sus_optimization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d16207a5-7247-42eb-a576-22ceea8c3903",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "from scipy import optimize\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import subprocess\n",
    "import sys\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "372323c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import sys\n",
    "#sys.path.append('/Users/evaalonsoortiz/Documents/python/susceptibility-to-fieldmap-fft/')\n",
    "#from functions import compute_fieldmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd209421",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the path to the folder so that it can always address data folder correctly\n",
    "home_path = \"C:/Users/User/msc_project/sus-optimization\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef8fbc8",
   "metadata": {},
   "source": [
    "## Cloning repositories </br>\n",
    "*Should be done only once, important to verify which version of chi to FM is being used*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf6067ad",
   "metadata": {},
   "source": [
    "### Hash where I had the fft bug aka template FM HZ created from: \n",
    "*d9f785b082fb145d547ff03ae53f23f1564ccc38*888"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2569c5f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\User\\msc_project\\sus-optimization\\susceptibility-to-fieldmap-fft\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'susceptibility-to-fieldmap-fft'...\n",
      "c:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\IPython\\core\\magics\\osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.\n",
      "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already up to date.\n",
      "branch 'mb/custom_pad' set up to track 'origin/mb/custom_pad'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Switched to a new branch 'mb/custom_pad'\n",
      "Note: switching to 'd9f785b082fb145d547ff03ae53f23f1564ccc38'.\n",
      "\n",
      "You are in 'detached HEAD' state. You can look around, make experimental\n",
      "changes and commit them, and you can discard any commits you make in this\n",
      "state without impacting any branches by switching back to a branch.\n",
      "\n",
      "If you want to create a new branch to retain commits you create, you may\n",
      "do so (now or later) by using -c with the switch command. Example:\n",
      "\n",
      "  git switch -c <new-branch-name>\n",
      "\n",
      "Or undo this operation with:\n",
      "\n",
      "  git switch -\n",
      "\n",
      "Turn off this advice by setting config variable advice.detachedHead to false\n",
      "\n",
      "HEAD is now at d9f785b Fix bug\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing c:\\users\\user\\msc_project\\sus-optimization\\susceptibility-to-fieldmap-fft\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Requirement already satisfied: click in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from susceptibility-to-fieldmap-fft==0.0.0) (8.1.7)\n",
      "Requirement already satisfied: numpy>=1.24.4 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from susceptibility-to-fieldmap-fft==0.0.0) (1.26.4)\n",
      "Requirement already satisfied: nibabel>=5.2.1 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from susceptibility-to-fieldmap-fft==0.0.0) (5.2.1)\n",
      "Requirement already satisfied: matplotlib>=3.7.5 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from susceptibility-to-fieldmap-fft==0.0.0) (3.8.4)\n",
      "Requirement already satisfied: scipy>=1.10.1 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from susceptibility-to-fieldmap-fft==0.0.0) (1.13.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from matplotlib>=3.7.5->susceptibility-to-fieldmap-fft==0.0.0) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from matplotlib>=3.7.5->susceptibility-to-fieldmap-fft==0.0.0) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from matplotlib>=3.7.5->susceptibility-to-fieldmap-fft==0.0.0) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from matplotlib>=3.7.5->susceptibility-to-fieldmap-fft==0.0.0) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from matplotlib>=3.7.5->susceptibility-to-fieldmap-fft==0.0.0) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from matplotlib>=3.7.5->susceptibility-to-fieldmap-fft==0.0.0) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from matplotlib>=3.7.5->susceptibility-to-fieldmap-fft==0.0.0) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from matplotlib>=3.7.5->susceptibility-to-fieldmap-fft==0.0.0) (2.9.0.post0)\n",
      "Requirement already satisfied: colorama in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from click->susceptibility-to-fieldmap-fft==0.0.0) (0.4.6)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\miniconda3\\envs\\mrsim\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.7.5->susceptibility-to-fieldmap-fft==0.0.0) (1.16.0)\n",
      "Building wheels for collected packages: susceptibility-to-fieldmap-fft\n",
      "  Building wheel for susceptibility-to-fieldmap-fft (setup.py): started\n",
      "  Building wheel for susceptibility-to-fieldmap-fft (setup.py): finished with status 'done'\n",
      "  Created wheel for susceptibility-to-fieldmap-fft: filename=susceptibility_to_fieldmap_fft-0.0.0-py3-none-any.whl size=23545 sha256=c0914fa0f741b67682a41f3aa055a6eba503caa7decedf66ce02e8ef6338f696\n",
      "  Stored in directory: c:\\users\\user\\appdata\\local\\pip\\cache\\wheels\\17\\ea\\51\\22427b7b5d1aced006c3db69245d8a8f88effcb4349a030d61\n",
      "Successfully built susceptibility-to-fieldmap-fft\n",
      "Installing collected packages: susceptibility-to-fieldmap-fft\n",
      "  Attempting uninstall: susceptibility-to-fieldmap-fft\n",
      "    Found existing installation: susceptibility-to-fieldmap-fft 0.0.0\n",
      "    Uninstalling susceptibility-to-fieldmap-fft-0.0.0:\n",
      "      Successfully uninstalled susceptibility-to-fieldmap-fft-0.0.0\n",
      "Successfully installed susceptibility-to-fieldmap-fft-0.0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# Above method uses the \"main\" simulation, so we need to load the latest\n",
    "!git clone https://github.com/shimming-toolbox/susceptibility-to-fieldmap-fft.git\n",
    "%cd susceptibility-to-fieldmap-fft\n",
    "!git pull \n",
    "!git checkout mb/custom_pad\n",
    "#!git checkout d9f785b082fb145d547ff03ae53f23f1564ccc38\n",
    "%pip install ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3565e9ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\User\\msc_project\\sus-optimization\\susceptibility-to-fieldmap-fft\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\IPython\\core\\magics\\osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.\n",
      "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
     ]
    }
   ],
   "source": [
    "%cd susceptibility-to-fieldmap-fft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9c838a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d9f785b082fb145d547ff03ae53f23f1564ccc38\n"
     ]
    }
   ],
   "source": [
    "!git rev-parse HEAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5e36b76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\msc_project\\sus-optimization\n",
      "README.md\n",
      "data\n",
      "evas_code.ipynb\n",
      "extract_metric.py\n",
      "internal_air_only.nii.gz\n",
      "output\n",
      "requirements.txt\n",
      "simulation\n",
      "soothsayer.ipynb\n",
      "spinal_cord_voxels_only.nii.gz\n",
      "sus_optimization.ipynb\n",
      "susceptibility-to-fieldmap-fft\n",
      "tissue-to-MRproperty\n"
     ]
    }
   ],
   "source": [
    "#Once we confirmed the head of the chi to fbfest we can go to home\n",
    "%cd {home_path}\n",
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "856d32fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_chi_to_fm_fft = \"C:/Users/User/msc_project/susceptibility-to-fieldmap-fft\"\n",
    "sys.path.append(path_to_chi_to_fm_fft)\n",
    "#sys.path.append('/Users/evaalonsoortiz/Documents/python/susceptibility-to-fieldmap-fft/')\n",
    "from functions import compute_fieldmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a737b875-042b-43da-9a63-93ced3187f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#meas_fmap_path = \"../data/fm_Hz.nii.gz\"\n",
    "#sim_chi_map_path = \"../data/chimap_crop_custom6.nii.gz\"\n",
    "#ROIs_map_path = \"../data/mask_crop_gre_fov_body_plus_trachea.nii.gz\" # Segmentations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587aa6df",
   "metadata": {},
   "source": [
    "## Importing functions for the optimization process </br>\n",
    "Importing function to automate command for chi to fm ppm and metric extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c84bde63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to reate the custom command that creates the fieldmap\n",
    "def chi_to_fm_ppm(input_file, output_file):\n",
    "\n",
    "    #Using a custom command for dB0 simulation\n",
    "\n",
    "    command = [\n",
    "    \"compute_fieldmap\",\n",
    "    \"-i\", input_file,\n",
    "    \"-o\", output_file,\n",
    "    \"-b\", \"50\",\n",
    "    \"-m\", \"b0SimISMRM\"\n",
    "    ]\n",
    "\n",
    "    # Run the command and capture output\n",
    "    result = subprocess.run(command, capture_output=True, text=True)\n",
    "\n",
    "    # We need to return the data from the fieldmap created\n",
    "\n",
    "    \n",
    "    # Check for errors\n",
    "    if result.returncode != 0:\n",
    "        print(\"Error occurred:\", result.stderr)\n",
    "\n",
    "    else:\n",
    "        print(\"Fieldmap simulated correctly\", result.stdout)\n",
    "        sim_fm_img = nib.load(output_file)\n",
    "        sim_fm_data = sim_fm_img.get_fdata()\n",
    "        return sim_fm_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4fef584",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to automate the subprocess of extractiing measure with SCT through the terminal\n",
    "from extract_metric import extract_metrics\n",
    "# Example of calling the function:\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b2e7017",
   "metadata": {},
   "source": [
    "## Begin! </br>\n",
    "*That which cannot be measured cannot be controlled; what cannot be controlled cannot be managed; What cannot be managed cannot be optimized* </br>\n",
    "-Sebastian Rios"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f75b98c",
   "metadata": {},
   "source": [
    "## Load variable paths!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c7cf161",
   "metadata": {},
   "outputs": [],
   "source": [
    "# New paths to cropped ISMRM\n",
    "sim_chi_map_path3 = \"data/crop_ISMRM/db0_030_wb_chimap_crop_ISMRM.nii.gz\"\n",
    "segmentations_cropped = \"data/crop_ISMRM/db0_030_segmentations_cropped_ISRMRM.nii.gz\"\n",
    "sc_final_mask = \"data/crop_ISMRM/sc_seg_c2t7_final_ISMRM.nii.gz\"\n",
    "measured_B0 = \"data/crop_ISMRM/B0_cropped_ISMRM.nii.gz\"\n",
    "demodulating_mask_path = \"data/crop_ISMRM/sc_seg_dilate2_10.nii.gz\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5420c69",
   "metadata": {},
   "source": [
    "## Objective function </br>\n",
    "Minimize the difference of the values on the spinal cord between B0meas and iterativaly changing fieldmaps with different chi values!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790b3381",
   "metadata": {},
   "source": [
    "### When same subject is used to measure a FM and generate segmentations for simulations </br>\n",
    "*i.e. all the images are registered to the same space*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64ae6e7d-6296-4453-a926-83cbac0d3ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "\n",
    "    print(\"Using chi: \",x)\n",
    "    # load simulated susceptibility map in ppm\n",
    "    chi_img = nib.load(sim_chi_map_path3)\n",
    "    chi_data = chi_img.get_fdata()\n",
    "    \n",
    "    # load segmentation labels map\n",
    "    ROIs_img = nib.load(segmentations_cropped)\n",
    "    ROIs_data = ROIs_img.get_fdata()\n",
    "\n",
    "    sc_seg_img = nib.load(sc_final_mask)\n",
    "    sc_seg_data = sc_seg_img.get_fdata()\n",
    "\n",
    "    demod_mask_img = nib.load(demodulating_mask_path)\n",
    "    demod_mask_data = demod_mask_img.get_fdata()\n",
    "\n",
    "\n",
    "    ind_sc = np.where(sc_seg_data == 1)\n",
    "    # find indicies that have label = 7 or 8, these voxels should correspond to the trachea (t) and lungs (l); ind_tl\n",
    "    ind_tl = np.where((ROIs_data == 7) | (ROIs_data == 8))\n",
    "    \n",
    "\n",
    "    # load simulated field map in Hz\n",
    "    b0meas_img = nib.load(measured_B0)\n",
    "    b0meas_data = b0meas_img.get_fdata()\n",
    "\n",
    "    # assign variable \"x\" to indicies corresponding to trachea/lungs\n",
    "    chi_data[ind_tl] = x\n",
    "\n",
    "    # compute the field distribution in ppm for the chi_data distribution using the compute_bz function from the susceptibility-to-fieldmap-fft package\n",
    "    #z =  compute_fieldmap.compute_bz(chi_data)\n",
    "    chi_dist, image_res , affine_matrix = compute_fieldmap.load_sus_dist(sim_chi_map_path3)\n",
    "    #z = chi_to_fm_ppm(sim_chi_map_path2, output\"output/slurp.nii.gz\")\n",
    "    # convert field distribution from ppm to Hz (assuming 3T)\n",
    "    z = compute_fieldmap.compute_bz(chi_data, image_resolution = image_res , buffer = 50, mode = \"b0SimISMRM\")\n",
    "    z = z*3*42.58 # deltaB0 [Hz] = deltaB0 [ppm] * B0 [T] * 1e-6 * 42.58*e6 [Hz/T]\n",
    "\n",
    "    # Demdoulate Z and b0meas to their own zeros\n",
    "    # Here we coul check if the voxel is bone or tissue (later on)\n",
    "    demod_factor_b0meas = np.mean(b0meas_data[demod_mask_data == 1])\n",
    "    print(\"Demod factor for B0meas: \", demod_factor_b0meas)\n",
    "    demod_factor_z = np.mean(z[demod_mask_data == 1])\n",
    "    print(\"Demod factor for Simulated: \", demod_factor_z)\n",
    "    demod_b0_meas = b0meas_data - demod_factor_b0meas\n",
    "    demod_z = z - demod_factor_z\n",
    "    difference = np.linalg.norm(demod_b0_meas[ind_sc] - demod_z[ind_sc])\n",
    "    print(\"minizing this difference: \", difference)\n",
    "    return (np.linalg.norm(demod_b0_meas[ind_sc] - demod_z[ind_sc]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4148129b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9d49a0",
   "metadata": {},
   "source": [
    "### Minimization using average of Verma paper dataset vs trust-worthy segmentation on PAM50 AMU data </br>\n",
    "*Not using Verma dataset values due to shimming conditions, using db0_030 measured FM*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d7d6d36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining average across different subjects - Verma paper dataset:\n",
    "verma_values_avg = [-183.359, -185.17767, -155.2222625,\t-100.69565,\t-35.3949375, 25.6912875, 7.387325, -39.7222375, -68.7842375, -61.1716625, -63.731925]\n",
    "\n",
    "# Using the values from ROMEO measured fieldmap, 2 datasets that differ in the mask used for averaging to zero\n",
    "\n",
    "\n",
    "# 1: Using a spinal cord mask (small) // Demodulation factor: 162.26 Hz\n",
    "romeo_sc_avg = []\n",
    "# 2: Usng a dilated spinal cord mask, includnig bone and bigger ROI // Demodulation factor: \n",
    "romeo_sc_dilated = []\n",
    "\n",
    "# Defining the vertebral levels to be used for optimization:\n",
    "vertebrae_levels_c2_t7 = [ 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'T1', 'T2', 'T3', 'T4', 'T5', 'T6', 'T7']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "971ff865",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demoded_b0meas_img.nii.gz -f data/amu_vs_meas/db0_030_only_sc.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/db0_030_metric_mask.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/demoded_b0maes_metrcs.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: db0_030_only_sc\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/demoded_b0maes_metrcs.csv\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# From an already demodulated with 162.26 HZ from a spinal cord mask\n",
    "# Used also as example for the how to use extract metric automated subprocess\n",
    "path_to_dmod_b0meas_db0_030 = \"data/amu_vs_meas/demoded_fms/demoded_b0meas_img.nii.gz\"\n",
    "path_to_db0_030_sc_mask = 'data/amu_vs_meas/db0_030_only_sc.nii.gz'\n",
    "path_to_db0_030_vert_levels = \"data/amu_vs_meas/db0_030_metric_mask.nii.gz\"\n",
    "path_to_db0_030_metrics = \"data/amu_vs_meas/metrics/demoded_b0maes_metrcs.csv\"\n",
    "extract_metrics(path_to_dmod_b0meas_db0_030, path_to_db0_030_sc_mask, path_to_db0_030_vert_levels, path_to_db0_030_metrics, method='wa', vert=\"2:14\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e642db7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_zero_dmod_b0meas_csv = pd.read_csv(path_to_db0_030_metrics)\n",
    "dmod_b0meas_values =  avg_zero_dmod_b0meas_csv['WA()']\n",
    "dmod_b0meas_values = dmod_b0meas_values[::-1]\n",
    "# Now this values are from C2 to T7 and will be used for comparison with the simulated from AMU VC subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8ac3c9cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the metric folder\n",
    "path_to_optimization_metrics = \"data/amu_vs_meas/metrics/\"\n",
    "path_to_optimization_demod = \"data/amu_vs_meas/demoded_fms/\"\n",
    "\n",
    "path_to_sim_vert_file = \"data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz\"\n",
    "path_to_amu_demod_mask = \"data/amu_vs_meas/demod_masks/c2_t7_sc_mask_AMU_VC_sim.nii.gz\"\n",
    "path_to_amu_chimap = \"data/amu_vs_meas/chimap_AMU_VC.nii.gz\"\n",
    "path_to_amu_segs = \"data/amu_vs_meas/amu_VC_segmentations.nii.gz\"\n",
    "path_to_amu_sc_mask = \"data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz\"\n",
    "\n",
    "# For every chi value we test we are going to create new files for demodulationn and for metrics\n",
    "# Becaause this is done in a loop we are going to need to create empty list:\n",
    "chi_tested = []\n",
    "counter = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e5bfde",
   "metadata": {},
   "source": [
    "### f_2 </br>\n",
    "*Implementation for minimizing the value of chi assuming that chi of lung should be equal to that of trachea*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ccdbfbcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def f_2(x):\n",
    "    global counter\n",
    "    np.append(chi_tested,x)\n",
    "    print('################')\n",
    "    value = x.size - 1 # This actually only works when x is of size 1. In f_goat its generalized\n",
    "    print(\"Iteration #\",counter)\n",
    "    print(\"Using chi:\",x[value])\n",
    "    # load simulated susceptibility map in ppm\n",
    "    # AMU VC for now\n",
    "    chi_img = nib.load(path_to_amu_chimap)\n",
    "    chi_data = chi_img.get_fdata()\n",
    "    \n",
    "    # load segmentation labels map - AMU chimap\n",
    "    ROIs_img = nib.load(path_to_amu_segs)\n",
    "    ROIs_data = ROIs_img.get_fdata()\n",
    "\n",
    "    #sc_seg_img = nib.load(sc_final_mask)\n",
    "    #sc_seg_data = sc_seg_img.get_fdata()\n",
    "\n",
    "    demod_mask_img = nib.load(path_to_amu_demod_mask)\n",
    "    demod_mask_data = demod_mask_img.get_fdata()\n",
    "\n",
    "    # All the above can be outside of the loop, save time \n",
    "\n",
    "    #ind_sc = np.where(sc_seg_data == 1)\n",
    "    # find indicies that have label = 4 or 6, these voxels should correspond to the trachea (t) and lungs (l); ind_tl\n",
    "    ind_tl = np.where((ROIs_data == 4) | (ROIs_data == 6))\n",
    "    \n",
    "\n",
    "    # load simulated field map in Hz\n",
    "    #b0meas_img = nib.load(measured_B0)\n",
    "    #b0meas_data = b0meas_img.get_fdata()\n",
    "\n",
    "    # assign variable \"x\" to indicies corresponding to trachea/lungs\n",
    "    chi_data[ind_tl] = x\n",
    "\n",
    "    # compute the field distribution in ppm for the chi_data distribution using the compute_bz function from the susceptibility-to-fieldmap-fft package\n",
    "    #z =  compute_fieldmap.compute_bz(chi_data)\n",
    "    chi_dist, image_res , affine_matrix = compute_fieldmap.load_sus_dist(path_to_amu_chimap)\n",
    "    #z = chi_to_fm_ppm(sim_chi_map_path2, output\"output/slurp.nii.gz\")\n",
    "\n",
    "    # convert field distribution from ppm to Hz (assuming 3T)\n",
    "\n",
