From 175be27fb842aadf44a4680e89eac7c487e2e145 Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com> Date: Sun, 27 Oct 2024 12:23:01 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- example_snia.html | 14 +++++++------- search.json | 2 +- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/.nojekyll b/.nojekyll index a09de50..986b388 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -dab3d479 \ No newline at end of file +2bc2bd33 \ No newline at end of file diff --git a/example_snia.html b/example_snia.html index 70a57d0..144866a 100644 --- a/example_snia.html +++ b/example_snia.html @@ -316,7 +316,7 @@ <h1 class="title">Supernova Type Ia Analysis</h1> <div class="hidden"> -<div id="007867b7" class="cell" data-execution_count="1"> +<div id="16ebce3c" class="cell" data-execution_count="1"> <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> doc_theme():</span> <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> theme_minimal() <span class="op">+</span> theme(</span> <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a> panel_grid_minor<span class="op">=</span>element_line(color<span class="op">=</span><span class="st">"gray"</span>, linetype<span class="op">=</span><span class="st">"--"</span>),</span> @@ -327,7 +327,7 @@ <h1 class="title">Supernova Type Ia Analysis</h1> <section id="computational-cosmology" class="level2 page-columns page-full"> <h2 class="anchored" data-anchor-id="computational-cosmology">Computational Cosmology</h2> <p>NumCosmo provides tools for calculating cosmological observables with precision. For example, the comoving distance in an XCDM cosmology up to a redshift of <span class="math inline">\(z = 3.0\)</span> can be computed with a few lines of code using the parameters <span class="math inline">\(\Omega_{c0} = 0.25\)</span>, <span class="math inline">\(\Omega_{b0} = 0.05\)</span>, and <span class="math inline">\(w\)</span> varying from <span class="math inline">\(-1.5\)</span> to <span class="math inline">\(-0.5\)</span>.</p> -<div id="f435784e" class="cell" data-execution_count="2"> +<div id="97e289c6" class="cell" data-execution_count="2"> <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span> <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span> <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> plotnine <span class="im">import</span> <span class="op">*</span></span> @@ -386,7 +386,7 @@ <h2 class="anchored" data-anchor-id="computational-cosmology">Computational Cosm <section id="the-modeling" class="level3"> <h3 class="anchored" data-anchor-id="the-modeling">The Modeling</h3> <p>NumCosmo’s computational objects are designed for direct use in statistical analyses. In the example above, the <code>HICosmoDEXcdm</code> class defines the cosmological model, which is a subclass of the <code>HICosmo</code> class representing a homogeneous isotropic cosmology. The model’s parameters can be accessed and managed within a model set, using the <code>MSet</code> class, which serves as the main container for all models in a given analysis.