https://github.com/predict-idlab/plotly-resampler

Visualize large time series data with plotly.py

Science Score: 36.0%

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Keywords

data-analysis data-science data-visualization plotly plotly-dash python time-series visualization

Keywords from Contributors

interactive transformers interface graph-computation

Last synced: 6 months ago · JSON representation

Repository

Visualize large time series data with plotly.py

Basic Info

Host: GitHub
Owner: predict-idlab
License: mit
Language: Python
Default Branch: main
Homepage: https://predict-idlab.github.io/plotly-resampler/latest
Size: 48.4 MB

Statistics

Stars: 1,144
Watchers: 17
Forks: 73
Open Issues: 62
Releases: 11

Topics

data-analysis data-science data-visualization plotly plotly-dash python time-series visualization

Created over 4 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing Funding License

README.md

plotly_resampler: visualize large sequential data by adding resampling functionality to Plotly figures

plotly-resampler improves the scalability of Plotly for visualizing large time series datasets. Specifically, our library dynamically aggregates time-series data respective to the current graph view, ensuring efficient and responsive updates during user interactions like panning or zooming via callbacks.

This core aggregation functionality is achieved by utilizing by time-series data point selection algorithms, for which plotly-resampler leverages the highly optimized implementations available in tsdownsample. Our default data aggregation method is MinMaxLTTB (and selects 1000 data points for plotting). For a deeper understanding of this method, you can consult to the algorithm's dedicated MinMaxLTTB repository and the associated research paper.

basic example gif

In this Plotly-Resampler demo over 110,000,000 data points are visualized!

🛠️ Installation

| pip | pip install plotly-resampler | | ---| ----|

👀 What is the difference between plotly-resampler figures and plain plotly figures?

plotly-resampler can be thought of as wrapper around plain plotly figures which adds visualization scalability to line-charts by dynamically aggregating the data w.r.t. the front-end view. plotly-resampler thus adds dynamic aggregation functionality to plain plotly figures.

❗ Important to know:

show always generates a static HTML view of the figure, prohibiting dynamic aggregation.
To have dynamic aggregation:
- Use show_dash with FigureResampler to initiate a Dash app to realize the dynamic aggregation with callbacks.
  (or output the object in a cell via IPython.display), which will also spawn a dash-web app
- with FigureWidgetResampler, you need to use IPython.display on the object, which uses widget-events to realize dynamic aggregation (via the running IPython kernel).

Other changes of plotly-resampler figures w.r.t. vanilla plotly:

double-clicking within a line-chart area does not Reset Axes, as it results in an “Autoscale” event. We decided to implement an Autoscale event as updating your y-range such that it shows all the data that is in your x-range.
- Note: vanilla Plotly figures their Autoscale result in Reset Axes behavior, in our opinion this did not make a lot of sense. It is therefore that we have overriden this behavior in plotly-resampler.

📋 Features

Convenient to use:
- just add either
  - register_plotly_resampler function to your notebook with the best suited mode argument.
  - FigureResampler decorator around a plotly Figure and call .show_dash()
  - FigureWidgetResampler decorator around a plotly Figure and output the instance in a cell
- allows all other plotly figure construction flexibility to be used!
Environment-independent
- can be used in Jupyter, vscode-notebooks, Pycharm-notebooks, Google Colab, DataSpell, and even as application (on a server)
Interface for various aggregation algorithms:
- ability to develop or select your preferred sequence aggregation method

🚀 Usage

Add dynamic aggregation to your plotly Figure (unfold your fitting use case) * 🤖 Automatically (minimal code overhead):

Use the registerplotlyresampler function

1. Import and call the `register_plotly_resampler` method
2. Just use your regular graph construction code

* **code example**:
  ```python
  import plotly.graph_objects as go; import numpy as np
  from plotly_resampler import register_plotly_resampler

  # Call the register function once and all Figures/FigureWidgets will be wrapped
  # according to the register_plotly_resampler its `mode` argument
  register_plotly_resampler(mode='auto')

  x = np.arange(1_000_000)
  noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000


  # auto mode: when working in an IPython environment, this will automatically be a 
  # FigureWidgetResampler else, this will be an FigureResampler
  f = go.Figure()
  f.add_trace({"y": noisy_sin + 2, "name": "yp2"})
  f
  ```

> **Note**: This wraps **all** plotly graph object figures with a 
> `FigureResampler` | `FigureWidgetResampler`. This can thus also be 
> used for the `plotly.express` interface. 🎉

