{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "159cba26",
   "metadata": {},
   "source": [
    "# IRAM Data Example\n",
    "\n",
    "Trey V. Wenger (c) April 2025\n",
    "\n",
    "Here we test `bayes_cn_hfs` on some real IRAM 30-m data and demonstrate a general procedure for determining the carbon isotopic ratio from observations of CN and 13CN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4888afb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-04T20:09:28.755454Z",
     "start_time": "2024-12-04T20:09:25.776282Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arviz version: 0.22.0dev\n",
      "pymc version: 5.21.1\n",
      "bayes_spec version: 1.7.8\n",
      "bayes_cn_hfs version: 2.0.0-staging+0.g7afe3a8.dirty\n"
     ]
    }
   ],
   "source": [
    "# General imports    \n",
    "import os\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import arviz as az\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pymc as pm\n",
    "\n",
    "print(\"arviz version:\", az.__version__)\n",
    "\n",
    "print(\"pymc version:\", pm.__version__)\n",
    "\n",
    "import bayes_spec\n",
    "print(\"bayes_spec version:\", bayes_spec.__version__)\n",
    "\n",
    "import bayes_cn_hfs\n",
    "print(\"bayes_cn_hfs version:\", bayes_cn_hfs.__version__)\n",
    "\n",
    "# Notebook configuration\n",
    "pd.options.display.max_rows = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0a6c4a",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3eb1045f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-05T01:40:09.396665Z",
     "start_time": "2024-12-05T01:40:07.779442Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bayes_spec import SpecData\n",
    "import pickle\n",
    "\n",
    "with open(\"iram_data.pkl\", \"rb\") as f:\n",
    "    iram_data = pickle.load(f)\n",
    "\n",
    "labels = [\"12CN-1/2\", \"12CN-3/2\", \"13CN-1/2\", \"13CN-3/2\"]\n",
    "data = {\n",
    "    label: SpecData(\n",
    "        iram_data[f\"frequency_{label}\"][500:-500],\n",
    "        iram_data[f\"spectrum_{label}\"][500:-500],\n",
    "        iram_data[f\"rms_{label}\"],\n",
    "        xlabel=r\"LSRK Frequency (MHz)\",\n",
    "        ylabel=r\"$T_A^*$ (K)\",\n",
    "    )\n",
    "    for label in labels\n",
    "}\n",
    "\n",
    "# subset of 12CN data\n",
    "data_12CN = {\n",
    "    label: data[label]\n",
    "    for label in labels if \"12CN\" in label\n",
    "}\n",
    "\n",
    "# Plot the data\n",
    "fig, axes = plt.subplots(4, layout=\"constrained\", figsize=(8, 12))\n",
    "for i, label in enumerate(labels):\n",
    "    axes[i].plot(data[label].spectral, data[label].brightness, 'k-')\n",
    "    axes[i].set_xlabel(data[label].xlabel)\n",
    "    axes[i].set_ylabel(data[label].ylabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7905a608-a4a7-481a-a9a6-81566e5272fa",
   "metadata": {},
   "source": [
    "## Inspecting the CN data\n",
    "\n",
    "We don't assume LTE, but we fix the kinetic temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ffd928e8-b654-44ee-8b0c-9786b5354e30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_cn_hfs import CNModel\n",
    "\n",
    "# Initialize and define the model\n",
    "baseline_degree = 0\n",
    "n_clouds = 2\n",
    "model = CNModel(\n",
    "    data_12CN,\n",
    "    molecule=\"CN\", # molecule (either \"CN\" or \"13CN\")\n",
    "    bg_temp = 2.7, # assumed background temperature (K)\n",
    "    Beff=0.78, # Main beam efficiency\n",
    "    Feff=0.94, # Forward efficiency\n",
    "    n_clouds=n_clouds,\n",
    "    baseline_degree=baseline_degree,\n",
    "    seed=1234,\n",
    "    verbose=True\n",
    ")\n",
    "model.add_priors(\n",
    "    prior_log10_N = [14.0, 1.0], # mean and width of log10 total column density prior (cm-2)\n",
    "    prior_log10_Tkin = None, # ignored\n",
    "    prior_velocity = [10.0, 3.0], # mean and width of velocity prior (km/s)\n",
    "    prior_fwhm_nonthermal = 1.0, # width of non-thermal broadening prior (km/s)\n",
    "    prior_fwhm_L = None, # assume Gaussian line profile\n",
    "    prior_rms = None, # do not infer spectral rms\n",
    "    prior_baseline_coeffs = None, # use default baseline priors\n",
    "    assume_LTE = False, # do not assume LTE\n",
    "    prior_log10_Tex = [0.75, 0.25], # mean and width of log10 excitation temperature prior (K)\n",
