Merge pull request #9 from EPCCed/data-generation

coarsening done

docs/data-generation.md

@@ -38,6 +38,10 @@ Following the structure given in the [general data generation](ML_training.md) c
 
     Edit line 17 (`for TRAJ_INDEX in {1..10}`) to give the desired number of trajectories. Edit `<account>` on line 9 and `x01` in lines containing paths for your project and submit via `sbatch fine_trajectories.sh`.
 
-3. Coarsen selected simulation snapshots. ## check this
+3. Coarsen selected simulation snapshots.
 
-    Fine-grained data must be coarsened to match the desired coarse-grained resolution. This can be done via interpolation for a general solution. Files in [files/coarsening](files/coarsening) perform this task.
+    Fine-grained data must be coarsened to match the desired coarse-grained resolution. This can be done via interpolation for a general solution. Files in [files/coarsening](files/coarsening) perform this task. Submit `submit-resize.sh` via `sbatch submit-resize.sh`.
+
+4. Single-timestep coarse simulations.
+
+

files/coarsening/resize_trajectory.py

@@ -7,7 +7,7 @@
 import restart
 
 ID_TRAJ    = sys.argv[1]
-INPUT_PATH = f'/scratch/space1/d175/data/training/ground-truth/trajectory_{ID_TRAJ}'
+INPUT_PATH = f'./{ID_TRAJ}'
 BASE_PATH  = Path(sys.argv[2])
 num_timesteps = 1001
 

files/coarsening/submit-resize.sh

@@ -13,7 +13,7 @@
 # activate conda environment for boutdata and xbout packages
 conda activate boutsmartsim
 
-cd /path/to/SiMLInt/files/coarsening
-TRAJECTORY=17
-OUTPUT_PATH=/path/to/${TRAJECTORY}
+cd /scratch/space1/x01/data/my-scratch-data
+TRAJECTORY=1
+OUTPUT_PATH=${TRAJECTORY}/extracted
 python resize_trajectory.py ${TRAJECTORY} ${OUTPUT_PATH}