RE: Relatively prime integers in NumPy

[AVI GROSS via Python-list]

Dmitry,

 

Efficiency of several kinds is hotly debated and sometimes it depends a lot on 
what is done within loops.

 

Many suggest a mild speed up of some comprehensions over loops but the loops 
are not gone but somewhat hidden and perhaps some aspects are faster for having 
been written in C carefully and not interpreted.

 

Comprehensions (and there are other versions that generate dictionaries and 
tuples and sets) may also be sped up a bit for other reasons like your fairly 
expensive APPPEND that has to keep finding the end o f a growing list and is 
not done the same way in a comprehension.

 

If you do a search, you find many opinions including on using functional 
programming techniques such as map/reduce. There are also 

 

Your particular case is interesting because it just makes all combination of 
three variables. Some languages, like R, have functions that do this for you, 
like expand.grd. Python has many modules, like itertools that do things 
including combinations but perhaps not designed for your case. 

 

Here is a version of your scenario:

 

import itertools

a = range(3)

b = range(4)

c = range(5)

 

list(itertools.product(a,b,c))

 

The result comes as tuples but as you are moving the result into numpy, does it 
matter:

 

>>> list(itertools.product(a,b,c))

[(0, 0, 0), (0, 0, 1), (0, 0, 2), (0, 0, 3), (0, 0, 4), (0, 1, 0), (0, 1, 1), 
(0, 1, 2), (0, 1, 3), (0, 1, 4), (0, 2, 0), (0, 2, 1), (0, 2, 2), (0, 2, 3), 
(0, 2, 4), (0, 3, 0), (0, 3, 1), (0, 3, 2), (0, 3, 3), (0, 3, 4), (1, 0, 0), 
(1, 0, 1), (1, 0, 2), (1, 0, 3), (1, 0, 4), (1, 1, 0), (1, 1, 1), (1, 1, 2), 
(1, 1, 3), (1, 1, 4), (1, 2, 0), (1, 2, 1), (1, 2, 2), (1, 2, 3), (1, 2, 4), 
(1, 3, 0), (1, 3, 1), (1, 3, 2), (1, 3, 3), (1, 3, 4), (2, 0, 0), (2, 0, 1), 
(2, 0, 2), (2, 0, 3), (2, 0, 4), (2, 1, 0), (2, 1, 1), (2, 1, 2), (2, 1, 3), 
(2, 1, 4), (2, 2, 0), (2, 2, 1), (2, 2, 2), (2, 2, 3), (2, 2, 4), (2, 3, 0), 
(2, 3, 1), (2, 3, 2), (2, 3, 3), (2, 3, 4)]

 

Or a atd easier to read pretty printed:

 

>>> import pprint

>>> pprint.pprint(list(itertools.product(a,b,c)))

[(0, 0, 0),

(0, 0, 1),

(0, 0, 2),

(0, 0, 3),

(0, 0, 4),

(0, 1, 0),

(0, 1, 1),

(0, 1, 2),

(0, 1, 3),

(0, 1, 4),

(0, 2, 0),

(0, 2, 1),

(0, 2, 2),

(0, 2, 3),

(0, 2, 4),

(0, 3, 0),

(0, 3, 1),

(0, 3, 2),

(0, 3, 3),

(0, 3, 4),

(1, 0, 0),

(1, 0, 1),

(1, 0, 2),

(1, 0, 3),

(1, 0, 4),

(1, 1, 0),

(1, 1, 1),

(1, 1, 2),

(1, 1, 3),

(1, 1, 4),

(1, 2, 0),

(1, 2, 1),

(1, 2, 2),

(1, 2, 3),

(1, 2, 4),

(1, 3, 0),

(1, 3, 1),

(1, 3, 2),

(1, 3, 3),

(1, 3, 4),

(2, 0, 0),

(2, 0, 1),

(2, 0, 2),

(2, 0, 3),

(2, 0, 4),

(2, 1, 0),

(2, 1, 1),

(2, 1, 2),

(2, 1, 3),

(2, 1, 4),

(2, 2, 0),

(2, 2, 1),

(2, 2, 2),

(2, 2, 3),

(2, 2, 4),

(2, 3, 0),

(2, 3, 1),

(2, 3, 2),

(2, 3, 3),

(2, 3, 4)]

 

I think that is close enough to what you want but is it faster? You can try a 
benchmarking method on alternatives.

 

 

 

 

From: Popov, Dmitry Yu  
Sent: Friday, July 12, 2024 11:10 PM
To: avi.e.gr...@gmail.com; 'Popov, Dmitry Yu via Python-list' 
; oscar.j.benja...@gmail.com
Subject: Re: Relatively prime integers in NumPy

 

Thank you very much. List comprehensions make code much more concise indeed. Do 
list comprehensions also improve the speed of calculations?

  _  

From: avi.e.gr...@gmail.com <mailto:avi.e.gr...@gmail.com>  
mailto:avi.e.gr...@gmail.com> >
Sent: Friday, July 12, 2024 6:57 PM
To: Popov, Dmitry Yu mailto:dpo...@anl.gov> >; 'Popov, Dmitry 
Yu via Python-list' mailto:python-list@python.org> >; 
oscar.j.benja...@gmail.com <mailto:oscar.j.benja...@gmail.com>  
mailto:oscar.j.benja...@gmail.com> >
Subject: RE: Relatively prime integers in NumPy 

 

Dmitry, I clearly did not understand what you wanted earlier as you had not 
made clear that in your example, you already had progressed to some level where 
you had the data and were now doing a second step. So, I hesitate to say much 
until 

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Dmitry,

 

I clearly did not understand what you wanted earlier as you had not made clear 
that in your example, you already had progressed to some level where you had 
the data and were now doing a second step. So, I hesitate to say much until 
either nobody else addressed the issue (as clearly some have) or you explain 
well enough.

 Ditr

I am guessing you have programming experience in other languages and are not as 
“pythonic” as some. The code you show may not be quite how others might do it. 
Some may write mch of your code as a single line of python using a list 
comprehension such as:

 

hkl_list = [ [h, k, l] for SOMETHING in RANGE  for SOMETHING2  in RANGE2 for 
SOMETHING3 in RANGE3] 

 

Where h, k. l come from the somethings.

 

Back to the real world.

 

 

From: Popov, Dmitry Y

Re: Relatively prime integers in NumPy

[Popov, Dmitry Yu via Python-list]

Thank you very much. List comprehensions make code much more concise indeed. Do 
list comprehensions also improve the speed of calculations?

From: avi.e.gr...@gmail.com 
Sent: Friday, July 12, 2024 6:57 PM
To: Popov, Dmitry Yu ; 'Popov, Dmitry Yu via Python-list' 
; oscar.j.benja...@gmail.com 

Subject: RE: Relatively prime integers in NumPy

Dmitry, I clearly did not understand what you wanted earlier as you had not 
made clear that in your example, you already had progressed to some level where 
you had the data and were now doing a second step. So, I hesitate to say much 
until
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Dmitry,



I clearly did not understand what you wanted earlier as you had not made clear 
that in your example, you already had progressed to some level where you had 
the data and were now doing a second step. So, I hesitate to say much until 
either nobody else addressed the issue (as clearly some have) or you explain 
well enough.



I am guessing you have programming experience in other languages and are not as 
“pythonic” as some. The code you show may not be quite how others might do it. 
Some may write mch of your code as a single line of python using a list 
comprehension such as:



hkl_list = [ [h, k, l] for SOMETHING in RANGE  for SOMETHING2  in RANGE2 for 
SOMETHING3 in RANGE3]



Where h, k. l come from the somethings.



Back to the real world.





From: Popov, Dmitry Yu 
Sent: Friday, July 12, 2024 1:13 PM
To: avi.e.gr...@gmail.com; 'Popov, Dmitry Yu via Python-list' 
; oscar.j.benja...@gmail.com; Popov, Dmitry Yu 

Subject: Re: Relatively prime integers in NumPy



Thank you very much, Oscar.



Using the following code looks like a much better solution than my current 
Python code indeed.

np.gcd.reduce(np.transpose(a))

or

np.gcd.reduce(a,1)



The next question is how I can generate ndarray of h,k,l indices. This can be 
easily done from a Python list by using the following code.



import numpy as np

hkl_list=[]

for h in range(0, max_h):

  for k in range(0, max_k):

for l in range(0, max_l):

  hkl_local=[]

  hkl_local.append(h)

  hkl_local.append(k)

  hkl_local.append(l)

  hkl_list.append(hkl_local)

hkl=np.array(hkl_list, dtype=np.int64)

This code will generate a two-dimensional ndarray of h,k,l indices but is it 
possible to make a faster routine with NumPy?



Regards,

Dmitry









From: Python-list 
mailto:python-list-bounces+dpopov=anl@python.org>>
 on behalf of Popov, Dmitry Yu via Python-list 
mailto:python-list@python.org>>
Sent: Thursday, July 11, 2024 2:25 PM
To: avi.e.gr...@gmail.com<mailto:avi.e.gr...@gmail.com> 
mailto:avi.e.gr...@gmail.com>>; 'Popov, Dmitry Yu via 
Python-list' mailto:python-list@python.org>>
Subject: Re: Relatively prime integers in NumPy



Thank you for your interest. My explanation is too concise indeed, sorry. So 
far, I have used Python code with three enclosed 'for' loops for this purpose 
which is pretty time consuming. I'm trying to develop a NumPy based code to 
make this

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This message came from outside your organization.



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Thank you for your interest. My explanation is too concise indeed, sorry. So 
far, I have used Python code with three enclosed 'for' loops for this purpose 
which is pretty time consuming. I'm trying to develop a NumPy based code to 
make this procedure faster. This routine is kind of 'heart' of the algorithm to 
index of X-ray Laue diffraction patterns. In our group we have to process huge 
amount of such patterns. They are collected at a synchrotron radiation 
facility. Faster indexation routine would help a lot.



This is the code I'm currently using. Any prompts how to implement it in NumPy 
would be highly appreciated.



for h in range(0, max_h):

  for k in range(0, max_k):

for l in range(0, max_l):

  chvec=1

  maxmult=2

  if h > 1: 

maxmult=h

  if k > 1:

maxmult=k

  if l > 1:

maxmult=l

  if h > 1:

if maxmult > h:

  maxmult=h

  if k > 1:

if maxmult > k:

  maxmult=k

  if l > 1:

if maxmult > l:

  maxmult=l

  maxmult=maxmult+1

  for innen in range(2, maxmult):

if h in range(0, (max_h+1), innen):

  

RE: Relatively prime integers in NumPy

[AVI GROSS via Python-list]

Dmitry,

 

I clearly did not understand what you wanted earlier as you had not made clear 
that in your example, you already had progressed to some level where you had 
the data and were now doing a second step. So, I hesitate to say much until 
either nobody else addressed the issue (as clearly some have) or you explain 
well enough.

 

I am guessing you have programming experience in other languages and are not as 
“pythonic” as some. The code you show may not be quite how others might do it. 
Some may write mch of your code as a single line of python using a list 
comprehension such as:

 

hkl_list = [ [h, k, l] for SOMETHING in RANGE  for SOMETHING2  in RANGE2 for 
SOMETHING3 in RANGE3] 

 

Where h, k. l come from the somethings.

 

Back to the real world.

 

 

From: Popov, Dmitry Yu  
Sent: Friday, July 12, 2024 1:13 PM
To: avi.e.gr...@gmail.com; 'Popov, Dmitry Yu via Python-list' 
; oscar.j.benja...@gmail.com; Popov, Dmitry Yu 

Subject: Re: Relatively prime integers in NumPy

 

Thank you very much, Oscar. 

 

Using the following code looks like a much better solution than my current 
Python code indeed.

np.gcd.reduce(np.transpose(a)) 
or 
np.gcd.reduce(a,1)  
 
The next question is how I can generate ndarray of h,k,l indices. This can be 
easily done from a Python list by using the following code.
 
import numpy as np
hkl_list=[]
for h in range(0, max_h):
  for k in range(0, max_k):
for l in range(0, max_l):
  hkl_local=[]
  hkl_local.append(h)
  hkl_local.append(k)
  hkl_local.append(l)
  hkl_list.append(hkl_local)
hkl=np.array(hkl_list, dtype=np.int64)
This code will generate a two-dimensional ndarray of h,k,l indices but is it 
possible to make a faster routine with NumPy? 
 
Regards,
Dmitry
 
 
 
  _  


From: Python-list mailto:python-list-bounces+dpopov=anl@python.org> > on behalf of Popov, 
Dmitry Yu via Python-list mailto:python-list@python.org> >
Sent: Thursday, July 11, 2024 2:25 PM
To: avi.e.gr...@gmail.com <mailto:avi.e.gr...@gmail.com>  
mailto:avi.e.gr...@gmail.com> >; 'Popov, Dmitry Yu via 
Python-list' mailto:python-list@python.org> >
Subject: Re: Relatively prime integers in NumPy 

 

Thank you for your interest. My explanation is too concise indeed, sorry. So 
far, I have used Python code with three enclosed 'for' loops for this purpose 
which is pretty time consuming. I'm trying to develop a NumPy based code to 
make this 

ZjQcmQRYFpfptBannerStart

This Message Is From an External Sender 

This message came from outside your organization. 

 

ZjQcmQRYFpfptBannerEnd

Thank you for your interest. My explanation is too concise indeed, sorry. So 
far, I have used Python code with three enclosed 'for' loops for this purpose 
which is pretty time consuming. I'm trying to develop a NumPy based code to 
make this procedure faster. This routine is kind of 'heart' of the algorithm to 
index of X-ray Laue diffraction patterns. In our group we have to process huge 
amount of such patterns. They are collected at a synchrotron radiation 
facility. Faster indexation routine would help a lot.
 
This is the code I'm currently using. Any prompts how to implement it in NumPy 
would be highly appreciated.
 
for h in range(0, max_h):
  for k in range(0, max_k):
for l in range(0, max_l):
  chvec=1
  maxmult=2
  if h > 1: 
maxmult=h
  if k > 1:
maxmult=k
  if l > 1:
maxmult=l
  if h > 1:
if maxmult > h:
  maxmult=h
  if k > 1:
if maxmult > k:
  maxmult=k
  if l > 1:
if maxmult > l:
  maxmult=l
  maxmult=maxmult+1
  for innen in range(2, maxmult):
if h in range(0, (max_h+1), innen):
  if k in range(0, (max_k+1), innen):
if l in range(0, (max_l+1), innen):
  chvec=0
  if chvec==1:
# Only relatively prime integers h,k,l pass to this 
block of the code
 
 

From: avi.e.gr...@gmail.com <mailto:avi.e.gr...@gmail.com>  
mailto:avi.e.gr...@gmail.com> >
Sent: Thursday, July 11, 2024 1:22 PM
To: Popov, Dmitry Yu mailto:dpo...@anl.gov> >; 'Popov, Dmitry 
Yu via Python-list' mailto:python-list@python.org> >
Subject: RE: Relatively prime integers in NumPy
 
Дмитрий, You may think you explained what you wanted but I do not see what 
result you expect from your examples. Your request is a bit too esote

Re: Relatively prime integers in NumPy

[Popov, Dmitry Yu via Python-list]

Thank you very much, Oscar.

Using the following code looks like a much better solution than my current 
Python code indeed.

np.gcd.reduce(np.transpose(a))
or
np.gcd.reduce(a,1)

The next question is how I can generate ndarray of h,k,l indices. This can be 
easily done from a Python list by using the following code.

import numpy as np
hkl_list=[]
for h in range(0, max_h):
  for k in range(0, max_k):
for l in range(0, max_l):
  hkl_local=[]
  hkl_local.append(h)
  hkl_local.append(k)
  hkl_local.append(l)
  hkl_list.append(hkl_local)
hkl=np.array(hkl_list, dtype=np.int64)

This code will generate a two-dimensional ndarray of h,k,l indices but is it 
possible to make a faster routine with NumPy?

Regards,
Dmitry





From: Python-list  on behalf of 
Popov, Dmitry Yu via Python-list 
Sent: Thursday, July 11, 2024 2:25 PM
To: avi.e.gr...@gmail.com ; 'Popov, Dmitry Yu via 
Python-list' 
Subject: Re: Relatively prime integers in NumPy

Thank you for your interest. My explanation is too concise indeed, sorry. So 
far, I have used Python code with three enclosed 'for' loops for this purpose 
which is pretty time consuming. I'm trying to develop a NumPy based code to 
make this
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This Message Is From an External Sender
This message came from outside your organization.

ZjQcmQRYFpfptBannerEnd

Thank you for your interest. My explanation is too concise indeed, sorry. So 
far, I have used Python code with three enclosed 'for' loops for this purpose 
which is pretty time consuming. I'm trying to develop a NumPy based code to 
make this procedure faster. This routine is kind of 'heart' of the algorithm to 
index of X-ray Laue diffraction patterns. In our group we have to process huge 
amount of such patterns. They are collected at a synchrotron radiation 
facility. Faster indexation routine would help a lot.

This is the code I'm currently using. Any prompts how to implement it in NumPy 
would be highly appreciated.

for h in range(0, max_h):
  for k in range(0, max_k):
for l in range(0, max_l):
  chvec=1
  maxmult=2
  if h > 1: 
maxmult=h
  if k > 1:
maxmult=k
  if l > 1:
maxmult=l
  if h > 1:
if maxmult > h:
  maxmult=h
  if k > 1:
if maxmult > k:
  maxmult=k
  if l > 1:
if maxmult > l:
  maxmult=l
  maxmult=maxmult+1
  for innen in range(2, maxmult):
if h in range(0, (max_h+1), innen):
  if k in range(0, (max_k+1), innen):
if l in range(0, (max_l+1), innen):
  chvec=0
  if chvec==1:
# Only relatively prime integers h,k,l pass to this 
block of the code



From: avi.e.gr...@gmail.com 
Sent: Thursday, July 11, 2024 1:22 PM
To: Popov, Dmitry Yu ; 'Popov, Dmitry Yu via Python-list' 

Subject: RE: Relatively prime integers in NumPy

Дмитрий, You may think you explained what you wanted but I do not see what 
result you expect from your examples. Your request is a bit too esoteric to be 
a great candidate for being built into a module like numpy for general purpose 
se but
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Дмитрий,

You may think you explained what you wanted but I do not see what result you
expect from your examples.

Your request is a bit too esoteric to be a great candidate for being built
into a module like numpy for general purpose se but I can imagine it could
be available in modules build on top of numpy.

Is there a reason you cannot solve this mostly outside numpy?

It looks like you could use numpy to select the numbers you want to compare,
then call one of many methods you can easily search for to see  how to use
python to make some list or other data structure for divisors of each number
involved and then use standard methods to compare the lists and exact common
divisors. If needed, you could then put the results back into your original
data structure using numpy albeit the number of matches can vary.

Maybe a better explanation is needed as I cannot see what your latter words
about -1 and 1 are about. Perhaps someone else knows.




-Original Message-
From: Python-list  On
Behalf Of Popov, Dmitry Yu via Python-list
Sent: Monday, July 8, 2024 3:10 PM
To: Popov, Dmitry Yu via Python-list 
Subject: Relatively pr

Re: Relatively prime integers in NumPy

[Popov, Dmitry Yu via Python-list]

Thank you for your interest. My explanation is too concise indeed, sorry. So 
far, I have used Python code with three enclosed 'for' loops for this purpose 
which is pretty time consuming. I'm trying to develop a NumPy based code to 
make this procedure faster. This routine is kind of 'heart' of the algorithm to 
index of X-ray Laue diffraction patterns. In our group we have to process huge 
amount of such patterns. They are collected at a synchrotron radiation 
facility. Faster indexation routine would help a lot.

This is the code I'm currently using. Any prompts how to implement it in NumPy 
would be highly appreciated.

for h in range(0, max_h):
  for k in range(0, max_k):
for l in range(0, max_l):
  chvec=1
  maxmult=2
  if h > 1: 
maxmult=h
  if k > 1:
maxmult=k
  if l > 1:
maxmult=l
  if h > 1:
if maxmult > h:
  maxmult=h
  if k > 1:
if maxmult > k:
  maxmult=k
  if l > 1:
if maxmult > l:
  maxmult=l
  maxmult=maxmult+1
  for innen in range(2, maxmult):
if h in range(0, (max_h+1), innen):
  if k in range(0, (max_k+1), innen):
if l in range(0, (max_l+1), innen):
  chvec=0
  if chvec==1:
# Only relatively prime integers h,k,l pass to this 
block of the code



From: avi.e.gr...@gmail.com 
Sent: Thursday, July 11, 2024 1:22 PM
To: Popov, Dmitry Yu ; 'Popov, Dmitry Yu via Python-list' 

Subject: RE: Relatively prime integers in NumPy

Дмитрий, You may think you explained what you wanted but I do not see what 
result you expect from your examples. Your request is a bit too esoteric to be 
a great candidate for being built into a module like numpy for general purpose 
se but
ZjQcmQRYFpfptBannerStart
This Message Is From an External Sender
This message came from outside your organization.

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Дмитрий,

You may think you explained what you wanted but I do not see what result you
expect from your examples.

Your request is a bit too esoteric to be a great candidate for being built
into a module like numpy for general purpose se but I can imagine it could
be available in modules build on top of numpy.

Is there a reason you cannot solve this mostly outside numpy?

It looks like you could use numpy to select the numbers you want to compare,
then call one of many methods you can easily search for to see  how to use
python to make some list or other data structure for divisors of each number
involved and then use standard methods to compare the lists and exact common
divisors. If needed, you could then put the results back into your original
data structure using numpy albeit the number of matches can vary.

Maybe a better explanation is needed as I cannot see what your latter words
about -1 and 1 are about. Perhaps someone else knows.




-Original Message-
From: Python-list  On
Behalf Of Popov, Dmitry Yu via Python-list
Sent: Monday, July 8, 2024 3:10 PM
To: Popov, Dmitry Yu via Python-list 
Subject: Relatively prime integers in NumPy

Dear Sirs.

Does NumPy provide a simple mechanism to identify relatively prime integers,
i.e. integers which don't have a common factor other than +1 or -1? For
example, in case of this array:
[[1,5,8],
  [2,4,8],
  [3,3,9]]
I can imagine a function which would return array of common factors along
axis 0: [1,2,3]. Those triples of numbers along axis 1 with the factor of1
or -1 would be relatively prime integers.

Regards,
Dmitry Popov

Argonne, IL
USA

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Re: Relatively prime integers in NumPy

[Peter J. Holzer via Python-list]

On 2024-07-08 19:09:45 +, Popov, Dmitry Yu via Python-list wrote:
> Does NumPy provide a simple mechanism to identify relatively prime
> integers, i.e. integers which don't have a common factor other than +1
> or -1?

Typing "numpy gcd" into my favourite search engine brings me to 
https://numpy.org/doc/stable/reference/generated/numpy.gcd.html

hp

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__/   | http://www.hjp.at/ |   challenge!"


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RE: Relatively prime integers in NumPy

[AVI GROSS via Python-list]

OK. That explains a bit more.

 

If I understand what you are looking for is a fast implementation and quite 
often in Pyhon it means using code written in another language such as C that 
is integrated carefully in a moule or two. Another tack is to replace many 
explicit loops with often much faster vectorized operations. Numpy provides 
advantages like the above if you use it as intended.

 

Of course there are other techniques in how code is refactored or the order of 
operations, or doing things in parallel.

 

Just as an example, your inner loop ear the top is operating one at a time or 
numbers between 0 and max_l and hen creates variables initialized and then 
possibly changed in chvec and maxmult. It uses various conditions to change 
those variables then goes on to do more things included in a fourth nested loop.

 

What would happen if, instead, you used two objects with the same names that 
were each a numpy array, or perhaps combined into a dataframe type object?

 

Using numpy (and perhaps pandas) you could have code that initialized one such 
array to hold the initial 1 or 2 as needed in an object whose length was 
max_l+1 and then the next operations, using numpy notation would be along the 
lines of replace the corresponding value depending on external variables you 
call h or k and so on. 

 

There would be several invisible loops, perhaps chained in some way, but 
probably running way faster than the explicit loop.

 

I am not going to write any specific code, but suggest you read some 
documentation on how to use numpy for some of the operations you want when 
operating on larger clusters of info. You can gain some speed even by changing 
a few parts. To refactor the entire thing would take more thought and if you 
come up with the idea  of operating on a multidimensional array, might take 
some care. 

 

But consider what would happen if you looked at your loops which are currently 
of a fixed size and created  a 3-D matrix with dimensions of max_h+1, max_k+1, 
and max_l+1 and simply initialized it with all possible initial values and then 
ran an algorithm to manipulate it, often asking numpy for various slices or 
whatever works for you as in axes.  This architecture may not work for ou but 
is an example of the kind of thinking it an take to make a problem use 
algorithms more efficiently.

 

I note the code did not actually help me understand what mathematical operation 
you want to perform. I assumed I might see some operations like division and t 
may be other parts of your code that implement what you want.

 

But if this is a common enough need, I suspect you may want to see if something 
similar enough is out there. Your code may be more complex and more like the 
sieve of Eratosthenes that attempts to test every possibility.

 

One algorithm I have seen simply takes the numbers you are evaluating and in a 
loop of the first N primes (or an open-ended generator) simply does an integer 
division by 2, as many times as it returns an integral result, then as many 
divisions by 3 then 5 and 7 and so on.  It aborts when it has been chopped down 
to size, or the prime being used is large enough (square root or so) ad at the 
end, you should have some sequence of divisors, or just  and the number if it 
is prime. Some such algorithm can be fairly fast and perhaps can even be done 
vectorized. 

 

One last comment is about memoization. If your data is of a nature where a 
relatively few numbers come up often, then you an use something, like perhaps a 
dictionary, to store the results of a computation like getting a list of prime 
factors for a specific number, or just recording whether it is prime or 
composite. Later calls to do calculations would always check if the result has 
already been saved and skip recalculating it.

 

Good Luck

 

 

From: Popov, Dmitry Yu  
Sent: Thursday, July 11, 2024 3:26 PM
To: avi.e.gr...@gmail.com; 'Popov, Dmitry Yu via Python-list' 

Subject: Re: Relatively prime integers in NumPy

 

Thank you for your interest. My explanation is too concise indeed, sorry. So 
far, I have used Python code with three enclosed 'for' loops for this purpose 
which is pretty time consuming. I'm trying to develop a NumPy based code to 
make this procedure faster. This routine is kind of 'heart' of the algorithm to 
index of X-ray Laue diffraction patterns. In our group we have to process huge 
amount of such patterns. They are collected at a synchrotron radiation 
facility. Faster indexation routine would help a lot. 

 

This is the code I'm currently using. Any prompts how to implement it in NumPy 
would be highly appreciated. 

 

for h in range(0, max_h):

  for k in range(0, max_k):

for l in range(0, max_l):

  chvec=1

  maxmult=2

  if h > 1: 

maxmult=h

  if k > 1:

maxmult=k

Re: Relatively prime integers in NumPy

[Oscar Benjamin via Python-list]

(posting on-list this time)

On Thu, 11 Jul 2024 at 15:18, Popov, Dmitry Yu via Python-list
 wrote:
>
> Dear Sirs.
>
> Does NumPy provide a simple mechanism to identify relatively prime integers, 
> i.e. integers which don't have a common factor other than +1 or -1? For 
> example, in case of this array:
> [[1,5,8],
>   [2,4,8],
>   [3,3,9]]
> I can imagine a function which would return array of common factors along 
> axis 0: [1,2,3]. Those triples of numbers along axis 1 with the factor of1 or 
> -1 would be relatively prime integers.

It sounds like you want the gcd (greatest common divisor) of each row.
The math module can do this:

In [1]: a = [[1,5,8],
   ...:   [2,4,8],
   ...:   [3,3,9]]

In [2]: import math

In [3]: [math.gcd(*row) for row in a]
Out[3]: [1, 2, 3]

NumPy can also do it apparently:

In [10]: np.gcd.reduce(np.transpose(a))
Out[10]: array([1, 2, 3])

https://en.wikipedia.org/wiki/Greatest_common_divisor

--
Oscar
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RE: Relatively prime integers in NumPy

[AVI GROSS via Python-list]

Дмитрий,

You may think you explained what you wanted but I do not see what result you
expect from your examples.

Your request is a bit too esoteric to be a great candidate for being built
into a module like numpy for general purpose se but I can imagine it could
be available in modules build on top of numpy.

Is there a reason you cannot solve this mostly outside numpy?

