SciQLopPlots
A high-performance scientific plotting library built on C++20/Qt6 with Python bindings via Shiboken6 (PySide6). Designed for the SciQLop data analysis platform but usable standalone.
Features
- Async resampling — render millions of points smoothly; data is downsampled in background threads via NeoQCP pipelines
- Multiple plot types — time series (line graphs), spectrograms (color maps), 2D histograms, parametric curves, N-D projection curves, waterfall plots
- Interactive — pan, zoom, data-driven callbacks, vertical/horizontal/rectangular spans, tracers, straight lines, text, shapes, pixmaps
- Multi-plot panels — synchronized axes, aligned margins, drag-and-drop from product trees
- Reactive pipelines — connect plot properties with
>>to build live data flows - Export — PDF (vector), PNG, JPG, BMP for both individual plots and panels
- Busy indicator — visual feedback when data is loading or being processed
- Runtime inspector — tree model/view for inspecting and editing plot properties
- Cross-platform — Linux, macOS, Windows
Architecture
graph TD
subgraph Python["Python Layer"]
PY[SciQLopPlots package]
SB[Shiboken6 bindings]
end
subgraph Plots["Plot Hierarchy"]
PI[SciQLopPlotInterface<br/><i>QFrame</i>]
SP[SciQLopPlot]
TSP[SciQLopTimeSeriesPlot]
NDP[SciQLopNDProjectionPlot]
end
subgraph Plotables["Plotables"]
PTI[SciQLopPlottableInterface<br/><i>QObject</i>]
GI[SciQLopGraphInterface]
CMI[SciQLopColorMapInterface]
LG[SciQLopLineGraph]
SLG[SciQLopSingleLineGraph]
CRV[SciQLopCurve]
WF[SciQLopWaterfallGraph]
NDC[SciQLopNDProjectionCurves]
CMB[SciQLopColorMapBase]
CM[SciQLopColorMap]
H2D[SciQLopHistogram2D]
end
subgraph Items["Overlay Items"]
VS[VerticalSpan]
HS[HorizontalSpan]
RS[RectangularSpan]
TR[Tracer]
SL[StraightLine]
end
subgraph MultiPlot["Multi-Plot"]
PPI[SciQLopPlotPanelInterface]
MPP[SciQLopMultiPlotPanel]
AX[AxisSynchronizer]
VA[VPlotsAlign]
end
PY --> SB --> PI
PI --> SP
PI --> NDP
SP --> TSP
PTI --> GI
PTI --> CMI
GI --> LG
GI --> SLG
GI --> CRV
GI --> WF
GI --> NDC
CMI --> CMB
CMB --> CM
CMB --> H2D
PPI --> MPP
MPP --> AX
MPP --> VA
SP -.->|contains| PTI
SP -.->|contains| Items
MPP -.->|contains| SP
Data flow
sequenceDiagram
participant User
participant Plot as SciQLopPlot
participant Resampler as Resampler<br/>(worker thread)
participant NeoQCP as NeoQCP
User->>Plot: pan / zoom
Plot->>Resampler: new visible range
Resampler->>Resampler: downsample data
Resampler->>NeoQCP: resampled points
NeoQCP->>Plot: render
For callback-driven data, the plot invokes a Python callable with (start, stop) on range change, and the returned arrays flow through the same resampling pipeline.
Quick start
The user guide walks through everything below and more: plot types, live data, time series, panels, overlays, pipelines and export. Every example in it is tested.
pip install SciQLopPlots
import numpy as np
from PySide6.QtWidgets import QApplication
from SciQLopPlots import SciQLopPlot
app = QApplication([])
plot = SciQLopPlot()
# Static data
x = np.arange(0, 1000, dtype=np.float64)
y = np.sin(x / 100) * np.cos(x / 10)
plot.plot(x, y, labels=["signal"])
# Data callback (called on pan/zoom with visible range)
def get_data(start, stop):
x = np.arange(start, stop, dtype=np.float64)
y = np.column_stack([np.sin(x / 100), np.cos(x / 100)])
return x, y
plot.plot(get_data, labels=["sin", "cos"])
plot.show()
app.exec()
Reactive pipelines
Connect plot properties with the >> operator to build live data pipelines:
from SciQLopPlots import SciQLopPlot
plot = SciQLopPlot()
graph = plot.plot(lambda start, stop: ..., labels=["signal"])
# Axis range changes automatically feed a transform, which pushes data to the graph
plot.x_axis().on.range >> get_data >> graph.on.data
# Direct property forwarding (no transform)
span.on.range >> plot.x_axis().on.range
# Chain multiple steps
source.on.range >> transform >> target.on.data
Run the gallery for a full feature showcase:
python tests/manual-tests/gallery.py
Runtime tracing
SciQLopPlots ships with a built-in tracer that emits Chrome trace JSON, viewable in
Perfetto, Speedscope, or
chrome://tracing. Always compiled in, runtime-toggled, ~1 ns when off.
