ift issueshttps://gitlab.mpcdf.mpg.de/groups/ift/-/issues2018-06-15T05:40:25Zhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/243feature request: fun with multi2018-06-15T05:40:25ZReimar H Leikefeature request: fun with multiMultifields and operators do not yet support some of the convenience functionality present for normal fields and operators. For example a MultiField that is multiplied with a BlockDiagonalOperator is not automatically converted to a Diag...Multifields and operators do not yet support some of the convenience functionality present for normal fields and operators. For example a MultiField that is multiplied with a BlockDiagonalOperator is not automatically converted to a Diagonal Operator the way it is done for fields. Minimal example attached.
[multi_issue.py](/uploads/f634d63059005da5f20731d673373e8c/multi_issue.py)Martin ReineckeMartin Reineckehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/244Update to NIFTy_52018-06-27T15:54:39ZPhilipp Arrasparras@mpa-garching.mpg.deUpdate to NIFTy_5- [x] Create NIFTy fork (@mtr @parras)
- [ ] Move KL and poisson demo from GlobalNewton to NIFTy fork. (@parras)
- [x] Revert documentation to NIFTy4 (@parras)
Use `Model` for constructing the following energies (@mtr, @jruestig, @reima...- [x] Create NIFTy fork (@mtr @parras)
- [ ] Move KL and poisson demo from GlobalNewton to NIFTy fork. (@parras)
- [x] Revert documentation to NIFTy4 (@parras)
Use `Model` for constructing the following energies (@mtr, @jruestig, @reimar, @pfrank):
- [ ] `GaussianLH`, `PoissonianLH` (new curvature)
- [ ] `NoiseModel`
- [ ] `make_correlated_field` (move from GlobalNewton)
- [ ] `make_amplitude_model` (move from GlobalNewton)
- [ ] `make_multifrequency_correlated_field` / `separable_correlations`(move from GlobalNewton and adopt)
To this end one probably needs to implement a Gaussian and a Poissonian likelihood which is to be used in all energies (see GlobalNewton repository). Please add documentation directly.
Documentation:
- [ ] Documentation on volume factors (see also #234, @reimar, @maxn).
- [ ] Add Models (@hutsch) and MultiFields (@mtr ) to top-level documentation.
- [ ] `__add__` and ` __sub__` of `Energy` (@mtr)
- [ ] `Constant` (@jruestig, @clienhar)
- [ ] `LocalModel` (@jruestig, @clienhar)
- [ ] `Model` (@jruestig, @clienhar)
- [ ] `Add` in models (@jruestig, @clienhar)
- [ ] `ScalarMul` (@jruestig, @clienhar)
- [ ] `LinearModel` (@jruestig, @clienhar)
- [ ] `Variable` (@jruestig, @clienhar)
- [ ] `ModelGradientOperator` (@clienhar)
- [ ] `SamplingEnabler` (@kjako)
- [ ] `SelectionOperator` (@lplatz)
Tests:
- [ ] Consistency checks for all linear operators (see `ift.extra`!). (@lplatz, @silvan, @mawandro)
- [ ] Gradient consistency checks for all energies in library (see `ift.extra`, do that when the energies are rewritten). (@lplatz, @silvan, @mawandro)
- [ ] Unit tests for all new functionality, especially for models. (@parras, @jruestig)
- [ ] Write `ift.extra.model_consistency_check` and check every model with it (@reimar, @silvan)
Demos:
- [X] Fix `demos/poisson_demo.py` or move poisson demo from GlobalNewton here.
- [X] Think about concept for demos. Which ones? How universal? Which features? MAP solution for multi-problems until Jakob's paper is out?
- [ ] Write demos (first `demo/advanced.py`, then `getting_started_123`, 1: R=mask, WF, 2: R=mask, PoissonLogNormal, 3: R=LOS, PosTanh, Gauss, AmplitudeReconstruction). Start when energies are ready. (@kjako, @natalia)
Internals:
- [ ] Add something like a `CurvatureInversionEnabler` class, which takes an `Energy`, an `IterationController` and (optionally) a preconditioner and adds invertability to the energy's curvature operator. Having such a class would probably allow for some code reductions in many energy classes. (This is now tracked as issue #246). (@mtr)
- [x] Move classes out of `model.py` except `Model`.
- [ ] Revert automatical casting of `Field` to `Model`. (@parras)
- [ ] Rewrite `__add__` of `Energy` such that no trees are created (@mtr).
To be discussed:
- [X] Automatically cast `Field` to `Model` when multiplied with a `Model`?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/245dtypes in UnitLogGauss2018-06-27T15:55:19ZPhilipp Arrasparras@mpa-garching.mpg.dedtypes in UnitLogGauss```
import numpy as np
import nifty5 as ift
space = ift.RGSpace(2)
f = ift.from_random('normal', space)
var_f = ift.Variable(f)
g = ift.from_random('normal', space, dtype=np.complex)
var_g = ift.Variable(g)
multi = ift.MultiField({'f':...```
import numpy as np
import nifty5 as ift
space = ift.RGSpace(2)
f = ift.from_random('normal', space)
var_f = ift.Variable(f)
g = ift.from_random('normal', space, dtype=np.complex)
var_g = ift.Variable(g)
multi = ift.MultiField({'f': f, 'g': g})
var = ift.Variable(multi)
op = ift.ScalingOperator(1j, space)
op_multi = ift.ScalingOperator(1j, var.value.domain)
lh_f = ift.library.UnitLogGauss(op(var_f))
lh_g = ift.library.UnitLogGauss(op(var_g))
lh = ift.library.UnitLogGauss(op_multi(var))
print(lh_f.value)
print(lh_f.gradient)
print()
print(lh_g.value)
print(lh_g.gradient)
print()
print(lh.value)
print(lh.gradient['f'])
print(lh.gradient['g'])
```
Results in:
```
2.230929182659062
nifty5.Field instance
- domain = DomainTuple, len: 1
RGSpace(shape=(2,), distances=(0.5,), harmonic=False)
- val = array([-1.52732309+0.j, -1.45915817+0.j])
0.30445727594179295
nifty5.Field instance
- domain = DomainTuple, len: 1
RGSpace(shape=(2,), distances=(0.5,), harmonic=False)
- val = array([0.25117854+0.27192986j, 0.45978435-0.51036889j])
2.535386458600855
nifty5.Field instance
- domain = DomainTuple, len: 1
RGSpace(shape=(2,), distances=(0.5,), harmonic=False)
- val = array([-1.52732309+0.j, -1.45915817+0.j])
nifty5.Field instance
- domain = DomainTuple, len: 1
RGSpace(shape=(2,), distances=(0.5,), harmonic=False)
- val = array([0.25117854+0.27192986j, 0.45978435-0.51036889j])
```
I would expect that the gradient has the same `dtype` as the input.https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/246Slight re-design of the `Energy` class2018-06-27T20:37:45ZMartin ReineckeSlight re-design of the `Energy` classCurrently, most energy classes take constructor arguments which allow their curvatures to be inverted. This only makes sense as long as we are not working very much with sums of energies. In this latter case, it doesn't matter if the ind...Currently, most energy classes take constructor arguments which allow their curvatures to be inverted. This only makes sense as long as we are not working very much with sums of energies. In this latter case, it doesn't matter if the individual curvatures are invertible or not, we just need to make sure that the summed-up curvature is invertible.
I'd like to propose the following changes:
- All inversion-related functionality is removed from the energy classes we currently have
- the `Energy` base class gets a new method `make_curvature_invertible()`, which takes an `IterationController` and optionally another `Energy` that serves as a preconditioner (we need an `Energy` here and not simply a `LinearOperator`, since the preconditioning might depend on position). This method returns a new `Energy` object, with invertible curvature.
