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Pabla007/bqplot_S.ipynb

Last active June 7, 2019 08:48
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bqplot_S.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import bqplot"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#import json\n",
"import numpy as np\n",
"import bqplot.pyplot as plt\n",
"#from ipywidgets.embed import embed_data\n",
"#from ipywidgets.embed import embed_minimal_html\n",
"#from ipywidgets.embed import embed_minimal_html, dependency_state"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3f020b6fc0ab466b99d87af7f85fe6f1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig_margin={'top':…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"size = 100\n",
"scale = 100.\n",
"np.random.seed(0)\n",
"x_data = np.arange(size)\n",
"y_data = np.cumsum(np.random.randn(size) * scale)\n",
"\n",
"fig = plt.figure(title='First Example')\n",
"plt.plot(y_data)\n",
"fig\n",
"#embed_minimal_html('export.html', views=[fig], state=dependency_state([fig]))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"html_template = '''<html><head>\n",
"\n",
"\n",
"<!-- Load require.js. Delete this if your page already loads require.js -->\n",
"<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js\" \n",
"integrity=\"sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=\" crossorigin=\"anonymous\">\n",
"</script>\n",
"<script src=\"https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js\" crossorigin=\"anonymous\"></script>\n",
"<script type=\"application/vnd.jupyter.widget-state+json\">\n",
"{\n",
" \"version_major\": 2,\n",
" \"version_minor\": 0,\n",
" \"state\": {\n",
" \"ccf41db1734c4b01a3f0d1626d88672c\": {\n",
" \"model_name\": \"LayoutModel\",\n",
" \"model_module\": \"@jupyter-widgets/base\",\n",
" \"model_module_version\": \"1.1.0\",\n",
" \"state\": {\n",
" \"min_width\": \"125px\"\n",
" }\n",
" },\n",
" \"50630ca5d7494397946e769624d75c7d\": {\n",
" \"model_name\": \"LinearScaleModel\",\n",
" \"model_module\": \"bqplot\",\n",
" \"model_module_version\": \"^0.4.5\",\n",
" \"state\": {\n",
" \"allow_padding\": false,\n",
" \"max\": 1,\n",
" \"min\": 0,\n",
" \"stabilized\": false\n",
" }\n",
" },\n",
" \"bbdddfa7678545d9a3551cfaf9ed0038\": {\n",
" \"model_name\": \"LinearScaleModel\",\n",
" \"model_module\": \"bqplot\",\n",
" \"model_module_version\": \"^0.4.5\",\n",
" \"state\": {\n",
" \"allow_padding\": false,\n",
" \"max\": 1,\n",
" \"min\": 0,\n",
" \"stabilized\": false\n",
" }\n",
" },\n",
" \"abbea5e8c9b5433d8943d32997dd46ee\": {\n",
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" \"model_module\": \"bqplot\",\n",
" \"model_module_version\": \"^0.4.5\",\n",
" \"state\": {\n",
" \"_dom_classes\": [],\n",
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" \"IPY_MODEL_8f1a60477c734bc78fc99e9dd06cce70\",\n",
" \"IPY_MODEL_d6b5bb73e2984d1fbe518d0ea5825f9a\"\n",
" ],\n",
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" \"marks\": [\n",
" \"IPY_MODEL_5f66b75c2b9b4cc29b7aae2dcbf3e000\"\n",
" ],\n",
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" \"scale_y\": \"IPY_MODEL_bbdddfa7678545d9a3551cfaf9ed0038\",\n",
" \"title\": \"First Example\"\n",
" }\n",
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" },\n",
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