{
 "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 detector 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 wich contrains 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": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# use `widget` for better user experience; `inline` is for documentation generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jerome/.venv/py311/lib/python3.11/site-packages/pyopencl/cache.py:495: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n",
      "  _create_built_program_from_source_cached(\n",
      "/home/jerome/.venv/py311/lib/python3.11/site-packages/pyopencl/cache.py:499: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n",
      "  prg.build(options_bytes, devices)\n"
     ]
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy\n",
    "import os\n",
    "import h5py\n",
    "from silx.resources import ExternalResources\n",
    "\n",
    "from pyFAI import goniometer\n",
    "from pyFAI.detectors import Detector\n",
    "from pyFAI.goniometer import ExtendedTransformation, GoniometerRefinement\n",
    "from pyFAI.control_points import ControlPoints\n",
    "from pyFAI.geometryRefinement import GeometryRefinement\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 import distance_matrix\n",
    "import time\n",
    "\n",
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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 downoaded 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": {},
   "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": {},
   "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": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 ms ± 69.2 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": {},
   "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": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/505442400.py:8: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7daf1f5099a440cdac20714b5f21a214",
       "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"
    }
   ],
   "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": {},
   "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": {},
   "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": {},
   "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": {},
   "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",
      "Cost function after refinement: 2.490988846910722e-11\n",
      "GonioParam(dist=0.719994724358983, poni1=0.031408577292064206, poni2=0.004, rot1=0.0, offset=-82.79995188659902, scale=1.0, nrj=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": {},
   "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": {},
   "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",
      " 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",
      "Cost function after refinement: 2.7342490266357463e-06\n",
      "GonioParam(dist=0.7230953421817191, poni1=0.03184537030470006, poni2=0.004, rot1=0.0, offset=-82.7999466162549, scale=1.0, nrj=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": {},
   "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",
      " 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",
      "Cost function after refinement: 2.689172533892251e-06\n",
      "GonioParam(dist=0.7230962438566039, poni1=0.03205370080101193, poni2=0.003984854883002208, rot1=1.0947770664950588e-05, offset=-82.79994398900047, scale=0.9994150576486731, nrj=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": {},
   "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]), array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
      "       1.27158634e+08, 1.31904151e+08, 1.31268033e+08]))}\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": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/954410272.py:6: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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": {},
   "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": {},
   "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 = distance_matrix(target.reshape(-1, 1 ),\n",
    "                         got.reshape(-1, 1), p=1)\n",
    "    \n",
    "    return target, target-got[d2.argmin(axis=-1)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29218\n",
