How can I plot the following 3 functions (i.e. sin, cos and the addition), on the domain t, in the same figure?
import numpy as np import matplotlib.pyplot as plt t = np.linspace(0, 2*np.pi, 400) a = np.sin(t) b = np.cos(t) c = a + b 
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5 Answers
To plot multiple graphs on the same figure you will have to do:
from numpy import * import math import matplotlib.pyplot as plt t = linspace(0, 2*math.pi, 400) a = sin(t) b = cos(t) c = a + b plt.plot(t, a, 'r') # plotting t, a separately plt.plot(t, b, 'b') # plotting t, b separately plt.plot(t, c, 'g') # plotting t, c separately plt.show() 
Comments
Perhaps a more pythonic way of doing so.
from numpy import * import math import matplotlib.pyplot as plt t = linspace(0,2*math.pi,400) a = sin(t) b = cos(t) c = a + b plt.plot(t, a, t, b, t, c) plt.show() 
Comments
Just use the function plot as follows
figure() ... plot(t, a) plot(t, b) plot(t, c) 
1 Comment
Bavafaali
just a little change : plt.figure()
A pretty concise method is to concatenate the function values horizontally to make an array of shape (len(t), 3) and call plot().
t = np.linspace(0, 2*np.pi, 400) a = np.sin(t) b = np.cos(t) c = a + b plt.plot(t, np.c_[a, b, c]); If the data doesn't come from a numpy array and you don't want the numpy dependency, zip() is your friend.
plt.plot(t, list(zip(a, b, c))); Since there are 3 different graphs on a single plot, perhaps it makes sense to insert a legend in to distinguish which is which. That can be done easily by passing the label.
plt.plot(t, np.c_[a, b, c], label=['sin', 'cos', 'sin+cos']); plt.legend(); 
Comments
If you want to work with figure, I give an example where you want to plot multiple ROC curves in the same figure:
from matplotlib import pyplot as plt plt.figure() for item in range(0, 10, 1): plt.plot(fpr[item], tpr[item]) plt.show() 2 Comments
Cheng
What is an ROC curve?
Joud C
Receiver operating characteristic. It's used in the context of stats to show how a hypothesis test behaves for a given threshold. For instance you may have a binary classifier that takes some input x, applies some function f(x) to it and predicts H1 if f(x) > t. t is your threshold that you use to decide whether to predict H0 or H1. Varying that threshold will yield different true positive rate-false positive rate pairs. The ROC curve captures that. The name comes from early applications of hypothesis testing in the military to decide whether a radar was raising a false alarm @Cheng