Compare commits

...

221 Commits

Author SHA1 Message Date
Mazzoleni Alice Francesca
f161df3591 fixed calculate eta 2025-04-16 09:30:26 +02:00
Mazzoleni Alice Francesca
15e52565a9 dont convert to byte 2025-04-11 14:35:20 +02:00
froejdh_e
e71569b15e resize before read 2025-04-11 13:38:33 +02:00
froejdh_e
92f5421481 np test 2025-04-10 16:58:47 +02:00
froejdh_e
113f34cc98 fixes 2025-04-10 16:50:04 +02:00
Mazzoleni Alice Francesca
53a90e197e added additional tests
All checks were successful
Build on RHEL9 / buildh (push) Successful in 1m52s
2025-04-10 10:41:58 +02:00
Mazzoleni Alice Francesca
76f050f69f solved merge conflict
Some checks failed
Build on RHEL9 / buildh (push) Failing after 1m22s
2025-04-10 09:21:50 +02:00
Mazzoleni Alice Francesca
a13affa4d3 changed template arguments added tests 2025-04-10 09:13:58 +02:00
Erik Fröjdh
8b0eee1e66 fixed warnings and removed ambiguous read_frame (#154)
All checks were successful
Build on RHEL9 / buildh (push) Successful in 1m47s
Fixed warnings:
- unused variable in Interpolator
- Narrowing conversions uint64-->int64

Removed an ambiguous function from JungfrauDataFile
- NDarry read_frame(header&=nullptr)
- Frame read_frame()

NDArray and NDView size() is now signed
2025-04-09 17:54:55 +02:00
froejdh_e
894065fe9c added utility plot
All checks were successful
Build on RHEL9 / buildh (push) Successful in 1m48s
2025-04-09 12:19:14 +02:00
Erik Fröjdh
f16273a566 Adding support for Jungfrau .dat files (#152)
All checks were successful
Build on RHEL9 / buildh (push) Successful in 1m48s
closes #150 

**Not addressed in this PR:** 

- pixels_per_frame, bytes_per_frame and tell should be made cost in
FileInterface
2025-04-08 15:31:04 +02:00
20d1d02fda function signature for push back (#153)
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 48s
This example now works:
```python
cl = Cluster3x3i(5,7,np.array((1,2,3,4,5,6,7,8,9), dtype = np.int32))
cv = ClusterVector_Cluster3x3i()
cv.push_back(cl)
```
2025-04-07 17:18:17 +02:00
froejdh_e
10e4e10431 function signature for push back 2025-04-07 15:33:37 +02:00
Mazzoleni Alice Francesca
017960d963 added push_back property
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 37s
2025-04-07 13:41:14 +02:00
Mazzoleni Alice Francesca
a12e43b176 underlying container of ClusterVcetor is now a std::vector 2025-04-07 12:27:44 +02:00
Mazzoleni Alice Francesca
9de84a7f87 added some python tests
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 41s
2025-04-04 17:19:15 +02:00
Mazzoleni Alice Francesca
885309d97c fix build
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 43s
2025-04-03 17:14:28 +02:00
Mazzoleni Alice Francesca
e24ed68416 fixed include 2025-04-03 16:50:02 +02:00
Mazzoleni Alice Francesca
248d25486f refactored python files 2025-04-03 16:38:12 +02:00
Erik Fröjdh
7db1ae4d94 Dev/gitea ci (#151)
All checks were successful
Build on RHEL9 / buildh (push) Successful in 1m41s
Build and test on internal PSI gitea
2025-04-03 13:18:55 +02:00
Mazzoleni Alice Francesca
a24bbd9cf9 started to do python refactoring
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 44s
2025-04-03 11:56:25 +02:00
Mazzoleni Alice Francesca
d7ef9bb1d8 missed some refactoring of datatypes
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 49s
2025-04-03 11:36:15 +02:00
Mazzoleni Alice Francesca
de9fc16e89 generalize is_selected 2025-04-03 09:28:54 +02:00
Mazzoleni Alice Francesca
85a6b5b95e suppress compiler warnings 2025-04-03 09:28:02 +02:00
Mazzoleni Alice Francesca
50eeba4005 restructured GainMap to have own class and generalized
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 40s
2025-04-02 17:58:26 +02:00
Mazzoleni Alice Francesca
98d2d6098e refactored other cpp files 2025-04-02 16:00:46 +02:00
Mazzoleni Alice Francesca
61af1105a1 templated eta and updated test 2025-04-02 14:42:38 +02:00
Mazzoleni Alice Francesca
240960d3e7 generalized FindCluster to read in general cluster sizes - assuming that finding cluster center is equal for all clusters 2025-04-02 12:05:16 +02:00
Mazzoleni Alice Francesca
04728929cb implemented sum_2x2() for general clusters, only one calculate_eta2 function for all clusters
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 37s
2025-04-01 18:29:08 +02:00
Mazzoleni Alice Francesca
3083d51699 merge conflict 2025-04-01 17:50:11 +02:00
Mazzoleni Alice Francesca
4240942cec solved merge conflict 2025-04-01 17:48:48 +02:00
Mazzoleni Alice Francesca
745d09fbe9 changed push_back to take Cluster as input argument 2025-04-01 15:30:10 +02:00
froejdh_e
8cad7a50a6 fixed py
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 42s
2025-04-01 15:00:03 +02:00
Erik Fröjdh
9d8e803474 Merge branch 'main' into developer 2025-04-01 14:35:27 +02:00
Erik Fröjdh
a42c0d645b added roi, noise and gain (#143)
- Moved definitions of Cluster_2x2 and Cluster_3x3 to it's own file
- Added optional members for ROI, noise_map and gain_map in ClusterFile

**API:**

After creating the ClusterFile the user can set one or all of: roi,
noise_map, gain_map

```python
f = ClusterFile(fname)
f.set_roi(roi) #aare.ROI
f.set_noise_map(noise_map) #numpy array
f.set_gain_map(gain_map) #numpy array
```

**When reading clusters they are evaluated in the order:**

1. If ROI is enabled check that the cluster is within the ROI
1. If noise_map is enabled check that the cluster meets one of the
conditions
    - Center pixel above noise
    - Highest 2x2 sum above 2x noise
    - 3x3 sum above 3x noise
1. If gain_map is set apply the gain map before returning the clusters
(not used for noise cut)

**Open questions:**
1. Check for out of bounds access in noise and gain map?

closes #139 
closes #135 
closes #90
2025-04-01 14:31:25 +02:00
Mazzoleni Alice Francesca
508adf5016 refactoring of remaining files
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 40s
Build the package using cmake then documentation / deploy (push) Has been skipped
2025-04-01 10:01:23 +02:00
Mazzoleni Alice Francesca
e038bd1646 refactored and put calculate_eta function in seperate file 2025-03-31 17:35:39 +02:00
Mazzoleni Alice Francesca
7e5f91c6ec added benchmark to time generalize calculate_eta - twice as long so will keep specific version for 2x2 and 3x3 clusters 2025-03-31 17:04:57 +02:00
Mazzoleni Alice Francesca
ed9ef7c600 removed analyze_cluster function as not used anymore
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 52s
Build the package using cmake then documentation / deploy (push) Has been skipped
2025-03-31 12:26:29 +02:00
57bb6c71ae ClusterSize should be larger than 1
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 51s
Build the package using cmake then documentation / deploy (push) Has been skipped
2025-03-28 14:49:55 +01:00
f8f98b6ec3 Generalized calculate_eta2 function to work with general cluster types 2025-03-28 14:29:20 +01:00
0876b6891a cpp Cluster and ClusterVector and ClusterFile are templated now, they support generic cluster types 2025-03-25 21:42:50 +01:00
Erik Fröjdh
6ad76f63c1 Fixed reading clusters with ROI (#142)
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 9s
Fixed incorrect reading of clusters with ROI


closes #141
2025-03-24 14:28:10 +01:00
6e7e81b36b complete mess but need to install RedHat 9 2025-03-21 16:32:54 +01:00
Erik Fröjdh
5d8ad27b21 Developer (#138)
All checks were successful
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Successful in 1m45s
- Fully functioning variable size cluster finder
- Added interpolation
- Bit reordering for ADC SAR 05

---------

Co-authored-by: Patrick <patrick.sieberer@psi.ch>
Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
Co-authored-by: Dhanya Thattil <dhanya.thattil@psi.ch>
Co-authored-by: xiangyu.xie <xiangyu.xie@psi.ch>
2025-03-20 12:52:04 +01:00
Erik Fröjdh
b529b6d33b Merge branch 'main' into developer
All checks were successful
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Successful in 1m33s
2025-03-19 19:29:15 +01:00
froejdh_e
602b04e49f bumped version number
All checks were successful
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Successful in 1m35s
2025-03-18 17:47:05 +01:00
Erik Fröjdh
11cd2ec654 Interpolate (#137)
- added eta based interpolation
2025-03-18 17:45:38 +01:00
froejdh_e
e59a361b51 removed workspace
Some checks failed
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Failing after 48s
Build the package using cmake then documentation / deploy (push) Has been skipped
2025-03-17 15:23:55 +01:00
Erik Fröjdh
1ad362ccfc added action for gitea (#136)
All checks were successful
Build the package using cmake then documentation / build (ubuntu-latest, 3.12) (push) Successful in 1m30s
2025-03-17 15:21:59 +01:00
froejdh_e
332bdeb02b modified algo 2025-03-14 11:07:09 +01:00
froejdh_e
3a987319d4 WIP 2025-03-05 21:51:23 +01:00
froejdh_e
5614cb4673 WIP 2025-03-05 17:40:08 +01:00
froejdh_e
8ae6bb76f8 removed warnings added clang-tidy 2025-02-21 11:18:39 +01:00
Xiangyu Xie
1d2c38c1d4 Enable VarClusterFinder (#134)
Co-authored-by: xiangyu.xie <xiangyu.xie@psi.ch>
2025-02-19 16:11:24 +01:00
Erik Fröjdh
b7a47576a1 Multi threaded fitting and returning chi2 (#132)
Co-authored-by: Patrick <patrick.sieberer@psi.ch>
Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
Co-authored-by: Dhanya Thattil <dhanya.thattil@psi.ch>
2025-02-19 07:19:59 +01:00
Erik Fröjdh
fc1c9f35d6 Merge branch 'main' into developer 2025-02-18 21:52:20 +01:00
froejdh_e
5d2f25a6e9 bumped version number 2025-02-18 21:44:03 +01:00
Erik Fröjdh
6a83988485 Added chi2 to fit results (#131)
- fit_gaus and fit_pol1 now return a dict
- calculate chi2 after fit
- cleaned up code
2025-02-18 21:13:27 +01:00
8abfc68138 fixed linking to lmfit (#130)
using "$<BUILD_INTERFACE:lmfit>" to exclude the target lmfit from being
included in the installed aare target
2025-02-18 15:54:52 +01:00
froejdh_e
8ff6f9f506 fixed linking to lmfit 2025-02-18 15:49:46 +01:00
Erik Fröjdh
dadf5f4869 Added fitting, fixed roi etc (#129)
Co-authored-by: Patrick <patrick.sieberer@psi.ch>
Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
2025-02-12 16:50:31 +01:00
froejdh_e
dcb9a98faa bumped version 2025-02-12 16:49:30 +01:00
Erik Fröjdh
7309cff47c Added fitting with lmfit (#128)
- added stand alone fitting using:
https://jugit.fz-juelich.de/mlz/lmfit.git
- fit_gaus, fit_pol1 with and without errors
- multi threaded fitting

---------

Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
2025-02-12 16:35:48 +01:00
Erik Fröjdh
c0c5e07ad8 added decoding of adc_sar_04 (#127) 2025-02-12 16:17:32 +01:00
froejdh_e
2faa317bdf removed debug line 2025-02-12 10:59:18 +01:00
Erik Fröjdh
f7031d7f87 Update CMakeLists.txt
Removed flto=auto which caused issues with gcc 8.5
2025-02-12 10:52:55 +01:00
Erik Fröjdh
d86cb533c8 Fix minor warnings (#126)
- Unused variables
- signed vs. unsigned
- added -flto=auto
2025-02-11 11:48:01 +01:00
Erik Fröjdh
4c750cc3be Fixing ROI read of RawFile (#125)
- Bugfixes
- New abstraction for detector geometry
- Tests for updating geo with ROI
2025-02-11 11:08:22 +01:00
froejdh_e
e96fe31f11 removed main and token 2025-02-05 15:55:55 +01:00
froejdh_e
cd5a738696 disable upload on dev 2025-02-05 15:44:45 +01:00
froejdh_e
1ba43b69d3 fix 2025-02-05 15:16:16 +01:00
froejdh_e
fff536782b disable auto upload 2025-02-05 15:13:53 +01:00
Erik Fröjdh
5a3ca2ae2d Decoding for ADC SAR 05 64->16bit (#124)
Co-authored-by: Patrick <patrick.sieberer@psi.ch>
2025-02-05 14:40:26 +01:00
froejdh_e
078e5d81ec docs 2025-01-15 16:40:34 +01:00
froejdh_e
6cde968c60 summing 2x2 2025-01-15 16:12:06 +01:00
froejdh_e
f6d736facd docs for ClusterFile 2025-01-15 09:15:41 +01:00
Erik Fröjdh
e1cc774d6c Multi threaded cluster finder (#117) 2025-01-14 21:36:25 +01:00
froejdh_e
d0f435a7ab bounds checking on subfiles 2025-01-10 19:02:50 +01:00
froejdh_e
7ce02006f2 clear pedestal 2025-01-10 17:26:23 +01:00
froejdh_e
7550a2cb97 fixing read bug 2025-01-10 15:33:56 +01:00
froejdh_e
caf7b4ecdb added docs for ClusterFinderMT 2025-01-10 10:22:04 +01:00
Erik Fröjdh
72d10b7735 Multi threaded cluster finder. (#115)
Added a prototype for the multi threaded cluster finder including python
bindings
2025-01-09 16:55:35 +01:00
froejdh_e
cc95561eda MultiThreaded Cluster finder 2025-01-09 16:53:22 +01:00
froejdh_e
dc9e10016d WIP 2025-01-08 16:45:24 +01:00
froejdh_e
21ce7a3efa bumped version 2025-01-07 16:33:16 +01:00
froejdh_e
acdce8454b moved pd to double 2025-01-07 15:01:43 +01:00
Erik Fröjdh
d07da42745 bitdepths 2025-01-07 12:27:01 +01:00
Erik Fröjdh
7d6223d52d Cluster finder improvements (#113) 2024-12-16 14:42:18 +01:00
Erik Fröjdh
da67f58323 Cluster finder improvements (#112) 2024-12-16 14:26:35 +01:00
froejdh_e
e6098c02ef bumped version 2024-12-16 14:24:46 +01:00
froejdh_e
29b1dc8df3 missing header 2024-12-13 14:57:36 +01:00
froejdh_e
f88b53387f WIP 2024-12-12 17:58:04 +01:00
froejdh_e
a0f481c0ee mod pedestal 2024-12-12 14:34:10 +01:00
froejdh_e
b3a9e9576b WIP 2024-12-11 16:27:36 +01:00
froejdh_e
60534add92 WIP 2024-12-11 09:54:33 +01:00
froejdh_e
7f2a23d5b1 accumulating clusters in one array 2024-12-10 22:00:12 +01:00
froejdh_e
6a150e8d98 WIP 2024-12-10 17:21:05 +01:00
froejdh_e
b43003966f build pkg on all branched deploy docs on main 2024-11-29 16:41:42 +01:00
froejdh_e
c2d039a5bd fix conda build 2024-11-29 16:37:42 +01:00
Erik Fröjdh
6fd52f6b8d added missing enums (#111)
- Missing enums
- Matching values to slsDetectorPackage
- tests
2024-11-29 15:28:19 +01:00
Erik Fröjdh
659f1f36c5 AARE_INSTALL_PYTHONEXT (#109)
- added AARE_INSTALL_PYTHONEXT option to install also python files in
aare folder
2024-11-29 15:28:02 +01:00
froejdh_e
0047d15de1 removed print flip 2024-11-29 15:00:18 +01:00
froejdh_e
a1b7fb8fc8 added missing enums 2024-11-29 14:56:39 +01:00
2e4a491d7a CMAKE_INSTALL_PREFIX not needed to specify destination folder and removed 2024-11-29 14:38:32 +01:00
fdce2f69b9 python 3.10 required in cmake 2024-11-29 11:07:05 +01:00
Erik Fröjdh
ada4d41f4a bugfix on iteration and returning master file (#110) 2024-11-29 08:52:04 +01:00
froejdh_e
115dfc0abf bugfix on iteration and returning master file 2024-11-28 21:14:40 +01:00
31b834c3fd added AARE_INSTALL_PYTHONEXT option to install python in make install, which also installs the python files in the aare folder 2024-11-28 15:18:13 +01:00
Erik Fröjdh
feed4860a6 Developer (#108)
- Support for very old moench
- read_n in RawFile
2024-11-27 21:22:01 +01:00
froejdh_e
0df8e4bb7d added support for old old moench files 2024-11-27 16:27:55 +01:00
froejdh_e
8bf9ac55ce modified read_n also for File and RawFile 2024-11-27 09:31:57 +01:00
Erik Fröjdh
2d33fd4813 Streamlined build, new transforms (#106) 2024-11-26 16:27:00 +01:00
froejdh_e
996a8861f6 roll back conda-build 2024-11-26 15:53:06 +01:00
06670a7e24 read_n returns remaining frames (#105)
Modified read_n to return the number of frames available if less than
the number of frames requested.

```python
#f is a CtbRawFile containing 10 frames

f.read_n(7) # you get 7 frames
f.read_n(7) # you get 3 frames
f.read_n(7) # RuntimeError
```

Also added support for chunk_size when iterating over a file:

```python
# The file contains 10 frames


with CtbRawFile(fname, chunk_size = 7) as f:
    for headers, frames in f:
        #do something with the data
        # 1 iteration 7 frames
        # 2 iteration 3 frames
        # 3 iteration stops
```
2024-11-26 14:07:21 +01:00
froejdh_e
8e3d997bed read_n returns remaining frames 2024-11-26 12:07:17 +01:00
Erik Fröjdh
a3f813f9b4 Modified moench05 transform (#103)
Moench05 transforms: 
- moench05: Works with the updated firmware and better data compression
(adcenable10g=0xFF0F)
- moench05_old: Works with the previous data and can be used with
adcenable10g=0xFFFFFFFF
- moench05_1g: For the 1g data acquisition only with adcenable=0x2202
2024-11-26 09:02:33 +01:00
d48482e9da Modified moench05 transform: new firmware (moench05), legacy firmware (moench05_old), 1g readout (moench05_1g) 2024-11-25 16:39:08 +01:00
Erik Fröjdh
8f729fc83e Developer (#102) 2024-11-21 10:27:26 +01:00
Erik Fröjdh
f9a2d49244 removed extra print 2024-11-21 10:22:22 +01:00
Erik Fröjdh
9f7cdbcb48 conversion warnings 2024-11-18 18:18:55 +01:00
Erik Fröjdh
3b0e13e41f added links (#101) 2024-11-18 16:19:15 +01:00
Erik Fröjdh
3af8182998 added links 2024-11-18 16:18:29 +01:00
Erik Fröjdh
99e829fd06 Latest changes (#100) 2024-11-18 15:42:37 +01:00
Erik Fröjdh
47e867fc1a Merge branch 'main' into developer 2024-11-18 15:38:15 +01:00
Erik Fröjdh
8ea4372cf1 fix 2024-11-18 15:33:38 +01:00
Erik Fröjdh
75f83e5e3b detecting need to link with stdfs 2024-11-18 15:33:09 +01:00
Erik Fröjdh
30d05f9203 detecting need to link with stdfs 2024-11-18 15:19:57 +01:00
Erik Fröjdh
37d3dfcf71 WIP 2024-11-18 14:46:28 +01:00
Erik Fröjdh
35c6706b3c docs 2024-11-18 14:39:46 +01:00
Erik Fröjdh
9ab61cac4e deps in pkg 2024-11-18 11:47:26 +01:00
Erik Fröjdh
13394c3a61 cmake targets 2024-11-18 11:30:33 +01:00
Erik Fröjdh
088288787a Merge branch 'developer' of github.com:slsdetectorgroup/aare into developer 2024-11-18 09:22:36 +01:00
Erik Fröjdh
9d4459eb8c linking json with PUBLIC to avoid errors 2024-11-18 09:22:28 +01:00
Erik Fröjdh
95ff77c8fc Cluster reading, expression templates (#99)
Co-authored-by: froejdh_e <erik.frojdh@psi.ch>
2024-11-15 16:32:36 +01:00
Erik Fröjdh
62a14dda13 Merge branch 'main' into developer 2024-11-15 16:19:34 +01:00
Erik Fröjdh
632c2ee0c8 bumped version 2024-11-15 16:15:04 +01:00
Erik Fröjdh
17f8d28019 frame reading for cluster file 2024-11-15 16:13:46 +01:00
Erik Fröjdh
e77b615293 Added expression templates (#98)
- Works with NDArray
- Works with NDView
2024-11-15 15:17:52 +01:00
Erik Fröjdh
0d058274d5 WIP 2024-11-14 17:03:16 +01:00
Erik Fröjdh
5cde7a99b5 WIP 2024-11-14 17:02:48 +01:00
froejdh_e
dcedb4fb13 added missing header 2024-11-14 16:37:24 +01:00
Erik Fröjdh
7ffd732d98 ported reading clusters (#95) 2024-11-14 16:22:38 +01:00
Erik Fröjdh
fbaf9dce89 Developer (#94) 2024-11-14 08:03:18 +01:00
Erik Fröjdh
dc889dab76 removed subfile from cmake 2024-11-14 07:48:59 +01:00
Erik Fröjdh
cb94d079af Merge branch 'developer' of github.com:slsdetectorgroup/aare into developer 2024-11-14 07:42:00 +01:00
Erik Fröjdh
13b2cb40b6 docs and reorder 2024-11-14 07:41:50 +01:00
Erik Fröjdh
17917ac7ea Merge branch 'main' into developer 2024-11-12 16:44:15 +01:00
Erik Fröjdh
db936b6357 improved documentation 2024-11-12 16:39:03 +01:00
froejdh_e
2ee1a5583e WIP 2024-11-12 09:27:01 +01:00
Erik Fröjdh
349e3af8e1 Brining in changes (#93) 2024-11-11 19:59:55 +01:00
Erik Fröjdh
a0b6c4cc03 Merge branch 'main' into developer 2024-11-11 18:52:23 +01:00
Erik Fröjdh
5f21759c8c removed prints, bumped version 2024-11-11 18:22:18 +01:00
froejdh_e
ecf1b2a90b WIP 2024-11-11 17:13:48 +01:00
froejdh_e
b172c7aa0a starting work on ROI 2024-11-07 16:24:48 +01:00
Erik Fröjdh
d8d1f0c517 Taking v1 as the first release (#92)
- file reading
- decoding master file
2024-11-07 10:14:20 +01:00
Erik Fröjdh
d5fb823ae4 added numpy variants 2024-11-07 09:16:49 +01:00
Erik Fröjdh
9c220bff51 added simple decoding of scan parameters 2024-11-07 08:14:33 +01:00
Erik Fröjdh
b2e5c71f9c MH02 1-4 counters 2024-11-06 21:32:03 +01:00
Erik Fröjdh
cbfd1f0b6c ClusterFinder 2024-11-06 12:41:41 +01:00
Erik Fröjdh
5b2809d6b0 working Moench03 detector type 2024-11-06 10:13:56 +01:00
Erik Fröjdh
4bb8487e2c added moench03 back 2024-11-06 09:18:57 +01:00
Erik Fröjdh
1cc7690f9a discard partial 2024-11-06 09:13:40 +01:00
Erik Fröjdh
25812cb291 RawFile is now using RawMasterFile 2024-11-06 09:10:09 +01:00
Erik Fröjdh
654c1db3f4 WIP 2024-11-05 16:01:22 +01:00
Erik Fröjdh
2efb763242 func to prop 2024-11-05 16:00:11 +01:00
Erik Fröjdh
7f244e22a2 extra methods in CtbRawFile 2024-11-05 15:55:17 +01:00
Erik Fröjdh
d98b45235f optional 2024-11-05 14:37:35 +01:00
Erik Fröjdh
80a39415de added CtbRawFile 2024-11-05 14:36:18 +01:00
Erik Fröjdh
b8a4498379 WIP 2024-10-31 18:03:17 +01:00
Erik Fröjdh
49da039ff9 working on 05 2024-10-31 15:35:43 +01:00
Erik Fröjdh
563c39c0dd decoding of old Moench03 2024-10-31 11:53:24 +01:00
Erik Fröjdh
ae1166b908 WIP 2024-10-31 10:39:52 +01:00
Erik Fröjdh
ec61132296 WIP 2024-10-31 10:29:07 +01:00
Erik Fröjdh
cee0d71b9c added check to prevent segfault on missing subfile 2024-10-31 09:43:41 +01:00
Erik Fröjdh
19c6a4091f improved docs and added PixelMap 2024-10-31 08:56:12 +01:00
Erik Fröjdh
92d9c28c73 numpy in conda env for docs 2024-10-30 18:35:54 +01:00
Erik Fröjdh
b7e6962e44 added numpy as dep 2024-10-30 18:09:29 +01:00
Erik Fröjdh
13ac6b0f37 added missing numpy dependency 2024-10-30 17:54:34 +01:00
Erik Fröjdh
79d924c2a3 docs and version bump 2024-10-30 17:48:50 +01:00
Erik Fröjdh
9b733fd0ec WIP 2024-10-30 17:41:45 +01:00
Erik Fröjdh
6505f37d87 added type bindings 2024-10-30 17:40:47 +01:00
Erik Fröjdh
a466887064 added variable cluster finder 2024-10-30 17:22:57 +01:00
Erik Fröjdh
dde92b993f xml back in 2024-10-30 16:29:43 +01:00
Erik Fröjdh
1b61155c5c another try 2024-10-30 16:15:44 +01:00
Erik Fröjdh
738934f2a0 added github token 2024-10-30 16:12:08 +01:00
Erik Fröjdh
6b8f2478b6 added deploy 2024-10-30 16:04:11 +01:00
Erik Fröjdh
41fbddb750 pinned sphinx version 2024-10-30 15:56:27 +01:00
Erik Fröjdh
504e8b4565 updated doxyfile 2024-10-30 15:53:40 +01:00
Erik Fröjdh
acdcaac338 fmt 2024-10-30 15:39:04 +01:00
Erik Fröjdh
8b43011fa1 modified action 2024-10-30 15:37:23 +01:00
Erik Fröjdh
801adccbd7 updated path for docs 2024-10-30 15:28:07 +01:00
Erik Fröjdh
da5ba034b8 WIP 2024-10-30 15:18:48 +01:00
Erik Fröjdh
1cbded04f8 doxygen 2024-10-30 15:14:55 +01:00
Erik Fröjdh
9b33ad0ee8 pybind 2024-10-30 15:13:20 +01:00
Erik Fröjdh
1f539a234b forgot json 2024-10-30 15:11:52 +01:00
Erik Fröjdh
29a42507d7 WIP 2024-10-30 15:09:35 +01:00
Erik Fröjdh
5035c20aa4 added action for docs 2024-10-30 15:07:34 +01:00
Erik Fröjdh
f754e0f769 file reading 2024-10-30 14:53:50 +01:00
Erik Fröjdh
be019b9769 updated readme 2024-10-30 10:26:53 +01:00
Erik Fröjdh
af4f000fe7 fetch content for json 2024-10-30 09:36:41 +01:00
Erik Fröjdh
b37f4845cf cmake defaults 2024-10-30 08:58:42 +01:00
Erik Fröjdh
b037aebc5f update 2024-10-30 08:36:38 +01:00
Erik Fröjdh
dea5aaf9cf slight mod 2024-10-30 08:33:22 +01:00
Erik Fröjdh
4cc6aa9d40 updated workflows 2024-10-30 08:11:38 +01:00
Erik Fröjdh
c3a5d22f83 added anaconda-client 2024-10-29 17:54:02 +01:00
Erik Fröjdh
a8afa04129 updated workflow 2024-10-29 17:50:44 +01:00
Erik Fröjdh
eb855fb9a3 updated workflow 2024-10-29 17:49:21 +01:00
Erik Fröjdh
b4fe044679 WIP 2024-10-29 17:00:58 +01:00
Erik Fröjdh
082d793161 WIP 2024-10-29 16:46:02 +01:00
Erik Fröjdh
9f29f173ff updated path 2024-10-29 13:09:14 +01:00
Erik Fröjdh
8a10bcbbdb workflow 2024-10-29 13:07:53 +01:00
Erik Fröjdh
c509e29b52 building with scikit build 2024-10-29 11:19:20 +01:00
Erik Fröjdh
1a16d4522e WIP 2024-10-28 16:50:38 +01:00
Erik Fröjdh
8a435cbe9b WIP 2024-10-28 16:26:14 +01:00
Erik Fröjdh
7f9151f270 WIP 2024-10-28 13:37:58 +01:00
Erik Fröjdh
abb1d20ca3 WIP 2024-10-28 12:25:47 +01:00
Erik Fröjdh
a4fb217e3f Files and structure for python interface 2024-10-28 11:22:12 +01:00
Erik Fröjdh
5d643dc133 added cluster finder 2024-10-25 16:18:36 +02:00
Erik Fröjdh
54dd88f070 added documentation 2024-10-25 13:54:36 +02:00
Erik Fröjdh
b1b020ad60 WIP 2024-10-25 10:23:34 +02:00
164 changed files with 17558 additions and 0 deletions

7
.clang-format Normal file
View File

@@ -0,0 +1,7 @@
BasedOnStyle: LLVM
IndentWidth: 4
UseTab: Never
ColumnLimit: 80
AlignConsecutiveAssignments: false
AlignConsecutiveMacros: true

42
.clang-tidy Normal file
View File

@@ -0,0 +1,42 @@
---
Checks: '*,
-altera-*,
-android-cloexec-fopen,
-cppcoreguidelines-pro-bounds-array-to-pointer-decay,
-cppcoreguidelines-pro-bounds-pointer-arithmetic,
-fuchsia*,
-readability-else-after-return,
-readability-avoid-const-params-in-decls,
-readability-identifier-length,
-cppcoreguidelines-pro-bounds-constant-array-index,
-cppcoreguidelines-pro-type-reinterpret-cast,
-llvm-header-guard,
-modernize-use-nodiscard,
-misc-non-private-member-variables-in-classes,
-readability-static-accessed-through-instance,
-readability-braces-around-statements,
-readability-isolate-declaration,
-readability-implicit-bool-conversion,
-readability-identifier-length,
-readability-identifier-naming,
-hicpp-signed-bitwise,
-hicpp-no-array-decay,
-hicpp-braces-around-statements,
-google-runtime-references,
-google-readability-todo,
-google-readability-braces-around-statements,
-modernize-use-trailing-return-type,
-llvmlibc-*'
HeaderFilterRegex: \.hpp
FormatStyle: none
CheckOptions:
- { key: readability-identifier-naming.NamespaceCase, value: lower_case }
# - { key: readability-identifier-naming.FunctionCase, value: lower_case }
- { key: readability-identifier-naming.ClassCase, value: CamelCase }
# - { key: readability-identifier-naming.MethodCase, value: CamelCase }
# - { key: readability-identifier-naming.StructCase, value: CamelCase }
# - { key: readability-identifier-naming.VariableCase, value: lower_case }
- { key: readability-identifier-naming.GlobalConstantCase, value: UPPER_CASE }
...

View File

@@ -0,0 +1,58 @@
name: Build the package using cmake then documentation
on:
workflow_dispatch:
permissions:
contents: read
pages: write
id-token: write
jobs:
build:
strategy:
fail-fast: false
matrix:
platform: [ubuntu-latest, ]
python-version: ["3.12", ]
runs-on: ${{ matrix.platform }}
defaults:
run:
shell: "bash -l {0}"
steps:
- uses: actions/checkout@v4
- name: Setup dev env
run: |
sudo apt-get update
sudo apt-get -y install cmake gcc g++
- name: Get conda
uses: conda-incubator/setup-miniconda@v3
with:
python-version: ${{ matrix.python-version }}
environment-file: etc/dev-env.yml
miniforge-version: latest
channels: conda-forge
conda-remove-defaults: "true"
- name: Build library
run: |
mkdir build
cd build
cmake .. -DAARE_SYSTEM_LIBRARIES=ON -DAARE_DOCS=ON
make -j 2
make docs

View File

@@ -0,0 +1,30 @@
name: Build on RHEL8
on:
workflow_dispatch:
permissions:
contents: read
jobs:
buildh:
runs-on: "ubuntu-latest"
container:
image: gitea.psi.ch/images/rhel8-developer-gitea-actions
steps:
- uses: actions/checkout@v4
- name: Install dependencies
run: |
dnf install -y cmake python3.12 python3.12-devel python3.12-pip
- name: Build library
run: |
mkdir build && cd build
cmake .. -DAARE_PYTHON_BINDINGS=ON -DAARE_TESTS=ON
make -j 2
- name: C++ unit tests
working-directory: ${{gitea.workspace}}/build
run: ctest

View File

@@ -0,0 +1,31 @@
name: Build on RHEL9
on:
push:
workflow_dispatch:
permissions:
contents: read
jobs:
buildh:
runs-on: "ubuntu-latest"
container:
image: gitea.psi.ch/images/rhel9-developer-gitea-actions
steps:
- uses: actions/checkout@v4
- name: Install dependencies
run: |
dnf install -y cmake python3.12 python3.12-devel python3.12-pip
- name: Build library
run: |
mkdir build && cd build
cmake .. -DAARE_PYTHON_BINDINGS=ON -DAARE_TESTS=ON
make -j 2
- name: C++ unit tests
working-directory: ${{gitea.workspace}}/build
run: ctest

View File

@@ -0,0 +1,42 @@
name: Build pkgs and deploy if on main
on:
push:
branches:
- main
jobs:
build:
strategy:
fail-fast: false
matrix:
platform: [ubuntu-latest, ] # macos-12, windows-2019]
python-version: ["3.12",]
runs-on: ${{ matrix.platform }}
# The setup-miniconda action needs this to activate miniconda
defaults:
run:
shell: "bash -l {0}"
steps:
- uses: actions/checkout@v4
- name: Get conda
uses: conda-incubator/setup-miniconda@v3.0.4
with:
python-version: ${{ matrix.python-version }}
channels: conda-forge
- name: Prepare
run: conda install conda-build=24.9 conda-verify pytest anaconda-client
- name: Enable upload
run: conda config --set anaconda_upload yes
- name: Build
env:
CONDA_TOKEN: ${{ secrets.CONDA_TOKEN }}
run: conda build conda-recipe --user slsdetectorgroup --token ${CONDA_TOKEN}

40
.github/workflows/build_conda.yml vendored Normal file
View File

@@ -0,0 +1,40 @@
name: Build pkgs and deploy if on main
on:
push:
branches:
- developer
jobs:
build:
strategy:
fail-fast: false
matrix:
platform: [ubuntu-latest, ] # macos-12, windows-2019]
python-version: ["3.12",]
runs-on: ${{ matrix.platform }}
# The setup-miniconda action needs this to activate miniconda
defaults:
run:
shell: "bash -l {0}"
steps:
- uses: actions/checkout@v4
- name: Get conda
uses: conda-incubator/setup-miniconda@v3.0.4
with:
python-version: ${{ matrix.python-version }}
channels: conda-forge
- name: Prepare
run: conda install conda-build=24.9 conda-verify pytest anaconda-client
- name: Disable upload
run: conda config --set anaconda_upload no
- name: Build
run: conda build conda-recipe

66
.github/workflows/build_docs.yml vendored Normal file
View File

@@ -0,0 +1,66 @@
name: Build the package using cmake then documentation
on:
workflow_dispatch:
push:
permissions:
contents: read
pages: write
id-token: write
jobs:
build:
strategy:
fail-fast: false
matrix:
platform: [ubuntu-latest, ]
python-version: ["3.12",]
runs-on: ${{ matrix.platform }}
defaults:
run:
shell: "bash -l {0}"
steps:
- uses: actions/checkout@v4
- name: Get conda
uses: conda-incubator/setup-miniconda@v3
with:
python-version: ${{ matrix.python-version }}
environment-file: etc/dev-env.yml
miniforge-version: latest
channels: conda-forge
conda-remove-defaults: "true"
- name: Build library
run: |
mkdir build
cd build
cmake .. -DAARE_SYSTEM_LIBRARIES=ON -DAARE_DOCS=ON
make -j 2
make docs
- name: Upload static files as artifact
id: deployment
uses: actions/upload-pages-artifact@v3
with:
path: build/docs/html/
deploy:
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
runs-on: ubuntu-latest
needs: build
if: github.ref == 'refs/heads/main'
steps:
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v4

24
.gitignore vendored Normal file
View File

@@ -0,0 +1,24 @@
install/
.cproject
.project
bin/
.settings
*.aux
*.log
*.out
*.toc
*.o
*.so
.*
build/
RELEASE.txt
Testing/
ctbDict.cpp
ctbDict.h
*.pyc
*/__pycache__/*

513
CMakeLists.txt Normal file
View File

@@ -0,0 +1,513 @@
cmake_minimum_required(VERSION 3.14)
project(aare
VERSION 1.0.0
DESCRIPTION "Data processing library for PSI detectors"
HOMEPAGE_URL "https://github.com/slsdetectorgroup/aare"
LANGUAGES C CXX
)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
if (${CMAKE_VERSION} VERSION_GREATER "3.24")
cmake_policy(SET CMP0135 NEW) #Fetch content download timestamp
endif()
cmake_policy(SET CMP0079 NEW)
include(GNUInstallDirs)
include(FetchContent)
#Set default build type if none was specified
include(cmake/helpers.cmake)
default_build_type("Release")
set_std_fs_lib()
message(STATUS "Extra linking to fs lib:${STD_FS_LIB}")
set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake" ${CMAKE_MODULE_PATH})
# General options
option(AARE_PYTHON_BINDINGS "Build python bindings" OFF)
option(AARE_TESTS "Build tests" OFF)
option(AARE_BENCHMARKS "Build benchmarks" OFF)
option(AARE_EXAMPLES "Build examples" OFF)
option(AARE_IN_GITHUB_ACTIONS "Running in Github Actions" OFF)
option(AARE_DOCS "Build documentation" OFF)
option(AARE_VERBOSE "Verbose output" OFF)
option(AARE_CUSTOM_ASSERT "Use custom assert" OFF)
option(AARE_INSTALL_PYTHONEXT "Install the python extension in the install tree under CMAKE_INSTALL_PREFIX/aare/" OFF)
option(AARE_ASAN "Enable AddressSanitizer" OFF)
# Configure which of the dependencies to use FetchContent for
option(AARE_FETCH_FMT "Use FetchContent to download fmt" ON)
option(AARE_FETCH_PYBIND11 "Use FetchContent to download pybind11" ON)
option(AARE_FETCH_CATCH "Use FetchContent to download catch2" ON)
option(AARE_FETCH_JSON "Use FetchContent to download nlohmann::json" ON)
option(AARE_FETCH_ZMQ "Use FetchContent to download libzmq" ON)
option(AARE_FETCH_LMFIT "Use FetchContent to download lmfit" ON)
#Convenience option to use system libraries only (no FetchContent)
option(AARE_SYSTEM_LIBRARIES "Use system libraries" OFF)
if(AARE_SYSTEM_LIBRARIES)
message(STATUS "Build using system libraries")
set(AARE_FETCH_FMT OFF CACHE BOOL "Disabled FetchContent for FMT" FORCE)
set(AARE_FETCH_PYBIND11 OFF CACHE BOOL "Disabled FetchContent for pybind11" FORCE)
set(AARE_FETCH_CATCH OFF CACHE BOOL "Disabled FetchContent for catch2" FORCE)
set(AARE_FETCH_JSON OFF CACHE BOOL "Disabled FetchContent for nlohmann::json" FORCE)
set(AARE_FETCH_ZMQ OFF CACHE BOOL "Disabled FetchContent for libzmq" FORCE)
# Still fetch lmfit when setting AARE_SYSTEM_LIBRARIES since this is not available
# on conda-forge
endif()
if(AARE_VERBOSE)
add_compile_definitions(AARE_VERBOSE)
endif()
if(AARE_CUSTOM_ASSERT)
add_compile_definitions(AARE_CUSTOM_ASSERT)
endif()
if(AARE_BENCHMARKS)
add_subdirectory(benchmarks)
endif()
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
if(AARE_FETCH_LMFIT)
#TODO! Should we fetch lmfit from the web or inlcude a tar.gz in the repo?
set(LMFIT_PATCH_COMMAND git apply ${CMAKE_CURRENT_SOURCE_DIR}/patches/lmfit.patch)
# For cmake < 3.28 we can't supply EXCLUDE_FROM_ALL to FetchContent_Declare
# so we need this workaround
if (${CMAKE_VERSION} VERSION_LESS "3.28")
FetchContent_Declare(
lmfit
GIT_REPOSITORY https://jugit.fz-juelich.de/mlz/lmfit.git
GIT_TAG main
PATCH_COMMAND ${LMFIT_PATCH_COMMAND}
UPDATE_DISCONNECTED 1
)
else()
FetchContent_Declare(
lmfit
GIT_REPOSITORY https://jugit.fz-juelich.de/mlz/lmfit.git
GIT_TAG main
PATCH_COMMAND ${LMFIT_PATCH_COMMAND}
UPDATE_DISCONNECTED 1
EXCLUDE_FROM_ALL 1
)
endif()
#Disable what we don't need from lmfit
set(BUILD_TESTING OFF CACHE BOOL "")
set(LMFIT_CPPTEST OFF CACHE BOOL "")
set(LIB_MAN OFF CACHE BOOL "")
set(LMFIT_CPPTEST OFF CACHE BOOL "")
set(BUILD_SHARED_LIBS OFF CACHE BOOL "")
if (${CMAKE_VERSION} VERSION_LESS "3.28")
if(NOT lmfit_POPULATED)
FetchContent_Populate(lmfit)
add_subdirectory(${lmfit_SOURCE_DIR} ${lmfit_BINARY_DIR} EXCLUDE_FROM_ALL)
endif()
else()
FetchContent_MakeAvailable(lmfit)
endif()
set_property(TARGET lmfit PROPERTY POSITION_INDEPENDENT_CODE ON)
else()
find_package(lmfit REQUIRED)
endif()
if(AARE_FETCH_ZMQ)
# Fetchcontent_Declare is deprecated need to find a way to update this
# for now setting the policy to old is enough
if (${CMAKE_VERSION} VERSION_GREATER_EQUAL "3.30")
cmake_policy(SET CMP0169 OLD)
endif()
set(ZMQ_PATCH_COMMAND git apply ${CMAKE_CURRENT_SOURCE_DIR}/patches/libzmq_cmake_version.patch)
FetchContent_Declare(
libzmq
GIT_REPOSITORY https://github.com/zeromq/libzmq.git
GIT_TAG v4.3.4
PATCH_COMMAND ${ZMQ_PATCH_COMMAND}
UPDATE_DISCONNECTED 1
)
# Disable unwanted options from libzmq
set(BUILD_TESTS OFF CACHE BOOL "Switch off libzmq test build")
set(BUILD_SHARED OFF CACHE BOOL "Switch off libzmq shared libs")
set(WITH_PERF_TOOL OFF CACHE BOOL "")
set(ENABLE_CPACK OFF CACHE BOOL "")
set(ENABLE_CLANG OFF CACHE BOOL "")
set(ENABLE_CURVE OFF CACHE BOOL "")
set(ENABLE_DRAFTS OFF CACHE BOOL "")
# TODO! Verify that this is what we want to do in aare
# Using GetProperties and Populate to be able to exclude zmq
# from install (not possible with FetchContent_MakeAvailable(libzmq))
FetchContent_GetProperties(libzmq)
if(NOT libzmq_POPULATED)
FetchContent_Populate(libzmq)
add_subdirectory(${libzmq_SOURCE_DIR} ${libzmq_BINARY_DIR} EXCLUDE_FROM_ALL)
endif()
else()
find_package(ZeroMQ 4 REQUIRED)
endif()
if (AARE_FETCH_FMT)
set(FMT_TEST OFF CACHE INTERNAL "disabling fmt tests")
FetchContent_Declare(
fmt
GIT_REPOSITORY https://github.com/fmtlib/fmt.git
GIT_TAG 10.2.1
GIT_PROGRESS TRUE
USES_TERMINAL_DOWNLOAD TRUE
)
set(FMT_INSTALL ON CACHE BOOL "")
# set(FMT_CMAKE_DIR "")
FetchContent_MakeAvailable(fmt)
set_property(TARGET fmt PROPERTY POSITION_INDEPENDENT_CODE ON)
install(TARGETS fmt
EXPORT ${project}-targets
LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
INCLUDES DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
)
else()
find_package(fmt 6 REQUIRED)
endif()
if (AARE_FETCH_JSON)
FetchContent_Declare(
json
URL https://github.com/nlohmann/json/releases/download/v3.11.3/json.tar.xz
)
set(JSON_Install ON CACHE BOOL "")
FetchContent_MakeAvailable(json)
set(NLOHMANN_JSON_TARGET_NAME nlohmann_json)
install(
TARGETS nlohmann_json
EXPORT "${TARGETS_EXPORT_NAME}"
)
message(STATUS "target: ${NLOHMANN_JSON_TARGET_NAME}")
else()
find_package(nlohmann_json 3.11.3 REQUIRED)
endif()
include(GNUInstallDirs)
# If conda build, always set lib dir to 'lib'
if($ENV{CONDA_BUILD})
set(CMAKE_INSTALL_LIBDIR "lib")
endif()
# Set lower / upper case project names
string(TOUPPER "${PROJECT_NAME}" PROJECT_NAME_UPPER)
string(TOLOWER "${PROJECT_NAME}" PROJECT_NAME_LOWER)
# Set targets export name (used by slsDetectorPackage and dependencies)
set(TARGETS_EXPORT_NAME "${PROJECT_NAME_LOWER}-targets")
set(namespace "aare::")
set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake" ${CMAKE_MODULE_PATH})
# Check if project is being used directly or via add_subdirectory
set(AARE_MASTER_PROJECT OFF)
if (CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_SOURCE_DIR)
set(AARE_MASTER_PROJECT ON)
endif()
add_library(aare_compiler_flags INTERFACE)
target_compile_features(aare_compiler_flags INTERFACE cxx_std_17)
if(AARE_PYTHON_BINDINGS)
add_subdirectory(python)
endif()
#################
# MSVC specific #
#################
if(MSVC)
add_compile_definitions(AARE_MSVC)
if(CMAKE_BUILD_TYPE STREQUAL "Release")
message(STATUS "Release build")
target_compile_options(aare_compiler_flags INTERFACE /O2)
else()
message(STATUS "Debug build")
target_compile_options(
aare_compiler_flags
INTERFACE
/Od
/Zi
/MDd
/D_ITERATOR_DEBUG_LEVEL=2
)
target_link_options(
aare_compiler_flags
INTERFACE
/DEBUG:FULL
)
endif()
target_compile_options(
aare_compiler_flags
INTERFACE
/w # disable warnings
)
else()
######################
# GCC/Clang specific #
######################
if(CMAKE_BUILD_TYPE STREQUAL "Release")
message(STATUS "Release build")
target_compile_options(aare_compiler_flags INTERFACE -O3)
else()
message(STATUS "Debug build")
endif()
# Common flags for GCC and Clang
target_compile_options(
aare_compiler_flags
INTERFACE
-Wall
-Wextra
-pedantic
-Wshadow
-Wold-style-cast
-Wnon-virtual-dtor
-Woverloaded-virtual
-Wdouble-promotion
-Wformat=2
-Wredundant-decls
-Wvla
-Wdouble-promotion
-Werror=return-type #important can cause segfault in optimzed builds
)
endif() #GCC/Clang specific
if(AARE_ASAN)
message(STATUS "AddressSanitizer enabled")
target_compile_options(
aare_compiler_flags
INTERFACE
-fsanitize=address,undefined,pointer-compare
-fno-omit-frame-pointer
)
target_link_libraries(
aare_compiler_flags
INTERFACE
-fsanitize=address,undefined,pointer-compare
-fno-omit-frame-pointer
)
endif()
if(AARE_TESTS)
enable_testing()
add_subdirectory(tests)
endif()
###------------------------------------------------------------------------------MAIN LIBRARY
###------------------------------------------------------------------------------------------
set(PUBLICHEADERS
include/aare/ArrayExpr.hpp
include/aare/CalculateEta.hpp
include/aare/Cluster.hpp
include/aare/ClusterFinder.hpp
include/aare/ClusterFile.hpp
include/aare/CtbRawFile.hpp
include/aare/ClusterVector.hpp
include/aare/decode.hpp
include/aare/defs.hpp
include/aare/Dtype.hpp
include/aare/File.hpp
include/aare/Fit.hpp
include/aare/FileInterface.hpp
include/aare/FilePtr.hpp
include/aare/Frame.hpp
include/aare/GainMap.hpp
include/aare/geo_helpers.hpp
include/aare/JungfrauDataFile.hpp
include/aare/NDArray.hpp
include/aare/NDView.hpp
include/aare/NumpyFile.hpp
include/aare/NumpyHelpers.hpp
include/aare/Pedestal.hpp
include/aare/PixelMap.hpp
include/aare/RawFile.hpp
include/aare/RawMasterFile.hpp
include/aare/RawSubFile.hpp
include/aare/VarClusterFinder.hpp
include/aare/utils/task.hpp
)
set(SourceFiles
${CMAKE_CURRENT_SOURCE_DIR}/src/CtbRawFile.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/defs.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Dtype.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/decode.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Frame.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/File.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/FilePtr.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Fit.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/geo_helpers.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/JungfrauDataFile.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/NumpyFile.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/NumpyHelpers.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Interpolator.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/PixelMap.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/RawFile.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/RawSubFile.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/RawMasterFile.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/utils/task.cpp
)
add_library(aare_core STATIC ${SourceFiles})
target_include_directories(aare_core PUBLIC
"$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
"$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>"
)
target_link_libraries(
aare_core
PUBLIC
fmt::fmt
nlohmann_json::nlohmann_json
${STD_FS_LIB} # from helpers.cmake
PRIVATE
aare_compiler_flags
$<BUILD_INTERFACE:lmfit>
)
set_target_properties(aare_core PROPERTIES
ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}
PUBLIC_HEADER "${PUBLICHEADERS}"
)
if (AARE_PYTHON_BINDINGS)
set_property(TARGET aare_core PROPERTY POSITION_INDEPENDENT_CODE ON)
endif()
if(AARE_TESTS)
set(TestSources
${CMAKE_CURRENT_SOURCE_DIR}/src/algorithm.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/defs.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Dtype.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Frame.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/geo_helpers.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/RawMasterFile.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/NDArray.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/NDView.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/ClusterFinder.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/ClusterVector.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Cluster.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/CalculateEta.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/ClusterFile.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Pedestal.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/JungfrauDataFile.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/NumpyFile.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/NumpyHelpers.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/RawFile.test.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/utils/task.test.cpp
)
target_sources(tests PRIVATE ${TestSources} )
endif()
###------------------------------------------------------------------------------------------
###------------------------------------------------------------------------------------------
if(AARE_MASTER_PROJECT)
install(TARGETS aare_core aare_compiler_flags
EXPORT "${TARGETS_EXPORT_NAME}"
LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
PUBLIC_HEADER DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/aare
)
endif()
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_INSTALL_RPATH $ORIGIN)
set(CMAKE_BUILD_WITH_INSTALL_RPATH FALSE)
# #Overall target to link to when using the library
# add_library(aare INTERFACE)
# target_link_libraries(aare INTERFACE aare_core aare_compiler_flags)
# target_include_directories(aare INTERFACE
# $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
# $<INSTALL_INTERFACE:include>
# )
# add_subdirectory(examples)
if(AARE_DOCS)
add_subdirectory(docs)
endif()
# custom target to run check formatting with clang-format
add_custom_target(
check-format
COMMAND find \( -name "*.cpp" -o -name "*.hpp" \) -not -path "./build/*" | xargs -I {} -n 1 -P 10 bash -c "clang-format -Werror -style=\"file:.clang-format\" {} | diff {} -"
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
COMMENT "Checking code formatting with clang-format"
VERBATIM
)
add_custom_target(
format-files
COMMAND find \( -name "*.cpp" -o -name "*.hpp" \) -not -path "./build/*" | xargs -I {} -n 1 -P 10 bash -c "clang-format -i -style=\"file:.clang-format\" {}"
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
COMMENT "Formatting with clang-format"
VERBATIM
)
if (AARE_IN_GITHUB_ACTIONS)
message(STATUS "Running in Github Actions")
set(CLANG_TIDY_COMMAND "clang-tidy-17")
else()
set(CLANG_TIDY_COMMAND "clang-tidy")
endif()
add_custom_target(
clang-tidy
COMMAND find \( -path "./src/*" -a -not -path "./src/python/*" -a \( -name "*.cpp" -not -name "*.test.cpp" \) \) -not -name "CircularFifo.hpp" -not -name "ProducerConsumerQueue.hpp" -not -name "VariableSizeClusterFinder.hpp" | xargs -I {} -n 1 -P 10 bash -c "${CLANG_TIDY_COMMAND} --config-file=.clang-tidy -p build {}"
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
COMMENT "linting with clang-tidy"
VERBATIM
)
if(AARE_MASTER_PROJECT)
set(CMAKE_INSTALL_DIR "share/cmake/${PROJECT_NAME}")
set(PROJECT_LIBRARIES aare-core aare-compiler-flags )
include(cmake/package_config.cmake)
endif()

View File

@@ -1,2 +1,71 @@
# aare
Data analysis library for PSI hybrid detectors
## Build and install
Prerequisites
- cmake >= 3.14
- C++17 compiler (gcc >= 8)
- python >= 3.10
### Development install (for Python)
```bash
git clone git@github.com:slsdetectorgroup/aare.git --branch=v1 #or using http...
mkdir build
cd build
#configure using cmake
cmake ../aare
#build (replace 4 with the number of threads you want to use)
make -j4
```
Now you can use the Python module from your build directory
```python
import aare
f = aare.File('Some/File/I/Want_to_open_master_0.json')
```
To run form other folders either add the path to your conda environment using conda-build or add it to your PYTHONPATH
### Install using conda/mamba
```bash
#enable your env first!
conda install aare=2024.10.29.dev0 -c slsdetectorgroup
```
### Install to a custom location and use in your project
Working example in: https://github.com/slsdetectorgroup/aare-examples
```bash
#build and install aare
git clone git@github.com:slsdetectorgroup/aare.git --branch=v1 #or using http...
mkdir build
cd build
#configure using cmake
cmake ../aare -DCMAKE_INSTALL_PREFIX=/where/to/put/aare
#build (replace 4 with the number of threads you want to use)
make -j4
#install
make install
#Now configure your project
cmake .. -DCMAKE_PREFIX_PATH=SOME_PATH
```
### Local build of conda pkgs
```bash
conda build . --variants="{python: [3.11, 3.12, 3.13]}"
```

27
benchmarks/CMakeLists.txt Normal file
View File

@@ -0,0 +1,27 @@
include(FetchContent)
FetchContent_Declare(
benchmark
GIT_REPOSITORY https://github.com/google/benchmark.git
GIT_TAG v1.8.3 # Change to the latest version if needed
)
# Ensure Google Benchmark is built correctly
set(BENCHMARK_ENABLE_TESTING OFF CACHE BOOL "" FORCE)
FetchContent_MakeAvailable(benchmark)
add_executable(benchmarks)
target_sources(benchmarks PRIVATE ndarray_benchmark.cpp calculateeta_benchmark.cpp)
# Link Google Benchmark and other necessary libraries
target_link_libraries(benchmarks PRIVATE benchmark::benchmark aare_core aare_compiler_flags)
# Set output properties
set_target_properties(benchmarks PROPERTIES
RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}
OUTPUT_NAME run_benchmarks
)

View File

@@ -0,0 +1,70 @@
#include "aare/CalculateEta.hpp"
#include "aare/ClusterFile.hpp"
#include <benchmark/benchmark.h>
using namespace aare;
class ClusterFixture : public benchmark::Fixture {
public:
Cluster<int, 2, 2> cluster_2x2{};
Cluster<int, 3, 3> cluster_3x3{};
private:
using benchmark::Fixture::SetUp;
void SetUp([[maybe_unused]] const benchmark::State &state) override {
int temp_data[4] = {1, 2, 3, 1};
std::copy(std::begin(temp_data), std::end(temp_data),
std::begin(cluster_2x2.data));
cluster_2x2.x = 0;
cluster_2x2.y = 0;
int temp_data2[9] = {1, 2, 3, 1, 3, 4, 5, 1, 20};
std::copy(std::begin(temp_data2), std::end(temp_data2),
std::begin(cluster_3x3.data));
cluster_3x3.x = 0;
cluster_3x3.y = 0;
}
// void TearDown(::benchmark::State& state) {
// }
};
BENCHMARK_F(ClusterFixture, Calculate2x2Eta)(benchmark::State &st) {
for (auto _ : st) {
// This code gets timed
Eta2 eta = calculate_eta2(cluster_2x2);
benchmark::DoNotOptimize(eta);
}
}
// almost takes double the time
BENCHMARK_F(ClusterFixture,
CalculateGeneralEtaFor2x2Cluster)(benchmark::State &st) {
for (auto _ : st) {
// This code gets timed
Eta2 eta = calculate_eta2<int, 2, 2>(cluster_2x2);
benchmark::DoNotOptimize(eta);
}
}
BENCHMARK_F(ClusterFixture, Calculate3x3Eta)(benchmark::State &st) {
for (auto _ : st) {
// This code gets timed
Eta2 eta = calculate_eta2(cluster_3x3);
benchmark::DoNotOptimize(eta);
}
}
// almost takes double the time
BENCHMARK_F(ClusterFixture,
CalculateGeneralEtaFor3x3Cluster)(benchmark::State &st) {
for (auto _ : st) {
// This code gets timed
Eta2 eta = calculate_eta2<int, 3, 3>(cluster_3x3);
benchmark::DoNotOptimize(eta);
}
}
// BENCHMARK_MAIN();

View File

@@ -0,0 +1,136 @@
#include <benchmark/benchmark.h>
#include "aare/NDArray.hpp"
using aare::NDArray;
constexpr ssize_t size = 1024;
class TwoArrays : public benchmark::Fixture {
public:
NDArray<int,2> a{{size,size},0};
NDArray<int,2> b{{size,size},0};
void SetUp(::benchmark::State& state) {
for(uint32_t i = 0; i < size; i++){
for(uint32_t j = 0; j < size; j++){
a(i, j)= i*j+1;
b(i, j)= i*j+1;
}
}
}
// void TearDown(::benchmark::State& state) {
// }
};
BENCHMARK_F(TwoArrays, AddWithOperator)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res = a+b;
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, AddWithIndex)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res(a.shape());
for (uint32_t i = 0; i < a.size(); i++) {
res(i) = a(i) + b(i);
}
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, SubtractWithOperator)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res = a-b;
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, SubtractWithIndex)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res(a.shape());
for (uint32_t i = 0; i < a.size(); i++) {
res(i) = a(i) - b(i);
}
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, MultiplyWithOperator)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res = a*b;
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, MultiplyWithIndex)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res(a.shape());
for (uint32_t i = 0; i < a.size(); i++) {
res(i) = a(i) * b(i);
}
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, DivideWithOperator)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res = a/b;
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, DivideWithIndex)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res(a.shape());
for (uint32_t i = 0; i < a.size(); i++) {
res(i) = a(i) / b(i);
}
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, FourAddWithOperator)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res = a+b+a+b;
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, FourAddWithIndex)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res(a.shape());
for (uint32_t i = 0; i < a.size(); i++) {
res(i) = a(i) + b(i) + a(i) + b(i);
}
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, MultiplyAddDivideWithOperator)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res = a*a+b/a;
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_F(TwoArrays, MultiplyAddDivideWithIndex)(benchmark::State& st) {
for (auto _ : st) {
// This code gets timed
NDArray<int,2> res(a.shape());
for (uint32_t i = 0; i < a.size(); i++) {
res(i) = a(i) * a(i) + b(i) / a(i);
}
benchmark::DoNotOptimize(res);
}
}
BENCHMARK_MAIN();

11
cmake/FindSphinx.cmake Normal file
View File

@@ -0,0 +1,11 @@
#Look for an executable called sphinx-build
find_program(SPHINX_EXECUTABLE
NAMES sphinx-build sphinx-build-3.6
DOC "Path to sphinx-build executable")
include(FindPackageHandleStandardArgs)
#Handle standard arguments to find_package like REQUIRED and QUIET
find_package_handle_standard_args(Sphinx
"Failed to find sphinx-build executable"
SPHINX_EXECUTABLE)