It looks like you could use numpy to select the numbers you want to compare,
then call one of many methods you can easily search for to see  how to use
python to make some list or other data structure for divisors of each number
involved and then use standard methods to compare the lists and exact common
divisors. If needed, you could then put the results back into your original
data structure using numpy albeit the number of matches can vary.

Maybe a better explanation is needed as I cannot see what your latter words
about -1 and 1 are about. Perhaps someone else knows.




-Original Message-
From: Python-list  On
Behalf Of Popov, Dmitry Yu via Python-list
Sent: Monday, July 8, 2024 3:10 PM
To: Popov, Dmitry Yu via Python-list 
Subject: Relatively prime integers in NumPy

Dear Sirs.

Does NumPy provide a simple mechanism to identify relatively prime integers,
i.e. integers which don't have a common factor other than +1 or -1? For
example, in case of this array:
[[1,5,8],
  [2,4,8],
  [3,3,9]]
I can imagine a function which would return array of common factors along
axis 0: [1,2,3]. Those triples of numbers along axis 1 with the factor of1
or -1 would be relatively prime integers.

Regards,
Dmitry Popov

Argonne, IL
USA

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Relatively prime integers in NumPy

[Popov, Dmitry Yu via Python-list]

Dear Sirs.

Does NumPy provide a simple mechanism to identify relatively prime integers, 
i.e. integers which don't have a common factor other than +1 or -1? For 
example, in case of this array:
[[1,5,8],
  [2,4,8],
  [3,3,9]]
I can imagine a function which would return array of common factors along axis 
0: [1,2,3]. Those triples of numbers along axis 1 with the factor of1 or -1 
would be relatively prime integers.

Regards,
Dmitry Popov

Argonne, IL
USA

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Re: Couldn't install numpy on Python 2.7

[Ethan Furman via Python-list]

Hey, everyone!

I believe the original question has been answered, and tempers seem to be flaring in sub-threads, so let's call this 
thread done and move on to other interesting topics.


Thank you for your support!

--
~Ethan~
Moderator
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Re: Couldn't install numpy on Python 2.7

[Chris Green via Python-list]

Chris Angelico  wrote:
> On Thu, 13 Jun 2024 at 10:58,  wrote:
> >
> > Chris,
> >
> > You seem to have perceived an insult that I remain unaware of.
> 
> If you're not aware that you're saying this, then don't say it.
> 
Er, um, that really makes no sense! :-)

How can one not say something that one isn't aware of saying?

-- 
Chris Green
·
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Re: Couldn't install numpy on Python 2.7

[Chris Angelico via Python-list]

On Thu, 13 Jun 2024 at 10:58,  wrote:
>
> Chris,
>
> You seem to have perceived an insult that I remain unaware of.

If you're not aware that you're saying this, then don't say it.

> I looked up FUD and sharply disagree with suggestions I am trying to somehow
> cause Fear, Uncertainty or Doubt. I simply asked if another such update ...
> as a hypothetical. Had I asked what impact Quantum Computers might have on
> existing languages, would that also be FUD, or just a speculation in a
> discussion.

What DID you intend by your comments? Were you trying to imply that
work spent upgrading to Python 3 would have to be redone any day now
when this hypothetical massively-incompatible Python 4 is released? Or
what? What WERE you trying to say?

If you don't understand how damaging it can be to say that sort of
thing, **don't say it**. Otherwise, expect responses like this.

I *detest* the attitude that you can make vague disparaging comments
and then hide behind claims that you had no idea how damaging you were
being.

ChrisA
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https://mail.python.org/mailman/listinfo/python-list

RE: Couldn't install numpy on Python 2.7

[AVI GROSS via Python-list]

Chris,

You seem to have perceived an insult that I remain unaware of.

I have no special knowledge, like you do, of plans made for changes to the
pthon language and implementation.

I was asking a hypothetical question about what some users would do if
python came out with a newer major version. I have seen people often wait
until some software that tries to get updated too frequently makes multiple
updates and then finally give in and skip the intermediates.

I wondered if something like a version 4.0 might get people still using
version 2 might finally come around and also if some version 3 users would
not be thrilled with something not stable enough.

I have no favorite ideas here and can see a balance between adding features
or fixing flaws and on the other side, not discomfiting many and especially
when in many cases, the original people who wrote software are no longer
there nor budgets to pay for changes.

I looked up FUD and sharply disagree with suggestions I am trying to somehow
cause Fear, Uncertainty or Doubt. I simply asked if another such update ...
as a hypothetical. Had I asked what impact Quantum Computers might have on
existing languages, would that also be FUD, or just a speculation in a
discussion.

Either way, I am taking any further discussion along these lines offline and
will not continue here.

-Original Message-
From: Python-list  On
Behalf Of Chris Angelico via Python-list
Sent: Wednesday, June 12, 2024 7:23 PM
To: python-list@python.org
Subject: Re: Couldn't install numpy on Python 2.7

On Thu, 13 Jun 2024 at 09:20,  wrote:
> My point was that version 4 COULD HAPPEN one day and I meant INCOMPATIBLE
> version not 4. Obviously we can make a version 4 that is not incompatible
> too.

This is still FUD. Back your words with something, or stop trying to
imply that there's another incompatible change just around the corner.

Do you realise how insulting you are being to the developers of Python
by these implications?

ChrisA
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Re: Couldn't install numpy on Python 2.7

[Chris Angelico via Python-list]

On Thu, 13 Jun 2024 at 09:20,  wrote:
> My point was that version 4 COULD HAPPEN one day and I meant INCOMPATIBLE
> version not 4. Obviously we can make a version 4 that is not incompatible
> too.

This is still FUD. Back your words with something, or stop trying to
imply that there's another incompatible change just around the corner.

Do you realise how insulting you are being to the developers of Python
by these implications?

ChrisA
-- 
https://mail.python.org/mailman/listinfo/python-list

RE: Couldn't install numpy on Python 2.7

[AVI GROSS via Python-list]

Chris,

I don't want to get off message and debate whether my "jokes" are jokes, let
alone funny. Obviously, they often aren't.

What I meant by joking here does seem relevant. As the years pass, there can
come a time when it is suggested that a language (any language including
python) is no longer useful to many in the community as it has not kept up
with changes in the industry or whatever. Suggestions are made for changes
and additions that that not be backward compatible. They can be somewhat
minor things like new keywords that have not been reserved and where
programs that exist might be scanned for use of that keyword, and you simply
replace those names with was_keyword or something and the programs will
generally  run.  But there can be major changes too and there can be a
choice to just create a new language that has some similarities to python 3
(or PERL version whatever) or just name it the same but a version higher
much like has happened.

My point was that version 4 COULD HAPPEN one day and I meant INCOMPATIBLE
version not 4. Obviously we can make a version 4 that is not incompatible
too.

I have experience in other languages where disconnects happen at various
levels. Some functions in a collection such as a package are removed perhaps
to replace them with a more abstract version that does much more. Do you
remove the old one immediately or do you make a new name for the new one and
perhaps in some way mark the old one for deprecation with a pointer to the
new one to be used as soon as reasonable? I have seen many approaches. I
have seen entire packages yanked. I have seen parts that used to be in the
distribution as if built-in and then taken out and vice versa.

The point is you do not need a 4.0 to be incompatible. The incompatibility,
or need to change, can happen anytime when you are importing things like
numpy which is released whenever they want to and is not part of the python
distribution. Also, as we have seen at times, other modules you may have
imported, in some languages, can mask names you are using in your program
that you may not even be aware are there. Much can go wrong with software
and keeping current can also give you problems when something released may
have inadvertent changes or bugs.

So much of our code is voluntary and as noted earlier, some python
variants/distributions simply may not have anyone interested in keeping them
up to date. You as a user, take your chances.


-Original Message-
From: Python-list  On
Behalf Of Chris Angelico via Python-list
Sent: Wednesday, June 12, 2024 5:52 PM
To: python-list@python.org
Subject: Re: Couldn't install numpy on Python 2.7

On Thu, 13 Jun 2024 at 07:36,  wrote:
> But if the goal was to deprecate python 2 and in some sense phase it out,
it
> is perhaps not working well for some. Frankly, issuing so many updates
like
> 2.7 and including backporting of new features has helped make it possible
to
> delay any upgrade.

The goal was to improve Python. I don't think anyone's ever tried to
"kill off" Python 2 - not in the sense of stopping people from using
it - but there haven't been any changes, not even security fixes, in
over four years.

> And, yes, I was KIDDING about python 4. I am simply suggesting that there
> may well be a time that another shift happens that may require another
> effort to get people on board a new and perhaps incompatible setup.

Kidding, eh? It sure sounded like you were trying to imply that there
would inevitably be another major breaking change. It definitely
smelled like FUD.

Maybe your jokes just aren't funny.

ChrisA
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Re: Couldn't install numpy on Python 2.7

[Oscar Benjamin via Python-list]

On Wed, 12 Jun 2024 at 23:52, Greg Ewing via Python-list
 wrote:
> On 13/06/24 10:09 am, Chris Angelico wrote:
>  > So if anyone
>  > actually does need to use pip with Python 2.7, they probably need to
>  > set up a local server
>
> You should also be able to download a .tar.gz from PyPI and use pip
> to install that. Although you'll have to track down the dependencies
> yourself in that case.

It is almost certainly better to download the wheel (.whl) file rather
than the sdist (.tar.gz) file. Building NumPy from source needs not
just compilers etc but also you first need to build/install a BLAS
library.

--
Oscar
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Re: Couldn't install numpy on Python 2.7

[Chris Angelico via Python-list]

On Thu, 13 Jun 2024 at 08:46, Oscar Benjamin via Python-list
 wrote:
> I don't know much about SSL and related networking things especially
> on Windows. I would be surprised if pip on old Python can't install
> from current PyPI though. I imagine that something strange has
> happened like a new version of pip running on an old version of Python
> or old Python on new OS (or old PyCharm...).
>
> There is no problem using Python 2.7 with pip and PyPI on this Linux
> machine but I guess it has a newer SSL library provided by the OS:

Sadly, I would NOT be surprised if this is indeed a problem on
Windows. You're exactly right - on Linux, it can use a newer SSL
library from the OS. Of course, this does assume that you've updated
your OS, which is far from a guarantee, but since this has security
implications there's a good chance you can update it while retaining a
legacy system.

On Thu, 13 Jun 2024 at 08:51, Greg Ewing via Python-list
 wrote:
> On 13/06/24 10:09 am, Chris Angelico wrote:
>  > So if anyone
>  > actually does need to use pip with Python 2.7, they probably need to
>  > set up a local server
>
> You should also be able to download a .tar.gz from PyPI and use pip
> to install that. Although you'll have to track down the dependencies
> yourself in that case.

Also a possibility; in my opinion, losing dependency management is too
big a cost, so I would be inclined to set up a local server. But then,
I would be using a newer SSL library and not have the problem in the
first place.

ChrisA
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Re: Couldn't install numpy on Python 2.7

[Greg Ewing via Python-list]

On 13/06/24 4:31 am, avi.e.gr...@gmail.com wrote:

It seems Microsoft is having a problem where something lik 2/3 of Windows
users have not upgraded from Windows 10 after many years


At least Python 3 is a clear improvement over Python 2 in many ways.
Whereas the only thing Microsoft seems to have done with Windows in
recent times is change it in ways that nobody wants, so there is
understandable resistance to upgrading even if it's possible.

On 13/06/24 10:09 am, Chris Angelico wrote:
> So if anyone
> actually does need to use pip with Python 2.7, they probably need to
> set up a local server

You should also be able to download a .tar.gz from PyPI and use pip
to install that. Although you'll have to track down the dependencies
yourself in that case.

--
Greg
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Re: Couldn't install numpy on Python 2.7

[Oscar Benjamin via Python-list]

On Wed, 12 Jun 2024 at 23:11, Chris Angelico via Python-list
 wrote:
>
> On Thu, 13 Jun 2024 at 07:57, Oscar Benjamin via Python-list
>  wrote:
> > They are seeing a warning that explicitly says "You can upgrade to a
> > newer version of Python to solve this". I don't know whether that SSL
> > warning is directly connected to pip not finding any versions of numpy
> > but with the available information so far that seems like the first
> > thing to consider.
>
> I think it is; AIUI, with an ancient SSL library, pip is unable to
> download packages safely from the current pypi server. So if anyone
> actually does need to use pip with Python 2.7, they probably need to
> set up a local server, using older encryption protocols (which should
> therefore NOT be made accessible to the internet). Since pip can't
> contact the upstream pypi, there's no available numpy for it to
> install.

I don't know much about SSL and related networking things especially
on Windows. I would be surprised if pip on old Python can't install
from current PyPI though. I imagine that something strange has
happened like a new version of pip running on an old version of Python
or old Python on new OS (or old PyCharm...).

There is no problem using Python 2.7 with pip and PyPI on this Linux
machine but I guess it has a newer SSL library provided by the OS:

$ pip install numpy
DEPRECATION: Python 2.7 reached the end of its life on January 1st,
2020. Please upgrade your Python as Python 2.7 is no longer
maintained. pip 21.0 will drop support for Python 2.7 in January 2021.
More details about Python 2 support in pip can be found at
https://pip.pypa.io/en/latest/development/release-process/#python-2-support
pip 21.0 will remove support for this functionality.
Collecting numpy
  Downloading numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl (17.0 MB)
 |████| 17.0 MB 14.3 MB/s
Installing collected packages: numpy
Successfully installed numpy-1.16.6

If it is actually the case that pip on Python 2.7 (on Windows) cannot
download from PyPI then an easier option rather than creating a local
server would just be to download the numpy wheels from PyPI using a
browser:

  https://pypi.org/project/numpy/1.15.4/#files

Then you can do

   pip install .\numpy-1.15.4-cp27-none-win_amd64.whl

Using a newer version of Python is still my primary suggestion though.

--
Oscar
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Re: Couldn't install numpy on Python 2.7

[Chris Angelico via Python-list]

On Thu, 13 Jun 2024 at 07:57, Oscar Benjamin via Python-list
 wrote:
> They are seeing a warning that explicitly says "You can upgrade to a
> newer version of Python to solve this". I don't know whether that SSL
> warning is directly connected to pip not finding any versions of numpy
> but with the available information so far that seems like the first
> thing to consider.

I think it is; AIUI, with an ancient SSL library, pip is unable to
download packages safely from the current pypi server. So if anyone
actually does need to use pip with Python 2.7, they probably need to
set up a local server, using older encryption protocols (which should
therefore NOT be made accessible to the internet). Since pip can't
contact the upstream pypi, there's no available numpy for it to
install.

ChrisA
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Re: Couldn't install numpy on Python 2.7

[Oscar Benjamin via Python-list]

On Wed, 12 Jun 2024 at 22:38, AVI GROSS via Python-list
 wrote:
>
> The discussion though was about a specific OP asking if they can fix their
> problem. One solution being suggested is to fix a deeper problem and simply
> make their code work with a recent version of python 3.

The OP has not replied with any explanation as to why they are using
Python 2.7 and has not said whether they have any existing code that
only works with Python 2.7. It is unclear at this point whether there
is any reason that they shouldn't just install a newer version of
Python.

They are seeing a warning that explicitly says "You can upgrade to a
newer version of Python to solve this". I don't know whether that SSL
warning is directly connected to pip not finding any versions of numpy
but with the available information so far that seems like the first
thing to consider.

It is entirely reasonable to start by suggesting to use a newer
version of Python until some reason is given for not doing that.

--
Oscar
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Re: Couldn't install numpy on Python 2.7

[Chris Angelico via Python-list]

On Thu, 13 Jun 2024 at 07:36,  wrote:
> But if the goal was to deprecate python 2 and in some sense phase it out, it
> is perhaps not working well for some. Frankly, issuing so many updates like
> 2.7 and including backporting of new features has helped make it possible to
> delay any upgrade.

The goal was to improve Python. I don't think anyone's ever tried to
"kill off" Python 2 - not in the sense of stopping people from using
it - but there haven't been any changes, not even security fixes, in
over four years.

> And, yes, I was KIDDING about python 4. I am simply suggesting that there
> may well be a time that another shift happens that may require another
> effort to get people on board a new and perhaps incompatible setup.

Kidding, eh? It sure sounded like you were trying to imply that there
would inevitably be another major breaking change. It definitely
smelled like FUD.

Maybe your jokes just aren't funny.

ChrisA
-- 
https://mail.python.org/mailman/listinfo/python-list

RE: Couldn't install numpy on Python 2.7

[AVI GROSS via Python-list]

Chris,

Since you misunderstood, my statement was that making an incompatible set of
changes to create Python 3 in the first place was a decision made by some
and perhaps not one that thrilled others who already had an embedded base of
programs or ones in the pipeline that would need much work to become
comparable.

And, of course, users of a program who continued to use python 2, also have
to find a way to ...

But if the goal was to deprecate python 2 and in some sense phase it out, it
is perhaps not working well for some. Frankly, issuing so many updates like
2.7 and including backporting of new features has helped make it possible to
delay any upgrade.

And, yes, I was KIDDING about python 4. I am simply suggesting that there
may well be a time that another shift happens that may require another
effort to get people on board a new and perhaps incompatible setup. I have
seen things like that happen in multiple phases including phases where the
new tools are not an upgrade, but brand new. An example might be if
accompany decided to switch to another existing language because they want
better error detection and faster execution or new features that may take
forever to arrive in what they are using or that supply various services by
humans to help them.

The discussion though was about a specific OP asking if they can fix their
problem. One solution being suggested is to fix a deeper problem and simply
make their code work with a recent version of python 3. But another solution
could be to step backward to a version of python 2 that still has numpy
support, or as was suggested, find out what other modules they are using are
interfering with the program being satisfied with the last version of numpy
being used and perhaps find a way to get ...

In the long run, though, continuing with python 2 will likely cause ever
more such headaches if you want the latest and greatest of things like
numpy.


-Original Message-
From: Python-list  On
Behalf Of Chris Angelico via Python-list
Sent: Wednesday, June 12, 2024 2:00 PM
To: python-list@python.org
Subject: Re: Couldn't install numpy on Python 2.7

On Thu, 13 Jun 2024 at 03:41, AVI GROSS via Python-list
 wrote:
>
> Change is hard even when it may be necessary.
>
> The argument often is about whether some things are necessary or not.
>
> Python made a decision but clearly not a unanimous one.

What decision? To not release any new versions of Python 2? That isn't
actually the OP's problem here - the Python interpreter runs just
fine. But there's no numpy build for the OP's hardware and Python 2.7.

So if you want to complain about Python 2.7 being dead, all you have
to do is go through all of the popular packages and build binaries for
all modern computers. If that sounds easy, go ahead and do it; if it
sounds hard, realise that open source is not a democracy, and you
can't demand that other people do more and more and more unpaid work
just because you can't be bothered upgrading your code.

> My current PC was not upgradable because of the new hardware requirement
> Microsoft decided was needed for Windows 11.

Yes, and that's a good reason to switch to Linux for the older computer.

> I mention this in the context of examples of why even people who are
fairly
> knowledgeable do not feel much need to fix what does not feel broken.

It doesn't feel broken, right up until it does. The OP has discovered
that it *IS* broken. Whining that it doesn't "feel broken" is nonsense
when it is, in fact, not working.

> When is Python 4 coming?

Is this just another content-free whine, or are you actually curious
about the planned future of Python? If the latter, there is **PLENTY**
of information out there and I don't need to repeat it here.

Please don't FUD.

ChrisA
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Re: Couldn't install numpy on Python 2.7

[Chris Angelico via Python-list]

On Thu, 13 Jun 2024 at 06:55, Thomas Passin via Python-list
 wrote:
> The project cannot move to a Python-3 compatible version because Jython
> 3.xx doesn't exist and may never exist.  The saving grace is that my
> project doesn't have to use packages like numpy, scipy, and so forth.

Exactly. If you don't need to ALSO use something newer, there's
nothing stopping you from continuing with the old version. And that's
fine! As long as you're okay with not getting updates, you're welcome
to do whatever you like - including running Windows 98 on an ancient
PC and editing your documents on that. (Yes, I know someone who did
that, long after Win 98 was dead to most of us.)

> Thunderbird and everything else worked perfectly for me during that
> week.  True, there were a few Windows-only programs I missed, but I used
> other similar programs even if I didn't like them as much.

It's true. And there ARE solutions to that, although it's a bit rough
trying to run them on low hardware (Wine works nicely for some
programs, less so for others; VirtualBox is really not gonna be happy
with a small fraction of your limited RAM). But if your needs are
simple, even a crazily low-end system is sufficient.

> It's amazing
> how little resources Linux installs need, even with a GUI.  Of course,
> 4GB RAM is limiting whether you are on Linux or Windows - you can't
> avoid shuffling all those GUI bits around - but with a little care it
> worked great.  And with the external SSD the laptop was a lot snappier
> than it ever was when it was new.

One of the big differences with Linux is that you have a choice of
desktop environments, from "none" (just boot straight into a terminal)
on up. Some of them are a bit of a compromise in terms of how well you
can get your work done, but let's say you had an even MORE ancient
system with maybe one gig of memory... I'd rather have a super-light
desktop environment even if it doesn't have everything I'm normally
accustomed to!

ChrisA
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Re: Couldn't install numpy on Python 2.7

[Thomas Passin via Python-list]

On 6/12/2024 1:59 PM, Chris Angelico via Python-list wrote:

On Thu, 13 Jun 2024 at 03:41, AVI GROSS via Python-list
 wrote:


Change is hard even when it may be necessary.

The argument often is about whether some things are necessary or not.

Python made a decision but clearly not a unanimous one.


What decision? To not release any new versions of Python 2? That isn't
actually the OP's problem here - the Python interpreter runs just
fine. But there's no numpy build for the OP's hardware and Python 2.7.

So if you want to complain about Python 2.7 being dead, all you have
to do is go through all of the popular packages and build binaries for
all modern computers. If that sounds easy, go ahead and do it; if it
sounds hard, realise that open source is not a democracy, and you
can't demand that other people do more and more and more unpaid work
just because you can't be bothered upgrading your code.


I support a Tomcat project that has some java code and most of the code 
is for Jython 2.7.  Jython 2.7 is approximately on a par with Python 
2.7.  Any Python-only code from the standard library will probably run, 
but of course any C extensions cannot.  The nice thing about using 
Jython in a java environment is that it can call any java object, and 
java code can call Jython objects and their methods.


The project cannot move to a Python-3 compatible version because Jython 
3.xx doesn't exist and may never exist.  The saving grace is that my 
project doesn't have to use packages like numpy, scipy, and so forth. 
Also, the project is very mature and almost certainly won't need to 
create functionality such packages would enable.  It would be nice to be 
able to use some newer parts of the standard library, but there it is. 
Jython does support "from __future__ import" and I make use of that for 
the print function and the like.



My current PC was not upgradable because of the new hardware requirement
Microsoft decided was needed for Windows 11.


Yes, and that's a good reason to switch to Linux for the older computer.


I have a 2012-vintage laptop that in modern terms has a very small 
supply of RAM and a very slow hard drive. When my newer Windows 10 
computer was going to be out of service for a while, I put a Linux 
distro on an external SSD and copied things I needed to work on to it, 
including my Thunderbird email profile directory.


Thunderbird and everything else worked perfectly for me during that 
week.  True, there were a few Windows-only programs I missed, but I used 
other similar programs even if I didn't like them as much.  It's amazing 
how little resources Linux installs need, even with a GUI.  Of course, 
4GB RAM is limiting whether you are on Linux or Windows - you can't 
avoid shuffling all those GUI bits around - but with a little care it 
worked great.  And with the external SSD the laptop was a lot snappier 
than it ever was when it was new.



I mention this in the context of examples of why even people who are fairly
knowledgeable do not feel much need to fix what does not feel broken.


It doesn't feel broken, right up until it does. The OP has discovered
that it *IS* broken. Whining that it doesn't "feel broken" is nonsense
when it is, in fact, not working.


When is Python 4 coming?


Is this just another content-free whine, or are you actually curious
about the planned future of Python? If the latter, there is **PLENTY**
of information out there and I don't need to repeat it here.

Please don't FUD.

ChrisA


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Re: Couldn't install numpy on Python 2.7

[Chris Angelico via Python-list]

On Thu, 13 Jun 2024 at 03:41, AVI GROSS via Python-list
 wrote:
>
> Change is hard even when it may be necessary.
>
> The argument often is about whether some things are necessary or not.
>
> Python made a decision but clearly not a unanimous one.

What decision? To not release any new versions of Python 2? That isn't
actually the OP's problem here - the Python interpreter runs just
fine. But there's no numpy build for the OP's hardware and Python 2.7.

So if you want to complain about Python 2.7 being dead, all you have
to do is go through all of the popular packages and build binaries for
all modern computers. If that sounds easy, go ahead and do it; if it
sounds hard, realise that open source is not a democracy, and you
can't demand that other people do more and more and more unpaid work
just because you can't be bothered upgrading your code.

> My current PC was not upgradable because of the new hardware requirement
> Microsoft decided was needed for Windows 11.

Yes, and that's a good reason to switch to Linux for the older computer.

> I mention this in the context of examples of why even people who are fairly
> knowledgeable do not feel much need to fix what does not feel broken.

It doesn't feel broken, right up until it does. The OP has discovered
that it *IS* broken. Whining that it doesn't "feel broken" is nonsense
when it is, in fact, not working.

> When is Python 4 coming?

Is this just another content-free whine, or are you actually curious
about the planned future of Python? If the latter, there is **PLENTY**
of information out there and I don't need to repeat it here.

Please don't FUD.

ChrisA
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https://mail.python.org/mailman/listinfo/python-list

RE: Couldn't install numpy on Python 2.7

[AVI GROSS via Python-list]

Change is hard even when it may be necessary.

The argument often is about whether some things are necessary or not.

Python made a decision but clearly not a unanimous one.

My current PC was not upgradable because of the new hardware requirement
Microsoft decided was needed for Windows 11. I bought a new one a while back
and turned it on in another room and then set it aside because replacing the
current one in the current position will be a pain, especially with getting
all my wires and so on, and since I do not want to use a full copy of my
data including many obsolete things, that will be another pain to get what I
need, if I can remember. Complicating issues also include licenses for
things in fixed amounts and the likelihood of messing up the
hardware/software I have that records shows from cable to my hard disk,
possibly needing to buy a new one.

I mention this in the context of examples of why even people who are fairly
knowledgeable do not feel much need to fix what does not feel broken.

I have wondered if instead of doing what Microsoft wants, if maybe switching
to Linux of some kinds makes as much sense. I suspect some may simply
upgrade to an Apple product.

And think of all the PC's that may effectively be discarded as they may not
even be usable if donated.

We live in a rapidly developing age and hence one with regularly and
irregularly scheduled rounds of obsolescence.

When is Python 4 coming?

-Original Message-
From: Python-list  On
Behalf Of MRAB via Python-list
Sent: Wednesday, June 12, 2024 12:56 PM
To: python-list@python.org
Subject: Re: Couldn't install numpy on Python 2.7

On 2024-06-12 17:31, AVI GROSS via Python-list wrote:
> I am sure there is inertia to move from an older product and some people
> need a reason like this where the old becomes untenable.
> 
> It seems Microsoft is having a problem where something lik 2/3 of Windows
> users have not upgraded from Windows 10 after many years and have set a
> deadline in a year or so for stopping updates. In that case, hardware was
a
> concern for some as Windows 11 did not work on their machines. With
> upgrading python, the main concern is having to get someone to examine old
> code and try to make it compatible.
> 
In the case of Windows, my PC is over 10 years old yet performs 
perfectly well for my needs. It can't run Windows 11. Therefore, I'm in 
the process of migrating to Linux, and I still have over a year to 
achieve that before support ends.

> But anyone doing new code in Python 2 in recent years should ...
> 
Indeed...

> -Original Message-
> From: Python-list 
On
> Behalf Of Gordinator via Python-list
> Sent: Wednesday, June 12, 2024 10:19 AM
> To: python-list@python.org
> Subject: Re: Couldn't install numpy on Python 2.7
> 
> On 12/06/2024 12:30, marc nicole wrote:
>> I am trying to install numpy library on Python 2.7.15 in PyCharm but the
>> error message I get is:
>> 
>> ERROR: Could not find a version that satisfies the requirement numpy
(from
>>> versions: none)
>>> ERROR: No matching distribution found for numpy
>>> c:\python27\lib\site-packages\pip\_vendor\urllib3\util\ssl_.py:164:
>>> InsecurePlatformWarning: A true SSLContext object is not available. This
>>> prevents urllib3 fro
>>> m configuring SSL appropriately and may cause certain SSL connections to
>>> fail. You can upgrade to a newer version of Python to solve this. For
> more
>>> information, see
>>>
https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings
>>>InsecurePlatformWarning,
>> 
>> 
>> Any clues?
> 
> Why are you using Python 2? Come on, it's been 16 years. Ya gotta move
> on at some point.

