{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PEtab application example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While the [PEtab yaml2sbml notebook](petab_yaml2sbml.ipynb) illustrates on a toy model the basics of the pyABC PEtab import, this notebook contains an application example based on real data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# install if not done yet\n", "!pip install pyabc --quiet" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import amici.petab_import\n", "import numpy as np\n", "import petab\n", "\n", "import pyabc\n", "from pyabc.petab import AmiciPetabImporter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We illustrate the usage of PEtab models using a model taken from the benchmark collection. The following cell clones the git repository." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into 'tmp/benchmark-models'...\n", "remote: Enumerating objects: 3511, done.\u001b[K\n", "remote: Counting objects: 100% (3511/3511), done.\u001b[K\n", "remote: Compressing objects: 100% (922/922), done.\u001b[K\n", "remote: Total 3511 (delta 2695), reused 3170 (delta 2564), pack-reused 0\u001b[K\n", "Receiving objects: 100% (3511/3511), 201.90 MiB | 20.18 MiB/s, done.\n", "Resolving deltas: 100% (2695/2695), done.\n", "Checking out files: 100% (3411/3411), done.\n" ] } ], "source": [ "!git clone --depth 1 https://github.com/LeonardSchmiester/Benchmark-Models.git \\\n", " tmp/benchmark-models || (cd tmp/benchmark-models && git pull)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can import a problem, here using the \"Boehm_JProteomer2014\" example, to AMICI and PEtab:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# read the petab problem from yaml\n", "petab_problem = petab.Problem.from_yaml(\n", " \"tmp/benchmark-models/hackathon_contributions_new_data_format/\"\n", " \"Boehm_JProteomeRes2014/Boehm_JProteomeRes2014.yaml\"\n", ")\n", "\n", "# compile the petab problem to an AMICI ODE model\n", "model = amici.petab_import.import_petab_problem(petab_problem)\n", "\n", "# the solver to numerically solve the ODE\n", "solver = model.getSolver()\n", "\n", "# import everything to pyABC\n", "importer = AmiciPetabImporter(petab_problem, model, solver)\n", "\n", "# extract what we need from the importer\n", "prior = importer.create_prior()\n", "model = importer.create_model()\n", "kernel = importer.create_kernel()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once everything has been compiled and imported, we can simply call the model:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'llh': -138.22199570334107}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model(petab_problem.x_nominal_free_scaled)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can run an analysis using pyABC's exact sequential sampler under the assumption of measurement noise. Note that the following cell takes, depending on the resources, minutes to hours to run through. Also, the resulting database is not provided here." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:Sampler:Parallelizing the sampling on 20 cores.\n", "INFO:History:Start \n", "INFO:ABC:Calibration sample before t=0.\n", "INFO:ABC:t: 0, eps: 49386005.386943504.\n", "INFO:ABC:Acceptance rate: 1000 / 3348 = 2.9869e-01, ESS=1.0000e+03.\n", "INFO:ABC:t: 1, eps: 3707.524037573809.\n", "INFO:ABC:Acceptance rate: 1000 / 3582 = 2.7917e-01, ESS=3.0162e+02.\n", "INFO:ABC:t: 2, eps: 189.0069260595768.\n", "INFO:ABC:Acceptance rate: 1000 / 3753 = 2.6645e-01, ESS=2.9065e+02.\n", "INFO:ABC:t: 3, eps: 94.5034630297884.\n", "INFO:ABC:Acceptance rate: 1000 / 6376 = 1.5684e-01, ESS=3.2931e+02.\n", "INFO:ABC:t: 4, eps: 47.2517315148942.\n", "INFO:ABC:Acceptance rate: 1000 / 16933 = 5.9056e-02, ESS=2.7417e+02.\n", "INFO:ABC:t: 5, eps: 23.6258657574471.\n", "INFO:ABC:Acceptance rate: 1000 / 6990 = 1.4306e-01, ESS=3.6511e+02.\n", "INFO:ABC:t: 6, eps: 11.81293287872355.\n", "INFO:ABC:Acceptance rate: 1000 / 50479 = 1.9810e-02, ESS=2.3205e+02.\n", "INFO:ABC:t: 7, eps: 5.906466439361775.\n", "INFO:ABC:Acceptance rate: 1000 / 141531 = 7.0656e-03, ESS=3.0510e+01.\n", "INFO:ABC:t: 8, eps: 2.9532332196808877.\n", "INFO:ABC:Acceptance rate: 1000 / 54532 = 1.8338e-02, ESS=2.7007e+01.\n", "INFO:ABC:t: 9, eps: 1.4766166098404439.\n", "INFO:ABC:Acceptance rate: 1000 / 21583 = 4.6333e-02, ESS=3.9807e+00.\n", "INFO:ABC:t: 10, eps: 1.0.\n", "INFO:ABC:Acceptance rate: 1000 / 12382 = 8.0762e-02, ESS=1.3817e+01.\n", "INFO:ABC:Stopping: minimum epsilon.