</p> -<div id="a3f772cb" class="cell" data-execution_count="4"> +<div id="09787363" class="cell" data-execution_count="4"> <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>mset <span class="op">=</span> Ncm.MSet.new_array([cosmo])</span> <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>mset.pretty_log()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <div class="cell-output cell-output-stdout"> @@ -410,7 +410,7 @@ <h3 class="anchored" data-anchor-id="the-modeling">The Modeling</h3> <section id="fitting-model-to-data" class="level2 page-columns page-full"> <h2 class="anchored" data-anchor-id="fitting-model-to-data">Fitting Model to Data</h2> <p>Once models are defined and the free parameters are set, they can be analyzed using a variety of statistical methods. For example, the best-fit parameters for a given model can be found by maximizing the likelihood function.</p> -<div id="1f251021" class="cell" data-execution_count="5"> +<div id="d53178d2" class="cell" data-execution_count="5"> <details class="code-fold"> <summary>Code</summary> <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>cosmo.props.w_fit <span class="op">=</span> <span class="va">True</span></span> @@ -437,7 +437,7 @@ <h2 class="anchored" data-anchor-id="fitting-model-to-data">Fitting Model to Dat # - differentiation: Numerical differentiantion (forward) #................ # Minimum found with precision: |df|/f = 1.00000e-08 and |dx| = 1.00000e-05 -# Elapsed time: 00 days, 00:00:00.8133440 +# Elapsed time: 00 days, 00:00:00.8876990 # iteration [000074] # function evaluations [000076] # gradient evaluations [000000] @@ -470,7 +470,7 @@ <h2 class="anchored" data-anchor-id="fitting-model-to-data">Fitting Model to Dat </div> </div> <p>The code above fits the XCDM model to the Union2.1 dataset, which contains supernova type Ia data. It computes the best-fit parameters for the model and the covariance matrix of the parameters using the Fisher matrix.</p> -<div id="fc42385c" class="cell" data-execution_count="6"> +<div id="9183a77f" class="cell" data-execution_count="6"> <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>lhr2d <span class="op">=</span> Ncm.LHRatio2d.new(</span> <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a> fit,</span> <span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> mset.fparam_get_pi_by_name(<span class="st">"Omegac"</span>),</span> @@ -538,7 +538,7 @@ <h2 class="anchored" data-anchor-id="fitting-model-to-data">Fitting Model to Dat <section id="running-a-mcmc-analysis" class="level2 page-columns page-full"> <h2 class="anchored" data-anchor-id="running-a-mcmc-analysis">Running a MCMC Analysis</h2> <p>NumCosmo also provides tools for running Markov Chain Monte Carlo (MCMC) analyses. The code below runs an MCMC analysis using the <code>APES</code> algorithm.</p> -<div id="43689f2d" class="cell" data-execution_count="8"> +<div id="fac337ac" class="cell" data-execution_count="8"> <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>init_sampler <span class="op">=</span> Ncm.MSetTransKernGauss.new(<span class="dv">0</span>)</span> <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>init_sampler.set_mset(mset)</span> <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>init_sampler.set_prior_from_mset()</span> diff --git a/search.json b/search.json index cab35fe..0392d17 100644 --- a/search.json +++ b/search.json @@ -30,7 +30,7 @@ "href": "example_snia.html#fitting-model-to-data", "title": "Supernova Type Ia Analysis", "section": "Fitting Model to Data", - "text": "Fitting Model to Data\nOnce models are defined and the free parameters are set, they can be analyzed using a variety of statistical methods. For example, the best-fit parameters for a given model can be found by maximizing the likelihood function.