I want to do linear regression to the data given by x and y. Everything seems to be fine when I use a linear plot, but when I want to plot it on a log scale the line does not look straight. I think I should divide the interval into finer grids rather than only six points. But I couldn’t do that.
How can I do line fitting on a log scale for the below script?
import numpy as np import matplotlib.pyplot as plt x = np.array([1560., 526., 408., 226., 448., 288.]) y = np.array([0.118, 0.124, 0.131, 0.160, 0.129, 0.138]) f = np.multiply(x,y**2) coefs = np.polyfit(x, f, 1) pred_f = coefs[1] + np.multiply(sorted(x), coefs[0]) fig, ax1 = plt.subplots(1, 1, figsize=(8,6)) ax1.scatter(x, f) ax1.plot(sorted(x), pred_f, 'k--') ax1.set_xscale('log') ax1.set_yscale('log') plt.show()
Thank you in advance.
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Answer
Indeed, “straight lines” (linear functions) don’t look straight on log-log plots:
import numpy as np import matplotlib.pyplot as plt x = np.linspace(0.1, 10) y = 2*x+3 # LINEAR! plt.plot(x, y) plt.xscale('log') plt.yscale('log') plt.show()
Result:
To fit on the log scale, run your regression on loagrithms of the original data:
coefs = np.polyfit(np.log(x), np.log(f), 1) # Now work with logarithms everywhere! pred_f = coefs[1] + np.multiply(sorted(np.log(x)), coefs[0]) fig, ax1 = plt.subplots(1, 1, figsize=(8,6)) ax1.scatter(np.log(x), np.log(f)) # logs here too! ax1.plot(sorted(np.log(x)), pred_f, 'k--') # pred_f is already in logs plt.show()
Plot:
Or have Matplotlib draw log ticks. Then you’ll need to exponentiate pred_f
to put it on the same scale as the data:
fig, ax1 = plt.subplots(1, 1, figsize=(8,6)) ax1.scatter(x, f) # original scale! ax1.plot(sorted(x), np.exp(pred_f), 'k--') # exponentiate pred_f ax1.set_xscale('log') ax1.set_yscale('log') plt.show()
The plot is the same, but it now uses the original scale of your data: