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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# GolemFlavor Tutorial"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this example, we will generate a fake measured flavour composition using a multivariate Gaussian distribution and sample from it using the [emcee](https://emcee.readthedocs.io/) MCMC algorithm."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from __future__ import absolute_import, division, print_function\n",
+ "\n",
+ "from functools import partial\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Astrophysical Neutrino Flavor Mixing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Source Flavor Composition"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The origins and accelerations mechanisms of astrophysically produced neutrinos is still an active puzzle, and is part of a bigger question on the origins of ultra-high-energy cosmic rays. The new but very active field of neutrino flavor physics can be used as a powerful probe to help idenfify these sources. The most common hypothesis of the neutrino flavor composition at the source is one produced by the decay of a pion, which results in the following source composition:\n",
+ "\n",
+ "$$\\pi\\:\\text{decay}\\rightarrow\\left(f_e:f_\\mu:f_\\tau\\right)_\\text{S}=\\left(1:2:0\\right)_\\text{S}$$\n",
+ "\n",
+ "where $f_\\alpha$ is the flavor composition of a neutrino with flavor $\\alpha\\in\\{e,\\mu,\\tau\\}$ and the subscript S represents that this is the flavour composition at the source. In the code below we normalize this to 1 for later calculations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Source composition = (1.00 : 0.00 : 0.00)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from golemflavor.fr import normalize_fr\n",
+ "\n",
+ "source_composition = normalize_fr((1, 0, 0))\n",
+ "print('Source composition = ({:.2f} : {:.2f} : {:.2f})'.format(*source_composition))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Neutrino Mixing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For the three massive neutrinos, the flavour eigenstates of the neutrino $|\\nu_\\alpha>$, $\\alpha\\in\\{e,\\mu,\\tau\\}$, are related to the mass eigenstates $|\\nu_i>$, $i\\in\\{1,2,3\\}$ via a unitary mixing matrix $U_{\\alpha i}$ known as the PMNS matrix:\n",
+ " \n",
+ "$$ |\\nu_\\alpha>=\\sum^3_{i=1}U^*_{\\alpha i}|\\nu_i> $$\n",
+ "\n",
+ "The determination of the values of this mixing matrix is currently a world-wide effort. We can import values of this mixing from GolemFlavor which are taken from a [global fit to world neutrino data](https://doi.org/10.1007/JHEP01(2017)087):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mixing Matrix =\n",
+ "[[ 0.82327921+0.j 0.54796108+0.j -0.09913534+0.11010079j]\n",
+ " [-0.30340559+0.06889398j 0.59033699+0.0458547j 0.74336952+0.j ]\n",
+ " [ 0.47090947+0.06045075j -0.58950774+0.04023502j 0.65226662+0.j ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from golemflavor.fr import NUFIT_U\n",
+ "\n",
+ "print('Mixing Matrix =\\n{}'.format(NUFIT_U))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This mixing matrix says that neutrinos can oscillation from one flavor state $\\alpha\\in\\{e,\\mu,\\tau\\}$ to another $\\beta\\in\\{e,\\mu,\\tau\\}$ as a function of the propagation distance. The oscillation probability gives the probability that a neutrino produced in a flavour state $\\alpha$ is then detected in a flavour state $\\beta$ after a propagation distance $L$:\n",
+ "\n",
+ "$$\n",
+ "\\begin{align}\n",
+ " P_{\\nu_\\alpha\\rightarrow\\nu_\\beta}\\left(L\\right) &= \\mid<\\nu_\\beta\\left(L\\right)|\\nu_\\alpha\\left(0\\right)>\\mid^2\\\\\n",
+ " &=\\mid\\sum_{i=1}^3U_{\\beta i}U_{\\alpha i}^*e^{-i\\frac{m_i^2L}{2E}}\\mid^2\n",
+ "\\end{align}\n",
+ "$$\n",
+ "\n",
+ "where $\\Delta m_{ij}^2=m_i^2-m_j^2$ is the mass-squared differences and $E$ is the neutrino energy."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Measured Flavor Composition"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Once an astrophysical neutrino escapes the source it was produced from, they are free to propagate in the vacuum. Astrophysical neutrinos have $\\mathcal{O}(\\text{Mpc})$ or higher baselines, large enough that the mass eigenstates completely decouple ($L\\rightarrow\\infty$). This is useful for us, because the above oscillation probability simplifies so that:\n",
+ "\n",
+ "$$\n",
+ "\\begin{align}\n",
+ " \\phi_{i,\\oplus}&=\\sum_\\alpha\\phi_{\\alpha,\\text{S}}\\mid{U_{\\alpha i}}\\mid^2\\\\\n",
+ " \\phi_{\\alpha,\\oplus}&=\\sum_{i,\\beta}\n",
+ " \\mid{U_{\\alpha i}}\\mid^2\\mid{U_{\\beta i}}\\mid^2\\phi_{\\beta,\\text{S}}\n",
+ "\\end{align}\n",
+ "$$\n",
+ "\n",
+ "This is nice because all the complicated interference terms drop out, and the oscillation depends only on the **square of the mixing matrix**. From this, the measured flavor composition on Earth is defined as $f_{\\alpha,\\oplus}=\\phi_{\\alpha,\\oplus}/\\sum_\\alpha\\phi_{\\alpha,\\oplus}$, where the $\\oplus$ subscript denotes as measured on Earth. We can compute this using GolemFlavor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Measured composition = (0.55 : 0.18 : 0.27)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from golemflavor.fr import u_to_fr\n",
+ "\n",
+ "measured_composition = u_to_fr(source_composition, NUFIT_U)\n",
+ "print('Measured composition = ({:.2f} : {:.2f} : {:.2f})'.format(*measured_composition))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The effect of neutrino mixing has modified the flavor composition from $\\left(1:0:0\\right)_\\text{S}\\rightarrow\\left(0.55:0.18:0.27\\right)_\\oplus$ at Earth!\n",
+ "\n",
+ "Here is listed the expected measured compositions from some other source composition models:\n",
+ "\n",
+ "$$\n",
+ "\\begin{align}\n",
+ " \\left(0:1:0\\right)_\\text{S}&\\rightarrow\\left(0.18:0.44:0.38\\right)_\\oplus\\\\\n",
+ " \\left(1:2:0\\right)_\\text{S}&\\rightarrow\\left(0.31:0.35:0.34\\right)_\\oplus\n",
+ "\\end{align}\n",
+ "$$\n",
+ "\n",
+ "This can be more easily visualized using a [ternary plot](https://zenodo.org/badge/latestdoi/19505/marcharper/python-ternary), with axes being the fraction of each neutrino flavor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x864 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import golemflavor.plot as plot_utils\n",
+ "# getdist package requires `%matplotlib inline` to come after the import for inline notebook figures.\n",
+ "%matplotlib inline\n",
+ "\n",
+ "nbins = 25\n",
+ "fontsize = 23\n",
+ "\n",
+ "# Figure\n",
+ "fig = plt.figure(figsize=(12, 12))\n",
+ "\n",
+ "# Axis\n",
+ "ax = fig.add_subplot(111)\n",
+ "tax = plot_utils.get_tax(ax, scale=nbins, rot_ax_labels=True)\n",
+ "\n",
+ "# Plot source composition\n",
+ "tax.scatter([normalize_fr([1, 2, 0])*nbins], marker='o', s=350, facecolors='red',\n",
+ " edgecolors='k', linewidth=2.3, label=r'$(1:2:0)_{\\rm S}$', zorder=3)\n",
+ "tax.scatter([np.array([0, 1, 0])*nbins], marker='s', s=350, facecolors='green',\n",
+ " edgecolors='k', linewidth=2.3, label=r'$(0:1:0)_{\\rm S}$', zorder=3)\n",
+ "tax.scatter([np.array([1, 0, 0])*nbins], marker='^', s=350, facecolors='blue',\n",
+ " edgecolors='k', linewidth=2.3, label=r'$(1:0:0)_{\\rm S}$', zorder=3)\n",
+ "\n",
+ "# Plot measured composition\n",
+ "tax.scatter([u_to_fr([1, 2, 0], NUFIT_U)*nbins], marker='o', s=350,\n",
+ " edgecolors='k', facecolors='white', linewidth=2.3, zorder=3)\n",
+ "tax.scatter([u_to_fr([0, 1, 0], NUFIT_U)*nbins], marker='s', s=350,\n",
+ " edgecolors='k', facecolors='white', linewidth=2.3, zorder=3)\n",
