diff --git a/.gitignore b/.gitignore index e7afe12..02cc876 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,10 @@ *._* synthpop/demo/*/*.png +synthpop/demo/*.png synthpop/demo/*/*.pdf synthpop/data/extinction synthpop/data/isochrones +run*.py outputfiles *.DS_Store diff --git a/TUTORIAL-spiseagen.ipynb b/TUTORIAL-spiseagen.ipynb new file mode 100644 index 0000000..4a949db --- /dev/null +++ b/TUTORIAL-spiseagen.ipynb @@ -0,0 +1,2505 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "832a172a-b16f-4e9f-8cad-666d2c6b5eb4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import synthpop \n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pdb\n", + "from astropy.table import vstack" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d97eb406-8463-44db-81db-7e68fb92afa1", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 8691 - Execution Date: 2026-01-09 02:13:46\n", + "\n", + "\n", + "################################ Settings #################################\n", + " 8694 - # reading default parameters from\n", + " 8699 - default_config_file = /System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/config_files/_default.synthpop_conf \n", + " 8702 - # read configuration from \n", + " 8704 - config_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/config_files/huston2025_defaults.synthpop_conf' \n", + "\n", + "\n", + "# copy the following to a config file to redo this model generation -------\n", + " 8706 - {\n", + " \"l_set\": null,\n", + " \"l_set_type\": null,\n", + " \"b_set\": null,\n", + " \"b_set_type\": null,\n", + " \"name_for_output\": \"mod2test\",\n", + " \"model_name\": \"Koshimoto2022\",\n", + " \"field_shape\": \"circle\",\n", + " \"field_scale\": null,\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 242505982,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 50000,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"SpiseaGenerator\",\n", + " \"extinction_map_kwargs\": {\n", + " \"name\": \"maps_from_dustmaps\",\n", + " \"dustmap_name\": \"marshall\"\n", + " },\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"SODC\",\n", + " \"R_V\": 2.5\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"SpiseaCluster\",\n", + " \"block_spisea_prints\": true\n", + " },\n", + " \"ifmr_kwargs\": {\n", + " \"name\": \"SpiseaIfmr\",\n", + " \"spisea_ifmr_name\": \"IFMR_N20_Sukhbold\"\n", + " },\n", + " \"multiplicity_kwargs\": {\n", + " \"name\": \"SpiseaMultiplicity\",\n", + " \"spisea_multiplicity_name\": \"MultiplicityResolvedDK\"\n", + " },\n", + " \"maglim\": null,\n", + " \"chosen_bands\": [\n", + " \"ubv\",\n", + " \"ukirt\"\n", + " ],\n", + " \"obsmag\": true,\n", + " \"combine_system_mags\": true,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": [\n", + " {\n", + " \"name\": \"popsycle_post_processing\"\n", + " }\n", + " ],\n", + " \"output_location\": \"outputfiles/default\",\n", + " \"output_filename_pattern\": \"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"csv\",\n", + " \"overwrite\": true\n", + "}\n", + " 8708 - Location or solid_angle_sr are not defined in the settings! Can not run main() or process_all()\n" + ] + } + ], + "source": [ + "mod2=0\n", + "mod2 = synthpop.SynthPop('huston2025_defaults.synthpop_conf',\n", + " extinction_map_kwargs={'name':'maps_from_dustmaps', \n", + " 'dustmap_name': 'marshall'},\n", + " chosen_bands = ['ubv', 'ukirt'],\n", + " maglim = None,\n", + " star_generator=\"SpiseaGenerator\",\n", + " evolution_class={\"name\":\"SpiseaCluster\", \"block_spisea_prints\":True},\n", + " ifmr_kwargs={\"name\":\"SpiseaIfmr\", \"spisea_ifmr_name\":\"IFMR_N20_Sukhbold\"},\n", + " multiplicity_kwargs={\"name\":\"SpiseaMultiplicity\", \"spisea_multiplicity_name\":\"MultiplicityResolvedDK\"},\n", + " post_processing_kwargs=[{\"name\": \"popsycle_post_processing\"}], #[{\"name\": \"CombineTables\"}],\n", + " name_for_output='mod2test',\n", + " combine_system_mags = True,\n", + " model_name='Koshimoto2022',\n", + " opt_iso_props=[\"log_L\", \"log_Teff\", \"log_g\",\"log_R\", \"phase\"],\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "aea54947-3723-417e-9be5-10d9f59c889d", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "######################### Initialize populations ##########################\n", + " 17231 - read Population files from Huston2025\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 17245 - # Initialize Population 0 (bulge) from \n", + " 17247 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/bulge.popjson'\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 17662 - # Initialize Population 1 (halo) from \n", + " 17664 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/halo.popjson'\n", + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 17769 - # Initialize Population 2 (nsd) from \n", + " 17772 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/nsd.popjson'\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 18692 - # Initialize Population 3 (thick_disk) from \n", + " 18694 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thick_disk.popjson'\n", + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 18780 - # Initialize Population 4 (thin_disk_1) from \n", + " 18782 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_1.popjson'\n", + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 18875 - # Initialize Population 5 (thin_disk_2) from \n", + " 18877 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_2.popjson'\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 18970 - # Initialize Population 6 (thin_disk_3) from \n", + " 18972 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_3.popjson'\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 19058 - # Initialize Population 7 (thin_disk_4) from \n", + " 19059 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_4.popjson'\n", + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 19146 - # Initialize Population 8 (thin_disk_5) from \n", + " 19148 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_5.popjson'\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 19242 - # Initialize Population 9 (thin_disk_6) from \n", + " 19244 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_6.popjson'\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 19347 - # Initialize Population 10 (thin_disk_7) from \n", + " 19349 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_7.popjson'\n", + " 19492 - # All populations are initialized\n" + ] + } + ], + "source": [ + "mod2.init_populations()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f49ccdb0-aaf5-451a-8350-6738020f8866", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 23592 - Execution Date: 2026-01-09 02:14:00\n", + "\n", + "\n", + "################################ Settings #################################\n", + "\n", + "\n", + "# Copy the following to a config file to redo this model generation: ------\n", + " 23599 - {\n", + " \"l_set\": [\n", + " 3\n", + " ],\n", + " \"l_set_type\": \"pairs\",\n", + " \"b_set\": [\n", + " -1\n", + " ],\n", + " \"b_set_type\": \"pairs\",\n", + " \"name_for_output\": \"mod2test\",\n", + " \"model_name\": \"Huston2025\",\n", + " \"field_shape\": \"box\",\n", + " \"field_scale\": 0.0003,\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 242505982,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 50000,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"SpiseaGenerator\",\n", + " \"extinction_map_kwargs\": {\n", + " \"name\": \"maps_from_dustmaps\",\n", + " \"dustmap_name\": \"marshall\"\n", + " },\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"SODC\",\n", + " \"R_V\": 2.5\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"SpiseaCluster\",\n", + " \"block_spisea_prints\": true\n", + " },\n", + " \"ifmr_kwargs\": {\n", + " \"name\": \"SpiseaIfmr\",\n", + " \"spisea_ifmr_name\": \"IFMR_N20_Sukhbold\"\n", + " },\n", + " \"multiplicity_kwargs\": {\n", + " \"name\": \"SpiseaMultiplicity\",\n", + " \"spisea_multiplicity_name\": \"MultiplicityResolvedDK\"\n", + " },\n", + " \"maglim\": null,\n", + " \"chosen_bands\": [\n", + " \"ubv\",\n", + " \"ukirt\"\n", + " ],\n", + " \"obsmag\": true,\n", + " \"combine_system_mags\": true,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": [\n", + " {\n", + " \"name\": \"popsycle_post_processing\"\n", + " }\n", + " ],\n", + " \"output_location\": \"outputfiles/default\",\n", + " \"output_filename_pattern\": \"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"csv\",\n", + " \"overwrite\": true\n", + "}\n", + "\n", + "\n", + "############################# Update location #############################\n", + " 23601 - # set location to: \n", + " 23643 - l, b = (3.00 deg, -1.00 deg)\n", + " 23647 - # set field scale to:\n", + " 23650 - field_scale = 3.000e-04 deg\n", + "\n", + "\n", + "############################# Generate Field ##############################\n", + " 23888 - Evolving test set from population 0 to estimate average initial->final mass ratio\n", + " 32591 - Evolving test set from population 1 to estimate average initial->final mass ratio\n", + " 42368 - Evolving test set from population 2 to estimate average initial->final mass ratio\n", + " 50380 - Evolving test set from population 3 to estimate average initial->final mass ratio\n", + " 58257 - Evolving test set from population 4 to estimate average initial->final mass ratio\n", + " 114535 - Evolving test set from population 5 to estimate average initial->final mass ratio\n", + "/opt/mambaforge3/envs/astro-synthpop/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:698: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 126823 - Evolving test set from population 6 to estimate average initial->final mass ratio\n", + " 312643 - Evolving test set from population 7 to estimate average initial->final mass ratio\n", + " 323147 - Evolving test set from population 8 to estimate average initial->final mass ratio\n", + " 333027 - Evolving test set from population 9 to estimate average initial->final mass ratio\n", + " 343672 - Evolving test set from population 10 to estimate average initial->final mass ratio\n" + ] + } + ], + "source": [ + "systems, companions = mod2.process_location(l_deg=3, b_deg=-1, field_shape='box', field_scale=0.0003, field_scale_unit='deg')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0ada6c97-b9a1-4313-a6ce-d9b0194a4e1e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'sys' has no attribute 'last_traceback'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pdb\u001b[38;5;241m.\u001b[39mpm()\n", + "File \u001b[0;32m~/anaconda3/lib/python3.11/pdb.py:1713\u001b[0m, in \u001b[0;36mpm\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1712\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpm\u001b[39m():\n\u001b[0;32m-> 1713\u001b[0m post_mortem(sys\u001b[38;5;241m.\u001b[39mlast_traceback)\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'sys' has no attribute 'last_traceback'" + ] + } + ], + "source": [ + "pdb.pm()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8a8cbc7d-d595-456d-8191-824f109f9766", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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m_ubv_Vm_ukirt_Hm_ubv_Im_ubv_Rlog_Teffm_ubv_Um_ukirt_Jphaselog_Rm_ukirt_Km_ubv_Blog_glog_Lsystem_idxiMassMasseccentricitylog_a
3NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN60.0991400.0991400.7479401.816843
6NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN340.0898370.0898370.2278430.206399
10NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN430.0482110.0482110.651220-0.172260
11NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN460.0900260.0900260.8651660.003385
16NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN880.0530700.0530700.866230-0.295654
.........................................................
2831NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN129210.0251590.0251590.444700-0.634171
2832NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN129250.0877820.0877820.798973-0.517232
2833NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN129290.0586320.0586320.4538860.009245
2839NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN129680.0601060.0601060.910518-0.407058
2840NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN129680.0221320.0221320.736371-0.551094
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1016 rows × 18 columns

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+ "2833 98.0 NaN NaN NaN NaN NaN 12929 0.058632 \n", + "2839 98.0 NaN NaN NaN NaN NaN 12968 0.060106 \n", + "2840 98.0 NaN NaN NaN NaN NaN 12968 0.022132 \n", + "\n", + " Mass eccentricity log_a \n", + "3 0.099140 0.747940 1.816843 \n", + "6 0.089837 0.227843 0.206399 \n", + "10 0.048211 0.651220 -0.172260 \n", + "11 0.090026 0.865166 0.003385 \n", + "16 0.053070 0.866230 -0.295654 \n", + "... ... ... ... \n", + "2831 0.025159 0.444700 -0.634171 \n", + "2832 0.087782 0.798973 -0.517232 \n", + "2833 0.058632 0.453886 0.009245 \n", + "2839 0.060106 0.910518 -0.407058 \n", + "2840 0.022132 0.736371 -0.551094 \n", + "\n", + "[1016 rows x 18 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companions[companions['iMass']<0.1]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bfcea464-e3c9-4556-94aa-3da46ba53a2a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(systems['Mass'],systems['system_Mass'], 'k.')\n", + "plt.xscale('log')\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cd623f1e-a96a-4625-8bbb-4fa75428c434", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([6.410e+03, 1.998e+03, 3.530e+02, 3.800e+01, 5.000e+00, 1.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00]), array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5]), )\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = plt.hist(systems['n_companions'], bins=np.linspace(-0.5,8.5,10))\n", + "print(x)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f46c47e8-151c-42bf-8dd7-1697767723db", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# systems1, companions1 = mod2.process_location(l_deg=4, b_deg=-1, field_shape='circle', field_scale=0.003, field_scale_unit='deg')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ef400507-a03d-4591-92ad-8998db08efd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.37521148246808567" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "systems.loc[0,'Mass']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "124c45a3-a2d7-4bcc-abf8-fbf29341e3b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([ 0, 1, 2, 3, 4, 5, 6, 7, 10, 13,\n", + " ...\n", + " 8789, 8791, 8792, 8793, 8796, 8797, 8798, 8799, 8803, 8804],\n", + " dtype='int64', length=5493)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "systems.index[systems.Mass>0.2]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ef11e0e0-9d8c-4626-98ac-3224e5ba2c1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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m_ubv_Vm_ukirt_Hm_ubv_Im_ubv_Rlog_Teffm_ubv_Um_ukirt_Jphaselog_Rm_ukirt_Km_ubv_Blog_glog_Lsystem_idxiMassMasseccentricitylog_a
037.60134420.46153623.25684033.9732173.69495449.75551222.5128170.0-0.11416019.48343845.3741924.556569-0.49584820.7826160.7825220.9346680.354885
1NaNNaNNaNNaNNaNNaNNaN101.0NaNNaNNaNNaNNaN41.5717710.5653230.8988301.480356
236.25501220.09235022.65808532.8436393.72403347.56614821.9987240.0-0.06664019.17171543.6344054.496704-0.28449350.8471170.8469720.834302-0.841084
3NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN60.0991400.0991400.7479401.816843
440.24125324.51290327.59582436.9598843.48772350.42340426.1826450.0-0.74606123.57409546.5872945.138216-2.588573140.1461870.1461860.8749900.187827
.........................................................
283850.20474124.77269428.82751044.8530843.55077967.83853727.6187670.0-0.44046023.22199761.4405454.895733-1.725149129550.3773520.3773450.2241991.234425
2839NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN129680.0601060.0601060.910518-0.407058
2840NaNNaNNaNNaNNaNNaNNaN98.0NaNNaNNaNNaNNaN129680.0221320.0221320.736371-0.551094
284152.46818327.89494932.06139247.3428843.47054068.70499330.4414410.0-0.82534826.44108562.6550595.200353-2.815882129830.1164010.1164000.4611061.228697
284244.80711124.34827027.85276940.5122683.53269758.75113426.6283730.0-0.52225323.09930853.6702384.952021-1.961062129910.2950950.2950910.7041810.111233
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2843 rows × 18 columns

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" + ], + "text/plain": [ + " m_ubv_V m_ukirt_H m_ubv_I m_ubv_R log_Teff m_ubv_U \\\n", + "0 37.601344 20.461536 23.256840 33.973217 3.694954 49.755512 \n", + "1 NaN NaN NaN NaN NaN NaN \n", + "2 36.255012 20.092350 22.658085 32.843639 3.724033 47.566148 \n", + "3 NaN NaN NaN NaN NaN NaN \n", + "4 40.241253 24.512903 27.595824 36.959884 3.487723 50.423404 \n", + "... ... ... ... ... ... ... \n", + "2838 50.204741 24.772694 28.827510 44.853084 3.550779 67.838537 \n", + "2839 NaN NaN NaN NaN NaN NaN \n", + "2840 NaN NaN NaN NaN NaN NaN \n", + "2841 52.468183 27.894949 32.061392 47.342884 3.470540 68.704993 \n", + "2842 44.807111 24.348270 27.852769 40.512268 3.532697 58.751134 \n", + "\n", + " m_ukirt_J phase log_R m_ukirt_K m_ubv_B log_g log_L \\\n", + "0 22.512817 0.0 -0.114160 19.483438 45.374192 4.556569 -0.495848 \n", + "1 NaN 101.0 NaN NaN NaN NaN NaN \n", + "2 21.998724 0.0 -0.066640 19.171715 43.634405 4.496704 -0.284493 \n", + "3 NaN 98.0 NaN NaN NaN NaN NaN \n", + "4 26.182645 0.0 -0.746061 23.574095 46.587294 5.138216 -2.588573 \n", + "... ... ... ... ... ... ... ... \n", + "2838 27.618767 0.0 -0.440460 23.221997 61.440545 4.895733 -1.725149 \n", + "2839 NaN 98.0 NaN NaN NaN NaN NaN \n", + "2840 NaN 98.0 NaN NaN NaN NaN NaN \n", + "2841 30.441441 0.0 -0.825348 26.441085 62.655059 5.200353 -2.815882 \n", + "2842 26.628373 0.0 -0.522253 23.099308 53.670238 4.952021 -1.961062 \n", + "\n", + " system_idx iMass Mass eccentricity log_a \n", + "0 2 0.782616 0.782522 0.934668 0.354885 \n", + "1 4 1.571771 0.565323 0.898830 1.480356 \n", + "2 5 0.847117 0.846972 0.834302 -0.841084 \n", + "3 6 0.099140 0.099140 0.747940 1.816843 \n", + "4 14 0.146187 0.146186 0.874990 0.187827 \n", + "... ... ... ... ... ... \n", + "2838 12955 0.377352 0.377345 0.224199 1.234425 \n", + "2839 12968 0.060106 0.060106 0.910518 -0.407058 \n", + "2840 12968 0.022132 0.022132 0.736371 -0.551094 \n", + "2841 12983 0.116401 0.116400 0.461106 1.228697 \n", + "2842 12991 0.295095 0.295091 0.704181 0.111233 \n", + "\n", + "[2843 rows x 18 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e119fa43-8be8-4e1e-a05c-59388ae35dc8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4518.014925377473, 2570.5936592374915)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(systems['Mass']) + np.sum(companions['Mass']), np.sum(systems['system_Mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8c465e65-3854-4629-9e1f-078b4978a6b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.4712328484178397" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mod2.populations[0].av_mass_corr #, mod0.populations[0].av_mass_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6a3e2452-4696-4eec-889a-69d5ddf94392", + "metadata": {}, + "outputs": [], + "source": [ + "import synthpop \n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pdb" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "791823da-b18e-4a38-b1a0-ec6b9cbc6980", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "################################ Settings #################################\n", + " 136889 - # reading default parameters from\n", + " 136891 - default_config_file = /Users/mhuston/code/synthpop/synthpop/config_files/_default.synthpop_conf \n", + " 136893 - # read configuration from \n", + " 136893 - config_file = '/Users/mhuston/code/synthpop/synthpop/config_files/huston2025_defaults.synthpop_conf' \n", + "\n", + "\n", + "# copy the following to a config file to redo this model generation -------\n", + " 136895 - {\n", + " \"l_set\": null,\n", + " \"l_set_type\": null,\n", + " \"b_set\": null,\n", + " \"b_set_type\": null,\n", + " \"name_for_output\": \"mod0test\",\n", + " \"model_name\": \"Huston2025\",\n", + " \"field_shape\": \"circle\",\n", + " \"field_scale\": null,\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 358778542,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 50000,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"StarGenerator\",\n", + " \"extinction_map_kwargs\": {\n", + " \"name\": \"maps_from_dustmaps\",\n", + " \"dustmap_name\": \"marshall\"\n", + " },\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"SODC\",\n", + " \"R_V\": 2.5\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"MIST\",\n", + " \"interpolator\": \"CharonInterpolator\"\n", + " },\n", + " \"ifmr_kwargs\": {\n", + " \"name\": \"PopsycleIfmrs\",\n", + " \"ifmr_name\": \"SukhboldN20\"\n", + " },\n", + " \"multiplicity_kwargs\": null,\n", + " \"maglim\": null,\n", + " \"chosen_bands\": [\n", + " \"Bessell_U\",\n", + " \"Bessell_B\",\n", + " \"Bessell_V\",\n", + " \"Bessell_R\",\n", + " \"Bessell_I\"\n", + " ],\n", + " \"obsmag\": true,\n", + " \"combine_system_mags\": false,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": [\n", + " {\n", + " \"name\": \"ConvertMistMags\",\n", + " \"conversions\": {\n", + " \"AB\": [\n", + " \"R062\",\n", + " \"Z087\",\n", + " \"Y106\",\n", + " \"J129\",\n", + " \"W146\",\n", + " \"H158\",\n", + " \"F184\"\n", + " ]\n", + " }\n", + " },\n", + " {\n", + " \"name\": \"RenameColumns\",\n", + " \"old_names\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\"\n", + " ],\n", + " \"new_names\": [\n", + " \"logL\",\n", + " \"logTeff\",\n", + " \"logg\",\n", + " \"Fe/H_evolved\",\n", + " \"log_radius\"\n", + " ]\n", + " }\n", + " ],\n", + " \"output_location\": \"outputfiles/default\",\n", + " \"output_filename_pattern\": \"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"csv\",\n", + " \"overwrite\": true\n", + "}\n", + " 136896 - Location or field size are not defined in the settings! Can not run main() or process_all()\n" + ] + } + ], + "source": [ + "mod0=0\n", + "mod0 = synthpop.SynthPop('huston2025_defaults.synthpop_conf',\n", + " extinction_map_kwargs={'name':'maps_from_dustmaps', \n", + " 'dustmap_name': 'marshall'},\n", + " chosen_bands = ['Bessell_U', 'Bessell_B', 'Bessell_V', 'Bessell_R', 'Bessell_I'],\n", + " maglim = None,\n", + " #multiplicity_kwargs={\"name\":\"Raghavan\"},\n", + " #post_processing_kwargs=[{\"name\": \"DeriveBinaryParameters\"}],\n", + " name_for_output='mod0test',\n", + " field_shape='circle', field_scale_unit='deg'\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "51bd3707-1de1-462f-a5e5-e784067eb7ed", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "######################### Initialize populations ##########################\n", + " 139221 - read Population files from Huston2025\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 139228 - # Initialize Population 0 (bulge) from \n", + " 139228 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/bulge.popjson'\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 140433 - # Initialize Population 1 (halo) from \n", + " 140434 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/halo.popjson'\n", + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 140893 - # Initialize Population 2 (nsd) from \n", + " 140893 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/nsd.popjson'\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 141713 - # Initialize Population 3 (thick_disk) from \n", + " 141713 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thick_disk.popjson'\n", + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 142173 - # Initialize Population 4 (thin_disk_1) from \n", + " 142173 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_1.popjson'\n", + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 142633 - # Initialize Population 5 (thin_disk_2) from \n", + " 142633 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_2.popjson'\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 143086 - # Initialize Population 6 (thin_disk_3) from \n", + " 143087 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_3.popjson'\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 143547 - # Initialize Population 7 (thin_disk_4) from \n", + " 143547 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_4.popjson'\n", + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 144020 - # Initialize Population 8 (thin_disk_5) from \n", + " 144020 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_5.popjson'\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 144493 - # Initialize Population 9 (thin_disk_6) from \n", + " 144493 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_6.popjson'\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 144954 - # Initialize Population 10 (thin_disk_7) from \n", + " 144955 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_7.popjson'\n", + " 145411 - # All populations are initialized\n" + ] + } + ], + "source": [ + "mod0.init_populations()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "27ed4615-0a3e-4969-a621-73ebac86404b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 145416 - Execution Date: 2026-02-03 13:44:01\n", + "\n", + "\n", + "################################ Settings #################################\n", + "\n", + "\n", + "# Copy the following to a config file to redo this model generation: ------\n", + " 145416 - {\n", + " \"l_set\": [\n", + " 3\n", + " ],\n", + " \"l_set_type\": \"pairs\",\n", + " \"b_set\": [\n", + " -1\n", + " ],\n", + " \"b_set_type\": \"pairs\",\n", + " \"name_for_output\": \"mod0test\",\n", + " \"model_name\": \"Huston2025\",\n", + " \"field_shape\": \"circle\",\n", + " \"field_scale\": 0.003,\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 358778542,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 50000,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"StarGenerator\",\n", + " \"extinction_map_kwargs\": {\n", + " \"name\": \"maps_from_dustmaps\",\n", + " \"dustmap_name\": \"marshall\"\n", + " },\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"SODC\",\n", + " \"R_V\": 2.5\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"MIST\",\n", + " \"interpolator\": \"CharonInterpolator\"\n", + " },\n", + " \"ifmr_kwargs\": {\n", + " \"name\": \"PopsycleIfmrs\",\n", + " \"ifmr_name\": \"SukhboldN20\"\n", + " },\n", + " \"multiplicity_kwargs\": null,\n", + " \"maglim\": null,\n", + " \"chosen_bands\": [\n", + " \"Bessell_U\",\n", + " \"Bessell_B\",\n", + " \"Bessell_V\",\n", + " \"Bessell_R\",\n", + " \"Bessell_I\"\n", + " ],\n", + " \"obsmag\": true,\n", + " \"combine_system_mags\": false,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": [\n", + " {\n", + " \"name\": \"ConvertMistMags\",\n", + " \"conversions\": {\n", + " \"AB\": [\n", + " \"R062\",\n", + " \"Z087\",\n", + " \"Y106\",\n", + " \"J129\",\n", + " \"W146\",\n", + " \"H158\",\n", + " \"F184\"\n", + " ]\n", + " }\n", + " },\n", + " {\n", + " \"name\": \"RenameColumns\",\n", + " \"old_names\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\"\n", + " ],\n", + " \"new_names\": [\n", + " \"logL\",\n", + " \"logTeff\",\n", + " \"logg\",\n", + " \"Fe/H_evolved\",\n", + " \"log_radius\"\n", + " ]\n", + " }\n", + " ],\n", + " \"output_location\": \"outputfiles/default\",\n", + " \"output_filename_pattern\": \"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"csv\",\n", + " \"overwrite\": true\n", + "}\n", + "\n", + "\n", + "############################# Update location #############################\n", + " 145416 - # set location to: \n", + " 145416 - l, b = (3.00 deg, -1.00 deg)\n", + " 145417 - # set field scale to:\n", + " 145417 - field_scale = 3.000e-03 deg\n", + "\n", + "\n", + "############################# Generate Field ##############################\n", + " 147076 - Evolving test set from population 0 to estimate average initial->final mass ratio\n", + " 148005 - Evolving test set from population 1 to estimate average initial->final mass ratio\n", + " 148783 - Evolving test set from population 2 to estimate average initial->final mass ratio\n", + " 149549 - Evolving test set from population 3 to estimate average initial->final mass ratio\n", + " 150333 - Evolving test set from population 4 to estimate average initial->final mass ratio\n", + " 151731 - Evolving test set from population 5 to estimate average initial->final mass ratio\n", + " 152691 - Evolving test set from population 6 to estimate average initial->final mass ratio\n", + " 153537 - Evolving test set from population 7 to estimate average initial->final mass ratio\n", + " 154331 - Evolving test set from population 8 to estimate average initial->final mass ratio\n", + " 155136 - Evolving test set from population 9 to estimate average initial->final mass ratio\n", + " 155948 - Evolving test set from population 10 to estimate average initial->final mass ratio\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 156788 - # From density profile (number density)\n", + " 156788 - expected_total_iMass = 6041.1823\n", + " 156788 - expected_total_eMass = 3621.8081\n", + " 156789 - average_iMass_per_star = 0.5739\n", + " 156789 - mass_loss_correction = 0.5995\n", + " 156789 - n_expected_stars = 10527.1663\n", + " 157027 - # From Generated Field:\n", + " 157027 - generated_stars = 10282\n", + " 157028 - generated_total_iMass = 5674.5584\n", + " 157032 - generated_total_eMass = 3400.3551\n", + " 157032 - det_mass_loss_corr = 0.5992\n", + " 157032 - # Done\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 157038 - # From density profile (number density)\n", + " 157038 - expected_total_iMass = 11.3803\n", + " 157038 - expected_total_eMass = 6.6042\n", + " 157039 - average_iMass_per_star = 0.5739\n", + " 157039 - mass_loss_correction = 0.5803\n", + " 157039 - n_expected_stars = 19.8309\n", + " 157081 - # From Generated Field:\n", + " 157081 - generated_stars = 21\n", + " 157082 - generated_total_iMass = 9.7678\n", + " 157085 - generated_total_eMass = 6.6964\n", + " 157086 - det_mass_loss_corr = 0.6856\n", + " 157086 - # Done\n", + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 157092 - # From density profile (number density)\n", + " 157098 - expected_total_iMass = 22.4651\n", + " 157100 - expected_total_eMass = 13.3395\n", + " 157100 - average_iMass_per_star = 0.5739\n", + " 157100 - mass_loss_correction = 0.5938\n", + " 157101 - n_expected_stars = 39.1469\n", + " 157280 - # From Generated Field:\n", + " 157280 - generated_stars = 40\n", + " 157281 - generated_total_iMass = 15.1659\n", + " 157284 - generated_total_eMass = 13.7503\n", + " 157284 - det_mass_loss_corr = 0.9067\n", + " 157285 - # Done\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 157290 - # From density profile (number density)\n", + " 157290 - expected_total_iMass = 121.4103\n", + " 157290 - expected_total_eMass = 71.2413\n", + " 157291 - average_iMass_per_star = 0.5739\n", + " 157291 - mass_loss_correction = 0.5868\n", + " 157291 - n_expected_stars = 211.5656\n", + " 157340 - # From Generated Field:\n", + " 157341 - generated_stars = 229\n", + " 157341 - generated_total_iMass = 82.7034\n", + " 157345 - generated_total_eMass = 64.9211\n", + " 157345 - det_mass_loss_corr = 0.7850\n", + " 157345 - # Done\n", + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 157351 - # From density profile (number density)\n", + " 157351 - expected_total_iMass = 1.6111\n", + " 157351 - expected_total_eMass = 1.3322\n", + " 157351 - average_iMass_per_star = 0.5739\n", + " 157352 - mass_loss_correction = 0.8269\n", + " 157352 - n_expected_stars = 2.8074\n", + " 157372 - # From Generated Field:\n", + " 157372 - generated_stars = 4\n", + " 157373 - generated_total_iMass = 1.1729\n", + " 157376 - generated_total_eMass = 1.1729\n", + " 157376 - det_mass_loss_corr = 1.0000\n", + " 157377 - # Done\n", + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 157382 - # From density profile (number density)\n", + " 157382 - expected_total_iMass = 31.1919\n", + " 157382 - expected_total_eMass = 22.4112\n", + " 157383 - average_iMass_per_star = 0.5739\n", + " 157383 - mass_loss_correction = 0.7185\n", + " 157383 - n_expected_stars = 54.3539\n", + " 157513 - # From Generated Field:\n", + " 157513 - generated_stars = 58\n", + " 157514 - generated_total_iMass = 31.9011\n", + " 157517 - generated_total_eMass = 22.1126\n", + " 157517 - det_mass_loss_corr = 0.6932\n", + " 157517 - # Done\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 157523 - # From density profile (number density)\n", + " 157523 - expected_total_iMass = 80.1899\n", + " 157523 - expected_total_eMass = 53.5507\n", + " 157524 - average_iMass_per_star = 0.5739\n", + " 157524 - mass_loss_correction = 0.6678\n", + " 157524 - n_expected_stars = 139.7364\n", + " 157595 - # From Generated Field:\n", + " 157595 - generated_stars = 142\n", + " 157595 - generated_total_iMass = 123.5942\n", + " 157599 - generated_total_eMass = 56.8900\n", + " 157599 - det_mass_loss_corr = 0.4603\n", + " 157600 - # Done\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 157605 - # From density profile (number density)\n", + " 157605 - expected_total_iMass = 113.1293\n", + " 157605 - expected_total_eMass = 72.7280\n", + " 157606 - average_iMass_per_star = 0.5739\n", + " 157606 - mass_loss_correction = 0.6429\n", + " 157606 - n_expected_stars = 197.1355\n", + " 157681 - # From Generated Field:\n", + " 157682 - generated_stars = 199\n", + " 157682 - generated_total_iMass = 88.5975\n", + " 157686 - generated_total_eMass = 73.7958\n", + " 157686 - det_mass_loss_corr = 0.8329\n", + " 157686 - # Done\n", + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 157691 - # From density profile (number density)\n", + " 157692 - expected_total_iMass = 298.9194\n", + " 157692 - expected_total_eMass = 189.5564\n", + " 157692 - average_iMass_per_star = 0.5739\n", + " 157693 - mass_loss_correction = 0.6341\n", + " 157693 - n_expected_stars = 520.8872\n", + " 157815 - # From Generated Field:\n", + " 157815 - generated_stars = 564\n", + " 157816 - generated_total_iMass = 269.7140\n", + " 157819 - generated_total_eMass = 188.3088\n", + " 157820 - det_mass_loss_corr = 0.6982\n", + " 157820 - # Done\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 157825 - # From density profile (number density)\n", + " 157825 - expected_total_iMass = 402.1573\n", + " 157825 - expected_total_eMass = 242.0188\n", + " 157826 - average_iMass_per_star = 0.5739\n", + " 157826 - mass_loss_correction = 0.6018\n", + " 157826 - n_expected_stars = 700.7861\n", + " 157938 - # From Generated Field:\n", + " 157938 - generated_stars = 704\n", + " 157939 - generated_total_iMass = 363.9586\n", + " 157943 - generated_total_eMass = 232.7892\n", + " 157943 - det_mass_loss_corr = 0.6396\n", + " 157943 - # Done\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 157949 - # From density profile (number density)\n", + " 157949 - expected_total_iMass = 923.8911\n", + " 157950 - expected_total_eMass = 535.3873\n", + " 157950 - average_iMass_per_star = 0.5739\n", + " 157950 - mass_loss_correction = 0.5795\n", + " 157950 - n_expected_stars = 1609.9423\n", + " 158107 - # From Generated Field:\n", + " 158108 - generated_stars = 1564\n", + " 158108 - generated_total_iMass = 944.3171\n", + " 158112 - generated_total_eMass = 533.2038\n", + " 158112 - det_mass_loss_corr = 0.5646\n", + " 158112 - # Done\n", + "\n", + "\n", + "########################### Combine Populations ###########################\n", + " 158114 - Number of star systems generated: 13807 (34 columns)\n", + " 158114 - included_columns = ['iMass', 'age', 'Fe/H_initial', 'n_companions', 'system_idx', 'Mass', 'system_Mass', 'Bessell_B', 'Bessell_I', '[Fe/H]', 'Bessell_R', 'log_Teff', 'star_mass', 'phase', 'log_R', 'Bessell_U', 'Bessell_V', 'log_g', 'log_L', 'x', 'y', 'z', 'Dist', 'l', 'b', 'vr_bc', 'mul', 'mub', 'U', 'V', 'W', 'VR_LSR', 'A_Ks', 'pop']\n", + "\n", + "\n", + "# Save result -------------------------------------------------------------\n", + " 158115 - write result to \"outputfiles/default/mod0test_l3.000_b-1.000.csv\"\n", + " 158412 - ---------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "systems2, companions2 = mod0.process_location(l_deg=3, b_deg=-1, field_scale=0.003)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "be1bb312-7bbb-42fc-980f-1263fb6aefbc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iMassageFe/H_initialn_companionssystem_idxMasssystem_MassBessell_BBessell_IFe/H_evolved...bvr_bcmulmubUVWVR_LSRA_Kspop
010.96283110.0000000.0972030.001.4118901.411890NaNNaNNaN...-0.999281-72.353405-1.6785780.912385-54.886640171.17803447.642478-62.7294801.1277150
10.30177310.0000000.4147180.010.3017670.30176754.77128435.0861410.498334...-0.997295-81.346780-5.8274320.892881-55.63483312.38365044.467385-71.7222051.0844440
20.17380110.0000000.3139380.020.1737990.17379949.21917933.4455200.381648...-1.000060-28.038029-9.281038-4.226658-0.422993-93.505317-145.590057-18.4147780.7478270
30.11543310.000000-0.4022200.030.1154320.11543258.83172637.731578-0.370915...-0.99940075.096595-9.7667061.838982113.483721-206.66786892.27931184.7207411.2238540
41.00682510.000000-0.3952260.040.5037440.503744NaNNaNNaN...-0.999075-127.824941-3.934595-1.420070-108.08997292.535456-42.617322-118.2011360.9720100
..................................................................
138021.3513629.260163-0.1744410.0138020.5412980.541298NaNNaNNaN...-1.001100-78.550139-9.4410071.240180-39.855349-225.51831070.691088-68.9269660.82841610
138030.3460697.610965-0.2590250.0138030.3460640.34606439.95361527.930924-0.224325...-1.000616-23.947118-5.1122993.725888-3.170410126.82493594.044406-14.3235970.63207510
138040.0891997.414431-0.9538820.0138040.0891990.08919931.54860121.4819620.089199...-1.00123463.777681-5.1752042.99821888.92052955.486010118.77998973.4011930.73213410
138050.0811589.411309-0.5328580.0138050.0811580.08115845.37075127.6474850.081158...-0.99796751.029651-7.3693340.05677385.989350-173.1506809.98984960.6537671.28899010
138060.1581387.992015-0.1988030.0138060.1581360.15813651.60478334.985851-0.162014...-1.000445-52.548231-6.870920-0.629655-14.707654-248.576859-36.331116-42.9249940.92000010
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13807 rows × 34 columns

\n", + "
" + ], + "text/plain": [ + " iMass age Fe/H_initial n_companions system_idx Mass \\\n", + "0 10.962831 10.000000 0.097203 0.0 0 1.411890 \n", + "1 0.301773 10.000000 0.414718 0.0 1 0.301767 \n", + "2 0.173801 10.000000 0.313938 0.0 2 0.173799 \n", + "3 0.115433 10.000000 -0.402220 0.0 3 0.115432 \n", + "4 1.006825 10.000000 -0.395226 0.0 4 0.503744 \n", + "... ... ... ... ... ... ... \n", + "13802 1.351362 9.260163 -0.174441 0.0 13802 0.541298 \n", + "13803 0.346069 7.610965 -0.259025 0.0 13803 0.346064 \n", + "13804 0.089199 7.414431 -0.953882 0.0 13804 0.089199 \n", + "13805 0.081158 9.411309 -0.532858 0.0 13805 0.081158 \n", + "13806 0.158138 7.992015 -0.198803 0.0 13806 0.158136 \n", + "\n", + " system_Mass Bessell_B Bessell_I Fe/H_evolved ... b \\\n", + "0 1.411890 NaN NaN NaN ... -0.999281 \n", + "1 0.301767 54.771284 35.086141 0.498334 ... -0.997295 \n", + "2 0.173799 49.219179 33.445520 0.381648 ... -1.000060 \n", + "3 0.115432 58.831726 37.731578 -0.370915 ... -0.999400 \n", + "4 0.503744 NaN NaN NaN ... -0.999075 \n", + "... ... ... ... ... ... ... \n", + "13802 0.541298 NaN NaN NaN ... -1.001100 \n", + "13803 0.346064 39.953615 27.930924 -0.224325 ... -1.000616 \n", + "13804 0.089199 31.548601 21.481962 0.089199 ... -1.001234 \n", + "13805 0.081158 45.370751 27.647485 0.081158 ... -0.997967 \n", + "13806 0.158136 51.604783 34.985851 -0.162014 ... -1.000445 \n", + "\n", + " vr_bc mul mub U V W \\\n", + "0 -72.353405 -1.678578 0.912385 -54.886640 171.178034 47.642478 \n", + "1 -81.346780 -5.827432 0.892881 -55.634833 12.383650 44.467385 \n", + "2 -28.038029 -9.281038 -4.226658 -0.422993 -93.505317 -145.590057 \n", + "3 75.096595 -9.766706 1.838982 113.483721 -206.667868 92.279311 \n", + "4 -127.824941 -3.934595 -1.420070 -108.089972 92.535456 -42.617322 \n", + "... ... ... ... ... ... ... \n", + "13802 -78.550139 -9.441007 1.240180 -39.855349 -225.518310 70.691088 \n", + "13803 -23.947118 -5.112299 3.725888 -3.170410 126.824935 94.044406 \n", + "13804 63.777681 -5.175204 2.998218 88.920529 55.486010 118.779989 \n", + "13805 51.029651 -7.369334 0.056773 85.989350 -173.150680 9.989849 \n", + "13806 -52.548231 -6.870920 -0.629655 -14.707654 -248.576859 -36.331116 \n", + "\n", + " VR_LSR A_Ks pop \n", + "0 -62.729480 1.127715 0 \n", + "1 -71.722205 1.084444 0 \n", + "2 -18.414778 0.747827 0 \n", + "3 84.720741 1.223854 0 \n", + "4 -118.201136 0.972010 0 \n", + "... ... ... ... \n", + "13802 -68.926966 0.828416 10 \n", + "13803 -14.323597 0.632075 10 \n", + "13804 73.401193 0.732134 10 \n", + "13805 60.653767 1.288990 10 \n", + "13806 -42.924994 0.920000 10 \n", + "\n", + "[13807 rows x 34 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "systems2" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c204d075-2836-44da-abb8-868205f5e396", + "metadata": {}, + "outputs": [], + "source": [ + "companions2" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "7a9c23d6-9dfd-4823-85bc-76bb918f82b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4593.996038489701, 4593.996038489701)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(systems2['Mass']), np.sum(systems2['system_Mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "63513a61-b394-42f1-a4b8-b7311a291534", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.stairs(*np.histogram(systems.m_ubv_I, bins=np.linspace(10,40,21)), label='with binaries')\n", + "plt.stairs(*np.histogram(systems2.Bessell_I, bins=np.linspace(10,40,21)), label='singles')\n", + "plt.legend()\n", + "#plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0630950-fca5-4dc0-b045-c70501602d74", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:astro-synthpop]", + "language": "python", + "name": "conda-env-astro-synthpop-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TUTORIAL-v2-inprogress.ipynb b/TUTORIAL-v2-inprogress.ipynb new file mode 100755 index 0000000..da0d4f1 --- /dev/null +++ b/TUTORIAL-v2-inprogress.ipynb @@ -0,0 +1,2160 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "832a172a-b16f-4e9f-8cad-666d2c6b5eb4", + "metadata": {}, + "outputs": [], + "source": [ + "import synthpop \n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pdb" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d97eb406-8463-44db-81db-7e68fb92afa1", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 60159 - Execution Date: 2025-12-12 01:01:48\n", + "\n", + "\n", + "################################ Settings #################################\n", + " 60162 - # reading default parameters from\n", + " 60164 - default_config_file = /System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/config_files/_default.synthpop_conf \n", + " 60173 - # read configuration from \n", + " 60176 - config_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/config_files/huston2025_defaults.synthpop_conf' \n", + "\n", + "\n", + "# copy the following to a config file to redo this model generation -------\n", + " 60179 - {\n", + " \"l_set\": null,\n", + " \"l_set_type\": null,\n", + " \"b_set\": null,\n", + " \"b_set_type\": null,\n", + " \"name_for_output\": \"mod2test\",\n", + " \"model_name\": \"Huston2025\",\n", + " \"field_shape\": \"circle\",\n", + " \"field_scale\": null,\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 1070455543,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 50000,\n", + " \"kinematics_at_the_end\": false,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"StarGenerator\",\n", + " \"extinction_map_kwargs\": {\n", + " \"name\": \"maps_from_dustmaps\",\n", + " \"dustmap_name\": \"marshall\"\n", + " },\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"SODC\",\n", + " \"R_V\": 2.5\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"MIST\",\n", + " \"interpolator\": \"CharonInterpolator\"\n", + " },\n", + " \"ifmr_kwargs\": {\n", + " \"name\": \"PopsycleIfmrs\",\n", + " \"ifmr_name\": \"SukhboldN20\"\n", + " },\n", + " \"multiplicity_kwargs\": {\n", + " \"name\": \"Raghavan\"\n", + " },\n", + " \"maglim\": [\n", + " \"Bessell_I\",\n", + " 99,\n", + " \"keep\"\n", + " ],\n", + " \"chosen_bands\": [\n", + " \"Bessell_V\",\n", + " \"Bessell_I\",\n", + " \"2MASS_Ks\"\n", + " ],\n", + " \"obsmag\": true,\n", + " \"combine_system_mags\": true,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": null,\n", + " \"output_location\": \"outputfiles/default\",\n", + " \"output_filename_pattern\": \"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"csv\",\n", + " \"overwrite\": true\n", + "}\n", + " 60181 - Location or solid_angle_sr are not defined in the settings! Can not run main() or process_all()\n" + ] + } + ], + "source": [ + "mod2=0\n", + "mod2 = synthpop.SynthPop('huston2025_defaults.synthpop_conf',\n", + " extinction_map_kwargs={'name':'maps_from_dustmaps', \n", + " 'dustmap_name': 'marshall'},\n", + " chosen_bands = ['Bessell_V','Bessell_I', '2MASS_Ks'],\n", + " maglim = ['Bessell_I', 99, \"keep\"],\n", + " multiplicity_kwargs={\"name\":\"Raghavan\"},\n", + " post_processing_kwargs=None, #[{\"name\": \"CombineTables\"}],\n", + " name_for_output='mod2test',\n", + " combine_system_mags = True,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "aea54947-3723-417e-9be5-10d9f59c889d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "######################### Initialize populations ##########################\n", + " 62423 - read Population files from Huston2025\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 62466 - # Initialize Population 0 (bulge) from \n", + " 62469 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/bulge.popjson'\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 67590 - # Initialize Population 1 (halo) from \n", + " 67593 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/halo.popjson'\n", + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 69152 - # Initialize Population 2 (nsd) from \n", + " 69154 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/nsd.popjson'\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 71211 - # Initialize Population 3 (thick_disk) from \n", + " 71213 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thick_disk.popjson'\n", + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 72840 - # Initialize Population 4 (thin_disk_1) from \n", + " 72842 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_1.popjson'\n", + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 74167 - # Initialize Population 5 (thin_disk_2) from \n", + " 74168 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_2.popjson'\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 75490 - # Initialize Population 6 (thin_disk_3) from \n", + " 75492 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_3.popjson'\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 77010 - # Initialize Population 7 (thin_disk_4) from \n", + " 77013 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_4.popjson'\n", + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 78392 - # Initialize Population 8 (thin_disk_5) from \n", + " 78393 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_5.popjson'\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 79733 - # Initialize Population 9 (thin_disk_6) from \n", + " 79735 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_6.popjson'\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 81091 - # Initialize Population 10 (thin_disk_7) from \n", + " 81092 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_7.popjson'\n", + " 82482 - # All populations are initialized\n" + ] + } + ], + "source": [ + "mod2.init_populations()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f49ccdb0-aaf5-451a-8350-6738020f8866", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 82493 - Execution Date: 2025-12-12 01:02:11\n", + "\n", + "\n", + "################################ Settings #################################\n", + "\n", + "\n", + "# Copy the following to a config file to redo this model generation: ------\n", + " 82504 - {\n", + " \"l_set\": [\n", + " 3\n", + " ],\n", + " \"l_set_type\": \"pairs\",\n", + " \"b_set\": [\n", + " -1\n", + " ],\n", + " \"b_set_type\": \"pairs\",\n", + " \"name_for_output\": \"mod2test\",\n", + " \"model_name\": \"Huston2025\",\n", + " \"field_shape\": \"box\",\n", + " \"field_scale\": 0.0003,\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 1070455543,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 50000,\n", + " \"kinematics_at_the_end\": false,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"StarGenerator\",\n", + " \"extinction_map_kwargs\": {\n", + " \"name\": \"maps_from_dustmaps\",\n", + " \"dustmap_name\": \"marshall\"\n", + " },\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"SODC\",\n", + " \"R_V\": 2.5\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"MIST\",\n", + " \"interpolator\": \"CharonInterpolator\"\n", + " },\n", + " \"ifmr_kwargs\": {\n", + " \"name\": \"PopsycleIfmrs\",\n", + " \"ifmr_name\": \"SukhboldN20\"\n", + " },\n", + " \"multiplicity_kwargs\": {\n", + " \"name\": \"Raghavan\"\n", + " },\n", + " \"maglim\": [\n", + " \"Bessell_I\",\n", + " 99,\n", + " \"keep\"\n", + " ],\n", + " \"chosen_bands\": [\n", + " \"Bessell_V\",\n", + " \"Bessell_I\",\n", + " \"2MASS_Ks\"\n", + " ],\n", + " \"obsmag\": true,\n", + " \"combine_system_mags\": true,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": null,\n", + " \"output_location\": \"outputfiles/default\",\n", + " \"output_filename_pattern\": \"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"csv\",\n", + " \"overwrite\": true\n", + "}\n", + "\n", + "\n", + "############################# Update location #############################\n", + " 82507 - # set location to: \n", + " 82560 - l, b = (3.00 deg, -1.00 deg)\n", + " 82563 - # set field scale to:\n", + " 82567 - field_scale = 3.000e-04 deg\n", + "\n", + "\n", + "############################# Generate Field ##############################\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 99463 - # From density profile (number density)\n", + " 99466 - expected_total_iMass = 14.0516\n", + " 99470 - expected_total_eMass = 11.5281\n", + " 99473 - average_iMass_per_star = 0.5739\n", + " 99476 - mass_loss_correction = 0.8204\n", + " 99479 - n_expected_stars = 24.4858\n", + " 99485 - # Determine velocities when position are generated \n", + " 99608 - # From Generated Field:\n", + " 99615 - generated_stars = 21\n", + " 99619 - generated_total_iMass = 11.9298\n", + " 99629 - generated_total_eMass = 7.9472\n", + " 99633 - det_mass_loss_corr = 0.6662\n", + " 99638 - # Done\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 100224 - # From density profile (number density)\n", + " 100228 - expected_total_iMass = 0.0262\n", + " 100232 - expected_total_eMass = 0.0210\n", + " 100239 - average_iMass_per_star = 0.5739\n", + " 100242 - mass_loss_correction = 0.8027\n", + " 100249 - n_expected_stars = 0.0456\n", + " 100261 - # Determine velocities when position are generated \n", + " 100269 - # From Generated Field:\n", + " 100273 - generated_stars = 0\n", + " 100276 - # Done\n", + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 101677 - # From density profile (number density)\n", + " 101682 - expected_total_iMass = 0.0516\n", + " 101685 - expected_total_eMass = 0.0425\n", + " 101689 - average_iMass_per_star = 0.5739\n", + " 101692 - mass_loss_correction = 0.8238\n", + " 101695 - n_expected_stars = 0.0898\n", + " 101701 - # Determine velocities when position are generated \n", + " 101707 - # From Generated Field:\n", + " 101712 - generated_stars = 0\n", + " 101718 - # Done\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 102213 - # From density profile (number density)\n", + " 102219 - expected_total_iMass = 0.2769\n", + " 102223 - expected_total_eMass = 0.2258\n", + " 102227 - average_iMass_per_star = 0.5739\n", + " 102234 - mass_loss_correction = 0.8155\n", + " 102241 - n_expected_stars = 0.4825\n", + " 102248 - # Determine velocities when position are generated \n", + " 102306 - # From Generated Field:\n", + " 102313 - generated_stars = 1\n", + " 102317 - generated_total_iMass = 0.7203\n", + " 102327 - generated_total_eMass = 0.7201\n", + " 102330 - det_mass_loss_corr = 0.9997\n", + " 102335 - # Done\n", + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 102854 - # From density profile (number density)\n", + " 102858 - expected_total_iMass = 0.0039\n", + " 102862 - expected_total_eMass = 0.0042\n", + " 102869 - average_iMass_per_star = 0.5739\n", + " 102875 - mass_loss_correction = 1.0859\n", + " 102879 - n_expected_stars = 0.0068\n", + " 102885 - # Determine velocities when position are generated \n", + " 102891 - # From Generated Field:\n", + " 102894 - generated_stars = 0\n", + " 102898 - # Done\n", + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 103458 - # From density profile (number density)\n", + " 103462 - expected_total_iMass = 0.0718\n", + " 103465 - expected_total_eMass = 0.0713\n", + " 103468 - average_iMass_per_star = 0.5739\n", + " 103471 - mass_loss_correction = 0.9942\n", + " 103474 - n_expected_stars = 0.1250\n", + " 103484 - # Determine velocities when position are generated \n", + " 103495 - # From Generated Field:\n", + " 103502 - generated_stars = 0\n", + " 103510 - # Done\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 104030 - # From density profile (number density)\n", + " 104033 - expected_total_iMass = 0.1853\n", + " 104037 - expected_total_eMass = 0.1705\n", + " 104040 - average_iMass_per_star = 0.5739\n", + " 104043 - mass_loss_correction = 0.9201\n", + " 104046 - n_expected_stars = 0.3228\n", + " 104052 - # Determine velocities when position are generated \n", + " 104059 - # From Generated Field:\n", + " 104063 - generated_stars = 0\n", + " 104069 - # Done\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 104619 - # From density profile (number density)\n", + " 104623 - expected_total_iMass = 0.2611\n", + " 104626 - expected_total_eMass = 0.2315\n", + " 104629 - average_iMass_per_star = 0.5739\n", + " 104636 - mass_loss_correction = 0.8867\n", + " 104640 - n_expected_stars = 0.4549\n", + " 104648 - # Determine velocities when position are generated \n", + " 104654 - # From Generated Field:\n", + " 104658 - generated_stars = 0\n", + " 104661 - # Done\n", + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 105210 - # From density profile (number density)\n", + " 105213 - expected_total_iMass = 0.6869\n", + " 105216 - expected_total_eMass = 0.6034\n", + " 105220 - average_iMass_per_star = 0.5739\n", + " 105223 - mass_loss_correction = 0.8784\n", + " 105231 - n_expected_stars = 1.1970\n", + " 105241 - # Determine velocities when position are generated \n", + "/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/star_generator.py:190: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " star_systems.loc[unique_pris,\"n_companions\"] = comp_count\n", + " 105305 - # From Generated Field:\n", + " 105308 - generated_stars = 2\n", + " 105312 - generated_total_iMass = 0.4848\n", + " 105321 - generated_total_eMass = 0.4848\n", + " 105325 - det_mass_loss_corr = 1.0000\n", + " 105329 - # Done\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 105915 - # From density profile (number density)\n", + " 105925 - expected_total_iMass = 0.9002\n", + " 105931 - expected_total_eMass = 0.7704\n", + " 105940 - average_iMass_per_star = 0.5739\n", + " 105953 - mass_loss_correction = 0.8558\n", + " 105962 - n_expected_stars = 1.5686\n", + " 105983 - # Determine velocities when position are generated \n", + "/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/star_generator.py:190: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " star_systems.loc[unique_pris,\"n_companions\"] = comp_count\n", + " 106053 - # From Generated Field:\n", + " 106056 - generated_stars = 1\n", + " 106059 - generated_total_iMass = 0.1942\n", + " 106068 - generated_total_eMass = 0.1942\n", + " 106071 - det_mass_loss_corr = 1.0000\n", + " 106076 - # Done\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 106625 - # From density profile (number density)\n", + " 106630 - expected_total_iMass = 1.9625\n", + " 106638 - expected_total_eMass = 1.5977\n", + " 106642 - average_iMass_per_star = 0.5739\n", + " 106645 - mass_loss_correction = 0.8141\n", + " 106648 - n_expected_stars = 3.4198\n", + " 106654 - # Determine velocities when position are generated \n", + " 106732 - # From Generated Field:\n", + " 106739 - generated_stars = 4\n", + " 106742 - generated_total_iMass = 1.0244\n", + " 106751 - generated_total_eMass = 1.0244\n", + " 106759 - det_mass_loss_corr = 1.0000\n", + " 106764 - # Done\n", + "\n", + "\n", + "########################### Combine Populations ###########################\n", + " 106777 - Number of star systems generated: 29 (32 columns)\n", + " 106780 - included_columns = ['iMass', 'age', 'Fe/H_initial', 'n_companions', 'system_idx', 'Mass', 'system_Mass', 'Bessell_V', 'phase', '[Fe/H]', 'log_Teff', '2MASS_Ks', 'Bessell_I', 'log_g', 'star_mass', 'log_L', 'log_R', 'x', 'y', 'z', 'Dist', 'l', 'b', 'vr_bc', 'mul', 'mub', 'U', 'V', 'W', 'VR_LSR', 'A_Ks', 'pop']\n", + "\n", + "\n", + "# Save result -------------------------------------------------------------\n", + " 106783 - write result to \"outputfiles/default/mod2test_l3.000_b-1.000.csv\"\n", + " 106792 - write result to \"outputfiles/default/mod2test_l3.000_b-1.000_companions.csv\"\n", + " 106814 - ---------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "systems, companions = mod2.process_location(l_deg=3, b_deg=-1, field_shape='box', field_scale=0.0003, field_scale_unit='deg')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f46c47e8-151c-42bf-8dd7-1697767723db", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# systems1, companions1 = mod2.process_location(l_deg=4, b_deg=-1, field_shape='circle', field_scale=0.003, field_scale_unit='deg')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ef400507-a03d-4591-92ad-8998db08efd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.13860702487190293" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "systems.loc[0,'Mass']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "124c45a3-a2d7-4bcc-abf8-fbf29341e3b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([1, 3, 4, 5, 6, 9, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 23, 26,\n", + " 27],\n", + " dtype='int64')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "systems.index[systems.Mass>0.2]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ef11e0e0-9d8c-4626-98ac-3224e5ba2c1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iMassMasssystem_idxperiodeccentricityBessell_Vphase[Fe/H]log_Teff2MASS_KsBessell_Ilog_gstar_masslog_Llog_Rpop
00.0528860.05288624.466588e+030.51076526.6130380.0528860.0528860.05288615.01683721.2323650.0528860.0528860.0528860.0528860.0
10.0205750.02057549.738295e+070.29961430.9109870.0205750.0205750.02057515.31001523.6720890.0205750.0205750.0205750.0205750.0
20.2295310.22952763.995588e+020.35876541.4515950.000000-0.6149673.56656322.65229532.6518965.0465230.229527-2.029220-0.6250590.0
30.0137100.01371082.571640e+020.47469730.3976480.0137100.0137100.01371015.34754923.4143560.0137100.0137100.0137100.0137100.0
40.2686810.26867791.803843e+050.36114540.0570570.0000000.3585823.47807822.65181231.5209324.9595770.268677-2.255531-0.5612440.0
50.1419380.141937101.930537e+050.72840942.5956000.0000000.2244353.45529624.03447233.4325345.1329680.141937-2.805300-0.7905640.0
60.4884150.488397112.139929e+030.10155535.7362310.000000-0.3298793.59425420.81830728.7918954.7842960.488397-1.326134-0.3288990.0
73.3249330.756418129.196913e+050.796021NaN101.000000NaNNaNNaNNaNNaN0.756418NaNNaN0.0
80.0276130.027613162.425340e+030.35133127.4450510.0276130.0276130.02761315.41435121.8627700.0276130.0276130.0276130.0276130.0
90.3503800.350372181.402913e+020.48942438.7922910.000000-0.4091853.56910222.23660131.0281054.9315920.350372-1.717874-0.4744650.0
100.2571470.257144197.229160e+000.00000046.5458450.0000000.3733813.47107523.65543235.4496824.9691780.257144-2.316249-0.5775970.0
110.1545700.154569221.156666e+010.00000044.7434060.0000000.0930173.48955724.73433635.0203755.1075590.154569-2.567829-0.7403528.0
120.0857920.085792237.742122e+040.22827932.5860600.0857920.0857920.08579216.90402325.3095480.0857920.0857920.0857920.0857928.0
130.0361280.036128242.237148e+030.38033830.0310230.0361280.0361280.03612816.21303823.6194360.0361280.0361280.0361280.0361289.0
140.0400380.040038252.222875e+080.41559228.8708150.0400380.0400380.04003815.81127622.8111490.0400380.0400380.0400380.04003810.0
150.1535880.153587265.707038e+040.40740751.3619750.000000-0.5314973.54410724.85570338.9225655.1560470.153587-2.401654-0.76636310.0
160.0340380.034038276.255048e+040.00752230.6180320.0340380.0340380.03403816.34494223.9952750.0340380.0340380.0340380.03403810.0
\n", + "
" + ], + "text/plain": [ + " iMass Mass system_idx period eccentricity Bessell_V \\\n", + "0 0.052886 0.052886 2 4.466588e+03 0.510765 26.613038 \n", + "1 0.020575 0.020575 4 9.738295e+07 0.299614 30.910987 \n", + "2 0.229531 0.229527 6 3.995588e+02 0.358765 41.451595 \n", + "3 0.013710 0.013710 8 2.571640e+02 0.474697 30.397648 \n", + "4 0.268681 0.268677 9 1.803843e+05 0.361145 40.057057 \n", + "5 0.141938 0.141937 10 1.930537e+05 0.728409 42.595600 \n", + "6 0.488415 0.488397 11 2.139929e+03 0.101555 35.736231 \n", + "7 3.324933 0.756418 12 9.196913e+05 0.796021 NaN \n", + "8 0.027613 0.027613 16 2.425340e+03 0.351331 27.445051 \n", + "9 0.350380 0.350372 18 1.402913e+02 0.489424 38.792291 \n", + "10 0.257147 0.257144 19 7.229160e+00 0.000000 46.545845 \n", + "11 0.154570 0.154569 22 1.156666e+01 0.000000 44.743406 \n", + "12 0.085792 0.085792 23 7.742122e+04 0.228279 32.586060 \n", + "13 0.036128 0.036128 24 2.237148e+03 0.380338 30.031023 \n", + "14 0.040038 0.040038 25 2.222875e+08 0.415592 28.870815 \n", + "15 0.153588 0.153587 26 5.707038e+04 0.407407 51.361975 \n", + "16 0.034038 0.034038 27 6.255048e+04 0.007522 30.618032 \n", + "\n", + " phase [Fe/H] log_Teff 2MASS_Ks Bessell_I log_g star_mass \\\n", + "0 0.052886 0.052886 0.052886 15.016837 21.232365 0.052886 0.052886 \n", + "1 0.020575 0.020575 0.020575 15.310015 23.672089 0.020575 0.020575 \n", + "2 0.000000 -0.614967 3.566563 22.652295 32.651896 5.046523 0.229527 \n", + "3 0.013710 0.013710 0.013710 15.347549 23.414356 0.013710 0.013710 \n", + "4 0.000000 0.358582 3.478078 22.651812 31.520932 4.959577 0.268677 \n", + "5 0.000000 0.224435 3.455296 24.034472 33.432534 5.132968 0.141937 \n", + "6 0.000000 -0.329879 3.594254 20.818307 28.791895 4.784296 0.488397 \n", + "7 101.000000 NaN NaN NaN NaN NaN 0.756418 \n", + "8 0.027613 0.027613 0.027613 15.414351 21.862770 0.027613 0.027613 \n", + "9 0.000000 -0.409185 3.569102 22.236601 31.028105 4.931592 0.350372 \n", + "10 0.000000 0.373381 3.471075 23.655432 35.449682 4.969178 0.257144 \n", + "11 0.000000 0.093017 3.489557 24.734336 35.020375 5.107559 0.154569 \n", + "12 0.085792 0.085792 0.085792 16.904023 25.309548 0.085792 0.085792 \n", + "13 0.036128 0.036128 0.036128 16.213038 23.619436 0.036128 0.036128 \n", + "14 0.040038 0.040038 0.040038 15.811276 22.811149 0.040038 0.040038 \n", + "15 0.000000 -0.531497 3.544107 24.855703 38.922565 5.156047 0.153587 \n", + "16 0.034038 0.034038 0.034038 16.344942 23.995275 0.034038 0.034038 \n", + "\n", + " log_L log_R pop \n", + "0 0.052886 0.052886 0.0 \n", + "1 0.020575 0.020575 0.0 \n", + "2 -2.029220 -0.625059 0.0 \n", + "3 0.013710 0.013710 0.0 \n", + "4 -2.255531 -0.561244 0.0 \n", + "5 -2.805300 -0.790564 0.0 \n", + "6 -1.326134 -0.328899 0.0 \n", + "7 NaN NaN 0.0 \n", + "8 0.027613 0.027613 0.0 \n", + "9 -1.717874 -0.474465 0.0 \n", + "10 -2.316249 -0.577597 0.0 \n", + "11 -2.567829 -0.740352 8.0 \n", + "12 0.085792 0.085792 8.0 \n", + "13 0.036128 0.036128 9.0 \n", + "14 0.040038 0.040038 10.0 \n", + "15 -2.401654 -0.766363 10.0 \n", + "16 0.034038 0.034038 10.0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "companions" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e119fa43-8be8-4e1e-a05c-59388ae35dc8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(13.481956152199869, 13.481956152199869)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(systems['Mass']) + np.sum(companions['Mass']), np.sum(systems['system_Mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8c465e65-3854-4629-9e1f-078b4978a6b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8204132984658864" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mod2.populations[0].av_mass_corr #, mod0.populations[0].av_mass_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "791823da-b18e-4a38-b1a0-ec6b9cbc6980", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "################################ Settings #################################\n", + " 106950 - # reading default parameters from\n", + " 106954 - default_config_file = /System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/config_files/_default.synthpop_conf \n", + " 106962 - # read configuration from \n", + " 106965 - config_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/config_files/huston2025_defaults.synthpop_conf' \n", + "\n", + "\n", + "# copy the following to a config file to redo this model generation -------\n", + " 106969 - {\n", + " \"l_set\": null,\n", + " \"l_set_type\": null,\n", + " \"b_set\": null,\n", + " \"b_set_type\": null,\n", + " \"name_for_output\": \"mod0test\",\n", + " \"model_name\": \"Huston2025\",\n", + " \"field_shape\": \"circle\",\n", + " \"field_scale\": null,\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 517216434,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 50000,\n", + " \"kinematics_at_the_end\": false,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"StarGenerator\",\n", + " \"extinction_map_kwargs\": {\n", + " \"name\": \"maps_from_dustmaps\",\n", + " \"dustmap_name\": \"marshall\"\n", + " },\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"SODC\",\n", + " \"R_V\": 2.5\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"MIST\",\n", + " \"interpolator\": \"CharonInterpolator\"\n", + " },\n", + " \"ifmr_kwargs\": {\n", + " \"name\": \"PopsycleIfmrs\",\n", + " \"ifmr_name\": \"SukhboldN20\"\n", + " },\n", + " \"multiplicity_kwargs\": null,\n", + " \"maglim\": [\n", + " \"Bessell_I\",\n", + " 99,\n", + " \"keep\"\n", + " ],\n", + " \"chosen_bands\": [\n", + " \"Bessell_U\",\n", + " \"Bessell_B\",\n", + " \"Bessell_V\",\n", + " \"Bessell_R\",\n", + " \"Bessell_I\"\n", + " ],\n", + " \"obsmag\": true,\n", + " \"combine_system_mags\": false,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": [\n", + " {\n", + " \"name\": \"ConvertMistMags\",\n", + " \"conversions\": {\n", + " \"AB\": [\n", + " \"R062\",\n", + " \"Z087\",\n", + " \"Y106\",\n", + " \"J129\",\n", + " \"W146\",\n", + " \"H158\",\n", + " \"F184\"\n", + " ]\n", + " }\n", + " },\n", + " {\n", + " \"name\": \"RenameColumns\",\n", + " \"old_names\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\"\n", + " ],\n", + " \"new_names\": [\n", + " \"logL\",\n", + " \"logTeff\",\n", + " \"logg\",\n", + " \"Fe/H_evolved\",\n", + " \"log_radius\"\n", + " ]\n", + " }\n", + " ],\n", + " \"output_location\": \"outputfiles/default\",\n", + " \"output_filename_pattern\": \"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"csv\",\n", + " \"overwrite\": true\n", + "}\n", + " 106972 - Location or solid_angle_sr are not defined in the settings! Can not run main() or process_all()\n" + ] + } + ], + "source": [ + "mod0=0\n", + "mod0 = synthpop.SynthPop('huston2025_defaults.synthpop_conf',\n", + " extinction_map_kwargs={'name':'maps_from_dustmaps', \n", + " 'dustmap_name': 'marshall'},\n", + " chosen_bands = ['Bessell_U', 'Bessell_B', 'Bessell_V', 'Bessell_R', 'Bessell_I'],\n", + " maglim = ['Bessell_I', 99, \"keep\"],\n", + " #multiplicity_kwargs={\"name\":\"Raghavan\"},\n", + " #post_processing_kwargs=[{\"name\": \"DeriveBinaryParameters\"}],\n", + " name_for_output='mod0test',\n", + " field_shape='circle', field_scale_unit='deg'\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "51bd3707-1de1-462f-a5e5-e784067eb7ed", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "######################### Initialize populations ##########################\n", + " 107048 - read Population files from Huston2025\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 107054 - # Initialize Population 0 (bulge) from \n", + " 107059 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/bulge.popjson'\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 109819 - # Initialize Population 1 (halo) from \n", + " 109822 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/halo.popjson'\n", + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 111133 - # Initialize Population 2 (nsd) from \n", + " 111136 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/nsd.popjson'\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 113208 - # Initialize Population 3 (thick_disk) from \n", + " 113211 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thick_disk.popjson'\n", + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 114537 - # Initialize Population 4 (thin_disk_1) from \n", + " 114549 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_1.popjson'\n", + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 115934 - # Initialize Population 5 (thin_disk_2) from \n", + " 115937 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_2.popjson'\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 117300 - # Initialize Population 6 (thin_disk_3) from \n", + " 117307 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_3.popjson'\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 118638 - # Initialize Population 7 (thin_disk_4) from \n", + " 118641 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_4.popjson'\n", + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 119962 - # Initialize Population 8 (thin_disk_5) from \n", + " 119966 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_5.popjson'\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 121290 - # Initialize Population 9 (thin_disk_6) from \n", + " 121293 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_6.popjson'\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 122595 - # Initialize Population 10 (thin_disk_7) from \n", + " 122602 - pop_file = '/System/Volumes/Data/mnt/g3/scratch/shepbrooke/code/synthpop/synthpop/models/Huston2025/thin_disk_7.popjson'\n", + " 123970 - # All populations are initialized\n" + ] + } + ], + "source": [ + "mod0.init_populations()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "27ed4615-0a3e-4969-a621-73ebac86404b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 123981 - Execution Date: 2025-12-12 01:02:52\n", + "\n", + "\n", + "################################ Settings #################################\n", + "\n", + "\n", + "# Copy the following to a config file to redo this model generation: ------\n", + " 123984 - {\n", + " \"l_set\": [\n", + " 3\n", + " ],\n", + " \"l_set_type\": \"pairs\",\n", + " \"b_set\": [\n", + " -1\n", + " ],\n", + " \"b_set_type\": \"pairs\",\n", + " \"name_for_output\": \"mod0test\",\n", + " \"model_name\": \"Huston2025\",\n", + " \"field_shape\": \"circle\",\n", + " \"field_scale\": 0.003,\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 517216434,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 50000,\n", + " \"kinematics_at_the_end\": false,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"StarGenerator\",\n", + " \"extinction_map_kwargs\": {\n", + " \"name\": \"maps_from_dustmaps\",\n", + " \"dustmap_name\": \"marshall\"\n", + " },\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"SODC\",\n", + " \"R_V\": 2.5\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"MIST\",\n", + " \"interpolator\": \"CharonInterpolator\"\n", + " },\n", + " \"ifmr_kwargs\": {\n", + " \"name\": \"PopsycleIfmrs\",\n", + " \"ifmr_name\": \"SukhboldN20\"\n", + " },\n", + " \"multiplicity_kwargs\": null,\n", + " \"maglim\": [\n", + " \"Bessell_I\",\n", + " 99,\n", + " \"keep\"\n", + " ],\n", + " \"chosen_bands\": [\n", + " \"Bessell_U\",\n", + " \"Bessell_B\",\n", + " \"Bessell_V\",\n", + " \"Bessell_R\",\n", + " \"Bessell_I\"\n", + " ],\n", + " \"obsmag\": true,\n", + " \"combine_system_mags\": false,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": [\n", + " {\n", + " \"name\": \"ConvertMistMags\",\n", + " \"conversions\": {\n", + " \"AB\": [\n", + " \"R062\",\n", + " \"Z087\",\n", + " \"Y106\",\n", + " \"J129\",\n", + " \"W146\",\n", + " \"H158\",\n", + " \"F184\"\n", + " ]\n", + " }\n", + " },\n", + " {\n", + " \"name\": \"RenameColumns\",\n", + " \"old_names\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\"\n", + " ],\n", + " \"new_names\": [\n", + " \"logL\",\n", + " \"logTeff\",\n", + " \"logg\",\n", + " \"Fe/H_evolved\",\n", + " \"log_radius\"\n", + " ]\n", + " }\n", + " ],\n", + " \"output_location\": \"outputfiles/default\",\n", + " \"output_filename_pattern\": \"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"csv\",\n", + " \"overwrite\": true\n", + "}\n", + "\n", + "\n", + "############################# Update location #############################\n", + " 123992 - # set location to: \n", + " 123999 - l, b = (3.00 deg, -1.00 deg)\n", + " 124002 - # set field scale to:\n", + " 124006 - field_scale = 3.000e-03 deg\n", + "\n", + "\n", + "############################# Generate Field ##############################\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 135955 - # From density profile (number density)\n", + " 135959 - expected_total_iMass = 6266.2368\n", + " 135963 - expected_total_eMass = 3620.9410\n", + " 135965 - average_iMass_per_star = 0.5739\n", + " 135969 - mass_loss_correction = 0.5778\n", + " 135972 - n_expected_stars = 10919.3389\n", + " 135978 - # Determine velocities when position are generated \n", + " 136264 - # From Generated Field:\n", + " 136272 - generated_stars = 10896\n", + " 136280 - generated_total_iMass = 6354.0252\n", + " 136291 - generated_total_eMass = 3714.6287\n", + " 136295 - det_mass_loss_corr = 0.5846\n", + " 136300 - # Done\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 136833 - # From density profile (number density)\n", + " 136837 - expected_total_iMass = 11.5051\n", + " 136840 - expected_total_eMass = 6.6045\n", + " 136843 - average_iMass_per_star = 0.5739\n", + " 136850 - mass_loss_correction = 0.5741\n", + " 136861 - n_expected_stars = 20.0484\n", + " 136866 - # Determine velocities when position are generated \n", + " 136952 - # From Generated Field:\n", + " 136955 - generated_stars = 20\n", + " 136959 - generated_total_iMass = 12.2489\n", + " 136969 - generated_total_eMass = 6.4685\n", + " 136972 - det_mass_loss_corr = 0.5281\n", + " 136977 - # Done\n", + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 138353 - # From density profile (number density)\n", + " 138357 - expected_total_iMass = 22.7895\n", + " 138360 - expected_total_eMass = 13.3393\n", + " 138363 - average_iMass_per_star = 0.5739\n", + " 138367 - mass_loss_correction = 0.5853\n", + " 138370 - n_expected_stars = 39.7123\n", + " 138377 - # Determine velocities when position are generated \n", + " 138781 - # From Generated Field:\n", + " 138783 - generated_stars = 38\n", + " 138787 - generated_total_iMass = 23.2551\n", + " 138802 - generated_total_eMass = 13.2491\n", + " 138805 - det_mass_loss_corr = 0.5697\n", + " 138809 - # Done\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 139407 - # From density profile (number density)\n", + " 139412 - expected_total_iMass = 122.8593\n", + " 139415 - expected_total_eMass = 70.9399\n", + " 139423 - average_iMass_per_star = 0.5739\n", + " 139426 - mass_loss_correction = 0.5774\n", + " 139433 - n_expected_stars = 214.0907\n", + " 139446 - # Determine velocities when position are generated \n", + " 139547 - # From Generated Field:\n", + " 139550 - generated_stars = 196\n", + " 139557 - generated_total_iMass = 97.1045\n", + " 139567 - generated_total_eMass = 62.3082\n", + " 139572 - det_mass_loss_corr = 0.6417\n", + " 139577 - # Done\n", + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 140177 - # From density profile (number density)\n", + " 140181 - expected_total_iMass = 1.6613\n", + " 140185 - expected_total_eMass = 1.3323\n", + " 140188 - average_iMass_per_star = 0.5739\n", + " 140192 - mass_loss_correction = 0.8020\n", + " 140195 - n_expected_stars = 2.8949\n", + " 140202 - # Determine velocities when position are generated \n", + " 140275 - # From Generated Field:\n", + " 140283 - generated_stars = 5\n", + " 140290 - generated_total_iMass = 1.0451\n", + " 140299 - generated_total_eMass = 1.0451\n", + " 140303 - det_mass_loss_corr = 1.0000\n", + " 140308 - # Done\n", + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 140930 - # From density profile (number density)\n", + " 140934 - expected_total_iMass = 30.9311\n", + " 140938 - expected_total_eMass = 22.4114\n", + " 140941 - average_iMass_per_star = 0.5739\n", + " 140944 - mass_loss_correction = 0.7246\n", + " 140947 - n_expected_stars = 53.8996\n", + " 140954 - # Determine velocities when position are generated \n", + " 141179 - # From Generated Field:\n", + " 141183 - generated_stars = 49\n", + " 141186 - generated_total_iMass = 14.5146\n", + " 141196 - generated_total_eMass = 14.5142\n", + " 141200 - det_mass_loss_corr = 1.0000\n", + " 141209 - # Done\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 141859 - # From density profile (number density)\n", + " 141863 - expected_total_iMass = 82.0298\n", + " 141866 - expected_total_eMass = 53.5505\n", + " 141869 - average_iMass_per_star = 0.5739\n", + " 141872 - mass_loss_correction = 0.6528\n", + " 141876 - n_expected_stars = 142.9424\n", + " 141883 - # Determine velocities when position are generated \n", + " 142027 - # From Generated Field:\n", + " 142030 - generated_stars = 156\n", + " 142033 - generated_total_iMass = 74.4942\n", + " 142044 - generated_total_eMass = 59.7687\n", + " 142048 - det_mass_loss_corr = 0.8023\n", + " 142052 - # Done\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 142674 - # From density profile (number density)\n", + " 142677 - expected_total_iMass = 113.4406\n", + " 142685 - expected_total_eMass = 72.7281\n", + " 142688 - average_iMass_per_star = 0.5739\n", + " 142692 - mass_loss_correction = 0.6411\n", + " 142695 - n_expected_stars = 197.6779\n", + " 142700 - # Determine velocities when position are generated \n", + " 142831 - # From Generated Field:\n", + " 142835 - generated_stars = 201\n", + " 142838 - generated_total_iMass = 135.4129\n", + " 142849 - generated_total_eMass = 80.8587\n", + " 142853 - det_mass_loss_corr = 0.5971\n", + " 142858 - # Done\n", + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 143474 - # From density profile (number density)\n", + " 143478 - expected_total_iMass = 307.7419\n", + " 143481 - expected_total_eMass = 189.5561\n", + " 143486 - average_iMass_per_star = 0.5739\n", + " 143491 - mass_loss_correction = 0.6160\n", + " 143498 - n_expected_stars = 536.2609\n", + " 143505 - # Determine velocities when position are generated \n", + " 143740 - # From Generated Field:\n", + " 143743 - generated_stars = 465\n", + " 143747 - generated_total_iMass = 251.5798\n", + " 143757 - generated_total_eMass = 157.4086\n", + " 143760 - det_mass_loss_corr = 0.6257\n", + " 143765 - # Done\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 144407 - # From density profile (number density)\n", + " 144411 - expected_total_iMass = 393.8240\n", + " 144415 - expected_total_eMass = 242.0194\n", + " 144425 - average_iMass_per_star = 0.5739\n", + " 144432 - mass_loss_correction = 0.6145\n", + " 144435 - n_expected_stars = 686.2648\n", + " 144441 - # Determine velocities when position are generated \n", + " 144641 - # From Generated Field:\n", + " 144648 - generated_stars = 675\n", + " 144652 - generated_total_iMass = 395.6062\n", + " 144662 - generated_total_eMass = 235.3720\n", + " 144666 - det_mass_loss_corr = 0.5950\n", + " 144671 - # Done\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 145293 - # From density profile (number density)\n", + " 145296 - expected_total_iMass = 843.5416\n", + " 145299 - expected_total_eMass = 501.9240\n", + " 145302 - average_iMass_per_star = 0.5739\n", + " 145305 - mass_loss_correction = 0.5950\n", + " 145311 - n_expected_stars = 1469.9279\n", + " 145321 - # Determine velocities when position are generated \n", + " 145628 - # From Generated Field:\n", + " 145639 - generated_stars = 1399\n", + " 145646 - generated_total_iMass = 845.8139\n", + " 145656 - generated_total_eMass = 488.4139\n", + " 145660 - det_mass_loss_corr = 0.5774\n", + " 145664 - # Done\n", + "\n", + "\n", + "########################### Combine Populations ###########################\n", + " 145670 - Number of star systems generated: 14100 (34 columns)\n", + " 145673 - included_columns = ['iMass', 'age', 'Fe/H_initial', 'n_companions', 'system_idx', 'Mass', 'system_Mass', 'Bessell_R', 'Bessell_V', 'phase', '[Fe/H]', 'log_Teff', 'Bessell_B', 'Bessell_U', 'Bessell_I', 'log_g', 'star_mass', 'log_L', 'log_R', 'x', 'y', 'z', 'Dist', 'l', 'b', 'vr_bc', 'mul', 'mub', 'U', 'V', 'W', 'VR_LSR', 'A_Ks', 'pop']\n", + "\n", + "\n", + "# Save result -------------------------------------------------------------\n", + " 145678 - write result to \"outputfiles/default/mod0test_l3.000_b-1.000.csv\"\n", + " 146379 - ---------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "systems2, companions2 = mod0.process_location(l_deg=3, b_deg=-1, field_scale=0.003)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "be1bb312-7bbb-42fc-980f-1263fb6aefbc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iMassageFe/H_initialn_companionssystem_idxMasssystem_MassBessell_RBessell_Vphase...bvr_bcmulmubUVWVR_LSRA_Kspop
00.49437310.000000-0.2156240.000.4943560.49435629.99014133.0031070.0...-1.00264720.851423-1.141347-0.36663934.817679222.855722-0.29422630.4741430.6385040
10.59822410.0000000.3123460.010.5982000.59820032.06078835.6356090.0...-0.9999994.497563-4.767811-1.64956722.199811140.371946-28.83896614.1208990.7937800
20.31207610.0000000.1396740.020.3120630.31206335.78811139.8921140.0...-0.999885-6.863255-0.463243-1.7997825.802534234.747785-32.7568922.7608860.8756200
30.50034810.0000000.4347240.030.5003350.50033535.01783039.1237250.0...-0.999599-54.807391-0.033170-4.812194-44.089990241.806832-109.640117-45.1834080.9098130
40.33488710.000000-0.2801560.040.3348810.33488135.22632639.2219210.0...-0.9994150.864536-3.2447111.26994518.521059166.44750238.79938410.4884300.9068260
..................................................................
140950.5236887.363215-0.2290170.0140950.5236720.52367236.95613440.8858800.0...-1.001740-32.300374-2.359379-0.085617-6.690491-1.065248-0.514827-22.6774930.92000010
140960.5129899.191717-0.3645450.0140960.5129690.51296941.94164447.0861230.0...-0.997993-19.367821-4.041722-0.04461716.757236-200.0789543.194714-9.7437701.28900010
140970.3736747.867828-0.1527650.0140970.3736670.37366743.55091548.8396290.0...-0.999036-44.022898-2.182506-0.043911-18.202149-3.8648963.633416-34.3990691.28900010
140980.3430198.368702-0.6348590.0140980.3430120.34301243.32513848.5560670.0...-0.999156-25.513031-3.7642210.0214199.891600-183.67645910.669237-15.8893211.28900010
140990.1063717.506652-0.5367450.0140990.1063710.10637141.96584746.2512430.0...-1.001384-10.167084-2.089534-0.04545315.493918-0.2352222.609823-0.5441300.92000010
\n", + "

14100 rows × 34 columns

\n", + "
" + ], + "text/plain": [ + " iMass age Fe/H_initial n_companions system_idx Mass \\\n", + "0 0.494373 10.000000 -0.215624 0.0 0 0.494356 \n", + "1 0.598224 10.000000 0.312346 0.0 1 0.598200 \n", + "2 0.312076 10.000000 0.139674 0.0 2 0.312063 \n", + "3 0.500348 10.000000 0.434724 0.0 3 0.500335 \n", + "4 0.334887 10.000000 -0.280156 0.0 4 0.334881 \n", + "... ... ... ... ... ... ... \n", + "14095 0.523688 7.363215 -0.229017 0.0 14095 0.523672 \n", + "14096 0.512989 9.191717 -0.364545 0.0 14096 0.512969 \n", + "14097 0.373674 7.867828 -0.152765 0.0 14097 0.373667 \n", + "14098 0.343019 8.368702 -0.634859 0.0 14098 0.343012 \n", + "14099 0.106371 7.506652 -0.536745 0.0 14099 0.106371 \n", + "\n", + " system_Mass Bessell_R Bessell_V phase ... b vr_bc \\\n", + "0 0.494356 29.990141 33.003107 0.0 ... -1.002647 20.851423 \n", + "1 0.598200 32.060788 35.635609 0.0 ... -0.999999 4.497563 \n", + "2 0.312063 35.788111 39.892114 0.0 ... -0.999885 -6.863255 \n", + "3 0.500335 35.017830 39.123725 0.0 ... -0.999599 -54.807391 \n", + "4 0.334881 35.226326 39.221921 0.0 ... -0.999415 0.864536 \n", + "... ... ... ... ... ... ... ... \n", + "14095 0.523672 36.956134 40.885880 0.0 ... -1.001740 -32.300374 \n", + "14096 0.512969 41.941644 47.086123 0.0 ... -0.997993 -19.367821 \n", + "14097 0.373667 43.550915 48.839629 0.0 ... -0.999036 -44.022898 \n", + "14098 0.343012 43.325138 48.556067 0.0 ... -0.999156 -25.513031 \n", + "14099 0.106371 41.965847 46.251243 0.0 ... -1.001384 -10.167084 \n", + "\n", + " mul mub U V W VR_LSR \\\n", + "0 -1.141347 -0.366639 34.817679 222.855722 -0.294226 30.474143 \n", + "1 -4.767811 -1.649567 22.199811 140.371946 -28.838966 14.120899 \n", + "2 -0.463243 -1.799782 5.802534 234.747785 -32.756892 2.760886 \n", + "3 -0.033170 -4.812194 -44.089990 241.806832 -109.640117 -45.183408 \n", + "4 -3.244711 1.269945 18.521059 166.447502 38.799384 10.488430 \n", + "... ... ... ... ... ... ... \n", + "14095 -2.359379 -0.085617 -6.690491 -1.065248 -0.514827 -22.677493 \n", + "14096 -4.041722 -0.044617 16.757236 -200.078954 3.194714 -9.743770 \n", + "14097 -2.182506 -0.043911 -18.202149 -3.864896 3.633416 -34.399069 \n", + "14098 -3.764221 0.021419 9.891600 -183.676459 10.669237 -15.889321 \n", + "14099 -2.089534 -0.045453 15.493918 -0.235222 2.609823 -0.544130 \n", + "\n", + " A_Ks pop \n", + "0 0.638504 0 \n", + "1 0.793780 0 \n", + "2 0.875620 0 \n", + "3 0.909813 0 \n", + "4 0.906826 0 \n", + "... ... ... \n", + "14095 0.920000 10 \n", + "14096 1.289000 10 \n", + "14097 1.289000 10 \n", + "14098 1.289000 10 \n", + "14099 0.920000 10 \n", + "\n", + "[14100 rows x 34 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "systems2" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c204d075-2836-44da-abb8-868205f5e396", + "metadata": {}, + "outputs": [], + "source": [ + "companions2" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7a9c23d6-9dfd-4823-85bc-76bb918f82b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4834.035778401899, 4834.035778401899)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(systems2['Mass']), np.sum(systems2['system_Mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "63513a61-b394-42f1-a4b8-b7311a291534", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.stairs(*np.histogram(systems.Bessell_I, bins=np.linspace(10,40,21)), label='with binaries')\n", + "plt.stairs(*np.histogram(systems2.Bessell_I, bins=np.linspace(10,40,21)), label='singles')\n", + "plt.legend()\n", + "#plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0630950-fca5-4dc0-b045-c70501602d74", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:astro-synthpop]", + "language": "python", + "name": "conda-env-astro-synthpop-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TUTORIAL.ipynb b/TUTORIAL.ipynb index 191def7..2b7f259 100755 --- a/TUTORIAL.ipynb +++ b/TUTORIAL.ipynb @@ -273,7 +273,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/source/configuration.rst b/docs/source/configuration.rst index 5f9d90c..0ddc812 100644 --- a/docs/source/configuration.rst +++ b/docs/source/configuration.rst @@ -32,7 +32,7 @@ For example the config file can look as follows:: "model_base_name":"my_generated_model", "l_set": [0, 2], "b_set": [-1], - "solid_angle":5e-7, + "field_scale":1e-3, "model_name":"besancon_Robin2003", "evolution_class":{ "name":"MIST", @@ -66,7 +66,7 @@ Configuration File Contents Our ``_default_config.json`` is sorted into different categories, which are not required but may be used for any configuration. Note that any arguments starting with an '#' are ignored, so we use these for comments. The optional category headings are: MANDITORY, SIGHTLINES, SEED, COORDINATE_SYSTEM, POPULATION_GENERATION, EXTINCTION_MAP, ISOCHRONE_INTERPOLATION, PHOTOMETRIC_OUTPUTS, and OUTPUT. -MANDITORY +MANDATORY ^^^^^^^^^ **model_name**: (string) model directory containing the population files @@ -89,9 +89,11 @@ SIGHTLINES * "pairs": treats the l and b sets as a list of pairs, i.e. ((l[0],b[0]),..., (l[i],b[i]) * "range": treats the l and b sets as arguments for np.arange([l/b]_set) -**solid_angle**: solid angle of cone FoV in units defined by **solid_angle_unit** +**field_shape**: shape of field on-sky (options: "circle" or "box") -**solid_angle_unit**: angle for solid angle of FoV (options: "deg^2" for square degree, "sr" for steradian) +**field_scale**: size of the field in units defined by **field_scale_unit** (radius for a circle, side length for a square box, or [l side length, b side length] for a rectangular box) + +**solid_angle_unit**: angular units for the **field_scale** (options: rad, deg, arcmin, arcsec) example:: @@ -100,8 +102,9 @@ example:: "l_set_type":"list", "b_set":[-1,0,1], "b_set_type":"list", - "solid_angle": 1e-2, - "solid_angle_unit": "deg^2" + "field_shape":"box" + "field_scale": 1e-2, + "field_scale_unit": "deg" } SEED @@ -160,17 +163,8 @@ POPULATION_GENERATION ^^^^^^^^^^^^^^^^^^^^^ **max_distance**: maximum distance for stars in catalog (kpc) -**distance_step_size**: step size for generation of stars as slices in distance (kpc) - -**window_type**: dictionary containing the following: - -* **window_type**: currently must be set to "cone" [note: other options are planned but not in the immediate future] -* **kwargs** - **mass_lims**: range of initial stellar masses to produce -**N_mc_totmass**: number of random points to sample to estimate average density in a slice - **lost_mass_option**: method to estimate correction for mass loss with four integer options: * 1: For each population, a test batch of N_av_mass stars is generated and evolved to estimate the total initial stellar mass required to meet the desired present day total stellar mass. These values are saved for all sightlines run with the initialized populations. @@ -180,11 +174,9 @@ POPULATION_GENERATION **N_av_mass**: number of stars to use to estimate average evolved stellar mass -**kinematics_at_the_end**: sets whether to determine stellar masses are evolved at the end of the process, instead of as stars are generated (boolean) - **scale_factor**: scale down number of generated stars as n_generated = (n_total/scale_factor) -**skip_lowmass_stars**: option to skip the generation of low mass stars which cannot be bright enough to reach the magnitude cut [note: improves runtimes and memory usage] +**skip_lowmass_stars**: option to skip the generation of low mass stars which cannot be bright enough to reach the magnitude cut [note: improves runtimes and memory usage but not available for certain configuration options] **chunk_size**: for computational feasibility, limit number of stars to evolve at once to this value @@ -192,13 +184,9 @@ example:: "POPULATION_GENERATION":{ "max_distance":25, - "distance_step_size":0.10, - "window_type":{"window_type":"cone"}, "mass_lims":{"min_mass":0.08,"max_mass":100}, - "N_mc_totmass":10000, "lost_mass_option": 1, "N_av_mass":20000, - "kinematics_at_the_end":true, "scale_factor": 1, "skip_lowmass_stars": false, "chunk_size": 250000 @@ -207,17 +195,19 @@ example:: EXTINCTION_MAP ^^^^^^^^^^^^^^ -**extinction_map_kwargs**: dictionary containing: +**extinction_map_kwargs**: None or dictionary containing: * **name**: name of extinction map module * ****: any kwargs required or optional for the selected module -**extinction_law_kwargs**: dictionary containing: +**extinction_law_kwargs**: None or dictionary containing: * **name**: name of extinction law module * **R_V**: total to selective extinction ratio [note: only used in select extinction laws, will be ignored if input for others] * ****: any kwargs required or optional for the selected module +Note: if either **extinction_map_kwargs** or **extinction_law_kwargs** is set to None, no extinction will be computed. + example:: "EXTINCTION_MAP": @@ -238,6 +228,14 @@ ISOCHRONE_INTERPOLATION * **name**: name of stellar evolution class * **interpolator**: name of isochrone interpolator class +**ifmr_kwargs**: None or dictionary containing: +* **name**: name of the initial-final mass relation module +* ****: any kwargs required or optional for the selected module + +**multiplicity_kwargs**: None or dictionary containing: +* **name**: name of the metallicity module +* ****: any kwargs required or optional for the selected module + example for single evolution class:: "ISOCHRONE_INTERPOLATION":{ @@ -269,9 +267,9 @@ example for evolution class determined by population:: PHOTOMETRIC_OUTPUTS ^^^^^^^^^^^^^^^^^^^ -**mag_lim**: list containing the band to select on, the magnitude limit in that band, and "keep" or "remove" for whether to drop stars dimmer than the limit +**mag_lim**: None, or list containing the band to select on and the magnitude limit -**chosen_bands**: list of filters to include for synthetic photometry +**chosen_bands**: list of magnitude systems or filters to include for synthetic photometry For the MIST evolution module, the following filters are available: @@ -330,26 +328,30 @@ For the MIST evolution module, the following filters are available: * - UVIT - UVIT_F148W, UVIT_F154W, UVIT_F169M, UVIT_F172M, UVIT_N242W, UVIT_N219M, UVIT_N245M, UVIT_N263M, UVIT_N279N -**eff_wavelengths**: dictionary specifying effective wavelength for each chosen filter [Note: use option {"json_file":"AAA_effective_wavelengths.json"} to load these from a pre-existing file] - **obs_mag**: boolean option to generate observed magnitudes (generates absolute magnitudes if set to false) +**combine_system_mags**: boolean option for whether the star systems table should include the summed system magnitude including any companions (for True) or only the magnitudes of the primary stars (for False). note: the companions table always holds only individual magnitudes for each companion. + **opt_iso_props**: optional stellar properties to save, with original column names from isochrones For MIST isochrone stellar property options, see `their documentation here `_ -**col_names**: columns names for output for the columns determined in **opt_iso_props** - -example:: +example 1:: "PHOTOMETRIC_OUTPUTS":{ - "maglim":["Bessell_I", 18, "remove"], + "maglim":["Bessell_I", 18], "chosen_bands": ["R062","Z087","Y106","J129","W146","H158","F184", "Bessell_U", "Bessell_B", "Bessell_V", "Bessell_R", "Bessell_I", "VISTA_J", "VISTA_H", "VISTA_Ks"], - "eff_wavelengths": {"json_file":"AAA_effective_wavelengths.json"}, "obsmag":true, + "opt_iso_props":["log_L", "log_Teff", "log_g", "[Fe/H]", "log_R", "phase"], + }, + +example 2:: - "opt_iso_props":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R"], - "col_names":["logL", "Teff", "logg" ,"Fe/H_evolved","log_radius"] + "PHOTOMETRIC_OUTPUTS":{ + "maglim":None, + "chosen_bands": ["UBVRIplus", "VISTA"], + "obsmag":true, + "opt_iso_props":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R", "phase"], }, OUTPUT @@ -370,7 +372,7 @@ OUTPUT example:: "OUTPUT":{ - "post_processing_kwargs": [{"name":"ProcessDarkCompactObjects", "remove":true}], + "post_processing_kwargs": [{"name":"ConvertMistMags", "conversions":{"AB": ["R062", "Z087", "Y106", "J129", "W146", "H158", "F184"]}}], "output_location":"outputfiles/testing", "output_filename_pattern": "{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}", "output_file_type": ["csv",{}], diff --git a/docs/source/models.rst b/docs/source/models.rst index ba18548..99d02f8 100644 --- a/docs/source/models.rst +++ b/docs/source/models.rst @@ -49,20 +49,52 @@ Additional kwargs can be used to define galactic warp (see Configuration info fo Available Models ---------------- .. note:: - additional models + descriptions coming soon + Because SynthPop uses different code frameworks than other models (e.g. different numerical techniques, approximations, and configuration options), these models will not exactly reproduce the catalogs and results that come from the cited works if the cited work did not use SynthPop itself. -synthpop_Huston2024 +Huston2024 ^^^^^^^^^^^^^^^^^^^ +Preliminary version presented by Huston et al. (in prep.), with citations therein to model components drawn from many existing models. This version was used by the Roman Galactic Exoplanet Survey Project Infrastructure Team for some preparations for microlensing science with the Roman Galactic Bulge Time Domain Survey. + +Huston2025 +^^^^^^^^^^^^^^^^^^^ +Final model version presented by Huston et al. (in prep.), with citations therein to model components drawn from many existing models. This version was used by the Roman Galactic Exoplanet Survey Project Infrastructure Team for some preparations for microlensing science with the Roman Galactic Bulge Time Domain Survey. There are two key differences from Huston2024: the inclusion of a nuclear stellar disk and 3-d handling of the dust. besancon_Robin2003 ^^^^^^^^^^^^^^^^^^ +SynthPop implementation of the Besancon Galactic model, as presented by `Robin et al. (2003) `__. GUMS_dr3 ^^^^^^^^ +SynthPop implementation of the Gaia Universe Model Snapshot (GUMS), DR3 version, as described in the `Gaia DR3 Documenation, Section 2.2 `__. GUMS_dr3_mod_dens ^^^^^^^^^^^^^^^^^ +SynthPop implementation of the Gaia Universe Model Snapshot (GUMS), DR3 version, as described in the `Gaia DR3 Documenation, Section 2.2 `__. This version is rescaled to better match their catalog with the SynthPop code implementation (see `Klüter & Huston et al. (2025) `__). -Koshimoto2021 +Koshimoto2022 ^^^^^^^^^^^^^ +Galactic model presented by `Koshimoto et al. (2021) `__ and `Koshimoto et al. (2022) `__ (`genstars `__), which provides multiple model versions. Here, we provide the E+Ex bulge model, the NEED TO UPDATE DISK VERSION, and their nuclear stellar disk model, which is based on `Sormani et al. (2022) `__. + +Additional Populations +---------------- +.. note:: We provide some additional population files under **models/spare_populations**, which should not be taken as complete models but may be used for population studies or in combination with other model components. + +Cao2013_bulge +^^^^^^^^^^^^^ +Galactic bulge density model based on OGLE-III red clump giants from `Cao et al. (2013) `__, with additional parameters from other models. + +Koshimoto2022_E_bulge_nsd +^^^^^^^^^^^^^^^^^^^^^^^^^ +Alternate E bulge model from `Koshimoto et al. (2021) `__, with corresponding NSD implementation from `Sormani et al. (2022) `__ with the IMF from `Koshimoto et al. (2021) `__ for this bulge parameterization and the age and metallicity used by `genstars `__. + +Koshimoto2022_G_bulge_nsd +^^^^^^^^^^^^^^^^^^^^^^^^^ +Alternate G bulge model from `Koshimoto et al. (2021) `__, with corresponding NSD implementation from `Sormani et al. (2022) `__ with the IMF from `Koshimoto et al. (2021) `__ for this bulge parameterization and the age and metallicity used by `genstars `__. + +Koshimoto2022_GxG_bulge_nsd +^^^^^^^^^^^^^^^^^^^^^^^^^ +Alternate G+Gx bulge model from `Koshimoto et al. (2021) `__, with corresponding NSD implementation from `Sormani et al. (2022) `__ with the IMF from `Koshimoto et al. (2021) `__ for this bulge parameterization and the age and metallicity used by `genstars `__. +Sormani2022_nsd +^^^^^^^^^^^^^^^ +Nuclear Stellar Disk population from `Sormani et al. (2022) `__. diff --git a/requirements.txt b/requirements.txt index 64db7c6..a94493e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy>=1.23.2 -scipy>=1.9.0 +scipy>=1.6.0 pandas>=1.4.0 requests>=2.28.1 tables>=3.7.0; diff --git a/synthpop/config_files/AAA_effective_wavelengths.json b/synthpop/config_files/AAA_effective_wavelengths.json deleted file mode 100644 index bdbd61a..0000000 --- a/synthpop/config_files/AAA_effective_wavelengths.json +++ /dev/null @@ -1,263 +0,0 @@ -{ - "#comment":"effective wavelengths in microns", - "CFHT_u":0.382671, - "CFHT_CaHK":0.395100, - "CFHT_g":0.476008, - "CFHT_r":0.620744, - "CFHT_i_new":0.768102, - "CFHT_i_old":0.768088, - "CFHT_z":0.980398, - "DECam_u":0.385688, - "DECam_g":0.476990, - "DECam_r":0.637044, - "DECam_i":0.777430, - "DECam_z":0.915488, - "DECam_Y":0.988645, - "GALEX_FUV":0.154885, - "GALEX_NUV":0.230337, - "ACS_HRC_F220W":0.225484, - "ACS_HRC_F250W":0.273938, - "ACS_HRC_F330W":0.336823, - "ACS_HRC_F344N":0.343395, - "ACS_HRC_F435W":0.434136, - "ACS_HRC_F475W":0.470887, - "ACS_HRC_F502N":0.502292, - "ACS_HRC_F550M":0.557304, - "ACS_HRC_F555W":0.532696, - "ACS_HRC_F606W":0.577643, - "ACS_HRC_F625W":0.624939, - "ACS_HRC_F658N":0.658591, - "ACS_HRC_F660N":0.660001, - "ACS_HRC_F775W":0.762432, - "ACS_HRC_F814W":0.797339, - "ACS_HRC_F850LP":0.900499, - "ACS_HRC_F892N":0.891661, - "ACS_WFC_F435W":0.434136, - "ACS_WFC_F475W":0.470887, - "ACS_WFC_F502N":0.502292, - "ACS_WFC_F550M":0.557304, - "ACS_WFC_F555W":0.532696, - "ACS_WFC_F606W":0.577643, - "ACS_WFC_F625W":0.624939, - "ACS_WFC_F658N":0.658591, - "ACS_WFC_F660N":0.660001, - "ACS_WFC_F775W":0.762432, - "ACS_WFC_F814W":0.797339, - "ACS_WFC_F850LP":0.900499, - "ACS_WFC_F892N":0.891661, - "WFC3_UVIS_F200LP":0.506944, - "WFC3_UVIS_F218W":0.221883, - "WFC3_UVIS_F225W":0.235870, - "WFC3_UVIS_F275W":0.271386, - "WFC3_UVIS_F280N":0.279694, - "WFC3_UVIS_F300X":0.286433, - "WFC3_UVIS_F336W":0.335895, - "WFC3_UVIS_F343N":0.343648, - "WFC3_UVIS_F350LP":0.553110, - "WFC3_UVIS_F373N":0.373135, - "WFC3_UVIS_F390M":0.390488, - "WFC3_UVIS_F390W":0.401986, - "WFC3_UVIS_F395N":0.395264, - "WFC3_UVIS_F410M":0.410863, - "WFC3_UVIS_F438W":0.432226, - "WFC3_UVIS_F467M":0.468087, - "WFC3_UVIS_F469N":0.468815, - "WFC3_UVIS_F475W":0.473144, - "WFC3_UVIS_F475X":0.485288, - "WFC3_UVIS_F487N":0.487339, - "WFC3_UVIS_F502N":0.500968, - "WFC3_UVIS_F547M":0.543583, - "WFC3_UVIS_F555W":0.523533, - "WFC3_UVIS_F600LP":0.727955, - "WFC3_UVIS_F606W":0.578081, - "WFC3_UVIS_F621M":0.620881, - "WFC3_UVIS_F625W":0.618765, - "WFC3_UVIS_F631N":0.630400, - "WFC3_UVIS_F645N":0.645315, - "WFC3_UVIS_F656N":0.656133, - "WFC3_UVIS_F657N":0.656646, - "WFC3_UVIS_F658N":0.658618, - "WFC3_UVIS_F665N":0.665633, - "WFC3_UVIS_F673N":0.676565, - "WFC3_UVIS_F680N":0.687460, - "WFC3_UVIS_F689M":0.687195, - "WFC3_UVIS_F763M":0.760129, - "WFC3_UVIS_F775W":0.760997, - "WFC3_UVIS_F814W":0.795484, - "WFC3_UVIS_F845M":0.842843, - "WFC3_UVIS_F850LP":0.914857, - "WFC3_UVIS_F953N":0.952895, - "WFC3_IR_F098M":0.982681, - "WFC3_IR_F105W":1.043083, - "WFC3_IR_F110W":1.120052, - "WFC3_IR_F125W":1.236355, - "WFC3_IR_F126N":1.258505, - "WFC3_IR_F127M":1.273237, - "WFC3_IR_F128N":1.283723, - "WFC3_IR_F130N":1.301012, - "WFC3_IR_F132N":1.319312, - "WFC3_IR_F139M":1.383606, - "WFC3_IR_F140W":1.373466, - "WFC3_IR_F153M":1.532628, - "WFC3_IR_F160W":1.527847, - "WFC3_IR_F164N":1.645145, - "WFC3_IR_F167N":1.667400, - "WFPC2_F218W":0.220127, - "WFPC2_F255W":0.261017, - "WFPC2_F300W":0.302867, - "WFPC2_F336W":0.334948, - "WFPC2_F439W":0.430961, - "WFPC2_F450W":0.454540, - "WFPC2_F555W":0.537054, - "WFPC2_F606W":0.589888, - "WFPC2_F622W":0.614554, - "WFPC2_F675W":0.668714, - "WFPC2_F791W":0.782050, - "WFPC2_F814W":0.792454, - "WFPC2_F850LP":0.909794, - "F070W":0.698843, - "F090W":0.898498, - "F115W":1.143362, - "F140M":1.402362, - "F150W2":1.479371, - "F150W":1.487256, - "F162M":1.624333, - "F164N":1.644618, - "F182M":1.838883, - "F187N":1.873722, - "F200W":1.968041, - "F210M":2.090835, - "F212N":2.121193, - "F250M":2.500580, - "F277W":2.727858, - "F300M":2.981832, - "F322W2":3.073138, - "F323N":3.236730, - "F335M":3.353723, - "F356W":3.528704, - "F360M":3.614893, - "F405N":4.051576, - "F410M":4.072318, - "F430M":4.278479, - "F444W":4.350426, - "F460M":4.626986, - "F466N":4.654048, - "F470N":4.707784, - "F480M":4.813911, - "LSST_u":0.375120, - "LSST_g":0.474066, - "LSST_r":0.617234, - "LSST_i":0.750097, - "LSST_z":0.867890, - "LSST_y":0.971182, - "PS_g":0.481016, - "PS_r":0.615547, - "PS_i":0.750303, - "PS_z":0.866836, - "PS_y":0.961360, - "PS_w":0.598070, - "PS_open":0.643187, - "SDSS_u":0.360804, - "SDSS_g":0.467178, - "SDSS_r":0.614112, - "SDSS_i":0.745789, - "SDSS_z":0.892278, - "SkyMapper_u":0.350022, - "SkyMapper_v":0.387868, - "SkyMapper_g":0.501605, - "SkyMapper_r":0.607685, - "SkyMapper_i":0.773283, - "SkyMapper_z":0.912025, - "IRAC_3.6":3.507483, - "IRAC_4.5":4.436556, - "IRAC_5.8":5.628062, - "IRAC_8.0":7.589054, - "SPLUS_uJAVA":0.354207, - "SPLUS_gSDSS":0.471583, - "SPLUS_rSDSS":0.620257, - "SPLUS_iSDSS":0.762701, - "SPLUS_zSDSS":0.891347, - "SPLUS_J0378":0.3770, - "SPLUS_J0395":0.3940, - "SPLUS_J0410":0.4094, - "SPLUS_J0515":0.5133, - "SPLUS_J0660":0.6614, - "SPLUS_J0861":0.8611, - "hsc_g":0.475871, - "hsc_r":0.616633, - "hsc_i":0.768236, - "hsc_z":0.890654, - "hsc_y":0.975962, - "hsc_nb816":0.816771, - "hsc_nb921":0.920205, - "INT_IPHAS_gR":0.615345, - "INT_IPHAS_Ha":0.656820, - "INT_IPHAS_gI":0.766326, - "Swift_UVW2":0.207569, - "Swift_UVM2":0.224656, - "Swift_UVW1":0.271568, - "Swift_U":0.352378, - "Swift_B":0.434596, - "Swift_V":0.541238, - "Bessell_U":0.365988, - "Bessell_B":0.438074, - "Bessell_V":0.544543, - "Bessell_R":0.641147, - "Bessell_I":0.798209, - "2MASS_Ks":2.152164, - "2MASS_J":1.228538, - "2MASS_H":1.638610, - "Kepler_Kp":0.597814, - "Hipparcos_Hp":0.798209, - "Tycho_B":0.428000, - "Tycho_V":0.534000, - "Gaia_G_DR2Rev":0.583631, - "Gaia_BP_DR2Rev":0.502092, - "Gaia_RP_DR2Rev":0.758883, - "Gaia_G_MAW":0.584430, - "Gaia_BP_MAWb":0.504200, - "Gaia_BP_MAWf":0.498269, - "Gaia_RP_MAW":0.759811, - "TESS":0.745564, - "Gaia_G_EDR3":0.673, - "Gaia_BP_EDR3":0.532, - "Gaia_RP_EDR3":0.797, - "UKIDSS_Z":0.881700, - "UKIDSS_Y":1.030500, - "UKIDSS_J":1.248300, - "UKIDSS_H":1.631300, - "UKIDSS_K":22010.00, - "UVIT_F148W": 0.148100, - "UVIT_F154W":0.154100, - "UVIT_F169M":0.1608, - "UVIT_F172M":0.1717, - "UVIT_N219M":0.2196, - "UVIT_N242W":0.2418, - "UVIT_N245M":0.2447, - "UVIT_N263M":0.2632, - "UVIT_N279N":0.2792, - "VISTA_Z":0.878860, - "VISTA_Y":1.019614, - "VISTA_J":1.255579, - "VISTA_H":1.649872, - "VISTA_Ks":2.157790, - "Washington_C":0.397351, - "Washington_M":0.509304, - "Washington_T1":0.6650281, - "Washington_T2":0.797884, - "Stromgren_u":0.348257, - "Stromgren_v":0.412435, - "Stromgren_b":0.466691, - "Stromgren_y":0.546474, - "R062":0.615778, - "Z087":0.863390, - "Y106":1.045809, - "J129":1.274700, - "W146":1.301380, - "H158":1.558247, - "F184":1.830067, - "WISE_W1":3.352600, - "WISE_W2":4.602800, - "WISE_W3":11.560800, - "WISE_W4":22.088300 -} diff --git a/synthpop/config_files/_default.synthpop_conf b/synthpop/config_files/_default.synthpop_conf index 0fa9127..979eb6f 100644 --- a/synthpop/config_files/_default.synthpop_conf +++ b/synthpop/config_files/_default.synthpop_conf @@ -1,178 +1,90 @@ -{ +{ "SEED":{"random_seed":null}, + "MANDATORY":{ - "#comment1": "default directory and base for the output files", - "name_for_output":null, - "#comment2": "directory containing population json files", - "model_name":null + "name_for_output":"defaultmodel", + "model_name":"Huston2025" }, "SIGHTLINES":{ - "#comment1":"These inputs are mandatory in order to run synthpop.main() or process_all() ", - - "#comment2":[ - "Here we specify Galactic longitude and latitude in degrees", - "If [l/b]_set_type=='pairs' it will treat l and b as pairs, i.e. ((l[0],b[0]),..., (l[i],b[i]) ", - "otherwise it will loop over l and b individually, i.e. ((l[0],b[0]),...,(l[0],b[i]),(l[1],b[0]),... ", - "if [l/b]_set_type=='range' it will use it as argument for np.arange([l/b]_set)" - ], "l_set":null, "l_set_type":null, "b_set":null, "b_set_type": null, - "#comment3":"Solid angle of cone/pyramid in units defined by solid_angle_unit [sr, or deg^2]", - "solid_angle": null, - "solid_angle_unit": "deg^2" + "field_shape": "circle", + "field_scale": null, + "field_scale_unit": "deg" }, - "SEED":{"random_seed":null}, - "COORDINATE_SYSTEM":{ "sun": { - "#comment": "Location of the Sun (kpc)", "x": -8.178, "y": 0.0, "z": 0.017, - "#comment2": "Motion of the Sun (km/s) from Reid & Brunthaler (2020)", "u": 12.9, "v": 245.6, "w": 7.78, - "#comment3": "direction of the solar apex in galactic coordinates in degree", "l_apex_deg": 56.24, - "b_apex_deg": 22.54 + "b_apex_deg": 22.54, + "l_gal_cen": 0.0, + "b_gal_cen": 0.0 }, "lsr":{ - "#comment": "Velocity (km/s) of the local standard of rest from Schönrich et al. (2010).", "u_lsr": 1.8, "v_lsr": 233.4, "w_lsr": 0.53 }, "warp": { - "#comment_1": "Settings for the warp can be overwritten from the population files.", - "#comment_2": "This default comes from Chen X. et al 2019.", - "#comment_3": "r_warp = radius when the warp starts in kpc", "r_warp": 7.72, "amp_warp": 0.060, - "#comment_4": "amp_warp_[pos/neg] can be used to specify different values for sin(phi_warp)>0 and sin(phi_warp)<0. If set to null(default), use amp_warp on both sides.", "amp_warp_pos": null, "amp_warp_neg": null, - "#comment_5": "alpha_warp = exponent in the power law", "alpha_warp": 1.33, - "#comment_6": "phi_warp_deg = angle for line of nodes; can also be in radian by specifying phi_warp_rad instead", "phi_warp_deg": 17.5 } - }, - "POPULATION_GENERATION":{ - "#comment1":"Maximum distance from Sun in kpc", - "max_distance":15, - "#comment2":"Step size in kpc", - "distance_step_size":0.10, - - "#comment3":"Window type - currently only allows cone", - "window_type":{"window_type":"cone" , "#kwargs for windowtype": null}, + "EXTINCTION_MAP": + { + "extinction_map_kwargs": {"name":"Surot", "project_3d":true, "dist_2d":8.15}, + "extinction_law_kwargs": [{"name":"SODC", "R_V":2.5}] + }, - "#comment4":"Limiting mass [min_mass, max_mass]", + "POPULATION_GENERATION":{ + "max_distance":25, "mass_lims":{"min_mass":0.08,"max_mass":100}, - "#comment5":"Number of points used for estimating the total mass in a slice ", - "N_mc_totmass":10000, - - "#comment6":"method to estimate the mass loss correction", "lost_mass_option": 1, - "N_av_mass":20000, - - "#comment7":"Flag for estimate the velocities for all stars at once", - "kinematics_at_the_end":false, - - "#comment8": "Scale down the number of generated stars n_gen = (n/scale_factor)", + "N_av_mass":50000, "scale_factor": 1, - "#comment9": "Reduce the generation of low-mass stars which are to faint", "skip_lowmass_stars": false, - "#comment10": "Evolve the number of stars in chunks of size", - "chunk_size": 250000 + "chunk_size": 250000, + "star_generator": "StarGenerator" }, - "EXTINCTION_MAP": - { - "#comment":"specify the extinction map and law", - "extinction_map_kwargs":{ - "name":"MapsFromDustmaps", "dustmap_name":"marshall" - }, - "extinction_law_kwargs": - [ - {"name":"ODonnell1994", "R_V":3.1} - ] - }, - "ISOCHRONE_INTERPOLATION":{ - "#comment":"This includes a default and a few commented out examples of how to use the code with different Isochrone ystems and Interpolators", - "#comment2":"If evolution_class is set to null, it will read the keywords from the population files.", "evolution_class": {"name":"MIST", "interpolator":"CharonInterpolator"}, - - "# comment3": ["An example for multi evolution_class; by default only the MIST isochrones are implemented.", - "The code iterates from the beginning of the list and uses the first appropriate class for the given star."], - "# evolution_class":[ - {"name":"MIST", "min_mass":0.2, "max_mass":0.3, "#comment":"use the MIST isochrones for masses between 0.1, 0.2 with the standard interpolator"}, - {"name":"MIST", "interpolator":"LagrangeInterpolator","min_mass":0.1, "max_mass":0.7}, - {"name":"MIST", "interpolator":"CharonInterpolator", "#comment":"use the MIST isochrones outside of the previous defined ranges"} - ], - "#comment4": "An example for using a different evolution_class for 'population_name'", - "# evolution_class":{ - "default":[ - {"name":"MIST", "interpolator":"CharonInterpolator"} - ], - "'population_name'":[ - {"name":"MIST", "interpolator":"LagrangeInterpolator" , "#comment" : "a different interpolator for 'population_name'" } - ] - } - }, + "ifmr_kwargs": {"name":"SukhboldN20"}, + "multiplicity_kwargs": null + }, "PHOTOMETRIC_OUTPUTS":{ - "#comment1":["Limiting magnitude- [band, limit, opt]", - "opt ='keep','remove'"], - "maglim":["2MASS_Ks",30, "keep"], - "#comment2":["Magnitude systems and bands to generate for each star.", - "use '[magsys,...]' to take all magnitudes from a given system", - "or '{magsys1:[filter1, filter2,...], magsys2:'all',...}' to avoid confusion"], - "chosen_bands": ["Bessell_B", "Bessell_V", "Bessell_I", "Gaia_G_EDR3", "Gaia_BP_EDR3", "Gaia_RP_EDR3", "2MASS_J", "2MASS_H", "2MASS_Ks", "Z087", "W146"], - "#comment3":["Either specify the effective wavelength in microns for each filter", - "or a string to specify a separate json file where this can be found"], - "eff_wavelengths": { - "json_file":"AAA_effective_wavelengths.json" - }, - "#comment4":"Set obsmag to true for observed magnitude, false for absolute magnitudes", + "maglim":null, + "chosen_bands": ["R062","Z087","Y106","J129","W146","H158","F184", "Bessell_U", "Bessell_B", "Bessell_V", "Bessell_R", "Bessell_I", "2MASS_J", "2MASS_H", "2MASS_Ks"], + "photsys": {"AB":["WFIRST"]}, "obsmag":true, + "combine_system_mags": true, + "effective_wavelengths": "vega_eff", - "#comment5": ["Optional properties from the isochrones; these can overlap with the default properties.", - "opt_iso_props are the original column names in the Isochrones", - "col_names are the column names in the output table "], - "opt_iso_props":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R","phase"], - "col_names":["logL", "logTeff", "logg" ,"Fe/H_evolved","log_radius", "phase"] + "opt_iso_props":["log_L", "log_Teff", "log_g", "[Fe/H]", "log_R", "phase"] }, - - "OUTPUT": { - "#comment1":"specify a script and function for advanced post processing", - "#comment2": "should be either a dictionary as follows or null", - - "# advanced_post_processing": { - "name": "sub class name", - "filename": "optional: path of the file ", - "further_keywords": "additional keyword arguments" - }, - "post_processing_kwargs": null, - - "#comment3": ["If output_location is '' or null it is replaced with the synthpop directory", - "If output_location ends with '/' it uses output_location/name_for_output as directory"], - "output_location": null, - "#comment4": ["Pattern for the output file (without directory and extension) interpreted by string.format() ", - "The following keys are available:", - "name_for_output (str), model_name (str), l_deg (float), b_deg(float), solid_angle (float), date (datetime.date object), time (datetime.time object)"], - "output_filename_pattern": "{model_name}_l{l_deg:.3f}_b{b_deg:.3f}", - "#comment9": ["Filetypes from pandas: csv, json, html, xml, excel, hdf5, feather, parquet, stata, pickle, sql", - "Filetypes astropy: fits and vot"], - "output_file_type": ["csv", - {"#Comment": "This can be used to add additional kwargs"}], - - "overwrite": false + + "OUTPUT":{ + "post_processing_kwargs": [{"name":"RenameColumns", "old_names":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R"], + "new_names":["logL", "logTeff", "logg" ,"Fe/H_evolved","log_radius"]}, + {"name":"ExtinctionEstimator"}], + + "output_location":"outputfiles/default", + "output_filename_pattern": "{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}", + "output_file_type": "csv", + "overwrite": true } } \ No newline at end of file diff --git a/synthpop/config_files/huston2025_defaults.synthpop_conf b/synthpop/config_files/huston2025_defaults.synthpop_conf index b6b9197..b37eecd 100644 --- a/synthpop/config_files/huston2025_defaults.synthpop_conf +++ b/synthpop/config_files/huston2025_defaults.synthpop_conf @@ -10,8 +10,9 @@ "l_set_type":null, "b_set":null, "b_set_type": null, - "solid_angle": null, - "solid_angle_unit": "deg^2" + "field_shape": "circle", + "field_scale": null, + "field_scale_unit": "deg" }, "COORDINATE_SYSTEM":{ @@ -23,7 +24,9 @@ "v": 245.6, "w": 7.78, "l_apex_deg": 56.24, - "b_apex_deg": 22.54 + "b_apex_deg": 22.54, + "l_gal_cen": 0.0, + "b_gal_cen": 0.0 }, "lsr":{ "u_lsr": 1.8, @@ -48,36 +51,36 @@ "POPULATION_GENERATION":{ "max_distance":25, - "distance_step_size":0.10, - "window_type":{"window_type":"cone"}, "mass_lims":{"min_mass":0.08,"max_mass":100}, - "N_mc_totmass":10000, "lost_mass_option": 1, "N_av_mass":50000, - "kinematics_at_the_end":false, "scale_factor": 1, "skip_lowmass_stars": false, - "chunk_size": 250000 + "chunk_size": 250000, + "star_generator": "StarGenerator" }, "ISOCHRONE_INTERPOLATION":{ - "evolution_class": {"name":"MIST", "interpolator":"CharonInterpolator"} + "evolution_class": {"name":"MIST", "interpolator":"CharonInterpolator"}, + "ifmr_kwargs": {"name":"SukhboldN20"}, + "multiplicity_kwargs": null }, "PHOTOMETRIC_OUTPUTS":{ - "maglim":["W146", 23.975533, "remove"], + "maglim":["W146", 23.975533], "chosen_bands": ["R062","Z087","Y106","J129","W146","H158","F184", "Bessell_U", "Bessell_B", "Bessell_V", "Bessell_R", "Bessell_I", "2MASS_J", "2MASS_H", "2MASS_Ks"], - "eff_wavelengths": { - "json_file":"AAA_effective_wavelengths.json" - }, + "photsys": null, "obsmag":true, + "combine_system_mags": false, + "effective_wavelengths": "vega_eff", - "opt_iso_props":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R", "phase"], - "col_names":["logL", "logTeff", "logg" ,"Fe/H_evolved","log_radius","phase"] + "opt_iso_props":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R", "phase"] }, "OUTPUT":{ - "post_processing_kwargs": [{"name":"ProcessDarkCompactObjects", "remove":false}, {"name":"ConvertMistMags", "conversions":{"AB": ["R062", "Z087", "Y106", "J129", "W146", "H158", "F184"]}}], + "post_processing_kwargs": [{"name":"ConvertMistMags", "conversions":{"AB": ["R062", "Z087", "Y106", "J129", "W146", "H158", "F184"]}}, + {"name":"RenameColumns", "old_names":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R"], + "new_names":["logL", "logTeff", "logg" ,"Fe/H_evolved","log_radius"]}], "output_location":"outputfiles/default", "output_filename_pattern": "{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}", diff --git a/synthpop/config_files/huston2026_defaults.synthpop_conf b/synthpop/config_files/huston2026_defaults.synthpop_conf new file mode 100644 index 0000000..f58f8fe --- /dev/null +++ b/synthpop/config_files/huston2026_defaults.synthpop_conf @@ -0,0 +1,88 @@ +{ "SEED":{"random_seed":null}, + + "MANDATORY":{ + "name_for_output":"Huston2026", + "model_name":"Huston2026" + }, + + "SIGHTLINES":{ + "l_set":null, + "l_set_type":null, + "b_set":null, + "b_set_type": null, + "field_shape": "circle", + "field_scale": null, + "field_scale_unit": "deg" + }, + + "COORDINATE_SYSTEM":{ + "sun": { + "x": -8.178, + "y": 0.0, + "z": 0.017, + "u": 12.9, + "v": 245.6, + "w": 7.78, + "l_apex_deg": 56.24, + "b_apex_deg": 22.54, + "l_gal_cen": -0.056, + "b_gal_cen": -0.046 + }, + "lsr":{ + "u_lsr": 1.8, + "v_lsr": 233.4, + "w_lsr": 0.53 + }, + "warp": { + "r_warp": 7.72, + "amp_warp": 0.060, + "amp_warp_pos": null, + "amp_warp_neg": null, + "alpha_warp": 1.33, + "phi_warp_deg": 17.5 + } + }, + + "EXTINCTION_MAP": + { + "extinction_map_kwargs": {"name":"Surot", "project_3d":true, "dist_2d":8.178}, + "extinction_law_kwargs": [{"name":"SODC", "R_V":2.5}] + }, + + "POPULATION_GENERATION":{ + "max_distance":25, + "mass_lims":{"min_mass":0.08,"max_mass":100}, + "lost_mass_option": 1, + "N_av_mass":50000, + "scale_factor": 1, + "skip_lowmass_stars": false, + "chunk_size": 250000, + "star_generator": "SpiseaGenerator" + }, + + "ISOCHRONE_INTERPOLATION":{ + "evolution_class": {"name":"SpiseaCluster"}, + "ifmr_kwargs": {"name":"SpiseaIfmr", "spisea_ifmr_name":"IFMR_N20_Sukhbold"}, + "multiplicity_kwargs": {"name":"SpiseaMultiplicity", "spisea_multiplicity_name":"MultiplicityResolvedDK"} + }, + + "PHOTOMETRIC_OUTPUTS":{ + "maglim": null, + "chosen_bands": ["roman", "bessell", "2mass"], + "photsys":"AB", + "obsmag":true, + "combine_system_mags": true, + "effective_wavelengths": "average", + "opt_iso_props":["log_L", "log_Teff", "log_g", "log_R", "phase"] + }, + + "OUTPUT":{ + "post_processing_kwargs": [{"name":"RenameColumns", "old_names":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R"], + "new_names":["logL", "logTeff", "logg" ,"Fe/H_evolved","log_radius"]}], + + "output_location":"outputfiles/default", + "output_filename_pattern": "{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}", + "output_file_type": "csv", + "overwrite": true + } +} \ No newline at end of file diff --git a/synthpop/config_files/my_config.synthpop_conf b/synthpop/config_files/my_config.synthpop_conf index ed345c4..b57f7f8 100644 --- a/synthpop/config_files/my_config.synthpop_conf +++ b/synthpop/config_files/my_config.synthpop_conf @@ -12,7 +12,7 @@ "extinction_law_kwargs": [{"name":"SODC", "R_V":2.5}], "evolution_class": {"name":"MIST", "interpolator":"CharonInterpolator"}, - "maglim":["2MASS_Ks",21, "remove"], + "maglim":["2MASS_Ks",21], "obsmag": true, "name_for_output":"default_synthpop", diff --git a/synthpop/config_files/popsycle_multiples_defaults.synthpop_conf b/synthpop/config_files/popsycle_multiples_defaults.synthpop_conf new file mode 100755 index 0000000..968eedd --- /dev/null +++ b/synthpop/config_files/popsycle_multiples_defaults.synthpop_conf @@ -0,0 +1,87 @@ +{ "SEED":{"random_seed":null}, + + "MANDATORY":{ + "name_for_output":"synthpop_multiples", + "model_name":"Huston2026" + }, + + "SIGHTLINES":{ + "l_set":null, + "l_set_type":null, + "b_set":null, + "b_set_type": null, + "field_shape": "box", + "field_scale": null, + "field_scale_unit": "deg" + }, + + "COORDINATE_SYSTEM":{ + "sun": { + "x": -8.178, + "y": 0.0, + "z": 0.017, + "u": 12.9, + "v": 245.6, + "w": 7.78, + "l_apex_deg": 56.24, + "b_apex_deg": 22.54, + "l_gal_cen": -0.056, + "b_gal_cen": -0.046 + }, + "lsr":{ + "u_lsr": 1.8, + "v_lsr": 233.4, + "w_lsr": 0.53 + }, + "warp": { + "r_warp": 7.72, + "amp_warp": 0.060, + "amp_warp_pos": null, + "amp_warp_neg": null, + "alpha_warp": 1.33, + "phi_warp_deg": 17.5 + } + }, + + "EXTINCTION_MAP": + { + "extinction_map_kwargs": {"name":"surot"}, + "extinction_law_kwargs": [{"name":"SODC", "R_V":2.5}] + }, + + "POPULATION_GENERATION":{ + "max_distance":25, + "mass_lims":{"min_mass":0.08,"max_mass":100}, + "N_mc_totmass":10000, + "lost_mass_option": 1, + "N_av_mass":50000, + "scale_factor": 1, + "skip_lowmass_stars": false, + "chunk_size": 250000, + "star_generator": "SpiseaGenerator" + }, + + "ISOCHRONE_INTERPOLATION":{ + "evolution_class": {"name":"SpiseaCluster", "block_spisea_prints":true}, + "ifmr_kwargs": {"name":"SpiseaIfmr", "spisea_ifmr_name":"IFMR_N20_Sukhbold"}, + "multiplicity_kwargs": {"name":"SpiseaMultiplicity", + "spisea_multiplicity_name":"MultiplicityResolvedDK"} + }, + + "PHOTOMETRIC_OUTPUTS":{ + "maglim":null, + "chosen_bands": ["bessell", "ukirt"], + "obsmag":false, + "combine_system_mags":true, + "opt_iso_props":["log_L", "log_Teff", "log_g", "log_R", "phase", "isWR"], + "photsys":"Vega" + }, + + "OUTPUT":{ + "post_processing_kwargs":[{"name": "extinction_estimator"}, + {"name": "popsycle_post_processing"}], + "save_data": false, + "output_location":"synthpop_output", + "output_filename_pattern":"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}" + } +} diff --git a/synthpop/config_files/popsycle_singles_defaults.synthpop_conf b/synthpop/config_files/popsycle_singles_defaults.synthpop_conf new file mode 100755 index 0000000..35bf39b --- /dev/null +++ b/synthpop/config_files/popsycle_singles_defaults.synthpop_conf @@ -0,0 +1,86 @@ +{ "SEED":{"random_seed":null}, + + "MANDATORY":{ + "name_for_output":"synthpop_singles", + "model_name":"Huston2026" + }, + + "SIGHTLINES":{ + "l_set":null, + "l_set_type":null, + "b_set":null, + "b_set_type": null, + "field_shape": "box", + "field_scale": null, + "field_scale_unit": "deg" + }, + + "COORDINATE_SYSTEM":{ + "sun": { + "x": -8.178, + "y": 0.0, + "z": 0.017, + "u": 12.9, + "v": 245.6, + "w": 7.78, + "l_apex_deg": 56.24, + "b_apex_deg": 22.54, + "l_gal_cen": -0.056, + "b_gal_cen": -0.046 + }, + "lsr":{ + "u_lsr": 1.8, + "v_lsr": 233.4, + "w_lsr": 0.53 + }, + "warp": { + "r_warp": 7.72, + "amp_warp": 0.060, + "amp_warp_pos": null, + "amp_warp_neg": null, + "alpha_warp": 1.33, + "phi_warp_deg": 17.5 + } + }, + + "EXTINCTION_MAP": + { + "extinction_map_kwargs": {"name":"surot"}, + "extinction_law_kwargs": [{"name":"SODC", "R_V":2.5}] + }, + + "POPULATION_GENERATION":{ + "max_distance":25, + "mass_lims":{"min_mass":0.08,"max_mass":100}, + "N_mc_totmass":10000, + "lost_mass_option": 1, + "N_av_mass":50000, + "scale_factor": 1, + "skip_lowmass_stars": false, + "chunk_size": 250000, + "star_generator": "SpiseaGenerator" + }, + + "ISOCHRONE_INTERPOLATION":{ + "evolution_class": {"name":"SpiseaCluster", "block_spisea_prints":true}, + "ifmr_kwargs": {"name":"SpiseaIfmr", "spisea_ifmr_name":"IFMR_N20_Sukhbold"}, + "multiplicity_kwargs": null + }, + + "PHOTOMETRIC_OUTPUTS":{ + "maglim":null, + "chosen_bands": ["bessell", "ukirt"], + "obsmag":false, + "combine_system_mags":true, + "opt_iso_props":["log_L", "log_Teff", "log_g", "log_R", "phase", "isWR"], + "photsys":"Vega" + }, + + "OUTPUT":{ + "post_processing_kwargs":[{"name": "extinction_estimator"}, + {"name": "popsycle_post_processing"}], + "save_data": false, + "output_location":"synthpop_output", + "output_filename_pattern":"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}" + } +} diff --git a/synthpop/config_files/version1_default.synthpop_conf b/synthpop/config_files/version1_default.synthpop_conf new file mode 100644 index 0000000..74ebace --- /dev/null +++ b/synthpop/config_files/version1_default.synthpop_conf @@ -0,0 +1,175 @@ +{ + "MANDATORY":{ + "#comment1": "default directory and base for the output files", + "name_for_output":null, + "#comment2": "directory containing population json files", + "model_name":null + }, + + "SIGHTLINES":{ + "#comment1":"These inputs are mandatory in order to run synthpop.main() or process_all() ", + + "#comment2":[ + "Here we specify Galactic longitude and latitude in degrees", + "If [l/b]_set_type=='pairs' it will treat l and b as pairs, i.e. ((l[0],b[0]),..., (l[i],b[i]) ", + "otherwise it will loop over l and b individually, i.e. ((l[0],b[0]),...,(l[0],b[i]),(l[1],b[0]),... ", + "if [l/b]_set_type=='range' it will use it as argument for np.arange([l/b]_set)" + ], + "l_set":null, + "l_set_type":null, + "b_set":null, + "b_set_type": null, + "#comment3":"Field shape - options are circle or box", + "field_shape": "circle", + "#comment4":"Field size - for circle, radius. for box, side half-width for square or two half-widths for (l, b)", + "field_scale": null, + "#comment5":"Field scale unit. Options are rad, deg, arcmin, arcsec", + "field_scale_unit": "deg" + }, + + "SEED":{"random_seed":null}, + + "COORDINATE_SYSTEM":{ + "sun": { + "#comment": "Location of the Sun (kpc)", + "x": -8.178, + "y": 0.0, + "z": 0.017, + "#comment2": "Motion of the Sun (km/s) from Reid & Brunthaler (2020)", + "u": 12.9, + "v": 245.6, + "w": 7.78, + "#comment3": "direction of the solar apex in galactic coordinates in degree", + "l_apex_deg": 56.24, + "b_apex_deg": 22.54 + }, + "lsr":{ + "#comment": "Velocity (km/s) of the local standard of rest from Schönrich et al. (2010).", + "u_lsr": 1.8, + "v_lsr": 233.4, + "w_lsr": 0.53 + }, + "warp": { + "#comment_1": "Settings for the warp can be overwritten from the population files.", + "#comment_2": "This default comes from Chen X. et al 2019.", + "#comment_3": "r_warp = radius when the warp starts in kpc", + "r_warp": 7.72, + "amp_warp": 0.060, + "#comment_4": "amp_warp_[pos/neg] can be used to specify different values for sin(phi_warp)>0 and sin(phi_warp)<0. If set to null(default), use amp_warp on both sides.", + "amp_warp_pos": null, + "amp_warp_neg": null, + "#comment_5": "alpha_warp = exponent in the power law", + "alpha_warp": 1.33, + "#comment_6": "phi_warp_deg = angle for line of nodes; can also be in radian by specifying phi_warp_rad instead", + "phi_warp_deg": 17.5 + } + + }, + + "POPULATION_GENERATION":{ + "#comment1":"Maximum distance from Sun in kpc", + "max_distance":15, + + "#comment4":"Limiting mass [min_mass, max_mass]", + "mass_lims":{"min_mass":0.08,"max_mass":100}, + + "#comment6":"method to estimate the mass loss correction", + "lost_mass_option": 1, + "N_av_mass":20000, + + "#comment8": "Scale down the number of generated stars n_gen = (n/scale_factor)", + "scale_factor": 1, + "#comment9": "Reduce the generation of low-mass stars which are to faint", + "skip_lowmass_stars": false, + "#comment10": "Evolve the number of stars in chunks of size", + "chunk_size": 250000, + + "#comment11": "Select star generator (options are standard StarGenerator or SpiseaGenerator).", + "star_generator": "StarGenerator" + }, + + "EXTINCTION_MAP": + { + "#comment":"specify the extinction map and law", + "extinction_map_kwargs":{ + "name":"MapsFromDustmaps", "dustmap_name":"marshall" + }, + "extinction_law_kwargs": + [ + {"name":"ODonnell1994", "R_V":3.1} + ] + }, + + "ISOCHRONE_INTERPOLATION":{ + "#comment":"This includes a default and a few commented out examples of how to use the code with different Isochrone ystems and Interpolators", + "#comment2":"If evolution_class is set to null, it will read the keywords from the population files.", + "evolution_class": {"name":"MIST", "interpolator":"CharonInterpolator"}, + + "# comment3": ["An example for multi evolution_class; by default only the MIST isochrones are implemented.", + "The code iterates from the beginning of the list and uses the first appropriate class for the given star."], + "# evolution_class":[ + {"name":"MIST", "min_mass":0.2, "max_mass":0.3, "#comment":"use the MIST isochrones for masses between 0.1, 0.2 with the standard interpolator"}, + {"name":"MIST", "interpolator":"LagrangeInterpolator","min_mass":0.1, "max_mass":0.7}, + {"name":"MIST", "interpolator":"CharonInterpolator", "#comment":"use the MIST isochrones outside of the previous defined ranges"} + ], + "#comment4": "An example for using a different evolution_class for 'population_name'", + "# evolution_class":{ + "default":[ + {"name":"MIST", "interpolator":"CharonInterpolator"} + ], + "'population_name'":[ + {"name":"MIST", "interpolator":"LagrangeInterpolator" , "#comment" : "a different interpolator for 'population_name'" } + ] + }, + "ifmr_kwargs": null, + "multiplicity_kwargs": null + }, + + "PHOTOMETRIC_OUTPUTS":{ + "#comment1":["Limiting magnitude- null or [band, limit]"], + "maglim":null, + "#comment2":["Magnitude system to use. Options are None, 'Vega', 'AB', and 'ST'.", + "For None, the default system for your isochrones will be used, which may vary per band."], + "photsys":null, + "#comment3":["Select photometric filters, listing select filter names or filter sets."], + "chosen_bands": ["Bessell_B", "Bessell_V", "Bessell_I", "Gaia_G_EDR3", "Gaia_BP_EDR3", "Gaia_RP_EDR3", "2MASS_J", "2MASS_H", "2MASS_Ks", "Z087", "W146"], + "#comment4":"Set obsmag to true for observed magnitude, false for absolute magnitudes", + "obsmag":true, + "#comment5":"Set combine_system_mags to true to show sytem magnitudes in the systems table, false for primary star mags", + "combine_system_mags": true, + "#comment6":"Set definition of effective wavelength per filter. Used for scaling extinction", + "#comment6.1":"options are vega_eff, pivot, and average, with vega_eff reproducing version1 behavior", + "effective_wavelengths": "vega_eff", + + "#comment7": "Optional properties from the isochrones; these can overlap with the default properties.", + "opt_iso_props":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R","phase"] + }, + + "OUTPUT": { + "#comment1":"specify a script and function for advanced post processing", + "#comment2": "should be either a dictionary as follows or null", + + "# advanced_post_processing": { + "name": "sub class name", + "filename": "optional: path of the file ", + "further_keywords": "additional keyword arguments" + }, + "post_processing_kwargs": [{"name":"RenameColumns", + "old_names":["log_L", "log_Teff", "log_g", "[Fe/H]","log_R"], + "new_names":["logL", "logTeff", "logg" ,"Fe/H_evolved","log_radius"]}], + + "#comment3": ["If output_location is '' or null it is replaced with the synthpop directory", + "If output_location ends with '/' it uses output_location/name_for_output as directory"], + "output_location": null, + "#comment4": ["Pattern for the output file (without directory and extension) interpreted by string.format() ", + "The following keys are available:", + "name_for_output (str), model_name (str), l_deg (float), b_deg(float), solid_angle (float), date (datetime.date object), time (datetime.time object)"], + "output_filename_pattern": "{model_name}_l{l_deg:.3f}_b{b_deg:.3f}", + "#comment9": ["Filetypes from pandas: csv, json, html, xml, excel, hdf5, feather, parquet, stata, pickle, sql", + "Filetypes astropy: fits and vot"], + "output_file_type": ["csv", + {"#Comment": "This can be used to add additional kwargs"}], + + "overwrite": false + } +} \ No newline at end of file diff --git a/synthpop/constants.py b/synthpop/constants.py index c4fc813..b472a76 100644 --- a/synthpop/constants.py +++ b/synthpop/constants.py @@ -21,18 +21,21 @@ # Note that the Isochrones also need the initial mass, age, and metallicity for the interpolation REQ_ISO_PROPS = ["star_mass"] -# Columnnames for output table +# Column names for output table # You can rename each column, but you must keep the order! # If you add further items to REQ_ISO_PROPS, you must add the column names at the end. -COL_NAMES = [ +REQ_COL_NAMES = [ "pop", "iMass", "age", "Fe/H_initial", "Mass", "In_Final_Phase", "Dist", "l", "b", - "vr_bc", "mul", "mub", "x", "y", "z", "U", "V", "W", "VR_LSR", "ExtinctionInMap"] + "vr_bc", "mul", "mub", "x", "y", "z", "U", "V", "W", "VR_LSR"] # ------- Physical Constants -------- c = 3e8 # m/s G = 6.674e-11 # m^3 kg^-1 s^-2 +sigma_sb = 5.670374419e-8 # W m^-2 K^-4 Msun_kg = 1.989e30 # solar mass in kg +Lsun_w = 3.846e26 # solar luminosity in W +Rsun_m = 695700000 # solar radius in m m_per_pc = 3.086e16 # meters per parsec Mbol_sun = 4.74 # Bolometric magnitude of the sun DEG2RAD = np.pi / 180 diff --git a/synthpop/data/moments/vasiliev2026_huston_nsc_grid.dat b/synthpop/data/moments/vasiliev2026_huston_nsc_grid.dat new file mode 100644 index 0000000..3c053e6 --- /dev/null +++ b/synthpop/data/moments/vasiliev2026_huston_nsc_grid.dat @@ -0,0 +1,10202 @@ + r z rho v_phi sigma_phi sigma_r sigma_z +0.0000 0.0000 4246044.0000 5.413024e+01 551.945664 506.785557 473.604878 +0.0001 0.0000 4246044.0000 5.646030e+01 307.684860 272.018470 268.788309 +0.0002 0.0000 1427311.0000 5.774550e+01 219.045271 190.462186 193.356239 +0.0003 0.0000 731720.2000 5.819259e+01 178.846753 154.729524 158.217024 +0.0004 0.0000 452588.7000 5.849324e+01 154.775497 133.965019 136.922950 +0.0005 0.0000 311915.8000 5.884885e+01 138.450929 120.259210 122.560952 +0.0006 0.0000 230553.4000 5.928834e+01 126.494207 110.546412 112.205416 +0.0007 0.0000 178952.2000 5.976353e+01 117.289385 103.306632 104.406744 +0.0008 0.0000 143986.0000 6.022537e+01 109.921872 97.687760 98.262917 +0.0009 0.0000 119081.5000 6.067377e+01 103.862691 93.196167 93.368335 +0.0010 0.0000 100642.9000 6.109714e+01 98.764385 89.532913 89.384568 +0.0011 0.0000 86559.4000 6.150194e+01 94.412873 86.498950 86.055877 +0.0012 0.0000 75516.6400 6.188229e+01 90.645635 83.942367 83.245915 +0.0013 0.0000 66666.9000 6.222026e+01 87.342653 81.755639 80.870134 +0.0014 0.0000 59443.6100 6.250979e+01 84.423571 79.852414 78.810909 +0.0015 0.0000 53455.3700 6.275388e+01 81.823055 78.186602 77.022214 +0.0016 0.0000 48424.1000 6.296965e+01 79.497884 76.718861 75.452576 +0.0017 0.0000 44146.8100 6.315347e+01 77.408910 75.417443 74.051104 +0.0018 0.0000 40471.5500 6.331518e+01 75.526690 74.263373 72.839510 +0.0019 0.0000 37283.7400 6.344826e+01 73.818318 73.220439 71.736869 +0.0020 0.0000 34495.6300 6.356055e+01 72.262528 72.278376 70.754591 +0.0021 0.0000 32038.9700 6.364139e+01 70.848483 71.420140 69.867976 +0.0022 0.0000 29859.9900 6.370281e+01 69.559448 70.638969 69.075274 +0.0023 0.0000 27915.7200 6.373722e+01 68.381631 69.923890 68.352086 +0.0024 0.0000 26171.5100 6.376042e+01 67.300749 69.266906 67.695213 +0.0025 0.0000 24599.0800 6.376780e+01 66.309439 68.660221 67.090628 +0.0026 0.0000 23175.1100 6.375989e+01 65.397605 68.100609 66.539265 +0.0027 0.0000 21879.9100 6.374486e+01 64.558712 67.584481 66.027092 +0.0028 0.0000 20696.9900 6.371572e+01 63.790142 67.104027 65.572032 +0.0029 0.0000 19612.5900 6.367966e+01 63.082183 66.658620 65.141181 +0.0030 0.0000 18615.0900 6.363373e+01 62.430382 66.244145 64.746159 +0.0031 0.0000 17694.6400 6.357944e+01 61.829119 65.858164 64.384995 +0.0032 0.0000 16842.8500 6.351952e+01 61.275743 65.499270 64.054525 +0.0033 0.0000 16052.4900 6.345424e+01 60.765656 65.160687 63.748202 +0.0034 0.0000 15317.3300 6.338353e+01 60.294934 64.846164 63.469835 +0.0035 0.0000 14631.9400 6.330689e+01 59.859969 64.550133 63.206458 +0.0036 0.0000 13991.6000 6.322549e+01 59.458276 64.270537 62.963598 +0.0037 0.0000 13392.1700 6.313997e+01 59.087514 64.007128 62.747580 +0.0038 0.0000 12830.0000 6.305038e+01 58.746094 63.759664 62.537309 +0.0039 0.0000 12301.8700 6.295613e+01 58.430881 63.525478 62.352856 +0.0040 0.0000 11804.9000 6.285997e+01 58.139536 63.305104 62.172506 +0.0041 0.0000 11336.5700 6.276175e+01 57.870724 63.097190 62.003825 +0.0042 0.0000 10894.6000 6.266047e+01 57.622824 62.900807 61.849315 +0.0043 0.0000 10476.9400 6.255807e+01 57.395266 62.715759 61.705699 +0.0044 0.0000 10081.7900 6.245486e+01 57.185642 62.540836 61.568754 +0.0045 0.0000 9707.4690 6.234986e+01 56.992187 62.375073 61.443567 +0.0046 0.0000 9352.4980 6.224412e+01 56.815583 62.219707 61.327996 +0.0047 0.0000 9015.5170 6.213781e+01 56.655338 62.072135 61.230139 +0.0048 0.0000 8695.2940 6.203063e+01 56.508390 61.933279 61.128837 +0.0049 0.0000 8390.7050 6.192387e+01 56.373558 61.802574 61.038697 +0.0050 0.0000 8100.7250 6.181840e+01 56.250966 61.678783 60.953746 +0.0051 0.0000 7824.4130 6.171248e+01 56.139668 61.562724 60.877218 +0.0052 0.0000 7560.9090 6.160584e+01 56.040251 61.454615 60.810750 +0.0053 0.0000 7309.4210 6.150038e+01 55.948448 61.351344 60.747008 +0.0054 0.0000 7069.2210 6.139477e+01 55.867324 61.254754 60.692767 +0.0055 0.0000 6839.6360 6.129071e+01 55.794911 61.164041 60.636862 +0.0056 0.0000 6620.0460 6.118613e+01 55.731309 61.079650 60.590086 +0.0057 0.0000 6409.8780 6.108264e+01 55.676365 61.000142 60.547109 +0.0058 0.0000 6208.5990 6.097970e+01 55.627478 60.925831 60.508986 +0.0059 0.0000 6015.7150 6.087719e+01 55.586196 60.855916 60.480435 +0.0060 0.0000 5830.7690 6.077485e+01 55.552067 60.792791 60.453675 +0.0061 0.0000 5653.3330 6.067498e+01 55.523321 60.732235 60.426962 +0.0062 0.0000 5483.0100 6.057560e+01 55.499769 60.677733 60.408565 +0.0063 0.0000 5319.4290 6.047681e+01 55.482322 60.625046 60.390700 +0.0064 0.0000 5162.2460 6.037955e+01 55.469486 60.579722 60.378213 +0.0065 0.0000 5011.1470 6.028271e+01 55.461703 60.536166 60.371185 +0.0066 0.0000 4865.8390 6.018789e+01 55.458351 60.496812 60.364337 +0.0067 0.0000 4726.0470 6.009305e+01 55.459505 60.461160 60.362449 +0.0068 0.0000 4591.5120 6.000041e+01 55.464526 60.428992 60.361952 +0.0069 0.0000 4461.9870 5.990754e+01 55.474027 60.399903 60.367685 +0.0070 0.0000 4337.2420 5.981668e+01 55.486266 60.374733 60.372823 +0.0071 0.0000 4217.0570 5.972656e+01 55.501594 60.352149 60.379435 +0.0072 0.0000 4101.2250 5.963606e+01 55.522695 60.332853 60.393971 +0.0073 0.0000 3989.5510 5.954891e+01 55.543338 60.315947 60.401658 +0.0074 0.0000 3881.8500 5.946214e+01 55.568193 60.302016 60.419989 +0.0075 0.0000 3777.9460 5.937625e+01 55.595437 60.290132 60.435803 +0.0076 0.0000 3677.6730 5.929214e+01 55.624582 60.281178 60.454816 +0.0077 0.0000 3580.8730 5.920662e+01 55.659239 60.274445 60.477898 +0.0078 0.0000 3487.3980 5.912510e+01 55.693582 60.269964 60.499763 +0.0079 0.0000 3397.1060 5.904335e+01 55.730363 60.267652 60.524979 +0.0080 0.0000 3309.8630 5.896140e+01 55.769624 60.267687 60.547112 +0.0081 0.0000 3225.5400 5.888158e+01 55.810169 60.268720 60.571905 +0.0082 0.0000 3144.0170 5.880197e+01 55.852845 60.274046 60.608220 +0.0083 0.0000 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1.005228e+01 97.694087 94.723155 105.236710 +0.0003 0.0010 39634.8000 1.524821e+01 97.700737 93.896017 103.194887 +0.0004 0.0010 40388.6600 2.025858e+01 97.093201 92.838927 100.825826 +0.0005 0.0010 40919.8400 2.495402e+01 95.997639 91.604349 98.279011 +0.0006 0.0010 41100.7200 2.924509e+01 94.529710 90.248878 95.673746 +0.0007 0.0010 40882.1400 3.310279e+01 92.805490 88.811359 93.092449 +0.0008 0.0010 40279.6300 3.652717e+01 90.928693 87.344938 90.613207 +0.0009 0.0010 39349.6800 3.954516e+01 88.974192 85.878283 88.273065 +0.0010 0.0010 38166.9400 4.219495e+01 86.996656 84.438089 86.054684 +0.0011 0.0010 36807.6400 4.450614e+01 85.045071 83.043571 84.066921 +0.0012 0.0010 35339.9900 4.652578e+01 83.142960 81.704999 82.205015 +0.0013 0.0010 33819.9000 4.828687e+01 81.313457 80.433983 80.500412 +0.0014 0.0010 32289.7600 4.982242e+01 79.566373 79.230427 78.931723 +0.0015 0.0010 30780.1100 5.116117e+01 77.907606 78.092436 77.519996 +0.0016 0.0010 29312.0800 5.233027e+01 76.342898 77.024715 76.207957 +0.0017 0.0010 27899.4600 5.335209e+01 74.869216 76.021565 75.008093 +0.0018 0.0010 26550.5300 5.424164e+01 73.486174 75.078760 73.911561 +0.0019 0.0010 25269.5700 5.501862e+01 72.190882 74.195683 72.898615 +0.0020 0.0010 24058.0000 5.569678e+01 70.980758 73.366838 71.976978 +0.0021 0.0010 22915.2600 5.629278e+01 69.853048 72.595291 71.128143 +0.0022 0.0010 21839.4800 5.681572e+01 68.800631 71.872701 70.348330 +0.0023 0.0010 20827.9700 5.727613e+01 67.820450 71.196060 69.629633 +0.0024 0.0010 19877.5000 5.767898e+01 66.908026 70.563085 68.967565 +0.0025 0.0010 18984.4600 5.803333e+01 66.059364 69.970601 68.359463 +0.0026 0.0010 18145.1500 5.834369e+01 65.269739 69.414804 67.797181 +0.0027 0.0010 17355.9400 5.861589e+01 64.534991 68.894129 67.276076 +0.0028 0.0010 16613.3400 5.885343e+01 63.851548 68.404804 66.791156 +0.0029 0.0010 15914.0600 5.905829e+01 63.216901 67.945127 66.342634 +0.0030 0.0010 15255.0100 5.923611e+01 62.624640 67.511741 65.925467 +0.0031 0.0010 14633.2900 5.938823e+01 62.075375 67.104034 65.530283 +0.0032 0.0010 14046.2500 5.951653e+01 61.562794 66.718391 65.170736 +0.0033 0.0010 13491.4200 5.962267e+01 61.090623 66.357551 64.846946 +0.0034 0.0010 12966.5400 5.971057e+01 60.647686 66.012576 64.529892 +0.0035 0.0010 12469.5100 5.978243e+01 60.237423 65.686811 64.240734 +0.0036 0.0010 11998.4100 5.983867e+01 59.857718 65.379281 63.971698 +0.0037 0.0010 11551.4900 5.988180e+01 59.504227 65.087779 63.717979 +0.0038 0.0010 11127.1300 5.991407e+01 59.177372 64.814037 63.488799 +0.0039 0.0010 10723.8400 5.993619e+01 58.873097 64.554650 63.268812 +0.0040 0.0010 10340.2500 5.994981e+01 58.591464 64.309357 63.067702 +0.0041 0.0010 9975.1280 5.995478e+01 58.331368 64.077620 62.877996 +0.0042 0.0010 9627.3180 5.995327e+01 58.090697 63.858331 62.706133 +0.0043 0.0010 9295.7560 5.994514e+01 57.867357 63.651644 62.542351 +0.0044 0.0010 8979.4580 5.993044e+01 57.661119 63.454975 62.390226 +0.0045 0.0010 8677.5100 5.991096e+01 57.470447 63.269405 62.248347 +0.0046 0.0010 8389.0670 5.988583e+01 57.294467 63.093892 62.117952 +0.0047 0.0010 8113.3450 5.985705e+01 57.132371 62.928103 61.995252 +0.0048 0.0010 7849.6170 5.982358e+01 56.984687 62.770472 61.881292 +0.0049 0.0010 7597.2060 5.978734e+01 56.848770 62.622601 61.774191 +0.0050 0.0010 7355.4840 5.974734e+01 56.723490 62.481217 61.677820 +0.0051 0.0010 7123.8640 5.970385e+01 56.610155 62.348683 61.587128 +0.0052 0.0010 6901.8030 5.965793e+01 56.507243 62.223726 61.504132 +0.0053 0.0010 6688.7910 5.960983e+01 56.413109 62.105372 61.429291 +0.0054 0.0010 6484.3540 5.955977e+01 56.328373 61.994606 61.358317 +0.0055 0.0010 6288.0480 5.950793e+01 56.252218 61.889660 61.296904 +0.0056 0.0010 6099.4570 5.945418e+01 56.184022 61.791149 61.239222 +0.0057 0.0010 5918.1950 5.939985e+01 56.123371 61.698019 61.182603 +0.0058 0.0010 5743.8950 5.934336e+01 56.069292 61.610476 61.136273 +0.0059 0.0010 5576.2180 5.928488e+01 56.023338 61.528825 61.093593 +0.0060 0.0010 5414.8420 5.922608e+01 55.982524 61.451691 61.054744 +0.0061 0.0010 5259.4660 5.916648e+01 55.949249 61.380174 61.024557 +0.0062 0.0010 5109.8070 5.910659e+01 55.920803 61.312927 60.992142 +0.0063 0.0010 4965.5990 5.904536e+01 55.898268 61.250406 60.965994 +0.0064 0.0010 4826.5980 5.898430e+01 55.879686 61.191648 60.946829 +0.0065 0.0010 4692.5780 5.892261e+01 55.866431 61.137674 60.927675 +0.0066 0.0010 4563.3220 5.886056e+01 55.857421 61.087239 60.911530 +0.0067 0.0010 4438.6270 5.879841e+01 55.852585 61.041257 60.899878 +0.0068 0.0010 4318.2980 5.873572e+01 55.852306 60.998398 60.891591 +0.0069 0.0010 4202.1500 5.867300e+01 55.855864 60.959766 60.885897 +0.0070 0.0010 4090.0080 5.861081e+01 55.863326 60.924745 60.880075 +0.0071 0.0010 3981.7030 5.854805e+01 55.872385 60.891577 60.878863 +0.0072 0.0010 3877.0760 5.848571e+01 55.885659 60.862436 60.880086 +0.0073 0.0010 3775.9770 5.842307e+01 55.902554 60.836519 60.885841 +0.0074 0.0010 3678.2600 5.835992e+01 55.922684 60.813067 60.893966 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60.752609 61.223025 +0.0090 0.0010 2479.9490 5.740205e+01 56.526361 60.764129 61.255479 +0.0091 0.0010 2423.1060 5.734657e+01 56.577331 60.777736 61.290923 +0.0092 0.0010 2367.9370 5.729165e+01 56.629371 60.792881 61.325894 +0.0093 0.0010 2314.3810 5.723732e+01 56.682163 60.809075 61.359156 +0.0094 0.0010 2262.3800 5.718435e+01 56.735633 60.826844 61.393278 +0.0095 0.0010 2211.8800 5.713074e+01 56.791056 60.846840 61.433430 +0.0096 0.0010 2162.8270 5.707839e+01 56.847621 60.867228 61.474256 +0.0097 0.0010 2115.1710 5.702675e+01 56.904341 60.889768 61.513470 +0.0098 0.0010 2068.8630 5.697602e+01 56.961452 60.912443 61.553445 +0.0099 0.0010 2023.8560 5.692535e+01 57.020509 60.937527 61.595970 +0.0100 0.0010 0.0000 5.687639e+01 57.078783 60.963425 61.634718 +0.0000 0.0011 31771.7700 0.000000e+00 74.323710 74.323223 83.735416 +0.0001 0.0011 31930.3200 4.508234e+00 94.022668 92.537341 103.537843 +0.0002 0.0011 32367.1200 9.299673e+00 94.775808 92.167493 102.271624 +0.0003 0.0011 32978.0700 1.413451e+01 94.857580 91.498197 100.559852 +0.0004 0.0011 33625.0700 1.884643e+01 94.411684 90.629809 98.554822 +0.0005 0.0011 34172.6800 2.331176e+01 93.534322 89.609201 96.377757 +0.0006 0.0011 34516.4300 2.745029e+01 92.319772 88.479498 94.115351 +0.0007 0.0011 34596.1700 3.122675e+01 90.861085 87.280844 91.859131 +0.0008 0.0011 34395.1800 3.462409e+01 89.242329 86.042797 89.663106 +0.0009 0.0011 33930.4400 3.766002e+01 87.526929 84.785214 87.555558 +0.0010 0.0011 33239.8300 4.035434e+01 85.770467 83.535494 85.571446 +0.0011 0.0011 32370.6800 4.273384e+01 84.012295 82.306016 83.702833 +0.0012 0.0011 31371.1800 4.482936e+01 82.277872 81.117163 81.990655 +0.0013 0.0011 30284.9200 4.667390e+01 80.591092 79.970693 80.388283 +0.0014 0.0011 29148.4600 4.829325e+01 78.964484 78.869804 78.900662 +0.0015 0.0011 27991.0700 4.971351e+01 77.409871 77.822276 77.533914 +0.0016 0.0011 26835.2500 5.096400e+01 75.935696 76.826608 76.279756 +0.0017 0.0011 25697.6200 5.205971e+01 74.536382 75.885154 75.123785 +0.0018 0.0011 24589.9900 5.302206e+01 73.218388 74.994143 74.029874 +0.0019 0.0011 23520.3200 5.386398e+01 71.977882 74.153262 73.070783 +0.0020 0.0011 22493.6400 5.460777e+01 70.815058 73.364112 72.165984 +0.0021 0.0011 21512.7300 5.526421e+01 69.727060 72.621555 71.331313 +0.0022 0.0011 20578.7600 5.584454e+01 68.709280 71.923527 70.560781 +0.0023 0.0011 19691.6900 5.635715e+01 67.757117 71.268826 69.844191 +0.0024 0.0011 18850.6100 5.680816e+01 66.870144 70.653144 69.189430 +0.0025 0.0011 18053.9400 5.720759e+01 66.041977 70.073478 68.587019 +0.0026 0.0011 17299.7600 5.755881e+01 65.270155 69.528989 68.023089 +0.0027 0.0011 16585.9800 5.786919e+01 64.550101 69.018228 67.502891 +0.0028 0.0011 15910.4000 5.814159e+01 63.880071 68.536036 67.019033 +0.0029 0.0011 15270.8400 5.837971e+01 63.255912 68.081939 66.568403 +0.0030 0.0011 14665.1500 5.858681e+01 62.674698 67.653058 66.149155 +0.0031 0.0011 14091.2600 5.876596e+01 62.132498 67.248062 65.759136 +0.0032 0.0011 13547.1900 5.892049e+01 61.627005 66.864739 65.390654 +0.0033 0.0011 13031.0800 5.905198e+01 61.157651 66.500792 65.045940 +0.0034 0.0011 12541.1500 5.916133e+01 60.721265 66.158669 64.735135 +0.0035 0.0011 12075.7600 5.925368e+01 60.315912 65.834632 64.447028 +0.0036 0.0011 11633.3800 5.932971e+01 59.938380 65.525815 64.172203 +0.0037 0.0011 11212.5700 5.939230e+01 59.589056 65.234921 63.913649 +0.0038 0.0011 10812.0000 5.944329e+01 59.262906 64.959486 63.676347 +0.0039 0.0011 10430.4300 5.948266e+01 58.961124 64.699350 63.458670 +0.0040 0.0011 10066.7200 5.951179e+01 58.681622 64.453553 63.255021 +0.0041 0.0011 9719.8140 5.953265e+01 58.422217 64.220211 63.061033 +0.0042 0.0011 9388.7330 5.954507e+01 58.182450 63.999987 62.884051 +0.0043 0.0011 9072.5580 5.955069e+01 57.959850 63.792040 62.718704 +0.0044 0.0011 8770.4340 5.954856e+01 57.754508 63.594372 62.563934 +0.0045 0.0011 8481.5630 5.954129e+01 57.563433 63.406917 62.416653 +0.0046 0.0011 8205.2020 5.952870e+01 57.387856 63.229666 62.281353 +0.0047 0.0011 7940.6590 5.951063e+01 57.226208 63.062460 62.156141 +0.0048 0.0011 7687.2860 5.948833e+01 57.077487 62.903452 62.037586 +0.0049 0.0011 7444.4790 5.946147e+01 56.941176 62.753434 61.930759 +0.0050 0.0011 7211.6760 5.943051e+01 56.817083 62.612031 61.829241 +0.0051 0.0011 6988.3490 5.939713e+01 56.702937 62.477264 61.736823 +0.0052 0.0011 6774.0050 5.936000e+01 56.598874 62.350590 61.650844 +0.0053 0.0011 6568.1830 5.932051e+01 56.504509 62.230725 61.574927 +0.0054 0.0011 6370.4520 5.927799e+01 56.419295 62.117605 61.504125 +0.0055 0.0011 6180.4050 5.923396e+01 56.342185 62.010497 61.436308 +0.0056 0.0011 5997.6640 5.918833e+01 56.272957 61.910589 61.370671 +0.0057 0.0011 5821.8710 5.914021e+01 56.211382 61.816016 61.319941 +0.0058 0.0011 5652.6920 5.909012e+01 56.157674 61.728023 61.271827 +0.0059 0.0011 5489.8110 5.903927e+01 56.109861 61.644156 61.225536 +0.0060 0.0011 5332.9320 5.898770e+01 56.068655 61.566011 61.183139 +0.0061 0.0011 5181.7750 5.893424e+01 56.032983 61.492177 61.146447 +0.0062 0.0011 5036.0770 5.887937e+01 56.004194 61.423628 61.116812 +0.0063 0.0011 4895.5920 5.882395e+01 55.979485 61.358747 61.087721 +0.0064 0.0011 4760.0940 5.876812e+01 55.960137 61.298516 61.062059 +0.0065 0.0011 4629.3700 5.871048e+01 55.946945 61.243550 61.045433 +0.0066 0.0011 4503.2190 5.865359e+01 55.937238 61.192590 61.031109 +0.0067 0.0011 4381.4500 5.859587e+01 55.932071 61.143528 61.017352 +0.0068 0.0011 4263.8790 5.853789e+01 55.930233 61.098968 61.000956 +0.0069 0.0011 4150.3330 5.847937e+01 55.932491 61.058727 60.994921 +0.0070 0.0011 4040.6460 5.842218e+01 55.938120 61.022719 60.988278 +0.0071 0.0011 3934.6600 5.836256e+01 55.947557 60.988506 60.990933 +0.0072 0.0011 3832.2240 5.830442e+01 55.960002 60.958285 60.989047 +0.0073 0.0011 3733.1960 5.824541e+01 55.975985 60.930669 60.993824 +0.0074 0.0011 3637.4370 5.818640e+01 55.994682 60.906219 60.999986 +0.0075 0.0011 3544.8190 5.812734e+01 56.016209 60.883795 61.006662 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60.837788 61.334118 +0.0091 0.0011 2403.6180 5.722178e+01 56.633431 60.850042 61.365398 +0.0092 0.0011 2349.2210 5.716895e+01 56.684913 60.864253 61.401238 +0.0093 0.0011 2296.4020 5.711657e+01 56.737408 60.880171 61.438269 +0.0094 0.0011 2245.1040 5.706493e+01 56.791004 60.897224 61.475953 +0.0095 0.0011 2195.2740 5.701389e+01 56.845502 60.915666 61.509004 +0.0096 0.0011 2146.8600 5.696280e+01 56.901924 60.935540 61.547578 +0.0097 0.0011 2099.8140 5.691401e+01 56.956730 60.956761 61.584643 +0.0098 0.0011 2054.0890 5.686479e+01 57.013827 60.978917 61.622862 +0.0099 0.0011 2009.6400 5.681722e+01 57.070200 61.002869 61.663743 +0.0100 0.0011 0.0000 5.676983e+01 57.128444 61.028491 61.705425 +0.0000 0.0012 26930.9700 5.780703e-07 71.569970 71.569451 80.528484 +0.0001 0.0012 27048.9700 4.202450e+00 91.636503 90.327790 100.890394 +0.0002 0.0012 27379.6600 8.673737e+00 92.335783 90.031067 99.784822 +0.0003 0.0012 27859.1700 1.320488e+01 92.464537 89.476302 98.316453 +0.0004 0.0012 28398.9800 1.765064e+01 92.127717 88.744616 96.580185 +0.0005 0.0012 28905.8400 2.190491e+01 91.415708 87.892224 94.704142 +0.0006 0.0012 29299.5300 2.589260e+01 90.397901 86.941987 92.735932 +0.0007 0.0012 29523.9500 2.956964e+01 89.147785 85.926152 90.753847 +0.0008 0.0012 29550.4800 3.292361e+01 87.738814 84.862769 88.794273 +0.0009 0.0012 29375.2100 3.594837e+01 86.220487 83.778590 86.907924 +0.0010 0.0012 29012.6900 3.865986e+01 84.645770 82.686536 85.096051 +0.0011 0.0012 28488.8600 4.107799e+01 83.051931 81.601218 83.378069 +0.0012 0.0012 27834.5900 4.322650e+01 81.464812 80.538566 81.768409 +0.0013 0.0012 27081.0900 4.513212e+01 79.904870 79.502213 80.267768 +0.0014 0.0012 26257.2200 4.681833e+01 78.391899 78.498920 78.863508 +0.0015 0.0012 25388.1100 4.830787e+01 76.936936 77.535986 77.569781 +0.0016 0.0012 24494.6200 4.962318e+01 75.540498 76.611434 76.351906 +0.0017 0.0012 23593.4400 5.078371e+01 74.215011 75.727973 75.240452 +0.0018 0.0012 22697.5200 5.180807e+01 72.959048 74.889489 74.197335 +0.0019 0.0012 21816.5800 5.271538e+01 71.771235 74.094482 73.231037 +0.0020 0.0012 20957.6700 5.351930e+01 70.655468 73.342537 72.356029 +0.0021 0.0012 20125.6800 5.423192e+01 69.606402 72.633402 71.535190 +0.0022 0.0012 19323.8300 5.486528e+01 68.623121 71.962935 70.779040 +0.0023 0.0012 18554.0000 5.542591e+01 67.700368 71.329433 70.074916 +0.0024 0.0012 17816.9900 5.592447e+01 66.837259 70.732638 69.423906 +0.0025 0.0012 17112.8300 5.636605e+01 66.029799 70.168423 68.822703 +0.0026 0.0012 16441.0500 5.675747e+01 65.275760 69.636980 68.259818 +0.0027 0.0012 15800.8000 5.710393e+01 64.572673 69.136968 67.743553 +0.0028 0.0012 15191.0000 5.741035e+01 63.915662 68.663039 67.258666 +0.0029 0.0012 14610.4000 5.768097e+01 63.301818 68.216066 66.809619 +0.0030 0.0012 14057.6800 5.791808e+01 62.729697 67.791847 66.386458 +0.0031 0.0012 13531.4900 5.812464e+01 62.196003 67.390095 65.990995 +0.0032 0.0012 13030.4800 5.830411e+01 61.698540 67.010183 65.624510 +0.0033 0.0012 12553.3100 5.845892e+01 61.234306 66.648791 65.281947 +0.0034 0.0012 12098.6900 5.859198e+01 60.803294 66.307233 64.963600 +0.0035 0.0012 11665.3800 5.870540e+01 60.400196 65.982027 64.661034 +0.0036 0.0012 11252.2000 5.880175e+01 60.027682 65.674237 64.384986 +0.0037 0.0012 10858.0200 5.888370e+01 59.680475 65.384816 64.126784 +0.0038 0.0012 10481.7800 5.895227e+01 59.358045 65.109167 63.887931 +0.0039 0.0012 10122.4800 5.900946e+01 59.058181 64.848923 63.661149 +0.0040 0.0012 9779.2020 5.905577e+01 58.779763 64.601387 63.451218 +0.0041 0.0012 9451.0710 5.909205e+01 58.521486 64.368306 63.259221 +0.0042 0.0012 9137.2670 5.912028e+01 58.280957 64.147176 63.075631 +0.0043 0.0012 8837.0150 5.913915e+01 58.059558 63.938638 62.904891 +0.0044 0.0012 8549.5880 5.915033e+01 57.855449 63.739162 62.748550 +0.0045 0.0012 8274.3010 5.915542e+01 57.666061 63.551229 62.600333 +0.0046 0.0012 8010.5120 5.915448e+01 57.490047 63.373065 62.461336 +0.0047 0.0012 7757.6180 5.914765e+01 57.328687 63.203626 62.334761 +0.0048 0.0012 7515.0540 5.913671e+01 57.179476 63.043833 62.213555 +0.0049 0.0012 7282.2880 5.911981e+01 57.043597 62.892485 62.105009 +0.0050 0.0012 7058.8210 5.910043e+01 56.917220 62.748657 61.997063 +0.0051 0.0012 6844.1860 5.907489e+01 56.803594 62.614196 61.904828 +0.0052 0.0012 6637.9420 5.904723e+01 56.698952 62.484666 61.814856 +0.0053 0.0012 6439.6760 5.901639e+01 56.603739 62.363305 61.733610 +0.0054 0.0012 6248.9990 5.898301e+01 56.517998 62.248443 61.656491 +0.0055 0.0012 6065.5450 5.894693e+01 56.439571 62.140165 61.589382 +0.0056 0.0012 5888.9710 5.890827e+01 56.370228 62.038556 61.523325 +0.0057 0.0012 5718.9510 5.886787e+01 56.307392 61.942187 61.468383 +0.0058 0.0012 5555.1810 5.882529e+01 56.252291 61.851916 61.414096 +0.0059 0.0012 5397.3710 5.878158e+01 56.202632 61.766822 61.367116 +0.0060 0.0012 5245.2510 5.873550e+01 56.161341 61.686696 61.325971 +0.0061 0.0012 5098.5640 5.868731e+01 56.125607 61.611804 61.286367 +0.0062 0.0012 4957.0670 5.863958e+01 56.095364 61.541169 61.252581 +0.0063 0.0012 4820.5320 5.858955e+01 56.070575 61.475213 61.222925 +0.0064 0.0012 4688.7530 5.853976e+01 56.049237 61.412235 61.194112 +0.0065 0.0012 4561.5330 5.848863e+01 56.033953 61.356125 61.171895 +0.0066 0.0012 4438.6840 5.843616e+01 56.023800 61.303282 61.154204 +0.0067 0.0012 4320.0290 5.838349e+01 56.017204 61.253558 61.139472 +0.0068 0.0012 4205.3950 5.833001e+01 56.014761 61.207513 61.126337 +0.0069 0.0012 4094.6220 5.827643e+01 56.015946 61.165382 61.116934 +0.0070 0.0012 3987.5530 5.822243e+01 56.020474 61.126778 61.110510 +0.0071 0.0012 3884.0410 5.816758e+01 56.029262 61.092308 61.109315 +0.0072 0.0012 3783.9440 5.811246e+01 56.041372 61.059706 61.106646 +0.0073 0.0012 3687.1270 5.805807e+01 56.055642 61.031141 61.107227 +0.0074 0.0012 3593.4620 5.800315e+01 56.073124 61.004821 61.111333 +0.0075 0.0012 3502.8240 5.794726e+01 56.093470 60.981059 61.118977 +0.0076 0.0012 3415.0970 5.789235e+01 56.116188 60.960577 61.124336 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60.928799 61.453578 +0.0092 0.0012 2328.9600 5.703854e+01 56.746514 60.941971 61.487785 +0.0093 0.0012 2276.9340 5.698935e+01 56.797583 60.956380 61.519269 +0.0094 0.0012 2226.3930 5.694018e+01 56.849977 60.972716 61.550994 +0.0095 0.0012 2177.2850 5.689061e+01 56.903769 60.990168 61.590074 +0.0096 0.0012 2129.5610 5.684205e+01 56.958595 61.009697 61.629146 +0.0097 0.0012 2083.1730 5.679457e+01 57.013868 61.030082 61.666814 +0.0098 0.0012 2038.0770 5.674732e+01 57.070558 61.051408 61.710164 +0.0099 0.0012 1994.2280 5.670098e+01 57.126526 61.073907 61.743322 +0.0100 0.0012 0.0000 5.665503e+01 57.183723 61.097914 61.777862 +0.0000 0.0013 23115.3500 1.272038e-13 69.223899 69.224426 77.751816 +0.0001 0.0013 23205.0600 3.944560e+00 89.652773 88.486350 98.673306 +0.0002 0.0013 23459.6800 8.149595e+00 90.303234 88.247228 97.698571 +0.0003 0.0013 23838.7000 1.241632e+01 90.450559 87.774419 96.408434 +0.0004 0.0013 24284.0900 1.663161e+01 90.193337 87.147635 94.902182 +0.0005 0.0013 24731.3900 2.069340e+01 89.600231 86.419529 93.258285 +0.0006 0.0013 25120.6800 2.453395e+01 88.729201 85.603889 91.524078 +0.0007 0.0013 25404.7000 2.810682e+01 87.640989 84.727904 89.766136 +0.0008 0.0013 25552.9500 3.139726e+01 86.397501 83.807529 88.018400 +0.0009 0.0013 25551.9300 3.439224e+01 85.043046 82.861799 86.306705 +0.0010 0.0013 25402.6100 3.710167e+01 83.622508 81.900778 84.651306 +0.0011 0.0013 25116.3900 3.953842e+01 82.168231 80.937007 83.073041 +0.0012 0.0013 24711.0600 4.172021e+01 80.708921 79.982797 81.572929 +0.0013 0.0013 24207.3900 4.366917e+01 79.263628 79.044526 80.154944 +0.0014 0.0013 23626.5400 4.540534e+01 77.852632 78.128728 78.829934 +0.0015 0.0013 22988.4300 4.694761e+01 76.483904 77.242899 77.593084 +0.0016 0.0013 22310.7800 4.831902e+01 75.168034 76.384125 76.428068 +0.0017 0.0013 21608.7500 4.953438e+01 73.907537 75.560444 75.349830 +0.0018 0.0013 20894.8600 5.061644e+01 72.709974 74.770286 74.341750 +0.0019 0.0013 20179.1800 5.157949e+01 71.575918 74.021561 73.414374 +0.0020 0.0013 19469.5700 5.243744e+01 70.505358 73.308993 72.553602 +0.0021 0.0013 18772.0500 5.320225e+01 69.493885 72.631447 71.751848 +0.0022 0.0013 18091.0500 5.388367e+01 68.542892 71.990217 71.008738 +0.0023 0.0013 17429.6700 5.449179e+01 67.649071 71.380569 70.310466 +0.0024 0.0013 16789.9400 5.503217e+01 66.810366 70.802665 69.669587 +0.0025 0.0013 16173.1000 5.551372e+01 66.025514 70.256219 69.072763 +0.0026 0.0013 15579.7500 5.594465e+01 65.288581 69.739166 68.507673 +0.0027 0.0013 15010.0400 5.632611e+01 64.601005 69.249068 67.993225 +0.0028 0.0013 14463.7600 5.666632e+01 63.957150 68.785147 67.512947 +0.0029 0.0013 13940.4700 5.696714e+01 63.354912 68.344386 67.052974 +0.0030 0.0013 13439.5300 5.723238e+01 62.792240 67.926998 66.632723 +0.0031 0.0013 12960.2300 5.746664e+01 62.266270 67.530041 66.240417 +0.0032 0.0013 12501.7300 5.767054e+01 61.776445 67.153335 65.873027 +0.0033 0.0013 12063.1900 5.784968e+01 61.317849 66.795448 65.524922 +0.0034 0.0013 11643.7300 5.800533e+01 60.890146 66.454733 65.197573 +0.0035 0.0013 11242.4800 5.813914e+01 60.494641 66.131884 64.899152 +0.0036 0.0013 10858.5800 5.825604e+01 60.124476 65.825942 64.617307 +0.0037 0.0013 10491.2000 5.835813e+01 59.780318 65.536166 64.354642 +0.0038 0.0013 10139.5200 5.844596e+01 59.459898 65.262242 64.108420 +0.0039 0.0013 9802.7770 5.851913e+01 59.163158 65.001797 63.882883 +0.0040 0.0013 9480.2410 5.858249e+01 58.886143 64.755118 63.665967 +0.0041 0.0013 9171.2150 5.863572e+01 58.627231 64.521341 63.469652 +0.0042 0.0013 8875.0330 5.867742e+01 58.390780 64.299747 63.283974 +0.0043 0.0013 8591.0580 5.871163e+01 58.168335 64.090279 63.109583 +0.0044 0.0013 8318.6850 5.873704e+01 57.963362 63.890176 62.945136 +0.0045 0.0013 8057.3390 5.875440e+01 57.774210 63.701302 62.794807 +0.0046 0.0013 7806.4770 5.876601e+01 57.598766 63.521718 62.654471 +0.0047 0.0013 7565.5830 5.877088e+01 57.438047 63.351605 62.522405 +0.0048 0.0013 7334.1710 5.877023e+01 57.288936 63.190192 62.401282 +0.0049 0.0013 7111.7800 5.876530e+01 57.151604 63.036403 62.282969 +0.0050 0.0013 6897.9770 5.875521e+01 57.025783 62.892379 62.175753 +0.0051 0.0013 6692.3500 5.874006e+01 56.911355 62.755035 62.079813 +0.0052 0.0013 6494.5120 5.872143e+01 56.806449 62.625457 61.990969 +0.0053 0.0013 6304.0970 5.870046e+01 56.709968 62.501576 61.900209 +0.0054 0.0013 6120.7610 5.867478e+01 56.622951 62.386191 61.826457 +0.0055 0.0013 5944.1750 5.864680e+01 56.544100 62.276417 61.753508 +0.0056 0.0013 5774.0320 5.861713e+01 56.472419 62.171966 61.684437 +0.0057 0.0013 5610.0400 5.858455e+01 56.408685 62.074717 61.622865 +0.0058 0.0013 5451.9230 5.854776e+01 56.353497 61.983078 61.568649 +0.0059 0.0013 5299.4210 5.850986e+01 56.304549 61.896341 61.519813 +0.0060 0.0013 5152.2850 5.847076e+01 56.262158 61.814277 61.473785 +0.0061 0.0013 5010.2830 5.842976e+01 56.225200 61.737893 61.435834 +0.0062 0.0013 4873.1930 5.838758e+01 56.193793 61.665593 61.398823 +0.0063 0.0013 4740.8070 5.834400e+01 56.167607 61.597919 61.364452 +0.0064 0.0013 4612.9380 5.829975e+01 56.145589 61.534058 61.335561 +0.0065 0.0013 4489.4030 5.825295e+01 56.130005 61.476086 61.315807 +0.0066 0.0013 4370.0310 5.820701e+01 56.118108 61.421579 61.293052 +0.0067 0.0013 4254.6550 5.815883e+01 56.110334 61.370129 61.277392 +0.0068 0.0013 4143.1190 5.811073e+01 56.106515 61.322774 61.262990 +0.0069 0.0013 4035.2710 5.806201e+01 56.106321 61.278879 61.249401 +0.0070 0.0013 3930.9670 5.801245e+01 56.110281 61.238939 61.239745 +0.0071 0.0013 3830.0680 5.796236e+01 56.117326 61.202170 61.232636 +0.0072 0.0013 3732.4430 5.791188e+01 56.127915 61.168401 61.229873 +0.0073 0.0013 3637.9660 5.786145e+01 56.141432 61.137967 61.229962 +0.0074 0.0013 3546.5150 5.781003e+01 56.157723 61.110209 61.230829 +0.0075 0.0013 3457.9760 5.775862e+01 56.176661 61.085615 61.235343 +0.0076 0.0013 3372.2370 5.770691e+01 56.198495 61.063583 61.243435 +0.0077 0.0013 3289.1930 5.765542e+01 56.222847 61.043731 61.250721 +0.0078 0.0013 3208.7420 5.760391e+01 56.249199 61.026729 61.258673 +0.0079 0.0013 3130.7880 5.755189e+01 56.278090 61.012546 61.272579 +0.0080 0.0013 3055.2370 5.750062e+01 56.308959 61.001025 61.289400 +0.0081 0.0013 2982.0020 5.744918e+01 56.342135 60.991683 61.303789 +0.0082 0.0013 2910.9960 5.739809e+01 56.376667 60.984510 61.321375 +0.0083 0.0013 2842.1380 5.734704e+01 56.413235 60.979159 61.339025 +0.0084 0.0013 2775.3510 5.729643e+01 56.451518 60.976991 61.361330 +0.0085 0.0013 2710.5590 5.724579e+01 56.491784 60.976503 61.383541 +0.0086 0.0013 2647.6900 5.719621e+01 56.532209 60.978082 61.408124 +0.0087 0.0013 2586.6760 5.714610e+01 56.576004 60.981447 61.434684 +0.0088 0.0013 2527.4510 5.709584e+01 56.621920 60.986711 61.461927 +0.0089 0.0013 2469.9520 5.704667e+01 56.667586 60.993836 61.488941 +0.0090 0.0013 2414.1180 5.699784e+01 56.714768 61.002378 61.518052 +0.0091 0.0013 2359.8900 5.694926e+01 56.763068 61.012634 61.548949 +0.0092 0.0013 2307.2140 5.690148e+01 56.812542 61.024743 61.580304 +0.0093 0.0013 2256.0350 5.685392e+01 56.863246 61.038031 61.614548 +0.0094 0.0013 2206.3010 5.680698e+01 56.914954 61.053223 61.647671 +0.0095 0.0013 2157.9650 5.676060e+01 56.967385 61.070043 61.678881 +0.0096 0.0013 2110.9770 5.671428e+01 57.020736 61.088094 61.715439 +0.0097 0.0013 2065.2930 5.666851e+01 57.076124 61.107721 61.753503 +0.0098 0.0013 2020.8690 5.662370e+01 57.130809 61.127622 61.787914 +0.0099 0.0013 1977.6630 5.657961e+01 57.186255 61.149636 61.822729 +0.0100 0.0013 0.0000 5.653570e+01 57.243182 61.172812 61.863162 +0.0000 0.0014 20052.5400 0.000000e+00 67.233504 67.232872 75.353399 +0.0001 0.0014 20122.0000 3.728054e+00 87.986087 86.952060 96.826690 +0.0002 0.0014 20321.0200 7.700349e+00 88.597022 86.750223 95.957830 +0.0003 0.0014 20623.1800 1.174390e+01 88.749928 86.337033 94.800807 +0.0004 0.0014 20989.5800 1.575422e+01 88.543788 85.790783 93.472360 +0.0005 0.0014 21375.2900 1.964013e+01 88.036549 85.155885 92.011656 +0.0006 0.0014 21736.0200 2.333909e+01 87.279138 84.443273 90.480303 +0.0007 0.0014 22033.8300 2.681063e+01 86.319427 83.676842 88.902122 +0.0008 0.0014 22240.6800 3.002601e+01 85.210462 82.867911 87.324322 +0.0009 0.0014 22339.7400 3.297990e+01 83.988801 82.031797 85.767130 +0.0010 0.0014 22324.7900 3.567003e+01 82.695893 81.180075 84.245891 +0.0011 0.0014 22198.2600 3.810976e+01 81.362234 80.316529 82.790956 +0.0012 0.0014 21968.8700 4.030721e+01 80.015016 79.456488 81.394091 +0.0013 0.0014 21649.2200 4.228490e+01 78.670247 78.604142 80.061214 +0.0014 0.0014 21253.7800 4.405554e+01 77.348270 77.766639 78.807174 +0.0015 0.0014 20797.3600 4.564141e+01 76.059960 76.947568 77.618489 +0.0016 0.0014 20294.0900 4.705468e+01 74.815582 76.153069 76.509980 +0.0017 0.0014 19756.7800 4.832012e+01 73.620579 75.385020 75.470035 +0.0018 0.0014 19196.6200 4.945213e+01 72.478372 74.647911 74.499462 +0.0019 0.0014 18623.1200 5.046370e+01 71.394773 73.941640 73.597885 +0.0020 0.0014 18044.1300 5.137061e+01 70.365149 73.266393 72.751174 +0.0021 0.0014 17465.9800 5.218255e+01 69.392551 72.622013 71.972564 +0.0022 0.0014 16893.6800 5.290829e+01 68.473525 72.007133 71.240357 +0.0023 0.0014 16331.0300 5.355927e+01 67.607594 71.422864 70.541473 +0.0024 0.0014 15780.8400 5.414000e+01 66.793130 70.866502 69.906381 +0.0025 0.0014 15245.1500 5.465978e+01 66.027364 70.336745 69.323831 +0.0026 0.0014 14725.3500 5.512466e+01 65.309991 69.834038 68.776162 +0.0027 0.0014 14222.3000 5.554064e+01 64.636380 69.356644 68.252842 +0.0028 0.0014 13736.5100 5.591143e+01 64.005328 68.902084 67.769034 +0.0029 0.0014 13268.1400 5.624172e+01 63.415024 68.470314 67.319857 +0.0030 0.0014 12817.1400 5.653504e+01 62.862444 68.059215 66.893882 +0.0031 0.0014 12383.2900 5.679409e+01 62.344194 67.667865 66.498194 +0.0032 0.0014 11966.2200 5.702357e+01 61.860625 67.294860 66.125075 +0.0033 0.0014 11565.5000 5.722611e+01 61.407416 66.939866 65.774614 +0.0034 0.0014 11180.6200 5.740330e+01 60.986872 66.602929 65.449952 +0.0035 0.0014 10811.0200 5.755921e+01 60.595320 66.282362 65.146164 +0.0036 0.0014 10456.1400 5.769685e+01 60.228802 65.979033 64.859715 +0.0037 0.0014 10115.4100 5.781821e+01 59.888064 65.691044 64.594235 +0.0038 0.0014 9788.2290 5.792373e+01 59.570586 65.416902 64.343046 +0.0039 0.0014 9474.0520 5.801614e+01 59.274609 65.157896 64.114513 +0.0040 0.0014 9172.3290 5.809563e+01 58.999542 64.911383 63.896947 +0.0041 0.0014 8882.5260 5.816362e+01 58.744407 64.678478 63.692633 +0.0042 0.0014 8604.1190 5.822101e+01 58.507265 64.456013 63.505997 +0.0043 0.0014 8336.6000 5.826963e+01 58.285456 64.245053 63.326649 +0.0044 0.0014 8079.4810 5.830939e+01 58.080783 64.045190 63.158049 +0.0045 0.0014 7832.2910 5.834017e+01 57.891290 63.855351 63.002501 +0.0046 0.0014 7594.5800 5.836382e+01 57.716373 63.674748 62.857434 +0.0047 0.0014 7365.9180 5.838056e+01 57.554853 63.503678 62.725621 +0.0048 0.0014 7145.8930 5.839194e+01 57.405853 63.341221 62.598616 +0.0049 0.0014 6934.1140 5.839607e+01 57.269690 63.187523 62.481758 +0.0050 0.0014 6730.2100 5.839632e+01 57.143143 63.041095 62.370893 +0.0051 0.0014 6533.8260 5.839213e+01 57.027365 62.901689 62.264054 +0.0052 0.0014 6344.6260 5.838369e+01 56.920465 62.771287 62.171026 +0.0053 0.0014 6162.2910 5.837022e+01 56.824620 62.647346 62.085653 +0.0054 0.0014 5986.5180 5.835348e+01 56.737675 62.529560 62.006415 +0.0055 0.0014 5817.0180 5.833385e+01 56.657452 62.417832 61.929567 +0.0056 0.0014 5653.5180 5.831103e+01 56.585879 62.312656 61.861903 +0.0057 0.0014 5495.7600 5.828607e+01 56.521313 62.213129 61.798089 +0.0058 0.0014 5343.4970 5.825922e+01 56.463846 62.119668 61.733659 +0.0059 0.0014 5196.4940 5.822850e+01 56.412248 62.031389 61.680213 +0.0060 0.0014 5054.5310 5.819605e+01 56.368722 61.948124 61.633524 +0.0061 0.0014 4917.3950 5.816127e+01 56.330781 61.869561 61.593389 +0.0062 0.0014 4784.8860 5.812520e+01 56.298440 61.796093 61.557107 +0.0063 0.0014 4656.8190 5.808735e+01 56.271515 61.726729 61.522955 +0.0064 0.0014 4533.0220 5.804866e+01 56.248933 61.661478 61.489924 +0.0065 0.0014 4413.3300 5.800815e+01 56.231970 61.601650 61.464655 +0.0066 0.0014 4297.5840 5.796734e+01 56.219026 61.545120 61.440914 +0.0067 0.0014 4185.6340 5.792540e+01 56.209083 61.491699 61.416901 +0.0068 0.0014 4077.3340 5.788208e+01 56.203867 61.443066 61.400568 +0.0069 0.0014 3972.5450 5.783798e+01 56.202893 61.397852 61.386481 +0.0070 0.0014 3871.1340 5.779284e+01 56.206100 61.357012 61.380966 +0.0071 0.0014 3772.9730 5.774749e+01 56.212115 61.318694 61.368756 +0.0072 0.0014 3677.9390 5.770162e+01 56.221346 61.283345 61.364810 +0.0073 0.0014 3585.9150 5.765535e+01 56.233778 61.251235 61.360354 +0.0074 0.0014 3496.7890 5.760828e+01 56.248623 61.221571 61.361092 +0.0075 0.0014 3410.4520 5.756105e+01 56.266053 61.195431 61.362062 +0.0076 0.0014 3326.8020 5.751320e+01 56.286771 61.171643 61.362336 +0.0077 0.0014 3245.7390 5.746517e+01 56.310404 61.150988 61.371017 +0.0078 0.0014 3167.1680 5.741705e+01 56.335471 61.132775 61.380863 +0.0079 0.0014 3090.9990 5.736899e+01 56.362947 61.117460 61.394476 +0.0080 0.0014 3017.1440 5.732083e+01 56.392594 61.104299 61.407636 +0.0081 0.0014 2945.5190 5.727273e+01 56.424360 61.093416 61.421403 +0.0082 0.0014 2876.0440 5.722465e+01 56.458280 61.085084 61.437448 +0.0083 0.0014 2808.6410 5.717707e+01 56.494043 61.077941 61.453686 +0.0084 0.0014 2743.2380 5.712923e+01 56.531388 61.074362 61.472896 +0.0085 0.0014 2679.7630 5.708202e+01 56.570388 61.072476 61.492789 +0.0086 0.0014 2618.1490 5.703464e+01 56.610942 61.072980 61.516179 +0.0087 0.0014 2558.3290 5.698768e+01 56.653566 61.075505 61.541962 +0.0088 0.0014 2500.2410 5.694103e+01 56.696903 61.079334 61.565889 +0.0089 0.0014 2443.8260 5.689457e+01 56.741670 61.084908 61.590606 +0.0090 0.0014 2389.0250 5.684949e+01 56.787104 61.091903 61.612919 +0.0091 0.0014 2335.7830 5.680250e+01 56.835293 61.101587 61.645698 +0.0092 0.0014 2284.0460 5.675724e+01 56.883681 61.112623 61.675628 +0.0093 0.0014 2233.7640 5.671197e+01 56.933572 61.124882 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5.656395e+01 56.779483 62.031493 62.202989 +0.0071 0.0019 3449.2070 5.654255e+01 56.778912 61.984830 62.180437 +0.0072 0.0019 3368.3780 5.652031e+01 56.780901 61.941808 62.164931 +0.0073 0.0019 3289.8370 5.649624e+01 56.786164 61.901425 62.150474 +0.0074 0.0019 3213.5120 5.647178e+01 56.794130 61.864390 62.138087 +0.0075 0.0019 3139.3350 5.644600e+01 56.805005 61.830074 62.127475 +0.0076 0.0019 3067.2390 5.641908e+01 56.818698 61.798946 62.120804 +0.0077 0.0019 2997.1580 5.639268e+01 56.832812 61.770540 62.114047 +0.0078 0.0019 2929.0290 5.636363e+01 56.853189 61.744684 62.113625 +0.0079 0.0019 2862.7920 5.633517e+01 56.874102 61.721617 62.111289 +0.0080 0.0019 2798.3870 5.630611e+01 56.897054 61.701451 62.113298 +0.0081 0.0019 2735.7570 5.627677e+01 56.922095 61.683297 62.117676 +0.0082 0.0019 2674.8470 5.624648e+01 56.949735 61.667626 62.122975 +0.0083 0.0019 2615.6020 5.621623e+01 56.979277 61.654368 62.131367 +0.0084 0.0019 2557.9720 5.618485e+01 57.010737 61.643354 62.143673 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61.705471 62.454649 +0.0100 0.0019 0.0000 5.568917e+01 57.693844 61.722096 62.488179 +0.0000 0.0020 9993.4200 0.000000e+00 60.198660 60.198188 66.377752 +0.0001 0.0020 10012.9900 2.900483e+00 82.204941 81.580486 90.547707 +0.0002 0.0020 10070.6100 5.972845e+00 82.551842 81.440902 89.948086 +0.0003 0.0020 10163.0800 9.119174e+00 82.633511 81.174805 89.217818 +0.0004 0.0020 10285.3200 1.227174e+01 82.509295 80.832611 88.400341 +0.0005 0.0020 10430.6800 1.537952e+01 82.203479 80.444514 87.528368 +0.0006 0.0020 10591.4600 1.840285e+01 81.746305 80.017594 86.605186 +0.0007 0.0020 10759.3800 2.130765e+01 81.161092 79.563896 85.646553 +0.0008 0.0020 10926.1400 2.407446e+01 80.468484 79.087432 84.664307 +0.0009 0.0020 11083.9600 2.668771e+01 79.690087 78.595145 83.678527 +0.0010 0.0020 11225.9300 2.913802e+01 78.846750 78.091479 82.693959 +0.0011 0.0020 11346.3700 3.142416e+01 77.960457 77.576564 81.723278 +0.0012 0.0020 11440.9700 3.354848e+01 77.040437 77.058174 80.758713 +0.0013 0.0020 11506.8200 3.551412e+01 76.106162 76.535066 79.814455 +0.0014 0.0020 11542.4000 3.732860e+01 75.164454 76.011543 78.904943 +0.0015 0.0020 11547.3500 3.899884e+01 74.229812 75.489922 78.025494 +0.0016 0.0020 11522.3400 4.053281e+01 73.307230 74.973238 77.172288 +0.0017 0.0020 11468.8500 4.193989e+01 72.405125 74.462258 76.360779 +0.0018 0.0020 11388.9200 4.322968e+01 71.524579 73.956181 75.582949 +0.0019 0.0020 11285.0000 4.441103e+01 70.671802 73.460855 74.817826 +0.0020 0.0020 11159.7400 4.549050e+01 69.852050 72.975600 74.108492 +0.0021 0.0020 11015.8400 4.647825e+01 69.060964 72.500867 73.423703 +0.0022 0.0020 10856.0000 4.737917e+01 68.305681 72.037511 72.779230 +0.0023 0.0020 10682.7800 4.820228e+01 67.582896 71.585788 72.160253 +0.0024 0.0020 10498.5800 4.895340e+01 66.894144 71.147416 71.573363 +0.0025 0.0020 10305.6000 4.963971e+01 66.238849 70.720250 71.013661 +0.0026 0.0020 10105.8300 5.026434e+01 65.617503 70.307658 70.488201 +0.0027 0.0020 9901.0290 5.083374e+01 65.028559 69.908329 69.981810 +0.0028 0.0020 9692.7630 5.135324e+01 64.468776 69.520702 69.511384 +0.0029 0.0020 9482.3820 5.182536e+01 63.942581 69.146700 69.064384 +0.0030 0.0020 9271.0540 5.225596e+01 63.444084 68.785120 68.636180 +0.0031 0.0020 9059.7780 5.264958e+01 62.975745 68.439419 68.233061 +0.0032 0.0020 8849.3980 5.300942e+01 62.534590 68.107537 67.856523 +0.0033 0.0020 8640.6260 5.333939e+01 62.119191 67.788628 67.498150 +0.0034 0.0020 8434.0510 5.363934e+01 61.730826 67.481843 67.161547 +0.0035 0.0020 8230.1600 5.391481e+01 61.365185 67.189594 66.845267 +0.0036 0.0020 8029.3620 5.416614e+01 61.021844 66.909284 66.548678 +0.0037 0.0020 7831.9940 5.439588e+01 60.700044 66.640013 66.266762 +0.0038 0.0020 7638.3190 5.460657e+01 60.398098 66.383624 65.994941 +0.0039 0.0020 7448.5400 5.479835e+01 60.114106 66.135720 65.744326 +0.0040 0.0020 7262.8070 5.497321e+01 59.847739 65.900720 65.506832 +0.0041 0.0020 7081.2240 5.513196e+01 59.600915 65.674658 65.284462 +0.0042 0.0020 6903.8590 5.527670e+01 59.368928 65.458038 65.067084 +0.0043 0.0020 6730.7470 5.540756e+01 59.153683 65.251343 64.872704 +0.0044 0.0020 6561.8960 5.552682e+01 58.951392 65.053554 64.683285 +0.0045 0.0020 6397.2950 5.563464e+01 58.763943 64.864655 64.507810 +0.0046 0.0020 6236.9140 5.573193e+01 58.588593 64.682317 64.340570 +0.0047 0.0020 6080.7060 5.581969e+01 58.425099 64.509843 64.182085 +0.0048 0.0020 5928.6160 5.589862e+01 58.273582 64.344946 64.035733 +0.0049 0.0020 5780.5770 5.596859e+01 58.133646 64.186670 63.896257 +0.0050 0.0020 5636.5160 5.603115e+01 58.003504 64.035868 63.762928 +0.0051 0.0020 5496.3540 5.608702e+01 57.882599 63.892303 63.638481 +0.0052 0.0020 5360.0060 5.613531e+01 57.771829 63.755067 63.524149 +0.0053 0.0020 5227.3870 5.617873e+01 57.668794 63.624761 63.416885 +0.0054 0.0020 5098.4080 5.621666e+01 57.573680 63.499411 63.310711 +0.0055 0.0020 4972.9770 5.624827e+01 57.487391 63.380192 63.211134 +0.0056 0.0020 4851.0060 5.627515e+01 57.409686 63.267877 63.124752 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62.133688 62.370196 +0.0072 0.0020 3300.7000 5.626124e+01 56.910021 62.087662 62.347183 +0.0073 0.0020 3225.0010 5.624196e+01 56.913950 62.046449 62.330551 +0.0074 0.0020 3151.3820 5.622176e+01 56.920095 62.008020 62.316960 +0.0075 0.0020 3079.7800 5.620075e+01 56.929501 61.972487 62.303632 +0.0076 0.0020 3010.1370 5.617847e+01 56.941253 61.939058 62.290851 +0.0077 0.0020 2942.3930 5.615562e+01 56.956167 61.908658 62.283909 +0.0078 0.0020 2876.4910 5.613190e+01 56.972867 61.881475 62.278006 +0.0079 0.0020 2812.3760 5.610689e+01 56.992725 61.857023 62.278280 +0.0080 0.0020 2749.9950 5.608194e+01 57.014346 61.835316 62.277605 +0.0081 0.0020 2689.2950 5.605624e+01 57.038310 61.815905 62.277566 +0.0082 0.0020 2630.2260 5.602998e+01 57.064064 61.798834 62.282903 +0.0083 0.0020 2572.7390 5.600314e+01 57.091852 61.784255 62.290013 +0.0084 0.0020 2516.7850 5.597625e+01 57.121351 61.771401 62.296401 +0.0085 0.0020 2462.3200 5.594874e+01 57.153134 61.760987 62.306349 +0.0086 0.0020 2409.2970 5.592147e+01 57.186226 61.752784 62.316471 +0.0087 0.0020 2357.6740 5.589380e+01 57.220714 61.746489 62.327884 +0.0088 0.0020 2307.4090 5.586557e+01 57.256914 61.742341 62.343083 +0.0089 0.0020 2258.4600 5.583721e+01 57.294721 61.740347 62.358738 +0.0090 0.0020 2210.7890 5.580920e+01 57.333628 61.739896 62.376906 +0.0091 0.0020 2164.3570 5.578092e+01 57.373632 61.740996 62.393787 +0.0092 0.0020 2119.1270 5.575247e+01 57.415036 61.745392 62.411434 +0.0093 0.0020 2075.0630 5.572430e+01 57.457585 61.750037 62.431976 +0.0094 0.0020 2032.1310 5.569539e+01 57.500779 61.757050 62.452577 +0.0095 0.0020 1990.2980 5.566719e+01 57.546500 61.764903 62.480524 +0.0096 0.0020 1949.5290 5.563934e+01 57.592224 61.774918 62.504645 +0.0097 0.0020 1909.7950 5.561173e+01 57.639155 61.786392 62.522471 +0.0098 0.0020 0.0000 5.558378e+01 57.686932 61.799332 62.546619 +0.0099 0.0020 0.0000 5.555612e+01 57.735254 61.813557 62.575182 +0.0100 0.0020 0.0000 5.552897e+01 57.784214 61.829024 62.602670 +0.0000 0.0021 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64.712397 65.349090 +0.0059 0.0029 3407.2580 5.304688e+01 58.834887 64.604188 65.238726 +0.0060 0.0029 3343.3180 5.311787e+01 58.767687 64.505355 65.146499 +0.0061 0.0029 3280.3740 5.318523e+01 58.704655 64.407998 65.048632 +0.0062 0.0029 3218.4370 5.324774e+01 58.646941 64.313837 64.960861 +0.0063 0.0029 3157.5210 5.330777e+01 58.593951 64.224820 64.873928 +0.0064 0.0029 3097.6330 5.336090e+01 58.546602 64.138260 64.792530 +0.0065 0.0029 3038.7780 5.341058e+01 58.504168 64.057504 64.721272 +0.0066 0.0029 2980.9580 5.345669e+01 58.465128 63.978752 64.652050 +0.0067 0.0029 2924.1750 5.349937e+01 58.430425 63.903769 64.584505 +0.0068 0.0029 2868.4260 5.353808e+01 58.400575 63.832073 64.523956 +0.0069 0.0029 2813.7080 5.357506e+01 58.373081 63.763163 64.462967 +0.0070 0.0029 2760.0150 5.360765e+01 58.349774 63.699089 64.409879 +0.0071 0.0029 2707.3420 5.363790e+01 58.329984 63.637252 64.358341 +0.0072 0.0029 2655.6800 5.366564e+01 58.313470 63.578940 64.310711 +0.0073 0.0029 2605.0190 5.369141e+01 58.300300 63.523734 64.264767 +0.0074 0.0029 2555.3510 5.371498e+01 58.290224 63.471599 64.222308 +0.0075 0.0029 2506.6630 5.373623e+01 58.283181 63.422428 64.183082 +0.0076 0.0029 2458.9450 5.375574e+01 58.279127 63.375922 64.146746 +0.0077 0.0029 2412.1830 5.377314e+01 58.278247 63.333061 64.116569 +0.0078 0.0029 2366.3640 5.378906e+01 58.279092 63.292469 64.084711 +0.0079 0.0029 2321.4760 5.380369e+01 58.283131 63.254115 64.054325 +0.0080 0.0029 2277.5030 5.381631e+01 58.289352 63.218367 64.031611 +0.0081 0.0029 2234.4310 5.382793e+01 58.297562 63.185471 64.009413 +0.0082 0.0029 2192.2470 5.383762e+01 58.307479 63.154847 63.987708 +0.0083 0.0029 2150.9340 5.384676e+01 58.321180 63.127420 63.972764 +0.0084 0.0029 2110.4790 5.385480e+01 58.335302 63.101664 63.958896 +0.0085 0.0029 2070.8650 5.386173e+01 58.351869 63.078047 63.946440 +0.0086 0.0029 2032.0770 5.386747e+01 58.369545 63.056537 63.933385 +0.0087 0.0029 1994.1010 5.387194e+01 58.389414 63.037323 63.924107 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87.845359 +0.0002 0.0030 4337.8160 4.545055e+00 79.265015 78.660996 87.381113 +0.0003 0.0030 4360.5030 6.932133e+00 79.246073 78.451392 86.881243 +0.0004 0.0030 4391.5380 9.336759e+00 79.109559 78.195451 86.330024 +0.0005 0.0030 4430.2140 1.172713e+01 78.874403 77.918479 85.761703 +0.0006 0.0030 4475.6530 1.407822e+01 78.552545 77.620629 85.177238 +0.0007 0.0030 4526.8310 1.636973e+01 78.154089 77.309298 84.579251 +0.0008 0.0030 4582.6040 1.858628e+01 77.690695 76.996441 83.971118 +0.0009 0.0030 4641.7430 2.071668e+01 77.172608 76.671700 83.347762 +0.0010 0.0030 4702.9680 2.275294e+01 76.610004 76.339934 82.722350 +0.0011 0.0030 4764.9870 2.469048e+01 76.013070 76.007185 82.090276 +0.0012 0.0030 4826.5350 2.652883e+01 75.388689 75.670508 81.459624 +0.0013 0.0030 4886.4030 2.826431e+01 74.746466 75.332518 80.825053 +0.0014 0.0030 4943.4730 2.990020e+01 74.090059 74.993254 80.203907 +0.0015 0.0030 4996.7410 3.143865e+01 73.431698 74.653875 79.588816 +0.0016 0.0030 5045.3350 3.288295e+01 72.774298 74.316356 78.981493 +0.0017 0.0030 5088.5260 3.423800e+01 72.121149 73.980134 78.385285 +0.0018 0.0030 5125.7340 3.550717e+01 71.475530 73.646733 77.801391 +0.0019 0.0030 5156.5280 3.669518e+01 70.842354 73.315840 77.228422 +0.0020 0.0030 5180.6180 3.780649e+01 70.222762 72.990575 76.662438 +0.0021 0.0030 5197.8460 3.884430e+01 69.620029 72.668563 76.130267 +0.0022 0.0030 5208.1740 3.981481e+01 69.036298 72.350836 75.601026 +0.0023 0.0030 5211.6680 4.072139e+01 68.471249 72.037020 75.088667 +0.0024 0.0030 5208.4820 4.156740e+01 67.926337 71.730281 74.598057 +0.0025 0.0030 5198.8460 4.235873e+01 67.401062 71.428431 74.119898 +0.0026 0.0030 5183.0450 4.309774e+01 66.895181 71.132447 73.659479 +0.0027 0.0030 5161.4110 4.378907e+01 66.411052 70.841465 73.209664 +0.0028 0.0030 5134.3090 4.443404e+01 65.948419 70.557849 72.784423 +0.0029 0.0030 5102.1280 4.503715e+01 65.506394 70.281749 72.365238 +0.0030 0.0030 5065.2670 4.560096e+01 65.081826 70.009722 71.966346 +0.0031 0.0030 5024.1320 4.612742e+01 64.678800 69.743945 71.589658 +0.0032 0.0030 4979.1200 4.662007e+01 64.294489 69.486609 71.223796 +0.0033 0.0030 4930.6240 4.708139e+01 63.928370 69.233382 70.866802 +0.0034 0.0030 4879.0180 4.751361e+01 63.578545 68.989961 70.531170 +0.0035 0.0030 4824.6630 4.791659e+01 63.248957 68.751242 70.209129 +0.0036 0.0030 4767.8990 4.829486e+01 62.934792 68.518289 69.899160 +0.0037 0.0030 4709.0450 4.864958e+01 62.636236 68.294695 69.599991 +0.0038 0.0030 4648.4000 4.898026e+01 62.354651 68.075543 69.317222 +0.0039 0.0030 4586.2380 4.929110e+01 62.087332 67.863186 69.039688 +0.0040 0.0030 4522.8140 4.958240e+01 61.832791 67.657090 68.778570 +0.0041 0.0030 4458.3640 4.985431e+01 61.594671 67.457301 68.532037 +0.0042 0.0030 4393.1000 5.011019e+01 61.367418 67.262880 68.289261 +0.0043 0.0030 4327.2180 5.034899e+01 61.154266 67.076371 68.063433 +0.0044 0.0030 4260.8940 5.057351e+01 60.952519 66.893463 67.843752 +0.0045 0.0030 4194.2880 5.078425e+01 60.761678 66.718330 67.634150 +0.0046 0.0030 4127.5460 5.098343e+01 60.581030 66.549451 67.413211 +0.0047 0.0030 4060.7960 5.116831e+01 60.412262 66.384689 67.221528 +0.0048 0.0030 3994.1560 5.134011e+01 60.253256 66.223651 67.059935 +0.0049 0.0030 3927.7300 5.150279e+01 60.103669 66.070015 66.885227 +0.0050 0.0030 3861.6110 5.165519e+01 59.963492 65.921102 66.722327 +0.0051 0.0030 3795.8820 5.179845e+01 59.832004 65.777323 66.565099 +0.0052 0.0030 3730.6180 5.193247e+01 59.707679 65.639362 66.412980 +0.0053 0.0030 3665.8820 5.205816e+01 59.591995 65.505253 66.266951 +0.0054 0.0030 3601.7330 5.217644e+01 59.483099 65.376142 66.127609 +0.0055 0.0030 3538.2200 5.228672e+01 59.381861 65.251606 65.997247 +0.0056 0.0030 3475.3890 5.238952e+01 59.287955 65.132353 65.871607 +0.0057 0.0030 3413.2790 5.248721e+01 59.198818 65.017442 65.754706 +0.0058 0.0030 3351.9270 5.257739e+01 59.117550 64.906782 65.643024 +0.0059 0.0030 3291.3650 5.266249e+01 59.041333 64.799559 65.534504 +0.0060 0.0030 3231.6210 5.274078e+01 58.971360 64.698322 65.432225 +0.0061 0.0030 3172.7170 5.281505e+01 58.905724 64.600047 65.332212 +0.0062 0.0030 3114.6720 5.288369e+01 58.846904 64.505955 65.238929 +0.0063 0.0030 3057.5030 5.294820e+01 58.792389 64.415780 65.151443 +0.0064 0.0030 3001.2230 5.300905e+01 58.740656 64.326146 65.066232 +0.0065 0.0030 2945.8410 5.306258e+01 58.697713 64.242688 64.990847 +0.0066 0.0030 2891.3640 5.311565e+01 58.656108 64.162513 64.913094 +0.0067 0.0030 2837.7970 5.316279e+01 58.620117 64.086408 64.843871 +0.0068 0.0030 2785.1440 5.320752e+01 58.587186 64.017261 64.780110 +0.0069 0.0030 2733.4040 5.324905e+01 58.557800 63.947571 64.718967 +0.0070 0.0030 2682.5770 5.328684e+01 58.532862 63.881656 64.660723 +0.0071 0.0030 2632.6610 5.332277e+01 58.510791 63.818686 64.606061 +0.0072 0.0030 2583.6510 5.335489e+01 58.493387 63.759261 64.555296 +0.0073 0.0030 2535.5420 5.338597e+01 58.477593 63.702519 64.504720 +0.0074 0.0030 2488.3280 5.341367e+01 58.465798 63.649272 64.460233 +0.0075 0.0030 2442.0010 5.343998e+01 58.456859 63.598737 64.418373 +0.0076 0.0030 2396.5530 5.346426e+01 58.451040 63.551424 64.379404 +0.0077 0.0030 2351.9760 5.348598e+01 58.447269 63.506822 64.345000 +0.0078 0.0030 2308.2590 5.350673e+01 58.446942 63.464462 64.311971 +0.0079 0.0030 2265.3920 5.352560e+01 58.448299 63.425235 64.281950 +0.0080 0.0030 2223.3640 5.354270e+01 58.452259 63.387819 64.254153 +0.0081 0.0030 2182.1640 5.355812e+01 58.458928 63.353499 64.228113 +0.0082 0.0030 2141.7790 5.357225e+01 58.467687 63.321569 64.208220 +0.0083 0.0030 2102.1990 5.358502e+01 58.478526 63.292148 64.188107 +0.0084 0.0030 2063.4100 5.359696e+01 58.490903 63.265397 64.168649 +0.0085 0.0030 2025.4000 5.360759e+01 58.505257 63.240530 64.151667 +0.0086 0.0030 1988.1560 5.361728e+01 58.521404 63.217122 64.136104 +0.0087 0.0030 1951.6650 5.362570e+01 58.539266 63.196279 64.124917 +0.0088 0.0030 1915.9140 5.363275e+01 58.558832 63.178178 64.117313 +0.0089 0.0030 1880.8910 5.363913e+01 58.580558 63.161053 64.104374 +0.0090 0.0030 1846.5810 5.364479e+01 58.603226 63.146284 64.097852 +0.0091 0.0030 1812.9720 5.364924e+01 58.628869 63.133547 64.098763 +0.0092 0.0030 1780.0510 5.365421e+01 58.653619 63.122691 64.094272 +0.0093 0.0030 1747.8050 5.365826e+01 58.680833 63.114131 64.088378 +0.0094 0.0030 1716.2200 5.366129e+01 58.709250 63.106619 64.089167 +0.0095 0.0030 1685.2830 5.366452e+01 58.738224 63.100478 64.088512 +0.0096 0.0030 0.0000 5.366610e+01 58.769211 63.096215 64.091472 +0.0097 0.0030 0.0000 5.366709e+01 58.802082 63.094008 64.098740 +0.0098 0.0030 0.0000 5.366857e+01 58.834870 63.093296 64.103318 +0.0099 0.0030 0.0000 5.367009e+01 58.867884 63.093790 64.113326 +0.0100 0.0030 0.0000 5.366972e+01 58.903650 63.096335 64.124830 +0.0000 0.0031 4022.3210 3.599933e-06 54.293113 54.292759 59.093487 +0.0001 0.0031 4026.4750 2.166532e+00 79.023908 78.703452 87.782236 +0.0002 0.0031 4038.8480 4.445487e+00 79.136139 78.568638 87.338124 +0.0003 0.0031 4059.1720 6.779014e+00 79.113453 78.356922 86.841652 +0.0004 0.0031 4087.0110 9.128919e+00 78.973505 78.108000 86.313716 +0.0005 0.0031 4121.7670 1.146891e+01 78.740664 77.832888 85.758321 +0.0006 0.0031 4162.6940 1.376894e+01 78.427200 77.543747 85.189836 +0.0007 0.0031 4208.9230 1.601371e+01 78.038969 77.244502 84.610819 +0.0008 0.0031 4259.4770 1.818589e+01 77.586489 76.933338 84.029251 +0.0009 0.0031 4313.3060 2.027586e+01 77.085712 76.616452 83.419764 +0.0010 0.0031 4369.3070 2.227674e+01 76.538491 76.295304 82.815439 +0.0011 0.0031 4426.3640 2.418255e+01 75.958527 75.969647 82.205088 +0.0012 0.0031 4483.3710 2.598956e+01 75.351716 75.641657 81.594287 +0.0013 0.0031 4539.2640 2.770104e+01 74.728389 75.313735 80.985272 +0.0014 0.0031 4593.0470 2.931611e+01 74.092702 74.983941 80.377857 +0.0015 0.0031 4643.8130 3.083731e+01 73.451399 74.654824 79.777481 +0.0016 0.0031 4690.7590 3.226757e+01 72.812068 74.327266 79.192817 +0.0017 0.0031 4733.2010 3.361149e+01 72.177195 74.001312 78.613433 +0.0018 0.0031 4770.5770 3.487379e+01 71.551304 73.677240 78.042994 +0.0019 0.0031 4802.4510 3.605425e+01 70.934152 73.359581 77.487670 +0.0020 0.0031 4828.5080 3.716143e+01 70.330263 73.042503 76.942233 +0.0021 0.0031 4848.5490 3.819819e+01 69.741815 72.730458 76.413402 +0.0022 0.0031 4862.4800 3.916714e+01 69.169534 72.422332 75.897335 +0.0023 0.0031 4870.3020 4.007492e+01 68.616132 72.118603 75.397968 +0.0024 0.0031 4872.1010 4.092642e+01 68.082784 71.821299 74.906475 +0.0025 0.0031 4868.0280 4.172012e+01 67.567995 71.527281 74.441807 +0.0026 0.0031 4858.2960 4.246367e+01 67.072793 71.239156 73.988347 +0.0027 0.0031 4843.1630 4.315983e+01 66.595993 70.956388 73.547780 +0.0028 0.0031 4822.9250 4.381016e+01 66.140545 70.680206 73.126004 +0.0029 0.0031 4797.9010 4.442001e+01 65.704170 70.409275 72.713221 +0.0030 0.0031 4768.4280 4.499169e+01 65.285850 70.144852 72.313477 +0.0031 0.0031 4734.8500 4.552532e+01 64.887057 69.886947 71.938958 +0.0032 0.0031 4697.5150 4.602591e+01 64.507640 69.633421 71.568529 +0.0033 0.0031 4656.7640 4.649508e+01 64.144419 69.386093 71.218168 +0.0034 0.0031 4612.9320 4.693448e+01 63.800114 69.147914 70.885540 +0.0035 0.0031 4566.3400 4.734766e+01 63.470659 68.913712 70.558814 +0.0036 0.0031 4517.2960 4.773318e+01 63.160762 68.686246 70.250960 +0.0037 0.0031 4466.0900 4.809585e+01 62.863670 68.465398 69.954356 +0.0038 0.0031 4412.9990 4.843496e+01 62.583486 68.250683 69.664806 +0.0039 0.0031 4358.2770 4.875403e+01 62.317045 68.041595 69.390669 +0.0040 0.0031 4302.1620 4.905516e+01 62.063960 67.838191 69.126941 +0.0041 0.0031 4244.8760 4.933522e+01 61.825670 67.640983 68.875937 +0.0042 0.0031 4186.6220 4.959905e+01 61.599595 67.449780 68.635036 +0.0043 0.0031 4127.5850 4.984598e+01 61.385783 67.263945 68.403250 +0.0044 0.0031 4067.9380 5.007977e+01 61.183030 67.084223 68.181823 +0.0045 0.0031 4007.8340 5.029785e+01 60.992481 66.910258 67.970080 +0.0046 0.0031 3947.4170 5.050264e+01 60.812341 66.741820 67.769681 +0.0047 0.0031 3886.8130 5.069511e+01 60.641809 66.579264 67.577940 +0.0048 0.0031 3826.1400 5.087938e+01 60.481217 66.419894 67.368222 +0.0049 0.0031 3765.5020 5.104768e+01 60.331229 66.265518 67.212266 +0.0050 0.0031 3704.9930 5.120742e+01 60.190021 66.118093 67.044093 +0.0051 0.0031 3644.6990 5.135803e+01 60.056221 65.974296 66.883218 +0.0052 0.0031 3584.6950 5.149924e+01 59.931195 65.836148 66.731503 +0.0053 0.0031 3525.0490 5.163264e+01 59.813346 65.702972 66.580666 +0.0054 0.0031 3465.8220 5.175743e+01 59.702597 65.573940 66.438257 +0.0055 0.0031 3407.0680 5.187450e+01 59.599422 65.449687 66.303758 +0.0056 0.0031 3348.8350 5.198538e+01 59.502317 65.328995 66.172459 +0.0057 0.0031 3291.1680 5.208786e+01 59.413265 65.213809 66.051232 +0.0058 0.0031 3234.1080 5.218524e+01 59.329109 65.103053 65.938910 +0.0059 0.0031 3177.6900 5.227627e+01 59.251393 64.996113 65.824817 +0.0060 0.0031 3121.9460 5.236167e+01 59.179239 64.893345 65.718983 +0.0061 0.0031 3066.9020 5.244074e+01 59.113582 64.794400 65.616277 +0.0062 0.0031 3012.5820 5.251628e+01 59.050611 64.699365 65.519608 +0.0063 0.0031 2959.0060 5.258550e+01 58.993585 64.607862 65.424824 +0.0064 0.0031 2906.1890 5.265162e+01 58.942241 64.520516 65.344056 +0.0065 0.0031 2854.1460 5.271255e+01 58.895409 64.436396 65.261126 +0.0066 0.0031 2802.8880 5.276982e+01 58.852202 64.355311 65.182325 +0.0067 0.0031 2752.4240 5.282327e+01 58.813670 64.277852 65.108658 +0.0068 0.0031 2702.7600 5.287484e+01 58.777965 64.201932 65.033540 +0.0069 0.0031 2653.9010 5.291978e+01 58.747828 64.133314 64.973296 +0.0070 0.0031 2605.8490 5.296299e+01 58.720301 64.066367 64.913764 +0.0071 0.0031 2558.6060 5.300335e+01 58.696171 64.002417 64.855756 +0.0072 0.0031 2512.1710 5.304192e+01 58.675546 63.941068 64.799272 +0.0073 0.0031 2466.5420 5.307649e+01 58.659171 63.883720 64.751884 +0.0074 0.0031 2421.7160 5.310939e+01 58.645476 63.829362 64.702851 +0.0075 0.0031 2377.6900 5.314012e+01 58.634468 63.777688 64.660458 +0.0076 0.0031 2334.4580 5.316892e+01 58.625593 63.728943 64.619273 +0.0077 0.0031 2292.0140 5.319536e+01 58.620614 63.682848 64.579363 +0.0078 0.0031 2250.3510 5.321974e+01 58.618001 63.639317 64.543729 +0.0079 0.0031 2209.4630 5.324310e+01 58.617432 63.598046 64.509964 +0.0080 0.0031 2169.3400 5.326486e+01 58.618905 63.559712 64.475691 +0.0081 0.0031 2129.9750 5.328452e+01 58.623468 63.523577 64.449197 +0.0082 0.0031 2091.3570 5.330295e+01 58.630616 63.490322 64.426026 +0.0083 0.0031 2053.4790 5.332012e+01 58.639745 63.459947 64.404759 +0.0084 0.0031 2016.3290 5.333589e+01 58.650194 63.431672 64.381555 +0.0085 0.0031 1979.8970 5.334997e+01 58.662774 63.405120 64.362768 +0.0086 0.0031 1944.1740 5.336312e+01 58.677341 63.380802 64.347800 +0.0087 0.0031 1909.1480 5.337538e+01 58.693754 63.358453 64.334112 +0.0088 0.0031 1874.8080 5.338658e+01 58.711143 63.338563 64.320902 +0.0089 0.0031 1841.1430 5.339719e+01 58.730472 63.320554 64.309332 +0.0090 0.0031 1808.1430 5.340624e+01 58.751728 63.304086 64.299743 +0.0091 0.0031 1775.7960 5.341456e+01 58.774652 63.289801 64.291901 +0.0092 0.0031 1744.0910 5.342191e+01 58.798971 63.277238 64.286655 +0.0093 0.0031 1713.0160 5.342915e+01 58.823645 63.266715 64.281577 +0.0094 0.0031 1682.5600 5.343539e+01 58.850365 63.258033 64.283191 +0.0095 0.0031 1652.7120 5.344114e+01 58.878380 63.251123 64.283467 +0.0096 0.0031 0.0000 5.344603e+01 58.907524 63.245721 64.283971 +0.0097 0.0031 0.0000 5.345022e+01 58.937959 63.241465 64.284903 +0.0098 0.0031 0.0000 5.345424e+01 58.969570 63.239433 64.289841 +0.0099 0.0031 0.0000 5.345822e+01 59.001926 63.239096 64.295606 +0.0100 0.0031 0.0000 5.346179e+01 59.035178 63.239998 64.301785 +0.0000 0.0032 3751.9760 0.000000e+00 54.068663 54.068086 58.768583 +0.0001 0.0032 3755.7100 2.120770e+00 78.931614 78.624618 87.748378 +0.0002 0.0032 3766.8350 4.350122e+00 79.027813 78.484309 87.300836 +0.0003 0.0032 3785.1230 6.633278e+00 78.996446 78.280036 86.822426 +0.0004 0.0032 3810.2020 8.932138e+00 78.859175 78.034018 86.311161 +0.0005 0.0032 3841.5600 1.121986e+01 78.629074 77.767259 85.775729 +0.0006 0.0032 3878.5620 1.347372e+01 78.320251 77.485994 85.222853 +0.0007 0.0032 3920.4610 1.567203e+01 77.942852 77.191157 84.658819 +0.0008 0.0032 3966.4200 1.780324e+01 77.502811 76.888694 84.088687 +0.0009 0.0032 4015.5320 1.985335e+01 77.014086 76.578444 83.508530 +0.0010 0.0032 4066.8470 2.181932e+01 76.482821 76.263818 82.917957 +0.0011 0.0032 4119.3910 2.369292e+01 75.919938 75.947813 82.329560 +0.0012 0.0032 4172.1970 2.547303e+01 75.331876 75.628775 81.737128 +0.0013 0.0032 4224.3280 2.716001e+01 74.728057 75.309078 81.148040 +0.0014 0.0032 4274.8940 2.875601e+01 74.110465 74.988452 80.561607 +0.0015 0.0032 4323.0790 3.026037e+01 73.488834 74.668323 79.977449 +0.0016 0.0032 4368.1480 3.167638e+01 72.867323 74.351385 79.407797 +0.0017 0.0032 4409.4640 3.300892e+01 72.248429 74.036043 78.843538 +0.0018 0.0032 4446.4910 3.425987e+01 71.635250 73.721560 78.289924 +0.0019 0.0032 4478.8000 3.543449e+01 71.034401 73.411146 77.747599 +0.0020 0.0032 4506.0640 3.653587e+01 70.445271 73.103609 77.215697 +0.0021 0.0032 4528.0580 3.757078e+01 69.869801 72.800704 76.692818 +0.0022 0.0032 4544.6500 3.853964e+01 69.310308 72.501483 76.189215 +0.0023 0.0032 4555.7930 3.944855e+01 68.770419 72.206931 75.704613 +0.0024 0.0032 4561.5170 4.029967e+01 68.246085 71.916373 75.224563 +0.0025 0.0032 4561.9150 4.109651e+01 67.741577 71.631351 74.763103 +0.0026 0.0032 4557.1400 4.184394e+01 67.254764 71.350583 74.315524 +0.0027 0.0032 4547.3880 4.254392e+01 66.786817 71.076018 73.878957 +0.0028 0.0032 4532.8940 4.319878e+01 66.338814 70.806561 73.462160 +0.0029 0.0032 4513.9190 4.381433e+01 65.907899 70.541813 73.059444 +0.0030 0.0032 4490.7440 4.439145e+01 65.495949 70.283945 72.668044 +0.0031 0.0032 4463.6620 4.493302e+01 65.101426 70.030977 72.285998 +0.0032 0.0032 4432.9690 4.543990e+01 64.725451 69.784151 71.924392 +0.0033 0.0032 4398.9660 4.591603e+01 64.367676 69.543072 71.576375 +0.0034 0.0032 4361.9450 4.636309e+01 64.026387 69.307846 71.240187 +0.0035 0.0032 4322.1930 4.678442e+01 63.698799 69.078283 70.911259 +0.0036 0.0032 4279.9860 4.717748e+01 63.392061 68.855399 70.606030 +0.0037 0.0032 4235.5890 4.754819e+01 63.097449 68.637695 70.302931 +0.0038 0.0032 4189.2530 4.789576e+01 62.817915 68.426601 70.016734 +0.0039 0.0032 4141.2120 4.822289e+01 62.552970 68.220384 69.743276 +0.0040 0.0032 4091.6880 4.852985e+01 62.301475 68.020097 69.479818 +0.0041 0.0032 4040.8880 4.881912e+01 62.062637 67.826023 69.225919 +0.0042 0.0032 3989.0020 4.909060e+01 61.836240 67.636034 68.982253 +0.0043 0.0032 3936.2080 4.934695e+01 61.620776 67.452977 68.748820 +0.0044 0.0032 3882.6680 4.958694e+01 61.418700 67.276291 68.525191 +0.0045 0.0032 3828.5320 4.981355e+01 61.227326 67.103114 68.310669 +0.0046 0.0032 3773.9370 5.002596e+01 61.046686 66.935377 68.107064 +0.0047 0.0032 3719.0090 5.022647e+01 60.874922 66.773466 67.909567 +0.0048 0.0032 3663.8600 5.041478e+01 60.714753 66.614954 67.724099 +0.0049 0.0032 3608.5950 5.059515e+01 60.561842 66.463204 67.521167 +0.0050 0.0032 3553.3080 5.076186e+01 60.418935 66.315722 67.348969 +0.0051 0.0032 3498.0850 5.091773e+01 60.284773 66.172726 67.204255 +0.0052 0.0032 3443.0010 5.106619e+01 60.157704 66.034248 67.047692 +0.0053 0.0032 3388.1270 5.120774e+01 60.037657 65.900526 66.888989 +0.0054 0.0032 3333.5250 5.133802e+01 59.926309 65.772440 66.748932 +0.0055 0.0032 3279.2520 5.146202e+01 59.821500 65.648308 66.609392 +0.0056 0.0032 3225.3580 5.157825e+01 59.723938 65.528189 66.481957 +0.0057 0.0032 3171.8940 5.168906e+01 59.631130 65.410908 66.352544 +0.0058 0.0032 3118.9010 5.179167e+01 59.545953 65.300836 66.234415 +0.0059 0.0032 3066.4170 5.188791e+01 59.466473 65.193428 66.121244 +0.0060 0.0032 3014.4770 5.197906e+01 59.392361 65.089583 66.016578 +0.0061 0.0032 2963.1110 5.206550e+01 59.323128 64.989873 65.908938 +0.0062 0.0032 2912.3450 5.214586e+01 59.259914 64.894064 65.808164 +0.0063 0.0032 2862.2020 5.222272e+01 59.199490 64.801959 65.709824 +0.0064 0.0032 2812.7020 5.229302e+01 59.146631 64.713392 65.622802 +0.0065 0.0032 2763.8610 5.235977e+01 59.096994 64.628171 65.536350 +0.0066 0.0032 2715.6940 5.242291e+01 59.052273 64.545604 65.453475 +0.0067 0.0032 2668.2140 5.248100e+01 59.011385 64.467719 65.378730 +0.0068 0.0032 2621.4290 5.253572e+01 58.974445 64.392862 65.308187 +0.0069 0.0032 2575.3480 5.258765e+01 58.941324 64.320957 65.237559 +0.0070 0.0032 2529.9760 5.263636e+01 58.911655 64.252915 65.172219 +0.0071 0.0032 2485.3180 5.268159e+01 58.886203 64.187533 65.109624 +0.0072 0.0032 2441.3760 5.272419e+01 58.863681 64.125743 65.054362 +0.0073 0.0032 2398.1510 5.276421e+01 58.844452 64.066552 64.999142 +0.0074 0.0032 2355.6440 5.280189e+01 58.828638 64.010940 64.950715 +0.0075 0.0032 2313.8540 5.283744e+01 58.815432 63.958504 64.903735 +0.0076 0.0032 2272.7770 5.287023e+01 58.805339 63.908497 64.860040 +0.0077 0.0032 2232.4110 5.290174e+01 58.797573 63.861038 64.817903 +0.0078 0.0032 2192.7520 5.293104e+01 58.792255 63.816079 64.779171 +0.0079 0.0032 2153.7950 5.295811e+01 58.790417 63.773925 64.742919 +0.0080 0.0032 2115.5340 5.298361e+01 58.790750 63.734081 64.708759 +0.0081 0.0032 2077.9640 5.300792e+01 58.792877 63.696225 64.675582 +0.0082 0.0032 2041.0770 5.303039e+01 58.797368 63.662153 64.647558 +0.0083 0.0032 2004.8660 5.305082e+01 58.805264 63.630029 64.624043 +0.0084 0.0032 1969.3240 5.307080e+01 58.813437 63.600527 64.599721 +0.0085 0.0032 1934.4430 5.308838e+01 58.824339 63.572404 64.581228 +0.0086 0.0032 1900.2140 5.310615e+01 58.836399 63.546303 64.559102 +0.0087 0.0032 1866.6280 5.312206e+01 58.850781 63.523173 64.541809 +0.0088 0.0032 1833.6780 5.313696e+01 58.866569 63.501977 64.526493 +0.0089 0.0032 1801.3520 5.315032e+01 58.884584 63.482353 64.517080 +0.0090 0.0032 1769.6440 5.316360e+01 58.903857 63.464835 64.504778 +0.0091 0.0032 1738.5420 5.317548e+01 58.924614 63.449273 64.495928 +0.0092 0.0032 1708.0370 5.318664e+01 58.946700 63.435035 64.490295 +0.0093 0.0032 1678.1200 5.319673e+01 58.970115 63.422836 64.481205 +0.0094 0.0032 1648.7810 5.320626e+01 58.995320 63.412663 64.477929 +0.0095 0.0032 0.0000 5.321510e+01 59.021507 63.404167 64.473189 +0.0096 0.0032 0.0000 5.322344e+01 59.048964 63.397069 64.470869 +0.0097 0.0032 0.0000 5.323112e+01 59.077501 63.392201 64.471133 +0.0098 0.0032 0.0000 5.323821e+01 59.107283 63.388512 64.474662 +0.0099 0.0032 0.0000 5.324470e+01 59.138022 63.386319 64.476193 +0.0100 0.0032 0.0000 5.325131e+01 59.169691 63.385899 64.478237 +0.0000 0.0033 3505.2810 5.818825e-07 53.856332 53.855799 58.461133 +0.0001 0.0033 3508.6510 2.077052e+00 78.852500 78.560702 87.720782 +0.0002 0.0033 3518.6970 4.258964e+00 78.935906 78.418823 87.281109 +0.0003 0.0033 3535.2220 6.493223e+00 78.897869 78.217009 86.818443 +0.0004 0.0033 3557.9060 8.744142e+00 78.760050 77.978077 86.319567 +0.0005 0.0033 3586.3080 1.098336e+01 78.533928 77.717462 85.799532 +0.0006 0.0033 3619.8800 1.319059e+01 78.232005 77.439716 85.265968 +0.0007 0.0033 3657.9800 1.534644e+01 77.861701 77.150895 84.714829 +0.0008 0.0033 3699.8830 1.743619e+01 77.434828 76.855965 84.167694 +0.0009 0.0033 3744.8030 1.944912e+01 76.960011 76.554680 83.602035 +0.0010 0.0033 3791.9130 2.138201e+01 76.445159 76.247828 83.029405 +0.0011 0.0033 3840.3650 2.322494e+01 75.899076 75.940607 82.459940 +0.0012 0.0033 3889.3070 2.497848e+01 75.328015 75.630056 81.889317 +0.0013 0.0033 3937.9100 2.664069e+01 74.741538 75.319337 81.318026 +0.0014 0.0033 3985.3830 2.821567e+01 74.142751 75.007632 80.748514 +0.0015 0.0033 4030.9880 2.970244e+01 73.538140 74.696662 80.186482 +0.0016 0.0033 4074.0580 3.110517e+01 72.932178 74.388530 79.626508 +0.0017 0.0033 4114.0010 3.242515e+01 72.329303 74.077164 79.078517 +0.0018 0.0033 4150.3130 3.366904e+01 71.732284 73.771681 78.539213 +0.0019 0.0033 4182.5770 3.483494e+01 71.146810 73.469045 78.008846 +0.0020 0.0033 4210.4670 3.593173e+01 70.571311 73.172404 77.491394 +0.0021 0.0033 4233.7410 3.696146e+01 70.009691 72.878032 76.986780 +0.0022 0.0033 4252.2430 3.792905e+01 69.462781 72.587063 76.490345 +0.0023 0.0033 4265.8910 3.883575e+01 68.932288 72.301681 76.009055 +0.0024 0.0033 4274.6720 3.968680e+01 68.418061 72.017537 75.543232 +0.0025 0.0033 4278.6360 4.048736e+01 67.921859 71.740436 75.076706 +0.0026 0.0033 4277.8840 4.123634e+01 67.443995 71.467121 74.641641 +0.0027 0.0033 4272.5630 4.193908e+01 66.982725 71.198694 74.211831 +0.0028 0.0033 4262.8560 4.259985e+01 66.540688 70.934963 73.799588 +0.0029 0.0033 4248.9750 4.321950e+01 66.116271 70.677059 73.397034 +0.0030 0.0033 4231.1510 4.380244e+01 65.711034 70.424988 73.012949 +0.0031 0.0033 4209.6300 4.435010e+01 65.320893 70.177451 72.629597 +0.0032 0.0033 4184.6690 4.486433e+01 64.948407 69.935752 72.270282 +0.0033 0.0033 4156.5240 4.534600e+01 64.594946 69.699892 71.927082 +0.0034 0.0033 4125.4540 4.580002e+01 64.256716 69.468461 71.589609 +0.0035 0.0033 4091.7120 4.622652e+01 63.933836 69.244514 71.268946 +0.0036 0.0033 4055.5450 4.662763e+01 63.626739 69.025189 70.959249 +0.0037 0.0033 4017.1910 4.700507e+01 63.334772 68.811075 70.658903 +0.0038 0.0033 3976.8780 4.736159e+01 63.055134 68.602743 70.369656 +0.0039 0.0033 3934.8210 4.769535e+01 62.791540 68.399783 70.092737 +0.0040 0.0033 3891.2230 4.801000e+01 62.541080 68.202593 69.827821 +0.0041 0.0033 3846.2780 4.830666e+01 62.302547 68.010676 69.576092 +0.0042 0.0033 3800.1630 4.858521e+01 62.076981 67.824285 69.331488 +0.0043 0.0033 3753.0440 4.884802e+01 61.862971 67.642524 69.097938 +0.0044 0.0033 3705.0760 4.909704e+01 61.658984 67.466683 68.866396 +0.0045 0.0033 3656.4010 4.933038e+01 61.467182 67.295980 68.652784 +0.0046 0.0033 3607.1520 4.955187e+01 61.283563 67.129389 68.442129 +0.0047 0.0033 3557.4470 4.975848e+01 61.113321 66.968365 68.244988 +0.0048 0.0033 3507.3990 4.995361e+01 60.950859 66.812096 68.057217 +0.0049 0.0033 3457.1090 5.013885e+01 60.798304 66.660597 67.874384 +0.0050 0.0033 3406.6690 5.031358e+01 60.652811 66.513440 67.696003 +0.0051 0.0033 3356.1630 5.048019e+01 60.516460 66.371709 67.507832 +0.0052 0.0033 3305.6690 5.063524e+01 60.388004 66.234450 67.346806 +0.0053 0.0033 3255.2570 5.077952e+01 60.268118 66.100961 67.213631 +0.0054 0.0033 3204.9880 5.091841e+01 60.153733 65.971265 67.061565 +0.0055 0.0033 3154.9230 5.104782e+01 60.047880 65.847157 66.923877 +0.0056 0.0033 3105.1140 5.117069e+01 59.947809 65.727479 66.790350 +0.0057 0.0033 3055.6130 5.128633e+01 59.854503 65.611084 66.662090 +0.0058 0.0033 3006.4620 5.139693e+01 59.765729 65.498346 66.533432 +0.0059 0.0033 2957.7030 5.149886e+01 59.684797 65.391302 66.418863 +0.0060 0.0033 2909.3710 5.159655e+01 59.608410 65.287213 66.306068 +0.0061 0.0033 2861.4990 5.168739e+01 59.537884 65.186836 66.200867 +0.0062 0.0033 2814.1150 5.177305e+01 59.472680 65.090423 66.101261 +0.0063 0.0033 2767.2450 5.185617e+01 59.409729 64.997552 65.998348 +0.0064 0.0033 2720.9110 5.193201e+01 59.354650 64.907706 65.906802 +0.0065 0.0033 2675.1330 5.200444e+01 59.303081 64.821365 65.817155 +0.0066 0.0033 2629.9270 5.207383e+01 59.253677 64.739203 65.731428 +0.0067 0.0033 2585.3090 5.213521e+01 59.214336 64.658981 65.651251 +0.0068 0.0033 2541.2900 5.219605e+01 59.174155 64.582929 65.574320 +0.0069 0.0033 2497.8810 5.225257e+01 59.138770 64.510816 65.504485 +0.0070 0.0033 2455.0910 5.230698e+01 59.106689 64.440556 65.432437 +0.0071 0.0033 2412.9260 5.235700e+01 59.078967 64.374911 65.371615 +0.0072 0.0033 2371.3920 5.240480e+01 59.054809 64.311761 65.307208 +0.0073 0.0033 2330.4930 5.244943e+01 59.033407 64.252000 65.253086 +0.0074 0.0033 2290.2310 5.249144e+01 59.015477 64.194883 65.199239 +0.0075 0.0033 2250.6080 5.253251e+01 59.000033 64.140278 65.146820 +0.0076 0.0033 2211.6230 5.256941e+01 58.987936 64.090037 65.104377 +0.0077 0.0033 2173.2770 5.260503e+01 58.978100 64.041247 65.060262 +0.0078 0.0033 2135.5660 5.263977e+01 58.970473 63.993033 65.011972 +0.0079 0.0033 2098.4900 5.267007e+01 58.967335 63.951558 64.982839 +0.0080 0.0033 2062.0450 5.269994e+01 58.965033 63.910268 64.942177 +0.0081 0.0033 2026.2260 5.272793e+01 58.965629 63.871915 64.907672 +0.0082 0.0033 1991.0290 5.275469e+01 58.968314 63.835355 64.876903 +0.0083 0.0033 1956.4490 5.277973e+01 58.972979 63.802821 64.847038 +0.0084 0.0033 1922.4810 5.280303e+01 58.980225 63.771502 64.821441 +0.0085 0.0033 1889.1180 5.282467e+01 58.989320 63.742014 64.798609 +0.0086 0.0033 1856.3540 5.284552e+01 58.999801 63.714857 64.775926 +0.0087 0.0033 1824.1830 5.286548e+01 59.011790 63.690060 64.758934 +0.0088 0.0033 1792.5970 5.288418e+01 59.025490 63.666907 64.737135 +0.0089 0.0033 1761.5880 5.290110e+01 59.041469 63.646527 64.724503 +0.0090 0.0033 1731.1500 5.291707e+01 59.059436 63.627328 64.710487 +0.0091 0.0033 1701.2740 5.293284e+01 59.078148 63.610403 64.698597 +0.0092 0.0033 1671.9530 5.294694e+01 59.098967 63.594738 64.688508 +0.0093 0.0033 1643.1780 5.296081e+01 59.120665 63.581344 64.681386 +0.0094 0.0033 1614.9410 5.297399e+01 59.143323 63.569662 64.671389 +0.0095 0.0033 0.0000 5.298621e+01 59.167694 63.559832 64.666425 +0.0096 0.0033 0.0000 5.299721e+01 59.193143 63.551446 64.663158 +0.0097 0.0033 0.0000 5.300753e+01 59.220556 63.545017 64.664291 +0.0098 0.0033 0.0000 5.301798e+01 59.248329 63.539878 64.662742 +0.0099 0.0033 0.0000 5.302810e+01 59.277380 63.536421 64.661048 +0.0100 0.0033 0.0000 5.303734e+01 59.307304 63.534484 64.661677 +0.0000 0.0034 3279.6220 5.613656e-07 53.657664 53.657093 58.172960 +0.0001 0.0034 3282.6760 2.034915e+00 78.790070 78.509481 87.715432 +0.0002 0.0034 3291.7840 4.171703e+00 78.863571 78.370022 87.282759 +0.0003 0.0034 3306.7750 6.359731e+00 78.819827 78.172184 86.830620 +0.0004 0.0034 3327.3700 8.563945e+00 78.680795 77.937581 86.342254 +0.0005 0.0034 3353.1890 1.075780e+01 78.456295 77.682585 85.841383 +0.0006 0.0034 3383.7550 1.292082e+01 78.161713 77.411138 85.319815 +0.0007 0.0034 3418.5100 1.503462e+01 77.802634 77.129696 84.790215 +0.0008 0.0034 3456.8240 1.708394e+01 77.386904 76.841472 84.259694 +0.0009 0.0034 3498.0130 1.906298e+01 76.924567 76.545857 83.707194 +0.0010 0.0034 3541.3520 2.096169e+01 76.421812 76.246031 83.151506 +0.0011 0.0034 3586.0960 2.277315e+01 75.895461 75.947219 82.603149 +0.0012 0.0034 3631.4960 2.450044e+01 75.341245 75.644227 82.049789 +0.0013 0.0034 3676.8170 2.614174e+01 74.768818 75.340262 81.492348 +0.0014 0.0034 3721.3500 2.769548e+01 74.187037 75.037908 80.940718 +0.0015 0.0034 3764.4330 2.916480e+01 73.598614 74.734644 80.395407 +0.0016 0.0034 3805.4560 3.055277e+01 73.007782 74.433869 79.852095 +0.0017 0.0034 3843.8760 3.186117e+01 72.420092 74.133884 79.313379 +0.0018 0.0034 3879.2200 3.309249e+01 71.840195 73.836790 78.791658 +0.0019 0.0034 3911.0910 3.425238e+01 71.266905 73.542793 78.272263 +0.0020 0.0034 3939.1650 3.534315e+01 70.703579 73.252160 77.764541 +0.0021 0.0034 3963.1980 3.636862e+01 70.155671 72.964442 77.271463 +0.0022 0.0034 3983.0150 3.733326e+01 69.619784 72.679096 76.786170 +0.0023 0.0034 3998.5110 3.823895e+01 69.099790 72.400309 76.315053 +0.0024 0.0034 4009.6410 3.908977e+01 68.595167 72.124941 75.849573 +0.0025 0.0034 4016.4200 3.989108e+01 68.107513 71.852893 75.391898 +0.0026 0.0034 4018.9110 4.064152e+01 67.637396 71.587176 74.968247 +0.0027 0.0034 4017.2190 4.134791e+01 67.184790 71.325286 74.547809 +0.0028 0.0034 4011.4840 4.201222e+01 66.748864 71.068365 74.132775 +0.0029 0.0034 4001.8740 4.263578e+01 66.329818 70.815833 73.736579 +0.0030 0.0034 3988.5800 4.322290e+01 65.929421 70.569061 73.354777 +0.0031 0.0034 3971.8090 4.377657e+01 65.544046 70.328647 72.980384 +0.0032 0.0034 3951.7770 4.429498e+01 65.177020 70.091029 72.622219 +0.0033 0.0034 3928.7060 4.478355e+01 64.825524 69.859058 72.276666 +0.0034 0.0034 3902.8210 4.524439e+01 64.488339 69.631958 71.939680 +0.0035 0.0034 3874.3450 4.567717e+01 64.170431 69.410304 71.617001 +0.0036 0.0034 3843.4990 4.608557e+01 63.864507 69.196248 71.311936 +0.0037 0.0034 3810.4950 4.646861e+01 63.575799 68.985431 71.012087 +0.0038 0.0034 3775.5380 4.683072e+01 63.298849 68.780197 70.724722 +0.0039 0.0034 3738.8250 4.717211e+01 63.034513 68.579725 70.445322 +0.0040 0.0034 3700.5440 4.749382e+01 62.785222 68.386550 70.182872 +0.0041 0.0034 3660.8690 4.779809e+01 62.546057 68.195868 69.921646 +0.0042 0.0034 3619.9690 4.808372e+01 62.320859 68.011754 69.678732 +0.0043 0.0034 3577.9970 4.835370e+01 62.106463 67.833799 69.441173 +0.0044 0.0034 3535.0990 4.860875e+01 61.903095 67.659075 69.216308 +0.0045 0.0034 3491.4090 4.884936e+01 61.710001 67.488798 68.996527 +0.0046 0.0034 3447.0530 4.907696e+01 61.527305 67.324203 68.786197 +0.0047 0.0034 3402.1460 4.929163e+01 61.354625 67.164333 68.582946 +0.0048 0.0034 3356.7950 4.949403e+01 61.191669 67.009061 68.390119 +0.0049 0.0034 3311.0980 4.968562e+01 61.037253 66.858481 68.207359 +0.0050 0.0034 3265.1440 4.986763e+01 60.890229 66.712212 68.026329 +0.0051 0.0034 3219.0180 5.003826e+01 60.753581 66.569936 67.853452 +0.0052 0.0034 3172.7930 5.020310e+01 60.622503 66.434265 67.662386 +0.0053 0.0034 3126.5400 5.035473e+01 60.500415 66.300933 67.516122 +0.0054 0.0034 3080.3220 5.049899e+01 60.385069 66.172234 67.366552 +0.0055 0.0034 3034.1990 5.063457e+01 60.276363 66.046578 67.236133 +0.0056 0.0034 2988.2250 5.076308e+01 60.175285 65.926576 67.099198 +0.0057 0.0034 2942.4500 5.088452e+01 60.079475 65.811276 66.967410 +0.0058 0.0034 2896.9210 5.099967e+01 59.990584 65.698532 66.843962 +0.0059 0.0034 2851.6790 5.110968e+01 59.905624 65.589489 66.716431 +0.0060 0.0034 2806.7600 5.121162e+01 59.828515 65.485458 66.605449 +0.0061 0.0034 2762.1990 5.130861e+01 59.755563 65.383945 66.490639 +0.0062 0.0034 2718.0260 5.140039e+01 59.687723 65.287503 66.389535 +0.0063 0.0034 2674.2680 5.148686e+01 59.625025 65.193652 66.288867 +0.0064 0.0034 2630.9500 5.156892e+01 59.566342 65.103814 66.193097 +0.0065 0.0034 2588.0920 5.164807e+01 59.511370 65.015769 66.096860 +0.0066 0.0034 2545.7150 5.171865e+01 59.463343 64.933051 66.009214 +0.0067 0.0034 2503.8350 5.178854e+01 59.418433 64.853107 65.929393 +0.0068 0.0034 2462.4670 5.185325e+01 59.377922 64.775325 65.849497 +0.0069 0.0034 2421.6220 5.191578e+01 59.339839 64.700561 65.769579 +0.0070 0.0034 2381.3130 5.197429e+01 59.306123 64.631055 65.701278 +0.0071 0.0034 2341.5480 5.203030e+01 59.275730 64.563871 65.632019 +0.0072 0.0034 2302.3340 5.208206e+01 59.249082 64.499875 65.570682 +0.0073 0.0034 2263.6780 5.213172e+01 59.226171 64.439155 65.511718 +0.0074 0.0034 2225.5850 5.217905e+01 59.206211 64.379869 65.453305 +0.0075 0.0034 2188.0570 5.222322e+01 59.189226 64.325666 65.400877 +0.0076 0.0034 2151.0980 5.226588e+01 59.174399 64.272512 65.349938 +0.0077 0.0034 2114.7090 5.230551e+01 59.163113 64.222572 65.302669 +0.0078 0.0034 2078.8910 5.234323e+01 59.153838 64.175397 65.260806 +0.0079 0.0034 2043.6420 5.237898e+01 59.147506 64.130540 65.218007 +0.0080 0.0034 2008.9610 5.241278e+01 59.143699 64.088562 65.181454 +0.0081 0.0034 1974.8480 5.244525e+01 59.141787 64.049011 65.144101 +0.0082 0.0034 1941.2980 5.247566e+01 59.142448 64.011254 65.111552 +0.0083 0.0034 1908.3100 5.250518e+01 59.145230 63.976928 65.077256 +0.0084 0.0034 1875.8790 5.253200e+01 59.150551 63.944186 65.046015 +0.0085 0.0034 1844.0000 5.255791e+01 59.156933 63.913595 65.023827 +0.0086 0.0034 1812.6700 5.258224e+01 59.166111 63.885122 64.998430 +0.0087 0.0034 1781.8830 5.260586e+01 59.176322 63.859270 64.974434 +0.0088 0.0034 1751.6340 5.262758e+01 59.188750 63.834774 64.955967 +0.0089 0.0034 1721.9180 5.264936e+01 59.201786 63.812264 64.934620 +0.0090 0.0034 1692.7270 5.266912e+01 59.217773 63.792118 64.921155 +0.0091 0.0034 1664.0560 5.268671e+01 59.236024 63.773441 64.908017 +0.0092 0.0034 1635.8990 5.270556e+01 59.253160 63.756693 64.890920 +0.0093 0.0034 1608.2480 5.272219e+01 59.273616 63.742047 64.881653 +0.0094 0.0034 1581.0970 5.273839e+01 59.294846 63.729086 64.875450 +0.0095 0.0034 0.0000 5.275401e+01 59.317101 63.717611 64.863812 +0.0096 0.0034 0.0000 5.276886e+01 59.340479 63.708097 64.854782 +0.0097 0.0034 0.0000 5.278281e+01 59.365723 63.700151 64.849477 +0.0098 0.0034 0.0000 5.279607e+01 59.391977 63.693809 64.848105 +0.0099 0.0034 0.0000 5.280874e+01 59.419251 63.688858 64.846262 +0.0100 0.0034 0.0000 5.282094e+01 59.447552 63.685661 64.844891 +0.0000 0.0035 3072.7380 1.889149e-07 53.474086 53.473716 57.903856 +0.0001 0.0035 3075.5170 1.994967e+00 78.743032 78.473612 87.720619 +0.0002 0.0035 3083.8050 4.088871e+00 78.805605 78.333891 87.298883 +0.0003 0.0035 3097.4540 6.231414e+00 78.758627 78.139958 86.855224 +0.0004 0.0035 3116.2210 8.391125e+00 78.620519 77.911023 86.383923 +0.0005 0.0035 3139.7730 1.054108e+01 78.399403 77.657977 85.892970 +0.0006 0.0035 3167.6940 1.266106e+01 78.109775 77.395101 85.387947 +0.0007 0.0035 3199.4950 1.473484e+01 77.757719 77.119439 84.869007 +0.0008 0.0035 3234.6270 1.674696e+01 77.356149 76.837460 84.347571 +0.0009 0.0035 3272.4870 1.869065e+01 76.904983 76.549904 83.821387 +0.0010 0.0035 3312.4400 2.055529e+01 76.418335 76.258654 83.284498 +0.0011 0.0035 3353.8290 2.234098e+01 75.902970 75.964882 82.742517 +0.0012 0.0035 3395.9910 2.404255e+01 75.363903 75.670197 82.210392 +0.0013 0.0035 3438.2700 2.565913e+01 74.808039 75.372030 81.671739 +0.0014 0.0035 3480.0350 2.719304e+01 74.241466 75.077728 81.137482 +0.0015 0.0035 3520.6860 2.864605e+01 73.667729 74.783606 80.606831 +0.0016 0.0035 3559.6710 3.001790e+01 73.093602 74.488435 80.076100 +0.0017 0.0035 3596.4880 3.131389e+01 72.520931 74.197992 79.554953 +0.0018 0.0035 3630.6990 3.253531e+01 71.952164 73.907531 79.039709 +0.0019 0.0035 3661.9250 3.368732e+01 71.394554 73.620734 78.538188 +0.0020 0.0035 3689.8560 3.477097e+01 70.845585 73.336375 78.042614 +0.0021 0.0035 3714.2440 3.579184e+01 70.307413 73.055643 77.556615 +0.0022 0.0035 3734.9080 3.675227e+01 69.783458 72.779303 77.081517 +0.0023 0.0035 3751.7240 3.765654e+01 69.271909 72.505462 76.613705 +0.0024 0.0035 3764.6280 3.850772e+01 68.775844 72.237306 76.159033 +0.0025 0.0035 3773.6060 3.930825e+01 68.298873 71.971876 75.702292 +0.0026 0.0035 3778.6910 4.006047e+01 67.835751 71.712009 75.293255 +0.0027 0.0035 3779.9520 4.076954e+01 67.389924 71.455033 74.867586 +0.0028 0.0035 3777.4960 4.143499e+01 66.961321 71.204304 74.467888 +0.0029 0.0035 3771.4550 4.206274e+01 66.547956 70.956511 74.071847 +0.0030 0.0035 3761.9850 4.265451e+01 66.152810 70.715182 73.691558 +0.0031 0.0035 3749.2550 4.321057e+01 65.773613 70.478322 73.329992 +0.0032 0.0035 3733.4510 4.373479e+01 65.408851 70.247358 72.971711 +0.0033 0.0035 3714.7620 4.422865e+01 65.062542 70.019861 72.628418 +0.0034 0.0035 3693.3840 4.469425e+01 64.729700 69.798039 72.293975 +0.0035 0.0035 3669.5120 4.513423e+01 64.411876 69.579402 71.969943 +0.0036 0.0035 3643.3410 4.554696e+01 64.109071 69.368214 71.665942 +0.0037 0.0035 3615.0610 4.593794e+01 63.819518 69.161083 71.366036 +0.0038 0.0035 3584.8570 4.630629e+01 63.544878 68.958544 71.078284 +0.0039 0.0035 3552.9070 4.665474e+01 63.281130 68.761548 70.795655 +0.0040 0.0035 3519.3810 4.698164e+01 63.032947 68.570319 70.534941 +0.0041 0.0035 3484.4420 4.729198e+01 62.795324 68.384652 70.277242 +0.0042 0.0035 3448.2420 4.758577e+01 62.568181 68.201042 70.026735 +0.0043 0.0035 3410.9260 4.786195e+01 62.354385 68.024085 69.789272 +0.0044 0.0035 3372.6300 4.812393e+01 62.150315 67.850336 69.558102 +0.0045 0.0035 3333.4800 4.837148e+01 61.957283 67.682539 69.339083 +0.0046 0.0035 3293.5930 4.860468e+01 61.773610 67.519489 69.127644 +0.0047 0.0035 3253.0810 4.882576e+01 61.600043 67.360983 68.925965 +0.0048 0.0035 3212.0440 4.903548e+01 61.435552 67.206708 68.727816 +0.0049 0.0035 3170.5770 4.923190e+01 61.280560 67.057753 68.543945 +0.0050 0.0035 3128.7670 4.942103e+01 61.133362 66.911704 68.360826 +0.0051 0.0035 3086.6940 4.959795e+01 60.993936 66.770635 68.187543 +0.0052 0.0035 3044.4310 4.976633e+01 60.862048 66.632877 68.017256 +0.0053 0.0035 3002.0480 4.992528e+01 60.737733 66.500482 67.859552 +0.0054 0.0035 2959.6060 5.007680e+01 60.620032 66.373486 67.682531 +0.0055 0.0035 2917.1670 5.022036e+01 60.509793 66.248741 67.538199 +0.0056 0.0035 2874.7830 5.035584e+01 60.404748 66.126696 67.410717 +0.0057 0.0035 2832.5060 5.048261e+01 60.308356 66.011106 67.277715 +0.0058 0.0035 2790.3820 5.060360e+01 60.216540 65.898559 67.149563 +0.0059 0.0035 2748.4510 5.071796e+01 60.130753 65.789732 67.023254 +0.0060 0.0035 2706.7530 5.082580e+01 60.050597 65.683812 66.900790 +0.0061 0.0035 2665.3220 5.092857e+01 59.977170 65.582944 66.792012 +0.0062 0.0035 2624.1890 5.102603e+01 59.906921 65.484693 66.682040 +0.0063 0.0035 2583.3830 5.111824e+01 59.841830 65.390590 66.579567 +0.0064 0.0035 2542.9300 5.120472e+01 59.781533 65.299649 66.482406 +0.0065 0.0035 2502.8520 5.128726e+01 59.725617 65.212625 66.387467 +0.0066 0.0035 2463.1710 5.136558e+01 59.674484 65.128063 66.295236 +0.0067 0.0035 2423.9050 5.144131e+01 59.624597 65.047078 66.203425 +0.0068 0.0035 2385.0700 5.150979e+01 59.583752 64.969514 66.125502 +0.0069 0.0035 2346.6800 5.157668e+01 59.544365 64.893064 66.042598 +0.0070 0.0035 2308.7490 5.163994e+01 59.508659 64.822005 65.973227 +0.0071 0.0035 2271.2870 5.170051e+01 59.476229 64.754549 65.902494 +0.0072 0.0035 2234.3040 5.175770e+01 59.447596 64.689795 65.834919 +0.0073 0.0035 2197.8070 5.181186e+01 59.422402 64.627193 65.770939 +0.0074 0.0035 2161.8040 5.186367e+01 59.399942 64.567930 65.711154 +0.0075 0.0035 2126.2990 5.191265e+01 59.381018 64.511596 65.654031 +0.0076 0.0035 2091.2970 5.195920e+01 59.364407 64.457993 65.600770 +0.0077 0.0035 2056.8010 5.200434e+01 59.350561 64.405898 65.548496 +0.0078 0.0035 2022.8140 5.204575e+01 59.339821 64.357618 65.503979 +0.0079 0.0035 1989.3360 5.208601e+01 59.331172 64.311925 65.458351 +0.0080 0.0035 1956.3690 5.212415e+01 59.325134 64.268423 65.414970 +0.0081 0.0035 1923.9130 5.216044e+01 59.321354 64.227680 65.378516 +0.0082 0.0035 1891.9650 5.219477e+01 59.320202 64.189172 65.343939 +0.0083 0.0035 1860.5260 5.222804e+01 59.320856 64.153241 65.309737 +0.0084 0.0035 1829.5920 5.225928e+01 59.323351 64.119354 65.275096 +0.0085 0.0035 1799.1610 5.228914e+01 59.328337 64.087285 65.247340 +0.0086 0.0035 1769.2310 5.231684e+01 59.335241 64.057666 65.223262 +0.0087 0.0035 1739.7980 5.234340e+01 59.344001 64.029731 65.195823 +0.0088 0.0035 1710.8570 5.236946e+01 59.353991 64.003860 65.170948 +0.0089 0.0035 1682.4040 5.239424e+01 59.365363 63.980603 65.150570 +0.0090 0.0035 1654.4360 5.241929e+01 59.377920 63.957241 65.126677 +0.0091 0.0035 1626.9470 5.243984e+01 59.394274 63.938790 65.117002 +0.0092 0.0035 1599.9310 5.246072e+01 59.411115 63.921054 65.102160 +0.0093 0.0035 1573.3840 5.248110e+01 59.429491 63.904657 65.086276 +0.0094 0.0035 0.0000 5.250072e+01 59.448866 63.890081 65.073323 +0.0095 0.0035 0.0000 5.251900e+01 59.469795 63.877413 65.064739 +0.0096 0.0035 0.0000 5.253736e+01 59.491268 63.866583 65.052260 +0.0097 0.0035 0.0000 5.255401e+01 59.514872 63.857501 65.046492 +0.0098 0.0035 0.0000 5.257082e+01 59.539005 63.848825 65.037505 +0.0099 0.0035 0.0000 5.258626e+01 59.564725 63.842540 65.029402 +0.0100 0.0035 0.0000 5.260027e+01 59.591677 63.838637 65.033592 +0.0000 0.0036 2882.6650 0.000000e+00 53.304982 53.304514 57.654712 +0.0001 0.0036 2885.2030 1.956584e+00 78.708454 78.451725 87.739273 +0.0002 0.0036 2892.7720 4.008769e+00 78.758791 78.311492 87.318269 +0.0003 0.0036 2905.2430 6.108447e+00 78.710823 78.119877 86.888173 +0.0004 0.0036 2922.4010 8.225151e+00 78.571544 77.895892 86.432508 +0.0005 0.0036 2943.9550 1.033338e+01 78.355009 77.648728 85.952794 +0.0006 0.0036 2969.5400 1.241270e+01 78.071784 77.390886 85.465669 +0.0007 0.0036 2998.7250 1.444762e+01 77.730056 77.121176 84.964292 +0.0008 0.0036 3031.0250 1.642269e+01 77.335073 76.846110 84.454907 +0.0009 0.0036 3065.9120 1.833186e+01 76.897275 76.564375 83.942835 +0.0010 0.0036 3102.8220 2.016773e+01 76.423826 76.279686 83.423181 +0.0011 0.0036 3141.1740 2.192337e+01 75.921671 75.993543 82.901737 +0.0012 0.0036 3180.3780 2.359926e+01 75.399565 75.705757 82.384300 +0.0013 0.0036 3219.8510 2.519473e+01 74.856702 75.417203 81.856552 +0.0014 0.0036 3259.0240 2.670821e+01 74.304177 75.127867 81.334406 +0.0015 0.0036 3297.3560 2.814282e+01 73.746041 74.840541 80.813652 +0.0016 0.0036 3334.3460 2.949990e+01 73.186704 74.553877 80.304439 +0.0017 0.0036 3369.5330 3.078346e+01 72.629665 74.268396 79.795560 +0.0018 0.0036 3402.5090 3.199463e+01 72.075293 73.985625 79.295566 +0.0019 0.0036 3432.9190 3.313695e+01 71.527571 73.704547 78.801304 +0.0020 0.0036 3460.4620 3.421449e+01 70.990326 73.427372 78.315952 +0.0021 0.0036 3484.8980 3.523025e+01 70.463492 73.153861 77.835637 +0.0022 0.0036 3506.0410 3.618650e+01 69.950059 72.883088 77.373191 +0.0023 0.0036 3523.7590 3.708708e+01 69.450150 72.616223 76.916937 +0.0024 0.0036 3537.9710 3.793792e+01 68.963842 72.353774 76.464225 +0.0025 0.0036 3548.6420 3.873757e+01 68.494201 72.095838 76.033986 +0.0026 0.0036 3555.7810 3.949232e+01 68.039020 71.840711 75.584540 +0.0027 0.0036 3559.4310 4.020098e+01 67.600626 71.590663 75.184416 +0.0028 0.0036 3559.6690 4.087070e+01 67.177782 71.342989 74.784275 +0.0029 0.0036 3556.5970 4.150089e+01 66.771119 71.103156 74.404793 +0.0030 0.0036 3550.3420 4.209465e+01 66.379852 70.864679 74.036826 +0.0031 0.0036 3541.0430 4.265428e+01 66.005338 70.633040 73.674054 +0.0032 0.0036 3528.8550 4.318312e+01 65.646328 70.404966 73.321437 +0.0033 0.0036 3513.9410 4.368195e+01 65.300336 70.182262 72.974775 +0.0034 0.0036 3496.4700 4.415257e+01 64.971614 69.963786 72.645378 +0.0035 0.0036 3476.6130 4.459743e+01 64.656504 69.750067 72.327375 +0.0036 0.0036 3454.5420 4.501584e+01 64.355674 69.540931 72.016105 +0.0037 0.0036 3430.4250 4.541215e+01 64.067585 69.337736 71.720123 +0.0038 0.0036 3404.4290 4.578685e+01 63.793433 69.138177 71.429703 +0.0039 0.0036 3376.7140 4.614009e+01 63.532458 68.944303 71.154471 +0.0040 0.0036 3347.4340 4.647521e+01 63.281512 68.756181 70.882298 +0.0041 0.0036 3316.7380 4.679144e+01 63.045591 68.571199 70.628365 +0.0042 0.0036 3284.7670 4.708990e+01 62.821046 68.391115 70.379207 +0.0043 0.0036 3251.6540 4.737327e+01 62.604151 68.214340 70.134442 +0.0044 0.0036 3217.5260 4.764161e+01 62.400968 68.043370 69.905183 +0.0045 0.0036 3182.5000 4.789513e+01 62.206898 67.877310 69.681500 +0.0046 0.0036 3146.6870 4.813571e+01 62.023196 67.714276 69.467994 +0.0047 0.0036 3110.1910 4.836250e+01 61.848773 67.557722 69.267181 +0.0048 0.0036 3073.1080 4.857826e+01 61.682773 67.404877 69.066222 +0.0049 0.0036 3035.5290 4.878318e+01 61.525477 67.255550 68.876049 +0.0050 0.0036 2997.5360 4.897631e+01 61.377548 67.110589 68.692222 +0.0051 0.0036 2959.2060 4.915925e+01 61.236969 66.970372 68.517701 +0.0052 0.0036 2920.6120 4.933317e+01 61.103539 66.833650 68.347604 +0.0053 0.0036 2881.8190 4.949850e+01 60.978054 66.701573 68.184241 +0.0054 0.0036 2842.8910 4.965567e+01 60.858569 66.573401 68.023479 +0.0055 0.0036 2803.8860 4.980562e+01 60.746542 66.450027 67.857088 +0.0056 0.0036 2764.8570 4.994616e+01 60.641073 66.329605 67.714039 +0.0057 0.0036 2725.8530 5.008114e+01 60.540705 66.212272 67.570994 +0.0058 0.0036 2686.9220 5.020492e+01 60.448688 66.098769 67.459075 +0.0059 0.0036 2648.1040 5.032466e+01 60.360900 65.989833 67.331216 +0.0060 0.0036 2609.4370 5.043843e+01 60.278606 65.884071 67.211402 +0.0061 0.0036 2570.9570 5.054755e+01 60.200838 65.781751 67.093076 +0.0062 0.0036 2532.6970 5.064904e+01 60.130147 65.683908 66.982003 +0.0063 0.0036 2494.6840 5.074723e+01 60.062201 65.588551 66.872546 +0.0064 0.0036 2456.9470 5.083854e+01 60.000324 65.497063 66.765680 +0.0065 0.0036 2419.5080 5.092677e+01 59.942266 65.408291 66.672737 +0.0066 0.0036 2382.3910 5.100966e+01 59.888511 65.323607 66.578931 +0.0067 0.0036 2345.6140 5.108918e+01 59.838985 65.241679 66.487412 +0.0068 0.0036 2309.1960 5.116441e+01 59.793547 65.163067 66.402592 +0.0069 0.0036 2273.1510 5.123604e+01 59.751973 65.088093 66.323223 +0.0070 0.0036 2237.4950 5.130438e+01 59.713964 65.015620 66.244867 +0.0071 0.0036 2202.2390 5.136951e+01 59.679461 64.945849 66.169856 +0.0072 0.0036 2167.3950 5.143132e+01 59.649136 64.879948 66.101820 +0.0073 0.0036 2132.9710 5.149048e+01 59.621543 64.816653 66.033738 +0.0074 0.0036 2098.9780 5.154641e+01 59.597078 64.756851 65.972155 +0.0075 0.0036 2065.4200 5.159906e+01 59.576756 64.698951 65.915041 +0.0076 0.0036 2032.3040 5.165110e+01 59.557473 64.643989 65.854534 +0.0077 0.0036 1999.6360 5.169964e+01 59.541702 64.591726 65.802746 +0.0078 0.0036 1967.4180 5.174643e+01 59.528247 64.541535 65.749017 +0.0079 0.0036 1935.6540 5.179007e+01 59.518392 64.494663 65.705487 +0.0080 0.0036 1904.3460 5.183246e+01 59.509945 64.449946 65.658434 +0.0081 0.0036 1873.4960 5.187296e+01 59.503891 64.407636 65.615979 +0.0082 0.0036 1843.1030 5.191135e+01 59.500246 64.368212 65.578719 +0.0083 0.0036 1813.1680 5.194882e+01 59.498463 64.329882 65.541432 +0.0084 0.0036 1783.6910 5.198377e+01 59.499961 64.295563 65.504785 +0.0085 0.0036 1754.6700 5.201713e+01 59.502769 64.262446 65.474687 +0.0086 0.0036 1726.1030 5.204879e+01 59.507593 64.231121 65.447515 +0.0087 0.0036 1697.9890 5.207932e+01 59.514225 64.202144 65.420600 +0.0088 0.0036 1670.3250 5.210834e+01 59.522841 64.175122 65.394459 +0.0089 0.0036 1643.1080 5.213702e+01 59.531885 64.150178 65.367197 +0.0090 0.0036 1616.3350 5.216323e+01 59.543682 64.127451 65.346307 +0.0091 0.0036 1590.0020 5.218894e+01 59.557504 64.106137 65.329397 +0.0092 0.0036 1564.1050 5.221359e+01 59.571944 64.086344 65.307602 +0.0093 0.0036 1538.6410 5.223726e+01 59.588083 64.069039 65.294287 +0.0094 0.0036 0.0000 5.226010e+01 59.605521 64.053553 65.280401 +0.0095 0.0036 0.0000 5.228146e+01 59.624782 64.039544 65.269098 +0.0096 0.0036 0.0000 5.230280e+01 59.644744 64.026893 65.254458 +0.0097 0.0036 0.0000 5.232394e+01 59.665323 64.015034 65.240919 +0.0098 0.0036 0.0000 5.234255e+01 59.688660 64.006288 65.236250 +0.0099 0.0036 0.0000 5.236164e+01 59.711579 63.999014 65.229377 +0.0100 0.0036 0.0000 5.237909e+01 59.737147 63.993030 65.224099 +0.0000 0.0037 2707.6900 1.014321e-07 53.149989 53.149549 57.423867 +0.0001 0.0037 2710.0140 1.919632e+00 78.683110 78.440515 87.762029 +0.0002 0.0037 2716.9500 3.932391e+00 78.728292 78.299603 87.358439 +0.0003 0.0037 2728.3810 5.992019e+00 78.674026 78.109881 86.930059 +0.0004 0.0037 2744.1190 8.066387e+00 78.536049 77.888587 86.485554 +0.0005 0.0037 2763.9070 1.013385e+01 78.324057 77.649310 86.026704 +0.0006 0.0037 2787.4200 1.217428e+01 78.046240 77.397144 85.550867 +0.0007 0.0037 2814.2800 1.417052e+01 77.712161 77.134524 85.063130 +0.0008 0.0037 2844.0550 1.611131e+01 77.328777 76.865312 84.567390 +0.0009 0.0037 2876.2780 1.798738e+01 76.901514 76.589033 84.058978 +0.0010 0.0037 2910.4500 1.979144e+01 76.440905 76.310784 83.565885 +0.0011 0.0037 2946.0510 2.152122e+01 75.951883 76.032396 83.061460 +0.0012 0.0037 2982.5570 2.317276e+01 75.441526 75.750063 82.552243 +0.0013 0.0037 3019.4450 2.474460e+01 74.916231 75.467821 82.045742 +0.0014 0.0037 3056.2020 2.623860e+01 74.379448 75.186366 81.540398 +0.0015 0.0037 3092.3410 2.765703e+01 73.835044 74.905071 81.032603 +0.0016 0.0037 3127.4040 2.899834e+01 73.288931 74.624495 80.534552 +0.0017 0.0037 3160.9680 3.026846e+01 72.743219 74.345028 80.035691 +0.0018 0.0037 3192.6550 3.146843e+01 72.202414 74.069362 79.547177 +0.0019 0.0037 3222.1310 3.260197e+01 71.667790 73.795471 79.069127 +0.0020 0.0037 3249.1130 3.367199e+01 71.141709 73.524198 78.586811 +0.0021 0.0037 3273.3650 3.468186e+01 70.625865 73.256417 78.121997 +0.0022 0.0037 3294.7040 3.563448e+01 70.122232 72.992172 77.664072 +0.0023 0.0037 3312.9910 3.653265e+01 69.632433 72.731729 77.215255 +0.0024 0.0037 3328.1350 3.738007e+01 69.156724 72.474816 76.780155 +0.0025 0.0037 3340.0860 3.817831e+01 68.693872 72.222107 76.352351 +0.0026 0.0037 3348.8340 3.893338e+01 68.245728 71.972157 75.919562 +0.0027 0.0037 3354.4000 3.964307e+01 67.814468 71.727302 75.528289 +0.0028 0.0037 3356.8390 4.031486e+01 67.398508 71.486107 75.111373 +0.0029 0.0037 3356.2280 4.094775e+01 66.997696 71.249518 74.725978 +0.0030 0.0037 3352.6660 4.154498e+01 66.611575 71.017828 74.353824 +0.0031 0.0037 3346.2700 4.210705e+01 66.240733 70.788374 74.017707 +0.0032 0.0037 3337.1670 4.263947e+01 65.884106 70.565492 73.667895 +0.0033 0.0037 3325.4990 4.314227e+01 65.543304 70.345060 73.324367 +0.0034 0.0037 3311.4080 4.361867e+01 65.214565 70.130750 72.990896 +0.0035 0.0037 3295.0460 4.406698e+01 64.903045 69.921214 72.678102 +0.0036 0.0037 3276.5620 4.449113e+01 64.604761 69.715614 72.370129 +0.0037 0.0037 3256.1070 4.489383e+01 64.317044 69.514619 72.068284 +0.0038 0.0037 3233.8280 4.527191e+01 64.045723 69.319003 71.784932 +0.0039 0.0037 3209.8710 4.563185e+01 63.785189 69.128190 71.501154 +0.0040 0.0037 3184.3740 4.597078e+01 63.535789 68.940545 71.232856 +0.0041 0.0037 3157.4740 4.629408e+01 63.299436 68.758289 70.977459 +0.0042 0.0037 3129.3000 4.659939e+01 63.073949 68.579460 70.728468 +0.0043 0.0037 3099.9730 4.688850e+01 62.858858 68.405715 70.487165 +0.0044 0.0037 3069.6120 4.716184e+01 62.655236 68.236347 70.250071 +0.0045 0.0037 3038.3240 4.742122e+01 62.460468 68.071911 70.029662 +0.0046 0.0037 3006.2160 4.766769e+01 62.276180 67.911091 69.813484 +0.0047 0.0037 2973.3830 4.790243e+01 62.099060 67.754160 69.602985 +0.0048 0.0037 2939.9160 4.812301e+01 61.932968 67.602417 69.403960 +0.0049 0.0037 2905.9020 4.833305e+01 61.774955 67.454154 69.211251 +0.0050 0.0037 2871.4180 4.853321e+01 61.624969 67.310352 69.027353 +0.0051 0.0037 2836.5390 4.872191e+01 61.482945 67.170749 68.847965 +0.0052 0.0037 2801.3340 4.890160e+01 61.348449 67.034805 68.677073 +0.0053 0.0037 2765.8670 4.907308e+01 61.220713 66.902598 68.509760 +0.0054 0.0037 2730.2000 4.923498e+01 61.100050 66.774309 68.347083 +0.0055 0.0037 2694.3890 4.938887e+01 60.986210 66.650351 68.195860 +0.0056 0.0037 2658.4870 4.953484e+01 60.879174 66.530685 68.049354 +0.0057 0.0037 2622.5420 4.967643e+01 60.777434 66.414130 67.888044 +0.0058 0.0037 2586.5980 4.980967e+01 60.681165 66.299861 67.748444 +0.0059 0.0037 2550.6980 4.993400e+01 60.592337 66.190886 67.620198 +0.0060 0.0037 2514.8790 5.005158e+01 60.508427 66.084252 67.513526 +0.0061 0.0037 2479.1770 5.016447e+01 60.429234 65.981957 67.396267 +0.0062 0.0037 2443.6240 5.027214e+01 60.355369 65.882749 67.278860 +0.0063 0.0037 2408.2490 5.037559e+01 60.285490 65.786912 67.164464 +0.0064 0.0037 2373.0800 5.047260e+01 60.221215 65.694870 67.062153 +0.0065 0.0037 2338.1420 5.056518e+01 60.161515 65.606059 66.957000 +0.0066 0.0037 2303.4570 5.065386e+01 60.105627 65.519677 66.861108 +0.0067 0.0037 2269.0450 5.073696e+01 60.054394 65.437731 66.771462 +0.0068 0.0037 2234.9270 5.081796e+01 60.006220 65.357624 66.678561 +0.0069 0.0037 2201.1170 5.089425e+01 59.962558 65.282352 66.600509 +0.0070 0.0037 2167.6320 5.096706e+01 59.923017 65.209217 66.521950 +0.0071 0.0037 2134.4840 5.103688e+01 59.886367 65.139149 66.443810 +0.0072 0.0037 2101.6870 5.110370e+01 59.853641 65.072211 66.368784 +0.0073 0.0037 2069.2510 5.116698e+01 59.823934 65.007708 66.300564 +0.0074 0.0037 2037.1860 5.122750e+01 59.797461 64.947154 66.235540 +0.0075 0.0037 2005.4990 5.128540e+01 59.773855 64.887992 66.175154 +0.0076 0.0037 1974.1980 5.134074e+01 59.753140 64.832237 66.116258 +0.0077 0.0037 1943.2890 5.139324e+01 59.735087 64.778740 66.057716 +0.0078 0.0037 1912.7780 5.144408e+01 59.720028 64.727819 66.005386 +0.0079 0.0037 1882.6680 5.149264e+01 59.707661 64.678990 65.953970 +0.0080 0.0037 1852.9630 5.153966e+01 59.697651 64.633264 65.905483 +0.0081 0.0037 1823.6670 5.158341e+01 59.689846 64.590396 65.859930 +0.0082 0.0037 1794.7800 5.162629e+01 59.682823 64.548576 65.815463 +0.0083 0.0037 1766.3040 5.166672e+01 59.679773 64.509867 65.780061 +0.0084 0.0037 1738.2400 5.170537e+01 59.679311 64.473212 65.740529 +0.0085 0.0037 1710.5880 5.174261e+01 59.680249 64.438694 65.702903 +0.0086 0.0037 1683.3470 5.177820e+01 59.682753 64.406359 65.671620 +0.0087 0.0037 1656.5180 5.181244e+01 59.687434 64.376237 65.646060 +0.0088 0.0037 1630.0970 5.184513e+01 59.694178 64.347899 65.619954 +0.0089 0.0037 1604.0850 5.187700e+01 59.701193 64.321568 65.590675 +0.0090 0.0037 1578.4780 5.190705e+01 59.711741 64.296910 65.565941 +0.0091 0.0037 1553.2750 5.193632e+01 59.722259 64.274341 65.542088 +0.0092 0.0037 1528.4720 5.196420e+01 59.735158 64.253796 65.524895 +0.0093 0.0037 0.0000 5.199202e+01 59.748094 64.235048 65.504699 +0.0094 0.0037 0.0000 5.201816e+01 59.763743 64.217996 65.486481 +0.0095 0.0037 0.0000 5.204305e+01 59.781016 64.202842 65.470607 +0.0096 0.0037 0.0000 5.206673e+01 59.799889 64.189105 65.454553 +0.0097 0.0037 0.0000 5.208950e+01 59.820585 64.176722 65.444546 +0.0098 0.0037 0.0000 5.211150e+01 59.841469 64.166571 65.440930 +0.0099 0.0037 0.0000 5.213304e+01 59.863491 64.157692 65.429775 +0.0100 0.0037 0.0000 5.215469e+01 59.886371 64.149722 65.417686 +0.0000 0.0038 2546.3090 0.000000e+00 53.007856 53.007469 57.210114 +0.0001 0.0038 2548.4450 1.884369e+00 78.669311 78.435212 87.797300 +0.0002 0.0038 2554.8200 3.857561e+00 78.707495 78.295240 87.402011 +0.0003 0.0038 2565.3310 5.877449e+00 78.649231 78.110488 86.986818 +0.0004 0.0038 2579.8100 7.913478e+00 78.511082 77.892841 86.551908 +0.0005 0.0038 2598.0290 9.941554e+00 78.302509 77.658329 86.106844 +0.0006 0.0038 2619.7000 1.194391e+01 78.031345 77.410153 85.643603 +0.0007 0.0038 2644.4850 1.390469e+01 77.705284 77.154750 85.171022 +0.0008 0.0038 2672.0030 1.581115e+01 77.331120 76.890031 84.687692 +0.0009 0.0038 2701.8360 1.765486e+01 76.915780 76.622645 84.204594 +0.0010 0.0038 2733.5380 1.943056e+01 76.466777 76.349894 83.714040 +0.0011 0.0038 2766.6470 2.113178e+01 75.992131 76.077551 83.225288 +0.0012 0.0038 2800.6910 2.275850e+01 75.495518 75.800688 82.729681 +0.0013 0.0038 2835.2000 2.430859e+01 74.982916 75.525287 82.234157 +0.0014 0.0038 2869.7130 2.578360e+01 74.459505 75.251163 81.742019 +0.0015 0.0038 2903.7880 2.718322e+01 73.928719 74.975512 81.249283 +0.0016 0.0038 2937.0050 2.851140e+01 73.395814 74.701484 80.754114 +0.0017 0.0038 2968.9790 2.976773e+01 72.863997 74.430118 80.278560 +0.0018 0.0038 2999.3570 3.095717e+01 72.334415 74.160069 79.798672 +0.0019 0.0038 3027.8290 3.208110e+01 71.812664 73.891767 79.325850 +0.0020 0.0038 3054.1260 3.314360e+01 71.298216 73.626794 78.860612 +0.0021 0.0038 3078.0240 3.414710e+01 70.793807 73.365278 78.398518 +0.0022 0.0038 3099.3390 3.509547e+01 70.299055 73.106488 77.949594 +0.0023 0.0038 3117.9340 3.599175e+01 69.817450 72.851251 77.503183 +0.0024 0.0038 3133.7090 3.683537e+01 69.351906 72.599051 77.069820 +0.0025 0.0038 3146.6050 3.763210e+01 68.897736 72.351339 76.664329 +0.0026 0.0038 3156.5950 3.838544e+01 68.457367 72.107789 76.253672 +0.0027 0.0038 3163.6860 3.909590e+01 68.032157 71.867732 75.853081 +0.0028 0.0038 3167.9110 3.976920e+01 67.621400 71.630038 75.457258 +0.0029 0.0038 3169.3290 4.040297e+01 67.225672 71.397768 75.082403 +0.0030 0.0038 3168.0160 4.100216e+01 66.844206 71.169441 74.715593 +0.0031 0.0038 3164.0680 4.156812e+01 66.478568 70.946122 74.359152 +0.0032 0.0038 3157.5920 4.210381e+01 66.125794 70.725947 74.009698 +0.0033 0.0038 3148.7050 4.261077e+01 65.788247 70.510560 73.672389 +0.0034 0.0038 3137.5340 4.309009e+01 65.463745 70.299970 73.344011 +0.0035 0.0038 3124.2070 4.354369e+01 65.153693 70.092803 73.026174 +0.0036 0.0038 3108.8560 4.397239e+01 64.856426 69.890994 72.719690 +0.0037 0.0038 3091.6150 4.438044e+01 64.569939 69.690593 72.411588 +0.0038 0.0038 3072.6150 4.476351e+01 64.300253 69.500989 72.134180 +0.0039 0.0038 3051.9870 4.512827e+01 64.040584 69.312285 71.854228 +0.0040 0.0038 3029.8560 4.547445e+01 63.790761 69.126297 71.583121 +0.0041 0.0038 3006.3450 4.580053e+01 63.555989 68.943900 71.321746 +0.0042 0.0038 2981.5720 4.611254e+01 63.329786 68.768446 71.074754 +0.0043 0.0038 2955.6500 4.640728e+01 63.115322 68.597260 70.825117 +0.0044 0.0038 2928.6870 4.668595e+01 62.910271 68.429927 70.597549 +0.0045 0.0038 2900.7840 4.695229e+01 62.714908 68.266497 70.370831 +0.0046 0.0038 2872.0370 4.720327e+01 62.530047 68.106643 70.156985 +0.0047 0.0038 2842.5390 4.744234e+01 62.354077 67.951383 69.945945 +0.0048 0.0038 2812.3730 4.766996e+01 62.185670 67.800122 69.741688 +0.0049 0.0038 2781.6210 4.788530e+01 62.026349 67.653474 69.547301 +0.0050 0.0038 2750.3570 4.809123e+01 61.874938 67.509657 69.359640 +0.0051 0.0038 2718.6520 4.828601e+01 61.729610 67.370214 69.173027 +0.0052 0.0038 2686.5720 4.847105e+01 61.595498 67.235547 69.003891 +0.0053 0.0038 2654.1800 4.864698e+01 61.466868 67.103993 68.837568 +0.0054 0.0038 2621.5340 4.881477e+01 61.345172 66.976371 68.674364 +0.0055 0.0038 2588.6890 4.897472e+01 61.228624 66.851930 68.514379 +0.0056 0.0038 2555.6950 4.912597e+01 61.119186 66.734190 68.371474 +0.0057 0.0038 2522.5990 4.927024e+01 61.016689 66.614668 68.223670 +0.0058 0.0038 2489.4460 4.940831e+01 60.918969 66.502064 68.081852 +0.0059 0.0038 2456.2760 4.954186e+01 60.825636 66.391858 67.927845 +0.0060 0.0038 2423.1270 4.966531e+01 60.740298 66.285991 67.800831 +0.0061 0.0038 2390.0330 4.978154e+01 60.659583 66.182273 67.697954 +0.0062 0.0038 2357.0260 4.989456e+01 60.583367 66.082600 67.580169 +0.0063 0.0038 2324.1370 5.000226e+01 60.512362 65.986308 67.462731 +0.0064 0.0038 2291.3930 5.010571e+01 60.444770 65.893024 67.353429 +0.0065 0.0038 2258.8180 5.020254e+01 60.383185 65.803595 67.248712 +0.0066 0.0038 2226.4360 5.029505e+01 60.326048 65.717364 67.150255 +0.0067 0.0038 2194.2680 5.038444e+01 60.271673 65.634729 67.056894 +0.0068 0.0038 2162.3330 5.046952e+01 60.222183 65.554385 66.961928 +0.0069 0.0038 2130.6490 5.055111e+01 60.176399 65.477455 66.878142 +0.0070 0.0038 2099.2310 5.062863e+01 60.134619 65.403524 66.794967 +0.0071 0.0038 2068.0950 5.070295e+01 60.095879 65.332758 66.718234 +0.0072 0.0038 2037.2540 5.077405e+01 60.061018 65.265477 66.642351 +0.0073 0.0038 2006.7180 5.084156e+01 60.029374 65.200331 66.569989 +0.0074 0.0038 1976.4990 5.090689e+01 60.000742 65.138207 66.501202 +0.0075 0.0038 1946.6060 5.096951e+01 59.974620 65.078582 66.433735 +0.0076 0.0038 1917.0470 5.102918e+01 59.951468 65.021502 66.371962 +0.0077 0.0038 1887.8300 5.108597e+01 59.931646 64.966551 66.313217 +0.0078 0.0038 1858.9600 5.114049e+01 59.914348 64.914741 66.258715 +0.0079 0.0038 1830.4440 5.119318e+01 59.899947 64.865567 66.206737 +0.0080 0.0038 1802.2860 5.124345e+01 59.887715 64.818010 66.156021 +0.0081 0.0038 1774.4890 5.129184e+01 59.878047 64.773381 66.107512 +0.0082 0.0038 1747.0580 5.133857e+01 59.870106 64.730426 66.062026 +0.0083 0.0038 1719.9930 5.138150e+01 59.864172 64.690772 66.011783 +0.0084 0.0038 1693.2980 5.142545e+01 59.861279 64.652525 65.979294 +0.0085 0.0038 1666.9740 5.146641e+01 59.860260 64.617301 65.942083 +0.0086 0.0038 1641.0200 5.150541e+01 59.861224 64.583732 65.906170 +0.0087 0.0038 1615.4380 5.154322e+01 59.863546 64.551822 65.874071 +0.0088 0.0038 1590.2280 5.157957e+01 59.868159 64.522810 65.845827 +0.0089 0.0038 1565.3880 5.161537e+01 59.873316 64.494606 65.817730 +0.0090 0.0038 1540.9170 5.164908e+01 59.880855 64.468635 65.788399 +0.0091 0.0038 1516.8150 5.168096e+01 59.890590 64.444757 65.766511 +0.0092 0.0038 1493.0790 5.171224e+01 59.901347 64.422807 65.742886 +0.0093 0.0038 0.0000 5.174194e+01 59.914187 64.402868 65.723069 +0.0094 0.0038 0.0000 5.177206e+01 59.927608 64.383748 65.700097 +0.0095 0.0038 0.0000 5.179987e+01 59.943211 64.367405 65.683436 +0.0096 0.0038 0.0000 5.182779e+01 59.959384 64.352400 65.664668 +0.0097 0.0038 0.0000 5.185413e+01 59.976947 64.339288 65.651647 +0.0098 0.0038 0.0000 5.187951e+01 59.995910 64.327106 65.637942 +0.0099 0.0038 0.0000 5.190360e+01 60.016364 64.316989 65.627795 +0.0100 0.0038 0.0000 5.192753e+01 60.037638 64.307938 65.618402 +0.0000 0.0039 2397.2040 0.000000e+00 52.876608 52.876118 57.011930 +0.0001 0.0039 2399.1730 1.850254e+00 78.664708 78.441366 87.838330 +0.0002 0.0039 2405.0490 3.787331e+00 78.695304 78.302302 87.451842 +0.0003 0.0039 2414.7420 5.768643e+00 78.633283 78.121365 87.045221 +0.0004 0.0039 2428.1010 7.764900e+00 78.499476 77.907260 86.626909 +0.0005 0.0039 2444.9210 9.755246e+00 78.293120 77.676941 86.190183 +0.0006 0.0039 2464.9480 1.172242e+01 78.027448 77.434825 85.739766 +0.0007 0.0039 2487.8780 1.364781e+01 77.708781 77.183047 85.277834 +0.0008 0.0039 2513.3710 1.552097e+01 77.343920 76.925741 84.809515 +0.0009 0.0039 2541.0530 1.733336e+01 76.940878 76.664502 84.345003 +0.0010 0.0039 2570.5240 1.907961e+01 76.503365 76.398052 83.864122 +0.0011 0.0039 2601.3710 2.075562e+01 76.040497 76.130712 83.384831 +0.0012 0.0039 2633.1670 2.235858e+01 75.558078 75.862864 82.906519 +0.0013 0.0039 2665.4900 2.388729e+01 75.055933 75.591502 82.420406 +0.0014 0.0039 2697.9220 2.534234e+01 74.547032 75.321749 81.943283 +0.0015 0.0039 2730.0600 2.672593e+01 74.028980 75.054537 81.462030 +0.0016 0.0039 2761.5230 2.803814e+01 73.508778 74.786070 80.983441 +0.0017 0.0039 2791.9540 2.928158e+01 72.989803 74.519840 80.515198 +0.0018 0.0039 2821.0300 3.045997e+01 72.473467 74.255266 80.044160 +0.0019 0.0039 2848.4600 3.157390e+01 71.962832 73.994171 79.586556 +0.0020 0.0039 2873.9910 3.262818e+01 71.459255 73.733592 79.131319 +0.0021 0.0039 2897.4090 3.362576e+01 70.964954 73.477866 78.681342 +0.0022 0.0039 2918.5360 3.456823e+01 70.481230 73.224429 78.240181 +0.0023 0.0039 2937.2350 3.546060e+01 70.008559 72.974467 77.799527 +0.0024 0.0039 2953.4020 3.630060e+01 69.550460 72.726783 77.369628 +0.0025 0.0039 2966.9690 3.709689e+01 69.104075 72.484846 76.973824 +0.0026 0.0039 2977.9010 3.784935e+01 68.670910 72.245726 76.570961 +0.0027 0.0039 2986.1910 3.855896e+01 68.252096 72.009933 76.176929 +0.0028 0.0039 2991.8560 3.923307e+01 67.846446 71.777044 75.783467 +0.0029 0.0039 2994.9370 3.986765e+01 67.457738 71.547699 75.410932 +0.0030 0.0039 2995.4950 4.046955e+01 67.079712 71.324711 75.048132 +0.0031 0.0039 2993.6060 4.103802e+01 66.717989 71.104513 74.694389 +0.0032 0.0039 2989.3590 4.157736e+01 66.369383 70.889158 74.345908 +0.0033 0.0039 2982.8540 4.208707e+01 66.035107 70.676801 74.012872 +0.0034 0.0039 2974.1970 4.256889e+01 65.713181 70.469644 73.690714 +0.0035 0.0039 2963.5010 4.302654e+01 65.405341 70.265812 73.370874 +0.0036 0.0039 2950.8830 4.345985e+01 65.110942 70.067869 73.065944 +0.0037 0.0039 2936.4590 4.387049e+01 64.827727 69.874391 72.769295 +0.0038 0.0039 2920.3460 4.425991e+01 64.556993 69.683142 72.481535 +0.0039 0.0039 2902.6620 4.462943e+01 64.298029 69.494805 72.203347 +0.0040 0.0039 2883.5190 4.497983e+01 64.050844 69.310958 71.934313 +0.0041 0.0039 2863.0300 4.531259e+01 63.814068 69.133029 71.673497 +0.0042 0.0039 2841.3010 4.562855e+01 63.589171 68.958705 71.420622 +0.0043 0.0039 2818.4350 4.592767e+01 63.373930 68.788156 71.175934 +0.0044 0.0039 2794.5330 4.621327e+01 63.168814 68.622899 70.943097 +0.0045 0.0039 2769.6870 4.648461e+01 62.973106 68.461025 70.711078 +0.0046 0.0039 2743.9870 4.674124e+01 62.787263 68.302358 70.496196 +0.0047 0.0039 2717.5180 4.698691e+01 62.609056 68.148448 70.283023 +0.0048 0.0039 2690.3610 4.721883e+01 62.440717 67.998349 70.081359 +0.0049 0.0039 2662.5890 4.743946e+01 62.280015 67.851368 69.882744 +0.0050 0.0039 2634.2740 4.765083e+01 62.127169 67.709363 69.692289 +0.0051 0.0039 2605.4840 4.785111e+01 61.982330 67.570872 69.509902 +0.0052 0.0039 2576.2810 4.804164e+01 61.842917 67.435475 69.326776 +0.0053 0.0039 2546.7250 4.822335e+01 61.714028 67.305065 69.161773 +0.0054 0.0039 2516.8720 4.839556e+01 61.590573 67.177531 68.998854 +0.0055 0.0039 2486.7740 4.856066e+01 61.473383 67.053389 68.836656 +0.0056 0.0039 2456.4800 4.871812e+01 61.361847 66.932905 68.681338 +0.0057 0.0039 2426.0360 4.886755e+01 61.256947 66.816662 68.537182 +0.0058 0.0039 2395.4830 4.900956e+01 61.158261 66.703161 68.395802 +0.0059 0.0039 2364.8630 4.914709e+01 61.061384 66.593013 68.255557 +0.0060 0.0039 2334.2110 4.927827e+01 60.974037 66.486485 68.106507 +0.0061 0.0039 2303.5610 4.940051e+01 60.892307 66.383648 67.981652 +0.0062 0.0039 2272.9450 4.951794e+01 60.814613 66.284813 67.866069 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65.155517 66.573749 +0.0078 0.0039 1806.0250 5.083560e+01 60.112612 65.102081 66.518513 +0.0079 0.0039 1779.0420 5.089159e+01 60.095332 65.051639 66.459878 +0.0080 0.0039 1752.3720 5.094626e+01 60.080064 65.003680 66.406888 +0.0081 0.0039 1726.0210 5.099848e+01 60.068216 64.957743 66.355678 +0.0082 0.0039 1699.9940 5.104901e+01 60.058128 64.914008 66.307837 +0.0083 0.0039 1674.2930 5.109726e+01 60.051118 64.872369 66.262295 +0.0084 0.0039 1648.9210 5.114332e+01 60.045805 64.833540 66.219667 +0.0085 0.0039 1623.8810 5.118828e+01 60.043460 64.797717 66.185048 +0.0086 0.0039 1599.1750 5.123085e+01 60.041907 64.761926 66.137837 +0.0087 0.0039 1574.8030 5.127243e+01 60.042384 64.729301 66.104401 +0.0088 0.0039 1550.7660 5.131274e+01 60.044117 64.698552 66.073720 +0.0089 0.0039 1527.0650 5.135092e+01 60.048105 64.669692 66.042840 +0.0090 0.0039 1503.6990 5.138796e+01 60.053725 64.642395 66.012972 +0.0091 0.0039 1480.6690 5.142390e+01 60.061052 64.617014 65.984557 +0.0092 0.0039 1457.9720 5.145835e+01 60.069849 64.593842 65.960644 +0.0093 0.0039 0.0000 5.149196e+01 60.080339 64.572186 65.936219 +0.0094 0.0039 0.0000 5.152432e+01 60.092337 64.552180 65.914052 +0.0095 0.0039 0.0000 5.155563e+01 60.105877 64.534054 65.892558 +0.0096 0.0039 0.0000 5.158633e+01 60.120519 64.517721 65.873556 +0.0097 0.0039 0.0000 5.161601e+01 60.135771 64.503109 65.859144 +0.0098 0.0039 0.0000 5.164506e+01 60.152510 64.489754 65.841264 +0.0099 0.0039 0.0000 5.167229e+01 60.171011 64.478111 65.828713 +0.0100 0.0039 0.0000 5.169885e+01 60.190988 64.467733 65.814449 +0.0000 0.0040 2259.2110 0.000000e+00 52.721364 52.720779 56.798064 +0.0001 0.0040 2261.0300 1.817071e+00 78.663919 78.452370 87.891887 +0.0002 0.0040 2266.4620 3.720286e+00 78.689411 78.314048 87.505843 +0.0003 0.0040 2275.4240 5.663635e+00 78.628180 78.136523 87.112235 +0.0004 0.0040 2287.7820 7.622977e+00 78.492237 77.927687 86.701423 +0.0005 0.0040 2303.3530 9.577345e+00 78.290945 77.702951 86.277033 +0.0006 0.0040 2321.9060 1.150640e+01 78.031112 77.467127 85.837213 +0.0007 0.0040 2343.1720 1.339871e+01 77.721815 77.222480 85.390965 +0.0008 0.0040 2366.8440 1.523996e+01 77.364821 76.969854 84.930135 +0.0009 0.0040 2392.5860 1.702257e+01 76.972624 76.714586 84.474448 +0.0010 0.0040 2420.0380 1.874119e+01 76.546252 76.455035 84.013679 +0.0011 0.0040 2448.8270 2.039160e+01 76.095819 76.191816 83.545191 +0.0012 0.0040 2478.5700 2.197046e+01 75.623909 75.928365 83.073601 +0.0013 0.0040 2508.8820 2.347718e+01 75.138309 75.663351 82.610470 +0.0014 0.0040 2539.3860 2.491366e+01 74.639911 75.400082 82.145889 +0.0015 0.0040 2569.7130 2.628015e+01 74.134958 75.136969 81.674815 +0.0016 0.0040 2599.5140 2.757763e+01 73.627849 74.875302 81.210060 +0.0017 0.0040 2628.4630 2.880796e+01 73.120427 74.615144 80.748695 +0.0018 0.0040 2656.2600 2.997419e+01 72.615985 74.356464 80.294856 +0.0019 0.0040 2682.6350 3.107967e+01 72.116692 74.099206 79.844434 +0.0020 0.0040 2707.3500 3.212630e+01 71.623504 73.845392 79.398130 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70.441935 73.711829 +0.0036 0.0040 2802.1090 4.295361e+01 65.365783 70.245635 73.410555 +0.0037 0.0040 2790.1500 4.336843e+01 65.084665 70.053518 73.113383 +0.0038 0.0040 2776.5760 4.376237e+01 64.814974 69.860374 72.821090 +0.0039 0.0040 2761.4920 4.413616e+01 64.557598 69.677498 72.549602 +0.0040 0.0040 2744.9990 4.449114e+01 64.311044 69.497797 72.282534 +0.0041 0.0040 2727.2000 4.482742e+01 64.074594 69.321664 72.017235 +0.0042 0.0040 2708.1900 4.515023e+01 63.848539 69.149021 71.765809 +0.0043 0.0040 2688.0640 4.545487e+01 63.632787 68.980073 71.519096 +0.0044 0.0040 2666.9140 4.574378e+01 63.428948 68.815881 71.286336 +0.0045 0.0040 2644.8260 4.602039e+01 63.232436 68.654456 71.059864 +0.0046 0.0040 2621.8830 4.628285e+01 63.045043 68.498275 70.831603 +0.0047 0.0040 2598.1630 4.653173e+01 62.866879 68.345720 70.621800 +0.0048 0.0040 2573.7410 4.676965e+01 62.696945 68.196143 70.416735 +0.0049 0.0040 2548.6880 4.699651e+01 62.534986 68.049821 70.216869 +0.0050 0.0040 2523.0710 4.721172e+01 62.381521 67.908603 70.024939 +0.0051 0.0040 2496.9520 4.741727e+01 62.235297 67.771100 69.840692 +0.0052 0.0040 2470.3920 4.761320e+01 62.095836 67.636391 69.660837 +0.0053 0.0040 2443.4480 4.780041e+01 61.963745 67.505525 69.486143 +0.0054 0.0040 2416.1730 4.797862e+01 61.838541 67.378445 69.317368 +0.0055 0.0040 2388.6160 4.814799e+01 61.719397 67.255153 69.158423 +0.0056 0.0040 2360.8250 4.831014e+01 61.606615 67.134747 68.998675 +0.0057 0.0040 2332.8420 4.846452e+01 61.499592 67.017914 68.853423 +0.0058 0.0040 2304.7100 4.861319e+01 61.397177 66.904186 68.706586 +0.0059 0.0040 2276.4650 4.875261e+01 61.303514 66.794483 68.572562 +0.0060 0.0040 2248.1450 4.888757e+01 61.212505 66.687293 68.434297 +0.0061 0.0040 2219.7820 4.901613e+01 61.127529 66.583782 68.304206 +0.0062 0.0040 2191.4070 4.913960e+01 61.047187 66.485248 68.165624 +0.0063 0.0040 2163.0490 4.925665e+01 60.972018 66.387811 68.045022 +0.0064 0.0040 2134.7350 4.936904e+01 60.901200 66.293072 67.932847 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65.239565 66.713311 +0.0080 0.0040 1703.2750 5.064737e+01 60.275274 65.190323 66.659273 +0.0081 0.0040 1678.3150 5.070352e+01 60.261416 65.143156 66.606135 +0.0082 0.0040 1653.6400 5.075745e+01 60.249428 65.098481 66.555123 +0.0083 0.0040 1629.2530 5.080960e+01 60.239921 65.055926 66.509099 +0.0084 0.0040 1605.1590 5.085978e+01 60.232824 65.015240 66.462253 +0.0085 0.0040 1581.3600 5.090807e+01 60.228325 64.977877 66.419570 +0.0086 0.0040 1557.8590 5.095427e+01 60.225194 64.941298 66.381604 +0.0087 0.0040 1534.6580 5.099980e+01 60.223548 64.907739 66.337823 +0.0088 0.0040 1511.7590 5.104303e+01 60.223499 64.875695 66.305750 +0.0089 0.0040 1489.1620 5.108496e+01 60.225013 64.844938 66.271134 +0.0090 0.0040 1466.8690 5.112537e+01 60.228847 64.816548 66.238564 +0.0091 0.0040 1444.8800 5.116441e+01 60.234465 64.790356 66.207708 +0.0092 0.0040 0.0000 5.120287e+01 60.240782 64.765138 66.177588 +0.0093 0.0040 0.0000 5.123960e+01 60.249660 64.742489 66.149661 +0.0094 0.0040 0.0000 5.127562e+01 60.258818 64.721157 66.126479 +0.0095 0.0040 0.0000 5.130913e+01 60.271140 64.701621 66.106532 +0.0096 0.0040 0.0000 5.134279e+01 60.283747 64.684967 66.085407 +0.0097 0.0040 0.0000 5.137544e+01 60.297513 64.668867 66.066952 +0.0098 0.0040 0.0000 5.140717e+01 60.312788 64.654159 66.049661 +0.0099 0.0040 0.0000 5.143798e+01 60.329172 64.641156 66.033485 +0.0100 0.0040 0.0000 5.146756e+01 60.346803 64.629235 66.019886 +0.0000 0.0041 2131.3000 9.948294e-14 52.526425 52.525702 56.555010 +0.0001 0.0041 2132.9860 1.784602e+00 78.674683 78.469308 87.938137 +0.0002 0.0041 2138.0200 3.652674e+00 78.693839 78.334545 87.566189 +0.0003 0.0041 2146.3280 5.562586e+00 78.630950 78.158366 87.179524 +0.0004 0.0041 2157.7890 7.485817e+00 78.495657 77.953775 86.778609 +0.0005 0.0041 2172.2380 9.404781e+00 78.297662 77.733742 86.364655 +0.0006 0.0041 2189.4690 1.130009e+01 78.044456 77.503519 85.944356 +0.0007 0.0041 2209.2360 1.315836e+01 77.740221 77.265900 85.506978 +0.0008 0.0041 2231.2650 1.496931e+01 77.393679 77.018313 85.061634 +0.0009 0.0041 2255.2520 1.672239e+01 77.009814 76.768082 84.614244 +0.0010 0.0041 2280.8720 1.841259e+01 76.596453 76.514364 84.169143 +0.0011 0.0041 2307.7870 2.003772e+01 76.156887 76.257282 83.714649 +0.0012 0.0041 2335.6500 2.159398e+01 75.699665 75.999890 83.261955 +0.0013 0.0041 2364.1130 2.307977e+01 75.222983 75.741411 82.803312 +0.0014 0.0041 2392.8290 2.449805e+01 74.738653 75.481852 82.345594 +0.0015 0.0041 2421.4660 2.584770e+01 74.246185 75.225854 81.889617 +0.0016 0.0041 2449.7010 2.712980e+01 73.750788 74.968729 81.435371 +0.0017 0.0041 2477.2350 2.834721e+01 73.254624 74.714799 80.986250 +0.0018 0.0041 2503.7900 2.950230e+01 72.761760 74.460113 80.539419 +0.0019 0.0041 2529.1140 3.059728e+01 72.272544 74.209354 80.098802 +0.0020 0.0041 2552.9850 3.163475e+01 71.790972 73.960151 79.662198 +0.0021 0.0041 2575.2090 3.261939e+01 71.315383 73.713228 79.225744 +0.0022 0.0041 2595.6220 3.355101e+01 70.852391 73.469823 78.809264 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0.000000 9.952048e-01 5.051544 13.707937 6.066338 +0.975 0.400 0.000000 9.973588e-01 5.048852 13.710392 6.031633 +0.980 0.400 0.000000 9.993456e-01 5.045575 13.711982 5.997030 +0.985 0.400 0.000000 1.001453e+00 5.041965 13.713863 5.962998 +0.990 0.400 0.000000 1.003475e+00 5.038903 13.715271 5.929067 +0.995 0.400 0.000000 1.005484e+00 5.034653 13.717385 5.895882 +1.000 0.400 0.000000 1.007591e+00 5.027792 13.719857 5.863396 \ No newline at end of file diff --git a/synthpop/demo/validation_spiseagen.ipynb b/synthpop/demo/validation_spiseagen.ipynb new file mode 100644 index 0000000..cbf8544 --- /dev/null +++ b/synthpop/demo/validation_spiseagen.ipynb @@ -0,0 +1,11808 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b92ac79f-bef3-4ab7-b0c2-908998704711", + "metadata": {}, + "source": [ + "# Validation - Star generation with SpiseaGenerator\n", + "\n", + "Macy Huston" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "832ced52-916a-4791-a3e7-4a8403396f23", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/pydantic/_internal/_config.py:341: UserWarning: Valid config keys have changed in V2:\n", + "* 'keep_untouched' has been renamed to 'ignored_types'\n", + " warnings.warn(message, UserWarning)\n" + ] + } + ], + "source": [ + "import sys\n", + "import os\n", + "\n", + "import numpy as np\n", + "import synthpop\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import pdb" + ] + }, + { + "cell_type": "markdown", + "id": "4452d42f-615b-41ef-b197-e33ff13d9850", + "metadata": {}, + "source": [ + "### Generate a few test catalogs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8d1a3be6-8365-4bad-9a44-df463b62b96f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 1396 - Execution Date: 30-09-2025 16:00:03\n", + "\n", + "\n", + "################################ Settings #################################\n", + " 1396 - # reading default parameters from\n", + " 1396 - default_config_file = /Users/mhuston/code/synthpop/synthpop/config_files/_default.synthpop_conf \n", + " 1397 - # read configuration from \n", + " 1397 - config_file = '/Users/mhuston/code/synthpop/synthpop/config_files/spisea_gen_test.synthpop_conf' \n", + "\n", + "\n", + "# copy the following to a config file to redo this model generation -------\n", + " 1398 - {\n", + " \"l_set\": [\n", + " 0,\n", + " 90,\n", + " 12.17,\n", + " 39.14\n", + " ],\n", + " \"l_set_type\": \"pairs\",\n", + " \"b_set\": [\n", + " 0,\n", + " 0,\n", + " 5.37,\n", + " 8.53\n", + " ],\n", + " \"b_set_type\": \"pairs\",\n", + " \"name_for_output\": \"Huston2025_spisea\",\n", + " \"model_name\": \"Huston2025\",\n", + " \"solid_angle\": [\n", + " 0.001,\n", + " 0.3,\n", + " 0.06,\n", + " 0.6\n", + " ],\n", + " \"solid_angle_unit\": \"deg^2\",\n", + " \"random_seed\": 1520086531,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"window_type\": {\n", + " \"window_type\": \"cone\"\n", + " },\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 50000,\n", + " \"kinematics_at_the_end\": false,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"SpiseaGenerator\",\n", + " \"extinction_map_kwargs\": {\n", + " \"name\": \"maps_from_dustmaps\",\n", + " \"dustmap_name\": \"marshall\"\n", + " },\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"SODC\",\n", + " \"R_V\": 2.5\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"SpiseaCluster\"\n", + " },\n", + " \"maglim\": [\n", + " \"2mass-Ks\",\n", + " 99,\n", + " \"keep\"\n", + " ],\n", + " \"chosen_bands\": [\n", + " \"2mass-J\",\n", + " \"2mass-H\",\n", + " \"2mass-Ks\"\n", + " ],\n", + " \"eff_wavelengths\": {\n", + " \"2mass-J\": 1.232024441939017,\n", + " \"2mass-H\": 1.642309415675878,\n", + " \"2mass-Ks\": 2.155751977206318\n", + " },\n", + " \"obsmag\": true,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"col_names\": [\n", + " \"logL\",\n", + " \"logTeff\",\n", + " \"logg\",\n", + " \"log_radius\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": null,\n", + " \"output_location\": \"outputfiles/default\",\n", + " \"output_filename_pattern\": \"{name_for_output}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"hdf5\",\n", + " \"overwrite\": true\n", + "}\n", + "\n", + "\n", + "########################## initialize population ##########################\n", + " 1398 - read Population files from Huston2025\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 1399 - # Initialize Population 0 (bulge) from \n", + " 1399 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/bulge.popjson'\n", + " 1743 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /Users/mhuston/NotBacked/spisea_data/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 2039 - # Initialize Population 1 (halo) from \n", + " 2039 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/halo.popjson'\n", + " 2041 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 2042 - # Initialize Population 2 (nsd) from \n", + " 2042 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/nsd.popjson'\n", + " 2394 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 2395 - # Initialize Population 3 (thick_disk) from \n", + " 2396 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thick_disk.popjson'\n", + " 2398 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 2398 - # Initialize Population 4 (thin_disk_1) from \n", + " 2398 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_1.popjson'\n", + " 2400 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 2401 - # Initialize Population 5 (thin_disk_2) from \n", + " 2401 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_2.popjson'\n", + " 2403 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 2403 - # Initialize Population 6 (thin_disk_3) from \n", + " 2404 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_3.popjson'\n", + " 2405 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 2406 - # Initialize Population 7 (thin_disk_4) from \n", + " 2406 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_4.popjson'\n", + " 2408 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 2408 - # Initialize Population 8 (thin_disk_5) from \n", + " 2408 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_5.popjson'\n", + " 2410 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 2410 - # Initialize Population 9 (thin_disk_6) from \n", + " 2411 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_6.popjson'\n", + " 2412 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 2413 - # Initialize Population 10 (thin_disk_7) from \n", + " 2413 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/Huston2025/thin_disk_7.popjson'\n", + " 2415 - Setting lost_mass_option to 3 for SpiseaGenerator.\n", + " 2415 - # all population are initialized\n" + ] + } + ], + "source": [ + "# Set up model object \n", + "model = synthpop.SynthPop(\"spisea_gen_test.synthpop_conf\",chosen_bands=['2mass-J','2mass-H','2mass-Ks'],\n", + " solid_angle_unit='deg^2', output_file_type='hdf5',\n", + " l_set=[0,90, 12.17, 39.14], b_set=[0, 0, 5.37, 8.53], \n", + " l_set_type=\"pairs\", b_set_type=\"pairs\",\n", + " #solid_angle=[1e-4,3e-2,6e-3, 6e-2])\n", + " solid_angle=[1e-3,3e-1,6e-2, 6e-1])\n", + "model.init_populations()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cf05812b-e65a-4a70-af68-639404886624", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "############################# update location #############################\n", + " 2419 - # set location to: \n", + " 2420 - l, b = (1.00 deg, 1.00 deg)\n", + " 2420 - # set solid_angle to:\n", + " 2420 - solid_angle = 1.000e-03 deg^2\n", + "\n", + "\n", + "############################# Generate Field ##############################\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 4180 - # From density profile (number density)\n", + " 4181 - expected_total_iMass = 236412.4844\n", + " 4181 - expected_total_eMass = 236412.4844\n", + " 4181 - average_iMass_per_star = 0.5739\n", + " 4181 - mass_loss_correction = 1.0000\n", + " 4182 - n_expected_stars = 411964.6463\n", + " 4182 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.7543483357110188 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.4990758306077128 for 46 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 568 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 4218 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 17560 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 38150 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 52239 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 96139 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 152282 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 52113 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.7543483357110188 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.4990758306077128 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 368 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 2957 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 12128 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 26313 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 36411 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 66895 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 105420 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 36031 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 6937 - # From Generated Field:\n", + " 6937 - generated_stars = 699857\n", + " 6945 - generated_total_iMass = 399346.5532\n", + " 6990 - generated_total_eMass = 236782.5483\n", + " 7002 - det_mass_loss_corr = 0.5929\n", + " 7531 - # Done\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 7878 - # From density profile (number density)\n", + " 7878 - expected_total_iMass = 28693.2977\n", + " 7879 - expected_total_eMass = 286.1756\n", + " 7879 - average_iMass_per_star = 0.5739\n", + " 7879 - mass_loss_correction = 1.0000\n", + " 7879 - n_expected_stars = 50000.0000\n", + " 7880 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-4.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-3.4990758306077128 for 67 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-3.0061603087048185 for 1291 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-2.4990758306077128 for 7354 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-2.0061603087048185 for 12684 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.7543483357110188 for 9828 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.4990758306077128 for 8534 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.249198357391113 for 5767 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.0061603087048185 for 2999 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.7543483357110189 for 1242 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.4990758306077128 for 393 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.24919835739111287 for 95 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 8198 - # From Generated Field:\n", + " 8198 - generated_stars = 870\n", + " 8198 - generated_total_iMass = 517.4998\n", + " 8202 - generated_total_eMass = 277.1649\n", + " 8202 - det_mass_loss_corr = 0.5356\n", + " 8205 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.006160308704818447 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 9154 - # From density profile (number density)\n", + " 9155 - expected_total_iMass = 28693.2977\n", + " 9155 - expected_total_eMass = 2948.0730\n", + " 9155 - average_iMass_per_star = 0.5739\n", + " 9156 - mass_loss_correction = 1.0000\n", + " 9156 - n_expected_stars = 50000.0000\n", + " 9156 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 79 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 523 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 2085 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 4693 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 6313 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 11553 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 18131 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 6398 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 9697 - # From Generated Field:\n", + " 9698 - generated_stars = 8751\n", + " 9698 - generated_total_iMass = 4668.3818\n", + " 9703 - generated_total_eMass = 2874.1771\n", + " 9703 - det_mass_loss_corr = 0.6157\n", + " 9708 - # Done\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 10055 - # From density profile (number density)\n", + " 10055 - expected_total_iMass = 28693.2977\n", + " 10055 - expected_total_eMass = 2634.1217\n", + " 10056 - average_iMass_per_star = 0.5739\n", + " 10056 - mass_loss_correction = 1.0000\n", + " 10056 - n_expected_stars = 50000.0000\n", + " 10057 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-2.0061603087048185 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.7543483357110188 for 135 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.4990758306077128 for 1103 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.249198357391113 for 5057 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.0061603087048185 for 12455 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.7543483357110189 for 16154 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.4990758306077128 for 10735 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.24919835739111287 for 3727 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 11380 - # From Generated Field:\n", + " 11380 - generated_stars = 8189\n", + " 11381 - generated_total_iMass = 4960.1463\n", + " 11385 - generated_total_eMass = 2732.9409\n", + " 11385 - det_mass_loss_corr = 0.5510\n", + " 11389 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.006160308704818447 for 678 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.24565166428898116 for 71 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 11722 - # From density profile (number density)\n", + " 11723 - expected_total_iMass = 28693.2977\n", + " 11723 - expected_total_eMass = 260.6200\n", + " 11723 - average_iMass_per_star = 0.5739\n", + " 11723 - mass_loss_correction = 1.0000\n", + " 11724 - n_expected_stars = 50000.0000\n", + " 11724 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.24919835739111287 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.006160308704818447 for 27 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=0.24565166428898116 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=-0.006160308704818447 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.15 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.15 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.2 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.2 [M/H]=-0.006160308704818447 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.2 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.25 [M/H]=-0.006160308704818447 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.25 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.3 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.3 [M/H]=-0.006160308704818447 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.3 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=-0.006160308704818447 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=-0.006160308704818447 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=-0.006160308704818447 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=-0.006160308704818447 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.006160308704818447 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.006160308704818447 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=-0.006160308704818447 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=-0.006160308704818447 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=-0.006160308704818447 for 30 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.006160308704818447 for 24 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.24919835739111287 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.006160308704818447 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.006160308704818447 for 32 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=0.24565166428898116 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.006160308704818447 for 29 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.24919835739111287 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.006160308704818447 for 38 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.006160308704818447 for 39 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.24919835739111287 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.006160308704818447 for 37 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=0.24565166428898116 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.006160308704818447 for 53 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=0.24565166428898116 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.24919835739111287 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.006160308704818447 for 52 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=0.24565166428898116 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.24919835739111287 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.006160308704818447 for 66 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=0.24565166428898116 for 23 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.24919835739111287 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.006160308704818447 for 62 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=0.24565166428898116 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.24919835739111287 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.006160308704818447 for 84 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=0.24565166428898116 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.24919835739111287 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.006160308704818447 for 91 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=0.24565166428898116 for 25 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.24919835739111287 for 21 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.006160308704818447 for 91 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=0.24565166428898116 for 27 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.24919835739111287 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.006160308704818447 for 128 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=0.24565166428898116 for 25 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.24919835739111287 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.006160308704818447 for 114 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=0.24565166428898116 for 27 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.24919835739111287 for 24 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.006160308704818447 for 143 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=0.24565166428898116 for 28 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.24919835739111287 for 31 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.006160308704818447 for 130 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=0.24565166428898116 for 36 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.24919835739111287 for 31 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.006160308704818447 for 141 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=0.24565166428898116 for 43 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.24919835739111287 for 36 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.006160308704818447 for 197 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=0.24565166428898116 for 66 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.24919835739111287 for 39 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.006160308704818447 for 200 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=0.24565166428898116 for 48 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.24919835739111287 for 47 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.006160308704818447 for 247 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=0.24565166428898116 for 51 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.24919835739111287 for 41 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.006160308704818447 for 269 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=0.24565166428898116 for 63 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.24919835739111287 for 62 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.006160308704818447 for 326 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=0.24565166428898116 for 88 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.24919835739111287 for 62 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.006160308704818447 for 353 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=0.24565166428898116 for 81 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.24919835739111287 for 58 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.006160308704818447 for 403 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=0.24565166428898116 for 82 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.24919835739111287 for 85 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.006160308704818447 for 459 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=0.24565166428898116 for 111 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.24919835739111287 for 72 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.006160308704818447 for 462 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=0.24565166428898116 for 130 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.24919835739111287 for 90 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.006160308704818447 for 564 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=0.24565166428898116 for 122 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.24919835739111287 for 115 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.006160308704818447 for 586 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=0.24565166428898116 for 136 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.24919835739111287 for 131 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.006160308704818447 for 703 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=0.24565166428898116 for 165 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.24919835739111287 for 136 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.006160308704818447 for 708 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=0.24565166428898116 for 206 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.24919835739111287 for 153 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.006160308704818447 for 863 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=0.24565166428898116 for 203 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.24919835739111287 for 195 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.006160308704818447 for 965 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.24565166428898116 for 224 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.24919835739111287 for 179 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.006160308704818447 for 1094 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=0.24565166428898116 for 262 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.24919835739111287 for 205 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.006160308704818447 for 1214 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=0.24565166428898116 for 314 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.24919835739111287 for 235 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.006160308704818447 for 1334 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=0.24565166428898116 for 308 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.24919835739111287 for 263 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.006160308704818447 for 1578 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=0.24565166428898116 for 351 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.24919835739111287 for 315 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.006160308704818447 for 1668 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.24565166428898116 for 379 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.4990758306077128 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.24919835739111287 for 353 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.006160308704818447 for 1836 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=0.24565166428898116 for 463 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.4990758306077128 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.24919835739111287 for 362 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.006160308704818447 for 2076 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.24565166428898116 for 491 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.24919835739111287 for 447 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.006160308704818447 for 2430 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.24565166428898116 for 584 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.24919835739111287 for 474 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.006160308704818447 for 2673 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=0.24565166428898116 for 633 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=0.5009241693922871 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.24919835739111287 for 579 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.006160308704818447 for 3027 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.24565166428898116 for 722 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.24919835739111287 for 618 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.006160308704818447 for 3323 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.24565166428898116 for 800 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.24919835739111287 for 719 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.006160308704818447 for 3709 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.24565166428898116 for 907 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 79 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 20 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 14936 - # From Generated Field:\n", + " 14937 - generated_stars = 560\n", + " 14937 - generated_total_iMass = 286.1731\n", + " 14941 - generated_total_eMass = 239.4102\n", + " 14941 - det_mass_loss_corr = 0.8366\n", + " 14945 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 15264 - # From density profile (number density)\n", + " 15264 - expected_total_iMass = 28693.2977\n", + " 15264 - expected_total_eMass = 1119.4327\n", + " 15265 - average_iMass_per_star = 0.5739\n", + " 15265 - mass_loss_correction = 1.0000\n", + " 15265 - n_expected_stars = 50000.0000\n", + " 15265 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 106 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 756 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 248 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.24919835739111287 for 117 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.006160308704818447 for 810 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.24565166428898116 for 252 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.24919835739111287 for 132 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.006160308704818447 for 913 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=0.24565166428898116 for 294 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.24919835739111287 for 154 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.006160308704818447 for 1017 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.24565166428898116 for 300 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.24919835739111287 for 172 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.006160308704818447 for 1140 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=0.24565166428898116 for 373 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.24919835739111287 for 196 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.006160308704818447 for 1360 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.24565166428898116 for 381 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.24919835739111287 for 204 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.006160308704818447 for 1535 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.24565166428898116 for 459 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.24919835739111287 for 256 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.006160308704818447 for 1622 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.24565166428898116 for 519 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.24919835739111287 for 264 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.006160308704818447 for 1897 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=0.24565166428898116 for 582 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.24919835739111287 for 288 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.006160308704818447 for 2020 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.24565166428898116 for 669 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.24919835739111287 for 322 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.006160308704818447 for 2422 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.24565166428898116 for 752 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.24919835739111287 for 378 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.006160308704818447 for 2597 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.24565166428898116 for 833 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.5009241693922871 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.24919835739111287 for 398 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.006160308704818447 for 2928 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.24565166428898116 for 905 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.5009241693922871 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.24919835739111287 for 422 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.006160308704818447 for 3302 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.24565166428898116 for 996 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.5009241693922871 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.24919835739111287 for 577 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.006160308704818447 for 3782 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.24565166428898116 for 1160 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.5009241693922871 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.24919835739111287 for 622 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.006160308704818447 for 4159 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.24565166428898116 for 1404 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.5009241693922871 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 329 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 2199 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 681 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 17212 - # From Generated Field:\n", + " 17212 - generated_stars = 2718\n", + " 17212 - generated_total_iMass = 1731.3056\n", + " 17216 - generated_total_eMass = 1136.4172\n", + " 17216 - det_mass_loss_corr = 0.6564\n", + " 17220 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 17569 - # From density profile (number density)\n", + " 17570 - expected_total_iMass = 28693.2977\n", + " 17570 - expected_total_eMass = 2399.0354\n", + " 17570 - average_iMass_per_star = 0.5739\n", + " 17570 - mass_loss_correction = 1.0000\n", + " 17571 - n_expected_stars = 50000.0000\n", + " 17571 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 154 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 2334 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 503 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.24919835739111287 for 388 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.006160308704818447 for 4943 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.24565166428898116 for 1110 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.24919835739111287 for 432 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.006160308704818447 for 5647 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.24565166428898116 for 1208 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.24919835739111287 for 454 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.006160308704818447 for 6373 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=0.24565166428898116 for 1394 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.24919835739111287 for 521 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.006160308704818447 for 7037 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.24565166428898116 for 1517 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.24919835739111287 for 643 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.006160308704818447 for 7769 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.24565166428898116 for 1755 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 363 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 4388 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 1013 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 19060 - # From Generated Field:\n", + " 19060 - generated_stars = 6381\n", + " 19060 - generated_total_iMass = 3292.9774\n", + " 19064 - generated_total_eMass = 2369.7213\n", + " 19064 - det_mass_loss_corr = 0.7196\n", + " 19068 - # Done\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 19438 - # From density profile (number density)\n", + " 19438 - expected_total_iMass = 28693.2977\n", + " 19439 - expected_total_eMass = 3050.9374\n", + " 19439 - average_iMass_per_star = 0.5739\n", + " 19439 - mass_loss_correction = 1.0000\n", + " 19439 - n_expected_stars = 50000.0000\n", + " 19440 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 598 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 4146 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 882 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.24919835739111287 for 1444 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.006160308704818447 for 9584 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.24565166428898116 for 1988 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.24919835739111287 for 1609 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.006160308704818447 for 10714 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.24565166428898116 for 2138 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 20876 - # From Generated Field:\n", + " 20877 - generated_stars = 8365\n", + " 20877 - generated_total_iMass = 4849.7974\n", + " 20881 - generated_total_eMass = 3072.4030\n", + " 20882 - det_mass_loss_corr = 0.6335\n", + " 20886 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.24919835739111287 for 1769 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.006160308704818447 for 12186 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.24565166428898116 for 2399 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 68 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 514 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 117 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 21201 - # From density profile (number density)\n", + " 21201 - expected_total_iMass = 28693.2977\n", + " 21201 - expected_total_eMass = 7564.6554\n", + " 21201 - average_iMass_per_star = 0.5739\n", + " 21202 - mass_loss_correction = 1.0000\n", + " 21202 - n_expected_stars = 50000.0000\n", + " 21202 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.7543483357110189 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.4990758306077128 for 402 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 2993 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 4306 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 1150 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.5009241693922871 for 50 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.7543483357110189 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.4990758306077128 for 440 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.24919835739111287 for 3411 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.006160308704818447 for 4992 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.24565166428898116 for 1377 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.5009241693922871 for 64 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.7543483357110189 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.4990758306077128 for 505 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.24919835739111287 for 3737 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.006160308704818447 for 5500 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.24565166428898116 for 1523 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.5009241693922871 for 57 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.7543483357110189 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.4990758306077128 for 540 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.24919835739111287 for 4277 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.006160308704818447 for 6143 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.24565166428898116 for 1685 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.5009241693922871 for 65 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 290 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 2179 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 3287 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 885 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 22700 - # From Generated Field:\n", + " 22700 - generated_stars = 21205\n", + " 22701 - generated_total_iMass = 12037.3915\n", + " 22705 - generated_total_eMass = 7621.6505\n", + " 22705 - det_mass_loss_corr = 0.6332\n", + " 22712 - # Done\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 23032 - # From density profile (number density)\n", + " 23032 - expected_total_iMass = 28693.2977\n", + " 23033 - expected_total_eMass = 9395.1498\n", + " 23033 - average_iMass_per_star = 0.5739\n", + " 23033 - mass_loss_correction = 1.0000\n", + " 23033 - n_expected_stars = 50000.0000\n", + " 23034 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 24 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 619 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 3452 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 3159 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 457 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.7543483357110189 for 35 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.4990758306077128 for 1322 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.24919835739111287 for 7261 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.006160308704818447 for 6603 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.24565166428898116 for 952 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.5009241693922871 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.7543483357110189 for 29 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.4990758306077128 for 1440 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.24919835739111287 for 8169 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.006160308704818447 for 7346 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.24565166428898116 for 1058 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.5009241693922871 for 27 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 667 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 3632 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 3191 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 462 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 24586 - # From Generated Field:\n", + " 24586 - generated_stars = 27431\n", + " 24587 - generated_total_iMass = 15268.0307\n", + " 24592 - generated_total_eMass = 9370.1156\n", + " 24592 - det_mass_loss_corr = 0.6137\n", + " 24601 - # Done\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 24939 - # From density profile (number density)\n", + " 24939 - expected_total_iMass = 28693.2977\n", + " 24940 - expected_total_eMass = 19452.2050\n", + " 24940 - average_iMass_per_star = 0.5739\n", + " 24940 - mass_loss_correction = 1.0000\n", + " 24940 - n_expected_stars = 50000.0000\n", + " 24941 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.0061603087048185 for 46 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 770 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 3195 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 3315 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 859 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 52 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.249198357391113 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.0061603087048185 for 91 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.7543483357110189 for 1444 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.4990758306077128 for 6016 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.24919835739111287 for 6115 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.006160308704818447 for 1498 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.24565166428898116 for 86 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.0061603087048185 for 102 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.7543483357110189 for 1633 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.4990758306077128 for 6519 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.24919835739111287 for 6869 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.006160308704818447 for 1800 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.24565166428898116 for 102 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 51 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 883 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 3622 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 3791 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 942 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 59 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.0061603087048185 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 121 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 538 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 528 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 143 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.249198357391113 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.0061603087048185 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.7543483357110189 for 214 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.4990758306077128 for 935 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.24919835739111287 for 951 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.006160308704818447 for 237 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.24565166428898116 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.249198357391113 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.0061603087048185 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.7543483357110189 for 256 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.4990758306077128 for 1089 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.24919835739111287 for 1133 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.006160308704818447 for 288 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 26924 - # From Generated Field:\n", + " 26924 - generated_stars = 57647\n", + " 26925 - generated_total_iMass = 32865.5303\n", + " 26932 - generated_total_eMass = 19450.2309\n", + " 26933 - det_mass_loss_corr = 0.5918\n", + " 26948 - # Done\n", + "\n", + "\n", + "########################### Combine Populations ###########################\n", + " 26974 - Number of stars generated: 841974 (28 columns)\n", + " 26975 - included_columns = ['pop', 'iMass', 'age', 'Fe/H_initial', 'Mass', 'In_Final_Phase', 'Dist', 'l', 'b', 'vr_bc', 'mul', 'mub', 'x', 'y', 'z', 'U', 'V', 'W', 'VR_LSR', 'A_Ks', 'logL', 'logTeff', 'logg', 'log_radius', 'phase', '2mass-J', '2mass-H', '2mass-Ks']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.24565166428898116 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 135 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 574 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 581 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 145 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Save result -------------------------------------------------------------\n", + " 27624 - write result to \"outputfiles/default/Huston2025_spisea_l1.000_b1.000.h5\"\n", + " 27799 - ---------------------------------------------------------------\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "( pop iMass age Fe/H_initial Mass In_Final_Phase \\\n", + " 0 0.0 0.087256 10.0 -1.75 0.087256 0.0 \n", + " 1 0.0 0.164623 10.0 -1.50 0.164620 0.0 \n", + " 2 0.0 0.175564 10.0 -1.50 0.175561 0.0 \n", + " 3 0.0 0.253208 10.0 -1.50 0.253203 0.0 \n", + " 4 0.0 0.162633 10.0 -1.50 0.162630 0.0 \n", + " ... ... ... ... ... ... ... \n", + " 841969 10.0 0.106719 10.0 0.25 0.106719 0.0 \n", + " 841970 10.0 0.120508 10.0 0.25 0.120508 0.0 \n", + " 841971 10.0 0.143050 10.0 0.25 0.143050 0.0 \n", + " 841972 10.0 0.227572 10.0 0.25 0.227572 0.0 \n", + " 841973 10.0 1.274976 10.0 0.25 0.532972 1.0 \n", + " \n", + " Dist l b vr_bc ... VR_LSR A_Ks \\\n", + " 0 4.101626 0.994749 0.991626 20.673140 ... 