{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# More Advanced Eryn Examples" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "from eryn.ensemble import EnsembleSampler\n", "from eryn.state import State\n", "from eryn.prior import ProbDistContainer, uniform_dist\n", "from eryn.moves import GaussianMove\n", "from eryn.backends import HDFBackend\n", "\n", "# make the plots look a bit nicer with some defaults\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "rcparams = {}\n", "rcparams[\"axes.linewidth\"] = 0.5\n", "rcparams[\"font.family\"] = \"serif\"\n", "rcparams[\"font.size\"] = 22\n", "rcparams[\"legend.fontsize\"] = 16\n", "rcparams[\"mathtext.fontset\"] = \"stix\"\n", "rcparams[\"text.usetex\"] = True \n", "mpl.rcParams.update(rcparams) # update plot parameters\n", "\n", "# set random seed\n", "np.random.seed(42)\n", "\n", "from chainconsumer import ChainConsumer\n", "from scipy import interpolate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Searching for Gaussian pulses in 2D \n", "\n", "In this simple example we generate a noisy figure, which contains signals in the shape of 2D Gaussian pulses. We will attempt to estimate the nubmer of signals, as well as the level of the synthetic noise. First, we generate the data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "num = 100 # the number of step for each dimension\n", "lowlim = -10 # Low limit on each axis\n", "highlim = 10 # high limit on each axis\n", "npulses = 10 # The # of injected pulses\n", "\n", "dx = (highlim - lowlim)/num # Get the discritization \n", "\n", "x, y = np.mgrid[lowlim:highlim:dx, lowlim:highlim:dx] # Generate the grid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define the parameters of the injection signals" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "Amp = np.random.uniform(.5, 1.5, size=(npulses)) # Draw an amplitude\n", "spread = .2 # Keep their spread as fixed for simplicity. \n", "sigma = spread * np.diag(np.ones(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the actual injection" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * Parameters injected: \n", "\n", " [[ 0.87454012 -7.67064809 7.51855763]\n", " [ 1.45071431 5.31908225 -4.60257423]\n", " [ 1.23199394 -5.09080052 -5.06552784]\n", " [ 1.09865848 -3.13212411 0.39610291]\n", " [ 0.65601864 -1.0888797 -3.34033376]\n", " [ 0.65599452 1.78964632 -5.76809823]\n", " [ 0.55808361 -3.32568562 -2.13821051]\n", " [ 1.36617615 -0.70288025 4.56281538]\n", " [ 1.10111501 -4.80521949 0.22775101]\n", " [ 1.20807258 1.4786331 -7.2567934 ]]\n" ] } ], "source": [ "edges = 2 # Utility parameter, in order to avoid having signals at the border of our data set\n", " \n", "# Draw the coordinates parameters\n", "inj_coordinates = np.random.uniform(lowlim+edges, highlim-edges, size=(npulses, 2))\n", "\n", "# Gather all parameters here\n", "gauss_inj_params = [ [AA, xy[0], xy[1]] for AA, xy in zip(Amp, inj_coordinates)]\n", "\n", "print(' * Parameters injected: \\n\\n', np.matrix(gauss_inj_params))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then can define the model and likelihood functions" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# First we compute some constant terms of the Gaussian models (reminder: we have assumed a fixed spread for each pulse)\n", "sigma_det = np.linalg.det(sigma)\n", "sigma_inv = np.linalg.inv(sigma)\n", "norm = np.sqrt((2*np.pi)**2 * sigma_det)\n", "\n", "def gaussian_flat(x, y, a, b, c):\n", " pos = np.empty(x.shape + (2,))\n", " pos[:, :, 0] = x; \n", " pos[:, :, 1] = y\n", " mu = [b, c]\n", " # This einsum call calculates (x-mu)T.Sigma-1.(x-mu) in a vectorized\n", " # way across all the input variables.\n", " fac = np.einsum('...k,kl,...l->...', pos-mu, sigma_inv, pos-mu)\n", " return a * np.exp(- fac / 2) / norm\n", "\n", "def gaussian(X, Y, a, b, c):\n", " x = X[:,0]\n", " y = Y[0,:]\n", " # breakpoint()\n", " A = np.exp(-((x[:, None] - b) ** 2) / (2 * sigma[0,0]))\n", " B = np.exp(-((y[:, None] - c) ** 2) / (2 * sigma[1,1])) \n", " # breakpoint()\n", " C = A*B[:,None,:] # (np.expand_dims(A,axis=0) * np.expand_dims(np.transpose(B),axis=2))\n", " \n", " return np.transpose( a * C / norm , axes=(1,0,2))\n", "\n", "def log_prob_fn(x, groups, X, Y, data, inds=None, fill_inds=[], fill_values=None):\n", "\n", " x1, x2 = x\n", " group1, group2 = groups\n", " a = x1[:, 0]\n", " b = x1[:, 1]\n", " c = x1[:, 2]\n", " n = num * num\n", "\n", " gauss_out = gaussian(X, Y, a, b, c)\n", " num_groups = int(group1.max() + 1)\n", " template = np.zeros((num_groups, num, num))\n", " for i in range(num_groups):\n", " inds1 = np.where(group1 == i)\n", " given_signal = gauss_out[:,:,inds1].squeeze().sum(axis=-1)\n", " template[i] += given_signal\n", "\n", " sig = np.atleast_2d(x2)[:,0]\n", " llh = - 0.5 * ( np.sum(((template - data)) ** 2, axis=(1, 2)) )\n", " llh *= 1/sig**2\n", " llh += - n*np.log(sig) - .5 * n * np.log(2.*np.pi)\n", " return llh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then generate the noise and add our injections." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "sigma_noise = [[0.2]] # The nosie variance\n", "\n", "noise = sigma_noise[0][0] * np.random.randn( num, num ) # Draw the random points for the noise\n", "\n", "# Generate the data-set\n", "injection = np.zeros( (num, num) )\n", "for params in gauss_inj_params:\n", " injection += gaussian_flat(x, y, *params)\n", "data = injection + noise\n", "\n", "params = np.array(gauss_inj_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot everything below" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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l1WqGQ3q+dl1rN+q4dqRaq7gbQqE+ai2kuPNyKWGjsp84c+YMNZvNYIS2ee/MmTOiMjc2NqjZbFKr1WLfNwL54uJiTyR//fXXR8ZrG4I2ACOMJDq7hEe1RNTW+mlLorMn7tymo+/9mK4eG4y6PnnjOt185FEi8gva8NIGAAAAQJ0Y4cgWMH1iktaPOqUdUrSil5tMzpyrEStK+BGHzteMaW5UtAS7/Ji4LW2LK6gf+/EdOnr7Jr19+CjdvfZWX5T2Tuc/0q1HHqW9Q/4xk3quS+ZOK+TZPscpAqzxCbbFbJ+4GkrQ59YtWaOhKPRYOaWSO3Lk3lfcNktEXN94h0RSiXd2Cf/mFBE75uWf0iaN9Y6pU3qvlDzFEnqqIgXf3KRcS74y7Kj2YW6ajjILCwtEdF9k5rh8+XLfsTHOnTtH586d877faDRoY2ODtra2aGZmRtHaeoCHNgD7jBSPauk5XCR2anT2xT95m9b+2VfpN/7pV+jkj/ojTk7+aJd+459+hdb+2Vfp0O3+D2qhtsJHGwAAAABVcPO9D/p+l4rZro+zz3s75MubS6p3sPHdTRWzuTJj2OJOSKRJEfJsMdcXtSy17yDyR28ThQXrFGH9rau7dOzHd+j13/8mvfFv/oA+eqs/6OXkj3bp//LbX6Hz/8//jibuxMdc6vec8h5ReK1UJVZJIsKrimSuUsweJTgx1XBl9/ZAgsQq/MBDbUpFuialXuRVEBpLnye7ec+QE7VelTe5wR1bRGcPMjc3RzMzM7S1teU9ptls0sLCgjop5LgCQRuAMUYSBd177093gxYfsaSLBls0zhGQj773Y5q6tUdPXe/Sb/zOGz1R++SPduk3fucNeup6l6Zu7dG//59/mFwHAAAAAEBpzJduTtiwBQWJkO2WlyuW5CRFi5UrRZMs7enJwwPCRayunKhULnJaI2IPtCVR1NaUbaxGJt7/MU3f/BF98tYefeXCfVH7o3s/oi9/4w36+PVrNHXzJh1578e98966ujvQRs0a4+bOt1mjKcOsU+l6NWUYQc4W5jRiJNcOad0+SjyFUPKareoewBESWFPO0/jl2+uvKlG7SjHetF9Sx7ATThrq2Kiy7YXAIOvr69RsNlkf7U6nQ51Oh9bX19lzl5aWaH5+Phjh7SPk2z1MIGgDMKJIk0EafAkXXSE7JGyniNoS3HL/6E936eqx4/RLP/d5euv4VE/U/l/+xz/tidlvHZ+iX/q5z7N2JAAAAAAAwyQmChqhxSdku6/ZYkCqqC0VglOjpiWkClwxAYM7P7RJEKIXXR/wljZicMy3mkgnaqdajRARvX30MfrCmb9C3zsy0RO1f+qH/5F+6/d+mz5+/Rp9//gJ+pXPfp5+eOy4qN1S7E0abrPGJ2pz5/nIFWFL+XW7xDzCS1rqxMqqWqiORVNLrm13Qy+04ZE6R1wSwZzyqiBmleReP3bb3XuhL2+AZmOv5NhUuQYhZsdZWFig5eVlWlpa6nu92+3S4uIira+vs9YgrVaLms0mtdttajab4vqMJ3fIt3uYwEMbgDHFJzYb/uhPd4Me1Zf/v39KNx95lE7/xJNJ9YdE7VjbDFcfm6Rf+rnP90Tsf/CN3yIiui9mP5afEPLtd2/Sk48fzS4HAAAAAECCLWYPvPdnQqURU22/UNenVCNsuyKD1o/WF0WrgbMECfVBKlxw5dpjq03IGGL3rXdo4v0fEx19rPea8aj+yI3rUY/qEthititM/+DIUfrCmb9CX7nwB/TJW3v01dYmEVFPzL762KRXzObGyPa17tXvmbvQenI92n2R3TlimDk/ZJtQl6jJ9ePpycMDlhvaMrjrVjJmZvxdgVyStJN7oiJlnnyWQJwXf4hQu6tOrGvIWUe++znR4FM7RHG/fRd3bLUboLbfOdHguk0h13IHntk6VldXqdlsUqPR6FmLmMhsn3f2wsICzc3NUbfbpbNnzwbLb7VatLKyQt1uty8p5NzcHE1PTwctT+oGgjYA+5SJO7dp7Z99laZu7Q2Iw8bWo3tkgr7+xQbdOVzWC+xTH5lQidp/b+Fne2I2EdHfW/jZImI2EUHMBgAAAECtmISKXCJCLirYCEic+BFLuuWKT3ZiNp9Arkk+lpKoTCo6aRPJ2eXaY6sVhMx5rqXHsR/fod/61/8vmrp5k/7Lv/pz9IOJx3p2JCd/tEtf/sYb1D16lF75z/+LZFFbkpzy1BMTvbY9dbJfoH7gxFP0A3qLVk6foa/+m/+h9/qXfub+Z2f3nL6yIyJ1SCwrkbQvtKak1gt1+lW7fQ5dD7lituQ8rk1Eed7gPmG7ClIEWBs3WW0VlFzj3D3YtL/UJlwuUjFbEr1vr+uUcXQ3OXOF9v3M2bNno8K0y/b2tui4hYUF8bHDBpYjAOxD/uhPd0fKo/ozT/ZHiH/mE/3i+t9tfbPv/b/b+uZAokgAAAAAgHHj9PRE78d93RfRmprE0UfMmzsmVGisCLQWA24iObvOGNy4xvCJ2UT3PKqnbt6kj1+/Rv/t7/8O/dSfaStGzHY9qg0+6xJf+yRttst86uRk74eI6GOPHqfVP273Hf9r3+r/7OzzBQ/Nj0YodNeTvVniirWuPQJnUZH7NEGKeCbdrNF4fae2RXueaUtq0lZfdHiOrUldUba+tedDY2GhnTvXy5srxx0X937sRtZr2iPdHPCVHxOMtden1GJI0jYAJCBCuyCtVqvPs6bb7VKj0aC5ubmRLBfsHzj/bONRbcTr3/idN+jvLfws/d3WNwc8qmedslwBuipscf2t41N97fuN33mjmO0IAAAAAEaP/fbZ2Y7G4yIJJUJmqehD8xi5JsI0Rkyk4NqujcjMsRrw1TPgucuI2UT3PKr/y7/6c/Tf/v7v0MevX6Mvf+MN+tLP/Cz92re+OeBRHSM219pIbcNPHSb68j//Hfr43nX6/vETfe378jfe6NmOEN0XtUNe4YbY+IZsFKTza68F7Txr7E+GSR1itsGei1wxW1tfTnm5UdpE+khtia2GZg4k97pQpDaR/4mZqqOSQ0/FGCRj4bOS8T31kTvnAHBA0C7EysoKtdvtAT+Z+fl5ajQadO7cuZEqF4wf2kSMRGU8qr/zwz361EfKPxL1M8fv0t96w0kA6bT3N37nDfqtX/xFsuX6kNg+e/JY8XYCAAAAoDzj/tnZ9yXdFbXtY224L/caQbhURGopYTBXiI/5M4eQCiUXd/a8YrbhBxOP0a989vO9iOz/qvlPiKjfo7qvbkYslkaNa0Xtj9y43muX3R67vV/+xhv0y5/7gkh0N2jnzqzxmB0OVza3niX1a32YNUgieFNFRomYF7sGQzYrkkSSwxD/67AG8d0XXQumkyeO0NVrt4KitnSMYn2KidpVkmv3ITnX13/f36lcv28AYkDQLkCr1aK1tTW6e/fuwHubm5s0OztLzz//vDoqpKpywcGiao/qVO4cOkTdI/c+8Nviui1qd49M0J1Dh4bZTAAAAAAUZtw/O0uiWVOTO+ZghDlXwMkRUiSCXGkxW1qeZlxt4dgnZhv+x9v3PKmNmE3U71Edwidm+/y+paI2EdHh4w/R7cnH6OpDD9A/+N+foxOTU3T1nb0+Ubt79CjdeuTRaDuJ9POWmjzO7bsRtlLXTWrEpy2omWtCakfB9T3liQW77mEIzaVFVW15JoFrHeKmPbcSUTuGdL0OQ7gNJQXm5kgr7Gs2nULXtmZs4J8NJEDQLsDKyoo32mNmZoYWFhZoZWVFnQ20qnLBwcLnUV21nYebGPIzTx7vs0a5c/gIff2LDfr3//MP6aoTRXL1sUn6pb/5BfpPnv1IMGFlFZHjAAAA6qVKe4hut0vnz5+nVqtF09PTREQ0NzdHq6urQ2kPuAc+O6cRE49MksWr18pGRo5aVF1KeyTCseGjez+iX/v9/s/Ov/atbw5EaHPR2Uaws38v1bbbhw/Tf/2Fv0OH79yh7mS/ncglIvrlz32Bbj3yKO0dOjzQttIJ6DRWNr6664jiLUVJcc1E7VYVPR3zuy8paueWV7o9o+LH7NrrcO2yBXdJUlwtvvWVG6UuQXNt2+MDERtoKSZof+Mb3yCiexkxjx+vx393FGi329Rut6nRaHiPmZubo7W1Nep2uzQ1NTXUcsH4MnvymNp2pIRHtU80tu09UuxQiO6J2rM/+Yk+D+/7TNId55W6vL0BAADUQ5X2EM1mk1ZWVmh9fb1PwDafr9bX12ttjws+O4/vZ+fYl/VUYYCzKPH5rIbEClso0UQkcpGvEvE4ZK1SmqrF9Y/u/Yh+6/d+W+RR7cOI2lIBXcPtw4fp9uFBcezUExNEAq/snPHTiE32eg1FVGvXTk77U8/19du9JjmPZKk3OXctp0T6Su49Kb7bobKNICnpv9nc8NlyGKRiv68P9j3Q3uDLEUy1azU2f6ltkVj9EPUnaAzNtWSc7TqlPvfueElsd4b59AIYTx4sVdAv/MIv0NLSEp04cYKee+45+uIXv0jf+MY36Pr1wWR1+4lWq0VE1Ivi4Zidne07dpjlgv0JJ/SevHG9T8z+pZ/7PP3xxz5Bv/Rzn6e3jk/1RO2fOX43WlYMn3e1K4bnCNLuuW7ZXBuefPxocn0AAACqxdhDcNGym5ub1Gg0qN1uJ5XdbDbp5Zdfpq2tLVpYWOh7b2VlhTY2NmptDwc+O4/3Z+dTk0fYRHUlvoibsn1itsFEeY47dfbh1BMTvWSJLk/evCdmf3LvOn1v4jj9ymc/T//+Y5+gX/ns5+n7x0/0RO2P3Ihfo1WI2ePIM8cPs9eKi73mQz+l2iTlyu7t3nUdu7a5cqXtLnENaO89mvuVxOebo8oNKImYbbiye7snIHP9NvfSUbmf1iXoptTDrenQPGuu3VJ/Q8HBoZig3Wq16Bd+4Rfo2WefpcuXL9P6+nrvQ/qZM2folVdeod/7vd8rVd3IcOHCBSIKf3g275ljh1ku2D+E7DY+84lJuvnIo9Q9MjGQANJ4VL91fIrenzqe5FHNiccaUVsrbMfEbAAAAOOH1B5CS7fbpaWlJVpdXWU/R3U6nVrb4wOfnfHZWUKKwOJG/j09eXjgx8VN3qVtoxEuq0SahND94eBE7b2HH6Wdw0fpexPH6W/99Ofof/yzYTQe1d8/fqLnUc3ZjcRwrTfs/mgsQS69s9f7qRLpfEoSE1YlFvrKDs29FCNm22WliNocqf7bqeVVda4EyTri2hAaS9sDPXZvs++Hdj2+fttryrdJESNnQ0szH7Ho7FCZkrXM/Rh8G02x8fPB3bvHxZIIDI9iliNzc3O9Rzd3d3ep1WrRt771Ldrc3KTt7W3a3t6mtbW13rGLi4t07tw5euaZZ0o1YSh0u10iop4vo+TYusu9c+cO3bnTb97w4YcfitsCxpPTP/EkLf9vfp6Ovvdj1qP6t37xF+nOoUNBj2otPlsU10+b6L5Ibftqc0jEb0RnAwDAeFGlPcTLL79MU1NTXnH68uXLtbYnVB4