDGZatKqJcDnMdAvlOOM9D/2BODoV6Jq3oT1jmOpdnZdrczfsL27Vj7O2sUlT71S9n6vQBVcKvm+nEYRCcVKyQzGazaHZgGtSssM7Jy3XupzwY02vBVrSylm3JwiCIPTjF0FJEMSViVJk4OYUB1YaMLhJv7w41Gq0cvRsNbILyjCyT4zqgjY6worXZ43AmL5x3oWnmkh+YmI/XHIDgAdjr4732RdQXgQrifBAEeiooxpy0Sm94lsvj63JwVuWEa2ESXyXUMADlLrqfT7PpyYPbPU5b8kv8ftnq/YAQ6kvoxaEQjEyzMrcmmPs5RpYlmv9wdQEpCc7JIWflFmUUyXiGGThvMcnCIIg/Afn8bS35QRhFJdL2tRh1KhRsFgsOHToUIBnRFxpSImo3YXlmLVil+axeBONHQvS8MWBM3h8Xa6uOTlFLq9SX3xv3D0CU4a0XpzqWcxq2U9quysVR1QoFk0bJHle+TRLwPwIuBTOy9edkaiWvz9bDmiVXioku6CM6b57YmISIsOCmUyi5k3ox2TA9drMYZg+rIfP7/TcS3LptfynovT+CYIgCHkGDRoEAIa1AUUoOyFdunSR/VtycnIAZ0JcqUhFBoxEmPholZG0x5LKOryzvRDpyV2x9fA5ycjc4k35sFjQanHKkj4nFtEVrgbMXcNWY2bU9bVzIS/elCLgd47qide2qoscLYijbHrgP9tdP5Rh7oc5pqYr84jTS4Ww1p4mXtW6xY08bHewVN2m1lRUpQirXHotQRAEEVhIUBLEFYqWeiQzxtKT3mnhgDk3JHqFFy9K9UR7+ONtyT8nu02xhNgTv7dbhnRnSsezcNLvUW4RbMT1tTNxtkra1IVHSpBUuBrwf1+otxTRg1CQ6b1neBfkV+4YLBlhNYJUeqkQf/RlHHt1PD7eewolVcrOsev2nMS8tH6S3wWsIl0tzV3t/RMEQRD+h0lQXn311d7XHMehoKBA8m9GEY9N6OPixYuSvx81alSAZ0K0V/SmcBodi8XkRIjHA7yzvRDDezcbpPCi9JHV2huks+IB8PSnBxEZakVlbSMWb1J+b3LpeBJdFnyOIbUINsN4JtYWgruu7Yk3v/7B8FhtAUvUSShINucVS0aCzYL/TMy4ZyanOPH63SPw3Po8lLta+mRynPFaVrlI5Mg+MbBwytejhWveLsjCMfVwHNM3DrNG91Y1czJD6LFGWLW2JSEIgiDMg0lQFhUVgeM4eDwecBwn+TejSI1N6MNmk05dslhMaztKdGC02v2bPdbkFCfSBnTDmJczUO5STv+TEheTU5x44+7hmLd2n+Ii2QgXahox+73vJP/G6nbJwrnqOp+ol9Gm9gBQ7mrAv/b8aHictoQX3Eu3/A+p/a6SjQTqNZzhANyX2hursk4qbscLLbPumc15xXjm84Ot2pqY4WQg9zBi74kK1fvE7WnebmzfOMUsAg+AKSnN0eHecWwpskaFnj8irARBEIS5MKe8Knn3kK8PQXQMzKxHMjLW3hMVqmJSOJY40jFlSHcsB4fH1vgvUqk0H6/bZaiy26UaRaU1GLckU/MYHAB7hFW23yLruW3vLN9WgOXbCmQjgXpcf/mx7OEhqoLS7QH2FJWbcs9sziv2S2SdjxoKW6wI0Rrhk8si4KOc72UV4b2sIsTarEzjGhV6LP1tld4/QRAE4X+YBOW2bdt0/Y0giPYFaz3S+1mFiI8MVawTM1LbpCdqId5nyhAn3rKwp8+aCf/esn/Q14ieF4TLMo7qim6+fvdwuN3Ar9fta5NWI4GmWBQV5iO6x85Kp/eL6RIajMXTB8FhD/dez+tzTzPtm11QZvieaXJ7sGiDf9y1PQCmDXXKilk9ET5hneqW/BKszCpqFeVUe2hhltBT628LKPfgJAiCIPwPk6C88cYbdf2NIIj2BauQEzqNykWHjNQ26YlaxHdpbVjjddAsKMPcNf5x0FRG+yJWuCjWIwYfTE2AxRKY6Gxb9caUgq9tXbQhHyVV2h4gXKy/BIc93Ptgo8ntQWm1sqGM75HVUbpndheWqxrYGEFYZyxGb4QvyMJhdGIsfvvvXNXj+1voKbn76qn7JgiCIMyFiuoI4gpCj5Dj68Q25xXrGktqu9GJscwpczxP/ju31RyAyw6aSc0OmhzkJV50hBUPj0/UIQHlGds3Dk57mOKY4vW0wx6G+elJsumqaqQN7IYXN/rH0VSMPbz5mWN7if1cqGnULCZ5+P025xVj3JJM1fYsHJqF4dir47UfS3TPBMIw5sWN+WiSKJbkI3xA689RTfixphTH2EJ8fnbYw0zvDTk5xYkdC9Kwds4YvDZzGNbOGYMdC9JITBIEQbQDDLcN+eCDD7yvJ0+ejK5duxodkiAIP6GnF6RcnZiR2qYgC4eXpqfgsTX7mOd+tqpetZ2EVBQjOsKK+69P9LYvGN47xnCarNft8uoWExM5HhyXiLQB3XxaTfzfF9rTH/ljwoOApfheqL2EJyYm4V/f/8h0TA7NLrNlAgfT9sLiLw7hSHEl3tleqHrtC4XWmMsPDbScc/E9syX/rM5Zs0WJ1Vpn6I3wsQrhhVMHwmEP19xORWsbFi3tRgiCIIjAYVhQ3nfffeA4Dl26dMHZs/r/o0kQhP/R0wsSkF6wqrXw8EA55W3KkO54+NQFvL29kHkOagYowiby2QVlADwYe3U8xvSN824vrA8rqapDaXU9lmceQ2XdJaZ5iKM6k1OceGh8ouz7ePfbQozsE4Ppw3oAAL48cAb/2HmC6VhCPGheuJe6/Jc6KcWb3xzHvdf1wae5Z3xaXUjhAbBo2iA8vs5/Drx6KXc1Ml9rYqE1baiTeV8e/p7ZebwUXx5sHVlnYepgJ/aeqGCOyioJQKn+nWoCjjULQZhOzIqZrYsIgiCItsWwoAwLC0N9fT0GDBiAsDCy7SaI9o7WXpBChAvbJrcH/ythM0WR4w9TkjG0Z0yrvnxysDQx35Jf4vPepFxCxZGOXrHhzA6cQrHR5PZgV0EZ/rXnlOI+LZGqEk1RWTGLNx3GzGt7695fD/WXPHg3q4h5+x/Ou9qdmNTCwqkDcV9qIoIsHJrcHizPPKZZTAr5NOeU7vOx6WAx7OHB+NmInvg4R/kaA3wFoFz0T4vwM9thlZ9TRn4J3pO4pvS0LiIIgiDaHsOC0ul0oqioSLb3IUEQ7Q9xtKK0ul61pgxoThsMtzaXXi/acEjRaIS1ncKUIU7clNJsrDPnn9+jpqFJdR5ykRi5foHFlXV4ZHUO5qf396a+iomWaMMRHWHFn25LQYwttNXCXCrCIgUvgnf9UGa49rGksg7LMo4iOsKKyprGdmOYw8MBWLVTv/gySmRYMKoZI81yxEeGej9fPQZAYlwM17MSlbWX8HHOKcXPXCzszIr+memwynK/aG1dRBAEQbQPDJvyJCcnw+PxoLCw7RYRBEFoh49WTB/WA/eOTUCsyFhDinJXIx5ZnYNHVueoulYKo4ksc7FYOCYxCbSOxGQXlOGznFN45rM8RZG1NOMoUl/Z6mPuw4tQKZOcyppGWATnaezl1Fl+Hy0RXrX2Eyzw741f3Le35bYH0G02ZATeQGfRLcmGx+oaGeb9fI2KyZgIK65NiDE8JwCAxyP5mYuFndy1KWeupQaf0eCw+2YgaTHe0XK/aPneIAiCINoHhiOUs2bNwqZNm3Dy5El8//33GDVqlBnzIggiQPCRA5aUUz0YaS8iRXS4VTESo0aJwNxnUrJDtmk9jzha0nDJrSpcpThVUaNxD2k8ACpqGvGzET2x5fBZVAa8VYo64VYLahvdAT3mC7cmwx6u/lBEDj7KN7JPDG58dZsp0d+KmkY4o8Jh4WA4DfhC7SXMT0/Cuj0/yhrrNLk9stcz/zs90T899Zc8SnNSIhDOuARBEIQ5GBaUP//5z/GXv/wFubm5mDt3LrZt24aIiAgz5kYQhJ+RSxE1EyPtRaS4PzXBJxKjd+4vbsxHZKiVqWk9X7O5Oa8Yz3x2ULWpuxgLB3yee0bnTKVhqalrKwIpJi0cMOeGRK+gYnExVkrf3HuiwjQXXQ7AH786jAfHJWLFt8azeBLibdixIE1W2LG0+VCrQZarvdTrsMraekSMnhZHBEEQRNtgOOXVarXi3//+N3r16oXvv/8eN954I/bt0286QRBEYNAbOWCFT0NkNezgDUCUYh4xEVbMS0syPHdeKGb/UMq0fUllrVfAahWTgPHoVGeB80OOrtsDvLO9EJvzihFk4bBw6kDZOkMOwMPjExXTN82MjPHXWZdQbT1X5egaGeaTqj5W4F4MsEf1tuSXSP6e79E5a8UuPL4uF7NW7MK4JZma02SFaD2fWr83CIIgiLbHtD6Ujz/+OF588UXs3bsXo0aNwvDhwzFu3Dj07dsXkZGRsFjYtOsvfvELo1MiCIIBvZEDLbAYdggjIjOv7Y1lGUclW5pwAF6eMRhBFs6UesSWUdVZvOkw3Jdr2Aj92MP9Zyb04sZ8uN0eWXMpYWroU5MHykb5/BEZW5mlHJ1kSYl1RIWqiizWua/PPYNnp/rem3IRf6POq1rOp1ajH4IgCKJ9YFofSh6O4+DxeLBv3z7NkUqO40hQEkSA8GeNEoujZHNLhuNYlVWIC4I6wOiI5miO0NxFPJ5Zcx/bNw6f5JxSTZH0V32pFByA8JAgZoMiVm4a1A3/OdS2vYIv1DRifnp/rNpZaKp5Dx8JVGrJsnCqfNsYIWqtMvSgVufKEsFeNG2QqsganRiLWJtVNYpe5mrwSXtVq73U4rwqTpkd2SeG+XyK+38SBEEQHQPDghIAPJ7W/5mQ+h1BEO0HPZEYYeRQKooIAPPTkzAvLUlx4bk5rxhPf3pQ1lnVc3mchHibpAFIvC1U89zFWDjg2oRYb1uE9oIHwJQUBz7OOW3amLE2K/pd1QX/QdsKSgBIiI/A3ucmYfa7u7Drh8A4eXIAFm/Kx00p6oJIqVWGP3kgNQGf7jst2brmlRmDmURWkIXD7cN6SPZ4FCN8KKOWrcDS/xWQb1cybagT72wvlKxd9aD5vU9KdjAb/RAEQRDtC8OCcvz48T4RSoIgOgYskRhxKh4fQQDA3OdOHLGocNUrRpL4iMi6PT9ix4I06QWmCV85bg+w90QFJiU78ER6Et759ge46s2NCurl45zTpjiD8pS7GvH61wXmDGaQT3NO4afJDhwprg7YMVkFEQ/fKkOrg7ARJiU78OzUZOz6oQzZBWUAPBh7dTzGiOok1UhPdjAJSuEDJTOcmJVSZt/ZXoiHxidiw/5iWYdagiAIouNiWFB+/fXXJkyDIIhAw9K0/G93DcfZ6jqcKK9Bn9gI3Ds2ASHBzfXQLG0EpCIWLGtjNQFQelG5ByYrGfkl+O2/cwMmGoRwALpFhTbXjW491urvndXI55ujpRj4/OY2ObaWVGlhq4y3vzmOr4+yGTjpgTehCbJwSO0XjzFXx3nvrd2F5Zoid2oPivj2KMJ6TKNOzCwpsxv2F+Ob30/A3hMVmluPEARBEO0bU1JeCYLoGIijhZOSHZKRGMflNLU/fnXY5/fv7ij0RhTU2gjIRSy0CCU5AWCWcQpLJMcf8Evo529JljWR4TEzUtkZ0ZKWqjVVOsjCYXRiLOauqWTa/mcjeiC1XzwWbzqsqe5WaEIjlzbKGsljeVAkNr3hRajag5UKV+sHOU1uD97PKmRKmd17okJX6xGCIAiifUOCkiCuEJQWquLedhWuesxds0+346NZLUnkhCPrAliJthRqfKqfPTxE9T24PcDCqQMRHxmKrpFh2PVDKV7bejxAM23/REdY4fHAx9hJjic/2o9F07SlWO4uLGdqFdMlNBhLfjb08vbsYnJ+en/vfMxyWpVL2ZVLMW1ut5KMx9Yo1xIv3nQYN11+mMTPV0tasD+NwAiCIIi2gwQlQVwByC1Uiyvr8MjqHDyYmoD0ZAduGdIdADBuSaZs+hoAPP3pQUSGWmXru8xoSWLhgJF9YiT/xkdhHjFgptNWYvKJiUm4NiEWpa56ZB1nS6M8c6EWD95wNYBmMf3+zhOqzqFXAqHBFlRocIs9W9V8vWsxgWEVQXeO6okgC6dJNEWHB2NeWj8A5jqtAr4pu+eq65qjs1xzunh2QVmr9x5jC1EdU5iGLvedooQ/WrIQBEEQbY9fBWVxcTFKS0tRWVkJt9uN8ePH+/NwBEFIwBItfC+rCO9lFcFpD8PMa3urisELNY2Y/d53sql4ZkQieNMcuRS5ySlOvHH3cMlIqhIxEVaM6B2NrUfOG56jHv6WeUyzmH0vqwjXJsZ6U43HJ8Vh4wHp5vRXEvWX3Jq250/7yqwirLx8vaulkrKKoIkDuiG7oAzHzl5kns+F2kvYkl+CySlOQ06r4lR2Xizyaemb84rxu4/3K6bRbslnu57OVddpzkCQqts0itx7JgiCIAKP6YIyKysLb7zxBrZt24azZ1ss6jmOw6VLl1pt//LLL6O6utnt79lnn4XNZjN7SgRxRaMlWlhSWYelGUeZx5ZLxTMrEsEvXuUWjjelOGGPyGPuafjslIHoER2m6DLrb/RGRvno1Jb8EhKTJsGSSsrihhwdYcWTH+1HSZW2BynCqKMep9XmXq7HsCqryCflVygWWdJogWaRzULXyDBN3ylydZtGMFpnShAEQZiLaYKyvLwcc+bMweeff+79HUsvyvLycvz1r38Fx3FISkrC/fffb9aUCIIANC1ytWoduVQ8s5rDF5XWYNySTNmF4+7CcmYx6bSH4ZfXJ+DGV7cZmFHbUVxZh10/lOHFjfltPZVOA0sqqZrJjQe4fA1qT0EWRh21Oq0q9XLlxeLrdw/H4k2HVdNoWftG8260Xxw4w7Q9YH5rELPqTAmCIAjzsJgxSFlZGW644QZ8/vnn8Hg83n/R0dEIC1P+j+TcuXO9r9euXWvGdAiCEJB1zL+pncJFMQ+/CNcLh+aoz7KMo60iIfzCcXNesabU2hduTcbeExVt0iLELLILyjr0/NsjUtevGN7kxmH3/e+ZPTwYZrRhPldd530IIzcchxZBx4squYcpvNh6bn0eUxptSRVbGx4+ysgqfhdOHYgdC9JME3hqdaZAs0BuIltkgiCIgGKKoJw9ezYOHz4Mj8eDyMhILFmyBKdOnUJ5eTluvPFGxX0TEhIwatQoeDwe7NixA/X15vSXIwiieQH23/yz6hvKoGWtLBZ3/CI8lsHsQ3xMfjmotnCM78LWBmJ+ehImpzg7gcskLZT9hdS10eT2ILugDOtzT8MeHoJvfj8Ba+eMwWszh2F+ehIu1F4CY3BPka6RYT4PYcT3nTBtFABT/aIHYHKnZeXB1ASvMGQVv/elJppa16ilzpQgCIIIHIYF5ZYtW/Df//4XHMfhqquuwu7du/H73/8e3bt3Zx6DF5319fXYv3+/0SkRxBUPvxBeuuUoqupa1y6zMD89qVVERgm5qIVV47eMwx7WvFhXSGXlF47uJg9ibVbF8Zz2MMxLS1KcY3uHX6CPvTq+rafSaRFfG5vzijFuSSZmrdiFx9flYtaKXbjx1W2orG3ALUO6Y92eH005rtMehpF9YpBdUIb6S248kd4f3aJ85+Kwh3lTOc1wUNZDerLD+5pV/JptkqOnzpQgCILwP4ZrKIVpqm+88QauueYazWMMGzbM+/ro0aMYPXq00WkRxBWL1t5wYnhHxnlpSZiXloRdP5Rh7oc5sn3+5BwctbYViI6w4vVZIzCmbxz+uImtTvDX6/YpzgvwXdiaVdtpNhwAe4QVlZdFtFQz+oVTBwIcEB1uZeq5SLAjblGjVqf3RHp/00TdyD4xuPHVbb79IqNC8cTEfmg2sfVg7NXxGHPZ2VWrWIq1haDC1SB5vfP3rsfjwdmqesVtxPc3S69Ls51YtdaZEgRBEIHBsKDcsWMHACA2NhZ33HGHrjG6du3qfX3+fNtY+RNEZ0BPbzghUiIstV88XrljMB693PNRSuyIoxFa2woAzcYmlstjfJ7LZvqhJKzkFrYzr+2NZRlHWxmsGCEiJAg1DU1M20oZuwDAKzMGA4DkAn3aUCcWbzrcYeontYreZ6cMxOvbjreZUBa2qGHpB7lqZ6Fpx/7iQHGr35VU1WPZ1uPen5dvK/CaUWkRS46oUDx/yyDMXSNtKAS0pNHKmQ7x24iFYJPbA3t4CJ6aPADlF+sRawuBwx7uFY3+cGJVeyDkj/YkBEEQhDqGBWVJSQk4jsPAgQN1jxEREeF9XVtba3RKBHFFokfEiZFzZGSJRgjRm5Z3rrq5/qnc1aDvDVwmztZc7xYSbJFc2EZHNKfJsjrEKvFAagLs4SFM7Vbmpydh7e6TPiYo3aJCsWjaIO85FDaj7xoZhgpXA+au0f+QINAsnDoQA5xRmP3ud0zbO+1heGBcInrFhht6GGIUPvLHUqdnxnWjFaFzK2uUve6SGxYLmO5dLfe3kljkxSSLE6vWCKaa4y7gn1RbgiAIQhnDgtLtbm4sHRQUpHuMyspK72u73W50SgRxRWKkturB1ASkJzsUF3STU5ytxI7c9nprmIpKa3TtJ6bM1YB/Zjc3rp+7Zl+rhW1lTSM8aBZ4CfE2xNtCMXetvGumEpMun7dVOwtl9+cjJ0ldIyFfddYM34weaH5IMG5JpqJwsIUGwVXPFh0NBPGRobg2IRaxthCmBwO8AJic4sTrdw/HvLX7dPfqNAIf+WO9dqPDraisbZT9bMyMgAMt0dHFmw5j4dRkyaijmMqaRq+A27EgTfHeZb2/1cQia6sSt9vTKurOEsHU+nCLIAiC8D+GBWW3bt1QWFiIkydP6h7jwIED3tcOh0NhS4Ig5NAj4qIjrHhlxmDZRZg4giCsM1NCbw3T0oyjeGJikq59xSzedBgWTt4plgOwbs+P2LEgDUEWDn+6bTAeW5Oj6RgxEVbvovuVGYPxyOrW+/PL8WlDnZKRxpIq+f55LA8J/CUmYyKsuHNUT7yzvVCTMPr26Hm8sP6Qavqq1LUXYwsNuJgUp0myPtS4PzUByzKOyYo6f7wN3owqxhYiKaqkthf22eQfVMghfJghBUs68HPr8xTdZfn38Niafa3+xtpLUsvDLYIgCML/GBaUKSkpKCwsRFFREQoKCtC3b1/NY3z22Wfe12PHjjU6JYK4ImEVcbcN646eMeFeow+5RZhUWpuFg8+CXy6iMDoxFo6oUOb+dkL+lnkM9vBgVNbqc6cVoiROhC0GxvaNQ4zG9iYAUFHTiC35JZic4sTkFCfekomcLJw6UDZqw89l0YZDmJTs8Pk82tKtsqKmEcN7x+DNe2KYTZ44Dvg457TiNtERVtx/fSLmpfVrde0F+v3yYvDmlGZxMrJPDNbuVn846ogKxby0JFzjiGx1bmIjrKi75FasqbVwwK/T+uE1QZ2kFs5V12H6sB6YlOzA+1mFWLzpsOy24uvcCCzpwEZalYgFsFr6q9H3QxAEQZiDYUE5depUbNy4EQDwf//3f/jHP/6haf/PPvsMe/bsAcdxSElJ0dRuhJDG5XJJ/t7tdsNiMaX1KNEOYTWs+Oudw1Sf5MultYkFmlxEIcjCYdbo3liacUzz+3B7YIqYZIUXMSWV2uu3xYtfucgJS6SxpKoeyzOP4/H0lghtW7pV8u9tx4I073vakl+ClVlF8lE5lbBcrM2KXX9IR0iw9PeQv9+v2CyI45rnvDKrCCuzihBrszIJolmje7f6vDPyS/BZ7mmm/d0eYHRiHJz2U7rS1PnzFGThEB/J1ouV9fpWqmsMhOA3UwATBEEQgcGwupg9e7Y3TXX16tX4y1/+wrzv9u3b8cADD3h/fuqpp4xOhwDQpUsXyX9Hjhxp66kRBhA2Wc8uKEOTSN2Z1RtOi7mPBy3RNfF8EuJtDCO0PfziXI8RkFQjdT5yMn1YD4y9HAFmXcwvzTiKzXktrp/8Q4K2QPje+Pf0/K2D8NY9I1r1J2XNNCx3NWLviQrZv49OjFXtK2qEv88ajrVzxuDB1AQArR+QsEbXhNd2kIVDZW0DVmYVaYrOZReUYkqK9hKP6HAr3B6P935jFeGLNx32ubakkOq9OW5Jpnc/1mPF2kJafQdphXpJEgRBdBwMC0qbzYa//OUv8Fx+NL1gwQL89Kc/xZdffomamta1KLW1tfj6669x//33Iz09HZWVleA4DuPGjcPdd99tdDoE0SlRW+jx8IYV4gW/sDG6GnrMffjompCO0AvOKaidi+3CFumRQm3xq0Wsvrgx3ysWgiwcpg1tW5ORc9V1Pg8zIkOt+PMdQzBvQj/Mm9AXz04ZoKnuUepc8eN/ceAMrkv0X1Tq12v3ocLVgC/zSgyNI7y29borL99WgPeyijQf+0JtI2a/+533/ucfOqgJuApXAx5dnSMrKvmsBPG9z2chsByLQ/M99dL0FO/P4r+z0hG+PwiCILSiFhzoqBhOeQWAu+++GwUFBXjhhRfAcRy2bt2KrVu3Nh8guOUQUVFRPumYvAjt27cvPvnkE3AcFdSbwcWLFyV/P2rUqADPhDCDLw8US5rFyKWbGjWs0BsZWJpxFNc4unjnopaC2x6YeW0v72tHlP4FrNTil08dLKmqw8FTlRJ7SSNM99ucV4x3tpvX81APRaUujFuSKfuQITpcW0RRfK6kanX9xYXaRs3GS2Kcoj6HRtyVjSC8//lWGkoo1SeymO3w+7G07Zic4sSbFrl64mQs3pRPvSQJgrji8Ed/3vaCKYISABYuXIj+/fvjkUce8WkDcunSJa9QlBI6N998M1avXo2YGDb3SEIdm0061ZDqJzseXx44g7kSboiA8gJRj2EFL4COna3WPV/hXIQ949orSzOOYd2eH/HCrclIG9CtlekQC9GXnV6FfHmg+LLbpb5+mnxU0GhfUaPERFhV62DV3FyFOO3NTsHZBWU4V12HolKXrjrbtmTmtb39ZpzE13SyILz/dyxIw5v3jMAznx1kclgV1yeymO3w+7G27VB6sGWxgHpJEgRxRcHan7ejYpqgBIC77roLN910E95++22sXr0a+fn53iikkPDwcPzkJz/B/PnzkZ6ebuYUCKLTsDmvWNJaX4hZBhab84qxaMMhXa6sQsRzmZzixEPjE/F2G0fZlOC/zJ9IT9LVsqKxye3z88tf5ht+v10jw9os8iXEbDE7bagTN766rc3flxES4iN8fjYzNZNVTHq3h6/Qq210Y/6/clX34x9Y8GKP9SESL55ZsyDkHmxRL0mCIK4ktGSBdNSHaaYKSgCIjo7GggULsGDBAlRUVODQoUMoKyuDy+WC3W5Ht27dMHToUFit/jNeIIiOTpPbg6c/Pci8vZEoyea8Ysn+iXoRzqXJ7cGG/cpGIGbAAbBHWBEWHISSKm3ngv8yX/HtD7qO7apvwq4fypDaLx5fHjhjWEzyUby/bW3byN3PRvTExzmnTBkrOsKKu3T0tBQTE2GFB8CFGv2tKYwiFpDtIbWbv+dY07bV0pjlEL53o207qJckQRBXClqyQDqqu7XpglJITEwMxo0b589DEESnZFdBmaZFc2l1Pdbnnta8KNMqXFkQLjoDEWXj3+krMwb7LFCLSl1Yu/skU9TVA+BivXzfQDWyC8ow5uo4PLc+T/cYPCP7xGD8nzMNR4uN4LSHIbVfnGZBKW7LEREShCkpTiy+LQVpf/1al+CaN6EvkrpFeq9tAD4ipMJVj8WbDgfkOpOq7ROmdsu1U/E3/D3H0joomiGNWWo/f9Q1Ui9JgiCuBFgf+ndkd2vdgvLgwYP46quvcPDgQZSVlSEkJARdu3bFddddh1tuuQXdunUzc54EcUWR/UMp87Yc4NPYXEuBt1bhqjYP8aIzEF+O4jQ54QJ1XloSdheWY9PBM1i9S71hvX6a0weNNHXn+eKA/yO6cghr2OzhIZr3f332CHxfVIFVWYW4UNuImoYmfJxzCpn/O6v73KT2u8rnMxU74t2U4sRNKU6vyIy3heLJj/bjbJV8xFBrraxabZ9cCqe/Ed9zSuKWn7VWwUt1jQRBEMZgLY3oyO7WmgVlSUkJ5syZgy+//FLy7++99x5CQ0Pxm9/8Bn/605/ICIYgdMG+cDNS4K1FuCoht+j055djdLgVr88egTFXN/d6lGvIXlnbgM/3nfHbPABg7NXxHfrJIo9QnH954Iwm4eW0h6GypgHLMo62uib1iEmpBxSsDnnP3zJQsv6YvzLn3JDodc8Viy4PmiOrNQ0tEWu12r4mtwf28BA8ddM1KHc1ILZLKArOXcTybccltzcDuXtOqT5x5rW9sTTjqKbjUF0jQRCEMViyRzq6u7UmQXn69Glcf/31OHXqFDwej2SbD4/Hg7q6Orz66qs4dOgQNm7caNpkCeJKYWzfON2LUbkCbynBpa0zXDO2kCBEhll9ahVjbSF48dZBsIeH4LOcU95FddfIUDiiQnG2ql53KqBcpOWVOwYjtV88AHmhMW2o03DdHsv8rk2Mxd4TFX48SmB4IDUB9ZfceC3jqOa0yNqGS3jm8zxTz7VQLLE65G3OK/aJ2AsRiqPhvWNaXTO8yyovJqPDrfjl9QkYnRiL0ov1yC4o8z6o4O+njPwSfJZ72kc0O+1hPi1p/IGS0JOrT/ziANuDlXkT+iGpWxeqayQIgjABluyRjp4FwnmkbFhlmDhxIrZt2+YVkh6PB1arFfHx8WhoaEB5eblXaPL//+c//xlPPvmk394Awc6gQYMAAIcOHWrjmRBqNLk9GPnSFsPpqGvnjPH2M5QSXHeN6oVlOsxf5tyQgE9yfBfRcvVj0RFWXKhplP0SfWh8IjbsL5aMOgFQjUjJCY1AsnbOGIxOjMW1f9xiStrrlU50uBWv3DHY+xk3uT2KJjL8092FU5Mxd438tfDG3SMwZUiLABOKwveyipjmxj+oEF+z4vmYeT1yAH51QwLSBjhQUlWH8ov1iLWFwGEP1yT4sgvKMGvFLtXt+O8NgiAIwjzaYx9Ks7QBs6DcuXMnxo0b5xWLQ4YMwR//+Eekp6cjNDQUAFBWVoZ//etfeP7551FRUQGPx4OrrroKp06dIlfXdgAJyo6FGe6rr80chtBgi6LgsoUEwdWg35CGFV5Y8gi/ROXSVQHpyKrwb3rcKs3mtZnDMH1YDyzeeIhZmASK24Z1BwB8nuvftF8z+fBX13mjzwC7EOoSGoyL9Zck/8aLzh0L0nwEWHu5htTgoPzwhWUxwr9XtbQr8TkiCIIgzEFpTdMWmKUNmFNe161b5309ZswYZGZmIizMtz4qLi4Ojz32GNLT0zFmzBhcuHABpaWl2Lp1KyZPnmxoogRxpTE5xYm37hmBRRvyNbfC4Im3heJ3H+9XjJYEQkxyAMKtQXj9wREoddW3+hJVcntU+lt76NUINNeKNrk96B4d3tZTacXPR/bCueq6DiEoeUEz5mpfE56s42y1vnJiEpC3ZW8v15AaHkCyJY2WmmmtaVftbeFDEATR0ems7tbMgvK7777zvn7jjTdaiUkh/fv3x1NPPYVnnnkGALBr1y4SlAShA3EtVGl1vWx9mBinPQzg0C4Wy/xi3mLhMH1YD9PGbS9GOJlHSvDbf+cGpHVFmNWC2kY38/aVtY1w2Nuf0BUjJWik0oOMsuP4eR+B1F6uIb1obYqtZNojTiVvb6lZBEEQRPuEWVAWFRUBAPr06YNhw4apbj9jxgyvoOT3JQhCO8KnWU1uD97dUcjURP2FW5NRerHtehlKYebivcntQWl1+3h/K74tCshxPAB+O+ka/PFLtocKHgBz1+Tg9btHwGkPaxcPF3jEDrJSgsYftbGvbyvwvnZedj7t6Ghtii1n2qPVAIkgCIIgAA2CsrKyEhzHISEhgWl74XaVlZVa50UQhAQsTdSjI6x4ZUazoUl2QVmgp6iIuI2I3pQ6f0SuOgrxXUIU7celWLwpHwunDsTcNfv87nhrj7CCA1ChYijl9gALpw5EfGSoZG3s/iNmdAAAlylJREFUixvz/W60VFJZp7mNRntGywMbubQrpXOvNRpKEARBXBkwN4lsaGgAAMVUVyEhIS1Nsevr20cUgSA6A3zKmsPuey9GR1gxP70/9j43yRs9GNknBu1lzWfhmufDszmvGOOWZGLWil14fF0uZq3YhXFLMrE5r1hxHD56ciWKSQBw2MO9DrgsHy0fvSqurMMDqQmItfkapGm5Ppz2MDw8PhGcxLG97VxmDMbztw5iGu9EeY3kg4RA1TW2pTOwPygqrTE8htq5F0ZDCYIgCALQ2IeSIIj2gVrKGs/eExXMjen9jdvTPB++jYlaSp3U+wMQkMhVe8Vpb/mcperglBDW3sbaQnDbsO6YlOxAhatBsdUGT5wtBN/8fgJCgi2SPRyFKauskfEPsk/gg+wTrWrzOnpdI/9+dheWY2UAXX/X7TmJeWn9DEUOWc99R/+MCIIgCPMgQUkQbYDeVE+t+5VU1po5bcOcq65jSqn7w6cH8cL6PJytbvD+zREVilmj+1yxkUkAmDbU6f28+YcK72cVMhs18VS4GrAyqwj2cCsS4m24Y0RPfJxzSnGfMleD94GA2gON0YmxrdrEKCGuzROnRrclHAewd2tuhu/GNXFAt4AKSi11lHKwnvv29BkRBEEQbQsJSoIIMHrdE7XutzmvWLPQ8Del1fVYuuV/qil1UvV3JVX1nareTQ8b9hfjqckDfdqt3JeayGzUxMNvtzTjmKbjZx0v9RGQZlmfi2vzRifGaq4T5RGb/RjlF2N64x/ZJzXtc7aqHo+uzsGUwd3MmwgjRiOHaueeb+3CZwwQBEEQBOfxsD17tVgs4DgOMTExGDJkCNPgX3/9taZ9OI7D1q1bmcYmtGNW81JCP3KpnnyMUc49Uet+/nLINIKciRChjbVzxrQScm3xeSs9zMguKMOsFbt0jcu/P/49Adqumzk3JOIn13TF3A9zcKGWLUKqRKwtBOWuBvUN2wlS1wegLbtB7tyrfU8RBEEQHQuztIHmCGVFRQW++eYbpm05jmPex+PxeLcniM6IXvdErfsFyiFTK+1tPh0VqQjU5BQnnkjvH9AIrlILCSNRMn5fuX6JahHILw4U4+mbB+KVOwbjkcuiyAgdSUw6ZSKHWrMbWHtVEgRBEASgUVAyBjMJgpBAi3uiMMKgdT9Wh0xbaBCsQRafOrfocKspUR3Cf/C1a+KIU+/Y8IDOQ/gwI21AN+w9UeGdS3yXUN3jCmvzxLWapdX1qmnc/L0wOcWJ+elJmtN6OzIzr+3t03pld2E5tuSXSNZxqvWUZDX+IgiCIAhmQfnLX/7Sn/MgiE6PXvdErfuxbv/SbYMxbWh3nwWj2+PB7He/Y9pfiC3EgpoGN4D2HYmMjrAiLDgIJVUt5yjWZoWrvgn1l9xtODN2KlwNkhGnWFuIwl7+gX+YMeblrT6RPEdUKCJCglDT0MQ8llxtnrBf4vrc00xj8fdAQryN+fidgYT4CABsfVpZekrK9aokCIIgCCHMgnLVqlX+nAdBdGhY6pP0uidq3Y91e0dUWKsFY5PbA6c9TLOT6l/vHAYArRaxtpAguDSICn9zoaYRz07pi+TudpRerEe8LRT5xVX445fty7xIiWc+P4jKmsZWwr2iDVMzxWmhJVXaeg/zd8oLtyYrRsD8dS90FrpGhmFzXjFzqq9cVgRBEARBaIFcXgnCIKz1SXrdE7Xux2+vJApjIqyStVZBFg4v3Jqsq/ZMnCL3w/mLeG3rcc3j+Js/fnkETnsYpg11YsP+4g7XhkSuFUd7jgyLEbfiYK3N03svKG3fLSoUd4zside3Feh9O+0Cpz0MI/vEYPSfMjTvSz0lCYIgCCNY2noCBNGR4d0QxaKEr0/anFfs/V2QhcPCqcmyC1tAOkITZOEwbahTUTAI9+O3V6KiphFb8ksk/8bXnmnhxY35aHJ7vBFPq8WCv2e2PzHJU1xZh7e3F2oSk9Ygqh0zC48HWDh1IF6bOQxr54zBjgVpTEYv/AMPoOWe4ZG6h1i2XzRtEMb1u0rfG2lHvHBrMt78uoC596eQKy2SSxAEQZgLCUqC0Ima+yrQIrQAvi9kvuRYDnuYYsuQd7YXys7jofGJrVqGKG3P8/QnB5F1vNQ7PyHz0pLgiGI3VuHT5vjjP7Ymx9RegO2BxqaO+YbaqwyOjwzF9GE9MLZvnCajF96B1GH3FUFy9xDL9nwks72eKyUsHPDG3SMwKdmBVVnq970QDr7OsE1uD7ILyrA+9zSyC8okvxu04o8xCYIgiPYFpbwShE60uK9W1jYo9glcOHWgpJhkaQEibHavpWXIhdpGzH73O8n03C35JajTaFKzJb8EoxNj8eJGadEcCPxVs9neakG10F6X76XV9Vife1qXe6hWB1K17flI5qOrc1T7pYrblii1MeFTcP/ys6EoddXjXFW96fW6y2cNx5QhTmQXlGlyaBZHdOWMnl6anoIpQ/S1CdHaroQgCILomJCgJAidsNYdlVTV4c+bj8guUjkAizcdxk0pzlYLYpYWIHpahvjMT9Q+gE/j1SpEPt93Gk57eJvWJLoamvCzET3wcQ6bG6gaYVYLpg524hOTxjODqLAgVNc1tVuhyALHwaf9hx6RodWBVG172d6LUaGYNbo3EuJt6BrZXKcobJFS4WrA3DXNNcfCz0Qo2FKT4gE0PyBalnHUlIcT4nOmtQ5SWLcqd8+Xuxrw2JocPHwqEX+YkqxpfLkx1dqVEARBEB0PEpQEoRPWuqPyi/W6+k8C/msZIj6+sJ8ga4RTTHlNY7twS40INe9rra7R3a7EJAA8OO5qLMs4phpJa8+IWxobERksDsussEY++fuUP/b9qQn4PPeMb+sUGZFsDbYABgXlwqkDcV9qok/PydJqNmfdm1Mc+MXYBO/7YslqeHt7IYb2jMaUId2ZjqFWDqDWroQgCILoWJCgJAidsDpOsvYHlBKDWtskxNv0NZTnRe0/s4s6nOupmD6xEW09BUViIoIx5up4fJUnbYqkRkK8TTKSZiZdQoNxsf6S7v21uujqFRn+SKlkjXxKp4hacfuwHkhPdkgK0d2F5bpMc3j47xShmGTpOckTE2HF8rtH+MyLNavhufV5klkUUmgpB6B2JQRBEB0fMuUhCJ2wOk6yikIpMahmFiI01dicV4wnP9rPdCw5ispchvZnwcIBy2cON90EhT8X945NaNcGKxU1l3SLSaD54cHkFCd2LEjD2jlj8GBqgmlzs3DAExP76RaT0eFWfPir67BjQRr+MCUZ3/x+AmJtVqZ9hSKDBS0Oy2Yjd+wKVyNWZhWhsrZBUngZac8h5WIrNw+5/V+eMbjVvEqq2OZU7mpk/my0ZlYQBEEQHRsSlARhACbHSVZlI7Edq2jdkl+CR1fnMC8O5fg894yh/Vn45dgExEWGYuHUgQDMcyH1AJiS4sCewnLcNaqnYgpfdIQVk5K7mnTkwCHsH8pH0hbeOghv3TMCTtE1qCeT0O0B/nf2ou75XahthIXjvKJl74kKlLu0ReTEIkPKJVSrw7KZGDm2kfYcYhdbLQZcThkH3M15xVj8xSHmObAKQK2ZFQRBEETHhlJeCcIganVXmYfPMo1TelG6BkrWLORyat+kZAfGLck0pZ6uuk5/mqMavBvmqp1FWLWzCE57GB4an8icFqkEX0/4XlYR3ssqkt3OFhKEh8b3xby0fgiycPjyQDGeW5/nU/vWnpH7jKWuwYrLhipayTxyztAchaJDrtepEkKRIZfSeueoXm2WUsmazvl+ViHiI0N9vg/U0uSF8Nf0A6kJmCSRQsuaqiqut+TRY77FKgBZywH4hyMEQRBEx4YEZSfE5ZJOW3S73bBYKCjtD+TqrjbnFSsKHCFKizU50QoA72cVdoi6R3HApqSyDu9sL8TEgV0Nz591UexqaMI1ji7exfWUIU7clNJ8XrOOl2L5tuNM44QGcahvg76UF2oaZUWS+Br88kAxOK61AY4a9RrbxYjhr+Mmt0dTxFssMuQET3FlHV7beoxpTH+kVLKOKediy9qeRM7UR+s84iNDW4lJLdFNHqcGAajUhkUqdZcgCILo2JCg7IR06dJF9m/Jydqs3wn98Is2FoSpjHKIBYMWM462RK5PH/+rjMPGImJaERu/8Od1dGIs1uw+wZSi2RZikodFSGzOK9YVnTRKrM2KkX1iADRH0LRGfnmRoUfwSOGPlEo9Y4pdbFnak6i51RpJK9XaXoiDdgGolllBLUMIgiA6DyQoCcJPaFm0aV046+0VGUj4yIQfytgMIZcKGWTh8NL0FDy2Zl8bzYwNOSHBt7Aoqaz1iY4FknJXI258dRteuDVZU6RT7Myqp5+q1Jj+SKnUkrbKI3axZW1PYmQeSmmlWiK3RlxzzXifBEEQRPuHBGUn5OJFaVONUaNGBXgmVzZaFm1KqYxizIre+BuHPQxTUhzMKb+BRO6zmTKkOx4+dQFvby805TjThnbHXdf2wpP/zkVJFVufQCVibVaUVNUhu6AMI/vEYO+JCpyrrkNRaQ3W7j5p2JTJDPho3BPpSUzbS9X4mZGqWtvYhC35JaZHwpTSOZUQ13WytifRMw+1tFLW6KZc/aXWeVJrEIIgiM4NCcpOiM1mk/w91U8GFq2pcayLaDOiN0pwADiZNFXxduJFrAfA/akJ6BkdjuhwK3JPXfDXNA2h9Nn8YUoyhvaMaWXW0yU0CBfrtTWk/+boeUwZ7MCiaYPwyGrjKajlrkbM/1cuAPlU4raGj8at3X0SjqgwnK1SjqBJCRYzUlUraxp90kzNRC6dkwUz6zr1ppWyRjeNikmCIAjiyoAEJUH4idGJsYi1WZnbJrAuov3Zu41fOs65IRHvXI7SSUU+pNxZHRqb2bcV4lRIPlVUmJJ3U4oD9nArsn8oBdAcYck6Xoo3vi7QdKzK2kY8sjoHD6Qm4JYhTnxxwLzeiO1RTPJ4AJRU1WN+en8syzjKFEETfg7xXULhiArF2ap63ZF4cZqpHmEkdW3w44jTOUur65lSjc2u69STVkqmOQRBEISZkKAkCD8RZOFw+7AeTCmf0eHqpjw8ZixI+UVkdIQVF2paBK8wsjG8d4xi5OOpyQNFbSrqMXfNvnafijuqTzS+OHDGO+fFmw77vMfoCCsA+JyXNbtPYnRijO5jrmyHab960ZLmmRAfwRRBkzKYio6wGr6WlFp4qCHXskQ4b2E6Z5Pbg3d3FDLVNCoJVSXk9tOTVkqmOQRBEIRZcB6PVlN5oqMyaNAgAMChQ+yNrAljZBeUYdaKXarb3TLEieV3j2Aas8ntwbglmZpMQcTE2qx4aXoKbkpxKi5sWRe+/Jzac2SSMAenPQwzr+2NpRlHVbddO2cMxvaNU7yOAm0wxWIyIzcn/sqXS6Pl9wOko35v3tN8j6sJVbk56dlPDb3iliAIguj4mKUNSFBeQZCgDDxNbg9SX9mqasjitIdhx4I05oXc5rxiwzV5sbYQ3Dasu2TTdFb4xaiWHo5Ex2B+ehIS4m2It4UCHFB6sd6n/6nSQw0OQLeoUPz1zmE++0n1Q0x9JVOTmdCcGxLxSc5pzS1JhHMD5EWh2sMRPtIod78qCT8AhoSq1v0IgiAIQgmztAGlvBKEHwmycJg1ujeWZig3YpdrZeFPyl0NWJlVhJVZRboiHR2lDyahnfnpSXg8vb/iNko1eB4AdZfcmP3ud97fS11jyzOPaRKTHIAvDhTj2SkD8eRH+5n3E6JWW6lmeiV2axUjV9MINItwKQGuNCclV2cz6kQJgiAIwihk+0kQfiYhXtp1Vwyr2Q6/wDST4so6PLI6B18ymsbwERO9YjI6PBi09G2fOO1hmJfWuuVHk9uD7IIyrM89jeyCMkxKduDNe0bAYfet6bVL1KACLe1ENuc1X2Ob84pVH7SI4cXchRp90UnxOLsLy1v9jfU+VNqOr2mcPqyHt0WIFqEqRO9+BEEQBBEoKEJJEH6G1USHdTt/tg2ZuyYHr2M4pgzpLrtNk9uDRRuM9cG85G6JrlDOvS/jk+Kx/VhpwI+r5O6plMa5Y0FaizurLfRy5LC1s7EwmpY2oJuhhyKxthDFthesSIlCs+9XpWOxbGeGwDUK1VkSBEEQSpCgJAg/w9rzjdXl1Z8LRw+Ax9bsw1sWTjb9VWuaohQX6y8BaI5mCSNZTnsYFk4diBhbKL7KK8YH2SdUx+osopQXaPWX3EyC0uz3LeXu2eT2YHnmMclIYvHliKOwfi+7oEzx2uCjaf/MLjL0UMRhD5dNudWClCg0+35VOpYU8V1Cde2nx/2ZRSj6ywyIIAiC6DyQoCQIP2NGzzfhwu/bo+f9OV0AwKINhyRrsvSkKcrBAQi3BuH1B0eg1NXauGV3YRnTOAlxESgsqzFlTmYRGmxB/SW37N/npyehd5wN5RfrEWsLgcMe7n3v2QVs79seHoQLtU2G5zpvQj+k9ov3OffNQvI4VmYVorJWvo+qB8AfPj3ovVZYH3acKNf3eQnFXJCFk2x7oXUcMf7q0agmVHme/HcuFk0b5BVr/hK4LEJRzgyoROJhAkEQBHHlQjWUBBEA+J5v4nozhz1MdVG2Oa8Y45ZkYtaKXXh8XS4+zjnt7+mipKq+VU2W2bWbfLTKYuF8as2a3B68liEdFZOivYlJAIpi8uHxiXg8vT9uH94DD95wNW4f0dP73oEWASEnVzgAcbYQw2KSQ7OAmD+pv8/xN+cVY+RLW7A046iimOSpqGnE8szmz4o1StYrJlzXfAFfMTc5xYkdC9Kwds4YLL1zKGJtIaq1uSyi0Mj9KgcvVIVzkOJsVb1PranSfnoFrlwNtLDOVc0MCGhOX25yd4b8AIIgCMIIFKEkCAbMqCGSc39UGifQPfqEiKNN/qrdFKZIbs4rxqINh1TbrLQ189P7I6lrFzy3Ps+nfYXTHobaxqZWhjQ8HIAN+4vx1OSBsp87S4Rs+rDuWJlVpDrPm1Mc2JxXAsiMIxYietvRrMoqwry0JKYoXJfQYLy3o1B1TPF7l0rJBVoMcAAgPCRINQ1Wbhwxeu5XOfjvj/pLbjyR3h9rd5+UTQ2Wcm7lBa44osj6XoTz2FVQhqc/OajqGhsZZjXkdksQBEFcOZCgJAgVzKwhEi5+1VCKEAQCcbRpS34J874Pj0/ER3tPodylHuFa/MUhhFstcLuBx9YY660ZKBLiIzBliBM3pbQIjvguocg/U4k/fnlEdj/WRbiagLCHhzAJyl+MTcD0Yd2ZhIiRCPSF2kbve1KrbbxYf8lbQysFL9dev3sEYmwhsmKOF0fZP5QCaL6veOdZ8fuNtVlx+7AeSNfYc1XL/SqGF5EZ+SX4LPe0z70Qc9kJVw6p68SowGVt88MfmzX12p813QRBEETHgAQlQSjQljVE/nRzVSMmItinJmtzXjGTgAGAW4Y48YcpyXjypwMw5uWtqg3oy12NeOSyAOko8GKbFxyb84rxu4/2M39eLItwJQHR5PYw19UFWTikDeiGf2YX4UR5DfrERuDesQkICfateDB6ve04dh6jE2NlxTAr3aJCfWoIpdicV4ynPz3oEwlevu04oiOseGXGYB/n2bZwJVUTbxUyEWwx4utEr8DVl+nAtrUeMyCCIAiic0GCkrgiYUlhbeuG4m355H/xtBQfgxYtkavvi8rR5PYgJNiCP92egkcvp1CqLU87SiUWByC7oBRujwelF+tRVOrSbFTEugiXExBajGOkxM27OwpbRSiNXm+vf12AT/ed9o6bNqAbxrycwRSlFvLXO4chtV+87N+V0nIv1DQ/nHirDc1izExTN0Os6c10GHt1PD7JOa2YvuzUYQZEEARBdD7IlIe44hCb3MxasQvjlmR6TTB42qqhON9A/tjZi6aOq4U/fnXEez60Rq5KqurxflYhmtweb7Qqxhbir6kGHA+Av2Uex+x3v8Pj63I1iUneCEfLIpy/HtbnnkZ2QZnXBGVyihOv3z0cMTbf9EmhcYyc+UpxZR0eWZ2D1zKOecczQ7wITV32nqjQLCYBSL5XnuYeqIdUx2grsxiz0tT562RknxjF88GC1vuXP/aYy+nL/O+kqG1s0pQKTxAEQXROKEJJXFFoSWENRENxcaS0wtWAxZv0pQqaifB8KDmWyrF402GfKFhtQxPm/3u/H2bacdDTIqaotKaVgQtfvws0n2ehaIu1hWDh1OZzziJulmYcxdrdJ7Bo2iBMSnbAaQ8zdO3xx3rms4OYOlhfhHD5tuPe1+Ja5d2F5UyGTcWVdXg/qxDxkaEBTXk1I02dn+W0oU7c+Oo2w7XbWr6fxNco/0BInF7MU1nTSO1DCIIgCBKUxJWD1hRWfzYUB9hNMtoC4fn4y8+G6hpDKEoddu1tIjob9vBgvHLHENXaQLVrouRydFGKClcD5q7JwZuWEbCHhzBdWyWX21S8ec8IvHBrsi6XVzHlrkb8c9dJw+OIH/RoEUeLNx32vtZroqXV3VnPw6VYm9XnoYDDHoZpQ514Z3uhKbXbWr6fxGZNTW4PIkOt8MhERgOR+k8QBEG0f0hQElcMWlJYx/aN81tDcaBt24Gwwp8PcIAjKlRzKw/hYvOb309gaupulJ/0j8PXR9ncKQMNx3GYlOyQ/TvrNaH0d+E5nzG8h6b5vbgxHzsWpGF+en8szTiqaV9/IRYseh/e6BFietydtc7PaQ/DN7+fgL0nKryidWSfGNz46jbVB19pA7r57Ccndkf2iYGFA5SyZTkAHzwwGtf3i/fpR6rFFZbahxAEQVy5UA0lccWgNYXVHw3FgbZvB6KV1btOoJzRlVIMv9jce6ICL9ya7Pf33F7FJNDs7ClXb2vmNcGf89e/LtC8z+7CcsxL6wdHVKgJMzEH4dxGJ8bqmht/XllrK+VqT4U1olKMToxFrIZ64RduTUZIsAVj+8Zh+rAeGNs3DntPVDA9+BrzcoZqHTgA7D1RoSgm+TGDgyw+YlLq/StB7UMIgiCuXEhQElcMelJY+Roih913X6HxiVbash2IHr7KK0GDjjpKIfxiMyIkyIwpdVjkFt3t5Zo4V12HIAuHRdMGgYO8GUtbIJybHlhNtNRS4wF5YRpk4TC8l51pPg+mJkh+f7AKM7HhkZzY1fogTe/Djfgu7echBEEQBBFYKOWVuGLQm8JqtKG4GH8/yY8KC0ZVnXzj+LZAT2uNzojwYYWwPu/Y2eo2nFUL/PyM9pL0B10jw9Dk9sAeHoIHUxPwr+9/xMX6Js3jSN1/ws+itLpeU2q8eJx9P15gmke6TPqz3rReuXpGrQ/SdD/c6CgpFwRBEITpkKAkrhi09O6T2tes+iB/NwJfdOsgvPrf/7ULIcCL9LW7jRu0mMlvJvTDyp2FioKEAxBqtaCu0Vh0lkfYLqQ9GjJFh1t9HqbwD1KWbjnq47waaPhrqMJVj3FLMn3OWZfQIPS9yoar47tgoCMKf9p8RHU8sahfnnkcq7IKcaFWW1q3lDDdXVjO1ColzhYiW3ut9uBLCSmxq/VBmt4HXqUubTXWBEEQROeBUl6JKwp/pLBqhV/g+Sud0BkdjhduTW7zdEX++DOv7a3Z0MdfcABiIqz4917l6BY/9wnXXGXasacNdSLIwumqTwsE6QO74osDZ3z6HQZZOKT2i2/jmTWfu7lr9rU6Zxfrm7D/VBU+yz2DlTuLEB1hlb3uxT1AN+cVY+RLW7A046hmMQk0p3iKe0SyirFpw7pjd2G5ZH9JpdptVoTz0FoLrveBl78flBEEQRDtlysiQrlz5068//772LFjB06dOgUA6NmzJ8aNG4f77rsP119/vd/nUFVVhc8//xyffPIJjhw5gjNnzqChoQFxcXHo378/xo0bh7S0NKSmpiI0lGpRtKDV2t/sFFat8As8M9ozCBFGGoIsnGTKYqzNijtG9MAXB0pMFTQxEVaEBlt8hCPfgmCPSs1aIPGg2RxHDX7ukaFWfJV31pRjb9hfjCd/OqBdGjJxHPBxzml8nHMagK+b6ejEWERHWCX7EPobpz0MC6cmY/Em9XN2tqolAqeWgbA5r1j3/ccBiI6w4sl/5/pc7057GGZe25tpjPW5p7Eqq8hnX6F7rFzKcZwtBGWuBtXxxeJObjxxmxBAe4TUiNs1QRAE0TngPB5Pe1vbmIbL5cJvfvMbrFy5UnG7Bx54AH/7299gs9n8Mo8PP/wQ8+fPx/nz51W3/eijj/Czn/3ML/MYNKjZzOLQoUN+Gb8t0GPt3x4wsqCVgl8wi6OsYrE9sk8M9p6oQElVc63Y8sxjqDSh3jI6woo/3ZaCGFuoz7He2HYcy7Yaq52MjrACQEAETXS4Fa/PHoExV8chyMKhye3ByJe2mHbshVMH+vRH1Et0uBWVtY1+E6bC6wmA6Q8/lIizhWD6sO6YlOzA6MRY7C4sx6wVu5j25cWe+OGG8Duhye1plTrLilioiv8GAPYIKyprtH02Wu7fG1/dppq+umNBmuQDMtaHb3wUHVAujZSbN0EQBNExMEsbdNoIZVNTE2bMmIH//ve/3t+Fh4dj0KBBCA4ORn5+PqqqqgAAK1euxOnTp7Fp0yYEBZnrQvn444/jb3/7m8/vevXqhV69eiEkJATnzp3D0aNHcelS+zJR6QjI9e3T03PO3wgXcvG2UCzakG/q+FKRBsC39nNzXjFufHWbX1ItL9Q04rE1+/BgagLSkx2ocDVg9J8yTBFiPx/ZE0/fPBDvZxWaIsaUuFDbCAvHeRfZQRYOr8wYbJqgOlFeY8o496cmYpmOXpHiSKNcf0Le4GXRhkMIlNfr9KFO3Hltb6+Y59FS08dHoD/81XWwcJykcDLiqOuwh6G2sUnyuubPGSd4LY6SyokzOUMdqdptvXXgcuNJIRfRFF8vct87BEEQxJVFpxWUCxcu9BGTc+bMwSuvvILY2Oa0HJfLhVdeeQUvvfQSAOA///kPnn/+efzxj380bQ7PPPOMV0xyHIdf/vKXWLBgAQYMGOCzXW1tLbZt24bVq1cjJIS9h9mVjJq1v9TizB9zYH3a7w8Dlicm9kPiVV2a7fo9zaYY2QVlkvOQE99m815WEd4TpPKZwYpvCzG8VzTuS03Eim8LUVLl39rDb4+dQ9bx8wCaF9+Tkh14654ReOrj/aiq0+4qKqRKR62eED4CNS+tH65xdMGiDYeY6lMdUaFYNG2QT6p3aXW9okD3AJprX1lTMqVYv78Y3x4vw0vTUzBlSItA0VObV3qxHtOH9ZD8m17TmfnpSRjVJxaz3/tOdhte0M5PT8K6PT+K0s2Vz42Se6wQLemrRpAqDeAzHNqiVIAgCIJov3TKlNfTp0+jX79+qKtr/o/tvffeiw8++EBy24ULF3pFZXh4OI4fP47u3bsbnkNWVhZuuOEGeDweWCwWvP/++7j33nsNj2uEzpTyml1QxpQGt3bOGNPcWYWwptr6U8g57WG4a1QvvL+zyMdURDwPIyl+7YVYmxV7np2E5ZnHsVRHZM4I0RFW3DWqJ97eXhjQ44qRSi/MOl6K2e/KCxyeD391XStznfW5p/H4ulxT5vaLsX1wc4pTNSWTlYfHJ+IPU5qNZPjrV8uYSvc963eHEF7IP3XTNZj/7/2q2782cxhuGdLdR4z991AxVu08wbSvnBgWorV2nCAIgiDEmKUNOqXL69/+9jevmIyIiMCyZctkt124cCF69eoFoDlS+Nprrxk+vsfjwUMPPQReq//ud79rczHZ2dDarNtM5Fw6xY3F9TYIZ6W4sg7Lth5r5VApnofeFD/eFfPDB6/DazOHYd6EvmZMWxflrkb89b//Q2HpxYAf+0JNY5uLSUDaibj0IlsEUWo7M105b05xYmzfOIQEW7yOokZ4e3shvjxwBoCvS6kaYidXKfS4LPPRw3LG6GvXyDBveun0YT0wOjEW6/cXM+/LgnB8XjyLXWeN0OT2mDoeQRAE0XnplILy008/9b6+8847vWmuUoSEhOD+++/3/vzZZ58ZPn5GRgby85tr5Ox2O55//nnDYxK+aG3WbRZqqbZAc6otHz1oi6igeB5GRPULtyYjNSke04f1QGo/81po6OGNrwvwee6ZNp1DWzE/vT92LEhrlc5YVOpi2l/qPuCFlRKOqFA4ouTFl5SAm5zixOt3D4fRYNlz6/O8IoZP81SaL0sNIWCsLUdsl1BFMSonaJv7U6qL0S6hQbrcUjfnFWPckkzMWrELj6/LxawVuzBuSab3oVJbj0cQBEF0bjqdoPzf//6H48dbmnBPnjxZdZ+bb77Z+/rYsWM4etRYSt27777rfX3HHXf4zT32SkYtysASqdCDmkgU1kH5IzrKinAeekR1dLgVT6T3R/0ltzc6MToxFo4o6jXXFvwjuxAb9p9B1rFSZB0vxfrc08g6Xoo136mnUMrdB0EWDtOGKtfbTR/WHYumyUcHPWjprykkxhYqafajhXJXI3YLWs5MTnFix4I0rJ0zBg+kJiDW5ltvrqWXrFw/WjUcUWGaejrysH4X6ClAYc2YkEMcifzygLHxCIIgiCuPTmfKs3+/b33L2LFjVfcZMWIEQkJC0NDQ4B2jf//+uueQkZHhfZ2WlqZ7HEIePsqg1+1QL1pSbdtDo+9z1XW4ZUh3OKLCNJnZNDY1+dQq8nWZs0b3DngNI9Esrub/K1fXvjOv7S3bQmKDShrmhv3FeGryQDw0PlE27fed7YUY3jvGR8iZ9TBlS36JTy0kn+Y5tm8cnp2abKiGUGg6U1JZi8WbDqPC1aDYjkOpx6uSKQ7rd4GroQm7CsqQmhSvvjGMm5NJ1YJbOGk32kCZnREEQRAdj04nKA8fbnEtDAkJ8dZHKsFvV1BQ0GoMrRw/fhzl5S1P1YcMGQIAOHjwIN5++21s2bIFp06dgtVqhdPpxPjx4zF79myMHz9e03FcLrZUNyFutxsWS+cJSqu5HU5KdiC7oMxU0wotqbYj+8Qg1hYim+rGAYiJsKLcj/0V+VourULQ1eD2+ZmPTtyfmmDyDAl/kxAfIfl7lpTs4so67PqhTFV48iKDH/fYWXNqXdfnnsGzU6UfDLG2wFBCOEZ4SBDzAyopB1Sl75fRibGIDre2qneWYu6aHLxyx2CmSKuWjAnxuZIzDFOKLLM60RIEQRBXFp1OUJ440ZIC1rNnT3Acm4Do3bu3V1AWFRXpPv6BAwd8fnY4HFi0aBFeeuklNDX5thyorKzEkSNH8M4772DatGn44IMPYLfbmY7TpUsXXfNLTjZumNGekFvYbckvaeVsKuXCqhU+1VatsXiFqx43vrpNUUwCwEu3DcbiTfmGXTGliI6welMd5UQFK3x0Yv0VWsPYkYm3hUr+njWKmF1QxiRalmcex7o9J02tGy5zNQRMvGhtx6FF0AZZONyfmoClGcdUt71Q28jcR1evOZlRw7C2TOcnCIIg2h+dTlBWVVV5X7OKMwCIioryvq6urtZ9/LKyMp+flyxZgr/+9a8AmntRJicno2vXrjh37hzy8/O9TrAbNmzADTfcgJ07d+oWi1cq4oWd3JN3PsrGWmcldyy1VNtpQ52Yu2af4mJNuEi1WIBHV+fomo8S91+f6I2YmJF+60HzAj/WZkWFq1Hx/TntYVg4dSDsESHILigD4EHxhTp8su+04Xm0JaHBFtRfcqtv2I548qP9WDTNt42Mtigim+xgjYDz98mgHlHIO12luC0QWPGiNfKohXlpSVi1swgXGDMSWFJL9ZqTGTUMaw/p/ARBEET7ofPkP15GmAoaFsb+H73w8HDJMbRSWVnp8zMvJidNmoRjx44hLy8PmZmZyMvLw/Hjx/HTn/7Uu+3Bgwcxd+5cpuNcvHhR878BAwbofl8dBS0urHqRM/Rw2MPw+t0jsGF/seISPNZmxTe/n+Bd4PPjxdqsuuckJjrCinlp/bw/62mVIMftl3vkyY1lCw3CwqkDMWVId6T2i8fvbroG8yddg6yCUhOO3rb4W0z6oyrtbFWLmYrQvXP5tuOK+/HGVmOvZqvnY4U3z3l2Clu2RKDFi7gdh1m1gkEWDq/MGMz0GQtTS5XQa06mV6T7y+yMIAiC6Nh0OkHZ2Njy9Dc4mD0AK9yWN+fRA9//UsiNN96ITZs2oW9f3z5+V199NTZt2oQJEyZ4f/fPf/4TR44cUT2OzWbT/K8z1U/KoaWmyAhCx8nXZg7D2jljsGNBGmJsIapP/stdjdh7ogJAi8Ni/SU3XrtrOGIizBGVr8wY7LMQNtIqQUx6sgNv3jMCdpm51tQ3Ye6afT5ukLsLy1FSxdYzsTMSGcb2XRQjci41A/7hxtOfHsQjEu6dUgjrBsf0jVNtL8LCvAl9vffJ5BRnmzk1tyX8w6PocLb7XE34Kd3XSuZkekS6P83OCIIgiI5Np1MYEREttWJS4k4O4bZG2nxI7fvGG2/AapVeQAQHB+PNN9/01np6PB68//77uo9/paO3pkgPUpEMLccX93q7d+Vuw3WUsTYr3pJJ6dXbKkFMhasek5IdCAsOkvw7/x6e/SwPn+WcQnZBmSaH2c5Idd0lpu2mD3W2aoehBgeo7uMBmFMtAd8WHEEWDgunDtQ0JymSukX6RPz0iiEjiFtkGMlU0Htce3gI/j5rONN+LMJPKWNCLr2fJWNBfNq1tGUhCIIgriw6XQ2lsP6wtraWeb+amhrJMYwcH2huSaJmhHPNNddg1KhR2LNnDwBg+/btuo9/paO3pijQxy8qdWFZxrFWArLSoOPrwlsGKS74hDViW/JLsDKrSPMxFm86DHtEiKJI5Ost5/+7uY2Pmem8nZlVO9X7Sgrh1/y3Deuu67MUM29CX6T2u6pV3WCMjLGPFqTuDa1GOErwtaFytY9SLTLMMOpSQ+q4jqgwREdYUVkjXYssbFPCgtbaT5Za8OWzhiPGFmp6LSlBEATR+eh0gjI+vqXep7iYvQFzSUmJ93VcnH5HQeHxgWZBycKIESO8gvKHH37QffwrnQpXPSycvPW93EJNbTHKCqsL7JrvTijWeeolniG6JezlNzoxFk9/cpCpnQFPcWXdZaMddspd/muN0llQum7l4IWXPTzEFEHJRxHFGInoq4kjM4xw1MSiP4261OYlddyzVS3fD2b10dXaRsVMMU8QBEFc2XQ6QXnNNdd4X5eVlaGmpsYnDVaOH3/80fvaiHnNwIG+qWGs4lS4XUVFhe7jX8lszitWdVcFWi/UzIxcsDz5H9knBl8cYH/YoYVfrNyNqUOceG3mcKbF6OQUJyJDrZj93ncajxSYVMHOAotYZBGTz04ZiOTuUSi9WO8jvJrcHsUHGazIRdj1RvRZxZGRnpJqYvH1u4dj8abDsg9wOLA5qmpFzSCMQ7N5Vmiwxae+OJCCzp+utgRBEMSVQ6cTlGJBl5ubi+uvv15xn9OnT+P8+fOyY2ihX79+CAkJ8Rr71NezGZEIazi1uNMSzbD0VbNwwPJZvpEIf0QulJ78TxvqxNvbCzWNpwUPgC8OFCPzyDn8vzuHtlosjuwTg70nKnx+BtfszOqqb1Idn2fs1fH4JOe0X/pnshAZFoTqupb5sjaNbyvMKtXrGhWK1H6tXVeVHmSwomSAwxJ5t0dYERYc5JMK7W9xxCLanlufpxgh54263s8qRHxkqGmiisUgrKKmER/+6jpYOK7NBJ0RMU8QBEEQQCcUlKNHj0ZoaKhXyO3YsUNVUH777bfe12FhYRg9erTu4wcHByM1NRXbtm0DABQWsomHoqIi7+tu3brpPv6VCktfNbfH10WTtcWI1shFk9sDe3gInrrpGpS7GhDbJRRdI0PhbvLg1+v2MY9jhJqGJjyyOgfREVYfMxZxpExPmqXTHoYxfeO8AqYtEIrJWFsIfjGmD5ZtVW8aH2iiI6yYMbyHKemoAHDsbDWyC8p8RAefrl1/yY0n0pOwdvdJXY66wiiiVAq4WuT9lRmDTYt2saags4g21nTrxZsOe1/zGQpG3g9rmnDpxXpMv9yKhyAIgiA6Ip1OUHbp0gUTJ07El19+CQD48MMP8dRTTynu8+GHH3pfT5w40ZDLKwDMmDHDKyi3b9+O+vp6hIbKm1o0NDT4GPGMGTPG0PE7E6wLSz3uriwilG8xwvoEXyp9Nvpyew0tLptmIT6mWDxqFZMcWoQHH4nVWoNpNhWuBizbekzR5KSteH3WCFgsnGmCcvm2AizfVuAVPG43H4FraXXkiArD/PT+SIiPQHyXUDz571ycraqXPS/iyL1SCvjrdw9vFfETRyGNRru0pKCb4dYsRXFlneQDGbl5SH1PtbVBGEEQBEEEik7XNgQA7rvvPu/rAwcOYOPGjbLb5uTk4KuvvpLcVy933XWX1+21oqICb7/9tuL2K1asQGlpS9P36dOnG55DZ0DcVmPWil0YtyTTp78hj57FG+tidEt+SavfSbUf4NNnxSL1Qk2jKWLSFiLdpiNQREdYW6UAT05x4vXZbMZT/oJPbeQxkizYJTQI3SKN94LkeyiOuWx8FG1Sf1EeXvA8tibHR0wCQElVHZZlHEVosAWp/eKxaNog75ykWD5rOKYMaRGTUtdwyeXjPfO5r5iMtYVg4VTzUlqVjv/o6pxW9z7rfR9rC9F1XYjvW6l5yH1PVbjqr7g+mwRBEMSVSacUlD/72c8wdOhQ788PP/wwjhw50mq74uJi3HPPPWhqak6fGzZsGO644w7JMb/++mtwHOf9p9Qr8qqrrsJvf/tb789/+MMfkJmZKTvuggULvD8PHDgQt99+u+L7uxLQurDU0ySddTG6PveMT786qQVk6iuZePrTg36NjgWqZ54cci1NxlwdZ7pg0grfZ/GJ9P6t+vGFBrN/zf35jiHY+Yd0rJ0zBq/NHIa1c8bgjbuHwyka02kPw8PjE8EhcD0UteBBc7p2k9sj26fQaQ/DW/eMwJQh3QGwpYCLBVaFqwFz1/jej3p7PbKmoAvHY73v/+/WQabcm+J5KH1PzV2zD9OGOr3zEM8LaNtrhCAIgiDMotOlvAIAx3F49913MX78eNTW1qK4uBjXXXcdHn30UYwfPx7BwcHYvXs3li9fjrNnzwIAwsPDsWLFCnCcOf9xX7BgAb766ivs2bMHNTU1mDRpEmbPno1p06ahW7duOHv2LDZu3IjVq1fD7XYDaK7fXL16NSyWTqnzmdFT28jiripevI1OjEWszapaY1XmavCmvcqa+Cj0ZDSLuktuvx9DDX+4YZpJQnwEdixIw+7CcmTkl+Cz3NOaWpbE2EIlTUpuSnFKpl4P7x0j23ZhUrID2QVlyDp+vk3SnYXp2ixuniwp4GLELqlb8kt0Oyaz1EOKU9BZ7vtpQ53441eHYRb8PHb9UKZqCLRhfzFev3sEFm+i1hwEQRBE56VTCkoAGDVqFD788EPMnj0btbW1qKqqwpIlS7BkyZJW24aHh+PDDz/EqFGjTDt+REQENm7ciEmTJuHgwYNwu9345z//iX/+85+S20dFReHf//43c9/KzgxrbePyzGN4PL2/93da+6oFWTjcPqwH3mOob/sqrxhutweLNig7yfqb0GAO9ZfaZgZCN8z7UhO9YmR3YbnfBdM91/XC6u9+VN2ua2QYgiwcKmsbsDKrSPNnxadBS9XESdUGioVafJdQwANsPXIWz3yW1yodNdAI07rV3Dz11iPy18XyzGNYlnFMk2Oy8DwfO3uR6Xjieaq5Kr+zvdAv92x2QRmTAI6xhXgfclBrDoIgCKIz0mkFJQDcfvvt2Lt3L379618jMzMTHo/vsoLjOEycOBF///vfDfWelKNbt27Ys2cPFi9ejLfeegtlZa2bwQcHB+POO+/E4sWLcfXVV5s+h44I68J2aUazq+e8tCTv4kxrX7X0ZAeToPwg+wQ+yD7B9gb8yNCe0dhd1LZ9ShdvOox3dxR6Rbq/jFGEjEyIw9Yj5xXbVjgupzSztJCRo2tkmLSxUrgV96cmYl5av1bXEi/UNucV43cf7dcc5fMnWgxfjJrDyAl4uV6PUudZ7zyl7vuRfWJw46vb/PgAiG3kc9V11JqDIAiC6NR0akEJNNckZmRk4Mcff8TOnTtx+vRpAECPHj1w/fXXo1evXkzj/OQnP2klSFkIDQ3FSy+9hBdeeAHbt2/HDz/8gPPnzyMqKgp9+vTBjTfeiKioKM3jdma0LGyXZhzD2t0/YtG0lgik3OJNKurEG6a0RUqiHn6dloRfr9unOl9/vyfeFOaNu4cHxKWya2Qoc0qzWuRIDqc9zFsTKL7TL9Q2YmnGUazaWYhXZgzG5BSnz/VUVOryPuBoL8TZQjQZvqj1mlSjUsHpV5yuKpc6roTwoYEU4vte73XAOo+xV8dj+bYC1e3JxZUgCILo7HR6QcnTq1cv3HXXXW12fKvViokTJ2LixIltNoeOAr+wZV0MllTJp9TxyLUiuGWIU9Zspr0RHWHF9f3i8cqMwXhEof/j/PQkzEtLwvLM41iacdSvc5q3dh9emzlc0+elC09LauOiDYd8+ix2iwrFommDvJ+93ojpLUMcWLxJObJ5oaYRj6zOwcPjE7Fhf3G7ikaKWTw9RVNapVI9olmcq67TFUHWY2Ljj8i5cB5j+sYpCnA1AUwQBEEQnYUr2/2FaJfwC1utiB0geeScGIsr67DiW/PqqzgAMRFWdIuU7zlqhFdmDPb2f3zrnhGSzqNv3TMCj6f3R5CFw6M/6YvocP+2GnF7gF+v3YeUHv6Nspe66gU/+QqK+ktuuAWfu96I0Cc5p5kF4tvbC9u1mHx4fKK3FYgW5BxheRdfObdSVrpGhuky/3HYwxQfGMkdy2yE8xB+T5GLK0EQBHElc8VEKImOxeQUJ+an92eOsEk5QALKjrFmwi8ZX54xGJGhVsx+7zvTxo6JCMaM4T1hDw9Bk9vjFZVKtaKb84rx9KcHcaG2ybR5KLEl/5xfx+drG6XSJCtqGvHYmn14+NQF/GFKMrN7rxit2xvhsRv7Yu2ek6iQiY7z0a2FU5NbOYTG2UKweHoKLBa0irrH2qx4aXqKtxWIHuSurf/kleC59b5GQ7G2EJQxGg/xbXu+OHCGaft5E/oiqVukbhMbtRRe7vL/qFUyPDGxHxKv6iI5D61GYARBEATRGSFBSbRb5qX1w9rdJ3zSG9UQp7npiYboQbiAXJ972tBYHJoX6tOGOrF+fzHKXQ14L6sI72UV+bRgkKsV3ZxXrJgS25HghdXIPjEY/2dlg5W3txdiaM8YTBnixEvTU/DYmn2BmqZmUvvFY0gvOx69/DnJ1YROTnHiphT5BwdSRjR7T1Rgfe5pSQEkVUcsFmpS22zJL8HiTfkiMWnFtKFOrNrJZlbFR+tYI4ep/a4yZGSj1lLEAzDl9V53dbziPFiNwFjOPUEQBEF0REhQEu2WIAuHRdMGaTLvEC9WA+FAunDqQJ82GlpS7eQMZn42sodkuwOlFgxA86J10YZDmubfXhEKqze/Ps7U63Ph+jzclOLAlCHd8fCpC3h7e6F/J6mTUlc9pg/rwRTdUnIIFf5tc14xbnx1m2wPSLk6YuGxJB1uZQyeKlyNzGJyfnp/7zFYIodm1R4qRRBvTnFgJYPDM8t3iJqLK8u5JwiCIIiOCglKol0jZ8QiRm4R6m+HxZgIq4+YBNgXzFLpjA57GJ69eQCeV2mYLm7BwLO7sFxTRLc9YeGaazJ5YmxW3D6sB/5XcpHZQbXM1eBNe/7DlGQM7RnTKk2zPcBfl3LtLpSijFLIpQPzDyAeGp+o+oACgOQYcm7B/HaWy2mjcg99HFGhmJfWz/uzWuQQYK89ZIn6yUUQdxeWMwlKo98hap+N1tpQgiAIgmhvkKAk2j38glDOtVRpEWq0FYIaFTWN2JJf4rMgZF0wS6UzVrga8Nz6g4r1fHL1ooC2iGx0uBUXFFo9BIKFUwciPjLUR0htyS/B57lnvKm+WhGegylDms/x0i1HsXzbcRNnrg+pBx9aooxSKNUJ87+TM5/if9cc1eZ03SP8QwC5a33RtEGSIs9o7aGWqJ9UBDEQkVK1z0bp4RBBEARBdBTI5ZXoEARZODyeniTpbqrkAKnkxGgG/IKwye1Bk9uD7IIyrM89DXt4CF6/u7Vbpniu/EJ3+rAeqKxt7oHIag6TdbwU63NPI7ugzOtuqyWa8vrsEZifnoTocCvzPmYTHxmK6cN6YGzfOIQEW1BZ24BVWUWGIoricxBk4ZDaL97oVJkZ3ita8vdq0Tc5N2I+krU5r1hyXJY6YQnzY99jVNUzpRTL8UBqguq1LmZyihM7FqRh7ZwxeG3mMKydMwY7FqQxi0k950pIIFxa1T4b4cMhgiAIguioUISS6FCwGmCI95GKhjjtYZg21GmonyC/IFyeeQzr9vzYavyFUwcixhaqms6ox41WGHHjIzOTkh2whwejsvaS4r5OexgqaxqwLOOY3x1wlRCKP7MceStcrVN+zYhUR4UFo6pO+bwCwFOTB6CytkFT9M1IJCsQdcJqTEp24NmpyZpNZ9RqD6UwM+rnb5dW1s+mPXyGBEEQBKEXEpREh0PPIlRJiD41eaD390WlLqzdfVJzHaJUjV9JZR3mrtmHN+8ZgenDeiimM9rDQwy50Qpr5dTEJABv/WZbiUmpdEKzHHkXbzqMmy674PIopSGzUlV3CbG2EFS4GlRTJIMsnKYHH1oiWeJr3991wkqI37MRV1YetbpII+dKCj0PqVhh/Wza8jMkCIIgCKOQoCTaBC0W+mbZ7csteMW/n5eWhOWZx7Eqq9BQjaEwWuJ2A3PXyBtz3J+aoPs4wmOt+JbN1fTYueqAtFNRYua1vfHFgTPez9SsKI2cmJCLRmnhtmHdsSqriMlMRovAYn3vWcdLW13/oxNjZZ1YA4HRtFAhLHWR/oj6mSWGxQTS0ZYgCIIg2goSlETA0WKmEWi7/Sa3R9b8Rw98tOS59XmKKXrrc9mavasdS61JO88qHWY3ZmILDfY5x057GGZe28u08eXEBB+N2vVDGVbvOoGv8ko0jTsp2YHRibGmp0iyRqiWbzuOT3JO+bXdBGsE18IBy2cNN20erG6oHSnqZ6ajLUEQBEG0V0hQEgFFi4W+Xrt9vRHNzXnFqu1J9KJkMuNBc7uLWJsVFa7GgKShtrW768V637Tc4so6LM04ZlqkTUlMbMkvwaIN+ZpMaIyks7KgpcZTfP3vLiw3LTo5Pz2pVS2wHG4PEGMLNeW4WuoiO1rUz991mgRBEATR1pCgJAKGlkUjLr/WaryhN6IpJ14Dye3DemClRDqlv4gOt6KyVlrAcgBibSF4bupAFJy/iOXbCgIwI+OoiQk9nzNrOquR1GwtNZ7i69+MVGH+vM1LS8K8tCTmNitmpSlrrYvsaFE/f9ZpEgRBEERbQ21DiIChZdGox25fbysBs9xFjZKe7MCb97RuNeIv7k9NBCDfMuGPt6fg9hE9kdrvqoDMBwAu1DTiZyN66tpXTUzo/ZzV2l8AzdfeuCWZmLViFx5fl4tZK3Zh3JJMpvYVPHwki+XzF17/ZqV28udNS5sVs46ttS5S7lyxfFZthbBF0Ni+cSQmCYIgiE4DRSiJgOEPMw1+WyOtBMxyF9WLVDrlroIyzF2Tw5yaqjWq6bSHYV5aP1zj6KKaimdGyw0tRIRaYOHkeydyAKIjrAgNtvikJ6ulEOr5nBdOHYj7UhMVF/9aU7OVIpl8JGvplv8xRYXPVdfhliHdVVNApc4Xj1QEP9BppXrqIinqRxAEQRDtAxKURMDwh5kGv62RVgJaBCwHIMYWolgTqQdxOqXFwmmqc3TYwzDz2t7MZkL88VgW5Wa03NDCB9knVbd5ecZgzWJC6+fssIepikmtDzJYUrKDLBysQUFM8+waGcZk/CI8XyWVtSh3NSC2SygcUdLnLdBmMnoFrL/cWQmCIAiCYIcEJREwtC4atWxrJPpZVOpiewNoXli75UJnArqEBrcynpGCA/D63a1T9Fjfzy/G9sHNKU7veVi356RiJLHZmdP3eCyLcjNabpiBeP4sYoKPCB47e1HTsVgEk5YHGZW1DUyRzM15xVim8mBAfP2zGr9oEV+BNJMhN1SCIAiC6LiQoCQChtZF4wu3JuOR1TmSY3lE2+qNfm7OK8bSjGNM+/IikSVy+JNrrsIXB9Tr5zxojniqzVOOOFuIj0hQiyQunzUcU4aoCwGptExhNHPd7hNYv5+9PtAsmp1FW58vAGi45MY/s4tworwGfWIjcO/YBGQeOatZBDuiQvH8Lcmwh4dgfe5pxegnq/AvqarDnzcfUY1kpg3oxlTnKb7+Af+kgAYyrZTcUAmCIAiiY0KCshPicklH3NxuNyyWtvVh8teikaXOzylKmePTFVlhiTjy7D1RAXtYMCrr1PeREiWsdYtLM47hGkek97zJnV8tvTvV0jLH9o3DjmPnVcfxF19dNroRCpuXv8zHim8LfeouX9p0WHN67vz0/kjq2gWLNymfv5aoZzXTuOUX65kimf/MLmISv/PTkyQ/S3+kgAYyrZTqIgmCIAii40GCshPSpUsX2b8lJycHcCbSsCwa1cSeuDZNGP2UY9pQp88x/GnGU1xZh5+N6ImPc06pbisVjWR5P4C02ZCRRTmrwQzHmbvA11Kb+UH2CXyQfcIr8vadrMDb2wtbbafVpOiFW5vvDbX3D4A56smnpsbKRFXFnCivYdouId7GtF1HhOoiCYIgCKJjQYKSaBPUFo16THYmpzjx0PhESXEBAO9sL8Tw3jGYnOJEk9uDrOOlht6DGqlJ8cg4clax6bw4aipkcooTT6T3VzTa4c/D0i3/Q2q/q7zCUU+fRC0GM2P7xjH1KZSCA9AtKgwlVb4R6oVTk7F4Uz6zmywv8owwb0Jf73kDgHFLMhXf/9OfHkRljXTvTjHCNG57OJug7BMbwbSdWe06WDHSY5MgCIIgiM4NCcpOyMWL0uYjo0aNCvBM9KPHZKfJ7cH6XOW6vhc35sPt9mDxpsN+N5dxRIXhlRmDZetAgdZRUzEJ8WwCY/m2AizfViCZ2trk9mB55jGsyiryqf8Ub8sq4t/PKsS9YxMQHWFVFMtK4/z150NhsXCtBIrFAmY3WTPcZpO6RXqFd3ZBmer71/J+hWncTW4Pk8nUvWMT8O6OwoC162CBxZlWDAlQgiAIgrhyaNuCOsIv2Gw2yX9tXT+pBdYITFFpS4rg8sxjPlEvMbwgemzNPr+KSQ4tkcdJyQ7cOsQhu+072wuxOU9eBGuNRBVX1uGR1TneMTfnFWPkS1uwNONYKzMhPsLHb8sq4hdvOowbX92Gu0b11DQ3IaWueskm73IN6/2F8PxqaSuiRpwtBN/8foJPKxA+pVYsq4SRzJBgC9N2esVZk9uD7IIyrM89jeyCMjSpOBbzKdDi+0V87Yj3GbckE7NW7MLj63Ixa8UujFuSqXidEwRBEATRcek4CoO4ohidGAtHVKjqduv2nEST26PJrdWfCBf9W/JLkPpKJjYeKJHd3gPgmc8O4rN90gt83pxHq3x4+tOD+PLAGTy6Okc2qsYf6cWN+WhyezSJ15LKOryzvRBp18RrnFkzSseanOLEjgVpmJ+ehAgrWz9GPYjTjc1MIy1zNWDviQqf38mJZYc9zFubqrRdt6hQPJGehPpLbiYxKEar0FNLgQZarh3hMbQKUIIgCIIgOjaU8kq0S4IsHGaN7q0qEosr67DzeCme+SzP1ONbOEDjeh1AS5ojIG3uIkW5qxHz/5ULQLrJ/bShTtm6UDku1DTiqU8OMKWNNtdgHkWQhsdLfE1h7qlKTfNiTdnckl+CZRnHTElrlUMc6WN11mVFKuLJapgk3q6otAZrd5/0uR+0OveymC0J0VrHrKUGl9JfCYIgCKLzQIKSaLewOlk+snovXA1Nph133oS+SHba8dga+dpHW2gQZo7qhbSB3QBPcwonLw4AeXMXNaSa3L+jUUzyXKxnPyd6DHY8aBbDsbYQlLsaVLdnTdlUEiZm8WBqQqsWIOeq6zDz2l5YmnFMsk+qB9BUNyoX8WR1MeW325xXjGUZRzWJQSF6hZ7WOmY9RloEQRAEQXR8SFAS7RbWFEQzxSQApPa7CpW1ygLJVd+ElVlFuDYxttViXs3cRQk9Te7bmtuGdcfKrCLV7Vh7jfqznQtPenJzXauU4Ux0hBWArwGPQ6VFiZiYCKspxjlmRP30Cj3W+4/fTo+RFkEQBEEQHR8SlES7xewURDX4dMyRfWJw46vbVLfn6x/TBnRDSHBLvqjRBbPWJvdtjVpLjAdTE5Ce7FB1+uQjhV8ZqLO7bagT/z18DjUyDxmEKbdyaaCVl4Xk/PT+SIiP8Ik8K/VGFWLW9WpG1E+v0FO7/8Tpy1oFKEEQBEEQnQMy5SHaLUrOmFph3f+FW5Ox90QFs5ArdzVizMtbfcxGzFowsza5bys4AI6oUKzdfVJxmy/zSlTFpNAw5oPsE7rndNfoPvh/dw4FB2WXVACqhjPr9pzELUO6e11otUROL9Q0YndhuY534IsZUT+9Qo/VmZb/XNUMpITuxwRBEARBdB5IUBLtGrPaSLBEjH4zMQn28BDNEbJyV4OPg6VeZ1YxvWLCDe0fczl10x/2J/yYs0b3ZmrVoiSu5JxBtc6HFyssbqpaIn88WiPPZqR2mhH1MyL0WJ1pAe0ClCAIgiCIzgGlvBLtnskpTrjdHjy2Zp/mfeNsIZjOWOP3j51FeG2r/tYjwlq2F25NxqOrc1qZu/CEWS2oa3RLjsOnEg5wROmeCy4f9+HxifjX96eYjWRY4WsK6y9JvwcxcuLKLAMeD4ApKc2uqLyoVHJT1RP50xp5NiNSrTXtVAql65FF6LE60/LbvnnPiFZ1qaz1swRBEARBdDxIUBLtnia3B4s3Hda8X6zNiuw/TMTeExVMgvJCrX7RJa5l4xfWT396sJWY4wBFMQk0L/C//t853fMBmmsB39leCHu41dA4TnsYFk5NRowtpJWgyC4oYxpDTlxpNeCRMszhW7y8l1WE97KKfNppyNUV6on8jU6MZXa0jbOFmJLaaVQM8hgVeqzOtPyxWAUoQRAEQRAdHxKURLtHq+jgl61/un0wQoItGNknBrE2K8pd5kbppBBHvqQig0rROH6BPynZgT98etDQXPjjGBHK89OTMC8tSVYMaImgCdtz8CJDa1poWLAFz9+SjBhbKLbkl2BlVlGrfqEs7TT0RP6CLByzo+30Yd1NE1BmRf0CKfS0CFCCIAiCIDo2JCiJdo9W0SFcaPNtIQIhJoGWiBafyqmFWJsV3/x+AkKCLcguKEOFyWmqeljz3QnMS0uS/TsfQXtktXTPTg+aI2hb8ktaCSKnPQwzr+2laT5nq+oxd80+vH73cHyVVyJ7TLV2Gnojf5OSHUyCctLltiRmYZYYJKFHEARBEITZkKAk2j2s6YnzJvRFar+rvAttubYQQqLDrUwRvNnX9cKG/cWorrsk+XdxREtPL8VyVyNeyziKWFsICkovatrXX5ytbjDciH7fyQq8s72w1edQUlmHpRnHYAsJYu4lyovF59bnKT4kYGmnoSfyx0c2lT5bfzmZkhgkCIIgCKI9QoKSaPewpifOn3SNN2LDYvYSZwvBsruG4d6Vu1XncMuQHrgh6So8ejkSpxbR0uvw+frXBbr28ydK74UlEislJoGWcyjXM1IOD8AccVb7HLRG/oSRTX4uPORkShAEQRDElQi1DSHaPXraEbBECMtcDbBwHHNLBS0tFDpT83ZhGm92QRnW555GdkGZtyZS7TyrObgadXhVguVz4CN/04f18PacVELLdUAQBEEQBNHZoQgl0a7hRUv9JTeeSO+PtbtP+vQ9lEtPZI0QlrrqNdXSsUa01KKqHYVukc1upXwtqrgGckqKubWCZsHSTsMI5GRKEARBEATRDAlKot0iJWIcUaGYn56EhHib4iJeS1uIsX3jNNXSsdSyiVMjOyovTk/BlvwSyVrUkso6vMdgUBNoApV6SjWNBEEQBEEQJCiJdoqcoc7ZqnosyziGN+8ZobiY19oWwh8Rp8kpTrx+9wjMW5vTqrVFe2Fkn2jknLwAj2h+ESFB+H93DsWkZAfGLclUrIE0Ct9H0iy0ttMgCIIgCIIg9EM1lESbIVWTx/9ezlCH/92LG/O920uhp+5Say0dCzG2kHYrJgFg74kLiAwN8vldeBAw54ZEr8DW6larlftT+5g21sKpA7FjQRqJSYIgCIIgiABBEUqiTZCryXvh1mTYw0MURYxaSwjfusuky3WX9d6/BzKCpdftNZBU1fm6rNY2Aa9tPY5/ZJ/AHcN7+P343e0Rpo0VHxlKdYwEQRAEQRABhAQlEXDk0llLKuvw6OocPJCawDSOlFiTrrsMw/z0/kiIj1BMZeWFqJkmKx3Z7fVCTWNAaiRPlNeYNpbR8631GtCyvT+uL4IgCIIgiLaGBCURUNTSWTkAn+WeZhpLLB7k6y7rsCzjqGTdJb/I35Jfgs9zz6Dc1eD9m9OESGZncHu1cIDH47/2Hn1ijUco+ZrYkX1ikF1Qpku0KUXNpa4BLdtrHZsgCIIgCKKjwHk8YjsOorMyaNAgAMChQ4fabA7ZBWWYtWKX6naxthBUuBoUDXV2LEjzioUmtwfjlmTKpspK7SO1yBfvA8Bwb0Fe6ALaRVlYsAV1l9y6j20m4rYqZoznsIfhm99PwI2vbtMtuvnP6aHxidiwv1iXaJN7GCF3DWjZXuvYBEEQBEEQgcAsbUCmPERAYa0pvG1YdwDshjpq5jHCukugZZGvtg+gbgCkxuQUJ968ZwQcdu3pmLNG94LTHtbqPASaB1ITdM1fDuHnGBJskTVQYsFhD8ND4xPxzvbCVp8nn0a9Oa9Ydn+tJlBq23sE25thMEUQBEEQBNGeoZRXIqCw1rhNSnZgdGKsZG/Imdf2Rv0lN7ILyrwpjaxC9Vx1neIiX4yaARAr4rYkpdX1WLzpsOp+Px3kxHVXx+HR1TmmRwi1MCnZgWenJnvnX1Rag2UZRwGdcxIbIzW3WBmO59bnodzVKLsffw6EvUhH9onBja9uU0yjfnFjPiYlOyTTX7U8jBjbN47J+ba4sg7LM