{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interpolate scalar data on mesh" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from vedo import dataurl, Mesh, Points, show, settings\n", "from vedo.pyplot import plot\n", "\n", "settings.default_backend = \"vtk\" # or k3d, ipyvtk, or 2d" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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(x/y/z) 		10.45 ... 1395
-702.5 ... 682.8
-834.5 ... 457.9
center of mass (641, 14.0, -281)
average size 631.596
nr. points / faces 1576 / 3031
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" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load a mesh of a mouse limb at 12 days of development\n", "msh = Mesh(dataurl + \"290.vtk\")\n", "msh" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Pick 100 points where we measure the value of a gene expression\n", "ids = np.random.randint(0, msh.npoints, 100)\n", "pts = msh.points()[ids] # slice the numpy array\n", "x = pts[:, 0] # x coordinates of the points\n", "gene = np.sin((x+150)/500)**2 # we are making this up!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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(....embl.es/examples/data/290.vtk)\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
bounds
(x/y/z) 		10.45 ... 1395
-702.5 ... 682.8
-834.5 ... 457.9
center of mass (641, 14.0, -281)
average size 631.596
nr. points / faces 1576 / 3031
point data array Scalars
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" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a set of points with those values\n", "points = Points(pts, r=10).cmap(\"Greens\", gene)\n", "\n", "# Interpolate the gene data onto the mesh, by averaging the 5 closest points\n", "msh.interpolate_data_from(points, n=5).cmap(\"Greens\").add_scalarbar()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Create a graph of the gene expression as function of x-position\n", "gene_plot = plot(x, gene, lw=0, title=\"Gene expression\").as2d(scale=0.5)\n", "\n", "# Show the mesh, the points and the graph\n", "show(msh, points, gene_plot).close()" ] } ], "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": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 2 }