    "    z = compute_fieldmap.compute_bz(chi_data, image_resolution = image_res , buffer = 50, mode = \"b0SimISMRM\")\n",
    "    z = z*3*42.58 # deltaB0 [Hz] = deltaB0 [ppm] * B0 [T] * 1e-6 * 42.58*e6 [Hz/T]\n",
    "\n",
    "    chi_val = x[counter]\n",
    "    chi_name = str(chi_val).replace(\".\",\"_\").strip(\"-\") # Susceptibility used to test transform to string for the name\n",
    "\n",
    "    demod_fm_name = f\"demod_db0_sim_{chi_name}_fm_Hz.nii.gz\"\n",
    "    path_to_demod_fm = os.path.join(path_to_optimization_demod, demod_fm_name)\n",
    "\n",
    "    # Demdoulate Z to their own zeros inside the Spinal Cord mask\n",
    "    # Here we coul check if the voxel is bone or tissue (later on)\n",
    "    #demod_factor_b0meas = np.mean(b0meas_data[demod_mask_data == 1])\n",
    "    #print(\"Demod factor for B0meas: \", demod_factor_b0meas)\n",
    "    #demod_b0_meas = b0meas_data - demod_factor_b0meas\n",
    "\n",
    "    demod_factor_z = np.mean(z[demod_mask_data == 1])\n",
    "\n",
    "    print(\"Demod factor for Simulated: \", demod_factor_z)\n",
    "    demod_z = z - demod_factor_z\n",
    "\n",
    "    demod_fm_Hz = nib.Nifti1Image(demod_z, affine = chi_img.affine)\n",
    "    nib.save(demod_fm_Hz, path_to_demod_fm)\n",
    "    print(\"Filename: \",path_to_demod_fm)\n",
    "\n",
    "    # Now to extract the metrics:\n",
    "\n",
    "    metrics_name = f\"metrics_db0_sim_{chi_name}_fm_Hz.csv\"\n",
    "    path_to_out_metrics = os.path.join(path_to_optimization_metrics,metrics_name)\n",
    "\n",
    "    extract_metrics(path_to_demod_fm, path_to_amu_sc_mask, path_to_sim_vert_file, path_to_out_metrics, method='wa', vert=\"2:14\")\n",
    "    \n",
    "    avg_zero_dmod_sim_csv = pd.read_csv(path_to_out_metrics)\n",
    "    dmod_simulated_vals =  avg_zero_dmod_sim_csv['WA()']\n",
    "    dmod_simulated_vals = dmod_simulated_vals[::-1]\n",
    "\n",
    "    # Perform the metric extraction\n",
    "\n",
    "    difference = np.linalg.norm(dmod_b0meas_values-dmod_simulated_vals)\n",
    "    print(\" Minimizing this difference: \", difference)\n",
    "    \n",
    "    return difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "58c6c8ba-c57d-48b3-bfae-41da22c36cef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################\n",
      "Iteration # 1\n",
      "Using chi: 0.4\n",
      "Demod factor for Simulated:  -106.53231631527589\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_4_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_4_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_4_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_4_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  178.47233499290346\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: 0.42000000000000004\n",
      "Demod factor for Simulated:  -106.61635395994426\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_42000000000000004_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_42000000000000004_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_42000000000000004_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_42000000000000004_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  178.80733527860684\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: 0.38\n",
      "Demod factor for Simulated:  -106.44827867060752\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_38_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_38_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_38_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_38_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  178.13811129942815\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: 0.3600000000000001\n",
      "Demod factor for Simulated:  -106.36424102593915\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_3600000000000001_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_3600000000000001_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_3600000000000001_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_3600000000000001_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  177.8046685775177\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: 0.3200000000000002\n",
      "Demod factor for Simulated:  -106.19616573660244\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_3200000000000002_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_3200000000000002_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_3200000000000002_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_3200000000000002_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  177.14014367896243\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: 0.28000000000000025\n",
      "Demod factor for Simulated:  -106.02809044726568\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_28000000000000025_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_28000000000000025_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_28000000000000025_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_28000000000000025_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  176.47879578506522\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: 0.2000000000000004\n",
      "Demod factor for Simulated:  -105.69193986859226\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_2000000000000004_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_2000000000000004_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_2000000000000004_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_2000000000000004_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  175.16577477788212\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: 0.12000000000000055\n",
      "Demod factor for Simulated:  -105.35578928991875\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_12000000000000055_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_12000000000000055_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_12000000000000055_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_12000000000000055_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  173.86589672310063\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -0.03999999999999915\n",
      "Demod factor for Simulated:  -104.6834881325718\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_03999999999999915_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_03999999999999915_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_03999999999999915_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_03999999999999915_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  171.306763196675\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -0.19999999999999885\n",
      "Demod factor for Simulated:  -104.01118697522492\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_19999999999999885_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_19999999999999885_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_19999999999999885_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_19999999999999885_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  168.8038405443672\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -0.5199999999999982\n",
      "Demod factor for Simulated:  -102.666584660531\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_5199999999999982_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_5199999999999982_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_5199999999999982_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_5199999999999982_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  163.97686632503692\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -0.8399999999999976\n",
      "Demod factor for Simulated:  -101.32198234583714\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_0_8399999999999976_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_0_8399999999999976_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_0_8399999999999976_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_0_8399999999999976_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  159.40633100689732\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -1.4799999999999964\n",
      "Demod factor for Simulated:  -98.6327777164494\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_1_4799999999999964_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_1_4799999999999964_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_1_4799999999999964_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_1_4799999999999964_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  151.12636573356508\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -2.119999999999995\n",
      "Demod factor for Simulated:  -95.94357308706164\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_2_119999999999995_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_2_119999999999995_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_2_119999999999995_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_2_119999999999995_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  144.15663962090838\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -3.399999999999993\n",
      "Demod factor for Simulated:  -90.56516382828612\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_3_399999999999993_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_3_399999999999993_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_3_399999999999993_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_3_399999999999993_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  134.92415727850246\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.67999999999999\n",
      "Demod factor for Simulated:  -85.18675456951057\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_67999999999999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_67999999999999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_67999999999999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_67999999999999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.9682397290621\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -7.239999999999984\n",
      "Demod factor for Simulated:  -74.42993605195956\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_7_239999999999984_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_7_239999999999984_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_7_239999999999984_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_7_239999999999984_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  150.96523163211086\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -3.3999999999999924\n",
      "Demod factor for Simulated:  -90.5651638282861\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_3_3999999999999924_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_3_3999999999999924_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_3_3999999999999924_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_3_3999999999999924_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  134.92415727850246\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -5.9599999999999875\n",
      "Demod factor for Simulated:  -79.80834531073508\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_5_9599999999999875_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_5_9599999999999875_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_5_9599999999999875_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_5_9599999999999875_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  138.5972958783334\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.039999999999991\n",
      "Demod factor for Simulated:  -87.87595919889836\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_039999999999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_039999999999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_039999999999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_039999999999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.98857605402992\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -5.319999999999989\n",
      "Demod factor for Simulated:  -82.49754994012284\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_5_319999999999989_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_5_319999999999989_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_5_319999999999989_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_5_319999999999989_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  134.86401471339417\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.3599999999999905\n",
      "Demod factor for Simulated:  -86.53135688420444\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3599999999999905_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3599999999999905_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_3599999999999905_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_3599999999999905_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7370042028093\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.039999999999991\n",
      "Demod factor for Simulated:  -87.87595919889836\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_039999999999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_039999999999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_039999999999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_039999999999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.98857605402992\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.519999999999991\n",
      "Demod factor for Simulated:  -85.85905572685752\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_519999999999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_519999999999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_519999999999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_519999999999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.79230525404944\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.19999999999999\n",
      "Demod factor for Simulated:  -87.20365804155139\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_19999999999999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_19999999999999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_19999999999999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_19999999999999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.80248727641066\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.439999999999991\n",
      "Demod factor for Simulated:  -86.19520630553099\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_439999999999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_439999999999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_439999999999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_439999999999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7495584200153\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.2799999999999905\n",
      "Demod factor for Simulated:  -86.86750746287794\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_2799999999999905_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_2799999999999905_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_2799999999999905_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_2799999999999905_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7546511681129\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.399999999999991\n",
      "Demod factor for Simulated:  -86.36328159486774\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_399999999999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_399999999999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_399999999999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_399999999999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.73950621601858\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.3199999999999905\n",
      "Demod factor for Simulated:  -86.69943217354121\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3199999999999905_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3199999999999905_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_3199999999999905_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_3199999999999905_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.74205280732093\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.37999999999999\n",
      "Demod factor for Simulated:  -86.44731923953606\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_37999999999999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_37999999999999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_37999999999999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_37999999999999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7373113786848\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.339999999999991\n",
      "Demod factor for Simulated:  -86.61539452887284\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_339999999999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_339999999999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_339999999999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_339999999999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.738584701497\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.36999999999999\n",
      "Demod factor for Simulated:  -86.48933806187031\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36999999999999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36999999999999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_36999999999999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_36999999999999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7369218303582\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.37999999999999\n",
      "Demod factor for Simulated:  -86.44731923953606\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_37999999999999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_37999999999999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_37999999999999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_37999999999999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7373113786848\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.36499999999999\n",
      "Demod factor for Simulated:  -86.51034747303738\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36499999999999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36499999999999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_36499999999999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_36499999999999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.73690402642345\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.3599999999999905\n",
      "Demod factor for Simulated:  -86.53135688420444\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3599999999999905_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3599999999999905_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_3599999999999905_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_3599999999999905_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7370042028093\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.367499999999991\n",
      "Demod factor for Simulated:  -86.49984276745386\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_367499999999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_367499999999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_367499999999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_367499999999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7368981808464\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.369999999999991\n",
      "Demod factor for Simulated:  -86.48933806187027\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_369999999999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_369999999999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_369999999999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_369999999999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.73692183035823\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.36624999999999\n",
      "Demod factor for Simulated:  -86.5050951202456\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36624999999999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36624999999999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_36624999999999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_36624999999999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.73689741674855\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.3649999999999896\n",
      "Demod factor for Simulated:  -86.51034747303738\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3649999999999896_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3649999999999896_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_3649999999999896_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_3649999999999896_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.73690402642345\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.3668749999999905\n",