49hdGKcIYMpgiAIgiCI9g4JSiKgaOkPGWThfERYUakLa3efxNLLQgZoSWlkFapdI8N0tcIww62Vb0sCNEe53t1RyHwe3rxnhGJ6rhzCdNAPvzuJi/VNittL4RTMQyh6rnF00TSneRP6IqlbpGRt4+a8YizedNhHTHYJDUawBbhQe8n7OymH3uyCMkOiTcvDCC3bL804hgdT5cWxnjkQBEEQBEG0N0hQEgGF7w8pFXGTSmflRczmvGIsyzgm6wz7+t3DVc1vYm1WlFTVoeBcteZ5qwlWrQ6ewvMgRuo8CCOcGfklzO6rQgFWVFaD/xw6y7SfcC7i9GIefk4LPt6Pj3PUjZRS+10lKejkagxd9c1CUs2hV6sgFKPlYYSW7QH9BlMEQRAEQRAdBRKURMDhawql0lmlDFRYnGEXbzqMhVOTMXeNfGpouasR8/+Vq2muwkihHEYcPO0RVlyo8Y1iRUdY8fKMwa325cX12L5xuFYiHdhpD8PCqQMRYwttJWw35xVrFpOs72HH8TLVsbpFhsDt8WB97mmfebF8tuv2nPQxUxKjVRCK0RI157ePlvjcpCh3NTIZTCldXwRBEARBEO0ZEpREmyCuKVSK6rHWuMXYQnSnhkohZwAkRK2nppyDp9x+AFDBIFS0nD9etLEQbg3CS9MHoXtMBFPLjd2F5SipUj/XrgY3Zr/7nfdnXqzaw0MM1xhqFYRitEbNtXLbsO5YlVXkl7EJgiAIgiDaGhKURMARp4feMqTZ0VVOHGlJaZw+rIdXaJVU1l6uy2tQ31kCuYip8H2oRdekzGDUTIHUTGR4xDWNTW6PZA9GLTWjS+8aqqmFBetnc7H+ks/PvOBOG3CV4eOYIQi1RM13F5YzRSd5lAymqA8lQRAEQRAdHRKURECRSg+NjrACgM8iXZhuqTWlkRda2QVlmsVkrM2K24f1QPplESAWgrzoje8Sivwzlbqia1pdRVlQSrutZ+xj+WBqgmZxo7f2jxfcmf87z7R9fJdQxb9rTaOWG4Ml6qvFQMcpYzDFUmdLEARBEATRESBBSQQMuTRPqWiPMGV0UrJD1XDHwgEVIvHIuvCfN6Efkrp1UVzkSwk2VsTzMGoiIzU3pbTbJ9KTmMZJT3YwbSdELd1UCY/3f1g3VkZLGrAc4qivFFpEtJTBFEEQBEEQRGfC0tYTIMzH5XJJ/nO72SJV/kBL70fAt+k7AG/jezncHmDuGt8G9qwL/9R+8Zg+rAfG9o2TFZOPrs7RXZcpnoceExk+nXV97mlkF5Shye3x/l4p7RYA1u4+CUdUGORkFYeWSJpW+HRTfhx/UeqqZ57P2L5xip+nUXgRrTSyhQPeuFu6fpYgCIIgCKIzQYKyE9KlSxfJf0eOHGmzOenp/ShM/eQb36vpgxc35nvFltrCn0VIaRXCYiwcMLJPjM/vtM5rc14xxi3JxKwVu/D4ulzMWrEL45ZkYnNeMVP6bElVPWaN7u0dW3wswJgxDJ9u6rD7CuWoMPMSINpTWw0WEb181nBMGUJikiAIgiCIzg8JSiIgGGnczu977NxFuBWUnVCAAsoLf1YhpUcIC3F7gL0nKnx+p2VectFRPp11S34J0zx6x4bjifT+sIdbfX5vD7fiifQkTNKR7ipkcooTC6cORKytZfyqukuKDwA4QPUBAQA4okLbXVsNORHttIfhrXtGYMploymCIAiCIIjODtVQdkIuXrwo+ftRo0YFeCYtGIkwdY0Mw+a8YizNOMa0vVC8GjVryWAUbEpkHS/11vSN7BODvScqUH/JjSfS+2Pt7pM+bTeE82JxkV2fe4ZpDmK3W94N9UJtI5ZmHMO6PT8achzdnFeMuWv2tZqr3AMAXkfOuSERb28vVBx70bRBpqeuip2G9RjkmFGzSRAEQRAE0dEhQdkJsdlskr+3WNouIK3XvMVpbxZhN766jXmf0up6rM897V3gSy38eWEn3E4sBDbnFeO9rCINs5Vm+bbj3tcWzldkOaJCMT89CQnxtlbzYElnLXM1ICbCigs1jYrnVex2q7VvphxNbg92FZTh6U8OKh6/1fsWCOfhvWPw9KcHW5kzRUdY8cqMwabXISo54mo9FhntEARBEARxpcN5PB695WFEB2PQoEEAgEOHDrXJ8eXcSJV4654RsIeHYNaKXUzbi4WLlFBgERRNbg/GLck0lO7KAi9hpYTc+tzTeHxdruoYESFBqGloatWDUQ9Oexh2LEhjirJpdb69d0xvjOgTC0dUawHf5PZg1w9lyC4oA+DB2KvjMcYPpjpy16DS50AQBEEQBNEZMUsbUA0lETD49NNYWwjT9vPT+2NyilNT/aU4xZKPvPHur2o1ifx2RmsnWRG62TaJJs+aJlzT0AQA+P/t3XlcVOX+B/DPsC8CIiiLIu4LobiQhXvgXlqGW4qKmprLzTJLLbu2/UrtetObXktyl9LMJXHJTBB3SXHHXVFEQAVEZGfm/P5Azp2BWc4swICf9+vFy3POPM9znjlzZpzvPJuLg7WOlLqlZOVjWbTursWGzHy74cRdLPrjCrLyCssFipYWMnRp5o5ZfVtiVt9W6NLcvUK6ueqaEVfd60BEREREmjGgpErVz98LJ+aGqEzeoo6nsy2mBzcDYNz4S+VAobBYITmgMGYSIX2VnUyolJTlKUrJANhbW2LD+E6obW9cYPndX9dVll8py5iZb8sG7sbStJyKOlK6EKt7HYiIiIhIMwaUVOlsrCzw9eA2kEH9LKcyqE7Eok9gpU5poLDheKLkgMLQILa2vTUG+HsYlLdsEKs8G6wupXX/MyEVj/OKdKbXRVtLnTGtt6ZsCdS2nIo6Un8kqMwfE4iIiIiqOwaUVCU0Lbvg6WJXbhyblGU2pLiTkSsp3YPsfHRqXEdy19whHerju2EB+PTV1pg/6AV09DVsiYvraU/LtbKVXieprY4bTtw16NxlaWupMzbgMkVLoNSuy8qk/khgTmteEhEREZk7zvJKVUafZRe0Lf8x4sWG+O6vazrP51vHQVK96jnZwdJChjfaeWO1hFleHz4twJe7E5CR87+WwbKTA0mxLOYGlsXcKDdBUD9/L1xOeYKlB27oKMG0NAWOpgq4DA1MpSyn8nlUAnr7earcS7pmGpah5H4ytzUviYiIiMwZA0qqUvosu6ApAAWATX/f1RkojA5qhJ+O3Na6dImFDMjMKQAA9PbzlBRQxl57VO6YMb05yy7h8cfFlEoPJgHNgaOhS8BILV8XfcZCKt9bpS3dUzbGl5sRtzTsnD/Qj+tIEhEREemBXV6pWikNQF9vVx9Bz5aVkNIldsSLDbH3YgpGvNhQa/kKAZj28xn8cTFFDJyMUTY2kRKrqJtIqLJ5aWmp03a9pZDpKF8XY8ZC6tPVmoiIiIh0Ywsl1QiausTWdrCGAKh0ia1tb4Ws/GJoW4G1tMvk/IF+eGdjvMH1UgjAp6+2hruTLeo52aGjrytO38nE0RsPsSzmpsZ8UicS0oeXix3yiuTIyi3S2bKoq6VO0/WWypiWQGPHQurT1ZqIiIiItGNASTVG2UAh8VEOvvur/JqKj/OKtZZTdtKY2g7WeJxr+Myp7k62eL1dfXE/qKmb5Fa2w9cfGnzeUtNfaYouzeqiU+M62J+QqrbLZ6naDtZY8GYbSS11ytc79Uk+vtx1SWUcqTplx4cawhRjIfXpak1EREREmjGgpBqlNFCQKwR0XRhtVFn7E1Kx5miiUeMEAfUtZVJb2Q6qGZ+pr+YeTmLwpK0ld1znxpge3EyvljrlwMze2gJTnrXmqrtm7/dqjunBzY1uCeRYSCIiIiLzwYCSaiRj1kostePsfaOCybItZXKFILaeuteyhaezLdKeFGhsZZMZMFOsOmWD14rq8qkpWDVFq6TUc3lWwLmIiIiISDMGlFQjGbNWogyAq6M1MnIKjaqDgP+1lP1xMUXj+E5NebWN8ZRCW9fPiuryWZnjEzkWkoiIiKjqMaCkKqPcYmfqYMDQJSlKzz64XX2skrBkiDa1HazR288Tf1xMwZSN8eWCR2PGZZZq7+OCM0lZ5Y5XZdfPyhyfyLGQRERERFWLASVVCXUtdqbsGill4hYXB2vYWVki9Un5LpMu9jZGB5SPc4tw4mY6Po9KMHocpiYf9WuNrLxCdv0kIiIioirBgJIqnaYWu9SsfEzZGG+S9QClTNyy4M02GrtMyhWC1oBUquO3Hpls2Y+ynO2skPI4D1617RH74Ss4fSeTXT+JiIiIqFJZVHUF6PkiVwgaW+xKj30elQC5CWajkbKIvaWFDJ0a10E9Jzs8yC5ZKkSuEMSAFPhfAGqYigvqnuQXY+aWc3gr4gR6fBuDrLxCvN6uPoKaulV4MClXCDh+Mx2/n03G8ZvpJnm9iIiIiKj6YQslVSpds68qrwEpdWyctrGYuiZu0dX1VtOspZ++6ocvdydofS5eLnYIauqGZTE3JD0PY6SYsHVXl4rurkxERERE1QcDSqpUUmdflZpOSnCjaeIWqV1vNQWk5+5l4sdDtzXWbVCAF15u4gYvF7sK6/Za1udRCejt5yl22y1bbwBGTYRUGd2ViYiIiKj6YEBJlUrq7KtS0kkJbrSNkdTW9VYG1eCsbEAqVwjYeS5Fa/12nkvBR/1aY/5AP7yzMV7n8zFWaevu2qO34eViX64FtbaDNQDV2WX1aVnU95oRERERUc3HgJIqlZTZVzWtnahMSnAzd9sFfLbzElKfFIiPeSnN4mpM11tdXXehlL+3nydq21vhcV6x1vSm8uXuy2qPq1umRJ+WxYrorkxERERE1Rsn5aFKpW2yG33WTpQS3GTmFqkEk8D/AqiIwzcl1VdT11upXXJTn5QEWFKCSTtrC0x7pamkck1Fn4mQTN1dmYiIiIiqPwaUVOmkzL6qi6FBi/DsL/rKQ0npNXW9ldp198tdl7A/IVVS2vwiBWQVOCusJsoti9qYsrsyEREREdUM7PJKVULX7Ku6VEbQ4qWl662urrulMnKKsPpooh5nrbrlN3QF6abqrkxERERENQdbKKnKlE52Y8jaiaXBTUW252nreqvPOpUyADKJFQ1q4o46jtbSK2lCuoJ0U3VXJiIiIqKagwElVUv6BHSGmNClkc6ut6Vdd10dbbSmEwAIEhoevVzs8HJTN3z1ur/OtC72Vgh7yUd3oRLIoL01VpkpuisTERERUc3BLq9UbZUGN+rWocwrkiMrt8jgDqS9/Dwl1yGvUI73fz2nM21Iq7o4oGHspgz/a90b0NYbk+891rjGpQzAwtC26O3niQNXHursdquNIS2LxnZXJiIiIqKagwElVWuagpv9CamYsjEeMug3KlHKOEC5QlA5Xz1naeM53+7WFKEdGmDe7xeRkaN9Lci5A/wQ0MD1WdpCjWnnD/ST/DxdHawhQHX5EE891qFUpm5tTiIiIiJ6/sgEQUpnPKoJXnjhBQDApUuXqrgmleOPiylqWy8HBXhh5bPWP+Wbv7R9TVvXTXVlejrbIr9YobFFtDRIPTI7GJYWsnIBqbbWPSlpNT3PT19tDVdHW5W8ANiySEREREQmiw0YUD5HnreAEtAckGkKwrS11v1xMQVTNsaXCxqVWwfLthRKCVJNQZ8glYiIiIjIVLEBu7xSjaapa6a+4wDlCgGfRyWobYEsPVbbwRp2VpZIfaLUeulihxEvNkRBsQLHb6ZXWKDHLqhEREREVBUYUNJzS1cQptzq9yi7QKU1U53HuUWInNABFhYyPMjOR+KjHPwSdxff/XVNTFPSFdUPro42bE0kIiIiomqPAWUNlJOTo/a4QqGAhQVXipFCXZdYKVKf5MO7tj3OJT3G6qOJ5R5PycrH1J/jVY7VcbTBV6/7Y0BbLrlBRERERNULx1DWQDKZ5tYuPz+/52oMpSE0jZWUwsnOCtn5xQadd3L3xpg7wM+gvERERERE+jDVGEo2VxEp0TZWUgpDg0kA+PHQbew5f9/g/ERERERElY1dXmugp0+fqj0eGBhYyTWpfuJuZ+jdzdWU5v1+EX39vTimkoiIiIiqBQaUNZCjo6Pa4xw/qduD7KoLJgEgI6cIcbczOGMrEREREVULjDCIlNRzsqvqKlR5UEtEREREJBUDSiIlnRrXgZeLHbR1OK3o3qjmENQSEREREUnBgJJIiaWFDPMHlsy0WjZulD37W/ZWB7zfq0WFnN/LpWRdSiIiIiKi6oABJVEZ/fy9sCKsAzxdVFsKPV3ssCKsA/r6e2LT33f1KnNCl0b4ZeLLmNitkcY0MgDzB/pxQh4iIiIiqjY4KQ+RGv38vdDbzxNxtzPwIDsf9ZxKWg4tLWQ4fjNd8kywXi52mD/QD/38vQAAQU3d0N7HFfN+v4iMnCKN6YiIiIiIqgMGlEQaWFrI1M62KnXSnOmvNMX7vVuWa3Ec0NYbff291AarRERERETVCQNKIj1JnTSnS7O6GoNETcEqEREREVF1wjGURHrSNROsDJxch4iIiIieDwwoifSkayZYgJPrEBEREdHzgQElkQF0zQTLyXWIiIiI6HnAMZREBtI2EywRERER0fOAASWRETi5DhERERE9z9jllYiIiIiIiAzCgJKIiIiIiIgMwoCSiIiIiIiIDMKAkoiIiIiIiAzCgJKIiIiIiIgMwoCSiIiIiIiIDMKAkoiIiIiIiAzCgJKIiIiIiIgMwoCSiIiIiIiIDMKAkoiIiIiIiAzyXASUx44dw6RJk+Dn5wdnZ2c4OzvDz88PkyZNwrFjxyq9PllZWfD29oZMJhP/wsPDK70eRERERERExrCq6gpUpJycHLz77rtYvXp1uccuX76My5cvIyIiAuPHj8d//vMfODo6Vkq9PvroI6SkpFTKuYiIiIiIiCpKjQ0o5XI53nzzTfz555/iMXt7e7zwwguwsrJCQkICnjx5AgBYvXo1kpOTsXv3blhaWlZovQ4fPoyIiIgKPQcREREREVFlqLFdXj/99FOVYHLixIm4d+8e/v77bxw/fhz379/HvHnzxMf37duHf/7znxVap4KCAkycOBGCIKBu3bpo27ZthZ6PiIiIiIioItXIgDI5ORnfffeduD969GisXLkSderUEY85Ojriyy+/VAkqv/vuO9y/f7/C6vXVV1/h6tWrAIDFixfD1dW1ws5FRERERERU0WpkQPmf//wH+fn5AAAHBwcsWbJEY9pPP/0UPj4+AIC8vDwsXbq0Qup08eJFLFy4EAAQHByM0aNHV8h5iIiIiIiIKkuNDCi3bdsmbg8bNkylZbIsGxsbjBs3Ttzfvn27yeujUCgwceJEFBUVwdbWFitWrDD5OYiIiIiIiCpbjQsor169ihs3boj7/fr105mnf//+4vb169dx7do1k9Zp+fLlOHHiBABg7ty5aNGihUnLJyIiIiIiqgo1LqA8d+6cyn5QUJDOPB06dICNjY3GMoyRlJSETz75BADQokULzJkzx2RlExERERERVaUat2zI5cuXxW0bGxtxfKQ2pelu3rxZrgxjTZ06FdnZ2QCAFStWwNbW1iTl5uTk6J1HoVDAwqLG/YZARERERERVpMYFlHfu3BG3GzRoAJlMJilfw4YNxYAyMTHRJHXZvHkzdu3aBaBkptng4GCTlAsAtWrVMiifn5+fyepARERERETPtxrXXPXkyRNx28XFRXI+Z2dncbu0RdEYmZmZmDFjBgCgTp06WLx4sdFlEhERERERmZMa10Kp3BXUzs5Ocj57e3u1ZRjqgw8+QFpaGgBg0aJFqFu3rtFlKnv69KneeQIDA01aByIiIiIier7VuICyqKhI3Laykv70lNMWFhYaVYfo6GisWbMGANC1a1eMHz/eqPLUcXR01DsPx08SEREREZEp1bgIw8HBQdzOz8+XnE85rSHBmnI5kydPBgBYW1vjhx9+kDyOk4iIiIiIqDqpcQGl8mQ1eXl5kvPl5uaqLUNfn332mbgO5qxZs/DCCy8YXBYREREREZE5q3EBpbu7u7idkpIiOV9qaqq47ebmZtC5k5KSxMl3GjdujE8//dSgcoiIiIiIiKqDGjeGsmXLluJ2eno6cnNzVbrBapKUlCRut2rVyqBzp6eno7i4GABw+/ZtSecttW7dOqxbt07cj4mJQc+ePQ2qhyZ3795FUVERW02JiIiIiJ5zN2/ehLW1tdHl1LgWytatW6vsnz17Vmee5ORkPHz4UGMZNYWjo6NJbprqTKFQICEhAQkJCVAoFFVdHbPxvF2XmvR8q+tzMfd6m3v9iMh88POCKpMp7zdra2uj5o4pVeNaKDt16gRbW1sUFBQAAI4cOYLOnTtrzXP48GFx287ODp06dTLo3FZWVnp1l83KyhJbNG1tbVXGblZE4Kfcrfd5lZOTI17nU6dOmeRNVBM8b9elJj3f6vpczL3e5l4/IjIf/LygymSO91uNa6GsVasWQkJCxP3IyEideZTThISEGPzC+Pv749GjR5L/unTpIuYdMWKExseIiIiIiIjMUY0LKAEgPDxc3D5//jyioqI0po2Pj8fevXvV5iUiIiIiIiLNamRAOWTIEAQEBIj7kydPxpUrV8qlS0lJQVhYGORyOQCgXbt2CA0NVVvmwYMHIZPJxL+1a9dWSN2JiIiIiIiqixo3hhIAZDIZfvrpJ3Tv3h15eXlISUnBSy+9hClTpqB79+6wsrJCXFwcli1bhrS0NACAvb09IiIiIJPJqrj2RERERERE1UONDCgBIDAwEJGRkRg1ahTy8vLw5MkTLFy4EAsXLiyX1t7eHpGRkQgMDKyCmhIREREREVVPNbLLa6nBgwfj9OnTCAkJUdvyKJPJ0KtXL8THx2Pw4MFVUEOqbI6OjhAEAYIgmMWsWObiebsuNen5VtfnUl3rTURUFj/PqDKZ4/0mEwRBqOpKVIakpCQcO3YMycnJAID69eujc+fO8PHxqeKaERGRuVGelv3p06dm8582ERGRuXluAkoiIiKpGFASERFJU6O7vBIREREREVHFYUBJREREREREBmFASURERERERAZhQElEREREREQGYUBJRBVm8ODBcHV1xZAhQ6q6KkR6Mcdp2Yno+ZCUlISePXvCz88Pbdu2xZYtW6q6SlTDGXvPcZZXIqowBw8eRHZ2NtatW4fffvutqqtDRERk9lJSUpCWloZ27dohNTUVHTt2xLVr1/jjFlUYY+85tlASUYXp2bMnnJycqroaRFUuKSkJS5cuxeuvv45GjRrB1tYWtWrVwgsvvIAZM2bg1q1bVV1FIjITXl5eaNeuHQDA09MT7u7uyMjIqNpKUY1m7D3HgJKoGkpOTkZYWBjc3Nxgb2+PNm3a4NSpUyYr/9ChQxg4cCC8vb0hk8mwY8cOtemWL1+ORo0awc7ODi+99BLi4uJMVgeimiIpKQm+vr547733sHPnTty5cwd2dnYoLCxEQkIC/vOf/8Df3x+//vprVVeVqEb75ptv8OKLL8LJyQn16tXDG2+8gatXr5r0HKb+//P06dOQy+Xw8fExaT2pcqxYsQJt27aFs7MznJ2dERQUhL1795r0HOZwzzGgJKpmMjMz0aVLF1hbW2Pv3r1ISEjA4sWL4erqqjb90aNHUVRUVO54QkIC0tLS1ObJyclBQEAAli9frrEemzdvxsyZMzF//nzEx8cjICAAffv2xYMHDwx7YkQ1lFwuBwD069cPv/zyCx4+fIisrCzk5uYiNjYWbdu2RV5eHsLCwnDhwoUqri1RzRUbG4tp06bhxIkT2L9/P4qKitCnTx/k5OSoTV/V/39mZGRgzJgxWLlypR7PksxJgwYNsGDBApw+fRqnTp1CcHAwXn/9dVy6dElt+mp7zwlEVK3Mnj1b6Nq1q6S0crlcCAgIEIYMGSIUFxeLx69cuSJ4eHgICxcu1FkGAGH79u3ljnfq1EmYNm2ayrm8vb2Fb775RiVdTEyMEBoaKqm+RDXR48ePhTNnzmh8PDU1Vahbt64AQBg3blzlVYzoOffgwQMBgBAbG1vusar+/zM/P1/o1q2bsH79ej2fFZk7V1dX4aeffip3vDrfc2yhJKpmdu7cicDAQAwdOhT16tVD+/btERERoTathYUF9uzZgzNnzmDMmDFQKBS4efMmgoOD8cYbb+Cjjz4yqA6FhYU4ffo0evXqpXKuXr164fjx4waVSVRTubi4iGNT1PHw8MCAAQMAlHQ1IqLKkZWVBQCoU6dOuceq8v9PQRAQHh6O4OBgjB492qDzkPmRy+XYtGkTcnJyEBQUVO7x6nzPMaAkqmZu3bqFFStWoHnz5ti3bx+mTJmCd999F+vWrVOb3tvbG9HR0Thy5AhGjhyJ4OBg9OrVCytWrDC4Do8ePYJcLoeHh4fKcQ8PD6Smpor7vXr1wtChQ7Fnzx40aNCAwSaRBu7u7gCA4uLiKq4J0fNBoVDgvffeQ5cuXeDv7682TVX9/3n06FFs3rwZO3bsQLt27dCuXTt2h6/GLly4gFq1asHW1hbvvPMOtm/fDj8/P7Vpq+s9Z2Vw7YioSigUCgQGBuLrr78GALRv3x4XL17EDz/8gLFjx6rN07BhQ2zYsAE9evRAkyZNsGrVKshksgqv619//VXh5yB6+PAhTp06hb///lv8V/mHjTVr1iA8PNzg8o8dO4a1a9fiyJEjuHfvHoCScTFdu3ZFeHg4OnfubOxTQGxsLABo/GJLRKY1bdo0XLx4EUeOHNGarir+/+zatSsUCkWFnoMqT8uWLXH27FlkZWXht99+w9ixYxEbG6sxqKyO9xxbKImqGS8vr3IfQq1bt8bdu3c15klLS8OkSZMwcOBA5Obm4v333zeqDu7u7rC0tCw3QDwtLQ2enp5GlU0kVWpqKho1aoR69ephwIABmD9/PqKiolSCSWPk5ORgwoQJ6NKlCyIiInD58mVkZ2cjOzsbly9fRkREBLp06YIJEyZonNRDih07doizNI8bN84kdScizaZPn45du3YhJiYGDRo00JqW/3+SsWxsbNCsWTN07NgR33zzDQICArB06VKN6avjPceAkqia6dKlS7lpzq9duwZfX1+16R89eoSQkBC0bt0a27Ztw4EDB7B582bMmjXL4DrY2NigY8eOOHDggHhMoVDgwIEDascFEFWE/Px83Llzp0LKlsvlePPNN7F69WrxmL29PQIDA/Hyyy/D2dlZPL569WqEhoaKs7nq4+7du5g0aRIA4PXXX0e/fv2MrzwRqSUIAqZPn47t27cjOjoajRs31pqe/39SRVAoFCgoKFD7WLW95/SexoeIqlRcXJxgZWUl/N///Z9w/fp1ITIyUnBwcBA2btxYLq1cLhcCAwOFAQMGCAUFBeLxs2fPCnXq1BH+/e9/qz1Hdna2cObMGeHMmTMCAOHf//63cObMGeHOnTtimk2bNgm2trbC2rVrhYSEBGHSpElC7dq1hdTUVNM/aSI1bt++LQAQAAh169YV+vXrJ8ybN0/4/fffxeMAhDVr1uhd9ty5c1XKmDhxopCeni4+/vTpU2HevHkqaT7++GO9zpGZmSm0bdtWACA0adJEpXwiMr0pU6YILi4uwsGDB4WUlBTxLzc3t1xa/v9JpjBnzhwhNjZWuH37tnD+/Hlhzpw5gkwmE/78889yaavzPceAkqgaioqKEvz9/QVbW1uhVatWwsqVKzWm/fPPP4W8vLxyx+Pj44WkpCS1eWJiYlS+KJf+jR07ViXd999/LzRs2FCwsbEROnXqJJw4ccKo50Wkj6ysLGHLli1CYmJiuceMCSjv3bsn2NnZiflHjx6tMa1yUGlvby8kJydLOkd2drbw8ssvCwAEb29v4ebNm3rVkYj0p+7/NW2fEfz/k4w1fvx4wdfXV7CxsRHq1q0rhISEqA0mS1XXe04mCIJgmrZOIiIi86A8gYG+k/LMnj0bixYtAgA4ODggKSlJ7bICQMl07M2aNUNSUhIA4KOPPsLChQu1lp+bm4v+/fvj0KFDqFu3LmJjY9G6dWvJ9SMiIjInHENJRESkZNu2beL2sGHDNAaTQMnYFOWJdLZv36617Pz8fAwcOBCHDh2Cq6sr9u/fz2CSiIiqNQaUREREz1y9ehU3btwQ96VMktO/f39x+/r167h27ZradAUFBXjjjTcQHR0NZ2dn/PHHHwgICDC+0kRERFWIASUREdEz586dU9mXMgNehw4dYGNjo7EMACgqKsKwYcOwb98+ODo6Ys+ePejUqZPxFSYiIqpiDCiJiIieuXz5srhtY2MDHx8fnXnKplMuAyhZgmTUqFHYuXMn7O3tERUVhS5dupiu0kRERFXIqqorQEREZC6U17Vs0KCByuQ+2jRs2BA3b94EACQmJqo8dvToUWzZsgVAydpfb731ltayUlNT9agxERFR1WJASURE9MyTJ0/EbRcXF8n5nJ2dxe3s7GyVxxQKhbhdUFCAtLQ0I2pIRERkXhhQEhERPZOTkyNu29nZSc5nb2+vtgwA6NmzJ7hCFxER1VQcQ0lERPRMUVGRuG1lJf03V+W0hYWFJq0TERGROWNASURE9IyDg4O4nZ+fLzmfclpHR0eT1omIiMicMaAkIiJ6platWuJ2Xl6e5Hy5ublqyyAiIqrpGFASERE94+7uLm6npKRIzqc8M6ubm5tJ60RERGTOGFASERE907JlS3E7PT1dpeVRm6SkJHG7VatWJq8XERGRuWJASURE9Ezr1q1V9s+ePaszT3JyMh4+fKixDCIiopqMASUREdEznTp1gq2trbh/5MgRnXkOHz4sbtvZ2aFTp04VUjciIiJzxICSiIjomVq1aiEkJETcj4yM1JlHOU1ISAhneSUioucKA0oiIiIl4eHh4vb58+cRFRWlMW18fDz27t2rNi8REdHzgAElERGRkiFDhiAgIEDcnzx5Mq5cuVIuXUpKCsLCwiCXywEA7dq1Q2hoaKXVk4iIyBzIBEEQqroSREREhpg4cSI2bNhQ7nhBQYG4bWVlBUtLy3Jp8vPzNZZ76tQpdO/eXVyL0tnZGVOmTEH37t1hZWWFuLg4LFu2DGlpaQAAe3t7HDp0CIGBgcY+JSIiomqFASUREVVb4eHhWLdunUF5df33t337dowaNUoMKjWxt7dHZGQkBg8ebFA9iIiIqjN2eSUiIlJj8ODBOH36NEJCQiCTyco9LpPJ0KtXL8THxzOYJCKi5xZbKImIiHRISkrCsWPHkJycDACoX78+OnfuDB8fnyquGRERUdViQElEREREREQGYZdXIiIiIiIiMggDSiIiIiIiIjIIA0oiIiIiIiIyCANKIiIiIiIiMggDSiIiIiIiIjIIA0oiIiIiIiIyCANKIiIiIiIiMggDSiIiIiIiIjIIA0oiIiIiIiIyCANKIiIiIiIiMggDSiIiIiIiIjIIA0oiIiIiIiIyCANKIqIqsnbtWshkMshkMvTs2bOqq0NE1ZTyZ0nZv3bt2lV19eiZHTt2aHydGjVqVNXVIzIYA0oiMrnExESN/2ka83fw4MGqfmpmLzw8vNx1Gz16tN7lDBw4sFw5s2bNqoAak7lo1KiRSd+v+jh48KBKXkdHR2RnZ5vkOaxatUqvMgoLC+Hm5launF27dunMKwgC9u3bh8mTJ6NDhw6oW7cubGxsYG9vDw8PD7z44osYNWoUvvvuO5w8eRIKhULv52hK6gJRX19fCIKgVzmLFy8uV05gYGAF1ZqIzI1VVVeAiKgyrV27FuPGjQMA9OjR47kIUrdv346nT5+iVq1aktI/ePAAf/zxRwXXivR18OBBvPLKKwAAX19fJCYmVm2FTGjdunUq+7m5udiyZQvGjx9vdNnr16/HhAkTJKfftWsXMjIy9D7PyZMnMX78eCQkJJR7rKioCPn5+Xjw4AFOnTqFn3/+GQDg5uaGo0ePomXLlnqfTxNXV1d06tRJ3G/atKle+e/evatyr0lR9vUj9Tw8PNC3b19xPyMjA3///XcV1ojINBhQEpHJ2dvbq/ynqU5eXh4OHTok7vv7+6N+/fpa89SpU8ck9Xve5OTkYOvWrRg7dqyk9JGRkSguLq7gWpG56dGjB9LS0gzKm5ycjIsXL4r7ut7LynJycvDbb7+VO75u3TqTBJSHDx9GYmKi5C6FhgRHUVFRCA0NRVFRkXhMJpOhSZMm8Pb2hpWVFTIyMnD9+nXk5uaKadLT0w1qidWmbdu2Rv8gtH79eskB5ZkzZ3DhwgWjzve8CAoKUnlt9A3cicwVA0oiMjkPDw+dX2gSExPRuHFjcf+DDz5AeHh4Bdfs+dKoUSOxFWv9+vWSA8r169cDKPlC3LBhQ9y5c6eiqkhmxNBWptzcXLz00kvivrW1NX799VfJ+bdt24anT58CALp06YLz588jOzsbhw8fxu3bt1U+J/RRev8LgoANGzbg008/1Znn0aNH2Lt3r0p+XZKSkjBixAgxmLS3t8cnn3yCiRMnol69eipp5XI5zp8/j+3bt2PTpk24fv26/k+sgjg7O8PS0hKZmZnYunUrli9fDgcHB535Sj8vAOnXjIhqFo6hJCKqoTp27IjWrVsDKPklPCkpSWeeCxcu4OzZswCArl27cqII0umdd95RaZ389ttv0blzZ8n5165dK26//fbbePPNNwGUjEdUDlb09dZbb8HS0hIAsGHDBkl5fv75ZzEwlDr2+OuvvxZbHa2srLBv3z588skn5YJJALC0tET79u3xxRdf4OrVq9i9ezc8PT0lnaeiWVtbY/jw4QCA7OxsbN++XWee4uJisfuug4MDQkNDK7SORGSeGFASEdVgpV+KFQoFNm7cqDO9civVmDFjKqxeVDP8+OOPKsHa0KFDMWPGDMn57969i5iYGAAlLXuhoaEICwsTH1+/fr3eE8SU8vb2RkhICADg+vXrOH78uM48ygGs1Pt/x44d4vbw4cPRrVs3SflkMhkGDBiABg0aSEpfGZSfs5Rg/o8//sCDBw8AAIMHD5Y8TpuIahYGlERULeTl5eHHH3/Eq6++Cl9fX9jb26N27dpo1aoVJk2ahAMHDmjNXzr7aemEPAAQGxur14yyRUVF2L9/P2bPno3g4GDUr18f9vb2sLe3R/369dGrVy98/fXXePjwoamfvsHCwsJgYVHyUa+rlUYulyMyMhIAYGdnh6FDh+p1rqysLGzevBmTJ0/Gyy+/LM5w6eTkhMaNG2PIkCFYvXo1CgsLJZepUCiwfft2jBw5Ei1btoSzszOsrKzEMnv37o25c+fiwIEDkMvlWsv666+/MH78ePj7+6N27dqwsrKCo6MjfHx80LNnT8ycORNRUVEoKCjQ63lrEx8fjwULFmDgwIFo2rQpatWqBRsbG3h4eKBTp06YNWsWLl26pLWMzz77DDKZTGWs1Z07dzTeu8otfhXp9OnTKsFjixYt9J5RdcOGDWLAOGjQIDg5OYnvLQC4desWjhw5YnAd9QmQLl26hNOnTwMoGevWrFkzneVnZ2cjNTVV3A8KCjKwpuYhKCgIzZs3BwAcOHAAKSkpWtMb8wOUKd7bPXv21Pu+V36vaOqeqzxb9meffSYe37dvH4YOHYomTZrAzs4O7u7u6NatG5YsWWLSzw2iakcgIqoCt2/fFgCIf2vWrNGYdt++fULDhg1V0qv769evn5CWlqa2jLFjx+rMr/wXExOjkj8mJkZwc3OTlNfR0VFYuXKlzmuwZs0aMU+PHj30uHqaKT/P0NBQQRAEITg4WDwWFxenMe+ePXvEdMOHDxcEQRB69OghHvvggw805v32228FW1tbSdfHx8dHOH78uM7ncufOHeHFF1+U/JotXLhQbTmZmZlC3759JZczZcoUnXXTJT09XWjevLmk88lkMmHKlClCYWGh2rLmz5+v172r7b1kKhkZGULjxo3Fczo4OAgXLlzQu5wWLVqIZezcuVM8PmvWLPH4hAkTJJfn6+sr5vv++++FnJwcwcnJSQAguLq6CgUFBRrzfvTRR2LeFStWCIIgqFzXqKiocnmSk5NV0ixYsECPZ286hn6WKOdzc3MTBEEQvvjiC/HYokWLNObNyMgQ3/Pe3t6CXC5XuVc7duyoMa+p3tvKn09S73vlcm/fvq02jfLn6Pz584Xs7GxhxIgRWuvYsmVLISkpSVIdSsXExIj5fX199cpLZE44KQ8RmbVt27apTHgBlEz606JFC+Tl5eHixYvIz88HUNL9qlu3boiJiYG3t7dKOW3atEHfvn1VZqMsO72+srIzyt67dw/p6enivqurK5o2bQpnZ2cUFhbi5s2b4q/5OTk5mDRpEgoLCzFt2jTjL4KRxo4di+joaAAlrTQvvvii2nTKrQ1SJ/ApdeXKFZVf6H18fFC/fn04Ojri6dOnuHLlCrKysgCUTGLyyiuv4MiRI+jYsaPa8nJzcxESEoIbN26IxxwdHdGqVSu4uroiPz8faWlpuHnzpriWn7o1/RQKBQYOHKjSymVnZ4dWrVrBzc0NRUVFePToEa5fvy7eY6ZYGzA3N1dlwhV7e3s0b94crq6ukMlkuH//Pq5fvw5BECAIAlasWIFHjx6pncymWbNm6Nu3r8oSA3Z2dujRo4fac+szw6ohBEHAmDFjcPv2bfHYjz/+CH9/f73KOXbsGK5duwYAcHd3R79+/cTHwsLC8K9//QsAsGXLFnz//fewt7fXu64ODg4YMmQI1qxZg8zMTHE21rKUu4Tb2tqKYwl1qVOnDmQymdjKevDgQcyePVvvepqT0aNHY/78+eJkRh9++KHadJs3bxbf88o9IXQx1Xu7ssjlcoSGhuLPP/8EAHh5eaFZs2aQy+U4d+4ccnJyAABXr17Fa6+9hlOnTsHKil+v6TlTpeEsET23pLRQ3rp1S6hVq5aYxtPTU9i+fbsgl8vFNFlZWcLcuXMFmUwmpuvVq5egUCjUntfQX/I3bNggtG3bVli6dKlw8+ZNtWnOnTsn9O/fXyzfzs5OuHXrlsYyK6uF8unTp4Kjo6MAQHB3d1fbEvb48WPBzs5OvM7FxcWCIEhvoZw4caLQv39/YePGjcKjR4/KPS6Xy4Xdu3cLLVu2FMtr3bq1ymup7F//+peYztnZWdiwYYPaeufk5Ai///67MGTIELWtKb/99ptYjo2NjbBkyRIhJyenXLqCggJh//79wrhx44QZM2ZofJ5SJSUlCZ6ensInn3winDp1SryeypKTk4WZM2eq3Ls///yzxjLNpTXj66+/VnnvvvPOOwaVM2nSJLGMqVOnlnu8TZs24uORkZGSyizbQikIqtdt0KBBavPt27dPTDNkyBDxuPLzVNdCKQiCEBAQoJLuX//6l8bPn4piyhZKQVB938fHx6vNGxQUJKa5ePGiIAiCpBZKU723y9azolooS3um+Pn5leu5kpubK8yYMUOlzJ9++klSPQTBfN7TRMZiQElEVUJKQDl48GDxcRcXF+Hy5csay1P+kgJA2LRpk9p0hn7xevr0qaR0crlcePPNN8VzzJo1S2PaygooBUEQRo8eLR7fsWNHuXwrV64UH585c6Z4XGpAKfX6pKenC02aNBHL3LVrl9p0yt10pX5BUxe0jR8/Xixn3rx5Bpejr8LCQq3dK5UtWbJErGNgYKDGdObw5TM6OlqwtLRUCRry8/P1LicvL0+oXbu2WM7Ro0fLpVm4cKH4eJ8+fSSVqy6gVCgU4nFra2vh4cOH5fKNHDlSzKfc9VZKQLls2TKVdACEVq1aCZ9//rkQGxsr+b1hDFMHlKtWrRKPv/fee+XyXbt2TXy8Q4cO4nEpAaWp3tuCUDkBZemPX5mZmRrLHDhwoJi2W7dukuohCObxniYyBU7KQ0RmKSkpCTt37hT3v/jiC7Rq1Upj+pkzZ+Lll18W95ctW2bS+jg6OkpKZ2FhgUWLFon7v//+u0nrYShdk5MYMrulMqnXp06dOvjkk0/EfU3X5969e+J2ly5dJJVdukRERZSjL2tra9jY2EhK++6776Jhw4YAgFOnTumcCKWqpKSk4K233hInSHF1dcVvv/0GW1tbvcv6/fff8fjxYwBAkyZN1C4zMnLkSLEb5V9//YXk5GSD6i2TycSZY4uKivDLL7+oPJ6dnS3O1Fq3bl30799fr/InT54sziZb6sqVK5g/fz569OgBFxcXtG3bFpMnT0ZkZCQePXpk0POoTEOHDhW7GP/yyy8oLi5WedyYz4uqek8a48cff0Tt2rU1Pv7++++L23FxceWuF1FNx4CSiMzSrl27xC+ujo6OmDBhgtb0MplMZcbJI0eOVNkXt6ZNm8Ld3R0AcOPGDfGLc1UKDg4WlyfYtWsXMjIyxMeUZ9IMCAhAQEBAhdblpZdeErdLxwSWZWdnJ26fP3/e4HOZqpyKJJPJVMbyxsXFVWFt1CsuLsbw4cORlpYGoKTOGzZsMHidUuXxuqNGjVKbpkGDBujZsyeAkjF0UteSVEfbDypbtmwR15EcOXKk3uPfrKysEBUVhYkTJ6odRyiXy3HhwgWsXLkSYWFh8Pb2xrBhw3D16lUDnknlcHJywuDBgwEAaWlp2Ldvn/iY8GxsJVDyw8nIkSP1Krs6vCeVtWrVSudSMEFBQeJrX1BQoDK+mOh5wFHDRGSWTp48KW53795dUgvYgAEDVCbIiIuLw4ABA0xet9IvWOfOnUNKSgqys7NVJg0CgKdPnwIo+fJ1//59rb9uVwYLCwuEhYVhwYIFKCwsxKZNmzB16lQAxrdOlnXp0iXExsbi4sWLSE9Px9OnT1Wm/c/LyxO3NbU6dezYUfyy+Y9//AOOjo7i66uPjh07ii3dn3/+Oby9vTFixIhKnTQjNzcXf/75J+Lj45GYmIgnT56goKBAZX3FCxcuiNuGtsRVpDlz5uDw4cPi/scff4xXX33VoLJSUlLECU4AqKw7WVZYWJg4odS6deswZ84cg87ZokULvPzyyzhx4gROnTqFhIQE+Pn5ATDN/W9vb4+VK1fi3XffxYoVK7BlyxaNywcVFRVhy5Yt2LFjB5YuXYopU6YYdM6KNmbMGPz8888ASq596esdGxuLO3fuAAD69euHunXr6lWuqd7blUXKUjB2dnZwc3MTX3Nz+BGRqDIxoCQis6Q8A2CbNm0k5XF2doavr6+4tphyGaaQnJyMmTNnYuvWrTrXPFRWOrtpVRszZgwWLFgAoORL9NSpU1VaGywtLTW2Fklx4sQJzJgxQ68WNk3XZvr06Vi/fj3kcjkePHiA1157DQ0aNED//v3Ro0cPdOvWTewmqs2ECRPw7bff4unTp8jNzcXo0aPx/vvvq5TTokULyfXVR15eHr788kssW7YM2dnZkvOZy/1Savv27Vi8eLG4HxISgi+++MLg8jZu3Ci+fwIDA7Ve/9DQUEybNg15eXm4cuUK4uLiNM7MrMuYMWNw4sQJACX3/4IFC5CYmIhDhw4BAF544QV06NDBoLJL+fv7Y/ny5Vi2bBkuXbqEY8eO4e+//0ZcXBwuXLig8iNCUVERpk6dCicnJ61BdVXp1asXvLy8kJKSgqioKDx+/Bi1a9c2au1JwHTv7cri6ekpKZ2Dg4O4XdriTfS8YJdXIjJLyr/wurm5Sc5X2tUUADIzM01Wn4SEBLRv3x6//vqrXsEkALNZ8Lp169YIDAwEUNICfO3aNRw5cgS3bt0CAPTp0wceHh4Glb1582Z07dpV7+6ahYWFao936NABq1evVhmfd+/ePURERCAsLAy+vr5o3rw5Zs+ejZs3b2osv379+ti6dStcXFzEY48ePcKGDRvw9ttvo2XLlvDx8cH06dNx7tw5vequTXZ2NoKDg/HNN9/oFUwC5nO/ACU/yowbN07cr1+/Pn755RfJS0SooxyQ6AqknJ2dMWjQILV59TVixAhxXGtkZKTYjbY0yDNF63wpmUwGf39/TJo0CRERETh37hzS0tKwZMkSeHl5qaR999138eTJE5Od21SUf2DKz8/Hr7/+itzcXGzduhVAyRjagQMH6l2uqd7blUXqWGhlyj8cED0PGFASkVlS/lKtz3/oymlN9cVcLpdj2LBhYncmW1tbTJgwAdu2bRPXVywsLBTXFBQEAb6+viY5t6kpry+5fv16o9aeLJWYmIjw8HAx0K5bty4+/vhj/PXXX7h9+7bY5bX02kgdXzRmzBgkJCRgypQpan9UuHHjBhYtWoRWrVph9uzZGifC6NOnD65evYqPPvqo3PqkQMmX2eXLl6N9+/YYN26cSVoXPvzwQ7E1DCjpGrhmzRqcO3cOjx49Qn5+vsr9Yui1r0h5eXkYMmSI2GJqZWWFzZs3693FUdnp06dx6dIlcX/evHmoXbu21j/liZs2bdqk8UcIXZQDoHv37iE6Olrs7lraJbwi1a1bFzNmzMCFCxdUel1kZmZiy5YtFXpuQ5X9vNi2bZv4A8nw4cMNmpAJMN17m4jMA7u8EpFZUm5R0qeFRzmtqcYtRkVFiV+Cra2tER0drXZWSk31MCdvvfUWZs6ciaKiIqxfv14MFlxcXPD6668bVOaSJUuQn58PoGTGzmPHjmlt6dTn2jRp0gT//e9/sWzZMpw9exYHDx5EbGwsYmJixHKKi4uxaNEi5OfnY+nSpWrL8fDwwMKFC7Fw4UIkJCSI5URHR4uTNwmCgLVr1yIjI8Oo2XnT09Px008/ifvffvstZs2apTWPOd4v06ZNU2m1XbRokeRZOTVZu3atyn7pWGOpMjIyEBUVhdDQUIPOP2bMGLGF7f333xe7xffq1Uvtjw0Vwc3NDStWrEDXrl3FY0ePHtU58VhV8Pf3R/v27XHmzBkcPXpUpeeIsS26pnpv60Pf3iVEJA1bKInILCm3gkht0Srb+mVMS4qy/fv3i9ujRo3SGUzm5uaa7aQMbm5u4kRFSUlJYle7oUOHqsy+qA/l6/PPf/5TZ7dZQyadsbCwQIcOHTBz5kz8/vvvePjwITZv3qwyy+jy5cvF8bPa+Pn5YerUqdi8eTNSU1OxZ88etG3bVnx8586dKhPQ6Cs6Olr84tq4cWN88MEHOvOY20Q8q1atwpo1a8T90NBQlaURDFFYWFhuyQ5DGNPttX///uLnwsWLF8XjpuzuKkXnzp3h5OQk7pvrUjGA6rUp/WGtefPmkiarkcKY97Zyj5SyE6OpY66fy0TVHQNKIjJLypNjSB2Xd+nSJZUWD3UTbCiP/ZI6zuXu3bvidukYRG1OnjwJhUIhqeyqoO7LszFfqPW9PsePHzf4XKVsbW0xbNgw7Nu3D9bW1gBKWh8OHDigVzmWlpbo378/Dhw4oDL+VnkWUn0pX4+OHTvqnL0yLy8PZ8+e1VmuIfeuIc6ePYvp06eL+y1atMDq1auNLnf37t1IT08HUPL6PXnyRKXbr7a/o0ePiuXs3bsXDx48MKgO1tbWGDFihMox5SUyKotMJlOZubr0HjZH6pZSGT16dIWdT5/3tnJQLmXMvPKPCERkOgwoicgsKa/7denSJZVlFTTZuHGjuF27dm34+/uXS6P8JU55+QptpPzyraxstz5z89prr6FOnTrifuPGjVW63+lLn+sjl8uNWk+wrBYtWojLPwAQ10nUl7u7u0p3TkPLAfS/XzZt2iRpvK8h966+srKyMGTIELELs4ODA7Zu3QpnZ2ejy1ZuWezTp49KMKBLUFAQ6tevD6CkG2TpchaGKDteNTQ0VGWGzsrw+PFjlWVFKqu7rSHq1auHfv36ifsymaxCA8pSUt7byrPBSlnT0piu7ESkGQNKIjJLvXv3RoMGDcT9uXPnak1/9+5dLFu2TNwPDw+HpaVluXTKU8DfunVLUkuP8qyMyi0l6pw8eVIlsDVHNjY2SE9PF1t/bt26ZdQacPpcn3//+9+SujDr0wKn3CqtHCjrW47yOMay5ehD+XqcPHlS67itx48f49NPP5VUrvK9++jRowpZXiQ8PFxlZs0VK1ao/WFGXw8fPsSePXvE/SFDhuiVXyaTqYybNKbba8eOHVVaP5W79upLoVBg06ZNevdI+OGHH1Tui1deecXgOlSGqKgo8XopFAqV7qj6MtV7GwDat28vbu/duxc5OTkay0pMTERERITkcxORdAwoicgsWVpaqixivnv3bsyZM0ftF7fU1FQMHDhQ/DJRq1YtvPfee2rLbdOmjdh9Kz09XWVRc0169Oghbv/6668au1XGx8dj0KBBZt3dtSIoX58vv/xS43jAdevW6fxhoFS7du0QGRkptpRpsmLFCpUAqHv37iqPh4SEYMWKFTqXZdi9ezcOHjyosRx9KOdNSkrCV199pTbdw4cPMWDAAMnjJ+vXr4969eoBKPlSbopJSpR9++232LFjh7g/adIkk40t/Pnnn8WWWxsbG5WlQKQaOnSouH327FlJLVIVTaFQ4K233kLbtm0RERGh8z4TBAEREREqPyJ4eXnhtddeq+iqmg1TvbcB4NVXXxXHUWZkZODDDz9UW9b9+/cxaNAgvSeBIiJpOMsrEZmtqVOnYtu2bYiOjgYALFy4ENHR0Rg/fjxatmyJ/Px8HD16FD/88IM4NgsAFi9erHHZDmdnZ7z22mviF+fw8HB8/fXXaNq0qcoED1999ZXYMjN8+HDMnTsXqampkMvl6N+/P95++23069cPrq6uSElJwZ49exAZGYni4mL07dsXCQkJSEpKqqArY17+8Y9/YP369RAEAffu3UO7du3wj3/8A0FBQbC2tsbNmzfxyy+/iIH422+/rTILqjrnz59HWFgYpkyZggEDBuCll15Cs2bN4OrqisLCQty4cQPbtm3Dvn37xDyDBw9W6SIHlLRCT506FTNnzkSfPn0QFBSE1q1bo06dOpDL5UhMTMSePXuwdetW8YeAwMBA9O3b1+Dr0bhxYwwaNAg7d+4EAHz22WeIi4vDqFGj4OPjg6ysLBw5cgQRERHIyMiAt7c32rVrp9KCp8nIkSOxZMkSAMD8+fOxatUqtG7dWmVCpXfffRfBwcF61fnKlSv4+OOPVY5du3ZNpaujvj744AP07t0bgGqLYkhIiEEzMHfp0gVeXl7iBDbr1q3D4sWLDa6fKV26dAmTJk3CjBkz0LNnT/E+c3Nzg6WlJdLT03Hu3Dls3bpVZRyfhYUF/vvf/6p0Z67pTPXeBkomGQsPD8fKlSsBlASh169fx7hx4+Dj44PMzEzExsYiIiIC2dnZCAsLM/seJETVkkBEVAVu374tABD/1qxZozbdkydPhJ49e6qk1fb37bffSjp3gwYNtJYTExOjkufAgQOCjY2NzvP7+fkJDx48EHx9fTWWVWrNmjVimh49euh3ATUYO3asWGZoaKjR5fXo0UMs74MPPtCY7ssvv5T0+gwbNky4deuWyjF1pL7epX8vvfSSkJGRUa4c5ddByl/z5s2FO3fuGH3d7t+/L/j4+Og8n4uLi3DkyBGV123+/Pkay338+LHg7++vtUxN7yVtYmJi9L7muv5K63H+/HmV46tWrTLsogqCMH36dLEcDw8PoaioSOVx5df7+++/N/g8pZTrHRUVVe7x4uJiwcLCwqDr4+zsLGzatMnoOgqC4Z8lyvnc3NyMrsf8+fPF8jp27Kg2jane26UyMjKE1q1b6yynW7duQm5ursqx27dvqy1T6vtRmZTP/LKU33e+vr6S8hCZI3Z5JSKz5uTkhP3792Pp0qUqY9PK6ty5M44cOaJzvT8AaNSoEc6dO4dvvvkG3bt3R7169VRaJ9UJDg7GwYMHVRYkV+bg4IB33nkHcXFxJluupDqZN28eVq1aJXbJLMvT0xNLlizB5s2bJY3XXLZsGfr06aNzshQfHx8sXLgQhw8fhqura7nHv/nmG7zxxhsq65qq4+7ujjlz5iA+Pl5log9DeXl54eTJkxg4cKDaxy0sLNC3b1+cOXNGr7UdXVxcEBcXh++//x69e/eGl5eXwcu9VBbl1kkrKyu88cYbBpel3O01LS0Nf/zxhzFVM5qlpSWSk5Px3//+F6+99pqkllcPDw+89957uHz5MoYPH17xlTQzpnpvl3J1dcXBgwc1jsutVasWZs+ejQMHDsDe3t6ouhORejJBqMC5x4mITEgQBPz999+4cOECHj58CFtbW3h6eqJbt24qE/hUdB1OnTqFU6dOITMzE66urvDx8UHPnj1Rq1atSqmDOcvPz8fhw4dx6dIl5OXloV69emjWrBm6du2qdpIkXYqLi3H+/Hlcu3YNKSkpyMnJgZ2dHerVq4eAgAC0adNGZTkNTRQKBRISEnD16lXcu3cP2dnZsLGxgZubG9q0aYP27dtX2NINt27dwqFDh5CSkgJ7e3vUr18fnTt3FmctpZpDoVDg+vXruHr1qrjOqyAIcHJygoeHB9q2bYsWLVpIumf1sXbtWowbNw5AyZhm5fHA5spU721lSUlJiImJEd9rvr6+CAkJMdvP5oMHD4oTMvn6+kpaR5fIHDGgJCIiIqrGqmNASQwoqebgpDxERERENcT58+dVJlNq2rQpli9fXoU1olLHjx/H559/Lu5nZGRUYW2ITIcBJREREVENkZmZqTJDakBAQBXWhpSlpaWpvDZENQUn5SEiIiIiIiKDcAwlERERERERGYQtlERERERERGQQBpRERERERERkEAaUREREREREZBAGlERERERERGQQBpRERERERERkEAaUREREREREZBAGlERERERERGQQBpRERERERERkEAaUREREREREZBAGlERERERERGSQ/wfCxifvf+5JnAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2320\n" - ] - } - ], - "source": [ - "\n", - "mask_M1isNS = (M1 <= 2.5) # M1 is a NS if mass is <= 2.5 Msun \n", - "mask_M2isNS = (M2 <= 2.5) # M2 is a NS if mass is <= 2.5 Msun \n", - "mask_BHBH = ((mask_M1isNS==0) & (mask_M2isNS==0)) # if true then the system is a BHBH\n", - "\n", - "\n", - "\n", - "\n", - "M1ZAMS = fsys['Mass@ZAMS_1'][...].squeeze() # mass in Msun of the compact object resulting from the *primary star*\n", - "M2ZAMS = fsys['Mass@ZAMS_2'][...].squeeze() # mass in Msun of the compact object resulting from the *secondary star*\n", - "\n", - "\n", - "# the separation At ZAMS is given by \n", - "separationZAMS = fsys['Separation@ZAMS'][()]\n", - "PeriodZAMS = separation_to_period_circular_case(separationZAMS*u.au, M1ZAMS*u.M_sun, M2ZAMS*u.M_sun)\n", - "\n", - "# the merger time is called the \"coalescence time\"\n", - "coalescence_time = fDCO['Coalescence_Time'][...].squeeze() * u.Myr # Myr \n", - "t_Hubble = 13.7 *u.Gyr\n", - "mask_tHubble = (coalescence_time < t_Hubble)\n", - "\n", - "# this is the parameter that describes the Wolf Rayet factor f_WR that was used. \n", - "WR_Multiplier = fsys['WR_Multiplier'][...].squeeze()\n", - "\n", - "# mask BBHs that merge in a Hubble time \n", - "mask_systemsOfInterest = (mask_BHBH==1) & (mask_tHubble==1)\n", - "\n", - "# add the mask of systems that are CHE. Since the CHE mask is based on systemParameters we have \n", - "# to match the systems from systemParameters that we want to mask with the DCO systems, we can do this using the SEED\n", - "# a system in systemParameters will have the same SEED in DoubleCompactObjects, if it exists in both\n", - "mask_DCO_that_are_CHE = np.in1d(SEED_DCO, SEED_CHE) \n", - "mask_DCO_that_are_BBH_and_CHE = (mask_DCO_that_are_CHE ==1) & (mask_systemsOfInterest==1)\n", - "\n", - "# we can mask for the f_WR = 1 factor that is used in Figure 4 of the paper. \n", - "mask_fWR_1 = (WR_Multiplier==1)\n", - "SEED_fWR_1 = SEED[mask_fWR_1]\n", - "mask_DCO_that_are_fWR_1 = np.in1d(SEED_DCO, SEED_fWR_1)\n", - "\n", - "# combine all the masks above\n", - "mask_DCO_that_are_BBH_and_CHE_and_fWR_1 = (mask_DCO_that_are_CHE ==1) & (mask_systemsOfInterest==1) & (mask_DCO_that_are_fWR_1==1)\n", - "\n", - "mask_Fig4 = np.in1d(SEED, SEED_DCO[mask_DCO_that_are_BBH_and_CHE_and_fWR_1])\n", - "\n", - "\n", - "\n", - "\n", - "# plot the systems \n", - "f, ax= plt.subplots(1, 1, figsize=(10,10)) \n", - "\n", - "plt.scatter((M1ZAMS+M2ZAMS)[mask_Fig4], PeriodZAMS[mask_Fig4].to(u.d))\n", - "\n", - "xlabel = 'Total Mass at ZAMS [Msun]'\n", - "ylabel = 'Period at ZAMS [day]'\n", - "layoutAxes(ax=ax, nameX=xlabel,nameY=ylabel)\n", - "plt.xscale('log') \n", - "\n", - "\n", - "plt.show()\n", - "\n", - "\n", - "print(len((M1+M2)[mask_DCO_that_are_BBH_and_CHE_and_fWR_02]))\n" - ] - }, - { - "cell_type": "markdown", - "id": "544dc9c9", - "metadata": {}, - "source": [ - "
\n", - "\n", - " \n", - "For the last part of this excersize we will use the code 'FastCosmicIntegrator'. Our goal will be to calculate the merger rate for BBHs, so that we can compare to the analytical estimate we made earlier on. \n", - "\n", - "## Question 7: \n", - " \n", - " \n", - "Using the code below, plot the merger rate of CHE BBHs as a function of redshift. You should at the end of running this code create a plot with four panels that show the properties of the CHE binaries \n", - " \n", - " - a) what do the panels show? What are the differences between the panels? \n", - " - b): please write down the local (z=0) BBH merger rate, and compare this with your analytically calculated rate \n", - " - c): compare both rates with the other BBH rates as reported in Mandel & Broekgaarden et al. (2021) (Fig 3)\n", - "\n", - " \n", - " - d): Repeat the excersize above, and answer 7a & 7b above, but now for all BBHs (including non CHE). You can do this by changing dco_type to 'BBH'. What are the differences\n", - "\n", - " \n", - " \n", - " *Hint* we can do an approximate calculation by combining the Wolf Rayet factors. If you want to do the more expert version you can modify the code in ClassCOMPAS (setCOMPASDCOmask) and add the mask of a specific f_WR factor" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "681a2767", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.11/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/floorbroekgaarden/Projects/GitHub/COMPAS/utils/Tutorial/Tutorial_reproduce_CHE_paper_teaching_demo_example/FastCosmicIntegration.py': [Errno 2] No such file or directory\r\n" - ] - } - ], - "source": [ - "# change the line to point to your path \n", - "\n", - "!python3 FastCosmicIntegration.py \\\n", - "--dco_type 'CHE_BBH' \\\n", - "--path '/Users/floorbroekgaarden/Downloads/COMPAS_Output.h5' \\\n", - "--maxz 15 \\\n", - "--dontAppend" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "285095f6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHE_evolution_demo.ipynb\r\n", - "CHE_evolution_demo_ANSWERS.ipynb\r\n", - "COMPAS_Documentation.pdf\r\n", - "Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png\r\n", - "SNR_Grid_IMRPhenomPv2_FD_all_noise.hdf5\r\n", - "\u001b[34m__pycache__\u001b[m\u001b[m\r\n", - "old_ClassCOMPAS.py\r\n", - "old_FastCosmicIntegration.py\r\n", - "old_selection_effects.py\r\n", - "old_totalMassEvolvedPerZ.py\r\n" - ] - } - ], - "source": [ - "!ls " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "0b87ce45", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show the image in the notebook:\n", - "Image(filename='./Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png') \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "919656e9", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 7 \n", - " \n", - " \n", - "The rates for CHE around redshift 0 are about 20 Gpc^-3 yr^-1 for the mixture model (note that we have to select either of the Wolf Rayet factors if we want to get an absolute rate for one fixed model. )\n", - "This is indeed about the mean of the Rates quoted by Riley +2021, and corresponds to the CHE rates as shown in Fig 3 of Mandel & Broekgaarden\n", - " \n", - "The CHE rates are somewhat lower compared to the isolated binary evoluton rates, and many of the other channel rates. This is because the CHE channel is a rare channel: you need very specific intitial conditions (masses, metallicities) to form CHE stars. \n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98ccf72f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a92fadd4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "15d8abef", - "metadata": {}, - "source": [ - "
\n", - "\n", - "## Extra Question 8: \n", - " \n", - " \n", - "Play around with some of the parameters in the code FastCosmicIntegrater, do the rates go up or down? Is this expected?" - ] - }, - { - "cell_type": "markdown", - "id": "8b34f7ba", - "metadata": {}, - "source": [ - "
\n", - "\n", - "# Answer 8\n", - " \n", - " \n", - "Doing this for 'BBH' should give you a rate of about 100 Gpc^-3 yr^-1 which is right on the ballpark of the observations\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "2f7452f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.11/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/floorbroekgaarden/Projects/GitHub/COMPAS/utils/Tutorial/Tutorial_reproduce_CHE_paper_teaching_demo_example/FastCosmicIntegration.py': [Errno 2] No such file or directory\r\n" - ] - } - ], - "source": [ - "!python3 FastCosmicIntegration.py \\\n", - "--dco_type 'BBH' \\\n", - "--path '../../data/COMPAS_Output_reduced.h5' \\\n", - "--maxz 15 \\\n", - "--dontAppend" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "a4c53c13", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show the image in the notebook:\n", - "Image(filename='./Rate_Infomu00.035_muz-0.23_alpha0.0_sigma00.39_sigmaz0.0.png') \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06928d4f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "93c376a5", - "metadata": {}, - "source": [ - "
\n", - "\n", - " \n", - " \n", - "\n", - "## EXTRA using the CHE data: \n", - " \n", - "If there is time left, try to plot some other BBH or ZAMS properties of the BBH, (or NSBH or BNS), examples include chirp mass, mass ratio, individual masses. How do these compare with LIGOs observations (paper is attached to this directory)\n", - "\n", - " \n", - " \n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a647b78", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c370cf85", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4a02ec4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab7e8335", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "3e78c305", - "metadata": {}, - "source": [ - "
\n", - " \n", - " # Extra material:" - ] - }, - { - "cell_type": "markdown", - "id": "0b84006f", - "metadata": {}, - "source": [ - "[//]: ## (grip -b README.md)\n", - "\n", - "\n", - "\n", - "# Compact Object Mergers: Population Astrophysics & Statistics\n", - "\n", - "[![Documentation](https://img.shields.io/badge/Documentation-latest-orange.svg?style=flat)](https://github.com/TeamCOMPAS/COMPAS/blob/Documentation/COMPAS_Documentation.pdf)\n", - "\n", - "[//]: ## (Outline features)\n", - "COMPAS is a publicly available rapid binary population synthesis code (https://compas.science/) that is designed so that evolution prescriptions and model parameters are easily \n", - "adjustable. COMPAS draws properties for a binary star system from a set of initial distributions, and evolves it from zero-age main sequence to the end of its life as two compact \n", - "remnants. It has been used for inference from observations of gravitational-wave mergers, Galactic neutron stars, X-ray binaries, and luminous red novae.\n", - "\n", - "## Documentation\n", - "https://compas.science/docs\n", - "\n", - "## Contact\n", - "Please email your queries to compas-user@googlegroups.com. You are also welcome to join the [COMPAS User Google Group](https://groups.google.com/forum/#!members/compas-user) to engage in discussions with COMPAS users and developers.\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "## Example of additional excersizes\n", - "\n", - "If you are interested, you can download the COMPAS code from Github and do any of the following excersizes: \n", - "\n", - "\n", - "1). Try to run any of the demos that are provided (I recommend the Chirp mass distribution demo, and/or the detailed evolution demo)\n", - "\n", - "2.) Try to use the code, and the data above, to plot a detailed evolution plot of a CHE BBH. \n", - "\n", - "3.) Run a larger COMPAS simulation with your own favorite settings, compare this to the data given in this demo. \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f54328e3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From e692e653785e2ce17b3872ed49f0e99015ad59c8 Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Wed, 25 Oct 2023 15:50:57 +1100 Subject: [PATCH 05/32] Remove spurious files --- src/changelog.h | 2 +- src/outfile | 117 ------------------------------------------------ 2 files changed, 1 insertion(+), 118 deletions(-) delete mode 100644 src/outfile diff --git a/src/changelog.h b/src/changelog.h index f0db15744..7c086b25d 100644 --- a/src/changelog.h +++ b/src/changelog.h @@ -1058,7 +1058,7 @@ // 02.39.01 LC - Sep 01, 2023 - Defect repair: // - Fix for issue #945 - made HeSD SN types a sub-class of SNIA types. // 02.40.00 JR - Oct 25, 2023 - Enhancement, a little cleanup: -// - Added naive tides implementation. Functionality enable with new option `--enable-tides`. default is no tides. +// - Added naive tides implementation. Functionality enabled with new option `--enable-tides`. Default is no tides. const std::string VERSION_STRING = "02.40.00"; diff --git a/src/outfile b/src/outfile deleted file mode 100644 index 0729592b1..000000000 --- a/src/outfile +++ /dev/null @@ -1,117 +0,0 @@ - -COMPAS v02.40.00 -Compact Object Mergers: Population Astrophysics and Statistics -by Team COMPAS (http://compas.science/index.html) -A binary star simulator - -Start generating binaries at Wed Oct 25 15:33:51 2023 - -0: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -1: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -2: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -3: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -4: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Neutron_Star) -5: Stars touching: (Main_Sequence_>_0.7 -> Massless_Remnant) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -6: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -7: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -8: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -9: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -10: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -11: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) -12: Stars merged: (Main_Sequence_>_0.7 -> Naked_Helium_Star_MS) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -13: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) -14: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -15: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -16: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -17: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -18: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -19: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -20: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) -21: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -22: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -23: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -24: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) -25: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -26: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -27: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Neutron_Star) -28: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -29: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -30: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) -31: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -32: Allowed time exceeded: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -33: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) -34: Allowed time exceeded: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) -35: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -36: Allowed time exceeded: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) -37: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) -38: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -39: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -40: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) -41: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -42: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) -43: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Neutron_Star) -44: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -45: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) -46: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -47: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) -48: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) -49: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -50: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) -51: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -52: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_<=_0.7 -> Main_Sequence_<=_0.7) -53: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) -54: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -55: Allowed time exceeded: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Black_Hole) -56: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -57: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) -58: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -59: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -60: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -61: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -62: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -63: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -64: Allowed time exceeded: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) -65: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -66: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) -67: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -68: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) -69: Allowed time exceeded: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -70: Stars merged: (Main_Sequence_>_0.7 -> Naked_Helium_Star_MS) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -71: Stars merged: (Main_Sequence_>_0.7 -> Naked_Helium_Star_MS) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -72: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) -73: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -74: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) -75: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -76: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) -77: Unbound binary: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Neutron_Star) -78: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) -79: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -80: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -81: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -82: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -83: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -84: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -85: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -86: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -87: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) -88: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -89: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -90: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -91: Stars merged: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Thermally_Pulsing_Asymptotic_Giant_Branch) -92: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Oxygen-Neon_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -93: Stars merged: (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -94: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -95: Unbound binary: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Neutron_Star) -96: Allowed time exceeded: (Main_Sequence_>_0.7 -> Neutron_Star) + (Main_Sequence_>_0.7 -> Main_Sequence_>_0.7) -97: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -98: Double White Dwarf formed: (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) + (Main_Sequence_>_0.7 -> Carbon-Oxygen_White_Dwarf) -99: DCO formed: (Main_Sequence_>_0.7 -> Black_Hole) + (Main_Sequence_>_0.7 -> Black_Hole) - -Generated 100 of 100 binaries requested - -Simulation completed - -End generating binaries at Wed Oct 25 15:33:53 2023 - -Clock time = 2.12871 CPU seconds -Wall time = 0000:00:02 (hhhh:mm:ss) From 12a21429c9d7c55e456e0f841d04b330428c8284 Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Thu, 26 Oct 2023 14:00:26 +1100 Subject: [PATCH 06/32] WIP --- src/BH.h | 2 +- src/BaseBinaryStar.cpp | 17 ++++++++++++++--- src/BaseBinaryStar.h | 7 ++++--- 3 files changed, 19 insertions(+), 7 deletions(-) diff --git a/src/BH.h b/src/BH.h index ea99dd1d5..b597c966a 100755 --- a/src/BH.h +++ b/src/BH.h @@ -56,7 +56,7 @@ class BH: virtual public BaseStar, public Remnants { double CalculateMassLossRate() { return 0.0; } // Ensure that BHs don't lose mass in winds - double CalculateMomentOfInertia(const double p_RemnantRadius = 0.0) const { return -(2.0 / 5.0) * m_Mass * m_Radius * m_Radius; } + double CalculateMomentOfInertia(const double p_RemnantRadius = 0.0) const { return (2.0 / 5.0) * m_Mass * m_Radius * m_Radius; } double CalculateMomentOfInertiaAU(const double p_RemnantRadius = 0.0) const { return CalculateMomentOfInertia(p_RemnantRadius * RSOL_TO_AU) * RSOL_TO_AU * RSOL_TO_AU; } double CalculateRadiusOnPhase() const { return CalculateRadiusOnPhase_Static(m_Mass); } // Use class member variables - returns radius in Rsol diff --git a/src/BaseBinaryStar.cpp b/src/BaseBinaryStar.cpp index 525fed3ab..3fc522f33 100644 --- a/src/BaseBinaryStar.cpp +++ b/src/BaseBinaryStar.cpp @@ -2313,16 +2313,26 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { */ // find omega assuming synchronisation - // rough guess is (m_Star1->Omega() + m_Star2->Omega()) / 2.0 + // use current value of m_Omega as best guess for root + // if m_Omega == 0.0 (should only happen on the first timestep), calculate m_Omega here + + if (utils::Compare(m_Omega, 0.0) == 0) { + m_Omega = std::sqrt(G1 * (m_Star1->Mass() + m_Star2->Mass()) / (m_SemiMajorAxis * m_SemiMajorAxis * m_SemiMajorAxis)); + } + m_Omega = OmegaAfterSynchronisation(m_Star1->Mass(), m_Star2->Mass(), m_Star1->CalculateMomentOfInertiaAU(), m_Star2->CalculateMomentOfInertiaAU(), - m_TotalAngularMomentum, - (m_Star1->Omega() + m_Star2->Omega()) / 2.0); + m_TotalAngularMomentum, + m_Omega); + //std::cout << "m_Omega returned = " << m_Omega << "\n"; + if (m_Omega > 0.0) { // root found? // yes + //std::cout << "Root found: " << m_Omega << "\n"; + m_Star1->SetOmega(m_Omega); // synchronise star 1 m_Star2->SetOmega(m_Omega); // synchronise star 2 @@ -2334,6 +2344,7 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { m_EccentricityPrev = m_Eccentricity; m_SemiMajorAxisPrev = m_SemiMajorAxis; } + //else std::cout << "No root found\n"; /* std::cout << "Total angular momentum after = " << m_TotalAngularMomentum << "\n"; diff --git a/src/BaseBinaryStar.h b/src/BaseBinaryStar.h index c0318033b..652ffc83c 100644 --- a/src/BaseBinaryStar.h +++ b/src/BaseBinaryStar.h @@ -658,12 +658,13 @@ class BaseBinaryStar { boost::math::tools::eps_tolerance tol(get_digits); // tolerance // define functor - // function: ax + bx^(1/3) + c = 0 - double a = p_I1 + p_I2; + // function: (I_1 + I_2) Omega + L(Omega) - p_Ltot = 0 + // where L(Omega) = b*Omega(-1/3) + double a = p_I1 + p_I2; // I_1 + I_2 double b = PPOW(G1, 2.0 / 3.0) * p_M1 * p_M2 / std::cbrt(p_M1 + p_M2); double c = -p_Ltot; - auto func = [this, a, b, c](double x) -> double { return (a * x) + (b / std::cbrt(x)) + c; }; // functor + auto func = [a, b, c](double x) -> double { return (a * x) + (b / std::cbrt(x)) + c; }; // functor // find root double factor = TIDES_OMEGA_SEARCH_FACTOR; // size of search steps From b359805857b9a16081e2a65b7816c02c639798ad Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Thu, 26 Oct 2023 14:08:27 +1100 Subject: [PATCH 07/32] WIP --- src/BaseBinaryStar.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/BaseBinaryStar.cpp b/src/BaseBinaryStar.cpp index 3fc522f33..a6ecb660a 100644 --- a/src/BaseBinaryStar.cpp +++ b/src/BaseBinaryStar.cpp @@ -2317,7 +2317,7 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { // if m_Omega == 0.0 (should only happen on the first timestep), calculate m_Omega here if (utils::Compare(m_Omega, 0.0) == 0) { - m_Omega = std::sqrt(G1 * (m_Star1->Mass() + m_Star2->Mass()) / (m_SemiMajorAxis * m_SemiMajorAxis * m_SemiMajorAxis)); + m_Omega = OrbitalAngularVelocity(); } m_Omega = OmegaAfterSynchronisation(m_Star1->Mass(), From b95bf6959ee26331f2927f7ad85811ea7c8d8c80 Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Sun, 29 Oct 2023 18:21:41 +1100 Subject: [PATCH 08/32] Minor fixes; updated docs; added links to splash string --- .../program-options-list-defaults.rst | 6 +- online-docs/pages/whats-new.rst | 5 ++ src/BaseBinaryStar.cpp | 85 +++++++------------ src/BaseBinaryStar.h | 7 +- src/changelog.h | 4 +- src/utils.cpp | 10 ++- 6 files changed, 53 insertions(+), 64 deletions(-) diff --git a/online-docs/pages/User guide/Program options/program-options-list-defaults.rst b/online-docs/pages/User guide/Program options/program-options-list-defaults.rst index 93c466d9b..5adde9958 100644 --- a/online-docs/pages/User guide/Program options/program-options-list-defaults.rst +++ b/online-docs/pages/User guide/Program options/program-options-list-defaults.rst @@ -349,6 +349,10 @@ Default = 0.0 Multiplication factor for Eddington accretion for NS & BH (i.e. > 1 is super-eddington and 0 is no accretion). |br| Default = 1.0 +**--enable-tides** |br| +Enables tides. |br| +Default = FALSE + **--enable-warnings** |br| Display warning messages to stdout. |br| Default = FALSE @@ -1256,7 +1260,7 @@ Go to :ref:`the top of this page ` for the full alphabetical --mass-transfer-rejuvenation-prescription, --mass-transfer-thermal-limit-accretor, --mass-transfer-thermal-limit-C, --retain-core-mass-during-caseA-mass-transfer, --stellar-zeta-prescription, --zeta-adiabatic-arbitrary, --zeta-main-sequence, --zeta-radiative-giant-star ---circulariseBinaryDuringMassTransfer, --angular-momentum-conservation-during-circularisation +--circulariseBinaryDuringMassTransfer, --angular-momentum-conservation-during-circularisation, --enable-tides --envelope-state-prescription, --common-envelope-alpha, --common-envelope-alpha-thermal, --common-envelope-formalism, --common-envelope-lambda-prescription, --common-envelope-lambda, diff --git a/online-docs/pages/whats-new.rst b/online-docs/pages/whats-new.rst index f30799adb..e15165878 100644 --- a/online-docs/pages/whats-new.rst +++ b/online-docs/pages/whats-new.rst @@ -6,6 +6,11 @@ Following is a brief list of important updates to the COMPAS code. A complete r **LATEST RELEASE** |br| +**02.30.00 Oct 30, 2023** + +* Added a naive tides implementation. +* Added program option ``enable-tides`` to enable the tides implementation (default is ``false``). + **02.39.00 Jul 4, 2023** * Added 'Evolution_Status' columns to both SSE and BSE default system parameters records - records final status of evolution (reason evolution stopped). diff --git a/src/BaseBinaryStar.cpp b/src/BaseBinaryStar.cpp index a6ecb660a..9cd9c44be 100644 --- a/src/BaseBinaryStar.cpp +++ b/src/BaseBinaryStar.cpp @@ -189,7 +189,7 @@ BaseBinaryStar::BaseBinaryStar(const unsigned long int p_Seed, const long int p_ ? new BinaryConstituentStar(m_RandomSeed, mass2, metallicity, kickParameters2, OPTIONS->RotationalFrequency2() * SECONDS_IN_YEAR) // yes - use it (convert from Hz to cycles per year - see BaseStar::CalculateZAMSAngularFrequency()) : new BinaryConstituentStar(m_RandomSeed, mass2, metallicity, kickParameters2); // no - let it be calculated - starToRocheLobeRadiusRatio1 = (m_Star1->Radius() * RSOL_TO_AU) / (m_SemiMajorAxis * CalculateRocheLobeRadius_Static(mass1, mass2)); //eccentricity already zero + starToRocheLobeRadiusRatio1 = (m_Star1->Radius() * RSOL_TO_AU) / (m_SemiMajorAxis * CalculateRocheLobeRadius_Static(mass1, mass2)); //eccentricity already zero starToRocheLobeRadiusRatio2 = (m_Star2->Radius() * RSOL_TO_AU) / (m_SemiMajorAxis * CalculateRocheLobeRadius_Static(mass2, mass1)); } @@ -241,8 +241,8 @@ void BaseBinaryStar::SetInitialValues(const unsigned long int p_Seed, const long m_EvolutionStatus = EVOLUTION_STATUS::CONTINUE; - if (OPTIONS->PopulationDataPrinting()) { // user wants to see details of binary? - SAY("Using supplied random seed " << m_RandomSeed << " for Binary Star id = " << m_ObjectId); // yes - show them + if (OPTIONS->PopulationDataPrinting()) { // user wants to see details of binary? + SAY("Using supplied random seed " << m_RandomSeed << " for Binary Star id = " << m_ObjectId); // yes - show them } } @@ -282,12 +282,14 @@ void BaseBinaryStar::SetRemainingValues() { m_Omega = 0.0; - if (OPTIONS->CHEMode() != CHE_MODE::NONE) { // CHE enabled? + if (OPTIONS->CHEMode() != CHE_MODE::NONE) { // CHE enabled? // CHE enabled, update rotational frequency for constituent stars - assume tidally locked - m_Star1->SetOmega(OrbitalAngularVelocity()); - m_Star2->SetOmega(OrbitalAngularVelocity()); + double omega = OrbitalAngularVelocity(); // orbital angular velocity + + m_Star1->SetOmega(omega); + m_Star2->SetOmega(omega); // check for CHE // @@ -320,6 +322,9 @@ void BaseBinaryStar::SetRemainingValues() { else { if (m_Star2->StellarType() != STELLAR_TYPE::MS_GT_07) (void)m_Star2->SwitchTo(STELLAR_TYPE::MS_GT_07, true); // MS > 0.7 Msol - switch if necessary } + + // if both stars evolving as chemically homegeneous stars set m_Omega for binary + if (HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS})) m_Omega = omega; } double totalMass = m_Star1->Mass() + m_Star2->Mass(); @@ -327,7 +332,7 @@ void BaseBinaryStar::SetRemainingValues() { m_OrbitalEnergy = CalculateOrbitalEnergy(reducedMass, totalMass, m_SemiMajorAxis); m_OrbitalEnergyPrev = m_OrbitalEnergy; - m_OrbitalAngularMomentum = CalculateOrbitalAngularMomentum(reducedMass, totalMass, m_SemiMajorAxis); + m_OrbitalAngularMomentum = CalculateOrbitalAngularMomentum(m_Star1->Mass(), m_Star2->Mass(), m_SemiMajorAxis, m_Eccentricity); m_OrbitalAngularMomentumPrev = m_OrbitalAngularMomentum; m_Time = DEFAULT_INITIAL_DOUBLE_VALUE; @@ -1604,8 +1609,12 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { } // if CHE enabled, update rotational frequency for constituent stars - assume tidally locked - if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star1->SetOmega(OrbitalAngularVelocity()); - if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star2->SetOmega(OrbitalAngularVelocity()); + double omega = OrbitalAngularVelocity(); // orbital angular velocity + if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star1->SetOmega(omega); + if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star2->SetOmega(omega); + + // if both stars evolving as chemically homegeneous stars set m_Omega for binary + if (HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS})) m_Omega = omega; m_Star1->SetPostCEEValues(); // squirrel away post CEE stellar values for star 1 m_Star2->SetPostCEEValues(); // squirrel away post CEE stellar values for star 2 @@ -2137,7 +2146,8 @@ double BaseBinaryStar::CalculateAngularMomentum(const double p_SemiMajorAxis, double Is1 = ks1 * m1 * R1 * R1; double Is2 = ks2 * m2 * R2 * R2; - double Jorb = ((m1 * m2) / (m1 + m2)) * std::sqrt(G1 * (m1 + m2) * p_SemiMajorAxis * (1.0 - (p_Eccentricity * p_Eccentricity))); + + double Jorb = CalculateOrbitalAngularMomentum(m1, m2, p_SemiMajorAxis, p_Eccentricity); return (Is1 * w1) + (Is2 * w2) + Jorb; } @@ -2161,10 +2171,11 @@ void BaseBinaryStar::CalculateEnergyAndAngularMomentum() { m_OrbitalAngularMomentumPrev = m_OrbitalAngularMomentum; m_TotalAngularMomentumPrev = m_TotalAngularMomentum; - double totalMass = m_Star1->Mass() + m_Star2->Mass(); - double reducedMass = (m_Star1->Mass() * m_Star2->Mass()) / totalMass; + double totalMass = m_Star1->Mass() + m_Star2->Mass(); + double reducedMass = (m_Star1->Mass() * m_Star2->Mass()) / totalMass; + m_OrbitalEnergy = CalculateOrbitalEnergy(reducedMass, totalMass, m_SemiMajorAxis); - m_OrbitalAngularMomentum = CalculateOrbitalAngularMomentum(reducedMass, totalMass, m_SemiMajorAxis); + m_OrbitalAngularMomentum = CalculateOrbitalAngularMomentum(m_Star1->Mass(), m_Star2->Mass(), m_SemiMajorAxis, m_Eccentricity); // Calculate total energy and angular momentum using regular conservation of energy, especially useful for checking tides and rotational effects m_TotalEnergy = CalculateTotalEnergy(); @@ -2214,8 +2225,12 @@ void BaseBinaryStar::ResolveMassChanges() { } // if CHE enabled, update rotational frequency for constituent stars - assume tidally locked - if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star1->SetOmega(OrbitalAngularVelocity()); - if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star2->SetOmega(OrbitalAngularVelocity()); + double omega = OrbitalAngularVelocity(); // orbital angular velocity + if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star1->SetOmega(omega); + if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star2->SetOmega(omega); + + // if both stars evolving as chemically homegeneous stars set m_Omega for binary + if (HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS})) m_Omega = omega; CalculateEnergyAndAngularMomentum(); // perform energy and angular momentum calculations @@ -2294,24 +2309,6 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { if (OPTIONS->EnableTides() && !m_Unbound) { - /* - std::cout << "\nTime = " << m_Time << "\n"; - std::cout << "Total angular momentum before = " << m_TotalAngularMomentum << "\n"; - std::cout << "Semi-major axis before = " << m_SemiMajorAxis << "\n"; - std::cout << "Eccentricity before = " << m_Eccentricity << "\n"; - std::cout << "Omega (star1) before = " << m_Star1->Omega() << "\n"; - std::cout << "Omega (star2) before = " << m_Star2->Omega() << "\n"; - std::cout << "Omega (binary) before = " << m_Omega << "\n"; - std::cout << "MoI (star1) before = " << m_Star1->CalculateMomentOfInertia() << "\n"; - std::cout << "MoI (star2) before = " << m_Star2->CalculateMomentOfInertia() << "\n"; - std::cout << "Mass (star1) before = " << m_Star1->Mass() << "\n"; - std::cout << "Mass (star2) before = " << m_Star2->Mass() << "\n"; - std::cout << "Radius (star1) before = " << m_Star1->Radius() << "\n"; - std::cout << "Radius (star2) before = " << m_Star2->Radius() << "\n"; - std::cout << "Gyration radius (star1) before = " << m_Star1->CalculateGyrationRadius() << "\n"; - std::cout << "Gyration radius (star2) before = " << m_Star2->CalculateGyrationRadius() << "\n"; - */ - // find omega assuming synchronisation // use current value of m_Omega as best guess for root // if m_Omega == 0.0 (should only happen on the first timestep), calculate m_Omega here @@ -2326,12 +2323,8 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { m_Star2->CalculateMomentOfInertiaAU(), m_TotalAngularMomentum, m_Omega); - - //std::cout << "m_Omega returned = " << m_Omega << "\n"; if (m_Omega > 0.0) { // root found? - // yes - //std::cout << "Root found: " << m_Omega << "\n"; m_Star1->SetOmega(m_Omega); // synchronise star 1 m_Star2->SetOmega(m_Omega); // synchronise star 2 @@ -2344,24 +2337,6 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { m_EccentricityPrev = m_Eccentricity; m_SemiMajorAxisPrev = m_SemiMajorAxis; } - //else std::cout << "No root found\n"; - - /* - std::cout << "Total angular momentum after = " << m_TotalAngularMomentum << "\n"; - std::cout << "Semi-major axis after = " << m_SemiMajorAxis << "\n"; - std::cout << "Eccentricity after = " << m_Eccentricity << "\n"; - std::cout << "Omega (star1) after = " << m_Star1->Omega() << "\n"; - std::cout << "Omega (star2) after = " << m_Star2->Omega() << "\n"; - std::cout << "Omega (binary) after = " << m_Omega << "\n"; - std::cout << "MoI (star1) after = " << m_Star1->CalculateMomentOfInertia() << "\n"; - std::cout << "MoI (star2) after = " << m_Star2->CalculateMomentOfInertia() << "\n"; - std::cout << "Mass (star1) after = " << m_Star1->Mass() << "\n"; - std::cout << "Mass (star2) after = " << m_Star2->Mass() << "\n"; - std::cout << "Radius (star1) after = " << m_Star1->Radius() << "\n"; - std::cout << "Radius (star2) after = " << m_Star2->Radius() << "\n"; - std::cout << "Gyration radius (star1) after = " << m_Star1->CalculateGyrationRadius() << "\n"; - std::cout << "Gyration radius (star2) after = " << m_Star2->CalculateGyrationRadius() << "\n"; - */ } m_Star1->UpdateMagneticFieldAndSpin(m_CEDetails.CEEnow, m_Dt * MYR_TO_YEAR * SECONDS_IN_YEAR, EPSILON_PULSAR); // update pulsar parameters for star1 diff --git a/src/BaseBinaryStar.h b/src/BaseBinaryStar.h index 652ffc83c..4e988d585 100644 --- a/src/BaseBinaryStar.h +++ b/src/BaseBinaryStar.h @@ -453,9 +453,10 @@ class BaseBinaryStar { void CalculateWindsMassLoss(); void InitialiseMassTransfer(); - double CalculateOrbitalAngularMomentum(const double p_Mu, - const double p_Mass, - const double p_SemiMajorAxis) const { return p_Mu * std::sqrt(G1 * p_Mass * p_SemiMajorAxis); } + double CalculateOrbitalAngularMomentum(const double p_Star1Mass, + const double p_Star2Mass, + const double p_SemiMajorAxis, + const double p_Eccentricity) const { return ((p_Star1Mass * p_Star2Mass) / (p_Star1Mass + p_Star2Mass)) * std::sqrt(G1 * (p_Star1Mass + p_Star2Mass) * p_SemiMajorAxis * (1.0 - (p_Eccentricity * p_Eccentricity))); } double CalculateOrbitalEnergy(const double p_Mu, const double p_Mass, diff --git a/src/changelog.h b/src/changelog.h index 7c086b25d..869b76887 100644 --- a/src/changelog.h +++ b/src/changelog.h @@ -1057,8 +1057,10 @@ // - Fixed a few typos, a little code cleanup. // 02.39.01 LC - Sep 01, 2023 - Defect repair: // - Fix for issue #945 - made HeSD SN types a sub-class of SNIA types. -// 02.40.00 JR - Oct 25, 2023 - Enhancement, a little cleanup: +// 02.40.00 JR - Oct 30, 2023 - Enhancement, a little cleanup: // - Added naive tides implementation. Functionality enabled with new option `--enable-tides`. Default is no tides. +// - Fixed CalculateOrbitalAngularMomentum() (now uses eccentricity) +// - Added links to online documentation to splash string const std::string VERSION_STRING = "02.40.00"; diff --git a/src/utils.cpp b/src/utils.cpp index 326ab4a47..063f9710e 100644 --- a/src/utils.cpp +++ b/src/utils.cpp @@ -175,8 +175,8 @@ namespace utils { * - Set both to zero for no tolerance - or #undef COMPARE_GLOBAL_TOLERANCE for performance * * If p_Tolerance is > 0.0 it will be used in preference to the global tolerance values - * If p_Tolerance is > 0.0, then p_Absolute determines if p_tolerance should be treated as an absolute - * torelace (p_Absolute = true), or a relative tolerance (p_Absolete = false). + * If p_Tolerance is > 0.0, then p_Absolute determines if p_Tolerance should be treated as an absolute + * tolerance (p_Absolute = true), or a relative tolerance (p_Absolute = false). * * * int Compare(const double p_X, const double p_Y) @@ -184,7 +184,7 @@ namespace utils { * @param [IN] p_X Floating-point value to be compared * @param [IN] p_Y Floating-point value to be compared * @param [IN] p_Tolerance Floating-point tolerance value - if > 0.0 supersedes global tolerance - * @param [IN] p_Absolute Boolean indicatin whether p_Tolerance should be treated as absolute tolerance (true) or relative tolerance (false) + * @param [IN] p_Absolute Boolean indicating whether p_Tolerance should be treated as absolute tolerance (true) or relative tolerance (false) * @return Integer indicating result of comparison: * -1 indicates p_X is less than p_Y * 0 indicates equality @@ -1444,7 +1444,9 @@ namespace utils { VERSION_STRING + "\nCompact Object Mergers: Population Astrophysics and Statistics" "\nby Team COMPAS (http://compas.science/index.html)" - "\nA binary star simulator\n"; + "\nA binary star simulator\n" + "\nGo to https://compas.readthedocs.io/en/latest/index.html for the online documentation" + "\nCheck https://compas.readthedocs.io/en/latest/pages/whats-new.html to see what's new in the latest release\n"; if (p_Print) std::cout << splashString << std::endl; // print the splash string if required From afbe0057e50110ac3fba94a1c622427cd27a6d2a Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Tue, 31 Oct 2023 04:41:20 +1100 Subject: [PATCH 09/32] Update COMPAS default yaml file --- compas_python_utils/preprocessing/compasConfigDefault.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/compas_python_utils/preprocessing/compasConfigDefault.yaml b/compas_python_utils/preprocessing/compasConfigDefault.yaml index 8aca96e24..b008572a7 100644 --- a/compas_python_utils/preprocessing/compasConfigDefault.yaml +++ b/compas_python_utils/preprocessing/compasConfigDefault.yaml @@ -1,5 +1,5 @@ ##~!!~## COMPAS option values -##~!!~## File Created Mon Jun 26 10:54:22 2023 by COMPAS v02.38.08 +##~!!~## File Created Tue Oct 31 04:36:43 2023 by COMPAS v02.40.00 ##~!!~## ##~!!~## The default COMPAS YAML file (``compasConfigDefault.yaml``), as distributed, has ##~!!~## all COMPAS option entries commented so that the COMPAS default value for the @@ -47,6 +47,9 @@ booleanChoices: # --common-envelope-lambda-nanjing-use-rejuvenated-mass: False # Default: False # --revised-energy-formalism-nandez-ivanova: False # Default: False + ### TIDES +# --enable-tides: False # Default: False + ### SUPERNOVAE, KICKS AND REMNANTS # --allow-non-stripped-ECSN: False # Default: False # --pair-instability-supernovae: True # Default: True From 03829eecaf06473514c9551b4ee8d407ab721a72 Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Tue, 31 Oct 2023 05:00:44 +1100 Subject: [PATCH 10/32] Corrected typos --- src/BaseBinaryStar.cpp | 6 +++--- src/BaseBinaryStar.h | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/BaseBinaryStar.cpp b/src/BaseBinaryStar.cpp index 9cd9c44be..c590e2883 100644 --- a/src/BaseBinaryStar.cpp +++ b/src/BaseBinaryStar.cpp @@ -323,7 +323,7 @@ void BaseBinaryStar::SetRemainingValues() { if (m_Star2->StellarType() != STELLAR_TYPE::MS_GT_07) (void)m_Star2->SwitchTo(STELLAR_TYPE::MS_GT_07, true); // MS > 0.7 Msol - switch if necessary } - // if both stars evolving as chemically homegeneous stars set m_Omega for binary + // if both stars evolving as chemically homogeneous stars set m_Omega for binary if (HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS})) m_Omega = omega; } @@ -1613,7 +1613,7 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star1->SetOmega(omega); if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star2->SetOmega(omega); - // if both stars evolving as chemically homegeneous stars set m_Omega for binary + // if both stars evolving as chemically homogeneous stars set m_Omega for binary if (HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS})) m_Omega = omega; m_Star1->SetPostCEEValues(); // squirrel away post CEE stellar values for star 1 @@ -2229,7 +2229,7 @@ void BaseBinaryStar::ResolveMassChanges() { if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star1->SetOmega(omega); if (OPTIONS->CHEMode() != CHE_MODE::NONE) m_Star2->SetOmega(omega); - // if both stars evolving as chemically homegeneous stars set m_Omega for binary + // if both stars evolving as chemically homogeneous stars set m_Omega for binary if (HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS})) m_Omega = omega; CalculateEnergyAndAngularMomentum(); // perform energy and angular momentum calculations diff --git a/src/BaseBinaryStar.h b/src/BaseBinaryStar.h index 4e988d585..4868d929d 100644 --- a/src/BaseBinaryStar.h +++ b/src/BaseBinaryStar.h @@ -641,8 +641,8 @@ class BaseBinaryStar { * * @param [IN] p_M1 Mass of star 1 * @param [IN] p_M2 Mass of star 2 - * @param [IN] p_I1 Moment of intertia of star 1 - * @param [IN] p_I2 Moment of intertia of star 1 + * @param [IN] p_I1 Moment of inertia of star 1 + * @param [IN] p_I2 Moment of inertia of star 1 * @param [IN] p_Ltot Total angular momentum for binary * @param [IN] p_Guess Initial guess for value of root * @return Root found: will be -1.0 if no real root found From a9f87249969eda9e059c9d0eaf749a872298363f Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Tue, 31 Oct 2023 10:54:30 +1100 Subject: [PATCH 11/32] yaml and grid files for BBH detailed output --- .../detailed_evolution/Grid_demo.txt | 3 + .../example_bbh_compas_config.yaml | 273 ++++++++++++++++++ 2 files changed, 276 insertions(+) create mode 100644 examples/methods_paper_plots/detailed_evolution/Grid_demo.txt create mode 100644 examples/methods_paper_plots/detailed_evolution/example_bbh_compas_config.yaml diff --git a/examples/methods_paper_plots/detailed_evolution/Grid_demo.txt b/examples/methods_paper_plots/detailed_evolution/Grid_demo.txt new file mode 100644 index 000000000..c155ff9a2 --- /dev/null +++ b/examples/methods_paper_plots/detailed_evolution/Grid_demo.txt @@ -0,0 +1,3 @@ +--initial-mass-1 35.0 --initial-mass-2 31.0 --metallicity 0.001 --eccentricity 0.000000e+00 --semi-major-axis 3.5 --kick-magnitude-1 0 --kick-magnitude-2 0 +--initial-mass-1 35.0 --initial-mass-2 31.0 --metallicity 0.001 --eccentricity 0.000000e+00 --semi-major-axis 3.5 --kick-magnitude-1 0 --kick-magnitude-2 0 + diff --git a/examples/methods_paper_plots/detailed_evolution/example_bbh_compas_config.yaml b/examples/methods_paper_plots/detailed_evolution/example_bbh_compas_config.yaml new file mode 100644 index 000000000..623bc7fb6 --- /dev/null +++ b/examples/methods_paper_plots/detailed_evolution/example_bbh_compas_config.yaml @@ -0,0 +1,273 @@ +##~!!~## COMPAS option values +##~!!~## File Created Tue Oct 31 10:11:32 2023 by COMPAS v02.40.00 +##~!!~## +##~!!~## The default COMPAS YAML file (``compasConfigDefault.yaml``), as distributed, has +##~!!~## all COMPAS option entries commented so that the COMPAS default value for the +##~!!~## option is used by default. To use a value other than the COMPAS default value, +##~!!~## users must uncomment the entry and change the option value to the desired value. + +booleanChoices: + + ### LOGISTICS +# --debug-to-file: False # Default: False + --detailed-output: True # Default: False # WARNING! this creates a data heavy file +# --enable-warnings: False # Default: False # option to enable/disable warning messages +# --errors-to-file: False # Default: False +# --evolve-unbound-systems: True # Default: True +# --population-data-printing: False # Default: False +# --print-bool-as-string: False # Default: False +# --quiet: False # Default: False +# --rlof-printing: True # Default: True +# --store-input-files: True # Default: True +# --switch-log: False # Default: False + + ### STELLAR PROPERTIES +# --check-photon-tiring-limit: False # Default: False +# --use-mass-loss: True # Default: True +# --expel-convective-envelope-above-luminosity-threshold: False # Default: False + + ### BINARY PROPERTIES +# --allow-touching-at-birth: False # Default: False # record binaries that have stars touching at birth in output files + + ### MASS TRANSFER +# --angular-momentum-conservation-during-circularisation: False # Default: False +# --allow-rlof-at-birth: True # Default: True # allow binaries that have one or both stars in RLOF at birth to evolve, particularly useful in the context of CHE binaries +# --circularise-binary-during-mass-transfer: True # Default: True +# --hmxr-binaries: False # Default: False +# --mass-transfer: True # Default: True +# --retain-core-mass-during-caseA-mass-transfer: True # Default: True + + ### COMMON ENVELOPE +# --common-envelope-allow-immediate-RLOF-post-CE-survive: False # Default: False +# --common-envelope-allow-main-sequence-survive: True # Default: True # Allow main sequence stars to survive CE +# --common-envelope-allow-radiative-envelope-survive: False # Default: False +# --common-envelope-lambda-nanjing-enhanced: False # Default: False +# --common-envelope-lambda-nanjing-interpolate-in-mass: False # Default: False +# --common-envelope-lambda-nanjing-interpolate-in-metallicity: False # Default: False +# --common-envelope-lambda-nanjing-use-rejuvenated-mass: False # Default: False +# --revised-energy-formalism-nandez-ivanova: False # Default: False + + ### TIDES +# --enable-tides: False # Default: False + + ### SUPERNOVAE, KICKS AND REMNANTS +# --allow-non-stripped-ECSN: False # Default: False +# --pair-instability-supernovae: True # Default: True +# --pulsational-pair-instability: True # Default: True + + ### PULSAR PARAMETERS +# --evolve-pulsars: False # Default: False + + ### WHITE DWARF PARAMETERS +# --evolve-double-white-dwarfs: False # Default: False + + +numericalChoices: + + ### LOGISTICS +# --debug-level: 0 # Default: 0 +# --logfile-common-envelopes-record-types: -1 # Default: -1 +# --logfile-detailed-output-record-types: -1 # Default: -1 +# --logfile-double-compact-objects-record-types: -1 # Default: -1 +# --logfile-pulsar-evolution-record-types: -1 # Default: -1 +# --logfile-rlof-parameters-record-types: -1 # Default: -1 +# --logfile-supernovae-record-types: -1 # Default: -1 +# --logfile-system-parameters-record-types: -1 # Default: -1 +# --grid-lines-to-process: 9223372036854775807 # Default: 9223372036854775807 +# --grid-start-line: 0 # Default: 0 +# --hdf5-chunk-size: 100000 # Default: 100000 +# --hdf5-buffer-size: 1 # Default: 1 +# --log-level: 0 # Default: 0 +# --maximum-evolution-time: 13700.00 # Default: 13700.00 # maximum physical time a system can be evolved [Myr] +# --maximum-number-timestep-iterations: 99999 # Default: 99999 +# --number-of-systems: 10 # Default: 10 # number of systems per batch +# --timestep-multiplier: 1.000000 # Default: 1.000000 # optional multiplier relative to default time step duration + + ### STELLAR PROPERTIES +# --cool-wind-mass-loss-multiplier: 1.000000 # Default: 1.000000 +# --initial-mass: 5.000000 # Default: 5.000000 # initial mass for SSE +# --initial-mass-min: 5.000000 # Default: 5.000000 # use 5.0 for DCOs [Msol] +# --initial-mass-max: 150.00 # Default: 150.00 # stellar tracks extrapolated above 50 Msol (Hurley+2000) [Msol] +# --initial-mass-power: 0.000000 # Default: 0.000000 +# --luminosity-to-mass-threshold: 4.200000 # Default: 4.200000 +# --metallicity: 0.014200 # Default: 0.014200 # metallicity for both SSE and BSE - Solar metallicity Asplund+2010 +# --metallicity-min: 0.000100 # Default: 0.000100 +# --metallicity-max: 0.030000 # Default: 0.030000 +# --luminous-blue-variable-multiplier: 1.500000 # Default: 1.500000 +# --overall-wind-mass-loss-multiplier: 1.000000 # Default: 1.000000 +# --random-seed: 0 # Default: 0 +# --rotational-frequency: 0.000000 # Default: 0.000000 +# --rotational-frequency-1: 0.000000 # Default: 0.000000 +# --rotational-frequency-2: 0.000000 # Default: 0.000000 +# --wolf-rayet-multiplier: 0.100000 # Default: 0.100000 + + ### BINARY PROPERTIES +# --eccentricity: 0.000000 # Default: 0.000000 # eccentricity for BSE +# --eccentricity-min: 0.000000 # Default: 0.000000 +# --eccentricity-max: 1.000000 # Default: 1.000000 +# --initial-mass-1: 5.000000 # Default: 5.000000 # primary initial mass for BSE +# --initial-mass-2: 5.000000 # Default: 5.000000 # secondary initial mass for BSE +# --mass-ratio: 1.000000 # Default: 1.000000 +# --mass-ratio-min: 0.010000 # Default: 0.010000 +# --mass-ratio-max: 1.000000 # Default: 1.000000 +# --minimum-secondary-mass: 0.100000 # Default: 0.100000 # Brown dwarf limit [Msol] +# --orbital-period: 0.100000 # Default: 0.100000 # orbital period for BSE +# --orbital-period-min: 1.100000 # Default: 1.100000 # [days] +# --orbital-period-max: 1000.00 # Default: 1000.00 # [days] +# --semi-major-axis: 0.100000 # Default: 0.100000 # semi-major axis for BSE +# --semi-major-axis-min: 0.010000 # Default: 0.010000 # [AU] +# --semi-major-axis-max: 1000.00 # Default: 1000.00 # [AU] + + ### MASS TRANSFER +# --convective-envelope-temperature-threshold: 5370.00 # Default: 5370.00 # Only if using envelope-state-prescription = 'FIXED_TEMPERATURE' +# --critical-mass-ratio-HG-degenerate-accretor: 0.210000 # Default: 0.210000 +# --critical-mass-ratio-HG-non-degenerate-accretor: 0.250000 # Default: 0.250000 +# --critical-mass-ratio-MS-high-mass-degenerate-accretor: 0.000000 # Default: 0.000000 +# --critical-mass-ratio-MS-high-mass-non-degenerate-accretor: 0.625000 # Default: 0.625000 +# --critical-mass-ratio-MS-low-mass-degenerate-accretor: 1.000000 # Default: 1.000000 +# --critical-mass-ratio-MS-low-mass-non-degenerate-accretor: 1.440000 # Default: 1.440000 +# --critical-mass-ratio-giant-degenerate-accretor: 0.870000 # Default: 0.870000 +# --critical-mass-ratio-giant-non-degenerate-accretor: -1.000000 # Default: -1.000000 +# --critical-mass-ratio-helium-HG-degenerate-accretor: 0.210000 # Default: 0.210000 +# --critical-mass-ratio-helium-HG-non-degenerate-accretor: 0.250000 # Default: 0.250000 +# --critical-mass-ratio-helium-MS-degenerate-accretor: 0.000000 # Default: 0.000000 +# --critical-mass-ratio-helium-giant-degenerate-accretor: 0.870000 # Default: 0.870000 +# --critical-mass-ratio-helium-MS-non-degenerate-accretor: 0.000000 # Default: 0.000000 +# --critical-mass-ratio-helium-giant-non-degenerate-accretor: 1.280000 # Default: 1.280000 +# --critical-mass-ratio-white-dwarf-degenerate-accretor: 1.600000 # Default: 1.600000 +# --critical-mass-ratio-white-dwarf-non-degenerate-accretor: 0.000000 # Default: 0.000000 +# --mass-transfer-fa: 0.500000 # Default: 0.500000 # Only if using mass-transfer-accretion-efficiency-prescription = 'FIXED' +# --mass-transfer-jloss: 1.000000 # Default: 1.000000 # Only if using mass-transfer-angular-momentum-loss-prescription = 'FIXED' +# --mass-transfer-jloss-macleod-linear-fraction: 0.500000 # Default: 0.500000 +# --mass-transfer-thermal-limit-C: 10.000000 # Default: 10.000000 +# --zeta-adiabatic-arbitrary: 10000.00 # Default: 10000.00 +# --zeta-main-sequence: 2.000000 # Default: 2.000000 +# --zeta-radiative-envelope-giant: 6.500000 # Default: 6.500000 + + ### COMMON ENVELOPE +# --common-envelope-alpha: 1.000000 # Default: 1.000000 +# --common-envelope-alpha-thermal: 1.000000 # Default: 1.000000 # lambda = alpha_th*lambda_b + (1-alpha_th)*lambda_g +# --common-envelope-lambda: 0.100000 # Default: 0.100000 # Only if using 'LAMBDA_FIXED' +# --common-envelope-lambda-multiplier: 1.000000 # Default: 1.000000 # Multiply common envelope lambda by some constant +# --common-envelope-mass-accretion-constant: 0.000000 # Default: 0.000000 +# --common-envelope-mass-accretion-max: 0.100000 # Default: 0.100000 # For 'MACLEOD+2014' [Msol] +# --common-envelope-mass-accretion-min: 0.040000 # Default: 0.040000 # For 'MACLEOD+2014' [Msol] +# --common-envelope-recombination-energy-density: 15000000000000.00 # Default: 15000000000000.00 +# --common-envelope-slope-kruckow: -0.833333 # Default: -0.833333 +# --maximum-mass-donor-nandez-ivanova: 2.000000 # Default: 2.000000 + + ### SUPERNOVAE, KICKS AND REMNANTS +# --eddington-accretion-factor: 1.000000 # Default: 1.000000 # multiplication Factor for eddington accretion onto NS&BH +# --fix-dimensionless-kick-magnitude: -1.000000 # Default: -1.000000 +# --fryer-22-fmix: 0.500000 # Default: 0.500000 # parameter describing mixing growth time when using the 'FRYER2022' remnant mass prescription +# --fryer-22-mcrit: 5.750000 # Default: 5.750000 # critical mass for BH formation when using the 'FRYER2022' remnant mass prescription +# --kick-direction-power: 0.000000 # Default: 0.000000 +# --kick-magnitude-sigma-CCSN-NS: 265.00 # Default: 265.00 # [km/s] +# --kick-magnitude-sigma-CCSN-BH: 265.00 # Default: 265.00 # [km/s] +# --kick-magnitude-max: -1.000000 # Default: -1.000000 +# --kick-magnitude-random: 0.000000 # Default: 0.000000 # (SSE) used to draw the kick magnitude for the star should it undergo a supernova event +# --kick-magnitude: 0.000000 # Default: 0.000000 # (SSE) (drawn) kick magnitude for the star should it undergo a supernova event [km/s] +# --kick-magnitude-random-1: 0.000000 # Default: 0.000000 # (BSE) used to draw the kick magnitude for the primary star should it undergo a supernova event +# --kick-magnitude-1: 0.000000 # Default: 0.000000 # (BSE) (drawn) kick magnitude for the primary star should it undergo a supernova event [km/s] +# --kick-theta-1: 0.000000 # Default: 0.000000 # (BSE) angle between the orbital plane and the 'z' axis of the supernova vector for the primary star should it undergo a supernova event [radians] +# --kick-phi-1: 0.000000 # Default: 0.000000 # (BSE) angle between 'x' and 'y', both in the orbital plane of the supernova vector, for the primary star should it undergo a supernova event [radians] +# --kick-mean-anomaly-1: 0.000000 # Default: 0.000000 # (BSE) mean anomaly at the instant of the supernova for the primary star should it undergo a supernova event - should be uniform in [0, 2pi) [radians] +# --kick-magnitude-random-2: 0.000000 # Default: 0.000000 # (BSE) used to draw the kick velocity for the secondary star should it undergo a supernova event +# --kick-magnitude-2: 0.000000 # Default: 0.000000 # (BSE) (drawn) kick magnitude for the secondary star should it undergo a supernova event [km/s] +# --kick-theta-2: 0.000000 # Default: 0.000000 # (BSE) angle between the orbital plane and the 'z' axis of the supernova vector for the secondary star should it undergo a supernova event [radians] +# --kick-phi-2: 0.000000 # Default: 0.000000 # (BSE) angle between 'x' and 'y', both in the orbital plane of the supernova vector, for the secondary star should it undergo a supernova event [radians] +# --kick-mean-anomaly-2: 0.000000 # Default: 0.000000 # (BSE) mean anomaly at the instant of the supernova for the secondary star should it undergo a supernova event - should be uniform in [0, 2pi) [radians] +# --kick-magnitude-sigma-ECSN: 30.000000 # Default: 30.000000 # [km/s] +# --kick-magnitude-sigma-USSN: 30.000000 # Default: 30.000000 # [km/s] +# --kick-scaling-factor: 1.000000 # Default: 1.000000 +# --maximum-neutron-star-mass: 2.500000 # Default: 2.500000 +# --mcbur1: 1.600000 # Default: 1.600000 +# --muller-mandel-kick-multiplier-BH: 200.00 # Default: 200.00 # scaling prefactor for BH kicks when using the 'MULLERMANDEL' kick magnitude distribution +# --muller-mandel-kick-multiplier-NS: 520.00 # Default: 520.00 # scaling prefactor for NS kicks when using the 'MULLERMANDEL' kick magnitude distribution +# --muller-mandel-sigma-kick: 0.300000 # Default: 0.300000 # kick scatter when using the 'MULLERMANDEL' kick magnitude distribution +# --neutrino-mass-loss-BH-formation-value: 0.100000 # Default: 0.100000 +# --pisn-lower-limit: 60.000000 # Default: 60.000000 # Minimum core mass for PISN [Msol] +# --pisn-upper-limit: 135.00 # Default: 135.00 # Maximum core mass for PISN [Msol] +# --ppi-lower-limit: 35.000000 # Default: 35.000000 # Minimum core mass for PPI [Msol] +# --ppi-upper-limit: 60.000000 # Default: 60.000000 # Maximum core mass for PPI [Msol] + + ### PULSAR PARAMETERS +# --pulsar-birth-magnetic-field-distribution-min: 11.000000 # Default: 11.000000 # [log10(B/G)] +# --pulsar-birth-magnetic-field-distribution-max: 13.000000 # Default: 13.000000 # [log10(B/G)] +# --pulsar-birth-spin-period-distribution-min: 10.000000 # Default: 10.000000 # [ms] +# --pulsar-birth-spin-period-distribution-max: 100.00 # Default: 100.00 # [ms] +# --pulsar-magnetic-field-decay-timescale: 1000.00 # Default: 1000.00 # [Myr] +# --pulsar-magnetic-field-decay-massscale: 0.025000 # Default: 0.025000 # [Msol] +# --pulsar-minimum-magnetic-field: 8.000000 # Default: 8.000000 # [log10(B/G)] + + +stringChoices: + + ### LOGISTICS +# --add-options-to-sysparms: 'GRID' # Default: 'GRID' # Options: ['NEVER','GRID','ALWAYS'] + --grid: 'Grid_demo.txt' # Default: '' # grid file name (e.g. 'mygrid.txt') +# --mode: 'BSE' # Default: 'BSE' # Options: ['BSE','SSE'] # evolving single (SSE) or binary stars (BSE) +# --notes: { } # Default: { } +# --notes-hdrs: { } # Default: { } +# --output-container: 'COMPAS_Output' # Default: 'COMPAS_Output' + + ### STELLAR PROPERTIES +# --chemically-homogeneous-evolution: 'PESSIMISTIC' # Default: 'PESSIMISTIC' # Options: ['PESSIMISTIC','OPTIMISTIC','NONE'] # chemically homogeneous evolution +# --envelope-state-prescription: 'LEGACY' # Default: 'LEGACY' # Options: ['FIXED_TEMPERATURE','HURLEY','LEGACY'] +# --initial-mass-function: 'KROUPA' # Default: 'KROUPA' # Options: ['KROUPA','UNIFORM','POWERLAW','SALPETER'] +# --luminous-blue-variable-prescription: 'HURLEY_ADD' # Default: 'HURLEY_ADD' # Options: ['BELCZYNSKI','HURLEY','HURLEY_ADD','NONE'] +# --mass-loss-prescription: 'VINK' # Default: 'VINK' # Options: ['VINK','HURLEY','NONE'] +# --metallicity-distribution: 'ZSOLAR' # Default: 'ZSOLAR' # Options: ['LOGUNIFORM','ZSOLAR'] +# --pulsational-pair-instability-prescription: 'MARCHANT' # Default: 'MARCHANT' # Options: ['FARMER','MARCHANT','STARTRACK','COMPAS'] + + ### BINARY PROPERTIES +# --eccentricity-distribution: 'ZERO' # Default: 'ZERO' # Options: ['SANA2012','DUQUENNOYMAYOR1991','GELLER+2013','THERMAL','FLAT','ZERO'] +# --mass-ratio-distribution: 'FLAT' # Default: 'FLAT' # Options: ['SANA2012','DUQUENNOYMAYOR1991','FLAT'] +# --orbital-period-distribution: 'FLATINLOG' # Default: 'FLATINLOG' # Options: ['FLATINLOG'] +# --rotational-velocity-distribution: 'ZERO' # Default: 'ZERO' # Options: ['VLTFLAMES','HURLEY','ZERO'] +# --semi-major-axis-distribution: 'FLATINLOG' # Default: 'FLATINLOG' # Options: ['SANA2012','DUQUENNOYMAYOR1991','FLATINLOG'] + + ### MASS TRANSFER +# --case-BB-stability-prescription: 'ALWAYS_STABLE' # Default: 'ALWAYS_STABLE' # Options: ['ALWAYS_UNSTABLE','TREAT_AS_OTHER_MT','ALWAYS_STABLE_ONTO_NSBH','ALWAYS_STABLE'] +# --critical-mass-ratio-prescription: 'NONE' # Default: 'NONE' # Options: ['HURLEY_HJELLMING_WEBBINK','GE20_IC','GE20','CLAEYS','NONE'] +# --stellar-zeta-prescription: 'SOBERMAN' # Default: 'SOBERMAN' # Options: ['NONE','ARBITRARY','HURLEY','SOBERMAN'] +# --mass-transfer-angular-momentum-loss-prescription: 'ISOTROPIC' # Default: 'ISOTROPIC' # Options: ['ARBITRARY','MACLEOD_LINEAR','CIRCUMBINARY','ISOTROPIC','JEANS'] +# --mass-transfer-accretion-efficiency-prescription: 'THERMAL' # Default: 'THERMAL' # Options: ['FIXED','THERMAL'] +# --mass-transfer-rejuvenation-prescription: 'STARTRACK' # Default: 'STARTRACK' # Options: ['STARTRACK','NONE'] +# --mass-transfer-thermal-limit-accretor: 'CFACTOR' # Default: 'CFACTOR' # Options: ['ROCHELOBE','CFACTOR'] + + ### COMMON ENVELOPE +# --common-envelope-formalism: 'ENERGY' # Default: 'ENERGY' # Options: ['TWO_STAGE','ENERGY'] +# --common-envelope-lambda-prescription: 'LAMBDA_NANJING' # Default: 'LAMBDA_NANJING' # Options: ['LAMBDA_DEWI','LAMBDA_KRUCKOW','LAMBDA_NANJING','LAMBDA_LOVERIDGE','LAMBDA_FIXED'] # Xu & Li 2010 +# --common-envelope-mass-accretion-prescription: 'ZERO' # Default: 'ZERO' # Options: ['CHEVALIER','MACLEOD','UNIFORM','CONSTANT','ZERO'] + + ### SUPERNOVAE, KICKS AND REMNANTS +# --black-hole-kicks: 'FALLBACK' # Default: 'FALLBACK' # Options: ['FALLBACK','ZERO','REDUCED','FULL'] +# --fryer-supernova-engine: 'DELAYED' # Default: 'DELAYED' # Options: ['DELAYED','RAPID'] +# --kick-magnitude-distribution: 'MULLERMANDEL' # Default: 'MULLERMANDEL' # Options: ['MULLERMANDEL','MULLER2016MAXWELLIAN','MULLER2016','BRAYELDRIDGE','MAXWELLIAN','FLAT','FIXED','ZERO'] +# --kick-direction: 'ISOTROPIC' # Default: 'ISOTROPIC' # Options: ['POLES','WEDGE','POWERLAW','PERPENDICULAR','INPLANE','ISOTROPIC'] +# --neutron-star-equation-of-state: 'SSE' # Default: 'SSE' # Options: ['ARP3','SSE'] +# --neutrino-mass-loss-BH-formation: 'FIXED_MASS' # Default: 'FIXED_MASS' # Options: ['FIXED_MASS','FIXED_FRACTION'] +# --pulsar-birth-magnetic-field-distribution: 'ZERO' # Default: 'ZERO' # Options: ['LOGNORMAL','UNIFORM','FLATINLOG','FIXED','ZERO'] +# --pulsar-birth-spin-period-distribution: 'ZERO' # Default: 'ZERO' # Options: ['NORMAL','UNIFORM','FIXED','ZERO'] +# --remnant-mass-prescription: 'MULLERMANDEL' # Default: 'MULLERMANDEL' # Options: ['SCHNEIDER2020ALT','SCHNEIDER2020','MULLERMANDEL','MULLER2016','FRYER2022','FRYER2012','BELCZYNSKI2002','HURLEY2000'] + + ### LOGFILES AND OUTPUTS +# --logfile-type: 'HDF5' # Default: 'HDF5' # Options: ['TXT','TSV','CSV','HDF5','NONE'] +# --logfile-name-prefix: '' # Default: '' +# --logfile-definitions: '' # Default: '' +# --logfile-common-envelopes: 'BSE_Common_Envelopes' # Default: 'BSE_Common_Envelopes' +# --logfile-detailed-output: 'BSE_Detailed_Output' # Default: 'BSE_Detailed_Output' +# --logfile-double-compact-objects: 'BSE_Double_Compact_Objects' # Default: 'BSE_Double_Compact_Objects' +# --logfile-pulsar-evolution: 'BSE_Pulsar_Evolution' # Default: 'BSE_Pulsar_Evolution' +# --logfile-rlof-parameters: 'BSE_RLOF' # Default: 'BSE_RLOF' +# --logfile-supernovae: 'BSE_Supernovae' # Default: 'BSE_Supernovae' +# --logfile-switch-log: 'BSE_Switch_Log' # Default: 'BSE_Switch_Log' +# --logfile-system-parameters: 'BSE_System_Parameters' # Default: 'BSE_System_Parameters' +# --output-path: '.' # Default: '.' + + +listChoices: + +# --log-classes: { } # Default: { } +# --debug-classes: { } # Default: { } From f836270ea7acf123957ee407f249424d1730b7f2 Mon Sep 17 00:00:00 2001 From: avi Date: Tue, 31 Oct 2023 16:59:32 +1300 Subject: [PATCH 12/32] use a separate config for the tests rather than the method paper config --- .github/workflows/compas-compile-ci.yml | 14 +- py_tests/conftest.py | 17 +- py_tests/test_data/README.md | 10 +- py_tests/test_data/fiducial_bbh_config.yaml | 250 ++++++++++++++++++++ py_tests/test_data/generate_test_data.sh | 4 - py_tests/test_data/grid.txt | 2 + 6 files changed, 279 insertions(+), 18 deletions(-) create mode 100644 py_tests/test_data/fiducial_bbh_config.yaml delete mode 100644 py_tests/test_data/generate_test_data.sh create mode 100644 py_tests/test_data/grid.txt diff --git a/.github/workflows/compas-compile-ci.yml b/.github/workflows/compas-compile-ci.yml index e7573faa4..220e5cc84 100644 --- a/.github/workflows/compas-compile-ci.yml +++ b/.github/workflows/compas-compile-ci.yml @@ -43,10 +43,16 @@ jobs: pip install --upgrade pip pip install -e .[dev] export COMPAS_ROOT_DIR=${GITHUB_WORKSPACE} - cd ${GITHUB_WORKSPACE}/misc/examples/methods_paper_plots/detailed_evolution - chmod 755 run.sh - cat run.sh - ./run.sh + cd ${GITHUB_WORKSPACE}/py_tests/test_data/ + echo "Run example COMPAS job using the config py_tests/test_data/fiducial_bbh_configs.yaml" + compas_run_submit fiducial_bbh_configs.yaml > example_bbh.log + cat example_bbh.log + echo "Generating detailed evolution plot" + compas_plot_detailed_evolution "./COMPAS_Output/Detailed_Output/BSE_Detailed_Output_0.h5" --dont-show >> example_bbh.log + echo "Out files:" + ls -l + mkdir test_artifacts + mv example_bbh.log test_artifacts/ - name: Run pytests # Run tests and collect coverage data diff --git a/py_tests/conftest.py b/py_tests/conftest.py index fa428600e..5b264848d 100644 --- a/py_tests/conftest.py +++ b/py_tests/conftest.py @@ -1,16 +1,15 @@ import os from typing import Any, Dict +import subprocess import h5py import pytest from compas_python_utils.cosmic_integration.binned_cosmic_integrator.bbh_population import \ generate_mock_bbh_population_file HERE = os.path.dirname(__file__) -DETAILED_EVOLUTION_PATH = os.path.join( - HERE, "../misc/examples/methods_paper_plots/detailed_evolution" -) - +TEST_CONFIG_DIR = os.path.join(HERE, "test_data") +TEST_CONFIG_FNAME = os.path.join(TEST_CONFIG_DIR, "fiducial_bbh_config.yaml") TEST_ARCHIVE_DIR = os.path.join(HERE, "test_artifacts") @@ -21,16 +20,20 @@ def example_compas_output_path(clean=False): (This is a fixture so it can passed as a parameter to other tests) """ compas_data_path = os.path.join( - DETAILED_EVOLUTION_PATH, "COMPAS_Output/COMPAS_Output.h5" + TEST_CONFIG_DIR, "COMPAS_Output/COMPAS_Output.h5" ) if not os.path.exists(compas_data_path) or clean: # Check if path exists curr_dir = os.getcwd() - os.chdir(DETAILED_EVOLUTION_PATH) - os.system("python runSubmitDemo.py") + os.chdir(TEST_CONFIG_DIR) + cmd = f"compas_run_submit {TEST_CONFIG_FNAME}" + # run the command in shell "compas_run_submit {TEST_CONFIG_FNAME}" with subprocess + subprocess.run(cmd, shell=True, check=True) + os.chdir(curr_dir) print("Generated COMPAS test data") + return compas_data_path diff --git a/py_tests/test_data/README.md b/py_tests/test_data/README.md index 87f142d97..156860ac5 100644 --- a/py_tests/test_data/README.md +++ b/py_tests/test_data/README.md @@ -1,8 +1,12 @@ # Test Data -This folder contains all the COMPAS test data generated manually by running: +This folder contains COMPAS configs used for testing. +COMPAS is run from within the pytest environment and the output is cached for the remainder of the tests. + +The pytest runs the following command: ```bash -bash generate_test_data.sh +compas_run_submit fiducial_bbh_configs.yaml ``` -These files may need to be periodically updated if the COMPAS code changes. \ No newline at end of file +The pytest cache is _not_ cleared locally (but will be cleared on the CI server). +Hence, this may need to be rerun locally if changes are made to COMPAS. \ No newline at end of file diff --git a/py_tests/test_data/fiducial_bbh_config.yaml b/py_tests/test_data/fiducial_bbh_config.yaml new file mode 100644 index 000000000..872c8a12c --- /dev/null +++ b/py_tests/test_data/fiducial_bbh_config.yaml @@ -0,0 +1,250 @@ +# yaml file which contains all default options from COMPAS + +booleanChoices: + ### LOGISTICS + --debug-to-file: False + --detailed-output: True # WARNING! this creates a data heavy file + --enable-warnings: False # option to enable/disable warning messages + --errors-to-file: False + --evolve-unbound-systems: False + --population-data-printing: False + --print-bool-as-string: False + --quiet: False + --rlof-printing: True + --store-input-files: True + --switch-log: False + ### STELLAR PROPERTIES + --check-photon-tiring-limit: False + --use-mass-loss: True + ### BINARY PROPERTIES + --allow-touching-at-birth: False # record binaries that have stars touching at birth in output files + ### MASS TRANSFER + --angular-momentum-conservation-during-circularisation: False + --allow-rlof-at-birth: True # allow binaries that have one or both stars in RLOF at birth to evolve, particularly useful in the context of CHE binaries. + --circularise-binary-during-mass-transfer: True + --hmxr-binaries: False + --mass-transfer: True + --retain-core-mass-during-caseA-mass-transfer: False + ### COMMON ENVELOPE + --common-envelope-allow-immediate-RLOF-post-CE-survive: False + --common-envelope-allow-main-sequence-survive: True # Allow main sequence stars to survive CE. Was previously False by default + --common-envelope-allow-radiative-envelope-survive: False + --common-envelope-lambda-nanjing-enhanced: False + --common-envelope-lambda-nanjing-interpolate-in-mass: False + --common-envelope-lambda-nanjing-interpolate-in-metallicity: False + --common-envelope-lambda-nanjing-use-rejuvenated-mass: False + --revised-energy-formalism-nandez-ivanova: False + ### SUPERNOVAE, KICKS AND REMNANTS + --allow-non-stripped-ECSN: True + --pair-instability-supernovae: True + --pulsational-pair-instability: True + ### PULSAR PARAMETERS + --evolve-pulsars: False + +numericalChoices: + ### LOGISTICS + --debug-level: 0 + --logfile-common-envelopes-record-types: -1 + --logfile-detailed-output-record-types: -1 + --logfile-double-compact-objects-record-types: -1 + --logfile-pulsar-evolution-record-types: -1 + --logfile-rlof-parameters-record-types: -1 + --logfile-supernovae-record-types: -1 + --logfile-system-parameters-record-types: -1 + --grid-lines-to-process: + --grid-start-line: 0 + --hdf5-chunk-size: 100000 + --hdf5-buffer-size: 1 + --log-level: 0 + --maximum-evolution-time: 13700.0 # maximum physical time a system can be evolved [Myrs] + --maximum-number-timestep-iterations: 99999 + --number-of-systems: 10 # number of systems per batch + --timestep-multiplier: 1 # optional multiplier relative to default time step duration + ### STELLAR PROPERTIES + --cool-wind-mass-loss-multiplier: 1.0 + --initial-mass: # initial mass for SSE + --initial-mass-min: 5.0 # use 5.0 for DCOs [Msol] + --initial-mass-max: 150.0 # stellar tracks extrapolated above 50 Msol (Hurley+2000) [Msol] + --initial-mass-power: 0.0 + --metallicity: 0.0142 # metallicity for both SSE and BSE - Solar metallicity Asplund+2010 + --metallicity-min: 0.0001 + --metallicity-max: 0.03 + --luminous-blue-variable-multiplier: 1.5 + --overall-wind-mass-loss-multiplier: 1.0 + --random-seed: 0 + --rotational-frequency: 0.0 + --rotational-frequency-1: 0.0 + --rotational-frequency-2: 0.0 + --wolf-rayet-multiplier: 1.0 + ### BINARY PROPERTIES + --eccentricity: # eccentricity for BSE + --eccentricity-min: 0.0 + --eccentricity-max: 1.0 + --initial-mass-1: # primary initial mass for BSE + --initial-mass-2: # secondary initial mass for BSE + --mass-ratio: + --mass-ratio-min: 0.01 + --mass-ratio-max: 1.0 + --minimum-secondary-mass: 0.1 # Brown dwarf limit [Msol] + --orbital-period: # orbital period for BSE + --orbital-period-min: 1.1 # [days] + --orbital-period-max: 1000 # [days] + --semi-major-axis: # semi-major axis for BSE + --semi-major-axis-min: 0.01 # [AU] + --semi-major-axis-max: 1000.0 # [AU] + ### MASS TRANSFER + --convective-envelope-temperature-threshold: 5370 # Only if using envelope-state-prescription = 'FIXED_TEMPERATURE' + --critical-mass-ratio-HG-degenerate-accretor: 0.21 + --critical-mass-ratio-HG-non-degenerate-accretor: 0.25 + --critical-mass-ratio-MS-high-mass-degenerate-accretor: 0.0 + --critical-mass-ratio-MS-high-mass-non-degenerate-accretor: 0.625 + --critical-mass-ratio-MS-low-mass-degenerate-accretor: 1.0 + --critical-mass-ratio-MS-low-mass-non-degenerate-accretor: 1.44 + --critical-mass-ratio-giant-degenerate-accretor: 0.87 + --critical-mass-ratio-giant-non-degenerate-accretor: -1.0 + --critical-mass-ratio-helium-HG-degenerate-accretor: 0.21 + --critical-mass-ratio-helium-HG-non-degenerate-accretor: 0.25 + --critical-mass-ratio-helium-MS-degenerate-accretor: 0.0 + --critical-mass-ratio-helium-giant-degenerate-accretor: 0.0 + --critical-mass-ratio-helium-MS-non-degenerate-accretor: 0.87 + --critical-mass-ratio-helium-giant-non-degenerate-accretor: 1.28 + --critical-mass-ratio-white-dwarf-degenerate-accretor: 1.6 + --critical-mass-ratio-white-dwarf-non-degenerate-accretor: 0.0 + --mass-transfer-fa: 0.5 # Only if using mass-transfer-accretion-efficiency-prescription = 'FIXED' + --mass-transfer-jloss: 1.0 # Only if using mass-transfer-angular-momentum-loss-prescription = 'FIXED' + --mass-transfer-jloss-macleod-linear-fraction: 0.5 + --mass-transfer-thermal-limit-C: 10.0 + --zeta-adiabatic-arbitrary: 1.0E5 + --zeta-main-sequence: 2.0 + --zeta-radiative-envelope-giant: 6.5 + ### COMMON ENVELOPE + --common-envelope-alpha: 1.0 + --common-envelope-alpha-thermal: 1.0 # lambda = alpha_th*lambda_b + (1-alpha_th)*lambda_g + --common-envelope-lambda: 0.1 # Only if using 'LAMBDA_FIXED' + --common-envelope-lambda-multiplier: 1.0 # Multiply common envelope lambda by some constant + --common-envelope-mass-accretion-constant: 0 + --common-envelope-mass-accretion-max: 0.10 # For 'MACLEOD+2014' [Msol] + --common-envelope-mass-accretion-min: 0.04 # For 'MACLEOD+2014' [Msol] + --common-envelope-recombination-energy-density: 1.5E13 + --common-envelope-slope-kruckow: -0.83333333333 + --maximum-mass-donor-nandez-ivanova: 2.0 + ### SUPERNOVAE, KICKS AND REMNANTS + --eddington-accretion-factor: 1 # multiplication Factor for eddington accretion onto NS&BH + --fix-dimensionless-kick-magnitude: -1.0 + --fryer-22-fmix: 0.5 # parameter describing mixing growth time when using the 'FRYER2022' remnant mass prescription + --fryer-22-mcrit: 5.75 # critical mass for BH formation when using the 'FRYER2022' remnant mass prescription + --kick-direction-power: 0.0 + --kick-magnitude-sigma-CCSN-NS: 265.0 # [km/s] + --kick-magnitude-sigma-CCSN-BH: 265.0 # [km/s] + --kick-magnitude-max: -1.0 + --kick-magnitude-random: # (SSE) used to draw the kick magnitude for the star should it undergo a supernova event + --kick-magnitude: # (SSE) (drawn) kick magnitude for the star should it undergo a supernova event [km/s] + --kick-magnitude-random-1: # (BSE) used to draw the kick magnitude for the primary star should it undergo a supernova event + --kick-magnitude-1: # (BSE) (drawn) kick magnitude for the primary star should it undergo a supernova event [km/s] + --kick-theta-1: # (BSE) angle between the orbital plane and the 'z' axis of the supernova vector for the primary star should it undergo a supernova event [radians] + --kick-phi-1: # (BSE) angle between 'x' and 'y', both in the orbital plane of the supernova vector, for the primary star should it undergo a supernova event [radians] + --kick-mean-anomaly-1: # (BSE) mean anomaly at the instant of the supernova for the primary star should it undergo a supernova event - should be uniform in [0, 2pi) [radians] + --kick-magnitude-random-2: # (BSE) used to draw the kick velocity for the secondary star should it undergo a supernova event + --kick-magnitude-2: # (BSE) (drawn) kick magnitude for the secondary star should it undergo a supernova event [km/s] + --kick-theta-2: # (BSE) angle between the orbital plane and the 'z' axis of the supernova vector for the secondary star should it undergo a supernova event [radians] + --kick-phi-2: # (BSE) angle between 'x' and 'y', both in the orbital plane of the supernova vector, for the secondary star should it undergo a supernova event [radians] + --kick-mean-anomaly-2: # (BSE) mean anomaly at the instant of the supernova for the secondary star should it undergo a supernova event - should be uniform in [0, 2pi) [radians] + --kick-magnitude-sigma-ECSN: 30.0 # [km/s] + --kick-magnitude-sigma-USSN: 30.0 # [km/s] + --kick-scaling-factor: 1.0 + --maximum-neutron-star-mass: 2.5 + --mcbur1: 1.6 + --muller-mandel-kick-multiplier-BH: 200.0 # scaling prefactor for BH kicks when using the 'MULLERMANDEL' kick magnitude distribution + --muller-mandel-kick-multiplier-NS: 400.0 # scaling prefactor for NS kicks when using the 'MULLERMANDEL' kick magnitude distribution + --muller-mandel-sigma-kick: 0.3 # kick scatter when using the 'MULLERMANDEL' kick magnitude distribution + --neutrino-mass-loss-BH-formation-value: 0.1 + --pisn-lower-limit: 60.0 # Minimum core mass for PISN [Msol] + --pisn-upper-limit: 135.0 # Maximum core mass for PISN [Msol] + --ppi-lower-limit: 35.0 # Minimum core mass for PPI [Msol] + --ppi-upper-limit: 60.0 # Maximum core mass for PPI [Msol] + ### PULSAR PARAMETERS + --pulsar-birth-magnetic-field-distribution-min: 11.0 # [log10(B/G)] + --pulsar-birth-magnetic-field-distribution-max: 13.0 # [log10(B/G)] + --pulsar-birth-spin-period-distribution-min: 10.0 # [ms] + --pulsar-birth-spin-period-distribution-max: 100.0 # [ms] + --pulsar-magnetic-field-decay-timescale: 1000.0 # [Myr] + --pulsar-magnetic-field-decay-massscale: 0.025 # [Msol] + --pulsar-minimum-magnetic-field: 8.0 # [log10(B/G)] + +stringChoices: + ### LOGISTICS + --grid: 'grid.txt' # grid file name (e.g. 'mygrid.txt') + --mode: 'BSE' # evolving single stars (SSE) or binaries (BSE) + --notes: + --notes-hdrs: + --output-container: + ### STELLAR PROPERTIES + --chemically-homogeneous-evolution: 'PESSIMISTIC' # chemically homogeneous evolution. Options are 'NONE', 'OPTIMISTIC' and 'PESSIMISTIC' + --envelope-state-prescription: 'LEGACY' + --initial-mass-function: 'KROUPA' + --luminous-blue-variable-prescription: 'HURLEY_ADD' + --mass-loss-prescription: 'FLEXIBLE2023' + --metallicity-distribution: 'ZSOLAR' + --pulsational-pair-instability-prescription: 'MARCHANT' + ### BINARY PROPERTIES + --eccentricity-distribution: 'ZERO' + --mass-ratio-distribution: 'FLAT' + --orbital-period-distribution: 'FLATINLOG' + --rotational-velocity-distribution: 'ZERO' + --semi-major-axis-distribution: 'FLATINLOG' + ### MASS TRANSFER + --case-BB-stability-prescription: 'ALWAYS_STABLE' + --critical-mass-ratio-prescription: 'NONE' + --stellar-zeta-prescription: 'SOBERMAN' + --mass-transfer-angular-momentum-loss-prescription: 'ISOTROPIC' + --mass-transfer-accretion-efficiency-prescription: 'THERMAL' + --mass-transfer-rejuvenation-prescription: 'STARTRACK' + --mass-transfer-thermal-limit-accretor: 'CFACTOR' + ### COMMON ENVELOPE + --common-envelope-formalism: 'ENERGY' + --common-envelope-lambda-prescription: 'LAMBDA_NANJING' # Xu & Li 2010 + --common-envelope-mass-accretion-prescription: 'ZERO' + ### SUPERNOVAE, KICKS AND REMNANTS + --black-hole-kicks: 'FALLBACK' + --fryer-supernova-engine: 'DELAYED' + --kick-magnitude-distribution: 'MAXWELLIAN' + --kick-direction: 'ISOTROPIC' + --neutron-star-equation-of-state: 'SSE' + --neutrino-mass-loss-BH-formation: "FIXED_MASS" # "FIXED_FRACTION" + --pulsar-birth-magnetic-field-distribution: 'ZERO' + --pulsar-birth-spin-period-distribution: "ZERO" + --remnant-mass-prescription: 'FRYER2012' + ### LOGFILES AND OUTPUTS + # set the logfile names here + # + # set to None (e.g. logfile_BSE_supernovae = None) to use the default filename + # set to a string (e.g. logfile_BSE_supernovae = 'mySNfilename') to use that string as the filename + # set to empty string (e.g. logfile_BSE_supernovae = '""') to disable logging for that file (the file will not be created) + # + # We don't really need the 'BSE' or 'SSE' prefixes any more - they were put there because + # prior to the implementation of the containing folder it was too hard to locate the files + # created by a COMPAS run - especially the detailed output files. Now that the output + # files are created inside a containing folder for each run there is really no need for + # the prefixes - and if we don't have the prefixes we can share some of the options + # (e.g. specifying the supernovae filename doesn't need to have separate options for + # SSE and BSE - we really just need one (we only ever run in one mode or the other)) + # + # For now though, I'll leave them as is - we can change this when (if) we decide to + # drop the prefixes + --logfile-type: 'HDF5' + --logfile-name-prefix: + --logfile-definitions: + --logfile-common-envelopes: + --logfile-detailed-output: + --logfile-double-compact-objects: + --logfile-pulsar-evolution: + --logfile-rlof-parameters: + --logfile-supernovae: + --logfile-switch-log: + --logfile-system-parameters: + --output-path: + +## listChoices +listChoices: + --log-classes: [] + --debug-classes: [] diff --git a/py_tests/test_data/generate_test_data.sh b/py_tests/test_data/generate_test_data.sh deleted file mode 100644 index cc7d982b9..000000000 --- a/py_tests/test_data/generate_test_data.sh +++ /dev/null @@ -1,4 +0,0 @@ -## Assumes that COMPAS is installed in the current environment - -# Generate a population of 10 binaries -COMPAS -n 10 \ No newline at end of file diff --git a/py_tests/test_data/grid.txt b/py_tests/test_data/grid.txt new file mode 100644 index 000000000..e8c5db9a5 --- /dev/null +++ b/py_tests/test_data/grid.txt @@ -0,0 +1,2 @@ +--initial-mass-1 35.0 --initial-mass-2 31.0 --metallicity 0.001 --eccentricity 0.000000e+00 --semi-major-axis 3.5 --kick-magnitude-1 0 --kick-magnitude-2 0 +--initial-mass-1 35.0 --initial-mass-2 31.0 --metallicity 0.001 --eccentricity 0.000000e+00 --semi-major-axis 3.6 --kick-magnitude-1 0 --kick-magnitude-2 0 \ No newline at end of file From 442cc092fd407f04b8896dfdc7fe941070ac64b9 Mon Sep 17 00:00:00 2001 From: avi Date: Tue, 31 Oct 2023 17:33:48 +1300 Subject: [PATCH 13/32] fix path --- .github/workflows/compas-compile-ci.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/compas-compile-ci.yml b/.github/workflows/compas-compile-ci.yml index 220e5cc84..d2f98018f 100644 --- a/.github/workflows/compas-compile-ci.yml +++ b/.github/workflows/compas-compile-ci.yml @@ -44,8 +44,8 @@ jobs: pip install -e .[dev] export COMPAS_ROOT_DIR=${GITHUB_WORKSPACE} cd ${GITHUB_WORKSPACE}/py_tests/test_data/ - echo "Run example COMPAS job using the config py_tests/test_data/fiducial_bbh_configs.yaml" - compas_run_submit fiducial_bbh_configs.yaml > example_bbh.log + echo "Run example COMPAS job using the config py_tests/test_data/fiducial_bbh_config.yaml" + compas_run_submit fiducial_bbh_config.yaml > example_bbh.log cat example_bbh.log echo "Generating detailed evolution plot" compas_plot_detailed_evolution "./COMPAS_Output/Detailed_Output/BSE_Detailed_Output_0.h5" --dont-show >> example_bbh.log From baf6ea977a8fc165bb424aeb36d4ef073eeda04c Mon Sep 17 00:00:00 2001 From: Avi Vajpeyi Date: Wed, 1 Nov 2023 09:36:22 +1300 Subject: [PATCH 14/32] add test comnfig --- py_tests/test_data/fiducial_bbh_config.yaml | 467 ++++++++++---------- py_tests/test_data/grid.txt | 2 +- 2 files changed, 246 insertions(+), 223 deletions(-) diff --git a/py_tests/test_data/fiducial_bbh_config.yaml b/py_tests/test_data/fiducial_bbh_config.yaml index 872c8a12c..8dff49154 100644 --- a/py_tests/test_data/fiducial_bbh_config.yaml +++ b/py_tests/test_data/fiducial_bbh_config.yaml @@ -1,250 +1,273 @@ -# yaml file which contains all default options from COMPAS +##~!!~## COMPAS option values +##~!!~## File Created Tue Oct 31 10:11:32 2023 by COMPAS v02.40.00 +##~!!~## +##~!!~## The default COMPAS YAML file (``compasConfigDefault.yaml``), as distributed, has +##~!!~## all COMPAS option entries commented so that the COMPAS default value for the +##~!!~## option is used by default. To use a value other than the COMPAS default value, +##~!!~## users must uncomment the entry and change the option value to the desired value. booleanChoices: + ### LOGISTICS - --debug-to-file: False - --detailed-output: True # WARNING! this creates a data heavy file - --enable-warnings: False # option to enable/disable warning messages - --errors-to-file: False - --evolve-unbound-systems: False - --population-data-printing: False - --print-bool-as-string: False - --quiet: False - --rlof-printing: True - --store-input-files: True - --switch-log: False +# --debug-to-file: False # Default: False + --detailed-output: True # Default: False # WARNING! this creates a data heavy file +# --enable-warnings: False # Default: False # option to enable/disable warning messages +# --errors-to-file: False # Default: False +# --evolve-unbound-systems: True # Default: True +# --population-data-printing: False # Default: False +# --print-bool-as-string: False # Default: False +# --quiet: False # Default: False +# --rlof-printing: True # Default: True +# --store-input-files: True # Default: True +# --switch-log: False # Default: False + ### STELLAR PROPERTIES - --check-photon-tiring-limit: False - --use-mass-loss: True +# --check-photon-tiring-limit: False # Default: False +# --use-mass-loss: True # Default: True +# --expel-convective-envelope-above-luminosity-threshold: False # Default: False + ### BINARY PROPERTIES - --allow-touching-at-birth: False # record binaries that have stars touching at birth in output files +# --allow-touching-at-birth: False # Default: False # record binaries that have stars touching at birth in output files + ### MASS TRANSFER - --angular-momentum-conservation-during-circularisation: False - --allow-rlof-at-birth: True # allow binaries that have one or both stars in RLOF at birth to evolve, particularly useful in the context of CHE binaries. - --circularise-binary-during-mass-transfer: True - --hmxr-binaries: False - --mass-transfer: True - --retain-core-mass-during-caseA-mass-transfer: False +# --angular-momentum-conservation-during-circularisation: False # Default: False +# --allow-rlof-at-birth: True # Default: True # allow binaries that have one or both stars in RLOF at birth to evolve, particularly useful in the context of CHE binaries +# --circularise-binary-during-mass-transfer: True # Default: True +# --hmxr-binaries: False # Default: False +# --mass-transfer: True # Default: True +# --retain-core-mass-during-caseA-mass-transfer: True # Default: True + ### COMMON ENVELOPE - --common-envelope-allow-immediate-RLOF-post-CE-survive: False - --common-envelope-allow-main-sequence-survive: True # Allow main sequence stars to survive CE. Was previously False by default - --common-envelope-allow-radiative-envelope-survive: False - --common-envelope-lambda-nanjing-enhanced: False - --common-envelope-lambda-nanjing-interpolate-in-mass: False - --common-envelope-lambda-nanjing-interpolate-in-metallicity: False - --common-envelope-lambda-nanjing-use-rejuvenated-mass: False - --revised-energy-formalism-nandez-ivanova: False +# --common-envelope-allow-immediate-RLOF-post-CE-survive: False # Default: False +# --common-envelope-allow-main-sequence-survive: True # Default: True # Allow main sequence stars to survive CE +# --common-envelope-allow-radiative-envelope-survive: False # Default: False +# --common-envelope-lambda-nanjing-enhanced: False # Default: False +# --common-envelope-lambda-nanjing-interpolate-in-mass: False # Default: False +# --common-envelope-lambda-nanjing-interpolate-in-metallicity: False # Default: False +# --common-envelope-lambda-nanjing-use-rejuvenated-mass: False # Default: False +# --revised-energy-formalism-nandez-ivanova: False # Default: False + + ### TIDES +# --enable-tides: False # Default: False + ### SUPERNOVAE, KICKS AND REMNANTS - --allow-non-stripped-ECSN: True - --pair-instability-supernovae: True - --pulsational-pair-instability: True +# --allow-non-stripped-ECSN: False # Default: False +# --pair-instability-supernovae: True # Default: True +# --pulsational-pair-instability: True # Default: True + ### PULSAR PARAMETERS - --evolve-pulsars: False +# --evolve-pulsars: False # Default: False + + ### WHITE DWARF PARAMETERS +# --evolve-double-white-dwarfs: False # Default: False + numericalChoices: + ### LOGISTICS - --debug-level: 0 - --logfile-common-envelopes-record-types: -1 - --logfile-detailed-output-record-types: -1 - --logfile-double-compact-objects-record-types: -1 - --logfile-pulsar-evolution-record-types: -1 - --logfile-rlof-parameters-record-types: -1 - --logfile-supernovae-record-types: -1 - --logfile-system-parameters-record-types: -1 - --grid-lines-to-process: - --grid-start-line: 0 - --hdf5-chunk-size: 100000 - --hdf5-buffer-size: 1 - --log-level: 0 - --maximum-evolution-time: 13700.0 # maximum physical time a system can be evolved [Myrs] - --maximum-number-timestep-iterations: 99999 - --number-of-systems: 10 # number of systems per batch - --timestep-multiplier: 1 # optional multiplier relative to default time step duration +# --debug-level: 0 # Default: 0 +# --logfile-common-envelopes-record-types: -1 # Default: -1 +# --logfile-detailed-output-record-types: -1 # Default: -1 +# --logfile-double-compact-objects-record-types: -1 # Default: -1 +# --logfile-pulsar-evolution-record-types: -1 # Default: -1 +# --logfile-rlof-parameters-record-types: -1 # Default: -1 +# --logfile-supernovae-record-types: -1 # Default: -1 +# --logfile-system-parameters-record-types: -1 # Default: -1 +# --grid-lines-to-process: 9223372036854775807 # Default: 9223372036854775807 +# --grid-start-line: 0 # Default: 0 +# --hdf5-chunk-size: 100000 # Default: 100000 +# --hdf5-buffer-size: 1 # Default: 1 +# --log-level: 0 # Default: 0 +# --maximum-evolution-time: 13700.00 # Default: 13700.00 # maximum physical time a system can be evolved [Myr] +# --maximum-number-timestep-iterations: 99999 # Default: 99999 +# --number-of-systems: 10 # Default: 10 # number of systems per batch +# --timestep-multiplier: 1.000000 # Default: 1.000000 # optional multiplier relative to default time step duration + ### STELLAR PROPERTIES - --cool-wind-mass-loss-multiplier: 1.0 - --initial-mass: # initial mass for SSE - --initial-mass-min: 5.0 # use 5.0 for DCOs [Msol] - --initial-mass-max: 150.0 # stellar tracks extrapolated above 50 Msol (Hurley+2000) [Msol] - --initial-mass-power: 0.0 - --metallicity: 0.0142 # metallicity for both SSE and BSE - Solar metallicity Asplund+2010 - --metallicity-min: 0.0001 - --metallicity-max: 0.03 - --luminous-blue-variable-multiplier: 1.5 - --overall-wind-mass-loss-multiplier: 1.0 - --random-seed: 0 - --rotational-frequency: 0.0 - --rotational-frequency-1: 0.0 - --rotational-frequency-2: 0.0 - --wolf-rayet-multiplier: 1.0 +# --cool-wind-mass-loss-multiplier: 1.000000 # Default: 1.000000 +# --initial-mass: 5.000000 # Default: 5.000000 # initial mass for SSE +# --initial-mass-min: 5.000000 # Default: 5.000000 # use 5.0 for DCOs [Msol] +# --initial-mass-max: 150.00 # Default: 150.00 # stellar tracks extrapolated above 50 Msol (Hurley+2000) [Msol] +# --initial-mass-power: 0.000000 # Default: 0.000000 +# --luminosity-to-mass-threshold: 4.200000 # Default: 4.200000 +# --metallicity: 0.014200 # Default: 0.014200 # metallicity for both SSE and BSE - Solar metallicity Asplund+2010 +# --metallicity-min: 0.000100 # Default: 0.000100 +# --metallicity-max: 0.030000 # Default: 0.030000 +# --luminous-blue-variable-multiplier: 1.500000 # Default: 1.500000 +# --overall-wind-mass-loss-multiplier: 1.000000 # Default: 1.000000 +# --random-seed: 0 # Default: 0 +# --rotational-frequency: 0.000000 # Default: 0.000000 +# --rotational-frequency-1: 0.000000 # Default: 0.000000 +# --rotational-frequency-2: 0.000000 # Default: 0.000000 +# --wolf-rayet-multiplier: 0.100000 # Default: 0.100000 + ### BINARY PROPERTIES - --eccentricity: # eccentricity for BSE - --eccentricity-min: 0.0 - --eccentricity-max: 1.0 - --initial-mass-1: # primary initial mass for BSE - --initial-mass-2: # secondary initial mass for BSE - --mass-ratio: - --mass-ratio-min: 0.01 - --mass-ratio-max: 1.0 - --minimum-secondary-mass: 0.1 # Brown dwarf limit [Msol] - --orbital-period: # orbital period for BSE - --orbital-period-min: 1.1 # [days] - --orbital-period-max: 1000 # [days] - --semi-major-axis: # semi-major axis for BSE - --semi-major-axis-min: 0.01 # [AU] - --semi-major-axis-max: 1000.0 # [AU] +# --eccentricity: 0.000000 # Default: 0.000000 # eccentricity for BSE +# --eccentricity-min: 0.000000 # Default: 0.000000 +# --eccentricity-max: 1.000000 # Default: 1.000000 +# --initial-mass-1: 5.000000 # Default: 5.000000 # primary initial mass for BSE +# --initial-mass-2: 5.000000 # Default: 5.000000 # secondary initial mass for BSE +# --mass-ratio: 1.000000 # Default: 1.000000 +# --mass-ratio-min: 0.010000 # Default: 0.010000 +# --mass-ratio-max: 1.000000 # Default: 1.000000 +# --minimum-secondary-mass: 0.100000 # Default: 0.100000 # Brown dwarf limit [Msol] +# --orbital-period: 0.100000 # Default: 0.100000 # orbital period for BSE +# --orbital-period-min: 1.100000 # Default: 1.100000 # [days] +# --orbital-period-max: 1000.00 # Default: 1000.00 # [days] +# --semi-major-axis: 0.100000 # Default: 0.100000 # semi-major axis for BSE +# --semi-major-axis-min: 0.010000 # Default: 0.010000 # [AU] +# --semi-major-axis-max: 1000.00 # Default: 1000.00 # [AU] + ### MASS TRANSFER - --convective-envelope-temperature-threshold: 5370 # Only if using envelope-state-prescription = 'FIXED_TEMPERATURE' - --critical-mass-ratio-HG-degenerate-accretor: 0.21 - --critical-mass-ratio-HG-non-degenerate-accretor: 0.25 - --critical-mass-ratio-MS-high-mass-degenerate-accretor: 0.0 - --critical-mass-ratio-MS-high-mass-non-degenerate-accretor: 0.625 - --critical-mass-ratio-MS-low-mass-degenerate-accretor: 1.0 - --critical-mass-ratio-MS-low-mass-non-degenerate-accretor: 1.44 - --critical-mass-ratio-giant-degenerate-accretor: 0.87 - --critical-mass-ratio-giant-non-degenerate-accretor: -1.0 - --critical-mass-ratio-helium-HG-degenerate-accretor: 0.21 - --critical-mass-ratio-helium-HG-non-degenerate-accretor: 0.25 - --critical-mass-ratio-helium-MS-degenerate-accretor: 0.0 - --critical-mass-ratio-helium-giant-degenerate-accretor: 0.0 - --critical-mass-ratio-helium-MS-non-degenerate-accretor: 0.87 - --critical-mass-ratio-helium-giant-non-degenerate-accretor: 1.28 - --critical-mass-ratio-white-dwarf-degenerate-accretor: 1.6 - --critical-mass-ratio-white-dwarf-non-degenerate-accretor: 0.0 - --mass-transfer-fa: 0.5 # Only if using mass-transfer-accretion-efficiency-prescription = 'FIXED' - --mass-transfer-jloss: 1.0 # Only if using mass-transfer-angular-momentum-loss-prescription = 'FIXED' - --mass-transfer-jloss-macleod-linear-fraction: 0.5 - --mass-transfer-thermal-limit-C: 10.0 - --zeta-adiabatic-arbitrary: 1.0E5 - --zeta-main-sequence: 2.0 - --zeta-radiative-envelope-giant: 6.5 +# --convective-envelope-temperature-threshold: 5370.00 # Default: 5370.00 # Only if using envelope-state-prescription = 'FIXED_TEMPERATURE' +# --critical-mass-ratio-HG-degenerate-accretor: 0.210000 # Default: 0.210000 +# --critical-mass-ratio-HG-non-degenerate-accretor: 0.250000 # Default: 0.250000 +# --critical-mass-ratio-MS-high-mass-degenerate-accretor: 0.000000 # Default: 0.000000 +# --critical-mass-ratio-MS-high-mass-non-degenerate-accretor: 0.625000 # Default: 0.625000 +# --critical-mass-ratio-MS-low-mass-degenerate-accretor: 1.000000 # Default: 1.000000 +# --critical-mass-ratio-MS-low-mass-non-degenerate-accretor: 1.440000 # Default: 1.440000 +# --critical-mass-ratio-giant-degenerate-accretor: 0.870000 # Default: 0.870000 +# --critical-mass-ratio-giant-non-degenerate-accretor: -1.000000 # Default: -1.000000 +# --critical-mass-ratio-helium-HG-degenerate-accretor: 0.210000 # Default: 0.210000 +# --critical-mass-ratio-helium-HG-non-degenerate-accretor: 0.250000 # Default: 0.250000 +# --critical-mass-ratio-helium-MS-degenerate-accretor: 0.000000 # Default: 0.000000 +# --critical-mass-ratio-helium-giant-degenerate-accretor: 0.870000 # Default: 0.870000 +# --critical-mass-ratio-helium-MS-non-degenerate-accretor: 0.000000 # Default: 0.000000 +# --critical-mass-ratio-helium-giant-non-degenerate-accretor: 1.280000 # Default: 1.280000 +# --critical-mass-ratio-white-dwarf-degenerate-accretor: 1.600000 # Default: 1.600000 +# --critical-mass-ratio-white-dwarf-non-degenerate-accretor: 0.000000 # Default: 0.000000 +# --mass-transfer-fa: 0.500000 # Default: 0.500000 # Only if using mass-transfer-accretion-efficiency-prescription = 'FIXED' +# --mass-transfer-jloss: 1.000000 # Default: 1.000000 # Only if using mass-transfer-angular-momentum-loss-prescription = 'FIXED' +# --mass-transfer-jloss-macleod-linear-fraction: 0.500000 # Default: 0.500000 +# --mass-transfer-thermal-limit-C: 10.000000 # Default: 10.000000 +# --zeta-adiabatic-arbitrary: 10000.00 # Default: 10000.00 +# --zeta-main-sequence: 2.000000 # Default: 2.000000 +# --zeta-radiative-envelope-giant: 6.500000 # Default: 6.500000 + ### COMMON ENVELOPE - --common-envelope-alpha: 1.0 - --common-envelope-alpha-thermal: 1.0 # lambda = alpha_th*lambda_b + (1-alpha_th)*lambda_g - --common-envelope-lambda: 0.1 # Only if using 'LAMBDA_FIXED' - --common-envelope-lambda-multiplier: 1.0 # Multiply common envelope lambda by some constant - --common-envelope-mass-accretion-constant: 0 - --common-envelope-mass-accretion-max: 0.10 # For 'MACLEOD+2014' [Msol] - --common-envelope-mass-accretion-min: 0.04 # For 'MACLEOD+2014' [Msol] - --common-envelope-recombination-energy-density: 1.5E13 - --common-envelope-slope-kruckow: -0.83333333333 - --maximum-mass-donor-nandez-ivanova: 2.0 +# --common-envelope-alpha: 1.000000 # Default: 1.000000 +# --common-envelope-alpha-thermal: 1.000000 # Default: 1.000000 # lambda = alpha_th*lambda_b + (1-alpha_th)*lambda_g +# --common-envelope-lambda: 0.100000 # Default: 0.100000 # Only if using 'LAMBDA_FIXED' +# --common-envelope-lambda-multiplier: 1.000000 # Default: 1.000000 # Multiply common envelope lambda by some constant +# --common-envelope-mass-accretion-constant: 0.000000 # Default: 0.000000 +# --common-envelope-mass-accretion-max: 0.100000 # Default: 0.100000 # For 'MACLEOD+2014' [Msol] +# --common-envelope-mass-accretion-min: 0.040000 # Default: 0.040000 # For 'MACLEOD+2014' [Msol] +# --common-envelope-recombination-energy-density: 15000000000000.00 # Default: 15000000000000.00 +# --common-envelope-slope-kruckow: -0.833333 # Default: -0.833333 +# --maximum-mass-donor-nandez-ivanova: 2.000000 # Default: 2.000000 + ### SUPERNOVAE, KICKS AND REMNANTS - --eddington-accretion-factor: 1 # multiplication Factor for eddington accretion onto NS&BH - --fix-dimensionless-kick-magnitude: -1.0 - --fryer-22-fmix: 0.5 # parameter describing mixing growth time when using the 'FRYER2022' remnant mass prescription - --fryer-22-mcrit: 5.75 # critical mass for BH formation when using the 'FRYER2022' remnant mass prescription - --kick-direction-power: 0.0 - --kick-magnitude-sigma-CCSN-NS: 265.0 # [km/s] - --kick-magnitude-sigma-CCSN-BH: 265.0 # [km/s] - --kick-magnitude-max: -1.0 - --kick-magnitude-random: # (SSE) used to draw the kick magnitude for the star should it undergo a supernova event - --kick-magnitude: # (SSE) (drawn) kick magnitude for the star should it undergo a supernova event [km/s] - --kick-magnitude-random-1: # (BSE) used to draw the kick magnitude for the primary star should it undergo a supernova event - --kick-magnitude-1: # (BSE) (drawn) kick magnitude for the primary star should it undergo a supernova event [km/s] - --kick-theta-1: # (BSE) angle between the orbital plane and the 'z' axis of the supernova vector for the primary star should it undergo a supernova event [radians] - --kick-phi-1: # (BSE) angle between 'x' and 'y', both in the orbital plane of the supernova vector, for the primary star should it undergo a supernova event [radians] - --kick-mean-anomaly-1: # (BSE) mean anomaly at the instant of the supernova for the primary star should it undergo a supernova event - should be uniform in [0, 2pi) [radians] - --kick-magnitude-random-2: # (BSE) used to draw the kick velocity for the secondary star should it undergo a supernova event - --kick-magnitude-2: # (BSE) (drawn) kick magnitude for the secondary star should it undergo a supernova event [km/s] - --kick-theta-2: # (BSE) angle between the orbital plane and the 'z' axis of the supernova vector for the secondary star should it undergo a supernova event [radians] - --kick-phi-2: # (BSE) angle between 'x' and 'y', both in the orbital plane of the supernova vector, for the secondary star should it undergo a supernova event [radians] - --kick-mean-anomaly-2: # (BSE) mean anomaly at the instant of the supernova for the secondary star should it undergo a supernova event - should be uniform in [0, 2pi) [radians] - --kick-magnitude-sigma-ECSN: 30.0 # [km/s] - --kick-magnitude-sigma-USSN: 30.0 # [km/s] - --kick-scaling-factor: 1.0 - --maximum-neutron-star-mass: 2.5 - --mcbur1: 1.6 - --muller-mandel-kick-multiplier-BH: 200.0 # scaling prefactor for BH kicks when using the 'MULLERMANDEL' kick magnitude distribution - --muller-mandel-kick-multiplier-NS: 400.0 # scaling prefactor for NS kicks when using the 'MULLERMANDEL' kick magnitude distribution - --muller-mandel-sigma-kick: 0.3 # kick scatter when using the 'MULLERMANDEL' kick magnitude distribution - --neutrino-mass-loss-BH-formation-value: 0.1 - --pisn-lower-limit: 60.0 # Minimum core mass for PISN [Msol] - --pisn-upper-limit: 135.0 # Maximum core mass for PISN [Msol] - --ppi-lower-limit: 35.0 # Minimum core mass for PPI [Msol] - --ppi-upper-limit: 60.0 # Maximum core mass for PPI [Msol] +# --eddington-accretion-factor: 1.000000 # Default: 1.000000 # multiplication Factor for eddington accretion onto NS&BH +# --fix-dimensionless-kick-magnitude: -1.000000 # Default: -1.000000 +# --fryer-22-fmix: 0.500000 # Default: 0.500000 # parameter describing mixing growth time when using the 'FRYER2022' remnant mass prescription +# --fryer-22-mcrit: 5.750000 # Default: 5.750000 # critical mass for BH formation when using the 'FRYER2022' remnant mass prescription +# --kick-direction-power: 0.000000 # Default: 0.000000 +# --kick-magnitude-sigma-CCSN-NS: 265.00 # Default: 265.00 # [km/s] +# --kick-magnitude-sigma-CCSN-BH: 265.00 # Default: 265.00 # [km/s] +# --kick-magnitude-max: -1.000000 # Default: -1.000000 +# --kick-magnitude-random: 0.000000 # Default: 0.000000 # (SSE) used to draw the kick magnitude for the star should it undergo a supernova event +# --kick-magnitude: 0.000000 # Default: 0.000000 # (SSE) (drawn) kick magnitude for the star should it undergo a supernova event [km/s] +# --kick-magnitude-random-1: 0.000000 # Default: 0.000000 # (BSE) used to draw the kick magnitude for the primary star should it undergo a supernova event +# --kick-magnitude-1: 0.000000 # Default: 0.000000 # (BSE) (drawn) kick magnitude for the primary star should it undergo a supernova event [km/s] +# --kick-theta-1: 0.000000 # Default: 0.000000 # (BSE) angle between the orbital plane and the 'z' axis of the supernova vector for the primary star should it undergo a supernova event [radians] +# --kick-phi-1: 0.000000 # Default: 0.000000 # (BSE) angle between 'x' and 'y', both in the orbital plane of the supernova vector, for the primary star should it undergo a supernova event [radians] +# --kick-mean-anomaly-1: 0.000000 # Default: 0.000000 # (BSE) mean anomaly at the instant of the supernova for the primary star should it undergo a supernova event - should be uniform in [0, 2pi) [radians] +# --kick-magnitude-random-2: 0.000000 # Default: 0.000000 # (BSE) used to draw the kick velocity for the secondary star should it undergo a supernova event +# --kick-magnitude-2: 0.000000 # Default: 0.000000 # (BSE) (drawn) kick magnitude for the secondary star should it undergo a supernova event [km/s] +# --kick-theta-2: 0.000000 # Default: 0.000000 # (BSE) angle between the orbital plane and the 'z' axis of the supernova vector for the secondary star should it undergo a supernova event [radians] +# --kick-phi-2: 0.000000 # Default: 0.000000 # (BSE) angle between 'x' and 'y', both in the orbital plane of the supernova vector, for the secondary star should it undergo a supernova event [radians] +# --kick-mean-anomaly-2: 0.000000 # Default: 0.000000 # (BSE) mean anomaly at the instant of the supernova for the secondary star should it undergo a supernova event - should be uniform in [0, 2pi) [radians] +# --kick-magnitude-sigma-ECSN: 30.000000 # Default: 30.000000 # [km/s] +# --kick-magnitude-sigma-USSN: 30.000000 # Default: 30.000000 # [km/s] +# --kick-scaling-factor: 1.000000 # Default: 1.000000 +# --maximum-neutron-star-mass: 2.500000 # Default: 2.500000 +# --mcbur1: 1.600000 # Default: 1.600000 +# --muller-mandel-kick-multiplier-BH: 200.00 # Default: 200.00 # scaling prefactor for BH kicks when using the 'MULLERMANDEL' kick magnitude distribution +# --muller-mandel-kick-multiplier-NS: 520.00 # Default: 520.00 # scaling prefactor for NS kicks when using the 'MULLERMANDEL' kick magnitude distribution +# --muller-mandel-sigma-kick: 0.300000 # Default: 0.300000 # kick scatter when using the 'MULLERMANDEL' kick magnitude distribution +# --neutrino-mass-loss-BH-formation-value: 0.100000 # Default: 0.100000 +# --pisn-lower-limit: 60.000000 # Default: 60.000000 # Minimum core mass for PISN [Msol] +# --pisn-upper-limit: 135.00 # Default: 135.00 # Maximum core mass for PISN [Msol] +# --ppi-lower-limit: 35.000000 # Default: 35.000000 # Minimum core mass for PPI [Msol] +# --ppi-upper-limit: 60.000000 # Default: 60.000000 # Maximum core mass for PPI [Msol] + ### PULSAR PARAMETERS - --pulsar-birth-magnetic-field-distribution-min: 11.0 # [log10(B/G)] - --pulsar-birth-magnetic-field-distribution-max: 13.0 # [log10(B/G)] - --pulsar-birth-spin-period-distribution-min: 10.0 # [ms] - --pulsar-birth-spin-period-distribution-max: 100.0 # [ms] - --pulsar-magnetic-field-decay-timescale: 1000.0 # [Myr] - --pulsar-magnetic-field-decay-massscale: 0.025 # [Msol] - --pulsar-minimum-magnetic-field: 8.0 # [log10(B/G)] +# --pulsar-birth-magnetic-field-distribution-min: 11.000000 # Default: 11.000000 # [log10(B/G)] +# --pulsar-birth-magnetic-field-distribution-max: 13.000000 # Default: 13.000000 # [log10(B/G)] +# --pulsar-birth-spin-period-distribution-min: 10.000000 # Default: 10.000000 # [ms] +# --pulsar-birth-spin-period-distribution-max: 100.00 # Default: 100.00 # [ms] +# --pulsar-magnetic-field-decay-timescale: 1000.00 # Default: 1000.00 # [Myr] +# --pulsar-magnetic-field-decay-massscale: 0.025000 # Default: 0.025000 # [Msol] +# --pulsar-minimum-magnetic-field: 8.000000 # Default: 8.000000 # [log10(B/G)] + stringChoices: + ### LOGISTICS - --grid: 'grid.txt' # grid file name (e.g. 'mygrid.txt') - --mode: 'BSE' # evolving single stars (SSE) or binaries (BSE) - --notes: - --notes-hdrs: - --output-container: +# --add-options-to-sysparms: 'GRID' # Default: 'GRID' # Options: ['NEVER','GRID','ALWAYS'] + --grid: 'Grid_demo.txt' # Default: '' # grid file name (e.g. 'mygrid.txt') +# --mode: 'BSE' # Default: 'BSE' # Options: ['BSE','SSE'] # evolving single (SSE) or binary stars (BSE) +# --notes: { } # Default: { } +# --notes-hdrs: { } # Default: { } +# --output-container: 'COMPAS_Output' # Default: 'COMPAS_Output' + ### STELLAR PROPERTIES - --chemically-homogeneous-evolution: 'PESSIMISTIC' # chemically homogeneous evolution. Options are 'NONE', 'OPTIMISTIC' and 'PESSIMISTIC' - --envelope-state-prescription: 'LEGACY' - --initial-mass-function: 'KROUPA' - --luminous-blue-variable-prescription: 'HURLEY_ADD' - --mass-loss-prescription: 'FLEXIBLE2023' - --metallicity-distribution: 'ZSOLAR' - --pulsational-pair-instability-prescription: 'MARCHANT' +# --chemically-homogeneous-evolution: 'PESSIMISTIC' # Default: 'PESSIMISTIC' # Options: ['PESSIMISTIC','OPTIMISTIC','NONE'] # chemically homogeneous evolution +# --envelope-state-prescription: 'LEGACY' # Default: 'LEGACY' # Options: ['FIXED_TEMPERATURE','HURLEY','LEGACY'] +# --initial-mass-function: 'KROUPA' # Default: 'KROUPA' # Options: ['KROUPA','UNIFORM','POWERLAW','SALPETER'] +# --luminous-blue-variable-prescription: 'HURLEY_ADD' # Default: 'HURLEY_ADD' # Options: ['BELCZYNSKI','HURLEY','HURLEY_ADD','NONE'] +# --mass-loss-prescription: 'VINK' # Default: 'VINK' # Options: ['VINK','HURLEY','NONE'] +# --metallicity-distribution: 'ZSOLAR' # Default: 'ZSOLAR' # Options: ['LOGUNIFORM','ZSOLAR'] +# --pulsational-pair-instability-prescription: 'MARCHANT' # Default: 'MARCHANT' # Options: ['FARMER','MARCHANT','STARTRACK','COMPAS'] + ### BINARY PROPERTIES - --eccentricity-distribution: 'ZERO' - --mass-ratio-distribution: 'FLAT' - --orbital-period-distribution: 'FLATINLOG' - --rotational-velocity-distribution: 'ZERO' - --semi-major-axis-distribution: 'FLATINLOG' +# --eccentricity-distribution: 'ZERO' # Default: 'ZERO' # Options: ['SANA2012','DUQUENNOYMAYOR1991','GELLER+2013','THERMAL','FLAT','ZERO'] +# --mass-ratio-distribution: 'FLAT' # Default: 'FLAT' # Options: ['SANA2012','DUQUENNOYMAYOR1991','FLAT'] +# --orbital-period-distribution: 'FLATINLOG' # Default: 'FLATINLOG' # Options: ['FLATINLOG'] +# --rotational-velocity-distribution: 'ZERO' # Default: 'ZERO' # Options: ['VLTFLAMES','HURLEY','ZERO'] +# --semi-major-axis-distribution: 'FLATINLOG' # Default: 'FLATINLOG' # Options: ['SANA2012','DUQUENNOYMAYOR1991','FLATINLOG'] + ### MASS TRANSFER - --case-BB-stability-prescription: 'ALWAYS_STABLE' - --critical-mass-ratio-prescription: 'NONE' - --stellar-zeta-prescription: 'SOBERMAN' - --mass-transfer-angular-momentum-loss-prescription: 'ISOTROPIC' - --mass-transfer-accretion-efficiency-prescription: 'THERMAL' - --mass-transfer-rejuvenation-prescription: 'STARTRACK' - --mass-transfer-thermal-limit-accretor: 'CFACTOR' +# --case-BB-stability-prescription: 'ALWAYS_STABLE' # Default: 'ALWAYS_STABLE' # Options: ['ALWAYS_UNSTABLE','TREAT_AS_OTHER_MT','ALWAYS_STABLE_ONTO_NSBH','ALWAYS_STABLE'] +# --critical-mass-ratio-prescription: 'NONE' # Default: 'NONE' # Options: ['HURLEY_HJELLMING_WEBBINK','GE20_IC','GE20','CLAEYS','NONE'] +# --stellar-zeta-prescription: 'SOBERMAN' # Default: 'SOBERMAN' # Options: ['NONE','ARBITRARY','HURLEY','SOBERMAN'] +# --mass-transfer-angular-momentum-loss-prescription: 'ISOTROPIC' # Default: 'ISOTROPIC' # Options: ['ARBITRARY','MACLEOD_LINEAR','CIRCUMBINARY','ISOTROPIC','JEANS'] +# --mass-transfer-accretion-efficiency-prescription: 'THERMAL' # Default: 'THERMAL' # Options: ['FIXED','THERMAL'] +# --mass-transfer-rejuvenation-prescription: 'STARTRACK' # Default: 'STARTRACK' # Options: ['STARTRACK','NONE'] +# --mass-transfer-thermal-limit-accretor: 'CFACTOR' # Default: 'CFACTOR' # Options: ['ROCHELOBE','CFACTOR'] + ### COMMON ENVELOPE - --common-envelope-formalism: 'ENERGY' - --common-envelope-lambda-prescription: 'LAMBDA_NANJING' # Xu & Li 2010 - --common-envelope-mass-accretion-prescription: 'ZERO' +# --common-envelope-formalism: 'ENERGY' # Default: 'ENERGY' # Options: ['TWO_STAGE','ENERGY'] +# --common-envelope-lambda-prescription: 'LAMBDA_NANJING' # Default: 'LAMBDA_NANJING' # Options: ['LAMBDA_DEWI','LAMBDA_KRUCKOW','LAMBDA_NANJING','LAMBDA_LOVERIDGE','LAMBDA_FIXED'] # Xu & Li 2010 +# --common-envelope-mass-accretion-prescription: 'ZERO' # Default: 'ZERO' # Options: ['CHEVALIER','MACLEOD','UNIFORM','CONSTANT','ZERO'] + ### SUPERNOVAE, KICKS AND REMNANTS - --black-hole-kicks: 'FALLBACK' - --fryer-supernova-engine: 'DELAYED' - --kick-magnitude-distribution: 'MAXWELLIAN' - --kick-direction: 'ISOTROPIC' - --neutron-star-equation-of-state: 'SSE' - --neutrino-mass-loss-BH-formation: "FIXED_MASS" # "FIXED_FRACTION" - --pulsar-birth-magnetic-field-distribution: 'ZERO' - --pulsar-birth-spin-period-distribution: "ZERO" - --remnant-mass-prescription: 'FRYER2012' +# --black-hole-kicks: 'FALLBACK' # Default: 'FALLBACK' # Options: ['FALLBACK','ZERO','REDUCED','FULL'] +# --fryer-supernova-engine: 'DELAYED' # Default: 'DELAYED' # Options: ['DELAYED','RAPID'] +# --kick-magnitude-distribution: 'MULLERMANDEL' # Default: 'MULLERMANDEL' # Options: ['MULLERMANDEL','MULLER2016MAXWELLIAN','MULLER2016','BRAYELDRIDGE','MAXWELLIAN','FLAT','FIXED','ZERO'] +# --kick-direction: 'ISOTROPIC' # Default: 'ISOTROPIC' # Options: ['POLES','WEDGE','POWERLAW','PERPENDICULAR','INPLANE','ISOTROPIC'] +# --neutron-star-equation-of-state: 'SSE' # Default: 'SSE' # Options: ['ARP3','SSE'] +# --neutrino-mass-loss-BH-formation: 'FIXED_MASS' # Default: 'FIXED_MASS' # Options: ['FIXED_MASS','FIXED_FRACTION'] +# --pulsar-birth-magnetic-field-distribution: 'ZERO' # Default: 'ZERO' # Options: ['LOGNORMAL','UNIFORM','FLATINLOG','FIXED','ZERO'] +# --pulsar-birth-spin-period-distribution: 'ZERO' # Default: 'ZERO' # Options: ['NORMAL','UNIFORM','FIXED','ZERO'] +# --remnant-mass-prescription: 'MULLERMANDEL' # Default: 'MULLERMANDEL' # Options: ['SCHNEIDER2020ALT','SCHNEIDER2020','MULLERMANDEL','MULLER2016','FRYER2022','FRYER2012','BELCZYNSKI2002','HURLEY2000'] + ### LOGFILES AND OUTPUTS - # set the logfile names here - # - # set to None (e.g. logfile_BSE_supernovae = None) to use the default filename - # set to a string (e.g. logfile_BSE_supernovae = 'mySNfilename') to use that string as the filename - # set to empty string (e.g. logfile_BSE_supernovae = '""') to disable logging for that file (the file will not be created) - # - # We don't really need the 'BSE' or 'SSE' prefixes any more - they were put there because - # prior to the implementation of the containing folder it was too hard to locate the files - # created by a COMPAS run - especially the detailed output files. Now that the output - # files are created inside a containing folder for each run there is really no need for - # the prefixes - and if we don't have the prefixes we can share some of the options - # (e.g. specifying the supernovae filename doesn't need to have separate options for - # SSE and BSE - we really just need one (we only ever run in one mode or the other)) - # - # For now though, I'll leave them as is - we can change this when (if) we decide to - # drop the prefixes - --logfile-type: 'HDF5' - --logfile-name-prefix: - --logfile-definitions: - --logfile-common-envelopes: - --logfile-detailed-output: - --logfile-double-compact-objects: - --logfile-pulsar-evolution: - --logfile-rlof-parameters: - --logfile-supernovae: - --logfile-switch-log: - --logfile-system-parameters: - --output-path: - -## listChoices +# --logfile-type: 'HDF5' # Default: 'HDF5' # Options: ['TXT','TSV','CSV','HDF5','NONE'] +# --logfile-name-prefix: '' # Default: '' +# --logfile-definitions: '' # Default: '' +# --logfile-common-envelopes: 'BSE_Common_Envelopes' # Default: 'BSE_Common_Envelopes' +# --logfile-detailed-output: 'BSE_Detailed_Output' # Default: 'BSE_Detailed_Output' +# --logfile-double-compact-objects: 'BSE_Double_Compact_Objects' # Default: 'BSE_Double_Compact_Objects' +# --logfile-pulsar-evolution: 'BSE_Pulsar_Evolution' # Default: 'BSE_Pulsar_Evolution' +# --logfile-rlof-parameters: 'BSE_RLOF' # Default: 'BSE_RLOF' +# --logfile-supernovae: 'BSE_Supernovae' # Default: 'BSE_Supernovae' +# --logfile-switch-log: 'BSE_Switch_Log' # Default: 'BSE_Switch_Log' +# --logfile-system-parameters: 'BSE_System_Parameters' # Default: 'BSE_System_Parameters' +# --output-path: '.' # Default: '.' + + listChoices: - --log-classes: [] - --debug-classes: [] + +# --log-classes: { } # Default: { } +# --debug-classes: { } # Default: { } diff --git a/py_tests/test_data/grid.txt b/py_tests/test_data/grid.txt index e8c5db9a5..060d93585 100644 --- a/py_tests/test_data/grid.txt +++ b/py_tests/test_data/grid.txt @@ -1,2 +1,2 @@ --initial-mass-1 35.0 --initial-mass-2 31.0 --metallicity 0.001 --eccentricity 0.000000e+00 --semi-major-axis 3.5 --kick-magnitude-1 0 --kick-magnitude-2 0 ---initial-mass-1 35.0 --initial-mass-2 31.0 --metallicity 0.001 --eccentricity 0.000000e+00 --semi-major-axis 3.6 --kick-magnitude-1 0 --kick-magnitude-2 0 \ No newline at end of file +--initial-mass-1 35.0 --initial-mass-2 31.0 --metallicity 0.001 --eccentricity 0.000000e+00 --semi-major-axis 3.5 --kick-magnitude-1 0 --kick-magnitude-2 0 \ No newline at end of file From 0fbe778418b45b215866224bce180c8d454f3613 Mon Sep 17 00:00:00 2001 From: Avi Vajpeyi Date: Wed, 1 Nov 2023 09:55:48 +1300 Subject: [PATCH 15/32] fix gridfn --- py_tests/test_data/fiducial_bbh_config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/py_tests/test_data/fiducial_bbh_config.yaml b/py_tests/test_data/fiducial_bbh_config.yaml index 8dff49154..7dd3d1779 100644 --- a/py_tests/test_data/fiducial_bbh_config.yaml +++ b/py_tests/test_data/fiducial_bbh_config.yaml @@ -205,7 +205,7 @@ stringChoices: ### LOGISTICS # --add-options-to-sysparms: 'GRID' # Default: 'GRID' # Options: ['NEVER','GRID','ALWAYS'] - --grid: 'Grid_demo.txt' # Default: '' # grid file name (e.g. 'mygrid.txt') + --grid: 'grid.txt' # Default: '' # grid file name (e.g. 'mygrid.txt') # --mode: 'BSE' # Default: 'BSE' # Options: ['BSE','SSE'] # evolving single (SSE) or binary stars (BSE) # --notes: { } # Default: { } # --notes-hdrs: { } # Default: { } From 2a2d7cd22129f123b4aea2e49eb49e18ec0df692 Mon Sep 17 00:00:00 2001 From: Avi Vajpeyi Date: Wed, 1 Nov 2023 11:10:42 +1300 Subject: [PATCH 16/32] change test to not require the example-COMPAS run to have DCOs that merge in hubble time --- py_tests/test_fast_cosmic_integration.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/py_tests/test_fast_cosmic_integration.py b/py_tests/test_fast_cosmic_integration.py index fef86f51a..210d46785 100644 --- a/py_tests/test_fast_cosmic_integration.py +++ b/py_tests/test_fast_cosmic_integration.py @@ -16,6 +16,7 @@ def test_fast_cosmic_integration(example_compas_output_path, test_archive_dir, COMPAS, ) = FastCosmicIntegration.find_detection_rate( path=example_compas_output_path, + merges_hubble_time=False, ) runtime = time.time() - t0 assert runtime < 10 @@ -38,3 +39,13 @@ def test_fast_cosmic_integration(example_compas_output_path, test_archive_dir, # check that the COMPAS object is a COMPASData object assert isinstance(COMPAS, COMPASData) + + +def test_compas_output_has_dcos(example_compas_output_path): + """Test that the COMPAS output has dco_type""" + COMPAS = COMPASData(path=example_compas_output_path, lazyData=False) + COMPAS.setCOMPASDCOmask(types='BBH', withinHubbleTime=False, pessimistic=True, noRLOFafterCEE=True) + COMPAS.setCOMPASData() + n_bin = len(COMPAS.seedsDCO) + assert n_bin > 1 + assert sum(COMPAS.DCOmask) == n_bin \ No newline at end of file From 0ddbb6f18bfc073a0cc44138603a4d6dce71a499 Mon Sep 17 00:00:00 2001 From: jeffriley Date: Thu, 2 Nov 2023 21:58:16 +1100 Subject: [PATCH 17/32] Update .dictionary.txt --- .dictionary.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/.dictionary.txt b/.dictionary.txt index 61763d433..d7428b4cb 100644 --- a/.dictionary.txt +++ b/.dictionary.txt @@ -13,3 +13,4 @@ accreting accrete accreted YHe +yhe From 3e27410b211a6dd8945a0a23c380bc3104dda183 Mon Sep 17 00:00:00 2001 From: jeffriley Date: Thu, 2 Nov 2023 22:02:07 +1100 Subject: [PATCH 18/32] Update compas-compile-ci.yml --- .github/workflows/compas-compile-ci.yml | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/.github/workflows/compas-compile-ci.yml b/.github/workflows/compas-compile-ci.yml index d2f98018f..ca073754d 100644 --- a/.github/workflows/compas-compile-ci.yml +++ b/.github/workflows/compas-compile-ci.yml @@ -43,16 +43,10 @@ jobs: pip install --upgrade pip pip install -e .[dev] export COMPAS_ROOT_DIR=${GITHUB_WORKSPACE} - cd ${GITHUB_WORKSPACE}/py_tests/test_data/ - echo "Run example COMPAS job using the config py_tests/test_data/fiducial_bbh_config.yaml" - compas_run_submit fiducial_bbh_config.yaml > example_bbh.log - cat example_bbh.log - echo "Generating detailed evolution plot" - compas_plot_detailed_evolution "./COMPAS_Output/Detailed_Output/BSE_Detailed_Output_0.h5" --dont-show >> example_bbh.log - echo "Out files:" - ls -l - mkdir test_artifacts - mv example_bbh.log test_artifacts/ + cd ${GITHUB_WORKSPACE}/misc/examples/methods_paper_plots/detailed_evolution + chmod 755 run.sh + cat run.sh + ./run.sh - name: Run pytests # Run tests and collect coverage data @@ -78,4 +72,4 @@ jobs: with: name: COMPAS-run-artifacts path: | - py_tests/test_artifacts \ No newline at end of file + py_tests/test_artifacts From a8ad338257762dc57406359dac866dfc1e1dc973 Mon Sep 17 00:00:00 2001 From: jeffriley Date: Thu, 2 Nov 2023 22:02:58 +1100 Subject: [PATCH 19/32] Update precommit-checks.yml --- .github/workflows/precommit-checks.yml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/.github/workflows/precommit-checks.yml b/.github/workflows/precommit-checks.yml index 62b6bc6dd..bc7f4762c 100644 --- a/.github/workflows/precommit-checks.yml +++ b/.github/workflows/precommit-checks.yml @@ -7,8 +7,10 @@ jobs: pre-commit: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 - - uses: actions/setup-python@v2 - - uses: pre-commit/action@v2.0.3 + - uses: actions/checkout@v4 + - uses: actions/setup-python@v4 + with: + python-version: 3.8 + - uses: pre-commit/action@v3.0.0 From 1390cffe69fb89b8219be2cadf46ad514f5d99d8 Mon Sep 17 00:00:00 2001 From: jeffriley Date: Thu, 2 Nov 2023 22:03:51 +1100 Subject: [PATCH 20/32] Update .pre-commit-config.yaml --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index aeb3440f0..12d820e72 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,4 +5,4 @@ repos: rev: v2.2.4 hooks: - id: codespell # Spellchecker - args: [-L, nd, --ignore-words=.dictionary.txt, --skip, '*.eps,*.svg,*.ipynb,./misc/*,paper.md,./online-docs/index.rst'] \ No newline at end of file + args: [--ignore-words=.dictionary.txt, --skip, '*.eps,*.svg,*.ipynb,./misc/*,paper.md,./online-docs/index.rst'] From 9cd502d19715f5bca3e23daf740ea7f964be94ca Mon Sep 17 00:00:00 2001 From: jeffriley Date: Thu, 2 Nov 2023 22:07:04 +1100 Subject: [PATCH 21/32] Update example_bbh_compas_config.yaml --- .../detailed_evolution/example_bbh_compas_config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/misc/examples/methods_paper_plots/detailed_evolution/example_bbh_compas_config.yaml b/misc/examples/methods_paper_plots/detailed_evolution/example_bbh_compas_config.yaml index bf10adc21..f0a4a60ae 100644 --- a/misc/examples/methods_paper_plots/detailed_evolution/example_bbh_compas_config.yaml +++ b/misc/examples/methods_paper_plots/detailed_evolution/example_bbh_compas_config.yaml @@ -183,7 +183,7 @@ stringChoices: --envelope-state-prescription: 'LEGACY' --initial-mass-function: 'KROUPA' --luminous-blue-variable-prescription: 'HURLEY_ADD' - --mass-loss-prescription: 'VINK' + --mass-loss-prescription: 'FLEXIBLE2023' --metallicity-distribution: 'ZSOLAR' --pulsational-pair-instability-prescription: 'MARCHANT' ### BINARY PROPERTIES From b3652722e9a6daf090e2769ca437efd2ea63408d Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Thu, 2 Nov 2023 22:27:14 +1100 Subject: [PATCH 22/32] Revert "Fix merge conflicts" This reverts commit 221478822e3880d78cb7d2d319b37fce1306cf5c, reversing changes made to a9f87249969eda9e059c9d0eaf749a872298363f. --- .../preprocessing/compasConfigDefault.yaml | 10 +- .../program-options-list-defaults.rst | 27 +- online-docs/pages/whats-new.rst | 11 +- src/BaseStar.cpp | 895 +++--------------- src/BaseStar.h | 25 +- src/EAGB.cpp | 6 +- src/GiantBranch.cpp | 36 +- src/HeMS.cpp | 93 +- src/HeMS.h | 6 +- src/MS_gt_07.cpp | 2 +- src/Options.cpp | 61 +- src/Options.h | 16 +- src/Remnants.h | 2 +- src/changelog.h | 10 +- src/constants.h | 71 +- src/yaml.h | 4 - 16 files changed, 183 insertions(+), 1092 deletions(-) diff --git a/compas_python_utils/preprocessing/compasConfigDefault.yaml b/compas_python_utils/preprocessing/compasConfigDefault.yaml index 3af898482..b008572a7 100644 --- a/compas_python_utils/preprocessing/compasConfigDefault.yaml +++ b/compas_python_utils/preprocessing/compasConfigDefault.yaml @@ -1,5 +1,5 @@ ##~!!~## COMPAS option values -##~!!~## File Created Thu Nov 2 21:41:13 2023 by COMPAS v02.41.00 +##~!!~## File Created Tue Oct 31 04:36:43 2023 by COMPAS v02.40.00 ##~!!~## ##~!!~## The default COMPAS YAML file (``compasConfigDefault.yaml``), as distributed, has ##~!!~## all COMPAS option entries commented so that the COMPAS default value for the @@ -99,7 +99,7 @@ numericalChoices: # --rotational-frequency: 0.000000 # Default: 0.000000 # --rotational-frequency-1: 0.000000 # Default: 0.000000 # --rotational-frequency-2: 0.000000 # Default: 0.000000 -# --wolf-rayet-multiplier: 1.000000 # Default: 1.000000 +# --wolf-rayet-multiplier: 0.100000 # Default: 0.100000 ### BINARY PROPERTIES # --eccentricity: 0.000000 # Default: 0.000000 # eccentricity for BSE @@ -216,11 +216,7 @@ stringChoices: # --envelope-state-prescription: 'LEGACY' # Default: 'LEGACY' # Options: ['FIXED_TEMPERATURE','HURLEY','LEGACY'] # --initial-mass-function: 'KROUPA' # Default: 'KROUPA' # Options: ['KROUPA','UNIFORM','POWERLAW','SALPETER'] # --luminous-blue-variable-prescription: 'HURLEY_ADD' # Default: 'HURLEY_ADD' # Options: ['BELCZYNSKI','HURLEY','HURLEY_ADD','NONE'] -# --mass-loss-prescription: 'FLEXIBLE2023' # Default: 'FLEXIBLE2023' # Options: ['FLEXIBLE2023','BELCZYNSKI2010','HURLEY','NONE'] -# --OB-mass-loss: 'VINK2021' # Default: 'VINK2021' # Options: ['KRTICKA2018','BJORKLUND2022','VINK2021','VINK2001','NONE'] -# --RSG-mass-loss: 'DECIN2023' # Default: 'DECIN2023' # Options: ['NJ90','KEE2021','YANG2023','DECIN2023','BEASOR2020','VINKSABHAHIT2023','NONE'] -# --VMS-mass-loss: 'SABHAHIT2023' # Default: 'SABHAHIT2023' # Options: ['SABHAHIT2023','BESTENLEHNER2020','VINK2011','NONE'] -# --WR-mass-loss: 'SANDERVINK2023' # Default: 'SANDERVINK2023' # Options: ['SHENAR2019','SANDERVINK2023','BELCZYNSKI2010'] +# --mass-loss-prescription: 'VINK' # Default: 'VINK' # Options: ['VINK','HURLEY','NONE'] # --metallicity-distribution: 'ZSOLAR' # Default: 'ZSOLAR' # Options: ['LOGUNIFORM','ZSOLAR'] # --pulsational-pair-instability-prescription: 'MARCHANT' # Default: 'MARCHANT' # Options: ['FARMER','MARCHANT','STARTRACK','COMPAS'] diff --git a/online-docs/pages/User guide/Program options/program-options-list-defaults.rst b/online-docs/pages/User guide/Program options/program-options-list-defaults.rst index ed88ee079..5adde9958 100644 --- a/online-docs/pages/User guide/Program options/program-options-list-defaults.rst +++ b/online-docs/pages/User guide/Program options/program-options-list-defaults.rst @@ -939,12 +939,6 @@ Default = 10 :ref:`Back to Top ` -**--OB-mass-loss** |br| -Main sequence mass loss prescription. |br| -Options: { NONE, VINK2001, VINK2021, BJORKLUND2022, KRTICKA2018 } |br| -NONE turns off mass loss for main sequence stars. Also available are Vink (2001, previous default), Vink (2021), Bjorklund (2022), and Krticka (2018). |br| -Default = VINK2021 - **--orbital-period** |br| Initial orbital period for a binary star when evolving in BSE mode (days). |br| Used only if the semi-major axis is not specified via ``--semi-major-axis``. |br| @@ -1111,12 +1105,6 @@ Options: { ZERO, HURLEY, VLTFLAMES } |br| ``ZERO`` sets all initial rotational velocities to 0, while ``HURLEY`` and ``VLTFLAMES`` sample them from the Hurley, Pols, Tout (2000) and Ramirez-Agudelo et al. (2013,2015), respectively |br| Default = ZERO -**--RSG-mass-loss** |br| -Red supergiant mass loss prescription. |br| -Options: { NONE, VINKSABHAHIT2023, BEASOR2020, DECIN2023, YANG2023, KEE2021, NJ90 } |br| -NONE turns off mass loss for giant (CHeB, FGB, AGB, TPAGB stellar types) stars below the RSG_MAXIMUM_TEMP. Also available are Vink and Sabhahit (2023), Beasor et al. (2020), Decin et al. (2023), Yang et al. (2023), Kee et. al (2021), and Nieuwenhuijzen and de Jager (1990, previous default). |br| -Default = DECIN2023 - .. _options-props-S: :ref:`Back to Top ` @@ -1185,12 +1173,6 @@ Default = TRUE **--version [ -v ]** |br| Prints COMPAS version string. -**--VMS-mass-loss** |br| -Very massive main sequence mass loss prescription. |br| -Options: { NONE, VINK2011, SABHAHIT2023, BESTENLEHNER2020 } |br| -Applied above the VERY_MASSIVE_MINIMUM_MASS (100 Msol by default). NONE turns off mass loss. Also available are Vink (2011), Bestenlehner (2020), and Sabhahit (2023). |br| -Default = SABHAHIT2023 - .. _options-props-W: :ref:`Back to Top ` @@ -1200,12 +1182,6 @@ Multiplicative constant for Wolf Rayet winds. Note that wind mass loss will also ``overall-wind-mass-loss-multiplier``. |br| Default = 1.0 -**--WR-mass-loss** |br| -Wolf-Rayet mass loss prescription. |br| -Options: { BELCZYNSKI2010, SANDERVINK2023, SHENAR2019 } |br| -Selects between Belczynski (2010), Sander and Vink (2021 updated), and Shenar (2019). |br| -Default = SANDERVINK2023 - .. _options-props-X: .. _options-props-Y: @@ -1263,8 +1239,7 @@ Go to :ref:`the top of this page ` for the full alphabetical --use-mass-loss, --check-photon-tiring-limit, --cool-wind-mass-loss-multiplier, --luminous-blue-variable-prescription, --luminous-blue-variable-multiplier, --mass-loss-prescription, --overall-wind-mass-loss-multiplier, --wolf-rayet-multiplier, ---expel-convective-envelope-above-luminosity-threshold, --luminosity-to-mass-threshold, ---OB-mass-loss, --RSG-mass-loss, --VMS-mass-loss, --WR-mass-loss +--expel-convective-envelope-above-luminosity-threshold, --luminosity-to-mass-threshold --chemically-homogeneous-evolution diff --git a/online-docs/pages/whats-new.rst b/online-docs/pages/whats-new.rst index 439a2c37e..e15165878 100644 --- a/online-docs/pages/whats-new.rst +++ b/online-docs/pages/whats-new.rst @@ -6,20 +6,11 @@ Following is a brief list of important updates to the COMPAS code. A complete r **LATEST RELEASE** |br| -**02.41.00 Nov 02, 2023** +**02.30.00 Oct 30, 2023** * Added a naive tides implementation. * Added program option ``enable-tides`` to enable the tides implementation (default is ``false``). -**02.40.00 Oct 20, 2023** - -* Added ``FLEXIBLE2023`` as a new default, and ``BELCZYNSKI2010`` as a replacement for the previous ``VINK`` mass loss prescription. The following new sub-wrappers are overridden when selecting ``BELCZYNSKI2010``: -* Added ``--OB-mass-loss`` program option, applying to main sequence stars, with default ``VINK2021``, and options ``NONE``, ``VINK2001`` (previous default), ``BJORKLUND2022``, and ``KRTICKA2018``. -* Added ``--RSG-mass-loss`` program option, applying to stars below 8kK in giant branch stellar types, with default ``DECIN2023``, and options ``NONE``, ``VINISABHAHIT2023``, ``BEASOR2020``, ``YANG2023``, ``KEE2021``, ``NJ90`` (previous default). -* Added ``--VMS-mass-loss`` program option, applying to stars over 100 Msol, with default ``SABHAHIT2023``, and options ``NONE``, ``VINK2011``, and ``BESTENLEHNER2020``. -* Added ``--WR-mass-loss`` program option, with default ``SANDERVINK2023``, and options ``BELCZYNSKI2010``, and ``SHENAR2019``. -* Changed default value for option ``--wolf-rayet-multiplier`` from 0.1 to 1.0 - **02.39.00 Jul 4, 2023** * Added 'Evolution_Status' columns to both SSE and BSE default system parameters records - records final status of evolution (reason evolution stopped). diff --git a/src/BaseStar.cpp b/src/BaseStar.cpp index 8d376e227..616d05c8a 100755 --- a/src/BaseStar.cpp +++ b/src/BaseStar.cpp @@ -1108,7 +1108,7 @@ double BaseStar::CalculateLambdaLoveridgeEnergyFormalism(const double p_EnvMass, */ double BaseStar::CalculateLambdaNanjing() const { - double mass = m_MZAMS; + double mass = m_MZAMS; double lambda = 0.0; if (OPTIONS->CommonEnvelopeLambdaNanjingUseRejuvenatedMass()) {mass = m_Mass0;} // Use rejuvenated mass to calculate lambda instead of true birth mass @@ -1183,7 +1183,7 @@ double BaseStar::CalculateMassInterpolatedLambdaNanjing(const double p_Mass, con double lambda = 0.0; std::vector ind = utils::binarySearch(NANJING_MASSES, p_Mass); int low = ind[0]; - int up = ind[1]; + int up = ind[1]; if ( (low < 0) && (up >= 0) ) { // Mass below range calculated by Xu & Li (2010) lambda = CalculateLambdaNanjingEnhanced(0, p_Zind); // Use lambda for minimum mass } @@ -1196,7 +1196,7 @@ double BaseStar::CalculateMassInterpolatedLambdaNanjing(const double p_Mass, con else { // Linear interpolation between upper and lower mass bins double lambdaLow = CalculateLambdaNanjingEnhanced(low, p_Zind); double lambdaUp = CalculateLambdaNanjingEnhanced(up, p_Zind); - lambda = lambdaLow + (p_Mass - NANJING_MASSES[low]) / (NANJING_MASSES[up] - NANJING_MASSES[low]) * (lambdaUp - lambdaLow); + lambda = lambdaLow + (p_Mass - NANJING_MASSES[low]) / (NANJING_MASSES[up] - NANJING_MASSES[low]) * (lambdaUp - lambdaLow); } return lambda; } @@ -1223,9 +1223,9 @@ double BaseStar::CalculateZInterpolatedLambdaNanjing(const double p_Z, const int } else { // Linear interpolation in logZ between pop. I and pop. II metallicities const double logZ = log(m_Metallicity); - double lambdaLow = CalculateLambdaNanjingEnhanced(p_MassInd, 0); - double lambdaUp = CalculateLambdaNanjingEnhanced(p_MassInd, 1); - lambda = lambdaLow + (logZ - LAMBDA_NANJING_POPII_LOGZ) / (LAMBDA_NANJING_POPI_LOGZ - LAMBDA_NANJING_POPII_LOGZ) * (lambdaUp - lambdaLow); + double lambdaLow = CalculateLambdaNanjingEnhanced(p_MassInd, 0); + double lambdaUp = CalculateLambdaNanjingEnhanced(p_MassInd, 1); + lambda = lambdaLow + (logZ - LAMBDA_NANJING_POPII_LOGZ) / (LAMBDA_NANJING_POPI_LOGZ - LAMBDA_NANJING_POPII_LOGZ) * (lambdaUp - lambdaLow); } return lambda; } @@ -1241,11 +1241,16 @@ double BaseStar::CalculateZInterpolatedLambdaNanjing(const double p_Z, const int */ double BaseStar::FindLambdaNanjingNearestMassIndex(const double p_Mass) const { - if (p_Mass < NANJING_MASSES_MIDPOINTS[0]) return 0; // M < 1.5 Msun, use lambda for the 1 Msun model - - if (p_Mass >= NANJING_MASSES_MIDPOINTS.back()) return NANJING_MASSES.size() - 1; // M >= 75 Msun, use lambda for the 100 Msun model - - return utils::binarySearch(NANJING_MASSES_MIDPOINTS, p_Mass)[1]; // Search for upper and lower mass bin edges + if (p_Mass < NANJING_MASSES_MIDPOINTS[0]) { // M < 1.5 Msun, use lambda for the 1 Msun model + return 0; + } + else if (p_Mass >= NANJING_MASSES_MIDPOINTS.back()) { // M >= 75 Msun, use lambda for the 100 Msun model + return NANJING_MASSES.size() - 1; + } + else { // Search for upper and lower mass bin edges + std::vector ind = utils::binarySearch(NANJING_MASSES_MIDPOINTS, p_Mass); + return ind[1]; + } } @@ -1393,12 +1398,12 @@ double BaseStar::InterpolateGe20QCrit( const QCRIT_PRESCRIPTION p_qCritPrescript // One of the following must be set if (p_qCritPrescription == QCRIT_PRESCRIPTION::GE20) { - qCritVectorLowerMass = std::get<1>(radiiQCritsZetasFromGe20[lowerMassInd]); - qCritVectorUpperMass = std::get<1>(radiiQCritsZetasFromGe20[upperMassInd]); + qCritVectorLowerMass = std::get<1>(radiiQCritsZetasFromGe20[lowerMassInd]); + qCritVectorUpperMass = std::get<1>(radiiQCritsZetasFromGe20[upperMassInd]); } else if (p_qCritPrescription == QCRIT_PRESCRIPTION::GE20_IC) { - qCritVectorLowerMass = std::get<2>(radiiQCritsZetasFromGe20[lowerMassInd]); - qCritVectorUpperMass = std::get<2>(radiiQCritsZetasFromGe20[upperMassInd]); + qCritVectorLowerMass = std::get<2>(radiiQCritsZetasFromGe20[lowerMassInd]); + qCritVectorUpperMass = std::get<2>(radiiQCritsZetasFromGe20[upperMassInd]); } // Get vector of radii from GE20_QCRIT_AND_ZETA for both lower and upper masses @@ -1442,8 +1447,8 @@ double BaseStar::InterpolateGe20QCrit( const QCRIT_PRESCRIPTION p_qCritPrescript double upperRadiusUpperMass = pow(10, logRadiusVectorUpperMass[upperRadiusUpperMassInd]); // Interpolate on the radii first, then the masses - double qCritLowerMass = qLowLow + (upperRadiusLowerMass - m_Radius)/(upperRadiusLowerMass - lowerRadiusLowerMass) * (qLowUpp - qLowLow); - double qCritUpperMass = qUppLow + (upperRadiusUpperMass - m_Radius)/(upperRadiusUpperMass - lowerRadiusUpperMass) * (qUppUpp - qUppLow); + double qCritLowerMass = qLowLow + (upperRadiusLowerMass - m_Radius)/(upperRadiusLowerMass - lowerRadiusLowerMass) * (qLowUpp - qLowLow); + double qCritUpperMass = qUppLow + (upperRadiusUpperMass - m_Radius)/(upperRadiusUpperMass - lowerRadiusUpperMass) * (qUppUpp - qUppLow); double interpolatedQCrit = qCritLowerMass + (upperMass - m_Mass)/(upperMass - lowerMass) * (qCritUpperMass - qCritLowerMass); return interpolatedQCrit; @@ -1653,6 +1658,7 @@ double BaseStar::CalculateInitialEnvelopeMass_Static(const double p_Mass) { } + /* * Calculate rejuvenation factor for stellar age based on mass lost/gained during mass transfer * @@ -1683,7 +1689,7 @@ double BaseStar::CalculateMassTransferRejuvenationFactor() const { break; default: // unknown prescription - use default Hurley et al. 2000 prescription = 1.0 - SHOW_WARN(ERROR::UNKNOWN_MT_REJUVENATION_PRESCRIPTION, "Using default fRej = 1.0"); // show warning + SHOW_WARN(ERROR::UNKNOWN_MT_REJUVENATION_PRESCRIPTION, "Using default fRej = 1.0"); // show warning fRej = 1.0; } @@ -1704,7 +1710,7 @@ double BaseStar::CalculateMassTransferRejuvenationFactor() const { double BaseStar::CalculateMassLossRateVassiliadisWood() const { double logP0 = min(3.3, (-2.07 - (0.9 * log10(m_Mass)) + (1.94 * log10(m_Radius)))); - double P0 = PPOW(10.0, (logP0)); // In their fortran code, Hurley et al. take P0 to be min(p0, 2000.0), implemented here as a minimum power + double P0 = PPOW(10.0, (logP0)); // In their fortran code, Hurley et al take P0 to be min(p0, 2000.0), implemented here as a minimum power double logMdot_VW = -11.4 + (0.0125 * (P0 - 100.0 * max((m_Mass - 2.5), 0.0))); double Mdot_VW = PPOW(10.0, (logMdot_VW)); @@ -1728,7 +1734,7 @@ double BaseStar::CalculateMassLossRateKudritzkiReimers() const { /* - * Calculate the mass-loss rate for massive stars (L > 4000 L_sol) using the + * Calculate the mass loss rate for massive stars (L > 4000 L_sol) using the * Nieuwenhuijzen & de Jager 1990 prescription, modified by a metallicity * dependent factor (Kudritzki et al 1989). * @@ -1737,7 +1743,7 @@ double BaseStar::CalculateMassLossRateKudritzkiReimers() const { * * double CalculateMassLossRateNieuwenhuijzenDeJagerStatic() * - * @return Nieuwenhuijzen & de Jager mass-loss rate for massive stars (in Msol yr^-1) + * @return Nieuwenhuijzen & de Jager mass loss rate for massive stars (in Msol yr^-1) */ double BaseStar::CalculateMassLossRateNieuwenhuijzenDeJager() const { double rate = 0.0; @@ -1750,70 +1756,6 @@ double BaseStar::CalculateMassLossRateNieuwenhuijzenDeJager() const { return rate; } - -/* - * Calculate the Eddington factor (L/L_Edd) as required by CalculateMassLossRateBjorklund - * see text surrounding Equation 6 in https://arxiv.org/abs/2203.08218 - * - * - * double CalculateMassLossRateBjorklundEddingtonFactor() - * - * @return Eddington factor - */ -double BaseStar::CalculateMassLossRateBjorklundEddingtonFactor() const { - - const double iHe = 2.0; - const double YHe = 0.1; // Assumed constant by Bjorklund et al. - double kappa_e = 0.4 * (1.0 + iHe * YHe) / (1.0 + 4.0 * YHe); // cm^2/g - double kappa_e_SI = kappa_e * OPACITY_CGS_TO_SI; // m^2/kg - double top = kappa_e_SI * m_Luminosity * LSOL; - double bottom = 4.0 * M_PI * G * C * m_Mass * MSOL_TO_KG; - - return top / bottom; -} - - -/* - * Calculate the mass loss rate for massive OB stars according to the prescription from Bjorklund et al. 2022 - * See Equation 7 and surrounding text in https://arxiv.org/abs/2203.08218 - * - * This prescription is calibrated to the following ranges: - * 10^4.5 < L / Lsol < 10^6 - * 15,000 < Teff / K < 50,000 - * 15 < M / Msol < 80 - * Z Zsol, Z_LMC = 0.5 * Zsol and Z_SMC = 0.2 * Zsol, with Zsol = 0.014 - * - * - * double CalculateMassLossRateOBBjorklund2022() - * - * @return Bjorklund mass-loss rate for massive stars (in Msol yr^-1) - */ -double BaseStar::CalculateMassLossRateOBBjorklund2022() const { - - double Gamma = CalculateMassLossRateBjorklundEddingtonFactor(); - - double logZ = log10(m_Metallicity / 0.014); - double logL = log10(m_Luminosity / 1.0E6); - double Teff = m_Temperature * TSOL; // Convert effective temperature to Kelvin - double logTeff = log10(Teff/45000.0); - - double Meff = m_Mass * (1.0 - Gamma); - double logMeff = log10(Meff / 45.0); - - // Constants, q depends on logTeff - const double constC = -5.52; - const double m = 2.39; - const double n = -1.48; - const double p = 2.12; - double q = 0.75 - (1.87 * logTeff); - - // Equation 7 in Bjorklund et al. 2022 - double logMdot = constC + (m * logL) + (n * logMeff) + (p * logTeff) + (q * logZ); - - return PPOW(10.0, logMdot); -} - - /* * Calculate LBV-like mass loss rate for stars beyond the Humphreys-Davidson limit (Humphreys & Davidson 1994) * @@ -1822,18 +1764,16 @@ double BaseStar::CalculateMassLossRateOBBjorklund2022() const { * * double CalculateMassLossRateLBV(const LBV_PRESCRIPTION p_LBV_prescription) * - * @param [IN] p_LBV_Prescription Which LBV prescription to use * @return LBV-like mass loss rate (in Msol yr^{-1}) */ -double BaseStar::CalculateMassLossRateLBV(const LBV_PRESCRIPTION p_LBV_Prescription) { - double rate = 0.0; // default return value - - double HD_limit_factor = m_Radius * std::sqrt(m_Luminosity) * 1.0E-5; // calculate factor by which the star is above the HD limit +double BaseStar::CalculateMassLossRateLBV(const LBV_PRESCRIPTION p_LBV_prescription) { + double rate = 0.0; + double HD_limit_factor = m_Radius * std::sqrt(m_Luminosity) * 1.0E-5; // calculate factor by which you are above the HD limit if ((utils::Compare(m_Luminosity, LBV_LUMINOSITY_LIMIT_STARTRACK) > 0) && (utils::Compare(HD_limit_factor, 1.0) > 0)) { // check if luminous blue variable m_LBVphaseFlag = true; // mark the star as LBV - m_DominantMassLossRate = MASS_LOSS_TYPE::LBV; + m_DominantMassLossRate = MASS_LOSS_TYPE::LUMINOUS_BLUE_VARIABLE; - switch (p_LBV_Prescription) { // decide which LBV prescription to use + switch (p_LBV_prescription) { // decide which LBV prescription to use case LBV_PRESCRIPTION::NONE: rate = 0.0; break; @@ -1855,20 +1795,18 @@ double BaseStar::CalculateMassLossRateLBV(const LBV_PRESCRIPTION p_LBV_Prescript return rate; } - /* * Calculate LBV-like mass loss rate for stars beyond the Humphreys-Davidson limit (Humphreys & Davidson 1994) * * Hurley+ 2000 Section 7.1 a few equation after Eq. 106 (Equation not labelled) - * * - * double CalculateMassLossRateLBVHurley(const double p_HD_LimitFactor) + * double CalculateMassLossRateLBVHurley(const double p_HD_limit_factor) * - * @param [IN] p_HD_LimitFactor Factor by which star is above Humphreys-Davidson limit + * @param [IN] p_HD_limit_factor Factor by which star is above Humphreys-Davidson limit * @return LBV-like mass loss rate (in Msol yr^{-1}) */ -double BaseStar::CalculateMassLossRateLBVHurley(const double p_HD_LimitFactor) const { - return 0.1 * PPOW((p_HD_LimitFactor - 1.0), 3.0) * ((m_Luminosity / 6.0E5) - 1.0); +double BaseStar::CalculateMassLossRateLBVHurley(const double p_HD_limit_factor) const { + return 0.1 * PPOW((p_HD_limit_factor - 1.0), 3.0) * ((m_Luminosity / 6.0E5) - 1.0); } @@ -1876,11 +1814,10 @@ double BaseStar::CalculateMassLossRateLBVHurley(const double p_HD_LimitFactor) c * Calculate LBV-like mass loss rate for stars beyond the Humphreys-Davidson limit (Humphreys & Davidson 1994) * * Belczynski et al. 2010, eq 8 - * * * double CalculateMassLossRateLBVBelczynski() * - * @return LBV-like mass loss rate (in Msol yr^{-1}) +* @return LBV-like mass loss rate (in Msol yr^{-1}) */ double BaseStar::CalculateMassLossRateLBVBelczynski() const { return OPTIONS->LuminousBlueVariableFactor() * 1.0E-4; @@ -1936,6 +1873,21 @@ double BaseStar::CalculateMassLossRateWolfRayetZDependent(const double p_Mu) con } +/* + * Calculate the Wolf-Rayet like mass loss rate independent of WR star composition as given by Nugis & Lamers 2000 + * + * Belczynski et al. 2010, eq 10. We do not use this equation by default. + * + * + * double CalculateMassLossRateWolfRayet3() + * + * @return Mass loss rate (in Msol yr^{-1}) + */ +double BaseStar::CalculateMassLossRateWolfRayet3() const { + return exp(-5.73 + (0.88 * log(m_Mass))); +} + + /* * Calculate mass loss rate for massive OB stars using the Vink et al 2001 prescription * @@ -1943,603 +1895,59 @@ double BaseStar::CalculateMassLossRateWolfRayetZDependent(const double p_Mu) con * Belczynski et al. 2010, eqs 6 & 7 * * - * double CalculateMassLossRateOBVink2001(const double prescription) + * double CalculateMassLossRateOB(const double p_Teff) * + * @param [IN] p_Teff Effective temperature in K * @return Mass loss rate for hot OB stars in Msol yr^-1 */ -double BaseStar::CalculateMassLossRateOBVink2001() const { - - double rate = 0.0; // default return value +double BaseStar::CalculateMassLossRateOB(const double p_Teff) { - double teff = m_Temperature * TSOL; + double rate; - if (utils::Compare(teff, VINK_MASS_LOSS_MINIMUM_TEMP) >= 0 && utils::Compare(teff, VINK_MASS_LOSS_BISTABILITY_TEMP) <= 0) { + if (utils::Compare(p_Teff, VINK_MASS_LOSS_MINIMUM_TEMP) >= 0 && utils::Compare(p_Teff, VINK_MASS_LOSS_BISTABILITY_TEMP) <= 0) { double V = 1.3; // v_inf/v_esc - double logMdotOB = -6.688 + + double logMdotOB = -6.688 + (2.210 * log10(m_Luminosity / 1.0E5)) - - (1.339 * log10(m_Mass / 30.0)) - - (1.601 * log10(V / 2.0)) + - (0.85 * LogMetallicityXi()) + - (1.07 * log10(teff / 20000.0)); + (1.339 * log10(m_Mass / 30.0)) - + (1.601 * log10(V / 2.0)) + + (0.85 * log10(m_Metallicity / ZSOL)) + + (1.07 * log10(p_Teff / 20000.0)); rate = PPOW(10.0, logMdotOB); - + m_DominantMassLossRate = MASS_LOSS_TYPE::VINK; } - else if (utils::Compare(teff, VINK_MASS_LOSS_BISTABILITY_TEMP) > 0) { - SHOW_WARN_IF(utils::Compare(teff, VINK_MASS_LOSS_MAXIMUM_TEMP) > 0, ERROR::HIGH_TEFF_WINDS); // show warning if winds being used outside comfort zone + else if (utils::Compare(p_Teff, VINK_MASS_LOSS_BISTABILITY_TEMP) > 0) { + SHOW_WARN_IF(utils::Compare(p_Teff, VINK_MASS_LOSS_MAXIMUM_TEMP) > 0, ERROR::HIGH_TEFF_WINDS); // show warning if winds being used outside comfort zone double V = 2.6; // v_inf/v_esc double logMdotOB = -6.697 + (2.194 * log10(m_Luminosity / 1.0E5)) - - (1.313 * log10(m_Mass / 30.0)) - - (1.226 * log10(V / 2.0)) + - (0.85 * LogMetallicityXi()) + - (0.933 * log10(teff / 40000.0)) - - (10.92 * log10(teff / 40000.0) * log10(teff/40000.0)); + (1.313 * log10(m_Mass / 30.0)) - + (1.226 * log10(V / 2.0)) + + (0.85 * log10(m_Metallicity / ZSOL)) + + (0.933 * log10(p_Teff / 40000.0)) - + (10.92 * log10(p_Teff / 40000.0) * log10(p_Teff/40000.0)); rate = PPOW(10.0, logMdotOB); - + m_DominantMassLossRate = MASS_LOSS_TYPE::VINK; } else { SHOW_WARN(ERROR::LOW_TEFF_WINDS, "Mass Loss Rate = 0.0"); // too cold to use winds - show warning. + rate = 0.0; } return rate; } -/* - * Calculate mass loss rate for massive OB stars using the Vink+Sander 2021 update - * https://arxiv.org/pdf/2103.12736.pdf - * features two bi-stability jumps, at T1 and T2 - * offset = {"cold":-5.99,"inter":-6.688,"hot":-6.697} - * - * - * double CalculateMassLossRateOBVinkSander2021(const double prescription) - * - * @return Mass loss rate for hot OB stars in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateOBVinkSander2021() const { - - double rate = 0.0; // default return value - - const double zExp2001 = 0.85; - const double zExp = 0.42; - - double teff = m_Temperature * TSOL; - double Gamma = 7.66E-5 * 0.325 * m_Luminosity / m_Mass; - double charrho = -14.94 + (3.1857 * Gamma) + (zExp * LogMetallicityXi()); - double T2 = ( 61.2 + (2.59 * charrho) ) * 1000.0; // typically around 25000.0, higher jump first as in Vink python recipe - double T1 = ( 100.0 + (6.0 * charrho) ) * 1000.0; // typically around 20000.0, has similar behavior when fixed - - double logL5 = log10(m_Luminosity / 1.0E5); - double logM30 = log10(m_Mass / 30.0); - double logT40 = log10(teff / 40000.0); - double logT20 = log10(teff / 20000.0); - - if (utils::Compare(teff, VINK_MASS_LOSS_MINIMUM_TEMP) >= 0 && utils::Compare(teff, T1) <= 0) { - - double V = 0.7; // v_inf/v_esc - double logMdotOB = -5.99 + - (2.210 * logL5) - - (1.339 * logM30) - - (1.601 * log10(V / 2.0)) + - (zExp2001 * LogMetallicityXi()) + - (1.07 * logT20); - - rate = PPOW(10.0, logMdotOB); - } - else if (utils::Compare(teff, T1) > 0 && utils::Compare(teff, T2) <= 0) { - SHOW_WARN_IF(utils::Compare(teff, VINK_MASS_LOSS_MAXIMUM_TEMP) > 0, ERROR::HIGH_TEFF_WINDS); // show warning if winds being used outside comfort zone - - double V = 1.3; // v_inf/v_esc - double logMdotOB = -6.688 + - (2.210 * logL5) - - (1.339 * logM30) - - (1.601 * log10(V / 2.0)) + - (zExp2001 * LogMetallicityXi()) + - (1.07 * logT20); - - rate = PPOW(10.0, logMdotOB); - } - else if (utils::Compare(teff, T2) > 0) { - SHOW_WARN_IF(utils::Compare(teff, VINK_MASS_LOSS_MAXIMUM_TEMP) > 0, ERROR::HIGH_TEFF_WINDS); // show warning if winds being used outside comfort zone - - double V = 2.6; // v_inf/v_esc - double logMdotOB = -6.697 + - (2.194 * logL5) - - (1.313 * logM30) - - (1.226 * log10(V / 2.0)) + - (zExp * LogMetallicityXi()) + - (0.933 * logT40) - - (10.92 * logT40 * logT40); - - rate = PPOW(10.0, logMdotOB); - } - else { - SHOW_WARN(ERROR::LOW_TEFF_WINDS, "Mass Loss Rate = 0.0"); // too cold to use winds - show warning. - } - - return rate; -} - - -/* - * Calculate mass loss rate for massive OB stars using the Krticka+ 2018 prescription - * - * https://arxiv.org/pdf/1712.03321.pdf - * - * - * double CalculateMassLossRateOBKrticka2018() - * - * @return Mass loss rate for hot OB stars in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateOBKrticka2018() const { - - double logZ = LogMetallicityXi(); - double logMdot = -5.70 + 0.50 * logZ + (1.61 - 0.12 * logZ) * log10(m_Luminosity / 1.0E6); - - return PPOW(10.0, logMdot); -} - - -/* - * Calculate mass loss rate for RSG stars using the Beasor+2020 prescription - * - * https://arxiv.org/pdf/2001.07222.pdf eq 4. - * - * fit corrected slightly in Decin 2023, eq E.1 - * https://arxiv.org/pdf/2303.09385.pdf - * - * corrected again by Beasor+2023, https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.2460B/abstract - * - * - * double CalculateMassLossRateRSGBeasor2020() - * - * @return Mass loss rate for RSG stars in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateRSGBeasor2020() const { - - double logMdot = (-21.5 - 0.15 * m_MZAMS) + (3.6 * log10(m_Luminosity)); //Further correction by Beasor+ - - return PPOW(10.0, logMdot); -} - - -/* - * Calculate mass loss rate for RSG stars using the Decin2023 prescription - * - * https://arxiv.org/pdf/2303.09385.pdf eq 6. - * - * - * double CalculateMassLossRateRSGDecin2023() - * - * @return Mass loss rate for RSG stars in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateRSGDecin2023() const { - return PPOW(10.0, -20.63 - 0.16 * m_MZAMS + 3.47 * log10(m_Luminosity)); -} - - -/* - * Calculate mass loss rate for RSG stars using the Yang 2023 prescription - * Third order polynomial in log Luminosity. - * https://arxiv.org/pdf/2303.09385.pdf eq 6. - * - * - * double CalculateMassLossRateRSGYang2023() - * - * @return Mass loss rate for RSG stars in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateRSGYang2023() const { - - double logL = log10(m_Luminosity); - double logMdot = 0.45 * logL * logL * logL - 5.26 * logL * logL + 20.93 * logL - 34.56; - - return PPOW(10.0, logMdot); -} - - -/* - * Calculate mass loss rate for RSG stars using the Kee + 2021 prescription - * - * https://arxiv.org/pdf/2101.03070.pdf eqs 5, 13, 14, 25. - * - * - * double CalculateMassLossRateRSGKee2021() - * - * @return Mass loss rate for RSG stars in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateRSGKee2021() const { - - const double vturb = 1.5E4; // turbulent velocity, m/s, for a typical RSG - const double k_b = 1.38E-23; // Boltzmann Constant in J K^-1 - const double sigma = 5.67E-8; // Stefan Boltzmann constant W m^-2 K^-4 - const double m_h = 1.67E-27; // mass of hydrogen in Kg - const double kappa = 0.01 * OPACITY_CGS_TO_SI; // Given after Eq. 16 - - double teff = TSOL * m_Temperature; // in K - - double R_SI = sqrt((m_Luminosity * LSOLW) / (4.0 * M_PI * sigma * PPOW(teff, 4.0))); - double M_SI = m_Mass * MSOL_TO_KG; - double cs = sqrt(k_b * teff / m_h); - double gamma = (kappa * m_Luminosity * LSOLW) / (4.0 * M_PI * G * C * M_SI); - double vesc = sqrt(2.0 * G * (M_SI) / (R_SI)); // m/s, not vesc,eff - - double Rpmod = G * (M_SI) * (1.0 - gamma) / (2.0 * ((cs * cs) + (vturb * vturb))); // modified parker radius, in m - double rho = (4.0 / 3.0) * (Rpmod / (kappa * (R_SI) * (R_SI))) * - (exp(-(2.0 * Rpmod / (R_SI)) + (3.0 / 2.0))) / (1.0 - exp(-2.0 * Rpmod / (R_SI))); - - double MdotAnalytical = 4.0 * M_PI * rho * sqrt(cs * cs + vturb * vturb) * Rpmod * Rpmod; // in kg/s - double factor = PPOW(((vturb / 17000.0) / (vesc / 60000.0)), 1.30); // non-isothermal correction factor - - return factor * MdotAnalytical * SECONDS_IN_YEAR / MSOL_TO_KG; -} - - -/* - * Calculate mass loss rate for RSG stars using the Vink and Sabhahit 2023 prescription - * A kinked function of L and M - * https://arxiv.org/pdf/2309.08657.pdf eqs 1 and 2 - * - * - * double CalculateMassLossRateRSGVinkSabhahit2023() - * - * @return Mass loss rate for RSG stars in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateRSGVinkSabhahit2023() const { - - const double logLkink = 4.6; - - double logL = log10(m_Luminosity); - double logM = log10(m_Mass); - - double logMdot; - if (utils::Compare(logL, logLkink) < 0) { - logMdot = -8.0 + 0.7 * logL - 0.7 * logM; - } - else if (utils::Compare(logL, logLkink) >= 0) { - logMdot = -24.0 + 4.77 * logL - 3.99 * logM; - } - - return PPOW(10.0, logMdot); -} - - -/* - * Calculate mass loss rate for very massive (>100 Msol) OB stars using the Bestenlehner 2020 prescription - * - * https://arxiv.org/pdf/2002.05168.pdf - * - * - * double CalculateMassLossRateVMSBestenlehner2020() - * - * @return Mass loss rate for hot OB stars in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateVMSBestenlehner2020() const { - - const double alpha = 0.39; // CAK force multiplier - const double logMdotZero = -4.78; // from substituting LogMdotTrans and Gamma_e trans into eq 12. - - double gamma = 7.66E-5 * 0.325 * m_Luminosity / m_Mass; // Eddington Parameter, not metallicity specific as in the publication - double logMdot = logMdotZero + ((1.0 / alpha) + 0.5) * log10(gamma) - (((1.0 - alpha) / alpha) + 2.0) * log10(1.0 - gamma); - - return PPOW(10.0, logMdot); -} - - -/* - * Calculate mass loss rate for very massive (>100 Msol) OB stars using a fit to the Vink 2011 mass loss rates - * - * https://arxiv.org/pdf/1105.0556.pdf - * - * - * double CalculateMassLossRateVMSVink2011() - * - * @return Mass loss rate for very massive stars in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateVMSVink2011() const { - - double rate; - - double Gamma = 7.66E-5 * 0.325 * m_Luminosity / m_Mass; - double rate2001 = CalculateMassLossRateOBVink2001(); - - double logMdotdiff; - if (utils::Compare(Gamma, 0.5) > 0) { // ensure that the prescription isn't extrapolated to low gamma - logMdotdiff = 0.04468 + (0.3091 * Gamma) + (0.2434 * Gamma * Gamma); - rate = PPOW(10.0, (logMdotdiff + log10(rate2001))); - } - else { - SHOW_WARN(ERROR::LOW_GAMMA, "Mass Loss Rate defaulting to Vink2001, low Gamma"); // gamma extrapolated outside fit range, default to Vink2001 - rate = rate2001; - } - - return rate; -} - - -/* - * Calculate mass loss rate for very massive stars using the Sabhahit 2023 prescription - * - * https://arxiv.org/pdf/2306.11785.pdf - * - * - * double CalculateMassLossRateVMSSabhahit2023() - * - * @return Mass loss rate in Msol yr^-1 - */ -double BaseStar::CalculateMassLossRateVMSSabhahit2023() const { - - double gamma = 7.66E-5 * 0.325 * m_Luminosity / m_Mass; // Eddington Parameter, independent of surface composition - double Mswitch = PPOW(m_Metallicity, -1.574) * 0.0615 + 18.10; // obtained from a powerlaw fit to table 2, given teff=45kK - double Lswitch = PPOW(10, (-1.91 * m_Log10Metallicity + 2.36)); // loglinear fits to table 2 - double Mdotswitch = PPOW(10, (-1.86 * m_Log10Metallicity - 8.90)); - double gammaswitch = 7.66E-5 * 0.325 * Lswitch / Mswitch; - - double Mdot; - if (utils::Compare(gamma, gammaswitch) > 0) { - Mdot = Mdotswitch * PPOW((m_Luminosity / Lswitch) , 4.77) * PPOW((m_Mass/Mswitch) , -3.99); - } - else { - Mdot = CalculateMassLossRateOBVink2001(); - } - - return Mdot; -} - - -/* - * Calculate mass loss for main sequence stars. - * Switches prescription based on program options. - * - * - * double CalculateMassLossRateOB(const OB_MASS_LOSS) - * - * @param [IN] p_OB_MassLoss Mass loss prescription to use - * @return Mass loss rate (in Msol yr^{-1}) - */ -double BaseStar::CalculateMassLossRateOB(const OB_MASS_LOSS p_OB_MassLoss) { - - double rate = 0.0; // default return value - - m_DominantMassLossRate = MASS_LOSS_TYPE::OB; - - switch (p_OB_MassLoss) { // decide which prescription to use - case OB_MASS_LOSS::NONE: - rate = 0.0; - break; - case OB_MASS_LOSS::VINK2001: - rate = CalculateMassLossRateOBVink2001(); - break; - case OB_MASS_LOSS::VINK2021: - rate = CalculateMassLossRateOBVinkSander2021(); - break; - case OB_MASS_LOSS::BJORKLUND2022: - rate = CalculateMassLossRateOBBjorklund2022(); - break; - case OB_MASS_LOSS::KRTICKA2018: - rate = CalculateMassLossRateOBKrticka2018(); - break; - default: - SHOW_WARN(ERROR::UNKNOWN_MASS_LOSS_PRESCRIPTION, "Using default value VINK2021"); - rate = CalculateMassLossRateOBVinkSander2021(); - break; - } - return rate; -} - - -/* - * Calculate mass loss for RSG stars (Red Supergiant). - * Switches prescription based on program options. - * - * - * double CalculateMassLossRateRSG(const RSG_MASS_LOSS) - * - * @param [IN] p_RSG_MassLoss Mass loss prescription to use - * @return Mass loss rate (in Msol yr^{-1}) - */ -double BaseStar::CalculateMassLossRateRSG(const RSG_MASS_LOSS p_RSG_MassLoss) const { - - double rate = 0.0; // default return value - - switch (p_RSG_MassLoss) { // decide which prescription to use - case RSG_MASS_LOSS::NONE: - rate = 0.0; - break; - case RSG_MASS_LOSS::VINKSABHAHIT2023: - rate = CalculateMassLossRateRSGVinkSabhahit2023(); - break; - case RSG_MASS_LOSS::BEASOR2020: - rate = CalculateMassLossRateRSGBeasor2020(); - break; - case RSG_MASS_LOSS::DECIN2023: - rate = CalculateMassLossRateRSGDecin2023(); - break; - case RSG_MASS_LOSS::YANG2023: - rate = CalculateMassLossRateRSGYang2023(); - break; - case RSG_MASS_LOSS::KEE2021: - rate = CalculateMassLossRateRSGKee2021(); - break; - case RSG_MASS_LOSS::NJ90: - rate = CalculateMassLossRateNieuwenhuijzenDeJager(); - break; - default: - SHOW_WARN(ERROR::UNKNOWN_MASS_LOSS_PRESCRIPTION, "Using default value NJ90"); - rate = CalculateMassLossRateNieuwenhuijzenDeJager(); - break; - } - return rate; -} - - -/* - * Calculate mass loss for very massive MS stars, >100Msol. - * Switches prescription based on program options. - * - * - * double CalculateMassLossRateVMS(const VMS_MASS_LOSS) - * - * @param [IN] p_VMS_MassLoss Mass loss prescription to use - * @return Mass loss rate (in Msol yr^{-1}) - */ -double BaseStar::CalculateMassLossRateVMS(const VMS_MASS_LOSS p_VMS_MassLoss) { - - double rate = 0.0; - - switch (p_VMS_MassLoss) { // decide which prescription to use - case VMS_MASS_LOSS::NONE: - rate = 0.0; - break; - case VMS_MASS_LOSS::BESTENLEHNER2020: - rate = CalculateMassLossRateVMSBestenlehner2020(); - break; - case VMS_MASS_LOSS::VINK2011: - rate = CalculateMassLossRateVMSVink2011(); - break; - case VMS_MASS_LOSS::SABHAHIT2023: - rate = CalculateMassLossRateVMSSabhahit2023(); - break; - default: - SHOW_WARN(ERROR::UNKNOWN_MASS_LOSS_PRESCRIPTION, "Using default value VINK2011"); - rate = CalculateMassLossRateVMSVink2011(); - break; - } - return rate; -} - - -/* - * Calculate the mass-loss rate for Wolf-Rayet stars according to the - * prescription of Sander & Vink 2020 (https://arxiv.org/abs/2009.01849) - * - * Use the luminosity prescription given by Equation 13 (see section 3.4.1) - * - * - * double CalculateMassLossRateWolfRayetSanderVink2020(const double p_Mu) - * - * @param [IN] p_Mu Small envelope parameter (see Hurley et al. 2000, eq 97 & 98) - * @return Mass loss rate (in Msol yr^{-1}) - */ -double BaseStar::CalculateMassLossRateWolfRayetSanderVink2020(const double p_Mu) const { - - double Mdot = 0.0; // default return value - - if (utils::Compare(p_Mu, 1.0) < 0) { - - double logL = log10(m_Luminosity); - double logZ = LogMetallicityXi(); - - // Calculate alpha, L0 and Mdot10 - double alpha = 0.32 * logZ + 1.4; // Equation 18 in Sander & Vink 2020 - double logL0 = -0.87 * logZ + 5.06; // Equation 19 in Sander & Vink 2020 - double logMdot10 = -0.75 * logZ - 4.06; // Equation 20 in Sander & Vink 2020 - - if (utils::Compare(logL0, logL) <= 0) { // No mass loss for L < L0 - // Equation 13 in Sander & Vink 2020 - double logMdot = alpha * log10(logL - logL0) + 0.75 * (logL - logL0 - 1.0) + logMdot10; - Mdot = PPOW(10.0, logMdot) * OPTIONS->WolfRayetFactor(); - } - } - return Mdot; -} - - -/* - * Calculate the correction to the mass-loss rates for Wolf-Rayet stars - * as a function of effective temperature, according to the - * prescription of Sander et al. 2023 (https://arxiv.org/abs/2301.01785) - * - * Use the correction given in Eq. 18, with the effective temperature - * (what they refer to as T_\star in Eq. 1) as T_eff,crit - * - * - * double CalculateMassLossRateWolfRayetTemperatureCorrectionSander2023(const double p_Mdot) - * - * @param [IN] p_Mdot Uncorrected mass-loss rate (in Msol yr^{-1}) - * @return Corrected mass-loss rate (in Msol yr^{-1}) - */ -double BaseStar::CalculateMassLossRateWolfRayetTemperatureCorrectionSander2023(const double p_Mdot) const { - - const double teffRef = 141.0E3; // reference effective temperature in Kelvin - const double teffMin = 100.0E3; // minimum effective temperature in Kelvin to apply correction - - double teff = m_Temperature * TSOL; // get effective temperature in Kelvin - double logMdotUncorrected = log10(p_Mdot); // uncorrected mass-loss rate - double logMdotCorrected = 0.0; - - // Only apply to sufficiently hot stars - if (utils::Compare(teff, teffMin) > 0) { - logMdotCorrected = logMdotUncorrected - 6.0 * log10(teff / teffRef); - } - else{ - logMdotCorrected = logMdotUncorrected; - } - - return PPOW(10.0, logMdotCorrected); -} - - -/* - * Calculate the mass-loss rate for helium stars according to the - * prescription of Vink 2017 (https://ui.adsabs.harvard.edu/abs/2017A%26A...607L...8V/abstract) - * - * See their Eq. 1 - * - * - * double CalculateMassLossRateHeliumStarVink2017() - * - * @return Mass loss rate (in Msol yr^{-1}) - */ -double BaseStar::CalculateMassLossRateHeliumStarVink2017() const { - - double logMdot = -13.3 + (1.36 * log10(m_Luminosity)) + (0.61 * LogMetallicityXi()); // Eq. 1. - - return PPOW(10.0, logMdot); -} - - -/* - * Calculate the mass-loss rate for Wolf--Rayet stars according to the - * prescription of Shenar et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019A%26A...627A.151S/abstract) - * - * See their Eq. 6 and Table 5 - * - * We use the fitting coefficients for hydrogen rich WR stars (e.g., WNh) - * The C4 (X_He) term is = 0 and is omitted - * - * - * double CalculateMassLossRateWolfRayetShenar2019() - * - * @return Mass loss rate (in Msol yr^{-1}) - */ -double BaseStar::CalculateMassLossRateWolfRayetShenar2019() const { - - double teff = m_Temperature * TSOL; - - // For H-rich WR stars (X_H > 0.4) - const double C1 = -6.78; - const double C2 = 0.66; - const double C3 = -0.12; - const double C5 = 0.74; - - double logMdot = C1 + (C2 * log10(m_Luminosity)) + (C3 * log10(teff)) + (C5 * m_Log10Metallicity); - - return PPOW(10.0, logMdot); -} - - /* * Calculate the dominant mass loss mechanism and associated rate for the star * at the current evolutionary phase. * * According to Hurley et al. 2000 * - * * double CalculateMassLossRateHurley() * * @return Mass loss rate in Msol per year @@ -2551,20 +1959,19 @@ double BaseStar::CalculateMassLossRateHurley() { /* * Calculate the dominant mass loss mechanism and associated rate for the star at the current evolutionary phase - * According to Vink - based on implementation in StarTrack + * According to Vink - based on implementation in StarTrack * - * - * double CalculateMassLossRateBelczynski2010() + * double CalculateMassLossRateVink() * * @return Mass loss rate in Msol per year */ -double BaseStar::CalculateMassLossRateBelczynski2010() { +double BaseStar::CalculateMassLossRateVink() { m_DominantMassLossRate = MASS_LOSS_TYPE::NONE; // reset dominant mass loss rate double LBVRate = CalculateMassLossRateLBV(OPTIONS->LuminousBlueVariablePrescription()); // start with LBV winds (can be, and is often, 0.0) double otherWindsRate = 0.0; - if (m_DominantMassLossRate != MASS_LOSS_TYPE::LBV || + if (m_DominantMassLossRate != MASS_LOSS_TYPE::LUMINOUS_BLUE_VARIABLE || OPTIONS->LuminousBlueVariablePrescription() == LBV_PRESCRIPTION::HURLEY_ADD ) { // check whether we should add other winds to the LBV winds (always for HURLEY_ADD prescription, only if not in LBV regime for others) double teff = m_Temperature * TSOL; // change to Kelvin so it can be compared with values as stated in Vink prescription @@ -2572,68 +1979,15 @@ double BaseStar::CalculateMassLossRateBelczynski2010() { otherWindsRate = CalculateMassLossRateHurley() * OPTIONS->CoolWindMassLossMultiplier(); // Apply cool wind mass loss multiplier } else { // hot stars, add Vink et al. 2001 winds (ignoring bistability jump) - otherWindsRate = CalculateMassLossRateOBVink2001(); - m_DominantMassLossRate = MASS_LOSS_TYPE::OB; + otherWindsRate = CalculateMassLossRateOB(teff); } if (utils::Compare(LBVRate, otherWindsRate) > 0) { - m_DominantMassLossRate = MASS_LOSS_TYPE::LBV; // set LBV dominant again in case Hurley or OB overwrote it + m_DominantMassLossRate = MASS_LOSS_TYPE::LUMINOUS_BLUE_VARIABLE; // set LBV dominant again in case Hurley or OB overwrote it } } - // BSE and StarTrack have some multiplier they apply here - return LBVRate + otherWindsRate; -} - - -/* - * Calculate the mass loss rate according to the updated prescription. The structure is similar to the Vink wrapper (previous default), which should be called Belczynski. - * Mass loss rates for hot, massive OB stars are given by Bjorklund et al. 2022 - * Mass loss rates for helium rich Wolf--Rayet stars are given by Sander (not yet implemented) - * Mass loss rates for red supergiants are given by Beasor and Davies (not yet implemented) - * Mass loss rates for luminous blue variables are still given as defined elsewhere in the code - * - * - * double CalculateMassLossRateFlexible2023() - * - * @return Mass loss rate in Msol per year - */ -double BaseStar::CalculateMassLossRateFlexible2023() { - - m_DominantMassLossRate = MASS_LOSS_TYPE::NONE; - - double LBVRate = CalculateMassLossRateLBV(OPTIONS->LuminousBlueVariablePrescription()); // start with LBV winds (can be, and is often, 0.0) - double otherWindsRate = 0.0; - - double teff = TSOL * m_Temperature; - - if (m_DominantMassLossRate != MASS_LOSS_TYPE::LBV || - OPTIONS->LuminousBlueVariablePrescription() == LBV_PRESCRIPTION::HURLEY_ADD ) { // check whether we should add other winds to the LBV winds (always for HURLEY_ADD prescription, only if not in LBV regime for others) - - - if ((utils::Compare(teff, RSG_MAXIMUM_TEMP) < 0) && (utils::Compare(m_MZAMS, 8.0) >= 0) && - IsOneOf(GIANTS)) { // RSG criteria, below 8kK, above 8Msol, and core helium burning giant(CHeB, FGB, EAGB, TPAGB) - otherWindsRate = CalculateMassLossRateRSG(OPTIONS->RSGMassLoss()); - m_DominantMassLossRate = MASS_LOSS_TYPE::RSG; - } - else if (utils::Compare(teff, VINK_MASS_LOSS_MINIMUM_TEMP) < 0) { // cool stars, add Hurley et al 2000 winds (NJ90) - otherWindsRate = CalculateMassLossRateHurley() * OPTIONS->CoolWindMassLossMultiplier(); // apply cool wind mass loss multiplier - } // change to Kelvin so it can be compared with values as stated in Vink prescription - else if (utils::Compare(m_MZAMS, VERY_MASSIVE_MINIMUM_MASS) >= 0) { - otherWindsRate = CalculateMassLossRateVMS(OPTIONS->VMSMassLoss()); - m_DominantMassLossRate = MASS_LOSS_TYPE::VMS; // massive MS, >100 Msol. Alternately could use Luminosity or Gamma and Mass threshold - } - - else { - otherWindsRate = CalculateMassLossRateOB(OPTIONS->OBMassLoss()); - m_DominantMassLossRate = MASS_LOSS_TYPE::OB; - } - - if (utils::Compare(LBVRate, otherWindsRate) > 0) { - m_DominantMassLossRate = MASS_LOSS_TYPE::LBV; // set LBV dominant again in case Hurley or OB overwrote it - } - - } + // BSE and StarTrack have some mulptilier they apply here return LBVRate + otherWindsRate; } @@ -2651,8 +2005,7 @@ double BaseStar::CalculateMassLossRateFlexible2023() { */ double BaseStar::CalculateMassLossRate() { - double mDot = 0.0; // default return value - + double mDot = 0.0; if (OPTIONS->UseMassLoss()) { double LBVRate; @@ -2661,36 +2014,28 @@ double BaseStar::CalculateMassLossRate() { switch (OPTIONS->MassLossPrescription()) { // which prescription? case MASS_LOSS_PRESCRIPTION::HURLEY: // HURLEY - LBVRate = CalculateMassLossRateLBV(LBV_PRESCRIPTION::HURLEY_ADD); + LBVRate = CalculateMassLossRateLBV(LBV_PRESCRIPTION::HURLEY_ADD); otherWindsRate = CalculateMassLossRateHurley(); if (utils::Compare(LBVRate, otherWindsRate) > 0) { - m_DominantMassLossRate = MASS_LOSS_TYPE::LBV; + m_DominantMassLossRate = MASS_LOSS_TYPE::LUMINOUS_BLUE_VARIABLE; } mDot = LBVRate + otherWindsRate; break; - case MASS_LOSS_PRESCRIPTION::BELCZYNSKI2010: // formerly named VINK mass-loss prescription - mDot = CalculateMassLossRateBelczynski2010(); + case MASS_LOSS_PRESCRIPTION::VINK: // VINK + mDot = CalculateMassLossRateVink(); break; - case MASS_LOSS_PRESCRIPTION::FLEXIBLE2023: // updated mass loss prescription - mDot = CalculateMassLossRateFlexible2023(); - break; - - case MASS_LOSS_PRESCRIPTION::NONE: // no mass loss prescription - mDot = 0.0; - break; - - default: // unknown mass-loss prescription + default: // unknown mass loss prescription SHOW_WARN(ERROR::UNKNOWN_MASS_LOSS_PRESCRIPTION, "Using HURLEY"); // show warning - LBVRate = CalculateMassLossRateLBV(LBV_PRESCRIPTION::HURLEY_ADD); + LBVRate = CalculateMassLossRateLBV(LBV_PRESCRIPTION::HURLEY_ADD); otherWindsRate = CalculateMassLossRateHurley(); if (utils::Compare(LBVRate, otherWindsRate) > 0) { - m_DominantMassLossRate = MASS_LOSS_TYPE::LBV; + m_DominantMassLossRate = MASS_LOSS_TYPE::LUMINOUS_BLUE_VARIABLE; } mDot = LBVRate + otherWindsRate; // use HURLEY } - mDot = mDot * OPTIONS->OverallWindMassLossMultiplier(); // apply overall wind mass loss multiplier + mDot = mDot * OPTIONS->OverallWindMassLossMultiplier(); // Apply overall wind mass loss multiplier } return mDot; @@ -2710,7 +2055,7 @@ double BaseStar::CalculateMassLossRate() { * @return Mass loss */ double BaseStar::CalculateMassLoss_Static(const double p_Mass, const double p_Mdot, const double p_Dt) { - return max(0.0, min(p_Mdot * p_Dt * 1.0E6, p_Mass * MAXIMUM_MASS_LOSS_FRACTION)); // mass loss rate given in Msol per year, times are in Myr so need to multiply by 10^6 + return max(0.0, min(p_Mdot * p_Dt * 1.0E6, p_Mass * MAXIMUM_MASS_LOSS_FRACTION)); // Mass loss rate given in Msol per year, times are in Myr so need to multiply by 10^6 } @@ -2736,29 +2081,29 @@ double BaseStar::CalculateMassLossValues(const bool p_UpdateMDot, const bool p_U double mDot = m_Mdot; double mass = m_Mass; - if (OPTIONS->UseMassLoss()) { // only if using mass loss (program option) + if (OPTIONS->UseMassLoss()) { // only if using mass loss (program option) - mDot = CalculateMassLossRate(); // calculate mass loss rate - double massLoss = CalculateMassLoss_Static(mass, mDot, dt); // calculate mass loss - limited to (mass * MAXIMUM_MASS_LOSS_FRACTION) + mDot = CalculateMassLossRate(); // calculate mass loss rate + double massLoss = CalculateMassLoss_Static(mass, mDot, dt); // calculate mass loss - limited to (mass * MAXIMUM_MASS_LOSS_FRACTION) if (OPTIONS->CheckPhotonTiringLimit()) { - double lim = m_Luminosity / (G_SOLAR_YEAR * m_Mass / m_Radius); // calculate the photon tiring limit in Msol yr^-1 using Owocki & Gayley 1997, equation slightly clearer in Owocki+2004 Eq. 20 - massLoss = std::min(massLoss, lim); // limit mass loss to the photon tiring limit + double lim = m_Luminosity / (G_SOLAR_YEAR * m_Mass / m_Radius); // calculate the photon tiring limit in Msol yr^-1 using Owocki & Gayley 1997, equation slightly clearer in Owocki+2004 Eq. 20 + massLoss = std::min(massLoss, lim); // limit mass loss to the photon tiring limit } // could do this without the test - we know the mass loss may already // have been limited. This way is probably marginally faster if (utils::Compare(massLoss, (mass * MAXIMUM_MASS_LOSS_FRACTION)) < 0) { - mass -= massLoss; // new mass based on mass loss + mass -= massLoss; // new mass based on mass loss } else { - dt = massLoss / (mDot * 1.0E6); // new timestep to match limited mass loss - mass -= massLoss; // new mass based on limited mass loss + dt = massLoss / (mDot * 1.0E6); // new timestep to match limited mass loss + mass -= massLoss; // new mass based on limited mass loss - if (p_UpdateMDt) m_Dt = dt; // update class member variable if necessary + if (p_UpdateMDt) m_Dt = dt; // update class member variable if necessary } - if (p_UpdateMDot) m_Mdot = mDot; // update class member variable if necessary + if (p_UpdateMDot) m_Mdot = mDot; // update class member variable if necessary } return mass; @@ -2782,10 +2127,10 @@ void BaseStar::ResolveMassLoss() { if (OPTIONS->UseMassLoss()) { m_Mass = CalculateMassLossValues(true, true); // calculate new values assuming mass loss applied - m_HeCoreMass = std::min(m_HeCoreMass, m_Mass); // update He mass if mass loss is happening from He stars + m_HeCoreMass=std::min(m_HeCoreMass,m_Mass); // update He mass if mass loss is happening from He stars - m_COCoreMass = std::min(m_COCoreMass, m_Mass); // not expected, only a precaution to avoid inconsistencies - m_CoreMass = std::min(m_CoreMass, m_Mass); + m_COCoreMass=std::min(m_COCoreMass,m_Mass); // Not expected, only a precaution to avoid inconsistencies + m_CoreMass=std::min(m_CoreMass, m_Mass); UpdateInitialMass(); // update effective initial mass (MS, HG & HeMS) UpdateAgeAfterMassLoss(); // update age (MS, HG & HeMS) @@ -2850,13 +2195,13 @@ DBL_DBL BaseStar::CalculateMassAcceptanceRate(const double p_DonorMassRate, cons case MT_ACCRETION_EFFICIENCY_PRESCRIPTION::THERMALLY_LIMITED: // thermally limited mass transfer: - acceptanceRate = min(OPTIONS->MassTransferCParameter() * p_AccretorMassRate, p_DonorMassRate); + acceptanceRate = min(OPTIONS->MassTransferCParameter() * p_AccretorMassRate, p_DonorMassRate); fractionAccreted = acceptanceRate / p_DonorMassRate; break; case MT_ACCRETION_EFFICIENCY_PRESCRIPTION::FIXED_FRACTION: // fixed fraction of mass accreted, as in StarTrack fractionAccreted = OPTIONS->MassTransferFractionAccreted(); - acceptanceRate = min(p_DonorMassRate, fractionAccreted * p_DonorMassRate); + acceptanceRate = min(p_DonorMassRate, fractionAccreted * p_DonorMassRate); break; @@ -3439,9 +2784,9 @@ double BaseStar::DrawKickMagnitudeDistributionFlat(const double p_MaxVK, const d * @return Drawn kick magnitude (km s^-1) */ double BaseStar::DrawKickMagnitudeBrayEldridge(const double p_EjectaMass, - const double p_RemnantMass, - const double p_Alpha, - const double p_Beta) const { + const double p_RemnantMass, + const double p_Alpha, + const double p_Beta) const { return p_Alpha * (p_EjectaMass / p_RemnantMass) + p_Beta; } @@ -4046,15 +3391,19 @@ std::string BaseStar::MassTransferDonorHistoryString() const { STYPE_VECTOR mtHistVec = m_MassTransferDonorHistory; std::string mtHistStr = ""; - if (mtHistVec.empty()) { // this star was never a donor for MT + if (mtHistVec.empty()) { // This star was never a donor for MT mtHistStr = "NA"; } - else { // this star was a donor, return the stellar type string + else { // This star was a donor, return the stellar type string + for (size_t ii = 0; ii < mtHistVec.size(); ii++) { - mtHistStr += std::to_string(static_cast(mtHistVec[ii])) + "-"; // create string of stellar type followed by dash + mtHistStr += std::to_string(static_cast(mtHistVec[ii])) + "-"; // Create string of stellar type followed by dash } - mtHistStr.pop_back(); // remove final dash + + mtHistStr.pop_back(); // Remove final dash + } + return mtHistStr; } @@ -4071,7 +3420,7 @@ void BaseStar::UpdateMassTransferDonorHistory() { if (m_MassTransferDonorHistory.empty()) { m_MassTransferDonorHistory.push_back(m_StellarType); } - else if (!utils::IsOneOf(m_StellarType, { m_MassTransferDonorHistory.back() })) { // the star has not yet MT'd as its current type, so new event + else if (!utils::IsOneOf(m_StellarType, { m_MassTransferDonorHistory.back() })) { // The star has not yet MT'd as its current type, so new event m_MassTransferDonorHistory.push_back(m_StellarType); } } @@ -4089,7 +3438,7 @@ STELLAR_TYPE BaseStar::EvolveOnPhase() { STELLAR_TYPE stellarType = m_StellarType; - if (ShouldEvolveOnPhase()) { // evolve timestep on phase + if (ShouldEvolveOnPhase()) { // Evolve timestep on phase m_Tau = CalculateTauOnPhase(); @@ -4099,7 +3448,7 @@ STELLAR_TYPE BaseStar::EvolveOnPhase() { m_Luminosity = CalculateLuminosityOnPhase(); - std::tie(m_Radius, stellarType) = CalculateRadiusAndStellarTypeOnPhase(); // radius and possibly new stellar type + std::tie(m_Radius, stellarType) = CalculateRadiusAndStellarTypeOnPhase(); // Radius and possibly new stellar type m_Mu = CalculatePerturbationMuOnPhase(); @@ -4107,7 +3456,7 @@ STELLAR_TYPE BaseStar::EvolveOnPhase() { m_Temperature = CalculateTemperatureOnPhase(); - STELLAR_TYPE thisStellarType = ResolveEnvelopeLoss(); // resolve envelope loss if it occurs - possibly new stellar type + STELLAR_TYPE thisStellarType = ResolveEnvelopeLoss(); // Resolve envelope loss if it occurs - possibly new stellar type if (thisStellarType != m_StellarType) { // thisStellarType overrides stellarType (from CalculateRadiusAndStellarTypeOnPhase()) stellarType = thisStellarType; } @@ -4129,10 +3478,10 @@ STELLAR_TYPE BaseStar::ResolveEndOfPhase() { STELLAR_TYPE stellarType = m_StellarType; - if (IsEndOfPhase()) { // end of phase + if (IsEndOfPhase()) { // End of phase - stellarType = ResolveEnvelopeLoss(); // resolve envelope loss if it occurs - if (stellarType == m_StellarType) { // staying on phase? + stellarType = ResolveEnvelopeLoss(); // Resolve envelope loss if it occurs + if (stellarType == m_StellarType) { // Staying on phase? m_Tau = CalculateTauAtPhaseEnd(); diff --git a/src/BaseStar.h b/src/BaseStar.h index 105ad886f..a9304cee1 100644 --- a/src/BaseStar.h +++ b/src/BaseStar.h @@ -478,31 +478,12 @@ class BaseStar { double CalculateMassLossRateLBVHurley(const double p_HD_limit_fac) const; double CalculateMassLossRateLBVBelczynski() const; double CalculateMassLossRateNieuwenhuijzenDeJager() const; - double CalculateMassLossRateBjorklundEddingtonFactor() const; - double CalculateMassLossRateOB(const OB_MASS_LOSS p_OB_mass_loss); - double CalculateMassLossRateOBBjorklund2022() const; - double CalculateMassLossRateOBVink2001() const; - double CalculateMassLossRateOBKrticka2018() const; - double CalculateMassLossRateOBVinkSander2021() const; - double CalculateMassLossRateRSG(const RSG_MASS_LOSS p_RSG_mass_loss) const; - double CalculateMassLossRateRSGVinkSabhahit2023() const; - double CalculateMassLossRateRSGBeasor2020() const; - double CalculateMassLossRateRSGDecin2023() const; - double CalculateMassLossRateRSGYang2023() const; - double CalculateMassLossRateRSGKee2021() const; + double CalculateMassLossRateOB(const double p_Teff); double CalculateMassLossRateVassiliadisWood() const; - double CalculateMassLossRateVMS(const VMS_MASS_LOSS p_VMS_mass_loss); - double CalculateMassLossRateVMSBestenlehner2020() const; - double CalculateMassLossRateVMSSabhahit2023() const; - double CalculateMassLossRateVMSVink2011() const; - virtual double CalculateMassLossRateBelczynski2010(); - virtual double CalculateMassLossRateFlexible2023(); + virtual double CalculateMassLossRateVink(); double CalculateMassLossRateWolfRayetZDependent(const double p_Mu) const; + double CalculateMassLossRateWolfRayet3() const; // JR: Never called - do we need it? double CalculateMassLossRateWolfRayet(const double p_Mu) const; - double CalculateMassLossRateWolfRayetSanderVink2020(const double p_Mu) const; - double CalculateMassLossRateWolfRayetTemperatureCorrectionSander2023(const double p_Mdot) const; - double