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Re: Couldn't install numpy on Python 2.7

[MRAB via Python-list]

On 2024-06-12 17:31, AVI GROSS via Python-list wrote:

I am sure there is inertia to move from an older product and some people
need a reason like this where the old becomes untenable.

It seems Microsoft is having a problem where something lik 2/3 of Windows
users have not upgraded from Windows 10 after many years and have set a
deadline in a year or so for stopping updates. In that case, hardware was a
concern for some as Windows 11 did not work on their machines. With
upgrading python, the main concern is having to get someone to examine old
code and try to make it compatible.

In the case of Windows, my PC is over 10 years old yet performs 
perfectly well for my needs. It can't run Windows 11. Therefore, I'm in 
the process of migrating to Linux, and I still have over a year to 
achieve that before support ends.



But anyone doing new code in Python 2 in recent years should ...


Indeed...


-Original Message-
From: Python-list  On
Behalf Of Gordinator via Python-list
Sent: Wednesday, June 12, 2024 10:19 AM
To: python-list@python.org
Subject: Re: Couldn't install numpy on Python 2.7

On 12/06/2024 12:30, marc nicole wrote:

I am trying to install numpy library on Python 2.7.15 in PyCharm but the
error message I get is:

ERROR: Could not find a version that satisfies the requirement numpy (from

versions: none)
ERROR: No matching distribution found for numpy
c:\python27\lib\site-packages\pip\_vendor\urllib3\util\ssl_.py:164:
InsecurePlatformWarning: A true SSLContext object is not available. This
prevents urllib3 fro
m configuring SSL appropriately and may cause certain SSL connections to
fail. You can upgrade to a newer version of Python to solve this. For

more

information, see
https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings
   InsecurePlatformWarning,



Any clues?


Why are you using Python 2? Come on, it's been 16 years. Ya gotta move
on at some point.


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RE: Couldn't install numpy on Python 2.7

[AVI GROSS via Python-list]

I am sure there is inertia to move from an older product and some people
need a reason like this where the old becomes untenable.

It seems Microsoft is having a problem where something lik 2/3 of Windows
users have not upgraded from Windows 10 after many years and have set a
deadline in a year or so for stopping updates. In that case, hardware was a
concern for some as Windows 11 did not work on their machines. With
upgrading python, the main concern is having to get someone to examine old
code and try to make it compatible. 

But anyone doing new code in Python 2 in recent years should ...

-Original Message-
From: Python-list  On
Behalf Of Gordinator via Python-list
Sent: Wednesday, June 12, 2024 10:19 AM
To: python-list@python.org
Subject: Re: Couldn't install numpy on Python 2.7

On 12/06/2024 12:30, marc nicole wrote:
> I am trying to install numpy library on Python 2.7.15 in PyCharm but the
> error message I get is:
> 
> ERROR: Could not find a version that satisfies the requirement numpy (from
>> versions: none)
>> ERROR: No matching distribution found for numpy
>> c:\python27\lib\site-packages\pip\_vendor\urllib3\util\ssl_.py:164:
>> InsecurePlatformWarning: A true SSLContext object is not available. This
>> prevents urllib3 fro
>> m configuring SSL appropriately and may cause certain SSL connections to
>> fail. You can upgrade to a newer version of Python to solve this. For
more
>> information, see
>> https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings
>>InsecurePlatformWarning,
> 
> 
> Any clues?

Why are you using Python 2? Come on, it's been 16 years. Ya gotta move 
on at some point.
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Re: Couldn't install numpy on Python 2.7

[Gordinator via Python-list]

On 12/06/2024 12:30, marc nicole wrote:

I am trying to install numpy library on Python 2.7.15 in PyCharm but the
error message I get is:

ERROR: Could not find a version that satisfies the requirement numpy (from

versions: none)
ERROR: No matching distribution found for numpy
c:\python27\lib\site-packages\pip\_vendor\urllib3\util\ssl_.py:164:
InsecurePlatformWarning: A true SSLContext object is not available. This
prevents urllib3 fro
m configuring SSL appropriately and may cause certain SSL connections to
fail. You can upgrade to a newer version of Python to solve this. For more
information, see
https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings
   InsecurePlatformWarning,



Any clues?


Why are you using Python 2? Come on, it's been 16 years. Ya gotta move 
on at some point.

--
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Re: Couldn't install numpy on Python 2.7

[Chris Angelico via Python-list]

On Wed, 12 Jun 2024 at 21:32, marc nicole via Python-list
 wrote:
>
> I am trying to install numpy library on Python 2.7.15 in PyCharm but the
> error message I get is:
>
> You can upgrade to a newer version of Python to solve this.

The answer is right there in the error message.

ChrisA
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Couldn't install numpy on Python 2.7

[marc nicole via Python-list]

I am trying to install numpy library on Python 2.7.15 in PyCharm but the
error message I get is:

ERROR: Could not find a version that satisfies the requirement numpy (from
> versions: none)
> ERROR: No matching distribution found for numpy
> c:\python27\lib\site-packages\pip\_vendor\urllib3\util\ssl_.py:164:
> InsecurePlatformWarning: A true SSLContext object is not available. This
> prevents urllib3 fro
> m configuring SSL appropriately and may cause certain SSL connections to
> fail. You can upgrade to a newer version of Python to solve this. For more
> information, see
> https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings
>   InsecurePlatformWarning,


Any clues?
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[Python-announce] NumPy 1.26. 2 released

[Charles R Harris]

Charles R Harris 
Sat, Oct 14, 3:03 PM
to numpy-discussion, SciPy, bcc: python-announce-list
Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.26.2. NumPy 1.26.2 is a maintenance release that fixes bugs and
regressions discovered after the 1.26.1 release. The Python versions
supported by this release are 3.9-3.12. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.26.2/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.26.2>. The release notes
have documentation of the new meson functionality.

*Contributors*

A total of 13 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - @stefan6419846
   - @thalassemia +
   - Andrew Nelson
   - Charles Bousseau +
   - Charles Harris
   - Marcel Bargull +
   - Mark Mentovai +
   - Matti Picus
   - Nathan Goldbaum
   - Ralf Gommers
   - Sayed Adel
   - Sebastian Berg
   - William Ayd +



*Pull requests merged*
A total of 25 pull requests were merged for this release.

   - #24814: MAINT: align test_dispatcher s390x targets with
   _umath_tests_mtargets
   - #24929: MAINT: prepare 1.26.x for further development
   - #24955: ENH: Add Cython enumeration for NPY_FR_GENERIC
   - #24962: REL: Remove Python upper version from the release branch
   - #24971: BLD: Use the correct Python interpreter when running tempita.py
   - #24972: MAINT: Remove unhelpful error replacements from
   ``import_array()``
   - #24977: BLD: use classic linker on macOS, the new one in XCode 15
   has...
   - #25003: BLD: musllinux_aarch64 [wheel build]
   - #25043: MAINT: Update mailmap
   - #25049: MAINT: Update meson build infrastructure.
   - #25071: MAINT: Split up .github/workflows to match main
   - #25083: BUG: Backport fix build on ppc64 when the baseline set to
   Power9...
   - #25093: BLD: Fix features.h detection for Meson builds [1.26.x
   Backport]
   - #25095: BUG: Avoid intp conversion regression in Cython 3 (backport)
   - #25107: CI: remove obsolete jobs, and move macOS and conda Azure
   jobs...
   - #25108: CI: Add linux_qemu action and remove travis testing.
   - #25112: MAINT: Update .spin/cmds.py from main.
   - #25113: DOC: Visually divide main license and bundled licenses in
   wheels
   - #25115: MAINT: Add missing ``noexcept`` to shuffle helpers
   - #25116: DOC: Fix license identifier for OpenBLAS
   - #25117: BLD: improve detection of Netlib libblas/libcblas/liblapack
   - #25118: MAINT: Make bitfield integers unsigned
   - #25119: BUG: Make n a long int for np.random.multinomial
   - #25120: BLD: change default of the ``allow-noblas`` option to true.
   - #25121: BUG: ensure passing ``np.dtype`` to itself doesn't crash


Cheers,

Charles Harris
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[Python-announce] NumPy 1.26.1 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.26.1. NumPy 1.26.1 is a maintenance release that fixes bugs and
regressions discovered after the 1.26.0 release. In addition, it adds new
functionality for detecting BLAS and LAPACK when building from source.
Highlights are:

   - Improved detection of BLAS and LAPACK libraries for meson builds
   - Pickle compatibility with the upcoming NumPy 2.0.

The Python versions supported by this release are 3.9-3.12. Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.26.1/>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.26.1>. The release notes
have documentation of the new meson functionality.


*Contributors*

A total of 13 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - Andrew Nelson
   - Anton Prosekin +
   - Charles Harris
   - Chongyun Lee +
   - Ivan A. Melnikov +
   - Jake Lishman +
   - Mahder Gebremedhin +
   - Mateusz Sokół
   - Matti Picus
   - Munira Alduraibi +
   - Ralf Gommers
   - Rohit Goswami
   - Sayed Adel



*Pull requests merged*
A total of 20 pull requests were merged for this release.

   - #24742: MAINT: Update cibuildwheel version
   - #24748: MAINT: fix version string in wheels built with setup.py
   - #24771: BLD, BUG: Fix build failure for host flags e.g.
   ``-march=native``...
   - #24773: DOC: Updated the f2py docs to remove a note on -fimplicit-none
   - #24776: BUG: Fix SIMD f32 trunc test on s390x when baseline is none
   - #24785: BLD: add libquadmath to licences and other tweaks (#24753)
   - #24786: MAINT: Activate ``use-compute-credits`` for Cirrus.
   - #24803: BLD: updated vendored-meson/meson for mips64 fix
   - #24804: MAINT: fix licence path win
   - #24813: BUG: Fix order of Windows OS detection macros.
   - #24831: BUG, SIMD: use scalar cmul on bad Apple clang x86_64 (#24828)
   - #24840: BUG: Fix DATA statements for f2py
   - #24870: API: Add ``NumpyUnpickler`` for backporting
   - #24872: MAINT: Xfail test failing on PyPy.
   - #24879: BLD: fix math func feature checks, fix FreeBSD build, add CI...
   - #24899: ENH: meson: implement BLAS/LAPACK auto-detection and many CI...
   - #24902: DOC: add a 1.26.1 release notes section for BLAS/LAPACK
   build...
   - #24906: MAINT: Backport ``numpy._core`` stubs. Remove
   ``NumpyUnpickler``
   - #24911: MAINT: Bump pypa/cibuildwheel from 2.16.1 to 2.16.2
   - #24912: BUG: loongarch doesn't use REAL(10)


Cheers,

Charles Harris
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[Python-announce] NumPy 1.26.0 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.26.0. The NumPy 1.26.0 release is a continuation of the 1.25.x release
cycle with the addition of Python 3.12.0 support. Python 3.12 dropped
distutils, consequently supporting it required finding a replacement for
the setup.py/distutils based build system NumPy was using. We have chosen
to use the Meson build system instead, and this is the first NumPy release
supporting it. This is also the first release that supports Cython 3.0 in
addition to retaining 0.29.X compatibility. Supporting those two upgrades
was a large project, over 100 files have been touched in this release. The
changelog doesn't capture the full extent of the work, special thanks to
Ralf Gommers, Sayed Adel, Stéfan van der Walt, and Matti Picus who did much
of the work in the main development branch.

The highlights of this release are:

   - Python 3.12.0 support.
   - Cython 3.0.0 compatibility.
   - Use of the Meson build system
   - Updated SIMD support
   - f2py fixes, meson and bind(x) support
   - Support for the updated Accelerate BLAS/LAPACK library

The Python versions supported by this release are 3.9-3.12. Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.26.0/>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.26.0>


*Contributors*

A total of 20 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.

   -  @DWesl
   -  Albert Steppi +
   -  Bas van Beek
   -  Charles Harris
   -  Developer-Ecosystem-Engineering
   -  Filipe Laíns +
   -  Jake Vanderplas
   -  Liang Yan +
   -  Marten van Kerkwijk
   -  Matti Picus
   -  Melissa Weber Mendonça
   -  Namami Shanker
   -  Nathan Goldbaum
   -  Ralf Gommers
   -  Rohit Goswami
   -  Sayed Adel
   -  Sebastian Berg
   -  Stefan van der Walt
   -  Tyler Reddy
   -  Warren Weckesser




*Build system changes*
In this release, NumPy has switched to Meson as the build system and
meson-python as the build backend. Installing NumPy or building a wheel can
be
done with standard tools like ``pip`` and ``pypa/build``. The following are
supported:

   - Regular installs: ``pip install numpy`` or (in a cloned repo) ``pip
   install .``
   - Building a wheel: ``python -m build`` (preferred), or ``pip wheel .``
   - Editable installs: ``pip install -e . --no-build-isolation``
   - Development builds through the custom CLI implemented with spin
   <https://github.com/scientific-python/spin>: ``spin build``.

All the regular ``pip`` and ``pypa/build`` flags (e.g.,
``--no-build-isolation``) should work as expected.


*NumPy-specific build customization*
Many of the NumPy-specific ways of customizing builds have changed.
The ``NPY_*`` environment variables which control BLAS/LAPACK, SIMD,
threading,
and other such options are no longer supported, nor is a ``site.cfg`` file
to
select BLAS and LAPACK. Instead, there are command-line flags that can be
passed to the build via ``pip``/``build``'s config-settings interface. These
flags are all listed in the ``meson_options.txt`` file in the root of the
repo.
Detailed documented will be available before the final 1.26.0 release; for
now,
see the SciPy building from source
<http://scipy.github.io/devdocs/building/index.html> docs since most build
customization
works in an almost identical way in SciPy as it does in NumPy.


*Build dependencies*
While the runtime dependencies of NumPy have not changed, the build
dependencies have. Because we temporarily vendor Meson and meson-python,
there are several new dependencies - please see the ``[build-system]``
section
of ``pyproject.toml`` for details.

*Troubleshooting*

This build system change is quite large. In case of unexpected issues, it is
still possible to use a ``setup.py``-based build as a temporary workaround
(on
Python 3.9-3.11, not 3.12), by copying ``pyproject.toml.setuppy`` to
``pyproject.toml``. However, please open an issue with details on the NumPy
issue tracker. We aim to phase out ``setup.py`` builds as soon as possible,
and
therefore would like to see all potential blockers surfaced early on in the
1.26.0 release cycle.


Cheers,

Charles Harris
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Member address: arch...@mail-archive.com

[Python-announce] NumPy 1.26.0rc1 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.26.0rc1. The NumPy 1.26.0 release is a continuation of the 1.25.x release
cycle with the addition of Python 3.12.0 support. Python 3.12 dropped
distutils, consequently supporting it required finding a replacement for
the setup.py/distutils based build system NumPy was using. We have chosen
to use the Meson build system instead, and this is the first NumPy release
supporting it. This is also the first release that supports Cython 3.0 in
addition to retaining 0.29.X compatibility. Supporting those two upgrades
was a large project, over 100 files have been touched in this release. The
changelog doesn't capture the full extent of the work, special thanks to
Ralf Gommers, Sayed Adel, Stéfan van der Walt, and Matti Picus who did much
of the work in the main development branch.

The highlights of this release are:

   - Python 3.12.0 support.
   - Cython 3.0.0 compatibility.
   - Use of the Meson build system
   - Updated SIMD support
   - f2py fixes, meson and bind(x) support
   - Support for the updated Accelerate BLAS/LAPACK library

The Python versions supported by this release are 3.9-3.12. Note that no 32
bit wheels are provided. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.26.0rc1/>; source archives, release
notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.26.0rc1>


*Contributors*

A total of 18 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

   - @DWesl
   - Albert Steppi +
   - Bas van Beek
   - Charles Harris
   - Developer-Ecosystem-Engineering
   - Jake Vanderplas
   - Marten van Kerkwijk
   - Matti Picus
   - Melissa Weber Mendonça
   - Namami Shanker
   - Nathan Goldbaum
   - Ralf Gommers
   - Rohit Goswami
   - Sayed Adel
   - Sebastian Berg
   - Stefan van der Walt
   - Tyler Reddy
   - Warren Weckesser



*Build system changes*
In this release, NumPy has switched to Meson as the build system and
meson-python as the build backend. Installing NumPy or building a wheel can
be
done with standard tools like ``pip`` and ``pypa/build``. The following are
supported:

   - Regular installs: ``pip install numpy`` or (in a cloned repo) ``pip
   install .``
   - Building a wheel: ``python -m build`` (preferred), or ``pip wheel .``
   - Editable installs: ``pip install -e . --no-build-isolation``
   - Development builds through the custom CLI implemented with spin
   <https://github.com/scientific-python/spin>: ``spin build``.

All the regular ``pip`` and ``pypa/build`` flags (e.g.,
``--no-build-isolation``) should work as expected.


*NumPy-specific build customization*
Many of the NumPy-specific ways of customizing builds have changed.
The ``NPY_*`` environment variables which control BLAS/LAPACK, SIMD,
threading,
and other such options are no longer supported, nor is a ``site.cfg`` file
to
select BLAS and LAPACK. Instead, there are command-line flags that can be
passed to the build via ``pip``/``build``'s config-settings interface. These
flags are all listed in the ``meson_options.txt`` file in the root of the
repo.
Detailed documented will be available before the final 1.26.0 release; for
now,
please see `the SciPy building from source
<http://scipy.github.io/devdocs/building/index.html> docs since most build
customization
works in an almost identical way in SciPy as it does in NumPy.


*Build dependencies*
While the runtime dependencies of NumPy have not changed, the build
dependencies have. Because we temporarily vendor Meson and meson-python,
there are several new dependencies - please see the ``[build-system]``
section
of ``pyproject.toml`` for details.

*Troubleshooting*

This build system change is quite large. In case of unexpected issues, it is
still possible to use a ``setup.py``-based build as a temporary workaround
(on
Python 3.9-3.11, not 3.12), by copying ``pyproject.toml.setuppy`` to
``pyproject.toml``. However, please open an issue with details on the NumPy
issue tracker. We aim to phase out ``setup.py`` builds as soon as possible,
and
therefore would like to see all potential blockers surfaced early on in the
1.26.0 release cycle.


Cheers,

Charles Harris
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Member address: arch...@mail-archive.com

[Python-announce] NumPy 1.26.0b1 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.26.0b1. The NumPy 1.26.0 release is a continuation of the 1.25.x release
cycle with the addition of Python 3.12.0 support. Python 3.12 dropped
distutils, consequently supporting it required finding a replacement for
the setup.py/distutils based build system NumPy was using. We have chosen
to use the Meson build system instead, and this is the first NumPy release
supporting it. This is also the first release that supports Cython 3.0 in
addition to retaining 0.29.X compatibility. Supporting those two upgrades
was a large project, over 100 files have been touched in this release. The
changelog doesn't capture the full extent of the work, special thanks to
Ralf Gommers, Sayed Adel, Stéfan van der Walt, and Matti Picus who did much
of the work in the main development branch.

The highlights of this release are:

   - Python 3.12.0 support.
   - Cython 3.0.0 compatibility.
   - Use of the Meson build system
   - Updated SIMD support

The Python versions supported by this release are 3.9-3.12. Note that no 32
bit wheels are provided. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.26.0b1/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.26.0b1>.


*Contributors*

A total of 11 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.

   - Bas van Beek
   - Charles Harris
   - Matti Picus
   - Melissa Weber Mendonça
   - Ralf Gommers
   - Sayed Adel
   - Sebastian Berg
   - Stefan van der Walt
   - Tyler Reddy
   - Warren Weckesser


*Pull requests merged*

A total of 18 pull requests were merged for this release.

   - #24305: MAINT: Prepare 1.26.x branch for development
   - #24308: MAINT: Massive update of files from main for numpy 1.26
   - #24322: CI: fix wheel builds on the 1.26.x branch
   - #24326: BLD: update openblas to newer version
   - #24327: TYP: Trim down the ``_NestedSequence.__getitem__`` signature
   - #24328: BUG: fix choose refcount leak
   - #24337: TST: fix running the test suite in builds without BLAS/LAPACK
   - #24338: BUG: random: Fix generation of nan by dirichlet.
   - #24340: MAINT: Dependabot updates from main
   - #24342: MAINT: Add back NPY_RUN_MYPY_IN_TESTSUITE=1
   - #24353: MAINT: Update ``extbuild.py`` from main.
   - #24356: TST: fix distutils tests for deprecations in recent
   setuptools...
   - #24375: MAINT: Update cibuildwheel to version 2.15.0
   - #24381: MAINT: Fix codespaces setup.sh script
   - #24403: ENH: Vendor meson for multi-target build support
   - #24404: BLD: vendor meson-python to make the Windows builds with
   SIMD...
   - #24405: BLD, SIMD: The meson CPU dispatcher implementation
   - #24406: MAINT: Remove versioneer



*Build system changes*
In this release, NumPy has switched to Meson as the build system and
meson-python as the build backend. Installing NumPy or building a wheel can
be
done with standard tools like ``pip`` and ``pypa/build``. The following are
supported:

   - Regular installs: ``pip install numpy`` or (in a cloned repo) ``pip
   install .``
   - Building a wheel: ``python -m build`` (preferred), or ``pip wheel .``
   - Editable installs: ``pip install -e . --no-build-isolation``
   - Development builds through the custom CLI implemented with spin
   <https://github.com/scientific-python/spin>: ``spin build``.

All the regular ``pip`` and ``pypa/build`` flags (e.g.,
``--no-build-isolation``) should work as expected.


*NumPy-specific build customization*
Many of the NumPy-specific ways of customizing builds have changed.
The ``NPY_*`` environment variables which control BLAS/LAPACK, SIMD,
threading,
and other such options are no longer supported, nor is a ``site.cfg`` file
to
select BLAS and LAPACK. Instead, there are command-line flags that can be
passed to the build via ``pip``/``build``'s config-settings interface. These
flags are all listed in the ``meson_options.txt`` file in the root of the
repo.
Detailed documented will be available before the final 1.26.0 release; for
now
please see `the SciPy building from source
<http://scipy.github.io/devdocs/building/index.html> docs since most build
customization
works in an almost identical way in SciPy as it does in NumPy.


*Build dependencies*
While the runtime dependencies of NumPy have not changed, the build
dependencies have. Because we temporarily vendor Meson and meson-python,
there are several new dependencies - please see the ``[build-system]``
section
of ``pyproject.toml`` for details.

*Troubleshooting*

This build system change is quite large. In case of unexpected issues, it is
still possible to use a ``setup.py``-based build as a temporary workaround
(on
Python 3.9-3.11, not 3.12), by copying ``pyproject.toml.setuppy`` to
``pyproject.toml``. However, please open an issue with details on the NumPy
issue track

[Python-announce] NumPy 1.25.2 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.25.2. NumPy 1.25.2 is a maintenance release that fixes bugs and
regressions discovered after the 1.25.1 release. This is the last planned
release in the 1.25.x series, the next final release will be 1.26.0, which
will use the meson build system and support Python 3.12.

The Python versions supported by this release are 3.9-3.11 Note that 32 bit
wheels are only provided for Windows, all other wheels are 64 bits on
account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit
support. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.25.2/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.25.2>.


*Contributors*

A total of 13 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.

   - Aaron Meurer
   - Andrew Nelson
   - Charles Harris
   - Kevin Sheppard
   - Matti Picus
   - Nathan Goldbaum
   - Peter Hawkins
   - Ralf Gommers
   - Randy Eckenrode +
   - Sam James +
   - Sebastian Berg
   - Tyler Reddy
   - dependabot[bot]



*Pull requests merged*
A total of 19 pull requests were merged for this release.

   - #24148: MAINT: prepare 1.25.x for further development
   - #24174: ENH: Improve clang-cl compliance
   - #24179: MAINT: Upgrade various build dependencies.
   - #24182: BLD: use ``-ftrapping-math`` with Clang on macOS
   - #24183: BUG: properly handle negative indexes in ufunc_at fast path
   - #24184: BUG: PyObject_IsTrue and PyObject_Not error handling in
   setflags
   - #24185: BUG: histogram small range robust
   - #24186: MAINT: Update meson.build files from main branch
   - #24234: MAINT: exclude min, max and round from ``np.__all__``
   - #24241: MAINT: Dependabot updates
   - #24242: BUG: Fix the signature for np.array_api.take
   - #24243: BLD: update OpenBLAS to an intermediate commit
   - #24244: BUG: Fix reference count leak in str(scalar).
   - #24245: BUG: fix invalid function pointer conversion error
   - #24255: BUG: Factor out slow ``getenv`` call used for memory policy
   warning
   - #24292: CI: correct URL in cirrus.star [skip cirrus]
   - #24293: BUG: Fix C types in scalartypes
   - #24294: BUG: do not modify the input to ufunc_at
   - #24295: BUG: Further fixes to indexing loop and added tests


Cheers,

Charles Harris
___
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Member address: arch...@mail-archive.com

[Python-announce] NumPy 1.25.1 release

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.25.1. NumPy 1.25.1 is a maintenance release that fixes bugs discovered
after the 1.24.3 release and updates the build infrastructure to stay
current with upstream changes.

The Python versions supported by this release are 3.9-3.11 Note that 32 bit
wheels are only provided for Windows, all other wheels are 64 bits on
account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit
support. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.25.1/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.25.1>.


*Contributors*

A total of 10 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

   - Andrew Nelson
   - Charles Harris
   - Developer-Ecosystem-Engineering
   - Hood Chatham
   - Nathan Goldbaum
   - Rohit Goswami
   - Sebastian Berg
   - Tim Paine +
   - dependabot[bot]
   - matoro +



*Pull requests merged*
A total of 14 pull requests were merged for this release.

   - #23968: MAINT: prepare 1.25.x for further development
   - #24036: BLD: Port long double identification to C for meson
   - #24037: BUG: Fix reduction `return NULL` to be `goto fail`
   - #24038: BUG: Avoid undefined behavior in array.astype()
   - #24039: BUG: Ensure `__array_ufunc__` works without any kwargs passed
   - #24117: MAINT: Pin urllib3 to avoid anaconda-client bug.
   - #24118: TST: Pin pydantic<2 in Pyodide workflow
   - #24119: MAINT: Bump pypa/cibuildwheel from 2.13.0 to 2.13.1
   - #24120: MAINT: Bump actions/checkout from 3.5.2 to 3.5.3
   - #24122: BUG: Multiply or Divides using SIMD without a full vector
   can\...
   - #24127: MAINT: testing for IS_MUSL closes #24074
   - #24128: BUG: Only replace dtype temporarily if dimensions changed
   - #24129: MAINT: Bump actions/setup-node from 3.6.0 to 3.7.0
   - #24134: BUG: Fix private procedures in f2py modules

Cheers,

Charles Harris
___
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Member address: arch...@mail-archive.com

[Python-announce] Release of NumPy 1.24. 4

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.24.4. NumPy 1.24.4 is a maintenance release that fixes a few bugs
discovered after the 1.24.3 release.

The Python versions supported by this release are 3.8-3.11 Note that 32 bit
wheels are only provided for Windows, all other wheels are 64 bits on
account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit
support. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.24.4/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.24.4>.


*Contributors*

A total of 4 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.

   - Bas van Beek
   - Charles Harris
   - Sebastian Berg
   - Hongyang Peng +



*Pull requests merged*
A total of 6 pull requests were merged for this release.

   - #23720: MAINT, BLD: Pin rtools to version 4.0 for Windows builds.
   - #23739: BUG: fix the method for checking local files for 1.24.x
   - #23760: MAINT: Copy rtools installation from install-rtools.
   - #23761: BUG: Fix masked array ravel order for A (and somewhat K)
   - #23890: TYP,DOC: Annotate and document the ``metadata`` parameter of...
   - #23994: MAINT: Update rtools installation


Cheers,

Charles Harris
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Member address: arch...@mail-archive.com

[Python-announce] NumPy 1.25.0 released

[Charles R Harris]

Hi All,

The NumPy 1.25.0 release continues the ongoing work to improve the handling
and promotion of dtypes, increase execution speed, and clarify the
documentation. There has also been work to prepare for the future NumPy
2.0.0
release, resulting in a large number of new and expired deprecation.
Highlights are:

   - Support for MUSL, there are now MUSL wheels.
   - Support the Fujitsu C/C++ compiler.
   - Object arrays are now supported in einsum
   - Support for inplace matrix multiplication (@=).

