Python Matplotlib：在一个基本的plot上找到给定y值对应的x值

Question

I have a plot generated using Matplotlib (it is a precision-recall curve of a histogram originally) and I need to compute the correct x value which corresponds to the y value with y = 0.9. The data are loaded from text files which are present in columns. This is the code which is used to create the plot:

import numpy as np
import matplotlib.pyplot as plt
import pylab
from sklearn import metrics

data1 = np.loadtxt('text1.txt')
data2 = np.loadtxt('text2.txt')

background = 1 - (1 + y) / 2.
signal = 1 - (1 + x) / 2.

classifier_output = np.concatenate([background,signal])
true_value = np.concatenate([np.zeros_like(background, dtype=int), np.ones_like(signal,  dtype=int)])

precision, recall, threshold = metrics.precision_recall_curve(true_value, classifier_output)
plt.plot(threshold, precision[:-1])
plt.savefig('Plot.pdf', dpi = 2000)
plt.show()

Is there any way to compute the correct value on the x axis which corresponds to y = 0.9?

Answer 1

You can use np.interp() in the form of x_interp = np.interp(y_vals, y, x) to interpret an x value.

If you want to interpret a y value instead, you need to switch to y_interp = np.interp(x_vals, x, y) .

I also added some annotations to the plot to better visualize the result. Since the data were not provided, I made up the data for demonstration purposes.

import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics

# make up some data
background = np.linspace(0, 1, 400)
signal = np.linspace(0, 2, 400)

classifier_output = np.concatenate([background, signal])
true_value = np.concatenate([np.zeros_like(background, dtype=int), np.ones_like(signal, dtype=int)])

fig, ax = plt.subplots()
precision, recall, threshold = metrics.precision_recall_curve(true_value, classifier_output)
plt.plot(threshold, precision[:-1])

# added code
y_interp = 0.9
x_interp = round(np.interp(y_interp, precision[:-1], threshold), 4)  # x_interp = np.interp(y_vals, y, x)
plt.plot(x_interp, y_interp, 'o')
ax.annotate(f'(x={x_interp}, y={y_interp})', (x_interp, y_interp), size=14, xytext=(x_interp * 1.05, y_interp))
print(f'y = {y_interp}, x = {x_interp}')
plt.show()

Result: