From mikofski at berkeley.edu  Wed Nov  6 14:18:31 2019
From: mikofski at berkeley.edu (Dr. Mark Alexander Mikofski PhD)
Date: Wed, 6 Nov 2019 11:18:31 -0800
Subject: [SciPy-User] [ANN] pvfactors: a view-factor network model for
 bifacial PV by SunPower
Message-ID: <CAEqRcW35TTwNPPhX11cdDCt70SCVR0M+ZHKUAX-_7MQc-F+FnQ@mail.gmail.com>

SunPower has just released the latest version of pvfactors (v1.3.0), a
Python package for modeling the incident irradiance on bifacial
photovoltaic (PV) using the network radiosity model. Bifacial PV are solar
panels that can generate energy from both the front and back surfaces.

PyPI: https://pypi.org/project/pvfactors/
GitHub: https://github.com/SunPower/pvfactors/releases


Thanks SunPower!

-- 
Mark Mikofski, PhD (2005)
*Fiat Lux*
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From me at iyanmv.com  Thu Nov  7 07:11:55 2019
From: me at iyanmv.com (=?ISO-8859-1?Q?Iy=E1n_M=E9ndez?= Veiga)
Date: Thu, 07 Nov 2019 13:11:55 +0100
Subject: [SciPy-User] Scipy and information theory
Message-ID: <20976252.W2eyRVhfPJ@theotp40>

Hi,

I discovered recently some interesting functions in scipy that are useful in 
the context of information theory. In particular, xlogy, entr and rel_entr. 
However, for the calculations I want to do I am interested in using log2 
instead of natural logarithm. I was expecting to be able to choose the base 
with this functions but it is not possible.

Do you think it would be a nice feature to add to scipy? Maybe not for a 
general base, but at least the bases that are available in numpy (log2, 
log10). 

The workaround I am using with xlogy(x,y) (and similar functions) is 
multiplying x array by log2(e).

Best wishes,
Iy?n



From ralf.gommers at gmail.com  Fri Nov  8 10:54:32 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Fri, 8 Nov 2019 10:54:32 -0500
Subject: [SciPy-User] Scipy and information theory
In-Reply-To: <20976252.W2eyRVhfPJ@theotp40>
References: <20976252.W2eyRVhfPJ@theotp40>
Message-ID: <CABL7CQh=LDNJfH+P7tJQz2wB8SyeY3uRKRK5NFYs4eWHxYnYTg@mail.gmail.com>

Hi Iy?n,


On Thu, Nov 7, 2019 at 12:21 PM Iy?n M?ndez Veiga <me at iyanmv.com> wrote:

> Hi,
>
> I discovered recently some interesting functions in scipy that are useful
> in
> the context of information theory. In particular, xlogy, entr and
> rel_entr.
> However, for the calculations I want to do I am interested in using log2
> instead of natural logarithm. I was expecting to be able to choose the
> base
> with this functions but it is not possible.
>
> Do you think it would be a nice feature to add to scipy? Maybe not for a
> general base, but at least the bases that are available in numpy (log2,
> log10).
>

This does sound like a useful feature to add to these functions. I'm not
sure how easy it is to add a keyword to those functions though, since the
ufunc machinery may be limited in flexibility. Maybe someone else can say
straight away. If you want to start looking into implementing this, here
are some notes of where to start:
http://scipy.github.io/devdocs/dev/core-dev/index.html#scipy-special

Cheers,
Ralf


> The workaround I am using with xlogy(x,y) (and similar functions) is
> multiplying x array by log2(e).
>
> Best wishes,
> Iy?n
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From rlucente at pipeline.com  Fri Nov  8 12:10:48 2019
From: rlucente at pipeline.com (Robert Lucente - Pipeline.Com)
Date: Fri, 8 Nov 2019 12:10:48 -0500
Subject: [SciPy-User] Scipy and information theory
Message-ID: <061801d59657$77e141f0$67a3c5d0$@pipeline.com>

> interested in using log2 instead of natural logarithm. I was expecting to
be able to choose the base with this functions but it is not possible
Why not use Python's built in fcn?

https://docs.python.org/3/library/math.html#math.log

math.log(x[, base])

    With one argument, return the natural logarithm of x (to base e).

