{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "d83c77f5-754b-4d44-9f9e-003d53b2c928", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import arviz\n", "import x3cflux\n", "import hopsy\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "13dd0ae2", "metadata": {}, "source": [ "# Quickstart: the Spiralus Model\n", "\n", "This notebook explains the high-level workflow for performing parameter estimation with `13CFLUX`. \n", "It assumes a model file is already available that defines the network structure, atom transitions, and isotope-labeling experimental details (for example, extracellular rates). For model-specification details, see the [libFluxML documentation](https://github.com/modsim/FluxML), the [FluxML paper](https://doi.org/10.3389/fmicb.2019.01022), or the [Data Objects](../api/data_objects.rst) section of this documentation.\n", "\n", "Here we us a simple toy model called the *Spiralus* from the [Primer to 13C Metabolic Flux Analysis](https://doi.org/10.1002/9783527697441.ch05).\n", "\n", "The input **A** has two carbon atoms which, of which the first position is $^{13}$C labeled, and we have a measurement of the uptake rate *u* as well as an MS measurement of **H**. You can download the model from [this location](https://jugit.fz-juelich.de/IBG-1/ModSim/Fluxomics/13CFLUX/-/blob/main/docs/source/examples/spiralus.fml).\n", "\n", "![Spiralus network image](../_static/images/spiralus.png)" ] }, { "cell_type": "markdown", "id": "f1dd93b2", "metadata": {}, "source": [ "## Building a simulator\n", "\n", "We start by building a **simulator object**, which is the central piece of `13CFLUX`. If you know the name of the configuration, you can directly create the object. Exper users may want to specify the argument `sim_method`, which describes the type of simulation variables used (`cumomer`, `emu`). By entering the default `auto`, the method with less initial unknowns is chosen." ] }, { "cell_type": "code", "execution_count": 2, "id": "4e182cd1-cb34-40ac-be5c-9470f8f5e922", "metadata": {}, "outputs": [], "source": [ "simulator = x3cflux.create_simulator_from_fml(\"spiralus.fml\", \"ms_STAT\", sim_method=\"auto\")" ] }, { "cell_type": "markdown", "id": "079c3ee6-3923-430a-b2c6-27c6ba08ac03", "metadata": {}, "source": [ "Alternatively, if the name of the configuration is not known we can print a list, and select the one we would like to choose" ] }, { "cell_type": "code", "execution_count": 3, "id": "545295cc-9de3-42bf-98d7-021ef1f12f7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ms_INST', 'ms_STAT']\n" ] } ], "source": [ "data = x3cflux.FluxMLParser().parse(\"spiralus.fml\")\n", "print([config.name for config in data.configurations])" ] }, { "cell_type": "markdown", "id": "13a404b0", "metadata": {}, "source": [ "select configuration and create the simulator object" ] }, { "cell_type": "code", "execution_count": 4, "id": "13807ba7", "metadata": {}, "outputs": [], "source": [ "config = data.configurations[1]\n", "simulator = x3cflux.create_simulator_from_data(data.network_data, config, sim_method=\"auto\")" ] }, { "cell_type": "markdown", "id": "55413d17", "metadata": {}, "source": [ "The simulator object automatically computes free/dependent parameters, reduces the labeling states by backtracing and pre-computes template labeling systems. Free parameters can be accessed by `simulator.parameter_space.free_parameter_names`. The order of free parameters is alway netto fluxes, exchange fluxes and pool sizes (for isotopically non-stationary only)." ] }, { "cell_type": "code", "execution_count": 5, "id": "698e1f35", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['p.n', 'u.n']\n" ] } ], "source": [ "print(simulator.parameter_space.free_parameter_names)" ] }, { "cell_type": "markdown", "id": "7111b606", "metadata": {}, "source": [ "`FluxMLData` objects can be used to store one parameter configuration (e.g. a suitable starting point for numerical computation). It can be retrieved from the parameter entries of a measurement configuration." ] }, { "cell_type": "code", "execution_count": 6, "id": "399a5f31", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.4 1. ]\n" ] } ], "source": [ "params = x3cflux.get_parameters(simulator.parameter_space, simulator.configurations[0].parameter_entries)\n", "print(params)" ] }, { "cell_type": "markdown", "id": "6bcc9021-323a-49ce-be27-4b16ba4f1694", "metadata": {}, "source": [ "## Accessing parameter inequality constraints\n", "\n", "Linear inequality constraints $\\mathbf{A} \\cdot \\mathbf{\\theta} \\leq \\mathbf{b}$ can are accessible through the parameter space object." ] }, { "cell_type": "code", "execution_count": 7, "id": "87ff0113-1527-4062-b116-ef1a181709b4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-1. 0.]\n", " [ 0. -1.]\n", " [ 0. 1.]\n", " [ 1. -1.]\n", " [ 1. -1.]\n", " [-1. 1.]\n", " [ 1. -1.]]\n", "[0. 0. 5. 0. 0. 5. 0.]\n" ] } ], "source": [ "ineq_sys = simulator.parameter_space.inequality_system\n", "print(ineq_sys.matrix)\n", "print(ineq_sys.bound)" ] }, { "cell_type": "markdown", "id": "a1f517ce", "metadata": {}, "source": [ "## Forward simulation\n", "\n", "Residuals to observed data can be evaluated via the `compute_loss` function. The simulator object also provides functions to \n", "compute the gradients (e.g. `compute_loss_gradient`) Jacobians and the approximate Hessian matrix (see documentation)." ] }, { "cell_type": "code", "execution_count": 8, "id": "07827948-193a-46ad-a57c-d2e5df198dc9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "at [0.4 1. ] loss : 1.31e-28 gradient [-1.33226763e-12 5.32907052e-13]\n", "at [0.5, 1] loss : 1.62e+02 gradient [ 3600. -1800.]\n", "at [0.8, 1] loss : 4.61e+03 gradient [ 30720. -24576.]\n", "at [0.6, 2] loss : 4.98e+02 gradient [-840. 1052.]\n" ] } ], "source": [ "for p in [params, [0.5, 1], [0.8, 1], [0.6, 2]]:\n", " loss = simulator.compute_loss(p)\n", " gradient = simulator.compute_loss_gradient(p)\n", " print(f\"at {p} loss : {loss:4.2e} gradient {gradient}\")" ] }, { "cell_type": "markdown", "id": "be99e3a4-9b06-4ef2-9093-8ab445e82a8e", "metadata": {}, "source": [ "simulated measurements and their Jacobian can be calculated using `compute_measurements` and `compute_jacobian`" ] }, { "cell_type": "code", "execution_count": 9, "id": "a021edae-b1b1-4729-8819-c226f96804a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "simulated measurements at [0.4 1. ]: ([array([-0. , 0.84, 0.16])], [1.0])\n", "Jacobian:\n", " [[-0. -0. ]\n", " [-0.8 0.32]\n", " [ 0.8 -0.32]\n", " [ 0. 