{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"**Warning:** This notebook needs a running kernel to be fully interactive, please run it locally or run it on [mybinder](https://mybinder.org/v2/gh/vaexio/vaex/master?filepath=docs%2Fsource%2Ftutorial_jupyter.ipynb).\n",
"\n",
"
\n",
"\n",
"[](https://mybinder.org/v2/gh/vaexio/vaex/latest?filepath=docs%2Fsource%2Fexample_jupyter_plotly.ipynb)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"**Dashboard:** Or get a dashboard by rendering this notebook with Voila:\n",
"\n",
"
\n",
"\n",
"\n",
"[](http://voila.vaex.io/voila/render/example_jupyter_plotly.ipynb)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Plotly heatmap\n",
"\n",
"\n",
"Make sure you go through the [Vaex-jupyter tutorial first](./tutorial_jupyter.html).\n",
"\n",
"The easiest way to create your own visualizations is to follow a similar approach as described in the [Vaex-jupyter](./tutorial_jupyter.html) tutorial where we used matplotlib to create the figures. When using plotly however, we can first construct the widgets, and at each callback update the relevant components. This is much more efficient than creating an entirely new widget on each update. \n",
"\n",
"To solve this two step process (initialization and updating), we write a wrapper class that implement the dunder call method, such that it acts as a callable (like a function)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:04:50.172520Z",
"start_time": "2020-05-17T13:04:47.969864Z"
}
},
"outputs": [],
"source": [
"import vaex\n",
"import numpy as np\n",
"import vaex.jupyter.model as vjm\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Fetch a dataset\n",
"df = vaex.datasets.helmi_de_zeeuw.fetch()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:04:50.181812Z",
"start_time": "2020-05-17T13:04:50.174618Z"
}
},
"outputs": [],
"source": [
"# Define the axes\n",
"extend = 50\n",
"x_axis = vjm.Axis(df=df, expression=df.x, shape=100, min=-extend, max=extend)\n",
"y_axis = vjm.Axis(df=df, expression=df.y, shape=140, min=-extend, max=extend)\n",
"# in this case we need to know the min and max directly\n",
"await vaex.jupyter.gather()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:04:52.574882Z",
"start_time": "2020-05-17T13:04:51.290211Z"
}
},
"outputs": [],
"source": [
"import plotly.graph_objs as go\n",
"\n",
"class PlotlyHeatmap:\n",
" def __init__(self, x_axis, y_axis, figure_height=500, figure_width=400, title=\"Hi vaex, hi plotly\"):\n",
" self.x_axis = x_axis\n",
" self.y_axis = y_axis\n",
" self.heatmap = go.Heatmap()\n",
" self.layout = go.Layout(height=figure_height,\n",
" width=figure_width,\n",
" title=title,\n",
" xaxis=go.layout.XAxis(title=str(x_axis.expression),\n",
" range=[x_axis.min, x_axis.max]\n",
" ),\n",
" yaxis=go.layout.YAxis(title=str(y_axis.expression),\n",
" range=[y_axis.min, y_axis.max]\n",
" )\n",
" )\n",
" self.fig = go.FigureWidget(data=[self.heatmap], layout=self.layout)\n",
" # we respond to zoom/pan\n",
" self.fig.layout.on_change(self._pan_and_zoom, 'xaxis.range', 'yaxis.range')\n",
"\n",
" def _pan_and_zoom(self, layout, xrange, yrange):\n",
" self.x_axis.min, self.x_axis.max = xrange\n",
" self.y_axis.min, self.y_axis.max = yrange\n",
"\n",
" def __call__(self, data_array):\n",
" ar = data_array.data # take the numpy array data\n",
" assert data_array.ndim == 2\n",
" dim_x = data_array.dims[0]\n",
" dim_y = data_array.dims[1]\n",
" x0, x1 = data_array.coords[dim_x].attrs['min'], data_array.coords[dim_x].attrs['max']\n",
" y0, y1 = data_array.coords[dim_y].attrs['min'], data_array.coords[dim_y].attrs['max']\n",
" dx = (x1 - x0)/data_array.shape[0]\n",
" dy = (y1 - y0)/data_array.shape[1]\n",
"\n",
" z = np.log1p(ar).T\n",
" self.fig.update_traces(dict(z=z, x0=x0, y0=y0, dx=dx, dy=dy))\n",
" heatmap_plotly.fig.update_layout(\n",
" xaxis=go.layout.XAxis(title=dim_x, range=[x0, x1]),\n",
" yaxis=go.layout.YAxis(title=dim_y, range=[y0, y1])\n",
" )\n",
" \n",
"\n",
"heatmap_plotly = PlotlyHeatmap(x_axis, y_axis)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:04:53.594047Z",
"start_time": "2020-05-17T13:04:53.158770Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "664ef789dd2f418aad2f994a8eafeebb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"DataArray(children=[Container(children=[ProgressCircularNoAnimation(color='#9ECBF5', size=30, text='', value=1…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "174a1dc420a64d43b568190b5b918f47",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FigureWidget({\n",
" 'data': [{'type': 'heatmap', 'uid': '709f37ba-c069-4ad4-a26e-5072a777d78e'}],\n",
" 'layout':…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# we use `heatmap_plotly` as a callable function\n",
"da_view = df.widget.data_array(axes=[x_axis, y_axis], display_function=heatmap_plotly)\n",
"\n",
"# we display the progress bar and possible output (stack traces)\n",
"display(da_view)\n",
"\n",
"# and the plotly figure widget\n",
"display(heatmap_plotly.fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also create expression widgets to directly edit the axis on the figure above"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:04:56.335945Z",
