A tooltip functionality for Dash.
Project description
Dash Tooltip
A module to add interactive editable tooltips to your Dash applications. Inspired by mplcursors
and Matlab's datatip
.
Installation
You can download the dash_tooltip.py
module and place it in your working directory.
Basic Usage
import numpy as np
import plotly.express as px
from dash import Dash, dcc, html
from dash.dependencies import Input, Output
from dash_tooltip import tooltip
# Sample Data
np.random.seed(20)
y1 = np.random.normal(0, 10, 50)
x1 = np.arange(0, 50)
fig1 = px.scatter(x=x1, y=y1)
fig1.update_layout(title_text="Editable Title", title_x=0.5)
app1 = Dash(__name__)
#makes graph items, including tooltips editable
app1.layout = html.Div([
dcc.Graph(
id='graph1',
figure=fig1,
config={
'editable': True,
'edits': {
'shapePosition': True,
'annotationPosition': True
}
}
)
])
# Add the tooltip functionality to the app
tooltip(app1)
Click on data points to add tooltips.
If dcc.Graph
is configured editatble, tolltips:
- can be dragged around
- text can be edited on click
- can be deleted: click, delete text, enter. In some occasions a tooltip arrow may remain due to a Dash bug (clientside_callback not firing). In this cas, click near arrow end (mouse cursor changes to pointer), enter some text and repeat deletion and enter.
Advanced Usage
If you want to customize the tooltips, hover templates, and more:
import pandas as pd
import numpy as np
import plotly.express as px
from dash import Dash, dcc, html
from dash.dependencies import Input, Output
from dash_tooltip import tooltip
# Generate random time series data
date_rng = pd.date_range(start='2020-01-01', end='2020-12-31', freq='h')
ts1 = pd.Series(np.random.randn(len(date_rng)), index=date_rng)
ts2 = pd.Series(np.random.randn(len(date_rng)), index=date_rng)
df = pd.DataFrame({'Time Series 1': ts1, 'Time Series 2': ts2})
template = "x: %{x}<br>y: %{y:.2f}<br>ID: %{pointNumber}<br>name: %{customdata[0]}<br>unit: %{customdata[1]}"
fig10 = px.line(df, x=df.index, y=df.columns, title="Time Series Plot")
for i, trace in enumerate(fig10.data):
trace.customdata = np.column_stack((np.repeat(df.columns[i], len(df)), np.repeat('#{}'.format(i+1), len(df))))
trace.hovertemplate = template
app10 = Dash(__name__)
app10.layout = html.Div([
dcc.Graph(
id="graph-id",
figure=fig10,
config={
'editable': True,
'edits': {
'shapePosition': True,
'annotationPosition': True
}
}
)
])
tooltip(app10, graph_ids=["graph-id"], template=template, debug=True)
Tooltip Templates with Formatting
Tooltips can be formatted using templates similar to Plotly's hovertemplates. The tooltip template allows custom formatting and the inclusion of text and values.
For example, you can use a template like "x: %{x:.2f}<br>y: %{y:.2f}"
to display the x and y values with two decimal places.
Refer to Plotly’s documentation on hover text and formatting for more details on how to construct and customize your tooltip templates.
Custom Styling
custom_style = {
"font": {"size": 12, "color":"red"},
"arrowcolor": "red",
'arrowsize': 5,
# ... any other customization
}
tooltip(app10, style=custom_style, graph_ids=["graph-id"], template=template, debug=True)
For more examples, refer to the provided dash_tooltip_demo.py
or its Jupyter counterpart dash_tooltip_demo.ipynb
.
Handling Log Axes
Due to a long-standing bug in Plotly (see Plotly Issue #2580), annotations (fig.add_annotation
) may not be placed correctly on log-scaled axes. The dash_tooltip
module provides an option to automatically correct the tooltip placement on log-scaled axes via the apply_log_fix
argument in the tooltip
function. By default, apply_log_fix
is set to True
to enable the fix.
Debugging
If you encounter any issues or unexpected behaviors, enable the debug mode by setting the debug
argument of the tooltip
function to True
. The log outputs will be written to dash_app.log
in the directory where your script or application is located.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Acknowledgements
- Inspired by
mplcursors
and Matlab'sdatatip
.
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