      "Demod factor for Simulated:  -86.50246894384973\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3668749999999905_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_3668749999999905_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_3668749999999905_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_3668749999999905_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.73689687707582\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.367499999999991\n",
      "Demod factor for Simulated:  -86.49984276745386\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_367499999999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_367499999999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_367499999999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_367499999999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7368981808464\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.36656249999999\n",
      "Demod factor for Simulated:  -86.50378203204764\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36656249999999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36656249999999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_36656249999999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_36656249999999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.73689691648178\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.367187499999991\n",
      "Demod factor for Simulated:  -86.50115585565177\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_367187499999991_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_367187499999991_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_367187499999991_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_367187499999991_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7368972985307\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.36671874999999\n",
      "Demod factor for Simulated:  -86.50312548794865\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36671874999999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36671874999999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_36671874999999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_36671874999999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.73689683917124\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.366562499999989\n",
      "Demod factor for Simulated:  -86.50378203204767\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_366562499999989_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_366562499999989_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_366562499999989_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_366562499999989_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.7368969164818\n",
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.36679687499999\n",
      "Demod factor for Simulated:  -86.5027972158992\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36679687499999_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_36679687499999_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_36679687499999_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_36679687499999_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.73689684372164\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 132.736897\n",
      "         Iterations: 23\n",
      "         Function evaluations: 46\n",
      "Minimum chi: -4.36671874999999\n"
     ]
    }
   ],
   "source": [
    "min_chi_tl = optimize.fmin(f_2, 0.4)\n",
    "print(\"Minimum chi:\", min_chi_tl[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71fca9f5",
   "metadata": {},
   "source": [
    "## Plotting </br>\n",
    "Final results and extra chi values on top!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "84c99e3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once it runs we can plot it next to the initial measured very ez \n",
    "# Just load the csv and its done\n",
    "#print(min_chi_tl[0]) # FINAL chi value\n",
    "# We know what the chi of the final metric file is so:\n",
    "final_chi_name = chi_name = str(min_chi_tl[0]).replace(\".\",\"_\").strip(\"-\")\n",
    "final_chi_fm_csv = f\"metrics_db0_sim_{final_chi_name}_fm_Hz.csv\"\n",
    "path_to_final_metrics = os.path.join(path_to_optimization_metrics,final_chi_fm_csv)\n",
    "\n",
    "# Load the csv and extrac values accross c2 through T7\n",
    "\n",
    "# dmod_b0meas_values = dmod_b0meas_values\n",
    "\n",
    "final_csv = pd.read_csv(path_to_final_metrics)\n",
    "final_sim_values =  final_csv['WA()']\n",
    "final_sim_values = final_sim_values[::-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "4f335ecc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:12: SyntaxWarning: invalid escape sequence '\\c'\n",
      "<>:12: SyntaxWarning: invalid escape sequence '\\c'\n",
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_22568\\271511628.py:12: SyntaxWarning: invalid escape sequence '\\c'\n",
      "  plt.title('Final comparison $\\chi$ = -4.366', fontsize=14)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Final plot of final chi value vs measured\n",
    "goat_params = [-108.04, -66.25, -6.59, 42.15, 81.38, 114.00, 107.09, 47.94, -21.12, -32.96, -40.55, -53.68, -53.91]\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# \n",
    "plt.plot(vertebrae_levels_c2_t7, dmod_b0meas_values , marker = 'x', color = '#cccc00', linestyle='-', linewidth = 2, markersize = 8, label ='Measured')\n",
    "plt.plot(vertebrae_levels_c2_t7, final_sim_values , marker = 'x', color = '#DBA5FD', linestyle='-', linewidth = 2, markersize = 8, label ='Simulated')\n",
    "plt.plot(vertebrae_levels_c2_t7, goat_params , marker = 'x', color = '#6666ff', linestyle='-', linewidth = 2, markersize = 8, label ='Doble optimiztion')\n",
    "\n",
    "\n",
    "# Add titles and labels\n",
    "plt.title('Final comparison $\\chi$ = -4.366', fontsize=14)\n",
    "plt.xlabel('Vertebral Level', fontsize=12)\n",
    "plt.ylabel('B0 [Hz]', fontsize=12)\n",
    "\n",
    "plt.legend()\n",
    "# Customize grid and display\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.7)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "405df6f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:56: SyntaxWarning: invalid escape sequence '\\c'\n",
      "<>:64: SyntaxWarning: invalid escape sequence '\\c'\n",
      "<>:65: SyntaxWarning: invalid escape sequence '\\c'\n",
      "<>:56: SyntaxWarning: invalid escape sequence '\\c'\n",
      "<>:64: SyntaxWarning: invalid escape sequence '\\c'\n",
      "<>:65: SyntaxWarning: invalid escape sequence '\\c'\n",
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_22568\\3542779391.py:56: SyntaxWarning: invalid escape sequence '\\c'\n",
      "  plt.plot(vertebrae_levels_c2_t7, final_sim_values , marker = '*', color = '#3333ff', linestyle='--', linewidth = 2, markersize = 8, label ='Optimized $\\chi$ -4.366')\n",
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_22568\\3542779391.py:64: SyntaxWarning: invalid escape sequence '\\c'\n",
      "  plt.plot(vertebrae_levels_c2_t7, data_amu_chi_0_2 , marker = 'o', color = '#669999', linestyle='--', linewidth = 2, markersize = 8, label ='Simulated $\\chi$ 0.2')\n",
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_22568\\3542779391.py:65: SyntaxWarning: invalid escape sequence '\\c'\n",
      "  plt.plot(vertebrae_levels_c2_t7, data_amu_chi_2_3 , marker = 'o', color = '#527a7a', linestyle='--', linewidth = 2, markersize = 8, label ='Simulated $\\chi$ -2.3')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "# Some cool extra chi values!\n",
    "extra_chis_to_plot = [\"0_3600000000000001\", \"2_119999999999995\", \"7_239999999999984\"]\n",
    "\n",
    "# Vertebral levels and colors, love colors!\n",
    "vertebrae_levels_c2_t7 = ['C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'T1', 'T2', 'T3', 'T4', 'T5', 'T6', 'T7']\n",
    "colors = ['#33C3FF','#DBA5FD','#008000','#4d79ff','996633'] # more to come\n",
    "gray_colors = [\"#85adad\",\"#669999\",\"#527a7a\"] # For the extra values\n",
    "color_index = 0\n",
    "\n",
    "processed_chis = set() # To avoid repetition of chi values while iterating over filenames\n",
    "\n",
    "# Initialize \n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Loop through each file in the folder\n",
    "for filename in os.listdir(path_to_optimization_metrics):\n",
    "    # Check if file is CSV\n",
    "    if filename.endswith('.csv'):\n",
    "        # Check for each extra chi value in the filename\n",
    "        for chi_str in extra_chis_to_plot:\n",
    "            \n",
    "            pattern = rf\"metrics_db0_sim_{chi_str}_fm_Hz\"\n",
    "\n",
    "            #print(f\"Searching for pattern '{pattern}' in filename '{filename}'\")  # debugging line\n",
    "            \n",
    "            # Adjust for negative values and plot nice chi value instead of np.float .15!\n",
    "            if re.search(pattern, filename) and chi_str not in processed_chis:\n",
    "                processed_chis.add(chi_str)\n",
    "                chi_val = str(chi_str).replace(\"_\",\".\")\n",
    "                if float(chi_val) > 1:\n",
    "                    chi_val = f\"-{chi_val}\"\n",
    "\n",
    "                #print(chi_val) # debugging line\n",
    "\n",
    "                chi_val = round(float(chi_val),3)\n",
    "                # Load the CSV file\n",
    "                file_path = os.path.join(path_to_optimization_metrics, filename)\n",
    "                data = pd.read_csv(file_path)\n",
    "\n",
    "                # Extract wa column\n",
    "                column_name = 'WA()' \n",
    "                column_data = data[column_name][::-1]  # Reverse the data to have plot from c2 to t7\n",
    "\n",
    "                # Plot the column data\n",
    "                #plt.plot(vertebrae_levels_c2_t7, column_data, marker='o', linestyle='--',\n",
    "                #         label=f'$\\chi$: {chi_val}', color=gray_colors[color_index % len(colors)])\n",
    "                color_index += 1\n",
    "                break # Stop after finding the matching chi value for this file\n",
    "\n",
    "# Add measured data to plot\n",
    "\n",
    "plt.plot(vertebrae_levels_c2_t7, dmod_b0meas_values , marker = 'p', color = '#ff471a', linestyle='-', linewidth = 2, markersize = 8, label ='Measured')\n",
    "plt.plot(vertebrae_levels_c2_t7, final_sim_values , marker = '*', color = '#3333ff', linestyle='--', linewidth = 2, markersize = 8, label ='Optimized $\\chi$ -4.366')\n",
    "\n",
    "data_amu_chi_2_3 = [-15.9589, -26.1912, -5.0900, 42.1113, 98.3414, 142.7416, 94.2893, 23.7413, -8.8719, -48.1439, -86.6387, -105.1905, -96.3517]\n",
    "data_meas = [-114.4866, -101.0895, -43.1777, 28.1947, 108.5242, 146.3648, 94.0083, 9.4665, -17.1480, -24.7593, -37.5776, -48.5010, -55.8265]\n",
    "vertebrae_levels_c2_t7 = [ 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'T1', 'T2', 'T3', 'T4', 'T5', 'T6', 'T7']\n",
    "data_amu_chi_0_2 = [1.64083, -4.58333, 21.199, 73.5591, 134.231, 174.292, 97.7184, 0.298155, -34.4296, -73.6813, -114.521, -132.822, -119.369]\n",
    "\n",
    "#plt.plot(vertebrae_levels_c2_t7, data_meas, marker = 'p', color = '#ff471a', linestyle='-', linewidth = 2, markersize = 8, label = \"Measured\")\n",
    "plt.plot(vertebrae_levels_c2_t7, data_amu_chi_0_2 , marker = 'o', color = '#669999', linestyle='--', linewidth = 2, markersize = 8, label ='Simulated $\\chi$ 0.2')\n",
    "plt.plot(vertebrae_levels_c2_t7, data_amu_chi_2_3 , marker = 'o', color = '#527a7a', linestyle='--', linewidth = 2, markersize = 8, label ='Simulated $\\chi$ -2.3')\n",
    "\n",
    "# Customize plot appearance\n",
    "#plt.title('Different chi values')\n",
    "plt.xlabel('Vertebral Level', fontsize=15)\n",
    "plt.tick_params(axis='both', which='major', labelsize=16)\n",
    "plt.ylabel('B0 [Hz]', fontsize=15)\n",
    "plt.legend(fontsize=15)\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.7)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b4ad8e",
   "metadata": {},
   "source": [
    "## Testing using more than 1 input for fmin?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "d4cc55cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# New outputs\n",
    "# Define the metric and dmoded folder. This need to change to not overwrite data!\n",
    "path_to_optimization_metrics_double = \"data/amu_vs_meas/test_double_optimize/metrics/\"\n",
    "path_to_optimization_demod_double = \"data/amu_vs_meas/test_double_optimize/demoded_fms/\"\n",
    "\n",
    "# This files should always be the same unless something in the pipeline wants to be changed\n",
    "path_to_sim_vert_file = \"data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz\"\n",
    "path_to_amu_demod_mask = \"data/amu_vs_meas/demod_masks/c2_t7_sc_mask_AMU_VC_sim.nii.gz\"\n",
    "path_to_amu_chimap = \"data/amu_vs_meas/chimap_AMU_VC.nii.gz\"\n",
    "path_to_amu_segs = \"data/amu_vs_meas/amu_VC_segmentations.nii.gz\"\n",
    "path_to_amu_sc_mask = \"data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz\"\n",
    "\n",
    "# For every chi value we test we are going to create new files for demodulationn and for metrics\n",
    "# Becaause this is done in a loop we are going to need to create empty list:\n",
    "chi_tested = []\n",
    "counter2 = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad83fb5",
   "metadata": {},
   "source": [
    "### f_goat </br>\n",
    "*Chi value of lungs can be different to chi value of trachea*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "efaad9fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def f_goat(x):\n",
    "\n",
    "    global counter2 # This works? \n",
    "    counter2 += 1  \n",
    "    print('################')\n",
    "    print(f\"Iteration #{counter2}\")\n",
    "    print(f\"Current values of variables: x[0] = {x[0]}, x[1] = {x[1]}\")\n",
    "\n",
    "    # load simulated susceptibility map in ppm\n",
    "    # AMU VC for now\n",
    "    chi_img = nib.load(path_to_amu_chimap)\n",
    "    chi_data = chi_img.get_fdata()\n",
    "    \n",
    "    # load segmentation labels map - AMU chimap\n",
    "    ROIs_img = nib.load(path_to_amu_segs)\n",
    "    ROIs_data = ROIs_img.get_fdata()\n",
    "\n",
    "    #sc_seg_img = nib.load(sc_final_mask)\n",
    "    #sc_seg_data = sc_seg_img.get_fdata()\n",
    "\n",
    "    demod_mask_img = nib.load(path_to_amu_demod_mask)\n",
    "    demod_mask_data = demod_mask_img.get_fdata()\n",
    "\n",
    "    # All the above can be outside of the loop, save time \n",
    "\n",
    "    #ind_sc = np.where(sc_seg_data == 1)\n",
    "    # find indicies that have label = 4 or 6, these voxels should correspond to the trachea (t) and lungs (l); ind_tl\n",
    "    ind_trachea = np.where((ROIs_data == 4))\n",
    "    ind_lung = np.where((ROIs_data == 6))\n",
    "    \n",
    "\n",
    "    # load simulated field map in Hz\n",
    "    #b0meas_img = nib.load(measured_B0)\n",
    "    #b0meas_data = b0meas_img.get_fdata()\n",
    "\n",
    "    # assign variable \"x\" to indicies corresponding to trachea/lungs\n",
    "    chi_data[ind_trachea] = x[0]\n",
    "    chi_data[ind_lung] = x[1]\n",
    "    # compute the field distribution in ppm for the chi_data distribution using the compute_bz function from the susceptibility-to-fieldmap-fft package\n",
    "    #z =  compute_fieldmap.compute_bz(chi_data)\n",
    "    chi_dist, image_res , affine_matrix = compute_fieldmap.load_sus_dist(path_to_amu_chimap)\n",
    "    #z = chi_to_fm_ppm(sim_chi_map_path2, output\"output/slurp.nii.gz\")\n",
    "\n",
    "    # convert field distribution from ppm to Hz (assuming 3T)\n",
    "\n",
    "    z = compute_fieldmap.compute_bz(chi_data, image_resolution = image_res , buffer = 50, mode = \"b0SimISMRM\")\n",
    "    z = z*3*42.58 # deltaB0 [Hz] = deltaB0 [ppm] * B0 [T] * 1e-6 * 42.58*e6 [Hz/T]\n",
    "\n",
    "    chi1 = x[0]\n",
    "    chi2 = x[1]\n",
    "    #chi_name = str(chi_val).replace(\".\",\"_\").strip(\"-\") # Susceptibility used to test transform to string for the name\n",
    "\n",
    "    chi1_name = str(chi1).replace(\".\",\"_\").strip(\"-\")\n",
    "    chi2_name = str(chi2).replace(\".\",\"_\").strip(\"-\")\n",
    "\n",
    "    demod_fm_name = f\"demod_db0_sim_t_{chi1_name}_l_{chi2_name}_fm_Hz.nii.gz\"\n",
    "    path_to_demod_fm = os.path.join(path_to_optimization_demod_double, demod_fm_name)\n",
    "\n",
    "    # Demdoulate Z to their own zeros inside the Spinal Cord mask\n",
    "    # Here we coul check if the voxel is bone or tissue (later on)\n",
    "    #demod_factor_b0meas = np.mean(b0meas_data[demod_mask_data == 1])\n",
    "    #print(\"Demod factor for B0meas: \", demod_factor_b0meas)\n",
    "    #demod_b0_meas = b0meas_data - demod_factor_b0meas\n",
    "\n",
    "    demod_factor_z = np.mean(z[demod_mask_data == 1])\n",
    "\n",
    "    print(\"Demod factor for Simulated: \", demod_factor_z)\n",
    "    demod_z = z - demod_factor_z\n",
    "\n",
    "    demod_fm_Hz = nib.Nifti1Image(demod_z, affine = chi_img.affine)\n",
    "    nib.save(demod_fm_Hz, path_to_demod_fm)\n",
    "    print(\"Filename: \",path_to_demod_fm)\n",
    "\n",
    "    # Now to extract the metrics:\n",
    "\n",
    "    metrics_name = f\"metrics_db0_sim_t_{chi1_name}_l_{chi2_name}_fm_Hz.csv\"\n",
    "    path_to_out_metrics = os.path.join(path_to_optimization_metrics_double,metrics_name)\n",
    "\n",
    "    extract_metrics(path_to_demod_fm, path_to_amu_sc_mask, path_to_sim_vert_file, path_to_out_metrics, method='wa', vert=\"2:14\")\n",
    "    \n",
    "    avg_zero_dmod_sim_csv = pd.read_csv(path_to_out_metrics)\n",
    "    dmod_simulated_vals =  avg_zero_dmod_sim_csv['WA()']\n",
    "    dmod_simulated_vals = dmod_simulated_vals[::-1]\n",
    "\n",
    "    # Perform the metric extraction\n",
    "\n",
    "    difference = np.linalg.norm(dmod_b0meas_values-dmod_simulated_vals)\n",
    "    print(\" Minimizing this difference: \", difference)\n",
    "    \n",
    "    return difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "15ad9819",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################\n",
      "Iteration #1\n",
      "Current values of variables: x[0] = 0.4, x[1] = -2.65\n",
      "Demod factor for Simulated:  -93.75755052013716\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4_l_2_65_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4_l_2_65_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4_l_2_65_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4_l_2_65_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  137.94889982496622\n",
      "################\n",
      "Iteration #2\n",
      "Current values of variables: x[0] = 0.42000000000000004, x[1] = -2.65\n",
      "Demod factor for Simulated:  -93.75781920877182\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_42000000000000004_l_2_65_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_42000000000000004_l_2_65_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_42000000000000004_l_2_65_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_42000000000000004_l_2_65_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  137.93895862025377\n",
      "################\n",
      "Iteration #3\n",
      "Current values of variables: x[0] = 0.4, x[1] = -2.7825\n",
      "Demod factor for Simulated:  -93.20258118641392\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4_l_2_7825_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4_l_2_7825_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4_l_2_7825_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4_l_2_7825_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  136.82082001888136\n",
      "################\n",
      "Iteration #4\n",
      "Current values of variables: x[0] = 0.42000000000000004, x[1] = -2.7825\n",
      "Demod factor for Simulated:  -93.20284987504861\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_42000000000000004_l_2_7825_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_42000000000000004_l_2_7825_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_42000000000000004_l_2_7825_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_42000000000000004_l_2_7825_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  136.8104225473561\n",
      "################\n",
      "Iteration #5\n",
      "Current values of variables: x[0] = 0.42999999999999994, x[1] = -2.84875\n",