    With two arguments, return the logarithm of x to the given base,
calculated as log(x)/log(base).



-----Original Message-----
From: SciPy-User <scipy-user-bounces+rlucente=pipeline.com at python.org> On
Behalf Of scipy-user-request at python.org
Sent: Friday, November 8, 2019 12:00 PM
To: scipy-user at python.org
Subject: SciPy-User Digest, Vol 195, Issue 2

Send SciPy-User mailing list submissions to
	scipy-user at python.org

To subscribe or unsubscribe via the World Wide Web, visit
	https://mail.python.org/mailman/listinfo/scipy-user
or, via email, send a message with subject or body 'help' to
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You can reach the person managing the list at
	scipy-user-owner at python.org

When replying, please edit your Subject line so it is more specific than
"Re: Contents of SciPy-User digest..."


Today's Topics:

   1. Scipy and information theory (Iy?n M?ndez Veiga)
   2. Re: Scipy and information theory (Ralf Gommers)


----------------------------------------------------------------------

Message: 1
Date: Thu, 07 Nov 2019 13:11:55 +0100
From: Iy?n M?ndez Veiga <me at iyanmv.com>
To: scipy-user at python.org
Subject: [SciPy-User] Scipy and information theory
Message-ID: <20976252.W2eyRVhfPJ at theotp40>
Content-Type: text/plain; charset="iso-8859-1"

Hi,

I discovered recently some interesting functions in scipy that are useful in
the context of information theory. In particular, xlogy, entr and rel_entr. 
However, for the calculations I want to do I am interested in using log2
instead of natural logarithm. I was expecting to be able to choose the base
with this functions but it is not possible.

Do you think it would be a nice feature to add to scipy? Maybe not for a
general base, but at least the bases that are available in numpy (log2,
log10). 

The workaround I am using with xlogy(x,y) (and similar functions) is
multiplying x array by log2(e).

Best wishes,
Iy?n




------------------------------

Message: 2
Date: Fri, 8 Nov 2019 10:54:32 -0500
From: Ralf Gommers <ralf.gommers at gmail.com>
To: SciPy Users List <scipy-user at python.org>
Subject: Re: [SciPy-User] Scipy and information theory
Message-ID:
	<CABL7CQh=LDNJfH+P7tJQz2wB8SyeY3uRKRK5NFYs4eWHxYnYTg at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Hi Iy?n,


On Thu, Nov 7, 2019 at 12:21 PM Iy?n M?ndez Veiga <me at iyanmv.com> wrote:

> Hi,
>
> I discovered recently some interesting functions in scipy that are 
> useful in the context of information theory. In particular, xlogy, 
> entr and rel_entr.
> However, for the calculations I want to do I am interested in using 
> log2 instead of natural logarithm. I was expecting to be able to 
> choose the base with this functions but it is not possible.
>
> Do you think it would be a nice feature to add to scipy? Maybe not for 
> a general base, but at least the bases that are available in numpy 
> (log2, log10).
>

This does sound like a useful feature to add to these functions. I'm not
sure how easy it is to add a keyword to those functions though, since the
ufunc machinery may be limited in flexibility. Maybe someone else can say
straight away. If you want to start looking into implementing this, here are
some notes of where to start:
http://scipy.github.io/devdocs/dev/core-dev/index.html#scipy-special

Cheers,
Ralf


> The workaround I am using with xlogy(x,y) (and similar functions) is 
> multiplying x array by log2(e).
>
> Best wishes,
> Iy?n
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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------------------------------

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_______________________________________________
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SciPy-User at python.org
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------------------------------

End of SciPy-User Digest, Vol 195, Issue 2
******************************************