\n", "INFO:History:Done \n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%script false --no-raise-error\n", "# Uncomment this line to run cell\n", "\n", "# this takes some time\n", "\n", "sampler = pyabc.MulticoreEvalParallelSampler(n_procs=20)\n", "\n", "temperature = pyabc.Temperature()\n", "acceptor = pyabc.StochasticAcceptor(\n", " pdf_norm_method = pyabc.ScaledPDFNorm())\n", "\n", "abc = pyabc.ABCSMC(model, prior, kernel, \n", " eps=temperature,\n", " acceptor=acceptor,\n", " sampler=sampler,\n", " population_size=1000)\n", "abc.new(\"sqlite:////tmp/petab_amici_boehm.db\", {})\n", "abc.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use pyABC's standard analysis and visualization routines to analyze the obtained posterior sample. In particular, we can extract boundaries and literature parameter values from the PEtab problem:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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RdHXu3E1hpcTRq6EhCUFdP31m9qC7b3P377v799x9q5k92OzgRPZ0mVyers4OOjp2LLnNmTOHOXPmVLyuVCzWDWUShlqrj3YDPcB4M9uXoQLxPsCBTY5NZI+XKbMpDcCUKVPKnD1ExWIJU62hoXOAecDrgScYSgQvATc3MS6RWCi3XzHAFVdcUfU6FYslTLVWH70RuNHM/tnd/7VFMYnERiZXvkdQS0eH0dXZoR6BhKLeCtXzZrY3gJldbmZLzeytTYxLJBYy2XzZTWlmz57N7Nmzq16rpaglLPUmgivc/WUzezcwE/g6cFvzwhKJh0pDQ1OnTmXq1KlVr+3RBvYSknrvIyj9tH0AWODu3zczLTEh0qBMtvzQ0CWXXFLz2u5Ugl71CCQE9fYInjWz2ykuR73czLp24loRqaBSIqhHOpkgox6BhKDeP+b/BPwAeL+7/w0YC1xc7QIzu9PMXjCz9RWOm5ndZGZPm9kvVHOQOKo0NHTKKadwyimnVL22RzUCCUm9dxb3mtnvgfeb2fuBH7v7D2tctpDiFNNvVTh+PPCG4N90ijWH6fXEI7KnqDRr6J3vfGfNa7uTCV7O5JoRlsRMvfsRXAB8ElgaPLXIzBZUm1Lq7j8ys4lVmj0J+Ja7O/CYmb3WzA5w9+fqC11k91dp1tBFF11U89p0MsGWl7X2ozSu3mLxJ4Dp7v4qgJl9EXgUaOTeggOBTcMebw6eUyKQ2Kg0NFQPTR+VsNT7E2gMzRwi+Lpl+xGY2VwzW2tma7ds2dKqlxVpukw2T3dqdI/gxBNP5MQTT6x6bU8qoUXnJBT19gi+ATxuZt8NHn+I4r0EjXgWGL7o+kHBc6O4+wJgAcC0adO8wdcVaQuFgtOfK5QdGjrmmGNqXt+tWUMSknqLxV8xs1XAu4OnznT3nzX42vcB55vZYopF4u2qD0ic9Ocqb1N5wQUX1Lw+ndTQkISjntVHPwUcBvwSuNXd65qmYGb/BsyguHLpZuAqIAng7l8FlgMnAE8DvWijG4mZTIVtKuvVk0qQKzjZfIFkQrf1yK6r1SP4JsV9in9Mcbrnf6W4GmlN7n5ajeMOnFdPWyJ7okyulAhG9wiOP/54AO6///6K1w/fnGZMWolAdl2tRPBGd38TgJl9Hfhp80MSiYfSNpXpMolg1qxZNa8fvjnNmHQy3OAkVmolgmzpC3fPmbVsopDIHq/a0NCnP/3pmtcP7lusgrE0qFYieLOZvRR8bUA6eGwUR3f2aWp0InuwUqG3q4G1hkD7Fkvjam1Ms2s/oSJS02CPoMz00ZkzZwKwcuXKitd3a5cyCUm99xGISMj6s