NDD_preprocessing_256_256.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e62b64a4-b2ac-48ea-a322-feb01c638b68",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:   0%|          | 0/209 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND01/outputs/physics_allparticle_275800.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:   1%|          | 2/209 [00:01<02:06,  1.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND03/outputs/physics_allparticle_254100.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:   2%|▏         | 4/209 [00:02<01:35,  2.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: Missing required fields ['phi'] in file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND05/outputs/physics_allparticle_222000.vtk'. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:   5%|▍         | 10/209 [00:04<01:19,  2.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND11/outputs/physics_allparticle_277400.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:  14%|█▍        | 30/209 [00:13<01:07,  2.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND31/outputs/physics_allparticle_225300.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:  15%|█▌        | 32/209 [00:14<01:17,  2.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND33/outputs/physics_allparticle_224800.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:  26%|██▋       | 55/209 [00:23<00:58,  2.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND56/outputs/physics_allparticle_306100.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:  29%|██▊       | 60/209 [00:24<00:52,  2.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND61/outputs/physics_allparticle_241400.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:  34%|███▍      | 71/209 [00:29<00:52,  2.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND72/outputs/physics_allparticle_234200.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:  35%|███▍      | 73/209 [00:30<00:57,  2.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND74/outputs/physics_allparticle_232000.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:  43%|████▎     | 90/209 [00:37<00:48,  2.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND91/outputs/physics_allparticle_208800.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds:  54%|█████▍    | 113/209 [00:51<01:17,  1.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_13/outputs/physics_allparticle_213300.vtk': could not convert string to float: ''. Skipping this file.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating global bounds: 100%|██████████| 209/209 [01:12<00:00,  2.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_166100.vtk' has a number <= 170000: 166100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_166000.vtk' has a number <= 170000: 166000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165900.vtk' has a number <= 170000: 165900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165800.vtk' has a number <= 170000: 165800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165700.vtk' has a number <= 170000: 165700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165600.vtk' has a number <= 170000: 165600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165500.vtk' has a number <= 170000: 165500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165400.vtk' has a number <= 170000: 165400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165300.vtk' has a number <= 170000: 165300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165200.vtk' has a number <= 170000: 165200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165100.vtk' has a number <= 170000: 165100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_165000.vtk' has a number <= 170000: 165000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164900.vtk' has a number <= 170000: 164900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164800.vtk' has a number <= 170000: 164800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164700.vtk' has a number <= 170000: 164700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164600.vtk' has a number <= 170000: 164600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164500.vtk' has a number <= 170000: 164500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164400.vtk' has a number <= 170000: 164400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164300.vtk' has a number <= 170000: 164300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164200.vtk' has a number <= 170000: 164200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164100.vtk' has a number <= 170000: 164100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_164000.vtk' has a number <= 170000: 164000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163900.vtk' has a number <= 170000: 163900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163800.vtk' has a number <= 170000: 163800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163700.vtk' has a number <= 170000: 163700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163600.vtk' has a number <= 170000: 163600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163500.vtk' has a number <= 170000: 163500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163400.vtk' has a number <= 170000: 163400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163300.vtk' has a number <= 170000: 163300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163200.vtk' has a number <= 170000: 163200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163100.vtk' has a number <= 170000: 163100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_163000.vtk' has a number <= 170000: 163000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162900.vtk' has a number <= 170000: 162900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162800.vtk' has a number <= 170000: 162800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162700.vtk' has a number <= 170000: 162700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162600.vtk' has a number <= 170000: 162600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162500.vtk' has a number <= 170000: 162500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162400.vtk' has a number <= 170000: 162400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162300.vtk' has a number <= 170000: 162300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162200.vtk' has a number <= 170000: 162200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162100.vtk' has a number <= 170000: 162100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_162000.vtk' has a number <= 170000: 162000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161900.vtk' has a number <= 170000: 161900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161800.vtk' has a number <= 170000: 161800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161700.vtk' has a number <= 170000: 161700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161600.vtk' has a number <= 170000: 161600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161500.vtk' has a number <= 170000: 161500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161400.vtk' has a number <= 170000: 161400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161300.vtk' has a number <= 170000: 161300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161200.vtk' has a number <= 170000: 161200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161100.vtk' has a number <= 170000: 161100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_161000.vtk' has a number <= 170000: 161000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_160900.vtk' has a number <= 170000: 160900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_160800.vtk' has a number <= 170000: 160800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_160700.vtk' has a number <= 170000: 160700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_160600.vtk' has a number <= 170000: 160600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_160500.vtk' has a number <= 170000: 160500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_160400.vtk' has a number <= 170000: 160400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_160300.vtk' has a number <= 170000: 160300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_160200.vtk' has a number <= 170000: 160200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1/outputs/physics_allparticle_160100.vtk' has a number <= 170000: 160100. Trying previous file.\n",
      "Error: No valid file with a number greater than 170000 was found in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_4'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_17'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_19'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_20'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_22'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_24'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_26'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_27'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_29'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_30'. Skipping this folder.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_164200.vtk' has a number <= 170000: 164200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_164100.vtk' has a number <= 170000: 164100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_164000.vtk' has a number <= 170000: 164000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163900.vtk' has a number <= 170000: 163900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163800.vtk' has a number <= 170000: 163800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163700.vtk' has a number <= 170000: 163700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163600.vtk' has a number <= 170000: 163600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163500.vtk' has a number <= 170000: 163500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163400.vtk' has a number <= 170000: 163400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163300.vtk' has a number <= 170000: 163300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163200.vtk' has a number <= 170000: 163200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163100.vtk' has a number <= 170000: 163100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_163000.vtk' has a number <= 170000: 163000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162900.vtk' has a number <= 170000: 162900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162800.vtk' has a number <= 170000: 162800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162700.vtk' has a number <= 170000: 162700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162600.vtk' has a number <= 170000: 162600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162500.vtk' has a number <= 170000: 162500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162400.vtk' has a number <= 170000: 162400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162300.vtk' has a number <= 170000: 162300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162200.vtk' has a number <= 170000: 162200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162100.vtk' has a number <= 170000: 162100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_162000.vtk' has a number <= 170000: 162000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161900.vtk' has a number <= 170000: 161900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161800.vtk' has a number <= 170000: 161800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161700.vtk' has a number <= 170000: 161700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161600.vtk' has a number <= 170000: 161600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161500.vtk' has a number <= 170000: 161500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161400.vtk' has a number <= 170000: 161400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161300.vtk' has a number <= 170000: 161300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161200.vtk' has a number <= 170000: 161200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161100.vtk' has a number <= 170000: 161100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_161000.vtk' has a number <= 170000: 161000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_160900.vtk' has a number <= 170000: 160900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_160800.vtk' has a number <= 170000: 160800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_160700.vtk' has a number <= 170000: 160700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_160600.vtk' has a number <= 170000: 160600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_160500.vtk' has a number <= 170000: 160500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_160400.vtk' has a number <= 170000: 160400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_160300.vtk' has a number <= 170000: 160300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_160200.vtk' has a number <= 170000: 160200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34/outputs/physics_allparticle_160100.vtk' has a number <= 170000: 160100. Trying previous file.\n",
      "Error: No valid file with a number greater than 170000 was found in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_35'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_37'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_40'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_41'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_43'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_44'. Skipping this folder.\n",
      "Warning: Not enough later files in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46'. Required: 20, Found: 7.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46/outputs/physics_allparticle_160700.vtk' has a number <= 170000: 160700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46/outputs/physics_allparticle_160600.vtk' has a number <= 170000: 160600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46/outputs/physics_allparticle_160500.vtk' has a number <= 170000: 160500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46/outputs/physics_allparticle_160400.vtk' has a number <= 170000: 160400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46/outputs/physics_allparticle_160300.vtk' has a number <= 170000: 160300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46/outputs/physics_allparticle_160200.vtk' has a number <= 170000: 160200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46/outputs/physics_allparticle_160100.vtk' has a number <= 170000: 160100. Trying previous file.\n",
      "Error: No valid file with a number greater than 170000 was found in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_47'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_48'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_50'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_0'. Skipping this folder.\n",
      "Warning: Not enough later files in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2'. Required: 20, Found: 12.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_161200.vtk' has a number <= 170000: 161200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_161100.vtk' has a number <= 170000: 161100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_161000.vtk' has a number <= 170000: 161000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_160900.vtk' has a number <= 170000: 160900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_160800.vtk' has a number <= 170000: 160800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_160700.vtk' has a number <= 170000: 160700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_160600.vtk' has a number <= 170000: 160600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_160500.vtk' has a number <= 170000: 160500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_160400.vtk' has a number <= 170000: 160400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_160300.vtk' has a number <= 170000: 160300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_160200.vtk' has a number <= 170000: 160200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2/outputs/physics_allparticle_160100.vtk' has a number <= 170000: 160100. Trying previous file.\n",
      "Error: No valid file with a number greater than 170000 was found in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_3'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_4'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_5'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_7'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_8'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_9'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_10'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_11'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_13'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_14'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_15'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_16'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_17'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_18'. Skipping this folder.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_169100.vtk' has a number <= 170000: 169100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_169000.vtk' has a number <= 170000: 169000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168900.vtk' has a number <= 170000: 168900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168800.vtk' has a number <= 170000: 168800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168700.vtk' has a number <= 170000: 168700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168600.vtk' has a number <= 170000: 168600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168500.vtk' has a number <= 170000: 168500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168400.vtk' has a number <= 170000: 168400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168300.vtk' has a number <= 170000: 168300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168200.vtk' has a number <= 170000: 168200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168100.vtk' has a number <= 170000: 168100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_168000.vtk' has a number <= 170000: 168000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167900.vtk' has a number <= 170000: 167900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167800.vtk' has a number <= 170000: 167800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167700.vtk' has a number <= 170000: 167700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167600.vtk' has a number <= 170000: 167600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167500.vtk' has a number <= 170000: 167500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167400.vtk' has a number <= 170000: 167400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167300.vtk' has a number <= 170000: 167300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167200.vtk' has a number <= 170000: 167200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167100.vtk' has a number <= 170000: 167100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_167000.vtk' has a number <= 170000: 167000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166900.vtk' has a number <= 170000: 166900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166800.vtk' has a number <= 170000: 166800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166700.vtk' has a number <= 170000: 166700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166600.vtk' has a number <= 170000: 166600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166500.vtk' has a number <= 170000: 166500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166400.vtk' has a number <= 170000: 166400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166300.vtk' has a number <= 170000: 166300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166200.vtk' has a number <= 170000: 166200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166100.vtk' has a number <= 170000: 166100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_166000.vtk' has a number <= 170000: 166000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165900.vtk' has a number <= 170000: 165900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165800.vtk' has a number <= 170000: 165800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165700.vtk' has a number <= 170000: 165700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165600.vtk' has a number <= 170000: 165600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165500.vtk' has a number <= 170000: 165500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165400.vtk' has a number <= 170000: 165400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165300.vtk' has a number <= 170000: 165300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165200.vtk' has a number <= 170000: 165200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165100.vtk' has a number <= 170000: 165100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_165000.vtk' has a number <= 170000: 165000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164900.vtk' has a number <= 170000: 164900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164800.vtk' has a number <= 170000: 164800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164700.vtk' has a number <= 170000: 164700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164600.vtk' has a number <= 170000: 164600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164500.vtk' has a number <= 170000: 164500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164400.vtk' has a number <= 170000: 