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delsimdelsim
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Added local serving notebook (#51)
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+163
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‎JupyterDash.ipynb‎

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@@ -20,7 +20,7 @@
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"import dash_html_components as html\n",
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"from dash.dependencies import Input, Output\n",
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"\n",
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"dd = JupyterDash('SomeID')\n",
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"dd = JupyterDash('SomeID', serve_locally=False)\n",
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"\n",
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"dd.layout = html.Div([dcc.RadioItems(id='item_list', \n",
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" options = [dict(label = k, value = k) for k in ['Hey', 'HiHi']]), \n",
@@ -46,12 +46,12 @@
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"data": {
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"text/html": [
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"<div>\n",
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" <iframe src=\"/app/endpoints/2f2216256833454083843ddf993755de/\" width=600 height=200 frameborder=\"0\"></iframe>\n",
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" <hr/><a href=\"/app/endpoints/2f2216256833454083843ddf993755de/\" target=\"_new\">Open in new window</a> for /app/endpoints/2f2216256833454083843ddf993755de/\n",
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" <iframe src=\"/app/endpoints/b721a9cd65a1465eb22b8dbc13875c4d/\" width=600 height=200 frameborder=\"0\"></iframe>\n",
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" <hr/><a href=\"/app/endpoints/b721a9cd65a1465eb22b8dbc13875c4d/\" target=\"_new\">Open in new window</a> for /app/endpoints/b721a9cd65a1465eb22b8dbc13875c4d/\n",
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"</div>"
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],
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"text/plain": [
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"<jupyter_plotly_dash.dash_wrapper.JupyterDash at 0x7f44e5bd7518>"
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"<jupyter_plotly_dash.dash_wrapper.JupyterDash at 0x7f9d9f80c1d0>"
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]
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},
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"execution_count": 3,
@@ -73,7 +73,7 @@
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{
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"data": {
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"text/plain": [
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"'/app/endpoints/2f2216256833454083843ddf993755de/'"
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"'/app/endpoints/b721a9cd65a1465eb22b8dbc13875c4d/'"
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]
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},
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"execution_count": 4,
@@ -104,33 +104,16 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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" <iframe src=\"/proxy/8055/SomeID/\" width=600 height=200 frameborder=\"0\"></iframe>\n",
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" <hr/><a href=\"/proxy/8055/SomeID/\" target=\"_new\">Open in new window</a> for /proxy/8055/SomeID/\n",
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"</div>"
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],
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"text/plain": [
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"<jupyter_plotly_dash.dash_wrapper.JupyterDash at 0x7f44e5bd7518>"
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]
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},
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"execution_count": 6,
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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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"outputs": [],
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"source": [
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"dd"
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -195,61 +178,27 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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" <iframe src=\"/app/endpoints/58cceed54a4c4c28988d2d255833b001/\" width=800 height=100 frameborder=\"0\"></iframe>\n",
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" <hr/><a href=\"/app/endpoints/58cceed54a4c4c28988d2d255833b001/\" target=\"_new\">Open in new window</a> for /app/endpoints/58cceed54a4c4c28988d2d255833b001/\n",
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"</div>"
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],
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"text/plain": [
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"<jupyter_plotly_dash.dash_wrapper.JupyterDash at 0x7f44c219a160>"
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]
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},
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"execution_count": 8,
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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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"outputs": [],
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"source": [
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"app.height=100\n",
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"app"
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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": 9,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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" <iframe src=\"/app/endpoints/ff6cf46043a848b696a66ffeedcee85b/\" width=800 height=100 frameborder=\"0\"></iframe>\n",
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" <hr/><a href=\"/app/endpoints/ff6cf46043a848b696a66ffeedcee85b/\" target=\"_new\">Open in new window</a> for /app/endpoints/ff6cf46043a848b696a66ffeedcee85b/\n",
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"</div>"
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],
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"text/plain": [
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"<jupyter_plotly_dash.dash_wrapper.JupyterDash at 0x7f44c219a3c8>"
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]
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},
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"execution_count": 9,
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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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"outputs": [],
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"source": [
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"a2.height = 100\n",
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"a2"
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -261,33 +210,16 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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" <iframe src=\"/app/endpoints/ff6cf46043a848b696a66ffeedcee85b/\" width=800 height=100 frameborder=\"0\"></iframe>\n",
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" <hr/><a href=\"/app/endpoints/ff6cf46043a848b696a66ffeedcee85b/\" target=\"_new\">Open in new window</a> for /app/endpoints/ff6cf46043a848b696a66ffeedcee85b/\n",
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"</div>"
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],
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"text/plain": [
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"<jupyter_plotly_dash.dash_wrapper.JupyterDash at 0x7f44c219a3c8>"
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]
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},
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"execution_count": 11,
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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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"outputs": [],
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"source": [
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"a2"
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -296,26 +228,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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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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"dash_app magic\n",
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"feed\n",
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"None\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"%dash_app feed"
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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": 14,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -324,44 +246,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": null,
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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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"{}"
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]
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},
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"execution_count": 15,
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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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"outputs": [],
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"source": [
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"app.app_state"
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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": 16,
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"execution_count": null,
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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": "e14393b0cbf4489ca495d227ab79f8ee",
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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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"Button(description='Click Me!', style=ButtonStyle())"
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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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"outputs": [],
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"source": [
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"import ipywidgets as widgets\n",
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"from IPython.display import display\n",
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": null,
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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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"{}"
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]
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},
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"execution_count": 17,
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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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"outputs": [],
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"source": [
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"dd.app_state"
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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": 18,
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"execution_count": null,
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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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"config dir: /home/mark/local/jpd/env/etc/jupyter\n",
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" jupyter_plotly_dash.serverext \u001b[32m enabled \u001b[0m\n",
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" - Validating...\n",
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" jupyter_plotly_dash.serverext \u001b[32mOK\u001b[0m\n",
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" nbserverproxy \u001b[32m enabled \u001b[0m\n",
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" - Validating...\n",
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" nbserverproxy \u001b[32mOK\u001b[0m\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"!jupyter serverextension list"
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]
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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": "3.6.8"
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"version": "3.7.6"
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}
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},
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"nbformat": 4,

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