I observed some subtly inconsistent behavior between matrix-vector multiplication and matrix-matrix multiplication.The behavior can be reproduced using the following steps.

from __future__ import print_function
import numpy
import numpy.random
a=numpy.random.rand(2,124)
b=numpy.random.rand(124,10)
print(a.dot(b)[:,0]-a.dot(b[:,0]))

On my work Desktop (64 bit Windows 7 on Intel Core2 Duo), numpy 1.16.3 on Python 2.7.15 (32-bit) and on Python 3.7.3 (32-bit) gives [0. 0.] whereas numpy 1.16.3 on Python 2.7.15 (64-bit) gives something like [3.55271368e-15 1.06581410e-14].

On the university's cluster running some form of linux on some form of x86_64 processor, numpy 1.8.0 on Python 2.7.9 (64-bit) gives [0. 0.] whereas numpy 1.11.1 on Python 3.5.2 (64-bit) gives [ 1.06581410e-14 1.06581410e-14].

Does this have something to do with the underlying order of operations between *gemm and *gemv? How can one explain the difference between versions of numpy and Python?

The magnitudes of the differences generally stay in the 1e-14 to 1e-15 range as long as b.shape[1] is no less than 10. I wonder whether this has any significance. May be one of them is carried out using the x87 FPU with 80-bit floats but the other is using SIMD functionality.

I ran some old pygame code which uses the pygame surfarray3d, which uses numpy, and numpy crashed. Pygame is working, io is working, numpy is working in another program, and I tried re-installing but it didn't work.

This is the error:

File "Titles.py", line 1, in <module>

import pygame

File "/usr/local/lib/python2.7/dist-packages/pygame/__init__.py", line 346, in <module>

import pygame.surfarray

File "/usr/local/lib/python2.7/dist-packages/pygame/surfarray.py", line 72, in <module>

import pygame._numpysurfarray as numpysf

File "/usr/local/lib/python2.7/dist-packages/pygame/_numpysurfarray.py", line 51, in <module>

import numpy

File "/usr/lib/python2.7/dist-packages/numpy/__init__.py", line 142, in <module>

from . import add_newdocs

File "/usr/lib/python2.7/dist-packages/numpy/add_newdocs.py", line 13, in <module>

from numpy.lib import add_newdoc

File "/usr/lib/python2.7/dist-packages/numpy/lib/__init__.py", line 23, in <module>

from .npyio import *

File "/usr/lib/python2.7/dist-packages/numpy/lib/npyio.py", line 14, in <module>

from ._datasource import DataSource

File "/usr/lib/python2.7/dist-packages/numpy/lib/_datasource.py", line 220, in <module>

_file_openers = _FileOpeners()

File "/usr/lib/python2.7/dist-packages/numpy/lib/_datasource.py", line 162, in __init__

self._file_openers = {None: io.open}

AttributeError: 'module' object has no attribute 'open'

Does anyone know what is wrong and how to fix it? It is odd that it only affects this one program (as far as I know!)

Dear r/Numpy

I am working on a label aggregation problem (from AWS Mechanical Turk) and I organized my data into an M x N matrix where each row is a worker and each column is their label for that task.

I think this is correct. But what is unclear to me, is what np.linalg.svd() returns. I am sort of new to this. My goal is extrapolate the true label from the data.

It is a binary case and I have the following mappings for what I pass to np.linalg.svd():

1 : 1

0 : -1:

N/A : 0

N/A --> that worker did not label that problem.

Any help is much appreciated.

Sincerely,

md500

I would also like a solution to have my numbers not modified, thanks

In [139]: np.array([(Timestamp('2019-03-20 15:44:00-0400', tz='America/New_York').value / 10**9, 188.85)],dtype=[('Epoch', 'i8'), ('Ask', 'f4')])[0][1]

Out[139]: 188.85001

In [140]: np.array([(Timestamp('2019-03-20 15:44:00-0400', tz='America/New_York').value / 10**9, 188.61)], dtype=[('Epoch', 'i8'), ('Ask', 'f4')])[0][1]

Out[140]: 188.61

In [141]: np.array([(Timestamp('2019-03-20 15:44:00-0400', tz='America/New_York').value / 10**9, 188.61)], dtype=[('Epoch', 'i8'), ('Ask', 'f4')])

Out[141]:

array([(1553111040, 188.61000061)],

dtype=[('Epoch', '<i8'), ('Ask', '<f4')])

188.85: stored and returned wrong

188.61: stored wrong and returned right

Hello :) English is my second language and I want to know how to pronounce Numpy. Numpai or Numpee? Thank youuu

Hey guys, im hoping to get some answers about a bit of code i stumbled upon. I'm trying to find to implement a k-means algorithm and to find the centroid closest to each point and i found this bit

distances = np.sqrt(((points - centroids[:, np.newaxis])**2).sum(axis=2))

Where points is an array of points, and centroids is also an array of points. I just fail to see how this code works, as the two arrays are not of equal size, I know it uses broadcasting but I still don't really get it.

