Matplotlib実践Tips：論文品質グラフのPython実装（plt.rcParams/savefig/colormap/subplots/tight_layout）

import matplotlib.pyplot as plt

plt.rcParams.update({
    'font.size': 12,
    'axes.labelsize': 14,
    'axes.titlesize': 14,
    'xtick.labelsize': 11,
    'ytick.labelsize': 11,
    'legend.fontsize': 11,
    'figure.figsize': (6, 4),
    'figure.dpi': 150,
    'lines.linewidth': 1.5,
    'axes.linewidth': 0.8,
    'axes.grid': True,
    'grid.alpha': 0.3,
})

plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams.update({
    'font.size': 12,
    'axes.labelsize': 14,
})

pip install japanize-matplotlib

import matplotlib.pyplot as plt
import japanize_matplotlib

plt.plot([1, 2, 3], [1, 4, 9])
plt.title("日本語タイトル")
plt.xlabel("X軸ラベル")
plt.ylabel("Y軸ラベル")
plt.show()

import matplotlib
from matplotlib import font_manager

# macOS
font_path = '/System/Library/Fonts/ヒラギノ角ゴシック W3.ttc'
# Linux
# font_path = '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc'

font_manager.fontManager.addfont(font_path)
matplotlib.rc('font', family='Hiragino Sans')

# フォント一覧の確認
fc-list :lang=ja

# Noto Sans CJKのインストール（Ubuntu/Debian）
sudo apt install fonts-noto-cjk

import matplotlib
matplotlib.font_manager._load_fontmanager(try_read_cache=False)

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.linspace(0, 10, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

fig, axes = plt.subplots(1, 3, figsize=(15, 4))
cmaps = ['viridis', 'cividis', 'plasma']

for ax, cmap in zip(axes, cmaps):
    im = ax.pcolormesh(X, Y, Z, cmap=cmap)
    ax.set_title(cmap)
    fig.colorbar(im, ax=ax)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)
x = np.linspace(0, 10, 100)

# tab10: Matplotlibのデフォルト配色（最大10色）
colors = plt.cm.tab10.colors

for i in range(5):
    plt.plot(x, np.sin(x + i), color=colors[i], label=f'Series {i+1}')

plt.legend()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)

# Machado, Oliveira & Fernandes (2009) の1型色覚(protanopia)シミュレーション行列
# (線形RGB空間に適用する)
PROTANOPIA_MATRIX = np.array([
    [0.152286, 1.052583, -0.204868],
    [0.114503, 0.786281, 0.099216],
    [-0.003882, -0.048116, 1.051998],
])


def simulate_protanopia(rgb):
    """sRGB画像 (0-1, shape=(H, W, 3)) に1型色覚シミュレーションを適用する"""
    linear = np.where(rgb <= 0.04045, rgb / 12.92, ((rgb + 0.055) / 1.055) ** 2.4)
    sim_linear = np.clip(linear @ PROTANOPIA_MATRIX.T, 0, 1)
    srgb = np.where(
        sim_linear <= 0.0031308,
        sim_linear * 12.92,
        1.055 * sim_linear ** (1 / 2.4) - 0.055,
    )
    return np.clip(srgb, 0, 1)


def plot_with_cycle(ax, colors, title):
    x = np.linspace(0, 4 * np.pi, 200)
    for i, c in enumerate(colors):
        ax.plot(x, np.sin(x + i * 0.6) + i * 0.18, color=c, linewidth=2.5, label=f"Series {i+1}")
    ax.set_title(title)


tab10 = list(plt.cm.tab10.colors[:5])
with plt.style.context("petroff10"):
    petroff10 = list(plt.rcParams["axes.prop_cycle"].by_key()["color"])[:5]

fig, axes = plt.subplots(1, 2, figsize=(9, 3.4))
plot_with_cycle(axes[0], tab10, "tab10 (Matplotlib default cycle)")
plot_with_cycle(axes[1], petroff10, "petroff10 (Matplotlib >= 3.10)")
fig.tight_layout()
fig.canvas.draw()

rgba = np.asarray(fig.canvas.buffer_rgba()).astype(float) / 255
simulated = simulate_protanopia(rgba[..., :3])
plt.imsave("simulated_protanopia.png", simulated)

plt.style.use('petroff10')

