{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "import logging\n", "import warnings\n", "\n", "import cmomy\n", "import numpy as np\n", "\n", "rng = cmomy.random.default_rng(0)\n", "np.set_printoptions(precision=4)\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "\n", "logger = logging.getLogger()\n", "logger.setLevel(logging.ERROR)" ] }, { "cell_type": "markdown", "id": "1", "metadata": {}, "source": [ "# Macrostate distribution extrapolation\n", "\n", "This notebook demonstrates how to perform temperature extrapolation (or interpolation) of macrostate log-probability distributions $\\ln \\Pi (N)$ for the number of particles from a flat-histogram grand canonical ensemble simulation." ] }, { "cell_type": "code", "execution_count": 2, "id": "2", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "3", "metadata": {}, "source": [ "We start by setting up functions to load in the data." ] }, { "cell_type": "code", "execution_count": 3, "id": "4", "metadata": {}, "outputs": [], "source": [ "# Need to load in some data, partially specifying information about simulations here\n", "# As well as functions to load data\n", "import glob\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "\n", "\n", "def get_sim_activity(Tr) -> float:\n", " if Tr == 0.700:\n", " return -6.250\n", " if Tr in {0.73, 0.74}:\n", " return -5.500\n", " if Tr == 0.850:\n", " return -4.800\n", " if Tr == 1.100:\n", " return -3.380\n", " if Tr == 1.200:\n", " return -3.000\n", "\n", " raise ValueError(\"Reduced temperature %f outside range in dataset.\" % Tr)\n", "\n", "\n", "def get_file_prefix(Tr, run_num=None):\n", " base_str = \"./SRS_data/lj.t%s.n12m6.v512.rc3.b.r\" % (\n", " str(\"%1.2f\" % Tr).replace(\".\", \"\")\n", " )\n", " if run_num is not None:\n", " return [\"%s%i\" % (base_str, run_num)]\n", " unstripped = glob.glob(\"%s*.energy.dat\" % base_str)\n", " return [u[:-11] for u in unstripped]\n", "\n", "\n", "def load_data(Tr, ref_mu=-4.0, run_num=None):\n", " f_prefixes = get_file_prefix(Tr, run_num=run_num)\n", " U_moms = np.array(\n", " [np.loadtxt(\"%s.energy.dat\" % f)[:, 1:] for f in f_prefixes]\n", " ) # Reduced units of energy/eps\n", " lnPis = np.array([np.loadtxt(\"%s.lnpi.dat\" % f)[:, 1] for f in f_prefixes])\n", " N_vals = np.array([np.loadtxt(\"%s.lnpi.dat\" % f)[:, 0] for f in f_prefixes])\n", " mu = Tr * get_sim_activity(\n", " Tr\n", " ) # Tr is kB*T/eps, activity is mu/kB*T, so getting mu/eps\n", "\n", " # Convert to x_arrays with proper labeling\n", " # Potential energy data is average U and average U^2 at each bin N for each repeat\n", " # So dimension are N_repeats by N by 2\n", " # And don't forget to include 0th order moments (all 1s)\n", " U_moms = np.concatenate([np.ones_like(lnPis)[..., None], U_moms], axis=-1)\n", " U_moms = xr.DataArray(U_moms, dims=[\"rec\", \"n\", \"umom\"])\n", " # For lnPi, adjust to a reference mu value\n", " # And subtract off N=0 bin\n", " lnPis = lnPis + (\n", " (1.0 / Tr) * (ref_mu - mu) * N_vals\n", " ) # Multiply by 1/Tr since want beta*mu\n", " lnPis = lnPis - lnPis[:, :1]\n", " lnPis = xr.DataArray(lnPis, dims=[\"rec\", \"n\"])\n", " # For mu need to add extra axis called comp\n", " ref_mu = xr.DataArray(ref_mu * np.ones((len(f_prefixes), 1)), dims=[\"rec\", \"comp\"])\n", "\n", " return {\n", " \"energy\": U_moms,\n", " \"lnPi\": lnPis,\n", " \"mu\": ref_mu,\n", " \"mu_sim\": mu,\n", " \"beta\": 1.0 / Tr,\n", " }" ] }, { "cell_type": "markdown", "id": "5", "metadata": {}, "source": [ "And actually load in the data to use for extrapolation/interpolation. This data is from simulations of in the grand canonical ensemble with flat-histogram methods employed to enhance sampling in the particle number. The potential considered is Lennard-Jones fluid, with more information found [here](https://www.nist.gov/mml/csd/chemical-informatics-group/lennard-jones-fluid-properties)." ] }, { "cell_type": "code", "execution_count": 4, "id": "6", "metadata": {}, "outputs": [], "source": [ "temps = np.array([0.70, 0.73, 0.74, 0.85, 1.10, 1.20])\n", "betas = (\n", " 1.0 / temps\n", ") # Reduced temperature is Tr = kB*T/eps, so beta is also dimensionless as eps/kB*T\n", "\n", "data_dicts = [load_data(t) for t in temps]" ] }, { "cell_type": "markdown", "id": "7", "metadata": {}, "source": [ "## Extrapolation\n", "\n", "We will start out by extrapolating from a central temperature value, moving both higher and lower." ] }, { "cell_type": "markdown", "id": "8", "metadata": {}, "source": [ "We need to import {mod}`thermoextrap` generally for access to data classes, then {mod}`~thermoextrap.lnpi` for special functions related to derivatives of macrostate distributions." ] }, { "cell_type": "code", "execution_count": 5, "id": "9", "metadata": {}, "outputs": [], "source": [ "import thermoextrap as xtrap" ] }, { "cell_type": "markdown", "id": "10", "metadata": {}, "source": [ "First, we need to create a data object to hold the data. Selecting the data, however, requires a bit of thought. Though we are interested in extrapolating $\\ln \\Pi (N)$, it turns out it is more convenient (and equivalent within an additive constant) to extrapolate $\\ln \\Pi (N) - \\ln \\Pi (0)$. The first two derivatives of this with respect to $\\beta = \\frac{1}{k_B T}$ are:\n", "\n", "    $\\frac{\\mathrm{d} \\ln ( \\Pi (N)/\\Pi(0) )}{\\mathrm{d} \\beta} = \\mu N - \\langle U \\rangle_N$
\n", "    $\\frac{\\mathrm{d}^2 \\ln ( \\Pi (N)/\\Pi(0) )}{\\mathrm{d} \\beta^2} = - \\frac{\\mathrm{d} \\langle U \\rangle_N}{\\mathrm{d} \\beta}$
\n", "\n", "The chemical potential is given by $\\mu$ and the angle brackets with a subscript of $N$ represent a canonical ensemble average at a particular macrostate with $N$ particles. While the first derivative involves $\\mu N$ and the potential energy, it is clear that for $k > 1$,\n", "\n", "    $\\frac{\\mathrm{d}^k \\ln ( \\Pi (N)/\\Pi(0) )}{\\mathrm{d} \\beta^k} = - \\frac{\\mathrm{d}^{k-1} \\langle U \\rangle_N}{\\mathrm{d} \\beta^{k-1}}$\n", "\n", "What this means is that all we need is a way to compute canonical ensemble derivatives of an average observable with respect to $\\beta$, which we have in {mod}`thermoextrap.beta`, and a custom derivative function to handle the special case of the 0$^\\mathrm{th}$ and 1$^\\mathrm{st}$ derivatives. This has been implemented in {mod}`thermoextrap.lnpi` for convenience.\n", "\n", "Based on the discussion above, we start by creating a custom data callback object that helps manage how derivatives are calculated and what information is provided to the function that calculates the derivatives." ] }, { "cell_type": "code", "execution_count": 6, "id": "11", "metadata": {}, "outputs": [], "source": [ "# for lnPi extrapolation, we need to setup a DataCallback\n", "# see definition in xtrap.lnpi\n", "ref = data_dicts[3]\n", "meta_lnpi = xtrap.lnpi.lnPiDataCallback(\n", " ref[\"lnPi\"], ref[\"mu\"], dims_n=[\"n\"], dims_comp=\"comp\"\n", ")" ] }, { "cell_type": "markdown", "id": "12", "metadata": {}, "source": [ "In the above callback, the first argument is the reference $\\ln \\Pi$ we're extrapolating from, the second argument is the chemical potential $\\mu$, `dims_n` is the dimension of the input {class}`~xarray.DataArray` over which the particle number $N$ changes, and if you really want to understand what `dims_comp` is you'll have to ask Bill.\n", "\n", "Next we need a data object to hold the potential energy moments, passing the callback as the `meta` argument. Note that because we're working with potential energies only, we can set `xu=None` and `x_is_u=True` to provide further accelerations for this special case." ] }, { "cell_type": "code", "execution_count": 7, "id": "13", "metadata": {}, "outputs": [], "source": [ "data_lnpi = xtrap.DataCentralMoments.from_ave_raw(\n", " u=ref[\"energy\"], xu=None, x_is_u=True, central=True, meta=meta_lnpi\n", ")" ] }, { "cell_type": "markdown", "id": "14", "metadata": {}, "source": [ "And