From avrahami.ben at gmail.com  Sun Nov 10 04:45:54 2019
From: avrahami.ben at gmail.com (Ben Avrahami)
Date: Sun, 10 Nov 2019 11:45:54 +0200
Subject: [SciPy-User] best way to get minimum and maximum support for
 generic random variable
Message-ID: <CALFWR8HhUAGRUL5tP5bhNHDfBg7HOP73D6GqGnkkaFQ38wUwHw@mail.gmail.com>

Hello,

I'm trying to come up with a simple universal method to get the minimum and
maximum values for any random variable (both rv_continuous and rv_discrete).

The best I came up with was rv.interval(1) but that is both more obtuse
than I'd like and error prone (as seen in the issue I opened:
https://github.com/scipy/scipy/issues/11026), also, it's not exactly was
the method is made for which makes me uneasy.

Is there any better way to get the smallest and largest possible values for
any rv?

regards, Ben.
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From rlucente at pipeline.com  Fri Nov  8 12:13:32 2019
From: rlucente at pipeline.com (Robert Lucente - Pipeline.Com)
Date: Fri, 8 Nov 2019 12:13:32 -0500
Subject: [SciPy-User] Scipy and information theory
Message-ID: <061901d59657$d980dd80$8c829880$@pipeline.com>

> interested in using log2 instead of natural logarithm. I was expecting 
> to be able to choose the base with this functions but it is not 
> possible

Why not use Python's built in fcn?

https://docs.python.org/3/library/math.html#math.log2

math.log2(x)

    Return the base-2 logarithm of x. This is usually more accurate than
log(x, 2).

    New in version 3.3.

-----Original Message-----
From: Robert Lucente - Pipeline.Com <rlucente at pipeline.com> 
Sent: Friday, November 8, 2019 12:11 PM
To: 'scipy-user at python.org' <scipy-user at python.org>
Cc: Robert Gmail Backup 2 Lucente (robert.backup.2.lucente at gmail.com)
<robert.backup.2.lucente at gmail.com>
Subject: Re: Scipy and information theory 

> interested in using log2 instead of natural logarithm. I was expecting 
> to be able to choose the base with this functions but it is not 
> possible
Why not use Python's built in fcn?

https://docs.python.org/3/library/math.html#math.log

math.log(x[, base])

    With one argument, return the natural logarithm of x (to base e).

    With two arguments, return the logarithm of x to the given base,
calculated as log(x)/log(base).



-----Original Message-----
From: SciPy-User <scipy-user-bounces+rlucente=pipeline.com at python.org> On
Behalf Of scipy-user-request at python.org
Sent: Friday, November 8, 2019 12:00 PM
To: scipy-user at python.org
Subject: SciPy-User Digest, Vol 195, Issue 2

Send SciPy-User mailing list submissions to
	scipy-user at python.org

To subscribe or unsubscribe via the World Wide Web, visit
	https://mail.python.org/mailman/listinfo/scipy-user
or, via email, send a message with subject or body 'help' to
	scipy-user-request at python.org

You can reach the person managing the list at
	scipy-user-owner at python.org

When replying, please edit your Subject line so it is more specific than
"Re: Contents of SciPy-User digest..."


Today's Topics:

   1. Scipy and information theory (Iy?n M?ndez Veiga)
   2. Re: Scipy and information theory (Ralf Gommers)


----------------------------------------------------------------------

Message: 1
Date: Thu, 07 Nov 2019 13:11:55 +0100
From: Iy?n M?ndez Veiga <me at iyanmv.com>
To: scipy-user at python.org
Subject: [SciPy-User] Scipy and information theory
Message-ID: <20976252.W2eyRVhfPJ at theotp40>
Content-Type: text/plain; charset="iso-8859-1"

Hi,

I discovered recently some interesting functions in scipy that are useful in
the context of information theory. In particular, xlogy, entr and rel_entr. 
However, for the calculations I want to do I am interested in using log2
instead of natural logarithm. I was expecting to be able to choose the base
with this functions but it is not possible.

