{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "a61d4fd0", "metadata": { "execution": { "iopub.execute_input": "2026-10-02T14:41:45.581367Z", "iopub.status.busy": "2026-10-02T14:41:45.581185Z", "iopub.status.idle": "2026-10-02T14:41:45.585262Z", "shell.execute_reply": "2026-10-02T14:41:45.584511Z" }, "papermill": { "duration": 0.006442, "end_time": "2026-10-02T14:41:45.585755+00:00", "exception": false, "start_time": "2026-10-02T14:41:45.579313+00:00", "status": "completed" }, "tags": [ "remove-input", "active-ipynb", "remove-output" ] }, "outputs": [], "source": [ "try:\n", " from openmdao.utils.notebook_utils import notebook_mode # noqa: F401\n", "except ImportError:\n", " !python -m pip install openmdao[notebooks]" ] }, { "cell_type": "markdown", "id": "8d2b93f3", "metadata": { "papermill": { "duration": 0.001186, "end_time": "2026-10-02T14:41:45.588446+00:00", "exception": false, "start_time": "2026-10-02T14:41:45.587260+00:00", "status": "completed" }, "tags": [] }, "source": [ "# VectorMagnitudeComp\n", "\n", "`VectorMagnitudeComp` computes the magnitude (L2 norm) of a single input of some given length.\n", "It may be vectorized to provide the result at one or more points simultaneously.\n", "\n", "$$\n", " \\lvert a_i \\rvert = \\sqrt{\\bar{a}_i \\cdot \\bar{a}_i}\n", "$$\n", "\n", "## VectorMagnitudeComp Options\n", "\n", "The default `vec_size` is 1, providing the magnitude of $a$ at a singlepoint. The length of $a$ is provided by option `length`.\n", "\n", "Other options for VectorMagnitudeComp allow the user to rename the input variable $a$ and the output $a\\_mag$, as well as specifying their units." ] }, { "cell_type": "code", "execution_count": 2, "id": "24182ec7", "metadata": { "execution": { "iopub.execute_input": "2026-10-02T14:41:45.591435Z", "iopub.status.busy": "2026-10-02T14:41:45.591300Z", "iopub.status.idle": "2026-10-02T14:41:46.784017Z", "shell.execute_reply": "2026-10-02T14:41:46.783255Z" }, "papermill": { "duration": 1.19487, "end_time": "2026-10-02T14:41:46.784514+00:00", "exception": false, "start_time": "2026-10-02T14:41:45.589644+00:00", "status": "completed" }, "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", " \n", "\n", "\n", "

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OptionDefaultAcceptable ValuesAcceptable TypesDescription
always_optFalse[True, False]['bool']If True, force nonlinear operations on this component to be included in the optimization loop even if this component is not relevant to the design variables and responses.
default_shape(1,)N/A['tuple']Default shape for variables that do not set val to a non-scalar value or set shape, shape_by_conn, copy_shape, or compute_shape. Default is (1,).
derivs_methodN/A['jax', 'cs', 'fd', None]N/AThe method to use for computing derivatives
distributedFalse[True, False]['bool']If True, set all variables in this component as distributed across multiple processes
in_nameaN/A['str']The variable name for input vector.
length3N/A['int']The length of the input vector at each point
mag_namea_magN/A['str']The variable name for output vector magnitude.
run_root_onlyFalse[True, False]['bool']If True, call compute, compute_partials, linearize, apply_linear, apply_nonlinear, solve_linear, solve_nonlinear, and compute_jacvec_product only on rank 0 and broadcast the results to the other ranks.
unitsN/AN/A['str']The units of the input vector.
use_jitTrue[True, False]['bool']If True, attempt to use jit on compute_primal, assuming jax or some other AD package capable of jitting is active.
vec_size1N/A['int']The number of points at which the vector magnitude is computed
\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import openmdao.api as om\n", "om.show_options_table(\"openmdao.components.vector_magnitude_comp.VectorMagnitudeComp\")" ] }, { "cell_type": "markdown", "id": "3ae67fa9", "metadata": { "papermill": { "duration": 0.001053, "end_time": "2026-10-02T14:41:46.786878+00:00", "exception": false, "start_time": "2026-10-02T14:41:46.785825+00:00", "status": "completed" }, "tags": [] }, "source": [ "## VectorMagnitudeComp Constructor\n", "\n", "The call signature for the `VectorMagnitudeComp` constructor is:\n", "\n", "```{eval-rst}\n", " .. automethod:: openmdao.components.vector_magnitude_comp.VectorMagnitudeComp.__init__\n", " :noindex:\n", "```\n", "\n", "## VectorMagnitudeComp Usage\n", "\n", "There are often situations when numerous magnitudes need to be computed, essentially in parallel.\n", "You can reduce the number of components required by having one `VectorMagnitudeComp` perform multiple operations.\n", "This is also convenient when the different operations have common inputs.\n", "\n", "The `add_magnitude` method is used to create additional magnitude calculations after instantiation.\n", "\n", "```{eval-rst}\n", " .. automethod:: openmdao.components.vector_magnitude_comp.VectorMagnitudeComp.add_magnitude\n", " :noindex:\n", "```\n", "\n", "## VectorMagnitudeComp Example\n", "\n", "In the following example VectorMagnitudeComp is used to compute the radius vector magnitude\n", "given a radius 3-vector at 100 points simultaneously. Note the use of `in_name` and `mag_name` to assign names to the inputs and outputs. Units are assigned using `units`. The units of the output magnitude are the same as those for the input." ] }, { "cell_type": "code", "execution_count": 3, "id": "54da8a72", "metadata": { "execution": { "iopub.execute_input": "2026-10-02T14:41:46.789553Z", "iopub.status.busy": "2026-10-02T14:41:46.789293Z", "iopub.status.idle": "2026-10-02T14:41:47.991513Z", "shell.execute_reply": "2026-10-02T14:41:47.990632Z" }, "papermill": { "duration": 1.205118, "end_time": "2026-10-02T14:41:47.992897+00:00", "exception": false, "start_time": "2026-10-02T14:41:46.787779+00:00", "status": "completed" }, "tags": [ "allow-assert" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1790952107.975225] [runnervm8df0l:6833 :0] ib_iface.c:1269 UCX ERROR mana_0: iface 0x5638e7e518f0 failed to create UD QP TX wr:256 sge:6 inl:64 resp:0 RX wr:4096 sge:1 resp:0 failed: Operation not supported\n", "[1790952107.975476] [runnervm8df0l:6833 :0] ucp_worker.c:1412 UCX ERROR uct_iface_open(ud_verbs/mana_0:1) failed: Input/output error\n", "[2.85750531 2.50174892 2.9155433 2.4823593 2.75900087 2.54721839\n", " 2.65788028 2.55844324 2.57807811 2.58960972 2.75723465 2.01360044\n", " 2.61203375 2.41654486 2.43476906 2.65317025 2.26166137 2.062405\n", " 2.6176164 2.58273795 2.60516257 2.29868961 2.41221897 2.76593447\n", " 2.61583202 2.3208024 2.37716736 3.0854658 2.35906415 2.73900957\n", " 2.72436912 2.91516313 2.39813078 2.92079569 3.06919622 2.89698033\n", " 