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)
fig, axes = plt.subplots(2, 2, figsize=(10, 8), sharex=True, sharey=True)

for ax in axes.flat:
    ax.plot(np.random.randn(50).cumsum())

fig.align_ylabels(axes[:, 0])
plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt


def build_figure(layout):
    np.random.seed(42)
    fig, axes = plt.subplots(2, 2, figsize=(5, 4.2), layout=layout)
    for i, ax in enumerate(axes.flat):
        ax.plot(np.random.randn(50).cumsum())
        ax.set_title(f"Very Long Subplot Title #{i}")
        ax.set_ylabel("Cumulative sum of\nrandom walk")
        ax.set_xlabel("Time step")
    return fig


for layout in [None, "tight", "constrained"]:
    fig = build_figure(layout)
    fig.savefig(f"layout_{layout or 'none'}.png", dpi=150)

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)
x = np.linspace(-3, 3, 60)
X, Y = np.meshgrid(x, x)
Z = np.sin(X) * np.cos(Y)

for layout in ["tight", "constrained"]:
    fig, axes = plt.subplots(2, 2, figsize=(6, 5), layout=layout)
    for i, ax in enumerate(axes.flat):
        im = ax.pcolormesh(X, Y, Z + 0.1 * i, cmap="viridis")
        ax.set_title(f"Panel {i}")
    fig.colorbar(im, ax=axes, shrink=0.8, label="Value")
    fig.suptitle(f"layout='{layout}'")
    fig.savefig(f"cbar_{layout}.png", dpi=150)

fig, axes = plt.subplots(
    2, 2,
    figsize=(10, 8),
    gridspec_kw={'hspace': 0.05, 'wspace': 0.05},
    sharex=True,
    sharey=True
)

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

np.random.seed(42)
t = np.linspace(0, 10, 200)
main_signal = np.sin(t) + 0.15 * np.random.randn(200)

fig = plt.figure(figsize=(10, 6), layout="constrained")
gs = GridSpec(2, 3, figure=fig)

ax_main = fig.add_subplot(gs[:, :2])  # 左側2/3を占める大きなプロット
ax_main.plot(t, main_signal)
ax_main.set_title("Main signal (spans 2 rows x 2 cols)")
ax_main.set_xlabel("Time [s]")
ax_main.set_ylabel("Amplitude")

ax_top = fig.add_subplot(gs[0, 2])  # 右上
ax_top.hist(main_signal, bins=15, color="tab:blue")
ax_top.set_title("Distribution")

ax_bottom = fig.add_subplot(gs[1, 2])  # 右下
ax_bottom.plot(t, np.gradient(main_signal, t), color="tab:orange")
ax_bottom.set_title("Derivative")

fig.savefig("gridspec_layout_demo.png", dpi=150)

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])

# PDF出力
fig.savefig('figure.pdf', bbox_inches='tight')

# SVG出力
fig.savefig('figure.svg', bbox_inches='tight')

fig.savefig('figure.png', dpi=300, bbox_inches='tight')

ax.scatter(x, y, rasterized=True)  # 散布図だけラスター化
fig.savefig('figure.pdf', dpi=300, bbox_inches='tight')

import numpy as np
import matplotlib.pyplot as plt

# グローバル設定
plt.rcParams.update({
    'font.size': 12,
    'axes.labelsize': 14,
    'axes.titlesize': 14,
    'xtick.labelsize': 11,
    'ytick.labelsize': 11,
    'legend.fontsize': 11,
    'figure.figsize': (6, 4),
    'figure.dpi': 150,
    'lines.linewidth': 1.5,
    'axes.linewidth': 0.8,
    'axes.grid': True,
    'grid.alpha': 0.3,
})

