I'm wondering if there is a way to use an index array with shape m x n to index into another array of shape m x n x p.
For instance, if I have the code below, is there any way to get d[y,x,i[y,x]]
for all y,x without explicitly iterating? The result would be a 2x2 array in the code below.
>>> i = numpy.array([[0,1],[2,3]])
>>> d = numpy.random.randn(2,2,4)
>>> d[1,0,i[1,0]]
1.5512465367574528
hello, how can i read this data correctly?
ncfile = Dataset("wrfout_d01_2021-04-11_00:00:00")
ncfile["Times"][0]
<ipython-input-42-0be7531eb713>:12: DeprecationWarning: np.bool
is a deprecated alias for the builtin bool
. To silence this warning, use bool
by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.bool_
here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecationsncfile["Times"][0]
maskedarray(data=[b'2', b'0', b'2', b'1', b'-', b'0', b'4', b'-', b'1',
b'1', b'', b'0', b'0', b':', b'0', b'0', b':', b'0',
b'0'],
mask=False,
fill_value=b'N/A',
dtype='|S1')
without the numpy 1.20 warning.
<ipython-input-42-0be7531eb713>:12: DeprecationWarning: np.bool
is a deprecated alias for the builtin bool
. To silence this warning, use bool
by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.bool_
here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecationsncfile["Times"][0]
Dataset
object is. It looks like the code you're using to load this data set is using np.bool
instead of np.bool_
or bool
Hello Team,
I am Ronnie Gandhi, a Computer Science undergrad from IIT Roorkee in my final year. I am interested in working on the problem statement of NumPy in this GSoD2021
I have successfully completed GSoD2020(my display name is Krezhairo) under LibreOffice, I have done GSoC 2019 , and 2020 under CGAL, and I have been contributing to open-source development for quite a while.
I have also done a Data Science internship at Microsoft, Hyderabad in 2019 and 2020.
I am very well versed with Python,and Jupyter Notebooks, and of course CLI tools. Also from my past GSoD2020 experience I know Markdown, reStructuredText, Sphinx as well.
For the mypy plugin, I also have the same problem (No module named 'numpy.typing.mypy_plugin'
). Actually, I don't see numpy/typing/mypy_plugin.py
in the installed package.
Environment: Python 3.9.4, CentOS 7, x86_64:
> pip list
Package Version
----------------- --------
mypy 0.812
mypy-extensions 0.4.3
numpy 1.20.2
pip 21.0.1
setuptools 54.2.0
typed-ast 1.4.3
typing-extensions 3.10.0.0
> ls .venv/lib/python3.9/site-packages/numpy/typing
__pycache__/ __init__.py _array_like.py _dtype_like.py _shape.py
tests/ _add_docstring.py _callable.py _scalars.py setup.py
main
on GitHuvb
I have a question about type annotations with numpy.
I have a function that takes a Sequence of floats. How can I type my numpy array such that it can meet the Sequence protocol?
from typing import Sequence
import numpy as np
def process(x: Sequence[float]) -> None: ...
my_array = np.empty(10, np.float16)
process(my_array)
# mypy: Argument 1 to "process" has incompatible type "ndarray"; expected "Sequence[float]" [arg-type]
Hi everyone, I am learning numpy from scratch right now and I have a (possibily very dumb) question. The most common(?) way to build a numpy array is the following: np.array([i for i in range(1_000_000)])
; however - if I am not wrong - this loads the array twice in memory: once when the list comprehension [...]
is executed and once when the ndarray
object is built (which copies the content of the python list in a "self-managed" area of the memory, if I well understood the docs). If this is true is, there a way to avoid the use of twice the memory?
Related question: I noticed that if I feed a generator to the np.array()
function (e.g. np.array(i for i in custom_generator())
) the generator object itself is used as a single element for the array (and elements "inside" the generator are not "extracted"). Why? Maybe because a generator does not allow to know its length a priori?
np.zeros(...)
, np.ones(...)
, etc., which can be used to generate arrays of a given length full of zeros or ones,
pip install https://github.com/numpy/numpy/archive/refs/heads/main.zip
dask
does something similar, but it implements it as parallelized operations on graphs. I was thinking maybe more on an architecture based on MPI.
ipdb> self.radex.impex.molfile = 'abcdef'+114*" "
ipdb> self.radex.impex.molfile
array(b'abcdef ',
dtype='|S120')
b'
as part of the string.
arr(1,2,3)
), it triggers a segfault. I'll look through the issues
b[0, 1] = False
, then how can a[b] still be 2 dimensional.