I need to print a numpy array with scientific notation regardless of the value of the components of the array.
To my knowledge, numpy.format_float_scientific acts only on scalars and returns error if applied to vectors - therefore, I need an alternative.
Thank you,
Davide
I would do smth like this:
np.set_printoptions(formatter={'all':np.format_float_scientific})
print(np.arange(5))
output:
[0.e+00 1.e+00 2.e+00 3.e+00 4.e+00]
Related
I have an array of strings in Matlab and I want to use it with python. I tried to do the same process as for numeric arrays i.e.
np_array = scipy.io.loadmat(mat_array)
which works with numeric data.
But when I try to use a string array like
mat_str_array = [“This”, “is”, “a”, “string”]
I get an error/warning stating
cannot be transformed using the same method
Any suggestions?
Thanks in advance.
Iam trying to get a FULL string representation of an 2D float32 512x512 array. I can either use numpys string2array(array) or repr(array). But the problem is that I always get a shortened output like that:
'...[2.0886018e-04 1.7029114e-04 2.8904244e-05 ... 4.1985390e-06
1.4982919e-06 4.7537060e-06]]'
Is there a possibility of full representation?
The answer is that numpy array2string has a threshold option which can be set to np.inf. Then a full representation is possible! :)
If I construct a numpy matrix like this:
A = array([[1,2,3],[4,5,6]])
and then type A.shape I get the result:
(2L, 3L)
Why am I getting a shape with the format long?
I can restart everything and I still have the same problem. And as far as I can see, it is only when I construct arrays I have this problem, otherwise I get short (regular) integers.
As #CédricJulien puts it on the comment, there is no problem with long numbers in this case - this should be treated as an implementation detail.
The real answer for your question can, of course, only be found inside numpy's source code, but the fact that the dimensions are long in this case should not matter for any use you have for the arrays or these indexes.
I have a function that I want to have quickly access the first (aka zeroth) element of a given Numpy array, which itself might have any number of dimensions. What's the quickest way to do that?
I'm currently using the following:
a.reshape(-1)[0]
This reshapes the perhaps-multi-dimensionsal array into a 1D array and grabs the zeroth element, which is short, sweet and often fast. However, I think this would work poorly with some arrays, e.g., an array that is a transposed view of a large array, as I worry this would end up needing to create a copy rather than just another view of the original array, in order to get everything in the right order. (Is that right? Or am I worrying needlessly?) Regardless, it feels like this is doing more work than what I really need, so I imagine some of you may know a generally faster way of doing this?
Other options I've considered are creating an iterator over the whole array and drawing just one element from it, or creating a vector of zeroes containing one zero for each dimension and using that to fancy-index into the array. But neither of these seems all that great either.
a.flat[0]
This should be pretty fast and never require a copy. (Note that a.flat is an instance of numpy.flatiter, not an array, which is why this operation can be done without a copy.)
You can use a.item(0); see the documentation at numpy.ndarray.item.
A possible disadvantage of this approach is that the return value is a Python data type, not a numpy object. For example, if a has data type numpy.uint8, a.item(0) will be a Python integer. If that is a problem, a.flat[0] is better--see #user2357112's answer.
np.hsplit(x, 2)[0]
Source: https://numpy.org/doc/stable/reference/generated/numpy.dsplit.html
Source:
https://numpy.org/doc/stable/reference/generated/numpy.hsplit.html
## y -- numpy array of shape (1, Ty)
if you want to get the first element:
use y.shape[0]
if you want to get the second element:
use y.shape[1]
Source:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.take.html
You can also use the take for more complicated extraction (to get few elements):
numpy.take(a, indices, axis=None, out=None, mode='raise')[source] Take
elements from an array along an axis.
If I construct a numpy matrix like this:
A = array([[1,2,3],[4,5,6]])
and then type A.shape I get the result:
(2L, 3L)
Why am I getting a shape with the format long?
I can restart everything and I still have the same problem. And as far as I can see, it is only when I construct arrays I have this problem, otherwise I get short (regular) integers.
As #CédricJulien puts it on the comment, there is no problem with long numbers in this case - this should be treated as an implementation detail.
The real answer for your question can, of course, only be found inside numpy's source code, but the fact that the dimensions are long in this case should not matter for any use you have for the arrays or these indexes.