{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Run a ParAMSJob for Lennard-Jones\n", "\n", "ParAMS uses PLAMS to run jobs through Python. PLAMS offers many functions for handling jobs. To run jobs through PLAMS, you can either\n", "\n", "* use the ``$AMSBIN/plams`` program\n", "* use the ``$AMSBIN/amspython`` program. You must then call ``init()`` before running jobs.\n", "\n", "Here, we use the second approach." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PLAMS working folder: /tmp/demo_paramsjob\n" ] } ], "source": [ "# first import all plams and params functions and classes\n", "from scm.plams import *\n", "from scm.params import *\n", "import os\n", "\n", "# call PLAMS init() to set up a new directory for running jobs\n", "# set path=None to use the current working directory\n", "# the default folder name is 'plams_workdir'\n", "init(path=\"/tmp\", folder=\"demo_paramsjob\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below it is shown how to set up and run a ParAMS job using a ``params.in`` file taken from the Getting Started tutorial. The job should take less than 2 minutes to finish." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22.03|13:45:53] JOB LJ_Ar STARTED\n", "[22.03|13:45:54] JOB LJ_Ar RUNNING\n", "[22.03|13:46:17] JOB LJ_Ar FINISHED\n", "[22.03|13:46:17] JOB LJ_Ar SUCCESSFUL\n" ] } ], "source": [ "# load all the settings for the job from a \"params.in\" file\n", "params_in_file = os.path.expandvars(\"$AMSHOME/scripting/scm/params/examples/LJ_Ar/params.in\")\n", "job = ParAMSJob.from_inputfile(params_in_file)\n", "\n", "# set a name for the job\n", "job.name = \"LJ_Ar\"\n", "\n", "# run the job\n", "job.run();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find out where the job and its results are stored:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The job was run in: /tmp/demo_paramsjob/LJ_Ar\n", "Contents of the job directory: ['LJ_Ar.out', 'LJ_Ar.run', 'LJ_Ar.dill', 'results', 'LJ_Ar.in', 'LJ_Ar.err']\n", "The results are stored in: /tmp/demo_paramsjob/LJ_Ar/results\n", "Contents of the results directory: ['settings_and_initial_data', 'optimization']\n" ] } ], "source": [ "print(f\"The job was run in: {job.path}\")\n", "print(f\"Contents of the job directory: {os.listdir(job.path)}\")\n", "print(f\"The results are stored in: {job.results.path}\")\n", "print(f\"Contents of the results directory: {os.listdir(job.results.path)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Access the results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When a job has finished, you would like to access the results. The job may have been run via the GUI or with the ParAMSJob as above. Typically, you would write **another** Python script and load the finished (or running) job:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# job = ParAMSJob.load_external(results_dir)\n", "\n", "# in this example it would be\n", "\n", "# job = ParAMSJob.load_external('/tmp/demo_paramsjob/LJ_Ar/results')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, there is no need to explicitly load the job again with ``load_external`` since the job was run in the same script, so the lines above are commented out.\n", "\n", "The results can be accessed with ``job.results``, which is of type ``ParAMSResults``.\n", "\n", "Below we print a table with the initial and best Lennard-Jones parameters eps and sigma, and the corresponding loss function values." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " eps rmin loss\n", "Initial 0.0003000 4.00000 572.18867\n", "Best 0.0001961 3.65375 0.00251\n" ] } ], "source": [ "# compare the results\n", "initial_interface = job.results.get_parameter_interface(source=\"initial\")\n", "initial_loss = job.results.get_loss(source=\"initial\")\n", "best_interface = job.results.get_parameter_interface(source=\"best\")\n", "best_loss = job.results.get_loss(source=\"best\")\n", "\n", "print(\"{:12s} {:>12s} {:>12s} {:>12s}\".format(\"\", \"eps\", \"rmin\", \"loss\"))\n", "print(\n", " \"{:12s} {:12.7f} {:12.5f} {:12.5f}\".format(\n", " \"Initial\", initial_interface[\"eps\"].value, initial_interface[\"rmin\"].value, initial_loss\n", " )\n", ")\n", "print(\n", " \"{:12s} {:12.7f} {:12.5f} {:12.5f}\".format(\n", " \"Best\", best_interface[\"eps\"].value, best_interface[\"rmin\"].value, best_loss\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also plot the running loss function value vs. evaluation number:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "evaluation, loss = job.results.get_running_loss()\n", "plt.plot(evaluation, np.log10(loss), \"-\")\n", "plt.ylabel(\"log10(loss)\")\n", "plt.xlabel(\"Evaluation number\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see the parameter values at different evaluations:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "evaluation, parameters = job.results.get_running_active_parameters()\n", "plt.plot(evaluation, parameters[\"rmin\"])\n", "plt.xlabel(\"Evaluation id\")\n", "plt.ylabel(\"Value of rmin\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can plot a scatter plot of reference vs. predicted forces with the help of the ``get_data_set_evaluator()`` function, which returns a ``DataSetEvaluator``:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dse = job.results.get_data_set_evaluator()\n", "forces = dse.results[\"forces\"]\n", "plt.plot(forces.reference_values, forces.predictions, \".\")\n", "plt.xlabel(f\"Reference force ({forces.unit})\")\n", "plt.ylabel(f\"Predicted force ({forces.unit})\")\n", "plt.xlim(auto=True)\n", "plt.autoscale(False)\n", "plt.plot([-10, 10], [-10, 10], linewidth=5, zorder=-1, alpha=0.3, c=\"red\")\n", "plt.show()" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "For all the ways the DataSetEvaluator can be used, see the :ref:`Data Set Evaluator` documentation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Call PLAMS finish()\n", "\n", "If you used PLAMS to run jobs, the ``finish()`` function [should be called at the end](../../../plams/started.html#running-plams), if ``init()`` was called at the beginning." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22.03|13:46:17] PLAMS run finished. Goodbye\n" ] } ], "source": [ "finish()" ] } ], "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }