{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "31a94bbd-34ef-404b-a9be-4f3a216a8c3c",
   "metadata": {},
   "source": [
    "# POD and fft from pointclouds \n",
    "\n",
    "We will now proceed to explain how to perform POD from point clouds. In this instance, we test only for POD in serial, as to perform in parallel, a parallel reader/writer is needed.\n",
    "\n",
    "If you have saved information in hdf5 and have habilitated mpi4py compilation of it, then you could use this code in parallel."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ad6337-6ba4-47c7-b687-0bf119a5b637",
   "metadata": {},
   "source": [
    "#### Import general modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b4ca2be-46e8-443b-ab44-6d1a60abfec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required modules\n",
    "from mpi4py import MPI #equivalent to the use of MPI_init() in C\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import h5py\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# Get mpi info\n",
    "comm = MPI.COMM_WORLD\n",
    "\n",
    "# Hide the log for the notebook. Not recommended when running in clusters as it is better you see what happens\n",
    "import os\n",
    "os.environ[\"PYSEMTOOLS_HIDE_LOG\"] = 'true'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae925826",
   "metadata": {},
   "source": [
    "## Set up the input parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f77a66fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_sequence = [f\"../4-interpolation/interpolated_fields{str(1+i).zfill(5)}.hdf5\" for i in range(0, 48)]\n",
    "pod_fields = [\"u\", \"v\", \"w\"]\n",
    "mesh_fname = \"../4-interpolation/points.hdf5\"\n",
    "mass_matrix_fname = \"../4-interpolation/points.hdf5\"\n",
    "mass_matrix_key = \"mass\"\n",
    "k = len(file_sequence)\n",
    "p = len(file_sequence)\n",
    "fft_axis = 1 # 0 for x, 1 for y, 2 for z (Depends on how the mesh was created)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62da10be",
   "metadata": {},
   "source": [
    "## Call the pysemtools routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e6c7262d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/adperez/software/pySEMTools/pysemtools/rom/fft_pod_wrappers.py:365: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  ioh[kappa].bm1sqrt[:, :] = np.copy(ioh[kappa].xi[:, :])\n"
     ]
    }
   ],
   "source": [
    "# Import the pysemtools routines\n",
    "from pysemtools.rom.fft_pod_wrappers import pod_fourier_1_homogenous_direction, physical_space\n",
    "\n",
    "# Perform the POD with your input data\n",
    "pod, ioh, _3d_bm_shape, number_of_frequencies, N_samples = pod_fourier_1_homogenous_direction(comm, file_sequence, pod_fields, mass_matrix_fname, mass_matrix_key, k, p, fft_axis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29aff816",
   "metadata": {},
   "source": [
    "# Write out data\n",
    "\n",
    "Write out modes that you specify in physical space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3be253e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing ./pod_kappa_0_mode0.vtk\n",
      "Writing ./pod_kappa_0_mode1.vtk\n",
      "Writing ./pod_kappa_0_mode2.vtk\n",
      "Writing ./pod_kappa_0_mode3.vtk\n",
      "Writing ./pod_kappa_0_mode4.vtk\n",
      "Writing ./pod_kappa_1_mode0.vtk\n",
      "Writing ./pod_kappa_1_mode1.vtk\n",
      "Writing ./pod_kappa_1_mode2.vtk\n",
      "Writing ./pod_kappa_1_mode3.vtk\n",
      "Writing ./pod_kappa_1_mode4.vtk\n",
      "Writing ./pod_kappa_2_mode0.vtk\n",
      "Writing ./pod_kappa_2_mode1.vtk\n",
      "Writing ./pod_kappa_2_mode2.vtk\n",
      "Writing ./pod_kappa_2_mode3.vtk\n",
      "Writing ./pod_kappa_2_mode4.vtk\n",
      "Writing ./pod_reconstructed_data_0\n",
      "Writing ./pod_reconstructed_data_1\n",
      "Writing ./pod_reconstructed_data_2\n",
      "Writing ./pod_reconstructed_data_3\n",
      "Writing ./pod_reconstructed_data_4\n",
      "Writing ./pod_reconstructed_data_5\n",
      "Writing ./pod_reconstructed_data_6\n",
      "Writing ./pod_reconstructed_data_7\n",
      "Writing ./pod_reconstructed_data_8\n",
      "Writing ./pod_reconstructed_data_9\n",
      "Writing ./pod_reconstructed_data_10\n",
      "Writing ./pod_reconstructed_data_11\n",
      "Writing ./pod_reconstructed_data_12\n",
      "Writing ./pod_reconstructed_data_13\n",
      "Writing ./pod_reconstructed_data_14\n",
      "Writing ./pod_reconstructed_data_15\n",
      "Writing ./pod_reconstructed_data_16\n",
      "Writing ./pod_reconstructed_data_17\n",
      "Writing ./pod_reconstructed_data_18\n",
      "Writing ./pod_reconstructed_data_19\n",
      "Writing ./pod_reconstructed_data_20\n",
      "Writing ./pod_reconstructed_data_21\n",
      "Writing ./pod_reconstructed_data_22\n",
      "Writing ./pod_reconstructed_data_23\n",
      "Writing ./pod_reconstructed_data_24\n",
      "Writing ./pod_reconstructed_data_25\n",
      "Writing ./pod_reconstructed_data_26\n",
      "Writing ./pod_reconstructed_data_27\n",
      "Writing ./pod_reconstructed_data_28\n",
      "Writing ./pod_reconstructed_data_29\n",
      "Writing ./pod_reconstructed_data_30\n",
      "Writing ./pod_reconstructed_data_31\n",
      "Writing ./pod_reconstructed_data_32\n",
      "Writing ./pod_reconstructed_data_33\n",
      "Writing ./pod_reconstructed_data_34\n",
      "Writing ./pod_reconstructed_data_35\n",
      "Writing ./pod_reconstructed_data_36\n",
      "Writing ./pod_reconstructed_data_37\n",
      "Writing ./pod_reconstructed_data_38\n",
      "Writing ./pod_reconstructed_data_39\n",
      "Writing ./pod_reconstructed_data_40\n",
      "Writing ./pod_reconstructed_data_41\n",
      "Writing ./pod_reconstructed_data_42\n",
      "Writing ./pod_reconstructed_data_43\n",
      "Writing ./pod_reconstructed_data_44\n",
      "Writing ./pod_reconstructed_data_45\n",
      "Writing ./pod_reconstructed_data_46\n",
      "Writing ./pod_reconstructed_data_47\n"
     ]
    }
   ],
   "source": [
    "from pysemtools.rom.fft_pod_wrappers import write_3dfield_to_file\n",
    "\n",
    "# Load the mesh\n",
    "with h5py.File(mesh_fname, 'r') as f:\n",
    "    x = f[\"x\"][:]\n",
    "    y = f[\"y\"][:]\n",
    "    z = f[\"z\"][:]\n",
    "\n",
    "# Write out 5 modes for the first 3 wavenumbers\n",
    "write_3dfield_to_file(\"pod.vtk\", x, y, z, pod, ioh, wavenumbers=[k for k in range(0, 3)], modes=[i for i in range(0, 5)], field_shape=_3d_bm_shape, fft_axis=fft_axis, field_names=pod_fields, N_samples=N_samples)\n",
    "\n",
    "# Reconstruct all the snapshots for the first 3 wavenumbers and 5 modes\n",
    "write_3dfield_to_file(\"pod.vtk\", x, y, z, pod, ioh, wavenumbers=[k for k in range(1, 3)], modes=[i for i in range(0, 5)], field_shape=_3d_bm_shape, fft_axis=fft_axis, field_names=pod_fields, N_samples=N_samples, snapshots=[i for i in range(0, len(file_sequence))])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52073306",
   "metadata": {},
   "source": [
    "## Save the data in fourier space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cadcf7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pysemtools.rom.fft_pod_wrappers import save_pod_state\n",
    "\n",
    "# Save the POD state\n",
    "save_pod_state(\"pod_state.hdf5\", pod)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bfed2f",
   "metadata": {},
   "source": [
    "## Reconstruct the snapshot\n",
    "\n",
    "Use the helper function to reconstruct the snapshots.\n",
    "\n",
    "Here you input the results you got. For this you must specify which snapshots to reconstruct the proper keyword. Then the wavenumbers and modes in each wavenumber to be used. At the moment, the support is only to reconstruct in the same modes in all chosen wavenumbers. This can be modified later quite easily by, for example, passing a directory with modes list, just as done for POD and IOH."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "caee6fd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "phys = physical_space(pod, ioh, wavenumbers=[k for k in range(0, number_of_frequencies)], modes=[i for i in range(0, k)], field_shape=_3d_bm_shape, fft_axis=fft_axis, field_names=pod_fields, N_samples=N_samples, snapshots=[i for i in range(0, len(file_sequence))])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7722c057",
   "metadata": {},
   "source": [
    "## Perform tests\n",
    "\n",
    "First check that the reconstruction of every snapshot matches and that the energy is kept in the time coefficients, not in the modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d011a42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing snapshot 0: ../4-interpolation/interpolated_fields00001.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 3.698958237844183e-05\n",