2.71236004 2.5786449 1.81112453 2.62062092 2.68481822 1.92651602\n", " 2.61526281 2.93331478 2.43387968 2.56269571 3.00352941 2.91792402\n", " 2.54916631 3.06822165 2.69448613 2.78274325 2.52322206 2.8988186\n", " 2.54131714 2.58739168 2.77786575 2.9947228 2.53623928 2.39182134\n", " 3.16377517 2.68640272 2.94467724 2.14113781 2.61543108 2.79727583\n", " 2.2461479 2.50412708 2.34574704 2.65378687 2.55950639 2.65955213\n", " 2.39464601 2.39544872 2.02763116 2.70984679 2.66352621 3.07833642\n", " 3.12885213 2.49176721 2.94116838 2.32141479 3.03835872 3.16834085\n", " 2.61251647 2.61968055 2.4901345 2.79433088 3.12724637 2.2906506\n", " 2.56365899 2.09319289 2.11784028 2.57540421 2.44545707 2.54641595\n", " 2.8340948 2.38880222 2.57454853 2.53027228]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[runnervm8df0l:06833] pml_ucx.c:313 Error: Failed to create UCP worker\n" ] } ], "source": [ "import numpy as np\n", "import openmdao.api as om\n", "\n", "n = 100\n", "\n", "p = om.Problem()\n", "\n", "comp = om.VectorMagnitudeComp(vec_size=n, length=3,\n", " in_name='r', mag_name='r_mag', units='km')\n", "\n", "p.model.add_subsystem(name='vec_mag_comp', subsys=comp,\n", " promotes_inputs=[('r', 'pos')])\n", "\n", "p.setup()\n", "\n", "p.set_val('pos', 1.0 + np.random.rand(n, 3))\n", "\n", "p.run_model()\n", "\n", "# Verify the results against numpy.dot in a for loop.\n", "expected = []\n", "for i in range(n):\n", " a_i = p.get_val('pos')[i, :]\n", " expected.append(np.sqrt(np.dot(a_i, a_i)))\n", "\n", " actual_i = p.get_val('vec_mag_comp.r_mag')[i]\n", " rel_error = np.abs(expected[i] - actual_i)/actual_i\n", " assert rel_error < 1e-9, f\"Relative error: {rel_error}\"\n", "\n", "print(p.get_val('vec_mag_comp.r_mag'))" ] }, { "cell_type": "code", "execution_count": 4, "id": "5ce8983c", "metadata": { "execution": { "iopub.execute_input": "2026-10-02T14:41:47.997211Z", "iopub.status.busy": "2026-10-02T14:41:47.997017Z", "iopub.status.idle": "2026-10-02T14:41:48.002972Z", "shell.execute_reply": "2026-10-02T14:41:48.002378Z" }, "papermill": { "duration": 0.00879, "end_time": "2026-10-02T14:41:48.003577+00:00", "exception": false, "start_time": "2026-10-02T14:41:47.994787+00:00", "status": "completed" }, "tags": [ "remove-input", "remove-output" ] }, "outputs": [ { "data": { "text/plain": [ "np.float64(6.39103915647494e-17)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from openmdao.utils.assert_utils import assert_near_equal\n", "\n", "assert_near_equal(p.get_val('vec_mag_comp.r_mag'), np.array(expected))" ] }, { "cell_type": "markdown", "id": "b7bdf3d0", "metadata": { "papermill": { "duration": 0.001412, "end_time": "2026-10-02T14:41:48.006583+00:00", "exception": false, "start_time": "2026-10-02T14:41:48.005171+00:00", "status": "completed" }, "tags": [] }, "source": [ "## VectorMagnitudeComp Example with Multiple Magnitudes\n", "\n", "Note that, when defining multiple magnitudes, an input name in one call to `add_magnitude` may not be an output name in another call, and vice-versa." ] }, { "cell_type": "code", "execution_count": 