diff --git a/clmm/theory/parent_class.py b/clmm/theory/parent_class.py index e5515f611..6c7a9196a 100644 --- a/clmm/theory/parent_class.py +++ b/clmm/theory/parent_class.py @@ -755,8 +755,6 @@ def eval_tangential_shear(self, r_proj, z_cl, z_src, z_src_info="discrete", verb ) gammat = beta_s_mean * gammat_inf - print("test") - gammat = 0 return gammat def eval_convergence(self, r_proj, z_cl, z_src, z_src_info="discrete", verbose=False): diff --git a/examples/demo_theory_functionality_diff_z_types.ipynb b/examples/demo_theory_functionality_diff_z_types.ipynb index 1e383048b..e4c48128a 100644 --- a/examples/demo_theory_functionality_diff_z_types.ipynb +++ b/examples/demo_theory_functionality_diff_z_types.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -71,20 +71,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.8.1'" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "clmm.__version__" ] @@ -98,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -224,34 +213,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\langle\\beta_s\\rangle = 0.440$" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/latex": [ - "$\\displaystyle \\langle\\beta_s^2\\rangle = 0.220$" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "z_inf = 1000\n", "\n", @@ -287,30 +251,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "z = np.linspace(0, zsrc_max, 1000)\n", "\n", @@ -368,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -377,32 +320,16 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0.00218744, 0.00176588, 0.00042673, ..., 0.00224765, 0.00186142,\n", - " 0.00032859]),\n", - " array([0.00898515, 0.01455188, 0.086497 , ..., 0.00915508, 0.0164856 ,\n", - " 0.13692621]),\n", - " array([-4.77048956e-18, -7.80625564e-18, -3.46944695e-17, ...,\n", - " -5.20417043e-18, -1.12757026e-17, -6.93889390e-17]))" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "gc_object.compute_tangential_and_cross_components()" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -419,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -445,18 +372,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 2.16 s, sys: 169 ms, total: 2.33 s\n", - "Wall time: 2.33 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "# in case we know the discrete source redshift, we can compute the reduced shear for each source galaxy\n", @@ -500,18 +418,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 553 ms, sys: 6.02 ms, total: 559 ms\n", - "Wall time: 558 ms\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "gt_distribution_no_approx = clmm.theory.compute_reduced_tangential_shear(\n", @@ -545,18 +454,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.26 ms, sys: 905 µs, total: 2.16 ms\n", - "Wall time: 1.73 ms\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "gt_beta_1 = clmm.theory.compute_reduced_tangential_shear(\n", @@ -575,18 +475,9 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.17 ms, sys: 21 µs, total: 1.19 ms\n", - "Wall time: 1.12 ms\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "gt_beta_2 = clmm.theory.compute_reduced_tangential_shear(\n", @@ -612,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -642,20 +533,9 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_cases(\n", " rr,\n", @@ -695,23 +575,9 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "UnboundLocalError", - "evalue": "cannot access local variable 'gammat' where it is not associated with a value", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[45], line 20\u001b[0m\n\u001b[1;32m 1\u001b[0m gammat_discrete \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\n\u001b[1;32m 2\u001b[0m [\n\u001b[1;32m 3\u001b[0m np\u001b[38;5;241m.