164400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164300.vtk' has a number <= 170000: 164300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164200.vtk' has a number <= 170000: 164200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164100.vtk' has a number <= 170000: 164100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_164000.vtk' has a number <= 170000: 164000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163900.vtk' has a number <= 170000: 163900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163800.vtk' has a number <= 170000: 163800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163700.vtk' has a number <= 170000: 163700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163600.vtk' has a number <= 170000: 163600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163500.vtk' has a number <= 170000: 163500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163400.vtk' has a number <= 170000: 163400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163300.vtk' has a number <= 170000: 163300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163200.vtk' has a number <= 170000: 163200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163100.vtk' has a number <= 170000: 163100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_163000.vtk' has a number <= 170000: 163000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162900.vtk' has a number <= 170000: 162900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162800.vtk' has a number <= 170000: 162800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162700.vtk' has a number <= 170000: 162700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162600.vtk' has a number <= 170000: 162600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162500.vtk' has a number <= 170000: 162500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162400.vtk' has a number <= 170000: 162400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162300.vtk' has a number <= 170000: 162300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162200.vtk' has a number <= 170000: 162200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162100.vtk' has a number <= 170000: 162100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_162000.vtk' has a number <= 170000: 162000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161900.vtk' has a number <= 170000: 161900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161800.vtk' has a number <= 170000: 161800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161700.vtk' has a number <= 170000: 161700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161600.vtk' has a number <= 170000: 161600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161500.vtk' has a number <= 170000: 161500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161400.vtk' has a number <= 170000: 161400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161300.vtk' has a number <= 170000: 161300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161200.vtk' has a number <= 170000: 161200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161100.vtk' has a number <= 170000: 161100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_161000.vtk' has a number <= 170000: 161000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_160900.vtk' has a number <= 170000: 160900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_160800.vtk' has a number <= 170000: 160800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_160700.vtk' has a number <= 170000: 160700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_160600.vtk' has a number <= 170000: 160600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_160500.vtk' has a number <= 170000: 160500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_160400.vtk' has a number <= 170000: 160400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_160300.vtk' has a number <= 170000: 160300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_160200.vtk' has a number <= 170000: 160200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19/outputs/physics_allparticle_160100.vtk' has a number <= 170000: 160100. Trying previous file.\n",
      "Error: No valid file with a number greater than 170000 was found in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_20'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_21'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_22'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_24'. Skipping this folder.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_168200.vtk' has a number <= 170000: 168200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_168100.vtk' has a number <= 170000: 168100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_168000.vtk' has a number <= 170000: 168000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167900.vtk' has a number <= 170000: 167900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167800.vtk' has a number <= 170000: 167800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167700.vtk' has a number <= 170000: 167700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167600.vtk' has a number <= 170000: 167600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167500.vtk' has a number <= 170000: 167500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167400.vtk' has a number <= 170000: 167400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167300.vtk' has a number <= 170000: 167300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167200.vtk' has a number <= 170000: 167200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167100.vtk' has a number <= 170000: 167100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_167000.vtk' has a number <= 170000: 167000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166900.vtk' has a number <= 170000: 166900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166800.vtk' has a number <= 170000: 166800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166700.vtk' has a number <= 170000: 166700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166600.vtk' has a number <= 170000: 166600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166500.vtk' has a number <= 170000: 166500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166400.vtk' has a number <= 170000: 166400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166300.vtk' has a number <= 170000: 166300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166200.vtk' has a number <= 170000: 166200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166100.vtk' has a number <= 170000: 166100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_166000.vtk' has a number <= 170000: 166000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165900.vtk' has a number <= 170000: 165900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165800.vtk' has a number <= 170000: 165800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165700.vtk' has a number <= 170000: 165700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165600.vtk' has a number <= 170000: 165600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165500.vtk' has a number <= 170000: 165500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165400.vtk' has a number <= 170000: 165400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165300.vtk' has a number <= 170000: 165300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165200.vtk' has a number <= 170000: 165200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165100.vtk' has a number <= 170000: 165100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_165000.vtk' has a number <= 170000: 165000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164900.vtk' has a number <= 170000: 164900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164800.vtk' has a number <= 170000: 164800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164700.vtk' has a number <= 170000: 164700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164600.vtk' has a number <= 170000: 164600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164500.vtk' has a number <= 170000: 164500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164400.vtk' has a number <= 170000: 164400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164300.vtk' has a number <= 170000: 164300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164200.vtk' has a number <= 170000: 164200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164100.vtk' has a number <= 170000: 164100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_164000.vtk' has a number <= 170000: 164000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163900.vtk' has a number <= 170000: 163900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163800.vtk' has a number <= 170000: 163800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163700.vtk' has a number <= 170000: 163700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163600.vtk' has a number <= 170000: 163600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163500.vtk' has a number <= 170000: 163500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163400.vtk' has a number <= 170000: 163400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163300.vtk' has a number <= 170000: 163300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163200.vtk' has a number <= 170000: 163200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163100.vtk' has a number <= 170000: 163100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_163000.vtk' has a number <= 170000: 163000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162900.vtk' has a number <= 170000: 162900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162800.vtk' has a number <= 170000: 162800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162700.vtk' has a number <= 170000: 162700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162600.vtk' has a number <= 170000: 162600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162500.vtk' has a number <= 170000: 162500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162400.vtk' has a number <= 170000: 162400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162300.vtk' has a number <= 170000: 162300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162200.vtk' has a number <= 170000: 162200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162100.vtk' has a number <= 170000: 162100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_162000.vtk' has a number <= 170000: 162000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161900.vtk' has a number <= 170000: 161900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161800.vtk' has a number <= 170000: 161800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161700.vtk' has a number <= 170000: 161700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161600.vtk' has a number <= 170000: 161600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161500.vtk' has a number <= 170000: 161500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161400.vtk' has a number <= 170000: 161400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161300.vtk' has a number <= 170000: 161300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161200.vtk' has a number <= 170000: 161200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161100.vtk' has a number <= 170000: 161100. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_161000.vtk' has a number <= 170000: 161000. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_160900.vtk' has a number <= 170000: 160900. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_160800.vtk' has a number <= 170000: 160800. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_160700.vtk' has a number <= 170000: 160700. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_160600.vtk' has a number <= 170000: 160600. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_160500.vtk' has a number <= 170000: 160500. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_160400.vtk' has a number <= 170000: 160400. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_160300.vtk' has a number <= 170000: 160300. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_160200.vtk' has a number <= 170000: 160200. Trying previous file.\n",
