python/src/module.cpp was one translation unit instantiating every cluster
size and value type; it compiled for 38 s and was the critical path of the
whole build. The bindings now live in bind_io.cpp (files, geometry, pixel
maps, calibration, test probes), bind_hist.cpp, bind_pedestal.cpp,
bind_fit.cpp, bind_eta.cpp and one bind_cluster_NxN.cpp per cluster size,
with the entry points declared in bindings.hpp and called from module.cpp
in the same order as before. The DEFINE_CLUSTER_BINDINGS macros moved to
bind_cluster_common.hpp.
Headers that several units include needed small fixes: allocate_image_data
and define_corner_enum are inline, the file-reader entry point is not, and
np_helper.hpp, bind_Defs.hpp, bind_FastPedestal.hpp,
bind_PedestalTrackingPixelHistogram.hpp and bind_ClusterFileSink.hpp now
include what they use, in particular pybind11/stl/filesystem.h for the
std::filesystem::path arguments they bind (previously supplied by include
order in module.cpp).
The module's exported names, types and docstrings are identical (verified
by dumping all 1948 of them), both test suites pass, and a clean Release
build with tests drops from 68 s to 42 s; the longest unit now takes 13 s
and the remaining 19 s is pybind11's LTO link.
- python string repr for Cluster and Eta
- Context manager for RawMasterFile
- slice operator for ROI
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
- Allowing the users more flexibility to play around with custom eta
functions without touching the c++ code
- passing vector of eta values to ``transform_eta_values``
```
from aare import Interpolator, ClusterVector, Etai, Cluster
import numpy as np
def custom_eta(cluster_pixel_coordinate_x, cluster_pixel_coordinate_y, cluster_data):
# dummy custom eta function that just returns the sum of the cluster data
eta = Etai()
eta.x = 0.1 # dummy x value
eta.y = 0.1 # dummy y value
eta.sum = np.sum(cluster_data) # sum of the cluster data as the "energy
return eta
# Create a dummy eta distribution and bins
eta_distribution = np.zeros((10, 10, 1)) # dummy eta distribution
etax_bins = np.linspace(0, 1.0, 11)
etay_bins = np.linspace(0, 1.0, 11)
e_bins = np.array([0., 10.]) # dummy energy bins
# Create the interpolator
interpolator = Interpolator(eta_distribution, etax_bins, etay_bins, e_bins)
# Create a dummy cluster vector
cluster_vector = ClusterVector()
cluster_vector.push_back(Cluster(10, 5, np.ones(shape=9, dtype = np.int32)))
cluster_vector.push_back(Cluster(20, 10, np.ones(shape=9, dtype = np.int32)))
# Create dummy etas for the clusters
cluster_array = np.array(cluster_vector)
etas = np.array([custom_eta(cluster["x"], cluster["y"], cluster["data"]) for cluster in cluster_array])
# transform eta values to uniform coordinates
uniform_coordinates = interpolator.transform_eta_values(etas)
# Interpolate to get the photon coordinates e.g. apply interpolation logic
photon_coordinates_x = cluster_array["x"] + uniform_coordinates["x"] # add to pixel coordinate
photon_coordinates_y = cluster_array["y"] + uniform_coordinates["y"] # add to pixel coordinate
```
advantage: full control over interpolation logic,
downside: inefficient quite some loops in python
- passing pre computed eta values to interpolate function
```
Interpolator.interpolate(cluster_vector, etas)
```
downside: less flexibility in interpolation logic.
downside: People might misuse it instead of using interpolate directly
with a pre compiled eta function implemented in c++
- 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>
Erik Fröjdh
mazzol_aandErik Fröjdh