Do you think it would be a nice feature to add to scipy? Maybe not for a
general base, but at least the bases that are available in numpy (log2,
log10). 

The workaround I am using with xlogy(x,y) (and similar functions) is
multiplying x array by log2(e).

Best wishes,
Iy?n




------------------------------

Message: 2
Date: Fri, 8 Nov 2019 10:54:32 -0500
From: Ralf Gommers <ralf.gommers at gmail.com>
To: SciPy Users List <scipy-user at python.org>
Subject: Re: [SciPy-User] Scipy and information theory
Message-ID:
	<CABL7CQh=LDNJfH+P7tJQz2wB8SyeY3uRKRK5NFYs4eWHxYnYTg at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Hi Iy?n,


On Thu, Nov 7, 2019 at 12:21 PM Iy?n M?ndez Veiga <me at iyanmv.com> wrote:

> Hi,
>
> I discovered recently some interesting functions in scipy that are 
> useful in the context of information theory. In particular, xlogy, 
> entr and rel_entr.
> However, for the calculations I want to do I am interested in using
> log2 instead of natural logarithm. I was expecting to be able to 
> choose the base with this functions but it is not possible.
>
> Do you think it would be a nice feature to add to scipy? Maybe not for 
> a general base, but at least the bases that are available in numpy 
> (log2, log10).
>

This does sound like a useful feature to add to these functions. I'm not
sure how easy it is to add a keyword to those functions though, since the
ufunc machinery may be limited in flexibility. Maybe someone else can say
straight away. If you want to start looking into implementing this, here are
some notes of where to start:
http://scipy.github.io/devdocs/dev/core-dev/index.html#scipy-special

Cheers,
Ralf


> The workaround I am using with xlogy(x,y) (and similar functions) is 
> multiplying x array by log2(e).
>
> Best wishes,
> Iy?n
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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------------------------------

Subject: Digest Footer

_______________________________________________
SciPy-User mailing list
SciPy-User at python.org
https://mail.python.org/mailman/listinfo/scipy-user


------------------------------

End of SciPy-User Digest, Vol 195, Issue 2
******************************************


From me at iyanmv.com  Sun Nov 10 07:30:20 2019
From: me at iyanmv.com (=?ISO-8859-1?Q?Iy=E1n_M=E9ndez?= Veiga)
Date: Sun, 10 Nov 2019 13:30:20 +0100
Subject: [SciPy-User] Scipy and information theory
In-Reply-To: <061901d59657$d980dd80$8c829880$@pipeline.com>
References: <061901d59657$d980dd80$8c829880$@pipeline.com>
Message-ID: <9721637.vL8ZEpCK8U@master>

El viernes, 8 de noviembre de 2019 18:13:32 (CET) Robert Lucente - 
Pipeline.Com escribi?:
> Why not use Python's built in fcn?
> 
> https://docs.python.org/3/library/math.html#math.log2
> 
> math.log2(x)

Because I want to avoid long (and slow) for loops and take advantage of numpy/
scipy element wise operations. That is why I prefer numpy.log2(x) over 
math.log2(x). My x is a high dimension array, not a scalar.

> This does sound like a useful feature to add to these functions. I'm not
> sure how easy it is to add a keyword to those functions though, since the
> ufunc machinery may be limited in flexibility. Maybe someone else can say
> straight away. If you want to start looking into implementing this, here are
> some notes of where to start:
> http://scipy.github.io/devdocs/dev/core-dev/index.html#scipy-special

Thanks Ralf, I'll have a look. Also waiting if someone else can say if it 
would be easy or hard to implement.