46
cmake/helpers.cmake Normal file
View File

@@ -0,0 +1,46 @@
function(default_build_type val)
if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
message(STATUS "No build type selected, default to Release")
set(CMAKE_BUILD_TYPE ${val} CACHE STRING "Build type (default ${val})" FORCE)
endif()
endfunction()
function(set_std_fs_lib)
# from pybind11
# Check if we need to add -lstdc++fs or -lc++fs or nothing
if(DEFINED CMAKE_CXX_STANDARD AND CMAKE_CXX_STANDARD LESS 17)
set(STD_FS_NO_LIB_NEEDED TRUE)
elseif(MSVC)
set(STD_FS_NO_LIB_NEEDED TRUE)
else()
file(
WRITE ${CMAKE_CURRENT_BINARY_DIR}/main.cpp
"#include <filesystem>\nint main(int argc, char ** argv) {\n std::filesystem::path p(argv[0]);\n return p.string().length();\n}"
)
try_compile(
STD_FS_NO_LIB_NEEDED ${CMAKE_CURRENT_BINARY_DIR}
SOURCES ${CMAKE_CURRENT_BINARY_DIR}/main.cpp
COMPILE_DEFINITIONS -std=c++17)
try_compile(
STD_FS_NEEDS_STDCXXFS ${CMAKE_CURRENT_BINARY_DIR}
SOURCES ${CMAKE_CURRENT_BINARY_DIR}/main.cpp
COMPILE_DEFINITIONS -std=c++17
LINK_LIBRARIES stdc++fs)
try_compile(
STD_FS_NEEDS_CXXFS ${CMAKE_CURRENT_BINARY_DIR}
SOURCES ${CMAKE_CURRENT_BINARY_DIR}/main.cpp
COMPILE_DEFINITIONS -std=c++17
LINK_LIBRARIES c++fs)
endif()
if(${STD_FS_NEEDS_STDCXXFS})
set(STD_FS_LIB stdc++fs PARENT_SCOPE)
elseif(${STD_FS_NEEDS_CXXFS})
set(STD_FS_LIB c++fs PARENT_SCOPE)
elseif(${STD_FS_NO_LIB_NEEDED})
set(STD_FS_LIB "" PARENT_SCOPE)
else()
message(WARNING "Unknown C++17 compiler - not passing -lstdc++fs")
set(STD_FS_LIB "")
endif()
endfunction()

View File

@@ -0,0 +1,35 @@
# This cmake code creates the configuration that is found and used by
# find_package() of another cmake project
# get lower and upper case project name for the configuration files
# configure and install the configuration files
include(CMakePackageConfigHelpers)
configure_package_config_file(
"${CMAKE_SOURCE_DIR}/cmake/project-config.cmake.in"
"${PROJECT_BINARY_DIR}/${PROJECT_NAME_LOWER}-config.cmake"
INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/${PROJECT_NAME_LOWER}
PATH_VARS CMAKE_INSTALL_DIR)
write_basic_package_version_file(
"${PROJECT_BINARY_DIR}/${PROJECT_NAME_LOWER}-config-version.cmake"
VERSION ${PROJECT_VERSION}
COMPATIBILITY SameMajorVersion
)
install(FILES
"${PROJECT_BINARY_DIR}/${PROJECT_NAME_LOWER}-config.cmake"
"${PROJECT_BINARY_DIR}/${PROJECT_NAME_LOWER}-config-version.cmake"
COMPONENT devel
DESTINATION ${CMAKE_INSTALL_DIR}
)
if (PROJECT_LIBRARIES OR PROJECT_STATIC_LIBRARIES)
install(
EXPORT "${TARGETS_EXPORT_NAME}"
FILE ${PROJECT_NAME_LOWER}-targets.cmake
DESTINATION ${CMAKE_INSTALL_DIR}
)
endif ()

View File

@@ -0,0 +1,28 @@
# Config file for @PROJECT_NAME_LOWER@
#
# It defines the following variables:
#
# @PROJECT_NAME_UPPER@_INCLUDE_DIRS - include directory
# @PROJECT_NAME_UPPER@_LIBRARIES - all dynamic libraries
# @PROJECT_NAME_UPPER@_STATIC_LIBRARIES - all static libraries
@PACKAGE_INIT@
include(CMakeFindDependencyMacro)
set(SLS_USE_HDF5 "@SLS_USE_HDF5@")
# List dependencies
find_dependency(Threads)
find_dependency(fmt)
find_dependency(nlohmann_json)
# Add optional dependencies here
if (SLS_USE_HDF5)
find_dependency(HDF5)
endif ()
set_and_check(@PROJECT_NAME_UPPER@_CMAKE_INCLUDE_DIRS "@PACKAGE_CMAKE_INSTALL_DIR@")
include("${CMAKE_CURRENT_LIST_DIR}/@TARGETS_EXPORT_NAME@.cmake")
check_required_components("@PROJECT_NAME@")

View File

@@ -0,0 +1,28 @@
python:
- 3.11
- 3.11
- 3.11
- 3.12
- 3.12
- 3.12
- 3.13
numpy:
- 1.26
- 2.0
- 2.1
- 1.26
- 2.0
- 2.1
- 2.1
zip_keys:
- python
- numpy
pin_run_as_build:
numpy: x.x
python: x.x

55
conda-recipe/meta.yaml Normal file
View File

@@ -0,0 +1,55 @@
package:
name: aare
version: 2025.4.1 #TODO! how to not duplicate this?
source:
path: ..
build:
number: 0
script:
- unset CMAKE_GENERATOR && {{ PYTHON }} -m pip install . -vv # [not win]
- {{ PYTHON }} -m pip install . -vv # [win]
requirements:
build:
- python {{python}}
- numpy {{ numpy }}
- {{ compiler('cxx') }}
host:
- cmake
- ninja
- python {{python}}
- numpy {{ numpy }}
- pip
- scikit-build-core
- pybind11 >=2.13.0
- fmt
- zeromq
- nlohmann_json
- catch2
run:
- python {{python}}
- numpy {{ numpy }}
test:
imports:
- aare
# requires:
# - pytest
# source_files:
# - tests
# commands:
# - pytest tests
about:
summary: An example project built with pybind11 and scikit-build.
# license_file: LICENSE

56
docs/CMakeLists.txt Normal file
View File

@@ -0,0 +1,56 @@
find_package(Doxygen REQUIRED)
find_package(Sphinx REQUIRED)
#Doxygen
set(DOXYGEN_IN ${CMAKE_CURRENT_SOURCE_DIR}/Doxyfile.in)
set(DOXYGEN_OUT ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile)
configure_file(${DOXYGEN_IN} ${DOXYGEN_OUT} @ONLY)
#Sphinx
set(SPHINX_SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src)
set(SPHINX_BUILD ${CMAKE_CURRENT_BINARY_DIR})
file(GLOB SPHINX_SOURCE_FILES CONFIGURE_DEPENDS "src/*.rst")
foreach(filename ${SPHINX_SOURCE_FILES})
get_filename_component(fname ${filename} NAME)
message(STATUS "Copying ${filename} to ${SPHINX_BUILD}/src/${fname}")
configure_file(${filename} "${SPHINX_BUILD}/src/${fname}")
endforeach(filename ${SPHINX_SOURCE_FILES})
configure_file(
"${CMAKE_CURRENT_SOURCE_DIR}/conf.py.in"
"${SPHINX_BUILD}/conf.py"
@ONLY
)
configure_file(
"${CMAKE_CURRENT_SOURCE_DIR}/static/extra.css"
"${SPHINX_BUILD}/static/css/extra.css"
@ONLY
)
add_custom_target(
docs
COMMAND ${DOXYGEN_EXECUTABLE} ${DOXYGEN_OUT}
COMMAND ${SPHINX_EXECUTABLE} -a -b html
-Dbreathe_projects.aare=${CMAKE_CURRENT_BINARY_DIR}/xml
-c "${SPHINX_BUILD}"
${SPHINX_BUILD}/src
${SPHINX_BUILD}/html
COMMENT "Generating documentation with Sphinx"
)
add_custom_target(
rst
COMMAND ${SPHINX_EXECUTABLE} -a -b html
-Dbreathe_projects.aare=${CMAKE_CURRENT_BINARY_DIR}/xml
-c "${SPHINX_BUILD}"
${SPHINX_BUILD}/src
${SPHINX_BUILD}/html
COMMENT "Generating documentation with Sphinx"
)

1917
docs/Doxyfile.in Normal file

File diff suppressed because it is too large Load Diff

63
docs/conf.py.in Normal file
View File

@@ -0,0 +1,63 @@
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
sys.path.insert(0, os.path.abspath('..'))
print(sys.path)
# -- Project information -----------------------------------------------------
project = 'aare'
copyright = '2024, CPS Detector Group'
author = 'CPS Detector Group'
version = '@PROJECT_VERSION@'
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['breathe',
'sphinx.ext.autodoc',
'sphinx.ext.napoleon',
]
breathe_default_project = "aare"
napoleon_use_ivar = True
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "furo"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['static']
def setup(app):
app.add_css_file('css/extra.css') # may also be an URL

7
docs/src/ClusterFile.rst Normal file
View File

@@ -0,0 +1,7 @@
ClusterFile
=============
.. doxygenclass:: aare::ClusterFile
:members:
:undoc-members:
:private-members:

View File

@@ -0,0 +1,7 @@
ClusterFinder
=============
.. doxygenclass:: aare::ClusterFinder
:members:
:undoc-members:

View File

@@ -0,0 +1,7 @@
ClusterFinderMT
==================
.. doxygenclass:: aare::ClusterFinderMT
:members:
:undoc-members:

View File

@@ -0,0 +1,6 @@
ClusterVector
=============
.. doxygenclass:: aare::ClusterVector
:members:
:undoc-members:

19
docs/src/Consume.rst Normal file
View File

@@ -0,0 +1,19 @@
Use from C++
========================
There are a few different way to use aare in your C++ project. Which one you choose
depends on how you intend to work with the library and how you manage your dependencies.
Install and use cmake with find_package(aare)
-------------------------------------------------
https://github.com/slsdetectorgroup/aare-examples
.. include:: _install.rst
Use as a submodule
-------------------
Coming soon...

7
docs/src/Dtype.rst Normal file
View File

@@ -0,0 +1,7 @@
Dtype
=============
.. doxygenclass:: aare::Dtype
:members:
:undoc-members:

8
docs/src/File.rst Normal file
View File

@@ -0,0 +1,8 @@
File
=============
.. doxygenclass:: aare::File
:members:
:undoc-members:
:private-members:

8
docs/src/Frame.rst Normal file
View File

@@ -0,0 +1,8 @@
Frame
=============
.. doxygenclass:: aare::Frame
:members:
:undoc-members:
:private-members:

106
docs/src/Installation.rst Normal file
View File

@@ -0,0 +1,106 @@
****************
Installation
****************
.. attention ::
- https://cliutils.gitlab.io/modern-cmake/README.html
conda/mamaba
~~~~~~~~~~~~~~~~~~
This is the recommended way to install aare. Using a package manager makes it easy to
switch between versions and is (one of) the most convenient way to install up to date
dependencies on older distributions.
.. note ::
aare is developing rapidly. Check for the latest release by
using: **conda search aare -c slsdetectorgroup**
.. code-block:: bash
# Install a specific version:
conda install aare=2024.11.11.dev0 -c slsdetectorgroup
cmake build (development install)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you are working on aare or want to test our a version that doesn't yet have
a conda package. Build using cmake and then run from the build folder.
.. code-block:: bash
git clone git@github.com:slsdetectorgroup/aare.git --branch=v1 #or using http...
mkdir build
cd build
#configure using cmake
cmake ../aare
#build (replace 4 with the number of threads you want to use)
make -j4
# add the build folder to your PYTHONPATH and then you should be able to
# import aare in python
cmake build + install and use in your C++ project
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. warning ::
When building aare with default settings we also include fmt and nlohmann_json.
Installation to a custom location is highly recommended.
.. note ::
It is also possible to install aare with conda and then use in your C++ project.
.. include:: _install.rst
cmake options
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For detailed options see the CMakeLists.txt file in the root directory of the project.
.. code-block:: bash
# usage (or edit with ccmake .)
cmake ../aare -DOPTION1=ON -DOPTION2=OFF
**AARE_SYSTEM_LIBRARIES "Use system libraries" OFF**
Use system libraries instead of using FetchContent to pull in dependencies. Default option is off.
**AARE_PYTHON_BINDINGS "Build python bindings" ON**
Build the Python bindings. Default option is on.
.. warning ::
If you have a newer system Python compared to the one in your virtual environment,
you might have to pass -DPython_FIND_VIRTUALENV=ONLY to cmake.
**AARE_TESTS "Build tests" OFF**
Build unit tests. Default option is off.
**AARE_EXAMPLES "Build examples" OFF**
**AARE_DOCS "Build documentation" OFF**
Build documentation. Needs doxygen, sphinx and breathe. Default option is off.
Requires a separate make docs.
**AARE_VERBOSE "Verbose output" OFF**
**AARE_CUSTOM_ASSERT "Use custom assert" OFF**
Enable custom assert macro to check for errors. Default option is off.

View File

@@ -0,0 +1,25 @@
JungfrauDataFile
==================
JungfrauDataFile is a class to read the .dat files that are produced by Aldo's receiver.
It is mostly used for calibration.
The structure of the file is:
* JungfrauDataHeader
* Binary data (256x256, 256x1024 or 512x1024)
* JungfrauDataHeader
* ...
There is no metadata indicating number of frames or the size of the image, but this
will be infered by this reader.
.. doxygenstruct:: aare::JungfrauDataHeader
:members:
:undoc-members:
:private-members:
.. doxygenclass:: aare::JungfrauDataFile
:members:
:undoc-members:
:private-members:

7
docs/src/NDArray.rst Normal file
View File

@@ -0,0 +1,7 @@
NDArray
=============
.. doxygenclass:: aare::NDArray
:members:
:undoc-members:

7
docs/src/NDView.rst Normal file
View File

@@ -0,0 +1,7 @@
NDView
=============
.. doxygenclass:: aare::NDView
:members:
:undoc-members:

8
docs/src/Pedestal.rst Normal file
View File

@@ -0,0 +1,8 @@
Pedestal
=============
.. doxygenclass:: aare::Pedestal
:members:
:undoc-members:
:private-members:

8
docs/src/RawFile.rst Normal file
View File

@@ -0,0 +1,8 @@
RawFile
===============
.. doxygenclass:: aare::RawFile
:members:
:undoc-members:
:private-members:

View File

@@ -0,0 +1,14 @@
RawMasterFile
===============
.. doxygenclass:: aare::RawMasterFile
:members:
:undoc-members:
:private-members:
.. doxygenclass:: aare::RawFileNameComponents
:members:
:undoc-members:
:private-members:

8
docs/src/RawSubFile.rst Normal file
View File

@@ -0,0 +1,8 @@
RawSubFile
===============
.. doxygenclass:: aare::RawSubFile
:members:
:undoc-members:
:private-members:

23
docs/src/Requirements.rst Normal file
View File

@@ -0,0 +1,23 @@
Requirements
==============================================
- C++17 compiler (gcc 8/clang 7)
- CMake 3.14+
**Internally used libraries**
.. note ::
These can also be picked up from the system/conda environment by specifying:
-DAARE_SYSTEM_LIBRARIES=ON during the cmake configuration.
- pybind11
- fmt
- nlohmann_json
- ZeroMQ
**Extra dependencies for building documentation**
- Sphinx
- Breathe
- Doxygen

47
docs/src/Tests.rst Normal file
View File

@@ -0,0 +1,47 @@
****************
Tests
****************
We test the code both from the C++ and Python API. By default only tests that does not require image data is run.
C++
~~~~~~~~~~~~~~~~~~
.. code-block:: bash
mkdir build
cd build
cmake .. -DAARE_TESTS=ON
make -j 4
export AARE_TEST_DATA=/path/to/test/data
./run_test [.files] #or using ctest, [.files] is the option to include tests needing data
Python
~~~~~~~~~~~~~~~~~~
.. code-block:: bash
#From the root dir of the library
python -m pytest python/tests --files # passing --files will run the tests needing data
Getting the test data
~~~~~~~~~~~~~~~~~~~~~~~~
.. attention ::
The tests needing the test data are not run by default. To make the data available, you need to set the environment variable
AARE_TEST_DATA to the path of the test data directory. Then pass either [.files] for the C++ tests or --files for Python
The image files needed for the test are large and are not included in the repository. They are stored
using GIT LFS in a separate repository. To get the test data, you need to clone the repository.
To do this, you need to have GIT LFS installed. You can find instructions on how to install it here: https://git-lfs.github.com/
Once you have GIT LFS installed, you can clone the repository like any normal repo using:
.. code-block:: bash
git clone https://gitea.psi.ch/detectors/aare-test-data.git

View File

@@ -0,0 +1,7 @@
VarClusterFinder
====================
.. doxygenclass:: aare::VarClusterFinder
:members:
:undoc-members:

23
docs/src/_install.rst Normal file
View File

@@ -0,0 +1,23 @@
.. code-block:: bash
#build and install aare
git clone git@github.com:slsdetectorgroup/aare.git --branch=developer #or using http...
mkdir build
cd build
#configure using cmake
cmake ../aare -DCMAKE_INSTALL_PREFIX=/where/to/put/aare
#build (replace 4 with the number of threads you want to use)
make -j4
#install
make install
#Go to your project
cd /your/project/source
#Now configure your project
mkdir build
cd build
cmake .. -DCMAKE_PREFIX_PATH=SOME_PATH

5
docs/src/algorithm.rst Normal file
View File

@@ -0,0 +1,5 @@
algorithm
=============
.. doxygenfile:: algorithm.hpp

66
docs/src/index.rst Normal file
View File

@@ -0,0 +1,66 @@
AARE
==============================================
.. note ::
**Examples:**
- `jupyter notebooks <https://github.com/slsdetectorgroup/aare-notebooks>`_
- `cmake+install <https://github.com/slsdetectorgroup/aare-examples>`_
- `git submodule <https://github.com/slsdetectorgroup/aare-submodule>`_
.. toctree::
:caption: Installation
:maxdepth: 3
Installation
Requirements
Consume
.. toctree::
:caption: Python API
:maxdepth: 1
pyFile
pyCtbRawFile
pyClusterFile
pyClusterVector
pyJungfrauDataFile
pyRawFile
pyRawMasterFile
pyVarClusterFinder
pyFit
.. toctree::
:caption: C++ API
:maxdepth: 1
algorithm
NDArray
NDView
Frame
File
Dtype
ClusterFinder
ClusterFinderMT
ClusterFile
ClusterVector
JungfrauDataFile
Pedestal
RawFile
RawSubFile
RawMasterFile
VarClusterFinder
.. toctree::
:caption: Developer
:maxdepth: 3
Tests

View File

@@ -0,0 +1,11 @@
ClusterFile
============
.. py:currentmodule:: aare
.. autoclass:: ClusterFile
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

View File

@@ -0,0 +1,33 @@
ClusterVector
================
The ClusterVector, holds clusters from the ClusterFinder. Since it is templated
in C++ we use a suffix indicating the data type in python. The suffix is
``_i`` for integer, ``_f`` for float, and ``_d`` for double.
At the moment the functionality from python is limited and it is not supported
to push_back clusters to the vector. The intended use case is to pass it to
C++ functions that support the ClusterVector or to view it as a numpy array.
**View ClusterVector as numpy array**
.. code:: python
from aare import ClusterFile
with ClusterFile("path/to/file") as f:
cluster_vector = f.read_frame()
# Create a copy of the cluster data in a numpy array
clusters = np.array(cluster_vector)
# Avoid copying the data by passing copy=False
clusters = np.array(cluster_vector, copy = False)
.. py:currentmodule:: aare
.. autoclass:: ClusterVector_i
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

11
docs/src/pyCtbRawFile.rst Normal file
View File

@@ -0,0 +1,11 @@
CtbRawFile
============
.. py:currentmodule:: aare
.. autoclass:: CtbRawFile
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

11
docs/src/pyFile.rst Normal file
View File

@@ -0,0 +1,11 @@
File
========
.. py:currentmodule:: aare
.. autoclass:: File
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

19
docs/src/pyFit.rst Normal file
View File

@@ -0,0 +1,19 @@
Fit
========
.. py:currentmodule:: aare
**Functions**
.. autofunction:: gaus
.. autofunction:: pol1
**Fitting**
.. autofunction:: fit_gaus
.. autofunction:: fit_pol1

View File

@@ -0,0 +1,10 @@
JungfrauDataFile
===================
.. py:currentmodule:: aare
.. autoclass:: JungfrauDataFile
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

10
docs/src/pyRawFile.rst Normal file
View File

@@ -0,0 +1,10 @@
RawFile
===================
.. py:currentmodule:: aare
.. autoclass:: RawFile
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

View File

@@ -0,0 +1,10 @@
RawMasterFile
===================
.. py:currentmodule:: aare
.. autoclass:: RawMasterFile
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

View File

@@ -0,0 +1,10 @@
VarClusterFinder
===================
.. py:currentmodule:: aare
.. autoclass:: VarClusterFinder
:members:
:undoc-members:
:show-inheritance:
:inherited-members:

4
docs/static/extra.css vendored Normal file
View File

@@ -0,0 +1,4 @@
/* override table no-wrap */
.wy-table-responsive table td, .wy-table-responsive table th {
white-space: normal;
}

15
etc/dev-env.yml Normal file
View File

@@ -0,0 +1,15 @@
name: dev-environment
channels:
- conda-forge
dependencies:
- anaconda-client
- doxygen
- sphinx=7.1.2
- breathe
- pybind11
- sphinx_rtd_theme
- furo
- nlohmann_json
- zeromq
- fmt
- numpy

View File

@@ -0,0 +1,99 @@
#pragma once
#include <cstdint> //int64_t
#include <cstddef> //size_t
#include <array>
#include <cassert>
namespace aare {
template <typename E, int64_t Ndim> class ArrayExpr {
public:
static constexpr bool is_leaf = false;
auto operator[](size_t i) const { return static_cast<E const &>(*this)[i]; }
auto operator()(size_t i) const { return static_cast<E const &>(*this)[i]; }
auto size() const { return static_cast<E const &>(*this).size(); }
std::array<int64_t, Ndim> shape() const { return static_cast<E const &>(*this).shape(); }
};
template <typename A, typename B, int64_t Ndim>
class ArrayAdd : public ArrayExpr<ArrayAdd<A, B, Ndim>, Ndim> {
const A &arr1_;
const B &arr2_;
public:
ArrayAdd(const A &arr1, const B &arr2) : arr1_(arr1), arr2_(arr2) {
assert(arr1.size() == arr2.size());
}
auto operator[](int i) const { return arr1_[i] + arr2_[i]; }
size_t size() const { return arr1_.size(); }
std::array<int64_t, Ndim> shape() const { return arr1_.shape(); }
};
template <typename A, typename B, int64_t Ndim>
class ArraySub : public ArrayExpr<ArraySub<A, B, Ndim>, Ndim> {
const A &arr1_;
const B &arr2_;
public:
ArraySub(const A &arr1, const B &arr2) : arr1_(arr1), arr2_(arr2) {
assert(arr1.size() == arr2.size());
}
auto operator[](int i) const { return arr1_[i] - arr2_[i]; }
size_t size() const { return arr1_.size(); }
std::array<int64_t, Ndim> shape() const { return arr1_.shape(); }
};
template <typename A, typename B, int64_t Ndim>
class ArrayMul : public ArrayExpr<ArrayMul<A, B, Ndim>,Ndim> {
const A &arr1_;
const B &arr2_;
public:
ArrayMul(const A &arr1, const B &arr2) : arr1_(arr1), arr2_(arr2) {
assert(arr1.size() == arr2.size());
}
auto operator[](int i) const { return arr1_[i] * arr2_[i]; }
size_t size() const { return arr1_.size(); }
std::array<int64_t, Ndim> shape() const { return arr1_.shape(); }
};
template <typename A, typename B, int64_t Ndim>
class ArrayDiv : public ArrayExpr<ArrayDiv<A, B, Ndim>, Ndim> {
const A &arr1_;
const B &arr2_;
public:
ArrayDiv(const A &arr1, const B &arr2) : arr1_(arr1), arr2_(arr2) {
assert(arr1.size() == arr2.size());
}
auto operator[](int i) const { return arr1_[i] / arr2_[i]; }
size_t size() const { return arr1_.size(); }
std::array<int64_t, Ndim> shape() const { return arr1_.shape(); }
};
template <typename A, typename B, int64_t Ndim>
auto operator+(const ArrayExpr<A, Ndim> &arr1, const ArrayExpr<B, Ndim> &arr2) {
return ArrayAdd<ArrayExpr<A, Ndim>, ArrayExpr<B, Ndim>, Ndim>(arr1, arr2);
}
template <typename A, typename B, int64_t Ndim>
auto operator-(const ArrayExpr<A,Ndim> &arr1, const ArrayExpr<B, Ndim> &arr2) {
return ArraySub<ArrayExpr<A, Ndim>, ArrayExpr<B, Ndim>, Ndim>(arr1, arr2);
}
template <typename A, typename B, int64_t Ndim>
auto operator*(const ArrayExpr<A, Ndim> &arr1, const ArrayExpr<B, Ndim> &arr2) {
return ArrayMul<ArrayExpr<A, Ndim>, ArrayExpr<B, Ndim>, Ndim>(arr1, arr2);
}
template <typename A, typename B, int64_t Ndim>
auto operator/(const ArrayExpr<A, Ndim> &arr1, const ArrayExpr<B, Ndim> &arr2) {
return ArrayDiv<ArrayExpr<A, Ndim>, ArrayExpr<B, Ndim>, Ndim>(arr1, arr2);
}
} // namespace aare

View File

@@ -0,0 +1,122 @@
#pragma once
#include "aare/Cluster.hpp"
#include "aare/ClusterVector.hpp"
#include "aare/NDArray.hpp"
namespace aare {
typedef enum {
cBottomLeft = 0,
cBottomRight = 1,
cTopLeft = 2,
cTopRight = 3
} corner;
typedef enum {
pBottomLeft = 0,
pBottom = 1,
pBottomRight = 2,
pLeft = 3,
pCenter = 4,
pRight = 5,
pTopLeft = 6,
pTop = 7,
pTopRight = 8
} pixel;
template <typename T> struct Eta2 {
double x;
double y;
int c;
T sum;
};
/**
* @brief Calculate the eta2 values for all clusters in a Clsutervector
*/
template <typename ClusterType,
typename = std::enable_if_t<is_cluster_v<ClusterType>>>
NDArray<double, 2> calculate_eta2(const ClusterVector<ClusterType> &clusters) {
NDArray<double, 2> eta2({static_cast<int64_t>(clusters.size()), 2});
for (size_t i = 0; i < clusters.size(); i++) {
auto e = calculate_eta2(clusters.at(i));
eta2(i, 0) = e.x;
eta2(i, 1) = e.y;
}
return eta2;
}
/**
* @brief Calculate the eta2 values for a generic sized cluster and return them
* in a Eta2 struct containing etay, etax and the index of the respective 2x2
* subcluster.
*/
template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
typename CoordType>
Eta2<T>
calculate_eta2(const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &cl) {
Eta2<T> eta{};
auto max_sum = cl.max_sum_2x2();
eta.sum = max_sum.first;
auto c = max_sum.second;
size_t index_bottom_left_max_2x2_subcluster =
(int(c / (ClusterSizeX - 1))) * ClusterSizeX + c % (ClusterSizeX - 1);
if ((cl.data[index_bottom_left_max_2x2_subcluster] +
cl.data[index_bottom_left_max_2x2_subcluster + 1]) != 0)
eta.x = static_cast<double>(
cl.data[index_bottom_left_max_2x2_subcluster + 1]) /
static_cast<double>(
(cl.data[index_bottom_left_max_2x2_subcluster] +
cl.data[index_bottom_left_max_2x2_subcluster + 1]));
if ((cl.data[index_bottom_left_max_2x2_subcluster] +
cl.data[index_bottom_left_max_2x2_subcluster + ClusterSizeX]) != 0)
eta.y =
static_cast<double>(
cl.data[index_bottom_left_max_2x2_subcluster + ClusterSizeX]) /
static_cast<double>(
(cl.data[index_bottom_left_max_2x2_subcluster] +
cl.data[index_bottom_left_max_2x2_subcluster + ClusterSizeX]));
eta.c = c; // TODO only supported for 2x2 and 3x3 clusters -> at least no
// underyling enum class
return eta;
}
// calculates Eta3 for 3x3 cluster based on code from analyze_cluster
// TODO only supported for 3x3 Clusters
template <typename T> Eta2<T> calculate_eta3(const Cluster<T, 3, 3> &cl) {
Eta2<T> eta{};
T sum = 0;
std::for_each(std::begin(cl.data), std::end(cl.data),
[&sum](T x) { sum += x; });
eta.sum = sum;
eta.c = corner::cBottomLeft;
if ((cl.data[3] + cl.data[4] + cl.data[5]) != 0)
eta.x = static_cast<double>(-cl.data[3] + cl.data[3 + 2]) /
(cl.data[3] + cl.data[4] + cl.data[5]);
if ((cl.data[1] + cl.data[4] + cl.data[7]) != 0)
eta.y = static_cast<double>(-cl.data[1] + cl.data[2 * 3 + 1]) /
(cl.data[1] + cl.data[4] + cl.data[7]);
return eta;
}
} // namespace aare

View File

@@ -0,0 +1,97 @@
#pragma once
#include <chrono>
#include <fmt/color.h>
#include <fmt/format.h>
#include <memory>
#include <thread>
#include "aare/ProducerConsumerQueue.hpp"
namespace aare {
template <class ItemType> class CircularFifo {
uint32_t fifo_size;
aare::ProducerConsumerQueue<ItemType> free_slots;
aare::ProducerConsumerQueue<ItemType> filled_slots;
public:
CircularFifo() : CircularFifo(100){};
CircularFifo(uint32_t size) : fifo_size(size), free_slots(size + 1), filled_slots(size + 1) {
// TODO! how do we deal with alignment for writing? alignas???
// Do we give the user a chance to provide memory locations?
// Templated allocator?
for (size_t i = 0; i < fifo_size; ++i) {
free_slots.write(ItemType{});
}
}
bool next() {
// TODO! avoid default constructing ItemType
ItemType it;
if (!filled_slots.read(it))
return false;
if (!free_slots.write(std::move(it)))
return false;
return true;
}
~CircularFifo() {}
using value_type = ItemType;
auto numFilledSlots() const noexcept { return filled_slots.sizeGuess(); }
auto numFreeSlots() const noexcept { return free_slots.sizeGuess(); }
auto isFull() const noexcept { return filled_slots.isFull(); }
ItemType pop_free() {
ItemType v;
while (!free_slots.read(v))
;
return std::move(v);
// return v;
}
bool try_pop_free(ItemType &v) { return free_slots.read(v); }
ItemType pop_value(std::chrono::nanoseconds wait, std::atomic<bool> &stopped) {
ItemType v;
while (!filled_slots.read(v) && !stopped) {
std::this_thread::sleep_for(wait);
}
return std::move(v);
}
ItemType pop_value() {
ItemType v;
while (!filled_slots.read(v))
;
return std::move(v);
}
ItemType *frontPtr() { return filled_slots.frontPtr(); }
// TODO! Add function to move item from filled to free to be used
// with the frontPtr function
template <class... Args> void push_value(Args &&...recordArgs) {
while (!filled_slots.write(std::forward<Args>(recordArgs)...))
;
}
template <class... Args> bool try_push_value(Args &&...recordArgs) {
return filled_slots.write(std::forward<Args>(recordArgs)...);
}
template <class... Args> void push_free(Args &&...recordArgs) {
while (!free_slots.write(std::forward<Args>(recordArgs)...))
;
}
template <class... Args> bool try_push_free(Args &&...recordArgs) {
return free_slots.write(std::forward<Args>(recordArgs)...);
}
};
} // namespace aare

121
include/aare/Cluster.hpp Normal file
View File

@@ -0,0 +1,121 @@
/************************************************
* @file Cluster.hpp
* @short definition of cluster, where CoordType (x,y) give
* the cluster center coordinates and data the actual cluster data
* cluster size is given as template parameters
***********************************************/
#pragma once
#include <algorithm>
#include <array>
#include <cstdint>
#include <numeric>
#include <type_traits>
namespace aare {
template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
typename CoordType = int16_t>
constexpr bool is_valid_cluster =
std::is_arithmetic_v<T> && std::is_integral_v<CoordType> &&
(ClusterSizeX > 0) && (ClusterSizeY > 0);
// requires clause c++20 maybe update
template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
typename CoordType = int16_t,
typename Enable = std::enable_if_t<
is_valid_cluster<T, ClusterSizeX, ClusterSizeY, CoordType>>>
struct Cluster {
CoordType x;
CoordType y;
T data[ClusterSizeX * ClusterSizeY];
T sum() const {
return std::accumulate(data, data + ClusterSizeX * ClusterSizeY, 0);
}
std::pair<T, int> max_sum_2x2() const {
constexpr size_t num_2x2_subclusters =
(ClusterSizeX - 1) * (ClusterSizeY - 1);
std::array<T, num_2x2_subclusters> sum_2x2_subcluster;
for (size_t i = 0; i < ClusterSizeY - 1; ++i) {
for (size_t j = 0; j < ClusterSizeX - 1; ++j)
sum_2x2_subcluster[i * (ClusterSizeX - 1) + j] =
data[i * ClusterSizeX + j] +
data[i * ClusterSizeX + j + 1] +
data[(i + 1) * ClusterSizeX + j] +
data[(i + 1) * ClusterSizeX + j + 1];
}
int index = std::max_element(sum_2x2_subcluster.begin(),
sum_2x2_subcluster.end()) -
sum_2x2_subcluster.begin();
return std::make_pair(sum_2x2_subcluster[index], index);
}
};
// Specialization for 2x2 clusters (only one sum exists)
template <typename T> struct Cluster<T, 2, 2, int16_t> {
int16_t x;
int16_t y;
T data[4];
T sum() const { return std::accumulate(data, data + 4, 0); }
std::pair<T, int> max_sum_2x2() const {
return std::make_pair(data[0] + data[1] + data[2] + data[3],
0); // Only one possible 2x2 sum
}
};
// Specialization for 3x3 clusters
template <typename T> struct Cluster<T, 3, 3, int16_t> {
int16_t x;
int16_t y;
T data[9];
T sum() const { return std::accumulate(data, data + 9, 0); }
std::pair<T, int> max_sum_2x2() const {
std::array<T, 4> sum_2x2_subclusters;
sum_2x2_subclusters[0] = data[0] + data[1] + data[3] + data[4];
sum_2x2_subclusters[1] = data[1] + data[2] + data[4] + data[5];
sum_2x2_subclusters[2] = data[3] + data[4] + data[6] + data[7];
sum_2x2_subclusters[3] = data[4] + data[5] + data[7] + data[8];
int index = std::max_element(sum_2x2_subclusters.begin(),
sum_2x2_subclusters.end()) -
sum_2x2_subclusters.begin();
return std::make_pair(sum_2x2_subclusters[index], index);
}
};
// Type Traits for is_cluster_type
template <typename T>
struct is_cluster : std::false_type {}; // Default case: Not a Cluster
template <typename T, uint8_t X, uint8_t Y, typename CoordType>
struct is_cluster<Cluster<T, X, Y, CoordType>> : std::true_type {}; // Cluster
template <typename T> constexpr bool is_cluster_v = is_cluster<T>::value;
template <typename ClusterType,
typename = std::enable_if_t<is_cluster_v<ClusterType>>>
struct extract_template_arguments; // Forward declaration
// helper struct to extract template argument
template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
typename CoordType>
struct extract_template_arguments<
Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>> {
using value_type = T;
static constexpr int cluster_size_x = ClusterSizeX;
static constexpr int cluster_size_y = ClusterSizeY;
using coordtype = CoordType;
};
} // namespace aare

View File

@@ -0,0 +1,54 @@
#pragma once
#include <atomic>
#include <thread>
#include "aare/ClusterFinderMT.hpp"
#include "aare/ClusterVector.hpp"
#include "aare/ProducerConsumerQueue.hpp"
namespace aare {
template <typename ClusterType,
typename = std::enable_if_t<is_cluster_v<ClusterType>>>
class ClusterCollector {
ProducerConsumerQueue<ClusterVector<ClusterType>> *m_source;
std::atomic<bool> m_stop_requested{false};
std::atomic<bool> m_stopped{true};
std::chrono::milliseconds m_default_wait{1};
std::thread m_thread;
std::vector<ClusterVector<ClusterType>> m_clusters;
void process() {
m_stopped = false;
fmt::print("ClusterCollector started\n");
while (!m_stop_requested || !m_source->isEmpty()) {
if (ClusterVector<ClusterType> *clusters = m_source->frontPtr();
clusters != nullptr) {
m_clusters.push_back(std::move(*clusters));
m_source->popFront();
} else {
std::this_thread::sleep_for(m_default_wait);
}
}
fmt::print("ClusterCollector stopped\n");
m_stopped = true;
}
public:
ClusterCollector(ClusterFinderMT<ClusterType, uint16_t, double> *source) {
m_source = source->sink();
m_thread = std::thread(&ClusterCollector::process, this);
}
void stop() {
m_stop_requested = true;
m_thread.join();
}
std::vector<ClusterVector<ClusterType>> steal_clusters() {
if (!m_stopped) {
throw std::runtime_error("ClusterCollector is still running");
}
return std::move(m_clusters);
}
};
} // namespace aare

View File

@@ -0,0 +1,454 @@
#pragma once
#include "aare/Cluster.hpp"
#include "aare/ClusterVector.hpp"
#include "aare/GainMap.hpp"
#include "aare/NDArray.hpp"
#include "aare/defs.hpp"
#include <filesystem>
#include <fstream>
#include <optional>
namespace aare {
/*
Binary cluster file. Expects data to be layed out as:
int32_t frame_number
uint32_t number_of_clusters
int16_t x, int16_t y, int32_t data[9] x number_of_clusters
int32_t frame_number
uint32_t number_of_clusters
....
*/
// TODO: change to support any type of clusters, e.g. header line with
// clsuter_size_x, cluster_size_y,
/**
* @brief Class to read and write cluster files
* Expects data to be laid out as:
*
*
* int32_t frame_number
* uint32_t number_of_clusters
* int16_t x, int16_t y, int32_t data[9] * number_of_clusters
* int32_t frame_number
* uint32_t number_of_clusters
* etc.
*/
template <typename ClusterType,
typename Enable = std::enable_if_t<is_cluster_v<ClusterType>>>
class ClusterFile {
FILE *fp{};
uint32_t m_num_left{}; /*Number of photons left in frame*/
size_t m_chunk_size{}; /*Number of clusters to read at a time*/
const std::string m_mode; /*Mode to open the file in*/
std::optional<ROI> m_roi; /*Region of interest, will be applied if set*/
std::optional<NDArray<int32_t, 2>>
m_noise_map; /*Noise map to cut photons, will be applied if set*/
std::optional<GainMap> m_gain_map; /*Gain map to apply to the clusters, will
be applied if set*/
public:
/**
* @brief Construct a new Cluster File object
* @param fname path to the file
* @param chunk_size number of clusters to read at a time when iterating
* over the file
* @param mode mode to open the file in. "r" for reading, "w" for writing,
* "a" for appending
* @throws std::runtime_error if the file could not be opened
*/
ClusterFile(const std::filesystem::path &fname, size_t chunk_size = 1000,
const std::string &mode = "r");
~ClusterFile();
/**
* @brief Read n_clusters clusters from the file discarding frame numbers.
* If EOF is reached the returned vector will have less than n_clusters
* clusters
*/
ClusterVector<ClusterType> read_clusters(size_t n_clusters);
/**
* @brief Read a single frame from the file and return the clusters. The
* cluster vector will have the frame number set.
* @throws std::runtime_error if the file is not opened for reading or the
* file pointer not at the beginning of a frame
*/
ClusterVector<ClusterType> read_frame();
void write_frame(const ClusterVector<ClusterType> &clusters);
/**
* @brief Return the chunk size
*/
size_t chunk_size() const { return m_chunk_size; }
/**
* @brief Set the region of interest to use when reading clusters. If set
* only clusters within the ROI will be read.
*/
void set_roi(ROI roi);
/**
* @brief Set the noise map to use when reading clusters. If set clusters
* below the noise level will be discarded. Selection criteria one of:
* Central pixel above noise, highest 2x2 sum above 2 * noise, total sum
* above 3 * noise.
*/
void set_noise_map(const NDView<int32_t, 2> noise_map);
/**
* @brief Set the gain map to use when reading clusters. If set the gain map
* will be applied to the clusters that pass ROI and noise_map selection.
*/
void set_gain_map(const NDView<double, 2> gain_map);
void set_gain_map(const GainMap &gain_map);
void set_gain_map(const GainMap &&gain_map);
/**
* @brief Close the file. If not closed the file will be closed in the
* destructor
*/
void close();
private:
ClusterVector<ClusterType> read_clusters_with_cut(size_t n_clusters);
ClusterVector<ClusterType> read_clusters_without_cut(size_t n_clusters);
ClusterVector<ClusterType> read_frame_with_cut();
ClusterVector<ClusterType> read_frame_without_cut();
bool is_selected(ClusterType &cl);
ClusterType read_one_cluster();
};
template <typename ClusterType, typename Enable>
ClusterFile<ClusterType, Enable>::ClusterFile(
const std::filesystem::path &fname, size_t chunk_size,
const std::string &mode)
: m_chunk_size(chunk_size), m_mode(mode) {
if (mode == "r") {
fp = fopen(fname.c_str(), "rb");
if (!fp) {
throw std::runtime_error("Could not open file for reading: " +
fname.string());
}
} else if (mode == "w") {
fp = fopen(fname.c_str(), "wb");
if (!fp) {
throw std::runtime_error("Could not open file for writing: " +
fname.string());
}
} else if (mode == "a") {
fp = fopen(fname.c_str(), "ab");
if (!fp) {
throw std::runtime_error("Could not open file for appending: " +
fname.string());
}
} else {
throw std::runtime_error("Unsupported mode: " + mode);
}
}
template <typename ClusterType, typename Enable>
ClusterFile<ClusterType, Enable>::~ClusterFile() {
close();
}
template <typename ClusterType, typename Enable>
void ClusterFile<ClusterType, Enable>::close() {
if (fp) {
fclose(fp);
fp = nullptr;
}
}
template <typename ClusterType, typename Enable>
void ClusterFile<ClusterType, Enable>::set_roi(ROI roi) {
m_roi = roi;
}
template <typename ClusterType, typename Enable>
void ClusterFile<ClusterType, Enable>::set_noise_map(
const NDView<int32_t, 2> noise_map) {
m_noise_map = NDArray<int32_t, 2>(noise_map);
}
template <typename ClusterType, typename Enable>
void ClusterFile<ClusterType, Enable>::set_gain_map(
const NDView<double, 2> gain_map) {
m_gain_map = GainMap(gain_map);
}
template <typename ClusterType, typename Enable>
void ClusterFile<ClusterType, Enable>::set_gain_map(const GainMap &gain_map) {
m_gain_map = gain_map;
}
template <typename ClusterType, typename Enable>
void ClusterFile<ClusterType, Enable>::set_gain_map(const GainMap &&gain_map) {
m_gain_map = gain_map;
}
// TODO generally supported for all clsuter types
template <typename ClusterType, typename Enable>
void ClusterFile<ClusterType, Enable>::write_frame(
const ClusterVector<ClusterType> &clusters) {
if (m_mode != "w" && m_mode != "a") {
throw std::runtime_error("File not opened for writing");
}
if (!(clusters.cluster_size_x() == 3) &&
!(clusters.cluster_size_y() == 3)) {
throw std::runtime_error("Only 3x3 clusters are supported");
}
int32_t frame_number = clusters.frame_number();
fwrite(&frame_number, sizeof(frame_number), 1, fp);
uint32_t n_clusters = clusters.size();
fwrite(&n_clusters, sizeof(n_clusters), 1, fp);
fwrite(clusters.data(), clusters.item_size(), clusters.size(), fp);
}
template <typename ClusterType, typename Enable>
ClusterVector<ClusterType>
ClusterFile<ClusterType, Enable>::read_clusters(size_t n_clusters) {
if (m_mode != "r") {
throw std::runtime_error("File not opened for reading");
}
if (m_noise_map || m_roi) {
return read_clusters_with_cut(n_clusters);
} else {
return read_clusters_without_cut(n_clusters);
}
}
template <typename ClusterType, typename Enable>
ClusterVector<ClusterType>
ClusterFile<ClusterType, Enable>::read_clusters_without_cut(size_t n_clusters) {
if (m_mode != "r") {
throw std::runtime_error("File not opened for reading");
}
ClusterVector<ClusterType> clusters(n_clusters);
clusters.resize(n_clusters);
int32_t iframe = 0; // frame number needs to be 4 bytes!
size_t nph_read = 0;
uint32_t nn = m_num_left;
uint32_t nph = m_num_left; // number of clusters in frame needs to be 4
auto buf = clusters.data();
// if there are photons left from previous frame read them first
if (nph) {
if (nph > n_clusters) {
// if we have more photons left in the frame then photons to read we
// read directly the requested number
nn = n_clusters;
} else {
nn = nph;
}
nph_read += fread((buf + nph_read), clusters.item_size(), nn, fp);
m_num_left = nph - nn; // write back the number of photons left
}
if (nph_read < n_clusters) {
// keep on reading frames and photons until reaching n_clusters
while (fread(&iframe, sizeof(iframe), 1, fp)) {
clusters.set_frame_number(iframe);
// read number of clusters in frame
if (fread(&nph, sizeof(nph), 1, fp)) {
if (nph > (n_clusters - nph_read))
nn = n_clusters - nph_read;
else
nn = nph;
nph_read +=
fread((buf + nph_read), clusters.item_size(), nn, fp);
m_num_left = nph - nn;
}
if (nph_read >= n_clusters)
break;
}
}
// Resize the vector to the number of clusters.
// No new allocation, only change bounds.
clusters.resize(nph_read);
if (m_gain_map)
m_gain_map->apply_gain_map(clusters);
return clusters;
}
template <typename ClusterType, typename Enable>
ClusterVector<ClusterType>
ClusterFile<ClusterType, Enable>::read_clusters_with_cut(size_t n_clusters) {
ClusterVector<ClusterType> clusters;
clusters.resize(n_clusters);
// if there are photons left from previous frame read them first
if (m_num_left) {
while (m_num_left && clusters.size() < n_clusters) {
ClusterType c = read_one_cluster();
if (is_selected(c)) {
clusters.push_back(c);
}
}
}
// we did not have enough clusters left in the previous frame
// keep on reading frames until reaching n_clusters
if (clusters.size() < n_clusters) {
// sanity check
if (m_num_left) {
throw std::runtime_error(
LOCATION + "Entered second loop with clusters left\n");
}
int32_t frame_number = 0; // frame number needs to be 4 bytes!
while (fread(&frame_number, sizeof(frame_number), 1, fp)) {
if (fread(&m_num_left, sizeof(m_num_left), 1, fp)) {
clusters.set_frame_number(
frame_number); // cluster vector will hold the last frame
// number
while (m_num_left && clusters.size() < n_clusters) {
ClusterType c = read_one_cluster();
if (is_selected(c)) {
clusters.push_back(c);
}
}
}
// we have enough clusters, break out of the outer while loop
if (clusters.size() >= n_clusters)
break;
}
}
if (m_gain_map)
m_gain_map->apply_gain_map(clusters);
return clusters;
}
template <typename ClusterType, typename Enable>
ClusterType ClusterFile<ClusterType, Enable>::read_one_cluster() {
ClusterType c;
auto rc = fread(&c, sizeof(c), 1, fp);
if (rc != 1) {
throw std::runtime_error(LOCATION + "Could not read cluster");
}
--m_num_left;
return c;
}
template <typename ClusterType, typename Enable>
ClusterVector<ClusterType> ClusterFile<ClusterType, Enable>::read_frame() {
if (m_mode != "r") {
throw std::runtime_error(LOCATION + "File not opened for reading");
}
if (m_noise_map || m_roi) {
return read_frame_with_cut();
} else {
return read_frame_without_cut();
}
}
template <typename ClusterType, typename Enable>
ClusterVector<ClusterType>
ClusterFile<ClusterType, Enable>::read_frame_without_cut() {
if (m_mode != "r") {
throw std::runtime_error("File not opened for reading");
}
if (m_num_left) {
throw std::runtime_error(
"There are still photons left in the last frame");
}
int32_t frame_number;
if (fread(&frame_number, sizeof(frame_number), 1, fp) != 1) {
throw std::runtime_error(LOCATION + "Could not read frame number");
}
int32_t n_clusters; // Saved as 32bit integer in the cluster file
if (fread(&n_clusters, sizeof(n_clusters), 1, fp) != 1) {
throw std::runtime_error(LOCATION +
"Could not read number of clusters");
}
ClusterVector<ClusterType> clusters(n_clusters);
clusters.set_frame_number(frame_number);
if (fread(clusters.data(), clusters.item_size(), n_clusters, fp) !=
static_cast<size_t>(n_clusters)) {
throw std::runtime_error(LOCATION + "Could not read clusters");
}
clusters.resize(n_clusters);
if (m_gain_map)
m_gain_map->apply_gain_map(clusters);
return clusters;
}
template <typename ClusterType, typename Enable>
ClusterVector<ClusterType>
ClusterFile<ClusterType, Enable>::read_frame_with_cut() {
if (m_mode != "r") {
throw std::runtime_error("File not opened for reading");
}
if (m_num_left) {
throw std::runtime_error(
"There are still photons left in the last frame");
}
int32_t frame_number;
if (fread(&frame_number, sizeof(frame_number), 1, fp) != 1) {
throw std::runtime_error("Could not read frame number");
}
if (fread(&m_num_left, sizeof(m_num_left), 1, fp) != 1) {
throw std::runtime_error("Could not read number of clusters");
}
ClusterVector<ClusterType> clusters;
clusters.reserve(m_num_left);
clusters.set_frame_number(frame_number);
while (m_num_left) {
ClusterType c = read_one_cluster();
if (is_selected(c)) {
clusters.push_back(c);
}
}
if (m_gain_map)
m_gain_map->apply_gain_map(clusters);
return clusters;
}
template <typename ClusterType, typename Enable>
bool ClusterFile<ClusterType, Enable>::is_selected(ClusterType &cl) {
// Should fail fast
if (m_roi) {
if (!(m_roi->contains(cl.x, cl.y))) {
return false;
}
}
auto cluster_size_x = extract_template_arguments<
std::remove_reference_t<decltype(cl)>>::cluster_size_x;
auto cluster_size_y = extract_template_arguments<
std::remove_reference_t<decltype(cl)>>::cluster_size_y;
size_t cluster_center_index =
(cluster_size_x / 2) + (cluster_size_y / 2) * cluster_size_x;
if (m_noise_map) {
auto sum_1x1 = cl.data[cluster_center_index]; // central pixel
auto sum_2x2 = cl.max_sum_2x2().first; // highest sum of 2x2 subclusters
auto total_sum = cl.sum(); // sum of all pixels
auto noise =
(*m_noise_map)(cl.y, cl.x); // TODO! check if this is correct
if (sum_1x1 <= noise || sum_2x2 <= 2 * noise ||
total_sum <= 3 * noise) {
return false;
}
}
// we passed all checks
return true;
}
} // namespace aare

View File

@@ -0,0 +1,62 @@
#pragma once
#include <atomic>
#include <filesystem>
#include <thread>
#include "aare/ClusterFinderMT.hpp"
#include "aare/ClusterVector.hpp"
#include "aare/ProducerConsumerQueue.hpp"
namespace aare {
template <typename ClusterType,
typename = std::enable_if_t<is_cluster_v<ClusterType>>>
class ClusterFileSink {
ProducerConsumerQueue<ClusterVector<ClusterType>> *m_source;
std::atomic<bool> m_stop_requested{false};
std::atomic<bool> m_stopped{true};
std::chrono::milliseconds m_default_wait{1};
std::thread m_thread;
std::ofstream m_file;
void process() {
m_stopped = false;
fmt::print("ClusterFileSink started\n");
while (!m_stop_requested || !m_source->isEmpty()) {
if (ClusterVector<ClusterType> *clusters = m_source->frontPtr();
clusters != nullptr) {
// Write clusters to file
int32_t frame_number =
clusters->frame_number(); // TODO! Should we store frame
// number already as int?
uint32_t num_clusters = clusters->size();
m_file.write(reinterpret_cast<const char *>(&frame_number),
sizeof(frame_number));
m_file.write(reinterpret_cast<const char *>(&num_clusters),
sizeof(num_clusters));
m_file.write(reinterpret_cast<const char *>(clusters->data()),
clusters->size() * clusters->item_size());
m_source->popFront();
} else {
std::this_thread::sleep_for(m_default_wait);
}
}
fmt::print("ClusterFileSink stopped\n");
m_stopped = true;
}
public:
ClusterFileSink(ClusterFinderMT<ClusterType, uint16_t, double> *source,
const std::filesystem::path &fname) {
m_source = source->sink();
m_thread = std::thread(&ClusterFileSink::process, this);
m_file.open(fname, std::ios::binary);
}
void stop() {
m_stop_requested = true;
m_thread.join();
m_file.close();
}
};
} // namespace aare

View File

@@ -0,0 +1,154 @@
#pragma once
#include "aare/core/defs.hpp"
#include <filesystem>
#include <fmt/format.h>
#include <string>
namespace aare {
struct ClusterHeader {
int32_t frame_number;
int32_t n_clusters;
std::string to_string() const {
return "frame_number: " + std::to_string(frame_number) +
", n_clusters: " + std::to_string(n_clusters);
}
};
struct ClusterV2_ {
int16_t x;
int16_t y;
std::array<int32_t, 9> data;
std::string to_string(bool detailed = false) const {
if (detailed) {
std::string data_str = "[";
for (auto &d : data) {
data_str += std::to_string(d) + ", ";
}
data_str += "]";
return "x: " + std::to_string(x) + ", y: " + std::to_string(y) +
", data: " + data_str;
}
return "x: " + std::to_string(x) + ", y: " + std::to_string(y);
}
};
struct ClusterV2 {
ClusterV2_ cluster;
int32_t frame_number;
std::string to_string() const {
return "frame_number: " + std::to_string(frame_number) + ", " +
cluster.to_string();
}
};
/**
* @brief
* important not: fp always points to the clusters header and does not point to
* individual clusters
*
*/
class ClusterFileV2 {
std::filesystem::path m_fpath;
std::string m_mode;
FILE *fp{nullptr};
void check_open() {
if (!fp)
throw std::runtime_error(
fmt::format("File: {} not open", m_fpath.string()));
}
public:
ClusterFileV2(std::filesystem::path const &fpath, std::string const &mode)
: m_fpath(fpath), m_mode(mode) {
if (m_mode != "r" && m_mode != "w")
throw std::invalid_argument("mode must be 'r' or 'w'");
if (m_mode == "r" && !std::filesystem::exists(m_fpath))
throw std::invalid_argument("File does not exist");
if (mode == "r") {
fp = fopen(fpath.string().c_str(), "rb");
} else if (mode == "w") {
if (std::filesystem::exists(fpath)) {
fp = fopen(fpath.string().c_str(), "r+b");
} else {
fp = fopen(fpath.string().c_str(), "wb");
}
}
if (fp == nullptr) {
throw std::runtime_error("Failed to open file");
}
}
~ClusterFileV2() { close(); }
std::vector<ClusterV2> read() {
check_open();
ClusterHeader header;
fread(&header, sizeof(ClusterHeader), 1, fp);
std::vector<ClusterV2_> clusters_(header.n_clusters);
fread(clusters_.data(), sizeof(ClusterV2_), header.n_clusters, fp);
std::vector<ClusterV2> clusters;
for (auto &c : clusters_) {
ClusterV2 cluster;
cluster.cluster = std::move(c);
cluster.frame_number = header.frame_number;
clusters.push_back(cluster);
}
return clusters;
}
std::vector<std::vector<ClusterV2>> read(int n_frames) {
std::vector<std::vector<ClusterV2>> clusters;
for (int i = 0; i < n_frames; i++) {
clusters.push_back(read());
}
return clusters;
}
size_t write(std::vector<ClusterV2> const &clusters) {
check_open();
if (m_mode != "w")
throw std::runtime_error("File not opened in write mode");
if (clusters.empty())
return 0;
ClusterHeader header;
header.frame_number = clusters[0].frame_number;
header.n_clusters = clusters.size();
fwrite(&header, sizeof(ClusterHeader), 1, fp);
for (auto &c : clusters) {
fwrite(&c.cluster, sizeof(ClusterV2_), 1, fp);
}
return clusters.size();
}
size_t write(std::vector<std::vector<ClusterV2>> const &clusters) {
check_open();
if (m_mode != "w")
throw std::runtime_error("File not opened in write mode");
size_t n_clusters = 0;
for (auto &c : clusters) {
n_clusters += write(c);
}
return n_clusters;
}
int seek_to_begin() { return fseek(fp, 0, SEEK_SET); }
int seek_to_end() { return fseek(fp, 0, SEEK_END); }
int32_t frame_number() {
auto pos = ftell(fp);
ClusterHeader header;
fread(&header, sizeof(ClusterHeader), 1, fp);
fseek(fp, pos, SEEK_SET);
return header.frame_number;
}
void close() {
if (fp) {
fclose(fp);
fp = nullptr;
}
}
};
} // namespace aare

View File

@@ -0,0 +1,164 @@
#pragma once
#include "aare/ClusterFile.hpp"
#include "aare/ClusterVector.hpp"
#include "aare/Dtype.hpp"
#include "aare/NDArray.hpp"
#include "aare/NDView.hpp"
#include "aare/Pedestal.hpp"
#include "aare/defs.hpp"
#include <cstddef>
namespace aare {
template <typename ClusterType = Cluster<int32_t, 3, 3>,
typename FRAME_TYPE = uint16_t, typename PEDESTAL_TYPE = double>
class ClusterFinder {
Shape<2> m_image_size;
const PEDESTAL_TYPE m_nSigma;
const PEDESTAL_TYPE c2;
const PEDESTAL_TYPE c3;
Pedestal<PEDESTAL_TYPE> m_pedestal;
ClusterVector<ClusterType> m_clusters;
static const uint8_t ClusterSizeX =
extract_template_arguments<ClusterType>::cluster_size_x;
static const uint8_t ClusterSizeY =
extract_template_arguments<ClusterType>::cluster_size_x;
using CT = typename extract_template_arguments<ClusterType>::value_type;
public:
/**
* @brief Construct a new ClusterFinder object
* @param image_size size of the image
* @param cluster_size size of the cluster (x, y)
* @param nSigma number of sigma above the pedestal to consider a photon
* @param capacity initial capacity of the cluster vector
*
*/
ClusterFinder(Shape<2> image_size, PEDESTAL_TYPE nSigma = 5.0,
size_t capacity = 1000000)
: m_image_size(image_size), m_nSigma(nSigma),
c2(sqrt((ClusterSizeY + 1) / 2 * (ClusterSizeX + 1) / 2)),
c3(sqrt(ClusterSizeX * ClusterSizeY)),
m_pedestal(image_size[0], image_size[1]), m_clusters(capacity) {};
void push_pedestal_frame(NDView<FRAME_TYPE, 2> frame) {
m_pedestal.push(frame);
}
NDArray<PEDESTAL_TYPE, 2> pedestal() { return m_pedestal.mean(); }
NDArray<PEDESTAL_TYPE, 2> noise() { return m_pedestal.std(); }
void clear_pedestal() { m_pedestal.clear(); }
/**
* @brief Move the clusters from the ClusterVector in the ClusterFinder to a
* new ClusterVector and return it.
* @param realloc_same_capacity if true the new ClusterVector will have the
* same capacity as the old one
*
*/
ClusterVector<ClusterType>
steal_clusters(bool realloc_same_capacity = false) {
ClusterVector<ClusterType> tmp = std::move(m_clusters);
if (realloc_same_capacity)
m_clusters = ClusterVector<ClusterType>(tmp.capacity());
else
m_clusters = ClusterVector<ClusterType>{};
return tmp;
}
void find_clusters(NDView<FRAME_TYPE, 2> frame, uint64_t frame_number = 0) {
// // TODO! deal with even size clusters
// // currently 3,3 -> +/- 1
// // 4,4 -> +/- 2
int dy = ClusterSizeY / 2;
int dx = ClusterSizeX / 2;
int has_center_pixel_x =
ClusterSizeX %
2; // for even sized clusters there is no proper cluster center and
// even amount of pixels around the center
int has_center_pixel_y = ClusterSizeY % 2;
m_clusters.set_frame_number(frame_number);
std::vector<CT> cluster_data(ClusterSizeX * ClusterSizeY);
for (int iy = 0; iy < frame.shape(0); iy++) {
for (int ix = 0; ix < frame.shape(1); ix++) {
PEDESTAL_TYPE max = std::numeric_limits<FRAME_TYPE>::min();
PEDESTAL_TYPE total = 0;
// What can we short circuit here?
PEDESTAL_TYPE rms = m_pedestal.std(iy, ix);
PEDESTAL_TYPE value = (frame(iy, ix) - m_pedestal.mean(iy, ix));
if (value < -m_nSigma * rms)
continue; // NEGATIVE_PEDESTAL go to next pixel
// TODO! No pedestal update???
for (int ir = -dy; ir < dy + has_center_pixel_y; ir++) {
for (int ic = -dx; ic < dx + has_center_pixel_x; ic++) {
if (ix + ic >= 0 && ix + ic < frame.shape(1) &&
iy + ir >= 0 && iy + ir < frame.shape(0)) {
PEDESTAL_TYPE val =
frame(iy + ir, ix + ic) -
m_pedestal.mean(iy + ir, ix + ic);
total += val;
max = std::max(max, val);
}
}
}
if ((max > m_nSigma * rms)) {
if (value < max)
continue; // Not max go to the next pixel
// but also no pedestal update
} else if (total > c3 * m_nSigma * rms) {
// pass
} else {
// m_pedestal.push(iy, ix, frame(iy, ix)); // Safe option
m_pedestal.push_fast(
iy, ix,
frame(iy,
ix)); // Assume we have reached n_samples in the
// pedestal, slight performance improvement
continue; // It was a pedestal value nothing to store
}
// Store cluster
if (value == max) {
// Zero out the cluster data
std::fill(cluster_data.begin(), cluster_data.end(), 0);
// Fill the cluster data since we have a photon to store
// It's worth redoing the look since most of the time we
// don't have a photon
int i = 0;
for (int ir = -dy; ir < dy + has_center_pixel_y; ir++) {
for (int ic = -dx; ic < dx + has_center_pixel_y; ic++) {
if (ix + ic >= 0 && ix + ic < frame.shape(1) &&
iy + ir >= 0 && iy + ir < frame.shape(0)) {
CT tmp =
static_cast<CT>(frame(iy + ir, ix + ic)) -
static_cast<CT>(
m_pedestal.mean(iy + ir, ix + ic));
cluster_data[i] =
tmp; // Watch for out of bounds access
i++;
}
}
}
ClusterType new_cluster{};
new_cluster.x = ix;
new_cluster.y = iy;
std::copy(cluster_data.begin(), cluster_data.end(),
new_cluster.data);
// Add the cluster to the output ClusterVector
m_clusters.push_back(new_cluster);
}
}
}
}
};
} // namespace aare

View File

@@ -0,0 +1,274 @@
#pragma once
#include <atomic>
#include <cstdint>
#include <memory>
#include <thread>
#include <vector>
#include "aare/ClusterFinder.hpp"
#include "aare/NDArray.hpp"
#include "aare/ProducerConsumerQueue.hpp"
namespace aare {
enum class FrameType {
DATA,
PEDESTAL,
};
struct FrameWrapper {
FrameType type;
uint64_t frame_number;
NDArray<uint16_t, 2> data;
};
/**
* @brief ClusterFinderMT is a multi-threaded version of ClusterFinder. It uses
* a producer-consumer queue to distribute the frames to the threads. The
* clusters are collected in a single output queue.
* @tparam FRAME_TYPE type of the frame data
* @tparam PEDESTAL_TYPE type of the pedestal data
* @tparam CT type of the cluster data
*/
template <typename ClusterType = Cluster<int32_t, 3, 3>,
typename FRAME_TYPE = uint16_t, typename PEDESTAL_TYPE = double>
class ClusterFinderMT {
using CT = typename extract_template_arguments<ClusterType>::value_type;
size_t m_current_thread{0};
size_t m_n_threads{0};
using Finder = ClusterFinder<ClusterType, FRAME_TYPE, PEDESTAL_TYPE>;
using InputQueue = ProducerConsumerQueue<FrameWrapper>;
using OutputQueue = ProducerConsumerQueue<ClusterVector<ClusterType>>;
std::vector<std::unique_ptr<InputQueue>> m_input_queues;
std::vector<std::unique_ptr<OutputQueue>> m_output_queues;
OutputQueue m_sink{1000}; // All clusters go into this queue
std::vector<std::unique_ptr<Finder>> m_cluster_finders;
std::vector<std::thread> m_threads;
std::thread m_collect_thread;
std::chrono::milliseconds m_default_wait{1};
std::atomic<bool> m_stop_requested{false};
std::atomic<bool> m_processing_threads_stopped{true};
/**
* @brief Function called by the processing threads. It reads the frames
* from the input queue and processes them.
*/
void process(int thread_id) {
auto cf = m_cluster_finders[thread_id].get();
auto q = m_input_queues[thread_id].get();
bool realloc_same_capacity = true;
while (!m_stop_requested || !q->isEmpty()) {
if (FrameWrapper *frame = q->frontPtr(); frame != nullptr) {
switch (frame->type) {
case FrameType::DATA:
cf->find_clusters(frame->data.view(), frame->frame_number);
m_output_queues[thread_id]->write(
cf->steal_clusters(realloc_same_capacity));
break;
case FrameType::PEDESTAL:
m_cluster_finders[thread_id]->push_pedestal_frame(
frame->data.view());
break;
}
// frame is processed now discard it
m_input_queues[thread_id]->popFront();
} else {
std::this_thread::sleep_for(m_default_wait);
}
}
}
/**
* @brief Collect all the clusters from the output queues and write them to
* the sink
*/
void collect() {
bool empty = true;
while (!m_stop_requested || !empty || !m_processing_threads_stopped) {
empty = true;
for (auto &queue : m_output_queues) {
if (!queue->isEmpty()) {
while (!m_sink.write(std::move(*queue->frontPtr()))) {
std::this_thread::sleep_for(m_default_wait);
}
queue->popFront();
empty = false;
}
}
}
}
public:
/**
* @brief Construct a new ClusterFinderMT object
* @param image_size size of the image
* @param cluster_size size of the cluster
* @param nSigma number of sigma above the pedestal to consider a photon
* @param capacity initial capacity of the cluster vector. Should match
* expected number of clusters in a frame per frame.
* @param n_threads number of threads to use
*/
ClusterFinderMT(Shape<2> image_size, PEDESTAL_TYPE nSigma = 5.0,
size_t capacity = 2000, size_t n_threads = 3)
: m_n_threads(n_threads) {
for (size_t i = 0; i < n_threads; i++) {
m_cluster_finders.push_back(
std::make_unique<
ClusterFinder<ClusterType, FRAME_TYPE, PEDESTAL_TYPE>>(
image_size, nSigma, capacity));
}
for (size_t i = 0; i < n_threads; i++) {
m_input_queues.emplace_back(std::make_unique<InputQueue>(200));
m_output_queues.emplace_back(std::make_unique<OutputQueue>(200));
}
// TODO! Should we start automatically?
start();
}
/**
* @brief Return the sink queue where all the clusters are collected
* @warning You need to empty this queue otherwise the cluster finder will
* wait forever
*/
ProducerConsumerQueue<ClusterVector<ClusterType>> *sink() {
return &m_sink;
}
/**
* @brief Start all processing threads
*/
void start() {
m_processing_threads_stopped = false;
m_stop_requested = false;
for (size_t i = 0; i < m_n_threads; i++) {
m_threads.push_back(
std::thread(&ClusterFinderMT::process, this, i));
}
m_collect_thread = std::thread(&ClusterFinderMT::collect, this);
}
/**
* @brief Stop all processing threads
*/
void stop() {
m_stop_requested = true;
for (auto &thread : m_threads) {
thread.join();
}
m_threads.clear();
m_processing_threads_stopped = true;
m_collect_thread.join();
}
/**
* @brief Wait for all the queues to be empty. Mostly used for timing tests.
*/
void sync() {
for (auto &q : m_input_queues) {
while (!q->isEmpty()) {
std::this_thread::sleep_for(m_default_wait);
}
}
for (auto &q : m_output_queues) {
while (!q->isEmpty()) {
std::this_thread::sleep_for(m_default_wait);
}
}
while (!m_sink.isEmpty()) {
std::this_thread::sleep_for(m_default_wait);
}
}
/**
* @brief Push a pedestal frame to all the cluster finders. The frames is
* expected to be dark. No photon finding is done. Just pedestal update.
*/
void push_pedestal_frame(NDView<FRAME_TYPE, 2> frame) {
FrameWrapper fw{FrameType::PEDESTAL, 0,
NDArray(frame)}; // TODO! copies the data!
for (auto &queue : m_input_queues) {
while (!queue->write(fw)) {
std::this_thread::sleep_for(m_default_wait);
}
}
}
/**
* @brief Push the frame to the queue of the next available thread. Function
* returns once the frame is in a queue.
* @note Spin locks with a default wait if the queue is full.
*/
void find_clusters(NDView<FRAME_TYPE, 2> frame, uint64_t frame_number = 0) {
FrameWrapper fw{FrameType::DATA, frame_number,
NDArray(frame)}; // TODO! copies the data!
while (!m_input_queues[m_current_thread % m_n_threads]->write(fw)) {
std::this_thread::sleep_for(m_default_wait);
}
m_current_thread++;
}
void clear_pedestal() {
if (!m_processing_threads_stopped) {
throw std::runtime_error("ClusterFinderMT is still running");
}
for (auto &cf : m_cluster_finders) {
cf->clear_pedestal();
}
}
/**
* @brief Return the pedestal currently used by the cluster finder
* @param thread_index index of the thread
*/
auto pedestal(size_t thread_index = 0) {
if (m_cluster_finders.empty()) {
throw std::runtime_error("No cluster finders available");
}
if (!m_processing_threads_stopped) {
throw std::runtime_error("ClusterFinderMT is still running");
}
if (thread_index >= m_cluster_finders.size()) {
throw std::runtime_error("Thread index out of range");
}
return m_cluster_finders[thread_index]->pedestal();
}
/**
* @brief Return the noise currently used by the cluster finder
* @param thread_index index of the thread
*/
auto noise(size_t thread_index = 0) {
if (m_cluster_finders.empty()) {
throw std::runtime_error("No cluster finders available");
}
if (!m_processing_threads_stopped) {
throw std::runtime_error("ClusterFinderMT is still running");
}
if (thread_index >= m_cluster_finders.size()) {
throw std::runtime_error("Thread index out of range");
}
return m_cluster_finders[thread_index]->noise();
}
// void push(FrameWrapper&& frame) {
// //TODO! need to loop until we are successful
// auto rc = m_input_queue.write(std::move(frame));
// fmt::print("pushed frame {}\n", rc);
// }
};
} // namespace aare

View File

@@ -0,0 +1,389 @@
#pragma once
#include "aare/Cluster.hpp" //TODO maybe store in seperate file !!!
#include <algorithm>
#include <array>
#include <cstddef>
#include <cstdint>
#include <numeric>
#include <vector>
#include <fmt/core.h>
#include "aare/Cluster.hpp"
#include "aare/NDView.hpp"
namespace aare {
template <typename ClusterType,
typename = std::enable_if_t<is_cluster_v<ClusterType>>>
class ClusterVector; // Forward declaration
/**
* @brief ClusterVector is a container for clusters of various sizes. It uses a
* contiguous memory buffer to store the clusters. It is templated on the data
* type and the coordinate type of the clusters.
* @note push_back can invalidate pointers to elements in the container
* @warning ClusterVector is currently move only to catch unintended copies, but
* this might change since there are probably use cases where copying is needed.
* @tparam T data type of the pixels in the cluster
* @tparam CoordType data type of the x and y coordinates of the cluster
* (normally int16_t)
*/
#if 0
template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
typename CoordType>
class ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>> {
std::byte *m_data{};
size_t m_size{0};
size_t m_capacity;
uint64_t m_frame_number{0}; // TODO! Check frame number size and type
/**
Format string used in the python bindings to create a numpy
array from the buffer
= - native byte order
h - short
d - double
i - int
*/
constexpr static char m_fmt_base[] = "=h:x:\nh:y:\n({},{}){}:data:";
public:
using value_type = T;
using ClusterType = Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>;
/**
* @brief Construct a new ClusterVector object
* @param capacity initial capacity of the buffer in number of clusters
* @param frame_number frame number of the clusters. Default is 0, which is
* also used to indicate that the clusters come from many frames
*/
ClusterVector(size_t capacity = 1024, uint64_t frame_number = 0)
: m_capacity(capacity), m_frame_number(frame_number) {
allocate_buffer(m_capacity);
}
~ClusterVector() { delete[] m_data; }
// Move constructor
ClusterVector(ClusterVector &&other) noexcept
: m_data(other.m_data), m_size(other.m_size),
m_capacity(other.m_capacity), m_frame_number(other.m_frame_number) {
other.m_data = nullptr;
other.m_size = 0;
other.m_capacity = 0;
}
// Move assignment operator
ClusterVector &operator=(ClusterVector &&other) noexcept {
if (this != &other) {
delete[] m_data;
m_data = other.m_data;
m_size = other.m_size;
m_capacity = other.m_capacity;
m_frame_number = other.m_frame_number;
other.m_data = nullptr;
other.m_size = 0;
other.m_capacity = 0;
other.m_frame_number = 0;
}
return *this;
}
/**
* @brief Reserve space for at least capacity clusters
* @param capacity number of clusters to reserve space for
* @note If capacity is less than the current capacity, the function does
* nothing.
*/
void reserve(size_t capacity) {
if (capacity > m_capacity) {
allocate_buffer(capacity);
}
}
/**
* @brief Add a cluster to the vector
*/
void push_back(const ClusterType &cluster) {
if (m_size == m_capacity) {
allocate_buffer(m_capacity * 2);
}
std::byte *ptr = element_ptr(m_size);
*reinterpret_cast<CoordType *>(ptr) = cluster.x;
ptr += sizeof(CoordType);
*reinterpret_cast<CoordType *>(ptr) = cluster.y;
ptr += sizeof(CoordType);
std::memcpy(ptr, cluster.data, ClusterSizeX * ClusterSizeY * sizeof(T));
m_size++;
}
ClusterVector &operator+=(const ClusterVector &other) {
if (m_size + other.m_size > m_capacity) {
allocate_buffer(m_capacity + other.m_size);
}
std::copy(other.m_data, other.m_data + other.m_size * item_size(),
m_data + m_size * item_size());
m_size += other.m_size;
return *this;
}
/**
* @brief Sum the pixels in each cluster
* @return std::vector<T> vector of sums for each cluster
*/
/*
std::vector<T> sum() {
std::vector<T> sums(m_size);
const size_t stride = item_size();
const size_t n_pixels = ClusterSizeX * ClusterSizeY;
std::byte *ptr = m_data + 2 * sizeof(CoordType); // skip x and y
for (size_t i = 0; i < m_size; i++) {
sums[i] =
std::accumulate(reinterpret_cast<T *>(ptr),
reinterpret_cast<T *>(ptr) + n_pixels, T{});
ptr += stride;
}
return sums;
}
*/
/**
* @brief Sum the pixels in the 2x2 subcluster with the biggest pixel sum in
* each cluster
* @return std::vector<T> vector of sums for each cluster
*/ //TODO if underlying container is a vector use std::for_each
/*
std::vector<T> sum_2x2() {
std::vector<T> sums_2x2(m_size);
for (size_t i = 0; i < m_size; i++) {
sums_2x2[i] = at(i).max_sum_2x2;
}
return sums_2x2;
}
*/
/**
* @brief Return the number of clusters in the vector
*/
size_t size() const { return m_size; }
uint8_t cluster_size_x() const { return ClusterSizeX; }
uint8_t cluster_size_y() const { return ClusterSizeY; }
/**
* @brief Return the capacity of the buffer in number of clusters. This is
* the number of clusters that can be stored in the current buffer without
* reallocation.
*/
size_t capacity() const { return m_capacity; }
/**
* @brief Return the size in bytes of a single cluster
*/
size_t item_size() const {
return 2 * sizeof(CoordType) + ClusterSizeX * ClusterSizeY * sizeof(T);
}
/**
* @brief Return the offset in bytes for the i-th cluster
*/
size_t element_offset(size_t i) const { return item_size() * i; }
/**
* @brief Return a pointer to the i-th cluster
*/
std::byte *element_ptr(size_t i) { return m_data + element_offset(i); }
/**
* @brief Return a pointer to the i-th cluster
*/
const std::byte *element_ptr(size_t i) const {
return m_data + element_offset(i);
}
std::byte *data() { return m_data; }
std::byte const *data() const { return m_data; }
/**
* @brief Return a reference to the i-th cluster casted to type V
* @tparam V type of the cluster
*/
ClusterType &at(size_t i) {
return *reinterpret_cast<ClusterType *>(element_ptr(i));
}
const ClusterType &at(size_t i) const {
return *reinterpret_cast<const ClusterType *>(element_ptr(i));
}
template <typename V> const V &at(size_t i) const {
return *reinterpret_cast<const V *>(element_ptr(i));
}
const std::string_view fmt_base() const {
// TODO! how do we match on coord_t?
return m_fmt_base;
}
/**
* @brief Return the frame number of the clusters. 0 is used to indicate
* that the clusters come from many frames
*/
uint64_t frame_number() const { return m_frame_number; }
void set_frame_number(uint64_t frame_number) {
m_frame_number = frame_number;
}
/**
* @brief Resize the vector to contain new_size clusters. If new_size is
* greater than the current capacity, a new buffer is allocated. If the size
* is smaller no memory is freed, size is just updated.
* @param new_size new size of the vector
* @warning The additional clusters are not initialized
*/
void resize(size_t new_size) {
// TODO! Should we initialize the new clusters?
if (new_size > m_capacity) {
allocate_buffer(new_size);
}
m_size = new_size;
}
private:
void allocate_buffer(size_t new_capacity) {
size_t num_bytes = item_size() * new_capacity;
std::byte *new_data = new std::byte[num_bytes]{};
std::copy(m_data, m_data + item_size() * m_size, new_data);
delete[] m_data;
m_data = new_data;
m_capacity = new_capacity;
}
};
#endif
/**
* @brief ClusterVector is a container for clusters of various sizes. It
* uses a contiguous memory buffer to store the clusters. It is templated on
* the data type and the coordinate type of the clusters.
* @note push_back can invalidate pointers to elements in the container
* @warning ClusterVector is currently move only to catch unintended copies,
* but this might change since there are probably use cases where copying is
* needed.
* @tparam T data type of the pixels in the cluster
* @tparam CoordType data type of the x and y coordinates of the cluster
* (normally int16_t)
*/
template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
typename CoordType>
class ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>> {
std::vector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>> m_data{};
uint64_t m_frame_number{0}; // TODO! Check frame number size and type
public:
using value_type = T;
using ClusterType = Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>;
/**
* @brief Construct a new ClusterVector object
* @param capacity initial capacity of the buffer in number of clusters
* @param frame_number frame number of the clusters. Default is 0, which is
* also used to indicate that the clusters come from many frames
*/
ClusterVector(size_t capacity = 300, int32_t frame_number = 0)
: m_frame_number(frame_number) {
m_data.reserve(capacity);
}
// Move constructor
ClusterVector(ClusterVector &&other) noexcept
: m_data(other.m_data), m_frame_number(other.m_frame_number) {
other.m_data.clear();
}
// Move assignment operator
ClusterVector &operator=(ClusterVector &&other) noexcept {
if (this != &other) {
m_data = other.m_data;
m_frame_number = other.m_frame_number;
other.m_data.clear();
other.m_frame_number = 0;
}
return *this;
}
/**
* @brief Reserve space for at least capacity clusters
* @param capacity number of clusters to reserve space for
* @note If capacity is less than the current capacity, the function does
* nothing.
*/
void reserve(size_t capacity) { m_data.reserve(capacity); }
void resize(size_t size) { m_data.resize(size); }
void push_back(const ClusterType &cluster) { m_data.push_back(cluster); }
ClusterVector &operator+=(const ClusterVector &other) {
m_data.insert(m_data.end(), other.begin(), other.end());
return *this;
}
/**
* @brief Return the number of clusters in the vector
*/
size_t size() const { return m_data.size(); }
uint8_t cluster_size_x() const { return ClusterSizeX; }
uint8_t cluster_size_y() const { return ClusterSizeY; }
/**
* @brief Return the capacity of the buffer in number of clusters. This is
* the number of clusters that can be stored in the current buffer without
* reallocation.
*/
size_t capacity() const { return m_data.capacity(); }
const auto begin() const { return m_data.begin(); }
const auto end() const { return m_data.end(); }
/**
* @brief Return the size in bytes of a single cluster
*/
size_t item_size() const {
return 2 * sizeof(CoordType) + ClusterSizeX * ClusterSizeY * sizeof(T);
}
ClusterType *data() { return m_data.data(); }
ClusterType const *data() const { return m_data.data(); }
/**
* @brief Return a reference to the i-th cluster casted to type V
* @tparam V type of the cluster
*/
ClusterType &at(size_t i) { return m_data[i]; }
const ClusterType &at(size_t i) const { return m_data[i]; }
/**
* @brief Return the frame number of the clusters. 0 is used to indicate
* that the clusters come from many frames
*/
uint64_t frame_number() const { return m_frame_number; }
void set_frame_number(uint64_t frame_number) {
m_frame_number = frame_number;
}
};
} // namespace aare

View File

@@ -0,0 +1,41 @@
#pragma once
#include "aare/FileInterface.hpp"
#include "aare/RawMasterFile.hpp"
#include "aare/Frame.hpp"
#include <filesystem>
#include <fstream>
namespace aare{
class CtbRawFile{
RawMasterFile m_master;
std::ifstream m_file;
size_t m_current_frame{0};
size_t m_current_subfile{0};
size_t m_num_subfiles{0};
public:
CtbRawFile(const std::filesystem::path &fname);
void read_into(std::byte *image_buf, DetectorHeader* header = nullptr);
void seek(size_t frame_index); //!< seek to the given frame index
size_t tell() const; //!< get the frame index of the file pointer
// in the specific class we can expose more functionality
size_t image_size_in_bytes() const;
size_t frames_in_file() const;
RawMasterFile master() const;
private:
void find_subfiles();
size_t sub_file_index(size_t frame_index) const {
return frame_index / m_master.max_frames_per_file();
}
void open_data_file(size_t subfile_index);
};
}

83
include/aare/Dtype.hpp Normal file
View File

@@ -0,0 +1,83 @@
#pragma once
#include <cstdint>
#include <map>
#include <string>
#include <typeinfo>
namespace aare {
// The format descriptor is a single character that specifies the type of the data
// - python documentation: https://docs.python.org/3/c-api/arg.html#numbers
// - py::format_descriptor<T>::format() (in pybind11) does not return the same format as
// written in python.org documentation.
// - numpy also doesn't use the same format. and also numpy associates the format
// with variable bitdepth types. (e.g. long is int64 on linux64 and int32 on win64)
// https://numpy.org/doc/stable/reference/arrays.scalars.html
//
// github issue discussing this:
// https://github.com/pybind/pybind11/issues/1908#issuecomment-658358767
//
// [IN LINUX] the difference is for int64 (long) and uint64 (unsigned long). The format
// descriptor is 'q' and 'Q' respectively and in the documentation it is 'l' and 'k'.
// in practice numpy doesn't seem to care when reading buffer info: the library
// interprets 'q' or 'l' as int64 and 'Q' or 'L' as uint64.
// for this reason we decided to use the same format descriptor as pybind to avoid
// any further discrepancies.
// in the following order:
// int8, uint8, int16, uint16, int32, uint32, int64, uint64, float, double
const char DTYPE_FORMAT_DSC[] = {'b', 'B', 'h', 'H', 'i', 'I', 'q', 'Q', 'f', 'd'};
// on linux64 & apple
const char NUMPY_FORMAT_DSC[] = {'b', 'B', 'h', 'H', 'i', 'I', 'l', 'L', 'f', 'd'};
/**
* @brief enum class to define the endianess of the system
*/
enum class endian {
#ifdef _WIN32
little = 0,
big = 1,
native = little
#else
little = __ORDER_LITTLE_ENDIAN__,
big = __ORDER_BIG_ENDIAN__,
native = __BYTE_ORDER__
#endif
};
/**
* @brief class to define the data type of the pixels
* @note only native endianess is supported
*/
class Dtype {
public:
enum TypeIndex { INT8, UINT8, INT16, UINT16, INT32, UINT32, INT64, UINT64, FLOAT, DOUBLE, ERROR, NONE };
uint8_t bitdepth() const;
size_t bytes() const;
std::string format_descr() const { return std::string(1, DTYPE_FORMAT_DSC[static_cast<int>(m_type)]); }
std::string numpy_descr() const { return std::string(1, NUMPY_FORMAT_DSC[static_cast<int>(m_type)]); }
explicit Dtype(const std::type_info &t);
explicit Dtype(std::string_view sv);
static Dtype from_bitdepth(uint8_t bitdepth);
// not explicit to allow conversions form enum to DType
Dtype(Dtype::TypeIndex ti); // NOLINT
bool operator==(const Dtype &other) const noexcept;
bool operator!=(const Dtype &other) const noexcept;
bool operator==(const std::type_info &t) const;
bool operator!=(const std::type_info &t) const;
// bool operator==(DType::TypeIndex ti) const;
// bool operator!=(DType::TypeIndex ti) const;
std::string to_string() const;
void set_type(Dtype::TypeIndex ti) { m_type = ti; }
private:
TypeIndex m_type{TypeIndex::ERROR};
};
} // namespace aare

66
include/aare/File.hpp Normal file
View File

@@ -0,0 +1,66 @@
#pragma once
#include "aare/FileInterface.hpp"
#include <memory>
namespace aare {
/**
* @brief RAII File class for reading, and in the future potentially writing
* image files in various formats. Minimal generic interface. For specail fuctions
* plase use the RawFile or NumpyFile classes directly.
* Wraps FileInterface to abstract the underlying file format
* @note **frame_number** refers the the frame number sent by the detector while **frame_index**
* is the position of the frame in the file
*/
class File {
std::unique_ptr<FileInterface> file_impl;
public:
/**
* @brief Construct a new File object
* @param fname path to the file
* @param mode file mode (r, w, a)
* @param cfg file configuration
* @throws std::runtime_error if the file cannot be opened
* @throws std::invalid_argument if the file mode is not supported
*
*/
File(const std::filesystem::path &fname, const std::string &mode="r", const FileConfig &cfg = {});
/**Since the object is responsible for managing the file we disable copy construction */
File(File const &other) = delete;
/**The same goes for copy assignment */
File& operator=(File const &other) = delete;
File(File &&other) noexcept;
File& operator=(File &&other) noexcept;
~File() = default;
// void close(); //!< close the file
Frame read_frame(); //!< read one frame from the file at the current position
Frame read_frame(size_t frame_index); //!< read one frame at the position given by frame number
std::vector<Frame> read_n(size_t n_frames); //!< read n_frames from the file at the current position
void read_into(std::byte *image_buf);
void read_into(std::byte *image_buf, size_t n_frames);
size_t frame_number(); //!< get the frame number at the current position
size_t frame_number(size_t frame_index); //!< get the frame number at the given frame index
size_t bytes_per_frame() const;
size_t pixels_per_frame() const;
size_t bytes_per_pixel() const;
size_t bitdepth() const;
void seek(size_t frame_index); //!< seek to the given frame index
size_t tell() const; //!< get the frame index of the file pointer
size_t total_frames() const;
size_t rows() const;
size_t cols() const;
DetectorType detector_type() const;
};
} // namespace aare

View File

@@ -0,0 +1,161 @@
#pragma once
#include "aare/Dtype.hpp"
#include "aare/Frame.hpp"
#include "aare/defs.hpp"
#include <filesystem>
#include <vector>
namespace aare {
/**
* @brief FileConfig structure to store the configuration of a file
* dtype: data type of the file
* rows: number of rows in the file
* cols: number of columns in the file
* geometry: geometry of the file
*/
struct FileConfig {
aare::Dtype dtype{typeid(uint16_t)};
uint64_t rows{};
uint64_t cols{};
bool operator==(const FileConfig &other) const {
return dtype == other.dtype && rows == other.rows && cols == other.cols && geometry == other.geometry &&
detector_type == other.detector_type && max_frames_per_file == other.max_frames_per_file;
}
bool operator!=(const FileConfig &other) const { return !(*this == other); }
// rawfile specific
std::string version{};
xy geometry{1, 1};
DetectorType detector_type{DetectorType::Unknown};
int max_frames_per_file{};
size_t total_frames{};
std::string to_string() const {
return "{ dtype: " + dtype.to_string() + ", rows: " + std::to_string(rows) + ", cols: " + std::to_string(cols) +
", geometry: " + geometry.to_string() + ", detector_type: " + ToString(detector_type) +
", max_frames_per_file: " + std::to_string(max_frames_per_file) +
", total_frames: " + std::to_string(total_frames) + " }";
}
};
/**
* @brief FileInterface class to define the interface for file operations
* @note parent class for NumpyFile and RawFile
* @note all functions are pure virtual and must be implemented by the derived classes
*/
class FileInterface {
public:
/**
* @brief one frame from the file at the current position
* @return Frame
*/
virtual Frame read_frame() = 0;
/**
* @brief read one frame from the file at the given frame number
* @param frame_number frame number to read
* @return frame
*/
virtual Frame read_frame(size_t frame_number) = 0;
/**
* @brief read n_frames from the file at the current position
* @param n_frames number of frames to read
* @return vector of frames
*/
virtual std::vector<Frame> read_n(size_t n_frames) = 0; // Is this the right interface?
/**
* @brief read one frame from the file at the current position and store it in the provided buffer
* @param image_buf buffer to store the frame
* @return void
*/
virtual void read_into(std::byte *image_buf) = 0;
/**
* @brief read n_frames from the file at the current position and store them in the provided buffer
* @param image_buf buffer to store the frames
* @param n_frames number of frames to read
* @return void
*/
virtual void read_into(std::byte *image_buf, size_t n_frames) = 0;
/**
* @brief get the frame number at the given frame index
* @param frame_index index of the frame
* @return frame number
*/
virtual size_t frame_number(size_t frame_index) = 0;
/**
* @brief get the size of one frame in bytes
* @return size of one frame
*/
virtual size_t bytes_per_frame() = 0;
/**
* @brief get the number of pixels in one frame
* @return number of pixels in one frame
*/
virtual size_t pixels_per_frame() = 0;
/**
* @brief seek to the given frame number
* @param frame_number frame number to seek to
* @return void
*/
virtual void seek(size_t frame_number) = 0;
/**
* @brief get the current position of the file pointer
* @return current position of the file pointer
*/
virtual size_t tell() = 0;
/**
* @brief get the total number of frames in the file
* @return total number of frames in the file
*/
virtual size_t total_frames() const = 0;
/**
* @brief get the number of rows in the file
* @return number of rows in the file
*/
virtual size_t rows() const = 0;
/**
* @brief get the number of columns in the file
* @return number of columns in the file
*/
virtual size_t cols() const = 0;
/**
* @brief get the bitdepth of the file
* @return bitdepth of the file
*/
virtual size_t bitdepth() const = 0;
virtual DetectorType detector_type() const = 0;
// function to query the data type of the file
/*virtual DataType dtype = 0; */
virtual ~FileInterface() = default;
protected:
std::string m_mode{};
// std::filesystem::path m_fname{};
// std::filesystem::path m_base_path{};
// std::string m_base_name{}, m_ext{};
// int m_findex{};
// size_t m_total_frames{};
// size_t max_frames_per_file{};
// std::string version{};
// DetectorType m_type{DetectorType::Unknown};
// size_t m_rows{};
// size_t m_cols{};
// size_t m_bitdepth{};
// size_t current_frame{};
};
} // namespace aare

30
include/aare/FilePtr.hpp Normal file
View File

@@ -0,0 +1,30 @@
#pragma once
#include <cstdio>
#include <filesystem>
namespace aare {
/**
* \brief RAII wrapper for FILE pointer
*/
class FilePtr {
FILE *fp_{nullptr};
public:
FilePtr() = default;
FilePtr(const std::filesystem::path& fname, const std::string& mode);
FilePtr(const FilePtr &) = delete; // we don't want a copy
FilePtr &operator=(const FilePtr &) = delete; // since we handle a resource
FilePtr(FilePtr &&other);
FilePtr &operator=(FilePtr &&other);
FILE *get();
int64_t tell();
void seek(int64_t offset, int whence = SEEK_SET) {
if (fseek(fp_, offset, whence) != 0)
throw std::runtime_error("Error seeking in file");
}
std::string error_msg();
~FilePtr();
};
} // namespace aare

92
include/aare/Fit.hpp Normal file
View File

@@ -0,0 +1,92 @@
#pragma once
#include <cmath>
#include <fmt/core.h>
#include <vector>
#include "aare/NDArray.hpp"
namespace aare {
namespace func {
double gaus(const double x, const double *par);
NDArray<double, 1> gaus(NDView<double, 1> x, NDView<double, 1> par);
double pol1(const double x, const double *par);
NDArray<double, 1> pol1(NDView<double, 1> x, NDView<double, 1> par);
} // namespace func
/**
* @brief Estimate the initial parameters for a Gaussian fit
*/
std::array<double, 3> gaus_init_par(const NDView<double, 1> x, const NDView<double, 1> y);
std::array<double, 2> pol1_init_par(const NDView<double, 1> x, const NDView<double, 1> y);
static constexpr int DEFAULT_NUM_THREADS = 4;
/**
* @brief Fit a 1D Gaussian to data.
* @param data data to fit
* @param x x values
*/
NDArray<double, 1> fit_gaus(NDView<double, 1> x, NDView<double, 1> y);
/**
* @brief Fit a 1D Gaussian to each pixel. Data layout [row, col, values]
* @param x x values
* @param y y vales, layout [row, col, values]
* @param n_threads number of threads to use
*/
NDArray<double, 3> fit_gaus(NDView<double, 1> x, NDView<double, 3> y,
int n_threads = DEFAULT_NUM_THREADS);
/**
* @brief Fit a 1D Gaussian with error estimates
* @param x x values
* @param y y vales, layout [row, col, values]
* @param y_err error in y, layout [row, col, values]
* @param par_out output parameters
* @param par_err_out output error parameters
*/
void fit_gaus(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
NDView<double, 1> par_out, NDView<double, 1> par_err_out,
double& chi2);
/**
* @brief Fit a 1D Gaussian to each pixel with error estimates. Data layout
* [row, col, values]
* @param x x values
* @param y y vales, layout [row, col, values]
* @param y_err error in y, layout [row, col, values]
* @param par_out output parameters, layout [row, col, values]
* @param par_err_out output parameter errors, layout [row, col, values]
* @param n_threads number of threads to use
*/
void fit_gaus(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
NDView<double, 3> par_out, NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
int n_threads = DEFAULT_NUM_THREADS
);
NDArray<double, 1> fit_pol1(NDView<double, 1> x, NDView<double, 1> y);
NDArray<double, 3> fit_pol1(NDView<double, 1> x, NDView<double, 3> y,
int n_threads = DEFAULT_NUM_THREADS);
void fit_pol1(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
NDView<double, 1> par_out, NDView<double, 1> par_err_out, double& chi2);
// TODO! not sure we need to offer the different version in C++
void fit_pol1(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
NDView<double, 3> par_out, NDView<double, 3> par_err_out,NDView<double, 2> chi2_out,
int n_threads = DEFAULT_NUM_THREADS);
} // namespace aare

124
include/aare/Frame.hpp Normal file
View File

@@ -0,0 +1,124 @@
#pragma once
#include "aare/Dtype.hpp"
#include "aare/NDArray.hpp"
#include "aare/defs.hpp"
#include <cstddef>
#include <cstdint>
#include <memory>
#include <vector>
namespace aare {
/**
* @brief Frame class to represent a single frame of data. Not much more than a
* pointer and some info. Limited interface to accept frames from many sources.
*/
class Frame {
uint32_t m_rows;
uint32_t m_cols;
Dtype m_dtype;
std::byte *m_data;
//TODO! Add frame number?
public:
/**
* @brief Construct a new Frame
* @param rows number of rows
* @param cols number of columns
* @param dtype data type of the pixels
* @note the data is initialized to zero
*/
Frame(uint32_t rows, uint32_t cols, Dtype dtype);
/**
* @brief Construct a new Frame
* @param bytes pointer to the data to be copied into the frame
* @param rows number of rows
* @param cols number of columns
* @param dtype data type of the pixels
*/
Frame(const std::byte *bytes, uint32_t rows, uint32_t cols, Dtype dtype);
~Frame(){ delete[] m_data; };
/** @warning Copy is disabled to ensure performance when passing
* frames around. Can discuss enabling it.
*
*/
Frame &operator=(const Frame &other) = delete;
Frame(const Frame &other) = delete;
// enable move
Frame &operator=(Frame &&other) noexcept;
Frame(Frame &&other) noexcept;
Frame clone() const; //<- Explicit copy
uint32_t rows() const;
uint32_t cols() const;
size_t bitdepth() const;
Dtype dtype() const;
uint64_t size() const;
size_t bytes() const;
std::byte *data() const;
/**
* @brief Get the pointer to the pixel at the given row and column
* @param row row index
* @param col column index
* @return pointer to the pixel
* @warning The user should cast the pointer to the appropriate type. Think
* twice if this is the function you want to use.
*/
std::byte *pixel_ptr(uint32_t row, uint32_t col) const;
/**
* @brief Set the pixel at the given row and column to the given value
* @tparam T type of the value
* @param row row index
* @param col column index
* @param data value to set
*/
template <typename T> void set(uint32_t row, uint32_t col, T data) {
assert(sizeof(T) == m_dtype.bytes());
if (row >= m_rows || col >= m_cols) {
throw std::out_of_range("Invalid row or column index");
}
std::memcpy(m_data + (row * m_cols + col) * m_dtype.bytes(), &data,
m_dtype.bytes());
}
template <typename T> T get(uint32_t row, uint32_t col) {
assert(sizeof(T) == m_dtype.bytes());
if (row >= m_rows || col >= m_cols) {
throw std::out_of_range("Invalid row or column index");
}
//TODO! add tests then reimplement using pixel_ptr
T data;
std::memcpy(&data, m_data + (row * m_cols + col) * m_dtype.bytes(),
m_dtype.bytes());
return data;
}
/**
* @brief Return an NDView of the frame. This is the preferred way to access
* data in the frame.
*
* @tparam T type of the pixels
* @return NDView<T, 2>
*/
template <typename T> NDView<T, 2> view() {
std::array<int64_t, 2> shape = {static_cast<int64_t>(m_rows),
static_cast<int64_t>(m_cols)};
T *data = reinterpret_cast<T *>(m_data);
return NDView<T, 2>(data, shape);
}
/**
* @brief Copy the frame data into a new NDArray. This is a deep copy.
*/
template <typename T> NDArray<T> image() {
return NDArray<T>(this->view<T>());
}
};
} // namespace aare

58
include/aare/GainMap.hpp Normal file
View File

@@ -0,0 +1,58 @@
/************************************************
* @file ApplyGainMap.hpp
* @short function to apply gain map of image size to a vector of clusters
***********************************************/
#pragma once
#include "aare/Cluster.hpp"
#include "aare/ClusterVector.hpp"
#include "aare/NDArray.hpp"
#include "aare/NDView.hpp"
#include <memory>
namespace aare {
class GainMap {
public:
explicit GainMap(const NDArray<double, 2> &gain_map)
: m_gain_map(gain_map) {};
explicit GainMap(const NDView<double, 2> gain_map) {
m_gain_map = NDArray<double, 2>(gain_map);
}
template <typename ClusterType,
typename = std::enable_if_t<is_cluster_v<ClusterType>>>
void apply_gain_map(ClusterVector<ClusterType> &clustervec) {
// in principle we need to know the size of the image for this lookup
size_t ClusterSizeX = clustervec.cluster_size_x();
size_t ClusterSizeY = clustervec.cluster_size_y();
using T = typename ClusterVector<ClusterType>::value_type;
int64_t index_cluster_center_x = ClusterSizeX / 2;
int64_t index_cluster_center_y = ClusterSizeY / 2;
for (size_t i = 0; i < clustervec.size(); i++) {
auto &cl = clustervec.at(i);
if (cl.x > 0 && cl.y > 0 && cl.x < m_gain_map.shape(1) - 1 &&
cl.y < m_gain_map.shape(0) - 1) {
for (size_t j = 0; j < ClusterSizeX * ClusterSizeY; j++) {
size_t x = cl.x + j % ClusterSizeX - index_cluster_center_x;
size_t y = cl.y + j / ClusterSizeX - index_cluster_center_y;
cl.data[j] = cl.data[j] * static_cast<T>(m_gain_map(y, x));
}
} else {
memset(cl.data, 0,
ClusterSizeX * ClusterSizeY *
sizeof(T)); // clear edge clusters
}
}
}
private:
NDArray<double, 2> m_gain_map{};
};
} // end of namespace aare

View File

@@ -0,0 +1,132 @@
#pragma once
#include "aare/CalculateEta.hpp"
#include "aare/Cluster.hpp"
#include "aare/ClusterFile.hpp" //Cluster_3x3
#include "aare/ClusterVector.hpp"
#include "aare/NDArray.hpp"
#include "aare/NDView.hpp"
#include "aare/algorithm.hpp"
namespace aare {
struct Photon {
double x;
double y;
double energy;
};
class Interpolator {
NDArray<double, 3> m_ietax;
NDArray<double, 3> m_ietay;
NDArray<double, 1> m_etabinsx;
NDArray<double, 1> m_etabinsy;
NDArray<double, 1> m_energy_bins;
public:
Interpolator(NDView<double, 3> etacube, NDView<double, 1> xbins,
NDView<double, 1> ybins, NDView<double, 1> ebins);
NDArray<double, 3> get_ietax() { return m_ietax; }
NDArray<double, 3> get_ietay() { return m_ietay; }
template <typename ClusterType,
typename Eanble = std::enable_if_t<is_cluster_v<ClusterType>>>
std::vector<Photon> interpolate(const ClusterVector<ClusterType> &clusters);
};
// TODO: generalize to support any clustertype!!! otherwise add std::enable_if_t
// to only take Cluster2x2 and Cluster3x3
template <typename ClusterType, typename Enable>
std::vector<Photon>
Interpolator::interpolate(const ClusterVector<ClusterType> &clusters) {
std::vector<Photon> photons;
photons.reserve(clusters.size());
if (clusters.cluster_size_x() == 3 || clusters.cluster_size_y() == 3) {
for (size_t i = 0; i < clusters.size(); i++) {
auto cluster = clusters.at(i);
auto eta = calculate_eta2(cluster);
Photon photon;
photon.x = cluster.x;
photon.y = cluster.y;
photon.energy = eta.sum;
// auto ie = nearest_index(m_energy_bins, photon.energy)-1;
// auto ix = nearest_index(m_etabinsx, eta.x)-1;
// auto iy = nearest_index(m_etabinsy, eta.y)-1;
// Finding the index of the last element that is smaller
// should work fine as long as we have many bins
auto ie = last_smaller(m_energy_bins, photon.energy);
auto ix = last_smaller(m_etabinsx, eta.x);
auto iy = last_smaller(m_etabinsy, eta.y);
// fmt::print("ex: {}, ix: {}, iy: {}\n", ie, ix, iy);
double dX, dY;
// cBottomLeft = 0,
// cBottomRight = 1,
// cTopLeft = 2,
// cTopRight = 3
switch (eta.c) {
case cTopLeft:
dX = -1.;
dY = 0;
break;
case cTopRight:;
dX = 0;
dY = 0;
break;
case cBottomLeft:
dX = -1.;
dY = -1.;
break;
case cBottomRight:
dX = 0.;
dY = -1.;
break;
}
photon.x += m_ietax(ix, iy, ie) * 2 + dX;
photon.y += m_ietay(ix, iy, ie) * 2 + dY;
photons.push_back(photon);
}
} else if (clusters.cluster_size_x() == 2 ||
clusters.cluster_size_y() == 2) {
for (size_t i = 0; i < clusters.size(); i++) {
auto cluster = clusters.at(i);
auto eta = calculate_eta2(cluster);
Photon photon;
photon.x = cluster.x;
photon.y = cluster.y;
photon.energy = eta.sum;
// Now do some actual interpolation.
// Find which energy bin the cluster is in
// auto ie = nearest_index(m_energy_bins, photon.energy)-1;
// auto ix = nearest_index(m_etabinsx, eta.x)-1;
// auto iy = nearest_index(m_etabinsy, eta.y)-1;
// Finding the index of the last element that is smaller
// should work fine as long as we have many bins
auto ie = last_smaller(m_energy_bins, photon.energy);
auto ix = last_smaller(m_etabinsx, eta.x);
auto iy = last_smaller(m_etabinsy, eta.y);
photon.x += m_ietax(ix, iy, ie) *
2; // eta goes between 0 and 1 but we could move the hit
// anywhere in the 2x2
photon.y += m_ietay(ix, iy, ie) * 2;
photons.push_back(photon);
}
} else {
throw std::runtime_error(
"Only 3x3 and 2x2 clusters are supported for interpolation");
}
return photons;
}
} // namespace aare

View File

@@ -0,0 +1,106 @@
#pragma once
#include <cstdint>
#include <filesystem>
#include <vector>
#include "aare/FilePtr.hpp"
#include "aare/defs.hpp"
#include "aare/NDArray.hpp"
#include "aare/FileInterface.hpp"
namespace aare {
struct JungfrauDataHeader{
uint64_t framenum;
uint64_t bunchid;
};
class JungfrauDataFile : public FileInterface {
size_t m_rows{}; //!< number of rows in the image, from find_frame_size();
size_t m_cols{}; //!< number of columns in the image, from find_frame_size();
size_t m_bytes_per_frame{}; //!< number of bytes per frame excluding header
size_t m_total_frames{}; //!< total number of frames in the series of files
size_t m_offset{}; //!< file index of the first file, allow starting at non zero file
size_t m_current_file_index{}; //!< The index of the open file
size_t m_current_frame_index{}; //!< The index of the current frame (with reference to all files)
std::vector<size_t> m_last_frame_in_file{}; //!< Used for seeking to the correct file
std::filesystem::path m_path; //!< path to the files
std::string m_base_name; //!< base name used for formatting file names
FilePtr m_fp; //!< RAII wrapper for a FILE*
using pixel_type = uint16_t;
static constexpr size_t header_size = sizeof(JungfrauDataHeader);
static constexpr size_t n_digits_in_file_index = 6; //!< to format file names
public:
JungfrauDataFile(const std::filesystem::path &fname);
std::string base_name() const; //!< get the base name of the file (without path and extension)
size_t bytes_per_frame() override;
size_t pixels_per_frame() override;
size_t bytes_per_pixel() const;
size_t bitdepth() const override;
void seek(size_t frame_index) override; //!< seek to the given frame index (note not byte offset)
size_t tell() override; //!< get the frame index of the file pointer
size_t total_frames() const override;
size_t rows() const override;
size_t cols() const override;
std::array<ssize_t,2> shape() const;
size_t n_files() const; //!< get the number of files in the series.
// Extra functions needed for FileInterface
Frame read_frame() override;
Frame read_frame(size_t frame_number) override;
std::vector<Frame> read_n(size_t n_frames=0) override;
void read_into(std::byte *image_buf) override;
void read_into(std::byte *image_buf, size_t n_frames) override;
size_t frame_number(size_t frame_index) override;
DetectorType detector_type() const override;
/**
* @brief Read a single frame from the file into the given buffer.
* @param image_buf buffer to read the frame into. (Note the caller is responsible for allocating the buffer)
* @param header pointer to a JungfrauDataHeader or nullptr to skip header)
*/
void read_into(std::byte *image_buf, JungfrauDataHeader *header = nullptr);
/**
* @brief Read a multiple frames from the file into the given buffer.
* @param image_buf buffer to read the frame into. (Note the caller is responsible for allocating the buffer)
* @param n_frames number of frames to read
* @param header pointer to a JungfrauDataHeader or nullptr to skip header)
*/
void read_into(std::byte *image_buf, size_t n_frames, JungfrauDataHeader *header = nullptr);
/**
* @brief Read a single frame from the file into the given NDArray
* @param image NDArray to read the frame into.
*/
void read_into(NDArray<uint16_t>* image, JungfrauDataHeader* header = nullptr);
JungfrauDataHeader read_header();
std::filesystem::path current_file() const { return fpath(m_current_file_index+m_offset); }
private:
/**
* @brief Find the size of the frame in the file. (256x256, 256x1024, 512x1024)
* @param fname path to the file
* @throws std::runtime_error if the file is empty or the size cannot be determined
*/
void find_frame_size(const std::filesystem::path &fname);
void parse_fname(const std::filesystem::path &fname);
void scan_files();
void open_file(size_t file_index);
std::filesystem::path fpath(size_t frame_index) const;
};
} // namespace aare

453
include/aare/NDArray.hpp Normal file
View File

@@ -0,0 +1,453 @@
#pragma once
/*
Container holding image data, or a time series of image data in contigious
memory.
TODO! Add expression templates for operators
*/
#include "aare/ArrayExpr.hpp"
#include "aare/NDView.hpp"
#include <algorithm>
#include <array>
#include <cmath>
#include <fmt/format.h>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <numeric>
namespace aare {
template <typename T, int64_t Ndim = 2>
class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
std::array<int64_t, Ndim> shape_;
std::array<int64_t, Ndim> strides_;
size_t size_{};
T *data_;
public:
/**
* @brief Default constructor. Will construct an empty NDArray.
*
*/
NDArray() : shape_(), strides_(c_strides<Ndim>(shape_)), data_(nullptr) {};
/**
* @brief Construct a new NDArray object with a given shape.
* @note The data is uninitialized.
*
* @param shape shape of the new NDArray
*/
explicit NDArray(std::array<int64_t, Ndim> shape)
: shape_(shape), strides_(c_strides<Ndim>(shape_)),
size_(std::accumulate(shape_.begin(), shape_.end(), 1,
std::multiplies<>())),
data_(new T[size_]) {}
/**
* @brief Construct a new NDArray object with a shape and value.
*
* @param shape shape of the new array
* @param value value to initialize the array with
*/
NDArray(std::array<int64_t, Ndim> shape, T value) : NDArray(shape) {
this->operator=(value);
}
/**
* @brief Construct a new NDArray object from a NDView.
* @note The data is copied from the view to the NDArray.
*
* @param v view of data to initialize the NDArray with
*/
explicit NDArray(const NDView<T, Ndim> v) : NDArray(v.shape()) {
std::copy(v.begin(), v.end(), begin());
}
template<size_t Size>
NDArray(const std::array<T, Size>& arr) : NDArray<T,1>({Size}) {
std::copy(arr.begin(), arr.end(), begin());
}
// Move constructor
NDArray(NDArray &&other) noexcept
: shape_(other.shape_), strides_(c_strides<Ndim>(shape_)),
size_(other.size_), data_(other.data_) {
other.reset(); // TODO! is this necessary?
}
// Copy constructor
NDArray(const NDArray &other)
: shape_(other.shape_), strides_(c_strides<Ndim>(shape_)),
size_(other.size_), data_(new T[size_]) {
std::copy(other.data_, other.data_ + size_, data_);
}
// Conversion operator from array expression to array
template <typename E>
NDArray(ArrayExpr<E, Ndim> &&expr) : NDArray(expr.shape()) {
for (size_t i = 0; i < size_; ++i) {
data_[i] = expr[i];
}
}
~NDArray() { delete[] data_; }
auto begin() { return data_; }
auto end() { return data_ + size_; }
auto begin() const { return data_; }
auto end() const { return data_ + size_; }
using value_type = T;
NDArray &operator=(NDArray &&other) noexcept; // Move assign
NDArray &operator=(const NDArray &other); // Copy assign
NDArray &operator+=(const NDArray &other);
NDArray &operator-=(const NDArray &other);
NDArray &operator*=(const NDArray &other);
//Write directly to the data array, or create a new one
template<size_t Size>
NDArray<T,1>& operator=(const std::array<T,Size> &other){
if(Size != size_){
delete[] data_;
size_ = Size;
data_ = new T[size_];
}
for (size_t i = 0; i < Size; ++i) {
data_[i] = other[i];
}
return *this;
}
// NDArray& operator/=(const NDArray& other);
template <typename V> NDArray &operator/=(const NDArray<V, Ndim> &other) {
// check shape
if (shape_ == other.shape()) {
for (uint32_t i = 0; i < size_; ++i) {
data_[i] /= other(i);
}
return *this;
}
throw(std::runtime_error("Shape of NDArray must match"));
}
NDArray<bool, Ndim> operator>(const NDArray &other);
bool operator==(const NDArray &other) const;
bool operator!=(const NDArray &other) const;
NDArray &operator=(const T & /*value*/);
NDArray &operator+=(const T & /*value*/);
NDArray operator+(const T & /*value*/);
NDArray &operator-=(const T & /*value*/);
NDArray operator-(const T & /*value*/);
NDArray &operator*=(const T & /*value*/);
NDArray operator*(const T & /*value*/);
NDArray &operator/=(const T & /*value*/);
NDArray operator/(const T & /*value*/);
NDArray &operator&=(const T & /*mask*/);
void sqrt() {
for (int i = 0; i < size_; ++i) {
data_[i] = std::sqrt(data_[i]);
}
}
NDArray &operator++(); // pre inc
template <typename... Ix>
std::enable_if_t<sizeof...(Ix) == Ndim, T &> operator()(Ix... index) {
return data_[element_offset(strides_, index...)];
}
template <typename... Ix>
std::enable_if_t<sizeof...(Ix) == Ndim, T &> operator()(Ix... index) const {
return data_[element_offset(strides_, index...)];
}
template <typename... Ix>
std::enable_if_t<sizeof...(Ix) == Ndim, T> value(Ix... index) {
return data_[element_offset(strides_, index...)];
}
// TODO! is int the right type for index?
T &operator()(int64_t i) { return data_[i]; }
const T &operator()(int64_t i) const { return data_[i]; }
T &operator[](int64_t i) { return data_[i]; }
const T &operator[](int64_t i) const { return data_[i]; }
T *data() { return data_; }
std::byte *buffer() { return reinterpret_cast<std::byte *>(data_); }
ssize_t size() const { return static_cast<ssize_t>(size_); }
size_t total_bytes() const { return size_ * sizeof(T); }
std::array<int64_t, Ndim> shape() const noexcept { return shape_; }
int64_t shape(int64_t i) const noexcept { return shape_[i]; }
std::array<int64_t, Ndim> strides() const noexcept { return strides_; }
size_t bitdepth() const noexcept { return sizeof(T) * 8; }
std::array<int64_t, Ndim> byte_strides() const noexcept {
auto byte_strides = strides_;
for (auto &val : byte_strides)
val *= sizeof(T);
return byte_strides;
}
/**
* @brief Create a view of the NDArray.
*
* @return NDView<T, Ndim>
*/
NDView<T, Ndim> view() const { return NDView<T, Ndim>{data_, shape_}; }
void Print();
void Print_all();
void Print_some();
void reset() {
data_ = nullptr;
size_ = 0;
std::fill(shape_.begin(), shape_.end(), 0);
std::fill(strides_.begin(), strides_.end(), 0);
}
};
// Move assign
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &
NDArray<T, Ndim>::operator=(NDArray<T, Ndim> &&other) noexcept {
if (this != &other) {
delete[] data_;
data_ = other.data_;
shape_ = other.shape_;
size_ = other.size_;
strides_ = other.strides_;
other.reset();
}
return *this;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator+=(const NDArray<T, Ndim> &other) {
// check shape
if (shape_ == other.shape_) {
for (size_t i = 0; i < size_; ++i) {
data_[i] += other.data_[i];
}
return *this;
}
throw(std::runtime_error("Shape of ImageDatas must match"));
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator-=(const NDArray<T, Ndim> &other) {
// check shape
if (shape_ == other.shape_) {
for (uint32_t i = 0; i < size_; ++i) {
data_[i] -= other.data_[i];
}
return *this;
}
throw(std::runtime_error("Shape of ImageDatas must match"));
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator*=(const NDArray<T, Ndim> &other) {
// check shape
if (shape_ == other.shape_) {
for (uint32_t i = 0; i < size_; ++i) {
data_[i] *= other.data_[i];
}
return *this;
}
throw(std::runtime_error("Shape of ImageDatas must match"));
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator&=(const T &mask) {
for (auto it = begin(); it != end(); ++it)
*it &= mask;
return *this;
}
template <typename T, int64_t Ndim>
NDArray<bool, Ndim> NDArray<T, Ndim>::operator>(const NDArray &other) {
if (shape_ == other.shape_) {
NDArray<bool, Ndim> result{shape_};
for (int i = 0; i < size_; ++i) {
result(i) = (data_[i] > other.data_[i]);
}
return result;
}
throw(std::runtime_error("Shape of ImageDatas must match"));
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator=(const NDArray<T, Ndim> &other) {
if (this != &other) {
delete[] data_;
shape_ = other.shape_;
strides_ = other.strides_;
size_ = other.size_;
data_ = new T[size_];
std::copy(other.data_, other.data_ + size_, data_);
}
return *this;
}
template <typename T, int64_t Ndim>
bool NDArray<T, Ndim>::operator==(const NDArray<T, Ndim> &other) const {
if (shape_ != other.shape_)
return false;
for (uint32_t i = 0; i != size_; ++i)
if (data_[i] != other.data_[i])
return false;
return true;
}
template <typename T, int64_t Ndim>
bool NDArray<T, Ndim>::operator!=(const NDArray<T, Ndim> &other) const {
return !((*this) == other);
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator++() {
for (uint32_t i = 0; i < size_; ++i)
data_[i] += 1;
return *this;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator=(const T &value) {
std::fill_n(data_, size_, value);
return *this;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator+=(const T &value) {
for (uint32_t i = 0; i < size_; ++i)
data_[i] += value;
return *this;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> NDArray<T, Ndim>::operator+(const T &value) {
NDArray result = *this;
result += value;
return result;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator-=(const T &value) {
for (uint32_t i = 0; i < size_; ++i)
data_[i] -= value;
return *this;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> NDArray<T, Ndim>::operator-(const T &value) {
NDArray result = *this;
result -= value;
return result;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator/=(const T &value) {
for (uint32_t i = 0; i < size_; ++i)
data_[i] /= value;
return *this;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> NDArray<T, Ndim>::operator/(const T &value) {
NDArray result = *this;
result /= value;
return result;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> &NDArray<T, Ndim>::operator*=(const T &value) {
for (uint32_t i = 0; i < size_; ++i)
data_[i] *= value;
return *this;
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> NDArray<T, Ndim>::operator*(const T &value) {
NDArray result = *this;
result *= value;
return result;
}
// template <typename T, int64_t Ndim> void NDArray<T, Ndim>::Print() {
// if (shape_[0] < 20 && shape_[1] < 20)
// Print_all();
// else
// Print_some();
// }
template <typename T, int64_t Ndim>
std::ostream &operator<<(std::ostream &os, const NDArray<T, Ndim> &arr) {
for (auto row = 0; row < arr.shape(0); ++row) {
for (auto col = 0; col < arr.shape(1); ++col) {
os << std::setw(3);
os << arr(row, col) << " ";
}
os << "\n";
}
return os;
}
template <typename T, int64_t Ndim> void NDArray<T, Ndim>::Print_all() {
for (auto row = 0; row < shape_[0]; ++row) {
for (auto col = 0; col < shape_[1]; ++col) {
std::cout << std::setw(3);
std::cout << (*this)(row, col) << " ";
}
std::cout << "\n";
}
}
template <typename T, int64_t Ndim> void NDArray<T, Ndim>::Print_some() {
for (auto row = 0; row < 5; ++row) {
for (auto col = 0; col < 5; ++col) {
std::cout << std::setw(7);
std::cout << (*this)(row, col) << " ";
}
std::cout << "\n";
}
}
template <typename T, int64_t Ndim>
void save(NDArray<T, Ndim> &img, std::string &pathname) {
std::ofstream f;
f.open(pathname, std::ios::binary);
f.write(img.buffer(), img.size() * sizeof(T));
f.close();
}
template <typename T, int64_t Ndim>
NDArray<T, Ndim> load(const std::string &pathname,
std::array<int64_t, Ndim> shape) {
NDArray<T, Ndim> img{shape};
std::ifstream f;
f.open(pathname, std::ios::binary);
f.read(img.buffer(), img.size() * sizeof(T));
f.close();
return img;
}
} // namespace aare

187
include/aare/NDView.hpp Normal file
View File

@@ -0,0 +1,187 @@
#pragma once
#include "aare/defs.hpp"
#include "aare/ArrayExpr.hpp"
#include <algorithm>
#include <array>
#include <cassert>
#include <cstdint>
#include <functional>
#include <iomanip>
#include <iostream>
#include <numeric>
#include <stdexcept>
#include <vector>
namespace aare {
template <int64_t Ndim> using Shape = std::array<int64_t, Ndim>;
// TODO! fix mismatch between signed and unsigned
template <int64_t Ndim> Shape<Ndim> make_shape(const std::vector<size_t> &shape) {
if (shape.size() != Ndim)
throw std::runtime_error("Shape size mismatch");
Shape<Ndim> arr;
std::copy_n(shape.begin(), Ndim, arr.begin());
return arr;
}
template <int64_t Dim = 0, typename Strides> int64_t element_offset(const Strides & /*unused*/) { return 0; }
template <int64_t Dim = 0, typename Strides, typename... Ix>
int64_t element_offset(const Strides &strides, int64_t i, Ix... index) {
return i * strides[Dim] + element_offset<Dim + 1>(strides, index...);
}
template <int64_t Ndim> std::array<int64_t, Ndim> c_strides(const std::array<int64_t, Ndim> &shape) {
std::array<int64_t, Ndim> strides{};
std::fill(strides.begin(), strides.end(), 1);
for (int64_t i = Ndim - 1; i > 0; --i) {
strides[i - 1] = strides[i] * shape[i];
}
return strides;
}
template <int64_t Ndim> std::array<int64_t, Ndim> make_array(const std::vector<int64_t> &vec) {
assert(vec.size() == Ndim);
std::array<int64_t, Ndim> arr{};
std::copy_n(vec.begin(), Ndim, arr.begin());
return arr;
}
template <typename T, int64_t Ndim = 2> class NDView : public ArrayExpr<NDView<T, Ndim>, Ndim> {
public:
NDView() = default;
~NDView() = default;
NDView(const NDView &) = default;
NDView(NDView &&) = default;
NDView(T *buffer, std::array<int64_t, Ndim> shape)
: buffer_(buffer), strides_(c_strides<Ndim>(shape)), shape_(shape),
size_(std::accumulate(std::begin(shape), std::end(shape), 1, std::multiplies<>())) {}
// NDView(T *buffer, const std::vector<int64_t> &shape)
// : buffer_(buffer), strides_(c_strides<Ndim>(make_array<Ndim>(shape))), shape_(make_array<Ndim>(shape)),
// size_(std::accumulate(std::begin(shape), std::end(shape), 1, std::multiplies<>())) {}
template <typename... Ix> std::enable_if_t<sizeof...(Ix) == Ndim, T &> operator()(Ix... index) {
return buffer_[element_offset(strides_, index...)];
}
template <typename... Ix> std::enable_if_t<sizeof...(Ix) == Ndim, T &> operator()(Ix... index) const {
return buffer_[element_offset(strides_, index...)];
}
ssize_t size() const { return static_cast<ssize_t>(size_); }
size_t total_bytes() const { return size_ * sizeof(T); }
std::array<int64_t, Ndim> strides() const noexcept { return strides_; }
T *begin() { return buffer_; }
T *end() { return buffer_ + size_; }
T const *begin() const { return buffer_; }
T const *end() const { return buffer_ + size_; }
T &operator()(int64_t i) const { return buffer_[i]; }
T &operator[](int64_t i) const { return buffer_[i]; }
bool operator==(const NDView &other) const {
if (size_ != other.size_)
return false;
for (uint64_t i = 0; i != size_; ++i) {
if (buffer_[i] != other.buffer_[i])
return false;
}
return true;
}
NDView &operator+=(const T val) { return elemenwise(val, std::plus<T>()); }
NDView &operator-=(const T val) { return elemenwise(val, std::minus<T>()); }
NDView &operator*=(const T val) { return elemenwise(val, std::multiplies<T>()); }
NDView &operator/=(const T val) { return elemenwise(val, std::divides<T>()); }
NDView &operator/=(const NDView &other) { return elemenwise(other, std::divides<T>()); }
template<size_t Size>
NDView& operator=(const std::array<T, Size> &arr) {
if(size() != static_cast<ssize_t>(arr.size()))
throw std::runtime_error(LOCATION + "Array and NDView size mismatch");
std::copy(arr.begin(), arr.end(), begin());
return *this;
}
NDView &operator=(const T val) {
for (auto it = begin(); it != end(); ++it)
*it = val;
return *this;
}
NDView &operator=(const NDView &other) {
if (this == &other)
return *this;
shape_ = other.shape_;
strides_ = other.strides_;
size_ = other.size_;
buffer_ = other.buffer_;
return *this;
}
NDView &operator=(NDView &&other) noexcept {
if (this == &other)
return *this;
shape_ = std::move(other.shape_);
strides_ = std::move(other.strides_);
size_ = other.size_;
buffer_ = other.buffer_;
other.buffer_ = nullptr;
return *this;
}
auto &shape() const { return shape_; }
auto shape(int64_t i) const { return shape_[i]; }
T *data() { return buffer_; }
void print_all() const;
private:
T *buffer_{nullptr};
std::array<int64_t, Ndim> strides_{};
std::array<int64_t, Ndim> shape_{};
uint64_t size_{};
template <class BinaryOperation> NDView &elemenwise(T val, BinaryOperation op) {
for (uint64_t i = 0; i != size_; ++i) {
buffer_[i] = op(buffer_[i], val);
}
return *this;
}
template <class BinaryOperation> NDView &elemenwise(const NDView &other, BinaryOperation op) {
for (uint64_t i = 0; i != size_; ++i) {
buffer_[i] = op(buffer_[i], other.buffer_[i]);
}
return *this;
}
};
template <typename T, int64_t Ndim> void NDView<T, Ndim>::print_all() const {
for (auto row = 0; row < shape_[0]; ++row) {
for (auto col = 0; col < shape_[1]; ++col) {
std::cout << std::setw(3);
std::cout << (*this)(row, col) << " ";
}
std::cout << "\n";
}
}
template <typename T, int64_t Ndim>
std::ostream& operator <<(std::ostream& os, const NDView<T, Ndim>& arr){
for (auto row = 0; row < arr.shape(0); ++row) {
for (auto col = 0; col < arr.shape(1); ++col) {
os << std::setw(3);
os << arr(row, col) << " ";
}
os << "\n";
}
return os;
}
} // namespace aare

119
include/aare/NumpyFile.hpp Normal file
View File

@@ -0,0 +1,119 @@
#pragma once
#include "aare/Dtype.hpp"
#include "aare/defs.hpp"
#include "aare/FileInterface.hpp"
#include "aare/NumpyHelpers.hpp"
#include <filesystem>
#include <iostream>
#include <numeric>
namespace aare {
/**
* @brief NumpyFile class to read and write numpy files
* @note derived from FileInterface
* @note implements all the pure virtual functions from FileInterface
* @note documentation for the functions can also be found in the FileInterface class
*/
class NumpyFile : public FileInterface {
public:
/**
* @brief NumpyFile constructor
* @param fname path to the numpy file
* @param mode file mode (r, w)
* @param cfg file configuration
*/
explicit NumpyFile(const std::filesystem::path &fname, const std::string &mode = "r", FileConfig cfg = {});
void write(Frame &frame);
Frame read_frame() override { return get_frame(this->current_frame++); }
Frame read_frame(size_t frame_number) override { return get_frame(frame_number); }
std::vector<Frame> read_n(size_t n_frames) override;
void read_into(std::byte *image_buf) override { return get_frame_into(this->current_frame++, image_buf); }
void read_into(std::byte *image_buf, size_t n_frames) override;
size_t frame_number(size_t frame_index) override { return frame_index; };
size_t bytes_per_frame() override;
size_t pixels_per_frame() override;
void seek(size_t frame_number) override { this->current_frame = frame_number; }
size_t tell() override { return this->current_frame; }
size_t total_frames() const override { return m_header.shape[0]; }
size_t rows() const override { return m_header.shape[1]; }
size_t cols() const override { return m_header.shape[2]; }
size_t bitdepth() const override { return m_header.dtype.bitdepth(); }
DetectorType detector_type() const override { return DetectorType::Unknown; }
/**
* @brief get the data type of the numpy file
* @return DType
*/
Dtype dtype() const { return m_header.dtype; }
/**
* @brief get the shape of the numpy file
* @return vector of type size_t
*/
std::vector<size_t> shape() const { return m_header.shape; }
/**
* @brief load the numpy file into an NDArray
* @tparam T data type of the NDArray
* @tparam NDim number of dimensions of the NDArray
* @return NDArray<T, NDim>
*/
template <typename T, size_t NDim> NDArray<T, NDim> load() {
NDArray<T, NDim> arr(make_shape<NDim>(m_header.shape));
if (fseek(fp, static_cast<int64_t>(header_size), SEEK_SET)) {
throw std::runtime_error(LOCATION + "Error seeking to the start of the data");
}
size_t rc = fread(arr.data(), sizeof(T), arr.size(), fp);
if (rc != static_cast<size_t>(arr.size())) {
throw std::runtime_error(LOCATION + "Error reading data from file");
}
return arr;
}
template <typename A, typename TYPENAME, A Ndim> void write(NDView<TYPENAME, Ndim> &frame) {
write_impl(frame.data(), frame.total_bytes());
}
template <typename A, typename TYPENAME, A Ndim> void write(NDArray<TYPENAME, Ndim> &frame) {
write_impl(frame.data(), frame.total_bytes());
}
template <typename A, typename TYPENAME, A Ndim> void write(NDView<TYPENAME, Ndim> &&frame) {
write_impl(frame.data(), frame.total_bytes());
}
template <typename A, typename TYPENAME, A Ndim> void write(NDArray<TYPENAME, Ndim> &&frame) {
write_impl(frame.data(), frame.total_bytes());
}
~NumpyFile() noexcept override;
private:
FILE *fp = nullptr;
size_t initial_header_len = 0;
size_t current_frame{};
uint32_t header_len{};
uint8_t header_len_size{};
size_t header_size{};
NumpyHeader m_header;
uint8_t major_ver_{};
uint8_t minor_ver_{};
size_t m_bytes_per_frame{};
size_t m_pixels_per_frame{};
size_t m_cols;
size_t m_rows;
size_t m_bitdepth;
void load_metadata();
void get_frame_into(size_t /*frame_number*/, std::byte * /*image_buf*/);
Frame get_frame(size_t frame_number);
void write_impl(void *data, uint64_t size);
};
} // namespace aare

View File

@@ -0,0 +1,55 @@
#pragma once
#include <algorithm>
#include <array>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <numeric>
#include <sstream>
#include <string>
#include <unordered_map>
#include <vector>
#include "aare/Dtype.hpp"
#include "aare/defs.hpp"
namespace aare {
struct NumpyHeader {
Dtype dtype{aare::Dtype::ERROR};
bool fortran_order{false};
std::vector<size_t> shape{};
std::string to_string() const;
};
namespace NumpyHelpers {
const constexpr std::array<char, 6> magic_str{'\x93', 'N', 'U', 'M', 'P', 'Y'};
const uint8_t magic_string_length{6};
std::string parse_str(const std::string &in);
/**
Removes leading and trailing whitespaces
*/
std::string trim(const std::string &str);
std::vector<std::string> parse_tuple(std::string in);
bool parse_bool(const std::string &in);
std::string get_value_from_map(const std::string &mapstr);
std::unordered_map<std::string, std::string> parse_dict(std::string in, const std::vector<std::string> &keys);
template <typename T, size_t N> bool in_array(T val, const std::array<T, N> &arr) {
return std::find(std::begin(arr), std::end(arr), val) != std::end(arr);
}
bool is_digits(const std::string &str);
aare::Dtype parse_descr(std::string typestring);
size_t write_header(const std::filesystem::path &fname, const NumpyHeader &header);
size_t write_header(std::ostream &out, const NumpyHeader &header);
} // namespace NumpyHelpers
} // namespace aare

209
include/aare/Pedestal.hpp Normal file
View File

@@ -0,0 +1,209 @@
#pragma once
#include "aare/Frame.hpp"
#include "aare/NDArray.hpp"
#include "aare/NDView.hpp"
#include <cstddef>
namespace aare {
/**
* @brief Calculate the pedestal of a series of frames. Can be used as
* standalone but mostly used in the ClusterFinder.
*
* @tparam SUM_TYPE type of the sum
*/
template <typename SUM_TYPE = double> class Pedestal {
uint32_t m_rows;
uint32_t m_cols;
uint32_t m_samples;
NDArray<uint32_t, 2> m_cur_samples;
//TODO! in case of int needs to be changed to uint64_t
NDArray<SUM_TYPE, 2> m_sum;
NDArray<SUM_TYPE, 2> m_sum2;
//Cache mean since it is used over and over in the ClusterFinder
//This optimization is related to the access pattern of the ClusterFinder
//Relies on having more reads than pushes to the pedestal
NDArray<SUM_TYPE, 2> m_mean;
public:
Pedestal(uint32_t rows, uint32_t cols, uint32_t n_samples = 1000)
: m_rows(rows), m_cols(cols), m_samples(n_samples),
m_cur_samples(NDArray<uint32_t, 2>({rows, cols}, 0)),
m_sum(NDArray<SUM_TYPE, 2>({rows, cols})),
m_sum2(NDArray<SUM_TYPE, 2>({rows, cols})),
m_mean(NDArray<SUM_TYPE, 2>({rows, cols})) {
assert(rows > 0 && cols > 0 && n_samples > 0);
m_sum = 0;
m_sum2 = 0;
m_mean = 0;
}
~Pedestal() = default;
NDArray<SUM_TYPE, 2> mean() {
return m_mean;
}
SUM_TYPE mean(const uint32_t row, const uint32_t col) const {
return m_mean(row, col);
}
SUM_TYPE std(const uint32_t row, const uint32_t col) const {
return std::sqrt(variance(row, col));
}
SUM_TYPE variance(const uint32_t row, const uint32_t col) const {
if (m_cur_samples(row, col) == 0) {
return 0.0;
}
return m_sum2(row, col) / m_cur_samples(row, col) -
mean(row, col) * mean(row, col);
}
NDArray<SUM_TYPE, 2> variance() {
NDArray<SUM_TYPE, 2> variance_array({m_rows, m_cols});
for (uint32_t i = 0; i < m_rows * m_cols; i++) {
variance_array(i / m_cols, i % m_cols) =
variance(i / m_cols, i % m_cols);
}
return variance_array;
}
NDArray<SUM_TYPE, 2> std() {
NDArray<SUM_TYPE, 2> standard_deviation_array({m_rows, m_cols});
for (uint32_t i = 0; i < m_rows * m_cols; i++) {
standard_deviation_array(i / m_cols, i % m_cols) =
std(i / m_cols, i % m_cols);
}
return standard_deviation_array;
}
void clear() {
m_sum = 0;
m_sum2 = 0;
m_cur_samples = 0;
m_mean = 0;
}
void clear(const uint32_t row, const uint32_t col) {
m_sum(row, col) = 0;
m_sum2(row, col) = 0;
m_cur_samples(row, col) = 0;
m_mean(row, col) = 0;
}
template <typename T> void push(NDView<T, 2> frame) {
assert(frame.size() == m_rows * m_cols);
// TODO! move away from m_rows, m_cols
if (frame.shape() != std::array<int64_t, 2>{m_rows, m_cols}) {
throw std::runtime_error(
"Frame shape does not match pedestal shape");
}
for (size_t row = 0; row < m_rows; row++) {
for (size_t col = 0; col < m_cols; col++) {
push<T>(row, col, frame(row, col));
}
}
}
/**
* Push but don't update the cached mean. Speeds up the process
* when initializing the pedestal.
*
*/
template <typename T> void push_no_update(NDView<T, 2> frame) {
assert(frame.size() == m_rows * m_cols);
// TODO! move away from m_rows, m_cols
if (frame.shape() != std::array<int64_t, 2>{m_rows, m_cols}) {
throw std::runtime_error(
"Frame shape does not match pedestal shape");
}
for (size_t row = 0; row < m_rows; row++) {
for (size_t col = 0; col < m_cols; col++) {
push_no_update<T>(row, col, frame(row, col));
}
}
}
template <typename T> void push(Frame &frame) {
assert(frame.rows() == static_cast<size_t>(m_rows) &&
frame.cols() == static_cast<size_t>(m_cols));
push<T>(frame.view<T>());
}
// getter functions
uint32_t rows() const { return m_rows; }
uint32_t cols() const { return m_cols; }
uint32_t n_samples() const { return m_samples; }
NDArray<uint32_t, 2> cur_samples() const { return m_cur_samples; }
NDArray<SUM_TYPE, 2> get_sum() const { return m_sum; }
NDArray<SUM_TYPE, 2> get_sum2() const { return m_sum2; }
// pixel level operations (should be refactored to allow users to implement
// their own pixel level operations)
template <typename T>
void push(const uint32_t row, const uint32_t col, const T val_) {
SUM_TYPE val = static_cast<SUM_TYPE>(val_);
if (m_cur_samples(row, col) < m_samples) {
m_sum(row, col) += val;
m_sum2(row, col) += val * val;
m_cur_samples(row, col)++;
} else {
m_sum(row, col) += val - m_sum(row, col) / m_samples;
m_sum2(row, col) += val * val - m_sum2(row, col) / m_samples;
}
//Since we just did a push we know that m_cur_samples(row, col) is at least 1
m_mean(row, col) = m_sum(row, col) / m_cur_samples(row, col);
}
template <typename T>
void push_no_update(const uint32_t row, const uint32_t col, const T val_) {
SUM_TYPE val = static_cast<SUM_TYPE>(val_);
if (m_cur_samples(row, col) < m_samples) {
m_sum(row, col) += val;
m_sum2(row, col) += val * val;
m_cur_samples(row, col)++;
} else {
m_sum(row, col) += val - m_sum(row, col) / m_cur_samples(row, col);
m_sum2(row, col) += val * val - m_sum2(row, col) / m_cur_samples(row, col);
}
}
/**
* @brief Update the mean of the pedestal. This is used after having done
* push_no_update. It is not necessary to call this function after push.
*/
void update_mean(){
m_mean = m_sum / m_cur_samples;
}
template<typename T>
void push_fast(const uint32_t row, const uint32_t col, const T val_){
//Assume we reached the steady state where all pixels have
//m_samples samples
SUM_TYPE val = static_cast<SUM_TYPE>(val_);
m_sum(row, col) += val - m_sum(row, col) / m_samples;
m_sum2(row, col) += val * val - m_sum2(row, col) / m_samples;
m_mean(row, col) = m_sum(row, col) / m_samples;
}
};
} // namespace aare

20
include/aare/PixelMap.hpp Normal file
View File

@@ -0,0 +1,20 @@
#pragma once
#include "aare/defs.hpp"
#include "aare/NDArray.hpp"
namespace aare {
NDArray<ssize_t, 2> GenerateMoench03PixelMap();
NDArray<ssize_t, 2> GenerateMoench05PixelMap();
NDArray<ssize_t, 2> GenerateMoench05PixelMap1g();
NDArray<ssize_t, 2> GenerateMoench05PixelMapOld();
//Matterhorn02
NDArray<ssize_t, 2>GenerateMH02SingleCounterPixelMap();
NDArray<ssize_t, 3> GenerateMH02FourCounterPixelMap();
//Eiger
NDArray<ssize_t, 2>GenerateEigerFlipRowsPixelMap();
} // namespace aare

View File

@@ -0,0 +1,203 @@
/*
* Copyright (c) Meta Platforms, Inc. and affiliates.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// @author Bo Hu (bhu@fb.com)
// @author Jordan DeLong (delong.j@fb.com)
// Changes made by PSD Detector Group:
// Copied: Line 34 constexpr std::size_t hardware_destructive_interference_size = 128; from folly/lang/Align.h
// Changed extension to .hpp
// Changed namespace to aare
#pragma once
#include <atomic>
#include <cassert>
#include <cstdlib>
#include <memory>
#include <stdexcept>
#include <type_traits>
#include <utility>
constexpr std::size_t hardware_destructive_interference_size = 128;
namespace aare {
/*
* ProducerConsumerQueue is a one producer and one consumer queue
* without locks.
*/
template <class T> struct ProducerConsumerQueue {
typedef T value_type;
ProducerConsumerQueue(const ProducerConsumerQueue &) = delete;
ProducerConsumerQueue &operator=(const ProducerConsumerQueue &) = delete;
ProducerConsumerQueue(ProducerConsumerQueue &&other){
size_ = other.size_;
records_ = other.records_;
other.records_ = nullptr;
readIndex_ = other.readIndex_.load(std::memory_order_acquire);
writeIndex_ = other.writeIndex_.load(std::memory_order_acquire);
}
ProducerConsumerQueue &operator=(ProducerConsumerQueue &&other){
size_ = other.size_;
records_ = other.records_;
other.records_ = nullptr;
readIndex_ = other.readIndex_.load(std::memory_order_acquire);
writeIndex_ = other.writeIndex_.load(std::memory_order_acquire);
return *this;
}
ProducerConsumerQueue():ProducerConsumerQueue(2){};
// size must be >= 2.
//
// Also, note that the number of usable slots in the queue at any
// given time is actually (size-1), so if you start with an empty queue,
// isFull() will return true after size-1 insertions.
explicit ProducerConsumerQueue(uint32_t size)
: size_(size), records_(static_cast<T *>(std::malloc(sizeof(T) * size))), readIndex_(0), writeIndex_(0) {
assert(size >= 2);
if (!records_) {
throw std::bad_alloc();
}
}
~ProducerConsumerQueue() {
// We need to destruct anything that may still exist in our queue.
// (No real synchronization needed at destructor time: only one
// thread can be doing this.)
if (!std::is_trivially_destructible<T>::value) {
size_t readIndex = readIndex_;
size_t endIndex = writeIndex_;
while (readIndex != endIndex) {
records_[readIndex].~T();
if (++readIndex == size_) {
readIndex = 0;
}
}
}
std::free(records_);
}
template <class... Args> bool write(Args &&...recordArgs) {
auto const currentWrite = writeIndex_.load(std::memory_order_relaxed);
auto nextRecord = currentWrite + 1;
if (nextRecord == size_) {
nextRecord = 0;
}
if (nextRecord != readIndex_.load(std::memory_order_acquire)) {
new (&records_[currentWrite]) T(std::forward<Args>(recordArgs)...);
writeIndex_.store(nextRecord, std::memory_order_release);
return true;
}
// queue is full
return false;
}
// move (or copy) the value at the front of the queue to given variable
bool read(T &record) {
auto const currentRead = readIndex_.load(std::memory_order_relaxed);
if (currentRead == writeIndex_.load(std::memory_order_acquire)) {
// queue is empty
return false;
}
auto nextRecord = currentRead + 1;
if (nextRecord == size_) {
nextRecord = 0;
}
record = std::move(records_[currentRead]);
records_[currentRead].~T();
readIndex_.store(nextRecord, std::memory_order_release);
return true;
}
// pointer to the value at the front of the queue (for use in-place) or
// nullptr if empty.
T *frontPtr() {
auto const currentRead = readIndex_.load(std::memory_order_relaxed);
if (currentRead == writeIndex_.load(std::memory_order_acquire)) {
// queue is empty
return nullptr;
}
return &records_[currentRead];
}
// queue must not be empty
void popFront() {
auto const currentRead = readIndex_.load(std::memory_order_relaxed);
assert(currentRead != writeIndex_.load(std::memory_order_acquire));
auto nextRecord = currentRead + 1;
if (nextRecord == size_) {
nextRecord = 0;
}
records_[currentRead].~T();
readIndex_.store(nextRecord, std::memory_order_release);
}
bool isEmpty() const {
return readIndex_.load(std::memory_order_acquire) == writeIndex_.load(std::memory_order_acquire);
}
bool isFull() const {
auto nextRecord = writeIndex_.load(std::memory_order_acquire) + 1;
if (nextRecord == size_) {
nextRecord = 0;
}
if (nextRecord != readIndex_.load(std::memory_order_acquire)) {
return false;
}
// queue is full
return true;
}
// * If called by consumer, then true size may be more (because producer may
// be adding items concurrently).
// * If called by producer, then true size may be less (because consumer may
// be removing items concurrently).
// * It is undefined to call this from any other thread.
size_t sizeGuess() const {
int ret = writeIndex_.load(std::memory_order_acquire) - readIndex_.load(std::memory_order_acquire);
if (ret < 0) {
ret += size_;
}
return ret;
}
// maximum number of items in the queue.
size_t capacity() const { return size_ - 1; }
private:
using AtomicIndex = std::atomic<unsigned int>;
char pad0_[hardware_destructive_interference_size];
// const uint32_t size_;
uint32_t size_;
// T *const records_;
T* records_;
alignas(hardware_destructive_interference_size) AtomicIndex readIndex_;
alignas(hardware_destructive_interference_size) AtomicIndex writeIndex_;
char pad1_[hardware_destructive_interference_size - sizeof(AtomicIndex)];
};
} // namespace aare

126
include/aare/RawFile.hpp Normal file
View File

@@ -0,0 +1,126 @@
#pragma once
#include "aare/FileInterface.hpp"
#include "aare/RawMasterFile.hpp"
#include "aare/Frame.hpp"
#include "aare/NDArray.hpp" //for pixel map
#include "aare/RawSubFile.hpp"
#include <optional>
namespace aare {
struct ModuleConfig {
int module_gap_row{};
int module_gap_col{};
bool operator==(const ModuleConfig &other) const {
if (module_gap_col != other.module_gap_col)
return false;
if (module_gap_row != other.module_gap_row)
return false;
return true;
}
};
/**
* @brief Class to read .raw files. The class will parse the master file
* to find the correct geometry for the frames.
* @note A more generic interface is available in the aare::File class.
* Consider using that unless you need raw file specific functionality.
*/
class RawFile : public FileInterface {
size_t n_subfiles{}; //f0,f1...fn
size_t n_subfile_parts{}; // d0,d1...dn
//TODO! move to vector of SubFile instead of pointers
std::vector<std::vector<RawSubFile *>> subfiles; //subfiles[f0,f1...fn][d0,d1...dn]
// std::vector<xy> positions;
ModuleConfig cfg{0, 0};
RawMasterFile m_master;
size_t m_current_frame{};
// std::vector<ModuleGeometry> m_module_pixel_0;
// size_t m_rows{};
// size_t m_cols{};
DetectorGeometry m_geometry;
public:
/**
* @brief RawFile constructor
* @param fname path to the master file (.json)
* @param mode file mode (only "r" is supported at the moment)
*/
RawFile(const std::filesystem::path &fname, const std::string &mode = "r");
virtual ~RawFile() override;
Frame read_frame() override;
Frame read_frame(size_t frame_number) override;
std::vector<Frame> read_n(size_t n_frames) override;
void read_into(std::byte *image_buf) override;
void read_into(std::byte *image_buf, size_t n_frames) override;
//TODO! do we need to adapt the API?
void read_into(std::byte *image_buf, DetectorHeader *header);
void read_into(std::byte *image_buf, size_t n_frames, DetectorHeader *header);
size_t frame_number(size_t frame_index) override;
size_t bytes_per_frame() override;
size_t pixels_per_frame() override;
size_t bytes_per_pixel() const;
void seek(size_t frame_index) override;
size_t tell() override;
size_t total_frames() const override;
size_t rows() const override;
size_t cols() const override;
size_t bitdepth() const override;
xy geometry();
size_t n_mod() const;
RawMasterFile master() const;
DetectorType detector_type() const override;
private:
/**
* @brief read the frame at the given frame index into the image buffer
* @param frame_number frame number to read
* @param image_buf buffer to store the frame
*/
void get_frame_into(size_t frame_index, std::byte *frame_buffer, DetectorHeader *header = nullptr);
/**
* @brief get the frame at the given frame index
* @param frame_number frame number to read
* @return Frame
*/
Frame get_frame(size_t frame_index);
/**
* @brief read the header of the file
* @param fname path to the data subfile
* @return DetectorHeader
*/
static DetectorHeader read_header(const std::filesystem::path &fname);
// void update_geometry_with_roi();
int find_number_of_subfiles();
void open_subfiles();
void find_geometry();
};
} // namespace aare

View File

@@ -0,0 +1,142 @@
#pragma once
#include "aare/defs.hpp"
#include <filesystem>
#include <fmt/format.h>
#include <fstream>
#include <optional>
#include <nlohmann/json.hpp>
using json = nlohmann::json;
namespace aare {
/**
* @brief Implementation used in RawMasterFile to parse the file name
*/
class RawFileNameComponents {
bool m_old_scheme{false};
std::filesystem::path m_base_path{};
std::string m_base_name{};
std::string m_ext{};
int m_file_index{}; // TODO! is this measurement_index?
public:
RawFileNameComponents(const std::filesystem::path &fname);
/// @brief Get the filename including path of the master file.
/// (i.e. what was passed in to the constructor))
std::filesystem::path master_fname() const;
/// @brief Get the filename including path of the data file.
/// @param mod_id module id run_d[module_id]_f0_0
/// @param file_id file id run_d0_f[file_id]_0
std::filesystem::path data_fname(size_t mod_id, size_t file_id) const;
const std::filesystem::path &base_path() const;
const std::string &base_name() const;
const std::string &ext() const;
int file_index() const;
void set_old_scheme(bool old_scheme);
};
class ScanParameters {
bool m_enabled = false;
std::string m_dac;
int m_start = 0;
int m_stop = 0;
int m_step = 0;
//TODO! add settleTime, requires string to time conversion
public:
ScanParameters(const std::string &par);
ScanParameters() = default;
ScanParameters(const ScanParameters &) = default;
ScanParameters &operator=(const ScanParameters &) = default;
ScanParameters(ScanParameters &&) = default;
int start() const;
int stop() const;
int step() const;
const std::string &dac() const;
bool enabled() const;
void increment_stop();
};
/**
* @brief Class for parsing a master file either in our .json format or the old
* .raw format
*/
class RawMasterFile {
RawFileNameComponents m_fnc;
std::string m_version;
DetectorType m_type;
TimingMode m_timing_mode;
size_t m_image_size_in_bytes{};
size_t m_frames_in_file{};
size_t m_total_frames_expected{};
size_t m_pixels_y{};
size_t m_pixels_x{};
size_t m_bitdepth{};
xy m_geometry{};
size_t m_max_frames_per_file{};
// uint32_t m_adc_mask{}; // TODO! implement reading
FrameDiscardPolicy m_frame_discard_policy{};
size_t m_frame_padding{};
// TODO! should these be bool?
uint8_t m_analog_flag{};
uint8_t m_digital_flag{};
uint8_t m_transceiver_flag{};
ScanParameters m_scan_parameters;
std::optional<size_t> m_analog_samples;
std::optional<size_t> m_digital_samples;
std::optional<size_t> m_transceiver_samples;
std::optional<size_t> m_number_of_rows;
std::optional<uint8_t> m_quad;
std::optional<ROI> m_roi;
public:
RawMasterFile(const std::filesystem::path &fpath);
std::filesystem::path data_fname(size_t mod_id, size_t file_id) const;
const std::string &version() const; //!< For example "7.2"
const DetectorType &detector_type() const;
const TimingMode &timing_mode() const;
size_t image_size_in_bytes() const;
size_t frames_in_file() const;
size_t pixels_y() const;
size_t pixels_x() const;
size_t max_frames_per_file() const;
size_t bitdepth() const;
size_t frame_padding() const;
const FrameDiscardPolicy &frame_discard_policy() const;
size_t total_frames_expected() const;
xy geometry() const;
std::optional<size_t> analog_samples() const;
std::optional<size_t> digital_samples() const;
std::optional<size_t> transceiver_samples() const;
std::optional<size_t> number_of_rows() const;
std::optional<uint8_t> quad() const;
std::optional<ROI> roi() const;
ScanParameters scan_parameters() const;
private:
void parse_json(const std::filesystem::path &fpath);
void parse_raw(const std::filesystem::path &fpath);
};
} // namespace aare

View File

@@ -0,0 +1,75 @@
#pragma once
#include "aare/Frame.hpp"
#include "aare/defs.hpp"
#include <cstdint>
#include <filesystem>
#include <map>
#include <optional>
namespace aare {
/**
* @brief Class to read a singe subfile written in .raw format. Used from RawFile to read
* the entire detector. Can be used directly to read part of the image.
*/
class RawSubFile {
protected:
std::ifstream m_file;
DetectorType m_detector_type;
size_t m_bitdepth;
std::filesystem::path m_fname;
size_t m_rows{};
size_t m_cols{};
size_t m_bytes_per_frame{};
size_t n_frames{};
uint32_t m_pos_row{};
uint32_t m_pos_col{};
std::optional<NDArray<ssize_t, 2>> m_pixel_map;
public:
/**
* @brief SubFile constructor
* @param fname path to the subfile
* @param detector detector type
* @param rows number of rows in the subfile
* @param cols number of columns in the subfile
* @param bitdepth bitdepth of the subfile
* @throws std::invalid_argument if the detector,type pair is not supported
*/
RawSubFile(const std::filesystem::path &fname, DetectorType detector,
size_t rows, size_t cols, size_t bitdepth, uint32_t pos_row = 0, uint32_t pos_col = 0);
~RawSubFile() = default;
/**
* @brief Seek to the given frame number
* @note Puts the file pointer at the start of the header, not the start of the data
* @param frame_index frame position in file to seek to
* @throws std::runtime_error if the frame number is out of range
*/
void seek(size_t frame_index);
size_t tell();
void read_into(std::byte *image_buf, DetectorHeader *header = nullptr);
void get_part(std::byte *buffer, size_t frame_index);
void read_header(DetectorHeader *header);
size_t rows() const;
size_t cols() const;
size_t frame_number(size_t frame_index);
size_t bytes_per_frame() const { return m_bytes_per_frame; }
size_t pixels_per_frame() const { return m_rows * m_cols; }
size_t bytes_per_pixel() const { return m_bitdepth / bits_per_byte; }
private:
template <typename T>
void read_with_map(std::byte *image_buf);
};
} // namespace aare

View File

@@ -0,0 +1,307 @@
#pragma once
#include <algorithm>
#include <map>
#include <unordered_map>
#include <vector>
#include "aare/NDArray.hpp"
const int MAX_CLUSTER_SIZE = 50;
namespace aare {
template <typename T> class VarClusterFinder {
public:
struct Hit {
int16_t size{};
int16_t row{};
int16_t col{};
uint16_t reserved{}; // for alignment
T energy{};
T max{};
// std::vector<int16_t> rows{};
// std::vector<int16_t> cols{};
int16_t rows[MAX_CLUSTER_SIZE] = {0};
int16_t cols[MAX_CLUSTER_SIZE] = {0};
double enes[MAX_CLUSTER_SIZE] = {0};
};
private:
const std::array<int64_t, 2> shape_;
NDView<T, 2> original_;
NDArray<int, 2> labeled_;
NDArray<int, 2> peripheral_labeled_;
NDArray<bool, 2> binary_; // over threshold flag
T threshold_;
NDView<T, 2> noiseMap;
bool use_noise_map = false;
int peripheralThresholdFactor_ = 5;
int current_label;
const std::array<int, 4> di{{0, -1, -1, -1}}; // row ### 8-neighbour by scaning from left to right
const std::array<int, 4> dj{{-1, -1, 0, 1}}; // col ### 8-neighbour by scaning from top to bottom
const std::array<int, 8> di_{{0, 0, -1, 1, -1, 1, -1, 1}}; // row
const std::array<int, 8> dj_{{-1, 1, 0, 0, 1, -1, -1, 1}}; // col
std::map<int, int> child; // heirachy: key: child; val: parent
std::unordered_map<int, Hit> h_size;
std::vector<Hit> hits;
// std::vector<std::vector<int16_t>> row
int check_neighbours(int i, int j);
public:
VarClusterFinder(Shape<2> shape, T threshold)
: shape_(shape), labeled_(shape, 0), peripheral_labeled_(shape, 0), binary_(shape), threshold_(threshold) {
hits.reserve(2000);
}
NDArray<int, 2> labeled() { return labeled_; }
void set_noiseMap(NDView<T, 2> noise_map) {
noiseMap = noise_map;
use_noise_map = true;
}
void set_peripheralThresholdFactor(int factor) { peripheralThresholdFactor_ = factor; }
void find_clusters(NDView<T, 2> img);
void find_clusters_X(NDView<T, 2> img);
void rec_FillHit(int clusterIndex, int i, int j);
void single_pass(NDView<T, 2> img);
void first_pass();
void second_pass();
void store_clusters();
std::vector<Hit> steal_hits() {
std::vector<Hit> tmp;
std::swap(tmp, hits);
return tmp;
};
void clear_hits() { hits.clear(); };
void print_connections() {
fmt::print("Connections:\n");
for (auto it = child.begin(); it != child.end(); ++it) {
fmt::print("{} -> {}\n", it->first, it->second);
}
}
size_t total_clusters() const {
// TODO! fix for stealing
return hits.size();
}
private:
void add_link(int from, int to) {
// we want to add key from -> value to
// fmt::print("add_link({},{})\n", from, to);
auto it = child.find(from);
if (it == child.end()) {
child[from] = to;
} else {
// found need to disambiguate
if (it->second == to)
return;
else {
if (it->second > to) {
// child[from] = to;
auto old = it->second;
it->second = to;
add_link(old, to);
} else {
// found value is smaller than what we want to link
add_link(to, it->second);
}
}
}
}
};
template <typename T> int VarClusterFinder<T>::check_neighbours(int i, int j) {
std::vector<int> neighbour_labels;
for (int k = 0; k < 4; ++k) {
const auto row = i + di[k];
const auto col = j + dj[k];
if (row >= 0 && col >= 0 && row < shape_[0] && col < shape_[1]) {
auto tmp = labeled_.value(i + di[k], j + dj[k]);
if (tmp != 0)
neighbour_labels.push_back(tmp);
}
}
if (neighbour_labels.size() == 0) {
return 0;
} else {
// need to sort and add to union field
std::sort(neighbour_labels.rbegin(), neighbour_labels.rend());
auto first = neighbour_labels.begin();
auto last = std::unique(first, neighbour_labels.end());
if (last - first == 1)
return *neighbour_labels.begin();
for (auto current = first; current != last - 1; ++current) {
auto next = current + 1;
add_link(*current, *next);
}
return neighbour_labels.back(); // already sorted
}
}
template <typename T> void VarClusterFinder<T>::find_clusters(NDView<T, 2> img) {
original_ = img;
labeled_ = 0;
peripheral_labeled_ = 0;
current_label = 0;
child.clear();
first_pass();
// print_connections();
second_pass();
store_clusters();
}
template <typename T> void VarClusterFinder<T>::find_clusters_X(NDView<T, 2> img) {
original_ = img;
int clusterIndex = 0;
for (int i = 0; i < shape_[0]; ++i) {
for (int j = 0; j < shape_[1]; ++j) {
if (use_noise_map)
threshold_ = 5 * noiseMap(i, j);
if (original_(i, j) > threshold_) {
// printf("========== Cluster index: %d\n", clusterIndex);
rec_FillHit(clusterIndex, i, j);
clusterIndex++;
}
}
}
for (const auto &h : h_size)
hits.push_back(h.second);
h_size.clear();
}
template <typename T> void VarClusterFinder<T>::rec_FillHit(int clusterIndex, int i, int j) {
// printf("original_(%d, %d)=%f\n", i, j, original_(i,j));
// printf("h_size[%d].size=%d\n", clusterIndex, h_size[clusterIndex].size);
if (h_size[clusterIndex].size < MAX_CLUSTER_SIZE) {
h_size[clusterIndex].rows[h_size[clusterIndex].size] = i;
h_size[clusterIndex].cols[h_size[clusterIndex].size] = j;
h_size[clusterIndex].enes[h_size[clusterIndex].size] = original_(i, j);
}
h_size[clusterIndex].size += 1;
h_size[clusterIndex].energy += original_(i, j);
if (h_size[clusterIndex].max < original_(i, j)) {
h_size[clusterIndex].row = i;
h_size[clusterIndex].col = j;
h_size[clusterIndex].max = original_(i, j);
}
original_(i, j) = 0;
for (int k = 0; k < 8; ++k) { // 8 for 8-neighbour
const auto row = i + di_[k];
const auto col = j + dj_[k];
if (row >= 0 && col >= 0 && row < shape_[0] && col < shape_[1]) {
if (use_noise_map)
threshold_ = peripheralThresholdFactor_ * noiseMap(row, col);
if (original_(row, col) > threshold_) {
rec_FillHit(clusterIndex, row, col);
} else {
// if (h_size[clusterIndex].size < MAX_CLUSTER_SIZE){
// h_size[clusterIndex].size += 1;
// h_size[clusterIndex].rows[h_size[clusterIndex].size] = row;
// h_size[clusterIndex].cols[h_size[clusterIndex].size] = col;
// h_size[clusterIndex].enes[h_size[clusterIndex].size] = original_(row, col);
// }// ? weather to include peripheral pixels
original_(row, col) = 0; // remove peripheral pixels, to avoid potential influence for pedestal updating
}
}
}
}
template <typename T> void VarClusterFinder<T>::single_pass(NDView<T, 2> img) {
original_ = img;
labeled_ = 0;
current_label = 0;
child.clear();
first_pass();
// print_connections();
// second_pass();
// store_clusters();
}
template <typename T> void VarClusterFinder<T>::first_pass() {
for (ssize_t i = 0; i < original_.size(); ++i) {
if (use_noise_map)
threshold_ = 5 * noiseMap(i);
binary_(i) = (original_(i) > threshold_);
}
for (int i = 0; i < shape_[0]; ++i) {
for (int j = 0; j < shape_[1]; ++j) {
// do we have someting to process?
if (binary_(i, j)) {
auto tmp = check_neighbours(i, j);
if (tmp != 0) {
labeled_(i, j) = tmp;
} else {
labeled_(i, j) = ++current_label;
}
}
}
}
}
template <typename T> void VarClusterFinder<T>::second_pass() {
for (ssize_t i = 0; i != labeled_.size(); ++i) {
auto cl = labeled_(i);
if (cl != 0) {
auto it = child.find(cl);
while (it != child.end()) {
cl = it->second;
it = child.find(cl);
// do this once before doing the second pass?
// all values point to the final one...
}
labeled_(i) = cl;
}
}
}
template <typename T> void VarClusterFinder<T>::store_clusters() {
// Accumulate hit information in a map
// Do we always have monotonic increasing
// labels? Then vector?
// here the translation is label -> Hit
std::unordered_map<int, Hit> h_map;
for (int i = 0; i < shape_[0]; ++i) {
for (int j = 0; j < shape_[1]; ++j) {
if (labeled_(i, j) != 0 || false
// (i-1 >= 0 and labeled_(i-1, j) != 0) or // another circle of peripheral pixels
// (j-1 >= 0 and labeled_(i, j-1) != 0) or
// (i+1 < shape_[0] and labeled_(i+1, j) != 0) or
// (j+1 < shape_[1] and labeled_(i, j+1) != 0)
) {
Hit &record = h_map[labeled_(i, j)];
if (record.size < MAX_CLUSTER_SIZE) {
record.rows[record.size] = i;
record.cols[record.size] = j;
record.enes[record.size] = original_(i, j);
} else {
continue;
}
record.size += 1;
record.energy += original_(i, j);
if (record.max < original_(i, j)) {
record.row = i;
record.col = j;
record.max = original_(i, j);
}
}
}
}
for (const auto &h : h_map)
hits.push_back(h.second);
}
} // namespace aare

111
include/aare/algorithm.hpp Normal file
View File

@@ -0,0 +1,111 @@
#pragma once
#include <algorithm>
#include <array>
#include <vector>
#include <aare/NDArray.hpp>
namespace aare {
/**
* @brief Index of the last element that is smaller than val.
* Requires a sorted array. Uses >= for ordering. If all elements
* are smaller it returns the last element and if all elements are
* larger it returns the first element.
* @param first iterator to the first element
* @param last iterator to the last element
* @param val value to compare
* @return index of the last element that is smaller than val
*
*/
template <typename T>
size_t last_smaller(const T* first, const T* last, T val) {
for (auto iter = first+1; iter != last; ++iter) {
if (*iter >= val) {
return std::distance(first, iter-1);
}
}
return std::distance(first, last-1);
}
template <typename T>
size_t last_smaller(const NDArray<T, 1>& arr, T val) {
return last_smaller(arr.begin(), arr.end(), val);
}
template <typename T>
size_t last_smaller(const std::vector<T>& vec, T val) {
return last_smaller(vec.data(), vec.data()+vec.size(), val);
}
/**
* @brief Index of the first element that is larger than val.
* Requires a sorted array. Uses > for ordering. If all elements
* are larger it returns the first element and if all elements are
* smaller it returns the last element.
* @param first iterator to the first element
* @param last iterator to the last element
* @param val value to compare
* @return index of the first element that is larger than val
*/
template <typename T>
size_t first_larger(const T* first, const T* last, T val) {
for (auto iter = first; iter != last; ++iter) {
if (*iter > val) {
return std::distance(first, iter);
}
}
return std::distance(first, last-1);
}
template <typename T>
size_t first_larger(const NDArray<T, 1>& arr, T val) {
return first_larger(arr.begin(), arr.end(), val);
}
template <typename T>
size_t first_larger(const std::vector<T>& vec, T val) {
return first_larger(vec.data(), vec.data()+vec.size(), val);
}
/**
* @brief Index of the nearest element to val.
* Requires a sorted array. If there is no difference it takes the first element.
* @param first iterator to the first element
* @param last iterator to the last element
* @param val value to compare
* @return index of the nearest element
*/
template <typename T>
size_t nearest_index(const T* first, const T* last, T val) {
auto iter = std::min_element(first, last,
[val](T a, T b) {
return std::abs(a - val) < std::abs(b - val);
});
return std::distance(first, iter);
}
template <typename T>
size_t nearest_index(const NDArray<T, 1>& arr, T val) {
return nearest_index(arr.begin(), arr.end(), val);
}
template <typename T>
size_t nearest_index(const std::vector<T>& vec, T val) {
return nearest_index(vec.data(), vec.data()+vec.size(), val);
}
template <typename T, size_t N>
size_t nearest_index(const std::array<T,N>& arr, T val) {
return nearest_index(arr.data(), arr.data()+arr.size(), val);
}
template <typename T>
std::vector<T> cumsum(const std::vector<T>& vec) {
std::vector<T> result(vec.size());
std::partial_sum(vec.begin(), vec.end(), result.begin());
return result;
}
} // namespace aare

13
include/aare/decode.hpp Normal file
View File

@@ -0,0 +1,13 @@
#pragma once
#include <cstdint>
#include <aare/NDView.hpp>
namespace aare {
uint16_t adc_sar_05_decode64to16(uint64_t input);
uint16_t adc_sar_04_decode64to16(uint64_t input);
void adc_sar_05_decode64to16(NDView<uint64_t, 2> input, NDView<uint16_t,2> output);
void adc_sar_04_decode64to16(NDView<uint64_t, 2> input, NDView<uint16_t,2> output);
} // namespace aare

266
include/aare/defs.hpp Normal file
View File

@@ -0,0 +1,266 @@
#pragma once
#include "aare/Dtype.hpp"
#include <array>
#include <stdexcept>
#include <cassert>
#include <cstdint>
#include <cstring>
#include <string>
#include <string_view>
#include <variant>
#include <vector>
/**
* @brief LOCATION macro to get the current location in the code
*/
#define LOCATION \
std::string(__FILE__) + std::string(":") + std::to_string(__LINE__) + \
":" + std::string(__func__) + ":"
#ifdef AARE_CUSTOM_ASSERT
#define AARE_ASSERT(expr)\
if (expr)\
{}\
else\
aare::assert_failed(LOCATION + " Assertion failed: " + #expr + "\n");
#else
#define AARE_ASSERT(cond)\
do { (void)sizeof(cond); } while(0)
#endif
namespace aare {
inline constexpr size_t bits_per_byte = 8;
void assert_failed(const std::string &msg);
class DynamicCluster {
public:
int cluster_sizeX;
int cluster_sizeY;
int16_t x;
int16_t y;
Dtype dt; // 4 bytes
private:
std::byte *m_data;
public:
DynamicCluster(int cluster_sizeX_, int cluster_sizeY_,
Dtype dt_ = Dtype(typeid(int32_t)))
: cluster_sizeX(cluster_sizeX_), cluster_sizeY(cluster_sizeY_),
dt(dt_) {
m_data = new std::byte[cluster_sizeX * cluster_sizeY * dt.bytes()]{};
}
DynamicCluster() : DynamicCluster(3, 3) {}
DynamicCluster(const DynamicCluster &other)
: DynamicCluster(other.cluster_sizeX, other.cluster_sizeY, other.dt) {
if (this == &other)
return;
x = other.x;
y = other.y;
memcpy(m_data, other.m_data, other.bytes());
}
DynamicCluster &operator=(const DynamicCluster &other) {
if (this == &other)
return *this;
this->~DynamicCluster();
new (this) DynamicCluster(other);
return *this;
}
DynamicCluster(DynamicCluster &&other) noexcept
: cluster_sizeX(other.cluster_sizeX),
cluster_sizeY(other.cluster_sizeY), x(other.x), y(other.y),
dt(other.dt), m_data(other.m_data) {
other.m_data = nullptr;
other.dt = Dtype(Dtype::TypeIndex::ERROR);
}
~DynamicCluster() { delete[] m_data; }
template <typename T> T get(int idx) {
(sizeof(T) == dt.bytes())
? 0
: throw std::invalid_argument("[ERROR] Type size mismatch");
return *reinterpret_cast<T *>(m_data + idx * dt.bytes());
}
template <typename T> auto set(int idx, T val) {
(sizeof(T) == dt.bytes())
? 0
: throw std::invalid_argument("[ERROR] Type size mismatch");
return memcpy(m_data + idx * dt.bytes(), &val, dt.bytes());
}
template <typename T> std::string to_string() const {
(sizeof(T) == dt.bytes())
? 0
: throw std::invalid_argument("[ERROR] Type size mismatch");
std::string s = "x: " + std::to_string(x) + " y: " + std::to_string(y) +
"\nm_data: [";
for (int i = 0; i < cluster_sizeX * cluster_sizeY; i++) {
s += std::to_string(
*reinterpret_cast<T *>(m_data + i * dt.bytes())) +
" ";
}
s += "]";
return s;
}
/**
* @brief size of the cluster in bytes when saved to a file
*/
size_t size() const { return cluster_sizeX * cluster_sizeY; }
size_t bytes() const { return cluster_sizeX * cluster_sizeY * dt.bytes(); }
auto begin() const { return m_data; }
auto end() const {
return m_data + cluster_sizeX * cluster_sizeY * dt.bytes();
}
std::byte *data() { return m_data; }
};
/**
* @brief header contained in parts of frames
*/
struct DetectorHeader {
uint64_t frameNumber;
uint32_t expLength;
uint32_t packetNumber;
uint64_t bunchId;
uint64_t timestamp;
uint16_t modId;
uint16_t row;
uint16_t column;
uint16_t reserved;
uint32_t debug;
uint16_t roundRNumber;
uint8_t detType;
uint8_t version;
std::array<uint8_t, 64> packetMask;
std::string to_string() {
std::string packetMaskStr = "[";
for (auto &i : packetMask) {
packetMaskStr += std::to_string(i) + ", ";
}
packetMaskStr += "]";
return "frameNumber: " + std::to_string(frameNumber) + "\n" +
"expLength: " + std::to_string(expLength) + "\n" +
"packetNumber: " + std::to_string(packetNumber) + "\n" +
"bunchId: " + std::to_string(bunchId) + "\n" +
"timestamp: " + std::to_string(timestamp) + "\n" +
"modId: " + std::to_string(modId) + "\n" +
"row: " + std::to_string(row) + "\n" +
"column: " + std::to_string(column) + "\n" +
"reserved: " + std::to_string(reserved) + "\n" +
"debug: " + std::to_string(debug) + "\n" +
"roundRNumber: " + std::to_string(roundRNumber) + "\n" +
"detType: " + std::to_string(detType) + "\n" +
"version: " + std::to_string(version) + "\n" +
"packetMask: " + packetMaskStr + "\n";
}
};
template <typename T> struct t_xy {
T row;
T col;
bool operator==(const t_xy &other) const {
return row == other.row && col == other.col;
}
bool operator!=(const t_xy &other) const { return !(*this == other); }
std::string to_string() const {
return "{ x: " + std::to_string(row) + " y: " + std::to_string(col) +
" }";
}
};
using xy = t_xy<uint32_t>;
/**
* @brief Class to hold the geometry of a module. Where pixel 0 is located and the size of the module
*/
struct ModuleGeometry{
int origin_x{};
int origin_y{};
int height{};
int width{};
int row_index{};
int col_index{};
};
/**
* @brief Class to hold the geometry of a detector. Number of modules, their size and where pixel 0
* for each module is located
*/
struct DetectorGeometry{
int modules_x{};
int modules_y{};
int pixels_x{};
int pixels_y{};
int module_gap_row{};
int module_gap_col{};
std::vector<ModuleGeometry> module_pixel_0;
};
struct ROI{
int64_t xmin{};
int64_t xmax{};
int64_t ymin{};
int64_t ymax{};
int64_t height() const { return ymax - ymin; }
int64_t width() const { return xmax - xmin; }
bool contains(int64_t x, int64_t y) const {
return x >= xmin && x < xmax && y >= ymin && y < ymax;
}
};
using dynamic_shape = std::vector<int64_t>;
//TODO! Can we uniform enums between the libraries?
/**
* @brief Enum class to identify different detectors.
* The values are the same as in slsDetectorPackage
* Different spelling to avoid confusion with the slsDetectorPackage
*/
enum class DetectorType {
//Standard detectors match the enum values from slsDetectorPackage
Generic,
Eiger,
Gotthard,
Jungfrau,
ChipTestBoard,
Moench,
Mythen3,
Gotthard2,
Xilinx_ChipTestBoard,
//Additional detectors used for defining processing. Variants of the standard ones.
Moench03=100,
Moench03_old,
Unknown
};
enum class TimingMode { Auto, Trigger };
enum class FrameDiscardPolicy { NoDiscard, Discard, DiscardPartial };
template <class T> T StringTo(const std::string &arg) { return T(arg); }
template <class T> std::string ToString(T arg) { return T(arg); }
template <> DetectorType StringTo(const std::string & /*name*/);
template <> std::string ToString(DetectorType arg);
template <> TimingMode StringTo(const std::string & /*mode*/);
template <> FrameDiscardPolicy StringTo(const std::string & /*mode*/);
using DataTypeVariants = std::variant<uint16_t, uint32_t>;
} // namespace aare

View File

@@ -0,0 +1,16 @@
#pragma once
#include "aare/defs.hpp"
#include "aare/RawMasterFile.hpp" //ROI refactor away
namespace aare{
/**
* @brief Update the detector geometry given a region of interest
*
* @param geo
* @param roi
* @return DetectorGeometry
*/
DetectorGeometry update_geometry_with_roi(DetectorGeometry geo, ROI roi);
} // namespace aare

View File

@@ -0,0 +1,18 @@
#include <thread>
#include <vector>
#include <utility>
namespace aare {
template<typename F>
void RunInParallel(F func, const std::vector<std::pair<int, int>>& tasks) {
// auto tasks = split_task(0, y.shape(0), n_threads);
std::vector<std::thread> threads;
for (auto &task : tasks) {
threads.push_back(std::thread(func, task.first, task.second));
}
for (auto &thread : threads) {
thread.join();
}
}
} // namespace aare

View File

@@ -0,0 +1,8 @@
#include <utility>
#include <vector>
namespace aare {
std::vector<std::pair<int, int>> split_task(int first, int last, int n_threads);
} // namespace aare

View File

@@ -0,0 +1,18 @@
diff --git a/CMakeLists.txt b/CMakeLists.txt
index dd3d8eb9..c0187747 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,11 +1,8 @@
# CMake build script for ZeroMQ
project(ZeroMQ)
-if(${CMAKE_SYSTEM_NAME} STREQUAL Darwin)
- cmake_minimum_required(VERSION 3.0.2)
-else()
- cmake_minimum_required(VERSION 2.8.12)
-endif()
+cmake_minimum_required(VERSION 3.15)
+message(STATUS "Patched cmake version")
include(CheckIncludeFiles)
include(CheckCCompilerFlag)

13
patches/lmfit.patch Normal file
View File

@@ -0,0 +1,13 @@
diff --git a/lib/CMakeLists.txt b/lib/CMakeLists.txt
index 4efb7ed..6533660 100644
--- a/lib/CMakeLists.txt
+++ b/lib/CMakeLists.txt
@@ -11,7 +11,7 @@ target_compile_definitions(${lib} PRIVATE "LMFIT_EXPORT") # for Windows DLL expo
target_include_directories(${lib}
PUBLIC
- $<BUILD_INTERFACE:${CMAKE_SOURCE_DIR}/>
+ $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/>
$<INSTALL_INTERFACE:include/>
)

23
pyproject.toml Normal file
View File

@@ -0,0 +1,23 @@
[build-system]
requires = ["scikit-build-core>=0.10", "pybind11", "numpy"]
build-backend = "scikit_build_core.build"
[project]
name = "aare"
version = "2025.4.1"
[tool.scikit-build]
cmake.verbose = true
[tool.scikit-build.cmake.define]
AARE_PYTHON_BINDINGS = "ON"
AARE_SYSTEM_LIBRARIES = "ON"
AARE_INSTALL_PYTHONEXT = "ON"
[tool.pytest.ini_options]
markers = [
"files: marks tests that need additional data (deselect with '-m \"not files\"')",
]

67
python/CMakeLists.txt Normal file
View File

@@ -0,0 +1,67 @@
find_package (Python 3.10 COMPONENTS Interpreter Development REQUIRED)
# Download or find pybind11 depending on configuration
if(AARE_FETCH_PYBIND11)
FetchContent_Declare(
pybind11
GIT_REPOSITORY https://github.com/pybind/pybind11
GIT_TAG v2.13.0
)
FetchContent_MakeAvailable(pybind11)
else()
find_package(pybind11 2.13 REQUIRED)
endif()
# Add the compiled python extension
pybind11_add_module(
_aare # name of the module
src/module.cpp # source file
)
set_target_properties(_aare PROPERTIES
LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}
)
target_link_libraries(_aare PRIVATE aare_core aare_compiler_flags)
# List of python files to be copied to the build directory
set( PYTHON_FILES
aare/__init__.py
aare/CtbRawFile.py
aare/func.py
aare/RawFile.py
aare/transform.py
aare/ScanParameters.py
aare/utils.py
)
# Copy the python files to the build directory
foreach(FILE ${PYTHON_FILES})
configure_file(${FILE} ${CMAKE_BINARY_DIR}/${FILE} )
endforeach(FILE ${PYTHON_FILES})
set_target_properties(_aare PROPERTIES
LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/aare
)
set(PYTHON_EXAMPLES
examples/play.py
examples/fits.py
)
# Copy the python examples to the build directory
foreach(FILE ${PYTHON_EXAMPLES})
configure_file(${FILE} ${CMAKE_BINARY_DIR}/${FILE} )
message(STATUS "Copying ${FILE} to ${CMAKE_BINARY_DIR}/${FILE}")
endforeach(FILE ${PYTHON_EXAMPLES})
if(AARE_INSTALL_PYTHONEXT)
install(TARGETS _aare
EXPORT "${TARGETS_EXPORT_NAME}"
LIBRARY DESTINATION aare
)
install(FILES ${PYTHON_FILES} DESTINATION aare)
endif()

191
python/aare/CtbRawFile.py Normal file
View File

@@ -0,0 +1,191 @@
from . import _aare
import numpy as np
from .ScanParameters import ScanParameters
class CtbRawFile(_aare.CtbRawFile):
"""File reader for the CTB raw file format.
Args:
fname (pathlib.Path | str): Path to the file to be read.
chunk_size (int): Number of frames to read at a time. Default is 1.
transform (function): Function to apply to the data after reading it.
The function should take a numpy array of type uint8 and return one
or several numpy arrays.
"""
def __init__(self, fname, chunk_size = 1, transform = None):
super().__init__(fname)
self._chunk_size = chunk_size
self._transform = transform
def read_frame(self, frame_index: int | None = None ) -> tuple:
"""Read one frame from the file and then advance the file pointer.
.. note::
Uses the position of the file pointer :py:meth:`~CtbRawFile.tell` to determine
which frame to read unless frame_index is specified.
Args:
frame_index (int): If not None, seek to this frame before reading.
Returns:
tuple: header, data
Raises:
RuntimeError: If the file is at the end.
"""
if frame_index is not None:
self.seek(frame_index)
header, data = super().read_frame()
if header.shape == (1,):
header = header[0]
if self._transform:
res = self._transform(data)
if isinstance(res, tuple):
return header, *res
else:
return header, res
else:
return header, data
def read_n(self, n_frames:int) -> tuple:
"""Read several frames from the file.
.. note::
Uses the position of the file pointer :py:meth:`~CtbRawFile.tell` to determine
where to start reading from.
If the number of frames requested is larger than the number of frames left in the file,
the function will read the remaining frames. If no frames are left in the file
a RuntimeError is raised.
Args:
n_frames (int): Number of frames to read.
Returns:
tuple: header, data
Raises:
RuntimeError: If EOF is reached.
"""
# Calculate the number of frames to actually read
n_frames = min(n_frames, self.frames_in_file - self.tell())
if n_frames == 0:
raise RuntimeError("No frames left in file.")
# Do the first read to figure out what we have
tmp_header, tmp_data = self.read_frame()
# Allocate arrays for
header = np.zeros(n_frames, dtype = tmp_header.dtype)
data = np.zeros((n_frames, *tmp_data.shape), dtype = tmp_data.dtype)
# Copy the first frame
header[0] = tmp_header
data[0] = tmp_data
# Do the rest of the reading
for i in range(1, n_frames):
header[i], data[i] = self.read_frame()
return header, data
def read(self) -> tuple:
"""Read the entire file.
Seeks to the beginning of the file before reading.
Returns:
tuple: header, data
"""
self.seek(0)
return self.read_n(self.frames_in_file)
def seek(self, frame_index:int) -> None:
"""Seek to a specific frame in the file.
Args:
frame_index (int): Frame position in file to seek to.
"""
super().seek(frame_index)
def tell(self) -> int:
"""Return the current frame position in the file.
Returns:
int: Frame position in file.
"""
return super().tell()
@property
def scan_parameters(self):
"""Return the scan parameters.
Returns:
ScanParameters: Scan parameters.
"""
return ScanParameters(self.master.scan_parameters)
@property
def master(self):
"""Return the master file.
Returns:
RawMasterFile: Master file.
"""
return super().master()
@property
def image_size_in_bytes(self) -> int:
"""Return the size of the image in bytes.
Returns:
int: Size of image in bytes.
"""
return super().image_size_in_bytes
def __len__(self) -> int:
"""Return the number of frames in the file.
Returns:
int: Number of frames in file.
"""
return super().frames_in_file
@property
def frames_in_file(self) -> int:
"""Return the number of frames in the file.
Returns:
int: Number of frames in file.
"""
return super().frames_in_file
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
pass
def __iter__(self):
return self
def __next__(self):
try:
if self._chunk_size == 1:
return self.read_frame()
else:
return self.read_n(self._chunk_size)
except RuntimeError:
# TODO! find a good way to check that we actually have the right exception
raise StopIteration

66
python/aare/RawFile.py Normal file
View File

@@ -0,0 +1,66 @@
from . import _aare
import numpy as np
from .ScanParameters import ScanParameters
class RawFile(_aare.RawFile):
def __init__(self, fname, chunk_size = 1):
super().__init__(fname)
self._chunk_size = chunk_size
def read(self) -> tuple:
"""Read the entire file.
Seeks to the beginning of the file before reading.
Returns:
tuple: header, data
"""
self.seek(0)
return self.read_n(self.total_frames)
@property
def scan_parameters(self):
"""Return the scan parameters.
Returns:
ScanParameters: Scan parameters.
"""
return ScanParameters(self.master.scan_parameters)
@property
def master(self):
"""Return the master file.
Returns:
RawMasterFile: Master file.
"""
return super().master
def __len__(self) -> int:
"""Return the number of frames in the file.
Returns:
int: Number of frames in file.
"""
return super().frames_in_file
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
pass
def __iter__(self):
return self
def __next__(self):
try:
if self._chunk_size == 1:
return self.read_frame()
else:
return self.read_n(self._chunk_size)
except RuntimeError:
# TODO! find a good way to check that we actually have the right exception
raise StopIteration

Some files were not shown because too many files have changed in this diff Show More