CalculateMassLossRateHeliumStarVink2017() const; - double CalculateMassLossRateWolfRayetShenar2019() const; virtual double CalculateMassTransferRejuvenationFactor() const; diff --git a/src/EAGB.cpp b/src/EAGB.cpp index 548717b47..8b9450813 100755 --- a/src/EAGB.cpp +++ b/src/EAGB.cpp @@ -877,20 +877,22 @@ double EAGB::CalculateMassLossRateHurley() { double rateVW = CalculateMassLossRateVassiliadisWood(); double rateWR = CalculateMassLossRateWolfRayet(m_Mu); double dominantRate; - m_DominantMassLossRate = MASS_LOSS_TYPE::GB; if (utils::Compare(rateNJ, rateKR) > 0) { + m_DominantMassLossRate = MASS_LOSS_TYPE::NIEUWENHUIJZEN_DE_JAGER; dominantRate = rateNJ; } else { + m_DominantMassLossRate = MASS_LOSS_TYPE::KUDRITZKI_REIMERS; dominantRate = rateKR; } if (utils::Compare(rateVW, dominantRate) > 0) { + m_DominantMassLossRate = MASS_LOSS_TYPE::VASSILIADIS_WOOD; dominantRate = rateVW; } if (utils::Compare(rateWR, dominantRate) > 0) { - m_DominantMassLossRate = MASS_LOSS_TYPE::WR; + m_DominantMassLossRate = MASS_LOSS_TYPE::WOLF_RAYET_LIKE; dominantRate = rateWR; } return dominantRate; diff --git a/src/GiantBranch.cpp b/src/GiantBranch.cpp index 6bb424a0b..3206f6630 100644 --- a/src/GiantBranch.cpp +++ b/src/GiantBranch.cpp @@ -902,15 +902,17 @@ double GiantBranch::CalculateMassLossRateHurley() { double rateKR = CalculateMassLossRateKudritzkiReimers(); double rateWR = CalculateMassLossRateWolfRayet(m_Mu); double dominantRate; - m_DominantMassLossRate = MASS_LOSS_TYPE::GB; + if (utils::Compare(rateNJ, rateKR) > 0) { dominantRate = rateNJ; + m_DominantMassLossRate = MASS_LOSS_TYPE::NIEUWENHUIJZEN_DE_JAGER; } else { dominantRate = rateKR; + m_DominantMassLossRate = MASS_LOSS_TYPE::KUDRITZKI_REIMERS; } if (utils::Compare(rateWR, dominantRate) > 0) { dominantRate = rateWR; - m_DominantMassLossRate = MASS_LOSS_TYPE::WR; + m_DominantMassLossRate = MASS_LOSS_TYPE::WOLF_RAYET_LIKE; } return dominantRate; @@ -1960,26 +1962,28 @@ STELLAR_TYPE GiantBranch::ResolvePulsationalPairInstabilitySN() { } break; case PPI_PRESCRIPTION::FARMER: { // Farmer et al. 2019 http://dx.doi.org/10.3847/1538-4357/ab518b - double totalMassPrePPISN = m_Mass; // save the total stellar mass - // three cases: - if (m_COCoreMass < FARMER_PPISN_UPP_LIM_LIN_REGIME) { - m_Mass = m_COCoreMass + 4.0; // a linear relation below CO core masses of 38 Msun - } - else if (m_COCoreMass < FARMER_PPISN_UPP_LIM_QUAD_REGIME) { // a quadratic relation in CO core mass for 38 =< CO_core < 60 - const double a1 = -0.096; - const double a2 = 8.564; - const double a3 = -2.07; - const double a4 = -152.97; - m_Mass = a1 * PPOW(m_COCoreMass, 2.0) + a2 * m_COCoreMass + a3 * m_Log10Metallicity + a4; + double totalMassPrePPISN = m_Mass; // Save the total stellar mass + // Three cases: + if (m_COCoreMass < FARMER_PPISN_UPP_LIM_LIN_REGIME){ + m_Mass = m_COCoreMass + 4.; // A linear relation below CO core masses of 38 Msun + } + + else if (m_COCoreMass < FARMER_PPISN_UPP_LIM_QUAD_REGIME) { // A quadratic relation in CO core mass for 38 =< CO_core < 60 + double a1 = -0.096; + double a2 = 8.564; + double a3 = -2.07; + double a4 = -152.97; + m_Mass = a1*pow(m_COCoreMass, 2.0) + a2*m_COCoreMass + a3*log10(m_Metallicity) + a4 ; } - else if (m_COCoreMass < FARMER_PPISN_UPP_LIM_INSTABILLITY) { // no remnant between 60 - 140 Msun - m_Mass = 0.0; + + else if (m_COCoreMass < FARMER_PPISN_UPP_LIM_INSTABILLITY) { // No remnant between 60 - 140 Msun + m_Mass = 0; } else { // BH mass becomes CO-core mass above the PISN gap m_Mass = m_COCoreMass; } - m_Mass = std::min(totalMassPrePPISN, m_Mass); // check if remnant mass is bigger than total mass + m_Mass = std::min(totalMassPrePPISN, m_Mass); // Check if your remnant mass is bigger than your total mass } break; diff --git a/src/HeMS.cpp b/src/HeMS.cpp index 8febed40a..cbef4be96 100755 --- a/src/HeMS.cpp +++ b/src/HeMS.cpp @@ -220,15 +220,17 @@ double HeMS::CalculateMassLossRateHurley() { double rateKR = CalculateMassLossRateKudritzkiReimers(); double rateWR = CalculateMassLossRateWolfRayet(0.0); // use mu=0.0 for Helium stars double dominantRate; - m_DominantMassLossRate = MASS_LOSS_TYPE::GB; + if (utils::Compare(rateNJ, rateKR) > 0) { dominantRate = rateNJ; + m_DominantMassLossRate = MASS_LOSS_TYPE::NIEUWENHUIJZEN_DE_JAGER; } else { dominantRate = rateKR; + m_DominantMassLossRate = MASS_LOSS_TYPE::KUDRITZKI_REIMERS; } if (utils::Compare(rateWR, dominantRate) > 0) { dominantRate = rateWR; - m_DominantMassLossRate = MASS_LOSS_TYPE::WR; + m_DominantMassLossRate = MASS_LOSS_TYPE::WOLF_RAYET_LIKE; } return dominantRate; @@ -240,96 +242,15 @@ double HeMS::CalculateMassLossRateHurley() { * * According to Vink - based on implementation in StarTrack * - * double CalculateMassLossRateBelczynski2010() + * double CalculateMassLossRateVink() * * @return Mass loss rate in Msol per year */ -double HeMS::CalculateMassLossRateBelczynski2010() { - m_DominantMassLossRate = MASS_LOSS_TYPE::WR; +double HeMS::CalculateMassLossRateVink() { + m_DominantMassLossRate = MASS_LOSS_TYPE::WOLF_RAYET_LIKE; return CalculateMassLossRateWolfRayetZDependent(0.0); } -/* - * Calculate the mass-loss rate for Wolf--Rayet stars according to the - * prescription of Shenar et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019A%26A...627A.151S/abstract) - * - * See their Eq. 6 and Table 5 - * - * We use the fitting coefficients for hydrogen poor WR stars - * The C4 (X_He) term is = 0 and is omitted - * - * double CalculateMassLossRateWolfRayetShenar2019() - * - * - * @return Mass loss rate (in Msol yr^{-1}) - */ -double HeMS::CalculateMassLossRateWolfRayetShenar2019() const { - - double logMdot = 0.0; - double Teff = m_Temperature * TSOL; - - // For H-poor WR stars (X_H < 0.05) - const double C1 = -7.99; - const double C2 = 0.97; - const double C3 = -0.07; - const double C5 = 0.89; - - logMdot = C1 + (C2 * log10(m_Luminosity)) + (C3 * log10(Teff)) + (C5 * m_Log10Metallicity); - - return PPOW(10.0, logMdot); // Mdot -} - - -/* - * Calculate the mass loss rate for helium stars in the updated prescription - * Uses Sander & Vink 2020 for Wolf--Rayet stars - * - * double CalculateMassLossRateFlexible2023() - * - * @return Mass loss rate in Msol per year - */ -double HeMS::CalculateMassLossRateFlexible2023() { - - m_DominantMassLossRate = MASS_LOSS_TYPE::WR; - - double MdotWR = 0.0; - - if(OPTIONS->WRMassLoss() == WR_MASS_LOSS::SANDERVINK2023){ - - // Calculate Sander & Vink 2020 mass-loss rate - double Mdot_SanderVink2020 = CalculateMassLossRateWolfRayetSanderVink2020(0.0); - - // Apply the Sander et al. 2023 temperature correction to the Sander & Vink 2020 rate - double Mdot_Sander2023 = CalculateMassLossRateWolfRayetTemperatureCorrectionSander2023(Mdot_SanderVink2020); - - // Calculate Vink 2017 mass-loss rate - double Mdot_Vink2017 = CalculateMassLossRateHeliumStarVink2017(); - - // Use whichever gives the highest mass loss rate -- will typically be Vink 2017 for - // low Mass or Luminosity, and Sander & Vink 2020 for high Mass or Luminosity - - MdotWR = std::max(Mdot_Sander2023, Mdot_Vink2017); - - } - else if(OPTIONS->WRMassLoss() == WR_MASS_LOSS::SHENAR2019){ - - // Mass loss rate for WR stars from Shenar+ 2019 - double Mdot_Shenar2019 = CalculateMassLossRateWolfRayetShenar2019(); - - // Calculate Vink 2017 mass-loss rate - double Mdot_Vink2017 = CalculateMassLossRateHeliumStarVink2017(); - - // Apply a minimum of Vink 2017 mass-loss rate to avoid extrapolating to low luminosity - MdotWR = std::max(Mdot_Shenar2019, Mdot_Vink2017); - - } - else{ - MdotWR = CalculateMassLossRateBelczynski2010(); - } - - return MdotWR; - -} /* * Determines if mass transfer is unstable according to the critical mass ratio. diff --git a/src/HeMS.h b/src/HeMS.h index 890cfee8a..7e0b21f14 100644 --- a/src/HeMS.h +++ b/src/HeMS.h @@ -80,10 +80,8 @@ class HeMS: virtual public BaseStar, public TPAGB { double CalculateLuminosityOnPhase() const { return CalculateLuminosityOnPhase(m_Mass, m_Tau); } // Use class member variables double CalculateMassLossRateHurley(); - double CalculateMassLossRateBelczynski2010(); - double CalculateMassLossRateFlexible2023(); - double CalculateMassLossRateWolfRayetShenar2019() const; - + double CalculateMassLossRateVink(); + double CalculateMassTransferRejuvenationFactor() const; double CalculateMomentOfInertia(const double p_RemnantRadius = 0.0) const { return MainSequence::CalculateMomentOfInertia(p_RemnantRadius); } // Use MainSequence diff --git a/src/MS_gt_07.cpp b/src/MS_gt_07.cpp index aeeb91652..a74ab8070 100755 --- a/src/MS_gt_07.cpp +++ b/src/MS_gt_07.cpp @@ -13,8 +13,8 @@ */ double MS_gt_07::CalculateMassLossRateHurley() { double rateNJ = CalculateMassLossRateNieuwenhuijzenDeJager(); - m_DominantMassLossRate = MASS_LOSS_TYPE::GB; if (utils::Compare(rateNJ, 0.0) > 0) { + m_DominantMassLossRate = MASS_LOSS_TYPE::NIEUWENHUIJZEN_DE_JAGER; } else { m_DominantMassLossRate = MASS_LOSS_TYPE::NONE; } diff --git a/src/Options.cpp b/src/Options.cpp index 2b3267487..c32f27991 100644 --- a/src/Options.cpp +++ b/src/Options.cpp @@ -359,29 +359,17 @@ void Options::OptionValues::Initialise() { m_ExpelConvectiveEnvelopeAboveLuminosityThreshold = false; m_LuminosityToMassThreshold = 4.2; // Podsiadlowski, private communication - m_MassLossPrescription.type = MASS_LOSS_PRESCRIPTION::FLEXIBLE2023; + m_MassLossPrescription.type = MASS_LOSS_PRESCRIPTION::VINK; m_MassLossPrescription.typeString = MASS_LOSS_PRESCRIPTION_LABEL.at(m_MassLossPrescription.type); m_LuminousBlueVariablePrescription.type = LBV_PRESCRIPTION::HURLEY_ADD; m_LuminousBlueVariablePrescription.typeString = LBV_PRESCRIPTION_LABEL.at(m_LuminousBlueVariablePrescription.type); - m_OBMassLoss.type = OB_MASS_LOSS::VINK2021; - m_OBMassLoss.typeString = OB_MASS_LOSS_LABEL.at(m_OBMassLoss.type); - - m_VMSMassLoss.type = VMS_MASS_LOSS::SABHAHIT2023; - m_VMSMassLoss.typeString = VMS_MASS_LOSS_LABEL.at(m_VMSMassLoss.type); - - m_RSGMassLoss.type = RSG_MASS_LOSS::DECIN2023; - m_RSGMassLoss.typeString = RSG_MASS_LOSS_LABEL.at(m_RSGMassLoss.type); - - m_WRMassLoss.type = WR_MASS_LOSS::SANDERVINK2023; - m_WRMassLoss.typeString = WR_MASS_LOSS_LABEL.at(m_WRMassLoss.type); - // Wind mass loss multiplicitive constants m_CoolWindMassLossMultiplier = 1.0; m_LuminousBlueVariableFactor = 1.5; m_OverallWindMassLossMultiplier = 1.0; - m_WolfRayetFactor = 1.0; + m_WolfRayetFactor = 0.1; // Mass transfer options @@ -1740,11 +1728,6 @@ bool Options::AddOptions(OptionValues *p_Options, po::options_description *p_Opt ("Neutron star equation of state to use (" + AllowedOptionValuesFormatted("neutron-star-equation-of-state") + ", default = '" + p_Options->m_NeutronStarEquationOfState.typeString + "')").c_str() ) - ( - "OB-mass-loss", - po::value(&p_Options->m_OBMassLoss.typeString)->default_value(p_Options->m_OBMassLoss.typeString), - ("OB mass loss prescription (" + AllowedOptionValuesFormatted("OB-mass-loss") + ", default = '" + p_Options->m_OBMassLoss.typeString + "')").c_str() - ) ( "orbital-period-distribution", po::value(&p_Options->m_OrbitalPeriodDistribution.typeString)->default_value(p_Options->m_OrbitalPeriodDistribution.typeString), @@ -1787,11 +1770,6 @@ bool Options::AddOptions(OptionValues *p_Options, po::options_description *p_Opt po::value(&p_Options->m_RotationalVelocityDistribution.typeString)->default_value(p_Options->m_RotationalVelocityDistribution.typeString), ("Initial rotational velocity distribution (" + AllowedOptionValuesFormatted("rotational-velocity-distribution") + ", default = '" + p_Options->m_RotationalVelocityDistribution.typeString + "')").c_str() ) - ( - "RSG-mass-loss", - po::value(&p_Options->m_RSGMassLoss.typeString)->default_value(p_Options->m_RSGMassLoss.typeString), - ("RSG mass loss prescription (" + AllowedOptionValuesFormatted("RSG-mass-loss") + ", default = '" + p_Options->m_RSGMassLoss.typeString + "')").c_str() - ) ( "semi-major-axis-distribution", @@ -1809,16 +1787,7 @@ bool Options::AddOptions(OptionValues *p_Options, po::options_description *p_Opt po::value(&p_Options->m_YAMLtemplate)->default_value(p_Options->m_YAMLtemplate), ("User-supplied YAML template filename (default = " + p_Options->m_YAMLtemplate + ")").c_str() ) - ( - "VMS-mass-loss", - po::value(&p_Options->m_VMSMassLoss.typeString)->default_value(p_Options->m_VMSMassLoss.typeString), - ("Very massive star mass loss prescription (" + AllowedOptionValuesFormatted("VMS-mass-loss") + ", default = '" + p_Options->m_VMSMassLoss.typeString + "')").c_str() - ) - ( - "WR-mass-loss", - po::value(&p_Options->m_WRMassLoss.typeString)->default_value(p_Options->m_WRMassLoss.typeString), - ("WR mass loss prescription (" + AllowedOptionValuesFormatted("WR-mass-loss") + ", default = '" + p_Options->m_WRMassLoss.typeString + "')").c_str() - ) + // vector (list) options - alphabetically @@ -2169,11 +2138,6 @@ std::string Options::OptionValues::CheckAndSetOptions() { COMPLAIN_IF(!found, "Unknown Neutron Star Equation of State"); } - if (!DEFAULTED("OB-mass-loss")) { // OB (main sequence) loss prescription - std::tie(found, m_OBMassLoss.type) = utils::GetMapKey(m_OBMassLoss.typeString, OB_MASS_LOSS_LABEL, m_OBMassLoss.type); - COMPLAIN_IF(!found, "Unknown OB Mass Loss Prescription"); - } - if (!DEFAULTED("pulsar-birth-magnetic-field-distribution")) { // pulsar birth magnetic field distribution std::tie(found, m_PulsarBirthMagneticFieldDistribution.type) = utils::GetMapKey(m_PulsarBirthMagneticFieldDistribution.typeString, PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION_LABEL, m_PulsarBirthMagneticFieldDistribution.type); COMPLAIN_IF(!found, "Unknown Pulsar Birth Magnetic Field Distribution"); @@ -2199,11 +2163,6 @@ std::string Options::OptionValues::CheckAndSetOptions() { COMPLAIN_IF(!found, "Unknown Rotational Velocity Distribution"); } - if (!DEFAULTED("RSG-mass-loss")) { // RSG mass loss prescription - std::tie(found, m_RSGMassLoss.type) = utils::GetMapKey(m_RSGMassLoss.typeString, RSG_MASS_LOSS_LABEL, m_RSGMassLoss.type); - COMPLAIN_IF(!found, "Unknown RSG Mass Loss Prescription"); - } - if (!DEFAULTED("semi-major-axis-distribution")) { // semi-major axis distribution std::tie(found, m_SemiMajorAxisDistribution.type) = utils::GetMapKey(m_SemiMajorAxisDistribution.typeString, SEMI_MAJOR_AXIS_DISTRIBUTION_LABEL, m_SemiMajorAxisDistribution.type); COMPLAIN_IF(!found, "Unknown Semi-Major Axis Distribution"); @@ -2214,16 +2173,6 @@ std::string Options::OptionValues::CheckAndSetOptions() { COMPLAIN_IF(!found, "Unknown stellar Zeta Prescription"); } - if (!DEFAULTED("VMS-mass-loss")) { // very massive mass loss prescription - std::tie(found, m_VMSMassLoss.type) = utils::GetMapKey(m_VMSMassLoss.typeString, VMS_MASS_LOSS_LABEL, m_VMSMassLoss.type); - COMPLAIN_IF(!found, "Unknown Very Massive Mass Loss Prescription"); - } - - if (!DEFAULTED("WR-mass-loss")) { // WR mass loss prescription - std::tie(found, m_WRMassLoss.type) = utils::GetMapKey(m_WRMassLoss.typeString, WR_MASS_LOSS_LABEL, m_WRMassLoss.type); - COMPLAIN_IF(!found, "Unknown WR Mass Loss Prescription"); - } - // constraint/value/range checks - alphabetically (where possible) COMPLAIN_IF(m_CommonEnvelopeAlpha < 0.0, "CE alpha (--common-envelope-alpha) < 0"); @@ -2469,18 +2418,14 @@ std::vector Options::AllowedOptionValues(const std::string p_Option case _("mode") : POPULATE_RET(EVOLUTION_MODE_LABEL); break; case _("neutrino-mass-loss-BH-formation") : POPULATE_RET(NEUTRINO_MASS_LOSS_PRESCRIPTION_LABEL); break; case _("neutron-star-equation-of-state") : POPULATE_RET(NS_EOSLabel); break; - case _("OB-mass-loss") : POPULATE_RET(OB_MASS_LOSS_LABEL); break; case _("orbital-period-distribution") : POPULATE_RET(ORBITAL_PERIOD_DISTRIBUTION_LABEL); break; case _("pulsar-birth-magnetic-field-distribution") : POPULATE_RET(PULSAR_BIRTH_MAGNETIC_FIELD_DISTRIBUTION_LABEL); break; case _("pulsar-birth-spin-period-distribution") : POPULATE_RET(PULSAR_BIRTH_SPIN_PERIOD_DISTRIBUTION_LABEL); break; case _("pulsational-pair-instability-prescription") : POPULATE_RET(PPI_PRESCRIPTION_LABEL); break; - case _("RSG-mass-loss") : POPULATE_RET(RSG_MASS_LOSS_LABEL); break; case _("remnant-mass-prescription") : POPULATE_RET(REMNANT_MASS_PRESCRIPTION_LABEL); break; case _("rotational-velocity-distribution") : POPULATE_RET(ROTATIONAL_VELOCITY_DISTRIBUTION_LABEL); break; case _("semi-major-axis-distribution") : POPULATE_RET(SEMI_MAJOR_AXIS_DISTRIBUTION_LABEL); break; case _("stellar-zeta-prescription") : POPULATE_RET(ZETA_PRESCRIPTION_LABEL); break; - case _("VMS-mass-loss") : POPULATE_RET(VMS_MASS_LOSS_LABEL); break; - case _("WR-mass-loss") : POPULATE_RET(WR_MASS_LOSS_LABEL); break; default: break; } return ret; diff --git a/src/Options.h b/src/Options.h index e17b1f034..37e2b84a7 100755 --- a/src/Options.h +++ b/src/Options.h @@ -502,7 +502,6 @@ class Options { "neutrino-mass-loss-BH-formation", "neutron-star-equation-of-state", - "OB-mass-loss", "orbital-period-distribution", "output-container", "c", "outputPath", "o", @@ -517,7 +516,6 @@ class Options { "quiet", - "RSG-mass-loss", "random-seed", "remnant-mass-prescription", "revised-energy-formalism-nandez-ivanova", @@ -531,11 +529,8 @@ class Options { "use-mass-loss", - "VMS-mass-loss", "version", "v", - "WR-mass-loss" - "yaml-template" }; @@ -810,11 +805,6 @@ class Options { ENUM_OPT m_LuminousBlueVariablePrescription; // Which LBV mass loss prescription to use double m_LuminousBlueVariableFactor; // Multiplicitive factor for luminous blue variable (LBV) mass loss rates when using Belczynski’s prescription double m_WolfRayetFactor; // Multiplicitive factor for Wolf-Rayet (WR) wind mass loss rates - ENUM_OPT m_OBMassLoss; - ENUM_OPT m_VMSMassLoss; // Which mass loss prescription for M > 100 Msol - ENUM_OPT m_RSGMassLoss; // Which mass loss prescription to use for RSG - ENUM_OPT m_WRMassLoss; // Which mass loss prescription to use for WR - // Mass transfer options bool m_UseMassTransfer; // Whether to use mass transfer (default = true) @@ -1395,7 +1385,6 @@ class Options { std::vector NotesHdrs() const { return m_CmdLine.optionValues.m_NotesHdrs; } size_t nObjectsToEvolve() const { return m_CmdLine.optionValues.m_ObjectsToEvolve; } - OB_MASS_LOSS OBMassLoss() const { return OPT_VALUE("OB-mass-loss", m_OBMassLoss.type, true); } bool OptimisticCHE() const { CHE_MODE che = OPT_VALUE("chemically-homogeneous-evolution", m_CheMode.type, true); return che == CHE_MODE::OPTIMISTIC; } double OrbitalPeriod() const { return OPT_VALUE("orbital-period", m_OrbitalPeriod, true); } @@ -1452,8 +1441,7 @@ class Options { double RotationalFrequency() const { return OPT_VALUE("rotational-frequency", m_RotationalFrequency, true); } double RotationalFrequency1() const { return OPT_VALUE("rotational-frequency-1", m_RotationalFrequency1, true); } double RotationalFrequency2() const { return OPT_VALUE("rotational-frequency-2", m_RotationalFrequency2, true); } - RSG_MASS_LOSS RSGMassLoss() const { return OPT_VALUE("RSG-mass-loss", m_RSGMassLoss.type, true); } - + double SemiMajorAxis() const { return OPT_VALUE("semi-major-axis", m_SemiMajorAxis, true); } SEMI_MAJOR_AXIS_DISTRIBUTION SemiMajorAxisDistribution() const { return OPT_VALUE("semi-major-axis-distribution", m_SemiMajorAxisDistribution.type, true); } double SemiMajorAxisDistributionMax() const { return OPT_VALUE("semi-major-axis-max", m_SemiMajorAxisDistributionMax, true); } @@ -1482,9 +1470,7 @@ class Options { bool UsePairInstabilitySupernovae() const { return OPT_VALUE("pair-instability-supernovae", m_UsePairInstabilitySupernovae, true); } bool UsePulsationalPairInstability() const { return OPT_VALUE("pulsational-pair-instability", m_UsePulsationalPairInstability, true); } - VMS_MASS_LOSS VMSMassLoss() const { return OPT_VALUE("VMS-mass-loss", m_VMSMassLoss.type, true); } double WolfRayetFactor() const { return OPT_VALUE("wolf-rayet-multiplier", m_WolfRayetFactor, true); } - WR_MASS_LOSS WRMassLoss() const { return OPT_VALUE("WR-mass-loss", m_WRMassLoss.type, true); } std::string YAMLfilename() const { return m_CmdLine.optionValues.m_YAMLfilename; } std::string YAMLtemplate() const { return m_CmdLine.optionValues.m_YAMLtemplate; } diff --git a/src/Remnants.h b/src/Remnants.h index c3c9d7003..f295ed3e8 100644 --- a/src/Remnants.h +++ b/src/Remnants.h @@ -56,7 +56,7 @@ class Remnants: virtual public BaseStar, public HeGB { const bool p_IsHeRich) { return CalculateMassAcceptanceRate(p_DonorMassRate, p_AccretorMassRate); } // Ignore the He content for non-WDs double CalculateMassLossRateHurley() { return 0.0; } - double CalculateMassLossRateBelczynski2010() { return 0.0; } + double CalculateMassLossRateVink() { return 0.0; } double CalculateMomentOfInertia(const double p_RemnantRadius = 0.0) const { return GiantBranch::CalculateMomentOfInertia(p_RemnantRadius); } // Default to GiantBranch double CalculateMomentOfInertiaAU(const double p_RemnantRadius = 0.0) const { return GiantBranch::CalculateMomentOfInertiaAU(p_RemnantRadius); } // Default to GiantBranch diff --git a/src/changelog.h b/src/changelog.h index b95e263c5..869b76887 100644 --- a/src/changelog.h +++ b/src/changelog.h @@ -1057,18 +1057,12 @@ // - Fixed a few typos, a little code cleanup. // 02.39.01 LC - Sep 01, 2023 - Defect repair: // - Fix for issue #945 - made HeSD SN types a sub-class of SNIA types. -// 02.40.00 JDM - Sep 29, 2023 - Enhancement: -// - Added 'FLEXIBLE2023' option to --mass-loss-prescription. Recover previous defaults via 'BELCZYNSKI2010' option. this applies the following prescriptions: -// - Added --OB-mass-loss program option. -// - Added --RSG-mass-loss. -// - Added --VMS-mass-loss. -// - Added --WR-mass-loss. -// 02.41.00 JR - Nov 02, 2023 - Enhancement, a little cleanup: +// 02.40.00 JR - Oct 30, 2023 - Enhancement, a little cleanup: // - Added naive tides implementation. Functionality enabled with new option `--enable-tides`. Default is no tides. // - Fixed CalculateOrbitalAngularMomentum() (now uses eccentricity) // - Added links to online documentation to splash string -const std::string VERSION_STRING = "02.41.00"; +const std::string VERSION_STRING = "02.40.00"; # endif // __changelog_h__ diff --git a/src/constants.h b/src/constants.h index 59bc67515..9d6e79599 100755 --- a/src/constants.h +++ b/src/constants.h @@ -229,9 +229,6 @@ constexpr double JOULES_TO_ERG = 1.0E7; constexpr double TESLA_TO_GAUSS = 1.0E4; // convert Tesla to Gauss constexpr double GAUSS_TO_TESLA = 1.0 / TESLA_TO_GAUSS; // convert Gauss to Tesla -// opacity -constexpr double OPACITY_CGS_TO_SI = 0.1; // cm^2 g^-1 to m^2 kg^-1 - // constants constexpr double _2_PI = M_PI * 2; // 2PI @@ -254,10 +251,9 @@ constexpr double G_SOLAR_YEAR = 3.14E7; constexpr double RSOL = 6.957E8; // Solar Radius (in m) constexpr double ZSOL = 0.02; // Solar Metallicity used in scalings constexpr double LOG10_ZSOL = -1.69897; // log10(ZSOL) - for performance -constexpr double ZSOL_ASPLUND = 0.0142; // Solar Metallicity (Asplund+ 2010) used in initial condition +constexpr double ZSOL_ASPLUND = 0.0142; // Solar Metallicity (Asplund+ 2010) used in initial condition constexpr double TSOL = 5778.0; // Solar Temperature in kelvin constexpr double LSOL = 3.844E33; // Solar Luminosity in erg/s -constexpr double LSOLW = 3.844E26; // Solar luminosity (in W) constexpr double AU = 149597870700.0; // 1 AU (Astronomical Unit) in metres constexpr double KM = 1000.0; // 1 km (Kilometre) in metres @@ -287,8 +283,6 @@ constexpr double MCBUR2 = 2.25; constexpr double NJ_MINIMUM_LUMINOSITY = 4.0E3; // Minimum luminosity in Lsun needed for Nieuwenhuijzen & de Jager wind mass loss constexpr double VINK_MASS_LOSS_MINIMUM_TEMP = 1.25E4; // Minimum temperature in K for Vink mass loss rates to be applied -constexpr double VERY_MASSIVE_MINIMUM_MASS = 100.0; // Minimum mass for applying Very Massive (VMS) mass rates to be applied -constexpr double RSG_MAXIMUM_TEMP = 8.0E3; // Upper temperature in K for Red Supergiant (RSG) mass loss rates to be applied constexpr double VINK_MASS_LOSS_BISTABILITY_TEMP = 2.5E4; // Temperature in K for bistability jump in Vink mass loss (assumed to be 25000K following Belczysnki+2010) constexpr double VINK_MASS_LOSS_MAXIMUM_TEMP = 5.0E4; // Maximum temperature in K for Vink mass loss rates to be applied (show warning above this) constexpr double LBV_LUMINOSITY_LIMIT_STARTRACK = 6.0E5; // STARTRACK LBV luminosity limit @@ -584,7 +578,6 @@ enum class ERROR: int { INVALID_TYPE_ZETA_CALCULATION, // invalid stellar type for Zeta calculation INVALID_VALUE_FOR_BOOLEAN_OPTION, // invalid values specified for boolean option LAMBDA_NOT_POSITIVE, // lambda is <= 0.0 - invalid - LOW_GAMMA, // very massive mass-loss prescription being extrapolated to low gamma (<0.5) LOW_TEFF_WINDS, // winds being used at low temperature MASS_NOT_POSITIVE_ONCE, // mass is <= 0.0 - invalid MAXIMUM_MASS_LOST, // (WARNING) maximum mass lost during mass loss calculations @@ -726,7 +719,6 @@ const COMPASUnorderedMap> ERROR_CATA { ERROR::INVALID_TYPE_ZETA_CALCULATION, { ERROR_SCOPE::ALWAYS, "Invalid stellar type for Zeta calculation" }}, { ERROR::INVALID_VALUE_FOR_BOOLEAN_OPTION, { ERROR_SCOPE::ALWAYS, "Invalid value specified for BOOLEAN option" }}, { ERROR::LAMBDA_NOT_POSITIVE, { ERROR_SCOPE::ALWAYS, "Lambda <= 0.0" }}, - { ERROR::LOW_GAMMA, { ERROR_SCOPE::ALWAYS, "Very massive prescription being extrapolated to low gamma (<0.5)" }}, { ERROR::LOW_TEFF_WINDS, { ERROR_SCOPE::ALWAYS, "Winds being used at low temperature" }}, { ERROR::MASS_NOT_POSITIVE_ONCE, { ERROR_SCOPE::FIRST_IN_FUNCTION, "Mass <= 0.0" }}, { ERROR::MAXIMUM_MASS_LOST, { ERROR_SCOPE::ALWAYS, "Maximum mass lost during mass loss calculations" }}, @@ -1048,52 +1040,12 @@ const COMPASUnorderedMap LBV_PRESCRIPTION_LABEL = { LBV_PRESCRIPTION::BELCZYNSKI, "BELCZYNSKI" } }; -// OB (main sequence) Mass loss prescriptions -enum class OB_MASS_LOSS: int { NONE, VINK2001, VINK2021, BJORKLUND2022, KRTICKA2018}; -const COMPASUnorderedMap OB_MASS_LOSS_LABEL = { - { OB_MASS_LOSS::NONE, "NONE" }, - { OB_MASS_LOSS::VINK2001, "VINK2001" }, - { OB_MASS_LOSS::VINK2021, "VINK2021" }, - { OB_MASS_LOSS::BJORKLUND2022, "BJORKLUND2022" }, - { OB_MASS_LOSS::KRTICKA2018, "KRTICKA2018" }, -}; - -// Very Massive Mass loss prescriptions -enum class VMS_MASS_LOSS: int { NONE, VINK2011, BESTENLEHNER2020, SABHAHIT2023}; -const COMPASUnorderedMap VMS_MASS_LOSS_LABEL = { - { VMS_MASS_LOSS::NONE, "NONE" }, - { VMS_MASS_LOSS::VINK2011, "VINK2011" }, - { VMS_MASS_LOSS::BESTENLEHNER2020, "BESTENLEHNER2020" }, - { VMS_MASS_LOSS::SABHAHIT2023, "SABHAHIT2023" }, -}; - -// RSG Mass loss prescriptions -enum class RSG_MASS_LOSS: int { NONE, VINKSABHAHIT2023, BEASOR2020, DECIN2023, YANG2023, KEE2021, NJ90}; -const COMPASUnorderedMap RSG_MASS_LOSS_LABEL = { - { RSG_MASS_LOSS::NONE, "NONE" }, - { RSG_MASS_LOSS::VINKSABHAHIT2023, "VINKSABHAHIT2023" }, - { RSG_MASS_LOSS::BEASOR2020, "BEASOR2020" }, - { RSG_MASS_LOSS::DECIN2023, "DECIN2023" }, - { RSG_MASS_LOSS::YANG2023, "YANG2023" }, - { RSG_MASS_LOSS::KEE2021, "KEE2021" }, - { RSG_MASS_LOSS::NJ90, "NJ90" }, -}; - -// WR Mass loss prescriptions -enum class WR_MASS_LOSS: int { BELCZYNSKI2010, SANDERVINK2023, SHENAR2019 }; -const COMPASUnorderedMap WR_MASS_LOSS_LABEL = { - { WR_MASS_LOSS::BELCZYNSKI2010, "BELCZYNSKI2010" }, - { WR_MASS_LOSS::SANDERVINK2023, "SANDERVINK2023" }, - { WR_MASS_LOSS::SHENAR2019, "SHENAR2019" }, -}; - // Mass loss prescriptions -enum class MASS_LOSS_PRESCRIPTION: int { NONE, HURLEY, BELCZYNSKI2010, FLEXIBLE2023 }; +enum class MASS_LOSS_PRESCRIPTION: int { NONE, HURLEY, VINK }; const COMPASUnorderedMap MASS_LOSS_PRESCRIPTION_LABEL = { - { MASS_LOSS_PRESCRIPTION::NONE, "NONE" }, - { MASS_LOSS_PRESCRIPTION::HURLEY, "HURLEY" }, - { MASS_LOSS_PRESCRIPTION::BELCZYNSKI2010, "BELCZYNSKI2010" }, - { MASS_LOSS_PRESCRIPTION::FLEXIBLE2023, "FLEXIBLE2023"} + { MASS_LOSS_PRESCRIPTION::NONE, "NONE" }, + { MASS_LOSS_PRESCRIPTION::HURLEY, "HURLEY" }, + { MASS_LOSS_PRESCRIPTION::VINK, "VINK" } }; @@ -1511,6 +1463,7 @@ const std::initializer_list ALL_HERTZSPRUNG_GAP = { STELLAR_TYPE::NAKED_HELIUM_STAR_HERTZSPRUNG_GAP }; + // (convenience) initializer list for COMPACT OBJECTS const std::initializer_list COMPACT_OBJECTS = { STELLAR_TYPE::HELIUM_WHITE_DWARF, @@ -1574,12 +1527,12 @@ enum class MASS_CUTOFF: int { // Symbolic names for mass loss rate type enum class MASS_LOSS_TYPE: int { NONE, - OB, - VMS, - GB, - RSG, - WR, - LBV + NIEUWENHUIJZEN_DE_JAGER, + KUDRITZKI_REIMERS, + VASSILIADIS_WOOD, + WOLF_RAYET_LIKE, + VINK, + LUMINOUS_BLUE_VARIABLE }; diff --git a/src/yaml.h b/src/yaml.h index 83a7def68..1f12b9464 100644 --- a/src/yaml.h +++ b/src/yaml.h @@ -283,10 +283,6 @@ namespace yaml { " --initial-mass-function", " --luminous-blue-variable-prescription", " --mass-loss-prescription", - " --OB-mass-loss", - " --RSG-mass-loss", - " --VMS-mass-loss", - " --WR-mass-loss", " --metallicity-distribution", " --pulsational-pair-instability-prescription", "", From 51e8ed1833d3cbda65c6ea991a1a49774efb2d16 Mon Sep 17 00:00:00 2001 From: Reinhold Willcox Date: Tue, 14 Nov 2023 16:42:19 +0100 Subject: [PATCH 23/32] cleaned up dev-git-workflow --- .../pages/Getting started/dev-git-workflow.rst | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/online-docs/pages/Getting started/dev-git-workflow.rst b/online-docs/pages/Getting started/dev-git-workflow.rst index 9709d57b9..60eab0c17 100644 --- a/online-docs/pages/Getting started/dev-git-workflow.rst +++ b/online-docs/pages/Getting started/dev-git-workflow.rst @@ -15,7 +15,7 @@ Contents of this document `Day to Day Commands <#day-to-day-commands>`__ -`Lifetime of a Project <#lifetime-of-a-project>`__ +`Lifetime of a New Feature <#lifetime-of-a-new-feature>`__ `COMPAS Git Workflow <#the-compas-git-workflow>`__ @@ -27,7 +27,7 @@ Contents of this document Introduction -============ +------------ Git & Github for COMPAS developers @@ -117,7 +117,7 @@ which is in common use in industry. Getting Set Up -============== +-------------- **Step-by-step directions for how to configure your local and remote git repositories** @@ -215,6 +215,7 @@ homepage `__ for an up-to-date list). Fork the main repo +------------------ As a COMPAS developer, you are highly encouraged to create your own @@ -262,7 +263,7 @@ The ```` is your choice, but should be informative, e.g Day to Day commands -=================== +------------------- Basic commands for navigating local git @@ -461,6 +462,7 @@ preferred text editor. Deleting branches +----------------- You should become comfortable deleting branches, or else your repos @@ -607,6 +609,7 @@ which will have an output that looks similar to: git pull +-------- If you have a branch which is "behind" the remote branch it is tracking @@ -697,7 +700,7 @@ called the "upstream" branch) with: Lifetime of a New Feature -========================= +------------------------- New feature branches @@ -808,7 +811,7 @@ before accepting it, so keep an eye on the pull request conversation. The COMPAS Git Workflow -======================= +----------------------- The above sections go over many of the available git commands that you might find useful. @@ -868,7 +871,7 @@ comparisons of key plots from different papers. Terminology -=========== +----------- - **Commit**: A single commit records a collection of edits to one or more files, with an associated commit message. From 8076e3f54e27f55688949e292a67a824e7247389 Mon Sep 17 00:00:00 2001 From: Reinhold Willcox Date: Wed, 15 Nov 2023 11:19:39 +0100 Subject: [PATCH 24/32] renamed G_SN to G_km_Msol_s and G1 to G_AU_Msol_yr --- src/BaseBinaryStar.cpp | 19 +++++++++++-------- src/BaseBinaryStar.h | 8 ++++---- src/BinaryConstituentStar.cpp | 4 ++-- src/constants.h | 4 ++-- src/utils.cpp | 2 +- 5 files changed, 20 insertions(+), 17 deletions(-) diff --git a/src/BaseBinaryStar.cpp b/src/BaseBinaryStar.cpp index c590e2883..3675a21c0 100644 --- a/src/BaseBinaryStar.cpp +++ b/src/BaseBinaryStar.cpp @@ -1220,7 +1220,7 @@ bool BaseBinaryStar::ResolveSupernova() { double aPrev_2 = aPrev * aPrev; double aPrev_3 = aPrev_2 * aPrev; - double omega = std::sqrt(G_SN * totalMassPrev / aPrev_3); // rad/s - Keplerian orbital frequency + double omega = std::sqrt(G_km_Msol_s * totalMassPrev / aPrev_3); // rad/s - Keplerian orbital frequency Vector3d separationVectorPrev = Vector3d(aPrev * (cosEccAnomaly - eccentricityPrev), aPrev * (sinEccAnomaly) * sqrt1MinusEccPrevSquared, @@ -1234,7 +1234,8 @@ bool BaseBinaryStar::ResolveSupernova() { Vector3d orbitalAngularMomentumVectorPrev = cross(separationVectorPrev, relativeVelocityVectorPrev); // km^2 s^-1 - Specific orbital angular momentum vector - Vector3d eccentricityVectorPrev = cross(relativeVelocityVectorPrev, orbitalAngularMomentumVectorPrev) / (G_SN * totalMassPrev) - separationVectorPrev.hat; // -- - Laplace-Runge-Lenz vector (magnitude = eccentricity) + Vector3d eccentricityVectorPrev = cross(relativeVelocityVectorPrev, orbitalAngularMomentumVectorPrev) / + (G_km_Msol_s * totalMassPrev) - separationVectorPrev.hat; // -- - Laplace-Runge-Lenz vector (magnitude = eccentricity) m_OrbitalVelocityPreSN = relativeVelocityVectorPrev.mag; // km/s - Set the Pre-SN orbital velocity and m_uK = m_Supernova->SN_KickMagnitude() / m_OrbitalVelocityPreSN; // -- - Dimensionless kick magnitude @@ -1268,11 +1269,12 @@ bool BaseBinaryStar::ResolveSupernova() { m_OrbitalAngularMomentumVector = orbitalAngularMomentumVector / orbitalAngularMomentum; // set unit vector here to make printing out the inclination vector easier Vector3d eccentricityVector = cross(relativeVelocityVector, orbitalAngularMomentumVector) / - (G_SN * totalMass) - separationVectorPrev / separationPrev; // PostSN Laplace-Runge-Lenz vector + (G_km_Msol_s * totalMass) - separationVectorPrev / separationPrev; // PostSN Laplace-Runge-Lenz vector m_Eccentricity = eccentricityVector.mag; // PostSN eccentricity double eccSquared = m_Eccentricity * m_Eccentricity; // useful function of eccentricity - double semiMajorAxis_km = (orbitalAngularMomentum * orbitalAngularMomentum) / (G_SN * totalMass * (1.0 - eccSquared)); // km - PostSN semi-major axis + double semiMajorAxis_km = (orbitalAngularMomentum * orbitalAngularMomentum) / + (G_km_Msol_s * totalMass * (1.0 - eccSquared)); // km - PostSN semi-major axis m_SemiMajorAxis = semiMajorAxis_km * KM_TO_AU; // AU - PostSN semi-major axis // Note: similar to above, @@ -1288,7 +1290,7 @@ bool BaseBinaryStar::ResolveSupernova() { m_Unbound = true; // Calculate the asymptotic Center of Mass velocity - double relativeVelocityAtInfinity = (G_SN*totalMass/orbitalAngularMomentum) * std::sqrt(eccSquared - 1.0); + double relativeVelocityAtInfinity = (G_km_Msol_s*totalMass/orbitalAngularMomentum) * std::sqrt(eccSquared - 1.0); Vector3d relativeVelocityVectorAtInfinity = relativeVelocityAtInfinity * (-1.0 * (eccentricityVector.hat / m_Eccentricity) + std::sqrt(1.0 - 1.0 / eccSquared) * cross(orbitalAngularMomentumVector.hat, eccentricityVector.hat)); @@ -1722,7 +1724,7 @@ double BaseBinaryStar::CalculateMassTransferOrbit(const double p double massD = p_DonorMass; // donor mass double massAtimesMassD = massA * massD; // accretor mass * donor mass double massAplusMassD = massA + massD; // accretor mass + donor mass - double jOrb = (massAtimesMassD / massAplusMassD) * std::sqrt(semiMajorAxis * G1 * massAplusMassD); // orbital angular momentum + double jOrb = (massAtimesMassD / massAplusMassD) * std::sqrt(semiMajorAxis * G_AU_Msol_yr * massAplusMassD); // orbital angular momentum double jLoss; // specific angular momentum carried away by non-conservative mass transfer if (utils::Compare(p_DeltaMassDonor, 0.0) < 0) { // mass loss from donor? @@ -2088,7 +2090,7 @@ double BaseBinaryStar::CalculateTotalEnergy(const double p_SemiMajorAxis, double Is1 = ks1 * m1 * R1 * R1 * RSOL_TO_AU_2; double Is2 = ks2 * m2 * R2 * R2 * RSOL_TO_AU_2; - return (0.5 * Is1 * w1 * w1) + (0.5 * Is2 * w2 * w2) - (0.5 * G1 * m1 * m2 / p_SemiMajorAxis); + return (0.5 * Is1 * w1 * w1) + (0.5 * Is2 * w2 * w2) - (0.5 * G_AU_Msol_yr * m1 * m2 / p_SemiMajorAxis); } @@ -2329,7 +2331,8 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { m_Star1->SetOmega(m_Omega); // synchronise star 1 m_Star2->SetOmega(m_Omega); // synchronise star 2 - m_SemiMajorAxis = std::cbrt(G1) * std::cbrt((m_Star1->Mass() + m_Star2->Mass())) / PPOW(m_Omega, 2.0 / 3.0); // re-calculate semi-major axis + m_SemiMajorAxis = std::cbrt(G_AU_Msol_yr) * std::cbrt((m_Star1->Mass() + m_Star2->Mass())) / + PPOW(m_Omega, 2.0 / 3.0); // re-calculate semi-major axis m_Eccentricity = 0.0; // circularise m_TotalAngularMomentum = CalculateAngularMomentum(); // re-calculate total angular momentum diff --git a/src/BaseBinaryStar.h b/src/BaseBinaryStar.h index 4868d929d..0386bfebf 100644 --- a/src/BaseBinaryStar.h +++ b/src/BaseBinaryStar.h @@ -220,7 +220,7 @@ class BaseBinaryStar { bool MergesInHubbleTime() const { return m_Flags.mergesInHubbleTime; } double Omega() const { return m_Omega; } bool OptimisticCommonEnvelope() const { return m_CEDetails.optimisticCE; } - double OrbitalAngularVelocity() const { return std::sqrt(G1 * (m_Star1->Mass() + m_Star2->Mass()) / (m_SemiMajorAxis * m_SemiMajorAxis * m_SemiMajorAxis)); } // rads/year + double OrbitalAngularVelocity() const { return std::sqrt(G_AU_Msol_yr * (m_Star1->Mass() + m_Star2->Mass()) / (m_SemiMajorAxis * m_SemiMajorAxis * m_SemiMajorAxis)); } // rads/year double OrbitalVelocityPreSN() const { return m_OrbitalVelocityPreSN; } double Periastron() const { return m_SemiMajorAxis * (1.0 - m_Eccentricity); } double PeriastronRsol() const { return Periastron() * AU_TO_RSOL; } @@ -456,11 +456,11 @@ class BaseBinaryStar { double CalculateOrbitalAngularMomentum(const double p_Star1Mass, const double p_Star2Mass, const double p_SemiMajorAxis, - const double p_Eccentricity) const { return ((p_Star1Mass * p_Star2Mass) / (p_Star1Mass + p_Star2Mass)) * std::sqrt(G1 * (p_Star1Mass + p_Star2Mass) * p_SemiMajorAxis * (1.0 - (p_Eccentricity * p_Eccentricity))); } + const double p_Eccentricity) const { return ((p_Star1Mass * p_Star2Mass) / (p_Star1Mass + p_Star2Mass)) * std::sqrt(G_AU_Msol_yr * (p_Star1Mass + p_Star2Mass) * p_SemiMajorAxis * (1.0 - (p_Eccentricity * p_Eccentricity))); } double CalculateOrbitalEnergy(const double p_Mu, const double p_Mass, - const double p_SemiMajorAxis) const { return -(G1 * p_Mu * p_Mass) / (2.0 * p_SemiMajorAxis); } + const double p_SemiMajorAxis) const { return -(G_AU_Msol_yr * p_Mu * p_Mass) / (2.0 * p_SemiMajorAxis); } double CalculateZetaRocheLobe(const double p_jLoss) const; @@ -662,7 +662,7 @@ class BaseBinaryStar { // function: (I_1 + I_2) Omega + L(Omega) - p_Ltot = 0 // where L(Omega) = b*Omega(-1/3) double a = p_I1 + p_I2; // I_1 + I_2 - double b = PPOW(G1, 2.0 / 3.0) * p_M1 * p_M2 / std::cbrt(p_M1 + p_M2); + double b = PPOW(G_AU_Msol_yr, 2.0 / 3.0) * p_M1 * p_M2 / std::cbrt(p_M1 + p_M2); double c = -p_Ltot; auto func = [a, b, c](double x) -> double { return (a * x) + (b / std::cbrt(x)) + c; }; // functor diff --git a/src/BinaryConstituentStar.cpp b/src/BinaryConstituentStar.cpp index 154e18cd3..613fb4285 100644 --- a/src/BinaryConstituentStar.cpp +++ b/src/BinaryConstituentStar.cpp @@ -358,7 +358,7 @@ double BinaryConstituentStar::CalculateCircularisationTimescale(const double p_S double rOverAPow21Over2 = rOverAPow10 * rOverA * std::sqrt(rOverA); // sqrt() is faster than pow() double secondOrderTidalCoeff = 1.592E-09 * PPOW(Mass(), 2.84); // aka E_2. - double freeFallFactor = std::sqrt(G1 * Mass() / rInAUPow3); + double freeFallFactor = std::sqrt(G_AU_Msol_yr * Mass() / rInAUPow3); timescale = 1.0 / ((21.0 / 2.0) * freeFallFactor * q2 * PPOW(1.0 + q2, 11.0/6.0) * secondOrderTidalCoeff * rOverAPow21Over2); } break; @@ -408,7 +408,7 @@ double BinaryConstituentStar::CalculateSynchronisationTimescale(const double p_S double e2 = 1.592E-9 * PPOW(Mass(), 2.84); // second order tidal coefficient (a.k.a. E_2) double rAU = Radius() * RSOL_TO_AU; double rAU_3 = rAU * rAU * rAU; - double freeFallFactor = std::sqrt(G1 * Mass() / rAU_3); + double freeFallFactor = std::sqrt(G_AU_Msol_yr * Mass() / rAU_3); timescale = 1.0 / (coeff2 * freeFallFactor * gyrationRadiusSquared_1 * q2 * q2 * PPOW(1.0 + q2, 5.0 / 6.0) * e2 * PPOW(rOverA, 17.0 / 2.0)); } break; diff --git a/src/constants.h b/src/constants.h index 59bc67515..33f275ad1 100755 --- a/src/constants.h +++ b/src/constants.h @@ -247,8 +247,8 @@ constexpr double HUBBLE_TIME = 1 / H0SI; constexpr double G = 6.67E-11; // Gravitational constant in m^3 kg^-1 s^-2 (more accurately known as G M_sol) constexpr double G_CGS = 6.6743E-8; // Gravitational constant in cm^3 g^-1 s^-2 -constexpr double G1 = 4.0 * PI_2; // Gravitational constant in AU^3 Msol^-1 yr^-2 -constexpr double G_SN = G * 1.0E-9 / KG_TO_MSOL; // Gravitational constant in km^3 Msol^-1 s^-2, for use in the ResolveSupernova() function +constexpr double G_AU_Msol_yr = 4.0 * PI_2; // Gravitational constant in AU^3 Msol^-1 yr^-2 +constexpr double G_km_Msol_s = G * 1.0E-9 / KG_TO_MSOL; // Gravitational constant in km^3 Msol^-1 s^-2, for use in the ResolveSupernova() function constexpr double G_SOLAR_YEAR = 3.14E7; // Gravitational constant in Lsol Rsol yr Msol^-2 for calculating photon tiring limit constexpr double RSOL = 6.957E8; // Solar Radius (in m) diff --git a/src/utils.cpp b/src/utils.cpp index 063f9710e..47103d267 100644 --- a/src/utils.cpp +++ b/src/utils.cpp @@ -233,7 +233,7 @@ namespace utils { double ConvertPeriodInDaysToSemiMajorAxisInAU(const double p_Mass1, const double p_Mass2, const double p_Period) { double a_cubed_SI_top = G * ((p_Mass1 * MSOL_TO_KG) + (p_Mass2 * MSOL_TO_KG)) * p_Period * p_Period * SECONDS_IN_DAY * SECONDS_IN_DAY; - double a_cubed_SI = a_cubed_SI_top / G1; + double a_cubed_SI = a_cubed_SI_top / G_AU_Msol_yr; double a_SI = std::cbrt(a_cubed_SI); return a_SI / AU; From e8edbe522861d30cbe5125c4b0bc6a6cfb01cf15 Mon Sep 17 00:00:00 2001 From: Reinhold Willcox Date: Wed, 15 Nov 2023 11:26:17 +0100 Subject: [PATCH 25/32] changed comment for G_km_Msol_s to remove specific reference to Supernova code --- src/constants.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constants.h b/src/constants.h index 33f275ad1..053230569 100755 --- a/src/constants.h +++ b/src/constants.h @@ -248,7 +248,7 @@ constexpr double HUBBLE_TIME = 1 / H0SI; constexpr double G = 6.67E-11; // Gravitational constant in m^3 kg^-1 s^-2 (more accurately known as G M_sol) constexpr double G_CGS = 6.6743E-8; // Gravitational constant in cm^3 g^-1 s^-2 constexpr double G_AU_Msol_yr = 4.0 * PI_2; // Gravitational constant in AU^3 Msol^-1 yr^-2 -constexpr double G_km_Msol_s = G * 1.0E-9 / KG_TO_MSOL; // Gravitational constant in km^3 Msol^-1 s^-2, for use in the ResolveSupernova() function +constexpr double G_km_Msol_s = G * 1.0E-9 / KG_TO_MSOL; // Gravitational constant in km^3 Msol^-1 s^-2 constexpr double G_SOLAR_YEAR = 3.14E7; // Gravitational constant in Lsol Rsol yr Msol^-2 for calculating photon tiring limit constexpr double RSOL = 6.957E8; // Solar Radius (in m) From fefd0614d9dcee9a6885729249a873b3eea11000 Mon Sep 17 00:00:00 2001 From: Mike Lau Date: Mon, 20 Nov 2023 00:42:30 +0100 Subject: [PATCH 26/32] (docs) correct typo in COMPAS_ROOT_DIR environment variable --- .../pages/User guide/Running COMPAS/running-via-python.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/online-docs/pages/User guide/Running COMPAS/running-via-python.rst b/online-docs/pages/User guide/Running COMPAS/running-via-python.rst index 6f774e467..bca32fc3b 100644 --- a/online-docs/pages/User guide/Running COMPAS/running-via-python.rst +++ b/online-docs/pages/User guide/Running COMPAS/running-via-python.rst @@ -6,7 +6,7 @@ specify the values of the program options. An example Python script is provided in the COMPAS suite on github: ``runSubmit.py``. Additionally, the default COMPAS options are specified on ``compasConfigDefault.yaml``. Users should copy the ``runSubmit.py`` and ``runSubmit.py`` scripts and modify the ``compasConfigDefault.yaml`` copy to match their experimental requirements. Refer to the :doc:`Getting started guide <../../Getting started/getting-started>` for more details. -To run COMPAS via Python using the ``runSubmit.py`` script provided, set the shell environment variable ``COMPAS-ROOT-DIR`` +To run COMPAS via Python using the ``runSubmit.py`` script provided, set the shell environment variable ``COMPAS_ROOT_DIR`` to the parent directory of the directory in which the COMPAS executable resides, then type `python /path-to-runSubmit/runSubmit.py`. For example, for Ubuntu Linux, type:: From bd107b9a645a914b1e5eed473a039e5837abc49e Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Mon, 20 Nov 2023 18:41:02 +1100 Subject: [PATCH 27/32] Changes for review --- src/BaseBinaryStar.cpp | 90 ++++++++++++++++++++++------------- src/BaseBinaryStar.h | 2 +- src/BaseStar.cpp | 6 +-- src/BaseStar.h | 1 - src/BinaryConstituentStar.cpp | 25 +++++----- src/NS.cpp | 38 +++++++-------- src/NS.h | 1 + src/changelog.h | 5 +- src/constants.h | 11 +++-- src/utils.cpp | 17 +++---- 10 files changed, 110 insertions(+), 86 deletions(-) diff --git a/src/BaseBinaryStar.cpp b/src/BaseBinaryStar.cpp index 3675a21c0..f428bdb87 100644 --- a/src/BaseBinaryStar.cpp +++ b/src/BaseBinaryStar.cpp @@ -313,7 +313,7 @@ void BaseBinaryStar::SetRemainingValues() { } // star 2 - if (utils::Compare(m_Star1->Omega(), m_Star2->OmegaCHE()) >= 0) { // star 2 CH? + if (utils::Compare(m_Star2->Omega(), m_Star2->OmegaCHE()) >= 0) { // star 2 CH? if (m_Star2->StellarType() != STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS) (void)m_Star2->SwitchTo(STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS, true); // yes, switch if not already Chemically Homogeneous } else if (m_Star2->MZAMS() <= 0.7) { // no - MS - initial mass determines actual type JR: don't use utils::Compare() here @@ -1290,7 +1290,7 @@ bool BaseBinaryStar::ResolveSupernova() { m_Unbound = true; // Calculate the asymptotic Center of Mass velocity - double relativeVelocityAtInfinity = (G_km_Msol_s*totalMass/orbitalAngularMomentum) * std::sqrt(eccSquared - 1.0); + double relativeVelocityAtInfinity = (G_km_Msol_s*totalMass/orbitalAngularMomentum) * std::sqrt(eccSquared - 1.0); Vector3d relativeVelocityVectorAtInfinity = relativeVelocityAtInfinity * (-1.0 * (eccentricityVector.hat / m_Eccentricity) + std::sqrt(1.0 - 1.0 / eccSquared) * cross(orbitalAngularMomentumVector.hat, eccentricityVector.hat)); @@ -1520,7 +1520,7 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { if( OPTIONS->CommonEnvelopeFormalism() == CE_FORMALISM::ENERGY ) { double k1 = m_Star1->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (lambda1 * alphaCE)) * m_Star1->Mass() * m_MassEnv1 / m_Star1->Radius(); double k2 = m_Star2->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (lambda2 * alphaCE)) * m_Star2->Mass() * m_MassEnv2 / m_Star2->Radius(); - double k3 = m_Star1->Mass() * m_Star2->Mass() / periastronRsol; //assumes immediate circularisation at periastron at start of CE + double k3 = m_Star1->Mass() * m_Star2->Mass() / periastronRsol; // assumes immediate circularisation at periastron at start of CE double k4 = (m_Mass1Final * m_Mass2Final); double aFinalRsol = k4 / (k1 + k2 + k3); m_SemiMajorAxis = aFinalRsol * RSOL_TO_AU; @@ -1538,7 +1538,7 @@ void BaseBinaryStar::ResolveCommonEnvelopeEvent() { // Stage 1: convective envelope removal on a dynamical timescale; assumes lambda = 1.5, motivated by bottom panel of Figure 3 of Hirai & Mandel 2022, including internal energy double k1 = m_Star1->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (1.5 * alphaCE)) * m_Star1->Mass() * convectiveEnvelopeMass1 / m_Star1->Radius(); double k2 = m_Star2->IsOneOf(COMPACT_OBJECTS) ? 0.0 : (2.0 / (1.5 * alphaCE)) * m_Star2->Mass() * convectiveEnvelopeMass2 / m_Star2->Radius(); - double k3 = m_Star1->Mass() * m_Star2->Mass() / periastronRsol; //assumes immediate circularisation at periastron at start of CE + double k3 = m_Star1->Mass() * m_Star2->Mass() / periastronRsol; // assumes immediate circularisation at periastron at start of CE double k4 = (endOfFirstStageMass1 * endOfFirstStageMass2); double aFinalRsol = k4 / (k1 + k2 + k3); m_SemiMajorAxis = aFinalRsol*RSOL_TO_AU; @@ -2264,50 +2264,50 @@ void BaseBinaryStar::ResolveMassChanges() { */ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { - CalculateMassTransfer(p_Dt); // calculate mass transfer if necessary + CalculateMassTransfer(p_Dt); // calculate mass transfer if necessary - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MT); // print (log) detailed output + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MT); // print (log) detailed output - CalculateWindsMassLoss(); // calculate mass loss dues to winds + CalculateWindsMassLoss(); // calculate mass loss dues to winds - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_WINDS); // print (log) detailed output + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_WINDS); // print (log) detailed output - if ((m_CEDetails.CEEnow || StellarMerger()) && // CEE or merger? - !(OPTIONS->CHEMode() != CHE_MODE::NONE && HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS}))) { // yes - avoid CEE if CH+CH + if ((m_CEDetails.CEEnow || StellarMerger()) && // CEE or merger? + !(OPTIONS->CHEMode() != CHE_MODE::NONE && HasTwoOf({STELLAR_TYPE::CHEMICALLY_HOMOGENEOUS}))) { // yes - avoid CEE if CH+CH - ResolveCommonEnvelopeEvent(); // resolve CEE - immediate event - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_CEE); // print (log) detailed output + ResolveCommonEnvelopeEvent(); // resolve CEE - immediate event + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_CEE); // print (log) detailed output } else if (m_Star1->IsSNevent() || m_Star2->IsSNevent()) { - EvaluateSupernovae(); // evaluate supernovae (both stars) - immediate event - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output + EvaluateSupernovae(); // evaluate supernovae (both stars) - immediate event + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output if (HasOneOf({ STELLAR_TYPE::NEUTRON_STAR })) { - (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters + (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters } } else { - ResolveMassChanges(); // apply mass loss and mass transfer as necessary - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MASS_RESOLUTION); // print (log) detailed output + ResolveMassChanges(); // apply mass loss and mass transfer as necessary + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MASS_RESOLUTION); // print (log) detailed output - if (HasStarsTouching()) { // if stars emerged from mass transfer as touching, it's a merger + if (HasStarsTouching()) { // if stars emerged from mass transfer as touching, it's a merger m_Flags.stellarMerger = true; // Set Roche lobe flags for both stars so that they show correct RLOF status - m_Star1->SetRocheLobeFlags(m_CEDetails.CEEnow, m_SemiMajorAxis, m_Eccentricity); // set Roche lobe flags for star1 - m_Star2->SetRocheLobeFlags(m_CEDetails.CEEnow, m_SemiMajorAxis, m_Eccentricity); // set Roche lobe flags for star2 - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MASS_RESOLUTION_MERGER); // print (log) detailed output + m_Star1->SetRocheLobeFlags(m_CEDetails.CEEnow, m_SemiMajorAxis, m_Eccentricity); // set Roche lobe flags for star1 + m_Star2->SetRocheLobeFlags(m_CEDetails.CEEnow, m_SemiMajorAxis, m_Eccentricity); // set Roche lobe flags for star2 + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_MASS_RESOLUTION_MERGER); // print (log) detailed output } } if ((m_Star1->IsSNevent() || m_Star2->IsSNevent())) { - EvaluateSupernovae(); // evaluate supernovae (both stars) if mass changes are responsible for a supernova - (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output + EvaluateSupernovae(); // evaluate supernovae (both stars) if mass changes are responsible for a supernova + (void)PrintDetailedOutput(m_Id, BSE_DETAILED_RECORD_TYPE::POST_SN); // print (log) detailed output if (HasOneOf({ STELLAR_TYPE::NEUTRON_STAR })) { - (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters + (void)PrintPulsarEvolutionParameters(PULSAR_RECORD_TYPE::DEFAULT); // print (log) pulsar evolution parameters } } - CalculateEnergyAndAngularMomentum(); // perform energy and angular momentum calculations + CalculateEnergyAndAngularMomentum(); // perform energy and angular momentum calculations if (OPTIONS->EnableTides() && !m_Unbound) { @@ -2326,20 +2326,46 @@ void BaseBinaryStar::EvaluateBinary(const double p_Dt) { m_TotalAngularMomentum, m_Omega); - if (m_Omega > 0.0) { // root found? + if (m_Omega >= 0.0) { // root found? - m_Star1->SetOmega(m_Omega); // synchronise star 1 - m_Star2->SetOmega(m_Omega); // synchronise star 2 + m_Star1->SetOmega(m_Omega); // synchronise star 1 + m_Star2->SetOmega(m_Omega); // synchronise star 2 - m_SemiMajorAxis = std::cbrt(G_AU_Msol_yr) * std::cbrt((m_Star1->Mass() + m_Star2->Mass())) / - PPOW(m_Omega, 2.0 / 3.0); // re-calculate semi-major axis - m_Eccentricity = 0.0; // circularise - m_TotalAngularMomentum = CalculateAngularMomentum(); // re-calculate total angular momentum + m_SemiMajorAxis = std::cbrt(G_AU_Msol_yr * (m_Star1->Mass() + m_Star2->Mass()) / m_Omega / m_Omega); // re-calculate semi-major axis + m_Eccentricity = 0.0; // circularise + m_TotalAngularMomentum = CalculateAngularMomentum(); // re-calculate total angular momentum // assign new values to "previous" values, for following timestep m_EccentricityPrev = m_Eccentricity; m_SemiMajorAxisPrev = m_SemiMajorAxis; } + else { // no (real) root found + + // no real root found - push the binary to a common envelope + // place the constiuent star closest to RLOF at RLOF and use that to + // calculate semi-major axis, then use that to calculate m_Omega + + double ratio1 = m_Star1->StarToRocheLobeRadiusRatio(m_SemiMajorAxis, m_Star1->Mass()); // star 1 ratio of radius to Roche lobe radius + double ratio2 = m_Star2->StarToRocheLobeRadiusRatio(m_SemiMajorAxis, m_Star2->Mass()); // star 2 ratio of radius to Roche lobe radius + + double radius; + double mass1; + double mass2; + if (ratio1 >= ratio2) { // star 1 closer to RLOF than star 2 (or same)? + radius = m_Star1->Radius(); // yes - use star 1 to calculate semi-major axis at RLOF + mass1 = m_Star1->Mass(); + mass2 = m_Star2->Mass(); + } + else { // no - star 2 closer to RLOF than star 1 + radius = m_Star2->Radius(); // use star 2 to calculate semi-major axis at RLOF + mass1 = m_Star2->Mass(); + mass2 = m_Star1->Mass(); + } + + m_Eccentricity = 0.0; // assume circular + m_SemiMajorAxis = radius * RSOL_TO_AU / CalculateRocheLobeRadius_Static(mass1, mass2); // new semi-major axis - should tip into CE + m_Omega = OrbitalAngularVelocity(); // m_Omega at new semi-major axis + } } m_Star1->UpdateMagneticFieldAndSpin(m_CEDetails.CEEnow, m_Dt * MYR_TO_YEAR * SECONDS_IN_YEAR, EPSILON_PULSAR); // update pulsar parameters for star1 diff --git a/src/BaseBinaryStar.h b/src/BaseBinaryStar.h index 0386bfebf..f6671dd8b 100644 --- a/src/BaseBinaryStar.h +++ b/src/BaseBinaryStar.h @@ -678,7 +678,7 @@ class BaseBinaryStar { catch(std::exception& e) { // catch generic boost root finding error root.first = -1.0; // set error return root.second = -1.0; - if (it < maxit) { // too many iterations? + if (it < maxit) { // not too many iterations? SHOW_ERROR(ERROR::ROOT_FINDER_FAILED, e.what()); // no - some other error - show it } } diff --git a/src/BaseStar.cpp b/src/BaseStar.cpp index 616d05c8a..a30c2fdac 100755 --- a/src/BaseStar.cpp +++ b/src/BaseStar.cpp @@ -141,7 +141,6 @@ BaseStar::BaseStar(const unsigned long int p_RandomSeed, m_DominantMassLossRate = MASS_LOSS_TYPE::NONE; m_Omega = m_OmegaZAMS; - m_AngularMomentum = DEFAULT_INITIAL_DOUBLE_VALUE; m_MinimumLuminosityOnPhase = DEFAULT_INITIAL_DOUBLE_VALUE; m_LBVphaseFlag = false; @@ -347,6 +346,7 @@ COMPAS_VARIABLE BaseStar::StellarPropertyValue(const T_ANY_PROPERTY p_Property) case ANY_STAR_PROPERTY::MDOT: value = Mdot(); break; case ANY_STAR_PROPERTY::MEAN_ANOMALY: value = SN_MeanAnomaly(); break; case ANY_STAR_PROPERTY::METALLICITY: value = Metallicity(); break; + case ANY_STAR_PROPERTY::MOMENT_OF_INERTIA: value = CalculateMomentOfInertia(); break; case ANY_STAR_PROPERTY::MZAMS: value = MZAMS(); break; case ANY_STAR_PROPERTY::NUCLEAR_TIMESCALE: value = CalculateNuclearTimescale(); break; case ANY_STAR_PROPERTY::OMEGA: value = Omega() / SECONDS_IN_YEAR; break; @@ -357,7 +357,7 @@ COMPAS_VARIABLE BaseStar::StellarPropertyValue(const T_ANY_PROPERTY p_Property) case ANY_STAR_PROPERTY::PULSAR_SPIN_FREQUENCY: value = Pulsar_SpinFrequency(); break; case ANY_STAR_PROPERTY::PULSAR_SPIN_PERIOD: value = Pulsar_SpinPeriod(); break; case ANY_STAR_PROPERTY::PULSAR_BIRTH_PERIOD: value = Pulsar_BirthPeriod(); break; - case ANY_STAR_PROPERTY::PULSAR_BIRTH_SPIN_DOWN_RATE: value = Pulsar_BirthSpinDownRate(); break; + case ANY_STAR_PROPERTY::PULSAR_BIRTH_SPIN_DOWN_RATE: value = Pulsar_BirthSpinDownRate(); break; case ANY_STAR_PROPERTY::RADIAL_EXPANSION_TIMESCALE: value = CalculateRadialExpansionTimescale(); break; case ANY_STAR_PROPERTY::RADIUS: value = Radius(); break; case ANY_STAR_PROPERTY::RANDOM_SEED: value = RandomSeed(); break; @@ -1094,7 +1094,7 @@ double BaseStar::CalculateLogBindingEnergyLoveridge(bool p_IsMassLoss) const { double BaseStar::CalculateLambdaLoveridgeEnergyFormalism(const double p_EnvMass, const double p_IsMassLoss) const { double bindingEnergy = PPOW(10.0, CalculateLogBindingEnergyLoveridge(p_IsMassLoss)); - return bindingEnergy > 0.0 ? (G_CGS * m_Mass * MSOL_TO_G * p_EnvMass * MSOL_TO_G) / (m_Radius * RSOL_TO_AU * AU_TO_CM * bindingEnergy) : 1E-20; + return bindingEnergy > 0.0 ? (G_CGS * m_Mass * MSOL_TO_G * p_EnvMass * MSOL_TO_G) / (m_Radius * RSOL_TO_AU * AU_TO_CM * bindingEnergy) : 1.0E-20; } diff --git a/src/BaseStar.h b/src/BaseStar.h index a9304cee1..a4ee52776 100644 --- a/src/BaseStar.h +++ b/src/BaseStar.h @@ -336,7 +336,6 @@ class BaseStar { MASS_LOSS_TYPE m_DominantMassLossRate; // Current dominant mass loss rate double m_Mu; // Current small envelope parameter mu double m_Omega; // Current angular frequency (yr^-1) - double m_AngularMomentum; // Current angular momentum double m_Radius; // Current radius (Rsol) double m_Tau; // Relative time double m_Temperature; // Current temperature (Tsol) diff --git a/src/BinaryConstituentStar.cpp b/src/BinaryConstituentStar.cpp index 613fb4285..1017c3ddf 100644 --- a/src/BinaryConstituentStar.cpp +++ b/src/BinaryConstituentStar.cpp @@ -343,9 +343,9 @@ double BinaryConstituentStar::CalculateCircularisationTimescale(const double p_S case ENVELOPE::CONVECTIVE: { // solve for stars with convective envelope, according to tides section (see Hurley et al. 2002, subsection 2.3.1) double tauConv = CalculateEddyTurnoverTimescale(); - double fConv = 1.0; // currently, as COMPAS doesn't have rotating stars tested, we set f_conv = 1 always. - double fConvOverTauConv = fConv / tauConv; - double rOverAPow8 = rOverA * rOverA * rOverA * rOverA * rOverA * rOverA * rOverA * rOverA; // use multiplication - pow() is slow + double fConv = 1.0; // currently, as COMPAS doesn't have rotating stars tested, we set f_conv = 1 always. + double fConvOverTauConv = fConv / tauConv; + double rOverAPow8 = rOverA * rOverA * rOverA * rOverA * rOverA * rOverA * rOverA * rOverA; // use multiplication - pow() is slow timescale = 1.0 / (fConvOverTauConv * ((Mass() - CoreMass()) / Mass()) * q2 * (1.0 + q2) * rOverAPow8); } break; @@ -357,10 +357,10 @@ double BinaryConstituentStar::CalculateCircularisationTimescale(const double p_S double rOverAPow10 = rOverA * rOverA * rOverA * rOverA * rOverA * rOverA * rOverA * rOverA * rOverA * rOverA; // use multiplication - pow() is slow double rOverAPow21Over2 = rOverAPow10 * rOverA * std::sqrt(rOverA); // sqrt() is faster than pow() - double secondOrderTidalCoeff = 1.592E-09 * PPOW(Mass(), 2.84); // aka E_2. - double freeFallFactor = std::sqrt(G_AU_Msol_yr * Mass() / rInAUPow3); + double secondOrderTidalCoeff = 1.592E-09 * PPOW(Mass(), 2.84); // aka E_2. + double freeFallFactor = std::sqrt(G_AU_Msol_yr * Mass() / rInAUPow3); - timescale = 1.0 / ((21.0 / 2.0) * freeFallFactor * q2 * PPOW(1.0 + q2, 11.0/6.0) * secondOrderTidalCoeff * rOverAPow21Over2); + timescale = 1.0 / ((21.0 / 2.0) * freeFallFactor * q2 * PPOW(1.0 + q2, 11.0/6.0) * secondOrderTidalCoeff * rOverAPow21Over2); } break; default: // all other envelope types (remnants?) @@ -399,7 +399,7 @@ double BinaryConstituentStar::CalculateSynchronisationTimescale(const double p_S double fConv = 1.0; // currently, as COMPAS doesn't have rotating stars tested, we set f_conv = 1 always. double kOverTc = (2.0 / 21.0) * (fConv / tauConv) * ((Mass() - CoreMass()) / Mass()); - timescale = 1.0 / (3.0 * kOverTc * q2 * gyrationRadiusSquared_1 * rOverA_6); + timescale = 1.0 / (3.0 * kOverTc * q2 * gyrationRadiusSquared_1 * rOverA_6); } break; case ENVELOPE::RADIATIVE: { // solve for stars with radiative envelope (see Hurley et al. 2002, subsection 2.3.2) @@ -410,7 +410,7 @@ double BinaryConstituentStar::CalculateSynchronisationTimescale(const double p_S double rAU_3 = rAU * rAU * rAU; double freeFallFactor = std::sqrt(G_AU_Msol_yr * Mass() / rAU_3); - timescale = 1.0 / (coeff2 * freeFallFactor * gyrationRadiusSquared_1 * q2 * q2 * PPOW(1.0 + q2, 5.0 / 6.0) * e2 * PPOW(rOverA, 17.0 / 2.0)); + timescale = 1.0 / (coeff2 * freeFallFactor * gyrationRadiusSquared_1 * q2 * q2 * PPOW(1.0 + q2, 5.0 / 6.0) * e2 * PPOW(rOverA, 17.0 / 2.0)); } break; default: // all other envelope types (remnants?) @@ -439,7 +439,7 @@ void BinaryConstituentStar::SetRocheLobeFlags(const bool p_CommonEnvelope, const double starToRocheLobeRadiusRatio = StarToRocheLobeRadiusRatio(p_SemiMajorAxis, p_Eccentricity); - if (utils::Compare(starToRocheLobeRadiusRatio, 1.0) >= 0) { // if star is equal to or larger than its Roche Lobe... + if (utils::Compare(starToRocheLobeRadiusRatio, 1.0) >= 0) { // if star is equal to or larger than its Roche Lobe... m_RLOFDetails.isRLOF = true; // ... it is currently Roche Lobe overflowing m_RLOFDetails.experiencedRLOF = true; // ... and for future checks, did Roche Lobe overflow } @@ -455,11 +455,10 @@ void BinaryConstituentStar::SetRocheLobeFlags(const bool p_CommonEnvelope, const * * @param [IN] p_SemiMajorAxis Semi major axis of the binary (in AU) * @param [IN] p_Eccentricity Eccentricity of the binary orbit - * @return Ratio of stars radius to its Roche lobe radius + * @return Ratio of star's radius to its Roche lobe radius */ -double BinaryConstituentStar::StarToRocheLobeRadiusRatio(const double p_SemiMajorAxis, const double p_Eccentricity) { - if ((utils::Compare(p_SemiMajorAxis, 0.0) <= 0) || (utils::Compare(p_Eccentricity, 1.0) > 0)) - return 0.0; // binary is unbound, so not in RLOF +double BinaryConstituentStar::StarToRocheLobeRadiusRatio(const double p_SemiMajorAxis, const double p_Eccentricity) { + if ((utils::Compare(p_SemiMajorAxis, 0.0) <= 0) || (utils::Compare(p_Eccentricity, 1.0) > 0)) return 0.0; // binary is unbound, so not in RLOF double rocheLobeRadius = BaseBinaryStar::CalculateRocheLobeRadius_Static(Mass(), m_Companion->Mass()); return (Radius() * RSOL_TO_AU) / (rocheLobeRadius * p_SemiMajorAxis * (1.0 - p_Eccentricity)); diff --git a/src/NS.cpp b/src/NS.cpp index 0b45843d3..8892d8c66 100755 --- a/src/NS.cpp +++ b/src/NS.cpp @@ -282,10 +282,11 @@ double NS::CalculateMomentOfInertiaCGS() const { // pow() is slow - use multiplication - double radius = m_Radius * RSOL_TO_KM; - double m_r = m_Mass / radius; + double r_km = m_Radius * RSOL_TO_KM; + double m_r = m_Mass / r_km; double m_r_4 = m_r * m_r * m_r * m_r; - double r_cm = m_Radius * KM_TO_CM; + + double r_cm = m_Radius * RSOL_TO_CM; double r_cm_2 = r_cm * r_cm; return 0.237 * m_Mass * MSOL_TO_G * r_cm_2 * (1.0 + (4.2 * m_r) + 90.0 * m_r_4); @@ -305,7 +306,7 @@ double NS::CalculateMomentOfInertiaCGS() const { * @param [IN] p_Omega Pulsar spin frequency. * @param [IN] p_MomentOfInteria Moment of Interia of the Neutron Star in kg m^2 * @param [IN] p_MagField Magnetic field in Tesla - * @param [IN] p_Radius Radius of the Neutron Star in metres + * @param [IN] p_Radius Radius of the Neutron Star in kilometres * @return Spin down rate (spin frequency derivative) of an isolated Neutron Star in s^(-2) */ double NS::CalculateSpinDownRate(const double p_Omega, const double p_MomentOfInteria, const double p_MagField, const double p_Radius) const { @@ -318,7 +319,7 @@ double NS::CalculateSpinDownRate(const double p_Omega, const double p_MomentOfIn double cgsMagField = p_MagField * TESLA_TO_GAUSS; // B field in G double magField_2 = cgsMagField * cgsMagField; constexpr double _8_PI_2 = 8.0 * PI_2; - constexpr double _3_C_3 = 3.0 * (C * 100.0) * (C * 100.0) * (C * 100.0); + constexpr double _3_C_3 = 3.0E6 * C * C * C; // 3.0 * (C * 100.0) * (C * 100.0) * (C * 100.0) double pDotTop = _8_PI_2 * radius_6 * magField_2; double pDotBottom = _3_C_3 * p_MomentOfInteria * period; double pDot = pDotTop / pDotBottom; // period derivative @@ -332,7 +333,7 @@ double NS::CalculateSpinDownRate(const double p_Omega, const double p_MomentOfIn * * Modifies the following class member variables: * - * m_AngularMomentum + * m_AngularMomentum_CGS * m_MomentOfInertia_CGS * m_PulsarDetails.birthPeriod * m_PulsarDetails.birthSpinDownRate @@ -348,15 +349,14 @@ void NS::CalculateAndSetPulsarParameters() { m_PulsarDetails.magneticField = PPOW(10.0, CalculatePulsarBirthMagneticField()) * GAUSS_TO_TESLA; // magnetic field in Gauss -> convert to Tesla m_PulsarDetails.spinPeriod = CalculatePulsarBirthSpinPeriod(); // spin period in ms m_PulsarDetails.spinFrequency = _2_PI / (m_PulsarDetails.spinPeriod * SECONDS_IN_MS); - m_PulsarDetails.birthPeriod = m_PulsarDetails.spinPeriod / 1000.0; // convert from ms to s + m_PulsarDetails.birthPeriod = m_PulsarDetails.spinPeriod * SECONDS_IN_MS; // convert from ms to s m_MomentOfInertia_CGS = CalculateMomentOfInertiaCGS(); // in CGS g cm^2 // Note we convert neutronStarMomentOfInertia from CGS to SI here - constexpr double factor = G_TO_KG * CM_TO_M * CM_TO_M; m_PulsarDetails.spinDownRate = CalculateSpinDownRate(m_PulsarDetails.spinFrequency, m_MomentOfInertia_CGS, m_PulsarDetails.magneticField, m_Radius * RSOL_TO_KM); m_PulsarDetails.birthSpinDownRate = m_PulsarDetails.spinDownRate; - m_AngularMomentum = _2_PI * m_MomentOfInertia_CGS / (m_PulsarDetails.spinPeriod * SECONDS_IN_MS) * factor; // in kg m^2 sec^-1 + m_AngularMomentum_CGS = m_MomentOfInertia_CGS * m_PulsarDetails.spinFrequency; // in CGS g cm^2 s^-1 } @@ -367,7 +367,7 @@ void NS::CalculateAndSetPulsarParameters() { * * Modifies the following class member variables: * - * m_AngularMomentum + * m_AngularMomentum_CGS * m_PulsarDetails.spinFrequency * m_PulsarDetails.magneticField * m_PulsarDetails.spinDownRate @@ -403,7 +403,7 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { double term1 = magFieldLowerLimit_G * magFieldLowerLimit_G * p_Stepsize; double term2 = tau * magFieldLowerLimit_G * ( m_PulsarDetails.magneticField * TESLA_TO_GAUSS - initialMagField_G); double term3 = (tau / 2.0) * (TESLA_TO_GAUSS * TESLA_TO_GAUSS * (m_PulsarDetails.magneticField * m_PulsarDetails.magneticField) - (initialMagField_G * initialMagField_G)); - double Psquared = 2 * constant2 * (term1 - term2 - term3) + (initialSpinPeriod * initialSpinPeriod); + double Psquared = 2.0 * constant2 * (term1 - term2 - term3) + (initialSpinPeriod * initialSpinPeriod); double P_f = std::sqrt(Psquared); m_PulsarDetails.spinFrequency = _2_PI / P_f; // pulsar spin frequency @@ -414,7 +414,7 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { double pDot = pDotTop / P_f; m_PulsarDetails.spinDownRate = -_2_PI * pDot / (P_f * P_f); - m_AngularMomentum = m_PulsarDetails.spinFrequency * m_MomentOfInertia_CGS; // angular momentum of star + m_AngularMomentum_CGS = m_PulsarDetails.spinFrequency * m_MomentOfInertia_CGS; // angular momentum of star in CGS } @@ -423,7 +423,7 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { * 1). * Modifies the following class member variables: * - * m_AngularMomentum + * m_AngularMomentum_CGS * m_PulsarDetails.spinFrequency * m_PulsarDetails.magneticField * m_PulsarDetails.spinDownRate @@ -444,8 +444,6 @@ void NS::SpinDownIsolatedPulsar(const double p_Stepsize) { */ void NS::UpdateMagneticFieldAndSpin(const bool p_CommonEnvelope, const bool p_RecycledNS, const double p_Stepsize, const double p_MassGainPerTimeStep, const double p_Epsilon) { - constexpr double unitsMoI = G_TO_KG * CM_TO_M * CM_TO_M; // converts CGS -> SI - double initialMagField = m_PulsarDetails.magneticField; // (in T) double magFieldLowerLimit = PPOW(10.0, OPTIONS->PulsarLog10MinimumMagneticField()) * GAUSS_TO_TESLA; double kappa = OPTIONS->PulsarMagneticFieldDecayMassscale() * MSOL_TO_KG; @@ -457,14 +455,14 @@ void NS::UpdateMagneticFieldAndSpin(const bool p_CommonEnvelope, const bool p_Re else if (utils::Compare(m_PulsarDetails.spinFrequency, _2_PI * 1000.0) < 0 && (p_RecycledNS || p_CommonEnvelope) && utils::Compare(p_MassGainPerTimeStep, 0.0) > 0) { - // his part of the code does pulsar recycling through acretion + // This part of the code does pulsar recycling through accretion // recycling happens for pulsar with spin period larger than 1 ms and in a binary system with mass transfer // the pulsar being recycled is either in a common envolope, or should have started the recycling process in previous time steps. double mass_kg = m_Mass * MSOL_TO_KG; // in kg - double r_m = m_Radius * RSOL_TO_KM * 1000.0; // in metres + double r_m = m_Radius * RSOL_TO_KM * KM_TO_M; // in metres - double MoI_SI = m_MomentOfInertia_CGS * unitsMoI; - double angularMomentum_SI = m_AngularMomentum * unitsMoI; + double MoI_SI = m_MomentOfInertia_CGS * CGS_SI; + double angularMomentum_SI = m_AngularMomentum_CGS * CGS_SI; double newPulsarMagneticField = (initialMagField - magFieldLowerLimit) * exp(-p_MassGainPerTimeStep / 1000.0 / kappa) + magFieldLowerLimit; @@ -492,7 +490,7 @@ void NS::UpdateMagneticFieldAndSpin(const bool p_CommonEnvelope, const bool p_Re m_PulsarDetails.magneticField = newPulsarMagneticField; m_PulsarDetails.spinFrequency = angularMomentum_SI / MoI_SI; m_PulsarDetails.spinDownRate = Jdot / MoI_SI; - m_AngularMomentum = angularMomentum_SI / unitsMoI; + m_AngularMomentum_CGS = angularMomentum_SI / CGS_SI; } else { SpinDownIsolatedPulsar(p_Stepsize); diff --git a/src/NS.h b/src/NS.h index ca46ca4f6..aad0adb40 100755 --- a/src/NS.h +++ b/src/NS.h @@ -60,6 +60,7 @@ class NS: virtual public BaseStar, public Remnants { CalculateAndSetPulsarParameters(); } + double m_AngularMomentum_CGS; // Current angular momentum in CGS - only required in NS class double m_MomentOfInertia_CGS; // MoI in CGS - only required in NS class diff --git a/src/changelog.h b/src/changelog.h index 869b76887..1d40d0903 100644 --- a/src/changelog.h +++ b/src/changelog.h @@ -1057,12 +1057,13 @@ // - Fixed a few typos, a little code cleanup. // 02.39.01 LC - Sep 01, 2023 - Defect repair: // - Fix for issue #945 - made HeSD SN types a sub-class of SNIA types. -// 02.40.00 JR - Oct 30, 2023 - Enhancement, a little cleanup: +// 02.41.00 JR - Nov 02, 2023 - Enhancement, a little cleanup: // - Added naive tides implementation. Functionality enabled with new option `--enable-tides`. Default is no tides. // - Fixed CalculateOrbitalAngularMomentum() (now uses eccentricity) // - Added links to online documentation to splash string +// - Constants 'G1' and 'G_SN' renamed to 'G_AU_Msol_yr' and 'G_km_Msol_s' respectively -const std::string VERSION_STRING = "02.40.00"; +const std::string VERSION_STRING = "02.41.00"; # endif // __changelog_h__ diff --git a/src/constants.h b/src/constants.h index 8c0aee696..34697a082 100755 --- a/src/constants.h +++ b/src/constants.h @@ -216,7 +216,9 @@ constexpr double KM_TO_AU = 1.0 / AU_TO_KM; // time constexpr double SECONDS_IN_YEAR = 31556926.0; // number of second in 1 year -constexpr double SECONDS_IN_DAY = SECONDS_IN_YEAR * 4.0 / 1461.0; // number of second in 1 day +constexpr double DAYS_IN_QUAD = 1461.0; // number of days in any 4-year period +constexpr double DAYS_IN_YEAR = DAYS_IN_QUAD / 4.0; // mean days per year (given DAYS_IN_QUAD) +constexpr double SECONDS_IN_DAY = SECONDS_IN_YEAR / DAYS_IN_YEAR; // number of second in 1 day constexpr double SECONDS_IN_MS = 1.0E-3; // number of second in 1 millisecond constexpr double SECONDS_IN_MYR = 31556926.0 * 1.0E6; // number of second in 1 Myr constexpr double MYR_TO_YEAR = 1.0E6; // convert Myr to year @@ -229,9 +231,12 @@ constexpr double JOULES_TO_ERG = 1.0E7; constexpr double TESLA_TO_GAUSS = 1.0E4; // convert Tesla to Gauss constexpr double GAUSS_TO_TESLA = 1.0 / TESLA_TO_GAUSS; // convert Gauss to Tesla +// systems +constexpr double CGS_SI = G_TO_KG * CM_TO_M * CM_TO_M; // convert CGS to SI + // constants -constexpr double _2_PI = M_PI * 2; // 2PI +constexpr double _2_PI = M_PI * 2.0; // 2PI constexpr double PI_2 = M_PI * M_PI; // PI squared constexpr double SQRT_M_2_PI = 0.79788456080286536; // sqrt(2/PI) constexpr double DEGREE = M_PI / 180.0; // 1 degree in radians @@ -315,7 +320,7 @@ constexpr double NEWTON_RAPHSON_EPSILON = 1.0E-5; constexpr double EPSILON_PULSAR = 1.0; // JR: todo: description -constexpr double MIN_HMXRB_STAR_TO_ROCHE_LOBE_RADIUS_RATIO = 0.8; // Minimum value of stellar radius | Roche Lobe radius for visible HMXRBs +constexpr double MIN_HMXRB_STAR_TO_ROCHE_LOBE_RADIUS_RATIO = 0.8; // Minimum value of stellar radius | Roche Lobe radius for visible HMXRBs constexpr double ADAPTIVE_RLOF_FRACTION_DONOR_GUESS = 0.001; // Fraction of donor mass to use as guess in BaseBinaryStar::MassLossToFitInsideRocheLobe() constexpr int ADAPTIVE_RLOF_MAX_ITERATIONS = 50; // Maximum number of iterations in BaseBinaryStar::MassLossToFitInsideRocheLobe() diff --git a/src/utils.cpp b/src/utils.cpp index 47103d267..3d04254dc 100644 --- a/src/utils.cpp +++ b/src/utils.cpp @@ -194,22 +194,22 @@ namespace utils { #ifdef COMPARE_GLOBAL_TOLERANCE if (p_Tolerance > 0.0) { // use tolerance passed? if (p_Absolute) { // yes - absolute tolerance? - return (std::abs(p_X - p_Y) <= p_Tolerance) ? 0 : (p_X < p_Y ? -1 : 1); // yes + return (fabs(p_X - p_Y) <= p_Tolerance) ? 0 : (p_X < p_Y ? -1 : 1); // yes } else { // no - relative tolerance - return (std::abs(p_X - p_Y) <= p_Tolerance * std::max(std::abs(p_X), fabs(p_Y))) ? 0 : (p_X < p_Y ? -1 : 1); + return (fabs(p_X - p_Y) <= p_Tolerance * std::max(fabs(p_X), fabs(p_Y))) ? 0 : (p_X < p_Y ? -1 : 1); } } else { // use global tolerance - return (std::abs(p_X - p_Y) <= std::max(FLOAT_TOLERANCE_ABSOLUTE, FLOAT_TOLERANCE_RELATIVE * std::max(std::abs(p_X), fabs(p_Y)))) ? 0 : (p_X < p_Y ? -1 : 1); + return (fabs(p_X - p_Y) <= std::max(FLOAT_TOLERANCE_ABSOLUTE, FLOAT_TOLERANCE_RELATIVE * std::max(fabs(p_X), fabs(p_Y)))) ? 0 : (p_X < p_Y ? -1 : 1); } #else if (p_Tolerance > 0.0) { // use tolerance passed? if (p_Absolute) { // yes - absolute tolerance? - return (std::abs(p_X - p_Y) <= p_Tolerance) ? 0 : (p_X < p_Y ? -1 : 1); // yes + return (fabs(p_X - p_Y) <= p_Tolerance) ? 0 : (p_X < p_Y ? -1 : 1); // yes } else { // no - relative tolerance - return (std::abs(p_X - p_Y) <= p_Tolerance * std::max(std::abs(p_X), fabs(p_Y))) ? 0 : (p_X < p_Y ? -1 : 1); + return (fabs(p_X - p_Y) <= p_Tolerance * std::max(fabs(p_X), fabs(p_Y))) ? 0 : (p_X < p_Y ? -1 : 1); } } else { // no tolerance @@ -231,12 +231,7 @@ namespace utils { * @return Semi-major axis in AU */ double ConvertPeriodInDaysToSemiMajorAxisInAU(const double p_Mass1, const double p_Mass2, const double p_Period) { - - double a_cubed_SI_top = G * ((p_Mass1 * MSOL_TO_KG) + (p_Mass2 * MSOL_TO_KG)) * p_Period * p_Period * SECONDS_IN_DAY * SECONDS_IN_DAY; - double a_cubed_SI = a_cubed_SI_top / G_AU_Msol_yr; - double a_SI = std::cbrt(a_cubed_SI); - - return a_SI / AU; + return return std::cbrt((p_Mass1 + m_Mass2) * p_Period * p_Period / DAYS_IN_YEAR / DAYS_IN_YEAR); } From 14ac5523be036fee059e494ab117eb0086d27154 Mon Sep 17 00:00:00 2001 From: jeffriley Date: Mon, 20 Nov 2023 18:46:28 +1100 Subject: [PATCH 28/32] Update whats-new.rst --- online-docs/pages/whats-new.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/online-docs/pages/whats-new.rst b/online-docs/pages/whats-new.rst index e15165878..2fd676ecf 100644 --- a/online-docs/pages/whats-new.rst +++ b/online-docs/pages/whats-new.rst @@ -6,7 +6,7 @@ Following is a brief list of important updates to the COMPAS code. A complete r **LATEST RELEASE** |br| -**02.30.00 Oct 30, 2023** +**02.41.00 Nov 02, 2023** * Added a naive tides implementation. * Added program option ``enable-tides`` to enable the tides implementation (default is ``false``). From 115d164a9e53b1777eea86c5b0257c48d8ca3cb8 Mon Sep 17 00:00:00 2001 From: jeffriley Date: Mon, 20 Nov 2023 18:47:03 +1100 Subject: [PATCH 29/32] Update whats-new.rst --- online-docs/pages/whats-new.rst | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/online-docs/pages/whats-new.rst b/online-docs/pages/whats-new.rst index 2fd676ecf..439a2c37e 100644 --- a/online-docs/pages/whats-new.rst +++ b/online-docs/pages/whats-new.rst @@ -11,6 +11,15 @@ Following is a brief list of important updates to the COMPAS code. A complete r * Added a naive tides implementation. * Added program option ``enable-tides`` to enable the tides implementation (default is ``false``). +**02.40.00 Oct 20, 2023** + +* Added ``FLEXIBLE2023`` as a new default, and ``BELCZYNSKI2010`` as a replacement for the previous ``VINK`` mass loss prescription. The following new sub-wrappers are overridden when selecting ``BELCZYNSKI2010``: +* Added ``--OB-mass-loss`` program option, applying to main sequence stars, with default ``VINK2021``, and options ``NONE``, ``VINK2001`` (previous default), ``BJORKLUND2022``, and ``KRTICKA2018``. +* Added ``--RSG-mass-loss`` program option, applying to stars below 8kK in giant branch stellar types, with default ``DECIN2023``, and options ``NONE``, ``VINISABHAHIT2023``, ``BEASOR2020``, ``YANG2023``, ``KEE2021``, ``NJ90`` (previous default). +* Added ``--VMS-mass-loss`` program option, applying to stars over 100 Msol, with default ``SABHAHIT2023``, and options ``NONE``, ``VINK2011``, and ``BESTENLEHNER2020``. +* Added ``--WR-mass-loss`` program option, with default ``SANDERVINK2023``, and options ``BELCZYNSKI2010``, and ``SHENAR2019``. +* Changed default value for option ``--wolf-rayet-multiplier`` from 0.1 to 1.0 + **02.39.00 Jul 4, 2023** * Added 'Evolution_Status' columns to both SSE and BSE default system parameters records - records final status of evolution (reason evolution stopped). From 47124f0abe6507fcb4d78b21732f193523983e18 Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Mon, 20 Nov 2023 18:49:28 +1100 Subject: [PATCH 30/32] Fixed typos --- src/utils.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/utils.cpp b/src/utils.cpp index 3d04254dc..17df9b1c6 100644 --- a/src/utils.cpp +++ b/src/utils.cpp @@ -231,7 +231,7 @@ namespace utils { * @return Semi-major axis in AU */ double ConvertPeriodInDaysToSemiMajorAxisInAU(const double p_Mass1, const double p_Mass2, const double p_Period) { - return return std::cbrt((p_Mass1 + m_Mass2) * p_Period * p_Period / DAYS_IN_YEAR / DAYS_IN_YEAR); + return std::cbrt((p_Mass1 + p_Mass2) * p_Period * p_Period / DAYS_IN_YEAR / DAYS_IN_YEAR); } From 60cb072e30e5cca021272dd2671489480c9fc3ab Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Mon, 20 Nov 2023 19:00:08 +1100 Subject: [PATCH 31/32] Fix changelog --- src/changelog.h | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/src/changelog.h b/src/changelog.h index 1d40d0903..e7ccc0694 100644 --- a/src/changelog.h +++ b/src/changelog.h @@ -1057,6 +1057,15 @@ // - Fixed a few typos, a little code cleanup. // 02.39.01 LC - Sep 01, 2023 - Defect repair: // - Fix for issue #945 - made HeSD SN types a sub-class of SNIA types. +// - Fix for issue #945 - made HeSD SN types a sub-class of SNIA types. +// +// 02.40.00 JDM - Sep 29, 2023 - Enhancement: +// - Added 'FLEXIBLE2023' option to --mass-loss-prescription. Recover previous defaults via 'BELCZYNSKI2010' option. this applies the following prescriptions: +// - Added --OB-mass-loss program option. +// - Added --RSG-mass-loss. +// - Added --VMS-mass-loss. +// - Added --WR-mass-loss. +// // 02.41.00 JR - Nov 02, 2023 - Enhancement, a little cleanup: // - Added naive tides implementation. Functionality enabled with new option `--enable-tides`. Default is no tides. // - Fixed CalculateOrbitalAngularMomentum() (now uses eccentricity) From 739c8f52f720851c9472cce575e406a5bad72b2f Mon Sep 17 00:00:00 2001 From: Jeff Riley Date: Tue, 21 Nov 2023 10:07:24 +1100 Subject: [PATCH 32/32] Check for passed tolerance taken outside ifdef in utils::Compare() --- src/utils.cpp | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/src/utils.cpp b/src/utils.cpp index 17df9b1c6..9c6507d04 100644 --- a/src/utils.cpp +++ b/src/utils.cpp @@ -191,7 +191,6 @@ namespace utils { * 1 indicates p_X is greater than p_Y */ int Compare(const double p_X, const double p_Y, const double p_Tolerance, const bool p_Absolute) { - #ifdef COMPARE_GLOBAL_TOLERANCE if (p_Tolerance > 0.0) { // use tolerance passed? if (p_Absolute) { // yes - absolute tolerance? return (fabs(p_X - p_Y) <= p_Tolerance) ? 0 : (p_X < p_Y ? -1 : 1); // yes @@ -201,21 +200,12 @@ namespace utils { } } else { // use global tolerance + #ifdef COMPARE_GLOBAL_TOLERANCE return (fabs(p_X - p_Y) <= std::max(FLOAT_TOLERANCE_ABSOLUTE, FLOAT_TOLERANCE_RELATIVE * std::max(fabs(p_X), fabs(p_Y)))) ? 0 : (p_X < p_Y ? -1 : 1); - } #else - if (p_Tolerance > 0.0) { // use tolerance passed? - if (p_Absolute) { // yes - absolute tolerance? - return (fabs(p_X - p_Y) <= p_Tolerance) ? 0 : (p_X < p_Y ? -1 : 1); // yes - } - else { // no - relative tolerance - return (fabs(p_X - p_Y) <= p_Tolerance * std::max(fabs(p_X), fabs(p_Y))) ? 0 : (p_X < p_Y ? -1 : 1); - } - } - else { // no tolerance return (p_X == p_Y) ? 0 : (p_X < p_Y ? -1 : 1); - } #endif + } }