\u001b[39mmean(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 17\u001b[0m ]\n\u001b[1;32m 18\u001b[0m )\n\u001b[0;32m---> 20\u001b[0m gammat_distribution \u001b[38;5;241m=\u001b[39m \u001b[43mclmm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtheory\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_tangential_shear\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43mrr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43mcluster_m\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43mconcentration\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43mcluster_z\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_z_distrib_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[43mcosmo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[43mdelta_mdef\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m500\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mmassdef\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcritical\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43mz_src_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdistribution\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[1;32m 31\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m gammat_beta \u001b[38;5;241m=\u001b[39m clmm\u001b[38;5;241m.\u001b[39mtheory\u001b[38;5;241m.\u001b[39mcompute_tangential_shear(\n\u001b[1;32m 34\u001b[0m rr,\n\u001b[1;32m 35\u001b[0m cluster_m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 42\u001b[0m z_src_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeta\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 43\u001b[0m )\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/clmm/theory/func_layer.py:637\u001b[0m, in \u001b[0;36mcompute_tangential_shear\u001b[0;34m(r_proj, mdelta, cdelta, z_cluster, z_source, cosmo, delta_mdef, halo_profile_model, massdef, alpha_ein, z_src_info, verbose, validate_input)\u001b[0m\n\u001b[1;32m 631\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmin(r_proj) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1.0e-11\u001b[39m:\n\u001b[1;32m 632\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 633\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRmin = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnp\u001b[38;5;241m.\u001b[39mmin(r_proj)\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2e\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Mpc/h! This value is too small \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 634\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mand may cause computational issues.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 635\u001b[0m )\n\u001b[0;32m--> 637\u001b[0m tangential_shear \u001b[38;5;241m=\u001b[39m \u001b[43m_modeling_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval_tangential_shear\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 638\u001b[0m \u001b[43m \u001b[49m\u001b[43mr_proj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 639\u001b[0m \u001b[43m \u001b[49m\u001b[43mz_cluster\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 640\u001b[0m \u001b[43m \u001b[49m\u001b[43mz_source\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 641\u001b[0m \u001b[43m \u001b[49m\u001b[43mz_src_info\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mz_src_info\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 642\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 643\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 645\u001b[0m _modeling_object\u001b[38;5;241m.\u001b[39mvalidate_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 646\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tangential_shear\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/clmm/theory/parent_class.py:757\u001b[0m, in \u001b[0;36mCLMModeling.eval_tangential_shear\u001b[0;34m(self, r_proj, z_cl, z_src, z_src_info, verbose)\u001b[0m\n\u001b[1;32m 752\u001b[0m gammat_inf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_eval_tangential_shear_core(\n\u001b[1;32m 753\u001b[0m r_proj\u001b[38;5;241m=\u001b[39mr_proj, z_cl\u001b[38;5;241m=\u001b[39mz_cl, z_src\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_inf\n\u001b[1;32m 754\u001b[0m )\n\u001b[1;32m 755\u001b[0m gammat \u001b[38;5;241m=\u001b[39m beta_s_mean \u001b[38;5;241m*\u001b[39m gammat_inf\n\u001b[0;32m--> 757\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgammat\u001b[49m\n", - "\u001b[0;31mUnboundLocalError\u001b[0m: cannot access local variable 'gammat' where it is not associated with a value" - ] - } - ], + "outputs": [], "source": [ "gammat_discrete = np.array(\n", " [\n", @@ -732,18 +598,6 @@ " ]\n", ")\n", "\n", - "gammat_distribution = clmm.theory.compute_tangential_shear(\n", - " rr,\n", - " cluster_m,\n", - " concentration,\n", - " cluster_z,\n", - " model_z_distrib_dict[\"func\"],\n", - " cosmo,\n", - " delta_mdef=500,\n", - " massdef=\"critical\",\n", - " z_src_info=\"distribution\",\n", - " validate_input=False\n", - ")\n", "\n", "gammat_beta = clmm.theory.compute_tangential_shear(\n", " rr,\n", @@ -768,7 +622,6 @@ " rr,\n", " base=(gammat_discrete, \"k.