On 23 Nov 2012 03:34, Charles R Harris charlesr.har...@gmail.com wrote:
Examples,
In [13]: ones(()).flags.writeable
Out[13]: True
In [14]: (-ones(())).flags.writeable
Out[14]: False
In [15]: (-1*ones(())).flags.writeable
Out[15]: False
In [16]: (1 + ones(())).flags.writeable
Out[16]:
I have a simple function defined in the following snippet:
--- start ---
import numpy
def chebyshev(x, m):
'''Calculates Chebyshev functions of the first kind using the
trigonometric identities.'''
theta = numpy.where(
numpy.abs(x)=1.0,
numpy.arccos(x),
Bob Dowling rjd4+numpy at cam.ac.uk writes:
[clip]
I'm guessing that numpy.where() is evaluating the complete arccos and
arccosh arrays and therefore getting invalid arguments.
Now, I can turn off the warnings with numpy.seterr(invalid='ignore') but
that's not what I would regard as good
You may want to use this:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.piecewise.html
Thank you. That's just what I needed.
Works a treat:
--- start ---
import numpy
def chebyshev(x, m):
'''Calculates Chebyshev functions of the first kind using the
trigonometric
On Fri, 2012-11-23 at 10:49 +, Nathaniel Smith wrote:
On 23 Nov 2012 03:34, Charles R Harris charlesr.har...@gmail.com
wrote:
Examples,
In [13]: ones(()).flags.writeable
Out[13]: True
In [14]: (-ones(())).flags.writeable
Out[14]: False
In [15]:
hi all,
I'm glad to inform you that stochastic programming and optimization addon
for FuncDesigner v. 0.421 has been released.
Now you can use gradient-based solvers for numerical optimization, such
as ALGENCAN, IPOPT, ralg, gsubg etc. Usually they work faster than
derivative-free (such as
On Fri, Nov 23, 2012 at 7:53 AM, Sebastian Berg
sebast...@sipsolutions.netwrote:
On Fri, 2012-11-23 at 10:49 +, Nathaniel Smith wrote:
On 23 Nov 2012 03:34, Charles R Harris charlesr.har...@gmail.com
wrote:
Examples,
In [13]: ones(()).flags.writeable
Out[13]: True
In
On Fri, Nov 23, 2012 at 4:38 AM, Bob Dowling rjd4+nu...@cam.ac.uk wrote:
I have a simple function defined in the following snippet:
--- start ---
import numpy
def chebyshev(x, m):
'''Calculates Chebyshev functions of the first kind using the
trigonometric identities.'''
On Thu, Nov 22, 2012 at 6:20 AM, Francesc Alted franc...@continuum.io wrote:
As Nathaniel said, there is not a difference in terms of *what* is
computed. However, the methods that you suggested actually differ on
*how* they are computed, and that has dramatic effects on the time
used. For
