离散颜色条缺少一种颜色
Discrete colorbar is missing a color
Matplotlibs 离散颜色条在我的颜色图中缺少一种颜色定义，并且也在绘图中使用。
在我的示例代码中，我有七种颜色，但颜色条只显示六种颜色，尽管创建颜色图和颜色条的代码似乎与我在 Internet 上找到的示例相同。缺少带有标签“180”的红色。即使我更改了边界并勾选了颜色栏中的米色或浅蓝色。
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pandas as pd
# 4 marker
# 7 color
n=100
c = np.random.randint(1,8,size=n)
m = np.random.randint(1,5,size=n)
x = np.random.uniform(size=n)
y = np.random.uniform(size=n)
d_data = {'P':x, 'f':y, 'node':c, 'arch':m}
df = pd.DataFrame(d_data)
# Creating a unique list of elements
l_arch = df.arch.unique()
l_node = df.node.unique()
# Sorting is needd for good colormap
l_arch.sort()
l_node.sort()
# Creating a markers dictionary
zti_markers = ["v","^","s","o","x","+","D"]
d_marker = dict(zip(l_arch,zti_markers[:len(l_arch)] ))
# Creating a colormap and a color dictionary; A little cheat here: I know how
many different colors I need.
color_list = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f']
cmap = matplotlib.colors.ListedColormap(color_list)
norm = matplotlib.colors.BoundaryNorm(l_node, cmap.N)
d_color = dict(zip(l_node, color_list))
fig, ax = plt.subplots()
df['color'] = df['node'].apply(lambda x: d_color[x])
df['marker'] = df['arch'].apply(lambda x: d_marker[x])
for idx, row in df.iterrows():
ax.scatter(row['P'], row['f'], color=row['color'], marker=row['marker'])
cax, _ = matplotlib.colorbar.make_axes(ax)
cb = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm,
spacing='uniform', orientation='vertical', extend='neither') #, ticks=l_node,
boundaries=l_node)
# cb = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm,
spacing='uniform', orientation='vertical', extend='neither', ticks=l_node, boundaries=l_node)
# cb = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm,
spacing='uniform', orientation='vertical', extend='neither', boundaries=[i-0.5 for i in l_node])
cb.set_ticklabels(['22','38','45','65','90','130','180'])
cb.set_ticks([0.5,1.5,2.5,3.5,4.5,5.5,6.5],update_ticks=True)
# cb.update_ticks()
cb.set_label('colorbar', rotation=90)
print(plt.gci()) # --> None
# gci(): Get the current colorable artist. Specifically, returns the current ScalarMappable instance (image or patch collection), or None if no images or patch collections have been defined.
plt.show()
如何修复颜色栏以包含缺失的红色？
问题是您需要用一个额外的值 0:
来定义 BoundaryNorm
norm = matplotlib.colors.BoundaryNorm([0] + list(l_node), cmap.N)
（如果你有7种颜色，你需要8个边界）。或者更一般地说：
norm = matplotlib.colors.BoundaryNorm([l_node[0]-1] + list(l_node), cmap.N)
输出：
顾名思义，BoundaryNorm定义了颜色映射的边界。您需要比颜色多一个边界。例如，如果您希望将 20 到 50 之间的所有值映射到颜色图的第一种颜色，并将 50 到 60 之间的所有值映射到颜色图的第二种颜色，则需要 BoundaryNorm([20,50,60], 2).
在您的情况下，您实际上并未执行任何映射，因此您需要做的就是确保边界数比颜色数多 1。
norm = matplotlib.colors.BoundaryNorm(np.arange(len(l_node)+1), cmap.N)
如果你想在某处实际使用映射，你可以定义
norm = matplotlib.colors.BoundaryNorm(np.arange(len(l_node)+1)-0.5, cmap.N)
并在
中使用它
ax.scatter(..., color=cmap(norm(row['node'])), )
我会在这里提供后者的完整代码，其中我也简化了一些东西，
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pandas as pd
# 4 marker
# 7 color
n=100
c = np.random.randint(1,8,size=n)
m = np.random.randint(1,5,size=n)
x = np.random.uniform(size=n)
y = np.random.uniform(size=n)
d_data = {'P':x, 'f':y, 'node':c, 'arch':m}
df = pd.DataFrame(d_data)
# Creating a unique list of elements
l_arch = df.arch.unique()
l_node = df.node.unique()
# Sorting is needd for good colormap
l_arch.sort()
l_node.sort()
# Creating a markers dictionary
zti_markers = ["v","^","s","o","x","+","D"]
d_marker = dict(zip(l_arch,zti_markers[:len(l_arch)] ))
# Creating a colormap and a color dictionary; A little cheat here: I know how
#many different colors I need.