30.347662 1.008129 \n", + " 1 4.443596 0.983578 0.998467 89.796099 ... 99.468451 1.040572 \n", + " 2 4.516957 1.004790 1.008739 115.730629 ... 125.411489 0.698197 \n", + " 3 4.578856 0.986780 0.999921 132.583515 ... 142.257132 1.053404 \n", + " 4 4.590119 1.005536 1.015721 68.760867 ... 78.443279 0.704340 \n", + " ... ... ... ... ... ... ... ... \n", + " 841969 24.567799 1.009179 0.989502 8.916735 ... 18.595314 1.475000 \n", + " 841970 24.515773 0.998180 0.986586 0.471410 ... 10.146042 1.465000 \n", + " 841971 24.515039 1.007495 0.998240 -41.148230 ... -31.468518 1.475000 \n", + " 841972 24.569481 1.000376 0.994683 -11.494525 ... -1.817687 1.475000 \n", + " 841973 24.540170 0.991357 1.002339 -33.766303 ... -24.090815 0.925000 \n", + " \n", + " logL logTeff logg log_radius phase 2mass-J 2mass-H \\\n", + " 0 NaN NaN NaN NaN 98.0 NaN NaN \n", + " 1 -2.229652 3.582798 5.164759 -0.756750 0.0 25.620295 23.272632 \n", + " 2 -2.171630 3.585718 5.145455 -0.733579 0.0 24.313721 22.540926 \n", + " 3 -1.853040 3.596846 5.034229 -0.596541 0.0 24.826884 22.458110 \n", + " 4 -2.240377 3.582264 5.168240 -0.761044 0.0 24.533154 22.749533 \n", + " ... ... ... ... ... ... ... ... \n", + " 841969 NaN NaN NaN NaN 98.0 NaN NaN \n", + " 841970 NaN NaN NaN NaN 98.0 NaN NaN \n", + " 841971 NaN NaN NaN NaN 98.0 NaN NaN \n", + " 841972 NaN NaN NaN NaN 98.0 NaN NaN \n", + " 841973 NaN NaN NaN NaN 101.0 NaN NaN \n", + " \n", + " 2mass-Ks \n", + " 0 NaN \n", + " 1 22.214299 \n", + " 2 21.770624 \n", + " 3 21.387901 \n", + " 4 21.974940 \n", + " ... ... \n", + " 841969 NaN \n", + " 841970 NaN \n", + " 841971 NaN \n", + " 841972 NaN \n", + " 841973 NaN \n", + " \n", + " [841974 rows x 28 columns],\n", + " {'bulge': {'distance_distribution': array([[5.00000000e-02, 2.16964157e-08],\n", + " [1.50000000e-01, 1.83973916e-07],\n", + " [2.50000000e-01, 6.18837919e-07],\n", + " [3.50000000e-01, 1.49894127e-06],\n", + " [4.50000000e-01, 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1.77568193e+01],\n", + " [2.43500000e+01, 1.71369419e+01],\n", + " [2.44500000e+01, 1.65410187e+01],\n", + " [2.45500000e+01, 1.59696104e+01],\n", + " [2.46500000e+01, 1.54067019e+01],\n", + " [2.47500000e+01, 1.48685669e+01],\n", + " [2.48500000e+01, 1.43516630e+01],\n", + " [2.49500000e+01, 1.38463531e+01]]),\n", + " 'distance_distribution_comment': 'pairs of distances in [kpc] and number of stars in the slice'}})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.process_location(1,1,0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e8bc365a-e12e-48df-bec2-456acafc4793", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "############################# update location #############################\n", + " 115506 - # set location to: \n", + " 115507 - l, b = (90.00 deg, 0.00 deg)\n", + " 115508 - # set solid_angle to:\n", + " 115508 - solid_angle = 3.000e-01 deg^2\n", + "\n", + "\n", + "############################# Generate Field ##############################\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 117119 - # From density profile (number density)\n", + " 117120 - expected_total_iMass = 28693.2977\n", + " 117120 - expected_total_eMass = 21.1696\n", + " 117120 - average_iMass_per_star = 0.5739\n", + " 117120 - mass_loss_correction = 1.0000\n", + " 117121 - n_expected_stars = 50000.0000\n", + " 117121 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.7543483357110188 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.4990758306077128 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 76 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 498 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 2168 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 4561 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 6248 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 11694 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 18538 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 6303 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 117459 - # From Generated Field:\n", + " 117460 - generated_stars = 57\n", + " 117460 - generated_total_iMass = 19.5394\n", + " 117464 - generated_total_eMass = 16.2988\n", + " 117464 - det_mass_loss_corr = 0.8342\n", + " 117467 - # Done\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 117862 - # From density profile (number density)\n", + " 117862 - expected_total_iMass = 28693.2977\n", + " 117862 - expected_total_eMass = 603.5701\n", + " 117863 - average_iMass_per_star = 0.5739\n", + " 117863 - mass_loss_correction = 1.0000\n", + " 117863 - n_expected_stars = 50000.0000\n", + " 117863 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-4.0061603087048185 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-3.4990758306077128 for 76 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-3.0061603087048185 for 1209 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-2.4990758306077128 for 7398 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-2.0061603087048185 for 12506 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.7543483357110188 for 9810 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.4990758306077128 for 8410 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 118292 - # From Generated Field:\n", + " 118292 - generated_stars = 1858\n", + " 118293 - generated_total_iMass = 1031.5564\n", + " 118296 - generated_total_eMass = 593.3868\n", + " 118296 - det_mass_loss_corr = 0.5752\n", + " 118299 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.249198357391113 for 5664 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.0061603087048185 for 3026 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.7543483357110189 for 1249 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.4990758306077128 for 419 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.24919835739111287 for 111 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.006160308704818447 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.7543483357110188 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 119208 - # From density profile (number density)\n", + " 119208 - expected_total_iMass = 0.0000\n", + " 119209 - expected_total_eMass = 0.0000\n", + " 119209 - average_iMass_per_star = 0.5739\n", + " 119209 - mass_loss_correction = 1.0000\n", + " 119209 - n_expected_stars = 0.0000\n", + " 119210 - # Determine velocities when position are generated \n", + " 119211 - # From Generated Field:\n", + " 119211 - generated_stars = 0\n", + " 119211 - # Done\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 119541 - # From density profile (number density)\n", + " 119542 - expected_total_iMass = 28693.2977\n", + " 119542 - expected_total_eMass = 22660.1600\n", + " 119542 - average_iMass_per_star = 0.5739\n", + " 119542 - mass_loss_correction = 1.0000\n", + " 119543 - n_expected_stars = 50000.0000\n", + " 119543 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-2.0061603087048185 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.7543483357110188 for 122 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.4990758306077128 for 1118 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.249198357391113 for 5033 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.0061603087048185 for 12571 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.7543483357110189 for 16075 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.4990758306077128 for 10856 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.24919835739111287 for 3611 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.006160308704818447 for 647 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.24565166428898116 for 49 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 121537 - # From Generated Field:\n", + " 121537 - generated_stars = 68763\n", + " 121538 - generated_total_iMass = 39558.4651\n", + " 121545 - generated_total_eMass = 22602.2266\n", + " 121546 - det_mass_loss_corr = 0.5714\n", + " 121565 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-2.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.7543483357110188 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.4990758306077128 for 400 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.249198357391113 for 1838 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.0061603087048185 for 4756 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.7543483357110189 for 5889 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.4990758306077128 for 4106 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.24919835739111287 for 1419 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.006160308704818447 for 243 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.24565166428898116 for 24 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 121925 - # From density profile (number density)\n", + " 121925 - expected_total_iMass = 31265.6991\n", + " 121925 - expected_total_eMass = 31265.6991\n", + " 121925 - average_iMass_per_star = 0.5739\n", + " 121926 - mass_loss_correction = 1.0000\n", + " 121926 - n_expected_stars = 54482.5825\n", + " 121926 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.24919835739111287 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.006160308704818447 for 29 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=-0.006160308704818447 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=-0.006160308704818447 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.15 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.2 [M/H]=-0.006160308704818447 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.25 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.25 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.3 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=-0.006160308704818447 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=-0.006160308704818447 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.006160308704818447 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=-0.006160308704818447 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=-0.006160308704818447 for 23 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.006160308704818447 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=-0.24919835739111287 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=-0.006160308704818447 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=-0.006160308704818447 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=-0.24919835739111287 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=-0.006160308704818447 for 17 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.006160308704818447 for 21 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.006160308704818447 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.24919835739111287 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.006160308704818447 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.006160308704818447 for 38 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=0.24565166428898116 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.006160308704818447 for 37 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.24919835739111287 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.006160308704818447 for 31 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=0.24565166428898116 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.006160308704818447 for 50 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=0.24565166428898116 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.24919835739111287 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.006160308704818447 for 60 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=0.24565166428898116 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.24919835739111287 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.006160308704818447 for 59 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=0.24565166428898116 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.24919835739111287 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.006160308704818447 for 72 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=0.24565166428898116 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.24919835739111287 for 17 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.006160308704818447 for 61 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=0.24565166428898116 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.24919835739111287 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.006160308704818447 for 83 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=0.24565166428898116 for 21 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.24919835739111287 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.006160308704818447 for 85 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=0.24565166428898116 for 23 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.24919835739111287 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.006160308704818447 for 99 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=0.24565166428898116 for 24 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.24919835739111287 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.006160308704818447 for 126 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=0.24565166428898116 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.24919835739111287 for 25 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.006160308704818447 for 134 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=0.24565166428898116 for 36 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.24919835739111287 for 28 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.006160308704818447 for 158 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=0.24565166428898116 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.24919835739111287 for 31 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.006160308704818447 for 144 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=0.24565166428898116 for 38 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.24919835739111287 for 32 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.006160308704818447 for 203 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=0.24565166428898116 for 47 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.24919835739111287 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.006160308704818447 for 199 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=0.24565166428898116 for 62 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.24919835739111287 for 48 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.006160308704818447 for 212 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=0.24565166428898116 for 68 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.24919835739111287 for 45 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.006160308704818447 for 226 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=0.24565166428898116 for 74 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.24919835739111287 for 58 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.006160308704818447 for 297 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=0.24565166428898116 for 73 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.24919835739111287 for 66 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.006160308704818447 for 346 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=0.24565166428898116 for 74 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.24919835739111287 for 77 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.006160308704818447 for 370 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=0.24565166428898116 for 92 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.24919835739111287 for 67 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.006160308704818447 for 420 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=0.24565166428898116 for 107 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.24919835739111287 for 97 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.006160308704818447 for 473 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=0.24565166428898116 for 108 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.24919835739111287 for 84 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.006160308704818447 for 534 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=0.24565166428898116 for 130 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.24919835739111287 for 93 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.006160308704818447 for 620 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=0.24565166428898116 for 144 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.24919835739111287 for 139 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.006160308704818447 for 658 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=0.24565166428898116 for 164 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.24919835739111287 for 159 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.006160308704818447 for 710 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=0.24565166428898116 for 184 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.24919835739111287 for 151 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.006160308704818447 for 790 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=0.24565166428898116 for 218 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.24919835739111287 for 171 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.006160308704818447 for 899 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=0.24565166428898116 for 197 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.24919835739111287 for 190 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.006160308704818447 for 1040 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.24565166428898116 for 235 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.24919835739111287 for 205 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.006160308704818447 for 1172 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=0.24565166428898116 for 285 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.24919835739111287 for 245 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.006160308704818447 for 1237 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=0.24565166428898116 for 326 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.24919835739111287 for 256 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.006160308704818447 for 1490 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=0.24565166428898116 for 345 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.24919835739111287 for 313 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.006160308704818447 for 1682 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=0.24565166428898116 for 401 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.24919835739111287 for 324 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.006160308704818447 for 1827 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.24565166428898116 for 444 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.24919835739111287 for 376 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.006160308704818447 for 2113 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=0.24565166428898116 for 509 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.24919835739111287 for 456 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.006160308704818447 for 2367 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.24565166428898116 for 571 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.24919835739111287 for 470 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.006160308704818447 for 2657 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.24565166428898116 for 603 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.24919835739111287 for 534 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.006160308704818447 for 2959 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=0.24565166428898116 for 698 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.4990758306077128 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.24919835739111287 for 619 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.006160308704818447 for 3298 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.24565166428898116 for 804 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.24919835739111287 for 719 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.006160308704818447 for 3698 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.24565166428898116 for 890 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.4990758306077128 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.24919835739111287 for 768 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.006160308704818447 for 4176 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.24565166428898116 for 1012 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 79 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 28 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.006160308704818447 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.2 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.25 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.3 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=-0.006160308704818447 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.006160308704818447 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.006160308704818447 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.006160308704818447 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.006160308704818447 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.006160308704818447 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.006160308704818447 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.006160308704818447 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.24919835739111287 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.006160308704818447 for 21 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.006160308704818447 for 21 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.006160308704818447 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=0.24565166428898116 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.006160308704818447 for 29 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.006160308704818447 for 29 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=0.24565166428898116 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.006160308704818447 for 37 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.006160308704818447 for 39 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.006160308704818447 for 49 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=0.24565166428898116 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.24919835739111287 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.006160308704818447 for 32 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.24919835739111287 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.006160308704818447 for 46 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=0.24565166428898116 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.24919835739111287 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.006160308704818447 for 57 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=0.24565166428898116 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.24919835739111287 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.006160308704818447 for 52 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=0.24565166428898116 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.24919835739111287 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.006160308704818447 for 59 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=0.24565166428898116 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.24919835739111287 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.006160308704818447 for 71 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=0.24565166428898116 for 24 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.24919835739111287 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.006160308704818447 for 77 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=0.24565166428898116 for 22 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.24919835739111287 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.006160308704818447 for 108 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=0.24565166428898116 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.24919835739111287 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.006160308704818447 for 118 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=0.24565166428898116 for 34 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.24919835739111287 for 24 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.006160308704818447 for 113 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=0.24565166428898116 for 29 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.24919835739111287 for 27 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.006160308704818447 for 114 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=0.24565166428898116 for 32 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.24919835739111287 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.006160308704818447 for 127 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=0.24565166428898116 for 38 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.24919835739111287 for 34 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.006160308704818447 for 141 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=0.24565166428898116 for 44 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.24919835739111287 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.006160308704818447 for 212 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=0.24565166428898116 for 46 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.24919835739111287 for 36 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.006160308704818447 for 236 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=0.24565166428898116 for 49 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.24919835739111287 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.006160308704818447 for 245 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.24565166428898116 for 62 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.24919835739111287 for 42 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.006160308704818447 for 252 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=0.24565166428898116 for 56 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.24919835739111287 for 49 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.006160308704818447 for 310 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=0.24565166428898116 for 73 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.24919835739111287 for 68 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.006160308704818447 for 342 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=0.24565166428898116 for 87 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.24919835739111287 for 72 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.006160308704818447 for 364 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=0.24565166428898116 for 107 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.24919835739111287 for 62 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.006160308704818447 for 426 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.24565166428898116 for 83 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.24919835739111287 for 87 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.006160308704818447 for 548 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=0.24565166428898116 for 124 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.24919835739111287 for 115 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.006160308704818447 for 515 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.24565166428898116 for 134 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.24919835739111287 for 97 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.006160308704818447 for 556 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.24565166428898116 for 150 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.24919835739111287 for 122 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.006160308704818447 for 688 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=0.24565166428898116 for 153 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.24919835739111287 for 138 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.006160308704818447 for 782 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.24565166428898116 for 182 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.24919835739111287 for 155 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.006160308704818447 for 805 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.24565166428898116 for 191 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.24919835739111287 for 173 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.006160308704818447 for 948 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.24565166428898116 for 230 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 18 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 126971 - # From Generated Field:\n", + " 126971 - generated_stars = 67094\n", + " 126972 - generated_total_iMass = 37181.8812\n", + " 126979 - generated_total_eMass = 31084.7126\n", + " 126980 - det_mass_loss_corr = 0.8360\n", + " 126996 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 127349 - # From density profile (number density)\n", + " 127349 - expected_total_iMass = 36487.3919\n", + " 127349 - expected_total_eMass = 36487.3919\n", + " 127349 - average_iMass_per_star = 0.5739\n", + " 127350 - mass_loss_correction = 1.0000\n", + " 127350 - n_expected_stars = 63581.7331\n", + " 127350 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 139 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 887 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 288 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.24919835739111287 for 172 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.006160308704818447 for 1130 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.24565166428898116 for 295 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.24919835739111287 for 181 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.006160308704818447 for 1181 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=0.24565166428898116 for 376 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.24919835739111287 for 187 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.006160308704818447 for 1280 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.24565166428898116 for 416 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.24919835739111287 for 212 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.006160308704818447 for 1455 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=0.24565166428898116 for 444 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.24919835739111287 for 246 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.006160308704818447 for 1718 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.24565166428898116 for 493 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.24919835739111287 for 249 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.006160308704818447 for 1874 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.24565166428898116 for 548 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.24919835739111287 for 315 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.006160308704818447 for 2119 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.24565166428898116 for 638 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.24919835739111287 for 339 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.006160308704818447 for 2400 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=0.24565166428898116 for 740 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=0.5009241693922871 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.24919835739111287 for 381 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.006160308704818447 for 2651 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.24565166428898116 for 793 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.5009241693922871 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.24919835739111287 for 468 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.006160308704818447 for 2984 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.24565166428898116 for 878 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.24919835739111287 for 491 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.006160308704818447 for 3290 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.24565166428898116 for 1020 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.5009241693922871 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.24919835739111287 for 546 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.006160308704818447 for 3784 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.24565166428898116 for 1125 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.5009241693922871 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.24919835739111287 for 610 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.006160308704818447 for 4253 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.24565166428898116 for 1384 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.24919835739111287 for 665 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.006160308704818447 for 4759 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.24565166428898116 for 1443 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.5009241693922871 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.24919835739111287 for 798 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.006160308704818447 for 5277 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.24565166428898116 for 1608 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.5009241693922871 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.4990758306077128 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 391 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 2950 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 853 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.5009241693922871 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 43 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 358 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 92 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.24919835739111287 for 51 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.006160308704818447 for 418 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.24565166428898116 for 131 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.24919835739111287 for 59 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.006160308704818447 for 511 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=0.24565166428898116 for 152 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.24919835739111287 for 92 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.006160308704818447 for 530 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.24565166428898116 for 136 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.24919835739111287 for 98 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.006160308704818447 for 549 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=0.24565166428898116 for 181 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.24919835739111287 for 73 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.006160308704818447 for 691 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.24565166428898116 for 207 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.24919835739111287 for 102 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.006160308704818447 for 764 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.24565166428898116 for 229 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.24919835739111287 for 133 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.006160308704818447 for 792 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.24565166428898116 for 273 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.24919835739111287 for 134 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.006160308704818447 for 925 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=0.24565166428898116 for 281 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.24919835739111287 for 145 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.006160308704818447 for 1045 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.24565166428898116 for 340 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.24919835739111287 for 163 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.006160308704818447 for 1216 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.24565166428898116 for 366 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.24919835739111287 for 164 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.006160308704818447 for 1353 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.24565166428898116 for 418 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.24919835739111287 for 240 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.006160308704818447 for 1516 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.24565166428898116 for 458 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.24919835739111287 for 237 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.006160308704818447 for 1673 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.24565166428898116 for 542 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.5009241693922871 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.24919835739111287 for 237 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.006160308704818447 for 1808 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.24565166428898116 for 562 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.24919835739111287 for 274 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.006160308704818447 for 2159 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.24565166428898116 for 666 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 160 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 1086 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 131011 - # From Generated Field:\n", + " 131012 - generated_stars = 89061\n", + " 131013 - generated_total_iMass = 49540.9213\n", + " 131021 - generated_total_eMass = 36324.7222\n", + " 131022 - det_mass_loss_corr = 0.7332\n", + " 131051 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 353 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 131492 - # From density profile (number density)\n", + " 131492 - expected_total_iMass = 35390.9953\n", + " 131492 - expected_total_eMass = 35390.9953\n", + " 131492 - average_iMass_per_star = 0.5739\n", + " 131493 - mass_loss_correction = 1.0000\n", + " 131493 - n_expected_stars = 61671.1884\n", + " 131493 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 215 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 2794 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 627 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.24919835739111287 for 527 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.006160308704818447 for 6163 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.24565166428898116 for 1346 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.24919835739111287 for 531 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.006160308704818447 for 6763 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.24565166428898116 for 1548 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.24919835739111287 for 589 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.006160308704818447 for 7788 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=0.24565166428898116 for 1829 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.24919835739111287 for 703 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.006160308704818447 for 8586 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.24565166428898116 for 1952 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.24919835739111287 for 763 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.006160308704818447 for 9733 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.24565166428898116 for 2142 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 463 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 5486 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 1203 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 108 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 1451 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 354 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.24919835739111287 for 240 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.006160308704818447 for 3088 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.24565166428898116 for 680 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.24919835739111287 for 263 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.006160308704818447 for 3496 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.24565166428898116 for 782 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.24919835739111287 for 294 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.006160308704818447 for 3973 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=0.24565166428898116 for 870 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 134578 - # From Generated Field:\n", + " 134578 - generated_stars = 93237\n", + " 134579 - generated_total_iMass = 54149.4028\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.24919835739111287 for 371 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.006160308704818447 for 4433 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.24565166428898116 for 1022 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.24919835739111287 for 387 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.006160308704818447 for 4844 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.24565166428898116 for 1171 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 205 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 2846 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 628 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 134587 - generated_total_eMass = 35638.9889\n", + " 134588 - det_mass_loss_corr = 0.6582\n", + " 134616 - # Done\n", + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 134970 - # From density profile (number density)\n", + " 134970 - expected_total_iMass = 33414.6535\n", + " 134970 - expected_total_eMass = 33414.6535\n", + " 134970 - average_iMass_per_star = 0.5739\n", + " 134971 - mass_loss_correction = 1.0000\n", + " 134971 - n_expected_stars = 58227.2799\n", + " 134971 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 767 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 4774 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 997 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.5009241693922871 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.24919835739111287 for 1654 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.006160308704818447 for 11094 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.24565166428898116 for 2151 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.24919835739111287 for 1888 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.006160308704818447 for 12438 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.24565166428898116 for 2421 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.24919835739111287 for 2125 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.006160308704818447 for 13985 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.24565166428898116 for 2767 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 89 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 640 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 122 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 415 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 2775 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 575 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.24919835739111287 for 952 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.006160308704818447 for 6379 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.24565166428898116 for 1323 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.24919835739111287 for 1058 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.006160308704818447 for 7193 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.24565166428898116 for 1379 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 137905 - # From Generated Field:\n", + " 137906 - generated_stars = 91257\n", + " 137907 - generated_total_iMass = 51541.9614\n", + " 137915 - generated_total_eMass = 33234.5598\n", + " 137916 - det_mass_loss_corr = 0.6448\n", + " 137942 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.24919835739111287 for 1192 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.006160308704818447 for 8084 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.24565166428898116 for 1516 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 55 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 351 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 74 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 138344 - # From density profile (number density)\n", + " 138345 - expected_total_iMass = 69120.9528\n", + " 138345 - expected_total_eMass = 69120.9528\n", + " 138345 - average_iMass_per_star = 0.5739\n", + " 138345 - mass_loss_correction = 1.0000\n", + " 138345 - n_expected_stars = 120447.9067\n", + " 138346 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.7543483357110189 for 22 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.4990758306077128 for 961 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 7030 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 9920 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 2848 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.5009241693922871 for 134 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.7543483357110189 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.4990758306077128 for 1070 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.24919835739111287 for 8369 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.006160308704818447 for 11821 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.24565166428898116 for 3279 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.5009241693922871 for 154 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.7543483357110189 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.4990758306077128 for 1203 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.24919835739111287 for 9257 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.006160308704818447 for 13452 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.24565166428898116 for 3735 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.5009241693922871 for 164 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-1.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.7543483357110189 for 31 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.4990758306077128 for 1339 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.24919835739111287 for 10388 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.006160308704818447 for 14830 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.24565166428898116 for 4116 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.5009241693922871 for 173 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 727 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 5423 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 7760 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 2119 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 91 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.7543483357110189 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.4990758306077128 for 577 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 4260 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 6128 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 1658 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.5009241693922871 for 89 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.7543483357110189 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.4990758306077128 for 664 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.24919835739111287 for 5020 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.006160308704818447 for 7216 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.24565166428898116 for 2043 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.5009241693922871 for 82 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.7543483357110189 for 23 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.4990758306077128 for 717 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.24919835739111287 for 5704 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.006160308704818447 for 8133 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.24565166428898116 for 2139 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.5009241693922871 for 93 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-1.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.7543483357110189 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.4990758306077128 for 833 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.24919835739111287 for 6358 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.006160308704818447 for 9186 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.24565166428898116 for 2518 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 144079 - # From Generated Field:\n", + " 144080 - generated_stars = 193916\n", + " 144082 - generated_total_iMass = 110669.6021\n", + " 144096 - generated_total_eMass = 68917.1673\n", + " 144099 - det_mass_loss_corr = 0.6227\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.5009241693922871 for 115 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 422 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 3375 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 4662 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 1317 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 59 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 144201 - # Done\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 144556 - # From density profile (number density)\n", + " 144556 - expected_total_iMass = 78794.2469\n", + " 144556 - expected_total_eMass = 78794.2469\n", + " 144556 - average_iMass_per_star = 0.5739\n", + " 144557 - mass_loss_correction = 1.0000\n", + " 144557 - n_expected_stars = 137304.2720\n", + " 144557 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-1.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 42 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 1758 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 9484 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 8556 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 1238 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 31 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-1.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.7543483357110189 for 104 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.4990758306077128 for 3584 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.24919835739111287 for 19998 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.006160308704818447 for 18090 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.24565166428898116 for 2580 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.5009241693922871 for 43 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.7543483357110189 for 106 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.4990758306077128 for 3957 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.24919835739111287 for 22587 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.006160308704818447 for 20076 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.24565166428898116 for 2950 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.5009241693922871 for 54 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 51 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 1747 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 9823 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 8831 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 1226 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 31 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 1151 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 6159 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 5531 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 817 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-1.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.7543483357110189 for 57 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.4990758306077128 for 2335 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.24919835739111287 for 13107 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.006160308704818447 for 11532 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.24565166428898116 for 1675 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.5009241693922871 for 35 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.7543483357110189 for 74 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.4990758306077128 for 2600 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.24919835739111287 for 14659 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.006160308704818447 for 13306 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.24565166428898116 for 1866 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.5009241693922871 for 31 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 1158 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 6321 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 5627 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 151198 - # From Generated Field:\n", + " 151198 - generated_stars = 225857\n", + " 151200 - generated_total_iMass = 130328.6659\n", + " 151216 - generated_total_eMass = 78506.5760\n", + " 151219 - det_mass_loss_corr = 0.6024\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 809 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 151357 - # Done\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 151718 - # From density profile (number density)\n", + " 151719 - expected_total_iMass = 162357.7584\n", + " 151719 - expected_total_eMass = 162357.7584\n", + " 151719 - average_iMass_per_star = 0.5739\n", + " 151720 - mass_loss_correction = 1.0000\n", + " 151720 - n_expected_stars = 282919.3081\n", + " 151720 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.249198357391113 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.0061603087048185 for 275 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 4431 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 18172 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 19020 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 4828 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 327 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.249198357391113 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.0061603087048185 for 490 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.7543483357110189 for 8119 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.4990758306077128 for 33785 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.24919835739111287 for 34731 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.006160308704818447 for 8800 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.24565166428898116 for 556 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.249198357391113 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.0061603087048185 for 537 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.7543483357110189 for 9371 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.4990758306077128 for 37382 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.24919835739111287 for 38484 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.006160308704818447 for 10061 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.24565166428898116 for 626 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.5009241693922871 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 7 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 329 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 5064 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 20656 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 20696 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 5262 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 349 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.249198357391113 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.0061603087048185 for 205 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 3064 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 12604 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 12969 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 3348 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 217 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.249198357391113 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.0061603087048185 for 331 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.7543483357110189 for 5593 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.4990758306077128 for 23172 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.24919835739111287 for 23722 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.006160308704818447 for 6304 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.24565166428898116 for 363 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.249198357391113 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.0061603087048185 for 394 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.7543483357110189 for 6383 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.4990758306077128 for 25928 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.24919835739111287 for 26556 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.006160308704818447 for 6864 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.24565166428898116 for 402 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 187 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 3413 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 14129 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 14494 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 3809 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 225 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 164849 - # From Generated Field:\n", + " 164849 - generated_stars = 477117\n", + " 164851 - generated_total_iMass = 274110.9917\n", + " 164873 - generated_total_eMass = 162078.7999\n", + " 164874 - det_mass_loss_corr = 0.5913\n", + " 164957 - # Done\n", + "\n", + "\n", + "########################### Combine Populations ###########################\n", + " 164989 - Number of stars generated: 1308217 (28 columns)\n", + " 164989 - included_columns = ['pop', 'iMass', 'age', 'Fe/H_initial', 'Mass', 'In_Final_Phase', 'Dist', 'l', 'b', 'vr_bc', 'mul', 'mub', 'x', 'y', 'z', 'U', 'V', 'W', 'VR_LSR', 'A_Ks', 'logL', 'logTeff', 'logg', 'log_radius', 'phase', '2mass-J', '2mass-H', '2mass-Ks']\n", + "\n", + "\n", + "# Save result -------------------------------------------------------------\n", + " 166028 - write result to \"outputfiles/default/Huston2025_spisea_l90.000_b0.000.h5\"\n", + " 166077 - ---------------------------------------------------------------\n", + "\n", + "\n", + "\n", + "############################# update location #############################\n", + " 166091 - # set location to: \n", + " 166091 - l, b = (12.17 deg, 5.37 deg)\n", + " 166092 - # set solid_angle to:\n", + " 166092 - solid_angle = 6.000e-02 deg^2\n", + "\n", + "\n", + "############################# Generate Field ##############################\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 167791 - # From density profile (number density)\n", + " 167791 - expected_total_iMass = 364527.7778\n", + " 167792 - expected_total_eMass = 364527.7778\n", + " 167792 - average_iMass_per_star = 0.5739\n", + " 167792 - mass_loss_correction = 1.0000\n", + " 167792 - n_expected_stars = 635214.1572\n", + " 167793 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.7543483357110188 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.4990758306077128 for 59 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 866 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 6546 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 27063 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 58952 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 80883 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 147048 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 232293 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 80573 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.4990758306077128 for 44 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 626 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 4625 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 18676 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 41125 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 56737 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 102508 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 161808 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 56087 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 171841 - # From Generated Field:\n", + " 171841 - generated_stars = 1076512\n", + " 171850 - generated_total_iMass = 616269.8659\n", + " 171916 - generated_total_eMass = 363888.7580\n", + " 171934 - det_mass_loss_corr = 0.5905\n", + " 172759 - # Done\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 173131 - # From density profile (number density)\n", + " 173132 - expected_total_iMass = 28693.2977\n", + " 173132 - expected_total_eMass = 1912.4300\n", + " 173132 - average_iMass_per_star = 0.5739\n", + " 173132 - mass_loss_correction = 1.0000\n", + " 173133 - n_expected_stars = 50000.0000\n", + " 173133 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-3.4990758306077128 for 71 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-3.0061603087048185 for 1193 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-2.4990758306077128 for 7461 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-2.0061603087048185 for 12597 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.7543483357110188 for 9709 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.4990758306077128 for 8564 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.249198357391113 for 5554 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.0061603087048185 for 2956 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.7543483357110189 for 1218 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 173475 - # From Generated Field:\n", + " 173476 - generated_stars = 5945\n", + " 173476 - generated_total_iMass = 3542.6780\n", + " 173480 - generated_total_eMass = 1866.8904\n", + " 173480 - det_mass_loss_corr = 0.5270\n", + " 173484 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.4990758306077128 for 396 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.24919835739111287 for 109 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.006160308704818447 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 174492 - # From density profile (number density)\n", + " 174493 - expected_total_iMass = 0.0000\n", + " 174493 - expected_total_eMass = 0.0000\n", + " 174493 - average_iMass_per_star = 0.5739\n", + " 174494 - mass_loss_correction = 1.0000\n", + " 174494 - n_expected_stars = 0.0000\n", + " 174494 - # Determine velocities when position are generated \n", + " 174495 - # From Generated Field:\n", + " 174495 - generated_stars = 0\n", + " 174496 - # Done\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 174818 - # From density profile (number density)\n", + " 174818 - expected_total_iMass = 56728.3656\n", + " 174818 - expected_total_eMass = 56728.3656\n", + " 174819 - average_iMass_per_star = 0.5739\n", + " 174819 - mass_loss_correction = 1.0000\n", + " 174819 - n_expected_stars = 98852.9904\n", + " 174820 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-2.0061603087048185 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.7543483357110188 for 232 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.4990758306077128 for 2031 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.249198357391113 for 10074 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.0061603087048185 for 24650 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.7543483357110189 for 32165 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.4990758306077128 for 21421 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.24919835739111287 for 7223 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.006160308704818447 for 1302 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.24565166428898116 for 120 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-2.0061603087048185 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.7543483357110188 for 181 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.4990758306077128 for 1549 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.249198357391113 for 7735 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.0061603087048185 for 18508 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.7543483357110189 for 23907 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 179529 - # From Generated Field:\n", + " 179530 - generated_stars = 173707\n", + " 179530 - generated_total_iMass = 98829.1201\n", + " 179541 - generated_total_eMass = 56827.5909\n", + " 179542 - det_mass_loss_corr = 0.5750\n", + " 179576 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.4990758306077128 for 15837 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.24919835739111287 for 5591 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.006160308704818447 for 1051 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.24565166428898116 for 97 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 179926 - # From density profile (number density)\n", + " 179926 - expected_total_iMass = 28693.2977\n", + " 179927 - expected_total_eMass = 11.6811\n", + " 179927 - average_iMass_per_star = 0.5739\n", + " 179927 - mass_loss_correction = 1.0000\n", + " 179927 - n_expected_stars = 50000.0000\n", + " 179928 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.006160308704818447 for 25 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.15 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.2 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.2 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.25 [M/H]=-0.006160308704818447 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.25 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.3 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=-0.006160308704818447 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.006160308704818447 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=-0.24919835739111287 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=-0.006160308704818447 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.006160308704818447 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=-0.006160308704818447 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=-0.006160308704818447 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=-0.006160308704818447 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=0.24565166428898116 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.006160308704818447 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.24919835739111287 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.006160308704818447 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.24919835739111287 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.006160308704818447 for 27 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=0.24565166428898116 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.006160308704818447 for 27 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.006160308704818447 for 44 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=0.24565166428898116 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.24919835739111287 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.006160308704818447 for 46 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=0.24565166428898116 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.24919835739111287 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.006160308704818447 for 42 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=0.24565166428898116 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.006160308704818447 for 50 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=0.24565166428898116 for 22 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.24919835739111287 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.006160308704818447 for 43 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=0.24565166428898116 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.24919835739111287 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.006160308704818447 for 59 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=0.24565166428898116 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.24919835739111287 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.006160308704818447 for 78 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=0.24565166428898116 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.24919835739111287 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.006160308704818447 for 73 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=0.24565166428898116 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.24919835739111287 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.006160308704818447 for 88 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=0.24565166428898116 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.24919835739111287 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.006160308704818447 for 81 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=0.24565166428898116 for 22 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.24919835739111287 for 22 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.006160308704818447 for 108 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=0.24565166428898116 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.24919835739111287 for 22 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.006160308704818447 for 105 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=0.24565166428898116 for 34 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.24919835739111287 for 22 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.006160308704818447 for 132 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=0.24565166428898116 for 44 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.24919835739111287 for 30 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.006160308704818447 for 159 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=0.24565166428898116 for 37 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.24919835739111287 for 30 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.006160308704818447 for 172 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=0.24565166428898116 for 42 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.24919835739111287 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.006160308704818447 for 177 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=0.24565166428898116 for 48 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.24919835739111287 for 37 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.006160308704818447 for 195 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=0.24565166428898116 for 56 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.24919835739111287 for 54 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.006160308704818447 for 227 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=0.24565166428898116 for 65 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.24919835739111287 for 55 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.006160308704818447 for 261 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=0.24565166428898116 for 70 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.24919835739111287 for 55 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.006160308704818447 for 304 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=0.24565166428898116 for 73 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.24919835739111287 for 62 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.006160308704818447 for 333 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=0.24565166428898116 for 90 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.24919835739111287 for 76 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.006160308704818447 for 378 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=0.24565166428898116 for 99 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.24919835739111287 for 73 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.006160308704818447 for 427 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=0.24565166428898116 for 100 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.24919835739111287 for 84 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.006160308704818447 for 483 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=0.24565166428898116 for 122 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.24919835739111287 for 108 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.006160308704818447 for 574 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=0.24565166428898116 for 153 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.24919835739111287 for 109 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.006160308704818447 for 586 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=0.24565166428898116 for 170 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.24919835739111287 for 126 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.006160308704818447 for 664 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=0.24565166428898116 for 173 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.24919835739111287 for 156 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.006160308704818447 for 749 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=0.24565166428898116 for 194 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.24919835739111287 for 143 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.006160308704818447 for 845 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=0.24565166428898116 for 191 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.24919835739111287 for 175 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.006160308704818447 for 979 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.24565166428898116 for 229 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.24919835739111287 for 190 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.006160308704818447 for 1108 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=0.24565166428898116 for 249 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.24919835739111287 for 219 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.006160308704818447 for 1212 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=0.24565166428898116 for 313 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.24919835739111287 for 236 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.006160308704818447 for 1323 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=0.24565166428898116 for 317 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.24919835739111287 for 268 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.006160308704818447 for 1489 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=0.24565166428898116 for 328 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.24919835739111287 for 318 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.006160308704818447 for 1687 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.24565166428898116 for 423 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.24919835739111287 for 360 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.006160308704818447 for 1908 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=0.24565166428898116 for 436 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.24919835739111287 for 400 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.006160308704818447 for 2107 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.24565166428898116 for 488 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.24919835739111287 for 447 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.006160308704818447 for 2343 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.24565166428898116 for 595 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.24919835739111287 for 477 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.006160308704818447 for 2700 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=0.24565166428898116 for 630 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=0.5009241693922871 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.24919835739111287 for 538 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.006160308704818447 for 2941 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.24565166428898116 for 753 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 183049 - # From Generated Field:\n", + " 183050 - generated_stars = 25\n", + " 183050 - generated_total_iMass = 10.4837\n", + " 183053 - generated_total_eMass = 10.4835\n", + " 183054 - det_mass_loss_corr = 1.0000\n", + " 183056 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.24919835739111287 for 558 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.006160308704818447 for 3428 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.24565166428898116 for 818 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.24919835739111287 for 710 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.006160308704818447 for 3741 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.24565166428898116 for 940 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 87 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 28 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 183378 - # From density profile (number density)\n", + " 183378 - expected_total_iMass = 28693.2977\n", + " 183378 - expected_total_eMass = 225.5096\n", + " 183378 - average_iMass_per_star = 0.5739\n", + " 183379 - mass_loss_correction = 1.0000\n", + " 183379 - n_expected_stars = 50000.0000\n", + " 183379 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 106 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 745 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 190 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.24919835739111287 for 105 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.006160308704818447 for 831 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.24565166428898116 for 271 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.24919835739111287 for 133 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.006160308704818447 for 949 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=0.24565166428898116 for 302 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.24919835739111287 for 153 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.006160308704818447 for 1114 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.24565166428898116 for 329 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.24919835739111287 for 163 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.006160308704818447 for 1183 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=0.24565166428898116 for 344 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.24919835739111287 for 191 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.006160308704818447 for 1317 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.24565166428898116 for 405 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.24919835739111287 for 235 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.006160308704818447 for 1539 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.24565166428898116 for 475 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.24919835739111287 for 257 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.006160308704818447 for 1596 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.24565166428898116 for 476 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.24919835739111287 for 278 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.006160308704818447 for 1888 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=0.24565166428898116 for 521 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=0.5009241693922871 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.24919835739111287 for 273 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.006160308704818447 for 2061 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.24565166428898116 for 641 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.24919835739111287 for 316 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.006160308704818447 for 2339 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.24565166428898116 for 721 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.24919835739111287 for 403 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.006160308704818447 for 2666 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.24565166428898116 for 811 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.5009241693922871 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.24919835739111287 for 412 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.006160308704818447 for 2979 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.24565166428898116 for 883 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.4990758306077128 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.24919835739111287 for 447 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.006160308704818447 for 3284 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.24565166428898116 for 1015 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.24919835739111287 for 541 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.006160308704818447 for 3628 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.24565166428898116 for 1178 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.5009241693922871 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.24919835739111287 for 602 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.006160308704818447 for 4187 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.24565166428898116 for 1301 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.5009241693922871 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 312 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 2267 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 185387 - # From Generated Field:\n", + " 185388 - generated_stars = 593\n", + " 185388 - generated_total_iMass = 307.9669\n", + " 185392 - generated_total_eMass = 231.2014\n", + " 185392 - det_mass_loss_corr = 0.7507\n", + " 185395 - # Done\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 734 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 185759 - # From density profile (number density)\n", + " 185760 - expected_total_iMass = 28693.2977\n", + " 185760 - expected_total_eMass = 750.2440\n", + " 185760 - average_iMass_per_star = 0.5739\n", + " 185761 - mass_loss_correction = 1.0000\n", + " 185761 - n_expected_stars = 50000.0000\n", + " 185761 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 211 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 2205 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 503 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.24919835739111287 for 409 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.006160308704818447 for 4906 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.24565166428898116 for 1062 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.24919835739111287 for 447 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.006160308704818447 for 5659 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.24565166428898116 for 1211 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.24919835739111287 for 467 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.006160308704818447 for 6307 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=0.24565166428898116 for 1399 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.24919835739111287 for 544 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.006160308704818447 for 7115 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.24565166428898116 for 1532 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.24919835739111287 for 628 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.006160308704818447 for 7944 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.24565166428898116 for 1767 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 365 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 4425 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 974 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 187323 - # From Generated Field:\n", + " 187323 - generated_stars = 1871\n", + " 187324 - generated_total_iMass = 944.0412\n", + " 187327 - generated_total_eMass = 696.3265\n", + " 187328 - det_mass_loss_corr = 0.7376\n", + " 187331 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 187671 - # From density profile (number density)\n", + " 187671 - expected_total_iMass = 28693.2977\n", + " 187672 - expected_total_eMass = 1519.3277\n", + " 187672 - average_iMass_per_star = 0.5739\n", + " 187672 - mass_loss_correction = 1.0000\n", + " 187672 - n_expected_stars = 50000.0000\n", + " 187673 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 633 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 4097 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 802 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.24919835739111287 for 1397 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.006160308704818447 for 9507 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.24565166428898116 for 1960 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.24919835739111287 for 1582 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.006160308704818447 for 10694 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 189330 - # From Generated Field:\n", + " 189330 - generated_stars = 4154\n", + " 189331 - generated_total_iMass = 2415.0127\n", + " 189334 - generated_total_eMass = 1532.6224\n", + " 189335 - det_mass_loss_corr = 0.6346\n", + " 189338 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.24565166428898116 for 2113 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.24919835739111287 for 1759 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.006160308704818447 for 12167 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.24565166428898116 for 2351 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 65 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 583 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 92 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 189723 - # From density profile (number density)\n", + " 189723 - expected_total_iMass = 28693.2977\n", + " 189723 - expected_total_eMass = 6678.0224\n", + " 189724 - average_iMass_per_star = 0.5739\n", + " 189724 - mass_loss_correction = 1.0000\n", + " 189724 - n_expected_stars = 50000.0000\n", + " 189725 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.7543483357110189 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.4990758306077128 for 394 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 2908 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 4221 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 1211 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.5009241693922871 for 50 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.7543483357110189 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.4990758306077128 for 450 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.24919835739111287 for 3494 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.006160308704818447 for 4829 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.24565166428898116 for 1407 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.5009241693922871 for 63 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.7543483357110189 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.4990758306077128 for 523 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.24919835739111287 for 3918 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.006160308704818447 for 5483 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.24565166428898116 for 1480 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.5009241693922871 for 70 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.7543483357110189 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.4990758306077128 for 549 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.24919835739111287 for 4346 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.006160308704818447 for 6263 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.24565166428898116 for 1658 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.5009241693922871 for 76 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-1.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 279 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 2170 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 3259 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 839 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 191462 - # From Generated Field:\n", + " 191462 - generated_stars = 18759\n", + " 191463 - generated_total_iMass = 10613.8131\n", + " 191467 - generated_total_eMass = 6688.1204\n", + " 191467 - det_mass_loss_corr = 0.6301\n", + " 191473 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.7543483357110189 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 191793 - # From density profile (number density)\n", + " 191793 - expected_total_iMass = 28693.2977\n", + " 191793 - expected_total_eMass = 13167.3181\n", + " 191793 - average_iMass_per_star = 0.5739\n", + " 191794 - mass_loss_correction = 1.0000\n", + " 191794 - n_expected_stars = 50000.0000\n", + " 191794 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 597 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 3468 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 3112 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 446 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.7543483357110189 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.4990758306077128 for 1285 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.24919835739111287 for 7301 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.006160308704818447 for 6577 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.24565166428898116 for 960 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.5009241693922871 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.7543483357110189 for 31 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.4990758306077128 for 1478 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.24919835739111287 for 8289 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.006160308704818447 for 7313 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.24565166428898116 for 1081 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.5009241693922871 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 640 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 3633 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 3272 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 502 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 193635 - # From Generated Field:\n", + " 193636 - generated_stars = 38140\n", + " 193638 - generated_total_iMass = 21473.7904\n", + " 193643 - generated_total_eMass = 13236.3801\n", + " 193644 - det_mass_loss_corr = 0.6164\n", + " 193659 - # Done\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.006160308704818447 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.24919835739111287 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.006160308704818447 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 193996 - # From density profile (number density)\n", + " 193996 - expected_total_iMass = 28693.2977\n", + " 193996 - expected_total_eMass = 28158.7120\n", + " 193996 - average_iMass_per_star = 0.5739\n", + " 193997 - mass_loss_correction = 1.0000\n", + " 193997 - n_expected_stars = 50000.0000\n", + " 193997 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.0061603087048185 for 49 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 810 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 3341 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 3353 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 869 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 53 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.249198357391113 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.0061603087048185 for 107 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.7543483357110189 for 1493 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.4990758306077128 for 6041 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.24919835739111287 for 6137 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.006160308704818447 for 1583 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.24565166428898116 for 105 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.249198357391113 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.0061603087048185 for 106 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.7543483357110189 for 1604 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.4990758306077128 for 6639 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.24919835739111287 for 6942 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.006160308704818447 for 1790 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.24565166428898116 for 106 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 47 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 844 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 3708 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 3638 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 978 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 56 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.249198357391113 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.0061603087048185 for 32 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 536 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 2082 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 2162 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 588 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.249198357391113 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.0061603087048185 for 45 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.7543483357110189 for 952 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.4990758306077128 for 3820 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.24919835739111287 for 3869 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.006160308704818447 for 963 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 196870 - # From Generated Field:\n", + " 196870 - generated_stars = 82743\n", + " 196871 - generated_total_iMass = 47408.0961\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.24565166428898116 for 53 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.249198357391113 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.0061603087048185 for 70 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.7543483357110189 for 1000 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.4990758306077128 for 4452 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.24919835739111287 for 4441 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.006160308704818447 for 1156 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.24565166428898116 for 80 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 22 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 583 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 2408 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 2314 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 627 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 43 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 196878 - generated_total_eMass = 28032.4403\n", + " 196880 - det_mass_loss_corr = 0.5913\n", + " 196907 - # Done\n", + "\n", + "\n", + "########################### Combine Populations ###########################\n", + " 196929 - Number of stars generated: 1402449 (28 columns)\n", + " 196930 - included_columns = ['pop', 'iMass', 'age', 'Fe/H_initial', 'Mass', 'In_Final_Phase', 'Dist', 'l', 'b', 'vr_bc', 'mul', 'mub', 'x', 'y', 'z', 'U', 'V', 'W', 'VR_LSR', 'A_Ks', 'logL', 'logTeff', 'logg', 'log_radius', 'phase', '2mass-J', '2mass-H', '2mass-Ks']\n", + "\n", + "\n", + "# Save result -------------------------------------------------------------\n", + " 198013 - write result to \"outputfiles/default/Huston2025_spisea_l12.170_b5.370.h5\"\n", + " 198065 - ---------------------------------------------------------------\n", + "\n", + "\n", + "\n", + "############################# update location #############################\n", + " 198078 - # set location to: \n", + " 198078 - l, b = (39.14 deg, 8.53 deg)\n", + " 198078 - # set solid_angle to:\n", + " 198078 - solid_angle = 6.000e-01 deg^2\n", + "\n", + "\n", + "############################# Generate Field ##############################\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 199758 - # From density profile (number density)\n", + " 199759 - expected_total_iMass = 28693.2977\n", + " 199759 - expected_total_eMass = 15669.2328\n", + " 199760 - average_iMass_per_star = 0.5739\n", + " 199760 - mass_loss_correction = 1.0000\n", + " 199760 - n_expected_stars = 50000.0000\n", + " 199761 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 75 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 516 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 2181 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 4516 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 6385 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 11479 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 18305 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 200162 - # From Generated Field:\n", + " 200163 - generated_stars = 46011\n", + " 200163 - generated_total_iMass = 27124.7558\n", + " 200169 - generated_total_eMass = 15603.7308\n", + " 200171 - det_mass_loss_corr = 0.5753\n", + " 200191 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 6299 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 25 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 200574 - # From density profile (number density)\n", + " 200574 - expected_total_iMass = 28693.2977\n", + " 200574 - expected_total_eMass = 4296.5860\n", + " 200575 - average_iMass_per_star = 0.5739\n", + " 200575 - mass_loss_correction = 1.0000\n", + " 200575 - n_expected_stars = 50000.0000\n", + " 200575 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-4.0061603087048185 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-3.4990758306077128 for 85 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-3.0061603087048185 for 1223 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-2.4990758306077128 for 7348 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-2.0061603087048185 for 12565 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.7543483357110188 for 9881 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.4990758306077128 for 8444 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.249198357391113 for 5606 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 200964 - # From Generated Field:\n", + " 200964 - generated_stars = 13346\n", + " 200965 - generated_total_iMass = 7730.7508\n", + " 200969 - generated_total_eMass = 4325.9997\n", + " 200969 - det_mass_loss_corr = 0.5596\n", + " 200974 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-1.0061603087048185 for 2922 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.7543483357110189 for 1262 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.4990758306077128 for 376 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.24919835739111287 for 100 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=-0.006160308704818447 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.15 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 2; nsd ------------------------------------------------------\n", + " 201929 - # From density profile (number density)\n", + " 201930 - expected_total_iMass = 0.0000\n", + " 201930 - expected_total_eMass = 0.0000\n", + " 201930 - average_iMass_per_star = 0.5739\n", + " 201930 - mass_loss_correction = 1.0000\n", + " 201931 - n_expected_stars = 0.0000\n", + " 201931 - # Determine velocities when position are generated \n", + " 201932 - # From Generated Field:\n", + " 201932 - generated_stars = 0\n", + " 201933 - # Done\n", + "\n", + "\n", + "# Population 3; thick_disk -----------------------------------------------\n", + " 202360 - # From density profile (number density)\n", + " 202360 - expected_total_iMass = 190367.0647\n", + " 202360 - expected_total_eMass = 190367.0647\n", + " 202360 - average_iMass_per_star = 0.5739\n", + " 202361 - mass_loss_correction = 1.0000\n", + " 202361 - n_expected_stars = 331727.4071\n", + " 202361 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-2.0061603087048185 for 39 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.7543483357110188 for 831 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.4990758306077128 for 6958 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.249198357391113 for 33438 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.0061603087048185 for 83365 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.7543483357110189 for 106643 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.4990758306077128 for 71012 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.24919835739111287 for 24903 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.006160308704818447 for 4358 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.24565166428898116 for 414 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.5009241693922871 for 23 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-2.0061603087048185 for 36 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.7543483357110188 for 526 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.4990758306077128 for 5271 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.249198357391113 for 25239 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-1.0061603087048185 for 61905 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.7543483357110189 for 80293 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.4990758306077128 for 53212 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.24919835739111287 for 18362 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=-0.006160308704818447 for 3348 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 216106 - # From Generated Field:\n", + " 216106 - generated_stars = 580510\n", + " 216111 - generated_total_iMass = 333948.5612\n", + " 216150 - generated_total_eMass = 190761.8756\n", + " 216159 - det_mass_loss_corr = 0.5712\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.24565166428898116 for 320 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.1 [M/H]=0.5009241693922871 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 216590 - # Done\n", + "\n", + "\n", + "# Population 4; thin_disk_1 ----------------------------------------------\n", + " 217003 - # From density profile (number density)\n", + " 217004 - expected_total_iMass = 28693.2977\n", + " 217004 - expected_total_eMass = 30.9219\n", + " 217004 - average_iMass_per_star = 0.5739\n", + " 217005 - mass_loss_correction = 1.0000\n", + " 217005 - n_expected_stars = 50000.0000\n", + " 217005 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=-0.006160308704818447 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.01 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.05 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.1 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.15 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.2 [M/H]=-0.006160308704818447 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.2 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.25 [M/H]=-0.006160308704818447 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.3 [M/H]=-0.006160308704818447 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.3 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.35 [M/H]=-0.006160308704818447 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=-0.006160308704818447 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.4 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=-0.006160308704818447 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.45 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=-0.006160308704818447 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.5 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=-0.006160308704818447 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.55 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=-0.006160308704818447 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.6 [M/H]=0.24565166428898116 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=-0.006160308704818447 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.65 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.24919835739111287 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=-0.006160308704818447 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.7 [M/H]=0.24565166428898116 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=-0.24919835739111287 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=-0.006160308704818447 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.75 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=-0.006160308704818447 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.8 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=-0.24919835739111287 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=-0.006160308704818447 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.85 [M/H]=0.24565166428898116 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=-0.006160308704818447 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.9 [M/H]=0.24565166428898116 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.24919835739111287 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=-0.006160308704818447 for 28 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=5.95 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.24919835739111287 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=-0.006160308704818447 for 24 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.0 [M/H]=0.24565166428898116 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.24919835739111287 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=-0.006160308704818447 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.05 [M/H]=0.24565166428898116 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.24919835739111287 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=-0.006160308704818447 for 42 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.1 [M/H]=0.24565166428898116 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=-0.006160308704818447 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.15 [M/H]=0.24565166428898116 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.24919835739111287 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=-0.006160308704818447 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.2 [M/H]=0.24565166428898116 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.24919835739111287 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=-0.006160308704818447 for 53 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.25 [M/H]=0.24565166428898116 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.24919835739111287 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=-0.006160308704818447 for 61 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.3 [M/H]=0.24565166428898116 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.24919835739111287 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=-0.006160308704818447 for 71 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.35 [M/H]=0.24565166428898116 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.24919835739111287 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=-0.006160308704818447 for 64 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.4 [M/H]=0.24565166428898116 for 17 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.24919835739111287 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=-0.006160308704818447 for 81 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.45 [M/H]=0.24565166428898116 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.24919835739111287 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=-0.006160308704818447 for 82 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=0.24565166428898116 for 12 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.5 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.24919835739111287 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=-0.006160308704818447 for 90 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.55 [M/H]=0.24565166428898116 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.24919835739111287 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=-0.006160308704818447 for 98 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.6 [M/H]=0.24565166428898116 for 28 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.24919835739111287 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=-0.006160308704818447 for 114 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=0.24565166428898116 for 32 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.65 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.24919835739111287 for 26 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=-0.006160308704818447 for 148 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.7 [M/H]=0.24565166428898116 for 36 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.24919835739111287 for 33 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=-0.006160308704818447 for 169 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.75 [M/H]=0.24565166428898116 for 39 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.24919835739111287 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=-0.006160308704818447 for 172 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.8 [M/H]=0.24565166428898116 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.24919835739111287 for 35 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=-0.006160308704818447 for 190 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.85 [M/H]=0.24565166428898116 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.24919835739111287 for 35 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=-0.006160308704818447 for 202 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=0.24565166428898116 for 50 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.9 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.24919835739111287 for 34 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=-0.006160308704818447 for 235 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=6.95 [M/H]=0.24565166428898116 for 61 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.24919835739111287 for 47 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=-0.006160308704818447 for 273 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.0 [M/H]=0.24565166428898116 for 70 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.24919835739111287 for 51 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=-0.006160308704818447 for 279 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.05 [M/H]=0.24565166428898116 for 69 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.24919835739111287 for 46 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=-0.006160308704818447 for 365 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.1 [M/H]=0.24565166428898116 for 83 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.24919835739111287 for 68 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=-0.006160308704818447 for 393 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.15 [M/H]=0.24565166428898116 for 85 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.24919835739111287 for 85 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=-0.006160308704818447 for 423 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.2 [M/H]=0.24565166428898116 for 95 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.24919835739111287 for 63 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=-0.006160308704818447 for 496 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.25 [M/H]=0.24565166428898116 for 145 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.24919835739111287 for 96 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=-0.006160308704818447 for 531 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=0.24565166428898116 for 127 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.3 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.24919835739111287 for 97 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=-0.006160308704818447 for 618 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=0.24565166428898116 for 128 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.35 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.24919835739111287 for 130 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=-0.006160308704818447 for 706 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=0.24565166428898116 for 152 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.4 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.24919835739111287 for 149 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=-0.006160308704818447 for 740 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=0.24565166428898116 for 182 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.45 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.24919835739111287 for 161 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=-0.006160308704818447 for 918 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=0.24565166428898116 for 210 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.5 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.24919835739111287 for 186 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=-0.006160308704818447 for 972 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.24565166428898116 for 199 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.55 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.24919835739111287 for 204 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=-0.006160308704818447 for 1074 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.6 [M/H]=0.24565166428898116 for 247 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.24919835739111287 for 230 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=-0.006160308704818447 for 1195 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=0.24565166428898116 for 277 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.65 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.24919835739111287 for 221 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=-0.006160308704818447 for 1404 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.7 [M/H]=0.24565166428898116 for 311 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.24919835739111287 for 282 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=-0.006160308704818447 for 1541 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=0.24565166428898116 for 375 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.75 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.24919835739111287 for 309 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=-0.006160308704818447 for 1703 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.24565166428898116 for 398 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.8 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.4990758306077128 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.24919835739111287 for 360 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=-0.006160308704818447 for 1843 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=0.24565166428898116 for 482 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.85 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.24919835739111287 for 397 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=-0.006160308704818447 for 2265 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.24565166428898116 for 512 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.9 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.24919835739111287 for 457 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=-0.006160308704818447 for 2499 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.24565166428898116 for 536 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=7.95 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.4990758306077128 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.24919835739111287 for 509 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=-0.006160308704818447 for 2549 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=0.24565166428898116 for 624 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.0 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.24919835739111287 for 610 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=-0.006160308704818447 for 2974 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.24565166428898116 for 733 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.05 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.24919835739111287 for 582 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=-0.006160308704818447 for 3354 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.24565166428898116 for 808 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.1 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.24919835739111287 for 704 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=-0.006160308704818447 for 3857 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.24565166428898116 for 916 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.15 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 90 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 21 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 220015 - # From Generated Field:\n", + " 220015 - generated_stars = 58\n", + " 220016 - generated_total_iMass = 46.4617\n", + " 220019 - generated_total_eMass = 28.9623\n", + " 220019 - det_mass_loss_corr = 0.6234\n", + " 220023 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 5; thin_disk_2 ----------------------------------------------\n", + " 220382 - # From density profile (number density)\n", + " 220383 - expected_total_iMass = 28693.2977\n", + " 220383 - expected_total_eMass = 577.0939\n", + " 220383 - average_iMass_per_star = 0.5739\n", + " 220383 - mass_loss_correction = 1.0000\n", + " 220384 - n_expected_stars = 50000.0000\n", + " 220384 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.24919835739111287 for 105 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=-0.006160308704818447 for 788 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.24565166428898116 for 207 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.2 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.24919835739111287 for 129 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=-0.006160308704818447 for 820 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.24565166428898116 for 242 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.25 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.24919835739111287 for 110 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=-0.006160308704818447 for 896 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=0.24565166428898116 for 272 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.3 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.24919835739111287 for 135 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=-0.006160308704818447 for 1070 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.24565166428898116 for 348 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.35 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.24919835739111287 for 163 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=-0.006160308704818447 for 1105 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=0.24565166428898116 for 332 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.4 [M/H]=0.5009241693922871 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.24919835739111287 for 195 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=-0.006160308704818447 for 1339 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.24565166428898116 for 386 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.45 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.24919835739111287 for 201 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=-0.006160308704818447 for 1422 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.24565166428898116 for 421 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.5 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.24919835739111287 for 212 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=-0.006160308704818447 for 1606 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.24565166428898116 for 523 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.55 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.24919835739111287 for 249 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=-0.006160308704818447 for 1855 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=0.24565166428898116 for 630 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.6 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.24919835739111287 for 297 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=-0.006160308704818447 for 2037 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.24565166428898116 for 670 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.65 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.24919835739111287 for 347 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=-0.006160308704818447 for 2354 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.24565166428898116 for 711 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.7 [M/H]=0.5009241693922871 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.24919835739111287 for 360 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=-0.006160308704818447 for 2575 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.24565166428898116 for 809 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.75 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.24919835739111287 for 411 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=-0.006160308704818447 for 2961 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.24565166428898116 for 892 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.8 [M/H]=0.5009241693922871 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.24919835739111287 for 460 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=-0.006160308704818447 for 3330 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.24565166428898116 for 1033 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.85 [M/H]=0.5009241693922871 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.4990758306077128 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.24919835739111287 for 587 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=-0.006160308704818447 for 3745 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.24565166428898116 for 1065 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.9 [M/H]=0.5009241693922871 for 14 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.24919835739111287 for 591 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=-0.006160308704818447 for 4134 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.24565166428898116 for 1267 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=8.95 [M/H]=0.5009241693922871 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 332 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 2219 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 651 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.5009241693922871 for 13 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 222353 - # From Generated Field:\n", + " 222353 - generated_stars = 1482\n", + " 222354 - generated_total_iMass = 995.4554\n", + " 222357 - generated_total_eMass = 642.3722\n", + " 222358 - det_mass_loss_corr = 0.6453\n", + " 222361 - # Done\n", + "\n", + "\n", + "# Population 6; thin_disk_3 ----------------------------------------------\n", + " 222695 - # From density profile (number density)\n", + " 222695 - expected_total_iMass = 28693.2977\n", + " 222696 - expected_total_eMass = 2001.4786\n", + " 222696 - average_iMass_per_star = 0.5739\n", + " 222696 - mass_loss_correction = 1.0000\n", + " 222696 - n_expected_stars = 50000.0000\n", + " 222697 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.24919835739111287 for 168 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=-0.006160308704818447 for 2310 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.0 [M/H]=0.24565166428898116 for 528 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.24919835739111287 for 409 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=-0.006160308704818447 for 4849 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.24565166428898116 for 1084 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.05 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.24919835739111287 for 431 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=-0.006160308704818447 for 5695 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.24565166428898116 for 1177 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.1 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.24919835739111287 for 504 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=-0.006160308704818447 for 6147 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=0.24565166428898116 for 1422 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.15 [M/H]=0.5009241693922871 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.24919835739111287 for 538 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=-0.006160308704818447 for 7080 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 224236 - # From Generated Field:\n", + " 224237 - generated_stars = 5310\n", + " 224237 - generated_total_iMass = 3080.2130\n", + " 224242 - generated_total_eMass = 2053.2591\n", + " 224243 - det_mass_loss_corr = 0.6666\n", + " 224246 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.24565166428898116 for 1477 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.2 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.24919835739111287 for 635 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.006160308704818447 for 7892 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.24565166428898116 for 1727 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 330 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 4450 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 979 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.25 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 7; thin_disk_4 ----------------------------------------------\n", + " 224648 - # From density profile (number density)\n", + " 224648 - expected_total_iMass = 28693.2977\n", + " 224649 - expected_total_eMass = 4030.7126\n", + " 224649 - average_iMass_per_star = 0.5739\n", + " 224649 - mass_loss_correction = 1.0000\n", + " 224649 - n_expected_stars = 50000.0000\n", + " 224649 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.24919835739111287 for 604 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 4206 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.24565166428898116 for 859 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.4990758306077128 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.24919835739111287 for 1451 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.006160308704818447 for 9680 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.24565166428898116 for 1947 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.4990758306077128 for 4 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.24919835739111287 for 1605 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.006160308704818447 for 10813 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.24565166428898116 for 2091 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 226147 - # From Generated Field:\n", + " 226147 - generated_stars = 10772\n", + " 226147 - generated_total_iMass = 6121.9135\n", + " 226151 - generated_total_eMass = 3916.2928\n", + " 226151 - det_mass_loss_corr = 0.6397\n", + " 226156 - # Done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.4990758306077128 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.24919835739111287 for 1770 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.006160308704818447 for 12037 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.24565166428898116 for 2309 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 77 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 537 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 91 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.3 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.35 [M/H]=-0.006160308704818447 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.4 [M/H]=-0.006160308704818447 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.45 [M/H]=-0.006160308704818447 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\n", + "# Population 8; thin_disk_5 ----------------------------------------------\n", + " 226593 - # From density profile (number density)\n", + " 226593 - expected_total_iMass = 28693.2977\n", + " 226593 - expected_total_eMass = 17445.7989\n", + " 226594 - average_iMass_per_star = 0.5739\n", + " 226594 - mass_loss_correction = 1.0000\n", + " 226594 - n_expected_stars = 50000.0000\n", + " 226595 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.7543483357110189 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.4990758306077128 for 394 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 2856 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 4131 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 1179 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.5009241693922871 for 45 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-1.0061603087048185 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.7543483357110189 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.4990758306077128 for 473 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.24919835739111287 for 3449 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.006160308704818447 for 4888 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.24565166428898116 for 1364 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.5009241693922871 for 53 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.7543483357110189 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.4990758306077128 for 530 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.24919835739111287 for 3824 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.006160308704818447 for 5562 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.24565166428898116 for 1537 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.5009241693922871 for 70 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.7543483357110189 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.4990758306077128 for 578 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.24919835739111287 for 4387 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.006160308704818447 for 6067 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.24565166428898116 for 1715 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.5009241693922871 for 71 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 308 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 2321 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 3233 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 870 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 228305 - # From Generated Field:\n", + " 228305 - generated_stars = 48863\n", + " 228307 - generated_total_iMass = 28035.0690\n", + " 228312 - generated_total_eMass = 17439.1575\n", + " 228313 - det_mass_loss_corr = 0.6220\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.4990758306077128 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.24919835739111287 for 60 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=-0.006160308704818447 for 79 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.5 [M/H]=0.24565166428898116 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.4990758306077128 for 8 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.24919835739111287 for 72 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=-0.006160308704818447 for 83 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.24565166428898116 for 25 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.55 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.4990758306077128 for 17 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.24919835739111287 for 67 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=-0.006160308704818447 for 95 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.6 [M/H]=0.24565166428898116 for 20 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.7543483357110189 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.4990758306077128 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.24919835739111287 for 81 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=-0.006160308704818447 for 117 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.24565166428898116 for 30 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.65 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 40 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 66 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 23 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 228326 - # Done\n", + "\n", + "\n", + "# Population 9; thin_disk_6 ----------------------------------------------\n", + " 228765 - # From density profile (number density)\n", + " 228765 - expected_total_iMass = 34398.5989\n", + " 228765 - expected_total_eMass = 34398.5989\n", + " 228765 - average_iMass_per_star = 0.5739\n", + " 228766 - mass_loss_correction = 1.0000\n", + " 228766 - n_expected_stars = 59941.8710\n", + " 228766 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 16 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 723 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 4177 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 3829 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 520 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.7543483357110189 for 45 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.4990758306077128 for 1573 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.24919835739111287 for 8764 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.006160308704818447 for 8141 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.24565166428898116 for 1123 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.5009241693922871 for 18 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.7543483357110189 for 35 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.4990758306077128 for 1762 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.24919835739111287 for 9826 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.006160308704818447 for 8832 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.24565166428898116 for 1272 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.5009241693922871 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 34 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 790 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 4168 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 3875 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 576 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 9 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.7543483357110189 for 10 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.4990758306077128 for 475 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.24919835739111287 for 2800 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=-0.006160308704818447 for 2409 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.24565166428898116 for 369 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.7 [M/H]=0.5009241693922871 for 7 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.7543483357110189 for 30 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.4990758306077128 for 1037 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.24919835739111287 for 5759 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=-0.006160308704818447 for 5111 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.24565166428898116 for 710 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.75 [M/H]=0.5009241693922871 for 19 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.7543483357110189 for 32 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.4990758306077128 for 1135 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.24919835739111287 for 6414 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=-0.006160308704818447 for 5711 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.24565166428898116 for 801 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.8 [M/H]=0.5009241693922871 for 11 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 15 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 485 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 2710 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 2391 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 357 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 231969 - # From Generated Field:\n", + " 231970 - generated_stars = 98940\n", + " 231971 - generated_total_iMass = 56678.5559\n", + " 231979 - generated_total_eMass = 34445.6950\n", + " 231980 - det_mass_loss_corr = 0.6077\n", + " 232011 - # Done\n", + "\n", + "\n", + "# Population 10; thin_disk_7 ---------------------------------------------\n", + " 232344 - # From density profile (number density)\n", + " 232345 - expected_total_iMass = 73611.9768\n", + " 232345 - expected_total_eMass = 73611.9768\n", + " 232345 - average_iMass_per_star = 0.5739\n", + " 232345 - mass_loss_correction = 1.0000\n", + " 232345 - n_expected_stars = 128273.8179\n", + " 232346 - # Determine velocities when position are generated \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.249198357391113 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.0061603087048185 for 107 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 2063 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 8202 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 8598 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 2204 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 149 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.249198357391113 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.0061603087048185 for 228 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.7543483357110189 for 3770 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.4990758306077128 for 15175 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.24919835739111287 for 15525 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.006160308704818447 for 4034 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.24565166428898116 for 255 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.249198357391113 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.0061603087048185 for 256 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.7543483357110189 for 4231 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.4990758306077128 for 17272 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.24919835739111287 for 17610 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.006160308704818447 for 4461 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.24565166428898116 for 303 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.5009241693922871 for 6 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.249198357391113 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 127 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 2293 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 9209 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 9618 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 2465 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 184 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.249198357391113 for 2 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-1.0061603087048185 for 90 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.7543483357110189 for 1400 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.4990758306077128 for 5717 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.24919835739111287 for 5835 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=-0.006160308704818447 for 1583 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.24565166428898116 for 80 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.85 [M/H]=0.5009241693922871 for 5 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-1.0061603087048185 for 186 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.7543483357110189 for 2672 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.4990758306077128 for 10764 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.24919835739111287 for 10768 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=-0.006160308704818447 for 2800 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.24565166428898116 for 173 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.9 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.249198357391113 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-1.0061603087048185 for 180 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.7543483357110189 for 2973 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.4990758306077128 for 11832 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.24919835739111287 for 12071 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=-0.006160308704818447 for 3142 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.24565166428898116 for 216 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=9.95 [M/H]=0.5009241693922871 for 3 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-1.0061603087048185 for 84 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.7543483357110189 for 1657 stars\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:712: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + " 238302 - # From Generated Field:\n", + " 238302 - generated_stars = 217467\n", + " 238303 - generated_total_iMass = 122831.2426\n", + " 238315 - generated_total_eMass = 73383.3399\n", + " 238315 - det_mass_loss_corr = 0.5974\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.4990758306077128 for 6434 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.24919835739111287 for 6532 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=-0.006160308704818447 for 1788 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.24565166428898116 for 115 stars\n", + "Keep low mass stars below grid and compact objects\n", + "Keep low mass stars below grid and compact objects\n", + "Starting SPISEA cluster generation for bin log_age=10.0 [M/H]=0.5009241693922871 for 1 stars\n", + "Keep low mass stars below grid and compact objects\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 238356 - # Done\n", + "\n", + "\n", + "########################### Combine Populations ###########################\n", + " 238378 - Number of stars generated: 1022759 (28 columns)\n", + " 238378 - included_columns = ['pop', 'iMass', 'age', 'Fe/H_initial', 'Mass', 'In_Final_Phase', 'Dist', 'l', 'b', 'vr_bc', 'mul', 'mub', 'x', 'y', 'z', 'U', 'V', 'W', 'VR_LSR', 'A_Ks', 'logL', 'logTeff', 'logg', 'log_radius', 'phase', '2mass-J', '2mass-H', '2mass-Ks']\n", + "\n", + "\n", + "# Save result -------------------------------------------------------------\n", + " 239164 - write result to \"outputfiles/default/Huston2025_spisea_l39.140_b8.530.h5\"\n", + " 239204 - ---------------------------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "# Only re-run if needed - catalog generation\n", + "if True:\n", + " for i,loc in enumerate(model.get_iter_loc()):\n", + " if i>0: #i==0:\n", + " # run synthpop for the given location and solid angle\n", + " model.process_location(*loc, model.parms.solid_angle[i], model.parms.solid_angle_unit,save_data=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6599724c-a040-4683-b445-e4a95f99fd53", + "metadata": {}, + "outputs": [], + "source": [ + "# Catalog load\n", + "data0 = pd.read_hdf(\"outputfiles/default/Huston2025_spisea_l0.000_b0.000.h5\")\n", + "data1 = pd.read_hdf(\"outputfiles/default/Huston2025_spisea_l90.000_b0.000.h5\")\n", + "data2 = pd.read_hdf(\"outputfiles/default/Huston2025_spisea_l12.170_b5.370.h5\")\n", + "data3 = pd.read_hdf(\"outputfiles/default/Huston2025_spisea_l39.140_b8.530.h5\")\n", + "catalogs = [data0,data1,data2,data3]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0c4f0588-121b-4cf2-85af-6f52aabeff74", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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popiMassageFe/H_initialMassIn_Final_PhaseDistlbvr_bc...VR_LSRA_KslogLlogTefflogglog_radiusphase2mass-J2mass-H2mass-Ks
00.00.25382610.0-1.500.2538200.03.455078359.9932320.014634122.744509...131.9921382.238285-1.8512833.5968925.033655-0.5957540.028.37879224.02203021.952436
10.00.08837010.0-1.500.0883700.04.0223750.0137130.00018512.497959...21.7487992.461908NaNNaNNaNNaN98.0NaNNaNNaN
20.00.12902710.0-1.500.1290250.04.0885100.0137940.002649-27.810827...-18.5596682.468231-2.5137343.5620285.255248-0.8572510.031.11235426.36901324.119329
30.00.10719810.0-1.500.1071970.04.1764940.0089370.0012784.183377...13.4331962.476643-2.7274803.5445495.321999-0.9291660.031.67154826.92301824.683262
40.00.11219410.0-1.500.1121930.04.353625359.9875590.002188118.772595...128.0173592.270699-2.6787083.5485475.306789-0.9127750.030.92665126.52076524.450412
..................................................................