    "    assume_CTEX = False, # do not assume CTEX\n",
    "    prior_log10_LTE_precision = [-6.0, 1.0], # offset and width of log10 LTE precision prior\n",
    "    fix_log10_Tkin = 0.5, # fix the kinetic temperature\n",
    "    clip_weights = 1.0e-9, # clip statistical weights between [clip_weights, 1-clip_weights]\n",
    "    clip_tau = -10.0, # clip optical depths below to prevent masers\n",
    "    ordered = False, # do not assume optically-thin\n",
    ")\n",
    "model.add_likelihood()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c75a7ca2-9f76-4ea9-a07f-28a2477805ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_spec.plots import plot_predictive\n",
    "\n",
    "# prior predictive check\n",
    "prior = model.sample_prior_predictive(\n",
    "    samples=1000,  # prior predictive samples\n",
    ")\n",
    "_ = plot_predictive(model.data, prior.prior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6bca25d-b15d-4e05-9c29-4c2035a69153",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "model.fit(\n",
    "    n = 100_000, # maximum number of VI iterations\n",
    "    draws = 1_000, # number of posterior samples\n",
    "    rel_tolerance = 0.05, # VI relative convergence threshold\n",
    "    abs_tolerance = 0.05, # VI absolute convergence threshold\n",
    "    learning_rate = 0.01, # VI learning rate\n",
    ")\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ee48b18-f7cd-4cf5-a324-49acad0e6851",
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = model.sample_posterior_predictive(\n",
    "    thin=10, # keep one in {thin} posterior samples\n",
    ")\n",
    "_ = plot_predictive(model.data, posterior.posterior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc56098a-c33d-481c-a623-3a702fa2b947",
   "metadata": {},
   "outputs": [],
   "source": [
    "pm.summary(model.trace.posterior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca3c45b2-47ee-4f25-8d45-5edb217b300e",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "init_kwargs = {\n",
    "    \"rel_tolerance\": 0.05,\n",
    "    \"abs_tolerance\": 0.05,\n",
    "    \"learning_rate\": 0.01,\n",
    "}\n",
    "model.sample(\n",
    "    init = \"advi+adapt_diag\", # initialization strategy\n",
    "    tune = 1000, # tuning samples\n",
    "    draws = 1000, # posterior samples\n",
    "    chains = 8, # number of independent chains\n",
    "    cores = 8, # number of parallel chains\n",
    "    init_kwargs = init_kwargs, # VI initialization arguments\n",
    "    nuts_kwargs = {\"target_accept\": 0.8}, # NUTS arguments\n",
    ")\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c56e3f2-e66e-4c91-b857-6ede00c09cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.solve(kl_div_threshold=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3b52ff7-0081-411f-b92c-0c5e24408992",
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = model.sample_posterior_predictive(\n",
    "    thin=10, # keep one in {thin} posterior samples\n",
    ")\n",
    "_ = plot_predictive(model.data, posterior.posterior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "672cbbc9-f2af-4be1-aa63-e25f8cf02f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pm.summary(model.trace.solution_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d6efe58-0d0e-4fd0-a3d5-8f908ad48ca6",
   "metadata": {},
   "source": [
    "## Number of cloud components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14ec0393-8db8-47bc-868b-c9d0572053a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_spec import Optimize\n",
    "from bayes_cn_hfs import CNModel\n",
    "\n",
    "max_n_clouds = 8\n",
    "baseline_degree = 0\n",
    "opt = Optimize(\n",
    "    CNModel,\n",
    "    data_12CN,\n",
    "    molecule=\"CN\", # molecule name\n",
    "    bg_temp = 2.7, # assumed background temperature (K)\n",
    "    Beff=0.78, # Main beam efficiency\n",
    "    Feff=0.94, # Forward efficiency\n",
    "    max_n_clouds=max_n_clouds,\n",
    "    baseline_degree=baseline_degree,\n",
    "    seed=1234,\n",
    "    verbose=True\n",
    ")\n",
    "opt.add_priors(\n",
    "    prior_log10_N = [14.0, 1.0], # mean and width of log10 total column density prior (cm-2)\n",
    "    prior_log10_Tkin = None, # ignored\n",
    "    prior_velocity = [10.0, 3.0], # mean and width of velocity prior (km/s)\n",