\n\n\nCode\ncosmo.props.w_fit = True\ncosmo.props.Omegac_fit = True\nmset.prepare_fparam_map()\n\nlh = Ncm.Likelihood(\n dataset=Ncm.Dataset.new_array(\n [Nc.DataDistMu.new_from_id(dist, Nc.DataSNIAId.SIMPLE_UNION2_1)]\n )\n)\nfit = Ncm.Fit.factory(\n Ncm.FitType.NLOPT, \"ln-neldermead\", lh, mset, Ncm.FitGradType.NUMDIFF_FORWARD\n)\nfit.run(Ncm.FitRunMsgs.SIMPLE)\nfit.log_info()\nfit.fisher()\nfit.log_covar()\n\n\n#----------------------------------------------------------------------------------\n# Model fitting. Interating using:\n# - solver: NLOpt:ln-neldermead\n# - differentiation: Numerical differentiantion (forward)\n#................\n# Minimum found with precision: |df|/f = 1.00000e-08 and |dx| = 1.00000e-05\n# Elapsed time: 00 days, 00:00:00.8133440\n# iteration [000074]\n# function evaluations [000076]\n# gradient evaluations [000000]\n# degrees of freedom [000577]\n# m2lnL = 562.219163105056 ( 562.21916 )\n# Fit parameters:\n# 0.230499952926549 -1.02959391499341 \n#----------------------------------------------------------------------------------\n# Data used:\n# - Union2.1 sample\n#----------------------------------------------------------------------------------\n# Model[03000]:\n# - NcHICosmo : XCDM - Constant EOS\n#----------------------------------------------------------------------------------\n# Model parameters\n# - H0[00]: 67.36 [FIXED]\n# - Omegac[01]: 0.230499952926549 [FREE]\n# - Omegax[02]: 0.7 [FIXED]\n# - Tgamma0[03]: 2.7245 [FIXED]\n# - Yp[04]: 0.24 [FIXED]\n# - ENnu[05]: 3.046 [FIXED]\n# - Omegab[06]: 0.05 [FIXED]\n# - w[07]: -1.02959391499341 [FREE]\n#----------------------------------------------------------------------------------\n# NcmMSet parameters covariance matrix\n# -------------------------------\n# Omegac[03000:01] = 0.2305 +/- 0.06233 | 1 | -0.9297 |\n# w[03000:07] = -1.03 +/- 0.08595 | -0.9297 | 1 |\n# -------------------------------\n\n\nThe code above fits the XCDM model to the Union2.1 dataset, which contains supernova type Ia data. It computes the best-fit parameters for the model and the covariance matrix of the parameters using the Fisher matrix.\n\nlhr2d = Ncm.LHRatio2d.new(\n fit,\n mset.fparam_get_pi_by_name(\"Omegac\"),\n mset.fparam_get_pi_by_name(\"w\"),\n 1.0e-3,\n)\n\nbest_fit = pd.DataFrame(\n {\n \"Omegac\": [cosmo.props.Omegac],\n \"w\": [cosmo.props.w],\n \"sigma\": \"Best-fit\",\n \"region\": \"Best-fit\",\n }\n)\n\n\nregions_pd_list = []\nfor i, sigma in enumerate(\n [Ncm.C.stats_1sigma(), Ncm.C.stats_2sigma(), Ncm.C.stats_3sigma()]\n):\n fisher_rg = lhr2d.fisher_border(sigma, 300.0, Ncm.FitRunMsgs.NONE)\n Omegac_a = np.array(fisher_rg.p1.dup_array())\n w_a = np.array(fisher_rg.p2.dup_array())\n regions_pd_list.append(\n pd.DataFrame(\n {\n \"Omegac\": Omegac_a,\n \"w\": w_a,\n \"sigma\": rf\"{i+1}$\\sigma$\",\n \"region\": \"Fisher\",\n }\n )\n )\n\nregions_pd = pd.concat(regions_pd_list)\n\nThe code above computes the confidence regions for the best-fit parameters using the Fisher matrix.\n\n\nCode\n(\n ggplot(regions_pd, aes(\"Omegac\", \"w\", fill=\"sigma\", color=\"sigma\"))\n + geom_polygon(alpha=0.3)\n + geom_point(data=best_fit)\n + labs(x=r\"$\\Omega_c$\", y=r\"$w$\", fill=r\"Confidence\")\n + guides(fill=guide_legend(), color=False)\n + doc_theme()\n)\n\n\n\n\n\n\n\n\nFigure 2: Best-fit parameters and confidence regions for the XCDM model.", + "text": "Fitting Model to Data\nOnce models are defined and the free parameters are set, they can be analyzed using a variety of statistical methods. For example, the best-fit parameters for a given model can be found by maximizing the likelihood function.