# matplotlib 误差棒图

matplotlib中使用errorbar()绘制误差棒图来表示被测量的误差范围。

```import numpy as np
import matplotlib.pyplot as plt
# example data
x = np.arange(0.1, 4, 0.5)
y = np.exp(-x)
fig, ax = plt.subplots()
ax.errorbar(x, y, xerr=0.2, yerr=0.4)
plt.show()```
```import matplotlib
from  matplotlib import ticker
import matplotlib.pyplot as plt
import numpy as np
matplotlib.rcParams["font.sans-serif"] = ["SimHei"]
matplotlib.rcParams["axes.unicode_minus"] = False
X = np.linspace(2,80,8)
Y= 100*np.sin(X)
xerror = 1* np.random.randn(X.shape[0])+3
yerror = 2*xerror
plt.errorbar(X, Y, fmt= "bo:", xerr = xerror, yerr = yerror)
plt.title("误差图示例",fontsize =18,color ="b")
plt.xlabel("x 轴标签",color ="b")
plt.ylabel("y 轴标签",color ="b")
plt.show()```
```import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Rectangle
# Number of data points
n = 5
# Dummy data
np.random.seed(10)
x = np.arange(0, n, 1)
y = np.random.rand(n) * 5.
# Dummy errors (above and below)
xerr = np.random.rand(2, n) + 0.1
yerr = np.random.rand(2, n) + 0.2

def make_error_boxes(ax, xdata, ydata, xerror, yerror, facecolor='r',
edgecolor='None', alpha=0.5):
# Create list for all the error patches
errorboxes = []
# Loop over data points; create box from errors at each point
for x, y, xe, ye in zip(xdata, ydata, xerror.T, yerror.T):
rect = Rectangle((x - xe[0], y - ye[0]), xe.sum(), ye.sum())
errorboxes.append(rect)
# Create patch collection with specified colour/alpha
pc = PatchCollection(errorboxes, facecolor=facecolor, alpha=alpha,
edgecolor=edgecolor)
# Add collection to axes
ax.add_collection(pc)
# Plot errorbars
artists = ax.errorbar(xdata, ydata, xerr=xerror, yerr=yerror,
fmt='None', ecolor='k')
return artists

# Create figure and axes
fig, ax = plt.subplots(1)
# Call function to create error boxes
_ = make_error_boxes(ax, x, y, xerr, yerr)
plt.show()
```
```import numpy as np
import matplotlib.pyplot as plt
# example data
x = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0])
y = np.exp(-x)
xerr = 0.1
yerr = 0.2
# lower & upper limits of the error
lolims = np.array([0, 0, 1, 0, 1, 0, 0, 0, 1, 0], dtype=bool)
uplims = np.array([0, 1, 0, 0, 0, 1, 0, 0, 0, 1], dtype=bool)
ls = 'dotted'
fig, ax = plt.subplots(figsize=(7, 4))
# standard error bars
ax.errorbar(x, y, xerr=xerr, yerr=yerr, linestyle=ls)
# including upper limits
ax.errorbar(x, y + 0.5, xerr=xerr, yerr=yerr, uplims=uplims,
linestyle=ls)
# including lower limits
ax.errorbar(x, y + 1.0, xerr=xerr, yerr=yerr, lolims=lolims,
linestyle=ls)
# including upper and lower limits
ax.errorbar(x, y + 1.5, xerr=xerr, yerr=yerr,
lolims=lolims, uplims=uplims,
marker='o', markersize=8,
linestyle=ls)
# Plot a series with lower and upper limits in both x & y
# constant x-error with varying y-error
xerr = 0.2
yerr = np.zeros(x.shape) + 0.2
yerr[[3, 6]] = 0.3
# mock up some limits by modifying previous data
xlolims = lolims
xuplims = uplims
lolims = np.zeros(x.shape)
uplims = np.zeros(x.shape)
lolims[[6]] = True  # only limited at this index
uplims[[3]] = True  # only limited at this index
# do the plotting
ax.errorbar(x, y + 2.1, xerr=xerr, yerr=yerr,
xlolims=xlolims, xuplims=xuplims,
uplims=uplims, lolims=lolims,
marker='o', markersize=8,
linestyle='none')
# tidy up the figure
ax.set_xlim((0, 5.5))
ax.set_title('Errorbar upper and lower limits')
plt.show()```

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