ift issueshttps://gitlab.mpcdf.mpg.de/groups/ift/-/issues2020-05-19T12:51:06Zhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/299Add possibility to log energies2020-05-19T12:51:06ZPhilipp Arrasparras@mpa-garching.mpg.deAdd possibility to log energiesI have implemented a method to log energies inside a IterationController such that they can be analyzed afterwards. @mtr how do you like this implementation? Do you think we could/should make this part of nifty? Or do you have other ideas how to approach the problem?
https://gitlab.mpcdf.mpg.de/ift/papers/rickandresolve/-/commit/8b0e15dc4a533dfba17f29361345309e336dfb8a
(Since this is part of an unpublished project, this link is only visible for ift group members. Sorry!)
@veberle @mtrI have implemented a method to log energies inside a IterationController such that they can be analyzed afterwards. @mtr how do you like this implementation? Do you think we could/should make this part of nifty? Or do you have other ideas how to approach the problem?
https://gitlab.mpcdf.mpg.de/ift/papers/rickandresolve/-/commit/8b0e15dc4a533dfba17f29361345309e336dfb8a
(Since this is part of an unpublished project, this link is only visible for ift group members. Sorry!)
@veberle @mtrhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/298Tests are taking too long2020-05-19T08:02:40ZMartin ReineckeTests are taking too longOn my laptop, the Nifty regression test are taking almost 10 minutes at the moment, which starts to get really problematic during development. Is there a chance of reducing test sizes and/or avoiding redundancies in the current tests?
The biggest problem by far is test_correlated_fields.py, which accounts for roughly 40% of the testing time. Next is test_jacobian.py with 20%; next is test_energy_gradients.py.
@parras, @pfrank, @phaimOn my laptop, the Nifty regression test are taking almost 10 minutes at the moment, which starts to get really problematic during development. Is there a chance of reducing test sizes and/or avoiding redundancies in the current tests?
The biggest problem by far is test_correlated_fields.py, which accounts for roughly 40% of the testing time. Next is test_jacobian.py with 20%; next is test_energy_gradients.py.
@parras, @pfrank, @phaimhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/297Really obsolete Nifty docs are still online2020-05-14T06:58:53ZMartin ReineckeReally obsolete Nifty docs are still onlineI searched online for "nifty documentation" today, and the second hit was
https://wwwmpa.mpa-garching.mpg.de/ift/nifty/start.html
Can we please remove these completely obsolete files from the public internet? Our potential users might be grateful :)I searched online for "nifty documentation" today, and the second hit was
https://wwwmpa.mpa-garching.mpg.de/ift/nifty/start.html
Can we please remove these completely obsolete files from the public internet? Our potential users might be grateful :)Torsten EnsslinTorsten Ensslinhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/296Releasing NIFTy 6?2020-05-22T07:46:13ZMartin ReineckeReleasing NIFTy 6?I have the impression that we have enough new features to justify a NIFTy 6 release.
Does anyone have feature suggestions which should go into the code before the release?I have the impression that we have enough new features to justify a NIFTy 6 release.
Does anyone have feature suggestions which should go into the code before the release?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/295Suggestion: Add dtype property to DomainTuple2020-04-26T15:24:18ZPhilipp Arrasparras@mpa-garching.mpg.deSuggestion: Add dtype property to DomainTupleI suggest to add a property called `dtype` to `DomainTuple`. When wrapping a numpy array as `Field` the constructor would check that the dtype of the array and the `DomainTuple` coincide. Functions like `from_random` do not need to take a dtype any more. In particular, sampling from e.g. `DiagonalOperator`s become unambiguous in terms of drawing either a real or a complex field. This in turn would simplify the KL because the `lh_sampling_dtype` would disappear.
I am not 100% sure how to treat `RGSpace.get_default_codomain()`. Would the default codomain of a real space be real again or complex? My suggestion is to add an optional argument to that function and take as default the same dtype as the original space.
Also I am not sure how to tell the `DomainTuple` its dtype. I suggest as an additional argument of `DomainTuple.make()`.
What do you think about that, @mtr @pfrank @reimar?I suggest to add a property called `dtype` to `DomainTuple`. When wrapping a numpy array as `Field` the constructor would check that the dtype of the array and the `DomainTuple` coincide. Functions like `from_random` do not need to take a dtype any more. In particular, sampling from e.g. `DiagonalOperator`s become unambiguous in terms of drawing either a real or a complex field. This in turn would simplify the KL because the `lh_sampling_dtype` would disappear.
I am not 100% sure how to treat `RGSpace.get_default_codomain()`. Would the default codomain of a real space be real again or complex? My suggestion is to add an optional argument to that function and take as default the same dtype as the original space.
Also I am not sure how to tell the `DomainTuple` its dtype. I suggest as an additional argument of `DomainTuple.make()`.
What do you think about that, @mtr @pfrank @reimar?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/293[GU] Simplify point-wise Field operations2020-04-14T06:46:41ZMartin Reinecke[GU] Simplify point-wise Field operationsCurrently, point-wise operations on Nifty objects are not really implemented in a beautiful way: they are injected via some nasty Python tricks into `Field`, `MultiField`, `Operator` and the `sugar` module, and a slightly more complicated variant is added to `Linearization`.
My plan is to have a single method in `Field`, `MultiField`, `Linearization` and `Operator`, which is called `ptw` (or `pointwise`), which takes a string describing the requested pointwise operation (like "sin", "exp", "one_over" etc.). Implementation of the actual operations would be fairly trivial via a dictionary mapping these names to their `numpy` equivalents, and also describing their derivatives via `numpy` or custom functions where necessary.
If we like, we can leave the global pointwise functions injected into `sugar.py` unchanged for aesthetic reasons :)
Opinions?Currently, point-wise operations on Nifty objects are not really implemented in a beautiful way: they are injected via some nasty Python tricks into `Field`, `MultiField`, `Operator` and the `sugar` module, and a slightly more complicated variant is added to `Linearization`.
My plan is to have a single method in `Field`, `MultiField`, `Linearization` and `Operator`, which is called `ptw` (or `pointwise`), which takes a string describing the requested pointwise operation (like "sin", "exp", "one_over" etc.). Implementation of the actual operations would be fairly trivial via a dictionary mapping these names to their `numpy` equivalents, and also describing their derivatives via `numpy` or custom functions where necessary.
If we like, we can leave the global pointwise functions injected into `sugar.py` unchanged for aesthetic reasons :)
Opinions?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/292Many Linearization methods explicitly expect Fields2020-04-03T07:53:56ZMartin ReineckeMany Linearization methods explicitly expect FieldsWhile working on Field/MultiField unification, I noticed that the largest part of `Linearization` members do not accept `MultiFields`; a clear example is `sinc`, which explicitly creates a `Field` for its output, but many other methods break in more obscure ways.
If we want unification, we need all `Linearization` methods to work on `Multifields`, which is quite a bit of tedious work. Any volunteers?While working on Field/MultiField unification, I noticed that the largest part of `Linearization` members do not accept `MultiFields`; a clear example is `sinc`, which explicitly creates a `Field` for its output, but many other methods break in more obscure ways.
If we want unification, we need all `Linearization` methods to work on `Multifields`, which is quite a bit of tedious work. Any volunteers?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/289Overflows after updating NIFTy2020-03-27T14:50:45ZPhilipp Arrasparras@mpa-garching.mpg.deOverflows after updating NIFTyWhen updating to the new interpolation scheme (https://gitlab.mpcdf.mpg.de/ift/nifty/commit/37a5691e3013ca36303ee4c3e78c74485f516793), I get overflows in the minimization where I did not get any before. I am confused since the both gradient tests work better than before and the accuracy of the approximation is better. Maybe the operator works better now so that actually an inconsistency in my model is triggered or so. I have the feeling that it is not the operator's fault... ;)
@gedenhof can you share your observations about the overflows? If it is the same commit as for me, then I will go ahead and try to assemble a minimal demonstration case but I think this might be a lot of work so maybe we can find the problem without that.
@gedenhof, @lerouWhen updating to the new interpolation scheme (https://gitlab.mpcdf.mpg.de/ift/nifty/commit/37a5691e3013ca36303ee4c3e78c74485f516793), I get overflows in the minimization where I did not get any before. I am confused since the both gradient tests work better than before and the accuracy of the approximation is better. Maybe the operator works better now so that actually an inconsistency in my model is triggered or so. I have the feeling that it is not the operator's fault... ;)
@gedenhof can you share your observations about the overflows? If it is the same commit as for me, then I will go ahead and try to assemble a minimal demonstration case but I think this might be a lot of work so maybe we can find the problem without that.
@gedenhof, @lerouhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/287Switch to new random interface of numpy2020-03-24T11:25:13ZPhilipp Arrasparras@mpa-garching.mpg.deSwitch to new random interface of numpy`np.random.seed` is legacy. The numpy docs say:
> The best practice is to **not** reseed a BitGenerator, rather to recreate a new one. This method is here for legacy reasons.
> This example demonstrates best practice.
```python
from numpy.random import MT19937
from numpy.random import RandomState, SeedSequence
rs = RandomState(MT19937(SeedSequence(123456789)))
# Later, you want to restart the stream
rs = RandomState(MT19937(SeedSequence(987654321)))
```
We might want to migrate eventually.`np.random.seed` is legacy. The numpy docs say:
> The best practice is to **not** reseed a BitGenerator, rather to recreate a new one. This method is here for legacy reasons.
> This example demonstrates best practice.
```python
from numpy.random import MT19937
from numpy.random import RandomState, SeedSequence
rs = RandomState(MT19937(SeedSequence(123456789)))
# Later, you want to restart the stream
rs = RandomState(MT19937(SeedSequence(987654321)))
```
We might want to migrate eventually.Martin ReineckeMartin Reineckehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/285Performance of Operator Jacobians2020-03-10T10:32:22ZReimar H LeikePerformance of Operator JacobiansMany of our operators call the Jacobian of Linearizations more than once, leading to exponential performance losses in chains of operators, as uncovered by the test_for_performance_issues branch.
TODOS:
- [x] extend the tests to not only cover energy operators
- [x] fix all uncovered performance issuesMany of our operators call the Jacobian of Linearizations more than once, leading to exponential performance losses in chains of operators, as uncovered by the test_for_performance_issues branch.
TODOS:
- [x] extend the tests to not only cover energy operators
- [x] fix all uncovered performance issueshttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/284Inconsistency of .sum() for Fields and Linearizations2020-03-11T23:04:51ZPhilipp Arrasparras@mpa-garching.mpg.deInconsistency of .sum() for Fields and LinearizationsIs it wanted that `Linearization.sum()` returns a scalar Field but `Field.sum()` returns an actual scalar, i.e. in most situations a `float`? `MultiField.sum()` inherits the behaviour from `Field`.
Linearizations: https://gitlab.mpcdf.mpg.de/ift/nifty/blob/NIFTy_6/nifty6/linearization.py#L237
Fields: https://gitlab.mpcdf.mpg.de/ift/nifty/blob/NIFTy_6/nifty6/field.py#L382
MultiFields: https://gitlab.mpcdf.mpg.de/ift/nifty/blob/NIFTy_6/nifty6/multi_field.py#L169Is it wanted that `Linearization.sum()` returns a scalar Field but `Field.sum()` returns an actual scalar, i.e. in most situations a `float`? `MultiField.sum()` inherits the behaviour from `Field`.
Linearizations: https://gitlab.mpcdf.mpg.de/ift/nifty/blob/NIFTy_6/nifty6/linearization.py#L237
Fields: https://gitlab.mpcdf.mpg.de/ift/nifty/blob/NIFTy_6/nifty6/field.py#L382
MultiFields: https://gitlab.mpcdf.mpg.de/ift/nifty/blob/NIFTy_6/nifty6/multi_field.py#L169https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/283Reduce licensing boilerplate2020-05-13T11:36:01ZGordian EdenhoferReduce licensing boilerplateIf we really feel the need to put licensing boilerplate in every source file, let's at least make it concise by e.g. using https://spdx.org/ids.If we really feel the need to put licensing boilerplate in every source file, let's at least make it concise by e.g. using https://spdx.org/ids.https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/282FieldZeroPadder2020-02-19T16:45:27ZVincent EberleFieldZeroPadderFieldZeropadding.adjoint doesn't work in 2 dimensions.FieldZeropadding.adjoint doesn't work in 2 dimensions.Philipp FrankPhilipp Frankhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/281[NIFTy6] Boost performance of Correlated Fields2019-12-06T13:53:49ZPhilipp Arrasparras@mpa-garching.mpg.de[NIFTy6] Boost performance of Correlated FieldsDo PowerDistributor only on one slice and copy it with ContractionOperator.adjoint afterwards. The power distributor appears to be the bottleneck of correlated field evaluations currently.Do PowerDistributor only on one slice and copy it with ContractionOperator.adjoint afterwards. The power distributor appears to be the bottleneck of correlated field evaluations currently.https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/280[NIFTy6] Implement partial insert operator for multi fields2019-12-06T16:54:00ZPhilipp Arrasparras@mpa-garching.mpg.de[NIFTy6] Implement partial insert operator for multi fieldshttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/279Consistent ordering of domain and non-domain arguments for standard operators2019-12-06T08:34:31ZGordian EdenhoferConsistent ordering of domain and non-domain arguments for standard operatorsCurrently, the order in which arguments shall be provided differs between operators, e.g. `ift.from_global_data` and `ift.full` both take the domain as first argument, while `ift.ScalingOperator` requires the factor with which to scale a field as first argument and the domain as second. Moving to NIFTY6 might be the perfect opportunity to fix this either by consistently requiring the domain to be e.g. the first argument or by making the `ift.ScalingOperator` more flexible by swapping arguments in a suitable manner during initialization.Currently, the order in which arguments shall be provided differs between operators, e.g. `ift.from_global_data` and `ift.full` both take the domain as first argument, while `ift.ScalingOperator` requires the factor with which to scale a field as first argument and the domain as second. Moving to NIFTY6 might be the perfect opportunity to fix this either by consistently requiring the domain to be e.g. the first argument or by making the `ift.ScalingOperator` more flexible by swapping arguments in a suitable manner during initialization.https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/278Getting rid of standard parallelization?2019-12-09T12:50:25ZMartin ReineckeGetting rid of standard parallelization?The default way of parallelizing large tasks with MPI is currently to distribute fields over MPI tasks along their first axis. While it appears to work (our tests run fine with MPI), it has not been used during the last few years, and other parallelization approaches seem more promising.
