Line plots¶

The default figure for most papers, and the one where the property cycle matters most: a reader who prints your paper in greyscale must still be able to tell the curves apart.

All data here is synthetic and all axis labels are deliberately generic — this notebook is about how a figure is built and styled, not about any particular measurement.

In [1]:

%matplotlib inline

# Jupyter crops every inline preview with bbox_inches="tight". That defeats the exact column widths
# this package exists to guarantee, and because the themes sit the axes close to the canvas edge, the
# crop slices through the top and right spines. Turn it off so previews match the files jf.save() writes.
%config InlineBackend.print_figure_kwargs = {"bbox_inches": None}

# jf.save() reports what it wrote through the "journalfig" logger instead of printing. Raise that one
# logger -- a blanket logging.basicConfig(level="INFO") also switches on fontTools, which logs a dozen
# subsetting lines per PDF.
import logging

logging.basicConfig(format="%(message)s")
logging.getLogger("journalfig").setLevel(logging.INFO)

import numpy as np
import matplotlib.pyplot as plt

import journalfig as jf

# The themes render previews at 600 dpi, which makes every embedded image in a notebook large. This
# only touches the on-screen preview; jf.save() exports at savefig.dpi regardless.
PREVIEW_DPI = 150


def preview(journal, **kwargs):
    """Apply a theme, then drop the on-screen DPI so this notebook stays light."""
    jf.use(journal, **kwargs)
    plt.rcParams["figure.dpi"] = PREVIEW_DPI


rng = np.random.default_rng(0)
print("journalfig", jf.__version__, "| themes:", jf.JOURNALS)

%matplotlib inline # Jupyter crops every inline preview with bbox_inches="tight". That defeats the exact column widths # this package exists to guarantee, and because the themes sit the axes close to the canvas edge, the # crop slices through the top and right spines. Turn it off so previews match the files jf.save() writes. %config InlineBackend.print_figure_kwargs = {"bbox_inches": None} # jf.save() reports what it wrote through the "journalfig" logger instead of printing. Raise that one # logger -- a blanket logging.basicConfig(level="INFO") also switches on fontTools, which logs a dozen # subsetting lines per PDF. import logging logging.basicConfig(format="%(message)s") logging.getLogger("journalfig").setLevel(logging.INFO) import numpy as np import matplotlib.pyplot as plt import journalfig as jf # The themes render previews at 600 dpi, which makes every embedded image in a notebook large. This # only touches the on-screen preview; jf.save() exports at savefig.dpi regardless. PREVIEW_DPI = 150 def preview(journal, **kwargs): """Apply a theme, then drop the on-screen DPI so this notebook stays light.""" jf.use(journal, **kwargs) plt.rcParams["figure.dpi"] = PREVIEW_DPI rng = np.random.default_rng(0) print("journalfig", jf.__version__, "| themes:", jf.JOURNALS)

journalfig 0.5.1 | themes: ('nature', 'aps', 'elsevier')

The property cycle¶

The themes pair colour with linestyle, so eight curves stay distinguishable without colour. The palette is Okabe–Ito and is identical across all three themes, so retargeting never changes colours.

Markers are deliberately left out of the cycle: a marker there would decorate every point of a dense curve. Use jf.MARKER_CYCLE when you want them.

In [2]:

preview("elsevier")

x = np.linspace(0, 10, 400)
fig, ax = jf.subplots("elsevier", width="double", ratio=0.42)
for i in range(8):
    ax.plot(x, np.sin(x + i * 0.4) + i * 0.6, label=f"Series {i + 1}")
ax.set_xlabel("x (a.u.)")
ax.set_ylabel("y (a.u.)")
ax.legend(ncols=4, loc="upper center", bbox_to_anchor=(0.5, 1.28))

preview("elsevier") x = np.linspace(0, 10, 400) fig, ax = jf.subplots("elsevier", width="double", ratio=0.42) for i in range(8): ax.plot(x, np.sin(x + i * 0.4) + i * 0.6, label=f"Series {i + 1}") ax.set_xlabel("x (a.u.)") ax.set_ylabel("y (a.u.)") ax.legend(ncols=4, loc="upper center", bbox_to_anchor=(0.5, 1.28))

Out[2]:

<matplotlib.legend.Legend at 0x11455ecf0>

Label the lines, not a legend box¶

A legend costs space and makes the reader look up a colour before they can read the plot. At 89 mm that lookup is the most expensive thing on the page. jf.label_lines() puts each name beside its own line instead — leave room for it with ax.margins(x=...), since the labels sit outside the axes.

In [3]:

preview("elsevier")
fig, axs = jf.subplots("elsevier", 1, 2, width="double", ratio=0.4)

for i in range(3):
    axs[0].plot(x, np.sin(x + i * 0.6), label=f"Series {i + 1}")
    axs[1].plot(x, np.sin(x + i * 0.6), label=f"Series {i + 1}")

axs[0].legend()
axs[0].set_title("legend")

axs[1].margins(x=0.22)          # room for the labels, which sit outside the axes
jf.label_lines(axs[1])
axs[1].set_title("jf.label_lines()")

for ax in axs:
    ax.set_xlabel("x (a.u.)")
axs[0].set_ylabel("y (a.u.)")

preview("elsevier") fig, axs = jf.subplots("elsevier", 1, 2, width="double", ratio=0.4) for i in range(3): axs[0].plot(x, np.sin(x + i * 0.6), label=f"Series {i + 1}") axs[1].plot(x, np.sin(x + i * 0.6), label=f"Series {i + 1}") axs[0].legend() axs[0].set_title("legend") axs[1].margins(x=0.22) # room for the labels, which sit outside the axes jf.label_lines(axs[1]) axs[1].set_title("jf.label_lines()") for ax in axs: ax.set_xlabel("x (a.u.)") axs[0].set_ylabel("y (a.u.)")

Out[3]:

Text(0, 0.5, 'y (a.u.)')

Does it survive greyscale?¶

Render the cycle, then desaturate it. The linestyles carry the distinction once the hues collapse.

In [4]:

preview("nature")
fig, axs = jf.subplots("nature", 1, 2, width="double", ratio=0.36)

for ax, grey in zip(axs, [False, True]):
    for i in range(4):
        colour = "0.35" if grey else jf.COLOR_CYCLE[i]
        ax.plot(x, np.sin(x + i * 0.5), color=colour, linestyle=jf.LINESTYLE_CYCLE[i], label=f"Series {i + 1}")
    ax.set_xlabel("x (a.u.)")
    ax.set_title("greyscale" if grey else "colour")
axs[0].set_ylabel("y (a.u.)")
axs[1].legend(fontsize=5)

preview("nature") fig, axs = jf.subplots("nature", 1, 2, width="double", ratio=0.36) for ax, grey in zip(axs, [False, True]): for i in range(4): colour = "0.35" if grey else jf.COLOR_CYCLE[i] ax.plot(x, np.sin(x + i * 0.5), color=colour, linestyle=jf.LINESTYLE_CYCLE[i], label=f"Series {i + 1}") ax.set_xlabel("x (a.u.)") ax.set_title("greyscale" if grey else "colour") axs[0].set_ylabel("y (a.u.)") axs[1].legend(fontsize=5)

Out[4]:

<matplotlib.legend.Legend at 0x1169096d0>

Minimum line weight¶

APS requires printed lines of at least 0.5 pt. A hairline that looks fine on screen disappears in print, and jf.check() reports it.

In [5]:

jf.use("aps")
fig, ax = jf.subplots("aps", width="single")
ax.plot(x, np.sin(x), linewidth=0.2)
ax.set_xlabel("x (a.u.)")
ax.set_ylabel("y (a.u.)")

for violation in jf.check(fig, warn=False):
    print(violation)
plt.close(fig)

jf.use("aps") fig, ax = jf.subplots("aps", width="single") ax.plot(x, np.sin(x), linewidth=0.2) ax.set_xlabel("x (a.u.)") ax.set_ylabel("y (a.u.)") for violation in jf.check(fig, warn=False): print(violation) plt.close(fig)

[line] a line is 0.20 pt, below the 0.50 pt minimum

Markers on a dense curve¶

markevery keeps a marked line legible instead of drawing a marker on all 400 points.

