{ "cells": [ { "cell_type": "markdown", "id": "31a94bbd-34ef-404b-a9be-4f3a216a8c3c", "metadata": {}, "source": [ "# Interpolation to query points\n", "\n", "In this example we show how to interpolate to an arbitrary set of query points." ] }, { "cell_type": "markdown", "id": "f9ad6337-6ba4-47c7-b687-0bf119a5b637", "metadata": {}, "source": [ "#### Import general modules" ] }, { "cell_type": "code", "execution_count": 1, "id": "8b4ca2be-46e8-443b-ab44-6d1a60abfec4", "metadata": {}, "outputs": [], "source": [ "# Import required modules\n", "from mpi4py import MPI #equivalent to the use of MPI_init() in C\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# Get mpi info\n", "comm = MPI.COMM_WORLD" ] }, { "cell_type": "markdown", "id": "1702ba96-7854-4a2f-89d2-7f2b2aaa61b1", "metadata": {}, "source": [ "#### Import modules from pynek" ] }, { "cell_type": "code", "execution_count": 2, "id": "09a4efe8-3557-4ff7-850e-0a26c8937bd6", "metadata": {}, "outputs": [], "source": [ "from pysemtools.io.ppymech.neksuite import pynekread\n", "from pysemtools.datatypes.msh import Mesh\n", "from pysemtools.datatypes.field import FieldRegistry" ] }, { "cell_type": "markdown", "id": "63764358", "metadata": {}, "source": [ "## Read the data and build objects\n", "\n", "In this instance, we create connectivity for the mesh object, given that we wish to use direct stiffness summation to reduce discontinuities." ] }, { "cell_type": "code", "execution_count": 3, "id": "116a2e64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-09-11 18:27:03,861 - Mesh - INFO - Initializing empty Mesh object.\n", "2024-09-11 18:27:03,863 - Field - INFO - Initializing empty Field object\n", "2024-09-11 18:27:03,864 - pynekread - INFO - Reading file: ../data/rbc0.f00001\n", "2024-09-11 18:27:03,876 - Mesh - INFO - Initializing Mesh object from x,y,z ndarrays.\n", "2024-09-11 18:27:03,876 - Mesh - INFO - Initializing common attributes.\n", "2024-09-11 18:27:03,877 - Mesh - INFO - Getting vertices\n", "2024-09-11 18:27:03,878 - Mesh - INFO - Getting vertices\n", "2024-09-11 18:27:03,888 - Mesh - INFO - Getting facet centers\n", "2024-09-11 18:27:03,896 - Mesh - INFO - Creating connectivity\n", "2024-09-11 18:27:04,376 - Mesh - INFO - Mesh object initialized.\n", "2024-09-11 18:27:04,377 - Mesh - INFO - Mesh data is of type: float64\n", "2024-09-11 18:27:04,378 - Mesh - INFO - Elapsed time: 0.502331771s\n", "2024-09-11 18:27:04,378 - pynekread - INFO - Reading field data\n", "2024-09-11 18:27:04,389 - pynekread - INFO - File read\n", "2024-09-11 18:27:04,389 - pynekread - INFO - Elapsed time: 0.525439139s\n" ] } ], "source": [ "msh = Mesh(comm, create_connectivity=True)\n", "fld = FieldRegistry(comm)\n", "fname = '../data/rbc0.f00001'\n", "pynekread(fname, comm, data_dtype=np.double, msh=msh, fld=fld)" ] }, { "cell_type": "markdown", "id": "45d0bf3a", "metadata": {}, "source": [ "## Building the set of points to use\n", "\n", "In this case, we will use a cylindrical mesh to interpolate the points in our domain.\n", "\n", "For this, we will use some tools to generate point distributions.\n", "\n", "Be mindful that in general, the interpolation routines will only takei nto considerations the points that are passed in rank 0.\n", "\n", "For this reason, we build all the points only in this rank and generate a dummy variable xyz in other ranks." ] }, { "cell_type": "code", "execution_count": 4, "id": "1eb170a7", "metadata": {}, "outputs": [], "source": [ "# Import helper functions\n", "import pysemtools.interpolation.utils as interp_utils\n", "import pysemtools.interpolation.pointclouds as pcs\n", "\n", "\n", "if comm.Get_rank() == 0 :\n", " # Generate the bounding box of the points\n", " x_bbox = [0, 0.05]\n", " y_bbox = [0, 2*np.pi]\n", " z_bbox = [0 , 1]\n", " nx = 30\n", " ny = 30\n", " nz = 80\n", " \n", " # Generate the 1D mesh\n", " x_1d = pcs.generate_1d_arrays(x_bbox, nx, mode=\"equal\")\n", " y_1d = pcs.generate_1d_arrays(y_bbox, ny, mode=\"equal\")\n", " z_1d = pcs.generate_1d_arrays(z_bbox, nz, mode=\"equal\")\n", "\n", " # Generate a 3D mesh\n", " r, th, z = np.meshgrid(x_1d, y_1d, z_1d, indexing='ij')\n", " x = r*np.cos(th)\n", " y = r*np.sin(th)\n", "\n", " # Array the points as a list of probes\n", " xyz = interp_utils.transform_from_array_to_list(nx,ny,nz,[x, y, z])\n", "\n", " # Write the points for future use\n", " with open('points.csv', 'w') as f:\n", " for i in range((xyz.shape[0])):\n", " f.write(f\"{xyz[i][0]},{xyz[i][1]},{xyz[i][2]}\\n\")\n", "else:\n", " xyz = 1\n" ] }, { "cell_type": "markdown", "id": "43f8de92", "metadata": {}, "source": [ "## Interpolate\n", "\n", "The module to use for the interpolations is:" ] }, { "cell_type": "code", "execution_count": 5, "id": "23a8b7ae", "metadata": {}, "outputs": [], "source": [ "from pysemtools.interpolation.probes import Probes" ] }, { "cell_type": "markdown", "id": "2c0dff3a", "metadata": {}, "source": [ "### Interpolate from points in memory\n", "\n", "We have created the xyz points and we have them in memory, so we can just interpolate form there.\n", "\n", "#### Finding the points\n", "\n", "The first step is to initialize the probe object, which will attempt to find the query points in the SEM mesh.\n", "\n", "There are many options to do this, so we recommend that you check the documentation for this class." ] }, { "cell_type": "code", "execution_count": 6, "id": "5e449707", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-09-11 18:27:05,085 - Probes - INFO - Initializing Probes object\n", "2024-09-11 18:27:05,087 - Probes - INFO - Probes provided as keyword argument\n", "2024-09-11 18:27:05,089 - Probes - INFO - Input probes are distributed: False\n", "2024-09-11 18:27:05,090 - Probes - INFO - Mesh provided as keyword argument\n", "2024-09-11 18:27:05,096 - Probes - INFO - Initializing interpolator\n", "2024-09-11 18:27:05,098 - Interpolator - INFO - Initializing Interpolator object\n", "2024-09-11 18:27:05,105 - Interpolator - INFO - Initializing point interpolator: multiple_point_legendre_numpy\n", "2024-09-11 18:27:05,118 - Interpolator - INFO - Allocating buffers in point interpolator\n", "2024-09-11 18:27:05,121 - Interpolator - INFO - Using device: cpu\n", "2024-09-11 18:27:05,126 - Interpolator - INFO - Interpolator initialized\n", "2024-09-11 18:27:05,127 - Interpolator - INFO - Elapsed time: 0.02208092599999989s\n", "2024-09-11 18:27:05,128 - Probes - INFO - Setting up global tree\n", "2024-09-11 18:27:05,129 - Interpolator - INFO - Using global_tree of type: rank_bbox\n", "2024-09-11 18:27:05,133 - Interpolator - INFO - Finding bounding boxes for each rank\n", "2024-09-11 18:27:05,136 - Interpolator - INFO - Creating global KD tree with rank centroids\n", "2024-09-11 18:27:05,137 - Interpolator - INFO - Elapsed time: 0.004132310999999778s\n", "2024-09-11 18:27:05,139 - Probes - INFO - Scattering probes to all ranks\n", "2024-09-11 18:27:05,140 - Interpolator - INFO - Scattering probes\n", "2024-09-11 18:27:05,157 - Interpolator - INFO - done\n", "2024-09-11 18:27:05,159 - Interpolator - INFO - Elapsed time: 0.017513389999999962s\n", "2024-09-11 18:27:05,159 - Probes - INFO - Finding points\n", "2024-09-11 18:27:05,160 - Interpolator - INFO - using communication pattern: point_to_point\n", "2024-09-11 18:27:05,164 - Interpolator - INFO - Finding points - start\n", "2024-09-11 18:27:05,165 - Interpolator - INFO - Finding bounding box of sem mesh\n", "2024-09-11 18:27:05,223 - Interpolator - INFO - Creating KD tree with local bbox centroids\n", "2024-09-11 18:27:05,235 - Interpolator - INFO - Obtaining candidate ranks and sources\n", "2024-09-11 18:27:05,376 - Interpolator - INFO - Send data to candidates and recieve from sources\n", "2024-09-11 18:27:05,382 - Interpolator - INFO - Find rst coordinates for the points\n", "2024-09-11 18:27:25,039 - Interpolator - INFO - Send data to sources and recieve from candidates\n", "2024-09-11 18:27:25,042 - Interpolator - INFO - Determine which points were found and find best candidate\n", "2024-09-11 18:27:25,406 - Interpolator - INFO - Finding points - finished\n", "2024-09-11 18:27:25,408 - Interpolator - INFO - Elapsed time: 20.242600142s\n", "2024-09-11 18:27:25,408 - Probes - INFO - Gathering probes to rank 0 after search\n", "2024-09-11 18:27:25,409 - Interpolator - INFO - Gathering probes\n", "2024-09-11 18:27:25,417 - Interpolator - INFO - done\n", "2024-09-11 18:27:25,418 - Interpolator - INFO - Elapsed time: 0.007374879000000334s\n", "2024-09-11 18:27:25,418 - Probes - INFO - Redistributing probes to found owners\n", "2024-09-11 18:27:25,419 - Interpolator - INFO - Scattering probes\n", "2024-09-11 18:27:25,427 - Interpolator - INFO - done\n", "2024-09-11 18:27:25,428 - Interpolator - INFO - Elapsed time: 0.008342505000001665s\n", "2024-09-11 18:27:25,429 - Probes - INFO - Writing probe coordinates to ./interpolated_fields.csv\n", "2024-09-11 18:27:26,224 - Probes - INFO - Writing points with warnings to ./warning_points_interpolated_fields.json\n", "2024-09-11 18:27:26,226 - Probes - INFO - Found 69760 points, 0 not found, 2240 with warnings\n", "2024-09-11 18:27:26,227 - Probes - WARNING - There are points with warnings. Check the warning file to see them (error code -10)\n", "2024-09-11 18:27:26,228 - Probes - WARNING - There are points with warnings. If test pattern is small, you can trust the interpolation\n", "2024-09-11 18:27:26,229 - Probes - INFO - Probes object initialized\n", "2024-09-11 18:27:26,230 - Probes - INFO - Elapsed time: 21.144440734s\n" ] } ], "source": [ "probes = Probes(comm, probes = xyz, msh = msh, point_interpolator_type='multiple_point_legendre_numpy', max_pts=256, find_points_comm_pattern='point_to_point')" ] }, { "cell_type": "markdown", "id": "a964a7db", "metadata": {}, "source": [ "### Interpolate a set of fields\n", "\n", "To interpolate a particular field, you must call the function and put the fields in a list, as follow:" ] }, { "cell_type": "code", "execution_count": 7, "id": "6b79551e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-09-11 18:27:26,237 - Probes - INFO - Interpolating fields from field list\n", "2024-09-11 18:27:26,239 - Probes - INFO - Interpolating field 0\n", "2024-09-11 18:27:26,239 - Interpolator - INFO - Interpolating field from rst coordinates\n", "2024-09-11 18:27:26,660 - Interpolator - INFO - Elapsed time: 0.42095929099999907s\n" ] } ], "source": [ "# The first input is the time to write in the file\n", "probes.interpolate_from_field_list(0, [fld.registry['w']], comm, write_data=False)" ] }, { "cell_type": "markdown", "id": "06cb8501", "metadata": {}, "source": [ "### Plot results\n", "\n", "The points have now been interpolated and are gathered once again in rank 0. Therefore one can proceed to perform operations such as plotting.\n", "\n", "Note that they are still arrayed as a list. If one whishes to use the structured mesh format, then the points must be mapped back to their original shape." ] }, { "cell_type": "code", "execution_count": 8, "id": "21b75019", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if comm.Get_rank() == 0:\n", "\n", " polar_fields = interp_utils.transform_from_list_to_array(nx,ny,nz,probes.interpolated_fields)\n", " w_polar = polar_fields[1]\n", "\n", " w_2d = np.mean(w_polar, axis=1)\n", "\n", " levels = 500\n", " levels = np.linspace(-0.07, 0.07, levels)\n", " cmapp='RdBu_r'\n", " fig, ax = plt.subplots(1, 1,figsize=(5, 5))\n", "\n", " c1 = ax.tricontourf(r[:,0,:].flatten(), z[:,0,:].flatten() ,w_2d.flatten(), levels=levels, cmap=cmapp)\n", " fig.colorbar(c1)\n", " ax.set_xlabel(\"r\")\n", " ax.set_ylabel(\"z\")\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "78bdf617", "metadata": {}, "source": [ "### Interpolate to points in a csv\n", "\n", "If the points are already in a csv file, we can choose to interpolate from those, instead of bulding points in memory (which we already did).