      "Warning: The file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25/outputs/physics_allparticle_160100.vtk' has a number <= 170000: 160100. Trying previous file.\n",
      "Error: No valid file with a number greater than 170000 was found in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_26'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_27'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_28'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_30'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_0'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_1'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_2'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_3'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_4'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_5'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_6'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_7'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_8'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_9'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_10'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_11'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_12'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_13'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_14'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_15'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_16'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_17'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_18'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_19'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_20'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_5_0'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_5_1'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_5_2'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_6_0'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_6_1'. Skipping this folder.\n",
      "Error: No files found with numbers >= 160001 in '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_6_2'. Skipping this folder.\n",
      "\n",
      "Problematic folders encountered:\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND01/outputs/physics_allparticle_275800.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND01'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND03/outputs/physics_allparticle_254100.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND03'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND05/outputs/physics_allparticle_222000.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND05'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND11/outputs/physics_allparticle_277400.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND11'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND31/outputs/physics_allparticle_225300.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND31'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND33/outputs/physics_allparticle_224800.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND33'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND56/outputs/physics_allparticle_306100.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND56'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND61/outputs/physics_allparticle_241400.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND61'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND72/outputs/physics_allparticle_234200.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND72'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND74/outputs/physics_allparticle_232000.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND74'. Skipping.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND91/outputs/physics_allparticle_208800.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND91'. Skipping.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_4' due to no valid later files.\n",
      "Error reading mesh data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_13/outputs/physics_allparticle_213300.vtk' in folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_13'. Skipping.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_17' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_19' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_20' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_22' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_24' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_26' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_27' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_29' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_30' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_34' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_35' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_37' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_40' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_41' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_43' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_44' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_47' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_48' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_50' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_0' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_3' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_4' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_5' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_7' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_8' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_9' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_10' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_11' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_13' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_14' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_15' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_16' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_17' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_18' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_20' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_21' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_22' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_24' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_26' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_27' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_28' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_30' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_0' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_1' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_2' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_3' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_4' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_5' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_6' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_7' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_8' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_9' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_10' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_11' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_12' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_13' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_14' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_15' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_16' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_17' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_18' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_19' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_20' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_5_0' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_5_1' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_5_2' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_6_0' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_6_1' due to no valid later files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_6_2' due to no valid later files.\n",
      "Global bounds determined: (np.float64(-126.0), np.float64(-120.0), np.float64(320.0), np.float64(290.0))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_ND01: 100%|██████████| 30/30 [00:13<00:00,  2.25file/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND01/outputs/physics_allparticle_275800.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND01/outputs/physics_allparticle_275800.vtk'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_ND02: 100%|██████████| 30/30 [00:15<00:00,  1.88file/s]\n",
      "Interpolating in io2D_ND03: 100%|██████████| 30/30 [00:13<00:00,  2.20file/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND03/outputs/physics_allparticle_254100.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND03/outputs/physics_allparticle_254100.vtk'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_ND04: 100%|██████████| 30/30 [00:12<00:00,  2.44file/s]\n",
      "Interpolating in io2D_ND05: 100%|██████████| 30/30 [00:14<00:00,  2.04file/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: Missing required fields ['phi'] in file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND05/outputs/physics_allparticle_222000.vtk'. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND05/outputs/physics_allparticle_222000.vtk'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_ND06: 100%|██████████| 30/30 [00:15<00:00,  1.93file/s]\n",
      "Interpolating in io2D_ND07: 100%|██████████| 30/30 [00:15<00:00,  1.95file/s]\n",