In [6]:

preview("elsevier")
fig, ax = jf.subplots("elsevier", width="single")
for i, marker in enumerate(jf.MARKER_CYCLE[:3]):
    ax.plot(x, np.sin(x) + i * 0.5, marker=marker, markevery=40, label=f"Series {i + 1}")
ax.set_xlabel("x (a.u.)")
ax.set_ylabel("y (a.u.)")
ax.legend()

preview("elsevier") fig, ax = jf.subplots("elsevier", width="single") for i, marker in enumerate(jf.MARKER_CYCLE[:3]): ax.plot(x, np.sin(x) + i * 0.5, marker=marker, markevery=40, label=f"Series {i + 1}") ax.set_xlabel("x (a.u.)") ax.set_ylabel("y (a.u.)") ax.legend()

Out[6]:

<matplotlib.legend.Legend at 0x116ccca50>

Bands and shaded regions¶

fill_between is the usual way to show a spread around a line. Transparency is preserved in PDF, SVG and PNG — but not in EPS, where the PostScript backend flattens it to opaque. No theme writes EPS by default for exactly that reason.

In [7]:

preview("nature")
y = np.sin(x)
fig, ax = jf.subplots("nature", width="single")
ax.plot(x, y, label="Series A")
ax.fill_between(x, y - 0.15, y + 0.15, alpha=0.25, linewidth=0, label="Spread")
ax.set_xlabel("x (a.u.)")
ax.set_ylabel("y (a.u.)")
ax.legend()

preview("nature") y = np.sin(x) fig, ax = jf.subplots("nature", width="single") ax.plot(x, y, label="Series A") ax.fill_between(x, y - 0.15, y + 0.15, alpha=0.25, linewidth=0, label="Spread") ax.set_xlabel("x (a.u.)") ax.set_ylabel("y (a.u.)") ax.legend()

Out[7]:

<matplotlib.legend.Legend at 0x116de3ed0>

Log scales¶

The themes turn minor ticks on, which is what you want on a log axis. axes.formatter.use_mathtext is on too, so offset text is typeset rather than printed as 1e-3.

In [8]:

preview("aps")
decades = np.logspace(-3, 2, 200)
fig, ax = jf.subplots("aps", width="single")
for i in range(3):
    ax.plot(decades, decades ** (0.5 + 0.25 * i), label=f"Series {i + 1}")
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlabel("x (a.u.)")
ax.set_ylabel("y (a.u.)")
ax.legend()

preview("aps") decades = np.logspace(-3, 2, 200) fig, ax = jf.subplots("aps", width="single") for i in range(3): ax.plot(decades, decades ** (0.5 + 0.25 * i), label=f"Series {i + 1}") ax.set_xscale("log") ax.set_yscale("log") ax.set_xlabel("x (a.u.)") ax.set_ylabel("y (a.u.)") ax.legend()

Out[8]:

<matplotlib.legend.Legend at 0x126094f50>

The same figure, three journals¶

One data set, one loop, three compliant figures. Note the sizes and font sizes change; the colours never do.

In [9]:

for journal in jf.JOURNALS:
    preview(journal)
    fig, ax = jf.subplots(journal, width="single")
    for i in range(3):
        ax.plot(x, np.sin(x + i * 0.5), label=f"Series {i + 1}")
    ax.set_xlabel("x (a.u.)")
    ax.set_ylabel("y (a.u.)")
    ax.set_title(jf.get_spec(journal).name)
    ax.legend()
    plt.show()

for journal in jf.JOURNALS: preview(journal) fig, ax = jf.subplots(journal, width="single") for i in range(3): ax.plot(x, np.sin(x + i * 0.5), label=f"Series {i + 1}") ax.set_xlabel("x (a.u.)") ax.set_ylabel("y (a.u.)") ax.set_title(jf.get_spec(journal).name) ax.legend() plt.show()