data-visualization

Create publication-quality figures with matplotlib/seaborn (Python) or ggplot2 (R). Covers multi-panel layouts, colorblind-safe palettes, and journal export settings.

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Repository: wookat/ai-research-skills
Last source activity: July 15, 2026 at 11:38
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name	data-visualization
description	Create publication-quality figures with matplotlib/seaborn (Python) or ggplot2 (R). Covers multi-panel layouts, colorblind-safe palettes, and journal export settings.
tags	["visualization","matplotlib","seaborn","ggplot2","publication-figures","color-palettes"]
version	1.0.0
authors	[{"name":"awesome-rosetta-skills contributors","github":"@awesome-rosetta-skills"}]
license	MIT
platforms	["claude-code","codex","gemini-cli","cursor"]
dependencies	{"python":["matplotlib>=3.7.0","seaborn>=0.12.0","numpy>=1.24.0","pandas>=2.0.0","scipy>=1.10.0","palettable>=3.3.3"]}
last_updated	2026-03-17

Data Visualization

Produce figures that meet journal submission standards: correct DPI, font sizes, line widths, colorblind-friendly palettes, and vector/raster export formats.

Journal Figure Standards

Journal Family	Single Column	Double Column	DPI	Preferred Format
Nature / Science	89 mm	183 mm	300	TIFF / EPS
Cell Press	85 mm	170 mm	300	PDF / TIFF
PLOS ONE	83 mm	171 mm	300	TIFF / EPS
ACS Journals	84 mm	176 mm	600	TIFF
General use	3.5 in	7 in	300	PDF / SVG

Setup

pip install matplotlib seaborn numpy pandas scipy palettable
# For LaTeX rendering in labels (optional but recommended)
# Requires a local LaTeX installation (e.g. TeX Live or MiKTeX)

Publication Style Foundation

"""
pub_style.py
Reusable publication-quality matplotlib configuration.
"""

import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
import numpy as np
import pandas as pd
from typing import Optional, Tuple, List, Union


# ─────────────────────────────────────────────
# Color Palettes (colorblind-friendly)
# ─────────────────────────────────────────────


OKABE_ITO = [
    ,  
    ,  
    ,  
    ,  
    ,  
    ,  
    ,  
    ,  
]


WONG = [
    ,
    ,
    ,
    ,
    ,
    ,
    ,
    ,
]


 () -> []:
    
    :
         palettable
        palette = (palettable.colorbrewer.qualitative, )
         palette.hex_colors
     Exception:
         OKABE_ITO[:n]






 () -> :
    
     color_palette  :
        color_palette = OKABE_ITO

    sns.set_theme(style=style, context=context, palette=color_palette,
                  font=font_family, font_scale=)

    rc_params = {
        
        :          font_size,
        :     font_size,
        :     font_size,
        :    font_size - ,
        :    font_size - ,
        :    font_size - ,
        : font_size,
        
        :    line_width * ,
        :     line_width,
        :    line_width,
        
        :   tick_major_size,
        :   tick_major_size,
        :   tick_major_size * ,
        :   tick_major_size * ,
        :  line_width,
        :  line_width,
        
        :  axes_spines_right,
        :    axes_spines_top,
        
        :        ,
        :       ,
        : ,
        
        :     ,
        : ,
        
        :        use_latex,
    }
    mpl.rcParams.update(rc_params)


 () -> :
    
     mm / 


 () -> :
    
     formats  :
        formats = [, ]
     fmt  formats:
        full_path = 
        fig.savefig(full_path, dpi=dpi, =fmt)
        ()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from pub_style import setup_publication_style, mm_to_inches, save_figure, OKABE_ITO

setup_publication_style(font_size=8)

rng = np.random.default_rng(42)
n = 80
x = rng.normal(5, 1.5, n)
y = 2.3 * x + rng.normal(0, 2, n)
group = rng.choice(["A", "B"], size=n)
df = pd.DataFrame({"x": x, "y": y, "group": group})

fig, ax = plt.subplots(figsize=(mm_to_inches(89), mm_to_inches(70)))

colors = {"A": OKABE_ITO[0], "B": OKABE_ITO[1]}
for grp, gdf in df.groupby("group"):
    ax.scatter(gdf["x"], gdf["y"], color=colors[grp], s=18, alpha=0.75,
               linewidths=0.3, edgecolors="white", label=grp, zorder=3)

