-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Dear all, I was wondering if a piece of code has been developped for derivative calculations, espacially for a sample (array of points) like for integration: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html with different methods (right or left first derivatives, second derivatives...) I didn't succeed in finding this in the documentation. Does it exist? If not, is it planned by someone? Thanks. Cheers, - -- François Boulogne. Membre de l'April - Promouvoir et défendre le logiciel libre http://www.april.org -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux) iQIcBAEBAgAGBQJOrXHDAAoJEKkn15fZnrRLxbYP/3/NUzU5metNhGc4tw6Uo0mw kIlAxf1cvtopbJ+JTXrmKkJpyYUI1bMsqVKpPniW073dgPAfAX3Bajymysh5Hgnd 5+0hh7v8JmsIvKm9HrEePSrENYrIVTfFyvRV+tBLhkfHJ9Vj7uUy7a3/lyRS6s7v FEItXLhkNYtqEir8h2eZ7uW178mwq6nBl6Zi5UOjOXq7u0SxcnusKkYyiL+CirSN KafcrU+rUdHw6khE1exj435GMSKx+N3+rV/kDAQWWQc0ncWmdX2Jr7PapT1DQbNN b3UK2NjYGDZE2NMRuZmRmeTIk2S+PVPqRxEu0x62CS4Y4JnTaZx1xSiPGP0d9cZC NG4/9h0LFM8rxj/kxYjBakbIs6LhwPnS1ZvYf00o3eaY4+tLGx3UozoLpEWSCOuQ GrONTWvdUlfVWsd3qOhwCj+NORkz9yXJNqJHulgz6T8fKnMyPL+R80XfRuS1alxj m166ySHxrPDzwiSlV5vWR/1ajrEMAI/GYev55dtzlIx6hxfLVCOryFei/LNtlrN+ lYK48QqateqvLDs+NUq+7azmCAkc8fW5UeD7bBLdoGwlXvYaOEZh8+lzxU2sc0T4 93kNZAzOzv/CdcI1uhnp2NP8ODGEyRLx5cUlcKqmF/iAqtM2zc5Zca9YiygR2w83 XQsGdZYw4rkVMnUTk6Ep =/Nlz -----END PGP SIGNATURE-----
2011/10/30 François Boulogne <boulogne.f@gmail.com>:
-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1
Dear all,
I was wondering if a piece of code has been developped for derivative calculations, espacially for a sample (array of points) like for integration: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html with different methods (right or left first derivatives, second derivatives...) I didn't succeed in finding this in the documentation. Does it exist? If not, is it planned by someone?
There is one helper function in scipy.optimize (scipy.optimize.optimize), nothing else in scipy. My standard recommendation for finite differences is numdifftools, it's on pypi. There is a ticket that asks for it's inclusion in scipy, IIRC. There are some packages on automatic differentiation. (and we have our own hacked together numdiff in statsmodels, just for optimization and Hessian calculations.) Josef
Thanks. Cheers,
- -- François Boulogne.
Membre de l'April - Promouvoir et défendre le logiciel libre http://www.april.org -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux)
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On Sun, Oct 30, 2011 at 5:03 PM, <josef.pktd@gmail.com> wrote:
2011/10/30 François Boulogne <boulogne.f@gmail.com>:
-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1
Dear all,
I was wondering if a piece of code has been developped for derivative calculations, espacially for a sample (array of points) like for integration: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html with different methods (right or left first derivatives, second
derivatives...)
I didn't succeed in finding this in the documentation. Does it exist? If not, is it planned by someone?
There is one helper function in scipy.optimize (scipy.optimize.optimize), nothing else in scipy.
My standard recommendation for finite differences is numdifftools, it's on pypi. There is a ticket that asks for it's inclusion in scipy, IIRC.
That's http://projects.scipy.org/scipy/ticket/1510, for those that are interested. Ralf
There are some packages on automatic differentiation.
(and we have our own hacked together numdiff in statsmodels, just for optimization and Hessian calculations.)
Josef
On Sun, Oct 30, 2011 at 11:03 AM, <josef.pktd@gmail.com> wrote:
2011/10/30 François Boulogne <boulogne.f@gmail.com>:
-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1
Dear all,
I was wondering if a piece of code has been developped for derivative calculations, espacially for a sample (array of points) like for integration: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html with different methods (right or left first derivatives, second
derivatives...)
I didn't succeed in finding this in the documentation. Does it exist? If not, is it planned by someone?
There is one helper function in scipy.optimize (scipy.optimize.optimize), nothing else in scipy.
Well, not exactly "nothing else"... (eat's email arrived as I was typing this, so it will echo some of what he said.) Functions that operate on a discrete sample: numpy.diff This can be used to compute a derivative by dividing by the appropriate power of dx. numpy.ediff1d Like numpy.diff, but strictly for 1D arrays. It also provides the option for specifying values to append to the ends of the array before computing the difference. numpy.gradient Return the gradient of an n-d array. scipy.fftpack.diff Derivative of a periodic sequence. See http://www.scipy.org/Cookbook/KdV for an example. Functions that operate on a callable function: scipy.misc.derivative Find the n-th derivative of a function at point x0. scipy.misc.central_difference_weights Return weights for an Np-point central derivative scipy.optimize.approx_fprime No docstring (sigh), but from the source code (use approx_fprime?? in ipython), it is pretty easy to figure out what it does. Having said that, I think a module specifically for computing derivatives (with good docs and tests), as being discussed in the ticket #1510 ( http://projects.scipy.org/scipy/ticket/1510) would be a nice addition. Warren
My standard recommendation for finite differences is numdifftools, it's on pypi. There is a ticket that asks for it's inclusion in scipy, IIRC.
There are some packages on automatic differentiation.
(and we have our own hacked together numdiff in statsmodels, just for optimization and Hessian calculations.)
Josef
Thanks. Cheers,
- -- François Boulogne.
Membre de l'April - Promouvoir et défendre le logiciel libre http://www.april.org -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux)
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On Sun, Oct 30, 2011 at 12:55 PM, Warren Weckesser < warren.weckesser@enthought.com> wrote:
On Sun, Oct 30, 2011 at 11:03 AM, <josef.pktd@gmail.com> wrote:
2011/10/30 François Boulogne <boulogne.f@gmail.com>:
-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1
Dear all,
I was wondering if a piece of code has been developped for derivative calculations, espacially for a sample (array of points) like for integration: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html with different methods (right or left first derivatives, second
derivatives...)
I didn't succeed in finding this in the documentation. Does it exist? If not, is it planned by someone?
There is one helper function in scipy.optimize (scipy.optimize.optimize), nothing else in scipy.
Well, not exactly "nothing else"... (eat's email arrived as I was typing this, so it will echo some of what he said.)
Functions that operate on a discrete sample:
numpy.diff This can be used to compute a derivative by dividing by the appropriate power of dx.
numpy.ediff1d Like numpy.diff, but strictly for 1D arrays. It also provides the option for specifying values to append to the ends of the array before computing the difference.
numpy.gradient Return the gradient of an n-d array.
scipy.fftpack.diff Derivative of a periodic sequence. See http://www.scipy.org/Cookbook/KdV for an example.
Functions that operate on a callable function:
scipy.misc.derivative Find the n-th derivative of a function at point x0.
scipy.misc.central_difference_weights Return weights for an Np-point central derivative
Correction: I put this in the wrong list; central_difference_weights is just a utility function for computing weights (as the name says). It does not compute derivatives of a callable function. Warren
scipy.optimize.approx_fprime No docstring (sigh), but from the source code (use approx_fprime?? in ipython), it is pretty easy to figure out what it does.
Having said that, I think a module specifically for computing derivatives (with good docs and tests), as being discussed in the ticket #1510 ( http://projects.scipy.org/scipy/ticket/1510) would be a nice addition.
Warren
My standard recommendation for finite differences is numdifftools, it's on pypi. There is a ticket that asks for it's inclusion in scipy, IIRC.
There are some packages on automatic differentiation.
(and we have our own hacked together numdiff in statsmodels, just for optimization and Hessian calculations.)
Josef
Thanks. Cheers,
- -- François Boulogne.
Membre de l'April - Promouvoir et défendre le logiciel libre http://www.april.org -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux)
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On Sun, Oct 30, 2011 at 1:57 PM, Warren Weckesser <warren.weckesser@enthought.com> wrote:
On Sun, Oct 30, 2011 at 12:55 PM, Warren Weckesser <warren.weckesser@enthought.com> wrote:
On Sun, Oct 30, 2011 at 11:03 AM, <josef.pktd@gmail.com> wrote:
2011/10/30 François Boulogne <boulogne.f@gmail.com>:
-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1
Dear all,
I was wondering if a piece of code has been developped for derivative calculations, espacially for a sample (array of points) like for integration: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html with different methods (right or left first derivatives, second derivatives...) I didn't succeed in finding this in the documentation. Does it exist? If not, is it planned by someone?
