# 误差条形图中的上限和下限 在matplotlib中,误差条可以有“限制”。对误差线应用限制实质上使误差单向。因此,可以分别通过``uplims``,``lolims``,``xuplims``和``xlolims``参数在y方向和x方向上应用上限和下限。 这些参数可以是标量或布尔数组。 例如,如果``xlolims``为``True``,则``x-error``条形将仅从数据扩展到递增值。如果``uplims``是一个填充了``False``的数组,除了第4和第7个值之外,所有y误差条都是双向的,除了第4和第7个条形,它们将从数据延伸到减小的y值。 ![条形图限制示例](https://matplotlib.org/_images/sphx_glr_errorbar_limits_001.png) ```python 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_like(x) + 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() ``` ## 下载这个示例 - [下载python源码: errorbar_limits.py](https://matplotlib.org/_downloads/errorbar_limits.py) - [下载Jupyter notebook: errorbar_limits.ipynb](https://matplotlib.org/_downloads/errorbar_limits.ipynb)