Add import/export of dataclasses to hdf5-based mrp files

[fzimmermann89]

Draft of a more efficient "mrpro native" data format matching our dataclass structure.
So instead of acquisitions as in ismrmrd, we write kdata.data, kdata.traj, etc as h5 groups.
This allows us to only store what we actually need in reduced form, speeds up loading for use in trainings and will allow us to store additional information not in ismrmrd.

The idea is to use this for temporary storage and caching. So one can load in all ismrmrd fileds required for a training, dump them to a temp drive and load randomly back into memory in a data loader..

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
src/mrpro
_version.py	6	2	67%	7–8
src/mrpro/algorithms/csm
inati.py	25	1	96%	43
src/mrpro/algorithms/dcf
dcf_voronoi.py	55	4	93%	15, 55–56, 89
src/mrpro/algorithms/optimizers
adam.py	30	6	80%	108, 125–129
cg.py	52	1	98%	139
pdhg.py	81	3	96%	180–181, 187
pgd.py	53	4	92%	108, 153–156
src/mrpro/algorithms/reconstruction
DirectReconstruction.py	28	6	79%	62, 65, 70, 77–79
IterativeSENSEReconstruction.py	13	1	92%	79
Reconstruction.py	50	13	74%	54–56, 80–87, 109, 112
RegularizedIterativeSENSEReconstruction.py	51	26	49%	104–108, 122–161
TotalVariationRegularizedReconstruction.py	52	25	52%	99–111, 125–153
src/mrpro/data
AcqInfo.py	165	7	96%	49, 56, 134–135, 137, 243, 367
CsmData.py	43	2	95%	233–235
Dataclass.py	437	46	89%	71, 332, 348, 414, 472–474, 487, 582, 602–603, 605, 620–621, 623, 670–671, 676–677, 760–761, 781–782, 794, 799–800, 809, 813–814, 823, 828, 833–834, 840, 847–848, 881, 885, 895, 1033–1034, 1059, 1066, 1071–1072, 1074
DcfData.py	33	1	97%	62
EncodingLimits.py	97	3	97%	37, 127, 130
IData.py	178	12	93%	138, 155–156, 217–225, 258, 304, 322
IHeader.py	174	8	95%	49, 98–101, 284, 288, 292, 296
KData.py	229	24	90%	121–122, 137, 144, 155–166, 177, 185, 196, 234, 256–258, 305–306, 378, 544, 546, 618
KHeader.py	176	13	93%	115–121, 148, 196, 203–204, 231–238
KNoise.py	22	1	95%	44
KTrajectory.py	95	3	97%	163, 165, 185
QData.py	32	1	97%	43
Rotation.py	743	42	94%	105, 203, 340, 438, 482, 500, 587, 589, 598, 635, 637, 700, 777, 782, 785, 800, 817, 822, 898, 1086, 1091, 1094, 1118, 1122, 1146, 1266, 1268, 1276–1277, 1341, 1423, 1627, 1634–1636, 1695, 1791, 1943, 1978, 1982, 2161, 2189
SpatialDimension.py	150	19	87%	34, 103, 146, 158, 278–280, 293–295, 329, 347, 360, 373, 386, 399, 408–409, 437
src/mrpro/data/traj_calculators
KTrajectoryCalculator.py	26	2	92%	84, 95
KTrajectoryCartesian.py	31	4	87%	129–132, 136
KTrajectoryIsmrmrd.py	19	1	95%	57
KTrajectorySpiral2D.py	57	13	77%	63–66, 69, 71, 73, 75, 77, 105, 107, 134–136
src/mrpro/operators
AveragingOp.py	36	2	94%	72, 113
CartesianSamplingOp.py	112	4	96%	152, 191, 266, 387
ConjugateGradientOp.py	89	7	92%	62, 64, 100, 106, 228, 230, 233
ConstraintsOp.py	85	4	95%	78, 80, 250, 255
EndomorphOperator.py	28	2	93%	71, 77
FiniteDifferenceOp.py	29	2	93%	40, 126
FourierOp.py	101	6	94%	186–187, 206, 251, 315, 320
Functional.py	70	2	97%	116, 118
GridSamplingOp.py	165	15	91%	72–73, 82–83, 90–91, 94, 96, 98, 282, 290–291, 306, 312–313
LinearOperator.py	202	6	97%	217, 255, 296, 305, 313, 330
LinearOperatorMatrix.py	174	19	89%	99, 136, 169, 178, 183, 192–195, 208–211, 219–220, 225–226, 238, 347, 377, 404
MultiIdentityOp.py	16	2	88%	58, 63
NonUniformFastFourierOp.py	195	10	95%	68, 95, 217, 219, 257, 259, 336, 393, 467, 472
Operator.py	88	3	97%	82, 115, 125
PatchOp.py	49	3	94%	93, 129, 144
ProximableFunctionalSeparableSum.py	44	3	93%	118, 213, 224
SliceProjectionOp.py	178	10	94%	45, 62, 64, 70, 154, 180, 216, 253, 290, 330
WaveletOp.py	121	5	96%	168, 186, 230, 235, 258
ZeroPadOp.py	18	1	94%	30
src/mrpro/operators/functionals
SSIM.py	73	7	90%	60–80, 82, 86, 114, 147
src/mrpro/operators/models
EPG.py	255	19	93%	31–32, 283, 288, 304–306, 326–327, 332, 356, 361, 386, 391, 546, 600, 739, 756, 783
src/mrpro/phantoms
EllipsePhantom.py	43	2	95%	66, 131
brainweb.py	297	40	87%	276, 290–294, 349–359, 398, 454–457, 479–480, 485–486, 488–489, 493, 501, 508–509, 550, 621, 624–625, 644, 653–656, 667, 669, 700–701, 715, 723
fastmri.py	106	10	91%	50–51, 59, 65, 162, 169–171, 174–175, 189
m4raw.py	74	4	95%	58–59, 74, 76
mdcnn.py	71	7	90%	58, 62–63, 70, 82, 88, 135
src/mrpro/utils
RandomGenerator.py	156	15	90%	23–24, 36, 38, 188, 212, 428, 446, 528, 799, 829–832, 895, 898
ema.py	42	4	90%	49, 74, 78, 82
filters.py	62	2	97%	44, 49
indexing.py	183	2	99%	34, 344
pad_or_crop.py	40	6	85%	41, 44, 47, 50, 67, 74
reshape.py	139	9	94%	112, 308, 420–422, 443, 445, 452, 467
slice_profiles.py	49	6	88%	21, 37, 119–122, 155
split_idx.py	10	2	80%	43, 47
summarize.py	57	6	89%	40–41, 70–73, 81
unit_conversion.py	72	15	79%	34, 44, 51, 53, 62, 69, 71, 78, 80, 89, 100, 121, 123, 144, 146
TOTAL	8033	556	93%

Tests	Skipped	Failures	Errors	Time
3018	0 💤	0 ❌	0 🔥	2m 19s ⏱️

[github-actions]

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[cubic-dev-ai]

mrge found 11 issues across 6 files. View them in mrge.io

[fzimmermann89]

@ckolbPTB I would like to add this to one of the example notebooks. Then if we break something and the mrp import does not work anymore with the old mrp file, we notice and would have to update the file on zenodo.

unit test here would then just do kdata->mrp>kdata roundtrip.

Maybe we can have a short notebook about indexing, rearranging, moving to cuda, importing and exporting data?
without any reconstruction etc, just basics?