    "    prior_fwhm_nonthermal = 1.0, # width of non-thermal broadening prior (km/s)\n",
    "    prior_fwhm_L = None, # assume Gaussian line profile\n",
    "    prior_rms = None, # do not infer spectral rms\n",
    "    prior_baseline_coeffs = None, # use default baseline priors\n",
    "    assume_LTE = False, # do not assume LTE\n",
    "    prior_log10_Tex = [0.75, 0.25], # mean and width of log10 excitation temperature prior (K)\n",
    "    assume_CTEX = False, # do not assume CTEX\n",
    "    prior_log10_LTE_precision = [-6.0, 1.0], # offset and width of log10 LTE precision prior\n",
    "    fix_log10_Tkin = 0.5, # fix the kinetic temperature\n",
    "    clip_weights = 1.0e-9, # clip statistical weights between [clip_weights, 1-clip_weights]\n",
    "    clip_tau = -10.0, # clip optical depths below to prevent masers\n",
    "    ordered = False, # do not assume optically-thin\n",
    ")\n",
    "opt.add_likelihood()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffcf2f31-62e3-4c14-ae3c-82105f3c1d1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_spec.plots import plot_predictive\n",
    "\n",
    "# prior predictive check\n",
    "prior = opt.models[1].sample_prior_predictive(\n",
    "    samples=1000,  # prior predictive samples\n",
    ")\n",
    "_ = plot_predictive(opt.models[1].data, prior.prior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49c5b11c-8f7a-4525-8be1-178f7601cf79",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "fit_kwargs = {\n",
    "    \"rel_tolerance\": 0.05,\n",
    "    \"abs_tolerance\": 0.05,\n",
    "    \"learning_rate\": 0.01,\n",
    "}\n",
    "opt.fit_all(**fit_kwargs)\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed4cf28f-66c7-47d7-9e84-004f4692fb34",
   "metadata": {},
   "outputs": [],
   "source": [
    "null_bic = opt.models[1].null_bic()\n",
    "n_clouds = np.arange(max_n_clouds+1)\n",
    "bics_vi = np.array([null_bic] + [model.bic(chain=0) for model in opt.models.values()])\n",
    "print(bics_vi)\n",
    "\n",
    "plt.plot(n_clouds[1:], bics_vi[1:], 'ko')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71db68fc-ef41-46d1-9368-c37e489144c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "fit_kwargs = {\n",
    "    \"rel_tolerance\": 0.05,\n",
    "    \"abs_tolerance\": 0.05,\n",
    "    \"learning_rate\": 0.01,\n",
    "}\n",
    "sample_kwargs = {\n",
    "    \"chains\": 8,\n",
    "    \"cores\": 8,\n",
    "    \"n_init\": 100_000,\n",
    "    \"init_kwargs\": fit_kwargs,\n",
    "    \"nuts_kwargs\": {\"target_accept\": 0.9},\n",
    "}\n",
    "opt.optimize(fit_kwargs=fit_kwargs, sample_kwargs=sample_kwargs, approx=False)\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a374f444-9752-44ff-8518-08a5804506ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "null_bic = opt.models[1].null_bic()\n",
    "n_clouds = np.arange(max_n_clouds+1)\n",
    "bics_mcmc = np.array([null_bic] + [model.bic() for model in opt.models.values()])\n",
    "print(bics_mcmc)\n",
    "\n",
    "plt.plot(n_clouds[1:], bics_vi[1:], 'ko', label=\"VI\")\n",
    "plt.plot(n_clouds[1:], bics_mcmc[1:], 'ro', label=\"MCMC\")\n",
    "plt.xlabel(\"Number of Clouds\")\n",
    "plt.ylabel(\"BIC\")\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdcbe7fb-5307-4b3b-8b4d-f546dc18e870",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = opt.models[2]\n",
    "print(model.n_clouds)\n",
    "pm.summary(model.trace.solution_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e25919d-5e05-4b61-95b7-b57a4c6de73d",
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = model.sample_posterior_predictive(\n",
    "    thin=100, # keep one in {thin} posterior samples\n",
    ")\n",
    "_ = plot_predictive(model.data, posterior.posterior_predictive)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fedc1586-57af-43b9-9492-9835cea1745f",
   "metadata": {},
   "source": [
    "## Ratio Model\n",
    "We start by assuming CTEX for 13CN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c01076d-7390-4838-a44f-c71a49e6f3cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_cn_hfs.cn_ratio_model import CNRatioModel\n",
    "\n",
    "# Initialize and define the model\n",
    "n_clouds = 2 # number of cloud components\n",
    "baseline_degree = 0 # polynomial baseline degree\n",
    "model = CNRatioModel(\n",
    "    data,\n",
    "    bg_temp = 2.7, # assumed background temperature (K)\n",
