NIFTy issueshttps://gitlab.mpcdf.mpg.de/ift/nifty/issues2019-10-17T13:44:44Zhttps://gitlab.mpcdf.mpg.de/ift/nifty/issues/274Restructure DOFDistributor2019-10-17T13:44:44ZJakob KnollmuellerRestructure DOFDistributorHi,
I just want to document the thought to change the DOFDistributor to a BinDistributor and build it analogous to the adjoint numpy bincount function. This should make things more clear and allow for operations on fields of any one-dimensional domain. We could get rid of the DOFSpace and change the default to an UnstructuredDomain
JakobHi,
I just want to document the thought to change the DOFDistributor to a BinDistributor and build it analogous to the adjoint numpy bincount function. This should make things more clear and allow for operations on fields of any one-dimensional domain. We could get rid of the DOFSpace and change the default to an UnstructuredDomain
Jakobhttps://gitlab.mpcdf.mpg.de/ift/nifty/issues/266Amplitude model2019-04-18T20:05:11ZPhilipp ArrasAmplitude modelI am back at considering what kind of default amplitude model is suitable for nifty. I think the current `SLAmplitude` operator does not provide a sensible prior on the zeromode. Therefore, I think we should change it.
What I do for Lognormal problems is the following: set the zeromode of the amplitude operator A constantly to one and have as sky model: exp(ht @ U @ (A*xi)), where U is an operator which turns a standard normal distribution into a flat distribution with a lower and an upper bound.
My problem is that `demos/getting_started_3.py` does not show how to properly set up a prior on the zero mode.
@reimar, @pfrank, do you have a suggestion how to deal with this?I am back at considering what kind of default amplitude model is suitable for nifty. I think the current `SLAmplitude` operator does not provide a sensible prior on the zeromode. Therefore, I think we should change it.
What I do for Lognormal problems is the following: set the zeromode of the amplitude operator A constantly to one and have as sky model: exp(ht @ U @ (A*xi)), where U is an operator which turns a standard normal distribution into a flat distribution with a lower and an upper bound.
My problem is that `demos/getting_started_3.py` does not show how to properly set up a prior on the zero mode.
@reimar, @pfrank, do you have a suggestion how to deal with this?https://gitlab.mpcdf.mpg.de/ift/nifty/issues/258Branch cleanup2019-05-21T06:55:16ZMartin ReineckeBranch cleanupI'd like to get a better understanding which branches are still used and which ones can be deleted:
- new_los (I'll adjust the code for NIFTy 5)
- new_sampling (@reimar is this still relevant?)
- symbolicDifferentiation (@parras do you want to keep this?)
- yango_minimizer (@reimar ?)
- addUnits (@parras ?)
- theo_master (I guess I'll convert this into a tag)
- nifty2go (is anyone still using this? Otherwise I'd convert it into a tag as well)
@ensslint, any comments?I'd like to get a better understanding which branches are still used and which ones can be deleted:
- new_los (I'll adjust the code for NIFTy 5)
- new_sampling (@reimar is this still relevant?)
- symbolicDifferentiation (@parras do you want to keep this?)
- yango_minimizer (@reimar ?)
- addUnits (@parras ?)
- theo_master (I guess I'll convert this into a tag)
- nifty2go (is anyone still using this? Otherwise I'd convert it into a tag as well)
@ensslint, any comments?Martin ReineckeMartin Reineckehttps://gitlab.mpcdf.mpg.de/ift/nifty/issues/255Unify SlopeOperator, OffsetOperator, Polynom-fitting demo into one Parametric...2019-01-31T19:41:56ZPhilipp ArrasUnify SlopeOperator, OffsetOperator, Polynom-fitting demo into one ParametricOperator@pfrank, just to keep track of this idea.@pfrank, just to keep track of this idea.https://gitlab.mpcdf.mpg.de/ift/nifty/issues/254Better support for partial inference2019-05-21T07:32:43ZMartin ReineckeBetter support for partial inferenceI have added the `partial-const` branch for tweaks that improve NIFTy's support for partial inference.
