Examples¶

Basic Charts¶

Line Chart — Multi-Series with Annotations¶

Multi-series line chart with legend and annotations

fig = (
    Figure(width=760, height=460)
    .set_title("Monthly Revenue vs Operating Costs  (2024)")
    .set_xlabel("Month").set_ylabel("USD ($M)")
    .add(LineSeries(months, revenue, color="#2563eb", label="Revenue", width=3))
    .add(LineSeries(months, costs,   color="#dc2626", label="Costs",   linestyle="dashed"))
    .set_legend("top-left")
    .annotate("Peak Revenue", x="Dec", y=3.00, arrow=True, color="#2563eb")
    .tight_layout()
)
fig.show()

Scatter with Colormap Encoding¶

ScatterSeries(x, y, c=z_values, cmap="viridis", size=7)

Histogram — Bimodal Distribution¶

import numpy as np
data = np.concatenate([np.random.normal(50,8,400), np.random.normal(78,6,200)])
Figure().add(HistogramSeries(data.tolist(), bins=28, color="#0891b2")).show()

Pie and Donut Charts¶

Heatmap — Correlation Matrix¶

Box Plot — Multi-Group¶

New 2-D Chart Types¶

Bubble Chart¶

from glyphx.bubble import BubbleSeries

fig = Figure().set_title("Market Cap vs Growth Rate")
fig.add(BubbleSeries(x, y, sizes=market_cap, c=growth_rate,
                     cmap="plasma", label="Companies"))
fig.show()

Sunburst Chart¶

from glyphx.sunburst import SunburstSeries

fig = Figure(width=600, height=600).set_title("Org Revenue Breakdown")
fig.add(SunburstSeries(
    labels=["Total", "EMEA", "APAC", "Americas",
            "UK", "DE", "JP", "AU", "US", "CA"],
    parents=["",     "Total","Total","Total",
             "EMEA","EMEA","APAC","APAC","Americas","Americas"],
    values= [0,      0,      0,      0,
             420,    310,    280,    190,   850,       320],
))
fig.show()

Parallel Coordinates¶

from glyphx.parallel_coords import ParallelCoordinatesSeries

fig = Figure(width=900, height=500).set_title("Iris Dataset")
fig.add(ParallelCoordinatesSeries(
    data=df[["sepal_length","sepal_width","petal_length","petal_width"]],
    labels=df["species"],
    cmap="viridis",
))
fig.show()

Diverging Bar Chart¶

from glyphx.diverging_bar import DivergingBarSeries

fig = Figure().set_title("YoY Growth by Region")
fig.add(DivergingBarSeries(
    categories=["North","South","East","West","Central"],
    values=    [  12,    -8,     21,    -3,      15],
    color_pos="#2563eb",
    color_neg="#dc2626",
))
fig.show()

3-D Charts¶

3-D Scatter¶

from glyphx import Figure3D
from glyphx.scatter3d import Scatter3DSeries
import numpy as np

rng = np.random.default_rng(42)
xs = rng.normal(0, 1, 500)
ys = rng.normal(0, 1, 500)
zs = np.sin(xs) + np.cos(ys)

fig = Figure3D(title="Gaussian Scatter", theme="dark",
               azimuth=45, elevation=25)
fig.add(Scatter3DSeries(xs, ys, zs, c=zs, cmap="plasma", size=4))
fig.show()

3-D Surface¶

from glyphx import Figure3D
from glyphx.surface3d import Surface3DSeries
import numpy as np

x = np.linspace(-3, 3, 80)
y = np.linspace(-3, 3, 80)
Z = np.sin(np.sqrt(x[None,:]**2 + y[:,None]**2))

fig = Figure3D(title="Sinc Surface", azimuth=30, elevation=35)
fig.add(Surface3DSeries(x, y, Z, cmap="viridis", wireframe=True))
fig.show()

3-D Line (Helix)¶

from glyphx import Figure3D
from glyphx.line3d import Line3DSeries
import numpy as np

t = np.linspace(0, 6 * np.pi, 1_000)
fig = Figure3D(title="Helix")
fig.add(Line3DSeries(np.cos(t), np.sin(t), t / (6 * np.pi),
                     color="#dc2626", width=2.5, label="Helix"))
fig.show()

Contour Plot¶

from glyphx import Figure3D
from glyphx.contour import ContourSeries
import numpy as np

x = np.linspace(-2, 2, 100)
y = np.linspace(-2, 2, 100)
Z = np.exp(-(x[None,:]**2 + y[:,None]**2))

fig = Figure3D(title="Gaussian Contour")
fig.add(ContourSeries(x, y, Z, levels=10, filled=True, cmap="coolwarm"))
fig.show()

