我使用条形图来表示每组的数据。其中一些条形图彼此显着不同。如何指示条形图中的显着差异？
import numpy as np
import matplotlib.pyplot as plt
menMeans = (5, 15, 30, 40)
menStd = (2, 3, 4, 5)
ind = np.arange(4) # the x locations for the groups
width=0.35
p1 = plt.bar(ind, menMeans, width=width, color='r', yerr=menStd)
plt.xticks(ind+width/2., ('A', 'B', 'C', 'D') )
我的目标是
我在这里做了几件事，我建议在处理复杂的情节时做这些事情。将自定义格式提取到字典中，当您想要更改参数时，它使生活变得简单 - 您可以将此字典传递给多个绘图。我还为annotateitervalues 编写了一个自定义函数，作为奖励，(A,C)如果你真的想要，它可以在两者之间进行注释（但我坚持我的评论，这不是正确的视觉方法）。一旦数据发生变化，它可能需要一些调整，但这应该会让你走上正确的轨道。
import numpy as np
import matplotlib.pyplot as plt
menMeans = (5, 15, 30, 40)
menStd = (2, 3, 4, 5)
ind = np.arange(4) # the x locations for the groups
width= 0.7
labels = ('A', 'B', 'C', 'D')
# Pull the formatting out here
bar_kwargs = {'width':width,'color':'y','linewidth':2,'zorder':5}
err_kwargs = {'zorder':0,'fmt':None,'linewidth':2,'ecolor':'k'} #for matplotlib >= v1.4 use 'fmt':'none' instead
fig, ax = plt.subplots()
ax.p1 = plt.bar(ind, menMeans, **bar_kwargs)
ax.errs = plt.errorbar(ind, menMeans, yerr=menStd, **err_kwargs)
# Custom function to draw the diff bars
def label_diff(i,j,text,X,Y):
x = (X[i]+X[j])/2
y = 1.1*max(Y[i], Y[j])
dx = abs(X[i]-X[j])
props = {'connectionstyle':'bar','arrowstyle':'-',\
'shrinkA':20,'shrinkB':20,'linewidth':2}
ax.annotate(text, xy=(X[i],y+7), zorder=10)
ax.annotate('', xy=(X[i],y), xytext=(X[j],y), arrowprops=props)
# Call the function
label_diff(0,1,'p=0.0370',ind,menMeans)
label_diff(1,2,'p<0.0001',ind,menMeans)
label_diff(2,3,'p=0.0025',ind,menMeans)
plt.ylim(ymax=60)
plt.xticks(ind, labels, color='k')
plt.show()
上面的答案启发我自己编写了一个小而灵活的函数：
def barplot_annotate_brackets(num1, num2, data, center, height, yerr=None, dh=.05, barh=.05, fs=None, maxasterix=None):
"""
Annotate barplot with p-values.
:param num1: number of left bar to put bracket over
:param num2: number of right bar to put bracket over
:param data: string to write or number for generating asterixes
:param center: centers of all bars (like plt.bar() input)
:param height: heights of all bars (like plt.bar() input)
:param yerr: yerrs of all bars (like plt.bar() input)
:param dh: height offset over bar / bar + yerr in axes coordinates (0 to 1)
:param barh: bar height in axes coordinates (0 to 1)
:param fs: font size
:param maxasterix: maximum number of asterixes to write (for very small p-values)
"""
if type(data) is str:
text = data
else:
# * is p < 0.05
# ** is p < 0.005
# *** is p < 0.0005
# etc.
text = ''
p = .05
while data < p:
text += '*'
p /= 10.
if maxasterix and len(text) == maxasterix:
break
if len(text) == 0:
text = 'n. s.'
lx, ly = center[num1], height[num1]
rx, ry = center[num2], height[num2]
if yerr:
ly += yerr[num1]
ry += yerr[num2]
ax_y0, ax_y1 = plt.gca().get_ylim()
dh *= (ax_y1 - ax_y0)
barh *= (ax_y1 - ax_y0)
y = max(ly, ry) + dh
barx = [lx, lx, rx, rx]
bary = [y, y+barh, y+barh, y]
mid = ((lx+rx)/2, y+barh)
plt.plot(barx, bary, c='black')
kwargs = dict(ha='center', va='bottom')
if fs is not None:
kwargs['fontsize'] = fs
plt.text(*mid, text, **kwargs)
这使我可以相对简单地获得一些不错的注释，例如：
heights = [1.8, 2, 3]
bars = np.arange(len(heights))
plt.figure()
plt.bar(bars, heights, align='center')
plt.ylim(0, 5)
barplot_annotate_brackets(0, 1, .1, bars, heights)
barplot_annotate_brackets(1, 2, .001, bars, heights)
barplot_annotate_brackets(0, 2, 'p < 0.0075', bars, heights, dh=.2)
如果您正在使用 matplotlib 并寻求箱线图注释，请使用我的代码作为函数：
def AnnoMe(x1, x2, ARRAY, TXT):
y, h, col = max(max(ARRAY[x1-1]),max(ARRAY[x2-1])) + 2, 2, 'k'
plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)
plt.text((x1+x2)*.5, y+h, TXT, ha='center', va='bottom', color=col)
其中“x1”和“x2”是您要比较的两列，“ARRAY”是用于说明箱线图的列表列表。而且，“TXT”是您的文本，例如 p 值或字符串格式的显着/不显着。
因此，调用它：
AnnoMe(1, 2, MyArray, "p-value=0.02")