"start_time": "2020-05-17T13:04:56.306185Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6fea44f84e174077be1eff4ba071f82e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Expression(label='Custom expression', placeholder='Enter a custom expression', prepend_icon='functions', succe…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e55dd91b551243f5a80075af1710a543",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Expression(label='Custom expression', placeholder='Enter a custom expression', prepend_icon='functions', succe…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_widget = df.widget.expression(x_axis)\n",
"y_widget = df.widget.expression(y_axis)\n",
"display(x_widget)\n",
"display(y_widget)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using [ipyvuetify](https://github.com/mariobuikhuizen/ipyvuetify) we can create pretty buttons and assign them some functionality:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:04:58.799606Z",
"start_time": "2020-05-17T13:04:58.745330Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8a17e1d5098f4abf9daab1a33679321d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Col(children=[Btn(children=['reset']), Btn(children=['fireball'])])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import ipyvuetify as v\n",
"\n",
"# A button to reset the figure ot its initial state\n",
"button_reset = v.Btn(children=['reset'])\n",
"\n",
"def reset(*ignore_arguments):\n",
" x_axis.expression = df.x\n",
" y_axis.expression = df.y\n",
"button_reset.on_event('click', reset)\n",
"\n",
"# A button that presents a specific figure\n",
"button_fireball = v.Btn(children=['fireball'])\n",
"\n",
"def fireball(*ignore_arguments):\n",
" x_axis.expression = np.log(df.x**2)\n",
" y_axis.expression = df.y\n",
"button_fireball.on_event('click', fireball)\n",
"\n",
"preset_widget = v.Col(children=[button_reset, button_fireball])\n",
"preset_widget\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Voila vuetify setup\n",
"\n",
"We can more elegantly present the visualisations created in this notebook using Voila."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:05:00.445189Z",
"start_time": "2020-05-17T13:05:00.441853Z"
}
},
"outputs": [],
"source": [
"from vaex.jupyter.widgets import ContainerCard, Html, LinkList"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:05:06.164470Z",
"start_time": "2020-05-17T13:05:06.147034Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ba2ce1ea92c471aa4fe41e5b2af419b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"LinkList(items=[{'title': 'Vaex', 'url': 'https://vaex.io', 'img': 'https://vaex.io/img/logos/logo-grey.svg'},…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LinkList(items=\n",
" [{'title': 'Vaex', 'url': 'https://vaex.io', 'img': 'https://vaex.io/img/logos/logo-grey.svg', },\n",
" {'title': 'Vaex on GitHub', 'url': 'https://github.com/vaexio/vaex', 'img': 'https://github.githubassets.com/pinned-octocat.svg'},\n",
" {'title': 'Vaex DataFrame server', 'url': 'http://dataframe.vaex.io/', 'icon': 'mdi-database'},\n",
" {'title': 'Voila (dashboard)', 'url': 'https://github.com/voila-dashboards/voila', 'icon': 'dashboard'},\n",
" {'title': 'Plotly', 'url': 'https://plotly.com/', 'img': 'https://plotly.com/img/favicon.ico'},\n",
" ], _metadata={'mount_id': 'content-nav'})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:05:07.915043Z",
"start_time": "2020-05-17T13:05:07.895880Z"
}
},
"outputs": [],
"source": [
"card_widget = ContainerCard(title=f'{len(df):,} Simulated stars',\n",
" subtitle=\"using vaex-jupyter\",\n",
" main=heatmap_plotly.fig,\n",
" controls=[x_widget, y_widget, preset_widget], \n",
" show_controls=True,\n",
" card_props={'style': 'width: 420px;', 'class': 'pa-2 ma-4'},\n",
" _metadata={'mount_id': 'content-main'}\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:05:09.143554Z",
"start_time": "2020-05-17T13:05:09.127962Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7f028cf3f9c04d9e875e70c136f7e452",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"ContainerCard(card_props={'style': 'width: 420px;', 'class': 'pa-2 ma-4'}, controls=[Expression(label='Custom …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# You do not have to render the widget for it to show up in voila-vuetify\n",
"card_widget"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T13:05:11.557260Z",
"start_time": "2020-05-17T13:05:11.539347Z"
}
},
"outputs": [],
"source": [
"Html(tag='span',\n",
" children=['Simulated stars'],\n",
" _metadata={'mount_id': 'content-bar'})\n",
"Html(tag='span',\n",
" children=['Resources'],\n",
" _metadata={'mount_id': 'content-title'});"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-14T20:59:01.403266Z",
"start_time": "2020-05-14T20:59:01.279583Z"
}
},
"source": [
"[screenshot](screenshot/example_jupyter_plotly.png)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
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"model_module_version": "^1.4.0",
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],
"_jupyter_vue": "IPY_MODEL_da2eee0d1307411f954e4fa579461649",
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