From e395201a43e83c42f14f3124972252d0053720a7 Mon Sep 17 00:00:00 2001 From: Hongwan Liu Date: Thu, 26 Sep 2019 18:06:11 -0400 Subject: [PATCH] Testing for successfully compiled code, bug fixed with logBeta_tab * Beta was accidentally being populated by Alpha values. Fixed in hyrec/hydrogen.c/interpolate_rates * Renamed files from RR to R to be compatible with previous naming convention. * Producing test_xe.dat to check that the recombination history is sensible. --- ...o2s_BB_n_250.dat => R_2pto2s_BB_n_250.dat} | 0 ...o2p_BB_n_250.dat => R_2sto2p_BB_n_250.dat} | 0 hyrec/hydrogen.c | 8 +- notebooks/recfast_fudge.ipynb | 136 +- notebooks/test_xe.dat | 108487 +++++++++++++++ source/thermodynamics.c | 8 + 6 files changed, 108525 insertions(+), 114 deletions(-) rename hyrec/{RR_2pto2s_BB_n_250.dat => R_2pto2s_BB_n_250.dat} (100%) rename hyrec/{RR_2sto2p_BB_n_250.dat => R_2sto2p_BB_n_250.dat} (100%) create mode 100644 notebooks/test_xe.dat diff --git a/hyrec/RR_2pto2s_BB_n_250.dat b/hyrec/R_2pto2s_BB_n_250.dat similarity index 100% rename from hyrec/RR_2pto2s_BB_n_250.dat rename to hyrec/R_2pto2s_BB_n_250.dat diff --git a/hyrec/RR_2sto2p_BB_n_250.dat b/hyrec/R_2sto2p_BB_n_250.dat similarity index 100% rename from hyrec/RR_2sto2p_BB_n_250.dat rename to hyrec/R_2sto2p_BB_n_250.dat diff --git a/hyrec/hydrogen.c b/hyrec/hydrogen.c index a907efe03..92cdfe0a7 100644 --- a/hyrec/hydrogen.c +++ b/hyrec/hydrogen.c @@ -204,10 +204,10 @@ void interpolate_rates(double Alpha[2], double Beta[2], double *R2p2s, double *R + rate_table->logAlpha_tab[l][iTR+2]*coeff2[3] ); Beta[l] = exp( - rate_table->logAlpha_tab[l][iTR-1]*coeff2[0] - + rate_table->logAlpha_tab[l][iTR]*coeff2[1] - + rate_table->logAlpha_tab[l][iTR+1]*coeff2[2] - + rate_table->logAlpha_tab[l][iTR+2]*coeff2[3] + rate_table->logBeta_tab[l][iTR-1]*coeff2[0] + + rate_table->logBeta_tab[l][iTR]*coeff2[1] + + rate_table->logBeta_tab[l][iTR+1]*coeff2[2] + + rate_table->logBeta_tab[l][iTR+2]*coeff2[3] ); } diff --git a/notebooks/recfast_fudge.ipynb b/notebooks/recfast_fudge.ipynb index e8c481fc1..0d3b8f2b4 100644 --- a/notebooks/recfast_fudge.ipynb +++ b/notebooks/recfast_fudge.ipynb @@ -26,9 +26,22 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "CosmoComputationError", + "evalue": "\n\nError in Class: perturb_init(L:284) :error in perturb_timesampling_for_sources(ppr, pba, pth, ppt);\n=>perturb_timesampling_for_sources(L:992) :condition (pvecback[pba->index_bg_a]* pvecback[pba->index_bg_H]/ pvecthermo[pth->index_th_dkappa] < ppr->start_sources_at_tau_c_over_tau_h) is true; your choice of initial time for computing sources is inappropriate: it corresponds to a time after recombination. You should decrease 'start_sources_at_tau_c_over_tau_h'\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mCosmoComputationError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m })\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mLambdaCDM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mhyrec_mod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mclassy.pyx\u001b[0m in \u001b[0;36mclassy.Class.compute\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mCosmoComputationError\u001b[0m: \n\nError in Class: perturb_init(L:284) :error in perturb_timesampling_for_sources(ppr, pba, pth, ppt);\n=>perturb_timesampling_for_sources(L:992) :condition (pvecback[pba->index_bg_a]* pvecback[pba->index_bg_H]/ pvecthermo[pth->index_th_dkappa] < ppr->start_sources_at_tau_c_over_tau_h) is true; your choice of initial time for computing sources is inappropriate: it corresponds to a time after recombination. You should decrease 'start_sources_at_tau_c_over_tau_h'\n" + ] + } + ], "source": [ "# create instance of the class \"Class\"\n", "from classy import Class\n", @@ -63,42 +76,9 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hongwan/anaconda/envs/python3/lib/python3.6/site-packages/ipykernel_launcher.py:13: RuntimeWarning: invalid value encountered in true_divide\n", - " del sys.path[0]\n", - "/Users/hongwan/anaconda/envs/python3/lib/python3.6/site-packages/ipykernel_launcher.py:14: RuntimeWarning: invalid value encountered in true_divide\n", - " \n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$(C_\\\\ell - C_\\\\ell^\\\\mathrm{ref})/C_\\\\ell^\\\\mathrm{ref} [\\\\%]$')" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cls_new = hyrec_mod.lensed_cl(2500)\n", "cls_std = LambdaCDM.lensed_cl(2500)\n", @@ -122,21 +102,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1089.192467412792\n", - "Hubble at rec.: 5.206417089278467\n", - "1089.0\n", - "Photon-baryon sound speed: 0.4546061312605719\n", - "Sound horizon at rec.: 144.70248662076526\n" - ] - } - ], + "outputs": [], "source": [ "z_rec = LambdaCDM.get_current_derived_parameters(['z_rec'])['z_rec']\n", "Hubble_rec = LambdaCDM.Hubble(z_rec)\n", @@ -151,32 +119,9 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "\n", @@ -195,54 +140,25 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", - " [1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", - " [2.00000000e+00, 1.56602399e-10, 7.65424447e-15],\n", - " ...,\n", - " [2.49800000e+03, 1.02201077e-17, 3.74715679e-19],\n", - " [2.49900000e+03, 1.01820974e-17, 3.74768248e-19],\n", - " [2.50000000e+03, 1.01443305e-17, 3.74816418e-19]])" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np.stack((ll_vec_new, cls_new['tt'], cls_new['ee']), axis=-1)" ] }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'dict' object has no attribute 'shape'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcls_new\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'shape'" - ] - } - ], + "outputs": [], "source": [ "cls_new.shape" ] }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/notebooks/test_xe.dat b/notebooks/test_xe.dat new file mode 100644 index 000000000..aa48b7d43 --- /dev/null +++ b/notebooks/test_xe.dat @@ -0,0 +1,108487 @@ +1.000000e+04 1.163639e+00 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+6.386902e-05 1.272947e-04 +-2.103280e-05 1.272945e-04 diff --git a/source/thermodynamics.c b/source/thermodynamics.c index c8c0884e8..e1df94747 100755 --- a/source/thermodynamics.c +++ b/source/thermodynamics.c @@ -2787,6 +2787,14 @@ int thermodynamics_recombination_with_hyrec( rec_build_history(¶m, &rate_table, &twog_params, xe_output, Tm_output); + FILE *ftest = fopen("./test_xe.dat", "w"); + + for(int iz=0; izthermodynamics_verbose > 0) printf(" by Y. Ali-Haïmoud & C. Hirata\n");