If there is general agreement, I propose to remove this parallelization from the code, which would make NIFTy much smaller and easier to use and maintain.The default way of parallelizing large tasks with MPI is currently to distribute fields over MPI tasks along their first axis. While it appears to work (our tests run fine with MPI), it has not been used during the last few years, and other parallelization approaches seem more promising.
If there is general agreement, I propose to remove this parallelization from the code, which would make NIFTy much smaller and easier to use and maintain.https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/277Changelog for NIFTy_62019-12-06T16:54:45ZLukas PlatzChangelog for NIFTy_6Dear @parras, @pfrank, @phaim and @mtr,
should we introduce a changelog for the NIFTy_6 branch?
Currently, there are 107 files changed w.r.t. NIFTy 5, but no indication which of these changes affect the user interface.
If there was a list of (compatibility breaking) changes to the UI, NIFTy 5 users who want to update their code (now or in the distant future) could do so without testing by trial and error which changes they need to make.
Currently, your memory of the changes is probably still fresh enough so you could create such a changelog without excessive trouble, and it would be a very valuable feature to everyone else (and spare you explaining it over and over again).
What do you think? Would you be willing to write up a short summary of your UI changes?
And how about a policy "No merges into NIFTy_6_without a UI changelog entry (if applicable)" for the future?
Thank you all for building the multi frequency capabilities and for considering this,
LukasDear @parras, @pfrank, @phaim and @mtr,
should we introduce a changelog for the NIFTy_6 branch?
Currently, there are 107 files changed w.r.t. NIFTy 5, but no indication which of these changes affect the user interface.
If there was a list of (compatibility breaking) changes to the UI, NIFTy 5 users who want to update their code (now or in the distant future) could do so without testing by trial and error which changes they need to make.
Currently, your memory of the changes is probably still fresh enough so you could create such a changelog without excessive trouble, and it would be a very valuable feature to everyone else (and spare you explaining it over and over again).
What do you think? Would you be willing to write up a short summary of your UI changes?
And how about a policy "No merges into NIFTy_6_without a UI changelog entry (if applicable)" for the future?
Thank you all for building the multi frequency capabilities and for considering this,
Lukashttps://gitlab.mpcdf.mpg.de/ift/nifty_gridder/-/issues/3Segfault when epsilon >= 1e-1 (roughly)2019-10-02T16:57:59ZPhilipp Arrasparras@mpa-garching.mpg.deSegfault when epsilon >= 1e-1 (roughly)I have edited the tests in order to trigger the bug. See branch `bugreport` and https://gitlab.mpcdf.mpg.de/ift/nifty_gridder/-/jobs/938200I have edited the tests in order to trigger the bug. See branch `bugreport` and https://gitlab.mpcdf.mpg.de/ift/nifty_gridder/-/jobs/938200https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/275MPI broken master branch2019-10-04T14:44:21ZReimar H LeikeMPI broken master branchthe recent merge of operator spectra to master broke the functionality of the MPI KL, since newtonCG now requires the metric of the KL as an oeprator.the recent merge of operator spectra to master broke the functionality of the MPI KL, since newtonCG now requires the metric of the KL as an oeprator.Reimar H LeikeReimar H Leike