- we add a new class `SamplingEnabledEnergy`, whose constructor takes two energies `prior` and `likelihood` and an `IterationController`. It returns an energy object that behaves like the sum of prior and likelihood, and returns a sampling-enabled curvature.https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/247[post Nifty5] make Fields and MultiFields immutable?2018-10-17T11:59:49ZMartin Reinecke[post Nifty5] make Fields and MultiFields immutable?Currently, `Field`s and `MultiField`s are the only NIFTy classes whose value can change after they have been constructed. It may be worthwhile to change this and make them immutable as well.
Pros:
- code will get quite a bit shorter (e....Currently, `Field`s and `MultiField`s are the only NIFTy classes whose value can change after they have been constructed. It may be worthwhile to change this and make them immutable as well.
Pros:
- code will get quite a bit shorter (e.g. no more explicit `lock()` calls)
- less potential for subtle usage errors
Cons:
- slight performance hit by additional copying of data (operators like += will not be as efficient any more)https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/248state of branches2019-02-01T08:30:53ZMartin Reineckestate of branchesWith NIFTy 5 around the corner, I'd like to get an overview which of the current branches we still need...
- try_different_sampling: @reimar, should we merge this?
- new_sampling: still has "krylov" files, can this be removed?
- complex...With NIFTy 5 around the corner, I'd like to get an overview which of the current branches we still need...
- try_different_sampling: @reimar, should we merge this?
- new_sampling: still has "krylov" files, can this be removed?
- complex_fixes: changes seem very small. @parras, should this be merged?
- symbolicDifferentiation: we should probably keep this, so that we don't forget how it is done if we ever need it again ;)
- yango_minimizer: @reimar, what's the status?
- krylov_probing: @reimar, ditto
- addUnits: will be kept since it might be useful in the future
- new_los: @kjako, I still need your opinion on thishttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/249Linear Operator-MultiFields bug2018-07-09T11:59:46ZReimar H LeikeLinear Operator-MultiFields bugThe new feature that one can add multifields that do not have exactly the same domain causes trouble with the check_input function of linear operators. One fix would be to allow ANY linear operator to operate on ANY MultiField, which is ...The new feature that one can add multifields that do not have exactly the same domain causes trouble with the check_input function of linear operators. One fix would be to allow ANY linear operator to operate on ANY MultiField, which is well defined since a linear operator applied to zero is always zero. Thoughts?
(minimal code example for NIFTy crashing below)
```
import nifty5 as ift
space = ift.RGSpace(4)
a = ift.full(space, 1.)
space2 = ift.RGSpace(5)
b = ift.full(space, 1.)
multi = ift.MultiField({'a':a, 'b':b})
var_a = ift.Variable(multi)['a']*2 # Multiplying this by 2 causes NIFTy to crash later
# Here it crashes due to domain mismatch
var_a.gradient.adjoint_times(a)
```https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/250[post Nifty6] new design for the `Field` class?2019-12-02T20:58:52ZMartin Reinecke[post Nifty6] 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 d...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/251Versioning problems when using Nifty in dependencies for other projects2018-07-03T15:04:57ZChristoph LienhardVersioning problems when using Nifty in dependencies for other projectsI am currently trying to set up a working setup.py for the HMCF project and have problems making pip install NIFTy on the fly.
I am not an expert and therefore the reason for my problem may lie somewhere else, but so far I think the sour...I am currently trying to set up a working setup.py for the HMCF project and have problems making pip install NIFTy on the fly.
I am not an expert and therefore the reason for my problem may lie somewhere else, but so far I think the source of it all is that the version is not specified directly in setup.py.
The problem arises, when I specify a dependency link to NIFTy on git in my HMCF [setup.py](https://gitlab.mpcdf.mpg.de/ift/HMCF/blob/master/setup.py):
setup(name=...
dependency_links=["git+https://gitlab.mpcdf.mpg.de/ift/NIFTy@NIFTy4#egg=nifty4-4.2"],
install_requires=["numpy>=1.10", "nifty4>=4.2", "scipy>=0.17", "h5py>=2.5.0"],
...)
This does not work (I also tried fiddling around with the egg argument, leaving it out, do not specify a version, etc. pp, but the problem remains).
> Could not find a version that satisfies the requirement nifty4 (from hmcf==1.0) (from versions: )
> No matching distribution found for nifty4 (from hmcf==1.0)
As far as I understand the problem: pip wants to have a version number.
In several stack overflow questions and related corners of the internet people state that pip looks for a version in the setup.py file of the dependency.
Since NIFTy does not specify the version in an obvious way, pip will not treat NIFTy as a valid package.
Possible Solutions:
===================
1. Reinstate NIFTy on python servers as done with [NIFTy1](https://pypi.org/project/ift_nifty)
2. Specify a version (hard-coded if you want to call it like this) and skip the `__version__` stuff in the setup.py file of NIFTy.
I know, this is handy, but I could not find a solution where pip dependencies work while NIFTy is only available via git
I tried the latter solution on a new nifty branch called [setup_version_test](https://gitlab.mpcdf.mpg.de/ift/NIFTy/tree/setup_version_test) and it works using
setup(name=...
dependency_links=["git+https://gitlab.mpcdf.mpg.de/ift/NIFTy@setup_version_test#egg=nifty4-4.2"],
...)
in the HMCF setup.py file.
Since we have a number of projects which depend on Nifty and simulate easy installation by issuing a setup.py file (talking about you, [starblade](https://gitlab.mpcdf.mpg.de/ift/starblade/blob/master/setup.py)),
I find it very important that it is actually possible to achieve an on-the-fly installation of Nifty.
Everything in here was done with MPA resources, python 3.5.1 and pip 9.0.1https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/252Constant model jacobian buggy2018-07-11T10:14:53ZReimar H LeikeConstant model jacobian buggyThe jacobian of the ConstantModel is defined as
```
self._jacobian = 0.
```
This already looks quite simplified and as one woould expect it easily causes trouble, e.g. as shown in this example:
```
import nifty5 as ift
space = ift.RGSpa...The jacobian of the ConstantModel is defined as
```
self._jacobian = 0.
```
This already looks quite simplified and as one woould expect it easily causes trouble, e.g. as shown in this example:
```
import nifty5 as ift
space = ift.RGSpace(4)
a = ift.full(space, 1.)
hspace = space.get_default_codomain()
ht = ift.HarmonicTransformOperator(hspace, target = space)
b = ift.full(hspace, 1.)
multia = ift.MultiField.from_dict({'a':a})
var_a = ift.Variable(multia)['a']
const_b = ift.Constant(multia, b)
sumabvalue = var_a.value + ht(const_b).value
# Here it crashes due to domain mismatch, even though you can add the values just fine
sumab = var_a + ht(const_b)
```
There are at least 2 possible ways to fix this issue. One possibility is to make the ConstantModel use the position again, such that it can return zeros in the correct domain. Another possibility is to have an operator that maps anything to an empty MultiDomain. (which then can be added with other fields)
I favor the last one, it is however incompatible with the way that martin wants to unify Field and MultiField.
Any thoughts @kjako @mtr @parras ?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/253inverter=2018-09-11T09:50:38ZChristoph Lienhardinverter=https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/254Better support for partial inference2020-06-30T14:00:51ZMartin 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 const...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/255Unify SlopeOperator, OffsetOperator, Polynom-fitting demo into one Parametric...2019-11-29T10:39:00ZPhilipp Arrasparras@mpa-garching.mpg.deUnify 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/256Tag release commit!2019-02-01T08:37:20ZPhilipp Arrasparras@mpa-garching.mpg.deTag release commit!Also:
- First demo runs, then build pagesAlso:
- First demo runs, then build pagesPhilipp Arrasparras@mpa-garching.mpg.dePhilipp Arrasparras@mpa-garching.mpg.dehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/257Demos do not work for python 3.5.52019-02-01T10:42:19ZJakob KnollmuellerDemos do not work for python 3.5.5Hi,
all demos do not run with python 3.5.5 because the f prefix before strings is not supported.
JakobHi,
all demos do not run with python 3.5.5 because the f prefix before strings is not supported.
JakobPhilipp Arrasparras@mpa-garching.mpg.dePhilipp Arrasparras@mpa-garching.mpg.dehttps://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 w...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/259bug in linearization with multifield as target2019-02-07T09:03:15ZReimar H Leikebug in linearization with multifield as targetIt is possible to contruct sensible operators out of Nifty routines that fail if called with Linearizations. [minimal_example.py](/uploads/064781a8a4c0a0af2872a52efb5c0814/minimal_example.py)It is possible to contruct sensible operators out of Nifty routines that fail if called with Linearizations. [minimal_example.py](/uploads/064781a8a4c0a0af2872a52efb5c0814/minimal_example.py)https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/260extending clip2019-02-25T10:55:09ZReimar H Leikeextending clipCurrently clip clips all values by the same clipping value. However, in numpy clip can also take an array, such that every value gets clipped by a diffent clipping value. Field.clip can in principle take a dobj and then clips differently...Currently clip clips all values by the same clipping value. However, in numpy clip can also take an array, such that every value gets clipped by a diffent clipping value. Field.clip can in principle take a dobj and then clips differently for every entry, however this does not seem intended.
It would be advantageous to support passing a Field/MultiField instead of a scalar for clip.Reimar H LeikeReimar H Leikehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/262Nifty citation2019-05-21T06:52:52ZPhilipp Arrasparras@mpa-garching.mpg.deNifty citationWhen citing NIFTy with A&A style I get in the references:
```
Martin Reinecke, Theo Steininger, Marco Selig. 2019, NIFTy – Numerical Information Field TheorY
```
without any link. Can this be fixed? Also: the year is missing in https://...When citing NIFTy with A&A style I get in the references:
```
Martin Reinecke, Theo Steininger, Marco Selig. 2019, NIFTy – Numerical Information Field TheorY
```
without any link. Can this be fixed? Also: the year is missing in https://gitlab.mpcdf.mpg.de/ift/nifty/blob/NIFTy_5/docs/source/citations.rsthttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/263Properly implementing a convolution2019-03-14T15:09:48ZJulia StadlerProperly implementing a convolutionMy response convolves the signal with a non-spherical PSF, which is specified by a field with the same domain as the signal. To avoid issues with periodic boundaries I apply a zero padding, so the exact steps are:
* zero-pad the PSF fro...My response convolves the signal with a non-spherical PSF, which is specified by a field with the same domain as the signal. To avoid issues with periodic boundaries I apply a zero padding, so the exact steps are:
* zero-pad the PSF from the center, apply a Fourier transform and construct a DiagonalOperator from the result
* zero-pad the signal from the right/top and apply a Fourier transform
* apply the PSF-operator to the signal field followed by an inverse Fourier transform
* apply the adjoint signal zero padding operation
* take the real value
The problem with this procedure is that it reduces the flux at the borders of the image, where the PSF smears the additional zeros out to the pixels I'm interested in. I attached two plots to illustrate the problem, on shows how point sources are smeared out to the opposite side of the image without zero padding, the other how the zero padding leaks into the image at the border. The signal contains two point sources which get brighter with with bin number and equally the PSF gets wider.
Has anyone encountered a similar problem before? Or are there any nifty tools to tackle this, I'm not aware of? My approach would be to extend the signal field beyond the data region and to include a mask in the response to account for that. .Or is there a smarter approach?![data_no-zero-padding](/uploads/90b1f2e428d6316b0f4886ec40535fe4/data_no-zero-padding.png)
![data_outer-zero-padding](/uploads/75851eacc3174eccb9be766b57bdad28/data_outer-zero-padding.png)