We will release NumPy 1.26 on top of the 1.25 code when Python 12 reaches
the rc stage. That is needed because distutils has been dropped by Python
12 and we will be switching to using meson for future builds. The next
mainline release will be NumPy 2.0.0. We plan that the 2.0 series will
still support downstream projects built against earlier
versions of NumPy.

The Python versions supported in this release are 3.9-3.11. Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.25.0/>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.25.0>.

*Contributors*

A total of 148 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.

   - @DWesl
   - @partev +
   - @pierreloicq +
   - @pkubaj +
   - @pmvz +
   - @pratiklp00 +
   - @tajbinjohn +
   - A Chethan Reddy +
   - Aaron Meurer
   - Aleksei Nikiforov +
   - Alex Rogozhnikov
   - Alexander Heger
   - Alexander Neumann +
   - Andrew Nelson
   - Arun Kota +
   - Bas van Beek
   - Ben Greiner +
   - Berke Kocaoğlu +
   - Bob Eldering
   - Brian Soto
   - Brian Walshe +
   - Brock Mendel
   - Charles Harris
   - Charles Young +
   - Chris Brown
   - Chris Sidebottom +
   - Christian Lorentzen +
   - Christian Veenhuis +
   - Christopher Sidebottom +
   - Chun-Wei Chen +
   - Clément Robert
   - Cédric Hannotier +
   - Daiki Shintani +
   - Daniel McCloy +
   - Derek Homeier
   - Developer-Ecosystem-Engineering
   - DhavalParmar61 +
   - Dimitri Papadopoulos Orfanos
   - Dmitry Belov +
   - Dominic Davis-Foster +
   - Eddie Darling +
   - Edward E +
   - Eero Vaher
   - Eric Wieser
   - Eugene Kim +
   - Evgeni Burovski
   - Facundo Batista +
   - Francesc Elies +
   - Ganesh Kathiresan
   - Hongyang Peng +
   - Hood Chatham
   - Ikko Ashimine
   - Ikko Eltociear Ashimine +
   - Inessa Pawson
   - Irit Katriel
   - Ivan Gonzalez
   - JP Ungaretti +
   - Jarrod Millman
   - Jean-François B +
   - Johana De La Rosa +
   - Johnson Sun +
   - Jonathan Kohler +
   - Jory Klaverstijn +
   - Joyce Brum +
   - Jules Kouatchou +
   - Kentaro Kawakami +
   - Khem Raj +
   - Kyle Sunden
   - Larry Bradley
   - Lars Grüter
   - Laurenz Kremeyer +
   - Lee Johnston +
   - Lefteris Loukas +
   - Leona Taric
   - Lillian Zha
   - Lu Yun Chi +
   - Malte Londschien +
   - Manuchehr Aminian +
   - Mariusz Felisiak
   - Mark Harfouche
   - Mark J. Olson +
   - Marko Pacak +
   - Marten van Kerkwijk
   - Matteo Raso
   - Matthew Muresan +
   - Matti Picus
   - Meekail Zain
   - Melissa Weber Mendonça
   - Michael Kiffer +
   - Michael Lamparski
   - Michael Siebert
   - Michail Gaganis +
   - Mike Toews
   - Mike-gag +
   - Miki Watanabe
   - Miki Watanabe (渡邉 美希)
   - Miles Cranmer
   - Muhammad Ishaque Nizamani +
   - Mukulika Pahari
   - Nathan Goldbaum
   - Nico Schlömer
   - Norwid Behrnd +
   - Noé Rubinstein +
   - Oleksandr Pavlyk
   - Oscar Gustafsson
   - Pamphile Roy
   - Panagiotis Zestanakis +
   - Paul Romano +
   - Paulo Almeida +
   - Pedro Lameiras +
   - Peter Hawkins
   - Pey Lian Lim
   - Peyton Murray +
   - Philip Holzmann +
   - Pierre Blanchard +
   - Pieter Eendebak
   - Pradipta Ghosh
   - Pratyay Banerjee +
   - Prithvi Singh +
   - Raghuveer Devulapalli
   - Ralf Gommers
   - Richie Cotton +
   - Robert Kern
   - Rohit Goswami
   - Ross Barnowski
   - Roy Smart +
   - Rustam Uzdenov +
   - Sadi Gulcelik +
   - Sarah Kaiser +
   - Sayed Adel
   - Sebastian Berg
   - Simon Altrogge +
   - Somasree Majumder
   - Stefan Behnel
   - Stefan van der Walt
   - Stefanie Molin
   - StepSecurity Bot +
   - Syam Gadde +
   - Sylvain Ferriol +
   - Talha Mohsin +
   - Taras Tsugrii +
   - Thomas A Caswell
   - Tyler Reddy
   - Warren Weckesser
   - Will Tirone +
   - Yamada Fuyuka +
   - Younes Sandi +
   - Yuki K +

Cheers,

Charles Harris
___
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Member address: arch...@mail-archive.com

[Python-announce] NumPy 1.25.0rc1 released

[Charles R Harris]

Hi All,

The NumPy 1.25.0 release continues the ongoing work to improve the handling
and promotion of dtypes, increase the execution speed, and clarify the
documentation. There has also been work to prepare for the future NumPy
2.0.0
release, resulting in a large number of new and expired deprecation.
Highlights are:

   - Support for MUSL, there are now MUSL wheels.
   - Support the Fujitsu C/C++ compiler.
   - Object arrays are now supported in einsum
   - Support for inplace matrix multiplication (@=).

We will release NumPy 1.26 on top of the 1.25 code when Python 12 reaches
the rc stage. That is needed because distutils has been dropped by Python
12 and we will be switching to using meson for future builds. The next
mainline release will be NumPy 2.0.0. We plan that the 2.0 series will
still support downstream projects built against earlier
versions of NumPy.

The Python versions supported in this release are 3.9-3.11. Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.25.0rc1/>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.25.0rc1>.

*Contributors*

A total of 145 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.

   - @DWesl
   - @partev +
   - @pierreloicq +
   - @pkubaj +
   - @pmvz +
   - @tajbinjohn +
   - A Chethan Reddy +
   - Aaron Meurer
   - Aleksei Nikiforov +
   - Alex Rogozhnikov
   - Alexander Heger
   - Alexander Neumann +
   - Andrew Nelson
   - Arun Kota +
   - Bas van Beek
   - Ben Greiner +
   - Berke Kocaoğlu +
   - Bob Eldering
   - Brian Soto
   - Brock Mendel
   - Charles Harris
   - Charles Young +
   - Chris Brown
   - Chris Sidebottom +
   - Christian Lorentzen +
   - Christian Veenhuis +
   - Christopher Sidebottom +
   - Chun-Wei Chen +
   - Clément Robert
   - Cédric Hannotier +
   - Daiki Shintani +
   - Daniel McCloy +
   - Derek Homeier
   - Developer-Ecosystem-Engineering
   - DhavalParmar61 +
   - Dimitri Papadopoulos Orfanos
   - Dmitry Belov +
   - Dominic Davis-Foster +
   - Eddie Darling +
   - Edward E +
   - Eero Vaher
   - Eric Wieser
   - Eugene Kim +
   - Evgeni Burovski
   - Facundo Batista +
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   - Ganesh Kathiresan
   - Hongyang Peng +
   - Hood Chatham
   - Ikko Ashimine
   - Ikko Eltociear Ashimine +
   - Inessa Pawson
   - Irit Katriel
   - Ivan Gonzalez
   - JP Ungaretti +
   - Jarrod Millman
   - Jean-François B +
   - Johana De La Rosa +
   - Johnson Sun +
   - Jonathan Kohler +
   - Jory Klaverstijn +
   - Joyce Brum +
   - Jules Kouatchou +
   - Kentaro Kawakami +
   - Khem Raj +
   - Kyle Sunden
   - Larry Bradley
   - Lars Grüter
   - Laurenz Kremeyer +
   - Lee Johnston +
   - Lefteris Loukas +
   - Leona Taric
   - Lillian Zha
   - Lu Yun Chi +
   - Malte Londschien +
   - Manuchehr Aminian +
   - Mariusz Felisiak
   - Mark Harfouche
   - Mark J. Olson +
   - Marko Pacak +
   - Marten van Kerkwijk
   - Matteo Raso
   - Matthew Muresan +
   - Matti Picus
   - Meekail Zain
   - Melissa Weber Mendonça
   - Michael Kiffer +
   - Michael Lamparski
   - Michael Siebert
   - Michail Gaganis +
   - Mike Toews
   - Mike-gag +
   - Miki Watanabe
   - Miki Watanabe (渡邉 美希)
   - Miles Cranmer
   - Muhammad Ishaque Nizamani +
   - Mukulika Pahari
   - Nathan Goldbaum
   - Nico Schlömer
   - Norwid Behrnd +
   - Noé Rubinstein +
   - Oleksandr Pavlyk
   - Oscar Gustafsson
   - Pamphile Roy
   - Panagiotis Zestanakis +
   - Paul Romano +
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   - Peter Hawkins
   - Peyton Murray +
   - Philip Holzmann +
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   - Pieter Eendebak
   - Pradipta Ghosh
   - Pratyay Banerjee +
   - Prithvi Singh +
   - Raghuveer Devulapalli
   - Ralf Gommers
   - Richie Cotton +
   - Robert Kern
   - Rohit Goswami
   - Ross Barnowski
   - Roy Smart +
   - Rustam Uzdenov +
   - Sadi Gulcelik +
   - Sarah Kaiser +
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   - Stefan Behnel
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   - StepSecurity Bot +
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   - Thomas A Caswell
   - Tyler Reddy
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   - Will Tirone +
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   - Yuki K +

Cheers,

Charles Harris
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[Python-announce] NumPy 1.24.3 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.24.3. NumPy 1.24.3 is a maintenance release that fixes bugs and
regressions discovered after the 1.24.2 release.

The Python versions supported by this release are 3.8-3.11 Note that 32 bit
wheels are only provided for Windows, all other wheels are 64 bits on
account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit
support. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.24.3/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.24.3>.

*Contributors*

A total of 12 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.

   - Aleksei Nikiforov +
   - Alexander Heger
   - Bas van Beek
   - Bob Eldering
   - Brock Mendel
   - Charles Harris
   - Kyle Sunden
   - Peter Hawkins
   - Rohit Goswami
   - Sebastian Berg
   - Warren Weckesser
   - dependabot[bot]



*Pull requests merged*
A total of 17 pull requests were merged for this release.

   - #23206: BUG: fix for f2py string scalars (#23194)
   - #23207: BUG: datetime64/timedelta64 comparisons return NotImplemented
   - #23208: MAINT: Pin matplotlib to version 3.6.3 for refguide checks
   - #23221: DOC: Fix matplotlib error in documentation
   - #23226: CI: Ensure submodules are initialized in gitpod.
   - #23341: TYP: Replace duplicate reduce in ufunc type signature with
   reduceat.
   - #23342: TYP: Remove duplicate CLIP/WRAP/RAISE in ``__init__.pyi``.
   - #23343: TYP: Mark ``d`` argument to fftfreq and rfftfreq as optional...
   - #23344: TYP: Add type annotations for comparison operators to
   MaskedArray.
   - #23345: TYP: Remove some stray type-check-only imports of ``msort``
   - #23370: BUG: Ensure like is only stripped for ``like=`` dispatched
   functions
   - #23543: BUG: fix loading and storing big arrays on s390x
   - #23544: MAINT: Bump larsoner/circleci-artifacts-redirector-action
   - #23634: BUG: Ignore invalid and overflow warnings in masked setitem
   - #23635: BUG: Fix masked array raveling when ``order="A"`` or
   ``order="K"``
   - #23636: MAINT: Update conftest for newer hypothesis versions
   - #23637: BUG: Fix bug in parsing F77 style string arrays.


Cheers,

Charles Harris
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/28/2023 1:50 PM, a a wrote:

On Tuesday, 28 March 2023 at 18:12:40 UTC+2, Thomas Passin wrote:

On 3/28/2023 8:47 AM, a a wrote:

Ok, I can export bookmarks to html file and open it in Firefox to get
a long list of clickable urls but icon of the bookmarked web page is missing.

When I open Bookmarks as right a side-bar I can view and identify an individual 
Boomarks by icon,
so I would like Firefox Library to export Bookmarks to html file, icons 
included 

Since accessing opened Tabs is my default use of history in Firefox and has 
worked fine for years
I paid no special interest to bookmark opened Tabs and assign labels to 
individual bookmark.

So, generally speaking, I am happy with 1,000+ opened Tabs in Firefox , not 
being sure if this number is for real or refers to every bookmark from the 
history + opened Tabs

But definitely I need a smarter solution and approach to manage 10,000+ opened 
Tabs in Firefox in a future 


I think you had better start using another name for this thread, if it
continues.

The HTML export file will contain the icons, but the HTML elements do
not provide for showing them.

I can't imagine how you can find anything among nor navigate through
1000 open tabs, let alone 10,000 in the future. I would think the memory
usage would be impossibly high. So I hope you are mostly using the
history and do not really have that many tabs open at once!



I am a plain guy, so if Firefox counted 1,000+ opened Tabs, I can be surprised, 
but have no idea how to check that number.

You are exactly right, icon URI and icon data come with saved opened Tabs,
a single example below.

So I am going to ask Firefox team to offer
export to html, modified to have :
icon, name of web page, url address
to appear in a single row (feature already supported by Firefox, when you open 
new Tab
and click: enter URL or search string - input field,
you get such list
List is limited in size for the reasons unknown to me, but feature works fine.


You should be aware that the HTML format for bookmarks is a standard 
developed back in the day by Netscape. It goes back to the early 1990s, 
I think. The FF folks will not be modifying it, since all browsers know 
how to generate it and consume it, and who knows how many software 
packages consume it.  No one can afford to have a change, even one 
that's supposed to be harmless, inadvertently break software that's 
worked for years.


They are going to need a lot of persuading.

Maybe there's something else they would be willing and able to do.  But 
you can expect that any proposed new feature will probably need to have 
some strong support.  Raymond Chen at Microsoft has written how each new 
feature proposal starts off with -100 points.  Only if the advantages 
get the score up above zero can the feature have any chance of getting 
adopted - and then it has to compete with other potential features that 
have their own scores.




--
So would prefer a horizontal list of opened Tabs
by htmlized, vertical list of the same opened Tabs,
featuring:
icon, name of web-site, URL address

Thank you for your excellent support


You're welcome.

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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Tuesday, 28 March 2023 at 18:12:40 UTC+2, Thomas Passin wrote:
> On 3/28/2023 8:47 AM, a a wrote: 
> > Ok, I can export bookmarks to html file and open it in Firefox to get 
> > a long list of clickable urls but icon of the bookmarked web page is 
> > missing. 
> > 
> > When I open Bookmarks as right a side-bar I can view and identify an 
> > individual Boomarks by icon,
> > so I would like Firefox Library to export Bookmarks to html file, icons 
> > included 
> > 
> > Since accessing opened Tabs is my default use of history in Firefox and has 
> > worked fine for years 
> > I paid no special interest to bookmark opened Tabs and assign labels to 
> > individual bookmark. 
> > 
> > So, generally speaking, I am happy with 1,000+ opened Tabs in Firefox , not 
> > being sure if this number is for real or refers to every bookmark from the 
> > history + opened Tabs 
> >
> > But definitely I need a smarter solution and approach to manage 10,000+ 
> > opened Tabs in Firefox in a future  
> 
> I think you had better start using another name for this thread, if it 
> continues. 
> 
> The HTML export file will contain the icons, but the HTML elements do 
> not provide for showing them. 
> 
> I can't imagine how you can find anything among nor navigate through 
> 1000 open tabs, let alone 10,000 in the future. I would think the memory 
> usage would be impossibly high. So I hope you are mostly using the 
> history and do not really have that many tabs open at once!


I am a plain guy, so if Firefox counted 1,000+ opened Tabs, I can be surprised, 
but have no idea how to check that number.

You are exactly right, icon URI and icon data come with saved opened Tabs,
a single example below.

So I am going to ask Firefox team to offer
export to html, modified to have :
icon, name of web page, url address
to appear in a single row (feature already supported by Firefox, when you open 
new Tab
and click: enter URL or search string - input field,
you get such list
List is limited in size for the reasons unknown to me, but feature works fine.

--
So would prefer a horizontal list of opened Tabs
by htmlized, vertical list of the same opened Tabs,
featuring:
icon, name of web-site, URL address

Thank you for your excellent support

darius


"https://www.nasa.gov/feature/goddard/2017/nasas-sdo-watches-a-sunspot-turn-toward-earth;
 add_date="1499899506" last_modified="1499899507" 
icon_uri="https://www.nasa.gov/favicon.ico; 

Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/28/2023 8:47 AM, a a wrote:

Ok, I can export bookmarks to html file and open it in Firefox to get
a long list of clickable urls but icon of the bookmarked web page is missing.

When I open Bookmarks as right a side-bar I can view and identify an individual 
Boomarks by icon,
so I would like Firefox Library to export Bookmarks to html file, icons 
included 

Since accessing opened Tabs is my default use of history in Firefox and has 
worked fine for years
I paid no special interest to bookmark opened Tabs and assign labels to 
individual bookmark.

So, generally speaking, I am happy with 1,000+ opened Tabs in Firefox , not 
being sure if this number is for real or refers to every bookmark from the 
history + opened Tabs

But definitely I need a smarter solution and approach to manage 10,000+ opened 
Tabs in Firefox in a future 


I think you had better start using another name for this thread, if it 
continues.


The HTML export file will contain the icons, but the HTML elements do 
not provide for showing them.


I can't imagine how you can find anything among nor navigate through 
1000 open tabs, let alone 10,000 in the future. I would think the memory 
usage would be impossibly high.  So I hope you are mostly using the 
history and do not really have that many tabs open at once!


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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Tuesday, 28 March 2023 at 06:33:44 UTC+2, Thomas Passin wrote:
> On 3/27/2023 8:37 PM, a a wrote: 
> >> To save the tabs, right click any one of them and select the "Select All 
> >> Tabs" item. They will all highlight. Right click on one of them and 
> >> select the "Bookmark Tabs" item. A dialog box will open with an entry 
> >> lone for the Name to use (like "Tabset1") and a location - a bookmark 
> >> folder - for them to go into. CAREFUL - if you just click "Save", you 
> >> may not be able to find them. Use the dropdown arrow to save them in 
> >> one of the top level folders, like "Bookmarks Toolbars". 
> > I can select All Opened Tabs (as from the given link) 
> > and get 1,000+ Opened Tabs ( I am afraid, this is s number of all saved 
> > bookmarks in the past) 
> > I go to menu, Bookmarks, Manage Boomarks and copy Tabs 
> > 
> > and 
> > https://www.textfixer.com/html/convert-url-to-html-link.php 
> > 
> > does the job, converting text urls into clickable web links 
> > 
> > I copy the result and past into Notepad++ to save file as html 
> > 
> > and what I get is web page of clickable Opened Tabs 
> > 
> > since icon and page name are lost
> I don't understand this. You don't really have 1000 tabs open at the 
> same time, do you? If you select all the open tabs - I think you wrote 
> that you only have 50 - then you can save them as bookmarks under a 
> folder name you choose. That folder will contain the 50 open links. I 
> tried it this evening, so I know that's how it works. (It happens that 
> I'm working on my own bookmark manager just now, so I've been messing 
> around with importing, exporting, and reading the bookmark files). 
> 
> Then you can export them and import the same bookmark file into another 
> browser on another computer. Whenever you want to reopen some of those 
> tabs, you would navigate to that part of the bookmarks and open the tabs 
> you want. 
> 
> Maybe you have something else in mind? Do you want to send the links of 
> the opened tab set to someone else, but not all your bookmarks? Please 
> explain more carefully what you want to do.

Ok, I can export bookmarks to html file and open it in Firefox to get
a long list of clickable urls but icon of the bookmarked web page is missing.

When I open Bookmarks as right a side-bar I can view and identify an individual 
Boomarks by icon,
so I would like Firefox Library to export Bookmarks to html file, icons 
included ;)

Since accessing opened Tabs is my default use of history in Firefox and has 
worked fine for years
I paid no special interest to bookmark opened Tabs and assign labels to 
individual bookmark.

So, generally speaking, I am happy with 1,000+ opened Tabs in Firefox , not 
being sure if this number is for real or refers to every bookmark from the 
history + opened Tabs

But definitely I need a smarter solution and approach to manage 10,000+ opened 
Tabs in Firefox in a future ;)

- I just build personal search engine resembling targets set by MyLifeBits 
Project by Microsoft in the past.

darius
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Tuesday, 28 March 2023 at 06:33:44 UTC+2, Thomas Passin wrote:
> On 3/27/2023 8:37 PM, a a wrote: 
> >> To save the tabs, right click any one of them and select the "Select All 
> >> Tabs" item. They will all highlight. Right click on one of them and 
> >> select the "Bookmark Tabs" item. A dialog box will open with an entry 
> >> lone for the Name to use (like "Tabset1") and a location - a bookmark 
> >> folder - for them to go into. CAREFUL - if you just click "Save", you 
> >> may not be able to find them. Use the dropdown arrow to save them in 
> >> one of the top level folders, like "Bookmarks Toolbars". 
> > I can select All Opened Tabs (as from the given link) 
> > and get 1,000+ Opened Tabs ( I am afraid, this is s number of all saved 
> > bookmarks in the past) 
> > I go to menu, Bookmarks, Manage Boomarks and copy Tabs 
> > 
> > and 
> > https://www.textfixer.com/html/convert-url-to-html-link.php 
> > 
> > does the job, converting text urls into clickable web links 
> > 
> > I copy the result and past into Notepad++ to save file as html 
> > 
> > and what I get is web page of clickable Opened Tabs 
> > 
> > since icon and page name are lost
> I don't understand this. You don't really have 1000 tabs open at the 
> same time, do you? If you select all the open tabs - I think you wrote 
> that you only have 50 - then you can save them as bookmarks under a 
> folder name you choose. That folder will contain the 50 open links. I 
> tried it this evening, so I know that's how it works. (It happens that 
> I'm working on my own bookmark manager just now, so I've been messing 
> around with importing, exporting, and reading the bookmark files). 
> 
> Then you can export them and import the same bookmark file into another 
> browser on another computer. Whenever you want to reopen some of those 
> tabs, you would navigate to that part of the bookmarks and open the tabs 
> you want. 
> 
> Maybe you have something else in mind? Do you want to send the links of 
> the opened tab set to someone else, but not all your bookmarks? Please 
> explain more carefully what you want to do.

Ok, I was not aware of the real number of the opened Tabs in Firefox, since I 
can jump from left to right and vice versa in real time, so the number given by 
me: 50 opened Tabs was my general estimate, but I can read the real number of 
opened Tabs from the same menu (line below) to be 1,000+

What I copy and paste into Notepad++ is 1,000+ -line file.
It's hard to verify if the above number is made of opened Tabs only or 
bookmarks are included, 
since I exactly use and keep multi Tabs opened as my live bookmarks and cache 
memory, when I work on my projects (watching, counting sunspots,   Earthquakes 
prediction in Turkey, ... )

I would like to fund the development of such smart Tabs Manager to replace 
boomarks, to let me group Tabs belonging to different projects.

It doesn't look to be complicated, if supported by the Firefox team.

Firefox 97. comes with alike functionality (when I open a new Tab)  but limited 
to 4 rows of web-page icons + names and 4 rows called: Recent activity

All I need is to replace opened Tabs by history of the Recent activity - 
default Firefox page, when I open a new Tab

It's hard to imagine, I can have 1,000+ Tabs live opened in Firefox
but I really need such feature, called in the past as: MyLifeBits by MS

So I have to ask Firefox team today  to lift 4 rows limit on web links and 4 
rows limit on the recent activity, coming with
New Tab opened


When I am busy on a project I can open 100+ web pages via search engine  in one 
day and would prefer
100+ opened Tabs to be saved in html format for the records as a reference.

Hope to get some support from Firefox team via Twitter.

Ok, smart bookmarks manager can offer the above functionality right now, so I 
go to search engine to get one.

darius

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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/27/2023 8:37 PM, a a wrote:

I can select All Opened Tabs (as from the given link)
and get 1,000+ Opened Tabs ( I am afraid, this is s number of all saved 
bookmarks in the past)
I go to menu, Bookmarks, Manage Boomarks and copy Tabs

and
https://www.textfixer.com/html/convert-url-to-html-link.php

does the job, converting text urls into clickable web links

I copy the result and past into Notepad++ to save file as html

and what I get is web page of clickable Opened Tabs
You can get that directly in Notepad++.  Load Firefox's HTML format 
bookmark file into Notepad++. View it in a browser using the "View/View 
Current File In" menu item. True, you could have opened it directly in 
the browser, but now you can edit the file and cut out the parts you 
don't need, and check to make sure you get what you intended. The 
structure of the HTML is very regular.


If you have saved your set of opened links under a distinctive heading 
near the top of the collection - as I suggested earlier - it should be 
easy to find the start and end points of their HTML elements, and delete 
all the ones you don't want. You could import this shortened bookmark 
file into another installation of Firefox and this would add them to the 
bookmarks in the other browser, all grouped by your custom heading.


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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/27/2023 8:37 PM, a a wrote:

To save the tabs, right click any one of them and select the "Select All
Tabs" item. They will all highlight. Right click on one of them and
select the "Bookmark Tabs" item. A dialog box will open with an entry
lone for the Name to use (like "Tabset1") and a location - a bookmark
folder - for them to go into. CAREFUL - if you just click "Save", you
may not be able to find them. Use the dropdown arrow to save them in
one of the top level folders, like "Bookmarks Toolbars".

I can select All Opened Tabs (as from the given link)
and get 1,000+ Opened Tabs ( I am afraid, this is s number of all saved 
bookmarks in the past)
I go to menu, Bookmarks, Manage Boomarks and copy Tabs

and
https://www.textfixer.com/html/convert-url-to-html-link.php

does the job, converting text urls into clickable web links

I copy the result and past into Notepad++ to save file as html

and what I get is web page of clickable Opened Tabs

since icon and page name are lost


I don't understand this. You don't really have 1000 tabs open at the 
same time, do you?  If you select all the open tabs - I think you wrote 
that you only have 50 - then you can save them as bookmarks under a 
folder name you choose. That folder will contain the 50 open links. I 
tried it this evening, so I know that's how it works. (It happens that 
I'm working on my own bookmark manager just now, so I've been messing 
around with importing, exporting, and reading the bookmark files).


Then you can export them and import the same bookmark file into another 
browser on another computer.  Whenever you want to reopen some of those 
tabs, you would navigate to that part of the bookmarks and open the tabs 
you want.


Maybe you have something else in mind?  Do you want to send the links of 
the opened tab set to someone else, but not all your bookmarks? Please 
explain more carefully what you want to do.


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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Tuesday, 28 March 2023 at 02:07:43 UTC+2, Thomas Passin wrote:
> On 3/27/2023 4:02 PM, Thomas Passin wrote:
> > On 3/27/2023 3:07 PM, a a wrote: 
> >> On Monday, 27 March 2023 at 19:19:41 UTC+2, Thomas Passin wrote: 
> >>> On 3/27/2023 10:07 AM, a a wrote: 
>  Ok, I know, I need to switch to Windows 10 run on another PC next to 
>  me. 
>  
>  I need to learn how to copy and move every web page opened in 
>  Firefox as a reference to social media, web sites for Python, chat 
>  and more (about 50 web pages live opened  
> >>> 
> >>> This sounds like you mean when you get a new Windows 10 PC, you will 
> >>> want to move your open tabs to the new machine. I see several 
> >>> possibilities for this. 
> >>> 
> >>> 1. Copy your Firefox profile folder to the new computer, and tell 
> >>> Firefox to use it as the default profile. I *think* this will include 
> >>> the open tabs, but I haven't tried it. Saving that folder is useful for 
> >>> backup anyway. (If you use Thunderbird for email, you really *must* 
> >>> back up its profile folder because all your email with its folder 
> >>> structure is there. BTW, you can even copy the profile over to a Linux 
> >>> machine that has Thunderbird, and presto, all your email will be there. 
> >>> The Firefox profile would probably transfer just as well). 
> >>> 
> >>> 2. Bookmark all your open tabs under a new heading "open tabs", then 
> >>> export the bookmarks. In the new machine, import them into Firefox 
> >>> there. They won't open in tabs, but it will be easy to find them and 
> >>> open them when you want to. You probably will want to copy over your 
> >>> bookmarks anyway, so this adds little effort. 
> >>> 
> >>> 3. There may be a specific record of open tabs that you can copy or 
> >>> export. I don't know about this but an internet search should help. 
> >>> 
> >>> Good luck. 
> >> 
> >> a nice solution comes from 
> >> 
> >> How to Copy URLs of All Open Tabs in Firefox 
> >> 
> >> https://www.howtogeek.com/723921/how-to-copy-urls-of-all-open-tabs-in-firefox/
> >>  
> >> 
> >> right clicking opened tab, all opened tabs can be selected 
> >> moving via menu to bookmarks/ booksmarks management 
> >> url bookmarks can be right-mouse clicked to copy urls 
> >> finally, urls can be pasted into Notepad++ 
> >> and saved as a file 
> >> unfortunately, saving as .html file 
> >> fails to generate html file with clickable web links 
> >> 
> >
> > Don't go pasting urls into a text file one by one.  Instead, do my #2 
> > above. That will import all the bookmarks, including the tabs you saved 
> > as bookmarks.  Then import the exported bookmark file into the new 
> > browser.  There's no reason to fuss around trying to get text copies of 
> > urls to open. 
> > 
> > For that matter, the exported bookmarks file is an HTML file and can be 
> > opened directly in a browser, with clickable links.
> All right, I think I've got the easiest way to go. You *can* bookmark 
> all the tabs at once - see below. Then, as I already proposed, export 
> the bookmarks and import them into Firefox on the new computer. 
> 
> To save the tabs, right click any one of them and select the "Select All 
> Tabs" item. They will all highlight. Right click on one of them and 
> select the "Bookmark Tabs" item. A dialog box will open with an entry 
> lone for the Name to use (like "Tabset1") and a location - a bookmark 
> folder - for them to go into. CAREFUL - if you just click "Save", you 
> may not be able to find them. Use the dropdown arrow to save them in 
> one of the top level folders, like "Bookmarks Toolbars".

I can select All Opened Tabs (as from the given link)
and get 1,000+ Opened Tabs ( I am afraid, this is s number of all saved 
bookmarks in the past)
I go to menu, Bookmarks, Manage Boomarks and copy Tabs

and 
https://www.textfixer.com/html/convert-url-to-html-link.php

does the job, converting text urls into clickable web links

I copy the result and past into Notepad++ to save file as html

and what I get is web page of clickable Opened Tabs

since icon and page name are lost 
I would prefer another solution already ofered by Firex to generate web page of 
recently visited web pages,
unfortunately coming with  limits on the number of visited
web pages,
so I contacted Firefox, Notepad++ for help
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/27/2023 4:02 PM, Thomas Passin wrote:

On 3/27/2023 3:07 PM, a a wrote:

On Monday, 27 March 2023 at 19:19:41 UTC+2, Thomas Passin wrote:

On 3/27/2023 10:07 AM, a a wrote:
Ok, I know, I need to switch to Windows 10 run on another PC next to 
me.


I need to learn how to copy and move every web page opened in 
Firefox as a reference to social media, web sites for Python, chat 
and more (about 50 web pages live opened 


This sounds like you mean when you get a new Windows 10 PC, you will
want to move your open tabs to the new machine. I see several
possibilities for this.

1. Copy your Firefox profile folder to the new computer, and tell
Firefox to use it as the default profile. I *think* this will include
the open tabs, but I haven't tried it. Saving that folder is useful for
backup anyway. (If you use Thunderbird for email, you really *must*
back up its profile folder because all your email with its folder
structure is there. BTW, you can even copy the profile over to a Linux
machine that has Thunderbird, and presto, all your email will be there.
The Firefox profile would probably transfer just as well).

2. Bookmark all your open tabs under a new heading "open tabs", then
export the bookmarks. In the new machine, import them into Firefox
there. They won't open in tabs, but it will be easy to find them and
open them when you want to. You probably will want to copy over your
bookmarks anyway, so this adds little effort.

3. There may be a specific record of open tabs that you can copy or
export. I don't know about this but an internet search should help.

Good luck.


a nice solution comes from

How to Copy URLs of All Open Tabs in Firefox

https://www.howtogeek.com/723921/how-to-copy-urls-of-all-open-tabs-in-firefox/

right clicking opened tab, all opened tabs can be selected
moving via menu to bookmarks/ booksmarks management
url bookmarks can be right-mouse clicked to copy urls
finally, urls can be pasted into Notepad++
and saved as a file
unfortunately, saving as .html file
fails to generate html file with clickable web links



Don't go pasting urls into a text file one by one.  Instead, do my #2 
above. That will import all the bookmarks, including the tabs you saved 
as bookmarks.  Then import the exported bookmark file into the new 
browser.  There's no reason to fuss around trying to get text copies of 
urls to open.


For that matter, the exported bookmarks file is an HTML file and can be 
opened directly in a browser, with clickable links.


All right, I think I've got the easiest way to go.  You *can* bookmark 
all the tabs at once - see below.  Then, as I already proposed, export 
the bookmarks and import them into Firefox on the new computer.


To save the tabs, right click any one of them and select the "Select All 
Tabs" item.  They will all highlight.  Right click on one of them and 
select the "Bookmark Tabs" item. A dialog box will open with an entry 
lone for the Name to use (like "Tabset1") and a location - a bookmark 
folder - for them to go into.  CAREFUL - if you just click "Save", you 
may not be able to find them.  Use the dropdown arrow to save them in 
one of the top level folders, like "Bookmarks Toolbars".


--
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/27/2023 3:07 PM, a a wrote:

On Monday, 27 March 2023 at 19:19:41 UTC+2, Thomas Passin wrote:

On 3/27/2023 10:07 AM, a a wrote:

Ok, I know, I need to switch to Windows 10 run on another PC next to me.

I need to learn how to copy and move every web page opened in Firefox as a 
reference to social media, web sites for Python, chat and more (about 50 web 
pages live opened 


This sounds like you mean when you get a new Windows 10 PC, you will
want to move your open tabs to the new machine. I see several
possibilities for this.

1. Copy your Firefox profile folder to the new computer, and tell
Firefox to use it as the default profile. I *think* this will include
the open tabs, but I haven't tried it. Saving that folder is useful for
backup anyway. (If you use Thunderbird for email, you really *must*
back up its profile folder because all your email with its folder
structure is there. BTW, you can even copy the profile over to a Linux
machine that has Thunderbird, and presto, all your email will be there.
The Firefox profile would probably transfer just as well).

2. Bookmark all your open tabs under a new heading "open tabs", then
export the bookmarks. In the new machine, import them into Firefox
there. They won't open in tabs, but it will be easy to find them and
open them when you want to. You probably will want to copy over your
bookmarks anyway, so this adds little effort.

3. There may be a specific record of open tabs that you can copy or
export. I don't know about this but an internet search should help.

Good luck.


a nice solution comes from

How to Copy URLs of All Open Tabs in Firefox

https://www.howtogeek.com/723921/how-to-copy-urls-of-all-open-tabs-in-firefox/

right clicking opened tab, all opened tabs can be selected
moving via menu to bookmarks/ booksmarks management
url bookmarks can be right-mouse clicked to copy urls
finally, urls can be pasted into Notepad++
and saved as a file
unfortunately, saving as .html file
fails to generate html file with clickable web links



Don't go pasting urls into a text file one by one.  Instead, do my #2 
above. That will import all the bookmarks, including the tabs you saved 
as bookmarks.  Then import the exported bookmark file into the new 
browser.  There's no reason to fuss around trying to get text copies of 
urls to open.


For that matter, the exported bookmarks file is an HTML file and can be 
opened directly in a browser, with clickable links.


--
https://mail.python.org/mailman/listinfo/python-list

Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Monday, 27 March 2023 at 19:19:41 UTC+2, Thomas Passin wrote:
> On 3/27/2023 10:07 AM, a a wrote: 
> > Ok, I know, I need to switch to Windows 10 run on another PC next to me. 
> >
> > I need to learn how to copy and move every web page opened in Firefox as a 
> > reference to social media, web sites for Python, chat and more (about 50 
> > web pages live opened  
> 
> This sounds like you mean when you get a new Windows 10 PC, you will 
> want to move your open tabs to the new machine. I see several 
> possibilities for this. 
> 
> 1. Copy your Firefox profile folder to the new computer, and tell 
> Firefox to use it as the default profile. I *think* this will include 
> the open tabs, but I haven't tried it. Saving that folder is useful for 
> backup anyway. (If you use Thunderbird for email, you really *must* 
> back up its profile folder because all your email with its folder 
> structure is there. BTW, you can even copy the profile over to a Linux 
> machine that has Thunderbird, and presto, all your email will be there. 
> The Firefox profile would probably transfer just as well). 
> 
> 2. Bookmark all your open tabs under a new heading "open tabs", then 
> export the bookmarks. In the new machine, import them into Firefox 
> there. They won't open in tabs, but it will be easy to find them and 
> open them when you want to. You probably will want to copy over your 
> bookmarks anyway, so this adds little effort. 
> 
> 3. There may be a specific record of open tabs that you can copy or 
> export. I don't know about this but an internet search should help. 
> 
> Good luck.

a nice solution comes from

How to Copy URLs of All Open Tabs in Firefox

https://www.howtogeek.com/723921/how-to-copy-urls-of-all-open-tabs-in-firefox/

right clicking opened tab, all opened tabs can be selected
moving via menu to bookmarks/ booksmarks management 
url bookmarks can be right-mouse clicked to copy urls
finally, urls can be pasted into Notepad++
and saved as a file
unfortunately, saving as .html file
fails to generate html file with clickable web links

Notepad++ keeps urls active, selectable but not ready to be opened in Firefox

so I need to learn how to make Notepad++ or another editor to save urls as
html file

BTW

Selecting all opened tabs I get 1,000+ active urls (opened web pages ), so 
something must be wrong
-- 
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/27/2023 10:07 AM, a a wrote:

Ok, I know, I need to switch to Windows 10 run on another PC next to me.

I need to learn how to copy and move every web page opened in Firefox as a 
reference to social media, web sites for Python, chat and more (about 50 web 
pages live opened 


This sounds like you mean when you get a new Windows 10 PC, you will 
want to move your open tabs to the new machine.  I see several 
possibilities for this.


1. Copy your Firefox profile folder to the new computer, and tell 
Firefox to use it as the default profile.  I *think* this will include 
the open tabs, but I haven't tried it.  Saving that folder is useful for 
backup anyway.  (If you use Thunderbird for email, you really *must* 
back up its profile folder because all your email with its folder 
structure is there. BTW, you can even copy the profile over to a Linux 
machine that has Thunderbird, and presto, all your email will be there. 
The Firefox profile would probably transfer just as well).


2. Bookmark all your open tabs under a new heading "open tabs", then 
export the bookmarks. In the new machine, import them into Firefox 
there.  They won't open in tabs, but it will be easy to find them and 
open them when you want to.  You probably will want to copy over your 
bookmarks anyway, so this adds little effort.


3. There may be a specific record of open tabs that you can copy or 
export.  I don't know about this but an internet search should help.


Good luck.
--
https://mail.python.org/mailman/listinfo/python-list

Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Thursday, 23 March 2023 at 22:15:10 UTC+1, Thomas Passin wrote:
> On 3/23/2023 3:38 PM, Mats Wichmann wrote: 
> > On 3/23/23 09:48, Thomas Passin wrote: 
> > 
> >> I didn't realize that Christoph Gohlke is still maintaining this site. 
> > 
> > Unless the the last-changed stuff stopped working, it's in a static state: 
> > 
> > by Christoph Gohlke. Updated on 26 June 2022 at 07:27 UTC
> I did see that. The OP needs a version that would work with Windows 7 
> and an older version of Python (3.7 or 3.8, IIRC), so things may work out.
Thank you Thomas for your excellent work you did for me.

Ok, I know, I need to switch to Windows 10 run on another PC next to me.

I need to learn how to copy and move every web page opened in Firefox as a 
reference to social media, web sites for Python, chat and more (about 50 web 
pages live opened ;)

I really love the limited functionality of w3schools to let me live open and 
run Python examples, especiallly Matplotlib examples.

Unfortunately chat forum at w3schools is low traffic, showing no interest to 
add more examples.


https://www.w3schools.com/python/trypython.asp?filename=demo_matplotlib_subplots3

https://www.w3schools.com/python/matplotlib_subplot.asp

thank you Thomas,

darius
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/23/2023 3:38 PM, Mats Wichmann wrote:

On 3/23/23 09:48, Thomas Passin wrote:

I didn't realize that Christoph Gohlke is still maintaining this site. 


Unless the the last-changed stuff stopped working, it's in a static state:

by Christoph Gohlke. Updated on 26 June 2022 at 07:27 UTC


I did see that.  The OP needs a version that would work with Windows 7 
and an older version of Python (3.7 or 3.8, IIRC), so things may work out.


--
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Mats Wichmann]

On 3/23/23 09:48, Thomas Passin wrote:

I didn't realize that Christoph Gohlke is still maintaining this site. 


Unless the the last-changed stuff stopped working, it's in a static state:

by Christoph Gohlke. Updated on 26 June 2022 at 07:27 UTC



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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/18/2023 3:05 PM, Thomas Passin wrote:

downloaded and run HWiNFO and AVE not supported, not greened out


That's too bad; you may be out of luck.  It's possible that someone
has compiled the .pyd library in such a way that it does not need the
 instruction set extensions.  I'm sorry but I don't know how to find
out except by trying internet searches - or by downgrading to earlier
 versions of Numpy hoping to find one that works and also can be used
by the other libraries/programs that need to use it.


Here's a possibility to try -

https://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy

"NumPy: a fundamental package needed for scientific computing with Python.

Numpy+MKL is linked to the Intel® Math Kernel Library and includes
required DLLs in the numpy.DLLs directory.

Numpy+Vanilla is a minimal distribution, which does not include any
optimized BLAS libray or C runtime DLLs."

I didn't realize that Christoph Gohlke is still maintaining this site. I 
haven't needed to use it since PyPi got so much more complete about 
packages with binary code. He has tons of binary packages for all kinds 
of Python libraries.  I think this one might work for you because it 
links to the Intel math library. So it may be able to use various or no 
instruction set extensions. If so, it could work with your old processor.


Worth trying, anyway.

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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/22/2023 8:09 AM, a a wrote:

On Saturday, 18 March 2023 at 20:12:22 UTC+1, Thomas Passin wrote:

On 3/17/2023 11:52 AM, a a wrote:

On Friday, 17 March 2023 at 16:32:53 UTC+1, a a wrote:

On Friday, 17 March 2023 at 16:03:14 UTC+1, Thomas Passin wrote:

On 3/16/2023 8:07 PM, a a wrote:

Crash report:

Problem Caption:
Problem Event Name: APPCRASH
Application name: python.exe
Application version: 3.8.7150.1013
Application time signature: 5fe0df5a
Error module name: _multiarray_umath.cp38-win32.pyd
Version of the module with the error: 0.0.0.0
Time signature of the module with the error: 63dfe4cf
Exception code: c01d
Exception offset: 000269c9
Operating system version: 6.1.7601.2.1.0.256.48
Regional Settings ID: 1045
Additional information 1: 0a9e
Additional information 2: 0a9e372d3b4ad19135b953a78882e789
Additional information 3: 0a9e
Additional information 4: 0a9e372d3b4ad19135b953a78882e789

This exception has been reported to have many causes, but one
possibility seems to be that your computer may not support an advanced
instruction set that the .pyd was compiled for. I found this one
specifically mentioned on the Internet: Advanced Vector Extensions. If
that were the case, you would either need to find a different version of
the module, or upgrade the computer/OS.

It would be worth trying to downgrade the multiarray version to an
earlier one and see if that fixes the problem.

Thank you Thomas
for your kind reply.

I am fully aware to be living on an old machine, old OS, Windows 7, 32-bit 
system
but I have visited every social chat support forum on the Internet: from Python 
to Matplotlib, Numpy, Twitter, Github.

As a newbie I am not aware how to downgrade "the multiarray version to an
earlier one

I simply tried to test Python code from


https://www.section.io/engineering-education/reading-and-processing-android-sensor-data-using-python-with-csv-read/


# Python program to read .csv file

import numpy as np
import matplotlib.pyplot as plt
import csv


"After importing the libraries, we now read the .csv file:

with open('accl1.csv', 'r') as f:
data = list(csv.reader(f, delimiter=',')) #reading csv file


Just read about AVE from Wikipedia

https://en.wikipedia.org/wiki/Advanced_Vector_Extensions



downloaded and run
HWiNFO
and AVE not supported, not greened out

That's too bad; you may be out of luck. It's possible that someone has
compiled the .pyd library in such a way that it does not need the
instruction set extensions. I'm sorry but I don't know how to find out
except by trying internet searches - or by downgrading to earlier
versions of Numpy hoping to find one that works and also can be used by
the other libraries/programs that need to use it.



Thank you Thomas for youre kind help.

You are the real Python PRO, you deserve Nobel Prize in Python.

:)


I operated an old Dell computer with Windows XP preinstalled
and upgraded XP to Windows 7 to get some web services to work.

Unfortunately I failed to find and install driver for video controller since 
none supported by Dell.

Visited many driver sites (Intel Driver Assistant included and more)
without any success.

So life with an old PC is not easy


I reused my 10-year-old Sony VAIO laptop (it had Windows 8, IIRC) to be 
a Linux machine - I got a 1T external solid state drive, set up the BIOS 
to boot from it, and installed Linux Mint.  If you are willing to tackle 
Linux, this might be a good way to go.  I recommend Mint for newcomers 
to Linux.  The computer is much snappier and pleasant to use than it was 
under Windows.


I mostly use it as a backup computer.  I had to to without my main 
computer for a week or so, and the old machine made a fine substitute. 
I even copied all my Thunderbird emails over and used email all the week 
without losing any messages. Actually, the keyboard on that old computer 
is much better than I've got on my new one, although a few keys are 
getting a little flaky.


I was able to compile some version of Python on it, though I forget why 
I needed to do that.  With this setup, you could install a newer version 
of Python, and Numpy would work - it might get compiled during 
installation, but that's not a problem.  It happens automatically.


If fact, I know that it works because I have Numpy working on the 
computer.  Of course, my computer has the instruction set extensions and 
your does not, so who knows if can be compiled for you.  But it would 
probably be your best bet.


Anyway, if you decide to try it out, let us know.  And if you hit any 
problems, I might be able to help you.  I'm not a Linux expert but I've 
installed various distributions maybe 20 times or more as virtual 
machines, and twice using an external drive, including running Tomcat 
and MySQL as services.  Once you get it installed and working, and 
learned some of its quirks (not too bad, mostly about installing 
programs and configuring the desktop to be more to your liking), it's 
not much diff

Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Saturday, 18 March 2023 at 20:12:22 UTC+1, Thomas Passin wrote:
> On 3/17/2023 11:52 AM, a a wrote: 
> > On Friday, 17 March 2023 at 16:32:53 UTC+1, a a wrote: 
> >> On Friday, 17 March 2023 at 16:03:14 UTC+1, Thomas Passin wrote: 
> >>> On 3/16/2023 8:07 PM, a a wrote: 
> >>>> Crash report: 
> >>>> 
> >>>> Problem Caption: 
> >>>> Problem Event Name: APPCRASH 
> >>>> Application name: python.exe 
> >>>> Application version: 3.8.7150.1013 
> >>>> Application time signature: 5fe0df5a 
> >>>> Error module name: _multiarray_umath.cp38-win32.pyd 
> >>>> Version of the module with the error: 0.0.0.0 
> >>>> Time signature of the module with the error: 63dfe4cf 
> >>>> Exception code: c01d 
> >>>> Exception offset: 000269c9 
> >>>> Operating system version: 6.1.7601.2.1.0.256.48 
> >>>> Regional Settings ID: 1045 
> >>>> Additional information 1: 0a9e 
> >>>> Additional information 2: 0a9e372d3b4ad19135b953a78882e789 
> >>>> Additional information 3: 0a9e 
> >>>> Additional information 4: 0a9e372d3b4ad19135b953a78882e789 
> >>> This exception has been reported to have many causes, but one 
> >>> possibility seems to be that your computer may not support an advanced 
> >>> instruction set that the .pyd was compiled for. I found this one 
> >>> specifically mentioned on the Internet: Advanced Vector Extensions. If 
> >>> that were the case, you would either need to find a different version of 
> >>> the module, or upgrade the computer/OS. 
> >>> 
> >>> It would be worth trying to downgrade the multiarray version to an 
> >>> earlier one and see if that fixes the problem. 
> >> Thank you Thomas 
> >> for your kind reply. 
> >> 
> >> I am fully aware to be living on an old machine, old OS, Windows 7, 32-bit 
> >> system 
> >> but I have visited every social chat support forum on the Internet: from 
> >> Python to Matplotlib, Numpy, Twitter, Github. 
> >> 
> >> As a newbie I am not aware how to downgrade "the multiarray version to an 
> >> earlier one 
> >> 
> >> I simply tried to test Python code from 
> >> 
> >> 
> >> https://www.section.io/engineering-education/reading-and-processing-android-sensor-data-using-python-with-csv-read/
> >>  
> >> 
> >>  
> >> # Python program to read .csv file 
> >> 
> >> import numpy as np 
> >> import matplotlib.pyplot as plt 
> >> import csv 
> >>  
> >> 
> >> "After importing the libraries, we now read the .csv file: 
> >> 
> >> with open('accl1.csv', 'r') as f: 
> >> data = list(csv.reader(f, delimiter=',')) #reading csv file 
> >> 
> >>  
> >> Just read about AVE from Wikipedia 
> >> 
> >> https://en.wikipedia.org/wiki/Advanced_Vector_Extensions 
> > 
> > 
> > downloaded and run 
> > HWiNFO 
> > and AVE not supported, not greened out
> That's too bad; you may be out of luck. It's possible that someone has 
> compiled the .pyd library in such a way that it does not need the 
> instruction set extensions. I'm sorry but I don't know how to find out 
> except by trying internet searches - or by downgrading to earlier 
> versions of Numpy hoping to find one that works and also can be used by 
> the other libraries/programs that need to use it.


Thank you Thomas for youre kind help.

You are the real Python PRO, you deserve Nobel Prize in Python.

I operated an old Dell computer with Windows XP preinstalled
and upgraded XP to Windows 7 to get some web services to work.

Unfortunately I failed to find and install driver for video controller since 
none supported by Dell.

Visited many driver sites (Intel Driver Assistant included and more)
without any success.

So life with an old PC is not easy



-- 
https://mail.python.org/mailman/listinfo/python-list

Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/17/2023 11:52 AM, a a wrote:

On Friday, 17 March 2023 at 16:32:53 UTC+1, a a wrote:

On Friday, 17 March 2023 at 16:03:14 UTC+1, Thomas Passin wrote:

On 3/16/2023 8:07 PM, a a wrote:

Crash report:

Problem Caption:
Problem Event Name: APPCRASH
Application name: python.exe
Application version: 3.8.7150.1013
Application time signature: 5fe0df5a
Error module name: _multiarray_umath.cp38-win32.pyd
Version of the module with the error: 0.0.0.0
Time signature of the module with the error: 63dfe4cf
Exception code: c01d
Exception offset: 000269c9
Operating system version: 6.1.7601.2.1.0.256.48
Regional Settings ID: 1045
Additional information 1: 0a9e
Additional information 2: 0a9e372d3b4ad19135b953a78882e789
Additional information 3: 0a9e
Additional information 4: 0a9e372d3b4ad19135b953a78882e789

This exception has been reported to have many causes, but one
possibility seems to be that your computer may not support an advanced
instruction set that the .pyd was compiled for. I found this one
specifically mentioned on the Internet: Advanced Vector Extensions. If
that were the case, you would either need to find a different version of
the module, or upgrade the computer/OS.

It would be worth trying to downgrade the multiarray version to an
earlier one and see if that fixes the problem.

Thank you Thomas
for your kind reply.

I am fully aware to be living on an old machine, old OS, Windows 7, 32-bit 
system
but I have visited every social chat support forum on the Internet: from Python 
to Matplotlib, Numpy, Twitter, Github.

As a newbie I am not aware how to downgrade "the multiarray version to an
earlier one

I simply tried to test Python code from


https://www.section.io/engineering-education/reading-and-processing-android-sensor-data-using-python-with-csv-read/


# Python program to read .csv file

import numpy as np
import matplotlib.pyplot as plt
import csv


"After importing the libraries, we now read the .csv file:

with open('accl1.csv', 'r') as f:
data = list(csv.reader(f, delimiter=',')) #reading csv file


Just read about AVE from Wikipedia

https://en.wikipedia.org/wiki/Advanced_Vector_Extensions



downloaded and run
HWiNFO
and AVE not supported, not greened out


That's too bad; you may be out of luck.  It's possible that someone has 
compiled the .pyd library in such a way that it does not need the 
instruction set extensions.  I'm sorry but I don't know how to find out 
except by trying internet searches - or by downgrading to earlier 
versions of Numpy hoping to find one that works and also can be used by 
the other libraries/programs that need to use it.


--
https://mail.python.org/mailman/listinfo/python-list

Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/17/2023 11:32 AM, a a wrote:

On Friday, 17 March 2023 at 16:03:14 UTC+1, Thomas Passin wrote:


It would be worth trying to downgrade the multiarray version to an 
earlier one and see if that fixes the problem.


Thank you Thomas for your kind reply.

I am fully aware to be living on an old machine, old OS, Windows 7,
32-bit system but I have visited every social chat support forum on
the Internet: from Python to Matplotlib, Numpy, Twitter, Github.

I mentioned the "multiarray" just because of its name in the error message:

"Error module name: _multiarray_umath.cp38-win32.pyd "

I assumed that the code you tried to run required an import from a
module or package whose name included "multiarray".  But I didn't try to
actually look for one.  Now I've checked, and I see it's included with
NumPy.


I simply tried to test Python code from


https://www.section.io/engineering-education/reading-and-processing-android-sensor-data-using-python-with-csv-read/

  # Python program to read .csv file

import numpy as np import matplotlib.pyplot as plt import csv 

"After importing the libraries, we now read the .csv file:

with open('accl1.csv', 'r') as f: data = list(csv.reader(f,
delimiter=',')) #reading csv file


You don't need numpy just to do this import, so you could remove the
numpy import just to test reading the csv file.  But I imagine you do
need numpy for later steps.



As a newbie I am not aware how to downgrade "the multiarray version
to an earlier one.
I just had to do this myself to work around a change in an import that 
broke one of my programs (not a numpy import).  If you can identify an 
earlier version that work - we will use proglib v 3.72 as an example - 
with pip you would use


python3 -m pip install --upgrade proglib<=3.72

To get exactly version 3.72, you would use "==3.72".

NOTE - no space allowed before the first "=" character!.
NOTE - you may need to type "python" or "py" instead of "python3".  Just 
use the one that runs the version of Python that you want to run.


To find which versions are available:

python3 -m pip install --upgrade proglib==

To find out which version you have installed on your computer:

python3 -m pip show numpy

After you downgrade to an earlier version, you can test it just by 
trying to import numpy.  In an interpreter session, just try to import it:


>>> import numpy

If that succeeds, chances are you will be all set.


 Just read about AVE from Wikipedia

https://en.wikipedia.org/wiki/Advanced_Vector_Extensions


I have read that there are other instruction set extensions that could 
be missing besides AVE, that could cause that exception code.  The fact 
that you have a relatively old computer suggests that could be the problem.


--
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Friday, 17 March 2023 at 16:03:14 UTC+1, Thomas Passin wrote:
> On 3/16/2023 8:07 PM, a a wrote: 
> > Crash report: 
> > 
> > Problem Caption: 
> > Problem Event Name: APPCRASH 
> > Application name: python.exe 
> > Application version: 3.8.7150.1013 
> > Application time signature: 5fe0df5a 
> > Error module name: _multiarray_umath.cp38-win32.pyd 
> > Version of the module with the error: 0.0.0.0 
> > Time signature of the module with the error: 63dfe4cf 
> > Exception code: c01d 
> > Exception offset: 000269c9 
> > Operating system version: 6.1.7601.2.1.0.256.48 
> > Regional Settings ID: 1045 
> > Additional information 1: 0a9e 
> > Additional information 2: 0a9e372d3b4ad19135b953a78882e789 
> > Additional information 3: 0a9e 
> > Additional information 4: 0a9e372d3b4ad19135b953a78882e789
> This exception has been reported to have many causes, but one 
> possibility seems to be that your computer may not support an advanced 
> instruction set that the .pyd was compiled for. I found this one 
> specifically mentioned on the Internet: Advanced Vector Extensions. If 
> that were the case, you would either need to find a different version of 
> the module, or upgrade the computer/OS. 
> 
> It would be worth trying to downgrade the multiarray version to an 
> earlier one and see if that fixes the problem.