    # Per-group regression line
    slope, intercept, r, p, se = stats.linregress(gdf["x"], gdf["y"])
    x_line = np.linspace(gdf["x"].min(), gdf["x"].max(), 100)
    ax.plot(x_line, slope * x_line + intercept, color=colors[grp],
            linewidth=1.2, linestyle="--")
    ax.annotate(f"r={r:.2f}", xy=(x_line[-1], slope * x_line[-1] + intercept),
                fontsize=6, color=colors[grp], ha="left")

ax.set_xlabel("Predictor variable (units)")
ax.set_ylabel("Outcome variable (units)")
ax.legend(title="Group", loc="upper left", markerscale=1.2)
ax.set_title("Figure 1: Group-stratified regression")

fig.tight_layout()
save_figure(fig, "figure1_scatter", formats=["pdf", "tiff"])
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pub_style import setup_publication_style, mm_to_inches, save_figure, OKABE_ITO

setup_publication_style(font_size=8)

rng = np.random.default_rng(7)
data = pd.DataFrame({
    "value": np.concatenate([
        rng.normal(5, 1.2, 40),
        rng.normal(6.5, 1.5, 40),
        rng.normal(4.2, 0.9, 40),
        rng.normal(7.0, 2.0, 40),
    ]),
    "condition": ["Control"] * 40 + ["Drug A"] * 40 + ["Drug B"] * 40 + ["Combo"] * 40,
})

fig, ax = plt.subplots(figsize=(mm_to_inches(120), mm_to_inches(80)))

palette = {c: OKABE_ITO[i] for i, c in enumerate(data["condition"].unique())}

# Violin (distribution)
sns.violinplot(
    data=data, x="condition", y="value",
    palette=palette, inner=None, linewidth=0.8,
    cut=0, ax=ax,
)

# Strip (individual data points)
sns.stripplot(
    data=data, x="condition", y="value",
    color="black", size=2.5, alpha=0.5, jitter=True,
    ax=ax, zorder=3,
)

# Median markers
for i, (cond, grp) in enumerate(data.groupby("condition")):
    ax.plot(i, grp["value"].median(), marker="D", color="white",
            markeredgecolor="black", markeredgewidth=0.8, markersize=5, zorder=4)

ax.set_xlabel("Treatment condition")
ax.set_ylabel("Response (arbitrary units)")
ax.set_title("Figure 2: Distribution of responses by condition")

# Significance bracket example
ax.annotate("", xy=(1, 9.5), xytext=(3, 9.5),
            arrowprops=dict(arrowstyle="-", color="black", lw=0.8))
ax.text(2, 9.7, "***", ha="center", va="bottom", fontsize=9)

fig.tight_layout()
save_figure(fig, "figure2_violin", formats=["pdf", "tiff"])
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
from scipy.cluster import hierarchy
from scipy.spatial.distance import pdist
from pub_style import setup_publication_style, mm_to_inches, save_figure, OKABE_ITO

setup_publication_style(font_size=8)

rng = np.random.default_rng(123)

# ── Data ──────────────────────────────────────────────────────────────────
n_genes, n_samples = 30, 20
expr_matrix = rng.normal(0, 1, (n_genes, n_samples))
# Add a biologically meaningful signal
expr_matrix[:10, :10] += 2.5

labels_col = ["Ctrl"] * 10 + ["Treat"] * 10
time_x = np.linspace(0, 4 * np.pi, 100)
curve_a = np.sin(time_x) + rng.normal(0, 0.15, 100)
curve_b = np.cos(time_x) + rng.normal(0, 0.15, 100)

