Python equivalent to 'hold on' in Matlab
PythonMatlabGraphMatplotlibPython Problem Overview
Is there an explicit equivalent command in Python's matplotlib for Matlab's hold on? I'm trying to plot all my graphs on the same axes. Some graphs are generated inside a for loop, and these are plotted separately from su and sl:
import numpy as np
import matplotlib.pyplot as plt
for i in np.arange(1,5):
z = 68 + 4 * np.random.randn(50)
zm = np.cumsum(z) / range(1,len(z)+1)
plt.plot(zm)
plt.axis([0,50,60,80])
plt.show()
n = np.arange(1,51)
su = 68 + 4 / np.sqrt(n)
sl = 68 - 4 / np.sqrt(n)
plt.plot(n,su,n,sl)
plt.axis([0,50,60,80])
plt.show()
Python Solutions
Solution 1 - Python
Just call plt.show() at the end:
import numpy as np
import matplotlib.pyplot as plt
plt.axis([0,50,60,80])
for i in np.arange(1,5):
z = 68 + 4 * np.random.randn(50)
zm = np.cumsum(z) / range(1,len(z)+1)
plt.plot(zm)
n = np.arange(1,51)
su = 68 + 4 / np.sqrt(n)
sl = 68 - 4 / np.sqrt(n)
plt.plot(n,su,n,sl)
plt.show()
Solution 2 - Python
You can use the following:
plt.hold(True)
Solution 3 - Python
The hold on feature is switched on by default in matplotlib.pyplot. So each time you evoke plt.plot() before plt.show() a drawing is added to the plot. Launching plt.plot() after the function plt.show() leads to redrawing the whole picture.
Solution 4 - Python
check pyplot docs. For completeness,
import numpy as np
import matplotlib.pyplot as plt
#evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)
# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()
Solution 5 - Python
Use plt.sca(ax) to set the current axes, where ax is the Axes object you'd like to become active.
For example:
In a first function: import numpy as np import matplotlib.pyplot as plt
for i in np.arange(1,5):
z = 68 + 4 * np.random.randn(50)
zm = np.cumsum(z) / range(1,len(z)+1)
plt.plot(zm)
plt.axis([0,50,60,80])
plt.show()
In the next function: def function2(...., ax=None)
if ax is None:
fig, ax = plt.subplots(1)
else:
plt.sca(ax)
n = np.arange(1,51)
su = 68 + 4 / np.sqrt(n)
sl = 68 - 4 / np.sqrt(n)
plt.plot(n,su,n,sl)
plt.axis([0,50,60,80])
plt.show()