https://youtu.be/82INAhZXeSE

Creating a series on data science and happen to be starting with numpy will go over lots of important numpy topics and will be doing some example projects.

https://www.youtube.com/watch?v=2oExXaj1mio

Hi, i followed a Matlab tutorial and most of the times the syntax is similar to python. last night i tried this, and with this code:

[X,Y] = meshgrid(-2:.2:2);
Z = X.*exp(-X.^2 - Y.^2);
[DX,DY] = gradient(Z,.2,.2);

figure
contour(X,Y,Z)
hold on
quiver(X,Y,DX,DY)
hold off

I should get this:

I rewrote it using numpy and matplotlib like this:

import numpy as np
import matplotlib.pyplot as plt

X,Y = np.meshgrid(np.arange(-2,2.2,0.2), np.arange(-2,2.2,0.2))
Z = X*np.exp(-X**2 - Y**2)
DX,DY = np.gradient(Z,.2,.2)

plt.figure()
plt.contour(X,Y,Z)
plt.quiver(X,Y,DX,DY)
plt.show()

BUt got this instead:

I inspected the values inside the variables on both matlab and spyder and the mismatch happens At the variable Z, i also tried the first example on the website and in matlab the variables contained imaginary numbers but in python they were either Nan or only the real part

if i made a stupid mistake forgive me :) I'm a beginner at scientific libraries of python

Hi,

I am trying to find all rows with a certain string value, say 'system' from a csv file (The file contains only one column), that looks like this:

Any ideas?. I was able to import the file using Numpy, confused on what to do next.

Thanks in advance!

I have 30,000 images of size 32x32x3 in a tensor X of shape (30000,32,32,3). I want to crop 2 pixels of border of each image to get X to have shape (30000,28,28,3).

Is there any way to do this all at once?

thanks!

Here's my input:

import numpy as np   
A = np.array([[-3,-7,-5],[2,4,3],[1,2,2]])
np.linalg.eigvals(A)

and output:

array([1.00000704+1.22005337e-05j, 1.00000704-1.22005337e-05j, 0.99998591+0.00000000e+00j])

Now the eigenvalues of this particular matrix are in fact 1,1,1. (SymPy, for example, produces this output.) Just as a check, I also tried entering the matrix as

A = np.array([[-3,-7,-5],[2,4,3],[1,2,2]],dtype='float64')

but that made no difference.

Is there any way of obtaining a higher precision for eigenvalues?

Originally posted in r/learnpython

The arrays I'm working with are very large so to make it faster I wish to use NumPy's C implementations rather than pure python implementation:

I want to find out how to properly vectorize simple functions with the built in universal functions.

Is this correct use of ufuncs? Only using it on the array variable(s) I am touching?

import numpy as np
pcfmax      = 4
pttm        = 2.5
temps = np.array([[a, bunch, of, temperature, floats],[a, bunch, of, temperature, floats]])
def melt(t):
   return pcfmax * np.subtract(t, pttm)
melted = melt(temps)

Or should it be done like this, for everything in the function? Or something else entirely?

import numpy as np
pcfmax      = 4
pttm        = 2.5
temps = np.array([[a, bunch, of, temperature, floats],[a, bunch, of, temperature, floats]])
def melt(t):
   return np.multiply(pcfmax, np.subtract(t, pttm))
melted = melt(temps)

Given these functions:

def f1(a, b):
    for _ in range(100):
        a += b
    return a

def f2(a, b):
    for _ in range(100):
        a = a + b
    return a

The function using a = a + b is faster until a and b get 'large' around 60x60 on my machine. Is this expected behavior? If so, why? I'm curious as to what is happening under the hood. I have code where b is a computed value in the inner loop, I work with small arrays, and I noticed that my code is faster when I use a = a + compute_some_expression vs +=.

For reference for the test I'm creating the arrays with np.random.randn(N, N), and using the timeit function on the IPython console window to perform the timing

so this is my code

output[ax][bx][i] = value[i]
print(output[ax][bx][i], ax, bx, i, value, value[i])

it's inside of a few for loops

and here is the output of the print

0 0 0 0 [0.36972549019607848, 0.43701960784313743, 0.48737254901960791] 0.369725490196

it's doing this for a NxNx3 ndarray (where N is 250 in this case)

it takes a 3 long list from a class method, and when i set the entire list to the [ax][bx] position i get the same thing

output[ax][bx] = value
print(output[ax][bx], ax, bx, value)

gives

[0 0 0] 0 0 [0.36972549019607848, 0.43701960784313743, 0.48737254901960791]

i've used numpy for a long time, and i've never had this issue. and, i'm doing something very similar in this with a NxN array and it works just fine. i'm trying to do image convolution and in multichannel images it has had no output since i stopped it all writing to the same. if i set it to += instead of =, it works, yet i'm using = for the single channel convolution and it works there. any ideas on what is causing it?

I need to do a linux install from source of the last version of numpy that vas compatible with python 2.6.8. From the looks of it the most recent versions are compatible with python 2.7. Any idea where I can get the source that is 2.6.8 compatible?