| | """ |
| | ======================================= |
| | Different ways of specifying error bars |
| | ======================================= |
| | |
| | Errors can be specified as a constant value (as shown in |
| | :doc:`/gallery/statistics/errorbar`). However, this example demonstrates |
| | how they vary by specifying arrays of error values. |
| | |
| | If the raw ``x`` and ``y`` data have length N, there are two options: |
| | |
| | Array of shape (N,): |
| | Error varies for each point, but the error values are |
| | symmetric (i.e. the lower and upper values are equal). |
| | |
| | Array of shape (2, N): |
| | Error varies for each point, and the lower and upper limits |
| | (in that order) are different (asymmetric case) |
| | |
| | In addition, this example demonstrates how to use log |
| | scale with error bars. |
| | """ |
| |
|
| | import matplotlib.pyplot as plt |
| | import numpy as np |
| |
|
| | |
| | x = np.arange(0.1, 4, 0.5) |
| | y = np.exp(-x) |
| |
|
| | |
| | error = 0.1 + 0.2 * x |
| |
|
| | fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True) |
| | ax0.errorbar(x, y, yerr=error, fmt='-o') |
| | ax0.set_title('variable, symmetric error') |
| |
|
| | |
| | |
| | lower_error = 0.4 * error |
| | upper_error = error |
| | asymmetric_error = [lower_error, upper_error] |
| |
|
| | ax1.errorbar(x, y, xerr=asymmetric_error, fmt='o') |
| | ax1.set_title('variable, asymmetric error') |
| | ax1.set_yscale('log') |
| | plt.show() |
| |
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