      "81 60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/55614379.py:7: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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 procdure for the second module ... and try to rationalize the procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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": {},
   "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": {},
   "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": {},
   "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",
      "Cost function after refinement: 2.343186420609047e-11\n",
      "GonioParam(dist=0.7200063780235467, poni1=0.03286839545438533, poni2=0.004, rot1=0.0, offset=-77.19998908851593, scale=1.0, nrj=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": {},
   "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": {},
   "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",
      " 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",
      "Cost function after refinement: 1.3797258269094543e-07\n",
      "GonioParam(dist=0.7200063394925221, poni1=0.033375469748006405, poni2=0.004, rot1=0.0, offset=-77.1999827124022, scale=1.0, nrj=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",
      " 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",
      "Cost function after refinement: 8.828503235714087e-10\n",
      "GonioParam(dist=0.720682072663415, poni1=0.03367823301601242, poni2=0.004058010685114263, rot1=-4.448556851210191e-05, offset=-77.19997879322797, scale=0.9989672333399332, nrj=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": {
    "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": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/3210620731.py:4: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27712\n",
      "72 53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/2659044189.py:7: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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 used 3 parameter 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": {},
   "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": {},
   "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",
      "Cost function after refinement: 1.8085719501136514e-11\n",
      "GonioParam(dist=0.7200189604924729, poni1=0.034089307266644636, poni2=0.004, rot1=0.0, offset=-71.59991818231154, scale=1.0, nrj=17.027082549190933)\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.034089307266644636\n",
      " 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",
      " 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",
      "Cost function after refinement: 1.1960984572759293e-07\n",
      "GonioParam(dist=0.7200183446946246, poni1=0.03456504217138479, poni2=0.004, rot1=0.0, offset=-71.5999122000852, scale=1.0, nrj=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",
      " 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",
      "Cost function after refinement: 6.570414650006303e-10\n",
      "GonioParam(dist=0.7206864291588817, poni1=0.03482474083006927, poni2=0.004043166284269385, rot1=-3.376838364866449e-05, offset=-71.59990881341272, scale=0.9989828730379465, nrj=17.027082549190933)\n",
      "maxdelta on: scale (5) 1.0 --> 0.9989828730379465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:99: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25659\n",
      "71 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:107: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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": {},
   "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",
      "Cost function after refinement: 2.4921937668954493e-11\n",
      "GonioParam(dist=0.7200067449756247, poni1=0.03274966118487238, poni2=0.004, rot1=0.0, offset=-65.7999350248995, scale=1.0, nrj=17.027082549190933)\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.03274966118487238\n",
      " 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",
      " 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",
      "Cost function after refinement: 9.923942799031425e-08\n",
      "GonioParam(dist=0.7204818161545966, poni1=0.033188632482365366, poni2=0.004, rot1=0.0, offset=-65.79992934723474, scale=1.0, nrj=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",
      " 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",
      "Cost function after refinement: 5.124871954470427e-10\n",