finally, use that data to create our extrapolation model." ] }, { "cell_type": "code", "execution_count": 8, "id": "15", "metadata": {}, "outputs": [], "source": [ "em_lnpi = xtrap.lnpi.factory_extrapmodel_lnPi(beta=ref[\"beta\"], data=data_lnpi, order=1)" ] }, { "cell_type": "markdown", "id": "16", "metadata": {}, "source": [ "We can make predictions at a range of beta values for which we have data to check (in the loaded \"samples\" data). Note that we set all $\\ln \\Pi (0)$ elements to zero." ] }, { "cell_type": "code", "execution_count": 9, "id": "17", "metadata": {}, "outputs": [], "source": [ "out_lnpi = em_lnpi.predict(betas).mean(dim=\"rec\")\n", "out_lnpi -= out_lnpi.sel(n=0)" ] }, { "cell_type": "code", "execution_count": 10, "id": "18", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the results\n", "fig, ax = plt.subplots()\n", "\n", "colors = plt.cm.coolwarm(np.linspace(0.0, 1.0, len(betas)))\n", "\n", "n_vals = np.arange(out_lnpi.sizes[\"n\"])\n", "\n", "for i, b in enumerate(betas):\n", " ax.plot(n_vals, out_lnpi.sel(beta=b), color=colors[i], label=\"%1.2f\" % temps[i])\n", " ax.plot(\n", " n_vals[::50],\n", " data_dicts[i][\"lnPi\"].mean(dim=\"rec\").values[::50],\n", " \"o\",\n", " color=colors[i],\n", " )\n", "\n", "ax.plot(n_vals, ref[\"lnPi\"].mean(dim=\"rec\"), \"k--\", label=\"Ref\")\n", "\n", "ax.set_xlabel(r\"$N$\")\n", "ax.set_ylabel(\"$\\\\ln \\\\Pi (N)$\")\n", "\n", "ax.legend()\n", "\n", "fig.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "19", "metadata": {}, "source": [ "This extrapolation misses too far away from the reference. Next we will try interpolation using up to first-order derivatives, which gives us a 3rd order interpolating polynomial." ] }, { "cell_type": "markdown", "id": "20", "metadata": {}, "source": [ "## Interpolation" ] }, { "cell_type": "markdown", "id": "21", "metadata": {}, "source": [ "Implementing interpolation is easy if we already have {class}`thermoextrap.models.ExtrapModel` objects. So just need to build a list of these for the end states (highest and lowest temperatures) we will use as our references." ] }, { "cell_type": "code", "execution_count": 11, "id": "22", "metadata": {}, "outputs": [], "source": [ "xems_interp = []\n", "for d in [data_dicts[0], data_dicts[-1]]:\n", " this_meta = xtrap.lnpi.lnPiDataCallback(\n", " d[\"lnPi\"], d[\"mu\"], dims_n=[\"n\"], dims_comp=\"comp\"\n", " )\n", " this_data = xtrap.DataCentralMoments.from_ave_raw(\n", " u=d[\"energy\"], xu=None, x_is_u=True, central=True, meta=this_meta\n", " )\n", " this_xem = xtrap.lnpi.factory_extrapmodel_lnPi(\n", " beta=d[\"beta\"], data=this_data, order=1\n", " )\n", " xems_interp.append(this_xem)\n", "\n", "xemi = xtrap.InterpModel(xems_interp)" ] }, { "cell_type": "code", "execution_count": 12, "id": "23", "metadata": {}, "outputs": [], "source": [ "out_xemi = xemi.predict(betas).mean(dim=\"rec\")\n", "out_xemi -= out_xemi.sel(n=0)" ] }, { "cell_type": "code", "execution_count": 13, "id": "24", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the results\n", "fig, ax = plt.subplots()\n", "\n", "colors = plt.cm.coolwarm(np.linspace(0.0, 1.0, len(betas)))\n", "\n", "n_vals = np.arange(out_xemi.sizes[\"n\"])\n", "\n", "for i, b in enumerate(betas):\n", " ax.plot(n_vals, out_xemi.sel(beta=b), color=colors[i], label=\"%1.2f\" % temps[i])\n", " ax.plot(\n", " n_vals[::50],\n", " data_dicts[i][\"lnPi\"].mean(dim=\"rec\").values[::50],\n", " \"o\",\n", " color=colors[i],\n", " )\n", "\n", "for d in [data_dicts[0], data_dicts[-1]]:\n", " ax.plot(n_vals, d[\"lnPi\"].mean(dim=\"rec\"), \"k--\", label=\"Ref\")\n", "\n", "ax.set_xlabel(r\"$N$\")\n", "ax.set_ylabel(\"$\\\\ln \\\\Pi (N)$\")\n", "\n", "ax.legend()\n", "\n", "fig.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "25", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Tags", "hide_input": false, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.8" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }