- Add bind_ClusterFinderCUDA.hpp with pybind11 bindings for
ClusterFinderCUDA
- Build CUDA bindings as separate _aare_cuda.so to avoid
segfaults from mixing nvcc and gcc compiled code in the
same shared object
- Re-export CUDA classes onto _aare in __init__.py so user
code uses `from aare import ClusterFinderCUDA` regardless
of which .so hosts the class
- Factory in ClusterFinder.py selects backend; RuntimeError
if GPU requested on CPU-only build
- Update python/CMakeLists.txt: _aare_cuda module gated
behind AARE_CUDA and AARE_PYTHON_BINDINGS
- Add validation notebook: ~20x speedup vs sequential ClusterFinder
## Unified Minuit2 fitting framework with FitModel API
### Models (`Models.hpp`)
Consolidate all model structs (Gaussian, RisingScurve, FallingScurve)
into a
single header. Each model provides: `eval`, `eval_and_grad`, `is_valid`,
`estimate_par`, `compute_steps`, and `param_info` metadata. No Minuit2
dependency.
### Chi2 functors (`Chi2.hpp`)
Generic `Chi2Model1DGrad` (analytic gradient) templated on the model
struct.
Replaces the separate Chi2Gaussian, Chi2GaussianGradient,
Chi2Scurves, and Chi2ScurvesGradient headers.
### FitModel (`FitModel.hpp`)
Configuration object wrapping `MnUserParameters`, strategy, tolerance,
and
user-override tracking. User constraints (fixed parameters, start
values, limits)
always take precedence over automatic data-driven estimates.
### Fit functions (`Fit.hpp`)
- `fit_pixel<Model, FCN>(model, x, y, y_err)` -> single-pixel,
self-contained
- `fit_pixel<Model, FCN>(model, upar_local, x, y, y_err)` -> pre-cloned
upar for hot loops
- `fit_3d<Model, FCN>(model, x, y, y_err, ..., n_threads)` ->
row-parallel over pixel grid
### Python bindings
- `Pol1`, `Pol2`, `Gaussian`, `RisingScurve`, `FallingScurve` model
classes with
`FixParameter`, `SetParLimits`, `SetParameter`, and properties for
`max_calls`, `tolerance`, `compute_errors`
- Single `fit(model, x, y, y_err, n_threads)` dispatch replacing the old
`fit_gaus_minuit`, `fit_gaus_minuit_grad`, `fit_scurve_minuit_grad`,
etc.
### Benchmarks
- Updated `fit_benchmark.cpp` (Google Benchmark) to use the new FitModel
API
- Jupyter notebooks for 1D and 3D S-curve fitting (lmfit vs Minuit2
analytic)
- ~1.8x speedup over lmfit, near-linear thread scaling up to physical
core count
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
Matterhorn10 Transform
some other Transformations from pyctbGUI
added method get_reading_mode for easier error handling in decoders
## TODO:
- proper error handling for all other decoders
- proper documentation for all other decoders
- refactoring all other decoders to store hard coded values in a Struct
ChipSpecification
This PR adds support for decoding digital data from the my320 test chip.
- Added BitOffset (strong type)
- Expand 24 to 32 bit
- Python bindings for decoding my302
- Improved docs
- added rosenblatttransform
- added 3x3 eta methods
- interpolation can be used with various eta functions
- added documentation for interpolation, eta calculation
- exposed full eta struct in python
- disable ClusterFinder for 2x2 clusters
- factory function for ClusterVector
---------
Co-authored-by: Dhanya Thattil <dhanya.thattil@psi.ch>
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
When refactoring the dispatch of the python binding for ClusterFile I
forgot chunk_size. Adding it back in.
Excluded from release notes since the bug was introduced after the last
release and now fixed before the next release.
1. added back chunk_size
2. removed a few commented out lines
closes #197
- added scurve fitting with two different signs (scurve, scurve2)
- at the moment no option to set initial parameters
---------
Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
Changes to be able to run the example notebooks:
- Invert gain map on setting (multiplication is faster but user supplies
ADU/energy)
- Cast after applying gain map not to loose precision (Important for
int32 clusters)
- "factor" for ClusterFileSink
- Cluster size available to be able to create the right file sink
New function apply_custom_weights (can we find a better name) that takes
a uint16 and a NDView<double,1> of bases for the conversion. For each
supplied weight it is used as base (instead of 2) to convert from bits
to a double.
---------
Co-authored-by: siebsi <sieb.patr@gmail.com>
kferjaoui
mazzol_a
heymes_j