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Add ESRF demo
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‎2019-01-10-ESRF/README.md

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Installation
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------------
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Building this presentation requires npm. From the talk directory, run:
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```bash
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npm install
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```

‎2019-01-10-ESRF/notebooks/01.0.ipywidgets.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Jupyter Interactive widgets\n",
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"The notebook comes alive with the interactive widgets:\n",
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"\n",
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"- Part of the Jupyter project\n",
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"- BSD Licensed\n",
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"\n",
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"**Installation for the legacy notebook:**\n",
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"\n",
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"```bash\n",
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"conda install -c conda-forge ipywidgets\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Speeding up the bottleneck in the REPL\n",
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"\n",
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"<img src=\"./images/Flow.svg\"></img>"
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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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"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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"100"
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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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"10 * 10"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def f(x):\n",
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" print(x * x)"
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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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"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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"81\n"
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]
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}
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],
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"source": [
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"f(9)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from ipywidgets import *\n",
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"from traitlets import dlink"
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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": 6,
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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": "6ff7ff03c89841958aecd220c17d0eb8",
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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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"interactive(children=(IntSlider(value=50, description='x'), Output()), _dom_classes=('widget-interact',))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"interact(f, x=(0, 100));"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Interactive Jupyter widgets"
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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": 7,
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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": "4c44962b96874b6b9a5921b9e46150aa",
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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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"FloatSlider(value=7.5, description='Input:', max=10.0, min=5.0)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"slider = FloatSlider(\n",
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" value=7.5,\n",
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" min=5.0,\n",
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" max=10.0,\n",
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" step=0.1,\n",
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" description='Input:',\n",
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")\n",
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"\n",
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"slider"
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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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"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": "4c44962b96874b6b9a5921b9e46150aa",
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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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"FloatSlider(value=7.5, description='Input:', max=10.0, min=5.0)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"slider"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"slider.value = 9"
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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": 11,
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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": "277cf7934f0b4ff9809e8c02ec933d01",
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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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"FloatText(value=9.0, description='Value')"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"text = FloatText(description='Value')\n",
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"dlink((slider, 'value'), (text, 'value'))\n",
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"text"
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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": 12,
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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": "4c44962b96874b6b9a5921b9e46150aa",
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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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"FloatSlider(value=9.0, description='Input:', max=10.0, min=5.0)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"slider"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Widgets are represented in the back-end by a single object. Each time a widget is displayed, a new representation of that same object is created in the front-end. These representations are called views.\n",
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"\n",
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"![Kernel & front-end diagram](./images/WidgetModelView.png)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Jupyter widgets as a framework\n",
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"\n",
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" - bqplot\n",
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" - ipyleaflet\n",
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" - pythreejs"
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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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"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",
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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": "3.7.1"
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},
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"widgets": {
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"state": {
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"0cb2d81d826b4e25aba3b1d39711ef3f": {
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"views": [
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{
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"cell_index": 6
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}
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]
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},
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"2cfe6521f9834ca69e54518716104cd2": {
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"views": [
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{
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"cell_index": 8
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},
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{
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"cell_index": 9
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},
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{
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"cell_index": 12
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}
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]
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},
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"889356d59ac543a49da33d134d791c3f": {
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"views": [
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{
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"cell_index": 11
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}
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]
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}
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},
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"version": "2.0.0-dev"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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