使用 python 和 matplotlib，事件的二维直方图与事件概率 x 轴和 y 轴的一维条形图不对齐
2D histogram of events is misaligned with 1D bar charts of event probability x and y axes using python and matplotlib
我想使用 matplotlib 绘制二维直方图，以便可视化两个变量对事件发生的影响。
在我的测试用例中，事件是“愿望成真”，变量x是流星的数量，y是仙女教母的参与。我想做的是绘制一个二维直方图，表示流星和仙女教母的愿望成真。然后在每个轴旁边，我想显示一个愿望成真的概率，事件/（事件+无事件），对于流星和仙女教母的每个容器（一维条形图包含每个直方图容器的概率）。条形图 bin 应与 2d 直方图 bin 对应并对齐。但是，条形图和直方图箱之间似乎存在轻微的错位。
为了正确对齐条形图，设置与第一个和最后一个 bin 边缘对应的轴的限制是否有效？一旦设置了这些限制，我可以将 bin 中心输入 plt.bar() 作为轴上的位置而不是索引吗？
我的代码和生成的图片如下：
import numpy as np
import matplotlib.pyplot as plt
from numpy import linspace
import cubehelix
# Create random events and non-events
x_noneve = 3.*np.random.randn(10000) +22.
np.random.seed(seed=41)
y_noneve = np.random.randn(10000)
np.random.seed(seed=45)
x_eve = 3.*np.random.randn(1000) +22.
np.random.seed(seed=33)
y_eve = np.random.randn(1000)
x_all = np.concatenate((x_eve,x_noneve),axis=0)
y_all = np.concatenate((y_eve,y_noneve),axis=0)
# Set up default x and y limits
xlims = [min(x_all),max(x_all)]
ylims = [min(y_all),max(y_all)]
# Set up your x and y labels
xlabel = 'Falling Star'
ylabel = 'Fairy Godmother'
# Define the locations for the axes
left, width = 0.12, 0.55
bottom, height = 0.12, 0.55
bottom_h = left_h = left+width+0.03
# Set up the geometry of the three plots
rect_wishes = [left, bottom, width, height] # dimensions of wish plot
rect_histx = [left, bottom_h, width, 0.25] # dimensions of x-histogram
rect_histy = [left_h, bottom, 0.25, height] # dimensions of y-histogram
# Set up the size of the figure
fig = plt.figure(1, figsize=(9.5,9))
fig.suptitle('Wishes coming true', fontsize=18, fontweight='bold')
cx1 = cubehelix.cmap(startHue=240,endHue=-300,minSat=1,maxSat=2.5,minLight=.3,maxLight=.8,gamma=.9)
# Make the three plots
axWishes = plt.axes(rect_wishes) # wishes plot
axStarx = plt.axes(rect_histx) # x bar chart
axFairy = plt.axes(rect_histy) # y bar chart
# Define the number of bins
nxbins = 50
nybins = 50
nbins = 100
xbins = linspace(start = xlims[0], stop = xlims[1], num = nxbins)
ybins = linspace(start = ylims[0], stop = ylims[1], num = nybins)
xcenter = (xbins[0:-1]+xbins[1:])/2.0
ycenter = (ybins[0:-1]+ybins[1:])/2.0
delx = np.around(xbins[1]-xbins[0], decimals=2,out=None)
dely = np.around(ybins[1]-ybins[0], decimals=2,out=None)
H, xedges,yedges = np.histogram2d(y_eve,x_eve,bins=(ybins,xbins))
X = xcenter
Y = ycenter
H = np.where(H==0,np.nan,H) # Remove 0's from plot
# Plot the 2D histogram
cax = (axWishes.imshow(H, extent=[xlims[0],xlims[1],ylims[0],ylims[1]],