5, "id": "16a89cbf", "metadata": { "execution": { "iopub.execute_input": "2026-10-02T14:41:48.010631Z", "iopub.status.busy": "2026-10-02T14:41:48.010335Z", "iopub.status.idle": "2026-10-02T14:41:48.023265Z", "shell.execute_reply": "2026-10-02T14:41:48.022723Z" }, "papermill": { "duration": 0.016099, "end_time": "2026-10-02T14:41:48.024196+00:00", "exception": false, "start_time": "2026-10-02T14:41:48.008097+00:00", "status": "completed" }, "tags": [ "allow_assert", "allow-assert" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2.94150942 2.38802442 2.96474423 2.96286864 2.93145956 2.37448568\n", " 2.66785445 2.68592757 3.10231324 2.20774032 3.16150831 2.67801845\n", " 2.35493889 2.5985426 2.84452695 2.57568393 2.51946056 2.6647638\n", " 3.38597844 2.83227089 2.79968191 2.55079835 2.36219258 2.97108226\n", " 2.62820447 2.7167298 2.56024763 3.12091922 3.05983716 2.3234788\n", " 2.98862055 2.64280412 2.73181378 3.04230545 2.27539892 2.40971498\n", " 2.81553032 2.38948478 2.48920531 2.33116376 2.81311334 2.39810696\n", " 2.7248686 2.86901389 2.66077466 2.48756816 2.40714374 3.34147257\n", " 3.02391174 2.70898673 2.59132995 2.86814649 2.45636438 2.20620658\n", " 3.14969345 2.59150227 2.43011744 2.79858915 2.36109504 2.70070795\n", " 2.79226762 3.08132426 3.08391196 3.10197039 3.20265938 2.50312371\n", " 2.13317725 2.91630068 2.8580269 2.14833487 2.40334094 2.72428637\n", " 2.92856938 2.60907714 2.296835 2.6070284 2.27316886 2.69177432\n", " 2.84323958 2.59094559 2.87381609 2.26458576 2.37670461 2.5978017\n", " 2.3109162 3.09358817 2.52866379 2.30601876 2.47865764 2.40813802\n", " 2.7931841 2.64195744 2.48969502 2.94986017 2.57442048 2.28693572\n", " 2.78809911 3.15379439 2.11473169 2.61203502]\n" ] } ], "source": [ "import numpy as np\n", "\n", "n = 100\n", "\n", "p = om.Problem()\n", "\n", "comp = om.VectorMagnitudeComp(vec_size=n, length=3,\n", " in_name='r', mag_name='r_mag', units='km')\n", "\n", "comp.add_magnitude(vec_size=n, length=3,\n", " in_name='b', mag_name='b_mag', units='ft')\n", "\n", "p.model.add_subsystem(name='vec_mag_comp', subsys=comp,\n", " promotes_inputs=['r', 'b'])\n", "\n", "p.setup()\n", "\n", "p.set_val('r', 1.0 + np.random.rand(n, 3))\n", "p.set_val('b', 1.0 + np.random.rand(n, 3))\n", "\n", "p.run_model()\n", "\n", "# Verify the results against numpy.dot in a for loop.\n", "expected_r = []\n", "expected_b = []\n", "for i in range(n):\n", " a_i = p.get_val('r')[i, :]\n", " expected_r.append(np.sqrt(np.dot(a_i, a_i)))\n", "\n", " actual_i = p.get_val('vec_mag_comp.r_mag')[i]\n", " rel_error = np.abs(expected_r[i] - actual_i)/actual_i\n", " assert rel_error < 1e-9, f\"Relative error: {rel_error}\"\n", "\n", " b_i = p.get_val('b')[i, :]\n", " expected_b.append(np.sqrt(np.dot(b_i, b_i)))\n", "\n", " actual_i = p.get_val('vec_mag_comp.b_mag')[i]\n", " rel_error = np.abs(expected_b[i] - actual_i)/actual_i\n", " assert rel_error < 1e-9, f\"Relative error: {rel_error}\"\n", "\n", "print(p.get_val('vec_mag_comp.r_mag'))" ] }, { "cell_type": "code", "execution_count": 