Best wishes,
Iy?n



From rlucente at pipeline.com  Sun Nov 10 10:00:42 2019
From: rlucente at pipeline.com (Robert Lucente - Pipeline.Com)
Date: Sun, 10 Nov 2019 10:00:42 -0500
Subject: [SciPy-User] Scipy and information theory
In-Reply-To: <9721637.vL8ZEpCK8U@master>
References: <061901d59657$d980dd80$8c829880$@pipeline.com>
 <9721637.vL8ZEpCK8U@master>
Message-ID: <08e201d597d7$9f6c5ee0$de451ca0$@pipeline.com>

> Because I want to avoid long (and slow) for loops and take advantage of
numpy/ scipy element wise operations
I am embarrassed

That was an amateur mistake on my part :-(

-----Original Message-----
From: SciPy-User <scipy-user-bounces+rlucente=pipeline.com at python.org> On
Behalf Of Iy?n M?ndez Veiga
Sent: Sunday, November 10, 2019 7:30 AM
To: SciPy Users List <scipy-user at python.org>
Subject: Re: [SciPy-User] Scipy and information theory

El viernes, 8 de noviembre de 2019 18:13:32 (CET) Robert Lucente -
Pipeline.Com escribi?:
> Why not use Python's built in fcn?
> 
> https://docs.python.org/3/library/math.html#math.log2
> 
> math.log2(x)

Because I want to avoid long (and slow) for loops and take advantage of
numpy/ scipy element wise operations. That is why I prefer numpy.log2(x)
over math.log2(x). My x is a high dimension array, not a scalar.

> This does sound like a useful feature to add to these functions. I'm 
> not sure how easy it is to add a keyword to those functions though, 
> since the ufunc machinery may be limited in flexibility. Maybe someone 
> else can say straight away. If you want to start looking into 
> implementing this, here are some notes of where to start:
> http://scipy.github.io/devdocs/dev/core-dev/index.html#scipy-special

Thanks Ralf, I'll have a look. Also waiting if someone else can say if it
would be easy or hard to implement.

Best wishes,
Iy?n


_______________________________________________
SciPy-User mailing list
SciPy-User at python.org
https://mail.python.org/mailman/listinfo/scipy-user


From ralf.gommers at gmail.com  Sun Nov 10 21:26:58 2019
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Sun, 10 Nov 2019 18:26:58 -0800
Subject: [SciPy-User] Scipy and information theory
In-Reply-To: <08e201d597d7$9f6c5ee0$de451ca0$@pipeline.com>
References: <061901d59657$d980dd80$8c829880$@pipeline.com>
 <9721637.vL8ZEpCK8U@master>
 <08e201d597d7$9f6c5ee0$de451ca0$@pipeline.com>
Message-ID: <CABL7CQh9TkKyUH3cn4V=vC-cCvzVoQCBcrLdZeUxcK7izAtUdQ@mail.gmail.com>

On Sun, Nov 10, 2019 at 5:25 PM Robert Lucente - Pipeline.Com <
rlucente at pipeline.com> wrote:

> > Because I want to avoid long (and slow) for loops and take advantage of
> numpy/ scipy element wise operations
> I am embarrassed
>
> That was an amateur mistake on my part :-(
>
> -----Original Message-----
> From: SciPy-User <scipy-user-bounces+rlucente=pipeline.com at python.org> On
> Behalf Of Iy?n M?ndez Veiga
> Sent: Sunday, November 10, 2019 7:30 AM
> To: SciPy Users List <scipy-user at python.org>
> Subject: Re: [SciPy-User] Scipy and information theory
>
> El viernes, 8 de noviembre de 2019 18:13:32 (CET) Robert Lucente -
> Pipeline.Com escribi?:
> > Why not use Python's built in fcn?
> >
> > https://docs.python.org/3/library/math.html#math.log2
> >
> > math.log2(x)
>
> Because I want to avoid long (and slow) for loops and take advantage of
> numpy/ scipy element wise operations. That is why I prefer numpy.log2(x)
> over math.log2(x). My x is a high dimension array, not a scalar.
>
> > This does sound like a useful feature to add to these functions. I'm
> > not sure how easy it is to add a keyword to those functions though,
> > since the ufunc machinery may be limited in flexibility. Maybe someone
> > else can say straight away. If you want to start looking into
> > implementing this, here are some notes of where to start:
> > http://scipy.github.io/devdocs/dev/core-dev/index.html#scipy-special
>
> Thanks Ralf, I'll have a look. Also waiting if someone else can say if it
> would be easy or hard to implement.
>