Just reading from search engine:

https://www.bing.com/search?q=how+to+downgrade+_multiarray_umath.cp38-win32.pyd+=QBLH=-1=0=how+to+downgrade+_multiarray_umath.cp38-win32.pyd+=1-50=n=
-- 
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Friday, 17 March 2023 at 16:03:14 UTC+1, Thomas Passin wrote:
> On 3/16/2023 8:07 PM, a a wrote: 
> > Crash report: 
> > 
> > Problem Caption: 
> > Problem Event Name: APPCRASH 
> > Application name: python.exe 
> > Application version: 3.8.7150.1013 
> > Application time signature: 5fe0df5a 
> > Error module name: _multiarray_umath.cp38-win32.pyd 
> > Version of the module with the error: 0.0.0.0 
> > Time signature of the module with the error: 63dfe4cf 
> > Exception code: c01d 
> > Exception offset: 000269c9 
> > Operating system version: 6.1.7601.2.1.0.256.48 
> > Regional Settings ID: 1045 
> > Additional information 1: 0a9e 
> > Additional information 2: 0a9e372d3b4ad19135b953a78882e789 
> > Additional information 3: 0a9e 
> > Additional information 4: 0a9e372d3b4ad19135b953a78882e789
> This exception has been reported to have many causes, but one 
> possibility seems to be that your computer may not support an advanced 
> instruction set that the .pyd was compiled for. I found this one 
> specifically mentioned on the Internet: Advanced Vector Extensions. If 
> that were the case, you would either need to find a different version of 
> the module, or upgrade the computer/OS. 
> 
> It would be worth trying to downgrade the multiarray version to an 
> earlier one and see if that fixes the problem.

Thank you Thomas
for your kind reply.

I am fully aware to be living on an old machine, old OS, Windows 7, 32-bit 
system
but I have visited every social chat support forum on the Internet: from Python 
to Matplotlib, Numpy, Twitter, Github.

As a newbie I am not aware how to downgrade "the multiarray version to an 
 earlier one

I simply tried to test Python code from


https://www.section.io/engineering-education/reading-and-processing-android-sensor-data-using-python-with-csv-read/


# Python program to read .csv file

import numpy as np
import matplotlib.pyplot as plt
import csv


"After importing the libraries, we now read the .csv file:

with open('accl1.csv', 'r') as f:
data = list(csv.reader(f, delimiter=',')) #reading csv file


Just read about AVE from Wikipedia

https://en.wikipedia.org/wiki/Advanced_Vector_Extensions

-- 
https://mail.python.org/mailman/listinfo/python-list

Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

On Friday, 17 March 2023 at 16:32:53 UTC+1, a a wrote:
> On Friday, 17 March 2023 at 16:03:14 UTC+1, Thomas Passin wrote: 
> > On 3/16/2023 8:07 PM, a a wrote: 
> > > Crash report: 
> > > 
> > > Problem Caption: 
> > > Problem Event Name: APPCRASH 
> > > Application name: python.exe 
> > > Application version: 3.8.7150.1013 
> > > Application time signature: 5fe0df5a 
> > > Error module name: _multiarray_umath.cp38-win32.pyd 
> > > Version of the module with the error: 0.0.0.0 
> > > Time signature of the module with the error: 63dfe4cf 
> > > Exception code: c01d 
> > > Exception offset: 000269c9 
> > > Operating system version: 6.1.7601.2.1.0.256.48 
> > > Regional Settings ID: 1045 
> > > Additional information 1: 0a9e 
> > > Additional information 2: 0a9e372d3b4ad19135b953a78882e789 
> > > Additional information 3: 0a9e 
> > > Additional information 4: 0a9e372d3b4ad19135b953a78882e789 
> > This exception has been reported to have many causes, but one 
> > possibility seems to be that your computer may not support an advanced 
> > instruction set that the .pyd was compiled for. I found this one 
> > specifically mentioned on the Internet: Advanced Vector Extensions. If 
> > that were the case, you would either need to find a different version of 
> > the module, or upgrade the computer/OS. 
> > 
> > It would be worth trying to downgrade the multiarray version to an 
> > earlier one and see if that fixes the problem.
> Thank you Thomas 
> for your kind reply. 
> 
> I am fully aware to be living on an old machine, old OS, Windows 7, 32-bit 
> system 
> but I have visited every social chat support forum on the Internet: from 
> Python to Matplotlib, Numpy, Twitter, Github. 
> 
> As a newbie I am not aware how to downgrade "the multiarray version to an 
> earlier one 
> 
> I simply tried to test Python code from 
> 
> 
> https://www.section.io/engineering-education/reading-and-processing-android-sensor-data-using-python-with-csv-read/
>  
> 
>  
> # Python program to read .csv file 
> 
> import numpy as np 
> import matplotlib.pyplot as plt 
> import csv 
>  
> 
> "After importing the libraries, we now read the .csv file: 
> 
> with open('accl1.csv', 'r') as f: 
> data = list(csv.reader(f, delimiter=',')) #reading csv file 
> 
>  
> Just read about AVE from Wikipedia 
> 
> https://en.wikipedia.org/wiki/Advanced_Vector_Extensions


downloaded and run
HWiNFO
and AVE not supported, not greened out
-- 
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Re: Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[Thomas Passin]

On 3/16/2023 8:07 PM, a a wrote:

Crash report:

Problem Caption:
   Problem Event Name:  APPCRASH
   Application name: python.exe
   Application version: 3.8.7150.1013
   Application time signature:  5fe0df5a
   Error module name:   _multiarray_umath.cp38-win32.pyd
   Version of the module with the error:0.0.0.0
   Time signature of the module with the error: 63dfe4cf
   Exception code: c01d
   Exception offset:000269c9
   Operating system version:6.1.7601.2.1.0.256.48
   Regional Settings ID:1045
   Additional information 1: 0a9e
   Additional information 2: 0a9e372d3b4ad19135b953a78882e789
   Additional information 3: 0a9e
   Additional information 4: 0a9e372d3b4ad19135b953a78882e789


This exception has been reported to have many causes, but one 
possibility seems to be that your computer may not support an advanced 
instruction set that the .pyd was compiled for.  I found this one 
specifically mentioned on the Internet: Advanced Vector Extensions.  If 
that were the case, you would either need to find a different version of 
the module, or upgrade the computer/OS.


It would be worth trying to downgrade the multiarray version to an 
earlier one and see if that fixes the problem.

--
https://mail.python.org/mailman/listinfo/python-list

Numpy, Matplotlib crash Python 3.8 Windows 7, 32-bit - can you help ?

[a a]

Crash report:

Problem Caption:
  Problem Event Name:   APPCRASH
  Application name: python.exe
  Application version:  3.8.7150.1013
  Application time signature:   5fe0df5a
  Error module name:_multiarray_umath.cp38-win32.pyd
  Version of the module with the error: 0.0.0.0
  Time signature of the module with the error:  63dfe4cf
  Exception code: c01d
  Exception offset: 000269c9
  Operating system version: 6.1.7601.2.1.0.256.48
  Regional Settings ID: 1045
  Additional information 1: 0a9e
  Additional information 2: 0a9e372d3b4ad19135b953a78882e789
  Additional information 3: 0a9e
  Additional information 4: 0a9e372d3b4ad19135b953a78882e789
-- 
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[Python-announce] NumPy 1.24. 2 Release

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.24.2. NumPy 1.24.2 is a maintenance release that fixes bugs and
regressions discovered after the 1.24.1 release.

The Python versions supported by this release are 3.8-3.11 Note that 32 bit
wheels are only provided for Windows, all other wheels are 64 bits on
account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit
support. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.24.2/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.24.2>.


*Contributors*

A total of 14 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - Bas van Beek
   - Charles Harris
   - Khem Raj +
   - Mark Harfouche
   - Matti Picus
   - Panagiotis Zestanakis +
   - Peter Hawkins
   - Pradipta Ghosh
   - Ross Barnowski
   - Sayed Adel
   - Sebastian Berg
   - Syam Gadde +
   - dmbelov +
   - pkubaj +



*Pull requests merged*
A total of 17 pull requests were merged for this release.

   - #22965: MAINT: Update python 3.11-dev to 3.11.
   - #22966: DOC: Remove dangling deprecation warning
   - #22967: ENH: Detect CPU features on FreeBSD/powerpc64*
   - #22968: BUG: np.loadtxt cannot load text file with quoted fields
   separated...
   - #22969: TST: Add fixture to avoid issue with randomizing test order.
   - #22970: BUG: Fix fill violating read-only flag. (#22959)
   - #22971: MAINT: Add additional information to missing scalar
   AttributeError
   - #22972: MAINT: Move export for scipy arm64 helper into main module
   - #22976: BUG, SIMD: Fix spurious invalid exception for sin/cos on
   arm64/clang
   - #22989: BUG: Ensure correct loop order in sin, cos, and arctan2
   - #23030: DOC: Add version added information for the strict parameter
   in...
   - #23031: BUG: use ``_Alignof`` rather than ``offsetof()`` on most
   compilers
   - #23147: BUG: Fix for npyv__trunc_s32_f32 (VXE)
   - #23148: BUG: Fix integer / float scalar promotion
   - #23149: BUG: Add missing  header.
   - #23150: TYP, MAINT: Add a missing explicit ``Any`` parameter to the
   ``npt.ArrayLike``...
   - #23161: BLD: remove redundant definition of npy_nextafter [wheel build]


Cheers,

Charles Harris
___
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To unsubscribe send an email to python-announce-list-le...@python.org
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Member address: arch...@mail-archive.com

[Python-announce] NumPy 1.24.1 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.24.1. NumPy 1.24.1 is a maintenance release that fixes bugs and
regressions discovered after the 1.24.0 release.

The Python versions supported by this release are 3.8-3.11 Note that 32 bit
wheels are only provided for Windows, all other wheels are 64 bits on
account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit
support. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.24.1/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.24.1>.


*Contributors*

A total of 12 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

   - Andrew Nelson
   - Ben Greiner +
   - Charles Harris
   - Clément Robert
   - Matteo Raso
   - Matti Picus
   - Melissa Weber Mendonça
   - Miles Cranmer
   - Ralf Gommers
   - Rohit Goswami
   - Sayed Adel
   - Sebastian Berg


*Pull requests merged*

A total of 18 pull requests were merged for this release.

   - #22820: BLD: add workaround in setup.py for newer setuptools
   - #22830: BLD: CIRRUS_TAG redux
   - #22831: DOC: fix a couple typos in 1.23 notes
   - #22832: BUG: Fix refcounting errors found using pytest-leaks
   - #22834: BUG, SIMD: Fix invalid value encountered in several ufuncs
   - #22837: TST: ignore more np.distutils.log imports
   - #22839: BUG: Do not use getdata() in np.ma.masked_invalid
   - #22847: BUG: Ensure correct behavior for rows ending in delimiter
   in\...
   - #22848: BUG, SIMD: Fix the bitmask of the boolean comparison
   - #22857: BLD: Help raspian arm + clang 13 about builtin_mul_overflow
   - #22858: API: Ensure a full mask is returned for masked_invalid
   - #22866: BUG: Polynomials now copy properly (#22669)
   - #22867: BUG, SIMD: Fix memory overlap in ufunc comparison loops
   - #22868: BUG: Fortify string casts against floating point warnings
   - #22875: TST: Ignore nan-warnings in randomized out tests
   - #22883: MAINT: restore npymath implementations needed for freebsd
   - #22884: BUG: Fix integer overflow in in1d for mixed integer dtypes
   #22877
   - #22887: BUG: Use whole file for encoding checks with
   `charset_normalizer`.


Cheers,

Charles Harris
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[Python-announce] NumPy 1.24.0 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.24.0. The NumPy 1.24.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase execution speed, and
clarify the documentation. There are also a large number of new and expired
deprecations due to changes in promotion and cleanups. This might be called
a deprecation release. Highlights are

   - Many new deprecations.
   - Many expired deprecations.
   - New f2py features and fixes.
   - New "dtype" and "casting" keywords for stacking functions.

The Python versions supported in this release are 3.8-3.11 Note that 32 bit
wheels are only provided for Windows, all other wheels are 64 bits on
account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit
support. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.24.0/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.24.0>.

*Contributors*

A total of 177 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - @DWesl
   - @amagicmuffin +
   - @dg3192 +
   - @h-vetinari
   - @juztamau5 +
   - @swagatip +
   - Aaron Meurer
   - Aayush Agrawal +
   - Adam Knapp +
   - Adarsh Singh +
   - Al-Baraa El-Hag
   - Alban Desmaison +
   - Ales Crocaro +
   - Alex Low +
   - Alexander Grund +
   - Alexander Kanavin +
   - Andras Deak
   - Andrew Nelson
   - Angéllica Araujo +
   - Anselm Hahn +
   - Ari Cooper Davis +
   - Axel Huebl +
   - Bas van Beek
   - Bhavuk Kalra
   - Bram Ton +
   - Brent Tan +
   - Brigitta Sipőcz
   - Callum O'Riley +
   - Charles Harris
   - Chiara Marmo
   - Christoph Reiter
   - Damien Caliste
   - Dan Schult +
   - Daniel da Silva
   - DanielHabenicht +
   - Dave Goel +
   - David Gilbertson +
   - Developer-Ecosystem-Engineering
   - Digya Acharya +
   - Dimitri Papadopoulos Orfanos
   - Eric-Shang +
   - Evgeni Burovski
   - Ewout ter Hoeven
   - Felix Hirwa Nshuti +
   - Frazer McLean +
   - Ganesh Kathiresan
   - Gavin Zhang +
   - Gaëtan de Menten
   - Greg Lucas
   - Greg Sadetsky +
   - Gregory P. Smith [Google LLC] +
   - Hameer Abbasi
   - Hannah Aizenman +
   - Hood Chatham
   - I-Shen Leong
   - Iantra Solari +
   - Ikko Ashimine +
   - Inessa Pawson
   - Jake Bowhay +
   - Jake Close +
   - Jarrod Millman
   - Jason Thai
   - JessePires +
   - Jessé Pires +
   - Jhonatan Cunha +
   - Jingxuan He +
   - Johnathon Cusick +
   - Jon Morris
   - Jordy Williams +
   - Josh Wilson
   - João Fontes Gonçalves +
   - Juan Luis Cano Rodríguez
   - Jyn Spring 琴春 +
   - KIU Shueng Chuan
   - Karl Otness +
   - Kevin Sheppard
   - Kinshuk Dua +
   - Leo Fang
   - Leona Taric +
   - Lev Maximov +
   - Lillian Zha +
   - Loïc Estève
   - M. Eric Irrgang +
   - Mariusz Felisiak
   - Mark Harfouche
   - Martin Zacho +
   - Matheus Santana Patriarca +
   - Matt Williams +
   - Matteo Raso +
   - Matthew Barber
   - Matthew Brett
   - Matthew Sterrett +
   - Matthias Bussonnier
   - Matthias Koeppe +
   - Matti Picus
   - Meekail Zain +
   - Melissa Weber Mendonça
   - Michael Osthege +
   - Michael Siebert +
   - Mike Toews
   - Miki Watanabe +
   - Miles Cranmer +
   - Monika Kubek +
   - Muhammad Jarir Kanji +
   - Mukulika Pahari
   - Namami Shanker
   - Nathan Goldbaum +
   - Nathan Rooy +
   - Navpreet Singh +
   - Noritada Kobayashi +
   - Oleksiy Kononenko +
   - Omar Ali +
   - Pal Barta +
   - Pamphile Roy
   - Patrick Hoefler +
   - Pearu Peterson
   - Pedro Nacht +
   - Petar Mlinarić +
   - Peter Hawkins
   - Pey Lian Lim
   - Pieter Eendebak
   - Pradipta Ghosh
   - Pranab Das +
   - Precision Wordcraft LLC +
   - PrecisionWordcraft +
   - Rafael CF Sousa +
   - Rafael Cardoso Fernandes Sousa
   - Rafael Sousa +
   - Raghuveer Devulapalli
   - Ralf Gommers
   - Rin Cat (鈴猫) +
   - Robert Kern
   - Rohit Davas +
   - Rohit Goswami
   - Ross Barnowski
   - Roy Smart +
   - Ruth Comer +
   - Sabiha Tahsin Soha +
   - Sachin Krishnan T V +
   - Sanjana M Moodbagil +
   - Sanya Sinha +
   - Sarah Coffland +
   - Saransh Chopra +
   - Satish Kumar Mishra +
   - Satish Mishra +
   - Sayed Adel
   - Schrijvers Luc +
   - Sebastian Berg
   - Serge Guelton
   - Seva Lotoshnikov +
   - Shashank Gupta +
   - Shoban Chiddarth +
   - Shreya Singh +
   - Shreyas Joshi +
   - Sicheng Zeng +
   - Simran Makandar +
   - Shuangchi He +
   - Srimukh Sripada +
   - Stefan van der Walt
   - Stefanie Molin +
   - Stuart Archibald
   - Tania Allard
   - Thomas A Caswell
   - Thomas Kastl +
   - Thomas Mansencal +
   - Tony Newton / Teng Liu +
   - Toshiki Kataoka
   - Tyler Reddy
   - Vardhaman Kalloli +
   - Warren Weckesser
   - Will Ayd +
   - William Stein +
   - XinRu Lin +
   - Yin Li +
   - Yunika Bajracharya +
   - Zachary Blackwood +
   - 渡邉 美希 +

Cheers,

Charles Harris
___
P

[Python-announce] NumPy 1.24.0rc2 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.24.0rc1. The NumPy 1.24.0 release continues the ongoing work to improve
the handling and promotion of dtypes, increase execution speed, and
clarify the documentation. There are also a large number of new and expired
deprecations due to changes in promotion and cleanups. This might be called
a deprecation release. Highlights are

   - Many new deprecations.
   - Many expired deprecations.
   - New f2py features and fixes.
   - New "dtype" and "casting" keywords for stacking functions.

The Python versions supported in this release are 3.8-3.11 Note that 32 bit
wheels are only provided for Windows, all other wheels are 64 bits on
account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit
support. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.24.0rc2/>; source archives, release
notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.24.0rc2>.

*Contributors*

A total of 175 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - @DWesl
   - @amagicmuffin +
   - @dg3192 +
   - @h-vetinari
   - @juztamau5 +
   - @swagatip +
   - Aaron Meurer
   - Aayush Agrawal +
   - Adam Knapp +
   - Adarsh Singh +
   - Al-Baraa El-Hag
   - Alban Desmaison +
   - Ales Crocaro +
   - Alex Low +
   - Alexander Grund +
   - Alexander Kanavin +
   - Andras Deak
   - Angéllica Araujo +
   - Anselm Hahn +
   - Ari Cooper Davis +
   - Axel Huebl +
   - Bas van Beek
   - Bhavuk Kalra
   - Bram Ton +
   - Brent Tan +
   - Brigitta Sipőcz
   - Callum O'Riley +
   - Charles Harris
   - Chiara Marmo
   - Christoph Reiter
   - Damien Caliste
   - Dan Schult +
   - Daniel da Silva
   - DanielHabenicht +
   - Dave Goel +
   - David Gilbertson +
   - Developer-Ecosystem-Engineering
   - Digya Acharya +
   - Dimitri Papadopoulos Orfanos
   - Eric-Shang +
   - Evgeni Burovski
   - Ewout ter Hoeven
   - Felix Hirwa Nshuti +
   - Frazer McLean +
   - Ganesh Kathiresan
   - Gavin Zhang +
   - Gaëtan de Menten
   - Greg Lucas
   - Greg Sadetsky +
   - Gregory P. Smith [Google LLC] +
   - Hameer Abbasi
   - Hannah Aizenman +
   - Hood Chatham
   - I-Shen Leong
   - Iantra Solari +
   - Ikko Ashimine +
   - Inessa Pawson
   - Jake Bowhay +
   - Jake Close +
   - Jarrod Millman
   - Jason Thai
   - JessePires +
   - Jessé Pires +
   - Jhonatan Cunha +
   - Jingxuan He +
   - Johnathon Cusick +
   - Jon Morris
   - Jordy Williams +
   - Josh Wilson
   - João Fontes Gonçalves +
   - Juan Luis Cano Rodríguez
   - Jyn Spring 琴春 +
   - KIU Shueng Chuan
   - Karl Otness +
   - Kevin Sheppard
   - Kinshuk Dua +
   - Leo Fang
   - Leona Taric +
   - Lev Maximov +
   - Lillian Zha +
   - Loïc Estève
   - M. Eric Irrgang +
   - Mariusz Felisiak
   - Mark Harfouche
   - Martin Zacho +
   - Matheus Santana Patriarca +
   - Matt Williams +
   - Matteo Raso +
   - Matthew Barber
   - Matthew Brett
   - Matthew Sterrett +
   - Matthias Bussonnier
   - Matthias Koeppe +
   - Matti Picus
   - Meekail Zain +
   - Melissa Weber Mendonça
   - Michael Osthege +
   - Michael Siebert +
   - Mike Toews
   - Miki Watanabe +
   - Miles Cranmer +
   - Monika Kubek +
   - Muhammad Jarir Kanji +
   - Mukulika Pahari
   - Namami Shanker
   - Nathan Goldbaum +
   - Nathan Rooy +
   - Navpreet Singh +
   - Noritada Kobayashi +
   - Oleksiy Kononenko +
   - Omar Ali +
   - Pal Barta +
   - Pamphile Roy
   - Patrick Hoefler +
   - Pearu Peterson
   - Pedro Nacht +
   - Petar Mlinarić +
   - Peter Hawkins
   - Pey Lian Lim
   - Pieter Eendebak
   - Pradipta Ghosh
   - Pranab Das +
   - Precision Wordcraft LLC +
   - PrecisionWordcraft +
   - Rafael CF Sousa +
   - Rafael Cardoso Fernandes Sousa
   - Rafael Sousa +
   - Raghuveer Devulapalli
   - Ralf Gommers
   - Rin Cat (鈴猫) +
   - Robert Kern
   - Rohit Davas +
   - Rohit Goswami
   - Ross Barnowski
   - Ruth Comer +
   - Sabiha Tahsin Soha +
   - Sachin Krishnan T V +
   - Sanjana M Moodbagil +
   - Sanya Sinha +
   - Sarah Coffland +
   - Saransh Chopra +
   - Satish Kumar Mishra +
   - Satish Mishra +
   - Sayed Adel
   - Schrijvers Luc +
   - Sebastian Berg
   - Serge Guelton
   - Seva Lotoshnikov +
   - Shashank Gupta +
   - Shoban Chiddarth +
   - Shreya Singh +
   - Shreyas Joshi +
   - Sicheng Zeng +
   - Simran Makandar +
   - Shuangchi He +
   - Srimukh Sripada +
   - Stefan van der Walt
   - Stefanie Molin +
   - Stuart Archibald
   - Tania Allard
   - Thomas A Caswell
   - Thomas Kastl +
   - Thomas Mansencal +
   - Tony Newton / Teng Liu +
   - Toshiki Kataoka
   - Tyler Reddy
   - Vardhaman Kalloli +
   - Warren Weckesser
   - Will Ayd +
   - William Stein +
   - XinRu Lin +
   - Yin Li +
   - Yunika Bajracharya +
   - Zachary Blackwood +
   - 渡邉 美希 +

Cheers,

Charles Harris
___
Python-announce-list maili

[Python-announce] NumPy 1.24.0rc1 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.24.0rc1. The NumPy 1.24.0 release continues the ongoing work to improve
the handling and promotion of dtypes, increase execution speed, and
clarify the documentation. There are also a large number of new and expired
deprecations due to changes in promotion and cleanups. This might be called
a deprecation release. Highlights are

   - Many new deprecations.
   - Many expired deprecations.
   - New f2py features and fixes.
   - New "dtype" and "casting" keywords for stacking functions.

The Python versions supported in this release are 3.8-3.11 Note that 32 bit
wheels are only provided for Windows, all other wheels are 64 bits on
account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit
support. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.24.0rc1/>; source archives, release
notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.24.0rc1>. The Python 3.8
aarch64 wheel is missing from this pre-release due to build problems.

*Contributors*

A total of 174 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.


   - @DWesl
   - @h-vetinari
   - Aaron Meurer
   - Aayush Agrawal +
   - Adam Knapp +
   - Adarsh Singh +
   - Al-Baraa El-Hag
   - Alban Desmaison +
   - Ales Crocaro +
   - Alex +
   - Alexander Grund +
   - Alexander Kanavin +
   - Andras Deak
   - Angéllica Araujo +
   - Anselm Hahn +
   - Ari Cooper Davis +
   - Axel Huebl +
   - Bas van Beek
   - Bhavuk Kalra
   - Bram Ton +
   - Brent Tan +
   - Brigitta Sipőcz
   - Callum O'Riley +
   - Charles Harris
   - Chiara Marmo
   - Christoph Reiter
   - Damien Caliste
   - Dan Schult +
   - Daniel da Silva
   - DanielHabenicht +
   - Dave Goel +
   - David Gilbertson +
   - Developer-Ecosystem-Engineering
   - Digya Acharya +
   - Dimitri Papadopoulos Orfanos
   - Eric-Shang +
   - Evgeni Burovski
   - Ewout ter Hoeven
   - Felix Hirwa Nshuti +
   - Frazer McLean +
   - Ganesh Kathiresan
   - Gavin Zhang +
   - Gaëtan de Menten
   - Greg Lucas
   - Greg Sadetsky +
   - Gregory P. Smith [Google LLC] +
   - Hameer Abbasi
   - Hannah Aizenman +
   - Hood Chatham
   - I-Shen Leong
   - Iantra Solari +
   - Ikko Ashimine +
   - Inessa Pawson
   - Jake Bowhay +
   - Jake Close +
   - Jarrod Millman
   - Jason Thai
   - Jessé Pires +
   - Jhonatan Cunha +
   - Jingxuan He +
   - Johnathon Cusick +
   - Jon Morris
   - Jordy Williams +
   - Josh Wilson
   - João Fontes Gonçalves +
   - Juan Luis Cano Rodríguez
   - Jyn Spring 琴春 +
   - KIU Shueng Chuan
   - Karl Otness +
   - Kevin Sheppard
   - Kinshuk Dua +
   - Leo Fang
   - Leona Taric +
   - Lev Maximov +
   - Lillian Zha +
   - Loïc Estève
   - M. Eric Irrgang +
   - Mariusz Felisiak
   - Mark Harfouche
   - Martin Zacho +
   - Matheus Santana Patriarca +
   - Matt Williams +
   - Matteo Raso +
   - Matthew Barber
   - Matthew Brett
   - Matthew Sterrett +
   - Matthias Bussonnier
   - Matthias Koeppe +
   - Matti Picus
   - Meekail Zain +
   - Melissa Weber Mendonça
   - Michael Osthege +
   - Michael Siebert +
   - Mike Toews
   - Miki Watanabe +
   - Miles Cranmer +
   - MilesCranmer +
   - Monika Kubek +
   - Muhammad Jarir Kanji +
   - Mukulika Pahari
   - Namami Shanker
   - Nathan Goldbaum +
   - Nathan Rooy +
   - Navpreet Singh +
   - Noritada Kobayashi +
   - Oleksiy Kononenko +
   - Omar +
   - Pal Barta +
   - Pamphile Roy
   - Patrick Hoefler +
   - Pearu Peterson
   - Pedro Nacht +
   - Petar Mlinarić +
   - Pey Lian Lim
   - Pieter Eendebak
   - Pradipta Ghosh
   - Pranab Das +
   - Precision Wordcraft LLC +
   - PrecisionWordcraft +
   - Rafael CF Sousa +
   - Rafael Cardoso Fernandes Sousa
   - Rafael Sousa +
   - Raghuveer Devulapalli
   - Ralf Gommers
   - Rin Cat (鈴猫) +
   - Robert Kern
   - Rohit Davas +
   - Rohit Goswami
   - Ross Barnowski
   - Ruth Comer +
   - Sabiha Tahsin Soha +
   - Sachin Krishnan T V +
   - Sanjana M Moodbagil +
   - Sanya Sinha +
   - Sarah Coffland +
   - Saransh Chopra +
   - Satish Kumar Mishra +
   - Satish Mishra +
   - Sayed Adel
   - Schrijvers Luc +
   - Sebastian Berg
   - Serge Guelton
   - Seva Lotoshnikov +
   - Shashank Gupta +
   - Shoban Chiddarth +
   - Shreya Singh +
   - Shreyas Joshi +
   - Sicheng Zeng +
   - Simran Makandar +
   - Srimukh Sripada +
   - Stefan van der Walt
   - Stefanie Molin +
   - Stuart Archibald
   - Tania Allard
   - Thomas A Caswell
   - Thomas Kastl +
   - Thomas Mansencal +
   - Tony Newton / Teng Liu +
   - Toshiki Kataoka
   - Tyler Reddy
   - Vardhaman Kalloli +
   - Warren Weckesser
   - Will Ayd +
   - William Stein +
   - XinRu Lin +
   - Yin Li +
   - Yulv-git +
   - Yunika Bajracharya +
   - Zachary Blackwood +
   - amagicmuffin +
   - dg3192 +
   - juztamau5 +
   - swagatip +
   - 渡邉 美希 +

Cheers,

Charles Harris
__

[Python-announce] Release of NumPy 1.23.5

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I am pleased to announce the release of
NumPy 1.23.5. NumPy 1.23.5 is a maintenance release that fixes bugs
discovered after the 1.23.4 release and keeps the build infrastructure
current. The Python versions supported for this release are 3.8-3.11.
Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.23.5>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.23.5>.

