{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibration of the 9-Mythen detector at the Cristal beamline at Soleil\n",
    "\n",
    "Mythen detectors are 1D-strip detectors sold by Dectris. \n",
    "On the Cristal beamline at Soleil, 9 of them are mounted on the goniometer. \n",
    "\n",
    "This notebook explains how to calibrate precisely their position (including the wavelength used) as function of the goniometer position.\n",
    "\n",
    "All input data are provided in a Nexus file which contains both the (approximate) energy, the goniometer positions (500 points have been measured) and the measured signal.\n",
    "\n",
    "As pyFAI is not made for 1D data, the Mythen detector will be considered as a 1x1280 image.\n",
    "\n",
    "We start by importing a whole bunch of modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:47.015223Z",
     "iopub.status.busy": "2026-09-15T09:25:47.015089Z",
     "iopub.status.idle": "2026-09-15T09:25:47.395799Z",
     "shell.execute_reply": "2026-09-15T09:25:47.395127Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# use `widget` for better user experience; `inline` is for documentation generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:47.397459Z",
     "iopub.status.busy": "2026-09-15T09:25:47.397321Z",
     "iopub.status.idle": "2026-09-15T09:25:48.648826Z",
     "shell.execute_reply": "2026-09-15T09:25:48.647951Z"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy\n",
    "import h5py\n",
    "from silx.resources import ExternalResources\n",
    "\n",
    "from pyFAI.detectors import Detector\n",
    "from pyFAI.goniometer import ExtendedTransformation, GoniometerRefinement\n",
    "from pyFAI.control_points import ControlPoints\n",
    "from pyFAI.gui import jupyter\n",
    "from pyFAI.units import hc\n",
    "from pyFAI.calibrant import get_calibrant\n",
    "from pyFAI.containers import Integrate1dResult\n",
    "\n",
    "import ipywidgets as widgets\n",
    "\n",
    "from scipy.signal import find_peaks_cwt\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.optimize import bisect, minimize\n",
    "from scipy.spatial.distance import cdist\n",
    "import time\n",
    "\n",
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:48.651632Z",
     "iopub.status.busy": "2026-09-15T09:25:48.651254Z",
     "iopub.status.idle": "2026-09-15T09:25:48.689234Z",
     "shell.execute_reply": "2026-09-15T09:25:48.688509Z"
    }
   },
   "outputs": [],
   "source": [
    "#Nota: Useful to configure a proxy if you are behind a firewall\n",
    "#os.environ[\"http_proxy\"] = \"http://proxy.company.fr:3128\"\n",
    "\n",
    "downloader = ExternalResources(\"detector_calibration\", \"http://www.silx.org/pub/pyFAI/gonio/\")\n",
    "mythen_ring_file = downloader.getfile(\"LaB6_17keV_att3_tth2C_24_01_2018_19-43-20_1555.nxs\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data file can be downloaded from:\n",
    "http://www.silx.org/pub/pyFAI/gonio/LaB6_17keV_att3_tth2C_24_01_2018_19-43-20_1555.nxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:48.690802Z",
     "iopub.status.busy": "2026-09-15T09:25:48.690704Z",
     "iopub.status.idle": "2026-09-15T09:25:48.695940Z",
     "shell.execute_reply": "2026-09-15T09:25:48.695405Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positions:  [90.00000001 89.79994445 89.5998889  89.39994445 89.19994445] ...\n"
     ]
    }
   ],
   "source": [
    "#Open the Nexus file and retrieve the actual positions:\n",
    "\n",
    "h5 = h5py.File(mythen_ring_file, mode=\"r\")\n",
    "position = h5[\"/LaB6_17keV_att3_1555/scan_data/actuator_1_1\"][:]\n",
    "print(\"Positions: \", position[:5], \"...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:48.743861Z",
     "iopub.status.busy": "2026-09-15T09:25:48.743759Z",
     "iopub.status.idle": "2026-09-15T09:25:48.764146Z",
     "shell.execute_reply": "2026-09-15T09:25:48.763320Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_01 (501, 5120)\n",
      "data_02 (501, 1280)\n",
      "data_03 (501, 1280)\n",
      "data_04 (501, 1280)\n",
      "data_05 (501, 1280)\n",
      "data_06 (501, 5120)\n",
      "data_07 (501, 1280)\n",
      "data_08 (501, 1280)\n",
      "data_09 (501, 1280)\n",
      "data_10 (501, 1280)\n",
      "data_11 (501, 1280)\n",
      "data_12 (501, 1280)\n",
      "['data_02', 'data_03', 'data_04', 'data_05', 'data_07', 'data_08', 'data_09', 'data_10', 'data_11', 'data_12']\n"
     ]
    }
   ],
   "source": [
    "#Read all data\n",
    "\n",
    "data = {}\n",
    "ds_names = []\n",
    "for idx in range(1,13):\n",
    "    name = \"data_%02i\"%idx\n",
    "    ds = h5[\"/LaB6_17keV_att3_1555/scan_data/\"+name][:]\n",
    "    print(name, ds.shape)\n",
    "    if ds.shape[1]<2000:\n",
    "        #Keep only the single modules\n",
    "        data[name] = ds\n",
    "        ds_names.append(name)\n",
    "\n",
    "print(ds_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:48.765689Z",
     "iopub.status.busy": "2026-09-15T09:25:48.765592Z",
     "iopub.status.idle": "2026-09-15T09:25:48.768454Z",
     "shell.execute_reply": "2026-09-15T09:25:48.767812Z"
    }
   },
   "outputs": [],
   "source": [
    "#Define a Mythen-detector mounted vertically:\n",
    "\n",
    "class MythenV(Detector):\n",
    "    \"Verical Mythen dtrip detector from Dectris\"\n",
    "    aliases = [\"MythenV 1280\"]\n",
    "    force_pixel = True\n",
    "    MAX_SHAPE = (1280, 1)\n",
    "\n",
    "    def __init__(self,pixel1=50e-6, pixel2=8e-3):\n",
    "        super(MythenV, self).__init__(pixel1=pixel1, pixel2=pixel2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:48.769981Z",
     "iopub.status.busy": "2026-09-15T09:25:48.769893Z",
     "iopub.status.idle": "2026-09-15T09:25:48.773849Z",
     "shell.execute_reply": "2026-09-15T09:25:48.772972Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_02 MythenV 1280\n",
      "data_03 MythenV 1280\n",
      "data_04 MythenV 1280\n",
      "data_05 MythenV 1280\n",
      "data_07 MythenV 1280\n",
      "data_08 MythenV 1280\n",
      "data_09 MythenV 1280\n",
      "data_10 MythenV 1280\n",
      "data_11 MythenV 1280\n",
      "data_12 MythenV 1280\n"
     ]
    }
   ],
   "source": [
    "#Define all modules as single detectors of class MythenV. \n",
    "# Each one has a mask defined from dummy-values in the dataset\n",
    "\n",
    "modules = {}\n",
    "for name, ds in data.items():\n",
    "    one_module = MythenV()\n",
    "    mask = ds[0]<0\n",
    "    #discard the first 20 and last 20 pixels as their intensities are less reliable\n",
    "    mask[:20] = True\n",
    "    mask[-20:] = True\n",
    "    one_module.mask = mask.reshape(-1,1)\n",
    "    modules[name] = one_module\n",
    "\n",
    "for k,v in modules.items():\n",
    "    print(k, v.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:48.775500Z",
     "iopub.status.busy": "2026-09-15T09:25:48.775412Z",
     "iopub.status.idle": "2026-09-15T09:25:57.154358Z",
     "shell.execute_reply": "2026-09-15T09:25:57.153108Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.3 ms ± 120 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "[[287.06072343   0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "# Define a peak-picking function based on the dataset-name and the frame_id:\n",
    "\n",
    "def peak_picking(module_name, frame_id,  \n",
    "                 threshold=500):\n",
    "    \"\"\"Peak-picking base on find_peaks_cwt from scipy plus \n",
    "    second-order tailor exapention refinement for sub-pixel resolution.\n",
    "    \n",
    "    The half-pixel offset is accounted here, i.e pixel #0 has its center at 0.5\n",
    "    \n",
    "    \"\"\"\n",
    "    module = modules[module_name]\n",
    "    msk = module.mask.ravel()\n",
    "    \n",
    "    spectrum = data[module_name][frame_id]\n",
    "    guess = find_peaks_cwt(spectrum, [20])\n",
    "    \n",
    "    valid = numpy.logical_and(numpy.logical_not(msk[guess]), \n",
    "                               spectrum[guess]>threshold)\n",
    "    guess = guess[valid]\n",
    "    \n",
    "    #Based on maximum is f'(x) = 0 ~ f'(x0) + (x-x0)*(f''(x0))\n",
    "    df = numpy.gradient(spectrum)\n",
    "    d2f = numpy.gradient(df)\n",
    "    bad = d2f==0\n",
    "    d2f[bad] = 1e-10 #prevent devision by zero. Discared later on\n",
    "    cor = df / d2f\n",
    "    cor[abs(cor)>1] = 0\n",
    "    cor[bad] = 0\n",
    "    ref = guess - cor[guess] + 0.5 #half a pixel offset\n",
    "    x = numpy.zeros_like(ref) + 0.5 #half a pixel offset\n",
    "    return numpy.vstack((ref,x)).T\n",
    "\n",
    "%timeit peak_picking(ds_names[0], 93)\n",
    "print(peak_picking(ds_names[0], 93))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:57.156835Z",
     "iopub.status.busy": "2026-09-15T09:25:57.156679Z",
     "iopub.status.idle": "2026-09-15T09:25:57.168013Z",
     "shell.execute_reply": "2026-09-15T09:25:57.167321Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy (keV):  17.027082549190933 \n",
      "Wavelength (A):  7.281587910025816e-11\n",
      "LaB6 Calibrant with 109 reflections at wavelength 7.281587910025816e-11\n"
     ]
    }
   ],
   "source": [
    "nrj = h5[\"/LaB6_17keV_att3_1555/CRISTAL/Monochromator/energy\"][0]\n",
    "wl = hc / nrj *1e-10\n",
    "print(\"Energy (keV): \",nrj, \"\\nWavelength (A): \",wl)\n",
    "\n",
    "LaB6 = get_calibrant(\"LaB6\")\n",
    "LaB6.wavelength = wl\n",
    "print(LaB6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:57.169845Z",
     "iopub.status.busy": "2026-09-15T09:25:57.169754Z",
     "iopub.status.idle": "2026-09-15T09:25:57.173607Z",
     "shell.execute_reply": "2026-09-15T09:25:57.172875Z"
    }
   },
   "outputs": [],
   "source": [
    "#This cell defines the transformation of coordinates for a simple goniometer mounted vertically.\n",
    "\n",
    "trans = ExtendedTransformation(dist_expr=\"dist\", \n",
    "                                   poni1_expr=\"poni1\", \n",
    "                                   poni2_expr=\"poni2\", \n",
    "                                   rot1_expr=\"rot1\", \n",
    "                                   rot2_expr=\"pi*(offset+scale*angle)/180.\", \n",
    "                                   rot3_expr=\"0.0\", \n",
    "                                   wavelength_expr=\"hc/nrj*1e-10\", \n",
    "                                   param_names=[\"dist\", \"poni1\", \"poni2\", \"rot1\", \"offset\", \"scale\", \"nrj\"], \n",
    "                                   pos_names=[\"angle\"], \n",
    "                                   constants={\"hc\": hc})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:57.175329Z",
     "iopub.status.busy": "2026-09-15T09:25:57.175227Z",
     "iopub.status.idle": "2026-09-15T09:25:57.178370Z",
     "shell.execute_reply": "2026-09-15T09:25:57.177582Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximated offset for the first module:  82.79994445106844\n"
     ]
    }
   ],
   "source": [
    "def get_position(idx):\n",
    "    \"Returns the postion of the goniometer for the given frame_id\"\n",
    "    return position[idx]\n",
    "\n",
    "#Approximate offset for the module #0 at 0°\n",
    "print(\"Approximated offset for the first module: \",get_position(36))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:57.179892Z",
     "iopub.status.busy": "2026-09-15T09:25:57.179804Z",
     "iopub.status.idle": "2026-09-15T09:25:57.348654Z",
     "shell.execute_reply": "2026-09-15T09:25:57.347870Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/505442400.py:8: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "304638d9f1e645e485f12bcb4d9e59ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=4, description='module_id', max=9), IntSlider(value=250, description='fr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This interactive plot lets one visualize any spectra acquired by any module\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "line = ax.plot(data[ds_names[0]][250])[0]\n",
    "ligne = plt.Line2D(xdata=[640,640], ydata=[-500, 1000], figure=fig, linestyle=\"--\", color='red', axes=ax)\n",
    "ax.add_line(ligne)\n",
    "ax.set_title(\"spectrum\")\n",
    "fig.show()\n",
    "\n",
    "def update(module_id, frame_id):\n",
    "   spectrum = data[ds_names[module_id]][frame_id]\n",
    "   line.set_data(numpy.arange(spectrum.size), spectrum)\n",
    "   ax.set_title(\"Module %i, Frame %i\"%(module_id, frame_id))\n",
    "   \n",
    "   fig.canvas.draw()\n",
    "\n",
    "    \n",
    "interactive_plot = widgets.interactive(update, \n",
    "                                       module_id=(0, len(data)-1), \n",
    "                                       frame_id=(0, data[ds_names[0]].shape[0]-1))\n",
    "display(interactive_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:57.350244Z",
     "iopub.status.busy": "2026-09-15T09:25:57.350145Z",
     "iopub.status.idle": "2026-09-15T09:25:57.353366Z",
     "shell.execute_reply": "2026-09-15T09:25:57.352654Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_02\n"
     ]
    }
   ],
   "source": [
    "#Work with the first module corresponding to:\n",
    "name = ds_names[0]\n",
    "print(name)\n",
    "ds = data[name]\n",
    "module = modules[name]\n",
    "\n",
    "#Use the previous widget to select:\n",
    "## the index where the beam-center is in the middle of the module\n",
    "zero_pos = 36\n",
    "\n",
    "## The frame index where the first LaB6 peak enters the right-hand side of the spectrum\n",
    "peak_zero_start = 74\n",
    "\n",
    "## The frame index where this first LaB6 leaves the spectrum or the second LaB6 peak appears:\n",
    "peak_zero_end = 94\n",
    "\n",
    "# The frames between peak_zero_start and peak_zero_end will be used to calibrate roughly the goniometer \n",
    "# and used later for finer peak extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:57.354867Z",
     "iopub.status.busy": "2026-09-15T09:25:57.354776Z",
     "iopub.status.idle": "2026-09-15T09:25:57.358244Z",
     "shell.execute_reply": "2026-09-15T09:25:57.357714Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GoniometerRefinement with 0 geometries labeled: .\n"
     ]
    }
   ],
   "source": [
    "param0 = {\"dist\": 0.72, \n",
    "          \"poni1\": 640*50e-6, \n",
    "          \"poni2\": 4e-3, \n",
    "          \"rot1\":0, \n",
    "          \"offset\": -get_position(zero_pos), \n",
    "          \"scale\":1, \n",
    "          \"nrj\": nrj}\n",
    "\n",
    "#Lock enegy for now and a couple of other parameters\n",
    "bounds0 = {\"nrj\": (nrj, nrj),\n",
    "           \"dist\": (0.71, 0.73),\n",
    "           \"poni2\": (4e-3, 4e-3),\n",
    "           \"rot1\": (0,0),\n",
    "           \"scale\":(1,1), \n",
    "          }\n",
    "\n",
    "gonioref0 = GoniometerRefinement(param0, \n",
    "                                 get_position, \n",
    "                                 trans, \n",
    "                                 detector=module, \n",
    "                                 wavelength=wl, \n",
    "                                 bounds=bounds0\n",
    "                                 )\n",
    "goniometers = {name:  gonioref0} \n",
    "print(gonioref0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:25:57.360060Z",
     "iopub.status.busy": "2026-09-15T09:25:57.359963Z",
     "iopub.status.idle": "2026-09-15T09:26:00.196621Z",
     "shell.execute_reply": "2026-09-15T09:26:00.195686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GoniometerRefinement with 20 geometries labeled: data_02_0074, data_02_0075, data_02_0076, data_02_0077, data_02_0078, data_02_0079, data_02_0080, data_02_0081, data_02_0082, data_02_0083, data_02_0084, data_02_0085, data_02_0086, data_02_0087, data_02_0088, data_02_0089, data_02_0090, data_02_0091, data_02_0092, data_02_0093.\n",
      "Residual error before fit:\n",
      "6.737408475887519e-07\n"
     ]
    }
   ],
   "source": [
    "# Extract the frames where only the peak zero from LaB6 is present.\n",
    "\n",
    "for i in range(peak_zero_start, peak_zero_end):\n",
    "    cp = ControlPoints(calibrant=LaB6, wavelength=wl)\n",
    "    peak = peak_picking(name, i)\n",
    "    if len(peak)!=1: \n",
    "        continue\n",
    "    cp.append([peak[0]], ring=0)\n",
    "    img = ds[i].reshape((-1,1)) #Images are vertical ... transpose the spectrum\n",
    "    sg = gonioref0.new_geometry(\"%s_%04i\"%(name,i), \n",
    "                                image=img, \n",
    "                                metadata=i, \n",
    "                                control_points=cp, \n",
    "                                calibrant=LaB6)\n",
    "    sg.geometry_refinement.data = numpy.array(cp.getList())\n",
    "\n",
    "print(gonioref0)\n",
    "print(\"Residual error before fit:\")\n",
    "print(gonioref0.chi2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:00.198325Z",
     "iopub.status.busy": "2026-09-15T09:26:00.198229Z",
     "iopub.status.idle": "2026-09-15T09:26:00.216602Z",
     "shell.execute_reply": "2026-09-15T09:26:00.215770Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost function before refinement: 6.737408475887519e-07\n",
      "[ 7.20000000e-01  3.20000000e-02  4.00000000e-03  0.00000000e+00\n",
      " -8.27999445e+01  1.00000000e+00  1.70270825e+01]\n",
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.490988846910722e-11\n",
      "           x: [ 7.200e-01  3.141e-02  4.000e-03  0.000e+00 -8.280e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [ 1.584e-07  2.919e-08        nan        nan  3.427e-11\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.490988846910722e-11\n",
      "GonioParam(dist=np.float64(0.719994724358983), poni1=np.float64(0.031408577292064206), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-82.79995188659902), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.031408577292064206\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.19994724e-01,  3.14085773e-02,  4.00000000e-03,  0.00000000e+00,\n",
       "       -8.27999519e+01,  1.00000000e+00,  1.70270825e+01])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First refinement:\n",
    "gonioref0.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:00.218150Z",
     "iopub.status.busy": "2026-09-15T09:26:00.218060Z",
     "iopub.status.idle": "2026-09-15T09:26:51.915419Z",
     "shell.execute_reply": "2026-09-15T09:26:51.914353Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of peaks found and used for refinement\n",
      "1203\n",
      "Residual error before fitting:  3.118662178645907e-06\n"
     ]
    }
   ],
   "source": [
    "#Here we extract all spectra for peaks,\n",
    "# If there are as many peaks as expected from the theoritical LaB6. perform the assignment.\n",
    "\n",
    "#Peaks from LaB6:\n",
    "tths = LaB6.get_2th()\n",
    "\n",
    "for i in range(peak_zero_end, ds.shape[0]):\n",
    "    peak = peak_picking(name, i)\n",
    "    ai=gonioref0.get_ai(get_position(i))\n",
    "    tth = ai.array_from_unit(unit=\"2th_rad\", scale=False)\n",
    "    tth_low = tth[20]\n",
    "    tth_hi = tth[-20]\n",
    "    ttmin, ttmax = min(tth_low, tth_hi), max(tth_low, tth_hi)\n",
    "    valid_peaks = numpy.logical_and(ttmin<=tths, tths<ttmax)\n",
    "    cnt = valid_peaks.sum()\n",
    "    if (len(peak) ==  cnt):    \n",
    "        cp = ControlPoints(calibrant=LaB6, wavelength=wl)\n",
    "        #revert the order of assignment if needed !!\n",
    "        if tth_hi < tth_low:\n",
    "            peak = peak[-1::-1]\n",
    "        for p, r in zip(peak, numpy.where(valid_peaks)[0]):\n",
    "            #print(p,r)\n",
    "            cp.append([p], ring=r)\n",
    "        img = ds[i].reshape((-1,1))\n",
    "        sg = gonioref0.new_geometry(\"%s_%04i\"%(name,i), \n",
    "                                    image=img, \n",
    "                                    metadata=i, \n",
    "                                    control_points=cp, \n",
    "                                    calibrant=LaB6)\n",
    "        sg.geometry_refinement.data = numpy.array(cp.getList())\n",
    "        #print(sg.label, len(sg.geometry_refinement.data))\n",
    "\n",
    "#print(gonioref0)\n",
    "print(\" Number of peaks found and used for refinement\")\n",
    "print(sum([len(sg.geometry_refinement.data) for sg in gonioref0.single_geometries.values()]))\n",
    "print(\"Residual error before fitting: \", gonioref0.chi2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:51.917346Z",
     "iopub.status.busy": "2026-09-15T09:26:51.917241Z",
     "iopub.status.idle": "2026-09-15T09:26:52.634523Z",
     "shell.execute_reply": "2026-09-15T09:26:52.633810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost function before refinement: 3.118662178645907e-06\n",
      "[ 7.19994724e-01  3.14085773e-02  4.00000000e-03  0.00000000e+00\n",
      " -8.27999519e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.7342490266357463e-06\n",
      "           x: [ 7.231e-01  3.185e-02  4.000e-03  0.000e+00 -8.280e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 7\n",
      "         jac: [-5.582e-08 -3.618e-10        nan        nan  6.365e-10\n",
      "                      nan        nan]\n",
      "        nfev: 30\n",
      "        njev: 7\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.7342490266357463e-06\n",
      "GonioParam(dist=np.float64(0.7230953421817191), poni1=np.float64(0.03184537030470006), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-82.7999466162549), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: dist (0) 0.719994724358983 --> 0.7230953421817191\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.23095342e-01,  3.18453703e-02,  4.00000000e-03,  0.00000000e+00,\n",
       "       -8.27999466e+01,  1.00000000e+00,  1.70270825e+01])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonioref0.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:52.636405Z",
     "iopub.status.busy": "2026-09-15T09:26:52.636309Z",
     "iopub.status.idle": "2026-09-15T09:26:53.299379Z",
     "shell.execute_reply": "2026-09-15T09:26:53.298766Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost function before refinement: 2.7342490266357463e-06\n",
      "[ 7.23095342e-01  3.18453703e-02  4.00000000e-03  0.00000000e+00\n",
      " -8.27999466e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.689172533892251e-06\n",
      "           x: [ 7.231e-01  3.205e-02  3.985e-03  1.095e-05 -8.280e+01\n",
      "                9.994e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [-5.145e-07 -1.241e-07  5.957e-07 -4.287e-07 -9.297e-10\n",
      "               -8.539e-08        nan]\n",
      "        nfev: 28\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.689172533892251e-06\n",
      "GonioParam(dist=np.float64(0.7230962438566039), poni1=np.float64(0.03205370080101193), poni2=np.float64(0.003984854883002208), rot1=np.float64(1.094777066495059e-05), offset=np.float64(-82.79994398900047), scale=np.float64(0.9994150576486731), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 1.0 --> 0.9994150576486731\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.23096244e-01,  3.20537008e-02,  3.98485488e-03,  1.09477707e-05,\n",
       "       -8.27999440e+01,  9.99415058e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonioref0.set_bounds(\"poni1\", -1, 1)\n",
    "gonioref0.set_bounds(\"poni2\", -1, 1)\n",
    "gonioref0.set_bounds(\"rot1\", -1, 1)\n",
    "gonioref0.set_bounds(\"scale\", 0.9, 1.1)\n",
    "gonioref0.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:53.301118Z",
     "iopub.status.busy": "2026-09-15T09:26:53.301022Z",
     "iopub.status.idle": "2026-09-15T09:26:54.663507Z",
     "shell.execute_reply": "2026-09-15T09:26:54.661959Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data_02': (array([9.50000113e-04, 2.85000034e-03, 4.75000057e-03, ...,\n",
      "       9.49952613e+01, 9.49971613e+01, 9.49990613e+01], shape=(50000,)), array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.2715863e+08,\n",
      "       1.3190415e+08, 1.3126803e+08], shape=(50000,), dtype=float32))}\n"
     ]
    }
   ],
   "source": [
    "# Perform the azimuthal intgration of all data for the first module:\n",
    "\n",
    "mg = gonioref0.get_mg(position)\n",
    "mg.radial_range = (0, 95)\n",
    "images = [i.reshape(-1, 1) for i in ds]\n",
    "res_mg = mg.integrate1d(images, 50000)\n",
    "results={name: res_mg}\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:54.665153Z",
     "iopub.status.busy": "2026-09-15T09:26:54.665054Z",
     "iopub.status.idle": "2026-09-15T09:26:55.317273Z",
     "shell.execute_reply": "2026-09-15T09:26:55.315847Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/954410272.py:6: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the integrated pattern vs expected peak positions:\n",
    "\n",
    "LaB6_new = get_calibrant(\"LaB6\")\n",
    "LaB6_new.wavelength = hc/gonioref0.param[-1]*1e-10\n",
    "p = jupyter.plot1d(res_mg, calibrant=LaB6_new)\n",
    "p.figure.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:55.319073Z",
     "iopub.status.busy": "2026-09-15T09:26:55.318973Z",
     "iopub.status.idle": "2026-09-15T09:26:55.324800Z",
     "shell.execute_reply": "2026-09-15T09:26:55.323668Z"
    }
   },
   "outputs": [],
   "source": [
    "#Peak profile function based on a bilinear interpolations: \n",
    "\n",
    "def calc_fwhm(integrate_result, calibrant, tth_min=None, tth_max=None):\n",
    "    \"calculate the tth position and FWHM for each peak\"\n",
    "    delta = integrate_result.intensity[1:] - integrate_result.intensity[:-1]\n",
    "    maxima = numpy.where(numpy.logical_and(delta[:-1]>0, delta[1:]<0))[0]\n",
    "    minima = numpy.where(numpy.logical_and(delta[:-1]<0, delta[1:]>0))[0]\n",
    "    maxima += 1\n",
    "    minima += 1\n",
    "    tth = []\n",
    "    FWHM = []\n",
    "    if tth_min is None:\n",
    "        tth_min = integrate_result.radial[0]\n",
    "    if tth_max is None:\n",
    "        tth_max = integrate_result.radial[-1]\n",
    "    for tth_rad in calibrant.get_2th():\n",
    "        tth_deg = tth_rad*integrate_result.unit.scale\n",
    "        if (tth_deg<=tth_min) or (tth_deg>=tth_max):\n",
    "            continue\n",
    "        idx_theo = abs(integrate_result.radial-tth_deg).argmin()\n",
    "        id0_max = abs(maxima-idx_theo).argmin()\n",
    "        id0_min = abs(minima-idx_theo).argmin()\n",
    "        I_max = integrate_result.intensity[maxima[id0_max]]\n",
    "        I_min = integrate_result.intensity[minima[id0_min]]\n",
    "        tth_maxi = integrate_result.radial[maxima[id0_max]]\n",
    "        I_thres = (I_max + I_min)/2.0\n",
    "        if minima[id0_min]>maxima[id0_max]:\n",
    "            if id0_min == 0:\n",
    "                min_lo = integrate_result.radial[0]\n",
    "            else:\n",
    "                min_lo = integrate_result.radial[minima[id0_min-1]]\n",
    "            min_hi = integrate_result.radial[minima[id0_min]]\n",
    "        else:\n",
    "            if id0_min == len(minima) -1:\n",
    "                min_hi = integrate_result.radial[-1]\n",
    "            else:\n",
    "                min_hi = integrate_result.radial[minima[id0_min+1]]\n",
    "            min_lo = integrate_result.radial[minima[id0_min]]\n",
    "            \n",
    "        f = interp1d(integrate_result.radial, integrate_result.intensity-I_thres)\n",
    "        try:\n",
    "            tth_lo = bisect(f, min_lo, tth_maxi)\n",
    "            tth_hi = bisect(f, tth_maxi, min_hi)\n",
    "        except:\n",
    "            pass\n",
    "        else:\n",
    "            FWHM.append(tth_hi-tth_lo)\n",
    "            tth.append(tth_deg)\n",
    "    return tth, FWHM\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:55.326637Z",
     "iopub.status.busy": "2026-09-15T09:26:55.326546Z",
     "iopub.status.idle": "2026-09-15T09:26:55.330770Z",
     "shell.execute_reply": "2026-09-15T09:26:55.329543Z"
    }
   },
   "outputs": [],
   "source": [
    "# Peak error:\n",
    "\n",
    "def calc_peak_error(integrate_result, calibrant, tth_min=10, tth_max=95):\n",
    "    \"calculate the tth position and FWHM for each peak\"\n",
    "    peaks = find_peaks_cwt(integrate_result.intensity, [10])\n",
    "    df = numpy.gradient(integrate_result.intensity)\n",
    "    d2f = numpy.gradient(df)\n",
    "    bad = d2f==0\n",
    "    d2f[bad] = 1e-10\n",
    "    cor = df / d2f\n",
    "    print((abs(cor)>1).sum())\n",
    "    cor[abs(cor)>1] = 0\n",
    "    cor[bad] = 0\n",
    "    got = numpy.interp(peaks-cor[peaks], \n",
    "                       numpy.arange(len(integrate_result.radial)), \n",