6Xpo6OHhk499dSa15d6BNrAXhqlRCASkaEawegewSc/+cma1/ekir++GhqSRmnOmUhEStNHy80aqkc6Vfz11dCQNEqJQCQimWzlO4tnzJjBjBkzql5fuk7LTEijNDQkEpHStM9yNYIzzjij5vWloSH1CKRRSgQiEal2Z3E9iUDTRyUsGhoSiUhpaKirc/SvYTabJZvNjnp+uNJ16hFIo9QjEIlIfzZPV2cH5e7YP/bYYwFYtWpVxes7Oqy4gb0SgTRIiUAkIplsfnC9oJHOPvvsutpIpxL0DmjfYmmMEoFIRDLZ8pvSAMyZM6euNtLJBH0DhTDDkhhSjUAkIn3ZfMW9CHp7e+nt7a3ZRjqloSFpnHoEIhHJZPNlZwwBnHDCCUD1GgEUewQaGpJGKRGIRCSTK1RcefTcc8+tqw1tVylhUCIQiUgmmyddYWionkXnALpTCbb3VZ9mKlKLagQiEemvMjS0fft2tm/fXrONnmRCS0xIw9QjEIlIXzbPARVmDZ100klAHTWCVILerGoE0hglApGIZLKFirOGPvOZz9TVRremj0oIlAhEIlJt1tDJJ59cVxs9mj4qIVCNQCQi1RLB1q1b2bp1a802StNH3T3s8CRG1CMQiUgmV6iYCD784Q8D9dUICg4D+QJdFeoNIrUoEYhEoFBwBnKVawQXXnhhXe0M7ls8oEQgu06JQCQC1fYiAJg1a1Zd7ZQWrevN5hhDMpzgJHZUIxCJwOA2lWX2IgB4/vnnef7552u2U+oR9OleAmmAegQiESjN9KnUI5g9ezZQu0ZQul7LTEgjlAhEIlArEVxyySV1tdOTUo9AGqdEIBKBwaGhConguOOOq6udUo1APQJphGoEIhEYKhaX/xXctGkTmzZtqtmOagQSBvUIRCJQWiiuUo/g9NNPB+q7jwDUI5DGKBGIRKDW9NHLL7+8rnbUI5AwNHVoyMyOM7MNZva0mY2qfpnZGWa2xczWBf/q27FbZDc3VCMo/ys4c+ZMZs6cWbOdtGYNSQia1iMwswRwC3AssBlYY2b3ufuvRpy6xN3Pb1YcIu1ocNZQhbuBN27cCMDkyZOrtqOhIQlDM4eG3gE87e4bAcxsMXASMDIRiMROqUdQ+kM+0llnnQXUrhF0dXZgpqEhaUwzE8GBwPBpD5uB6WXOO8XM3gv8Fvif7j5qqoSZzQXmAhx88MFNCFWktWr1CK655pq62jGz4r7FSgTSgKinjy4DJrr7EcADwDfLneTuC9x9mrtP22+//VoaoEgzlIZyuirUCI4++miOPvroutrqSWkDe2lMMxPBs8CEYY8PCp4b5O7b3L0/ePg14G1NjEekbfRn85gVh3bK2bBhAxs2bKirrW71CKRBzRwaWgO8wcwmUUwAs4GPDj/BzA5w9+eChycCv25iPCJtI5MrBOP7Vvb4OeecA9SuEUBx5pB6BNKIpiUCd8+Z2fnAD4AEcKe7P2Vm1wJr3f0+4DNmdiKQA14EzmhWPCLtJJPND079LOfzn/983W2lNTQkDWrqDWXuvhxYPuK5K4d9fSlwaTNjEGlH1bapBDjqqKPqbkvFYmlU1MVikVjKZCtvUwmwfv161q9fX1db6hFIo7TEhEgE+rL5ioVigPPPL95jWXeNQD0CaYASgUgEag0NfelLX6q7LfUIpFFKBCIR6M9W3rge4O1vf3vdbalHII1SjUAkAplc9VlD69atY926dXW1pemj0ij1CEQiUGtoaN68eUB9NYLSncXuXvG+BJFqlAhEIlBr1tANN9xQd1vdqQTu0J+r3qZIJUoEIhHoy+ar1gimTp1ad1ulIabegeq9DJFKVCMQiUAmm6erwsqjAGvWrGHNmjV1tXXAmG4Antn6SiixSfyoRyASgf4aQ0MXX3wxUF+N4O0TxwLw2MYXedshY0OJT+JFiUCkxfIFZyBfqDpr6Oabb667vXGv6eLvX/caHtu4jfP+4bAwQpSYUSIQabH+wY3rK4/MHn744TvV5vRJ47jnyc1k8wWSCY34ys7RT4xIi5Vu/qo2NLR69WpWr15dd5vTJ4+ldyDP+me3NxyfxI96BCItlskV9yuu1iO47LLLgPpqBFDsEQA8/syLvOXgfRsLUGJHiUCkxQb3K67SI7j99tt3qs399u7i0P324rGN2/jU0Yc2FJ/EjxKBSIvVkwimTJmy0+1OnzyO+9b9mVy+QKfqBLIT9NMi0mKZbGloqHIieOSRR3jkkUd2qt0jJ4/jlf4cv3rupYbik/hRj0CkxfpLPYIq+xFcddVVQP01AoAjJ5XuJ9jGEQe9dtcDlNhRIhBpsb46hobuvPPOnW737/bpZtL4vXh844vMfa/qBFI/JQKRFqtnaGjy5Mm71PaRk8fyvV88R77gJDq0EqnURzUCkRYbKhZX/vVbuXIlK1eu3Om2p08ax8uZHL9WnUB2gnoEIi2WCe4srrbExHXXXQfAzJkzd6rt6ZOH6gSHHzhmFyOUuFEiEGmx0tBQV5VEcNddd+1S2weMSXPIuB4ef+ZFzn7Prg0vSfwoEYi0WD1DQxMmTNjl9qdPGssPnvoLhYLToTqB1EE1ApEWy2TzmEGqyk1fK1asYMWKFbvU/vRJ49jel+U3z7+8qyFKzKhHINJimWye7s5E1f2F58+fD8Bxxx230+2X6gSPP7ONN75+n10LUmJFiUCkxYr7FVfvjC9evHiX2z9o3x4O2jfN4xtf5Mx3TdrldiQ+lAhEWiyTzVedMQSw//77N/Qa0yeN46HfqE4g9VGNQKTFMrnq21QCLFu2jGXLlu3yaxw5eSx/7c3yuxe0j7HUph6BSItlsvmqU0cBvvzlLwMwa9asXXqNIyeX9ifYxpT9996lNiQ+lAhEWiyTzdesEdx9990NvcZB+6Z5/ZhuHtu4jY+/c2JDbcmeT4lApMVKs4aqGT9+fEOvYWYcOXkcP/rdFty96gwlEdUIRFqsnllDS5cuZenSpQ29zvTJY9n6ygC/36I6gVTX1ERgZseZ2QYze9rMLilzvMvMlgTHHzezic2MR6QdZLJ50qnqPYKbbrqJm266qaHXKe1j/NjGFxtqR/Z8TRsaMrMEcAtwLLAZWGNm97n7r4ad9gngr+5+mJnNBr4InNqsmESiks0X+Ftvlr/2DvBSJltzaOjee+9t+DUPGdfD/vt08+jGbXxs+sEaHpKKmlkjeAfwtLtvBDCzxcBJwPBEcBJwdfD13cDNZmbu7pUafWbrq3z0jseaE7FISNyLG9D8tXeAF18d4OVMbofjY/dKVb1+zJjGVw4t1gnG8h/r/szyXz7HXqlO0qkEe6US9KQ66UklSCY6UH6QZiaCA4FNwx5vBqZXOsfdc2a2HRgHbB1+kpnNBeYC7HXAoWTzhWbFLBKavbs7OWRcD/v2pNi3J8XYvZLsu1eKsT0p3nrIvlWvXbJkCQCnntpYB/nCf5zClP33oXcgx6v9efqyxf97B/K82p8jV9Dvkuwms4bcfQGwAGDatGn+7586KuKIRJrrtttuAxpPBBPG9nDuDG1bKWDnVj7WzETwLDB8Ld2DgufKnbPZzDqBMcC2JsYksltYvnx51CFIjDRz1tAa4A1mNsnMUsBs4L4R59wH/I/g6w8DD1WrD4jERU9PDz09PVGHITHRtB5BMOZ/PvADIAHc6e5Pmdm1wFp3vw/4OnCXmT0NvEgxWYjE3qJFiwCYM2dOxJFIHNju9gF82rRpvnbt2qjDEGmqGTNmALBq1apI45A9h5k94e7Tyh3bLYrFInHzwAMPRB2CxIgSgUgbSiaTUYcgMaK1hkTa0MKFC1m4cGHUYUhMKBGItCElAmml3a5YbGZbgD9GHQcwnhF3QMeY3oshei+G6L0Y0g7vxSHuvl+5A7tdImgXZra2UgU+bvReDNF7MUTvxZB2fy80NCQiEnNKBCIiMadEsOsWRB1AG9F7MUTvxRC9F0Pa+r1QjUBEJObUIxARiTklAhGRmFMiaJCZXWhmbmbjo44lKmb2JTP7jZn9wsy+a2avjTqmVjOz48xsg5k9bWaXRB1PVMxsgpk9bGa/MrOnzOyCqGOKmpklzOxnZva9qGOpRImgAWY2AfhH4E9RxxKxB4DD3f0I4LfApRHH01JmlgBuAY4H3gicZmZvjDaqyOSAC939jcCRwHkxfi9KLgB+HXUQ1SgRNOb/Ap8FYl1xd/cfuntpd/bHKO5GFyfvAJ52943uPgAsBk6KOKZIuPtz7v5k8PXLFP8AHhhtVNExs4OADwBfizqWapQIdpGZnQQ86+4/jzqWNnMWcH/UQbTYgcCmYY83E+M/fiVmNhF4C/B4tJFE6gaKHxYLUQdSjZahrsLMVgL7lzn0L8BlFIeFYqHae+Hu9wbn/AvFoYFvtzI2aT9m9hrgHmCeu78UdTxRMLMPAi+4+xNmNiPqeKpRIqjC3WeWe97M3gRMAn5uZlAcCnnSzN7h7s+3MMSWqfRelJjZGcAHgWNiuO/0s8CEYY8PCp6LJTNLUkwC33b3pVHHE6F3ASea2QlAN7CPmS1y97bbf1Q3lIXAzP4ATHP3qFcXjISZHQd8BTja3bdEHU+rmVknxSL5MRQTwBrgo+7+VKSBRcCKn4y+Cbzo7vOijqddBD2Ci9z9g1HHUo5qBBKGm4G9gQfMbJ2ZfTXqgFopKJSfD/yAYnH0O3FMAoF3AacD7wt+FtYFn4iljalHICISc+oRiIjEnBKBiEjMKRGIiMScEoGISMwpEYiIxJwSgYhIzCkRyB7HzGbsypK/ZtZhZjeZ2Xoz+6WZrTGzSWb2eDAf/k9mtmXY/HbZ0bgAAANcSURBVPiJwXVTg6XIjwsejxt2zvNm9uywxykz+0PQ/jozWzsihs7gNeaH8V4EbV5tZheF1Z7sebTEhMiQU4HXA0e4eyFYOfJVd58Og8toTHP380dcdxrwk+D/Fe6+DZgaXHM18Iq7X186OViW5B8q3Il+LMW7lD9iZpfWu1yHmSXcPV/3dyoyjHoEstsxs73M7Ptm9vPg0/upwcYwvzGzJ4GTa1x/tZndZWaPmtnvzOyTwaEDgOfcvQDg7pvd/a812jLgI8AZwLFm1t3gt3cacCPFPS7eWeO1/2BmXwy+54+Y2aFmtsLMnjCzH5vZf2kwFokJ9Qhkd3Qc8Gd3/wCAmY0B1gPvA54GltTRxhEUN07ZC/iZmX0f+A7wEzN7D/AgsMjdf1ajnaOAZ9z992a2iuLa8/fUuMaBH5qZA7e7+4Lg++gGZgLnAK+lmBRW12hrm7u/Nbj+QeBT7v47M5sO3ErxPRGpSj0C2R39kuKn7y8Gf7QnUfxj/LtgKGVRHW3c6+59wfDMw8A73H0zMIXiDmsF4EEzO6ZGO6dR3IiG4P/T6njtdwd/vI+nuIPXe4PnPwg87O59FJPJh4Ldz6pZAoPLPh8F/LuZrQNup9jDEalJPQLZ7bj7b83srcAJwHUUP73vdDPlHrt7P8WNde43s78AH6rUfvBH+hTgpGAvBgPGmdnewe5cleJ/Nvj/BTP7LsUdzn5EMYm8O1jNFmAcxU/0D1T5Pl4N/u8A/ubuU6ucK1KWegSy2zGz1wO97r4I+BLFT8ITzezQ4JR6PpWfZGbdZjYOmAGsMbO3Bm1jZh0Uh4/+WKWNY4BfuPsEd5/o7odQ/CT/36vEvpeZ7V36muLmRuvNbB/gPcDBQVsTgfPq/F4INn95xsw+ErRtZvbmeq4VUSKQ3dGbgJ8GQyBXAZcDc4HvB4XTF+po4xcUh4QeAz7n7n8G/g5YZmbrg+M5iktsV3Ia8N0Rz91D9T/er6NYh/g58FPg++6+gmLyeCjokZTcC8wys646vh+AjwGfCNp+ipjumyw7T8tQS+yUm9IpEmfqEYiIxJyKxbLHMrMzgQtGPP3/3f28KOLZVUFBedKIp/+3u/8ginhkz6OhIRGRmNPQkIhIzCkRiIjEnBKBiEjMKRGIiMTcfwLo4cFg447CWgAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%%script false --no-raise-error\n", "# Uncomment this line to run cell\n", "\n", "h = pyabc.History(\"sqlite:///tmp/petab_amici_boehm.db\", _id=1)\n", "refval = {k: v for k,v in zip(petab_problem.x_free_ids, petab_problem.x_nominal_free_scaled)}\n", "for i, par in enumerate(petab_problem.x_free_ids):\n", " pyabc.visualization.plot_kde_1d_highlevel(\n", " h, x=par,\n", " xmin=petab_problem.get_lb(scaled=True,fixed=False)[i],\n", " xmax=petab_problem.get_ub(scaled=True,fixed=False)[i],\n", " refval=refval, refval_color='k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apparently, in this case seven out of the nine parameters can be estimated with high confidence, while two other parameters can only be bounded." ] } ], "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }