Recon examples of Cartesian Siemens scans

[hdillinger]

Fixes #518

Added file to illustrate the use of matplotlib's imshow for non-isotropic pixel/voxel data.

The output:

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
src/mrpro/algorithms/csm
inati.py	24	1	96%	44
walsh.py	16	1	94%	34
src/mrpro/algorithms/dcf
dcf_voronoi.py	53	4	92%	15, 48–49, 76
src/mrpro/algorithms/optimizers
adam.py	20	1	95%	69
src/mrpro/algorithms/reconstruction
DirectReconstruction.py	28	16	43%	51–71, 85
IterativeSENSEReconstruction.py	42	23	45%	77–78, 88–98, 113–124, 138–149
Reconstruction.py	51	24	53%	41, 53–55, 79–86, 103–114
src/mrpro/data
AcqInfo.py	128	2	98%	174, 214
CsmData.py	28	3	89%	14, 81–83
DcfData.py	44	8	82%	17, 65, 77–82
IData.py	67	9	87%	119, 125, 129, 159–167
IHeader.py	75	7	91%	75, 109, 127–131
KHeader.py	164	17	90%	24, 126–130, 157, 207, 218, 225–226, 229, 236, 275–286
KNoise.py	31	15	52%	39–52, 56–61
KTrajectory.py	69	5	93%	178–182
MoveDataMixin.py	126	14	89%	14, 109, 125, 139–141, 202, 265, 279, 358, 378–379, 396–397
QData.py	39	7	82%	42, 65–73
Rotation.py	447	22	95%	96, 277, 362, 364, 391, 446, 451, 454, 469, 486, 491, 634, 639, 642, 658, 662, 736, 738, 746–747, 987, 1069
SpatialDimension.py	46	2	96%	64, 103
TrajectoryDescription.py	14	1	93%	23
acq_filters.py	10	1	90%	47
src/mrpro/data/_kdata
KData.py	105	16	85%	108–109, 118, 126, 180–181, 216, 221–222, 241–252
KDataRemoveOsMixin.py	29	2	93%	44, 46
KDataSelectMixin.py	20	2	90%	47, 63
KDataSplitMixin.py	48	3	94%	50, 80, 89
src/mrpro/data/traj_calculators
KTrajectoryCalculator.py	25	2	92%	23, 45
KTrajectoryIsmrmrd.py	13	2	85%	41, 50
KTrajectoryPulseq.py	29	1	97%	54
src/mrpro/operators
CartesianSamplingOp.py	50	9	82%	49–50, 55–56, 61–62, 88, 91, 114
ConstraintsOp.py	60	2	97%	46, 48
EndomorphOperator.py	64	2	97%	225, 231
FiniteDifferenceOp.py	27	2	93%	40, 105
FourierOp.py	77	1	99%	131
Functional.py	43	2	95%	108, 110
GridSamplingOp.py	136	9	93%	72–73, 82–83, 90–91, 94, 96, 98
LinearOperator.py	160	12	92%	54, 90, 189, 217, 258, 267, 275, 284, 292, 303, 401, 406
MultiIdentityOp.py	13	2	85%	44, 49
Operator.py	76	2	97%	24, 67
SliceProjectionOp.py	173	8	95%	44, 61, 63, 69, 206, 227, 260, 300
WaveletOp.py	120	5	96%	152, 170, 205, 210, 233
ZeroPadOp.py	16	1	94%	30
src/mrpro/utils
filters.py	62	2	97%	44, 49
modify_acq_info.py	17	1	94%	12
slice_profiles.py	46	6	87%	20, 36, 113–116, 149
sliding_window.py	34	1	97%	34
split_idx.py	10	2	80%	43, 47
summarize_tensorvalues.py	11	9	18%	20–29
typing.py	12	7	42%	6–15
zero_pad_or_crop.py	31	6	81%	26, 30, 54, 57, 60, 63
TOTAL	3975	302	92%

Tests	Skipped	Failures	Errors	Time
1547	0 💤	0 ❌	0 🔥	1m 26s ⏱️

[fzimmermann89]

Not sure if this is helpful to have in mrpro in this form:
If we want to have an example that shows how properly plot to somebody how does not know matplotlib in mrpro (not sure if we do),
I would instead provide the simplest example without subplots and loops.
And separate creating the random data, recon_matrix (not encoding AFAIK) with the proper type etc from plotting it..
The main point of this example gets somehow lost in the code..

Or is this just an example to showcase something during the hackathon and not meant to end up in main

Edit: OK.. The latter. :D

Outdated

[fzimmermann89]

related: #518

[ckolbPTB]

Superseded by #562