颜色、色图与 colorbar

import matplotlib as mpl

for c in ['#1f77b4', 'tab:blue', 'C0', 'crimson', '0.5', (1, .5, 0), (1, .5, 0, .3)]:
    print(f'{str(c):18}', mpl.colors.to_rgba(c))
# 输出: #1f77b4            (0.12156862745098039, 0.4666666666666667, 0.7058823529411765, 1.0)
# 输出: tab:blue           (0.12156862745098039, 0.4666666666666667, 0.7058823529411765, 1.0)
# 输出: C0                 (0.12156862745098039, 0.4666666666666667, 0.7058823529411765, 1.0)
# 输出: crimson            (0.8627450980392157, 0.0784313725490196, 0.23529411764705882, 1.0)
# 输出: 0.5                (0.5, 0.5, 0.5, 1.0)
# 输出: (1, 0.5, 0)        (1.0, 0.5, 0.0, 1)
# 输出: (1, 0.5, 0, 0.3)   (1.0, 0.5, 0.0, 0.3)

import matplotlib as mpl

print(len(mpl.colors.CSS4_COLORS), len(mpl.colors.BASE_COLORS), len(mpl.colors.TABLEAU_COLORS))
# 输出: 148 8 10

print(list(mpl.colors.BASE_COLORS))
# 输出: ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']

print(mpl.colors.CSS4_COLORS['crimson'])
# 输出: #DC143C

import matplotlib as mpl

print(mpl.colors.to_rgba('0.5'))
# 输出: (0.5, 0.5, 0.5, 1.0)

print(mpl.colors.to_rgba(0.5))
# ValueError: Invalid RGBA argument: 0.5

import matplotlib as mpl

for bad in ['#C0', '#c0']:
    try:
        mpl.colors.to_rgba(bad)
    except ValueError as e:
        print(e)
# 输出: Invalid RGBA argument: '#C0'
# 输出: Invalid RGBA argument: '#c0'

rcParams["axes.prop_cycle"] = cycler(
    color=["#1f77b4", ...]      # 颜色列表，必须是真实颜色（不能是 'C0'）
)
Axes.set_prop_cycle(
    color=[...],                # 只设颜色
    linestyle=["--", ":"],      # 同时轮换线型
    linewidth=[1, 2, 3],        # 还能轮换线宽
    **kwargs                    # 任意 Artist 属性都能进 cycle
)

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from cycler import cycler

mpl.rcParams['axes.prop_cycle'] = cycler(color=['tab:blue', 'tab:red', 'k'])

fig, ax = plt.subplots()
for i in range(4):
    ax.plot(np.arange(10), np.arange(10) + i)
plt.show()

import matplotlib as mpl

cols = mpl.rcParams['axes.prop_cycle'].by_key()['color']
print(len(cols))
# 输出: 10
print(cols[0])
# 输出: #1f77b4

import matplotlib as mpl
from cycler import cycler

try:
    mpl.rcParams['axes.prop_cycle'] = cycler(color=['C0', 'C1'])
except ValueError as e:
    print(e)
# 输出: Key axes.prop_cycle: Cannot put cycle reference ('C0') in prop_cycler

import matplotlib as mpl
from cycler import cycler

try:
    mpl.rcParams['axes.prop_cycle'] = cycler(color=['okabe_ito'])
except ValueError as e:
    print(e)
# 输出: Key axes.prop_cycle: 'okabe_ito' does not look like a color arg

import matplotlib as mpl
from cycler import cycler

OI = [mpl.colors.to_hex(mpl.colormaps['tab10'](i / 9)) for i in range(10)]
print(OI[:3])
# 输出: ['#1f77b4', '#ff7f0e', '#2ca02c']
mpl.rcParams['axes.prop_cycle'] = cycler(color=OI)