+O8uOrbtc9rPz3Xo/O+eKf0bE8SW946xIfIkL7bbE7ApCNgs5fZKK2SGMDcipJybo0jt79NTJyb4EkTcePUQv/9WfpYn3f0xvH32MiO4lkHzq5CRdfWyyz6M6F9MfrY+0K2JfemcvKq5zNg9aSnkdx8ooIeaVwBaziXiBMvTkRIpIzJ1nbCtKe027hMov9dQJ0aANh21Hoe2jJKGnHZXNIbFf4WwwfNetvU7M/zVWGxpyvcsN2iSrvvUduh7cOm0gWoMcikVo20xOTtLnPvc5Wl9fp52dnV7UyWc/+1m6e/cutdtt+tKXvkSzs7P01/7aX6PvfOc7VTSjFnZ2dqLHmA/Wvoigqss9f/48TU5O9v1c7gx+mQOjhVaQ5YTf0z/xJM3+5CcGj/3EJF2fnBoQsyWR0L5I7FRMxLbvxwXR2QAAMP5UZQ/R7Xap2WwO2IwMqz1S8Nm5n1H87Hz1rf+fuC051P3Yu68uIzJw3ra+BGfmda0Hd0gwK2k1IYmavPHooZ6Y7fLDY8dZMVsaXZ1rQ1J1RLYGqcgZi551ka5/7hjpJofddtuKwn7NPsbMm/lXEq2tvYYlY+M7LlVwdseGi+otHb0t8WK2kc7zyRNH6OSJ/vtEyKM6RUQtfV92o8Wl4x27RkLivaYOG/ca5v4ucMfa63VU7FzA/qISQdvl2WefpZdffpk2NzfpT/7kT+jZZ5+l1dVV+umf/mn61re+RXNzc/RP/+k/raMpxdFEjgyr3FdeeYV2d3f7fmZn+DR8YLRxxWSpR/VnPjHZ98MeUyjhotR6JAWpwI7obAAAGG2qsocwYvOZM2dGoj2p4LPzcMvlPjuffOrPFSufgxMpJCJgacEpFs3rE0WMeMSJ2vbrXHkuEs9krdjmiskmqtnnp23e4963I6LNeGksQ1LhIrE11icaWxlJVKnvdxtbVExNKinBJ7TF1rHdNk7ctrHnuKqoUl/7zetc+1KFQm5sNKJnLqmbXaZ99lhcvXarL/ljibpTyrDXSOyeUFVUdulNUd/fIEkdPrsXAEpRzHJEyszMDG1tbdHGxgZtbW1Rt9ullZUVOnv2LDWbTfqbf/Nv1t2kfc+hQ4fokOOT/OCDtexlgCHwmSePR208uHNccqKzzXGu/Ygp07UfieETw0tHiwMAAMjjww8/HEhqyH0Oqcp2wiR0nJubIyLqWXYQEb377rt05swZOnv2rLeO0u0pAT471w/72fmB6j47+4SBK7u3+0RtV8wyAgH3qL404tOgjar21esrsw4kNgB9xyvEYMmxxtKkNEYYM3YpkmPN8SUJCVKhtcCJsLkWGr41b+rSrGEi3lbHLsc3lqF+hKxH3HPcMbLbv58iW13LEek82de1OceMrfZel4pUkLWvwbrvgTZS65vQ5mPO2ErFbNuOBgAtxQXt69ev0/Hj4UjPZ599tvdlYGpqqpcE56WXXqIXXnghev64UsJvsc5ywfB58vGj9Pa7N/tec32qff7UUlG7tJjtnuPz1DaExO1QVLevPYjOBgCAfk4cerjYUzgxNr77XZqc7I9ofPXVV+m1117re60q2wn72EajQaurq32fk5aWluhrX/sabW5u1tIeCfjs7Ge/f3YuIVS5AhGHJjLWxm2fL1qSe/zcPrYqb16u766oLRF1jae2/bsELgKzKlHbrk9aPtc+iWhUlQjHRXbHvIu1a8QIcNrzYsf7xjJlrCRJNLXtT/XtHgap42afT3Tfd9sQEl9zxqfKca163rTlx8Rsyd8S38Yr0eD9R7K5cSlaIzjIFAs1+Lf/9t/S9PQ0nThxgk6dOkVf/vKXByJ0bNwvDgsLC7S+vk7nz58v1aRaMB+INV+Ehlku2J9wwq/Pgzr2fmnf7Ni5n/rIhPdHWybEbAAAGC7PPffcgFXDK6+8MnBcVVHORmze3NwcELPN661Wi1ZWVmppTwh8dj4Yn53N49/uY+ApEdX2MXbCuku7t8TCToiQzUcsOtdHTMy+tHtLHZlnn1Mqqu/UExO9n1xCVgNasdsnmtdhcaIlZA8R8/X1keIVbM6LCcdmHaWsQR+xa0+6iWWOG5fobO4e56IdY/se4SvPvvelbtyF6s8Rm4cZne0jtNZLRWbbxO7ziMoGuRQTtF9++WV68cUX6ad/+qfpT/7kT2h5eZlOnDhBf/Ev/kX6x//4H9Obb77ZO/Yb3/gGG91y9uxZunbtWqkm1YLxUwx9ETJ91USDVFUu2B9wgq5PAJYmW/SVUcLWY/bksWL2ILAZAQCA0eXBBx+k48eP9/241g11sLOz4/189OKLL9La2tpQRGwbfHbueo/ZL5+dfQnNuARa5v8uPrsGKRKRJyaaSepzj9H6uLrChm8jwBx3cWdvIEmfIVVIMkKx++M7NlZWCImQY/uZuz92e0cJW8DWrNPcBIv2OncT0UmTRfpEt9h6klhfpPgalzw+Jtq74r5GaAwlCKyDEqI2l6BRArc2tIlsNeNVKpqbu98afOvYN7aSTcsU6k6QDMaTYoL2888/T7/5m79JW1tbdO3aNTp//jw988wz9O1vf5vOnTtHs7Oz9NBDD9FDDz1ES0tLbLQOEdEDDzxQqkm1YJIOhaJBzHuaBEVVlQvGD03UcU7ixRJJG2PkRnqHzkd0NgAA7E804qM5dnFx0XvM/Pw8ERF9/etfr7w9IfDZef9/duZEEi4ZHfd/G0nCOt952na6r2tFyVRB08a3EUBEfWKu9NxUcoXj0DkxwStWHydslxC3RzGqlCgtSaQrbOegESklgvqowVn35JDa55R7B9fWlOSZJSKy67h+UtvoCsv2776/STGqspMqVQbY/xTNbmIek5ycnKTl5WW6fPkybW9v05e+9CV64YUX6Nlnn6XPfe5z9K1vfYs++9nPEtG9xy0ff/xx+vKXv0xERHfv3i3ZpMpZWFggorCf4uXLl/uOHWa5YDzhxFqfuJsiTKcmXXzy8aN9PxKMMC2N2pYcBzEbAADGi6rsIcyxJlo3dMz29nbl7YmBz848++mzs08ksV/XiNXSL/luQq5hiQMp9cYS6bmitvk9Vzjk7EBsgSpFOHaPPz094RUP3fIlAplWQHOjvHPK4kgVcCXnuJY9OfPt62vKeJt2+KLES8BtFnHXiW9cpGupVHQx17YqxV67HaWtR0J1EYXHVrJOS94jtcdJoqdTNlOlSJ9+AMBHsaSQq6urtLy83Ht00vDpT3+aPv3pT9Ov/uqves+9du0aLS8v0w9/+MNgRM0oMjc318s+f+7cOfaYZrNJCwsLqoieqsoF+wtJ0kWiwcSLEtE7JRravO4mskytRwLEbAAAGD9mZmao3W5XZjshwRY+q2pPCHx2xmdnonhivBAmAeKpySNeQceNEh+HaNEQJsmZ3Xfzuo2bHFLKxZ29Smw8QuK73R+DRvwzx4b6W5WAbVPF2nLXbKiOK7u3s4Rk6XiUHLdcwc6cH0vMypGbkNC1qZCKnimbML61XWoupNee7W2ea5NDlDf/mnMl90L7WjPXUoqQ7baLm79RfRIEjB/FBO3JyUn6zd/8Tfr7f//v0+rqKjUajV4kSYhPf/rTtLW1Re12mxYWFujTn/50qSbVxvr6Oi0uLlK32x34gNzpdKjT6dDW1hZ77tLSEnU6Hdrc3Bz4EpZTLth/PPn4UVYoNoIwJ2wbNFHbJaKhU4RtLRCyAQBgfDlz5gw1m83i9hDGTiQUpWuYm5urvD0h8Nl5vD47H33koegxrsgRExHMl3qfKOVSV9RjCfHBlHFq8siAKJkr4tmitvldw+npib5o7Evv3P8/lxQyVRznyiHyi4+h6F/JmEnnKVXIDM1hiqAaw5xf1UaMuymSQqhdIUEwZWzsa4orTzoH7gZB7lpwNxFc72S7vW7bU4Rq972ctWHXbzazfGtCMr51CNlaYvcuu7+uqB1DKnqnXmNIGgliFLUcISL61V/9Vfrd3/1deuGFF8TnvPDCC/Srv/qrY/mBnOjeY4vLy8u0tLTU93q326XFxUVaX19nI4ZarRY1m01qt9vUbDaLlQv2LyERNzfxYhXWHhorEm25AAAAxpeqbSfMuRycMD1Muwp8dr7PKH92Pvzwg0Gxwn3PTl7ow02+FhI5Sj2W7r7mCvC+ZHk2scfo7ePN/0sLOHakZAhpkkfDpXf4ZJO5Gwcx8d0d09jvqdjjpimTE/O4MkpZbYTWS6gObUSpRLgsCWcVEkvE6L7nO1ZiQcLNVU7yScl8axJw+t5PsUORtstOMOtLNGv6zHmza68lyb0wVm5K8k4D9zdJ+nfIhpv7kvd5iNlAQrEIbZfJycmqih5JVldXqdlsUqPR6EWEdDodWl9f937xWVhYoLm5Oep2u3T27Nli5YL9jS9S2+CzIQkhEcJzRORSEdsQsgEAYH9QlT3EzMwMzc3NUavV8h6zublJU1NTfZ+jRsGuAp+dx+Ozc0pUXq6NhcQ6QHtOCbixKCVCSKJySwiPJjr7rau79NTJ8DWYGqldwpOYKO4tLj3GjarURIGHMAKXKT81+lciZpt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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import cm\n", "\n", "plt.figure(figsize=(17,7))\n", "plt.subplot(121)\n", "cf = plt.contourf(x, y, injection, 10, cmap=cm.PuBu)\n", "plt.scatter(params[:,1], params[:,2], marker='x', color='#DC143C')\n", "plt.colorbar(cf)\n", "plt.xlabel(r'$x$')\n", "plt.ylabel(r'$y$')\n", "plt.subplot(122)\n", "cf = plt.contourf(x, y, data, 10, cmap=cm.PuBu)\n", "plt.scatter(params[:,1], params[:,2], marker='x', color='#DC143C')\n", "plt.xlabel(r'$x$')\n", "plt.ylabel(r'$y$')\n", "plt.colorbar(cf)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then need to configure models and set-up our sample, in a very similar manner as we did for the simpler case before. Like in the previous case, we have two models that we need to sample for. One refers to the pulse signals, which we need to also estimate their total number in the data, and the other is the overal noise (no dynamical parameter space here). " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "ndims = [3, 1] # The dimensions of the two models we sample for here (# of parameter for each pulse, and # of parameters for the noise)\n", "nleaves_max = [2*npulses, 1] # Maximum number of components for each model type (noise is 1, because we don't want to use RJ MCMC on it).