528910810.00.10985910.00.250.1098590.024.9544910.012388-0.010701-8.985492...0.2637062.772000NaNNaNNaNNaN98.0NaNNaNNaN
528910910.00.56020510.00.250.5601850.024.906523359.999096-0.009612-33.844011...-24.5978812.656000-1.2832863.5776374.732755-0.2732450.032.58897427.42877425.006842
528911010.03.70212310.00.250.7975311.024.994276359.996758-0.01373722.935469...32.1805402.656000NaNNaNNaNNaN101.0NaNNaNNaN
528911110.01.42228610.00.250.5490291.024.952371359.999402-0.0124527.976619...17.2224802.656000NaNNaNNaNNaN101.0NaNNaNNaN
528911210.00.61836810.00.250.6183390.024.9431340.0039330.002087-30.028957...-20.7802493.113000-1.1170503.5946714.677518-0.2241960.033.83520227.89454625.109114
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5289113 rows × 28 columns

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" + ], + "text/plain": [ + " pop iMass age Fe/H_initial Mass In_Final_Phase \\\n", + "0 0.0 0.253826 10.0 -1.50 0.253820 0.0 \n", + "1 0.0 0.088370 10.0 -1.50 0.088370 0.0 \n", + "2 0.0 0.129027 10.0 -1.50 0.129025 0.0 \n", + "3 0.0 0.107198 10.0 -1.50 0.107197 0.0 \n", + "4 0.0 0.112194 10.0 -1.50 0.112193 0.0 \n", + "... ... ... ... ... ... ... \n", + "5289108 10.0 0.109859 10.0 0.25 0.109859 0.0 \n", + "5289109 10.0 0.560205 10.0 0.25 0.560185 0.0 \n", + "5289110 10.0 3.702123 10.0 0.25 0.797531 1.0 \n", + "5289111 10.0 1.422286 10.0 0.25 0.549029 1.0 \n", + "5289112 10.0 0.618368 10.0 0.25 0.618339 0.0 \n", + "\n", + " Dist l b vr_bc ... VR_LSR \\\n", + "0 3.455078 359.993232 0.014634 122.744509 ... 131.992138 \n", + "1 4.022375 0.013713 0.000185 12.497959 ... 21.748799 \n", + "2 4.088510 0.013794 0.002649 -27.810827 ... -18.559668 \n", + "3 4.176494 0.008937 0.001278 4.183377 ... 13.433196 \n", + "4 4.353625 359.987559 0.002188 118.772595 ... 128.017359 \n", + "... ... ... ... ... ... ... \n", + "5289108 24.954491 0.012388 -0.010701 -8.985492 ... 0.263706 \n", + "5289109 24.906523 359.999096 -0.009612 -33.844011 ... -24.597881 \n", + "5289110 24.994276 359.996758 -0.013737 22.935469 ... 32.180540 \n", + "5289111 24.952371 359.999402 -0.012452 7.976619 ... 17.222480 \n", + "5289112 24.943134 0.003933 0.002087 -30.028957 ... -20.780249 \n", + "\n", + " A_Ks logL logTeff logg log_radius phase 2mass-J \\\n", + "0 2.238285 -1.851283 3.596892 5.033655 -0.595754 0.0 28.378792 \n", + "1 2.461908 NaN NaN NaN NaN 98.0 NaN \n", + "2 2.468231 -2.513734 3.562028 5.255248 -0.857251 0.0 31.112354 \n", + "3 2.476643 -2.727480 3.544549 5.321999 -0.929166 0.0 31.671548 \n", + "4 2.270699 -2.678708 3.548547 5.306789 -0.912775 0.0 30.926651 \n", + "... ... ... ... ... ... ... ... \n", + "5289108 2.772000 NaN NaN NaN NaN 98.0 NaN \n", + "5289109 2.656000 -1.283286 3.577637 4.732755 -0.273245 0.0 32.588974 \n", + "5289110 2.656000 NaN NaN NaN NaN 101.0 NaN \n", + "5289111 2.656000 NaN NaN NaN NaN 101.0 NaN \n", + "5289112 3.113000 -1.117050 3.594671 4.677518 -0.224196 0.0 33.835202 \n", + "\n", + " 2mass-H 2mass-Ks \n", + "0 24.022030 21.952436 \n", + "1 NaN NaN \n", + "2 26.369013 24.119329 \n", + "3 26.923018 24.683262 \n", + "4 26.520765 24.450412 \n", + "... ... ... \n", + "5289108 NaN NaN \n", + "5289109 27.428774 25.006842 \n", + "5289110 NaN NaN \n", + "5289111 NaN NaN \n", + "5289112 27.894546 25.109114 \n", + "\n", + "[5289113 rows x 28 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data0" + ] + }, + { + "cell_type": "markdown", + "id": "92ad938a-77e1-4b44-b4fc-11a5a077cd01", + "metadata": {}, + "source": [ + "## Let's look at stellar density" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "439537fd-7506-43e8-8d20-a9bf034f8a3c", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Set up histogram bins, and points for density function plotting.\n", + "dist_bins = np.arange(0.0,25.01, 0.5)\n", + "dist_binc = dist_bins[:-1] + np.diff(dist_bins)/2\n", + "dist_func_pts = np.arange(0.0,25.0001, 0.05)\n", + "ccycle = ['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3', '#999999', '#e41a1c', '#dede00']" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "92eedecb-2b19-46ef-9cc3-70b81ee44458", + "metadata": {}, + "outputs": [], + "source": [ + "# Generic plotting code we can use for any population\n", + "def plot_pop_dens(pops, poplabel=None, ylim=None, legend=True):\n", + " # Cycle through sight lines\n", + " for sl in range(len(model.parms.l_set)):\n", + " sum_catalog = np.zeros(len(dist_binc))\n", + " sum_func = np.zeros(len(dist_func_pts))\n", + " # Cycle through populations\n", + " for pop in pops:\n", + " # Bin catalog data - sum mass and divide by volume\n", + " data_sl = catalogs[sl]\n", + " l,b = model.parms.l_set[sl], model.parms.b_set[sl]\n", + " cone_rads = np.sqrt(model.parms.solid_angle[sl]/np.pi) *np.pi/180\n", + " cone_vol = np.pi * (cone_rads*dist_bins)**2 * dist_bins / 3\n", + " cone_chunks = np.diff(cone_vol)\n", + " binned_dists = pd.cut(data_sl.Dist, dist_bins)\n", + " if model.populations[pop].population_density.density_unit=='number':\n", + " sum_catalog += np.histogram(data_sl.where(data_sl['pop']==pop).Dist, bins=dist_bins)[0]\n", + " else:\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists, observed=False).Mass.sum()\n", + " # Get density function from model object\n", + " r,phi,z = model.populations[pop].coord_trans.dlb_to_rphiz(dist_func_pts, l,b)\n", + " sum_func += model.populations[pop].population_density.density(r,phi,z)\n", + " sl_lab = '('+str(l)+', '+str(b)+')'\n", + " # Plot the summed data\n", + " plt.step(dist_binc, sum_catalog/cone_chunks, where='mid', c=ccycle[sl])\n", + " plt.plot(dist_func_pts, sum_func, \n", + " c=ccycle[sl], linewidth=5, alpha=0.25, label=sl_lab)\n", + " # Plot labeling/formatting\n", + " plt.yscale('log')\n", + " print(model.populations[pop].population_density.density_unit)\n", + " if model.populations[pop].population_density.density_unit=='number':\n", + " plt.ylabel(r'Stellar Density [*/kpc$^3$]')\n", + " else:\n", + " plt.ylabel(r'Stellar Density [M$_\\odot$/kpc$^3$]')\n", + " plt.xlabel('Distance [kpc]')\n", + " plt.xlim(0,25); plt.ylim(ylim)\n", + " if legend: plt.legend()\n", + " plt.title(poplabel, loc='left')\n", + " plt.tight_layout()\n", + " plt.savefig('validation_figures/spisea_'+poplabel.replace(' ','_')+'.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ebb6c8a9-6214-432c-b00f-761711ae923d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mass\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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H42hvb18QUfehHzyKZ3uLL1guthMtV5NELta0uSnqCCEHISPF5Afl7uFg3jFkcpXsE44n1SgydssSUl1UdJ7u/vvvx7nnnotly5apg8uvfvWrg/a5+eabsXr1ajgcDpx44ol49NFHp69ra2vD5ZdfjpUrV6r7OOOMM7Bu3bqyr1MidaWIunQnWqEIXzyRxHO949g3GlLeU7m2TL+qBheHfRNCstPotuHQTm9R5uQDExFsHwioZi1CSPVQ0RG7YDCIzZs348ILL8Rb3/rWg66/8847cdlll+GWW25Rou6GG27AWWedhW3btqnI3NjYGP7nf/4Hu3fvhtPpxNlnn63EoqRkF4rff/o1cOZxdJc6lnO+/peC9xOKxnHxfzyqCp8JIaRcuOzGGLKt+8YLmhkPTyYxGY5hTbukcs30vySkCqhoYSdCTLZcXH/99bj44otxwQUXqPMi8H73u9/h1ltvxRVXXIG7774b69evR3Nzs7r+DW94g6qxW0hhJ6JuvvNXJ0IxvLh/omRRx2kShJBikIjd+g7v9HmaGRNSO1S0sMtHLBbD448/js997nPTl5lMJpVuffjhh9X57u5uPPTQQ6rGTpon/vKXv+BDH/pQzvuMRqNqS+P3+7HYDEyEsXc0rOpcCk2T6GqUJgnH9Hn+miaEFIvbbsGR3Q14tnei5BKS+f54JYQsHFX76RweHkYymURHR8eMy+X8iy++qE6fdNJJOOecc3D00Ucr0Xf66afjTW96U877vOaaa3D11VdjKUildOwZCWI0EDvoutnTJGQupMx5lHoZQgiZC/Ij8HsXnaiE2lgwhp6RYM6u+0wz457hIA7p9KluW0JI5VG1wq5YvvrVr6qtGCT6JzV7mRE7ifotNPFESqVdg9H8JqKC3SKTJDz8xUwIKauZsTRe5ZtUkWY0GJs2M7YW0YRBCFlcqlYdtLa2wmw2Y2BgYMblcr6zs3NO92m329W2mIiYK+ZgKnCSBCFkIVOzh3X5ivqRKdeLmbEcj+R2hJDKoWoVgs1mw7HHHot77rln+rJUKqXOz9eAWCxUZFLF8ccfj4VE0h8v9fmLEnVtXrsqdqaoI4QsFBKBEzuUYiZVyHFLjl+jgeI8Ngkhi0NF/9QKBALYvn379Pldu3bhqaeeUl2u4k0nadPzzz8fxx13HE444QRldyIWKeku2blyySWXqE1SsQ0NDVgo9gwHZ9TO5UJMQle2uhdsHYQQMntShcsWwd6x/NNupMlr11BQ1elJMxebtwhZeipa2P3973/HaaedNn0+Xf8mYu7222/HO97xDjU27KqrrkJ/fz+2bNmCP/7xjwc1VFQSczH7bPMd6HwlhJDFoL3BAbvVpIRbIfrGI8qjUwShNHcRQpaOihZ2r3nNa9RIrXxceumlaqsGwrGEKjouZApKCCGVgDRUHNZlLsrMeNCfhD8cV2MN5XaM3hGyNFS0sFsqpMZONrFTKWc9naReM+vpijUFJYSQpcJhM+PQrtLMjDet8OEHHzyJ4o6QJYCV+FmQ+rqtW7fisccem/d9ScRx/1hIdb7K4G2b2YS17cXXy3GaBCFkqXHbrcrMuFi27vWjd6RwCpcQUn4YsVvgerrdw0FMhOIHLtQ0fOrsjYjNqrWTH7bdzS40e2barXCaBCGkksyMpQu2dzR7U0WmkfG+MWOCzsoWN82MCVlEKOwWiEgsiR2Dk4jEszRLaNqMbliZ20g/KEJINZgZL2+2oNFlV8e3eDJ/DfRIIKaOgTQzJmTxoJJYACZCMdVJJqnXYkyH17QVf9BLpBKIJWOIpWJIphJIpJJI6gkk9SRSekr9itZhPK5J02DSTDBpZpg1M6wmK6xmq/prN9vVdYQQUipuhwWHLWvAzsFJBKPJ4syM2zzqdoSQhYWfsjI3T/RPhDEayEi95qHNZ1fp12yp1mQqiXAirLZIMoxIIopYMqrEWzmQx7SZbXCYHXBanHBbPeov076EkGKQTMOhnb6cM64PMjPu96u0bIt3caf7EFJvUNiV2aC4fzxS0HRYg46OJgtaPGZEk4Zru4i2UDyEYCKo/kpUTpAIm4bCYkuidPFkcYIyfb/RRFRtE9EJdZlE8FxWN3w2n9pkH0IIyYXUzknGwWULq5q6fEgCQ2qOw/GkMl3nj0hCFgYKuzJQih+d1azhhzu/ipeefrGo/TvdXfjQkR/OK+5E1H3v2e+iP9hX9DpWelfhQ0fNvF+JBgZik2rbj31wWV1otDeh0dGoUrmEEJKNjganavTa1ucvuO/AhJgZJ5Qg5IhEQsoPhV0Z6ukyTYfzYbMm4XWH8dJ4caJOELH2pUe+iHLTM7kHwXgQNlPumZASNRyPjMMetKPN2Y52Vzt/ZRNCsiKmxDJntpgfvMOTaTNjD5rcNDMmpJxQ2M2DgQkj/ZCvSSKWjCCUnITVFoHFBoxED6RLP3fCv+QUVnOJwhUT3ZOmi2se/ao6nf5bbHQvlAhhlW8Vmy4IIVmxZ3huFmvAvmm5Dz+4mGbGhJQLCrs5kErp6BkJqlb+XGIumPArQRdPxdDus6PRdbCAE1EnDQy5uHTLx0qumytUjyf7iFCTiF2xyL6yDknR9k72qNvzIEwImY2kY8VU/ZmeAyPICrF1nx+7hwNY03Yg2kcImTsUdiV2xcYTKewYDKgW/kw0UwyaJYx9oR3QTMbtpHyku9mpvJ/mgoi02cLPbDKry2wmuxJpFrEyMVlUDZxZRdK0adElNXPGllQ2KfFUHLFkHJdu+WcE44GCj58Z3Uvjj/rRi150e7sp7gghM5BjwncvPEEZGadHKcqP4EJmxtJ0pusaVrW6YaaZMSHzgsKuhK5YEXMyGiw97zWRiiOYmMBYZAwW16BxmR6DFWY4rCZ0NbpUs8RcD5DiNScWJA6L0/hrdihhVw7E9046YiW9KiIvGA8pX7ximIiOq3TsCu+KsqyFEFJ7RsaC/JVshRw3oxlzsrMhIlDEnpi1Z6Z0CSGlQWFXJDJGZ8+wYTocSQbhj48inDRmIcb1mZE9n9OCdp9DGQSXdDC0uOC2yuaBy+Iqm4jLhkT4pOtVtlZnq5ppK80U/phfbfEpu5VcjEVGldFxh7tjwdZICKl+XHYLNnT5sHMogEAk/4/HUCw5bWbsddJuiZC5QGFXBPtHQ5hMxVR0TgSd1M3lotVjV63/xWAxWdDkaIbX5oXH6llQIVeMsPTYPGrr0rswHh3H7oldeW8zGBpQ6eBmR/OirZMQUn3IZJ1DOrzYOxrC0KTh3ZmLRFLHywOTyry9zedYtDUSUitQ2BXBtuG9SNnjSCFfKkEHtCRcDsMmJBeZdWmHNm2A01qcCFxMZI1NjqaD6u2yIeJPpmSIOBUkfczaO0JINjPjla1uOG1m9I6G8u4rNXk9IyGEY0msaHap2xJCioPCrgj0gV3wOt1Gv2lKg1n0nQak5H+aSS7C5tZnEXMO4z8e+guSWhIJLYWYlkDclEBKy26HUukCSOr60hRrjbKxeROuPfW6iv+3EUKWBonCibjbus+YeJMPie7JpIq1JczTJqTeobAroivW7R+FK2GMy5GG1y3PrTzoNq/EW9BjH8Kty+5Roi+TpBJ5cURMcUTMMYRNMaxtWgV7NACInYnZDsj4rgoTQxJ9E6H2wujWom8j+8qYNIeFKRRCSHY8Dis2dBVnZjwaSGIyHMfadjea3MwIEFIITZeqeZKVdFfs//34FrhdzrzCLk3/8X1ocblhLVAvZ7U4odlM0MTNZGrTRAyJvYnFDki0TM5Lqladti+J8JO3hwg1qbnbN7m3KGuUu974cwo7QkheZKzYaV+9p6Tb0MyY1LLWmJiYgM/nm/f9MWJXIikT8NQRPfA6rMqM0wQN5qQZ7Y8aDQSdj90Iayr/MGyFdzn0E/4Z+tSuYkGn2yPQ7Loh8tLHLSWQJOWrGadF5InYs7oBqwuwuY1o3wIhB1ARaZ2WTtVJW8okDEIIKbeZsViniCUKxR0h2aGwKxGp4W3w2OC0WtBgccFjdiAcMmxPDCT8ljutMM3kPuCez06f1TP+akhCxyg0jEBrOwQ450bjinjY2GbrRonypUWe+usxtjJ32ba52lRH8Eh4JO9+cv1y7/KyPjYhpLbNjCdCcewZDiBVwMx40B9Rf1e3iZMAxR0hs6GwKwGrCWhzu9Fi88AlM16z/mK8GvFianz1vbDqN2a/Sv1/OXQsB4bGYBqfAFRtSY77kvq/6MSB6N70gjNEnt1r/J1ndK/LvQzRZEyNF8vFQKhfddWKRx4hhJRiZrx9cBLReH4z4/FQHC/1+bGuwwsbmyoImQGFXZH4bDas9DTCbbYhngwjnjxgtBlPhdFvj2BltK34O9RWIK5dV3g/fSesv/4iNIjdiEy3kEhejrLIpnXA2Tcd0HYi+GZH10T8icDzdAL2hqnInqmkA/FK70rsmNiuJldkXbKuq/my6xsPUV59hBBSDA6bGYd1+bBrKAh/OF7QzHibiLt2jzJBJoQY8NNQBF2r1uHwVd3SaoJodALvffLfoOmaqq8z6TLRVYO2VoMrZcfX17wPPpsHeiIpIymMrOwsHRZ/DEDhUa0G2lpAt6m70LECGmTSQ/+UwJvF2A7gx+cUd79tRxgpXs08FdHzHdis+RsfxEh5lXcVdkzsUB522Ygn4+id7MVq32rWwhBCisZiNmF9hwf7xsIYmDDSrrmQ8Y4v9U9iTZsbDa6Zc7UJqVco7Ipg5REnQEt3qoRGEDNlH4vTFBiD7ajXwuSYOsCkUkAqDj0WAyJRIBqFHonD2hIBIjFAon5i/CuWJyKQMgWg6ML7p06f9mVgqlzOEHjSQasDbh2aY+qCP3zMEHbFMvQcEB43RFw8CAQHDlwn9iuOBsDRaET1XC0HpZ3tFge6vSvzTqeQdO1QeAjtrvbi10UIqXvkx6AYE4vfXc9wZg3zwciYx+0DATWpor2B3fiEUNgV4WOXid3RhLvO/OGMy8LxBN5yw1/x95QbWmZaU06b7NDEqsRleDal5ZEeSwKROPRQAogkVPpSRKASeTK5Iiq/VKfElnTAajPFpOyuBwAtBmg+QHvT94FE/l+3ingEuOutxun030K0Hwm87SeAs8kQe1PTMmTaRLurQ40Wy4VcJ+PSWG9HCCmVFo9ddc9u3Ve4c1amWUgjRneLi1kCUtdQ2GXhkksuUVvaWyYTEW4OiWBloMcSiKQOmG0Wg2aTFKhZiTI9mYIWjkMPxFVED3ABFikenhJMzRuMdlwRfMmoIeCkvi0ZgR6JQg/r0Fwa4HMWboSVGjtJw0rErlgGnwUm9gDBwan7kIheE+BsRrujCaFECKM5OmWNerseHNJ4yJLOwiWEVCduuwWHdhZnZrxvLAR/OIbVbW647VYKPFKXUNhVAJrZBHjs0Dx26PEk9EAM+tgBT5P4Xc8Xd0ctVtjO9cFkEluUXLMYNaO2rtTonpxGRlNGZBwY36UikN02NyajuX9Rx5Mx9AX3Y4W3u7h/ByGEZGCzHPhRmLY9KcQR3Q34/kUnUtyRuoPCrsLQrGZoTU6kGuzAMg+wv9guCzGQk3q+ZUg1u6A1WKFJ7VwsYGzRSaOWTg0aEbPjA3NgiyJP2lbeRCssNuCIM4wLxvcArmbA1gCYjbfYWGQMHpsXjfbG0h6XEFL3zMXM+LneCYwEomj1su6O1BcUdhWKyWSC/aJjjVq8UBy6dIfl8nZKpg5E9aT2Tgw8xSpA3Nl9GalkaeYQ/7mo/8CWw7Kk1LStK7M7NjRkiEj0GvWB0oDhaMD+wD64LW5YF3BSBiGk9s2Mh/wR1TWbjUwz4+0DkzCbTGhys2OW1A8UdhV+MNPEn8lugd7okDZT6OMRVZNXCEnpYv8kIIXEHtuBZg7V7Zoh9iTFmin0RPhNjw8uIW0rEcHt/6lOxvSE0fErJCeAyAQwIe82G3ZO9mFV65Gwu9pmNpoQQkiRZsYrWz3K3kT87qQrNhdyKJMRZMubnOhsLDFLQUiVQmFXJag6Ea8dcFslrwlMxqDnMirOaFzQh4PQIgloLc7stSZidyKbZ8qSRCJvIsSkhk42EXrFpG2lo3eKa3r/UHD3je4VuHbLZdA8HYbQZB0MIaQERNgd2mnCjsGA8rPLh0T3JNq3qtXNmjtS81DYlYiIpXQ6IE1Y0qWLhES5tBYXdK9NhrJCj2b31MtED0SlgwFodxuNGvmQzlWpj5Mtl9CbjugdwK5ZsDEwihc8U7crwAvBvYhO7IYj0GfMunW3Ae52wyCZB15CSBHIxAmZVLFjcBLBaP7j8EgghngyhTVtHmWCTEitQmFXIiLqTvvqPUu9DGiSkuj0QPNHkRrK6IDNkabV4zHoErlrdRnpXaupuF+us4WepFhF4IVHjW0qTatZnbh2IoDoThmrAey1uzEhDRWziJnMuGbNscbpeBAOMUMWGxf/PmNTnn9tgETyZL4tIYTkwWox4ZBOH3YPBdQM2Xz4wwk1qWI9Z8ySGobCroxI15Z0by0WSpg1OGZE4Yq1RtG6fbB94OjS0xLS5epuNTYhFjIEXmQM2hv+HY4pm5XVehIvhweR0Gf9io6Fgf3/q07uiY7gMFsDrJn+dtLM4d9rbDa3MdPW3aHq8wghJBtmk4a17R7sHwujZyT/pArJsHDGLKll+K4ucfKECLd7/+X0rLeT65aifkOTursVXmDvZNG30Xv9RpetGCXPB5vL2BpWGF230Qk1ds0SGsZyzYw9kaGZ+1sOPKexWBi7g71Y62iDWcvy61n88kLDgGWHMkNWIs/VajSBEEJIBnLsXd7sMqb4FDAzjiWSeLZ3DKvbPPA5rUt27CZkIdD0zE8BmUF68sTExAR86VmxFYp6GeMp6KEY9JEwkKtTLMMaxfbh46BJ3d1CHdCiAewbfh6jk73ThsmxeAhX9/xWnf7Cjkdh01NwJ+NYHZ5ETrkmlivSnStduiaJGLYD3mWA3bMw6yaEVC3hWKLkchnJtoidCsUdqQWtwYhdjaAOSDKizOaE7rZBHwwalid50P1RwKwZligLcUCze7Bs2QmI+dsRkHStRPOCwwftFjRb0evwYGUkMD1Ldwbioye1fNKdm0oAk/uNTRotROCJ0GMUjxAyRzNj2Vfqp9N2KoRUM3wX1+j0CnR5gKEQdDEqzoOML1OuKa0LI+7kPlf6VmK3nkRIjIlFjKU583o171bwiyWBxY0V9qbsI82ykfbeG91upGlF5ElamBBSt2SaGYsNyq7BAMI5fuRmmhnvGQ7i0E4ZycioHaluKOxqFGX+2+5WnndqEkUe9KCIOx1oW5i0rFkzY5VvNXZN7ERMOmDTtG2UtltA5sxGxjAWC8KSjKIz29ixzFm12RidBEZfNsSdr/tAFy8hpG7NjJ024MiVTcqk2F/gR+5YMKYmVUgTBu1QSDVDYVfrkyuandAtJqRGMyxRsqCLTcBg0PC6WwBxZzFZsNq3Bi+MvnBwl600RMiWjGMoPAaLnkCrNuutmS9yl60ez+YBfCuMjlqmaQmp647Z9R0e9I6EMDSZZ4QigMlIQnXMih2KfREdDghZcmH3m9/8puTbvO51r4PTyZEuS4Hms8Nk1qAPh/LOqlBp2wUUdzIjdpVv1fT5WCojepfG2Yg90hBrbUCj1NV1HAX7wDPZa+/y1ePJLYa3AWO7AN9yI5LHGbWE1CWqJKTVrbzrcs2YTROJpww7lA4v3LRDIVXInN615513Xskfqpdffhlr166dy8ORMqDJEGwRd/vyW6IocTcUXLC0rF0Miae45tGvFr5B1wps3PAqXHvIe6CJMXIuaZqrHk9SvyLuxvcA3i6gYaVhgkwIqTtkXqyIO6mnyzNiFvGkjpf7J7G61Y1GOXYSUkXM+edIf38/2tun5osWwOvlBIFKQHNYlb1JwSkVE1HoCf3AfNlip1QUKew2Nm/CC6Nbi77NC/6diHZtgUOicJN9gIwhEyPjXOSqx5Mmi9EdRqNFQ7fhjUd7A0LqimaPXYm77QOBvPslUzp2DgWwIulCu8+xaOsjZEmE3fnnn19SWvW9731vxfvA1QtqnNhiTanIdl+ahmtPvQ7RZBT+6AT2BvbOMBTNRFK1M6J6EmlrWg00rlImyJjcB4THDr5hsfV4nUcDF/7VmHBBCKkbPA6rmjH7/L78lihyaJLaPOmuXd409UOXkFoUdrfddltJ+3/nO9+Zy8OQhUCib90+Y/LEYk+pmEIOjg6LQ21mkwX7A/sK3iaRko62qV/NcnBNjzWLhw/42knjhNTYFUv/k8CevxpCsXE1YGUNKCH1gsNmxiGd3oJTKoTekSD84Rg2dPpgZscsqTVhNzY2piIszc3NGBoawl//+lds2LABhx9++MKskJQVEVUSfVNTKmJJ6IMBIFl4SsVC0eJsQSKVwGBoIO9+e/x7cFjzRpgz58oKIsaa1xnC7L2/N9KtqnkiDzPq8XQgMAAEBwFPl3E/nEtLSF1gzRBpaT+7fKzv9OC2i0+C1cKOWVK5lPTT4z/+4z9w7LHH4rjjjlNRuLe85S2455578M53vlNdR6rIBsVmhsljg2m5D5rdrCJ5B20ZBz0ZVbZQdLg70OxsybtPJBFBz+QepPTsdYEQwSd1c6tfAyw7zrA5kUkV2TarI3vORaJ+ex8BRncCyUSZ/nWEkEqfUlEs2/sDeKbXmFJBSE1E7G688UY8//zzCIfDWLlyJXbt2oW2tjY13+zVr341PvjBDy7cSsnC1dy1e4CBQM5aN0EfDkOX5gvHwrT/L3MvU+lWv0ySyEEgFsDeyV50e1fmrnVRado2Y4tMABM9Rj1esYhwlNuIyJMOWrFKmR0lJITU3JQKYcgfyWqHkjmhQurtlB1Ku0fV6hFS1RE7i8WimiYkDbt+/Xol6gQZXsui0upFxJp0y2r53OJ0vaj5s3Neg6Ypweay5m9kmIhOoC+4v7g7dTQAHUdORfCM92rWtGwifPAWmwSGngd232dE8FI5IoWEkBqZUmHBylYPNi5rUJE8m+XANtusODFlhyLTKgipNEoKv5jNZkQiETgcDtx3333TlwcC+dvGq42bb75Zbclk/YTbNacVaHMZ82VzeMXpIm4GgmoOrbYABcQmzYTVvtXYObFDpV5zMRIegcVkRburOLsd2D1A++FALGhE48TTrtQO2o7NwAX3GWKREFKziG/doWYftg9OKgGXC/HBk1FlK5qd6Ghg4xWpHEr6dr777rtht9uno3RpQqEQvve976FWuOSSS7B161Y89thjqDcTY/Guy4eekIaLYN607XyQ5ggZPWY1529gGAj2YzQyWtqdi62JzKdddaphdVIKA08DvQ8Bg1sLz60lhFQ1bocFG7p8cEitcQH2jobRM7Jwx0RCFjRilynmMhGj4mLNikllo3nt0KRj1p9bvOjRhITNoLW6FmQNMnpsjW81Xhh9Me9+YpMiM2h9Nl/pAu/DjwPBIWB8NxDOU4M3e6KFdM+Gho1GDanBY/0dITWJpGNF3O0YDGA0kD97M+SPIp5IYXWbR82mJWQpmXcl/DXXXIOOjg5ceOGFMy6/9dZblR3KZz/72fk+BFlktCaHsjrRx/OIu0AUus2s5tAuBHaLAyu9K/PPlZUutbGXVfo2XZsnky2KqveUfTztxhbxA2M7ATWyrMgGC0nnik1K83rDT48QUnNYzCYc0uHFtowa21x+d4P+JCYjcaxt9ygbFRGGrD0nVSnsvvvd7+LHP/7xQZeLr53YoFDYVR/qYNTqgh7Ob/mhj4YBm0mNKlsIXFZXaXNlATWuTCZblHRAdfiAri1AaBQY3wVE88/TnUbqAAefA1wtQPMh2W1UCCFVjcmkYVWruyS/O0FsVKTjluKOLDbzroCXmbFdXV0HXS4ds319ffO9e7KUXnfSTJE5VzY+c5MO2dT+SaRCccPsWLYy1pmk58qWgsyglXFlc8LVDHQdY9ThyfiyYjto/XuBPfcBQy8AqfppuCGkXpCO2VL87oRneuh3R6o0Ytfd3Y0HH3wQa9asmXG5XLZs2bL53j1ZQjI7X5d6ruxweAgDwdzTKQ6aKzv3BwU8HYCrFZjoBUa2ldZB234U8IE/G1E8QkjN+d1NhOLYMxxQXbHZyPS8EzsUEYWELCbzfsddfPHF+MQnPoF4PI7Xvva16jKZRvGZz3wGn/rUp8qxRrJUVNBc2RXebpg0M0bCwwVvk3M6RSlIU0TTasDTaXTQylzZYhh8Btj3KNCwyqi/43gyQmrO787rtGLHwCTieexQhD3DQZg0DZ2NtEMhVSTsPv3pT2NkZAQf/ehHEYsZBe7icye1dZ/73OfKsUZSCXNlJyJ5mykWY65sl7tLzZWdiOZvctgf2I/1TevL86BSNycdtIF+o8Ei12SMbN2zkTGgaR3g7SzPWgghFYHbbtihbB+YRER+yOZBJlnItIruFhfr7Uh1CDt5o/7rv/4rrrzySrzwwgtqMsUhhxwy7XdHqht1IJLu1zY35PClL6HTuqxlhXcFknpCjRfLhQi//mAfOt1d5XpgwNtlRO/SAi8ZL3w72Wf4RUBSyC0b2FxBSA0h0yhE3IlJ8WQkf6PZ0GQUsWQKa2iHQhaBso0P8Hg8OO6441Q3LEVdbaLJL84iUqx6ruKTMk2nWOldBYclf2pjKDRUVNp2TgJv+QnG32IJjwH7HzNq9mhiSkhN2aGs7/Ci2VO45EJq817u9yu/O0IqXtj94Ac/wBFHHKFSsLLJ6f/4j/8ox12TCkIzacZM2QK/OPWR0IKuw5hOsVoZGRdKyfqjEwuwACvQusHooLV5iruNdMuO7gD6ngCitTWCj5B6t0ORSFxXoyNrI0UscWCTZopnescwGogiHEtMb5xaQSoqFXvVVVfh+uuvx8c+9jGcfPLJ6rKHH34Yn/zkJ9HT04MvfelL5VgnqRA0ixlodUMfzCNOxP5kIgKtYeFSjyLqZPSYzJVN5rEY6ZnsxRqTBe4pA+OyIv53y44F/PuA4RcOXJ5v5JjYo8jEC5laIeKQkysIqQmWNblgs5hVVC4NPe9IVQq773znO/j+97+Pd73rXdOXvelNb8JRRx2lxB6FXe2huaxKtIl4y4U+FgEcFmj2hWv1l25ZScvu9u/OvQ49hT3+PVjXsFZNsyg7ciBuWAFkmCkXZYsidBwFXPgAYPeWf12EkEWn1WuHxdSAde0eNYqsVM87WqOQcjDvd5HYnEht3WyOPfZYJBL5C0pJ9aJJ2iGagJ6jaFiHDgyHgC5vwdTtfPDYPOj2dqvRYrlIphLY5d+NdY3rYDUtzJQMOJuA7lOA3geLv83AM0DPg4YhskTw+GudkKqn0W3D9y46AS/s9yMuxu55yPS8I6RihN373vc+FbWTdGwm3/ve9/Ce97xnvndPKhSVMmhzA/snoec4eMlkCoyGobVmRLMWgAZ7Q8EO2Hgyhj3+3VjTsBZmbQHSn/J8XPhXIB4CUgmjc1Y6aAtao+jA2C4gNAy0HgbYFiBlTAhZVNwOK45a2aTsUMKx4qZPsM6OlAtLuZon/u///g8nnXSSOv+3v/1N1de9//3vx2WXXTa932zxR2pgMoWMHesPijzJih6IQndaoLkX1qi3xdkyYwpFNmLJGLanXlbpWxGmMrKsrDUtyhpmSph1HQ2Ex43JFfFw4dvKfNr9jwONq4GGbkbvCKlybBYTDu30YtdQAP4Cc7cFsU05bFmD6rQlZEmF3XPPPYdjjjlGnd6xY4f629raqja5Lg2LQmsTzWGF3mA30q75umTtZqPxYhEodrSYzKGVkWUL9t50NgLLjgfGdwP+IqxOZGKGRPpCQ4zeEVIDiEhb1+5Fz0gQI4H8HqDihfdy/yTWdXiVKCRkyYTdvffei0pl27ZteMc73jHj/B133IHzzjtvSddVi/V2+mQsv6/dUAjo9CyYiJLomwi1F0a3Fn0b2Vfm0EoTxoJhMgHNawF3m2FWHAsWvg2jd4TUlB3K6jYPbJYw+sbzR+9DsSS29flV84VrARvPSG0z73eOCKXMjtjZ48a+/vWvY6nYsGEDnnrqKXU6EAhg9erVeN3rXrdk66lVRKwVqqPTowlALFAWaGairEGibyLUxP5kl38XoonsXbuSqi02qlc2pPO161gjejfyUnHWKEPPA/69hi2Ks5kCj5AqZlmTE3aLCXtGgnmD9zJ+7KX+Saxpc6PBxVnTZAmE3Uc+8hE0Njbi7LPPnnG5+Nj95Cc/WVJhl8lvfvMbnH766XC7md5aCLTM1EGuZorhEFJmk2GBYjWVPXon95eOvm1o2oAdE9sRLzD6azI2ubARu2zRO6uzdGsUSele/DeKO0KqmBavHVaLSdXTJfNM6JHrxC5lZYtbWagQUgrzTuT/6Ec/UhG7Bx54YPoy8a+766675p2mvf/++3Huuedi2bJl6kv7V7/61UH73HzzzSoSJxMvTjzxRDz66KNZ70vWk5mWJQtH/K7nEb/j2YO3Hz+L2LceRvTavyJ2+5ML2gWWNjCWKRX52DvZi5B0si4mMnN2xStKu42MJNv/9+Jm1BJCKhaf04oNXYXr6OTwuGc4iP1ji3x8IlXPvCN2b3jDG/Dtb39bmRL/6U9/Uh2yv/71r5WoO/TQQ+d138FgEJs3b8aFF16It7714MjGnXfeqbpub7nlFiXqbrjhBpx11lmqlq69vX16P7/fj4ceekhFEMkCIRG4bh/03gOu6/lQ+8VTQBGzZ+dvYLwrp4hM6SllcCwed1KntyhI1O2iBwxrlKjfSM3mEpeZ1ihiibLvMaOxwtW8OGslhJQdMSLe0OXD1r3jBfftG48gmkhhVYtb1esRUoiyVGe++93vxvj4OE455RS0tbXhvvvuw/r16+d9v5LenZ3izUTsUy6++GJccMEF6rwIvN/97ne49dZbccUVV0zvJ0LzzDPPVFG9fESjUbVlCkJSHBJRtX3gaCXWpJ5OHwgoi7aDSKZURG+xEAPjFd5u9Pp78hoY757YhXWN62ExLVLBctoaRTZ3BzC+C5joLXy7ZMwwNvYuA5rXcSQZIVWKROzWdxY3dWY0EEMimVIzaWmHQgoxp2+xTG+6TETUifWJRPAW2rsuFovh8ccfx+c+97npy0wmE8444ww1q3Z2GvZDH/pQwfu85pprcPXVVy/IeusBVTNnM0OzmaEndaQKdIBJt+xi/P5stDci7o6jP9iXc5/YlIHx6oY1C2NgXLD2bh3gagWGXgByNH3MYHI/EBk3oncys5YQUnWYMyJwMoUiH8OTSQQiCWxc5oODo8dIHub07njyySezXi5ROolypa9fSO+64eFhJJNJdHR0zLhczr/44ovT5ycmJlTd3c9//vOC9ykiMVO0yr+lu7u7zCuvExrs0EJx6LHcxpz6WFiNHFsM2lxtiKdiGAmP5NxHau2k5i5tYLzoOBqA5ccDo9uBydwidBpJ3/Y/CTSsAhpXsbGCkCqm2NFi6zo8+O6FJ8DjWKDxiKQ+hZ3Uz1155ZV485vfnHVObCXR0NCAgYGBova12+1qI+UaOeYyRo7lapIIxKCH4tBci3OA6nIvQyKVwJAYAOfAH/Wjz7QfyzzLsSRIalXZm7QYUysSBaZWyHMrFirhUWPmbGbHLSGkonFYzThqZSOe6Slca5dmx0AAz+8dV1MqaIdCsjHneO6+fftwzjnnwGazqc5VEXmvfe1r1fnFQCZbmM3mg0SbnO/s7FyUNZD8aFYz0OSAPhouMJXCa4wnW+j1aJqqtwvG85sES1TParKpKN+S4W4F7D4jIleM550IQGmuaF4PNK1h9I6QKkCOSRJ9i2SkYQOROHYNBQ+yQ5FUbTqqJ1eJHUp3swttvkWyayK1L+ykQSGVSuHBBx/Eb3/7W3z84x9HX1+fMgAWkffGN74Rzc0L17knAvLYY4/FPffcMz1JQtYj5y+99NJ53bdYqMgmqV4yT7xTKdlI9pSsLp53Y2ForYvjL2jSTOj2riy4n9Tj2cw2NNgbsGRYbED7EaV73nUebXjemZmqIaQaxJ10yaaR016HDdsHJlU3bL5gfc9ISBkai/kxx3aSNPMKk0izwqmnnorrrrtOWYz87W9/U7Yj3/3ud5X33Kte9Sp84xvfUNG9uSDTImRyRHp6xK5du9Tpnh6jw1Hq4b7//e/jP//zP/HCCy8os2SxSEl3yc6VSy65BFu3bsVjjz02r/shB6ZS5Dvo6JKSDS+eP1tm56tMoZDGiWzbjvHtGA2PIJKILKjnXl6sLqD7lNJuI1G+ngeACLu6CalGHDazskNx2ws3cvVPRLB7KIhUHsNjUl+UtbVm48aNavvMZz6DoaEhNe1BNuHyyy8v+f7+/ve/47TTTps+n25sOP/883H77bcrw2F5nKuuugr9/f3YsmUL/vjHPx7UUEGWFs1iBpqdRto1BzKVAst90BbZp6nY0WIyh1ZGli36r2J5vAv/ajRKiLj09wLje+QZy+95J521IvDUvNmVTM0SUmXIhIpDOn3YNRTARCj/D9/RYExF99Z30A6FAJq+ZKGIyke6YqX5QjprfT5aSsyX1EAAuj+qplAI1ncdqYyN02heO0wt+WfOlgN5y1/x18/ghdGtJd3urjf+fPHGj+UjOmnYosw2NZY6ux+dY5x+z+8Bi/NAt22rNFZUwNoJISUfr3pHQ9g3GsIn//sJddm179gMu9QwZ/HGW9vuUU0ZsjE9W59ag2Y4WWCN3cKgtbhU2jUX+mQUutsGzbGwb0s52En0LZo0zKgl3dqXw+NOUrXpqF5Kr5D3g90LLDsWGN1h+NkVIjJhjCNrORTwHJjIQgipfOR4JTNjM2MwxVijSLetNGZQ3NUfjNlmgTV2C4NmMUFrzB81kpTsYgSR5WAn0TfZlnmXY7l3uWqWOGgzHejy3ju5b+lq7bLaohwKtB8OFDMtI5UAhrYCQy8CqQoRqISQohFxt2l58dEcsVDJ7LYl9cOChUakieGll15SBr9iTUKIwpPfDkdPJIHxCLSmxfVj63B1IpaMYyKa209qMuZHX3AJPe6y4W4zbFEkNRsIF7ZGkdFlgX5jYoWng7V3hFQJ8mP0BxefhOHJqKq7m22Hks0WZf9YWKVmGbWrLxZE2H3ta19T82JPOOEE1S3r8XhUatPppHlqvVPUAWYiCt1lhWZfvEoBw+NuBeKpOEJ5fO7E485mtqPVWUE/Vix2oHMzMOwozRql6zjgQ49S3BFSJchxSnzrvE4rdgxMIhLPbYciDPojMGkaVrW6YVrkxjRSQ6nY2267DaOjo/jf//1ffPnLX1ZzWs8++2x84hOfKPdDkWpHPOziB296PIlUfwCpaAJ6LLlo6U/xuFvlWwW7CKU89AX2YyI6gYpCxJlMrFh+YvG36fs7sO9RILl4VjOEkPkjjRFih+Itoh5ZOmZf6p9EPI8nHqktyh4SufPOO/Hf//3f6vSFF16Ia665Bv/4j/+o/lYLbJ5YHOJ3PV/Uflq3D7YPHL0o6QTxuFvtW6M87GT8WC56J3vVvm7r4hgrF4U8Px982OialXmzwRyj9DJtUWQUmTRWtG0yumcJIVWB2Jqs7/CiZySIkTxNaUIwmsC2Pr/aXzzySG1T9oid1WpFLGa8yU455RS4XK7pqRDVApsnFhCrSQm1UtB7/UY0b5GQpolVvtUqgpdzTXoKe/x7pjtrK0rcOXzAsmOArmMBm9ewPcncZtueJKJA/1OGP16lNIcQQgoi6dXVbR41eaIQ4nP3Yp8f/kU0gyc1ErH74Ac/qAyKZRrERRddpC771re+pdKxhEjUTaJvaaGmj0egT+Qo9E+mio7qlRuX1YVu30psH3s55z7JVAK7J3ZhXeP6GdMsKgaxNpHGiuEXDMuTfIigG9sFRMYNzzsZZ0YIqQq6Gp2wW0zYM5x/DrY0XMioMumwbfXmLzkh1UvZv41kTqx0xJ5++ulYtWqVmh/7ile8Al/5ylfK/VCkSlEp1XQ6oM0FxJOqrq7S8Nl86HR15t1HRo/t8e/G6oY1MGsVmOKQ6FznFmB899TEigKEx6ZSsxsBZ9NirJAQUgaaPXZlULx130TB33AiAKV7ljNma5MFCTO8+93vVps0UTQ1NfGNQ3Ki3hstTuj9gbz76SkdS/Euana2FNwnFA9h72QvVnpXVeZ7XdbUtAZwNBq2KDKhIh/JGND/tDGKTG5Xif8mQshBeBxWHNrpnT4v4i0XUps3EY6pjlmXzVKZxy4yJxY0f9Tc3IxqhM0Ti4vmsELz2PJPpRiPAJ0eVCr+qB99pj4s8yxDxSIRuGXHGTNkC/ndCSPbpjzvNgDOZgo8QqqAzFFjxUyoEI7obsD3LzqR4q5GqMDCoMponpAtPb+NLDzKkDiUgJ6ryUbGjTU5FtXbbjZmk0WlXvPZoFhNVrS52lCxSO1c2+Gl+d0Jy04ALn6E4o6QKrBCkXFiMnmiWJ7rncBYMKbSuaT6obAjFYFmNgHNDjVSLBf6SAjo8i7Zr8qrHvqXgvtIOvYrp3wNjZL2rFRsbqD7FKD3weJvs/9RYOBZoP0IacVbyNURQuaBHB9lRmx6nFj/RBj9kvEoMKVCmioOM5vgc1oXdb2kgoSdeNQVw6233jrXhyB1huaxQwvEoEey+8eJWbE2GQV8+efNlhO72Y6NzZvwwujWovbvmdyDnRM7cJh5o+qsrUhEGF/4VyAeMmbIDm8DwiOFPe8m9xn7y3zaAibOhJClFXdOm/H1vqbNiwanTTVM5JhCpmDHbO0wZ2F3++23q67Xo48+unIGo5OqR2txAfsnc76n9LGIeJFAs5gX7QB57anXzfCrCyfC2DWxc8YaY6kYrnn0q8YadR27/buxrnGdEoYViepMnjJXXnEC4N8HjO6QLpX8t4v6ja5ZsURxVWcNLSH12jG7YzCARDL39zU7Zutc2H3kIx/BHXfcgV27duGCCy7Ae9/73qptliCVg2Y1Q2+wAzlSB0pMjUagtS/exAc5uDksB6KEctqsrUfPZE9OAVrxHnez8S0H7A3A0PNAvFDXbBwYeAZoXAU0rmbdHSFV0jErY8iKmTHbPxFRhsbSMWvmjNmqY87FMtI1Kh51Ykb829/+Ft3d3Xj729+uZsQygkfmg9bgUAIvF3ooBn2J3dN99gZ0ubuK8rhLFYqCVQp2j9E16+kobn/xxROBl8g/zogQUvkzZiVKF0sc2AYmwni2dwz+cAzhWGJ64/d75TOvUILdbse73vUute3Zs0elZz/60Y8ikUjg+eefh8dTufYU+aDdSYV424Vyizd9JAwsX1rvpRZnK2KpOIZDQ3k97noneyrX4242JrNhTuxoAkZzT9042NB4E+Cs4IYRQshBM2b7xsMlW6NIx600Z1TF8axOKVt7m8lkUi+0qPlqF0ScFVsZ3nZw5x5rpSeSwMTSz2mVyRQNksIs5HEX7ENV4e0Euo4BMhtApJFCzI1nb9EJYO8jwNCLQDTAebOEVMmMWUm1ri2xrEVsVNIdt6QGI3bRaBS/+MUvVOfrAw88gDe+8Y3493//d7z+9a9XQo+Q+aA15e9+VTNmPYvXSJEN+TGzwtuNYDz/jMaR8DBsZhtana2oGqS5ovPo0j3vVpwMXPQga+8IqXCWNblw0/uPw8sD0rCWf99MaxRSo8JOUq4/+clPVG2dWJ9II0VraxV9aZHq8LbLg9FIEYbWvrQpf5NmQrd3ZcH90gbGhSJ8FYXdW7rn3d6HgcCAEfUjhFQ0LV6HmlZRqGM2kxSj8rUp7G655RasXLkSa9euxX333ae2bEhEj5B5k8zegKBPRJGyW6ClTTWtRknAYpPZ+SrWJ7nYMb4dq31rlMedWKFUfJ1KpudddBIYllmzkcKedwNPA3rS6LYlhFR8x+xh0jE7GEA4VjjNunMggI3LG1S9HqkhYff+97+/8r+USM0Qv+v5ovbTun2wfeDoJX1vpv3sCiHGx+KRV/Gfo7TnnWyuVmD4RSA0nP820gk88jIQmTBmzUpTBiGkYpGo3aGdXuwaCsJfwHUgEE3gxT4/1rd74bDxs11TBsWELCgSfev2Qe/1F30Tta94NC3ywabUCRWC7CvGx5keeRWP2QJ0HAFM9AJjOws3SgQHAak/lFFkVudirZIQMueOWQ96R0MY8udvTovGU0rcrW33cAxZLQi7Z555BkcccUTRDRJifbJhwwZYLFVg1EoqBolkSfRNhJqeTEHfPynFHdl3TqUQv7O4qN5iTKgYDY/k7ITNnFJRNR53s2noBuw+YPB5IFnAxy4WBPY/btiouFoWa4WEkDkey2SsmHje7R0N5f3tlh5D1t3sQtsijnok+ZlTglzGiI2M5JgtmYWTTz4ZPT09qBbEw27Tpk04/vjjl3opdY8cZDSbGSanFaY2t4riZd0qoAs7PaFCtmXe5WqTTtiDNtMBG5f9gX3Va/jpaDAMjZ1NhfeVGbMDzwJju2iHQkgV0O5zYF27p+DkCfk494yE0DsSrN5jWY0xpxCavHhXXnklXK7ihpzHYrGq87GTze/3o6GhijoYax2vDVogCr2I4t5KQDzu4skYJsTnLQdy3UBoAJ3uKu0gtdiAjqOMKRTSWJHZSJENqc+T9GzLBsBsNXzyKr3GkJA6pcFlw4YuE3YMBNQ0inwM+qNqDNmatsJikFSgsHvVq16Fbdu2lRSxczpZX0PKMZHCBb1vsuAPj0o4rKQ97uKpuJpAkYuh0KCK5DU7qnTWsrwuTatnNkgU63knVirSdUtxR0hF4rRZcNgyH7buy/0DNc1EKI5tfX4V6ZNmDFJFwu4vf/lL+VdCSBFoYm3isUMP5CnslaLftsqo5xSPu1W+1crmRGbH5kJSsuJx57V5UbX4VhjmxOJjVyzijyeiVzpuCSEV21SxrsMzw6w4FxLZe6Z3TEXuWjxVYOlUg1TGtx8hpU6kCMWg52ik0P1R6E1OaJalr7tLe9ytFnE3sRNJqTXLEWXs8e/B2sZ1cFqqNLotB3CZOBELAKPbgUB/7n0zPe/EFkXSuRVQJ0kIyY4pQ6AVO4Fi03IffnDxSRR3iwyPpKQqJ1JoTXnET0qHPnZguHUlYLc4sMq3CpqW+yMnHbJ7/LsRT+b3kKpo5AAu0yq6jgY6twBWNyBCdfZmzeigC/QB/U8CiaWf/UsIyY50yR61srGk22zd58fuocCCrYlkhxE7Up14bNAmczdS6MEYdGm2cFSOv5Lb6sYK7wrsGNuecx8Rdbv9u7G2YS3M1W7q6+0CbB5g8LnCok2mWuz/O9C2qbguW0LIoiJRt+9eeAIiU2nYUDSBnUPZx5BlzpXtl5ne0LCq1Q0TmyoWBUbsSPXaoDTn78rWR8MV137faG9Eh7tjhqed1N5lbv7oBLaPv4xwPIxIIqK2Svt3FI1E74q1RJFI5cAzhvkxIaQij7vSTCGbzJjdvLIZjS4bbBbzjG1248RoMIaX+icRT1Spb2eVwYgdqVo0hwWa26aic9mQaJ4WiAFeOyqJVmdb7Y4fy4bYmkgNnXjYTRTwsxQBO7rDiOBxFBkhFY3NYsKhXT6Vbh0P5S8hCUYTqmNWJlW47JQeFR2xO//883H//feXZzWElIjU2uUTO/pYRE2tqCRk/NhhTRtLuk16/FjVIq9R81pjtFgxYk287vqezO2HRwipCMSzTsRaZ0PhyRPicyeRu4lQdXnbVhvzls0TExM444wzsGrVKlxwwQVK6C1fvhzVjEyekC2ZrA4j3HpGdb42OqAPBrNer6dSwHgEWktxZtqLgQjRf33V1xFOhLBrYpdKteYic/xYTeBuBazHGvV0aXKJt0QY6HnAGEXmaKSZMSEVihzTlje7VIPFnpGZx+Js1ijiibesyammW6SR21ZlRqIC0fQyFO8MDQ3hv/7rv/Cf//mf2Lp1qxJ6F110Ed785jfDaq2c4vVSSU+eEPHq8/mWejkkB/IWTu0ZR/yHRrGu9V1HGmPGptDkv2UeaLbKC/9Ls8SOie05O2Gl5u7qR76gTt/1xp+rcWU1QcQPXFviVBeaGRNS8UyG43hh/wT++YePl3Q76biV5ox6FHf+MmuNsjRPtLW14bLLLsPTTz+Nv/3tb1i/fj3e9773YdmyZfjkJz+Jl19+uRwPQ0juRoo89ie6/DdSWfYnaaxmqzIwFiPjQlR1KjZbU4UItVLonfLII4RULF6nFUd2N2J9hqFxMTzTMz7dcUvmR1lDGH19ffjTn/6kNrPZjHPOOQfPPvssNm3ahOuuu06JPEIWAs2ZPzKsRxOqyUKaLSoNMSRe6VulPOzyBdD3+PdgY8tGNaGi6pFf5RJ9k6kT4TFjhmwqXtjMuP9pYPnxgKWyGmIIIQeQrtnbPnQSXuzzIxDJbsqezRqFlId5R+zi8Th+/vOf441vfKOqs/vpT3+KT3ziE9i/f79Kzd59992466678KUvfak8KyZkjqhGigq1DZFRYss8+WtT48mYmk6R1GvkV62IOxkl1rACWPlKwNVW2Mw4Jn53jwORwnMrCSFLh9VixhErGrG8yXWQHUouaxQ2VVRIxK6rqwupVArvete78Oijj2LLli0H7XPaaaehsbE0x2pCyo2eSCpTY2QU7FYSzY5mVVM3FBrMuU8oHsLeyV6s9K6qrVoUEW8yrWJ4m9ERmw+Zudv/FNB8COBbtlgrJISUiByjVra64bCZsXc0pNyM8rFrSBovNHQ2VulYxVqJ2H384x/H3r17VRdppqhTsy97DM8qEXW7du2a70MRUhxy9Iinsm6p4RBSkYTyuKvE6F2nuxON0gGaB3/Uj75gH2oOsUFp3wQ0rS28r7x2Iy8Bwy8B0vlMCKlYpPt1fYdXWaMUYt9YGLuGAkjlmAVOFiFi98UvfhEf/vCH4XLNtJMYHR3FmjVraBlCFp34T54raj+t2wfbB46uuMjXcs8K1SUbjGe3cBFGwsOwm21ocbai5mhcaYwiG9oKpPLX52ByPyDPU9vhgKXy6icJIQY+pxWHdfmwfXASUfmhnYfRQEzts67dA6tYWpGSmPczlivqEQgE4HBUZsqL1CBWkxJqpaD3+o1IXoUhHbLSTGEv0CAgUTsZP1aTuJqBZccaNXiFkHq7vseBKDtmCalkJCUr4s7ntORspIgljG0sGMXTPWMYmYwgHEtMb5WYaamZiJ3YmwgS7bjqqqtmROwkSie2J9nq7QhZCOR9KNG3tFDTw/GcpsVIphC/63lUMhaTBat9a/DCyNac+6hyh8lerDVZ4RLz3lrD6gS6jjGaJYoxMxY7lJYNQOMqet0RUqFYzCaVlu0dDWHIP9PCqZju2Hr2u1twYffkk09Of7mIpYnNdiANIqc3b96Myy+/fK53T0jJqA+6zeiw0mxmpETcFWi1r2RsZhu6vSvz7qPrKWWTsq5xvdq/Juvu2jYdOJ+2PclH17HAxY8CJqZwCKnYpooWN5xWM3pGgljb7sbOXD/Ec/jdiaUKyc6cn5l7771X/ZUxYv/2b//GyQyk4hDTYr1vEtVMZiROxotlQzppXxrdhjUNa2E2mdUs2pr6NSvpWDEzlohcMUhaVjaJ9hUzl5YQsiS0+RzK7uQzb9iEcAFzYvrdFc+8Je9tt90237sgZEHQ7BZoLhv0PN5IEtGT6F41UOzM2I3Nm3DtqdfVjrjLNDOWTZoq5G8+I+PQMND3JNBxJM2MCan0porlDdgxMIlIkTXPsUQKzhpMUCypsJP6ui9/+ctwu93TtXa5uP766+e6NkLmjdbkEPM3NVYsG/p4GPBV7he/RN9EqL0wmrvWbjayr4wfq5m5splmxrI5TjHEXXg0/21k/JjU57UfATiYUSCkUnFYzdjQ5VM+dv5wjgk0GbzU78em5Y1w25mOzYZlrvV1MnEifToX1RoxEE8+2WjVUv1oVjN0rw0QY+JsRJMVO2os/RmS6JsINaln7Z3sxWTMn3VfSdUWG9WraswWIxI3thOY6C3OzLjlUMDbuVgrJITMqanCo4yMB2c1VcwmkdTxUp8fq1rdaPZU7g/zqhJ26fq62adrhUsuuURtfr8fDQ0NS70cMk+0RgcQiOVsk5dRY3BZK/aHiKwrHX1b17QOuyZ2IhwP573NeGQMnZ4u1CzyWjWvA6xuYGSbYVicCz1lzKIVvzsxP67Q15mQekeOdd3SVGGzqKaKfB9r8S+WCJ80UnQ1Oiv2+L0UzLttLBwOIxQ6UO+yZ88e3HDDDfi///u/+d41IWVBM5ugNTjyjhrDZHXMKDRrZqzyrYa1QAfs/uB+BGSuaq0jUbjOLYDZWnhfie4NPgckq7dTmpB6oNVrxyEdXljMhcVa33hECbwkJ1WUT9i9+c1vxg9/+EN1enx8HCeccAK++c1vqsu/853vzPfuCSkPPrsSeLnQxyPQq2Q0ldVkxRrfatUBm9/jrgeRRA7ft1rC0WBYnMi0ikKERoD+J4ECEU9CyNLinZpU4SyiuW0sGFOpWWmqIGUQdk888QROPfVUdfpnP/sZOjs7VdROxN6NN95YjjUSMm80k2akZHMgok6fyF/XUUnYLQ6s9K7Km35IppLY7d+lxpPVPFYH0LF5ZoesmBZn26RjtudBwL8vfwqXELKkiBXKoZ1eNLisOSdUpLfxUAxP94xiOGNSRb1OqZh3S4mkYb1erzot6de3vvWtMJlMOOmkk5TAI6Ri8Nig+aPQc7XUy3VeO7QqmU3osXnUXNm9k7kbCETU7fbvxtrGtSqNW9NkRjCLMTIWlh1nmBmzPoeQikSaKmRmLDKcDYr1szuqTqdUzPsbbP369fjVr36F3t5e/O///i/OPPNMdfng4CBNi0lFIR9uZX+SA/l1JynZaqLJ0YQOd/5uz0gijF5/b+3/ehUzZzEyLoX9fwcGnmXkjpAKP3ava/di4/LSNMUzU1Mq6o15R+xkTuy73/1ufPKTn8Tpp5+Ok08+eTp6d/TRR5djjYSUDTEsRj7vI+melXq8KjEtFtpd7Wr6xECwP+c+YpEiDRXLPctRs2QaGQvRScPvLhnNb2Y8uc+4rYwu46QKQipW3N168UkYnoxi11Agb7NENGNKRbJKaqcrSti97W1vwytf+Ur09fWp+bBpROS95S1vme/dE1J28kbtJNw/FobWUUQhfgUhgi0QD+TdZzQ8ApvJhjZXG2qWtJGxIH+dTcDg80A0u/ffjKYKTqogpOLFnYwhk2kVOwcDCMUKR+Ne6ptUZsaOKvqxPl/KYtssDROyZSLdsYRU6qixfOjhOPRIHJqjCAuNSvJ/8nQXnCvb498DXU/BZ2+ovZmy2RCRJnYo4nUXGMi/LydVEFI9TRVdPuweCmA8lL85LJpI4cU+P9a0udEgGZs6oCzC7p577lGb1NWlZoU9b7311nI8BCELQzJ7mD41EITW6TkgfKymihdBmfYnxUygqLmZsrkwmYC2jYaZsUyrKGZShezvruHIJiFVjtmkYW27B33jYeVll49kSseOwQCWNznR0eBErTNvYXf11VfjS1/6Eo477jh0dXXV/pcEqSnidz1f1H5atw+2Dxxd0e/vUufK1uRM2Xw0rgSsTmDohfz7yaQKSd82rQEaVy3W6gghJSLH42VNLjVrds9wUE2jyFZvl0bSt+J5193igmnWsVzuo5KP74sq7G655RbcfvvteN/73leeFRGy0Ej0rdsHvbdA3VUGal+xSangOo3MubIydUIMirN1wmbOlE2k6mwKg0ThRMjue7TwvmO7DCNjmTMrUT9CSEUi82IlPSvCLTbLpLgerVHmLexisRhe8YpXlGc1hCwC8sGV6JsINX0iktfiRHNbEfuPJ1AtpOfKyqZpJuwP7Mu7v4i/w5oPg0mrI+Fi9wKdR8/skM3F+C4gPGp0zMqEixo46BNSi7jtFjWpYsdgQNkXrW13Y+dgsGRrFJlTW+3M+1/wwQ9+ED/+8Y9x5ZVXlmdFhCwC6leZRN9aXVJdCz3HKBo9VL0RrRZnC+KpGIZCQzn3CcdDyuC427uyJn6pFk1m52uxZsbLTwI++BDFHSEVitViUpMqekaC+NTZGxHLUUOdRrkceR14z7cfQi0xb2EXiUTwve99D3fffTeOOuooWK0zOwmvv/76+T4EIQuGEjNNTuhDOX7ZVblxbYerE7FkHBPR8Zz7TEQnYDX3o8vdhbohbWbc+2Dxt9n3iDGGrGHFQq6MEDIPTCYNq9s8cNks2DsWKngIH/RXlyn9ogi7Z555Blu2bFGnn3vuuRnX1VUEgFQtmntq1Fi0eqNzuZDP4ArvCsRTcYTiudMSw6Eh5XEnUb66YLaZcaAfGHl5xtiirGbGg8/J4GHAt2xx10sIKYn2BofyrpO6u3xmxpnUipnxvIXdvffeW56VELKEaM1O6H2TqEWkfm6VbxV2jG9XEypy0RfcD6vZCp+tTjzcMs2Mm9cBzmajGzZvQ4kOjLwEJMJA01qmZQmpYHxOKw5b5sOOgUlEcs0Ir0Ez4zqqmCYkv2mxRO7yoYdyi6JKx2KyYHXDGphNuX/LSQdtr78HoXQUq96QKRVdxxiWKIWY6J0SgfU3h5KQakJsTDZ0+dDgKmw47w/H8UzvGAYmwgjHEjm3Sp+7XZb2j7/+9a/47ne/ix07duBnP/sZli9fjv/6r//CmjVr1LixpWTXrl248MILMTAwALPZjEceeQRu99SvdEJmjxoLxXN+aKV7Vm+QbtPqjNKIz91q32q8OJrbxy2lp7DHvxvrGtfDZq4Pl/YZ2FyGuBPRFsldl6gIDRtmxu0yhqwOnytCqgSL2YR17R7sHwujfyJ3TV2tWKPMO2L385//HGeddRacTieefPJJRKPGwO2JiQl87Wtfw1LzgQ98QBkob926Fffddx/sds6BJNnRLGbAl+f9IaF8f5aB8lWEy+rCCk/+4n/xthNxl6zXaJTZCnQcBXhmjknMSnQS6HsCiBVvq0AIWXw0TcPyZhdWt7lhytBjNrNJWaOUQtoapWYjdl/5yleUSfH73/9+/OQnP5m+/JRTTlHXLSXPP/+86tI99dRT1fnm5uYlXQ+pfLQGOxCIQc/RJi++d/DYoJmrt4pB5sQWmikrtXgvj7+Mld6VqkavLubKHjSG7DCje3bo+fyed1Jv1/sQ0LrRSOcKcrt6er4IqRJaPHaVnp02M9a0vNYoLpsZa9o9sJpNCMeSOOfrf0GlM29ht23bNrzqVa866PKGhgaMjxdIZRTg/vvvx9e//nU8/vjj6Ovrwy9/+Uucd955M/a5+eab1T79/f3YvHkzbrrpJpxwwgnqupdffhkejwfnnnsu9u3bh7e97W34/Oc/P681kdpGky/0Rgf0kex1Zrp0V41HoLW4UAsUM1O2rubKZhtDJiPGSvW8EysV6bqtt+eLkCozMw6KG4KY1kvGJgui/fYMh9RcWmeVNFXMO+zQ2dmJ7du3H3T5Aw88gLVr187rvoPBoBJrIt6yceedd+Kyyy7DF77wBTzxxBNqX0kLDw4OqusTiYSq//v2t7+Nhx9+GH/605/URkheJCJnzfMBnoxBj1VuGL7YmbKlkJ4rW5fIvNjlJ5Z2G/HHq9cmFEKqyMy4xVO4PlYiey/1+TEaOHAMlOhdvgaLpWyymHfE7uKLL8bHP/5x3HrrrerX/P79+5WIuvzyy+c9jeLss89WWy7E/Fge/4ILLlDnJSX8u9/9Tq3liiuuUE0cxx13HLq7u9X155xzDp566im87nWvy3p/Uh+YrhEU/P7iZ4mS2kFFpcT+ZCCQ9XpdLC9GQ9A6vahGMmfKpvQkdk/sRljSiVnInCtbt8j74YMPG6PFpKkijx/gDM+7zEgfIaSCzYwjBc2MJVnTk5HJKSYlu1RNFvOO2ImAeve7343TTz8dgUBApWVlzNiHP/xhfOxjH8NCITNqJUV7xhlnTF9mMpnUeRGWwvHHH6+id2NjY0ilUiq1u3Hjxpz3ec0116gUcnpLC0JSf2hOq9pyoUcS0IPVa3+SninrsrpxSPOhcNs8qgv2oM104NdsMJ+gqXXkwOxqAbpfAXiXARZn9s3qOHAbMTNO1p7pNSG1aGa8rt0Dc2ZXRRZKbbRYqiYLSzm+IP7lX/4Fn/70p1VKVsTdpk2bVG3bQjI8PIxkMomOjo4Zl8v5F198UZ22WCyqM1fEpoREzzzzTLzxjW/MeZ+f+9znVGo3M2JHcVfnpsWTudOP+mgYEAFY4GBQ6VhNVmWDsnNiR95O2N7JHritbiUI6xazxbA3EZNimVaRD7FLkY7ZjiOL88YjhCwZDS4bDltmzm9mnNFoYbeYVFOFNGLMZqmbLOYl7CQKdvvtt+MXv/gFdu/erUSeeNdJk8L73ve+iii0LpTOzUSsUGiHQtJInZ3mzf1+UJ2zExFoTdX/pS1iTTpgd/t356wLEdEn169rXKfEYN0y3THrBMZ25d9X6uxE3IkYdNTJRA9CqtzMePdwEBOhePadphot5CgpTRVr2txKFFYSc07FysH/TW96k0q7SsfpkUceicMPPxx79uxR3nFvectbsJC0trYqw2ExHs5EzktDByFloaFAdEpmzFawn1EpeGxeLPcsz7tPPBkzPO702vg3z7upom2jMTs2H8m4YWQcHFqslRFC5mlm3Fno2K9+7OrYPhBA33j2GuWqE3YSqZOatXvuuUcZE99xxx3Kx+7pp5/G3XffjT//+c/44Q9/iIXCZrPh2GOPVY+fGUGU8yeffPK87lu6cCWdLDV6pL6ZkWaVCF185ibdsanBoPqrtgofNVOIJkcz2l0zyxtmE46H0evvrfp/a1nwdBhmxnlGtU03UkjjhYwiI4RUpZlxLmSihfjiidCrBOacihUhJ55wp5122kHXvfa1r1VNFT/60Y+UcfFckXq9TCsVGQ8mXa1iNLxy5UpVD3f++eerzlfxrrvhhhuURUq6S3auXHLJJWqTGjtpoiBEiN+VYVSbA63bB9sHjq6IMoS50uHuQDwVw1hkLOc+kzE/+oL7saxAhK8ucDYaY8gGnjHMivOZGUtDhXTXNq0zGjJoZExI9ZgZ52EsGEM0nlR+d1Ur7J555hlcd911Oa+XurYbb7wR8+Hvf//7DOGYbmwQMScRw3e84x0YGhrCVVddpQyKt2zZgj/+8Y8HNVQQMmesJiXW9N7irG/UfhLNqxIjy1yIYJPpE7LlYiQ8ApvZjlZn66KuraJnzO5/rDQzYxoZE1JdZsZ5CMWSeLHPj64i0rgVKexGR0fzCii5TmxG5sNrXvOagumeSy+9VG2ELAQSeZMInIi1lHgYBXIInWSqqIhetSBjxFb5ViOUw98uTV9gv2qkaMgYU1a3WGyGkXHn0UD/k6UZGdtKm1VJCFl8M+Pe0RCG8zglCImkrkRgVQo7sRoRO5FcSGODTH6oRqTGTjb5NxKi0qo2M0ztbtUoocaK1QFmk1l1yhaid7JXiTuXpBXrHbFD+dDfjfmy/r3FGRlT2BFSFWbGq1rdaqzY3tH8ZsaZpJagFnnOwk4iadL9msseJHOCQ7XBGjuSDc08NUdW/OvqBDEpLoSup6ZtUGRcWd0jdijiXedsBka3yxOUf3/pmF1+AuDgsYaQSqfd54BT6u6GAio6V4jt/ZPYuLwRNsu850EsvLCTOrdCzKdxgpCKxGuHJrNi81ic6JE4tCqvsctGPJXD10lIxvDy6EtY3bBGmRhXc/NI2fAtB8TMeWgrkMf4GakE0P+0YZ3iblvMFRJC5oDXaVV1d9JUIXV1Bevu9k+opgqPw1rZwu62224r70oIqYE5soI+GoEuArDGxM3XHv1KUfsd1rQR//qqr9fcv39OyBiyzi3AwLNK/Ba0Q2leDzSsWMwVEkLmgN1qxqFdPvQMBzFaYLxkPKnjpf5JrGxxozWP6X25WLzYICG1NEc2n9O4RPMmqrcUIRNJrW5s3lTSbV4cewGRRBarj3rF7jU6Zoupo5PU7eiOwulbQsiSYzZpaqzYiubC04fkI71nOIiekeCCe4DOe1ZsLcLmCVLMHFmE4zk/oPpEBHBb1Viyakaibteeeh2iSUOoyr93b6AX/ujB9i+xVAzXPPpVdXowNIBVDasXfb0Vi9VhdMuKj53MkM2HmBgnokDrYUa9HiGkoulocCq/u11DwYNMisXbLpN9oyFMBGNqdJltgb4fKOyywOYJUghNCmGbcjdSKME3GobWsfRmleUQdzJLNs26xvXYNbETIenmzMFweBheuw/NjuZFWmWVdMzKlIrhF4GJPfn3DQ4CIqbbjwDMdTyXl5AqocFlw2HLzNgxMIlY4oCYu+LOp7Pu/+/nH4eNyxuUT1654c9BQuaK1NHlaZLQJaJXoPaimj3uCnXM7g/sw2RsctHWVRVIBE6aJHzdhfeNTAB9T2afYEEIqTgcVrOKxLV57Vjbnr/0Ip5M4aU+P0YD5S/bYcSOkDmimgNaXND7cosXFdFzWqDVWErNYrJgtW81dkzsRFK6OnNELXv8e7C2cR2clsI1KHWDvG+a1hw4n0+4iUG0mBhL5M7dzgkVhFQ4FrMJ6zq8+Ma7jsGekWDO/WxmEyRrK+lblxarHGEXj8fx+te/HrfccgsOOeSQ8q2KkCpBs1ugee25U7LJFDAegdZce+a9dosDq3yrsGtil/Kyy0ZKT2GPeNw1rIeVKcXsFDN6TFh2AnDxIxR3hFTBj/5lzS647BbsHj647m42g/7yRuXnFUawWq1qZmytIY0TmzZtwvHHH7/USyFVgNbkAMy5v2x1fxR6gRmD1Yp41q3w5rfniCfjysA4mc/Lrd6QKR0yJ7YU9j8KjO1aqBURQspMo9uGDV1e2K2Lm7GZdyr2ve99L37wgx/g2muvRa3A5glSCpJm1Rrzpxr14RCwzFuT3m6N9kbE3TH0+Hty7hNJhNEz2aPSt7X4HJSMPAcX/tUYJybIX+mYzWYTkzl+bGQbYDIDjasWd72EkDnhtFmUmfGuoQD84UR1CDuZB3vrrbfi7rvvxrHHHgu3e2bB4PXXXz/fhyCk4tE8+RsJ1KSKiUhBAVittLnaMRnLP/g6EJvEvsC+ghG+ukHNIJ46XspfieANPgtECzScSNROBGDLoUzLElIldXfrO7zYNxbGgFhhLfTjzfcOnnvuORxzzDHq9EsvvTTjOv4yJ3WJ1NXliNqlLOYDnbRWU019RrrcXTM87bIxEOxX9XgiBNMGyLX0HMwLi82YUiEjyEIj+fed7DMmWbRtMiJ4hJCKRtM0rGh2wWkzq2kVBcrullbY3XvvveVZCSE1Qvyu54vaT+v2wfaBo2tG2GT+O9JGxYWQqRZigFwrz8G8EZEmHbAjLxniLR8i/vqfAtqPNEQhIaTiafHYlS2KzJmNJbIHASrG7mTr1q3o6elBLHbgl7ocrM8999xyPQQhlYtE37p90HsPnsiQC7VvPCV976il8WMvjG4t+jayr0y1yDRArntE5LZuAOQ5KdQsIWnb/icN42Nrbab5Cak13Haj7m7nUACBSKLyhN3OnTvxlre8Bc8++6wScukRS+lf4BzLReoBeb9L9E2EmnTA6gMBIFeoPaUjfudzqDUyx49Js4TYoIjdSTYyx4+RHEiDhNluROXyed4pr7uHgLbDjbm06a5bRkEJqVisFhMO6fBi72gIgckKE3Yf//jHsWbNGtxzzz3q76OPPoqRkRF86lOfwje+8Q1UI5wVS+aC+jFjM2roUiLwcnkTSZSuRkmPH5PtEJNF2ZwUGnidSMXFs33R1lhVeDuB1BGle95JI4Z03VLcEVKxmEwaVra6kYgEy3u/872Dhx9+GF/60pfQ2toKk8mktle+8pW45ppr8M///M+oRsTqRFLLjz322FIvhVQpWqMDmqU2UqxzxWPzYplnecH9xCYlqfNHVE58y4HlJ5V2G5lWkWeWLyGkcmj1OSorYidRLa/XCP+LuNu/fz82bNiAVatWYdu2beVYIyFVh2aScWNOIyWbBz2RyjtvttppdjQjloxhKDSYc59wIoxef6+aYsEmiizIc/LBh4DwqOF1l0+wZXreEULqknlH7I444gg8/fTT6vSJJ56I6667Dg8++KCK4q1du7YcaySkKtGcVmgee9599JFQwVRltdPp7kSjozHvPpMxP/qC+xdtTVUp7lwtQPcrAE8nILN3s23WjF/+oztkYO9SrpoQUo3C7v/9v/+HVMqoGRIxt2vXLpx66qn4/e9/jxtvvLEcaySkatGaHdDMeT5m0hE1GUWts9yzQo0fy8dIeARDoaFFW1NVIvN2OzYDrtbC+07uA4ZeAKaOz4SQ+mDeqdizzjpr+vT69evx4osvYnR0FE1NTUyrkLpHxo2hxQV9MHdKVh+LABLds9ZuStakmbDKtxo7JrYjmsgtZPuDfbCZbWiwc5RfTuQ91X44MPIyMFkgyhkcBKQ5RTpmzWVztyKEVDALMpm2ubmZoo6QKTRX/pSspGJlKkWtp2TNJjNW+9bAYsovMHonexFi4X8RXneHAo2rC+8bHjMsUxLZp4EQQmqLOf2Eu+yyy4rel7NiCZGUrBOIxFWzRDbE+06biAKNtW37IdE4idxtG30x7/ixl8ZewtqGNbCJj9sUHD+WhabVgNkGjL6cv55O5vj2PQF0bqaRMSE1zpyE3ZNPPlnUftV6EKaPHVmQLtlWF/T+PCnZcUnJWqDZaztl5rK6sMLbPX2e48fmiW+ZIe5kxmw+EhFD3MmUirSRMSGk5pjTN0itz4cVHzvZ/H4/GhpY60PKg+awQvPZoY+Es16vy6iK4RDQ5TWEYA3T6mzF+sZDsH385aJvw/FjeXC3AubNwL5H8++XjBtpWam5czUv1uoIIYtIbYcGCKkwtCYndH/uWic9ngRGw9BaXahlJOr2zVd/C3v8ezASHs67r93iwOcf+Oyira1qcTQYHbP5xo+lEQHYcijg6eD4MUJqDNbYEbLIgkZryV/jpAei0CUl67ah1p8LMSU2aRomohM594tKCpEUhy3DUobjxwipS1hjR8giM6OGLpm9mSLVH4AmKVnLVOO61VSTnyf5N0m9XTwVZydsOZDomwg1GSlWLLJvLAjYPQu5MkLIIsEaO0KWkPhdzxe1n9btg+0DR9ekuJv2uBvfrsaP5WMyNskau3zI+0OibyKS9RQw/CIQGi48fkz26zrG8MgjhFQ1ZfkU//Wvf8V73/tevOIVr8C+ffvUZf/1X/+FBx54oBx3T0htIdG3bl9JN9F7/UC8dicIiLfd6oY1MBfwuNtLj7vixJ2kZKXzddlxQPP6wuPHZOLHwDNAMrGUKyeEVELzxM9//nO8733vw3ve8x488cQTiEYNV/mJiQl87WtfU6PFCCEHkKibRN9EqOnBmDInzkkyVXRUr9oRnzqpuds1sQu6RJuykNJT2OPfjXWN65UnHilC5LUcYtihjO3Kv29k3OiYFTsUC59bQuo2YveVr3wFt9xyC77//e/DarVOX37KKacooUcIydFEYTPD1OSE1uRQUbysW745szWIzJPtzvC4y0YilVDiLpmiz2TRNK4CWjcUbpAQI+P+J4F4dkseQkjlM+9vjW3btuFVr3rVQZeL/9v4+Ph8756QmkdrdkGzFJ4TW+sjx9LInNhOd1fefSKJiBJ3EsEjReLtAtqPELfs/PuJqOt7EojmNtMmhNSwsOvs7MT27dsPulzq69auXYtqRKZObNq0Cccff/xSL4XUAWJGrLW5oKFANEUmU9QJba42tDhbDrpcxo9Jg4VsY5Ex7BzfqURe5lYvAnhOuFqAzi2A+UB2JSvSxCKRuzB/nBNSdzV2F198MT7+8Y/j1ltvVeml/fv34+GHH8bll1+OK6+8EtUIJ0+QJbFAaXZCH81db6f7o9A9tpr3t0vT5V6GWDI+w8C4mPFjHD1WAIcP6Dwa2Pe3/PtJqlsaKto2Au62xVodIWSphd0VV1yBVCqF008/HaFQSKVl7Xa7EnYf+9jH5nv3hNQNMm5MiyZUQ0Uu9JEQYDNDsxZO3VY7Isy6fYbH3UrvKvRM7inqdhw9VgQ2V/FTKvY/bnTWSiqXUyoIqXg0vUx5i1gsplKygUBApTE9nuo3u0xH7KTD1+crzZ6CkLmgp3TofZPGaDEhnkL8jmfVSeu7jjSsUqxmw7y4xufJphFht2NsO4LxYF4R2OHuxEfu/pA6f9cbf05hVwgxJf5aicdpTqkgpOK1xrxr7Hp6elRNi81mU4LuhBNOmBZ1ch0hpNR6O3feNKKIvnwp21rDarIqjzun1aksTrJtss9QaHCpl1qdUypKIT2lghBSu6nYNWvWoK+vD+3t7TMuHxkZUdclk7QkIKQUxAYFLc68/nZ6IAbdboHmtaMekOibpGN3+8XjLnuSgfYn85lSoQMjLwHBgez7ckoFIVXDvD+ZcpDNFl2QlKzDwVQIIXNB89jVlg99NAw9Wj+TAjw2D5Z7VhS1b0qnyCttSoUHWHYM0LKhuCkVg88azRWEkNqJ2F122WXqr4g66X51uVzT10mU7m9/+xu2bNlSnlUSUodoErUL5WmkkCjLUBCQers6MTJucjSpmruBYH/e/Xone3Fo0wZ2xpZK81rDCmV0R/79wmMHplQUsk4hhFSHsHvyySenv1yeffZZVWOXRk5v3rxZdcYSQuYxnaLVnXcfPSGD3kNAe/66vFqi3dU+5WM3mnOfQCyA/cH9WO5Zvqhrqwkauo0RZJJyzddbF500jIxF3GVG9Agh1Sns7r33XvX3ggsuwL/927+xa5SQBUCzZETiktmnLOjxKCAdsg1TX67SOVvjIk8EWzwVUwIuF6PhEdhMVrS5Ztb/kiLwdAAmKzD4nLRq595P6vP6p8SdpHQJIdUr7MSEWBokbrvttunLfvjDH+ILX/gCgsEgzjvvPNx0003K044QMn/idz1f1H5atw+2Dxxd0+JO/m3STLFzYoeaNpGL/mA/rGYbGu2Ni7q+msDVbEypEJPifCSiU5G7IwEHDd0JWWrmXJhz9dVX4/nnD3zRSDr2oosuwhlnnKFMi3/729/immuuKdc6CalPJPrWXVo0XO/1K/+7WsdsMmO1bw2sBWq89k7uzRvZIwWmVHQdDZjtMztkE+GZW2wS2PsIMN5j2KFwrBsh1Rexe/rpp/GVr3xl+vxPfvITnHjiifj+97+vznd3d6vo3Re/+MXyrJSQOkQiUxJ9E6GmJ1PQ+wOA1NVlI5kqOqpXK4ioW+VbjZ3juYv9dT2lplasa1gHO02LS0dSrJ0ZUyrStif5oJExIdUXsRsbG0NHR8f0+fvuuw9nn3329Pnjjz8evb29818hIXWOaqKwmWFyWmFa5jXGiVlNB2910hk7G6fFiZW+VXlTz+Jxt8u/W3XUkjngbAZWnFyakbHU3xFCqidiJ6Ju165dKjIn48SeeOIJlZ5NMzk5Cau1Otvgb775ZrXRXJlUGprdAjQ7lIddPmTerGZzol7w2rzoci+bPh9LHWwTI520L4+9jNW+VTBpxqxdu9le07WIZUOeo4seNDphh18AwqOFjYzHdgLtRzBqR0i1CLtzzjlH1dL967/+K371q18pH7tTTz11+vpnnnkG69atQzVyySWXqC09v42QSkLzOaBFk0q85TUv9tiM6F6dIB53aa559KtF3WZj8yZce+p1FHfFIM+R1NwtOx4Y2QYEckypSOPfC5gsQOthFHeELCJzzt18+ctfhsViwatf/WpVVydbppfdrbfeijPPPLNc6ySEZKC1uPKLtpQOfTAIPVX7TRRpJPomQq0UXhjdimgyumBrqklklJiINV8RU0BE/IllCqdUEFL5EbvW1lbcf//9mJiYgMfjgdk880vmpz/9qbqcEFJ+NPGta3MDfZM5Z6fq8SQwVD/mxfJvlOhbOBFGj38PgvHcw+olVVtsVI9kQd5PLesNI2NJueYjNAL0P23YoXBKBSELzryrrSVVOVvUCc3NzTMieISQ8iINFRK5y4cejkMfz+3zVovizmV1YX3TIfDavbCZbdk3E49NZaFxJdC6oXCqNeo3RpCJ5x0hZEGpzzY6QmoETerovPlNwPWJCPRA7nq8WsRisiiPO/lbiBC7N+eHtwtoO1zCyPn3E387MTKO8fkmZCGhsCOkytGanUa3bB70kRD0aAL1hETmxONOKyA4eiZ7WGc3X9ytxlixQkJapoTICDLpriWEVFaNHSGkMlD1c+1u6JHcwk3V4Q0GAfHBqyO/O0nLrvR2Y49/T859kqkE9vh3Y23DuqIifAuB+OwlUnHlsydbIpVAQk8ipafUdcmp07IZ6Oo/dUrXYdKM+cAaTOq0SdPUX7NmUf8ms2ZWkzosmhlWkxUWs1VdVlacjYa4K/iPjRtp2fYjjdsQQsoKhR0hNYCINa01o94ueXA3rC7TK/ZNQuuYaqaQcWV10FThszdgmWcZ9gf259wnmogq8bemYY0SRItBPBnHUHgI/pgf8WT+VLmIONm/lIkcIvPyIULParIpoWczy2ZXncWyyWVzem/YPDM97fKx729Gd23DStqhEFJGKOwIqREy07HFjBaTGbQyrqwexF2LsxXRZAwj4eGc+4TiQTVXttvbveDPyXh0HPsm92ZE4PKLuu8+cwt6J3uKvv9Odxc+dOSH84u7JBBGOKsIlPS13WybEnoOOC0OOCxOld4ummJGjwnii3fx3yjuCCkTFHaE1AoSgev2Qe/1F7W72i+ekmI01ANd7i6V7pyITsy4PHNKxVBoUM2W7XB3HnT7ckypECHXH+zDSHik6NtIpK4UUSfIY3zpkeLmdK/0rsKHjpopAuU5iCQiagMmZkT5HGZD5InYc1lcM+fvWl3GnFgZKVYs+x8DRl4yumsJIfOGwo6QGkFEh0Tg9FgSuvjXhXOk7pKpoiJ6tfj8rPB2qxo2GS+22FMq5DFFoKW7cItNr2YKz8+d8C95rVrkPr/37HeVsCuWnsk9ah3FROOk3i+YCs7wCDSbLErgST2jSwTfBX+BOW1rIhHJIRlBNlJ4/JjJCjSvLXrdhJDsUNgRUkOoAnq7BfoyD/S+gGFSnAc9Eld+ePWC1M+t9K1CPJlQkSoRNaVOqXBkRqiKROro9k72KmE01/SqIKKukAC7dMvHihaM5TBpluaTyZhfbZnPsyH23HC0Hgrz6E6Yg0MzRbH4n5rMsKeSRqxwogdIxYGWQ5mWJWQeUNgRUoNoMvapXSZTBPKOFZPInu6w1pW4k8aANQ2r8dHNl6gpFbkQcSLNFCIy3v+H98zpsaRjdTA0iMHQwLzTqyJEpR6uEJJSLakWrkiKiTCWHDE86iysDk3g0tAATBYHNHme/HugNa1Vz7shBOVfJH91FW0VjPNyffp0xqVTtzEuOXBedQpPdw3P3JwWl0ozS6dwPdScktqGwo6QGkXNkhVxNxDIOXbMmCkbADq90Cz1Y4MidWGrGlZj18QuVU+Wi/5QP5a7i5iJmgWxKJEGidk1fbNJp1dFtKU7UsWixKJZYDKZp2xLzHBanEp8pIXMtKDRDAEp/46knjKMUKZOi22KrEP+pvQk4mKjMisVPTvdW64Ub7HsdjVgMhmBTX6MCMF+QFK9TSKqTfOKcBZLZp2hPMcmzQyLEnqGVYxl6rJUKmm8NmozXqdcXdTlqMkkZC5Q2BFSw2gOC9Dqgj6Ue26qnkgp8YcujxHpqxPcVjdWeFeg159bLEiEak8J6do0IpzEPiWSJyKYRpoRVjesQZOjCYtJUCZBTFHuubl5u3LDo4B/L2KJEK7p/UP2O4hNAqPbAYncmSxzinCWQmadofINRBKZVQxzEZaHNm7AV075KmwW+6JZ6BAiUNgRUuNobhtM8RRS47lFhqrFGwgCHSLu6ifK0GhvRNwdQ79EiXIQVZ2hxROIBbB9fLuqPctFZoRsbeM6NNgbsNhIs4M0hEjtYFltVAr56Hk6AalTHH5h+qKYngCSs56vpN9ovGhai5imF91AUgrF1hnORVi+NL4NTw49qdYqkT2rmlFsnfINtCsRKZYyEvljdI+UEwo7QuoArdEBLZGCHsg9OkuNHJPIXvuUgXGd0OZqRywVx2gJFiS5EBuTLzz0/9SYsmKRL/WlQF5j6fItdZya7B9OhFR3r9QoFuPFdxCORqBh1fTZnJG7OTaQzIV86ehSOpMzxWKxkVCZa/ypYz+tGnMMwWeH3WIv/3QQUhdQ2BFSJ2gtTkDEXTyPuBOLlOGQSt/Wk7hb5l6mojKZnZ3ZkEaIlb6VWa8bCA6oztdSRJ1EzJZK2AnyGpfa5Sv7pyOMUtsXSUaUyAslgurv7Pq9XFgdTVgZ9qPH6St7A8lcKFaEFRKWsr5SO653+3dhODx00P1KlM/wDTSMouW5l/cLU7ukroXd6tWr4fP5YDKZ0NTUhHvvvXepl0TIEs6UdRmRuTzowZiYk0FrdqKenptuXzd2ju/MWxcnBsZemwdNjubpy0Tc7AvsxVhkbMa+2SI74vkms2vFBkSo9hScrF2aOmRrQYu6TDpXReCJ1100GUFCzbqVpo3EjCYeue2H9m1FvASRYm2ZgFbEONqi769EEVaMsJQUtDRilMNyRkbNyTaZoZXleUtPBBGhJ5s8/5LiJaQuhJ3w0EMPwePJmGFISJ0izRGadMoWQPdHoJs0lcKtFyTttdq3Gjsmtuf9Ut4X2Ke+RD02r+o47fH3ICDF/gUiOzKtYZVv1YKkESsJeW4kopetblB176quXV116aJpI1J7Hz5wvaZBpF+G/DPOT2lfPfSoPAL0hhVyZ1P7GnsbonHqsmkBeeC8/CePKanj9Cav3yeO+SQiyahxXnUPp+Y1g3ehLWdiiZiabQzMjC5LHZ8IPHmfidhrtDXCZqnt9xqpY2FHCDmAZs6IkCRzf4mlhoKqLk/z2Y1xZVUcWSoW+eKWeqedEzumzYRnIyJB0q0rvSuVHUo4XrjzVUTOcu+Kuq+ZUgbaIow0wAwzcOFfgalJHBAxPfic0RGbb0rFeA8gHcTergVZY1rwyeuftosxziem7GIMyxiJTBo2MnOoMcxCuS1nJLr40S2XTkdUjckgLiUASW1T0a/w/fffj69//et4/PHH0dfXh1/+8pc477zzZuxz8803q336+/uxefNm3HTTTTjhhBNmHEhe/epXq1TsJz7xCbznPXMzGiWkFil2tJjMoJVxZfUg7iTaIaJtt393Tv8/+dIXD7xiaHd1oN3VXhfPXcnIc2LLiCCvOMkQd5HxPDfSgeFtgHQdN3SXfUlp0+JiU5vKJzApQi8xJfbiqhlHag3jqRhiyXher8SFspyR9HI4HlKCNDOiLJHEtMhTk0HMDr43a4yKFnbBYFCJtQsvvBBvfevUr7UM7rzzTlx22WW45ZZbcOKJJ+KGG27AWWedhW3btqG9vV3t88ADD2D58uVKGJ5xxhk48sgjcdRRZSzSIKTakOhbtw96b/5GgUzUvvGUfCugHpA063LPcuyd3Dvv2bSL7U9X1ZgtQMdRwNBWIDScf9/RHUaUb4nny0oU1mwxI1cLjPw4SOgJld6XqJwIvlgyqrqL5balNloUspwpVLdnPH4M4zDEszF5wzlD7DGqV91U9Kt39tlnqy0X119/PS6++GJccMEF6rwIvN/97ne49dZbccUVV6jLRNQJXV1dOOecc/DEE0/kFHbRaFRtafz+4r/4CKkWRHBI9E0JNVW3FIc+HMwsbDpAMjUd1dMDsbpqqJAGCYm2SKdrsemyzOvlC5uibg6ocXiHAyMvAZMF0o5VMF9WPm9WzfCvc8F10PXfes2/YTI2qcSevH9E8EWnxF+2iHGxdX7FvF8zo47S7JIZtRYDbxF58peNGdVFRQu7fMRiMZWi/dznPjd9maRbJSr38MMPT0f8UqkUvF4vAoEA/vznP+Ptb397zvu85pprcPXVVy/K+glZSlTqZSr6JnNidYsJKRF3edBHQ9DtZmV4XC90uDsQiE/OKV3msbFha87I+7N1g5o6gYkDwjorIv4kLdu60RCFVYYItWbngS7rNFK7JyIvkoio7uJIIqqsZaRLtliKfb9mjlQT5DFlE1/GdPrWbfXAbXWpv7XeAFTtVK2wGx4eRjKZREdHx4zL5fyLL76oTg8MDOAtb3mLOi37SnTv+OOPz3mfIhIltZsZsevuLn8NByGVhuaxwSSdiiNThezZ0AF9KKT+yv71gjRTrPGtxS7/zqrxp6sZmtcBEi0aKlALGhwyxF37EYCpNsoF0ilS2WZH16QrVsyhReiJWbSIvnQd31x89GRfidjlMl6W1K1MVEnfvwg7jxJ6bnis3gXzFiR1JuyKYe3atXj66aeL3t9ut6uNkHpE89ph0oHUaG5xp0wjhoNKBMr+9YAMhf/mq7+FF8dezDteTL7kur0r1f7V7k9XUTSuNNKthQiPAf1PGTV6NSw0pC5P1cNZD6R100bR4sEogk8sXPwxf87mn/lMyUhH98aSY9PejTIlQ4SeIfY86jNAlo6qFXatra0wm80qKpeJnO/s7FyydRFSzYi1iUn8vkbzW3hIZE9EoLJCqQMsZgsObToUO8a3K4uL2TTYG7HCu4ITARYKmS+baX2SCzGX7n3IiNxJerNOxHWmUXS6qlNE3fT4t0RYmUZLSjdT7M01uieNIJnpWIkgyiap2/Ra3FNCTwQoPxeLS9UKO5vNhmOPPRb33HPPtAWK1NPJ+UsvvXRe9y0WKrJJ+paQekPzOYzI3VgBcTcq4k6H1lAfJsbyRba2YS16A73T3nXyhdXqbKOdyWKS9rMrxPKTgA8+VDfiLte4ONmaMtK4YoGSFnqhRKhsUzLSiHBU9x0PYQiD0DSTimb7bF7Vbc4ShToXdtLwsH379unzu3btwlNPPYXm5masXLlS1cOdf/75OO6445R3ndidSMNEukt2rlxyySVqkxq7hoaD3dMJqXVErClxN5S/oULEn4rc1cmECrvFgXUN69UXoviD0RpikZC0Y/cpQO+Dxd9m3yNAYADwMoOTmcYVcSVbGonqpUVeMB6YmmpRnm5bVXunp5SPXtpLT34geW1eeG0+JfgYzSs/FX1E+vvf/47TTjtt+ny6sUHE3O233453vOMdGBoawlVXXaUMirds2YI//vGPBzVUEEJKR8SaFk8WnFKhJlTI/FnZX6IjNT6lQv5t8oVEFhF5P2VOqZC/A88CyWj+KRUDzwAWO+Ck7UwujLmzdjRNxfUyZ/3Klm128ly7bdONGJKyNdK2RjRPCT2rR/1wIvNH0wtVV9Yx6YjdxMQEfD7fUi+HkEVHjyURvfavJd2mnqZUkCVEIkv9Tx8Qe9OXh4EfnWOcfs/vARHhbRsBd9uSLLPakSkrwUQQwVhQCbrd/uImrqT53An/krPbdnZ0TwSmRPIkbSs1evVyDPGXWWtUdMRuqWCNHSFTcEoFqVQkEtd1tBGVi2aZL5tGbEBkkkXq0AWbL1vLSIerT4ktH/7ttJtUM0ZQRfQCCMSDWTvF59NtGwsPYyQ8rB5XrFR8dh+8Vi87bUuAwi4LrLEj5OApFTJ5Qs/nc5c5pSKZgiZD3glZSKSGq3OLMV9W7E5yoS/sfNl6Oh5IXalsbWibTt1KylZ87gLxgDJQLke3rUQKJ6LjakuXPxjRPB8NkgtAYUcIKWpKhYwT02VKxXBI+dnlQx8MQl/ug2ZhYTRZYCSS034kMPyCYVRcaL6siLumNYu1uppHxo012hvVlm7GEJH36eM+g7HomBJo8+22lYoxJRxjAfRhv+r0TYs8sVapl5RtsVDYEUKKRk2cEK02FMpvfhpLQu+blJlc0Gw8zJAFRkaJtW0CTC8BYwUmhIzvAcTeo+WQurVCWfBmDKcdLc4WrNRXqW5bmYUro/nSNkHz6baVaGB65NlQaFCdN1LFDSqqp/E1pbAjhJSG5rIBHRogUblUbnEn6Vj0BYA2NzRX7U4CIBU2XzbTky2XmfHYDiDqN/YXuw2xU6EgKDvpFKrRRd6p0rZiezI5lbYVy6BMiqnHm91pK+nbdJet2WRRIq/B3gCXxaUer1hqaVoMhV0W2DxBSH40h9Q2eYCBoCHgcqCieoNBoNlZN1MqyBKTmWYt1sxYPPLETqVGvtgrOW3b5GhWmxwbZPyZjD6TeczFdttmm3yRRoTiWGQUo5ERfO+Z75ZU4yfzna899bqaEHcUdllg8wQhhVEp1rS4S8z6EZQh9iSmpw8EoIXjB7zu0tS45x2pEjNj2VdsU2z0J1zcRgxj5q1020qXrUTyJmN+Fc2bXepR7OQLQYRfKaJOeGF0q6oPlPq9aofCjhAyZzSrGegScReALhYnU6S7Ywvenp53ZCHNjKWZYvjFqZ8XyG9mnJT6Lgq7pUA+/+mJGF3oUqPPgrEA/Cpt6z9oPnOhWrxYxvWzffRk0oUYIvvsDWqWrez7/j+8B7UEhR0hZF5oZhPQ6YU+KPV0LtVYUSz0vCMLgurkdhub3QcMPQ8U6M5UZscrTgKs1R+xqYXRZyK8ZEunbIfDBzqei43cCSLqZqdt5f7UfZrMNTm7lsKOEDJvNJMGU4cH1n88HBjLUbCeSUpH/M7nFmNppN5xNQMdmw0j41mRn4MmVvQ/CXQcxZRsBaZsuy0rVR2cpEyLZaV3lTGvNgdixTIeH58+vz+wD+2u9qqfekFhlwU2TxBSOnIgNLe6ldddajS/rYGK0hGyWDh8xpQKicqplGu+MWVPGb54chtSUccXaW6QOrg0iVRcpWulASMUDx5Ul2c1W2fMqS3EWGRMmS1bTBbVWdtgb1TdtdUm8ijsssDmCULmjuZzwGQxQS/kdTdFajgEU1d1/0ImVYBE4dIjyPL5qYldysDTQPsRgLNpMVdICiDHiJnNDQ5Vl7cMy1QdntTjTUTTzRdz//Eo95W2UBFxaBgwN1VNYwWFHSFkYbzuOk2G110eOxRFMGaYGYvfnTRjELJQWJ1Ap4i7p4FYMPd+Uo838KxheuxuXcwVkjkiUba0lUoylcRkfBL+6IQyR07NQ+RJh+1QaEhtDotzespGvhTvUkNhRwhZEDS7BejyGuIulqe2KZmCHoxDD09Aa3EaojAbtEYh5cAiPzq2GMJN6urymRnv/zvQcijg6TDO08i4KjCbzNMCTDpsZRTZRHRCpWzzRfIKdtsmY0os9pn3q85auX+ZeCGPV0lQ2BFCFgw1K1bsUEZC0APZD5q0RiGLjkRbOjcbwq0UM2MaGVdlh22DqpdrUCJPIngT0XHlmTdb5BXbbdvp7sKHjvwwRsOj6ngkIk/q8cQ+RexUlnqaBYUdIWRBkQObNtVUoY9GoIunmAg+WqOQpUSiLMuOM1Kz0g1bDDQyrnqR15iO5KWS8McmMB4ZV92zpRga9wf78KVHvlix0ywo7Aghi9ZUAamhGzJmzFpff4hUKee+gVmD1uJSUb/oNx9azKWSehJ3H/q7UXMX6M+9X6aRcRENQaTyMZvM0zV533rNv2E4PIyJ2ETW7to08qP0e89+Vwm7YhBrFkkDe+1eLCYUdlmg3QkhC4PmtBp1dyLuYklVN5cPfSwMyG0IWShMUi5wNDC6E5joKby/TLLoOsa4HakJrGYrujwy86ILcbFQiU5gPGqIvNlcuuVjqqEiH5njz14a24ZmZwuaHE3w2XwzUrULBYVdFmh3QshCjyHzAiNh6IEDnlS50P2F9yFk3jSvBUwWYGxn/v1CQ8Dgc0D74UbEj9QUVpMVLc5WtYlnnjRdjEfHEBWPQzl+QTtokkUhArFJtUmUUGrxmuxNynR5oaCwI4QsUd2dC7rDDF0EXpHpLWnA0JqdC74+Uqc0rjQaK4a35d8vPGr44YmRsZlfo7WK3WxXkyhkC8VDGI+Oq8aL2bNri0Xq+kbDI2qzW+xK4Ik/XrnhO5IQsmRoHjtgsxip2Xjh0gd9JIRUMmXYojAVRhYCb5cRuRvamr+eLjJhTKmQEWRioUJqGpfVpbYud5cyQJYonj/qn7NHnkQA+xP9GAgNIBUub90mhR0hZEnRpMtVLFGGw9BD+X2klOfdeMSI3Im4c2Spv6PfHZkv7jZAOxIYet4wK85FLDA1X3YzYK2OqQRkfqTtTWRLepJK3EkkL6imXZQu0OQ2cttyQmFHCFlyJPqmtbuh+y2qYSLXAbIYzzv63ZGy4Go2BJukXPMh48mUuDuKNih1aJ/S5GhSmzRdiHWKRPLEyLiQ6XGpc2xLgcKOEFIxaD474JiVmi3R845+d6RsOHxGx+zeR/JPqJAJFr0PGw0Vdp9xGadU1F3TRZurTW1jkbGCpsdfOOnqkpswioXCLgu0OyFkiVOzy7zAaBj6ZFTa0Ap73mmGlUrsB08s5lJJPSBROInclTKhQuCUirql0d6ozInFx24p0PS5JIXrhLTdycTEBHy+qV9hhJBFQw/FoQ+HoKeKKFCOpxC/41l10n7FqYZAJKQcyNfkD04B9j5c2u0+H2B6tk7RdV3ZpaRJSKpWWaeMI5qIzEjFBiYDeMW6U8qmNRixI4RULJrLakTvRNxF8puCZqKPh4E2N+vsSHmQ99FFDxqdsNJQEfUXN6WC1C2apsFhyWyoccBj82KFd8WUdcqYEnligVJuKOwIIRWNjBRDhxvaZBT6WKSozjN9IipVy0Brjs5ZQuYi7pyNwIqTDIPi8IE6qpz49wGthy7G6kgVWqd0urswGZtET6SIiSclQCMoQkh1GBr7HNCWeaHZi/s9qieSSPUHkCo2lUtIMci0CTEmFkuUQoztAMZ2LcaqSBVi0kxosDdgVcOq8t5vWe+NEEIWeByZ1umBqclZtFWAjC3T900W9sgjpFjEHLttk2FmXIjxPcDIy/nNjgkpI0zFEkKqClU31+AAnBaj9i6WpUYlOTNCp8dTStzBZYUmolDSuwLNjMlckfdN6wZjSsVEb+GUrAyOb9vILlmy4FDYEUKqEk1GkXV5gYkIMB6FXqKRsboPmhmT+dK8DjBZgbGd+fcLDgJ60oj0STqXkAWCqVhCSNUigszU6FS1d3BbDSPjEpg2MyZkPjSuBFqKaJIIjRiTLJJzGyJPSDEwYkcIqXrEs87U5YXt3Ucp3zukCtQzJVPTUb3USAimdjc0M3/nknngW2akZfsKmGSLZUr/U8YIMsvCTB4g9Q2FHSGkdjpnGxzQ3Vbow+Hife8CMVWDhyYH4LExLUvmjqfdGCuWb/zY9Aiyh4COIwHxOuP4MVJGKOyywJFihFQvmsXonFXdsDKWrFD0TlKyqRT0kRC0QAxocRr1e4TMBWfzgdMcP0aWAOYesnDJJZdg69ateOyxx5Z6KYSQOaJ57NCW+6C5i0936dEEUvsnDe+7WZ21hBSFRN9EqJVC74NAPLRQKyJ1Bn+WEkJqFqmb09rc0D02I3oXLy4KL9E+iO9dowPw2pmeJcUj7xWJvolQi4eBwWeBRKTw+LHIOOfKkrJAYUcIqXk0p8yctUCbiEKfiMywRsmFpHBFDGqTMaDZadwHIcWKOxFpsnW/wuiEjQXz30bGlJntgLt1sVZJahSmYgkh9dNc0eiAttwLZIo0SbnGc296KI7UXj+S/ZNFR/wImcZiBzq3AHZf/v30FDD0PDDZv1grIzUKI3aEkLprrhB7k1LNjMUjz3r2IcbM2kY7NBkrRUgxmK1A52YjKhcey72fjB0bfhFIJYCGFYu5QlJD8MhECKk/ZJRYd4EIymyGpGYqBd0fMWbPTkahc/4nKRaZNtF+JOBuK7zv6HZgbNdirIrUIIzYEULqMi0ro8TSUyckxaqPR4BQPK+ZcRrpmFX2KBNRw/9OZtCywYIUQqK8aqTYS8BkX/59x/cYkbvm9bRBISVBYUcIqUuUELOZpydXwG2DHo6X1j2bSEIfCqruWzFGVtYqNjNFHsmNvDdaNxhTKkZeyr+vf58h7loPo7gjRUNhRwghs7tnJc06HinK3FhQnnf+KHR/FDBpgNUMzWEBHJb8o8okJcwv7PqkeR2QLGI6SmDAEHcq0mf8ECEkHxR2hBCSgRJaPoeK4EHE3Vi4+BvrQPx/XgJkXm0xj9XtUylhirs6paG78Pgxwb8XiE4a48qku5bvF5IHCjtCCMllbtziQsqecZgsNI1Cri9S1Al6r9+I8rmsgIXRu7qm2PFjK04GLnqQ4o7khMKOEELyoOrvSrVGkSzr2w8HcqVhMxoy9MEgdEnJQoNuMan0rBJ5clo2uQ+Lpv5S+NXo+DEZKVYsex8GwqOAq2UhV0aqGAo7QggpwhpFomtF0+YCJNJXjA6bigKqaj5p2pjK/B5U3SdiL0PkwaypLktN/dWmzxt/jY1CsIrGjwmRCcOkWGrq8o0f638aWHESYHMt7npJVUBhl4Wbb75ZbckkXeYJqXdmW6MIqqkiFIcejAGRLF/CEmkrUlOVZJD8+kMAtYwDx6aDBKBckJha65TAUwJC/T1wmZa+3DYlGLWM62eflsCjTO6gUFy48WOC/JUaOhlBlozlvk0yCvQ/CXQcBdi9i7ZUUh1oOh02c+L3+9HQ0ICJiQn4fCWamRJC6gJdRFQwpqxSEElCL2YSrTRZ/PFlw/S4BKzvOtJI1ea73z+8XFKdn/jwWV+/Xr4OiliA6YAgzBB8uU5P75u+bvZ5isbsxMNGVC6R0VCRCAM/Osc4/Z7fAxbnAdNjZ+OSLZVUntZgxI4QQuaBqoVrcEBrcEBPpaCFE9AlihdNALFUdqEnjigSfUtH1vKRaZBc5uYNxVgE8TueKy1qqBVpA1PCMpSwyycCMyONebaaEIhWJ9B1tBG5iwVz75dKGvuIFYq7dTFXSCoYCjtCCCkTan6s22YYFauUbQpaNAldibyk2lSET+1sRMBKoWzNG8bqEP+jjK7KY7MxG4kwyr8l7/2WyFTaWiWPZEvNTywqYZeZgs6oQVSvT46axIoThBY70LkFGHgWiOap79RTU3V5GwBv52KukFQoFHaEELJAKCHhNBnGx1OI2JsWeVK3J1siCSQktqdnFz7SjFFK2rbI5g3rGzeUHDUsRVyWPRVcRO2iEohJ2bJcl+d2RiQwU/gZp1X9oVyW8VftuxiYrUDnZmDwOWAynN/zTmruousA3/IDHbeVJlbJokBhRwghiy32HLJZZ2gUESRa4oDQ0xMiTuR0CtZzN6j6PRXRKoZimzeKjRrORVwuaCq4/MtQDTGS2pzVC5PtGVfCbpbgMzqWF8CeJl1Hl9lMUYznndioSMctxV3dQWFHCCEVgBIBVrOxYabomzG6TG1TUamUbkQAp04bfw+cL1tvXCk1gUUzx1SwrKHEFPaCicCMiWCzn2nlSyjCL0PsqXrMtACUrdgUsPwY6DoW6DzaiMwVg3jjiY1KuuOW1A0UdoQQUiUYacGZoiafLDhQtzZVu5bKPJ8WhnKZ2nnq+szTU+fTp03m8onFOaaCCzaQLCTFRkLl6ZL0+qxRsAc9cyLq5D4dZmgyXzjDoFr5FoqgSyOnP/y4kZaVEWO5yPS7o+lFXUJhRwghNcqMTtNc+5R4n9NiMVPw5Tqdmto/4zxmnxcRU0J0sew1fmWvB5xDJHIqxazPulsl7DKFnurAPgww+6BN7ip8v9JUsex4QxSSuoHCjhBCSOliUTAXsX8J951LBKqo4jIPsD+AJaWUesBSyJFiVmn2qGxT56ev8UGLrACCe43XQPpkpv7KTtPPuYweG3jaqNEz8+u+XuArTQghpHJEo9Slzb4cgP2iY2dM/yhcA5e5ZdQhilBMZr+8rFG4YqJ7mSnmEtGdMivWDEzsASIZoi8pc4c3QalBvwYtOgGEnwSWHQnN4ZjTY5HqgsKOEEJIdYg+WxEhwjmkl2fYpKTF3lSTitGwosP6tsOBaNK4PK8ILH2s3JxrBy0+wLMaGN99QNlJ+hvSMGEFwhp0aaadDAIjT0JrWQc4napBR7OZppt1VIqX1AwUdoQQQuoeJRzFpmRWQWLW7uTMaF8iQwDKaXU+lduXcMFrB+2A6TpA3wtrNHbgWz4WhR55DmhaDZjtM1fmdEET30Ob0cQh84OV4CunETVZNCjsCCGEkBIwzIzNxjeofeqyWfsoX0IRgBliT3kTTgu/lNG0ulD+gNoKxB/MdkXPwRdZ+mF955lqqkim4FPCTqKkIvim/qomDnrjVTQUdoQQQshC1QuKOMoj/mwXHQOEE9MCUI2cS2/FpHwzSUaB0Z2I/3k/kGov/naJTiAaVpG7GesTARqWLT4t+OTfpc8We3KaYq9ioLAjhBBClgARQ5pNUqDZv4qn07vxtOgTbzzjvOqYnY3VCXQcCuuZJiA8WejBAXsr4v87NYc2/TgF0KUeT+YFZ0T3lBmzdPSKwLOL0JtK6y7W6DUyAwo7QgghpJINqbNE/JToS4+fU2JvSvRJRK31UBW5U5Mn8hEZBmBTJ+O/3DHnbl8l8OTxQwcie+pqp1XV7hlij5G9xYLCjhBCCKnaKSSW7DOHO44C+l6AHpww5t8mpOlj1p2YxDNwJ6CtXdDZvnogI7InAs8+lcYVwSdNGhR7ZYXCjhBCCKnFmcNrN0MbfgEIDqnr9CQOiDz5G9Vh1W8AdAfwhluAxlULOttXdQnHEmqbUbMnAi8d2ZO/7MadF3Uh7EKhEDZu3Ih//Md/xDe+8Y2lXg4hhBCy8MgosbZNgOklYLLPmE4hm30qkSpdungWunjeOQcAiwO6Z9V0Wnf2mLeFmO2rHiGWVA0k6ZFqmmUqqicWLFNpXEb1iqcuhN1Xv/pVnHTSSUu9DEIIIWRxEUHUugEwWYGJLFYnKkUan6rjG4QmYq5zI3Rp7BCBF0tBF+GlNhFfpUXTivbnm0rbiuJUTSJSOxiMzezElWieg1E91Luwe/nll/Hiiy/i3HPPxXPPLcCMP0IIIaTSaV4LmK3AaIEmCUnbphLQ2o+Y6m6d1bQhEbspoZcWfEqIZSKds6X68+WYl6seU8RmuhPXnxHVc5ihOSSqZzGMlUnlC7v7778fX//61/H444+jr68Pv/zlL3HeeefN2Ofmm29W+/T392Pz5s246aabcMIJJ0xff/nll6vrH3rooSX4FxBCCCEVQkM3YLIAIy+JWsq9X3gM6H8a6DjSEIMZqPFjsrms04JPWa+IyItOib1o0oi+lTltmzmqTYnJQBJ6YCqqp7qHJaJnUYKvnpsyKlrYBYNBJdYuvPBCvPWtbz3o+jvvvBOXXXYZbrnlFpx44om44YYbcNZZZ2Hbtm1ob2/Hr3/9axx66KFqo7AjhBBS93i7jLTs0Nb8+0X9QN+TQOdmwDLlt5IDTWr5HLJliL1kptiTaFvSuGy+adssdivq8cTqJZKAPjF1gXjoua0wOayAiurVj9CraGF39tlnqy0X119/PS6++GJccMEF6rwIvN/97ne49dZbccUVV+CRRx7BT37yE/z0pz9FIBBAPB6Hz+fDVVddlfX+otGo2tJMTBjvEL9/KvZLCCGEVD02wLkKGHsCSH/ljY0CVses/UaA0UGjAUPMjwWry6jbKxbJrMrdOjToSe2A0EuLPonYeVLAcLi4++sLAreNFLdvowPW14mVy9R6bQdStwcJPevSjUpLa4zZzSpzRq8SZKm//OUvp89Ho1HdbDbPuEx4//vfr7/pTW866Pa33Xab/qlPfSrvY3zhC19Qj8ONGzdu3Lhx44ZF3Hbs2FEWvVTREbt8DA8PI5lMoqOjY8blcl6aJebC5z73OZXaTTM+Po5Vq1ahp6cHDQ0N814zWfhfPd3d3ejt7VWRWVLZ8PWqLvh6VQ98raoLyQ6uXLkSzc3NZbm/qhV2pfKBD3yg4D52u11tsxFRxw9H9SCvFV+v6oGvV3XB16t64GtVXZikVrEc94MqpbW1FWazGQMDAzMul/OdnZ1Lti5CCCGEkKWiaoWdzWbDsccei3vuuWf6slQqpc6ffPLJS7o2QgghhJCloKJTsdLJun379unzu3btwlNPPaXy0JKPlnq4888/H8cdd5zyrhO7E7FISXfJzhdJy37hC1/Imp4llQdfr+qCr1d1wdereuBrVd+vlzbVcVqR/OUvf8Fpp5120OUi5m6//XZ1+t///d+nDYq3bNmCG2+8UXnaEUIIIYTUGxUt7AghhBBCSB3U2BFCCCGEkJlQ2BFCCCGE1AgUdnm4+eabsXr1ajgcDlW39+ijjy71kkgWvvjFL6pRMJnbYYcdttTLIlPcf//9OPfcc7Fs2TL12vzqV7+acb1Ug8iYv66uLjidTpxxxhl4+eWXl2y99Uyh10r8QGd/1l7/+tcv2XrrnWuuuQbHH388vF6vmo9+3nnnqVnpmUQiEVxyySVoaWmBx+PBP/zDPxxkE0Yq5/V6zWtec9Bn7J/+6Z9KehwKuxzceeedqutWOlWeeOIJbN68GWeddRYGBweXemkkC4cffjj6+vqmtwceeGCpl0SmkE51+fzID6VsXHfddarpSWY9/+1vf4Pb7VafNflCIpX1Wgki5DI/a3fccceirpEc4L777lOiTeai/+lPf1Lz0M8880z1Oqb55Cc/id/+9rdqZrrsv3//frz1rW9d0nXXK/cV8XoJF1988YzPmBwjS6Isg8lqkBNOOEG/5JJLps8nk0l92bJl+jXXXLOk6yLZZ/xu3rx5qZdBikAOOZnznVOplN7Z2al//etfn75sfHxct9vt+h133LFEqyTZXivh/PPP19/85jcv2ZpIfgYHB9Xrdt99901/lqxWq/7Tn/50ep8XXnhB7fPwww8v4UpJttdLePWrX61//OMf1+cDI3ZZiMViePzxx1VKKHPUh5x/+OGHl3RtJDuSupP00dq1a/Ge97xHzfcllY94U4pVUeZnTUb4SekDP2uVa0MlaaQNGzbgIx/5CEZGRpZ6SSRj5qiQnjkq32MSFcr8fEmZivjA8vNVea9Xmh/96EdqutYRRxyhZtiHQqHaMSheKoaHh5FMJtHR0THjcjn/4osvLtm6SHZEBIivoXzRSNj66quvxqmnnornnntO1TKQykVEnZDts5a+jlQOkoaVNN6aNWuwY8cOfP7zn8fZZ5+tRIKMeCRLh0xe+sQnPoFTTjlFCQJBPkMypamxsXHGvvx8VebrJbz73e/GqlWrVKDimWeewWc/+1lVh/eLX/yi6PumsCNVj3yxpDnqqKOU0JMPxl133YWLLrpoSddGSC3xzne+c/r0kUceqT5v69atU1G8008/fUnXVu9I7Zb8mGV9cXW/Xh/60IdmfMakqUw+W/JDSj5rxcBUbBYkBCq/Pmd3Dsn5zs7OJVsXKQ75dXrooYfOGEdHKpP054mftepESh/keMnP2tJy6aWX4n/+539w7733YsWKFdOXy2dISovGx8dn7M/PV2W+XtlIT9Iq5TNGYZcFCV0fe+yxuOeee2aETeX8ySefvKRrI8XNGJZfN/JLh1Q2ktKTL5jMz5rf71fdsfysVT579+5VNXb8rC0N0uMiIuGXv/wl/vznP6vPUybyPWa1Wmd8viStJzXI/HxV3uuVjaeeekr9LeUzxlRsDsTqRGbSHnfccTjhhBNwww03qJbkCy64YKmXRmZx+eWXK+8tSb9KK79Y1EjE9V3vetdSL41MCe3MX5vSMCEHKykYliJuqTP5yle+gkMOOUQd6K688kpVXyIeT6RyXivZpH5VfNBEjMuPp8985jNYv369sqchS5PO+/GPf4xf//rXqp44XTcnDUjiCSl/pRxFvs/k9fP5fPjYxz6mRN1JJ5201MuvOy4p8HrJZ0quP+ecc5TvoNTYiV3Nq171KlX2UDTz6qmtcW666SZ95cqVus1mU/YnjzzyyFIviWThHe94h97V1aVep+XLl6vz27dvX+plkSnuvfde1dI/exPrjLTlyZVXXql3dHQom5PTTz9d37Zt21Ivuy7J91qFQiH9zDPP1Nva2pSFxqpVq/SLL75Y7+/vX+pl1y3ZXivZbrvttul9wuGw/tGPflRvamrSXS6X/pa3vEXv6+tb0nXXKyjwevX09OivetWr9ObmZnUsXL9+vf7pT39an5iYKOlxtKkHI4QQQgghVQ5r7AghhBBCagQKO0IIIYSQGoHCjhBCCCGkRqCwI4QQQgipESjsCCGEEEJqBAo7QgghhJAagcKOEEIIIaRGoLAjhBBCCKkRKOwIIVWJpmn41a9+hVrjAx/4gPq3Zf77du/erc6n50YuBLfffvv048qYN0JIdUJhRwipSFEjw8s7Ojrwute9DrfeeitSqdSMffv6+nD22WfXpAh8/etfX9K/rxy84x3vUI/J4fCEVDcUdoSQihQ1EqX6wx/+gNNOOw0f//jH8cY3vhGJRGJ6PxlEb7fbUYvIv2ux/30yhFwe02azLdpjEkLKD4UdIaQiRc3y5ctxzDHH4POf/zx+/etfK5En6cJsUbhYLIZLL70UXV1dcDgcWLVqFa655hp13erVq9Xft7zlLeo26fM7duzAm9/8ZhUV9Hg8OP7443H33XfPWIvs+7WvfQ0XXnghvF4vVq5cie9973sz9tm7dy/e9a53obm5GW63G8cddxz+9re/TV8va5d/h6xr7dq1uPrqq2cI1LmQTCbVmg477DD09PRMPx/f+c53VJRPRJo81s9+9rOS1koIqX4o7AghFc9rX/tabN68Gb/4xS+yXn/jjTfiN7/5De666y5s27YNP/rRj6YF3GOPPab+3nbbbSoSmD4fCARwzjnn4J577sGTTz6pIoXnnnvutFBK881vflMJINnnox/9KD7ykY+ox0jfx6tf/Wrs27dPPf7TTz+Nz3zmM9Np47/+9a94//vfryKOW7duxXe/+10lTr/61a/O+bmIRqP4x3/8R1VvJ/cvYjPNlVdeiX/4h39Q63jPe96Dd77znXjhhReKWishpDawLPUCCCGkGCQ69cwzz2S9TsTYIYccgle+8pUqciURuzRtbW3qb2Njo4oEphGhKFuaL3/5y/jlL3+pRI9E/9KI+BNBJ3z2s5/Ft771Ldx7773YsGEDfvzjH2NoaEiJRYmCCevXr5++rUTnrrjiCpx//vnqvETR5HFEUH3hC18o+TkQcfaGN7xBiTtZQ0NDw4zrRfB98IMfnP73/OlPf8JNN92Eb3/72wXXSgipDRixI4RUBbquK9GWq+lCIlgitv75n/8Z//d//1eUSLr88suxceNGJfokHSvRrdkRu6OOOmr6tDy+iMPBwUF1Xh7z6KOPnhZKs5Go2Je+9CV13+nt4osvVpHDUChU4jMAlUYNBoPq3zdb1AmzGx/kfDpiV2ithJDagMKOEFIViEBZs2ZN1uukhm3Xrl0qShUOh/H2t78db3vb2/Len4g6idBJDZ2kNEX4HHnkkapeLxPpzs1ExF06fSm1bIXEo0Tt5L7T27PPPouXX35Z1dyVikQPJWr58MMPl3zbQmslhNQGFHaEkIrnz3/+sxJEUj+WC5/Ppyw7vv/97+POO+/Ez3/+c4yOjk6LM2k4yOTBBx9UkT5pqhBBJ5E46cQtBYnmiVhLP042wSn1eJLynL2ZTKUffqW+79prr8Wb3vQm3HfffQdd/8gjjxx0XiKSxayVEFIbsMaOEFJRSP1Yf3+/EmIDAwP44x//qDpcxe5EGhGycf3116uOWEk1imD66U9/qoSapFgFaaSQJolTTjlFdd02NTWpmjxpxpCGCYnCSeNBqY0EkhqViN95552n1ihrkCaLZcuWqTToVVddpdYtDQ4SQZS1SXr2ueeew1e+8pU5PT8f+9jH1HMj9yudwlJXmEb+3dLoIZdJA8mjjz6KH/zgB0WtlRBSGzBiRwipKETIiegQMSadqtIkIF2vYhtiNpuz3kasSK677jolasS2RCJvv//976ejYtLZKo0E3d3dSvylxaAIvFe84hVK3J111lkqwlYK4vkm9W7t7e0qTSqRP4mopdcp9/k///M/ah9Z10knnaSaLzKbO+aCTIaQFK885kMPPTR9uVz2k5/8REXnfvjDH+KOO+7Apk2bilorIaQ20HSpSCaEEFIRSHp4fHy85EkZEnWUmkGJyM2H17zmNdiyZQtuuOGGed0PIWRpYMSOEEIqDInySQet/F0sJHUrjymNJISQ6oURO0IIqSDESsXv96vTkpKWCRGLEbGbnJxUNY2C1Ca2trbO6X4IIUsLhR0hhBBCSI3AVCwhhBBCSI1AYUcIIYQQUiNQ2BFCCCGE1AgUdoQQQgghNQKFHSGEEEJIjUBhRwghhBBSI1DYEUIIIYTUCBR2hBBCCCE1AoUdIYQQQghqg/8PK2nnuxIlpygAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_pop_dens([4,5,6,7,8,9,10], ylim=(1e4,5e8), poplabel='thin disk', legend=False)" + ] + }, + { + "cell_type": "markdown", + "id": "6005498f-12b3-4fee-87bf-32283106ff60", + "metadata": {}, + "source": [ + "## Next: kinematics" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "9f234c04-1155-4513-9533-43a2935570c8", + "metadata": {}, + "outputs": [], + "source": [ + "kine_func_pts = np.arange(0.0,22.1501, 0.05)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "2c7f6ee5-301f-47b0-a2ca-ff3000de3593", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_kinematics_distr(sl, pop,ds=1):\n", + " l,b = model.parms.l_set[sl], model.parms.b_set[sl]\n", + " data = catalogs[sl][::ds]\n", + " x,y,z = model.populations[pop].coord_trans.dlb_to_xyz(kine_func_pts, l,b)\n", + " r,phi_rad,z2 = model.populations[pop].coord_trans.dlb_to_rphiz(kine_func_pts, l,b)\n", + " \n", + "# u1,v1,w1 = model.populations[pop].kinematics.mean_galactic_uvw(x,y,z)\n", + "# u1p = u1 + model.populations[pop].kinematics.sigma_u * np.exp((r-model.populations[pop].kinematics.sun.r)*model.populations[pop].kinematics.disp_grad/2)\n", + "# v1p,w1p = v1 + model.populations[pop].kinematics.sigma_v, w1 + model.populations[pop].kinematics.sigma_w\n", + "# u1m = u1 - model.populations[pop].kinematics.sigma_u * np.exp((r-model.populations[pop].kinematics.sun.r)*model.populations[pop].kinematics.disp_grad/2)\n", + "# v1m,w1m = v1 - model.populations[pop].kinematics.sigma_v, w1 - model.populations[pop].kinematics.sigma_w\n", + " \n", + "# u,v,w = u1 * np.cos(phi_rad) + v1 * np.sin(phi_rad), -u1 * np.sin(phi_rad) + v1 * np.cos(phi_rad), w1\n", + "# u_p,v_p,w_p = u1p * np.cos(phi_rad) + v1p * np.sin(phi_rad), -u1p * np.sin(phi_rad) + v1p * np.cos(phi_rad), w1p\n", + "# u_m,v_m,w_m = u1m * np.cos(phi_rad) + v1m * np.sin(phi_rad), -u1m * np.sin(phi_rad) + v1m * np.cos(phi_rad), w1m\n", + " \n", + " plt.subplots(nrows=1,ncols=3,figsize=(15,5))\n", + " plt.subplot(131)\n", + " plt.plot(data.where(data['pop']==pop).Dist, np.abs(data.where(data['pop']==pop)['V']), marker='.', markersize=0.2, linestyle='none', c='k')\n", + " # plt.plot(kine_func_pts, np.abs(v), c='c', label='Function mean')\n", + " # plt.plot(kine_func_pts, np.abs(v_p), c='c', linestyle='--', label=r'Function $\\pm$ 1-sigma')\n", + " # plt.plot(kine_func_pts, np.abs(v_m), c='c', linestyle='--')\n", + " plt.ylabel('|V| (km/s)'); plt.xlabel('Distance (kpc)')\n", + " plt.ylim(0,400)\n", + " plt.xlim(0,25)\n", + " \n", + " plt.subplot(132)\n", + " plt.plot(data.where(data['pop']==pop).Dist, data.where(data['pop']==pop)['U'], marker='.', markersize=0.2, linestyle='none', c='k')\n", + " # plt.plot(kine_func_pts, u, c='c', label='Function mean')\n", + " # plt.plot(kine_func_pts, u_p, c='c', linestyle='--', label=r'Function $\\pm$ 1-sigma')\n", + " # plt.plot(kine_func_pts, u_m, c='c', linestyle='--')\n", + " plt.ylabel('U (km/s)'); plt.xlabel('Distance (kpc)')\n", + " plt.ylim(-250,250)\n", + " plt.xlim(0,25)\n", + " \n", + " plt.subplot(133)\n", + " plt.plot(data.where(data['pop']==pop).Dist, data.where(data['pop']==pop)['W'], marker='.', markersize=0.2, linestyle='none', c='k')\n", + " # plt.plot(kine_func_pts, w, c='c', label='Function mean')\n", + " # plt.plot(kine_func_pts, w_p, c='c', linestyle='--', label=r'Function $\\pm$ 1$\\sigma$')\n", + " # plt.plot(kine_func_pts, w_m, c='c', linestyle='--')\n", + " plt.ylabel('W (km/s)'); plt.xlabel('Distance (kpc)')\n", + " plt.legend()\n", + " plt.ylim(-110,110)\n", + " plt.xlim(0,25)\n", + " \n", + " plt.tight_layout()\n", + " plt.savefig('validation_figures/kinematics_sl'+str(sl)+'_pop'+str(pop)+'_ds'+str(ds)+'.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "77b20a5c-6093-482a-9173-b1327563c89c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_74178/3132407858.py:42: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + " plt.legend()\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_kinematics_distr(0,6,ds=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8baa82eb-2bee-470f-a411-ac1ff9f6171f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a495e307-758f-4bb4-b107-74ec8243ce1f", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/synthpop/demo/validation_stargen.ipynb b/synthpop/demo/validation_stargen.ipynb index 1d74bf4..5cea5ea 100755 --- a/synthpop/demo/validation_stargen.ipynb +++ b/synthpop/demo/validation_stargen.ipynb @@ -14,10 +14,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "832ced52-916a-4791-a3e7-4a8403396f23", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhuston/anaconda3/lib/python3.11/site-packages/pydantic/_internal/_config.py:341: UserWarning: Valid config keys have changed in V2:\n", + "* 'keep_untouched' has been renamed to 'ignored_types'\n", + " warnings.warn(message, UserWarning)\n" + ] + } + ], "source": [ "import sys\n", "import os\n", @@ -25,7 +35,8 @@ "import numpy as np\n", "import synthpop\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd" + "import pandas as pd\n", + "import pdb" ] }, { @@ -38,50 +49,1496 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "8d1a3be6-8365-4bad-9a44-df463b62b96f", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 1564 - Execution Date: 2026-02-02 15:40:24\n", + "\n", + "\n", + "################################ Settings #################################\n", + " 1564 - # reading default parameters from\n", + " 1564 - default_config_file = /Users/mhuston/code/synthpop/synthpop/config_files/_default.synthpop_conf \n", + "\n", + "\n", + "# copy the following to a config file to redo this model generation -------\n", + " 1565 - {\n", + " \"l_set\": [\n", + " 0,\n", + " 90,\n", + " 12.17,\n", + " 39.14\n", + " ],\n", + " \"l_set_type\": \"pairs\",\n", + " \"b_set\": [\n", + " 0,\n", + " 0,\n", + " 5.37,\n", + " 8.53\n", + " ],\n", + " \"b_set_type\": \"pairs\",\n", + " \"name_for_output\": \"Besancon_validation\",\n", + " \"model_name\": \"besancon_Robin2003\",\n", + " \"field_shape\": \"circle\",\n", + " \"field_scale\": [\n", + " 0.01784576525620624,\n", + " 0.3090977212369663,\n", + " 0.1382327032752274,\n", + " 0.43713018947193605\n", + " ],\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 480373423,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 20000,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"StarGenerator\",\n", + " \"extinction_map_kwargs\": null,\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"ODonnell1994\",\n", + " \"R_V\": 3.1\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"MIST\",\n", + " \"interpolator\": \"CharonInterpolator\"\n", + " },\n", + " \"ifmr_kwargs\": null,\n", + " \"multiplicity_kwargs\": null,\n", + " \"maglim\": null,\n", + " \"chosen_bands\": [\n", + " \"Bessell_B\",\n", + " \"Bessell_V\",\n", + " \"Bessell_I\",\n", + " \"Gaia_G_EDR3\",\n", + " \"Gaia_BP_EDR3\",\n", + " \"Gaia_RP_EDR3\",\n", + " \"2MASS_J\",\n", + " \"2MASS_H\",\n", + " \"2MASS_Ks\",\n", + " \"Z087\",\n", + " \"W146\"\n", + " ],\n", + " \"obsmag\": false,\n", + " \"combine_system_mags\": true,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": [\n", + " {\n", + " \"name\": \"RenameColumns\",\n", + " \"old_names\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\"\n", + " ],\n", + " \"new_names\": [\n", + " \"logL\",\n", + " \"logTeff\",\n", + " \"logg\",\n", + " \"Fe/H_evolved\",\n", + " \"log_radius\"\n", + " ]\n", + " }\n", + " ],\n", + " \"output_location\": null,\n", + " \"output_filename_pattern\": \"{model_name}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"hdf5\",\n", + " \"overwrite\": true\n", + "}\n", + "\n", + "\n", + "######################### Initialize populations ##########################\n", + " 1566 - read Population files from besancon_Robin2003\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 1569 - # Initialize Population 0 (bulge) from \n", + " 1569 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/bulge.popjson'\n", + " 2952 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + "\n", + "\n", + "# Population 1; halo -----------------------------------------------------\n", + " 2956 - # Initialize Population 1 (halo) from \n", + " 2956 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/halo.popjson'\n", + " 3471 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + "\n", + "\n", + "# Population 2; thick_disk -----------------------------------------------\n", + " 3473 - # Initialize Population 2 (thick_disk) from \n", + " 3474 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/thick_disk.popjson'\n", + " 3990 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + "\n", + "\n", + "# Population 3; thin_disk_1 ----------------------------------------------\n", + " 3993 - # Initialize Population 3 (thin_disk_1) from \n", + " 3993 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/thin_disk_1.popjson'\n", + " 4512 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + "\n", + "\n", + "# Population 4; thin_disk_2 ----------------------------------------------\n", + " 4515 - # Initialize Population 4 (thin_disk_2) from \n", + " 4515 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/thin_disk_2.popjson'\n", + " 5032 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + "\n", + "\n", + "# Population 5; thin_disk_3 ----------------------------------------------\n", + " 5033 - # Initialize Population 5 (thin_disk_3) from \n", + " 5034 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/thin_disk_3.popjson'\n", + " 5555 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + "\n", + "\n", + "# Population 6; thin_disk_4 ----------------------------------------------\n", + " 5556 - # Initialize Population 6 (thin_disk_4) from \n", + " 5556 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/thin_disk_4.popjson'\n", + " 6077 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + "\n", + "\n", + "# Population 7; thin_disk_5 ----------------------------------------------\n", + " 6078 - # Initialize Population 7 (thin_disk_5) from \n", + " 6079 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/thin_disk_5.popjson'\n", + " 6602 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + "\n", + "\n", + "# Population 8; thin_disk_6 ----------------------------------------------\n", + " 6603 - # Initialize Population 8 (thin_disk_6) from \n", + " 6603 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/thin_disk_6.popjson'\n", + " 7126 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + "\n", + "\n", + "# Population 9; thin_disk_7 ----------------------------------------------\n", + " 7128 - # Initialize Population 9 (thin_disk_7) from \n", + " 7128 - pop_file = '/Users/mhuston/code/synthpop/synthpop/models/besancon_Robin2003/thin_disk_7.popjson'\n", + " 7655 - Extinction Map and/or Law is set to None -- no extinction will be applied.\n", + " 7656 - # All populations are initialized\n" + ] + } + ], "source": [ "# Set up model object \n", "model = synthpop.SynthPop(model_name='besancon_Robin2003',name_for_output='Besancon_validation',obsmag=False, \n", - " maglim=['2MASS_Ks', 999999, 'keep'], extinction_map_kwargs={'name':'NoExtinction'}, \n", + " maglim=None, extinction_map_kwargs=None, \n", " l_set=[0,90, 12.17, 39.14], b_set=[0, 0, 5.37, 8.53], l_set_type=\"pairs\", b_set_type=\"pairs\",\n", - " solid_angle=[1e-3,3e-1,6e-2, 6e-1], solid_angle_unit='deg^2',\n", + " field_scale=list(np.sqrt(np.array([1e-3,3e-1,6e-2, 6e-1])/3.14)), \n", + " field_scale_unit='deg', field_shape='circle',\n", " max_distance=25, overwrite=True, output_file_type='hdf5')\n", "model.init_populations()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "e8bc365a-e12e-48df-bec2-456acafc4793", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 7661 - Execution Date: 2026-02-02 15:40:30\n", + "\n", + "\n", + "################################ Settings #################################\n", + "\n", + "\n", + "# Copy the following to a config file to redo this model generation: ------\n", + " 7661 - {\n", + " \"l_set\": [\n", + " 0\n", + " ],\n", + " \"l_set_type\": \"pairs\",\n", + " \"b_set\": [\n", + " 0\n", + " ],\n", + " \"b_set_type\": \"pairs\",\n", + " \"name_for_output\": \"Besancon_validation\",\n", + " \"model_name\": \"besancon_Robin2003\",\n", + " \"field_shape\": \"circle\",\n", + " \"field_scale\": 0.01784576525620624,\n", + " \"field_scale_unit\": \"deg\",\n", + " \"random_seed\": 480373423,\n", + " \"sun\": {\n", + " \"x\": -8.178,\n", + " \"y\": 0.0,\n", + " \"z\": 0.017,\n", + " \"u\": 12.9,\n", + " \"v\": 245.6,\n", + " \"w\": 7.78,\n", + " \"l_apex_deg\": 56.24,\n", + " \"b_apex_deg\": 22.54\n", + " },\n", + " \"lsr\": {\n", + " \"u_lsr\": 1.8,\n", + " \"v_lsr\": 233.4,\n", + " \"w_lsr\": 0.53\n", + " },\n", + " \"warp\": {\n", + " \"r_warp\": 7.72,\n", + " \"amp_warp\": 0.06,\n", + " \"amp_warp_pos\": null,\n", + " \"amp_warp_neg\": null,\n", + " \"alpha_warp\": 1.33,\n", + " \"phi_warp_deg\": 17.5\n", + " },\n", + " \"max_distance\": 25,\n", + " \"distance_step_size\": 0.1,\n", + " \"mass_lims\": {\n", + " \"min_mass\": 0.08,\n", + " \"max_mass\": 100\n", + " },\n", + " \"N_mc_totmass\": 10000,\n", + " \"lost_mass_option\": 1,\n", + " \"N_av_mass\": 20000,\n", + " \"scale_factor\": 1,\n", + " \"skip_lowmass_stars\": false,\n", + " \"chunk_size\": 250000,\n", + " \"star_generator\": \"StarGenerator\",\n", + " \"extinction_map_kwargs\": null,\n", + " \"extinction_law_kwargs\": [\n", + " {\n", + " \"name\": \"ODonnell1994\",\n", + " \"R_V\": 3.1\n", + " }\n", + " ],\n", + " \"evolution_class\": {\n", + " \"name\": \"MIST\",\n", + " \"interpolator\": \"CharonInterpolator\"\n", + " },\n", + " \"ifmr_kwargs\": null,\n", + " \"multiplicity_kwargs\": null,\n", + " \"maglim\": null,\n", + " \"chosen_bands\": [\n", + " \"Bessell_B\",\n", + " \"Bessell_V\",\n", + " \"Bessell_I\",\n", + " \"Gaia_G_EDR3\",\n", + " \"Gaia_BP_EDR3\",\n", + " \"Gaia_RP_EDR3\",\n", + " \"2MASS_J\",\n", + " \"2MASS_H\",\n", + " \"2MASS_Ks\",\n", + " \"Z087\",\n", + " \"W146\"\n", + " ],\n", + " \"obsmag\": false,\n", + " \"combine_system_mags\": true,\n", + " \"opt_iso_props\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\",\n", + " \"phase\"\n", + " ],\n", + " \"post_processing_kwargs\": [\n", + " {\n", + " \"name\": \"RenameColumns\",\n", + " \"old_names\": [\n", + " \"log_L\",\n", + " \"log_Teff\",\n", + " \"log_g\",\n", + " \"[Fe/H]\",\n", + " \"log_R\"\n", + " ],\n", + " \"new_names\": [\n", + " \"logL\",\n", + " \"logTeff\",\n", + " \"logg\",\n", + " \"Fe/H_evolved\",\n", + " \"log_radius\"\n", + " ]\n", + " }\n", + " ],\n", + " \"output_location\": null,\n", + " \"output_filename_pattern\": \"{model_name}_l{l_deg:.3f}_b{b_deg:.3f}\",\n", + " \"output_file_type\": \"hdf5\",\n", + " \"overwrite\": true\n", + "}\n", + "\n", + "\n", + "############################# Update location #############################\n", + " 7661 - # set location to: \n", + " 7661 - l, b = (0.00 deg, 0.00 deg)\n", + " 7662 - # set field scale to:\n", + " 7662 - field_scale = 1.785e-02 deg\n", + "\n", + "\n", + "############################# Generate Field ##############################\n", + " 8317 - Evolving test set from population 0 to estimate average initial->final mass ratio\n", + " 8677 - Evolving test set from population 1 to estimate average initial->final mass ratio\n", + " 8998 - Evolving test set from population 2 to estimate average initial->final mass ratio\n", + " 9327 - Evolving test set from population 3 to estimate average initial->final mass ratio\n", + " 10124 - Evolving test set from population 4 to estimate average initial->final mass ratio\n", + " 10615 - Evolving test set from population 5 to estimate average initial->final mass ratio\n", + " 10981 - Evolving test set from population 6 to estimate average initial->final mass ratio\n", + " 11337 - Evolving test set from population 7 to estimate average initial->final mass ratio\n", + " 11705 - Evolving test set from population 8 to estimate average initial->final mass ratio\n", + " 12063 - Evolving test set from population 9 to estimate average initial->final mass ratio\n", + "\n", + "\n", + "# Population 0; bulge ----------------------------------------------------\n", + " 12438 - # From density profile (number density)\n", + " 12438 - expected_total_iMass = 917247.1505\n", + " 12438 - expected_total_eMass = 917135.8904\n", + " 12439 - average_iMass_per_star = 0.5739\n", + " 12439 - mass_loss_correction = 0.9999\n", + " 12439 - n_expected_stars = 1598364.8193\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "992d2861282c401792dc70b03fdf3454", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1597543 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_pop_dens([0], poplabel='bulge', ylim=(1e4,3e10), legend=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "a47ad1cf-5b63-4e10-939e-6043a70fde47", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_pop_dens([1],poplabel='halo',ylim=(1e3,2e7), legend=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "76cf120b-cea6-406e-94ba-a5a572766e39", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_pop_dens([2], ylim=(1e4,1e8), poplabel='thick disk', legend=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "4c39863f-4b67-4fa0-83cd-ebce62aa478e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n", + "/var/folders/_z/fn__fgrs2wqgsmbvw8bvrr0r0000gn/T/ipykernel_27009/3964421722.py:19: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " sum_catalog += data_sl.where(data_sl['pop']==pop).groupby(binned_dists).Mass.sum()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_pop_dens([3,4,5,6,7,8,9], ylim=(1e4,5e8), poplabel='thin disk', legend=False)" ] @@ -207,7 +1798,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "9f234c04-1155-4513-9533-43a2935570c8", "metadata": {}, "outputs": [], @@ -217,7 +1808,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "2c7f6ee5-301f-47b0-a2ca-ff3000de3593", "metadata": {}, "outputs": [], @@ -273,10 +1864,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "77b20a5c-6093-482a-9173-b1327563c89c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_kinematics_distr(0,9,ds=2)" ] @@ -288,13 +1890,29 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "293dc641-c7cc-4a51-b195-89d613037121", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12562d65-3bcd-4820-93bb-c799eea3de58", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:astro-synthpop] *", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-astro-synthpop-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -306,7 +1924,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/synthpop/models/GUMS_dr3/disk3.popjson b/synthpop/models/GUMS_dr3/disk3.popjson index 4b5cfc8..c42465d 100644 --- a/synthpop/models/GUMS_dr3/disk3.popjson +++ b/synthpop/models/GUMS_dr3/disk3.popjson @@ -12,7 +12,6 @@ "gamma_flare": 0.0001, "radius_flare": 10, "disk_cutoff": 100 - }, "imf_func_kwargs" : { @@ -43,6 +42,7 @@ "sigma_v": 11.283, "sigma_w": 9.182 }, + "warp":{ "r_warp": 9.8065, "amp_warp_pos": 0.61675, diff --git a/synthpop/models/GUMS_dr3/disk4.popjson b/synthpop/models/GUMS_dr3/disk4.popjson index 39fdfeb..11135bc 100644 --- a/synthpop/models/GUMS_dr3/disk4.popjson +++ b/synthpop/models/GUMS_dr3/disk4.popjson @@ -12,7 +12,6 @@ "gamma_flare": 0.0001, "radius_flare": 10, "disk_cutoff": 100 - }, "imf_func_kwargs" : { @@ -43,7 +42,8 @@ "sigma_v": 13.851, "sigma_w": 11.272 }, - "warp":{ + + "warp":{ "r_warp": 9.5515, "amp_warp_pos": 0.47175, "amp_warp_neg": 0.26700, diff --git a/synthpop/models/GUMS_dr3/disk5.popjson b/synthpop/models/GUMS_dr3/disk5.popjson index 12877d0..0b3460d 100644 --- a/synthpop/models/GUMS_dr3/disk5.popjson +++ b/synthpop/models/GUMS_dr3/disk5.popjson @@ -34,7 +34,6 @@ "low_bound": -0.725, "high_bound": 0.625, "radial_gradient": -0.07 - }, "kinematics_func_kwargs" : { @@ -43,7 +42,8 @@ "sigma_v": 17.257, "sigma_w": 14.044 }, - "warp":{ + + "warp":{ "r_warp": 9.169, "amp_warp_pos": 0.45300, "amp_warp_neg": 0.39000, diff --git a/synthpop/models/GUMS_dr3/disk6.popjson b/synthpop/models/GUMS_dr3/disk6.popjson index c269cb4..e835915 100644 --- a/synthpop/models/GUMS_dr3/disk6.popjson +++ b/synthpop/models/GUMS_dr3/disk6.popjson @@ -42,7 +42,8 @@ "sigma_v": 20.968, "sigma_w": 17.063 }, - "warp":{ + + "warp":{ "r_warp": 8.659, "amp_warp_pos": 0.45300, "amp_warp_neg": 0.39000, diff --git a/synthpop/models/GUMS_dr3/halo.popjson b/synthpop/models/GUMS_dr3/halo.popjson index ac0708b..4314231 100644 --- a/synthpop/models/GUMS_dr3/halo.popjson +++ b/synthpop/models/GUMS_dr3/halo.popjson @@ -15,7 +15,7 @@ "name": "PiecewisePowerlaw", "alphas": [0.5] }, - "imf_func_kwargs": {"name": "Kroupa"}, + "age_func_kwargs" : { "name" : "SingleValue", "age_value": 14 diff --git a/synthpop/models/GUMS_dr3_mod_dens/bar.popjson b/synthpop/models/GUMS_dr3_mod_dens/bar.popjson index e5fa2f5..e8c3173 100644 --- a/synthpop/models/GUMS_dr3_mod_dens/bar.popjson +++ b/synthpop/models/GUMS_dr3_mod_dens/bar.popjson @@ -24,7 +24,6 @@ "alphas":[0.5] }, - "age_func_kwargs" : { "name" : "SingleValue", "age_value": 10 diff --git a/synthpop/models/GUMS_dr3_mod_dens/disk3.popjson b/synthpop/models/GUMS_dr3_mod_dens/disk3.popjson index f9dd46e..682d5ba 100644 --- a/synthpop/models/GUMS_dr3_mod_dens/disk3.popjson +++ b/synthpop/models/GUMS_dr3_mod_dens/disk3.popjson @@ -42,6 +42,7 @@ "sigma_v": 11.283, "sigma_w": 9.182 }, + "warp":{ "r_warp": 9.8065, "amp_warp_pos": 0.61675, diff --git a/synthpop/models/GUMS_dr3_mod_dens/disk4.popjson b/synthpop/models/GUMS_dr3_mod_dens/disk4.popjson index 83ebef9..3391e5a 100644 --- a/synthpop/models/GUMS_dr3_mod_dens/disk4.popjson +++ b/synthpop/models/GUMS_dr3_mod_dens/disk4.popjson @@ -42,7 +42,8 @@ "sigma_v": 13.851, "sigma_w": 11.272 }, - "warp":{ + + "warp":{ "r_warp": 9.5515, "amp_warp_pos": 0.47175, "amp_warp_neg": 0.26700, diff --git a/synthpop/models/GUMS_dr3_mod_dens/disk5.popjson b/synthpop/models/GUMS_dr3_mod_dens/disk5.popjson index bee54ba..f45e4e2 100644 --- a/synthpop/models/GUMS_dr3_mod_dens/disk5.popjson +++ b/synthpop/models/GUMS_dr3_mod_dens/disk5.popjson @@ -43,7 +43,8 @@ "sigma_v": 17.257, "sigma_w": 14.044 }, - "warp":{ + + "warp":{ "r_warp": 9.169, "amp_warp_pos": 0.45300, "amp_warp_neg": 0.39000, diff --git a/synthpop/models/GUMS_dr3_mod_dens/disk6.popjson b/synthpop/models/GUMS_dr3_mod_dens/disk6.popjson index 6553c8c..612b6c3 100644 --- a/synthpop/models/GUMS_dr3_mod_dens/disk6.popjson +++ b/synthpop/models/GUMS_dr3_mod_dens/disk6.popjson @@ -42,7 +42,8 @@ "sigma_v": 20.968, "sigma_w": 17.063 }, - "warp":{ + + "warp":{ "r_warp": 8.659, "amp_warp_pos": 0.45300, "amp_warp_neg": 0.39000, diff --git a/synthpop/models/GUMS_dr3_mod_dens/halo.popjson b/synthpop/models/GUMS_dr3_mod_dens/halo.popjson index 4746670..3e37009 100644 --- a/synthpop/models/GUMS_dr3_mod_dens/halo.popjson +++ b/synthpop/models/GUMS_dr3_mod_dens/halo.popjson @@ -15,7 +15,7 @@ "name": "PiecewisePowerlaw", "alphas": [0.5] }, - "imf_func_kwargs": {"name": "Kroupa"}, + "age_func_kwargs" : { "name" : "SingleValue", "age_value": 14 diff --git a/synthpop/models/Huston2024/bulge.popjson b/synthpop/models/Huston2024/bulge.popjson index 7686370..2ce8759 100644 --- a/synthpop/models/Huston2024/bulge.popjson +++ b/synthpop/models/Huston2024/bulge.popjson @@ -38,13 +38,13 @@ "population_density_kwargs" : { "name" : "triaxial_bulge", - "triaxial_type" : "E3", - "density_unit" : "mass", - "x0" : 0.67, - "y0" : 0.29, - "z0" : 0.27, - "rho0" : 1.32585e10, - "bar_angle" : 29.4 + "triaxial_type" : "E3", + "density_unit" : "mass", + "x0" : 0.67, + "y0" : 0.29, + "z0" : 0.27, + "rho0" : 1.32585e10, + "bar_angle" : 29.4 } } diff --git a/synthpop/models/Huston2024/thin_disk_1.popjson b/synthpop/models/Huston2024/thin_disk_1.popjson index 0e81fff..7c48b7b 100644 --- a/synthpop/models/Huston2024/thin_disk_1.popjson +++ b/synthpop/models/Huston2024/thin_disk_1.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 5.2e6, - "R0" : 5.0, - "z0" : 0.061, - "z45" : 0.036, - "Rbreak" : 5.3 + "rho_sun" : 5.2e6, + "R" : 5.0, + "z_sun" : 0.061, + "z_45" : 0.036, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2024/thin_disk_2.popjson b/synthpop/models/Huston2024/thin_disk_2.popjson index a05bf53..c31af2d 100644 --- a/synthpop/models/Huston2024/thin_disk_2.popjson +++ b/synthpop/models/Huston2024/thin_disk_2.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 5.2e6, - "R0" : 2.6, - "z0" : 0.141, - "z45" : 0.085, - "Rbreak" : 5.3 + "rho_sun" : 5.2e6, + "R" : 2.6, + "z_sun" : 0.141, + "z_45" : 0.085, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2024/thin_disk_3.popjson b/synthpop/models/Huston2024/thin_disk_3.popjson index 4b39185..15e49f8 100644 --- a/synthpop/models/Huston2024/thin_disk_3.popjson +++ b/synthpop/models/Huston2024/thin_disk_3.popjson @@ -32,10 +32,11 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 4.1e6, - "R0" : 2.6, - "z0" : 0.224, - "z45" : 0.134, - "Rbreak" : 5.3 + "rho_sun" : 4.1e6, + "R" : 2.6, + "z_sun" : 0.224, + "z_45" : 0.134, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2024/thin_disk_4.popjson b/synthpop/models/Huston2024/thin_disk_4.popjson index 057d019..b88aad8 100644 --- a/synthpop/models/Huston2024/thin_disk_4.popjson +++ b/synthpop/models/Huston2024/thin_disk_4.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 3.5e6, - "R0" : 2.6, - "z0" : 0.292, - "z45" : 0.175, - "Rbreak" : 5.3 + "rho_sun" : 3.5e6, + "R" : 2.6, + "z_sun" : 0.292, + "z_45" : 0.175, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2024/thin_disk_5.popjson b/synthpop/models/Huston2024/thin_disk_5.popjson index 47174e7..15cfa71 100644 --- a/synthpop/models/Huston2024/thin_disk_5.popjson +++ b/synthpop/models/Huston2024/thin_disk_5.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 6.7e6, - "R0" : 2.6, - "z0" : 0.372, - "z45" : 0.223, - "Rbreak" : 5.3 + "rho_sun" : 6.7e6, + "R" : 2.6, + "z_sun" : 0.372, + "z_45" : 0.223, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2024/thin_disk_6.popjson b/synthpop/models/Huston2024/thin_disk_6.popjson index 3433e2d..781c4ee 100644 --- a/synthpop/models/Huston2024/thin_disk_6.popjson +++ b/synthpop/models/Huston2024/thin_disk_6.popjson @@ -32,10 +32,11 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 7.3e6, - "R0" : 2.6, - "z0" : 0.440, - "z45" : 0.264, - "Rbreak" : 5.3 + "rho_sun" : 7.3e6, + "R" : 2.6, + "z_sun" : 0.440, + "z_45" : 0.264, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2024/thin_disk_7.popjson b/synthpop/models/Huston2024/thin_disk_7.popjson index 99f4803..a3749e8 100644 --- a/synthpop/models/Huston2024/thin_disk_7.popjson +++ b/synthpop/models/Huston2024/thin_disk_7.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 1.5e7, - "R0" : 2.6, - "z0" : 0.445, - "z45" : 0.267, - "Rbreak" : 5.3 + "rho_sun" : 1.5e7, + "R" : 2.6, + "z_sun" : 0.445, + "z_45" : 0.267, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2025/bulge.popjson b/synthpop/models/Huston2025/bulge.popjson index 7686370..77ba688 100644 --- a/synthpop/models/Huston2025/bulge.popjson +++ b/synthpop/models/Huston2025/bulge.popjson @@ -24,7 +24,7 @@ "kinematics_func_kwargs" : { "name" : "Koshimoto2021Bulge", "v0_stream" : 50.292048949082, - "y0_stream" : 341.648699391874, + "y0_stream" : 0.341648699391874, "C_par_r" : 1.02368618322272, "C_perp_r" : 4.77951649927811, "C_par_z" : 1.03692771363475, @@ -33,18 +33,19 @@ "h0_z" : [0.599661429898468,3.49164867887895,2.49084715817117], "sigma_i0" : [59.862204994578, 74.3069119713283, 70.358527922527], "sigma_i1" : [159.378160538468, 78.1318838272984, 80.8867483955919], - "omega_p" : 50.357033142405 + "omega_p" : 50.357033142405, + "bar_angle" : 29.4 }, "population_density_kwargs" : { "name" : "triaxial_bulge", - "triaxial_type" : "E3", - "density_unit" : "mass", - "x0" : 0.67, - "y0" : 0.29, - "z0" : 0.27, - "rho0" : 1.32585e10, - "bar_angle" : 29.4 + "triaxial_type" : "E3", + "density_unit" : "mass", + "x0" : 0.67, + "y0" : 0.29, + "z0" : 0.27, + "rho0" : 1.32585e10, + "bar_angle" : 29.4 } } diff --git a/synthpop/models/Huston2025/thin_disk_1.popjson b/synthpop/models/Huston2025/thin_disk_1.popjson index 0e81fff..7c48b7b 100644 --- a/synthpop/models/Huston2025/thin_disk_1.popjson +++ b/synthpop/models/Huston2025/thin_disk_1.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 5.2e6, - "R0" : 5.0, - "z0" : 0.061, - "z45" : 0.036, - "Rbreak" : 5.3 + "rho_sun" : 5.2e6, + "R" : 5.0, + "z_sun" : 0.061, + "z_45" : 0.036, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2025/thin_disk_2.popjson b/synthpop/models/Huston2025/thin_disk_2.popjson index a05bf53..c31af2d 100644 --- a/synthpop/models/Huston2025/thin_disk_2.popjson +++ b/synthpop/models/Huston2025/thin_disk_2.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 5.2e6, - "R0" : 2.6, - "z0" : 0.141, - "z45" : 0.085, - "Rbreak" : 5.3 + "rho_sun" : 5.2e6, + "R" : 2.6, + "z_sun" : 0.141, + "z_45" : 0.085, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2025/thin_disk_3.popjson b/synthpop/models/Huston2025/thin_disk_3.popjson index 4b39185..15e49f8 100644 --- a/synthpop/models/Huston2025/thin_disk_3.popjson +++ b/synthpop/models/Huston2025/thin_disk_3.popjson @@ -32,10 +32,11 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 4.1e6, - "R0" : 2.6, - "z0" : 0.224, - "z45" : 0.134, - "Rbreak" : 5.3 + "rho_sun" : 4.1e6, + "R" : 2.6, + "z_sun" : 0.224, + "z_45" : 0.134, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2025/thin_disk_4.popjson b/synthpop/models/Huston2025/thin_disk_4.popjson index 057d019..b88aad8 100644 --- a/synthpop/models/Huston2025/thin_disk_4.popjson +++ b/synthpop/models/Huston2025/thin_disk_4.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 3.5e6, - "R0" : 2.6, - "z0" : 0.292, - "z45" : 0.175, - "Rbreak" : 5.3 + "rho_sun" : 3.5e6, + "R" : 2.6, + "z_sun" : 0.292, + "z_45" : 0.175, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2025/thin_disk_5.popjson b/synthpop/models/Huston2025/thin_disk_5.popjson index 47174e7..15cfa71 100644 --- a/synthpop/models/Huston2025/thin_disk_5.popjson +++ b/synthpop/models/Huston2025/thin_disk_5.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 6.7e6, - "R0" : 2.6, - "z0" : 0.372, - "z45" : 0.223, - "Rbreak" : 5.3 + "rho_sun" : 6.7e6, + "R" : 2.6, + "z_sun" : 0.372, + "z_45" : 0.223, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2025/thin_disk_6.popjson b/synthpop/models/Huston2025/thin_disk_6.popjson index 3433e2d..781c4ee 100644 --- a/synthpop/models/Huston2025/thin_disk_6.popjson +++ b/synthpop/models/Huston2025/thin_disk_6.popjson @@ -32,10 +32,11 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 7.3e6, - "R0" : 2.6, - "z0" : 0.440, - "z45" : 0.264, - "Rbreak" : 5.3 + "rho_sun" : 7.3e6, + "R" : 2.6, + "z_sun" : 0.440, + "z_45" : 0.264, + "R_break" : 5.3, + "linear_z": true } } diff --git a/synthpop/models/Huston2025/thin_disk_7.popjson b/synthpop/models/Huston2025/thin_disk_7.popjson index 99f4803..59cf16a 100644 --- a/synthpop/models/Huston2025/thin_disk_7.popjson +++ b/synthpop/models/Huston2025/thin_disk_7.popjson @@ -32,11 +32,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 1.5e7, - "R0" : 2.6, - "z0" : 0.445, - "z45" : 0.267, - "Rbreak" : 5.3 + "rho_sun" : 1.6e7, + "R" : 2.6, + "z_sun" : 0.445, + "z_45" : 0.267, + "R_break" : 5.3, + "linear_z" : true } } diff --git a/synthpop/models/Huston2026/bulge.popjson b/synthpop/models/Huston2026/bulge.popjson new file mode 100644 index 0000000..77ba688 --- /dev/null +++ b/synthpop/models/Huston2026/bulge.popjson @@ -0,0 +1,52 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 10.0 + }, + + "metallicity_func_kwargs" : { + "#comment":"From Gonzalez, O.A.; et al. 2015", + "name" : "double_gaussian", + "weight" : 0.323, + "mean1" : -0.31, + "std1" : 0.31, + "mean2" : 0.26, + "std2" : 0.20 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Bulge", + "v0_stream" : 50.292048949082, + "y0_stream" : 0.341648699391874, + "C_par_r" : 1.02368618322272, + "C_perp_r" : 4.77951649927811, + "C_par_z" : 1.03692771363475, + "C_perp_z" : 4.38840062081552, + "h0_r" : [0.860976289672363,8.44373519664828,0.923663895777159], + "h0_z" : [0.599661429898468,3.49164867887895,2.49084715817117], + "sigma_i0" : [59.862204994578, 74.3069119713283, 70.358527922527], + "sigma_i1" : [159.378160538468, 78.1318838272984, 80.8867483955919], + "omega_p" : 50.357033142405, + "bar_angle" : 29.4 + }, + + "population_density_kwargs" : { + "name" : "triaxial_bulge", + "triaxial_type" : "E3", + "density_unit" : "mass", + "x0" : 0.67, + "y0" : 0.29, + "z0" : 0.27, + "rho0" : 1.32585e10, + "bar_angle" : 29.4 + } +} + + diff --git a/synthpop/models/Huston2026/halo.popjson b/synthpop/models/Huston2026/halo.popjson new file mode 100644 index 0000000..a4e7928 --- /dev/null +++ b/synthpop/models/Huston2026/halo.popjson @@ -0,0 +1,32 @@ +{ + "name" : "halo", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 14.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -1.78, + "std" : 0.50 + }, + + "kinematics_func_kwargs" : { + "name" : "velocity_gradient", + "sigma_u" : 131.0, + "sigma_v" : 106.0, + "sigma_w" : 85.0, + "const_V_ad": 226 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Halo" + } +} + diff --git a/synthpop/models/Huston2026/nsc.popjson b/synthpop/models/Huston2026/nsc.popjson new file mode 100644 index 0000000..4fef3a2 --- /dev/null +++ b/synthpop/models/Huston2026/nsc.popjson @@ -0,0 +1,34 @@ +{ + "name" : "nsc", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 10.0 + }, + + "metallicity_func_kwargs" : { + "#comment":"From Schultheis+26", + "name" : "double_gaussian", + "weight" : 0.171, + "mean1" : -0.77, + "std1" : 0.24, + "mean2" : 0.26, + "std2" : 0.10 + }, + + "kinematics_func_kwargs" : { + "name" : "kinematics_from_grid", + "moment_file":"vasiliev2026_huston_nsc_grid.dat" + }, + + "population_density_kwargs" : { + "name" : "density_from_grid", + "moment_file":"vasiliev2026_huston_nsc_grid.dat", + "population_density_name":"nsc" + } +} diff --git a/synthpop/models/Huston2026/nsd.popjson b/synthpop/models/Huston2026/nsd.popjson new file mode 100644 index 0000000..9b6dc33 --- /dev/null +++ b/synthpop/models/Huston2026/nsd.popjson @@ -0,0 +1,36 @@ +{ + "name" : "nsd", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 10.0 + }, + + "metallicity_func_kwargs" : { + "#comment":"From Nogueras-Lara+24 based on Fritz+21 data", + "name" : "double_gaussian", + "weight" : 0.21, + "mean1" : -0.19, + "std1" : 0.73, + "mean2" : 0.11, + "std2" : 0.26 + }, + + "kinematics_func_kwargs" : { + "name" : "kinematics_from_grid", + "moment_file":"vasiliev2026_huston_nsd_grid.dat" + }, + + "population_density_kwargs" : { + "name" : "density_from_grid", + "moment_file":"vasiliev2026_huston_nsd_grid.dat", + "population_density_name":"nsd" + } +} + + diff --git a/synthpop/models/Huston2026/thick_disk.popjson b/synthpop/models/Huston2026/thick_disk.popjson new file mode 100644 index 0000000..2637333 --- /dev/null +++ b/synthpop/models/Huston2026/thick_disk.popjson @@ -0,0 +1,34 @@ +{ + "name" : "thick_disk", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 12.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -0.78, + "std" : 0.30 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 75.0, + "sigma_z_sun" : 49.2, + "beta_r" : 0.0, + "beta_z" : 0.0, + "R_sigma_r" : 180.0, + "R_sigma_z" : 9.4, + "pop_age" : 12.0 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thickdisk" + } +} diff --git a/synthpop/models/Huston2026/thin_disk_1.popjson b/synthpop/models/Huston2026/thin_disk_1.popjson new file mode 100644 index 0000000..7c48b7b --- /dev/null +++ b/synthpop/models/Huston2026/thin_disk_1.popjson @@ -0,0 +1,44 @@ +{ + "name" : "thin_disk_1", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 0.0, + "high_bound" : 0.15 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : 0.01, + "std" : 0.12 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 0.075 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 5.2e6, + "R" : 5.0, + "z_sun" : 0.061, + "z_45" : 0.036, + "R_break" : 5.3, + "linear_z": true + } +} + + diff --git a/synthpop/models/Huston2026/thin_disk_2.popjson b/synthpop/models/Huston2026/thin_disk_2.popjson new file mode 100644 index 0000000..c31af2d --- /dev/null +++ b/synthpop/models/Huston2026/thin_disk_2.popjson @@ -0,0 +1,43 @@ +{ + "name" : "thin_disk_2", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 0.15, + "high_bound" : 1.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : 0.03, + "std" : 0.12 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 0.575 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 5.2e6, + "R" : 2.6, + "z_sun" : 0.141, + "z_45" : 0.085, + "R_break" : 5.3, + "linear_z": true + } +} + diff --git a/synthpop/models/Huston2026/thin_disk_3.popjson b/synthpop/models/Huston2026/thin_disk_3.popjson new file mode 100644 index 0000000..15e49f8 --- /dev/null +++ b/synthpop/models/Huston2026/thin_disk_3.popjson @@ -0,0 +1,42 @@ +{ + "name" : "thin_disk_3", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 1.0, + "high_bound" : 2.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : 0.03, + "std" : 0.10 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 1.5 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 4.1e6, + "R" : 2.6, + "z_sun" : 0.224, + "z_45" : 0.134, + "R_break" : 5.3, + "linear_z": true + } +} diff --git a/synthpop/models/Huston2026/thin_disk_4.popjson b/synthpop/models/Huston2026/thin_disk_4.popjson new file mode 100644 index 0000000..b88aad8 --- /dev/null +++ b/synthpop/models/Huston2026/thin_disk_4.popjson @@ -0,0 +1,44 @@ +{ + "name" : "thin_disk_4", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 2.0, + "high_bound" : 3.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : 0.01, + "std" : 0.11 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 2.5 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 3.5e6, + "R" : 2.6, + "z_sun" : 0.292, + "z_45" : 0.175, + "R_break" : 5.3, + "linear_z": true + } +} + + diff --git a/synthpop/models/Huston2026/thin_disk_5.popjson b/synthpop/models/Huston2026/thin_disk_5.popjson new file mode 100644 index 0000000..15cfa71 --- /dev/null +++ b/synthpop/models/Huston2026/thin_disk_5.popjson @@ -0,0 +1,43 @@ +{ + "name" : "thin_disk_5", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 3.0, + "high_bound" : 5.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -0.07, + "std" : 0.18 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 4.0 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 6.7e6, + "R" : 2.6, + "z_sun" : 0.372, + "z_45" : 0.223, + "R_break" : 5.3, + "linear_z": true + } +} + diff --git a/synthpop/models/Huston2026/thin_disk_6.popjson b/synthpop/models/Huston2026/thin_disk_6.popjson new file mode 100644 index 0000000..781c4ee --- /dev/null +++ b/synthpop/models/Huston2026/thin_disk_6.popjson @@ -0,0 +1,42 @@ +{ + "name" : "thin_disk_6", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 5.0, + "high_bound" : 7.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -0.14, + "std" : 0.17 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 6.0 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 7.3e6, + "R" : 2.6, + "z_sun" : 0.440, + "z_45" : 0.264, + "R_break" : 5.3, + "linear_z": true + } +} diff --git a/synthpop/models/Huston2026/thin_disk_7.popjson b/synthpop/models/Huston2026/thin_disk_7.popjson new file mode 100644 index 0000000..59cf16a --- /dev/null +++ b/synthpop/models/Huston2026/thin_disk_7.popjson @@ -0,0 +1,43 @@ +{ + "name" : "thin_disk_7", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 7.0, + "high_bound" : 10.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -0.37, + "std" : 0.20 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 8.5 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 1.6e7, + "R" : 2.6, + "z_sun" : 0.445, + "z_45" : 0.267, + "R_break" : 5.3, + "linear_z" : true + } +} + diff --git a/synthpop/models/Koshimoto2021/bulge.popjson b/synthpop/models/Koshimoto2021/bulge.popjson index 79a13b4..ad1d89c 100644 --- a/synthpop/models/Koshimoto2021/bulge.popjson +++ b/synthpop/models/Koshimoto2021/bulge.popjson @@ -3,43 +3,47 @@ "imf_func_kwargs" : { "name" : "PiecewisePowerlaw", - "alphas" : [0.22,1.16,2.32], - "splitpoints" : [0.08,0.90] + "alphas" : [0.18,1.13,2.32], + "splitpoints" : [0.08,0.86] }, "age_func_kwargs" : { "name" : "single_value", "#comment" : "units in gigayears", - "age_value" : 10.0 + "age_value" : 9.0 }, "metallicity_func_kwargs" : { - "#comment":"From Gonzalez, O.A.; et al. 2015", - "name" : "double_gaussian", - "weight" : 0.323, - "mean1" : -0.31, - "std1" : 0.31, - "mean2" : 0.26, - "std2" : 0.20 + "name" : "single_value", + "met_value" : 0.0 }, "kinematics_func_kwargs" : { "name" : "Koshimoto2021Bulge", - "v0_stream" : 50.292048949082, - "y0_stream" : 341.648699391874, - "C_par_r" : 1.02368618322272, - "C_perp_r" : 4.77951649927811, - "C_par_z" : 1.03692771363475, - "C_perp_z" : 4.38840062081552, - "h0_r" : [0.860976289672363,8.44373519664828,0.923663895777159], - "h0_z" : [0.599661429898468,3.49164867887895,2.49084715817117], - "sigma_i0" : [59.862204994578, 74.3069119713283, 70.358527922527], - "sigma_i1" : [159.378160538468, 78.1318838272984, 80.8867483955919], - "omega_p" : 50.357033142405 + "v0_stream" : 43, + "y0_stream" : 0.407, + "C_par_r" : 1.0, + "C_perp_r" : 4.3, + "C_par_z" : 1.1, + "C_perp_z" : 3.7, + "h0_r" : [0.86, 3.22, 0.95], + "h0_z" : [0.56, 2.00, 3.82], + "sigma_i0" : [64, 76, 71], + "sigma_i1" : [152, 78, 82], + "omega_p" : 47.4 }, "population_density_kwargs" : { - "name" : "Koshimoto2021BulgeB" + "name" : "Koshimoto2021Bulge", + "parameterization" : "E", + "rho0" : 4.12e9, + "x0" : 0.93, + "y0" : 0.37, + "z0" : 0.24, + "C_perp" : 1.2, + "C_par" : 4.1, + "R_c" : 2.6, + "X_shape" : false } } diff --git a/synthpop/models/Koshimoto2021/bulge_x.popjson b/synthpop/models/Koshimoto2021/bulge_x.popjson new file mode 100644 index 0000000..84c029a --- /dev/null +++ b/synthpop/models/Koshimoto2021/bulge_x.popjson @@ -0,0 +1,51 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.18,1.13,2.32], + "splitpoints" : [0.08,0.86] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 9.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.0 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Bulge", + "v0_stream" : 43, + "y0_stream" : 0.407, + "C_par_r" : 1.0, + "C_perp_r" : 4.3, + "C_par_z" : 1.1, + "C_perp_z" : 3.7, + "h0_r" : [0.86, 3.22, 0.95], + "h0_z" : [0.56, 2.00, 3.82], + "sigma_i0" : [64, 76, 71], + "sigma_i1" : [152, 78, 82], + "omega_p" : 47.4 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Bulge", + "parameterization" : "E", + "rho0" : 5.93e9, + "x0" : 0.28, + "y0" : 0.18, + "z0" : 0.29, + "C_perp" : 1.3, + "C_par" : 2.2, + "R_c" : 1.3, + "X_shape" : true, + "b_X" : 1.38 + } +} + + diff --git a/synthpop/models/Koshimoto2021/thick_disk.popjson b/synthpop/models/Koshimoto2021/thick_disk.popjson index 2637333..7d610be 100644 --- a/synthpop/models/Koshimoto2021/thick_disk.popjson +++ b/synthpop/models/Koshimoto2021/thick_disk.popjson @@ -12,9 +12,8 @@ }, "metallicity_func_kwargs" : { - "name" : "gaussian", - "mean" : -0.78, - "std" : 0.30 + "name" : "single_value", + "met_value" : -0.78 }, "kinematics_func_kwargs" : { diff --git a/synthpop/models/Koshimoto2021/thin_disk_1.popjson b/synthpop/models/Koshimoto2021/thin_disk_1.popjson index 0e81fff..72da9c4 100644 --- a/synthpop/models/Koshimoto2021/thin_disk_1.popjson +++ b/synthpop/models/Koshimoto2021/thin_disk_1.popjson @@ -6,16 +6,14 @@ }, "age_func_kwargs" : { - "name" : "uniform", + "name" : "single_value", "#comment" : "units in gigayears", - "low_bound" : 0.0, - "high_bound" : 0.15 + "age_value" : 0.1 }, "metallicity_func_kwargs" : { - "name" : "gaussian", - "mean" : 0.01, - "std" : 0.12 + "name" : "single_value", + "met_value" : 0.01 }, "kinematics_func_kwargs" : { @@ -26,17 +24,18 @@ "beta_z" : 0.77, "R_sigma_r" : 14.3, "R_sigma_z" : 5.9, - "pop_age" : 0.075 + "pop_age" : 0.1 }, "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 5.2e6, - "R0" : 5.0, - "z0" : 0.061, - "z45" : 0.036, - "Rbreak" : 5.3 + "rho_sun" : 5.2e6, + "R" : 5.0, + "z_sun" : 0.061, + "z_45" : 0.036, + "R_break" : 5.3, + "linear_z" : false } } diff --git a/synthpop/models/Koshimoto2021/thin_disk_2.popjson b/synthpop/models/Koshimoto2021/thin_disk_2.popjson index a05bf53..3be2ec7 100644 --- a/synthpop/models/Koshimoto2021/thin_disk_2.popjson +++ b/synthpop/models/Koshimoto2021/thin_disk_2.popjson @@ -6,16 +6,14 @@ }, "age_func_kwargs" : { - "name" : "uniform", + "name" : "single_value", "#comment" : "units in gigayears", - "low_bound" : 0.15, - "high_bound" : 1.0 + "age_value" : 0.6 }, "metallicity_func_kwargs" : { - "name" : "gaussian", - "mean" : 0.03, - "std" : 0.12 + "name" : "single_value", + "met_value" : 0.00 }, "kinematics_func_kwargs" : { @@ -26,17 +24,18 @@ "beta_z" : 0.77, "R_sigma_r" : 14.3, "R_sigma_z" : 5.9, - "pop_age" : 0.575 + "pop_age" : 0.6 }, "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 5.2e6, - "R0" : 2.6, - "z0" : 0.141, - "z45" : 0.085, - "Rbreak" : 5.3 + "rho_sun" : 5.3e6, + "R" : 2.6, + "z_sun" : 0.141, + "z_45" : 0.085, + "R_break" : 5.3, + "linear_z" : false } } diff --git a/synthpop/models/Koshimoto2021/thin_disk_3.popjson b/synthpop/models/Koshimoto2021/thin_disk_3.popjson index 4b39185..474e6b2 100644 --- a/synthpop/models/Koshimoto2021/thin_disk_3.popjson +++ b/synthpop/models/Koshimoto2021/thin_disk_3.popjson @@ -6,16 +6,14 @@ }, "age_func_kwargs" : { - "name" : "uniform", + "name" : "single_value", "#comment" : "units in gigayears", - "low_bound" : 1.0, - "high_bound" : 2.0 + "age_value" : 1.5 }, "metallicity_func_kwargs" : { - "name" : "gaussian", - "mean" : 0.03, - "std" : 0.10 + "name" : "single_value", + "met_value" : -0.02 }, "kinematics_func_kwargs" : { @@ -32,10 +30,11 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 4.1e6, - "R0" : 2.6, - "z0" : 0.224, - "z45" : 0.134, - "Rbreak" : 5.3 + "rho_sun" : 4.1e6, + "R" : 2.6, + "z_sun" : 0.224, + "z_45" : 0.134, + "R_break" : 5.3, + "linear_z" : false } } diff --git a/synthpop/models/Koshimoto2021/thin_disk_4.popjson b/synthpop/models/Koshimoto2021/thin_disk_4.popjson index 057d019..73e4fe8 100644 --- a/synthpop/models/Koshimoto2021/thin_disk_4.popjson +++ b/synthpop/models/Koshimoto2021/thin_disk_4.popjson @@ -6,16 +6,14 @@ }, "age_func_kwargs" : { - "name" : "uniform", + "name" : "single_value", "#comment" : "units in gigayears", - "low_bound" : 2.0, - "high_bound" : 3.0 + "age_value" : 2.5 }, "metallicity_func_kwargs" : { - "name" : "gaussian", - "mean" : 0.01, - "std" : 0.11 + "name" : "single_value", + "met_value" : -0.03 }, "kinematics_func_kwargs" : { @@ -32,11 +30,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 3.5e6, - "R0" : 2.6, - "z0" : 0.292, - "z45" : 0.175, - "Rbreak" : 5.3 + "rho_sun" : 3.6e6, + "R" : 2.6, + "z_sun" : 0.292, + "z_45" : 0.175, + "R_break" : 5.3, + "linear_z" : false } } diff --git a/synthpop/models/Koshimoto2021/thin_disk_5.popjson b/synthpop/models/Koshimoto2021/thin_disk_5.popjson index 47174e7..ed3a6f0 100644 --- a/synthpop/models/Koshimoto2021/thin_disk_5.popjson +++ b/synthpop/models/Koshimoto2021/thin_disk_5.popjson @@ -6,16 +6,14 @@ }, "age_func_kwargs" : { - "name" : "uniform", + "name" : "single_value", "#comment" : "units in gigayears", - "low_bound" : 3.0, - "high_bound" : 5.0 + "age_value" : 4.0 }, "metallicity_func_kwargs" : { - "name" : "gaussian", - "mean" : -0.07, - "std" : 0.18 + "name" : "single_value", + "met_value" : -0.05 }, "kinematics_func_kwargs" : { @@ -32,11 +30,12 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 6.7e6, - "R0" : 2.6, - "z0" : 0.372, - "z45" : 0.223, - "Rbreak" : 5.3 + "rho_sun" : 6.7e6, + "R" : 2.6, + "z_sun" : 0.372, + "z_45" : 0.223, + "R_break" : 5.3, + "linear_z" : false } } diff --git a/synthpop/models/Koshimoto2021/thin_disk_6.popjson b/synthpop/models/Koshimoto2021/thin_disk_6.popjson index 78dbc66..b587753 100644 --- a/synthpop/models/Koshimoto2021/thin_disk_6.popjson +++ b/synthpop/models/Koshimoto2021/thin_disk_6.popjson @@ -6,16 +6,14 @@ }, "age_func_kwargs" : { - "name" : "uniform", + "name" : "single_value", "#comment" : "units in gigayears", - "low_bound" : 5.0, - "high_bound" : 7.0 + "age_value" : 6.0 }, "metallicity_func_kwargs" : { - "name" : "gaussian", - "mean" : -0.14, - "std" : 0.17 + "name" : "single_value", + "met_value" : -0.09 }, "kinematics_func_kwargs" : { @@ -32,10 +30,11 @@ "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 7.3e6, - "R0" : 2.6, - "z0" : 0.440, - "z45" : 0.264, - "Rbreak" : 5.3 + "rho_sun" : 7.4e6, + "R" : 2.6, + "z_sun" : 0.440, + "z_45" : 0.264, + "R_break" : 5.3, + "linear_z" : false } } diff --git a/synthpop/models/Koshimoto2021/thin_disk_7.popjson b/synthpop/models/Koshimoto2021/thin_disk_7.popjson index 99f4803..87e9225 100644 --- a/synthpop/models/Koshimoto2021/thin_disk_7.popjson +++ b/synthpop/models/Koshimoto2021/thin_disk_7.popjson @@ -6,16 +6,14 @@ }, "age_func_kwargs" : { - "name" : "uniform", + "name" : "single_value", "#comment" : "units in gigayears", - "low_bound" : 7.0, - "high_bound" : 10.0 + "age_value" : 8.7 }, "metallicity_func_kwargs" : { - "name" : "gaussian", - "mean" : -0.37, - "std" : 0.20 + "name" : "single_value", + "met_value" : -0.12 }, "kinematics_func_kwargs" : { @@ -26,17 +24,18 @@ "beta_z" : 0.77, "R_sigma_r" : 14.3, "R_sigma_z" : 5.9, - "pop_age" : 8.5 + "pop_age" : 8.7 }, "population_density_kwargs" : { "name" : "Koshimoto2021Thindisk", "#comment" : "rho0 in Msun/kpc**3 (10**9)", - "rho0" : 1.5e7, - "R0" : 2.6, - "z0" : 0.445, - "z45" : 0.267, - "Rbreak" : 5.3 + "rho_sun" : 1.6e7, + "R" : 2.6, + "z_sun" : 0.445, + "z_45" : 0.267, + "R_break" : 5.3, + "linear_z" : false } } diff --git a/synthpop/models/Koshimoto2022/bulge.popjson b/synthpop/models/Koshimoto2022/bulge.popjson new file mode 100644 index 0000000..ad1d89c --- /dev/null +++ b/synthpop/models/Koshimoto2022/bulge.popjson @@ -0,0 +1,50 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.18,1.13,2.32], + "splitpoints" : [0.08,0.86] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 9.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.0 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Bulge", + "v0_stream" : 43, + "y0_stream" : 0.407, + "C_par_r" : 1.0, + "C_perp_r" : 4.3, + "C_par_z" : 1.1, + "C_perp_z" : 3.7, + "h0_r" : [0.86, 3.22, 0.95], + "h0_z" : [0.56, 2.00, 3.82], + "sigma_i0" : [64, 76, 71], + "sigma_i1" : [152, 78, 82], + "omega_p" : 47.4 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Bulge", + "parameterization" : "E", + "rho0" : 4.12e9, + "x0" : 0.93, + "y0" : 0.37, + "z0" : 0.24, + "C_perp" : 1.2, + "C_par" : 4.1, + "R_c" : 2.6, + "X_shape" : false + } +} + + diff --git a/synthpop/models/Koshimoto2022/bulge_x.popjson b/synthpop/models/Koshimoto2022/bulge_x.popjson new file mode 100644 index 0000000..84c029a --- /dev/null +++ b/synthpop/models/Koshimoto2022/bulge_x.popjson @@ -0,0 +1,51 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.18,1.13,2.32], + "splitpoints" : [0.08,0.86] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 9.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.0 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Bulge", + "v0_stream" : 43, + "y0_stream" : 0.407, + "C_par_r" : 1.0, + "C_perp_r" : 4.3, + "C_par_z" : 1.1, + "C_perp_z" : 3.7, + "h0_r" : [0.86, 3.22, 0.95], + "h0_z" : [0.56, 2.00, 3.82], + "sigma_i0" : [64, 76, 71], + "sigma_i1" : [152, 78, 82], + "omega_p" : 47.4 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Bulge", + "parameterization" : "E", + "rho0" : 5.93e9, + "x0" : 0.28, + "y0" : 0.18, + "z0" : 0.29, + "C_perp" : 1.3, + "C_par" : 2.2, + "R_c" : 1.3, + "X_shape" : true, + "b_X" : 1.38 + } +} + + diff --git a/synthpop/models/Koshimoto2022/nsd.popjson b/synthpop/models/Koshimoto2022/nsd.popjson new file mode 100644 index 0000000..86f398e --- /dev/null +++ b/synthpop/models/Koshimoto2022/nsd.popjson @@ -0,0 +1,32 @@ +{ + "name" : "nsd", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.18,1.13,2.32], + "splitpoints" : [0.08,0.86] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 7.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.10 + }, + + "kinematics_func_kwargs" : { + "name" : "kinematics_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + }, + + "population_density_kwargs" : { + "name" : "density_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + } +} + + diff --git a/synthpop/models/Koshimoto2022/thick_disk.popjson b/synthpop/models/Koshimoto2022/thick_disk.popjson new file mode 100644 index 0000000..7d610be --- /dev/null +++ b/synthpop/models/Koshimoto2022/thick_disk.popjson @@ -0,0 +1,33 @@ +{ + "name" : "thick_disk", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 12.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : -0.78 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 75.0, + "sigma_z_sun" : 49.2, + "beta_r" : 0.0, + "beta_z" : 0.0, + "R_sigma_r" : 180.0, + "R_sigma_z" : 9.4, + "pop_age" : 12.0 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thickdisk" + } +} diff --git a/synthpop/models/Koshimoto2022/thin_disk_1.popjson b/synthpop/models/Koshimoto2022/thin_disk_1.popjson new file mode 100644 index 0000000..72da9c4 --- /dev/null +++ b/synthpop/models/Koshimoto2022/thin_disk_1.popjson @@ -0,0 +1,42 @@ +{ + "name" : "thin_disk_1", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 0.1 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.01 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 0.1 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 5.2e6, + "R" : 5.0, + "z_sun" : 0.061, + "z_45" : 0.036, + "R_break" : 5.3, + "linear_z" : false + } +} + + diff --git a/synthpop/models/Koshimoto2022/thin_disk_2.popjson b/synthpop/models/Koshimoto2022/thin_disk_2.popjson new file mode 100644 index 0000000..3be2ec7 --- /dev/null +++ b/synthpop/models/Koshimoto2022/thin_disk_2.popjson @@ -0,0 +1,41 @@ +{ + "name" : "thin_disk_2", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 0.6 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.00 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 0.6 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 5.3e6, + "R" : 2.6, + "z_sun" : 0.141, + "z_45" : 0.085, + "R_break" : 5.3, + "linear_z" : false + } +} + diff --git a/synthpop/models/Koshimoto2022/thin_disk_3.popjson b/synthpop/models/Koshimoto2022/thin_disk_3.popjson new file mode 100644 index 0000000..474e6b2 --- /dev/null +++ b/synthpop/models/Koshimoto2022/thin_disk_3.popjson @@ -0,0 +1,40 @@ +{ + "name" : "thin_disk_3", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 1.5 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : -0.02 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 1.5 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 4.1e6, + "R" : 2.6, + "z_sun" : 0.224, + "z_45" : 0.134, + "R_break" : 5.3, + "linear_z" : false + } +} diff --git a/synthpop/models/Koshimoto2022/thin_disk_4.popjson b/synthpop/models/Koshimoto2022/thin_disk_4.popjson new file mode 100644 index 0000000..73e4fe8 --- /dev/null +++ b/synthpop/models/Koshimoto2022/thin_disk_4.popjson @@ -0,0 +1,42 @@ +{ + "name" : "thin_disk_4", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 2.5 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : -0.03 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 2.5 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 3.6e6, + "R" : 2.6, + "z_sun" : 0.292, + "z_45" : 0.175, + "R_break" : 5.3, + "linear_z" : false + } +} + + diff --git a/synthpop/models/Koshimoto2022/thin_disk_5.popjson b/synthpop/models/Koshimoto2022/thin_disk_5.popjson new file mode 100644 index 0000000..ed3a6f0 --- /dev/null +++ b/synthpop/models/Koshimoto2022/thin_disk_5.popjson @@ -0,0 +1,41 @@ +{ + "name" : "thin_disk_5", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 4.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : -0.05 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 4.0 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 6.7e6, + "R" : 2.6, + "z_sun" : 0.372, + "z_45" : 0.223, + "R_break" : 5.3, + "linear_z" : false + } +} + diff --git a/synthpop/models/Koshimoto2022/thin_disk_6.popjson b/synthpop/models/Koshimoto2022/thin_disk_6.popjson new file mode 100644 index 0000000..b587753 --- /dev/null +++ b/synthpop/models/Koshimoto2022/thin_disk_6.popjson @@ -0,0 +1,40 @@ +{ + "name" : "thin_disk_6", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 6.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : -0.09 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 6.0 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 7.4e6, + "R" : 2.6, + "z_sun" : 0.440, + "z_45" : 0.264, + "R_break" : 5.3, + "linear_z" : false + } +} diff --git a/synthpop/models/Koshimoto2022/thin_disk_7.popjson b/synthpop/models/Koshimoto2022/thin_disk_7.popjson new file mode 100644 index 0000000..87e9225 --- /dev/null +++ b/synthpop/models/Koshimoto2022/thin_disk_7.popjson @@ -0,0 +1,41 @@ +{ + "name" : "thin_disk_7", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 8.7 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : -0.12 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Disk", + "sigma_r_sun" : 42.0, + "sigma_z_sun" : 24.4, + "beta_r" : 0.32, + "beta_z" : 0.77, + "R_sigma_r" : 14.3, + "R_sigma_z" : 5.9, + "pop_age" : 8.7 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Thindisk", + "#comment" : "rho0 in Msun/kpc**3 (10**9)", + "rho_sun" : 1.6e7, + "R" : 2.6, + "z_sun" : 0.445, + "z_45" : 0.267, + "R_break" : 5.3, + "linear_z" : false + } +} + diff --git a/synthpop/models/Vasiliev2026/nsc.popjson b/synthpop/models/Vasiliev2026/nsc.popjson new file mode 100644 index 0000000..4fef3a2 --- /dev/null +++ b/synthpop/models/Vasiliev2026/nsc.popjson @@ -0,0 +1,34 @@ +{ + "name" : "nsc", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 10.0 + }, + + "metallicity_func_kwargs" : { + "#comment":"From Schultheis+26", + "name" : "double_gaussian", + "weight" : 0.171, + "mean1" : -0.77, + "std1" : 0.24, + "mean2" : 0.26, + "std2" : 0.10 + }, + + "kinematics_func_kwargs" : { + "name" : "kinematics_from_grid", + "moment_file":"vasiliev2026_huston_nsc_grid.dat" + }, + + "population_density_kwargs" : { + "name" : "density_from_grid", + "moment_file":"vasiliev2026_huston_nsc_grid.dat", + "population_density_name":"nsc" + } +} diff --git a/synthpop/models/Vasiliev2026/nsd.popjson b/synthpop/models/Vasiliev2026/nsd.popjson new file mode 100644 index 0000000..9b6dc33 --- /dev/null +++ b/synthpop/models/Vasiliev2026/nsd.popjson @@ -0,0 +1,36 @@ +{ + "name" : "nsd", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 10.0 + }, + + "metallicity_func_kwargs" : { + "#comment":"From Nogueras-Lara+24 based on Fritz+21 data", + "name" : "double_gaussian", + "weight" : 0.21, + "mean1" : -0.19, + "std1" : 0.73, + "mean2" : 0.11, + "std2" : 0.26 + }, + + "kinematics_func_kwargs" : { + "name" : "kinematics_from_grid", + "moment_file":"vasiliev2026_huston_nsd_grid.dat" + }, + + "population_density_kwargs" : { + "name" : "density_from_grid", + "moment_file":"vasiliev2026_huston_nsd_grid.dat", + "population_density_name":"nsd" + } +} + + diff --git a/synthpop/models/besancon_Robin2003/bulge.popjson b/synthpop/models/besancon_Robin2003/bulge.popjson index 51f540f..f1c0539 100644 --- a/synthpop/models/besancon_Robin2003/bulge.popjson +++ b/synthpop/models/besancon_Robin2003/bulge.popjson @@ -33,10 +33,6 @@ "z0":0.424, "Rc":2.54, "n0": 1.370e10 - }, - - "av_mass_corr" : 0.563529, - "n_star_corr" : 1.77453 + } } - diff --git a/synthpop/models/besancon_Robin2003/halo.popjson b/synthpop/models/besancon_Robin2003/halo.popjson index d4788ae..4bf0e88 100644 --- a/synthpop/models/besancon_Robin2003/halo.popjson +++ b/synthpop/models/besancon_Robin2003/halo.popjson @@ -27,9 +27,5 @@ "population_density_kwargs" : { "name" : "Besancon2003Halo" - }, - - "av_mass_corr" : 0.549629, - "n_star_corr" : 1.81941 + } } - diff --git a/synthpop/models/besancon_Robin2003/thick_disk.popjson b/synthpop/models/besancon_Robin2003/thick_disk.popjson index 8c1a9e5..10c59b7 100644 --- a/synthpop/models/besancon_Robin2003/thick_disk.popjson +++ b/synthpop/models/besancon_Robin2003/thick_disk.popjson @@ -31,9 +31,5 @@ "hr" : 2.5, "hz" : 0.8, "xl" : 0.4 - - }, - - "av_mass_corr" : 0.546120, - "n_star_corr" : 1.83110 + } } \ No newline at end of file diff --git a/synthpop/models/besancon_Robin2003/thin_disk_1.popjson b/synthpop/models/besancon_Robin2003/thin_disk_1.popjson index 775be93..bc619a2 100644 --- a/synthpop/models/besancon_Robin2003/thin_disk_1.popjson +++ b/synthpop/models/besancon_Robin2003/thin_disk_1.popjson @@ -37,10 +37,7 @@ "power": 2, "flare_flag" : true - }, - - "av_mass_corr" : 0.825737, - "n_star_corr" : 1.21104 + } } diff --git a/synthpop/models/besancon_Robin2003/thin_disk_2.popjson b/synthpop/models/besancon_Robin2003/thin_disk_2.popjson index 36acbd6..5045486 100644 --- a/synthpop/models/besancon_Robin2003/thin_disk_2.popjson +++ b/synthpop/models/besancon_Robin2003/thin_disk_2.popjson @@ -36,9 +36,6 @@ "offset":0.5, "power":1, "flare_flag" : true - }, - - "mass_corr" : 0.691252, - "n_star_corr" : 1.44665 + } } diff --git a/synthpop/models/besancon_Robin2003/thin_disk_3.popjson b/synthpop/models/besancon_Robin2003/thin_disk_3.popjson index c3fe452..6fea9ef 100644 --- a/synthpop/models/besancon_Robin2003/thin_disk_3.popjson +++ b/synthpop/models/besancon_Robin2003/thin_disk_3.popjson @@ -36,8 +36,5 @@ "hrm" : 1.320, "offset":0.5, "power":1 - }, - - "av_mass_corr" : 0.635764, - "n_star_corr" : 1.57291 + } } diff --git a/synthpop/models/besancon_Robin2003/thin_disk_4.popjson b/synthpop/models/besancon_Robin2003/thin_disk_4.popjson index e2816a7..8bb9835 100644 --- a/synthpop/models/besancon_Robin2003/thin_disk_4.popjson +++ b/synthpop/models/besancon_Robin2003/thin_disk_4.popjson @@ -36,10 +36,7 @@ "hrm" : 1.320, "offset":0.5, "power":1 - }, - - "av_mass_corr" : 0.620140, - "n_star_corr" : 1.61254 + } } diff --git a/synthpop/models/besancon_Robin2003/thin_disk_5.popjson b/synthpop/models/besancon_Robin2003/thin_disk_5.popjson index 75db9e2..d712002 100644 --- a/synthpop/models/besancon_Robin2003/thin_disk_5.popjson +++ b/synthpop/models/besancon_Robin2003/thin_disk_5.popjson @@ -36,9 +36,6 @@ "hrm" : 1.320, "offset":0.5, "power":1 - }, - - "av_mass_corr" : 0.588717, - "n_star_corr" : 1.69861 + } } diff --git a/synthpop/models/besancon_Robin2003/thin_disk_6.popjson b/synthpop/models/besancon_Robin2003/thin_disk_6.popjson index ff17727..27c8905 100644 --- a/synthpop/models/besancon_Robin2003/thin_disk_6.popjson +++ b/synthpop/models/besancon_Robin2003/thin_disk_6.popjson @@ -36,8 +36,5 @@ "hrm" : 1.320, "offset":0.5, "power":1 - }, - - "av_mass_corr" : 0.564627, - "n_star_corr" : 1.77108 + } } \ No newline at end of file diff --git a/synthpop/models/besancon_Robin2003/thin_disk_7.popjson b/synthpop/models/besancon_Robin2003/thin_disk_7.popjson index 86e5b95..36d989c 100644 --- a/synthpop/models/besancon_Robin2003/thin_disk_7.popjson +++ b/synthpop/models/besancon_Robin2003/thin_disk_7.popjson @@ -36,9 +36,6 @@ "hrm" : 1.320, "offset":0.5, "power":1 - }, - - "av_mass_corr" : 0.567811, - "n_star_corr" : 1.76115 + } } diff --git a/synthpop/models/galaxia_Sharma2011/bulge.popjson b/synthpop/models/galaxia_Sharma2011/bulge.popjson new file mode 100644 index 0000000..d12b370 --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/bulge.popjson @@ -0,0 +1,40 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [2.35], + "splitpoints" : [] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 10.0 + }, + + "metallicity_func_kwargs" : { + "#comment":"From Gonzalez, O.A.; et al. 2015", + "name" : "gaussian", + "mean" : 0.00, + "std" : 0.40 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 113.0, + "sigma_v" : 115.0, + "sigma_w" : 100.0, + "const_V_ad" : 79 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Bulge", + "x0":1.59, + "y0":0.424, + "z0":0.424, + "Rc":2.54, + "n0": 1.370e10 + } +} + diff --git a/synthpop/models/galaxia_Sharma2011/halo.popjson b/synthpop/models/galaxia_Sharma2011/halo.popjson new file mode 100644 index 0000000..bc10cbb --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/halo.popjson @@ -0,0 +1,33 @@ +{ + "name" : "halo", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.5], + "splitpoints" : [] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 14.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -1.78, + "std" : 0.50 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 131.0, + "sigma_v" : 106.0, + "sigma_w" : 85.0, + "const_V_ad" : 226 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Halo" + } +} diff --git a/synthpop/models/galaxia_Sharma2011/thick_disk.popjson b/synthpop/models/galaxia_Sharma2011/thick_disk.popjson new file mode 100644 index 0000000..625748f --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/thick_disk.popjson @@ -0,0 +1,37 @@ +{ + "name" : "thick_disk", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.5], + "splitpoints" : [] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 11.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -0.78, + "std" : 0.30 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 67.0, + "sigma_v" : 51.0, + "sigma_w" : 42.0 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Thickdisk", + "#comment" : "units in kpc, rho0 in Msun/kpc**3 (10**9)", + "rho0" : 0.00134E9, + "hr" : 2.5, + "hz" : 0.8, + "xl" : 0.4 + } +} diff --git a/synthpop/models/galaxia_Sharma2011/thin_disk_1.popjson b/synthpop/models/galaxia_Sharma2011/thin_disk_1.popjson new file mode 100644 index 0000000..3d820c0 --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/thin_disk_1.popjson @@ -0,0 +1,43 @@ +{ + "name" : "thin_disk_1", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [1.6,3.0], + "splitpoints" : [1.0]}, + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 0.0, + "high_bound" : 0.15 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : 0.01, + "std" : 0.12 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 16.7, + "sigma_v" : 10.8, + "sigma_w" : 6.0, + "disp_grad" : -0.2 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Thindisk", + "#comment" : "p0 in Msun/kpc**3 (10**9)", + "p0" : 0.003999E9, + "e" : 0.0140, + "hrp" : 5.0, + "hrm" : 3.0, + "power": 2, + "flare_flag" : true + } +} + + diff --git a/synthpop/models/galaxia_Sharma2011/thin_disk_2.popjson b/synthpop/models/galaxia_Sharma2011/thin_disk_2.popjson new file mode 100644 index 0000000..13811e7 --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/thin_disk_2.popjson @@ -0,0 +1,43 @@ +{ + "name" : "thin_disk_2", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [1.6,3.0], + "splitpoints" : [1.0]}, + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 0.15, + "high_bound" : 1.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : 0.03, + "std" : 0.12 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 19.8, + "sigma_v" : 12.8, + "sigma_w" : 8.0, + "disp_grad" : -0.2 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Thindisk", + "#comment" : "p0 in Msun/kpc**3 (10**9)", + "p0" : 0.007902E9, + "e" : 0.0268, + "hrp" : 2.530, + "hrm" : 1.320, + "offset":0.5, + "power":1, + "flare_flag" : true + } +} + diff --git a/synthpop/models/galaxia_Sharma2011/thin_disk_3.popjson b/synthpop/models/galaxia_Sharma2011/thin_disk_3.popjson new file mode 100644 index 0000000..2c2250e --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/thin_disk_3.popjson @@ -0,0 +1,42 @@ +{ + "name" : "thin_disk_3", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [1.6,3.0], + "splitpoints" : [1.0]}, + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 1.0, + "high_bound" : 2.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : 0.03, + "std" : 0.10 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 27.2, + "sigma_v" : 17.6, + "sigma_w" : 10.0, + "disp_grad" : -0.2 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Thindisk", + "#comment" : "p0 in Msun/kpc**3 (10**9)", + "p0" : 0.006224E9, + "e" : 0.0375, + "flare_flag" : true, + "hrp" : 2.530, + "hrm" : 1.320, + "offset":0.5, + "power":1 + } +} diff --git a/synthpop/models/galaxia_Sharma2011/thin_disk_4.popjson b/synthpop/models/galaxia_Sharma2011/thin_disk_4.popjson new file mode 100644 index 0000000..b5b7726 --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/thin_disk_4.popjson @@ -0,0 +1,44 @@ +{ + "name" : "thin_disk_4", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [1.6,3.0], + "splitpoints" : [1.0]}, + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 2.0, + "high_bound" : 3.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : 0.01, + "std" : 0.11 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 30.2, + "sigma_v" : 19.5, + "sigma_w" : 13.2, + "disp_grad" : -0.2 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Thindisk", + "#comment" : "p0 in Msun/kpc**3 (10**9)", + "p0" : 0.004020E9, + "e" : 0.0551, + "flare_flag" : true, + "hrp" : 2.530, + "hrm" : 1.320, + "offset":0.5, + "power":1 + } +} + + diff --git a/synthpop/models/galaxia_Sharma2011/thin_disk_5.popjson b/synthpop/models/galaxia_Sharma2011/thin_disk_5.popjson new file mode 100644 index 0000000..9180bbb --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/thin_disk_5.popjson @@ -0,0 +1,43 @@ +{ + "name" : "thin_disk_5", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [1.6,3.0], + "splitpoints" : [1.0]}, + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 3.0, + "high_bound" : 5.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -0.07, + "std" : 0.18 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 36.7, + "sigma_v" : 13.7, + "sigma_w" : 15.8, + "disp_grad" : -0.2 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Thindisk", + "#comment" : "p0 in Msun/kpc**3 (10**9)", + "p0" : 0.0058140E9, + "e" : 0.0696, + "flare_flag" : true, + "hrp" : 2.530, + "hrm" : 1.320, + "offset":0.5, + "power":1 + } +} + diff --git a/synthpop/models/galaxia_Sharma2011/thin_disk_6.popjson b/synthpop/models/galaxia_Sharma2011/thin_disk_6.popjson new file mode 100644 index 0000000..99595b8 --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/thin_disk_6.popjson @@ -0,0 +1,42 @@ +{ + "name" : "thin_disk_6", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [1.6,3.0], + "splitpoints" : [1.0]}, + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 5.0, + "high_bound" : 7.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -0.14, + "std" : 0.17 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 43.1, + "sigma_v" : 27.8, + "sigma_w" : 17.4, + "disp_grad" : -0.2 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Thindisk", + "#comment" : "p0 in Msun/kpc**3 (10**9)", + "p0" : 0.004928E9, + "e" : 0.0785, + "flare_flag" : true, + "hrp" : 2.530, + "hrm" : 1.320, + "offset":0.5, + "power":1 + } +} diff --git a/synthpop/models/galaxia_Sharma2011/thin_disk_7.popjson b/synthpop/models/galaxia_Sharma2011/thin_disk_7.popjson new file mode 100644 index 0000000..da3ee11 --- /dev/null +++ b/synthpop/models/galaxia_Sharma2011/thin_disk_7.popjson @@ -0,0 +1,43 @@ +{ + "name" : "thin_disk_7", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [1.6,3.0], + "splitpoints" : [1.0] + }, + + "age_func_kwargs" : { + "name" : "uniform", + "#comment" : "units in gigayears", + "low_bound" : 7.0, + "high_bound" : 10.0 + }, + + "metallicity_func_kwargs" : { + "name" : "gaussian", + "mean" : -0.37, + "std" : 0.20 + }, + + "kinematics_func_kwargs" : { + "name" : "Besancon2003", + "sigma_u" : 43.1, + "sigma_v" : 27.8, + "sigma_w" : 17.5, + "disp_grad" : -0.2 + }, + + "population_density_kwargs" : { + "name" : "Besancon2003Thindisk", + "#comment" : "p0 in Msun/kpc**3 (10**9)", + "p0" : 0.006590E9, + "e" : 0.0791, + "flare_flag" : true, + "hrp" : 2.530, + "hrm" : 1.320, + "offset":0.5, + "power":1 + } +} + diff --git a/synthpop/models/spare_populations/Cao2013_bulge/bulge.json b/synthpop/models/spare_populations/Cao2013_bulge/bulge.json new file mode 100644 index 0000000..3d8809c --- /dev/null +++ b/synthpop/models/spare_populations/Cao2013_bulge/bulge.json @@ -0,0 +1,56 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 10.0 + }, + + "metallicity_func_kwargs" : { + "#comment":"Metallicity not taken directly from Cao et al 2013", + "#comment":"From Gonzalez, O.A.; et al. 2015", + "name" : "double_gaussian", + "weight" : 0.323, + "mean1" : -0.31, + "std1" : 0.31, + "mean2" : 0.26, + "std2" : 0.20 + }, + + "kinematics_func_kwargs" : { + "#comment" : "Kinematics not provided in Cao et al 2013, use Koshimoto2021 unpublished results", + "#comment" : "Bessell function best-fit kinematics model provided by Naoki Koshimoto", + "name" : "koshimoto2021_bulge", + "v0_stream" : 50.292048949082, + "y0_stream" : 341.648699391874, + "C_par_r" : 1.02368618322272, + "C_perp_r" : 4.77951649927811, + "C_par_z" : 1.03692771363475, + "C_perp_z" : 4.38840062081552, + "h0_r" : [0.860976289672363,8.44373519664828,0.923663895777159], + "h0_z" : [0.599661429898468,3.49164867887895,2.49084715817117], + "sigma_i0" : [59.862204994578, 74.3069119713283, 70.358527922527], + "sigma_i1" : [159.378160538468, 78.1318838272984, 80.8867483955919], + "omega_p" : 50.357033142405 + }, + + "population_density_kwargs" : { + "name" : "triaxial_bulge", + "triaxial_type" : "E3", + "density_unit" : "mass", + "x0" : 0.67, + "y0" : 0.29, + "z0" : 0.27, + "rho0" : 1.32585e10, + "bar_angle" : 29.4 + }, + + "n_star_corr" : 1.77453 +} + + diff --git a/synthpop/models/spare_populations/Koshimoto2022_E_bulge_nsd/bulge.popjson b/synthpop/models/spare_populations/Koshimoto2022_E_bulge_nsd/bulge.popjson new file mode 100644 index 0000000..576274d --- /dev/null +++ b/synthpop/models/spare_populations/Koshimoto2022_E_bulge_nsd/bulge.popjson @@ -0,0 +1,50 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.18,1.10,2.31], + "splitpoints" : [0.08,0.84] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 9.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.0 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Bulge", + "v0_stream" : 49, + "y0_stream" : 0.393, + "C_par_r" : 1.0, + "C_perp_r" : 3.8, + "C_par_z" : 1.0, + "C_perp_z" : 3.0, + "h0_r" : [0.82, 9.29, 0.86], + "h0_z" : [0.51, 2.90, 2.19], + "sigma_i0" : [64, 75, 72], + "sigma_i1" : [156, 84, 86], + "omega_p" : 49.5 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Bulge", + "parameterization" : "E", + "rho0" : 9.72e9, + "x0" : 0.67, + "y0" : 0.28, + "z0" : 0.24, + "C_perp" : 1.4, + "C_par" : 3.3, + "R_c" : 2.8, + "X_shape" : false + } +} + + diff --git a/synthpop/models/spare_populations/Koshimoto2022_E_bulge_nsd/nsd.popjson b/synthpop/models/spare_populations/Koshimoto2022_E_bulge_nsd/nsd.popjson new file mode 100644 index 0000000..8b3e605 --- /dev/null +++ b/synthpop/models/spare_populations/Koshimoto2022_E_bulge_nsd/nsd.popjson @@ -0,0 +1,32 @@ +{ + "name" : "nsd", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.18,1.10,2.31], + "splitpoints" : [0.08,0.84] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 7.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.10 + }, + + "kinematics_func_kwargs" : { + "name" : "kinematics_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + }, + + "population_density_kwargs" : { + "name" : "density_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + } +} + + diff --git a/synthpop/models/spare_populations/Koshimoto2022_G_bulge_nsd/bulge.popjson b/synthpop/models/spare_populations/Koshimoto2022_G_bulge_nsd/bulge.popjson new file mode 100644 index 0000000..5dd6dc9 --- /dev/null +++ b/synthpop/models/spare_populations/Koshimoto2022_G_bulge_nsd/bulge.popjson @@ -0,0 +1,50 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.17,1.18,2.40], + "splitpoints" : [0.08,0.90] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 9.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.0 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Bulge", + "v0_stream" : 12, + "y0_stream" : 0.020, + "C_par_r" : 1.0, + "C_perp_r" : 4.9, + "C_par_z" : 1.0, + "C_perp_z" : 2.3, + "h0_r" : [1.03, 2.15, 0.73], + "h0_z" : [0.52, 1.44, 1.10], + "sigma_i0" : [76, 68, 75], + "sigma_i1" : [136, 109, 101], + "omega_p" : 40.5 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Bulge", + "parameterization" : "G", + "rho0" : 2.43e9, + "x0" : 1.03, + "y0" : 0.46, + "z0" : 0.40, + "C_perp" : 2.0, + "C_par" : 4.0, + "R_c" : 4.8, + "X_shape" : false + } +} + + diff --git a/synthpop/models/spare_populations/Koshimoto2022_G_bulge_nsd/nsd.popjson b/synthpop/models/spare_populations/Koshimoto2022_G_bulge_nsd/nsd.popjson new file mode 100644 index 0000000..9703114 --- /dev/null +++ b/synthpop/models/spare_populations/Koshimoto2022_G_bulge_nsd/nsd.popjson @@ -0,0 +1,32 @@ +{ + "name" : "nsd", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.17,1.18,2.40], + "splitpoints" : [0.08,0.90] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 7.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.10 + }, + + "kinematics_func_kwargs" : { + "name" : "kinematics_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + }, + + "population_density_kwargs" : { + "name" : "density_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + } +} + + diff --git a/synthpop/models/spare_populations/Koshimoto2022_GxG_bulge_nsd/bulge.popjson b/synthpop/models/spare_populations/Koshimoto2022_GxG_bulge_nsd/bulge.popjson new file mode 100644 index 0000000..a7a03dc --- /dev/null +++ b/synthpop/models/spare_populations/Koshimoto2022_GxG_bulge_nsd/bulge.popjson @@ -0,0 +1,50 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.22,1.16,2.32], + "splitpoints" : [0.08,0.90] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 9.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.0 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Bulge", + "v0_stream" : 28, + "y0_stream" : 0.011, + "C_par_r" : 1.0, + "C_perp_r" : 4.6, + "C_par_z" : 1.0, + "C_perp_z" : 4.8, + "h0_r" : [0.94, 4.23, 0.88], + "h0_z" : [0.70, 1.73, 2.03], + "sigma_i0" : [64, 75, 70], + "sigma_i1" : [155, 78, 83], + "omega_p" : 45.9 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Bulge", + "parameterization" : "G", + "rho0" : 0.88e9, + "x0" : 1.56, + "y0" : 0.72, + "z0" : 0.49, + "C_perp" : 1.2, + "C_par" : 3.1, + "R_c" : 2.8, + "X_shape" : false + } +} + + diff --git a/synthpop/models/spare_populations/Koshimoto2022_GxG_bulge_nsd/bulge_x.popjson b/synthpop/models/spare_populations/Koshimoto2022_GxG_bulge_nsd/bulge_x.popjson new file mode 100644 index 0000000..ff42ba0 --- /dev/null +++ b/synthpop/models/spare_populations/Koshimoto2022_GxG_bulge_nsd/bulge_x.popjson @@ -0,0 +1,51 @@ +{ + "name" : "bulge", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.22,1.16,2.32], + "splitpoints" : [0.08,0.90] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 9.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.0 + }, + + "kinematics_func_kwargs" : { + "name" : "Koshimoto2021Bulge", + "v0_stream" : 28, + "y0_stream" : 0.011, + "C_par_r" : 1.0, + "C_perp_r" : 4.6, + "C_par_z" : 1.0, + "C_perp_z" : 4.8, + "h0_r" : [0.94, 4.23, 0.88], + "h0_z" : [0.70, 1.73, 2.03], + "sigma_i0" : [64, 75, 70], + "sigma_i1" : [155, 78, 83], + "omega_p" : 45.9 + }, + + "population_density_kwargs" : { + "name" : "Koshimoto2021Bulge", + "parameterization" : "G", + "rho0" : 2.64e9, + "x0" : 0.76, + "y0" : 0.31, + "z0" : 0.40, + "C_perp" : 1.2, + "C_par" : 1.3, + "R_c" : 5.2, + "X_shape" : true, + "b_X" : 1.38 + } +} + + diff --git a/synthpop/models/spare_populations/Koshimoto2022_GxG_bulge_nsd/nsd.popjson b/synthpop/models/spare_populations/Koshimoto2022_GxG_bulge_nsd/nsd.popjson new file mode 100644 index 0000000..71c1561 --- /dev/null +++ b/synthpop/models/spare_populations/Koshimoto2022_GxG_bulge_nsd/nsd.popjson @@ -0,0 +1,32 @@ +{ + "name" : "nsd", + + "imf_func_kwargs" : { + "name" : "PiecewisePowerlaw", + "alphas" : [0.22,1.16,2.32], + "splitpoints" : [0.08,0.90] + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "units in gigayears", + "age_value" : 7.0 + }, + + "metallicity_func_kwargs" : { + "name" : "single_value", + "met_value" : 0.10 + }, + + "kinematics_func_kwargs" : { + "name" : "kinematics_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + }, + + "population_density_kwargs" : { + "name" : "density_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + } +} + + diff --git a/synthpop/models/spare_populations/README.md b/synthpop/models/spare_populations/README.md new file mode 100644 index 0000000..83c1b73 --- /dev/null +++ b/synthpop/models/spare_populations/README.md @@ -0,0 +1 @@ +This directory contains some population models for incomplete or swappable Galactic models. A user may construct their own model directory with components selected or swapped in from these. \ No newline at end of file diff --git a/synthpop/models/spare_populations/Sormani2022_nsd/nsd.popjson b/synthpop/models/spare_populations/Sormani2022_nsd/nsd.popjson new file mode 100644 index 0000000..87dbed3 --- /dev/null +++ b/synthpop/models/spare_populations/Sormani2022_nsd/nsd.popjson @@ -0,0 +1,35 @@ +{ + "name" : "nsd", + + "imf_func_kwargs" : { + "name" : "kroupa" + }, + + "age_func_kwargs" : { + "name" : "single_value", + "#comment" : "NOT pulled from Sormani et al 2022", + "age_value" : 10.0 + }, + + "metallicity_func_kwargs" : { + "#comment":"From Gonzalez, O.A.; et al. 2015, not Sormani et al 2022", + "name" : "double_gaussian", + "weight" : 0.323, + "mean1" : -0.31, + "std1" : 0.31, + "mean2" : 0.26, + "std2" : 0.20 + }, + + "kinematics_func_kwargs" : { + "name" : "kinematics_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + }, + + "population_density_kwargs" : { + "name" : "density_from_grid", + "moment_file":"sormani2022_koshimoto_grid.dat" + } +} + + diff --git a/synthpop/models/validation_model/pop_0.popjson b/synthpop/models/validation_model/pop_0.popjson index 2caf647..86b116d 100644 --- a/synthpop/models/validation_model/pop_0.popjson +++ b/synthpop/models/validation_model/pop_0.popjson @@ -1,5 +1,6 @@ { "name" : "test_population_0", + "imf_func_kwargs" : { "name" : "Chabrier" }, diff --git a/synthpop/models/validation_model/pop_1.popjson b/synthpop/models/validation_model/pop_1.popjson index b3c4723..dc59178 100644 --- a/synthpop/models/validation_model/pop_1.popjson +++ b/synthpop/models/validation_model/pop_1.popjson @@ -1,5 +1,6 @@ { "name" : "test_population_1", + "imf_func_kwargs" : { "name" : "kroupa" }, @@ -27,14 +28,17 @@ "sigma_v" : 12.8, "sigma_w" : 8.0 }, - "population_density_kwargs" : { + + "population_density_kwargs" : { "name" : "Besancon2003Halo", "p0" : 3.9e5, "e" : 0.76, "ac" : 0.5, "power" : -2.44 - }, "kinematics_func_kwargs" : { + }, + + "kinematics_func_kwargs" : { "name" : "VelocityGradient", "sigma_u" : 19.8, "sigma_v" : 12.8, diff --git a/synthpop/models/validation_model/pop_3.popjson b/synthpop/models/validation_model/pop_3.popjson index 8630a13..3d739b9 100644 --- a/synthpop/models/validation_model/pop_3.popjson +++ b/synthpop/models/validation_model/pop_3.popjson @@ -31,7 +31,6 @@ "hr" : 2.5, "hz" : 0.8, "xl" : 0.4 - }, "av_mass_corr" : 0.546120, diff --git a/synthpop/models/validation_model/pop_4.popjson b/synthpop/models/validation_model/pop_4.popjson index 181fde1..b8d49d1 100644 --- a/synthpop/models/validation_model/pop_4.popjson +++ b/synthpop/models/validation_model/pop_4.popjson @@ -1,24 +1,29 @@ { "name": "test_population_4", + "imf_func_kwargs": { "name": "kroupa" }, + "age_func_kwargs": { "name": "single_value", "#comment": "units in gigayears", "age_value": 10.0 }, + "metallicity_func_kwargs": { "#comment": "From Gonzalez, O.A.; et al. 2015", "name": "SingleValue", "met_value": -0.5 }, + "kinematics_func_kwargs" : { "name" : "VelocityGradient", "sigma_u" : 19.8, "sigma_v" : 12.8, "sigma_w" : 8.0 }, + "population_density_kwargs" : { "name" : "Besancon2003Thindisk", "#comment" : "p0 in Msun/kpc**3 (10**9)", diff --git a/synthpop/models/validation_model/pop_5.popjson b/synthpop/models/validation_model/pop_5.popjson index 2620b20..aea44bc 100644 --- a/synthpop/models/validation_model/pop_5.popjson +++ b/synthpop/models/validation_model/pop_5.popjson @@ -1,24 +1,29 @@ { "name" : "test_population_5", + "imf_func_kwargs" : { "name" : "kroupa" }, + "age_func_kwargs" : { "name" : "single_value", "#comment" : "units in gigayears", "age_value" : 9.135 }, + "metallicity_func_kwargs": { "#comment": "From Gonzalez, O.A.; et al. 2015", "name": "SingleValue", "met_value": -0.32 }, + "kinematics_func_kwargs" : { "name" : "VelocityGradient", "sigma_u" : 19.8, "sigma_v" : 12.8, "sigma_w" : 8.0 }, + "population_density_kwargs" : { "name" : "Besancon2003Thindisk", "#comment" : "p0 in Msun/kpc**3 (10**9)", diff --git a/synthpop/models/validation_model/pop_6.popjson b/synthpop/models/validation_model/pop_6.popjson index 8abc4c6..96aaa60 100644 --- a/synthpop/models/validation_model/pop_6.popjson +++ b/synthpop/models/validation_model/pop_6.popjson @@ -1,18 +1,22 @@ { "name" : "test_population_6", + "imf_func_kwargs" : { "name" : "kroupa" }, + "age_func_kwargs" : { "name" : "single_value", "#comment" : "units in gigayears", "age_value" : 4.23 }, + "metallicity_func_kwargs": { "#comment": "From Gonzalez, O.A.; et al. 2015", "name": "SingleValue", "met_value": -1.14 }, + "kinematics_func_kwargs" : { "name" : "VelocityGradient", "sigma_u" : 19.8, diff --git a/synthpop/modules/evolution/_evolution.py b/synthpop/modules/evolution/_evolution.py index b436751..73fd626 100644 --- a/synthpop/modules/evolution/_evolution.py +++ b/synthpop/modules/evolution/_evolution.py @@ -22,7 +22,7 @@ import numpy as np -from .. import const +from synthpop import constants as const ISOCHRONES_DIR = const.ISOCHRONES_DIR EVOLUTION_DIR = os.path.dirname(__file__) diff --git a/synthpop/modules/evolution/charon_interpolator.py b/synthpop/modules/evolution/charon_interpolator.py index 9aa9032..7a668f9 100644 --- a/synthpop/modules/evolution/charon_interpolator.py +++ b/synthpop/modules/evolution/charon_interpolator.py @@ -550,8 +550,8 @@ def get_evolved_props( raise ValueError("inter_age must either be log or linear") # split properties - props_no_charon = props.intersection(self.props_no_charon) - props_with_charon = props.difference(self.props_no_charon) + props_no_charon = set(props).intersection(self.props_no_charon) + props_with_charon = set(props).difference(self.props_no_charon) in_grid = None @@ -568,7 +568,7 @@ def get_evolved_props( result = (w1 * p_f1 + w2 * p_f2 + w3 * p_f3 + w4 * p_f4) / (w1 + w2 + w3 + w4) - in_grid = np.product([in_grid1, in_grid2, in_grid3, in_grid4]) + in_grid = np.prod([in_grid1, in_grid2, in_grid3, in_grid4]) else: result = None diff --git a/synthpop/modules/evolution/mist.py b/synthpop/modules/evolution/mist.py index 9cfa8ac..d435b04 100644 --- a/synthpop/modules/evolution/mist.py +++ b/synthpop/modules/evolution/mist.py @@ -22,9 +22,10 @@ import json import tqdm import sys +from astropy import table import numpy as np -import pandas +import pandas as pd import requests from ._evolution import EvolutionIsochrones, ISOCHRONES_DIR, EVOLUTION_DIR @@ -84,6 +85,8 @@ class MIST(EvolutionIsochrones, CharonInterpolator): # folder where isochrone files can be found FOLDER = f"{ISOCHRONES_DIR}/mist" + mag_system_conversions = pd.read_csv(f"{EVOLUTION_DIR}/mist_magnitude_systems.dat", + sep='\s+', index_col='filter') # lowest and highest mass where MIST isochrones should be used # (can be outside the covered range, in such cases the closets grid_points are used) @@ -92,9 +95,10 @@ class MIST(EvolutionIsochrones, CharonInterpolator): min_mass = 0.1 isochrones_name = 'MIST' - def __init__(self, columns, use_global=True, **kwargs): + def __init__(self, columns, mist_version='1.2', photsys=None, + alpha=0.0, use_global=True, effective_wavelengths=None, **kwargs): """ - pull out all the information from the isochrone file + Pull out all the information from the isochrone file and puts it into tracks under the index of the lowest non-magnitude properties in MIST isochrones @@ -102,17 +106,49 @@ def __init__(self, columns, use_global=True, **kwargs): ---------- columns : list list of columns + mist_version : str + version of the MIST isochrones to use (only '1.2' available currently) + alpha : float + [alpha/Fe] alpha enhancement (only 0.0 available currently) use_global : Bool store or use isochrones as global variable """ + + if mist_version != '1.2': + ValueError(f"Invalid mist_version {mist_version}. Only '1.2' is available at this time.") + if alpha != 0.0: + warnings.warn("MIST v1.2 only uses solar-scaled alpha abundances. Setting [alpha/Fe] to 0.0.") self.magsys, self.none_mag_cols, self.bands = self.get_mag_systems(columns) + with open(f"{EVOLUTION_DIR}/mist_effective_wavelengths.json") as f: + self.eff_wavelengths = json.load(f)[effective_wavelengths] + + # Deal with magnitude systems + self.photsys = photsys + if self.photsys is None: + self.photsys_dict = {band: self.mag_system_conversions.loc[band,'system'] for band in self.bands} + elif self.photsys in ['Vega','AB','ST']: + self.photsys_dict = {band: self.photsys for band in self.bands} + elif isinstance(self.photsys, dict): + self.photsys_dict = {band: self.mag_system_conversions.loc[band,'system'] for band in self.bands} + for key, val in self.photsys.items(): + if key not in ['Vega', 'AB','ST']: + raise ValueError("Dict input for photsys must be of structure e.g. {'AB':['W146','Z087']} or {'AB':['WFIRST']} " + "with photometric systems being Vega, AB, ST, and using any valid filter sets or names from MIST v1.2.") + for item in val: + if item in self.bands: + self.photsys_dict.update({item: key}) + elif item in self.magsys: + self.photsys_dict.update({band: key for band in self.magsys[item] if (band in self.bands)}) + else: + raise ValueError(f"Invalid input in photsys_dict: {item} not found as band or band set.") + else: + raise ValueError("Input for photsys must be 'Vega', 'AB', 'ST', None, or dict.") # Check for isochrone directory and create if needed os.makedirs(self.FOLDER, exist_ok=True) - # Check for isochrones in requested magnitude systems - # download from MIST if needed + # Check for isochrones in requested magnitude systems & download from MIST if needed for msys in self.magsys: if not os.path.isdir(f"{self.FOLDER}/{msys}"): self.download_isochrones(msys) @@ -150,7 +186,7 @@ def __init__(self, columns, use_global=True, **kwargs): # Get mass range for each metallicity and age self.mass_range = self.get_mass_ranges(self.isochrones_grouped) - # call super after loading the isochrones so the interpolator has axcess to the data + # call super after loading the isochrones so the interpolator has access to the data super().__init__(**kwargs) @@ -271,7 +307,7 @@ def get_mass_ranges(isochrones_grouped): min_values = isochrones_grouped['initial_mass'].max() min_values.name='min_mass' max_values.name='max_mass' - mass_range = pandas.concat([min_values,max_values], axis=1) + mass_range = pd.concat([min_values,max_values], axis=1) return mass_range @staticmethod @@ -324,7 +360,7 @@ def load_isochrones(self, magsys): else: if os.path.isfile(f'{filename}.h5'): # file exist in a Hierarchical Data Format (.h5) - df = pandas.read_hdf(f'{filename}.h5', 'data') + df = pd.read_hdf(f'{filename}.h5', 'data') else: # convert ascii to Hierarchical Data Format @@ -340,8 +376,20 @@ def load_isochrones(self, magsys): isochrones[file_met] = df[use_columns].copy() else: isochrones[file_met][use_columns] = df[use_columns] - - return pandas.concat(isochrones.values()) + + if self.photsys is not None: + for band in self.bands: + if self.mag_system_conversions.loc[band, 'system'] == self.photsys_dict[band]: + continue + elif self.mag_system_conversions.loc[band, 'system'] == 'Vega': + isochrones[file_met][band] += self.mag_system_conversions[f'mag(Vega/{self.photsys_dict[band]})'] + elif (self.mag_system_conversions.loc[band, 'system'] == 'AB') and (self.photsys_dict[band]=='Vega'): + isochrones[file_met][band] -= self.mag_system_conversions[f'mag(Vega/AB)'] + elif (self.mag_system_conversions.loc[band, 'system'] == 'AB') and (self.photsys_dict[band]=='ST'): + isochrones[file_met][band] -= self.mag_system_conversions[f'mag(Vega/AB)'] + isochrones[file_met][band] += self.mag_system_conversions[f'mag(Vega/ST)'] + + return pd.concat(isochrones.values()) @staticmethod def get_columns(filename): @@ -358,7 +406,7 @@ def read_csv(cls, filename): # get column names cols = cls.get_columns(filename) # load table from ascii file - df = pandas.read_csv(filename, sep='\s+', comment='#', + df = pd.read_csv(filename, sep='\s+', comment='#', skip_blank_lines=True, low_memory=False, header=None, names=cols) return df @@ -422,3 +470,140 @@ def download_isochrones(self, magsys_name): os.remove(f'{self.FOLDER}/MIST_v1.2_vvcrit0.4_{magsys_name}.txz') print('Isochrones downloaded: %s' % magsys_name) + +def generate_effective_wavelengths_json(): + from astropy import units as u + from astroquery.svo_fps import SvoFps + + with open(f'{EVOLUTION_DIR}/mist_columns.json') as f: + columns_dict = json.load(f) + + filters_list = [] + for k,v in columns_dict.items(): + if v not in ['cmd','basic','full']: + filters_list.append(k) + + all_effs = {'vega_eff': {}, + 'pivot': {}, + 'average': {}} + + for f in filters_list: + pre_str = columns_dict[f] + f_str = f.replace('_','.') + if pre_str=='UBVRIplus': pre_str=f.split('_')[0] + if pre_str=='IPHAS': pre_str, f_str = 'INT', '.'.join(f_str.split('.')[1:]) + if pre_str=='Swift': f_str = 'UVOT.'+f_str.split('.')[1] + if pre_str=='Bessell': pre_str = 'Generic' + if pre_str=='DECam': pre_str = 'CTIO' + if pre_str=='VISTA': pre_str = 'Paranal' + if pre_str=='UKIDSS': pre_str = 'UKIRT' + if pre_str=='WFIRST': f_str = 'WFI.'+f_str + if pre_str=='UVIT': pre_str = 'Astrosat' + if pre_str=='Gaia' and f_str.split('.')[2]=='EDR3': + if f_str.split('.')[1]=='G': f_str='Gaia3.'+f_str.split('.')[1] + else: f_str='Gaia3.G'+f_str.split('.')[1].lower() + elif pre_str=='Gaia' and f_str.split('.')[2]=='DR2Rev': + if f_str.split('.')[1]=='G': f_str='Gaia2r.'+f_str.split('.')[1] + else: f_str='Gaia2r.G'+f_str.split('.')[1].lower() + elif pre_str=='Gaia' and ("MAW" in f_str.split('.')[2]): + f_str = {"Gaia_G_MAW": 'Gaia2m.G', "Gaia_RP_MAW": 'Gaia2m.Grp', + "Gaia_BP_MAWb": 'Gaia2m.Gbp_bright', "Gaia_BP_MAWf":'Gaia2m.Gbp_faint'}[f] + if pre_str=='SPITZER': + f_str = 'IRAC.' + {'3.6':'I1', '4.5':'I2', '5.8':'I3', '8.0':'I4'}[f.split('_')[-1]] + + if pre_str=='WashDDOuvby' and f.split('_')[0]=='Washington': + pre_str = 'GCPD'; f_str += '_pe' + elif pre_str=='WashDDOuvby' and f.split('_')[0]=='DDO51': pre_str,f_str = 'KPNO','Mosaic.D51' + elif pre_str=='WashDDOuvby' and f.split('_')[0]=='Stromgren': pre_str='Generic' + + if pre_str=='TESS': f_str +='.red' + if pre_str=='Kepler' and f_str=='Kepler.Kp': f_str='Kepler.K' + if pre_str=='HSC': + pre_str='Subaru' + if f_str.split('.')[-1][:2]=='nb': f_str+='_filter' + if pre_str=='SDSSugriz': pre_str='Sloan' + if pre_str=='PanSTARRS': pre_str,f_str = 'PAN-STARRS', 'PS1.'+f.split('_')[-1] + if pre_str=='JWST': f_str = 'NIRCam.'+f.split('_')[-1] + if pre_str[:3]=='HST': + pre_str='HST'; f_str = f_str.replace('.','_',1) + if f_str[:9]=='WFC3_UVIS': f_str = f_str.replace('.','1.') + elif f_str[:5]=='WFPC2': f_str = f_str.replace('_','-WF.') + if pre_str=='SPLUS': + pre_str = 'CTIO' + if ('SDSS' in f_str) or ('JAVA' in f_str): f_str = 'S-PLUS.'+f_str.split('.')[-1][0] + else: f_str = 'S-PLUS.F'+f_str.split('.')[-1][2:] + + if pre_str=='CFHT': + f_str = 'Megaprime.'+f.split('_')[1] + if f.split('_')[-1] != 'CaHK': f_str +='S' + if f.split('_')[-1]=='new': f_str += '2' + + try: + filt_id = f"{pre_str}/{f_str}" + all_tab = SvoFps.get_filter_metadata(filt_id) + vals = [(all_tab[col].to(u.micron)).value.round(10) for col + in ['WavelengthEff', 'WavelengthPivot', 'WavelengthMean']] + all_effs['vega_eff'][f] = vals[0] + all_effs['pivot'][f] = vals[1] + all_effs['average'][f] = vals[2] + except: + print(filt_id, f) + + json_object = json.dumps(all_effs, indent=4) + with open(f"{EVOLUTION_DIR}/mist_effective_wavelengths.json", "w") as outfile: + outfile.write(json_object) + return + +def get_spisea_obs_str(filt): + """ + Get SPISEA/pysynphot obs_str for filter if available + """ + from spisea.synthetic import get_obs_str, get_filter_info + + with open(f'{EVOLUTION_DIR}/mist_columns.json') as f: + mist_columns = json.load(f) + + with open(f'{EVOLUTION_DIR}/spisea_filters.json') as f: + spisea_filters = json.load(f) + + mist_filter_set = mist_columns[filt] + # Try generic method + try: + str_spl = filt.split('_') + try_str = '_'.join(['m']+[substr.lower() for substr in str_spl[:-1]]+[str_spl[-1]]) + f_str = get_obs_str(try_str) + get_filter_info(f_str) + return f_str + except: + pass + + # Handle special cases + name_subs = {"LSST":'rubin', "PS":'ps1', "UKIDSS":'ukirt'} + if str_spl[0] in name_subs: + f_str = name_subs[str_spl[0]]+','+str_spl[-1] + elif mist_filter_set.startswith('HST_'): + str_spl[1] = str_spl[1]+'1' + f_str = ','.join([substr.lower() for substr in str_spl[:-1]]+[str_spl[-1]]) + elif mist_filter_set=='JWST': + f_str = 'jwst,'+filt + elif mist_filter_set=='WFIRST': + f_str = 'roman,wfi,f'+filt[1:] + elif mist_filter_set=='HSC': + f_str = 'subaru,'+f_str + elif str_spl[0]=='Gaia': + str_spl = [str_spl[0], str_spl[2], str_spl[1]] + if str_spl[2] != 'G': + str_spl[2] = 'G'+str_spl[2].lower() + if str_spl[1] == 'DR2Rev': + str_spl[1] = 'dr2_rev' + f_str = ','.join([substr.lower() for substr in str_spl[:-1]]+[str_spl[-1]]) + elif filt=='TESS': + f_str = 'tess,tess' + else: + raise ValueError(f"No corresponding SPISEA obs_str for filter {filt}") + + try: + get_filter_info(f_str) + return f_str + except: + raise ValueError(f"No corresponding SPISEA obs_str for filter {filt}, {f_str}") diff --git a/synthpop/modules/evolution/mist_columns.json b/synthpop/modules/evolution/mist_columns.json index 76a1373..bb33129 100644 --- a/synthpop/modules/evolution/mist_columns.json +++ b/synthpop/modules/evolution/mist_columns.json @@ -331,10 +331,14 @@ "center_si28": "full", "pp": "full", "cno": "full", - "tri_alfaburn_cburn_nburn_o": "full", + "tri_alfa": "full", + "burn_c": "full", + "burn_n": "full", + "burn_o": "full", "c12_c12": "full", "delta_nu": "full", - "delta_Pgnu_max": "full", + "delta_Pg": "full", + "nu_max": "full", "acoustic_cutoff": "full", "max_conv_vel_div_csound": "full", "max_gradT_div_grada": "full", diff --git a/synthpop/modules/evolution/mist_effective_wavelengths.json b/synthpop/modules/evolution/mist_effective_wavelengths.json new file mode 100644 index 0000000..21acc2b --- /dev/null +++ b/synthpop/modules/evolution/mist_effective_wavelengths.json @@ -0,0 +1,794 @@ +{ + "vega_eff": { + "CFHT_u": 0.3883644534, + "CFHT_CaHK": 0.3949552324, + "CFHT_g": 0.4798561579, + "CFHT_r": 0.6217056854, + "CFHT_i_new": 0.7511546289, + "CFHT_i_old": 0.763727317, + "CFHT_z": 0.88550891, + "DECam_u": 0.3856881744, + "DECam_g": 0.4769904356, + "DECam_r": 0.6370442336, + "DECam_i": 0.7774301665, + "DECam_z": 0.9154883663, + "DECam_Y": 0.9886452082, + "GALEX_FUV": 0.1548848966, + "GALEX_NUV": 0.2303366368, + "ACS_HRC_F220W": 0.22548366, + "ACS_HRC_F250W": 0.2739378175, + "ACS_HRC_F330W": 0.3368231383, + "ACS_HRC_F344N": 0.343394607, + "ACS_HRC_F435W": 0.4341356205, + "ACS_HRC_F475W": 0.473824383, + "ACS_HRC_F502N": 0.5022926165, + "ACS_HRC_F550M": 0.5573036002, + "ACS_HRC_F555W": 0.5326964157, + "ACS_HRC_F606W": 0.57764327, + "ACS_HRC_F625W": 0.6249393996, + "ACS_HRC_F658N": 0.6585914269, + "ACS_HRC_F660N": 0.660000976, + "ACS_HRC_F775W": 0.7624316459, + "ACS_HRC_F814W": 0.8019736638, + "ACS_HRC_F850LP": 0.9115281675, + "ACS_HRC_F892N": 0.89179931, + "ACS_WFC_F435W": 0.4341618987, + "ACS_WFC_F475W": 0.4708865269, + "ACS_WFC_F502N": 0.5022915137, + "ACS_WFC_F550M": 0.5574749381, + "ACS_WFC_F555W": 0.5331751979, + "ACS_WFC_F606W": 0.5809259775, + "ACS_WFC_F625W": 0.6266199682, + "ACS_WFC_F658N": 0.6585955913, + "ACS_WFC_F660N": 0.6600024155, + "ACS_WFC_F775W": 0.7652436431, + "ACS_WFC_F814W": 0.7973389948, + "ACS_WFC_F850LP": 0.9004993171, + "ACS_WFC_F892N": 0.8916611845, + "WFC3_UVIS_F200LP": 0.5121915879, + "WFC3_UVIS_F218W": 0.2222481852, + "WFC3_UVIS_F225W": 0.2372812061, + "WFC3_UVIS_F275W": 0.2720032446, + "WFC3_UVIS_F280N": 0.2796946442, + "WFC3_UVIS_F300X": 0.2879281894, + "WFC3_UVIS_F336W": 0.3358950517, + "WFC3_UVIS_F343N": 0.3436477269, + "WFC3_UVIS_F350LP": 0.5546112248, + "WFC3_UVIS_F373N": 0.3731348883, + "WFC3_UVIS_F390M": 0.3905108992, + "WFC3_UVIS_F390W": 0.4022155518, + "WFC3_UVIS_F395N": 0.3952682835, + "WFC3_UVIS_F410M": 0.4108740102, + "WFC3_UVIS_F438W": 0.4323349441, + "WFC3_UVIS_F467M": 0.4680867533, + "WFC3_UVIS_F469N": 0.4688149496, + "WFC3_UVIS_F475W": 0.4732439707, + "WFC3_UVIS_F475X": 0.4855994158, + "WFC3_UVIS_F487N": 0.4873391774, + "WFC3_UVIS_F502N": 0.5009683308, + "WFC3_UVIS_F547M": 0.5436083962, + "WFC3_UVIS_F555W": 0.5235748333, + "WFC3_UVIS_F600LP": 0.7291349526, + "WFC3_UVIS_F606W": 0.578220165, + "WFC3_UVIS_F621M": 0.6208806497, + "WFC3_UVIS_F625W": 0.6188226808, + "WFC3_UVIS_F631N": 0.6303995476, + "WFC3_UVIS_F645N": 0.6453152828, + "WFC3_UVIS_F656N": 0.6561331002, + "WFC3_UVIS_F657N": 0.6566489527, + "WFC3_UVIS_F658N": 0.6586186045, + "WFC3_UVIS_F665N": 0.6656356585, + "WFC3_UVIS_F673N": 0.6765682201, + "WFC3_UVIS_F680N": 0.6874793439, + "WFC3_UVIS_F689M": 0.6872213018, + "WFC3_UVIS_F763M": 0.7602853311, + "WFC3_UVIS_F775W": 0.7612784141, + "WFC3_UVIS_F814W": 0.7964249363, + "WFC3_UVIS_F845M": 0.8430201269, + "WFC3_UVIS_F850LP": 0.9154107654, + "WFC3_UVIS_F953N": 0.9529021042, + "WFC3_IR_F098M": 0.982680921, + "WFC3_IR_F105W": 1.0430829312, + "WFC3_IR_F110W": 1.1200521482, + "WFC3_IR_F125W": 1.2363546256, + "WFC3_IR_F126N": 1.2585052746, + "WFC3_IR_F127M": 1.2732367538, + "WFC3_IR_F128N": 1.2837230274, + "WFC3_IR_F130N": 1.3010115674, + "WFC3_IR_F132N": 1.3193122874, + "WFC3_IR_F139M": 1.3836058256, + "WFC3_IR_F140W": 1.3734664302, + "WFC3_IR_F153M": 1.5326281441, + "WFC3_IR_F160W": 1.5278466535, + "WFC3_IR_F164N": 1.6451450279, + "WFC3_IR_F167N": 1.6673999339, + "WFPC2_F218W": 0.2201266051, + "WFPC2_F255W": 0.2610167759, + "WFPC2_F300W": 0.3028665602, + "WFPC2_F336W": 0.3349476086, + "WFPC2_F439W": 0.4309612576, + "WFPC2_F450W": 0.4545400116, + "WFPC2_F555W": 0.5370539464, + "WFPC2_F606W": 0.5898876613, + "WFPC2_F622W": 0.6145562037, + "WFPC2_F675W": 0.6687537615, + "WFPC2_F791W": 0.7823461052, + "WFPC2_F814W": 0.792985073, + "WFPC2_F850LP": 0.9101954392, + "INT_IPHAS_gR": 0.6153453901, + "INT_IPHAS_Ha": 0.6568196032, + "INT_IPHAS_gI": 0.7663258935, + "F070W": 0.6988427277, + "F090W": 0.8984981217, + "F115W": 1.1433622504, + "F140M": 1.4023618738, + "F150W2": 1.4793713184, + "F150W": 1.4872561807, + "F162M": 1.6243330064, + "F164N": 1.6446179234, + "F182M": 1.8388832224, + "F187N": 1.8737223679, + "F200W": 1.9680410202, + "F210M": 2.0908350068, + "F212N": 2.1211927275, + "F250M": 2.5005800774, + "F277W": 2.7278576094, + "F300M": 2.981831926, + "F322W2": 3.0731382702, + "F323N": 3.2367304317, + "F335M": 3.3537232323, + "F356W": 3.5287040492, + "F360M": 3.6148930727, + "F405N": 4.0515758914, + "F410M": 4.0723184169, + "F430M": 4.2784788064, + "F444W": 4.3504264674, + "F460M": 4.6269863826, + "F466N": 4.6540477223, + "F470N": 4.7077839402, + "F480M": 4.8139105124, + "LSST_u": 0.3751204675, + "LSST_g": 0.4740662917, + "LSST_r": 0.6172344033, + "LSST_i": 0.7500974822, + "LSST_z": 0.8678903964, + "LSST_y": 0.9711821219, + "PS_g": 0.4810159608, + "PS_r": 0.6155465981, + "PS_i": 0.75030305, + "PS_z": 0.8668363561, + "PS_y": 0.9613603597, + "PS_w": 0.5980702689, + "PS_open": 0.6431865942, + "SDSS_u": 0.3608040315, + "SDSS_g": 0.4671782214, + "SDSS_r": 0.6141123004, + "SDSS_i": 0.7457889036, + "SDSS_z": 0.8922779724, + "SkyMapper_u": 0.3500223051, + "SkyMapper_v": 0.3878683052, + "SkyMapper_g": 0.5016052513, + "SkyMapper_r": 0.6076849415, + "SkyMapper_i": 0.7732832996, + "SkyMapper_z": 0.9120253912, + "IRAC_3.6": 3.5074833471, + "IRAC_4.5": 4.4365562895, + "IRAC_5.8": 5.6280616704, + "IRAC_8.0": 7.5890539882, + "hsc_g": 0.4758712304, + "hsc_r": 0.6166333537, + "hsc_i": 0.7682361188, + "hsc_z": 0.8906543372, + "hsc_y": 0.9759623535, + "hsc_nb816": 0.8167707804, + "hsc_nb921": 0.9202050859, + "Swift_UVW2": 0.2083951775, + "Swift_UVM2": 0.2245028767, + "Swift_UVW1": 0.2681668169, + "Swift_U": 0.3520878963, + "Swift_B": 0.4345281337, + "Swift_V": 0.5411445324, + "Bessell_U": 0.3659880363, + "Bessell_B": 0.4380740665, + "Bessell_V": 0.5445429409, + "Bessell_R": 0.6411469926, + "Bessell_I": 0.7982092673, + "2MASS_J": 1.235, + "2MASS_H": 1.662, + "2MASS_Ks": 2.159, + "Kepler_Kp": 0.5978137605, + "Hipparcos_Hp": 0.4901702944, + "Tycho_B": 0.428, + "Tycho_V": 0.534, + "Gaia_G_DR2Rev": 0.5829579934, + "Gaia_BP_DR2Rev": 0.5017693781, + "Gaia_RP_DR2Rev": 0.7587213836, + "TESS": 0.7452636342, + "Gaia_G_EDR3": 0.5822388714, + "Gaia_BP_EDR3": 0.5035750275, + "Gaia_RP_EDR3": 0.7619959993, + "Gaia_G_MAW": 0.5837721475, + "Gaia_BP_MAWb": 0.5038941213, + "Gaia_BP_MAWf": 0.4979228484, + "Gaia_RP_MAW": 0.7596555743, + "UKIDSS_Z": 0.8817, + "UKIDSS_Y": 1.0305, + "UKIDSS_J": 1.2483, + "UKIDSS_H": 1.6313, + "UKIDSS_K": 2.201, + "VISTA_Z": 0.8788990452, + "VISTA_Y": 1.0196438027, + "VISTA_J": 1.2481040342, + "VISTA_H": 1.6347740041, + "VISTA_Ks": 2.1435422067, + "Washington_C": 0.4007454455, + "Washington_M": 0.5013242919, + "Washington_T1": 0.6341341587, + "Washington_T2": 0.7936057257, + "DDO51_vac": 0.5142992741, + 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AB -0.424539 0.367125 + CFHT_CaHK AB -0.736321 -0.027845 + CFHT_g AB -0.357651 -0.091810 + CFHT_r AB 0.440900 0.154376 + CFHT_i_new AB 1.055841 0.361670 + CFHT_i_old AB 1.119709 0.385491 + CFHT_z AB 1.558980 0.513837 + DECam_u AB -0.420184 0.340835 + DECam_g AB -0.366396 -0.089596 + DECam_r AB 0.529093 0.182467 + DECam_i AB 1.176206 0.405898 + DECam_z AB 1.634043 0.516983 + DECam_Y AB 1.843140 0.564316 + GALEX_FUV AB -0.631684 2.123122 + GALEX_NUV AB -0.216471 1.665903 + ACS_HRC_F220W Vega -0.242516 1.683496 + ACS_HRC_F250W Vega -0.026944 1.495635 + ACS_HRC_F330W Vega 0.123693 1.182286 + ACS_HRC_F344N Vega 0.137867 1.151053 + ACS_HRC_F435W Vega -0.605858 -0.086591 + ACS_HRC_F475W Vega -0.390985 -0.093936 + ACS_HRC_F502N Vega -0.270890 -0.083596 + ACS_HRC_F550M Vega 0.065311 0.024283 + ACS_HRC_F555W Vega -0.055068 -0.007055 + ACS_HRC_F606W Vega 0.238048 0.080791 + ACS_HRC_F625W Vega 0.463040 0.160116 + ACS_HRC_F658N Vega 0.776888 0.376572 + ACS_HRC_F660N Vega 0.684204 0.278863 + ACS_HRC_F775W Vega 1.113686 0.383335 + ACS_HRC_F814W Vega 1.279988 0.425512 + ACS_HRC_F850LP Vega 1.635112 0.521485 + ACS_HRC_F892N Vega 1.546341 0.487505 + ACS_WFC_F435W Vega -0.612699 -0.101679 + ACS_WFC_F475W Vega -0.408606 -0.097875 + ACS_WFC_F502N Vega -0.270926 -0.083681 + ACS_WFC_F550M Vega 0.066147 0.024511 + ACS_WFC_F555W Vega -0.052478 -0.006312 + ACS_WFC_F606W Vega 0.255801 0.086500 + ACS_WFC_F625W Vega 0.471701 0.163258 + ACS_WFC_F658N Vega 0.776774 0.376382 + ACS_WFC_F660N Vega 0.684181 0.278710 + ACS_WFC_F775W Vega 1.126465 0.387955 + ACS_WFC_F814W Vega 1.263223 0.423663 + ACS_WFC_F850LP Vega 1.607517 0.519586 + ACS_WFC_F892N Vega 1.547434 0.488893 + WFC3_UVIS_F200LP Vega 0.240419 0.451333 + WFC3_UVIS_F218W Vega -0.260883 1.691151 + WFC3_UVIS_F225W Vega -0.155178 1.660654 + WFC3_UVIS_F275W Vega -0.028405 1.498659 + WFC3_UVIS_F280N Vega 0.002335 1.432770 + WFC3_UVIS_F300X Vega -0.018331 1.420732 + WFC3_UVIS_F336W Vega 0.122049 1.185760 + WFC3_UVIS_F343N Vega 0.143427 1.155674 + WFC3_UVIS_F350LP Vega 0.303192 0.156765 + WFC3_UVIS_F373N Vega 0.061798 0.895206 + WFC3_UVIS_F390M Vega -0.638936 0.099307 + WFC3_UVIS_F390W Vega -0.507217 0.215957 + WFC3_UVIS_F395N Vega -0.727198 -0.020987 + WFC3_UVIS_F410M Vega -0.779296 -0.155917 + WFC3_UVIS_F438W Vega -0.663431 -0.152048 + WFC3_UVIS_F467M Vega -0.494367 -0.154733 + WFC3_UVIS_F469N Vega -0.494313 -0.157247 + WFC3_UVIS_F475W Vega -0.394892 -0.097092 + WFC3_UVIS_F475X Vega -0.272098 -0.049307 + WFC3_UVIS_F487N Vega -0.071910 0.181882 + WFC3_UVIS_F502N Vega -0.279955 -0.086936 + WFC3_UVIS_F547M Vega -0.011458 -0.000371 + WFC3_UVIS_F555W Vega -0.091758 -0.024420 + WFC3_UVIS_F600LP Vega 0.992958 0.324082 + WFC3_UVIS_F606W Vega 0.240576 0.083013 + WFC3_UVIS_F621M Vega 0.422610 0.146192 + WFC3_UVIS_F625W Vega 0.432396 0.148078 + WFC3_UVIS_F631N Vega 0.463927 0.157768 + WFC3_UVIS_F645N Vega 0.546515 0.189560 + WFC3_UVIS_F656N Vega 1.013088 0.620403 + WFC3_UVIS_F657N Vega 0.720202 0.325542 + WFC3_UVIS_F658N Vega 0.762428 0.362080 + WFC3_UVIS_F665N Vega 0.660589 0.236611 + WFC3_UVIS_F673N Vega 0.698476 0.238895 + WFC3_UVIS_F680N Vega 0.752419 0.257328 + WFC3_UVIS_F689M Vega 0.771601 0.276873 + WFC3_UVIS_F763M Vega 1.094690 0.378800 + WFC3_UVIS_F775W Vega 1.106567 0.380746 + WFC3_UVIS_F814W Vega 1.249656 0.418257 + WFC3_UVIS_F845M Vega 1.439277 0.500252 + WFC3_UVIS_F850LP Vega 1.640322 0.520800 + WFC3_UVIS_F953N Vega 1.817663 0.614161 + WFC3_IR_F098M Vega 1.839929 0.561553 + WFC3_IR_F105W Vega 2.069729 0.645302 + WFC3_IR_F110W Vega 2.377424 0.759489 + WFC3_IR_F125W Vega 2.691129 0.901052 + WFC3_IR_F126N Vega 2.728329 0.921130 + WFC3_IR_F127M Vega 2.795047 0.961204 + WFC3_IR_F128N Vega 2.886508 1.037116 + WFC3_IR_F130N Vega 2.854722 0.976109 + WFC3_IR_F132N Vega 2.906179 0.997375 + WFC3_IR_F139M Vega 3.091872 1.078618 + WFC3_IR_F140W Vega 3.102928 1.076328 + WFC3_IR_F153M Vega 3.488218 1.253686 + WFC3_IR_F160W Vega 3.492650 1.251445 + WFC3_IR_F164N Vega 3.767375 1.384753 + WFC3_IR_F167N Vega 3.775500 1.361575 + WFPC2_F218W Vega -0.278397 1.696001 + WFPC2_F255W Vega -0.050464 1.565908 + WFPC2_F300W Vega 0.026125 1.336668 + WFPC2_F336W Vega 0.122630 1.183504 + WFPC2_F439W Vega -0.664368 -0.145544 + WFPC2_F450W Vega -0.484289 -0.084954 + WFPC2_F555W Vega -0.015900 -0.001464 + WFPC2_F606W Vega 0.297727 0.100171 + WFPC2_F622W Vega 0.406463 0.141358 + WFPC2_F675W Vega 0.683640 0.239690 + WFPC2_F791W Vega 1.201484 0.410745 + WFPC2_F814W Vega 1.243529 0.416817 + WFPC2_F850LP Vega 1.628746 0.518736 + INT_IPHAS_gR Vega 0.422955 0.146731 + INT_IPHAS_Ha Vega 0.753818 0.358673 + INT_IPHAS_gI Vega 1.143547 0.393872 + JWST_F070W Vega 0.807113 0.277827 + JWST_F090W Vega 1.596532 0.511469 + JWST_F115W Vega 2.397871 0.777727 + JWST_F140M Vega 3.150233 1.103238 + JWST_F150W2 Vega 3.638258 1.228996 + JWST_F150W Vega 3.395463 1.205652 + JWST_F162M Vega 3.717215 1.351846 + JWST_F164N Vega 3.780254 1.391996 + JWST_F182M Vega 4.196167 1.558015 + JWST_F187N Vega 4.295891 1.624210 + JWST_F200W Vega 4.476147 1.675761 + JWST_F210M Vega 4.694407 1.780225 + JWST_F212N Vega 4.741969 1.801134 + JWST_F250M Vega 5.417244 2.116563 + JWST_F277W Vega 5.818282 2.295891 + JWST_F300M Vega 6.147738 2.457418 + JWST_F322W2 Vega 6.385763 2.533096 + JWST_F323N Vega 6.464886 2.606257 + JWST_F335M Vega 6.620067 2.680412 + JWST_F356W Vega 6.845848 2.781285 + JWST_F360M Vega 6.933009 2.830344 + JWST_F405N Vega 7.427233 3.081301 + JWST_F410M Vega 7.434069 3.072036 + JWST_F430M Vega 7.636414 3.170645 + JWST_F444W Vega 7.733486 3.207505 + JWST_F460M Vega 7.969036 3.333257 + JWST_F466N Vega 8.022737 3.375634 + JWST_F470N Vega 8.035230 3.363519 + JWST_F480M Vega 8.128712 3.407118 + LSST_u AB -0.213066 0.658455 + LSST_g AB -0.374080 -0.091776 + LSST_r AB 0.418363 0.145008 + LSST_i AB 1.057461 0.363627 + LSST_z AB 1.509448 0.507514 + LSST_y AB 1.786473 0.543658 + PS_g AB -0.351542 -0.087801 + PS_r AB 0.413845 0.143531 + PS_i AB 1.056578 0.363226 + PS_z AB 1.506211 0.507125 + PS_y AB 1.764576 0.539014 + PS_w AB 0.425688 0.125908 + PS_open AB 0.715310 0.199410 + SDSS_u AB -0.005824 0.931096 + SDSS_g AB -0.431000 -0.100592 + SDSS_r AB 0.403808 0.142469 + SDSS_i AB 1.036204 0.355853 + SDSS_z AB 1.584227 0.517963 + SkyMapper_u AB 0.143267 1.104895 + SkyMapper_v AB -0.460789 0.311950 + SkyMapper_g AB -0.223002 -0.058233 + SkyMapper_r AB 0.376120 0.127869 + SkyMapper_i AB 1.160368 0.400876 + SkyMapper_z AB 1.640164 0.525982 + IRAC_3.6 Vega 6.848464 2.785147 + IRAC_4.5 Vega 7.833123 3.258136 + IRAC_5.8 Vega 8.855130 3.750716 + IRAC_8.0 Vega 10.192009 4.391595 + hsc_g AB -0.385232 -0.095381 + hsc_r AB 0.418631 0.143814 + hsc_i AB 1.123724 0.386480 + hsc_z AB 1.600675 0.513929 + hsc_y AB 1.806233 0.548435 + hsc_nb816 AB 1.340376 0.469517 + hsc_nb921 AB 1.671034 0.540824 + Swift_UVW2 AB -0.390787 1.734154 + Swift_UVM2 AB -0.247102 1.686980 + Swift_UVW1 AB -0.121676 1.510161 + Swift_U AB 0.020865 1.013124 + Swift_B AB -0.615727 -0.115885 + Swift_V AB -0.024917 -0.004726 + Bessell_U Vega -0.112035 0.800527 + Bessell_B Vega -0.594221 -0.107512 + Bessell_V Vega 0.011340 0.006521 + Bessell_R Vega 0.568462 0.190278 + Bessell_I Vega 1.249636 0.431372 + 2MASS_J Vega 2.656686 0.889176 + 2MASS_H Vega 3.753842 1.364157 + 2MASS_Ks Vega 4.815035 1.834505 + Kepler_Kp Vega 0.428473 0.122666 + Kepler_D51 Vega -0.178119 -0.052236 + Hipparcos_Hp Vega -0.043887 0.007642 + Tycho_B Vega -0.641996 -0.060855 + Tycho_V Vega -0.084969 -0.014228 + Gaia_G_DR2Rev Vega 0.410114 0.123891 + Gaia_BP_DR2Rev Vega -0.110591 0.065590 + Gaia_RP_DR2Rev Vega 1.120561 0.368669 + TESS Vega 1.154300 0.363778 + Gaia_G_EDR3 Vega 0.405022 0.128961 + Gaia_BP_EDR3 Vega -0.119837 0.030233 + Gaia_RP_EDR3 Vega 1.133129 0.373349 + UKIDSS_Z Vega 1.549954 0.513127 + UKIDSS_Y Vega 1.990089 0.614878 + UKIDSS_J Vega 2.708005 0.915245 + UKIDSS_H Vega 3.729479 1.352530 + UKIDSS_K Vega 4.897422 1.871458 + VISTA_Z Vega 1.836233 0.565932 + VISTA_Y Vega 2.008116 0.604697 + VISTA_J Vega 2.746411 0.920785 + VISTA_H Vega 3.748443 1.360424 + VISTA_Ks Vega 4.782263 1.817194 + Washington_C Vega -0.432850 0.332454 + Washington_M Vega -0.205646 -0.051436 + Washington_T1 Vega 0.531087 0.185742 + Washington_T2 Vega 1.297327 0.449972 + DDO51_vac Vega -0.192974 -0.057579 + DDO51_f31 Vega -0.200442 -0.059975 + Stromgren_u Vega 0.150280 1.135368 + Stromgren_v Vega -0.756072 -0.132136 + Stromgren_b Vega -0.500662 -0.152966 + Stromgren_y Vega -0.003578 0.002927 + R062 Vega 0.429702 0.137095 + Z087 Vega 1.495606 0.487379 + Y106 Vega 2.090705 0.653780 + J129 Vega 2.827025 0.958363 + W146 Vega 3.132416 1.024467 + H158 Vega 3.589896 1.287404 + F184 Vega 4.189278 1.551332 + WISE_W1 Vega 6.610497 2.665543 + WISE_W2 Vega 7.935423 3.305247 + WISE_W3 Vega 11.856446 5.139422 + WISE_W4 Vega 14.653819 6.614602 + SPLUS_uJAVA AB 0.144112 1.095256 + SPLUS_gSDSS AB -0.399119 -0.094412 + SPLUS_rSDSS AB 0.439725 0.151759 + SPLUS_iSDSS AB 1.115829 0.383721 + SPLUS_zSDSS AB 1.580157 0.515161 + SPLUS_J0340 AB -0.661364 -0.132665 + SPLUS_J0378 AB -0.223843 0.584698 + SPLUS_J0395 AB -0.688661 0.025523 + SPLUS_J0410 AB -0.785238 -0.154419 + SPLUS_J0515 AB -0.199907 -0.059776 + SPLUS_J0660 AB 0.715373 0.305152 + SPLUS_J0861 AB 1.512292 0.530025 + UVIT_F148W AB -0.511478 2.301383 + UVIT_F154W AB -0.507695 2.318347 + UVIT_F169M AB -0.655051 2.014355 + UVIT_F172M AB -0.605145 1.913212 + UVIT_F242W AB -0.149361 1.628923 + UVIT_N219M AB -0.275667 1.693668 + UVIT_N245M AB -0.086757 1.662303 + UVIT_N263M AB -0.040787 1.538884 + UVIT_N279N AB 0.019904 1.482277 diff --git a/synthpop/modules/evolution/spisea_cluster.py b/synthpop/modules/evolution/spisea_cluster.py new file mode 100644 index 0000000..c7b2ec3 --- /dev/null +++ b/synthpop/modules/evolution/spisea_cluster.py @@ -0,0 +1,260 @@ +""" +Evolution module to store information for the SpiseaGenerator. Not valid for a standard StarGenerator. +""" + +__all__ = ["SpiseaCluster", ] +__author__ = "M.J. Huston" +__date__ = "2025-05-28" + +try: + from ._evolution import EvolutionIsochrones, EvolutionInterpolator, EVOLUTION_DIR +except: + from _evolution import EvolutionIsochrones, EvolutionInterpolator, EVOLUTION_DIR +import numpy as np +import json +from spisea import evolution as spisea_evolution +from spisea import atmospheres as spisea_atmospheres +from spisea import synthetic as spisea_synthetic +import pdb + +class SpiseaCluster(EvolutionIsochrones,EvolutionInterpolator): + """ + Placeholder object to store modules and values for use by the SpiseaGenerator, which + will generate and evolve stars as binned SPISEA clusters. + """ + def __init__(self, columns, n_proc=1, photsys=None, + spisea_evolution_name="MISTv1", block_spisea_prints=True, + spisea_evolution_kwargs={"version":1.2, "synthpop_extension":True}, + spisea_atm_func_name="get_merged_atmosphere", spisea_wd_atm_func_name="get_wd_atmosphere", + min_mass=0, max_mass=1000, effective_wavelengths='pivot', + bbh_frac=0.1, **kwargs): + self.name='SpiseaCluster' + if not n_proc>=1: + raise ValueError("n_proc for SpiseaCluster must be at least 1") + self.n_proc = n_proc + self.block_spisea_prints=block_spisea_prints + + self.spisea_evolution = getattr(spisea_evolution, spisea_evolution_name)(**spisea_evolution_kwargs) + + self.spisea_atm_func = getattr(spisea_atmospheres, spisea_atm_func_name) + self.spisea_wd_atm_func = getattr(spisea_atmospheres, spisea_wd_atm_func_name) + self.min_mass = min_mass + self.max_mass = max_mass + + self.allowed_non_mag_cols = ["[Fe/H]_init", "log10_isochrone_age_yr", 'phase', + 'star_mass', 'initial_mass', 'log_L', 'log_R', 'log_Teff', 'log_g', 'isWR'] + + self.magsys, self.non_mag_cols, self.bands, self.bands_obs_str = self.get_cols(columns) + with open(f"{EVOLUTION_DIR}/spisea_effective_wavelengths.json") as f: + all_eff_wavelengths = json.load(f)[effective_wavelengths] + self.eff_wavelengths = {self.bands[i]:all_eff_wavelengths[self.bands_obs_str[i]] for i in range(len(self.bands))} + + # Deal with magnitude systems + self.photsys = photsys + if self.photsys is None: + self.photsys = 'Vega' # All mags default to Vega in SPISEA + if self.photsys in ['Vega','AB','ST']: + self.photsys_dict = {band: self.photsys for band in self.bands} + elif isinstance(self.photsys, dict): + self.photsys_dict = {band: 'Vega' for band in self.bands} + for key, val in self.photsys.items(): + if key not in ['Vega', 'AB','ST']: + raise ValueError("Dict input for photsys must be of structure e.g. {'AB':['m_roman_f146','m_roman_f087']} or {'AB':['roman']} " + "with photometric systems being Vega, AB, ST, and using any valid filter sets or names from MIST v1.2.") + for item in val: + if item in self.bands: + self.photsys_dict.update({item: key}) + elif item in self.all_spisea_filters: + self.photsys_dict.update({band: key for band in self.bands if ('m_'+item+'_' in band)}) + else: + raise ValueError(f"Invalid input in photsys_dict: {item} not found as band or band set.") + else: + raise ValueError("photsys must be 'Vega', 'AB', 'ST', None, or dict") + + with open(f"{EVOLUTION_DIR}/spisea_photometric_system_conversions.json") as f: + all_photsys_convert = json.load(f) + self.photsys_convert = {} + for i,band in enumerate(self.bands): + if self.photsys_dict[band] == 'Vega': + self.photsys_convert[band] = 0.0 + else: + self.photsys_convert[band] = all_photsys_convert[self.photsys_dict[band]][self.bands_obs_str[i]] + + # Binary evolution + self.bbh_frac=bbh_frac + + if spisea_evolution_name=='MISTv1': + self.feh_list = np.array([-4.0,-3.5,-3.0,-2.5,-2.0,-1.75,-1.5,-1.25, + -1.0,-0.75,-0.5,-0.25,0,0.25,0.5]) + self.log_age_list = np.linspace(5.0,10.3,54) + self.log_age_list[0] = 5.01 + elif spisea_evolution_name=='MergedBaraffePisaEkstromParsec': + self.feh_list = np.array([0.0]) + self.log_age_list = np.linspace(6.0,10.1,83) + self.log_age_list[-1] = 10.9 + Warning(f"Evolution module {spisea_evolution_name} only includes solar metallicy. All stars will be assigned solar metallicity.") + elif spisea_evolution_name=='COSMIC': + self.feh_list = np.array([-2.3,-2.0,-1.75,-1.5,-1.25, + -1.0,-0.75,-0.5,-0.25,0,0.176]) + self.log_age_list = np.linspace(5.0,10.3,54) + if self.bbh_frac<1.0: + self.bbh_frac=1.0 + Warning("Setting bbh_frac to 1.0 to let COSMIC handle binary evolution.") + if spisea_atm_func_name != "get_merged_atmosphere_w_bb_supplement": + self.spisea_atm_func = spisea_atmospheres.get_merged_atmosphere_w_bb_supplement + Warning("Setting amosphere model to get_merged_atmosphere_w_bb_supplement for COSMIC.") + self.spisea_wd_atm_func = None + else: + raise ValueError("Invalid SPISEA evolution_model. Only MISTv1, MergedBaraffePisaEkstromParsec," + " and COSMIC are available at this time.") + + def get_cols(self, columns): + with open(f"{EVOLUTION_DIR}/spisea_filters.json") as f: + self.all_spisea_filters = json.load(f) + magsys = {} + all_bands = [] + non_mag_cols = [] + + for column in columns: + column_split = column.split(',') + # Non-magnitude columns + if column in self.allowed_non_mag_cols: + non_mag_cols.append(column) + # Magnitude systems + elif column in self.all_spisea_filters.keys(): + magsys = self.all_spisea_filters[column] + # Magnitude systems with nested categories & need all + if hasattr(magsys, "keys"): + for magsys_subset in magsys.keys(): + for band in magsys[magsys_subset]: + all_bands.append(column+','+magsys_subset+','+band) + # Magnitude systems with no nested catagories & need all + else: + for band in magsys: + all_bands.append(column+','+band) + # Magnitude systems with nested or band selections + elif (len(column_split) in [2,3]) and (column_split[0] in self.all_spisea_filters.keys()): + magsys = self.all_spisea_filters[column_split[0]] + # Specified nested band set, use all filters + if (len(column_split)==2) and hasattr(magsys, "keys"): + if column_split[1] in magsys.keys(): + for band in magsys[column_split[1]]: + all_bands.append(column+','+band) + else: + raise ValueError('Invalid column '+column+' for SPISEA isochrones.') + # Specified nested band set, select filters + elif len(column_split)==3: + if column_split[1] in magsys.keys(): + if column_split[2] in magsys[column_split[1]]: + all_bands.append(column) + else: + raise ValueError('Invalid column '+column+' for SPISEA isochrones.') + else: + raise ValueError('Invalid column '+column+' for SPISEA isochrones.') + elif len(column_split)==2: + if column_split[1] in magsys: + all_bands.append(column) + else: + raise ValueError('Invalid column '+column+' for SPISEA isochrones.') + else: + raise ValueError('Invalid column '+column+' for SPISEA isochrones.') + else: + raise ValueError('Invalid column '+column+' for SPISEA isochrones.') + + all_bands_obs_str = list(np.unique(all_bands)) + all_bands = ['m_'+spisea_synthetic.get_filter_col_name(band) for band in all_bands_obs_str] + + return None, list(np.unique(non_mag_cols)), all_bands, all_bands_obs_str + + def get_evolved_props(**kwargs): + raise ValueError("SpiseaCluster Evolution module is only compatible with SpiseaGenerator, not StarGenerator") + return + +def generate_effective_wavelengths_json(): + import pysynphot + with open(f'{EVOLUTION_DIR}/spisea_filters.json') as f: + d = json.load(f) + def do_filt_effs(sys,flts): + efflamsi = {} + for flt in flts: + flt_name = sys+','+flt + filt = spisea_synthetic.get_filter_info(flt_name) + obs = pysynphot.Observation(pysynphot.Vega, filt).efflam() + assert str(filt.waveunits) == 'angstrom' + efflamsi[flt_name] = obs*1e-4 + #pdb.set_trace() + print(flt_name, obs*1e-4) + return efflamsi + def do_filt_pivs(sys,flts): + efflamsi = {} + for flt in flts: + flt_name = sys+','+flt + filt = spisea_synthetic.get_filter_info(flt_name) + obs = filt.pivot() + assert str(filt.waveunits) == 'angstrom' + efflamsi[flt_name] = obs*1e-4 + #pdb.set_trace() + print(flt_name, obs*1e-4) + return efflamsi + def do_filt_avgs(sys,flts): + efflamsi = {} + for flt in flts: + flt_name = sys+','+flt + filt = spisea_synthetic.get_filter_info(flt_name) + obs = filt.avgwave() + assert str(filt.waveunits) == 'angstrom' + efflamsi[flt_name] = obs*1e-4 + #pdb.set_trace() + print(flt_name, obs*1e-4) + return efflamsi + + all_effs = {} + typ = ['vega_eff','pivot','average'] + funcs = [do_filt_effs, do_filt_pivs, do_filt_avgs] + for tp in range(3): + efflams = {} + do_filts = funcs[tp] + for sys in d: + flts = d[sys] + if isinstance(flts,dict): + for subsys in flts: + efflams.update(do_filts(sys+','+subsys,flts[subsys])) + else: + efflams.update(do_filts(sys,flts)) + all_effs[typ[tp]] = efflams + + json_object = json.dumps(all_effs, indent=4) + with open(f"{EVOLUTION_DIR}/spisea_effective_wavelengths.json", "w") as outfile: + outfile.write(json_object) + return + +def generate_photsys_conversions(): + with open(f'{EVOLUTION_DIR}/spisea_filters.json') as f: + d = json.load(f) + + def do_filts(sys,flts): + convs_ab = {} + convs_st = {} + for flt in flts: + flt_name = sys+','+flt + convs_ab.update({flt_name: spisea_synthetic.calc_ab_vega_filter_conversion(flt_name)}) + convs_st.update({flt_name: spisea_synthetic.calc_st_vega_filter_conversion(flt_name)}) + return convs_ab, convs_st + + all_convs = {"AB":{}, "ST":{}} + for sys in d: + flts = d[sys] + if isinstance(flts,dict): + for subsys in flts: + convs_ab, convs_st = do_filts(sys+','+subsys,flts[subsys]) + all_convs["AB"].update(convs_ab) + all_convs["ST"].update(convs_st) + else: + convs_ab, convs_st = do_filts(sys,flts) + all_convs["AB"].update(convs_ab) + all_convs["ST"].update(convs_st) + + json_object = json.dumps(all_convs, indent=4) + with open(f"{EVOLUTION_DIR}/spisea_photometric_system_conversions.json", "w") as outfile: + outfile.write(json_object) + return diff --git a/synthpop/modules/evolution/spisea_effective_wavelengths.json b/synthpop/modules/evolution/spisea_effective_wavelengths.json new file mode 100644 index 0000000..4a49694 --- /dev/null +++ b/synthpop/modules/evolution/spisea_effective_wavelengths.json @@ -0,0 +1,1025 @@ +{ + "vega_eff": { + "2mass,J": 1.2320244419390167, + "2mass,H": 1.642309415675878, + "2mass,Ks": 2.155751977206318, + "ctio_osiris,H": 1.6209597437306624, + "ctio_osiris,K": 2.1689069827209218, + "decam,u": 0.365235070798187, + "decam,g": 0.46737217223042626, + "decam,r": 0.635423461014996, + "decam,i": 0.7784397371598968, + "decam,z": 0.9232402932139733, + "decam,Y": 1.0072050884636148, + "euclid,VIS": 0.6874712436874423, + "euclid,Y": 1.0716327969103672, + "euclid,J": 1.3425944374630965, + "euclid,H": 1.7388853994479971, + "gaia,dr1,G": 0.585139627758609, + "gaia,dr1,Gbp": 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1.2290447513916476, + "subaru,hsc,g": -0.39474118869027564, + "subaru,hsc,r": 0.41815092644969454, + "subaru,hsc,i": 1.1510728056377282, + "subaru,hsc,z": 1.5902177580831207, + "subaru,hsc,Y": 1.8290983150854032, + "subaru,hsc,nb387": -0.6786195735814395, + "subaru,hsc,nb468": -0.47760066542426216, + "subaru,hsc,nb515": -0.1744459178717399, + "subaru,hsc,nb527": -0.11432270871215877, + "subaru,hsc,nb656": 0.7454828673710949, + "subaru,hsc,nb718": 0.9089991209897335, + "subaru,hsc,nb816": 1.3560631257039653, + "subaru,hsc,nb921": 1.6841304384211355, + "subaru,hsc,nb926": 1.706938760354511, + "subaru,hsc,nb973": 1.7739754491543067, + "tycho,B": -0.6435395221812641, + "tycho,V": -0.08681799217211505, + "washington,C": -0.406676599007298, + "washington,M": -0.19598963862826224, + "washington,T1": 0.5417956384104627, + "washington,T2": 1.319247530456412, + "nsfcam,L": 6.80606509710465 + } +} \ No newline at end of file diff --git a/synthpop/modules/extinction/_extinction.py b/synthpop/modules/extinction/_extinction.py index d853f65..6f30eb7 100644 --- a/synthpop/modules/extinction/_extinction.py +++ b/synthpop/modules/extinction/_extinction.py @@ -395,4 +395,40 @@ def set_bands(self, bands: List[str], eff_wavelengths: Dict[str, float]): eff_wavelengths[band], band) for band in bands} + def validate_extinction(self): + if self.multi_laws: + for i,b in enumerate(self.bands): + law_i = self.ext_law_dict[self.ext_law_index[i]] + assert((law_i.min_wavelength<=self.eff_wavelengths[b]) & + (law_i.max_wavelength>=self.eff_wavelengths[b])), \ + f"Invalid filter {self.bands[i]} with wavelength {self.eff_wavelengths[b]} microns for extinction law." \ + f" {law_i.extinction_law_name} is valid from {law_i.min_wavelength}-{law_i.max_wavelength} microns." + law_i = self.ext_law_dict[self.get_ext_law_index(self.ref_wavelength, self.A_or_E_type)] + assert ((law_i.min_wavelength<=self.ref_wavelength) & + (law_i.max_wavelength>=self.ref_wavelength)), \ + f"Invalid extinction map quantity {self.A_or_E_type} for extinction law." \ + f" {law_i.extinction_law_name} is valid from {law_i.min_wavelength}-{law_i.max_wavelength} microns." + if self.ref_wavelength2 is not None: + law_i = self.ext_law_dict[self.get_ext_law_index(self.ref_wavelength2, self.A_or_E_type)] + assert ((law_i.min_wavelength<=self.ref_wavelength2) & + (law_i.max_wavelength>=self.ref_wavelength2)), \ + f"Invalid extinction map quantity {self.A_or_E_type} for extinction law." \ + f" {law_i.extinction_law_name} is valid from {law_i.min_wavelength}-{law_i.max_wavelength} microns." + + else: + for i,b in enumerate(self.bands): + assert((self.min_wavelength<=self.eff_wavelengths[b]) & + (self.max_wavelength>=self.eff_wavelengths[b])), \ + f"Invalid filter {self.bands[i]} with wavelength {self.eff_wavelengths[b]} microns for extinction law." \ + f" {self.extinction_law_name} is valid from {self.min_wavelength}-{self.max_wavelength} microns." + assert ((self.min_wavelength<=self.ref_wavelength) & + (self.max_wavelength>=self.ref_wavelength)), \ + f"Invalid extinction map quantity {self.A_or_E_type} for extinction law." \ + f" {self.extinction_law_name} is valid from {self.min_wavelength}-{self.max_wavelength} microns." + if self.ref_wavelength2 is not None: + assert ((self.min_wavelength<=self.ref_wavelength) & + (self.max_wavelength>=self.ref_wavelength)), \ + f"Invalid extinction map quantity {self.A_or_E_type} for extinction law." \ + f" {self.extinction_law_name} is valid from {self.min_wavelength}-{self.max_wavelength} microns." + return Extinction diff --git a/synthpop/modules/extinction/galaxia_3d.py b/synthpop/modules/extinction/galaxia_3d.py index 8f17d18..2fb4bcd 100644 --- a/synthpop/modules/extinction/galaxia_3d.py +++ b/synthpop/modules/extinction/galaxia_3d.py @@ -5,10 +5,10 @@ Publication DOI: 10.1088/0004-637X/730/1/3 (galaxia), 10.1086/305772 (2-d map) -Data available at: http://bhs.astro.berkeley.edu/GalaxiaData.tar.gz +Data available in .ebf form at: http://bhs.astro.berkeley.edu/GalaxiaData.tar.gz """ -__all__ = ["Galaxia_3D", ] +__all__ = ["Galaxia_3d", ] __author__ = "M.J. Huston" __date__ = "2024-04-18" @@ -20,12 +20,12 @@ except ImportError: from _extinction import ExtinctionMap import time -import ebf import tarfile import os import requests +import h5py -class Galaxia_3D(ExtinctionMap): +class Galaxia_3d(ExtinctionMap): """ Extinction map used in Galaxia @@ -41,48 +41,57 @@ def __init__(self, **kwargs): self.ref_wavelength = 0.4361 self.ref_wavelength2 = 0.5448 self.A_or_E_type = 'E(B-V)' - - if (not os.path.isfile(f'{const.EXTINCTIONS_DIR}/Galaxia_ExMap3d_1024.ebf')) or (not os.path.isfile(f'{const.EXTINCTIONS_DIR}/Galaxia_Schlegel_4096.ebf')): - print("Missing Galaxia map data - download and arrangement will take a few minutes.") - if not os.path.isdir(f'{const.EXTINCTIONS_DIR}'): - os.mkdir(f'{const.EXTINCTIONS_DIR}') - if not os.path.isfile(f'{const.EXTINCTIONS_DIR}/GalaxiaData.tar.gz'): - with open(f'{const.EXTINCTIONS_DIR}/GalaxiaData.tar.gz', "wb") as f: - r = requests.get("http://bhs.astro.berkeley.edu/GalaxiaData.tar.gz") - f.write(r.content) - print('Data retrieved.') - with tarfile.open(f'{const.EXTINCTIONS_DIR}/GalaxiaData.tar.gz', "r") as f: - f.extract('GalaxiaData/Extinction/ExMap3d_1024.ebf', f'{const.EXTINCTIONS_DIR}/') - f.extract('GalaxiaData/Extinction/Schlegel_4096.ebf', f'{const.EXTINCTIONS_DIR}/') - os.rename(f'{const.EXTINCTIONS_DIR}/GalaxiaData/Extinction/ExMap3d_1024.ebf', - f'{const.EXTINCTIONS_DIR}/Galaxia_ExMap3d_1024.ebf') - os.rename(f'{const.EXTINCTIONS_DIR}/GalaxiaData/Extinction/Schlegel_4096.ebf', - f'{const.EXTINCTIONS_DIR}/Galaxia_Schlegel_4096.ebf') - os.remove(f'{const.EXTINCTIONS_DIR}/GalaxiaData.tar.gz') - os.rmdir(f'{const.EXTINCTIONS_DIR}/GalaxiaData/Extinction') - os.rmdir(f'{const.EXTINCTIONS_DIR}/GalaxiaData') - print('Extinction file setup complete.') - # Set up 3D grid - mapfile_3d = ebf.read(f'{const.EXTINCTIONS_DIR}/Galaxia_ExMap3d_1024.ebf') - map_grid_3d = mapfile_3d['exmap3d.xmms'] - map_data_3d = mapfile_3d['exmap3d.data'] - # Set up 3D interpolation + # Check for files and download if needed + if not os.path.isdir(f'{const.EXTINCTIONS_DIR}'): + os.mkdir(f'{const.EXTINCTIONS_DIR}') + map_filename_3d = f'{const.EXTINCTIONS_DIR}/Galaxia_ExMap3d_1024.h5' + if not os.path.isfile(map_filename_3d): + print('Fetching 3-d extinction map file.') + map_url = 'https://lsu.box.com/shared/static/3hifnqy9u6lko3ebqdqwwt5ockim27t3' + try: + with open(map_filename_3d, "wb") as f: + r = requests.get(map_url, stream=True) + for chunk in r.iter_content(chunk_size=8192): + f.write(chunk) + print('Map retrieved.') + except: + print(f'There was an error fetching the extinction map. Try downloading the file at {map_url} manually and placing it in {const.EXTINCTIONS_DIR}') + raise + map_filename_schlegel = f'{const.EXTINCTIONS_DIR}/Galaxia_Schlegel_4096.h5' + if not os.path.isfile(map_filename_schlegel): + print('Fetching Schlegel extinction map file.') + map_url = 'https://lsu.box.com/shared/static/x3he8q1ybjrg4x98le551dmczj3ys1dx' + try: + with open(map_filename_schlegel, "wb") as f: + r = requests.get(map_url, stream=True) + for chunk in r.iter_content(chunk_size=8192): + f.write(chunk) + print('Map retrieved.') + except: + print(f'There was an error fetching the extinction map. Try downloading the file at {map_url} manually and placing it in {const.EXTINCTIONS_DIR}') + raise + + # Set up 3D grid interpolation for distance scaling + mapfile_3d = h5py.File(map_filename_3d, 'r') + map_grid_3d = np.array(mapfile_3d['xmms']) + map_data_3d = np.array(mapfile_3d['data']) + mapfile_3d.close() l_grid_3d = np.append(np.arange(*map_grid_3d[0]),map_grid_3d[0][1]) b_grid_3d = np.append(np.arange(*map_grid_3d[1]),map_grid_3d[1][1]) self.r_grid = 10**np.append(np.arange(*map_grid_3d[2]),map_grid_3d[2][1]) self.grid_interpolator_3d = RegularGridInterpolator((l_grid_3d,b_grid_3d,self.r_grid), - map_data_3d, bounds_error=False, fill_value=None, method='nearest') + map_data_3d, bounds_error=False, fill_value=None, method='linear') - # Set up 3d grid - mapfile_2d = ebf.read(f'{const.EXTINCTIONS_DIR}/Galaxia_Schlegel_4096.ebf') - map_grid_2d = mapfile_2d['exmap2d.xmms'] - map_data_2d = mapfile_2d['exmap2d.data'] - # set up interpolator + # Set up 2D grid interpolation for Schlegel extinction map + mapfile_2d = h5py.File(map_filename_schlegel, 'r') + map_grid_2d = np.array(mapfile_2d['xmms']) + map_data_2d = np.array(mapfile_2d['data']) + mapfile_2d.close() l_grid_2d = np.append(np.arange(*map_grid_2d[0]),map_grid_2d[0][1]) b_grid_2d = np.append(np.arange(*map_grid_2d[1]),map_grid_2d[1][1]) self.grid_interpolator_2d = RegularGridInterpolator((l_grid_2d,b_grid_2d), - map_data_2d, bounds_error=False, fill_value=None, method='nearest') + map_data_2d, bounds_error=False, fill_value=None, method='linear') def extinction_in_map(self, l_deg, b_deg, dist): """ @@ -107,8 +116,4 @@ def extinction_in_map(self, l_deg, b_deg, dist): mapval_2d = self.grid_interpolator_2d((use_l, b_deg)) # 2D scaling - return mapval_2d*mapval_3d - - - - + return mapval_2d*mapval_3d \ No newline at end of file diff --git a/synthpop/modules/extinction/gums.py b/synthpop/modules/extinction/gums.py index f2a48fa..68d57f0 100644 --- a/synthpop/modules/extinction/gums.py +++ b/synthpop/modules/extinction/gums.py @@ -17,11 +17,11 @@ import numpy as np try: from ._extinction import ExtinctionMap - from .lallement import Lallement + from .lallement2019 import Lallement2019 from .. import const except ImportError: from _extinction import ExtinctionMap - from lallement import Lallement + from lallement2019 import Lallement2019 import constants as const import time import os @@ -33,7 +33,7 @@ # empty dictionary to store dustmaps query _query_dict = {} -class Gums(Lallement,ExtinctionMap): +class Gums(Lallement2019,ExtinctionMap): """ Extinction map from Gaia Universe Model Snapshot diff --git a/synthpop/modules/extinction/lallement.py b/synthpop/modules/extinction/lallement2019.py similarity index 99% rename from synthpop/modules/extinction/lallement.py rename to synthpop/modules/extinction/lallement2019.py index 250705b..6838444 100644 --- a/synthpop/modules/extinction/lallement.py +++ b/synthpop/modules/extinction/lallement2019.py @@ -13,7 +13,7 @@ Data file FTP: https://cdsarc.cds.unistra.fr/ftp/J/A+A/625/A135/ """ -__all__ = ["Lallement", ] +__all__ = ["Lallement2019", ] __author__ = "M.J. Huston" __date__ = "2024-11-06" @@ -34,7 +34,7 @@ current_map_name = None current_map_data = None -class Lallement(ExtinctionMap): +class Lallement2019(ExtinctionMap): """ Extinction map from Lallement et al. 2019 diff --git a/synthpop/modules/extinction/lallement2022.py b/synthpop/modules/extinction/lallement2022.py new file mode 100644 index 0000000..fb01b68 --- /dev/null +++ b/synthpop/modules/extinction/lallement2022.py @@ -0,0 +1,179 @@ +""" +Extinction map from Lallement et al. (2022), for dust within 6 kpc based on +Gaia & 2MASS observations. + +Extinction is given as 'A0', or total extinction at reference wavelength 5500 Angstroms. + +The user may determine whether to integrate over the map individually for each star, or to +integrate for the average sightline and interpolate for distances. The user can also adjust +the dr used for extinction integration. + +Publication DOI: 10.1051/0004-6361/202142846 + +Data file FTP: http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/661/A147#/browse +""" + +__all__ = ["Lallement2022", ] +__author__ = "M.J. Huston" +__date__ = "2026-02-17" + +import gzip +import h5py +import shutil +import numpy as np +try: + from ._extinction import ExtinctionMap + from .. import const +except ImportError: + from _extinction import ExtinctionMap + import const +import time +import os +import requests +import pdb +from astropy.io import fits + +current_map_name = None +current_map_data = None + +class Lallement2022(ExtinctionMap): + """ + Extinction map from Lallement et al. 2019 + + Attributes + ---------- + dr=0.001 : float + step size for extinction integration in kpc + per_sightline=True : boolean + calculate extinction integral once per sightline if True (faster) + calculate extinciton integral individually per star if False (slower) + + Methods + ------- + lallement_ext_func(l_deg, b_deg, dist): + get extinction value in map for list of star locations + extinction_in_map(l_deg, b_deg, dist): + equivalent to lallement_ext_func + """ + + def __init__(self, dr=0.001, per_sightline=True, **kwargs): + super().