Issues with Simulation and running the model

[PhindyChimele20]

Hi, I read your paper and it is pretty cool. I am interested in trying the analysis in our SNPs dataset, so I have been following the instructions on the documentation and tried to run the analysis on the wolves example dataset that is provided before I can run it on my own data. However, I have been encountering problems 1)When I run the simulation with the benchmark.slim file I am getting an error: "Finished generation" "99" "; N=" "10006"
"Finished generation" "100" "; N=" "9951"
ERROR (EidosSymbolTable::_GetValue): undefined identifier De.

Error on script line 120, character 22:

        De[i,j] = De[i,j] + 1;

2. When I tried to run the Empirical analysis using the examples dataset provided, running the simulation with the wolves.slim file gives; slim -d "MAP_FILE_0='Examples/Empirical/cookie_123_disp.csv'" -d "MAP_FILE_1='Examples/Empirical/cookie_123_dens.csv'" -d "OUTNAME='Examples/Empirical/sim'" -d SEED=2 SLiM_recipes/wolves.slim
   // Initial random seed:
   6770018400048796296

// RunInitializeCallbacks():
initializeSLiMModelType(modelType = 'nonWF');
initializeSLiMOptions(dimensionality = 'xy');
initializeTreeSeq(checkCoalescence = T);
#WARNING (Eidos_ExecuteFunction_readCSV): function readCSV() could not read file at path /home/chriscs/Software/disperseNN3/Wolves/genetic_map.txt.
ERROR (EidosInterpreter::Evaluate_Call): operand type NULL is not supported by the '.' operator.

Error on script line 25, character 14:

ends = map.getValue("ends");

I am not sure if there's an issue with the file?

3. So does running the mapnn --processing step, it seems to be giving an issue: I ran the script provided as the example python mapnn.py --preprocess --out Examples/Empirical/ --simid 1 --seed 1 --num_snps 10627 --n 94 --tree_list /home/pntuli/lustre/mapNN/tempout/tree_list.txt --target_list /home/pntuli/lustre/mapNN/tempout/map_list.txt --map_width 50 --slim_width 7109.537608030495 --habitat_map Examples/Empirical/wolf_distribution.png --empirical Examples/Empirical/wolves_n94 --chroms 38
   2024-10-10 15:31:06.155360: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
   2024-10-10 15:31:06.227482: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
   2024-10-10 15:31:06.228105: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
   To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
   2024-10-10 15:31:07.464549: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
   starting pre-processing pipeline
   getting mean from training, on sim 0
   getting sd from training, on sim 0
   Traceback (most recent call last):
   File "/mnt/lustre/users/pntuli/mapNN/mapnn.py", line 1250, in
   preprocess()
   File "/mnt/lustre/users/pntuli/mapNN/mapnn.py", line 958, in preprocess
   geno_mat, locs = training_generator.sample_ts(trees[args.simid-1], args.seed) # -1 for 0-indexing
   File "/mnt/lustre/users/pntuli/mapNN/data_generation.py", line 209, in sample_ts
   tss.append(tskit.load(fp))
   File "/apps/chpc/bio/anaconda3-2020.02/envs/mapnn/lib/python3.9/site-packages/tskit/trees.py", line 3289, in load
   return TreeSequence.load(
   File "/apps/chpc/bio/anaconda3-2020.02/envs/mapnn/lib/python3.9/site-packages/tskit/trees.py", line 4084, in load
   file, local_file = util.convert_file_like_to_open_file(file_or_path, "rb")
   File "/apps/chpc/bio/anaconda3-2020.02/envs/mapnn/lib/python3.9/site-packages/tskit/util.py", line 692, in convert_file_like_to_open_file
   _file = open(path, mode)
   FileNotFoundError: [Errno 2] No such file or directory: 'tempout/recap_123_chr1.trees'

Please assist, as I am interested in trying this analysis on my SNPs dataset.

[andrewkern]

thanks for your interest in the software. you have a bunch of questions here so let's just start with the first one, which seems to be some difficulty in running the benchmark.slim SLiM recipe. What version of SLiM are you running?

[PhindyChimele20]

I am running SLiM version 4.0.1

[andrewkern]

I was able to run the benchmark.slim recipe with SLiM version 4.0.1-- can you post the complete command line you are using?