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milanofthemilanofthe
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updated the examples in the welcome modal
1 parent 2093834 commit bba5fa5

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Lines changed: 732 additions & 1487 deletions

‎src/lib/nodes/generated/blocks.ts‎

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// Auto-generated by scripts/extract-blocks.py - DO NOT EDIT
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// Re-run 'python scripts/extract-blocks.py' to update
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import type { NodeCategory } from '$lib/types';
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import type { NodeCategory } from '../types';
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export interface ExtractedParam {
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type: string;
@@ -1158,7 +1158,7 @@ export const extractedBlocks: Record<string, ExtractedBlock> =
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"Scope": {
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"blockClass": "Scope",
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"description": "Block for recording time domain data with variable sampling rate.",
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"docstringHtml": "<p>Block for recording time domain data with variable sampling rate.</p>\n<p>A time threshold can be set by <cite>t_wait</cite> to start recording data after the simulation\ntime is larger then the specified waiting time, i.e. <cite>t - t_wait &gt; 0</cite>.\nThis is useful for recording data only after all the transients have settled.</p>\n<p>The block uses an interal <cite>Schedule</cite> event, when <cite>sampling_rate</cite> is provided,\notherwise it just samples at every simulation timestep.</p>\n<div class=\"section\" id=\"parameters\">\n<h3>Parameters</h3>\n<dl class=\"docutils\">\n<dt>sampling_rate <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">int, None</span></dt>\n<dd>number of samples per time unit, default is every timestep</dd>\n<dt>t_wait <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">float</span></dt>\n<dd>wait time before starting recording, optional</dd>\n<dt>labels <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">list[str]</span></dt>\n<dd>labels for the scope traces, and for the csv, optional</dd>\n</dl>\n</div>\n<div class=\"section\" id=\"attributes\">\n<h3>Attributes</h3>\n<dl class=\"docutils\">\n<dt>recording <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">dict</span></dt>\n<dd>recording, where key is time, and value the recorded values</dd>\n<dt>_sample_next_timestep <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">bool</span></dt>\n<dd>flag to indicate this is a timestep to sample, only used for event based sampling\nwhen <cite>sampling_rate</cite> is provided as an arg</dd>\n<dt>events <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">list[Schedule]</span></dt>\n<dd>internal scheduled event for periodic input sampling when <cite>sampling_rate</cite> is provided</dd>\n</dl>\n</div>\n",
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"docstringHtml": "<p>Block for recording time domain data with variable sampling rate.</p>\n<p>A time threshold can be set by <cite>t_wait</cite> to start recording data after the simulation\ntime is larger then the specified waiting time, i.e. <cite>t - t_wait &gt; 0</cite>.\nThis is useful for recording data only after all the transients have settled.</p>\n<p>The block uses an interal <cite>Schedule</cite> event, when <cite>sampling_rate</cite> is provided,\notherwise it just samples at every simulation timestep.</p>\n<div class=\"section\" id=\"parameters\">\n<h3>Parameters</h3>\n<dl class=\"docutils\">\n<dt>sampling_rate <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">int, None</span></dt>\n<dd>number of samples per time unit, default is every timestep</dd>\n<dt>t_wait <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">float</span></dt>\n<dd>wait time before starting recording, optional</dd>\n<dt>labels <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">list[str]</span></dt>\n<dd>labels for the scope traces, and for the csv, optional</dd>\n</dl>\n</div>\n<div class=\"section\" id=\"attributes\">\n<h3>Attributes</h3>\n<dl class=\"docutils\">\n<dt>recording_time <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">list[float]</span></dt>\n<dd>recorded time points</dd>\n<dt>recording_data <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">list[float]</span></dt>\n<dd>regorded data points</dd>\n<dt>_incremental_idx <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">int</span></dt>\n<dd>index for incremental reading of accumulated data since last\ncall of incremental read</dd>\n<dt>_sample_next_timestep <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">bool</span></dt>\n<dd>flag to indicate this is a timestep to sample, only used for\nevent based sampling when <cite>sampling_rate</cite> is provided as an arg</dd>\n<dt>events <span class=\"classifier-delimiter\">:</span> <span class=\"classifier\">list[Schedule]</span></dt>\n<dd>internal scheduled event for periodic input sampling when\n<cite>sampling_rate</cite> is provided</dd>\n</dl>\n</div>\n",
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"params": {
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"sampling_rate": {
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"type": "any",

‎static/examples/bouncing-ball.json‎

Lines changed: 33 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -1,10 +1,10 @@
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{
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"version": "1.0.0",
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"metadata": {
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"created": "2025-12-22T23:21:53.588Z",
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"modified": "2025-12-22T23:21:53.588Z",
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"created": "2026-01-14T10:21:54.899Z",
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"modified": "2026-01-14T10:21:54.899Z",
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"name": "Bouncing Ball",
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"description": "Gravity and collision events"
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"name": "Gravity, Collisions and discrete Events"
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},
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"graph": {
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"nodes": [
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"targetNodeId": "78f720fe-8774-42c3-be6b-5a4ef30e00df",
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"targetPortIndex": 0
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}
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],
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"annotations": [
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{
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"id": "deeecd86-71ac-4a13-a289-c64c946e667c",
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"position": {
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"x": -45,
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"y": 90
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},
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"content": "# Bouncing Ball Physics\n\nThis is an example of a hybrid system with continuous dynamics (free falling ball) and discrete events (bounces).\n\nIntegrating the gravitational acceleration gives the ball velocity in free fall:\n\n$$ v(t) = - \\int_0^t g \\, d\\tau $$\n\nAnd the position is the integrated velocity:\n\n$$ x(t) = \\int_0^t v(\\tau) \\, d\\tau $$\n\nWe use an event node to define the discrete dynamics of the ball hitting the ground. Whenever the position crosses zero\n\n$$ |x(t)| = 0 $$\n\nthe sign of the velocity is flipped and a coefficient of restitution ($b$) is applied to model the energy loss from the deformation of the ball:\n\n$$ y(t) \\leftarrow -b \\, y(t) $$\n\nWhen the total energy drops below a certain threshold we consider that a Zeno state and stop the simulation.",
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"width": 425,
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"height": 370
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}
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]
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},
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"events": [
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"type": "pathsim.events.ZeroCrossing",
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"name": "Bounce",
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"position": {
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"x": 960,
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"x": 945,
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"y": 225
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},
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"params": {
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"func_evt": "bounce_detect",
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"func_act": "bounce_resolve"
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}
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},
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{
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"id": "eda13d11-f264-4861-ba54-e17c48884905",
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"type": "pathsim.events.Condition",
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"name": "Zeno",
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"position": {
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"x": 1200,
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"y": 225
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},
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"params": {
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"func_evt": "zeno_detect",
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"func_act": "zeno_resolve"
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}
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}
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],
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"codeContext": {
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"code": "# initial position \nx0 = 1\n\n# mass\nm = 1\n\n# gravity\ng = 9.81\n\n# bounce back coefficient (randomized)\nb = 0.85 + 0.1*np.random.rand()\n\n# event detection function\ndef bounce_detect(t):\n return pos.engine.get()\n\n# event resolution function\ndef bounce_resolve(t):\n pos.engine.set(-pos.engine.get())\n vel.engine.set(-b*vel.engine.get())"
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"code": "# initial position \nx0 = 1\n\n# mass\nm = 1\n\n# gravity\ng = 9.81\n\n# bounce back coefficient (randomized)\nb = 0.85 + 0.1*np.random.rand()\n\n# zeno energy threshold\ne_th = 0.5\n\n# event detection function\ndef bounce_detect(t):\n return pos.engine.state\n\n# event resolution function\ndef bounce_resolve(t):\n pos.engine.set(-pos.engine.state)\n vel.engine.set(-b*vel.engine.state)\n\n# zeno state detection\ndef zeno_detect(t):\n e = total_energy.outputs[0]\n return e < e_th\n\n# stop simulation \ndef zeno_resolve(t):\n sim.stop()\n"
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},
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"simulationSettings": {
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"duration": "5",
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"duration": "50",
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"dt": "",
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"solver": "RKBS32",
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"adaptive": true,
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"ftol": "",
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"dt_min": "",
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"dt_max": "0.05",
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"ghostTraces": 6,
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"ghostTraces": 8,
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"plotResults": true
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}
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}
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}