There is one helper function in scipy.optimize (scipy.optimize.optimize), nothing else in scipy.
Well, not exactly "nothing else"... (eat's email arrived as I was typing this, so it will echo some of what he said.)
Functions that operate on a discrete sample:
numpy.diff This can be used to compute a derivative by dividing by the appropriate power of dx.
numpy.ediff1d Like numpy.diff, but strictly for 1D arrays. It also provides the option for specifying values to append to the ends of the array before computing the difference.
numpy.gradient Return the gradient of an n-d array.
scipy.fftpack.diff Derivative of a periodic sequence. See http://www.scipy.org/Cookbook/KdV for an example.
Functions that operate on a callable function:
scipy.misc.derivative Find the n-th derivative of a function at point x0.
scipy.misc.central_difference_weights Return weights for an Np-point central derivative
Correction: I put this in the wrong list; central_difference_weights is just a utility function for computing weights (as the name says). It does not compute derivatives of a callable function.
Warren
scipy.optimize.approx_fprime No docstring (sigh), but from the source code (use approx_fprime?? in ipython), it is pretty easy to figure out what it does.
Having said that, I think a module specifically for computing derivatives (with good docs and tests), as being discussed in the ticket #1510 (http://projects.scipy.org/scipy/ticket/1510) would be a nice addition.
Warren
Ok. I take back the "nothing else" for the original question since in reading to fast, I misinterpreted the initial question with what I usually need, derivatives of a function in several arguments. Josef
My standard recommendation for finite differences is numdifftools, it's on pypi. There is a ticket that asks for it's inclusion in scipy, IIRC.
There are some packages on automatic differentiation.
(and we have our own hacked together numdiff in statsmodels, just for optimization and Hessian calculations.)
Josef
Thanks. Cheers,
- -- François Boulogne.
Membre de l'April - Promouvoir et défendre le logiciel libre http://www.april.org -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux)
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Hi, On Sun, Oct 30, 2011 at 7:55 PM, Warren Weckesser < warren.weckesser@enthought.com> wrote:
On Sun, Oct 30, 2011 at 11:03 AM, <josef.pktd@gmail.com> wrote:
2011/10/30 François Boulogne <boulogne.f@gmail.com>:
-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1
Dear all,
I was wondering if a piece of code has been developped for derivative calculations, espacially for a sample (array of points) like for integration: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html with different methods (right or left first derivatives, second
derivatives...)
I didn't succeed in finding this in the documentation. Does it exist? If not, is it planned by someone?
There is one helper function in scipy.optimize (scipy.optimize.optimize), nothing else in scipy.
Well, not exactly "nothing else"... (eat's email arrived as I was typing this, so it will echo some of what he said.)
Functions that operate on a discrete sample:
numpy.diff This can be used to compute a derivative by dividing by the appropriate power of dx.
numpy.ediff1d Like numpy.diff, but strictly for 1D arrays. It also provides the option for specifying values to append to the ends of the array before computing the difference.
numpy.gradient Return the gradient of an n-d array.
scipy.fftpack.diff Derivative of a periodic sequence. See http://www.scipy.org/Cookbook/KdV for an example.
Functions that operate on a callable function:
scipy.misc.derivative Find the n-th derivative of a function at point x0.
scipy.misc.central_difference_weights Return weights for an Np-point central derivative
scipy.optimize.approx_fprime No docstring (sigh), but from the source code (use approx_fprime?? in ipython), it is pretty easy to figure out what it does.
Having said that, I think a module specifically for computing derivatives (with good docs and tests), as being discussed in the ticket #1510 ( http://projects.scipy.org/scipy/ticket/1510) would be a nice addition.
Perhaps it would be quite reasonable to upgrade the scipy documentation with a new 'derivation topic'. Above outline would be already a nice skeleton for it. Thanks, eat
Warren
My standard recommendation for finite differences is numdifftools, it's on pypi. There is a ticket that asks for it's inclusion in scipy, IIRC.
There are some packages on automatic differentiation.
(and we have our own hacked together numdiff in statsmodels, just for optimization and Hessian calculations.)
Josef
Thanks. Cheers,
- -- François Boulogne.
Membre de l'April - Promouvoir et défendre le logiciel libre http://www.april.org -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux)
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-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Le 30/10/2011 18:55, Warren Weckesser a écrit :
Having said that, I think a module specifically for computing
derivatives (with good docs and tests), as being discussed in the ticket #1510 (http://projects.scipy.org/scipy/ticket/1510) would be a nice addition. Thank to all of you for your responses. I'll follow the discussion on the bug tracker. Regards, - -- François Boulogne. Membre de l'April - Promouvoir et défendre le logiciel libre http://www.april.org -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux) iQIcBAEBAgAGBQJOrvbrAAoJEKkn15fZnrRLnRAP/3mB+lbggEegl7Bfvswv1KaO qm8b+TKqoNSNJ42zhq3dg5sRb4RiobAX+nXhS8SOifotnPm4M+BLybEq2bu+TovC 0i83KM86aK7c9fLV5jmN/rhLlFTEZB9Ga722ZYWc5PAg56QSrvjiTPFp4b6cJJLC utOGLCadB7HB6S6m8XzJQ/G66eAMcz2CUCcBvAyOzY+wRLhXLqRqPZhcwrb1QeSf ff8xOQiOAMmcHtnqJfxo6PpLuoItqUmCOXAsfC5yRjdY1AjO82voZ7ZDNuAhhFOl GGJg7z/MEHQRx68gWJ70BxlTIqDhfvc5TI0E7/SFsO4yPd+kYpS5w6Uf6j916WGE X7DdHD6etsqSSpuApx2vXPGgl/ozi4gM+W/H0Ey/8m+KM0N6shkrWEVg8WejUAX0 dfrAR1txo4TioFrx0VwbFtKSsqjyxztbT0nqlO2XSJ5pwGgc6zrLoArJFT1uL25w oMt4pB2UGvLhko8F2LM6Cirr0jfC7c1bjhHkRFpq/8TOvLg+DcRtu9Ag7C1KmskX xpl6s9WtGxFKTPzj5IXNXGboNHaeq454tO5NTd/lRod7r7q0VO1gIldv16AhC7IO UBWfmEIZRXMhKj4ocrRLnyijpUnxZ7ruZBQwMkAJ+SX7pTC5f2/qOFT4ERBjEvyW lL0Qzj8p12lA6DN4Th6t =x1nX -----END PGP SIGNATURE-----
Hi, There is the Theano software in the python scientific tools that can do symbolic differentiation. With it, you can compute the gradient, the hessien and/or the jacobian. You can also compute efficiently jacobian times a vector, without explicitly computing the jacobian using R operation or L operation. http://deeplearning.net/software/theano/tutorial/gradients.html HTH Fred 2011/10/31 François Boulogne <boulogne.f@gmail.com>:
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Le 30/10/2011 18:55, Warren Weckesser a écrit :
Having said that, I think a module specifically for computing
derivatives (with good docs and tests), as being discussed in the ticket #1510 (http://projects.scipy.org/scipy/ticket/1510) would be a nice addition.
Thank to all of you for your responses. I'll follow the discussion on the bug tracker.
Regards,
- -- François Boulogne.
Membre de l'April - Promouvoir et défendre le logiciel libre http://www.april.org -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux)
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Hi, 2011/10/30 François Boulogne <boulogne.f@gmail.com>
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Dear all,
I was wondering if a piece of code has been developped for derivative calculations, espacially for a sample (array of points) like for integration: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html with different methods (right or left first derivatives, second derivatives...) I didn't succeed in finding this in the documentation. Does it exist? If not, is it planned by someone?
Some other (possible relevant) links: http://docs.scipy.org/doc/numpy/reference/generated/numpy.diff.html http://docs.scipy.org/doc/numpy/reference/generated/numpy.ediff1d.html http://docs.scipy.org/doc/numpy/reference/generated/numpy.gradient.html http://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.central_diff_... Regards, eat
Thanks. Cheers,
- -- François Boulogne.
Membre de l'April - Promouvoir et défendre le logiciel libre http://www.april.org -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux)
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participants (6)
-
eat -
François Boulogne -
Frédéric Bastien -
josef.pktd＠gmail.com -
Ralf Gommers -
Warren Weckesser