Hi everyone,
I wondered how to express a numpy float exactly in terms of format, and
found `%r` quite useful: `float(repr(a)) == a` is guaranteed for Python
`float`s. When trying the same thing with lower-precision Python floats, I
found this identity not quite fulfilled:
```
import numpy
b = nump
0][0] +a[1][1] - a[0][1] - a[1][0]`). :)
Cheers,
Nico
On Sun, Mar 5, 2017 at 3:53 PM Sebastian Berg
wrote:
On Thu, 2017-03-02 at 10:27 +, Nico Schlömer wrote:
> Hi everyone,
>
> When trying to speed up my code, I noticed that simply by reordering
> my data I could get more tha
Hi everyone,
When trying to speed up my code, I noticed that simply by reordering my
data I could get more than twice as fast for the simplest operations:
```
import numpy
a = numpy.random.rand(50, 50, 50)
%timeit a[0] + a[1]
100 loops, best of 3: 1.7 µs per loop
%timeit a[:, 0] + a[:, 1]
10
Hi everyone,
I noticed a funny behavior in numpy's array_equal. The two arrays
```
a1 = numpy.array(
[3.14159265358979320],
dtype=numpy.float64
)
a2 = numpy.array(
[3.14159265358979329],
dtype=numpy.float64
)
```
(differing the in the 18th overall digit) are reported equal
