Reload a grid of line traces as one labeled xarray.Dataset, analyze, and replot

plotpress.load_data_xarray() isn’t limited to mesh grids – a uniform grid where every panel holds exactly one line series (the other half of its supported scope; see the mesh version of this example, Reload a mesh grid as one labeled xarray.Dataset, analyze, and replot) comes back the same way, dimensioned by the figure’s own row/col grid, with every panel’s y trace stacked into one (row, col, point) array instead of a title-keyed dict one panel at a time.

The source figure below is a 3x4 grid of independent sensor traces, each its own damped oscillation at a different frequency – standing in for a bank of channel recordings saved earlier, with none of the code that built them still around. This example loads it back, mean-centers every channel’s trace in one broadcast subtraction, and replots the whole grid of centered traces into a figure rebuilt from ds.attrs["template"] (the same dict load_data() returns under "template", reachable straight off the Dataset – no second, separate load_data() call just to get it), so every channel’s title/labels come back already applied, not re-typed by hand. A small before/after figure in the middle pictures that same transformation on one channel, with an arrow from its original trace to its centered one.

• 
• 
• 

This figure has 12 panels — too many to usefully embed at a fixed size.

Open the full interactive example in a new page to pick a tool, then zoom, pan, point-pick or annotate any panel.

View this figure’s Vega export ↗ — the raw JSON spec, rendered live by a real Vega engine.

View this figure’s Vega-Lite export ↗ — the raw JSON spec(s), rendered live by a real Vega-Lite engine.

Live figure — pick a tool, then zoom, pan, point-pick or annotate. Nothing is active until a tool is selected.

View this figure’s Vega export ↗ — the raw JSON spec, rendered live by a real Vega engine.

View this figure’s Vega-Lite export ↗ — the raw JSON spec(s), rendered live by a real Vega-Lite engine.

This figure has 12 panels — too many to usefully embed at a fixed size.

Open the full interactive example in a new page to pick a tool, then zoom, pan, point-pick or annotate any panel.

View this figure’s Vega export ↗ — the raw JSON spec, rendered live by a real Vega engine.

View this figure’s Vega-Lite export ↗ — the raw JSON spec(s), rendered live by a real Vega-Lite engine.

<xarray.Dataset> Size: 42kB
Dimensions:   (row: 3, col: 4, point: 400)
Coordinates:
    title     (row, col) object 96B 'channel 0' 'channel 1' ... 'channel 11'
    xlabel    (row, col) object 96B '' '' '' '' '' '' '' '' '' '' '' ''
    ylabel    (row, col) object 96B '' '' '' '' '' '' '' '' '' '' '' ''
    has_data  (row, col) bool 12B True True True True ... True True True True
  * point     (point) float64 3kB 0.0 0.01003 0.02005 0.03008 ... 3.98 3.99 4.0
Dimensions without coordinates: row, col
Data variables:
    y         (row, col, point) float64 38kB 0.2 0.2626 0.3241 ... 0.1741 0.1467
Attributes:
    figsize:   [14.0, 7.0]
    title:     None
    template:  {'style': {'facecolor': '#ffffff', 'dpi': 100.0, 'axes_facecol...

import os
import tempfile

import numpy as np
import plotpress

fig, axes = plotpress.subplots(3, 4, figsize=(14, 7))
t = np.linspace(0, 4, 400)
grid = np.asarray(axes)
for i, ax in enumerate(grid.ravel()):
    freq = 1.0 + 0.4 * i
    # A nonzero baseline (+0.2), so the mean-centering analysis below has
    # something real to remove from each channel.
    y = np.exp(-0.6 * t) * np.sin(2 * np.pi * freq * t) + 0.2
    ax.plot(t, y, color="C0")
    ax.set_title(f"channel {i}", fontsize=9)
    ax.tick_params(labelsize=6)
fig.tight_layout()
path = os.path.join(tempfile.gettempdir(), "plotpress_gallery_line_grid_xarray.html")
fig.save(path, interactive=True)

# ---------------------------------------------------------------------------
# Load: every channel's trace comes back as one (row, col, point) array --
# ds["y"] -- sharing the single "point" coordinate every panel plotted
# against, since they all used the same `t`.
# ---------------------------------------------------------------------------
ds = plotpress.load_data_xarray(path)
print(ds)

# Analyze: mean-center every channel's own trace in one broadcast
# subtraction across the whole grid -- the kind of per-panel operation a
# title-keyed dict of dicts has no native way to express at all.
# `centered` keeps the full (row, col, point) shape, so it can be
# replotted channel-for-channel below.
centered = ds["y"] - ds["y"].mean(dim="point")

# ---------------------------------------------------------------------------
# Picture the transformation on one channel before replotting the whole
# grid below: channel (0, 0)'s own trace on the left, its centered version
# (the exact same subtraction applied to and replotted for every channel)
# on the right, an arrow between them standing in for "load_data_xarray()
# -> subtract the channel's own mean".
# ---------------------------------------------------------------------------
fig_arrow, (ax_before, ax_arrow, ax_after) = plotpress.subplots(1, 3, figsize=(11, 3.2))
ax_before.plot(ds["point"].values, ds["y"].values[0, 0], color="C0")
ax_before.set_title(f"before: {ds['title'].values[0, 0]}", fontsize=9)

ax_arrow.set_xlim(0, 1); ax_arrow.set_ylim(0, 1)
ax_arrow.set_axis_off()
ax_arrow.annotate("", xy=(0.92, 0.5), xytext=(0.08, 0.5), arrowprops={"color": "#555"})
ax_arrow.text(0.5, 0.72, "subtract the\nchannel's own mean", ha="center", fontsize=9, color="#555")

ax_after.plot(ds["point"].values, centered.values[0, 0], color="C3")
ax_after.set_title("after: centered", fontsize=9)
fig_arrow.tight_layout()

# Replot: rebuild the same 3x4 grid and every axes' own title from
# ds.attrs["template"] -- only the trace itself (and tick_params, one of the
# few things a template deliberately doesn't carry -- see
# figure_from_template()'s own docstring) needs setting by hand below.
nrows, ncols = ds.sizes["row"], ds.sizes["col"]
fig2, axes2 = plotpress.figure_from_template(ds.attrs["template"], figsize=(14, 7))
for r in range(nrows):
    for c in range(ncols):
        ax = axes2[r, c]
        ax.plot(ds["point"].values, centered.values[r, c], color="C3")
        ax.tick_params(labelsize=6)
fig2.suptitle("Every channel's trace, mean-centered")
fig2.tight_layout()