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Add demo notebooks and environment
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‎.gitignore‎

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@@ -2,3 +2,5 @@ node_modules/
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output/
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package-lock.json
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subshells.pdf
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__pycache__/
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.ipynb_checkpoints/

‎README.md‎

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@@ -25,3 +25,15 @@ npm run pdf
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```
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which generates `subshells.pdf`.
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## Serve demos locally
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Create and activate a `micromamba` (or `pixi` or `conda` etc) environment and run `jupyter lab`:
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```bash
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micromamba create -f environment.yml
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micromamba activate subshells
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cd notebooks
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jupyter lab
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```

‎environment.yml‎

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name: subshells
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channels:
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- conda-forge
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dependencies:
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- ipykernel >= 7.1.0
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- ipywidgets
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- jupyterlab >= 4.4.0
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- numpy
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- pillow
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- python

‎index.md‎

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@@ -158,7 +158,7 @@ mermaid diagram similar to that in https://github.com/ipython/ipykernel/pull/124
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- `ipykernel` is the reference implementation.
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- Later releases fix problems, mostly caused by new shell channel thread rather than subshells themselves
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- `jupyterlab >= 4.4.0`
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- For `ipywidgets` support need `jupyterlab >= 4.4.0`
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- For `ipywidgets` support need `jupyterlab >= 4.4.4`
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- For `%matplotlib ipympl` support need `ipympl >= 0.9.8`
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---

‎notebooks/demo_1_simple.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": null,
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"id": "090c6acc-e56f-4c1f-8024-c413639a1724",
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"metadata": {},
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"outputs": [],
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"source": [
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"a = [2, 3, 4, 5]\n",
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"a"
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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": "a62bcaad-0ef5-4c9e-9e8c-ce9a239d2db1",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os, threading as t\n",
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"print({\"pid\": os.getpid(), \"thread_count\": t.active_count(), \"thread_id\": t.current_thread()})"
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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": "c72811d3-ebf7-44ee-a787-c3c501d61745",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"%subshell"
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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": "34e331d0-1772-45ce-83ed-5b388225bcc4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Simulate a long-running task.\n",
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"import time\n",
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"for b in range(10):\n",
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" print('b =', b)\n",
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" time.sleep(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": null,
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"id": "a94a83c7-e120-4c34-b465-3c7e633b375d",
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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": "3.14.0"
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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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}

‎notebooks/demo_2_raytracer.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": null,
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"id": "1f697483-ce2b-49cc-9e3d-feec5c1de98c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.display import display\n",
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"import time\n",
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"from raytracer import RayTracer\n",
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"\n",
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"ray_tracer = RayTracer(700, 500)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bf4a9a3a-5ac7-4d96-9c42-35314729655a",
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"metadata": {},
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"source": [
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"## Run ray tracer\n",
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"\n",
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"### With just a single thread in this notebook we cannot plot the results until the calculation has finished"
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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": "49866843-8013-4110-8a40-44be22722a76",
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"metadata": {},
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"outputs": [],
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"source": [
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"ray_tracer.run_random()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9c312380-f822-4fd3-9ec4-fed5e407b80a",
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"metadata": {},
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"source": [
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"## Plot ray traced image"
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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": "e6219511-ac58-414e-80e8-00b9aa126531",
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"metadata": {},
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"outputs": [],
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"source": [
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"handle = display(ray_tracer.as_image(), display_id=True)"
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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": "5eb89836-3147-4778-b1da-915a8dfeff5e",
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"metadata": {},
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"outputs": [],
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"source": [
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"ray_tracer.reset()"
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]
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},
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{
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"cell_type": "raw",
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"id": "24068e26-4c1b-49e3-acd5-f514010968cc",
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"metadata": {},
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"source": [
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"# code to run in subshell\n",
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"while not ray_tracer.completed:\n",
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" handle.update(ray_tracer.as_image())\n",
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" time.sleep(0.5)\n",
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"handle.update(ray_tracer.as_image())"
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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": "f8b1bbd4-fc98-49eb-aafd-bb1899bb5536",
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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": "3.14.0"
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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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}

‎notebooks/demo_3_ipywidgets.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": "a3600d23-63b9-4a8f-86be-7c2a453ad462",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ipywidgets import HBox, IntSlider\n",
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"import time\n",
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"from traitlets import link"
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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": "cf42830b-bdc4-48ac-977d-9abaf0e19aef",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "84129ec96ab44ec6afcfd4d3774271bc",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(IntSlider(value=0, orientation='vertical'), IntSlider(value=0, orientation='vertical'), IntSlid…"
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]
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},
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"execution_count": 2,
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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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"n = 10\n",
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"sliders = [IntSlider(orientation='vertical') for i in range(n)]\n",
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"HBox(sliders)"
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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": "131fad3f-40ca-47b1-9089-58925686dd4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Link all the sliders in python\n",
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"links = [link((sliders[i], 'value'), (sliders[i+1], 'value')) for i in range(n-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": 4,
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"id": "eb2d735d-5ecc-49eb-b154-7a5d34601699",
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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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"0 0\n",
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"0 0\n",
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"0 0\n",
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"0 0\n",
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"0 0\n",
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"0 0\n",
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"0 0\n",
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"0 0\n",
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"0 0\n",
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"0 0\n"
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]
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}
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],
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"source": [
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"for i in range(10):\n",
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" time.sleep(1)\n",
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" print(sliders[0].value, sliders[-1].value)"
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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": "45ccce73-ec13-4a11-84b1-2d74b42a89dc",
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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": "3.14.0"
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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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}

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