matplotlib学习笔记

import matplotlib.pyplot as plt

fig.get_layout_engine().set(...)

legend = plt.legend()
for line in legend.get_lines():
    line.set_linewidth(1.0)

ax = plt.gca()

ax.set_ylim(bottom=0)

ax.set_yscale('log')
# 对数坐标有时候副刻度会显示label，看着很挤，可以强制不显示副刻度的label
ax_sa.tick_params(axis='y', which='minor', labelleft=False)

ax.ticklabel_format(style='sci', scilimits=(4, 4), useMathText=True)

ax1.xaxis.set_major_formatter(lambda x, pos: str(x-5))

from matplotlib.ticker import ScalarFormatter
y_formatter = ScalarFormatter()
y_formatter.set_powerlimits((-9, -9))
ax.yaxis.set_major_formatter(y_formatter)

ax.yaxis.get_offset_text().set_fontsize(8)

ax.xaxis.set_label_coords(0.1, -0.19)

# https://stackoverflow.com/a/68344604
ax.set_axisbelow(True)

ax.grid(axis='y')

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm, colors

ax = plt.gca()
cmap = plt.get_cmap('coolwarm')

# need to normalize because color maps are defined in [0, 1]
norm = colors.TwoSlopeNorm(1, vmin=0, vmax=5)

norm_cmap = cm.ScalarMappable(norm=norm, cmap=cmap)

for i in np.linspace(0, 5, 100):
    plt.scatter(i, i, color=norm_cmap.to_rgba(i))
cb = plt.colorbar(norm_cmap, ax=ax, ticks=[0, 0.5, 1, 2, 3, 4, 5])
cb.ax.tick_params(labelsize=8)
plt.show()

from matplotlib import gridspec

# https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure
fig = plt.figure(dpi=300, figsize=(xx, xx), constrained_layout=True)
fig_sa_max.get_layout_engine().set(
    # 上下的padding
    h_pad=0.01,
    # 左右的padding
    w_pad=0.01,
    # 左，下，宽，高。一般上面预留出来放legend。
    rect=(0, 0, 1, 0.74),
)
# 1行2列
gs = gridspec.GridSpec(1, 2, figure=fig)
for col in range(2):
    ax = plt.subplot(gs[0, col])
    ...
fig.legend(
    loc='upper center',
    # 精确定位
    bbox_to_anchor=(0.53, 1.04),
    ...
)
fig.savefig(pdf_path, metadata={'CreationDate': None})

from matplotlib import gridspec

fig = plt.figure(dpi=300, figsize=(xx, xx), constrained_layout=True)
# 2行3列
gs = gridspec.GridSpec(2, 3, figure=fig)
# https://matplotlib.org/stable/api/layout_engine_api.html#matplotlib.layout_engine.ConstrainedLayoutEngine.set
fig.get_layout_engine().set(
    # 左右的padding
    w_pad=0.01,
    # 子图纵向间距，可以用来放子图title。
    hspace=0.13,
    # 左，下，宽，高。一般下面预留出来放子图title。上面预留出来放legend。
    rect=(0, 0.05, 1, 0.85),
)
for row in range(2):
    for col in range(3):
        ax = plt.subplot(gs[row, col])
        ...
    # 放title一般用fig.text
    fig.text(x, y, title, ...)
fig.legend(
    loc='upper center',
    # 精确定位
    bbox_to_anchor=(0.54, 1.0),
    # 跟图片上沿的距离
    borderaxespad=0.01,
    ...
)
...
fig.savefig(pdf_path, metadata={'CreationDate': None})

fig = plt.figure(dpi=300, figsize=(7, 2.8))
# 外面是2行2列的大子图
# https://matplotlib.org/stable/api/_as_gen/matplotlib.gridspec.GridSpec.html
outer_grid = gridspec.GridSpec(2, 2, figure=fig,
    # 左边距
    left=0.06,
    # 下边距
    bottom=0.12,
    # 右边距
    right=0.998,
    # 上边距
    top=0.93,
    # 子图横向距离
    wspace=0.2,
    # 子图纵向距离
    hspace=0.4,
)
for row in range(2):
    for col in range(2):
        # 每个子图又分为1行3列的小子图
        inner_grid = gridspec.GridSpecFromSubplotSpec(1, 3, subplot_spec=outer_grid[row, col], wspace=0.4)
        for i in range(3):
            ax = fig.add_subplot(inner_grid[i])
            ...
        # 给每个大子图加title可以这样加
        bbox = outer_grid[row, col].get_position(fig)
        fig.text(bbox.x0 + bbox.width/2, bbox.y0 - 0.07, title, ha='center', va='top')
...
fig.legend(
    loc='upper center',
    # 精确定位
    bbox_to_anchor=(0.5, 1.02),
    ...
)
fig.savefig(pdf_path, metadata={'CreationDate': None})

plt.locator_params(axis='y', nbins=4)

plt.yscale('log')
# 设置成log scale似乎会清空ticks，所以要把设置ticks放后面
plt.yticks([1e5, 1e6, 1e7], fontsize=8)
# plt.yticks似乎会消除minor ticks，所以还得把它们补上
# 其中numticks比较玄学，似乎大于3就行。这里直接设置成一个大数，就肯定不会有问题了。
ax.yaxis.set_minor_locator(LogLocator(base=10, subs=np.arange(2, 10) * 0.1, numticks=233))

ax.tick_params(axis='y', which='major', pad=0.1)

# 手动控制颜色，不然两个ax上画出的线会出现相同颜色
color_list = plt.rcParams['axes.prop_cycle'].by_key()['color']
color_index = 0

ax1 = plt.gca()
ax1.set_ylabel('ylabel1', fontsize=8)
ax1.plot(x, y, label='legend1', color=color_list[color_index])
color_index += 1

ax2 = ax1.twinx()
ax2.set_ylabel('ylabel2', fontsize=8)
ax2.plot(x, y, label='legend2', color=color_list[color_index])
color_index += 1

lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, fontsize=8)

plt.show()