\n", "\n", "One option is to of course read them and assign them to the xyz array we indicated before. The same can be done with the mesh.\n", "\n", "Simply write the path to the file where the mesh and/or the probes are in the appropiate keyword argument." ] }, { "cell_type": "markdown", "id": "d6130d19", "metadata": {}, "source": [ "#### Initializing the probes object\n", "\n", "Now we can initialize the probes object" ] }, { "cell_type": "code", "execution_count": 9, "id": "cb8bb4a6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-09-11 18:27:27,207 - Probes - INFO - Initializing Probes object\n", "2024-09-11 18:27:27,208 - Probes - INFO - Reading probes from ./points.csv\n", "2024-09-11 18:27:27,388 - Probes - INFO - Input probes are distributed: False\n", "2024-09-11 18:27:27,388 - Probes - INFO - Reading mesh from ../data/rbc0.f00001\n", "2024-09-11 18:27:27,389 - Mesh - INFO - Initializing empty Mesh object.\n", "2024-09-11 18:27:27,390 - pynekread - INFO - Reading file: ../data/rbc0.f00001\n", "2024-09-11 18:27:27,393 - Mesh - INFO - Initializing Mesh object from x,y,z ndarrays.\n", "2024-09-11 18:27:27,394 - Mesh - INFO - Initializing common attributes.\n", "2024-09-11 18:27:27,394 - Mesh - INFO - Getting vertices\n", "2024-09-11 18:27:27,395 - Mesh - INFO - Getting vertices\n", "2024-09-11 18:27:27,403 - Mesh - INFO - Getting facet centers\n", "2024-09-11 18:27:27,409 - Mesh - INFO - Mesh object initialized.\n", "2024-09-11 18:27:27,410 - Mesh - INFO - Mesh data is of type: float32\n", "2024-09-11 18:27:27,410 - Mesh - INFO - Elapsed time: 0.016976128999999673s\n", "2024-09-11 18:27:27,411 - pynekread - INFO - File read\n", "2024-09-11 18:27:27,412 - pynekread - INFO - Elapsed time: 0.022214138999999022s\n", "2024-09-11 18:27:27,412 - Probes - INFO - Initializing interpolator\n", "2024-09-11 18:27:27,413 - Interpolator - INFO - Initializing Interpolator object\n", "2024-09-11 18:27:27,413 - Interpolator - INFO - Initializing point interpolator: multiple_point_legendre_numpy\n", "2024-09-11 18:27:27,418 - Interpolator - INFO - Allocating buffers in point interpolator\n", "2024-09-11 18:27:27,418 - Interpolator - INFO - Using device: cpu\n", "2024-09-11 18:27:27,419 - Interpolator - INFO - Interpolator initialized\n", "2024-09-11 18:27:27,420 - Interpolator - INFO - Elapsed time: 0.0066051529999988645s\n", "2024-09-11 18:27:27,421 - Probes - INFO - Setting up global tree\n", "2024-09-11 18:27:27,421 - Interpolator - INFO - Using global_tree of type: rank_bbox\n", "2024-09-11 18:27:27,422 - Interpolator - INFO - Finding bounding boxes for each rank\n", "2024-09-11 18:27:27,424 - Interpolator - INFO - Creating global KD tree with rank centroids\n", "2024-09-11 18:27:27,424 - Interpolator - INFO - Elapsed time: 0.002681530000000265s\n", "2024-09-11 18:27:27,425 - Probes - INFO - Scattering probes to all ranks\n", "2024-09-11 18:27:27,425 - Interpolator - INFO - Scattering probes\n", "2024-09-11 18:27:27,434 - Interpolator - INFO - done\n", "2024-09-11 18:27:27,435 - Interpolator - INFO - Elapsed time: 0.008592331000002673s\n", "2024-09-11 18:27:27,435 - Probes - INFO - Finding points\n", "2024-09-11 18:27:27,436 - Interpolator - INFO - using communication pattern: point_to_point\n", "2024-09-11 18:27:27,436 - Interpolator - INFO - Finding points - start\n", "2024-09-11 18:27:27,437 - Interpolator - INFO - Finding bounding box of sem mesh\n", "2024-09-11 18:27:27,467 - Interpolator - INFO - Creating KD tree with local bbox centroids\n", "2024-09-11 18:27:27,476 - Interpolator - INFO - Obtaining candidate ranks and sources\n", "2024-09-11 18:27:27,597 - Interpolator - INFO - Send data to candidates and recieve from sources\n", "2024-09-11 18:27:27,602 - Interpolator - INFO - Find rst coordinates for the points\n", "2024-09-11 18:27:45,167 - Interpolator - INFO - Send data to sources and recieve from