__init__(**kwargs) + # name of the extinction map used + self.extinction_map_name = "Lallement" + # A0 = value at 5500 angstroms + self.ref_wavelength = 0.55 + self.A_or_E_type = 'A0' + self.per_sightline = per_sightline # Calculate the extinction integral once per sightline, otherwise, individually for each star + self.dr = dr #: step size for extinction integration in kpc + if not os.path.isfile(f'{const.EXTINCTIONS_DIR}/lallement2022_cube_ext.fits'): + if not os.path.isdir(f'{const.EXTINCTIONS_DIR}'): + os.mkdir(f'{const.EXTINCTIONS_DIR}') + print("Missing Lallement et al. (2022) extinction table. Download and unpacking may take several minutes but only needs done once.") + print(f"If this fails, try downloading the file at http://cdsarc.u-strasbg.fr/ftp/J/A+A/661/A147/cube_ext.fits.gz"+ + f", placing it in {const.EXTINCTIONS_DIR}, and initializing your model again.") + map_url = 'http://cdsarc.u-strasbg.fr/ftp/J/A+A/661/A147/cube_ext.fits.gz' + map_filename = f'{const.EXTINCTIONS_DIR}/lallement2022_cube_ext.fits.gz' + if not os.isfile(map_filename): + with open(map_filename, "wb") as f: + r = requests.get(map_url) + f.write(r.content) + print('Map retrieved.') + with gzip.open(map_filename,'rb') as f_in, open(map_filename[:-3],'wb') as f_out: + shutil.copyfileobj(f_in,f_out) + os.remove(map_filename) + print('File unzipped; ready to use.') + + # Check whether the map is already loaded from the prior population + global current_map_name, current_map_data + if (current_map_name is not None) and (current_map_data is not None): + if current_map_name==self.extinction_map_name: + self.map_data = current_map_data + else: + self.map_data = fits.open(f'{const.EXTINCTIONS_DIR}/lallement2022_cube_ext.fits')[0].data + current_map_name = self.extinction_map_name + current_map_data = self.map_data + else: + self.map_data = fits.open(f'{const.EXTINCTIONS_DIR}/lallement2022_cube_ext.fits')[0].data + current_map_name = self.extinction_map_name + current_map_data = self.map_data + + #pdb.set_trace() + # 10 pc grid spacing: -3 to 3 kpc in x,y; -400 to 400 pc in z + self.grid_dr = 0.010 + self.grid_x_mid = 300 + self.grid_y_mid = 300 + self.grid_z_mid = 40 + self.x_extent=3.0 + self.y_extent=3.0 + self.z_extent=0.4 + # Data units are dmag/dpc at A0, 5500 angstrom - maybe ??? + + def lallement_ext_func(self,l_deg,b_deg,dist): + ''' + Get extinction value from Lallement et al. (2019) map + for an array of star positions. + + Parameters + ---------- + l_deg: ndarray [degrees] + galactic longitude + b_deg: ndarray [degrees] + galactic latitude + dist: ndarray [kpc] + radial distance from the Sun + + Returns + ------- + extinction_value: ndarray [mag] + extinction at each star position defined as self.A_or_E_type + ''' + dist_max = np.max(dist) + # Take mean sightline for all stars, or do each star individually + if self.per_sightline: + dist_pts = np.arange(0,dist_max,self.dr) + # Convert to nearest neighbor map array indices + l_rad, b_rad = np.mean(l_deg)*np.pi/180, np.mean(b_deg)*np.pi/180 + else: + dist_pts = np.arange(0,dist_max,self.dr)[np.newaxis,:] + # Convert to nearest neighbor map array indices + l_rad, b_rad = l_deg[:,np.newaxis]*np.pi/180, b_deg[:,np.newaxis]*np.pi/180 + xm_dists = dist_pts*np.cos(b_rad)*np.cos(l_rad) + ym_dists = dist_pts*np.cos(b_rad)*np.sin(l_rad) + zm_dists = dist_pts*np.sin(b_rad) + xm_pts = np.maximum(np.minimum(np.around(xm_dists/self.grid_dr).astype(int), self.grid_x_mid), -self.grid_x_mid) + self.grid_x_mid + ym_pts = np.maximum(np.minimum(np.around(ym_dists/self.grid_dr).astype(int), self.grid_y_mid), -self.grid_y_mid) + self.grid_y_mid + zm_pts = np.maximum(np.minimum(np.around(zm_dists/self.grid_dr).astype(int), self.grid_z_mid), -self.grid_z_mid) + self.grid_z_mid + # Find where each line exits the grid + within_grid = (np.abs(xm_dists) float: + """ + Given an effective wavelength lambda_eff, calculate the extinction ratio A_lambda/A_ref + + Parameters + ---------- + eff_wavelength : float + wavelength to compute extinction ratio at [microns] + """ + obsc = np.interp(eff_wavelength*1e4, self.spisea_red_law.wave, self.spisea_red_law.obscuration, + left=np.nan, right=np.nan) + + return obsc/self.spisea_red_law.obscuration[0] diff --git a/synthpop/modules/extinction/surot.py b/synthpop/modules/extinction/surot.py index b116a54..a9b1431 100755 --- a/synthpop/modules/extinction/surot.py +++ b/synthpop/modules/extinction/surot.py @@ -1,7 +1,7 @@ """ Extinction map from Surot et al 2020. This is a 2-d map which may be used -as a screen at some distance, or pushed into 3-d following the scheme used -in Galaxia (see galaxia_3d module / Sharma et al. 2011) +as a screen at some distance, or projected into 3-d following the scheme used +in Galaxia (see galaxia_3d module / Sharma et al. 2011). Extinction is provided as total extinction A_Ks at 2.15 microns @@ -20,11 +20,11 @@ from scipy.spatial import KDTree from ._extinction import ExtinctionMap import time -import ebf from scipy.interpolate import RegularGridInterpolator import requests import os import tarfile +import h5py current_map_name = None current_map_data = None @@ -60,61 +60,69 @@ def __init__(self, project_3d=True, dist_2d=8.15, **kwargs): map_url = 'https://cdsarc.cds.unistra.fr/ftp/J/A+A/644/A140/ejkmap.dat.gz' # Fetch extinction map data if needed - if not os.path.isfile(f'{const.EXTINCTIONS_DIR}/surot_A_Ks_table.h5'): + surot_map_file = f'{const.EXTINCTIONS_DIR}/surot_A_Ks_table.h5' + if not os.path.isfile(surot_map_file): if not os.path.isdir(f'{const.EXTINCTIONS_DIR}'): os.mkdir(f'{const.EXTINCTIONS_DIR}') - if not os.path.isfile(f'{const.EXTINCTIONS_DIR}/surot_'+map_url.split("/")[-1]): - print("Missing Surot table. Download and formatting may take several minutes.") - print('Downloading map file from VizieR...') - map_filename = f'{const.EXTINCTIONS_DIR}/surot_'+map_url.split("/")[-1] + print('Retrieving Surot extinction map file. This may take a couple minutes the first time.') + try: + map_url = 'https://lsu.box.com/shared/static/cwitks7jtzne0w32nhc00yj9pcpcrjoe' + with open(surot_map_file, "wb") as f: + r = requests.get(map_url, stream=True) + for chunk in r.iter_content(chunk_size=8192): + f.write(chunk) + print('Map retrieved.') + except: + print(f'There was an error fetching the extinction map.') + raise + #### Outdated code for fetching and converting file. We have a quicker method now using box. + # if not os.path.isdir(f'{const.EXTINCTIONS_DIR}'): + # os.mkdir(f'{const.EXTINCTIONS_DIR}') + # if not os.path.isfile(f'{const.EXTINCTIONS_DIR}/surot_'+map_url.split("/")[-1]): + # print("Missing Surot table. Download and formatting may take several minutes.") + # print('Downloading map file from VizieR...') + # map_filename = f'{const.EXTINCTIONS_DIR}/surot_'+map_url.split("/")[-1] + # try: + # with open(map_filename, "wb") as f: + # r = requests.get(map_url) + # f.write(r.content) + # print('Map retrieved.') + # except: + # print(f'There was an error fetching the map. This happens occasionally due to the extremely large file. Consider manually downloading https://cdsarc.cds.unistra.fr/ftp/J/A+A/644/A140/surot_ejkmap.dat.gz and placing it in {const.EXTINCTIONS_DIR}.') + # raise + # else: + # map_filename = f'{const.EXTINCTIONS_DIR}/surot_ejkmap.dat.gz' + # print('Reading table...') + # E_JKs_map_df = pd.read_fwf(map_filename,compression='gzip', header=None) + # print('Reformatting values...') + # E_JKs_map = E_JKs_map_df.to_numpy() + # A_Ks_vals = 0.422167 * E_JKs_map[:,2] #conversion from Surot2020 paper + # entries = E_JKs_map.shape[0] + # print('Saving hdf5 version') + # map_output = 'surot_A_Ks_table.h5' + # surot_2d = np.zeros((entries, 3)) + # surot_2d[:,0] = E_JKs_map[:,0] + # surot_2d[:,1] = E_JKs_map[:,1] + # surot_2d[:,2] = A_Ks_vals + # surot_2d_df = pd.DataFrame(surot_2d, columns=['l','b','A_Ks']) + # surot_2d_df.to_hdf(f'{const.EXTINCTIONS_DIR}/'+map_output, key='data', index=False, mode='w') + # print('File 2D version saved as '+map_output) + + if project_3d: + map_filename_3d = f'{const.EXTINCTIONS_DIR}/Galaxia_ExMap3d_1024.h5' + if not os.path.isfile(map_filename_3d): + print('Fetching 3-d extinction map file.') + map_url = 'https://lsu.box.com/shared/static/3hifnqy9u6lko3ebqdqwwt5ockim27t3' try: - with open(map_filename, "wb") as f: - r = requests.get(map_url) - f.write(r.content) + with open(map_filename_3d, "wb") as f: + r = requests.get(map_url, stream=True) + for chunk in r.iter_content(chunk_size=8192): + f.write(chunk) print('Map retrieved.') except: - print(f'There was an error fetching the map. This happens occasionally due to the extremely large file. Consider manually downloading https://cdsarc.cds.unistra.fr/ftp/J/A+A/644/A140/surot_ejkmap.dat.gz and placing it in {const.EXTINCTIONS_DIR}.') + print(f'There was an error fetching the extinction map. Try downloading the file at {map_url} manually and placing it in {const.EXTINCTIONS_DIR}') raise - else: - map_filename = f'{const.EXTINCTIONS_DIR}/surot_ejkmap.dat.gz' - print('Reading table...') - E_JKs_map_df = pd.read_fwf(map_filename,compression='gzip', header=None) - print('Reformatting values...') - E_JKs_map = E_JKs_map_df.to_numpy() - A_Ks_vals = 0.422167 * E_JKs_map[:,2] #conversion from Surot2020 paper - entries = E_JKs_map.shape[0] - print('Saving hdf5 version') - map_output = 'surot_A_Ks_table.h5' - surot_2d = np.zeros((entries, 3)) - surot_2d[:,0] = E_JKs_map[:,0] - surot_2d[:,1] = E_JKs_map[:,1] - surot_2d[:,2] = A_Ks_vals - surot_2d_df = pd.DataFrame(surot_2d, columns=['l','b','A_Ks']) - surot_2d_df.to_hdf(f'{const.EXTINCTIONS_DIR}/'+map_output, key='data', index=False, mode='w') - print('File 2D version saved as '+map_output) - - # Fetch 3-d projection data if needed - if project_3d: - if (not os.path.isfile(f'{const.EXTINCTIONS_DIR}/Galaxia_ExMap3d_1024.ebf')) or (not os.path.isfile(f'{const.EXTINCTIONS_DIR}/Galaxia_Schlegel_4096.ebf')): - print("Missing Galaxia map data - download and arrangement will take a few minutes (significantly faster than the Surot+20 data download).") - if not os.path.isdir(f'{const.EXTINCTIONS_DIR}'): - os.mkdir(f'{const.EXTINCTIONS_DIR}') - if not os.path.isfile(f'{const.EXTINCTIONS_DIR}/GalaxiaData.tar.gz'): - with open(f'{const.EXTINCTIONS_DIR}/GalaxiaData.tar.gz', "wb") as f: - r = requests.get("http://bhs.astro.berkeley.edu/GalaxiaData.tar.gz") - f.write(r.content) - print('Data retrieved.') - with tarfile.open(f'{const.EXTINCTIONS_DIR}/GalaxiaData.tar.gz', "r") as f: - f.extract('GalaxiaData/Extinction/ExMap3d_1024.ebf', f'{const.EXTINCTIONS_DIR}/') - f.extract('GalaxiaData/Extinction/Schlegel_4096.ebf', f'{const.EXTINCTIONS_DIR}/') - os.rename(f'{const.EXTINCTIONS_DIR}/GalaxiaData/Extinction/ExMap3d_1024.ebf', - f'{const.EXTINCTIONS_DIR}/Galaxia_ExMap3d_1024.ebf') - os.rename(f'{const.EXTINCTIONS_DIR}/GalaxiaData/Extinction/Schlegel_4096.ebf', - f'{const.EXTINCTIONS_DIR}/Galaxia_Schlegel_4096.ebf') - os.remove(f'{const.EXTINCTIONS_DIR}/GalaxiaData.tar.gz') - os.rmdir(f'{const.EXTINCTIONS_DIR}/GalaxiaData/Extinction') - os.rmdir(f'{const.EXTINCTIONS_DIR}/GalaxiaData') - print('Extinction file setup complete.') + print('Extinction file setup complete.') # Grab saved data from last population, if same map used. global current_map_name, current_map_data @@ -135,7 +143,7 @@ def __init__(self, project_3d=True, dist_2d=8.15, **kwargs): else: current_map_name = self.extinction_map_name # Data from surot_A_Ks_table1.csv - tmp = pd.read_hdf(f'{const.EXTINCTIONS_DIR}/surot_A_Ks_table.h5', key='data') + tmp = pd.read_hdf(surot_map_file, key='data') #pd.read_csv(f'{const.EXTINCTIONS_DIR}/surot_A_Ks_table_2D.csv', # usecols=[0, 1, 2], sep=',', names=['l','b','A_Ks']) self.coord_tree = KDTree(np.transpose(np.array([tmp['l'],tmp['b']]))) @@ -143,16 +151,16 @@ def __init__(self, project_3d=True, dist_2d=8.15, **kwargs): current_map_data = [self.coord_tree, self.A_Ks_list] if self.project_3d: - # Set up 3D grid - mapfile_3d = ebf.read(f'{const.EXTINCTIONS_DIR}/Galaxia_ExMap3d_1024.ebf') - map_grid_3d = mapfile_3d['exmap3d.xmms'] - map_data_3d = mapfile_3d['exmap3d.data'] - # Set up 3D interpolation + # Set up 3D grid interpolation for distance scaling + mapfile_3d = h5py.File(f'{const.EXTINCTIONS_DIR}/Galaxia_ExMap3d_1024.h5', 'r') + map_grid_3d = np.array(mapfile_3d['xmms']) + map_data_3d = np.array(mapfile_3d['data']) + mapfile_3d.close() l_grid_3d = np.append(np.arange(*map_grid_3d[0]),map_grid_3d[0][1]) b_grid_3d = np.append(np.arange(*map_grid_3d[1]),map_grid_3d[1][1]) self.r_grid = 10**np.append(np.arange(*map_grid_3d[2]),map_grid_3d[2][1]) - self.grid_interpolator_3d = RegularGridInterpolator((l_grid_3d,b_grid_3d,self.r_grid), - map_data_3d, bounds_error=False, fill_value=0) + self.grid_interpolator_3d = RegularGridInterpolator((l_grid_3d,b_grid_3d,self.r_grid), + map_data_3d, bounds_error=False, fill_value=None, method='linear') def extinction_in_map(self, l_deg, b_deg, dist): """ @@ -185,4 +193,4 @@ def extinction_in_map(self, l_deg, b_deg, dist): else: scale_factor = (dist>self.dist_2d) - return ext_value * scale_factor + return ext_value * scale_factor \ No newline at end of file diff --git a/synthpop/modules/initial_final_mass_relation/__init__.py b/synthpop/modules/initial_final_mass_relation/__init__.py new file mode 100644 index 0000000..b551766 --- /dev/null +++ b/synthpop/modules/initial_final_mass_relation/__init__.py @@ -0,0 +1 @@ +from ._initial_final_mass_relation import * diff --git a/synthpop/modules/initial_final_mass_relation/_initial_final_mass_relation.py b/synthpop/modules/initial_final_mass_relation/_initial_final_mass_relation.py new file mode 100644 index 0000000..f2e31f9 --- /dev/null +++ b/synthpop/modules/initial_final_mass_relation/_initial_final_mass_relation.py @@ -0,0 +1,39 @@ +""" +This file contains the base class for the initial-final mass relation +""" + +__all__ = ["InitialFinalMassRelation"] +__author__ = "M.J. Huston" +__credits__ = ["M.J. Huston"] +__date__ = "2025-10-15" + +from typing import Union, Callable +from types import ModuleType + +import numpy as np +from scipy import integrate, interpolate +from abc import ABC, abstractmethod + + +class InitialFinalMassRelation(ABC): + """ + The initial-final mass relation (IFMR) class for Population class. + A keyword name is given upon initialization to select the form + of the IFMR. + + Methods: + -------- + process_compact_objects(mass, metallicity, age) - returns final mass + phase (remnant type) + """ + + def __init__(self, logger: ModuleType = None, **kwargs): + """ + Initialize the IFMR class for a Population class + """ + self.logger = logger + + # This is only a placeholder. The function should be defined in a subclass + @abstractmethod + def process_compact_objects(self, m_init: Union[np.ndarray, float], + feh_init: Union[np.ndarray, float]): + raise NotImplementedError('No IFMR specified') diff --git a/synthpop/modules/initial_final_mass_relation/raithel18.py b/synthpop/modules/initial_final_mass_relation/raithel18.py new file mode 100644 index 0000000..d9dd5a8 --- /dev/null +++ b/synthpop/modules/initial_final_mass_relation/raithel18.py @@ -0,0 +1,171 @@ +""" +Assign final compact object types and masses based on PopSyCLE (Rose et al 2022). +""" + +__all__ = ["Raithel18", ] +__author__ = "M.J. Huston" +__date__ = "2025-10-14" + +import pandas +import numpy as np +from ._initial_final_mass_relation import InitialFinalMassRelation +#from scipy.stats import maxwell, uniform_direction +#from synthpop.synthpop_utils.coordinates_transformation import CoordTrans +import pdb +from typing import Set, Tuple, Dict, Union + +class Raithel18(InitialFinalMassRelation): + """ + Post-processing to account for dim compact objects, based on Raithel18 PopSyCLE (Rose et al 2022). + """ + + def __init__(self, logger, ifmr_name='SukhboldN20', **kwargs): + super().__init__(logger, **kwargs) + #: initial-final mass relation name to determine compact object masses. + self.name='Raithel18' + self.spisea_ifmr_name = "IFMR_Raithel18" + + def mass_bh(self, m_zams, feh, f_ej=0.9): + """ + Black hole mass calculation for Raithel18 and SukhboldN20 + + Parameters + ---------- + m_zams + float or array of float values for initial stellar mass in units of solar mass + f_ej + float value representing the ejection fraction, + or how much of the star's envelope is ejected in the supernova + default value 0.9 adopted from Lam et al. (2020) + + Returns + ------- + m_bh + float or array of float values for final black hole mass in units of solar mass + """ + m_bh_core_i = -2.049 + 0.4140 * m_zams + m_bh_all_i = 15.52 - 0.3294 * (m_zams - 25.97) - 0.02121 * ( + m_zams - 25.97) ** 2 + 0.003120 * (m_zams - 25.97) ** 3 + # branch ii + m_bh_core_ii = 5.697 + 7.8598 * 10 ** 8 * m_zams ** -4.858 + # branch determination: 0 for i and 1 for ii + branch = (m_zams > 42.21).astype(int) + m_bh = (f_ej * m_bh_core_i + (1 - f_ej) * m_bh_all_i) * (1 - branch) + m_bh_core_ii * branch + return m_bh + + def mass_ns(self, m_zams): + """ + Neutron star final mass calculation, adopting the 1.36 Msun average + with a standard deviation of 0.09. + Based on PopSyCLE (Rose et al, 2022) + + Parameters + ---------- + m_zams + float or array of float values for initial stellar mass in units of solar mass + + Returns + ------- + m_ns + float or array of float values for final neutron star mass in units of solar mass + """ + return np.random.normal(1.36, 0.09, len(m_zams)) + + def mass_wd(self, m_zams): + """ + White dwarf final mass calculation. + Based on PopSyCLE (Rose et al. 2022) + + Parameters + ---------- + m_zams + float or array of float values for initial stellar mass in units of solar mass + + Returns + ------- + m_wd + float or array of float values for final white dwarf mass in units of solar mass + """ + return 0.109 * m_zams + 0.394 + + def compact_type_validation(self, m_type, m_prelim, m_ns): + """ + Reassign low-mass BHs to NSs + Based on PopSyCLE (Lam et al 2020; Rose et al 2022) + + Parameters + ---------- + m_type + int value for assigned compact object type + m_prelim + float value for compact object mass in units of solar mass + m_ns + float neutron star mass generated for the objects + Returns + ------- + m_type + integer value indicating updated object type + 2 = neutron star + 3 = black hole + m_compact + updated mass of NS or BH + """ + bh_to_ns = (m_type==3) & (m_prelim<3.0) + m_type[bh_to_ns] = 2 + m_final = m_prelim + m_final[bh_to_ns] = m_ns[bh_to_ns] + return m_type, m_final + + def compact_type_from_initial(self, m_zams, feh): + """ + Probabilistic drawing of compact object types + Based on PopSyCLE (Lam et al 2020; Rose et al 2022) + Which pulls from Rathiel et al 2018 and Sukhbold et al 2020 + + Parameters + ---------- + m_zams + array of float values for initial stellar mass in units of solar mass + feh + array of float values for initial metallicity [Fe/H] + Returns + ------- + m_type + array of integer values indicating object type + 0 = non-compact object or luminous white dwarf + 1 = dim white dwarf + 2 = neutron star + 3 = black hole + """ + # Draw random numbers for bins that can be either NS or BH + n_rand = np.random.uniform(size=len(m_zams)) + # Start with pre-CO objects, then go through mass bins, and assign appropriate type + result = np.zeros(len(m_zams)) + result += ((m_zams>0.5) & (m_zams<=9)) * 1 + result += ((m_zams>9) & (m_zams<=15)) * 2 + result += ((m_zams>15) & (m_zams<=17.8)) * ((n_rand<0.679)*2 + (n_rand>=0.679)*3) + result += ((m_zams>17.8) & (m_zams<=18.5)) * ((n_rand<0.833)*2 + (n_rand>=0.833)*3) + result += ((m_zams>18.5) & (m_zams<=21.7)) * ((n_rand<0.500)*2 + (n_rand>=0.500)*3) + result += ((m_zams>21.7) & (m_zams<=25.2)) * 3 + result += ((m_zams>25.2) & (m_zams<=27.5)) * ((n_rand<0.652)*2 + (n_rand>=0.652)*3) + result += ((m_zams>27.5) & (m_zams<=60)) * 3 + result += ((m_zams>60) & (m_zams<=120)) * ((n_rand<0.400)*2 + (n_rand>=0.400)*3) + return result + + def process_compact_objects(self, m_init: Union[np.ndarray, float], + feh_init: Union[np.ndarray, float]): + """ + Get the final masses and types for compact objects + """ + # Probabilistic determination of object types + m_type = self.compact_type_from_initial(m_init, feh_init) + # Get possible masses and select by type + m_wd = self.mass_wd(m_init) + m_ns = self.mass_ns(m_init) + m_bh = self.mass_bh(m_init, feh_init) + m_compact = (m_bh * (m_type == 3).astype(int) + + m_ns * (m_type == 2).astype(int) + + m_wd * (m_type == 1).astype(int)) + m_type, m_compact = self.compact_type_validation(m_type, m_compact, m_ns) + + return m_compact, 100+m_type diff --git a/synthpop/modules/initial_final_mass_relation/spera15.py b/synthpop/modules/initial_final_mass_relation/spera15.py new file mode 100644 index 0000000..59929c3 --- /dev/null +++ b/synthpop/modules/initial_final_mass_relation/spera15.py @@ -0,0 +1,140 @@ +""" +Assign final compact object types and masses based on PopSyCLE (Rose et al 2022). +""" + +__all__ = ["Spera15", ] +__author__ = "M.J. Huston" +__date__ = "2025-10-14" + +import pandas +import numpy as np +from ._initial_final_mass_relation import InitialFinalMassRelation +#from scipy.stats import maxwell, uniform_direction +#from synthpop.synthpop_utils.coordinates_transformation import CoordTrans +import pdb +from typing import Set, Tuple, Dict, Union + +class Spera15(InitialFinalMassRelation): + """ + Post-processing to account for dim compact objects, based on the Spera15 IFMR from PopSyCLE (Rose et al 2022). + """ + + def __init__(self, logger, ifmr_name='SukhboldN20', **kwargs): + super().__init__(logger, **kwargs) + #: initial-final mass relation name to determine compact object masses. + self.name='Spera15' + self.spisea_ifmr_name = "IFMR_Spera15" + + def mass_spera15(self, m_zams, feh): + """ + Remnant mass calculation from Spera et al 2015, appendix C + Takes in the m_zams and Fe/H as lists + """ + # First, calculate M_CO, based on M_ZAMS and Z + # Note: z equation from Rose et al 2022, + # with z_sun from Ekstroem et al 2012 + z = 0.014*10**feh + + # First, calculate m_co + + # C13, 14, 15 + z_cat_1 = np.array([(z>4.0e-3).astype(int), + ((z<=4.0e-3)&(z>=1.0e-3)).astype(int), + ((z<1.0e-3)).astype(int)]) + b1s = np.array([59.63 - 2.969e3*z + 4.988e4*z**2, + 40.98 + 3.415e4*z - 8.064e6*z**2, + np.repeat(67.07, len(z))]) + k1s = np.array([45.04 - 2.176e3*z + 3.806e4*z**2, + 35.17 + 1.548e4*z - 3.759e6*z**2, + np.repeat(46.89, len(z))]) + k2s = np.array([138.9 - 4.664e3*z + 5.106e4*z**2, + 20.36 + 1.162e5*z - 2.276e7*z**2, + np.repeat(113.8, len(z))]) + d1s = np.array([2.790e-2 - 1.780e-2*z + 77.05*z**2, + 2.500e-2 - 4.346*z + 1.340e3*z**2, + np.repeat(2.199e-2, len(z))]) + d2s = np.array([6.730e-3 + 2.690*z - 52.39*z**2, + 1.750e-2 + 11.39*z - 2.902e3*z**2, + np.repeat(2.602e-2, len(z))]) + + b1 = b1s[0]*z_cat_1[0] + b1s[1]*z_cat_1[1] + b1s[2]*z_cat_1[2] + k1 = k1s[0]*z_cat_1[0] + k1s[1]*z_cat_1[1] + k1s[2]*z_cat_1[2] + k2 = k2s[0]*z_cat_1[0] + k2s[1]*z_cat_1[1] + k2s[2]*z_cat_1[2] + d1 = d1s[0]*z_cat_1[0] + d1s[1]*z_cat_1[1] + d1s[2]*z_cat_1[2] + d2 = d2s[0]*z_cat_1[0] + d2s[1]*z_cat_1[1] + d2s[2]*z_cat_1[2] + + # C12 + g1 = 0.5 / (1 + 10**((k1-m_zams)*d1)) + g2 = 0.5 / (1 + 10**((k2-m_zams)*d2)) + # C11 + m_co = -2.0 + (b1+2.0)*(g1+g2) + + # Then, m_rem + # Outer z condition for C1-3 vs C4-10 + z_cat_2 = np.array([(z<=5e-4).astype(int), (z>5e-4).astype(int)]) + # M_co condition for C4 and C1 + z_cat_2_1 = np.array([(m_co<5).astype(int), ((m_co>=5) & (m_co<10)).astype(int), (m_co>=10).astype(int)]) + # Inner z condition for C8-10 + z_cat_2_2 = np.array([(z>2e-3).astype(int), ((z<=2e-3) & (z>1e-3)).astype(int), (z<=1e-2).astype(int)]) + # Inner z condition for C6-7 + z_cat_2_3 = np.array([(z>1e-3).astype(int), (z<=1e-3).astype(int)]) + + # m_rem for z<5e-4 + # C2-3 + m = -6.476e2*z + 1.911 + q = 2.300e3*z + 11.67 + p = -2.333 + 0.1559*m_co + 0.2700*m_co**2 + f = m*m_co + q + m_rem_low_z = z_cat_2_1[0] * np.maximum(p, 1.27) + \ + z_cat_2_1[1] * p + \ + z_cat_2_1[2] * np.minimum(p, f) + + # m_rem for z>=5e-4 + m = z_cat_2_2[0]*np.repeat(1.217, len(z)) + z_cat_2_2[1]*(-43.82*z + 1.340) + z_cat_2_2[2]*(-6.476e2*z + 1.911) + q = z_cat_2_2[0]*np.repeat(1.061, len(z)) + z_cat_2_2[1]*(-1.296e4*z + 26.98) + z_cat_2_2[2]*(2.300e3*z + 11.67) + a1 = z_cat_2_3[0]*(1.340 - 29.46 / (1 + (z/1.110e-3)**2.361)) + z_cat_2_3[1]*(1.105e5*z - 1.258e2) + a2 = z_cat_2_3[0]*(80.22 - 74.73 * z**0.965 / (2.720e-3 + z**0.965)) + z_cat_2_3[1]*(91.56 - 1.957e4*z - 1.558e7*z**2) + l = z_cat_2_3[0]*(5.683 + 3.533 / (1 + (z/7.430e-3)**1.993)) + z_cat_2_3[1]*(1.134e4*z - 2.143) + eta = z_cat_2_3[0]*(1.066 - 1.121 / (1 + (z/2.558e-2)**0.609)) + z_cat_2_3[1]*(3.090e-2 - 22.30*z + 7.363e4*z**2) + + h = a1 + (a2-a1)/(1+10**((l-m_co)*eta)) + f = m*m_co+q + + m_rem_high_z = z_cat_2_1[0] * np.maximum(h, 1.27) + \ + z_cat_2_1[1] * h + \ + z_cat_2_1[2] * np.maximum(h, f) + + m_rem = z_cat_2[0]*m_rem_low_z + z_cat_2[1]*m_rem_high_z + + return m_rem + + def compact_type_from_final(self, m_fin): + """ + Determination of compact object type from final mass + Based on PopSyCLE (Lam et al 2020; Rose et al 2022) + Which pulls from Spera et al 2015 + + Parameters + ---------- + m_fin + float value for final mass in units of solar mass + Returns + ------- + m_type + integer value indicating object type + 1 = dim white dwarf + 2 = neutron star + 3 = black hole + """ + return (m_fin<1.4).astype(int)*1 + ((m_fin>=1.4) & (m_fin<3)).astype(int) * 2 + (m_fin>=3).astype(int)*3 + + def process_compact_objects(self, m_init: Union[np.ndarray, float], + feh_init: Union[np.ndarray, float]): + """ + Get the final masses and types for compact objects + """ + # Cycle through evolved stars, calculating mass & type + m_compact = self.mass_spera15(m_init,feh_init) + m_type = self.compact_type_from_final(m_compact) + + return m_compact, 100+m_type diff --git a/synthpop/modules/initial_final_mass_relation/spisea_ifmr.py b/synthpop/modules/initial_final_mass_relation/spisea_ifmr.py new file mode 100644 index 0000000..a9625f6 --- /dev/null +++ b/synthpop/modules/initial_final_mass_relation/spisea_ifmr.py @@ -0,0 +1,35 @@ +""" +Assign final compact object types and masses based on SPISEA (Hosek et al 2020; Rose et al 2022). +""" + +__all__ = ["SpiseaIfmr", ] +__author__ = "M.J. Huston" +__date__ = "2025-12-10" + +import pandas +import numpy as np +from ._initial_final_mass_relation import InitialFinalMassRelation +import pdb +from typing import Set, Tuple, Dict, Union +from spisea import ifmr as spisea_ifmr + +class SpiseaIfmr(InitialFinalMassRelation): + """ + Post-processing to account for dim compact objects, based on PopSyCLE (Rose et al 2022). + + Attributes + ---------- + spisea_ifmr_name='IFMR_N20_Sukhbold' : string + selected initial-final mass relation, must exist in SPISEA + """ + + def __init__(self, logger, spisea_ifmr_name='IFMR_N20_Sukhbold', **kwargs): + super().__init__(logger, **kwargs) + #: initial-final mass relation name to determine compact object masses within SPISEA. + self.name='SpiseaIfmr' + self.spisea_ifmr = getattr(spisea_ifmr, spisea_ifmr_name)() + + def process_compact_objects(self, m_init: Union[np.ndarray, float], + feh_init: Union[np.ndarray, float]): + raise ValueError("SpiseaIfmr is only compatible with SpiseaGenerator, not StarGenerator") + return diff --git a/synthpop/modules/initial_final_mass_relation/sukhbold_n20.py b/synthpop/modules/initial_final_mass_relation/sukhbold_n20.py new file mode 100644 index 0000000..eae921e --- /dev/null +++ b/synthpop/modules/initial_final_mass_relation/sukhbold_n20.py @@ -0,0 +1,171 @@ +""" +Assign final compact object types and masses based on PopSyCLE (Rose et al 2022). +""" + +__all__ = ["SukhboldN20", ] +__author__ = "M.J. Huston" +__date__ = "2025-10-14" + +import pandas +import numpy as np +from ._initial_final_mass_relation import InitialFinalMassRelation +#from scipy.stats import maxwell, uniform_direction +#from synthpop.synthpop_utils.coordinates_transformation import CoordTrans +import pdb +from typing import Set, Tuple, Dict, Union + +class SukhboldN20(InitialFinalMassRelation): + """ + Post-processing to account for dim compact objects, the SukhboldN20 IFMR from PopSyCLE (Rose et al 2022). + """ + + def __init__(self, logger, **kwargs): + super().__init__(logger, **kwargs) + #: initial-final mass relation name to determine compact object masses. + self.name='SukhboldN20' + self.spisea_ifmr_name = "IFMR_N20_Sukhbold" + + def mass_bh(self, m_zams, feh, f_ej=0.9): + """ + Black hole mass calculation + + Parameters + ---------- + m_zams + float or array of float values for initial stellar mass in units of solar mass + f_ej + float value representing the ejection fraction, + or how much of the star's envelope is ejected in the supernova + default value 0.9 adopted from Lam et al. (2020) + + Returns + ------- + m_bh + float or array of float values for final black hole mass in units of solar mass + """ + f_z = np.minimum(10**feh, np.ones(len(feh))) + m_bh_0 = 0.4652*m_zams - 3.2917 + m_bh_zsun = -0.271*m_zams + 24.743 + branch = (m_zams > 39.6).astype(int) + m_bh_prelim = (1-branch)*m_bh_0 + branch*((1-f_z)*m_bh_0 + f_z*m_bh_zsun) + # Assign any BHs < 3.0Msun to NS instead + m_ns_backup = (m_bh_prelim<3.0).astype(int)*self.mass_ns(m_zams) + m_bh = np.maximum(m_bh_prelim, m_ns_backup) + return m_bh + + def mass_ns(self, m_zams): + """ + Neutron star final mass calculation, adopting the 1.36 Msun average + with a standard deviation of 0.09. + Based on PopSyCLE (Rose et al, 2022) + + Parameters + ---------- + m_zams + float or array of float values for initial stellar mass in units of solar mass + + Returns + ------- + m_ns + float or array of float values for final neutron star mass in units of solar mass + """ + return np.random.normal(1.36, 0.09, len(m_zams)) + + def mass_wd(self, m_zams): + """ + White dwarf final mass calculation. + Based on PopSyCLE (Rose et al. 2022) + + Parameters + ---------- + m_zams + float or array of float values for initial stellar mass in units of solar mass + + Returns + ------- + m_wd + float or array of float values for final white dwarf mass in units of solar mass + """ + return 0.109 * m_zams + 0.394 + + def compact_type_validation(self, m_type, m_prelim, m_ns): + """ + Reassign low-mass BHs to NSs + Based on PopSyCLE (Lam et al 2020; Rose et al 2022) + + Parameters + ---------- + m_type + int value for assigned compact object type + m_prelim + float value for compact object mass in units of solar mass + m_ns + float neutron star mass generated for the objects + Returns + ------- + m_type + integer value indicating updated object type + 2 = neutron star + 3 = black hole + m_compact + updated mass of NS or BH + """ + bh_to_ns = (m_type==3) & (m_prelim<3.0) + m_type[bh_to_ns] = 2 + m_final = m_prelim + m_final[bh_to_ns] = m_ns[bh_to_ns] + return m_type, m_final + + def compact_type_from_initial(self, m_zams, feh): + """ + Probabilistic drawing of compact object types + Based on PopSyCLE (Lam et al 2020; Rose et al 2022) + Which pulls from Rathiel et al 2018 and Sukhbold et al 2020 + + Parameters + ---------- + m_zams + array of float values for initial stellar mass in units of solar mass + feh + array of float values for initial metallicity [Fe/H] + Returns + ------- + m_type + array of integer values indicating object type + 0 = non-compact object or luminous white dwarf + 1 = dim white dwarf + 2 = neutron star + 3 = black hole + """ + # Get value for metallicity dependence + f_z = np.minimum(10**feh, np.ones(len(feh))) + # Draw random numbers for bins that can be either NS or BH + n_rand = np.random.uniform(size=len(m_zams)) + # Start with pre-CO objects, then go through mass bins, and assign appropriate type + result = np.zeros(len(m_zams)) + result += ((m_zams>0.5) & (m_zams<=9)) * 1 + result += ((m_zams>9) & (m_zams<=15)) * 2 + result += ((m_zams>15) & (m_zams<=21.8)) * ((n_rand<0.75)*2 + (n_rand>=0.75)*3) + result += ((m_zams>21.8) & (m_zams<=25.2)) * 3 + result += ((m_zams>25.2) & (m_zams<=27.4)) * 2 + result += ((m_zams>27.4) & (m_zams<=60)) * 3 + result += ((m_zams>60) & (m_zams<=120)) * ((n_rand<0.80*f_z)*2 + (n_rand>=0.80*f_z)*3) + return result + + def process_compact_objects(self, m_init: Union[np.ndarray, float], + feh_init: Union[np.ndarray, float]): + """ + Get the final masses and types for compact objects + """ + # Probabilistic determination of object types + m_type = self.compact_type_from_initial(m_init, feh_init) + # Get possible masses and select by type + m_wd = self.mass_wd(m_init) + m_ns = self.mass_ns(m_init) + m_bh = self.mass_bh(m_init, feh_init) + m_compact = (m_bh * (m_type == 3).astype(int) + + m_ns * (m_type == 2).astype(int) + + m_wd * (m_type == 1).astype(int)) + m_type, m_compact = self.compact_type_validation(m_type, m_compact, m_ns) + + return m_compact, 100+m_type diff --git a/synthpop/modules/initial_mass_function/_initial_mass_function.py b/synthpop/modules/initial_mass_function/_initial_mass_function.py index bbba22b..0bd2f3b 100644 --- a/synthpop/modules/initial_mass_function/_initial_mass_function.py +++ b/synthpop/modules/initial_mass_function/_initial_mass_function.py @@ -43,6 +43,7 @@ def __init__(self, min_mass=None, max_mass=None, logger: ModuleType = None): """ Initialize the IMF class for a Population class """ + self.spisea_imf = None self.logger = logger # default mass limits if min_mass is None: min_mass = 0.01 diff --git a/synthpop/modules/initial_mass_function/piecewise_powerlaw.py b/synthpop/modules/initial_mass_function/piecewise_powerlaw.py index 61d42ba..78033f8 100644 --- a/synthpop/modules/initial_mass_function/piecewise_powerlaw.py +++ b/synthpop/modules/initial_mass_function/piecewise_powerlaw.py @@ -44,7 +44,7 @@ def __init__( alphas: tuple[float] = (1), splitpoints: tuple[float] = (), **kwargs ): super().__init__(min_mass, max_mass) - self.imf_name = 'Piecewise Powerlaw' + self.imf_name = 'PiecewisePowerlaw' self.alphas = alphas self.splitpoints = splitpoints diff --git a/synthpop/modules/initial_mass_function/single_value.py b/synthpop/modules/initial_mass_function/single_value.py new file mode 100644 index 0000000..6e5bad2 --- /dev/null +++ b/synthpop/modules/initial_mass_function/single_value.py @@ -0,0 +1,61 @@ +""" +Single value IMF (made for PBHs) +""" + +__all__ = ['SingleValue', ] + +import numpy as np +from scipy.special import erf, erfinv + +try: + from ._initial_mass_function import InitialMassFunction +except ImportError: + from _initial_mass_function import InitialMassFunction + + +class SingleValue(InitialMassFunction): + """ + Single value initial mass function (IMF) + """ + + def __init__( + self, mass: float, **kwargs + ): + """ + Parameters + ---------- + mass : float [Msun] + mass value for all objects + """ + super().__init__(min_mass, max_mass) + self.imf_name = 'SingleValue' + + # setup control parameters + self.mass = mass + + def imf(self, m_in): + """ initial mass function """ + if not isinstance(m_in, np.ndarray): + m = np.array([m_in]) + else: + m = m_in + # 0.4342.. == 1/ln(10) + prob = m==self.mass + if not isinstance(m_in, np.ndarray): + return prob[0] + return prob + + def average_mass(self, + min_mass: Union[np.ndarray, float, None] = None, + max_mass: Union[float, None] = None + ) -> float: + return self.mass + + def draw_random_mass( + self, + min_mass: Union[np.ndarray, float, None] = None, + max_mass: Union[float, None] = None, + N: Union[float, None] = None + ) -> Union[np.ndarray, float]: + + return np.ones(N)*self.mass diff --git a/synthpop/modules/initial_mass_function/spisea_imf.py b/synthpop/modules/initial_mass_function/spisea_imf.py new file mode 100644 index 0000000..ce39948 --- /dev/null +++ b/synthpop/modules/initial_mass_function/spisea_imf.py @@ -0,0 +1,82 @@ +""" +Initial mass function for a piecewise power law, e.g.: + + For M ≤ m1: ξ(M) ∝ M^-a0 + + For m1 < M ≤ m2: ξ(M) ∝ M^-a1 + + For m2 < M: ξ(M) ∝ M^-a2 + +The number of splitpoints is modifiable. +""" + +__all__ = ["SpiseaImf", ] +__date__ = "2025-12-10" + +import numpy as np + +try: + from ._initial_mass_function import InitialMassFunction +except ImportError: + from _initial_mass_function import InitialMassFunction + +from typing import Callable +from spisea.imf import imf as spisea_imf +from spisea.imf import spisea_multiplicity as spisea_multiplicity + +class SpiseaImf(InitialMassFunction): + """ + Initial mass function generator for a piecewise power law + + Attributes: + ----------- + min_mass : float + lower initial mass limit + max_mass : float + upper initial mass limit + alphas : ndarray [float] + power law indices for the mass chunks from lower mass to higher + splitpoints : ndarray [float] + mass values where pieces split; + length must be length alphas minus 1 + """ + + def __init__(self, min_mass=None, max_mass=None, spisea_imf_name='Kroupa_2001', spisea_multiplicity_name=None, + spisea_imf_kwargs={}): + super().__init__(min_mass, max_mass) + self.imf_name = 'SpiseaImf' + self.min_mass=min_mass + self.max_mass=max_mass + self.spisea_imf_kwargs = spisea_imf_kwargs + self.spisea_imf_name = spisea_imf_name + self.spisea_multiplicity = None + if spisea_multiplicity_name is not None: + self.spisea_multiplicity = getattr(spisea_multiplicity, spisea_multiplicity_name) + self.spisea_imf = getattr(spisea_imf, spisea_imf_name)(massLimits=np.array([min_mass, max_mass]), + multiplicity=self.spisea_multiplicity, **spisea_imf_kwargs) + + # returns the number of stars of that initial mass + def imf(self, m_in): + """ + Initial mass function + + Parameters + ---------- + m_in: initial mass + + Returns + ------- + prob: probability at the initial mass + + """ + if not isinstance(m_in, np.ndarray): + m = np.array([m_in]) + else: + m = m_in + + prob = np.sum([i(m) for i in self.imf_parts], axis=0) + + if not isinstance(m_in, np.ndarray): + return prob[0] + return prob + diff --git a/synthpop/modules/kinematics/kinematics_from_grid.py b/synthpop/modules/kinematics/kinematics_from_grid.py index 7f1fd0f..bd63b7d 100644 --- a/synthpop/modules/kinematics/kinematics_from_grid.py +++ b/synthpop/modules/kinematics/kinematics_from_grid.py @@ -1,5 +1,6 @@ """ -Kinematic module that interpolates values from a grid +Kinematic module that interpolates values from a grid. Created for NSD and NSC models +tabulated from AGAMA, but can use other files with the same regular grid format. """ __all__ = ['KinematicsFromGrid'] @@ -13,7 +14,7 @@ from .. import const from ._kinematics import Kinematics from .. import default_sun -from scipy.interpolate import LinearNDInterpolator +from scipy.interpolate import RegularGridInterpolator class KinematicsFromGrid(Kinematics): """ @@ -38,14 +39,22 @@ def __init__( # Open the file and create interpolators for rotational velocity and velocity dispersions dat = pd.read_csv(const.MOMENTS_DIR + '/' + moment_file, sep='\s+', comment='#') - self.interpolate_v_phi = LinearNDInterpolator(list(zip(dat['r'],dat['z'])), - dat['v_phi'], fill_value=0.0, rescale=False) - self.interpolate_sigma_phi = LinearNDInterpolator(list(zip(dat['r'],dat['z'])), - dat['sigma_phi'], fill_value=0.0, rescale=False) - self.interpolate_sigma_r = LinearNDInterpolator(list(zip(dat['r'],dat['z'])), - dat['sigma_r'], fill_value=0.0, rescale=False) - self.interpolate_sigma_z = LinearNDInterpolator(list(zip(dat['r'],dat['z'])), - dat['sigma_z'], fill_value=0.0, rescale=False) + v_phi = dat.pivot(index='r', columns='z', values='v_phi') + sigma_phi = dat.pivot(index='r', columns='z', values='sigma_phi') + sigma_r = dat.pivot(index='r', columns='z', values='sigma_r') + sigma_z = dat.pivot(index='r', columns='z', values='sigma_z') + self.interpolate_v_phi = RegularGridInterpolator((v_phi.index.to_numpy(), + v_phi.columns.to_numpy()), v_phi.to_numpy(), + bounds_error=False, fill_value=None) + self.interpolate_sigma_phi = RegularGridInterpolator((sigma_phi.index.to_numpy(), + sigma_phi.columns.to_numpy()), sigma_phi.to_numpy(), + bounds_error=False, fill_value=None) + self.interpolate_sigma_r = RegularGridInterpolator((sigma_r.index.to_numpy(), + sigma_r.columns.to_numpy()), sigma_r.to_numpy(), + bounds_error=False, fill_value=None) + self.interpolate_sigma_z = RegularGridInterpolator((sigma_z.index.to_numpy(), + sigma_z.columns.to_numpy()), sigma_z.to_numpy(), + bounds_error=False, fill_value=None) self.kinematics_func_name = 'kinematics_from_grid' self.sun = sun if sun is not None else default_sun @@ -71,10 +80,10 @@ def draw_random_velocity( r, phi_rad, z = self.coord_trans.xyz_to_rphiz(x, y, z) absz = np.abs(z) - sigma_r = self.interpolate_sigma_r(list(zip(r,absz))) - sigma_phi = self.interpolate_sigma_phi(list(zip(r,absz))) - sigma_z = self.interpolate_sigma_z(list(zip(r,absz))) - v_rot = self.interpolate_v_phi(list(zip(r,absz))) + sigma_r = self.interpolate_sigma_r(np.column_stack([r,absz])) + sigma_phi = self.interpolate_sigma_phi(np.column_stack([r,absz])) + sigma_z = self.interpolate_sigma_z(np.column_stack([r,absz])) + v_rot = self.interpolate_v_phi(np.column_stack([r,absz])) # Draw random deviations from circular velocity dv_r = np.random.normal(0, sigma_r) @@ -91,3 +100,4 @@ def draw_random_velocity( w = dv_z return u, v, w + diff --git a/synthpop/modules/kinematics/koshimoto2021_bulge.py b/synthpop/modules/kinematics/koshimoto2021_bulge.py index 247796f..2ced9ee 100644 --- a/synthpop/modules/kinematics/koshimoto2021_bulge.py +++ b/synthpop/modules/kinematics/koshimoto2021_bulge.py @@ -17,7 +17,7 @@ class Koshimoto2021Bulge(Kinematics): def __init__( self, v0_stream, y0_stream, C_par_r, C_perp_r, C_par_z, C_perp_z, h0_r, h0_z, sigma_i0, - sigma_i1, omega_p, bar_angle=27, **kwargs + sigma_i1, omega_p, bar_angle=27, bar_plane_angle=0, **kwargs ): super().__init__(**kwargs) # initialises self.coord_transform & self.density_class self.v0_stream = v0_stream # km/s @@ -32,7 +32,7 @@ def __init__( self.sigma_i1 = sigma_i1 # km/s self.omega_p = omega_p # km/s/kpc self.bar_ang = bar_angle*np.pi/180 #radians - + self.bar_plane_ang = bar_plane_angle*np.pi/180 def vel_disp(self, xp, yp, zp, i): if i < 2: @@ -67,15 +67,18 @@ def draw_random_velocity(self, x, y, z, **kwargs): R = np.sqrt(x ** 2 + y ** 2) # Rotate to be in plane of galactic bar -> xp axis aligned with major axis of bar alpha=self.bar_ang - xp = x * np.cos(alpha) - y * np.sin(alpha) - yp = x * np.sin(alpha) + y * np.cos(alpha) - zp = z + xp0 = x * np.cos(alpha) - y * np.sin(alpha) + yp0 = x * np.sin(alpha) + y * np.cos(alpha) + zp0 = z + xp = xp0 * np.cos(self.bar_plane_ang) + zp0 * np.sin(self.bar_plane_ang) + yp = yp0 + zp = - xp0 * np.sin(self.bar_plane_ang) + zp0 * np.cos(self.bar_plane_ang) # Stream velocityy v_x_stream = self.v0_stream * (1 - np.exp(-(yp / self.y0_stream)**2)) * (-1) ** ( 1 - (yp > 0).astype(int)) # Solid body velocity - v_y_sb = self.omega_p * R * (-1) ** (1 - (xp < 0).astype(int)) + v_sb = self.omega_p * R # velocity dispersions sigma_x = self.vel_disp(abs(xp), abs(yp), abs(zp), 0) @@ -88,8 +91,8 @@ def draw_random_velocity(self, x, y, z, **kwargs): dvz = np.random.normal(0, sigma_z) # Calculate velocities in bar frame - vxp = v_x_stream + dvx - vyp = v_y_sb + dvy + vxp = v_sb*(yp/R) + v_x_stream + dvx + vyp = v_sb*(-xp/R) + dvy vzp = dvz # Convert back into Galactic frame @@ -101,7 +104,7 @@ def draw_random_velocity(self, x, y, z, **kwargs): return vx, vy, vz - def get_mean_velocity(self, x, y, z, **kwargs): + def mean_galactic_uvw(self, x, y, z, **kwargs): """ Generate a random u,v,w velocity vector given galactic x,y,z coordinates @@ -130,19 +133,17 @@ def get_mean_velocity(self, x, y, z, **kwargs): v_x_stream = self.v0_stream * (1 - np.exp(-(yp / self.y0_stream)**2)) * (-1) ** ( 1 - (yp > 0).astype(int)) # Solid body velocity - v_y_sb = self.omega_p * R * (-1) ** (1 - (xp < 0).astype(int)) + v_sb = self.omega_p * R # Calculate velocities in bar frame - vxp = v_x_stream - vyp = v_y_sb + vxp = v_sb*(yp/R) + v_x_stream + vyp = v_sb*(-xp/R) vzp = np.repeat(0, len(z)) - # Convert back into Galactic frame + #Convert back into Galactic frame rot = -alpha vx = vxp * np.cos(rot) - vyp * np.sin(rot) vy = vxp * np.sin(rot) + vyp * np.cos(rot) vz = vzp - return vx, vy, vz - - + return vx, vy, vz \ No newline at end of file diff --git a/synthpop/modules/kinematics/koshimoto2021_disk.py b/synthpop/modules/kinematics/koshimoto2021_disk.py index 5012d85..44d50fa 100644 --- a/synthpop/modules/kinematics/koshimoto2021_disk.py +++ b/synthpop/modules/kinematics/koshimoto2021_disk.py @@ -87,7 +87,7 @@ def draw_random_velocity( # Convert to Galactocentric coordinates r, phi_rad, z = self.coord_trans.xyz_to_rphiz(x, y, z) - # Set parameters from Koshimoto+21 + # Set fixed parameters from Koshimoto+21 T_min, T_max = 0.01, 10 R_d = 2.6 c1,c2,c3,c4 = 3.822, 0.524, 0.00567, 2.13 diff --git a/synthpop/modules/metallicity/double_gaussian.py b/synthpop/modules/metallicity/double_gaussian.py index 7614ba7..5e89d34 100644 --- a/synthpop/modules/metallicity/double_gaussian.py +++ b/synthpop/modules/metallicity/double_gaussian.py @@ -21,7 +21,7 @@ class DoubleGaussian(Metallicity): metallicity_func_name : string A class attribute for the name of the _MetallicityBase subclass that this is. weight : float - percentage of stars belonging to the first Gaussian distribution ( A1/(A1+A2)) + fraction of stars belonging to the first Gaussian distribution ( A1/(A1+A2)) mean1 : float [[Fe/H]] the mean value of the first Gaussian distribution std1 : float [[Fe/H]] diff --git a/synthpop/modules/multiplicity/__init__.py b/synthpop/modules/multiplicity/__init__.py new file mode 100644 index 0000000..172db69 --- /dev/null +++ b/synthpop/modules/multiplicity/__init__.py @@ -0,0 +1 @@ +from ._multiplicity import * diff --git a/synthpop/modules/multiplicity/_multiplicity.py b/synthpop/modules/multiplicity/_multiplicity.py new file mode 100644 index 0000000..d399e34 --- /dev/null +++ b/synthpop/modules/multiplicity/_multiplicity.py @@ -0,0 +1,37 @@ +""" +This file contains the base class for multiplicity +""" + +__all__ = ["Multiplicity"] +__author__ = "M.J. Huston" +__credits__ = ["M.J. Huston"] +__date__ = "2025-10-15" + +from typing import Union, Callable +from types import ModuleType + +import numpy as np +from scipy import integrate, interpolate +from abc import ABC, abstractmethod + + +class Multiplicity(ABC): + """ + Multiplicity base class + + Methods: + -------- + generate_companions + """ + + def __init__(self, logger: ModuleType = None, **kwargs): + """ + Initialize the IFMR class for a Population class + """ + self.logger = logger + + # This is only a placeholder. The function should be defined in a subclass + @abstractmethod + def generate_companions(self, m_init: Union[np.ndarray, float], + feh_init: Union[np.ndarray, float]): + raise NotImplementedError('No Multiplicity specified') diff --git a/synthpop/modules/multiplicity/raghavan.py b/synthpop/modules/multiplicity/raghavan.py new file mode 100644 index 0000000..3d0cb5e --- /dev/null +++ b/synthpop/modules/multiplicity/raghavan.py @@ -0,0 +1,146 @@ +""" +Binary companion generator, based on Raghavan et al. 2010 + +NOTE: The eccentricity is very hacky, there's not mathematical +ditstribution provided in the paper. +""" + +__all__ = ["Multiplicity"] +__author__ = "M. Newman, M.J. Huston" +__credits__ = ["M. Newman, M.J. Huston"] +__date__ = "2025-10-15" + +import pandas as pd +import numpy as np +from ._multiplicity import Multiplicity +import pdb +import synthpop.constants as const + +try: + from constants import (SYNTHPOP_DIR, DEFAULT_MODEL_DIR, DEFAULT_CONFIG_FILE, DEFAULT_CONFIG_DIR) +except (ImportError, ValueError): + from synthpop.constants import (SYNTHPOP_DIR, DEFAULT_MODEL_DIR, DEFAULT_CONFIG_FILE, DEFAULT_CONFIG_DIR) + +class Raghavan(Multiplicity): + def __init__(self, **kwargs): + """ + Hi + """ + super().__init__(**kwargs) + self.name='Raghavan' + + #@staticmethod + def check_is_binary(self, pri_masses): + """ + Probabilistic determination of binary star status based on temperature bins and probabilities. + + Parameters + ---------- + pri_masses + masses of the primaries + + Returns + ------- + is_binary + array of boolean values indicating whether each star is a binary + """ + + # Function from Duchene 2013 + binary_frac = (pri_masses<=0.1)*0.2 + \ + (pri_masses>0.1)*0.3836*pri_masses**0.27 + + is_binary = np.random.rand(len(pri_masses)) < binary_frac + + return (is_binary, binary_frac) + + #@staticmethod + def draw_companion_m_ratios(self, n): + """ + Mass ratio (M2/M1) calculation from toy probability function based on Figure 16 of Raghavan et. al 2010 + + Returns + ------- + random_mass_ratio + float value for the mass ratio (M2/M1) of the binary system + """ + # Normalization factor (maximum value of N on Figure 16 of Raghavan 2010) + #normalization_factor = 13 + + # Separate systems into 3 different sections based on Raghavan 2010, Figure 16 + probability_bins = [0.103743, 0.778075+0.103743, 1.] # Add previous bins to get *cumulative* values + random_numbers = np.random.rand(n) + bin_nos = np.searchsorted(probability_bins, random_numbers) + + random_mass_ratio = np.zeros(n) + random_mass_ratio[bin_nos==0] = np.sqrt(2*np.random.rand(len(np.where(bin_nos==0)[0]))/50) + random_mass_ratio[bin_nos==1] = np.random.uniform(0.2, 0.95, len(np.where(bin_nos==1)[0])) + random_mass_ratio[bin_nos==2] = np.random.uniform(0.95, 1.0, len(np.where(bin_nos==2)[0])) + + return random_mass_ratio + + def draw_periods(self, n): + """ + Find binary orbital period from Gaussian distribution in Figure 13 of Raghavan et al. 2010 + + Returns + ------- + periods + float value of the period in days + """ + # Draw a log(Period) from the Raghavan Figure 13 Gaussian + logP = np.repeat(-1, n) + mu = 5.03 # Value from Raghavan Figure 13 + sigma = 2.28 # Value from Raghavan Figure 13 + logP = np.random.normal(loc = mu, scale = sigma, size=n) + + # Keep drawing if we get a period less than 1 day (logP < 0) + while np.any(logP <= 0) or np.any(logP >= 9): + redraw_idx = ((logP <= 0) | (logP >= 9)) + logP[redraw_idx] = np.random.normal(loc=mu, scale=sigma, size=len(np.where(redraw_idx)[0])) + + return 10**logP + + def draw_eccentricities(self, periods): + """ + Draw random eccentricities based on periods + """ + e_arr = np.zeros(len(periods)) + not_circularized = (periods > 12) + n_draw = np.sum(not_circularized) + n_rand = np.random.rand(n_draw) + e_drawn = n_rand * 0.6/0.8 * (n_rand<=0.8) + (1-np.sqrt((1-n_rand)/0.2)*0.4)*(n_rand>0.8) + e_arr[not_circularized] = e_drawn + return e_arr + + def generate_companions(self, pri_masses): + """ + Generates companion stars + + Parameters + ---------- + pri_masses : ndarray + primary star masses + + Returns + ------- + companions_table : dataframe + companion properties + """ + + n_pri_stars = len(pri_masses) + + # Identify primary stars with companions + (binary_flags, binary_frac) = self.check_is_binary(pri_masses) + n_sec_stars = sum(binary_flags) + pri_id = np.arange(n_pri_stars)[binary_flags] + pri_masses_with_sec = pri_masses[binary_flags] + + # Draw an initial mass + mass_ratios = self.draw_companion_m_ratios(n_sec_stars) + sec_masses = mass_ratios * pri_masses_with_sec + + # Draw periods for binary stars + periods = self.draw_periods(n_sec_stars) + eccentricities = self.draw_eccentricities(periods) + + return pri_id, sec_masses, periods, eccentricities diff --git a/synthpop/modules/multiplicity/spisea_multiplicity.py b/synthpop/modules/multiplicity/spisea_multiplicity.py new file mode 100644 index 0000000..0a0a149 --- /dev/null +++ b/synthpop/modules/multiplicity/spisea_multiplicity.py @@ -0,0 +1,36 @@ +""" +Spisea multiplicity class holder +""" + +__all__ = ["SpiseaMultiplicity"] +__author__ = "M.J. Huston" +__credits__ = ["M. Newman, M.J. Huston"] +__date__ = "2025-10-15" + +import pandas as pd +import numpy as np +from ._multiplicity import Multiplicity +import pdb +import synthpop.constants as const +from spisea.imf import multiplicity as spisea_multiplicity + +try: + from constants import (SYNTHPOP_DIR, DEFAULT_MODEL_DIR, DEFAULT_CONFIG_FILE, DEFAULT_CONFIG_DIR) +except (ImportError, ValueError): + from synthpop.constants import (SYNTHPOP_DIR, DEFAULT_MODEL_DIR, DEFAULT_CONFIG_FILE, DEFAULT_CONFIG_DIR) + +class SpiseaMultiplicity(Multiplicity): + def __init__(self, spisea_multiplicity_name="MultiplicityResolvedDK", spisea_multiplicity_kwargs={}, **kwargs): + """ + Hi + """ + super().__init__(**kwargs) + self.name='SpiseaMultiplicity' + self.spisea_multiplicity = getattr(spisea_multiplicity, spisea_multiplicity_name)(**spisea_multiplicity_kwargs) + + def generate_companions(self, pri_masses): + """ + not valid here + """ + raise ValueError("SpiseaMultiplicity can only be used by SpiseaGenerator") + diff --git a/synthpop/modules/population_density/_population_density.py b/synthpop/modules/population_density/_population_density.py index bd4b18e..55b8b56 100644 --- a/synthpop/modules/population_density/_population_density.py +++ b/synthpop/modules/population_density/_population_density.py @@ -12,6 +12,9 @@ import numpy as np from .. import const, default_sun +from ... import synthpop_utils as sp_utils +import pdb +from scipy.integrate import cumulative_trapezoid, trapezoid class PopulationDensity(ABC): """ @@ -40,11 +43,13 @@ class PopulationDensity(ABC): def __init__(self, sun: ModuleType = None, - coord_trans: ModuleType = None, + coord_trans: ModuleType = sp_utils.coordinates_transformation.CoordTrans(), gamma_flare: float = None, h_flare: float = None, radius_flare: float = 0, + grid_resolution: float = 0.010, logger: ModuleType = None, + max_gc_dist: float = None, **kwargs): """ Initialize the Population Density class @@ -58,9 +63,11 @@ def __init__(self, coord_trans: ModuleType the coordinate transformation package see synthpop_utils/coordinate_transformations - gamma_flare, radius_flare: float + gamma_flare, h_flare, radius_flare: float parameters to implement the flare of the milky way - + grid_resolution: float + required spatial resolution in kpc for the density integration/interpolation + grid. for angular dimensions, use this cutoff at 10 kpc or max_distance if shorter. """ # sun sun sun, here it comes self.logger = logger @@ -73,6 +80,23 @@ def __init__(self, self.gamma_flare = 0 if gamma_flare is None and h_flare is None else gamma_flare self.h_flare = h_flare self.radius_flare = radius_flare + + # define coordinates of center of the field in degrees and radians + self.l_deg = None + self.l_rad = None + self.b_deg = None + self.b_rad = None + # define the field + self.field_shape = None + self.lb_radius_deg = None + self.l_length_deg = None + self.b_length_deg = None + self.required_grid_resolution = grid_resolution + self.initial_grid_resolution = np.maximum(0.01, grid_resolution) + + # Set limits for where we need to look for stars + self.max_gc_dist = max_gc_dist + @abstractmethod def density(self, r: np.ndarray, phi_rad: np.ndarray, z: np.ndarray) -> np.ndarray: """ @@ -102,7 +126,7 @@ def get_kappa_flare(self, h_flare: float = None, radius_flare: float = None ) -> np.ndarray or float: """ - Estimates the correction factor for the Warp. + Estimates the correction factor for the flare. The scale height should then be multiplied by kappa_flare Parameters @@ -173,3 +197,632 @@ def gradient(self, r_kpc: np.ndarray or float, - self.density(r_kpc, phi_rad, z_kpc - eps[2])) / (2 * eps[2]) return dRho_dR, dRho_dPhi, dRho_dz + + def update_location(self, l_deg: float, b_deg: float, field_shape: str, field_scale_deg: float, + max_distance: float): + """ + Set the location and solid_angle + + Parameters + ---------- + l_deg, b_deg : float [deg] + galactic longitude and latitude in degrees + solid_angle : float [sr] + size of the cone + """ + self.l_deg = l_deg + self.l_rad = l_deg * np.pi / 180. + self.b_deg = b_deg + self.b_rad = b_deg * np.pi / 180. + self.field_shape = field_shape + # convert solid angle to half cone angle using wiki formula: + if self.field_shape=='circle': + self.lb_radius_deg = field_scale_deg + elif self.field_shape=='box': + if isinstance(field_scale_deg, (list, tuple, set)): + self.l_length_deg = field_scale_deg[0] + self.b_length_deg = field_scale_deg[1] + else: + self.l_length_deg = field_scale_deg + self.b_length_deg = field_scale_deg + self.max_distance = max_distance + self.current_grid_resolution = self.initial_grid_resolution + + if self.logger is not None: + self.logger.debug("setting up density grid") + + #Distance initial bounds-- not dependent on field shape + d_min, d_max = 0, self.max_distance + if self.max_gc_dist is not None: + # Apply a 5% buffer to be safe + d_min = self.sun.gal_dist-self.max_gc_dist*1.05 + d_max = self.sun.gal_dist+self.max_gc_dist*1.05 + + if self.field_shape == 'circle': + self.make_density_grid_circle(d_min, d_max, 0, 2*np.pi, 0, self.lb_radius_deg*np.pi/180) + if self.total_mass>0.0: + # Adjust distance grid for zero density regions if relevant + nz_indices = np.flatnonzero(self.density_int_st_dir) + dmin_idx = np.maximum(nz_indices[0]-1, 0) + dmax_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_d_pts)) + nz_indices = np.flatnonzero(trapezoid(self.density_int_st_rad, x=self.density_grid_d_pts, axis=1)) + stdir_min_idx = np.maximum(nz_indices[0]-1, 0) + stdir_max_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_st_dir)-1) + nz_indices = np.flatnonzero(trapezoid(trapezoid(self.density_grid_vscaled, x=self.density_grid_st_dir, + axis=0), x=self.density_grid_d_pts, axis=0)) + strad_min_idx = np.maximum(nz_indices[0]-1, 0) + strad_max_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_st_rad)-1) + while dmin_idx>0 or dmax_idx<(len(self.density_grid_d_pts)-1) \ + or stdir_min_idx>0 or stdir_max_idx<(len(self.density_grid_st_dir)-1) \ + or strad_min_idx>0 or strad_max_idx<(len(self.density_grid_st_rad)-1) : + self.current_grid_resolution = np.maximum(self.current_grid_resolution/3, + self.current_grid_resolution*(dmax_idx-dmin_idx)/len(self.density_grid_d_pts)) + self.make_density_grid_circle(self.density_grid_d_pts[dmin_idx], self.density_grid_d_pts[dmax_idx], + self.density_grid_st_dir[stdir_min_idx], self.density_grid_st_dir[stdir_max_idx], + self.density_grid_st_rad[strad_min_idx], self.density_grid_st_rad[strad_max_idx]) + nz_indices = np.flatnonzero(self.density_int_st_dir) + dmin_idx = np.maximum(nz_indices[0]-1, 0) + dmax_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_d_pts)) + nz_indices = np.flatnonzero(trapezoid(self.density_int_st_rad, x=self.density_grid_d_pts, axis=1)) + stdir_min_idx = np.maximum(nz_indices[0]-1, 0) + stdir_max_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_st_dir)-1) + nz_indices = np.flatnonzero(trapezoid(trapezoid(self.density_grid_vscaled, x=self.density_grid_st_dir, + axis=0), x=self.density_grid_d_pts, axis=0)) + strad_min_idx = np.maximum(nz_indices[0]-1, 0) + strad_max_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_st_rad)-1) + #pdb.set_trace() + # If needed, zoom to required resolution + if self.current_grid_resolution>self.required_grid_resolution: + self.current_grid_resolution = self.required_grid_resolution + self.make_density_grid_circle(self.density_grid_d_pts[dmin_idx], self.density_grid_d_pts[dmax_idx], + self.density_grid_st_dir[stdir_min_idx], self.density_grid_st_dir[stdir_max_idx], + self.density_grid_st_rad[strad_min_idx], self.density_grid_st_rad[strad_max_idx]) + + elif self.field_shape == 'box': + dl_min, dl_max = -self.l_length_deg/2 * np.pi/180, self.l_length_deg/2 * np.pi/180 + db_min, db_max = -self.b_length_deg/2 * np.pi/180, self.b_length_deg/2 * np.pi/180 + # Create the initial density grid + self.make_density_grid_box(d_min, d_max, dl_min, dl_max, db_min, db_max) + if (self.total_mass>0.0): + # Adaptively adjust distance grid for zero density regions if relevant + nz_indices = np.flatnonzero(self.density_int_l) + dmin_idx = np.maximum(nz_indices[0]-1, 0) + dmax_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_d_pts)-1) + nz_indices = np.flatnonzero(trapezoid(self.density_int_b, x=self.density_grid_d_pts, axis=1)) + lmin_idx = np.maximum(nz_indices[0]-1, 0) + lmax_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_dl_pts)-1) + nz_indices = np.flatnonzero(trapezoid(trapezoid(self.density_grid_vscaled, x=self.density_grid_dl_pts, + axis=0), x=self.density_grid_d_pts, axis=0)) + bmin_idx = np.maximum(nz_indices[0]-1, 0) + bmax_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_db_pts)-1) + while dmin_idx>0 or dmax_idx<(len(self.density_grid_d_pts)-1) \ + or lmin_idx>0 or lmax_idx<(len(self.density_grid_dl_pts)-1) \ + or bmin_idx>0 or bmax_idx<(len(self.density_grid_db_pts)-1): + self.current_grid_resolution = np.maximum(self.current_grid_resolution/3, + self.current_grid_resolution*(dmax_idx-dmin_idx)/len(self.density_grid_d_pts)) + self.make_density_grid_box(self.density_grid_d_pts[dmin_idx], self.density_grid_d_pts[dmax_idx], + self.density_grid_dl_pts[lmin_idx], self.density_grid_dl_pts[lmax_idx], + self.density_grid_db_pts[bmin_idx], self.density_grid_db_pts[bmax_idx]) + nz_indices = np.flatnonzero(self.density_int_l) + dmin_idx = np.maximum(nz_indices[0]-1, 0) + dmax_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_d_pts)-1) + nz_indices = np.flatnonzero(trapezoid(self.density_int_b, x=self.density_grid_d_pts, axis=1)) + lmin_idx = np.maximum(nz_indices[0]-1, 0) + lmax_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_dl_pts)-1) + nz_indices = np.flatnonzero(trapezoid(trapezoid(self.density_grid_vscaled, x=self.density_grid_dl_pts, + axis=0), x=self.density_grid_d_pts, axis=0)) + bmin_idx = np.maximum(nz_indices[0]-1, 0) + bmax_idx = np.minimum(nz_indices[-1]+1,len(self.density_grid_db_pts)-1) + # If needed, get into required resolution + if self.current_grid_resolution>self.required_grid_resolution: + self.current_grid_resolution = self.required_grid_resolution + self.make_density_grid_box(self.density_grid_d_pts[dmin_idx], self.density_grid_d_pts[dmax_idx], + self.density_grid_dl_pts[lmin_idx], self.density_grid_dl_pts[lmax_idx], + self.density_grid_db_pts[bmin_idx], self.density_grid_db_pts[bmax_idx]) + + def make_density_grid_circle(self, d_min, d_max, stdir_min, stdir_max, strad_min, strad_max): + # Set resolution + grid_d_n_pts = int(np.ceil((d_max-d_min)/self.current_grid_resolution)) + 1 + grid_d_n_pts = np.maximum(grid_d_n_pts, 51) + self.density_grid_d_pts = np.linspace(d_min, d_max, grid_d_n_pts) + + # Set up a grid + res_dist = np.minimum(self.sun.gal_dist,d_max) + grid_st_dir_n_pts = int(np.ceil(res_dist*strad_max/self.current_grid_resolution)) + grid_st_dir_n_pts = np.maximum(grid_st_dir_n_pts, 50) + self.density_grid_st_dir = np.linspace(stdir_min, stdir_max, grid_st_dir_n_pts) + grid_st_rad_n_pts = int(np.ceil(res_dist*(strad_max-strad_min)/self.current_grid_resolution)) + grid_st_rad_n_pts = np.maximum(grid_st_rad_n_pts,25) + self.density_grid_st_rad = np.linspace(np.sqrt(strad_min), np.sqrt(strad_max), grid_st_rad_n_pts)**2 + d_grid, st_dir_grid, st_rad_grid = np.meshgrid(self.density_grid_d_pts, self.density_grid_st_dir, + self.density_grid_st_rad) + self.current_grid_resolution = np.max([(self.density_grid_d_pts[1]-self.density_grid_d_pts[0]), + (self.density_grid_st_dir[1]-self.density_grid_st_dir[0])*strad_max*res_dist, + (self.density_grid_st_rad[-1]-self.density_grid_st_rad[-2])*res_dist]) + grid_shape = d_grid.shape + + # Get into physically meaningful coordinates + delta_l_rad = st_rad_grid * np.sin(st_dir_grid) + delta_b_rad = st_rad_grid * np.cos(st_dir_grid) + l_grid_ravel, b_grid_ravel = self.rotate_00_to_lb(delta_l_rad.ravel(), delta_b_rad.ravel()) + r_flat, phi_flat, z_flat = self.coord_trans.dlb_to_rphiz(d_grid.ravel(), + l_grid_ravel*180/np.pi, b_grid_ravel*180/np.pi) + + # Get density at points and integrate + self.density_grid = self.density(r_flat, phi_flat, z_flat).reshape(grid_shape) + vol_elem = d_grid**2 * np.sin(st_rad_grid) + self.density_grid_vscaled = self.density_grid*vol_elem + self.density_int_st_rad = trapezoid(self.density_grid_vscaled, x=self.density_grid_st_rad, axis=2) + self.density_int_st_dir = trapezoid(self.density_int_st_rad, x=self.density_grid_st_dir, axis=0) + self.total_mass = trapezoid(self.density_int_st_dir, x=self.density_grid_d_pts, axis=0) + + def make_density_grid_box(self, d_min, d_max, dl_min, dl_max, db_min, db_max): + # Set resolution + grid_d_n_pts = int(np.ceil((d_max-d_min)/self.current_grid_resolution))+1 + grid_d_n_pts = np.maximum(grid_d_n_pts, 50) + self.density_grid_d_pts = np.linspace(d_min, d_max, grid_d_n_pts) + + # Set up a grid + res_dist = np.minimum(self.sun.gal_dist,d_max) + delta_l_rad_n_pts = int(np.ceil(res_dist*(dl_max-dl_min)/self.current_grid_resolution)) + delta_l_rad_n_pts = np.maximum(40, delta_l_rad_n_pts) + delta_b_rad_n_pts = int(np.ceil(res_dist*(db_max-db_min)/self.current_grid_resolution)) + delta_b_rad_n_pts = np.maximum(40, delta_b_rad_n_pts) + self.density_grid_dl_pts = np.linspace(dl_min, dl_max, delta_l_rad_n_pts) + self.density_grid_db_pts = np.linspace(db_min, db_max, delta_b_rad_n_pts) + d_grid, dl_grid, db_grid = np.meshgrid(self.density_grid_d_pts, self.density_grid_dl_pts, + self.density_grid_db_pts) + self.current_grid_resolution = np.max([(self.density_grid_d_pts[1]-self.density_grid_d_pts[0]), + (self.density_grid_dl_pts[1]-self.density_grid_dl_pts[0])*res_dist, + (self.density_grid_db_pts[1]-self.density_grid_db_pts[0])*res_dist]) + grid_shape = d_grid.shape + + # Get into physically meaningful coordinates + l_grid_ravel, b_grid_ravel = self.rotate_00_to_lb(dl_grid.ravel(), db_grid.ravel()) + r_flat, phi_flat, z_flat = self.coord_trans.dlb_to_rphiz(d_grid.ravel(), + l_grid_ravel*180/np.pi, b_grid_ravel*180/np.pi) + + # Evaluate the density across the grid, then integrate + self.density_grid = self.density(r_flat, phi_flat, z_flat).reshape(grid_shape) + vol_elem = d_grid**2 * np.cos(b_grid_ravel.reshape(grid_shape)*np.pi/180) + self.density_grid_vscaled = self.density_grid*vol_elem + self.density_int_b = trapezoid(self.density_grid_vscaled, x=self.density_grid_db_pts, axis=2) + self.density_int_l = trapezoid(self.density_int_b, x=self.density_grid_dl_pts, axis=0) + self.total_mass = trapezoid(self.density_int_l, x=self.density_grid_d_pts, axis=0) + + def cumulative_integral(self, y, x=None, axis=-1, initial=0.0): + # I had initially used simpson here, but it was unstable for the NSC + # I think a more reliable method is to increase grid points and do trapezoid + res = cumulative_trapezoid(y, x=x, axis=axis, initial=initial) + return res + + def draw_random_positions(self, n_stars: int = 1) \ + -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + Draw points from the density distribution. Uses a grid of density points and + CDF inversion in 3 dimensions. + + Parameters + ---------- + dist_max : float [kpc] + upper distance limit + n_stars : int, None, optional + number of stars drawn + if None return one position as float + + Returns + ------- + + x : float, ndarray [kpc] + Cartesian X coordinate (centered at the galactic center) of the drawn positions + y : float, ndarray [kpc] + Cartesian Y coordinate (centered at the galactic center) of the drawn positions + z : float, ndarray [kpc] + Cartesian Z coordinate (centered at the galactic center) of the drawn positions + + d_kpc : float, ndarray [kpc] + distances of the drawn positions + star_l_deg : float, ndarray [deg] + galactic longitude of the drawn positions + star_b_deg : float, ndarray [deg] + galactic latitude of the drawn positions + """ + if n_stars == 0: + return np.empty(0), np.empty(0), np.empty(0), np.empty(0), np.empty(0), np.empty(0) + + idx_stars = np.arange(n_stars) + if self.field_shape == 'circle': + + # First, select distances + # Use quadratic CDF inversion w/ density point + cumulative density information + # + # Set up cumulative density grid and select surrounding points for each + d_cum_dens = self.cumulative_integral(self.density_int_st_dir, x=self.density_grid_d_pts) + rand_pts_d = np.random.rand(n_stars) * d_cum_dens[-1] + near_d_hi = np.clip(np.searchsorted(d_cum_dens, rand_pts_d), 1, len(self.density_grid_d_pts) - 1) + near_d_lo = near_d_hi - 1 + d1 = self.density_grid_d_pts[near_d_lo] + d2 = self.density_grid_d_pts[near_d_hi] + cum_dens_d_lo = d_cum_dens[near_d_lo] + cum_dens_d_hi = d_cum_dens[near_d_hi] + rho1_d = self.density_int_st_dir[near_d_lo] + rho2_d = self.density_int_st_dir[near_d_hi] + cum_dens_d_diff = cum_dens_d_hi - cum_dens_d_lo + assert np.all(cum_dens_d_diff > 0.0), f"Density CDF must monotonically increase." + # Get the random point in cumulative density and invert + u_cell_d = np.clip((rand_pts_d - cum_dens_d_lo) / cum_dens_d_diff, 0.0, 1.0) + delta_rho_d = rho2_d - rho1_d + flat_mask_d = delta_rho_d==0.0 + inside_sqrt_d = np.maximum(0.0, rho1_d**2 + u_cell_d * (rho2_d**2 - rho1_d**2)) + frac_quad_d = (np.sqrt(inside_sqrt_d) - rho1_d) / np.where(flat_mask_d, 1.0, delta_rho_d) + frac_d = np.where(flat_mask_d, u_cell_d, frac_quad_d) + # Get the distance placement within the cell + d_kpc = d1 + frac_d * (d2 - d1) + + # Second, select radial directions + # Use quadratic CDF inversion w/ density point + cumulative density information + # + # Interpolate the density slices for the surrounding d_kpc + t_d = (d_kpc - d1) / (d2 - d1) + int_rad_lo = self.density_int_st_rad[:, near_d_lo] + int_rad_hi = self.density_int_st_rad[:, near_d_hi] + int_rad_interp = (1.0 - t_d) * int_rad_lo + t_d * int_rad_hi + # Set up cumulative density grid and select surrounding points for each + st_dir_cum_dens = self.cumulative_integral(int_rad_interp, x=self.density_grid_st_dir, axis=0) + rand_pts_dir = np.random.rand(n_stars) * st_dir_cum_dens[-1, :] + near_dir_hi = np.clip((st_dir_cum_dens <= rand_pts_dir[np.newaxis, :]).sum(axis=0), 1, len(self.density_grid_st_dir) - 1) + near_dir_lo = np.maximum(near_dir_hi - 1, 0) + dens_near_dir_lo = self.density_grid_st_dir[near_dir_lo] + dens_near_dir_hi = self.density_grid_st_dir[near_dir_hi] + cum_dens_dir_lo = st_dir_cum_dens[near_dir_lo, idx_stars] + cum_dens_dir_hi = st_dir_cum_dens[near_dir_hi, idx_stars] + rho1_dir = int_rad_interp[near_dir_lo, idx_stars] + rho2_dir = int_rad_interp[near_dir_hi, idx_stars] + cum_dens_dir_diff = cum_dens_dir_hi - cum_dens_dir_lo + assert np.all(cum_dens_dir_diff > 0.0), f"Directional CDF must monotonically increase." + # Get the random point in cumulative density and invert + u_cell_dir = np.clip((rand_pts_dir - cum_dens_dir_lo) / cum_dens_dir_diff, 0.0, 1.0) + delta_rho_dir = rho2_dir - rho1_dir + flat_mask_dir = delta_rho_dir==0.0 + inside_sqrt_dir = np.maximum(0.0, rho1_dir**2 + u_cell_dir * (rho2_dir**2 - rho1_dir**2)) + frac_quad_dir = (np.sqrt(inside_sqrt_dir) - rho1_dir) / np.where(flat_mask_dir, 1.0, delta_rho_dir) + frac_dir = np.where(flat_mask_dir, u_cell_dir, frac_quad_dir) + # Get the directional placement within the cell + star_st_dir = dens_near_dir_lo + frac_dir * (dens_near_dir_hi - dens_near_dir_lo) + + # Third, we select angular radial distance + # Use quadratic CDF inversion w/ density point + cumulative density information + # + # Interpolate accounting for star_st_dir and d_kpc + idx_st_dir_hi = np.clip((self.density_grid_st_dir < star_st_dir[:, np.newaxis]).sum(axis=1), 1, len(self.density_grid_st_dir) - 1) + idx_st_dir_lo = np.maximum(idx_st_dir_hi - 1, 0) + st_dir_lo = self.density_grid_st_dir[idx_st_dir_lo] + st_dir_hi = self.density_grid_st_dir[idx_st_dir_hi] + w_dir = np.nan_to_num((star_st_dir - st_dir_lo) / (st_dir_hi - st_dir_lo))[:, np.newaxis] + w_d = t_d[:, np.newaxis] + rho_00 = self.density_grid_vscaled[idx_st_dir_lo, near_d_lo, :] + rho_10 = self.density_grid_vscaled[idx_st_dir_hi, near_d_lo, :] + rho_01 = self.density_grid_vscaled[idx_st_dir_lo, near_d_hi, :] + rho_11 = self.density_grid_vscaled[idx_st_dir_hi, near_d_hi, :] + rho_interp = ((1.0 - w_dir) * (1.0 - w_d) * rho_00 + + w_dir * (1.0 - w_d) * rho_10 + + (1.0 - w_dir) * w_d * rho_01 + + w_dir * w_d * rho_11) + # Set up cumulative density grid and select surrounding points for each + st_rad_cum_dens = self.cumulative_integral(rho_interp, x=self.density_grid_st_rad, axis=1) + rand_pts_rad = np.random.rand(n_stars) * st_rad_cum_dens[:, -1] + near_pts_hi = np.clip((st_rad_cum_dens <= rand_pts_rad[:, np.newaxis]).sum(axis=1), 1, len(self.density_grid_st_rad) - 1) + near_pts_lo = np.maximum(near_pts_hi - 1, 0) + r1 = self.density_grid_st_rad[near_pts_lo] + r2 = self.density_grid_st_rad[near_pts_hi] + cum_dens_rad_lo = st_rad_cum_dens[idx_stars, near_pts_lo] + cum_dens_rad_hi = st_rad_cum_dens[idx_stars, near_pts_hi] + rho1_rad = rho_interp[idx_stars, near_pts_lo] + rho2_rad = rho_interp[idx_stars, near_pts_hi] + cum_dens_rad_diff = cum_dens_rad_hi - cum_dens_rad_lo + assert np.all(cum_dens_rad_diff > 0.0), f"Radial CDF must monotonically increase." + # Get the random point in cumulative density and invert + u_cell_rad = np.clip((rand_pts_rad - cum_dens_rad_lo) / cum_dens_rad_diff, 0.0, 1.0) + delta_rho_rad = rho2_rad - rho1_rad + flat_mask_rad = delta_rho_rad==0.0 + inside_sqrt_rad = np.maximum(0.0, rho1_rad**2 + u_cell_rad * (rho2_rad**2 - rho1_rad**2)) + frac_quad_rad = (np.sqrt(inside_sqrt_rad) - rho1_rad) / np.where(flat_mask_rad, 1.0, delta_rho_rad) + frac_rad = np.where(flat_mask_rad, u_cell_rad, frac_quad_rad) + # Get the angular radial placement within the cell + star_st_rad = r1 + frac_rad * (r2 - r1) + + # Put into physical coordinates + delta_l_rad = star_st_rad * np.sin(star_st_dir) + delta_b_rad = star_st_rad * np.cos(star_st_dir) + + elif self.field_shape == 'box': + + # First, select distances + # Use quadratic CDF inversion w/ density point + cumulative density information + # + # Set up cumulative density grid and select surrounding points for each + d_cum_dens = self.cumulative_integral(self.density_int_l, x=self.density_grid_d_pts) + rand_pts_d = np.random.rand(n_stars) * d_cum_dens[-1] + near_d_hi = np.clip(np.searchsorted(d_cum_dens, rand_pts_d), 1, len(self.density_grid_d_pts) - 1) + near_d_lo = near_d_hi - 1 + d1 = self.density_grid_d_pts[near_d_lo] + d2 = self.density_grid_d_pts[near_d_hi] + cum_dens_d_lo = d_cum_dens[near_d_lo] + cum_dens_d_hi = d_cum_dens[near_d_hi] + rho1_d = self.density_int_l[near_d_lo] + rho2_d = self.density_int_l[near_d_hi] + cum_dens_d_diff = cum_dens_d_hi - cum_dens_d_lo + assert np.all(cum_dens_d_diff > 0.0), f"Density CDF must monotonically increase." + # Get the random point in cumulative density and invert + u_cell_d = np.clip((rand_pts_d - cum_dens_d_lo) / cum_dens_d_diff, 0.0, 1.0) + delta_rho_d = rho2_d - rho1_d + flat_mask_d = delta_rho_d==0 + inside_sqrt_d = np.maximum(0.0, rho1_d**2 + u_cell_d * (rho2_d**2 - rho1_d**2)) + frac_quad_d = (np.sqrt(inside_sqrt_d) - rho1_d) / np.where(flat_mask_d, 1.0, delta_rho_d) + frac_d = np.where(flat_mask_d, u_cell_d, frac_quad_d) + # Get the distance placement within the cell + d_kpc = d1 + frac_d * (d2 - d1) + + # Second, select longitude offset + # Use quadratic CDF inversion w/ density point + cumulative density information + # + # Interpolate the density slices for the surrounding d_kpc + t_d = (d_kpc - d1) / (d2 - d1) + int_l_lo = self.density_int_b[:, near_d_lo] + int_l_hi = self.density_int_b[:, near_d_hi] + int_l_interp = (1.0 - t_d) * int_l_lo + t_d * int_l_hi + # Set up cumulative density grid and select surrounding points for each + l_cum_dens = self.cumulative_integral(int_l_interp, x=self.density_grid_dl_pts, axis=0) + rand_pts_l = np.random.rand(n_stars) * l_cum_dens[-1, :] + near_l_hi = np.clip((l_cum_dens <= rand_pts_l[np.newaxis, :]).sum(axis=0), 1, len(self.density_grid_dl_pts) - 1) + near_l_lo = np.maximum(near_l_hi - 1, 0) + l1 = self.density_grid_dl_pts[near_l_lo] + l2 = self.density_grid_dl_pts[near_l_hi] + cum_dens_l_lo = l_cum_dens[near_l_lo, idx_stars] + cum_dens_l_hi = l_cum_dens[near_l_hi, idx_stars] + rho1_l = int_l_interp[near_l_lo, idx_stars] + rho2_l = int_l_interp[near_l_hi, idx_stars] + cum_dens_l_diff = cum_dens_l_hi - cum_dens_l_lo + assert np.all(cum_dens_l_diff > 0.0), f"Longitude CDF must monotonically increase." + # Get the random point in cumulative density and invert + u_cell_l = np.clip((rand_pts_l - cum_dens_l_lo) / cum_dens_l_diff, 0.0, 1.0) + delta_rho_l = rho2_l - rho1_l + flat_mask_l = delta_rho_l==0 + inside_sqrt_l = np.maximum(0.0, rho1_l**2 + u_cell_l * (rho2_l**2 - rho1_l**2)) + frac_quad_l = (np.sqrt(inside_sqrt_l) - rho1_l) / np.where(flat_mask_l, 1.0, delta_rho_l) + frac_l = np.where(flat_mask_l, u_cell_l, frac_quad_l) + # Get the longitude placement within the cell + delta_l_rad = l1 + frac_l * (l2 - l1) + + # Third, select latitude offset + # Use quadratic CDF inversion w/ density point + cumulative density information + # + # Interpolate accounting for delta_l_rad and d_kpc + idx_l_hi = np.clip((self.density_grid_dl_pts < delta_l_rad[:, np.newaxis]).sum(axis=1), 1, len(self.density_grid_dl_pts) - 1) + idx_l_lo = np.maximum(idx_l_hi - 1, 0) + l_lo = self.density_grid_dl_pts[idx_l_lo] + l_hi = self.density_grid_dl_pts[idx_l_hi] + w_l = np.nan_to_num((delta_l_rad - l_lo) / (l_hi - l_lo))[:, np.newaxis] + w_d = t_d[:, np.newaxis] + rho_00 = self.density_grid_vscaled[idx_l_lo, near_d_lo, :] + rho_10 = self.density_grid_vscaled[idx_l_hi, near_d_lo, :] + rho_01 = self.density_grid_vscaled[idx_l_lo, near_d_hi, :] + rho_11 = self.density_grid_vscaled[idx_l_hi, near_d_hi, :] + rho_interp = ((1.0 - w_l) * (1.0 - w_d) * rho_00 + + w_l * (1.0 - w_d) * rho_10 + + (1.0 - w_l) * w_d * rho_01 + + w_l * w_d * rho_11) + # Set up cumulative density grid and select surrounding points for each + b_cum_dens = self.cumulative_integral(rho_interp, x=self.density_grid_db_pts, axis=1) + + rand_pts_b = np.random.rand(n_stars) * b_cum_dens[:, -1] + near_b_hi = np.clip((b_cum_dens <= rand_pts_b[:, np.newaxis]).sum(axis=1), 1, len(self.density_grid_db_pts) - 1) + near_b_lo = np.maximum(near_b_hi - 1, 0) + b1 = self.density_grid_db_pts[near_b_lo] + b2 = self.density_grid_db_pts[near_b_hi] + cum_dens_b_lo = b_cum_dens[idx_stars, near_b_lo] + cum_dens_b_hi = b_cum_dens[idx_stars, near_b_hi] + rho1_b = rho_interp[idx_stars, near_b_lo] + rho2_b = rho_interp[idx_stars, near_b_hi] + cum_dens_b_diff = cum_dens_b_hi - cum_dens_b_lo + assert np.all(cum_dens_b_diff > 0.0), f"Latitude CDF must monotonically increase." + # Get the random point in cumulative density and invert + u_cell_b = np.clip((rand_pts_b - cum_dens_b_lo) / cum_dens_b_diff, 0.0, 1.0) + delta_rho_b = rho2_b - rho1_b + flat_mask_b = delta_rho_b==0 + inside_sqrt_b = np.maximum(0.0, rho1_b**2 + u_cell_b * (rho2_b**2 - rho1_b**2)) + frac_quad_b = (np.sqrt(inside_sqrt_b) - rho1_b) / np.where(flat_mask_b, 1.0, delta_rho_b) + frac_b = np.where(flat_mask_b, u_cell_b, frac_quad_b) + # Get the latitude placement within the cell + delta_b_rad = b1 + frac_b * (b2 - b1) + + # Convert from deltas from window center to real l & b + star_l_rad, star_b_rad = self.rotate_00_to_lb(delta_l_rad, delta_b_rad) + star_l_deg = star_l_rad * 180/np.pi + star_b_deg = star_b_rad * 180/np.pi + if np.abs(self.l_deg)<90: + star_l_deg -= 360*(star_l_deg>180) + # estimate galactocentric coordinates + x, y, z = self.coord_trans.dlb_to_xyz(d_kpc, star_l_deg, star_b_deg) + + return x, y, z, d_kpc, star_l_deg, star_b_deg + + + def draw_random_positions_rejection_method(self, n_stars: int = 1) \ + -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + FOR TESTING PURPOSES ONLY + Draw points randomly uniformly then use rejection method to select according + to density distribution. + + Parameters + ---------- + dist_max : float [kpc] + upper distance limit + n_stars : int, None, optional + number of stars drawn + if None return one position as float + + Returns + ------- + + x : float, ndarray [kpc] + Cartesian X coordinate (centered at the galactic center) of the drawn positions + y : float, ndarray [kpc] + Cartesian Y coordinate (centered at the galactic center) of the drawn positions + z : float, ndarray [kpc] + Cartesian Z coordinate (centered at the galactic center) of the drawn positions + + d_kpc : float, ndarray [kpc] + distances of the drawn positions + star_l_deg : float, ndarray [deg] + galactic longitude of the drawn positions + star_b_deg : float, ndarray [deg] + galactic latitude of the drawn positions + """ + if n_stars==0: + return np.empty(0),np.empty(0),np.empty(0),np.empty(0),np.empty(0),np.empty(0) + density_grid_max = np.max(self.density_grid) + + # Draw a bunch of points uniformly in the full window + coords = np.array(self.draw_random_point_in_slice(0,self.max_distance, n_stars*1000)) + # Calculate density at these locations + r, phi, z = self.coord_trans.dlb_to_rphiz(*coords[3:]) + rho_at_draws = self.density(r, phi, z)/density_grid_max + # Draw random numbers to compare to the drawn densities to select kept stars + keep_stars = np.where(rho_at_draws > np.random.rand(len(rho_at_draws)))[0] + if len(keep_stars)>n_stars: + keep_stars = keep_stars[:n_stars] + # See if we have any stars to keep + if len(keep_stars)==0: + pos_list = np.array([[],[],[],[],[],[]]) + else: + pos_list = coords[:, keep_stars] + while len(pos_list[0]) < n_stars: + #print(f"random positions drawn: {len(pos_list[0])} / {n_stars}") + # Draw a bunch of points uniformly in the full window + coords = np.array(self.draw_random_point_in_slice(0,self.max_distance, n_stars*100)) + # Calculate density at these locations + r, phi, z = self.coord_trans.dlb_to_rphiz(*coords[3:]) + rho_at_draws = self.density(r, phi, z)/density_grid_max + # Draw random numbers to compare to the drawn densities to select kept stars + keep_stars = np.where(rho_at_draws > np.random.rand(len(rho_at_draws)))[0] + if len(keep_stars)>(n_stars-len(pos_list[0])): + keep_stars = keep_stars[:n_stars] + if len(keep_stars)>0: + pos_list = np.concatenate([pos_list, coords[:, keep_stars]], axis=1) + return pos_list + + + + def draw_random_point_in_slice(self, dist_inner: float, dist_outer: float, n_stars: int = 1, + population_density_func=None) \ + -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + DEPRECATED / UNUSED IN VERSION 2 + Draw one or more random point in a slice + given coordinates (self.l, self.b) [degrees], + solid angle[steradians], and distance range[kpc] + + To get distance d, we draw from a cumulative quadratic distribution. To do so, + we found the integrated r**2 such that our CDF is + Prob(x) = (x**3 - dist_inner**3)/(dist_outer**3 - dist_inner**3) + Then, we invert for x(Prob) so that we can draw Prob from Uniform(0,1) + x = ((r_max**3 - r_min**3)*Prob + r_min**3)**(1/3) + + Parameters + ---------- + dist_inner : float [kpc] + lower distance + dist_outer : float [kpc] + upper distance + n_stars : int, None, optional + number of stars drawn + if None return one position as float + + Returns + ------- + + x : float, ndarray [kpc] + Cartesian X coordinate (centered at the galactic center) of the drawn positions + y : float, ndarray [kpc] + Cartesian Y coordinate (centered at the galactic center) of the drawn positions + z : float, ndarray [kpc] + Cartesian Z coordinate (centered at the galactic center) of the drawn positions + + d_kpc : float, ndarray [kpc] + distances of the drawn positions + star_l_deg : float, ndarray [deg] + galactic longitude of the drawn positions + star_b_deg : float, ndarray [deg] + galactic latitude of the drawn positions + """ + if n_stars==0: + return np.empty(0),np.empty(0),np.empty(0),np.empty(0),np.empty(0),np.empty(0) + + # generate a cone uniformly around l=0, b=0 with solidAngle as covered area + # Phi in the paper + if (population_density_func is None) or (self.field_shape=='circle'): + d_kpc = np.cbrt(np.random.uniform(dist_inner ** 3, dist_outer ** 3, size=n_stars)) + if self.field_shape=='circle': + st_dir = np.random.uniform(0, 2 * np.pi, size=n_stars) + # Theta in the paper + st_rad = np.arccos(np.random.uniform(np.cos(self.lb_radius_deg*np.pi/180), 1, size=n_stars)) + # Estimate offset to center in ra and dec + delta_l_rad = st_rad * np.sin(st_dir) + delta_b_rad = st_rad * np.cos(st_dir) + if self.field_shape=='box': + delta_l_rad = np.pi/180 * self.l_length_deg/2 * np.random.uniform(-1, 1, size=n_stars) + delta_b_rad = np.pi/180 * self.b_length_deg/2 * np.random.uniform(-1, 1, size=n_stars) + + star_l_rad, star_b_rad = self.rotate_00_to_lb(delta_l_rad, delta_b_rad) + star_l_deg = star_l_rad * 180 / np.pi + star_b_deg = star_b_rad * 180 / np.pi + + # estimate galactocentric coordinates + x, y, z = self.coord_trans.dlb_to_xyz(d_kpc, star_l_deg, star_b_deg) + + return x, y, z, d_kpc, star_l_deg, star_b_deg + + def rotate_00_to_lb(self, delta_l: np.ndarray, delta_b: np.ndarray) \ + -> Tuple[np.ndarray, np.ndarray]: + """ + Rotates coordinate system such that 0, 0 lands on self.l ,self.b + + Parameters + ---------- + delta_l : float, ndarray [radians] + difference in galactic longitude + delta_b : float, ndarray [radians] + difference in galactic longitude + + Returns + ------- + star_l_rad : float, ndarray [radians] + galactic longitude + star_b_rad : float, ndarray [radians] + galactic latitude + """ + # calculate sin and cos. + sin_theta, cos_theta = np.sin(delta_b), np.cos(delta_b) + sin_phi, cos_phi = np.sin(delta_l), np.cos(delta_l) + + # estimate rotation matrix, + # by rotating first around y-axis and then around z-axis + mat = np.matmul( + sp_utils.rotation_matrix(self.l_rad, axis='z'), + sp_utils.rotation_matrix(self.b_rad, axis='y') + ) + # convert to spherical coordinates + vec = np.array([cos_theta * cos_phi, cos_theta * sin_phi, sin_theta]) + + # apply rotation matrix + vec = np.dot(mat, vec) + + # convert to galactic coordinates + star_b_rad = np.arcsin(vec[2]) + star_l_rad = np.arctan2(vec[1], vec[0]) + star_l_rad += 2 * np.pi * (star_l_rad < 0) # only if phi_prime_rad < 0 + # this way it works with both ndarray and floats + + return star_l_rad, star_b_rad diff --git a/synthpop/modules/population_density/besancon2003_bulge.py b/synthpop/modules/population_density/besancon2003_bulge.py index 39ebb09..7ee8781 100644 --- a/synthpop/modules/population_density/besancon2003_bulge.py +++ b/synthpop/modules/population_density/besancon2003_bulge.py @@ -27,7 +27,7 @@ class Besancon2003Bulge(PopulationDensity): """ def __init__(self, x0=1.59, y0=0.424, z0=0.424, Rc=2.54, n0=1.37e10, bar_angle=11.1, **kwargs): - super().__init__() + super().__init__(**kwargs) self.population_density_name = "Besancon2003Bulge" self.density_unit = 'number' self.x0 = x0 diff --git a/synthpop/modules/population_density/besancon2003_dark.py b/synthpop/modules/population_density/besancon2003_dark.py index 2cbc809..6e38892 100644 --- a/synthpop/modules/population_density/besancon2003_dark.py +++ b/synthpop/modules/population_density/besancon2003_dark.py @@ -24,6 +24,7 @@ class Besancon2003Dark(PopulationDensity): def __init__(self, e=1, pc=1.079e8, Rc=2.697, **kwargs): super().__init__(**kwargs) + self.population_density_name = 'Besancon2003Dark' self.density_unit = 'mass' self.e = e self.pc = pc diff --git a/synthpop/modules/population_density/besancon2003_thickdisk.py b/synthpop/modules/population_density/besancon2003_thickdisk.py index dfe011e..c82a964 100644 --- a/synthpop/modules/population_density/besancon2003_thickdisk.py +++ b/synthpop/modules/population_density/besancon2003_thickdisk.py @@ -30,7 +30,7 @@ class Besancon2003Thickdisk(PopulationDensity): def __init__(self, rho0, hr, hz, xl, flare_flag=False, **kwargs): super().__init__(**kwargs) - self.population_density_name = "ThickDisk" + self.population_density_name = "Besancon2003Thickdisk" self.density_unit = 'mass' self.rho0 = rho0 self.xl = xl diff --git a/synthpop/modules/population_density/besancon2003_thindisk.py b/synthpop/modules/population_density/besancon2003_thindisk.py index e16c6b3..45bb1bd 100644 --- a/synthpop/modules/population_density/besancon2003_thindisk.py +++ b/synthpop/modules/population_density/besancon2003_thindisk.py @@ -84,8 +84,6 @@ def density(self, r: np.ndarray, phi_rad: np.ndarray, z: np.ndarray) -> np.ndarr a = np.sqrt(r ** 2 + (z / k_flare / self.e) ** 2) a0 = np.sqrt(self.sun.r ** 2 + (self.sun.z / k_flare0 / self.e) ** 2) - - def exp_arg(x): # function to estimate the argument for exp() return -np.sqrt(self.offset ** 2 + x ** 2) ** self.power diff --git a/synthpop/modules/population_density/chatzopoulos2015_nsc.py b/synthpop/modules/population_density/chatzopoulos2015_nsc.py new file mode 100644 index 0000000..0a0f772 --- /dev/null +++ b/synthpop/modules/population_density/chatzopoulos2015_nsc.py @@ -0,0 +1,43 @@ +""" +NSC Density profile from Chatzopoulos et al. (2015) +""" + +__all__ = ["Chatzopoulos2015Nsc", ] +__author__ = "M.J. Huston" +__date__ = "2026-02-06" + +import numpy as np +import scipy.special +from ._population_density import PopulationDensity + +class Chatzopoulos2015Nsc(PopulationDensity): + """ + NSC density profile + + Attributes + ---------- + gamma : float + q : float + a0 : float [kpc] + M : float [Msun] + """ + + def __init__( + self, gamma=0.71, q=0.73, a0=0.0059, M=6.1e7, + **kwargs): + super().__init__(**kwargs) + self.population_density_name = 'Chatzopoulos2015Nsc' + self.gamma = gamma + self.q = q + self.a0 = a0 + self.M = M + + @staticmethod + def a_func(r,z): + return np.sqrt(r**2 + z**2/self.q**2) + + def density(self, r, phi_rad, z): + a = self.a_func(r,z) + rho = (3-self.gamma)*self.M / (4*np.pi*self.q) \ + * self.a0/(a**self.gamma * (a+self.a0)**(4-self.gamma)) + return rho diff --git a/synthpop/modules/population_density/constant.py b/synthpop/modules/population_density/constant.py new file mode 100644 index 0000000..9f0c08e --- /dev/null +++ b/synthpop/modules/population_density/constant.py @@ -0,0 +1,50 @@ +""" +Constant density model, for testing purposes only. +""" + +__all__ = ["Constant", ] +__author__ = "M.J. Huston" +__date__ = "2026-01-29" + +import numpy as np +import scipy.special +from .. import const +from ._population_density import PopulationDensity + +class Constant(PopulationDensity): + """ + Constant density + + Attributes + ---------- + rho : float + density at all positions in [density_unit]/kpc^2 + """ + + def __init__(self, density_unit = 'mass', rho = 1e8, **kwargs): + super().__init__(**kwargs) + self.population_density_name = "Constant" + self.density_unit = density_unit + self.rho = rho + + def density(self, r: np.ndarray, phi_rad: np.ndarray, z: np.ndarray) -> np.ndarray: + """ + + Estimates the density at the given position + + Parameters + ---------- + r : ndarray ['kpc'] + Distance to z axis + phi_rad : ndarray ['rad'] + azimuth angle of the stars. phi_rad = 0 is pointing towards sun. + z : height above the galactic plane (corrected for warp of the galaxy) + + Returns + ------- + rho : ndarray [M_sun/kpc^3 or #/kpc^3] + density at the given location, either in number density evolved + mass density or initial mass density should be specified in density_unit. + + """ + return np.ones(r.shape)*self.rho diff --git a/synthpop/modules/population_density/density_from_grid.py b/synthpop/modules/population_density/density_from_grid.py index aa51648..bd6e37f 100644 --- a/synthpop/modules/population_density/density_from_grid.py +++ b/synthpop/modules/population_density/density_from_grid.py @@ -1,5 +1,6 @@ """ -Density function that interpolates over a grid +Density function that interpolates over a grid. Created for NSD and NSC models +tabulated from AGAMA, but can use other files with the same format. """ __all__ = ["density_from_grid"] @@ -8,7 +9,7 @@ import numpy as np import pandas as pd -from scipy.interpolate import LinearNDInterpolator +from scipy.interpolate import LinearNDInterpolator, RegularGridInterpolator from ._population_density import PopulationDensity from .. import const @@ -24,22 +25,31 @@ class density_from_grid(PopulationDensity): file must be whitespace delimited and have comments marked with '#' density_unit : str "mass" or "number" to specify units for the provided density + abs_z : str + if True, take the absolute value of the z coordinate before evaluating """ def __init__( - self, moment_file=None, density_unit='mass',abs_z=True, + self, moment_file=None, density_unit='mass', abs_z=True, + population_density_name='DensityFromGrid', **kwargs ): - super().__init__(**kwargs) dat = pd.read_csv(const.MOMENTS_DIR + '/' + moment_file, sep='\s+', comment='#') - self.interpolate_rho = LinearNDInterpolator(list(zip(dat['r'],dat['z'])), - dat['rho'], fill_value=0.0, rescale=False) + super().__init__(max_gc_dist=np.max(dat['r']), **kwargs) + rho = dat.pivot(index='r', columns='z', values='rho') + self.required_grid_resolution = np.minimum(np.min(np.diff(rho.index.to_numpy())), + np.min(np.diff(rho.columns.to_numpy()))) + self.piv = rho + self.interpolate_rho = RegularGridInterpolator((rho.index.to_numpy(), + rho.columns.to_numpy()), rho.to_numpy(), + bounds_error=False, fill_value=0.0) self.density_unit = density_unit self.abs_z=abs_z + self.population_density_name = population_density_name def density(self, r, theta, z): if self.abs_z: z = np.abs(z) - return self.interpolate_rho(list(zip(r,z))) * 1e9 + return self.interpolate_rho(np.column_stack([r,z])) * 1e9 diff --git a/synthpop/modules/population_density/gallego_cano2020_nsd.py b/synthpop/modules/population_density/gallego_cano2020_nsd.py new file mode 100644 index 0000000..774d723 --- /dev/null +++ b/synthpop/modules/population_density/gallego_cano2020_nsd.py @@ -0,0 +1,57 @@ +""" +NSD Density profile from Gallego-Cano et al. (2020) +""" + +__all__ = ["GallegoCano2020Nsd", ] +__author__ = "M.J. Huston" +__date__ = "2026-02-06" + +import numpy as np +import scipy.special +from ._population_density import PopulationDensity + +class GallegoCano2020Nsd(PopulationDensity): + """ + NSD density profile + + Attributes + ---------- + rho2 : float [Msun per kpc^3] + central density of the second component + rho1_rho2 : float + ratio of rho1 to rho2, the central density of the second component + R1, R2 : float [kpc] + radial scale length of each component + q : float + uhh + n1 : float + exponent for radial scaling + n2 : float + exponent for height scaling + """ + + def __init__( + self, rho2=170e10, rho1_rho2=1.311, + R1=0.00506, R2=0.0246, + q=0.37, n1=0.72, n2=0.79, + **kwargs): + super().__init__(**kwargs) + self.population_density_name = 'GallegoCano2020Nsd' + self.rho1 = rho1_rho2*rho2 + self.rho2 = rho2 + self.R1 = R1 + self.R2 = R2 + self.q = q + self.n1 = n1 + self.n2 = n2 + + + @staticmethod + def a_func(r,z): + return np.sqrt(r**2 + z**2/self.q**2) + + def density(self, r, phi_rad, z): + a = a_func(r,z) + rho = self.rho1*np.exp(-(a/self.R1)**self.n1) + self.rho2*np.exp(-(a/self.R2)**self.n2) + + return rho diff --git a/synthpop/modules/population_density/gums_bar.py b/synthpop/modules/population_density/gums_bar.py index 27054d9..ca80a70 100644 --- a/synthpop/modules/population_density/gums_bar.py +++ b/synthpop/modules/population_density/gums_bar.py @@ -31,6 +31,7 @@ class GumsBar(PopulationDensity): def __init__(self, n0, x0, y0, z0, alpha, beta, gamma, c_perp, c_para, dz_bone=0, x_bone=0, r_max=np.inf, sigma_cut_of=1e-10, **kwargs): super().__init__(**kwargs) + self.population_density_name = 'GumsBar' self.density_unit = 'number' self.n0 = n0 self.x0 = x0 diff --git a/synthpop/modules/population_density/koshimoto2021_bulge.py b/synthpop/modules/population_density/koshimoto2021_bulge.py new file mode 100644 index 0000000..ab9e54e --- /dev/null +++ b/synthpop/modules/population_density/koshimoto2021_bulge.py @@ -0,0 +1,121 @@ +""" +Bulge density profile from Koshimoto et al. (2021) +""" + +__all__ = ["Koshimoto2021Bulge", ] +__author__ = "M.J. Huston" +__date__ = "2022-02-02" + +import numpy as np +import scipy.special +from ._population_density import PopulationDensity + +class Koshimoto2021Bulge(PopulationDensity): + """ + Bulge density distribution options from Koshimoto+21 (+ unpublished related work, + N. Koshimoto, private communication) + + Attributes + ---------- + parameterization : str + parameterization options, with 'E' for Exponential, 'G' for Gaussian, + and 'B' for Bessel function. + x0 : float [kpc] + scale length along x' axis + y0 : float [kpc] + scale length along y' axis + z0 : float [kpc] + scale length along z' axis + C_par : float + bar shape parameter + C_perp : float + bar shape parameter + bar_angle : float [deg] + angle of the bar from the GC line of sight (within plane) in degrees + bar_plane_angle : float [deg] + angle of the bar from the GC line of sight out of the plane in degrees + R_c : float [kpc] + cutoff radius + X_shape : boolean + if true, apply as an X-shaped structure + b_X : float + the slope of the X-shaped structure + """ + + def __init__( + self, parameterization, rho0, x0, y0, z0, C_perp, C_par, R_c, + X_shape=False, b_X=None, bar_angle=27, bar_plane_angle=0, **kwargs + ): + # these were the defaults we phased out: + # parameterization='B', x0=0.849918751795326, y0=0.339420928043361, z0=0.286256780667543, + # rho0=7.53034e9, C_perp=1.28032639342739, C_par=3.24013809549932, bar_angle_deg=27, + super().__init__(**kwargs) + assert (parameterization in ['E', 'G', 'B']), f"Invalid Koshimoto2021Bulge parameterization" \ + f" '{parameterization}'. Options are 'E' (exponential), 'G' (gaussian), or 'B' (bessell)." + self.parameterization = parameterization + self.population_density_name = 'Koshimoto2021Bulge' + self.x0 = x0 + self.y0 = y0 + self.z0 = z0 + self.rho0 = rho0 + self.C_par = C_par + self.C_perp = C_perp + self.density_unit = 'mass' + self.bar_ang = bar_angle * np.pi / 180 + self.bar_plane_ang = bar_plane_angle * np.pi / 180 + param_functions = {'E':self.param_function_e, + 'G':self.param_function_g, + 'B':self.param_function_b} + self.param_function = param_functions[parameterization] + self.X_shape = X_shape + self.b_X = b_X + self.R_c = R_c + + @staticmethod + def param_function_e(rs): + return np.exp(-rs) + + @staticmethod + def param_function_g(rs): + return np.exp(-0.5*rs**2) + + @staticmethod + def param_function_b(rs): + return scipy.special.kn(0, rs) + + @staticmethod + def cutoff_function(x): + # Eqn 14 + return np.exp(-x**2) ** (x>0) + + def rs_function(self, xp, yp, zp): + # Eqn 16 + rs = ((np.abs(xp / self.x0) ** self.C_perp + + np.abs(yp / self.y0) ** self.C_perp) ** (self.C_par/self.C_perp) + + np.abs(zp / self.z0) ** self.C_par ) ** (1/self.C_par) + return rs + + def density(self, r, phi_rad, z): + + # Align coordinates with the bar, + xp0 = -r * np.cos(phi_rad - self.bar_ang) + yp0 = r * np.sin(phi_rad - self.bar_ang) + zp0 = z + xp = xp0 * np.cos(self.bar_plane_ang) + zp0 * np.sin(self.bar_plane_ang) + yp = yp0 + zp = - xp0 * np.sin(self.bar_plane_ang) + zp0 * np.cos(self.bar_plane_ang) + + # Do the math + if not self.X_shape: + # Eqn 13 + rs = self.rs_function(xp, yp, zp) + rho = self.rho0 * self.param_function(rs) * self.cutoff_function((r-self.R_c)/0.5) + else: + # Eqn 17 + rs1 = self.rs_function(xp-self.b_X*zp, yp, zp) + rs2 = self.rs_function(xp+self.b_X*zp, yp, zp) + rho = self.rho0 * \ + (self.param_function(rs1) + self.param_function(rs2)) * \ + self.cutoff_function((r-self.R_c)/0.5) + + return rho diff --git a/synthpop/modules/population_density/koshimoto2021_bulge_b.py b/synthpop/modules/population_density/koshimoto2021_bulge_b.py deleted file mode 100644 index 9252223..0000000 --- a/synthpop/modules/population_density/koshimoto2021_bulge_b.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -Bulge density profile from from unpublished work related to Koshimoto et al 2021 -""" - -__all__ = ["Koshimoto2021BulgeB", ] -__author__ = "M.J. Huston" -__date__ = "2022-02-02" - -import numpy as np -import scipy.special -from ._population_density import PopulationDensity - -class Koshimoto2021BulgeB(PopulationDensity): - """ - Bessell function bulge density distributions from Koshimoto for - (private communication, unpublished work related to Koshimoto+21) - - Attributes - ---------- - x0 : float - y0 : float - z0 : float - C_par : float - C_perp : float - bar_ang : float - """ - - def __init__( - self, x0=0.849918751795326, y0=0.339420928043361, z0=0.286256780667543, n0=7.53034e9, - C_perp=1.28032639342739, C_par=3.24013809549932, bar_angle=27, **kwargs - ): - super().__init__(**kwargs) - self.x0 = x0 - self.y0 = y0 - self.z0 = z0 - self.n0 = n0 - self.C_par = C_par - self.C_perp = C_perp - self.density_unit = 'mass' - self.bar_ang = bar_angle * np.pi / 180 - - def density(self, r, theta, z): - alpha = np.pi/2.0 - self.bar_ang - - # switch to cartestian coordinates - x = r * np.cos(theta) - y = r * np.sin(theta) - - # Rotation to account for rotation angle - xf = x * np.cos(alpha) + y * np.sin(alpha) - yf = -x * np.sin(alpha) + y * np.cos(alpha) - zf = z - - #calculations - rs=((np.abs(xf / self.x0) ** self.C_perp - + np.abs(yf / self.y0) ** self.C_perp) ** (self.C_par/self.C_perp) - + np.abs(zf / self.z0) ** self.C_par ) ** (1/self.C_par) - rho = self.n0 * scipy.special.kn(0, rs) - - return rho diff --git a/synthpop/modules/population_density/koshimoto2021_thickdisk.py b/synthpop/modules/population_density/koshimoto2021_thickdisk.py index 278a4f4..41f6ed6 100644 --- a/synthpop/modules/population_density/koshimoto2021_thickdisk.py +++ b/synthpop/modules/population_density/koshimoto2021_thickdisk.py @@ -16,25 +16,25 @@ class Koshimoto2021Thickdisk(PopulationDensity): Attributes ---------- - R0 : float [kpc] + R : float [kpc] disk scale length - z0 : float [kpc] + z_sun : float [kpc] disk scale height - rho0 : float [M_sum/kpc^3] + rho_sun : float [M_sum/kpc^3] mass density at the solar position - Rbreak : float [kpc] + R_break : float [kpc] distance within which surface density is flat """ - def __init__( - self, rho0=(1.7e-3 + 4.4e-4) * 10 ** 9, z0=0.903, R0=2.200, Rbreak=5.300, **kwargs - ): + def __init__(self, rho_sun=(1.7e-3 + 4.4e-4 + 9.1e-6) * 10 ** 9, + z_sun=0.903, R=2.200, R_break=5.300, **kwargs): super().