Add AveragingOp

[fzimmermann89]

Calculates the avarage over a dimenion or over subets aong a dimension
Useful for qMRI if the images hve been reconstruced using a sliding windows.
Example cmrf (pseudocode):

kdata = kdata.split_k1_into_other(split_indices)
mrf_model = SignalAverage(split_indices) @ CardiacFingerprinting()

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
src/mrpro
_version.py	6	2	67%	7–8
src/mrpro/algorithms/csm
inati.py	24	1	96%	44
walsh.py	16	1	94%	53
src/mrpro/algorithms/dcf
dcf_voronoi.py	55	4	93%	15, 55–56, 89
src/mrpro/algorithms/optimizers
adam.py	22	1	95%	104
pdhg.py	81	3	96%	178–179, 185
pgd.py	53	4	92%	107, 152–155
src/mrpro/algorithms/reconstruction
DirectReconstruction.py	28	16	43%	59–79, 93
IterativeSENSEReconstruction.py	13	1	92%	79
Reconstruction.py	50	22	56%	42, 54–56, 80–87, 108–117
RegularizedIterativeSENSEReconstruction.py	41	17	59%	97–101, 115–140
src/mrpro/data
AcqInfo.py	145	5	97%	42, 121–122, 124, 233
CsmData.py	29	3	90%	15, 84–86
DcfData.py	45	8	82%	18, 66, 78–83
EncodingLimits.py	81	3	96%	33, 123, 126
IData.py	59	8	86%	118, 132, 159–167
IHeader.py	136	12	91%	69–72, 253, 257, 261, 265, 299–303
KData.py	201	29	86%	113–114, 129, 136, 147–157, 166, 174, 183, 217, 239–241, 277–278, 333–344, 473, 475, 545
KHeader.py	141	21	85%	24, 109–115, 142, 190, 197–198, 201, 208, 225–232, 240–251
KNoise.py	31	15	52%	39–52, 56–61
KTrajectory.py	108	3	97%	166, 168, 188
MoveDataMixin.py	140	18	87%	28, 126, 142, 156–158, 220, 336–338, 351, 430, 450–451, 453, 468–469, 471
QData.py	39	7	82%	42, 65–73
Rotation.py	719	35	95%	101, 199, 336, 434, 478, 496, 583, 585, 594, 628, 630, 693, 770, 775, 778, 793, 810, 815, 891, 1079, 1084, 1087, 1111, 1115, 1243, 1245, 1253–1254, 1318, 1400, 1703, 1855, 1890, 1894, 2084
SpatialDimension.py	232	20	91%	34, 104, 148, 154, 274–276, 289–291, 325, 343, 356, 369, 382, 395, 404–405, 420, 429
acq_filters.py	12	1	92%	47
src/mrpro/data/traj_calculators
KTrajectoryCalculator.py	27	1	96%	84
KTrajectoryIsmrmrd.py	19	1	95%	57
src/mrpro/operators
AveragingOp.py	34	2	94%	53, 78
CartesianSamplingOp.py	96	4	96%	124, 163, 238, 305
ConstraintsOp.py	60	2	97%	46, 48
EndomorphOperator.py	32	2	94%	52, 58
FiniteDifferenceOp.py	27	2	93%	40, 105
FourierOp.py	90	3	97%	185, 257, 262
Functional.py	77	9	88%	20–22, 117, 119, 226–228, 242
GridSamplingOp.py	136	9	93%	71–72, 81–82, 89–90, 93, 95, 97
LinearOperator.py	201	8	96%	107, 217, 244, 251, 292, 301, 309, 326
LinearOperatorMatrix.py	162	19	88%	82, 119, 152, 161, 166, 175–178, 191–194, 202–203, 208–209, 221, 310, 337, 364
MultiIdentityOp.py	13	2	85%	43, 48
NonUniformFastFourierOp.py	188	10	95%	69, 96, 206, 208, 241, 243, 319, 373, 423, 428
Operator.py	79	2	97%	32, 88
ProximableFunctionalSeparableSum.py	39	3	92%	50, 103, 110
SliceProjectionOp.py	174	8	95%	45, 62, 64, 70, 206, 227, 260, 300
WaveletOp.py	119	5	96%	151, 169, 204, 209, 232
ZeroPadOp.py	16	1	94%	30
src/mrpro/phantoms
brainweb.py	275	31	89%	325–335, 371, 427–430, 452–453, 458–459, 461–462, 466, 474, 481–482, 520, 586–587, 606–609, 620, 622, 657–658, 671
src/mrpro/utils
filters.py	62	2	97%	44, 49
indexing.py	170	1	99%	331
reshape.py	95	2	98%	110, 301
slice_profiles.py	47	6	87%	21, 37, 116–119, 152
sliding_window.py	34	1	97%	34
split_idx.py	10	2	80%	43, 47
summarize_tensorvalues.py	12	3	75%	23, 25, 27
typing.py	69	55	20%	9–235
unit_conversion.py	60	14	77%	32, 40, 42, 49, 51, 58, 60, 69, 80, 82, 101, 103, 124, 126
zero_pad_or_crop.py	31	6	81%	26, 30, 55, 58, 61, 64
TOTAL	5954	476	92%

Tests	Skipped	Failures	Errors	Time
2751	0 💤	0 ❌	0 🔥	1m 48s ⏱️

[github-actions]

📚 Documentation

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[fzimmermann89]

Not sure if this is the best abstraction - open for suggestions and docstring changes

We could have a reduction op supporting min, max, mean etc - but only mean is linear.

We could use the maximum index for the domain size if domain_sze has not been set (still ofc with a warning) - assuming that you want to use all data somewhere. But the cmrf reco already breaks that assumption.