Right after I wrote that Josh just posted
https://github.com/scipy/scipy/issues/11029#issuecomment-551968035, which
states "to add a keyword argument ... and the current Cython special
codegen can?t handle that automatically". So I think that confirms what I
suspected: it's not really doable right now. Adding a new function is not
justified; a Python wrapper could be but it's not great for performance -
you could try that and keep the overhead for the current default behavior
is low as possible, then report some benchmark result. That should help us
decide. Probably best to open an issue or PR for that for further
discussion.

Cheers,
Ralf
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From charlesr.harris at gmail.com  Sun Nov 10 21:32:41 2019
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Sun, 10 Nov 2019 19:32:41 -0700
Subject: [SciPy-User] NumPy 1.17.4 release.
Message-ID: <CAB6mnx+446Kpt_HyJ0MHNEWmGkp6+AAAePQACc-Lnr2Fk67rjg@mail.gmail.com>

Hi All,

On behalf of the NumPy team I am pleased to announce that NumPy 1.17.4 has
been released. This is a bugfix release. The Python versions supported in
this release are 3.5-3.8.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support
and OpenBLAS >= 3.7 to avoid wrong results on the Skylake architecture. The
NumPy Wheels for this release can be downloaded from PyPI
<https://pypi.org/project/numpy/1.17.4/>, source archives and release notes
are available from Github
<https://github.com/numpy/numpy/releases/tag/v1.17.4>.

*Highlights*

   - Fixed `random.random_integers` biased generation of 8 and 16 bit
   integers.
   - Fixed `np.einsum` regression on Power9 and z/Linux.
   - Fixed histogram problem with signed integer arrays

*Contributors*

A total of 5 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

   - Charles Harris
   - Chris Burr +
   - Matti Picus
   - Qiming Sun +
   - Warren Weckesser


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

   - gh-14758: BLD: Declare support for python 3.8
   - gh-14781: BUG: Random: biased samples from integers() with 8 or 16
   bit...
   - gh-14851: BUG: Fix `_ctypes` class circular reference. (gh-13808)
   - gh-14852: BLD: Add 'apt update' to shippable
   - gh-14855: BUG: Fix `np.einsum` errors on Power9 Linux and z/Linux
   - gh-14857: BUG: Fix histogram problem with signed integer arrays.
   - gh-14858: BLD: Prevent -flto from optimising long double
   representation...
   - gh-14866: MAINT: move buffer.h -> npy_buffer.h to avoid conflicts


Cheers,

Charles Harris
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From me at iyanmv.com  Wed Nov 13 08:00:23 2019
From: me at iyanmv.com (=?ISO-8859-1?Q?Iy=E1n_M=E9ndez?= Veiga)
Date: Wed, 13 Nov 2019 14:00:23 +0100
Subject: [SciPy-User] numpy.longdouble and scipy status?
Message-ID: <2242250.9r6hlQATtY@theotp40>

Hi,

How is the current status of SciPy when using np.longdouble (np.float128)? I am 
doing some calculations where I would like to get a resolution of 1e-18. 
np.float128 in my computer would be ok for that, at least based on the output 
of numpy.finfo(numpy.float128).

However, some scipy functions always output numpy.float64 even though the input 
is numpy.float128. For example, try the following code:

import numpy as np
from scipy.linalg import expm

I = np.eye(3, dtype=np.float128)
A = expm(I)
print(A.dtype)

>> float128
>> float64

Is this behavious expected? I couldn't find anything on scipy docs, sorry if I 
overlooked.

Thanks,
Iy?n