{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient based optimization (Adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "from jax import config\n",
    "#config.update(\"jax_enable_x64\", True)\n",
    "#config.update('jax_num_cpu_devices', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CpuDevice(id=0)]\n"
     ]
    }
   ],
   "source": [
    "#NBVAL_SKIP\n",
    "import os\n",
    "\n",
    "# Tell XLA to fake 2 host CPU devices\n",
    "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n",
    "\n",
    "# Only make GPU 0 and GPU 1 visible to JAX:\n",
    "#os.environ['CUDA_VISIBLE_DEVICES'] = '1,2'\n",
    "\n",
    "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"]   = \"false\"\n",
    "\n",
    "import jax\n",
    "\n",
    "# Now JAX will list two CpuDevice entries\n",
    "print(jax.devices())\n",
    "# → [CpuDevice(id=0), CpuDevice(id=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "import os\n",
    "#os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n",
    "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n",
    "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n",
    "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load ssp template from FSPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-11 10:14:51,323 - rubix - INFO - \n",
      "   ___  __  _____  _____  __\n",
      "  / _ \\/ / / / _ )/  _/ |/_/\n",
      " / , _/ /_/ / _  |/ /_>  <\n",
      "/_/|_|\\____/____/___/_/|_|\n",
      "\n",
      "\n",
      "2025-11-11 10:14:51,324 - rubix - INFO - Rubix version: 0.0.post507+g27646941c\n",
      "2025-11-11 10:14:51,325 - rubix - INFO - JAX version: 0.4.38\n",
      "2025-11-11 10:14:51,325 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n",
      "2025-11-11 10:14:51,326 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "from rubix.spectra.ssp.factory import get_ssp_template\n",
    "ssp_fsps = get_ssp_template(\"FSPS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(107,)\n",
      "(12,)\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "age_values = ssp_fsps.age\n",
    "print(age_values.shape)\n",
    "\n",
    "metallicity_values = ssp_fsps.metallicity\n",
    "print(metallicity_values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start age: 15.848933219909668, start metallicity: 0.025251565501093864\n",
      "target age: 3.1622776985168457, target metallicity: 0.014199999161064625\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "index_age = 90\n",
    "index_metallicity = 9\n",
    "\n",
    "#initial_metallicity_index = 5\n",
    "#initial_age_index = 70\n",
    "initial_metallicity_index = 10\n",
    "initial_age_index = 104\n",
    "\n",
    "initial_age_index2 = 90\n",
    "initial_metallicity_index2 = 6\n",
    "\n",
    "initial_age_index3 = 99\n",
    "initial_metallicity_index3 = 11\n",
    "\n",
    "learning_all = 5e-3\n",
    "tol = 1e-10\n",
    "\n",
    "print(f\"start age: {age_values[initial_age_index]}, start metallicity: {metallicity_values[initial_metallicity_index]}\")\n",
    "print(f\"target age: {age_values[index_age]}, target metallicity: {metallicity_values[index_metallicity]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configure pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "from rubix.core.pipeline import RubixPipeline\n",
    "import os\n",
    "config = {\n",
    "    \"pipeline\":{\"name\": \"calc_gradient\",},\n",
    "    \n",
    "    \"logger\": {\n",
    "        \"log_level\": \"DEBUG\",\n",
    "        \"log_file_path\": None,\n",
    "        \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n",
    "    },\n",
    "    \"data\": {\n",
    "        \"name\": \"IllustrisAPI\",\n",
    "        \"args\": {\n",
    "            \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n",
    "            \"particle_type\": [\"stars\"],\n",
    "            \"simulation\": \"TNG50-1\",\n",
    "            \"snapshot\": 99,\n",
    "            \"save_data_path\": \"data\",\n",
    "        },\n",
    "        \n",
    "        \"load_galaxy_args\": {\n",
    "        \"id\": 14,\n",
    "        \"reuse\": True,\n",
    "        },\n",
    "        \n",
    "        \"subset\": {\n",
    "            \"use_subset\": True,\n",
    "            \"subset_size\": 2,\n",
    "        },\n",
    "    },\n",
    "    \"simulation\": {\n",
    "        \"name\": \"IllustrisTNG\",\n",
    "        \"args\": {\n",
    "            \"path\": \"data/galaxy-id-14.hdf5\",\n",
    "        },\n",
    "    \n",
    "    },\n",
    "    \"output_path\": \"output\",\n",
    "\n",
    "    \"telescope\":\n",
    "        {\"name\": \"TESTGRADIENT\",\n",
    "         \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n",
    "         \"lsf\": {\"sigma\": 1.2},\n",
    "         \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},\n",
    "         },\n",
    "    \"cosmology\":\n",
    "        {\"name\": \"PLANCK15\"},\n",
    "        \n",
    "    \"galaxy\":\n",
    "        {\"dist_z\": 0.1,\n",
    "         \"rotation\": {\"type\": \"edge-on\"},\n",
    "        },\n",
    "        \n",
    "    \"ssp\": {\n",
    "        \"template\": {\n",
    "            \"name\": \"FSPS\"\n",
    "        },\n",
    "        \"dust\": {\n",
    "                \"extinction_model\": \"Cardelli89\",\n",
    "                \"dust_to_gas_ratio\": 0.01,\n",
    "                \"dust_to_metals_ratio\": 0.4,\n",
    "                \"dust_grain_density\": 3.5,\n",
    "                \"Rv\": 3.1,\n",
    "            },\n",
    "    },        \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:52,007 - rubix - INFO - Getting rubix data...\n",
      "2025-11-11 10:14:52,008 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/jax/_src/numpy/lax_numpy.py:188: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n",
      "  return asarray(x, dtype=self.dtype)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/core/data.py:537: UserWarning: Explicitly requested dtype <class 'jax.numpy.float64'> requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n",
      "  rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n",
      "2025-11-11 10:14:52,081 - rubix - INFO - Centering stars particles\n",
      "2025-11-11 10:14:53,068 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2 for stars\n",
      "2025-11-11 10:14:53,071 - rubix - INFO - Data loaded with 2 star particles and 0 gas particles.\n",
      "2025-11-11 10:14:53,072 - rubix - INFO - Setting up the pipeline...\n",
      "2025-11-11 10:14:53,073 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n",
      "2025-11-11 10:14:53,074 - rubix - DEBUG - Roataion Type found: edge-on\n",
      "2025-11-11 10:14:53,078 - rubix - INFO - Calculating spatial bin edges...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:53,102 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:53,278 - rubix - INFO - Calculating spatial bin edges...\n",
      "2025-11-11 10:14:53,288 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:14:53,341 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:53,447 - rubix - DEBUG - SSP Wave: (5994,)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:53,465 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:53,533 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:53,742 - rubix - INFO - Assembling the pipeline...\n",
      "2025-11-11 10:14:53,743 - rubix - INFO - Compiling the expressions...\n",
      "2025-11-11 10:14:53,744 - rubix - INFO - Number of devices: 1\n",
      "2025-11-11 10:14:53,813 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n",
      "2025-11-11 10:14:53,814 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n",
      "2025-11-11 10:14:53,814 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n",
      "2025-11-11 10:14:53,904 - rubix - INFO - Assigning particles to spaxels...\n",
      "2025-11-11 10:14:53,921 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n",
      "2025-11-11 10:14:54,059 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n",
      "2025-11-11 10:14:54,060 - rubix - INFO - Convolving with PSF...\n",
      "2025-11-11 10:14:54,063 - rubix - INFO - Convolving with LSF...\n",
      "2025-11-11 10:14:54,068 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n",
      "2025-11-11 10:14:57,373 - rubix - INFO - Pipeline run completed in 4.30 seconds.\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "pipe = RubixPipeline(config)\n",
    "inputdata = pipe.prepare_data()\n",
    "output = pipe.run_sharded(inputdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "import jax.numpy as jnp\n",
    "\n",
    "inputdata.stars.age = jnp.array([age_values[index_age], age_values[index_age]])\n",
    "inputdata.stars.metallicity = jnp.array([metallicity_values[index_metallicity], metallicity_values[index_metallicity]])\n",
    "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n",
    "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
    "inputdata.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-11 10:14:57,494 - rubix - INFO - Setting up the pipeline...\n",
      "2025-11-11 10:14:57,495 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n",
      "2025-11-11 10:14:57,496 - rubix - DEBUG - Roataion Type found: edge-on\n",
      "2025-11-11 10:14:57,498 - rubix - INFO - Calculating spatial bin edges...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:57,511 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:14:57,523 - rubix - INFO - Calculating spatial bin edges...\n",
      "2025-11-11 10:14:57,533 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:14:57,577 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:57,617 - rubix - DEBUG - SSP Wave: (5994,)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:57,630 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:57,684 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:14:57,731 - rubix - INFO - Assembling the pipeline...\n",
      "2025-11-11 10:14:57,732 - rubix - INFO - Compiling the expressions...\n",
      "2025-11-11 10:14:57,733 - rubix - INFO - Number of devices: 1\n",
      "2025-11-11 10:14:57,809 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n",
      "2025-11-11 10:14:57,810 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n",
      "2025-11-11 10:14:57,810 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n",
      "2025-11-11 10:14:57,871 - rubix - INFO - Assigning particles to spaxels...\n",
      "2025-11-11 10:14:57,873 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n",
      "2025-11-11 10:14:57,884 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n",
      "2025-11-11 10:14:57,884 - rubix - INFO - Convolving with PSF...\n",
      "2025-11-11 10:14:57,887 - rubix - INFO - Convolving with LSF...\n",
      "2025-11-11 10:14:57,889 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n",
      "2025-11-11 10:15:00,996 - rubix - INFO - Pipeline run completed in 3.50 seconds.\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "targetdata = pipe.run_sharded(inputdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(466,)\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "print(targetdata[0,0,:].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set initial datracube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "inputdata.stars.age = jnp.array([age_values[initial_age_index], age_values[initial_age_index]])\n",
    "inputdata.stars.metallicity = jnp.array([metallicity_values[initial_metallicity_index], metallicity_values[initial_metallicity_index]])\n",
    "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n",
    "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
    "inputdata.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-11 10:15:01,085 - rubix - INFO - Setting up the pipeline...\n",
      "2025-11-11 10:15:01,086 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n",
      "2025-11-11 10:15:01,086 - rubix - DEBUG - Roataion Type found: edge-on\n",
      "2025-11-11 10:15:01,088 - rubix - INFO - Calculating spatial bin edges...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:01,100 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:15:01,110 - rubix - INFO - Calculating spatial bin edges...\n",
      "2025-11-11 10:15:01,119 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:15:01,152 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:01,183 - rubix - DEBUG - SSP Wave: (5994,)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:01,195 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:01,258 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:01,317 - rubix - INFO - Assembling the pipeline...\n",
      "2025-11-11 10:15:01,318 - rubix - INFO - Compiling the expressions...\n",
      "2025-11-11 10:15:01,319 - rubix - INFO - Number of devices: 1\n",
      "2025-11-11 10:15:01,395 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n",
      "2025-11-11 10:15:01,396 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n",
      "2025-11-11 10:15:01,397 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n",
      "2025-11-11 10:15:01,456 - rubix - INFO - Assigning particles to spaxels...\n",
      "2025-11-11 10:15:01,458 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n",
      "2025-11-11 10:15:01,468 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n",
      "2025-11-11 10:15:01,469 - rubix - INFO - Convolving with PSF...\n",
      "2025-11-11 10:15:01,471 - rubix - INFO - Convolving with LSF...\n",
      "2025-11-11 10:15:01,474 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n",
      "2025-11-11 10:15:04,414 - rubix - INFO - Pipeline run completed in 3.33 seconds.\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "initialdata = pipe.run_sharded(inputdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adam optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:04,493 - rubix - INFO - Setting up the pipeline...\n",
      "2025-11-11 10:15:04,493 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n",
      "2025-11-11 10:15:04,494 - rubix - DEBUG - Roataion Type found: edge-on\n",
      "2025-11-11 10:15:04,496 - rubix - INFO - Calculating spatial bin edges...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:04,505 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:15:04,516 - rubix - INFO - Calculating spatial bin edges...\n",
      "2025-11-11 10:15:04,525 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:15:04,569 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:04,602 - rubix - DEBUG - SSP Wave: (5994,)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:04,615 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:15:04,654 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "from rubix.pipeline import linear_pipeline as pipeline\n",
    "\n",
    "pipeline_instance = RubixPipeline(config)\n",
    "\n",
    "pipeline_instance._pipeline = pipeline.LinearTransformerPipeline(\n",
    "    pipeline_instance.pipeline_config, \n",
    "    pipeline_instance._get_pipeline_functions()\n",
    ")\n",
    "pipeline_instance._pipeline.assemble()\n",
    "pipeline_instance.func = pipeline_instance._pipeline.compile_expression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "import optax\n",
    "\n",
    "def loss_only_wrt_age_metallicity(age, metallicity, base_data, target):\n",
    "        \n",
    "        base_data.stars.age = age*20\n",
    "        base_data.stars.metallicity = metallicity*0.05\n",
    "\n",
    "        output = pipeline_instance.func(base_data)\n",
    "        #loss = jnp.sum((output.stars.datacube - target) ** 2)\n",
    "        #loss = jnp.sum(optax.l2_loss(output.stars.datacube, target.stars.datacube))\n",
    "        #loss = jnp.sum(optax.huber_loss(output.stars.datacube, target.stars.datacube))\n",
    "        loss = jnp.sum(optax.cosine_distance(output.stars.datacube, target))\n",
    "        \n",
    "        return jnp.log10(loss) #loss#/0.03 #jnp.log10(loss #/5e-5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import optax\n",
    "\n",
    "\n",
    "def adam_optimization_multi(loss_fn, params_init, data, target, learning=learning_all, tol=1e-3, max_iter=500):\n",
    "    \"\"\"\n",
    "    Optimizes both age and metallicity.\n",
    "\n",
    "    Args:\n",
    "        loss_fn: function with signature loss_fn(age, metallicity, data, target)\n",
    "        params_init: dict with keys 'age' and 'metallicity', each a JAX array\n",
    "        data: base data for the loss function\n",
    "        target: target data for the loss function\n",
    "        learning_rate: learning rate for Adam\n",
    "        tol: tolerance for convergence (based on update norm)\n",
    "        max_iter: maximum number of iterations\n",
    "\n",
    "    Returns:\n",
    "        params: final parameters (dict)\n",
    "        params_history: list of parameter values for each iteration\n",
    "        loss_history: list of loss values for each iteration\n",
    "    \"\"\"\n",
    "    params = params_init  # e.g., {'age': jnp.array(...), 'metallicity': jnp.array(...)}\n",
    "    optimizers = {\n",
    "        'age': optax.adam(learning),\n",
    "        'metallicity': optax.adam(learning)\n",
    "    }\n",
    "    # Create a parameter label pytree matching the structure of params\n",
    "    param_labels = {'age': 'age', 'metallicity': 'metallicity'}\n",
    "    \n",
    "    # Combine the optimizers with multi_transform\n",
    "    optimizer = optax.multi_transform(optimizers, param_labels)\n",
    "    optimizer_state = optimizer.init(params)\n",
    "    \n",
    "    age_history = []\n",
    "    metallicity_history = []\n",
    "    loss_history = []\n",
    "    \n",
    "    for i in range(max_iter):\n",
    "        # Compute loss and gradients with respect to both parameters\n",
    "        loss, grads = jax.value_and_grad(lambda p: loss_fn(p['age'], p['metallicity'], data, target))(params)\n",
    "        loss_history.append(float(loss))\n",
    "        # Save current parameters (convert from JAX arrays to floats)\n",
    "        age_history.append(float(params['age'][0]))\n",
    "        metallicity_history.append(float(params['metallicity'][0]))\n",
    "        #params_history.append({\n",
    "        #    'age': params['age'],\n",
    "        #    'metallicity': params['metallicity']\n",
    "        #})\n",
    "        \n",
    "        # Compute updates and apply them\n",
    "        updates, optimizer_state = optimizer.update(grads, optimizer_state)\n",
    "        params = optax.apply_updates(params, updates)\n",
    "        \n",
    "        # Optionally clip the parameters to enforce physical constraints:\n",
    "        #params['age'] = jnp.clip(params['age'], 0.0, 1.0)\n",
    "        #params['metallicity'] = jnp.clip(params['metallicity'], 0.0, 1.0)\n",
    "        # For metallicity, uncomment and adjust the limits as needed:\n",
    "        # params['metallicity'] = jnp.clip(params['metallicity'], metallicity_lower_bound, metallicity_upper_bound)\n",
    "        \n",
    "        # Check convergence based on the global norm of updates\n",
    "        if optax.global_norm(updates) < tol:\n",
    "            print(f\"Converged at iteration {i}\")\n",
    "            break\n",
    "\n",
    "    return params, age_history, metallicity_history, loss_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-11 10:15:04,803 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n",
      "2025-11-11 10:15:04,804 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n",
      "2025-11-11 10:15:04,804 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n",
      "2025-11-11 10:15:04,888 - rubix - INFO - Assigning particles to spaxels...\n",
      "2025-11-11 10:15:04,903 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-11 10:15:05,278 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n",
      "2025-11-11 10:15:05,279 - rubix - INFO - Convolving with PSF...\n",
      "2025-11-11 10:15:05,282 - rubix - INFO - Convolving with LSF...\n",
      "2025-11-11 10:15:05,287 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Array(nan, dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "loss_only_wrt_age_metallicity(inputdata.stars.age, inputdata.stars.metallicity, inputdata, targetdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial parameters: {'age': Array([0.7924467, 0.7924467], dtype=float32), 'metallicity': Array([0.5050313, 0.5050313], dtype=float32)}\n",
      "Optimized Age: [nan nan]\n",
      "Optimized Metallicity: [nan nan]\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "data = inputdata  # Replace with your actual data if needed\n",
    "target_value = targetdata  # Replace with your actual target\n",
    "\n",
    "# Define initial guesses for both age and metallicity.\n",
    "# Adjust the initialization as needed for your problem.\n",
    "age_init = jnp.array([age_values[initial_age_index]/20, age_values[initial_age_index]/20])\n",
    "metallicity_init = jnp.array([metallicity_values[initial_metallicity_index]/0.05, metallicity_values[initial_metallicity_index]/0.05])\n",
    "\n",
    "\n",
    "# Pack both initial parameters into a dictionary.\n",
    "params_init = {'age': age_init, 'metallicity': metallicity_init}\n",
    "print(f\"Initial parameters: {params_init}\")\n",
    "\n",
    "# Call the new optimizer function that handles both parameters.\n",
    "optimized_params, age_history, metallicity_history, loss_history = adam_optimization_multi(\n",
    "    loss_only_wrt_age_metallicity,\n",
    "    params_init,\n",
    "    data,\n",
    "    target_value,\n",
    "    learning=learning_all,\n",
    "    tol=tol,\n",
    "    max_iter=5000,\n",
    ")\n",
    "\n",
    "print(f\"Optimized Age: {optimized_params['age']}\")\n",
    "print(f\"Optimized Metallicity: {optimized_params['metallicity']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-11 10:17:53,403 - rubix - INFO - Getting rubix data...\n",
      "2025-11-11 10:17:53,405 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/jax/_src/numpy/lax_numpy.py:188: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n",
      "  return asarray(x, dtype=self.dtype)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/core/data.py:537: UserWarning: Explicitly requested dtype <class 'jax.numpy.float64'> requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n",
      "  rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n",
      "2025-11-11 10:17:53,446 - rubix - INFO - Centering stars particles\n",
      "2025-11-11 10:17:53,785 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2 for stars\n",
      "2025-11-11 10:17:53,786 - rubix - INFO - Data loaded with 2 star particles and 0 gas particles.\n",
      "2025-11-11 10:17:53,801 - rubix - INFO - Setting up the pipeline...\n",
      "2025-11-11 10:17:53,802 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n",
      "2025-11-11 10:17:53,803 - rubix - DEBUG - Roataion Type found: edge-on\n",
      "2025-11-11 10:17:53,806 - rubix - INFO - Calculating spatial bin edges...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:17:53,840 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:17:53,855 - rubix - INFO - Calculating spatial bin edges...\n",
      "2025-11-11 10:17:53,865 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:17:53,906 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:17:53,960 - rubix - DEBUG - SSP Wave: (5994,)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:17:53,977 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:17:54,039 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:17:54,094 - rubix - INFO - Assembling the pipeline...\n",
      "2025-11-11 10:17:54,095 - rubix - INFO - Compiling the expressions...\n",
      "2025-11-11 10:17:54,096 - rubix - INFO - Number of devices: 1\n",
      "2025-11-11 10:17:54,177 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n",
      "2025-11-11 10:17:54,177 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n",
      "2025-11-11 10:17:54,178 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n",
      "2025-11-11 10:17:54,236 - rubix - INFO - Assigning particles to spaxels...\n",
      "2025-11-11 10:17:54,238 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n",
      "2025-11-11 10:17:54,251 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n",
      "2025-11-11 10:17:54,251 - rubix - INFO - Convolving with PSF...\n",
      "2025-11-11 10:17:54,253 - rubix - INFO - Convolving with LSF...\n",
      "2025-11-11 10:17:54,256 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n",
      "2025-11-11 10:17:57,635 - rubix - INFO - Pipeline run completed in 3.83 seconds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial parameters: {'age': Array([0.15811388, 0.15811388], dtype=float32), 'metallicity': Array([0.05050315, 0.05050315], dtype=float32)}\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "inputdata2 = pipe.prepare_data()\n",
    "\n",
    "inputdata2.stars.age = jnp.array([age_values[initial_age_index2], age_values[initial_age_index2]])\n",
    "inputdata2.stars.metallicity = jnp.array([metallicity_values[initial_metallicity_index2], metallicity_values[initial_metallicity_index2]])\n",
    "inputdata2.stars.mass = jnp.array([[1.0], [1.0]])\n",
    "inputdata2.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
    "inputdata2.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
    "\n",
    "initialdata2 = pipe.run_sharded(inputdata2)\n",
    "\n",
    "data2 = inputdata2  # Replace with your actual data if needed\n",
    "target_value = targetdata  # Replace with your actual target\n",
    "\n",
    "# Define initial guesses for both age and metallicity.\n",
    "# Adjust the initialization as needed for your problem.\n",
    "age_init2 = jnp.array([age_values[initial_age_index2]/20, age_values[initial_age_index2]/20])\n",
    "metallicity_init2 = jnp.array([metallicity_values[initial_metallicity_index2]/0.05, metallicity_values[initial_metallicity_index2]/0.05])\n",
    "\n",
    "\n",
    "# Pack both initial parameters into a dictionary.\n",
    "params_init2 = {'age': age_init2, 'metallicity': metallicity_init2}\n",
    "print(f\"Initial parameters: {params_init2}\")\n",
    "\n",
    "# Call the new optimizer function that handles both parameters.\n",
    "optimized_params2, age_history2, metallicity_history2, loss_history2 = adam_optimization_multi(\n",
    "    loss_only_wrt_age_metallicity,\n",
    "    params_init2,\n",
    "    data2,\n",
    "    target_value,\n",
    "    learning=learning_all,\n",
    "    tol=tol,\n",
    "    max_iter=5000,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-11 10:20:28,016 - rubix - INFO - Getting rubix data...\n",
      "2025-11-11 10:20:28,019 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/jax/_src/numpy/lax_numpy.py:188: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n",
      "  return asarray(x, dtype=self.dtype)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/core/data.py:537: UserWarning: Explicitly requested dtype <class 'jax.numpy.float64'> requested in array is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n",
      "  rubixdata.galaxy.center = jnp.array(data[\"subhalo_center\"], dtype=jnp.float64)\n",
      "2025-11-11 10:20:28,056 - rubix - INFO - Centering stars particles\n",
      "2025-11-11 10:20:28,372 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2 for stars\n",
      "2025-11-11 10:20:28,373 - rubix - INFO - Data loaded with 2 star particles and 0 gas particles.\n",
      "2025-11-11 10:20:28,386 - rubix - INFO - Setting up the pipeline...\n",
      "2025-11-11 10:20:28,387 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n",
      "2025-11-11 10:20:28,388 - rubix - DEBUG - Roataion Type found: edge-on\n",
      "2025-11-11 10:20:28,390 - rubix - INFO - Calculating spatial bin edges...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:20:28,424 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:20:28,440 - rubix - INFO - Calculating spatial bin edges...\n",
      "2025-11-11 10:20:28,450 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:20:28,537 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:20:28,594 - rubix - DEBUG - SSP Wave: (5994,)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:20:28,606 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:20:28,674 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:20:28,731 - rubix - INFO - Assembling the pipeline...\n",
      "2025-11-11 10:20:28,733 - rubix - INFO - Compiling the expressions...\n",
      "2025-11-11 10:20:28,734 - rubix - INFO - Number of devices: 1\n",
      "2025-11-11 10:20:28,815 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n",
      "2025-11-11 10:20:28,816 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n",
      "2025-11-11 10:20:28,817 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n",
      "2025-11-11 10:20:28,875 - rubix - INFO - Assigning particles to spaxels...\n",
      "2025-11-11 10:20:28,877 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n",
      "2025-11-11 10:20:28,889 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n",
      "2025-11-11 10:20:28,890 - rubix - INFO - Convolving with PSF...\n",
      "2025-11-11 10:20:28,892 - rubix - INFO - Convolving with LSF...\n",
      "2025-11-11 10:20:28,894 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n",
      "2025-11-11 10:20:32,060 - rubix - INFO - Pipeline run completed in 3.67 seconds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial parameters: {'age': Array([0.44562545, 0.44562545], dtype=float32), 'metallicity': Array([0.8980868, 0.8980868], dtype=float32)}\n"
     ]
    }
   ],
   "source": [
    "#NBVAL_SKIP\n",
    "inputdata3 = pipe.prepare_data()\n",
    "\n",
    "inputdata3.stars.age = jnp.array([age_values[initial_age_index3], age_values[initial_age_index3]])\n",
    "inputdata3.stars.metallicity = jnp.array([metallicity_values[initial_metallicity_index3], metallicity_values[initial_metallicity_index3]])\n",
    "inputdata3.stars.mass = jnp.array([[1.0], [1.0]])\n",
    "inputdata3.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
    "inputdata3.stars.coords = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
    "\n",
    "initialdata3 = pipe.run_sharded(inputdata3)\n",
    "\n",
    "data3 = inputdata3  # Replace with your actual data if needed\n",
    "target_value = targetdata  # Replace with your actual target\n",
    "\n",
    "age_init3 = jnp.array([age_values[initial_age_index3]/20, age_values[initial_age_index3]/20])\n",
    "metallicity_init3 = jnp.array([metallicity_values[initial_metallicity_index3]/0.05, metallicity_values[initial_metallicity_index3]/0.05])\n",
    "\n",
    "params_init3 = {'age': age_init3, 'metallicity': metallicity_init3}\n",
    "print(f\"Initial parameters: {params_init3}\")\n",
    "\n",
    "optimized_params3, age_history3, metallicity_history3, loss_history3 = adam_optimization_multi(\n",
    "    loss_only_wrt_age_metallicity,\n",
    "    params_init3,\n",
    "    data3,\n",
    "    target_value,\n",
    "    learning=learning_all,\n",
    "    tol=tol,\n",
    "    max_iter=5000,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of iterations: 5000\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Configure matplotlib to use LaTeX for all text\n",
    "#mpl.rcParams.update({\n",
    "#    \"text.usetex\": True,                # Use LaTeX for text rendering\n",
    "#    \"font.family\": \"serif\",             # Use serif fonts\n",
    "    # Here \"txfonts\" is not directly available as a font in matplotlib,\n",
    "    # but you can set the serif list to a font that closely resembles it.\n",
    "    # Alternatively, you can try using:\n",
    "#    \"font.serif\": [\"Times\", \"Palatino\", \"New Century Schoolbook\"],\n",
    "#    \"font.size\": 16,                    # Set the base font size (adjust to match your document)\n",
    "#    \"text.latex.preamble\": r\"\\usepackage{txfonts}\",  # Use txfonts to match your Overleaf document\n",
    "#})\n",
    "\n",
    "\n",
    "# Convert histories to NumPy arrays if needed\n",
    "loss_history_np = np.array(loss_history)\n",
    "age_history_np = np.array(age_history)\n",
    "metallicity_history_np = np.array(metallicity_history)\n",
    "\n",
    "# Create an x-axis based on the number of iterations (assumed same for all)\n",
    "iterations = np.arange(len(loss_history_np))\n",
    "print(f\"Number of iterations: {len(iterations)}\")\n",
    "\n",
    "# Create a figure with three subplots in one row and shared x-axis.\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True)\n",
    "\n",
    "# Plot the loss history (convert log-loss back to loss if needed)\n",
    "axs[0].plot(iterations, 10**loss_history_np, marker='o', linestyle='-')\n",
    "axs[0].set_xlabel(\"Iteration\")\n",
    "axs[0].set_ylabel(\"Loss\")\n",
    "axs[0].set_title(\"Loss History\")\n",
    "axs[0].grid(True)\n",
    "\n",
    "# Plot the age history, multiplying by 20 for the physical scale.\n",
    "axs[1].plot(iterations, age_history_np * 20, marker='o', linestyle='-')\n",
    "# Draw a horizontal line for the target age\n",
    "axs[1].hlines(y=age_values[index_age], xmin=0, xmax=iterations[-1], color='r', linestyle='-')\n",
    "axs[1].set_xlabel(\"Iteration\")\n",
    "axs[1].set_ylabel(\"Age\")\n",
    "axs[1].set_title(\"Age History\")\n",
    "axs[1].grid(True)\n",
    "\n",
    "# Plot the metallicity history, multiplying by 0.05 for the physical scale.\n",
    "axs[2].plot(iterations, metallicity_history_np *0.05, marker='o', linestyle='-')\n",
    "# Draw a horizontal line for the target metallicity\n",
    "axs[2].hlines(y=metallicity_values[index_metallicity], xmin=0, xmax=iterations[-1], color='r', linestyle='-')\n",
    "axs[2].set_xlabel(\"Iteration\")\n",
    "axs[2].set_ylabel(\"Metallicity\")\n",
    "axs[2].set_title(\"Metallicity History\")\n",
    "axs[2].grid(True)\n",
    "\n",
    "axs[0].set_xlim(-5, 900)\n",
    "axs[1].set_xlim(-5, 900)\n",
    "axs[2].set_xlim(-5, 900)\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"output/optimisation_history.jpg\", dpi=1000)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:09,750 - rubix - INFO - Setting up the pipeline...\n",
      "2025-11-11 10:23:09,751 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n",
      "2025-11-11 10:23:09,752 - rubix - DEBUG - Roataion Type found: edge-on\n",
      "2025-11-11 10:23:09,753 - rubix - INFO - Calculating spatial bin edges...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:09,764 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:23:09,779 - rubix - INFO - Calculating spatial bin edges...\n",
      "2025-11-11 10:23:09,789 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:23:09,842 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:09,890 - rubix - DEBUG - SSP Wave: (5994,)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:09,902 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:09,954 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:10,022 - rubix - INFO - Assembling the pipeline...\n",
      "2025-11-11 10:23:10,023 - rubix - INFO - Compiling the expressions...\n",
      "2025-11-11 10:23:10,024 - rubix - INFO - Number of devices: 1\n",
      "2025-11-11 10:23:10,108 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n",
      "2025-11-11 10:23:10,109 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n",
      "2025-11-11 10:23:10,109 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n",
      "2025-11-11 10:23:10,168 - rubix - INFO - Assigning particles to spaxels...\n",
      "2025-11-11 10:23:10,170 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n",
      "2025-11-11 10:23:10,182 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n",
      "2025-11-11 10:23:10,183 - rubix - INFO - Convolving with PSF...\n",
      "2025-11-11 10:23:10,185 - rubix - INFO - Convolving with LSF...\n",
      "2025-11-11 10:23:10,188 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n",
      "2025-11-11 10:23:13,509 - rubix - INFO - Pipeline run completed in 3.76 seconds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 466)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "#run the pipeline with the optimized age\n",
    "#rubixdata.stars.age = optimized_age\n",
    "i = 0\n",
    "inputdata.stars.age = jnp.array([age_history[i]*20, age_history[i]*20])\n",
    "inputdata.stars.metallicity = jnp.array([metallicity_history[i]*0.05, metallicity_history[i]*0.05])\n",
    "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n",
    "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
    "\n",
    "pipe = RubixPipeline(config)\n",
    "rubixdata = pipe.run_sharded(inputdata)\n",
    "\n",
    "#plot the target and the optimized spectra\n",
    "import matplotlib.pyplot as plt\n",
    "wave = pipe.telescope.wave_seq\n",
    "\n",
    "spectra_target = targetdata\n",
    "spectra_optimitzed = rubixdata\n",
    "print(rubixdata.shape)\n",
    "\n",
    "\n",
    "plt.plot(wave, spectra_target[0,0,:], label=f\"Target age = {age_values[index_age]:.2f}, metal. = {metallicity_values[index_metallicity]:.4f}\")\n",
    "plt.plot(wave, spectra_optimitzed[0,0,:], label=f\"Optimized age = {age_history[i]*20:.2f}, metal. = {metallicity_history[i]*0.05:.4f}\")\n",
    "plt.xlabel(\"Wavelength [Å]\")\n",
    "plt.ylabel(\"Luminosity [L/Å]\")\n",
    "plt.title(\"Difference between target and optimized spectra\")\n",
    "#plt.title(f\"Loss {loss_history[i]:.2e}\")\n",
    "plt.legend()\n",
    "#plt.ylim(0.00003, 0.00008)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:13,726 - rubix - INFO - Setting up the pipeline...\n",
      "2025-11-11 10:23:13,728 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n",
      "2025-11-11 10:23:13,729 - rubix - DEBUG - Roataion Type found: edge-on\n",
      "2025-11-11 10:23:13,730 - rubix - INFO - Calculating spatial bin edges...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:13,740 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:23:13,750 - rubix - INFO - Calculating spatial bin edges...\n",
      "2025-11-11 10:23:13,759 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:23:13,789 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:13,848 - rubix - DEBUG - SSP Wave: (5994,)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:13,866 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:13,916 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:23:13,982 - rubix - INFO - Assembling the pipeline...\n",
      "2025-11-11 10:23:13,982 - rubix - INFO - Compiling the expressions...\n",
      "2025-11-11 10:23:13,983 - rubix - INFO - Number of devices: 1\n",
      "2025-11-11 10:23:14,061 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n",
      "2025-11-11 10:23:14,062 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n",
      "2025-11-11 10:23:14,062 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n",
      "2025-11-11 10:23:14,119 - rubix - INFO - Assigning particles to spaxels...\n",
      "2025-11-11 10:23:14,121 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n",
      "2025-11-11 10:23:14,134 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n",
      "2025-11-11 10:23:14,135 - rubix - INFO - Convolving with PSF...\n",
      "2025-11-11 10:23:14,137 - rubix - INFO - Convolving with LSF...\n",
      "2025-11-11 10:23:14,139 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n",
      "2025-11-11 10:23:17,643 - rubix - INFO - Pipeline run completed in 3.92 seconds.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "#run the pipeline with the optimized age\n",
    "#rubixdata.stars.age = optimized_age\n",
    "i = 850\n",
    "inputdata.stars.age = jnp.array([age_history[i]*20, age_history[i]*20])\n",
    "inputdata.stars.metallicity = jnp.array([metallicity_history[i]*0.05, metallicity_history[i]*0.05])\n",
    "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n",
    "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
    "\n",
    "pipe = RubixPipeline(config)\n",
    "rubixdata = pipe.run_sharded(inputdata)\n",
    "\n",
    "#plot the target and the optimized spectra\n",
    "import matplotlib.pyplot as plt\n",
    "wave = pipe.telescope.wave_seq\n",
    "\n",
    "spectra_target = targetdata #.stars.datacube\n",
    "spectra_optimitzed = rubixdata #.stars.datacube\n",
    "\n",
    "plt.plot(wave, spectra_target[0,0,:], label=f\"Target age = {age_values[index_age]:.2f}, metal. = {metallicity_values[index_metallicity]:.4f}\")\n",
    "plt.plot(wave, spectra_optimitzed[0,0,:], label=f\"Optimized age = {age_history[i]*20:.2f}, metal. = {metallicity_history[i]*0.05:.4f}\")\n",
    "plt.xlabel(\"Wavelength [Å]\")\n",
    "plt.ylabel(\"Luminosity [L/Å]\")\n",
    "plt.title(\"Difference between target and optimized spectra\")\n",
    "#plt.title(f\"Loss {loss_history[i]:.2e}\")\n",
    "plt.legend()\n",
    "#plt.ylim(0.00003, 0.00008)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a figure with two subplots, sharing the x-axis.\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, gridspec_kw={'height_ratios': [4, 1]}, figsize=(7, 5))\n",
    "\n",
    "# Plot target and optimized spectra in the upper subplot.\n",
    "ax1.plot(wave, spectra_target[0, 0, :], label=f\"Target age = {age_values[index_age]:.2f}, metallicity = {metallicity_values[index_metallicity]:.4f}\")\n",
    "ax1.plot(wave, spectra_optimitzed[0, 0, :], label=f\"Optimized age = {age_history[i]*20:.2f}, metallicity = {metallicity_history[i]*0.05:.4f}\")\n",
    "ax1.set_ylabel(\"Luminosity [L/Å]\")\n",
    "#ax1.set_title(\"Target vs Optimized Spectra\")\n",
    "ax1.legend()\n",
    "ax1.grid(True)\n",
    "\n",
    "# Compute the residual (difference between target and optimized spectra).\n",
    "residual = (spectra_target[0, 0, :] - spectra_optimitzed[0, 0, :]) #/spectra_target[0, 0, :]\n",
    "\n",
    "# Plot the residual in the lower subplot.\n",
    "ax2.plot(wave, residual, 'k-')\n",
    "ax2.set_xlabel(\"Wavelength [Å]\")\n",
    "ax2.set_ylabel(\"Residual\")\n",
    "ax2.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"output/optimisation_spectra.jpg\", dpi=1000)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate loss landscape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "import optax\n",
    "\n",
    "def loss_only_wrt_age_metallicity(age, metallicity, base_data, target):\n",
    "\n",
    "    # Create 2D arrays for age and metallicity.\n",
    "    # For example, if there are two stars, you might do:\n",
    "    base_data.stars.age = jnp.array([age*20, age*20])\n",
    "    base_data.stars.metallicity = jnp.array([metallicity*0.05, metallicity*0.05])\n",
    "\n",
    "    output = pipeline_instance.func(base_data)\n",
    "    #loss = jnp.sum((output.stars.datacube - target) ** 2)\n",
    "    loss = jnp.sum(optax.cosine_distance(output.stars.datacube, target))\n",
    "    return loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Number of grid points\n",
    "num_steps = 100\n",
    "\n",
    "# Define physical ranges\n",
    "physical_ages = jnp.linspace(0, 1, num_steps)         # Age from 0 to 10\n",
    "physical_metals = jnp.linspace(0, 1, num_steps)    # Metallicity from 1e-4 to 0.05\n",
    "\n",
    "# Use nested vmap to compute the loss at every grid point.\n",
    "# Note: loss_only_wrt_age_metallicity takes physical values directly.\n",
    "#vectorized_loss = jax.vmap(\n",
    "#    lambda age: jax.vmap(\n",
    "#        lambda metal: loss_only_wrt_age_metallicity(age, metal, inputdata, targetdata)\n",
    "#    )(physical_metals)\n",
    "#)(physical_ages)\n",
    "\n",
    "# Convert the result to a NumPy array for plotting\n",
    "#loss_map = jnp.array(vectorized_loss)\n",
    "\n",
    "loss_map = []\n",
    "\n",
    "for age in physical_ages:\n",
    "    row = []\n",
    "    for metal in physical_metals:\n",
    "        loss = loss_only_wrt_age_metallicity(age, metal, inputdata, targetdata)\n",
    "        row.append(loss)\n",
    "    loss_map.append(jnp.stack(row))\n",
    "\n",
    "loss_map = jnp.stack(loss_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ucfsc6ZGenYJPhp75nIL3DND3d1YEc3sjQTu5azcqOxt9nwHx56Ut4WQMToOpHhrbk9QsplWt8RIEQRCBw8waLyGGHC9BEAShiQIGJzlen9P2tZIWkCWFO5jr8IjEHfxpqenwFwok1yFsw2TXoee8XpyQXYevbPXUzslk1yHsq+OanJBUh1GMvm965zI+rrH3v2EOyXUYxZv5VP11/P1EKJBdh3dzS67DcF+T192WaLzjNXoAtMbrCbrjJQiCIHwOrfGKIcdLEARBaGImuIoQQ47XLCY27YtwlwVFMiEvUSp80gsdayyqMSU3SY2JElfoiJD1YfSyv8Y1E4GsJ6JaNJen+YyP600ULhfhHoCoX1V/E38/T3KznuhnkdysJ/LZXW42k2ykNaJcPoz2ITxDixgEQRCEJs7LwVVGD4DWeD1Bd7wEQRCEJk7WlHvZSB+A1ng9QY63laGWiFtu7zQhL+rFqITpHuXKS4/qhB/+lYGB5lKwyyZdcr25+UJFduYxk3BD7/yiKGdvlhB4Gdpo0g1vEm6EW6QzSc3+Ibw+RQRBEAQRZOiOlyAIgtBE8WLve0v79glyvGL8JMuKaCkhBt+y6XxTH5WkzMu13LBm8zaLIqF15RA2ISG7oydC1hsZWDWHwcjnUJSdG8Y1GEFscmnCTPSz2cj1QMvO4YDCGg6jfQjPkNRMEARBaOKe9U3vAVBUsyfojpcgCILQxJs0q43tKapZDDlegiAIQhNv8nx7kxc83CDHaxJVQQSDa0PijFTu1RcMGqVrbu1144b5uNe4LFaGsx3pXLMTtdOzLmx0vRcwvuYbiPVePXi3juz9mmuorPfy6F37NVPn11NhBVr/JXwBOV6CIAhCEzNSMyGGHC9BEAShiTflEZ1+sqUtQY7XG4L4g85o5ipFJcX655+E6h+mDmnafeuUrqIOPpKdAXNSsBnZ2dN8gdm+JJBv9WwH86HsbHRu1Tgesp7pwcyWo4b5w2vbEfNijZfRGm+L0HYigiAIQhPaTuQf6I6XIAiC0MTJZGGgm7hPw/9pO5EYcrwBwF/h9bz8qqc2ry8LJpiVHkX4q56vCHcpuBGjtXb11vXVIxEHOoraaPSxp89zW6jzq1eC9hT9TBCeIMdLEARBaKJAEv5AEfdp+2vfZiHHSxAEQWhC24n8Azles/ghaYZ7G39U+1CN2Uxu5aS2ACfT0IMvI5yFcxguThB6srM7/qrtq57f+yUIs5HPvqrzazbyuS3h3Rov3fG2BDlegiAIQpMGqZnKAvoacrwEQRCEJooXCTRojbdlyPEGEW+inc0kLJcD/ENUr+woko515WE2KDt7GktoXwjKzjy+nEM9nznpV/RZbc2Rz+EuPRO+gRwvQRAEoQmt8foHylxFEARBaKJA9uoAKHOVJ+iOV4DUCuIDRHmbRYkyAp232RtZMBCYkh5NyM6AfxJi+EvaFs8XepHPem3xZbnBcJCdnUwynCiksT1lrhJDjpcgCILQxLvqRCQ1twQ5XoIgCEIThcke94Zr9yHH2xLkePXgrrTw8pixKn1+I5jRy0bxJsrYVxHOLdlixCZv8jz7Ow+zWWmbx0zkc8Pc3ud9Nlt60Ey5QW9kZ4IwAjlegiAIQhOSmv0DOV6CIAhCEwXGqzC1/ZAz85Dj5fCXQsv//mM6cjV7fC0EZWRflgj0Ri72xziBiHwORPk/byKezcynntt76dfsZyrQsnNbhd8eZKQP4RlyvARBEIQm3iXQIMfbEuR4CYIgCE2oSIJ/IMfrBb5KriES0ERyNBEcfCU7A/4p/+eN7Mzjr/nUc3uf99nTMoweGdrM3GbzfBOEFuR4CYIgCE1IavYP9A4RBEEQmjRuJzJ6hBqlpaUYN24cMjMzMWjQILz//vtBtYfueE0iCaQuSt7iP4xKv+5rTmaipU3n+vVD+T9PmYX8kevZ7HzquUMj77PeucMt4llhkqlSpKFCREQECgoKkJWVhbKyMgwdOhSTJ09Gu3btgmNPUGYlCIIgQh7FizvYUNxOlJaWhrS0NABAamoqkpKScPbs2aA53tB7hwiCIIiQoDFXs9HDKBs2bMCUKVPQuXNnSJKElStXNmtTWFiIjIwMREVFIScnB1u3bvXqmnbs2AGn04n09HSv+vsCuuMVIJKQvYGPUlZCRIIWlQ4MB0TbHXwlQftSdlbNEYDyf2Yin72ZTz1365WdCXNUV1dj8ODBmDVrFm677bZmr69YsQJ5eXlYvHgxcnJyUFBQgIkTJ+LAgQNITk4GAGRlZcHhcDTru2bNGnTu3BkAcPbsWUyfPh1Llizx7wW1ADlegiAIQhMnJOGPMU99AKCyslJ13mazwWazafaZNGkSJk2aJBxz0aJFuP/++zFz5kwAwOLFi7Fq1SosXboUc+bMAQAUFRV5tKuurg5Tp07FnDlzMHLkSL2X4xfoJxtBEAShiRmpOT09HR06dHAd+fn5Xtlgt9uxY8cOTJgwwXVOlmVMmDABmzdv1jUGYwz33HMPrr32Wtx9991e2eFL6I7XG3wkzfLSGCXN8A5fRpn6SoJuDbIzT2jO533Si4a5vS83aFbybks4IV5+8NQHaNjCExcX5zovutttidOnT8PpdCIlJUV1PiUlBfv379c1xjfffIMVK1Zg0KBBrvXj//f//h8GDhzolU1mIcdLEARBaOJNsFRj+7i4OJXjDSajR4+GooTOj6igS81GI9UKCgrQp08fREdHIz09HY888ghqa2sDZC1BEET40Ji5yugBANnZ2cjMzERhYaEpG5KSkmCxWFBeXq46X15ejtTUVFNjB4ug3vHqiVTjeffddzFnzhwsXboUI0eOxMGDB3HPPfdAkiQsWrQoCFdAEGIsdU4MWPMTen9Rjujz9bgUH4lD16Zg38RUOG2WYJtHEH5l27ZtPrnjtVqtGDp0KNatW4epU6cCABRFwbp16zB79mzT4weDoDpePZFqPJs2bcKoUaNw1113AQAyMjJw55134ttvv/XaBm/2nKnQsfzBfLiWq7bXWNYm0Vpl88xOeuwwtnXDm60eRmvq+iurkDdrv72/LMctTxYhutIBRQZkBVBkoO/n5Zjw/F58siALR8d30p7P4NqopzU4fxRACPR86rndihb4uc6v2flaO8yL6kTMi+pEVVVVOHz4sOt5cXExioqKkJiYiK5duyIvLw8zZszAsGHDMHz4cBQUFKC6utrlO1obQZOavYlUGzlyJHbs2OGSo48ePYpPP/0UkydPFs5TV1eHyspK1UEQ/qT3l+W44+HtiLrYsKdQvvxd3fj/qIsO3PHb7ej1ZblgBIIIDQIlNW/fvh1DhgzBkCFDAAB5eXkYMmQInn76aQDAtGnT8NJLL+Hpp59GVlYWioqKsHr16mYBV62FoN3xehOpdtddd+H06dMYPXo0GGNwOBx48MEH8fjjjwvnyc/Px/z5831qO0GIsNQ5ccuTRQDEwe8SA5gE3PzE93j1y2tJdiZCFjO5mo1IzePGjQNrIcH97NmzW6207E7Qg6uMsH79eixcuBB/+ctfsHPnTnz00UdYtWoVnnvuOWGfuXPn4sKFC66jtLRU2FaWmOuQJHAHUx16YExyHUZpLD7tOpik+Q9AfN771G3u/fk5/FV9xP2XsqZN3PthdEytX+O+oNnfCRL6rilDdKWjxR1nEgOiKx248rNy3TZ583dtTICgd0uI2RSAgZ/P++o4on8//pqvNdJWqhOFGkG74/UmUu2pp57C3Xffjfvuuw8AMHDgQFRXV+OBBx7AE088AVlu/gf3lC2FIHxN3y/KXGu6LaHIQJ8vyvHDlCv8bxhBeIGZO97s7GxYLBbk5uYiNzfXH+a1WoLmeL2JVKupqWnmXC2WBpmuJZmCIAJB9Pl6XU4XaHDO0eft/jWIIIKEr6Ka2yJBjWpuKVJt+vTp6NKliyvV2JQpU7Bo0SIMGTIEOTk5OHz4MJ566ilMmTLF5YBbE+riCSajnfVEL6siOk1NpwtRBiD3a+WjS/VEJhuNdm5mlw4p09uo6EvxkYbueC/FW5vZpKvGsBeRwWYKIARzPh79cxvLPmUm01VbRoFsuMxfKJYFDDWC6ninTZuGiooKPP300ygrK0NWVpYqUq2kpER1h/vkk09CkiQ8+eSTOHHiBDp16oQpU6ZgwYIFwboEglCx/9pU9Pu8TFdbWQEOXNs6ozKJ8MDJJFUlM719CM8EPWWkp0i19evXq55HRERg3rx5mDdvXgAsIwjjvHfFjRhvO4y4umqPd+NMAmrbR2DfDWkBtI4gjEFrvP4h6I43HOCXn/VEOXv6oBvdzC6qu+spEYfhxAJMW8ozK9MZlZ1Vc5uores+twh3m3bvGYxte0bg9zdHYslHz4FBe0tR45/kkwVZmluJRHML3wMPtvqjAEKg5/M0t78LLnhaFgkHmBe7I9jl9rTGK4bEeILwAbv3DsK2XSMAAGdvk/HeK8NQ2z4SQMNaLv//2vaRWPG/2Tg0jmRmgghH6I6XIEzyw75B2LazobD2kEHbMGTQDhxEKhZ90QmZa06i7xdlrlzN+69Nxd4b0uC0WUzflROEvzGyJ5vvQ3iGHK8AvYkyzKDojGoWvSbqr4r65brqjQZWybcCSdpofmZI4ihTPflzDUf96vzHb8b5OZmMg4f7YuuOBqc7eOAODBq4q+l1mwW7p1yB3YJ9uoHIRe2rPMze1OAN9Hw8RiVos3V+2yoKM37NyuU/Ha3xiiHHSxBeUnysJzZ/OwYAMCCzCIMH7giyRQThW8zU46U1XjHkeAnCC348kY6vN40HIKFP7z24KmsrpPC6GSLCACNpWvk+hGfI8eqgWSQyr6AKPmOiSGY9so17G1F/9UZ1LkEF+Pa8POkUzOGWDYxrpwdRhLNQNvZQas2o7Kwax4ukF3q+JNxl4LLyNKz/+nowJqNHxiHkZH+j+hx4ioj2VXS2N++BOVnWeCSyr+bj8dfcZiKf2zK0j9c/UFQzQRig4nQnrFs/EU5nBNKvOIZRI9bTnS5BEIagO16C0Mm58wn4/MtJcDisSEv9EWNHr4MsU2Qy0XYxs8ZLiCHHG2D4r2leQtb79a2OFNY+L5Zotfu6y5midkYjnPXmv9Wb07ml+XwpQfMokFBd3Q5rv5gMuz0KSUnlGHvNWkgWxSUR642ONhOdbTTyWfcchmVZ8S2+v/Mw+3JuM8k3GuZr+zJ0Y1lSo30Aimr2BDlegmiBujor1n05CZcutUOHDudw7bjPEBnpCLZZBOF3mBfBVexye4pqFkOOlyA84HRasH7DDbhwIQHR0dW4bvx/YLPVBdssgggIZnI1E2LI8QrwZQINUX5mRTCFe3tFFVGqfV5PMg0+lk4c4axup8rjrEN29pQoQzSfqiygjqQGvpKgPWGRFDAGfLNpHE6dSkNkpB3XjV+Ndu2qtW3yIme04ZzMAZhDNV+rKwXom5zT3pQebKvQGq9/oHeIIARs33k1jpf0gCw7MfaatUhIOBtskwiCaAPQHS9BaLB330Ds3z8QADByxFdIS/0pyBYRROAhqdk/kOPl8GnJL9WHr2lcsewsTrIhlJF1RDgr3CZTXpIURi5Df4RuS+hNrCGKyDaa1MCoBC3i+PHu2LHzagBAVtZWdO1WrCnZepMz2h85mY1GPns1hw9lYB5/RCL7cj539M7fVqDMVf6BpGaC4KioSMbmzdcAAK68cg/69dsdZIsIIng03vEaPYCG7USZmZkoLCwM8lWEHnTHSxCXqayMw1dfXQ9FicAVVxzHVVd9S1mpiLDGjNRM24nEkOP1BoNKLNMRvSySoD314VHLcdoRy6Jygc3HEuVxNhbhLJSNmVqu49vpSgTiIwmap7Y2CuvXT4TdHoXExAqMHPlli1mpvMqX7OeczJ5kPl/mfXbNZ1KW9VUksjfz8fir7GFrh9Z4/QNJzUTY43BY8PXX16GqKg7t2l3E2LFrEBFhrFAEQRCEXuiOlwhrGAO2bLkGZ06nIDKyDuPGfYbo6Npgm0UQIQHd8foHcrwC+LU992QaaomY76T50DCeopr1RC+L5FpRuUC4S7Qe8ji3hGhcT5HFQnuNJsrworRbUVE2fiztDll2Ysw169A+rtLjF4eeCGmzZQH9Eflsdg4e3fMZzosc2FKAZucOh0QRDMajlKlsSMuQ4yXClkMH++HA5b26w3O+RnJyWZAtIojQgu54/QM5XiIsOXEiHTt35gAABg7ajoyMo0G2iCBCD3K8/oEcr58QRR/rjV4Wwcs+ko4IZ4XTwsXRwx7mMxjhLJIw1XK5B4mOacuCvpSgz57piE3fjANjMnr0PIDMzO/F9rhhNkmHHonYTOSzN7KznjlMzxcipQDNzq1X9m4rkOP1D21/kYIgOKqr22HjhglwOiORknoCw4Ztor26BEEEFHK8RNhgt1uxcf0E1NVGo0P8WYwYvb7FvboEEc5Q5ir/QFKzAK/KAqrkVO3+onE9yTN65Gk9iSfE0cPuv79EiTL4NsYSa/CynjdlAY1en/v4ilPGpq/Ho7Kyoa7u6LGfIzKyXldpN09R0XrmFuGPnMzeRFQbncOn8wW4FKBo7kDM1xphTDK8PNbYnjJXiSHHS7R5mCJh65YxqDiVhogIO0aP/RwxMTXBNosgQh4qkuAfyPESbRrGgF07rkZpSXdIshMjRq9HfMK5YJtFEK0CCq7yD+R4iTbNnt1DcPRwHwAMOSO+Rmoa1dUlCL2YkZoJMeR4OYS/1Dx9jgSZq8zgaeVItdapykTlfRar5hPya4/GchYL6/x6WE/TU49XtN7L476eenB/Jg7sHQQAGDJsM7qkH/e4vizCm4xYIluFW55CsQaviS1H3synmjuINXjNzkcQLUGOl2iTHDvaC7uLsgEAAwbtQI9eh4JsEUG0Pkhq9g/keIk2R8mx7tixbQQAoHffH3Blvx+CbBFBtE5IavYP5Hh14J5ggReu+A+Z5KftBfwvSIvhvtpZrHg8b3kRSceqEbixdGRjcttCItpqJKzny+EuQf9Y0g3bvh0NMBkZPQ6i/6CdHhNkmMlE5Y0EravesIkavAGRgU3Ox9MWa/62JZgXd7zkeFuGHC/RZjhR2hXbt4wBmIxu3Q8ha9gWykpFECZgcKvAprMP4RlyvESb4KcT6di2+RowJiM94wiGZG8mp0sQJlEgQaJ9vD6HHK8AbzJX8b8M1fV8fWDQZUTRy3zBBD31bYURzoAXGapabm/xOJ++DFdNbdQSdNlPXbBtU4PTvaLrUQzNVudf9qa+rq8kaLOys2pcEwUWAOPZrryJfDZav9mM5A0Et+ZvuMnOhO+gXM1Eq+an0nRs++YaMMWCLunHMDTnG0iUf5kgfEJjcJXRI9Q4f/48hg0bhqysLAwYMABLliwJqj10x0u0WkqPdUfR1hFgTEbn9GMYdvXXVPSAIHyIwiRh+VFPfUKN9u3bY8OGDYiJiUF1dTUGDBiA2267DR07dgyKPWHveHV9SJrJgILkEz76zne3SVUR12DBBF6OlgVtmqGjvq6eufXIzg2viCOeXSO5SdDHj/TC7h3DAUhIzziCrOwtYLKkKSXqlXvV8/lGgnaPfBbZYnhckzV4/VZ310d1fnkCXfPXG9m5rcKYF8FVIfjb12KxICYmBgBQV1cHxhhYEA1t+58cos1x9GAf7N6RA0BCRq8DyBq+meRlgvADgZKaN2zYgClTpqBz586QJAkrV65s1qawsBAZGRmIiopCTk4Otm7damiO8+fPY/Dgwbjiiivwhz/8AUlJSYbt9BXkeIlWA2PAob39sbdoGACgR5+9GHjVNopeJgg/ESjHW11djcGDBwtr965YsQJ5eXmYN28edu7cicGDB2PixIk4deqUq03j+q378dNPDfnZ4+Pj8d1336G4uBjvvvsuysvLvXtTfEDYS80iJMFjvaiTbOho78WHVZS3GYJIZkUg/XqKqNUjHeuRptWynFr6E0U882M5FRn7vhuCY4f6AgCu7P8demf+AAZJ8/3Vk3wD8J30q7e90aQbZhJuAMajn/0R+ezNfHrm1ju/ryKfgfCowesrKisrVc9tNhtsNptm20mTJmHSpEnCsRYtWoT7778fM2fOBAAsXrwYq1atwtKlSzFnzhwAQFFRkS67UlJSMHjwYHz99de4/fbbdfXxNXTHS4Q8TqeMom9Hupxu5uAduLL/D3SnSxB+pjFXs9EDANLT09GhQwfXkZ+f75UNdrsdO3bswIQJE1znZFnGhAkTsHnzZl1jlJeX4+LFiwCACxcuYMOGDejTp49X9vgCuuMlQpr6+gjs3DQGZ06lQpKcGDT8W6R3Kw62WQQRFpgJriotLUVcXJzrvOhutyVOnz4Np9OJlJQU1fmUlBTs379f1xjHjx/HAw884AqqeuihhzBw4ECv7PEF5Hg5RFJes2Qa/J2W0vRElUBDIEnxkrJaXmaC8wATSMeqpBmCZBqiRBneRDibSayhNx80LzvXXIrGjq/H4uKFBFgi6jF05NdISinXJR3qTU5hRvrl0StB64l+NiM7NxvXz+UGTcvAPiw9qGduM5HPQPiVAmxwvEZzNTf8Py4uTuV4g8nw4cN1S9GBgKRmIiSputgeW764HhcvJMBqu4Srx61DUkrwgiEIIhwxE1yVnZ2NzMxMYcCUXpKSkmCxWJoFQ5WXlyM1NdXU2MGC7niJkOPMqWQUbRqF+nobYmIrMWzMV4iNrQq2WQQRdjAYT0/Q2H7btm0+ueO1Wq0YOnQo1q1bh6lTpwIAFEXBunXrMHv2bNPjB4Owd7wiyU6vlKeC68I4CVoRyNGqiGNepnZPoKEqPcjZKMrPDMF5lWQtjjL26tpbQBwdrZb5jh/pif27hoIxGR0ST2Po6K9htdUJJVCjsrM7ehNfNGJWBtYVpayzBKIWniV978sNivAkCfsyB7Se+czMrTcZBuVn1k92djYsFgtyc3ORm5vrsW1VVRUOHz7sel5cXIyioiIkJiaia9euyMvLw4wZMzBs2DAMHz4cBQUFqK6udkU5tzbC3vESoQFjEg5+PxglhxoiDVPTj2Ng9rewWOiLjiCChTf7chvbG7nj3b59O8aPH+96npeXBwCYMWMGli1bhmnTpqGiogJPP/00ysrKkJWVhdWrVzcLuGotBH2N12g2kvPnzyM3NxdpaWmw2Wy48sor8emnnwbIWsIf1Nsjseub0S6n2zNzNwYM30JOlyCCDfPyMMi4ceNcEcf8sWzZMleb2bNn4/jx46irq8O3336LnJwcU5cWTIJ6x9uYjWTx4sXIyclBQUEBJk6ciAMHDiA5OblZe7vdjuuvvx7Jycn44IMP0KVLFxw/fhzx8fH+NdSDfKeOZJa0XxBEMquTbGjL0c1eMygDKwI5Wp30QtxHhYkIZ5FEePF8B+zeMhKXqmMhy070z/4WqemlAMTRznpkZ5V1HuRBw6X5DEYfN7dFO0Jd1MZfc6vG9YMkDBhPxqFnbr21Xv0xNxAe+ZlVeJOJKgSLJIQaQXW8erKR8CxduhRnz57Fpk2bEBkZCQDIyMgIpMmEDykr6Yp9O4dCcUYgKqYKg0ZsQoeEc8E2iyCIy5jZx2tkjTfcCJrjbcxGMnfuXNe5lrKRfPLJJxgxYgRyc3Pxz3/+E506dcJdd92Fxx57DBaLRbNPXV0d6urqXM/d05gRgUdxyji0exB+PNIbANAx5ST6D/8Wkdb6IFtGEARPoNZ4w42gOV5vspEcPXoUX3zxBX75y1/i008/xeHDh/Gb3/wG9fX1mDdvnmaf/Px8zJ8/X5dNfKKMZkkzdMBHMvP5DFVyryrauUm2UifJUM/NRzXzPy/0JNOwQFvaFEc4A7LkhL+oqYrFnq05qDqfAADo1ncvembucb1dRvPq6kn40Pz6fFSaz2Dks/scZvI+m51bNC6PaVnWRFS0nrk9zR/MuQmiJVpVVLOiKEhOTsYbb7wBi8WCoUOH4sSJE3jxxReFjnfu3LmuCDmg4Y43PT09UCYTHGUl6ThYdBWcjkhEWuvQd+g2JKWVUc5lgghVmGR8zZbWeFskaI7Xm2wkaWlpiIyMVMnK/fr1Q1lZGex2O6xWa7M+nipiEIHB6bDgQFEWykoyAADxSRXIzP4Wtuja4BpGEIRHaI3XPwTN8XqTjWTUqFF49913oSgKZLlBDjp48CDS0tI0na6v8HhHxv+6Y6Lzgqhmrr1TIBu7t1PJy1wbPck0ROUC3aVGj7mbtSb3EOFcdaEDDmwdjktV7QEwdOu3F9377lO9p/pK32lHOKvs1pln2Gj0s68kaMBcEgwzyTfc5xaNK8KMHA0EPklHqMzd6jGRuorWeMUE9ZOTl5eHJUuW4J133sG+ffvw61//WpWNZPr06argq1//+tc4e/YsHn74YRw8eBCrVq3CwoUL6ddUCMIY8NORnvhu/ThcqmoPa1QNBo/ZgIx++0laJohWgplczYSYoK7xtpSNpKSkxHVnCzTUd/zss8/wyCOPYNCgQejSpQsefvhhPPbYY8G6BEIDe60Nh3dehfOnGv6OCakn0W/odkTa7EG2jCAIw/g+g2zYE/TgqtmzZwul5fXr1zc7N2LECGzZssUvtsg6o5r511StBLmamaJ9no9qlmVxWUA+ylmUTMNX5QIbBuYji41FOJ8tS8HhnVfBYbdBlp3IGLgbad2PIkIWz6cnGpmXgZ2CX9SiJBvu6I1+1ppb3d6LfMkmkmDoSb7hqY8nGdoXNnnCX0k6VLYEOGGH3rzR4Qqt8YoJuuMl2gaKU8bxPf1RXtwDABATdwF9sreiXdzFIFtGEIS30D5e/0COlzBN9YU4HN4xFJcuNvwjS+t5GN0y9yIiwhFkywiCMIWZuoCEkLB3vHqSNOhOpqGKZObkYV5e5uVePlpZ0ZaHATfpWCAp8z20c3ipZWqz+YQbxgPKj/bAiX19wRQLIm216DlkF+JTTl3uq53D2T0qWSXncS+JknyI/mZ6Ip+bzcdhVIJW9ZVajlZuaOd9Egxv5F4zuajN2KR7PhMSdDNbdEa1t2SH3rn1SuCtGwkwfJ3h8L6YI+wdL+Ed9lobju3KQmVFQzGL+JQydB9SBJutroWeBEG0GuiO1y+Q4yUMc74sBSXfDYLDboMkO9F1wB4kZxyjbUIE0dYgx+sXwmAHOOErFIeMku8H4Oi2bDjsNkTHXUDm2A1I6U5OlyAINdnZ2cjMzERhYWGwTQk56I6XQ7Qm1cypiJwMEz3WzmLFr+vKsnj9lc9KxQSP+axUwrVcVXMPmatUa64Nv80uXWiP0qLBqK1qDwBI6XkEXfruh6yzWL1ovRfwsA1I+Mu55W0/oi1H7ujdgtQ0s7mtSP4ogKB3HdfMliA926B8Op8Xa66quU2s9xIcJnI1U1SzGHK8hEcYA86WpuOnPf1cAVTdhhQhPrki2KYRBOFnzORqJsSQ4yWEKE4ZJ3/IxLkfrwAAxKWUI2NwESJsVDeXIMICWuP1C+R4/QWfuUqVeYpro0dChls9X64/LxczQXuRpNzS1qK66hj8uDMLdRcbihuk9DmI1F6HIUktZE0SFE/wVP9XtNVIJBeK59cXsmBUkjYjR3vaiqRnC5JRCdqb7T1mthzplZ2N4o28bFRSDsQcrR4qC+gXyPESzagsS8ZP3w+E4oiAxVqH9CHfITbpLAVQEUSYITFV+IjuPoRnyPESLpgi4dSB3jh3LAMAEJ1wDl2vKkJkFO3NJQiC8BVh73g9ZThqxD1zlfDOTzQUJzurCikIavO6ByeoZGRFUDiAOy/JTedFdXrdI5wddVacLBqES+cSAACJ3Y+h05WHEBnh5PqLI5M10SE76x5X8J7rkaA9zycY109ytEiG1lOIQU8NXk/Sr56xjMrOetEzlr+kXz3jeiMhe2Nvq8PEGi8VSRDjteO12+0oLi5Gz549ERER9v67VVN7oT1+2pUFR20UZIsDaYN+QPvUU8E2iyCIYEPbifyC4QQaNTU1uPfeexETE4P+/fujpKQEAPDQQw/h+eef97mBhH+5+FMKSr/NhqM2CpHtqtF1xLfkdAmCaIB5eRAeMex4586di++++w7r169HVFSU6/yECROwYsUKnxoXsjRGHLjLZ42/DpsdcB1MkZoOcC9dLr/V0qGoDnBH03l+XKVZn4bDqUg4faAXTu0eAKZYEJN0Guk5WxHZ7pKwjwLuYDJ3CNoL+3o6ZM3DySTNQzWu6NBjn047jNrnjthG7XFVfTn7RDghqw4ResYKRWQw1SHCIimuQ89YesZxP8ICcrwAgHfeeQerVq1yPf/jH/+I+Ph4jBw5EsePHzc8nmHHu3LlSrz22msYPXo0JG6xs3///jhy5IhhA4jAo9RHoHznYFw41g0AkNC9GJ2vKoIl0ljRe4IgiHBg4cKFiI6OBgBs3rwZhYWFeOGFF5CUlIRHHnnE8HiGF2crKiqQnJzc7Hx1dbXKEROhSX11DE4VDYSjJgaS7ERS/32I71wWbLMIgghFKIEGAKC0tBS9evUC0HDz+fOf/xwPPPAARo0ahXHjxhkez7DjHTZsGFatWoWHHnoIAFzO9s0338SIESMMGxBseJlJJDk1i2pWPdHxKRPkalYn0OBOuyfQ4KKiJYt2Egy+vyqBBjdOXUVHnN6dCeaMgCWqFp0G74YtrqpZFK4sGEuFKmLZRLSzO7qTbniez2jSC0Bvkg7f2AfoTRaiHfmsN4LbTLILf0U48/gy4tjMWHqlYz31u9sUlEADABAbG4szZ86ga9euWLNmDfLy8gAAUVFRuHTpkuHxDDvehQsXYtKkSdi7dy8cDgdeeeUV7N27F5s2bcJXX31l2ADC/zAGVP/YGZWHegKQYIs/j06Df4DFSqkfCYIQQwk0Grj++utx3333YciQITh48CAmT54MANizZw8yMjIMj2d4jXf06NEoKiqCw+HAwIEDsWbNGiQnJ2Pz5s0YOnSoYQMI/8IUCRcO9kLloV4AJLTrfBLJV31HTpcgiJah4CoAQGFhIUaMGIGKigp8+OGH6NixIwBgx44duPPOOw2P59UG3J49e2LJkiXedG01yB7UEnfpWROm/VglKXOqFZOafgMpbnIWv3YuyumscAM3JtNQHBZU7e0D+7kEAAxxPYvRruuPcEJS5XBuJlsKygcKMSM7w02y1VEW0KzEy2MuD3PLcq+7faLEHGZkZ55mJSUFuZ5F+Z2NSsrukdNG5Wyz5f98RajYQYQm8fHxeO2115qdnz9/vlfjGb7jrays1DwuXrwIu93ulRGE73FeisKFXYNgP5cASXYiYeBexHb7kfItEwQRELKzs5GZmYnCwsJgm2Ka1atXY+PGja7nhYWFyMrKwl133YVz584ZHs+w442Pj0dCQkKzIz4+HtHR0ejWrRvmzZsHRaFfjcGi/nwczu8cBGdNDGRbHToOLUJUpzPBNosgiFaGBHXaAl3H5b7btm3D3r1720S6yD/84Q+orKwEAOzevRu///3vMXnyZBQXF7sCrYxgWGpetmwZnnjiCdxzzz0YPnw4AGDr1q1455138OSTT6KiogIvvfQSbDYbHn/8ccMGBRpeWhNJyF7dJfJD8bmaFYFszHVolquZl4W5/k7OsEZJuK68Ey4d7gEwGZb2FxE/YC9kWz2cigyLrC0vepKTZU721PVTyqTsLBrLrMSrbtdyHmZRDm+RDKxq40G6FcnF/pCdPdliVHY2G0Wtq+yhQO7Vm/vaKfibi8YS/f08yc568ny3KSiqGQBQXFyMzMxMAMCHH36Im2++GQsXLsTOnTtdgVZGMOx433nnHbz88su44447XOemTJmCgQMH4vXXX8e6devQtWtXLFiwoFU43rYCY0Dt8a6o+7ELAMCadBqxfQ7DEukIsmUEQbRaaB8vAMBqtaKmpgYA8Pnnn2P69OkAgMTERNedsBEMO95NmzZh8eLFzc4PGTIEmzdvBtAQ+dyYw5nwP8wpo+ZQb9SfTQQARHctRXS3UlrPJQjCHOR4ATT4tLy8PIwaNQpbt251pUc+ePAgrrjiCsPjGdZN0tPT8dZbbzU7/9ZbbyE9PR0AcObMGSQkJBg2JpSQJOY6vOrPJNehQpVIWXIdqhzOiqw6FEVyHe65m521Vlz6IbPB6UoKonsfQlS30st5msW5mlW5nT3lg+byCYtzRhvL++vJFmE7QY5jUXvP82vnQubxlG/ZNY7ADr2I7BDNLb7ulq+noZ32+yPK7Wz4b+R2uOeNNjKHk8m6Dj25ukV965nFdYj68m3qmUU4VlvF8PquF/t+WwOvvfYaIiIi8MEHH+Cvf/0runRpUBb/85//4MYbbzQ8nuE73pdeegn/9V//hf/85z/Izs4GAGzfvh379u3Dhx9+CKBhUX3atGmGjSGM4axqh7r9vcHqrZAi6xHT9wAi4qqCbRZBEESbomvXrvj3v//d7Pz//M//eDWeYcd7yy234MCBA1i8eDEOHjwIAJg0aRJWrlyJqqqGL/1f//rXXhlD6Md5Lh71R7oDigVSdA1iMw9AjqoLtlkEQbQlSGp24XQ6sXLlSuzbtw9AQ2GgW265BRaLxfBYXiXQyMjIcNXeraysxD/+8Q9MmzYN27dvh9PZuirc8BGhPs1Hq4pq5h9zkcyi87LaDvc8zo6yZDhLrwAgQe5wHrbeR8CsDpc0ycvbohzQzEPEqioWmX+NjzLmTRSNJYhwdo/C1RelLIi2FUb96ksKoSc6WBRxrAdPCS3MoCfa2R1RrmdRtDOPnghl97HEeb5blmad3NvkKSmHwpq+9ETvLf+NJEqOoRrHg+fQM1abghwvAODw4cOYPHkyTpw4gT59+gAA8vPzkZ6ejlWrVqFnz56GxvN6cWLDhg2YMWMGOnfujJdffhnjx4/Hli1bvB2O0AFjgKPkCjhL0wFIiEg+BVvfQ5AiwuALgCCIgENrvA389re/Rc+ePVFaWoqdO3di586dKCkpQffu3fHb3/7W8HiG7njLysqwbNkyvPXWW6isrMQdd9yBuro6rFy50rXHifAPTJGgFHcHuxAPALBc8SMiu5ykyGWCIPwH7eMFAHz11VfYsmULEhMTXec6duyI559/HqNGjTI8nm7HO2XKFGzYsAE33XQTCgoKcOONN8JisWhuLWqt8FKVnsQaelFJvyp5mdeBuYeKW+RovRVKcXegph0gKbBkHIeceO5yhHGjjS3ncObtUD3WaTuPLklRNRD32EP+Yh5flaXzl9yrmkNHYgaP/Q0m1vBmbqMlBlXokIcBuH3ARXYY+7sqaFlOBtTyNI9aJm95PU7v58PbaPZWBUnNAACbzYaLFy82O19VVQWr1Wp4PN1S83/+8x/ce++9mD9/Pm666SavFpQJ47BaG5RDvRucrsUBy5WHICcazw1KEAQR7tTU1KBbt2549NFHDfW7+eab8cADD+Dbb78FYwyMMWzZsgUPPvggbrnlFsN26Ha8GzduxMWLFzF06FDk5OTgtddew+nTpw1PSOiHVbUDjvQC7DbAWge59yHIsdXBNosgiDChra3xLliwAFdffbXhfv/7v/+Lnj17YsSIEYiKikJUVBRGjhyJXr16oaCgwPB4uqXmq6++GldffTUKCgqwYsUKLF26FHl5eVAUBWvXrkV6ejrat29v2IBgY/GTvCyUWwQ5nFXysgTgfDxw4ooGWTamGlKPo0CEUxylzD12CiRlPRHOgJsUyMvWKtP5NtBsI8RdoRPkdDaaK1i/5CrKR+ybCGdPduiRWfXIzjx6SxvqjX7WmltvchTdkvRl9EYv67FD+H5CW50TRkF7+KdvVLZu9bQhqfnQoUPYv38/pkyZgh9++MFQ3/j4ePzzn//E4cOHXduJ+vXrh169enlli+HtRO3atcOsWbMwa9YsHDhwAG+99Raef/55zJkzB9dffz0++eQTrwwhLsMAVHQCytMansedh5RxHJIcop9mgiDaLt7cwXrxVbVhwwa8+OKL2LFjB06ePImPP/4YU6dOVbUpLCzEiy++iLKyMgwePBivvvqqq1CPHh599FG8+OKL2LRpk672LVUd+vLLL12PFy1apNsOwMt9vI306dMHL7zwAvLz8/Gvf/0LS5cuNTMcwQCUdQbOd2x43rECSD1JTpcgiOAQoDve6upqDB48GLNmzcJtt93W7PUVK1YgLy8PixcvRk5ODgoKCjBx4kQcOHAAycnJAICsrCw4HM2LwqxZswbbtm3DlVdeiSuvvFK34921a5eudpIXW0tMOd5GLBYLpk6d2uwXSmvGo+yseq3ptPDzxiloquhj/u/lkIGT6ZCq4hpGSj0JJJ4BmKSSoRm3LK9wUiA/Lv/Yqcjc+abt/6poZUUtD4rKBxpNrKF7d7EvSwlqjemGnkQbIinWrPTrq/KGeuxoyRbNcQ3K0c0x8zczl/PYV7K1x4QrAtm6zWLC8bpX7bHZbLDZbJpdJk2ahEmTJgmHXLRoEe6//37MnDkTALB48WKsWrUKS5cuxZw5cwAARUVFwv5btmzB8uXL8f7776Oqqgr19fWIi4vD008/LezD39H6mrab3bs14ZSBHzMgVcWBSQpwRUmD0yUIgmilpKeno0OHDq4jPz/fq3Hsdjt27NiBCRMmuM7JsowJEya4KuK1RH5+PkpLS3Hs2DG89NJLuP/++z06XX/jkztewgSOCOBkV0j1UWCyE+hyHGhPkcsEQQQfb6KUG9uXlpYiLi7OdV50t9sSp0+fhtPpREpKiup8SkoK9u/f79WYwYYcL4coEtZdkFJJ+no+lbykxYcW261AWVdIDiuYpb7B6drqVE0AqKOfBUOJIpxVOZkVgYzrpnuIkmaobeI6ybyWzp32qeysmlzvaC3PwaPrC8ac9Cua21cSdHMMvldmc0KYKpHnw9SnZmRrHUlAiJa57rrrYLFYkJubi9zc3GCb4+Kee+4JtgnkeINGXRRQkQ5JiQCLrGtwupH1wbaKIAiiCRNrvNu2bVPd8XpLUlISLBYLysvLVefLy8uRmppqevxgQGu8weBSLHCqW4PTtV0COh8jp0sQRMgRCgk0rFYrhg4dinXr1rnOKYqCdevWYcSIEb6dLEDQHW+gqeoAnE0DIIFFXwRSfnSrsUcQBBFCePn1lJ2drVtqrqqqwuHDh13Pi4uLUVRUhMTERHTt2hV5eXmYMWMGhg0bhuHDh6OgoADV1dWuKOfWRtg7Xj3ruu7bC2TOUfJ7bNV1dPnsUZfXY6sSgYsNe84Qcx7o9FPDY8VtQrciCfxr/BxMMba1iF+5EmW0angu6AMBflrvVWWA4v8G3DoiX2dClG1KL8L5eASn9dSFbZxFCyc3tzAjlmprl95vQ6OilrH30N1WU4UDTK0Pe5lpy9W36TpENYnd0bNlKZwxIjVv374d48ePdz1vTF4xY8YMLFu2DNOmTUNFRQWefvpplJWVISsrC6tXr24WcNVaCHvHGwgYA1CZDFRfLikVexroUGE+kIUgCMKfBCiBxrhx48CaRZWqmT17NmbPnm188BCEHK+/YQDOpwGXOjQ871AOtD8bVJMIgiD0YGY7ESGGHC8HL1V5ylzFP1ftWpDcHjMAZ7oAly5no4o/CSmmsklK5pQqTwmbVBI2v7WIl50FW4sUrr1kYZptnM0UsyZj+CxWTvdmWqjkb+0CFO4yKf9LV9RHNYVJCVpfoQJBRisdcrQnSVifJK1DwtSrlhj+EvSvNO2O0cxa7pjJtOWNNO2NJN2qMXHHa2SNN9wgx+svGICKK4Da9gAUIPEnSFFVwbaKIAhCN2bueH21nagtEhI/2QoLC5GRkYGoqCjk5ORg69atuvotX74ckiSFZo7o88kNTldSgMQfyekSBNH6YF4ehEeCfserp+qEFseOHcOjjz6KMWPG+MwWWSBzukvNfFSz6udg4/mLccDFyxWGkk5AiqjR/jA6+ejjptPu9XH5dqoIZ4PFExRB9LF72neF162VlmVn0b8z3eUOdEQ/65KHBTKwe91UtTzJnTcYFa3HJk/qqUiS1iNH67VbT7S0Cr9L0+4ENoraTE3ihj4UEUmYJ+h3vHzViczMTCxevBgxMTEeSww6nU788pe/xPz589GjR48AWqsDBuDU5WwqcWeAGLrTJQiilWLijjc7OxuZmZkoLCwMuNmhTlDveBurTsydO9d1Tk/ViWeffRbJycm499578fXXX3uco66uDnV1da7n7qWqfE5tNCSHFZCcDVuGCIIgWim0xusfgup4vak6sXHjRrz11lseay/y5OfnY/78+cLXRTKdpJKdxa+pXpIA2KMAoCEVpEW5fFq7aC8/tSovu6cEGoJ2TCBBK8IizYKkF4A4IYZAdtYjFpqVnVXm6ZjPI4K3RCXxGpSjxXV93erxGpSk9SXN8F3BBP9L094We3D1MDyfv2oSm03Y0ioI0D7ecCPoUrMRLl68iLvvvhtLlixBUlKSrj5z587FhQsXXEdpaal/jYy0N/zfHtVQZ5cgCKK1QsFVfiGod7xGq04cOXIEx44dw5QpU1znFKXhV2dERAQOHDiAnj17qvrYbDav60B6RXQNmLUOkt0GVpYBJJaDSZcgvPEkCIIIUcxIzbSPV0xQHS9fdaJxS1Bj1Qmt1GB9+/bF7t27VeeefPJJXLx4Ea+88grS09MN2yCKZOYfW9ylWA5VxLPEAAsDupSAlWRAqo8CyrsBsgMsshaIrIVkqQMi6gCL3U1/5SKc3RNo8E7byZ8X1N3lvTw/rqqWr3YyDUCc01nhfspKKglU0BfauOsAondXFKXMS9B87Vv36HMRZqKRRXK0WytddvjOJn05nPXnk3b1aLGF080of8jT3kjTuusjX8arRB4mc0u3dWiNV0zQtxO1VHVi+vTp6NKlC/Lz8xEVFYUBAwao+sfHxwNAs/NBJaoW6HIU7HwSUBUPSYkA6mKButim7xy5Hoi6CLQ7C8niCKa1BEEQ2tAar18IuuNtqepESUkJZLkV/rK0OIGO5UDiKaAqGqiPajjsNsBhA5RIoCYRuNQBLP4nIKY62BYTBEGooFzN/iHojhfwXHVi/fr1HvsuW7bM9wYBiFAlofDik9SoVkkMsNU2HABkx2Vp1x4DVpUEOKKBc10A+Riky4FZzZQx9xzQjfBRzXwktCqIWjuxhsc4Y9VL2pHMqhwbgq7MQ3SvcDr+vKi/YCizEjSPv+ToYCbsUKHrLfHmB29oRE77K+e02dzSrQ664/ULrfBWsvUjSQySrRpSwnHAWgVABi62zrqSBEG0YSiq2S+Q4w0ikgRI7csAMMDeDswRGWyTCIIgXEheHgBlrvJESEjNwcTCSUyi/Mzusp5IulRFDate4J+pkjJDkp1gkZeA+hhIdTGQ5Avq3MzNuzShinDmfkNxEqYwsYbKbvV8qhKDnO2iUoIWbmpRRLSnpBI8uhNtaKFDgnbHTAoE09KvICrWV3K0J7vMljcU4weJ1xtFV4e5eqKl3d9/PRHSRAMU1SyG7nhDgYiG9V/miAqyIQRBEBwkNfuFsL/jDQUkS33DZ1WhPwdBEKEDRTX7B/qm5+DlNJXs7NZOFWQsKhGoeqwtTzU2YY1ymyJBUiQ0+8nI99clO2tLyvw4qhTMHtNqtVxKkC8jKIOXo7X7ukubQulZJFV7sFYTj3Jvy3Kqnghpb3I165F7/SFHe4UPo6WNSrx65V09EcfqPMyCNjoTdlCuZg99CI+Q4w0paP2IIIgQgxypzyHHSxAEQWhCUrN/CHvHqydXs7sMyCeSkDmpWbJwEcC8QiW6kXX/gDIAijoauHlD7cHUUdQCaZqLllZUWrHeO+2WywXyUdB8tLNT0KbBXO5948+LzBDIuiJJ2JeRvnqkaW8ESKFUbUKO9t4a/TRbNgiAxKvu07IkbUaC9pTT2nhJw/CCiiSICXvHGxI0/tumn4oEQYQSJtZ4aTuRGHK8IUHjL2xyvARBhA4kNfuHsHe8aklZW5ZrLjVzcqqFy+nMRzirop2bHqpSDjP1SanxXLMPLi+PcbKsxEurfAtO1hVmUla4R3oL9fFoy85qmZzPd83Z5xZFzUv3IrlYJEELZUEPcqFIkvQkT2uNJZSBPcjRZnNIa+JhpcBMGUO9SU90IUpuYlCCdoeXe0X/fvn3XFduaNX4+pYp2iwU1ewXwt7xhhb0iSUIInSgO17/QI43FGj8VU27iQiCCCXojtcvhL3j5XM1RwqkKndJUB3VrP3YyUnNjI989pSsgjFA6xcm/1wgY6qiNfkBuEhmtWomzoqskp75xM1CCbqpveq9UngZkJfh3cZRtPvzFookWmFUMz+8XrlQRzSrnshpkRzt/pooh7Qv45A92eJCGJ1rToIWtdNjk9koY6Oys2rZwIPn0GsLQXgi7B1vSOD6kqDtCQRBhBB0x+sXyPGGApcdL1NtriUIgggutMbrH8jxcniTQIOXq1R5K3ipWZXcufljvkiCdDmJBgRdVDgF8h1niOjfgPq8OKpZ9VvAyUVLcxI0/5iX21WKNT+6U/0Dg38/RdHPKgmb8fI5027D4SmqWfTe6pEUjQm0GhgsYxgICVot8Rq/Ql3yssGIcRUevtTN5IDWk2QD0BeF3aagO16/QNpmKGCpb/g/i1TVvCUIgggmEmNeHUBD5qrMzEwUFhYG+SpCD7rjDQEk2QlI9Q2O1xkFSb4UbJMIgiAoc5WfoDveEEGKqAEAsPrYIFtCEARB+JOwv+MVr9+J13h5VFtb+G1Dgu1ETFVIgVtrtF4Eq+8ApT4OlsgzkES1Prm6u6qlKG69VxIUaBDW7202iSDDFW+vao1X4R5rrwOr3hu395Nx24n4tTYmWD9X9RWsA4u2GWnNr9WHR89aJX+eechOZTgjFloe19N6tFHb9Wyr8bRtx9S6rhdrv74rvqD9780905Wn9d+2CAVX+Qe64w0RpIhqQLYDLAJOe8dgm0MQBNEkNRs9CI+Q4w0RJAmQo08BAJT6RDjraW2EIIjg0njHa/QgPBP2UrNFWBiBy0jl9hNOJDbx8p9K7uXlZcFjOCXItmow51mw2kQ469LAmBWy9TQsfL1clVzMNM9LKjlaUJvXUwIt/jEnzfH1hvnrkwSZrkRbjtzlV1m19Uq7HW+uuqgCZ4eerUjQtw1IJR3zbUzIzlq2tNQ/EPhKdm7ozy1N+Epe9vRh1aP86no7tT8ViltfUUasNgttJ/ILYe94Qw05ugLM2XDXq9g7QnG0g2Qtgyzbg20aQRBhBq3x+geSmkMMSQIsURWwRJ0AJAegRKG+tiuc9R3UaZMJgiCIVgnd8XLwMlKEKpuSm1QpkkNFj1WRzE3jcKVS1XV6JUC2VkGKuARnbSqYIxaO+hTISjQiIss4SVW7MIIkOK+SX7nrYe6ZKvnL5W3kjeTqEPPt9UQ+y24/9/hxZdX7LpCLRcUXOBTeKDepUhgJreqvjdEate728dcqkrZV4+rIbqWnjrBeG4X9ddTTbejfcsaogGd88pkcDYTdvQpJzX4hzD5FrQtJdsISfQKWyFMAFCjOODjqU+nOlyCIgEGBVb6HHG+II0lAROR5RNh+AsCgOOOgODsE2yyCIMIBxrw7CI+EvdTMS2X8Znlefotwqx/Lv2bhND91Ag2+WAD3+4bXCFWJNbgJHM21MYulBiyyAs76ZDjqk2CRLkJW+MQVKvHY9UgVmaqSfsX6m/CfDa/e6qnZy8nOvDysuAm56uQYgtq83HR8b35mSRCd2yyJBZ8chXt/9NT8NSpBe6oFrEfaFqL6c+usJWt0Dh14qjesasd9wD3ZyI+sa34d0eC62qDlNs3GCgNNtS0FV2VkZCAuLg6yLCMhIQFffvll0GwJe8fbmrBEnIfiiANjUXA4ExAZcSbYJhEEQbQaNm3ahNjY4KflJam5FSFJQITlLADA6YynSkYEQfgX5uVBeCTsHa9FUlyHDOY6IiTFdcgSUx0WWXEd7q+52liaDpk7wB2MP2Q0HRa3Q2o6ZFQBqAdgadjj62xImCHVNx2yXWo6HOAOyXVIDnCHJDyg8AeaDqfkOpgiOtB0MKnpUGT1wS0NKYrkOvg+/Hn1ITcdTHIdTv5QZNXB93EqkutQGFwHP7d7/8ZD2Iab2/37iLfRociuQ8+4or6q80xWHQok1+FksuvQc3165nM/9LQT2ccfDmZxHQqTdR0OxeI6TNmn81p5e9sqkuLdYZQNGzZgypQp6Ny5MyRJwsqVK5u1KSwsREZGBqKiopCTk4OtW7cauxZJwtixY5GdnY2///3vxo30ISQ1tzIkCbDIlXAqHeFkcYhAVbBNIgiirRKg7UTV1dUYPHgwZs2ahdtuu63Z6ytWrEBeXh4WL16MnJwcFBQUYOLEiThw4ACSk5MBAFlZWXA4HM36rlmzBp07d8bGjRvRpUsXnDx5EhMmTMDAgQMxaNAg48b6AHK8rZBGx6uwdmDMAkmVI5IgCMI3mAmuqqysVJ232Wyw2WyafSZNmoRJkyYJx1y0aBHuv/9+zJw5EwCwePFirFq1CkuXLsWcOXMAAEVFRR7t6tKlCwAgLS0NkydPxs6dO8nxhgL8Jv9IucmZRbg5NnVUs6L5WBYk4BAm04jgzrtFNTN1tgTIUj0k6RIYi4ZTaY9I6bzqHweXqwISl+dZ4RPPqiJvoUbwD00VeS1qI4zV5aO53aQ5hU/8rB1BzEc+qxNwqOKatY1y++ZQRS+LciwLvm1E+aDVbcTjiHI1q+LCdeQvVvX1FMFtcCzVeUEEr3uZRR49yTzMlgIU2suhJ4paUX0kjeujAU8EEgy82R50uX16errq9Lx58/DMM88YNsFut2PHjh2YO3eu65wsy5gwYQI2b96sa4zq6mooioL27dujqqoKX3zxBe644w7DtvgKcrytFItcCYczGg4Wh0jpfLDNIQiCUFFaWoq4uKYqa6K73ZY4ffo0nE4nUlJSVOdTUlKwf/9+XWOUl5fj1ltvBQA4nU7cf//9yM7O9soeX0COt5VikS/C4UwGQxQUZoUMKqJAEIRvMSM1X3fddbBYLMjNzUVubq7vjTNAjx498N133wXVBp6wd7y8DKVObqB9HlCXErSoZGde8mtqL0ymwZcFtHDSb4SbPOlseo25VG8Fcn0VFLSHA3GwKae59k19edkZnOysUtbcpCSmCGRoXoVU2agtuYmSbDC3+VQlBvlcI3w6aF7iFSS6UL3nHuRXUTtVK34O7rQomYbIDpEc3WxuD/Zq2seht6Qg/5pILlb0SNCezoukYx1StVGZWm87PckxRNI0lQWE18FV27ZtU93xektSUhIsFgvKy8tV58vLy5Gammp6/GAQ9tuJWjMRckPwgoN1AIN7pQOCIAhzGM3T7I98zVarFUOHDsW6detc5xRFwbp16zBixAjfThYgyPG2YmRUQ0IdAAvqkEL71gmC8C0mcjVnZ2cjMzMThYWFLU5TVVWFoqIiV2RycXExioqKUFJSAgDIy8vDkiVL8M4772Dfvn349a9/jerqaleUc2sj7KXmSLlp35dFkB/WXZ5SSdICiVBWRThz+YdV+Zm1H7v/VRQnLxFz0rZTQqRSBnt9VzilWNTiClhxCpEObr2X29bGXSoUPlBbUct1ilU7spVXiFUSHx+RzQ/ED6MjItr9Nb7EIATvuXs5Re3H6utTS7kty9Y8iuDvLemURkVzOFVyqHZ7/k8m+ty5y7gieVkkgOuJnOYRSdN6xxVFUavamIyoNhNF3VzaDi9lycwarxGpefv27Rg/frzreV5eHgBgxowZWLZsGaZNm4aKigo8/fTTKCsrQ1ZWFlavXt0s4Kq1EPaOt7Ujy3WIjPgJ9Y40KIhBLTJgj6iDRalGBKuGhV1qw3l1CIJoC4wbN65Z7Ic7s2fPxuzZswNkkX8hx9sGsFiqEeE4Djs6won2UCQbFIsN9UiExOywOc8iQqlseSCCIAgeE8FVhJiwd7wWUVSz4LH7c1ECDVWJQUHpQGbhyvpxUc3M7a/COHlZ4SOTI3nZuR5WlIGxU2D1MXCiHRxSLJhkRW1EKiKc7RBTVwbp8vVKnOzsHqjp5KVtm7YEB1GJQT3ysptap+rPvz/cHLwsK5Kg+Tt7lZzcTO7VlpfVsrNgLB3StKfoXElgrzh5h/Z8epJ3NB9L3M7VXvOs8UQczcY1IVXrjdQWSdWiOylvoqj1vtZWMCM1Z2dnh8x2olAj7B1vW0OSFESwKkSiCoydgkOJR11EEhyW9qi2yWhX95PL+RIEQXiksWqI0T7w3XaitghFNbdhJDDYHOcQU1cKMAVOSztcsiYH2yyCIFoLzMuD8Ajd8XJEcpkn+I3y7rleI7jXIgTJNCI4GdmhND12cud52ZhFcHqvov49xCerUAR5mPmkGfxjuR6IQC1i6n9CTWQX1Ed0gMN+EZF11U1tnG5yKJfZTRUJy0vSVs4+3nS+DffpUsnn7rIeLy/zly4LJGjuFzifcEMd4awtRwMeJGlVI15G9l6abp68QyAd8418lLwD0BctrZ7amDTtKYqax4xULbIJMCdVi3I4e4qi1iNPtyUkeCE1+8WStgXd8YYJEUoNbPZzAIBLUclg9M+DIAg/YmQfb7gREo7XSIHjJUuWYMyYMUhISEBCQgImTJhguCByuBJVdxqS4oAiW2G3dgi2OQRBhDomEmhs27YNe/fupcAqDYIuNespcMyzfv163HnnnRg5ciSioqLw5z//GTfccAP27NnjqrdoBJWkzEcr87KxrC4LyD+3yE0hupGWpvMOTi6OkLWlZj4nsuqxW65mVVQmL+vy0cdcH1mV95mzu54hyn4al6JSUWvtiKjqSshMAat3k0NVsnXTWE5FcJ5LuKEuQ8hdA399Frfr46OcBRHLTCQ7C/Jjq+RJTwk0JG25WFe0tGpQPdK0PknaV8k7AL3yNCetCqRpHuF7AM+ysOZ8OvJM65V0/ZH8o3ku6pbna0uYiWomxAT9jpcvcJyZmYnFixcjJiYGS5cu1Wz/97//Hb/5zW+QlZWFvn374s0333Tl7SRaxlp/AbJiB5MjUBudEGxzCIIIZSi4yi8E1fE2FjieMGGC65zRAsc1NTWor69HYmKi5ut1dXWorKxUHeGMBCCqrgIAcCk6EYoUXinwCILQj8SYVwdAa7yeCKrU7IsCx4899hg6d+6sct48+fn5mD9/vrA/n0CDj2qO4B5b3LQTXjrW85iXl51Ndf1UUc0yJ6e5b5tTy9DceScnI0Zy/XmpmJem6xvaWFANS/0lOCOjUdOuIzqcVZfbYnbOLjsnYTuazjuiuDmcgrkjoXnePd0ti+Cuj5OO+VKJqvOCiGVVRLRKTnabTyQpC85DIA+rzwvGVE8tTOwhlqChiV6Zml8k8RRtLbK3EZGs6kl2Fr3mjYTdkh2e5zN2rXrnCwepGQogSGHvuQ9oH68ngi41m+H555/H8uXL8fHHHyMqKkqzzdy5c3HhwgXXUVpaGmArQw8JQExNw11vnS0e9ZHa7x1BEAThe4J6x2umwPFLL72E559/Hp9//jkGDRokbGez2WCz2YSvhyuRjkuw1lXCbotDZUJnJFQcg8yM/rQlCKItw0vHRvoQngmq4+ULHE+dOhVAU4FjT1UoXnjhBSxYsACfffYZhg0bZsqGSD5pMQcvI0W4JTPmN9jzjy2SttTsUEnQfMQxF+GsynesFsGcinZyDcblauZ9pjD6mLuxlR1AlL0c9ZHRcEZYcb5jOjqc+xEyUxBZ0zRAZE1TH0dt01iWaE52jtaew2nlJGteduYeA4DC/QlUOau56GdJFsjOovMq7RZi+AhpkdTMoW6j3V4l98rqccxI1foiqt1eEkrEetp439djf0F7PdJtIKRtb+TsNos3wVJh9hZ5Q9Cl5pYKHE+fPh1z5851tf/zn/+Mp556CkuXLkVGRgbKyspQVlaGqqqqYF1Cq0WGgnaXTkBSnHBYo3G+Y1fYrdHBNosgiFDBxD5eQkzQ9/G2VOC4pKREVUj+r3/9K+x2O26//XbVOPPmzcMzzzwTSNPbBBFKHeLPluBCwhVwRthwIbEraqOqEFN5DlE1VZTfiiDCGKpO5B+C7ngBzwWO169fr3p+7Ngx/xsUZkQ47Eg4fRzVsR1RGxOPuphY1MXEQnbUo92Fc2hXeQ6yQuu/BBF2eHMHy2WuoqhmbULC8QYTfj2H306keuwhc5XqMbefRVinl7t7l7nz/F09k9VOjt9CpCqswP2DUPlF7t+JU/VvRrSOzAA4EaWcQmT1OThZB1yK6QAlIhIXOyajKr4j4svLkVBa4RrBGdlkrz0+AlZnPa798XuMKt+DDnU1uGCLwZq+w7Gqz0iAXQJs9iabrOr7aNX1CQor8FuFmGpdlxtIVGzBfTuRjrVgdRvxWE3nBWvFbmu8hteIzWxrcu8vmtpD7WLhuFp9DbRr+byxcTyOJWgvq95z49ulRGvHBNESYe94CTUWVo/oygq0q6xAbXQcamMSUR8VhXOdO+NSu/ZILv4RkXV2VZ/RP+3Bk1tXIK7+EpyQYAGDU5Jw3fHdmLt+BR6d/Fts6ZkUpCsiCMJbJEWdC0BvH8IzQQ+uIkITCUD0pUqkHjmChJMnISkKaju0R8mgvqjI6AJ7lA01ce0xyPETnv/mHcTWXwLQlJDEcvluPK6uGm98nI9RR0oo2JEgWhsUXOUXwv6O18KlZeG3A4myWAGAjZOXHfxjxcL10a7Ty0tX/HknJy8rbhqWxG+rUW1BamrDuN9Qoh+cqh1LvFxrUc/HZ8FyWiXIqESHc5dwKSoZde1iUZmShMqUJNgcdjz77zkAxL/gZDAokPDn/7yJST9/DszpRH079Xz8diSFq/OrysbFZbtSSdDceUVVbIG/VrVNjNcxBfV8hduMdLQXStZur4mkZj1ytJ42gBeytcBW4TjiLm52+E7OFvXxh5ztcW7hK20I2k7kF+iOl9CFRalH0slSJP14DNZL1QCAKd9/ifi6KmFB8UZkMMTXVuH64h2BMJUgCB9hJlczIYYcL2EIW+0ldDpRgl5f7cDPtn8Op+i2wQ2nJGF8yXd+to4gCJ9iQmqmIgliSGoWyMt8xKN7JhtZlaFKO8KZr9MrinB2cnKvhc/YpLP+qCqQmZc3edlZILmqJFq3TFJOG5eVKqbpsb1DU6fq5FhEldTDck7fr1sLY0isOIuEjSWAzap6rT4t3vW4LqHptfrYJoMdUZz8zWUAVcnREfzjpjbM7VMueh+EkdOc1K+Oouba64mO9tifP99yRLUw6toNJpCXVWPxHXS09yg162mnR1IWthFObTyCW9DX0xx6ClgQDdB2IjFh73gJ77lgi4FTklyBVJ5wQsJFmYoxEESrgsF4dSJSmluEpGbCaz67cqQupws0RDtviurpZ4sIgvAltMbrH8L+jlctL2vLzpFuUc12qeltswgkaVFt3kjusXs0sQg9idkVXjrUEb3MeInWobZD4qRcJ19ft55rVBeNT2JuwBPr/w9xddUeA6wUAFXWaHz482thj4iEpVb9euQl7n3jCjREnW6aUHbwci8fBd3029HJydEOG3feLWGHIijYIJaq+feQay+SqVXR1W7JQvhoaUF/XsNU/y35NqIoaqgR5UzRIykLxxFLwqJ8LUJEkdqicTz9WzDRX2/Utq5o8LYEgxeZq/xiSZuC7ngJwzAGOOpTUBdhxe9vfgQAoAi+9RrcqISnrr0T9ohIzTYEQYQotI/XL5DjJQyj1CeAsYbb4nW9cvDAbXNxMaph/bYxyrnx/xdt0Xh4yixsyOgfHGMJgvAexcuD8EjYS808Fk4j8SQ1R6iSa2gnzYhQ5Xrmoow5uVcRaGt6o5p5+B4KL9/xOaA5SdlTAg2Jl1a5er7S5RtWpSa+qbFsx5dD0zBiyDxM+uF73LB7NzpcqsGF6Bh83mcgVmcOhj0yEnzZY9mtBLLM2SXXc7ms6y3cY2g+ttRzUr9D+3xEnfibQCVb8zKySnZuOVpadd6iPab7a+KIau3HwihqVXu3KGphVLR2FLY42lnQxlNuaE/tjI6rNb6H/qI2Iplc9z85PdI4QbQAOV7COLITcAKIrIIcWwYJTtgtkfjnkKH4ZODQpmYO+mYiiNaMN8FSFFzVMuR4CcNICSWQ7BGQLJdvMUlaIoi2iYmygISYsHe8fK5mWZVfmT+v/iDx0rNDR4lAK2s6r5KX+XffEaF9HoDE1fZTJwNoeuzkJEanIPmDIpCQmaK+M+Wfq15TZexwap6X+Pa8TM1HR7s5aolvx4/lFLQRnef68pUcJTdpm++vase/z6pr4jrzHwV+PlXBJi4fN9wQRilrnxdJwmJp2k1lMDqfHrnWU8lFQSIRPdHV4vzTgvE9tdPTRkeUd3MpXfC4rWLC8WZnZ8NisSA3Nxe5ubl+MK71EvaOlyAIghBgwvFS5iox5HgJgiAIbRQYv7OnpacWCXvHK0qaIZKdG17jImk5UYsvF6g0y2SgNbd28o16p1qgjOAikx0WLtczl4jZ4eQSRnD6mMK1URRJ87F7FLXqBy7TbqdqomhrfEwwXzP9jn97VdK29rhqOVtwXiVZu0VtC2RkoczNjasKcNczjrusLnpN1F/1RnPn+ShxdX1Icwi+ZJtJvKL2BiOZjUvT7nZxT0SSuY6xxLm21fN5fI0gdBL2jpcgCILQhqKa/QM5XoIgCEIbimr2C2HveC2qCFR9uZr55wqXzYFPm8hHL8uCCGnZKcrnrJ5PJSlz+pbqvKItNfNtmOq8WGp2iuRl1eOm9opqDmi39yhta4+rR8JWSeGiOonua05Me1yhnK2KZOZkZ0GEsyRor7uPyg7tx/w1qKTwZtcq6m9sbqFN7t+xevrzMr7KDm3JXGRfS695jQe53FNEd5tEYZ5zV4v6EB4Je8dLEARBCKA7Xr9AjpcgCIIQ4E3RA3K8LRH2jtc9Ytl1XpC3GQBs7smGNeCjnflkGpHcfFY+WplL6utwC5fkZWu+HS9tq6RmPpJZ0IaXd91zRvPPRbKzIjzPjcPbIYiIbtZfIEkL5WjBeVE0drP5hf215xCdF83d/GJ1tBO1EYwjlMV1jiuUa/XIvcyDlC4aS4d9esYB3GR2PXPrkM/V47s9FywVEIQRwt7xEgRBEAJIavYL5HgJgiAIbRQGw9IxBVe1CDleDnV+Zu0I54YXtfvzEct8lDIv8dbLvKTc9Liel5Dd5Lt6xku22hHOajlau70o6tqT1KySgXX0F8nRTg/z+UPCFsnUDf29l7AZL9erLkI7urpZiUfV3EzzvCgftCgBsaoUoAcpVoXou1FXtLM+aVskKQtzX+uxwwP8W62aW0fSC9Hb5GnysCgLyBS37QI6+4QgxcXFmDVrFsrLy2GxWLBlyxa0a9cuKLaQ4yUIgiC0aUNS8z333IM//elPGDNmDM6ePQubzRY0W8jxEgRBENq0Eal5z549iIyMxJgxYwAAiYmJQbUn7B1vJJf0lpeUo6SmxBgWWS2d1LMIzT61SiTXhpORpabHEZwMo7CmuRWunJsT4qhmkXTsFLRRJdzQER3tPp9IthbJ3CKpWNTevZ0nCdzVXmBrM1m3hXE89xF2abGv0TYN83mvW4be15xnAq3Qupf1JEKPDRs24MUXX8SOHTtw8uRJfPzxx5g6daqqTWFhIV588UWUlZVh8ODBePXVVzF8+HBd4x86dAixsbGYMmUKTpw4gdtvvx2PP/64H65EH2HveAmCIAgBAZKaq6urMXjwYMyaNQu33XZbs9dXrFiBvLw8LF68GDk5OSgoKMDEiRNx4MABJCcnAwCysrLgcDTf6rlmzRo4HA58/fXXKCoqQnJyMm688UZkZ2fj+uuvN2yrLyDHSxAEQWjD4IXjbfhfZWWl6rTNZhOuq06aNAmTJk0SDrlo0SLcf//9mDlzJgBg8eLFWLVqFZYuXYo5c+YAAIqKioT9u3TpgmHDhiE9PR0AMHnyZBQVFZHjDRbqRBlNv5acXJmwWhap6lOjWF2PeUnZKZCB1fPxiW7F8qseZEEIqYUvbyiKMLTUa58PMywkQ/oUZ1iE+oYRJu54G51cI/PmzcMzzzxj2AS73Y4dO3Zg7ty5rnOyLGPChAnYvHmzrjGys7Nx6tQpnDt3Dh06dMCGDRvwq1/9yrAtviLsHS9BEAQhQFFgOEWX0tC+tLQUcXFxrtPeRhGfPn0aTqcTKSkpqvMpKSnYv3+/rjEiIiKwcOFCXHPNNWCM4YYbbsDNN9/slT2+gBwvQRAEoY2JO97rrrsOFosFubm5yM3N9YNxxmhJzg4k5HgJgiAIn7Nt2zbVHa+3JCUlwWKxoLy8XHW+vLwcqamppscPBmHvePl1XX47UDvZrvkYAOotVf43zA+4b1My3N/E+p1iem7v+5u9bjNbfczPbfJ9M7F5J5hz+9QOg/1F8RlhSQgk0LBarRg6dCjWrVvn2mKkKArWrVuH2bNn+3SuQBH2jpcgCIIQYCKBRnZ2tm6puaqqCocPH3Y9Ly4uRlFRERITE9G1a1fk5eVhxowZGDZsGIYPH46CggJUV1e7opxbG+R4CYIgCE0YU8AM5l5ubG9Eat6+fTvGjx/vep6XlwcAmDFjBpYtW4Zp06ahoqICTz/9NMrKypCVlYXVq1c3C7hqLYS9403lZOOOco3rMS+TWdx+8fGZcEQyJN9fJLnxEponWU4kV+qR4Hwl9/kTMzKyNxK20fck0Pb5ww6P4wbgM2JWLtaDWVlfc8xw3x7FmPEUkF5IzePGjVMXDdFg9uzZrVZadifsHS9BEAQhgHkhNYdokYRQwv8/QwmCIIiwIzs7G5mZmSgsLAy2KSFH2N/xdrI0rV9cVJoyPumW3/iMUYJfhvyvGz2rJXrnNhdl7B8JLRSlbTNRyb7GV++PL99nozKw2bmNSsLeyNR6bPRGuvfl0kGrQFHcCijrwIs13nAj7B0vQRAEIYCkZr9AjpcgCILQhCkKmME7XqNR0OFI2DveWKkpaYZdakqUUW/yV5tFoHRFap+GRfVM39wWU6pX+PwqdQbxF7iz5SYhRVv8ynSGz0fd95i44zWyjzfcCHvHSxAEQfgeWuMVQ46XIAiC0EZhgNHSmbTG2yJh73hjZKvm+RpWzz1Wf5Dquafi5BhNjXhFmJceRXK0Rfv05T7anUSqs0VHdKcsGNMTesYVzuenyFDRexNK+Ova2zJKmxTAWwmMwfACBDneFqFvAYIgCEITpjCvDoD28XoiJBxvYWEhMjIyEBUVhZycHGzdutVj+/fffx99+/ZFVFQUBg4ciE8//TRAlhIEQYQRTPHuQMMa7969eymwSoOgS80rVqxAXl4eFi9ejJycHBQUFGDixIk4cOAAkpOTm7XftGkT7rzzTuTn5+Pmm2/Gu+++i6lTp2Lnzp0YMGCA4fnl1EOux7Hc+djmTQmCIMIKpjAwg2u8LeVcJgCJBfldysnJQXZ2Nl577TUADXUW09PT8dBDD2HOnDnN2k+bNg3V1dX497//7Tp39dVXIysrC4sXL25xvsrKSnTo0AEXLlygiDuCINoEvv5eaxxvnHQrIiTRJkhtHKwe69nH9B3rgaDe8drtduzYsQNz5851nZNlGRMmTMDmzZs1+2zevNlVMqqRiRMnYuXKlZrt6+rqUFdX53p+4cIFAA0fLIIgiLZA4/eZr++jHKzOJR3r7oP6lhuFOUF1vKdPn4bT6WxWUzElJQX79+/X7FNWVqbZvqysTLN9fn4+5s+f3+x8enq6l1YTBEGEJmfOnEGHDh1Mj2O1WpGamoqNZd7Fz6SmpsJq1d4xQoTAGq+/mTt3ruoO+fz58+jWrRtKSkp88gFtLVRWViI9PR2lpaVhJf/QddN1hwMXLlxA165dkZiY6JPxoqKiUFxcDLvd3nJjDaxWK6KionxiS1skqI43KSkJFosF5eXlqvPl5eVITU3V7JOammqovc1mg81ma3a+Q4cOYfUPs5G4uDi67jCCrju8kGXfbVSJiooi5+kngrqdyGq1YujQoVi3bp3rnKIoWLduHUaMGKHZZ8SIEar2ALB27Vphe4IgCIIIJYIuNefl5WHGjBkYNmwYhg8fjoKCAlRXV2PmzJkAgOnTp6NLly7Iz88HADz88MMYO3YsXn75Zdx0001Yvnw5tm/fjjfeeCOYl0EQBEEQugi64502bRoqKirw9NNPo6ysDFlZWVi9erUrgKqkpEQln4wcORLvvvsunnzySTz++OPo3bs3Vq5cqXsPr81mw7x58zTl57YMXTdddzhA1x1e191aCfo+XoIgCIIIJ0IiZSRBEARBhAvkeAmCIAgigJDjJQiCIIgAQo6XIAiCIAJIm3S84Vpm0Mh1L1myBGPGjEFCQgISEhIwYcKEFt+nUMXo37uR5cuXQ5IkTJ061b8G+gmj133+/Hnk5uYiLS0NNpsNV155Zav8rBu97oKCAvTp0wfR0dFIT0/HI488gtra2gBZ6xs2bNiAKVOmoHPnzpAkSZibnmf9+vW46qqrYLPZ0KtXLyxbtszvdhI6YW2M5cuXM6vVypYuXcr27NnD7r//fhYfH8/Ky8s123/zzTfMYrGwF154ge3du5c9+eSTLDIyku3evTvAlpvD6HXfddddrLCwkO3atYvt27eP3XPPPaxDhw7sxx9/DLDl5jB63Y0UFxezLl26sDFjxrCf/exngTHWhxi97rq6OjZs2DA2efJktnHjRlZcXMzWr1/PioqKAmy5OYxe99///ndms9nY3//+d1ZcXMw+++wzlpaWxh555JEAW26OTz/9lD3xxBPso48+YgDYxx9/7LH90aNHWUxMDMvLy2N79+5lr776KrNYLGz16tWBMZjwSJtzvMOHD2e5ubmu506nk3Xu3Jnl5+drtr/jjjvYTTfdpDqXk5PDfvWrX/nVTl9j9LrdcTgcrH379uydd97xl4l+wZvrdjgcbOTIkezNN99kM2bMaJWO1+h1//Wvf2U9evRgdrs9UCb6BaPXnZuby6699lrVuby8PDZq1Ci/2ulP9DjeP/7xj6x///6qc9OmTWMTJ070o2WEXtqU1NxYZnDChAmuc3rKDPLtgYYyg6L2oYg31+1OTU0N6uvrfZZkPRB4e93PPvsskpOTce+99wbCTJ/jzXV/8sknGDFiBHJzc5GSkoIBAwZg4cKFcDqdgTLbNN5c98iRI7Fjxw6XHH306FF8+umnmDx5ckBsDhZt4XutLRP0zFW+JBBlBkMRb67bncceewydO3du9o81lPHmujdu3Ii33noLRUVFAbDQP3hz3UePHsUXX3yBX/7yl/j0009x+PBh/OY3v0F9fT3mzZsXCLNN481133XXXTh9+jRGjx4NxhgcDgcefPBBPP7444EwOWiIvtcqKytx6dIlREdHB8kyAmijwVWEMZ5//nksX74cH3/8cZuuRnLx4kXcfffdWLJkCZKSkoJtTkBRFAXJycl44403MHToUEybNg1PPPEEFi9eHGzT/Mr69euxcOFC/OUvf8HOnTvx0UcfYdWqVXjuueeCbRoRxrSpO95AlBkMRby57kZeeuklPP/88/j8888xaNAgf5rpc4xe95EjR3Ds2DFMmTLFdU5RFABAREQEDhw4gJ49e/rXaB/gzd87LS0NkZGRsFgsrnP9+vVDWVkZ7HZ7qyha7s11P/XUU7j77rtx3333AQAGDhyI6upqPPDAA3jiiSd8WkYvlBB9r8XFxdHdbgjQpj514Vpm0JvrBoAXXngBzz33HFavXo1hw4YFwlSfYvS6+/bti927d6OoqMh13HLLLRg/fjyKioqQnp4eSPO9xpu/96hRo3D48GHXDw0AOHjwINLS0lqF0wW8u+6amppmzrXxxwdrw2nq28L3Wpsm2NFdvmb58uXMZrOxZcuWsb1797IHHniAxcfHs7KyMsYYY3fffTebM2eOq/0333zDIiIi2EsvvcT27dvH5s2b12q3Exm57ueff55ZrVb2wQcfsJMnT7qOixcvBusSvMLodbvTWqOajV53SUkJa9++PZs9ezY7cOAA+/e//82Sk5PZn/70p2BdglcYve558+ax9u3bs3/84x/s6NGjbM2aNaxnz57sjjvuCNYleMXFixfZrl272K5duxgAtmjRIrZr1y52/Phxxhhjc+bMYXfffberfeN2oj/84Q9s3759rLCwkLYThRBtzvEyxtirr77KunbtyqxWKxs+fDjbsmWL67WxY8eyGTNmqNq/99577Morr2RWq5X179+frVq1KsAW+wYj192tWzcGoNkxb968wBtuEqN/b57W6ngZM37dmzZtYjk5Ocxms7EePXqwBQsWMIfDEWCrzWPkuuvr69kzzzzDevbsyaKiolh6ejr7zW9+w86dOxd4w03w5Zdfav57bbzWGTNmsLFjxzbrk5WVxaxWK+vRowd7++23A243oQ2VBSQIgiCIANKm1ngJgiAIItQhx0sQBEEQAYQcL0EQBEEEEHK8BEEQBBFAyPESBEEQRAAhx0sQBEEQAYQcL0EQBEEEEHK8BEEQBBFAyPEShJ84duwYJElylSBcv349JEnC+fPnAQDLli1DfHy87vEyMjJQUFDgczsJgggs5HiJNsk999wDSZLw4IMPNnstNzcXkiThnnvu0TWWu8P0FdOmTcPBgwd1t9+2bRseeOAB13NJkrBy5Uqf2kQQhP8hx0u0WdLT07F8+XJcunTJda62thbvvvsuunbtGkTLGoiOjkZycrLu9p06dUJMTIwfLSIIIhCQ4yXaLFdddRXS09Px0Ucfuc599NFH6Nq1K4YMGeI6pygK8vPz0b17d0RHR2Pw4MH44IMPADTIxePHjwcAJCQkqO6UV69ejdGjRyM+Ph4dO3bEzTffjCNHjui2T0tq/te//oXs7GxERUUhKSkJt956q+s1XmrOyMgAANx6662QJAkZGRk4duwYZFnG9u3bVWMWFBSgW7duqpKABEEED3K8RJtm1qxZePvtt13Ply5dipkzZ6ra5Ofn429/+xsWL16MPXv24JFHHsF///d/46uvvkJ6ejo+/PBDAMCBAwdw8uRJvPLKKwCA6upq5OXlYfv27Vi3bh1kWcatt97qtYNbtWoVbr31VkyePBm7du3CunXrMHz4cM2227ZtAwC8/fbbOHnyJLZt24aMjAxMmDBBdb2Nbe655542W/SdIFobEcE2gCD8yX//939j7ty5OH78OADgm2++wfLly7F+/XoAQF1dHRYuXIjPP//cVSS8R48e2LhxI15//XWMHTsWiYmJAIDk5GTVHerPf/5z1VxLly5Fp06dsHfvXgwYMMCwrQsWLMAvfvELzJ8/33Vu8ODBmm07deoEAIiPj0dqaqrr/H333YcHH3wQixYtgs1mw86dO7F7927885//NGwPQRD+gX4CE22aTp064aabbsKyZcvw9ttv46abbkJSUpLr9cOHD6OmpgbXX389YmNjXcff/va3FmXjQ4cO4c4770SPHj0QFxfnkn9LSkq8srWoqAjXXXedV30bmTp1KiwWCz7++GMADXL2+PHjXbYRBBF86I6XaPPMmjULs2fPBgAUFhaqXquqqgLQIPN26dJF9ZrNZvM47pQpU9CtWzcsWbIEnTt3hqIoGDBgAOx2u1d2RkdHe9WPx2q1Yvr06Xj77bdx22234d1333VJ4wRBhAbkeIk2z4033gi73Q5JkjBx4kTVa5mZmbDZbCgpKcHYsWM1+1utVgCA0+l0nTtz5gwOHDiAJUuWYMyYMQCAjRs3mrJz0KBBWLduXbM1aBGRkZEqmxq57777MGDAAPzlL3+Bw+HAbbfdZsougiB8Czleos1jsViwb98+12Oe9u3b49FHH8UjjzwCRVEwevRoXLhwAd988w3i4uIwY8YMdOvWDZIk4d///jcmT56M6OhoJCQkoGPHjnjjjTeQlpaGkpISzJkzx5Sd8+bNw3XXXYeePXviF7/4BRwOBz799FM89thjmu0zMjKwbt06jBo1CjabDQkJCQCAfv364eqrr8Zjjz2GWbNm+eROmiAI30FrvERYEBcXh7i4OM3XnnvuOTz11FPIz89Hv379cOONN2LVqlXo3r07AKBLly6YP38+5syZg5SUFMyePRuyLGP58uXYsWMHBgwYgEceeQQvvviiKRvHjRuH999/H5988gmysrJw7bXXYuvWrcL2L7/8MtauXYv09HTV9igAuPfee2G32zFr1ixTNhEE4XskxhgLthEEQfiW5557Du+//z6+//77YJtCEIQbdMdLEG2Iqqoq/PDDD3jttdfw0EMPBdscgiA0IMdLEG2I2bNnY+jQoRg3bhzJzAQRopDUTBAEQRABhO54CYIgCCKAkOMlCIIgiABCjpcgCIIgAgg5XoIgCIIIIOR4CYIgCCKAkOMlCIIgiABCjpcgCIIgAgg5XoIgCIIIIP8fT4bMo7cw12wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "# Plot the loss landscape using imshow.\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "plt.figure(figsize=(5, 4))\n",
    "plt.imshow(loss_map, origin='lower', extent=[0,1,0,1], aspect='auto', norm=colors.LogNorm())#, vmin=-3.5, vmax=-2.5)#extent=[1e-4, 0.05, 0, 10]\n",
    "plt.xlabel('Metallicity')\n",
    "plt.ylabel('Age')\n",
    "plt.title('Loss Landscape')\n",
    "plt.colorbar(label='loss')\n",
    "# Plot a red dot at the desired coordinates.\n",
    "plt.plot(metallicity_history[:], age_history[:])#, 'bx', markersize=8)\n",
    "#plt.plot(metallicity_history[::100], age_history[::100], 'bx', markersize=8)\n",
    "plt.plot(metallicity_values[index_metallicity]/0.05, age_values[index_age]/20, 'ro', markersize=8)\n",
    "plt.plot(metallicity_values[initial_metallicity_index]/0.05, age_values[initial_age_index]/20, 'ro', markersize=8)\n",
    "plt.savefig(f\"output/optimisation_losslandscape.jpg\", dpi=1000)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_SKIP\n",
    "metallicity_history = np.array(metallicity_history)*0.05\n",
    "age_history = np.array(age_history)*20\n",
    "metallicity_history2 = np.array(metallicity_history2)*0.05\n",
    "age_history2 = np.array(age_history2)*20\n",
    "metallicity_history3 = np.array(metallicity_history3)*0.05\n",
    "age_history3 = np.array(age_history3)*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "\n",
    "# Update the extent to the physical values: metallicity from 0 to 0.05 and age from 0 to 20.\n",
    "plt.imshow(loss_map, origin='lower', extent=[0, 0.05, 0, 20], aspect='auto', norm=colors.LogNorm())\n",
    "\n",
    "plt.xlabel('Metallicity')\n",
    "plt.ylabel('Age')\n",
    "plt.title('Loss Landscape')\n",
    "plt.colorbar(label='loss')\n",
    "\n",
    "# Plot the history in physical coordinates by multiplying the normalized values.\n",
    "plt.plot(metallicity_history[:], age_history[:])#, 'bx', markersize=8)\n",
    "plt.plot(metallicity_history2[:], age_history2[:])#, 'gx', markersize=8\n",
    "plt.plot(metallicity_history3[:], age_history3[:])#, 'mx', markersize=8)\n",
    "\n",
    "# Plot the red dots in physical coordinates\n",
    "plt.plot(metallicity_values[index_metallicity], age_values[index_age], marker='*', color='yellow', markersize=8)\n",
    "plt.plot(metallicity_values[initial_metallicity_index], age_values[initial_age_index], 'wo', markersize=8)\n",
    "plt.plot(metallicity_values[initial_metallicity_index2], age_values[initial_age_index2], 'wo', markersize=8)\n",
    "plt.plot(metallicity_values[initial_metallicity_index3], age_values[initial_age_index3], 'wo', markersize=8)\n",
    "\n",
    "plt.savefig(\"output/optimisation_losslandscape.jpg\", dpi=1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NBVAL_SKIP\n",
    "# plot loss history for all three runs\n",
    "\n",
    "loss_history_np = np.array(loss_history)\n",
    "loss_history2 = np.array(loss_history2)\n",
    "loss_history3 = np.array(loss_history3)\n",
    "iterations = np.arange(len(loss_history_np))\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.plot(iterations, loss_history_np, label='Run 1')\n",
    "plt.plot(iterations, loss_history2, label='Run 2')\n",
    "plt.plot(iterations, loss_history3, label='Run 3')\n",
    "#plt.yscale('log')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('log(Loss)')\n",
    "plt.title('Loss History for Three Runs')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(\"output/optimisation_loglosshistory.jpg\", dpi=1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NBVAL_SKIP\n",
    "# plot loss history for all three runs\n",
    "\n",
    "loss_history_np = np.array(loss_history)\n",
    "loss_history2 = np.array(loss_history2)\n",
    "loss_history3 = np.array(loss_history3)\n",
    "iterations = np.arange(len(loss_history_np))\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.plot(iterations, 10**loss_history_np, label='Run 1')\n",
    "plt.plot(iterations, 10**loss_history2, label='Run 2')\n",
    "plt.plot(iterations, 10**loss_history3, label='Run 3')\n",
    "#plt.yscale('log')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss History for Three Runs')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(\"output/optimisation_losshistory.jpg\", dpi=1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NBVAL_SKIP\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import numpy as np\n",
    "\n",
    "# Prepare loss histories\n",
    "loss_history_np = np.array(loss_history)\n",
    "loss_history2 = np.array(loss_history2)\n",
    "loss_history3 = np.array(loss_history3)\n",
    "iterations = np.arange(len(loss_history_np))\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(8, 3))\n",
    "\n",
    "# --- Left: Loss Landscape ---\n",
    "im = axs[0].imshow(\n",
    "    loss_map,\n",
    "    origin='lower',\n",
    "    extent=[0, 0.05, 0, 20],\n",
    "    aspect='auto',\n",
    "    norm=colors.LogNorm()\n",
    ")\n",
    "axs[0].set_xlabel('Metallicity')\n",
    "axs[0].set_ylabel('Age (Gyrs)')\n",
    "axs[0].set_xlim(0, 0.045)\n",
    "#axs[0].set_title('Loss Landscape')\n",
    "fig.colorbar(im, ax=axs[0], label='log(loss)')\n",
    "\n",
    "# Plot the history in physical coordinates\n",
    "axs[0].plot(metallicity_history[:], age_history[:], color='orange')\n",
    "axs[0].plot(metallicity_history2[:], age_history2[:], color='purple')\n",
    "axs[0].plot(metallicity_history3[:], age_history3[:], color='red')\n",
    "\n",
    "# Plot the red dots in physical coordinates\n",
    "axs[0].plot(metallicity_values[index_metallicity], age_values[index_age], marker='*', color='yellow', markersize=8)\n",
    "axs[0].plot(metallicity_values[initial_metallicity_index], age_values[initial_age_index], 'wo', markersize=8)\n",
    "axs[0].plot(metallicity_values[initial_metallicity_index2], age_values[initial_age_index2], 'wo', markersize=8)\n",
    "axs[0].plot(metallicity_values[initial_metallicity_index3], age_values[initial_age_index3], 'wo', markersize=8)\n",
    "\n",
    "# --- Right: Loss History ---\n",
    "axs[1].plot(iterations, 10**loss_history_np, label='Run 1', color='orange')\n",
    "axs[1].plot(iterations, 10**loss_history2, label='Run 2', color='purple')\n",
    "axs[1].plot(iterations, 10**loss_history3, label='Run 3', color='red')\n",
    "axs[1].set_xlabel('Iteration')\n",
    "axs[1].set_ylabel('Loss')\n",
    "#axs[1].set_title('Loss History for Three Runs')\n",
    "axs[1].legend()\n",
    "axs[1].grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"output/optimisation_landscape_and_history.jpg\", dpi=1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:28:16,917 - rubix - INFO - Setting up the pipeline...\n",
      "2025-11-11 10:28:16,918 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n",
      "2025-11-11 10:28:16,919 - rubix - DEBUG - Roataion Type found: edge-on\n",
      "2025-11-11 10:28:16,920 - rubix - INFO - Calculating spatial bin edges...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:28:16,930 - rubix - INFO - Getting cosmology...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-11 10:28:16,940 - rubix - INFO - Calculating spatial bin edges...\n",
      "2025-11-11 10:28:16,949 - rubix - INFO - Getting cosmology...\n",
      "2025-11-11 10:28:16,981 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:28:17,040 - rubix - DEBUG - SSP Wave: (5994,)\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:28:17,060 - rubix - INFO - Getting cosmology...\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:28:17,114 - rubix - DEBUG - Method not defined, using default method: cubic\n",
      "/home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubixcpu2/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n",
      "  warnings.warn(\n",
      "2025-11-11 10:28:17,176 - rubix - INFO - Assembling the pipeline...\n",
      "2025-11-11 10:28:17,177 - rubix - INFO - Compiling the expressions...\n",
      "2025-11-11 10:28:17,178 - rubix - INFO - Number of devices: 1\n",
      "2025-11-11 10:28:17,251 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n",
      "2025-11-11 10:28:17,252 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n",
      "2025-11-11 10:28:17,252 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n",
      "2025-11-11 10:28:17,309 - rubix - INFO - Assigning particles to spaxels...\n",
      "2025-11-11 10:28:17,311 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n",
      "2025-11-11 10:28:17,322 - rubix - DEBUG - Datacube shape: (1, 1, 466)\n",
      "2025-11-11 10:28:17,323 - rubix - INFO - Convolving with PSF...\n",
      "2025-11-11 10:28:17,325 - rubix - INFO - Convolving with LSF...\n",
      "2025-11-11 10:28:17,328 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n",
      "2025-11-11 10:28:20,803 - rubix - INFO - Pipeline run completed in 3.89 seconds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 466)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_SKIP\n",
    "#run the pipeline with the optimized age\n",
    "#rubixdata.stars.age = optimized_age\n",
    "i = 200\n",
    "inputdata.stars.age = jnp.array([age_history[i]*20, age_history[i]*20])\n",
    "inputdata.stars.metallicity = jnp.array([metallicity_history[i]*0.05, metallicity_history[i]*0.05])\n",
    "inputdata.stars.mass = jnp.array([[1.0], [1.0]])\n",
    "inputdata.stars.velocity = jnp.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
    "\n",
    "pipe = RubixPipeline(config)\n",
    "rubixdata = pipe.run_sharded(inputdata)\n",
    "\n",
    "#plot the target and the optimized spectra\n",
    "import matplotlib.pyplot as plt\n",
    "wave = pipe.telescope.wave_seq\n",
    "\n",
    "spectra_target = targetdata\n",
    "spectra_optimitzed = rubixdata\n",
    "print(rubixdata.shape)\n",
    "\n",
    "\n",
    "# Create a figure with two subplots, sharing the x-axis.\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, gridspec_kw={'height_ratios': [4, 1]}, figsize=(7, 5))\n",
    "\n",
    "# Plot target and optimized spectra in the upper subplot.\n",
    "ax1.plot(wave, spectra_target[0, 0, :], label=f\"Target age = {age_values[index_age]:.2f}, metallicity = {metallicity_values[index_metallicity]:.4f}\")\n",
    "ax1.plot(wave, spectra_optimitzed[0, 0, :], label=f\"Optimized age = {age_history[i]*20:.2f}, metallicity = {metallicity_history[i]*0.05:.4f}\")\n",
    "ax1.set_ylabel(\"Luminosity [L/Å]\")\n",
    "#ax1.set_title(\"Target vs Optimized Spectra\")\n",
    "ax1.legend()\n",
    "ax1.grid(True)\n",
    "\n",
    "# Compute the residual (difference between target and optimized spectra).\n",
    "residual = (spectra_target[0, 0, :] - spectra_optimitzed[0, 0, :]) #/spectra_target[0, 0, :]\n",
    "\n",
    "# Plot the residual in the lower subplot.\n",
    "ax2.plot(wave, residual, 'k-')\n",
    "ax2.set_xlabel(\"Wavelength [Å]\")\n",
    "ax2.set_ylabel(\"Residual\")\n",
    "ax2.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"output/optimisation_spectra.jpg\", dpi=1000)\n",
    "plt.show()"
   ]
  }
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