      "Interpolating in io2D_ND08: 100%|██████████| 30/30 [00:11<00:00,  2.58file/s]\n",
      "Interpolating in io2D_ND09: 100%|██████████| 30/30 [00:12<00:00,  2.34file/s]\n",
      "Interpolating in io2D_ND10: 100%|██████████| 30/30 [00:16<00:00,  1.84file/s]\n",
      "Interpolating in io2D_ND11: 100%|██████████| 30/30 [00:12<00:00,  2.32file/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND11/outputs/physics_allparticle_277400.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND11/outputs/physics_allparticle_277400.vtk'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND31/outputs/physics_allparticle_225300.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND31/outputs/physics_allparticle_225300.vtk'.\n"
     ]
    },
    {
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     ]
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     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND33/outputs/physics_allparticle_224800.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND33/outputs/physics_allparticle_224800.vtk'.\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND56/outputs/physics_allparticle_306100.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND56/outputs/physics_allparticle_306100.vtk'.\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
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     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND61/outputs/physics_allparticle_241400.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND61/outputs/physics_allparticle_241400.vtk'.\n"
     ]
    },
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     ]
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     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND72/outputs/physics_allparticle_234200.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND72/outputs/physics_allparticle_234200.vtk'.\n"
     ]
    },
    {
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     ]
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     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND74/outputs/physics_allparticle_232000.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND74/outputs/physics_allparticle_232000.vtk'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND91/outputs/physics_allparticle_208800.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_ND91/outputs/physics_allparticle_208800.vtk'.\n"
     ]
    },
    {
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     ]
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     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_1' due to insufficient files.\n"
     ]
    },
    {
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     ]
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     ]
    },
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     ]
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     "text": [
      "Error processing file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_13/outputs/physics_allparticle_213300.vtk': could not convert string to float: ''. Skipping this file.\n",
      "Info: Using previous valid data for file '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_13/outputs/physics_allparticle_213300.vtk'.\n"
     ]
    },
    {
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     ]
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     ]
    },
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     "text": [
      "Interpolating in io2D_2_36: 100%|██████████| 30/30 [00:27<00:00,  1.07file/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_37' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_2_38: 100%|██████████| 30/30 [00:26<00:00,  1.14file/s]\n",
      "Interpolating in io2D_2_39: 100%|██████████| 30/30 [00:27<00:00,  1.11file/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_40' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_41' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_2_42: 100%|██████████| 30/30 [00:28<00:00,  1.07file/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_43' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_44' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_2_45: 100%|██████████| 30/30 [00:29<00:00,  1.03file/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_46' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_47' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_48' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_2_49: 100%|██████████| 30/30 [00:29<00:00,  1.02file/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_2_50' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_0' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_3_1: 100%|██████████| 30/30 [00:31<00:00,  1.05s/file]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_2' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_3' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_4' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_5' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_3_6: 100%|██████████| 30/30 [00:33<00:00,  1.13s/file]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_7' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_8' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_9' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_10' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_11' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_3_12: 100%|██████████| 30/30 [00:32<00:00,  1.08s/file]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_13' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_14' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_15' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_16' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_17' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_18' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_19' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_20' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_21' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_22' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_3_23: 100%|██████████| 30/30 [00:34<00:00,  1.14s/file]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_24' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_25' due to insufficient files.\n",
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      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_27' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_28' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Interpolating in io2D_3_29: 100%|██████████| 30/30 [00:28<00:00,  1.05file/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_3_30' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_0' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_1' due to insufficient files.\n",
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      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_8' due to insufficient files.\n",
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      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_10' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_11' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_12' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_13' due to insufficient files.\n",
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      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_15' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_16' due to insufficient files.\n",
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      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_19' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_4_20' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_5_0' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_5_1' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_5_2' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_6_0' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_6_1' due to insufficient files.\n",
      "Skipping folder '/ocean/projects/eng170006p/ussqww/GenNDD_data/io2D_6_2' due to insufficient files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import re\n",
    "import gc\n",
    "import my_utils as myu  # Ensure 'myu' is defined with necessary functions\n",
    "\n",
    "# root_folder_path = \"/mnt/e/NeuronGrowth_2024/TTNG_05052024\"\n",
    "# root_folder_path = \"/mnt/e/GenNDD_data\"\n",
    "\n",
    "# root_folder_path = \"/ocean/projects/eng170006p/ussqww/GenNDD_data\"\n",
    "# ending_healthy = 160000\n",
    "# ending_iterations = 190000\n",
    "\n",
    "root_folder_path = \"/ocean/projects/eng170006p/ussqww/GenNDD_data\"\n",
    "ending_healthy = 160000\n",
    "ending_iterations = 170000\n",
    "\n",
    "# root_folder_path = \"/ocean/projects/eng170006p/ussqww/GenNDD_data_Dc\"\n",
    "# ending_healthy = 100000\n",
    "# ending_iterations = 180000\n",
    "\n",
    "def check_files_for_folder(folder_path, required_early=10, required_later=20, min_later_number=ending_healthy+1, last_later_threshold=ending_iterations):\n",
    "    \"\"\"Check the number of early and later files in the folder and ensure the last later file's number > 200,000.\"\"\"\n",
    "    all_vtk_files = glob.glob(os.path.join(folder_path, 'outputs/*.vtk'))  # Get all VTK files in the folder\n",
    "    file_details = []\n",
    "    messages = []  # List to store messages instead of printing them directly\n",
    "\n",
    "    for file in all_vtk_files:\n",
    "        try:\n",
    "            num = int(file.split('_')[-1].split('.')[0])  # Extract frame number from file name\n",
    "            file_details.append((file, num))\n",
    "        except ValueError:\n",
    "            messages.append(f\"Warning: Unable to parse file number from '{file}'. Skipping this file.\")\n",
    "\n",
    "    file_details_sorted = sorted(file_details, key=lambda x: x[1])  # Sort files based on frame number\n",
    "\n",
    "    # Separate early files (<= ending_healthy) and later files (>= min_later_number)\n",
    "    early_files = [(file, num) for file, num in file_details_sorted if num <= ending_healthy]\n",
    "    later_files = [(file, num) for file, num in file_details_sorted if num >= min_later_number]\n",
    "\n",
    "    # Select a limited number of early files (maximum 10)\n",
    "    if len(early_files) >= required_early:\n",
    "        selected_indices_early = np.linspace(0, len(early_files) - 1, required_early, dtype=int)  # Select required_early evenly spaced files\n",
    "        early_selected = [early_files[i][0] for i in selected_indices_early]  # Select only file paths\n",
    "    else:\n",
    "        messages.append(f\"Warning: Not enough early files in '{folder_path}'. Required: {required_early}, Found: {len(early_files)}.\")\n",
    "        early_selected = [file for file, num in early_files]  # Select all available file paths\n",
    "\n",