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from cycler import cycler

mpl.rcParams['axes.prop_cycle'] = (cycler(color=['r', 'g', 'b'])
                                   + cycler(linestyle=['-', '--', ':']))
print(len(mpl.rcParams['axes.prop_cycle']))
# 输出: 3

fig, ax = plt.subplots()
for i in range(3):
    ax.plot(np.arange(5), np.arange(5) + i)
plt.show()

from cycler import cycler

try:
    cycler(color=['r', 'g']) + cycler(linewidth=[1, 2, 3])
except ValueError as e:
    print(e)
# 输出: Can only add equal length cycles, not 2 and 3

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
ax.plot(np.arange(5), label='改之前')
ax.set_prop_cycle(color=['r', 'g'], linestyle=['--', ':'])
ax.plot(np.arange(5), np.arange(5) + 1, label='改之后')
ax.legend()
plt.show()

import matplotlib as mpl
import matplotlib.pyplot as plt

print('okabe_ito' in plt.colormaps(), 'petroff6' in plt.colormaps())
# 输出: False False
print(hasattr(mpl.colormaps['Blues'], 'with_alpha'))
# 输出: False

import matplotlib as mpl

print(mpl.colors.to_hex(mpl.colormaps['Blues'](0.0)),
      mpl.colors.to_hex(mpl.colormaps['Blues'](1.0)))
# 输出: #f7fbff #08306b

print(mpl.colors.to_hex(mpl.colormaps['Blues_r'](0.0)))
# 输出: #08306b          ← 和 Blues(1.0) 是同一个色

import matplotlib as mpl
import numpy as np

cm = mpl.colormaps['viridis']
print([mpl.colors.to_hex(cm(t)) for t in (0, .5, 1)])
# 输出: ['#440154', '#21918c', '#fde725']
print(cm(np.array([0, 0.5, 1.0])).shape)
# 输出: (3, 4)

matplotlib.colors.Normalize(vmin=None, vmax=None, clip=False)
matplotlib.colors.LogNorm(vmin=None, vmax=None, clip=False)
matplotlib.colors.SymLogNorm(linthresh, linscale=1, vmin=None, vmax=None, clip=False)
matplotlib.colors.PowerNorm(gamma, vmin=None, vmax=None, clip=False)
matplotlib.colors.TwoSlopeNorm(vcenter=0, vmin=None, vmax=None)
matplotlib.colors.CenteredNorm(vcenter=0, halfrange=None, clip=False)
matplotlib.colors.BoundaryNorm(boundaries, ncolors, clip=False, extend="neither")

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
sc = ax.scatter(np.arange(10), np.arange(10) ** 2, c=np.arange(10) ** 2, s=60)
sc.set_norm(mpl.colors.Normalize(vmin=0, vmax=100))
cb = fig.colorbar(sc, ax=ax, label='平方值')
plt.show()

import matplotlib as mpl

try:
    mpl.colors.TwoSlopeNorm(vcenter=5, vmin=-2, vmax=2)
except ValueError as e:
    print(e)
# 输出: vmin, vcenter, and vmax must be in ascending order

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

bounds = [-2.5, -1.5, -0.5, 0.5, 1.5, 2.5]
cmap = mpl.colormaps['coolwarm'].resampled(len(bounds) - 1)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

fig, ax = plt.subplots()
im = ax.imshow(np.linspace(-3, 3, 144).reshape(12, 12), cmap=cmap, norm=norm)
fig.colorbar(im, ax=ax, ticks=bounds, boundaries=bounds, spacing='proportional')
plt.show()

Figure.colorbar(
    mappable,               # ★ 带 cmap+norm 的对象：imshow/scatter/contourf 的返回值
    cax=None,               # ★ 给色条专用的 axes；给了就完全手动
    ax=None,                # ★ 从这些 axes 里「抢」空间；可以传 list
    use_gridspec=None,      # True 时用 gridspec 切，配合 constrained_layout
    location=None,          # 'left' / 'right' / 'top' / 'bottom'
    orientation=None,       # 'vertical' / 'horizontal'
    fraction=0.15,          # 从 ax 抢走多大比例
    shrink=1.0,             # 色条长度缩放
    aspect=20,              # 色条长宽比
    pad=0.05,               # 与主图的间距
    anchor=(0.0, 0.5),      # 色条在分配框里的锚点
    extend="neither",       # 'neither'/'both'/'min'/'max'，两端要不要三角
    extendfrac=None,        # 三角占多长
    ticks=None,             # 刻度位置
    boundaries=None,        # 分段边界（配 BoundaryNorm）
    spacing='uniform',      # 'uniform' / 'proportional'
    label="",               # 色条标题
)

