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| #! /bin/bash | ||
| #SBATCH -A mp107 | ||
| #SBATCH -C cpu | ||
| #SBATCH -n 1 # node count | ||
| #SBATCH -c 128 | ||
| #SBATCH -q debug | ||
| #SBATCH -t 0:30:00 | ||
| #SBATCH --output=slurmoutput/R-%j.out | ||
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| cd /pscratch/sd/x/xzackli/so/PSpipe_ana_cov_comp/project/ana_cov_comp | ||
| base_dir=/pscratch/sd/x/xzackli/so/envs/ps | ||
| pyenv_dir=${base_dir}/pyenv/perlmutter | ||
| export SOFIND_SYSTEM=perlmutter | ||
| export PYTHONPATH=$PYTHONPATH:${software_dir} | ||
| source ${pyenv_dir}/bin/activate | ||
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| export OMP_NUM_THREADS=128 | ||
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| $1 | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,19 @@ | ||
| #! /bin/bash | ||
| #SBATCH -A mp107 | ||
| #SBATCH -C cpu | ||
| #SBATCH -n 1 # node count | ||
| #SBATCH -c 128 | ||
| #SBATCH -q regular | ||
| #SBATCH -t 1:00:00 | ||
| #SBATCH --output=slurmoutput/R-%j.out | ||
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| cd /pscratch/sd/x/xzackli/so/PSpipe_ana_cov_comp/project/ana_cov_comp | ||
| base_dir=/pscratch/sd/x/xzackli/so/envs/ps | ||
| pyenv_dir=${base_dir}/pyenv/perlmutter | ||
| export SOFIND_SYSTEM=perlmutter | ||
| export PYTHONPATH=$PYTHONPATH:${software_dir} | ||
| source ${pyenv_dir}/bin/activate | ||
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| export OMP_NUM_THREADS=128 | ||
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| $1 |
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| This script reads the recipe from get_4pt_coupling_matrices_recipe.py and then cooks! | ||
| # For production on all, do something like | ||
| alias shortjob="sbatch paramfiles/1perlmutternode.slurm $1" # short QOS | ||
| for i in {0..57}; do \ | ||
| shortjob "srun --ntasks 1 --cpus-per-task 128 --cpu-bind=cores python -u \ | ||
| python/get_4pt_coupling_result.py paramfiles/cov_dr6_v4_20231128.dict $((50*i)) $((50*i+50))" | ||
| done | ||
| # For testing only: | ||
| alias testjob="sbatch paramfiles/1perlmutterdebug.slurm $1" # test QOS | ||
| testjob "srun --ntasks 1 --cpus-per-task 128 --cpu-bind=cores python -u \ | ||
| python/get_4pt_coupling_result.py paramfiles/cov_dr6_v4_20231128.dict 1 2" | ||
| """ | ||
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| import sys | ||
| import numpy as np | ||
| from pspipe_utils import log, covariance as psc | ||
| from pspy import so_dict, so_map, so_mcm, pspy_utils | ||
| from itertools import product, combinations_with_replacement as cwr | ||
| import os | ||
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| d = so_dict.so_dict() | ||
| d.read_from_file(sys.argv[1]) | ||
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| log = log.get_logger(**d) | ||
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| ewin_alms_dir = d['ewin_alms_dir'] | ||
| couplings_dir = d['couplings_dir'] | ||
| pspy_utils.create_directory(couplings_dir) | ||
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| surveys = d['surveys'] | ||
| arrays = {sv: d[f'arrays_{sv}'] for sv in surveys} | ||
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| if d['use_toeplitz_cov'] == True: | ||
| log.info('we will use the toeplitz approximation') | ||
| l_exact, l_band, l_toep = 800, 2000, 2750 | ||
| else: | ||
| l_exact, l_band, l_toep = None, None, None | ||
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| lmax = d['lmax'] | ||
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| recipe = np.load(f'{couplings_dir}/4pt_recipe.npy', allow_pickle=True)[()] | ||
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| unrolled = recipe['S_only'] + recipe['SxN'] + recipe['N_only'] | ||
| total_couplings = len(unrolled) | ||
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| if len(sys.argv) == 4: | ||
| log.info(f"computing only the covariance matrices : " + | ||
| f"{int(sys.argv[2])}:{int(sys.argv[3])} of {total_couplings}") | ||
| unrolled = unrolled[int(sys.argv[2]):int(sys.argv[3])] | ||
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| for k in unrolled: | ||
| spintype, filename1, filename2, coupling_fn = k['spintype'], k['w1'], k['w2'], k['coupling_fn'] | ||
| w1 = np.load(filename1) | ||
| w2 = np.load(filename2) | ||
| log.info(f"{spintype} {coupling_fn} from {filename1} {filename2}") | ||
| coupling = so_mcm.coupling_block(spintype, win1=w1, win2=w2, | ||
| lmax=lmax, input_alm=True, | ||
| l_exact=l_exact, l_toep=l_toep, | ||
| l_band=l_band) | ||
| np.save(coupling_fn, coupling) |