ift issueshttps://gitlab.mpcdf.mpg.de/groups/ift/-/issues2021-06-10T10:44:36Zhttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/332Rename `Likelihood` -> `LogLikelihood`2021-06-10T10:44:36ZPhilipp Arrasparras@mpa-garching.mpg.deRename `Likelihood` -> `LogLikelihood`NIFTy7 releasehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/331Build html coverage report of both release and develop branch2021-06-09T15:51:21ZPhilipp Arrasparras@mpa-garching.mpg.deBuild html coverage report of both release and develop branchNIFTy7 releasehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/329Performance check (in ift.check_operator) broken for BernoulliEnergy2021-06-07T10:42:14ZLukas PlatzPerformance check (in ift.check_operator) broken for BernoulliEnergyThe operator performance check introduced in [`a50ec021`](https://gitlab.mpcdf.mpg.de/ift/nifty/-/commit/a50ec021e457dc8f4e199c6331c67167eec0fdb7) changes the operator test position [by a factor of two](https://gitlab.mpcdf.mpg.de/ift/ni...The operator performance check introduced in [`a50ec021`](https://gitlab.mpcdf.mpg.de/ift/nifty/-/commit/a50ec021e457dc8f4e199c6331c67167eec0fdb7) changes the operator test position [by a factor of two](https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/a50ec021e457dc8f4e199c6331c67167eec0fdb7/nifty6/extra.py#L146) for the 'application to a linearization' test. This breaks the test for the `BernoulliEnergy`, as it creates input values outside the interval of `[0, 1)`.
@reimar, since you coded this function: does the factor of two serve a valuable purpose, or can we safely delete it?NIFTy7 releasehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/325Update general documentation to GeoVI2021-05-31T08:43:03ZPhilipp Arrasparras@mpa-garching.mpg.deUpdate general documentation to GeoVIAt the end of https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_7/docs/source/ift.rst MGVI is explicitly discussed. Let's add a discussion on GeoVI here or alternatively add a new file to the documentation for GeoVI alone.At the end of https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_7/docs/source/ift.rst MGVI is explicitly discussed. Let's add a discussion on GeoVI here or alternatively add a new file to the documentation for GeoVI alone.NIFTy7 releasehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/326Check NIFTy_7 documentation generation for error messages2021-05-30T17:30:29ZPhilipp Arrasparras@mpa-garching.mpg.deCheck NIFTy_7 documentation generation for error messagesNIFTy7 releasePhilipp Arrasparras@mpa-garching.mpg.dePhilipp Arrasparras@mpa-garching.mpg.dehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/322Update contributors list2021-05-30T17:28:48ZPhilipp Arrasparras@mpa-garching.mpg.deUpdate contributors listNIFTy7 releasePhilipp Arrasparras@mpa-garching.mpg.dePhilipp Arrasparras@mpa-garching.mpg.dehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/327Improvement: Simplify for const could be more potent2021-05-30T17:20:04ZPhilipp Arrasparras@mpa-garching.mpg.deImprovement: Simplify for const could be more potentThe following script demonstrates that the `simplify_for_const` machinery is not able to efficiently deal with partially constant `MultiField`s.
```python
import nifty7 as ift
import numpy as np
dom = ift.RGSpace(10)
e = ift.VariableCo...The following script demonstrates that the `simplify_for_const` machinery is not able to efficiently deal with partially constant `MultiField`s.
```python
import nifty7 as ift
import numpy as np
dom = ift.RGSpace(10)
e = ift.VariableCovarianceGaussianEnergy(dom, "a", "b", np.float64)
fa = ift.FieldAdapter(dom, "a")
fb = ift.FieldAdapter(dom, "b")
e1 = e(fa.adjoint@fa + fb.adjoint@fb)
print(e1)
inp = ift.from_random(fa.domain)
print(e1.simplify_for_constant_input(inp))
```https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/324Update `demos/mgvi_visualized.py` to GeoVI2021-05-30T14:49:38ZPhilipp Arrasparras@mpa-garching.mpg.deUpdate `demos/mgvi_visualized.py` to GeoVINIFTy7 releasePhilipp Arrasparras@mpa-garching.mpg.dePhilipp Arrasparras@mpa-garching.mpg.dehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/321Windows compatibility2021-05-05T17:08:08ZLukas PlatzWindows compatibilityA collaborator of mine just tried to install Nifty on a Windows machine in an Anaconda environment and had the problem that the symlink from `nifty7` to `src` was apparently breaking the setup. Once she removed it and renamed `src` to `n...A collaborator of mine just tried to install Nifty on a Windows machine in an Anaconda environment and had the problem that the symlink from `nifty7` to `src` was apparently breaking the setup. Once she removed it and renamed `src` to `nifty7`, the setup worked.
I have not checked if this is the general behavior under Windows or if it is just her setup, but assume it is the former.
Has anybody else experience with this and can weigh in?
What was the rationale behind changing the source location and introducing the symlink?
Cheers,
Lukashttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/308Fisher test for VariableCovarianceGaussianEnergy not sensitive2021-04-09T12:56:45ZPhilipp Arrasparras@mpa-garching.mpg.deFisher test for VariableCovarianceGaussianEnergy not sensitiveOn the branch `metric_tests` I have introduced a breaking factor (949578182c660faee1ea7344d79993d9d1b35310) and the test does not break. I am not sure how to fix it. Is it even possible to fix it in this case? Do we need two test cases, ...On the branch `metric_tests` I have introduced a breaking factor (949578182c660faee1ea7344d79993d9d1b35310) and the test does not break. I am not sure how to fix it. Is it even possible to fix it in this case? Do we need two test cases, one where the mean is sampled and one where the variance is sampled?Reimar H LeikeReimar H Leikehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/320Is `ift.operators.operator._FunctionApplier` exposed to the NIFTy namespace? ...2021-04-07T10:02:09ZLukas PlatzIs `ift.operators.operator._FunctionApplier` exposed to the NIFTy namespace? If not, why?I just had the case where I wanted to prepend a pointwise operator to a give operator. For *ap*pending a pointwise operator, we have the syntax `op.ptw()`, but do we also have a direct way to *pre*pend an operator?
What I came up with i...I just had the case where I wanted to prepend a pointwise operator to a give operator. For *ap*pending a pointwise operator, we have the syntax `op.ptw()`, but do we also have a direct way to *pre*pend an operator?
What I came up with in a hunch was `op @ ift.ScalingOperator(op.domain, 1.).abs()`, but that is just horrible.
Is there a simple way to do this that I forgot? If not, why don't we expose `operator._FunctionApplier` as `ift.FunctionApplier`?
Cheers!https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/291Problematic MultiField methods2021-03-24T09:26:17ZMartin ReineckeProblematic MultiField methodsCurrently, `MultiField` has methods like `s_sum`, `clip`, and many transcendental functions. I'm wondering whether they make any sense: what's the point of computing the sum over all values in several fields ... or computing the sine of ...Currently, `MultiField` has methods like `s_sum`, `clip`, and many transcendental functions. I'm wondering whether they make any sense: what's the point of computing the sum over all values in several fields ... or computing the sine of all field entries?
We currently use `MultiField`'s `norm` property in minimization, but given that the components of the `MultiField` may have vastly different scales, is this actually a clever thing to do? This basically means that we stop minimizing once the field component with the largest values has converged ... not necessarily what we want.
@parras, @pfrank, @reimar, @kjakohttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/317MaternKernel implementation2021-03-24T09:12:11ZVincent EberleMaternKernel implementation# Matern Kernel
I thought it is a good idea to create this issue to keep everyone up-to-date who is involved in developement or already uses the Matern_kernel.
https://gitlab.mpcdf.mpg.de/ift/nifty/-/tree/matern_kernel
@matteani @jroth...# Matern Kernel
I thought it is a good idea to create this issue to keep everyone up-to-date who is involved in developement or already uses the Matern_kernel.
https://gitlab.mpcdf.mpg.de/ift/nifty/-/tree/matern_kernel
@matteani @jroth @parras @sding @gedenhof @vkainz
If I forgot somebody, just mention them to make sure that they are notified.