\n", "branch_names = [\"pulse\", \"noise\"] \n", "\n", "priors = {\n", " \"pulse\": {\n", " 0: uniform_dist(0.5, 2.),\n", " 1: uniform_dist(lowlim, highlim),\n", " 2: uniform_dist(lowlim, highlim),\n", " },\n", " \"noise\": {\n", " 0: uniform_dist(0.000001, 2.),\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we generate the coordinates for each walker, as done before. First we need to define the number of Temperatures and walkers we want to use." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "ntemps = 10\n", "nwalkers = 30" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "coords = {\n", " name: np.zeros((ntemps, nwalkers, nleaf, ndim))\n", " for nleaf, ndim, name in zip(nleaves_max, ndims, branch_names)\n", "}\n", "\n", "sig1 = 0.0000001\n", "for nleaf, ndim, name in zip(nleaves_max, ndims, branch_names):\n", " for nn in range(nleaf):\n", " if name == \"pulse\":\n", " if nn >= len(gauss_inj_params):\n", " kk = np.random.randint(low=0, high=npulses)\n", " else: \n", " kk = nn\n", " coords[name][:, :, kk] = np.random.multivariate_normal(gauss_inj_params[kk], np.diag(np.ones(3) * sig1), size=(ntemps, nwalkers))\n", " else:\n", " coords[name][:, :, nn] = np.random.multivariate_normal(sigma_noise[nn], np.diag(np.ones(1) * sig1), size=(ntemps, nwalkers)) \n", "\n", "\n", "inds = {\n", " name: np.random.randint(low=0, high=1, size=(ntemps, nwalkers, nleaf), dtype=bool)\n", " for nleaf, name in zip(nleaves_max, branch_names)\n", "}\n", "\n", "inds['pulse'][:, :, :3] = True\n", "\n", "for name, inds_temp in inds.items():\n", " inds_fix = np.where(np.sum(inds_temp, axis=-1) == 0)\n", "\n", " for ind1, ind2 in zip(inds_fix[0], inds_fix[1]):\n", " inds_temp[ind1, ind2, 0] = True\n", "\n", "groups = {\n", " name: np.arange(coords[name].shape[0] * coords[name].shape[1]).reshape(\n", " coords[name].shape[:2]\n", " )[:, :, None]\n", " for name in coords\n", "}\n", "\n", "groups = {\n", " name: np.repeat(groups[name], coords[name].shape[2], axis=-1) for name in groups\n", "}\n", "\n", "coords_in = {name: coords[name][inds[name]] for name in coords}\n", "groups_in = {name: groups[name][inds[name]] for name in groups}\n", "\n", "# We compute the initial likelihood value at the injected coordinates:\n", "log_prob = log_prob_fn(\n", " [coords_in[\"pulse\"], coords_in[\"noise\"]],\n", " [groups_in[\"pulse\"], groups_in[\"noise\"]],\n", " x,\n", " y,\n", " data,\n", " fill_inds=[],\n", " fill_values=None,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What remains is to sample the dynamical parameter space! One last thing: Define proposal and initial temperature ladder:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "factor = 0.0001\n", "cov = {\"pulse\": np.diag(np.ones(3)) * factor, \n", " \"noise\": np.diag(np.ones(1)) * factor}\n", "moves = GaussianMove(cov)\n", "betas = np.linspace(1.0, 0.0, ntemps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Begin sampling. This might take a while, so you can reduce the `burnin` and `nsteps` to get a quick result. With current settings it ight take between 2 - 5 minutes." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:14<00:00, 7.01it/s]\n", "100%|██████████| 500/500 [02:26<00:00, 3.41it/s]\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "ensemble = EnsembleSampler(\n", " nwalkers,\n", " ndims, # assumes ndim_max\n", " log_prob_fn,\n", " priors,\n", " args=[x, y, data],\n", " tempering_kwargs=dict(betas=betas),\n", " nbranches=len(branch_names),\n", " branch_names=branch_names,\n", " nleaves_max=nleaves_max,\n", " provide_groups=True,\n", " update_iterations=1,\n", " plot_iterations=-1,\n", " vectorize=True,\n", " moves=moves,\n", " rj_moves=True,\n", ")\n", "\n", "nsteps = 500 # Number of samples per walker\n", "\n", "state = State(coords, log_like=log_prob.reshape(ntemps, nwalkers), betas=betas, blobs=None, inds=inds)\n", "ensemble.run_mcmc(state, nsteps, burn=100, progress=True, thin_by=1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then plot the results below" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def get_clean_chain(coords, ndim, temp=0):\n", " \"\"\"Simple utility function to extract the squeezed chains for all the parameters\n", " \"\"\"\n", " naninds = np.logical_not(np.isnan(coords[:, temp, :, :, 0].flatten()))\n", " samples_in = np.zeros((coords[:, temp, :, :, 0].flatten()[naninds].shape[0], ndim)) # init the chains to plot\n", " # get the samples to plot\n", " for d in range(ndim):\n", " givenparam = coords[:, temp, :, :, d].flatten()\n", " samples_in[:, d] = givenparam[\n", " np.logical_not(np.isnan(givenparam))\n", " ] # Discard the NaNs, each time they change the shape of the samples_in\n", " return samples_in" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "pulse_parameters = [\"$A$\", r\"$x$\", r\"$y$\"]\n", "noise_parameters = [\"$\\sigma_\\mathrm{noise}$\"]\n", "\n", "samples_pulses = get_clean_chain(ensemble.backend.get_chain(thin=1)['pulse'], ndims[0])\n", "samples_noise = get_clean_chain(ensemble.backend.get_chain(thin=1)['noise'], ndims[1])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:chainconsumer:Configure has been called 2 times - this is not good - it should be once!