+ "tax.scatter([u_to_fr([1, 0, 0], NUFIT_U)*nbins], marker='^', s=350,\n",
+ " edgecolors='k', facecolors='white', linewidth=2.3, zorder=3)\n",
+ "\n",
+ "# Draw arrows\n",
+ "ax.annotate(\"\", xy=np.array([0.415, 0.44])*nbins, xytext=np.array([0.499, 0.83])*nbins,\n",
+ " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n",
+ "ax.annotate(\"\", xy=np.array([0.505, 0.335])*nbins, xytext=np.array([0.64, 0.55])*nbins,\n",
+ " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n",
+ "ax.annotate(\"\", xy=np.array([0.67, 0.14])*nbins, xytext=np.array([0.975, 0.014])*nbins,\n",
+ " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n",
+ "\n",
+ "# Legend\n",
+ "l_size = fontsize\n",
+ "legend = plt.legend(loc=(0.7, 0.75), title=r'Source composition',\n",
+ " fontsize=l_size, prop={'size': fontsize})\n",
+ "plt.setp(legend.get_title(), fontsize=l_size)\n",
+ "ax.add_artist(legend)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The coloured circle, square and triangle show the source flavor compositions. The arrows show the effect of neutrino mixing on the flavor composition. The unfilled circle, square and triangle show the corresponding measured flavor composition. Neutrino mixing during propagation has the effect of averaging out the flavor contributions, which is why the arrows point towards the center of the triangle."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Generate Fake Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Using the things we have learned above, we can start to generate some data! Usually, this comes in the form of a likelihood fit comparing IceCube data to our models. GolemFlavor has built in hooks to the [`GolemFit` package](https://github.com/IceCubeOpenSource/GolemFit) for this, however `GolemFit` is only accessible to IceCube collaborators as it contains proprietary code/data. Instead, we can generate some fake data using a multivariate Gaussian likelihood. GolemFlavor has a convenient function to do such a task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running without GolemFit\n",
+ "Help on function multi_gaussian in module golemflavor.llh:\n",
+ "\n",
+ "multi_gaussian(fr, fr_bf, smearing, offset=-320)\n",
+ " Multivariate Gaussian log likelihood.\n",
+ " \n",
+ " Parameters\n",
+ " ----------\n",
+ " fr : List[float], length 3\n",
+ " The flavour composition to evaluate at.\n",
+ " fr_bf : List[float], length 3\n",
+ " The bestfit / injected flavour composition.\n",
+ " smearing : float\n",
+ " The amount of smearing.\n",
+ " offset : float, optional\n",
+ " An amount to offset the magnitude of the log likelihood.\n",
+ " \n",
+ " Returns\n",
+ " ----------\n",
+ " llh : float\n",
+ " The log likelihood evaluated at `fr`.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from golemflavor.llh import multi_gaussian\n",
+ "help(multi_gaussian)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Smearing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In reality, a measurement does not have an arbitrary precision due to effects such as mis-reconstruction and model uncertainties. These effects are said to *smear* the data, and it can be described as in our Gaussian likelihood using the `smearing` keyword. Here we set the amount of smearing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "smearing = 0.02"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Anarchic Sampling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we have everything we need to do scan over our likelihood, from which we will be able to visualize the effect of this smearing. However, scanning directly in the space of the flavour composition would not be the correct way to do the scan. This particular parameterization has degeneracies, since the total flavor composition must add up to 1, $\\sum_{\\alpha}f_\\alpha=1$, which introduces an unwanted prior dependence."