Why error bars in log-scale matplotlib bar plot are lopsided?
Question:
I’m trying to plot some bar plots, where each y-value is averaged over some series. Consequently, I’m also trying to add the error bars (standard deviations) for each bar.
The magnitudes generally seem right, even in log scale, but for several of the bars, the error bar drops down (- direction) almost indefinitely, while the + direction error is the right magnitude. I don’t think its just the log scaling, but any input is greatly appreciated. Here is a link to the plot
I’ve checked and the + direction error bars are correct, just not sure why/how they are are dropping down to the x-axis occasionally. Below is a simplified example.
y = [99.79999999999997, 0.11701249999999999, 0.00011250000000000004, 0.013393750000000001,0.007743750000000001,
0.01, 0.033906250000000006, 0.0009687500000000002, 0.04187500000000001, 0.0218, 0.0018062499999999997, 0.0005187500000000001]
std =[0.013662601021279521, 0.1500170651403811, 3.4156502553198664e-05, 0.001310709095617076,0.0006239324215543433,
0.0, 0.0021671698133741164,0.0018750000000000001, 0.005302515126491074,0.007984401459512583,0.0006297817082132506,4.0311288741492725e-05]
plt.figure() # Powder plot
plt.bar(np.arange(len(y)), y, yerr=std)
plt.yscale('log')
‘key_list’ is just a list of strings that will become the x-tick labels. ‘width’ is the bar offset to fit in pairs. ‘cm’ and ‘kk’ are just dictionaries of lists. This honestly seems like a rendering issue, but am mostly curious if any of you have encountered this.
Answers:
Like mentioned in the comment, it is because your std is larger than y (for example std[1] > y[1], hence the log scale goes banana. You can fix this by introduce a small tolerance to the lower std:
tor = 1e-9
lower_std = [a - tor if a<b else b for a,b in zip(y,std)]
plt.figure()
plt.bar(np.arange(len(y)), y, yerr=(lower_std,std))
plt.yscale('log')
plt.show()
Output:
You should look at the relative error rather than trying to plot the standard deviation, or any other measure of variability.
To illustrate this with an example:
In your linear space, you will have x +/- delta_x to display.
Projected into your logarithmic space, this becomes: log(x) +/- log(delta_x). But remember that log(x) – log(y) = log(x/y).
Hence, your non-symmetric error bar, for example. If you learn more about relative error, you will find an appropriate symmetric error bar.
Enjoy your learning 🙂
I’m trying to plot some bar plots, where each y-value is averaged over some series. Consequently, I’m also trying to add the error bars (standard deviations) for each bar.
The magnitudes generally seem right, even in log scale, but for several of the bars, the error bar drops down (- direction) almost indefinitely, while the + direction error is the right magnitude. I don’t think its just the log scaling, but any input is greatly appreciated. Here is a link to the plot
I’ve checked and the + direction error bars are correct, just not sure why/how they are are dropping down to the x-axis occasionally. Below is a simplified example.
y = [99.79999999999997, 0.11701249999999999, 0.00011250000000000004, 0.013393750000000001,0.007743750000000001,
0.01, 0.033906250000000006, 0.0009687500000000002, 0.04187500000000001, 0.0218, 0.0018062499999999997, 0.0005187500000000001]
std =[0.013662601021279521, 0.1500170651403811, 3.4156502553198664e-05, 0.001310709095617076,0.0006239324215543433,
0.0, 0.0021671698133741164,0.0018750000000000001, 0.005302515126491074,0.007984401459512583,0.0006297817082132506,4.0311288741492725e-05]
plt.figure() # Powder plot
plt.bar(np.arange(len(y)), y, yerr=std)
plt.yscale('log')
‘key_list’ is just a list of strings that will become the x-tick labels. ‘width’ is the bar offset to fit in pairs. ‘cm’ and ‘kk’ are just dictionaries of lists. This honestly seems like a rendering issue, but am mostly curious if any of you have encountered this.
Like mentioned in the comment, it is because your std is larger than y (for example std[1] > y[1], hence the log scale goes banana. You can fix this by introduce a small tolerance to the lower std:
tor = 1e-9
lower_std = [a - tor if a<b else b for a,b in zip(y,std)]
plt.figure()
plt.bar(np.arange(len(y)), y, yerr=(lower_std,std))
plt.yscale('log')
plt.show()
Output:
You should look at the relative error rather than trying to plot the standard deviation, or any other measure of variability.
To illustrate this with an example:
In your linear space, you will have x +/- delta_x to display.
Projected into your logarithmic space, this becomes: log(x) +/- log(delta_x). But remember that log(x) – log(y) = log(x/y).
Hence, your non-symmetric error bar, for example. If you learn more about relative error, you will find an appropriate symmetric error bar.
Enjoy your learning 🙂