color_list = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f']
cmap = matplotlib.colors.ListedColormap(color_list)
norm = matplotlib.colors.BoundaryNorm(np.arange(len(l_node)+1)-0.5, cmap.N)
d_color = dict(zip(l_node, color_list))
fig, ax = plt.subplots()
df['marker'] = df['arch'].apply(lambda x: d_marker[x])
for idx, row in df.iterrows():
ax.scatter(row['P'], row['f'], color=cmap(norm(row['node'])), marker=row['marker'])
sm = matplotlib.cm.ScalarMappable(cmap=cmap, norm=norm)
cb = fig.colorbar(sm, spacing='uniform', extend='neither')
cb.set_ticklabels(['22','38','45','65','90','130','180'])
cb.set_ticks(np.arange(len(l_node)), update_ticks=True)
cb.set_label('colorbar', rotation=90)
plt.show()
以上假定 "nodes" 是从 0 开始的后续整数。如果不是这种情况，则定义边界会稍微复杂一些，例如取中间值，
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pandas as pd
# 4 marker
# 7 color
n=100
c = np.random.choice([5,8,19,23,44,61,87], size=n)
m = np.random.randint(1,5,size=n)
x = np.random.uniform(size=n)
y = np.random.uniform(size=n)
d_data = {'P':x, 'f':y, 'node':c, 'arch':m}
df = pd.DataFrame(d_data)
# Creating a unique list of elements
l_arch = df.arch.unique()
l_node = df.node.unique()
# Sorting is needd for good colormap
l_arch.sort()
l_node.sort()
# Creating a markers dictionary
zti_markers = ["v","^","s","o","x","+","D"]
d_marker = dict(zip(l_arch,zti_markers[:len(l_arch)] ))
# Creating a colormap and a color dictionary; A little cheat here: I know how
#many different colors I need.
color_list = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f']
cmap = matplotlib.colors.ListedColormap(color_list)
bounds = np.concatenate(([l_node[0]-1], l_node[:-1] + np.diff(l_node)/2,[l_node[-1]+1] ))
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
d_color = dict(zip(l_node, color_list))
fig, ax = plt.subplots()
df['marker'] = df['arch'].apply(lambda x: d_marker[x])
for idx, row in df.iterrows():
ax.scatter(row['P'], row['f'], color=cmap(norm(row['node'])), marker=row['marker'])
sm = matplotlib.cm.ScalarMappable(cmap=cmap, norm=norm)
cb = fig.colorbar(sm, spacing='uniform', extend='neither')
cb.set_ticklabels(['22','38','45','65','90','130','180'])
cb.set_ticks(bounds[:-1]+np.diff(bounds)/2, update_ticks=True)
cb.set_label('colorbar', rotation=90)
plt.show()
Matplotlibs 离散颜色条在我的颜色图中缺少一种颜色定义，并且也在绘图中使用。
在我的示例代码中，我有七种颜色，但颜色条只显示六种颜色，尽管创建颜色图和颜色条的代码似乎与我在 Internet 上找到的示例相同。缺少带有标签“180”的红色。即使我更改了边界并勾选了颜色栏中的米色或浅蓝色。
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pandas as pd
# 4 marker
# 7 color
n=100
c = np.random.randint(1,8,size=n)
m = np.random.randint(1,5,size=n)
x = np.random.uniform(size=n)
y = np.random.uniform(size=n)
d_data = {'P':x, 'f':y, 'node':c, 'arch':m}
df = pd.DataFrame(d_data)
# Creating a unique list of elements
l_arch = df.arch.unique()
l_node = df.node.unique()
# Sorting is needd for good colormap
l_arch.sort()
l_node.sort()
# Creating a markers dictionary
zti_markers = ["v","^","s","o","x","+","D"]
d_marker = dict(zip(l_arch,zti_markers[:len(l_arch)] ))
# Creating a colormap and a color dictionary; A little cheat here: I know how
many different colors I need.
color_list = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f']
cmap = matplotlib.colors.ListedColormap(color_list)
norm = matplotlib.colors.BoundaryNorm(l_node, cmap.N)
d_color = dict(zip(l_node, color_list))
fig, ax = plt.subplots()
df['color'] = df['node'].apply(lambda x: d_color[x])
df['marker'] = df['arch'].apply(lambda x: d_marker[x])
for idx, row in df.iterrows():
ax.scatter(row['P'], row['f'], color=row['color'], marker=row['marker'])
cax, _ = matplotlib.colorbar.make_axes(ax)
cb = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm,
spacing='uniform', orientation='vertical', extend='neither') #, ticks=l_node,
boundaries=l_node)
# cb = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm,
spacing='uniform', orientation='vertical', extend='neither', ticks=l_node, boundaries=l_node)
# cb = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm,
spacing='uniform', orientation='vertical', extend='neither', boundaries=[i-0.5 for i in l_node])
cb.set_ticklabels(['22','38','45','65','90','130','180'])
cb.set_ticks([0.5,1.5,2.5,3.5,4.5,5.5,6.5],update_ticks=True)
# cb.update_ticks()
cb.set_label('colorbar', rotation=90)
print(plt.gci()) # --> None
# gci(): Get the current colorable artist. Specifically, returns the current ScalarMappable instance (image or patch collection), or None if no images or patch collections have been defined.
plt.show()
如何修复颜色栏以包含缺失的红色？
问题是您需要用一个额外的值 0:
BoundaryNorm
norm = matplotlib.colors.BoundaryNorm([0] + list(l_node), cmap.N)
（如果你有7种颜色，你需要8个边界）。或者更一般地说：
norm = matplotlib.colors.BoundaryNorm([l_node[0]-1] + list(l_node), cmap.N)
输出：
顾名思义，BoundaryNorm定义了颜色映射的边界。您需要比颜色多一个边界。例如，如果您希望将 20 到 50 之间的所有值映射到颜色图的第一种颜色，并将 50 到 60 之间的所有值映射到颜色图的第二种颜色，则需要 BoundaryNorm([20,50,60], 2).
在您的情况下，您实际上并未执行任何映射，因此您需要做的就是确保边界数比颜色数多 1。
norm = matplotlib.colors.BoundaryNorm(np.arange(len(l_node)+1), cmap.N)
如果你想在某处实际使用映射，你可以定义
norm = matplotlib.colors.BoundaryNorm(np.arange(len(l_node)+1)-0.5, cmap.N)
并在
中使用它ax.scatter(..., color=cmap(norm(row['node'])), )
我会在这里提供后者的完整代码，其中我也简化了一些东西，
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pandas as pd
# 4 marker
# 7 color
n=100
c = np.random.randint(1,8,size=n)
m = np.random.randint(1,5,size=n)
x = np.random.uniform(size=n)
y = np.random.uniform(size=n)
d_data = {'P':x, 'f':y, 'node':c, 'arch':m}
df = pd.DataFrame(d_data)
# Creating a unique list of elements
l_arch = df.arch.unique()
l_node = df.node.unique()
# Sorting is needd for good colormap
l_arch.sort()
l_node.sort()
# Creating a markers dictionary
zti_markers = ["v","^","s","o","x","+","D"]
d_marker = dict(zip(l_arch,zti_markers[:len(l_arch)] ))
# Creating a colormap and a color dictionary; A little cheat here: I know how
#many different colors I need.
color_list = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f']
cmap = matplotlib.colors.ListedColormap(color_list)
norm = matplotlib.colors.BoundaryNorm(np.arange(len(l_node)+1)-0.5, cmap.N)
d_color = dict(zip(l_node, color_list))
fig, ax = plt.subplots()
df['marker'] = df['arch'].apply(lambda x: d_marker[x])
for idx, row in df.iterrows():
ax.scatter(row['P'], row['f'], color=cmap(norm(row['node'])), marker=row['marker'])
sm = matplotlib.cm.ScalarMappable(cmap=cmap, norm=norm)
cb = fig.colorbar(sm, spacing='uniform', extend='neither')
cb.set_ticklabels(['22','38','45','65','90','130','180'])
cb.set_ticks(np.arange(len(l_node)), update_ticks=True)
cb.set_label('colorbar', rotation=90)
plt.show()
以上假定 "nodes" 是从 0 开始的后续整数。如果不是这种情况，则定义边界会稍微复杂一些，例如取中间值，
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pandas as pd
# 4 marker
# 7 color
n=100
c = np.random.choice([5,8,19,23,44,61,87], size=n)
m = np.random.randint(1,5,size=n)
x = np.random.uniform(size=n)
y = np.random.uniform(size=n)
d_data = {'P':x, 'f':y, 'node':c, 'arch':m}
df = pd.DataFrame(d_data)
# Creating a unique list of elements
l_arch = df.arch.unique()
l_node = df.node.unique()
# Sorting is needd for good colormap
l_arch.sort()
l_node.sort()
# Creating a markers dictionary
zti_markers = ["v","^","s","o","x","+","D"]
d_marker = dict(zip(l_arch,zti_markers[:len(l_arch)] ))
# Creating a colormap and a color dictionary; A little cheat here: I know how
#many different colors I need.
color_list = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f']
cmap = matplotlib.colors.ListedColormap(color_list)
bounds = np.concatenate(([l_node[0]-1], l_node[:-1] + np.diff(l_node)/2,[l_node[-1]+1] ))
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
d_color = dict(zip(l_node, color_list))
fig, ax = plt.subplots()
df['marker'] = df['arch'].apply(lambda x: d_marker[x])
for idx, row in df.iterrows():
ax.scatter(row['P'], row['f'], color=cmap(norm(row['node'])), marker=row['marker'])
sm = matplotlib.cm.ScalarMappable(cmap=cmap, norm=norm)
cb = fig.colorbar(sm, spacing='uniform', extend='neither')
cb.set_ticklabels(['22','38','45','65','90','130','180'])
cb.set_ticks(bounds[:-1]+np.diff(bounds)/2, update_ticks=True)
cb.set_label('colorbar', rotation=90)
plt.show()