v0.10.0

New features

🚨 Nan handling has been delegated to the aggregators, this implies that plotly-resampler does not perform any nan-checks anymore (making it faster) 🐎.

Consequently, we removed the check_nans argument of the FigureResampler constructor and its add_traces method. This argument was used to check for NaNs in the input data, but this is now handled by the nan_policy argument of specific aggregators (see for instance the constructor of the MinMax and MinMaxLTTB aggregator). 🔍

What's Changed

• Address FutureWarning: 'H' is deprecated and will be removed in a future version. Please use 'h' instead of 'H'. by @t-jakubek in #291
• 🚀 Python 3.12 support by @jvdd in #292
• 🔥 delegate nan behavior to aggregators by @jonasvdd in #294

New Contributors

• @t-jakubek made their first contribution in #291

Full Changelog: v0.9.2...v0.10.0

v0.9.2

New Features / major changes

⚡ overview / rangeslider support 🎉

• ➡️ code example:
• 🖍️ high level docs
• 🔍 API docs
  • make sure to take a look at the doc strings of the create_overview, overview_row_idxs, and overview_kwargs arguments of the FigureResampler its constructor.
    

💨 remove traceUpdater dash component as a dependency.

context: see #281 #271
traceUpdater was developed during a period when Dash did not yet contain the Patch feature for partial property updates. As such, traceUpdater has become somewhat redundant is now effectively replaced with Patch.

What's Changed

• Support nested admonitions by @jonasvdd in #245
• 👷 build: create codeql.yml by @NielsPraet in #248
• ✨ first draft of improved xaxis filtering by @jonasvdd in #250
• ⬆️ update dependencies by @jvdd in #260
• 💪 update dash-extensions by @jonasvdd in #261
• fix for #263 by @jonasvdd in #264
• Rangeslider support by @jonasvdd in #254
• 🙏 fix mkdocs by @jvdd in #268
• ✈️ fix for #270 by @jonasvdd in #272
• 🔍 adding init kwargs to show dash - fix for #265 by @jonasvdd in #269
• Refactor/remove trace updater by @jonasvdd in #281
• Bug/pop rangeselector by @jonasvdd in #279
• ✨ fix for #275 by @jonasvdd in #286
• Bug/rangeselector by @jonasvdd in #287

Full Changelog: v0.9.1...v0.9.2

v0.9.1: support for multiple axes

Major changes:

Support for multiple axes.

The .GIF below demonstrates how multiple axes on a subplots can be used to enhance the number of visible traces, without using more (vertical) screen space 🔥!

Make sure to take a look at our examples

What's Changed (generated)

• 🔥 multiple y-axes support by @jonasvdd in #244

Full Changelog: v0.9.0...v0.9.1

v0.9.0

Major changes:

Even faster aggregation 🐎

We switched our aggregation backend to tsdownsample, which alleviates the need to compile our C code on non-supported devices, and has parallelization capabilities.
tsdownsample leverages the argminmax crate, which has SIMD-optimized instruction to find vertical extrema really fast!

With parallelization enabled, you should clearly see a bump in perfomance when visualizing (multiple) large traces! 🐎

Versioned docs! :party:

We restyled our documentation and added versioning! 🎉

https://predict-idlab.github.io/plotly-resampler/latest/

Go check it out! ☝️

Other Features

• Support for log-scale axes (and thus log-bin-based aggregators) - check this pull-request
  

The above image shows how the log aggregator (row2) will use log-scale bins. This can be seen in the 1-1000 range when comparing both subplots.
Note: the shown data has a fixed delta-x of 1. Hence, here are no exact equally spaced bins for the left part of the LogLTTB.

• Add a fill-value option to gap handlers
  

The above image shows how the fill_value option can be used to fill gaps with a specific value.
This can be of greate use, when you use the fill='tozeroy' option in plotly and gaps occur in your data, as this will, combined with line_shape='vh', fill the area between the trace and the x-axis and gaps will be a flat zero-line.

Bugfixes

• support for pandas2.0 intricacies

What's Changed (generated)

• fix: handle bool dtype for x in LTTB_core_py by @jvdd in #183
• fix: add colors to streamlit example 🎨 by @jvdd in #187
• docs: describe solution in FAQ for slow datetime arrays by @jvdd in #184
• Rework aggregator interface by @jvdd in #186
• 🚀 integrate with tsdownsample by @jvdd in #191
• refactor: use composition for gap handling by @jvdd in #199
• ✨ np.array interface implementation by @jonasvdd in #154
• 🧹 fix typo in docstring + remove LTTB from MinMaxLTTB + remove interleave_gaps by @jonasvdd in #201
• chore: use ruff instead of isort by @jvdd in #200
• 🌈 adding marker props by @jonasvdd in #148
• Datetime bugfix by @jonasvdd in #209
• Fixes #210 by @jonasvdd in #211
• Log support by @jonasvdd in #207
• Datetime range by @jonasvdd in #213
• ✨ add fill_value option to gap handlers by @jonasvdd in #218
• ✨ fix limit_to_view=True but no gaps inserted bug by @jonasvdd in #220
• 🐛 convert trace props to array + check for nan removal by @jvdd in #225
• Figurewidget datetime bug by @jonasvdd in #232
• ♻️ deprecate JupyterDash in favor for updated Dash version by @NielsPraet in #233
• 👀 comment out reset layout by @jvdd in #228
• Docs/versioned docs (#236) by @jonasvdd in #237

