Python equivalent to 'hold on' in Matlab

Question

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()

Solution 1

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

You can use the following:

plt.hold(True)

Solution 3

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

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()

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Comments

[Jyotirmay]

This function is deprecated, Any alternative solution like this?

[ZZZ]

Note: This doesn't work in the case of interactive input from the command line. Works fine if run within a script.

[ImportanceOfBeingErnest]

@Jyotirmay The hold function in matplotlib does not serve any purpose and was hence deprecated. pyplot always acts as if hold was set to True anyways.

[ThatNewGuy]

How would you get around this when using IPython?

[freude]

use backend %matplotlib inline and all plt.plot() within one cell will be output in the same frame. If you put plt.show() between plt.plot() functions within a cell it will create more frames, new one for each plt.show()