# ── Layout: 2 rows × 3 cols ───────────────────────────────────────────────
fig = plt.figure(figsize=(mm_to_inches(183), mm_to_inches(120)))
gs = gridspec.GridSpec(
    2, 3,
    figure=fig,
    hspace=0.45,
    wspace=0.38,
    width_ratios=[1.5, 1, 1],
)

# Panel A — heatmap with clustering
ax_a = fig.add_subplot(gs[:, 0])   # spans both rows
linkage = hierarchy.linkage(pdist(expr_matrix), method="ward")
order = hierarchy.leaves_list(linkage)
cmap = sns.diverging_palette(220, 20, as_cmap=True)
im = ax_a.imshow(
    expr_matrix[order, :], aspect="auto", cmap=cmap,
    vmin=-3, vmax=3, interpolation="nearest",
)
ax_a.set_xlabel("Sample")
ax_a.set_ylabel("Gene (clustered)")
ax_a.set_title("A", loc="left", fontweight="bold")
cbar = fig.colorbar(im, ax=ax_a, fraction=0.046, pad=0.04)
cbar.set_label("Z-score", fontsize=7)
cbar.ax.tick_params(labelsize=6)

# Panel B — line plot
ax_b = fig.add_subplot(gs[0, 1])
ax_b.plot(time_x, curve_a, color=OKABE_ITO[0], linewidth=1.0, label="Condition A")
ax_b.plot(time_x, curve_b, color=OKABE_ITO[1], linewidth=1.0, linestyle="--", label="Condition B")
ax_b.fill_between(time_x, curve_a - 0.3, curve_a + 0.3, color=OKABE_ITO[0], alpha=0.15)
ax_b.set_xlabel("Time (s)")
ax_b.set_ylabel("Signal")
ax_b.legend(loc="upper right", fontsize=6)
ax_b.set_title("B", loc="left", fontweight="bold")

# Panel C — bar chart with error bars
ax_c = fig.add_subplot(gs[0, 2])
means = rng.uniform(3, 8, 4)
sems  = rng.uniform(0.3, 1.0, 4)
bars = ax_c.bar(
    range(4), means, yerr=sems,
    color=OKABE_ITO[:4],
    capsize=3, linewidth=0.7, edgecolor="black", width=0.6,
    error_kw={"linewidth": 0.8},
)
ax_c.set_xticks(range(4))
ax_c.set_xticklabels(["Ctrl", "A", "B", "C"], fontsize=7)
ax_c.set_ylabel("Mean ± SEM")
ax_c.set_title("C", loc="left", fontweight="bold")

# Panel D — scatter
ax_d = fig.add_subplot(gs[1, 1])
x4 = rng.normal(0, 1, 60)
y4 = 0.7 * x4 + rng.normal(0, 0.8, 60)
ax_d.scatter(x4, y4, color=OKABE_ITO[2], s=15, alpha=0.7, linewidths=0)
ax_d.set_xlabel("Variable X")
ax_d.set_ylabel("Variable Y")
ax_d.set_title("D", loc="left", fontweight="bold")

# Panel E — histogram
ax_e = fig.add_subplot(gs[1, 2])
data_e = rng.normal(5, 1.5, 200)
ax_e.hist(data_e, bins=20, color=OKABE_ITO[3], edgecolor="white", linewidth=0.4)
ax_e.axvline(data_e.mean(), color="black", linestyle="--", linewidth=0.9, label="Mean")
ax_e.set_xlabel("Value")
ax_e.set_ylabel("Count")
ax_e.legend(fontsize=6)
ax_e.set_title("E", loc="left", fontweight="bold")

save_figure(fig, "figure3_multipanel", formats=["pdf", "tiff"])
plt.show()

# publication_figure.R
# Requires: ggplot2, cowplot, ggbeeswarm, RColorBrewer

library(ggplot2)
library(cowplot)
library(dplyr)