    "    Beff=0.78, # Main beam efficiency\n",
    "    Feff=0.94, # Forward efficiency\n",
    "    n_clouds=n_clouds,\n",
    "    baseline_degree=baseline_degree,\n",
    "    seed=1234,\n",
    "    verbose=True\n",
    ")\n",
    "model.add_priors(\n",
    "    prior_log10_N_12CN = [14.5, 0.5], # mean and width of log10 12CN total column density prior (cm-2)\n",
    "    prior_ratio_12C_13C = [50.0, 25.0], # mean and width of 12C/13C ratio prior\n",
    "    prior_log10_Tkin = None, # kinetic temperature is fixed\n",
    "    prior_velocity = [9.0, 2.0], # mean and width of velocity prior (km/s)\n",
    "    prior_fwhm_nonthermal = 1.0, # width of non-thermal broadening prior (km/s)\n",
    "    prior_fwhm_L = 1.0, # width of latent Lorentzian FWHM prior (km/s) TIP: set to typical feature separation\n",
    "    prior_rms = None, # do not infer spectral rms\n",
    "    prior_baseline_coeffs = None, # use default baseline priors\n",
    "    assume_LTE = False, # do not assume LTE\n",
    "    prior_log10_Tex = [0.6, 0.15], # mean and width of excitation temperature prior (K)\n",
    "    assume_CTEX_12CN = False, # do not assume CTEX\n",
    "    prior_LTE_precision = 100.0, # width of LTE precision prior\n",
    "    assume_CTEX_13CN = True, # assume CTEX for 13CN\n",
    "    fix_log10_Tkin = 1.5, # kinetic temperature is fixed (K)\n",
    "    ordered = False, # do not assume optically-thin\n",
    ")\n",
    "model.add_likelihood()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cb94ca2-d7e8-431b-9219-0e6021185758",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_spec.plots import plot_predictive\n",
    "\n",
    "# prior predictive check\n",
    "prior = model.sample_prior_predictive(\n",
    "    samples=100,  # prior predictive samples\n",
    ")\n",
    "_ = plot_predictive(model.data, prior.prior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "722bb329-cf58-4fb1-a98a-c257250d8c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "model.fit(\n",
    "    n = 100_000, # maximum number of VI iterations\n",
    "    draws = 1_000, # number of posterior samples\n",
    "    rel_tolerance = 0.01, # VI relative convergence threshold\n",
    "    abs_tolerance = 0.1, # VI absolute convergence threshold\n",
    "    learning_rate = 0.01, # VI learning rate\n",
    ")\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35918b9e-6867-4818-bcbb-7dcfdabd15cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = model.sample_posterior_predictive(\n",
    "    thin=100, # keep one in {thin} posterior samples\n",
    ")\n",
    "_ = plot_predictive(model.data, posterior.posterior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "546b1d95-58b2-4956-9bd2-e131111040dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "model.sample(\n",
    "    init = \"advi+adapt_diag\", # initialization strategy\n",
    "    tune = 1000, # tuning samples\n",
    "    draws = 1000, # posterior samples\n",
    "    chains = 8, # number of independent chains\n",
    "    cores = 8, # number of parallel chains\n",
    "    init_kwargs = {\"rel_tolerance\": 0.01, \"abs_tolerance\": 0.1, \"learning_rate\": 0.01}, # VI initialization arguments\n",
    "    nuts_kwargs = {\"target_accept\": 0.9}, # NUTS arguments\n",
    ")\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32a150de-8008-4550-ac3e-a3c5326ec275",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.solve(kl_div_threshold=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10c12a81-5500-455d-9bd2-f37fbe84ee23",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"solutions:\", model.solutions)\n",
    "\n",
    "pm.summary(model.trace.solution_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a663943f-c688-44db-8239-f6fdf91760a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = model.sample_posterior_predictive(\n",
    "    thin=100, # keep one in {thin} posterior samples\n",
    ")\n",
    "_ = plot_predictive(model.data, posterior.posterior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dec3d765-0f94-4743-a45e-94ef08b0ddbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 12C/13C ratio over all clouds\n",
    "for solution in model.solutions:\n",
    "    model.trace[f\"solution_{solution}\"][\"ratio_12C_13C_total\"] = (\n",
    "        (10.0**model.trace[f\"solution_{solution}\"][\"log10_N_12CN\"]).sum(dim=\"cloud\") / \n",
    "    model.trace[f\"solution_{solution}\"][\"N_13CN\"].sum(dim=\"cloud\")\n",
    "    )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac1a963a-4ffd-4260-ad37-1cc7c1838333",
   "metadata": {},
   "outputs": [],
   "source": [
    "pm.summary(model.trace.solution_0, var_names=[\"ratio_12C_13C_total\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ada77014-dd61-4425-a8ac-1f20065c9416",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"/staging/twenger2/iram_trace.pkl\", \"wb\") as f:\n",
    "    pickle.dump(model.trace, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d42034c-82dd-4f2e-bfae-4cb397337a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_spec.plots import plot_pair\n",
    "\n",
    "var_names = [\n",
    "    param for param in model.cloud_freeRVs\n",
    "    if not set(model.model.named_vars_to_dims[param]).intersection(set(\n",
    "        [\"transition_12CN\", \"state_12CN\", \"transition_13CN\", \"state_13CN\"]\n",
    "    ))\n",
    "]\n",
    "_ = plot_pair(\n",
    "    model.trace.solution_0, # samples\n",
    "    var_names, # var_names to plot\n",
    "    labeller=model.labeller, # label manager\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb287bbc-6f80-482f-87ae-b9ca07cb4b0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ignore transition and state dependent parameters\n",
    "var_names = [\n",
    "    param for param in model.cloud_deterministics + [\"ratio_12C_13C\"]\n",
    "    if not set(model.model.named_vars_to_dims[param]).intersection(set(\n",
    "        [\"transition_12CN\", \"state_12CN\", \"transition_13CN\", \"state_13CN\"]\n",
    "    )) and param not in [\"fwhm_thermal_12CN\", \"fwhm_thermal_13CN\"]\n",
    "]\n",
    "print(var_names)\n",
    "_ = plot_pair(\n",
    "    model.trace.solution_0.sel(cloud=0), # samples\n",
    "    var_names + model.hyper_deterministics, # var_names to plot\n",
    "    labeller=model.labeller, # label manager\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94fa8780-f2e9-444e-90d3-ed9229aa88c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = plot_pair(\n",
    "    model.trace.solution_0.sel(cloud=1), # samples\n",
    "    var_names + model.hyper_deterministics, # var_names to plot\n",
    "    labeller=model.labeller, # label manager\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dd1aabe-ee1b-430a-ab84-ac866cceb3fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "var_names = model.cloud_deterministics + model.baseline_freeRVs + model.hyper_freeRVs + model.hyper_deterministics + [\"ratio_12C_13C\", \"ratio_12C_13C_total\"]\n",
    "point_stats = az.summary(model.trace.solution_0, var_names=var_names, kind='stats', hdi_prob=0.68)\n",
    "display(point_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71c8db9-0072-4ac0-b35f-c3a738dafb12",
   "metadata": {},
   "source": [
    "## Assumption about 13CN Excitation Temperature\n",
    "There is no evidence for CTEX in the 13CN data, and we are unable to constrain the excitation temperature of 13CN because we only detect the brightest transitions. Here we relax the 13CN CTEX assumption and instead assume that it suffers from hyperfine anomalies the same as 12CN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c91a9eac-bcc0-4a68-9b7e-27e7485eec30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_cn_hfs.cn_ratio_model import CNRatioModel\n",
    "\n",
    "# Initialize and define the model\n",
    "n_clouds = 2 # number of cloud components\n",
    "baseline_degree = 0 # polynomial baseline degree\n",
    "model = CNRatioModel(\n",
    "    data,\n",
    "    bg_temp = 2.7, # assumed background temperature (K)\n",
    "    Beff=0.78, # Main beam efficiency\n",
    "    Feff=0.94, # Forward efficiency\n",
    "    n_clouds=n_clouds,\n",
    "    baseline_degree=baseline_degree,\n",
    "    seed=1234,\n",
    "    verbose=True\n",
    ")\n",
    "model.add_priors(\n",
    "    prior_log10_N_12CN = [14.5, 0.5], # mean and width of log10 12CN total column density prior (cm-2)\n",
    "    prior_ratio_12C_13C = [50.0, 25.0], # mean and width of 12C/13C ratio prior\n",
    "    prior_log10_Tkin = None, # kinetic temperature is fixed\n",
    "    prior_velocity = [9.0, 2.0], # mean and width of velocity prior (km/s)\n",
    "    prior_fwhm_nonthermal = 1.0, # width of non-thermal broadening prior (km/s)\n",