    "    # Select a limited number of later files (maximum 20)\n",
    "    if len(later_files) >= required_later:\n",
    "        selected_indices_later = np.linspace(0, len(later_files) - 1, required_later, dtype=int)  # Select required_later evenly spaced files\n",
    "        later_selected = [later_files[i][0] for i in selected_indices_later]  # Select only file paths\n",
    "    elif len(later_files) > 0:\n",
    "        messages.append(f\"Warning: Not enough later files in '{folder_path}'. Required: {required_later}, Found: {len(later_files)}.\")\n",
    "        later_selected = [file for file, num in later_files]  # Select all available file paths\n",
    "    else:\n",
    "        messages.append(f\"Error: No files found with numbers >= {min_later_number} in '{folder_path}'. Skipping this folder.\")\n",
    "        return None, None, None, messages  # Skip the folder if no valid files are found\n",
    "\n",
    "    # Handle the last file in later_files and check if it's corrupted or has an issue\n",
    "    last_file, last_number = None, None\n",
    "    valid_last_file = False\n",
    "\n",
    "    for idx in reversed(range(len(later_files))):  # Loop backwards through the later_files list\n",
    "        try:\n",
    "            # Try to get the file and its number\n",
    "            last_file = later_files[idx][0]\n",
    "            last_number = later_files[idx][1]\n",
    "\n",
    "            # Check if the file has a number > ending_iterations\n",
    "            if last_number > last_later_threshold:\n",
    "                valid_last_file = True\n",
    "                # messages.append(f\"The file '{last_file}' has a valid number > {last_later_threshold}: {last_number}.\")\n",
    "                break  # If the file is valid, exit the loop\n",
    "            else:\n",
    "                messages.append(f\"Warning: The file '{last_file}' has a number <= {last_later_threshold}: {last_number}. Trying previous file.\")\n",
    "\n",
    "        except (IndexError, ValueError, OSError) as e:\n",
    "            # Handle the case where the file is corrupted or unreadable\n",
    "            messages.append(f\"Error: Failed to process file '{last_file}' in '{folder_path}'. Error: {e}. Trying previous file.\")\n",
    "    \n",
    "    # If no valid last file was found, skip the folder\n",
    "    if not valid_last_file:\n",
    "        messages.append(f\"Error: No valid file with a number greater than {last_later_threshold} was found in '{folder_path}'. Skipping this folder.\")\n",
    "        return None, None, None, messages\n",
    "\n",
    "    return early_selected, later_selected, last_number, messages\n",
    "\n",
    "def find_global_bounds(subfolders):\n",
    "    \"\"\"Calculate global bounds based on the 30th VTK file in the later files across all subfolders.\"\"\"\n",
    "    global_min_x, global_min_y = float('inf'), float('inf')  # Initialize min values to infinity\n",
    "    global_max_x, global_max_y = float('-inf'), float('-inf')  # Initialize max values to negative infinity\n",
    "\n",
    "    folder_file_info = {}  # Dictionary to store file information for each folder\n",
    "    problematic_folders = []  # List to collect problematic folders and issues\n",
    "    all_messages = []  # List to collect all messages instead of printing\n",
    "\n",
    "    # Loop through each subfolder to find the global bounds and gather file information\n",
    "    for subfolder in tqdm(subfolders, desc=\"Calculating global bounds\"):\n",
    "        early_files, later_files, last_later_number, messages = check_files_for_folder(subfolder)\n",
    "        all_messages.extend(messages)  # Collect messages from check_files_for_folder\n",
    "\n",
    "        # Skip folder if the last_later_number is smaller than ending_iterations\n",
    "        if last_later_number is not None and last_later_number < ending_iterations:\n",
    "            problematic_folders.append(f\"Skipping folder '{subfolder}' as last_later_number is less than ending_iterations: {last_later_number}.\")\n",
    "            continue\n",
    "\n",
    "        if later_files is None:  # Skip the folder if no valid files are found\n",
    "            problematic_folders.append(f\"Skipping folder '{subfolder}' due to no valid later files.\")\n",
    "            continue\n",
    "\n",
    "        folder_file_info[subfolder] = (early_files, later_files, last_later_number)\n",
    "\n",
    "        # Loop through later_files in reverse to find a valid file for bounds calculation\n",
    "        valid_file_found = False\n",
    "        for idx in reversed(range(len(later_files))):\n",
    "            last_file = later_files[idx]\n",
    "            try:\n",
    "                data = myu.read_mesh_cellCon_exception(last_file, 1, 0)  # Read the mesh and field data\n",
    "                if data:\n",
    "                    points = data[0]  # Extract points data\n",
    "                    # Validate points to ensure they are numeric and finite\n",
    "                    if np.isfinite(points).all():  # Ensure there are no `inf` or `nan` values in points\n",
    "                        min_x, min_y = np.min(points, axis=0)  # Get min x and y values\n",
    "                        max_x, max_y = np.max(points, axis=0)  # Get max x and y values\n",
    "                        # Update the global bounds based on the current file\n",
    "                        global_min_x = min(global_min_x, min_x)\n",
    "                        global_min_y = min(global_min_y, min_y)\n",
    "                        global_max_x = max(global_max_x, max_x)\n",
    "                        global_max_y = max(global_max_y, max_y)\n",
    "                        valid_file_found = True  # A valid file was found\n",
    "                        break  # Exit loop after finding a valid file\n",
    "                    else:\n",
    "                        problematic_folders.append(f\"Error: Invalid (non-finite) points in file '{last_file}'. Skipping.\")\n",
    "                else:\n",
    "                    problematic_folders.append(f\"Error reading mesh data for file '{last_file}' in folder '{subfolder}'. Skipping.\")\n",
    "\n",
    "            except (IndexError, ValueError, OSError) as e:\n",
    "                # Handle the case where the last file is corrupted or unreadable\n",
    "                problematic_folders.append(f\"Error processing file '{last_file}' in folder '{subfolder}'. Error: {e}. Trying previous file.\")\n",
    "\n",
    "        # If no valid file was found, skip the folder\n",
    "        if not valid_file_found:\n",
    "            problematic_folders.append(f\"Error: No valid file found for bounds calculation in '{subfolder}'. Skipping this folder.\")\n",
    "            continue\n",
    "\n",
    "    # Print out problematic folders at the end\n",
    "    if problematic_folders:\n",
    "        all_messages.append(\"\\nProblematic folders encountered:\")\n",
    "        all_messages.extend(problematic_folders)\n",
    "\n",
    "    # Print all messages at once\n",
    "    if all_messages:\n",
    "        print(\"\\n\".join(all_messages))\n",
    "\n",
    "    # Check if bounds are valid (ensure no inf bounds)\n",
    "    if not np.isfinite([global_min_x, global_min_y, global_max_x, global_max_y]).all():\n",
    "        raise ValueError(\"Error: Invalid global bounds detected. Check the input data.\")\n",
    "\n",
    "    # Return the global bounds and the collected file information\n",
    "    return global_min_x, global_min_y, global_max_x, global_max_y, folder_file_info\n",
    "\n",
    "def interpolate_data_for_folder(folder_path, global_bounds, file_info, grid_size=1.0):\n",
    "    \"\"\"Interpolate selected VTK files within a folder using specified global bounds and file information.\"\"\"\n",
    "    global_min_x, global_min_y, global_max_x, global_max_y = global_bounds\n",
    "    x_coords = np.arange(global_min_x, global_max_x + grid_size, grid_size)\n",
    "    y_coords = np.arange(global_min_y, global_max_y + grid_size, grid_size)\n",
    "    grid_x, grid_y = np.meshgrid(x_coords, y_coords)\n",
    "    grid_points = np.vstack([grid_x.ravel(), grid_y.ravel()]).T.astype(np.float32)  # Use float32 to save memory\n",
    "\n",
    "    early_files, later_files, last_later_number = file_info  # Use the pre-collected file information\n",
    "\n",
    "    # Skip folder if the last_later_number is smaller than ending_iterations\n",
    "    if last_later_number is not None and last_later_number < ending_iterations:\n",
    "        print(f\"Skipping folder '{folder_path}' as last_later_number is less than ending_iterations: {last_later_number}.\")\n",
    "        return None, None, None\n",
    "\n",
    "    selected_files = early_files + later_files\n",
    "\n",
    "    if not selected_files:\n",
    "        print(f\"Error: No valid files selected in '{folder_path}'. Skipping this folder.\")\n",
    "        return None, None, None\n",
    "\n",
    "    interpolated_data = []\n",
    "    last_valid_step_data = None\n",
    "\n",
    "    folder_name = os.path.basename(folder_path)  # Get the folder name for display\n",
    "    desc = f'Interpolating in {folder_name}'  # Description for progress bar\n",
    "\n",
    "    # Process each selected file\n",
    "    with tqdm(total=len(selected_files), desc=desc, unit='file') as progress:\n",
    "        for filename in selected_files:\n",
    "            data = myu.read_mesh_cellCon_exception(filename, 1, 0)  # Read the mesh and field data\n",
    "            if data:\n",
    "                points, fields, _, _ = data  # Extract points and scalar fields\n",
    "                interpolated_step_data = {}\n",
    "                for field_name, values in fields.items():\n",
    "                    try:\n",
    "                        # Interpolate each field using the grid points\n",
    "                        interpolated_values = myu.interpolate_features_cKD(points, {field_name: values}, grid_points, k=3)\n",
    "                        interpolated_matrix = interpolated_values[field_name].reshape(len(y_coords), len(x_coords))\n",
    "                        interpolated_step_data[field_name] = interpolated_matrix  # Store the interpolated matrix\n",
    "                    except Exception as e:\n",
    "                        print(f\"Warning: Interpolation failed for field '{field_name}' in file '{filename}': {e}\")\n",
    "                        interpolated_step_data[field_name] = np.full((len(y_coords), len(x_coords)), np.nan)  # Assign NaNs on failure\n",
    "                interpolated_data.append(interpolated_step_data)  # Append interpolated data for this file\n",
    "                last_valid_step_data = interpolated_step_data  # Update last valid data\n",