6, "id": "6815465e", "metadata": { "execution": { "iopub.execute_input": "2026-10-02T14:41:48.028588Z", "iopub.status.busy": "2026-10-02T14:41:48.028395Z", "iopub.status.idle": "2026-10-02T14:41:48.033207Z", "shell.execute_reply": "2026-10-02T14:41:48.032590Z" }, "papermill": { "duration": 0.007731, "end_time": "2026-10-02T14:41:48.033901+00:00", "exception": false, "start_time": "2026-10-02T14:41:48.026170+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2.96069145 3.13130979 2.46960323 2.24185923 3.14236443 2.7162824\n", " 2.67084699 2.65208076 2.23138507 2.96495719 2.21283452 2.96075738\n", " 2.7791782 2.45658923 2.32385805 2.61976552 3.07065177 3.02596357\n", " 2.81495593 2.79496595 2.69827459 2.17279619 3.12760617 2.52198103\n", " 2.69469765 2.1651186 2.44447024 2.82418528 2.79381759 2.24740186\n", " 2.04800647 2.45960075 2.73787187 2.78086296 2.73710427 2.18965073\n", " 2.40401628 2.7300819 3.05246641 2.66080812 2.20449301 2.68046025\n", " 2.15475355 2.22855567 2.38393625 2.49604276 2.69421345 2.52955125\n", " 2.50563237 2.71117168 2.22085459 2.29111274 2.65386564 2.74080244\n", " 2.79412452 2.61396614 2.38182512 2.46355725 1.91068459 2.02098145\n", " 2.23456152 2.55308248 2.45104384 2.28304056 2.69883242 2.69168668\n", " 2.77406375 2.96496458 2.08479141 2.60693208 2.51590045 2.83919379\n", " 2.47032324 2.8424662 2.59189145 2.75896828 2.90934579 2.99116046\n", " 2.65137125 1.9195302 2.84364137 2.46645639 2.12549131 3.08559738\n", " 2.86335082 2.64536696 2.27241003 2.32738122 2.77713957 2.85329487\n", " 2.24610566 1.93326468 2.37727492 2.34248477 2.95801972 2.09113215\n", " 2.73661374 2.64484663 3.0714567 2.33691541]\n" ] } ], "source": [ "print(p.get_val('vec_mag_comp.b_mag'))" ] }, { "cell_type": "code", "execution_count": 7, "id": "95216bda", "metadata": { "execution": { "iopub.execute_input": "2026-10-02T14:41:48.038366Z", "iopub.status.busy": "2026-10-02T14:41:48.038188Z", "iopub.status.idle": "2026-10-02T14:41:48.041784Z", "shell.execute_reply": "2026-10-02T14:41:48.041148Z" }, "papermill": { "duration": 0.00768, "end_time": "2026-10-02T14:41:48.043063+00:00", "exception": false, "start_time": "2026-10-02T14:41:48.035383+00:00", "status": "completed" }, "tags": [ "remove-input", "remove-output" ] }, "outputs": [ { "data": { "text/plain": [ "np.float64(6.060962970202032e-17)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "assert_near_equal(p.get_val('vec_mag_comp.r_mag'), np.array(expected_r))\n", "assert_near_equal(p.get_val('vec_mag_comp.b_mag'), np.array(expected_b))" ] } ], "metadata": { "celltoolbar": "Tags", "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": "3.13.14" }, "orphan": true, "papermill": { "default_parameters": {}, "duration": 3.745256, "end_time": "2026-10-02T14:41:48.660288+00:00", "environment_variables": {}, "exception": null, "input_path": "/home/runner/work/OpenMDAO/OpenMDAO/openmdao/docs/openmdao_book/features/building_blocks/components/vector_magnitude_comp.ipynb", "output_path": "/home/runner/work/OpenMDAO/OpenMDAO/openmdao/docs/_executed_book/features/building_blocks/components/vector_magnitude_comp.ipynb", "parameters": {}, "start_time": "2026-10-02T14:41:44.915032+00:00", "version": "2.7.0" } }, "nbformat": 4, "nbformat_minor": 5 }