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ayghriayghri
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removed refinable mesh
1 parent b1c41ef commit 77d9946

27 files changed

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‎examples/Discrete_Laplacian.ipynb‎

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‎examples/faces.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "219aa85c-7c68-456b-998e-7f33597df555",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "d588a3a1-7cc2-498f-839f-4adb924b9e2e",
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"metadata": {},
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"outputs": [],
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"source": [
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"n = 500000\n",
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"k = 5"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "26da88a6-4316-4c68-bd91-044e7cdfbc36",
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"metadata": {},
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"outputs": [],
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"source": [
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"faces = np.random.randint(0,n,(n,k))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "e36b486d-c793-45c6-b91a-f8d0c1028b89",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(500000, 5)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"faces.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "b7bb21ef-d4b8-4063-90bd-4f3d8c9d6a71",
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"metadata": {},
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"outputs": [],
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"source": [
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"num_edges_per_face = k "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "1da88955-e4c2-4bb5-8955-d2eb605c0676",
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext memory_profiler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "3b62d7fd-9893-4cf9-a487-3ed05b67c156",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"peak memory: 148.63 MiB, increment: 0.00 MiB\n"
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]
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}
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],
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"source": [
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"%%memit\n",
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"indices = np.stack(\n",
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" [np.arange(num_edges_per_face), (np.arange(num_edges_per_face) + 1) % num_edges_per_face], \n",
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" axis=1\n",
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")\n",
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"\n",
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"# Use advanced indexing to select face pairs\n",
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"es = faces[:, indices].reshape(-1, 2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "78255cc0-20c1-4f55-99a2-f2b144f098da",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"peak memory: 148.58 MiB, increment: 0.00 MiB\n"
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]
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}
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],
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"source": [
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"%%memit\n",
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"es = np.concatenate( [\n",
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" faces[:, [i, (i + 1) % num_edges_per_face]]\n",
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" for i in range(num_edges_per_face)\n",
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" ],\n",
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" axis=0,\n",
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" )\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "13a599b9-ded1-4660-8a8d-6a98569382f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"target_vertices = np.arange(n)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "e91e6804-b698-4ef0-888c-7c588c3fc32e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.13 ms ± 23.9 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"a = np.tile(target_vertices[:,None], [1,3])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "21a85517-0762-43c2-a86f-2ae25eb3aa9f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.25 ms ± 103 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"repeated_arr = np.repeat(target_vertices[:, np.newaxis], 3, axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "263446a5-a9ac-48fc-9b87-d361befc6a12",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3.12 ms ± 170 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"repeated_arr = np.stack([target_vertices]*3,axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "a55153ae-0754-4b98-b5c6-38659cd7879e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(500000, 3)"
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]
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},
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"repeated_arr = np.stack([target_vertices]*3,axis=1)\n",
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"repeated_arr.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "54f1f7c4-e9ee-468d-a226-9f419d6b527d",
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"metadata": {},
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"outputs": [],
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"source": [
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"nearest_triangles = np.random.randint(0,n, (3,3))\n",
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"\n",
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"target_nodes = np.tile(np.arange(3)[:, None], (1, 3))\n",
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"res = np.stack(\n",
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" [nearest_triangles[..., None], target_nodes[..., None]], axis=-1\n",
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" ).reshape(-1, 2)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "1f1023ad-6dbe-416a-9096-c98b16977cc8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[310133, 67769, 2770],\n",
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" [ 48884, 167275, 180288],\n",
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" [433041, 57137, 122329]])"
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]
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},
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"execution_count": 35,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nearest_triangles"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "50942dc6-8038-43de-a5d1-289a32573ddd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0, 0, 0],\n",
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" [1, 1, 1],\n",
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" [2, 2, 2]])"
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]
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},
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"execution_count": 36,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"target_nodes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"id": "042a3169-631c-4eb2-9084-547c02ed68a3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[310133, 0],\n",
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" [ 67769, 0],\n",
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" [ 2770, 0],\n",
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" [ 48884, 1],\n",
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" [167275, 1],\n",
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" [180288, 1],\n",
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" [433041, 2],\n",
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" [ 57137, 2],\n",
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" [122329, 2]])"
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]
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},
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"res"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a248e01d-73d3-45a3-9d43-d7b8a16762f8",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.8"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎examples/icosphere_transfer.ipynb‎

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‎examples/length_distribution.py‎

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from sphedron import Icosphere
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from sphedron.helpers import compute_edges_angles
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import matplotlib.pyplot as plt
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import numpy as np
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mesh = Icosphere(refine_factor=16, refine_by_angle=False)
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distances = compute_edges_angles(mesh.nodes, mesh.edges)
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print("mean:", np.mean(distances))
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print("std:", np.std(distances))
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print("max:", np.max(distances))
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print("min:", np.min(distances))
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# print(plt.hist(distances, bins=40))
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plt.show()

‎examples/mesh_transfer.ipynb‎

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