\n", "WARNING:chainconsumer:To avoid this, load your chains in first, then call analysis/plotting methods\n", "WARNING:chainconsumer:Parameter $A$ in chain Pulses is not constrained\n", "WARNING:chainconsumer:Parameter $x$ in chain Pulses is not constrained\n", "WARNING:chainconsumer:Parameter $y$ in chain Pulses is not constrained\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c = ChainConsumer()\n", "\n", "c.add_chain(samples_pulses, parameters=pulse_parameters, name='Pulses', color='#6495ed')\n", "c.configure(bar_shade=True, tick_font_size=8, label_font_size=12, max_ticks=8, usetex=True, serif=True)\n", "\n", "for ii in range(npulses):\n", " c.add_marker([gauss_inj_params[ii][0], gauss_inj_params[ii][1], gauss_inj_params[ii][2]], \\\n", " parameters=pulse_parameters, marker_style=\"x\", \\\n", " marker_size=100, color='#DC143C') \n", "\n", "fig = c.plotter.plot(figsize=(8,8), legend=False);" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c = ChainConsumer()\n", "\n", "c.add_chain(samples_noise, parameters=noise_parameters, name='noise', color='orange')\n", "c.configure(bar_shade=True, tick_font_size=8, label_font_size=12, max_ticks=8, usetex=True, serif=True)\n", "\n", "fig = c.plotter.plot(figsize=(4,4), legend=False, truth=sigma_noise[0]);" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def get_clean_k_chains(backend, temp=0):\n", " \"\"\" A simple function to get the chains of model order k\n", "\n", " Args:\n", " backend: The Eryn backend\n", "\n", " Returns:\n", " k_chain: the chains of the k order \n", " \"\"\"\n", " inds = backend.get_value(\"inds\") # Get the leaves out\n", " branches = {name: np.sum(inds[name], axis=-1, dtype=int) for name in inds}\n", " for (branch) in (branches): # Get the total number of components/branches per temperature\n", " if branch == list(branches.keys())[0]:\n", " k_chain = branches[branch][:, temp].flatten()\n", " else:\n", " k_chain += branches[branch][:, temp].flatten()\n", " return k_chain" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gauss_k_chain_baseline = get_clean_k_chains(ensemble.backend)\n", "\n", "bns = (np.arange(1, nleaves_max[0] + 2) - 0.5) # Get maximum allowed number of leaves for the given branch\n", "\n", "fig = plt.figure(figsize=(5, 5))\n", "plt.hist(\n", " gauss_k_chain_baseline-1,\n", " bins=bns,\n", " color='#6495ed',\n", " alpha=0.9,\n", " lw=2,\n", " histtype='step',\n", " density=True,\n", " hatch='///'\n", " )\n", "\n", "plt.axvline(x=int(npulses), linestyle='--', lw=2, color='#DC143C')\n", "\n", "plt.xticks(fontsize=12)\n", "plt.yticks([])\n", "plt.xlim(1, 20)\n", "plt.xlabel(\"$\\#$ of peaks in the data\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Choosing the optimal B-Spline order\n", "\n", "In this section we will attempt to fit a spectrum using a shape-agnostic model based on B-spline interpolation. The motivation of the problem is to determine the optimal number of free knots that are best describing the data, avoiding over-fitting situations. A very useful package is [this](https://github.com/karnesis/spectral) one, which is a simple package for performing spectral analysis. Has a lot of window functions, but more importantly, it contains the log-PSD function, which we will use in order to reduce the computational complexity of the problem. For more invormation about the `lpsd` method you can check [this](https://pure.mpg.de/rest/items/item_150688/component/file_150687/content) paper. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we set-up the type of measurement. The duration, cadence, also try to define the total number of steps for our sampler." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "Tobs = 1e6 # Duration of data \n", "dt = 10 # Delta-t [s]\n", "fs = 1/dt # Sampling frequency\n", "Ndata = int(Tobs/dt) # Number of data points\n", "tvec = np.arange(0, Tobs, dt) # Get the time vector\n", "df = 1.0/Tobs # Define Delta-f\n", "burnin=1000\n", "nsteps=2000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then need to simulate some time-series. We could totally do this directly in frequency domain, but we will generate synthetic time series in order to use the `lpsd` function, which is useful for now. We start by defining our \"true\", or \"theoretical\" PSD of the data, which we will use to generate our data-set." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/yz/xf637mpx56g2dvdv9d8v1fnm0000gn/T/ipykernel_3250/1282363938.py:14: RuntimeWarning: divide by zero encountered in true_divide\n", " noisemodel = lambda f : 1e-3/f + 50*f + amp * (f**(n1 + n2) / (fstar**n1 * f**n2 + fstar**n2 * f**n1) )\n", "/var/folders/yz/xf637mpx56g2dvdv9d8v1fnm0000gn/T/ipykernel_3250/1282363938.py:14: RuntimeWarning: divide by zero encountered in power\n", " noisemodel = lambda f : 1e-3/f + 50*f + amp * (f**(n1 + n2) / (fstar**n1 * f**n2 + fstar**n2 * f**n1) )\n" ] } ], "source": [ "\n", "if (Ndata % 2)==0: # Get the number of requencies\n", " nfft = int((Ndata/2)+1)\n", "else:\n", " nfft = int((Ndata+1)/2)\n", "\n", "F = df*nfft # make the positive frequency vector\n", "fvec = np.arange(0, F, df)\n", "\n", "# Define a smooth model for the PSD of the noise\n", "fstar=8e-3\n", "n1=-5\n", "n2=5\n", "amp = 1\n", "noisemodel = lambda f : 1e-3/f + 50*f + amp * (f**(n1 + n2) / (fstar**n1 * f**n2 + fstar**n2 * f**n1) )\n", "\n", "# Get the PSD of the noise using hte model above\n", "Sn = noisemodel(fvec)\n", "\n", "# Remove first nan (assume also zero-mean)\n", "Sn[0] = 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the spectral module for convenience. It contains tons of windows and some spectral utility functions." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from spectral import genTimeSeriesFromPSD, lpsd, welchpsd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We generate the time-series by `ifft`-ing from the PSD model we provided. We then compute the PSD of the data using hte Welch method. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " # Generating noisy data with \n", "\t Tobs: 1000000.0 \n", "\tNdata: 100000 \n", "\t dt: 10 \n", "\t df: 1e-06\n", "\t \n" ] } ], "source": [ "navs = 30\n", "\n", "## Generate time-series here\n", "print('\\n # Generating noisy data with \\n\\t Tobs: {} \\n\\tNdata: {} \\n\\t dt: {} \\n\\t df: {}\\n\\t '.format(Tobs, Ndata, dt, df))\n", "my_data = genTimeSeriesFromPSD(Sn,fs) # I divide by two because the function assumes the 2-sided spectrum\n", "\n", "f20, S20 = welchpsd(my_data, fs, int(Ndata/navs), win='nuttall4c')\n", "fr1, S1 = welchpsd(my_data, fs, Ndata, win='nuttall4c')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to save some computatational time, we use the `lpsd` method, which is basically a method to compute the PSD of time-series, using the optimal number of averages at each frequeny. The frequency grid is equally spaced in log, and pre-defined. For more information, you can tcheck [this](https://www.sciencedirect.com/science/article/abs/pii/S026322410500117X?via%3Dihub) paper." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "f0 = 1e-4\n", "f1 = 5e-2\n", "\n", "fl, Sl, Sle, _, Kavs = lpsd(my_data, fs, Kdes=100, Jdes=200, flims=[f0, f1], win='nuttall4b', winalpha=0., olap=0.0, order=0, errrype='std', DOPLOT=False, VERBOSE=False)\n", "errs = [np.absolute(Sle[0]), np.absolute(Sle[1])]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,5))\n", "plt.loglog(fr1, S1, label='Numerically calculated PSD, with N=1', color='grey', alpha=.3)\n", "plt.loglog(f20, S20, label=f'Numerically calculated PSD, with N={navs}', color='cornflowerblue')\n", "plt.errorbar(fl, np.absolute(Sl), yerr=errs, fmt='.', label='logPSD', color='crimson')\n", "\n", "plt.loglog(fvec, Sn, alpha=0.5, color='k', linewidth=3, label='My PSD theoretical model')\n", "plt.xlim(1e-5, fvec[-1])\n", "plt.ylim(1e-3, 1e2)\n", "plt.ylabel('[1/Hz]')\n", "plt.xlabel('Frequency [Hz]')\n", "plt.legend(loc='lower left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we setting up the sampler." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "maxmodels = 20\n", "ntemps = 10\n", "nwalkers = 10\n", "backendname = 'a_test'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this investigation, we need two models. One model concerns the knots at the edges of the spectrum, which we only do parameter estimation for their respective amplitude. So, no reversible jump for this one. We do this by simply setting the maximum number of leaves to `1`. The second model is the one that is dynamic, each component having an amplitude and a position parameter. We call those models `edges` and `knots` respectively. " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max: 2.2400506683151953\n", "min: -0.7418475507541406\n" ] } ], "source": [ "ndims = [2, 2] # The first is control point + knot, the second two control points (knots are the edges)\n", "nleaves_max = [maxmodels, 1]\n", "nleaves_min = [2, 1]\n", "branch_names = [\"knots\", \"edges\"]\n", "\n", "# Restrict the frequency band\n", "i_band = np.where((fl>=f0) & (fl<=f1))[0]\n", "\n", "mx = np.max(np.log(noisemodel(fl[i_band])))\n", "mn = np.min(np.log(noisemodel(fl[i_band])))\n", "\n", "print('max:', mx)\n", "print('min:', mn)\n", "\n", "#The prior on the knot amplitude could be dependent on the frequency, but I choose\n", "#to be simple here\n", "priors = {\n", " \"knots\": {\n", " 0: uniform_dist(np.log(f0), np.log(f1)),\n", " 1: uniform_dist(1.5*mn, 1.2*mx),\n", " },\n", " \"edges\": {\n", " 0: uniform_dist(1.5*mn, 1.2*np.log(noisemodel(fl[0]))),\n", " 1: uniform_dist(1.5*mn, 1.3*np.log(noisemodel(fl[-1]))),\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting up the input coordinates. This might be easier in the future, but hte functionality should remain." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# knots: Max models = 20, dim = 2\n", "# edges: Max models = 1, dim = 2\n" ] } ], "source": [ "\n", "# Initialize the knots fr, \n", "coords = {\n", " name: np.zeros((ntemps, nwalkers, nleaf, ndim))\n", " for nleaf, ndim, name in zip(nleaves_max, ndims, branch_names)\n", "}\n", "\n", "\n", "sig1 = 0.0001\n", "for nleaf, ndim, name in zip(nleaves_max, ndims, branch_names):\n", " print('# {}: Max models = {}, dim = {}'.format(name, nleaf, ndim))\n", " for nn in range(nleaf):\n", " if name == \"knots\":\n", " for dd in range(ndim):\n", " coords[name][:, :, nn, dd] = priors['knots'][dd].rvs(size=(ntemps,nwalkers))\n", " else:\n", " for dd in range(ndim):\n", " coords[name][:, :, nn, dd] = priors['edges'][dd].rvs(size=(ntemps,nwalkers)) # np.random.multivariate_normal(edges_starting_params[nn], np.diag(np.ones(2) * sig1), size=(ntemps, nwalkers)) \n", "\n", "indxs = {\n", " name: np.random.randint(low=0, high=1, size=(ntemps, nwalkers, nleaf), dtype=bool)\n", " for nleaf, name in zip(nleaves_max, branch_names)\n", "}\n", "\n", "indxs['knots'][:, :, :3] = True\n", "\n", "for name, inds_temp in indxs.items():\n", " inds_fix = np.where(np.sum(inds_temp, axis=-1) == 0)\n", "\n", " for ind1, ind2 in zip(inds_fix[0], inds_fix[1]):\n", " inds_temp[ind1, ind2, 0] = True\n", "\n", "groups = {\n", " name: np.arange(coords[name].shape[0] * coords[name].shape[1]).reshape(\n", " coords[name].shape[:2]\n", " )[:, :, None]\n", " for name in coords\n", "}\n", "\n", "groups = {\n", " name: np.repeat(groups[name], coords[name].shape[2], axis=-1) for name in groups\n", "}\n", "\n", "coords_in = {name: coords[name][indxs[name]] for name in coords}\n", "groups_in = {name: groups[name][indxs[name]] for name in groups}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to define the likelihood function. It hadles" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "class SplineLikelihood:\n", "\n", " def __init__(self, freq, per, N, inf=1e14, expmax = 500, kind='cubic', ftol=.1):\n", " \"\"\"\n", " Gaussian likelihood model using a log-periodogram.