What you are trying to do actually corresponds to np.arange.

time = np.arange(dt, T, dt)

To include the T time you will need to change the stop boundary to T + dt.

Answer from Guimoute on Stack Overflow
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NumPy
numpy.org › doc › stable › reference › generated › numpy.linspace.html
numpy.linspace — NumPy v2.5 Manual
The type of the output array. If dtype is not given, the data type is inferred from start and stop. The inferred dtype will never be an integer; float is chosen even if the arguments would produce an array of integers.
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Stack Overflow
stackoverflow.com › questions › 65893323 › pass-in-a-float-to-np-linspace
python - Pass in a float to np.linspace - Stack Overflow
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What you are trying to do actually corresponds to np.arange.

time = np.arange(dt, T, dt)

To include the T time you will need to change the stop boundary to T + dt.

2 of 2
2

I agree with Dwight in the comments, but I thought I could tell you anyway, just to give you another option, that numpy.arange(start, stop, step) may be also useful to you, and should do the same trick.

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I have an array with dtype=numpy.complex128. I want to use numpy.linspace to evenly space out points over this array. But whenever I do that it does it with a 64 bit float accuracy. How do I make it return evenly spaced out points with a 128 bit float accuracy?

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NumPy doesn't really support true 128-bit floats. On some platforms (Windows not among them), there might be a padded 80-bit float type available. But note that complex128 also only uses 64-bit floats, it just uses two of them for the real and the imaginary parts, totalling 128 bits. If you want a linspace with the same float precision as your complex array, you should just use float64 (and complex128 would work, as well).
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I can't help you with that question, but why do you need 128 bit accuracy?
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This is nothing to do with numpy, consider:

>>> temp = np.linspace(1,2,11)
>>> temp
array([1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. ])
>>> #                                     ^ look, numpy displays it fine
>>> for t in temp:
...     print(t)
... 
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7000000000000002
1.8
1.9
2.0

The "issue" is with how computers represent floats in general. See: https://docs.python.org/3/tutorial/floatingpoint.html.

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