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
im = ax.imshow(np.arange(144).reshape(12, 12), cmap='viridis')
cb = fig.colorbar(im, ax=ax, label='得分')
print(cb.vmin, cb.vmax)
# 输出: 0.0 143.0
plt.show()

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
im = ax.imshow(np.arange(25).reshape(5, 5))

# ① ax=：从主图身上抢走 15% 宽度，主图被压窄
cb = fig.colorbar(im, ax=ax)

# ② cax=：完全手动，主图尺寸不变
cax = ax.inset_axes([1.04, 0.05, 0.04, 0.9])
cb2 = fig.colorbar(im, cax=cax)
plt.show()

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
im = ax.imshow(np.arange(25).reshape(5, 5))
cax = fig.add_axes([1.02, 0, .05, 1])
cb = fig.colorbar(im, ax=ax, cax=cax)
print(cb.ax is cax)
# 输出: True

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
im = ax.imshow(np.arange(25).reshape(5, 5))
try:
    fig.colorbar([im, im], cax=fig.add_axes([1.02, 0, .05, 1]))
except AttributeError as e:
    print(e)
# 输出: 'list' object has no attribute 'cmap'

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
im = ax.imshow(np.arange(25).reshape(5, 5))
cb = fig.colorbar(im, ax=ax, fraction=.08, pad=.02, shrink=.9, aspect=25)
plt.show()

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
im = ax.imshow(np.arange(25).reshape(5, 5))
cb = fig.colorbar(im, ax=ax, extend='both', extendfrac=.12)
print(cb.extend)
# 输出: both

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
im = ax.imshow(np.arange(25).reshape(5, 5))
cb = fig.colorbar(im, ax=ax, ticks=[0, 12, 24], boundaries=[0, 6, 12, 18, 24])
print(cb.boundaries)
# 输出: [0, 6, 12, 18, 24]

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
im = ax.imshow(np.random.default_rng(0).normal(size=(8, 8)))
cb = fig.colorbar(im, ax=ax)
cb.outline.set_linewidth(0.8)
cb.ax.tick_params(labelsize=8, length=2)
cb.set_label('得分', rotation=270, labelpad=14)
cb.ax.set_title('K', fontsize=9, pad=6)
for s in cb.ax.spines.values():
    s.set_visible(False)
plt.show()

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

rng = np.random.default_rng(1)
Z = rng.normal(size=(14, 14))
fig, axs = plt.subplots(1, 3, figsize=(13.5, 3.6))

# ① ax= 自动挤压
ax = axs[0]
im = ax.imshow(Z, cmap='viridis')
fig.colorbar(im, ax=ax, label='自动挤压')
ax.set_title('ax=', fontsize=9)

# ② cax= 精确控制
ax = axs[1]
im = ax.imshow(Z, cmap='magma')
cax = ax.inset_axes([1.04, 0.05, 0.04, 0.9])
cb = fig.colorbar(im, cax=cax)
cb.set_label('手动', rotation=270, labelpad=12)
ax.set_title('cax=', fontsize=9)

# ③ 分段 + extend
ax = axs[2]
bounds = [-2.5, -1.5, -0.5, 0.5, 1.5, 2.5]
cmap = mpl.colormaps['coolwarm'].resampled(len(bounds) - 1)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
im = ax.imshow(np.clip(Z, -3, 3), cmap=cmap, norm=norm)
fig.colorbar(im, ax=ax, ticks=bounds, boundaries=bounds,
             spacing='proportional', extend='both')
ax.set_title('BoundaryNorm + extend', fontsize=9)

plt.tight_layout()
plt.show()

matplotlib.colors.LinearSegmentedColormap.from_list(
    name,                   # 色图名，随便取
    colors,                 # ['#...', ...] 或 [(位置, '#...'), ...]
    N=256,                  # 采样级数
)
matplotlib.colors.ListedColormap(
    colors,                 # 颜色列表
    name="from_list",       # 名字
    N=None,                 # 级数，默认等于颜色数
)
matplotlib.colormaps['Blues'].resampled(6)     # 连续 → 离散