-\", dict(label=\"discrete\")),\n", " others=(\n", - " (gammat_distribution, \"rx-\", dict(label=\"distribution, no approx\")),\n", " (gammat_beta, \"b--\", dict(label=\"beta, no approx\")),\n", " ),\n", " ylabel=\"$\\gamma_t$\",\n", @@ -800,18 +653,6 @@ " ]\n", ")\n", "\n", - "kappa_distribution = clmm.theory.compute_convergence(\n", - " rr,\n", - " cluster_m,\n", - " concentration,\n", - " cluster_z,\n", - " model_z_distrib_dict[\"func\"],\n", - " cosmo,\n", - " delta_mdef=500,\n", - " massdef=\"critical\",\n", - " z_src_info=\"distribution\",\n", - " beta_kwargs={\"zmax\": zsrc_max},\n", - ")\n", "\n", "kappa_beta = clmm.theory.compute_convergence(\n", " rr,\n", @@ -822,8 +663,7 @@ " cosmo,\n", " delta_mdef=500,\n", " massdef=\"critical\",\n", - " z_src_info=\"beta\",\n", - " beta_kwargs={\"zmax\": zsrc_max},\n", + " z_src_info=\"beta\"\n", ")" ] }, @@ -837,7 +677,6 @@ " rr,\n", " base=(kappa_discrete, \"k.-\", dict(label=\"discrete\")),\n", " others=(\n", - " (kappa_distribution, \"rx-\", dict(label=\"distribution, no approx\")),\n", " (kappa_beta, \"b--\", dict(label=\"beta, no approx\")),\n", " ),\n", " ylabel=\"$\\kappa_t$\",\n", @@ -869,19 +708,6 @@ " ]\n", ")\n", "\n", - "mu_distribution = clmm.theory.compute_magnification(\n", - " rr,\n", - " cluster_m,\n", - " concentration,\n", - " cluster_z,\n", - " model_z_distrib_dict[\"func\"],\n", - " cosmo,\n", - " delta_mdef=500,\n", - " massdef=\"critical\",\n", - " z_src_info=\"distribution\",\n", - " approx=None,\n", - " beta_kwargs={\"zmax\": zsrc_max},\n", - ")\n", "\n", "mu_beta_1 = clmm.theory.compute_magnification(\n", " rr,\n", @@ -893,8 +719,7 @@ " delta_mdef=500,\n", " massdef=\"critical\",\n", " z_src_info=\"beta\",\n", - " approx=\"order1\",\n", - " beta_kwargs={\"zmax\": zsrc_max},\n", + " approx=\"order1\"\n", ")\n", "\n", "mu_beta_2 = clmm.theory.compute_magnification(\n", @@ -907,8 +732,7 @@ " delta_mdef=500,\n", " massdef=\"critical\",\n", " z_src_info=\"beta\",\n", - " approx=\"order2\",\n", - " beta_kwargs={\"zmax\": zsrc_max},\n", + " approx=\"order2\"\n", ")" ] }, @@ -922,7 +746,6 @@ " rr,\n", " base=(mu_discrete, \"k.-\", dict(label=\"discrete\")),\n", " others=(\n", - " (mu_distribution, \"rs-\", dict(label=\"distribution, no approx\")),\n", " (mu_beta_1, \"bx-\", dict(label=\"beta, order 1 approx\")),\n", " (mu_beta_2, \"b--\", dict(label=\"beta, order 2 approx\")),\n", " ),\n", @@ -958,20 +781,6 @@ " ]\n", ")\n", "\n", - "mu_bias_distribution = clmm.theory.compute_magnification_bias(\n", - " rr,\n", - " alpha,\n", - " cluster_m,\n", - " concentration,\n", - " cluster_z,\n", - " model_z_distrib_dict[\"func\"],\n", - " cosmo,\n", - " delta_mdef=500,\n", - " massdef=\"critical\",\n", - " z_src_info=\"distribution\",\n", - " approx=None,\n", - " beta_kwargs={\"zmax\": zsrc_max},\n", - ")\n", "\n", "mu_bias_beta_1 = clmm.theory.compute_magnification_bias(\n", " rr,\n", @@ -984,8 +793,7 @@ " delta_mdef=500,\n", " massdef=\"critical\",\n", " z_src_info=\"beta\",\n", - " approx=\"order1\",\n", - " beta_kwargs={\"zmax\": zsrc_max},\n", + " approx=\"order1\"\n", ")\n", "\n", "mu_bias_beta_2 = clmm.theory.compute_magnification_bias(\n", @@ -999,8 +807,7 @@ " delta_mdef=500,\n", " massdef=\"critical\",\n", " z_src_info=\"beta\",\n", - " approx=\"order2\",\n", - " beta_kwargs={\"zmax\": zsrc_max},\n", + " approx=\"order2\"\n", ")" ] }, @@ -1014,7 +821,6 @@ " rr,\n", " base=(mu_bias_discrete, \"k.-\", dict(label=\"discrete\")),\n", " others=(\n", - " (mu_bias_distribution, \"rs-\", dict(label=\"distribution, no approx\")),\n", " (mu_bias_beta_1, \"bx-\", dict(label=\"beta, order 1 approx\")),\n", " (mu_bias_beta_2, \"b--\", dict(label=\"beta, order 2 approx\")),\n", " ),\n", @@ -1028,76 +834,6 @@ "source": [ "When making approximation, there may be large differences (~few 10% level) in the inner regions (compare to when using the exact redshift of the sources), especially for magnification and magnification bias. However, these approaches are very fast to compute. The user as to chose the appropriate method depending on the use case." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {