- Add assert to flip
- Only one SensorPlacement for strixel_to_pixel_maps
- Pass order_map as NDView to ApplyRemap
Additional:
- Check output.shape() matches order_map.shape (correct buffer allocation)
- Chash nrows and ncols
- pass NDViews by value (copying is cheap and it makes the intent more clear that it only takes a snapshot of the arrays
- Move Chi2.hpp from include/aare/ to src/ (private)
- Pimpl on FitModel<Model>: MnUserParameters/MnStrategy behind opaque
src/FitModelImpl.hpp, no Minuit2 includes in public headers
- Move fit_pixel/fit_3d bodies to Fit.cpp with explicit instantiations
for all 8 models; drop FCN template param from public API
- CMake: aare::Minuit2 wrapped in $<BUILD_INTERFACE:...> (hidden from
exported targets, same pattern as lmfit), MINUIT2_INSTALL OFF, Chi2.hpp
removed from PUBLICHEADERS
- Update python bindings and benchmark callsites accordingly
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
Co-authored-by: Alice <alice.mazzoleni@psi.ch>
Collection of small improvements in usability:
- NDView works for large arrays
- Direct subtraction of Pedestal from np.array
- len() support for python bindings of files
- reshape image directly in decoder such that first dimension is num
counters
- take into account chip artefact in decoder.
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
With C++20 `fmt::print(s)` expects a compile time format string and
otherwise fails complaining about consteval. To get runtime formatting
use `fmt::print(fmt::runtime(s))`
Multi threaded filling of per pixel histograms for example for detector calibration
1. PixelHistogram - Generic variant expects already pedestal subtracted
data
2. PedestalTrackingHistogram - Terrible name, useful class. Keeps it's
own pedestal and does conversion and pedestal tracking in the worker
threads.
---------
Co-authored-by: Lars Erik Fröjd <froejdh_e@pc-jungfrau-02.psi.ch>
To improve codebase quality and reduce human error, this PR introduces
the pre-commit framework. This ensures that all code adheres to project
standards before it is even committed, maintaining a consistent style
and catching common mistakes early.
Key Changes:
- Code Formatting: Automated C++ formatting using clang-format (based on
the project's .clang-format file).
- Syntax Validation: Basic checks for file integrity and syntax.
- Spell Check: Automated scanning for typos in source code and comments.
- CMake Formatting: Standardization of CMakeLists.txt and .cmake
configuration files.
- GitHub Workflow: Added a CI action that validates every Pull Request
against the pre-commit configuration to ensure compliance.
The configuration includes a [ci] block to handle automated fixes within
the PR. Currently, this is disabled. If we want the CI to automatically
commit formatting fixes back to the PR branch, this can be toggled to
true in .pre-commit-config.yaml.
```yaml
ci:
autofix_commit_msg: [pre-commit] auto fixes from pre-commit hooks
autofix_prs: false
autoupdate_schedule: monthly
```
The last large commit with the fit functions, for example, was not
formatted according to the clang-format rules. This PR would allow to
avoid similar mistakes in the future.
Python fomat with `ruff` for tests and sanitiser for `.ipynb` notebooks
can be added as well.
## 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
Reading multiple ROI's for aare
- read_frame, read_n etc throws for multiple ROIs
- new functions read_ROIs, read_n_ROIs
- read_roi_into (used for python bindings - to not copy)
all these functions use get_frame or get_frame_into where one passes the
roi_index
## Refactoring:
- each roi keeps track of its subfiles that one has to open e.g.
subfiles can be opened several times
- refactored class DetectorGeometry - keep track of the updated module
geometries in new class ROIGeometry.
- ModuleGeometry updates based on ROI
## ROIGeometry:
- stores number of modules overlapping with ROI and its indices
- size of ROI
Note: only tested size of the resulting frames not the actual values
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
Co-authored-by: Erik Fröjdh <erik.frojdh@gmail.com>
- automatically run python tests
- automatically run test using data files on local runner from gitea
- fixed some of the workflows
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
hinger_v
lunin_landGitHub