\n", "\n", " Parameters\n", " ----------\n", " fr : (ndarray) frequency array of the estimation domain, size nf\n", " per : (ndarray) periodogram array, size nf \n", " N : (ndarray) array of averages per frequency, size nf \n", " var : (float) variance of the log-periodogram bins\n", " inf : (float or np.inf) if the likelihood diverges, it will be set equal to - inf\n", " expmax : (float) maximum value allowed in the exponential function. If this is reached, the log-likelihood will return -infinity.\n", " kind : (string) Interpolation kind. Set to 'cubic' by default.\n", " ftol : (float [ 0 < ftol]) Tolerance on proximity of knots in logspace\n", " \"\"\"\n", "\n", " # Frequency of data\n", " self.freq = freq\n", " self._Nsegs = N\n", " self._N = len(freq)\n", " self.logfr = np.log(freq)\n", " # Discrete Fourier transform of data (already normalized)\n", " self.per = per\n", " # Maximum value allowed in the exponential function\n", " self.expmax = expmax\n", " # Interpolation kind\n", " self.kind = kind\n", " # tolerance on proximity of knots in logspace\n", " self.ftol = ftol\n", " self.inf = inf\n", "\n", " def get_noise_psd_model(self, x, groups):\n", " \"\"\"Get the spline model for the noise PSD, given some knots\n", "\n", " Parameters\n", " ----------\n", " x, groups : (ndarray) PSD parameters\n", "\n", " Returns\n", " -------\n", " psd_model : interpolate.interp1d evaluated\n", " \"\"\"\n", " \n", " # I will consider two models. One handling the internal knots, and one for the edges\n", " internal_knots_parameters, control_points_edges = x\n", " group1, group2 = groups\n", " knots = internal_knots_parameters[:, 0]\n", " control_points = internal_knots_parameters[:, 1]\n", "\n", " num_groups = int(group1.max() + 1)\n", " # log_psd_model = np.zeros((num_groups, len(self.freq)))\n", " log_psd_model = np.empty((num_groups, len(self.freq)))\n", " log_psd_model[:] = np.nan\n", "\n", " # Loop over the temperatures vs walkers\n", " for i in range(num_groups):\n", " inds1 = np.where(group1 == i)\n", " knots_i = knots[inds1]\n", " control_points_i = control_points[inds1]\n", "\n", " inds2 = np.where(group2 == i)\n", " control_points_edges_i = np.squeeze(control_points_edges[inds2])\n", "\n", " # Remove zeros \n", " knots_i = knots_i[knots_i != 0.]\n", " control_points_i = control_points_i[control_points_i != 0.]\n", "\n", " knots_list = np.array([self.logfr[0]] + list(knots_i) + [self.logfr[-1]])\n", " control_pts = np.array([control_points_edges_i[0]] + list(control_points_i) + [control_points_edges_i[-1]])\n", " \n", " # Control for knots very close to each other\n", " if not np.any(np.diff(np.array(knots_list)) < self.ftol):\n", " # Change the data and reset the spline class\n", " interp_model = interpolate.interp1d(knots_list, control_pts, \n", " \t kind=self.kind, axis=-1, copy=True, \n", " \t bounds_error=False,\n", " \t fill_value=\"extrapolate\",\n", " \t assume_sorted=False)\n", " log_psd_model[i] = interp_model(self.logfr)\n", "\n", " # To prevent overflow\n", " if np.any(log_psd_model[i] > self.expmax):\n", " print('[Overflow!]')\n", " i_over = np.where((log_psd_model[i] > self.expmax) | (np.isnan(log_psd_model[i])))\n", " log_psd_model[i][i_over] = np.nan\n", " # Return the correct quantity\n", " return np.exp(log_psd_model)\n", "\n", " def evaluate(self, x, groups):\n", " \"\"\"\n", " Calculate the log-likelihood.\n", "\n", " Parameters\n", " ----------\n", " x : (ndarray) vector of spline parameters.\n", " groups : (ndarray) vector of groups parameters.\n", "\n", " Returns\n", " -------\n", " L : (ndarray) log-likelihood value at x, groups computed.\n", " \"\"\"\n", " # Compute PSD model\n", " C = self.get_noise_psd_model(x, groups)\n", " \n", " # Get the normalisation constants\n", " log2pi = - self._N * np.log(2.0*np.pi)\n", " \n", " # Log-likelihood calculation\n", " L = -1.* self.per / C\n", " \n", " # Sum with the normalization factor here\n", " L = np.nan_to_num(.5 * self._Nsegs * ( L - np.log(C) + log2pi), nan= -np.inf)\n", " L = np.sum(L, axis=-1) \n", " L[~np.isfinite(L)] = -self.inf\n", " return L" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "llh shape = (100,)\n", "liekeval = [-4.29905751e+06 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -4.37617254e+06 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -4.30751294e+06 -4.31923024e+06 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -4.31360092e+06 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -4.32508567e+06 -1.00000000e+14 -4.45639724e+06 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -4.46121449e+06 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14\n", " -1.00000000e+14 -1.00000000e+14 -1.00000000e+14 -1.00000000e+14]\n" ] } ], "source": [ "# Instantiate and initalize likelihood again with different ftol\n", "psdlike = SplineLikelihood(fl[i_band].squeeze(), np.real(Sl[i_band]), Kavs[i_band], ftol=.05)\n", "\n", "liekeval = psdlike.evaluate([coords_in[\"knots\"], coords_in[\"edges\"]], [groups_in[\"knots\"], groups_in[\"edges\"]])\n", "\n", "print('llh shape = ', liekeval.shape)\n", "print('liekeval = ', liekeval)\n", "\n", "log_prob = liekeval.reshape(ntemps, nwalkers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now run the sampler!" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * Started sampling ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 18%|█▊ | 184/1000 [00:03<00:15, 53.85it/s]/var/folders/yz/xf637mpx56g2dvdv9d8v1fnm0000gn/T/ipykernel_3250/1577252010.py:111: RuntimeWarning: divide by zero encountered in true_divide\n", " L = -1.* self.per / C\n", "/var/folders/yz/xf637mpx56g2dvdv9d8v1fnm0000gn/T/ipykernel_3250/1577252010.py:111: RuntimeWarning: overflow encountered in true_divide\n", " L = -1.* self.per / C\n", "/var/folders/yz/xf637mpx56g2dvdv9d8v1fnm0000gn/T/ipykernel_3250/1577252010.py:114: RuntimeWarning: divide by zero encountered in log\n", " L = np.nan_to_num(.5 * self._Nsegs * ( L - np.log(C) + log2pi), nan= -np.inf)\n", "/var/folders/yz/xf637mpx56g2dvdv9d8v1fnm0000gn/T/ipykernel_3250/1577252010.py:114: RuntimeWarning: invalid value encountered in subtract\n", " L = np.nan_to_num(.5 * self._Nsegs * ( L - np.log(C) + log2pi), nan= -np.inf)\n", "/Users/nikos/work/programs/anaconda3/lib/python3.9/site-packages/numpy/core/fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n", " return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n", " 61%|██████ | 612/1000 [00:11<00:07, 51.11it/s]/var/folders/yz/xf637mpx56g2dvdv9d8v1fnm0000gn/T/ipykernel_3250/1577252010.py:114: RuntimeWarning: overflow encountered in multiply\n", " L = np.nan_to_num(.5 * self._Nsegs * ( L - np.log(C) + log2pi), nan= -np.inf)\n", "100%|██████████| 1000/1000 [00:19<00:00, 52.54it/s]\n", " 46%|████▌ | 911/2000 [00:27<00:32, 33.08it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[Overflow!]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [01:00<00:00, 33.08it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " * Finished sampling!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "betas = np.linspace(1.0, 0.0, ntemps)\n", "factor = 0.0001\n", "cov = {\"knots\": np.diag(np.ones(2)) * factor, \n", " \"edges\": np.diag(np.ones(2)) * factor}\n", "\n", "moves = GaussianMove(cov)\n", "\n", "backend = HDFBackend(backendname + \".h5\")\n", "\n", "ensemble = EnsembleSampler(\n", " nwalkers,\n", " ndims, # assumes ndim_max\n", " psdlike.evaluate,\n", " priors,\n", " args=None,\n", " tempering_kwargs=dict(betas=betas),\n", " nbranches=len(branch_names),\n", " branch_names=branch_names,\n", " nleaves_max=nleaves_max,\n", " nleaves_min=nleaves_min,\n", " provide_groups=True,\n", " update_iterations=1,\n", " plot_iterations=-1,\n", " moves=moves,\n", " rj_moves=True,\n", " vectorize=True,\n", " backend=backend, \n", ")\n", "\n", "print(' * Started sampling ...')\n", "state = State(coords, log_like=log_prob, betas=betas, blobs=None, inds=indxs)\n", "\n", "ensemble.run_mcmc(state, nsteps, burn=burnin, progress=True, thin_by=1)\n", "\n", "print(' * Finished sampling!')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "baseline_run_backend = HDFBackend(backendname + \".h5\") # Load the data from disk" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "temp = 0 \n", "\n", "ndim_knots = 2 # Set the dimensionality of each model\n", "ndim_edges = 2\n", "\n", "samples_knots = get_clean_chain(baseline_run_backend.get_chain()['knots'], ndim_knots)\n", "samples_edges = get_clean_chain(baseline_run_backend.get_chain()['edges'], ndim_edges)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the \"squeezed\" posterior surface for our parameters, for all `k` models." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "paramnames = [r'$\\log f_{j,k}$',r'$\\log S_{j,k}$'] # Define parameter names\n", "\n", "i_band = np.where((f20>=f0) & (f20<=f1))[0] # Keep only the range which we fitted in\n", "\n", "c = ChainConsumer()\n", "c.add_chain(samples_knots, parameters=paramnames, name='Knots in-between', color='orange')\n", "c.configure(bar_shade=True, plot_hists=False)\n", "fig = c.plotter.plot(figsize=(4, 4));\n", "ff = np.linspace(np.log(f20[i_band][0]),np.log(f20[i_band][-1]), num=1000)\n", "ax_list = fig.axes\n", "ax_list[0].plot(ff, np.log(noisemodel(np.exp(ff))), color='k', lw=1.5,linestyle='--')\n", "plt.show()\n", "\n", "mpl.rcParams['text.usetex']=True # Update this becasue chaincnsumer is annoyinh" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, '$\\\\#$ of free knots')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "knots_k_chain = get_clean_k_chains(baseline_run_backend)\n", "\n", "bns = (np.arange(1, maxmodels + 2) - 0.5) # Get maximum allowed number of leaves for the given branch \n", "\n", "mpl.rcParams['text.usetex']=True # Update this becasue chaincnsumer is annoying\n", "\n", "fig = plt.figure(figsize=(6, 6))\n", "probs, bins, rects = plt.hist(\n", " knots_k_chain-1,\n", " bins=bns,\n", " facecolor=None,\n", " histtype='step',\n", " edgecolor='orange',\n", " hatch='////',\n", " alpha=1,\n", " lw=2,\n", " density=True,\n", " label='knots in-between'\n", " )\n", "\n", "plt.legend(loc='upper left')\n", "plt.xticks(np.arange(0, maxmodels))\n", "plt.xlim(0, 11)\n", "plt.xlabel(r\"$\\#$ of free knots\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\sim fin \\sim$" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.1" }, "vscode": { "interpreter": { "hash": "b9ba5a1b1b3609803c0e96492d0690add6c33090ea6cb0b49993a0abfd7cd473" } } }, "nbformat": 4, "nbformat_minor": 4 }