# TODO
- [x] Implementation of a**b by Simon and Philipp
- [x] Tests
- [x] Cosmetics
- [x] Demo
- [x] Make statistics_summary work for Matern Kernel fluctuationshttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/276testing differentiability for complex operators2021-03-08T08:36:30ZReimar H Leiketesting differentiability for complex operatorsThe routine extra.check_jacobian_consistency only checks derivatives in real direction. There are two other interesting cases: differentiability in real and imaginary direction and complex derifferentiability (which is the former with th...The routine extra.check_jacobian_consistency only checks derivatives in real direction. There are two other interesting cases: differentiability in real and imaginary direction and complex derifferentiability (which is the former with the additional requirement that df/d(Imag) = i*df/d(Re)).Reimar H LeikeReimar H Leikehttps://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/319AttributeError: 'OperatorAdapter' object has no attribute 'duckape'2021-03-07T22:57:53ZGordian EdenhoferAttributeError: 'OperatorAdapter' object has no attribute 'duckape'OperatorAdapter, e.g. the adjoint of an operator, should support ducktaping.OperatorAdapter, e.g. the adjoint of an operator, should support ducktaping.https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/318Ducc dependency2021-03-07T13:33:04ZJakob RothDucc dependencySo far DUCC was an optional dependency of nifty7. Now it is strictly required because of the import in https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_7/src/library/nft.py#L20
Is this intended?So far DUCC was an optional dependency of nifty7. Now it is strictly required because of the import in https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_7/src/library/nft.py#L20
Is this intended?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/310Unexpected behaviour of ducktape and ducktape_left2021-01-22T14:13:41ZPhilipp FrankUnexpected behaviour of ducktape and ducktape_leftI've noticed an unexpected behaviour of `ducktape_left` and `ducktape` in combination with `Linearization`.
The following example code:
a = ift.Linearization.make_var(ift.from_random(ift.RGSpace(3))).ducktape_left('a')
does not pr...I've noticed an unexpected behaviour of `ducktape_left` and `ducktape` in combination with `Linearization`.
The following example code:
a = ift.Linearization.make_var(ift.from_random(ift.RGSpace(3))).ducktape_left('a')
does not produce an error but produces something that is not an instance of `Linearization` any more. Instead it returns an `_OpChain`. The same is true if we replace `ducktape_left` with `ducktape`.
I suspect this is due to the fact that `Linearization` is now an instance of `Operator` but does not implement `ducktape` itself.
In contrast if we try this with `Field`:
a = ift.from_random(ift.RGSpace(3)).ducktape_left('a')
we get an error.
I see two possible solutions for this: either we disable the (currently unintended?) support of `Linearization` in `ducktape` and `ducktape_left` or we implement a proper version of them for `Field` and `Linearization`.
@all does somebody know what is going on here?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/312Unexpected amplification of fluctuations in the correlated field model by add...2021-01-13T18:02:56ZGordian EdenhoferUnexpected amplification of fluctuations in the correlated field model by adding axesI am working on a problem in which I might dynamically add axes and as such was relying on the normalization of the zero-mode in the correlated field model. I was surprised to notice that with each added axis the fluctuations of the corr...I am working on a problem in which I might dynamically add axes and as such was relying on the normalization of the zero-mode in the correlated field model. I was surprised to notice that with each added axis the fluctuations of the correlated field get amplified by the inverse of `offset_std_mean`. I am using NIFTy6 @ afc3e2df5e6b6d5f90eb414f07beeeefec2a085d .
The following code snippets reproduces the described amplification
```python
cf_axes = {
"offset_mean": 0.,
"offset_std_mean": 1e-3,
"offset_std_std": 1e-4,
"prefix": ''
}
temporal_axis_fluctuations = {
'fluctuations_mean': 1.,
'fluctuations_stddev': 0.1,
'loglogavgslope_mean': -1.0,
'loglogavgslope_stddev': 0.5,
'flexibility_mean': 2.5,
'flexibility_stddev': 1.0,
'asperity_mean': 0.5,
'asperity_stddev': 0.5,
'prefix': 'temporal_axis'
}
fish_axis_fluctuations = {
'fluctuations_mean': 1.,
'fluctuations_stddev': 0.1,
'loglogavgslope_mean': -1.5,
'loglogavgslope_stddev': 0.5,
'flexibility_mean': 2.5,
'flexibility_stddev': 1.0,
'asperity_mean': 0.5,
'asperity_stddev': 0.3,
'prefix': 'fish_axis'
}
cfmaker = ift.CorrelatedFieldMaker.make(**cf_axes)
cfmaker.add_fluctuations(ift.RGSpace(7638), **temporal_axis_fluctuations)
# cfmaker.add_fluctuations(ift.RGSpace(8), **fish_axis_fluctuations)
```
yields
```
cf = cfmaker.finalize()
np.mean([cf(ift.from_random(cf.domain)).s_std() for _ in range(100)]) # \approx 1
```
which is what I would expect. However, if I uncomment the last line in the second cell above, the result becomes
```
cf = cfmaker.finalize()
np.mean([cf(ift.from_random(cf.domain)).s_std() for _ in range(100)]) # \approx 1e+3
```
This does not make sense to me and I was expecting the same result as in the previous cell.
In short: Am I missing something or is there a bug in the correlated field model?https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/316Possibly unexpected behavior of correlated_field2021-01-13T11:13:02ZJakob RothPossibly unexpected behavior of correlated_field@gedenhof and I stumbled over the following behavior of the correlated field. We expected that the correlation structure in the correct units keeps unchanged when changing the target space's distances. Or in other words, if we increase ...@gedenhof and I stumbled over the following behavior of the correlated field. We expected that the correlation structure in the correct units keeps unchanged when changing the target space's distances. Or in other words, if we increase the pixel distance, we just zoom out. And when we decrease the pixel distance, we zoom in. However, this doesn't seem to be the case, as you can see here: In the right plot, the pixel distance is a factor of 10 larger.
![correlated_field](/uploads/bd5c783c172b52b23a347e2e70b7d87d/correlated_field.png)
When we create fields with a fixed power spectrum, everything works as expected. Again, in the right plot, the pixel distance is a factor of 10 larger. Here the right plot is a zoomed out version of the left.
![fixed_power_spec](/uploads/b3299fd32f0100ff9ea306f6939ab805/fixed_power_spec.png)
To us, this seems unintended, but we could not figure out how to change the correlated_field operator. @parras, @pfrank Could one of you help us to fix this, or explain why this is intended to be like this?
[pix_dist.py](/uploads/496ad8a62ee83f1c81a8fa39ba09a843/pix_dist.py)https://gitlab.mpcdf.mpg.de/ift/nifty/-/issues/315Unexpected get_sqrt behaviour2020-12-02T18:48:49ZSebastian HutschenreuterUnexpected get_sqrt behaviourThe following code raises a ValueError claiming no positive definiteness of the operator
```python
import nifty7 as ift
ds = ift.makeDomain(ift.RGSpace(100))
dd = ift.DiagonalOperator(ds, 4).get_sqrt()
```
while
```python
sd = ift.Sca...The following code raises a ValueError claiming no positive definiteness of the operator
```python
import nifty7 as ift
ds = ift.makeDomain(ift.RGSpace(100))
dd = ift.DiagonalOperator(ds, 4).get_sqrt()
```
while
```python
sd = ift.ScalingOperator(ift.full(ds, 4)).get_sqrt()
```
is fine.
Presumably that's a bug and the 'not' in the following code line in
[1](https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_7/src/operators/diagonal_operator.py#L170) is superfluous?