👷 Manually (higher data aggregation configurability, more speedup possibilities):
- Within a jupyter environment without creating a web application
- wrap the plotly Figure with FigureWidgetResampler
- output the FigureWidgetResampler instance in a cell ```python import plotly.graphobjects as go; import numpy as np from plotlyresampler import FigureResampler, FigureWidgetResampler
x = np.arange(1000000) noisysin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1000

# OPTION 1 - FigureWidgetResampler: dynamic aggregation via FigureWidget.layout.on_change fig = FigureWidgetResampler(go.Figure()) fig.addtrace(go.Scattergl(name='noisy sine', showlegend=True), hfx=x, hfy=noisysin)

fig * Using a web-application with <a href="https://github.com/plotly/dash">dash</a> callbacks 1. wrap the plotly Figure with `FigureResampler` 2. call `.show_dash()` on the `Figure`python import plotly.graphobjects as go; import numpy as np from plotlyresampler import FigureResampler, FigureWidgetResampler

x = np.arange(1000000) noisysin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1000

# OPTION 2 - FigureResampler: dynamic aggregation via a Dash web-app fig = FigureResampler(go.Figure()) fig.addtrace(go.Scattergl(name='noisy sine', showlegend=True), hfx=x, hfy=noisysin)

fig.show_dash(mode='inline') ```

Tip 💡: For significant faster initial loading of the Figure, we advise to wrap the constructor of the plotly Figure and add the trace data as hf_x and hf_y

Note: Any plotly Figure can be wrapped with FigureResampler and FigureWidgetResampler! 🎉 But only the go.Scatter/go.Scattergl traces are resampled.

💭 Important considerations & tips

When running the code on a server, you should forward the port of the FigureResampler.show_dash() method to your local machine.
Note that you can add dynamic aggregation to plotly figures with the FigureWidgetResampler wrapper without needing to forward a port!
The FigureWidgetResampler uses the IPython main thread for its data aggregation functionality, so when this main thread is occupied, no resampling logic can be executed. For example; if you perform long computations within your notebook, the kernel will be occupied during these computations, and will only execute the resampling operations that take place during these computations after finishing that computation.
In general, when using downsampling one should be aware of (possible) aliasing effects. The [R] in the legend indicates when the corresponding trace is being resampled (and thus possibly distorted) or not. Additionally, the ~<range> suffix represent the mean aggregation bin size in terms of the sequence index.
The plotly autoscale event (triggered by the autoscale button or a double-click within the graph), does not reset the axes but autoscales the current graph-view of plotly-resampler figures. This design choice was made as it seemed more intuitive for the developers to support this behavior with double-click than the default axes-reset behavior. The graph axes can ofcourse be resetted by using the reset_axis button. If you want to give feedback and discuss this further with the developers, see issue #49.

📜 Citation and papers

The paper about the plotly-resampler toolkit itself (preprint): https://arxiv.org/abs/2206.08703

bibtex @inproceedings{van2022plotly, title={Plotly-resampler: Effective visual analytics for large time series}, author={Van Der Donckt, Jonas and Van Der Donckt, Jeroen and Deprost, Emiel and Van Hoecke, Sofie}, booktitle={2022 IEEE Visualization and Visual Analytics (VIS)}, pages={21--25}, year={2022}, organization={IEEE} }

Related papers: - Visual representativeness of time series data point selection algorithms (preprint): https://arxiv.org/abs/2304.00900
code: https://github.com/predict-idlab/ts-datapoint-selection-vis - MinMaxLTTB - an efficient data point selection algorithm (preprint): https://arxiv.org/abs/2305.00332
code: https://github.com/predict-idlab/MinMaxLTTB

👤 Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost

Owner

Name: PreDiCT.IDLab
Login: predict-idlab
Kind: organization
Location: Ghent - Belgium

Website: http://predict.idlab.ugent.be/
Repositories: 55
Profile: https://github.com/predict-idlab

Repositories of the IDLab PreDiCT research group

GitHub Events

Total

Issues event: 25
Watch event: 114
Delete event: 4
Issue comment event: 48
Push event: 44
Pull request review comment event: 8
Pull request review event: 16
Pull request event: 18
Fork event: 11
Create event: 3

Last Year

Issues event: 25
Watch event: 114
Delete event: 4
Issue comment event: 48
Push event: 44
Pull request review comment event: 8
Pull request review event: 16
Pull request event: 18
Fork event: 11
Create event: 3

Committers

Last synced: 9 months ago

All Time

Total Commits: 713
Total Committers: 14
Avg Commits per committer: 50.929
Development Distribution Score (DDS): 0.366

Past Year

Commits: 20
Committers: 4
Avg Commits per committer: 5.0
Development Distribution Score (DDS): 0.45