*Contributors*

A total of 7 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.

   - @DWesl
   - Aayush Agrawal +
   - Adam Knapp +
   - Charles Harris
   - Navpreet Singh +
   - Sebastian Berg
   - Tania Allard


*Pull requests merged*

A total of 10 pull requests were merged for this release.

   - #22489: TST, MAINT: Replace most setup with setup_method (also
   teardown)
   - #22490: MAINT, CI: Switch to cygwin/cygwin-install-action@v2
   - #22494: TST: Make test_partial_iteration_cleanup robust but require
   leak...
   - #22592: MAINT: Ensure graceful handling of large header sizes
   - #22593: TYP: Spelling alignment for array flag literal
   - #22594: BUG: Fix bounds checking for ``random.logseries``
   - #22595: DEV: Update GH actions and Dockerfile for Gitpod
   - #22596: CI: Only fetch in actions/checkout
   - #22597: BUG: Decrement ref count in gentype_reduce if allocated
   memory...
   - #22625: BUG: Histogramdd breaks on big arrays in Windows

Cheers,

Charles Harris
___
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[Python-announce] NumPy 1.23.4 released.

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I am pleased to announce the release of
NumPy 1.23.4. NumPy 1.23.4 is a maintenance release that fixes bugs
discovered after the 1.23.3 release and keeps the build infrastructure
current. The main improvements are fixes for some annotation corner cases,
a fix for a long time *nested_iters* memory leak, and a fix of complex
vector dot for very large arrays. The Python versions supported for this
release are 3.8-3.11. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.23.4>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.23.4>.

Note that the mypy version needs to be 0.981+ if you test using Python
3.10.7, otherwise the typing tests will fail.


*Contributors*

A total of 8 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

   - Bas van Beek
   - Charles Harris
   - Matthew Barber
   - Matti Picus
   - Ralf Gommers
   - Ross Barnowski
   - Sebastian Berg
   - Sicheng Zeng +



*Pull requests merged*
A total of 13 pull requests were merged for this release.

   - #22368: BUG: Add ``__array_api_version__`` to ``numpy.array_api``
   namespace
   - #22370: MAINT: update sde toolkit to 9.0, fix download link
   - #22382: BLD: use macos-11 image on azure, macos-1015 is deprecated
   - #22383: MAINT: random: remove ``get_info`` from "extending with
   Cython"...
   - #22384: BUG: Fix complex vector dot with more than NPY_CBLAS_CHUNK
   elements
   - #22387: REV: Loosen ``lookfor``'s import try/except again
   - #22388: TYP,ENH: Mark ``numpy.typing`` protocols as runtime checkable
   - #22389: TYP,MAINT: Change more overloads to play nice with pyright
   - #22390: TST,TYP: Bump mypy to 0.981
   - #22391: DOC: Update delimiter param description.
   - #22392: BUG: Memory leaks in numpy.nested_iters
   - #22413: REL: Prepare for the NumPy 1.23.4 release.
   - #22424: TST: Fix failing aarch64 wheel builds.

Cheers,

Charles Harris
___
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[Python-announce] NumPy 1.23. 3 Release

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I am pleased to announce the release of
NumPy 1.23.3. NumPy 1.23.3 is a maintenance release that fixes bugs
discovered after the 1.23.2 release. There is no major theme for this
release, the main improvements are for some downstream builds and some
annotation corner cases. The Python versions supported for this release are
3.8-3.11. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.23.3>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.23.3>.

Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the 10.15
version currently used will no longer be supported by our build
infrastructure at that point.

*Contributors*

A total of 16 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - Aaron Meurer
   - Bas van Beek
   - Charles Harris
   - Ganesh Kathiresan
   - Gavin Zhang +
   - Iantra Solari+
   - Jyn Spring 琴春 +
   - Matti Picus
   - Rafael Cardoso Fernandes Sousa
   - Rafael Sousa +
   - Ralf Gommers
   - Rin Cat (鈴猫) +
   - Saransh Chopra +
   - Sayed Adel
   - Sebastian Berg
   - Serge Guelton



*Pull requests merged*
A total of 14 pull requests were merged for this release.

   - #22136: BLD: Add Python 3.11 wheels to aarch64 build
   - #22148: MAINT: Update setup.py for Python 3.11.
   - #22155: CI: Test NumPy build against old versions of GCC(6, 7, 8)
   - #22156: MAINT: support IBM i system
   - #22195: BUG: Fix circleci build
   - #22214: BUG: Expose heapsort algorithms in a shared header
   - #22215: BUG: Support using libunwind for backtrack
   - #22216: MAINT: fix an incorrect pointer type usage in f2py
   - #0: BUG: change overloads to play nice with pyright.
   - #1: TST,BUG: Use fork context to fix MacOS savez test
   - #2: TYP,BUG: Reduce argument validation in C-based
   ``__class_getitem__``
   - #3: TST: ensure ``np.equal.reduce`` raises a ``TypeError``
   - #4: BUG: Fix the implementation of numpy.array_api.vecdot
   - #22230: BUG: Better report integer division overflow (backport)


Cheers,

Charles Harris
___
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[Python-announce] NumPy 1.23.2 release

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I am pleased to announce the release of
NumPy 1.23.2. NumPy 1.23.2 is a maintenance release that fixes bugs
discovered after the 1.23.1 release. Notable features are:

   - Typing changes needed for Python 3.11
   - Wheels for Python 3.11.0rc1

The Python versions supported for this release are 3.8-3.11. Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.23.2>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.23.2>.

*Contributors*

A total of 9 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

   - Alexander Grund +
   - Bas van Beek
   - Charles Harris
   - Jon Cusick +
   - Matti Picus
   - Michael Osthege +
   - Pal Barta +
   - Ross Barnowski
   - Sebastian Berg


*Pull requests merged*
A total of 15 pull requests were merged for this release.

   - #22030: ENH: Add ``__array_ufunc__`` typing support to the ``nin=1``
   ufuncs
   - #22031: MAINT, TYP: Fix ``np.angle`` dtype-overloads
   - #22032: MAINT: Do not let ``_GenericAlias`` wrap the underlying
   classes'...
   - #22033: TYP,MAINT: Allow ``einsum`` subscripts to be passed via
   integer...
   - #22034: MAINT,TYP: Add object-overloads for the ``np.generic`` rich
   comparisons
   - #22035: MAINT,TYP: Allow the ``squeeze`` and ``transpose`` method to...
   - #22036: BUG: Fix subarray to object cast ownership details
   - #22037: BUG: Use ``Popen`` to silently invoke f77 -v
   - #22038: BUG: Avoid errors on NULL during deepcopy
   - #22039: DOC: Add versionchanged for converter callable behavior.
   - #22057: MAINT: Quiet the anaconda uploads.
   - #22078: ENH: reorder includes for testing on top of system
   installations...
   - #22106: TST: fix test_linear_interpolation_formula_symmetric
   - #22107: BUG: Fix skip condition for test_loss_of_precision[complex256]
   - #22115: BLD: Build python3.11.0rc1 wheels.

Cheers,

Charles Harris
___
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[Python-announce] NumPy 1.23.1 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.23.1. NumPy 1.23.1 is a maintenance release that fixes bugs discovered
after the 1.23.0 release. Notable fixes are:

   - Fix searchsorted for float16 NaNs
   - Fix compilation on Apple M1
   - Fix KeyError in crackfortran operator support (Slycot)

The Python versions supported for this release are 3.8-3.10. Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.23.1>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.23.1>.

*Contributors*

A total of 7 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

   - Charles Harris
   - Matthias Koeppe +
   - Pranab Das +
   - Rohit Goswami
   - Sebastian Berg
   - Serge Guelton
   - Srimukh Sripada +

*Pull requests merged*

A total of 8 pull requests were merged for this release.

   - #21866: BUG: Fix discovered MachAr (still used within valgrind)
   - #21867: BUG: Handle NaNs correctly for float16 during sorting
   - #21868: BUG: Use ``keepdims`` during normalization in ``np.average``
   and...
   - #21869: DOC: mention changes to ``max_rows`` behaviour in
   ``np.loadtxt``
   - #21870: BUG: Reject non integer array-likes with size 1 in delete
   - #21949: BLD: Make can_link_svml return False for 32bit builds on x86_64
   - #21951: BUG: Reorder extern "C" to only apply to function
   declarations...
   - #21952: BUG: Fix KeyError in crackfortran operator support

Cheers,

Charles Harris
___
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[Python-announce] NumPy 1.23.0 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.23.0. The NumPy 1.23.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, clarify the
documentation, and expire old deprecations. The highlights are:


   - Implementation of ``loadtxt`` in C, greatly improving its performance.
   - Exposing DLPack at the Python level for easy data exchange.
   - Changes to the promotion and comparisons of structured dtypes.
   - Improvements to f2py.

The Python versions supported in this release are 3.8-3.10, 3.11 will be
supported when it comes out. Note that 32 bit wheels are only provided for
Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and
other Linux distributions dropping 32 bit support. All 64 bit wheels are
also linked with 64 bit OpenBLAS. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.23.0/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.23.0>.

*Contributors*

A total of 151 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - @DWesl
   - @GalaxySnail +
   - @code-review-doctor +
   - @h-vetinari
   - Aaron Meurer
   - Alexander Shadchin
   - Alexandre de Siqueira
   - Allan Haldane
   - Amrit Krishnan
   - Andrei Batomunkuev
   - Andrew J. Hesford +
   - Andrew Murray +
   - Andrey Andreyevich Bienkowski +
   - André Elimelek de Weber +
   - Andy Wharton +
   - Arryan Singh
   - Arushi Sharma
   - Bas van Beek
   - Bharat Raghunathan
   - Bhavuk Kalra +
   - Brigitta Sipőcz
   - Brénainn Woodsend +
   - Burlen Loring +
   - Caio Agiani +
   - Charles Harris
   - Chiara Marmo
   - Cornelius Roemer +
   - Dahyun Kim +
   - Damien Caliste
   - David Prosin +
   - Denis Laxalde
   - Developer-Ecosystem-Engineering
   - Devin Shanahan +
   - Diego Wang +
   - Dimitri Papadopoulos Orfanos
   - Ding Liu +
   - Diwakar Gupta +
   - Don Kirkby +
   - Emma Simon +
   - Eric Wieser
   - Evan Miller +
   - Evgeni Burovski
   - Evgeny Posenitskiy +
   - Ewout ter Hoeven +
   - Felix Divo
   - Francesco Andreuzzi +
   - Ganesh Kathiresan
   - Gaëtan de Menten
   - Geoffrey Gunter +
   - Hans Meine
   - Harsh Mishra +
   - Henry Schreiner
   - Hood Chatham +
   - I-Shen Leong
   - Ilhan Polat
   - Inessa Pawson
   - Isuru Fernando
   - Ivan Gonzalez +
   - Ivan Meleshko +
   - Ivan Yashchuk +
   - Janus Heide +
   - Jarrod Millman
   - Jason Thai +
   - Jeremy Volkman +
   - Jesús Carrete Montaña +
   - Jhong-Ken Chen (陳仲肯) +
   - John Kirkham
   - John-Mark Gurney +
   - Jonathan Deng +
   - Joseph Fox-Rabinovitz
   - Jouke Witteveen +
   - Junyan Ou +
   - Jérôme Richard +
   - Kassian Sun +
   - Kazuki Sakamoto +
   - Kenichi Maehashi
   - Kevin Sheppard
   - Kilian Lieret +
   - Kushal Beniwal +
   - Leo Singer
   - Logan Thomas +
   - Lorenzo Mammana +
   - Margret Pax
   - Mariusz Felisiak +
   - Markus Mohrhard +
   - Mars Lee
   - Marten van Kerkwijk
   - Masamichi Hosoda +
   - Matthew Barber
   - Matthew Brett
   - Matthias Bussonnier
   - Matthieu Darbois
   - Matti Picus
   - Melissa Weber Mendonça
   - Michael Burkhart +
   - Morteza Mirzai +
   - Motahhar Mokf +
   - Muataz Attaia +
   - Muhammad Motawe +
   - Mukulika Pahari
   - Márton Gunyhó +
   - Namami Shanker +
   - Nihaal Sangha +
   - Niyas Sait
   - Omid Rajaei +
   - Oscar Gustafsson +
   - Ovee Jawdekar +
   - P. L. Lim +
   - Pamphile Roy +
   - Pantelis Antonoudiou +
   - Pearu Peterson
   - Peter Andreas Entschev
   - Peter Hawkins
   - Pierre de Buyl
   - Pieter Eendebak +
   - Pradipta Ghosh +
   - Rafael Cardoso Fernandes Sousa +
   - Raghuveer Devulapalli
   - Ralf Gommers
   - Raphael Kruse
   - Raúl Montón Pinillos
   - Robert Kern
   - Rohit Goswami
   - Ross Barnowski
   - Ruben Garcia +
   - Sadie Louise Bartholomew +
   - Saswat Das +
   - Sayed Adel
   - Sebastian Berg
   - Serge Guelton
   - Simon Surland Andersen +
   - Siyabend Ürün +
   - Somasree Majumder +
   - Soumya +
   - Stefan van der Walt
   - Stefano Miccoli +
   - Stephan Hoyer
   - Stephen Worsley +
   - Tania Allard
   - Thomas Duvernay +
   - Thomas Green +
   - Thomas J. Fan
   - Thomas Li +
   - Tim Hoffmann
   - Ting Sun +
   - Tirth Patel
   - Toshiki Kataoka
   - Tyler Reddy
   - Warren Weckesser
   - Yang Hau
   - Yoon, Jee Seok +

Cheers,

Charles Harris
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[Python-announce] NumPy 1.23.0rc3 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.23.0rc2. The NumPy 1.23.0 release continues the ongoing work to improve
the handling and promotion of dtypes, increase the execution speed, clarify
the documentation, and expire old deprecations. The highlights are:


   - Implementation of ``loadtxt`` in C, greatly improving its performance.
   - Exposing DLPack at the Python level for easy data exchange.
   - Changes to the promotion and comparisons of structured dtypes.
   - Improvements to f2py.

The Python versions supported in this release are 3.8-3.10, 3.11 will be
supported when it comes out. Note that 32 bit wheels are only provided for
Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and
other Linux distributions dropping 32 bit support. All 64 bit wheels are
also linked with 64 bit OpenBLAS. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.23.0rc3/>; source archives, release
notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.23.0rc3>.

*Contributors*

A total of 151 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - @DWesl
   - @GalaxySnail +
   - @code-review-doctor +
   - @h-vetinari
   - Aaron Meurer
   - Alexander Shadchin
   - Alexandre de Siqueira
   - Allan Haldane
   - Amrit Krishnan
   - Andrei Batomunkuev
   - Andrew J. Hesford +
   - Andrew Murray +
   - Andrey Andreyevich Bienkowski +
   - André Elimelek de Weber +
   - Andy Wharton +
   - Arryan Singh
   - Arushi Sharma
   - Bas van Beek
   - Bharat Raghunathan
   - Bhavuk Kalra +
   - Brigitta Sipőcz
   - Brénainn Woodsend +
   - Burlen Loring +
   - Caio Agiani +
   - Charles Harris
   - Chiara Marmo
   - Cornelius Roemer +
   - Dahyun Kim +
   - Damien Caliste
   - David Prosin +
   - Denis Laxalde
   - Developer-Ecosystem-Engineering
   - Devin Shanahan +
   - Diego Wang +
   - Dimitri Papadopoulos Orfanos
   - Ding Liu +
   - Diwakar Gupta +
   - Don Kirkby +
   - Emma Simon +
   - Eric Wieser
   - Evan Miller +
   - Evgeni Burovski
   - Evgeny Posenitskiy +
   - Ewout ter Hoeven +
   - Felix Divo
   - Francesco Andreuzzi +
   - Ganesh Kathiresan
   - Gaëtan de Menten
   - Geoffrey Gunter +
   - Hans Meine
   - Harsh Mishra +
   - Henry Schreiner
   - Hood Chatham +
   - I-Shen Leong
   - Ilhan Polat
   - Inessa Pawson
   - Isuru Fernando
   - Ivan Gonzalez +
   - Ivan Meleshko +
   - Ivan Yashchuk +
   - Janus Heide +
   - Jarrod Millman
   - Jason Thai +
   - Jeremy Volkman +
   - Jesús Carrete Montaña +
   - Jhong-Ken Chen (陳仲肯) +
   - John Kirkham
   - John-Mark Gurney +
   - Jonathan Deng +
   - Joseph Fox-Rabinovitz
   - Jouke Witteveen +
   - Junyan Ou +
   - Jérôme Richard +
   - Kassian Sun +
   - Kazuki Sakamoto +
   - Kenichi Maehashi
   - Kevin Sheppard
   - Kilian Lieret +
   - Kushal Beniwal +
   - Leo Singer
   - Logan Thomas +
   - Lorenzo Mammana +
   - Margret Pax
   - Mariusz Felisiak +
   - Markus Mohrhard +
   - Mars Lee
   - Marten van Kerkwijk
   - Masamichi Hosoda +
   - Matthew Barber
   - Matthew Brett
   - Matthias Bussonnier
   - Matthieu Darbois
   - Matti Picus
   - Melissa Weber Mendonça
   - Michael Burkhart +
   - Morteza Mirzai +
   - Motahhar Mokf +
   - Muataz Attaia +
   - Muhammad Motawe +
   - Mukulika Pahari
   - Márton Gunyhó +
   - Namami Shanker +
   - Nihaal Sangha +
   - Niyas Sait
   - Omid Rajaei +
   - Oscar Gustafsson +
   - Ovee Jawdekar +
   - P. L. Lim +
   - Pamphile Roy +
   - Pantelis Antonoudiou +
   - Pearu Peterson
   - Peter Andreas Entschev
   - Peter Hawkins
   - Pierre de Buyl
   - Pieter Eendebak +
   - Pradipta Ghosh +
   - Rafael Cardoso Fernandes Sousa +
   - Raghuveer Devulapalli
   - Ralf Gommers
   - Raphael Kruse
   - Raúl Montón Pinillos
   - Robert Kern
   - Rohit Goswami
   - Ross Barnowski
   - Ruben Garcia +
   - Sadie Louise Bartholomew +
   - Saswat Das +
   - Sayed Adel
   - Sebastian Berg
   - Serge Guelton
   - Simon Surland Andersen +
   - Siyabend Ürün +
   - Somasree Majumder +
   - Soumya +
   - Stefan van der Walt
   - Stefano Miccoli +
   - Stephan Hoyer
   - Stephen Worsley +
   - Tania Allard
   - Thomas Duvernay +
   - Thomas Green +
   - Thomas J. Fan
   - Thomas Li +
   - Tim Hoffmann
   - Ting Sun +
   - Tirth Patel
   - Toshiki Kataoka
   - Tyler Reddy
   - Warren Weckesser
   - Yang Hau
   - Yoon, Jee Seok +

Cheers,

Charles Harris
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[Python-announce] NumPy 1.23.0rc2 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.23.0rc2. The NumPy 1.23.0 release continues the ongoing work to improve
the handling and promotion of dtypes, increase the execution speed, clarify
the documentation, and expire old deprecations. The highlights are:


   - Implementation of ``loadtxt`` in C, greatly improving its performance.
   - Exposing DLPack at the Python level for easy data exchange.
   - Changes to the promotion and comparisons of structured dtypes.
   - Improvements to f2py.

The Python versions supported in this release are 3.8-3.10, 3.11 will be
supported when it comes out. Note that 32 bit wheels are only provided for
Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and
other Linux distributions dropping 32 bit support. All 64 bit wheels are
also linked with 64 bit OpenBLAS. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.23.0rc2/>; source archives, release
notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.23.0rc2>.

*Contributors*

A total of 150 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - @DWesl
   - @GalaxySnail +
   - @code-review-doctor +
   - @h-vetinari
   - Aaron Meurer
   - Alexander Shadchin
   - Alexandre de Siqueira
   - Allan Haldane
   - Amrit Krishnan
   - Andrei Batomunkuev
   - Andrew J. Hesford +
   - Andrew Murray +
   - Andrey Andreyevich Bienkowski +
   - André Elimelek de Weber +
   - Andy Wharton +
   - Arryan Singh
   - Arushi Sharma
   - Bas van Beek
   - Bharat Raghunathan
   - Bhavuk Kalra +
   - Brigitta Sipőcz
   - Brénainn Woodsend +
   - Burlen Loring +
   - Caio Agiani +
   - Charles Harris
   - Chiara Marmo
   - Cornelius Roemer +
   - Dahyun Kim +
   - Damien Caliste
   - David Prosin +
   - Denis Laxalde
   - Developer-Ecosystem-Engineering
   - Devin Shanahan +
   - Diego Wang +
   - Dimitri Papadopoulos Orfanos
   - Ding Liu +
   - Diwakar Gupta +
   - Don Kirkby +
   - Emma Simon +
   - Eric Wieser
   - Evan Miller +
   - Evgeni Burovski
   - Evgeny Posenitskiy +
   - Ewout ter Hoeven +
   - Felix Divo
   - Francesco Andreuzzi +
   - Ganesh Kathiresan
   - Gaëtan de Menten
   - Geoffrey Gunter +
   - Hans Meine
   - Harsh Mishra +
   - Henry Schreiner
   - Hood Chatham +
   - Ilhan Polat
   - Inessa Pawson
   - Isuru Fernando
   - Ivan Gonzalez +
   - Ivan Meleshko +
   - Ivan Yashchuk +
   - Janus Heide +
   - Jarrod Millman
   - Jason Thai +
   - Jeremy Volkman +
   - Jesús Carrete Montaña +
   - Jhong-Ken Chen (陳仲肯) +
   - John Kirkham
   - John-Mark Gurney +
   - Jonathan Deng +
   - Joseph Fox-Rabinovitz
   - Jouke Witteveen +
   - Junyan Ou +
   - Jérôme Richard +
   - Kassian Sun +
   - Kazuki Sakamoto +
   - Kenichi Maehashi
   - Kevin Sheppard
   - Kilian Lieret +
   - Kushal Beniwal +
   - Leo Singer
   - Logan Thomas +
   - Lorenzo Mammana +
   - Margret Pax
   - Mariusz Felisiak +
   - Markus Mohrhard +
   - Mars Lee
   - Marten van Kerkwijk
   - Masamichi Hosoda +
   - Matthew Barber
   - Matthew Brett
   - Matthias Bussonnier
   - Matthieu Darbois
   - Matti Picus
   - Melissa Weber Mendonça
   - Michael Burkhart +
   - Morteza Mirzai +
   - Motahhar Mokf +
   - Muataz Attaia +
   - Muhammad Motawe +
   - Mukulika Pahari
   - Márton Gunyhó +
   - Namami Shanker +
   - Nihaal Sangha +
   - Niyas Sait
   - Omid Rajaei +
   - Oscar Gustafsson +
   - Ovee Jawdekar +
   - P. L. Lim +
   - Pamphile Roy +
   - Pantelis Antonoudiou +
   - Pearu Peterson
   - Peter Andreas Entschev
   - Peter Hawkins
   - Pierre de Buyl
   - Pieter Eendebak +
   - Pradipta Ghosh +
   - Rafael Cardoso Fernandes Sousa +
   - Raghuveer Devulapalli
   - Ralf Gommers
   - Raphael Kruse
   - Raúl Montón Pinillos
   - Robert Kern
   - Rohit Goswami
   - Ross Barnowski
   - Ruben Garcia +
   - Sadie Louise Bartholomew +
   - Saswat Das +
   - Sayed Adel
   - Sebastian Berg
   - Serge Guelton
   - Simon Surland Andersen +
   - Siyabend Ürün +
   - Somasree Majumder +
   - Soumya +
   - Stefan van der Walt
   - Stefano Miccoli +
   - Stephan Hoyer
   - Stephen Worsley +
   - Tania Allard
   - Thomas Duvernay +
   - Thomas Green +
   - Thomas J. Fan
   - Thomas Li +
   - Tim Hoffmann
   - Ting Sun +
   - Tirth Patel
   - Toshiki Kataoka
   - Tyler Reddy
   - Warren Weckesser
   - Yang Hau
   - Yoon, Jee Seok +

Cheers,

Charles Harris
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[Python-announce] NumPy 1.23.0rc1 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.23.0rc1. The NumPy 1.23.0 release continues the ongoing work to improve
the handling and promotion of dtypes, increase the execution speed, clarify
the documentation, and expire old deprecations. The highlights are:


   - Implementation of ``loadtxt`` in C, greatly improving its performance.
   - Exposing DLPack at the Python level for easy data exchange.
   - Changes to the promotion and comparisons of structured dtypes.
   - Improvements to f2py.

The Python versions supported in this release are 3.8-3.10, 3.11 will be
supported when it comes out. Note that 32 bit wheels are only provided for
Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and
other Linux distributions dropping 32 bit support. All 64 bit wheels are
also linked with 64 bit OpenBLAS. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.23.0rc1/>; source archives, release
notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.23.0rc1>.