‎static/examples/cascade-subsystem.json‎

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{
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"version": "1.0.0",
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"metadata": {
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"created": "2025-12-23T14:57:43.382Z",
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"modified": "2025-12-23T14:57:43.382Z",
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"name": "Cascade Control",
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"description": "Nested PID control loops"
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"created": "2026-01-14T10:23:07.377Z",
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"modified": "2026-01-14T10:23:07.377Z",
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"name": "Cascade PI Controler",
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"name": "Cascade Control of a Plant modeled as nested Subsystems"
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},
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"graph": {
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"nodes": [
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}
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],
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"params": {
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"operations": null,
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"_rotation": 1
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}
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},
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"type": "WhiteNoise",
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"name": "Noise",
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"position": {
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"x": 255,
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"y": 0
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"x": 105,
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"y": -15
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},
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"inputs": [],
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"outputs": [
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],
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"params": {
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"spectral_density": "noise_psd",
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"sampling_rate": null,
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"_rotation": 1
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"sampling_rate": "0.5",
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"_rotation": 0
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}
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},
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{
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"id": "44861add-91bd-4091-bebe-f89b308c9c45",
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"type": "Scope",
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"name": "Disturbed Signal",
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"position": {
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"x": 345,
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"y": 210
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"x": 405,
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"y": 120
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},
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"inputs": [
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{
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"targetNodeId": "44861add-91bd-4091-bebe-f89b308c9c45",
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"targetPortIndex": 0
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}
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],
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"annotations": [
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{
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"id": "c3103ad7-22d5-455d-8078-579809c95ad6",
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"position": {
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"x": 345,
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"y": -15
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},
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"content": "# Disturbance\n\nThe inline disturbance adds a white noise signal on top of the input signal.",
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"width": 155,
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"height": 100
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}
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]
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}
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}
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"targetNodeId": "4154466b-5a0b-42f7-bb32-27e649d59245",
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"targetPortIndex": 0
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}
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],
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"annotations": [
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{
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"id": "4dc50a79-58b7-4cbd-b049-110bfbbd117e",
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"position": {
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"x": -600,
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"y": -255
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},
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"content": "# Plant\n\nThe plant contains two transfer functions and an additive inline disturbance...\n\n... which is modeled by a subsystem too.",
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"width": 140,
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"height": 160
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}
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]
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}
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},
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"type": "Scope",
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"name": "Plant vs Setpoint",
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"position": {
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"x": 1170,
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"y": 495
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"x": 1095,
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"y": 510
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},
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"inputs": [
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{
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],
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"outputs": [],
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"params": {
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"sampling_rate": null,
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"t_wait": "0.0",
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"labels": null,
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"_rotation": 1
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}
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},
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],
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"outputs": [],
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"params": {
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"sampling_rate": null,
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"t_wait": "0.0",
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"labels": null,
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"_rotation": 3
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}
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},
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],
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"outputs": [],
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"params": {
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"sampling_rate": null,
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"t_wait": "0.0",
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"labels": null,
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"_rotation": 3
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}
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}
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"targetNodeId": "e13e27ba-96ec-4b02-a6d6-6cea244e49dc",
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"targetPortIndex": 0
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}
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],
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"annotations": [
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{
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"id": "416e7281-7496-4b94-8c9f-ab802029d5e9",
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"position": {
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"x": 720,
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"y": 225
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},
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"content": "# Cascade PI Controler\n\nThis example is a cascade controler. The plant is modeled as a **Subsystem**. Double click to enter.",
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"width": 230,
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"height": 115
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}
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]
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},
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"events": [],
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"codeContext": {
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"code": "# spectral density of noise source in plant\nnoise_psd = 1e-5"
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"code": "# spectral density of noise source in plant\nnoise_psd = 1e-4"
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},
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"simulationSettings": {
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"duration": "100.0",
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"dt": "0.1",
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"solver": "RK4",
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"solver": "SSPRK22",
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"adaptive": true,
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"atol": "1e-6",
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"rtol": "1e-4",
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"ftol": "1e-9",
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"dt_min": "",
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"dt_max": "",
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"ghostTraces": 6,
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"ghostTraces": 3,
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"plotResults": true
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}
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}
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}

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