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License: MIT License
AlgoRithm for Charge Transfer Inefficiency (CTI) Correction
License: MIT License
Hello everybody,
I discovered that it's not possible to install Arctic on a MacBook computer with a M1/M2 Arm processor.
What happened:
During the installation process, the autoconf
package version 2022.7.11.1 is installed (see
Line 19 in 750bdfb
Unfortunately, this package is installing pyyaml
version 5.4.1 which is definitely not compatible with a M1/M2 Arm processor.
Possible solution:
Using package autoconf
version 2023.9.18.4 solves the issue. This package will install pyyaml
version >=6.0.1 which is compatible with a M1/M2 Arm processor.
See the following code snippet to fix the issue in pyproject.toml
:
dependencies = [
"autoconf==2023.9.18.4", # <= This is new
"numpy ~=1.21",
]
Cheers,
Frederic
name: Holding into too much memory
about: File a bug/issue
title: '[BUG] Not dumping uneccessary data from memory '
labels: Bug, Question
Using parallelisation software to run through arctic multiple times. Each run increases the memory usage of the workers adn does not release memory, causing crashes for larger simulations.
This has worked fine before with previous master commit #0a9ff64fb8a263d8d8a85924c7b6c11cbb276bbd - workers didn't have memory usage beyond roughly 0.5GB. Only new master seems to have this problem.
Hi all,
I have just tried to install the latest version of arctic from source using the installatiion instructions from the readme but when I come to the make commands I get the following error:
I am not sure if the readme or the code (or even my poor C installation skills!) that need reviewing though.
Thanks!
It's not possible to compile ArCTIC under MacOS with the clang compiler.
Here is the error message:
$ make gsl
... everything is fine ...
$ make lib
...
d: unknown option: -rpath=/System/Volumes/Data/work/sw/fred_arctic/arctic/gsl/lib/
clang: error: linker command failed with exit code 1 (use -v to see invocation)
make: *** [libarctic.so] Error 1
ICs of pixel bounce spread countinertuitively between kA and kv parameters. They work, but aren't split properly.
If I pass ROEChargeInjection to the wrapper, it does not calls it set_express_matrix_from_rows_and_express
and set_store_trap_states_matrix
methods, instead reverting to the methods of the default ROE
class.
I have put a print(roe->type) before the following line in
add_cti.cpp`:
roe->set_express_matrix_from_rows_and_express(n_rows, express, offset);
And the type is indeed type 1, indicating that the Python wrapper appears to be passing the correct type.
My best guess is that the inheritance structure is failing to overwrite the methods correctly?
Building the library 'gsl' with the command make gsl
is running in one core.
This compilation process could be easily reduced by compiling 'gsl' in parallel mode (see https://www.gnu.org/software/make/manual/html_node/Parallel.html)
To activate parallel mode, all you have to do is replace all make <something>
in get_gsl.sh
by make -j <something>
.
Dear all,
I just found a 'nasty' bug.
When 'arctic' is used (for example) to remove CTI, lots of debug information is shown even when the verbosity level is set to 0 .
So when we run our Python script using Arctic from the command line, we get a lot of logging information (it's fine) and the script works (that's good). But when we run the script using JupyterLab, the code is blocking (not good !) because there is too many logging information (even if it's not displayed in JupyterLab)
The only solution I found is to replace in the C++ code the following statements print_v(0, "...");
with print_v(1, "...")
.
I will create a new pull request for a possible solution.
Cheers,
Frederic
The following script compares two datasets arctic clocking.
https://github.com/Jammy2211/autocti_workspace_test/blob/main/imaging_ci/profiling/arctic/noise.py
The datasets are identical, except one has Gaussian noise added to it whereas the other does not.
The clocking times for arctic for these two datasets are:
Clocking Time = 2.2919435501098633
Clocking Time No Noise = 7.8446900844573975
Experiments I did previously the latter time was going up to 12 seconds, albeit I cant reproduce this now. This likely depends on the exact nature of the image I simulate, but the general slow-down for noiseless data is likely a generic problem.
I'll send the files on teams as github wont let me.
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