Add PEX signal model

[fzimmermann89]

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[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%	178–179, 185
pgd.py	53	4	92%	107, 152–155
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
src/mrpro/data
AcqInfo.py	165	7	96%	49, 56, 134–135, 137, 243, 367
CsmData.py	43	2	95%	233–235
Dataclass.py	312	26	92%	59, 320, 336, 402, 460–462, 475, 570, 590–591, 593, 608–609, 611, 658–659, 664–665, 852–853, 878, 885, 890–891, 893
DcfData.py	33	1	97%	62
EncodingLimits.py	97	3	97%	37, 127, 130
IData.py	138	6	96%	125, 185, 231, 244, 249, 293
IHeader.py	130	7	95%	69–72, 255, 259, 263, 267
KData.py	228	24	89%	122–123, 138, 145, 156–167, 178, 186, 197, 236, 258–260, 304–305, 377, 543, 545, 617
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	735	42	94%	104, 202, 339, 437, 481, 499, 586, 588, 597, 631, 633, 696, 773, 778, 781, 796, 813, 818, 894, 1082, 1087, 1090, 1114, 1118, 1142, 1262, 1264, 1272–1273, 1337, 1419, 1623, 1630–1632, 1691, 1787, 1939, 1974, 1978, 2154, 2175
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, 303, 309–310
LinearOperator.py	202	6	97%	217, 255, 296, 305, 313, 330
LinearOperatorMatrix.py	174	19	89%	98, 135, 168, 177, 182, 191–194, 207–210, 218–219, 224–225, 237, 346, 376, 403
MultiIdentityOp.py	16	2	88%	58, 63
NonUniformFastFourierOp.py	194	10	95%	67, 94, 216, 218, 256, 258, 335, 393, 467, 472
Operator.py	88	3	97%	82, 115, 125
PatchOp.py	49	3	94%	93, 129, 144
ProximableFunctionalSeparableSum.py	44	3	93%	117, 208, 219
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	211	44	79%	31–32, 105–120, 168–172, 192–195, 216–221, 284, 289, 305–307, 327–328, 333, 357, 362, 387, 392, 508, 609, 636
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
filters.py	62	2	97%	44, 49
indexing.py	177	1	99%	321
pad_or_crop.py	32	6	81%	27, 31, 62, 65, 68, 71
reshape.py	139	9	94%	112, 306, 418–420, 441, 443, 450, 465
slice_profiles.py	49	6	88%	21, 37, 119–122, 155
split_idx.py	10	2	80%	43, 47
summarize.py	57	7	88%	40–41, 70–73, 77, 81
unit_conversion.py	84	19	77%	34, 44, 51, 53, 62, 69, 71, 78, 80, 89, 100, 109, 111, 118, 120, 139, 141, 162, 164
TOTAL	7670	529	93%

Tests	Skipped	Failures	Errors	Time
2991	0 💤	0 ❌	0 🔥	2m 1s ⏱️

[github-actions]

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[fzimmermann89]

i removred the example script for now and only keep the pex signal model. i moved t1 to the forward. this allows it tbe be inuded in a dictionary for dcitionary matching.

[mxlutz]

Looks good, I renamed a to b1 (as it basically is b1...) and changed the dimension of prep_delay, about this I am not sure whether it is a good idea

[mxlutz]

For me the test_pex_t1_recovery Test was failing due to dimension issues in the signal function (prep_delay and t1 are arrays). This is fixed with moving this to the same dimension as voltages, however I am not sure whether this is a good idea? Usually one of them should only be scalar, could add some assert statement.