\n\n\nCode\ncosmo.props.w_fit = True\ncosmo.props.Omegac_fit = True\nmset.prepare_fparam_map()\n\nlh = Ncm.Likelihood(\n dataset=Ncm.Dataset.new_array(\n [Nc.DataDistMu.new_from_id(dist, Nc.DataSNIAId.SIMPLE_UNION2_1)]\n )\n)\nfit = Ncm.Fit.factory(\n Ncm.FitType.NLOPT, \"ln-neldermead\", lh, mset, Ncm.FitGradType.NUMDIFF_FORWARD\n)\nfit.run(Ncm.FitRunMsgs.SIMPLE)\nfit.log_info()\nfit.fisher()\nfit.log_covar()\n\n\n#----------------------------------------------------------------------------------\n# Model fitting. Interating using:\n# - solver: NLOpt:ln-neldermead\n# - differentiation: Numerical differentiantion (forward)\n#................\n# Minimum found with precision: |df|/f = 1.00000e-08 and |dx| = 1.00000e-05\n# Elapsed time: 00 days, 00:00:00.8876990\n# iteration [000074]\n# function evaluations [000076]\n# gradient evaluations [000000]\n# degrees of freedom [000577]\n# m2lnL = 562.219163105056 ( 562.21916 )\n# Fit parameters:\n# 0.230499952926549 -1.02959391499341 \n#----------------------------------------------------------------------------------\n# Data used:\n# - Union2.1 sample\n#----------------------------------------------------------------------------------\n# Model[03000]:\n# - NcHICosmo : XCDM - Constant EOS\n#----------------------------------------------------------------------------------\n# Model parameters\n# - H0[00]: 67.36 [FIXED]\n# - Omegac[01]: 0.230499952926549 [FREE]\n# - Omegax[02]: 0.7 [FIXED]\n# - Tgamma0[03]: 2.7245 [FIXED]\n# - Yp[04]: 0.24 [FIXED]\n# - ENnu[05]: 3.046 [FIXED]\n# - Omegab[06]: 0.05 [FIXED]\n# - w[07]: -1.02959391499341 [FREE]\n#----------------------------------------------------------------------------------\n# NcmMSet parameters covariance matrix\n# -------------------------------\n# Omegac[03000:01] = 0.2305 +/- 0.06233 | 1 | -0.9297 |\n# w[03000:07] = -1.03 +/- 0.08595 | -0.9297 | 1 |\n# -------------------------------\n\n\nThe code above fits the XCDM model to the Union2.1 dataset, which contains supernova type Ia data. It computes the best-fit parameters for the model and the covariance matrix of the parameters using the Fisher matrix.\n\nlhr2d = Ncm.LHRatio2d.new(\n fit,\n mset.fparam_get_pi_by_name(\"Omegac\"),\n mset.fparam_get_pi_by_name(\"w\"),\n 1.0e-3,\n)\n\nbest_fit = pd.DataFrame(\n {\n \"Omegac\": [cosmo.props.Omegac],\n \"w\": [cosmo.props.w],\n \"sigma\": \"Best-fit\",\n \"region\": \"Best-fit\",\n }\n)\n\n\nregions_pd_list = []\nfor i, sigma in enumerate(\n [Ncm.C.stats_1sigma(), Ncm.C.stats_2sigma(), Ncm.C.stats_3sigma()]\n):\n fisher_rg = lhr2d.fisher_border(sigma, 300.0, Ncm.FitRunMsgs.NONE)\n Omegac_a = np.array(fisher_rg.p1.dup_array())\n w_a = np.array(fisher_rg.p2.dup_array())\n regions_pd_list.append(\n pd.DataFrame(\n {\n \"Omegac\": Omegac_a,\n \"w\": w_a,\n \"sigma\": rf\"{i+1}$\\sigma$\",\n \"region\": \"Fisher\",\n }\n )\n )\n\nregions_pd = pd.concat(regions_pd_list)\n\nThe code above computes the confidence regions for the best-fit parameters using the Fisher matrix.\n\n\nCode\n(\n ggplot(regions_pd, aes(\"Omegac\", \"w\", fill=\"sigma\", color=\"sigma\"))\n + geom_polygon(alpha=0.3)\n + geom_point(data=best_fit)\n + labs(x=r\"$\\Omega_c$\", y=r\"$w$\", fill=r\"Confidence\")\n + guides(fill=guide_legend(), color=False)\n + doc_theme()\n)\n\n\n\n\n\n\n\n\nFigure 2: Best-fit parameters and confidence regions for the XCDM model.", "crumbs": [ "About NumCosmo", "Examples",