    "                       integrate_result.radial)\n",
    "    mask = numpy.logical_and(got>=tth_min,\n",
    "                             got<=tth_max)\n",
    "    got = got[mask]\n",
    "    target = numpy.array(calibrant.get_2th())*integrate_result.unit.scale\n",
    "    mask = numpy.logical_and(target>=tth_min,\n",
    "                             target<=tth_max)\n",
    "    target = target[mask]\n",
    "    print(len(got), len(target))\n",
    "    d2 = cdist(target.reshape(-1, 1 ),\n",
    "               got.reshape(-1, 1), \"minkowski\", p=1)\n",
    "    \n",
    "    return target, target-got[d2.argmin(axis=-1)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:55.332236Z",
     "iopub.status.busy": "2026-09-15T09:26:55.332147Z",
     "iopub.status.idle": "2026-09-15T09:26:56.829671Z",
     "shell.execute_reply": "2026-09-15T09:26:56.828265Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29218\n",
      "81 60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/55614379.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(*calc_fwhm(res_mg, LaB6_new), \"o\", label=\"FWHM\")\n",
    "ax.plot(*calc_peak_error(res_mg, LaB6_new), \"o\", label=\"offset\")\n",
    "ax.set_title(\"Peak shape & error as function of the angle\")\n",
    "ax.set_xlabel(res_mg.unit.label)\n",
    "ax.legend()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module 1 \n",
    "\n",
    "We can apply the same procedure for the second module ... and try to rationalize the procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:56.831561Z",
     "iopub.status.busy": "2026-09-15T09:26:56.831464Z",
     "iopub.status.idle": "2026-09-15T09:26:56.834656Z",
     "shell.execute_reply": "2026-09-15T09:26:56.833449Z"
    }
   },
   "outputs": [],
   "source": [
    "module_id = 1\n",
    "name = ds_names[module_id]\n",
    "ds = data[name]\n",
    "zero_pos = 64\n",
    "frame_start = 103\n",
    "frame_stop = 123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:56.836193Z",
     "iopub.status.busy": "2026-09-15T09:26:56.836099Z",
     "iopub.status.idle": "2026-09-15T09:26:56.840209Z",
     "shell.execute_reply": "2026-09-15T09:26:56.839039Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GoniometerRefinement with 0 geometries labeled: .\n"
     ]
    }
   ],
   "source": [
    "param1 = {\"dist\": 0.72, \n",
    "          \"poni1\": 640*50e-6, \n",
    "          \"poni2\": 4e-3, \n",
    "          \"rot1\":0, \n",
    "          \"offset\": -get_position(zero_pos), \n",
    "          \"scale\":1, \n",
    "          \"nrj\": nrj}\n",
    "\n",
    "#Lock enegy for now and a couple of other parameters\n",
    "bounds1 = {\"nrj\": (nrj, nrj),\n",
    "           \"dist\": (0.7, 0.8),\n",
    "           \"poni2\": (4e-3, 4e-3),\n",
    "           \"rot1\": (0,0),\n",
    "           \"scale\":(1,1), }\n",
    "\n",
    "gonioref1 = GoniometerRefinement(param1, \n",
    "                                 get_position, \n",
    "                                 trans, \n",
    "                                 detector=modules[name], \n",
    "                                 wavelength=wl, \n",
    "                                 bounds=bounds1\n",
    "                                 )\n",
    "print(gonioref1)\n",
    "goniometers[name]=gonioref1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:26:56.841845Z",
     "iopub.status.busy": "2026-09-15T09:26:56.841755Z",
     "iopub.status.idle": "2026-09-15T09:27:00.051846Z",
     "shell.execute_reply": "2026-09-15T09:27:00.050365Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GoniometerRefinement with 20 geometries labeled: data_03_0103, data_03_0104, data_03_0105, data_03_0106, data_03_0107, data_03_0108, data_03_0109, data_03_0110, data_03_0111, data_03_0112, data_03_0113, data_03_0114, data_03_0115, data_03_0116, data_03_0117, data_03_0118, data_03_0119, data_03_0120, data_03_0121, data_03_0122.\n",
      "1.4524664758918932e-06\n"
     ]
    }
   ],
   "source": [
    "#Exctract frames with peak#0\n",
    "for i in range(frame_start, frame_stop):\n",
    "    cp = ControlPoints(calibrant=LaB6, wavelength=wl)\n",
    "    peak = peak_picking(name, i)\n",
    "    if len(peak)!=1: \n",
    "        continue\n",
    "    cp.append([peak[0]], ring=0)\n",
    "    img = (ds[i]).reshape((-1,1))\n",
    "    sg = gonioref1.new_geometry(\"%s_%04i\"%(name,i), \n",
    "                                image=img, \n",
    "                                metadata=i, \n",
    "                                control_points=cp, \n",
    "                                calibrant=LaB6)\n",
    "    sg.geometry_refinement.data = numpy.array(cp.getList())\n",
    "\n",
    "print(gonioref1)\n",
    "print(gonioref1.chi2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:27:00.053696Z",
     "iopub.status.busy": "2026-09-15T09:27:00.053601Z",
     "iopub.status.idle": "2026-09-15T09:27:00.071264Z",
     "shell.execute_reply": "2026-09-15T09:27:00.070216Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost function before refinement: 1.4524664758918932e-06\n",
      "[ 7.20000000e-01  3.20000000e-02  4.00000000e-03  0.00000000e+00\n",
      " -7.72000000e+01  1.00000000e+00  1.70270825e+01]\n",
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.343186420609047e-11\n",
      "           x: [ 7.200e-01  3.287e-02  4.000e-03  0.000e+00 -7.720e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [ 1.374e-07  1.035e-07        nan        nan  9.897e-10\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.343186420609047e-11\n",
      "GonioParam(dist=np.float64(0.7200063780235467), poni1=np.float64(0.03286839545438533), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-77.19998908851593), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.03286839545438533\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20006378e-01,  3.28683955e-02,  4.00000000e-03,  0.00000000e+00,\n",
       "       -7.71999891e+01,  1.00000000e+00,  1.70270825e+01])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonioref1.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:27:00.073034Z",
     "iopub.status.busy": "2026-09-15T09:27:00.072946Z",
     "iopub.status.idle": "2026-09-15T09:27:56.372754Z",
     "shell.execute_reply": "2026-09-15T09:27:56.371265Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of peaks found and used for refinement\n",
      "1183\n",
      "Residual error before fitting:  6.334637850688808e-07\n"
     ]
    }
   ],
   "source": [
    "#Exctract all frames with peak>0\n",
    "tths = LaB6.get_2th()\n",
    "#print(tths)\n",
    "for i in range(frame_stop, ds.shape[0]):\n",
    "    frame_name = \"%s_%04i\"%(name, i)\n",
    "    if frame_name in gonioref1.single_geometries:\n",
    "        continue\n",
    "    peak = peak_picking(name, i)\n",
    "    ai=gonioref1.get_ai(get_position(i))\n",
    "    tth = ai.array_from_unit(unit=\"2th_rad\", scale=False)\n",
    "    tth_low = tth[20]\n",
    "    tth_hi = tth[-20]\n",
    "    ttmin, ttmax = min(tth_low, tth_hi), max(tth_low, tth_hi)\n",
    "    valid_peaks = numpy.logical_and(ttmin<=tths, tths<ttmax)\n",
    "    cnt = valid_peaks.sum()\n",
    "    if (len(peak) ==  cnt) and cnt>0:    \n",
    "        cp = ControlPoints(calibrant=LaB6, wavelength=wl)\n",
    "        #revert the order of assignment if needed !!\n",
    "        if tth_hi < tth_low:\n",
    "            peak = peak[-1::-1]\n",
    "        for p, r in zip(peak, numpy.where(valid_peaks)[0]):\n",
    "            cp.append([p], ring=r)\n",
    "        img = ds[i].reshape((-1,1))\n",
    "        sg = gonioref1.new_geometry(frame_name, \n",
    "                                    image=img, \n",
    "                                    metadata=i, \n",
    "                                    control_points=cp, \n",
    "                                    calibrant=LaB6)\n",
    "        sg.geometry_refinement.data = numpy.array(cp.getList())\n",
    "        #print(frame_name, len(sg.geometry_refinement.data))\n",
    "\n",
    "print(\" Number of peaks found and used for refinement\")\n",
    "print(sum([len(sg.geometry_refinement.data) for sg in gonioref1.single_geometries.values()]))\n",
    "print(\"Residual error before fitting: \", gonioref1.chi2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:27:56.374494Z",
     "iopub.status.busy": "2026-09-15T09:27:56.374396Z",
     "iopub.status.idle": "2026-09-15T09:27:58.695089Z",
     "shell.execute_reply": "2026-09-15T09:27:58.693631Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost function before refinement: 6.334637850688808e-07\n",
      "[ 7.20006378e-01  3.28683955e-02  4.00000000e-03  0.00000000e+00\n",
      " -7.71999891e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 1.3797258269094543e-07\n",
      "           x: [ 7.200e-01  3.338e-02  4.000e-03  0.000e+00 -7.720e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-4.626e-07  2.408e-08        nan        nan  1.330e-11\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 1.3797258269094543e-07\n",
      "GonioParam(dist=np.float64(0.7200063394925221), poni1=np.float64(0.033375469748006405), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-77.1999827124022), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.03286839545438533 --> 0.033375469748006405\n",
      "Cost function before refinement: 1.3797258269094543e-07\n",
      "[ 7.20006339e-01  3.33754697e-02  4.00000000e-03  0.00000000e+00\n",
      " -7.71999827e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 8.828503235714087e-10\n",
      "           x: [ 7.207e-01  3.368e-02  4.058e-03 -4.449e-05 -7.720e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 13\n",
      "         jac: [ 6.561e-08  6.554e-07 -1.755e-07  1.262e-07  7.935e-09\n",
      "                2.914e-07        nan]\n",
      "        nfev: 91\n",
      "        njev: 13\n",
      " multipliers: []\n",
      "Cost function after refinement: 8.828503235714087e-10\n",
      "GonioParam(dist=np.float64(0.720682072663415), poni1=np.float64(0.03367823301601242), poni2=np.float64(0.004058010685114263), rot1=np.float64(-4.448556851210192e-05), offset=np.float64(-77.19997879322797), scale=np.float64(0.9989672333399332), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 1.0 --> 0.9989672333399332\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20682073e-01,  3.36782330e-02,  4.05801069e-03, -4.44855685e-05,\n",
       "       -7.71999788e+01,  9.98967233e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonioref1.refine2()\n",
    "gonioref1.set_bounds(\"poni1\", -1, 1)\n",
    "gonioref1.set_bounds(\"poni2\", -1, 1)\n",
    "gonioref1.set_bounds(\"rot1\", -1, 1)\n",
    "gonioref1.set_bounds(\"scale\", 0.9, 1.1)\n",
    "gonioref1.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:27:58.696874Z",
     "iopub.status.busy": "2026-09-15T09:27:58.696773Z",
     "iopub.status.idle": "2026-09-15T09:28:00.086506Z",
     "shell.execute_reply": "2026-09-15T09:28:00.084524Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mg1 = gonioref1.get_mg(position)\n",
    "mg1.radial_range = (0, 95)\n",
    "images = [i.reshape(-1, 1) for i in data[name]]\n",
    "res_mg1 = mg1.integrate1d(images, 50000)\n",
    "results[name] = res_mg1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:28:00.088643Z",
     "iopub.status.busy": "2026-09-15T09:28:00.088544Z",
     "iopub.status.idle": "2026-09-15T09:28:00.252626Z",
     "shell.execute_reply": "2026-09-15T09:28:00.250745Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/3210620731.py:4: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LaB6_new = get_calibrant(\"LaB6\")\n",
    "LaB6_new.wavelength = hc/gonioref1.param[-1]*1e-10\n",
    "p = jupyter.plot1d(res_mg1, calibrant=LaB6_new)\n",
    "p.figure.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:28:00.254461Z",
     "iopub.status.busy": "2026-09-15T09:28:00.254361Z",
     "iopub.status.idle": "2026-09-15T09:28:01.707936Z",
     "shell.execute_reply": "2026-09-15T09:28:01.705977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27712\n",
      "72 53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/2659044189.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(*calc_fwhm(res_mg1, LaB6_new, 10, 88), \"o\", label=\"FWHM\")\n",
    "ax.plot(*calc_peak_error(res_mg1, LaB6_new, 10, 88), \"o\", label=\"error\")\n",
    "ax.set_title(\"Peak shape & error as function of the angle\")\n",
    "ax.set_xlabel(res_mg.unit.label)\n",
    "ax.legend()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All other Modules\n",
    "\n",
    "We define now an automatic procedure for any module. \n",
    "The detection uses 3 parameters visually extracted from the Figure1: \n",
    "\n",
    "* zero_pos: the frame where the beam-stop is in the center of the module\n",
    "* frame_start: the frame where the first peak of LaB6 appears (positive)\n",
    "* frame_stop: the frame where the second peak of LaB6 appears (positive)\n",
    "\n",
    "This is enough for boot-strapping the goniometer configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:28:01.709978Z",
     "iopub.status.busy": "2026-09-15T09:28:01.709877Z",
     "iopub.status.idle": "2026-09-15T09:28:01.718493Z",
     "shell.execute_reply": "2026-09-15T09:28:01.716816Z"
    }
   },
   "outputs": [],
   "source": [
    "def add_module(name,\n",
    "               zero_pos,\n",
    "               frame_start,\n",
    "               frame_stop,\n",
    "                ):\n",
    "    ds = data[name]\n",
    "    param = {\"dist\": 0.72, \n",
    "          \"poni1\": 640*50e-6, \n",
    "          \"poni2\": 4e-3, \n",
    "          \"rot1\":0, \n",
    "          \"offset\": -get_position(zero_pos), \n",
    "          \"scale\":1, \n",
    "          \"nrj\": nrj}\n",
    "\n",
    "    #Lock enegy for now and a couple of other parameters\n",
    "    bounds = {\"nrj\": (nrj, nrj),\n",
    "              \"dist\": (0.7, 0.8),\n",
    "              \"poni2\": (4e-3, 4e-3),\n",
    "              \"rot1\": (0,0),\n",
    "              \"scale\": (1,1)}\n",
    "\n",
    "    gonioref = GoniometerRefinement(param, \n",
    "                                    get_position, \n",
    "                                    trans, \n",
    "                                    detector=modules[name], \n",
    "                                    wavelength=wl, \n",
    "                                    bounds=bounds\n",
    "                                      )\n",
    "    goniometers[name] = gonioref\n",
    "    \n",
    "    for i in range(frame_start, frame_stop):\n",
    "        cp = ControlPoints(calibrant=LaB6, wavelength=wl)\n",
    "        peak = peak_picking(name, i)\n",
    "        if len(peak)!=1: \n",
    "            continue\n",
    "        cp.append([peak[0]], ring=0)\n",
    "        img = (ds[i]).reshape((-1,1))\n",
    "        sg = gonioref.new_geometry(\"%s_%04i\"%(name, i), \n",
    "                                    image=img, \n",
    "                                    metadata=i, \n",
    "                                    control_points=cp, \n",
    "                                    calibrant=LaB6)\n",
    "        sg.geometry_refinement.data = numpy.array(cp.getList())\n",
    "\n",
    "    print(gonioref.chi2())\n",
    "    gonioref.refine2()\n",
    "        \n",
    "    tths = LaB6.get_2th()\n",
    "    #print(tths)\n",
    "    for i in range(frame_stop, ds.shape[0]):\n",
    "        frame_name = \"%s_%04i\"%(name, i)\n",
    "        if frame_name in gonioref.single_geometries:\n",
    "            continue\n",
    "        peak = peak_picking(name, i)\n",
    "        ai=gonioref.get_ai(get_position(i))\n",
    "        tth = ai.array_from_unit(unit=\"2th_rad\", scale=False)\n",
    "        tth_low = tth[20]\n",
    "        tth_hi = tth[-20]\n",
    "        ttmin, ttmax = min(tth_low, tth_hi), max(tth_low, tth_hi)\n",
    "        valid_peaks = numpy.logical_and(ttmin<=tths, tths<ttmax)\n",
    "        cnt = valid_peaks.sum()\n",
    "        if (len(peak) ==  cnt) and cnt>0:    \n",
    "            cp = ControlPoints(calibrant=LaB6, wavelength=wl)\n",
    "            #revert the order of assignment if needed !!\n",
    "            if tth_hi < tth_low:\n",
    "                peak = peak[-1::-1]\n",
    "\n",
    "            for p, r in zip(peak, numpy.where(valid_peaks)[0]):\n",
    "                cp.append([p], ring=r)\n",
    "            img = (ds[i]).reshape((-1,1))\n",
    "            sg = gonioref.new_geometry(frame_name, \n",
    "                                        image=img, \n",
    "                                        metadata=i, \n",
    "                                        control_points=cp, \n",
    "                                        calibrant=LaB6)\n",
    "            sg.geometry_refinement.data = numpy.array(cp.getList())\n",
    "            #print(frame_name, len(sg.geometry_refinement.data))\n",
    "\n",
    "\n",
    "    print(\" Number of peaks found and used for refinement\")\n",
    "    print(sum([len(sg.geometry_refinement.data) for sg in gonioref.single_geometries.values()]))\n",
    "\n",
    "    gonioref.refine2()\n",
    "    gonioref.set_bounds(\"poni1\", -1, 1)\n",
    "    gonioref.set_bounds(\"poni2\", -1, 1)\n",
    "    gonioref.set_bounds(\"rot1\", -1, 1)\n",
    "    gonioref.set_bounds(\"scale\", 0.9, 1.1)\n",
    "    gonioref.refine2()\n",
    "    \n",
    "    mg = gonioref.get_mg(position)\n",
    "    mg.radial_range = (0, 95)\n",
    "    images = [i.reshape(-1, 1) for i in ds]\n",
    "    res_mg = mg.integrate1d(images, 50000)\n",
    "    results[name] = res_mg\n",
    "    \n",
    "    LaB6_new = get_calibrant(\"LaB6\")\n",
    "    LaB6_new.wavelength = hc/gonioref.param[-1]*1e-10\n",
    "    p = jupyter.plot1d(res_mg, calibrant=LaB6_new)\n",
    "    p.figure.show()\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(*calc_fwhm(res_mg, LaB6_new), \"o\", label=\"FWHM\")\n",
    "    ax.plot(*calc_peak_error(res_mg, LaB6_new, 10, 89), \"o\", label=\"error\")\n",
    "    ax.set_title(\"Peak shape & error as function of the angle\")\n",
    "    ax.set_xlabel(res_mg.unit.label)\n",
    "    ax.legend()\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:28:01.719932Z",
     "iopub.status.busy": "2026-09-15T09:28:01.719839Z",
     "iopub.status.idle": "2026-09-15T09:29:11.830583Z",
     "shell.execute_reply": "2026-09-15T09:29:11.828633Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.40633024472856e-06\n",
      "Cost function before refinement: 8.40633024472856e-06\n",
      "[ 7.20000000e-01  3.20000000e-02  4.00000000e-03  0.00000000e+00\n",
      " -7.15999445e+01  1.00000000e+00  1.70270825e+01]\n",
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 1.8085719501136514e-11\n",
      "           x: [ 7.200e-01  3.409e-02  4.000e-03  0.000e+00 -7.160e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [ 4.574e-08  8.170e-07        nan        nan  9.916e-09\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 1.8085719501136514e-11\n",
      "GonioParam(dist=np.float64(0.7200189604924729), poni1=np.float64(0.034089307266644636), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-71.59991818231154), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.034089307266644636\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of peaks found and used for refinement\n",
      "1093\n",
      "Cost function before refinement: 5.557202432497131e-07\n",
      "[ 7.20018960e-01  3.40893073e-02  4.00000000e-03  0.00000000e+00\n",
      " -7.15999182e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 1.1960984572759293e-07\n",
      "           x: [ 7.200e-01  3.457e-02  4.000e-03  0.000e+00 -7.160e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-9.442e-07  2.309e-08        nan        nan -1.668e-11\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 1.1960984572759293e-07\n",
      "GonioParam(dist=np.float64(0.7200183446946246), poni1=np.float64(0.03456504217138479), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-71.5999122000852), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.034089307266644636 --> 0.03456504217138479\n",
      "Cost function before refinement: 1.1960984572759293e-07\n",
      "[ 7.20018345e-01  3.45650422e-02  4.00000000e-03  0.00000000e+00\n",
      " -7.15999122e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 6.570414650006303e-10\n",
      "           x: [ 7.207e-01  3.482e-02  4.043e-03 -3.377e-05 -7.160e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 10\n",
      "         jac: [ 2.182e-08  1.739e-08 -1.608e-07  1.158e-07 -1.824e-10\n",
      "                8.080e-09        nan]\n",
      "        nfev: 70\n",
      "        njev: 10\n",
      " multipliers: []\n",
      "Cost function after refinement: 6.570414650006303e-10\n",
      "GonioParam(dist=np.float64(0.7206864291588817), poni1=np.float64(0.03482474083006927), poni2=np.float64(0.004043166284269385), rot1=np.float64(-3.3768383648664404e-05), offset=np.float64(-71.59990881341272), scale=np.float64(0.9989828730379465), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 1.0 --> 0.9989828730379465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:99: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25659\n",
      "71 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:107: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_module(ds_names[2],\n",
    "           92,\n",
    "           131,\n",
    "           151)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:29:11.832786Z",
     "iopub.status.busy": "2026-09-15T09:29:11.832685Z",
     "iopub.status.idle": "2026-09-15T09:30:16.490869Z",
     "shell.execute_reply": "2026-09-15T09:30:16.488798Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0825377842185958e-06\n",
      "Cost function before refinement: 1.0825377842185958e-06\n",
      "[ 7.20000000e-01  3.20000000e-02  4.00000000e-03  0.00000000e+00\n",
      " -6.57999444e+01  1.00000000e+00  1.70270825e+01]\n",
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.4921937668954493e-11\n",
      "           x: [ 7.200e-01  3.275e-02  4.000e-03  0.000e+00 -6.580e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-8.600e-08  7.994e-08        nan        nan  6.189e-10\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.4921937668954493e-11\n",
      "GonioParam(dist=np.float64(0.7200067449756247), poni1=np.float64(0.03274966118487238), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-65.7999350248995), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.03274966118487238\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of peaks found and used for refinement\n",
      "978\n",
      "Cost function before refinement: 4.7051020473788555e-07\n",
      "[ 7.20006745e-01  3.27496612e-02  4.00000000e-03  0.00000000e+00\n",
      " -6.57999350e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 9.923942799031425e-08\n",
      "           x: [ 7.205e-01  3.319e-02  4.000e-03  0.000e+00 -6.580e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 7\n",
      "         jac: [-1.340e-09 -8.952e-12        nan        nan -3.955e-10\n",
      "                      nan        nan]\n",
      "        nfev: 29\n",
      "        njev: 7\n",
      " multipliers: []\n",
      "Cost function after refinement: 9.923942799031425e-08\n",
      "GonioParam(dist=np.float64(0.7204818161545966), poni1=np.float64(0.033188632482365366), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-65.79992934723474), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: dist (0) 0.7200067449756247 --> 0.7204818161545966\n",
      "Cost function before refinement: 9.923942799031425e-08\n",
      "[ 7.20481816e-01  3.31886325e-02  4.00000000e-03  0.00000000e+00\n",
      " -6.57999293e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 5.124871954470427e-10\n",
      "           x: [ 7.205e-01  3.341e-02  3.978e-03  1.591e-05 -6.580e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 5\n",
      "         jac: [-5.053e-07 -5.371e-09 -1.298e-07  9.555e-08 -4.364e-10\n",
      "                2.566e-09        nan]\n",
      "        nfev: 36\n",
      "        njev: 5\n",
      " multipliers: []\n",
      "Cost function after refinement: 5.124871954470427e-10\n",
      "GonioParam(dist=np.float64(0.7204829253887527), poni1=np.float64(0.03340994866191066), poni2=np.float64(0.003977911642428385), rot1=np.float64(1.5909827962256308e-05), offset=np.float64(-65.79992655958921), scale=np.float64(0.9989991199293132), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 1.0 --> 0.9989991199293132\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:99: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24026\n",
      "64 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:107: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAeqlJREFUeJztnXeYE1Xbxu9kewOR3hFEcBVB4QVsL0VFBcHeP4qdYgEUBbviK2ADOyqKghQbqIiAAiuKFKlKL8LCwtLL9s3uJuf7IySbMpmWmUzI3r/r2ovMmafcc06WPDszmccmhBAghBBCCIkR7FYLIIQQQggxEhY3hBBCCIkpWNwQQgghJKZgcUMIIYSQmILFDSGEEEJiChY3hBBCCIkpWNwQQgghJKZgcUMIIYSQmILFDSHEcPbs2YMJEyYgNzfXailRAeeDkMgSb7UAQkhkKC8vx6JFi7B161ZkZmaiR48eQTYHDx7EzJkzvdsJCQmoVq0amjdvjnbt2iEtLU1Vri1btmDYsGHo0KEDGjRooFnrv//+izlz5uCOO+5AvXr1NPtbgZzmcOeDEKINnrkhJMYRQuD+++9H48aN8cYbb2DYsGGYPn26pG12djaGDRuGWbNmITs7G9u2bcMvv/yChx56CHXq1MEDDzyAo0ePKuZs1qwZHnvsMTRs2FCX5g0bNmDYsGHIzs7W5W8FcprDnQ9CiDZ45oaQGEcIgc6dO+O1115DYmIiMjIyFH369OmDJ554wm/st99+w5133omsrCysWLECtWrVCunfunVrTJgwIVzpMQPng5DIwuKGkBjHbrfj/vvvBwAUFhbqjtO1a1dMmzYNV1xxBZ5//nl88MEHIW337NmD2bNn47bbbvNehtm+fTt+/vln3H333UhPT8fcuXNx7NgxdOrUCe3atfP6rlu3DnPmzAEAfPXVV1ixYgUAoEePHsjMzPTa5eXlISsrC7m5uahduzauvPJK1KhRI0jLP//8gxUrVqBatWro1asXysrKMHXqVFxzzTVo3bq1pLZ58+Zh3759uP/++1FUVIRp06YBAGw2G5KTk9GyZUv897//RXx8vCrNUvPhYd++fViyZAny8/PRrFkzdO/eHUlJSd79aueNEFIJL0sRQlTTvXt3tGrVCjNnzoTL5Qpp57nHZNeuXd6xtWvXYtiwYVi2bBmuuOIK/PDDD5g1axYuvPBCjBkzxmuXn5+Pw4cPAwAOHDiA7OxsZGdn+xVmU6ZMQePGjfHyyy9j7dq1GD9+PJo1a+YtMDw89thjaNeuHWbNmoV58+ahW7duyMrKwrBhw7B69eogbUuWLMF///tfzJo1Cx9//DHy8/NRVlbm1bB7924sXboUd911F1q3bo39+/er0iw1HwDw0ksv4ayzzsInn3yClStXYtCgQWjZsiXWrFmjed4IIT4IQkiVoaCgQAAQ/fv3l9y/fPlyAUC8/vrrIWPccccdAoDIzs4OaTNv3jwBQPzxxx/esRkzZggAokuXLuLw4cPe8SFDhojk5GRx4MAB79js2bMFALF8+fKg2FlZWcJut4tRo0YJl8vlHX/88cdFWlqayMnJEUIIMW3aNAFATJo0yWtz5MgRcfnllwsAYurUqUHaLr74Yq+O/Px8UVhYKHl8BQUFIjMzU9x6662qNEvNx9dffy0AiHHjxnnH8vLyRIcOHUT9+vW9ubXMGyHEDc/cwP2XUVZWVkTiGJWLEKuoXr06APdlIT3ccccdqF27tne7X79+KC0txcqVK1X5v/HGGzjzzDPx8ssvw2azecdffPFFlJSUeC8hffLJJ2jRogXuvfder02tWrVw5513hox96623er/plJGR4f12mMvlwvLly/Hpp5/i7bffxqRJk3DmmWfizz//VH/gAXz44YeoX78+hg8f7h2rVq0aXnzxRRw4cACzZ8/2sw933gipSlTpe24mT56M8ePH4+DBg8jPz0dpaalpcYzKRYjVeC61qLkxWYrzzz/fb9tzD4rnEo8Sa9asQY0aNTBx4kQA7humPT/p6enYunUrAGDTpk245JJL/Aogqfy+XHDBBUFjOTk56NWrF/bv349u3bqhXr16iI+PR3FxsfdSlB42bdqEdu3aee/b8XDhhRcCADZu3CirW+u8EVKVqNLFTW5uLqZOnYpVq1bh4YcfDmlXXl6Of/75B4D7Ww+Bz/pQE0dtLkKinY0bNyIjIwONGzfW5Z+enu637flwLysrU+VfXl4Op9OJnTt3Bu275557cNFFFwFwn20JLBx880lRs2bNoLERI0bg4MGD2Lx5M+rWresd79+/P/7++29VmqVwOp2y+pxOp994uPNGSFWiShc3zzzzDABg1apVIW0WLlyIfv36oUaNGkhNTcW///6Ld999F3fffbemOGpsCIl21q1bh7///ht9+/aVLRLCxW4PfcU8MzMThw4dUvxq9dlnn41t27YFjW/fvl2TlnXr1qFTp05+hQ0ArF+/3m9bTrMULVu2xJYtW4LGN2/e7N1PCNEH77mRITc3FzfffDPef/99bNq0CatWrcK3336LBx98ELt377ZaHiERZefOnbjzzjtRp04djB492tRcderUAQAcO3YsaN8jjzyC7du3Y9KkSUH7Dh48iJycHABA3759sXHjRsyfP9+7v7S0FFOmTNGkpVmzZti6dSsqKiq8YzNmzMCePXtUa5aif//+2L17N2bMmOEdq6iowJgxY5CRkYGbb75Zk05CSCVV+syNEtOmTcOZZ56JGjVqICsrC0IIAMCZZ56JrKwsnHXWWRYrJEQdX331FQ4cOOC9hLF582bvmQ/f5714+OOPPxAfHw+Xy4WTJ09i3bp1WLBgAbp27YqffvoJTZs2NVVvu3bt0KxZMzz99NPYsGEDkpOTvc+MufXWWzF27FgMGjQI3377LTp37gyn04nNmzdj3bp1+Pbbb9G4cWMMHDgQv/zyC2644Qbcc889qFOnDhYtWoT+/ftj8eLFqs+0PP/887jyyivRtWtXXHvttdi2bRtyc3PRv39/vP/++6o0S/Hggw9i2bJl6Nu3L3755Rc0a9YM8+bNw8aNGzFz5kzJS2SEEHWwuJFhx44dKCgowCuvvOI33qpVKyQnJ1ukihDteJ69Arif/QLAu11UVOS1q1+/vt/++Ph4VKtWDbfffjveeecd1QW9VLuBVq1a4bHHHvP7xg8ApKWl4bHHHvPeSAsAycnJWLZsGb7++mvk5OSgoqLC7zk3Tz31FO666y789NNPyM7OxhlnnIF+/fphxowZSExMBADExcXh+++/x5w5c7B8+XKkpKTgyy+/9J5x8b2HJZQ2ALj00kuxefNmzJo1C8eOHUOvXr1w0003Yd68eX6X5uQ0S82H3W7HlClTMGjQICxatAj5+fno168fbr75Zr9LYFrmjRDixiY8pyOqMJMmTcLDDz8c9A2mYcOGYeHChdiwYUNYcbTaEELMY+LEiRg0aBC2bt2KVq1aWS2HEGICvOdGhu7du2PTpk1+TzIF3N9OKC4utkgVIUQtgd+oys/Px9tvv422bduysCEkhqnSl6U2btyIgwcPYsuWLXC5XFi4cCEAoGPHjqhWrRquu+463Hzzzbj22msxatQotGjRAtu2bcPnn3+OH374wfttBqU4am0IIcYycuRIlJSUoGPHjsjPz8esWbNQVFSEX375xWpphBATqdKXpf73v/9JPi343XffxbnnngvA/ayMGTNmYO7cuSgqKkJmZiYefPBBv3sP1MRRY0MIMRYhBH799VesWbMGeXl5aNWqFW666SbvU5YJIbFJlS5uCCGEEBJ78J4bQgghhMQULG4IIYQQElNUyRuKXS4XcnNzkZGREdRUjxBCCCHRiRACBQUFaNCggeyDOKtkcZObm6u76R8hhBBCrCUnJweNGjUKub9KFjcZGRkA3JPDr2GfoqgIaNDA/To3FwjofB7ReHp8tfiYZRuuPiUbuf1m+Rq5FlaMG7HWauOqzRUJ38AxwDg/PZrU5Ndro6Qn0EdPHjUxzNCh9HuspEPP2oS7bcRcSMU8FSMfQGNUfo6HokoWN55LUdWqVWNx4yEurvJ1tWrhFzfhxNPjq8XHLNtw9SnZyO03y9fItbBi3Ii1VhtXba5I+AaO+RKunx5NavLrtVHSE+ijJ4+aGGboUPo9VtKhZ23C3TZiLqRi+sYAFG8p4Q3FhBBCCIkpquSZGyJBfDzQv3/layvj6fHV4mOWbbhxlGzk9pvla+RaWDFuxFqrjas2VyR8pcaM8tOrySwbNXqMyGOVDq0xleyNONZIzEUo3WVlwIwZ0nl8qJIP8cvPz0f16tWRl5fHy1KEEELIaYLaz29eliKEEEJITMHLUsSNEICn03lqKhDu83/CiafHV4uPWbbh6lOykdtvlq+Ra2HFuBFrrTau2lyR8A0cA4zz06NJTX69Nkp6An305FETwwwdSr/HSjr0rE2420bMhVRMT4yiouD4ErC4IW6Ki4H0dPfrwsLwvy0VTjw9vlp8zLINV5+Sjdx+s3yNXAsrxo1Ya7Vx1eaKhG/gGGCcnx5NavLrtVHSE+ijJ4+aGGboUPo9VtKhZ23C3TZiLqRi+sZQAS9LEUIIISSmYHFDCCGEkJiCxQ0hhBBCYgoWN4QQQgiJKVjcEEIIISSmYHFDCCGEkJiCXwUnbuLigFtuqXwdJsJux8mefZCeFI8ErfH0aNHiY5ZtuHGUbOT2m+Vr5FpYMW7EWquNqzZXJHylxozy06vJLBs1eozIY5UOrTGV7I041kjMRSjd5eXADz9I5/GB7RfYfsEU5vydi0dmrEPDM1Lw58juVsshhBASA7D9ArGUuf8cAADsP1lisRJCCCFVDRY3hBBCCIkpWNwQN0VF7v4dNpvq3h1yJDpKkD3uOmSPu057PD1atPiYZRtuHCUbuf1m+Rq5FlaMG7HWauOqzRUJ38AxI/30aDLTRkmPEXms0qE1phFrEw1zIae7enXpHAGwuCGEEEJITMHihhBCCCExBYsbQgghhMQULG4IIYQQElOwuCGEEEJITMHihhBCCCExBdsvEDdxcUDPnpWvw8Rls2Nx8w4AgO562i9o1aLFxyzbcOMo2cjtN8vXyLWwYtyItVYbV22uSPhKjRnlp1eTWTZq9BiRxyodWmMq2RtxrJGYi1C6y8uBX3+VzuMD2y+w/YIpDJy6BvM3HQQAZI/tZbEaQgghsQDbLxBCCCGkSsLihhBCCCExBYsb4qaoCEhLc/8Y1H5h81s3Y/NbN+trv6BVixYfs2zDjaNkI7ffLF8j18KKcSPWWm1ctbki4Rs4ZqSfHk1m2ijpMSKPVTq0xjRibaJhLuR016snnSMA3lBMKikuNjRcarlDv7MeLVp8zLINN46Sjdx+s3yNXAsrxo1Ya7Vx1eaKhG/gmJF+ZuU3S48ReazSoTWmmnjhHmsk5iLM32WeuSGmIFDl7lMnhBASJbC4IYQQQkhMweKGEEIIITEFixtCCCGExBQsbgghhBASU/DbUsSN3Q506VL5OkyEzYYVjc8HAHTWGk+PFi0+ZtmGG0fJRm6/Wb5GroUV40astdq4anNFwldqzCg/vZrMslGjx4g8VunQGlPJ3ohjjcRchNJdUQH8+ad0Hh/YfoHtF0yB7RcIIYQYjdrP76g4c7Nu3Tps27YN3bt3R506dRTtjx8/jjVr1iA+Ph7t2rVDjRo1IqCSEEIIIacDlhY3CxcuxLPPPovjx49jx44dyMrKki1uhBAYPHgwfvzxR5x33nkoLi7GP//8gwkTJuDee++NoHJCCCGERCuW3lBcWFiI8ePHY/HixarshRBo27Ytdu/ejV9++QVLly7FuHHj8NBDD2Hv3r0mq41xioqA2rXdPwa1X1jzzl1Y885d+tovaNWixccs23DjKNnI7TfL18i1sGLciLVWG1dtrkj4Bo4Z6adHk5k2SnqMyGOVDq0xjVibaJgLOd1nnSWdIwBLz9zccMMNAIB9+/apsrfb7Rg4cKDf2M0334zBgwdj48aNaNKkidESqxZHjxoarmZJvn5nPVq0+JhlG24cJRu5/Wb5GrkWVowbsdZq46rNFQnfwDEj/czKb5YeI/JYpUNrTDXxwj3WSMxFmL/LUXHPTTgsXLgQNpsNmZmZIW0cDgccjso+R/n5YXzoEkIIISSqOa2fc5OdnY2hQ4di0KBBaNasWUi7MWPGoHr16t6fxo0bR04kIYQQQiLKaVvc5Obm4qqrrkKnTp0wYcIEWdtRo0YhLy/P+5OTkxMZkYQQQgiJOKflZanc3Fx069YN55xzDr799lskJCTI2iclJSEpKSlC6gghhBBiJVF/5mbVqlWYP3++d/vAgQPo1q0bzj77bMyaNYtFCyGEEEL8sPTMze7du7Fy5UocP34cALB48WIcPHgQ559/Ps4/3/3o/o8++ggrVqzANddcA4fDgSuuuAInT57EbbfdhtmzZ3tjdezYEc2bN7fkOGICux3o0KHydZi4bMDf9VoCANrqab+gVYsWH7Nsw42jZCO33yxfI9fCinEj1lptXLW5IuErNWaUn15NZtmo0WNEHqt0aI2pZG/EsUZiLkLpdjqBdeuk8/hgafuFJUuW4MMPPwwav+WWW3DLLbcAAD7++GPs2LEDr7/+OgoKCvDAAw9IxnrooYfQrVs3VXnZfsF8Hpq6Ggs2HQLA9guEEEKMQe3nN3tLsbgxBRY3hBBCjEbt53fU33NDCCGEEKIFFjfETXEx0KyZ+6e4OOxwCY5SLP3wXiz98F7t8fRo0eJjlm24cZRs5Pab5WvkWlgxbsRaq42rNlckfAPHjPTTo8lMGyU9RuSxSofWmEr2RhxrJOZCTvep+3GVOC2/Ck5MQAhgz57K12Fig0Cj/MP64unRosXHLNtw4yjZyO03y9fItbBi3Ii1VhtXba5I+EqNGeWnV5NZNmr0GJHHKh1aYyrZG3GskZgLOd0q4JkbQgghhMQULG4IIYQQElOwuCGEEEJITMHihhBCCCExBYsbYgo22KyWQAghpIrCb0sRNzYbkJlZ+TpMBGzYXrMJAOAcrfH0aNHiY5ZtuHGUbOT2m+Vr5FpYMW7EWquNqzZXJHylxozy06vJLBs1eozIY5UOrTGV7I041kjMRSjdTiewbZt0Ht+UfEIxn1BsBgOnrsH8TQcB8AnFhBBCjIFPKCaEEEJIlYTFDSGEEEJiChY3xE1xMXDeee4fg9ov/DJpMH6ZNFhf+wWtWrT4mGUbbhwlG7n9ZvkauRZWjBux1mrjqs0VCd/AMSP99Ggy00ZJjxF5rNKhNaYRaxMNcyGnu2NH6RyBiCpIXl6eACDy8vKslhI9FBYK4X7Atft1mDz8ye/64+nRosXHLNtw4yjZyO03y9fItbBi3Ii1VhtXba5I+AaOGemnR5OZNkp6jMhjlQ6tMY1Ym2iYCxndeYBQ8/nNMzeEEEIIiSlY3BBCCCEkpmBxQwghhJCYgsUNIYQQQmIKFjeEEEIIiSnYfoG4sdmApk0rX4eJgA37qtUBADTS035BqxYtPmbZhhtHyUZuv1m+Rq6FFeNGrLXauGpzRcJXaswoP72azLJRo8eIPFbp0BpTyd6IY43EXITS7XIBOTnSeXxTCiGEolWMwfYL5vPQ1NVYsOkQALZfIIQQYgxsv0AIIYSQKgmLG0IIIYTEFCxuiJuSEuA//3H/lJSEHS6hrBQ/fDEMP3wxTHs8PVq0+JhlG24cJRu5/Wb5GrkWVowbsdZq46rNFQnfwDEj/fRoMtNGSY8ReazSoTWmEWsTDXMhp7trV+kcgcg+vzhGYfsFCdh+wTgteuOw/QLbLxjly/YL4bU9YPsFa+eC7RcIIYQQQvxhcUMIIYSQmILFDSGEEEJiChY3xBRsCP9BgIQQQogeWNwQQgghJKZg+wVSSa1ahoUSEDiW4n56ZM1IadHiY5ZtuHGUbOT2m+Vr5FpYMW7EWquNqzZXJHwDx4z0Myu/WXqMyGOVDq0x1cQL91gjMRehdLtcwPHjiunYfoHtF0yB7RcIIYQYDdsvEEIIIaRKwuKGEEIIITEFixvipqTE/Vjrrl0Na78wc/pIzJw+Ul/7Ba1atPiYZRtuHCUbuf1m+Rq5FlaMG7HWauOqzRUJ38AxI/30aDLTRkmPEXms0qE1ppK9EccaibmQ092zp3SOQGSfXxyjsP2CBGy/YJwWvXHYfoHtF4zyZfuF8NoesP2CtXPB9guEEEIIIf6wuCGEEEJITMHihhBCCCExBYsbQgghhMQUlhY3RUVFmDRpEjp06ID09HT88ccfqvw+/vhjnHfeeahVqxa6deuGNWvWmKyUEEIIIacLlrZfGD16NI4cOYJnn30WN954I5xOp6LPtGnT8Oijj+Lzzz/HxRdfjNdffx1XXHEFNm/ejAYNGkRAdQyTmmpouOKEJHfYSGnR4mOWbbhxlGzk9pvla+RaWDFuxFqrjas2VyR8A8eM9DMrv1l6jMhjlQ6tMdXEC/dYIzEXoXQLoeqxFFHRfmHfvn1o3LgxsrKy0LVrV1nb888/H5dffjk+/PBDAIDL5ULDhg1x//33Y/To0arysf2C+bD9AiGEEKOJyfYLJ0+exKZNm3DFFVd4x+x2O7p3746lS5daqIwQQggh0cJp1RU8NzcXAFCnTh2/8Tp16sjed+NwOOBwOLzb+fn55ggkhBBCiOWcVmduPNjt9qBtuatrY8aMQfXq1b0/jRs3Nlvi6UdpKdCrl/untDTscPFlDnz2zYv47JsXtcfTo0WLj1m24cZRspHbb5avkWthxbgRa602rtpckfANHDPST48mM22U9BiRxyodWmMasTbRMBdyum+5RTpHILLPL44QOTk5AoDIysqStTty5IgAIGbNmuU33rdvX3HZZZeF9CstLRV5eXneH08+tl/wweD2C0M+ZvsFzXHYfoHtF4zyZfuF8NoesP2CtXNR1dov1KpVC2effTZ+//13v/ElS5agc+fOIf2SkpJQrVo1vx9CCCGExCZRX9w8/PDD6Nixo3d76NCh+PTTT7FkyRKUlpZi9OjROHz4MAYOHGihSkIIIYREC5YWN9OmTUN6ejpatWoFALj22muRnp6OV1991WtTWlqK4uJi7/aQIUPwxBNP4MYbb0RaWhqmTZuGH3/8ES1atIi4fhIam81qBYQQQqoqln5b6vbbb8f1118fNJ6YmOh9/f7778Plcvntf/755/H888+jvLwcCQkJpuskhBBCyOmDpcVNfHw80tPTZW2SkpJC7mNhQwghhJBAov6eG0IIIYQQLURF+4VIw/YL5sP2C4QQQowmJtsvEEIIIYQoweKGEEIIITEFixviprQUuPVW9084bQZOEV/mwPvfj8H734/RHk+PFi0+ZtmGG0fJRm6/Wb5GroUV40astdq4anNFwjdwzEg/PZrMtFHSY0Qeq3RojWnE2kTDXMjp7tdPOkcgss8vjlHy8vJUPb65SmFw+4WHP2H7Bc1x2H6B7ReM8mX7hfDaHrD9grVzUdXaLxBCCCGEKMHihhBCCCExBYsbQgghhMQULG4IIYQQElOwuCGmIITVCgghhFRVWNwQQgghJKZg+wW2X3AjBFBc7H6dmgrYbGGFe/CLVfjj770AgC1v3qQtnh4tWnzMsg1Xn5KN3H6zfI1cCyvGjVhrtXHV5oqEb+AYYJyfHk1q8uu1UdIT6KMnj5oYZuhQ+j1W0qFnbcLdNmIupGKeipGfn4/qDRoofn6zuGFxYwoPTlmNXzaztxQhhBDjYG8pQgghhFRJWNwQNw4HMGCA+8fhCDtcfHkZ3pg7Hm/MHa89nh4tWnzMsg03jpKN3H6zfI1cCyvGjVhrtXHV5oqEb+CYkX56NJlpo6THiDxW6dAaU8neiGONxFzI6R44UDpHILLPL45R2H5BAoPbLwz5mO0XNMdh+wW2XzDKl+0XQvuw/UJ4x8r2C6QqE+b9yIQQQohuWNwQQgghJKZgcUMIIYSQmILFDSGEEEJiChY3hBBCCIkpWNwQQgghJKaIt1oAiRJSU4HDhytfh0lZYjIuemQaAGCt1nh6tGjxMcs23DhKNnL7zfI1ci2sGDdirdXGVZsrEr5SY0b56dVklo0aPUbksUqH1phK9kYcayTmIpTuggKgRQvpPD6w/QLbL5jCQ1NXY8GmQwDYfoEQQogxsP0CIYQQQqokLG6IG4cDGDLE/WNA+4W4sjK8/MuHePmXD/W1X9CqRYuPWbbhxlGykdtvlq+Ra2HFuBFrrTau2lyR8A0cM9JPjyYzbZT0GJHHKh1aYxqxNtEwF3K6H39cOkcgss8vjlHYfkECtl8wToveOGy/wPYLRvmy/UJ4bQ/YfsHauWD7BUIIIYQQf1jcEEIIISSmYHFDCCGEkJiCxQ0hhBBCYgoWN4QQQgiJKVjcEEIIISSmYPsF4iYlBdi9u/J1mJQnJOGygZ8CAJZqjadHixYfs2zDjaNkI7ffLF8j18KKcSPWWm1ctbki4Ss1ZpSfXk1m2ajRY0Qeq3Rojalkb8SxRmIuQukuKAAuuEA6jw9sv8D2C6bw4JTV+GXzIQBsv0AIIcQY2H6BEEIIIVUSFjfETVkZMGKE+6esLOxwcRXlGJX1GUZlfaY9nh4tWnzMsg03jpKN3H6zfI1cCyvGjVhrtXHV5oqEb+CYkX56NJlpo6THiDxW6dAa04i1iYa5kNP97LPSOQKRfX5xjML2CxIY3H7h4U/YfkFzHLZfYPsFo3zZfiG8tgdsv2DtXBjQfsHyG4oPHDiAL7/8EocOHUKbNm1w1113ISEhQdZn3bp1mDdvHk6cOIEmTZrgjjvuQO3atSOkmBBCCCHRjKWXpbZv3442bdogKysLGRkZ+N///ocePXrA6XSG9Pnggw/QuXNn7N+/H3Xq1MGcOXPQsmVLbNmyJYLKCSGEEBKtWFrcPPXUUzj//PMxd+5cvPDCC1i8eDGWLVuGadOmhfR5++23MXjwYLz//vsYMWIE5s+fjzPPPBNffPFFBJUTQgghJFqxrLgpLy/HvHnzcNddd8FmswEAGjVqhK5du+KHH34I6VevXj3k5eV5tx0OB0pLS1G/fn3TNRP1CGG1AkIIIVUVy+652bt3LxwOB8466yy/8ebNm+PPP/8M6Td58mQMHDgQV111FZo2bYq//voLd999NwYPHhzSx+FwwOFweLfz8/PDPwBCCCGERCWWnbkpKSkBAGRkZPiNV6tWDcXFxSH9Nm3ahI0bN6J58+Y455xzULduXfz22284fPhwSJ8xY8agevXq3p/GjRsbcxCEEEIIiTosO3OTnp4OADh58qTf+IkTJ0I+ddDhcKBfv3548sknMWrUKADAiBEjcPHFF+PJJ58Mea/OqFGjMHz4cO92fn4+C5xAUlKAjRsrX4dJeUISrrr3fQDAr3raL2jVosXHLNtw4yjZyO03y9fItbBi3Ii1VhtXba5I+EqNGeWnV5NZNmr0GJHHKh1aYyrZG3GskZiLULoLC4HOnaXz+GBZ+wWXy4Xq1avj5ZdfxrBhw7zjl19+OZo2bYovv/wyyCcnJwdNmjTBggUL0KNHD+/4oEGDsGrVKqxevVpVbrZfMB+2XyCEEGI0Ud9+wW6349Zbb8Xnn3/uvUS1fv16LFu2DLfffrvXbvr06Xj11VcBAA0bNkSNGjUwf/58736Hw4HffvsNbdq0iewBEEIIISQqsfQhfmPHjkXXrl3Rvn17tGvXDvPnz8eAAQPQu3dvr83ixYuxYsUKPP3007Db7fjkk08wYMAArF27Fs2bN8fvv/8Ou92O0aNHW3gkMUBZGXCqiMTTTwOJiWGFi6sox9Clpy4Tll2lLZ4eLVp8zLINV5+Sjdx+s3yNXAsrxo1Ya7Vx1eaKhG/gGGCcnx5NavLrtVHSE+ijJ4+aGGboUPo9VtKhZ23C3TZiLqRiemL4fDlIFtnnF0eA0tJS8cMPP4iPP/5YLF++PGj/4sWLxfTp0/3GDh8+LGbPni0mTZokfv31V1FeXq4pJ9svSGBw+4UhH7P9guY4bL/A9gtG+bL9QnhtD9h+wdq5iIX2C0lJSejTp0/I/d26dQsaq127Nm644QYTVRFCCCHkdEXXPTeee2QIIYQQQqINXcVN/fr1MXDgQPz1119G6yGEEEIICQtdxc27776Lbdu2oXPnzmjTpg3Gjx+PI0eOGK2NEEIIIUQzuoqbvn37IisrCzt37sQNN9yA8ePHo2HDhrj55psxd+5c2a7eJDpxuoTVEgghhBBDCOs5N82bN8fo0aORnZ2NN998Ez/99BOuu+46NGnSBOPGjfPr50Sim9+2hm5fQQghhJxOhPVtqcLCQnz99df47LPPsHz5cnTr1g33338/Dh8+jAkTJmDFihWYPXu2UVqJiRTY4tGn31sAgB+Tk8OOV56QqD9ecjLguZ9Lra8WH7Nsw42jZCO33yxfI9fCinEj1lptXLW5IuErNWaUn15NZtmo0WNEHqt0aI2pZG/EsUZiLkLpLiwEuneXzuODrvYLS5cuxWeffYavv/4a1atXx4ABA3D//ff7dfg+dOgQmjRpEpVnb9h+IZjv1+3H0K/WAzCmXQLbLxBCCDEatZ/fus7cdO3aFT179sSMGTPQs2dPxMXFBdnUrVsX99xzj57whBBCCCG60VXcPP3003j55Zcl973xxht44oknAAATJ07Ur4xEFFt5GR5c+Z17Q2u7BAniKsr1xysrA95+2/36scfUP/JfrY9ZtuHqU7KR22+Wr5FrYcW4EWutNq7aXJHwDRwDjPPTo0lNfr02SnoCffTkURPDDB1Kv8dKOvSsTbjbRsyFVExPjNLS4PhSyD6/OARybjpDRhS2Xwjmxz+3G9Nm4BRsv6AjDtsvsP2CUb5svxBe2wO2X7B2Lgxov2BoV/B///0XNWvWNDIkIYQQQogmNF2WOvvssyVfA4DL5UJubi7uvvtuY5QRQgghhOhAU3HjuZdm0KBB3tceEhIS0KxZM8lGl4REK/uOF6NRWprVMgghhBiIpuJm4MCBAIBatWrhlltuMUUQIZHk2e834vNHWJATQkgsoeueGxY2sYeAsFqCJZwoLrNaAiGEEINRfeamWbNmAIDs7Gzv61BkZ2eHIYkQQgghRD+qi5tnn31W8jWJDVyJybjjzlcBADMNar+gO15yMpCVVfnaaJ/kymMtT1B4boseLXrjKNnI7TfL18i1sGJc4/sirLhqc0XCV2rMKD+9msyyUaPHiDxW6dAaU8neiGONxFyE0l1UBFx3nXQeH3S1XzjdYfuFYKpa+4VmI+cCAFrXy8D8of+1WA0hhBA1qP381nXPzZEjR/DBBx94tz/77DO0bt0avXr1wsGDB/WEJIQQQggxBF3tF0aMGIGrrroKAHDw4EE8/PDDeOqpp7By5Uo8/vjjmDZtmqEiifnYysvRd+1P7o3yHkBCQljx4ioq9McrLwc+/tj9+sEH1flq8fE51rVX32q8Fr1xlGzk9pvla+RaWDGu8X0RVly1uSLhGzgGGOenR5Oa/HptlPQE+ujJoyaGGTqUfo+VdOhZm3C3jZgLqZieGCUlwfGlkH1+cQhq164tjh8/LoQQYvLkyaJnz55CCCH2798v6tSpoydkRGH7hWCqavuFPmPnG69Fbxy2X2D7BaN82X4hvLYHbL9g7VxY1X6hrKwM5eXlAICFCxfiyiuvBACkp6fD4XDoCUkIIYQQYgi6iptLL70UQ4YMwQcffIDZs2ejd+/eAICVK1eic+fOhgokxExsNpvVEgghhBiMruLm/fffR2FhId577z289tpr3j5T77zzDr8mTgDgtHkkoBCni1JCCCFq0XVDcbNmzTBv3ryg8Tlz5oQtiFhDVX1CMSGEkNhD15kbQgghhJBoRVdxs3fvXtx4442oU6cO4uPjg34IIYQQQqxCVyVy7733oqysDG+//TZq1KhhtCZiASIhCffc8gIAYHJSUtjxKuIT9MdLSgJ++qnytdE+SZXHWh6v8NwWPVr0xlGykdtvlq+Ra2HFuMb3RVhx1eaKhK/UmFF+ejWZZaNGjxF5rNKhNaaSvRHHGom5CKW7uBi47TbpPD7oar+Qnp6OnTt3ol69elpdowK2XwiG7RcIIYREO6a2X6hfvz7sdt6uQwghhJDoQ/dlqWeeeQbvv/8+EhMVuiqT0wJbeTlu2bDQvWFQ+wXd8crLAU8Lj7vvVv/If7U+Pse6pXZv47XojaNkI7ffLF8j18KKcY3vi7Diqs0VCd/AMcA4Pz2a1OTXa6OkJ9BHTx41MczQofR7rKRDz9qEu23EXEjF9MQws/3CueeeKwCIjIwMkZmZKc477zy/n2iH7ReCqartF64ft8B4LXrjsP0C2y8Y5cv2C+G1PWD7BWvnwoD2C7rO3DzoaWxFCCGEEBJl6Cpuhg4darAMQgghhBBjCOuu4JKSEmzZssUoLcRCquoTioWomsdNCCGxjK7iprCwEP/3f/+H9PR0ZGZmesdvu+02rFmzxjBxhBBCCCFa0VXcjBo1Cvv378eqVav8xgcMGICXXnrJEGGEEEIIIXrQdc/N7Nmz8fvvv6N58+Z+4507d8btt99uiDBCCCGEED3oKm6OHj2KOnXqAABsNpt3vKSkhPcwnKa4EhIx+PqRAIAPDGq/oDteUhLw9deVr432SUryalPVfkGrFr1xlGzk9pvla+RaWDGu8X0RVly1uSLhKzVmlJ9eTWbZqNFjRB6rdGiNqWRvxLFGYi5C6S4uBgYMkM7jg672C507d8bw4cNx2223IS4uDk6nEwDw5JNPYs2aNVi0aJGmeMeOHcORI0fQrFkzJCcnq/bLzs5GSkoK6tatqykf2y8EY3T7hQemrMavbL9ACCHEQNR+fus6c/PKK6/gpptuwrJlywAA48aNw/z58/Hnn39i8eLFquOUl5fj/vvvx8yZM1GnTh3k5eVhwoQJuPfee2X95s+fj8GDB6O4uBipqam44IILMHnyZDbxJJrxPfNICCEkNtBV3Fx55ZVYsGABxo4di3r16mHChAm46KKLsGTJElx88cWq4/zvf//DL7/8gm3btqFZs2aYPn06+vbtiwsvvBAXXnihpM/KlSvRu3dvvPnmm3jkkUdgs9kwd+5c5OTksLgJA1tFBXpuXereqLgaiNf11vBid4YRr6ICmD3b/frGG9X5avHxOdY9dXoYr0VvHCUbuf1m+Rq5FlaMa3xfhBVXba5I+AaOAcb56dGkJr9eGyU9gT568qiJYYYOpd9jJR161ibcbSPmQiqmJ0ZxcXB8KWSfXxyC119/Xde+QBo0aCCeffZZv7Fzzz1XDB48OKRPjx49RLdu3VTnkILtF4Ixuv3CYLZf0B6H7RfYfsEoX7ZfCK/tAdsvWDsXBrRf0PVV8BEjRuja58uBAweQm5uLjh07+o137twZa9eulfQpKyvDb7/9huuvvx4OhwPbtm1DQUGBeuGEEEIIiXnCu/YQwL///ouaNWuqsj127BgABNnXrFkTR48elfQ5evQoysrK8O+//+Kss85Ceno6cnJycO2112Ly5MmoXr26pJ/D4YDD4fBu5+fnq9JYlaiqTygmhBASe2gqbs4++2zJ1wDgcrmQm5uLuz3tyhVIONUW3bfo8GwnSLVMBxAXFwfA/Zydv/76C40aNcKBAwdwySWX4Mknn8RHH30k6TdmzBg+XJBIIgSLOkIIiTU0FTdPPPEEAGDQoEHe1x4SEhLQrFkzdOvWTVWshg0bwmaz4cCBA37jubm5aNy4saRP7dq1kZKSgltvvRWNGjUCANSvXx933nknvvnmm5C5Ro0aheHDh3u38/PzQ+Yg1pNXUg7POThHhRPhP3WHEEJIVUJTcTNw4EAAQK1atXDLLbeElTg9PR0dO3bEzz//jLvuugsAUFpaikWLFmHUqFFeu5ycHBQXF6NVq1aw2+3o3r07Dh8+7Bfr8OHDOPPMM0PmSkpKQpIBD6YjkaGwtLK4KXcKFjeEEEI0oeuem3ALGw8vv/wyevXqhXPPPRcXX3wxJkyYgIyMDDz00ENem5deegkrVqzAxo0bvdtdunTB66+/jksvvRQrV67ElClT8MUXXxiiiRgDnx5DCCHEKnQVN3v37sVjjz2GP//8E8ePHw/aX1FRoSpOjx498PPPP+Pdd9/Fjz/+iDZt2mDp0qV+NwY3adIEJ0+e9G63b98eWVlZeOONN/Ddd9+hSZMm+Omnn9Cjh8LzSogsrvgEPNFzKADgjcTEsOM54/THE4mJXt8X1fomJgKTJ1e+VrD1xHcmKNuqjhtuHCUbuf1m+eo5/lA+VoxrfF+EFVdtrkj4So0Z5adXk1k2avQYkccqHVpjKtkbcayRmItQuktKgMGDpfP4oKv9wpVXXomysjIMGjRI8sF511xzjdaQEYXtF4Ixuv3Cg1NW4xed7Rf2nSjGZeOyAAAbX7oa6UmGfqkPQGX7hXPrV8O8xy43PD4hhBDjMbX9wooVK7Bz507Uq1dPt0AS2/A7SIQQQqxCV3FTv3592O26nv9HohRbRQW6/bvKvWFQ+wXd8fy0XAGoOXNTUQEsWOB+fbVCPp/4h+t0MS5uuHGUbOT2m+Wr5/hD+VgxrvF9EVZctbki4Rs4Bhjnp0eTmvx6bZT0BProyaMmhhk6lH6PlXToWZtwt42YC6mYnhhmtl949dVXxf333y8cDoced8th+4Vgoqn9wr6cw17fgmMn1Tmx/YI5vmy/wPYLet63bL/A9gvhzIUB7Rd0/Rk6depUbNmyBV999RUaN24c1FnZ880mcvrAJxQTQgiJFXQVNw8++KDROgghhBBCDEFXcTN06FCDZRBiDULwjBUhhMQamoqbwsJCVXbp6em6xBBCCCGEhIum4iYjI0OVHf8aJqcLgfeLEUIIOf3RVNzINackhBBCCIkGNBU3RvWUItGHKz4Bz13lbow62qD2C3rjicREr+9TWh75/957la8VbD3xK+ITjIsbbhwlG7n9ZvnqOf5QPlaMa3xfhBVXba5I+EqNGeWnV5NZNmr0GJHHKh1aYyrZG3GskZiLULpLSoARI6Tz+KCr/cLpDtsvBDN73T4M++pvAMa0X3hgymr8yvYLhBBCDETt5zcfM0wAADb28SaEEBIjGP8nMTk9cTrRee8/p15fA8TFWRfPz/dKqHqbOp3AH3+4X19+uXw+n/gFdS82Lm64cZRs5Pab5avn+EP5WDGu8X0RVly1uSLhGzgGGOenR5Oa/HptlPQE+ujJoyaGGTqUfo+VdOhZm3C3jZgLqZieGEVFwfGlkH1+cYzC9gvBGN1+YdBHbL+gOQ7bL7D9glG+bL8QXtsDtl+wdi4MaL/Ay1IEANh+gRBCSMzA4oYQQgghMQWLG0IIIYTEFCxuCCGEEBJTsLghpsCuBoQQQqyCxQ2JOgTvbSaEEBIGfM4NAQC44uPxatd7AABPJyi0JFCBM05/PJGQ4PV9VK1vQgLw2muVrxVsPfGdcQq/AlrihhtHyUZuv1m+eo4/lI8V4xrfF2HFVZsrEr5SY0b56dVklo0aPUbksUqH1phK9kYcayTmIpTu0lLg+eel8/jA9gtsvwCA7RcIIYREP2y/QDTB9guEEEJiBV6WIm6cTlxwYPup1+G3X7C5wojnp0VD+4W1a92vL7pI8TH7nvjOuhcaFzfcOEo2cvvN8tVz/KF8rBjX+L4IK67aXJHwDRwDjPPTo0lNfr02SnoCffTkURPDDB1Kv8dKOvSsTbjbRsyFVExPjMLC4PhSyD6/OEZh+4VgfvhzmzFtBk5xurRfuIHtF9h+wai4bL/A9gtG69Aa04i1iYa5YPsFQgghhBB/WNyQKo2wWoAPOw4VWC2BEEJiAhY3hEQJX6/OsVoCIYTEBCxuCCGEEBJTsLghhBBCSEzB4oYQQgghMQWfc0MAuNsvTLj0TgDAUIPaL+iNJxISvL73a3nk/wsvVL5WsPXEV9V+QW3cMOO44hLk50wuhlJ8vb56jj+UjxXjGt8XYcVVmysSvlJjRvnp1WSWjRo9RuSxSofWmEr2RhxrJOYilG6HAxg7VjqPD2y/wPYLAIxvv3D/F6uxcAvbL2jhxR834fNl2QCMWQNCCIk12H6BaKKqtl+omkdNCCGxDS9LEQCAcDnR8sge94bLBdjDq3ttLpf+eIG+Kn2wZYv79bnnyufziW+re55xccOMozhncjGU4uv11XP8oXysGNf4vggrrtpckfANHAOM89OjSU1+vTZKegJ99ORRE8MMHUq/x0o69KxNuNtGzIVUTE8Mtl8IDdsvBMP2CwbEDTPOK1/9ZU4LhXB82X6B7Re0Plpfb362X2D7BRVzw/YLxGKE1QIIIYRUUVjckCoNSzBCCIk9WNwQEiUIwVKLEEKMgMUNMQl+D4kQQog1WF7cZGVl4eabb8Zll12GQYMGYf/+/ap9p0yZgg4dOuCtt94yUSEhhBBCTicsLW4WLVqEHj16oE2bNnjuueewd+9eXHrppcjPz1f03bJlC5599lkcOnQIe/fujYBaQszFZuPZLkIIMQJLn3Pz3HPP4dZbb8WLL74IAPjvf/+L+vXr46OPPsKIESNC+pWWluL222/H+PHjMXr06AipjW1c8fH4qONNAICHDGq/oDeeSEjw+t6t5ZH/TzxR+VrB1hPfpab9gtq4YcZx2hXmTC6GUny9vnqOP5SPFeMa3xdhxVWbKxK+UmNG+enVZJaNGj1G5LFKh9aYSvZGHGsk5iKUbocDePdd6Tw+WNZ+oaioCBkZGZg6dSruvvtu7/jNN9+MoqIizJ8/P6TvoEGDUFZWhk8//RTt2rVD165dMWHCBNW52X4hmKrafiGzfjX8HCXtF174YSO+WO5+iB/bLxBCSDBqP78tO3Ozb98+CCHQoEEDv/EGDRpg0aJFIf1mzZqFX3/9FevXr1edy+FwwOFweLfVXPYihBBCyOmJZcVNeXk5ACApKclvPCUlxbsvkD179mDgwIGYM2cO0tPTVecaM2YMXnrpJf1iqwIuFxrlHfK+NqL9gu54gb4qfeC596pJE8XH7Hvi2+rKv4/Kyirw6fQl6HTWmbjo8rbhtV9Q0Kc4Z3IxlOLr9dUyr0o+VoxrfF+EFVdtrkj4Bo4Bxvnp0aQmv14bJT2BPnryqIlhhg6l32MlHXrWJtxtI+ZCKqYnRkFBcHwpZJ9fbCK5ubkCgPjxxx/9xu+55x7RqVMnSZ93331XpKWlifbt23t/UlJSRJ06dUT79u1FRUWFpF9paanIy8vz/uTk5Kh6fHNVgu0XpJm8YIMx86JC38szV5rTQiEcX7ZfYPsFrY/W15uf7RfYfkHF3Khtv2DZmZv69eujfv36WLVqFXr37u0dX7lyJbp06SLpc/vtt6Nz585+Y3fffTfat2+P4cOHIy4uTtIvKSkp6AwRIWrIPlZktQRCCCEasfTbUvfddx8mTZqEBx54AI0bN8ZXX32FLVu2YMqUKV6bV155BRs3bsTMmTNRu3Zt1K5d2y9GSkoK6tSpgw4dOkRaPpFFWC1AFdGkkl8EJ4QQY7D8q+D//vsvWrZsiQYNGuDw4cOYOHEi2rdv77XJzs7Gxo0bLVRJCCGEkNMJS4ubxMRETJ8+HYcOHcLhw4fRokULpKam+tk899xzKCwsDBlj+vTpyMjIMFsqIaYTTWeRCCHkdMbS4sZD3bp1UbduXcl9TZs2lfXNzMw0QxKpIvBSECGExB6W95YiRA4heD6DEEKINqLizA2xHhEXhykXup+K2y8+/LeFy64/noiP9/reqNY3Ph4YPLjytYKtJ74zxDfsPLji4o2ZFxX6FHPJxVCKr9dXy7wq+VgxrvF9EVZctbki4Ss1ZpSfXk1m2ajRY0Qeq3Rojalkb8SxRmIuQukuKwMmTZLO44Nl7ReshO0Xgpm1dh+Gf21k+4VVWLjlsK54vu0XNrzYAxnJ4fe6CkRt+4VnZm/AtJXuh0mZ3RLh+R82YgrbLxBCSEjUfn7zshQhhBBCYgpeliIAABsEzizOc28IAdjCvNVWhBEv0FelD44edb+uVUs+n198+W/aiXCOQ6s+pVxyMZTi6/XVMq9KPlaMa3xfhBVXba5I+AaOAcb56dGkJr9eGyU9gT568qiJYYYOpd9jJR161ibcbSPmQiqmJ0a0t1+wkry8PFWPb65KGN9+YYnueL7tF/KPnlDnZFL7heenrzBmXth+ge0XjNDJ9gva9bD9grHHepq0X+BlKVKlEQr7I/lVcaEkhhBCiCpY3BCT4BNkjKasQmWHdEIIqeKwuCEmof80RCTPYCiVYNF0MuXbNTlWSyCEkNMCFjckqomm4sJqjhQ6rJZACCGnBSxuiEnovywV7he1CCGEVG1Y3BAiA+ssQgg5/eBzbggAd7uEb8+/AgBwi0HtF/TGE/HxXt8eWh75379/5WsFW098Ne0XDJkXFfqUcrlsMvuV4svt17svFKF8rBjX+L4IK67aXJHwlRozyk+vJrNs1OgxIo9VOrTGVLI34lgjMRehdJeVATNmSOfxge0X2H4BgPHtFx6Yshq/bj6kK55v+4V/XuyBairbLxwpcCApwa7KXm37hadnb8D0CLVfeO77jZi6InT7hTcWbMN7WTsjooUQQqIRtZ/fPHNDYoK84nL8538LAcTuBz/vRSKEEHWwuCEA3O0XUspK3RtChP1JahNhxAv0VcG2g/nq8/nFl2+/EKQlnPYLxcXu16mpknGEcMnmEi4ZLUrx5fbr3af1WK0Y16I/3Lhqc0XCN3AMMM5PjyY1+fXaKOkJ9NGTR00MM3Qo/R4r6dCzNuFuGzEXUjE9MYqKguNLIfv84hiF7ReCMbr9wpCPf9cdL2dvZfuFPJXtF1Zt3Ks+n8+jvW98Tb79wgsRbL/w4gz5XBO+Xxt6P9svsP2C3BjbL4SXh+0XIjsXbL9AYh0hTu/4WrDxu1mEEGIILG6IKYRzVUuPrzCpSomi2ocQQohKWNwQANF1BoMQQggJBxY3JCawxcBXiQTPExFCiCGwuCFEhtO/ZCKEkKoHixsSE5h1z01Vh/NKCDkd4XNuCABA2O2Y2+pSAEAvhZYEanDZ9McTcXFe38tU+vr6KObzsXXZ5et7l11DXIWcuOWWytcSCIVcTrk1Uoovt19m38LtR1HW6lLE2Wy4Ru3xh4pnxbiKeTcsrtpckfCVGjPKT68ms2zU6DEij1U6tMZUsjfiWCMxF6F0l5cDP/wgnccHtl9g+wUAxrdfGDh1DeZvOqgrXs7xYlz+WhYA4O8XeqB6inI7hZW7juH2j1eoznc6tl94fcFWvJ/1b0S0eBg1awNm/BWZ4yeEECXUfn7zshQBEMXfllKpS+8NxdF0H7LSDcV8Dg4hhKiDxQ0xhVh5zk00Yc23qWJ/XgkhMYjs84tjFLZfCOb7pca2X3j0kz90x9uXU9l+4eThE6p8/tqwR9dj9k+n9gvjv18Ter9J7Reem7Zc+/Gz/QLbL7D9AtsvhDMXbL9ASHgIobA/MjIIIYQYCIsbYgrRdC8LIYSQqgWLG0IIIYTEFCxuCADlyzOEEELI6QKLGxJ1RFOhxatrhBBy+sHihkQ1VjeTjKI6yxKiqdAkhBC1sP0CAQAIux2Lm3cAAHQ3oP2CsMfpjxdf6XuRhvYLqvP52Io4+fo+rOMIyImePStf68jlktuvFF9uv8w+XccfKp4V4yrm3bC4anNFwldqzCg/vZrMslGjx4g8VunQGlPJ3ohjjcRchNJdXg78+qt0Hh/YfoHtFwAA363Zh8e/Ma79wpBpazF3wwFd8fadKMZl47IAAOufvwpnpCYq+uhtv3Beg2qY+2h0tF94ZvYGTJPJZUX7hZHf/YOZq3IimpMQQkLB9guEEEIIqZKwuCHmwDtxLcHlqnInYgkhJBjZ5xfHKGy/EMzsP7aJooQkUZSQZEj7haGfLtUdb1/OYa/vicPHVfn8tWGP+nyFhV7bG8bJt194fvoKY+alsFCI1FT3j0z7Bblcb81eE3p/YaEoT04RRQlJ4o91u7Xll9n3zNTl2o8/VDwrxlXMu2Fx1eaKhG/gmJF+ejSZaaOkx4g8VunQGtOItYmGuZDRnZeSourz2/IbisvKyrBo0SIcOnQIbdq0Qfv27RV99u7di5UrVyI+Ph4dO3ZEw4YNI6A09kktd0RNPI9vmUn5zLKVpbg47Fxy++NLSxAPYNCXa7GhXTNt+WX26Tr+UPGsGFcx74bFVZsrEr6BY0b6mZXfLD1G5LFKh9aYauKFe6yRmIswf5ctvSx15MgRtG/fHkOHDsXcuXNx5ZVXYuDAgSHthRC488470bVrV3zzzTf49NNP0bJlS0yYMCFyomMUo9slRPqqlN6LMbyII49ZbTR4+YwQYiaWnrkZNWoUAGDdunVITU3FmjVr8J///Ae9e/dGr17B38wQQuD666/HtGnTYLe767KpU6diwIAB6N27N1q0aBFR/cQcoun7e9GkxQrMOv4Vu47hkrbp5gQnhFR5LDtz43K58PXXX+Oee+5BamoqAKB9+/a45JJLMHPmTEkfu92OO+64w1vYAMDVV18Nl8uFbdu2RUR3rBKtjS6rUnFhq0J3YZc5nVZLIITEMJaducnJyUFBQQEyMzP9xjMzM7F69WrVcX766SfExcXhggsuCGnjcDjgcFTeN5Cfn69dMNGELYxqSY+rWWVBJIu+aC0wzaAqFXKEkMhj2ZkbT4Fxxhln+I3XqFFDdfGxZcsWDB8+HMOHD0ejRo1C2o0ZMwbVq1f3/jRu3Fi3bhKd6L7npiqdGiKEkCqCZWduUlJSAAAFBQV+4/n5+d7LVHLs2rULPXr0QM+ePTF27FhZ21GjRmH48OF+OVjgBGCzY0Xj8wEAne3h17zCbtMdT9grtbRS6evro5jPx1bY5G1dMGhe7HagS5fK1xIIhTWQ3e9zTC6pU0By+WX2KWmSJFQ8H42B46Hswx5XMe+GxVWbKxK+UmNG+enVZJaNGj1G5LFKh9aYSvZGHGsk5iKU7ooK4M8/pfP4YFn7hfLycqSnp+Pdd9/Fgw8+6B2/5pprkJqailmzZoX03b17N7p06YKLL74Y06dPR5zGnj9svxDM9+v2Y+hX6wEY85j9R2asw5y/c3XF822/sO65q1AjTbn9wopdx3CHjvYLretlYP7Q/4a0GzVrA2b8FZn2C8//sBFTlu8Jmeu1+VvxwW+h2y94jinebsPOV3saounJb//G16v3hcypFY/GyQP+g26t64QdjxBStYj69gsJCQm45pprMHPmTO+lgdzcXGRlZaFPnz5euyVLluDrr7/2bmdnZ6Nr1664+OKLMW3aNM2FDZGmKt3v4Us0XZWqoktACCGGY+lXwceNG4dLLrkE119/PTp37owpU6agU6dO+L//+z+vzdSpU7FixQrcdtttKC0tRbdu3VBaWor//ve/mDRpkteua9euaN26tRWHQSSIlQ/qyN5QHCuzRggh1mJpcdO6dWv8888/mDJlCg4dOoQnnngC/fr1Q3x8payuXbuiadOmAACn04mrr74aALBhwwa/WG3atImc8BgkrqQYa965y73xXC6QlhZWvMTSEv3xCosqfYdnAyouS9mLi9TnK6q0vef5r2Tjxhs1L0VFQLNm7tfZ2ZJxEkrlc8XL7fc5pm5DJmvLL7MvQc86horno3Hj7etV2WsZLz2Zj4omTZEQb0dSzl73uIp516VD7ZjaPEb7Bo4Bxvnp0aQmv14bJT2BPnryqIlhho7AuFrnQ8/ahLttxFxIxfTEcLmC40tgefuFRo0a4emnnw653/csTlpaGiZOnBgJWVWSmiXGfkU+nHge3+Mm5TPLVpajR8POJbffsy/kpTa5/DL7dB1/iHghY4XKr2F85l97MaDgpPoY4epQOxZOPCO1GOlnVn6z9BiRxyodWmOqiRfusUZiLsL5HQG7ghOTCOcKC6/OSFMVpuXfw4W6fY8VaelERgiJZVjcEABV934PK24oPlJgbIPSQE6HpQwlcc4/+yOqQy/sjUVIdMPihpjCafD5ahlv/iLdKkSpKLHi4zSavk0WTWw/XKBsRAixDBY3BEDVLUas+OzOKym3ICsxEherPkKiGhY3JCbgZ01swJ5ThBAjsPzbUiQ6EDYb/q7XEgDQ1qD2C3rjCbvd69tYwyP/VefzsXUqXAsybF58copQOW1xsrmETeYYfeKHbL/QoUPla5X7dK1jqHg+GkON+7XDkIkjNS45P3LHrUF30Hg4uaTsjPaVGjPKT68ms2zU6DEij1U6tMZUsjfiWCMxF6F0O53AunXSeXywrP2ClbD9QjBz/s7FIzPcbxgjHrM/dOY6fL9eX/uFnOPFuPw1d/uFtc9dhTNNbL/QvHYaFj/eNaTdqFn/YMZfOarjqsl55bl1MKn/f4L2vzxnMz77c3fIXOPmb8WHKtovJMTZsON/xrRfGPHN3/hmjfHtFz6/5z/o2qpO0Pgj3c/G4z1a6Yo9dt5WTFwSen6MZFNuHnq9sxQAsHtMzyp7Qz4hkSbq2y+Q6CJa/2+uSrW3lWsQap7Nmv3TvRioQm9LQk5LWNwQAMbf6xDOh5evq9rPEPM+bE7vD2E1OCqcuPKtJRgyba3VUk5LWOgQEoWIKkheXp4AIPLy8qyWEjX8vGKnyKlWR+RUqyNchYVhxxvxxTJvPFFUpMl3//6jXt/DB4+r8lmxYa96/UVFXttrXp0na/rc9BW6jyNUzkEf/S5p8uo3q2VzvT5rbej9PvHPH/GdZH7RtKn7J8D3t3W7Q8YdOXW59uMPlctH4+/rsyXHJ/ywTlUcqXHJ+ZE5bi26A8c37sj15nIWFGrLJWVntG/gmJF+ejSZaaOkx4g8VunQGlPJ3ohjjcRcyOjOa9xY1ec3bygmANyn8BrlHwZgzKUgm6iMp/lPWyG8voeFuj4iNgj1+n3i2zTYhvUnum+cENghn8smt9/vmKTzY88eaV9X6Li61jFULh+N2SHGbfAfDxVH+lhcwVrljluD7qBxH81OrblCxDPUV2rMKD+9msyyUaPHiDxW6dAaU8neiGONxFzI6VYBL0uRIIw4zW7YxRye8vdymt+mQiTgk44JMQcWNyQm0FuQRdNHi1XFi4iqWahazFq3z2oJhMQkLG5IEIZ81Bn0QW32x64Rl+AixWkkVTdhHaJF8xPOe+i3bUcMVEII8cDihgDwr0Ws/sD3y69DSlUoAiKFWXPJS2xuOA+EmAOLGwIgev+TVXvJRK/+aKqDTvdnvxjB6TgD0fQeIoS44belCABA2IDtNZsAAM4y4kPWZvPGO0drPB/fDJUfdwKVPmcr5fOJLxTi+8bVfBwhcob6CFfMJTenvsckFd5mAzIzK1+rzOv7vlB9/KFy+c5BqHH4j4eKIzUueRwyx61Fd9C4j+aztOaSev9p8JXSomrMKD+9msyyUaPHiDxW6dAaU8neiGONxFyE0u10Atu2SefxTSmsvgZhAWy/EMz8jQcw8Ev3Q9x2/O9aJMSFd1Jv+NfrMWvtfgDaH4W/70QxLhvnbr/w58juaHhGiqLP8n+P4c5P3O0Xdr3aE3a7/AeZ53H/Tc5Mxe9PdgtpN/K7fzBzlbHtF67KrItP+nUI2j/m5y346PddIXOZ1X5h0ZZDuO+L1ZJxn/jmb3xrQvuFL+/rhMta1goaf7T72Rius/2C0vwZyYZ9eej9nrv9wvZXrkVivLbfF8/xXnNePUzs295wfYTEKmy/QHQTTeWu2bW3mj/mowWFes2L1qdNR9N6E0KIEbC4Iacw9lPcqHYOej54XRqclE2jp7oxukWGupyR5XSss/hVekKiENnnF8cobL8QzIK//hXbajYR22o2EaV5+WHHe2rKcm88rW0L9u074vXNyTmiymflxr1en7L8AnnjoiKv7ZWjf5Y1fX7GSt3HESrnkI//kDR5bdYa2Vzjv18Xer9P/DYjZknmF5mZ7p8A30Wrd4WM+/SXOtYxRC5XYaE31p9/75HUHtR+QUpzUZE41LiF2N+wuV+rjde+k5g/meNWq1tqfMP23ODfF7W5fI73kUlLNfsG2akZM9JPjyYzbZT0GJHHKh1aYxqxNtEwFzK681q1YvsFoh47gHOO7QUAlBrw1FTfeHraL3h896n0tfn4lCnp97G1KfzV7Rs33PYL3jghctogn8tmk9nve0yh2i9s3izpK2SO0SZ0rGOoXD55joYYnxfY9iBEnDo57nuPdhwqQMvmaW6tUvMnc9xadAeN+2j2/r6ozeW3Vtp9pbSoGjPKT68ms2zU6DEij1U6tMZUsjfiWCMxF3K6VcDLUgRANF18Cf5cUYPv16iNvEwQyXtuDLuUF6WXScy4t8epIWhpudN4AWESTfd0ERJLsLghUY2e59wY+SFqxn0uej/QTPsclJkvsz58Q8VVs94ixAIreY7/dbtibLVEUwG560hh0FheSbkFSio5Wujwvo7GopLEPixuSFSj+syNDh9A+UMquv6yVvnMHyOLOwOPX40sNdr1XjWdv+mAPscoJ/dkSdDY2wuNK+T0UFpWWdBoucGfEKNgcUOCiKb/i9RK8TtzY+RlKcMiqcil9LV0lXGMXT7jZiDUGZdw4vg9a0/BL5LfNss+WqTKzixNe08EFzxWYcW3/AhhcUPcRNH/P/733Ki9LFV5AAbcDy0Z1yhCHZJRmYz8S9m0y1IhxkMp930fhFpfpbVS+5wgrUjN94b9eap8jSjEo+hvEUKiBn5birix2bCvWh0AwJkGfAjY7PDGa6Sj/YLHV2h45L/Hp7qW+EolhY+t5uMIESfUR7uAfC6bXWa/T3xXiPxo2rTytQ8um8xa6Tn+ELlEqHX11S6kx//dcRRdLkw/5QvpuZTS6jvvNoW/5ULNkcS4zWb3xq2B4FyyhZbUPMisj5JGgeD1EyJ4rLxxExQ7KuAsLseZaSFySo01bQohd0ySmgL+P9GQS7ONCj2G5LFKh9aYSvZGHGsk5iKUbpcLyMmRzuMDixsCABApqbhs0GcAgE0pqWHHK09K8cbLTtUWz5Wagi6nfBeq1OKr/x+lfKmVto0Tk2VNK5L1H0eonFeGyKmUS3a/T/xQ+ZGdLbnL5TN3gXEr9KxjqFw+Gr9MTpUcfyg5WXL8iRPl6HJq2HetF6SmVWqVmh+fGA0V1lpOd9C4T9wNKSlBY2+nyLQM8bG71qNJZn2UtEitX1lSctDYef0moqzChR7zduLjfh1CHlfg2NQZv+HtRTvwZX4FWku9BSR8RGoqLvf9/yQpXlUuXTYq9BiSxyodWmMq2RtxrJGYi1C68/OB6op/wvKyFHETXTfOasfvq+CSpy6kUbqCY879AvouJFjyhGIjbyj2vdwYag5UXHLyjeOrz6zLTkqEcxnUiCuIakOUVbh/MdbnnNQU/7kfNuFoYRme+m6DBk3C57V+jhY60GP8Ekz6Y1cYUUhVhMUNCcKIa/hhPu/Od0uVj+8HW7Q/5ybkPTdRWGCaVVCF9f7wWV9fdZEs/vwfPRB8MGrv1Yqmr5QronPRwrmR/J1FO7D9UCFembtFdwwplu44ips/XIbthwoMjUuiCNnnF8cobL8QzOK1u8X6ei3F+notRcGJ8NsvjPxyhTeeKC7W5Ju994jXd8fuQ6p8Nmw/4PU5fuSkvHFxsde2m0L7hTHfrtF9HKFyPvjR75Im439YL5vrvbn/hN7vE/+c4d9J5hcdOrh/AnwXrNoVMu5LX63SfvzFxeJ4ZltR1PZCP5/S/AJvrCXrsyW1j5u1VnL8w3kbvMMFJ/Il3x8TfpSYPw1rHXKOJMY37zzojXvM837zyTVnxU7ZPB67wZ/84R1zdeggHBdeJD/PElp+W5cddNwD3v/Nf8wn52Uv/BT6eAPHfPxueXOhak17cip/h/OO56nOFWjz9Jcrg44j1Ps4pB6ZY7r61fmqfZS06tGhOCdaY6pYm7CPNRJzIaM778ILVX1+854b4sblQtuDOwAABc7wH7plcwlvPLg0XCcCIHy07FDrKyp9jinp94lvV7iGZYdL93GEyhkqjk3I29jlYvgdk8Rfyi4XsHq1pK/cWilpkmLFzsPovPnvIB/hrIy1xDeWj/YFLqfk+Aq/OE7J94ekVp8YcdK3Wvvl88xRXqED1U/dN5NX5ED1wLnzyXXU837zybVf7myF7zp67Fwu2FavRiJOzV+bpooapY7RM2YXwWvq2bYJl2yswDGvX6izTAqa8pwu1bmCtqXWNMT7WFZPiGM66XnYoBotSts6dKiaE60xleyNONZIzIWcbhXwshQBEJ2XRAC9z7kxDlO+Cq7TL9JPCwb0fT19x6HgJ+YGIkI9ZVjF5IQ2MW6Cxs3f6n39zqLgB+L5Xk6S+iq46mcSSfh+vWqfSu/QWP37rOdxDkpxgvedRpf0SMRhcUOCsPq/DN//tPTck6DpCcWKNxQbjxX/J2/YfzLkPt8PQlfA3bF6ijtVBYq2+4n9i1ffkz4+HkbeULzzcOW9GPtPlgbt19P/TAopX0MKapkYUrsC193I9AaH9rJu7wlzApOYgMUNAeB/M6Yh3+Aw6D80PXEM/YvOjBuKdeYK50Nv+b/HQu6L8/lfIFKPyjfqhmLfs+BKNxRruqro9+0sebHhvN8kH02iMYbUHwByMaTmafWe44p59L77KgImvshRoTOSPyeKDOifdeqg/tx5JPxYJKpgcUNMIZxvgfh66ipudGcOxpRv4IQ4KDOvJMjNo2/RFNhlW089FSqVmrMdWs/6aHmfaSlCfA9b6syDbyip/WrfN9JnblS5SsbwHKPWs1hqmltq+b3ynZPA29pe+nGT6jiyS6bjvRnqDNW3q/drD0aiGhY3BEDAh4TV16V80FUkGXniJoL3Llg17X4f5GHcM+1BTRERsgBSMQu+Z5dCPfNG2k8xtBctxYHUB2Y4xb3Wt5zdtzg9pUXuLF9gAatk70HNum7KzQPgv0aBZ27m/KO+ganR94MtDTxDIzz/RNF/esQQ+G0pAsD9IXEspRoA494Unng1w9Cix0fNpRW18W3QfxyhcoaUJ+Rz2Wzy+5WOKZSv3Wbz7ksKPHMDm67jl/IRED5zICTtQ8XxLSAEQs+lVF4j3heBcf2PJdhOqoCQiickxtRqLDlejEZpabDbK9cozSUQD+n3rdTce8Y8hUJeSTls6WcgMd6O5CA/ZU0PTlmNP0f3gRDCZ+1kjq9WLdltqfe81NwpxfXOWZnLb9slMRfe946CtqBtFTpUxVCKq1WHmhxGb6vRoXduXC7guPJl1KgpblwuF+x2bSeS9PgQaSpS0tD+0ekAgPVp4bdfKEtK8cbLTktTsPZHpFVq+SlFna8zJdXr86dSPp/4DRNlHpMPoMInrtbjCJXz0iTpNgBlyfJz5vRZo6D9PvGlKJeLnZbu3fd3QLsLPcfvu35+Pj7jH/rm8RkfkCQ9PsxnXKSmocOp8e994khq9YlRKyFJXriPbTuf90WFxNy5fNbiN0+rBx//8cky7ysfu+5JKUFjfZLU+X5VbkcjADaf9dt4aj6k1tuzfWZCIgB3iwTP2NRTNh+sOoCPhnwp6ZeZEKJ9he9779RtMM7UyrHFKSmS70+RmorXp/6Bxmem4k7Peh05AiGE90ySU2JNPdsfhWrNkpYGHPE/Q+N936Wm+mlJT3B/BDqk/r8KiKG4rUKH4raUj9aYSvZ6dFkxF6F0ny7tF8aMGYO6desiISEBbdq0weLFi03xIfIY/bXK8G5M1e7rf6nCuGOJ5DebzPpWCSB/HL5/HwTOnZ5T/3Ehrun43YehMabT9zk3Pt5ODZOm5X3hayt1PELita+PU+3jmSQ0yZ318bX3FAC+61dxKrHcZSbP74rv1Hle55eEvklXy++0Syiv0T/78vDBb/9i1KzKtg6fLt2N9q8s9D452B4w90JFXDkCPTw6A/OQ0x9Li5uJEyfi1VdfxbRp05CXl4ebbroJ1113HXbv3m2oD1HG74PHgA/ZMrX/u0vg66pWi179CXEK37AxoeAI52Zavcj95+1782vgB4ae4w9V3PjGDiUn5PNv/GwqX+v9wFXCKVFE+Onx2e+J66crjDeO0xnat9wZPIe+99xUuJRvKPasg++9MJ7iUe6eKy3vT984oYq1PIlCavRPm3G8qAwvzXHfdBwXMPcVPvMqV9zkl5Z798sVRJ7teBY3MYell6Xeeust3HfffbjyyisBAC+++CImT56MiRMnYty4cYb5WIHTJfDLpoP4dfMh7DtZgkP5pYi325CWFI/khDgkJ8QhwW5DYrwdCXHuHwGBeLsNSfFxiLPbEGd3f+zY7TbYbO7/xOw294eR3eb+T9c7JrFtt9lOjXlew38bleMLVu/GzOkjAQB/3tga9erWQGK8HXF2GxLi7LDbbEhOsCPebkdSvB3xcTbE29374+22oA9PV1GxNx5e6A7IdUkOoKKwqNL3vl8AKJ+CRIlPvod/A86UubRWUuK1fXHwG7Jh40pLdB9HqJwfPPmuvlwlMnPqE7//rS8FxU6pKJPxrYzrHP47kF55+cau4/gTHA6vj3i+G2ynLts4fd4TBbf+KKl9UYfPJceXXzLVOyyKK8dd/eYBOBMAEOcoDdbqE+PRfq/KC/exHfvIeO9wcrkjKK7wscWgRQDS4SyuPL6cnt+pyvPpM+8HjU15/sOQrhVFlb8bzr6njt1n/SqGutcvsSxAM+DdHvJ//3PbFlb6Fd02x32sFaH9Rg95U/F4PO894TMXzvt/BUoSgmzifdar7JmuSIy3e7ffP/U7Eufwf/+Vl1V4t4/d+L2knP0HjiOnUxeckZqI1uuWwul0eX0OXz/bT+8Dd44GACQFrjEAXHut+99585S3pX4vSkq0xwj0Cfo/QCGmkr1UDq3HGom5kIrpiVGh7lEClhU3x44dw44dO9ClSxfvmM1mQ5cuXbB8+XLDfCLJws2HUOAoR2m5C1OW78GWA/lWS1JNSlkpJuVsBACc+9V6lCSGuL4eApvN/deP569Ie3ExJp6Kd8mrv8KWlg6bzV30pSTEIT7OU4i5H+ouhIBLCLgEcOTgcfx9yrf79DWoW78W7Hb/Ys2bF+73wO49h5B1yueG6WtQu577A08IASHcf10LuM9EJJYWe491z+FC3Pf5KneBGW9HnM19lsBus0EIgQV/7cKIU7ZPfb0eZckp/pdqbACE+69TT8Hoi+ev+viSErxxKs49u45h6Mx1qHAJlDtdKKtwoczpwtrN+725Hp22BiItzRMedhuwZG021p/aP3zGWlSkpMJmc/9FnVBagjdP7bMLgUdnrIM4Jc8lBBat3o0tp/aP+GodypJT4HQJOF0CG7bvx9JT+/rOXIf0M6uj3OlCuVPgr405ePbUvgGTVqA8JQU22LzfYrGdOmabrXItlv+z15tr2Mx1QFoaXEKg4NhJfOZZ15824acdJ1HhFBBFhfjAMzfLsnG4wn1COdlRinGe8d/+xd4Sd86jh45j6qnxLl+vQ7vW7mvzv/y1y5v3iZnrUJ6SgsTSErx+aqyguAyPzlgHpxBwOoX731Nz4BICccXF+PyU7bYDeXho6mo4KlxYuekg3vWJW5acgr825mCF5/321Xo0blwbzoKCyuP4fgOycgq88Z0ugQqfPFM8dtuO4OHpa5FYWoK3PGObD+Hh6WuD5tclgJKT+d73bsdpa9Dx/MPYsvMAFp0au236GtSueyYW+2geNn0tYAPGn9ouLi3H/V+sxskjJ/DtqbELZqxFh3+OYPk/e/GSx2+GW4PHb9ehAu/7ypfEgPfekGlrsXvPIfx8auyar9Yis3517/HZhcDD09di/dZ93vfdfVNW4YzURG+ce3Ydw5Dpa7F49V686KPnWKHDO3eZX6/HN5uPIT0p3u//Ed/3+uMz1+FgXgmmef5v++ZvZG055D2minIn7pn8F1Zs2I/XPDbP/AwA2LJkCQBg7t/ur4n3ktl2Stz/E1dSLOsjFSPQJzCuUkwle6kcSrq0bvtquOrcukhJjHOfyjtl4z2tp3XbN4YKLCtuDh06BACoXbu233idOnXw119/GeYDAA6HAw6Hw7udn29O0fHM9xtwKN/hN9arTX20bVwdFzQ6Ay4hUFruREmZCyXlTlQ4XXBUuFDudKHCJXCs0IG0pHjvf7Yuz4ey8Hz4S2+7hP+HuHcbCIgjYXNqO760xKv5nLrpyLMnoqzChXKXQIXTBadLoLTCBdep/6QDEcJzyty9z7d2P1FUjpLykiCfUKRUVJ7TPpDnwK6S0A+g8/qUVc77toMFWH889L0DKWX+T5xdtPVwaFuf1z/+nau56PPN6XuO6Pv1ubK5ft18KCiX77zM23jQb39KWSl8/67+8W//+L6xf/rnQIBv5Vytzj6BktwSSb+Vu4+rOn5fn/k+On3n/UCeA7tOzUHgevzgM+57Lnb2uv1B9ofzyyrtfWznbnAfY0pZKV73GQ+cFz/dAToWbDokE7dyzjzvt0D/nzccVJXnp38OIKWsFG8FjCn5FpRWeH09bNiXh5LDDv812OTWMd5nbOGWQ35+5U6BxVsPB61doJ/U/AW+9+Zu8Ne051gJ9hwrCTo+3zlcduohk35x/jkgqceDEMBv24JvZPX1+XlD8DzO33jQ75iyth2BxLkGL098/TcAoJfMttTvRUpZqayPVIxAn6D/AxRiKtlL5VDSpXXbV8PyUd2RovClDbOw/NtSroCLvC6XS/GZC1p9xowZg5deCj5VbzQdz6qJk8VlSIqPQ3KCHec1qI6BXZqb0p/IcIqKgMHulz88fJn7zvQQOE+dcfD8Jep0CpS7XN6/UIUAkhwl3v8Vvxt8McqSUr3Xvsud7oLJ5T3jUXl5zG6zIb6k2Os7/rZ2cCS7f1kCCzPgVCklgLjSSp+Xrz8PZae+reJ7Kc9zWc43/rhb2qA0IQWOU2dQvHGF+6/lZJ/jGHH1OahISfXee+A5a+LJA7j1ec9q+JzHSfDRN/yqc1CamIy0pHgkxtuRGO++1JfsKPXaPNOzNcqSU/1uVvXVMura1qhIST2Vz/+YnunZGo7kVK+f3WbDGaLMu/+pa1qhIiXVe+kzobQy7shrW8OenoaEODvi4+xILqvc98ZtbVGRnOIuruEpsuH9i9mzFvaSoqBcAPw0PnVNK5QnpyIhzobk8lJJe3txcBz3WarKOE9e3QrOU5e90iocQfaBOZ2paYizAXFxdsTZKi+pxtttSHD4z0NcRjqS4u1I9Yk78tS81/CZT89a+eYadW1rID3NPcc2m3eu4+w2JPrM97O9zkVZcgriiov9cvj+9euZX5vNhjifOfG8BxJ95uPZXufCmZoKe1GR35hLCO/2c9edC6SlI91Z6jcWn5GBNKfD77hsNpt3+/nrzkXxqQ8qzztbAEgIOG6Rlua3Rs/0bO1+b5zafrpna5Qnp/r9HzHq2tbuFz5z4EoNXr/kxDjv9uM9zkFcRrr3/wXP/yGBc5mRHB9Sy4t9zoMtPc3vfR5I5+ZnKm47JL7hluQo8bNREyPQJzCuUkwle6kcSrq0bvtqSPR9/Hmkke0ZbiLHjx8XAMQ333zjN37XXXeJLl26GOYjhBClpaUiLy/P+5OTkyMA5ZbpVYrCQu9nlSgstDaeHl8tPmbZhhtHyUZuv1m+Rq6FFeNGrLXauGpzRcI3cMxIPz2azLRR0mNEHqt0aI1pxNpEw1zI6M6Du95X+vy2rKyqUaMGMjMzkZWV5R1zuVzIysrCpZde6h2rqKhAWVmZJp9AkpKSUK1aNb8fQgghhMQmln4V/KmnnsJnn32G7777Drm5uRg+fDgKCwsxaNAgr83AgQNx0UUXafIhhBBCSNXF0ntu+vXrh8LCQowaNQqHDh1CmzZt8Ouvv6JRo0Zem4SEBCQlJWnyITpJDf/JxIbF0+Orxccs23DjKNnI7TfL18i1sGLciLVWG1dtrkj4Bo4Z6WdWfrP0GJHHKh1aY6qJF+6xRmIuQukWwv21cAVsQgihrCq2yM/PR/Xq1ZGXl8dLVIQQQshpgtrPb8vbLxBCCCGEGAmLG0IIIYTEFCxuiJvSUqBXL/dPaamyvZnx9Phq8THLNtw4SjZy+83yNXItrBg3Yq3VxlWbKxK+gWNG+unRZKaNkh4j8lilQ2tMI9YmGuZCTvctt0jnCET2i+IxSl5enqrvyVcpjHqeixHxjHy2SiRtw41j1rNqwvE1ci2sGDdirfU+I0ZLHqN99T5TxYhnmOjNb5YeI/JYpUNrTCPWJhrmQkZ31D/nhhBCCCHEDFjcEEIIISSmYHFDCCGEkJiCxQ0hhBBCYgoWN4QQQgiJKSxtv2AVQggA7icdklMUFVW+zs8HnE7r4unx1eJjlm24+pRs5Pab5WvkWlgxbsRaq42rNlckfAPHfAnXT48mNfn12ijpCfTRk0dNDDN0KP0eK+nQszbhbhsxF1IxT8Xw7PV8joeiSrZf2LdvHxo3bmy1DEIIIYToICcnR7anZJUsblwuF3Jzc5GRkQGbzWZY3Pz8fDRu3Bg5OTnsWRVhOPfWwbm3Ds69dXDurUEIgYKCAjRo0AB2e+g7a6rkZSm73W5qF/Fq1arxzW4RnHvr4NxbB+feOjj3kad69eqKNryhmBBCCCExBYsbQgghhMQULG4MJCkpCS+88AKSkpKsllLl4NxbB+feOjj31sG5j26q5A3FhBBCCIldeOaGEEIIITEFixtCCCGExBQsbgghhBASU7C4MZAtW7Zg3bp1KC8vt1pKTLN7925s2rQJJSUlIW2OHj2KVatW4dChQxFUVjVwOBxYunQptmzZIrn/0KFDWLVqFY4ePRphZbGNy+XC5s2bsWPHjpA2ubm5WLVqFU6cOBFBZbGNy+XCrl27sGbNGhw5ciSk3d69e7F69Wq29YkWBAmb7OxsccEFF4hatWqJZs2aibp164qsrCyrZcUcX375pTj77LNFs2bNRGZmpqhWrZp4++23g+yeeuopkZSUJDIzM0VSUpIYPHiwcLlcFiiOTQYNGiTsdru4/vrr/cZdLpcYPHiw39w/9dRT1oiMMebPny8aN24smjZtKtq2bSsuvfRSsW/fPu/+8vJy0bdvX5GcnOyd+//9738WKo4N1q5dK1q1aiXq1asnLrroIpGamipuueUWUVJS4rUpLi4W119/vUhNTRWtW7cWKSkp4v3337dQNRFCCBY3BnDZZZeJK664QpSVlQkhhHj88cdFzZo1RV5ensXKYouxY8eKf//917s9a9YsAUD89ttv3rGZM2eKpKQk8ddffwkhhNiwYYNIS0sTH330UcT1xiLfffeduOCCC8Q111wTVNxMnDhRZGRkiI0bNwohhFi5cqVITEwUX331lQVKY4e1a9eKhIQE8eabb3rHVq5cKVatWuXdHjt2rKhVq5bYtWuXEEKIRYsWCbvdLhYsWBBxvbFEx44dRa9evUR5ebkQQojdu3eLjIwMMX78eK/NE088IZo0aSIOHjwohBDim2++ETabTaxevdoKyeQULG7CZPv27QKAWLhwoXfs6NGjIj4+XkydOtVCZVWDevXq+f2F2qNHD3HDDTf42fzf//2f6NSpU6SlxRx79uwR9evXFxs2bBDXX399UHHTsWNHMWDAAL+x6667Tlx99dURVBl73HjjjaJjx46yNuecc44YOnSo39hll10mbr/9djOlxTxnnXWWePHFF/3GWrVqJZ5++mkhhPtsZc2aNcUrr7ziZ3PuueeKIUOGREwnCYb33ITJunXrAADt27f3jtWsWRPNmzf37iPmsGfPHhw5cgRnn322d2zdunV+awEAHTt2xPr16yH4SCfdOJ1O3HnnnRg5ciTOP//8oP1CCPz999+Sc8/fg/BYtGgRevfujeLiYqxZswb79u3z219UVITt27dz7k1g9OjR+OSTT/DZZ59h4cKFePzxx1FRUYGBAwcCcHemPnbsWNDc/+c//+HcW0yVbJxpJMePH0dcXFxQI6+aNWvi+PHjFqmKfcrLyzFgwABkZmbihhtu8I4fP34cNWvW9LOtWbMmHA4HiouLkZaWFmGlscHzzz+PjIwMPPLII5L7i4qK4HA4JOeevwf6KSoqQn5+Pnbt2oVzzjkHderUwa5du3DBBRdg5syZaNCggffmYc698VxxxRXo0KEDRo0ahYYNG2LXrl145plnvI2XPfMrNfd//fVXxPWSSljchElCQgKcTifKy8uRmJjoHS8pKfHbJsbhdDrRt29f7Ny5E7///rvfPCckJKC0tNTP3vOtKq6HPtavX48333wTX375Jf78808Alf+pL126FB06dEBCQgIASM49510/nnn9+eefsXr1ajRq1Aj5+fno2rUrhgwZgtmzZ3PuTUIIgauvvhrnnHMO9u3bh4SEBOzZswcdO3aE0+nEyJEjOfdRDC9LhUnTpk0BuL+C6Utubi6aNGlihaSYxul0ol+/fli6dCmysrJw1lln+e1v2rQp9u/f7ze2f/9+1KtXz/sfEdFGcXExOnTogAkTJmDkyJEYOXIkNm3ahE2bNmHkyJE4duwYkpKSULduXcm55++BfhITE1G/fn306tXLe7agWrVquPPOO/HHH38AAGrXro3U1FTOvcHs3bsX//zzDx588EHv/x1NmzbFddddhx9//BEA0KRJE9hsNs59FMLiJkwuvvhipKWled/sALB8+XIcPnwYV111lYXKYg+Xy4UBAwZgyZIlyMrK8rvXxsNVV12FuXPnwuVyecd+/PFHrkUYXHLJJVi6dKnfz+WXX47LL78cS5cuRcOGDQG4537OnDleP6fTiZ9++olzHyZXX3110Ifnvn37ULt2bQCA3W5H9+7d/f4PKisrw7x58zj3YVCrVi3YbLage5xycnK8c5+RkYFOnTr5zX1BQQEWL17Mubcai29ojgnGjRsn0tLSxIcffihmzpwpWrRoIW688UarZcUcDzzwgEhMTBSTJ08Wf/zxh/fH9+vhe/fuFTVr1hR33nmn+PHHH8W9994rMjIyxNatWy1UHntIfVtq69atIiMjQ9x3333ixx9/FHfccYeoWbOm2Lt3rzUiY4SdO3eKGjVqiBEjRogFCxaIcePGiaSkJDF58mSvzZo1a0RycrJ49NFHxY8//ij69OkjGjRoII4cOWKd8BjgvvvuE7Vr1xYTJ04UCxYsEMOGDRM2m83vK/aLFi0S8fHx4umnnxbff/+96N69u2jZsqUoLCy0UDlhV3CDmDZtGr766is4HA50794dQ4cORVJSktWyYoqbbroJhw8fDhq/+eabMWzYMO/2zp078dprr+Hff/9F06ZN8fjjj+O8886LpNSY5+mnnwYAvPrqq37jmzZtwptvvok9e/agRYsWePLJJyXPsBFt7NixA2+88QZ27tyJRo0aoV+/frjiiiv8bNasWYO3334b+/fvR+vWrfHUU0/x0kiYOJ1OfP755/jll19w4sQJNG3aFA888AA6duzoZ/fHH3/ggw8+wOHDh9G2bVuMHDkSderUsUg1AQAWN4QQQgiJKXjPDSGEEEJiChY3hBBCCIkpWNwQQgghJKZgcUMIIYSQmILFDSGEEEJiChY3hBBCCIkpWNwQQgghJKZgcUMIMZXjx48jOzvbahmGcfLkSezevdtqGbJs374d5eXlsjYVFRXYtm1bhBQREllY3BAS5ZSVlWHv3r0oKyszPPaBAwdw4MCBoHxbt26F0+k0JMdnn32GG264wZBY0cCXX36Ja6+91pBY+fn5OHTokKzN5MmT0alTJ7Ro0QJDhgyBw+GQtf/hhx9w2223IT4+HgDgcDjwzDPPIDMzEx06dMDPP/8MAIiLi8Mdd9yBWbNmGXIshEQTLG4IiVKOHz+OO+64AxkZGbj88stRq1Yt9OnTB3///bdhOUaNGoURI0b4jW3fvh3nnnsujhw5YkiOmjVrBnVvr+rMnTsXnTp1QpMmTXDBBRegQYMG+PLLL4PsnnnmGbz55pt47bXXMGXKFHz//fcYM2ZMyLgulwsjRozACy+8AJvNBgAYMmQI9u7dixkzZmDMmDEYMGAAXC4XbDYbnnvuOYwYMcKv0SwhsQCLG0KilIceegjbtm1DdnY29uzZgxMnTmDQoEFYunRpkG1xcTH27NkD324qQghs3boVW7duxc6dO4P+4j9y5Ajy8vKQn5/vtSssLPRectm5cye2bt0a1JG6qKgI2dnZqKio8BsvLS3F1q1bIYRASUkJdu7cieLiYlx//fUYP3681873bFFBQYFkvzAPe/fuRXFxsff10aNHQ9oeP37cexyBZ6MAd5+grVu3oqysDEII7N+/P+RZkKKiIuzbtw9CCL/jksPhcCA7O1vxzArg7kX03nvv4cSJEzh06BBefvll9O/fHytXrvTaLF26FG+++SbmzJmDLl264NJLL8UDDzwge6Zl7ty5yMvLQ+/evQG43xe//PILPv/8c7Rt2xZXXnklEhMTvYVP7969kZ+fj7lz5ypqJuS0wqqOnYQQeWrVqiXGjBkja5Ofny/69u0rEhMTRYMGDUTdunXFd999J4QQwuFwiFatWolWrVqJ5s2bi+TkZPF///d/oqioSAjh7mZfrVo1Ua1aNa/dwoULxVlnnSUAiLPPPlu0atVKDBkyRAghRFFRkRgwYIBIS0sTTZo0EWlpaWLkyJHC5XIJIYRYvny5ACBGjRolqlevLs455xyxbNky8frrr4u2bdt6Nd99993iyiuvFF27dhX169cXqampolOnTn4drLdt2yYyMzNFSkqKqFWrlrjuuutEu3btxAsvvBByLj7++GPvcdSpU8dvLoQQ4sCBAwKAGDFihKhbt65o2LChSEpKEhMmTPCLM3bsWJGUlCTq1asnatasKR577DEBQBQUFAghhHj33XdFq1atvPZOp1M888wzIiMjQzRu3FikpqaKfv36ae4KXadOHb/1vvbaa0X//v39bN555x1Ru3btkDH69+8v7rzzTr9jbtiwoSgsLBQVFRXimWeeCYp56623Bo0RcrrD4oaQKKVTp06iQ4cOYseOHSFtbrrpJnH++eeL3bt3CyGEOHz4sHjrrbckbQ8ePCjatWsnXnrpJe9Y//79xd133+1nt2HDBgFAHDhwwG+8f//+4uqrrxbHjh0TQgiRnZ0tmjZtKiZOnCiEqCxuevXq5S2ghBCSxY3dbhc//fSTEEKIkydPiszMTDFixAivzSWXXCJ69eolSkpKhMvlEs8++6wAIFvcBDJz5kyRnp4ucnNzhRCVxc3ll18ujh8/LoQQYsaMGSI+Pl7s27dPCCHEsmXLhN1uF3PnzhVCCJGTkyNatGghW9yMGTNGnH/++WLPnj1CCCGOHz8uLr74YjF06FDVWg8cOCASEhLE1KlTvTHi4uLEhAkTxIYNG7w/Q4cOFc2bNw8Zp3Xr1mLcuHF+Y0OGDBFpaWkiLS1N9OnTR5w8edJv/5gxY0TLli1VayXkdICXpQiJUj755BM4HA60bNkSZ511Fu666y58/fXX3ssjOTk5mDVrFt588000a9YMAFC7dm0MGzbML05FRQVycnJw4sQJXHvttfj11181azl69CimTp2Khx56CMePH8eOHTvgcDjQu3fvoMsko0ePRmpqqmy87t27o1evXgCA6tWro3fv3li3bh0AYOvWrVi2bBnGjh2L5ORk770hNWrUUKU1Pz8fO3bsQNu2bVGtWjUsW7bMb/8zzzzjjXX77bfDZrNh48aNAIBPP/0UV111FXr27AkAaNSoER5//HHZfBMmTED//v1RVlaGHTt24MiRI7jllltU36jrcrnw4IMPokmTJrjpppsAAMuXL4fT6cTjjz+Odu3aeX/efvttnHPOOSFjHThwALVq1fIbe++993Dw4EEcPHgQP/zwA6pXr+63v3bt2pKX8Qg5nYm3WgAhRJo2bdrgn3/+waZNm/Dnn39i0aJFuOuuuzB79mzMmDEDW7duBQBcdNFFkv4ulwuPPvooPv30U5xxxhmoVq0a8vPzkZKSolnL1q1b4XK58NRTT8Fu9/+bqEWLFn7bzZs3V4zXsGFDv+309HQUFBQAAP7991/Y7Xa0bt3auz8xMRFnn322bMx169bhvvvuw9atW1G3bl0kJSXh6NGjQfcM+ea22WxITU31y33hhRf62Z933nkhcx49ehSHDh3Ce++9h0mTJvntS0tLk9XrYciQIVi2bBmWLFniLQq3bt2Khg0bYt++fX62rVu3xqWXXhoyVmJiouS36tLT00P6lJWVITExUZVWQk4XWNwQEuWcd955OO+88/Dggw/ivffewyOPPIJx48YhKSkJALw33AYyefJkzJo1Cxs3bvQWIOPHj8fbb7+tWUNCQgIA4Pvvv0dmZqasbVxcnOb4vqSkpMDlcqGsrMz7dWYAKCkpkfXr168funbtiuXLl3vnpmnTppq+CZSSkoLS0lK/Mbm8nnl56623vGddtPDII4/gm2++waJFi/yKqPz8fNSsWdPPdsuWLdi2bRtuvvnmkPGaNWsWVMwpsX//fn6bjcQcvCxFSJQi9Rd4kyZNALjPylx44YVISUnxPrfEg+dbTFu2bMFFF13kd2Zl0aJFfrbJyclBD3tLTk4GAL/xCy64ABkZGfj222+DNCk9LE4rmZmZiI+Px++//+4dO3ToELZv3x7SRwiBbdu24frrr/cWNjt27EBOTo6m3G3btsUff/zhNxa47Uv16tVx/vnn65qXRx99FNOnT8fChQvRtm1bv321atVCXl6e39jrr7+OPn364Nxzzw0Zs0uXLlixYoVs3kBWrlyJbt26afIhJNrhmRtCopT//Oc/6N27Ny677DLUq1cP27Ztw9NPP43u3bt777F54YUX8OSTT6K8vBydO3fGhg0bsGjRIkybNg0XX3wxPvjgA0yfPh0tW7bEzJkzMX/+fDRq1Mibo3Xr1nj77bexYsUKnHHGGWjatCkaNWqEtLQ0TJ8+HX369EG1atXQsGFDjB07FsOHD4fNZsPVV1+N48ePY86cOahVqxZeeuklw467Xr16uPfeezFo0CC88847qF69Op5//nnvs1mksNls6NSpE8aOHYuMjAwcO3YMTz75ZNAlNCUeffRRvPfeexg0aBAGDBiAv//+G++++643hxRvvvkmevfujUceeQR33HEHysrKsHjxYuzduxdffPGFpM+IESPw0Ucf4YsvvkBycrL3EmONGjVQt25dXHPNNRg6dCg+//xz9OnTBx999BEWLFiAv/76S1Z/v3798M477+Do0aNB995IcezYMfz+++946623FG0JOa2w+o5mQog0x44dE2PHjhXXXHONaNeunejRo4d4/fXXvd/a8TBz5kzRo0cPceGFF4oHHnjA++0gIdxfHb7kkkvEhRdeKAYNGiTef/990b17d+/+goICMXjwYNGhQwfRqlUrsWbNGiGEEN9//73o3r27yMzM9H4VXAgh5s2bJ2644QZxwQUXiJ49e4qJEyeK8vJyIYQQ69evF61atfL7ppQQQnz22Wfihhtu8G6PGDFCjBw50s/mww8/FLfffrt3u7S0VIwcOVJceOGF4qqrrhKTJk0S7du3F2PHjg05Xzk5OaJv376ibdu2olu3buLzzz8XvXv3Fl988YUQQogjR46IVq1aie3bt/v5/ec//xELFizwbq9atUr06tVLXHTRRaJv377is88+EwC8x/nll1+Ka6+91i/GqlWrxF133SXatm0rrrjiCvHqq6/KfhW8S5cu3q+t+/6MHj3aazNp0iTvV+X79Okjdu3aFTKeLz179pSdJ19ee+01cc0116iyJeR0wiaEwpOpCCEkwjidTr97d06cOIHGjRtjxowZ3gfURSr3q6++ii+++OK06cO0fft23HvvvVi0aJH3Ep0U5eXl6N69Oz755BO/m7cJiQVY3BBCoo4vvvgCO3fuRM+ePVFYWIhXXnkFBw8exIYNG0z/Zs+tt96KW2+9FS1atMCyZcvw9NNPY8yYMXj44YdNzUsIMQ4WN4SQqKOiogJvvPEG5s2bB6fTiY4dO2p61k04bNq0CWPHjsXmzZvRsGFD9OvXD7fccovpeQkhxsHihhBCCCExBb8KTgghhJCYgsUNIYQQQmIKFjeEEEIIiSlY3BBCCCEkpmBxQwghhJCYgsUNIYQQQmIKFjeEEEIIiSlY3BBCCCEkpmBxQwghhJCYgsUNIYQQQmKK/wcEuXO1Wm734QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_module(ds_names[3],\n",
    "           121,\n",
    "           159,\n",
    "           179)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:30:16.493140Z",
     "iopub.status.busy": "2026-09-15T09:30:16.493037Z",
     "iopub.status.idle": "2026-09-15T09:31:17.862846Z",
     "shell.execute_reply": "2026-09-15T09:31:17.860948Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3193001243857842e-07\n",
      "Cost function before refinement: 2.3193001243857842e-07\n",
      "[ 7.20000000e-01  3.20000000e-02  4.00000000e-03  0.00000000e+00\n",
      " -6.00000556e+01  1.00000000e+00  1.70270825e+01]\n",
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.085662476197838e-10\n",
      "           x: [ 7.200e-01  3.165e-02  4.000e-03  0.000e+00 -6.000e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-2.720e-07  2.346e-08        nan        nan -9.145e-11\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.085662476197838e-10\n",
      "GonioParam(dist=np.float64(0.7199968842266145), poni1=np.float64(0.03165314623342505), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-60.00005992107673), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.03165314623342505\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of peaks found and used for refinement\n",
      "864\n",
      "Cost function before refinement: 3.8832764505567803e-07\n",
      "[ 7.19996884e-01  3.16531462e-02  4.00000000e-03  0.00000000e+00\n",
      " -6.00000599e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 8.355695005974523e-08\n",
      "           x: [ 7.206e-01  3.205e-02  4.000e-03  0.000e+00 -6.000e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 7\n",
      "         jac: [-2.493e-09 -1.943e-11        nan        nan -2.948e-10\n",
      "                      nan        nan]\n",
      "        nfev: 29\n",
      "        njev: 7\n",
      " multipliers: []\n",
      "Cost function after refinement: 8.355695005974523e-08\n",
      "GonioParam(dist=np.float64(0.7206442268536376), poni1=np.float64(0.03204960860571386), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-60.0000548000565), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: dist (0) 0.7199968842266145 --> 0.7206442268536376\n",
      "Cost function before refinement: 8.355695005974523e-08\n",
      "[ 7.20644227e-01  3.20496086e-02  4.00000000e-03  0.00000000e+00\n",
      " -6.00000548e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 3.338833374910402e-10\n",
      "           x: [ 7.206e-01  3.224e-02  3.976e-03  1.754e-05 -6.000e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 5\n",
      "         jac: [-3.349e-07 -1.351e-09 -1.135e-07  8.314e-08 -3.434e-10\n",
      "                3.682e-09        nan]\n",
      "        nfev: 36\n",
      "        njev: 5\n",
      " multipliers: []\n",
      "Cost function after refinement: 3.338833374910402e-10\n",
      "GonioParam(dist=np.float64(0.7206453081721997), poni1=np.float64(0.03223711680104726), poni2=np.float64(0.0039756515739672454), rot1=np.float64(1.7542241829967912e-05), offset=np.float64(-60.00005243766022), scale=np.float64(0.9990118655636463), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990118655636463\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:99: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22251\n",
      "56 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:107: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_module(ds_names[4],\n",
    "           150,\n",
    "           188,\n",
    "           208)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:31:17.864821Z",
     "iopub.status.busy": "2026-09-15T09:31:17.864721Z",
     "iopub.status.idle": "2026-09-15T09:32:16.313484Z",
     "shell.execute_reply": "2026-09-15T09:32:16.311562Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4965819410817478e-06\n",
      "Cost function before refinement: 1.4965819410817478e-06\n",
      "[ 7.20000000e-01  3.20000000e-02  4.00000000e-03  0.00000000e+00\n",
      " -5.43998333e+01  1.00000000e+00  1.70270825e+01]\n",
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.0171535049901094e-10\n",
      "           x: [ 7.200e-01  3.288e-02  4.000e-03  0.000e+00 -5.440e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-2.790e-07  1.127e-07        nan        nan  1.017e-09\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.0171535049901094e-10\n",
      "GonioParam(dist=np.float64(0.7200081377942467), poni1=np.float64(0.03288139794526324), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-54.39982225664511), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.03288139794526324\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of peaks found and used for refinement\n",
      "754\n",
      "Cost function before refinement: 3.084364539629558e-07\n",
      "[ 7.20008138e-01  3.28813979e-02  4.00000000e-03  0.00000000e+00\n",
      " -5.43998223e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 6.550962572089796e-08\n",
      "           x: [ 7.205e-01  3.324e-02  4.000e-03  0.000e+00 -5.440e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 7\n",
      "         jac: [-1.598e-09 -1.620e-11        nan        nan -3.332e-10\n",
      "                      nan        nan]\n",
      "        nfev: 29\n",
      "        njev: 7\n",
      " multipliers: []\n",
      "Cost function after refinement: 6.550962572089796e-08\n",
      "GonioParam(dist=np.float64(0.7205268320603014), poni1=np.float64(0.03323636624652024), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-54.399817662277066), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: dist (0) 0.7200081377942467 --> 0.7205268320603014\n",
      "Cost function before refinement: 6.550962572089796e-08\n",
      "[ 7.20526832e-01  3.32363662e-02  4.00000000e-03  0.00000000e+00\n",
      " -5.43998177e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.980133018674163e-10\n",
      "           x: [ 7.205e-01  3.339e-02  3.973e-03  1.948e-05 -5.440e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 5\n",
      "         jac: [-6.288e-07 -6.165e-09 -8.418e-08  6.319e-08 -4.255e-10\n",
      "                1.871e-09        nan]\n",
      "        nfev: 36\n",
      "        njev: 5\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.980133018674163e-10\n",
      "GonioParam(dist=np.float64(0.7205282661392393), poni1=np.float64(0.03339481351901525), poni2=np.float64(0.003972951169717649), rot1=np.float64(1.9483670504509455e-05), offset=np.float64(-54.39981566521699), scale=np.float64(0.9990259510567647), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990259510567647\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:99: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20548\n",
      "54 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:107: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_module(ds_names[5],\n",
    "           178,\n",
    "           216,\n",
    "           236)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:32:16.315311Z",
     "iopub.status.busy": "2026-09-15T09:32:16.315212Z",
     "iopub.status.idle": "2026-09-15T09:33:10.082235Z",
     "shell.execute_reply": "2026-09-15T09:33:10.080427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.41484379835199e-08\n",
      "Cost function before refinement: 7.41484379835199e-08\n",
      "[ 7.20000000e-01  3.20000000e-02  4.00000000e-03  0.00000000e+00\n",
      " -4.85998889e+01  1.00000000e+00  1.70270825e+01]\n",
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 5.651340369404608e-11\n",
      "           x: [ 7.200e-01  3.180e-02  4.000e-03  0.000e+00 -4.860e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-2.813e-07  1.905e-08        nan        nan -1.234e-10\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 5.651340369404608e-11\n",
      "GonioParam(dist=np.float64(0.7199985717445607), poni1=np.float64(0.03180385768846998), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-48.599891358723), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.03180385768846998\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of peaks found and used for refinement\n",
      "626\n",
      "Cost function before refinement: 2.383303034849477e-07\n",
      "[ 7.19998572e-01  3.18038577e-02  4.00000000e-03  0.00000000e+00\n",
      " -4.85998914e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 5.1983308105299717e-08\n",
      "           x: [ 7.206e-01  3.211e-02  4.000e-03  0.000e+00 -4.860e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 7\n",
      "         jac: [-2.199e-09 -2.335e-11        nan        nan -2.599e-10\n",
      "                      nan        nan]\n",
      "        nfev: 29\n",
      "        njev: 7\n",
      " multipliers: []\n",
      "Cost function after refinement: 5.1983308105299717e-08\n",
      "GonioParam(dist=np.float64(0.7206068542793277), poni1=np.float64(0.03211384891528438), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-48.599887342572686), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: dist (0) 0.7199985717445607 --> 0.7206068542793277\n",
      "Cost function before refinement: 5.1983308105299717e-08\n",
      "[ 7.20606854e-01  3.21138489e-02  4.00000000e-03  0.00000000e+00\n",
      " -4.85998873e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.556084121448774e-10\n",
      "           x: [ 7.206e-01  3.225e-02  3.969e-03  2.238e-05 -4.860e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 6\n",
      "         jac: [-6.637e-07  8.690e-08 -6.746e-08  5.130e-08  7.875e-10\n",
      "                2.881e-09        nan]\n",
      "        nfev: 42\n",
      "        njev: 6\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.556084121448774e-10\n",
      "GonioParam(dist=np.float64(0.7206092934239683), poni1=np.float64(0.03224725723057559), poni2=np.float64(0.003968935506155581), rot1=np.float64(2.237548771450148e-05), offset=np.float64(-48.59988566030428), scale=np.float64(0.9990409081096999), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990409081096999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:99: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18650\n",
      "54 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:107: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAHHCAYAAAAf2DoOAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAU4xJREFUeJzt3XlYVGX7B/DvgIIQDgoqiyIo4oILLrjkhuK+gJpvlGZavWYulablUplbpWa+aZlp5k/DFjU1Q000zTVz3wVcUUAQVxgRZH1+f0ycGJiBmWFW5vu5Lq46z3nOc+5zzsjcnOU+MiGEABEREZGJ2Jk7ACIiIrItTD6IiIjIpJh8EBERkUkx+SAiIiKTYvJBREREJsXkg4iIiEyKyQcRERGZFJMPIiIiMqlK5g6ALNPdu3fx008/YeDAgWjQoIHVjU/WLTs7G3/88Qdu3rwJR0dHvP766+YOSaOff/4ZTk5OGDx4sLlDKZUh9unatWtRs2ZNDBgwwAgRWg5rOabWjMmHFTh+/DiOHDkiTVeqVAmenp7o2rUratWqZZR1JiQk4J133kGdOnWMkhwYe3xTuX79Oo4ePQohBJ599ln4+/ubOySrl5mZieDgYMhkMoSGhqJ69ermDgk//PAD5HI5wsPDS8xbvHgxatSoYdFfVLrs09K29eOPP0bLli0rfPJhDcfU2jH5sAK7d+/GzJkzMXz4cNSsWROZmZn4+uuvcePGDSxcuBCTJk0yd4g2af78+Zg1axZ69uyJmjVrYty4cXj11Vfx5Zdfmjs0q/bzzz8jNjYWt27dQt26dc0dDgBgwYIF8PPzU/uFPHz4cDzzzDNmiEp7uuzT0raVyFCYfFiR9957Dy1btgQA5OXlYcCAAZg8eTJCQkLQqlUr8wZnY27fvo0PPvgA77//Pj7++GMAwNSpUzF27FgzR2b9rl27Bjs7O4tJPMoyefJkc4dQJmvbp1TxMfmwUpUqVcJ///tf7N69G3v27JGSj6ysLOzfvx83b96EXC5HaGgovLy8pOVyc3Px9ddfS9OOjo6oW7cuQkND4eTkVOZ6Y2NjsWvXLgQFBaF79+4a++Xm5uLPP/9EQkICqlevjnbt2mn8xZefn4/o6GgkJiaiadOm6NKlS4mxtIm56H0kdevWxc6dO5GSkoIWLVqgY8eOatdd1v7SJDU1FUIIeHp6Sm1NmzZFZGRkmcvqGkPR7fLz88Pu3btx48YNPPfcc0hISMDRo0fx1ltvIT09HdHR0VAoFFISlJeXhwMHDuDq1atwdnZGp06dSlwaKnod//z58zh27BgCAgLQrVs3tfFGRUXhxo0bAAB7e3u4u7ujc+fOao/v2bNnceHCBeTl5aFp06Zo166dxv0ghMDSpUulS4xLliwBAHTo0AEdOnTAsmXL0Lx5c4SEhJSIR6FQYMSIEWq36dy5czh27Bjc3NzQr18/jWcpzp07h9OnT0Mmk6Ft27Zo2rQpAGD58uV48OABhBBSTNWqVcMrr7wCQPP9Abrue23jLK609ZS1T4sra1uL7y9t4j19+jTOnDmD/Px8tG7dGsHBwWVuk7afMV33X0ZGBnbt2oUHDx4gODgYrVu3xrZt2/Do0SOMHDmyzLj03R4qicmHFatcuTIA5S8fANizZw9GjBgBV1dXdOrUCbdv38bo0aPxv//9D+PGjQOg/GV08+ZNaYy0tDR8/vnnyMjIQHR0dKlnUHbv3o2IiAh07dq11JvVrl69itDQUDzzzDPo3LkzMjMz8f777+PFF1/E3LlzVfqmp6ejb9++cHNzQ15eHiZMmIBXXnkFq1evlvpoG3PhfSRVqlTBDz/8gDp16uDp06eYOHEiwsLC8PPPP0v7TNv9pUlQUBCCgoKwYMECDBo0CD4+PgCAevXqlbpccdrEULhdTk5O+Omnn1CrVi0kJyejZcuW2LNnD+bMmYOmTZti6tSpaNGiBU6fPo2xY8fiypUrGDRoENLS0tCrVy/cvXsXr732GsaPH4+lS5dCJpMB+Pc6/v79+3HixAl4eXkhNjZWY/KRmpoqHY/s7GzExsZi5MiRmD9/Pt577z0AyoTyhRdewB9//IH+/fvDxcUFq1atgkwmw++//w5XV9cS4xYeZ4VCoXLMGzduDAB49913MXbs2BLJx7fffoubN2+qJB+F23T8+HEcPnwYdevWRXR0NJycnHD06FGVe6VSU1MxbNgwHDt2DD169ECNGjXwv//9D23btsXq1atx69Yt5ObmIisrS4qpZs2a0vLq7g/Qdd9rE6c6Za2nrH1aXFnbWmjWrFllxnv37l28+OKLOHnyJPr06QNHR0dMnz4dXbt2lRI2TbT5jOm6/86dO4d+/fqhcuXK6N69OzZs2IDWrVvj6tWriIuLKzP5KM/2kBqCLN68efMEAHHmzBmV9pdeekkAEAcOHBBXr14Vzs7OYtiwYSInJ0fq88033wg7Oztx9OhRjePn5eWJXr16iVatWkltJ06cEADEL7/8IoQQYsWKFaJSpUpi0qRJIj8/v9R4X375ZdGgQQOVOPLy8sTu3btLjB8YGCiuXLkitX/xxRcCgDh58mSp6ygt5oYNG4q4uDipfc+ePUImk4m5c+dKbeXZX0IIER8fLwIDA0VQUJDw9/cXCQkJKvPv378vvvjiCxETE6NxDG1jKNyugIAAERsbK4QQIj8/X6SmpopZs2YJAOI///mPyMjIEEIIkZKSIvLy8kSTJk2Ev7+/SE1NlcaOjIwUAMSXX34ptfn7+wsvLy/x+eefS20pKSmlbn9x3377rbC3t5e2d8eOHQKAOHLkiEq/v//+Wzx69KjUsSZOnCjs7e1LtDs6OoqJEyeWaB8wYIBo2rSpSpu/v7+oXbu2ynbevHlTODg4iHfffVelb8eOHYWXl5fK51AIofJ5bdq0qRgwYIDaeNu0aSP69OkjTeu677WNszhd1qNpn6pT2rbqEm/Xrl2Ft7e3yr+N+Ph4Ua1aNTF58mStYimq+GdMl3jy8vJEQECACAoKEmlpaVL7559/Ljw9PUWjRo1U1lX8mBpje2wdkw8rUJh8vPvuu+KLL74Qn376qejdu7cAICZMmCCEUP5ykclk4u7duyrLFhQUiNq1a4vRo0ertJ89e1asXbtWLF26VHzxxRdiyJAhQiaTiSdPnggh/v3C27Bhg3jnnXdEpUqVxDfffKNVvAMHDhT169eXvgzVKRx/ypQpKu0PHjwQAFS+CHWN+c033yyxbJ8+fYS3t7c0rev+KkqhUIiAgADxxhtviJycHNGrVy/h5+cn4uPjpT5RUVECgPjzzz81jqNtDIXbNX78+BJjFCYfv//+u0r73r17BQCxcuXKEsu0bNlS5Zetv7+/8PDwUEmAynLnzh3xyy+/iGXLlokvvvhCzJ07VwAQ//d//yeEEOKHH34oc/s1MVTy4ePjUyJR7tWrlwgODpamDx8+XOKLWh1dkg9d9702caqjy3oMmXxoE+/ff/8tAIhly5aVGOOdd94Rrq6uZf4RU9ZnTJd49uzZIwCIn376SaVfbm6uqFWrVpnJhyG2h1TxsosVSU5ORm5uLipVqoSePXti/vz5aN26NQDg1KlTcHV1xYYNGwAoT2EX/jg6OiIuLg4AoFAoMHjwYBw/fhyhoaGoU6cOHBwckJ6eDiEE7t27B19fX2mdU6ZMQXJyMrZv345+/fppFeeECRMwZMgQ+Pn5ISwsDCEhIejVqxe8vb1L9G3WrJnKtJubGxwdHXH79m2pTdeYg4KCSqwnKCgIu3btwqNHj1C9enWt95c6q1evxtWrV3HgwAFUrlwZW7ZsQUhICEJCQvDnn3/C398fJ0+ehLOzs9rr6oV0jaFFixYaxyo+79KlSwCg9jJaq1atsGbNGuTm5kqXoZo0aaJySao0hU/5tGnTBk2bNoWLiwuys7MBKE9NA8DAgQPRqFEj9O7dGz179kRoaCh69OiBVq1aSZccjC0wMBB2dqp1FL29vXHx4kVp+syZMwBQ6r0outJ132sTpyHWYyjaxHvq1CkAykfRly1bJn2uASAxMRHp6elISUlB7dq11a5Dm8+YLvEU7qvmzZur9KtUqRIaN26M1NTUUre5vNtDJTH5sCJFn3YpLjc3F/b29rh27VqJeWFhYdKNWp999hkOHz6Ms2fPIjAwUOozb948/Pnnn9I/qEKdO3fG5s2bsWbNGvTo0QMODg5lxtm3b1/ExcXhl19+wf79+zFx4kRkZGTg3XffxYIFC1T6uri4lFi+UqVKyMnJkaZ1jdne3l7tmABQUFAAQPv9pc7p06fxzDPPSDeFuri4YOfOnejYsSNCQkIQHR2N7777DsOHDy/1OrCuMbi7u2scq/i8/Px8AP9ud1HF90VZYxd18eJFvP/++5g7dy5mzpwptV+9ehUrVqyQjoWrqyvOnj2LzZs3Y8+ePVi5ciWmTp2K9u3bY/v27ahRo4ZW6yvK3t5eJeZCWVlZavtr89kqbT/pS9d9r02chliPoWgTb25uLgDg3r17JWKoXbs2Jk6cCEdHR7Xja/sZ0yWewhhK21elKc/2kHpMPiqIwMBAnD9/Hp999lmpCcKZM2fg7++v8iVe2K7O0KFDMXLkSAwdOhTh4eHYsmULnJ2dy4zH19cX7777Lt59913k5ORgwoQJWLhwIUaMGFHibEdZdI1Z3VmL2NhYuLm5wc3NDYD2+0sdNzc3PHnyBAkJCVKCUKtWLezatQsdO3ZEcHAw7Ozs8MEHH5Q6TnliKEtAQAAA5XYX/8s4JiYGPj4+ev2yLNznxWtAqDsWVapUwUsvvYSXXnoJAPD7779jwIABWLJkifR4si68vb1L/NUrhMCVK1fU3sCqjcLP1Llz59CmTRuN/Yr/ZV0aY+17U61Hl23VpHC/hoeH4/nnn9dpWV0+Y9oqLGR4+fJllZtthRDSU0KlKc/2kHp8t0sFMX78eOTk5GDWrFkl5ikUCly5cgUA4Ofnh6SkJDx69Eiaf/ToURw4cEDj2P369cPu3btx9OhR9O7dG2lpaaXGcvLkSZW/ThwcHKTT2k+fPtVls/SK+ddff8W9e/ek6UuXLmHbtm14+eWXpVP+2u4vdUaNGgU7Ozu888470l9EAODv74833ngD2dnZcHd3LzOhKE8MZenZsye8vb3xv//9T+XMwL59+/DXX3/h1Vdf1WtcPz8/AMCFCxektsePH2P58uUq/eLi4vD48WOVtk6dOsHOzk6vzwAAtG/fHnv37kVmZqbUFhkZiYyMDL3GA4DQ0FA0adIECxYswIMHD1TmxcTESP9fq1atEvM1Mda+N9V6dNlWTXr06IHAwEDMmzevxOdACCFdxlBH28+YLnr27AlPT08sWbJEejoQADZs2ACFQlHm8uXZHlKPZz4qiODgYERGRmLMmDHYv38/QkND4ejoiMuXL+Pw4cNYtmwZGjZsiClTpmD9+vXo0qULXnjhBdy+fRsnT57ElClTSv1LvXPnzjhw4AD69OmDbt26YdeuXfDw8FDbd/ny5Th+/DhCQkJQt25dJCUl4fvvv8ewYcP0eiZe15jHjBmD8PBwdOvWDdnZ2Vi7di1atWqFefPm6by/1GnVqhXWrVuH119/Ha1atUJ4eDicnZ3xxx9/4NixY3j11VexYcMGhIaGYv/+/Sq1QIoqTwxlcXR0xMaNGxEeHo727dtj8ODBuHfvHtauXYv+/fvj/fff12vczp07o1+/fhg3bhxOnz4NJycn7NixA9OmTVNJBi9duoQ+ffqga9euaNSoEXJzc7Fp0ybUrVsXb731ll7r/uCDDxAVFYXOnTtjyJAhuHr1KuRyOTp16qTyKLYu7O3tsXXrVgwYMADNmjVDREQE3N3dceLECeTl5WHnzp0AgCFDhuDtt9/GxIkTUa9ePY21LwDj7XtTrUeXbdXE3t4eUVFRGDx4MAICAhAREQEvLy8kJCTg4MGD6Nq1q8YzTdp+xnRR+Ph9eHg4OnXqhP79+yMxMRFPnz5Ft27dykz0y7M9pB6TDyvQvn17TJw4Ue3z9kUNHz4cffr0QVRUlPSPKSwsDCtXrpSui9avXx+XL1/Gzz//jOTkZLRr1w6LFy/GuXPnMHHiRMjlcgCAh4cHJk6cKJ3aBZQ3bR46dAjLly/H5s2bMX78eLVx/N///R+uXLmCPXv2ICEhAb6+vti3b5/KP0514xeaMGGCyg2A2sZcyM/PD1FRUfjhhx9w584dfPXVV4iIiChx4502+6u0fd2rVy9s3boV165dQ1ZWFkaPHo0tW7bA3d0do0ePxsaNG7Ft27ZSa6JoE0Np+6pDhw6YOHGi2uvWnTp1wrVr17B582ZcvXoVHh4e2L59O3r06KHS79VXX9X6RjmZTIbt27dj8+bNOHfuHKpWrYrNmzfD29sbEydORNu2bQEoL9f17t0b27ZtQ2xsLBwcHDB37lyEh4eXeQNkSEiI2vt2mjRpggsXLmD9+vVIS0vD888/j7CwMKxatQoPHz7Uapt69epVIhls2LChdHas8NT++PHjVW6wnjBhAnx9fXHs2DHcunULT548keapK69e3n2vLk51tF2Ppn2qTmnbqku8/v7+OHv2LHbv3o0TJ05AoVCgZcuWmDJlSqnvc9L2M6ZrPD169EBcXBzWr1+Phw8fol+/fhgyZAhCQkJK/HtXd0z13R5STyaK371DZKVOnjyJtm3b4pdffsF//vMfc4dDRBYuPz8ftWvXRq9evbBu3Tpzh2NTeM8HERFVeElJSdLjuoVWrlyJ1NRUvPDCC2aKynbxsgsREVV4t27dQo8ePaRLMqdPn8avv/6KcePGYeDAgeYOz+bwsgtVGImJiVi8eDFeffVVtYXGiMi23b59Gzt27MCNGzcgl8vRs2dPgxaYI+0x+SAiIiKT4j0fREREZFJMPoiIiMikLPKG04KCAiQnJ6Nq1aomewkVERERlY8QAo8fP4a3t3eppfotMvlITk6Gj4+PucMgIiIiPSQmJqJOnToa51tk8lG1alUAyuCLV68kIiIiy6RQKODj4yN9j2tikclH4aUWuVzO5IOIiMjKlHXLBG84JSIiIpNi8kFEREQmxeSDiIiITMoi7/nQRkFBAXJycswdhs2oXLmy1q/kJiIiKo1VJh85OTmIj49HQUGBuUOxKdWqVYOnpydrrxARUblYXfIhhEBKSgrs7e3h4+NTahETMgwhBDIzM3H37l0AgJeXl5kjIiIia2Z1yUdeXh4yMzPh7e0NZ2dnc4djM5ycnAAAd+/eRa1atXgJhoiI9GZ1pw3y8/MBAA4ODmaOxPYUJnu5ublmjoSIiKyZ1SUfhXjfgelxnxMRkSFY3WUXIiKiCqMgH7h1BMhIBVw8AN+OgF3Fv6zN5MNEtm7diu3bt5dof+ONNwAA3377LZYtWwZHR0dp3qxZs5CVlYXPPvtMahNCYPz48YiIiED37t2xdetWHD9+HJ9++qnKuA8ePMC0adMwZcoUNGnSRCWGtm3bSustdOzYMaxatQq1a9fGnDlzDLbdRESkQUwUED0NUCT/2yb3BvouBALDzReXCVjtZZfyyi8Q+Pv6A/x29jb+vv4A+QXCqOs7efIkoqKi0KFDB5Ufd3d3VK9eHd999x2OHj0q9c/IyMCnn36KJUuW4M6dO1L7uXPnsGLFCun+i5MnT2Ljxo0l1vf48WOsXr0at2/fVonhxx9/xLRp05CVlaXS//PPP8ePP/6IzZs3G3rTiYiouJgoYONI1cQDABQpyvaYKPPEZSI2eeYj+mIK5myLQUr6U6nNy7UKZoUFom8z4z1GKpfLMXr0aLXzfHx8sG/fPoSEhAAADh06BH9/f9SqVQv79+/Hiy++CADYt28f5HI5goOD9YrB398fdnZ22LJlC1566SUAwP3797F9+3YMHjwYFy5c0GtcIiLSUkG+8owH1P3RKwDIgOjpQOMBFfYSjM2d+Yi+mIJxP5xWSTwA4E76U4z74TSiL6aYJa7u3btj37590vS+ffvQrVs3hISElGjv2rVruR51fe2117B69Wpp+vvvv0fnzp3h6+ur95hERKSlW0dKnvFQIQDFbWW/CsqmznzkFwjM2RZTWq6JOdti0CvQE/Z2hn+y4969eypnPlxdXbF48WIAyuRj7NixyMrKgpOTE/bv34/JkyejRo0amDBhAgBlSflDhw5h5syZpY4LKC/baPLyyy9jxowZuHHjBurXr4/Vq1fjo48+wtmzZw20pUREpFFGqmH7WSGbSj6Oxz8sccajKAEgJf0pjsc/xLP+7gZfv6OjIzp06CBNFy2S1r17d2RnZ+Pvv/9GcHAwTp8+jZCQELi6uiI+Ph63b9/GnTt3kJaWhtDQ0FLHBZQ3nG7YsEFtHO7u7ggLC8OaNWvQr18/pKamYsiQIUw+iIhMwcXDsP2skE0lH3cfa0489Omnq9Lu+fD19UW9evWwb98+ZGZmokGDBlIZ87Zt22L//v1ISUmBm5sbgoKCyhz35s2bmD59usZY/vvf/2L06NFITEzESy+9pPKUDRERGZFvR+VTLYoUqL/vQ6ac79vR1JGZjE0lH7WqVjFoP0MrvO8jMzMT3bp1k9oL7/tISUlBt27dDFLsq1evXrCzs0NkZCTOnDlT7vGIiEhLdvbKx2k3joTygn/RBOSf3+99F1TYm00BG7vhtF09N3i5VoGmr24ZlE+9tKvnZsqwJKGhoTh+/Dh+//33EsnH3r17cejQoRKXXPRlZ2eHdevWYd26dSXOpBARkZEFhgMRkYC82BOWcm9lewWv82FTZz7s7WSYFRaIcT+c1pRrYlZYoFFuNtVG9+7dkZubi7i4OOmRWwDo1KkTkpKSkJeXh+7duxtsfV27djXYWEREpKPAcOXjtKxwWvH1beaFb0a0LlHnw9PIdT6GDBlS5hkGb29vrFu3DoDqa+tdXFzw448/IiMjA4GBgVqNW6NGDaxatUql/5AhQ9C6dWuN6x86dCjatWun1fYQEZEB2NkD9bqYOwqTkwkhjFvaUw8KhQKurq5IT0+HXC5Xmff06VPEx8ejXr16qFJF/3sz8gsEjsc/xN3HT1GrqvJSi7nOeFgLQ+17IiKqmEr7/i7K5s58FLK3kxnlcVoiIiIqnU3dcEpERETmx+SDiIiITIrJBxEREZkUkw8iIiIyKSYfREREZFJMPoiIiMikmHwQERGRSdlsnQ8iIiKjKci3ybLp2mLyQUREZEgxUUD0NECR/G+b3Fv5JtsK/sI4bdnuZZeCfCD+EHBhk/K/BfnmjoiIiKxdTBSwcaRq4gEAihRle0yUeeKyMLZ55sOMWWlsbCyuX78OHx8fNG/eHHZ2/+Z/J0+ehBACgYGBOHbsGHJzc9GnTx/s3LkTTZo0gZOTE06dOgUPDw+0adMGAJCcnIzTp0/DxcUFHTp0UHnnSnp6Ov744w+Eh4fjypUruH79Otq3bw9PT0+jbiMRkU0qyFd+t0DdK9MEABkQPV35JlsbvwRje8lHYVZa/MNRmJVGRBolAcnIyMCwYcNw6tQptGzZElevXoW7uzt+++03eHh4AACWLVuG8+fP48mTJ/Dx8UGTJk3Qp08fjBs3Do0aNUJcXByaN2+OgQMHok2bNvjss88we/ZstGvXDnfv3kVaWhq2bdsmJSbx8fF4/vnnMWjQIFy+fBmBgYGoU6cOkw8iImO4daTkGQ8VAlDcVvazwTfZFmVbyYcZs9L33nsPubm5iI+Ph6OjI/Lz8/Gf//wH7733HiIjI6V+58+fx7Fjx6QEotC1a9dw5swZuLm5AQDOnTuHGTNmYNu2bejfvz+EEHj55Zfx2muv4cyZMypnVNzc3BATEwOZjG/tJSIymoxUw/arwGwr+TBTVpqXl4d169ZhwoQJ2LlzJ4QQEELAz88PGzduVOnbpUuXEokHAIwcOVJKPABgw4YNCAoKQv/+/QEAMpkMH374IZo0aYJLly6hefPmUt+JEycy8SAiMjYXD8P2q8BsK/kwU1Z69+5dPHnyBCdOnMD169dV5nXu3Fll2tvbW+0Yxdtv3ryJ+vXrq7T5+/tL84omH5rGJCIiA/LtqLx/UJEC9WfYZcr5vh1NHZnFsa3kw0xZqYuLC2QyGcaOHYuIiIhS+2o6Q1G83d3dHbGxsSptjx49kuZpMyYRERmQnb3ywYWNIwHIoJqA/PN7uO8Cm7/ZFLC1R20Ls1Jo+jKWAfLaBs9K5XI5OnTogFWrVpWYl5KSoteYnTt3xpEjR3D37l2pbcuWLXB1dUWzZs30jpWIiMohMFz54ILcS7Vd7m20BxqskW2d+TBjVrp8+XL06NEDPXv2REREBJ4+fYq9e/eiTp06+Prrr3UeLyIiAsuWLUNoaCjeeust3LlzBwsWLMCiRYsgl8sNHj8REZWieEXTt88BicdY4VQD20o+gH+zUrV1PhYYLStt2bIlLl26hP/7v//DwYMHUaNGDYwZMwYDBgyQ+rRt2xb5+SWLnfXv3x/16tVTaZPJZNi9ezdWrVqFQ4cO4ZlnnsHWrVvRp08fqU+1atUwdOhQODo6GmWbiIgIpdeOav4f88VlwWRCCHV3xZiVQqGAq6sr0tPTS/wV//TpU8THx6NevXoqBbV0xrr7OjPYviciqig01Y4qPJtuY5daSvv+Lsr2znwUsrO3+SIvRERUDqxoqjedk49Dhw7hwIEDyM3NRdu2bTFw4ECV+bt27cK2bdtU2pydnfHZZ5+VL1IiIiJLwoqmetMp+ejTpw+ePn2KLl26wN7eHmPGjEGbNm3w22+/SRU1T5w4gR07dmDKlCnScrzngIiIKhxWNNWbTsnHokWL0KJFC2l62LBhaNKkCXbu3Kly46SHhwfefPNNw0VJRERkaVjRVG86JR9FEw8ACAgIgIODA+7cuaPSnpqaihkzZqBKlSpo164d+vXrV/5IiYiILAkrmuqtXEXGfvjhB+Tl5ZUoEV67dm04OztDoVBg+PDhGDRoEAoKCjSOk52dDYVCofJTFgt8SKfC4z4nIiqisHYUgJLFK1nRtDR6Jx9nz57Fm2++iRkzZqBRo0ZS+6hRo3D48GHMnDkTixcvxpEjRxAdHY3vv/9e41jz58+Hq6ur9OPj46Oxr7298iDm5OToGzrpKTMzEwBQuXJlM0dCRGQhWNFUL3rV+bh06RK6d++OwYMHY+XKlWW+O6Rjx45o0qQJVq9erXZ+dnY2srOzpWmFQgEfHx+1zwkLIZCQkIDc3Fx4e3urvDqejEMIgczMTNy9exfVqlWDl5dX2QsREdkS1o4CYMQ6HzExMQgNDcWgQYO0SjwAZXKRl5encb6jo6PWT8TIZDJ4eXkhPj4et27d0jpuKr9q1arB09PT3GEQEVke1o7SiU7JR1xcHEJDQxEeHo5vv/1WbeKxa9culRLfe/fuxZkzZ1QevS0vBwcHBAQE8NKLCVWuXFm65EVERFQeOtf5ePLkCRwcHPDWW29J7f3790f//v0BAOvXr8fUqVPRsmVL3L9/H3v37sWkSZMwbNgwgwZuZ2fHEt9ERERWSKfkY8aMGWovn9SoUUP6/zVr1uDKlSs4fvw4nJ2d8fXXX8PPz6/cgRIREVHFYHUvliMiIiLLpO33Nx8VISIiIpNi8kFEREQmxeSDiIiITIrJBxEREZkUkw8iIiIyKSYfREREZFI6l1cnIiKyGXxni1Ew+SAiIlInJgqIngYokv9tk3sDfRfybbXlxMsuRERExcVEARtHqiYeAKBIUbbHRJknrgqCyQcREVFRBfnKMx5QVwD8n7bo6cp+pBcmH0REREXdOlLyjIcKAShuK/uRXph8EBERFZWRath+VAKTDyIioqJcPAzbj0pg8kFERFSUb0flUy2QaeggA+S1lf1IL0w+iIiIirKzVz5OC6BkAvLPdN8FrPdRDkw+iIiIigsMByIiAbmXarvcW9nOOh/lwiJjREREhYpXNH37HJB4jBVODYzJBxEREVB6RdPm/zFfXBUQL7sQERGxoqlJMfkgIiLbxoqmJsfkg4iIbBsrmpockw8iIrJtrGhqckw+iIjItrGiqckx+SAiItvGiqYmx+SDiIhsGyuamhyTDyIiIlY0NSkWGSMiIgKUCUbjAaoVTlnR1CiYfBARkW0qXkq9MNGo18XckVV4TD6IiMj2lFZKnZdYjI73fBARkW1hKXWzY/JBRES2g6XULQKTDyIish0spW4RmHwQEZHtYCl1i8Dkg4iIbAdLqVsEJh9ERGQ7WErdIjD5ICIi28FS6haByQcREdkWllI3OxYZIyIi28NS6mbF5IOIiCo+llK3KEw+iIioYmMpdYvDez6IiKjiYil1i8Tkg4iIKiaWUrdYTD6IiKhiYil1i8Xkg4iIKiaWUrdYTD6IiKhiYil1i8Xkg4iIKiaWUrdYTD6IiKhiYil1i8Xkg4iIKi6WUrdIOhcZu3z5Mg4dOoTc3Fy0bdsWwcHBJfo8ffoUUVFRuHXrFgICAhAWFgZ7e2aWRERkBiylbnFkQgh1D0Cr9fLLL+PUqVPo2LEj7O3tsX79ejz//PP47rvvpD5paWkICQlBXl4eunXrhujoaNSpUwe7d++Go6OjVutRKBRwdXVFeno65HK57ltFREREJqft97dOZz5GjRqFyMhIyGTKa2WjR49Gu3btMGzYMPTo0QMAMH/+fCgUCpw/fx5Vq1bFnTt30LhxY3zzzTeYNGmS/ltEREREFYJO93z07NlTSjwAIDg4GA4ODrhx44bUtmnTJkRERKBq1aoAAE9PT4SFhWHTpk0GCpmIiIisWbleLLd161bk5OSgffv2AICcnBzEx8ejYcOGKv0aNmyInTt3ahwnOzsb2dnZ0rRCoShPWERERGTB9H7a5fr16xgzZgzGjh2LFi1aAACePHkCIQRcXV1V+larVg0ZGRkax5o/fz5cXV2lHx8fH33DIiIiIgunV/KRkJCAnj17okuXLvjqq6+kdmdnZwAlz1ykp6fjmWee0TjejBkzkJ6eLv0kJibqExYRERFZAZ0vuyQmJqJbt25o2bIlNmzYgEqV/h3C0dERfn5+uHbtmsoyV69eRaNGjTSO6ejoqPWTMERERGTddDrzkZSUhG7duiEoKAgbN25E5cqVS/R57rnnsHHjRmRmZgIA7t27h23btuG5554zTMRERERk1XSq89GkSRMkJSVh8uTJKolH165d0bVrVwDAw4cP0alTJ1SpUgWhoaHYsWMHqlevjj///BNOTk5arYd1PoiIiKyPUep8vPjii8jNzUV+fj7y8/Ol9ry8POn/3dzccOrUKfzyyy9ISEjA7NmzMXToULVnSYiIiMj26HTmw1R45oOIiLRWkM/S6RbCKGc+iIiILEpMFBA9DVAk/9sm91a+zZYvjbNYfKstERFZp5goYONI1cQDABQpyvaYKPPERWVi8kFERNanIF95xgPq7hz4py16urIfWRwmH0REZH1uHSl5xkOFABS3lf3I4jD5ICIi65ORath+ZFJMPoiIyPq4eBi2H5kUkw8iIrI+vh2VT7VApqGDDJDXVvYji8Pkg4iIrI+dvfJxWgAlE5B/pvsuYL0PC8Xkg4iIrFNgOBARCci9VNvl3sp21vmwWCwyRkRE1iswHGg8gBVOrQyTDyIish6aSqnX62LuyEgHTD6IiMg6sJR6hcF7PoiIyPKxlHqFwuSDiIgsG0upVzhMPoiIyLKxlHqFw+SDiIgsG0upVzhMPoiIyLKxlHqFw+SDiIgsG0upVzhMPoiIyLKxlHqFw+SDiIgsH0upVygsMkZERJapeDXTxgNYSr2CYPJBRESWh9VMKzRediEiIsvCaqYVHpMPIiKyHKxmahOYfBARkeVgNVObwOSDiIgsB6uZ2gQmH0REZDlYzdQmMPkgIiLLwWqmNoHJBxERWQ5WM7UJTD6IiMiysJpphcciY0REZHkCw1nNtAJj8kFEROZXvJR6YaJRr4u5IyMjYPJBRETmxVLqNof3fBARkfmwlLpNYvJBRETmwVLqNovJBxERmQdLqdssJh9ERGQeLKVus5h8EBGRebCUus1i8kFERObBUuo2i8kHERGZB0up2ywmH0REZD4spW6TWGSMiIjMi6XUbQ6TDyIiMi2WUrd5TD6IiMh0WEqdwHs+iIjIVFhKnf7B5IOIiIyPpdSpCCYfRERkfCylTkUw+SAiIuNjKXUqQq8bTs+dO4czZ86ga9euqF+/vsq88+fP4/Tp0yptDg4OGD58uP5REhGRdWMpdSpCp+Tj6NGjmDJlChQKBS5evIh169aVSD6ioqKwdOlSDBgwQGpzdnZm8kFEZMsKS6krUqD+vg+Zcj5LqdsEnZKP7OxsLFy4EJ07d4ZMpqkWP+Dv74+1a9eWNzYiIqooCkupbxwJZen0ogkIS6nbGp2Sj5CQEK36ZWRkYPPmzahSpQpatWoFb29vvYIjIqIKpLCUuto6HwtY58OGGKXIWEpKCiIjI5GWloajR4/igw8+wEcffaSxf3Z2NrKzs6VphUJhjLCIiMjcWEqdYITko3fv3pg0aRJcXFwAKO8BGTRoENq2bYt+/fqpXWb+/PmYM2eOoUMhIiJzYyl1UkMmhFB350/ZC8pkWLduHUaMGFFm31atWqFTp05YtmyZ2vnqznz4+PggPT0dcrlcn/CIiMjcWErd5igUCri6upb5/W2Sd7s4OzvjwYMHGuc7OjrC0dHRFKEQEZEpFJZSL/5kS2Ep9YhIJiA2zOBFxhISElSmr1+/jtOnT6NDhw6GXhUREVkillKnMuh05iMlJQW7du2Spg8dOoS8vDw0bNgQHTsqn80ePnw4GjZsiFatWuH+/ftYsWIF2rdvjzFjxhg2ciIisky6lFLnvR82SaczH48ePcL+/fuxf/9+jBo1CtnZ2di/fz8uX74s9Tlw4AB69eqF69evIzc3FytXrsS+ffvg5ORk8OCJiMgCsZQ6lUHvG06NSdsbVoiIyALFHwK+H1h2v1HbeeajgtH2+5svliMiIsMqLKUOTZWwZYC8Nkup2zAmH0REZFiFpdQBlExAWEqdmHwQEZExFJZSl3uptsu9+ZgtmabOBxER2SCWUicNmHwQEZHhqCunzptKqRgmH0REZBgsp05a4j0fRERUfoXl1IsXFysspx4TZZ64yCIx+SAiovJhOXXSEZMPIiIqH13KqROByQcREZUXy6mTjph8EBFR+bh4GLYfVXhMPoiIqHxYTp10xOSDiIjKh+XUSUdMPoiIqPxYTp10wCJjRERkGCynTlpi8kFERIZjZ89y6lQmXnYhIiIik2LyQURERCbF5IOIiIhMiskHERERmRSTDyIiIjIpJh9ERERkUkw+iIiIyKSYfBAREZFJMfkgIiIik2LyQURERCbF8upERFS2gny+s4UMhskHERGVLiYKiJ4GKJL/bZN7A30X8m21pBdediEiIs1iooCNI1UTDwBQpCjbY6LMExdZNSYfRESkXkG+8owHhJqZ/7RFT1f2I9IBkw8iIlLv1pGSZzxUCEBxW9mPSAdMPoiISL2MVMP2I/oHkw8iIlLPxcOw/Yj+weSDiIjU8+2ofKoFMg0dZIC8trIfkQ6YfBARkXp29srHaQGUTED+me67gPU+SGdMPoiISLPAcCAiEpB7qbbLvZXtrPNBemCRMSIiKl1gONB4ACucksEw+SAiIlWaSqnX62LuyKiCYPJBRET/Yil1MgHe80FEREospU4mwuSDiIhYSp1MiskHERGxlDqZFJMPIiJiKXUyKSYfRETEUupkUkw+iIiIpdTJpJh8EBERS6mTSTH5ICIiJZZSJxNhkTEiIvoXS6mTCeiVfNy5cwcXL15Es2bN4OnpqbbPrVu3kJCQgAYNGsDLy0ttHyIiMjOWUicz0Omyy6VLl/DCCy8gODgYvXr1wp49e0r0yc/Px6hRoxAYGIhJkyahfv36mDJlisECJiIiA4mJApY0A74fCGz+r/K/S5qxkikZnU7JR3x8PIYMGYIbN25o7LNs2TJERUXh3LlzOHXqFA4dOoRly5Zh48aN5Q6WiIgMhKXUyYx0Sj4GDhyIF198EQ4ODhr7rF27FhEREWjQoAEAIDg4GL1798batWvLFSgRERkIS6mTmRn0aZe8vDxcvHgRrVu3Vmlv3bo1zp49q3G57OxsKBQKlR8iIjISllInMzNo8pGRkYG8vDy4ubmptLu7u+PRo0cal5s/fz5cXV2lHx8fH0OGRURERbGUOpmZQZOPwssxWVlZKu2ZmZmlXqqZMWMG0tPTpZ/ExERDhkVEREWxlDqZmUHrfDg7O6NmzZpISkpSaU9KSoKfn5/G5RwdHeHo6GjIUIiISJPCUuqKFKi/70OmnM9S6mQkBq9w2rt3b/z222/SdG5uLrZv347evXsbelVERKQPllInM9Mp+Xj48CH27Nkj1fe4dOkS9uzZg9jYWKnPzJkzERcXh1GjRuGXX37B888/j+zsbNb6ICKyJCylTmYkE0KoO+em1pkzZ/Dee++VaO/fvz8mT54sTcfFxWHp0qVISEhAQEAAJk+ejLp162odlEKhgKurK9LT0yGXy7VejoiIdKSpwimRHrT9/tYp+TAVJh9EREbARIOMTNvvb75YjojIFsREKQuLFa3vIfdW3vvBSyxkYga/4ZSIiCwMS6mThWHyQURUkbGUOlkgJh9ERBUZS6mTBWLyQURUkbGUOlkgJh9ERBUZS6mTBWLyQURUkRWWUi9RybSQDJDXZil1MikmH0REFRlLqZMFYvJBRFTRsZQ6WRgWGSMisgWB4UDjAaxwShaByQcRka2wswfqdTF3FERMPoiIKiS+x4UsGJMPIqKKhu9xIQvHG06JiCoSvseFrACTDyKiioLvcSErweSDiKii4HtcyEow+SAiqij4HheyEkw+iIgqCr7HhawEkw8iooqC73EhK8Hkg4ioouB7XMhKMPkgIqpI+B4XsgIsMkZEVNHwPS5k4Zh8EBFZO02l1PkeF7JQTD6IiKwZS6mTFeI9H0RE1oql1MlKMfkgIrJGLKVOVozJBxGRNWIpdbJiTD6IiKwRS6mTFWPyQURkjVhKnawYkw8iImvEUupkxZh8EBFZI5ZSJyvG5IOIyFqxlDpZKRYZIyKyZiylTlaIyQcRkTVhKXWqAJh8EBFZC5ZSpwqC93wQEVkDllKnCoTJBxGRpWMpdapgmHwQEVk6llKnCobJBxGRpWMpdapgmHwQEVk6llKnCobJBxGRpWMpdapgmHwQEVk6llKnCobJBxGRNWApdapAWGSMiMhasJQ6VRBMPoiIrAlLqVMFwMsuREREZFJMPoiIiMikmHwQERGRSRn8no8HDx7g3r17Km329vYICAgw9KqIiIjIChk8+fjmm2/wySefwNfXV2qTy+U4fvy4oVdFREREVsgoT7sEBQXh6NGjxhiaiIiIrJzR7vm4ffs2Hjx4YKzhiYiIyEoZJfk4duwYWrdujbp168Lf3x/bt283xmqIiIjIChk8+WjatClOnz6N1NRUpKenY/jw4Xjuuedw7tw5jctkZ2dDoVCo/BAREVHFZPDkY8iQIWjVqhUAoFKlSpg7dy7q1q2LH3/8UeMy8+fPh6urq/Tj4+Nj6LCIiIjIQhi9zodMJoOPjw8SEhI09pkxYwbS09Oln8TERGOHRURERGZi8OQjNzdXZTo9PR3nz59Hw4YNNS7j6OgIuVyu8kNEZDMK8oH4Q8CFTcr/FuSbOyIiozL4o7bdunXDqFGj0KpVK9y/fx8ff/wxHB0dMX78eEOviojI+sVEAdHTAEXyv21yb6DvQuVbbIkqIIOf+diwYQMuXbqEt956C4sWLUKXLl1w4cIFeHp6GnpVRETWLSYK2DhSNfEAAEWKsj0myjxxERmZTAghzB1EcQqFAq6urkhPT+clGCKqmArygSXNSiYeEpnyDMikC4CdvUlDI9KXtt/ffLEcEZE53DpSSuIBAAJQ3Fb2I6pgmHwQEZlDRqph+xFZESYfRETm4OJh2H5EVoTJBxGROfh2VN7TAZmGDjJAXlvZj6iCYfJBRGQOdvbKx2kBlExA/pnuu4A3m1KFxOSDiMhcAsOBiEhA7qXaLvdWtrPOB1VQBi8yRkREOggMBxoPUD7VkpGqvMfDtyPPeFCFxuSDiMjc7OyBel3MHQWRyTD5ICIypYJ8nuUgm8fkg4jIVPgeFyIAvOGUiMg0+B4XIgmTDyIiYyvIV57xgLpXaf3TFj1d2Y/IBjD5ICIyNr7HhUgFkw8iImPje1yIVDD5ICIyNr7HhUgFkw8iImPje1yIVDD5ICIyNr7HhUgFkw8iIlPge1yIJCwyRkRkKnyPCxEAJh9ERKbF97gQMfkgIjIavseFSC0mH0RExsD3uBBpxBtOiYgMje9xISoVkw8iIkPie1yIysTkg4jIkPgeF6IyMfkgIjIkvseFqExMPoiIDInvcSEqE5MPIiJD4ntciMrE5IOIyJD4HheiMjH5ICIyNL7HhahULDJGRGQMfI8LkUZMPoiIjIXvcSFSi8kHEZEh8D0uRFpj8kFEVF58jwuRTnjDKRFRefA9LkQ6Y/JBRKQvvseFSC9MPoiI9MX3uBDphckHEZG++B4XIr0w+SAi0hff40KkFyYfRET64ntciPTC5IOISF98jwuRXph8EBGVB9/jQqQzFhkjIiovvseFSCdMPoiIDIHvcSHSGpMPIiJd8B0uROXG5IOISFt8hwuRQfCGUyIibfAdLkQGw+SDiKgsfIcLkUEZ9bJLVlYWnJycjLkKreUXCByPf4i7j5+iVtUqaFfPDfZ2mgoDmX7s8oyh67LG7m/ImLXpV1YfY49hrGNn6HmGXkbXbdKlHYBBPqO6xF9aHPk3DqKztu9w4U2nRGUySvLx1VdfYd68eXj48CE8PT2xcOFCvPTSS8ZYlVaiL6ZgXtQF+GScQy2k4S6qIdElCDPDm6NvM6+yBzDy2OUZQ9dljd3fkNurTb+y+hh7DABGOXaljavPvIEt62D72SSDLaNp2zRtk6ax1LVfcWwGAGiYfbFcn1Fd4lc3RtE4+todR2ctflteuX4Nsen1S02itG3TJ0kydZs2fzC08a2OU7cemT02ffebuvj13W5t/8DSJgZ9Y9fls2ZMMiGEuvOIevv111/x4osv4pdffkG/fv2wbt06jBkzBgcOHECnTp20GkOhUMDV1RXp6emQy+Xliif6Ygq2/rQCH1WOhLfsodSeLNwwN3ckBg8fW64v0fKOXZ4xdF3W2P21oe2Y2vQDUGof384v4NbhDUYbY07uSADALAMfu9LG1XdeVF5HhFc6YpBlNG1badukaSx17Q+FCwDATZZR5nr1WWfxcTSNoS6OsryY8yGOFgQCAKo5VwYApGXmSvO1bfNyrYJZYYFq9/GcbTFISX+q85iGblMXo7r47GRAQZFvGXPEVp79Vjx+fbdb2+W0iUHf2HX5rOlL2+9vgycfISEh8PT0xIYNG6S2Tp06wcfHB+vXr9dqDEMlH/kFAh98+ik+zf0MgPJAFCo8IO9XnopP3n9fr8sk5R27PGPouqyx+2tD2zHnTp+BjxbML6PfewBkpfZZlT8Qr9tvN+oYMiiv+Bv62JU2rj7zCidlBlqm+LaVtU2axlLXXvgbqaz16rvOouMA0DiGujiEUJ0uOu4duKNz9lIUGOBWusJVfDOitcoX6LgfTqu968QcisdoSfEVjQ2AQePSd7stdX+p+6yVh7bf3wa94VQIgRMnTqBLF9VrniEhITh27JghV6WV49fv4e3c7wCo/lIpOv127mocv37PLGOXZwxdlzV2f21oO+aPf13Rqt/EMvqMtv/dqGPIivVVN74+x660cfWdB5T80tR3GaDktmlzbDWNVbxdJtNuveVZZ+E4pY2hLg6Z7N+kpFBhQjMn92WDJB7Av7e1ztkWg/wCgfwCgTnbYsz+RVVU0Rhz8gosKr7COGZHXcLsKMPGpe92W+r+Kv5ZMxWDJh+PHz9GVlYWatSoodJeq1Yt3L17V+Ny2dnZUCgUKj+GkH/zL3jLHpb4pVLITgZ4yx4g/+ZfZhm7PGPouqyx+2tD2zE9Lv+oRb+H8Cqjj72swKhjqPtyKr4t+hy70sYtzzxDLaNu27TZJl3atVmvvussOk5ZY2gz7h24Y1zuJOwqaKf9IFoQAFLSn+J4/EMcj3+ocmreUhTGuO7vmxYXnwBwR5GNOwrDx6Xvdlvq/ir6WTMVo9xwWlBQoDKdl5cHWSm/ZebPn485c+YYPI5asjSD9jP02OUZQ9dljd3fkH197VK1HtPSlefYWbqi22HKbdL1M1rWOPpam9cb0QXtcLygscHOeKhz97HlfElpcuthprlDMAt9t9tS95cpP2sG/RdTtWpVVK1aFampql8ed+/ehbe3t8blZsyYgfT0dOknMTHRIPH41/c3aD9Dj12eMXRd1tj9Ddm3UeMgrce0dOU5dpau6HaYcpt0/YyWNk55xoguaIejBYFGTTwAoFbVKqhVtYpR11Fevm7O5g7BLPTdbkvdX6b8nBn0X41MJkOnTp2wd+9elfY//vgDnTt31rico6Mj5HK5yo8h2Pt1QpaTJzRdxioQQJaTJ+z9tHsKx9Bjl2cMXZc1dn9taDtmpQ6vl92vigeynDxK7VMAO6OOIUTJ6//Ft0WfY1fauOWZZ6hl1G2bNtukS7s269V3nUXHKWsMTcsnC3ccL2is/UJ6kEH5JEK7em5oV88NXq5VoMPVIZMojPHlZ/0sLj4ZAE+5Izzlho9L3+221P1V9LNmKgZP2adNm4bo6Gh8+eWXiI+Px0cffYTY2FhMnjzZ0Ksqm509nMIWQSaToaDYrAIokyWnsEX6vRTKEGOXZwxdlzV2f21oO2Ylh7L7hX8Op7DPS+1j1/FNo44BmfLH0MeutHHLM6/496u+y6jdNi22SdP6i7cLaLlePdepMk4pY6iNwwg3l6pT+KU0KywQ9nYy2NvJMCssUGWeuRWN0aGSnUXFVxjD7PCmmB1u2Lj03W5L3V/FP2umYvB/Pd26dcOmTZuwbt06dOjQAX/++Sd27tyJZs2aGXpV2gkMhywiEjK56mUfmbw2ZBGR5XsZlCHGLs8Yui5r7P7a0HZMbfqV1af3PCOPsU75Y/BjV9q4es7r+LbyBWgGWUbDtpW2TZrGUtMOJzfljzbr1WedxcfRMIa6OLKdPfF+5akqN5d6uVbBG13rwctV9ZR1NefKUl0FXds8XauUePSxbzMvfDOiNTwNuB5DxqgpvuLfZ6aOrbz7rXj8+m63tstpE4O+sWv7WTMFg9f5MARDFhmTGPM12IYYuzxj6LqssfsbMmZt+pXVx9hjGOvYGXqeoZfRdZt0aQcM8xnVJX4t48iHXbnKxGvbxgqnrHBqjRVOzVZkzBCMknwQERGRUZmlyBgRERFRWZh8EBERkUkx+SAiIiKTYvJBREREJsXkg4iIiEyKyQcRERGZFJMPIiIiMikmH0RERGRSlcwdgDqFdc8UCoWZIyEiIiJtFX5vl1W/1CKTj8ePHwMAfHx8zBwJERER6erx48dwdXXVON8iy6sXFBQgOTkZVatWhUxm7nf+WT6FQgEfHx8kJiayHL2F47GyHjxW1oPHynIIIfD48WN4e3vDzk7znR0WeebDzs4OderUMXcYVkcul/MfnpXgsbIePFbWg8fKMpR2xqMQbzglIiIik2LyQURERCbF5KMCcHR0xKxZs+Do6GjuUKgMPFbWg8fKevBYWR+LvOGUiIiIKi6e+SAiIiKTYvJBREREJsXkg4iIiEyKyYcVefLkCc6ePYvk5GSNfQoKCnDhwgWcP38e+fn5JoyO1ElKSsLhw4dx//59tfPj4uJw5swZ5OTkmDgyKionJwdnz55FUlKSxj7Xr1/HqVOnkJmZacLIqCghBG7cuIFTp07h7t27GvslJibi5MmTfEWHJRNk8ZKSksRLL70kXF1dRcuWLYWrq6sICQkRiYmJKv3Onz8v/P39hZeXl6hdu7bw9fUVp0+fNlPUlJaWJvz9/QUA8fPPP6vMS0hIEEFBQcLd3V3Uq1dP1KxZU+zZs8dMkdq2lStXimrVqonGjRuLRo0aieeff148efJEmv/w4UMREhIiqlatKgICAoRcLhfr1683Y8S26dy5c6Jx48bCw8NDtG7dWjg7O4vnnntOZGZmSn2ysrLEc889J5ycnETjxo2Fk5OT+PLLL80YNWnC5MMKHD58WPz4448iLy9PCCGEQqEQHTp0EL169ZL65Ofni8aNG4sXXnhBFBQUCCGEGDFihKhfv77Izc01S9y2LiIiQkybNk1t8tGtWzfRrVs3kZ2dLYQQYtq0aaJ69eri0aNHZojUdq1fv15UqlRJbN++XWrbsmWLSE5OlqZHjBghmjVrJtLT04UQQnz11VfCwcFBxMfHmzpcm9axY0fRp08f6ffZrVu3hKurq1i0aJHUZ/r06aJOnTrS8fv1118FAHH06FGzxEyaMfmwUsuWLRPOzs7S9MGDBwUAcfHiRant8uXLAoD4448/zBGiTVu5cqXo0KGDePz4cYnk48aNGwKAiI6OltoePXokKleuLNasWWOGaG1XQECAeO211zTOf/z4sXBwcBDfffed1JaXlydq1Kgh5s2bZ4oQ6R8BAQHiww8/VGlr2rSpmDp1qjTt4eEhZs+erdKnWbNm4o033jBJjKQ93vNhpU6cOAF/f39p+syZM3B0dETTpk2ltoYNG0Iul+PMmTPmCNFmxcTEYObMmfjhhx9QqVLJ1ycVHo82bdpIbdWqVUNAQACPlQklJibi6tWrCAsLw8OHD3Hq1Cncu3dPpU9MTAxycnJUjpW9vT1at27NY2Vic+fOxZo1a7B69Wrs2bMHU6dORWZmJsaPHw8ASE5ORmpqqsqxAoB27drxWFkgi3yxHJXu999/x7p167B+/Xqp7eHDh3B3dy/R193dHQ8fPjRleDYtKysLL7zwAhYuXAh/f388ffq0RJ/C4+Hm5qbSzmNlWoU3bu/duxdjx46Ft7c34uLiEBYWhu+//x5VqlSRjkfxf1vu7u5ISUkxecy2LDQ0FO3atcOMGTNQp04d3LhxA9OnT0fdunUBoNRjxX9XlofJh5X566+/EBERgZkzZ+L555+X2itXrqz2iy4rKwsODg6mDNGmzZs3D/b29mjQoAEOHz4sPcVy+fJlnDt3DkFBQahcuTIAIDs7G05OTtKyPFamVXgcjh49iqtXr6Jq1apISEhAcHAwPvnkE8ybN0/qU/zfFo+VaQkh0K9fP/j6+iIpKQkODg5ITExEu3btkJeXhw8//JDHysrwsosV+fvvv9GvXz9MmjQJs2fPVpnn6+uLR48eqTwGmJ2djQcPHkh/GZDxOTk5wcXFBdOnT8f06dMxc+ZMAMDPP/+Mr776CoDyWAHA7du3VZZNTk7msTKhwuMwfPhwVK1aFQBQt25d9OvXD4cOHVLpU/xY3b59m8fKhJKTk3H69Gm8/vrrUiLh4+OD8PBwREVFSdN2dnY8VlaCyYeVOHr0KPr27Yu33noLH3/8cYn5oaGhsLOzw/bt26W233//HXl5eejRo4cpQ7VpM2fOxOHDh6WfvXv3AgBmz56N7777DgDQvn17VK1aVfqlCSjv4UlOTkavXr3MErctcnd3R3BwcIkvq6SkJNSsWRMA0KBBA9SrV0/lWCUlJeHUqVM8Vibk5uYGOzu7EnVYEhMTpWPl7OyMjh07qhyrJ0+eYM+ePTxWFoiXXazAhQsX0LdvXzz77LPo168fDh8+LM179tlnYW9vD29vb7z99tuYMGECMjMzYW9vj/feew/jxo2Dn5+f+YKnEpycnDB79mzMnDkTVapUQY0aNfDhhx8iPDwcHTt2NHd4NmXhwoUYNGgQPD090aJFC+zatQsHDx7EwYMHpT7z58/HiBEjUKtWLTRq1Aiffvop2rRpg6FDh5oxctvi5OSE119/He+//z7y8/NRv3597N69G9HR0dixY4fU7+OPP0avXr0wY8YMPPvss/jqq69Qq1YtjBkzxozRkzp8q60V+O2337Bo0SK186Kjo+Hi4gJAWd3022+/RVRUFIQQGDBgAMaNGwd7e3tThktF5OTkIDQ0FHPmzClxBurnn3/G+vXrkZWVhe7du+Odd95BlSpVzBSp7Tpy5AiWL1+O1NRU1K9fH2+++SaaN2+u0mfnzp1YvXo10tLS0L59e0ydOhWurq5mitg25efnIzIyErt27cKDBw/g6+uL0aNHo0OHDir9/vrrL3z99ddITU1F8+bNMX36dHh6epopatKEyQcRERGZFO/5ICIiIpNi8kFEREQmxeSDiIiITIrJBxEREZkUkw8iIiIyKSYfREREZFJMPoiIiMikmHwQ2bjHjx/j2rVr5g7DYDIyMnDlyhVzh1GqGzduICsrq8x+ly9fRn5+vgkiIjItJh9E5ZSXl4fExES1bxUur/v37yMhIUGlraCgAHFxcQZb344dO0pUibRme/bsQevWrQ0yVmZmJpKTk1FaLcatW7eia9euqFevHkaMGIH09PRSxzx58iS6desmTRcUFGDBggVo0aIFWrRogZ9++kmaN3nyZHz99dfl3g4iS8Pkg0hPT58+xZgxY1C1alV07NgRnp6e6N69u/RGVENYsmQJRo4cqdKmUCjQpEkTXLx40SDrkMvlCAgIMMhYFcXhw4cRGhoKT09PBAcHw93dHUuWLCnR75tvvsHEiRMxY8YMbNq0CWfOnMGUKVNKHXvKlCl477334OTkBAD46KOPcPjwYaxZswYrVqzAxIkToVAoAACzZs3C7NmzkZGRYfBtJDIrQUR6mTRpkvD19RXXrl0TQghRUFAgDh06JD755JMSfZ8+fSpu3rwp8vLyVNqvXr0qYmNjxeXLl8WTJ09U5j18+FCMHTtWtG3bVsTGxorY2FihUCjEiRMnBACxadMmERsbK27evKmyXFZWloiPjxfZ2dkq7bm5uSI2NlZkZ2eL7Oxscf36dZGWliYUCoW4evWq1O/u3bsiISFBCCHEkydPRHJyssZ9kJSUJNLT04UQQiQnJ4uUlBSNfdPT06XtSEhIEAUFBSX6xMXFiYyMDGm84vuk6DbeunVL5Ofni7y8PGm7hBDi119/Fc8880yJZXJyckR8fLzGMYv65JNPxL59+0R+fr4QQoht27YJe3t78euvv0p9rly5IhwdHcXff/8tta1atUpUq1ZN47inT58Wjo6O4uHDh1Kbt7e3tM1CCBEQECAeP34sTQcGBorly5eXGTORNWHyQaSnli1bigkTJpTaJzs7W7z99tuiSpUqwsvLS7i5uYmVK1dK8zt06CAaNWokGjRoIJycnMSAAQPEvXv3hBBCfP/998Ld3V04OTmJRo0aiUaNGokdO3aIxo0bCwDC19dXNGrUSAwdOlQIoUwupkyZIlxcXISPj49wdnYWY8aMEVlZWUIIIRITEwUA8e6774rq1auLBg0aiC1btoiff/5ZuLu7SzFNmzZNtGnTRgwePFh4eHgIuVwumjRpIq5fvy71SUlJER06dBAODg6iVq1aokuXLqJ79+7iv//9r8Z9ERUVJW2Ht7e3cHV1VdkXQghhb28v3nzzTeHj4yPq1KkjKlWqJKZPn67SZ82aNcLFxUXUrFlTuLq6ivfee08AELGxsUII9cnH559/LqpXry7q1KkjXFxcxODBg8X9+/dLPXbFtW7dWuV4v/7666JHjx4qfbZs2SIAlEj8Cn3wwQeiS5cu0nROTo6oXr26uH//vigoKBBLly4Vffr0UVnmnXfeKbEeImvH5INIT0OHDhX+/v7i7NmzGvu8/fbbwsfHR5w/f14IIYRCoRCffvqp2r5paWmiZ8+e4vXXX5faPvjgAxESEqLS79GjRwKAOHHihEr7Bx98IIKDg8Xt27eFEMozGK1atRIffvihEOLf5KN9+/biwYMH0nLqkg8AYtWqVUII5VmGkJAQMWzYMKlPRESEaN++vUhLSxNCCPHNN98IAKUmH8X9+eefwsnJSdo3QiiTj8DAQGkbDh48KGQymTh16pQQQogbN26ISpUqSbE9fPhQBAcHl5p8rFmzRvj4+EjzMzIyxIABA8SLL76odaxPnjwR7u7uYv78+UIIIfLz80X16tXF9OnTxYULF6Sf+fPnC0dHR43j9OzZU7z11lsqbXPmzBHPPPOMcHFxEd27dy9xpmndunXCyclJ61iJrAGTDyI93bx5U3Tq1EkAEN7e3uK5554Tq1evFrm5uUIIITIzM4Wjo6NYvXp1qePk5eWJpKQkERsbK5YsWSL8/PykedomH7m5uUIul4vly5eLq1eviitXrojLly+LefPmicDAQCHEv8lHVFSUynjqko/GjRur9Pnqq6+ktvT0dCGTycTOnTtV+jRq1Eir5CMjI0O63NS6dWuxZMkSaZ69vb347rvvVPr7+vpKbbNmzRJNmzZVmR8VFVVq8tGiRQsxbdo0lf0SGRkpqlSpIl1WKcsbb7whatSoIe7cuSOEEOLcuXMCgLC3t1f5kclkolmzZhrHadq0qZgzZ06J9szMTCmRKy46OloA0DifyBpVMv1dJkQVg6+vLw4fPozr16/j0KFDOHDgAMaPH4/IyEjs3bsXN2/eRHZ2dqlPXsyePRuLFy+Gk5MTqlWrhqysLKSmpuocS1JSEhQKBRYtWoSlS5eqzHNxcVGZrl+/fpnj1a5du8QYjx8/BgDEx8dDCIHAwECVPsWni4uPj8crr7yCo0ePwsPDA87OzkhKSsLt27e1Xvf169dLrKdp06alrjcmJgYPHjzA1q1bVdp9fX3x6NEjuLu7l7r8xx9/jHXr1mHnzp3w8PAAAMTFxcHe3h7Z2dmwt7eX+vbs2RMNGjTQOJaDgwNycnJKtDs5OUk3oBZX2N/BwaHUOImsCZMPonLy9/eHv78/XnnlFQwaNAhDhgzBsWPH4OnpCUD5uKY6u3btwueff46DBw9KCcovv/yCYcOG6RxD5cqVAQArVqxA7969S+1b9MtSH4VfksUf9S2rbsX48eNRq1YtPHz4EM888wwAoFOnTigoKNBp3cUfZS1rvZUrV8b777+P8ePHa72eQgsWLMCnn36K7du3o2vXrlK7QqGAq6uryr588OABDh48iOnTp2scz8/Pr0SyVZbbt2/Dw8NDY3JCZI34qC2RntT9BVu3bl0AytoN9erVg5eXF37//XeVPnl5eQCA2NhYNGjQQOXMyN69e1X6VqlSBbm5uSptjo6OAKDSXrt2bfj5+WHTpk0lYiq+fHn5+fnB1dUVBw8elNqePn2KkydPlrpcbGwsBgwYICUe9+/fx/nz53Vad1BQEI4dO4bs7GypraxHmzt16qTXfvnss88wd+5cbNu2DaGhoSrzatSogSdPnkjHEgCWLl2KFi1aoEePHhrHDAkJwdGjR0tdb3HHjh1D9+7ddVqGyNLxzAeRngYMGIBmzZqhR48e8PHxwc2bNzF79mw0b94cbdu2hUwmw2effYbRo0fDyckJvXr1wo0bN7Bu3TqpsNe7776Lb775Bu3atcPOnTuxdu1alXU0btwYixcvxoEDB+Dh4YHatWujatWq8PX1xaZNmyCXy+Hi4gJfX1988cUXiIiIgFwux9ChQ5GZmYk//vgDaWlpWLFihcG228HBAVOmTMGMGTPg4uKCunXrYvHixUhLS4NMJtO43LPPPosvv/wS/v7+yM3NxcyZM7Wq8lnUK6+8gk8//RQjRozA5MmTpX0OQOO658+fj5CQELz88ssYPXo07OzscPjwYRw5cgTbtm1Tu8zSpUsxY8YMfPXVV6hduzbi4uIAAFWrVkXt2rUREhKCypUrY9GiRRg3bhw2bdqE5cuXY//+/aXugxdffBFTp05FTExMmZepAGWCtH37dkRGRpbZl8iqmPumEyJr9fjxY/Hll1+KsLAwERQUJHr27Clmz55d4hHO6OhoERYWJlq2bCleeukllZoaP/74o+jatasICgoSo0aNEpGRkSo3VObl5Ylp06aJdu3aicaNG4sdO3YIIYQ4cOCA6Nu3r2jatKn0qK0QQvz111/ihRdeEEFBQaJXr15i0aJFIjMzUwghxJ07d0SjRo3EjRs3VOLbsWOH6NChgzS9ePFi8eqrr6r02bJli8qNr/n5+WL+/PkiODhYdO/eXXz++eciLCxMvPnmmxr314MHD8TYsWNFq1atROfOncWSJUvEf//7X7Fw4UKpT9OmTcWhQ4dUlgsLCxPff/+9NB0XFyeGDh0qWrVqJSIiIsSmTZsEAKneyZ49e0Tr1q1VxoiJiRGvvfaaaNWqlejatav48MMPVWptFPfCCy9IjwUX/Sn6qO3WrVuFn5+fqFKliggNDRXnzp3TOF5RY8aMKXU/FfXTTz+JFi1aqK2JQmTNZEKUUjeYiEiN/Px8lfsdcnJy4O/vj/fffx/jxo0z6bojIyPx5ptv4tGjR+W+n8UU7t27h4EDB2Lnzp1wc3Mrte+gQYMwefJkhISEmCg6ItNg8kFEOtu9eze2b9+O559/Hvn5+ViyZAn+/vtvxMbGlvmFWl6jR49Gly5d0KxZM5w/fx7Tpk3Da6+9hgULFhh1vURkOEw+iEhnQgisWLECmzdvxpMnTxAUFISZM2eWeEzWGG7duoWPP/4YZ8+eRc2aNTFkyBCMHj261HstiMiyMPkgIiIik+KjtkRERGRSTD6IiIjIpJh8EBERkUkx+SAiIiKTYvJBREREJsXkg4iIiEyKyQcRERGZFJMPIiIiMikmH0RERGRSTD6IiIjIpP4fNP5yjAF6msQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_module(ds_names[6],\n",
    "           207,\n",
    "           245,\n",
    "           266)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:33:10.084218Z",
     "iopub.status.busy": "2026-09-15T09:33:10.084117Z",
     "iopub.status.idle": "2026-09-15T09:33:59.677596Z",
     "shell.execute_reply": "2026-09-15T09:33:59.675885Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.377487894658635e-06\n",
      "Cost function before refinement: 4.377487894658635e-06\n",
      "[ 7.20000000e-01  3.20000000e-02  4.00000000e-03  0.00000000e+00\n",
      " -4.27998889e+01  1.00000000e+00  1.70270825e+01]\n",
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 9.109058146293743e-11\n",
      "           x: [ 7.200e-01  3.049e-02  4.000e-03  0.000e+00 -4.280e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-3.538e-07  3.502e-09        nan        nan -3.652e-10\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 9.109058146293743e-11\n",
      "GonioParam(dist=np.float64(0.7199860337927957), poni1=np.float64(0.030492505461750096), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-42.79990784449265), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.030492505461750096\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of peaks found and used for refinement\n",
      "543\n",
      "Cost function before refinement: 1.911534453472336e-07\n",
      "[ 7.19986034e-01  3.04925055e-02  4.00000000e-03  0.00000000e+00\n",
      " -4.27999078e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 4.121300333570348e-08\n",
      "           x: [ 7.207e-01  3.077e-02  4.000e-03  0.000e+00 -4.280e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 7\n",
      "         jac: [-3.061e-09 -2.322e-11        nan        nan -3.626e-10\n",
      "                      nan        nan]\n",
      "        nfev: 29\n",
      "        njev: 7\n",
      " multipliers: []\n",
      "Cost function after refinement: 4.121300333570348e-08\n",
      "GonioParam(dist=np.float64(0.720705554602103), poni1=np.float64(0.03076843090598898), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-42.799904173872896), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: dist (0) 0.7199860337927957 --> 0.720705554602103\n",
      "Cost function before refinement: 4.121300333570348e-08\n",
      "[ 7.20705555e-01  3.07684309e-02  4.00000000e-03  0.00000000e+00\n",
      " -4.27999042e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 1.905573974207547e-10\n",
      "           x: [ 7.207e-01  3.086e-02  3.965e-03  2.507e-05 -4.280e+01\n",
      "                9.991e-01  1.703e+01]\n",
      "         nit: 5\n",
      "         jac: [-5.207e-07 -3.451e-07 -5.785e-08  4.380e-08 -4.683e-09\n",
      "                9.352e-08        nan]\n",
      "        nfev: 35\n",
      "        njev: 5\n",
      " multipliers: []\n",
      "Cost function after refinement: 1.905573974207547e-10\n",
      "GonioParam(dist=np.float64(0.7207071581128385), poni1=np.float64(0.030863392388519827), poni2=np.float64(0.003965206217913014), rot1=np.float64(2.5069661472187663e-05), offset=np.float64(-42.799902973828175), scale=np.float64(0.9990506059528655), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990506059528655\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:99: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16803\n",
      "45 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:107: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_module(ds_names[7],\n",
    "           236,\n",
    "           273,\n",
    "           293)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:33:59.679580Z",
     "iopub.status.busy": "2026-09-15T09:33:59.679479Z",
     "iopub.status.idle": "2026-09-15T09:34:44.801951Z",
     "shell.execute_reply": "2026-09-15T09:34:44.800078Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.734136493812279e-08\n",
      "Cost function before refinement: 9.734136493812279e-08\n",
      "[ 7.20000000e-01  3.20000000e-02  4.00000000e-03  0.00000000e+00\n",
      " -3.72000000e+01  1.00000000e+00  1.70270825e+01]\n",
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 1.383834659393779e-10\n",
      "           x: [ 7.200e-01  3.178e-02  4.000e-03  0.000e+00 -3.720e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-5.987e-07  2.236e-08        nan        nan -2.112e-10\n",
      "                      nan        nan]\n",
      "        nfev: 9\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 1.383834659393779e-10\n",
      "GonioParam(dist=np.float64(0.7199980533133431), poni1=np.float64(0.0317753490052087), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-37.20000282728478), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.0317753490052087\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of peaks found and used for refinement\n",
      "454\n",
      "Cost function before refinement: 1.4308139309115986e-07\n",
      "[ 7.19998053e-01  3.17753490e-02  4.00000000e-03  0.00000000e+00\n",
      " -3.72000028e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 3.226788268415189e-08\n",
      "           x: [ 7.208e-01  3.201e-02  4.000e-03  0.000e+00 -3.720e+01\n",
      "                1.000e+00  1.703e+01]\n",
      "         nit: 7\n",
      "         jac: [-3.925e-09 -5.222e-11        nan        nan -3.043e-10\n",
      "                      nan        nan]\n",
      "        nfev: 29\n",
      "        njev: 7\n",
      " multipliers: []\n",
      "Cost function after refinement: 3.226788268415189e-08\n",
      "GonioParam(dist=np.float64(0.7208093285809686), poni1=np.float64(0.03201295264935034), poni2=np.float64(0.004), rot1=np.float64(0.0), offset=np.float64(-37.19999969779676), scale=np.float64(1.0), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: dist (0) 0.7199980533133431 --> 0.7208093285809686\n",
      "Cost function before refinement: 3.226788268415189e-08\n",
      "[ 7.20809329e-01  3.20129526e-02  4.00000000e-03  0.00000000e+00\n",
      " -3.71999997e+01  1.00000000e+00  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 1.8511707669927584e-10\n",
      "           x: [ 7.208e-01  3.208e-02  3.960e-03  2.909e-05 -3.720e+01\n",
      "                9.991e-01  1.703e+01]\n",
      "         nit: 5\n",
      "         jac: [-5.574e-07  1.084e-09 -4.859e-08  3.728e-08 -3.498e-10\n",
      "                3.440e-09        nan]\n",
      "        nfev: 35\n",
      "        njev: 5\n",
      " multipliers: []\n",
      "Cost function after refinement: 1.8511707669927584e-10\n",
      "GonioParam(dist=np.float64(0.720810786218182), poni1=np.float64(0.0320817564491065), poni2=np.float64(0.003959633634965548), rot1=np.float64(2.9090593451096452e-05), offset=np.float64(-37.199998826623414), scale=np.float64(0.9990588539443465), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990588539443465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:99: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16875\n",
      "45 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/465879643.py:107: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_module(ds_names[8],\n",
    "           264,\n",
    "           302,\n",
    "           322)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:34:44.804059Z",
     "iopub.status.busy": "2026-09-15T09:34:44.803928Z",
     "iopub.status.idle": "2026-09-15T09:34:44.808339Z",
     "shell.execute_reply": "2026-09-15T09:34:44.806507Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(goniometers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:34:44.810116Z",
     "iopub.status.busy": "2026-09-15T09:34:44.809978Z",
     "iopub.status.idle": "2026-09-15T09:34:44.814984Z",
     "shell.execute_reply": "2026-09-15T09:34:44.812928Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_02 7.2310e-01 3.2054e-02 3.9849e-03 1.0948e-05 -8.2800e+01 9.9942e-01 1.7027e+01\n",
      "data_03 7.2068e-01 3.3678e-02 4.0580e-03 -4.4486e-05 -7.7200e+01 9.9897e-01 1.7027e+01\n",
      "data_04 7.2069e-01 3.4825e-02 4.0432e-03 -3.3768e-05 -7.1600e+01 9.9898e-01 1.7027e+01\n",
      "data_05 7.2048e-01 3.3410e-02 3.9779e-03 1.5910e-05 -6.5800e+01 9.9900e-01 1.7027e+01\n",
      "data_07 7.2065e-01 3.2237e-02 3.9757e-03 1.7542e-05 -6.0000e+01 9.9901e-01 1.7027e+01\n",
      "data_08 7.2053e-01 3.3395e-02 3.9730e-03 1.9484e-05 -5.4400e+01 9.9903e-01 1.7027e+01\n",
      "data_09 7.2061e-01 3.2247e-02 3.9689e-03 2.2375e-05 -4.8600e+01 9.9904e-01 1.7027e+01\n",
      "data_10 7.2071e-01 3.0863e-02 3.9652e-03 2.5070e-05 -4.2800e+01 9.9905e-01 1.7027e+01\n",
      "data_11 7.2081e-01 3.2082e-02 3.9596e-03 2.9091e-05 -3.7200e+01 9.9906e-01 1.7027e+01\n",
      "data_12 7.2081e-01 3.2082e-02 3.9596e-03 2.9091e-05 -3.7200e+01 9.9906e-01 1.7027e+01\n"
     ]
    }
   ],
   "source": [
    "# print all the parameters to be able to compare them visually\n",
    "goniometers[\"data_12\"] = goniometers[\"data_11\"]\n",
    "for name in ds_names:\n",
    "    print(name, *[\"{:8.4e}\".format(i) for i in goniometers[name].param])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the negative part of the spectrum ...\n",
    "\n",
    "Until now, we used only the data where 2th >0 \n",
    "For the last modules, this throws away half of the data.\n",
    "\n",
    "We setup here a way to assign the peaks for the negative part of the spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:34:44.816747Z",
     "iopub.status.busy": "2026-09-15T09:34:44.816626Z",
     "iopub.status.idle": "2026-09-15T09:34:44.822531Z",
     "shell.execute_reply": "2026-09-15T09:34:44.820871Z"
    }
   },
   "outputs": [],
   "source": [
    "def complete_gonio(module_id=None, name=None):\n",
    "    \"Scan missing frames for un-indexed peaks\"\n",
    "    if name is None:\n",
    "        name = ds_names[module_id]\n",
    "    gonioref = goniometers[name]\n",
    "    ds = data[name]\n",
    "    print(\"Number of peaks previously found:\",\n",
    "           sum([len(sg.geometry_refinement.data) for sg in gonioref.single_geometries.values()]))\n",
    "\n",
    "    tths = LaB6.get_2th()\n",
    "\n",
    "    for i in range(ds.shape[0]):\n",
    "        frame_name = \"%s_%04i\"%(name, i)\n",
    "        if frame_name in gonioref.single_geometries:\n",
    "                continue\n",
    "        peak = peak_picking(name, i)\n",
    "        ai=gonioref.get_ai(get_position(i))\n",
    "        tth = ai.array_from_unit(unit=\"2th_rad\", scale=False)\n",
    "        tth_low = tth[20]\n",
    "        tth_hi = tth[-20]\n",
    "        ttmin, ttmax = min(tth_low, tth_hi), max(tth_low, tth_hi)\n",
    "        valid_peaks = numpy.logical_and(ttmin<=tths, tths<ttmax)\n",
    "        cnt = valid_peaks.sum()\n",
    "        if (len(peak) ==  cnt) and cnt>0:    \n",
    "            cp = ControlPoints(calibrant=LaB6, wavelength=wl)\n",
    "            #revert the order of assignment if needed !!\n",
    "            if tth_hi < tth_low:\n",
    "                peak = peak[-1::-1]\n",
    "            for p, r in zip(peak, numpy.where(valid_peaks)[0]):\n",
    "                cp.append([p], ring=r)\n",
    "            img = ds[i].reshape((-1,1))\n",
    "            sg = gonioref.new_geometry(frame_name, \n",
    "                                        image=img, \n",
    "                                        metadata=i, \n",
    "                                        control_points=cp, \n",
    "                                        calibrant=LaB6)\n",
    "            sg.geometry_refinement.data = numpy.array(cp.getList())\n",
    "            #print(frame_name, len(sg.geometry_refinement.data))\n",
    "\n",
    "    print(\"Number of peaks found after re-scan:\",\n",
    "            sum([len(sg.geometry_refinement.data) for sg in gonioref.single_geometries.values()]))\n",
    "    return gonioref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:34:44.824222Z",
     "iopub.status.busy": "2026-09-15T09:34:44.824126Z",
     "iopub.status.idle": "2026-09-15T09:35:34.435931Z",
     "shell.execute_reply": "2026-09-15T09:35:34.434071Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 454\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1006\n",
      "Cost function before refinement: 4.707082320757526e-09\n",
      "[ 7.20810786e-01  3.20817564e-02  3.95963363e-03  2.90905935e-05\n",
      " -3.71999988e+01  9.99058854e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 9.802713851439468e-10\n",
      "           x: [ 7.208e-01  3.208e-02  3.962e-03  2.771e-05 -3.720e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [-8.293e-07  1.798e-08 -2.226e-07  1.638e-07 -1.374e-10\n",
      "               -3.004e-08        nan]\n",
      "        nfev: 29\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 9.802713851439468e-10\n",
      "GonioParam(dist=np.float64(0.7208134358051861), poni1=np.float64(0.03208491868712114), poni2=np.float64(0.003961539924237118), rot1=np.float64(2.7705794268123677e-05), offset=np.float64(-37.19999878573076), scale=np.float64(0.9989913574692074), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 0.9990588539443465 --> 0.9989913574692074\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20813436e-01,  3.20849187e-02,  3.96153992e-03,  2.77057943e-05,\n",
       "       -3.71999988e+01,  9.98991357e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonio8 = complete_gonio(module_id=8)\n",
    "gonio8.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:35:34.437835Z",
     "iopub.status.busy": "2026-09-15T09:35:34.437738Z",
     "iopub.status.idle": "2026-09-15T09:36:18.650797Z",
     "shell.execute_reply": "2026-09-15T09:36:18.648924Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 543\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1004\n"
     ]
    }
   ],
   "source": [
    "gonio7 = complete_gonio(module_id=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:36:18.652536Z",
     "iopub.status.busy": "2026-09-15T09:36:18.652437Z",
     "iopub.status.idle": "2026-09-15T09:36:19.429912Z",
     "shell.execute_reply": "2026-09-15T09:36:19.428145Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost function before refinement: 2.0497716259840994e-09\n",
      "[ 7.20707158e-01  3.08633924e-02  3.96520622e-03  2.50696615e-05\n",
      " -4.27999030e+01  9.99050606e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 6.982166320635817e-10\n",
      "           x: [ 7.207e-01  3.087e-02  3.967e-03  2.399e-05 -4.280e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [-6.616e-07  1.821e-08 -2.311e-07  1.692e-07 -1.059e-10\n",
      "               -2.989e-08        nan]\n",
      "        nfev: 29\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 6.982166320635817e-10\n",
      "GonioParam(dist=np.float64(0.720709214422471), poni1=np.float64(0.03086624488803945), poni2=np.float64(0.003966690771283648), rot1=np.float64(2.399141761751779e-05), offset=np.float64(-42.799902936946246), scale=np.float64(0.9990066875067236), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 0.9990506059528655 --> 0.9990066875067236\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20709214e-01,  3.08662449e-02,  3.96669077e-03,  2.39914176e-05,\n",
       "       -4.27999029e+01,  9.99006688e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonio7.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:36:19.431692Z",
     "iopub.status.busy": "2026-09-15T09:36:19.431595Z",
     "iopub.status.idle": "2026-09-15T09:36:59.495047Z",
     "shell.execute_reply": "2026-09-15T09:36:59.493142Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 626\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 990\n",
      "Cost function before refinement: 9.054149840019781e-10\n",
      "[ 7.20609293e-01  3.22472572e-02  3.96893551e-03  2.23754877e-05\n",
      " -4.85998857e+01  9.99040908e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 6.093707047210183e-10\n",
      "           x: [ 7.206e-01  3.225e-02  3.970e-03  2.155e-05 -4.860e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [-8.022e-07  3.637e-08 -2.332e-07  1.713e-07  1.531e-10\n",
      "               -6.906e-08        nan]\n",
      "        nfev: 29\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 6.093707047210183e-10\n",
      "GonioParam(dist=np.float64(0.7206117522126192), poni1=np.float64(0.03224903534974467), poni2=np.float64(0.003970067520032141), rot1=np.float64(2.154981768904857e-05), offset=np.float64(-48.5998856370124), scale=np.float64(0.999018042153128), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 0.9990409081096999 --> 0.999018042153128\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20611752e-01,  3.22490353e-02,  3.97006752e-03,  2.15498177e-05,\n",
       "       -4.85998856e+01,  9.99018042e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonio6 = complete_gonio(module_id=6)\n",
    "gonio6.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:36:59.496843Z",
     "iopub.status.busy": "2026-09-15T09:36:59.496745Z",
     "iopub.status.idle": "2026-09-15T09:37:33.005412Z",
     "shell.execute_reply": "2026-09-15T09:37:33.003567Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 754\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1038\n",
      "Cost function before refinement: 5.322293562205405e-10\n",
      "[ 7.20528266e-01  3.33948135e-02  3.97295117e-03  1.94836705e-05\n",
      " -5.43998157e+01  9.99025951e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 5.247777068807799e-10\n",
      "           x: [ 7.205e-01  3.340e-02  3.974e-03  1.898e-05 -5.440e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [-6.548e-07  1.292e-07 -1.627e-07  1.198e-07  1.289e-09\n",
      "               -2.774e-07        nan]\n",
      "        nfev: 29\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 5.247777068807799e-10\n",
      "GonioParam(dist=np.float64(0.7205306818871419), poni1=np.float64(0.03339533863359239), poni2=np.float64(0.003973636919054829), rot1=np.float64(1.8979820018941434e-05), offset=np.float64(-54.39981565738007), scale=np.float64(0.9990218992357166), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 0.9990259510567647 --> 0.9990218992357166\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20530682e-01,  3.33953386e-02,  3.97363692e-03,  1.89798200e-05,\n",
       "       -5.43998157e+01,  9.99021899e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonio5 = complete_gonio(module_id=5)\n",
    "gonio5.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:37:33.007636Z",
     "iopub.status.busy": "2026-09-15T09:37:33.007538Z",
     "iopub.status.idle": "2026-09-15T09:38:01.940618Z",
     "shell.execute_reply": "2026-09-15T09:38:01.938459Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 864\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1081\n",
      "Cost function before refinement: 4.522890520775561e-10\n",
      "[ 7.20645308e-01  3.22371168e-02  3.97565157e-03  1.75422418e-05\n",
      " -6.00000524e+01  9.99011866e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 4.329909092681409e-10\n",
      "           x: [ 7.206e-01  3.224e-02  3.976e-03  1.742e-05 -6.000e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [-3.236e-07 -1.338e-08 -8.079e-08  5.953e-08 -4.927e-10\n",
      "                3.166e-08        nan]\n",
      "        nfev: 29\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 4.329909092681409e-10\n",
      "GonioParam(dist=np.float64(0.7206464175851856), poni1=np.float64(0.032235988249254756), poni2=np.float64(0.0039758195842887565), rot1=np.float64(1.7416684299562833e-05), offset=np.float64(-60.0000524507336), scale=np.float64(0.9990197113116526), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 0.9990118655636463 --> 0.9990197113116526\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20646418e-01,  3.22359882e-02,  3.97581958e-03,  1.74166843e-05,\n",
       "       -6.00000525e+01,  9.99019711e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonio4 = complete_gonio(module_id=4)\n",
    "gonio4.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:38:01.942452Z",
     "iopub.status.busy": "2026-09-15T09:38:01.942353Z",
     "iopub.status.idle": "2026-09-15T09:38:24.889848Z",
     "shell.execute_reply": "2026-09-15T09:38:24.887744Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 978\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1156\n",
      "Cost function before refinement: 7.053066247292853e-10\n",
      "[ 7.20482925e-01  3.34099487e-02  3.97791164e-03  1.59098280e-05\n",
      " -6.57999266e+01  9.98999120e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 6.316703213409536e-10\n",
      "           x: [ 7.205e-01  3.341e-02  3.978e-03  1.605e-05 -6.580e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [-4.998e-07 -1.632e-08  9.666e-09 -4.950e-09 -5.754e-10\n",
      "                3.901e-08        nan]\n",
      "        nfev: 29\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 6.316703213409536e-10\n",
      "GonioParam(dist=np.float64(0.7204847939312276), poni1=np.float64(0.033407488249092), poni2=np.float64(0.003977700789209715), rot1=np.float64(1.605421293874649e-05), offset=np.float64(-65.79992658915752), scale=np.float64(0.9990156265501503), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 0.9989991199293132 --> 0.9990156265501503\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20484794e-01,  3.34074882e-02,  3.97770079e-03,  1.60542129e-05,\n",
       "       -6.57999266e+01,  9.99015627e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonio3 = complete_gonio(module_id=3)\n",
    "gonio3.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:38:24.891656Z",
     "iopub.status.busy": "2026-09-15T09:38:24.891558Z",
     "iopub.status.idle": "2026-09-15T09:38:42.309378Z",
     "shell.execute_reply": "2026-09-15T09:38:42.307315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 1093\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1229\n",
      "Cost function before refinement: 8.306328970111712e-10\n",
      "[ 7.20686429e-01  3.48247408e-02  4.04316628e-03 -3.37683836e-05\n",
      " -7.15999088e+01  9.98982873e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 7.771718764214187e-10\n",
      "           x: [ 7.207e-01  3.482e-02  4.043e-03 -3.357e-05 -7.160e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [ 9.152e-08 -1.052e-09  5.616e-08 -4.083e-08 -4.257e-10\n",
      "                2.851e-09        nan]\n",
      "        nfev: 29\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 7.771718764214187e-10\n",
      "GonioParam(dist=np.float64(0.7206860334374249), poni1=np.float64(0.03482262187953887), poni2=np.float64(0.004042896898550854), rot1=np.float64(-3.357268964891834e-05), offset=np.float64(-71.5999088383266), scale=np.float64(0.9989982448525314), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 0.9989828730379465 --> 0.9989982448525314\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20686033e-01,  3.48226219e-02,  4.04289690e-03, -3.35726896e-05,\n",
       "       -7.15999088e+01,  9.98998245e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonio2 = complete_gonio(module_id=2)\n",
    "gonio2.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:38:42.310958Z",
     "iopub.status.busy": "2026-09-15T09:38:42.310856Z",
     "iopub.status.idle": "2026-09-15T09:38:53.722872Z",
     "shell.execute_reply": "2026-09-15T09:38:53.721150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 1183\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1285\n",
      "Cost function before refinement: 9.822430107504146e-10\n",
      "[ 7.20682073e-01  3.36782330e-02  4.05801069e-03 -4.44855685e-05\n",
      " -7.71999788e+01  9.98967233e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 9.683583561116745e-10\n",
      "           x: [ 7.207e-01  3.368e-02  4.058e-03 -4.454e-05 -7.720e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [ 1.052e-07 -1.409e-09 -2.647e-09  1.496e-09 -3.432e-10\n",
      "                4.363e-09        nan]\n",
      "        nfev: 29\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 9.683583561116745e-10\n",
      "GonioParam(dist=np.float64(0.7206815883863531), poni1=np.float64(0.03367666939027231), poni2=np.float64(0.004058082314538765), rot1=np.float64(-4.453529530230962e-05), offset=np.float64(-77.19997881135367), scale=np.float64(0.9989759056428863), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 0.9989672333399332 --> 0.9989759056428863\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20681588e-01,  3.36766694e-02,  4.05808231e-03, -4.45352953e-05,\n",
       "       -7.71999788e+01,  9.98975906e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonio1 = complete_gonio(module_id=1)\n",
    "gonio1.refine2()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:38:53.724354Z",
     "iopub.status.busy": "2026-09-15T09:38:53.724256Z",
     "iopub.status.idle": "2026-09-15T09:38:58.595312Z",
     "shell.execute_reply": "2026-09-15T09:38:58.593558Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 1203\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1255\n",
      "Cost function before refinement: 2.580092915588822e-06\n",
      "[ 7.23096244e-01  3.20537008e-02  3.98485488e-03  1.09477707e-05\n",
      " -8.27999440e+01  9.99415058e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 2.5799879786031964e-06\n",
      "           x: [ 7.231e-01  3.207e-02  3.981e-03  1.351e-05 -8.280e+01\n",
      "                9.994e-01  1.703e+01]\n",
      "         nit: 4\n",
      "         jac: [-1.439e-07  3.613e-08  5.528e-07 -3.992e-07  1.022e-09\n",
      "               -1.146e-07        nan]\n",
      "        nfev: 29\n",
      "        njev: 4\n",
      " multipliers: []\n",
      "Cost function after refinement: 2.5799879786031964e-06\n",
      "GonioParam(dist=np.float64(0.7230969945154274), poni1=np.float64(0.03206584438250582), poni2=np.float64(0.003981302101258431), rot1=np.float64(1.3513759442931611e-05), offset=np.float64(-82.79994383887343), scale=np.float64(0.9993962397251773), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: scale (5) 0.9994150576486731 --> 0.9993962397251773\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.23096995e-01,  3.20658444e-02,  3.98130210e-03,  1.35137594e-05,\n",
       "       -8.27999438e+01,  9.99396240e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonio0 = complete_gonio(module_id=0)\n",
    "gonio0.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:38:58.597095Z",
     "iopub.status.busy": "2026-09-15T09:38:58.596999Z",
     "iopub.status.idle": "2026-09-15T09:40:28.893885Z",
     "shell.execute_reply": "2026-09-15T09:40:28.892107Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1250\n",
      "Cost function before refinement: 9.454139650039214e-07\n",
      "[ 7.23096995e-01  3.20658444e-02  3.98130210e-03  1.35137594e-05\n",
      " -8.27999438e+01  9.99396240e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 9.315197979050413e-07\n",
      "           x: [ 7.220e-01  3.220e-02  3.944e-03  4.514e-05 -8.280e+01\n",
      "                9.991e-01  1.703e+01]\n",
      "         nit: 9\n",
      "         jac: [-1.206e-08 -1.145e-08  1.037e-07 -7.484e-08  1.341e-10\n",
      "               -9.527e-09        nan]\n",
      "        nfev: 64\n",
      "        njev: 9\n",
      " multipliers: []\n",
      "Cost function after refinement: 9.315197979050413e-07\n",
      "GonioParam(dist=np.float64(0.7219737518819529), poni1=np.float64(0.032200315169332513), poni2=np.float64(0.003943811455063223), rot1=np.float64(4.514209223007645e-05), offset=np.float64(-82.79994226212274), scale=np.float64(0.9991407871114438), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: dist (0) 0.7230969945154274 --> 0.7219737518819529\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.21973752e-01,  3.22003152e-02,  3.94381146e-03,  4.51420922e-05,\n",
       "       -8.27999423e+01,  9.99140787e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rescan module0 which looks much different:\n",
    "gonio0.single_geometries.clear()\n",
    "gonio0 = complete_gonio(module_id=0)\n",
    "gonio0.refine2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discard wrongly assigned peaks\n",
    "\n",
    "We have seen previously that some modules have a much higher residual error, while all have almost the same number of peaks recorded and fitted.\n",
    "\n",
    "Some frames are contributing much more than all the other in those badly-fitted data. \n",
    "Let's spot them and re-assign them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:40:28.895901Z",
     "iopub.status.busy": "2026-09-15T09:40:28.895802Z",
     "iopub.status.idle": "2026-09-15T09:40:28.910563Z",
     "shell.execute_reply": "2026-09-15T09:40:28.909115Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_02_0455 6.313036491714458e-07\n",
      "data_02_0464 6.523609473021892e-07\n",
      "data_02_0465 6.891634832505096e-07\n",
      "data_02_0457 7.370411378714757e-07\n",
      "data_02_0456 7.376222637955442e-07\n",
      "data_02_0460 7.458553094683675e-07\n",
      "data_02_0466 7.671412675214738e-07\n",
      "data_02_0461 7.935692964121192e-07\n",
      "data_02_0462 7.971540266865079e-07\n",
      "data_02_0480 0.0011307661026103302\n"
     ]
    }
   ],
   "source": [
    "#search for mis-assigned peaks in module #0\n",
    "labels = []\n",
    "errors = []\n",
    "\n",
    "for lbl,sg in gonio0.single_geometries.items():\n",
    "    labels.append(lbl)\n",
    "    errors.append(sg.geometry_refinement.chi2())\n",
    "\n",
    "s = numpy.argsort(errors)\n",
    "for i in s[-10:]:\n",
    "    print(labels[i], errors[i])\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:40:28.912299Z",
     "iopub.status.busy": "2026-09-15T09:40:28.912210Z",
     "iopub.status.idle": "2026-09-15T09:40:32.776411Z",
     "shell.execute_reply": "2026-09-15T09:40:32.774631Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ControlPoints instance containing 4 group of point:\n",
      "LaB6 Calibrant with 109 reflections at wavelength 7.281587910025816e-11\n",
      "Containing 4 groups of points:\n",
      "#psg ring 52: 1 points\n",
      "#psh ring 53: 1 points\n",
      "#psi ring 54: 1 points\n",
      "#psj ring 55: 1 points\n",
      "Cost function before refinement: 8.041213618478122e-09\n",
      "[ 7.21973752e-01  3.22003152e-02  3.94381146e-03  4.51420922e-05\n",
      " -8.27999423e+01  9.99140787e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 9.214244602861188e-10\n",
      "           x: [ 7.206e-01  3.228e-02  4.043e-03 -2.053e-05 -8.280e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 9\n",
      "         jac: [-3.087e-08 -3.829e-08 -1.829e-07  1.319e-07 -8.443e-10\n",
      "               -1.615e-08        nan]\n",
      "        nfev: 64\n",
      "        njev: 9\n",
      " multipliers: []\n",
      "Cost function after refinement: 9.214244602861188e-10\n",
      "GonioParam(dist=np.float64(0.7205957894276002), poni1=np.float64(0.032284408312060156), poni2=np.float64(0.004042525656543217), rot1=np.float64(-2.0533943479941588e-05), offset=np.float64(-82.79994103817624), scale=np.float64(0.9989751747980284), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: dist (0) 0.7219737518819529 --> 0.7205957894276002\n",
      "Number of peaks previously found: 1246\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1263\n",
      "Cost function before refinement: 9.385075964190633e-10\n",
      "[ 7.20595789e-01  3.22844083e-02  4.04252566e-03 -2.05339435e-05\n",
      " -8.27999410e+01  9.98975175e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 9.382098584456135e-10\n",
      "           x: [ 7.206e-01  3.228e-02  4.043e-03 -2.057e-05 -8.280e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 1\n",
      "         jac: [-2.666e-08 -1.466e-06 -1.817e-07  1.310e-07 -1.881e-08\n",
      "               -4.389e-08        nan]\n",
      "        nfev: 9\n",
      "        njev: 1\n",
      " multipliers: []\n",
      "Cost function after refinement: 9.382098584456135e-10\n",
      "GonioParam(dist=np.float64(0.7205957964378075), poni1=np.float64(0.03228479377833516), poni2=np.float64(0.004042573433038651), rot1=np.float64(-2.056839946764658e-05), offset=np.float64(-82.79994103322964), scale=np.float64(0.9989751863383792), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032284408312060156 --> 0.03228479377833516\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20595796e-01,  3.22847938e-02,  4.04257343e-03, -2.05683995e-05,\n",
       "       -8.27999410e+01,  9.98975186e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove wrongly assigned peak for frame 480\n",
    "print(gonio0.single_geometries.pop(\"data_02_0480\").control_points)\n",
    "gonio0.refine2()\n",
    "gonio0 = complete_gonio(module_id=0)\n",
    "\n",
    "gonio0.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:40:32.778290Z",
     "iopub.status.busy": "2026-09-15T09:40:32.778192Z",
     "iopub.status.idle": "2026-09-15T09:40:39.192423Z",
     "shell.execute_reply": "2026-09-15T09:40:39.190628Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_11_0495 1.4653264249059768e-06 1.4653264249059768e-06 1.0\n",
      "data_11_0496 1.4360706723755973e-06 1.4653264249059768e-06 1.02037208411337\n",
      "data_11_0499 1.4166274579074244e-06 1.4360706723755973e-06 1.01372500184127\n",
      "data_11_0497 1.408429897528043e-06 1.4166274579074244e-06 1.0058203538520227\n",
      "data_11_0498 1.3832745250176507e-06 1.408429897528043e-06 1.0181853797315261\n",
      "data_11_0500 1.3708837484730059e-06 1.3832745250176507e-06 1.0090385319385737\n",
      "data_11_0492 1.3295635096461316e-06 1.3708837484730059e-06 1.031078048191825\n",
      "data_11_0489 1.3287444389906674e-06 1.3295635096461316e-06 1.000616424521849\n",
      "data_11_0491 1.3215889751725488e-06 1.3287444389906674e-06 1.0054142883699408\n",
      "data_11_0490 1.301797579301906e-06 1.3215889751725488e-06 1.0152031284934913\n",
      "data_11_0494 1.2865330382410883e-06 1.301797579301906e-06 1.0118648651896938\n",
      "data_11_0493 1.2861198600876661e-06 1.2865330382410883e-06 1.0003212594457518\n",
      "data_11_0483 1.2244247451147827e-06 1.2861198600876661e-06 1.0503870206960737\n",
      "data_11_0486 1.2073493928202727e-06 1.2244247451147827e-06 1.0141428424912058\n",
      "data_11_0485 1.2017864447477076e-06 1.2073493928202727e-06 1.0046288990002155\n",
      "data_11_0484 1.1994579208818936e-06 1.2017864447477076e-06 1.001941313509441\n",
      "data_11_0487 1.1764421082529778e-06 1.1994579208818936e-06 1.0195639143375226\n",
      "data_11_0477 1.1389376208111523e-06 1.1764421082529778e-06 1.032929360446549\n",
      "data_11_0478 1.1156529806479392e-06 1.1389376208111523e-06 1.0208708626849992\n",
      "data_11_0481 1.1104608438547017e-06 1.1156529806479392e-06 1.0046756594993609\n",
      "data_11_0479 1.1057542147544931e-06 1.1104608438547017e-06 1.004256487596797\n",
      "data_11_0480 1.100219756303158e-06 1.1057542147544931e-06 1.0050303209150975\n",
      "data_11_0482 8.673122808931536e-07 1.100219756303158e-06 1.2685393491374957\n",
      "Cost function before refinement: 1.0594738330129545e-09\n",
      "[ 7.20813436e-01  3.20849187e-02  3.96153992e-03  2.77057943e-05\n",
      " -3.71999988e+01  9.98991357e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 1.058942401027041e-09\n",
      "           x: [ 7.208e-01  3.209e-02  3.962e-03  2.762e-05 -3.720e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 1\n",
      "         jac: [-8.451e-07 -1.253e-06 -2.518e-07  1.849e-07 -1.614e-08\n",
      "                9.174e-08        nan]\n",
      "        nfev: 9\n",
      "        njev: 1\n",
      " multipliers: []\n",
      "Cost function after refinement: 1.058942401027041e-09\n",
      "GonioParam(dist=np.float64(0.7208138055232528), poni1=np.float64(0.032085466901616906), poni2=np.float64(0.003961650091808378), rot1=np.float64(2.7624888069974958e-05), offset=np.float64(-37.199998778669595), scale=np.float64(0.9989913173312018), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.03208491868712114 --> 0.032085466901616906\n",
      "Number of peaks previously found: 918\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1006\n",
      "Cost function before refinement: 9.804575919262705e-10\n",
      "[ 7.20813806e-01  3.20854669e-02  3.96165009e-03  2.76248881e-05\n",
      " -3.71999988e+01  9.98991317e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 9.797990703945884e-10\n",
      "           x: [ 7.208e-01  3.209e-02  3.962e-03  2.759e-05 -3.720e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 1\n",
      "         jac: [-8.288e-07  2.044e-06 -2.226e-07  1.638e-07  2.536e-08\n",
      "                1.004e-06        nan]\n",
      "        nfev: 9\n",
      "        njev: 1\n",
      " multipliers: []\n",
      "Cost function after refinement: 9.797990703945884e-10\n",
      "GonioParam(dist=np.float64(0.7208139925217619), poni1=np.float64(0.03208500579368141), poni2=np.float64(0.003961700325261782), rot1=np.float64(2.758792257199817e-05), offset=np.float64(-37.19999878439213), scale=np.float64(0.9989910907857577), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.032085466901616906 --> 0.03208500579368141\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20813993e-01,  3.20850058e-02,  3.96170033e-03,  2.75879226e-05,\n",
       "       -3.71999988e+01,  9.98991091e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def search_outliers(module_id=None, name=None, threshold=1.2):\n",
    "    \"Search for wrongly assigned peaks\"\n",
    "    if name is None:\n",
    "        name = ds_names[module_id]\n",
    "    gonioref = goniometers[name]\n",
    "    labels = []\n",
    "    errors = []\n",
    "\n",
    "    for lbl,sg in gonioref.single_geometries.items():\n",
    "        labels.append(lbl)\n",
    "        errors.append(sg.geometry_refinement.chi2())\n",
    "    s = numpy.argsort(errors)\n",
    "    last = errors[s[-1]]\n",
    "    to_remove = []\n",
    "    for i in s[-1::-1]:\n",
    "        lbl = labels[i]\n",
    "        current = errors[i]\n",
    "        print(lbl , current, last, last/current)\n",
    "        if threshold*current<last:\n",
    "            break\n",
    "        last=current\n",
    "        to_remove.append(lbl)\n",
    "    return to_remove\n",
    "\n",