interpolation='nearest', origin='lower',aspect="auto",cmap=cx1))
#Plot the axes labels
axWishes.set_xlabel(xlabel,fontsize=14)
axWishes.set_ylabel(ylabel,fontsize=14)
#Set up the plot limits
axWishes.set_xlim(xlims)
axWishes.set_ylim(ylims)
#Set up the probability bins
x_eve_hist, xoutbins = np.histogram(x_eve, bins=xbins)
y_eve_hist, youtbins = np.histogram(y_eve, bins=ybins)
x_noneve_hist, xoutbins = np.histogram(x_noneve, bins=xbins)
y_noneve_hist, youtbins = np.histogram(y_noneve, bins=ybins)
probax = [eve/(eve+noneve+0.0) if eve+noneve>0 else 0 for eve,noneve in zip(x_eve_hist,x_noneve_hist)]
probay = [eve/(eve+noneve+0.0) if eve+noneve>0 else 0 for eve,noneve in zip(y_eve_hist,y_noneve_hist)]
probax = probax/np.sum(probax)
probay = probay/np.sum(probay)
probax = np.round(probax*100., decimals=0, out=None)
probay = np.round(probay*100., decimals=0, out=None)
#Plot the bar charts
#Set up the limits
axStarx.set_xlim( xlims[0], xlims[1])
axFairy.set_ylim( ylims[0], ylims[1])
axStarx.bar(xcenter, probax, align='center', width =delx, color = 'royalblue')
axFairy.barh(ycenter,probay,align='center', height=dely, color = 'mediumorchid')
#Show the plot
plt.show()
虽然我的原始代码可以正常运行，但二维直方图和条形图的限制并未使用直方图箱定义。因此，对 bin 的任何更改都会导致图形对齐不佳。为了确保图形的限制始终对应于直方图 bin 的限制，我更改了
cax = (axWishes.imshow(H, extent=[xmin,xmax,ymin,ymax],
interpolation='nearest', origin='lower',aspect="auto",cmap=cx1))
到
cax = (axWishes.imshow(H, extent=[xbins[0],xbins[-1],ybins[0],ybins[-1]],
interpolation='nearest', origin='lower',aspect="auto",cmap=cx1))
和
axStarx.set_xlim( xlims[0], xlims[1])
axFairy.set_ylim( ylims[0], ylims[1])
到
axStarx.set_xlim(axWishes.get_xlim())
axFairy.set_ylim(axWishes.get_ylim())
有关信息，条形图可以接受沿轴的索引或值作为条形位置。当条形对应于 bin 而不是分类变量时，设置轴限制并正确定义条形宽度很重要。这些都是用 histo 自动完成的。但是，如果您希望按 bin 探索成员数以外的变量，则必须使用条形图并手动定义限制。
我想使用 matplotlib 绘制二维直方图，以便可视化两个变量对事件发生的影响。
在我的测试用例中，事件是“愿望成真”，变量x是流星的数量，y是仙女教母的参与。我想做的是绘制一个二维直方图，表示流星和仙女教母的愿望成真。然后在每个轴旁边，我想显示一个愿望成真的概率，事件/（事件+无事件），对于流星和仙女教母的每个容器（一维条形图包含每个直方图容器的概率）。条形图 bin 应与 2d 直方图 bin 对应并对齐。但是，条形图和直方图箱之间似乎存在轻微的错位。
为了正确对齐条形图，设置与第一个和最后一个 bin 边缘对应的轴的限制是否有效？一旦设置了这些限制，我可以将 bin 中心输入 plt.bar() 作为轴上的位置而不是索引吗？
我的代码和生成的图片如下：
import numpy as np
import matplotlib.pyplot as plt
from numpy import linspace
import cubehelix
# Create random events and non-events
x_noneve = 3.*np.random.randn(10000) +22.
np.random.seed(seed=41)
y_noneve = np.random.randn(10000)
np.random.seed(seed=45)
x_eve = 3.*np.random.randn(1000) +22.