    "    prior_fwhm_L = 1.0, # width of latent Lorentzian FWHM prior (km/s) TIP: set to typical feature separation\n",
    "    prior_rms = None, # do not infer spectral rms\n",
    "    prior_baseline_coeffs = None, # use default baseline priors\n",
    "    assume_LTE = False, # do not assume LTE\n",
    "    prior_log10_Tex = [0.6, 0.15], # mean and width of excitation temperature prior (K)\n",
    "    assume_CTEX_12CN = False, # do not assume CTEX\n",
    "    prior_LTE_precision = 100.0, # width of LTE precision prior\n",
    "    assume_CTEX_13CN = False, # assume CTEX for 13CN\n",
    "    fix_log10_Tkin = 1.5, # kinetic temperature is fixed (K)\n",
    "    ordered = False, # do not assume optically-thin\n",
    ")\n",
    "model.add_likelihood()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b02d941c-1467-404b-8f21-99edfeaf757e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_spec.plots import plot_predictive\n",
    "\n",
    "# prior predictive check\n",
    "prior = model.sample_prior_predictive(\n",
    "    samples=100,  # prior predictive samples\n",
    ")\n",
    "_ = plot_predictive(model.data, prior.prior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a458cea-adda-4220-987b-f8f88fe7861b",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "model.fit(\n",
    "    n = 100_000, # maximum number of VI iterations\n",
    "    draws = 1_000, # number of posterior samples\n",
    "    rel_tolerance = 0.01, # VI relative convergence threshold\n",
    "    abs_tolerance = 0.1, # VI absolute convergence threshold\n",
    "    learning_rate = 0.01, # VI learning rate\n",
    ")\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40155216-4686-4879-88b0-2d1466084094",
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = model.sample_posterior_predictive(\n",
    "    thin=100, # keep one in {thin} posterior samples\n",
    ")\n",
    "_ = plot_predictive(model.data, posterior.posterior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbf316d0-f9b7-4244-9404-c65a297a4407",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "model.sample(\n",
    "    init = \"advi+adapt_diag\", # initialization strategy\n",
    "    tune = 1000, # tuning samples\n",
    "    draws = 1000, # posterior samples\n",
    "    chains = 8, # number of independent chains\n",
    "    cores = 8, # number of parallel chains\n",
    "    init_kwargs = {\"rel_tolerance\": 0.01, \"abs_tolerance\": 0.1, \"learning_rate\": 0.01}, # VI initialization arguments\n",
    "    nuts_kwargs = {\"target_accept\": 0.9}, # NUTS arguments\n",
    ")\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "628d6af6-85eb-43e1-82b3-66d0fd0799ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.solve(kl_div_threshold=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82116ae6-5cb1-451a-a640-a59feb00c5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"solutions:\", model.solutions)\n",
    "\n",
    "pm.summary(model.trace.solution_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "612ce86a-a338-43e1-918b-533b43f68f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior = model.sample_posterior_predictive(\n",
    "    thin=100, # keep one in {thin} posterior samples\n",
    ")\n",
    "_ = plot_predictive(model.data, posterior.posterior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcde0ba5-6ec9-489b-89ad-439829540203",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 12C/13C ratio over all clouds\n",
    "for solution in model.solutions:\n",
    "    model.trace[f\"solution_{solution}\"][\"ratio_12C_13C_total\"] = (\n",
    "        (10.0**model.trace[f\"solution_{solution}\"][\"log10_N_12CN\"]).sum(dim=\"cloud\") / \n",
    "    model.trace[f\"solution_{solution}\"][\"N_13CN\"].sum(dim=\"cloud\")\n",
    "    )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56256c04-d4f2-43cb-9935-0dbca779384d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pm.summary(model.trace.solution_0, var_names=[\"ratio_12C_13C_total\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b49284d-ddea-45c8-9eef-f9aa4b55bb0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"/staging/twenger2/iram_trace_noCTEX.pkl\", \"wb\") as f:\n",
    "    pickle.dump(model.trace, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efeb29bb-9b1f-4042-b703-8e1b8df071f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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