ift issueshttps://gitlab.mpcdf.mpg.de/groups/ift/-/issues2019-09-24T09:00:52Zhttps://gitlab.mpcdf.mpg.de/ift/nifty_gridder/-/issues/1Feature wishlist2019-09-24T09:00:52ZPhilipp Arrasparras@mpa-garching.mpg.deFeature wishlist- [x] Add single precision mode
- [x] Compute holograpic matrix as sparse matrix
- [x] Set number of threads for fft from outside
- [x] Compute w-screen on the fly and apply it in dirty2grid and grid2dirty
- [x] Exploit symmetries in w-...- [x] Add single precision mode
- [x] Compute holograpic matrix as sparse matrix
- [x] Set number of threads for fft from outside
- [x] Compute w-screen on the fly and apply it in dirty2grid and grid2dirty
- [x] Exploit symmetries in w-screen
- [x] Add primary beam to gridder? No, at least not now.
- [x] Add weighted versions for vis2grid, grid2vis, apply_holo
- [x] Test wstacking vs modified DFThttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/273Negative distances2019-07-30T07:52:44ZPhilipp Arrasparras@mpa-garching.mpg.deNegative distancesIs it intended that distances of RGSpaces can be negative? I can imagine that this leads to unexpected behaviour...Is it intended that distances of RGSpaces can be negative? I can imagine that this leads to unexpected behaviour...https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/272Representation of ScalingOperator.ducktape()2019-06-25T12:46:21ZLukas PlatzRepresentation of ScalingOperator.ducktape()The representation of `ift.ScalingOperator(20., domain).ducktape('test')` is generated with swappend operators:
> ChainOperator:
> FieldAdapter <- ('test',)
> ScalingOperator (20.0)
Other operators do not exhibit this behavior ...The representation of `ift.ScalingOperator(20., domain).ducktape('test')` is generated with swappend operators:
> ChainOperator:
> FieldAdapter <- ('test',)
> ScalingOperator (20.0)
Other operators do not exhibit this behavior (tested DiagonalOperator, InverseGammaPrior). The domain and target of the resulting ChainOperator are correct, so I think this is just a quirk in the representation generation.https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/271Should we "fix" exact zeros in PoissonianEnergy?2019-06-25T13:09:05ZMartin ReineckeShould we "fix" exact zeros in PoissonianEnergy?Several people seem to encounter problems when applying `PoissonianEnergy` to a field that contains exact zeros.
Would it be acceptable to replace these zeros by `np.finfo(x.dtype).tiny` in `PoissonianEnergy.apply()` in order to address...Several people seem to encounter problems when applying `PoissonianEnergy` to a field that contains exact zeros.
Would it be acceptable to replace these zeros by `np.finfo(x.dtype).tiny` in `PoissonianEnergy.apply()` in order to address the problem, or should this be done somewhere else?
@lplatz, @hutsch, @reimar, @parrashttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/270Accuracy of nfft2019-05-21T07:33:44ZPhilipp Arrasparras@mpa-garching.mpg.deAccuracy of nfftI have investigated the accuracy of our nfft implementation for 1 and for 100 data points:
## Current implementation
### 1 data point
![nfft_accuracy_old1](/uploads/e59d21c7e258153ef95ce0d5585a8f1a/nfft_accuracy_old1.png)
### 100 data...I have investigated the accuracy of our nfft implementation for 1 and for 100 data points:
## Current implementation
### 1 data point
![nfft_accuracy_old1](/uploads/e59d21c7e258153ef95ce0d5585a8f1a/nfft_accuracy_old1.png)
### 100 data points
![nfft_accuracy_old](/uploads/771abb3bb5fd94ba5efb0e27114e06fc/nfft_accuracy_old.png)
---
## Alternative
We use the following criterion to decide on the oversampling factor:
```python
rat = 3 if eps < 1e-11 else 2
```
If one always takes an oversampling factor of 2 we arrive at the following results
### 1 data point
![nfft_accuracy_new1](/uploads/6b88362864b0ef494b893647b53a46c7/nfft_accuracy_new1.png)
### 100 data points
![nfft_accuracy_new](/uploads/31055329393f439ea64dd8fdfa00d9db/nfft_accuracy_new.png)
Therefore, my suggestion is to drop oversampling with a factor of three since it does not add relevant accuracy.
Here is the original paper for the code. Chapter 4 describes the algorithm which we use.
[nufft_paper.pdf](/uploads/85e154c34d7be2d7c7f0c6a71acfde1f/nufft_paper.pdf)
This is the code which produces the plots.
[gridding_accuracy.py](/uploads/72a8ebab21724318d786821f07948bff/gridding_accuracy.py)
@mtr, @phaimhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/269Unneeded branches?2019-10-08T22:34:59ZMartin ReineckeUnneeded branches?What is the status of the branches `mf_plus_add` and `mfcorrelatedfield_withzeromodeprior`? They appear to be superseded by `multi_freq_plus_gridder`. Can they be removed, @jruestig ?What is the status of the branches `mf_plus_add` and `mfcorrelatedfield_withzeromodeprior`? They appear to be superseded by `multi_freq_plus_gridder`. Can they be removed, @jruestig ?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/268Do we still need NFFT?2019-04-29T08:25:22ZMartin ReineckeDo we still need NFFT?I'd like to remove the `nifty5.library.NFFT` class, since it will most likely not be used in the future and has a fairly inconvenient dependency on `pynfft` and the nfft package.
Any objections? @parras?I'd like to remove the `nifty5.library.NFFT` class, since it will most likely not be used in the future and has a fairly inconvenient dependency on `pynfft` and the nfft package.
Any objections? @parras?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/267SumOperator is not imported with nifty2019-04-26T13:29:43ZPhilipp HaimSumOperator is not imported with niftyWhen importing nifty5, SumOperator is not included. Is that intentional?When importing nifty5, SumOperator is not included. Is that intentional?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/266Amplitude model2019-11-29T10:39:48ZPhilipp Arrasparras@mpa-garching.mpg.deAmplitude 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 Log...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/265MaskOperator in getting_started_12019-04-16T07:49:17ZJulia StadlerMaskOperator in getting_started_1The demo getting_started_1 uses a DiagonalOperator to implement the chessboard mask. I think replacing it by a MaskOperator would be helpful for people new to the code (I checked the demos first for how to implement masking ...)The demo getting_started_1 uses a DiagonalOperator to implement the chessboard mask. I think replacing it by a MaskOperator would be helpful for people new to the code (I checked the demos first for how to implement masking ...)https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/264Bug in metric2019-04-16T09:34:36ZPhilipp Arrasparras@mpa-garching.mpg.deBug in metricThe following script crashes in the current version of nifty and even on the branch `opsumdomains` the last example crashes. Anyone any ideas how to fix this?
```
import nifty5 as ift
dom = ift.RGSpace(10)
diffuse = ift.ducktape(dom, N...The following script crashes in the current version of nifty and even on the branch `opsumdomains` the last example crashes. Anyone any ideas how to fix this?
```
import nifty5 as ift
dom = ift.RGSpace(10)
diffuse = ift.ducktape(dom, None, 'xi')
diffuse2 = ift.ducktape(dom, None, 'xi2')
e1 = ift.GaussianEnergy(domain=diffuse.target)
ops = []
ops.append((e1 + e1) @ diffuse)
ops.append(e1 @ diffuse)
ops.append(e1 @ diffuse + e1 @ diffuse2)
for op in ops:
pos = ift.full(op.domain, 0)
lin = ift.Linearization.make_var(pos, True)
print(op(lin).metric)
assert isinstance(op(lin).metric.domain, ift.MultiDomain)
assert op(lin).metric.domain is op.domain
```https://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)https://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/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/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/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/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/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/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/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?