With Octave I am able to plot arrays to the terminal, for example, plotting an array with values for the function x^2 gives this output in my terminal:
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32
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10000 ++---------+-----------+----------+-----------+---------++2
++ + + + + ++3
|+ : : : : +|4
|++ : : : : ++|5
| + : : : : + |6
| ++ : : : : ++ |7
8000 ++.+..+.++8
| ++ : : : : ++ |9
| ++ : : : : ++ |10
| + : : : : + |11
| ++ : : : : ++ |12
| + : : : : + |13
6000 ++.++++.++14
| ++ : : : : ++ |15
| + : : : : + |16
| ++ : : : : ++ |17
| ++: : : :++ |18
4000 ++..++.++..++19
| + : : + |20
| ++ : : ++ |21
| :++ : : ++: |22
| : ++ : : ++ : |23
| : ++ : : ++ : |24
2000 ++.++++.++25
| : ++ : : ++ : |26
| : +++ : : +++ : |27
| : ++ : : ++ : |28
| : +++: :+++ : |29
+ + ++++ ++++ + +30
0 ++---------+-----------+----------+-----------+---------++31
0 20000 40000 60000 80000 10000032
Is there some way I can do something similar in Python, specifically with matplotlib? bashplotlib seems to offer some of this functionality but appears to be quite basic compared to Octave’s offering.
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Answer
As few answers already suggested the gnuplot is a great choice.
However, there is no need to call a gnuplot subprocess, it might be much easier to use a python gnuplotlib library.
Example (from: https://github.com/dkogan/gnuplotlib):
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>>> import numpy as np2
>>> import gnuplotlib as gp3
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>>> x = np.linspace(-5,5,100)5
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>>> gp.plot( x, np.sin(x) )7
[ graphical plot pops up showing a simple sinusoid ]8
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>>> gp.plot( (x, np.sin(x), {'with': 'boxes'}),11
(x, np.cos(x), {'legend': 'cosine'}),12
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_with = 'lines',14
terminal = 'dumb 80,40',15
unset = 'grid')16
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[ ascii plot printed on STDOUT]18
1 +-+---------+----------+-----------+-----------+----------+---------+-+19
+ +|||+ + + +++++ +++|||+ + +20
| |||||+ + + +|||||| cosine +-----+ |21
0.8 +-+ |||||| + + ++||||||+ +-+22
| ||||||+ + ++||||||||+ |23
| ||||||| + ++||||||||| |24
| |||||||+ + ||||||||||| |25
0.6 +-+ |||||||| + +||||||||||+ +-+26
| ||||||||+ | ++||||||||||| |27
| ||||||||| + ||||||||||||| |28
0.4 +-+ ||||||||| | ++||||||||||||+ +-+29
| ||||||||| + +|||||||||||||| |30
| |||||||||+ + ||||||||||||||| |31
| ||||||||||+ | ++||||||||||||||+ + |32
0.2 +-+ ||||||||||| + ||||||||||||||||| + +-+33
| ||||||||||| | +||||||||||||||||+ | |34
| ||||||||||| + |||||||||||||||||| + |35
0 +-+ +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +-+36
| + ||||||||||||||||||+ | ++|||||||||| |37
| | +||||||||||||||||| + ||||||||||| |38
| + ++|||||||||||||||| | +|||||||||| |39
-0.2 +-+ + ||||||||||||||||| + ||||||||||| +-+40
| | ++||||||||||||||+ | ++||||||||| |41
| + ||||||||||||||| + ++|||||||| |42
| | +|||||||||||||| + ||||||||| |43
-0.4 +-+ + ++||||||||||||+ | +|||||||| +-+44
| + ||||||||||||| + ||||||||| |45
| | +|||||||||||+ + ++||||||| |46
-0.6 +-+ + ++|||||||||| | +||||||| +-+47
| + ||||||||||| + ++|||||| |48
| + +|||||||||+ + ||||||| |49
| + ++|||||||| + +++||||| |50
-0.8 +-+ + + ++||||||+ + + +||||| +-+51
| + + +|||||| + + ++|||| |52
+ + + ++ ++|||++ + + ++ + + ++||| +53
-1 +-+---------+----------+-----------+-----------+----------+---------+-+54
-6 -4 -2 0 2 4 655