1. ]]\n" ] } ], "source": [ "print(f\"simulated measurements at {params}: {simulator.compute_measurements(params)}\")\n", "print(f\"Jacobian:\\n {simulator.compute_jacobian(params)}\")" ] }, { "cell_type": "markdown", "id": "d7c201c3-8a53-4d5f-bb59-3020e1102d1e", "metadata": {}, "source": [ "The order of measurements is always Labeling measurements, flux measurements and pool size measurements.\n", "Here we compar the simulated measurements to the ones reported in the configuration." ] }, { "cell_type": "code", "execution_count": 10, "id": "16d21aec-4820-4253-b9ed-194b6b80eab8", "metadata": {}, "outputs": [], "source": [ "def compare_meas(simulator, ps):\n", " names = simulator.measurement_names[0]\n", " real_meas = simulator.measurement_data[0]\n", " real_meas_stddev = simulator.measurement_standard_deviations[0]\n", " sim_meas = simulator.compute_measurements(ps)[0]\n", " width = 0.35 # width of the bars\n", "\n", " for m in range(len(names)):\n", " n = len(real_meas[m])\n", " x = np.arange(n) # the label locations\n", "\n", " fig, ax = plt.subplots(figsize=(8, 4))\n", " ax.bar(x - width/2, real_meas[m], width, label='Real', yerr=real_meas_stddev[m], capsize=5, color='tab:blue')\n", " ax.bar(x + width/2, sim_meas[m], width, label='Sim', color='tab:orange')\n", "\n", " ax.set_xlabel('Mass shift')\n", " ax.set_ylabel('Fractonal labeling enrichment')\n", " ax.set_title(f'{names[m]}')\n", " ax.set_xticks(x)\n", " plt.ylim([0, 1])\n", " ax.set_xticklabels([f\"M+{i}\" for i in range(n)])\n", " ax.legend()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "id": "f934f933-5aeb-4cb0-bd43-258d32340a35", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simulated measurements at [0.5, 1]. Residual 1.6e+02\n" ] }, { "data": { "image/png": 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T1bhxY5UuXVrvv/++9uzZU7gfQoRZAACA+56Li4vGjBmjyMhIHTlyRG5uboqPj1eLFi3y7P/999+rSZMmGjhwoLnt2LFjhVWuBXYzAAAAgDp37ixXV1ctWLBAw4cP17Bhw7R8+XIdO3ZMBw4c0Jw5c8x709aoUUP79+/XV199pSNHjmjs2LHat2+fQ+pmZhYAAMDGjD7EoCgoVqyYIiIi9N577+nEiRPy9fXV1KlTdfz4cfn4+KhevXoaM2aMJOnVV1/VDz/8oC5dushkMqlbt24aOHCgvvzyy0Kv25STk5NT6Fd1oLS0NHl7eys1NVVeXl6OLgcAADip9PR0nThxQtWqVXO6p2YVBbf7/RnJaywzAAAAgNMizAIAAMBpEWYBAADgtAizAAAAcFqEWQAAADgtwiwAAACcFmEWAAAAToswCwAAAKdFmAUAAIDT4nG2AAAAtjbBu5Cvl2rzU5pMJm3YsEEdO3a0+bltiZlZAACA+9CFCxc0YMAAValSRW5ubvL391ebNm30/fffS5ISEhL05JNPOrjKO2NmFgAA4D7UqVMnZWZmavny5QoODlZiYqJiYmJ08eJFSZK/v7+DK7QOM7MAAAD3mZSUFO3cuVPvvvuuWrVqpapVq6phw4YaPXq0OnToIOnmMoMvvvhCknTy5EmZTCatWbNGzZs3l4eHhx599FEdOXJE+/btU4MGDeTp6aknn3xSFy5cKNTPQpgFAAC4z3h6esrT01NffPGFMjIyrB43fvx4vfXWWzpw4ICKFSumF198USNHjtSHH36onTt36ujRoxo3bpwdK8+NMAsAAHCfKVasmJYtW6bly5fLx8dHTZs21ZgxY/TTTz/ddtzw4cPVpk0bPfTQQxo6dKji4uI0duxYNW3aVHXr1tVLL72k7du3F9KnuIkwCwAAcB/q1KmTzp49q40bN6pt27basWOH6tWrp2XLluU7pk6dOuaf/fz8JEmPPPKIRdv58+ftVnNeCLMAAAD3KXd3dz3++OMaO3asdu/erT59+mj8+PH59i9evLj5Z5PJlGdbdna2/QrOA2EWAAAAkqRatWrpypUrji7DELbmAgAAuM9cvHhRnTt3Vt++fVWnTh2VLl1a+/fv13vvvadnnnnG0eUZQpgFAACwNTs8kcuWPD091ahRI82cOVPHjh3T9evXFRgYqH79+mnMmDGOLs8QU05OTo6jiyhMaWlp8vb2Vmpqqry8vBxdDoAiLCEhQQkJCYbHBQQEKCAgwA4VAShK0tPTdeLECVWrVk3u7u6OLsfp3O73ZySvMTMLAPlYsGCBJk6caHjc+PHjNWHCBNsXBADIhTALAPl49dVXzU/CueXatWtq1qyZJGnXrl3y8PDINY5ZWQAoPIRZAMhHXssF/nqXb2hoqEqVKlXYZQEA/oIwC6DICxq1xdElmGVnppt/fmjsNrmUKBrr5E5Oa+foEgDAIdhnFgAA4C7cZ/fS24ytfm+GZ2ZXrFihLl26yM3NzaI9MzNTq1evVq9evWxSGAA42o3Lycq6nGzRlnM90/xzZuJxmYqXyDXO1bOsinmWtXt9ABzr1pOvrl69muf6edxeZubNf09dXV3v6jyGt+ZydXVVQkKCKlSoYNF+8eJFVahQQVlZWXdVkL2xNRfgfBy1zCBl10qlfv9Pw+O8m3aTT7PudqgofywzABwjISFBKSkpqlChgkqWLGl+xCtuLzs7W2fPnlXx4sVVpUqVXL83u27NlZOTk+d/qNOnT8vb29vo6QCgyPIMfVIe1RsZHufKrCxw3/D395cknT9/3sGVOB8XF5c8g6xRVofZunXrymQyyWQyqXXr1ipW7L9Ds7KydOLECbVt2/auigGAoqQYywUA3IHJZFJAQIAqVKig69evO7ocp1KiRAm5uNz97VtWh9mOHTtKkg4ePKg2bdrI09PTopigoCB16tTprgsCAABwNq6urne99hMFY3WYHT9+vCQpKChIXbp04bFtAAAAcDjDc7u9e/eWu7u7MjMzdfr0acXHx1u8jJo3b56CgoLk7u6uRo0aae/evbftP2vWLD344IPy8PBQYGCghg0bpvT09NuOAQAAwL3J8A1gv//+u/r27avdu3dbtN+6MczIbgafffaZIiMjFRUVpUaNGmnWrFlq06aNDh8+nGu3BElatWqVRo0apSVLlqhJkyY6cuSI+vTpI5PJpBkzZhj9KAAAAHByhsNsnz59VKxYMW3evFkBAQF3dQfajBkz1K9fP4WHh0uSoqKitGXLFi1ZskSjRo3K1X/37t1q2rSpXnzxRUk3lzx069ZNe/bsKXANAAAAcF6Gw+zBgwcVFxenmjVr3tWFMzMzFRcXp9GjR5vbXFxcFBYWptjY2DzHNGnSRJ9++qn27t2rhg0b6vjx49q6dat69uyZ73UyMjKUkZFhfp+WlnZXdQMAAKDoMBxma9WqpaSkpLu+cFJSkrKysuTn52fR7ufnp0OHDuU55sUXX1RSUpKaNWumnJwc3bhxQ/3799eYMWPyvc7UqVM1ceLEu64XAAAARY/hG8DeffddjRw5Ujt27NDFixeVlpZm8bKnHTt2aMqUKfroo4904MABrV+/Xlu2bNHbb7+d75jRo0crNTXV/Dp16pRdawQAAEDhMTwzGxYWJklq3bq1RbvRG8DKly8vV1dXJSYmWrQnJiaan6bxv8aOHauePXvq5ZdfliQ98sgjunLlil555RW9+eabeW686+bmJjc3N6tqAgAAgHMxHGa3b99ukwuXKFFC9evXV0xMjPmBDNnZ2YqJiVFERESeY65evZorsN7aoDgnJ8cmdQEAAMB5GA6zLVq0sNnFIyMj1bt3bzVo0EANGzbUrFmzdOXKFfPuBr169VKlSpU0depUSVL79u01Y8YM1a1bV40aNdLRo0c1duxYtW/fnqduAAAA3IcMh1lJ2rlzpxYsWKDjx4/r888/V6VKlfTJJ5+oWrVqatasmdXn6dKliy5cuKBx48bp3LlzCg0N1bZt28w3hcXHx1vMxL711lsymUx66623dObMGfn6+qp9+/Z65513CvIxAAAA4ORMOQb/Pr9u3Tr17NlT3bt31yeffKJff/1VwcHBmjt3rrZu3aqtW7faq1abSEtLk7e3t1JTU+Xl5eXocgBYIWjUFkeXUOSdnNbO0SUAgM0YyWuGdzOYPHmyoqKitGjRIhUvXtzc3rRpUx04cMB4tQAAAEABGQ6zhw8f1mOPPZar3dvbWykpKbaoCQAAALCK4TDr7++vo0eP5mrftWuXgoODbVIUAAAAYA3DYbZfv34aOnSo9uzZI5PJpLNnz2rlypUaPny4BgwYYI8aAQAAgDwZ3s1g1KhRys7OVuvWrXX16lU99thjcnNz0/DhwzV48GB71AgAAADkyXCYNZlMevPNNzVixAgdPXpUly9fVq1ateTp6WmP+gAAAIB8FWifWenmE7xq1aply1oAAAAAQwyH2fT0dM2ZM0fbt2/X+fPnlZ2dbXGc7bkAAABQWAyH2Zdeeklff/21nn/+eTVs2FAmk8kedQEAAAB3ZDjMbt68WVu3blXTpk3tUQ8AoCAmeDu6gqJvQqqjKwBgB4a35qpUqZJKly5tj1oAAAAAQwyH2enTp+uNN97QH3/8YY96AAAAAKsZXmbQoEEDpaenKzg4WCVLllTx4sUtjicnJ9usOAAAAOB2DIfZbt266cyZM5oyZYr8/Py4AQwAAAAOYzjM7t69W7GxsQoJCbFHPQAAAIDVDK+ZrVmzpq5du2aPWgAAAABDDIfZadOm6fXXX9eOHTt08eJFpaWlWbwAAACAwmJ4mUHbtm0lSa1bt7Zoz8nJkclkUlZWlm0qAwAAAO7AcJjdvn27PeoAAAAADDMcZlu0aGGPOgAAAADDDIdZSUpJSdHevXt1/vx5ZWdnWxzr1auXTQoDAAAA7sRwmN20aZO6d++uy5cvy8vLy2KfWZPJRJgFAABAoTG8m8Hrr7+uvn376vLly0pJSdGff/5pfvH0LwAAABQmw2H2zJkzGjJkiEqWLGmPegAAAACrGQ6zbdq00f79++1RCwAAAGCIVWtmN27caP65Xbt2GjFihH799Vc98sgjKl68uEXfDh062LZCAAAAIB9WhdmOHTvmaps0aVKuNh6aAAAAgMJkVZj93+23AAAAgKLA8JpZAAAAoKgwHGaHDBmi2bNn52qfO3euXnvtNVvUBAAAAFjFcJhdt26dmjZtmqu9SZMmWrt2rU2KAgAAAKxhOMxevHhR3t7eudq9vLyUlJRkk6IAAAAAaxgOs9WrV9e2bdtytX/55ZcKDg62SVEAAACANazazeCvIiMjFRERoQsXLugf//iHJCkmJkbTp0/XrFmzbF0fAAAAkC/DYbZv377KyMjQO++8o7fffluSFBQUpPnz56tXr142LxAAAADIj+EwK0kDBgzQgAEDdOHCBXl4eMjT09PWdQEAAAB3VKAwe4uvr6+t6gAAAAAMsyrM1qtXTzExMSpTpozq1q0rk8mUb98DBw7YrDgAAADgdqwKs88884zc3NwkSR07drRnPQAAAIDVrAqz48ePlyRlZWWpVatWqlOnjnx8fOxZFwAAAHBHhvaZdXV11RNPPKE///zTXvUAAAAAVjP80ITatWvr+PHj9qgFAAAAMMRwmJ08ebKGDx+uzZs3KyEhQWlpaRYvAAAAoLAY3prrqaeekiR16NDBYleDnJwcmUwmZWVl2a46AAAA4DYMh9nt27fbow4AAADAMMNhtkWLFvaoAwAAADCsQE8AS0lJ0d69e3X+/HllZ2dbHOvVq5dNCgMAAADuxHCY3bRpk7p3767Lly/Ly8vLYt2syWQizAIAAKDQGN7N4PXXX1ffvn11+fJlpaSk6M8//zS/kpOT7VEjAAAAkCfDYfbMmTMaMmSISpYsaY96AAAAAKsZDrNt2rTR/v377VELAAAAYIjhMNuuXTuNGDFCEyZM0Lp167Rx40aLl1Hz5s1TUFCQ3N3d1ahRI+3du/e2/VNSUjRo0CAFBATIzc1NDzzwgLZu3Wr4ugAAAHB+hm8A69evnyRp0qRJuY4ZfWjCZ599psjISEVFRalRo0aaNWuW2rRpo8OHD6tChQq5+mdmZurxxx9XhQoVtHbtWlWqVEl//PGHfHx8jH4MAAAA3AMMh9n/3YrrbsyYMUP9+vVTeHi4JCkqKkpbtmzRkiVLNGrUqFz9lyxZouTkZO3evVvFixeXJAUFBdmsHgAAADgXw8sM/io9Pb3AYzMzMxUXF6ewsLD/FuPiorCwMMXGxuY5ZuPGjWrcuLEGDRokPz8/1a5dW1OmTLntbHBGRobS0tIsXgAAALg3GA6zWVlZevvtt1WpUiV5enrq+PHjkqSxY8fq448/tvo8SUlJysrKkp+fn0W7n5+fzp07l+eY48ePa+3atcrKytLWrVs1duxYTZ8+XZMnT873OlOnTpW3t7f5FRgYaHWNAAAAKNoMh9l33nlHy5Yt03vvvacSJUqY22vXrq3FixfbtLj/lZ2drQoVKmjhwoWqX7++unTpojfffFNRUVH5jhk9erRSU1PNr1OnTtm1RgAAABQew2F2xYoVWrhwobp37y5XV1dze0hIiA4dOmT1ecqXLy9XV1clJiZatCcmJsrf3z/PMQEBAXrggQcsrvvQQw/p3LlzyszMzHOMm5ubvLy8LF4AAAC4NxTooQnVq1fP1Z6dna3r169bfZ4SJUqofv36iomJsThHTEyMGjdunOeYpk2b6ujRoxY3oR05ckQBAQEWs8QAAAC4PxgOs7Vq1dLOnTtzta9du1Z169Y1dK7IyEgtWrRIy5cv12+//aYBAwboypUr5t0NevXqpdGjR5v7DxgwQMnJyRo6dKiOHDmiLVu2aMqUKRo0aJDRjwEAAIB7gOGtucaNG6fevXvrzJkzys7O1vr163X48GGtWLFCmzdvNnSuLl266MKFCxo3bpzOnTun0NBQbdu2zXxTWHx8vFxc/pu3AwMD9dVXX2nYsGGqU6eOKlWqpKFDh+qNN94w+jEAAABwDzDl5OTkGB20c+dOTZo0ST/++KMuX76sevXqady4cXriiSfsUaNNpaWlydvbW6mpqayfBZxE0Kgtji6hyDvp/qKjSyj6JqQ6ugIAVjKS1wzPzEpS8+bNFR0dXaDiAAAAAFu5q4cmAAAAAI5EmAUAAIDTIswCAADAaRFmAQAA4LQIswAAAHBahncziIyMzLPdZDLJ3d1d1atX1zPPPKOyZcvedXEAAADA7RgOsz/88IMOHDigrKwsPfjgg5JuPlLW1dVVNWvW1EcffaTXX39du3btUq1atWxeMAAAAHCL4WUGzzzzjMLCwnT27FnFxcUpLi5Op0+f1uOPP65u3brpzJkzeuyxxzRs2DB71AsAAACYGX4CWKVKlRQdHZ1r1vWXX37RE088oTNnzujAgQN64oknlJSUZNNibYEngAHOhyeA3RlPALMCTwADnIaRvGZ4ZjY1NVXnz5/P1X7hwgWlpaVJknx8fJSZmWn01AAAAIAhBVpm0LdvX23YsEGnT5/W6dOntWHDBr300kvq2LGjJGnv3r164IEHbF0rAAAAYMHwDWALFizQsGHD1LVrV924cePmSYoVU+/evTVz5kxJUs2aNbV48WLbVgoAAAD8D8Nh1tPTU4sWLdLMmTN1/PhxSVJwcLA8PT3NfUJDQ21WIAAAAJAfw2H2Fk9PT9WpU8eWtQAAAACGGA6zV65c0bRp0xQTE6Pz588rOzvb4vit2VoAAADA3gyH2Zdffln//ve/1bNnTwUEBMhkMtmjLgAAAOCODIfZL7/8Ulu2bFHTpk3tUQ8AAABgNcNbc5UpU0Zly5a1Ry0AAACAIYbD7Ntvv61x48bp6tWr9qgHAAAAsJrhZQbTp0/XsWPH5Ofnp6CgIBUvXtzi+IEDB2xWHAAAAHA7hsPsrad8AQAAAI5mOMyOHz/eHnUAAAAAhhleMwsAAAAUFVbNzJYtW1ZHjhxR+fLlVaZMmdvuLZucnGyz4gAAAIDbsSrMzpw5U6VLl5YkzZo1y571AAAAAFazKsz27t07z58BAAAAR7IqzKalpVl9Qi8vrwIXAwAAABhhVZj18fG57TpZScrJyZHJZFJWVpZNCgMAAADuxKowu337dnvXAQAAABhmVZht0aKFvesAAAAADCvQPrM7d+5Ujx491KRJE505c0aS9Mknn2jXrl02LQ4AAAC4HcNhdt26dWrTpo08PDx04MABZWRkSJJSU1M1ZcoUmxcIAAAA5MdwmJ08ebKioqK0aNEiFS9e3NzetGlTHThwwKbFAQAAALdjOMwePnxYjz32WK52b29vpaSk2KImAAAAwCqGw6y/v7+OHj2aq33Xrl0KDg62SVEAAACANQyH2X79+mno0KHas2ePTCaTzp49q5UrV2r48OEaMGCAPWoEAAAA8mTV1lx/NWrUKGVnZ6t169a6evWqHnvsMbm5uWn48OEaPHiwPWoEAAAA8mQ4zJpMJr355psaMWKEjh49qsuXL6tWrVry9PS0R30AAABAvgyH2VtKlCih0qVLq3Tp0gRZAAAAOIThNbM3btzQ2LFj5e3traCgIAUFBcnb21tvvfWWrl+/bo8aAQAAgDwZnpkdPHiw1q9fr/fee0+NGzeWJMXGxmrChAm6ePGi5s+fb/MiAQAAgLwYDrOrVq3S6tWr9eSTT5rb6tSpo8DAQHXr1o0wCwAAgEJjeJmBm5ubgoKCcrVXq1ZNJUqUsEVNAAAAgFUMh9mIiAi9/fbbysjIMLdlZGTonXfeUUREhE2LAwAAAG7HqmUGzz33nMX7b775RpUrV1ZISIgk6ccff1RmZqZat25t+woBAACAfFgVZr29vS3ed+rUyeJ9YGCg7SoCAAAArGRVmF26dKm96wAAAAAMM7xmFgAAACgqCvQEsLVr12rNmjWKj49XZmamxbEDBw7YpDAAAADgTgzPzM6ePVvh4eHy8/PTDz/8oIYNG6pcuXI6fvy4xd6zAAAAgL0ZDrMfffSRFi5cqDlz5qhEiRIaOXKkoqOjNWTIEKWmphaoiHnz5ikoKEju7u5q1KiR9u7da9W41atXy2QyqWPHjgW6LgAAAJyb4TAbHx+vJk2aSJI8PDx06dIlSVLPnj31z3/+03ABn332mSIjIzV+/HgdOHBAISEhatOmjc6fP3/bcSdPntTw4cPVvHlzw9cEAADAvcFwmPX391dycrIkqUqVKvq///s/SdKJEyeUk5NjuIAZM2aoX79+Cg8PV61atRQVFaWSJUtqyZIl+Y7JyspS9+7dNXHiRAUHBxu+JgAAAO4NhsPsP/7xD23cuFGSFB4ermHDhunxxx9Xly5d9Oyzzxo6V2ZmpuLi4hQWFvbfglxcFBYWptjY2HzHTZo0SRUqVNBLL710x2tkZGQoLS3N4gUAAIB7g+HdDBYuXKjs7GxJ0qBBg1SuXDnt3r1bHTp00KuvvmroXElJScrKypKfn59Fu5+fnw4dOpTnmF27dunjjz/WwYMHrbrG1KlTNXHiREN1AQAAwDkYDrMuLi5ycfnvhG7Xrl3VtWtXmxaVn0uXLqlnz55atGiRypcvb9WY0aNHKzIy0vw+LS2NJ5YBAADcI6wKsz/99JPVJ6xTp47VfcuXLy9XV1clJiZatCcmJsrf3z9X/2PHjunkyZNq3769ue3WLHGxYsV0+PBh/e1vf7MY4+bmJjc3N6trAgAAgPOwKsyGhobKZDLd8QYvk8mkrKwsqy9eokQJ1a9fXzExMebttbKzsxUTE6OIiIhc/WvWrKn//Oc/Fm1vvfWWLl26pA8//JAZVwAAgPuMVWH2xIkTdisgMjJSvXv3VoMGDdSwYUPNmjVLV65cUXh4uCSpV69eqlSpkqZOnSp3d3fVrl3bYryPj48k5WoHAADAvc+qMFu1alW7FdClSxdduHBB48aN07lz5xQaGqpt27aZbwqLj4+3WKMLAAAA3GLKKcjmsE4sLS1N3t7eSk1NlZeXl6PLAWCFoFFbHF1CkXfS/UVHl1D0TSjYUyoBFD4jeY0pTwAAADgtwiwAAACcFmEWAAAAToswCwAAAKdl1W4GZcqUkclksuqEycnJd1UQAAAAYC2rwuysWbPsXAYAAABgnFVhtnfv3vauAwAAADDMqjCbn/T0dGVmZlq0sXcrAAAACovhG8CuXLmiiIgIVahQQaVKlVKZMmUsXgAAAEBhMRxmR44cqW+//Vbz58+Xm5ubFi9erIkTJ6pixYpasWKFPWoEAAAA8mR4mcGmTZu0YsUKtWzZUuHh4WrevLmqV6+uqlWrauXKlerevbs96gQAAAByMTwzm5ycrODgYEk318fe2oqrWbNm+u6772xbHQAAAHAbhsNscHCwTpw4IUmqWbOm1qxZI+nmjK2Pj49NiwMAAABux3CYDQ8P148//ihJGjVqlObNmyd3d3cNGzZMI0aMsHmBAAAAQH4Mr5kdNmyY+eewsDAdOnRIcXFxql69uurUqWPT4gAAAIDbuat9ZiWpatWqqlq1qi1qAQAAAAwpUJiNiYlRTEyMzp8/r+zsbItjS5YssUlhAAAAwJ0YDrMTJ07UpEmT1KBBAwUEBMhkMtmjLgAAAOCODIfZqKgoLVu2TD179rRHPQAAAIDVDO9mkJmZqSZNmtijFgAAAMAQw2H25Zdf1qpVq+xRCwAAAGCI4WUG6enpWrhwob755hvVqVNHxYsXtzg+Y8YMmxUHAAAA3I7hMPvTTz8pNDRUkvTzzz9bHONmMAAAABQmw2F2+/bt9qgDAAAAMMzwmtm/On36tE6fPm2rWgAAAABDDIfZ7OxsTZo0Sd7e3uanf/n4+Ojtt9/O9QAFAAAAwJ4MLzN488039fHHH2vatGlq2rSpJGnXrl2aMGGC0tPT9c4779i8SAAAACAvhsPs8uXLtXjxYnXo0MHcVqdOHVWqVEkDBw4kzAIAAKDQGF5mkJycrJo1a+Zqr1mzppKTk21SFAAAAGANw2E2JCREc+fOzdU+d+5chYSE2KQoAAAAwBqGlxm89957ateunb755hs1btxYkhQbG6tTp05p69atNi8QAAAAyI/hmdkWLVroyJEjevbZZ5WSkqKUlBQ999xzOnz4sJo3b26PGgEAAIA8GZ6ZjY+PV2BgYJ43esXHx6tKlSo2KQwAAAC4E8Mzs9WqVdOFCxdytV+8eFHVqlWzSVEAAACANQyH2ZycHJlMplztly9flru7u02KAgAAAKxh9TKDyMhISZLJZNLYsWNVsmRJ87GsrCzt2bNHoaGhNi8QAAAAyI/VYfaHH36QdHNm9j//+Y9KlChhPlaiRAmFhIRo+