    "\n",
    "                # Release memory after each file is processed\n",
    "                del points, fields, interpolated_step_data\n",
    "                gc.collect()\n",
    "\n",
    "            else:\n",
    "                if last_valid_step_data is not None:\n",
    "                    print(f\"Info: Using previous valid data for file '{filename}'.\")\n",
    "                    interpolated_data.append(last_valid_step_data.copy())  # Use the last valid data\n",
    "                else:\n",
    "                    print(f\"Warning: No previous valid data to duplicate for file '{filename}'. Assigning NaNs.\")\n",
    "                    # Assign NaNs for all fields if no previous data exists\n",
    "                    duplicated_step_data = {field: np.full((len(y_coords), len(x_coords)), np.nan) for field in myu.REQUIRED_FIELDS}\n",
    "                    interpolated_data.append(duplicated_step_data)\n",
    "\n",
    "            progress.update(1)  # Update progress bar\n",
    "\n",
    "    return interpolated_data, grid_x, grid_y  # Return the interpolated data and grid\n",
    "\n",
    "def process_all_cases(base_path):\n",
    "    \"\"\"Process all cases (subfolders) in the specified base path.\"\"\"\n",
    "    # Get all subfolders matching the pattern io2D_ND* or io2D_* (covers both ND and numbered folders)\n",
    "    subfolders = glob.glob(os.path.join(base_path, 'io2D_*'))  # Get all subfolders matching the pattern\n",
    "\n",
    "    # Sort subfolders based on the numeric part of the folder name.\n",
    "    # Handle folders like io2D_2_28, io2D_3_1, and io2D_ND01.\n",
    "    def sort_key(folder):\n",
    "        basename = os.path.basename(folder)\n",
    "        match = re.search(r'ND(\\d+)', basename)  # Match ND pattern\n",
    "        if match:\n",
    "            return int(match.group(1))  # Sort ND folders by their number\n",
    "        match = re.search(r'io2D_(\\d+)_(\\d+)', basename)  # Match io2D_2_28 pattern\n",
    "        if match:\n",
    "            return int(match.group(1)) * 100 + int(match.group(2))  # Sort by major and minor numbers\n",
    "        return float('inf')  # Place unknown patterns at the end\n",
    "    \n",
    "    subfolders.sort(key=sort_key)\n",
    "\n",
    "    # Unpack all four global bounds (min_x, min_y, max_x, max_y) and file info\n",
    "    global_min_x, global_min_y, global_max_x, global_max_y, folder_file_info = find_global_bounds(subfolders)\n",
    "    \n",
    "    global_bounds = (global_min_x, global_min_y, global_max_x, global_max_y)  # Pack global bounds into a tuple\n",
    "    print(f\"Global bounds determined: {global_bounds}\")\n",
    "    \n",
    "    cases_data = {}  # Dictionary to store the interpolated data for each case\n",
    "\n",
    "    # Process each subfolder\n",
    "    for subfolder in subfolders:\n",
    "        file_info = folder_file_info.get(subfolder)  # Retrieve file info for this folder\n",
    "        if file_info:  # Only proceed if valid file info is available\n",
    "            interpolated_data, grid_x, grid_y = interpolate_data_for_folder(subfolder, global_bounds, file_info)\n",
    "            if interpolated_data is not None:\n",
    "                case_name = os.path.basename(subfolder)  # Get the case name (subfolder name)\n",
    "                cases_data[case_name] = (interpolated_data, grid_x, grid_y)  # Store the interpolated data\n",
    "            else:\n",
    "                print(f\"Warning: No data interpolated for folder '{subfolder}'.\")\n",
    "        else:\n",
    "            print(f\"Skipping folder '{subfolder}' due to insufficient files.\")\n",
    "\n",
    "    return cases_data  # Return the processed data for all cases\n",
    "\n",
    "all_cases_data = process_all_cases(root_folder_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "111c7408",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.ndimage import zoom\n",
    "\n",
    "def downsample_all_cases_data(all_cases_data, target_size=(256, 256)):\n",
    "    \"\"\"\n",
    "    Downsamples each dataset from (241, 241) to (160, 280) for each image and the grid in all_cases_data.\n",
    "\n",
    "    Args:\n",
    "    all_cases_data (dict): Dictionary with keys as case identifiers and values as tuples of\n",
    "                           (interpolated_data, grid_x, grid_y) where interpolated_data is a list of dictionaries\n",
    "                           of shape (#time, #channel, 241, 241).\n",
    "    target_size (tuple): Target downsample size, default (160, 280).\n",
    "\n",
    "    Returns:\n",
    "    dict: A dictionary with the same keys and downsampled data as values. Each case will have:\n",
    "          (downsampled_interpolated_data, downsampled_grid_x, downsampled_grid_y).\n",
    "    \"\"\"\n",
    "    downsampled_data = {}\n",
    "\n",
    "    for key, (interpolated_data, grid_x, grid_y) in all_cases_data.items():\n",
    "        time_steps = len(interpolated_data)\n",
    "        channels = interpolated_data[0].keys()  # Assuming all time steps have the same channels\n",
    "        downsampled_tensor = []\n",
    "\n",
    "        # Downsample the grid_x and grid_y to (160, 280)\n",
    "        zoom_factor_grid_x = target_size[0] / grid_x.shape[0]\n",
    "        zoom_factor_grid_y = target_size[1] / grid_y.shape[1]\n",
    "        downsampled_grid_x = zoom(grid_x, zoom_factor_grid_x, order=1)  # Bilinear interpolation\n",
    "        downsampled_grid_y = zoom(grid_y, zoom_factor_grid_y, order=1)\n",
    "\n",
    "        # Downsample the interpolated_data (each time step and channel)\n",
    "        for time_step in interpolated_data:\n",
    "            downsampled_channels = {}\n",
    "            for channel in channels:\n",
    "                image = time_step[channel]  # Get the image for this channel\n",
    "                # Calculate the zoom factor for the image\n",
    "                zoom_factor_image_x = target_size[0] / image.shape[0]\n",
    "                zoom_factor_image_y = target_size[1] / image.shape[1]\n",
    "                # Downsample the image\n",
    "                downsampled_image = zoom(image, (zoom_factor_image_x, zoom_factor_image_y), order=1)  # Bilinear interpolation (order=1)\n",
    "                downsampled_channels[channel] = downsampled_image\n",
    "            downsampled_tensor.append(downsampled_channels)\n",
    "\n",
    "        # Store the downsampled data for this case\n",
    "        downsampled_data[key] = (downsampled_tensor, downsampled_grid_x, downsampled_grid_y)\n",
    "\n",
    "    return downsampled_data\n",
    "\n",
    "# Example usage\n",
    "# Assuming `all_cases_data` is your loaded dataset\n",
    "all_cases_data = downsample_all_cases_data(all_cases_data, target_size=(256, 256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a59e92c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All cases have consistent dimensions for all channels.\n"
     ]
    }
   ],
   "source": [
    "def check_consistent_dimensions_all_channels(all_cases_data):\n",
    "    \"\"\"\n",
    "    Check if the dimensions of all channels are consistent across all cases in the dataset.\n",
    "\n",
    "    Args:\n",
    "        all_cases_data (dict): Dictionary containing all cases.\n",
    "\n",
    "    Returns:\n",
    "        bool: True if dimensions are consistent for all channels, False otherwise.\n",
    "    \"\"\"\n",
    "\n",
    "    # Initialize a variable to store reference shapes for all channels\n",
    "    reference_shapes = None\n",
    "\n",
    "    for case, case_data in all_cases_data.items():\n",
    "        try:\n",
    "            current_shapes = {channel: case_data[0][0][channel].shape for channel in case_data[0][0]}  # Get shapes for all channels\n",
    "            \n",
    "            # Set the first case's shapes as the reference\n",
    "            if reference_shapes is None:\n",
    "                reference_shapes = current_shapes\n",
    "            # Compare the current shapes with the reference shapes\n",
    "            else:\n",
    "                for channel, current_shape in current_shapes.items():\n",
    "                    if channel in reference_shapes:\n",
    "                        if current_shape != reference_shapes[channel]:\n",
    "                            print(f\"Warning: Shape of '{channel}' in {case} is inconsistent. Expected {reference_shapes[channel]}, but got {current_shape}.\")\n",
    "                            return False\n",
    "                    else:\n",
    "                        print(f\"Warning: Channel '{channel}' found in {case} but missing in the reference case.\")\n",
    "                        return False\n",
    "        except KeyError:\n",
    "            print(f\"Warning: Some channels are missing in {case}.\")\n",
    "            return False\n",
    "\n",
    "    print(f\"All cases have consistent dimensions for all channels.\")\n",
    "    return True\n",
    "\n",
    "def print_all_cases_data_dimensions(all_cases_data):\n",
    "    \"\"\"\n",
    "    Prints the dimensions of the data stored in all_cases_data.\n",
    "\n",
    "    Args:\n",
    "        all_cases_data (dict): Dictionary containing data for all cases. \n",
    "                               Each case is a tuple with (interpolated_data, grid_x, grid_y).\n",
    "\n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    for case_name, (interpolated_data, grid_x, grid_y) in all_cases_data.items():\n",
    "        print(f\"Case: {case_name}\")\n",
    "        # Print dimensions of interpolated_data\n",
    "        if isinstance(interpolated_data, list):\n",
    "            print(f\"  Interpolated Data: {len(interpolated_data)} time steps\")\n",
    "            if len(interpolated_data) > 0 and isinstance(interpolated_data[0], dict):\n",
    "                for channel, data_array in interpolated_data[0].items():\n",
    "                    print(f\"    Channel '{channel}': {data_array.shape}\")\n",
    "        else:\n",
    "            print(f\"  Interpolated Data: {interpolated_data.shape}\")\n",
    "\n",
    "        # Print dimensions of grid_x and grid_y\n",
    "        print(f\"  Grid X: {grid_x.shape}\")\n",
    "        print(f\"  Grid Y: {grid_y.shape}\")\n",
    "        print(\"-\" * 50)\n",
    "\n",
    "# print_all_cases_data_dimensions(all_cases_data)\n",
    "consistent = check_consistent_dimensions_all_channels(all_cases_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f79c7cb1-7dae-483d-a64c-c8c2555b57a1",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2500x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x2500 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "def plot_interpolated_time_steps(time_step_data, channel, split_at=5, columns_per_row=3):\n",
    "    \"\"\"\n",
    "    Plots the specified channel from each time step's interpolated data, splitting the plots into two figures.\n",
    "\n",
    "    Args:\n",
    "        time_step_data (list of dicts): Each dict contains channel names as keys and 2D arrays of data as values.\n",
    "        channel (str): Name of the channel to plot.\n",
    "        split_at (int): Index at which to split the plots into a second figure.\n",
    "        columns_per_row (int): Number of columns per row in the plot.\n",
    "\n",
    "    Returns:\n",
    "        None: Displays the plots.\n",
    "    \"\"\"\n",
    "    \n",
    "    num_time_steps = len(time_step_data)\n",
    "    first_batch = min(num_time_steps, split_at)\n",
    "    second_batch = num_time_steps - first_batch\n",
    "\n",
    "    # Function to create plots for a given range of time steps\n",
    "    def plot_batch(start_index, end_index, title):\n",
    "        num_plots = end_index - start_index\n",
    "        rows = (num_plots + columns_per_row - 1) // columns_per_row  # Calculate rows needed\n",
    "        fig, axes = plt.subplots(rows, columns_per_row, figsize=(5 * columns_per_row, 5 * rows))\n",
    "        if not isinstance(axes, np.ndarray):\n",
    "            axes = np.array([axes])  # Handle the case where there is only one subplot\n",
    "        axes = axes.flatten()\n",
    "\n",
    "        for i in range(num_plots):\n",
    "            ax = axes[i]\n",
    "            data_index = start_index + i\n",
    "            if channel in time_step_data[data_index]:\n",
    "                c = ax.imshow(time_step_data[data_index][channel], cmap='viridis', aspect='auto')\n",
    "                fig.colorbar(c, ax=ax)\n",
    "                ax.set_title(f'Time Step: {data_index + 1}')\n",
    "                ax.set_xlabel('X Coordinate')\n",
    "                ax.set_ylabel('Y Coordinate')\n",
    "            else:\n",
    "                ax.set_title(f'Channel {channel} not found')\n",
    "                ax.set_xlabel('X Coordinate')\n",
    "                ax.set_ylabel('Y Coordinate')\n",
    "                print(f\"Warning: Channel '{channel}' not found in time step {data_index + 1}.\")\n",
    "\n",
    "        for j in range(i + 1, len(axes)):  # Hide unused axes\n",
    "            axes[j].axis('off')\n",
    "\n",
    "        plt.suptitle(title)\n",
    "        plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "        plt.show()\n",
    "\n",
    "    # Plot the first batch of time steps\n",
    "    plot_batch(0, first_batch, \"First Batch of Time Steps\")\n",
    "\n",
    "    # Plot the second batch of time steps if there are any\n",
    "    if second_batch > 0:\n",
    "        plot_batch(first_batch, num_time_steps, \"Second Batch of Time Steps\")\n",
    "\n",
    "# Randomly select a case key from all_cases_data\n",
    "case_key = random.choice(list(all_cases_data.keys()))  # Randomly pick a case key\n",
    "channel_to_plot = 'phi'  # Example channel to plot\n",
    "\n",
    "# Now use the variable to access data and call the plotting function\n",
    "plot_interpolated_time_steps(all_cases_data[case_key][0], channel_to_plot, columns_per_row=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4767234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "io2D_ND27\n",
      "io2D_ND82\n",
      "io2D_ND86\n",
      "io2D_ND88\n",
      "io2D_2_49\n",
      "io2D_ND21\n",
      "io2D_ND25\n",
      "io2D_ND02\n",
      "io2D_ND100\n",
      "io2D_ND41\n",
      "io2D_ND23\n",
      "io2D_ND45\n",
      "io2D_3_12\n",
      "io2D_ND33\n",
      "io2D_ND70\n",
      "io2D_ND13\n",
      "io2D_ND35\n",
      "io2D_ND67\n",
      "io2D_ND22\n",
      "io2D_ND64\n",
      "io2D_ND39\n",
      "io2D_ND03\n",
      "io2D_ND60\n",
      "io2D_ND38\n",
      "io2D_2_12\n",
      "io2D_ND32\n",
      "io2D_ND81\n",
      "io2D_ND57\n",
      "io2D_ND09\n",
      "io2D_ND36\n",
      "io2D_ND08\n",
      "io2D_ND91\n",
      "io2D_ND49\n",
      "io2D_3_29\n",
      "io2D_ND61\n",
      "io2D_ND95\n",
      "io2D_ND73\n",
      "io2D_2_9\n",
      "io2D_2_28\n",
      "io2D_2_3\n",
      "io2D_ND66\n",
      "io2D_ND87\n",
      "io2D_ND65\n",
      "io2D_3_23\n",
      "io2D_ND69\n",
      "io2D_ND43\n",
      "io2D_2_42\n",
      "io2D_ND94\n",
      "io2D_ND04\n",
      "io2D_2_13\n",
      "io2D_ND74\n",
      "io2D_ND26\n",
      "io2D_ND96\n",
      "io2D_ND53\n",
      "io2D_ND77\n",
      "io2D_ND90\n",
      "io2D_2_14\n",
      "io2D_2_33\n",
      "io2D_ND71\n",
      "io2D_ND93\n",
      "io2D_ND84\n",
      "io2D_ND92\n",
      "io2D_ND28\n",
      "io2D_2_25\n",
      "io2D_ND85\n",
      "io2D_ND83\n",
      "io2D_ND52\n",
      "io2D_ND54\n",
      "io2D_ND15\n",
      "io2D_ND76\n",
      "io2D_3_6\n",
      "io2D_2_18\n",
      "io2D_ND72\n",
      "io2D_2_31\n",
      "io2D_ND99\n",
      "io2D_2_32\n",
      "io2D_ND16\n",
      "io2D_ND51\n",
      "io2D_ND19\n",
      "io2D_ND55\n",
      "io2D_ND47\n",
      "io2D_ND18\n",
      "io2D_2_0\n",
      "io2D_ND07\n",
      "io2D_2_7\n",
      "io2D_ND56\n",
      "io2D_ND46\n",
      "io2D_2_8\n",
      "io2D_ND40\n",
      "io2D_ND20\n",
      "io2D_ND42\n",
      "io2D_ND68\n",
      "io2D_ND17\n",
      "io2D_ND75\n",
      "io2D_ND34\n",
      "io2D_2_6\n",
      "io2D_2_11\n",
      "io2D_ND37\n",
      "io2D_ND24\n",
      "io2D_ND01\n",
      "io2D_ND14\n",
      "io2D_2_45\n",
      "io2D_ND05\n",
      "io2D_ND79\n",
      "io2D_ND12\n",
      "io2D_2_38\n",
      "io2D_ND30\n",
      "io2D_ND89\n",
      "io2D_ND11\n",
      "io2D_2_2\n",
      "io2D_ND50\n",
      "io2D_ND29\n",
      "io2D_2_15\n",
      "io2D_ND97\n",
      "io2D_ND10\n",
      "io2D_ND62\n",
      "io2D_ND59\n",
      "io2D_3_1\n",
      "io2D_2_39\n",
      "io2D_ND44\n",
      "io2D_ND98\n",
      "io2D_2_10\n",
      "io2D_ND63\n",
      "io2D_ND58\n",
      "io2D_2_36\n",
      "io2D_2_23\n",
      "io2D_ND78\n",
      "io2D_ND31\n",
      "io2D_ND48\n",
      "io2D_2_16\n",
      "io2D_2_21\n",
      "io2D_ND80\n",
      "io2D_2_5\n",
      "io2D_ND06\n",
      "X_train shape: (93, 10, 1, 256, 256)\n",
      "Y_train shape: (93, 20, 1, 256, 256)\n",
      "X_val shape: (20, 10, 1, 256, 256)\n",
      "Y_val shape: (20, 20, 1, 256, 256)\n",
      "X_test shape: (21, 10, 1, 256, 256)\n",
      "Y_test shape: (21, 20, 1, 256, 256)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "\n",
    "def select_and_structure_data(all_cases_data, num_X_steps=10, num_Y_steps=20, train_frac=0.7, val_frac=0.15, channels=None):\n",
    "    \"\"\"\n",
    "    Structured data extraction for ML training from multidimensional time-series data.\n",
    "\n",
    "    Args:\n",
    "        all_cases_data (dict): The main dataset containing all cases.\n",
    "        num_X_steps (int): Number of time steps to use for X.\n",
    "        num_Y_steps (int): Number of time steps to use for Y.\n",
    "        train_frac (float): Fraction of data to be used for training.\n",
    "        val_frac (float): Fraction of data to be used for validation.\n",
    "        channels (list): List of channel names to include in the output arrays.\n",
    "\n",
    "    Returns:\n",
    "        dict: Dataset containing structured data for training, validation, and testing.\n",
    "    \"\"\"\n",
    "\n",
    "    # Extract keys for all cases\n",
    "    case_keys = list(all_cases_data.keys())\n",
    "    random.shuffle(case_keys)\n",
    "\n",
    "    # Determine split indices\n",
    "    num_train = int(len(case_keys) * train_frac)\n",
    "    num_val = int(len(case_keys) * val_frac)\n",
    "    \n",
    "    train_keys = case_keys[:num_train]\n",
    "    val_keys = case_keys[num_train:num_train + num_val]\n",
    "    test_keys = case_keys[num_train + num_val:]\n",
    "\n",
    "    # Define function to extract data for X and Y\n",
    "    def extract_data(cases, num_X_steps, num_Y_steps, channels):\n",
    "        X, Y = [], []\n",
    "        for case in cases:\n",
    "            case_data = all_cases_data[case][0]  # Assuming the data structure is (list of dicts with channel data, grid_x, grid_y)\n",
    "            # print(case_data)\n",
    "            print(case)\n",
    "            case_data[0]['phi']\n",
    "            if len(case_data) >= num_X_steps + num_Y_steps:\n",
    "                x_segments = np.stack([np.array([time_step[channel] for time_step in case_data[:num_X_steps]]) for channel in channels if channel in case_data[0]], axis=1)\n",
    "                y_segments = np.stack([np.array([time_step[channel] for time_step in case_data[num_X_steps:num_X_steps + num_Y_steps]]) for channel in channels if channel in case_data[0]], axis=1)\n",
    "\n",
    "                X.append(x_segments)\n",
    "                Y.append(y_segments)\n",
    "\n",
    "        return np.array(X), np.array(Y)\n",
    "\n",
    "    # Extract data for each dataset\n",
    "    X_train, Y_train = extract_data(train_keys, num_X_steps, num_Y_steps, channels)\n",
    "    X_val, Y_val = extract_data(val_keys, num_X_steps, num_Y_steps, channels)\n",
    "    X_test, Y_test = extract_data(test_keys, num_X_steps, num_Y_steps, channels)\n",
    "\n",
    "    return {\n",
    "        'X_train': X_train, 'Y_train': Y_train,\n",
    "        'X_val': X_val, 'Y_val': Y_val,\n",
    "        'X_test': X_test, 'Y_test': Y_test\n",
    "    }\n",
    "\n",
    "# Example usage: specify which channels to include\n",
    "# channels_to_include = ['phi', 'synaptogenesis', 'tubulin', 'tips', 'theta']  # Add your specific channels here\n",
    "channels_to_include = ['phi']  # Add your specific channels here\n",
    "dataset = select_and_structure_data(all_cases_data, channels=channels_to_include)\n",
    "\n",
    "# Display the shapes of the datasets\n",
    "for key in dataset:\n",
    "    print(f\"{key} shape: {dataset[key].shape if dataset[key].size else 'Empty'}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b1468fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the dataset to a .pkl file using pickle\n",
    "# with open('./reformatedNDDs/dataset_16k_20k.pkl', 'wb') as f:\n",
    "# with open('./reformatedNDDs/dataset_16k_20k_all.pkl', 'wb') as f:\n",
    "# with open('./reformatedNDDs/dataset_16k_20k_all_10042024.pkl', 'wb') as f:\n",
    "# with open('./reformatedNDDs/dataset_16k_20k_all_10152024.pkl', 'wb') as f:\n",
    "# with open('./reformatedNDDs/dataset_16k_20k_Dc.pkl', 'wb') as f:\n",
    "with open('./reformatedNDDs/dataset_16k_20k_al_256_11282024.pkl', 'wb') as f:\n",
    "    pickle.dump(dataset, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7be25f3d-e3aa-4515-b774-4b581beda5cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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