Full Changelog: v0.8.3.2...v0.9.0

v0.8.3.2 - dependency updates + bug fixes

What's Changed

• 🐛 return None when rounding 0 by @jvdd @jonasvdd in #172
• test: add tests for #123 by @jvdd in #176
• Remove locked werkzeug dependency due to CVE-2023-25577 by @imichaelmoore in #174
• ✨ making flask_cors & kaleido optional by @jvdd @jonasvdd in #177

New Contributors

• @imichaelmoore made their first contribution in #174

Full Changelog: v0.8.3.1...v0.8.3.2

v0.8.3.1 - Cleanup & tuple bugfix

What's Changed

• 🙈 bugifx for #144 by @jonasvdd in #145
• 🎨 formatting + linting in CI-CD by @jvdd in #143
• ⬆️ support Python 3.11 by @jvdd in #150
• 🧹 refurb cleanup + one typo by @jayceslesar in #152
• 🧹 clean up CONTRIBUTING.md by @jvdd in #160
• Improve monotonic assert + fix black formatting in vscode by @jonasvdd in #162
• ✨ add confidence bound example + more figure retention in basic_example.py. by @jonasvdd in #164

New Contributors

• @jayceslesar made their first contribution in #152

Full Changelog: v0.8.3...v0.8.3.1

v.0.8.3 - object dtype index parsing

Main changes:

• Try to parse the object dtype of the hf_x property in plotly-resampler, see #116 #120 #115
• Add the check_nan option to the add_trace(s) methods. Setting this variable to True allows for graph construction speedups when no Nans are present in your data.

What's Changed

• 🖊️ add contributing guide + changelog by @jvdd in #111
• 🔧 Tweaks - improve code quality, fix type-checking bug when IPywidgets is not installed & loosen up plotly-version by @jonasvdd in #114
• 🐛 update layout axes range bug by @jvdd in #126
• ✨ fix + test for #124 by @jonasvdd in #127
• 💨 making orjson non-option and fixating werkzeug #123 by @jonasvdd in #128
• 💪🏼 making orjson serialization more robust, see #118 by @jonasvdd in #131
• Resample bug, see #137 by @jonasvdd in #138
• ✨ add check_nans to add_trace(s) by @jvdd in #140
• 🐛 parse object arrays for hf_x by @jvdd in #116

Full Changelog: v0.8.0...v0.8.3

v0.8.0

Major changes

Faster aggregation 🐎

the lttbc dependency is removed; and we added our own (faster) lttb C implementation. Additionally we provide a Python fallback when this lttb-C building fails. In the near future, we will look into CIBuildWheels to build the wheels for the major OS & Python matrix versions.
A well deserved s/o to dgoeris/lttbc, who heavily inspired our implementation!

Figure Output serialization 📸

Plotly-resampler now also has the option to store the output figure as an Image in notebook output. As long the notebook is connected, the interactive plotly-resampler figure is shown; but once the figure / notebook isn't connected anymore, a static image will be rendered in the notebook output.

What's Changed (generated)

• 🐛 return self when calling add_traces by @jvdd in #75
• 🔥 add streamlit integration example by @jvdd in #80
• ✨ adding convert_traces_kwargs by @jonasvdd in #81
• Fix numeric hf_y input as dtype object by @jonasvdd in #90
• 🔥 add support for figure dict input + propagate _grid_str by @jvdd in #92
• 🙏 fix tests for all OS by @jvdd in #95
• Add python3dot10 by @jvdd in #96
• 🌅 FigureResampler display improvements by @jvdd in #97
• 📦 serialization support + 🎚️ update OS & python version in test-matrix by @jvdd in #87
• Lttbv2 🍒 ⛏️ branch by @jonasvdd in #103
• 🤖 hack together output retention in notebooks by @jvdd in #105
• 📦 improve docs by @jvdd in #104

& some other minor bug fixes 🙈

Full Changelog: v0.7.0...v0.8.0

v0.7.0 - composability & `register_plotly_resampler`

What's Changed

You can register plotly_resampler; this adds dynamic resampling functionality under the hood to plotly.py! 🥳
As a result, you can stop wrapping plotly figures with a plotly-resampler decorator (as this all happens automatically)

You only need to call the register_plotly_resampler method and all plotly figures will be wrapped (under the hood) according to that method's configuration.

-> More info in the README and docs!

Aditionally, all resampler Figures are now composable; implying that they can be decorated by themselves and all other types of plotly-(resampler) figures. This eases the switching from a FigureResampler to FigureWidgetResampler and vice-versa.

What's Changed (PR's)

• 🦌 Adding reset-axes functionality by @jonasvdd in #48
• 🐛 Small bugfixes by @jonasvdd in #52
• 🔍 investigating gap-detection methodology by @jonasvdd in #53
• 🔍 fix float index problem of #63 by @jonasvdd in #64
• 🔧 hotfix for rounding error by @jonasvdd in #66
• 🗳️ Compose figs by @jonasvdd in #72
• ✨ register plotly-resampler by @jvdd in #70
• 🤖 update dependencies + new release by @jvdd in #74

Full Changelog: v0.6.0...v0.7.0

v0.6.0 support for go.FigureWidget

What's Changed

Dynamically adjusting raw data 🔧

The hf_data property now allows adjusting the hf_traces their data; documentation 📖

fig.hf_data[-1]["y"] = - sin ** 2

FigureWidget support 👀

plotly-resampler can now wrap plotly's FigureWidget graph-object with the FigureWidgetResampler (see #47).

This has several advantages

1. ✔️ Able to use the on_click callback and thus create annotation app 👉🏼 see this example notebook.
2. ✔️ No web-application with dash callbacks need to be started

You can just seamlessly use plolty-resampler within your jupyter environment, remote or local.