__init__(**kwargs) + self.population_density_name = 'Koshimoto2021Thickdisk' self.density_unit = 'mass' - self.rho0 = rho0 - self.z0 = z0 - self.R0 = R0 - self.Rbreak = Rbreak + self.rho_sun = rho_sun + self.z_sun = z_sun + self.R = R + self.R_break = R_break def density(self, r: np.ndarray, phi_rad: np.ndarray, z: np.ndarray) -> np.ndarray: """ @@ -56,8 +56,8 @@ def density(self, r: np.ndarray, phi_rad: np.ndarray, z: np.ndarray) -> np.ndarr mass density or initial mass density should be specified in density_unit. """ - r_past_break = (np.array(r) > self.Rbreak).astype(int) - rho_past = self.rho0 * np.exp(-(r - self.sun.r) / self.R0) * np.exp(-abs(z) / self.z0) - rho_pre = self.rho0 * np.exp(-(self.Rbreak - self.sun.r) / self.R0) * np.exp( - -abs(z) / self.z0) + r_past_break = (np.array(r) > self.R_break) + rho_past = self.rho_sun * np.exp(-(r - self.sun.r) / self.R) * np.exp(-abs(z) / self.z_sun) + rho_pre = self.rho_sun * np.exp(-(self.R_break - self.sun.r) / self.R) * np.exp( + -abs(z) / self.z_sun) return rho_past * r_past_break + rho_pre * (1 - r_past_break) diff --git a/synthpop/modules/population_density/koshimoto2021_thindisk.py b/synthpop/modules/population_density/koshimoto2021_thindisk.py index 17a3b1b..5e5840a 100644 --- a/synthpop/modules/population_density/koshimoto2021_thindisk.py +++ b/synthpop/modules/population_density/koshimoto2021_thindisk.py @@ -16,26 +16,31 @@ class Koshimoto2021Thindisk(PopulationDensity): Attributes ---------- - R0 : float [kpc] + R : float [kpc] disk scale length - z0 : float [kpc] - disk scale height at the solar position for linear scale height model - z45 : float [kpc] + z_sun : float [kpc] + disk scale height at the solar position for linear scale height model + and everywhere for the flat scale height model + z_45 : float [kpc] disk scale height at 4.5 kpc for linear scale height model rho0 : float [M_sum/kpc^3] mass density at the solar position - Rbreak : float [kpc] + R_break : float [kpc] distance within which surface density is flat + linear_z : boolean + if True, use linear scale height; if False, use flat """ - def __init__(self, R0, z0, z45, rho0, Rbreak=5.3, **kwargs): + def __init__(self, R, z_sun, z_45, rho_sun, R_break=5.3, linear_z=False, **kwargs): super().__init__(**kwargs) self.density_unit = 'mass' - self.R0 = R0 - self.z0 = z0 - self.z45 = z45 - self.rho0 = rho0 - self.Rbreak = Rbreak + self.R = R + self.z_sun = z_sun + self.z_45 = z_45 + self.rho_sun = rho_sun + self.R_break = R_break + self.linear_z = linear_z + self.population_density_name = 'Koshimoto2021Thindisk' def density(self, r: np.ndarray, phi_rad: np.ndarray, z: np.ndarray) -> np.ndarray: """ @@ -56,14 +61,17 @@ def density(self, r: np.ndarray, phi_rad: np.ndarray, z: np.ndarray) -> np.ndarr mass density or initial mass density should be specified in density_unit. """ - r_greater_45 = r >= 4.5 - zR = r_greater_45 * (self.z0 - (self.z0 - self.z45) * (self.sun.r - r) / ( - self.sun.r - 4.5)) + (1 - r_greater_45) * self.z45 + r_greater_45 = (r > 4.5) + if self.linear_z: + zR = r_greater_45 * (self.z_sun - (self.z_sun - self.z_45) * (self.sun.r - r) / ( + self.sun.r - 4.5)) + (1 - r_greater_45) * self.z_45 + else: + zR = self.z_sun - r_greater_break = r >= self.Rbreak - rho = self.rho0 * self.z0 / zR * ( - r_greater_break * np.exp(-(r - self.sun.r) / self.R0) * ( + r_greater_break = (r > self.R_break) + rho = self.rho_sun * self.z_sun / zR * ( + r_greater_break * np.exp(-(r - self.sun.r) / self.R) * ( 1 / np.cosh(-abs(z) / zR)) ** 2 + (1 - r_greater_break) * np.exp( - -(self.Rbreak - self.sun.r) / self.R0) * (1 / np.cosh(-abs(z) / zR)) ** 2) + -(self.R_break - self.sun.r) / self.R) * (1 / np.cosh(-abs(z) / zR)) ** 2) return rho diff --git a/synthpop/modules/population_density/launhardt2002_nsd.py b/synthpop/modules/population_density/launhardt2002_nsd.py new file mode 100644 index 0000000..76e2908 --- /dev/null +++ b/synthpop/modules/population_density/launhardt2002_nsd.py @@ -0,0 +1,52 @@ +""" +NSD Density profile from Launhardt et al. (2002) +""" + +__all__ = ["Launhardt2002Nsd", ] +__author__ = "M.J. Huston" +__date__ = "2026-02-06" + +import numpy as np +import scipy.special +from ._population_density import PopulationDensity + +class Launhardt2002Nsd(PopulationDensity): + """ + NSD density profile + + Attributes + ---------- + rho1 : float [Msun per kpc^3] + central density of the first component + rho1_rho2 : float + ratio of rho1 to rho2, the central density of the second component + R1, R2 : float [kpc] + radial scale length of each component + z0 : float [kpc] + scale height + n_R : float + exponent for radial scaling + n_z : float + exponent for height scaling + """ + + def __init__( + self, rho1=15.2e10, rho1_rho2=3.9, + R1=0.120, R2=0.220, z0=0.45, + n_R=5, n_z=1.4, + **kwargs): + super().__init__(**kwargs) + self.rho1 = rho1 + self.rho2 = rho1/rho1_rho2 + self.R1 = R1 + self.R2 = R2 + self.z0 = z0 + self.n_R = n_R + self.n_z = n_z + self.population_density_name='Launhardt2002Nsd' + + def density(self, r, phi_rad, z): + rho = self.rho1 * np.exp(-0.693* ((r/self.R1)**self.n_R + (np.abs(z)/self.z0)**self.n_z)) \ + +self.rho2 * np.exp(-0.693* ((r/self.R2)**self.n_R + (np.abs(z)/self.z0)**self.n_z)) + + return rho diff --git a/synthpop/modules/population_density/triaxial_bulge.py b/synthpop/modules/population_density/triaxial_bulge.py index f172a89..67d66bd 100644 --- a/synthpop/modules/population_density/triaxial_bulge.py +++ b/synthpop/modules/population_density/triaxial_bulge.py @@ -30,12 +30,15 @@ class TriaxialBulge(PopulationDensity): Rmax : float [kpc] cutoff radius for bulge bar_angle : float [degrees] - angle of the bar + angle of the bar within the plane + bar_plane_angle : float [degrees] + angle of the bar out of the plane """ - def __init__(self, triaxial_type: str, density_unit: str, x0: float, y0: float, z0: float, rho0: float, Rmax=np.inf, bar_angle=29.4, **kwargs): - super().__init__() - self.population_density_name = "Bulge_Density" + def __init__(self, triaxial_type: str, density_unit: str, x0: float, y0: float, z0: float, rho0: float, + Rmax=np.inf, bar_angle=29.4, bar_plane_angle=0.0, **kwargs): + super().__init__(**kwargs) + self.population_density_name = "TriaxialBulge" self.density_unit = density_unit self.x0 = x0 self.y0 = y0 @@ -43,6 +46,7 @@ def __init__(self, triaxial_type: str, density_unit: str, x0: float, y0: float, self.rho0 = rho0 self.Rmax = Rmax self.bar_ang = bar_angle * np.pi / 180 + self.bar_plane_ang = bar_plane_angle * np.pi / 180 #Functional form if triaxial_type not in ['G1','G2','G3','E1','E2','E3','P1','P2','P3']: @@ -76,7 +80,9 @@ def rho_P3(x,y,z): rr = np.sqrt((x/self.x0)**2 + (y/self.y0)**2 + (z/self.z0)**2) return self.rho0 * 1/(1+r**2)**2 # Select proper function - rho_func_dict = {'G1':rho_G1, 'G2':rho_G2, 'G3':rho_G3, 'E1':rho_E1, 'E2':rho_E2, 'E3':rho_E3, 'P1':rho_P1, 'P2':rho_P2, 'P3':rho_P3} + rho_func_dict = {'G1':rho_G1, 'G2':rho_G2, 'G3':rho_G3, + 'E1':rho_E1, 'E2':rho_E2, 'E3':rho_E3, + 'P1':rho_P1, 'P2':rho_P2, 'P3':rho_P3} self.rho_func = rho_func_dict[triaxial_type] @@ -87,11 +93,12 @@ def density(self, r: np.ndarray, phi_rad: np.ndarray, z: np.ndarray) -> np.ndarr Parameters ---------- - r : ndarray ['kpc'] + r : ndarray [kpc] Distance to z axis - phi_rad : ndarray ['rad'] + phi_rad : ndarray [rad] azimuth angle of the stars. phi_rad = 0 is pointing towards sun. - z : height above the galactic plane (corrected for warp of the galaxy) + z : ndarray [kpc] + height above the galactic plane (corrected for warp of the galaxy) Returns ------- @@ -100,10 +107,14 @@ def density(self, r: np.ndarray, phi_rad: np.ndarray, z: np.ndarray) -> np.ndarr mass density or initial mass density should be specified in density_unit. """ - # Align coordinates with the bar, - xb = -r * np.cos(phi_rad - self.bar_ang) - yb = r * np.sin(phi_rad - self.bar_ang) - zb = z + # Align coordinates with bar within the plane + xb0 = -r * np.cos(phi_rad - self.bar_ang) + yb0 = r * np.sin(phi_rad - self.bar_ang) + zb0 = z + # Align to out-of-plane tilt + xb = xb0 * np.cos(self.bar_plane_ang) + zb0 * np.sin(self.bar_plane_ang) + yb = yb0 + zb = - xb0 * np.sin(self.bar_plane_ang) + zb0 * np.cos(self.bar_plane_ang) # Apply cutoff radius (Eqn 5) # 1 when r pandas.DataFrame: + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): """ This is a placeholder for the postprocessing. It must accept the pandas data frame and must return a pandas data frame. Parameters ---------- - dataframe : dataframe - original SynthPop output as pandas data frame + system_df : pandas dataframe + original SynthPop output for star systems + (may be all individual stars if no multiplicity) + companion_df : pandas dataframe + original SynthPop output for companions + (may be None for no multiplicity) Returns ------- - dataframe : dataframe - modified pandas data frame + system_df : pandas dataframe + modified star systems table + companion_df : pandas dataframe + modified companions table """ - return dataframe + return system_df, companion_df diff --git a/synthpop/modules/post_processing/additional_cuts.py b/synthpop/modules/post_processing/additional_cuts.py index d5dfb9a..f78f2dc 100644 --- a/synthpop/modules/post_processing/additional_cuts.py +++ b/synthpop/modules/post_processing/additional_cuts.py @@ -6,7 +6,7 @@ __author__ = "M.J. Huston" __date__ = "2024-05-14" -import pandas +import pandas as pd import numpy as np from ._post_processing import PostProcessing @@ -39,26 +39,30 @@ def __init__(self, model, logger, standard_cuts=None, difference_cuts=None, **kw self.standard_cuts = standard_cuts self.difference_cuts = difference_cuts - def do_post_processing(self, dataframe: pandas.DataFrame) -> pandas.DataFrame: + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): """ - Make the cuts and return the modified catalog. + Make the cuts based on the system table parameters + and applies to both tables. """ if not self.standard_cuts==None: for cut in self.standard_cuts: if cut[1]=='min': - dataframe = dataframe[dataframe[cut[0]]>cut[2]] + system_df = system_df[system_df[cut[0]]>cut[2]] elif cut[1]=='max': - dataframe = dataframe[dataframe[cut[0]]cut[3]] + system_df = system_df[(system_df[cut[0]]-system_df[cut[1]])>cut[3]] elif cut[2]=='max': - dataframe = dataframe[(dataframe[cut[0]]-dataframe[cut[1]]) pandas.DataFrame: - dataframe.reset_index(inplace=True, drop=True) - delta_l = np.array(dataframe['l'] - self.model.l_deg) * np.cos(self.model.b_deg*np.pi/180.0) * 3600 - delta_b = np.array(dataframe['b'] - self.model.b_deg) * 3600 + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): + + delta_l = np.array(system_df['l'] - self.model.l_deg) * np.cos(self.model.b_deg*np.pi/180.0) * 3600 + delta_b = np.array(system_df['b'] - self.model.b_deg) * 3600 pts = np.transpose([delta_l, delta_b]) kdt = KDTree(pts) res = kdt.query_ball_point(pts, self.blend_radius) + if (not self.model.parms.combine_system_mags) and (companion_df is not None): + system_df_new = combine_system_mags(system_df.copy(), companion_df, self.model.populations[0].bands) + else: + system_df_new = system_df for filt in self.filters: if self.model.parms.obsmag: - mags = np.array(dataframe[filt]) + mags = np.array(system_df_new[filt]) else: - mags = self.get_obs_mags(dataframe, filt) - mag_arr = np.array(list(map(lambda i: -2.5*np.log10(np.sum(10**(-0.4*mags[i]))), res))) - dataframe[filt+'_bl'] = mag_arr + mags = self.get_obs_mags(system_df_new, filt) + + mag_arr = np.array(list(map(lambda i: add_magnitudes(mags[i]), res))) + system_df[filt+'_bl'] = mag_arr - return dataframe + return system_df, companion_df diff --git a/synthpop/modules/post_processing/combine_tables.py b/synthpop/modules/post_processing/combine_tables.py new file mode 100644 index 0000000..c34d217 --- /dev/null +++ b/synthpop/modules/post_processing/combine_tables.py @@ -0,0 +1,74 @@ +""" +Add some derived and additional parameters to the multi-star systems +""" + +__all__ = ["CombineTables", ] +__author__ = "M. Newman, M.J. Huston" +__date__ = "2025-10-15" + +import pandas as pd +import numpy as np +from ._post_processing import PostProcessing +from scipy.stats import maxwell, uniform_direction +from synthpop.synthpop_utils.coordinates_transformation import CoordTrans +import pdb + +class CombineTables(PostProcessing): + """ + Post-processing to mimic old file format with binaries. + + Attributes + ---------- + + """ + + def __init__(self, model, logger, **kwargs): + super().__init__(model, logger, **kwargs) + + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): + """ + Perform the post-processing and return the modified DataFrame. + """ + + if np.any(system_df.n_companions>1): + raise NotImplementedError("THIS DOESNT WORK YET for higher order than binary systems") + # Do some renames + system_df.rename(columns={'system_Mass':'total_mass', 'system_idx':'ID'}, inplace=True) + companion_df.rename(columns={'system_Mass': 'total_mass', 'system_idx':'primary_ID'}, inplace=True) + system_df.loc[:,'primary_ID'] = system_df['ID'] + companion_df.loc[:,'logP'] = np.log10(companion_df['period']) + companion_df.loc[:,'ID'] = np.max(system_df['ID']) + 1 + np.arange(len(companion_df)) + + # Set up combo columns + system_df.loc[:,'combined_logL'] = np.nan + system_df.loc[:,'combined_logP'] = np.nan + system_df.loc[:,'q'] = np.nan + system_df.loc[:,'Is_Binary'] = 0 + + # Match up companions and primaries for combined quantities + system_df.set_index('ID', inplace=True) + companion_system_idxs = companion_df['primary_ID'].to_numpy() + companion_df.loc[:,'q'] = companion_df['Mass'] / system_df['Mass'][companion_system_idxs].to_numpy() + copy_props = ['mul','mub','vr_bc','U','V','W','age','pop','l','b','Dist', 'x','y','z','A_Ks', 'total_mass'] + for prop in copy_props: + companion_df.loc[:,prop] = system_df[prop][companion_system_idxs].to_numpy() + # combined_logL = np.log10(10**companion_df['log_L'] + 10**system_df['log_L'][companion_system_idxs].to_numpy()) + combined_logL = np.log10(10**system_df['log_L'][companion_system_idxs].fillna(-np.inf).to_numpy() + \ + 10**companion_df['log_L'].fillna(-np.inf)) + companion_df.loc[:,'combined_logL'] = combined_logL + companion_df.loc[:, 'Is_Binary'] = 2 + + # Update primaries combined values + system_df.loc[companion_system_idxs, 'combined_logL'] = combined_logL.to_numpy() + system_df.loc[companion_system_idxs, 'combined_logP'] = companion_df['logP'].to_numpy() + system_df.loc[companion_system_idxs, 'Is_Binary'] = 1 + system_df.loc[companion_system_idxs, 'eccentricity'] = companion_df['eccentricity'].to_numpy() + system_df.reset_index(inplace=True) + + # Combine table + system_df = pd.concat([system_df, companion_df]) + system_df.sort_values(['primary_ID', 'Is_Binary'], inplace=True) + system_df = system_df.fillna(np.nan) + + return system_df, None diff --git a/synthpop/modules/post_processing/combined_csv.py b/synthpop/modules/post_processing/combined_csv.py index 8df0324..2391aca 100644 --- a/synthpop/modules/post_processing/combined_csv.py +++ b/synthpop/modules/post_processing/combined_csv.py @@ -7,7 +7,7 @@ __date__ = "2023-01-23" import os -import pandas +import pandas as pd from ._post_processing import PostProcessing class CombinedCsv(PostProcessing): @@ -26,22 +26,39 @@ def __init__(self, model, logger, combined_filename=None, **kwargs): else: #: File name for combined output self.combined_filename = combined_filename + split_fname = self.combined_filename.split('.') + self.combined_companion_filename = '.'.join( + split_fname[:-1]+['_companion', split_fname[-1]]) if os.path.isfile(self.combined_filename): os.remove(self.combined_filename) - def do_post_processing(self, dataframe: pandas.DataFrame) -> pandas.DataFrame: + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): """ Combine all catalogs into one output csv file, and returns the unchanged DataFrame. """ # check if the file exist, if so it will not write a new header file_exist = os.path.isfile(self.combined_filename) # convert dataframe to csv sting - csv_data = dataframe.to_csv(index=False, header=file_exist) + csv_data = system_df.to_csv(index=False, header=file_exist) # open the file in append mode - self.logger.info(f"attach {dataframe.shape[0]} to {self.combined_filename}") + self.logger.info(f"attach {system_df.shape[0]} to {self.combined_filename}") with open(self.combined_filename, "a") as f: # write the data to the file f.write(csv_data) + + if companion_df is not None + # check if the file exist, if so it will not write a new header + file_exist = os.path.isfile(self.combined_companion_filename) + # convert dataframe to csv sting + csv_data = companion_df.to_csv(index=False, header=file_exist) - return dataframe + # open the file in append mode + self.logger.info(f"attach {companion_df.shape[0]} to" + \ + f"{self.combined_companion_filename}") + with open(self.combined_companion_filename, "a") as f: + # write the data to the file + f.write(csv_data) + + return system_df, companion_df diff --git a/synthpop/modules/post_processing/convert_mist_mags.py b/synthpop/modules/post_processing/convert_mist_mags.py deleted file mode 100644 index c3d20ce..0000000 --- a/synthpop/modules/post_processing/convert_mist_mags.py +++ /dev/null @@ -1,285 +0,0 @@ -""" -Postprocessing module to convert magnitude systems for any -filters provided by MIST. -""" - -__all__ = ["ConvertMistMags", ] -__author__ = "M.J. Huston" -__date__ = "2024-02-23" - -import pandas -import numpy as np -from ._post_processing import PostProcessing - -class ConvertMistMags(PostProcessing): - """ - Postprocessing module to convert magnitude systems for any - filters provided by MIST. Allowed systems are Vega, AB, and ST. - - Attributes - ---------- - conversions : dict - dictionary defining which filters to convert to which systems; - format: {'NEW_SYSTEM':['FILTER1', 'FILTER2']} - """ - - def __init__(self, model, conversions, logger, **kwargs): - super().__init__(model,logger, **kwargs) - self.conversions = conversions - - def do_post_processing(self, dataframe: pandas.DataFrame) -> pandas.DataFrame: - """ - Perform the magnitude conversions and returns the modified DataFrame. - """ - - # MIST data - # TODO find a better way to do this from the file - # - requires dealing with Roman filter naming discrepancy - mist_filters = np.array(['CFHT_u', 'CFHT_CaHK', 'CFHT_g', 'CFHT_r', 'CFHT_i_new', - 'CFHT_i_old', 'CFHT_z', 'DECam_u', 'DECam_g', 'DECam_r', 'DECam_i', - 'DECam_z', 'DECam_Y', 'GALEX_FUV', 'GALEX_NUV', 'ACS_HRC_F220W', - 'ACS_HRC_F250W', 'ACS_HRC_F330W', 'ACS_HRC_F344N', 'ACS_HRC_F435W', - 'ACS_HRC_F475W', 'ACS_HRC_F502N', 'ACS_HRC_F550M', 'ACS_HRC_F555W', - 'ACS_HRC_F606W', 'ACS_HRC_F625W', 'ACS_HRC_F658N', 'ACS_HRC_F660N', - 'ACS_HRC_F775W', 'ACS_HRC_F814W', 'ACS_HRC_F850LP', - 'ACS_HRC_F892N', 'ACS_WFC_F435W', 'ACS_WFC_F475W', 'ACS_WFC_F502N', - 'ACS_WFC_F550M', 'ACS_WFC_F555W', 'ACS_WFC_F606W', 'ACS_WFC_F625W', - 'ACS_WFC_F658N', 'ACS_WFC_F660N', 'ACS_WFC_F775W', 'ACS_WFC_F814W', - 'ACS_WFC_F850LP', 'ACS_WFC_F892N', 'WFC3_UVIS_F200LP', - 'WFC3_UVIS_F218W', 'WFC3_UVIS_F225W', 'WFC3_UVIS_F275W', - 'WFC3_UVIS_F280N', 'WFC3_UVIS_F300X', 'WFC3_UVIS_F336W', - 'WFC3_UVIS_F343N', 'WFC3_UVIS_F350LP', 'WFC3_UVIS_F373N', - 'WFC3_UVIS_F390M', 'WFC3_UVIS_F390W', 'WFC3_UVIS_F395N', - 'WFC3_UVIS_F410M', 'WFC3_UVIS_F438W', 'WFC3_UVIS_F467M', - 'WFC3_UVIS_F469N', 'WFC3_UVIS_F475W', 'WFC3_UVIS_F475X', - 'WFC3_UVIS_F487N', 'WFC3_UVIS_F502N', 'WFC3_UVIS_F547M', - 'WFC3_UVIS_F555W', 'WFC3_UVIS_F600LP', 'WFC3_UVIS_F606W', - 'WFC3_UVIS_F621M', 'WFC3_UVIS_F625W', 'WFC3_UVIS_F631N', - 'WFC3_UVIS_F645N', 'WFC3_UVIS_F656N', 'WFC3_UVIS_F657N', - 'WFC3_UVIS_F658N', 'WFC3_UVIS_F665N', 'WFC3_UVIS_F673N', - 'WFC3_UVIS_F680N', 'WFC3_UVIS_F689M', 'WFC3_UVIS_F763M', - 'WFC3_UVIS_F775W', 'WFC3_UVIS_F814W', 'WFC3_UVIS_F845M', - 'WFC3_UVIS_F850LP', 'WFC3_UVIS_F953N', 'WFC3_IR_F098M', - 'WFC3_IR_F105W', 'WFC3_IR_F110W', 'WFC3_IR_F125W', 'WFC3_IR_F126N', - 'WFC3_IR_F127M', 'WFC3_IR_F128N', 'WFC3_IR_F130N', 'WFC3_IR_F132N', - 'WFC3_IR_F139M', 'WFC3_IR_F140W', 'WFC3_IR_F153M', 'WFC3_IR_F160W', - 'WFC3_IR_F164N', 'WFC3_IR_F167N', 'WFPC2_F218W', 'WFPC2_F255W', - 'WFPC2_F300W', 'WFPC2_F336W', 'WFPC2_F439W', 'WFPC2_F450W', - 'WFPC2_F555W', 'WFPC2_F606W', 'WFPC2_F622W', 'WFPC2_F675W', - 'WFPC2_F791W', 'WFPC2_F814W', 'WFPC2_F850LP', 'INT_IPHAS_gR', - 'INT_IPHAS_Ha', 'INT_IPHAS_gI', 'JWST_F070W', 'JWST_F090W', - 'JWST_F115W', 'JWST_F140M', 'JWST_F150W2', 'JWST_F150W', - 'JWST_F162M', 'JWST_F164N', 'JWST_F182M', 'JWST_F187N', - 'JWST_F200W', 'JWST_F210M', 'JWST_F212N', 'JWST_F250M', - 'JWST_F277W', 'JWST_F300M', 'JWST_F322W2', 'JWST_F323N', - 'JWST_F335M', 'JWST_F356W', 'JWST_F360M', 'JWST_F405N', - 'JWST_F410M', 'JWST_F430M', 'JWST_F444W', 'JWST_F460M', - 'JWST_F466N', 'JWST_F470N', 'JWST_F480M', 'LSST_u', 'LSST_g', - 'LSST_r', 'LSST_i', 'LSST_z', 'LSST_y', 'PS_g', 'PS_r', 'PS_i', - 'PS_z', 'PS_y', 'PS_w', 'PS_open', 'SDSS_u', 'SDSS_g', 'SDSS_r', - 'SDSS_i', 'SDSS_z', 'SkyMapper_u', 'SkyMapper_v', 'SkyMapper_g', - 'SkyMapper_r', 'SkyMapper_i', 'SkyMapper_z', 'IRAC_3.6', - 'IRAC_4.5', 'IRAC_5.8', 'IRAC_8.0', 'hsc_g', 'hsc_r', 'hsc_i', - 'hsc_z', 'hsc_y', 'hsc_nb816', 'hsc_nb921', 'Swift_UVW2', - 'Swift_UVM2', 'Swift_UVW1', 'Swift_U', 'Swift_B', 'Swift_V', - 'Bessell_U', 'Bessell_B', 'Bessell_V', 'Bessell_R', 'Bessell_I', - '2MASS_J', '2MASS_H', '2MASS_Ks', 'Kepler_Kp', 'Kepler_D51', - 'Hipparcos_Hp', 'Tycho_B', 'Tycho_V', 'Gaia_G_DR2Rev', - 'Gaia_BP_DR2Rev', 'Gaia_RP_DR2Rev', 'TESS', 'Gaia_G_EDR3', - 'Gaia_BP_EDR3', 'Gaia_RP_EDR3', 'UKIDSS_Z', 'UKIDSS_Y', 'UKIDSS_J', - 'UKIDSS_H', 'UKIDSS_K', 'VISTA_Z', 'VISTA_Y', 'VISTA_J', 'VISTA_H', - 'VISTA_Ks', 'Washington_C', 'Washington_M', 'Washington_T1', - 'Washington_T2', 'DDO51_vac', 'DDO51_f31', 'Stromgren_u', - 'Stromgren_v', 'Stromgren_b', 'Stromgren_y', 'R062', - 'Z087', 'Y106', 'J129', 'W146', - 'H158', 'F184', 'WISE_W1', 'WISE_W2', 'WISE_W3', - 'WISE_W4', 'SPLUS_uJAVA', 'SPLUS_gSDSS', 'SPLUS_rSDSS', - 'SPLUS_iSDSS', 'SPLUS_zSDSS', 'SPLUS_J0340', 'SPLUS_J0378', - 'SPLUS_J0395', 'SPLUS_J0410', 'SPLUS_J0515', 'SPLUS_J0660', - 'SPLUS_J0861', 'UVIT_F148W', 'UVIT_F154W', 'UVIT_F169M', - 'UVIT_F172M', 'UVIT_F242W', 'UVIT_N219M', 'UVIT_N245M', - 'UVIT_N263M', 'UVIT_N279N']) - mist_systems = np.array(['AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', - 'AB', 'AB', 'AB', 'AB', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'AB', 'AB', - 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', - 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', - 'Vega', 'Vega', 'Vega', 'Vega', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', - 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', 'Vega', - 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', - 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB', 'AB']) - mist_mag_vega_ab = np.array([ 3.671250e-01, -2.784500e-02, -9.181000e-02, 1.543760e-01, - 3.616700e-01, 3.854910e-01, 5.138370e-01, 3.408350e-01, - -8.959600e-02, 1.824670e-01, 4.058980e-01, 5.169830e-01, - 5.643160e-01, 2.123122e+00, 1.665903e+00, 1.683496e+00, - 1.495635e+00, 1.182286e+00, 1.151053e+00, -8.659100e-02, - -9.393600e-02, -8.359600e-02, 2.428300e-02, -7.055000e-03, - 8.079100e-02, 1.601160e-01, 3.765720e-01, 2.788630e-01, - 3.833350e-01, 4.255120e-01, 5.214850e-01, 4.875050e-01, - -1.016790e-01, -9.787500e-02, -8.368100e-02, 2.451100e-02, - -6.312000e-03, 8.650000e-02, 1.632580e-01, 3.763820e-01, - 2.787100e-01, 3.879550e-01, 4.236630e-01, 5.195860e-01, - 4.888930e-01, 4.513330e-01, 1.691151e+00, 1.660654e+00, - 1.498659e+00, 1.432770e+00, 1.420732e+00, 1.185760e+00, - 1.155674e+00, 1.567650e-01, 8.952060e-01, 9.930700e-02, - 2.159570e-01, -2.098700e-02, -1.559170e-01, -1.520480e-01, - -1.547330e-01, -1.572470e-01, -9.709200e-02, -4.930700e-02, - 1.818820e-01, -8.693600e-02, -3.710000e-04, -2.442000e-02, - 3.240820e-01, 8.301300e-02, 1.461920e-01, 1.480780e-01, - 1.577680e-01, 1.895600e-01, 6.204030e-01, 3.255420e-01, - 3.620800e-01, 2.366110e-01, 2.388950e-01, 2.573280e-01, - 2.768730e-01, 3.788000e-01, 3.807460e-01, 4.182570e-01, - 5.002520e-01, 5.208000e-01, 6.141610e-01, 5.615530e-01, - 6.453020e-01, 7.594890e-01, 9.010520e-01, 9.211300e-01, - 9.612040e-01, 1.037116e+00, 9.761090e-01, 9.973750e-01, - 1.078618e+00, 1.076328e+00, 1.253686e+00, 1.251445e+00, - 1.384753e+00, 1.361575e+00, 1.696001e+00, 1.565908e+00, - 1.336668e+00, 1.183504e+00, -1.455440e-01, -8.495400e-02, - -1.464000e-03, 1.001710e-01, 1.413580e-01, 2.396900e-01, - 4.107450e-01, 4.168170e-01, 5.187360e-01, 1.467310e-01, - 3.586730e-01, 3.938720e-01, 2.778270e-01, 5.114690e-01, - 7.777270e-01, 1.103238e+00, 1.228996e+00, 1.205652e+00, - 1.351846e+00, 1.391996e+00, 1.558015e+00, 1.624210e+00, - 1.675761e+00, 1.780225e+00, 1.801134e+00, 2.116563e+00, - 2.295891e+00, 2.457418e+00, 2.533096e+00, 2.606257e+00, - 2.680412e+00, 2.781285e+00, 2.830344e+00, 3.081301e+00, - 3.072036e+00, 3.170645e+00, 3.207505e+00, 3.333257e+00, - 3.375634e+00, 3.363519e+00, 3.407118e+00, 6.584550e-01, - -9.177600e-02, 1.450080e-01, 3.636270e-01, 5.075140e-01, - 5.436580e-01, -8.780100e-02, 1.435310e-01, 3.632260e-01, - 5.071250e-01, 5.390140e-01, 1.259080e-01, 1.994100e-01, - 9.310960e-01, -1.005920e-01, 1.424690e-01, 3.558530e-01, - 5.179630e-01, 1.104895e+00, 3.119500e-01, -5.823300e-02, - 1.278690e-01, 4.008760e-01, 5.259820e-01, 2.785147e+00, - 3.258136e+00, 3.750716e+00, 4.391595e+00, -9.538100e-02, - 1.438140e-01, 3.864800e-01, 5.139290e-01, 5.484350e-01, - 4.695170e-01, 5.408240e-01, 1.734154e+00, 1.686980e+00, - 1.510161e+00, 1.013124e+00, -1.158850e-01, -4.726000e-03, - 8.005270e-01, -1.075120e-01, 6.521000e-03, 1.902780e-01, - 4.313720e-01, 8.891760e-01, 1.364157e+00, 1.834505e+00, - 1.226660e-01, -5.223600e-02, 7.642000e-03, -6.085500e-02, - -1.422800e-02, 1.238910e-01, 6.559000e-02, 3.686690e-01, - 3.637780e-01, 1.289610e-01, 3.023300e-02, 3.733490e-01, - 5.131270e-01, 6.148780e-01, 9.152450e-01, 1.352530e+00, - 1.871458e+00, 5.659320e-01, 6.046970e-01, 9.207850e-01, - 1.360424e+00, 1.817194e+00, 3.324540e-01, -5.143600e-02, - 1.857420e-01, 4.499720e-01, -5.757900e-02, -5.997500e-02, - 1.135368e+00, -1.321360e-01, -1.529660e-01, 2.927000e-03, - 1.370950e-01, 4.873790e-01, 6.537800e-01, 9.583630e-01, - 1.024467e+00, 1.287404e+00, 1.551332e+00, 2.665543e+00, - 3.305247e+00, 5.139422e+00, 6.614602e+00, 1.095256e+00, - -9.441200e-02, 1.517590e-01, 3.837210e-01, 5.151610e-01, - -1.326650e-01, 5.846980e-01, 2.552300e-02, -1.544190e-01, - -5.977600e-02, 3.051520e-01, 5.300250e-01, 2.301383e+00, - 2.318347e+00, 2.014355e+00, 1.913212e+00, 1.628923e+00, - 1.693668e+00, 1.662303e+00, 1.538884e+00, 1.482277e+00]) - mist_mag_vega_st = np.array([-4.2453900e-01, -7.3632100e-01, -3.5765100e-01, 4.4090000e-01, - 1.0558410e+00, 1.1197090e+00, 1.5589800e+00, -4.2018400e-01, - -3.6639600e-01, 5.2909300e-01, 1.1762060e+00, 1.6340430e+00, - 1.8431400e+00, -6.3168400e-01, -2.1647100e-01, -2.4251600e-01, - -2.6944000e-02, 1.2369300e-01, 1.3786700e-01, -6.0585800e-01, - -3.9098500e-01, -2.7089000e-01, 6.5311000e-02, -5.5068000e-02, - 2.3804800e-01, 4.6304000e-01, 7.7688800e-01, 6.8420400e-01, - 1.1136860e+00, 1.2799880e+00, 1.6351120e+00, 1.5463410e+00, - -6.1269900e-01, -4.0860600e-01, -2.7092600e-01, 6.6147000e-02, - -5.2478000e-02, 2.5580100e-01, 4.7170100e-01, 7.7677400e-01, - 6.8418100e-01, 1.1264650e+00, 1.2632230e+00, 1.6075170e+00, - 1.5474340e+00, 2.4041900e-01, -2.6088300e-01, -1.5517800e-01, - -2.8405000e-02, 2.3350000e-03, -1.8331000e-02, 1.2204900e-01, - 1.4342700e-01, 3.0319200e-01, 6.1798000e-02, -6.3893600e-01, - -5.0721700e-01, -7.2719800e-01, -7.7929600e-01, -6.6343100e-01, - -4.9436700e-01, -4.9431300e-01, -3.9489200e-01, -2.7209800e-01, - -7.1910000e-02, -2.7995500e-01, -1.1458000e-02, -9.1758000e-02, - 9.9295800e-01, 2.4057600e-01, 4.2261000e-01, 4.3239600e-01, - 4.6392700e-01, 5.4651500e-01, 1.0130880e+00, 7.2020200e-01, - 7.6242800e-01, 6.6058900e-01, 6.9847600e-01, 7.5241900e-01, - 7.7160100e-01, 1.0946900e+00, 1.1065670e+00, 1.2496560e+00, - 1.4392770e+00, 1.6403220e+00, 1.8176630e+00, 1.8399290e+00, - 2.0697290e+00, 2.3774240e+00, 2.6911290e+00, 2.7283290e+00, - 2.7950470e+00, 2.8865080e+00, 2.8547220e+00, 2.9061790e+00, - 3.0918720e+00, 3.1029280e+00, 3.4882180e+00, 3.4926500e+00, - 3.7673750e+00, 3.7755000e+00, -2.7839700e-01, -5.0464000e-02, - 2.6125000e-02, 1.2263000e-01, -6.6436800e-01, -4.8428900e-01, - -1.5900000e-02, 2.9772700e-01, 4.0646300e-01, 6.8364000e-01, - 1.2014840e+00, 1.2435290e+00, 1.6287460e+00, 4.2295500e-01, - 7.5381800e-01, 1.1435470e+00, 8.0711300e-01, 1.5965320e+00, - 2.3978710e+00, 3.1502330e+00, 3.6382580e+00, 3.3954630e+00, - 3.7172150e+00, 3.7802540e+00, 4.1961670e+00, 4.2958910e+00, - 4.4761470e+00, 4.6944070e+00, 4.7419690e+00, 5.4172440e+00, - 5.8182820e+00, 6.1477380e+00, 6.3857630e+00, 6.4648860e+00, - 6.6200670e+00, 6.8458480e+00, 6.9330090e+00, 7.4272330e+00, - 7.4340690e+00, 7.6364140e+00, 7.7334860e+00, 7.9690360e+00, - 8.0227370e+00, 8.0352300e+00, 8.1287120e+00, -2.1306600e-01, - -3.7408000e-01, 4.1836300e-01, 1.0574610e+00, 1.5094480e+00, - 1.7864730e+00, -3.5154200e-01, 4.1384500e-01, 1.0565780e+00, - 1.5062110e+00, 1.7645760e+00, 4.2568800e-01, 7.1531000e-01, - -5.8240000e-03, -4.3100000e-01, 4.0380800e-01, 1.0362040e+00, - 1.5842270e+00, 1.4326700e-01, -4.6078900e-01, -2.2300200e-01, - 3.7612000e-01, 1.1603680e+00, 1.6401640e+00, 6.8484640e+00, - 7.8331230e+00, 8.8551300e+00, 1.0192009e+01, -3.8523200e-01, - 4.1863100e-01, 1.1237240e+00, 1.6006750e+00, 1.8062330e+00, - 1.3403760e+00, 1.6710340e+00, -3.9078700e-01, -2.4710200e-01, - -1.2167600e-01, 2.0865000e-02, -6.1572700e-01, -2.4917000e-02, - -1.1203500e-01, -5.9422100e-01, 1.1340000e-02, 5.6846200e-01, - 1.2496360e+00, 2.6566860e+00, 3.7538420e+00, 4.8150350e+00, - 4.2847300e-01, -1.7811900e-01, -4.3887000e-02, -6.4199600e-01, - -8.4969000e-02, 4.1011400e-01, -1.1059100e-01, 1.1205610e+00, - 1.1543000e+00, 4.0502200e-01, -1.1983700e-01, 1.1331290e+00, - 1.5499540e+00, 1.9900890e+00, 2.7080050e+00, 3.7294790e+00, - 4.8974220e+00, 1.8362330e+00, 2.0081160e+00, 2.7464110e+00, - 3.7484430e+00, 4.7822630e+00, -4.3285000e-01, -2.0564600e-01, - 5.3108700e-01, 1.2973270e+00, -1.9297400e-01, -2.0044200e-01, - 1.5028000e-01, -7.5607200e-01, -5.0066200e-01, -3.5780000e-03, - 4.2970200e-01, 1.4956060e+00, 2.0907050e+00, 2.8270250e+00, - 3.1324160e+00, 3.5898960e+00, 4.1892780e+00, 6.6104970e+00, - 7.9354230e+00, 1.1856446e+01, 1.4653819e+01, 1.4411200e-01, - -3.9911900e-01, 4.3972500e-01, 1.1158290e+00, 1.5801570e+00, - -6.6136400e-01, -2.2384300e-01, -6.8866100e-01, -7.8523800e-01, - -1.9990700e-01, 7.1537300e-01, 1.5122920e+00, -5.1147800e-01, - -5.0769500e-01, -6.5505100e-01, -6.0514500e-01, -1.4936100e-01, - -2.7566700e-01, -8.6757000e-02, -4.0787000e-02, 1.9904000e-02]) - - for system_new in self.conversions.keys(): - for filt in self.conversions[system_new]: - if filt in dataframe.keys(): - idx = np.where(mist_filters==filt)[0][0] - system_old = mist_systems[idx] - if system_old==system_new: - print(filt,'already in system',system_new) - elif system_old=='Vega' and system_new=='AB': - dataframe[filt] = dataframe[filt] + mist_mag_vega_ab[idx] - elif system_old=='Vega' and system_new=='ST': - dataframe[filt] = dataframe[filt] + mist_mag_vega_st[idx] - elif system_old=='AB' and system_new=='Vega': - dataframe[filt] = dataframe[filt] - mist_mag_vega_ab[idx] - elif system_old=='ST' and system_new=='Vega': - dataframe[filt] = dataframe[filt] - mist_mag_vega_st[idx] - elif system_old=='AB' and system_new=='ST': - dataframe[filt] = dataframe[filt] - mist_mag_vega_ab[idx] + mist_mag_vega_st[idx] - elif system_old=='ST' and system_new=='AB': - dataframe[filt] = dataframe[filt] - mist_mag_vega_st[idx] + mist_mag_vega_ab[idx] - else: - raise ValueError('Invalid magnitude system conversion: '+system_old+ - ' -> '+system_new) - return dataframe diff --git a/synthpop/modules/post_processing/equatorial_coordinates.py b/synthpop/modules/post_processing/equatorial_coordinates.py index 9d77051..72c44aa 100644 --- a/synthpop/modules/post_processing/equatorial_coordinates.py +++ b/synthpop/modules/post_processing/equatorial_coordinates.py @@ -6,7 +6,7 @@ __author__ = "M.J. Huston" __date__ = "2025-06-30" -import pandas +import pandas as pd import numpy as np from ._post_processing import PostProcessing from synthpop.synthpop_utils.coordinates_transformation import lb_to_ad, uvw_to_vrmuad @@ -25,29 +25,30 @@ def __init__(self, model, logger, keep_galactic=False, **kwargs): super().__init__(model,logger, **kwargs) self.keep_galactic = keep_galactic - def do_post_processing(self, dataframe: pandas.DataFrame) -> pandas.DataFrame: + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): """ Perform the magnitude conversions and returns the modified DataFrame. """ - # Get ra, dec from l, b & add to dataframe - l_arr, b_arr = dataframe['l'].to_numpy(), dataframe['b'].to_numpy() + # Get ra, dec from l, b & add to system_df + l_arr, b_arr = system_df['l'].to_numpy(), system_df['b'].to_numpy() ra_arr, dec_arr = lb_to_ad(l_arr,b_arr) - l_idx = np.where(dataframe.columns=='l')[0][0] - dataframe.insert(l_idx, 'ra', ra_arr) - dataframe.insert(l_idx+1, 'dec', dec_arr) + l_idx = np.where(system_df.columns=='l')[0][0] + system_df.insert(l_idx, 'ra', ra_arr) + system_df.insert(l_idx+1, 'dec', dec_arr) - # Get mura, mudec from coords and kinemaitcs & add to dataframe - dist_arr = dataframe['Dist'].to_numpy() - u_arr, v_arr, w_arr = dataframe['U'].to_numpy(), dataframe['V'].to_numpy(), dataframe['W'].to_numpy() + # Get mura, mudec from coords and kinemaitcs & add to system_df + dist_arr = system_df['Dist'].to_numpy() + u_arr, v_arr, w_arr = system_df['U'].to_numpy(), system_df['V'].to_numpy(), system_df['W'].to_numpy() vr_arr, mura_arr, mudec_arr = uvw_to_vrmuad(l_arr,b_arr,dist_arr, u_arr,v_arr,w_arr) - mul_idx = np.where(dataframe.columns=='mul')[0][0] - dataframe.insert(mul_idx, 'mura', mura_arr) - dataframe.insert(mul_idx+1, 'mudec', mudec_arr) + mul_idx = np.where(system_df.columns=='mul')[0][0] + system_df.insert(mul_idx, 'mura', mura_arr) + system_df.insert(mul_idx+1, 'mudec', mudec_arr) # Remove galactic coordinates if selected if not self.keep_galactic: - dataframe.drop(columns=['l','b'],inplace=True) - dataframe.drop(columns=['mul','mub'],inplace=True) + system_df.drop(columns=['l','b'],inplace=True) + system_df.drop(columns=['mul','mub'],inplace=True) - return dataframe + return system_df, companion_df diff --git a/synthpop/modules/post_processing/extinction_correction_table.dat b/synthpop/modules/post_processing/extinction_correction_table.dat new file mode 100644 index 0000000..92b9da6 --- /dev/null +++ b/synthpop/modules/post_processing/extinction_correction_table.dat @@ -0,0 +1,90 @@ +Title: +Authors: +Table: +================================================================================ +Byte-by-byte Description of file: table.dat +-------------------------------------------------------------------------------- + Bytes Format Units Label Explanations +-------------------------------------------------------------------------------- + 1- 32 A32 --- Filter Description of Filter + 34- 70 A37 --- Color1 Description of Color1 + 72-104 A33 --- Color2 ? Description of Color2 +106-138 A33 --- Color3 ? Description of Color3 +140-158 F19.16 --- a_0 [0.99/36.95] Description of a_0 +160-181 F22.18 --- a_1 [-27.78/1.11] Description of a_1 +183-205 E23.16 --- a_2 [-2.29/12.37] Description of a_2 +207-228 E22.16 --- a_3 [-2.57/0.97] Description of a_3 +230-253 E24.17 --- a_4 [-0.1/0.2] Description of a_4 +255-276 F22.18 --- b_110 [-11.5/2.44] Description of b_110 +278-299 F22.19 --- b_111 [-0.38/9.52] Description of b_111 +301-322 E22.16 --- b_112 [-1.74/0.08] Description of b_112 +324-347 E24.17 --- b_113 [-0.01/0.13] Description of b_113 +349-371 F23.20 --- b_120 [-2.33/4.4] Description of b_120 +373-394 E22.16 --- b_121 [-0.54/0.71] Description of b_121 +396-419 E24.17 --- b_122 [-0.05/0.03] Description of b_122 +421-444 E24.17 --- b_130 [-2.45/5.3] Description of b_130 +446-468 E23.16 --- b_131 [-0.33/0.25] Description of b_131 +470-492 F23.20 --- b_140 [-2.24/3.49] Description of b_140 +494-515 F22.19 --- b_210 ? Description of b_210 +517-537 F21.18 --- b_211 ? Description of b_211 +539-560 F22.19 --- b_212 ? Description of b_212 +562-584 E23.16 --- b_213 ? Description of b_213 +586-606 F21.17 --- b_220 ? Description of b_220 +608-628 F21.18 --- b_221 ? Description of b_221 +630-651 F22.19 --- b_222 ? Description of b_222 +653-674 F22.18 --- b_230 ? Description of b_230 +676-697 F22.19 --- b_231 ? Description of b_231 +699-720 F22.19 --- b_240 ? Description of b_240 +722-740 F19.16 --- b_310 ? Description of b_310 +742-761 F20.17 --- b_311 ? Description of b_311 +763-782 F20.17 --- b_312 ? Description of b_312 +784-804 F21.18 --- b_313 ? Description of b_313 +806-826 F21.18 --- b_320 ? Description of b_320 +828-847 F20.17 --- b_321 ? Description of b_321 +849-870 F22.19 --- b_322 ? Description of b_322 +872-891 F20.17 --- b_330 ? Description of b_330 +893-913 F21.18 --- b_331 ? Description of b_331 +915-935 F21.18 --- b_340 ? Description of b_340 +-------------------------------------------------------------------------------- +Notes: +-------------------------------------------------------------------------------- +roman_wfi_f062 roman_wfi_f062-roman_wfi_f087-abs 14.7123663260254087 -2.596867985740326734 9.806053943160686e-01 -1.852356320170750e-01 1.3328763057688089e-02 -1.069045322554924349 0.5772518421376340303 -1.317858599029247e-01 1.0641112578956561e-02 0.12700401881981115970 -4.724768881088771e-02 5.4357039395773873e-03 -3.5176539059845573e-02 2.384723640354900e-03 0.00675347281993690478 +roman_wfi_f087 roman_wfi_f062-roman_wfi_f087-abs roman_wfi_f087-roman_wfi_f106-abs 8.1857959032684580 -0.944610769301591491 2.748909040594562e-01 -4.341468269134552e-02 2.7493030214887780e-03 0.049654247123488877 0.0123573243809215524 -9.181487374697619e-03 1.2083971403008217e-03 -0.09773936952102713072 2.090221593539373e-02 -2.0402645563374188e-03 2.2812415952436747e-02 -1.125236797015310e-03 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b/synthpop/modules/post_processing/extinction_estimator.py new file mode 100644 index 0000000..c088451 --- /dev/null +++ b/synthpop/modules/post_processing/extinction_estimator.py @@ -0,0 +1,308 @@ +""" +Postprocessing module to improve the estimation extinction in photometric filters +with a polynomial function of A_Ks and absolute colors. +""" + +__all__ = ["ExtinctionEstimator", ] +__author__ = "M.J. Huston" +__date__ = "2026-04-26" + +import pandas as pd +import numpy as np +from ._post_processing import PostProcessing +import os.path +import json +import warnings +import pdb +from astropy.table import Table +from ...synthpop_utils.utils_functions import combine_system_mags, get_primary_mags +from ..evolution._evolution import EVOLUTION_DIR + +class ExtinctionEstimator(PostProcessing): + """ + Postprocessing module to apply a correction to F146 extinction + + Attributes + ---------- + """ + + def __init__(self, model, logger, **kwargs): + super().__init__(model,logger, **kwargs) + # Load up the fit results + current_dir = os.path.dirname(os.path.abspath(__file__)) + ext_cor_tab = Table.read(f'{current_dir}/extinction_correction_table.dat', format='ascii.mrt') + ext_cor_tab = ext_cor_tab.filled(999) + self.fit_dict = {} + color_cols = [col for col in ext_cor_tab.colnames if col.startswith('Color')] + a_cols = [col for col in ext_cor_tab.colnames if col.startswith('a_')] + coeff_cols = [col for col in ext_cor_tab.colnames if (col[:2] in ['a_', 'b_'])] + self.fit_order = len(a_cols)-1 + for i in range(len(ext_cor_tab)): + filt = ext_cor_tab['Filter'][i] + self.fit_dict[filt] = {} + self.fit_dict[filt]['colors'] = [ext_cor_tab[col][i] for col in color_cols if (ext_cor_tab[col][i]!="999")] + self.fit_dict[filt]['order'] = self.fit_order + self.fit_dict[filt]['coefficients'] = [ext_cor_tab[col][i] for col in coeff_cols if ~(ext_cor_tab[col][i]==999.0)] + with open(f"{EVOLUTION_DIR}/spisea_photometric_system_conversions.json") as f: + self.photsys_convert = json.load(f) + + @staticmethod + def generic_extinction_polynomial(AKs_C, coeffs, order): + """ + Generic polynomial function of flexible order. Any number of colors + may be input, and cross-terms are computed between A_Ks and each color, + not between different colors. + + Identical function to that used to run the fits. + + Parameters: + ----------- + AKs_C : ndarray + array of extinction and color values to compute extinction estimate + coeffs : ndarray + array of coefficients for the polynomial function + order : int + polynomial order for the extinction coefficient function + + Returns: + -------- + ext_ests : ndarray + extinction estimates for each star in the AKs_C table + """ + n_colors = AKs_C.shape[1] - 1 + n_terms_AKs = order + 1 + n_terms_per_color = order * (order + 1) // 2 + n_terms = n_terms_AKs + (n_colors * n_terms_per_color) + assert n_terms == len(coeffs) + + AKs = AKs_C[:,0] + var_terms = [AKs**p for p in range(order+1)] + n_colors = AKs_C.shape[1]-1 + for i in range(0, n_colors): + Ci = AKs_C[:, i+1] + for p in range(1, order+1): + for q in range(order+1): + if p + q <= order: + var_terms.append((Ci**p) * (AKs**q)) + terms_mat = np.column_stack(var_terms) + val = terms_mat @ np.array(coeffs) + return val * AKs + + def get_roman_extinction_sim(self, catalog): + """ + Roman extinction estimator for simulations. Assumes all Roman filter photometry + is provided and in absolute AB mags. + + Parameters: + ----------- + catalog : pd.DataFrame, astropy.table.Table, or similar + required columns: A_Ks, f062, f087, f106, f129, f158, f184, f213, and f146 + low_extinction=False : boolean + if all A_Ks<=1, use the alternate lower order correction. if any A_Ks>1, a warning + will be printed, and the higher order correction will be used + + Returns: + -------- + extinctions : dict + entries of '':[, , ...] for each filter + """ + # Select the appropriate fit_dict + # fit_dict = self.roman_ext_fits + # if self.use_low_extinction and np.all(catalog['A_Ks']<=1): + # fit_dict = self.roman_ext_fits_lowext + # elif self.use_low_extinction: + # warnings.warn("low_extinction set to True, but some A_Ks > 1. " + # "switching to 0 <= A_Ks <= 5 fit.") + + # Iterate over the filters + catalog = self.convert_mags_to_ab(catalog) + result = {} + for filt in self.filter_list: + filt_valid = (filt in catalog) + filt_fit = self.fit_dict[filt] + colors = filt_fit['colors'] + coeffs = filt_fit['coefficients'] + order = filt_fit['order'] + self.logger.debug(f"Estimating {filt} extinction using {colors} and order={order} function") + + columns = [catalog['A_Ks']] + for c in colors: + f1,f2,_ = c.split('-') + filt_valid *= (f1 in catalog) + filt_valid *= (f2 in catalog) + if filt_valid: + columns.append(catalog[f1]-catalog[f2]) + else: + columns.append(np.ones(len(catalog))*np.nan) + AKs_C = np.stack(columns,axis=1) + + ext_filt = self.generic_extinction_polynomial(AKs_C, coeffs, order) + result['A_'+filt] = ext_filt + + return result + + def convert_mags_to_ab(self, ext_est_dict): + for i, f in enumerate(self.full_filter_list_obs_str): + if self.model.parms.photsys_dict[self.model.parms.bands[i]] == 'AB': + pass + elif self.model.parms.photsys_dict[self.model.parms.bands[i]] == 'Vega': + ext_est_dict[self.full_filter_list[i]] += self.photsys_convert['AB'][f] + elif self.model.parms.photsys_dict[self.model.parms.bands[i]] == 'ST': + ext_est_dict[self.full_filter_list[i]] -= self.photsys_convert['ST'][f] + ext_est_dict[self.full_filter_list[i]] += self.photsys_convert['AB'][f] + return ext_est_dict + + def do_post_processing(self, systems: pd.DataFrame, companions: pd.DataFrame): + """ + Run the process + """ + + # Catch case where we don't have K213 and need to swap in 2MASS_Ks + if 'W146' in systems: + if "2MASS_Ks" in systems: + systems.loc[:,"K213"] = systems['2MASS_Ks'] + if companions is not None: + companions.loc[:,"K213"] = companions['2MASS_Ks'] + warnings.warn("K213 missing from MISTv1, estimating from 2MASS_Ks.") + self.model.parms.eff_wavelengths['K213'] = self.model.parms.eff_wavelengths['2MASS_Ks'] + self.model.parms.photsys_dict["K213"] = self.model.parms.photsys_dict["2MASS_Ks"] + self.model.parms.bands += ["K213"] + elif "VISTA_Ks" in systems: + systems.loc[:,"K213"] = systems['VISTA_Ks'] + if companions is not None: + companions.loc[:,"K213"] = companions['VISTA_Ks'] + warnings.warn("K213 missing from MISTv1, estimating from VISTA_Ks.") + self.model.parms.eff_wavelengths['K213'] = self.model.parms.eff_wavelengths['VISTA_Ks'] + self.model.parms.photsys_dict["K213"] = self.model.parms.photsys_dict["VISTA_Ks"] + self.model.parms.bands += ["K213"] + + # Set up filter sets + if self.model.parms.star_generator=='SpiseaGenerator': + from spisea.synthetic import get_obs_str + self.full_filter_list_obs_str = [get_obs_str(f) for f in self.model.parms.bands] + else: + from ..evolution.mist import get_spisea_obs_str + self.full_filter_list_obs_str = [get_spisea_obs_str(f) for f in self.model.parms.bands] + self.full_filter_list = [f.replace(',','_') for f in self.full_filter_list_obs_str] + self.correct_mag_cols = self.model.parms.bands + self.filter_list = [f for f in self.full_filter_list if (f in self.fit_dict.keys())] + if len(self.filter_list) pandas.DataFrame: - """ - - Parameters - ---------- - dataframe : dataframe - original SynthPop output as pandas data frame - - Returns - ------- - dataframe : dataframe - modified pandas data frame - - """ + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): + if companion_df is not None: + raise ValueError("Must run combine_tables postproc before"+ \ + "gulls_post_processing") + filtlist = self.model.parms.chosen_bands + # convert l, b to ra, dec - dataframe["l(deg)"] = dataframe.iloc[:, 7] # l - dataframe["b(deg)"] = dataframe.iloc[:, 8] # b - ra, dec = lb_to_ad(dataframe["l(deg)"], dataframe["b(deg)"]) - dataframe["RA2000.0"] = ra - dataframe["DEC2000.0"] = dec + system_df["l(deg)"] = system_df['l'] # l + system_df["b(deg)"] = system_df['b'] # b + ra, dec = lb_to_ad(system_df["l(deg)"], system_df["b(deg)"]) + system_df["RA2000.0"] = ra + system_df["DEC2000.0"] = dec # convert columns - dataframe["[Fe/H]"] = dataframe["Fe/H_evolved"] - dataframe["Mbol"] = -2.5 * dataframe["logL"] + 4.75 - dataframe["Teff"] = 10 ** dataframe["logTeff"] - dataframe["[alpha/Fe]"] = 0 - dataframe["Radius"] = 10 ** dataframe["log_radius"] - dataframe["CL"] = dataframe["phase"] - dataframe["Vr"] = dataframe['vr_bc'] + system_df["logg"] = system_df["log_g"] + system_df["Mbol"] = -2.5 * system_df["log_L"] + 4.75 + system_df["Teff"] = 10 ** system_df["log_Teff"] + system_df["[alpha/Fe]"] = 0 + system_df["Radius"] = 10 ** system_df["log_R"] + system_df["CL"] = system_df["phase"] + system_df["Vr"] = system_df['vr_bc'] + + # For binary systems, get the primary magnitudes + if 'Is_Binary' in system_df.columns: + # Copy original magitude columns to combined magnitude columns + for filt in filtlist: + system_df[f"combined_{filt}"] = system_df[filt] - # compute an approximate magnitude for Roman F213 from 2MASS Ks - k213 = dataframe["2MASS_Ks"] + 1.834505 - + # Add in an index column before copying, this let's you map the primaries back later + # have to do a weird thing to make sure they're unique -- I think this is because of something + # that carries over from combine_tables maybe? Since SP doesn't save the dataframe between + # the post-processing steps, I'm guessing those indices never get reset + if not system_df.index.is_unique: + system_df = system_df.reset_index(drop=True) + system_df["original_df_pos"] = np.arange(len(system_df)) + # Create separate tables for primaries and secondaries -- this makes operations more efficient + # despite creating a need to re-index results, because they're performed across smaller dateframes + primaries = system_df[system_df['Is_Binary'] == 1].copy() + secondaries = system_df[system_df['Is_Binary'] == 2].copy() + + # Set the index to the primary ID so it can be used in a join + # Would've been unnecessary if I didn't need to do the reset_index above, but I can't figure + # out a better way to make sure they get inserted back into the right place w/o looping over rows + # (and defeating the purpose of all this) + primaries = primaries.set_index('primary_ID') + secondaries = secondaries.set_index('primary_ID') + + # Combine primary and secondary tables on primary_ID and add columns for system vs secondary + joined = primaries.join(secondaries, lsuffix='_sys', rsuffix='_sec', how='inner') + + # Get indices for mapping values back + # System + idx_system = joined['original_df_pos_sys'].to_numpy() + row_pos_system = system_df.index.get_indexer(idx_system) + valid_system = (row_pos_system!= -1) + + # Secondaries + idx_secondary = joined['original_df_pos_sec'].to_numpy() + row_pos_secondary = system_df.index.get_indexer(idx_secondary) + valid_secondary = (row_pos_secondary != -1) + + # Assign the primaries the secondaries' q and map back + joined['q_sys'] = joined['q_sec'] + q_col_pos = system_df.columns.get_loc('q') + if valid_system.any(): + system_df.iloc[row_pos_system[valid_system], q_col_pos] = joined['q_sys'].to_numpy() + + # Assign the secondaries the primaries' combined_logP and map back + joined['combined_logP_sec'] = joined['combined_logP_sys'] + logP_col_pos = system_df.columns.get_loc('combined_logP') + if valid_secondary.any(): + system_df.iloc[row_pos_secondary[valid_secondary], logP_col_pos] = joined['combined_logP_sec'].to_numpy() + + # Separate out primary's magnitude and replace original magnitude + for filt in filtlist: + # Set columns, more efficient access this way since we added the suffixes on join + sys_col = f"{filt}_sys" + sec_col = f"{filt}_sec" + + # Assign the secondaries the combined magnitude + joined[f"combined_{filt}_sec"] = joined[f"{filt}_sys"] + + # Get primary's flux -> magnitude + flux_system = 10.0 ** (-0.4 * joined[sys_col].values) + flux_secondary = 10.0 ** (-0.4 * joined[sec_col].values) + + with np.errstate(divide='ignore', invalid='ignore'): + # mag_primary = -2.5 * np.log10(flux_system - flux_secondary) + # TODO: write this more similarly to how single stars are done, if possible + # If flux_secondary is NaN for a given object, treat the + # system flux as just the primary flux. Otherwise use + # the difference (system - secondary). + flux_primary = flux_system - flux_secondary + mag_primary = np.empty_like(flux_primary) # make an array same length as flux array + mag_primary.fill(np.nan) # make nan by default + sec_nan_mask = np.isnan(flux_secondary) # make mask for where secondary is NaN + # If secondary flux is NaN, calculate primary as just the system + if sec_nan_mask.any(): + mag_primary[sec_nan_mask] = -2.5 * np.log10(flux_system[sec_nan_mask]) + # If secondary flux isn't NaN, calculate primary normally + sec_not_nan_mask = ~sec_nan_mask + if sec_not_nan_mask.any(): + mag_primary[sec_not_nan_mask] = -2.5 * np.log10(flux_primary[sec_not_nan_mask]) + + # The error state thing makes it a numpy array, need to go back to Series + mag_primary = pd.Series(mag_primary, index=joined[sys_col].index) + + # Forcing the calculation results in some infs, so need to get + # rid of those. Also need to make sure any cases where the + # secondary is the non-dark object are accounted for. Doing this + # weird pandas masking gives the same behavior as the subtract_magnitudes + # function in utils_functions, but in a pandas-friendly way that + # let's me keep everything "vectorized" or whatever + mag_primary = (mag_primary.replace([np.inf, -np.inf], np.nan) + .mask(np.isclose(joined[sys_col], + joined[sec_col], + equal_nan=True)) + ) + + + # This + conversion back to mag could be one operation, but I think doing it this way + # keeps it vectorized longer? + # flux_primary = flux_system - flux_secondary + # # If flux > 0, convert to magnitude; nan if not + # mag_primary = np.where(flux_primary > 0, + # -2.5*np.log10(flux_primary), + # np.nan) + + + # I don't fully understand why, but according to StackOverflow, I need to do this to + # stop it from complaining about equal len keys and values when trying to map back the primaries + # idx_system = joined['original_df_pos_sys'].to_numpy() + # row_pos_system = system_df.index.get_indexer(idx_system) + # valid_system = (row_pos_system != -1) + filt_col_pos_system = system_df.columns.get_loc(filt) + if valid_system.any(): + system_df.iloc[row_pos_system[valid_system], filt_col_pos_system] = mag_primary + + # idx_secondary = joined['original_df_pos_sec'].to_numpy() + # row_pos_secondary = system_df.index.get_indexer(idx_secondary) + # valid_secondary = (row_pos_secondary != -1) + comb_col_pos_secondary = system_df.columns.get_loc(f"combined_{filt}") + if valid_secondary.any(): + system_df.iloc[row_pos_secondary[valid_secondary], comb_col_pos_secondary] = joined[f"combined_{filt}_sec"] + # reduce data frame to the needed columns - filtlist = self.model.parms.chosen_bands cols = filtlist + ["mul", "mub", "Vr", "U", "V", "W", "iMass", "CL", "age", "Teff", "logg", "pop", "Mass", "Mbol", "Radius", "[Fe/H]","l", "b", "RA2000.0", "DEC2000.0", "Dist", "x", "y", "z", "A_Ks", "[alpha/Fe]"] - dataframe = dataframe[cols] + + # gulls needs additional columns when handling binaries + # if running catalogs to include in a gulls run that includes binaries, + # need to include these columns. + # HOWEVER, it does not need them and will not be able to process them + # if running gulls simulations that are only single-sources or single + # (non-planetary) lenses. So, don't want to just universally include these. + if 'Is_Binary' in system_df.columns: + binary_cols = ['Is_Binary', 'primary_ID', 'ID', + 'total_mass', 'q', 'combined_logP', 'eccentricity'] + for filt in filtlist: + binary_cols.append(f"combined_{filt}") + + cols = cols + binary_cols + + system_df = system_df[cols] + # compute an approximate magnitude for Roman F213 from 2MASS Ks + k213 = system_df["2MASS_Ks"] + 1.834505 # Get index of F184 band and insert K213 after that - idx = dataframe.columns.get_loc("F184")+1 - dataframe.insert(idx, "K213", k213) + idx = system_df.columns.get_loc("F184")+1 + system_df.insert(idx, "K213", k213) + + # Also add to the primary magnitudes if binary + if 'Is_Binary' in system_df.columns: + combined_k213 = system_df["combined_2MASS_Ks"] + 1.834505 + idx = system_df.columns.get_loc("combined_F184") + 1 + system_df.insert(idx, "combined_K213", combined_k213) # Replace NaNs with 99 for magnitude columns, and a non-physical # very small value for other parameters. for filt in filtlist: - dataframe.loc[:, filt] = dataframe.loc[:,filt].fillna(99) - dataframe.loc[:, "K213"] = dataframe.loc[:,"K213"].fillna(99) - dataframe.loc[:, "Mbol"] = dataframe.loc[:,"Mbol"].fillna(99) - dataframe = dataframe.fillna(value=2e-50) + system_df.loc[:, filt] = system_df.loc[:,filt].fillna(99) + system_df.loc[:, "K213"] = system_df.loc[:,"K213"].fillna(99) + system_df.loc[:, "Mbol"] = system_df.loc[:,"Mbol"].fillna(99) + if 'Is_Binary' in system_df.columns: + for filt in filtlist: + system_df.loc[:, f"combined_{filt}"] = system_df.loc[:, f"combined_{filt}"].fillna(99) + system_df.loc[:, "combined_K213"] = system_df.loc[:, "combined_K213"].fillna(99) + system_df = system_df.fillna(value=2e-50) + system_df = system_df.replace(-np.inf, 2e-50) # takes care of -inf from combining logL when it's a NaN single or both-NaN binary # Impose magnitude lower limits based on Roman expectations @@ -99,15 +239,15 @@ def do_post_processing(self, dataframe: pandas.DataFrame) -> pandas.DataFrame: pass elif self.cat_type == "bright": - dataframe = dataframe.loc[dataframe["W146"] > 12] + system_df = system_df.loc[system_df["W146"] > 12] elif self.cat_type == "mid1": - dataframe = dataframe.loc[dataframe["W146"] > 17] + system_df = system_df.loc[system_df["W146"] > 17] elif self.cat_type == "mid2": - dataframe = dataframe.loc[dataframe["W146"] > 20] + system_df = system_df.loc[system_df["W146"] > 20] elif self.cat_type == "faint": - dataframe = dataframe.loc[dataframe["W146"] > 24] + system_df = system_df.loc[system_df["W146"] > 24] - return dataframe + return system_df, companion_df diff --git a/synthpop/modules/post_processing/natal_kicks.py b/synthpop/modules/post_processing/natal_kicks.py new file mode 100644 index 0000000..82a4475 --- /dev/null +++ b/synthpop/modules/post_processing/natal_kicks.py @@ -0,0 +1,79 @@ +""" +Post-processing to add random kick velocities to neutron stars and black holes, according to +a Maxwellian distribution with a user-input mean. +""" + +__all__ = ["NatalKicks", ] +__author__ = "M.J. Huston" +__date__ = "2025-10-15" + +import pandas as pd +import numpy as np +from ._post_processing import PostProcessing +from scipy.stats import maxwell, uniform_direction +from synthpop.synthpop_utils.coordinates_transformation import CoordTrans +import pdb + +class NatalKicks(PostProcessing): + """ + Post-processing to add kicks to NSs and BHs, based on PopSyCLE (Rose et al 2022). + + Attributes + ---------- + kick_mean_bh=100 : float + mean of the maxwellian kick distribution for black holes (km/s) + kick_mean_ns=350 : float + mean of the maxwellian kick distribution for neutron stars (km/s) + """ + + def __init__(self, model, logger, kick_mean_bh=100, kick_mean_ns=350, **kwargs): + super().__init__(model, logger, **kwargs) + self.kick_mean_ns = kick_mean_ns + self.kick_mean_bh = kick_mean_bh + + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): + """ + Perform the post-processing and return the modified DataFrame. + """ + + # Pick out which stars need processed + phase = system_df['phase'].to_numpy().astype(int) + proc_stars = system_df.index + + # Apply birth kicks + kick_idxs = proc_stars[phase>=102] + kick_mtypes = phase[phase>=102] + # Generate random velocities + kick_vel = maxwell.rvs(size=len(kick_idxs), scale=1, loc=0) * \ + self.kick_mean_ns**(kick_mtypes==102).astype(int) * \ + self.kick_mean_bh**(kick_mtypes==103).astype(int) + # Generate random directions + rand_dir = uniform_direction.rvs(dim=3, size=len(kick_idxs)) + # Update cartesian velocities + l_deg = system_df['l'][kick_idxs].to_numpy() + b_deg = system_df['b'][kick_idxs].to_numpy() + u_new = system_df['U'][kick_idxs].to_numpy() + kick_vel * rand_dir[:,0] + v_new = system_df['V'][kick_idxs].to_numpy() + kick_vel * rand_dir[:,1] + w_new = system_df['W'][kick_idxs].to_numpy() + kick_vel * rand_dir[:,2] + coord_trans = CoordTrans(sun=self.model.parms.sun) + system_df.loc[kick_idxs, 'U'] = u_new + system_df.loc[kick_idxs, 'V'] = v_new + system_df.loc[kick_idxs, 'W'] = w_new + # Convert to and update proper motion/radial velocities + kick_ls = system_df['l'][kick_idxs].to_numpy() + kick_bs = system_df['b'][kick_idxs].to_numpy() + kick_dists = system_df['Dist'][kick_idxs].to_numpy() + if 'mul' in system_df: + vr_new, mul_new, mub_new = coord_trans.uvw_to_vrmulb(kick_ls, kick_bs, kick_dists, u_new, v_new, w_new) + system_df.loc[kick_idxs, 'vr_bc'] = vr_new + system_df.loc[kick_idxs, 'mul'] = mul_new + system_df.loc[kick_idxs, 'mub'] = mub_new + if 'mura' in system_df: + vr_new, mura_new, mudec_new = coord_trans.uvw_to_vrmulb(kick_ls, kick_bs, kick_dists, u_new, v_new, w_new) + system_df.loc[kick_idxs, 'vr_bc'] = vr_new + system_df.loc[kick_idxs, 'mura'] = mura_new + system_df.loc[kick_idxs, 'mudec'] = mudec_new + system_df.loc[kick_idxs, 'VR_LSR'] = coord_trans.vr_bc_to_vr_lsr(l_deg,b_deg,vr_new) + + return system_df, companion_df diff --git a/synthpop/modules/post_processing/popsycle_post_processing.py b/synthpop/modules/post_processing/popsycle_post_processing.py new file mode 100644 index 0000000..6595495 --- /dev/null +++ b/synthpop/modules/post_processing/popsycle_post_processing.py @@ -0,0 +1,246 @@ +""" +Post-processing to convert the output into the PopSyCLE input format, +ready to be plugged in at the calc_events stage. +""" + +__author__ = "M.J. Huston, S. Brooke, R. Patlak, A. Kim" + +from ._post_processing import PostProcessing +import time +import pandas as pd +import numpy as np +import h5py +import os +from popsycle.synthetic import _get_bin_edges, _bin_lb_hdf5 +import pdb + +synthpop_nonmag_cols = ['l', 'b', 'Dist', + 'x', 'y', 'z', + 'vr_bc', 'mul','mub', + 'U', 'V', 'W', + 'iMass','Mass', + 'log_L', 'log_g', 'log_Teff', 'Fe/H_initial', 'age', + 'pop', 'phase', 'isWR', 'n_companions', 'system_Mass'] +popsycle_nonmag_cols = ['glat', 'glon', 'rad', + 'px', 'py', 'pz', + 'vr', 'mu_lcosb', 'mu_b', + 'vx', 'vy', 'vz', + 'zams_mass', 'mass', 'systemMass', + 'mbol', 'grav', 'Teff', 'L', 'feh', 'age', + 'exbv', 'popid','isWR', + 'isMultiple', 'N_companions', 'rem_id', 'obj_id'] + +synthpop_nonmag_bin_cols = ['system_idx', 'period', 'eccentricity', '2MASS_Ks', + 'star_mass', 'log_R', 'log_a', 'isWR'] +popsycle_nonmag_bin_cols = ['system_idx', 'zams_mass', 'Teff', 'L', + 'logg', 'isWR', 'mass', 'phase', 'metallicity', + 'log_a', 'e', 'i', 'Omega', 'omega', 'zams_mass_prim', + 'glat','glon'] + +class PopsyclePostProcessing(PostProcessing): + + def __init__(self, model, logger, bin_edges_number=None, **kwargs): + super().__init__(model, logger, **kwargs) + self.bin_edges_number = bin_edges_number + + if hasattr(model.parms, "binning_procedure"): + self.binning_procedure = True + else: + self.binning_procedure = False + self.multiplicity = model.parms.multiplicity_kwargs + + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): + """ + Converts DataFrame into format needed for input to PopSyCLE as a replacement + for Galaxia, saving the file to the set file name + '_psc.h5'. + """ + + # Collect column names + self.synthpop_mag_cols = self.model.parms.bands + if self.model.parms.star_generator=='SpiseaGenerator': + self.mag_cols = [f[2:] for f in self.synthpop_mag_cols] # strip out the leading 'm_' + self.mag_cols_bin = self.synthpop_mag_cols.copy() + else: + # TODO might wanna genericize this at some point..... + from ..evolution.mist import get_spisea_obs_str + from spisea.synthetic import get_filter_col_name + self.mag_cols_bin = [get_filter_col_name(get_spisea_obs_str(f)) for f in self.synthpop_mag_cols] + self.mag_cols = [f[2:] for f in self.mag_cols_bin] + + for i in range(len(self.synthpop_mag_cols)): + if self.mag_cols[i].startswith('bessell_'): + self.mag_cols[i] = self.mag_cols[i].replace('bessell_', 'ubv_') + self.mag_cols_bin[i] = self.mag_cols_bin[i].replace('m_bessell_', 'm_ubv_') + elif self.mag_cols[i].startswith('ukirt_'): + self.mag_cols[i] = self.mag_cols[i].replace('ukirt_', 'ubv_') + + self.synthpop_ext_cols = ['A_'+f for f in self.synthpop_mag_cols] + self.ext_cols = ['A_'+f for f in self.mag_cols] + self.ext_cols_bin = ['A_'+f for f in self.mag_cols_bin] + + if self.multiplicity == None: + companion_df = pd.DataFrame(columns=synthpop_nonmag_bin_cols + synthpop_nonmag_cols) # Empty Dataframe for operations if singles only + + self.output_root = f"{self.model.get_filename(self.model.l_deg, self.model.b_deg)}_psc" + + # Drop unused data + cols_to_cut = [] + for col in system_df.keys(): + if col not in (synthpop_nonmag_cols+self.synthpop_mag_cols+ + self.synthpop_ext_cols+synthpop_nonmag_bin_cols+ + [self.model.populations[0].extinction.A_or_E_type]): + cols_to_cut.append(col) + + system_df.drop(columns=cols_to_cut, inplace=True) + + bin_cols_to_cut = [] + for col in companion_df.keys(): + if col not in (synthpop_nonmag_cols+self.synthpop_mag_cols+self.synthpop_ext_cols+ + synthpop_nonmag_bin_cols+ [self.model.populations[0].extinction.A_or_E_type]): + bin_cols_to_cut.append(col) + companion_df.drop(columns=bin_cols_to_cut, inplace=True) + + system_df.loc[:,'isMultiple'] = (system_df['n_companions'].to_numpy()>0).astype(int) + + if system_df['isMultiple'].any(): + map = system_df.set_index('system_idx')['iMass'].squeeze() + companion_df['zams_mass_prim'] = companion_df['system_idx'].map(map) # Primary star mass tracked in companion dataframe + + # Translate extinction to Ebv extinction + if not self.model.populations[0].extinction.A_or_E_type=="E(B-V)": + extinction = self.model.populations[0].extinction + extinction_type = self.model.populations[0].extinction.A_or_E_type + system_df.rename(columns={extinction_type:'ext_orig'}, inplace=True) + #ext_in_map = system_df[extinction_type].to_numpy() + Av_Aref = extinction.extinction_at_lambda(0.544579, 1.0) + Ab_Aref = extinction.extinction_at_lambda(0.438074, 1.0) + pd.eval("exbv = (Ab_Aref - Av_Aref)*system_df.ext_orig", target=system_df, inplace=True) + system_df.drop(columns=['ext_orig'], inplace=True) + else: + system_df.rename(columns={"E(B-V)":'exbv'}, inplace=True) + + # create log (with same info as galaxia log) + #dtype = [('latitude', 'f8'), ('longitude', 'f8'), ('surveyArea', 'f8')] + #log = np.zeros(1, dtype=dtype) + latitude = self.model.l_deg + longitude = self.model.b_deg + surveyArea = self.model.field_scale + if self.model.field_scale_unit=='sr': + surveyArea *= (180/np.pi)**2 + + # Write parameter file + os.makedirs('/'.join(self.output_root.split('/')[:-1]), exist_ok=True) + if not self.binning_procedure: + if os.path.exists(self.output_root + '_synthpop_params.txt'): + with open(self.output_root + '_synthpop_params.txt', 'r') as params_file: + lines = params_file.read() + else: + lines = "" + with open(self.output_root + '_synthpop_params.txt', 'w') as params_file: + params_file.write(f"seed {self.model.parms.random_seed}\n") + params_file.write(lines) + + # Rename columns to match PopSyCLE output + system_df.rename(columns={'iMass': 'zams_mass','Mass': 'mass', + 'x': 'px', 'y': 'py', 'z': 'pz', + 'U': 'vx', 'V': 'vy', 'W': 'vz', + 'vr_bc': 'vr', 'mul': 'mu_lcosb', 'mub': 'mu_b', + 'pop': 'popid', + 'b': 'glat', 'l': 'glon', 'Dist': 'rad', + 'log_g': 'grav', 'log_Teff': 'teff', 'Fe/H_initial': 'feh', + 'n_companions':'N_companions', 'system_idx':'obj_id', 'system_Mass':'systemMass', + 'log_L':'L'}, + inplace=True) + companion_df.rename(columns={'iMass':'zams_mass', 'log_Teff': 'teff', 'log_L':'L', + 'log_g':'logg', 'Mass':'mass', 'Fe/H_initial':'metallicity', + 'eccentricity':'e'}, inplace=True, errors='ignore') + + system_df['L'] = 10**system_df['L'] # Delogging teff and L + system_df['teff'] = 10**system_df['teff'] + companion_df['L'] = 10**companion_df['L'] + companion_df['teff'] = 10**companion_df['teff'] + + pd.eval('age = log10(system_df.age*1e9)', target=system_df, inplace=True) + + # Calculate log_a if it isn't included in columns + combined_mass = companion_df["zams_mass"] + companion_df["zams_mass_prim"] + if 'log_a' not in companion_df.columns: + semimajor_axis = np.cbrt(companion_df["period"]** 2 * combined_mass) + companion_df["log_a"] = np.log10(semimajor_axis) + x = np.random.uniform(0, 1, size=companion_df.shape[0]) + y = np.random.uniform(0, 1, size=companion_df.shape[0]) + z = np.random.uniform(0, 1, size=companion_df.shape[0]) + companion_df["i"] = np.arccos(x) * 180 / np.pi + companion_df["Omega"] = 360 * y + companion_df["omega"] = 360 * z + + pd.eval('glon = system_df.glon - (system_df.glon>180)*360', target=system_df, inplace=True) + pd.eval("mbol = -2.5 * system_df.L + 4.75", target=system_df, inplace=True) + + # Match companion coordinates to corresponding primary coordinates + if system_df['isMultiple'].any(): + map = system_df.set_index('obj_id')['glat'].squeeze() + companion_df['glat'] = companion_df['system_idx'].map(map) + map = system_df.set_index('obj_id')['glon'].squeeze() + companion_df['glon'] = companion_df['system_idx'].map(map) + + else: + pd.eval("systemMass = system_df.mass", target=system_df, inplace=True) + + # Rename filters and extinction + system_df.rename(columns=dict(zip(self.synthpop_mag_cols,self.mag_cols)), + inplace=True) + system_df.rename(columns=dict(zip(self.synthpop_ext_cols,self.ext_cols)), + inplace=True) + + companion_df.rename(columns=dict(zip(self.synthpop_mag_cols, self.mag_cols_bin)), + inplace=True, errors='ignore') + companion_df.rename(columns=dict(zip(self.synthpop_ext_cols, self.ext_cols_bin)), + inplace=True, errors='ignore') + + # system_df.loc[:, 'isMultiple'] = np.zeros(system_df.shape[0], dtype=int) + # system_df.loc[:, 'N_companions'] = np.zeros(system_df.shape[0], dtype=int) + + phases = np.nan_to_num(system_df['phase'].to_numpy()) + phases[phases == 10] = 101 + phases = phases.astype(int) + system_df.loc[:, 'rem_id'] = (phases*(phases>100)).astype(int) + # system_df.loc[:, 'obj_id'] = np.arange(0, len(system_df)) + + _, lat_bin_edges, long_bin_edges = _get_bin_edges(latitude, longitude, surveyArea, self.bin_edges_number) + + # Cut unused columns + cols_to_cut = [] + for col in system_df.keys(): + if col not in (popsycle_nonmag_cols + self.mag_cols + self.ext_cols): + cols_to_cut.append(col) + popsycle_df = system_df.drop(columns=cols_to_cut) + + cols_to_cut = [] + for col in companion_df.keys(): + if col not in (popsycle_nonmag_bin_cols + self.mag_cols_bin + self.ext_cols_bin): + cols_to_cut.append(col) + popsycle_bin_df = companion_df.drop(columns=cols_to_cut) + + if self.binning_procedure: + return popsycle_df, popsycle_bin_df + else: + if system_df['isMultiple'].any(): + with h5py.File(f"{self.output_root}_companions.h5", 'w') as h5file: + h5file['lat_bin_edges'] = lat_bin_edges + h5file['long_bin_edges'] = long_bin_edges + + _bin_lb_hdf5(lat_bin_edges, long_bin_edges, popsycle_bin_df, f"{self.output_root}_companions") + + with h5py.File(f"{self.output_root}.h5", 'w') as h5file: + h5file['lat_bin_edges'] = lat_bin_edges + h5file['long_bin_edges'] = long_bin_edges + + _bin_lb_hdf5(lat_bin_edges, long_bin_edges, popsycle_df, self.output_root) + self.logger.info(f"PopSyCLE formatted output saved in {self.output_root}.h5") + + if system_df['isMultiple'].any(): + return system_df, companion_df + else: + return system_df, None diff --git a/synthpop/modules/post_processing/process_dark_compact_objects.py b/synthpop/modules/post_processing/process_dark_compact_objects.py deleted file mode 100644 index 1144e8b..0000000 --- a/synthpop/modules/post_processing/process_dark_compact_objects.py +++ /dev/null @@ -1,292 +0,0 @@ -""" -Post-processing to account for dim compact objects, based on PopSyCLE (Rose et al 2022). - -The module will take all objects that have evolved past the MIST grid, and assign them a final -mass, removing their photometry. Optionally, one can just remove all of these objects instead. -""" - -__all__ = ["ProcessDarkCompactObjects", ] -__author__ = "M.J. Huston" -__date__ = "2024-05-23" - -import pandas -import numpy as np -from ._post_processing import PostProcessing - -class ProcessDarkCompactObjects(PostProcessing): - """ - Post-processing to account for dim compact objects, based on PopSyCLE (Rose et al 2022). - - Attributes - ---------- - remove=False : boolean - if true, remove dark compact objects from the catalog - ifmr_name='SukhboldN20' : string - selected initial-final mass relation; - options are 'SukhboldN20', 'Raithel18', 'Spera15' - """ - - def __init__(self, model, logger, remove=False, ifmr_name='SukhboldN20', **kwargs): - super().__init__(model, logger, **kwargs) - self.remove = remove - #: initial-final mass relation name to determine compact object masses. - #: options are Raithel18, SukhboldN20, Spera15 - self.ifmr_name= ifmr_name - - def mass_bh(self, m_zams, feh, f_ej=0.9): - """ - Black hole mass calculation for Raithel18 and SukhboldN20 - - Parameters - ---------- - m_zams - float or array of float values for initial stellar mass in units of solar mass - f_ej - float value representing the ejection fraction, - or how much of the star's envelope is ejected in the supernova - default value 0.9 adopted from Lam et al. (2020) - - Returns - ------- - m_bh - float or array of float values for final black hole mass in units of solar mass - """ - if self.ifmr_name=='Raithel18': - m_bh_core_i = -2.049 + 0.4140 * m_zams - m_bh_all_i = 15.52 - 0.3294 * (m_zams - 25.97) - 0.02121 * ( - m_zams - 25.97) ** 2 + 0.003120 * (m_zams - 25.97) ** 3 - # branch ii - m_bh_core_ii = 5.697 + 7.8598 * 10 ** 8 * m_zams ** -4.858 - # branch determination: 0 for i and 1 for ii - branch = (m_zams > 42.21).astype(int) - m_bh = (f_ej * m_bh_core_i + (1 - f_ej) * m_bh_all_i) * (1 - branch) + m_bh_core_ii * branch - elif self.ifmr_name=='SukhboldN20': - f_z = np.minimum(10**feh, np.ones(len(feh))) - m_bh_0 = 0.4652*m_zams - 3.2917 - m_bh_zsun = -0.271*m_zams + 24.743 - branch = (m_zams > 39.6).astype(int) - m_bh_prelim = (1-branch)*m_bh_0 + branch*((1-f_z)*m_bh_0 + f_z*m_bh_zsun) - # Assign any BHs < 3.0Msun to NS instead - m_ns_backup = (m_bh_prelim<3.0).astype(int)*self.mass_ns(m_zams) - m_bh = np.maximum(m_bh_prelim, m_ns_backup) - return m_bh - - def mass_ns(self, m_zams): - """ - Neutron star final mass calculation, adopting the 1.36 Msun average - with a standard deviation of 0.09. - Based on PopSyCLE (Rose et al, 2022) - - Parameters - ---------- - m_zams - float or array of float values for initial stellar mass in units of solar mass - - Returns - ------- - m_ns - float or array of float values for final neutron star mass in units of solar mass - """ - return np.random.normal(1.36, 0.09, len(m_zams)) - - def mass_wd(self, m_zams): - """ - White dwarf final mass calculation. - Based on PopSyCLE (Rose et al. 2022) - - Parameters - ---------- - m_zams - float or array of float values for initial stellar mass in units of solar mass - - Returns - ------- - m_wd - float or array of float values for final white dwarf mass in units of solar mass - """ - return 0.109 * m_zams + 0.394 - - def mass_spera15(self, m_zams, feh): - """ - Remnant mass calculation from Spera et al 2015, appendix C - Takes in the m_zams and Fe/H as lists - """ - # First, calculate M_CO, based on M_ZAMS and Z - # Note: z equation from Rose et al 2022, - # with z_sun from Ekstroem et al 2012 - z = 0.014*10**feh - - # First, calculate m_co - - # C13, 14, 15 - z_cat_1 = np.array([(z>4.0e-3).astype(int), ((z<=4.0e-3)&(z>=1.0e-3)).astype(int), ((z<1.0e-3)).astype(int)]) - b1s = np.array([59.63 - 2.969e3*z + 4.988e4*z**2, 40.98 + 3.415e4*z - 8.064e6*z**2, np.repeat(67.07, len(z))]) - k1s = np.array([45.04 - 2.176e3*z + 3.806e4*z**2, 35.17 + 1.548e4*z - 3.759e6*z**2, np.repeat(46.89, len(z))]) - k2s = np.array([138.9 - 4.664e3*z + 5.106e4*z**2, 20.36 + 1.162e5*z - 2.276e7*z**2, np.repeat(113.8, len(z))]) - d1s = np.array([2.790e-2 - 1.780e-2*z + 77.05*z**2, 2.500e-2 - 4.346*z + 1.340e3*z**2, np.repeat(2.199e-2, len(z))]) - d2s = np.array([6.730e-3 + 2.690*z - 52.39*z**2, 1.750e-2 + 11.39*z - 2.902e3*z**2, np.repeat(2.602e-2, len(z))]) - - b1 = b1s[0]*z_cat_1[0] + b1s[1]*z_cat_1[1] + b1s[2]*z_cat_1[2] - k1 = k1s[0]*z_cat_1[0] + k1s[1]*z_cat_1[1] + k1s[2]*z_cat_1[2] - k2 = k2s[0]*z_cat_1[0] + k2s[1]*z_cat_1[1] + k2s[2]*z_cat_1[2] - d1 = d1s[0]*z_cat_1[0] + d1s[1]*z_cat_1[1] + d1s[2]*z_cat_1[2] - d2 = d2s[0]*z_cat_1[0] + d2s[1]*z_cat_1[1] + d2s[2]*z_cat_1[2] - - # C12 - g1 = 0.5 / (1 + 10**((k1-m_zams)*d1)) - g2 = 0.5 / (1 + 10**((k2-m_zams)*d2)) - # C11 - m_co = -2.0 + (b1+2.0)*(g1+g2) - - # Then, m_rem - # Outer z condition for C1-3 vs C4-10 - z_cat_2 = np.array([(z<=5e-4).astype(int), (z>5e-4).astype(int)]) - # M_co condition for C4 and C1 - z_cat_2_1 = np.array([(m_co<5).astype(int), ((m_co>=5) & (m_co<10)).astype(int), (m_co>=10).astype(int)]) - # Inner z condition for C8-10 - z_cat_2_2 = np.array([(z>2e-3).astype(int), ((z<=2e-3) & (z>1e-3)).astype(int), (z<=1e-2).astype(int)]) - # Inner z condition for C6-7 - z_cat_2_3 = np.array([(z>1e-3).astype(int), (z<=1e-3).astype(int)]) - - # m_rem for z<5e-4 - # C2-3 - m = -6.476e2*z + 1.911 - q = 2.300e3*z + 11.67 - p = -2.333 + 0.1559*m_co + 0.2700*m_co**2 - f = m*m_co + q - m_rem_low_z = z_cat_2_1[0] * np.maximum(p, 1.27) + \ - z_cat_2_1[1] * p + \ - z_cat_2_1[2] * np.minimum(p, f) - - # m_rem for z>=5e-4 - m = z_cat_2_2[0]*np.repeat(1.217, len(z)) + z_cat_2_2[1]*(-43.82*z + 1.340) + z_cat_2_2[2]*(-6.476e2*z + 1.911) - q = z_cat_2_2[0]*np.repeat(1.061, len(z)) + z_cat_2_2[1]*(-1.296e4*z + 26.98) + z_cat_2_2[2]*(2.300e3*z + 11.67) - a1 = z_cat_2_3[0]*(1.340 - 29.46 / (1 + (z/1.110e-3)**2.361)) + z_cat_2_3[1]*(1.105e5*z - 1.258e2) - a2 = z_cat_2_3[0]*(80.22 - 74.73 * z**0.965 / (2.720e-3 + z**0.965)) + z_cat_2_3[1]*(91.56 - 1.957e4*z - 1.558e7*z**2) - l = z_cat_2_3[0]*(5.683 + 3.533 / (1 + (z/7.430e-3)**1.993)) + z_cat_2_3[1]*(1.134e4*z - 2.143) - eta = z_cat_2_3[0]*(1.066 - 1.121 / (1 + (z/2.558e-2)**0.609)) + z_cat_2_3[1]*(3.090e-2 - 22.30*z + 7.363e4*z**2) - - h = a1 + (a2-a1)/(1+10**((l-m_co)*eta)) - f = m*m_co+q - - m_rem_high_z = z_cat_2_1[0] * np.maximum(h, 1.27) + \ - z_cat_2_1[1] * h + \ - z_cat_2_1[2] * np.maximum(h, f) - - m_rem = z_cat_2[0]*m_rem_low_z + z_cat_2[1]*m_rem_high_z - - return m_rem - - def compact_type_from_final(self, m_fin): - """ - Determination of compact object type from final mass - Based on PopSyCLE (Lam et al 2020; Rose et al 2022) - Which pulls from Spera et al 2015 - - Parameters - ---------- - m_fin - float value for final mass in units of solar mass - Returns - ------- - m_type - integer value indicating object type - 1 = dim white dwarf - 2 = neutron star - 3 = black hole - """ - return (m_fin<1.4).astype(int)*1 + ((m_fin>=1.4) & (m_fin<3)).astype(int) * 2 + (m_fin>=3).astype(int)*3 - - def compact_type_from_initial(self, m_zams, feh): - """ - Probabilistic drawing of compact object types - Based on PopSyCLE (Lam et al 2020; Rose et al 2022) - Which pulls from Rathiel et al 2018 and Sukhbold et al 2020 - - Parameters - ---------- - m_zams - array of float values for initial stellar mass in units of solar mass - feh - array of float values for initial metallicity [Fe/H] - Returns - ------- - m_type - array of integer values indicating object type - 0 = non-compact object or luminous white dwarf - 1 = dim white dwarf - 2 = neutron star - 3 = black hole - """ - if self.ifmr_name=='Raithel18': - # Draw random numbers for bins that can be either NS or BH - n_rand = np.random.uniform(size=len(m_zams)) - # Start with pre-CO objects, then go through mass bins, and assign appropriate type - result = np.zeros(len(m_zams)) - result += ((m_zams>0.5) & (m_zams<=9)) * 1 - result += ((m_zams>9) & (m_zams<=15)) * 2 - result += ((m_zams>15) & (m_zams<=17.8)) * ((n_rand<0.679)*2 + (n_rand>=0.679)*3) - result += ((m_zams>17.8) & (m_zams<=18.5)) * ((n_rand<0.833)*2 + (n_rand>=0.833)*3) - result += ((m_zams>18.5) & (m_zams<=21.7)) * ((n_rand<0.500)*2 + (n_rand>=0.500)*3) - result += ((m_zams>21.7) & (m_zams<=25.2)) * 3 - result += ((m_zams>25.2) & (m_zams<=27.5)) * ((n_rand<0.652)*2 + (n_rand>=0.652)*3) - result += ((m_zams>27.5) & (m_zams<=60)) * 3 - result += ((m_zams>60) & (m_zams<=120)) * ((n_rand<0.400)*2 + (n_rand>=0.400)*3) - elif self.ifmr_name=='SukhboldN20': - # Get value for metallicity dependence - f_z = np.minimum(10**feh, np.ones(len(feh))) - # Draw random numbers for bins that can be either NS or BH - n_rand = np.random.uniform(size=len(m_zams)) - # Start with pre-CO objects, then go through mass bins, and assign appropriate type - result = np.zeros(len(m_zams)) - result += ((m_zams>0.5) & (m_zams<=9)) * 1 - result += ((m_zams>9) & (m_zams<=15)) * 2 - result += ((m_zams>15) & (m_zams<=21.8)) * ((n_rand<0.75)*2 + (n_rand>=0.75)*3) - result += ((m_zams>21.8) & (m_zams<=25.2)) * 3 - result += ((m_zams>25.2) & (m_zams<=27.4)) * 2 - result += ((m_zams>27.4) & (m_zams<=60)) * 3 - result += ((m_zams>60) & (m_zams<=120)) * ((n_rand<0.80*f_z)*2 + (n_rand>=0.80*f_z)*3) - return result - - def do_post_processing(self, dataframe: pandas.DataFrame) -> pandas.DataFrame: - """ - Perform the post-processing and return the modified DataFrame. - """ - - # Pick out which stars need processed - in_final_phase = np.array(dataframe['In_Final_Phase']) - proc_stars = dataframe[dataframe['In_Final_Phase']==1] - # If we want to remove compact objects, do so and return - if self.remove: - return dataframe.drop(proc_stars.index) - # Otherwise, we need to handle the compact objects properly. - m_init = np.array(dataframe['iMass']) - feh_init = np.array(dataframe['Fe/H_initial']) - m_final_pre = np.array(dataframe['Mass']) - - # For IFMRs with probabilistic object types - if self.ifmr_name in ['Raithel18', 'SukhboldN20']: - # Probabilistic determination of object types - m_type = self.compact_type_from_initial(m_init, feh_init) - # Get possible masses and select by type - m_compact = (self.mass_bh(m_init, feh_init) * (m_type == 3).astype(int) + - self.mass_ns(m_init) * (m_type == 2).astype(int) + - self.mass_wd(m_init) * (m_type == 1).astype(int)) - m_type = self.compact_type_from_final(m_compact) - # For IFMRs with analytic mass determination, then type assigned by mass - elif self.ifmr_name in ['Spera15']: - # Cycle through evolved stars, calculating mass & type - m_compact = self.mass_spera15(m_init,feh_init) - m_type = self.compact_type_from_final(m_compact) - - # Results into data frame - m_final = (in_final_phase * m_compact + - (1 - in_final_phase) * m_final_pre) - dataframe['Mass'] = m_final - dataframe['Dim_Compact_Object_Flag'] = m_type*in_final_phase - - # Set dim object magnitudes to nan - for magcol in self.model.parms.chosen_bands: - dataframe.loc[proc_stars.index, magcol] = np.nan - - return dataframe diff --git a/synthpop/modules/post_processing/recalculate_kinematics.py b/synthpop/modules/post_processing/recalculate_kinematics.py index 98cfe16..7574312 100644 --- a/synthpop/modules/post_processing/recalculate_kinematics.py +++ b/synthpop/modules/post_processing/recalculate_kinematics.py @@ -4,9 +4,9 @@ __all__ = ["RecalculateKinematics", ] __author__ = "M.J. Huston" -__date__ = "2024-05-14" +__date__ = "2025-05-14" -import pandas +import pandas as pd import numpy as np from ._post_processing import PostProcessing @@ -25,27 +25,28 @@ def __init__(self, model, logger, pop_ids=None, **kwargs): super().__init__(model,logger, **kwargs) self.pop_ids = pop_ids - def do_post_processing(self, dataframe: pandas.DataFrame) -> pandas.DataFrame: + def do_post_processing(self, system_df: pd.DataFrame, + companion_df: pd.DataFrame): """ Replace the kinematic columns and return the modified catalog. """ if self.pop_ids is not None: pop_ids = self.pop_ids else: - pop_ids = np.unique(dataframe['pop'].to_numpy()) + pop_ids = np.unique(system_df['pop'].to_numpy()) for pop in pop_ids: - idxs = dataframe[dataframe['pop']==float(pop)].index.to_numpy() + idxs = system_df[system_df['pop']==float(pop)].index.to_numpy() u, v, w, vr, mu_l, mu_b, vr_lsr = self.model.populations[int(pop)].do_kinematics( - dataframe.Dist[idxs].to_numpy(), dataframe.l[idxs].to_numpy(), dataframe.b[idxs].to_numpy(), - dataframe.x[idxs].to_numpy(), dataframe.y[idxs].to_numpy(), dataframe.x[idxs].to_numpy()) - dataframe.loc[idxs, 'U'] = u - dataframe.loc[idxs, 'V'] = v - dataframe.loc[idxs, 'W'] = w - dataframe.loc[idxs, 'mul'] = mu_l - dataframe.loc[idxs, 'mub'] = mu_b - dataframe.loc[idxs, 'vr_bc'] = vr - dataframe.loc[idxs, 'VR_LSR'] = vr_lsr - - return dataframe + system_df.Dist[idxs].to_numpy(), system_df.l[idxs].to_numpy(), system_df.b[idxs].to_numpy(), + system_df.x[idxs].to_numpy(), system_df.y[idxs].to_numpy(), system_df.x[idxs].to_numpy()) + system_df.loc[idxs, 'U'] = u + system_df.loc[idxs, 'V'] = v + system_df.loc[idxs, 'W'] = w + system_df.loc[idxs, 'mul'] = mu_l + system_df.loc[idxs, 'mub'] = mu_b + system_df.loc[idxs, 'vr_bc'] = vr + system_df.loc[idxs, 'VR_LSR'] = vr_lsr + + return system_df, companion_df diff --git a/synthpop/modules/post_processing/rename_columns.py b/synthpop/modules/post_processing/rename_columns.py new file mode 100644 index 0000000..35db784 --- /dev/null +++ b/synthpop/modules/post_processing/rename_columns.py @@ -0,0 +1,41 @@ +""" +Post-processing module to replace certain output column names. This replaces the "col_names" +configuration line from float: - """ - Monte Carlo integration of the total mass in a slice, - given near and far bounding radii r_inner and r_outer. - Want to add some catch for large error in the mass, - then increase value of N. - Recall that for a Monte Carlo integration: - Q_N = Volume * (1/N) * sum_N f(x) = V*, - the expected error in Q_N decreases as 1/sqrt(N). - - Parameters - ---------- - r_inner : float [kpc] - inner radius of the slice - r_outer : float [kpc] - outer radius of the slice - n_picks : int - number of picks for the integration - - Returns - ------- - total_mass : float - total mass in the slice - """ - # calculate volume of slice, needs to be kpc^3, r_inner and r_outer in kpc - volume = (1 / 3) * self.solid_angle_sr * (r_outer ** 3 - r_inner ** 3) - - # MC draws of density - d, lstar_deg, bstar_deg = self.position.draw_random_point_in_slice(r_inner, - r_outer, n_picks)[3:] - r, phi_rad, z = self.coord_trans.dlb_to_rphiz(d, lstar_deg, bstar_deg) - mean_density = np.mean(self.population_density.density(r, phi_rad, z)) - # then find mass from volume* - total_mass = volume * mean_density - - return total_mass - - def central_totmass(self, r_inner: float, r_outer: float) -> float: - """ - Returns the mass using the denisty in the center of a slice - - Parameters - ---------- - r_inner : float [kpc] - inner radius of the slice - r_outer : float [kpc] - outer radius of the slice - - Returns - ------- - totmass: float [Msun] - mass of a slice using the central density - """ - - # calculate volume of slice, needs to be kpc-3, r_inner and r_outer in kpc - volume = (1 / 3) * self.solid_angle_sr * (r_outer ** 3 - r_inner ** 3) - r, phi_rad, z = self.coord_trans.dlb_to_rphiz((r_outer + r_inner) / 2, self.l_deg, self.b_deg) - density = self.population_density.density(r, phi_rad, z) + self.population_density.update_location(l_deg, b_deg, field_shape, self.field_scale_deg, + self.max_distance) - totmass = volume * density + logger.debug(f"{self.name} : position is set to {l_deg: .3f}, {b_deg: .3f}") + if field_shape=='circle': + logger.debug(f"{self.field_scale_deg} deg radius circle. ({field_scale} {field_scale_unit})") + if field_shape=='box': + logger.debug(f"{self.population_density.l_length_deg}, {self.population_density.b_length_deg} degree l, b length box") - return totmass + if self.extinction is not None: + assert not np.any(np.isnan(self.extinction.get_extinctions(np.array([l_deg]), np.array([b_deg]), + np.array([self.max_distance]))[0])), \ + fr"{self.extinction.extinction_map_name} not valid in direction l_deg={l_deg}, b_deg={b_deg} " \ + f"at max distance {self.max_distance}. Check the map's sky coverage." def estimate_field(self, **kwargs) -> Tuple[float, float]: """ - estimate the field in the current pointing + Estimate the total mass and number of stars in the current field """ - if self.position.l_deg is None: + if self.population_density.l_deg is None: msg = ("coordinates where not set." - "You must run set_position(l_deg, b_deg, solid_angle_sr)" + "You must run set_position(l_deg, b_deg, field_shape, field_scale, field_scale_unit)" "before running 'estimate_field' or 'generate_field'") logger.critical(msg) raise AttributeError(msg) average_star_mass = self.imf.average_mass(min_mass=self.min_mass, max_mass=self.max_mass) - # radii we will step over - radii = np.arange(0, self.max_distance + self.step_size, self.step_size) - # find total mass in cone - # sum over all slices - total_stellar_mass = sum(self.mc_totmass(inner_radii, outer_radii, n_picks=1000) - for inner_radii, outer_radii in zip(radii, radii[1:])) + # total mass is computed in population_density.update_location + total_stellar_mass = self.population_density.total_mass + if total_stellar_mass < self.min_mass: + return 0, 0 + if self.evolution is None: + return total_stellar_mass, total_stellar_mass/average_star_mass # find total number of stars if self.population_density.density_unit == 'init_mass': av_mass_corr = 1 @@ -569,9 +565,8 @@ def estimate_average_mass_correction( **kwargs ) -> float: """ - Estimates the ratio between the average evolved mass and initialmass - Generates and evolve N stars in the cone, - Evolve them and estimates the average mass + Estimates the ratio between the average evolved mass and initial mass by + generating and evolving n_stars stars in the field andevolving them. Parameters ---------- @@ -590,29 +585,26 @@ def estimate_average_mass_correction( # use previously estimate values return self.av_mass_corr + logger.info(f"Evolving test set from population {self.popid} to estimate average initial->final mass ratio") # generate positions for a test sample positions = np.array( - self.position.draw_random_point_in_slice(0, self.max_distance, n_stars)) + self.population_density.draw_random_positions(n_stars)) - (m_initial, age, met, _, s_props, _, _, not_evolved - ) = self.generator.generate_star_at_location(positions[0:3].T, - {const.REQ_ISO_PROPS[0], self.glbl_params.maglim[0]}, + star_sample, _ = self.generator.generate_star_at_location(positions[0:4], + {'star_mass'}, min_mass=self.min_mass, max_mass=self.max_mass) # get evolved mass - mass_evolved = s_props[const.REQ_ISO_PROPS[0]] - # assume no mass loss for not evolved stars - mass_evolved[not_evolved] = m_initial[not_evolved] - # get average mass from imf - average_m_initial = self.imf.average_mass(min_mass=self.min_mass, max_mass=self.max_mass) + average_m_evolved = star_sample['system_Mass'].mean() + # get average mass + average_m_initial = star_sample['iMass'].mean() # estimate mass loss correction - av_mass_corr = np.mean(mass_evolved) / average_m_initial + av_mass_corr = average_m_evolved / average_m_initial self.av_mass_corr = av_mass_corr return av_mass_corr def get_mass_loss_for_option(self, lost_mass_option): - if lost_mass_option == 1: # estimate the av_mass_corr in the estimate field # and use it for different positions @@ -640,88 +632,90 @@ def get_mass_loss_for_option(self, lost_mass_option): return av_mass_corr - def get_n_star_expected(self, radii, average_imass_per_star, av_mass_corr): - """ estimates the number of stars in each slice """ - - # estimate the mass/numbers in each slice - mass_per_slice = np.array([self.mc_totmass(radii_inner, radii_outer, self.N_mc_totmass) - for radii_inner, radii_outer in zip(radii, radii[1:])]) - ################################################################ - # Translate density into number of generated stars # - ################################################################ + def get_n_star_expected(self, average_imass_per_star, av_mass_corr): + """ estimates the number of stars needed """ if self.population_density.density_unit == "number": - n_star_expected = mass_per_slice # density returns n_star_expected - mass_per_slice = n_star_expected * average_imass_per_star * av_mass_corr + n_star_expected = self.population_density.total_mass # density returns n_star_expected if self.population_density.density_unit == 'init_mass': - n_star_expected = mass_per_slice / average_imass_per_star + n_star_expected = self.population_density.total_mass / average_imass_per_star else: - n_star_expected = mass_per_slice / (average_imass_per_star * av_mass_corr) - - return n_star_expected, mass_per_slice - - @staticmethod - def convert_to_dataframe( - popid, initial_parameters, m_evolved, final_phase_flag, - galactic_coordinates, proper_motions, cartesian_coordinates, velocities, - vr_lsr, extinction_in_map, props, user_props, mags, headers - ): - """ convert data to a pandas data_frame""" - df = pandas.DataFrame(np.column_stack( - [np.repeat(popid, len(final_phase_flag)), # pop, - initial_parameters, # iMass, age, Fe/H, - m_evolved, final_phase_flag, # Mass, In_Final_Phase - galactic_coordinates, proper_motions, # Dist, l, b, Vr, mu_l, mu_b, - cartesian_coordinates, velocities, # x, y, z, U, V, W, - vr_lsr, extinction_in_map, # VR_LSR, extinction_in_map - props, user_props, mags # all specified props and magnitudes - ] - ), columns=headers) - return df - - def generate_field(self) -> Tuple[pandas.DataFrame, Dict]: + n_star_expected = self.population_density.total_mass / (average_imass_per_star * av_mass_corr) + + return n_star_expected + + # @staticmethod + # def convert_to_dataframe( + # popid, initial_parameters, m_evolved, final_phase_flag, + # galactic_coordinates, proper_motions, cartesian_coordinates, velocities, + # vr_lsr, extinction_in_map, props, user_props, mags, headers + # ): + # """ convert data to a pandas data_frame""" + # df = pd.DataFrame(np.column_stack( + # [np.repeat(popid, len(final_phase_flag)), # pop, + # initial_parameters, # iMass, age, Fe/H, + # m_evolved, final_phase_flag, # Mass, In_Final_Phase + # galactic_coordinates, proper_motions, # Dist, l, b, Vr, mu_l, mu_b, + # cartesian_coordinates, velocities, # x, y, z, U, V, W, + # vr_lsr, extinction_in_map, # VR_LSR, extinction_in_map + # props, user_props, mags # all specified props and magnitudes + # ] + # ), columns=headers) + # return df + + def generate_field(self) -> pd.DataFrame: """ - Generate the stars in the field - estimates the number of stars from a density distribution - and generates stars based on the individual distributions - estimates evolved properties by interpolating the isochrones + Generate the stars in the field. The number of stars is determined by + the density distribution, and the individual evolved properties come + from isochrone interpolation. Returns ------- population_df : DataFrame collected stars for this population - distribution : dict - collected distributions, by now only the distance distribution + population_companions_df : DataFrame + companions for the population_df stars (None if no multiplicity) """ - if self.position.l_deg is None: - msg = ("coordinates where not set." - "You must run set_position(l_deg, b_deg, solid_angle_sr)" + if self.population_density.l_deg is None: + msg = ("Coordinates were not set." + "You must run set_position(l_deg, b_deg, field_shape, field_scale, field_scale_unit)" "before running 'estimate_field' or 'generate_field'") logger.critical(msg) raise AttributeError(msg) logger.create_info_subsection(f"Population {self.popid}; {self.name}") + if self.population_density.total_mass < self.min_mass: + logger.critical(f'Total stellar mass for {self.name} in field less than min mass per star.'+ + '\nNo stars generated.') + companions = pd.DataFrame() if (self.mult is not None) else None + return pd.DataFrame(), companions + + if (self.evolution is None) and (self.glbl_params.maglim is not None): + logger.critical(f'Set maglim is not None. Population {self.name} with evolution=None'+ + ' provides no magnitudes and will not be included') + companions = pd.DataFrame() if (self.mult is not None) else None + return pd.DataFrame(), companions + + # array of radii to use - radii = np.arange(0, self.max_distance + self.step_size, self.step_size) + radii = np.linspace(0, self.max_distance, int(round(self.max_distance/0.1))+1) - # placeholder to collect distributions - distribution = {} # placeholder to collect data frames df_list = [] + comp_df_list = [] # collect all the column names # required_properties + optional_properties + magnitudes - headers = const.COL_NAMES + self.glbl_params.col_names + self.bands - # replace "ExtinctionInMap" with the output of the extinction map - if 'ExtinctionInMap' in headers: - extinction_index = headers.index("ExtinctionInMap") - headers[extinction_index] = self.extinction.A_or_E_type +# headers = const.REQ_COL_NAMES + self.glbl_params.opt_iso_props + self.bands +# # replace "ExtinctionInMap" with the output of the extinction map +# if self.extinction is not None: +# headers.append(self.extinction.A_or_E_type) # requested properties - props_list = set(const.REQ_ISO_PROPS + self.glbl_params.opt_iso_props + self.bands) + props_list = list(dict.fromkeys(const.REQ_ISO_PROPS + self.glbl_params.opt_iso_props)) + self.bands # get average_mass from imf average_imass_per_star = self.imf.average_mass(min_mass=self.min_mass, @@ -730,34 +724,33 @@ def generate_field(self) -> Tuple[pandas.DataFrame, Dict]: # get initial av_mass_corr # might be estimated on the fly av_mass_corr = self.get_mass_loss_for_option(self.lost_mass_option) - n_star_expected, mass_per_slice = self.get_n_star_expected( - radii, average_imass_per_star, av_mass_corr) + n_star_expected = self.get_n_star_expected(average_imass_per_star, av_mass_corr) - if (self.lost_mass_option == 3) and (self.population_density.density_unit != 'number') and (sum(n_star_expected)>0): - if np.sum(n_star_expected) < self.N_av_mass: - n_star_expected *= self.N_av_mass / np.sum(n_star_expected) +# if (self.lost_mass_option == 3) and (self.population_density.density_unit != 'number') and (n_star_expected>0): +# if n_star_expected < self.N_av_mass: +# n_star_expected = self.N_av_mass mass_limit = np.ones(radii[:-1].shape) * self.min_mass # new min mass for each frac_lowmass = (0., 0.) # average mass/ fraction of stars - if (self.glbl_params.maglim[1] > 50) \ + if (self.glbl_params.maglim is None) \ + or (self.glbl_params.maglim[1] > 50) \ or (not self.skip_lowmass_stars) \ or (not self.glbl_params.obsmag): pass elif self.skip_lowmass_stars and isinstance(self.evolution, list): logger.warning("skip_lowmass_stars is not implemented for multiple isochrones") + self.skip_lowmass_stars = False + + elif self.skip_lowmass_stars and self.mult is not None: + logger.warning("skip_lowmass_stars is not implemented for models with stellar multiplicity") + self.skip_lowmass_stars = False elif self.skip_lowmass_stars: + #raise NotImplementedError("SORRY I BROKE THIS OPTION TEMPORARILY - skip_lowmass_stars not available") logger.info(f"{self.name} : estimate minimum mass for magnitude limit") max_age = self.age.get_maximum_age() - '''if self.glbl_params.obsmag: - self.extinction.update_extinction_in_map(radius=radii[:-1]) - _,ext_dict_temp = self.extinction.get_extinctions(self.l_deg*np.ones(len(radii)-1),self.b_deg*np.ones(len(radii)-1),radii[:-1]) - extinction_at_slice_fronts = ext_dict_temp[self.glbl_params.maglim[0]] - else: - extinction_at_slice_fronts = None''' - #print('ext slices',extinction_at_slice_fronts) mass_limit = self.evolution.get_mass_min( self.glbl_params.maglim[0], self.glbl_params.maglim[1], radii[:-1], @@ -772,34 +765,27 @@ def generate_field(self) -> Tuple[pandas.DataFrame, Dict]: ((self.imf.F_imf(mass_limit) - self.imf.F_imf(self.min_mass)) / (self.imf.F_imf(self.max_mass) - self.imf.F_imf(self.min_mass))) ) # average_mass, fraction of stars - + #pdb.set_trace() # reduce number of stars by the scale factor and fract_above_min_mass - total_stars = np.random.poisson(n_star_expected * (1 - frac_lowmass[1]) / self.scale_factor) + total_stars = np.random.poisson(n_star_expected / self.scale_factor) expected_total_imass = n_star_expected * average_imass_per_star - distribution["distance_distribution"] = np.vstack( - [(radii[1:] + radii[:-1]) / 2, n_star_expected]).T - distribution["distance_distribution_comment"] = \ - "pairs of distances in [kpc] and number of stars in the slice" - logger.info("# From density profile (number density)") - logger.info(f"expected_total_iMass = {np.sum(expected_total_imass):.4f}") - logger.info(f"expected_total_eMass = {np.sum(mass_per_slice):.4f}") + logger.info(f"expected_total_iMass = {expected_total_imass:.4f}") + logger.info(f"expected_total_eMass = {self.population_density.total_mass:.4f}") logger.info(f"average_iMass_per_star = {average_imass_per_star:.4f}") logger.info(f"mass_loss_correction = {np.sum(av_mass_corr):.4f}") - logger.info(f"n_expected_stars = {np.sum(n_star_expected):.4f}") - if self.skip_lowmass_stars: - logger.info(f"without_lm_stars = {np.sum(n_star_expected * (1 - frac_lowmass[1])):.4f}") + logger.info(f"n_expected_stars = {n_star_expected:.4f}") + # no longer meaningful when slicing is ONLY used for mass cut, not count + # if self.skip_lowmass_stars: + # logger.info(f"without_lm_stars = {np.sum(n_star_expected * (1 - frac_lowmass[1])):.4f}") logger.debug(f"{self.name} : Lost mass option: %s", self.lost_mass_option) - logger.debug(f"{self.name} : Generate ~{sum(total_stars)} stars") + logger.debug(f"{self.name} : Generate ~{total_stars} stars") self.population_density.average_mass = average_imass_per_star * av_mass_corr self.population_density.av_mass_corr = av_mass_corr - if not self.glbl_params.kinematics_at_the_end: - logger.info("# Determine velocities when position are generated ") - ################################################################ # Generate Stars # ################################################################ @@ -812,47 +798,44 @@ def generate_field(self) -> Tuple[pandas.DataFrame, Dict]: all_m_evolved = [] all_r_inner = [] opt3_mass_loss_done=False - use_pbar = np.sum(total_stars)>self.glbl_params.chunk_size + use_pbar = (np.sum(total_stars)>self.glbl_params.chunk_size) * (self.generator.generator_name!