Top Committers

Name	Email	Commits
jonvdrdo	j**t@u**e	452
jvdd	b**d@g**m	243
Emiel Deprost	e**t@u**e	5
Niels Praet	6****t	2
Jayce Slesar	4****r	2
Alexander Braekevelt	a**t@g**m	1
Emmanuel Ferdman	e**n@g**m	1
Mathieu De Meue	m**u@d**e	1
Maxim Ivanov	i**g@g**m	1
Michael Moore	1****e	1
Sofie Van Hoecke	s**e@u**e	1
Tomasz Jakubek	T**k@c**m	1
mstojche	m**a@u**e	1
Mathieu De Meue	m**e@c**e	1

Committer Domains (Top 20 + Academic)

ugent.be: 4 calibrate.be: 1 carrier.com: 1 demeue.be: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 129
Total pull requests: 112
Average time to close issues: 2 months
Average time to close pull requests: 28 days
Total issue authors: 78
Total pull request authors: 11
Average comments per issue: 2.89
Average comments per pull request: 1.74
Merged pull requests: 89
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 19
Pull requests: 19
Average time to close issues: 3 days
Average time to close pull requests: 11 days
Issue authors: 15
Pull request authors: 4
Average comments per issue: 0.84
Average comments per pull request: 0.47
Merged pull requests: 12
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

jonasvdd (27)
jvdd (8)
kszlim (4)
Jeremy38100 (3)
firmai (2)
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Velip09 (2)
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Broundal (2)
jayceslesar (2)
auxym (2)
Olivier0330 (2)
Alexander-Serov (2)

Pull Request Authors

jonasvdd (57)
jvdd (30)
NielsPraet (7)
jayceslesar (4)
ivanovmg (4)
yzhaoinuw (2)
lemikhovalex (2)
t-jakubek (2)
emmanuel-ferdman (2)
ISX2 (1)
imichaelmoore (1)

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Pull Request Labels

enhancement (22) bug (12) dependencies (4) documentation (4) Optimization (2) integration (1) new feature (1)

Packages

Total packages: 4
Total downloads:
- pypi 404,650 last-month
Total docker downloads: 2,335

Total dependent packages: 11
(may contain duplicates)
Total dependent repositories: 195
(may contain duplicates)
Total versions: 89
Total maintainers: 2

pypi.org: plotly-resampler

Visualizing large time series with plotly

Homepage: https://github.com/predict-idlab/plotly-resampler
Documentation: https://predict-idlab.github.io/plotly-resampler/latest
License: MIT
Latest release: 0.11.0
published 6 months ago

Versions: 65
Dependent Packages: 11
Dependent Repositories: 193
Downloads: 404,650 Last month
Docker Downloads: 2,335

Rankings

Downloads: 0.6%

Dependent repos count: 1.1%

Dependent packages count: 1.3%

Stargazers count: 2.2%

Average: 2.2%

Docker downloads count: 2.4%

Forks count: 5.7%

Maintainers (2)

jvdd jonvdrdo

Last synced: 6 months ago

proxy.golang.org: github.com/predict-idlab/plotly-resampler

Documentation: https://pkg.go.dev/github.com/predict-idlab/plotly-resampler#section-documentation
License: mit
Latest release: v0.11.0
published 6 months ago

Versions: 9
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 9.0%

Average: 9.6%

Dependent repos count: 10.2%

Last synced: 6 months ago

conda-forge.org: plotly-resampler

Homepage: https://github.com/predict-idlab/plotly-resampler
License: LicenseRef-plotly-resampler
Latest release: 0.8.1
published over 3 years ago

Versions: 14
Dependent Packages: 0
Dependent Repositories: 1

Rankings

Stargazers count: 15.3%

Dependent repos count: 24.4%

Forks count: 26.6%

Average: 29.5%

Dependent packages count: 51.6%

Last synced: 6 months ago

anaconda.org: plotly-resampler

Plotly is an awesome interactive visualization library, however it can get pretty slow when a lot of data points are visualized (100 000+ datapoints). Plotly-resampler solves this by downsampling (aggregating) the data respective to the view and then plotting the aggregated points. When you interact with the plot (panning, zooming, ...), callbacks are used to aggregate data and update the figure.

Homepage: https://github.com/predict-idlab/plotly-resampler
License: MIT
Latest release: 0.8.3.2
published almost 3 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 1

Rankings

Stargazers count: 27.2%

Forks count: 39.3%

Average: 42.3%

Dependent packages count: 51.2%

Dependent repos count: 51.4%

Last synced: 6 months ago

Dependencies

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examples/requirements.txt pypi

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pyproject.toml pypi

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