The current idea is that one first builds the full Hamiltonian (as before), and then obtains a simplified version for partially constant inputs by calling its method `simplify_for_constant_input()` with the constant input components.
All that needs to be done (I hope) is to specialize this method for all composed operators, i.e. those that call other operators internally.
@kjako, @reimar, @parras, @pfrank: does the principal idea look sound to you?I have added the `partial-const` branch for tweaks that improve NIFTy's support for partial inference.
The current idea is that one first builds the full Hamiltonian (as before), and then obtains a simplified version for partially constant inputs by calling its method `simplify_for_constant_input()` with the constant input components.
All that needs to be done (I hope) is to specialize this method for all composed operators, i.e. those that call other operators internally.
@kjako, @reimar, @parras, @pfrank: does the principal idea look sound to you?https://gitlab.mpcdf.mpg.de/ift/nifty/issues/250[post Nifty5] new design for the `Field` class?2018-06-26T12:59:51ZMartin Reinecke[post Nifty5] new design for the `Field` class?I'm wondering whether we can solve most of our current problems regarding fields and multifields by introducing a single new `Field` class that
- unifies the current `Field` and `MultiField` classes (a classic field would have a single dictionary entry with an empty string as name)
- is immutable
- can contain scalars or array-like objects to describe field content (maybe even functions?)
If we have such a class we can again demand that all operations on fields require identical domains, and we can still avoid writing huge zero-filled arrays by writing scalar zeros instead.
@reimar, @kjako, @parras please let me know what you think.I'm wondering whether we can solve most of our current problems regarding fields and multifields by introducing a single new `Field` class that
- unifies the current `Field` and `MultiField` classes (a classic field would have a single dictionary entry with an empty string as name)
- is immutable
- can contain scalars or array-like objects to describe field content (maybe even functions?)
If we have such a class we can again demand that all operations on fields require identical domains, and we can still avoid writing huge zero-filled arrays by writing scalar zeros instead.
@reimar, @kjako, @parras please let me know what you think.https://gitlab.mpcdf.mpg.de/ift/nifty/issues/235Improved LOS response?2018-04-30T09:19:16ZMartin ReineckeImproved LOS response?@kjako mentioned some time ago that the current LOSResponse exhibits some artifacts. I suspect that they are caused by our method to compute the "influence" of lines of sight on individual data points.
Currently, the influence of a line of sight on a data point is simply proportional to the length of its intersection with the cell around the data point. It doesn't matter whether the LOS cuts the cell near the edges or goes straight through the center. This also implies that tiny shifts of a line of sight may lead to dramatic changes of its influences on data points, which is probably not desirable.
I propose to use an approach that is more similar to SPH methods:
- each data point has a sphere of influence with a given R_max (similar to the cell distances)
- it interacts with all lines of sight that intersect its sphere of influence
- the interaction strength scales with the integral along the line of sight over a function f(r) around the data point, which falls to zero at R_max.
My expectation is that this will reduce grid-related artifacts.
@ensslint, @kjako: does this sound worthwhile to try?@kjako mentioned some time ago that the current LOSResponse exhibits some artifacts. I suspect that they are caused by our method to compute the "influence" of lines of sight on individual data points.
Currently, the influence of a line of sight on a data point is simply proportional to the length of its intersection with the cell around the data point. It doesn't matter whether the LOS cuts the cell near the edges or goes straight through the center. This also implies that tiny shifts of a line of sight may lead to dramatic changes of its influences on data points, which is probably not desirable.
I propose to use an approach that is more similar to SPH methods:
- each data point has a sphere of influence with a given R_max (similar to the cell distances)
- it interacts with all lines of sight that intersect its sphere of influence
- the interaction strength scales with the integral along the line of sight over a function f(r) around the data point, which falls to zero at R_max.
My expectation is that this will reduce grid-related artifacts.
@ensslint, @kjako: does this sound worthwhile to try?