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

cmap = mpl.colors.LinearSegmentedColormap.from_list(
    'brand', ['#0d3b66', '#faf0ca', '#f4d35e', '#ee964b', '#f95738'])
print(cmap.N)
# 输出: 256

fig, ax = plt.subplots()
ax.imshow(np.linspace(0, 1, 256).reshape(1, -1), aspect='auto', cmap=cmap)
plt.show()

import matplotlib as mpl

even = mpl.colors.LinearSegmentedColormap.from_list(
    'even', ['#2c7bb6', '#f7f7f7', '#d7191c'])

explicit = mpl.colors.LinearSegmentedColormap.from_list(
    'explicit', [(0, '#2c7bb6'), (.5, '#f7f7f7'), (1, '#d7191c')])

skewed = mpl.colors.LinearSegmentedColormap.from_list(
    'skewed', [(0, '#2c7bb6'), (.12, '#f7f7f7'), (1, '#d7191c')])

print(even.N, explicit.N, skewed.N)
# 输出: 256 256 256

import matplotlib as mpl

lc = mpl.colors.ListedColormap(['#003f5c', '#58508d', '#bc5090'])
lc.set_bad('k')
lc.set_under('darkblue')
lc.set_over('purple')
print(mpl.colors.to_hex(lc.get_bad()), mpl.colors.to_hex(lc.get_under()))
# 输出: #000000 #00008b

import matplotlib as mpl

try:
    mpl.colors.ListedColormap(['r', 'g', 'b'], bad='k')
except TypeError as e:
    print(e)
# 输出: ListedColormap.__init__() got an unexpected keyword argument 'bad'

import matplotlib as mpl
from matplotlib.colors import LinearSegmentedColormap, ListedColormap
import matplotlib.pyplot as plt
import numpy as np

rng = np.random.default_rng(3)
Z = rng.normal(size=(14, 14))
fig, axs = plt.subplots(1, 3, figsize=(13.5, 3.6))

# ① 品牌发散色图：中点锚在 .5，两端不等距
div = LinearSegmentedColormap.from_list(
    'brand_div', [(0, '#0d3b66'), (.45, '#faf0ca'), (.55, '#faf0ca'), (1, '#f95738')])
ax = axs[0]
im = ax.imshow(Z, cmap=div, norm=mpl.colors.CenteredNorm())
fig.colorbar(im, ax=ax, label='相对 0')
ax.set_title('品牌发散色图', fontsize=9)

# ② 离散分级
bounds = [-2, -1, 0, 1, 2]
cmap = ListedColormap(['#2c7bb6', '#abd9e9', '#ffffbf', '#fdae61', '#d7191c'])
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
ax = axs[1]
im = ax.imshow(np.clip(Z, -2.5, 2.5), cmap=cmap, norm=norm)
fig.colorbar(im, ax=ax, ticks=bounds, boundaries=bounds, spacing='proportional')
ax.set_title('ListedColormap 分级', fontsize=9)

# ③ 带 bad：NaN 单独上色
ax = axs[2]
Z2 = Z.copy()
Z2[np.abs(Z2) < .5] = np.nan
cm3 = mpl.colormaps['coolwarm'].copy()
cm3.set_bad('#4d4d4d')
im = ax.imshow(Z2, cmap=cm3, norm=mpl.colors.CenteredNorm())
fig.colorbar(im, ax=ax, label='|x| < 0.5 为灰')
ax.set_title('set_bad 处理 NaN', fontsize=9)

plt.tight_layout()
plt.show()