(Full disclosure: I work on Numba...)

s2 = np.exp(s1)

s3 = np.sin(x)

y = s3 * s2

However, memory allocation is still pretty fast compared to special math functions (exp and sin), which dominate that calculation. I find this expression takes around 20 milliseconds for a million elements on my older laptop, so that might be negligible in your program execution time unless you need to recreate this decaying exponential thousands of times. Tools like Numba or numexpr will be useful to fuse loops so you only do one allocation, but they aren't necessary unless this becomes the bottleneck in your code.

If you are getting started with NumPy, I would suggest not worrying about these issues too much, and focus on making good use of arrays, NumPy array functions, and array expressions in your code. If you have to write for loops (if there is no good way to do the operation with existing NumPy functions), I would reach for something like Numba, and if you want to speed up complex array expressions, both Numba and Numexpr will do a good job.

On Tue, Jul 23, 2019 at 10:38 AM Hameer Abbasi <einstein.edison@gmail.com> wrote:

_______________________________________________Hi Ram,

No, NumPy doesn’t have a way. And it newer versions, it probably won’t create two arrays if all the dtypes match, it’ll do some magic to re use the existing ones, although it will use multiple loops instead of just one.

You might want to look into NumExpr or Numba if you want an efficient implementation.

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From:NumPy-Discussion <numpy-discussion-bounces+einstein.edison=gmail.com@python.org> on behalf of Ram Rachum <ram@rachum.com>

Sent:Tuesday, July 23, 2019 7:29 pm

To:numpy-discussion@python.org

Subject:[Numpy-discussion] Creating a sine wave with exponential decayHi everyone! Total Numpy newbie here.

I'd like to create an array with a million numbers, that has a sine wave with exponential decay on the amplitude.

In other words, I want the value of each cell

`n`

to be`sin(n) * 2 ** (-n * factor)`

.What would be the most efficient way to do that?

Someone suggested I do something like this:

`y = np.sin(x) * np.exp(newfactor * x)`

But this would create 2 arrays, wouldn't it? Isn't that wasteful? Does Numpy provide an efficient way of doing that without creating a redundant array?

Thanks for your help,

Ram Rachum.

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