      "Reconstruction energy: 3.6993163022397004e-05\n",
      "=======================================\n",
      "Testing snapshot 1: ../4-interpolation/interpolated_fields00002.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 3.8898814924962444e-05\n",
      "Reconstruction energy: 3.8902239959461035e-05\n",
      "=======================================\n",
      "Testing snapshot 2: ../4-interpolation/interpolated_fields00003.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 4.153454569587408e-05\n",
      "Reconstruction energy: 4.153817337208489e-05\n",
      "=======================================\n",
      "Testing snapshot 3: ../4-interpolation/interpolated_fields00004.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 4.4425076415412405e-05\n",
      "Reconstruction energy: 4.442864337674737e-05\n",
      "=======================================\n",
      "Testing snapshot 4: ../4-interpolation/interpolated_fields00005.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 4.688317219673442e-05\n",
      "Reconstruction energy: 4.688600091533262e-05\n",
      "=======================================\n",
      "Testing snapshot 5: ../4-interpolation/interpolated_fields00006.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 4.878401448156997e-05\n",
      "Reconstruction energy: 4.878710853476647e-05\n",
      "=======================================\n",
      "Testing snapshot 6: ../4-interpolation/interpolated_fields00007.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 5.026805630070726e-05\n",
      "Reconstruction energy: 5.027085874214742e-05\n",
      "=======================================\n",
      "Testing snapshot 7: ../4-interpolation/interpolated_fields00008.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 5.149475955390006e-05\n",
      "Reconstruction energy: 5.1498217100047447e-05\n",
      "=======================================\n",
      "Testing snapshot 8: ../4-interpolation/interpolated_fields00009.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 5.237488647324383e-05\n",
      "Reconstruction energy: 5.237815402427038e-05\n",
      "=======================================\n",
      "Testing snapshot 9: ../4-interpolation/interpolated_fields00010.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 5.263423351722945e-05\n",
      "Reconstruction energy: 5.26386214147448e-05\n",
      "=======================================\n",
      "Testing snapshot 10: ../4-interpolation/interpolated_fields00011.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 5.187294605439943e-05\n",
      "Reconstruction energy: 5.1878309034501663e-05\n",
      "=======================================\n",
      "Testing snapshot 11: ../4-interpolation/interpolated_fields00012.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 4.988241310699623e-05\n",
      "Reconstruction energy: 4.988923186557772e-05\n",
      "=======================================\n",
      "Testing snapshot 12: ../4-interpolation/interpolated_fields00013.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 4.697643854683935e-05\n",
      "Reconstruction energy: 4.698326450473892e-05\n",
      "=======================================\n",
      "Testing snapshot 13: ../4-interpolation/interpolated_fields00014.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 4.3863242905911426e-05\n",
      "Reconstruction energy: 4.386992609602263e-05\n",
      "=======================================\n",
      "Testing snapshot 14: ../4-interpolation/interpolated_fields00015.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 4.04654975068024e-05\n",
      "Reconstruction energy: 4.0472264813275435e-05\n",
      "=======================================\n",
      "Testing snapshot 15: ../4-interpolation/interpolated_fields00016.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 3.708331604360593e-05\n",
      "Reconstruction energy: 3.708800095581567e-05\n",
      "=======================================\n",
      "Testing snapshot 16: ../4-interpolation/interpolated_fields00017.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 3.361937142993228e-05\n",
      "Reconstruction energy: 3.3622823972522094e-05\n",
      "=======================================\n",