Large-Data 3-D (auto-downsampled)¶

GlyphX automatically downsamples 3-D series; the render stays fast regardless of input size:

import numpy as np
from glyphx import Figure3D
from glyphx.scatter3d import Scatter3DSeries

rng = np.random.default_rng(0)
n = 500_000
xs, ys, zs = rng.uniform(0, 1, n), rng.uniform(0, 1, n), rng.uniform(0, 1, n)

fig = Figure3D(title="500k Points — auto-thinned")
s3 = Scatter3DSeries(xs, ys, zs, cmap="plasma")
fig.add(s3)
fig.render_svg()

print(s3.last_downsample_info)
# {'algorithm': 'voxel-3D', 'original_n': 500000, 'thinned_n': 4913}

Themes¶

Dark Theme¶

Figure(theme="dark")

Colorblind-Safe Theme¶

Figure(theme="colorblind")   # Okabe-Ito palette

Line Styles¶

LineSeries(x, y, linestyle="solid")
LineSeries(x, y, linestyle="dashed")
LineSeries(x, y, linestyle="dotted")
LineSeries(x, y, linestyle="longdash")

Colormap Encoding¶

Layout¶

2×2 Subplot Grid¶

fig = Figure(rows=2, cols=2, width=800, height=580)
fig.add_axes(0,0).add_series(LineSeries(...))
fig.add_axes(0,1).add_series(BarSeries(...))
fig.add_axes(1,0).add_series(ScatterSeries(...))
fig.add_axes(1,1).add_series(HistogramSeries(...))

Dual Y-Axis¶

fig.add(LineSeries(x, prices, label="Price (left)"))
fig.add(BarSeries(x, volume, label="Volume (right)"), use_y2=True)

DataFrame Accessor¶

import glyphx  # registers df.glyphx

# Basic
df.glyphx.bar(x="month", y="revenue", title="Monthly Revenue")
df.glyphx.bar(groupby="region", y="revenue", agg="sum")

# Hue splitting — one series per region, auto-colored
df.glyphx.bar(x="month", y="revenue", hue="region",
              title="Revenue by Month and Region")

Statistical Charts¶

ECDF¶

from glyphx.ecdf import ECDFSeries

fig = (
    Figure()
    .add(ECDFSeries(ctrl.tolist(),  color="#3b82f6", label="Control"))
    .add(ECDFSeries(treat.tolist(), color="#ef4444", label="Treatment"))
)

Raincloud Plot¶

from glyphx.raincloud import RaincloudSeries

fig.add(RaincloudSeries(groups, categories=["Control","Low","High"], seed=42))

Significance Brackets¶

fig.add_stat_annotation("Control", "Drug A", p_value=0.0002)
fig.add_stat_annotation("Control", "Drug B", p_value=0.028, y_offset=32)

Financial Charts¶

Candlestick OHLC¶

Waterfall Bridge Chart¶

Treemap¶

Streaming Data¶

from glyphx.streaming import StreamingSeries

stream = StreamingSeries(max_points=120, color="#2563eb", label="Sensor A")
fig.add(stream)

with stream.live(fig, fps=10) as s:
    for reading in sensor_generator():
        s.push(reading)

Downsampling Examples¶

See also Large-Data Downsampling for the complete downsampling API reference.

Two-stage line pipeline (M4 + LTTB)¶

from glyphx.downsample import maybe_downsample_line
import numpy as np

x = np.linspace(0, 1, 2_000_000)
y = np.sin(x * 500) + np.random.normal(0, 0.1, len(x))

x_down, y_down = maybe_downsample_line(x, y, pixel_width=800)
print(f"{len(x):,} → {len(x_down):,} points")

Per-series threshold override¶

from glyphx import Figure
from glyphx.series import LineSeries

x = list(range(100_000))
y = [i**0.5 for i in x]

# Keep at most 500 points for this series
fig = Figure()
fig.add(LineSeries(x, y, label="sqrt(x)", threshold=500))
fig.show()

Inspect downsampling metadata¶

from glyphx import Figure
from glyphx.series import ScatterSeries
import numpy as np

rng = np.random.default_rng(0)
n = 50_000
sc = ScatterSeries(rng.uniform(0,1,n).tolist(),
                   rng.uniform(0,1,n).tolist())
Figure().add(sc).render_svg()

info = sc.last_downsample_info
print(f"Algorithm : {info['algorithm']}")
print(f"Original  : {info['original_n']:,}")
print(f"Thinned   : {info['thinned_n']:,}")