    "\n",
    "        \n",
    "for lbl in search_outliers(8):\n",
    "    gonio8.single_geometries.pop(lbl)\n",
    "gonio8.refine2()\n",
    "gonio8 = complete_gonio(module_id=8)\n",
    "gonio8.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:40:39.194142Z",
     "iopub.status.busy": "2026-09-15T09:40:39.194046Z",
     "iopub.status.idle": "2026-09-15T09:40:45.202150Z",
     "shell.execute_reply": "2026-09-15T09:40:45.200323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.982166320635817e-10\n",
      "data_10_0292 4.478313610295236e-06 4.478313610295236e-06 1.0\n",
      "data_10_0291 4.441618075732054e-06 4.478313610295236e-06 1.008261749195339\n",
      "data_10_0288 4.421114039991817e-06 4.441618075732054e-06 1.0046377531895274\n",
      "data_10_0285 4.4151969079424084e-06 4.421114039991817e-06 1.0013401739883365\n",
      "data_10_0290 4.4114083219467785e-06 4.4151969079424084e-06 1.0008588155344365\n",
      "data_10_0289 4.40450577270408e-06 4.4114083219467785e-06 1.0015671563618955\n",
      "data_10_0284 4.386350135873239e-06 4.40450577270408e-06 1.0041391216543243\n",
      "data_10_0275 4.376873169865872e-06 4.386350135873239e-06 1.0021652366060354\n",
      "data_10_0286 4.374944729160871e-06 4.376873169865872e-06 1.0004407920155303\n",
      "data_10_0283 4.360029737297362e-06 4.374944729160871e-06 1.0034208463616476\n",
      "data_10_0281 4.3581187741366675e-06 4.360029737297362e-06 1.0004384834970619\n",
      "data_10_0282 4.3552714424913155e-06 4.3581187741366675e-06 1.0006537667474804\n",
      "data_10_0278 4.352333646868408e-06 4.3552714424913155e-06 1.0006749932016406\n",
      "data_10_0280 4.3515501506852325e-06 4.352333646868408e-06 1.0001800499031481\n",
      "data_10_0277 4.349866613398178e-06 4.3515501506852325e-06 1.0003870319337769\n",
      "data_10_0276 4.3396700589153925e-06 4.349866613398178e-06 1.0023496151422475\n",
      "data_10_0279 4.338090818701843e-06 4.3396700589153925e-06 1.0003640403761813\n",
      "data_10_0274 4.337324373540883e-06 4.338090818701843e-06 1.0001767092094\n",
      "data_10_0287 4.319689618966426e-06 4.337324373540883e-06 1.004082412425427\n",
      "data_10_0496 1.950808600710568e-06 4.319689618966426e-06 2.2143072454124972\n",
      "Cost function before refinement: 6.590245000704119e-10\n",
      "[ 7.20709214e-01  3.08662449e-02  3.96669077e-03  2.39914176e-05\n",
      " -4.27999029e+01  9.99006688e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 6.580239090129342e-10\n",
      "           x: [ 7.207e-01  3.087e-02  3.967e-03  2.397e-05 -4.280e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-6.411e-07  8.001e-09 -1.445e-07  1.068e-07 -2.377e-10\n",
      "               -3.751e-07        nan]\n",
      "        nfev: 15\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 6.580239090129342e-10\n",
      "GonioParam(dist=np.float64(0.7207093528191252), poni1=np.float64(0.030865644602940744), poni2=np.float64(0.003966722983761047), rot1=np.float64(2.3967642448708747e-05), offset=np.float64(-42.7999029444283), scale=np.float64(0.9990064423875449), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.03086624488803945 --> 0.030865644602940744\n",
      "Number of peaks previously found: 985\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks found after re-scan: 1004\n",
      "Cost function before refinement: 6.991408555023671e-10\n",
      "[ 7.20709353e-01  3.08656446e-02  3.96672298e-03  2.39676424e-05\n",
      " -4.27999029e+01  9.99006442e-01  1.70270825e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 6.980019785354917e-10\n",
      "           x: [ 7.207e-01  3.087e-02  3.967e-03  2.393e-05 -4.280e+01\n",
      "                9.990e-01  1.703e+01]\n",
      "         nit: 2\n",
      "         jac: [-6.609e-07  1.522e-07 -2.321e-07  1.700e-07  1.580e-09\n",
      "                9.613e-08        nan]\n",
      "        nfev: 15\n",
      "        njev: 2\n",
      " multipliers: []\n",
      "Cost function after refinement: 6.980019785354917e-10\n",
      "GonioParam(dist=np.float64(0.7207094971168743), poni1=np.float64(0.03086623823197484), poni2=np.float64(0.003966772613855158), rot1=np.float64(2.3931290270607455e-05), offset=np.float64(-42.79990293688394), scale=np.float64(0.9990067735018262), nrj=np.float64(17.027082549190933))\n",
      "maxdelta on: poni1 (1) 0.030865644602940744 --> 0.03086623823197484\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20709497e-01,  3.08662382e-02,  3.96677261e-03,  2.39312903e-05,\n",
       "       -4.27999029e+01,  9.99006774e-01,  1.70270825e+01])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(gonio7.chi2())\n",
    "for lbl in search_outliers(7):\n",
    "    gonio7.single_geometries.pop(lbl)\n",
    "gonio7.refine2()\n",
    "gonio7 = complete_gonio(module_id=7)\n",
    "gonio7.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:40:45.203996Z",
     "iopub.status.busy": "2026-09-15T09:40:45.203899Z",
     "iopub.status.idle": "2026-09-15T09:40:45.250285Z",
     "shell.execute_reply": "2026-09-15T09:40:45.248826Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.382098584456135e-10\n",
      "data_02_0462 7.971540266865079e-07 7.971540266865079e-07 1.0\n",
      "data_02_0461 7.935692964121192e-07 7.971540266865079e-07 1.0045172240037459\n",
      "data_02_0466 7.671412675214738e-07 7.935692964121192e-07 1.0344500159351753\n",
      "data_02_0460 7.458553094683675e-07 7.671412675214738e-07 1.0285389911191738\n",
      "data_02_0456 7.376222637955442e-07 7.458553094683675e-07 1.011161601373661\n",
      "data_02_0457 7.370411378714757e-07 7.376222637955442e-07 1.0007884579221\n",
      "data_02_0465 6.891634832505096e-07 7.370411378714757e-07 1.069472129305439\n",
      "data_02_0464 6.523609473021892e-07 6.891634832505096e-07 1.0564143762751521\n",
      "data_02_0455 6.313036491714458e-07 6.523609473021892e-07 1.033355261225529\n",
      "data_02_0463 6.240792201950713e-07 6.313036491714458e-07 1.0115761408849928\n",
      "data_02_0459 6.163883776139301e-07 6.240792201950713e-07 1.0124772673536007\n",
      "data_02_0448 6.082012730300175e-07 6.163883776139301e-07 1.013461176335796\n",
      "data_02_0454 5.868915115543744e-07 6.082012730300175e-07 1.036309541126612\n",
      "data_02_0439 5.868103847559679e-07 5.868915115543744e-07 1.0001382504476983\n",
      "data_02_0435 5.859903705274693e-07 5.868103847559679e-07 1.0013993646819837\n",
      "data_02_0426 5.755879595006237e-07 5.859903705274693e-07 1.0180726696157278\n",
      "data_02_0458 5.724203268031596e-07 5.755879595006237e-07 1.0055337529943331\n",
      "data_02_0453 5.590326059354276e-07 5.724203268031596e-07 1.0239480143476254\n",
      "data_02_0434 5.570139568262492e-07 5.590326059354276e-07 1.0036240548094704\n",
      "data_02_0444 5.52479378594903e-07 5.570139568262492e-07 1.0082076877563808\n",
      "data_02_0430 5.489352109509736e-07 5.52479378594903e-07 1.0064564407114447\n",
      "data_02_0443 5.459467109203509e-07 5.489352109509736e-07 1.0054739775345192\n",
      "data_02_0447 5.424204366818411e-07 5.459467109203509e-07 1.0065009981188784\n",
      "data_02_0468 5.360711310359359e-07 5.424204366818411e-07 1.0118441476854674\n",
      "data_02_0425 5.340449210573293e-07 5.360711310359359e-07 1.0037940815439177\n",
      "data_02_0467 5.066722866428126e-07 5.340449210573293e-07 1.0540243371033504\n",
      "data_02_0438 5.014369370144399e-07 5.066722866428126e-07 1.0104406940173654\n",
      "data_02_0429 4.99403819968963e-07 5.014369370144399e-07 1.0040710882940447\n",
      "data_02_0442 4.988909715188969e-07 4.99403819968963e-07 1.0010279770117\n",
      "data_02_0421 4.887204776036332e-07 4.988909715188969e-07 1.0208104517435677\n",
      "data_02_0452 4.818394342566045e-07 4.887204776036332e-07 1.0142807808116514\n",
      "data_02_0441 4.7080818883309074e-07 4.818394342566045e-07 1.0234304451051603\n",
      "data_02_0433 4.6907497808393685e-07 4.7080818883309074e-07 1.0036949546024256\n",
      "data_02_0437 4.674409673906393e-07 4.6907497808393685e-07 1.0034956514453985\n",
      "data_02_0420 4.599762568493357e-07 4.674409673906393e-07 1.0162284692528132\n",
      "data_02_0451 4.5329033193163294e-07 4.599762568493357e-07 1.0147497628930484\n",
      "data_02_0446 4.4537883049631956e-07 4.5329033193163294e-07 1.0177635327357095\n",
      "data_02_0428 4.42456525822236e-07 4.4537883049631956e-07 1.0066047272523622\n",
      "data_02_0424 4.3749902020266474e-07 4.42456525822236e-07 1.0113314667933995\n",
      "data_02_0432 4.3734551969408895e-07 4.3749902020266474e-07 1.0003509822363863\n",
      "data_02_0436 4.331866832386705e-07 4.3734551969408895e-07 1.0096005639516095\n",
      "data_02_0450 4.262100092982173e-07 4.331866832386705e-07 1.0163690992427437\n",
      "data_02_0445 4.1944397535287247e-07 4.262100092982173e-07 1.0161309598967363\n",
      "data_02_0427 4.1840555392472044e-07 4.1944397535287247e-07 1.0024818538339453\n",
      "data_02_0423 4.1311968681592694e-07 4.1840555392472044e-07 1.0127950017331144\n",
      "data_02_0419 3.8768788546419296e-07 4.1311968681592694e-07 1.0655986485656717\n",
      "data_02_0408 3.5423439589395346e-07 3.8768788546419296e-07 1.0944388516699954\n",
      "data_02_0418 3.436567691349336e-07 3.5423439589395346e-07 1.0307796258041018\n",
      "data_02_0412 3.4365078751307176e-07 3.436567691349336e-07 1.0000174061054978\n",
      "data_02_0398 3.357911671477211e-07 3.4365078751307176e-07 1.0234062748943396\n",
      "data_02_0389 3.2715497286197265e-07 3.357911671477211e-07 1.0263978695179181\n",
      "data_02_0394 3.213836275958599e-07 3.2715497286197265e-07 1.017957807338494\n",
      "data_02_0416 3.19468056079038e-07 3.213836275958599e-07 1.005996128502901\n",
      "data_02_0403 3.0713355118444586e-07 3.19468056079038e-07 1.0401600699338276\n",
      "data_02_0411 3.0688035367483995e-07 3.0713355118444586e-07 1.0008250691403797\n",
      "data_02_0407 3.0628805841467744e-07 3.0688035367483995e-07 1.0019337850232497\n",
      "data_02_0402 3.049021454711834e-07 3.0628805841467744e-07 1.0045454351964376\n",
      "data_02_0393 2.980908450430102e-07 3.049021454711834e-07 1.0228497471205142\n",
      "data_02_0388 2.945097819229645e-07 2.980908450430102e-07 1.012159402980314\n",
      "data_02_0397 2.9208255748953456e-07 2.945097819229645e-07 1.0083100629297828\n",
      "data_02_0384 2.8246444537231956e-07 2.9208255748953456e-07 1.0340507000962094\n",
      "data_02_0406 2.817235681006301e-07 2.8246444537231956e-07 1.0026298022443931\n",
      "data_02_0401 2.815310734578888e-07 2.817235681006301e-07 1.0006837420835186\n",
      "data_02_0417 2.810858349891032e-07 2.815310734578888e-07 1.001583994685477\n",
      "data_02_0392 2.739812164821265e-07 2.810858349891032e-07 1.025931042274353\n",
      "data_02_0410 2.7025873203608476e-07 2.739812164821265e-07 1.0137737804732418\n",
      "data_02_0415 2.6501792601213064e-07 2.7025873203608476e-07 1.0197752887995744\n",
      "data_02_0405 2.6433960353472077e-07 2.6501792601213064e-07 1.0025661023484163\n",
      "data_02_0414 2.5775846420670583e-07 2.6433960353472077e-07 1.0255321948331337\n",
      "data_02_0396 2.570083223372203e-07 2.5775846420670583e-07 1.002918745442419\n",
      "data_02_0391 2.485179006235142e-07 2.570083223372203e-07 1.0341642259668387\n",
      "data_02_0387 2.4757387983640684e-07 2.485179006235142e-07 1.0038130871791933\n",
      "data_02_0383 2.436324190464806e-07 2.4757387983640684e-07 1.0161778994985649\n",
      "data_02_0400 2.4112108284998156e-07 2.436324190464806e-07 1.0104152493295724\n",
      "data_02_0395 2.386285458278207e-07 2.4112108284998156e-07 1.0104452592354953\n",
      "data_02_0399 2.3072425986983e-07 2.386285458278207e-07 1.0342585819213381\n",
      "data_02_0386 2.2832961753069427e-07 2.3072425986983e-07 1.0104876553687294\n",
      "data_02_0409 2.2099254926714062e-07 2.2832961753069427e-07 1.0332005232207373\n",
      "data_02_0404 2.200632149826795e-07 2.2099254926714062e-07 1.0042230332976563\n",
      "data_02_0390 2.1855316188101852e-07 2.200632149826795e-07 1.0069093171138062\n",
      "data_02_0382 2.1738312055544068e-07 2.1855316188101852e-07 1.005382392720227\n",
      "data_02_0381 2.0686219013436962e-07 2.1738312055544068e-07 1.0508596105176933\n",
      "data_02_0413 2.0388152591107816e-07 2.0686219013436962e-07 1.0146195895384433\n",
      "data_02_0385 2.0140303101946426e-07 2.0388152591107816e-07 1.0123061449426467\n",
      "data_02_0360 1.9864896885838632e-07 2.0140303101946426e-07 1.0138639640412193\n",
      "data_02_0375 1.9263546030346346e-07 1.9864896885838632e-07 1.0312170383658836\n",
      "data_02_0370 1.920301959625982e-07 1.9263546030346346e-07 1.003151922737105\n",
      "data_02_0365 1.911260411714131e-07 1.920301959625982e-07 1.0047306729404508\n",
      "data_02_0380 1.896072216244721e-07 1.911260411714131e-07 1.0080103465149082\n",
      "data_02_0369 1.7565559633410337e-07 1.896072216244721e-07 1.0794260221794028\n",
      "data_02_0355 1.710407884427107e-07 1.7565559633410337e-07 1.026980744963874\n",
      "data_02_0374 1.6999503262094926e-07 1.710407884427107e-07 1.0061516845853562\n",
      "data_02_0364 1.694072168719141e-07 1.6999503262094926e-07 1.0034698388881484\n",
      "data_02_0350 1.6598943575407027e-07 1.694072168719141e-07 1.0205903532493936\n",
      "data_02_0363 1.574813568972464e-07 1.6598943575407027e-07 1.0540259432891173\n",
      "data_02_0373 1.5391536774940503e-07 1.574813568972464e-07 1.0231685061731282\n",
      "data_02_0354 1.5253343428684397e-07 1.5391536774940503e-07 1.009059872473351\n",
      "data_02_0358 1.5111664301393935e-07 1.5253343428684397e-07 1.0093754813807896\n",
      "data_02_0349 1.4524820316105622e-07 1.5111664301393935e-07 1.040402839588838\n",
      "data_02_0368 1.4209150109826283e-07 1.4524820316105622e-07 1.0222159808179547\n",
      "data_02_0359 1.3720532981184415e-07 1.4209150109826283e-07 1.0356121099167162\n",
      "data_02_0379 1.370664499810907e-07 1.3720532981184415e-07 1.001013229939001\n",
      "data_02_0367 1.3261331246263957e-07 1.370664499810907e-07 1.0335798679314772\n",
      "data_02_0378 1.3251851947293534e-07 1.3261331246263957e-07 1.0007153188103914\n",
      "data_02_0362 1.2827293192504197e-07 1.3251851947293534e-07 1.033098078325475\n",
      "data_02_0372 1.274859702116593e-07 1.2827293192504197e-07 1.0061729279863196\n",
      "data_02_0344 1.2738008290496092e-07 1.274859702116593e-07 1.0008312705117124\n",
      "data_02_0353 1.270493714215481e-07 1.2738008290496092e-07 1.0026030155026546\n",
      "data_02_0345 1.2601084004776139e-07 1.270493714215481e-07 1.0082416034477122\n",
      "data_02_0343 1.216743457485912e-07 1.2601084004776139e-07 1.0356401694415556\n",
      "data_02_0357 1.1836469442237941e-07 1.216743457485912e-07 1.02796147400509\n",
      "data_02_0348 1.1627475678612285e-07 1.1836469442237941e-07 1.0179741303617673\n",
      "data_02_0371 1.1505415954033668e-07 1.1627475678612285e-07 1.0106088928089405\n",
      "data_02_0366 1.144487086545646e-07 1.1505415954033668e-07 1.0052901504341083\n",
      "data_02_0352 1.1166503411858944e-07 1.144487086545646e-07 1.0249287931351803\n",
      "data_02_0356 1.1066798028785224e-07 1.1166503411858944e-07 1.0090094156244998\n",
      "data_02_0377 1.0535396343349566e-07 1.1066798028785224e-07 1.0504396482217875\n",
      "data_02_0351 1.024389106065953e-07 1.0535396343349566e-07 1.0284564996800414\n",
      "data_02_0361 1.015376647151082e-07 1.024389106065953e-07 1.0088759761613169\n",
      "data_02_0347 1.013401128814537e-07 1.015376647151082e-07 1.0019493942530497\n",
      "data_02_0376 9.83230223927681e-08 1.013401128814537e-07 1.030685493745639\n",
      "data_02_0342 9.289928398112562e-08 9.83230223927681e-08 1.0583829947789956\n",
      "data_02_0329 9.16005199927433e-08 9.289928398112562e-08 1.0141785656728282\n",
      "data_02_0346 9.116941174584917e-08 9.16005199927433e-08 1.0047286500882109\n",
      "data_02_0111 9.09158982411786e-08 9.116941174584917e-08 1.002788439751187\n",
      "data_02_0074 9.069441044346989e-08 9.09158982411786e-08 1.0024421328351514\n",
      "data_02_0137 9.032976894843263e-08 9.069441044346989e-08 1.004036781000131\n",
      "data_02_0095 8.745284939511447e-08 9.032976894843263e-08 1.0328968075164728\n",
      "data_02_0148 8.648921735282278e-08 8.745284939511447e-08 1.0111416436844451\n",
      "data_02_0125 8.424420227332979e-08 8.648921735282278e-08 1.0266488971218346\n",
      "data_02_0334 8.326862448084429e-08 8.424420227332979e-08 1.0117160310809497\n",
      "data_02_0112 8.320697701582237e-08 8.326862448084429e-08 1.0007408929783639\n",
      "data_02_0075 8.138269794869593e-08 8.320697701582237e-08 1.02241605541606\n",
      "data_02_0126 8.103535619957762e-08 8.138269794869593e-08 1.0042862987886776\n",
      "data_02_0096 8.045292747184154e-08 8.103535619957762e-08 1.007239372711949\n",
      "data_02_0076 8.031608553391842e-08 8.045292747184154e-08 1.0017037924223202\n",
      "data_02_0341 7.826189678569166e-08 8.031608553391842e-08 1.0262476228227875\n",
      "data_02_0138 7.734409220420543e-08 7.826189678569166e-08 1.0118665117829948\n",
      "data_02_0313 7.682169383541839e-08 7.734409220420543e-08 1.0068001412453391\n",
      "data_02_0097 7.680679419341013e-08 7.682169383541839e-08 1.0001939885939093\n",
      "data_02_0176 7.667729640743856e-08 7.680679419341013e-08 1.0016888673967252\n",
      "data_02_0328 7.592389914455753e-08 7.667729640743856e-08 1.0099230581064675\n",
      "data_02_0113 7.555667819876745e-08 7.592389914455753e-08 1.0048602050082724\n",
      "data_02_0327 7.544624001244896e-08 7.555667819876745e-08 1.001463799737406\n",
      "data_02_0127 7.131653040419096e-08 7.544624001244896e-08 1.0579067655822936\n",
      "data_02_0312 6.911490652182211e-08 7.131653040419096e-08 1.0318545447453324\n",
      "data_02_0139 6.90931164939152e-08 6.911490652182211e-08 1.000315371906966\n",
      "data_02_0340 6.902050869991086e-08 6.90931164939152e-08 1.0010519741939317\n",
      "data_02_0333 6.774036313340146e-08 6.902050869991086e-08 1.018897825569497\n",
      "data_02_0307 6.766457491542204e-08 6.774036313340146e-08 1.0011200575496728\n",
      "data_02_0185 6.763925664301622e-08 6.766457491542204e-08 1.0003743132858398\n",
      "data_02_0150 6.745630599854259e-08 6.763925664301622e-08 1.0027121355337423\n",
      "data_02_0177 6.735831856953238e-08 6.745630599854259e-08 1.0014547190471963\n",
      "data_02_0193 6.662205552089285e-08 6.735831856953238e-08 1.0110513409243076\n",
      "data_02_0208 6.626147902975876e-08 6.662205552089285e-08 1.0054417211389464\n",
      "data_02_0098 6.59226867430583e-08 6.626147902975876e-08 1.0051392366336789\n",
      "data_02_0302 6.582977316743616e-08 6.59226867430583e-08 1.0014114217800176\n",
      "data_02_0140 6.576202833197386e-08 6.582977316743616e-08 1.001030151246557\n",
      "data_02_0077 6.529938573091631e-08 6.576202833197386e-08 1.0070849456833177\n",
      "data_02_0201 6.303184379764337e-08 6.529938573091631e-08 1.0359745455099272\n",
      "data_02_0114 6.245919214643206e-08 6.303184379764337e-08 1.009168412711275\n",
      "data_02_0318 6.239184702937052e-08 6.245919214643206e-08 1.0010793897002255\n",
      "data_02_0178 6.168572229146186e-08 6.239184702937052e-08 1.0114471341451146\n",
      "data_02_0326 6.156213199765569e-08 6.168572229146186e-08 1.0020075700726363\n",
      "data_02_0215 6.154481021597962e-08 6.156213199765569e-08 1.0002814499161712\n",
      "data_02_0311 6.138471473893103e-08 6.154481021597962e-08 1.0026080674599447\n",
      "data_02_0099 6.073035821178917e-08 6.138471473893103e-08 1.0107747845790713\n",
      "data_02_0301 6.069712736926435e-08 6.073035821178917e-08 1.0005474862479513\n",
      "data_02_0186 6.06185298751725e-08 6.069712736926435e-08 1.001296591887888\n",
      "data_02_0078 6.055512834877297e-08 6.06185298751725e-08 1.001047005070064\n",
      "data_02_0079 5.949127153350898e-08 6.055512834877297e-08 1.0178825697928604\n",
      "data_02_0306 5.912441694744221e-08 5.949127153350898e-08 1.006204789916032\n",
      "data_02_0332 5.727315593013976e-08 5.912441694744221e-08 1.0323233631399773\n",
      "data_02_0141 5.719417876988802e-08 5.727315593013976e-08 1.0013808601146192\n",
      "data_02_0115 5.7162648432152425e-08 5.719417876988802e-08 1.0005515898685664\n",
      "data_02_0128 5.695548290502778e-08 5.7162648432152425e-08 1.003637323687872\n",
      "data_02_0168 5.679705522484517e-08 5.695548290502778e-08 1.0027893643350951\n",
      "data_02_0100 5.659624831555914e-08 5.679705522484517e-08 1.0035480604326705\n",
      "data_02_0216 5.525232626983928e-08 5.659624831555914e-08 1.0243233567968968\n",
      "data_02_0217 5.4764481465418027e-08 5.525232626983928e-08 1.0089080511924378\n",
      "data_02_0278 5.440683377108302e-08 5.4764481465418027e-08 1.0065735803674922\n",
      "data_02_0194 5.33628932458377e-08 5.440683377108302e-08 1.0195630420643795\n",
      "data_02_0169 5.3331628241844825e-08 5.33628932458377e-08 1.0005862375671544\n",
      "data_02_0325 5.3061665141943205e-08 5.3331628241844825e-08 1.0050877238620284\n",
      "data_02_0284 5.276552648610089e-08 5.3061665141943205e-08 1.0056123510095236\n",
      "data_02_0151 5.2367054005389306e-08 5.276552648610089e-08 1.0076092208790393\n",
      "data_02_0116 5.235341210012136e-08 5.2367054005389306e-08 1.0002605733746992\n",
      "data_02_0129 5.1837395523233295e-08 5.235341210012136e-08 1.0099545235959393\n",
      "data_02_0209 5.18370655523612e-08 5.1837395523233295e-08 1.0000063655391866\n",
      "data_02_0101 5.166973621609745e-08 5.18370655523612e-08 1.0032384399170131\n",
      "data_02_0331 5.107872558725756e-08 5.166973621609745e-08 1.011570582900121\n",
      "data_02_0117 5.101371818979342e-08 5.107872558725756e-08 1.001274312082532\n",
      "data_02_0170 4.9929646401594325e-08 5.101371818979342e-08 1.0217119860909827\n",
      "data_02_0202 4.979995439523293e-08 4.9929646401594325e-08 1.0026042595407236\n",
      "data_02_0187 4.912204765516747e-08 4.979995439523293e-08 1.0138004576849953\n",
      "data_02_0195 4.909544260249187e-08 4.912204765516747e-08 1.000541904732197\n",
      "data_02_0179 4.884857389359304e-08 4.909544260249187e-08 1.0050537546794422\n",
      "data_02_0152 4.8736577849243335e-08 4.884857389359304e-08 1.0022979874519737\n",
      "data_02_0338 4.863240901971703e-08 4.8736577849243335e-08 1.0021419631810562\n",
      "data_02_0080 4.7763667151372616e-08 4.863240901971703e-08 1.0181883410583028\n",
      "data_02_0300 4.7307711300649533e-08 4.7763667151372616e-08 1.0096380872840243\n",
      "data_02_0289 4.716164970271535e-08 4.7307711300649533e-08 1.0030970417458864\n",
      "data_02_0305 4.694853706025366e-08 4.716164970271535e-08 1.0045392818563905\n",
      "data_02_0130 4.6783797253514985e-08 4.694853706025366e-08 1.003521300458917\n",
      "data_02_0317 4.67657228043495e-08 4.6783797253514985e-08 1.0003864892507082\n",
      "data_02_0142 4.632766607135677e-08 4.67657228043495e-08 1.0094556184271837\n",
      "data_02_0203 4.572492026730186e-08 4.632766607135677e-08 1.013181997924356\n",
      "data_02_0180 4.5156858808528775e-08 4.572492026730186e-08 1.0125797381341723\n",
      "data_02_0236 4.490529842713039e-08 4.5156858808528775e-08 1.0056020200334845\n",
      "data_02_0210 4.400391313944352e-08 4.490529842713039e-08 1.0204842075028755\n",
      "data_02_0081 4.364523998775825e-08 4.400391313944352e-08 1.0082179213995814\n",
      "data_02_0131 4.354491436440689e-08 4.364523998775825e-08 1.0023039573005421\n",
      "data_02_0310 4.337830735352795e-08 4.354491436440689e-08 1.0038407909631215\n",
      "data_02_0153 4.31781464846463e-08 4.337830735352795e-08 1.0046356984997684\n",
      "data_02_0339 4.314212949863592e-08 4.31781464846463e-08 1.0008348448819966\n",
      "data_02_0196 4.23775505358902e-08 4.314212949863592e-08 1.0180420754167514\n",
      "data_02_0188 4.2228755701222894e-08 4.23775505358902e-08 1.0035235429554226\n",
      "data_02_0229 4.2089051413528255e-08 4.2228755701222894e-08 1.0033192548418834\n",
      "data_02_0290 4.153886120248492e-08 4.2089051413528255e-08 1.0132451924563215\n",
      "data_02_0172 4.115914233205105e-08 4.153886120248492e-08 1.0092256264081134\n",
      "data_02_0102 4.110162871665345e-08 4.115914233205105e-08 1.0013993025871089\n",
      "data_02_0330 4.090697306450536e-08 4.110162871665345e-08 1.0047584956198332\n",
      "data_02_0337 4.052173813148013e-08 4.090697306450536e-08 1.0095068708991521\n",
      "data_02_0309 3.9744054468036604e-08 4.052173813148013e-08 1.0195672956333373\n",
      "data_02_0232 3.958823021675607e-08 3.9744054468036604e-08 1.003936125722907\n",
      "data_02_0171 3.9523838882891047e-08 3.958823021675607e-08 1.00162917711652\n",
      "data_02_0230 3.9452295301997494e-08 3.9523838882891047e-08 1.0018134199885178\n",
      "data_02_0242 3.9216491298397226e-08 3.9452295301997494e-08 1.0060128786587774\n",
      "data_02_0316 3.909765321726841e-08 3.9216491298397226e-08 1.003039519545289\n",
      "data_02_0239 3.897254103689109e-08 3.909765321726841e-08 1.0032102648954526\n",
      "data_02_0237 3.875842749117757e-08 3.897254103689109e-08 1.005524309410186\n",
      "data_02_0299 3.859061375036245e-08 3.875842749117757e-08 1.0043485636663019\n",
      "data_02_0324 3.826965217976124e-08 3.859061375036245e-08 1.0083868431595244\n",
      "data_02_0249 3.810910320273194e-08 3.826965217976124e-08 1.0042128773320962\n",
      "data_02_0143 3.8107526237647436e-08 3.810910320273194e-08 1.0000413819851404\n",
      "data_02_0118 3.810108545745867e-08 3.8107526237647436e-08 1.0001690445327591\n",
      "data_02_0189 3.8002440706154374e-08 3.810108545745867e-08 1.0025957477854395\n",
      "data_02_0082 3.774230173608561e-08 3.8002440706154374e-08 1.0068925041161452\n",
      "data_02_0238 3.7560085646901294e-08 3.774230173608561e-08 1.004851322515537\n",
      "data_02_0323 3.7515177979767095e-08 3.7560085646901294e-08 1.0011970532875631\n",
      "data_02_0181 3.70626977416921e-08 3.7515177979767095e-08 1.0122085078973082\n",
      "data_02_0231 3.682097870952214e-08 3.70626977416921e-08 1.0065647095933234\n",
      "data_02_0103 3.677844957399585e-08 3.682097870952214e-08 1.0011563602060147\n",
      "data_02_0255 3.628478587594827e-08 3.677844957399585e-08 1.013605253169616\n",
      "data_02_0204 3.616482722772049e-08 3.628478587594827e-08 1.003316997685968\n",
      "data_02_0272 3.599235964365634e-08 3.616482722772049e-08 1.0047917831943132\n",
      "data_02_0154 3.593898072303098e-08 3.599235964365634e-08 1.001485265289985\n",
      "data_02_0083 3.5895020334709865e-08 3.593898072303098e-08 1.0012246932279518\n",
      "data_02_0315 3.5408373834389536e-08 3.5895020334709865e-08 1.0137438251922115\n",
      "data_02_0271 3.540525678042462e-08 3.5408373834389536e-08 1.0000880392983518\n",
      "data_02_0132 3.539145384780218e-08 3.540525678042462e-08 1.0003900075052525\n",
      "data_02_0211 3.5318224781573855e-08 3.539145384780218e-08 1.0020734073323676\n",
      "data_02_0283 3.508224638393394e-08 3.5318224781573855e-08 1.0067264335087729\n",
      "data_02_0304 3.46939405835159e-08 3.508224638393394e-08 1.0111923233246825\n",
      "data_02_0266 3.438015300024862e-08 3.46939405835159e-08 1.009126997871854\n",
      "data_02_0282 3.4240456183147185e-08 3.438015300024862e-08 1.004079876049379\n",
      "data_02_0308 3.402620683267746e-08 3.4240456183147185e-08 1.0062965981345875\n",
      "data_02_0259 3.3943817851300015e-08 3.402620683267746e-08 1.0024272161056949\n",
      "data_02_0119 3.3927967732563694e-08 3.3943817851300015e-08 1.0004671697067522\n",
      "data_02_0253 3.3906643227995556e-08 3.3927967732563694e-08 1.0006289181864672\n",
      "data_02_0197 3.380954203638034e-08 3.3906643227995556e-08 1.0028720055276328\n",
      "data_02_0251 3.359311746412807e-08 3.380954203638034e-08 1.0064425271778772\n",
      "data_02_0144 3.324769307344446e-08 3.359311746412807e-08 1.0103894243104496\n",
      "data_02_0182 3.304671658618233e-08 3.324769307344446e-08 1.006081587159741\n",
      "data_02_0336 3.2916902236036773e-08 3.304671658618233e-08 1.0039436988697994\n",
      "data_02_0264 3.273171534614387e-08 3.2916902236036773e-08 1.0056577202854944\n",
      "data_02_0104 3.252847857592453e-08 3.273171534614387e-08 1.0062479642183375\n",
      "data_02_0298 3.2495936136271714e-08 3.252847857592453e-08 1.0010014310563744\n",
      "data_02_0250 3.2424764651418045e-08 3.2495936136271714e-08 1.0021949730589812\n",
      "data_02_0256 3.2363282384610147e-08 3.2424764651418045e-08 1.0018997537418248\n",
      "data_02_0173 3.233329242773447e-08 3.2363282384610147e-08 1.0009275256128867\n",
      "data_02_0258 3.226936799352002e-08 3.233329242773447e-08 1.0019809633156522\n",
      "data_02_0243 3.217054250479955e-08 3.226936799352002e-08 1.0030719248426017\n",
      "data_02_0190 3.214097411475764e-08 3.217054250479955e-08 1.0009199593620386\n",
      "data_02_0262 3.1932167695326754e-08 3.214097411475764e-08 1.0065390618458214\n",
      "data_02_0269 3.1518173981986e-08 3.1932167695326754e-08 1.0131350792586324\n",
      "data_02_0120 3.1505053151953135e-08 3.1518173981986e-08 1.0004164674780767\n",
      "data_02_0191 3.141979540670374e-08 3.1505053151953135e-08 1.0027135041506732\n",
      "data_02_0261 3.124495944635774e-08 3.141979540670374e-08 1.0055956532971715\n",
      "data_02_0218 3.055803606392995e-08 3.124495944635774e-08 1.022479304003395\n",
      "data_02_0252 3.018652396532365e-08 3.055803606392995e-08 1.0123072169234548\n",
      "data_02_0322 3.011743875604037e-08 3.018652396532365e-08 1.0022938607045202\n",
      "data_02_0314 3.0097930406807425e-08 3.011743875604037e-08 1.0006481624805847\n",
      "data_02_0265 3.00515531663903e-08 3.0097930406807425e-08 1.0015432560227533\n",
      "data_02_0268 2.9905630412435414e-08 3.00515531663903e-08 1.004879440825772\n",
      "data_02_0155 2.979168606021535e-08 2.9905630412435414e-08 1.0038247030392895\n",
      "data_02_0267 2.959674115223327e-08 2.979168606021535e-08 1.0065867017919088\n",
      "data_02_0198 2.8804746879771473e-08 2.959674115223327e-08 1.027495269295978\n",
      "data_02_0084 2.8541798370207324e-08 2.8804746879771473e-08 1.0092127519840735\n",
      "data_02_0303 2.8462045177166852e-08 2.8541798370207324e-08 1.0028020893278762\n",
      "data_02_0257 2.84619849607659e-08 2.8462045177166852e-08 1.0000021156781944\n",
      "data_02_0281 2.844564031443896e-08 2.84619849607659e-08 1.0005745923152465\n",
      "data_02_0219 2.8276171686765055e-08 2.844564031443896e-08 1.0059933370596001\n",
      "data_02_0174 2.7830204276391705e-08 2.8276171686765055e-08 1.0160245827139567\n",
      "data_02_0212 2.7693017298617995e-08 2.7830204276391705e-08 1.0049538472566713\n",
      "data_02_0133 2.7647612743784636e-08 2.7693017298617995e-08 1.0016422595055179\n",
      "data_02_0297 2.742399964524251e-08 2.7647612743784636e-08 1.0081539199764729\n",
      "data_02_0205 2.7192009285789882e-08 2.742399964524251e-08 1.0085315637036747\n",
      "data_02_0321 2.6960862044686667e-08 2.7192009285789882e-08 1.0085734365881958\n",
      "data_02_0277 2.6917629218358815e-08 2.6960862044686667e-08 1.001606115678953\n",
      "data_02_0105 2.6824132024842366e-08 2.6917629218358815e-08 1.0034855626802708\n",
      "data_02_0199 2.6596840541936688e-08 2.6824132024842366e-08 1.0085458076325755\n",
      "data_02_0145 2.6322012612096534e-08 2.6596840541936688e-08 1.010440992255807\n",
      "data_02_0134 2.588661423462738e-08 2.6322012612096534e-08 1.0168194408709788\n",
      "data_02_0244 2.5860850926336772e-08 2.588661423462738e-08 1.0009962281737748\n",
      "data_02_0288 2.5814316096620205e-08 2.5860850926336772e-08 1.001802675288487\n",
      "data_02_0280 2.5567500765371216e-08 2.5814316096620205e-08 1.0096534789815388\n",
      "data_02_0200 2.5161849095525086e-08 2.5567500765371216e-08 1.0161216955203134\n",
      "data_02_0286 2.5066600585479665e-08 2.5161849095525086e-08 1.003799817598745\n",
      "data_02_0085 2.5060021490244493e-08 2.5066600585479665e-08 1.0002625335033226\n",
      "data_02_0270 2.5014775517562304e-08 2.5060021490244493e-08 1.0018087698868383\n",
      "data_02_0263 2.4817548038766007e-08 2.5014775517562304e-08 1.0079470976942693\n",
      "data_02_0156 2.4613448459163313e-08 2.4817548038766007e-08 1.0082921976553314\n",
      "data_02_0276 2.4560452898896405e-08 2.4613448459163313e-08 1.0021577598949443\n",
      "data_02_0285 2.4538549254818378e-08 2.4560452898896405e-08 1.0008926218029668\n",
      "data_02_0121 2.4058596989283354e-08 2.4538549254818378e-08 1.0199493040158916\n",
      "data_02_0296 2.405839871997145e-08 2.4058596989283354e-08 1.0000082411682594\n",
      "data_02_0207 2.3903476052081983e-08 2.405839871997145e-08 1.0064811773631548\n",
      "data_02_0449 2.3732450458975782e-08 2.3903476052081983e-08 1.0072064026174556\n",
      "data_02_0275 2.3517628219451086e-08 2.3732450458975782e-08 1.0091345197534427\n",
      "data_02_0183 2.3500585579617803e-08 2.3517628219451086e-08 1.000725200645556\n",
      "data_02_0206 2.3061826926576378e-08 2.3500585579617803e-08 1.0190253206928632\n",
      "data_02_0220 2.2994985104397943e-08 2.3061826926576378e-08 1.0029067999772547\n",
      "data_02_0146 2.2740994949298897e-08 2.2994985104397943e-08 1.0111688233371194\n",
      "data_02_0221 2.2573021854433983e-08 2.2740994949298897e-08 1.0074413207034538\n",
      "data_02_0295 2.2222325010870083e-08 2.2573021854433983e-08 1.0157812849642132\n",
      "data_02_0106 2.2203302797213507e-08 2.2222325010870083e-08 1.0008567290114587\n",
      "data_02_0287 2.1985349022305534e-08 2.2203302797213507e-08 1.0099135917599873\n",
      "data_02_0335 2.1740527315368754e-08 2.1985349022305534e-08 1.0112610749217528\n",
      "data_02_0157 2.1600870527428213e-08 2.1740527315368754e-08 1.0064653314672298\n",
      "data_02_0086 2.1281183987317782e-08 2.1600870527428213e-08 1.0150220279238666\n",
      "data_02_0135 2.060532963668915e-08 2.1281183987317782e-08 1.032799977605077\n",
      "data_02_0245 2.021471536951278e-08 2.060532963668915e-08 1.0193232632780713\n",
      "data_02_0147 2.0122437868152768e-08 2.021471536951278e-08 1.0045858012813675\n",
      "data_02_0122 2.0049348713409756e-08 2.0122437868152768e-08 1.0036454627922216\n",
      "data_02_0107 1.9387318513901423e-08 2.0049348713409756e-08 1.0341475897780104\n",
      "data_02_0184 1.932982128871685e-08 1.9387318513901423e-08 1.0029745347525865\n",
      "data_02_0233 1.9242217938137572e-08 1.932982128871685e-08 1.004552663880064\n",
      "data_02_0175 1.9131995590831726e-08 1.9242217938137572e-08 1.0057611526608685\n",
      "data_02_0192 1.906093179977536e-08 1.9131995590831726e-08 1.0037282432885681\n",
      "data_02_0214 1.866853198836985e-08 1.906093179977536e-08 1.0210193180508231\n",
      "data_02_0246 1.8085096524630115e-08 1.866853198836985e-08 1.0322605667570064\n",
      "data_02_0224 1.7865147451066153e-08 1.8085096524630115e-08 1.0123116293423504\n",
      "data_02_0123 1.778411411472574e-08 1.7865147451066153e-08 1.0045565011457789\n",
      "data_02_0234 1.7743073771607667e-08 1.778411411472574e-08 1.0023130345759903\n",
      "data_02_0087 1.768951320824764e-08 1.7743073771607667e-08 1.0030278144304758\n",
      "data_02_0136 1.6856225078377287e-08 1.768951320824764e-08 1.0494350381533095\n",
      "data_02_0222 1.685223922581369e-08 1.6856225078377287e-08 1.0002365176823202\n",
      "data_02_0223 1.6841136609881915e-08 1.685223922581369e-08 1.000659255737244\n",
      "data_02_0108 1.6692846185776305e-08 1.6841136609881915e-08 1.0088834715455515\n",
      "data_02_0124 1.649784658805764e-08 1.6692846185776305e-08 1.0118197000243547\n",
      "data_02_0274 1.6468218420670362e-08 1.649784658805764e-08 1.0017991118790415\n",
      "data_02_0158 1.609224391228375e-08 1.6468218420670362e-08 1.0233637092773382\n",
      "data_02_0240 1.5068601573472125e-08 1.609224391228375e-08 1.0679321391451295\n",
      "data_02_0320 1.5036954071778313e-08 1.5068601573472125e-08 1.0021046484243248\n",
      "data_02_0247 1.4173929697596535e-08 1.5036954071778313e-08 1.0608881511757549\n",
      "data_02_0248 1.4169017637897149e-08 1.4173929697596535e-08 1.0003466760946256\n",
      "data_02_0088 1.387531962674457e-08 1.4169017637897149e-08 1.0211669366222367\n",
      "data_02_0235 1.3468344564201617e-08 1.387531962674457e-08 1.0302171555385269\n",
      "data_02_0160 1.3120041044574954e-08 1.3468344564201617e-08 1.0265474413108397\n",
      "data_02_0161 1.2298607229417375e-08 1.3120041044574954e-08 1.0667907999527597\n",
      "data_02_0260 1.2167259676309402e-08 1.2298607229417375e-08 1.0107951631346963\n",
      "data_02_0159 1.1999983455730615e-08 1.2167259676309402e-08 1.0139397042667508\n",
      "data_02_0294 1.1975930661632139e-08 1.1999983455730615e-08 1.0020084279692378\n",
      "data_02_0273 1.1970561201325617e-08 1.1975930661632139e-08 1.000448555436643\n",
      "data_02_0241 1.1800054102163205e-08 1.1970561201325617e-08 1.0144496879155118\n",
      "data_02_0109 1.1756306999668193e-08 1.1800054102163205e-08 1.0037211602671015\n",
      "data_02_0254 1.1681362011672839e-08 1.1756306999668193e-08 1.0064157747975333\n",
      "data_02_0089 1.0944841611492818e-08 1.1681362011672839e-08 1.0672938381681671\n",
      "data_02_0319 1.0710357720931692e-08 1.0944841611492818e-08 1.0218931894406165\n",
      "data_02_0291 9.6476348944768e-09 1.0710357720931692e-08 1.1101537151932743\n",
      "data_02_0110 9.297536383721325e-09 9.6476348944768e-09 1.0376549761470628\n",
      "data_02_0090 8.946140193618794e-09 9.297536383721325e-09 1.0392790837721477\n",
      "data_02_0440 8.921394731124998e-09 8.946140193618794e-09 1.0027737212890564\n",
      "data_02_0292 8.845594599634719e-09 8.921394731124998e-09 1.0085692522573224\n",
      "data_02_0293 8.013187887764542e-09 8.845594599634719e-09 1.1038795949289035\n",
      "data_02_0091 5.400193227259109e-09 8.013187887764542e-09 1.4838705858367347\n",
      "376\n"
     ]
    }
   ],
   "source": [
    "print(gonio0.chi2())\n",
    "print(len(search_outliers(0)))\n",
    "# for lbl in search_outliers(7):\n",
    "#     gonio7.single_geometries.pop(lbl)\n",
    "# gonio7.refine2()\n",
    "# gonio7 = complete_gonio(module_id=7)\n",
    "# gonio7.refine2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overlay of the different results\n",
    "\n",
    "We are getting to an end. Here are the first actually integrated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:40:45.251947Z",
     "iopub.status.busy": "2026-09-15T09:40:45.251856Z",
     "iopub.status.idle": "2026-09-15T09:41:04.759037Z",
     "shell.execute_reply": "2026-09-15T09:41:04.757040Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/3999147500.py:22: RuntimeWarning: invalid value encountered in divide\n",
      "  ax.plot(radial, summed/counted, label=\"Merged\")\n",
      "/tmp/ipykernel_3717616/3999147500.py:24: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "summed, counted, radial = None, None, None\n",
    "\n",
    "for i in range(9):\n",
    "    name = ds_names[i]\n",
    "    ds = data[name]\n",
    "    gonioref = goniometers[name]\n",
    "    mg = gonioref.get_mg(position)\n",
    "    mg.radial_range = (0, 95)\n",
    "    images = [i.reshape(-1, 1) for i in ds]\n",
    "    res_mg = mg.integrate1d(images, 50000)\n",
    "    results[name] = res_mg    \n",
    "    if summed is None:\n",
    "        summed = res_mg.sum\n",
    "        counted = res_mg.count\n",
    "    else:\n",
    "        summed += res_mg.sum\n",
    "        counted += res_mg.count\n",
    "    radial = res_mg.radial\n",
    "    jupyter.plot1d(res_mg, label=\"%i %s\"%(i, name), calibrant=LaB6, ax=ax )\n",
    "    \n",
    "ax.plot(radial, summed/counted, label=\"Merged\")\n",
    "ax.legend()\n",
    "fig.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Gonio fit\n",
    "\n",
    "Can we fit everything together?\n",
    "Just assume energy and scale parameter of the goniometer are the same for all modules and fit everything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:41:04.760891Z",
     "iopub.status.busy": "2026-09-15T09:41:04.760785Z",
     "iopub.status.idle": "2026-09-15T09:41:04.768431Z",
     "shell.execute_reply": "2026-09-15T09:41:04.767000Z"
    }
   },
   "outputs": [],
   "source": [
    "class MultiGoniometer:\n",
    "    def __init__(self, list_of_goniometers,\n",
    "                param_name_split,\n",
    "                param_name_common):\n",
    "        self.nb_gonio = len(list_of_goniometers)\n",
    "        self.goniometers = list_of_goniometers\n",
    "        self.names_split = param_name_split\n",
    "        self.names_common = param_name_common\n",
    "        self.param = None\n",
    "        \n",
    "    def init_param(self):\n",
    "        param = []\n",
    "        for gonio in self.goniometers:\n",
    "            param += list(gonio.param[:len(self.names_split)])\n",
    "        param += list(gonio.param[len(self.names_split):])\n",
    "        self.param = numpy.array(param)\n",
    "    \n",
    "    def residu2(self, param):\n",
    "        \"Actually performs the calulation of the average of the error squared\"\n",
    "        sumsquare = 0.0\n",
    "        for idx, gonio in enumerate(self.goniometers):\n",
    "            gonio_param = numpy.concatenate((param[len(self.names_split)*idx:len(self.names_split)*(1+idx)],\n",
    "                                             param[len(self.names_split)*len(self.goniometers):]))\n",
    "            sumsquare += gonio.residu2(gonio_param)\n",
    "        return sumsquare\n",
    "\n",
    "    def chi2(self, param=None):\n",
    "        \"\"\"Calculate the average of the square of the error for a given parameter set\n",
    "        \"\"\"\n",
    "        if param is not None:\n",
    "            return self.residu2(param)\n",
    "        else:\n",
    "            if self.param is None:\n",
    "                self.init_param()\n",
    "            return self.residu2(self.param)\n",
    "    def refine2(self, method=\"slsqp\", **options):\n",
    "        \"\"\"Geometry refinement tool\n",
    "\n",
    "        See https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.minimize.html\n",
    "\n",
    "        :param method: name of the minimizer\n",
    "        :param options: options for the minimizer\n",
    "        \"\"\"\n",
    "        if method.lower() in [\"simplex\", \"nelder-mead\"]:\n",
    "            method = \"Nelder-Mead\"\n",
    "\n",
    "        former_error = self.chi2()\n",
    "        print(\"Cost function before refinement: {}\".format(former_error))\n",
    "        param = numpy.asarray(self.param, dtype=numpy.float64)\n",
    "        print(param)\n",
    "        res = minimize(self.residu2, param, method=method,\n",
    "                       tol=1e-12,\n",
    "                       options=options)\n",
    "        print(res)\n",
    "        newparam = res.x\n",
    "        new_error = res.fun\n",
    "        print(\"Cost function after refinement: {}\".format(new_error))\n",
    "\n",
    "        if new_error < former_error:\n",
    "            self.param = newparam\n",
    "        return self.param\n",
    "    \n",
    "    def integrate(self, list_of_dataset, npt=50000, radial_range=(0,100)):\n",
    "        summed = None\n",
    "        counted = None\n",
    "        param = self.param\n",
    "        for idx, ds in enumerate(list_of_dataset):\n",
    "            gonio = self.goniometers[idx]\n",
    "            gonio_param = numpy.concatenate((param[len(self.names_split)*idx:len(self.names_split)*(1+idx)],\n",
    "                                             param[len(self.names_split)*len(self.goniometers):]))\n",
    "            print(gonio_param)\n",
    "            gonio.param = gonio_param\n",
    "            mg = gonio.get_mg(position)\n",
    "            mg.radial_range = radial_range\n",
    "            images = [i.reshape(-1, 1) for i in ds]\n",
    "            res_mg = mg.integrate1d(images, 50000)\n",
    "            if summed is None:\n",
    "                summed = res_mg.sum\n",
    "                counted = res_mg.count\n",
    "            else:\n",
    "                summed += res_mg.sum\n",
    "                counted += res_mg.count\n",
    "            radial = res_mg.radial\n",
    "        res = Integrate1dResult(radial, summed/numpy.maximum(counted, 1e-10))\n",
    "        res._set_unit(res_mg.unit)\n",
    "        res._set_count(counted)\n",
    "        res._set_sum(summed)\n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:41:04.769832Z",
     "iopub.status.busy": "2026-09-15T09:41:04.769737Z",
     "iopub.status.idle": "2026-09-15T09:41:04.772757Z",
     "shell.execute_reply": "2026-09-15T09:41:04.771382Z"
    }
   },
   "outputs": [],
   "source": [
    "multigonio = MultiGoniometer([goniometers[ds_names[i]] for i in range(9)],\n",
    "                 [\"dist\", \"poni1\", \"poni2\", \"rot1\", \"offset\"], \n",
    "                [\"scale\", \"nrj\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:41:04.774262Z",
     "iopub.status.busy": "2026-09-15T09:41:04.774153Z",
     "iopub.status.idle": "2026-09-15T09:41:05.292328Z",
     "shell.execute_reply": "2026-09-15T09:41:05.290542Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.603024734828662e-09\n",
      "CPU times: user 236 ms, sys: 16 ms, total: 252 ms\n",
      "Wall time: 254 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.139505955765795e-09\n",
      "CPU times: user 232 ms, sys: 9.88 ms, total: 242 ms\n",
      "Wall time: 243 ms\n"
     ]
    }
   ],
   "source": [
    "%time print(multigonio.chi2())\n",
    "multigonio.param = numpy.array([ 7.20594053e-01,  3.22408604e-02,  4.05228023e-03, -2.75578440e-05,\n",
    "       -8.27999414e+01,  7.20612302e-01,  3.36369797e-02,  4.02094516e-03,\n",
    "       -1.74996556e-05, -7.71999791e+01,  7.20636130e-01,  3.47920978e-02,\n",
    "        4.01341931e-03, -1.21330600e-05, -7.15999090e+01,  7.20757808e-01,\n",
    "        3.33850817e-02,  3.95036100e-03,  3.46517345e-05, -6.57999267e+01,\n",
    "        7.20813915e-01,  3.22167822e-02,  3.97128822e-03,  2.00055269e-05,\n",
    "       -6.00000525e+01,  7.20881596e-01,  3.33801850e-02,  3.97760147e-03,\n",
    "        1.47074593e-05, -5.43998157e+01,  7.21048510e-01,  3.22346939e-02,\n",
    "        4.02104962e-03, -1.69519259e-05, -4.85998856e+01,  7.21074630e-01,\n",
    "        3.08484557e-02,  4.09385968e-03, -6.91378973e-05, -4.27999030e+01,\n",
    "        7.21154891e-01,  3.20619921e-02,  4.24950906e-03, -1.81328256e-04,\n",
    "       -3.71999987e+01,  9.99038595e-01,  1.70266104e+01])\n",
    "%time print(multigonio.chi2())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:41:05.294170Z",
     "iopub.status.busy": "2026-09-15T09:41:05.294073Z",
     "iopub.status.idle": "2026-09-15T09:41:17.008836Z",
     "shell.execute_reply": "2026-09-15T09:41:17.007096Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost function before refinement: 6.139505955765795e-09\n",
      "[ 7.20594053e-01  3.22408604e-02  4.05228023e-03 -2.75578440e-05\n",
      " -8.27999414e+01  7.20612302e-01  3.36369797e-02  4.02094516e-03\n",
      " -1.74996556e-05 -7.71999791e+01  7.20636130e-01  3.47920978e-02\n",
      "  4.01341931e-03 -1.21330600e-05 -7.15999090e+01  7.20757808e-01\n",
      "  3.33850817e-02  3.95036100e-03  3.46517345e-05 -6.57999267e+01\n",
      "  7.20813915e-01  3.22167822e-02  3.97128822e-03  2.00055269e-05\n",
      " -6.00000525e+01  7.20881596e-01  3.33801850e-02  3.97760147e-03\n",
      "  1.47074593e-05 -5.43998157e+01  7.21048510e-01  3.22346939e-02\n",
      "  4.02104962e-03 -1.69519259e-05 -4.85998856e+01  7.21074630e-01\n",
      "  3.08484557e-02  4.09385968e-03 -6.91378973e-05 -4.27999030e+01\n",
      "  7.21154891e-01  3.20619921e-02  4.24950906e-03 -1.81328256e-04\n",
      " -3.71999987e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     message: Optimization terminated successfully\n",
      "     success: True\n",
      "      status: 0\n",
      "         fun: 6.139505955765795e-09\n",
      "           x: [ 7.206e-01  3.224e-02 ...  9.990e-01  1.703e+01]\n",
      "         nit: 1\n",
      "         jac: [ 5.910e-08 -8.339e-08 ... -4.210e-08  6.458e-07]\n",
      "        nfev: 48\n",
      "        njev: 1\n",
      " multipliers: []\n",
      "Cost function after refinement: 6.139505955765795e-09\n",
      "CPU times: user 11.3 s, sys: 355 ms, total: 11.7 s\n",
      "Wall time: 11.7 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.20594053e-01,  3.22408604e-02,  4.05228023e-03, -2.75578440e-05,\n",
       "       -8.27999414e+01,  7.20612302e-01,  3.36369797e-02,  4.02094516e-03,\n",
       "       -1.74996556e-05, -7.71999791e+01,  7.20636130e-01,  3.47920978e-02,\n",
       "        4.01341931e-03, -1.21330600e-05, -7.15999090e+01,  7.20757808e-01,\n",
       "        3.33850817e-02,  3.95036100e-03,  3.46517345e-05, -6.57999267e+01,\n",
       "        7.20813915e-01,  3.22167822e-02,  3.97128822e-03,  2.00055269e-05,\n",
       "       -6.00000525e+01,  7.20881596e-01,  3.33801850e-02,  3.97760147e-03,\n",
       "        1.47074593e-05, -5.43998157e+01,  7.21048510e-01,  3.22346939e-02,\n",
       "        4.02104962e-03, -1.69519259e-05, -4.85998856e+01,  7.21074630e-01,\n",
       "        3.08484557e-02,  4.09385968e-03, -6.91378973e-05, -4.27999030e+01,\n",
       "        7.21154891e-01,  3.20619921e-02,  4.24950906e-03, -1.81328256e-04,\n",
       "       -3.71999987e+01,  9.99038595e-01,  1.70266104e+01])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time multigonio.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:41:17.011102Z",
     "iopub.status.busy": "2026-09-15T09:41:17.010997Z",
     "iopub.status.idle": "2026-09-15T09:41:17.020124Z",
     "shell.execute_reply": "2026-09-15T09:41:17.018487Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LaB6 Calibrant with 109 reflections at wavelength 7.281789822635113e-11 \n",
      " LaB6 Calibrant with 109 reflections at wavelength 7.281789829007909e-11\n"
     ]
    }
   ],
   "source": [
    "LaB6_new = get_calibrant(\"LaB6\")\n",
    "LaB6_new.wavelength = 1e-10*hc/multigonio.param[-1]\n",
    "print(LaB6,\"\\n\", LaB6_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:41:17.021581Z",
     "iopub.status.busy": "2026-09-15T09:41:17.021485Z",
     "iopub.status.idle": "2026-09-15T09:41:32.428885Z",
     "shell.execute_reply": "2026-09-15T09:41:32.427273Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.20594053e-01  3.22408604e-02  4.05228023e-03 -2.75578440e-05\n",
      " -8.27999414e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.20612302e-01  3.36369797e-02  4.02094516e-03 -1.74996556e-05\n",
      " -7.71999791e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.20636130e-01  3.47920978e-02  4.01341931e-03 -1.21330600e-05\n",
      " -7.15999090e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.20757808e-01  3.33850817e-02  3.95036100e-03  3.46517345e-05\n",
      " -6.57999267e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.20813915e-01  3.22167822e-02  3.97128822e-03  2.00055269e-05\n",
      " -6.00000525e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.20881596e-01  3.33801850e-02  3.97760147e-03  1.47074593e-05\n",
      " -5.43998157e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.21048510e-01  3.22346939e-02  4.02104962e-03 -1.69519259e-05\n",
      " -4.85998856e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.21074630e-01  3.08484557e-02  4.09385968e-03 -6.91378973e-05\n",
      " -4.27999030e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.21154891e-01  3.20619921e-02  4.24950906e-03 -1.81328256e-04\n",
      " -3.71999987e+01  9.99038595e-01  1.70266104e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.3 s, sys: 1min 12s, total: 1min 29s\n",
      "Wall time: 15.4 s\n"
     ]
    }
   ],
   "source": [
    "%time res = multigonio.integrate([data[ds_names[i]] for i in range(9)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:41:32.430570Z",
     "iopub.status.busy": "2026-09-15T09:41:32.430468Z",
     "iopub.status.idle": "2026-09-15T09:41:32.615481Z",
     "shell.execute_reply": "2026-09-15T09:41:32.613511Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/1083240546.py:2: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  ax.figure.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = jupyter.plot1d(res, calibrant=LaB6_new)\n",
    "ax.figure.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:41:32.617434Z",
     "iopub.status.busy": "2026-09-15T09:41:32.617333Z",
     "iopub.status.idle": "2026-09-15T09:41:34.012098Z",
     "shell.execute_reply": "2026-09-15T09:41:34.010085Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28454\n",
      "68 60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717616/344732789.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(*calc_fwhm(res, LaB6_new, 10, 95), \"o\", label=\"FWHM\")\n",
    "ax.plot(*calc_peak_error(res, LaB6_new, 10, 95), \"o\", label=\"error\")\n",
    "ax.set_title(\"Peak shape & error as function of the angle\")\n",
    "ax.set_xlabel(res.unit.label)\n",
    "ax.legend()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T09:41:34.013918Z",
     "iopub.status.busy": "2026-09-15T09:41:34.013818Z",
     "iopub.status.idle": "2026-09-15T09:41:34.017712Z",
     "shell.execute_reply": "2026-09-15T09:41:34.016071Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total run time:  945.3684816360474\n"
     ]
    }
   ],
   "source": [
    "print(\"total run time: \", time.time()-start_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The calibration works and the FWHM of every single peak is pretty small: 0.02°. \n",
    "The geometry has been refined with the wavelength: \n",
    "The goniometer scale parameter refines to 0.999 instead of 1 and the wavelength is fitted with a change at the 5th digit which is pretty precise."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "0ad6f26946f74446b20e41ec4a5e0e74": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "SliderStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "SliderStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "description_width": "",
       "handle_color": null
      }
     },
     "2615930a16494eeca45ae61536cb239c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "304638d9f1e645e485f12bcb4d9e59ef": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "VBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "VBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_c1b6bd1f11164802898ac3514b15bf9e",
        "IPY_MODEL_ab16906162bf4ea5b1c5586a4a2d7a3b",
        "IPY_MODEL_3c5aaf47f3ff4c7d82f643ade070b423"
       ],
       "layout": "IPY_MODEL_53a2410d9704474b962f08509b804970",
       "tabbable": null,
       "tooltip": null
      }
     },
     "3c5aaf47f3ff4c7d82f643ade070b423": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/output",
       "_model_module_version": "1.0.0",
       "_model_name": "OutputModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/output",
       "_view_module_version": "1.0.0",
       "_view_name": "OutputView",
       "layout": "IPY_MODEL_96132320872a4be5bf834ab4869ebc53",
       "msg_id": "",
       "outputs": [],
       "tabbable": null,
       "tooltip": null
      }
     },
     "53a2410d9704474b962f08509b804970": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "5bb8a89bcff24384aa6bf701ef9a45e3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "96132320872a4be5bf834ab4869ebc53": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "ab16906162bf4ea5b1c5586a4a2d7a3b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "IntSliderModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "IntSliderModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "IntSliderView",
       "behavior": "drag-tap",
       "continuous_update": true,
       "description": "frame_id",
       "description_allow_html": false,
       "disabled": false,
       "layout": "IPY_MODEL_5bb8a89bcff24384aa6bf701ef9a45e3",
       "max": 500,
       "min": 0,
       "orientation": "horizontal",
       "readout": true,
       "readout_format": "d",
       "step": 1,
       "style": "IPY_MODEL_0ad6f26946f74446b20e41ec4a5e0e74",
       "tabbable": null,
       "tooltip": null,
       "value": 250
      }
     },
     "af8df9ae131242768f3a82c018e7f110": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "SliderStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "SliderStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "description_width": "",
       "handle_color": null
      }
     },
     "c1b6bd1f11164802898ac3514b15bf9e": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "IntSliderModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "IntSliderModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "IntSliderView",
       "behavior": "drag-tap",
       "continuous_update": true,
       "description": "module_id",
       "description_allow_html": false,
       "disabled": false,
       "layout": "IPY_MODEL_2615930a16494eeca45ae61536cb239c",
       "max": 9,
       "min": 0,
       "orientation": "horizontal",
       "readout": true,
       "readout_format": "d",
       "step": 1,
       "style": "IPY_MODEL_af8df9ae131242768f3a82c018e7f110",
       "tabbable": null,
       "tooltip": null,
       "value": 4
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}