candidates\n", "2024-09-11 18:27:45,170 - Interpolator - INFO - Determine which points were found and find best candidate\n", "2024-09-11 18:27:45,529 - Interpolator - INFO - Finding points - finished\n", "2024-09-11 18:27:45,530 - Interpolator - INFO - Elapsed time: 18.093211668s\n", "2024-09-11 18:27:45,530 - Probes - INFO - Gathering probes to rank 0 after search\n", "2024-09-11 18:27:45,531 - Interpolator - INFO - Gathering probes\n", "2024-09-11 18:27:45,536 - Interpolator - INFO - done\n", "2024-09-11 18:27:45,536 - Interpolator - INFO - Elapsed time: 0.00425026900000347s\n", "2024-09-11 18:27:45,537 - Probes - INFO - Redistributing probes to found owners\n", "2024-09-11 18:27:45,537 - Interpolator - INFO - Scattering probes\n", "2024-09-11 18:27:45,543 - Interpolator - INFO - done\n", "2024-09-11 18:27:45,544 - Interpolator - INFO - Elapsed time: 0.005580473000001973s\n", "2024-09-11 18:27:45,544 - Probes - INFO - Writing probe coordinates to ./interpolated_fields.csv\n", "2024-09-11 18:27:46,229 - Probes - INFO - Writing points with warnings to ./warning_points_interpolated_fields.json\n", "2024-09-11 18:27:46,232 - Probes - INFO - Found 69760 points, 0 not found, 2240 with warnings\n", "2024-09-11 18:27:46,233 - Probes - WARNING - There are points with warnings. Check the warning file to see them (error code -10)\n", "2024-09-11 18:27:46,234 - Probes - WARNING - There are points with warnings. If test pattern is small, you can trust the interpolation\n", "2024-09-11 18:27:46,235 - Probes - INFO - Probes object initialized\n", "2024-09-11 18:27:46,235 - Probes - INFO - Elapsed time: 19.027900337s\n" ] } ], "source": [ "probes2 = Probes(comm, probes=\"./points.csv\", msh = '../data/rbc0.f00001', point_interpolator_type='multiple_point_legendre_numpy', max_pts=256, find_points_comm_pattern='point_to_point')" ] }, { "cell_type": "markdown", "id": "546b31c5", "metadata": {}, "source": [ "After this, you can simply follow the same steps to interpolate fields. You can write the fields to the same data type by using the appropiate command.\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "b6c610cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-09-11 18:27:46,242 - Probes - INFO - Interpolating fields from field list\n", "2024-09-11 18:27:46,244 - Probes - INFO - Interpolating field 0\n", "2024-09-11 18:27:46,245 - Interpolator - INFO - Interpolating field from rst coordinates\n", "2024-09-11 18:27:46,703 - Interpolator - INFO - Elapsed time: 0.4582567080000004s\n", "2024-09-11 18:27:46,706 - Probes - INFO - Writing interpolated fields to ./interpolated_fields.csv\n" ] } ], "source": [ "probes2.interpolate_from_field_list(0, [fld.registry['w']], comm, write_data=True)" ] }, { "cell_type": "markdown", "id": "a9025c9c", "metadata": {}, "source": [ "## Reading probes results from CSV\n", "\n", "To read probes, we can use the probe reader object. Note that we will do this in rank 0. This is also the format of Neko probes, therefore you will be able to read those too." ] }, { "cell_type": "code", "execution_count": 11, "id": "292e9334", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import the module\n", "from pysemtools.io.read_probes import ProbesReader\n", "\n", "if comm.Get_rank() == 0:\n", "\n", " pr = ProbesReader(file_name = './interpolated_fields.csv')\n", "\n", " # The name of the field is the index in the list when interpolated by default\n", " w_polar2 = interp_utils.transform_from_list_to_array(nx,ny,nz,pr.fields['0'])\n", " \n", " w_2d2 = np.mean(w_polar2[0], axis=1)\n", "\n", " levels = 500\n", " levels = np.linspace(-0.07, 0.07, levels)\n", "\n", " cmapp='RdBu_r'\n", " fig, ax = plt.subplots(1, 1,figsize=(5, 5))\n", "\n", " c1 = ax.tricontourf(r[:,0,:].flatten(), z[:,0,:].flatten() ,w_2d2.flatten(), levels=levels, cmap=cmapp)\n", " fig.colorbar(c1)\n", " ax.set_xlabel(\"r\")\n", " ax.set_ylabel(\"z\")\n", " plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 }