      "Demod factor for Simulated:  -92.92549955250429\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_42999999999999994_l_2_84875_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_42999999999999994_l_2_84875_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_42999999999999994_l_2_84875_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_42999999999999994_l_2_84875_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  136.26564867767968\n",
      "################\n",
      "Iteration #6\n",
      "Current values of variables: x[0] = 0.4099999999999999, x[1] = -2.9812499999999997\n",
      "Demod factor for Simulated:  -92.37026153014641\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4099999999999999_l_2_9812499999999997_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4099999999999999_l_2_9812499999999997_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4099999999999999_l_2_9812499999999997_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4099999999999999_l_2_9812499999999997_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  135.2481788859049\n",
      "################\n",
      "Iteration #7\n",
      "Current values of variables: x[0] = 0.4049999999999998, x[1] = -3.1468749999999988\n",
      "Demod factor for Simulated:  -91.6764826908337\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4049999999999998_l_3_1468749999999988_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4049999999999998_l_3_1468749999999988_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4049999999999998_l_3_1468749999999988_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4049999999999998_l_3_1468749999999988_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  134.0628311644922\n",
      "################\n",
      "Iteration #8\n",
      "Current values of variables: x[0] = 0.4349999999999997, x[1] = -3.2131249999999985\n",
      "Demod factor for Simulated:  -91.39940105692408\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4349999999999997_l_3_2131249999999985_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4349999999999997_l_3_2131249999999985_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4349999999999997_l_3_2131249999999985_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4349999999999997_l_3_2131249999999985_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  133.60047600148167\n",
      "################\n",
      "Iteration #9\n",
      "Current values of variables: x[0] = 0.45249999999999946, x[1] = -3.4284374999999985\n",
      "Demod factor for Simulated:  -90.49781099217913\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_45249999999999946_l_3_4284374999999985_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_45249999999999946_l_3_4284374999999985_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_45249999999999946_l_3_4284374999999985_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_45249999999999946_l_3_4284374999999985_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  132.26760286599065\n",
      "################\n",
      "Iteration #10\n",
      "Current values of variables: x[0] = 0.4274999999999993, x[1] = -3.7265624999999973\n",
      "Demod factor for Simulated:  -89.24879413050853\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4274999999999993_l_3_7265624999999973_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4274999999999993_l_3_7265624999999973_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4274999999999993_l_3_7265624999999973_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4274999999999993_l_3_7265624999999973_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  130.77483195609176\n",
      "################\n",
      "Iteration #11\n",
      "Current values of variables: x[0] = 0.42624999999999913, x[1] = -4.165468749999997\n",
      "Demod factor for Simulated:  -87.41044141951062\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_42624999999999913_l_4_165468749999997_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_42624999999999913_l_4_165468749999997_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_42624999999999913_l_4_165468749999997_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_42624999999999913_l_4_165468749999997_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  129.25791701735955\n",
      "################\n",
      "Iteration #12\n",
      "Current values of variables: x[0] = 0.4737499999999988, x[1] = -4.447031249999997\n",
      "Demod factor for Simulated:  -86.23176972085608\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4737499999999988_l_4_447031249999997_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4737499999999988_l_4_447031249999997_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4737499999999988_l_4_447031249999997_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4737499999999988_l_4_447031249999997_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.69918491637233\n",
      "################\n",
      "Iteration #13\n",
      "Current values of variables: x[0] = 0.5081249999999984, x[1] = -5.097109374999995\n",
      "Demod factor for Simulated:  -83.50941323586729\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_5081249999999984_l_5_097109374999995_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_5081249999999984_l_5_097109374999995_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_5081249999999984_l_5_097109374999995_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_5081249999999984_l_5_097109374999995_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.8373845704211\n",
      "################\n",
      "Iteration #14\n",
      "Current values of variables: x[0] = 0.44749999999999845, x[1] = -5.184062499999995\n",
      "Demod factor for Simulated:  -83.14440014818751\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_44749999999999845_l_5_184062499999995_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_44749999999999845_l_5_184062499999995_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_44749999999999845_l_5_184062499999995_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_44749999999999845_l_5_184062499999995_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  129.06032686413758\n",
      "################\n",
      "Iteration #15\n",
      "Current values of variables: x[0] = 0.4949999999999981, x[1] = -5.465624999999994\n",
      "Demod factor for Simulated:  -81.965728449533\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4949999999999981_l_5_465624999999994_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4949999999999981_l_5_465624999999994_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4949999999999981_l_5_465624999999994_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4949999999999981_l_5_465624999999994_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  129.79381370136912\n",
      "################\n",
      "Iteration #16\n",
      "Current values of variables: x[0] = 0.4434374999999989, x[1] = -4.490507812499996\n",
      "Demod factor for Simulated:  -86.04926317701623\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4434374999999989_l_4_490507812499996_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4434374999999989_l_4_490507812499996_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4434374999999989_l_4_490507812499996_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4434374999999989_l_4_490507812499996_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.67496715997962\n",
      "################\n",
      "Iteration #17\n",
      "Current values of variables: x[0] = 0.46968749999999915, x[1] = -3.7534765624999986\n",
      "Demod factor for Simulated:  -89.13663274968474\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_46968749999999915_l_3_7534765624999986_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_46968749999999915_l_3_7534765624999986_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_46968749999999915_l_3_7534765624999986_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_46968749999999915_l_3_7534765624999986_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  130.62839752922403\n",
      "################\n",
      "Iteration #18\n",
      "Current values of variables: x[0] = 0.45304687499999863, x[1] = -4.8264160156249964\n",
      "Demod factor for Simulated:  -84.64245829856182\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_45304687499999863_l_4_8264160156249964_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_45304687499999863_l_4_8264160156249964_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_45304687499999863_l_4_8264160156249964_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_45304687499999863_l_4_8264160156249964_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.58186475120917\n",
      "################\n",
      "Iteration #19\n",
      "Current values of variables: x[0] = 0.4227343749999988, x[1] = -4.869892578124995\n",
      "Demod factor for Simulated:  -84.45995175472197\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4227343749999988_l_4_869892578124995_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4227343749999988_l_4_869892578124995_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4227343749999988_l_4_869892578124995_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4227343749999988_l_4_869892578124995_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.634652524017\n",
      "################\n",
      "Iteration #20\n",
      "Current values of variables: x[0] = 0.43234374999999853, x[1] = -5.205800781249995\n",
      "Demod factor for Simulated:  -83.05314687626765\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_43234374999999853_l_5_205800781249995_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_43234374999999853_l_5_205800781249995_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_43234374999999853_l_5_205800781249995_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_43234374999999853_l_5_205800781249995_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  129.12161805319263\n",
      "################\n",
      "Iteration #21\n",
      "Current values of variables: x[0] = 0.4406640624999988, x[1] = -4.669331054687496\n",
      "Demod factor for Simulated:  -85.30023410182906\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4406640624999988_l_4_669331054687496_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4406640624999988_l_4_669331054687496_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4406640624999988_l_4_669331054687496_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4406640624999988_l_4_669331054687496_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.56813288531737\n",
      "################\n",
      "Iteration #22\n",
      "Current values of variables: x[0] = 0.47097656249999864, x[1] = -4.625854492187498\n",
      "Demod factor for Simulated:  -85.48274064566894\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_47097656249999864_l_4_625854492187498_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_47097656249999864_l_4_625854492187498_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_47097656249999864_l_4_625854492187498_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_47097656249999864_l_4_625854492187498_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.55610620644322\n",
      "################\n",
      "Iteration #23\n",
      "Current values of variables: x[0] = 0.4950976562499987, x[1] = -4.50383544921875\n",
      "Demod factor for Simulated:  -85.9941350911424\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4950976562499987_l_4_50383544921875_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4950976562499987_l_4_50383544921875_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4950976562499987_l_4_50383544921875_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4950976562499987_l_4_50383544921875_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.61993519990082\n",
      "################\n",
      "Iteration #24\n",
      "Current values of variables: x[0] = 0.4585937499999988, x[1] = -4.468769531249997\n",
      "Demod factor for Simulated:  -86.14051644893618\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4585937499999988_l_4_468769531249997_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4585937499999988_l_4_468769531249997_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4585937499999988_l_4_468769531249997_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4585937499999988_l_4_468769531249997_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.68594968203584\n",
      "################\n",
      "Iteration #25\n",
      "Current values of variables: x[0] = 0.4544335937499987, x[1] = -4.7370043945312466\n",
      "Demod factor for Simulated:  -85.01697283615543\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4544335937499987_l_4_7370043945312466_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_4544335937499987_l_4_7370043945312466_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4544335937499987_l_4_7370043945312466_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_4544335937499987_l_4_7370043945312466_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.55319212829164\n",
      "################\n",
      "Iteration #26\n",
      "Current values of variables: x[0] = 0.48474609374999855, x[1] = -4.693527832031248\n",
      "Demod factor for Simulated:  -85.19947937999525\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_48474609374999855_l_4_693527832031248_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_48474609374999855_l_4_693527832031248_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_48474609374999855_l_4_693527832031248_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_48474609374999855_l_4_693527832031248_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.52753918668378\n",
      "################\n",
      "Iteration #27\n",
      "Current values of variables: x[0] = 0.5067871093749985, x[1] = -4.705626220703124\n",
      "Demod factor for Simulated:  -85.1491020190784\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_5067871093749985_l_4_705626220703124_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_5067871093749985_l_4_705626220703124_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_5067871093749985_l_4_705626220703124_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_5067871093749985_l_4_705626220703124_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.5081286782813\n",
      "################\n",
      "Iteration #28\n",
      "Current values of variables: x[0] = 0.49024414062499855, x[1] = -4.816776123046873\n",
      "Demod factor for Simulated:  -84.68333420956489\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_49024414062499855_l_4_816776123046873_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_49024414062499855_l_4_816776123046873_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_49024414062499855_l_4_816776123046873_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_49024414062499855_l_4_816776123046873_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.5452275140564\n",
      "################\n",
      "Iteration #29\n",
      "Current values of variables: x[0] = 0.5425976562499983, x[1] = -4.78539794921875\n",
      "Demod factor for Simulated:  -84.81546339248786\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_5425976562499983_l_4_78539794921875_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_5425976562499983_l_4_78539794921875_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_5425976562499983_l_4_78539794921875_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_5425976562499983_l_4_78539794921875_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.48831463824493\n",
      "################\n",
      "Iteration #30\n",
      "Current values of variables: x[0] = 0.5866796874999982, x[1] = -4.809594726562503\n",
      "Demod factor for Simulated:  -84.71470867065405\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_5866796874999982_l_4_809594726562503_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_5866796874999982_l_4_809594726562503_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_5866796874999982_l_4_809594726562503_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_5866796874999982_l_4_809594726562503_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.45942959230018\n",
      "################\n",
      "Iteration #31\n",
      "Current values of variables: x[0] = 0.6032226562499982, x[1] = -4.698444824218754\n",
      "Demod factor for Simulated:  -85.18047648016753\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_6032226562499982_l_4_698444824218754_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_6032226562499982_l_4_698444824218754_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_6032226562499982_l_4_698444824218754_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_6032226562499982_l_4_698444824218754_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.42760120694305\n",
      "################\n",
      "Iteration #32\n",
      "Current values of variables: x[0] = 0.6597119140624981, x[1] = -4.639279174804695\n",
      "Demod factor for Simulated:  -85.42904761546887\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_6597119140624981_l_4_639279174804695_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_6597119140624981_l_4_639279174804695_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_6597119140624981_l_4_639279174804695_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_6597119140624981_l_4_639279174804695_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.39429013101557\n",
      "################\n",
      "Iteration #33\n",
      "Current values of variables: x[0] = 0.7396044921874978, x[1] = -4.743247680664075\n",
      "Demod factor for Simulated:  -84.99465426704452\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_7396044921874978_l_4_743247680664075_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_7396044921874978_l_4_743247680664075_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_7396044921874978_l_4_743247680664075_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_7396044921874978_l_4_743247680664075_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.31263013693737\n",
      "################\n",
      "Iteration #34\n",
      "Current values of variables: x[0] = 0.8560131835937475, x[1] = -4.762058410644549\n",
      "Demod factor for Simulated:  -84.91743039102761\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_8560131835937475_l_4_762058410644549_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_8560131835937475_l_4_762058410644549_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_8560131835937475_l_4_762058410644549_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_8560131835937475_l_4_762058410644549_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.21649421983878\n",
      "################\n",
      "Iteration #35\n",
      "Current values of variables: x[0] = 0.9290454101562473, x[1] = -4.591742858886741\n",
      "Demod factor for Simulated:  -85.6317693358425\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_9290454101562473_l_4_591742858886741_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_0_9290454101562473_l_4_591742858886741_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_9290454101562473_l_4_591742858886741_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_0_9290454101562473_l_4_591742858886741_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.19596200619182\n",
      "################\n",
      "Iteration #36\n",
      "Current values of variables: x[0] = 1.1002282714843719, x[1] = -4.48281692504886\n",
      "Demod factor for Simulated:  -86.09029966843667\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_1_1002282714843719_l_4_48281692504886_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_1_1002282714843719_l_4_48281692504886_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_1_1002282714843719_l_4_48281692504886_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_1_1002282714843719_l_4_48281692504886_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  128.15448976852525\n",
      "################\n",
      "Iteration #37\n",
      "Current values of variables: x[0] = 1.2965295410156212, x[1] = -4.605596160888714\n",
      "Demod factor for Simulated:  -85.5786824439954\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_1_2965295410156212_l_4_605596160888714_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_1_2965295410156212_l_4_605596160888714_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_1_2965295410156212_l_4_605596160888714_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_1_2965295410156212_l_4_605596160888714_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  127.8860797578217\n",
      "################\n",
      "Iteration #38\n",
      "Current values of variables: x[0] = 1.6149383544921827, x[1] = -4.5887546539307245\n",
      "Demod factor for Simulated:  -85.65349985825867\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_1_6149383544921827_l_4_5887546539307245_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_1_6149383544921827_l_4_5887546539307245_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_1_6149383544921827_l_4_5887546539307245_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_1_6149383544921827_l_4_5887546539307245_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  127.6377812129769\n",
      "################\n",
      "Iteration #39\n",
      "Current values of variables: x[0] = 1.859153442382807, x[1] = -4.309513168335036\n",
      "Demod factor for Simulated:  -86.82636913566773\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_1_859153442382807_l_4_309513168335036_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_1_859153442382807_l_4_309513168335036_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_1_859153442382807_l_4_309513168335036_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_1_859153442382807_l_4_309513168335036_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  127.83865479364316\n",
      "################\n",
      "Iteration #40\n",
      "Current values of variables: x[0] = 2.3738635253906177, x[1] = -4.4154508972169\n",
      "Demod factor for Simulated:  -86.38956932548973\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_2_3738635253906177_l_4_4154508972169_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_2_3738635253906177_l_4_4154508972169_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_2_3738635253906177_l_4_4154508972169_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_2_3738635253906177_l_4_4154508972169_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  127.24309033311961\n",
      "################\n",
      "Iteration #41\n",
      "Current values of variables: x[0] = 3.0106811523437402, x[1] = -4.38176788330092\n",
      "Demod factor for Simulated:  -86.53920415401626\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_3_0106811523437402_l_4_38176788330092_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_3_0106811523437402_l_4_38176788330092_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_3_0106811523437402_l_4_38176788330092_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_3_0106811523437402_l_4_38176788330092_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  126.81136899271424\n",
      "################\n",
      "Iteration #42\n",
      "Current values of variables: x[0] = 2.766466064453116, x[1] = -4.661009368896609\n",
      "Demod factor for Simulated:  -85.36633487660717\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_2_766466064453116_l_4_661009368896609_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_2_766466064453116_l_4_661009368896609_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_2_766466064453116_l_4_661009368896609_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_2_766466064453116_l_4_661009368896609_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  126.65179240002517\n",
      "################\n",
      "Iteration #43\n",
      "Current values of variables: x[0] = 3.22012237548827, x[1] = -4.836757469177396\n",
      "Demod factor for Simulated:  -84.63631774707694\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_3_22012237548827_l_4_836757469177396_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_3_22012237548827_l_4_836757469177396_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_3_22012237548827_l_4_836757469177396_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_3_22012237548827_l_4_836757469177396_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  126.23720678790235\n",
      "################\n",
      "Iteration #44\n",
      "Current values of variables: x[0] = 4.615865173339828, x[1] = -4.629770698547592\n",
      "Demod factor for Simulated:  -85.52202204283452\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_4_615865173339828_l_4_629770698547592_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_4_615865173339828_l_4_629770698547592_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_4_615865173339828_l_4_629770698547592_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_4_615865173339828_l_4_629770698547592_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  125.19203236434363\n",
      "################\n",
      "Iteration #45\n",
      "Current values of variables: x[0] = 6.116328582763652, x[1] = -4.650278720856026\n",
      "Demod factor for Simulated:  -85.45628313512243\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_6_116328582763652_l_4_650278720856026_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_6_116328582763652_l_4_650278720856026_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_6_116328582763652_l_4_650278720856026_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_6_116328582763652_l_4_650278720856026_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  123.98942094207368\n",
      "################\n",
      "Iteration #46\n",
      "Current values of variables: x[0] = 6.325769805908181, x[1] = -5.105268306732501\n",
      "Demod factor for Simulated:  -83.55339672818316\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_6_325769805908181_l_5_105268306732501_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_6_325769805908181_l_5_105268306732501_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_6_325769805908181_l_5_105268306732501_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_6_325769805908181_l_5_105268306732501_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  123.76124895109409\n",
      "################\n",
      "Iteration #47\n",
      "Current values of variables: x[0] = 7.983314132690401, x[1] = -5.467018518448292\n",
      "Demod factor for Simulated:  -82.06049301526662\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_7_983314132690401_l_5_467018518448292_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_7_983314132690401_l_5_467018518448292_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_7_983314132690401_l_5_467018518448292_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_7_983314132690401_l_5_467018518448292_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  122.92387633118796\n",
      "################\n",
      "Iteration #48\n",
      "Current values of variables: x[0] = 10.879520339965783, x[1] = -5.280539770126921\n",
      "Demod factor for Simulated:  -82.88045840331203\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_10_879520339965783_l_5_280539770126921_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_10_879520339965783_l_5_280539770126921_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_10_879520339965783_l_5_280539770126921_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_10_879520339965783_l_5_280539770126921_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  120.10111921568941\n",
      "################\n",
      "Iteration #49\n",
      "Current values of variables: x[0] = 14.70921932220454, x[1] = -5.502430920601684\n",
      "Demod factor for Simulated:  -82.00252873142969\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_14_70921932220454_l_5_502430920601684_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_14_70921932220454_l_5_502430920601684_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_14_70921932220454_l_5_502430920601684_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_14_70921932220454_l_5_502430920601684_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  117.20455241394271\n",
      "################\n",
      "Iteration #50\n",
      "Current values of variables: x[0] = 16.576204872131292, x[1] = -6.31917071819395\n",
      "Demod factor for Simulated:  -78.60673861157385\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_16_576204872131292_l_6_31917071819395_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_16_576204872131292_l_6_31917071819395_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_16_576204872131292_l_6_31917071819395_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_16_576204872131292_l_6_31917071819395_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  118.38631637471757\n",
      "################\n",
      "Iteration #51\n",
      "Current values of variables: x[0] = 23.30211006164543, x[1] = -6.354583120347343\n",
      "Demod factor for Simulated:  -78.54877432773687\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_23_30211006164543_l_6_354583120347343_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_23_30211006164543_l_6_354583120347343_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_23_30211006164543_l_6_354583120347343_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_23_30211006164543_l_6_354583120347343_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  112.49539126476365\n",
      "################\n",
      "Iteration #52\n",
      "Current values of variables: x[0] = 30.961508026122946, x[1] = -6.798365421296868\n",
      "Demod factor for Simulated:  -76.792914983972\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_30_961508026122946_l_6_798365421296868_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_30_961508026122946_l_6_798365421296868_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_30_961508026122946_l_6_798365421296868_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_30_961508026122946_l_6_798365421296868_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  108.17254761649146\n",
      "################\n",
      "Iteration #53\n",
      "Current values of variables: x[0] = 29.094522476196197, x[1] = -5.981625623704602\n",
      "Demod factor for Simulated:  -80.18870510382789\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_29_094522476196197_l_5_981625623704602_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_29_094522476196197_l_5_981625623704602_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_29_094522476196197_l_5_981625623704602_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_29_094522476196197_l_5_981625623704602_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  106.55869526739484\n",
      "################\n",
      "Iteration #54\n",
      "Current values of variables: x[0] = 35.35368127822865, x[1] = -5.812853076459927\n",
      "Demod factor for Simulated:  -80.9796883499549\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_35_35368127822865_l_5_812853076459927_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_35_35368127822865_l_5_812853076459927_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_35_35368127822865_l_5_812853076459927_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_35_35368127822865_l_5_812853076459927_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  102.0379186969226\n",
      "################\n",
      "Iteration #55\n",
      "Current values of variables: x[0] = 51.60596998214705, x[1] = -7.108787577155111\n",
      "Demod factor for Simulated:  -75.77007460249727\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_51_60596998214705_l_7_108787577155111_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_51_60596998214705_l_7_108787577155111_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_51_60596998214705_l_7_108787577155111_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_51_60596998214705_l_7_108787577155111_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  94.04534279358656\n",
      "################\n",
      "Iteration #56\n",
      "Current values of variables: x[0] = 70.05434531211831, x[1] = -7.911965905431824\n",
      "Demod factor for Simulated:  -72.65384753803107\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_70_05434531211831_l_7_911965905431824_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_70_05434531211831_l_7_911965905431824_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_70_05434531211831_l_7_911965905431824_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_70_05434531211831_l_7_911965905431824_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  87.63356545434533\n",
      "################\n",
      "Iteration #57\n",
      "Current values of variables: x[0] = 74.44651856422402, x[1] = -6.926453560594883\n",
      "Demod factor for Simulated:  -76.84062090401392\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_74_44651856422402_l_6_926453560594883_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_74_44651856422402_l_6_926453560594883_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_74_44651856422402_l_6_926453560594883_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_74_44651856422402_l_6_926453560594883_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  83.85257940616368\n",
      "################\n",
      "Iteration #58\n",
      "Current values of variables: x[0] = 96.18902383327458, x[1] = -6.99049763024389\n",
      "Demod factor for Simulated:  -76.86447386403489\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_96_18902383327458_l_6_99049763024389_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_96_18902383327458_l_6_99049763024389_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_96_18902383327458_l_6_99049763024389_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_96_18902383327458_l_6_99049763024389_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  84.45646590655458\n",
      "################\n",
      "Iteration #59\n",
      "Current values of variables: x[0] = 109.1471825981137, x[1] = -9.02556638956678\n",
      "Demod factor for Simulated:  -68.51478009209006\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_109_1471825981137_l_9_02556638956678_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_109_1471825981137_l_9_02556638956678_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_109_1471825981137_l_9_02556638956678_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_109_1471825981137_l_9_02556638956678_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  85.61639974168055\n",
      "################\n",
      "Iteration #60\n",
      "Current values of variables: x[0] = 113.5393558502194, x[1] = -8.040054044729839\n",
      "Demod factor for Simulated:  -72.70155345807294\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_113_5393558502194_l_8_040054044729839_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_113_5393558502194_l_8_040054044729839_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_113_5393558502194_l_8_040054044729839_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_113_5393558502194_l_8_040054044729839_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  85.57428158168486\n",
      "################\n",
      "Iteration #61\n",
      "Current values of variables: x[0] = 78.8386918163297, x[1] = -5.940941215757942\n",
      "Demod factor for Simulated:  -81.02739426999679\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_78_8386918163297_l_5_940941215757942_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_78_8386918163297_l_5_940941215757942_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_78_8386918163297_l_5_940941215757942_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_78_8386918163297_l_5_940941215757942_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  89.02862731985334\n",
      "################\n",
      "Iteration #62\n",
      "Current values of variables: x[0] = 101.57005990266771, x[1] = -8.25441009611457\n",
      "Demod factor for Simulated:  -71.64293363656677\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_101_57005990266771_l_8_25441009611457_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_101_57005990266771_l_8_25441009611457_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_101_57005990266771_l_8_25441009611457_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_101_57005990266771_l_8_25441009611457_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  82.44157565741955\n",
      "################\n",
      "Iteration #63\n",
      "Current values of variables: x[0] = 62.477222616672336, x[1] = -7.1408096119796145\n",
      "Demod factor for Simulated:  -75.78200108250773\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_62_477222616672336_l_7_1408096119796145_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_62_477222616672336_l_7_1408096119796145_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_62_477222616672336_l_7_1408096119796145_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_62_477222616672336_l_7_1408096119796145_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  87.99741551647698\n",
      "################\n",
      "Iteration #64\n",
      "Current values of variables: x[0] = 100.77382254183263, x[1] = -7.815242936542283\n",
      "Demod factor for Simulated:  -73.47166536418163\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_100_77382254183263_l_7_815242936542283_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_100_77382254183263_l_7_815242936542283_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_100_77382254183263_l_7_815242936542283_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_100_77382254183263_l_7_815242936542283_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  82.33395183052298\n",
      "################\n",
      "Iteration #65\n",
      "Current values of variables: x[0] = 127.89736388027632, x[1] = -9.14319947206197\n",
      "Demod factor for Simulated:  -68.27397809673451\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_127_89736388027632_l_9_14319947206197_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_127_89736388027632_l_9_14319947206197_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_127_89736388027632_l_9_14319947206197_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_127_89736388027632_l_9_14319947206197_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  90.84957381214619\n",
      "################\n",
      "Iteration #66\n",
      "Current values of variables: x[0] = 87.8092298932371, x[1] = -7.4806400384616545\n",
      "Demod factor for Simulated:  -74.69896020219407\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_87_8092298932371_l_7_4806400384616545_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_87_8092298932371_l_7_4806400384616545_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_87_8092298932371_l_7_4806400384616545_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_87_8092298932371_l_7_4806400384616545_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.71262133715504\n",
      "################\n",
      "Iteration #67\n",
      "Current values of variables: x[0] = 87.01299253240202, x[1] = -7.041472878889367\n",
      "Demod factor for Simulated:  -76.52769192980898\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_87_01299253240202_l_7_041472878889367_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_87_01299253240202_l_7_041472878889367_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_87_01299253240202_l_7_041472878889367_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_87_01299253240202_l_7_041472878889367_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  82.70493207970766\n",
      "################\n",
      "Iteration #68\n",
      "Current values of variables: x[0] = 97.93079306010128, x[1] = -7.95117579180827\n",
      "Demod factor for Simulated:  -72.86412320987733\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_97_93079306010128_l_7_95117579180827_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_97_93079306010128_l_7_95117579180827_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_97_93079306010128_l_7_95117579180827_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_97_93079306010128_l_7_95117579180827_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.86094268273685\n",
      "################\n",
      "Iteration #69\n",
      "Current values of variables: x[0] = 84.96620041150575, x[1] = -7.616572893727641\n",
      "Demod factor for Simulated:  -74.09141804788973\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_84_96620041150575_l_7_616572893727641_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_84_96620041150575_l_7_616572893727641_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_84_96620041150575_l_7_616572893727641_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_84_96620041150575_l_7_616572893727641_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.92681222084411\n",
      "################\n",
      "Iteration #70\n",
      "Current values of variables: x[0] = 88.91810594408746, x[1] = -7.666240404431301\n",
      "Demod factor for Simulated:  -73.9364798769627\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_88_91810594408746_l_7_666240404431301_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_88_91810594408746_l_7_666240404431301_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_88_91810594408746_l_7_666240404431301_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_88_91810594408746_l_7_666240404431301_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.61831017187711\n",
      "################\n",
      "Iteration #71\n",
      "Current values of variables: x[0] = 78.7965427772233, x[1] = -7.195704651084686\n",
      "Demod factor for Simulated:  -75.77131686927946\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_78_7965427772233_l_7_195704651084686_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_78_7965427772233_l_7_195704651084686_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_78_7965427772233_l_7_195704651084686_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_78_7965427772233_l_7_195704651084686_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  82.77895268366294\n",
      "################\n",
      "Iteration #72\n",
      "Current values of variables: x[0] = 93.14723048938178, x[1] = -7.762308006627373\n",
      "Demod factor for Simulated:  -73.59092162472784\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_93_14723048938178_l_7_762308006627373_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_93_14723048938178_l_7_762308006627373_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_93_14723048938178_l_7_762308006627373_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_93_14723048938178_l_7_762308006627373_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.58207144936694\n",
      "################\n",
      "Iteration #73\n",
      "Current values of variables: x[0] = 94.25610654023214, x[1] = -7.94790837259702\n",
      "Demod factor for Simulated:  -72.82844129949649\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_94_25610654023214_l_7_94790837259702_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_94_25610654023214_l_7_94790837259702_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_94_25610654023214_l_7_94790837259702_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_94_25610654023214_l_7_94790837259702_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.68696782566853\n",
      "################\n",
      "Iteration #74\n",
      "Current values of variables: x[0] = 92.64438737848337, x[1] = -7.831091289063179\n",
      "Demod factor for Simulated:  -73.29607102517092\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_92_64438737848337_l_7_831091289063179_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_92_64438737848337_l_7_831091289063179_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_92_64438737848337_l_7_831091289063179_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_92_64438737848337_l_7_831091289063179_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.59297426485219\n",
      "################\n",
      "Iteration #75\n",
      "Current values of variables: x[0] = 96.87351192377771, x[1] = -7.927158891259252\n",
      "Demod factor for Simulated:  -72.95051277293602\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_96_87351192377771_l_7_927158891259252_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_96_87351192377771_l_7_927158891259252_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_96_87351192377771_l_7_927158891259252_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_96_87351192377771_l_7_927158891259252_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.77321501952588\n",
      "################\n",
      "Iteration #76\n",
      "Current values of variables: x[0] = 90.90695743901003, x[1] = -7.731470026138289\n",
      "Demod factor for Simulated:  -73.68998810095606\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_90_90695743901003_l_7_731470026138289_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_90_90695743901003_l_7_731470026138289_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_90_90695743901003_l_7_731470026138289_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_90_90695743901003_l_7_731470026138289_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.57057854597359\n",
      "################\n",
      "Iteration #77\n",
      "Current values of variables: x[0] = 91.40980054990843, x[1] = -7.662686743702482\n",
      "Demod factor for Simulated:  -73.98483870051305\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_40980054990843_l_7_662686743702482_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_40980054990843_l_7_662686743702482_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_40980054990843_l_7_662686743702482_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_40980054990843_l_7_662686743702482_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.57288482018423\n",
      "################\n",
      "Iteration #78\n",
      "Current values of variables: x[0] = 89.16952749953667, x[1] = -7.631848763213396\n",
      "Demod factor for Simulated:  -74.08390517674117\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_89_16952749953667_l_7_631848763213396_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_89_16952749953667_l_7_631848763213396_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_89_16952749953667_l_7_631848763213396_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_89_16952749953667_l_7_631848763213396_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.60506263214285\n",
      "################\n",
      "Iteration #79\n",
      "Current values of variables: x[0] = 92.1528047419205, x[1] = -7.729693195773879\n",
      "Demod factor for Simulated:  -73.71416751273117\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_92_1528047419205_l_7_729693195773879_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_92_1528047419205_l_7_729693195773879_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_92_1528047419205_l_7_729693195773879_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_92_1528047419205_l_7_729693195773879_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56618874767854\n",
      "################\n",
      "Iteration #80\n",
      "Current values of variables: x[0] = 91.64996163102211, x[1] = -7.798476478209686\n",
      "Demod factor for Simulated:  -73.41931691317423\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_64996163102211_l_7_798476478209686_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_64996163102211_l_7_798476478209686_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_64996163102211_l_7_798476478209686_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_64996163102211_l_7_798476478209686_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.5844957738468\n",
      "################\n",
      "Iteration #81\n",
      "Current values of variables: x[0] = 91.46984082018685, x[1] = -7.696634177329283\n",
      "Demod factor for Simulated:  -73.84345825367829\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_46984082018685_l_7_696634177329283_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_46984082018685_l_7_696634177329283_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_46984082018685_l_7_696634177329283_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_46984082018685_l_7_696634177329283_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56469786746088\n",
      "################\n",
      "Iteration #82\n",
      "Current values of variables: x[0] = 92.71568812309732, x[1] = -7.6948573469648744\n",
      "Demod factor for Simulated:  -73.86763766545347\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_92_71568812309732_l_7_6948573469648744_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_92_71568812309732_l_7_6948573469648744_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_92_71568812309732_l_7_6948573469648744_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_92_71568812309732_l_7_6948573469648744_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.58697536636518\n",
      "################\n",
      "Iteration #83\n",
      "Current values of variables: x[0] = 91.35914011003186, x[1] = -7.722316856344936\n",
      "Demod factor for Simulated:  -73.73440049208041\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_35914011003186_l_7_722316856344936_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_35914011003186_l_7_722316856344936_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_35914011003186_l_7_722316856344936_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_35914011003186_l_7_722316856344936_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56396468469194\n",
      "################\n",
      "Iteration #84\n",
      "Current values of variables: x[0] = 90.6761761882982, x[1] = -7.68925783790034\n",
      "Demod factor for Simulated:  -73.86369123302751\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_90_6761761882982_l_7_68925783790034_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_90_6761761882982_l_7_68925783790034_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_90_6761761882982_l_7_68925783790034_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_90_6761761882982_l_7_68925783790034_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56895658640157\n",
      "################\n",
      "Iteration #85\n",
      "Current values of variables: x[0] = 91.78364760351494, x[1] = -7.719584356305495\n",
      "Demod factor for Simulated:  -73.75154844280527\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_78364760351494_l_7_719584356305495_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_78364760351494_l_7_719584356305495_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_78364760351494_l_7_719584356305495_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_78364760351494_l_7_719584356305495_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56385340227777\n",
      "################\n",
      "Iteration #86\n",
      "Current values of variables: x[0] = 91.67294689335995, x[1] = -7.745267035321147\n",
      "Demod factor for Simulated:  -73.64249068120736\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_67294689335995_l_7_745267035321147_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_67294689335995_l_7_745267035321147_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_67294689335995_l_7_745267035321147_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_67294689335995_l_7_745267035321147_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56526893838016\n",
      "################\n",
      "Iteration #87\n",
      "Current values of variables: x[0] = 91.52061733848012, x[1] = -7.708792391827249\n",
      "Demod factor for Simulated:  -73.79321636056058\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_52061733848012_l_7_708792391827249_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_52061733848012_l_7_708792391827249_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_52061733848012_l_7_708792391827249_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_52061733848012_l_7_708792391827249_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56359990659047\n",
      "################\n",
      "Iteration #88\n",
      "Current values of variables: x[0] = 91.9451248319632, x[1] = -7.706059891787808\n",
      "Demod factor for Simulated:  -73.81036431128543\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_9451248319632_l_7_706059891787808_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_9451248319632_l_7_706059891787808_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_9451248319632_l_7_706059891787808_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_9451248319632_l_7_706059891787808_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56675412779823\n",
      "################\n",
      "Iteration #89\n",
      "Current values of variables: x[0] = 91.5056362905147, x[1] = -7.718252615205653\n",
      "Demod factor for Simulated:  -73.75339144688165\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5056362905147_l_7_718252615205653_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5056362905147_l_7_718252615205653_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5056362905147_l_7_718252615205653_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5056362905147_l_7_718252615205653_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56333985451141\n",
      "################\n",
      "Iteration #90\n",
      "Current values of variables: x[0] = 91.24260602547989, x[1] = -7.707460650727407\n",
      "Demod factor for Simulated:  -73.79505936463696\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_24260602547989_l_7_707460650727407_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_24260602547989_l_7_707460650727407_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_24260602547989_l_7_707460650727407_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_24260602547989_l_7_707460650727407_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56403368115262\n",
      "################\n",
      "Iteration #91\n",
      "Current values of variables: x[0] = 91.64838720900617, x[1] = -7.716553429910973\n",
      "Demod factor for Simulated:  -73.76242617326321\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_64838720900617_l_7_716553429910973_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_64838720900617_l_7_716553429910973_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_64838720900617_l_7_716553429910973_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_64838720900617_l_7_716553429910973_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56347618522723\n",
      "################\n",
      "Iteration #92\n",
      "Current values of variables: x[0] = 91.63340616104074, x[1] = -7.726013653289378\n",
      "Demod factor for Simulated:  -73.72260125958431\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_63340616104074_l_7_726013653289378_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_63340616104074_l_7_726013653289378_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_63340616104074_l_7_726013653289378_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_63340616104074_l_7_726013653289378_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56344188507856\n",
      "################\n",
      "Iteration #93\n",
      "Current values of variables: x[0] = 91.49065524254927, x[1] = -7.7277128385840586\n",
      "Demod factor for Simulated:  -73.71356653320274\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_49065524254927_l_7_7277128385840586_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_49065524254927_l_7_7277128385840586_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_49065524254927_l_7_7277128385840586_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_49065524254927_l_7_7277128385840586_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56379490661516\n",
      "################\n",
      "Iteration #94\n",
      "Current values of variables: x[0] = 91.60895421739195, x[1] = -7.719343282079244\n",
      "Demod factor for Simulated:  -73.75021126324809\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_60895421739195_l_7_719343282079244_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_60895421739195_l_7_719343282079244_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_60895421739195_l_7_719343282079244_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_60895421739195_l_7_719343282079244_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.5633415613658\n",
      "################\n",
      "Iteration #95\n",
      "Current values of variables: x[0] = 91.48118434686592, x[1] = -7.711582243995519\n",
      "Demod factor for Simulated:  -73.78100145054543\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_48118434686592_l_7_711582243995519_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_48118434686592_l_7_711582243995519_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_48118434686592_l_7_711582243995519_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_48118434686592_l_7_711582243995519_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56343453221864\n",
      "################\n",
      "Iteration #96\n",
      "Current values of variables: x[0] = 91.51923980040962, x[1] = -7.715190096318985\n",
      "Demod factor for Simulated:  -73.76640140280517\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_51923980040962_l_7_715190096318985_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_51923980040962_l_7_715190096318985_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_51923980040962_l_7_715190096318985_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_51923980040962_l_7_715190096318985_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56334503450336\n",
      "################\n",
      "Iteration #97\n",
      "Current values of variables: x[0] = 91.59535070749703, x[1] = -7.7224058009659124\n",
      "Demod factor for Simulated:  -73.73720130732458\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_59535070749703_l_7_7224058009659124_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_59535070749703_l_7_7224058009659124_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_59535070749703_l_7_7224058009659124_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_59535070749703_l_7_7224058009659124_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56334871093745\n",
      "################\n",
      "Iteration #98\n",
      "Current values of variables: x[0] = 91.53826752718147, x[1] = -7.7169940224807165\n",
      "Demod factor for Simulated:  -73.75910137893501\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_53826752718147_l_7_7169940224807165_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_53826752718147_l_7_7169940224807165_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_53826752718147_l_7_7169940224807165_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_53826752718147_l_7_7169940224807165_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.5633231196136\n",
      "################\n",
      "Iteration #99\n",
      "Current values of variables: x[0] = 91.43494960030421, x[1] = -7.715903355607125\n",
      "Demod factor for Simulated:  -73.76228156256856\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_43494960030421_l_7_715903355607125_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_43494960030421_l_7_715903355607125_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_43494960030421_l_7_715903355607125_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_43494960030421_l_7_715903355607125_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56342762936133\n",
      "################\n",
      "Iteration #100\n",
      "Current values of variables: x[0] = 91.56545306312002, x[1] = -7.718483300461214\n",
      "Demod factor for Simulated:  -73.7532288380782\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_56545306312002_l_7_718483300461214_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_56545306312002_l_7_718483300461214_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_56545306312002_l_7_718483300461214_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_56545306312002_l_7_718483300461214_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331812000899\n",
      "################\n",
      "Iteration #101\n",
      "Current values of variables: x[0] = 91.59808429978679, x[1] = -7.717224707736278\n",
      "Demod factor for Simulated:  -73.75893877013155\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_59808429978679_l_7_717224707736278_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_59808429978679_l_7_717224707736278_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_59808429978679_l_7_717224707736278_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_59808429978679_l_7_717224707736278_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.5633590713024\n",
      "################\n",
      "Iteration #102\n",
      "Current values of variables: x[0] = 91.52874829283272, x[1] = -7.7179956383383095\n",
      "Demod factor for Simulated:  -73.75477827769413\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_52874829283272_l_7_7179956383383095_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_52874829283272_l_7_7179956383383095_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_52874829283272_l_7_7179956383383095_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_52874829283272_l_7_7179956383383095_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56332177922796\n",
      "################\n",
      "Iteration #103\n",
      "Current values of variables: x[0] = 91.55593382877127, x[1] = -7.719484916318808\n",
      "Demod factor for Simulated:  -73.74890573683733\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55593382877127_l_7_719484916318808_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55593382877127_l_7_719484916318808_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55593382877127_l_7_719484916318808_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55593382877127_l_7_719484916318808_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331860693587\n",
      "################\n",
      "Iteration #104\n",
      "Current values of variables: x[0] = 91.59263859905857, x[1] = -7.719972578441712\n",
      "Demod factor for Simulated:  -73.74735629722142\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_59263859905857_l_7_719972578441712_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_59263859905857_l_7_719972578441712_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_59263859905857_l_7_719972578441712_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_59263859905857_l_7_719972578441712_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56332642343567\n",
      "################\n",
      "Iteration #105\n",
      "Current values of variables: x[0] = 91.54472086938918, x[1] = -7.71848987336416\n",
      "Demod factor for Simulated:  -73.75292278257596\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_54472086938918_l_7_71848987336416_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_54472086938918_l_7_71848987336416_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_54472086938918_l_7_71848987336416_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_54472086938918_l_7_71848987336416_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.5633173473967\n",
      "################\n",
      "Iteration #106\n",
      "Current values of variables: x[0] = 91.55424010373794, x[1] = -7.717488257506566\n",
      "Demod factor for Simulated:  -73.75724588381684\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55424010373794_l_7_717488257506566_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55424010373794_l_7_717488257506566_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55424010373794_l_7_717488257506566_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55424010373794_l_7_717488257506566_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.5633212127623\n",
      "################\n",
      "Iteration #107\n",
      "Current values of variables: x[0] = 91.55551039751293, x[1] = -7.718985751615747\n",
      "Demod factor for Simulated:  -73.75099077358222\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55551039751293_l_7_718985751615747_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55551039751293_l_7_718985751615747_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55551039751293_l_7_718985751615747_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55551039751293_l_7_718985751615747_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331671763854\n",
      "################\n",
      "Iteration #108\n",
      "Current values of variables: x[0] = 91.5347782037821, x[1] = -7.718992324518693\n",
      "Demod factor for Simulated:  -73.75068471807998\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5347782037821_l_7_718992324518693_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5347782037821_l_7_718992324518693_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5347782037821_l_7_718992324518693_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5347782037821_l_7_718992324518693_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56332306920373\n",
      "################\n",
      "Iteration #109\n",
      "Current values of variables: x[0] = 91.55778434828554, x[1] = -7.718610556475584\n",
      "Demod factor for Simulated:  -73.75259280807867\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55778434828554_l_7_718610556475584_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55778434828554_l_7_718610556475584_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55778434828554_l_7_718610556475584_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55778434828554_l_7_718610556475584_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331651335243\n",
      "################\n",
      "Iteration #110\n",
      "Current values of variables: x[0] = 91.56857387640929, x[1] = -7.719106434727172\n",
      "Demod factor for Simulated:  -73.75066079908491\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_56857387640929_l_7_719106434727172_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_56857387640929_l_7_719106434727172_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_56857387640929_l_7_719106434727172_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_56857387640929_l_7_719106434727172_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331762731116\n",
      "################\n",
      "Iteration #111\n",
      "Current values of variables: x[0] = 91.5506841211442, x[1] = -7.718644013704912\n",
      "Demod factor for Simulated:  -73.75235728670322\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5506841211442_l_7_718644013704912_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5506841211442_l_7_718644013704912_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5506841211442_l_7_718644013704912_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5506841211442_l_7_718644013704912_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331661991625\n",
      "################\n",
      "Iteration #112\n",
      "Current values of variables: x[0] = 91.5529580719168, x[1] = -7.718268818564749\n",
      "Demod factor for Simulated:  -73.7539593211996\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5529580719168_l_7_718268818564749_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5529580719168_l_7_718268818564749_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5529580719168_l_7_718268818564749_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5529580719168_l_7_718268818564749_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331684601851\n",
      "################\n",
      "Iteration #113\n",
      "Current values of variables: x[0] = 91.5548723161139, x[1] = -7.718806518352998\n",
      "Demod factor for Simulated:  -73.75173291048655\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5548723161139_l_7_718806518352998_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5548723161139_l_7_718806518352998_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5548723161139_l_7_718806518352998_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5548723161139_l_7_718806518352998_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.5633164711717\n",
      "################\n",
      "Iteration #114\n",
      "Current values of variables: x[0] = 91.56197254325522, x[1] = -7.7187730611236685\n",
      "Demod factor for Simulated:  -73.751968431862\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_56197254325522_l_7_7187730611236685_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_56197254325522_l_7_7187730611236685_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_56197254325522_l_7_7187730611236685_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_56197254325522_l_7_7187730611236685_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331675145047\n",
      "################\n",
      "Iteration #115\n",
      "Current values of variables: x[0] = 91.55350622667196, x[1] = -7.718676275559601\n",
      "Demod factor for Simulated:  -73.7522600729929\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55350622667196_l_7_718676275559601_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55350622667196_l_7_718676275559601_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55350622667196_l_7_718676275559601_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55350622667196_l_7_718676275559601_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331643871555\n",
      "################\n",
      "Iteration #116\n",
      "Current values of variables: x[0] = 91.55059419450032, x[1] = -7.718872237437015\n",
      "Demod factor for Simulated:  -73.75140017540079\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55059419450032_l_7_718872237437015_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55059419450032_l_7_718872237437015_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55059419450032_l_7_718872237437015_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55059419450032_l_7_718872237437015_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331701204871\n",
      "################\n",
      "Iteration #117\n",
      "Current values of variables: x[0] = 91.55598680983923, x[1] = -7.718675976715941\n",
      "Demod factor for Simulated:  -73.7522946499092\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55598680983923_l_7_718675976715941_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55598680983923_l_7_718675976715941_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55598680983923_l_7_718675976715941_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55598680983923_l_7_718675976715941_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639976922\n",
      "################\n",
      "Iteration #118\n",
      "Current values of variables: x[0] = 91.5546207203973, x[1] = -7.718545733922544\n",
      "Demod factor for Simulated:  -73.75282181241555\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5546207203973_l_7_718545733922544_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5546207203973_l_7_718545733922544_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5546207203973_l_7_718545733922544_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5546207203973_l_7_718545733922544_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331644749247\n",
      "################\n",
      "Iteration #119\n",
      "Current values of variables: x[0] = 91.55468361932645, x[1] = -7.718610930030156\n",
      "Demod factor for Simulated:  -73.75254958693328\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55468361932645_l_7_718610930030156_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55468361932645_l_7_718610930030156_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55468361932645_l_7_718610930030156_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55468361932645_l_7_718610930030156_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331641039105\n",
      "################\n",
      "Iteration #120\n",
      "Current values of variables: x[0] = 91.55716420249372, x[1] = -7.718610631186496\n",
      "Demod factor for Simulated:  -73.75258416384959\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55716420249372_l_7_718610631186496_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55716420249372_l_7_718610631186496_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55716420249372_l_7_718610631186496_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55716420249372_l_7_718610631186496_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331647548828\n",
      "################\n",
      "Iteration #121\n",
      "Current values of variables: x[0] = 91.5544207206274, x[1] = -7.718659864466325\n",
      "Demod factor for Simulated:  -73.75234109570707\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5544207206274_l_7_718659864466325_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5544207206274_l_7_718659864466325_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5544207206274_l_7_718659864466325_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5544207206274_l_7_718659864466325_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331640624222\n",
      "################\n",
      "Iteration #122\n",
      "Current values of variables: x[0] = 91.55572391114018, x[1] = -7.71872491115211\n",
      "Demod factor for Simulated:  -73.75208615868296\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55572391114018_l_7_71872491115211_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55572391114018_l_7_71872491115211_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55572391114018_l_7_71872491115211_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55572391114018_l_7_71872491115211_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639975605\n",
      "################\n",
      "Iteration #123\n",
      "Current values of variables: x[0] = 91.55624405704702, x[1] = -7.718781901713086\n",
      "Demod factor for Simulated:  -73.75185444455781\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55624405704702_l_7_718781901713086_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55624405704702_l_7_718781901713086_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55624405704702_l_7_718781901713086_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55624405704702_l_7_718781901713086_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331641745834\n",
      "################\n",
      "Iteration #124\n",
      "Current values of variables: x[0] = 91.557290000352, x[1] = -7.718741023401726\n",
      "Demod factor for Simulated:  -73.75203971288508\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_557290000352_l_7_718741023401726_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_557290000352_l_7_718741023401726_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_557290000352_l_7_718741023401726_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_557290000352_l_7_718741023401726_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331641741613\n",
      "################\n",
      "Iteration #125\n",
      "Current values of variables: x[0] = 91.55513804055855, x[1] = -7.718680154200175\n",
      "Demod factor for Simulated:  -73.75226575000157\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55513804055855_l_7_718680154200175_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55513804055855_l_7_718680154200175_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55513804055855_l_7_718680154200175_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55513804055855_l_7_718680154200175_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639765648\n",
      "################\n",
      "Iteration #126\n",
      "Current values of variables: x[0] = 91.5548751418595, x[1] = -7.718729088636344\n",
      "Demod factor for Simulated:  -73.75205725877534\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5548751418595_l_7_718729088636344_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5548751418595_l_7_718729088636344_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5548751418595_l_7_718729088636344_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5548751418595_l_7_718729088636344_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331641421009\n",
      "################\n",
      "Iteration #127\n",
      "Current values of variables: x[0] = 91.5557088928443, x[1] = -7.7186892546960415\n",
      "Demod factor for Simulated:  -73.75223530212571\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5557088928443_l_7_7186892546960415_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5557088928443_l_7_7186892546960415_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5557088928443_l_7_7186892546960415_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5557088928443_l_7_7186892546960415_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639626184\n",
      "################\n",
      "Iteration #128\n",
      "Current values of variables: x[0] = 91.55512302226268, x[1] = -7.718644497744107\n",
      "Demod factor for Simulated:  -73.75241489344434\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55512302226268_l_7_718644497744107_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55512302226268_l_7_718644497744107_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55512302226268_l_7_718644497744107_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55512302226268_l_7_718644497744107_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331640071546\n",
      "################\n",
      "Iteration #129\n",
      "Current values of variables: x[0] = 91.5555736889208, x[1] = -7.718704807800109\n",
      "Demod factor for Simulated:  -73.75216834237327\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5555736889208_l_7_718704807800109_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5555736889208_l_7_718704807800109_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5555736889208_l_7_718704807800109_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5555736889208_l_7_718704807800109_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639702915\n",
      "################\n",
      "Iteration #130\n",
      "Current values of variables: x[0] = 91.55614454120655, x[1] = -7.718713908295975\n",
      "Demod factor for Simulated:  -73.75213789449747\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55614454120655_l_7_718713908295975_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55614454120655_l_7_718713908295975_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55614454120655_l_7_718713908295975_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55614454120655_l_7_718713908295975_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639854304\n",
      "################\n",
      "Iteration #131\n",
      "Current values of variables: x[0] = 91.55538966572055, x[1] = -7.718688592724125\n",
      "Demod factor for Simulated:  -73.75223378612557\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55538966572055_l_7_718688592724125_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55538966572055_l_7_718688592724125_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55538966572055_l_7_718688592724125_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55538966572055_l_7_718688592724125_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.5633163964927\n",
      "################\n",
      "Iteration #132\n",
      "Current values of variables: x[0] = 91.55552486964403, x[1] = -7.7186730396200565\n",
      "Demod factor for Simulated:  -73.75230074587797\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55552486964403_l_7_7186730396200565_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55552486964403_l_7_7186730396200565_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55552486964403_l_7_7186730396200565_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55552486964403_l_7_7186730396200565_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639682112\n",
      "################\n",
      "Iteration #133\n",
      "Current values of variables: x[0] = 91.55553707446322, x[1] = -7.71868098166507\n",
      "Demod factor for Simulated:  -73.75226764500178\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55553707446322_l_7_71868098166507_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55553707446322_l_7_71868098166507_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55553707446322_l_7_71868098166507_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55553707446322_l_7_71868098166507_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639625733\n",
      "################\n",
      "Iteration #134\n",
      "Current values of variables: x[0] = 91.55585630158697, x[1] = -7.7186816436369865\n",
      "Demod factor for Simulated:  -73.75226916100199\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55585630158697_l_7_7186816436369865_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55585630158697_l_7_7186816436369865_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55585630158697_l_7_7186816436369865_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55585630158697_l_7_7186816436369865_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639759678\n",
      "################\n",
      "Iteration #135\n",
      "Current values of variables: x[0] = 91.55550632468716, x[1] = -7.71868685545234\n",
      "Demod factor for Simulated:  -73.75224262984466\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55550632468716_l_7_71868685545234_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55550632468716_l_7_71868685545234_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55550632468716_l_7_71868685545234_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55550632468716_l_7_71868685545234_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639613491\n",
      "################\n",
      "Iteration #136\n",
      "Current values of variables: x[0] = 91.5553345063061, x[1] = -7.718678582421369\n",
      "Demod factor for Simulated:  -73.75227497272073\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5553345063061_l_7_718678582421369_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_5553345063061_l_7_718678582421369_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5553345063061_l_7_718678582421369_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_5553345063061_l_7_718678582421369_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639655788\n",
      "################\n",
      "Iteration #137\n",
      "Current values of variables: x[0] = 91.55561529620974, x[1] = -7.718686586627373\n",
      "Demod factor for Simulated:  -73.7522452197745\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55561529620974_l_7_718686586627373_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55561529620974_l_7_718686586627373_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55561529620974_l_7_718686586627373_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55561529620974_l_7_718686586627373_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639614228\n",
      "################\n",
      "Iteration #138\n",
      "Current values of variables: x[0] = 91.55558454643366, x[1] = -7.718692460414644\n",
      "Demod factor for Simulated:  -73.75222020461732\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55558454643366_l_7_718692460414644_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55558454643366_l_7_718692460414644_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55558454643366_l_7_718692460414644_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55558454643366_l_7_718692460414644_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.56331639613454\n",
      "################\n",
      "Iteration #139\n",
      "Current values of variables: x[0] = 91.55560828241886, x[1] = -7.718698199789429\n",
      "Demod factor for Simulated:  -73.7521964844251\n",
      "Filename:  data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55560828241886_l_7_718698199789429_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/test_double_optimize/demoded_fms/demod_db0_sim_t_91_55560828241886_l_7_718698199789429_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55560828241886_l_7_718698199789429_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/test_double_optimize/metrics/metrics_db0_sim_t_91_55560828241886_l_7_718698199789429_fm_Hz.csv\n",
      "\n",
      "\n",
      " Minimizing this difference:  81.5633163963502\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 81.563316\n",
      "         Iterations: 74\n",
      "         Function evaluations: 139\n",
      "Minimum chi trachea:  91.55558454643366 & chi lungs: -7.718692460414644\n"
     ]
    }
   ],
   "source": [
    "goated_params = optimize.fmin(f_goat, [0.4,-2.65])\n",
    "print(\"Minimum chi trachea: \", goated_params[0], \"& chi lungs:\", goated_params[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34a7b9d",
   "metadata": {},
   "source": [
    "# Extras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "03e68621",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################\n",
      "Iteration # 1\n",
      "Using chi: -4.2\n",
      "Demod factor for Simulated:  -87.20365804155139\n",
      "Filename:  data/amu_vs_meas/demoded_fms/demod_db0_sim_4_2_fm_Hz.nii.gz\n",
      "Metrics extracted successfully: \n",
      "--\n",
      "Spinal Cord Toolbox (6.2)\n",
      "\n",
      "sct_extract_metric -i data/amu_vs_meas/demoded_fms/demod_db0_sim_4_2_fm_Hz.nii.gz -f data/amu_vs_meas/spinal_cord_AMU_VC.nii.gz -method wa -vert 2:14 -vertfile data/amu_vs_meas/vertfile_AMU_VC_c2t7.nii.gz -perlevel 1 -o data/amu_vs_meas/metrics/metrics_db0_sim_4_2_fm_Hz.csv\n",
      "--\n",
      "\n",
      "\n",
      "Load metric image...\n",
      "Estimation for label: spinal_cord_AMU_VC\n",
      "\n",
      "Done! To view results, type:\n",
      "start data/amu_vs_meas/metrics/metrics_db0_sim_4_2_fm_Hz.csv\n",
      "\n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_22568\\4250949314.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mbounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmin_chi_tl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moptimize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf_2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m4.2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbounds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Minimum chi:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_chi_tl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Optimization success:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_chi_tl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuccess\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Check if the optimization was successful\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Message:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_chi_tl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\scipy\\optimize\\_minimize.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m    709\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'newton-cg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    710\u001b[0m         res = _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,\n\u001b[0;32m    711\u001b[0m                                  \u001b[1;33m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    712\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'l-bfgs-b'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 713\u001b[1;33m         res = _minimize_lbfgsb(fun, x0, args, jac, bounds,\n\u001b[0m\u001b[0;32m    714\u001b[0m                                \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'tnc'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    716\u001b[0m         res = _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,\n",
      "\u001b[1;32mc:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\scipy\\optimize\\_lbfgsb_py.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[0;32m    343\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m             \u001b[0miprint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdisp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    345\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    346\u001b[0m     \u001b[1;31m# _prepare_scalar_function can use bounds=None to represent no bounds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 347\u001b[1;33m     sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,\n\u001b[0m\u001b[0;32m    348\u001b[0m                                   \u001b[0mbounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbounds\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    349\u001b[0m                                   \u001b[0mfinite_diff_rel_step\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfinite_diff_rel_step\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\scipy\\optimize\\_optimize.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess)\u001b[0m\n\u001b[0;32m    284\u001b[0m         \u001b[0mbounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m     \u001b[1;31m# ScalarFunction caches. Reuse of fun(x) during grad\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    287\u001b[0m     \u001b[1;31m# calculation reduces overall function evaluations.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 288\u001b[1;33m     sf = ScalarFunction(fun, x0, args, grad, hess,\n\u001b[0m\u001b[0;32m    289\u001b[0m                         \u001b[0mfinite_diff_rel_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0msf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon)\u001b[0m\n\u001b[0;32m    162\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    163\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfun_wrapped\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_fun_impl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_fun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m         \u001b[1;31m# Gradient evaluation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    169\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_update_fun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_updated\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_fun_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    263\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_updated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m    162\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 163\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfun_wrapped\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    141\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnfev\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    142\u001b[0m             \u001b[1;31m# Send a copy because the user may overwrite it.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    143\u001b[0m             \u001b[1;31m# Overwriting results in undefined behaviour because\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m             \u001b[1;31m# fun(self.x) will change self.x, with the two no longer linked.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 145\u001b[1;33m             \u001b[0mfx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    146\u001b[0m             \u001b[1;31m# Make sure the function returns a true scalar\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    147\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_22568\\26259791.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m     \u001b[1;31m# Perform the metric extraction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m     \u001b[0mdifference\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdmod_b0meas_values\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdmod_simulated_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" Minimizing this difference: \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdifference\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdifference\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\numpy\\linalg\\linalg.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(x, ord, axis, keepdims)\u001b[0m\n\u001b[0;32m   2539\u001b[0m     \u001b[1;31m# Immediately handle some default, simple, fast, and common cases.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2540\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2541\u001b[0m         \u001b[0mndim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2542\u001b[0m         if ((ord is None) or\n\u001b[1;32m-> 2543\u001b[1;33m             \u001b[1;33m(\u001b[0m\u001b[0mord\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'f'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'fro'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2544\u001b[0m             \u001b[1;33m(\u001b[0m\u001b[0mord\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2545\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2546\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'K'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\User\\miniconda3\\envs\\mrsim\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1575\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1576\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mNoReturn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1577\u001b[1;33m         raise ValueError(\n\u001b[0m\u001b[0;32m   1578\u001b[0m             \u001b[1;33mf\"\u001b[0m\u001b[1;33mThe truth value of a \u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m is ambiguous. \u001b[0m\u001b[1;33m\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1579\u001b[0m             \u001b[1;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1580\u001b[0m         \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "dbounds = [(-5,0.4)]\n",
    "min_chi_tl = optimize.minimize(f_2, -4.2, bounds=bounds)\n",
    "print(\"Minimum chi:\", min_chi_tl.x[0])\n",
    "print(\"Optimization success:\", min_chi_tl.success)  # Check if the optimization was successful\n",
    "print(\"Message:\", min_chi_tl.message) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f7ef590",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi: [-0.20583366]\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum chi:\", min_chi_tl.x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "678e362c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using chi:  [0.]\n",
      "Demod factor for B0meas:  132.99112910454772\n",
      "Demod factor for Simulated:  -298.65534837507926\n",
      "Using chi:  [-1.e-08]\n",
      "Demod factor for B0meas:  132.99112910454772\n",
      "Demod factor for Simulated:  -298.6553483744052\n",
      "Minimum chi: 0.0\n",
      "Optimization success: True\n",
      "Message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL\n"
     ]
    }
   ],
   "source": [
    "bounds = [(-9.05,0)]\n",
    "min_chi_tl = optimize.minimize(f, 0.35, bounds=bounds)\n",
    "print(\"Minimum chi:\", min_chi_tl.x[0])\n",
    "print(\"Optimization success:\", min_chi_tl.success)  # Check if the optimization was successful\n",
    "print(\"Message:\", min_chi_tl.message) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "630c1504",
   "metadata": {},
   "source": [
    "# Testing everything\n",
    "For the new chimaps, there is no spinal cord added but we have the spina cord segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b305e000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Demod factor for Simulated:  nan\n"
     ]
    }
   ],
   "source": [
    "    # load simulated susceptibility map in ppm\n",
    "chi_img = nib.load(sim_chi_map_path3)\n",
    "chi_data = chi_img.get_fdata()\n",
    "    \n",
    "    # load segmentation labels map\n",
    "ROIs_img = nib.load(segmentations_cropped)\n",
    "ROIs_data = ROIs_img.get_fdata()\n",
    "\n",
    "sc_seg_img = nib.load(sc_final_mask)\n",
    "sc_seg_data = sc_seg_img.get_fdata()\n",
    "\n",
    "demod_mask_img = nib.load(demodulating_mask_path)\n",
    "demod_mask_data = demod_mask_img.get_fdata()\n",
    "\n",
    "\n",
    "ind_sc = np.where(sc_seg_data == 1)\n",
    "    # find indicies that have label = 7 or 8, these voxels should correspond to the trachea (t) and lungs (l); ind_tl\n",
    "ind_tl = np.where((ROIs_data == 7) | (ROIs_data == 8))\n",
    "    \n",
    "\n",
    "    # load simulated field map in Hz\n",
    "b0meas_img = nib.load(measured_B0)\n",
    "b0meas_data = b0meas_img.get_fdata()\n",
    "\n",
    "    # assign variable \"x\" to indicies corresponding to trachea/lungs\n",
    "chi_data[ind_tl] = -4.2 # x in the loop above\n",
    "\n",
    "    # compute the field distribution in ppm for the chi_data distribution using the compute_bz function from the susceptibility-to-fieldmap-fft package\n",
    "    #z =  compute_fieldmap.compute_bz(chi_data)\n",
    "chi_dist, image_res , affine_matrix = compute_fieldmap.load_sus_dist(sim_chi_map_path3)\n",
    "    #z = chi_to_fm_ppm(sim_chi_map_path2, output\"output/slurp.nii.gz\")\n",
    "    # convert field distribution from ppm to Hz (assuming 3T)\n",
    "z = compute_fieldmap.compute_bz(chi_data, image_resolution = image_res , buffer = 50, mode = \"b0SimISMRM\")\n",
    "z = z*3*42.58 # deltaB0 [Hz] = deltaB0 [ppm] * B0 [T] * 1e-6 * 42.58*e6 [Hz/T]\n",
    "\n",
    "demod_factor_z = np.mean(z[demod_mask_data == 1])\n",
    "print(\"Demod factor for Simulated: \", demod_factor_z)\n",
    "demod_z = z - demod_factor_z\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c009580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(191, 285, 843)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "38d1d201",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quickly check if the ind_tl is actually taking the lungs or not\n",
    "ind_tl = np.where((ROIs_data == 7) | (ROIs_data == 8))\n",
    "\n",
    "only_internal_air = np.zeros(np.shape(ROIs_data))\n",
    "only_internal_air[ind_tl] = 1\n",
    "\n",
    "only_air_img = nib.Nifti1Image(only_internal_air, affine=ROIs_img.affine)\n",
    "nib.save(only_air_img, \"internal_air_only.nii.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4261ef71",
   "metadata": {},
   "source": [
    "### Using subprocess to test if we are using the correct version of compute_bz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5e93c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For sure that compute_bz is not returning the same even using the same chi value\n",
    "# Lets test calling with the custom function\n",
    "def chi_to_fm_ppm(input_file, output_file):\n",
    "\n",
    "    #Using a custom command for dB0 simulation\n",
    "\n",
    "    command = [\n",
    "    \"compute_fieldmap\",\n",
    "    \"-i\", input_file,\n",
    "    \"-o\", output_file,\n",
    "    \"-b\", \"50\",\n",
    "    \"-m\", \"b0SimISMRM\"\n",
    "    ]\n",
    "\n",
    "    # Run the command and capture output\n",
    "    result = subprocess.run(command, capture_output=True, text=True)\n",
    "    \n",
    "    # Check for errors\n",
    "    if result.returncode != 0:\n",
    "        print(\"Error occurred:\", result.stderr)\n",
    "    else:\n",
    "        print(\"FM created successfully:\", result.stdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0df7325",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FM created successfully: Start\n",
      "Susceptibility distribution loaded\n",
      "Fieldmap simulated\n",
      "Saving to NIfTI format\n",
      "End. Runtime: 20.40 seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "chi_to_fm_ppm(sim_chi_map_path3,\"output/please_please_please.nii.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2b2d7964",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_img_demod = nib.Nifti1Image(demod_z, affine = chi_img.affine)\n",
    "nib.save(z_img_demod, \"output/z_demoded_img.nii.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6a393e64",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_voxels_only = np.zeros_like(sc_seg_data)\n",
    "\n",
    "# Fill the spinal cord voxels\n",
    "sc_voxels_only[ind_sc] = demod_z[ind_sc]\n",
    "\n",
    "# Save the new mask as a NIfTI file for visual inspection\n",
    "masked_img = nib.Nifti1Image(sc_voxels_only, affine=sc_seg_img.affine)\n",
    "nib.save(masked_img, \"spinal_cord_voxels_only.nii.gz\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98765034",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f923fff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using chi:  [-4.2]\n",
      "Demod factor for B0meas:  132.99112910454772\n",
      "Demod factor for Simulated:  -298.37221739257666\n",
      "Using chi:  [-4.19999999]\n",
      "Demod factor for B0meas:  132.99112910454772\n",
      "Demod factor for Simulated:  -298.37221739325076\n",
      "Using chi:  [0.4]\n",
      "Demod factor for B0meas:  132.99112910454772\n",
      "Demod factor for Simulated:  -298.6823132305558\n",
      "Using chi:  [0.39999999]\n",
      "Demod factor for B0meas:  132.99112910454772\n",
      "Demod factor for Simulated:  -298.6823132298815\n",
      "Minimum chi: 0.4\n",
      "Optimization success: True\n",
      "Message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL\n"
     ]
    }
   ],
   "source": [
    "bounds = [(-5,0.4)]\n",
    "min_chi_tl = optimize.minimize(f, -4.2, bounds=bounds)\n",
    "print(\"Minimum chi:\", min_chi_tl.x[0])\n",
    "print(\"Optimization success:\", min_chi_tl.success)  # Check if the optimization was successful\n",
    "print(\"Message:\", min_chi_tl.message) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f5e6b3da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# What I want to do is mask this voxels out and see if they are literally the same \n",
    "masked_b0meas = np.zeros(np.shape(b0meas_data))\n",
    "masked_chi_dist = np.zeros(np.shape(chi_data))\n",
    "\n",
    "masked_b0meas = b0meas_data[sc_seg_data == 1]\n",
    "\n",
    "masked_b0_img = nib.Nifti1Image(masked_b0meas, affine=b0meas_img.affine)\n",
    "nib.save(masked_b0_img, \"output/masked/masked_B0meas.nii.gz\")\n",
    "\n",
    "masked_chimap = chi_data[ind_sc]\n",
    "\n",
    "masked_chimap_img = nib.Nifti1Image(masked_chimap, affine=chi_img.affine)\n",
    "nib.save(masked_chimap_img, \"output/masked/mask_chimap.nii.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2cf4a903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(191, 285, 843)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(b0meas_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "245e8caf",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_img = nib.nifti1Image(z, affine = chi_img.affine)\n",
    "nib.save(z_img, \"output/z_image_chi.nii.gz\")"
   ]
  }
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