PDhtq8QAAAAyIfVYXb79u2SpPDwcH344Yfy8vKyW1EAAACANQyvmZ01a5Zu3LiRqz05OVlpaWk2KQoAAACwhuGtubp27ar27dtr4MCBFu1r1qzRxo0beXACAABwPhO8HV1B0Tch1dEV5MnwzOyePXvUqlWrXO0tW7bUnj17bFIUAAAAYA3DYTYjIyPPZQbXr1/XtWvXbFIUAAAAYA3DYbZhw4ZauHBhrvaoqCjVr1/fJkUBAAAA1jC8Znby5MkKCwvTjz/+qNatW0uSYmJitG/fPn399dc2LxAAAADIj+GZ2aZNmyo2NlaBgYFas2aNNm3apOrVq+unn35S8+bN7VEjAAAAkCfDM7OSFBoaqpUrV9q6FgAAAMCQAoXZW9LT05WZmWnRxsMUAAAAUFgMLzO4evWqIiIiVKFCBZUqVUplypSxeAEAAACFxXCYHTFihL799lvNnz9fbm5uWrx4sSZOnKiKFStqxYoV9qgRAAAAyJPhZQabNm3SihUr1LJlS4WHh6t58+aqXr26qlatqpUrV6p79+72qBMAAADIxfDMbHJysoKDgyXdXB+bnJwsSWrWrJm+++67AhUxb948BQUFyd3dXY0aNdLevXvz7bto0SI1b97cvKwhLCzstv0BAABw7zIcZoODg3XixAlJUs2aNbVmzRpJN2dsfXx8DBfw2WefKTIyUuPHj9eBAwcUEhKiNm3a6Pz583n237Fjh7p166bt27ebtwh74okndObMGcPXBgAAgHMzHGbDw8P1448/SpJGjRqlefPmyd3dXcOGDdOIESMMFzBjxgz169dP4eHhqlWrlqKiolSyZEktWbIkz/4rV67UwIEDFRoaqpo1a2rx4sXKzs5WTEyM4WsDAADAuRleMzts2DDzz2FhYTp06JDi4uJUvXp11alTx9C5MjMzFRcXp9GjR5vbXFxcFBYWptjYWKvOcfXqVV2/fl1ly5bN83hGRoYyMjLM79PS0gzVCAAAgKLL0Mzs9evX1bp1a/3+++/mtqpVq+q5554zHGQlKSkpSVlZWfLz87No9/Pz07lz56w6xxtvvKGKFSsqLCwsz+NTp06Vt7e3+RUYGGi4TgAAABRNhsJs8eLF9dNPP9mrFsOmTZum1atXa8OGDXJ3d8+zz+jRo5Wammp+nTp1qpCrBAAAgL0YXjPbo0cPffzxxza5ePny5eXq6qrExESL9sTERPn7+9927AcffKBp06bp66+/vu2ssJubm7y8vCxeAAAAuDcYXjN748YNLVmyRN98843q16+vUqVKWRyfMWOG1ecqUaKE6tevr5iYGHXs2FGSzDdzRURE5Dvuvffe0zvvvKOvvvpKDRo0MPoRAAAAcI8wHGZ//vln1atXT5J05MgRi2Mmk8lwAZGRkerdu7caNGighg0batasWbpy5YrCw8MlSb169VKlSpU0depUSdK7776rcePGadWqVQoKCjKvrfX09JSnp6fh6wMAAMB5WR1mjx8/rmrVqmn79u02LaBLly66cOGCxo0bp3Pnzik0NFTbtm0z3xQWHx8vF5f/roaYP3++MjMz9fzzz1ucZ/z48ZowYYJNawMAAEDRZnWYrVGjhhISElShQgVJN0Po7Nmzc+1EUBARERH5LivYsWOHxfuTJ0/e9fUAAABwb7D6BrCcnByL91u3btWVK1dsXhAAAABgLcNrZgEAAKyRkJCghIQEw+MCAgIUEBBgh4pwL7I6zJpMplw3eBXkhi8AAHB/WLBggSZOnGh4HPfBwAirw2xOTo769OkjNzc3SVJ6err69++fa2uu9evX27ZCAADglF599VV16NDBou3atWtq1qyZJGnXrl3y8PDINY5ZWRhhdZjt3bu3xfsePXrYvBgAAHDvyGu5wF/vtwkNDc01KQYYZXWYXbp0qT3rAAAAdhI0aoujSzDLzkw3//zQ2G1yKZH34+gL28miUQYKwPDjbAEAAICigjALAAAAp8XWXAAAwC5uXE5W1uVki7ac65nmnzMTj8tUvESuca6eZVXMs6zd68O9gTALAADs4vLBL5X6/T/zPZ64amSe7d5Nu8mnWXd7lYV7DGEWAADYhWfok/Ko3sjwOFdmZWEAYRYAANhFMZYLoBBwAxgAAACcFmEWAAAAToswCwAAAKdFmAUAAIDTIswCAADAaRFmAQAA4LQIswAAAHBahFkAAAA4LcIsAAAAnBZhFgAAAE6LMAsAAACnRZgFAACA0yLMAgAAwGkRZgEAAOC0CLMAAABwWoRZAAAAOC3CLAAAAJwWYRYAAABOizALAAAAp0WYBQAAgNMizAIAAMBpEWYBAADgtAizAAAAcFqEWQAAADgtwiwAAACcFmEWAAAAToswCwAAAKdFmAUAAIDTIswCAADAaRFmAQAA4LQIswAAAHBahFkAAAA4LcIsAAAAnBZhFgAAAE6LMAsAAACnRZgFAACA0yLMAgAAwGkRZgEAAOC0CLMAAABwWsUcXYAkzZs3T++//77OnTunkJAQzZkzRw0bNsy3/+eff66xY8fq5MmTqlGjht5991099dRThVixc0hISFBCQoLhcQEBAQoICLBDRQAAALbl8DD72WefKTIyUlFRUWrUqJFmzZqlNm3a6PDhw6pQoUKu/rt371a3bt00depUPf3001q1apU6duyoAwcOqHbt2g74BEXXggULNHHiRMPjxo8frwkTJti+IAAAABsz5eTk5DiygEaNGunRRx/V3LlzJUnZ2dkKDAzU4MGDNWrUqFz9u3TpoitXrmjz5s3mtr///e8KDQ1VVFTUHa+XlpYmb29vpaamysvLy3YfpAjKa2b22rVratasmSRp165d8vDwyDWOmVkUNUGjtji6hCLvpPuLji6h6JuQ6ugKHIbv0J3xHbJCIX6HjOQ1h87MZmZmKi4uTqNHjza3ubi4KCwsTLGxsXmOiY2NVWRkpEVbmzZt9MUXX+TZPyMjQxkZGeb3qak3/0OkpaXdZfVFX6lSpVS9enWLtitXrph/Dg4OVqlSpfIcez/8fuA8sjOuOrqEIi/N5NB5CedwH/+7xnfozvgOWaEQv0O3cog1c64ODbNJSUnKysqSn5+fRbufn58OHTqU55hz587l2f/cuXN59p86dWqef2oPDAwsYNX3jooVKzq6BAA24u3oApzBNH5LyB//d1jBAd+hS5cuydv79td1+JpZexs9erTFTG52draSk5NVrlw5mUwmB1aGv0pLS1NgYKBOnTp1zy//AGyN7w9wd/gOFT05OTm6dOmSVRNvDg2z5cuXl6urqxITEy3aExMT5e/vn+cYf39/Q/3d3Nzk5uZm0ebj41PwomFXXl5e/EMCFBDfH+Du8B0qWu40I3uLQ/eZLVGihOrXr6+YmBhzW3Z2tmJiYtS4ceM8xzRu3NiivyRFR0fn2x8AAAD3LocvM4iMjFTv3r3VoEEDNWzYULNmzdKVK1cUHh4uSerVq5cqVaqkqVOnSpKGDh2qFi1aaPr06WrXrp1Wr16t/fv3a+HChY78GAAAAHAAh4fZLl266MKFCxo3bpzOnTun0NBQbdu2zXyTV3x8vFxc/juB3KRJE61atUpvvfWWxowZoxo1auiLL75gj1kn5+bmpvHjx+daEgLgzvj+AHeH75Bzc/g+swAAAEBBOXTNLAAAAHA3CLMAAABwWoRZAAAAOC3CLAAAAJwWYRY21adPH5lMJvXv3z/XsUGDBslkMqlPnz4FOndycrK6d+8uLy8v+fj46KWXXtLly5fvsmKg6LDn9+edd95RkyZNVLJkSR4cg3uWvb5DJ0+e1EsvvaRq1arJw8NDf/vb3zR+/HhlZmbaoGrcLcIsbC4wMFCrV6/WtWvXzG3p6elatWqVqlSpku+4li1batmyZfke7969u3755RdFR0dr8+bN+u677/TKK6/YsnTA4ez1/cnMzFTnzp01YMAAW5YLFDn2+A4dOnRI2dnZWrBggX755RfNnDlTUVFRGjNmjK3LRwEQZmFz9erVU2BgoNavX29uW79+vapUqaK6desW6Jy//fabtm3bpsWLF6tRo0Zq1qyZ5syZo9WrV+vs2bO2Kh1wOHt8fyRp4sSJGjZsmB555BFblAkUWfb4DrVt21ZLly7VE088oeDgYHXo0EHDhw+3uAYchzALu+jbt6+WLl1qfr9kyRLzU90KIjY2Vj4+PmrQoIG5LSwsTC4uLtqzZ89d1QoUNbb+/gD3m8L4DqWmpqps2bI2PScKhjALu+jRo4d27dqlP/74Q3/88Ye+//579ejRo8DnO3funCpUqGDRVqxYMZUtW1bnzp2723KBIsXW3x/gfmPv79DRo0c1Z84cvfrqqzY7JwqOMAu78PX1Vbt27bRs2TItXbpU7dq1U/ny5S36TJkyRZ6enubXzp071b9/f4u2+Ph4B30CwHH4/gB3x57foTNnzqht27bq3Lmz+vXrV1gfCbdRzNEF4N7Vt29fRURESJLmzZuX63j//v31wgsvmN93795dnTp10nPPPWduq1ixoiTJ399f58+ftxh/48YNJScny9/f3x7lAw5ly+8PcD+yx3fo7NmzatWqlZo0aaKFCxfaqXIYRZiF3bRt21aZmZkymUxq06ZNruNly5a1WG/k4eGhChUqqHr16rn6Nm7cWCkpKYqLi1P9+vUlSd9++62ys7PVqFEj+30IwEFs+f0B7ke2/g6dOXNGrVq1Uv369bV06VK5uPDH7aKCMAu7cXV11W+//Wb++W489NBDatu2rfr166eoqChdv35dERER6tq1K7NPuCfZ8vsjSfHx8UpOTlZ8fLyysrJ08OBBSVL16tXl6el51+cHihpbfofOnDmjli1bqmrVqvrggw904cIF8zH+Ouh4hFnYlZeXl83OtXLlSkVERKh169ZycXFRp06dNHv2bJudHyhqbPn9GTdunJYvX25+f2uLou3bt6tly5Y2uw5QlNjqOxQdHa2jR4/q6NGjqly5ssWxnJwcm1wDBWfK4b8CAAAAnBQLPgAAAOC0CLMAAABwWoRZAAAAOC3CLAAAAJwWYRYAAABOizALAAAAp0WYBQAAgNMizAIAAMBpEWYB4D61bNky+fj43LZPnz591LFjR/P7nJwcvfLKKypbtqxMJpP5sbgA4CiEWQC4S3369JHJZFL//v1zHRs0aJBMJpP69OlT+IXZwIcffqhly5aZ32/btk3Lli3T5s2blZCQoNq1a8tkMumLL75wWI0A7m+EWQCwgcDAQK1evVrXrl0zt6Wnp2vVqlWqUqWKAyu7O97e3hazt8eOHVNAQICaNGkif39/FStWzHHFAYAIswBgE/Xq1VNgYKDWr19vblu/fr2qVKmiunXrWvTdtm2bmjVrJh8fH5UrV05PP/20jh07Zj6emZmpiIgIBQQEyN3dXVWrVtXUqVMl3fwz/4QJE1SlShW5ubmpYsWKGjJkSL51/fjjj2rVqpVKly4tLy8v1a9fX/v377fo89VXX+mhhx6Sp6en2rZtq4SEBPOxvy4z6NOnjwYPHqz4+HiZTCYFBQUpKChIkvTss8+a2wCgMBFmAcBG+vbtq6VLl5rfL1myROHh4bn6XblyRZGRkdq/f79iYmLk4uKiZ599VtnZ2ZKk2bNna+PGjVqzZo0OHz6slStXmkPiunXrNHPmTC1YsEC///67vvjiCz3yyCP51tS9e3dVrlxZ+/btU1xcnEaNGqXixYubj1+9elUffPCBPvnkE3333XeKj4/X8OHD8zzXhx9+qEmTJqly5cpKSEjQvn37tG/fPknS0qVLzW0AUJj4+xAA2EiPHj00evRo/fHHH5Kk77//XqtXr9aOHTss+nXq1Mni/ZIlS+Tr66tff/1VtWvXVnx8vGrUqKFmzZrJZDKpatWq5r7x8fHy9/dXWFiYihcvripVqqhhw4b51hQfH68RI0aoZs2akqQaNWpYHL9+/bqioqL0t7/9TZIUERGhSZMm5Xkub29vlS5dWq6urvL397c45uPjk6sNAAoDM7MAYCO+vr5q166dli1bpqVLl6pdu3YqX758rn6///67unXrpuDgYHl5eZlnXePj4yXd/HP+wYMH9eCDD2rIkCH6+uuvzWM7d+6sa9euKTg4WP369dOGDRt048aNfGuKjIzUyy+/rLCwME2bNs1iOYMklSxZ0hxkJSkgIEDnz5+/m18DABQqwiwA2FDfvn21bNkyLV++XH379s2zT/v27ZWcnKxFixZpz5492rNnj6Sba2Wlm+tvT5w4obffflvXrl3TCy+8oOeff17SzRvNDh8+rI8++kgeHh4aOHCgHnvsMV2/fj3Pa02YMEG//PKL2rVrp2+//Va1atXShg0bzMf/uuRAkkwmk3Jycu769wAAhYUwCwA21LZtW2VmZur69etq06ZNruMXL17U4cOH9dZbb6l169Z66KGH9Oeff+bq5+XlpS5dumjRokX67LPPtG7dOiUnJ0uSPDw81L59e82ePVs7duxQbGys/vOf/+Rb0wMPPKBhw4bp66+/1nPPPWexrtcWihcvrqysLJueEwCsxZpZALAhV1dX/fbbb+af/1eZMmVUrlw5LVy4UAEBAYqPj9eoUaMs+syYMUMBAQGqW7euXFxc9Pnnn8vf318+Pj5atmyZsrKy1KhRI5UsWVKffvqpPDw8LNbV3nLt2jWNGDFCzz//vKpVq6bTp09r3759udbs3q2goCDFxMSoadOmcnNzU5kyZWx6fgC4HWZmAcDGvLy85OXllecxFxcXrV69WnFxcapdu7aGDRum999/36JP6dKl9d5776lBgwZ69NFHdfLkSW3dulUuLi7y8fHRokWL1LRpU9WpU0fffPONNm3apHLlyuW6lqurqy5evKhevXrpgQce0AsvvKAnn3xSEydOtOnnnT59uqKjoxUYGJhrGzIAsDdTDoujAAAA4KSYmQUAAIDTIswCAADAaRFmAQAA4LQIswAAAHBahFkAAAA4LcIsAAAAnBZhFgAAAE6LMAsAAACnRZgFAACA0yLMAgAAwGkRZgEAAOC0/h9Zk43Lgd1NrgAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Simulated