Tidy up Bu2019 injection

examples/KN/bash.sh

@@ -0,0 +1,27 @@
+#!/bin/bash -l
+#Set job requirements
+#SBATCH -N 1
+#SBATCH -n 1
+#SBATCH -p gpu
+#SBATCH -t 00:30:00
+#SBATCH --gpus-per-node=1
+#SBATCH --cpus-per-gpu=1
+#SBATCH --mem-per-gpu=5G
+#SBATCH --output=outdir_injection_Bu2019lm/log.out
+#SBATCH --job-name=injection_Bu2019lm
+
+now=$(date)
+echo "$now"
+
+# Loading modules
+# module load 2024
+# module load Python/3.10.4-GCCcore-11.3.0
+conda activate /home/twouters2/miniconda3/envs/ninjax
+
+# Display GPU name
+nvidia-smi --query-gpu=name --format=csv,noheader
+
+# Run the script
+python injection_Bu2019lm.py
+
+echo "DONE"

examples/KN/injection_Bu2019lm.py

@@ -16,7 +16,7 @@
 
 from fiesta.inference.lightcurve_model import BullaLightcurveModel
 from fiesta.inference.likelihood import EMLikelihood
-from fiesta.inference.prior import Uniform, Composite
+from fiesta.inference.prior import Uniform, CompositePrior
 from fiesta.inference.injection import InjectionRecovery
 from fiesta.inference.fiesta import Fiesta
 from fiesta.utils import load_event_data
@@ -72,7 +72,7 @@
 ###########################
 
 model = BullaLightcurveModel(name,
-                             f"../trained_models/{name}/",
+                             f"../../lightcurve_models/KN/{name}/",
                              filters)
 
 injection_dict = {"KNtheta": 30.0,
@@ -105,7 +105,7 @@
               luminosity_distance
               ]
 
-prior = Composite(prior_list)
+prior = CompositePrior(prior_list)
 
 detection_limit = None
 likelihood = EMLikelihood(model,
@@ -193,7 +193,7 @@
     ax.set_ylabel(filter_name)
     ax.invert_yaxis()
 
-plt.savefig(f"./figures/test_injection_{name}_data.png", bbox_inches = 'tight')
+plt.savefig(os.path.join(outdir, f"test_injection_{name}_data.png"), bbox_inches = 'tight')
 plt.close()
 
 # Fixed names: do not include them in the plotting, as will break corner

examples/KN/outdir_injection_Bu2019lm/log.out

@@ -0,0 +1,37 @@
+Thu Dec 19 16:49:10 CET 2024
+NVIDIA A100-SXM4-40GB
+GPU found? [cuda(id=0)]
+Loaded SurrogateLightcurveModel with filters ['ps1__g', 'ps1__r', 'ps1__i', 'ps1__z', 'ps1__y', '2massj', '2massh', '2massks', 'sdssu']
+Creating injection with filters: ['ps1__g', 'ps1__r', 'ps1__i', 'ps1__z', 'ps1__y', '2massj', '2massh', '2massks', 'sdssu']
+Converting error budget to dictionary.
+NOTE: No detection limit is given. Putting it to infinity.
+Loading and preprocessing observations in likelihood . . .
+Loading and preprocessing observations in likelihood . . . DONE
+INFO: Using MALA as local sampler
+Setting up a single Gaussian distribution
+No autotune found, use input sampler_params
+Training normalizing flow
+Tuning global sampler:   0%|          | 0/3 [00:00<?, ?it/s]Tuning global sampler:  33%|███▎      | 1/3 [01:13<02:26, 73.07s/it]Tuning global sampler:  67%|██████▋   | 2/3 [01:15<00:31, 31.51s/it]Tuning global sampler: 100%|██████████| 3/3 [01:17<00:00, 18.22s/it]Tuning global sampler: 100%|██████████| 3/3 [01:17<00:00, 25.96s/it]
+Compiling MALA body
+Starting Production run
+Production run:   0%|          | 0/3 [00:00<?, ?it/s]Production run:  33%|███▎      | 1/3 [00:00<00:00,  3.28it/s]Production run:  67%|██████▋   | 2/3 [00:00<00:00,  3.29it/s]Production run: 100%|██████████| 3/3 [00:00<00:00,  3.31it/s]Production run: 100%|██████████| 3/3 [00:00<00:00,  3.30it/s]
+Saving injection dict
+Saving samples to ./outdir_injection_Bu2019lm/results_training.npz
+Saving samples to ./outdir_injection_Bu2019lm/results_production.npz
+DONE
+Total runtime: 1.0 m 27.07 s
+DONE
+
+JOB STATISTICS
+==============
+Job ID: 9118840
+Cluster: snellius
+User/Group: twouters2/twouters2
+State: COMPLETED (exit code 0)
+Nodes: 1
+Cores per node: 18
+CPU Utilized: 00:01:44
+CPU Efficiency: 5.56% of 00:31:12 core-walltime
+Job Wall-clock time: 00:01:44
+Memory Utilized: 1.40 GB
+Memory Efficiency: 27.96% of 5.00 GB