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The effective number of parameters of the flavor composition can be reduced from three to two due to the requirement $\\sum_\\alpha f_\\alpha=1$. Therefore, in order to make sure we have an unbiased prior, the parameters in which to sample in must be determined by the [*Haar measure*](https://doi.org/10.1016/j.physletb.2003.08.045) of the flavor composition volume element, $\\text{d}f_{e,\\oplus}\\wedge\\text{d} f_{\\mu,\\oplus}\\wedge\\text{d}f_{\\tau,\\oplus}$. The following *flavor angles* parameterization was created for this reason:\n",
+ "\\begin{gather}\n",
+ " f_{\\alpha,\\oplus}=\n",
+ " \\begin{pmatrix}\n",
+ " f_{e,\\oplus} \\\\ f_{\\mu,\\oplus} \\\\ f_{\\tau,\\oplus}\n",
+ " \\end{pmatrix}=\n",
+ " \\begin{pmatrix}\n",
+ " \\sin^2\\phi_\\oplus\\,\\cos^2\\psi_\\oplus \\\\\n",
+ " \\sin^2\\phi_\\oplus\\,\\sin^2\\psi_\\oplus \\\\\n",
+ " \\cos^2\\phi_\\oplus\n",
+ " \\end{pmatrix} \\\\\n",
+ " \\text{d} f_{e,\\oplus}\\wedge\\text{d} f_{\\mu,\\oplus}\\wedge\\text{d} f_{\\tau,\\oplus}=\n",
+ " \\text{d}\\left(\\sin^4\\phi_\\oplus\\right)\\wedge\n",
+ " \\text{d}\\left(\\cos\\left(2\\psi_\\oplus\\right)\\right)\n",
+ "\\end{gather}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This basically tells us that we should scan in the parameter space defined by $\\sin^4\\phi_\\oplus$ and $\\cos\\left(2\\psi_\\oplus\\right)$. GolemFlavor contains a convenient function `fr_to_angles` to convert from flavor compositions to flavor angles."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Measured composition = (0.55 : 0.18 : 0.27)\n",
+ "Measured flavor angles = (0.54, 0.50)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from golemflavor.fr import fr_to_angles\n",
+ "\n",
+ "measured_angles = fr_to_angles(measured_composition)\n",
+ "print('Measured composition = ({:.2f} : {:.2f} : {:.2f})'.format(*measured_composition))\n",
+ "print('Measured flavor angles = ({:.2f}, {:.2f})'.format(*measured_angles))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Markov Chain Monte Carlo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can define the wrappers to the [`emcee` package](https://emcee.readthedocs.io/en/stable/) which will sample over the flavor angles using an affine invariant MCMC algorithm. To do this, it is convenient to define our parameters using the GolemFlavor `ParamSet` class, as so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from golemflavor.enums import ParamTag\n",
+ "from golemflavor.param import Param, ParamSet\n",
+ "\n",
+ "# Convert from flavor composition to flavor angles\n",
+ "measured_flavor_angles = fr_to_angles(measured_composition)\n",
+ "\n",
+ "# Params can be tagged for later convenience\n",
+ "tag = ParamTag.BESTFIT\n",
+ "\n",
+ "# Define the asimov `ParamSet`, with `Param` objects containing information such as name, value and ranges.\n",
+ "# Note: std defines the Prior standard deviation, however default behaviour is to use a flat prior.\n",
+ "# The assignment of `std=smearing` is just a placeholder for later. See `measurement.ipynb` example for further details.\n",
+ "asimov_paramset = [\n",
+ " Param(name='measured_angle1', value=measured_flavor_angles[0], ranges=[ 0., 1.], std=smearing, tag=tag, tex=r'\\sin^4\\phi_\\oplus'),\n",
+ " Param(name='measured_angle2', value=measured_flavor_angles[1], ranges=[-1., 1.], std=smearing, tag=tag, tex=r'\\cos(2\\psi_\\oplus)')\n",
+ "]\n",
+ "asimov_paramset = ParamSet(asimov_paramset)\n",
+ "\n",
+ "# Define the llh `ParamSet`, with `Param` objects containing information such as name, value and ranges.\n",
+ "tag = ParamTag.BESTFIT\n",
+ "src_compositions = [\n",
+ " Param(name='measured_angle1', value=0, ranges=[ 0., 1.], tag=tag, tex=r'\\sin^4\\phi_\\oplus'),\n",
+ " Param(name='measured_angle2', value=0, ranges=[-1., 1.], tag=tag, tex=r'\\cos(2\\psi_\\oplus)')\n",
+ "]\n",
+ "llh_paramset = ParamSet(src_compositions)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we have 2 `ParamSet` objects:\n",
+ "* `asimov_paramset` contains the measured parameters\n",
+ "* `llh_paramset` contains the model parameter values\n",
+ "\n",
+ "In this example, they contain the same parameters since we are doing a simple scan over the measured flavor angles to generate some fake data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we wrap our `multi_gaussian` likelihood into a function that accepts input parameters `theta` from the MCMC:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from golemflavor.fr import angles_to_fr\n",
+ "\n",
+ "def triangle_llh(theta, asimov_paramset, llh_paramset):\n",
+ " \"\"\"Log likelihood function for a given theta.\"\"\"\n",
+ " if len(theta) != len(llh_paramset):\n",
+ " raise AssertionError(\n",
+ " 'Length of MCMC scan is not the same as the input '\n",
+ " 'params\\ntheta={0}\\nparamset]{1}'.format(theta, llh_paramset)\n",
+ " )\n",
+ "\n",
+ " # Set llh_parameters values to the sampled parameters\n",
+ " for idx, param in enumerate(llh_paramset):\n",
+ " param.value = theta[idx]\n",
+ "\n",
+ " # Convert flavor angles to flavor compositions for the model parameters\n",
+ " measured_angles = llh_paramset.from_tag(ParamTag.BESTFIT, values=True)\n",
+ " measured_composition = angles_to_fr(measured_angles)\n",
+ "\n",
+ " # Convert flavor angles to flavor compositions for the injected parameters\n",
+ " bestfit_measured_angles = asimov_paramset.from_tag(ParamTag.BESTFIT, values=True)\n",
+ " bestfit_measured_comp = angles_to_fr(bestfit_measured_angles)\n",
+ "\n",
+ " # Get the value of `smearing`\n",
+ " smearing = asimov_paramset['measured_angle1'].std\n",
+ "\n",
+ " # Calculate the log likelihood using `multi_gaussian`\n",
+ " llh = multi_gaussian(measured_composition, bestfit_measured_comp, smearing)\n",
+ " return llh"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Last thing we need to setup is our prior distribution, which in this case is simply the bounds on the flavor angles. As we have defined this already in the `ParamSet` object using the `ranges` keyword, we can use the GolemFlavor function `lnprior` to do the work for us:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from golemflavor.llh import lnprior\n",
+ "\n",
+ "def ln_prob(theta, asimov_paramset, llh_paramset):\n",
+ " \"\"\"Posterior function for a given theta.\"\"\"\n",
+ " # Get the value of the log prior (in this case it will be either 0 or -inf)\n",
+ " lp = lnprior(theta, paramset=llh_paramset)\n",
+ " if not np.isfinite(lp):\n",
+ " return -np.inf\n",
+ " \n",
+ " # Return the log prior + log likelihood\n",
+ " return lp + triangle_llh(theta, asimov_paramset, llh_paramset)\n",
+ "\n",
+ "# Evalaute the posterior using the defined `asimov_paramset` and `llh_paramset`\n",
+ "ln_prob_eval = partial(\n",
+ " ln_prob,\n",
+ " asimov_paramset=asimov_paramset,\n",
+ " llh_paramset=llh_paramset\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we simply define the number of walkers, burnin period and number of steps to run the MCMC and GolemFlavor takes care of the rest!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running burn-in\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "452f652aa7de4ef28490f49960eeef75",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Finished burn-in\n",
+ "Running\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "360511dff3c84a7cae899f1e06e4f78a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(FloatProgress(value=0.0, max=10000.0), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Finished\n",
+ "acceptance fraction [0.709 0.7268 0.7083 0.7115 0.7145 0.7176 0.7141 0.7107 0.7298 0.7072\n",
+ " 0.7214 0.7113 0.7142 0.7157 0.7175 0.711 0.7138 0.7181 0.7187 0.7142\n",
+ " 0.7116 0.7245 0.7139 0.7166 0.7115 0.7171 0.7185 0.71 0.7142 0.7243\n",
+ " 0.7138 0.719 0.7144 0.716 0.7179 0.7159 0.7255 0.7212 0.7164 0.7199\n",
+ " 0.7107 0.7169 0.7224 0.7098 0.7096 0.7212 0.7166 0.7037 0.7166 0.7214\n",
+ " 0.7166 0.7198 0.7133 0.7299 0.7168 0.7053 0.7132 0.7147 0.7171 0.7108]\n",
+ "sum of acceptance fraction 42.954\n",
+ "np.unique(samples[:,0]).shape (429552,)\n",
+ "autocorrelation [31.88913292 30.6185495 ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import golemflavor.mcmc as mcmc_utils\n",
+ "\n",
+ "# Reduce these values for a quicker runtime\n",
+ "nwalkers = 60\n",
+ "burnin = 1000\n",
+ "nsteps = 10000\n",
+ "\n",
+ "# Generate initial seed using a flat distribution\n",
+ "p0 = mcmc_utils.flat_seed(\n",
+ " llh_paramset, nwalkers=nwalkers\n",
+ ")\n",
+ "\n",
+ "# Run the MCMC!\n",
+ "samples = mcmc_utils.mcmc(\n",
+ " p0 = p0,\n",
+ " ln_prob = ln_prob_eval,\n",
+ " ndim = len(llh_paramset),\n",
+ " nwalkers = nwalkers,\n",
+ " burnin = burnin,\n",
+ " nsteps = nsteps,\n",
+ " threads = 4\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualize Fake Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have generated the fake data, let's see if we can visualize it in a ternary plot. First we convert the data from flavor angles into flavor compositions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "measured_angles = samples\n",
+ "measured_compositions = np.array(\n",
+ " list(map(angles_to_fr, measured_angles))\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can display the 90%/99% credibility regions on the ternary plot to show how our fake data is distributed:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x864 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "nbins = 25\n",
+ "fontsize = 23\n",
+ "\n",
+ "# Figure\n",
+ "fig = plt.figure(figsize=(12, 12))\n",
+ "\n",
+ "# Axis\n",
+ "ax = fig.add_subplot(111)\n",
+ "tax = plot_utils.get_tax(ax, scale=nbins, rot_ax_labels=True)\n",
+ "\n",
+ "# Plot source composition\n",
+ "tax.scatter([np.array([1, 0, 0])*nbins], marker='^', s=350, facecolors='blue',\n",
+ " edgecolors='k', linewidth=2.3, label=r'$(1:0:0)_{\\rm S}$', zorder=3)\n",
+ "\n",
+ "# Plot measured composition posteriors\n",
+ "coverages = [(99, 'cornflowerblue'), (90, 'royalblue')]\n",
+ "for cov, color in coverages:\n",
+ " plot_utils.flavor_contour(\n",
+ " frs=measured_compositions,\n",
+ " fill=True,\n",
+ " ax=ax,\n",
+ " nbins=nbins,\n",
+ " coverage=cov,\n",
+ " linewidth=2.5,\n",
+ " color=color,\n",
+ " alpha=0.7,\n",
+ " oversample=8\n",
+ " )\n",
+ "\n",
+ "# Draw arrow\n",
+ "ax.annotate(\"\", xy=np.array([0.67, 0.14])*nbins, xytext=np.array([0.975, 0.014])*nbins,\n",
+ " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n",
+ "\n",
+ "# Legend\n",
+ "l_size = fontsize\n",
+ "handles, labels = ax.get_legend_handles_labels()\n",
+ "legend = plt.legend(handles=[handles[-1]], labels=[labels[-1]], loc=(0.7, 0.85),\n",
+ " title=r'Source composition', fontsize=l_size, prop={'size': fontsize})\n",
+ "plt.setp(legend.get_title(), fontsize=l_size)\n",
+ "ax.add_artist(legend)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What a measurement! In truth, the accuracy shown here has not been reached thus far, however the field of neutrino flavor physics is developing quickly. If you are interested in comparing this to a real flavor contour then you can checkout [this paper](https://doi.org/10.1088/0004-637X/809/1/98) by the IceCube collaboration.\n",
+ "\n",
+ "Thanks for reading! In the next example, `inference.ipynb`, we will see if we can make an inference of the source flavor composition using this fake data."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}