measurements at [0.4 1. ]. Residual 1.3e-28\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for p in [[0.5 ,1], params]:\n", " print(f\"Simulated measurements at {p}. Residual {simulator.compute_loss(p):4.2g}\")\n", " compare_meas(simulator, p)" ] }, { "cell_type": "markdown", "id": "f853cb92", "metadata": {}, "source": [ "## Optimization\n", "\n", "The simulator object can be directly used for optimization. 13CFLUX provides convenience functions for IPOPT, but is generally compatible with any solver library that support (linear) inequality constraints." ] }, { "cell_type": "markdown", "id": "4ee457e9-cbd4-47ad-afc4-2ccfb54ca7cf", "metadata": {}, "source": [ "The convenience function must be supplied with the simulator and a starting point. Additionally, custom inequality constraints can be supplied (skipped in this case).Finally, keyword arguments can be supplied to alter the behavior of the IPOPT optimizer (`max_iter` and `tol` in our case)." ] }, { "cell_type": "code", "execution_count": 12, "id": "c0ea485d-b3b7-4567-bbd7-e147da26fbc3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "******************************************************************************\n", "\n", "Found optimum at [0.40001103 1.00002669] with a loss of 2.864624265163987e-07\n" ] } ], "source": [ "optimum, obj_val = x3cflux.run_optimization(simulator,\n", " starting_point=np.array([0.1, 0.2]),\n", " max_iter=100,\n", " tol=1e-3)\n", "print(f\"Found optimum at {optimum} with a loss of {obj_val}\")" ] }, { "cell_type": "markdown", "id": "c7f67f21-40d5-49f6-af70-409c219220a4", "metadata": {}, "source": [ "As expected from the low loss, the simulated measurements are very close to the real measurements" ] }, { "cell_type": "code", "execution_count": 13, "id": "469c1e43-9bed-44fd-98f4-cb07140dee15", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "compare_meas(simulator, optimum)" ] }, { "cell_type": "markdown", "id": "27fb10d6-8d43-4e59-82bf-998c0877c82a", "metadata": {}, "source": [ "### Multi-start\n", "\n", "For real life problems it is it is recommended to start the optimization from several well dispersed points, to avoid getting stuck in local minima. For this we draw uniform samples from the feasible flux space" ] }, { "cell_type": "code", "execution_count": 14, "id": "f5bfb9c2-8d87-4d1d-a37d-f6f536abfa34", "metadata": {}, "outputs": [], "source": [ "samples = x3cflux.run_uniform_sampling(simulator, num_samples=100)" ] }, { "cell_type": "markdown", "id": "00bf5f14-a94a-4c44-8d98-e8ed8c1c9ade", "metadata": {}, "source": [ "These samples then serve as starting points for the optimization. To make use of parallel compute ressources this can be done in several independent processes (`num_procs`).\n", "\n", "We have a look at the top 5 optima, which all essentially agree" ] }, { "cell_type": "code", "execution_count": 15, "id": "a7aaf46f-6d4d-47bd-8405-c2a297ed0ab4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "******************************************************************************\n", "\n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "******************************************************************************\n", "\n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "******************************************************************************\n", "\n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "******************************************************************************\n", "\n", "Top 5 optima\n", "Optima at [0.4 1.00000001]: 3.6444120192693757e-14\n", "Optima at [0.40000002 1.00000004]: 7.965485760259464e-13\n", "Optima at [0.40000002 1.00000005]: 1.0061853728121022e-12\n", "Optima at [0.39999997 0.99999989]: 6.453825700791117e-12\n", "Optima at [0.39999993 0.99999984]: 1.063600193539763e-11\n" ] } ], "source": [ "optima, obj_val = x3cflux.run_multi_optimization(simulator, starting_points=samples,\n", " max_iter=100, tol=1e-3, num_procs=4)\n", "sorted_idx = np.argsort(obj_val)\n", "print(\"Top 5 optima\")\n", "for i in range(5):\n", " print(f\"Optima at {optima.transpose()[sorted_idx[i]]}: {obj_val[sorted_idx[i]]}\")" ] }, { "cell_type": "markdown", "id": "f3ddc4ab", "metadata": {}, "source": [ "## Statistics\n", "\n", "The simulator object can also be directly used for statistics. `13CFLUX` provides multiple ways for computing statistics, e.g. based on the computation of the asymptotic covariance matrix or profile likelihoods." ] }, { "cell_type": "markdown", "id": "5983471c", "metadata": {}, "source": [ "### Linearized or Fisherian statistics\n", "Covariance matrices must be computed in the optimum and give a rough approximation to the parameter uncertainty. `13CFLUX` not only computes the matrix, but also outputs a list of parameters that are **locally** non-identifiable. In our simple case, all parameters can be identified." ] }, { "cell_type": "code", "execution_count": 16, "id": "7feafd7e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Covariance Matrix at [0.40001103 1.00002669]:\n", " [[0.00047813 0.001 ]\n", " [0.001 0.0025 ]]\n", "List of non-identifiable parameters: []\n" ] } ], "source": [ "cov, non_ident = x3cflux.compute_free_parameter_covariance(simulator, optimum)\n", "print(f\"Covariance Matrix at {optimum}:\\n {cov}\")\n", "print(f\"List of non-identifiable parameters: {non_ident}\")" ] }, { "cell_type": "markdown", "id": "c919acbe", "metadata": {}, "source": [ "### Profile likelihood or parameter continuation\n", "\n", "Another aproach uses profile likelihoods. Confidence intervals from profile likelihoods are still based on local approximation, but tend to cope better with non-linear shapes. In `13CFLUX`, a specifically robust version using binary search is implemented. \n", "\n", "The method computes the confidence intervals for a given confidence level (`alpha`). It is possible to compute the confidence intervals only for specific paramters by specifying `names`." ] }, { "cell_type": "code", "execution_count": 17, "id": "0c795099", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "******************************************************************************\n", "\n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "******************************************************************************\n", "\n", "\n", "******************************************************************************\n", "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", " For more information visit http://projects.coin-or.org/Ipopt\n", "******************************************************************************\n", "\n", "95.0% confidence intervals\n", "0.9023678310474468 < u.n < 1.0976822856779214\n", "0.35782236324289546 < p.n < 0.44324822720607215\n", "0.5393695468836666 < q.n < 0.6617830193370621\n" ] } ], "source": [ "alpha=0.95\n", "names=[\"u.n\", \"p.n\", \"q.n\"]\n", "pl_intervals = x3cflux.run_profile_likelihood_cis(simulator, optimum, alpha=alpha, names=names, num_procs=4)\n", "print(f\"{100*alpha}% confidence intervals\")\n", "for n,i in zip(names, pl_intervals):\n", " print(f\"{i[0]} < {n} < {i[1]}\")" ] }, { "cell_type": "markdown", "id": "511a15cd-62f4-4fc8-a5f2-647fa1fe73c1", "metadata": {}, "source": [ "### Bayesian statistics\n", "A third, more advanced method to perform uncertainty quantification is Bayesian statistics. Bayesian statistics is based on a different statistical foundation than Frequentist methods. The result of Bayesian statistics is a multivariate probability distribution (*posterior*) that encodes likely parameter combinations by placing probability mass on them. These probability distributions are numerically computed by non-uniform Markov Chain Monte Carlo sampling." ] }, { "cell_type": "code", "execution_count": 18, "id": "4c266cf6-4f9d-49de-bc26-d1caeae396c9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "chain 0: 100%|██████████| 5000/5000 [00:02<00:00, 1729.65it/s]\n" ] } ], "source": [ "samples = x3cflux.run_non_uniform_sampling(simulator, num_samples=5_000, thinning=2, starting_point=np.array([.5, 1.4]), \n", " proposal=hopsy.CSmMALAProposal, progress_bar=True)" ] }, { "cell_type": "markdown", "id": "6df20d8b-5ba7-4254-b2ed-cd4151acd11c", "metadata": {}, "source": [ "The samples from the posterior distribution can e.g. be visualized and further analized by expert software packages like `arviz`" ] }, { "cell_type": "code", "execution_count": 19, "id": "f9d4a8b1-d695-445a-a48b-5b21b1ae2b41", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "arviz.plot_pair(arviz.from_dict({simulator.parameter_space.free_parameter_names[i]: samples[:, i, :].flatten() \n", " for i in range(simulator.parameter_space.num_free_parameters)}), \n", " marginals=True, kind=\"kde\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "cf14dc5a-7463-4ca0-bf13-32602f925ebb", "metadata": {}, "source": [ "### Comparison of uncertainty estimates\n", "\n", "we can now compare the uncertainty estiamtes for 95% confidence/credible interval" ] }, { "cell_type": "code", "execution_count": 20, "id": "501b6dca-51f3-41e4-a778-4c2d46882352", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.40001103 1.00002669]\n", "[[0.35627867 0.90002669]\n", " [0.44374338 1.10002669]]\n", "[[0.35782236 0.90236783]\n", " [0.44324823 1.09768229]]\n" ] } ], "source": [ "linearized_intervals = np.vstack([optimum - 2*np.sqrt(np.diag(cov)), optimum + 2*np.sqrt(np.diag(cov))])\n", "plike_intervals = np.flip(np.reshape(pl_intervals[:2], (2,2)).transpose(),axis=1)\n", "credible_intervals = np.quantile(samples[0],[0.025,0.975], axis=1)\n", "print(optimum)\n", "print(linearized_intervals)\n", "print(plike_intervals)" ] }, { "cell_type": "code", "execution_count": 21, "id": "1d26fe26-2236-4480-896d-f5c94f1b1af0", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "variables = simulator.parameter_space.free_parameter_names\n", "x = np.arange(len(variables)) # x locations for the groups\n", "width = 0.2\n", "fig, ax = plt.subplots()\n", "\n", "ax.bar(x - width, linearized_intervals[1, :] - linearized_intervals[0, :], width, \n", " bottom=linearized_intervals[0, :], label='Linearized')\n", "ax.bar(x, plike_intervals[1, :] - plike_intervals[0, :], width,\n", " bottom=plike_intervals[0, :], label='Profile likelihood')\n", "ax.bar(x + width, credible_intervals[1, :] - credible_intervals[0, :], width, \n", " bottom=credible_intervals[0, :], label='Bayesian')\n", "\n", "ax.set_xlabel('Variables')\n", "ax.set_ylabel('Values')\n", "plt.legend()\n", "ax.set_xticks(x)\n", "_ = ax.set_xticklabels(variables)" ] }, { "cell_type": "markdown", "id": "42ce4c3f-8ef3-4090-bff8-560aae893282", "metadata": {}, "source": [ "Which, in this simple case, are virtually identical.\n", "This was evident from the elliptical and highly symmetrical shape of the Bayesian posterior." ] }, { "cell_type": "markdown", "id": "a779941a-acd6-400d-869e-8212f7c6e444", "metadata": {}, "source": [ "## INST\n", "\n", "13CFLUX can also handle isotopically non-stationary 13C-MFA through the same interface" ] }, { "cell_type": "code", "execution_count": 22, "id": "99c1163b-55ab-4684-9c7f-2a870c151e75", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m\u001b[33mPool size of metabolite \"G\" is non-determinable given the measurement configuration, as it does not impact the simulation outcome. Consider fixing the pool size in FluxML to reduce the ill-conditionedness of the inverse problem.\u001b[0m\n" ] } ], "source": [ "simulator_inst = x3cflux.create_simulator_from_fml(\"spiralus.fml\", \"ms_INST\")" ] }, { "cell_type": "markdown", "id": "bff5dbcf-0b33-467d-aca1-2432d399a117", "metadata": {}, "source": [ "The simulator informs us, that the pool size of **G** has no influence on the simulated measurements. We verify this by trying two different values for it." ] }, { "cell_type": "code", "execution_count": 23, "id": "dadaba02-6d04-4951-a348-199d837bcef4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "current parameter values\n", "q.n: 0.6\n", "u.n: 1.0\n", "G: 1.0\n", "B: 1.0\n", "\u001b[1m\u001b[33mPool size \"G\" not set in initial configuration (set to 1)\u001b[0m\n" ] } ], "source": [ "param_names_inst = simulator_inst.parameter_space.free_parameter_names\n", "params_inst = x3cflux.get_parameters(simulator_inst.parameter_space, simulator_inst.configurations[0].parameter_entries)\n", "print(\"current parameter values\")\n", "for n, v in zip(param_names_inst, params_inst):\n", " print(f\"{n}: {v}\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "1d034c18-9733-4724-8503-32cc230142db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "residual at [0.6, 1, 0.2, 1]: 0.00011629149758229874\n", "residual at [0.6, 1, 1, 1]: 0.00011629149758229874\n", "residual at [0.6, 1, 5, 1]: 0.00011629149758229874\n" ] } ], "source": [ "for p in [[0.6, 1, 0.2, 1],[0.6, 1, 1, 1], [0.6, 1, 5, 1]]:\n", " print(f\"residual at {p}: {simulator_inst.compute_loss(p)}\")" ] }, { "cell_type": "code", "execution_count": 25, "id": "26bc7d83-eba8-4bc2-9adb-b25d667c5ab9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.00011629149758229874" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simulator_inst.compute_loss([0.6, 1, 5, 1])" ] }, { "cell_type": "markdown", "id": "99de3648-b9a4-41d2-8ceb-423f1795607f", "metadata": {}, "source": [ "Indeed, the residual is identical regardless whether the pool size of **G** is 0.2, 1 or 5.\n", "\n", "Hence, we fix it to 1 and also remove it from the parameters of the simulation." ] }, { "cell_type": "code", "execution_count": 26, "id": "40930cec-e1f6-42c0-a576-24f91b63f449", "metadata": {}, "outputs": [], "source": [ "config = simulator_inst.configurations[0]\n", "pool_constr = config.pool_size_constraints\n", "# Add constraint for G\n", "def_constr = [constr for constr in pool_constr.definition_constraints] + [x3cflux.DefinitionConstraint(name=\"manual_constraint\", parameter_name=\"G\", parameter_value=1)]\n", "# build new configuration\n", "pool_constr = x3cflux.ParameterConstraints(def_constr, pool_constr.equality_constraints, pool_constr.inequality_constraints)\n", "new_config = x3cflux.MeasurementConfiguration(\n", " config.name,\n", " config.comment,\n", " config.stationary,\n", " config.substrates,\n", " config.measurements,\n", " config.net_flux_constraints,\n", " config.exchange_flux_constraints,\n", " pool_constr,\n", " [x for x in simulator_inst.configurations[0].parameter_entries if x.name != 'G']) # remove G from parameters\n", "simulator_inst = x3cflux.create_simulator_from_data(simulator_inst.network_data, new_config)" ] }, { "cell_type": "markdown", "id": "18179c13-caeb-492a-aefa-92da5ed9de28", "metadata": {}, "source": [ "The simulator no longer indicates a non-determinable pool size and our parameters are now:" ] }, { "cell_type": "code", "execution_count": 27, "id": "24ac3cac-4455-480a-87a3-136979b43c1e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "q.n: 0.6\n", "u.n: 1.0\n", "B: 1.0\n" ] } ], "source": [ "param_names_inst = simulator_inst.parameter_space.free_parameter_names\n", "params_inst = x3cflux.get_parameters(simulator_inst.parameter_space, simulator_inst.configurations[0].parameter_entries)\n", "for n, v in zip(param_names_inst, params_inst):\n", " print(f\"{n}: {v}\")" ] }, { "cell_type": "markdown", "id": "166a12a7-4f78-42e2-bd97-34a62210e93b", "metadata": {}, "source": [ "### Configuring the simulator" ] }, { "cell_type": "markdown", "id": "8601102b-8075-4840-a629-84bb19d638f2", "metadata": {}, "source": [ "13CFLUX offers two different solvers for the differential equations (\"bdf\", \"sdirk\"), and allows to set the relative and absolute solver tolerances." ] }, { "cell_type": "code", "execution_count": 28, "id": "133d86ca-d2f5-4069-aa18-06e287027027", "metadata": {}, "outputs": [], "source": [ "simulator_inst.builder.set_solver(\"bdf\")\n", "simulator_inst.builder.solver.relative_tolerance = 1e-4\n", "simulator_inst.builder.solver.absolute_tolerance = 1e-7\n", "simulator_inst.builder.derivative_solver.relative_tolerance = 1e-4\n", "simulator_inst.builder.derivative_solver.absolute_tolerance = 1e-7" ] }, { "cell_type": "markdown", "id": "cec7b76d-2948-4c81-90da-3e6e6ed0f2cd", "metadata": {}, "source": [ "### Forward simulation\n", "\n", "In case of INST, also the times at which the forward simulation is evaluated can be set.\n", "\n", "First we define some common settings" ] }, { "cell_type": "code", "execution_count": 29, "id": "9c3cc229-9d70-4efb-af5b-59e4e7343a9b", "metadata": {}, "outputs": [], "source": [ "offset = 0.2\n", "times = np.geomspace(start=offset, stop=20+offset, num=50)-offset\n", "meas_times = simulator_inst.measurement_time_stamps[0]\n", "almost_infty = 1e5" ] }, { "cell_type": "markdown", "id": "2bcf7910-5ab0-4b90-b625-d72cfd455752", "metadata": {}, "source": [ "Now for the actual simulation" ] }, { "cell_type": "code", "execution_count": 30, "id": "836d648f-a641-4c4b-9bd7-54ba8605d587", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 26.0)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ms=simulator_inst.compute_measurements(params=params_inst, time_stamps=times)[0]\n", "ms_infty=simulator_inst.compute_measurements(params=params_inst, time_stamps=[almost_infty])[0][0]\n", "ax = pd.DataFrame(np.vstack(ms),columns=[\"M+0\", \"M+1\", \"M+2\"], index=times).plot()\n", "ax.scatter([25,25,25],[ms_infty], marker='x', color=[\n", " ax.get_lines()[0].get_color(),\n", " ax.get_lines()[1].get_color(),\n", " ax.get_lines()[2].get_color()])\n", "ax.set_ylim([0,1])\n", "ax.set_xticks(np.arange(0,26,5))\n", "_ = ax.set_xticklabels([0,5,10,15,20, \"∞\"])\n", "for t in meas_times:\n", " ax.plot([t,t], [0,1], 'k--')\n", "ax.set_xlim([0,26])" ] }, { "cell_type": "markdown", "id": "4b74051a-ccaa-4b6b-8029-c5d6475a743d", "metadata": {}, "source": [ "The colored lines show the timecourse of the labeling, dashed lines indicate time points of measurements. the x-es at ∞ show the stationary solution.\n", "\n", "If we now increase the pool size of **B** (the first pool after the input), the labeling dynamics is significantly slowed down" ] }, { "cell_type": "code", "execution_count": 31, "id": "a6c19ae0-7c95-4baf-a74f-26328babcffe", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "params_inst2 = params_inst.copy()\n", "params_inst2[2] = 10 # set B to 10\n", "ms=simulator_inst.compute_measurements(params=params_inst2, time_stamps=times)[0]\n", "ms_infty=simulator_inst.compute_measurements(params=params_inst2, time_stamps=[almost_infty])[0][0]\n", "ax = pd.DataFrame(np.vstack(ms),columns=[\"M+0\", \"M+1\", \"M+2\"], index=times).plot()\n", "ax.scatter([25,25,25],[ms_infty], marker='x', color=[\n", " ax.get_lines()[0].get_color(),\n", " ax.get_lines()[1].get_color(),\n", " ax.get_lines()[2].get_color()])\n", "ax.set_ylim([0,1])\n", "ax.set_xticks(np.arange(0,26,5))\n", "_ = ax.set_xticklabels([0,5,10,15,20, \"∞\"])\n", "for t in meas_times:\n", " ax.plot([t,t], [0,1], 'k--')\n", "_ = ax.set_xlim([0,26])" ] }, { "cell_type": "markdown", "id": "38ae182e-72b7-46b9-abb8-ed70a84144e2", "metadata": {}, "source": [ "The stationary solution, as expected, is not affected by the change in pool size." ] }, { "cell_type": "markdown", "id": "6afae871-cd6e-4f6b-b5bd-710d5216c376", "metadata": {}, "source": [ "### Optimization + Statistics\n", "\n", "Optimization and statistics work exactly the same as for the stationary case:\n", "\n", "- Fitting" ] }, { "cell_type": "code", "execution_count": 32, "id": "1dbc851d-b8c7-4ea3-818c-2aa572c818dc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimum at [0.6002588 1.00019754 1.00134517] with and objective value of 6.429128063154018e-05\n" ] } ], "source": [ "optimum_inst, obj_val = x3cflux.run_optimization(simulator_inst,\n", " starting_point=np.array([0.5, 1.2, 0.5]),\n", " max_iter=100,\n", " tol=1e-3)\n", "print(f\"Optimum at {optimum_inst} with and objective value of {obj_val}\")" ] }, { "cell_type": "markdown", "id": "1ffe5e48-a143-40e4-8e51-072ce6d03741", "metadata": {}, "source": [ "- Linearized statistics" ] }, { "cell_type": "code", "execution_count": 33, "id": "6c11cd0b-4454-46d0-9e02-992a3ae79c61", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Covariance Matrix at [0.40001103 1.00002669]:\n", " [[0.00116501 0.00090038 0.00707814]\n", " [0.00090038 0.00115914 0.00603928]\n", " [0.00707814 0.00603928 0.04600468]]\n", "List of non-identifiable parameters: []\n" ] } ], "source": [ "cov_inst, non_ident = x3cflux.compute_free_parameter_covariance(simulator_inst, optimum_inst)\n", "print(f\"Covariance Matrix at {optimum}:\\n {cov_inst}\")\n", "print(f\"List of non-identifiable parameters: {non_ident}\")" ] }, { "cell_type": "markdown", "id": "e9356c4b-d3f4-4088-b060-51128ab4dc6c", "metadata": {}, "source": [ "- Profile likelihood" ] }, { "cell_type": "code", "execution_count": 34, "id": "3494bba9-9da3-4400-910e-46aa3d4920b7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "95.0% confidence intervals\n", "0.5448638261590698 < q.n < 0.6824317044057978\n", "0.9406154569582612 < u.n < 1.0753891358262595\n", "0.6533094368930802 < B < 1.5170761781099686\n" ] } ], "source": [ "alpha=0.95\n", "names=[\"q.n\", \"u.n\", \"B\"]\n", "pl_intervals_inst = x3cflux.run_profile_likelihood_cis(simulator_inst, optimum_inst, alpha=alpha, names=names, num_procs=4)\n", "print(f\"{100*alpha}% confidence intervals\")\n", "for n,i in zip(names, pl_intervals_inst):\n", " print(f\"{i[0]} < {n} < {i[1]}\")" ] }, { "cell_type": "markdown", "id": "e4e1d277-a7c7-4167-a7e7-2799389b390d", "metadata": {}, "source": [ "- Bayesian statistics" ] }, { "cell_type": "code", "execution_count": 35, "id": "9c443dcd-28b8-4990-984c-2757038126a6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "chain 0: 100%|██████████| 5000/5000 [02:09<00:00, 38.74it/s]\n" ] } ], "source": [ "samples_inst = x3cflux.run_non_uniform_sampling(simulator_inst, num_samples=5_000, thinning=3, starting_point=np.array([.3, 1.1, 0.9]), \n", " proposal=hopsy.CSmMALAProposal, progress_bar=True)" ] }, { "cell_type": "code", "execution_count": 36, "id": "9ea6197a-9fef-482e-b20d-1bdc38ddcf2f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "arviz.plot_pair(arviz.from_dict({simulator_inst.parameter_space.free_parameter_names[i]: samples_inst[:, i, :].flatten() \n", " for i in range(simulator_inst.parameter_space.num_free_parameters)}), \n", " marginals=True, kind=\"kde\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "61f1c610-8ce4-476e-b292-bfba2ed08596", "metadata": {}, "source": [ "Finally, we can compare them again:" ] }, { "cell_type": "code", "execution_count": 37, "id": "8c081a1f-3280-4921-9ce9-27b0279e845e", "metadata": {}, "outputs": [], "source": [ "linearized_intervals_inst = np.vstack([optimum_inst - 2*np.sqrt(np.diag(cov_inst)), optimum_inst + 2*np.sqrt(np.diag(cov_inst))])\n", "plike_intervals_inst = np.transpose(pl_intervals_inst)\n", "credible_intervals_inst = np.quantile(samples_inst[0],[0.025,0.975], axis=1)" ] }, { "cell_type": "code", "execution_count": 38, "id": "ae1b4065-c0d3-479d-8619-fdbdd015fedb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "variables_inst = simulator_inst.parameter_space.free_parameter_names\n", "x = np.arange(len(variables_inst)) # x locations for the groups\n", "width = 0.2\n", "fig, ax = plt.subplots()\n", "\n", "ax.bar(x - width, linearized_intervals_inst[1, :] - linearized_intervals_inst[0, :], width, \n", " bottom=linearized_intervals_inst[0, :], label='Linearized')\n", "ax.bar(x, plike_intervals_inst[1, :] - plike_intervals_inst[0, :], width,\n", " bottom=plike_intervals_inst[0, :], label='Profile likelihood')\n", "ax.bar(x + width, credible_intervals_inst[1, :] - credible_intervals_inst[0, :], width, \n", " bottom=credible_intervals_inst[0, :], label='Bayesian')\n", "\n", "ax.set_xlabel('Variables')\n", "ax.set_ylabel('Values')\n", "plt.legend()\n", "ax.set_xticks(x)\n", "_= ax.set_xticklabels(variables_inst)" ] } ], "metadata": { "kernelspec": { "display_name": "flux", "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": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 5 }