      "Testing snapshot 17: ../4-interpolation/interpolated_fields00018.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 3.042199960394419e-05\n",
      "Reconstruction energy: 3.0424717509798353e-05\n",
      "=======================================\n",
      "Testing snapshot 18: ../4-interpolation/interpolated_fields00019.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.764625401122964e-05\n",
      "Reconstruction energy: 2.764854026761965e-05\n",
      "=======================================\n",
      "Testing snapshot 19: ../4-interpolation/interpolated_fields00020.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.5278143358328515e-05\n",
      "Reconstruction energy: 2.528007609408604e-05\n",
      "=======================================\n",
      "Testing snapshot 20: ../4-interpolation/interpolated_fields00021.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.3436562845034242e-05\n",
      "Reconstruction energy: 2.3438276147769336e-05\n",
      "=======================================\n",
      "Testing snapshot 21: ../4-interpolation/interpolated_fields00022.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.2002654602291965e-05\n",
      "Reconstruction energy: 2.2004310326422838e-05\n",
      "=======================================\n",
      "Testing snapshot 22: ../4-interpolation/interpolated_fields00023.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.0920245682595327e-05\n",
      "Reconstruction energy: 2.09217571517706e-05\n",
      "=======================================\n",
      "Testing snapshot 23: ../4-interpolation/interpolated_fields00024.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.013654268431107e-05\n",
      "Reconstruction energy: 2.0137912485458836e-05\n",
      "=======================================\n",
      "Testing snapshot 24: ../4-interpolation/interpolated_fields00025.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 1.9632578477660025e-05\n",
      "Reconstruction energy: 1.9633680618611167e-05\n",
      "=======================================\n",
      "Testing snapshot 25: ../4-interpolation/interpolated_fields00026.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 1.933656650853822e-05\n",
      "Reconstruction energy: 1.93375327210833e-05\n",
      "=======================================\n",
      "Testing snapshot 26: ../4-interpolation/interpolated_fields00027.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 1.9193896285463495e-05\n",
      "Reconstruction energy: 1.919484401965993e-05\n",
      "=======================================\n",
      "Testing snapshot 27: ../4-interpolation/interpolated_fields00028.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 1.9209913696544347e-05\n",
      "Reconstruction energy: 1.921084152200394e-05\n",
      "=======================================\n",
      "Testing snapshot 28: ../4-interpolation/interpolated_fields00029.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 1.9433633251162603e-05\n",
      "Reconstruction energy: 1.9434414649136665e-05\n",
      "=======================================\n",
      "Testing snapshot 29: ../4-interpolation/interpolated_fields00030.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 1.9843626264053833e-05\n",
      "Reconstruction energy: 1.9844286776115457e-05\n",
      "=======================================\n",
      "Testing snapshot 30: ../4-interpolation/interpolated_fields00031.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.0264037570696985e-05\n",
      "Reconstruction energy: 2.0264694456842263e-05\n",
      "=======================================\n",
      "Testing snapshot 31: ../4-interpolation/interpolated_fields00032.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.061492121971811e-05\n",
      "Reconstruction energy: 2.061561527274048e-05\n",
      "=======================================\n",
      "Testing snapshot 32: ../4-interpolation/interpolated_fields00033.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.0873392702196558e-05\n",
      "Reconstruction energy: 2.0874141067124846e-05\n",
      "=======================================\n",
      "Testing snapshot 33: ../4-interpolation/interpolated_fields00034.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.100146581923816e-05\n",
      "Reconstruction energy: 2.1002249619839244e-05\n",
      "=======================================\n",
      "Testing snapshot 34: ../4-interpolation/interpolated_fields00035.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.1002631484979843e-05\n",
      "Reconstruction energy: 2.1003356850805536e-05\n",
      "=======================================\n",
      "Testing snapshot 35: ../4-interpolation/interpolated_fields00036.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.0926017391762055e-05\n",
      "Reconstruction energy: 2.092671514261269e-05\n",
      "=======================================\n",
      "Testing snapshot 36: ../4-interpolation/interpolated_fields00037.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.0846074698639117e-05\n",
      "Reconstruction energy: 2.0846812180790863e-05\n",
      "=======================================\n",
      "Testing snapshot 37: ../4-interpolation/interpolated_fields00038.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.0866571618859654e-05\n",
      "Reconstruction energy: 2.086747072367675e-05\n",
      "=======================================\n",
      "Testing snapshot 38: ../4-interpolation/interpolated_fields00039.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.101439294141517e-05\n",
      "Reconstruction energy: 2.1015443956099017e-05\n",
      "=======================================\n",
      "Testing snapshot 39: ../4-interpolation/interpolated_fields00040.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.1135889588706728e-05\n",
      "Reconstruction energy: 2.1137121811861234e-05\n",
      "=======================================\n",
      "Testing snapshot 40: ../4-interpolation/interpolated_fields00041.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.1184459915354247e-05\n",
      "Reconstruction energy: 2.1185893188943514e-05\n",
      "=======================================\n",
      "Testing snapshot 41: ../4-interpolation/interpolated_fields00042.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.1250999040067878e-05\n",
      "Reconstruction energy: 2.1252544393961218e-05\n",
      "=======================================\n",
      "Testing snapshot 42: ../4-interpolation/interpolated_fields00043.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.1377532850036896e-05\n",
      "Reconstruction energy: 2.1378960026407462e-05\n",
      "=======================================\n",
      "Testing snapshot 43: ../4-interpolation/interpolated_fields00044.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.1529543422705357e-05\n",
      "Reconstruction energy: 2.153077292308808e-05\n",
      "=======================================\n",
      "Testing snapshot 44: ../4-interpolation/interpolated_fields00045.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.161399267113895e-05\n",
      "Reconstruction energy: 2.1615112232324585e-05\n",
      "=======================================\n",
      "Testing snapshot 45: ../4-interpolation/interpolated_fields00046.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.1637133597399283e-05\n",
      "Reconstruction energy: 2.163809363808384e-05\n",
      "=======================================\n",
      "Testing snapshot 46: ../4-interpolation/interpolated_fields00047.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.1440817225395135e-05\n",
      "Reconstruction energy: 2.1441751635941706e-05\n",
      "=======================================\n",
      "Testing snapshot 47: ../4-interpolation/interpolated_fields00048.hdf5\n",
      "Field u passed: True\n",
      "Field v passed: True\n",
      "Field w passed: True\n",
      "Snapshot energy: 2.0948984478946528e-05\n",
      "Reconstruction energy: 2.0949807869368654e-05\n",
      "=======================================\n",
      "total snapshot energy: 0.001434071843040977\n",
      "Total POD energy: 0.0014341827489845003\n",
      "=======================================\n"
     ]
    }
   ],
   "source": [
    "# Load the mass matrix\n",
    "with h5py.File(mass_matrix_fname, 'r') as f:\n",
    "    bm_v = f[\"mass\"][:]\n",
    "\n",
    "# Go through the snapshots in the file sequence\n",
    "total_snapshot_energy = 0\n",
    "for j, fname in enumerate(file_sequence):\n",
    "    print(f\"Testing snapshot {j}: {fname}\")\n",
    "    \n",
    "    with h5py.File(fname, 'r') as f:\n",
    "\n",
    "        # Check one snapshot at a time\n",
    "        # Here one could also just use the phys that has been previously computed.\n",
    "        phys = physical_space(pod, ioh, wavenumbers=[k for k in range(0, number_of_frequencies)], modes=[i for i in range(0, k)], field_shape=_3d_bm_shape, fft_axis=fft_axis, field_names=pod_fields, N_samples=N_samples, snapshots=[j])\n",
    "\n",
    "        # Check if the reconstruction is accurate\n",
    "        for field in pod_fields:\n",
    "            passed = np.allclose(phys[j][field], f[field][:])\n",
    "            print(f\"Field {field} passed: {passed}\")\n",
    "\n",
    "        # Check if the energy was accurately captured\n",
    "        snapshot_energy = 0\n",
    "        for field in pod_fields:\n",
    "            snapshot_energy += np.sum(f[field][:]**2*bm_v)\n",
    "        total_snapshot_energy += snapshot_energy\n",
    "\n",
    "        reconstruction_energy = 0\n",
    "        for kappa in range(0, number_of_frequencies):\n",
    "            a_i = np.diag(pod[kappa].d_1t)@pod[kappa].vt_1t[:,j]\n",
    "            reconstruction_energy += np.sum(np.abs(a_i)**2)\n",
    "\n",
    "        print(f\"Snapshot energy: {snapshot_energy}\")\n",
    "        print(f\"Reconstruction energy: {reconstruction_energy}\")\n",
    "    \n",
    "    print(\"=======================================\")\n",
    "\n",
    "# Check if the total energy is kept in the singular values\n",
    "print(f\"total snapshot energy: {total_snapshot_energy}\")\n",
    "\n",
    "total_pod_energy = 0\n",
    "for kappa in range(0, number_of_frequencies):\n",
    "    total_pod_energy += np.sum(pod[kappa].d_1t**2)\n",
    "print(f\"Total POD energy: {total_pod_energy}\")\n",
    "print(\"=======================================\")   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a08c840",
   "metadata": {},
   "source": [
    "Then check if the left and right singular vectors are indeed orthogonal with respect to the others along the wavenumbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b773b936",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The modes are orthogonal: True\n",
      "The right singular vectors are orthogonal: True\n"
     ]
    }
   ],
   "source": [
    "from pysemtools.rom.fft_pod_wrappers import degenerate_scaling\n",
    "\n",
    "# Check the left singular vectors\n",
    "all_passed = True\n",
    "for kappa in range(0, number_of_frequencies):\n",
    "    \n",
    "    kappa_i = 0\n",
    "    kappa_j = kappa_i # These are the modes for the fourier coefficients. Thus, they orthogonals in a wave number. The fourier modes are orthogonal among fourier modes\n",
    "\n",
    "    for i in range(0, k):\n",
    "        for j in range(0, k):\n",
    "\n",
    "            passed = True\n",
    "\n",
    "            scaled_mode_i = pod[kappa_i].u_1t[:,i]*ioh[kappa_i].bm1sqrt[:,0]*degenerate_scaling(kappa_i)\n",
    "            scaled_mode_j = pod[kappa_j].u_1t[:,j]*ioh[kappa_j].bm1sqrt[:,0]*degenerate_scaling(kappa_j)\n",
    "\n",
    "            norm = np.abs(scaled_mode_i.T@scaled_mode_j)\n",
    "\n",
    "            if (j==i) and (np.allclose(norm, 1)):\n",
    "                passed = True\n",
    "            elif (j!=i) and (np.allclose(norm, 0)):\n",
    "                passed = True\n",
    "            else:\n",
    "                passed = False\n",
    "\n",
    "            if not passed:              \n",
    "                print(f\"Mode {i} and mode {j} in wavenumber {kappa} are not orthogonal\")\n",
    "                print(f\"Norm: {norm}\")\n",
    "                all_passed = False\n",
    "                break\n",
    "\n",
    "print(f\"The modes are orthogonal: {all_passed}\")\n",
    "\n",
    "# Check the right singular vectors\n",
    "all_passed = True\n",
    "for kappa in range(0, number_of_frequencies):\n",
    "    \n",
    "    kappa_i = 0\n",
    "    kappa_j = kappa_i # These are the modes for the fourier coefficients. Thus, they orthogonals in a wave number. The fourier modes are orthogonal among fourier modes\n",
    "\n",
    "    for i in range(0, k):\n",
    "        for j in range(0, k):\n",
    "\n",
    "            passed = True\n",
    "\n",
    "            scaled_mode_i = pod[kappa_i].vt_1t[i,:]\n",
    "            scaled_mode_j = pod[kappa_j].vt_1t[j,:]\n",
    "\n",
    "            norm = np.abs(scaled_mode_i.T@scaled_mode_j)\n",
    "\n",
    "            if (j==i) and (np.allclose(norm, 1)):\n",
    "                passed = True\n",
    "            elif (j!=i) and (np.allclose(norm, 0)):\n",
    "                passed = True\n",
    "            else:\n",
    "                passed = False\n",
    "\n",
    "            if not passed:              \n",
    "                print(f\"Right singular vector {i} and right singular vector {j} in wavenumber {kappa} are not orthogonal\")\n",
    "                print(f\"Norm: {norm}\")\n",
    "                all_passed = False\n",
    "                break\n",
    "\n",
    "print(f\"The right singular vectors are orthogonal: {all_passed}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e120b544",
   "metadata": {},
   "source": [
    "## Visualize the data\n",
    "\n",
    "We have already written the data to vtk in previous steps. This means that you can readily use paraview or visit, for example.\n",
    "\n",
    "However one can also plot the data in python. For this we use the pyvista module.\n",
    "\n",
    "First we can get a dictionary with the reconstruction of the snapshots and also the modes in physical space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9fe37e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "phys = physical_space(pod, ioh, wavenumbers=[k for k in range(0, number_of_frequencies)], modes=[i for i in range(0, k)], field_shape=_3d_bm_shape, fft_axis=fft_axis, field_names=pod_fields, N_samples=N_samples, snapshots=[i for i in range(0, len(file_sequence))])\n",
    "phys_modes = physical_space(pod, ioh, wavenumbers=[k for k in range(0, number_of_frequencies)], modes=[i for i in range(0, k)], field_shape=_3d_bm_shape, fft_axis=fft_axis, field_names=pod_fields, N_samples=N_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e948308c",
   "metadata": {},
   "source": [
    "Then we can start plotting!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7529a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyvista as pv\n",
    "\n",
    "# Input parameters\n",
    "kappa = 1\n",
    "mode = 0\n",
    "fields = [\"u\",\"v\",\"w\"]\n",
    "isosurfaces = np.array([-8, 8])\n",
    "\n",
    "# Create a structured mesh\n",
    "mesh_1 = pv.StructuredGrid(x, y, z)\n",
    "mesh_2 = pv.StructuredGrid(x, y, z)\n",
    "\n",
    "# Add the field to the mesh\n",
    "for field in fields:\n",
    "    values = phys_modes[kappa][mode][field]\n",
    "    mesh_1.point_data[field] = values.ravel(order='F')\n",
    "    values2 = phys_modes[kappa+1][mode][field]\n",
    "    mesh_2.point_data[field] = values2.ravel(order='F')\n",
    "\n",
    "# Plot\n",
    "pl = pv.Plotter(shape=(2, 3), window_size=[1920,1080]) # Size in pixels\n",
    "pl.add_axes()\n",
    "for i, field in enumerate(fields):\n",
    "    \n",
    "    # Plot first row\n",
    "    pl.subplot(0, i)\n",
    "    # Obtain the isosurfaces\n",
    "    isos = mesh_1.contour(scalars = field, isosurfaces = isosurfaces)\n",
    "    pl.add_mesh(mesh_1.outline(), color=\"k\")\n",
    "    pl.add_mesh(isos, opacity=1, cmap=\"coolwarm\", scalar_bar_args={'title': f\"\"})\n",
    " \n",
    "    # Change the settings a bit\n",
    "    pl.add_text(f\"{field}: wavenumber {kappa} mode {mode}\", font = \"courier\", font_size=10, position=\"upper_left\")\n",
    "    \n",
    "    # Plot second row\n",
    "    pl.subplot(1, i)\n",
    "    # Obtain the isosurfaces\n",
    "    isos = mesh_2.contour(scalars = field, isosurfaces = isosurfaces)\n",
    "    pl.add_mesh(mesh_2.outline(), color=\"k\")\n",
    "    pl.add_mesh(isos, opacity=1, cmap=\"coolwarm\", scalar_bar_args={'title': f\"\"})\n",
    " \n",
    "    # Change the settings a bit\n",
    "    pl.add_text(f\"{field}: wavenumber {kappa+1} mode {mode}\", font = \"courier\", font_size=10, position=\"upper_left\")\n",
    "\n",
    "# Capture the plot as an image and show it (This is just to make the notebook render better on github)\n",
    "from IPython.display import Image, display\n",
    "image_path = \"static_plot.png\"\n",
    "pl.screenshot(image_path)\n",
    "pl.close()\n",
    "display(Image(filename=image_path))\n",
    "#pl.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}