np.random.seed(seed=33)
y_eve = np.random.randn(1000)
x_all = np.concatenate((x_eve,x_noneve),axis=0)
y_all = np.concatenate((y_eve,y_noneve),axis=0)
# Set up default x and y limits
xlims = [min(x_all),max(x_all)]
ylims = [min(y_all),max(y_all)]
# Set up your x and y labels
xlabel = 'Falling Star'
ylabel = 'Fairy Godmother'
# Define the locations for the axes
left, width = 0.12, 0.55
bottom, height = 0.12, 0.55
bottom_h = left_h = left+width+0.03
# Set up the geometry of the three plots
rect_wishes = [left, bottom, width, height] # dimensions of wish plot
rect_histx = [left, bottom_h, width, 0.25] # dimensions of x-histogram
rect_histy = [left_h, bottom, 0.25, height] # dimensions of y-histogram
# Set up the size of the figure
fig = plt.figure(1, figsize=(9.5,9))
fig.suptitle('Wishes coming true', fontsize=18, fontweight='bold')
cx1 = cubehelix.cmap(startHue=240,endHue=-300,minSat=1,maxSat=2.5,minLight=.3,maxLight=.8,gamma=.9)
# Make the three plots
axWishes = plt.axes(rect_wishes) # wishes plot
axStarx = plt.axes(rect_histx) # x bar chart
axFairy = plt.axes(rect_histy) # y bar chart
# Define the number of bins
nxbins = 50
nybins = 50
nbins = 100
xbins = linspace(start = xlims[0], stop = xlims[1], num = nxbins)
ybins = linspace(start = ylims[0], stop = ylims[1], num = nybins)
xcenter = (xbins[0:-1]+xbins[1:])/2.0
ycenter = (ybins[0:-1]+ybins[1:])/2.0
delx = np.around(xbins[1]-xbins[0], decimals=2,out=None)
dely = np.around(ybins[1]-ybins[0], decimals=2,out=None)
H, xedges,yedges = np.histogram2d(y_eve,x_eve,bins=(ybins,xbins))
X = xcenter
Y = ycenter
H = np.where(H==0,np.nan,H) # Remove 0's from plot
# Plot the 2D histogram
cax = (axWishes.imshow(H, extent=[xlims[0],xlims[1],ylims[0],ylims[1]],
interpolation='nearest', origin='lower',aspect="auto",cmap=cx1))
#Plot the axes labels
axWishes.set_xlabel(xlabel,fontsize=14)
axWishes.set_ylabel(ylabel,fontsize=14)
#Set up the plot limits
axWishes.set_xlim(xlims)
axWishes.set_ylim(ylims)
#Set up the probability bins
x_eve_hist, xoutbins = np.histogram(x_eve, bins=xbins)
y_eve_hist, youtbins = np.histogram(y_eve, bins=ybins)
x_noneve_hist, xoutbins = np.histogram(x_noneve, bins=xbins)
y_noneve_hist, youtbins = np.histogram(y_noneve, bins=ybins)
probax = [eve/(eve+noneve+0.0) if eve+noneve>0 else 0 for eve,noneve in zip(x_eve_hist,x_noneve_hist)]
probay = [eve/(eve+noneve+0.0) if eve+noneve>0 else 0 for eve,noneve in zip(y_eve_hist,y_noneve_hist)]
probax = probax/np.sum(probax)
probay = probay/np.sum(probay)
probax = np.round(probax*100., decimals=0, out=None)
probay = np.round(probay*100., decimals=0, out=None)
#Plot the bar charts
#Set up the limits
axStarx.set_xlim( xlims[0], xlims[1])
axFairy.set_ylim( ylims[0], ylims[1])
axStarx.bar(xcenter, probax, align='center', width =delx, color = 'royalblue')
axFairy.barh(ycenter,probay,align='center', height=dely, color = 'mediumorchid')
#Show the plot
plt.show()
虽然我的原始代码可以正常运行，但二维直方图和条形图的限制并未使用直方图箱定义。因此，对 bin 的任何更改都会导致图形对齐不佳。为了确保图形的限制始终对应于直方图 bin 的限制，我更改了
cax = (axWishes.imshow(H, extent=[xmin,xmax,ymin,ymax],
interpolation='nearest', origin='lower',aspect="auto",cmap=cx1))
到
cax = (axWishes.imshow(H, extent=[xbins[0],xbins[-1],ybins[0],ybins[-1]],
interpolation='nearest', origin='lower',aspect="auto",cmap=cx1))
和
axStarx.set_xlim( xlims[0], xlims[1])
axFairy.set_ylim( ylims[0], ylims[1])
到
axStarx.set_xlim(axWishes.get_xlim())
axFairy.set_ylim(axWishes.get_ylim())
有关信息，条形图可以接受沿轴的索引或值作为条形位置。当条形对应于 bin 而不是分类变量时，设置轴限制并正确定义条形宽度很重要。这些都是用 histo 自动完成的。但是，如果您希望按 bin 探索成员数以外的变量，则必须使用条形图并手动定义限制。