*Contributors*

A total of 150 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - @DWesl
   - @GalaxySnail +
   - @code-review-doctor +
   - @h-vetinari
   - Aaron Meurer
   - Alexander Shadchin
   - Alexandre de Siqueira
   - Allan Haldane
   - Amrit Krishnan
   - Andrei Batomunkuev
   - Andrew J. Hesford +
   - Andrew Murray +
   - Andrey Andreyevich Bienkowski +
   - André Elimelek de Weber +
   - Andy Wharton +
   - Arryan Singh
   - Arushi Sharma
   - Bas van Beek
   - Bharat Raghunathan
   - Bhavuk Kalra +
   - Brigitta Sipőcz
   - Brénainn Woodsend +
   - Burlen Loring +
   - Caio Agiani +
   - Charles Harris
   - Chiara Marmo
   - Cornelius Roemer +
   - Dahyun Kim +
   - Damien Caliste
   - David Prosin +
   - Denis Laxalde
   - Developer-Ecosystem-Engineering
   - Devin Shanahan +
   - Diego Wang +
   - Dimitri Papadopoulos Orfanos
   - Ding Liu +
   - Diwakar Gupta +
   - Don Kirkby +
   - Emma Simon +
   - Eric Wieser
   - Evan Miller +
   - Evgeni Burovski
   - Evgeny Posenitskiy +
   - Ewout ter Hoeven +
   - Felix Divo
   - Francesco Andreuzzi +
   - Ganesh Kathiresan
   - Gaëtan de Menten
   - Geoffrey Gunter +
   - Hans Meine
   - Harsh Mishra +
   - Henry Schreiner
   - Hood Chatham +
   - Ilhan Polat
   - Inessa Pawson
   - Isuru Fernando
   - Ivan Gonzalez +
   - Ivan Meleshko +
   - Ivan Yashchuk +
   - Janus Heide +
   - Jarrod Millman
   - Jason Thai +
   - Jeremy Volkman +
   - Jesús Carrete Montaña +
   - Jhong-Ken Chen (陳仲肯) +
   - John Kirkham
   - John-Mark Gurney +
   - Jonathan Deng +
   - Joseph Fox-Rabinovitz
   - Jouke Witteveen +
   - Junyan Ou +
   - Jérôme Richard +
   - Kassian Sun +
   - Kazuki Sakamoto +
   - Kenichi Maehashi
   - Kevin Sheppard
   - Kilian Lieret +
   - Kushal Beniwal +
   - Leo Singer
   - Logan Thomas +
   - Lorenzo Mammana +
   - Margret Pax
   - Mariusz Felisiak +
   - Markus Mohrhard +
   - Mars Lee
   - Marten van Kerkwijk
   - Masamichi Hosoda +
   - Matthew Barber
   - Matthew Brett
   - Matthias Bussonnier
   - Matthieu Darbois
   - Matti Picus
   - Melissa Weber Mendonça
   - Michael Burkhart +
   - Morteza Mirzai +
   - Motahhar Mokf +
   - Muataz Attaia +
   - Muhammad Motawe +
   - Mukulika Pahari
   - Márton Gunyhó +
   - Namami Shanker +
   - Nihaal Sangha +
   - Niyas Sait
   - Omid Rajaei +
   - Oscar Gustafsson +
   - Ovee Jawdekar +
   - P. L. Lim +
   - Pamphile Roy +
   - Pantelis Antonoudiou +
   - Pearu Peterson
   - Peter Andreas Entschev
   - Peter Hawkins
   - Pierre de Buyl
   - Pieter Eendebak +
   - Pradipta Ghosh +
   - Rafael Cardoso Fernandes Sousa +
   - Raghuveer Devulapalli
   - Ralf Gommers
   - Raphael Kruse
   - Raúl Montón Pinillos
   - Robert Kern
   - Rohit Goswami
   - Ross Barnowski
   - Ruben Garcia +
   - Sadie Louise Bartholomew +
   - Saswat Das +
   - Sayed Adel
   - Sebastian Berg
   - Serge Guelton
   - Simon Surland Andersen +
   - Siyabend Ürün +
   - Somasree Majumder +
   - Soumya +
   - Stefan van der Walt
   - Stefano Miccoli +
   - Stephan Hoyer
   - Stephen Worsley +
   - Tania Allard
   - Thomas Duvernay +
   - Thomas Green +
   - Thomas J. Fan
   - Thomas Li +
   - Tim Hoffmann
   - Ting Sun +
   - Tirth Patel
   - Toshiki Kataoka
   - Tyler Reddy
   - Warren Weckesser
   - Yang Hau
   - Yoon, Jee Seok +

Cheers,

Charles Harris
___
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[Python-announce] NumPy 1.22.4 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.22.4. NumPy 1.22.4 is a maintenance release that fixes bugs discovered
after the 1.22.3 release. In addition, the wheels for this release are
built using the recently released Cython 0.29.30, which should fix the
reported problems with debugging
<https://github.com/numpy/numpy/issues/21008>.

The Python versions supported in this release are 3.8-3.10.  Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.22.4>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.22.4>. Note that the Mac
wheels are based on OS X 10.15 rather than 10.9 that was used in previous
NumPy release cycles.

*Contributors*

A total of 12 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - Alexander Shadchin
   - Bas van Beek
   - Charles Harris
   - Hood Chatham
   - Jarrod Millman
   - John-Mark Gurney +
   - Junyan Ou +
   - Mariusz Felisiak +
   - Ross Barnowski
   - Sebastian Berg
   - Serge Guelton
   - Stefan van der Walt

*Pull requests merged*

A total of 22 pull requests were merged for this release.

   - #21191: TYP, BUG: Fix ``np.lib.stride_tricks`` re-exported under the...
   - #21192: TST: Bump mypy from 0.931 to 0.940
   - #21243: MAINT: Explicitly re-export the types in ``numpy._typing``
   - #21245: MAINT: Specify sphinx, numpydoc versions for CI doc builds
   - #21275: BUG: Fix typos
   - #21277: ENH, BLD: Fix math feature detection for wasm
   - #21350: MAINT: Fix failing simd and cygwin tests.
   - #21438: MAINT: Fix failing Python 3.8 32-bit Windows test.
   - #21444: BUG: add linux guard per #21386
   - #21445: BUG: Allow legacy dtypes to cast to datetime again
   - #21446: BUG: Make mmap handling safer in frombuffer
   - #21447: BUG: Stop using PyBytesObject.ob_shash deprecated in Python
   3.11.
   - #21448: ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS
   - #21472: BUG: Ensure compile errors are raised correctly
   - #21473: BUG: Fix segmentation fault
   - #21474: MAINT: Update doc requirements
   - #21475: MAINT: Mark ``npy_memchr`` with ``no_sanitize("alignment")``
   on clang
   - #21512: DOC: Proposal - make the doc landing page cards more similar...
   - #21525: MAINT: Update Cython version to 0.29.30.
   - #21536: BUG: Fix GCC error during build configuration
   - #21541: REL: Prepare for the NumPy 1.22.4 release.
   - #21547: MAINT: Skip tests that fail on PyPy.

Cheers,

Charles Harris
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[Python-announce] NumPy 1.21.6 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team I am pleased to announce the release of NumPy
1.21.6. NumPy 1.21.6 is a very small release that achieves two things:

   - Backs out the mistaken backport of C++ code into 1.21.5.
   - Provides a 32 bit Windows wheel for Python 3.10.

The provision of the 32 bit wheel is intended to make life easier for
oldest-supported-numpy.

The Python versions supported in this release are 3.7-3.10. If you want to
compile your own version using gcc-11 you will need to use gcc-11.2+ to
avoid problems. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.21.6/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.21.6>. Linux users will
need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014
wheels. A recent version of pip is needed to install the universal2
macos wheels.

Cheers,

Charles Harris
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[issue224943] NumPy URL update

[admin]

Change by admin :


--
github: None -> 33563

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[issue224943] NumPy URL update

[admin]

Change by admin :


--
github: None -> 33563

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[issue47082] No protection: `import numpy` in two different threads can lead to race-condition

[Sebastian Berg]

Sebastian Berg  added the comment:

Thanks, so there should already be a lock in place (sorry, I missed that).  But 
somehow we seem to get around it?

Do you know what may cause the locking logic to fail in this case?  Recursive 
imports in NumPy itself?  Or Cython using low-level C-API?

I.e. can you think of something to investigate that may help NumPy/Cython to 
make sure that locking is successful?


/* Cythons Import code (slightly cleaned up for Python 3 only): */
static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
PyObject *empty_list = 0;
PyObject *module = 0;
PyObject *global_dict = 0;
PyObject *empty_dict = 0;
PyObject *list;
if (from_list)
list = from_list;
else {
empty_list = PyList_New(0);
if (!empty_list)
goto bad;
list = empty_list;
}
global_dict = PyModule_GetDict(__pyx_m);
if (!global_dict)
goto bad;
empty_dict = PyDict_New();
if (!empty_dict)
goto bad;
{
if (level == -1) {
if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
module = PyImport_ImportModuleLevelObject(
name, global_dict, empty_dict, list, 1);
if (!module) {
if (!PyErr_ExceptionMatches(PyExc_ImportError))
goto bad;
PyErr_Clear();
}
}
level = 0;
}
if (!module) {
module = PyImport_ImportModuleLevelObject(
name, global_dict, empty_dict, list, level);
}
}
bad:
Py_XDECREF(empty_list);
Py_XDECREF(empty_dict);
return module;
}

--

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[issue47082] No protection: `import numpy` in two different threads can lead to race-condition

[Christian Heimes]

Christian Heimes  added the comment:

Python used to have a global import lock. The global import lock prevented 
recursive imports from other threads while the current thread was importing. 
The import lock was source of issues and dead locks. Antoine replaced it with a 
fine grained import lock in bpo-9260.

--
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[issue47082] No protection: `import numpy` in two different threads can lead to race-condition

[Sebastian Berg]

Sebastian Berg  added the comment:

To add to this: it would seem to me that the side-effects of importing should 
be guaranteed to only be called once?

However, IO or other operations could be part of the import side-effects and 
release the GIL.  So even a simple, pure-Python, package could run into this 
same issue and probably won't even realize that they can run into it.
(Assuming I understand how this is happening correctly.)

So it would seem to me that either:
* Python should lock on the thread level or maybe the `sys.modules` dictionary?
* The `threading` module could somehow ensure safety by hooking into
  the import machinery?
* Packages that may release the GIL or have side-effects that must
  only be run once have to lock (i.e. NumPy).
  (But it seems to me that many packages will not even be aware of
  possible issues.)

--
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[issue47082] No protection: `import numpy` in two different threads can lead to race-condition

[Jens Henrik Goebbert]

New submission from Jens Henrik Goebbert :

While using [Xpra](https://github.com/Xpra-org/xpra) we came across a bug which 
might be a Python or a NumPy issue.
Perhaps some of you can help us understanding some internals.

Calling `import numpy` at the same time in two different threads of the Python 
program can lead to a race-condition.
This happens for example with Xpra when loading the encoder nvjpeg:
```
2022-03-20 12:54:59,298  cannot load enc_nvjpeg (nvjpeg encoder)
Traceback (most recent call last):
  File "/lib/python3.9/site-packages/xpra/codecs/loader.py", line 
52, in codec_import_check
ic =  __import__(class_module, {}, {}, classnames)
  File "xpra/codecs/nvjpeg/encoder.pyx", line 8, in init 
xpra.codecs.nvjpeg.encoder
  File "/lib/python3.9/site-packages/numpy/__init__.py", line 150, 
in 
from . import core
  File "/lib/python3.9/site-packages/numpy/core/__init__.py", line 
51, in 
del os.environ[envkey]
  File "/lib/python3.9/os.py", line 695, in __delitem__
raise KeyError(key) from None
KeyError: 'OPENBLAS_MAIN_FREE'
```

Here the environment variable OPENBLAS_MAIN_FREE is set in the `numpy` code:
https://github.com/numpy/numpy/blob/maintenance/1.21.x/numpy/core/__init__.py#L18
and short after that it is deleted
https://github.com/numpy/numpy/blob/maintenance/1.21.x/numpy/core/__init__.py#L51
But this deletion fails ... perhaps because the initialization runs at the same 
time in two threads :thinking:

Shouldn't Python protect us by design?

@seberg comments 
[HERE](https://github.com/numpy/numpy/issues/21223#issuecomment-1074008386):
```
So, my current hypothesis (and I have briefly checked the Python code) is that 
Python does not do manual locking. But it effectively locks due to this going 
into C and thus holding the GIL. But somewhere during the import of NumPy, 
NumPy probably releases the GIL briefly and that could allow the next thread to 
go into the import machinery.
[..]
NumPy may be doing some worse than typical stuff here, but right now it seems 
to me that Python should be protecting us.
```

Can anyone comment on this?

--
components: Interpreter Core
messages: 415687
nosy: jhgoebbert
priority: normal
severity: normal
status: open
title: No protection: `import numpy` in two different threads can lead to 
race-condition
type: behavior
versions: Python 3.11

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[Python-announce] NumPy 1.22.3 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.22.3. NumPy 1.22.3 is a maintenance release that fixes bugs discovered
after the 1.22.2 release. The most noticeable fixes may be those for
DLPack. One that may cause some problems is disallowing strings as inputs
to logical ufuncs. It is still undecided how strings should be treated in
those functions and it was thought best to simply disallow them until a
decision was reached. That should not cause problems with older code.

The Python versions supported in this release are 3.8-3.10.  Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.22.3>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.22.3>. Note that the Mac
wheels are now based on OS X 10.14 rather than 10.9 that was used in
previous NumPy release cycles. 10.14 is the oldest release supported by
Apple. Linux users will need pip >= 0.19.3 in order to install the
manylinux2014 wheels. A recent version of pip is needed to install the
universal2 macos wheels.

*Contributors*

A total of 9 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.


   - @GalaxySnail +
   - Alexandre de Siqueira
   - Bas van Beek
   - Charles Harris
   - Melissa Weber Mendonça
   - Ross Barnowski
   - Sebastian Berg
   - Tirth Patel
   - Matthieu Darbois


*Pull requests merged*

A total of 10 pull requests were merged for this release.


   - #21048: MAINT: Use "3.10" instead of "3.10-dev" on travis.
   - #21106: TYP,MAINT: Explicitly allow sequences of array-likes in
   ``np.concatenate``
   - #21137: BLD,DOC: skip broken ipython 8.1.0
   - #21138: BUG, ENH: np._from_dlpack: export correct device information
   - #21139: BUG: Fix numba DUFuncs added loops getting picked up
   - #21140: BUG: Fix unpickling an empty ndarray with a non-zero
   dimension...
   - #21141: BUG: use ThreadPoolExecutor instead of ThreadPool
   - #21142: API: Disallow strings in logical ufuncs
   - #21143: MAINT, DOC: Fix SciPy intersphinx link
   - #21148: BUG,ENH: np._from_dlpack: export arrays with any strided
   size-1...

Cheers,

Charles Harris
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[Python-announce] NumPy 1.22.2 released

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.22.2. NumPy 1.22.2 fixes several bugs discovered after the 1.22.1
release. Notable fixes are:

   - Build related fixes for downstream projects and other platforms.
   - Various annotation fixes/additions.
   - Fixes for the CVE-2021-41495 complaint.
   - Toolchain fixes on Windows. Numpy wheels now use the 1.41 tool chain,
   fixing downstream link problems for projects using NumPy provided libraries
   on Windows.

Note that OSX wheels are provided for both the x86_64 and arm64
architectures, but there are no universal2 wheels. The minimum supported OS
X version is now 10.14 for the former and 11.0 for the latter. The Python
versions supported in this release are 3.8-3.10.  Wheels can be downloaded
from PyPI <https://pypi.org/project/numpy/1.22.2>; source archives, release
notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.22.2>. Linux users will
need pip >= 0.19.3 in order to install the manylinux2014 wheels.

*Contributors*

A total of 14 people contributed to this release.  People with a "+" by
their names contributed a patch for the first time.

   - Andrew J. Hesford +
   - Bas van Beek
   - Brénainn Woodsend +
   - Charles Harris
   - Hood Chatham
   - Janus Heide +
   - Leo Singer
   - Matti Picus
   - Mukulika Pahari
   - Niyas Sait
   - Pearu Peterson
   - Ralf Gommers
   - Sebastian Berg
   - Serge Guelton

*Pull requests merged*

A total of 21 pull requests were merged for this release.

   - #20842: BLD: Add NPY_DISABLE_SVML env var to opt out of SVML
   - #20843: BUG: Fix build of third party extensions with Py_LIMITED_API
   - #20844: TYP: Fix pyright being unable to infer the ``real`` and
   ``imag``...
   - #20845: BUG: Fix comparator function signatures
   - #20906: BUG: Avoid importing ``numpy.distutils`` on import
   numpy.testing
   - #20907: MAINT: remove outdated mingw32 fseek support
   - #20908: TYP: Relax the return type of ``np.vectorize``
   - #20909: BUG: fix f2py's define for threading when building with Mingw
   - #20910: BUG: distutils: fix building mixed C/Fortran extensions
   - #20912: DOC,TST: Fix Pandas code example as per new release
   - #20935: TYP, MAINT: Add annotations for ``flatiter.__setitem__``
   - #20936: MAINT, TYP: Added missing where typehints in
   ``fromnumeric.pyi``
   - #20937: BUG: Fix build_ext interaction with non numpy extensions
   - #20938: BUG: Fix missing intrinsics for windows/arm64 target
   - #20945: REL: Prepare for the NumPy 1.22.2 release.
   - #20982: MAINT: f2py: don't generate code that triggers
   ``-Wsometimes-uninitialized``.
   - #20983: BUG: Fix incorrect return type in reduce without initial value
   - #20984: ENH: review return values for PyArray_DescrNew
   - #20985: MAINT: be more tolerant of setuptools >= 60
   - #20986: BUG: Fix misplaced return.
   - #20992: MAINT: Further small return value validation fixes

Cheers,

Charles Harris
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Re: Pandas or Numpy

[Marco Sulla]

On Mon, 24 Jan 2022 at 05:37, Dennis Lee Bieber  wrote:
> Note that the comparison warns that /indexing/ in pandas can be slow.
> If your manipulation is always "apply operationX to columnY" it should be
> okay -- but "apply operationX to the nth row of columnY", and repeat for
> other rows, is going to be slow.

In my small way, I can confirm. In one of my previous works, we used
numpy and Pandas. Writing the code in Pandas is quick, but they just
realised that was really slow, and they tried to transform as much
Panda code to numpy code as possible.

Furthermore, I saw that they were so accustomed with Pandas that they
used it for all, even for a simple csv creation, when the csv builtin
module is enough.
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Re: Pandas or Numpy

[Dennis Lee Bieber]

On Sun, 23 Jan 2022 07:34:26 -0800, Tobiah  declaimed the
following:

I'm going to do a little rearranging of your paragraphs, since most of
them are domain specific, whereas the last (original) paragraph actually
gets to a core...

Caveat: I've not written anything making use of either package, so my
only basis for commenting is what I've read on web sites (like the pandas
documentation site)

>
>It seems like both libraries are possible choices.  Would one
>be the obvious choice for me?
>
pandas USES numpy internally but expands on it...

https://www.geeksforgeeks.org/difference-between-pandas-vs-numpy/
"""
A numpy array is a grid of values (of the same type) that are indexed by a
tuple of positive integers,
"""
Pandas provide high performance, fast, easy to use data structures and data
analysis tools for manipulating numeric data and time series.
"""

Pandas, I believe, might get closer to what one might find in
statistical packages (like R) in that it supports tables/data-frames in
which each column may be a different data type. I don't know if it actually
has the statistics concepts of "factors" (eg: a column containing
"male"/"female" is not really a text column but closer to an enumeration
type).



>I need to compose large (hundreds, thousands, maybe millions) lists
>and be able to do math on, or possibly sort by various columns, among other
>operations.  A common requirement would be to do the same math operation
>on each value in a column, or redistribute the values according to an
>exponential curve, etc.

En-mass operations should be supported; not sure about the
"redistribute" -- if you can define a function that takes one input
parameter (the existing value) and returns the redistributed value, I'd
think it should be feasible.

>
>One wrinkle is that the first column of a Csound score is actually a
>single character.  I was thinking if the data types all had to be the
>same, then I'd make a translation table or just use the ascii value
>of the character, but if I could mix types that might be a smidge better.
>

Based upon the comparison I linked, pandas should be applicable for
this. For pure numpy, you'd likely be better off maintaining a separate
list (though sorting will require some tricks to keep the numpy array in
sync with the character list).

Note that the comparison warns that /indexing/ in pandas can be slow.
If your manipulation is always "apply operationX to columnY" it should be
okay -- but "apply operationX to the nth row of columnY", and repeat for
other rows, is going to be slow.



-- 
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wlfr...@ix.netcom.comhttp://wlfraed.microdiversity.freeddns.org/
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Re: Pandas or Numpy

[Avi Gross via Python-list]

Definitely it sounds like you may use both. Quite a bit of what people do using 
DataFrame objects includes working on copies of individual columns, which often 
are numpy Series or the like and in the other direction, can be used to create 
or amend a pandas DataFrame. Plus, many operations used to select some subset 
of rows will use things like  a data structure holding the integer indexes you 
want or a boolean where True means take it and False means ignore it. 
Many real life applications just incorporate both numpy as np and pandas as pd 
and sometimes also use other Python functionality such as lists or matrices 
which unfortunately generally are just a list of lists. 
Python was built with a different philosophy than some languages like R in 
which much of what is in numpy and pandas is built in, in  other ways, and has 
been extended by packages. Python built mostly on flexible lists so the modules 
you are asking about do make faster and perhaps better versions. And, to be 
fair, python has lots of nifty features that R is largely missing and had to be 
added externally. 
Both used properly can do a nice job on the kind of things you want but with a 
warning. Your description suggests some of the data you will be using or making 
can get quite large. So make sure you look into the dtypes for parts of your 
data so you do not store small integers in full sized integers but in signed or 
unsigned bytes. Data with only two possible values, might be stored as boolean. 
And note many operations can be done in place, rather than creating a new 
object. If you are worried about space usage, or time spent on garbage 
collection, as in any programming language, there are recognized ideas about 
how you might tighten up your code using existing paradigms. I do admit some 
tricks have costs and it takes real testing to see if it even matters to try. 
But in general, much of numpy and pandas are already optimized in lots of 
compiled code  so using these rather than python lists and other data 
structures can already be a big plus.
Good luck.

-Original Message-
From: Julius Hamilton 
To: Chris Angelico 
Cc: python-list@python.org
Sent: Sun, Jan 23, 2022 1:05 pm
Subject: Re: Pandas or Numpy

Hey,


I don’t know but in case you don’t get other good answers, I’m pretty sure
Numpy is more of a mathematical library and Pandas is definitely for
handling spreadsheet data.


So maybe both.


Julius

On Sun 23. Jan 2022 at 18:28, Chris Angelico  wrote:

> On Mon, 24 Jan 2022 at 04:10, Tobiah  wrote:
> >
> > I know very little about either.  I need to handle score input files
> > for Csound.  Each line is a list of floating point values where each
> > column has a particular meaning to the program.
> >
> > I need to compose large (hundreds, thousands, maybe millions) lists
> > and be able to do math on, or possibly sort by various columns, among
> other
> > operations.  A common requirement would be to do the same math operation
> > on each value in a column, or redistribute the values according to an
> > exponential curve, etc.
> >
> > One wrinkle is that the first column of a Csound score is actually a
> > single character.  I was thinking if the data types all had to be the
> > same, then I'd make a translation table or just use the ascii value
> > of the character, but if I could mix types that might be a smidge better.
> >
> > It seems like both libraries are possible choices.  Would one
> > be the obvious choice for me?
> >
>
> I'm not an expert, but that sounds like a job for Pandas to me. It's
> excellent at handling tabular data, and yes, it's fine with a mixture
> of types. Everything else you've described should work fine (not sure
> how to redistribute on an exponential curve, but I'm sure it's not
> hard).
>
> BTW, Pandas is built on top of Numpy, so it's kinda "both".
>
> ChrisA
> --
> https://mail.python.org/mailman/listinfo/python-list
>
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Re: Pandas or Numpy

[Julius Hamilton]

Hey,


I don’t know but in case you don’t get other good answers, I’m pretty sure
Numpy is more of a mathematical library and Pandas is definitely for
handling spreadsheet data.


So maybe both.


Julius

On Sun 23. Jan 2022 at 18:28, Chris Angelico  wrote:

> On Mon, 24 Jan 2022 at 04:10, Tobiah  wrote:
> >
> > I know very little about either.  I need to handle score input files
> > for Csound.  Each line is a list of floating point values where each
> > column has a particular meaning to the program.
> >
> > I need to compose large (hundreds, thousands, maybe millions) lists
> > and be able to do math on, or possibly sort by various columns, among
> other
> > operations.  A common requirement would be to do the same math operation
> > on each value in a column, or redistribute the values according to an
> > exponential curve, etc.
> >
> > One wrinkle is that the first column of a Csound score is actually a
> > single character.  I was thinking if the data types all had to be the
> > same, then I'd make a translation table or just use the ascii value
> > of the character, but if I could mix types that might be a smidge better.
> >
> > It seems like both libraries are possible choices.  Would one
> > be the obvious choice for me?
> >
>
> I'm not an expert, but that sounds like a job for Pandas to me. It's
> excellent at handling tabular data, and yes, it's fine with a mixture
> of types. Everything else you've described should work fine (not sure
> how to redistribute on an exponential curve, but I'm sure it's not
> hard).
>
> BTW, Pandas is built on top of Numpy, so it's kinda "both".
>
> ChrisA
> --
> https://mail.python.org/mailman/listinfo/python-list
>
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Re: Pandas or Numpy

[Chris Angelico]

On Mon, 24 Jan 2022 at 04:10, Tobiah  wrote:
>
> I know very little about either.  I need to handle score input files
> for Csound.  Each line is a list of floating point values where each
> column has a particular meaning to the program.
>
> I need to compose large (hundreds, thousands, maybe millions) lists
> and be able to do math on, or possibly sort by various columns, among other
> operations.  A common requirement would be to do the same math operation
> on each value in a column, or redistribute the values according to an
> exponential curve, etc.
>
> One wrinkle is that the first column of a Csound score is actually a
> single character.  I was thinking if the data types all had to be the
> same, then I'd make a translation table or just use the ascii value
> of the character, but if I could mix types that might be a smidge better.
>
> It seems like both libraries are possible choices.  Would one
> be the obvious choice for me?
>

I'm not an expert, but that sounds like a job for Pandas to me. It's
excellent at handling tabular data, and yes, it's fine with a mixture
of types. Everything else you've described should work fine (not sure
how to redistribute on an exponential curve, but I'm sure it's not
hard).

BTW, Pandas is built on top of Numpy, so it's kinda "both".

ChrisA
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Pandas or Numpy

[Tobiah]

I know very little about either.  I need to handle score input files
for Csound.  Each line is a list of floating point values where each
column has a particular meaning to the program.

I need to compose large (hundreds, thousands, maybe millions) lists
and be able to do math on, or possibly sort by various columns, among other
operations.  A common requirement would be to do the same math operation
on each value in a column, or redistribute the values according to an
exponential curve, etc.

One wrinkle is that the first column of a Csound score is actually a
single character.  I was thinking if the data types all had to be the
same, then I'd make a translation table or just use the ascii value
of the character, but if I could mix types that might be a smidge better.

It seems like both libraries are possible choices.  Would one
be the obvious choice for me?


Thanks!
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[Python-announce] NumPy 1.22.1 has been released.

[Charles R Harris]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of NumPy
1.22.1. NumPy 1.22.1 fixes several bugs discovered after the 1.22.0
release. Notable fixes are:

   - Fix for f2PY docstring problems (SciPy)
   - Fix for reduction type problems (AstroPy)
   - Fixes for various typing bugs.

The Python versions supported in this release are 3.8-3.10.  Wheels can be
downloaded from PyPI <https://pypi.org/project/numpy/1.22.1>; source
archives, release notes, and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.22.1>. Linux users will
need pip >= 0.19.3 in order to install the manylinux2014 wheels. A recent
version of pip is needed to install the universal2 macos wheels.

*Contributors*

A total of 14 people contributed to this release.  People with a "+" by
their
names contributed a patch for the first time.

   - Arryan Singh
   - Bas van Beek
   - Charles Harris
   - Denis Laxalde
   - Isuru Fernando
   - Kevin Sheppard
   - Matthew Barber
   - Matti Picus
   - Melissa Weber Mendonça
   - Mukulika Pahari
   - Omid Rajaei +
   - Pearu Peterson
   - Ralf Gommers
   - Sebastian Berg


*Pull requests merged*
A total of 20 pull requests were merged for this release.

   - #20702: MAINT, DOC: Post 1.22.0 release fixes.
   - #20703: DOC, BUG: Use pngs instead of svgs.
   - #20704: DOC: Fixed the link on user-guide landing page
   - #20714: BUG: Restore vc141 support
   - #20724: BUG: Fix array dimensions solver for multidimensional
   arguments...
   - #20725: TYP: change type annotation for ``__array_namespace__`` to
   ModuleType
   - #20726: TYP, MAINT: Allow ``ndindex`` to accept integer tuples
   - #20757: BUG: Relax dtype identity check in reductions
   - #20763: TYP: Allow time manipulation functions to accept ``date`` and
   ``timedelta``...
   - #20768: TYP: Relax the type of ``ndarray.__array_finalize__``
   - #20795: MAINT: Raise RuntimeError if setuptools version is too recent.
   - #20796: BUG, DOC: Fixes SciPy docs build warnings
   - #20797: DOC: fix OpenBLAS version in release note
   - #20798: PERF: Optimize array check for bounded 0,1 values
   - #20805: BUG: Fix that reduce-likes honor out always (and live in the...
   - #20806: BUG: ``array_api.argsort(descending=True)`` respects
   relative...
   - #20807: BUG: Allow integer inputs for pow-related functions in
   ``array_api``
   - #20814: DOC: Refer to NumPy, not pandas, in main page
   - #20815: DOC: Update Copyright to 2022 [License]
   - #20819: BUG: Return correctly shaped inverse indices in array_api
   set...

Cheers,

Charles Harris
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