='SpiseaGenerator') if use_pbar: - pbar = tqdm(total=sum(missing_stars)) + pbar = tqdm(total=missing_stars) neg_missing_stars = np.minimum(missing_stars,0) gen_missing_stars = np.maximum(missing_stars,0) - while any(missing_stars > 0): - if (sum(gen_missing_stars)>self.glbl_params.chunk_size) and not (self.generator.generator_name=='SpiseaGenerator'): + current_max_id = -1 + while (missing_stars > 0): + if (gen_missing_stars>self.glbl_params.chunk_size) and not (self.generator.generator_name=='SpiseaGenerator'): final_expected_loop=False - idx_cs = np.searchsorted(np.cumsum(gen_missing_stars), self.glbl_params.chunk_size) - rem_chunk = self.glbl_params.chunk_size - (np.cumsum(gen_missing_stars)[idx_cs-1])*(idx_cs>0) - gen_stars_chunk = gen_missing_stars * (np.cumsum(gen_missing_stars) Tuple[pandas.DataFrame, Dict]: else: missing_stars -= gen_stars_chunk - # Convert Table to pd.DataFrame - df = self.convert_to_dataframe( - self.popid, initial_parameters, m_evolved, final_phase_flag, - position[:, 3:6], proper_motions, position[:, 0:3], velocities, - vr_lsr, extinction_in_map, props, user_props, mags, headers - ) - - # add to previous drawn data - if (self.glbl_params.maglim[-1] != "keep") and (not self.glbl_params.kinematics_at_the_end) and (not self.glbl_params.lost_mass_option==3): + # Filter for mag limits + if (self.glbl_params.maglim is not None) and (not self.glbl_params.lost_mass_option==3): + # TODO: probably need to make this not a slice + # TODO: confirm this drops nans also df = df[df[self.glbl_params.maglim[0]]0) and (self.mult is not None): + comp_df = comp_df[np.isin(comp_df['system_idx'].to_numpy(), df['system_idx'].to_numpy())] + elif (len(df)==0) and (self.mult is not None): + comp_df.drop(comp_df.index, inplace=True) + # If IFMR is None, we will have some zero mass objects to remove + if np.any(df['Mass']==0.0): + df = df[df['Mass']>0.0] + if self.mult is not None and len(comp_df)>0: + comp_df = comp_df[np.isin(comp_df['system_idx'].to_numpy(), df['system_idx'].to_numpy())] + if (self.mult is not None) and np.any(comp_df['Mass']==0.0): + unique_pris_orig = np.unique(comp_df['system_idx'].to_numpy()) + comp_df = comp_df[comp_df['Mass']>0.0] + # Update companion counts + unique_pris, comp_count = np.unique(comp_df['system_idx'].to_numpy(), return_counts=True) + df.loc[unique_pris,"n_companions"] = comp_count + # Update companion counts for those that go to zero + no_comp_pris = np.setdiff1d(unique_pris_orig, unique_pris, assume_unique=True) + df.loc[no_comp_pris,"n_companions"] = 0 + + # add to previous drawn data + df.loc[:,'system_idx'] += (current_max_id + 1) + if self.mult is not None: + comp_df.loc[:,'system_idx'] += (current_max_id + 1) + if len(df)>0: + current_max_id = int(np.max(df['system_idx'])) + #df.drop(columns=['r_inner'], inplace=True) df_list.append(df) + comp_df_list.append(comp_df) loop_counts += 1 if use_pbar: - pbar.update(np.sum(gen_stars_chunk)) + pbar.update(gen_stars_chunk) neg_missing_stars = np.minimum(missing_stars,0) gen_missing_stars = np.maximum(missing_stars,0) # combine the results from the different loops - if len(df_list) == 0: - population_df = pandas.DataFrame(columns=headers, dtype=float) + if len(df_list)==0: + population_df=pd.DataFrame() + population_comp_df=pd.DataFrame() else: - population_df = pandas.concat(df_list, ignore_index=True) + population_df = pd.concat(df_list, ignore_index=True) + if self.mult is not None: + population_comp_df = pd.concat(comp_df_list, ignore_index=True) + if self.mult is None: + population_comp_df = None # Remove any excess stars - if self.lost_mass_option==3: - r_inner=radii[np.searchsorted(radii, population_df['Dist'])-1] - population_df = self.remove_stars(population_df, r_inner, neg_missing_stars, radii) - population_df.reset_index(drop=True,inplace=True) + if (self.lost_mass_option==3) and (len(population_df)>0): + population_df, population_comp_df = self.remove_stars(population_df, population_comp_df, + neg_missing_stars) + if len(population_df)>0: + population_df.loc[:, 'pop'] = self.popid + #pdb.set_trace() to = time.time() # end timer ################################################################ @@ -904,71 +916,117 @@ def generate_field(self) -> Tuple[pandas.DataFrame, Dict]: if len(population_df) != 0: logger.info(f'generated_total_iMass = {population_df["iMass"].sum():.4f}') - gg = population_df.groupby(pandas.cut(population_df.Dist, radii), observed=False) - if self.skip_lowmass_stars: - im_incl = (gg["iMass"].sum() - + gg.size() * frac_lowmass[0] * frac_lowmass[1] - / (1 - frac_lowmass[1]) - ).sum() + gg = population_df.groupby(pd.cut(population_df.Dist, radii), observed=False) + # if self.skip_lowmass_stars: + # im_incl = (gg["iMass"].sum() + # + gg.size() * frac_lowmass[0] * frac_lowmass[1] + # / (1 - frac_lowmass[1]) + # ).sum() - logger.info(f'generated_total_iMass_incl_lowmass = {im_incl.sum():.4f}') + # logger.info(f'generated_total_iMass_incl_lowmass = {im_incl.sum():.4f}') logger.info(f'generated_total_eMass = {population_df["Mass"].sum():.4f}') - if self.skip_lowmass_stars: - em_incl = (gg["Mass"].sum() - + gg.size() * frac_lowmass[0] * frac_lowmass[1] - / (1 - frac_lowmass[1]) - ).sum() - logger.info(f'generated_total_eMass_incl_lowmass = {em_incl:.4f}') + # if self.skip_lowmass_stars: + # em_incl = (gg["Mass"].sum() + # + gg.size() * frac_lowmass[0] * frac_lowmass[1] + # / (1 - frac_lowmass[1]) + # ).sum() + # logger.info(f'generated_total_eMass_incl_lowmass = {em_incl:.4f}') logger.info(f'det_mass_loss_corr = ' f'{population_df["Mass"].sum() / population_df["iMass"].sum():.4f}') - if self.skip_lowmass_stars: - logger.info(f'det_mass_loss_corr_incl_lowmass = {em_incl / im_incl}') + # if self.skip_lowmass_stars: + # logger.info(f'det_mass_loss_corr_incl_lowmass = {em_incl / im_incl}') logger.debug(f'average_mass_per_star = {population_df["Mass"].mean():.4f}') - if self.skip_lowmass_stars: - mean_mass = em_incl * ((1 - frac_lowmass[1]) / gg.size()).sum() - logger.debug(f'average_mass_per_star_incl_lowmass = {mean_mass:.4f}') + # if self.skip_lowmass_stars: + # mean_mass = em_incl * ((1 - frac_lowmass[1]) / gg.size()).sum() + # logger.debug(f'average_mass_per_star_incl_lowmass = {mean_mass:.4f}') - if self.glbl_params.maglim[-1] != 'keep': - criteria = population_df[self.glbl_params.maglim[0]] < self.glbl_params.maglim[1] - else: - criteria = None - - sp_utils.log_basic_statistics(population_df, f"stats_{self.name}", criteria) logger.log(25, '# Done') logger.flush() - return population_df, distribution + return population_df, population_comp_df + + def generate_stars(self, missing_stars, mass_limit, props, radii=None): + # Drawing star positions takes more computational resources than before. + # Let's bin them up to make sure the density grid doesn't get too big. + gen_pos = missing_stars + position_bins = [] + while gen_pos > 0: + gen_stars = np.minimum(self.glbl_params.chunk_size, gen_pos) + position_bins.append(self.population_density.draw_random_positions(gen_stars)) + gen_pos -= gen_stars + position = np.hstack(position_bins) + min_mass = mass_limit + + u, v, w, vr_bc, mu_l, mu_b, vr_lsr = self.do_kinematics( + position[3], position[4], position[5], + position[0], position[1], position[2] + ) + + # generate star at the positions + star_systems, companions = self.generator.generate_star_at_location( + position[0:4], props, min_mass, self.max_mass, radii=radii, + avg_mass_per_star=self.population_density.average_mass, + skip_lowmass_stars=self.skip_lowmass_stars) + + # add kicks if relevant + if 'kick_x' in star_systems: + u += star_systems['kick_x'] + v += star_systems['kick_y'] + w += star_systems['kick_z'] + vr_bc, mu_l, mu_b = self.coord_trans.uvw_to_vrmulb(position[4], + position[5], position[3], u, v, w) + vr_lsr = self.coord_trans.vr_bc_to_vr_lsr(position[4], position[5], vr_bc) + + star_systems.loc[:,'x'] = position[0] + star_systems.loc[:,'y'] = position[1] + star_systems.loc[:,'z'] = position[2] + star_systems.loc[:,'Dist'] = position[3] + star_systems.loc[:,'l'] = position[4] + star_systems.loc[:,'b'] = position[5] + star_systems.loc[:,'vr_bc'] = vr_bc + star_systems.loc[:,'mul'] = mu_l + star_systems.loc[:,'mub'] = mu_b + star_systems.loc[:,'U'] = u + star_systems.loc[:,'V'] = v + star_systems.loc[:,'W'] = w + star_systems.loc[:,'VR_LSR'] = vr_lsr + + if self.obsmag: + dist_modulus = 5*np.log10(position[3] * 100) + for band in self.bands: + star_systems.loc[:,band] += dist_modulus + if companions is not None: + sys_idxs = star_systems['system_idx'].to_numpy() + dist_modulus_series = pd.Series(dist_modulus, index=sys_idxs) + companions.loc[:,band] += dist_modulus_series[ + companions['system_idx'].to_numpy()].to_numpy() + + return star_systems, companions @staticmethod def remove_stars( - df: pandas.DataFrame, - radii_star: np.ndarray, + df: pd.DataFrame, + comp_df: pd.DataFrame, missing_stars: np.ndarray, - radii: np.ndarray - ) -> pandas.DataFrame: + ) -> pd.DataFrame: """ - Removes stars form data frame in the corresponding slice - if missing_stars is < 0 + Removes stars form data frame if missing_stars is < 0 """ - preserve = np.ones(len(radii_star), bool) - for r, n in zip(radii, missing_stars): - if n < 0: - t = preserve[radii_star == r] - t[n:] = False - preserve[radii_star == r] = t - return df[preserve] + if missing_stars<0: + df = df.sample(n=len(df)+missing_stars, replace=False) + return df, comp_df[np.isin(comp_df['system_idx'], df['system_idx'])] + else: + return df, comp_df def check_field( self, - radii: np.ndarray, average_imass_per_star: float, m_initial: np.ndarray, m_evolved: np.ndarray, - radii_star: np.ndarray, - mass_per_slice: np.ndarray, + total_mass: float, fract_mass_limit: Union[Tuple[float, float], Tuple[np.ndarray, np.ndarray]], ) -> np.ndarray: """ @@ -978,16 +1036,14 @@ def check_field( Parameters ---------- - radii : ndarray m_initial : ndarray m_evolved : ndarray - radii_star : ndarray - mass_per_slice : ndarray + total_mass: float fract_mass_limit : ndarray or float Returns ------- missing_stars : ndarray - number of missing stars in each slice + number of missing stars """ # estimate current initial mass #m_in = np.array([np.sum(m_initial[radii_star == r]) for r in radii[:-1]]) @@ -995,7 +1051,7 @@ def check_field( #m_evo = np.array([np.sum(m_evolved[radii_star == r]) for r in radii[:-1]]) if self.population_density.density_unit in ['number', 'init_mass']: - return np.zeros(len(mass_per_slice), dtype=int) + return 0 # option 3. # if the sample is large enough otherwise determine the average mass before. @@ -1005,14 +1061,12 @@ def check_field( fract_mass_limit[0] * fract_mass_limit[1] # not generated + (1 - fract_mass_limit[1]) * np.mean(m_evolved) # generated ) - n_star_expected = mass_per_slice / average_emass_per_star_mass + n_star_expected = total_mass / average_emass_per_star_mass total_stars = np.random.poisson( n_star_expected * (1 - fract_mass_limit[1]) / self.scale_factor) # reduce number of stars by the scale factor - exist = np.array([np.sum(radii_star == r) for r in radii[:-1]]) - missing_stars = total_stars - exist - #pdb.set_trace() + missing_stars = total_stars - len(m_evolved) return missing_stars def do_kinematics( @@ -1054,42 +1108,12 @@ def do_kinematics( vr, mu_l, mu_b = self.coord_trans.uvw_to_vrmulb(star_l_deg, star_b_deg, dist, u, v, w) # correct for motion of the sun - # following Beaulieu et al. (2000) - vr_lsr = ( - vr - + self.sun.v_lsr * np.sin(star_l_deg * np.pi / 180) - * np.sin(star_b_deg * np.pi / 180) - + self.sun.v_pec * ( - np.sin(star_b_deg * np.pi / 180) * np.sin(self.sun.b_apex_deg * np.pi / 180) - + np.cos(star_b_deg * np.pi / 180) * np.cos(self.sun.b_apex_deg * np.pi / 180) - * np.cos(star_l_deg * np.pi / 180 - self.sun.l_apex_deg * np.pi / 180) - ) - ) + vr_lsr = self.coord_trans.vr_bc_to_vr_lsr(star_l_deg, star_b_deg, vr) return u, v, w, vr, mu_l, mu_b, vr_lsr - def extract_magnitudes( - self, radii_inner, galactic_coordinates, ref_mag, - props, inside_grid=None, not_evolved=None - ): - - if inside_grid is None: - inside_grid = np.ones(len(ref_mag), bool) - if not_evolved is None: - not_evolved = np.zeros(len(ref_mag), bool) - - - mags = np.full((len(ref_mag), len(self.bands)), 9999.) - extinction_in_map = np.zeros(len(ref_mag)) - - dist_module = 5 * np.log10(galactic_coordinates[:, 0] * 100) - - for i, band in enumerate(self.bands): - mags[:, i] = props[band] - - if self.glbl_params.obsmag: - ref_mag[inside_grid] += dist_module[inside_grid] - mags[inside_grid] += dist_module[inside_grid, np.newaxis] + def apply_extinction(self, df, comp_df): + galactic_coordinates = df[['Dist', 'l','b']].to_numpy() extinction_in_map, extinction_dict = self.extinction.get_extinctions( galactic_coordinates[:, 1], @@ -1097,48 +1121,16 @@ def extract_magnitudes( galactic_coordinates[:, 0]) if self.glbl_params.obsmag: - ext_mag = extinction_dict.get(self.glbl_params.maglim[0], 0) - ref_mag[:] += ext_mag for i, band in enumerate(self.bands): - mags[:, i] += extinction_dict.get(band, 0) + ext_band = extinction_dict.get(band, 0) + df.loc[:,band] += ext_band + if comp_df is not None and (len(comp_df)>0): + sys_idxs = df['system_idx'].to_numpy() + ext_band_series = pd.Series(ext_band, index=sys_idxs) + comp_df.loc[:,band] += ext_band_series[comp_df['system_idx'].to_numpy()].to_numpy() - mag_le_limit = ref_mag < self.glbl_params.maglim[1] + df.loc[:,self.extinction.A_or_E_type] = extinction_in_map - mags[np.logical_not(mag_le_limit)] = np.nan - mags[np.logical_not(inside_grid)] = np.nan - mags[not_evolved] = np.nan - - return mags, extinction_in_map - - @staticmethod - def extract_properties( - m_initial, props, req_keys, user_keys, - inside_grid=None, not_evolved=None - ): - # default masks - if inside_grid is None: - inside_grid = np.ones(len(ref_mag), bool) - if not_evolved is None: - not_evolved = np.zeros(len(ref_mag), bool) - - # setup numpy array to store data - default_props = np.zeros((len(m_initial), len(req_keys))) - user_props = np.zeros((len(m_initial), len(user_keys))) - - # store key into default_props and user_props - for i, key in enumerate(req_keys): - default_props[:, i] = props[key] - for i, key in enumerate(user_keys): - user_props[:, i] = props[key] - - # replace data outside grid with nan - default_props[np.logical_not(inside_grid), 1:] = np.nan - default_props[not_evolved, 1:] = np.nan - default_props[not_evolved, 0] = m_initial[not_evolved] - - user_props[np.logical_not(inside_grid)] = np.nan - user_props[not_evolved] = np.nan - - return default_props[:, 0], default_props[:, 1:], user_props + return df, comp_df diff --git a/synthpop/position.py b/synthpop/position.py deleted file mode 100644 index a8e990f..0000000 --- a/synthpop/position.py +++ /dev/null @@ -1,196 +0,0 @@ -""" -This file includes the Position class. -It handles the generation of star positions within a given cone. -""" - -__all__ = ['Position'] -__author__ = "J. Klüter, S. Johnson, M.J. Huston" -__credits__ = ["J. Klüter", "S. Johnson", "M.J. Huston", "A. Aronica", "M. Penny"] -__date__ = "2022-07-06" - -from typing import Tuple -import numpy as np -try: - from . import synthpop_utils as sp_utils -except ImportError: - import synthpop_utils as sp_utils - -class Position: - """ - Position class for a Population class. - This contains methods to randomly generate positions within a field/slice, - - Attributes - ---------- - l_deg : float [degree] - Galactic longitude of the current field in degrees. - l_rad : float [radian] - Galactic longitude of the current field in radians. - b_deg : float [degree] - Galactic latitude of the current field in degrees. - b_rad : float [radian] - Galactic latitude of the current field in radians. - cone_angle : float [radian] - opening angle of the cone - - Methods - ------- - __init__(l_deg: float, b_deg: float, solid_angle_sr: float) : None - Initialize the Class - update(*args, **kwargs) : None - Update the class with new coordinates, passes arguments to __init__() - draw_random_point_in_slice(dist_inner: float, dist_outer: float, N: int = 1): tuple - generate N points within the slice - rotate_00_to_lb(delta_l: ndarray, delta_b: ndarray) : Tuple[ndarray, ndarray] - rotates a cone from pointing toward 0,0 to (l,b) - """ - - # placeholder for transformation matrix between the rectangular equatorial coordinate system - # and the rectangular Galactic coordinate - - def __init__(self, coord_trans, **kwargs): - """ - Initialization - - Parameters - ---------- - l_deg, b_deg : float [deg] - galactic longitude and latitude in degrees - solid_angle : float [sr] - size of the cone - **kwargs : - Future keyword arguments to specify the shape of the field. - """ - - self.coord_trans = coord_trans - # define coordinates of center of the field in degrees and radians - self.l_deg = None - self.l_rad = None - self.b_deg = None - self.b_rad = None - # convert solid angle to half cone angle using wiki formula: - self.cone_angle = None - - def update_location(self, l_deg: float, b_deg: float, solid_angle: float): - """ - Set the location and solid_angle - - Parameters - ---------- - l_deg, b_deg : float [deg] - galactic longitude and latitude in degrees - solid_angle : float [sr] - size of the cone - """ - self.l_deg = l_deg - self.l_rad = l_deg * np.pi / 180. - self.b_deg = b_deg - self.b_rad = b_deg * np.pi / 180. - # convert solid angle to half cone angle using wiki formula: - self.cone_angle = sp_utils.solidangle_to_half_cone_angle(solid_angle) - - def draw_random_point_in_slice(self, dist_inner: float, dist_outer: float, n_stars: int = 1) \ - -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: - """ - Draw one or more random point in a slice - given coordinates (self.l, self.b) [degrees], - solid angle[steradians], and distance range[kpc] - - To get distance d, we draw from a cumulative quadratic distribution. To do so, - we found the integrated r**2 such that our CDF is - Prob(x) = (x**3 - dist_inner**3)/(dist_outer**3 - dist_inner**3) - Then, we invert for x(Prob) so that we can draw Prob from Uniform(0,1) - x = ((r_max**3 - r_min**3)*Prob + r_min**3)**(1/3) - - Parameters - ---------- - dist_inner : float [kpc] - lower distance - dist_outer : float [kpc] - upper distance - n_stars : int, None, optional - number of stars drawn - if None return one position as float - - Returns - ------- - - x : float, ndarray [kpc] - Cartesian X coordinate (centered at the galactic center) of the drawn positions - y : float, ndarray [kpc] - Cartesian Y coordinate (centered at the galactic center) of the drawn positions - z : float, ndarray [kpc] - Cartesian Z coordinate (centered at the galactic center) of the drawn positions - - d_kpc : float, ndarray [kpc] - distances of the drawn positions - star_l_deg : float, ndarray [deg] - galactic longitude of the drawn positions - star_b_deg : float, ndarray [deg] - galactic latitude of the drawn positions - """ - # draw as described above - d_kpc = np.cbrt(np.random.uniform(dist_inner ** 3, dist_outer ** 3, size=n_stars)) - - # generate a cone uniformly around l=0, b=0 with solidAngle as covered area - # Phi in the paper - st_dir = np.random.uniform(0, 2 * np.pi, size=n_stars) - # Theta in the paper - st_rad = np.arccos(np.random.uniform(np.cos(self.cone_angle), 1, size=n_stars)) - - # Estimate offset to center in ra and dec - delta_l_rad = st_rad * np.sin(st_dir) - delta_b_rad = st_rad * np.cos(st_dir) - - # rotate cone to l_deg, b_deg - star_l_rad, star_b_rad = self.rotate_00_to_lb(delta_l_rad, delta_b_rad) - - star_l_deg = star_l_rad * 180 / np.pi - star_b_deg = star_b_rad * 180 / np.pi - # estimate galactocentric coordinates - x, y, z = self.coord_trans.dlb_to_xyz(d_kpc, star_l_deg, star_b_deg) - - return x, y, z, d_kpc, star_l_deg, star_b_deg - - def rotate_00_to_lb(self, delta_l: np.ndarray, delta_b: np.ndarray) \ - -> Tuple[np.ndarray, np.ndarray]: - """ - Rotates coordinate system such that 0, 0 lands on self.l ,self.b - - Parameters - ---------- - delta_l : float, ndarray [radians] - difference in galactic longitude - delta_b : float, ndarray [radians] - difference in galactic longitude - - Returns - ------- - star_l_rad : float, ndarray [radians] - galactic longitude - star_b_rad : float, ndarray [radians] - galactic latitude - """ - # calculate sin and cos. - sin_theta, cos_theta = np.sin(delta_b), np.cos(delta_b) - sin_phi, cos_phi = np.sin(delta_l), np.cos(delta_l) - - # estimate rotation matrix, - # by rotating first around y-axis and then around z-axis - mat = np.matmul( - sp_utils.rotation_matrix(self.l_rad, axis='z'), - sp_utils.rotation_matrix(self.b_rad, axis='y') - ) - # convert to spherical coordinates - vec = np.array([cos_theta * cos_phi, cos_theta * sin_phi, sin_theta]) - - # apply rotation matrix - vec = np.dot(mat, vec) - - # convert to galactic coordinates - star_b_rad = np.arcsin(vec[2]) - star_l_rad = np.arctan2(vec[1], vec[0]) - star_l_rad += 2 * np.pi * (star_l_rad < 0) # only if phi_prime_rad < 0 - # this way it works with both ndarray and floats - - return star_l_rad, star_b_rad diff --git a/synthpop/spisea_generator.py b/synthpop/spisea_generator.py new file mode 100644 index 0000000..cbb7a22 --- /dev/null +++ b/synthpop/spisea_generator.py @@ -0,0 +1,385 @@ +""" +This file contains the SPISEA-based alternative to StarGenerator. +It bins stars by age and metallicity and generates SPISEA clusters, + then assigns them locations based on the population_density. +""" + +__all__ = ["SpiseaGenerator"] +__author__ = "M.J. Huston" +__credits__ = ["M.J. Huston"] +__date__ = "2025-05-28" + +from typing import Set, Tuple, Dict +import numpy as np +import time +from spisea import evolution as spisea_evolution +from spisea import atmospheres as spisea_atmospheres +from spisea.imf import imf as spisea_imf +from spisea.imf import multiplicity as spisea_multiplicity +from spisea import synthetic as spisea_synthetic +from spisea import ifmr as spisea_ifmr +import os, sys +import pdb +import pandas as pd +from astropy.table import vstack +from multiprocessing import Pool + +# Local Imports +# used to allow running as main and importing to another script +try: + from . import constants as const + from . import synthpop_utils as sp_utils + from .star_generator import StarGenerator + from .synthpop_utils.synthpop_logging import logger + from .synthpop_utils import coordinates_transformation as coord_trans + from .synthpop_utils import Parameters + from .modules.extinction import ExtinctionLaw, ExtinctionMap, CombineExtinction + from .modules.age import Age + from .modules.initial_mass_function import InitialMassFunction + from .modules.kinematics import Kinematics + from .modules.metallicity import Metallicity + from .modules.population_density import PopulationDensity +except ImportError: + import constants as const + import synthpop_utils as sp_utils + from synthpop_utils.synthpop_logging import logger + from synthpop_utils import coordinates_transformation as coord_trans + from synthpop_utils import Parameters + from modules.extinction import ExtinctionLaw, ExtinctionMap, CombineExtinction + from modules.age import Age + from modules.initial_mass_function import InitialMassFunction + from modules.kinematics import Kinematics + from modules.metallicity import Metallicity + from modules.population_density import PopulationDensity + from star_generator import StarGenerator + +class BlockSpiseaPrints: + def __init__(self, block_prints): + self.block_prints=block_prints + + def __enter__(self): + if self.block_prints: + self._original_stdout = sys.stdout + sys.stdout = open(os.devnull, 'w') + + def __exit__(self, exc_type, exc_val, exc_tb): + if self.block_prints: + sys.stdout.close() + sys.stdout = self._original_stdout + +class SpiseaGenerator(StarGenerator): + def __init__(self, density_module, imf_module, age_module, met_module, evolution_module, + glbl_params, max_mass, ifmr_module, mult_module, bands, logger): + # General synthpop things + spisea_dir=const.ISOCHRONES_DIR+'/spisea/' + self.generator_name = 'SpiseaGenerator' + self.density_module = density_module + self.imf_module = imf_module + self.ifmr_module = ifmr_module + self.mult_module = mult_module + self.age_module = age_module + self.met_module = met_module + self.evolution_module = evolution_module + self.chunk_size = glbl_params.chunk_size + self.bands = bands + self.obsmag = glbl_params.obsmag + self.max_mass = max_mass + self.logger = logger + self.system_mags = True + + # SPISEA specific setings + self.spisea_dir = spisea_dir+evolution_module.spisea_evolution.model_version_name+'/' + os.makedirs(self.spisea_dir, exist_ok=True) + if self.evolution_module.name != 'SpiseaCluster': + raise ValueError("To use SpiseaGenerator, the evolution class must be SpiseaCluster.") + if (self.mult_module is not None) and (self.mult_module.name!='SpiseaMultiplicity'): + raise ValueError("Only SpiseaMultiplicity Multiplicity objects can be used by SpiseaGenerator") + if self.imf_module.imf_name=='Kroupa': + if self.mult_module is not None: + self.imf_module.spisea_imf = spisea_imf.Kroupa_2001(multiplicity=self.mult_module.spisea_multiplicity) + else: + self.imf_module.spisea_imf = spisea_imf.Kroupa_2001() + elif self.imf_module.imf_name=='PiecewisePowerlaw': + if self.mult_module is not None: + self.imf_module.spisea_imf = spisea_imf.IMF_broken_powerlaw([imf_module.min_mass, *imf_module.splitpoints, imf_module.max_mass], + -np.array(imf_module.alphas), multiplicity=self.mult_module.spisea_multiplicity) + else: + self.imf_module.spisea_imf = spisea_imf.IMF_broken_powerlaw([imf_module.min_mass, *imf_module.splitpoints, imf_module.max_mass], + -np.array(imf_module.alphas)) + elif self.imf_module.imf_name=='SpiseaImf': + if (self.imf_module.spisea_multiplicity is None) and (self.mult_module is None): + pass + # Re-initialize IMF module with proper multiplicity if needed + elif (self.imf_module.spisea_multiplicity is None) and (self.mult_module.multiplicity_name=='SpiseaMultiplicity'): + self.imf_module.spisea_imf = getattr(spisea_imf, self.imf_module.spisea_imf_name)( + massLimits=np.array([self.imf_module.min_mass, self.imf_module.max_mass]), + multiplicity=self.mult_module.spisea_multiplicity, **self.imf_module.spisea_imf_kwargs) + else: + raise ValueError("Invalid IMF for SPISEA Generator; must use Kroupa, PiecewisePowerlaw, or SpiseaImf") + + # TODO clean this up later + if self.evolution_module.spisea_evolution.model_version_name=='COSMIC': + self.mh_list = self.evolution_module.feh_list + else: + self.mh_list = np.log10(np.array(self.evolution_module.spisea_evolution.z_list) / self.evolution_module.spisea_evolution.z_solar) + + self.n_proc = evolution_module.n_proc + + def generate_star_at_location(self, position, props, + min_mass=None, max_mass=None, radii=None, avg_mass_per_star=None, skip_lowmass_stars=None): + """ + Generates stars at the given positions + """ + if avg_mass_per_star is None: + avg_mass_per_star = 1 #self.synthpop_imf_module.average_mass(min_mass=min_mass, max_mass=self.max_mass) + + n_stars = len(position[0]) + + # First - check whether the age distribution is uniform + single_age = (self.age_module.age_func_name=='single_value') + single_feh = (self.met_module.metallicity_func_name=='single_value') + + # NOTE: We check Fe/H then convert to M/H for SPISEA + if single_age and single_feh: + age_all = np.clip(np.log10(self.age_module.age_value*1e9), + np.min(self.evolution_module.log_age_list), + np.max(self.evolution_module.log_age_list)) + mh_all = self.mh_list[np.argmin(np.abs(self.evolution_module.feh_list-self.met_module.metallicity_value))] + comb_bin_idxs = np.zeros(n_stars,dtype=int) + bins2d = [[age_all, mh_all, n_stars]] + elif single_age: + # Sample metallicities in [Fe/H], then bin by nearest grid point + age_all = np.clip(np.log10(self.age_module.age_value*1e9), + np.min(self.evolution_module.log_age_list), + np.max(self.evolution_module.log_age_list)) + fehs = self.met_module.draw_random_metallicity( + N=n_stars, x=position[0], y=position[1], z=position[2], age=10**age_all/1e9) + feh_bins, comb_bin_idxs, feh_bin_cts = np.unique(np.argmin(np.abs(self.evolution_module.feh_list - fehs[:, None]), axis=1), + return_inverse=True, return_counts=True) + bins2d = np.transpose([np.ones(len(feh_bins))*age_all, self.mh_list[feh_bins], feh_bin_cts]) + elif single_feh: + # Sample ages in log10(yr), then bin by nearest grid point + ages = np.log10(self.age_module.draw_random_age(n_stars)*1e9) + age_bins, comb_bin_idxs, age_bin_cts = np.unique(np.argmin(np.abs(self.evolution_module.log_age_list - ages[:, None]), axis=1), + return_inverse=True, return_counts=True) + mh_all = self.mh_list[np.argmin(np.abs(self.evolution_module.feh_list-self.met_module.metallicity_value))] + bins2d = np.transpose([self.evolution_module.log_age_list[age_bins], np.ones(len(age_bins))*mh_all, age_bin_cts]) + else: + ages = np.log10(self.age_module.draw_random_age(n_stars)*1e9) + fehs = self.met_module.draw_random_metallicity( + N=n_stars, x=position[0], y=position[1], z=position[2], age=10**np.mean(ages)/1e9) + comb_vals = np.transpose([np.argmin(np.abs(self.evolution_module.log_age_list - ages[:, None]), axis=1), np.argmin(np.abs(self.evolution_module.feh_list-fehs[:,None]), axis=1)]) + comb_bins, comb_bin_idxs, comb_bin_cts = np.unique(comb_vals, axis=0, return_inverse=True, return_counts=True) + bin_ages = self.evolution_module.log_age_list[comb_bins[:,0]] + bin_mhs = self.mh_list[comb_bins[:,1]] + bins2d = np.transpose([bin_ages, bin_mhs, comb_bin_cts]) + + # Serial case + if self.n_proc==1: + res = [] + for i_bin, bin2d in enumerate(bins2d): + system_idxs = np.where(comb_bin_idxs==i_bin)[0] + res.append(generate_spisea_cluster_stars(system_idxs, bin2d[0], bin2d[1], + self.evolution_module.feh_list[np.argmin(np.abs(self.mh_list-bin2d[1]))], + avg_mass_per_star, + self.evolution_module.block_spisea_prints, + self.evolution_module.spisea_evolution, + self.evolution_module.spisea_atm_func, + self.evolution_module.spisea_wd_atm_func, + np.min(min_mass), max_mass, + self.evolution_module.bands_obs_str, + self.imf_module.spisea_imf, + self.ifmr_module.spisea_ifmr, + self.spisea_dir, props, + self.evolution_module.bands, + self.evolution_module.bbh_frac)) + + # Parallel case: + else: + # Split up some bins for efficiency if relevant... + if np.any(bins2d[:,2]>self.chunk_size): + bins2d_new = [] + comb_bin_idxs_new = [] + for i_bin, bin2d in enumerate(bins2d): + comb_bin_idxs_split = np.split(comb_bin_idxs[i_bin], + int(np.ciel(self.chunk_size/bin2d[2]))) + bins2d_new += [[bin2d[0],bin2d[1],len(idxs)] for idxs in comb_bin_idxs_split] + comb_bin_idxs_new += comb_bin_idxs_split + bins2d = bins2d_new + comb_bin_idxs = comb_bin_idxs_new + + # Iterate through the bins and set up inputs for generate_spisea_cluster_stars + cluster_inputs = [] + for i_bin, bin2d in enumerate(bins2d): + system_idxs = np.where(comb_bin_idxs==i_bin)[0] + cluster_inputs.append((system_idxs, bin2d[0], bin2d[1], + self.evolution_module.feh_list[np.argmin(np.abs(self.mh_list-bin2d[1]))], + avg_mass_per_star, + self.evolution_module.block_spisea_prints, + self.evolution_module.spisea_evolution, + self.evolution_module.spisea_atm_func, + self.evolution_module.spisea_wd_atm_func, + np.min(min_mass), max_mass, + self.evolution_module.bands_obs_str, + self.imf_module.spisea_imf, + self.ifmr_module.spisea_ifmr, + self.spisea_dir, props, + self.evolution_module.bands, + self.evolution_module.bbh_frac)) + + # Do the parallel generation + with Pool(self.n_proc) as p: + res = p.starmap_async(generate_spisea_cluster_stars, cluster_inputs).get() + + # Bring together all the clusters + star_systems = pd.concat([res_i[0] for res_i in res]) + star_systems.sort_index(inplace=True) + companions = pd.concat([res_i[1] for res_i in res]) if (self.imf_module.spisea_imf.make_multiples) else None + # Convert magnitude system if needed + if (self.evolution_module.photsys_convert is not None) \ + and (self.evolution_module.bands[0] in props): + for i,band in enumerate(self.evolution_module.bands): + star_systems.loc[:,band] += self.evolution_module.photsys_convert[band] + if companions is not None: + companions.sort_index(inplace=True) + if (self.evolution_module.photsys_convert is not None) \ + and (self.evolution_module.bands[0] in props): + for i,band in enumerate(self.evolution_module.bands): + companions.loc[:,band] += self.evolution_module.photsys_convert[band] + + star_systems.loc[:,'system_Mass'] = star_systems['Mass'] + if self.imf_module.spisea_imf.make_multiples: + if len(companions)>0: + comp_mass_sums = companions.groupby("system_idx")['Mass'].sum() + primary_idxs = star_systems.index[star_systems['n_companions']>0] + star_systems.loc[primary_idxs,'system_Mass'] += comp_mass_sums[ + primary_idxs] + companions.reset_index(inplace=True) + star_systems.reset_index(inplace=True) + + return star_systems, companions + +def spisea_props_to_synthpop(tab): + renames = {'mass_current': 'Mass', 'mass':'iMass', 'logg':'log_g', + 'N_companions': 'n_companions', 'e':"eccentricity"} + for col in list(tab.columns): + if col in renames: + tab.rename_column(col, renames[col]) + lums = np.array(tab['L']) + teffs = np.array(tab['Teff']) + tab['log_L'] = np.log10(lums/const.Lsun_w) + tab['log_Teff'] = np.log10(teffs) + tab['log_R'] = np.log10((np.sqrt(lums/(4*np.pi*const.sigma_sb*teffs**4)))/const.Rsun_m) + nan_mass = np.isnan(tab['Mass']) + tab['Mass'][nan_mass] = tab['iMass'][nan_mass] + tab['star_mass'] = tab['Mass'] + if 'systemMass' in tab.columns: + tab.remove_column('systemMass') + tab.remove_column('Teff') + tab.remove_column('L') + + return tab + +def generate_spisea_cluster_stars(system_idxs, log_age, mh, feh, + avg_mass_per_star, + block_spisea_prints, + evo_model, atm_func, wd_atm_func, + min_mass, max_mass, bands_obs_str, + imf, ifmr, iso_dir, props, + bands, bbh_frac): + """ + Separated function to run SPISEA clusters for parallelization + """ + max_system_idx = -1 + star_systems_list_bin = [] + companions_list_bin = [] + n_bin = len(system_idxs) + logger.debug(f"Starting SPISEA cluster generation for bin log_age={log_age:.2f}" + f" [M/H]={mh:.2f} for {n_bin} stars") + cluster_stars_needed = n_bin + # Use a minimum mass per cluster of 100.0 so we don't get an error + generate_mass = np.maximum(cluster_stars_needed*avg_mass_per_star*1.1, 100.0) + # Loop until we have enough stars + while cluster_stars_needed > 0: + with BlockSpiseaPrints(block_prints=block_spisea_prints): + if evo_model.model_version_name=='COSMIC': + isochrone = spisea_synthetic.IsochronePhotExternalEvolution(logAge=log_age, AKs=0, + distance=10, metallicity=mh, + evo_model=evo_model, atm_func=atm_func, + wd_atm_func=wd_atm_func, atm_grid_dir=iso_dir, + min_mass=min_mass, max_mass=max_mass, + filters=bands_obs_str) + else: + isochrone = spisea_synthetic.IsochronePhot(logAge=log_age, AKs=0, + distance=10, metallicity=mh, + evo_model=evo_model, atm_func=atm_func, + wd_atm_func=wd_atm_func, iso_dir=iso_dir, + min_mass=min_mass, max_mass=max_mass, + filters=bands_obs_str) + cluster=spisea_synthetic.ResolvedCluster(isochrone, imf, generate_mass, + ifmr=ifmr, keep_low_mass_stars=True) + star_systems_i = cluster.star_systems + star_systems_i['system_idx'] = np.arange(len(star_systems_i)) + max_system_idx + 1 + if "companions" in cluster.__dir__(): + companions_i = cluster.companions + companions_i['system_idx'] += (max_system_idx + 1) + if len(star_systems_i)>0: + max_system_idx = star_systems_i['system_idx'].max() + keep_idx = ((star_systems_i['mass']>min_mass) & (star_systems_i['mass']0: + companions_bin = vstack(companions_list_bin) + else: + companions_bin = None + # Drop any excess stars + if cluster_stars_needed<0: + star_systems_bin = star_systems_bin[:cluster_stars_needed] + # Get the data into the expected form + star_systems_bin = spisea_props_to_synthpop(star_systems_bin) + + # Little column adjustments + star_systems_bin = star_systems_bin[list(props)+['iMass','Mass','system_idx', 'n_companions']] + star_systems_bin['age'] = 10**log_age / 1e9 + star_systems_bin['Fe/H_initial'] = feh + # Get companion stars in expected form + if (companions_bin is not None) and (len(companions_bin)>0): + # Drop any companions whose systems got dropped + companions_bin['Fe/H_initial'] = feh + companions_bin = companions_bin[np.isin(companions_bin['system_idx'], star_systems_bin['system_idx'])] + companions_bin = spisea_props_to_synthpop(companions_bin) + companions_bin = companions_bin[list(props)+['iMass','Mass','system_idx', 'eccentricity', 'log_a']] + if len(companions_bin)>0: + companions_bin['Fe/H_initial'] = feh + elif (companions_bin is not None): + companions_bin = spisea_props_to_synthpop(companions_bin) + companions_bin = companions_bin[list(props)+['iMass','Mass','system_idx', 'eccentricity', 'log_a']] + + # Deal with indexing + orig_idxs = np.array(star_systems_bin['system_idx']) + idxs_map = pd.Series(data=system_idxs, index=orig_idxs, dtype=int) + star_systems_bin['system_idx'] = system_idxs + if (companions_bin is not None): + companions_bin['system_idx'] = idxs_map[companions_bin['system_idx']] + companions_bin = companions_bin.to_pandas(index='system_idx') + + return star_systems_bin.to_pandas(index='system_idx'), companions_bin diff --git a/synthpop/star_generator.py b/synthpop/star_generator.py index 5c9ce1c..84c8219 100644 --- a/synthpop/star_generator.py +++ b/synthpop/star_generator.py @@ -1,7 +1,6 @@ """ -This file contains the StarGenerator, -It generates stars based on the provided initial distributions, -evolves them and applies the extinction. +This file contains the StarGenerator, which generates stars based on the provided +initial distributions and evolves them according to isochrones. """ __all__ = ["StarGenerator"] @@ -13,6 +12,7 @@ import numpy as np import time import pandas +import pdb # Local Imports # used to allow running as main and importing to another script @@ -21,33 +21,21 @@ except ImportError: import constants as const import synthpop_utils as sp_utils - from position import Position from synthpop_utils.synthpop_logging import logger from synthpop_utils import coordinates_transformation as coord_trans from synthpop_utils import Parameters from modules.extinction import ExtinctionLaw, ExtinctionMap, CombineExtinction from modules.evolution import EvolutionIsochrones, EvolutionInterpolator, \ CombineEvolution, MUST_HAVE_COLUMNS - from modules.age import Age - from modules.initial_mass_function import InitialMassFunction - from modules.kinematics import Kinematics - from modules.metallicity import Metallicity - from modules.population_density import PopulationDensity else: # continue import when if synthpop is imported from . import synthpop_utils as sp_utils - from .position import Position from .synthpop_utils.synthpop_logging import logger from .synthpop_utils import coordinates_transformation as coord_trans from .synthpop_utils import Parameters from .modules.extinction import ExtinctionLaw, ExtinctionMap, CombineExtinction from .modules.evolution import EvolutionIsochrones, EvolutionInterpolator, \ CombineEvolution, MUST_HAVE_COLUMNS - from .modules.age import Age - from .modules.initial_mass_function import InitialMassFunction - from .modules.kinematics import Kinematics - from .modules.metallicity import Metallicity - from .modules.population_density import PopulationDensity class StarGenerator: @@ -60,81 +48,106 @@ class StarGenerator: age_module met_module evolution_module - kinematics_at_end : bool - if true, wait until all stars are generated to calculate kinematics - chunk_size : int - number of stars to generate per chunk to limit memory use - ref_band : str - primary photometric filter for catalog - position + glbl_params : dict + global model parameters max_mass : float maximum allowed stellar mass + ifmr_module + mult_module + bands : list + photometric filters + logger """ - def __init__(self, imf_module, age_module, met_module, evolution_module, - glbl_params, position, max_mass, logger): + def __init__(self, density_module, imf_module, age_module, met_module, evolution_module, + glbl_params, max_mass, ifmr_module, mult_module, bands, logger): self.generator_name = 'StarGenerator' + self.density_module = density_module self.imf_module = imf_module + self.ifmr_module = ifmr_module + self.mult_module = mult_module self.age_module = age_module self.met_module = met_module if isinstance(evolution_module, list): self.evolution_module = evolution_module + elif evolution_module is None: + self.evolution_module = None else: self.evolution_module = (evolution_module,) - self.kinematics_at_the_end = glbl_params.kinematics_at_the_end self.chunk_size = glbl_params.chunk_size - self.ref_band = glbl_params.maglim[0] - self.position=position + self.bands = bands + self.obsmag = glbl_params.obsmag self.max_mass = max_mass self.logger = logger + self.system_mags = False - def generate_stars(self, radii, missing_stars, mass_limit, - do_kinematics, props): - position = np.vstack([ - np.column_stack(self.position.draw_random_point_in_slice(r_inner, r_outer, n_stars)) - for r_inner, r_outer, n_stars in zip(radii, radii[1:], missing_stars) - ]) - - min_mass = np.repeat(mass_limit, missing_stars) - r_inner = np.repeat(radii[:-1], missing_stars) - - if self.kinematics_at_the_end: - proper_motions = np.full((len(position), 3), np.nan) - velocities = np.full((len(position), 3), np.nan) - vr_lsr = np.repeat(np.nan, len(position)) - else: - u, v, w, vr_hc, mu_l, mu_b, vr_lsr = do_kinematics( - position[:, 3], position[:, 4], position[:, 5], - position[:, 0], position[:, 1], position[:, 2] - ) - proper_motions = np.column_stack([vr_hc, mu_l, mu_b]) - velocities = np.column_stack([u, v, w, ]) - - # generate star at the positions - return position, r_inner, proper_motions, velocities, vr_lsr, \ - self.generate_star_at_location( - position[:, 0:3], props, min_mass, self.max_mass) - - def generate_star_at_location(self, position, props, min_mass=None, max_mass=None): + def generate_star_at_location(self, position, props, + min_mass=None, max_mass=None, radii=None, avg_mass_per_star=None, + skip_lowmass_stars=False): """ - generates stars at the given positions + Generate stars at the given positions with observed properties. """ - n_stars = len(position) - # generate mass + n_stars = len(position[0]) + # Generate base properties: intial mass, age, metallicity m_initial = self.imf_module.draw_random_mass( - min_mass=min_mass, max_mass=max_mass, N=n_stars) - - # generate age + min_mass=np.min(min_mass), max_mass=max_mass, N=n_stars) age = self.age_module.draw_random_age(n_stars) - - # generate metallicity met = self.met_module.draw_random_metallicity( - N=n_stars, x=position[:,0], y=position[:,1], z=position[:,2], age=age) - - ref_mag, s_props, final_phase_flag, inside_grid, not_evolved = self.get_evolved_props( - m_initial, met, age, props) + N=n_stars, x=position[0], y=position[1], z=position[2], age=age) + + # Decide which stars to evolve + skip_lowmass_idx = np.zeros(n_stars, bool) + if skip_lowmass_stars: + radii_idx = np.searchsorted(radii, position[3])-1 + skip_lowmass_idx = m_initial < min_mass[radii_idx] + # Generate evolved properties + s_props, final_phase_flag = self.get_evolved_props(m_initial, met, age, props, + skip_lowmass_idx) + + # If assigned, apply IFMR to handle NS and BH evolution + s_props = self.apply_ifmr(m_initial, met, s_props, final_phase_flag) + + # If assigned, generate companions + if self.mult_module is not None: + assert skip_lowmass_stars==False + # Adopt metallicity and age of primary; generate init mass and orbits + pri_ids, m_initial_companions, periods, eccentricities = \ + self.mult_module.generate_companions(m_initial) + # Evolve companion stars + comp_s_props, comp_final_phase_flag = self.get_evolved_props( + m_initial_companions, met[pri_ids], age[pri_ids], props, + np.zeros(len(m_initial_companions), bool)) + # Apply IFMR if present + comp_s_props = self.apply_ifmr(m_initial_companions, + met[pri_ids], comp_s_props, comp_final_phase_flag) + + # Compile star systems table for output + m_final = s_props['star_mass'] + star_dict = {"iMass": m_initial, "age": age, "Fe/H_initial":met, + "n_companions":np.zeros(len(m_initial)), + "system_idx": np.arange(len(m_initial)), + "Mass": m_final, + "system_Mass": m_final} + star_dict.update(s_props) + star_systems = pandas.DataFrame.from_dict(star_dict) + + # If assigned, generate companions table and adjust systems table + if self.mult_module is not None: + unique_pris, comp_count = np.unique(pri_ids, return_counts=True) + m_final_companions = comp_s_props['star_mass'] + comp_dict = {"iMass": m_initial_companions, "Mass": m_final_companions, + "system_idx": pri_ids, "period": periods, + "eccentricity": eccentricities} + comp_dict.update(comp_s_props) + companions = pandas.DataFrame.from_dict(comp_dict) + # Update systems table + star_systems.loc[unique_pris,"n_companions"] = comp_count + comp_mass_sums = companions.groupby("system_idx")['Mass'].sum() + star_systems.loc[comp_mass_sums.index, "system_Mass"] += comp_mass_sums + else: + companions = None - return m_initial, age, met, ref_mag, s_props, final_phase_flag, inside_grid, not_evolved + return star_systems, companions def get_evolved_props( self, @@ -142,12 +155,13 @@ def get_evolved_props( met: np.ndarray, age: np.ndarray, props: Set, + skip_lowmass_idx: np.ndarray, **kwargs ) -> Tuple[np.ndarray, Dict, np.ndarray]: """ - evolve the stars using a list of evolution classes given by self.evolution - each evolution class have a min and max mass range where it should be used. - the used class are ranked by the order in the list. + Evolve the stars using a list of evolution classes given by self.evolution. + Each evolution class can have a min and max mass range; + the first class in the list which allows the star's given mass is used. Parameters ---------- @@ -164,10 +178,8 @@ def get_evolved_props( Returns ------- - mag: ndarray - list of the main magnitude s_track: dict - collection of ndarratys for each of the interpolated properties + collection of ndarrays for each of the interpolated properties inside_grid: ndarray used to check if the the star is inside the isochrone grid """ @@ -176,13 +188,14 @@ def get_evolved_props( # placeholders s_track = {p: np.ones(len(m_init)) * np.nan for p in props} - mag = np.nan * np.ones(len(m_init)) inside_grid = np.ones(len(m_init), bool) in_final_phase = np.zeros(len(m_init), bool) not_performed = np.ones(len(m_init), bool) + if self.evolution_module is None: + s_track['star_mass'] = m_init + return s_track, in_final_phase # check if multiple evolution classes are sepecified - for i, evolution_i in enumerate(self.evolution_module): # check if evolution has an atribute which says if numpy arrays can be used if hasattr(evolution_i, 'accept_np_arrays'): @@ -193,38 +206,21 @@ def get_evolved_props( # find the stars which fall into the mass range of the current evolution class if i != len(self.evolution_module) - 1: which = np.where(not_performed & (m_init > evolution_i.min_mass) & ( - m_init < evolution_i.max_mass))[0] - else: - which = np.where(not_performed & (m_init > evolution_i.min_mass))[0] - - # check if there are any stars for this step - if len(which) == 0: - continue - # loop over bunches of at most chunk_size to reduce memory usage - '''if len(which) > self.chunk_size * 6 / 5: - chunk_size = self.chunk_size - use_chunks = True + m_init < evolution_i.max_mass) & ~skip_lowmass_idx)[0] else: - chunk_size = len(which) + 1 - use_chunks = False - count_c = 0 + which = np.where(not_performed & (m_init > evolution_i.min_mass) + & ~skip_lowmass_idx)[0] - if use_chunks: - print(count_c, "/", len(which), end="")''' - - #for which2 in np.array_split(which, len(which) // chunk_size + 1): - # evolve the stars - which2=which if accept_np_arrays: s_props_i, inside_grid_i, in_final_phase_i = evolution_i.get_evolved_props( - m_init[which2], met[which2], age[which2], props, **kwargs) + m_init[which], met[which], age[which], props, **kwargs) else: # This can be used if self.evolution.get_evolved_props # can not handle multiple stars and numpy array: - m_initial = m_init[which2] - metallicity = met[which2] - age2 = age[which2] + m_initial = m_init[which] + metallicity = met[which] + age2 = age[which] s_props_array, inside_grid_i, in_final_phase_i = np.array([ evolution_i.get_evolved_props( m_initial[i], metallicity[i], age2[i] * 1e9, props, **kwargs) @@ -236,31 +232,50 @@ def get_evolved_props( key: np.array([i[key] for i in s_props_array]) for key in s_props_array[0].keys()} - # update results to data array - # update primary magnitude (used for limits etc) - mag_i = s_props_i.get(self.ref_band) - mag[which2] = mag_i - # update flags - inside_grid[which2] = inside_grid_i - in_final_phase[which2] = in_final_phase_i + inside_grid[which] = inside_grid_i + in_final_phase[which] = in_final_phase_i # update properties for key in s_track.keys(): - s_track[key][which2] = s_props_i[key] - - #count_c += len(which2) - #if use_chunks: - # print("\r", count_c, "/", len(which), end='') - # End loop we are removing here + s_track[key][which] = s_props_i[key] # update the list of not performed stars not_performed[which] = False - #if use_chunks: print('') - # check if anything left to do - #if not any(not_performed): - # break + if not any(not_performed): + break self.logger.debug(f"used time = {time.time() - ti:.2f}s") - return mag, s_track, in_final_phase, inside_grid, not_performed + for prop in props: + if prop == 'star_mass': + s_track[prop][np.logical_not(inside_grid)] = \ + m_init[np.logical_not(inside_grid)] + s_track[prop][not_performed] = m_init[not_performed] + else: + s_track[prop][np.logical_not(inside_grid)] = np.nan + s_track[prop][not_performed] = np.nan + + return s_track, in_final_phase + + def apply_ifmr(self, m_init, met, s_props, final_phase_flag): + """ + Apply the IFMR to catch stars evolved past the grid and make them + the appropriate dark remnant. + """ + if self.ifmr_module is None: + for key in s_props: + s_props[key][final_phase_flag] = 0.0 + else: + m_compact, m_phase = self.ifmr_module.process_compact_objects( + m_init[final_phase_flag], met[final_phase_flag]) + for key in s_props: + if key=='star_mass': + s_props[key][final_phase_flag] = m_compact + elif key=='phase': + s_props[key][final_phase_flag] = m_phase + else: + s_props[key][final_phase_flag] = np.nan + return s_props + + diff --git a/synthpop/synthpop_main.py b/synthpop/synthpop_main.py index b1aba98..a65942b 100644 --- a/synthpop/synthpop_main.py +++ b/synthpop/synthpop_main.py @@ -1,11 +1,11 @@ """ SynthPop is a modular framework to generate synthetic galaxy population models. -For usage see README.md! +For usage see README.md and our ReadTheDocs site! -This file contains the main SynthPop class and main function. -Which handles the setting of synthpop, data collection -from the different populations and saving process. -The generation process for each population is performed by +This file contains the main SynthPop class and main function, +which handles the setting of model parameters, data collection +from the populations, and file saving. +The generation process for each population is performed within the Population class defined in population.py. """ @@ -14,7 +14,7 @@ __credits__ = ["J. Klüter", "S. Johnson", "M.J. Huston", "A. Aronica", "M. Penny"] __data__ = "2023-01-09" __license__ = "GPLv3" -__version__ = "1.0.0" +__version__ = "2.0.0" # Standard Imports import os @@ -26,19 +26,22 @@ # Non-Standard Imports import pandas import numpy as np +import pdb -# check if astropy is installed +# Check if astropy is installed: +# only needed if the output format is VoTable or FITS-table. if importlib.util.find_spec("astropy") is not None: import astropy.table as astrotable - else: pass - # astropy is only needed if the output format is either VoTable or FITS-table - + # Local Imports try: from . import constants as const - + from .modules.post_processing import PostProcessing + from . import synthpop_utils as sp_utils + from .population import Population + from .synthpop_utils.synthpop_logging import logger except (ImportError, ValueError) as e: import constants as const import synthpop_utils as sp_utils @@ -46,13 +49,6 @@ from population import Population from synthpop_utils.synthpop_logging import logger -else: - from .modules.post_processing import PostProcessing - from . import synthpop_utils as sp_utils - from .population import Population - from .synthpop_utils.synthpop_logging import logger - - class SynthPop: """ Model class for generating catalogs of stars. @@ -65,8 +61,6 @@ class SynthPop: list of population.json files detected in the model directory population : list list of the initialized population objects - solid_angle : float [sr] - size of the cone min_mass, max_mass : float [Msun] minimum and maximum mass of the Star generation filename_base : str @@ -82,8 +76,6 @@ class SynthPop: wrapper to update the location in all the populations estimate_field_population() : None wrapper to estimate the field output for each population - do_kinematics(field_df: pandas.DataFrame) : pandas.DataFrame - wrapper to call the kinematics generation in each population generate_fields() : None wrapper to generate all the populations write_astrotable(filename: str, df: pandas.DataFrame, extension: str) : None @@ -93,7 +85,7 @@ class SynthPop: get_filename(l_deg: float, b_deg: float, solid_angle_sr: float) : None generate the base of the filename (i.e. without extension) for a given location process_location(l_deg: float, b_deg: float, - solid_angle_sr: float, save_data: bool) : Pandas.DataFrame Dict + solid_angle_sr: float, save_data: bool) : Pandas.DataFrame, Pandas.DataFrame process a given location. process_all() : None process all locations as specified in the configuration @@ -144,8 +136,9 @@ def __init__(self, *args, **kwargs): self.l_deg = None self.b_deg = None - self.solid_angle = None - self.solid_angle_unit = None + self.field_shape = self.parms.field_shape + self.field_scale = self.parms.field_scale + self.field_scale_unit = self.parms.field_scale_unit def get_iter_loc(self) -> Iterator[Tuple[float, float]]: """ returns an iterator for the defined locations """ @@ -156,7 +149,6 @@ def init_populations(self, forced: bool = False) -> None: Wrapper function that initializes all the populations for the model. Can initialize with coordinates, but it is not necessary. - Parameters ---------- forced : bool @@ -172,7 +164,7 @@ def init_populations(self, forced: bool = False) -> None: logger.debug("populations are already initialized") return - logger.create_info_section('initialize population') + logger.create_info_section('Initialize populations') # get the model directory if not os.path.split(self.parms.model_name)[0]: @@ -218,12 +210,12 @@ def init_populations(self, forced: bool = False) -> None: Population(pop_params, pop_id, self.parms) for pop_id, pop_params in self.population_params.items() ] - logger.info("# all population are initialized") + logger.info("# All populations are initialized") self.populations_are_initialized = True def update_location( - self, l_deg: float, b_deg: float, solid_angle: float, solid_angle_unit: str = "deg^2", - **positional_kwargs + self, l_deg: float, b_deg: float, field_shape: str, + field_scale: float or tuple or np.ndarray, field_scale_unit: str ) -> None: """ Simple wrapper to update filename, logfile, and all @@ -235,18 +227,18 @@ def update_location( galactic longitude for the center of the cone b_deg : float ['deg'] galactic longitude for the center of the cone - solid_angle : float [deg^2] - steradians for the cone size - solid_angle_unit : str - unit for steradians - positional_kwargs : - any keyword to be passed to Position + field_shape : str + shape of the field: may be 'circle' or 'box' + field_scale : float or tuple of floats + scale of the field: radius for circle, half-wdith for square, or l and b half-width for rectangle + field_scale_unit : str + unit for field scale """ if not self.save_data: self.filename_base = f'dump_file{np.random.randint(0, 999999):06d}' else: - self.filename_base = self.get_filename(l_deg, b_deg, solid_angle) + self.filename_base = self.get_filename(l_deg, b_deg) if not self.parms.overwrite: if os.path.isfile(ff := f"{self.filename_base}.{self.parms.output_file_type[0].lower()}"): @@ -254,13 +246,15 @@ def update_location( logger.critical(msg) raise FileExistsError(msg) - logger.update_location(f"{self.filename_base}.log", no_log_file=(not self.save_data)) - logger.create_info_section('update location') + logger.update_location(f"{self.filename_base}.log", self.parms.parameters_dict, + l_deg, b_deg, field_shape, field_scale, field_scale_unit, + no_log_file=(not self.save_data)) + logger.create_info_section('Update location') # update populations with the coordinates for the field logger.log(25, f"# set location to: ") logger.log(25, f"l, b = ({l_deg:.2f} deg, {b_deg:.2f} deg)") - logger.log(25, f"# set solid_angle to:") - logger.log(25, f"solid_angle = {solid_angle:.3e} {solid_angle_unit}") + logger.log(25, f"# set field scale to:") + logger.log(25, f"field_scale = {field_scale:.3e} {field_scale_unit}") # placeholder for future position kwargs # (e.g. when we have the option for a pyramid cone) @@ -268,15 +262,16 @@ def update_location( # update position in populations for population in self.populations: population.set_position( - l_deg, b_deg, solid_angle, solid_angle_unit, **positional_kwargs) + l_deg, b_deg, field_shape, field_scale, field_scale_unit) self.l_deg = l_deg self.b_deg = b_deg - self.solid_angle = solid_angle - self.solid_angle_unit = solid_angle_unit + self.field_shape = field_shape + self.field_scale = field_scale + self.field_scale_unit = field_scale_unit logger.debug("All Populations updated to (l,b) = " f"({self.populations[0].b_deg:.2f},{self.populations[0].b_deg:.2f}) " - f"and Solid Angle = {self.populations[0].solid_angle_sr} sr.") + f"and Field Scale = {self.populations[0].field_scale_deg} deg.") def estimate_field_population(self) -> None: """ @@ -288,59 +283,7 @@ def estimate_field_population(self) -> None: logger.debug(f"Population {population.name} estimates: {mass_est:0.2f} M_sun " f"will produce {stars_est:.1f} stars.") - def do_kinematics(self, field_df: pandas.DataFrame) -> pandas.DataFrame: - """ - - A simple wrapper to call the kinematic generation of each population - - Note that the change is performed in place. I.e. It will overwrite - what the kinematics in field_df is - - Parameters - ---------- - field_df : DataFrame - Generated stars - - Returns - ------- - field_df - dataframe of the generated stars - """ - - logger.info("# Determine velocities after all stars are evolved ") - - # extract columns - all_mass = field_df.iloc[:, 4].to_numpy() - all_dist, all_l_deg, all_b_deg = field_df.iloc[:, [6, 7, 8]].to_numpy().T - all_x, all_y, all_z = field_df.iloc[:, [12, 13, 14]].to_numpy().T - all_density_classes = tuple(pop.population_density for pop in self.populations) - - for pop_id, population in enumerate(self.populations): - # select stars which belongs to population - stars_with_pop_id = field_df[const.COL_NAMES[0]] == pop_id - - # call do_kinematics for the current population - u, v, w, vr, mul, mub, vr_lsr = population.do_kinematics( - dist=all_dist[stars_with_pop_id], - star_l_deg=all_l_deg[stars_with_pop_id], star_b_deg=all_b_deg[stars_with_pop_id], - x=all_x[stars_with_pop_id], y=all_y[stars_with_pop_id], z=all_z[stars_with_pop_id], - mass=all_mass[stars_with_pop_id], - all_x=all_x, all_y=all_y, all_z=all_z, all_mass=all_mass, - all_density_classes=all_density_classes, pop_id=pop_id) - - # update velocities in population file - field_df.loc[stars_with_pop_id, const.COL_NAMES[9]] = vr - field_df.loc[stars_with_pop_id, const.COL_NAMES[10]] = mul - field_df.loc[stars_with_pop_id, const.COL_NAMES[11]] = mub - - field_df.loc[stars_with_pop_id, const.COL_NAMES[15]] = u - field_df.loc[stars_with_pop_id, const.COL_NAMES[16]] = v - field_df.loc[stars_with_pop_id, const.COL_NAMES[17]] = w - field_df.loc[stars_with_pop_id, const.COL_NAMES[18]] = vr_lsr - - return field_df - - def generate_fields(self) -> Tuple[pandas.DataFrame, Dict]: + def generate_fields(self) -> pandas.DataFrame: """ calls the generate_field for all the populations and collects the data in a common pandas dataframe. @@ -349,8 +292,6 @@ def generate_fields(self) -> Tuple[pandas.DataFrame, Dict]: ------- field_df: DataFrame DataFrame including the generated Stars from all populations - distributions: dict - collection of intrinsic distributions for each population """ logger.create_info_section('Generate Field') @@ -359,43 +300,61 @@ def generate_fields(self) -> Tuple[pandas.DataFrame, Dict]: self.estimate_field_population() # Placeholder to collect all the data_frames from the populations field_list = [] - distributions = {} + field_companions_list = [] + max_star_id = -1 for population in self.populations: # for each population, generate the field - population_df, pop_distributions = population.generate_field() - - # collect distribution under the population name - # be careful if two populations share the same name - distributions[population.name] = pop_distributions + population_df, population_comp_df = population.generate_field() logger.debug( - "%s : Number of stars generated: %i (%i columns)", + "%s : Number of star systems generated: %i (%i columns)", population.name, *population_df.shape) + if population_comp_df is not None and (len(population_comp_df)>0): + population_comp_df.loc[:, 'system_idx'] += (max_star_id + 1) + if len(population_df)>0: + population_df.loc[:,'system_idx'] += (max_star_id + 1) + max_star_id = int(np.max(population_df['system_idx'])) + # collect data frame into field_list field_list.append(population_df) + field_companions_list.append(population_comp_df) # combine them into one common data frame logger.create_info_section('Combine Populations') field_df = pandas.concat(field_list, ignore_index=True) + if self.parms.multiplicity_kwargs is not None: + field_companions_df = pandas.concat(field_companions_list, ignore_index=True) + else: + field_companions_df = None - logger.info('Number of stars generated: %i (%i columns)', *field_df.shape) - # check if velocities should be generated after all positions are generated - if self.parms.kinematics_at_the_end: - field_df = self.do_kinematics(field_df) + logger.info('Number of star systems generated: %i (%i columns)', *field_df.shape) # check if faint stars and stars outside the grid should be kept or removed - if self.parms.maglim[-1] != 'keep': - logger.info('remove stars which are outside of the isochrone grid ') + if self.parms.maglim is not None: + logger.info('remove stars which are too faint ') field_df = field_df[field_df[self.parms.maglim[0]] < self.parms.maglim[1]] - logger.info('cleand field: Number of stars generated: %i (%i columns)', *field_df.shape) + if field_companions_df is not None: + field_companions_df = field_companions_df[np.isin(field_companions_df[ + 'system_idx'].to_numpy(), field_df['system_idx'].to_numpy())] + + logger.info('cleaned field: Number of stars generated: %i (%i columns)', *field_df.shape) + + # reset object ids + #field_df.reset_index(drop=True, inplace=True) +# if len(field_df) None: """ @@ -431,7 +390,7 @@ def write_astrotable(self, filename: str, df: pandas.DataFrame, extension: str) # save table tab.write(filename, format=extension, overwrite=True) - def write_to_file(self, df: pandas.DataFrame) -> str: + def write_to_file(self, df: pandas.DataFrame, companions=False) -> str: """ write the results to disc @@ -473,6 +432,8 @@ def write_to_file(self, df: pandas.DataFrame) -> str: kwargs.update(output_save_kwargs) # add extension to filename_base. filename = f"{self.filename_base}.{extension}" + if companions: + filename = f"{self.filename_base}_companions.{extension}" logger.log(25, 'write result to "%s"', filename) if extension.startswith('SAVE_ERROR'): @@ -484,7 +445,7 @@ def write_to_file(self, df: pandas.DataFrame) -> str: return filename - def get_filename(self, l_deg: float, b_deg: float, solid_angle: float) -> str: + def get_filename(self, l_deg: float, b_deg: float) -> str: """ create a file name for a given position @@ -492,8 +453,6 @@ def get_filename(self, l_deg: float, b_deg: float, solid_angle: float) -> str: ---------- l_deg, b_deg : float [deg] galactic coordinates - solid_angle: float [rad] - solid angle of the cone Returns ------- @@ -510,7 +469,6 @@ def get_filename(self, l_deg: float, b_deg: float, solid_angle: float) -> str: "date": datetime.datetime.now().date(), "l_deg": l_deg, "b_deg": b_deg, - "solid_angle_sr": solid_angle, "model_name": self.parms.model_name, "name_for_output": self.parms.name_for_output, } @@ -522,9 +480,12 @@ def get_filename(self, l_deg: float, b_deg: float, solid_angle: float) -> str: return filename_base def process_location( - self, l_deg: float, b_deg: float, solid_angle: float, solid_angle_unit: str = 'deg^2', - save_data: bool = True - ) -> Tuple[pandas.DataFrame, Dict]: + self, l_deg: float, b_deg: float, + field_shape: str = None, + field_scale: float = None, + field_scale_unit: str = None, + save_data: bool = True, **kwargs + ) -> pandas.DataFrame: """ Performs the field generation for a given position. @@ -532,10 +493,12 @@ def process_location( ---------- l_deg, b_deg : float [deg] galactic coordinates - solid_angle : float [deg^2] - Area of the cone - solid_angle_unit : str - Unit of the provided solid_angle + field_shape : str + shape of the field + field_scale : float or tuple or np.ndarray + scale of the field (radius or half-width(s)) + field_scale_unit : str + Unit of the provided field_scale save_data : bool If True the DataFrame is saved to disk If False the DataFrame are only returned, @@ -544,9 +507,13 @@ def process_location( ------- field_df : DataFrame Generated stars as Pandas Dataframe - distributions: dict - collection of intrinsic distributions for each population """ + if field_shape is None: + field_shape = self.parms.field_shape + if field_scale is None: + field_scale = self.parms.field_scale + if field_scale_unit is None: + field_scale_unit = self.parms.field_scale_unit # store boolean if data should be stored to disc self.save_data = save_data @@ -558,11 +525,12 @@ def process_location( # Step 1: Set the location and cone size self.update_location(l_deg=l_deg, b_deg=b_deg, - solid_angle=solid_angle, solid_angle_unit=solid_angle_unit) + field_shape=field_shape, field_scale=field_scale, + field_scale_unit=field_scale_unit) # Step 2: Generate all the fields for each population ti = time.time() - field_df, distributions = self.generate_fields() + field_df, field_companions_df = self.generate_fields() t1 = time.time() - ti # Step 3: Save the results @@ -570,24 +538,26 @@ def process_location( if isinstance(self.post_processing, list): # have multiple post-processing for post_processing in self.post_processing: - field_df = post_processing(field_df) + field_df, field_companions_df = post_processing(field_df, field_companions_df) else: # have single postprocessing - field_df = self.post_processing(field_df) + field_df, field_companions_df = self.post_processing(field_df, field_companions_df) if self.save_data: logger.create_info_subsection('Save result') - self.write_to_file(field_df) + self.write_to_file(field_df, companions=False) + if field_companions_df is not None: + self.write_to_file(field_companions_df, companions=True) t2 = time.time() - ti # log end statement - logger.log(15, f"{solid_angle}, {l_deg:.3f}, {b_deg:.3f}, done") + logger.log(15, f"{l_deg:.3f}, {b_deg:.3f}, done") logger.debug("---------------------------------------------------------------") logger.debug(f"Took total time {time.process_time()}") logger.debug(f'generate field: {t1:.1f}s | save field {t2:.1f}s') logger.info("---------------------------------------------------------------\n") - return field_df, distributions + return field_df, field_companions_df def process_all(self, forced=False) -> None: """ @@ -601,11 +571,12 @@ def process_all(self, forced=False) -> None: # create output location, if it does not exist os.makedirs(self.parms.output_location, exist_ok=True) - # Go through each b and l combination + # Go through each b and l combination for l_b_deg in self.parms.loc: self.process_location( - *l_b_deg, solid_angle=self.parms.solid_angle, - solid_angle_unit=self.parms.solid_angle_unit) + *l_b_deg, field_shape=self.parms.field_shape, + field_scale=self.parms.field_scale, + field_scale_unit=self.parms.field_scale_unit) def main(configfile: str = None, **kwargs): diff --git a/synthpop/synthpop_utils/coordinates_transformation.py b/synthpop/synthpop_utils/coordinates_transformation.py index 15f09ac..662b011 100644 --- a/synthpop/synthpop_utils/coordinates_transformation.py +++ b/synthpop/synthpop_utils/coordinates_transformation.py @@ -1,5 +1,5 @@ """ -Functions for coordinates transformations following Bovy 2011. +Functions for coordinates transformations following Bovy (2011). """ __all__ = ["get_trans_matrix", "getA", "lb_to_ad", "ad_to_lb", "dlb_to_xyz", "xyz_to_rphiz", "dlb_to_rphiz", "uvw_to_vrmulb", "uvw_to_vrmuad", "CoordTrans"] @@ -38,7 +38,7 @@ def get_trans_matrix() -> np.ndarray: def getA(longitude_rad: np.ndarray or float, latitude_rad: np.ndarray or float) \ -> np.ndarray: """ - determines the Position Matrix A from Bovy 2011 + Determine the Position Matrix A from Bovy (2011) Note that R^T is equivalent to A Parameters @@ -94,15 +94,17 @@ def warp_correction(self, r_kpc, phi_rad): def dlb_to_rphiz(self, d_kpc: np.ndarray, l_deg: np.ndarray, b_deg: np.ndarray) \ -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ - translates d, l, b into r, phi, z - - phi increases along the galactic rotation with a zero point at the position of the sun + Transform d, l, b into r, phi, z + (phi increases along the galactic rotation with a zero point at the position of the Sun) Parameters ---------- - d_kpc : float, ndarray [kpc] - l_deg : float, ndarray [degree] - b_deg : float, ndarray [degree] + d_kpc : float ndarray, [kpc] + distance + l_deg : float, ndarray, [degrees] + galactic longitude + b_deg : float, ndarray, [degrees] + galactic latitude Returns ------- @@ -127,12 +129,13 @@ def dlb_to_xyz(self, d_kpc: np.ndarray, l_deg: np.ndarray, b_deg: np.ndarray) \ Parameters ---------- + d_kpc : float ndarray, [kpc] + distance l_deg : float, ndarray, [degrees] galactic longitude b_deg : float, ndarray, [degrees] galactic latitude - d_kpc : float ndarray, [kpc] - distance + Returns ------- x,y,z : float nd_array [kpc] @@ -140,8 +143,8 @@ def dlb_to_xyz(self, d_kpc: np.ndarray, l_deg: np.ndarray, b_deg: np.ndarray) \ """ # convert to radian - l_rad = l_deg * np.pi / 180. - b_rad = b_deg * np.pi / 180. + l_rad = (l_deg-self.sun.l_gal_cen) * np.pi / 180. + b_rad = (b_deg-self.sun.b_gal_cen) * np.pi / 180. # convert to heliocentric cartesian cl = np.cos(l_rad) @@ -161,23 +164,23 @@ def dlb_to_xyz(self, d_kpc: np.ndarray, l_deg: np.ndarray, b_deg: np.ndarray) \ def rphiz_to_xyz(self, r_kpc: np.ndarray, phi_rad: np.ndarray, z_kpc: np.ndarray) \ -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ - Translate from cylindrical cartesian coordinates to + Translate from cylindrical cartesian coordinates to + galactocentric cartesian coordinates + the zero point of z follows the warp of the galaxy. + + Parameters + ---------- + r_kpc : float, ndarray [kpc] + phi_rad : float, ndarray [rad] + polar angle follows the rotation of the galaxy + zero point is at the sun. + z_kpc : float, ndarray [kpc] + hight above/below the galactic plane + + Returns + ------- + x_kpc,y_kpc,z_kpc : float, ndarray [kpc] galactocentric cartesian coordinates - the zero point of z follows the warp of the galaxy. - - Parameters - ---------- - r_kpc : float, ndarray [kpc] - phi_rad : float, ndarray [rad] - polar angle follows the rotation of the galaxy - zero point is at the sun. - z_kpc : float, ndarray [kpc] - hight above/below the galactic plane - - Returns - ------- - x_kpc,y_kpc,z_kpc : float, ndarray [kpc] - galactocentric cartesian coordinates """ # remove correction of warp z_kpc += self.warp_correction(r_kpc, phi_rad) @@ -368,34 +371,33 @@ def uvw_to_vrmuad( u_kmps: np.ndarray, v_kmps: np.ndarray, w_kmps: np.ndarray ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ + Conversion from u,v,w + to v_r, mu_l, and mu_b - Conversion from u,v,w - to v_r, mu_l, and mu_b - - Parameters - ---------- - l_deg : float, ndarray [degrees] - galactic longitude - b_deg : float, ndarray [degrees] - galactic latitude - dist_kpc : float, ndarray [kpc] - distance - u_kmps : float, ndarray [km/s] - rectangular velocity with_out correction for the motion of the sun - v_kmps : float, ndarray [km/s] - rectangular velocity with_out correction for the motion of the sun - w_kmps : float, ndarray [km/s] - rectangular velocity with_out correction for the motion of the sun - - Returns - ------- - vr_kmps : float, ndarray[km/s] - radial velocity - mu_a_maspyr : float, ndarray [mas/yr] - proper motion in galactic longitude cos(delta) is applied - mu_d_maspyr : float, ndarray [mas/yr] - proper motion in galactic latitude - """ + Parameters + ---------- + l_deg : float, ndarray [degrees] + galactic longitude + b_deg : float, ndarray [degrees] + galactic latitude + dist_kpc : float, ndarray [kpc] + distance + u_kmps : float, ndarray [km/s] + rectangular velocity with_out correction for the motion of the sun + v_kmps : float, ndarray [km/s] + rectangular velocity with_out correction for the motion of the sun + w_kmps : float, ndarray [km/s] + rectangular velocity with_out correction for the motion of the sun + + Returns + ------- + vr_kmps : float, ndarray[km/s] + radial velocity + mu_a_maspyr : float, ndarray [mas/yr] + proper motion in galactic longitude cos(delta) is applied + mu_d_maspyr : float, ndarray [mas/yr] + proper motion in galactic latitude + """ # Galactic to Equatorial coordinate transform. a_deg, d_deg = self.lb_to_ad(l_deg, b_deg) @@ -424,6 +426,37 @@ def uvw_to_vrmuad( return vr, mu_a_maspyr, mu_d_maspyr + def vr_bc_to_vr_lsr(self, l_deg: np.ndarray, b_deg: np.ndarray, + vr: np.ndarray) -> np.ndarray: + """ + Conversion of radial velocity from barycentric frame to + local standard of rest frame following Beaulieu et al. (2000) + + Parameters + ---------- + l_deg : float, ndarray [degrees] + galactic longitude + b_deg : float, ndarray [degrees] + galactic latitude + vr : float, ndarray [km/s] + barycentric radial velocity + + Returns + ------- + vr_lsr : float, ndarray[km/s] + radial velocity relative to the LSR + """ + + return (vr + + self.sun.v_lsr * np.sin(l_deg * np.pi / 180) + * np.sin(b_deg * np.pi / 180) + + self.sun.v_pec * ( + np.sin(b_deg * np.pi / 180) * np.sin(self.sun.b_apex_deg * np.pi / 180) + + np.cos(b_deg * np.pi / 180) * np.cos(self.sun.b_apex_deg * np.pi / 180) + * np.cos(l_deg * np.pi / 180 - self.sun.l_apex_deg * np.pi / 180) + ) + ) + # create wrappers for the default instance. _coord_trans = CoordTrans() @@ -474,6 +507,7 @@ def dlb_to_xyz(d_kpc: np.ndarray, l_deg: np.ndarray, b_deg: np.ndarray) \ galactic latitude d_kpc : float ndarray, [kpc] distance + Returns ------- x,y,z : float nd_array [kpc] @@ -485,23 +519,23 @@ def dlb_to_xyz(d_kpc: np.ndarray, l_deg: np.ndarray, b_deg: np.ndarray) \ def rphiz_to_xyz(r_kpc: np.ndarray, phi_rad: np.ndarray, z_kpc: np.ndarray)\ -> ndarray: """ - Translate from cylindrical cartesian coordinates to - galactocentric cartesian coordinates - the zero point of z follows the warp of the galaxy. + Translate from cylindrical cartesian coordinates to + galactocentric cartesian coordinates + the zero point of z follows the warp of the galaxy. - Parameters - ---------- - r_kpc : float, ndarray [kpc] - phi_rad : float, ndarray [rad] - polar angle follows the rotation of the galaxy - zero point is at the sun. - z_kpc : float, ndarray [kpc] - hight above/below the galactic plane + Parameters + ---------- + r_kpc : float, ndarray [kpc] + phi_rad : float, ndarray [rad] + polar angle follows the rotation of the galaxy + zero point is at the sun. + z_kpc : float, ndarray [kpc] + hight above/below the galactic plane - Returns - ------- - x_kpc,y_kpc,z_kpc : float, ndarray [kpc] - galactocentric cartesian coordinates + Returns + ------- + x_kpc,y_kpc,z_kpc : float, ndarray [kpc] + galactocentric cartesian coordinates """ return _coord_trans.rphiz_to_xyz(r_kpc, phi_rad, z_kpc) @@ -615,32 +649,31 @@ def uvw_to_vrmuad( ) \ -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ + Conversion from u,v,w + to v_r, mu_l, and mu_b - Conversion from u,v,w - to v_r, mu_l, and mu_b - - Parameters - ---------- - l_deg : float, ndarray [degrees] - galactic longitude - b_deg : float, ndarray [degrees] - galactic latitude - dist_kpc : float, ndarray [kpc] - distance - u_kmps : float, ndarray [km/s] - rectangular velocity with_out correction for the motion of the sun - v_kmps : float, ndarray [km/s] - rectangular velocity with_out correction for the motion of the sun - w_kmps : float, ndarray [km/s] - rectangular velocity with_out correction for the motion of the sun + Parameters + ---------- + l_deg : float, ndarray [degrees] + galactic longitude + b_deg : float, ndarray [degrees] + galactic latitude + dist_kpc : float, ndarray [kpc] + distance + u_kmps : float, ndarray [km/s] + rectangular velocity with_out correction for the motion of the sun + v_kmps : float, ndarray [km/s] + rectangular velocity with_out correction for the motion of the sun + w_kmps : float, ndarray [km/s] + rectangular velocity with_out correction for the motion of the sun - Returns - ------- - vr_kmps : float, ndarray[km/s] - radial velocity - mu_a_maspyr : float, ndarray [mas/yr] - proper motion in galactic longitude cos(delta) is applied - mu_d_maspyr : float, ndarray [mas/yr] - proper motion in galactic latitude - """ + Returns + ------- + vr_kmps : float, ndarray[km/s] + radial velocity + mu_a_maspyr : float, ndarray [mas/yr] + proper motion in galactic longitude cos(delta) is applied + mu_d_maspyr : float, ndarray [mas/yr] + proper motion in galactic latitude + """ return _coord_trans.uvw_to_vrmuad(l_deg, b_deg, dist_kpc, u_kmps, v_kmps, w_kmps) diff --git a/synthpop/synthpop_utils/get_subclass.py b/synthpop/synthpop_utils/get_subclass.py index b710b38..d4ed0b1 100644 --- a/synthpop/synthpop_utils/get_subclass.py +++ b/synthpop/synthpop_utils/get_subclass.py @@ -71,6 +71,11 @@ def __call__( # get subclass_name, filename and not_a_sub_class from kwargs # if isinstance(modul_kwargs, dict): # modul_kwargs = ModuleKwargs.parse_obj(modul_kwargs) + if (modul_kwargs is None) and (ParentClass.__name__ in + ['InitialFinalMassRelation','Multiplicity', + 'ExtinctionMap', 'ExtinctionLaw']): + logger.debug('setting %s class to None', ParentClass.__name__) + return None kwargs = modul_kwargs.init_kwargs subclass_name = modul_kwargs.name filename = modul_kwargs.filename diff --git a/synthpop/synthpop_utils/sun_info.py b/synthpop/synthpop_utils/sun_info.py index a29cf45..0607a57 100644 --- a/synthpop/synthpop_utils/sun_info.py +++ b/synthpop/synthpop_utils/sun_info.py @@ -34,6 +34,11 @@ class SunInfo(BaseModel): l_apex_deg: float = 53. b_apex_deg: float = 25. + # degree direction of Galactic Center (Sag A*) in Galactic coordinates + # from Reid & Brunthaler (2004) + l_gal_cen: float = -0.056 + b_gal_cen: float = -0.046 + class Config(): try: #pydantic version compatibility keep_untouched = (cached_property,) diff --git a/synthpop/synthpop_utils/synthpop_control.py b/synthpop/synthpop_utils/synthpop_control.py index cab5032..5469b7c 100644 --- a/synthpop/synthpop_utils/synthpop_control.py +++ b/synthpop/synthpop_utils/synthpop_control.py @@ -16,6 +16,7 @@ import argparse import numpy as np import pydantic +import pdb if pydantic.__version__.startswith("2"): from pydantic import BaseModel, model_validator @@ -64,8 +65,10 @@ class PopParams(BaseModel, extra="allow"): metallicity_func_kwargs: ModuleKwargs population_density_kwargs: ModuleKwargs kinematics_func_kwargs: ModuleKwargs + ifmr_kwargs: ModuleKwargs = None + multiplicity_kwargs: ModuleKwargs = None - evolution_kwargs: Optional[Union[ModuleKwargs, List[ModuleKwargs]]] = None + evolution_kwargs: Optional[Union[ModuleKwargs, List[ModuleKwargs], Dict[str, List[ModuleKwargs]]]] = None av_mass_corr: Optional[float] = None n_star_corr: Optional[float] = None @@ -109,12 +112,16 @@ def __init__( self.l_set_type = None self.b_set = None self.b_set_type = None + self.field_scale = None logger.create_info_section('Settings') self._categories = {} # read settings form config files a kwargs arguments + #pdb.set_trace() config_dir = self.read_default_config(default_config) + #pdb.set_trace() self.read_specific_config(specific_config, config_dir) + #pdb.set_trace() self.read_kwargs_config(kwargs) # generate random seed if not none @@ -122,21 +129,19 @@ def __init__( if not self.random_seed: self.random_seed = np.random.randint(0, 2 ** 31 - 1) - if len(self.chosen_bands) < len(self.eff_wavelengths): - tmp_eff_wavelengths = {} - for band in self.chosen_bands: - tmp_eff_wavelengths[band] = self.eff_wavelengths[band] - self.eff_wavelengths = tmp_eff_wavelengths - # log settings to file + self.parameters_dict = {key: item for key, item in self.__dict__.items() if not key.startswith('_')} self.log_settings() # check if Settings are ok - if not self.validate_input(): + if not self.validate_mandatory_input(): msg = "Settings Validation failed!." \ " Please ensure that all mandatory parameters are set." logger.critical(msg) raise ValueError(msg) + if hasattr(self, "col_names"): + logger.critical("WARNING: col_names input is no longer used in SynthPop v1.1+. Use " \ + "RenameColumns post-processing module to change column names instead.") # transfer l, b into a a location generator. self.loc = self.location_generator() @@ -148,27 +153,63 @@ def __init__( if self.output_location.endswith(os.sep) or self.output_location.endswith("/"): self.output_location = os.path.join(self.output_location, self.name_for_output) - if getattr(self, 'skip_lowmass_stars', False) \ - and getattr(self, 'kinematics_at_the_end', False): - raise ValueError("'skip_lowmass_stars' and 'kinematics_at_the_end' " - "can not be set to true simultaneously") + if getattr(self, 'kinematics_at_the_end', False): + raise ValueError("'kinematics_at_the_end' option has been removed in " \ + "SynthPop versions >=2.0.0") + + if (self.maglim is not None) and ("keep" in self.maglim): + raise ValueError("In SynthPop >=2.0.0, the \"keep\"/\"remove\" maglim options have been " \ + "removed for clarity. Use maglim=None to keep all stars or e.g. maglim=['Bessell_I', " \ + "21] to trim a catalog. The old behavior of \"keep\" would keep all stars in the catalog" \ + " but set photometry of stars dimmer than the magnitude limit to nans. The new behavior of" \ + " maglim=None keeps all stars without manually setting any mags to nan.") + if (self.maglim is not None) and ("remove" in self.maglim): + logger.critical("WARNING: In SynthPop >=2.0.0, the \"keep\"/\"remove\" maglim options have been " \ + "removed for clarity. Use maglim=None to keep all stars or e.g. maglim=['Bessell_I', " \ + "21] to trim a catalog. This catalog will be trimmed.") + + if hasattr(self, "eff_wavelengths"): + logger.critical("WARNING: eff_wavelengths configuration kwarg is no longer used. Effective wavelengths are" + " handled by the isochrone module for its respective photometric filters.") # self.sun = SunInfo(**self.sun, **self.lsr) # convert to ModuleKwargs BaseModels - if isinstance(self.evolution_class, list): + if self.evolution_class is None: + pass + elif isinstance(self.evolution_class, list): self.evolution_class = [ ModuleKwargs.parse_obj(ev) for ev in self.evolution_class] + elif ('default' in self.evolution_class) or ('name' not in self.evolution_class): + tmp = self.evolution_class.copy() + self.evolution_class = {} + for key in tmp: + if tmp[key] is None: + self.evolution_class[key] = None + elif isinstance(tmp[key], list): + if None in tmp[key]: + self.evolution_class[key] = None + if len(tmp[key])>1: + raise ValueError("evolution_class list cannot contain None and real options") + self.evolution_class[key] = [ModuleKwargs.parse_obj(ev) for ev in tmp[key]] + else: + self.evolution_class[key] = ModuleKwargs.parse_obj(tmp[key]) else: self.evolution_class = ModuleKwargs.parse_obj(self.evolution_class) - self.extinction_map_kwargs = ModuleKwargs.parse_obj(self.extinction_map_kwargs) - - if isinstance(self.extinction_law_kwargs, list): - self.extinction_law_kwargs = [ - ExtLawKwargs.parse_obj(ext_law) for ext_law in self.extinction_law_kwargs - ] - else: - self.extinction_law_kwargs = ExtLawKwargs.parse_obj(self.extinction_law_kwargs) + if self.extinction_map_kwargs is not None: + self.extinction_map_kwargs = ModuleKwargs.parse_obj(self.extinction_map_kwargs) + if self.ifmr_kwargs is not None: + self.ifmr_kwargs = ModuleKwargs.parse_obj(self.ifmr_kwargs) + if self.multiplicity_kwargs is not None: + self.multiplicity_kwargs = ModuleKwargs.parse_obj(self.multiplicity_kwargs) + + if self.extinction_law_kwargs is not None: + if isinstance(self.extinction_law_kwargs, list): + self.extinction_law_kwargs = [ + ExtLawKwargs.parse_obj(ext_law) for ext_law in self.extinction_law_kwargs + ] + else: + self.extinction_law_kwargs = ExtLawKwargs.parse_obj(self.extinction_law_kwargs) if isinstance(self.post_processing_kwargs, list): self.post_processing_kwargs = [ @@ -179,7 +220,7 @@ def __init__( if isinstance(self.output_file_type, str): self.output_file_type = [self.output_file_type, {}] - def validate_input(self): + def validate_mandatory_input(self): """ checks if all Mandatory files are provided""" out = True for key in self._categories["MANDATORY"]: @@ -193,14 +234,23 @@ def location_generator(self) -> Iterator[Tuple[float, float]]: converts l_set and b_set into a location generator object as defined by the l/b_set_type """ - if (self.l_set is None) or (self.b_set is None) or (self.solid_angle is None): - logger.critical("Location or solid_angle_sr are not defined in the settings! " + #pdb.set_trace() + if (self.field_scale is None) and ('solid_angle' in self.__dict__): + logger.critical("WARNING: In SynthPop >=v2.0.0, solid_angle is no longer the expected input for a field " + "size. Assuming circular window and assigning field_scale according to the given solid_angle.") + self.field_scale = np.sqrt(self.solid_angle/np.pi) + self.field_scale_unit = 'deg' + if ('solid_angle_unit' in self.__dict__) and (self.solid_angle_unit=='sr'): + self.field_scale *= (180/np.pi)**2 + + if (self.l_set is None) or (self.b_set is None) or (self.field_scale is None): + logger.critical("Location or field size are not defined in the settings! " "Can not run main() or process_all()") # create def no_location(): - logger.critical("Location or solid_angle_sr are not defined in the settings! " + logger.critical("Location or field size are not defined in the settings! " "Can not run main() or process_all()") for _ in []: yield 0, 0 @@ -229,9 +279,7 @@ def log_settings(self): """ logger.create_info_subsection('copy the following to a config file' ' to redo this model generation', 20) - json_object = json.dumps( - {key: item for key, item in self.__dict__.items() if not key.startswith('_')}, - indent=4) + json_object = json.dumps(self.parameters_dict, indent=4) logger.info(json_object) def read_default_config(self, default_config_file: str): @@ -292,6 +340,7 @@ def read_specific_config(self, config_file: str or None, config_dir: str = "."): logger.info("# read configuration from ") logger.info(f"{config_file = !r} ") specified = json_loader(config_file) + #pdb.set_trace() for cat, items in self._categories.items(): spec_dict = specified.get(cat, specified) @@ -299,6 +348,22 @@ def read_specific_config(self, config_file: str or None, config_dir: str = "."): if item in spec_dict: self.__dict__.update({item: spec_dict[item]}) + # We want to allow the old style of window setting to still work, with a warning. + if 'solid_angle' in specified: + self.__dict__['solid_angle'] = specified['solid_angle'] + if 'solid_angle' in specified: + self.__dict__['solid_angle_unit'] = specified['solid_angle_unit'] + if 'SIGHTLINES' in specified: + if 'solid_angle' in specified['SIGHTLINES']: + self.__dict__['solid_angle'] = specified['SIGHTLINES']['solid_angle'] + if 'solid_angle_unit' in specified['SIGHTLINES']: + self.__dict__['solid_angle_unit'] = specified['SIGHTLINES']['solid_angle_unit'] + # Also check for effecive wavelengths + if "eff_wavelengths" in specified: + self.__dict__["eff_wavelengths"] = None + if ("PHOTOMETRIC_OUTPUTS" in specified) and ("eff_wavelengths" in specified["PHOTOMETRIC_OUTPUTS"]): + self.__dict__["eff_wavelengths"] = None + def read_kwargs_config(self, kwargs: dict): """ reads settings from keyword arguments @@ -309,11 +374,6 @@ def read_kwargs_config(self, kwargs: dict): dictionary of specifications """ - # for cat, items in self._categories.items(): - # kwarg_dict = kwargs.get(cat, kwargs) - # for item in items: - # if item in kwarg_dict: - # self.__dict__.update({item:kwarg_dict[item]}) self.__dict__.update(kwargs) diff --git a/synthpop/synthpop_utils/synthpop_logging.py b/synthpop/synthpop_utils/synthpop_logging.py index bff5154..0957841 100644 --- a/synthpop/synthpop_utils/synthpop_logging.py +++ b/synthpop/synthpop_utils/synthpop_logging.py @@ -1,8 +1,8 @@ """ -This module consist the logging class of the SynthPopFramework. -It mainly works like a standard python logger. -But can change the logging location and provided function -to create sections in the logfile . +This module consist the logging class of the SynthPop framework. +It mainly works like a standard python logger +but can change the logging location and provided function +to create sections in the logfile. """ __all__ = ["SynthpopLogger", "logger", "log_basic_statistics"] @@ -16,6 +16,7 @@ import os import tempfile import numpy as np +import json try: from constants import SYNTHPOP_DIR @@ -24,7 +25,6 @@ LENGTH = 75 - class SynthpopLogger(logging.Logger): def __init__( self, @@ -102,7 +102,7 @@ def setup_file_logging(self, stream_level, file_level=None): # log date and time now = datetime.now() - self.info(f'Execution Date: {now.strftime("%d-%m-%Y %H:%M:%S")}') + self.info(f'Execution Date: {now.strftime("%Y-%m-%d %H:%M:%S")}') def create_info_section(self, msg): if len(msg) > LENGTH - 6: @@ -130,11 +130,13 @@ def create_info_subsection(self, msg, level=25): self.stream_logger.stream.write(f"\n\n{updated_msg}\n") def save_log_file(self, file_path): - self.current_file.seek(0) + #self.current_file.seek(0) with open(file_path, 'w') as f: - shutil.copyfileobj(self.current_file, f) + pass + #pdb.set_trace() - def update_location(self, filename, no_log_file=False): + def update_location(self, filename, parms_dict, l_deg, b_deg, field_shape, + field_scale, field_scale_unit, no_log_file=False): """ Copy the logfile header to a new location and continues logging at the new location @@ -163,6 +165,18 @@ def update_location(self, filename, no_log_file=False): self.filelogger.setLevel(self.stream_level) self.filelogger.setFormatter(self.file_formatter) self.addHandler(self.filelogger) + + if not no_log_file: + log_dict = parms_dict.copy() + log_dict.update({'l_set': [l_deg], 'b_set': [b_deg], 'l_set_type':'pairs', + 'b_set_type':'pairs', 'field_shape': field_shape, + 'field_scale': field_scale, 'field_scale_unit': field_scale_unit, + }) + now = datetime.now() + self.info(f'Execution Date: {now.strftime("%Y-%m-%d %H:%M:%S")}') + self.create_info_section("Settings") + self.create_info_subsection("Copy the following to a config file to redo this model generation:") + self.info(json.dumps(log_dict, indent=4)) def cleanup(self): if self.file_logging_enabled: diff --git a/synthpop/synthpop_utils/utils_functions.py b/synthpop/synthpop_utils/utils_functions.py index 6d40313..69d7ef3 100644 --- a/synthpop/synthpop_utils/utils_functions.py +++ b/synthpop/synthpop_utils/utils_functions.py @@ -1,15 +1,19 @@ -""" This file contains several utils function """ -__all__ = ['solidangle_to_half_cone_angle', 'half_cone_angle_to_solidangle', "rotation_matrix"] -__credits__ = ["J. Klüter", "S. Johnson", "M.J. Huston", "A. Aronica", "M. Penny"] +""" +This file contains several utility functions. +""" +__all__ = ["solidangle_to_half_cone_angle", "half_cone_angle_to_solidangle", + "rotation_matrix", "combine_system_mags", "get_primary_mags"] +__credits__ = ["J. Klüter", "S. Johnson", "M.J. Huston", "A. Aronica", "M. Penny"] import numpy as np - +import pandas as pd +import warnings +import pdb def solidangle_to_half_cone_angle(solid_angle): return np.arccos(1 - solid_angle / (2. * np.pi)) - def half_cone_angle_to_solidangle(cone_angle): return (2. * np.pi) * (1 - np.cos(cone_angle)) @@ -71,3 +75,43 @@ def rotation_matrix( return np.array([[ct, zero, -st], [zero, one, zero], [st, zero, ct]]) if axis == 'z': return np.array([[ct, -st, zero], [st, ct, zero], [zero, zero, one]]) + + +def combine_system_mags(df, comp_df, filters): + all_systems = df['system_idx'].to_numpy() + idx_map = pd.Series(np.arange(len(all_systems)), index=all_systems) + comp_rows = idx_map.loc[comp_df['system_idx']].to_numpy() + + comp_mags = np.ma.masked_invalid(comp_df[filters].to_numpy()) + comp_flux = (10.0 ** (-0.4 * comp_mags)).filled(0.0) + + main_mags = np.ma.masked_invalid(df[filters].to_numpy()) + system_flux = (10.0 ** (-0.4 * main_mags)).filled(0.0) + np.add.at(system_flux, comp_rows, comp_flux) + + masked_system_flux = np.ma.masked_less_equal(system_flux, 0.0) + df[filters] = (-2.5 * np.ma.log10(masked_system_flux)).filled(np.nan) + + return df + + +def get_primary_mags(df, comp_df, filters): + primary_idxs = (df['n_companions'] > 0).to_numpy() + if not np.any(primary_idxs): + return df + + primary_systems = df.loc[primary_idxs, 'system_idx'].to_numpy() + idx_map = pd.Series(np.arange(len(primary_systems)), index=primary_systems) + comp_rows = idx_map.loc[comp_df['system_idx']].to_numpy() + comp_mags = np.ma.masked_invalid(comp_df[filters].to_numpy()) + comp_flux = (10.0 ** (-0.4 * comp_mags)).filled(0.0) + + system_mags = np.ma.masked_invalid(df.loc[primary_idxs, filters].to_numpy()) + primary_flux = (10.0 ** (-0.4 * system_mags)).filled(0.0) + np.add.at(primary_flux, comp_rows, -comp_flux) + + masked_primary_flux = np.ma.masked_less_equal(primary_flux, 0.0) + df.loc[primary_idxs, filters] = (-2.5 * np.ma.log10(masked_primary_flux)).filled(np.nan) + + return df + diff --git a/synthpop/tests/test_population_density.py b/synthpop/tests/test_population_density.py new file mode 100644 index 0000000..e2045dc --- /dev/null +++ b/synthpop/tests/test_population_density.py @@ -0,0 +1,72 @@ +import synthpop.modules.population_density.constant as constant_density_module +import pdb +import numpy as np +import pytest + +def check_precision(v_true, v_calc, prec): + assert np.abs(v_calc-v_true)/v_true < prec, \ + f"Required precision {prec} not met for "+ \ + f"value {v_true} estimate of {v_calc}." + print(v_true, v_calc) + +def test_mass_integration(): + """ + Test the integration used to determine total stellar mass of a population in a given + field. Test two different field sizes each for each shape, using a constant density. + """ + constant_density = constant_density_module.Constant() + # Small circular field + field_scale_deg = 0.01 + constant_density.update_location(l_deg=0.0, b_deg=0.0, field_shape='circle', + field_scale_deg=field_scale_deg, max_distance=25) + total_mass_integ = constant_density.total_mass + total_mass_ana = constant_density.rho * 4./3*np.pi*constant_density.max_distance**3 * \ + np.pi*field_scale_deg**2*(np.pi/180)**2/(4*np.pi) + check_precision(total_mass_ana, total_mass_integ, 1e-4) + + # Larger circular field + field_scale_deg = 1.0 + constant_density.update_location(l_deg=0.0, b_deg=0.0, field_shape='circle', + field_scale_deg=field_scale_deg, max_distance=25) + #pdb.set_trace() + total_mass_integ = constant_density.total_mass + total_mass_ana = constant_density.rho * 4./3*np.pi*constant_density.max_distance**3 * \ + np.pi*field_scale_deg**2*(np.pi/180)**2/(4*np.pi) + check_precision(total_mass_ana, total_mass_integ, 1e-4) + + # Small square field + field_scale_deg = 0.02 + constant_density.update_location(l_deg=0.0, b_deg=0.0, field_shape='box', + field_scale_deg=field_scale_deg, max_distance=25) + total_mass_integ = constant_density.total_mass + total_mass_ana = constant_density.rho * 4./3*np.pi*constant_density.max_distance**3 * \ + field_scale_deg**2*(np.pi/180)**2/(4*np.pi) + check_precision(total_mass_ana, total_mass_integ, 1e-4) + + # Larger square field + field_scale_deg = 2.0 + constant_density.update_location(l_deg=0.0, b_deg=0.0, field_shape='box', + field_scale_deg=field_scale_deg, max_distance=25) + total_mass_integ = constant_density.total_mass + total_mass_ana = constant_density.rho * 4./3*np.pi*constant_density.max_distance**3 * \ + field_scale_deg**2*(np.pi/180)**2/(4*np.pi) + check_precision(total_mass_ana, total_mass_integ, 1e-4) + + # Small rectangular field + field_scale_deg = [0.02,0.01] + constant_density.update_location(l_deg=0.0, b_deg=0.0, field_shape='box', + field_scale_deg=field_scale_deg, max_distance=25) + total_mass_integ = constant_density.total_mass + total_mass_ana = constant_density.rho * 4./3*np.pi*constant_density.max_distance**3 * \ + field_scale_deg[0]*field_scale_deg[1]*(np.pi/180)**2/(4*np.pi) + check_precision(total_mass_ana, total_mass_integ, 1e-4) + + # Larger rectangular field + field_scale_deg = [2.0,1.0] + constant_density.update_location(l_deg=0.0, b_deg=0.0, field_shape='box', + field_scale_deg=field_scale_deg, max_distance=25) + total_mass_integ = constant_density.total_mass + total_mass_ana = constant_density.rho * 4./3*np.pi*constant_density.max_distance**3 * \ + field_scale_deg[0]*field_scale_deg[1]*(np.pi/180)**2/(4*np.pi) + check_precision(total_mass_ana, total_mass_integ, 1e-4) +