We have no code in aare to deal with RawFiles that have missing packets
if they are not padded to full size or frames with packet loss dropped.
Added:
- check that we don't try to read files that are not supported
- python bindings for frame discard policy
at least we get the check in:
closes #171
---------
Co-authored-by: AliceMazzoleni99 <alice.mazzoleni@psi.ch>
- error upon warning when building documentation (warnings often
indicate malrendered documentation)
- fixed some warnings
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
- seek to the edge of a file + read_into would select the wrong file
- Visible for example in f.read_frame(index) in python
Added fix, release notes and a small test.
closes #371
Co-authored-by: AliceMazzoleni99 <alice.mazzoleni@psi.ch>
- renamed pedestal.hpp to bind_Pedestal.hpp for python bindings
- variance is now always calculated in double and made private
- cost correctness in a few places
- removed push_fast and explicit updates of mean from Pedestal. If
performance is needed use FastPedestal
- Pedestal no longer caches std
Improved API for ClusterFile
- read_frame now returns a std::optional<ClusterVector> to differentiate
between an empty frame and end of file
- f.frames() produces an iterator over frames
- f.chunks() produces an iterator reading chunks of clusters
- python string repr for Cluster and Eta
- Context manager for RawMasterFile
- slice operator for ROI
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
- Updated error message to mention out of range and which file it
applies to
- If there are different number of frames in the subfiles, take the
minimum and print a warning
Closes: #363
Closes: #362
- fixed test in ClusterFinderMT (use minimum one pedestal to accumulate
dummy pedestal)
- added minimum_pedestal_samples as additional constructor argument in
ClusterFinder and ClusterFinderMT
- bugfix: race conditions for empty queues in collect() - check directly
when needed
- Default `ClusterVector` move operations simplifying the code and
fixing a bug.
- Fixed inconsistent frame number type (uint64/int32)
- Validate Python masks as one-dimensional, C-contiguous Boolean arrays,
handle empty masks safely, and reserve filtered storage based on the
selected cluster count.
- Align the C++ and Python API documentation with the implementation,
including concise `hitmap` and reduction documentation and a correctly
rendered constructor example.
- handles file reading of disabled udp ports
- adds members disabled_udp_ports in master file as an optional
- adds member diasbled_udp_port_types in master file as an optional
- Treats enabled udp ports as ROIs
- merges/combines consecutive ROis into one
### To discuss:
- right now can only handle either ROI or disabled udp ports (disabled
udp ports has higher precedence)
- Should Frames in File be removed from Master File as value is nonsense
for disabled udp ports
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
Optimized pedestal tracking with FastPedestal and performance improvements to the cluster finder.
- ~2x faster hitting 14k FPS on benchmark dataset on 16 core threadripper pro
**Changes**
- No bounds check on interior of frame. (only within half of a cluster)
(~15% improvement)
- Cache threshold (std*n_rms) Biggest improvement comes from not
computing std for each access
- Cache pedestal subtracted frame
- Switched to FastPedestal which assumes we reached steady state
**Things tried but rejected:**
- Always store cluster values, commit on val==max (~15% drop in frame
rate)
**Options**
- using 16 bit clusters and 16 bit pedestal. (slows down the single
threaded case but allow us to reach 12k with multi threading)
**Other notes on performance**
- Passing in memory frames from python and doing nothing: 0.8us/frame
- Passing + pedestal subtraction: 28us/frame
- Passing + pedestal + cluster finding (no store): 652us/frame
- Passing + pedestal + cluster finding: 886 us/frame
Minor API change:
- NDView<T> implicitly converts to NDView<const T> (as std::span in
C++20)
- expand4to8bit and expand24to32bit now takes a view over const uint8_t
to show that the input is not changes
closes #291
Before v8.0 we should always try to read analog samples. This bug
resulted in old Mönch03 data not being reordered.
1. Added test for master file parsing
2. Fixed code
closing #348
- Reading files from nfs shares tops out at ~1GB/s for single threaded
reads.
- MultiThreadedFileReader uses our File wrapper to read a generic file
in parallel
- Placed in aare::experimental to show that it's not production ready
- 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))`
If AARE_FETCH_MINUIT is set to OFF we first look for a standalone
Minuit2 and if that is not found we try to find Minuit2 as a part of
ROOT.
In both cases we make an alias to allow for simpler use of the target
later.
It still doesn't solve the issue that we install Minuit to when we fetch
it but that can be addressed in a separate PR.
closes #316
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>
Add options for var cluster_finder_X:
1. number of neighbors (for better segmentation of clusters)
2. option to empty the surrounding pixels
---------
Co-authored-by: xiangyu.xie <xiangyu.xie@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++
Erik Frojdh
maliakal_d
lunin_landmazzol_a