Enable for a session, then open the file in Perfetto:
from SciQLopPlots import tracing
with tracing.session("/tmp/sciqlop.json"):
panel.zoom_in_a_lot() # whatever's slow
Annotate Python work to land alongside the C++ zones (plot.replot,
setdata.colormap, resample.async_2d, …):
with tracing.zone("speasy.fetch", cat="data", product="amda/mms_fgm"):
data = speasy.get_data(...)
@tracing.traced("layer.eval", cat="layer")
def render_layer(...): ...
tracing.counter("queue_depth", q.size(), cat="fetch")
You can also auto-enable at process start with the SCIQLOP_TRACE env var:
SCIQLOP_TRACE=/tmp/sciqlop.json python my_script.py
When tracy_enable=true is also passed at build time, the same PROFILE_HERE_N
sites feed both the Chrome JSON tracer and the Tracy live-streaming view.
Building from source
Requires Qt6, PySide6 == 6.11.0, a C++20 compiler, and Meson.
# Development build (recommended — plain `debug` disables optimizations
# and makes the resampling pipelines noticeably sluggish)
meson setup build --buildtype=debugoptimized
meson compile -C build
# Install as editable Python package
pip install -e . --no-build-isolation
Build options
| Option | Type | Default | Description |
|---|---|---|---|
trace_refcount |
bool | false | Enable reference count tracing |
tracy_enable |
bool | false | Enable Tracy profiling |
with_opengl |
bool | true | Enable OpenGL support |
Contributing
Fork the repository, make your changes and submit a pull request. Bug reports and feature requests are welcome.
Credits
Development is supported by CDPP. We acknowledge support from Plas@Par.
Thanks
- PySide6 — Qt bindings and Shiboken6 binding generator
- NeoQCP — fork of QCustomPlot with async pipelines, multi-dtype support, and QRhi rendering
1 maintainer
Metadata
1 maintainer
Release files for SciQLopPlots 0.42.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sciqlopplots-0.42.2.tar.gz | 1.9 MB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| sciqlopplots-0.42.2-cp311-abi3-win_arm64.whl | CPython 3.11 | abi3 | Windows ARM64 | Details |
| sciqlopplots-0.42.2-cp311-abi3-win_amd64.whl | CPython 3.11 | abi3 | Windows x86-64 | Details |
| sciqlopplots-0.42.2-cp311-abi3-manylinux_2_39_aarch64.whl | CPython 3.11 | abi3 | Linux glibc 2.39+ ARM64 | Details |
| sciqlopplots-0.42.2-cp311-abi3-manylinux_2_34_x86_64.whl | CPython 3.11 | abi3 | Linux glibc 2.34+ x86-64 | Details |
| sciqlopplots-0.42.2-cp311-abi3-macosx_13_0_x86_64.whl | CPython 3.11 | abi3 | macOS 13.0+ x86-64 | Details |
| sciqlopplots-0.42.2-cp311-abi3-macosx_13_0_arm64.whl | CPython 3.11 | abi3 | macOS 13.0+ ARM64 | Details |
Total release size: 37.2 MB
Release files / sciqlopplots-0.42.2.tar.gz
| Download URL | sciqlopplots-0.42.2.tar.gz |
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| Size | 1.9 MB |
| Tags | Source |
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| Download URL | sciqlopplots-0.42.2-cp311-abi3-win_arm64.whl |
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| Download URL | sciqlopplots-0.42.2-cp311-abi3-win_amd64.whl |
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| Size | 4.6 MB |
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Release files / sciqlopplots-0.42.2-cp311-abi3-manylinux_2_39_aarch64.whl
| Download URL | sciqlopplots-0.42.2-cp311-abi3-manylinux_2_39_aarch64.whl |
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| Size | 6.8 MB |
| Tags | CPython 3.11 Linux glibc 2.39+ ARM64 abi3 |
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| Download URL | sciqlopplots-0.42.2-cp311-abi3-manylinux_2_34_x86_64.whl |
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| Download URL | sciqlopplots-0.42.2-cp311-abi3-macosx_13_0_x86_64.whl |
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| Size | 6.2 MB |
| Tags | CPython 3.11 abi3 macOS 13.0+ x86-64 |
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| Download URL | sciqlopplots-0.42.2-cp311-abi3-macosx_13_0_arm64.whl |
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