# Colorblind-safe palette (Okabe-Ito)
okabe_ito <- c(
  "#E69F00", "#56B4E9", "#009E73",
  "#F0E442", "#0072B2", "#D55E00", "#CC79A7"
)

# ── Theme ─────────────────────────────────────────────────────────────────
pub_theme <- theme_cowplot(font_size = 8) +
  theme(
    axis.line        = element_line(linewidth = 0.4),
    axis.ticks       = element_line(linewidth = 0.4),
    axis.ticks.length = unit(2, "pt"),
    legend.key.size  = unit(8, "pt"),
    plot.title       = element_text(face = "bold", size = 8),
    strip.text       = element_text(size = 7),
  )

# ── Simulate data ─────────────────────────────────────────────────────────
set.seed(42)
df <- data.frame(
  group    = rep(c("Control", "Drug A", "Drug B"), each = 50),
  response = c(rnorm(50, 5, 1.2), rnorm(50, 7, 1.5), rnorm(50, 4, 0.9)),
  time     = rep(1:50, 3)
)

# ── Panel 1: Violin + jitter ──────────────────────────────────────────────
p1 <- ggplot(df, aes(x = group, y = response, fill = group)) +
  geom_violin(trim = FALSE, alpha = 0.7, linewidth = 0.4) +
  geom_jitter(width = 0.15, size = 0.8, alpha = 0.5, color = "black") +
  stat_summary(fun = median, geom = "crossbar",
               width = 0.4, linewidth = 0.6, color = "white") +
  scale_fill_manual(values = okabe_ito[1:3]) +
  labs(x = NULL, y = "Response (a.u.)", title = "A") +
  pub_theme +
  theme(legend.position = "none")

# ── Panel 2: Line plot with ribbon ───────────────────────────────────────
df_time <- df %>%
  group_by(group, time) %>%
  summarise(mean = mean(response), se = sd(response) / sqrt(n()), .groups = "drop")

p2 <- ggplot(df_time, aes(x = time, y = mean, color = group, fill = group)) +
  geom_ribbon(aes(ymin = mean - se, ymax = mean + se), alpha = 0.15, linewidth = 0) +
  geom_line(linewidth = 0.6) +
  scale_color_manual(values = okabe_ito[1:3]) +
  scale_fill_manual(values  = okabe_ito[1:3]) +
  labs(x = "Time (arbitrary)", y = "Mean ± SE", title = "B",
       color = "Group", fill = "Group") +
  pub_theme +
  theme(legend.position = c(0.7, 0.85))

# ── Combine panels ────────────────────────────────────────────────────────
combined <- plot_grid(p1, p2, ncol = 2, align = "hv", axis = "tblr")

# ── Save ──────────────────────────────────────────────────────────────────
ggsave("figure_ggplot2.pdf",  combined, width = 183, height = 90, units = "mm", dpi = 300)
ggsave("figure_ggplot2.tiff", combined, width = 183, height = 90, units = "mm", dpi = 300,
       compression = "lzw")

Format	Best For	Notes
PDF	Vector; LaTeX inclusion	Infinitely scalable, small file
SVG	Web, Inkscape editing	Editable in vector tools
TIFF	Journal submission	Use LZW compression to reduce size
EPS	Legacy journal systems	Avoid transparency
PNG	Web / presentations	300 DPI minimum for print

from pub_style import OKABE_ITO, get_colorbrewer

# Colorblind-safe qualitative (≤8 groups)
colors = OKABE_ITO

# Sequential (e.g. heatmaps)
import matplotlib.pyplot as plt
plt.cm.viridis   # perceptually uniform, colorblind-safe
plt.cm.cividis   # optimised for deuteranopia / protanopia

# Diverging (e.g. correlation matrices, fold-change)
plt.cm.RdBu_r    # red–white–blue reversed
plt.cm.bwr       # blue–white–red

# From ColorBrewer
cb_colors = get_colorbrewer("Set2", n=8)   # qualitative
cb_seq    = get_colorbrewer("Blues", n=9)  # sequential (via palettable)