# データ生成
np.random.seed(42)
x = np.linspace(0, 2 * np.pi, 100)
signals = {
    'Signal A': np.sin(x) + np.random.normal(0, 0.1, 100),
    'Signal B': np.cos(x) + np.random.normal(0, 0.1, 100),
    'Signal C': np.sin(2 * x) + np.random.normal(0, 0.1, 100),
}

# カテゴリ用パレット
colors = plt.cm.Set2.colors

# サブプロット作成
fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))

# 左: 折れ線グラフ
ax = axes[0]
for i, (label, data) in enumerate(signals.items()):
    ax.plot(x, data, color=colors[i], label=label)
ax.set_xlabel('Time [s]')
ax.set_ylabel('Amplitude')
ax.set_title('Time Series Comparison')
ax.legend()

# 右: ヒートマップ
ax = axes[1]
X, Y = np.meshgrid(x, x)
Z = np.sin(X) * np.cos(Y)
im = ax.pcolormesh(X, Y, Z, cmap='cividis', rasterized=True)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('2D Heatmap')
fig.colorbar(im, ax=ax)

plt.tight_layout()

# ベクター形式で保存
fig.savefig('publication_figure.pdf', bbox_inches='tight')
fig.savefig('publication_figure.svg', bbox_inches='tight')
plt.show()

import matplotlib.pyplot as plt
import numpy as np

plt.rcParams.update({
    'text.usetex': True,              # システムの LaTeX を使用
    'font.family': 'serif',
    'font.serif': ['Computer Modern Roman'],
    'text.latex.preamble': r'\usepackage{amsmath}\usepackage{siunitx}',
    'axes.labelsize': 12,
    'figure.figsize': (5.5, 3.5),
    'pdf.fonttype': 42,               # TrueType でフォント埋め込み
    'ps.fonttype': 42,
    'savefig.bbox': 'tight',
    'savefig.pad_inches': 0.02,
})

t = np.linspace(0, 2 * np.pi, 200)
fig, ax = plt.subplots()
ax.plot(t, np.sin(t) * np.exp(-t / 5),
        label=r'$x(t) = \sin(t)\,e^{-t/5}$')
ax.set_xlabel(r'Time $t$ [\si{\second}]')
ax.set_ylabel(r'$x(t)$')
ax.set_title(r'Damped Oscillation: $\omega_0 = 1\,\si{\radian\per\second}$')
ax.legend(frameon=False)
fig.savefig('damped.pdf')   # PDF (LaTeX 数式入り、ベクター)
fig.savefig('damped.svg')   # SVG

import seaborn as sns
sns.set_context('paper', font_scale=1.1)
sns.set_style('whitegrid', {'grid.alpha': 0.3})
plt.rcParams.update({'figure.dpi': 200, 'savefig.dpi': 300})

import matplotlib

matplotlib.use("MacOSX")  # 実際に事象を再現するには対話的なGUIバックエンドが必要
import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)

# NG: show()の後にsavefig()を呼ぶ
plt.plot(np.arange(20), np.random.randn(20).cumsum())
plt.title("My Figure")
plt.show()  # ウィンドウを閉じるとFigureが破棄される
plt.savefig("wrong_blank.png")  # 新しい空のFigureが保存される

plt.close("all")

# OK: FigureオブジェクトとAxesを保持し、show()より前に保存する
fig, ax = plt.subplots()
ax.plot(np.arange(20), np.random.randn(20).cumsum())
ax.set_title("My Figure")
fig.savefig("right_correct.png")  # 先に保存
plt.show()

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)

for dpi in [72, 150, 300]:
    plt.rcParams.update({'font.size': 11})
    fig, ax = plt.subplots(figsize=(4, 3), layout='constrained')  # インチ単位、3パターンとも同じ
    x = np.linspace(0, 2 * np.pi, 100)
    ax.plot(x, np.sin(x) + 0.05 * np.random.randn(100))
    ax.set_title('Measured Signal')
    ax.set_xlabel('Time [s]')
    ax.set_ylabel('Amplitude')
    fig.savefig(f'signal_dpi{dpi}.png', dpi=dpi)