      "GonioParam(dist=0.7204829253887527, poni1=0.03340994866191066, poni2=0.003977911642428385, rot1=1.5909827962256308e-05, offset=-65.79992655958921, scale=0.9989991199293132, nrj=17.027082549190933)\n",
      "maxdelta on: scale (5) 1.0 --> 0.9989991199293132\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:99: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24026\n",
      "64 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:107: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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[3],\n",
    "           121,\n",
    "           159,\n",
    "           179)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "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",
      "Cost function after refinement: 2.085662476197838e-10\n",
      "GonioParam(dist=0.7199968842266145, poni1=0.03165314623342505, poni2=0.004, rot1=0.0, offset=-60.00005992107673, scale=1.0, nrj=17.027082549190933)\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.03165314623342505\n",
      " 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",
      " 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",
      "Cost function after refinement: 8.355695005974523e-08\n",
      "GonioParam(dist=0.7206442268536376, poni1=0.03204960860571386, poni2=0.004, rot1=0.0, offset=-60.0000548000565, scale=1.0, nrj=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",
      " 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",
      "Cost function after refinement: 3.338833374910402e-10\n",
      "GonioParam(dist=0.7206453081721997, poni1=0.03223711680104726, poni2=0.0039756515739672454, rot1=1.7542241829967905e-05, offset=-60.00005243766022, scale=0.9990118655636463, nrj=17.027082549190933)\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990118655636463\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:99: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22251\n",
      "56 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:107: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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fcrZLS4ElS6THqADLLxBDaDZiKQDgipQa+OHFf4TZDSGEkMoOyy8QQgghpMrC5IYQQgghUQWTG+KkoMBZE8RkUlS/w5/W4bdvx+G3b4fVVhQaL0pi5PbVeU0Cahm5X22s2vkr1dOjXY85aNXVclzpGStXX22cUZ708GPUOHJignkJhaZWD+HyLXc7KUlaXwImN4QQQgiJKpjcEEIIISSqYHJDCCGEkKiCyQ0hhBBCogomN4QQQgiJKpjcEEIIISSqYPkF4sRiAXr3Ln+tUevnizsCABxmFfmzGi9KYuT21XlNAmoZuV9trNr5K9XTo12POWjV1XJc6R0rp4/aOCM9afWj1oseMXK8hEJTq4dw+ZazXVoKrFghPUYFWH6BGALLLxBCCNETll8ghBBCSJWFyQ0hhBBCogomN8RJQQFQrZrzR4dSA7um3INdU+5RX35BqRclMXL76rwmAbWM3K82Vu38lerp0a7HHLTqajmu9IyVq682zihPevgxahw5McG8hEJTq4dw+Za7nZoqrS8Bbygm5RQW6iaVWGrTJqDGi5IYuX11XJOgWkbuVxurdv5K9fRo12MOWnW1HFd6xsrVVxtnlLYefowaR06MVg09NLV6UBMTirVQ+LuIZ26IoZhM4XZACCGkqhHW5OaXX37BHXfcgYYNG8JkMmHx4sUB+3/99dfo0aMH6tWrh5o1a6Jz58744YcfQmOWEEIIIZWCsCY3BQUFaNu2LaZPny6r/y+//IIePXpg2bJl2LJlC2666Sbccccd2LZtm8FOCSGEEFJZCOs9N7169UKvXr1k9586darX9oQJE7BkyRJ8++23aN++vc7uCCGEEFIZqdT33DgcDpw/fx516tQJtxVCCCGERAiV+ttS77zzDvLz83H//ff77WOz2WCzlX9zJy8vLxTWKh9mM9C1a/lrjVq/Nm4FAHCoyZ/VeFESI7evzmsSUMvI/Wpj1c5fqZ4e7XrMQauuluNK71g5fdTGGelJqx+1XvSIkeMlFJpaPYTLt5ztsjJg3TrpMSoQMeUXTCYTFi1ahH79+snqP2/ePAwYMABLlixB9+7d/fZ7/fXXMW7cOJ92ll8wFpZfIIQQoidRX35h/vz5ePrpp/HFF18ETGwAYOTIkcjNzXX/ZGVlhcglIYQQQsJBpbss9fnnn+PJJ5/E/Pnz0adPn6D9rVYrrFZrCJwRQgghJBII65mb/Px8ZGZmIjMzEwBw6NAhZGZm4siRIwCcZ1369+/v7j9v3jz0798fkydPxrXXXovs7GxkZ2cjNzc3HPaji4ICoF49548OpQa2/PthbPn3w+rLLyj1oiRGbl+d1ySglpH71caqnb9SPT3a9ZiDVl0tx5WesXL11cYZ5UkPP0aNIycmmJdQaGr1EC7fcrebN5fWlyCsZ242b96Mm266yb09bNgwAEBaWhpmz56N48ePuxMdAPjoo49QVlaGQYMGYdCgQe52V3+ikdOndZOqW6Txxm01XpTEyO2r45oE1TJyv9pYtfNXqqdHux5z0Kqr5bjSM1auvto4o7T18GPUOHJitGrooanVg5qYUKyFwt9FYU1uunXrhkD3M1dMWFatWmWsIUIIIYRUeirlDcWEEEIIIf5gckMIIYSQqILJDSGEEEKiCiY3xFBMpnA7IIQQUtWodM+5IQZhNgMdO5a/1qi1PfUyAIAwqSy/oNSLkhi5fXVek4BaRu5XG6t2/kr19GjXYw5adbUcV3rHyumjNs5IT1r9qPWiR4wcL6HQ1OohXL7lbNvtwLZt0mNUIGLKL4QKJY9vJupxlV9okVoDy19g+QVCCCHaiPryC4QQQggh/mByQwghhJCogskNcVJYCDRr5vwpLNSstfbDJ7H2wydhLSkOjRclMXL76rwmAbWM3K82Vu38lerp0a7HHLTqajmu9IyVq682zihPevgxahw5McG8hEJTq4dw+Za73aqVtL4EvKGYOBEC+Ouv8tcatS7KO6leS40XJTFy++q8JgG1jNyvNlbt/JXq6dGuxxy06mo5rvSOldNHbZyRnrT6UetFjxg5XkKhqdVDuHzL3ZYJz9wQQgghJKpgckMIIYSQqILJDSGEEEKiCiY3hBBCCIkqmNwQQ6laj4gkhBASCfDbUsSJyQS0bFn+WqPW3rpN1Gup8aIkRm5fndckoJaR+9XGqp2/Uj092vWYg1ZdLceV3rFy+qiNM9KTVj9qvegRI8dLKDS1egiXbznbdjuwZ4/0GBWHZPkFYgSu8gtXpNTADy+y/AIhhBBtsPwCIYQQQqosTG6IoWi9mkMIIYQohckNcVJYCFx1lfNHh1IDP34yED9+MlB9+QWlXpTEyO2r85oE1DJyv9pYtfNXqqdHux5z0Kqr5bjSM1auvto4ozzp4ceoceTEBPMSCk2tHsLlW+52p07S+lKIKkZubq4AIHJzc8NtJbLIzxfC+eUm52udtO6cuDw0XpTEyO1r0JpIahm5X22s2vkr1dOjXY85aNXVclzpGStXX22cUZ708GPUOHJignkJhaZWD+HyLXM7FxByP7955oYQQgghUQWTG0IIIYREFUxuCCGEEBJVMLkhhBBCSFTB5IYQQgghUQXLLxAnJhPQtGn5a41af9esr15LjRclMXL76rwmAbWM3K82Vu38lerp0a7HHLTqajmu9I6V00dtnJGetPpR60WPGDleQqGp1UO4fMvZdjiArCzpMSoOKYQQsnpGCSy/EBpc5RdapNbA8hdYfoEQQog2WH6BEEIIIVUWJjeEEEIIiSqY3BAnRUXANdc4f4qKNGstmfMilsx5EXElttB4URIjt6/OaxJQy8j9amPVzl+pnh7tesxBq66W40rPWLn6auOM8qSHH6PGkRMTzEsoNLV6CJdvudvduknrSxH0GcZRBssv+IHlF/TxoVaL5Re0tbP8gvKyBSy/wPILLL9ACCGEEFI5YHJDCCGEkKiCyQ0hhBBCogomN4QQQgiJKpjcEEIIISSqYPkFUk5ysm5SZxI0Pv1ZjRclMXL76rgmQbWM3K82Vu38lerp0a7HHLTqajmu9IyVq682zihtPfwYNY6cGK0aemhq9aAmJhRrkZzsLL9w9mzwscDyC+G2E7Ww/AIhhBA9YfkFQgghhFRZmNwQQgghJKpgckOcFBU5H23drZsupQbmzxuB+fNGqC+/oNSLkhi5fXVek4BaRu5XG6t2/kr19GjXYw5adbUcV3rGytVXG2eUJz38GDWOnJhgXkKhqdVDuHzL3e7dW1pfiqDPMDaQ1atXi9tvv100aNBAABCLFi0KGrNy5UrRvn17ERcXJy655BIxa9YsRWOy/IIfWH5BHx9qtVh+QVs7yy8oL1vA8gssv8DyC8ZQUFCAtm3bYvr06bL6Hzp0CH369MFNN92EzMxMvPDCC3j66afxww8/GOyUEEIIIZWFsH4VvFevXujVq5fs/jNmzEDz5s0xefJkAMCVV16JtWvX4t1330XPnj2NskkIIYSQSkSluudmw4YN6N69u1dbz549sWHDBr8xNpsNeXl5Xj+EEEIIiV4qVXKTnZ2NlJQUr7aUlBTk5eWhyM+NTOnp6UhKSnL/NG7cOBRWCSGEEBImKlVyo4aRI0ciNzfX/ZOVlRVuS4QQQggxkEpVfiE1NRUnTpzwajtx4gRq1qyJhIQEyRir1Qqr1RoKe5WfxETdpApjNa65Gi9KYuT21XFNgmoZuV9trNr5K9XTo12POWjV1XJc6RkrV19tnFHaevgxahw5MVo19NDU6kFNTCjWIjHR+f0pmY+liJjyCyaTCYsWLUK/fv389nnllVewbNky/PHHH+62hx9+GGfPnsXy5ctljcPyC6GB5RcIIYToSaUpv5Cfn4/MzExkZmYCcH7VOzMzE0eOHAHgvKTUv39/d//nnnsOBw8exMsvv4zdu3fjgw8+wBdffIEXX3wxHPYJIYQQEoGENbnZvHkz2rdvj/bt2wMAhg0bhvbt22PMmDEAgOPHj7sTHQBo3rw5li5dihUrVqBt27aYPHkyPvnkE34NnBBCCCFuwprcdOvWDUIIn5/Zs2cDAGbPno1Vq1b5xGzbtg02mw0HDhzA448/HnLfUUlxMdCnj/OnuFiz1syFr2PmwtcRW6qi/IIaL0pi5PbVeU0Cahm5X22s2vkr1dOjXY85aNXVclzpGStXX22cUZ708GPUOHJignkJhaZWD+HyLXf73nul9aUI+gzjKIPlF/zA8gv6+FCrxfIL2tpZfkF52QKWX2D5BZZfIIQQQgipHDC5IYQQQkhUweSGEEIIIVEFkxtCCCGERBVMbgghhBASVTC5IYQQQkhUETHlF0IFyy+EBpZfIIQQoieVpvwCIYQQQojeMLkhhBBCSFTB5IY4KS4G7rvP+aNDqYHpi9MxfXG6+vILSr0oiZHbV+c1Cahl5H61sWrnr1RPj3Y95qBVV8txpWesXH21cUZ50sOPUePIiQnmJRSaWj2Ey7fcbY9C2kEJ+gzjKIPlF/zA8gv6+FCrxfIL2tpZfkF52QKWX2D5BZZfIIQQQgipHDC5IYQQQkhUweSGEEIIIVEFkxtCCCGERBVMbgghhBASVTC5IYQQQkhUwfILxIkQQGGh83ViImAyadK68p9fAwCaNk7G8he7Gu9FSYzcvjqvSUAtI/erjVU7f6V6erTrMQetulqOKz1jAXn6auOM8lSxjxo/arzo5S3YugTT0EMz2HoYMXc9fMvczsvLQ1LDhrI+v5ncEENgbSlCCCF6wtpShBBCCKmyMLkhTmw24PHHnT82FSUTKmi9s/RdvLP0XcSWlYTGi5IYuX11XpOAWkbuVxurdv5K9fRo12MOWnW1HFd6xsrVVxtnlCc9/Bg1jpyYYF5CoanVQ7h8y91+7jlpfSmCPsM4ymD5BT+w/II+PtRqsfyCtnaWX1D2SH0tcUZ5YvkF7ZpaPYTLN8svEEIIIYQEhskNIYQQQqIKJjeEEEIIiSqY3BBCCCEkqmByQwghhJCogskNIYQQQqKKmHAbIBFCYiJw8mT5a41aVz8/FwBQPy4+NF6UxMjtq/OaBNQycr/aWLXzV6qnR7sec9Cqq+W40jtWTh+1cUZ60upHrRc9YuR4CYWmVg/h8i1n+/x54JJLpMeoAMsvEENg+QVCCCF6wvILhBBCCKmyMLkhTmw2YNAg548OpQbe+PFDvPHjh+rLLyj1oiRGbl+d1ySglpH71caqnb9SPT3a9ZiDVl0tx5WesXL11cYZ5UkPP0aNIycmmJdQaGr1EC7fcrf/+U9pfSmCPsM4ymD5BT+w/II+PtRqsfyCtnaWX1BetoDlF1h+geUXCCGEEEIqB0xuCCGEEBJVMLkhhBBCSFTB5IYQQgghUQWTG0IIIYREFUxuCCGEEBJVsPwCcZKQABw6VP5ao9YNz/0XAFAj1hoaL0pi5PbVeU0Cahm5X22s2vkr1dOjXY85aNXVclzpHSunj9o4Iz1p9aPWix4xcryEQlOrh3D5lrN9/jzQpo30GBUIe/mF6dOn41//+heys7PRtm1bvP/+++jUqZPf/lOnTsWHH36II0eOIDk5Gffeey/S09MRHy+vhhHLL4QGll8ghBCiJ5Wm/MKCBQswbNgwjB07Flu3bkXbtm3Rs2dPnHQVzKrAvHnzMGLECIwdOxZ//vkn/vvf/2LBggV49dVXQ+ycyMVkMoXbAiGEkCpGWJObKVOmYMCAAXjiiSfQsmVLzJgxA4mJiZg5c6Zk//Xr16NLly54+OGH0axZM9x666146KGHsGnTphA7j0JKSoDhw50/JSpKJlTQGrlyJkaunImYstLQeFESI7evzmsSUMvI/Wpj1c5fqZ4e7XrMQauuluNKz1i5+mrjjPKkhx+jxpETE8xLKDS1egiXb7nbo0ZJ60sR9BnGBmGz2YTFYhGLFi3yau/fv7+48847JWPmzp0rkpKSxMaNG4UQQhw4cEC0aNFCjB8/3u84xcXFIjc31/2TlZUl+/HNVQqDSg30ffuH0Hhh+QX/+1l+Qf1xwfILyh+tr1ab5Re0a2r1EC7fBpRfCNsNxadPn4bdbkdKSopXe0pKCnbv3i0Z8/DDD+P06dO44YYbIIRAWVkZnnvuuYCXpdLT0zFu3DhdvRNCCCEkcqlUXwVftWoVJkyYgA8++ABbt27F119/jaVLl+LNN9/0GzNy5Ejk5ua6f7KyskLomAghwm2BEEJIFSNsZ26Sk5NhsVhw4sQJr/YTJ04gNTVVMmb06NF47LHH8PTTTwMAWrdujYKCAjzzzDN47bXXYDb75mpWqxVWq4qvIxNCCCGkUhK2MzdxcXHo0KEDMjIy3G0OhwMZGRno3LmzZExhYaFPAmOxWADwDAEhhBBCnIT1IX7Dhg1DWloaOnbsiE6dOmHq1KkoKCjAE088AQDo378/GjVqhPT0dADAHXfcgSlTpqB9+/a49tprsX//fowePRp33HGHO8khhBBCSNUmrMnNAw88gFOnTmHMmDHIzs5Gu3btsHz5cvdNxkeOHPE6UzNq1CiYTCaMGjUKR48eRb169XDHHXdg/Pjx4ZoCIYQQQiKMsJdfGDx4MAYPHiy5b9WqVV7bMTExGDt2LMaOHRsCZ1WMhARgx47y1xq1ejw5HQAQo7b8glIvSmLk9tV5TQJqGblfbaza+SvV06Ndjzlo1dVyXOkdK6eP2jgjPWn1o9aLHjFyvIRCU6uHcPmWs52fD1x3nfQYFQh7+YVQw/ILoYHlFwghhOhJpSm/QAghhBCiN2G/LEUihJISYMIE5+tXXwXi4jRpvbB2LgDgp35PhcaLkhi5fXVek4BaRu5XG6t2/kr19GjXYw5adbUcV3rGAvL01cYZ5aliHzV+1HjRy1uwdQmmoYdmsPUwYu56+Ja7bbP5avsj6DOMo4zc3FzZj2+uUhhUauDOictD44XlF/zvZ/kF9ccFyy8of7S+Wm2WX9CuqdVDuHwbUH6Bl6UIIYQQElUwuSGGYjKZwm2BEEJIFYPJDSGEEEKiCiY3hBBCCIkqVCU3aWlp+OWXX/T2QsLI+eLScFsghBBCdEFVcpObm4vu3bvjsssuw4QJE3D06FG9fZEQM/LrP8JtgRBCCNEFVc+5Wbx4MU6dOoX//e9/mDNnDsaOHYvu3bvjqaeeQt++fREbG6u3T2Iwy/adw539pwAAvomP1yYWH+/WsseqeDZMfDywaVP5a71j5PZV40OtlpH71caqnb9SPT3a9ZiDVl0tx5XesXL6qI0z0pNWP2q96BEjx0soNLV6CJdvOdv5+cDNN0uPUQFdyi9s3boVs2bNwieffILq1avj0UcfxcCBA3HZZZdpldYdll+QxlUuAQAOT+yjm96VDWri+6E3atYjhBBStQlp+YXjx49jxYoVWLFiBSwWC3r37o0//vgDLVu2xLvvvqtVnhBCCCFEEaouS5WWluKbb77BrFmz8OOPP6JNmzZ44YUX8PDDD7uzqUWLFuHJJ5/Eiy++qKthYgyx9lI8sfkb50ZJD82lBp7Z+BUAYMPtj6qKx3vvOV8PHSq//ILcGLl91fhQO6aR+9XGqp2/Uj092vWYg1ZdLceVnrGAPH21cUZ5qthHjR81XvTyFmxdgmnooRlsPYyYux6+5W4XF/tq+yPoM4wlqFu3rqhdu7YYOHCg2LZtm2Sfc+fOiWbNmqmRNxSWX5CmxYtfGlJqoO/bP2iKZ/kFHfaz/IL644LlF5Q/Wl+tNssvaNfU6iFcvg0ov6DqzM27776L++67D/EBbnyrVasWDh06pEaeEEIIIUQ1qu65WblyJUpLfZ+LUlBQgCeffFKzKRI9CCHCbYEQQkgVQ1VyM2fOHBQVFfm0FxUV4dNPP9VsihBCCCFELYouS+Xl5UEIASEEzp8/73VZym63Y9myZahfv77uJgnREyEEXOU87Q4BS1jdEEII0RtFyU2tWrVgMplgMplw+eWX++w3mUwYN26cbuYIMYIyh4DrMZNZZwvRrEb1sPohhBCiL4qSm5UrV0IIgZtvvhlfffUV6tSp494XFxeHpk2bomHDhrqbJJUXk8kUvBMhhBCiI4qSm65duwIADh06hCZNmvCDK4qwxcTiwYcmAADm61BqwKVVqrb8wsqV5a/1jvHwlx7sUfdKfajVMnK/2li181eqp0e7HnPQqit3rFDEyumjNs5IT1r9qPWiR4wcL6HQ1OohXL7lbBcUALffLj1GBWSXX/j999/RqlUrmM1m/P777wH7tmnTRtbg4YDlF6SpSuUXSu0OXPba9wCAlS91Q/PkamF2RAghJBhKPr9ln7lp164dsrOzUb9+fbRr1w4mk0nya74mkwl2u125a0IIIYQQHZCd3Bw6dAj16tVzvybRRYy9DA9tX+7cKL0V0FLZvbQUj239DgCw7bb7VcXjo4+cr595Rp4XJTEe/lDaRV8farWM3K82Vu38lerp0a7HHLTqyh3L6FhAnr7aOKM8Veyjxo8aL3p5C7YuwTT00Ay2HkbMXQ/fcrclHkHjl6DPMI4yWH5BmqpUfqEkN8/d99DhE/r6UKvF8gva2ll+QXnZApZfYPmFKC6/oPohfkuXlt+j8fLLL6NWrVq4/vrr8ddff6mRJCRkCBFuB4QQQoxEVXIzYcIEJCQkAAA2bNiAadOmYdKkSUhOTmYVcFKp4Pf9CCEk+lBVODMrKwuXXnopAGDx4sW499578cwzz6BLly7o1q2bnv4IIYQQQhSh6sxN9erVcebMGQDAjz/+iB49egAA4uPjJWtOERKp8AoVIYREH6rO3PTo0QNPP/002rdvj71796J3794AgJ07d6JZs2Z6+iNEd/jsSUIIiW5UnbmZPn06OnfujFOnTuGrr75C3bp1AQBbtmzBQw89pKtBQgghhBAlqDpzU6tWLUybNs2nnUUzKy8lMbF44t6xAIBZVqs2MavVrVUao+LZMFYr8N135a/1jvHwNyZQXzU+1GoZuV9trNr5K9XTo12POWjVlTtWKGLl9FEbZ6QnrX7UetEjRo6XUGhq9RAu33K2CwuB++U9O012+YWK5OTkYNOmTTh58iQcDke5oMmExx57TI1kSGD5BWlYfoEQQkgkY0j5BU++/fZbPPLII8jPz0fNmjW9CmhGenJDCJ9zQwgh0Y2q5Oaf//wnnnzySUyYMAGJiYl6eyJhIMZehn67Vjk3dCi/cO8fPwEAdte/U1U85s51vn7kEfnlF+TGePgzBSu/oNSHWi0j96uNVTt/pXp6tOsxB626cscyOhaQp682zihPFfuo8aPGi17egq1LMA09NIOthxFz18O33G2jyy8kJiaKAwcOqAkNOyy/II1R5Rf6RWD5BVtOefmFwyy/wPILeumy/ALLLwTywvIL6jVDVX6hZ8+e2Lx5s5pQUsUQ4TZACCGkyqHqslSfPn0wfPhw7Nq1C61bt0ZshdNQd96p4lIEIYQQQogOqEpuBgwYAAB44403fPaZTCbY7XZtrggJETyzRAgh0Yeq5Mbzq9+EVDb4hGJCCIluVN1z40lxcbGm+OnTp6NZs2aIj4/Htddei02bNgXsn5OTg0GDBqFBgwawWq24/PLLsWzZMk0eCCGEEBI9qEpu7HY73nzzTTRq1AjVq1fHwYMHAQCjR4/Gf//7X9k6CxYswLBhwzB27Fhs3boVbdu2Rc+ePXHy5EnJ/iUlJejRowcOHz6ML7/8Env27MHHH3+MRo0aqZkGqaIIXosihJCoRtVlqfHjx2POnDmYNGmS+/4bAGjVqhWmTp2Kp556SpbOlClTMGDAADzxxBMAgBkzZmDp0qWYOXMmRowY4dN/5syZOHv2LNavX+++iZmFOvWhJCYWA/s61/wDHUoNuLRUl1/44ovy13rHePh7Odij7pX6UKtl5H61sWrnr1RPj3Y95qBVV+5YoYiV00dtnJGetPpR60WPGDleQqGp1UO4fMvZLiwEHn9ceowKqCq/cOmll+I///kPbrnlFtSoUQPbt2/HxRdfjN27d6Nz5844d+5cUI2SkhIkJibiyy+/RL9+/dztaWlpyMnJwZIlS3xievfujTp16iAxMRFLlixBvXr18PDDD+OVV16BxWKR5Z3lF6SpSuUXSsocuHyUs/zCqpe6oRnLLxBCSMRjePmFo0eP4tJLL/VpdzgcKC0tlaVx+vRp2O12pKSkeLWnpKRg9+7dkjEHDx7Ezz//jEceeQTLli3D/v37MXDgQJSWlmLs2LGSMTabDTabzb2dl5cnyx8hhBBCKieqkpuWLVtizZo1aNq0qVf7l19+ifbt2+tiTAqHw4H69evjo48+gsViQYcOHXD06FH861//8pvcpKens1q5DCwOO3ru3eDcKOsJxKg6NC7El6H37rUAgL/q36oqHosWOV/fdZc8L0piPPyh7AZ9fajVMnK/2li181eqp0e7HnPQqit3LKNjAXn6auOM8lSxjxo/arzo5S3YugTT0EMz2HoYMXc9fMvdLiz01fZH0GcYS7B48WKRlJQkJk6cKBITE8W//vUv8fTTT4u4uDjx448/ytKw2WzCYrGIRYsWebX3799f3HnnnZIx//jHP8Qtt9zi1bZs2TIBQNhsNsmY4uJikZub6/7JysqS/fjmqoRR5Rf6svyCPC2WX9DWzvILyssWsPwCyy+w/II3ffv2xbfffouffvoJ1apVw5gxY/Dnn3/i22+/RY8ePWRpxMXFoUOHDsjIyHC3ORwOZGRkoHPnzpIxXbp0wf79+72es7N37140aNAAcXFxkjFWqxU1a9b0+iGEEEJI9KL6PPuNN96IFStWaBp82LBhSEtLQ8eOHdGpUydMnToVBQUF7m9P9e/fH40aNUJ6ejoA4P/+7/8wbdo0DB06FM8//zz27duHCRMmYMiQIZp8kKqLCLcBQgghuqMqubn44ovx22+/oW7dul7tOTk5uPrqq93PvQnGAw88gFOnTmHMmDHIzs5Gu3btsHz5cvdNxkeOHIHZXH5yqXHjxvjhhx/w4osvok2bNmjUqBGGDh2KV155Rc00SIRSandAxRfIZSOY0hBCSFSjKrk5fPiwZP0om82Go0ePKtIaPHgwBg8eLLlv1apVPm2dO3fGr7/+qmgMEj7UVDqYufYQntXdiTSsxEAIIdGHouTmm2++cb/+4YcfkJSU5N622+3IyMjgQ/WIZlbuORmy5IYQQkj0oSi5cT1sz2QyIS0tzWtfbGwsmjVrhsmTJ+tmjhBCCCFEKYqSG9e3lJo3b47ffvsNycnJhpgioafUEoOXer8AAHjHzzfPZBMX59YqU1F+oSwmVrmXuDhg1qzy1zL9DQ7UV4mmVn9G7lcbq3b+SvX0aNdjDlp15Y4Vilg5fdTGGelJqx+1XvSIkeMlFJpaPYTLt5ztoiJg4EDpMSqgqvxCZYblF6QxqvxCywY1sUxh+YW7PliHbUdydPNSEVuZHVeMWg6A5RcIIaSyYHj5BQDIyMhARkYGTp486fXcGcBZ4JIQQgghJByoSm7GjRuHN954Ax07dkSDBg1gMvE7J5Udi8OOfxza6tzQofzCTQd+AwCcqt9VcbjZXh4v20tZGfDDD87XPYPElHnqBym/IFdTqz8j96uNVTt/pXp6tOsxB626cscyOhaQp682zihPFfuo8aPGi17egq1LMA09NIOthxFz18O33G2jyy+kpqaKTz/9VE1o2MnNzZX9+OaqRCSVX3hgygrlXhQ8Zr/oXK67L8svBNjP8gssv8DyCyy/EAlrEaryCyUlJbj++uvVhBISFCHC7YAQQkhlRlVy8/TTT2PevHl6eyGEEEII0YyqmwiKi4vx0Ucf4aeffkKbNm0QG+v9dd8pU6boYo5UTXgLFyGEEC2oSm5+//13tGvXDgCwY8cOPf2QKIN5CiGEkFCjKrlZuXKl3j4IIYQQQnRBUXJz9913B+1jMpnw1VdfqTZECCGEEKIFRcmNZ6FMEl2UWmIwusdzAIA3dSg14NJSW35BsZe4OGDatPLXMv09FexR93I1tfozcr/aWLXzV6qnR7sec9CqK3esUMTK6aM2zkhPWv2o9aJHjBwvodDU6iFcvuVsFxUBw4dLj1EBll8gACKr/EK/6euQmZWjm5eKFJfa0WI0yy8QQkhlQsnnt6qvghMSLfCbWYQQEn1oeJ48iSbMDjs6/b3TuWG/DbBY1IvZ7bjuyO8AgPyUzqq8uOJle7HbgTVrnK9vvDFwjN1TP8BZJSWaWv0ZuV9trNr5K9XTo12POWjVlTuW0bGAPH21cUZ5qthHjR81XvTyFmxdgmnooRlsPYyYux6+5W4XFPhq+yPoM4yjDJZfkMao8gv9VJRfuH8yyy/oup/lF+TNgeUX1Pli+QV568LyC+o1Q1V+gRAj4aUiQgghWmByQwghhJCogskNIYQQQqIKJjeEEEIIiSqY3BBDUXP/jBD6+wilPiGEkPDC5IZUaXjzMiGERB98zg0BAJRZLJjQ7QkAwKuxyksmeBEb69ayW5QfYnZLjHIvsbHApEnlr2X6eyRQXyWaWv0ZuV9trNr5K9XTo12POWjVlTtWKGLl9FEbZ6QnrX7UetEjRo6XUGhq9RAu33K2i4uBMWOkx6gAyy8QAMaVX7iqYU0sHRJZ5ReKSuy4coyz/MLq4d3QtC7LLxBCSKTD8guEEEIIqbLwshQB4Cx50OrEAeeGDuUX2hzfCwAQqe0Vh1tEebyi8gtbtzpfX3110Mfsu/2VBSm/IFdTqz8j96uNVTt/pXp6tOsxB626cscyOhaQp682zihPFfuo8aPGi17egq1LMA09NIOthxFz18O33O38fF9tfwR9hnGUwfIL0kRS+YUHphhbfqHwLMsvyNrP8gssv8DyCyy/EAlrwfILJBoQItwOCCGEVGaY3JAqhwCzJ0IIiWaY3JAqDZ9zQwgh0QeTG0IIIYREFUxuiKHwzAghhJBQw+SGEEIIIVEFn3NDADjLL0zt8hAA4AUdSg24tNSWX1DsJTYWGDu2/LVMf3cFe9S9XE2t/mJjceLFV7D1r3NonV+Kiyo+NFlGvN/9amPVzl+pnh7tesxBq67csUIRK6eP2jgjPWn1o9aLHjFyvIRCU6uHcPmWs22zARMnSo9RAZZfIAAiq/zCXR+sw7YjObp5qUhhSRlajvkBQGSVX3CtWfsmtbBoYJcwuyGEkMiC5RdIpSaU6XYkpvaHTheE2wIhhFRqeFmKAABMwoFLT2c5NxwOwKwh73U4cNmpv5y6qVcp9+IRL9uLwwH8+afz9ZVXBowR9gr6Omhq9ucx59ONL1YV73e/2li181eqp0e7HnPQqit3LKNjAXn6auOM8lSxjxo/arzo5S3YugTT0EMz2HoYMXc9fMvdZvkF/7D8gjRGlV+4a5Ly8gv3Tza2/EL+mRx337/+irzyC9e+tkRTPMsvsPyCLH2WX2D5BZZfIIQQQgipHDC5IYQQQkhUERHJzfTp09GsWTPEx8fj2muvxaZNm2TFzZ8/HyaTCf369TPWICGEEEIqDWFPbhYsWIBhw4Zh7Nix2Lp1K9q2bYuePXvi5MmTAeMOHz6Ml156CTfeqOxrxoQQQgiJbsKe3EyZMgUDBgzAE088gZYtW2LGjBlITEzEzJkz/cbY7XY88sgjGDduHC6+WOKbJSRiYPkFQgghoSasyU1JSQm2bNmC7t27u9vMZjO6d++ODRs2+I174403UL9+fTz11FNBx7DZbMjLy/P6IYQQQkj0Etbn3Jw+fRp2ux0pKSle7SkpKdi9e7dkzNq1a/Hf//4XmZmZssZIT0/HuHHjtFqNesosFvyn090AgGd1KDXg0lJbfkGxl9hY4KWXyl8HQHj4uy3Yo+5lamr257lmZouqeL/71caqnb9SPT3a9ZiDVl25Y4UiVk4ftXFGetLqR60XPWLkeAmFplYP4fItZ9tmA95/X3qMCoS1/MKxY8fQqFEjrF+/Hp07d3a3v/zyy1i9ejU2btzo1f/8+fNo06YNPvjgA/Tq1QsA8PjjjyMnJweLFy+WHMNms8Fms7m38/Ly0LhxY5ZfqIBR5RdaNaqJ755Xdl9Uv+nrkJmVo5uXiuTbytBqbOSWX6iVGIvMMbeG2Q0hhEQWSsovhPXMTXJyMiwWC06cOOHVfuLECaSmpvr0P3DgAA4fPow77rjD3ea48ITZmJgY7NmzB5dccolXjNVqhdVqNcA9IYQQQiKRsCY3cXFx6NChAzIyMtxf53Y4HMjIyMDgwYN9+rdo0QJ//PGHV9uoUaNw/vx5vPfee2jcuHEobEclJuFAo7xTzg0dyi9clHshYU2trtyLZ7yS8gtHjjhfN2kS9DH7Xvp6aGr15+GpIL6hqni/+9XGqp2/Uj092vWYg1ZduWMZHQvI01cbZ5Snin3U+FHjRS9vwdYlmIYemsHWw4i56+Fb7vb5877a/gj6DGODmT9/vrBarWL27Nli165d4plnnhG1atUS2dnZQgghHnvsMTFixAi/8WlpaaJv376yx2P5BWmqUvmF8yy/IG8/yy+w/ALLL7D8QiSshYryC2EvnPnAAw/g1KlTGDNmDLKzs9GuXTssX77cfZPxkSNHYNbyFzMhOrDzWC6uuiw09+bw2/OEEKKNsCc3ADB48GDJy1AAsGrVqoCxs2fP1t8Q0Q0hVMTob0MzQz7fhowxEpeLCCGERBw8JUKIDApK7CEbKxKTO0IIqUwwuSERh9GXZYSa00mEEEIqDUxuSJXGFIF3uESeI0IIqVwwuSFEBkw4CCGk8hARNxST8GM3W/Bpe+fTgPvHaDwsYmLcWnaLRCkBI7zExAADB5a/lumva0wAfx79yrR+Yy+YP881kxpLRrzf/WpjlaypFj092vWYg1ZduWOFIlZOH7VxRnrS6ketFz1i5HgJhaZWD+HyLWe7pAT45BPpMSoQ1vIL4UDJ45urEkaVX7iyQU18PzSyyi+cLy5F69d/BBC8/EIoSyKw/AIhhPhHyec3L0uRiCMSs21eliKEkMoDL0sRJ0KgTlGe+zVMGj7OhUCdwtwLr2tojJfpRQjg9Gnn6+TkwDEV9WX0Ewl1ZRjX4C/YWDLi/e5XG6tkTbXo6dGuxxy06sody+hYQJ6+2jijPFXso8aPGi96eQu2LsE09NAMth5GzF0P33K3K1P5hVDD8gvSGFV+4c6JyxWH32dw+YW80+fcfeWWX7hOqiSCEhQ8Jl1yLJZfCNzO8gvKHqmvJc4oTyy/oF1Tq4dw+Tag/AIvS5Eqh1ARo+VEFiGEkNDC5IZEHKHMI+Q+50aoyYgIIYSEBSY3hFQyNhw4HW4LhBAS0TC5IUQGkXRZasCnW8JtgRBCIhomN4RUMiIozyKEkIiEyQ2JOEJ5e4uIyKfqEEII0QKfc0MAOEsefNnqFgDAvTqUX3BpOVSWX1DsJSYGSEsrfy3T3zVBHnXvnodZ+TwU+fMYy1/5hfL9El4C6csYW3K/kjXVoqdHux5z0Kord6xQxMrpozbOSE9a/aj1okeMHC+h0NTqIVy+5WyXlACffy49RgVYfoEAMK78QovUGlj+wj8Uxfadvg7bFZZfyLeV4eutf+PWlqlITYoP2DevuBRtFJZfqFMtDltH95DlRS2usWonxmKbRPkF1/4Yswn7J/Q21AshhEQaLL9AqhzjvtmJMUt24u4P1gXtW7XSeUIIqXrwshRxIgQSSm3u11rLLySUFDtfOqpripfrZeXuE0goKca5U8XBYzz0TYESHc9+ibFy3fvVQmGh83ViouRj0t1zTpD4Z+mxv9QqcWYqkL6MsSX3B4vzh1I9Pdr1mINWXbljGR0LyNNXG2eUp4p91PhR40Uvb8HWJZiGHprB1sOIuevhW+52QYGvtj+CPsM4ymD5BWmMKr9wR7ry8gv3qyi/0GX0EtkxOafOufse+euk/44e87h+VOSUX2j1z6+U6bP8grw5sPyCOl8svyBvXVh+Qb0myy8QEv1E0jN3CCEkEmFyQwxGhCDCeEKZUAQbS0TiAhFCSATB5IYQGYQyoWDyQggh2mByQ0glg5elCCEkMExuiKFE+lkIPqGYEEKiDyY3pOqhIp+p6mdL1u0/FW4LhBAiGz7nhgAAHGYzll7RBQDQR0XJBC8sFreWw6I8f1bjxWGyyI/x8NfaEuCfgOc8TNrXBPfeW/46wFhCqryCx37J8gyB9GWMLbnfY8yJi3dhTdtmUjNTpGdYu1INub718KBlHLWxcvqojTPSk1Y/ar3oESPHSyg0tXoIl28526WlwJIl0mNUgOUXCADjyi9cVr86VgzrqihWTfmFjm+twOn8ElkxuYWlaPuGs/zCL8NvQpO6iX77uuaRXD0Om0dFRvmFWIsJ+8aHpvyCa8yUmlZsfLV7SMYkhBApWH6BVGqq+BUgQgghGmFyQyIONacSq9b5R0IIIQEJ+gzjKIPlF6QxqvxCnwnfKw6/T0X5hetHKSi/cPKcu6/c8gtdRoeu/EJnqVIPHvvbvPS1Mn0dyi/8Y+w32ufK8guhiWX5Bf3HYfmF8K4Fyy+QqkpV+jYTv75OCCGBYXJDCAmKiXdCEUIqEUxuiKEIEZqzDEqG4ZkP5XDNCCGVCSY3hFQyeBaFEEICw+SGRAVV6Z6bcMCEihBSmWByQwgJCi9LEUIqEyy/QAA4Sx78fHFHAMDNOpRfcGnZpUoJGODFDov8GA9/lwZ51L2rny7lF3r3Ln+tdCyvNfVTfsGfvoyxJfernX8APcPalWrI9a2HBy3jqI2V00dtnJGetPpR60WPGDleQqGp1UO4fMvZLi0FVqyQHqMCLL9AABhXfuGSetWQ8c9uimLVlF/o8OYKnCmQV34hp7AE7d5w/gORX37Bis2jjC0/4BqrbrU4bBntW+rBtT/OYsbe8b0M9VJxzNSa8fj11VtCMiYhhEjB8gskYghV5mz8PTeh+xtA7Ui2Mjtyi0p19UIIIZURJjeERAk3vr0Sbcf9iLMXzmARQkiVJegzjEPAtGnTRNOmTYXVahWdOnUSGzdu9Nv3o48+EjfccIOoVauWqFWrlrjlllsC9q8Iyy9I0+LFL0VBrFUUxFp1Kb/g0uo1fpni8Psmr1Ds5fpRS2THnD1x1t03WPkFVz9dyi8kJjp//Dwm3TWWv/ILrv3+yi+49v+06YDisSX3e2h2Hfut9rnm54uyhEThkGj31192u1INub718KBlHDWxcvXVxhnlSQ8/Ro0jJyaYl1BoavUQLt8yt3MTEmR/fof9huIFCxZg2LBhmDFjBq699lpMnToVPXv2xJ49e1C/fn2f/qtWrcJDDz2E66+/HvHx8Xj77bdx6623YufOnWjUqFEYZhA9JJbaIkZLTbySGFffs0EuZ+m5JigslDWWP0vBvATcH2Rsf/tVz19Cb93+U+hS5MeHP39K2pVqGKErdyyjY+Xqq40zSlsPP0aNIydGq4Yemlo9qIkJxVoo+XeMCLgsNWXKFAwYMABPPPEEWrZsiRkzZiAxMREzZ86U7D937lwMHDgQ7dq1Q4sWLfDJJ5/A4XAgIyMjxM5JJGH8PTd8zotW1h84E24LhJAqQliTm5KSEmzZsgXdu5d/C8VsNqN79+7YsGGDLI3CwkKUlpaiTp06RtkkoSaEX+CLxO8KBrVUSfMsEYmLTQiJSsJ6Wer06dOw2+1ISUnxak9JScHu3btlabzyyito2LChV4Lkic1mg81Wfmo9Ly9PvWESsRj/uckPZkIIqSyE/bKUFiZOnIj58+dj0aJFiI+Pl+yTnp6OpKQk90/jxo1D7LJqoyolYC2FwDDPIoSQgIQ1uUlOTobFYsGJEye82k+cOIHU1NSAse+88w4mTpyIH3/8EW3atPHbb+TIkcjNzXX/ZGVl6eKdVDVCl3AxtSOEEG2E9bJUXFwcOnTogIyMDPTr1w8A3DcHDx482G/cpEmTMH78ePzwww/o2LFjwDGsViusVquetqMSh8mEXxu3AgBcJ/V4fyWYzW4tYVKuJaDCi8eYQWM8+jYK1FfjPCpqoWvX8tdKx/LY7zBLpD8e+2Ex++wLNrbkfrXz96MnTH7eowDjy25XqiHXtx4etIyjNlZOH7VxRnrS6ketFz1i5HgJhaZWD+HyLWe7rAxYt056jAqEvfzCggULkJaWhv/85z/o1KkTpk6dii+++AK7d+9GSkoK+vfvj0aNGiE9PR0A8Pbbb2PMmDGYN28eunTp4tapXr06qlevHnQ8ll+QxqjyC82Tq2HlS90Uxfadthbb/85V5EVJ+YWzBSW4+s1KXH4hxoy9b/mWX3Dt/7h/R/RomeKzX4snPcovjF+6Cx+vOQRAn2OMEFK1UPL5Hfbn3DzwwAM4deoUxowZg+zsbLRr1w7Lly9332R85MgRmD2ywQ8//BAlJSW49957vXTGjh2L119/PZTWSRQQibf3ROItNXqsE78sRQgJFWFPbgBg8ODBfi9DrVq1ymv78OHDxhsihFQ6Tufb8OPOE7izXUNUt0bErzZCSLgI+gzjKIPlF6Rp8eKX4nRCTXE6oaYu5RdcWj3fWqo4/N53flTspcvoJbJjzpw46+6bdSRw+QVXvy6jv1EyBUktkZzs/PHzmHTXWNe9Jl1+wbW/zXDp8guu/T/9JlF+IcjYkvs9NLu9rrD8goTexIW/Sb9H+fnCkZwsHBLj+/Pl056fL3KqJYnTCTXFS7PWyZ97MN8KPcgay+hYufpq44zypIcfo8aRExPMSyg0tXoIl2+Z27l16lSe8gskcqhbpN8zgLRqqYlXEuPq+7cBPvxy+rSmsTTtDzK2v/2q5y+hJ/zo5RaWIsmfPwXtSQXO+7RW7DoRtK9fpPoq8SZ3LKNj5eqrjTNKWw8/Ro0jJ0arhh6aWj2oiQnFWij5d4xK/pwbQlyovZ8jGu8DicDbiAKy6dDZcFtQzMaDLCVBSCTD5IYYiprcobJ9OOtNsDWLvvWpfBnm2G92hNsCISQATG5IVKD22zxC5gdrJH2rqvKlAqElkt4rQkh4YHJDqhxCxbUorZevFI0ZpdlLqC4BhmKcKH2LCIkamNwQQ1H1QaPiT+9Iv+fm6Lki92tbmT00g1ZReOaGEMJvSxEAzvIL21MvAwC01aH8gkvLoaZsgcms2ItQEuPhLynIo+5d/bSWX7CjfH1b+HlMumu/ZOrj5UW6/IJrv88j0M1mwFWmxN8j2qX2q52/Hz1/75Ew+zn2Avjyafc65kzBNeT69hPvkJqL3LEUjKM6Vk4ftXFGetLqR60XPWLkeAmFplYP4fItZ9tuB7Ztkx6jAmEvvxBqWH5BGqPKLzStm4jVw29SFNtv+jpkZuUo8tLxrRU4nS+v/MKZfBs6vPUTAGDlS93QPLma376uedSrYcVvr6kvv3D4dAG6vbMKALDnrdtgjbH4HatmfAx+f72n3/3WGDP2BCi/8N+0jrjlSn3LLzRIiseGkdrKL7zx7S7MXOdbfmH5juN47rOtPu1KcXmNjzVj95u+66MnXf+1En+dKQTAUhKEhAoln9+8LEUMJdJT50jM7bU6UjqlrLOFGPL5Nuw4mqtxZEIIiQyY3JCIQ809E6rvuTFYvzLw3Gdb8M32Y7j9/bVhGT+a15YQEiaCPsM4ymD5BWla/PNLkVWzvsiqWV+IggJtYgUFbq3uby5THP7Auz8p9nL9qG9kx5w+cdbdd/+hE/47eszjeo3lFw7/ddKtVZybF3CsjiMXB9zf5mWJ8gse+3/ectBnn2ja1PkjsTatX/5aeu08NLu+/p38yfoZ762Fv0mOs3zjfr/jS/qWavfw2vaVr4NryPXtJ777m8t8PcsdS8E4qmLl6quNM8qTHn6MGkdOTDAvodDU6iFcvmVu5zZuzPILRBkmAVyUd9K5of17z+VacKjwIhR7MZkUxHjoHwjU16OfSevFIg8tm9SYnmNJDaVg/76K+kIAf/1V/roCft97D02zkvn7Gc/f+xpofEnfUu3+1ifI3IP69jcXSMxF7lgKxlEdK6eP2jgjPWn1o9aLHjFyvIRCU6uHcPmWuy0TXpYiVQ4RYMtvTAgvnYT6Ko2cq4CV6cqRKcKf4Xwm3+Z+7XBUppUlpPLA5IZEHKYIfM6Nnh9BkXaPSajshOwhfhGeip0rKHG/jmynhFRemNwQAMbVK1LzgRbKv7sr4w3F4bAS2edCCCHEGyY3hEQYIpIyKWIofK8JMQYmNwSAuktBkYRa/7LP3KhSVxcftG+QDka8lfwI9kbLpS+uJSHGw29LEQCAMAF76zYBAFyu9dPRZHJrCTUXNMwm5V5MCmK8/Mnsp/UTyUOrsdSaeOyX/H6Z536pr0t57PfJbkwmoGVL6X1wroHk2nmOKRS8j/7G8/MeOfwdewF0fNo93ytPq0HmHtS3n3gBibnIHcvDa3MVsbI8yumjNs5IT1r9qPWiR4wcL6HQ1OohXL7lbNvtwJ490mNUHFJUsfOiLL8gzSWvLoP9wjc39Cy/cFHtBKx95WZFsffP2IBNh88q8nLN+J9w6rxNVszpfBs6Xii/8P3QG3FlA//HQbCSCHI5dLoAN72zCgDw5xu3ISHOf/mFhFgL/nzzNr/7Y8wm7J/Q2+/+mY93xM0t5JdfuPy171Fid6ZUFdfOpZlS04qNr6ovPwEAr3+zE7PXH/YZZ9kfxzFwrn7lF/yVp9CTGyf9jKyzzmKoSj3vPXEet777CwBg3/heiLXwBDohcmD5BVLlMPqimq7floq0CxMyFk+PP4FCdeUzwlbXh4qP7CGE6A+TGwKg8n8bRskHZ6R/uARLfnS3LEMwkm7JyreV4Z9fbMfKPSeDd45wIi7RJSRaCPoM4yiD5Rekueqlr8Seuk3EnrpNdCm/4NK6adxSxeGP/PtnxV7+MfY72TEns8+6++7cf8x/R495SJZEUMDBwyfcWgXnJI49j7H8lVdw7b/yn18G3L9yq0T5hZYtnT9S5ReGfy29dh6aN4z5Vv5k/Yw3fuFmyXG+37jf7/hSOpO+3uLb38Nr6+FfB9WQ7dtP/C1vLpX0IGesPQey3bHuUhxyfcrxKHceauOM8qSHH6PGkRMTzEsoNLV6CJdvmdu5V1wh+/ObNxQTAM5TeJefOeLc0KH8gktLTdkCs0e87PILUBDjob8z0BNivXzIsiFLq0BqTM81kxpLwf5jUuUXdu0qf10Bk/Dz3nvOX2n5BYnxzP7eI4f/8aV0jp8r8u3vb32CzD2obz/xksebzLEEHLjiQmyxQ1msbI9y+qiNk+GpoLgU1dRoa/Wjdp56xMjxEgpNrR7C5Vvutkx4WYpEBUZfNhERdP0qmBMjrOpyz412CVn4u9RzIq84RA5Cy487sv3uKyqxh9BJOe+ukPeNFkKMgskNISEmaHISbL+MExBVGX/zP5ZTFFojIWLMtzv97pu2cl8InZSTmZUblnEJccHkhkQcas7CqD0rEI7aUkYTqV71Orumdn5Gn907dCpfcYwuiWgAjS1/ndNhAEIqH0xuCIDIqqSsxov6JxTL+3TR82yI4Ze4DJHXLhqqp2CHIrmTOkZ/2On/8lC4iJx/1YSEFiY3xFAi6V4VSWSfuQnhPLTez62PC29NA6fvL+fxeyYk6GU56Q5mHZMrKSk1a6THcRWJ/8b4FXcSbvhtKQIAEGbg75r1AQAX6VB+waWlpvyC8IiX60VAfowwlc9VBHnUvaufZEkEJXho1fBTfsE9lp/yCuWefXcLAEf9eTWZgKZNy19XxN97r/Z99Duen/fIJD3+H8fyECvRLqT6+1kfz3WRVX6hom8Fc5F73ApR3q82go0T3KNDYj18jm8Pb6acIjSqVk16zArbAsDpug0ghECy8PPXsJ9181kLOWsbbDvYOsl9D9WME2xbjpdQaGr1EC7fcrYdDiArS3qMCjC5IQCA4th43PB/MwEAhxMTtYklJrq1GlkTFIeXWpV7scUpiPHwtzghQF+PftYYbSc5TdWqubW2S43pMVasReIXhMf+YF5nJCT47MPhw35D/a6dh2aduDj/Y0t4kRqvLCFBchyRWE2y3e6nf6lVot3Dq+fvV5FQ3r4k0Hvtz7efuZRIeCiLl/ZbEeHhdaeH/0DvUSA/Uv92XdsdXP/+PMb8vNiERv7GrLBti4vHNU9/DABYbQOaVlfvSdbaVtg+8fturNh1AndZ4lDNX0wwTRnj6BIjx0soNLV6CJdvOdt5eUBSUuCxLsDkhkQcqm4oVhLjcWJE7il9rSfZPf05DLiMoOWpy6G638rfOF7JiBDue3P0WCbPtdbzlh8pKXWXpXRA6fut8mne4bgv794Z65F1tgg7juZi4j1tQj4+qbzwnhtS5RB+XssOUoHnB0Ow5EbzMxQV9jfLqi0lXzXfVoZvtx9Dga3Mq93vGWuP14Geqej2omCGDqM+nCUvDcrz5S/RNeLeGakZK/s7wJjkUC6u4qQ/7678pTZIiAn6DOMog+UXpGn98iKRmXqZyEy9TIjCQm1ihYVurW5vLFMcnjZ9pWIvN7+1THbM8eNn3X237jnqv6PHPK4aLlESQQFHsk67tU6cOBtwrJYvSYzlsf/yYV/57C7LL3DvX7bxgE+s6NjR+SOxNlePXCK9dh5jdhr1jey5PvfRGpGZepk41Lyll97kxdskx/lpyyF3e8n5fHf7onV7JfsPmbnWt93P+hTnnXe3/7H3eGDjhYUi56q24txVbb10i9pdLU5c0VoU5OS5u/aa+IOPh39/t13WMbjn4Al3v3Onc4QQzvdvb5MW4mCzKwMfvxLvpc/757EWD7/3s8/6bNyZJa0loZ1/Ls8dl/X3adme+k5a4ePJ5xgMNr6H5xtf/87vWMG8yBlHl5hgXkKhqdVDuHzL3M5t31725zcvSxEAzpIHbbMvPPDLofH2WYfDrWVRcSuuRUCxF7NwyI4xefjbZg/Q16OfWeNf1cJhd2udkBrTYyyT1FjBvHjsP1Zx/g4HsHlz+esK+H3vPT3JOaVygVV/nsCHEnomP++R5/tR4rk2DmlfZqnjw8/6CHt5+w4R+LiwlZQiaed2AEBOfjFqJSQADgfiM7ciHsD4H3bjtfuvcY7hkJiLXd4x6Hmsnr0w373Hc3Hlkd1BY6XeS4vE+1d+LDkurEP58bfZtT5Sx0WFbYdH3N/+fEnomCGxFhXHkjG+z3sa5FiW3C9jHF1i5HgJhaZWD+HyLXdbJrwsRQBEVtVndQ/xkx8kVFyYivTn3HhqBlJfuNn3mwaheu8DfZHChZz7kZR8pdvrXqQg77WtrPwXc6FE2YLMrBzZ48rFfiFp1HR5StZlxfLXrsuQpYES+wsoyGm9Lel8TEXS7ydSOWByQwwlEn8pqbn5Vms64lUnUuVzWmSPFWDfmCW+j+qX8xbpkZDJu6HY47UfHTn3CLlwyBGU8qRiv1x5r9RaYl2VJhRylkPqxuoFm4746V2O2vdd75uPI/DXCIlwmNwQQwnZN3EMHkbPhMOh9s9huWMp9CrnycG6OPZ7Q7H8m60DyEjite4KbuSWfGSIgnEDjuPx2uXJO/lV+P4FyPbKzwx5RQAAjpwrDKrtGafEVqBD6vDpAvlChKiEyQ0BoP70c6Sg9gNPzV/bWgn6IatCU5O/CPqzWFZyo+iyVLmekjNmwZJyKQsW2Q+c9PXk2WZX+I8x0Jksl5Taxw94xtllaLjWMNBK7D5+XrGPSv7riYQBJjfEiUG/PVSdUVERpPYDL1RPrlf2Iat1LGX9Q5Xb+B3H656b8tcWP7+dlBwe3mceAi+MZ1IhWV4hyFj+/ProeJ6lcbiSAXlnr9btP+XTFii5cSl5a/qeLarIybxinzg5SdeafU5/gf49qvmdYMSzoUh0w29LEQDOvxzPJNQEANTVQc+lpTVeiRclMa6+wT7wXP30SABcWv5+UbvH8jNYoDUVwmNOEh/Dwd4Pf2vnjlP42SKlJ/y0mzzaLR5rYzaZJPv7a5eco/Bc98CeHUJaw9/7VtGDyY8vH0vCV9NkKm+Lsfs3mvHnSbTwGcN33IrHt+fcPO8j9hf36qI/8MnAbl5eA525cfXJKy7zmU9FbdfhnVdcitik2oiLscDiEkpOltT1eu8q9PFBan/FtmDbamO0auihqdWDmphQrEVysvObU2fPBh8LTG7IBQpj49FhyDwAwOFq1bSJVavm1moap7z8gi1OuRdbXILsGEdiub8FiQH6eszDouQuVgnsHmP+JPVo/mBjeewP5nVKfKLffVIU+1tvj7jqcfJ/VRT50Su1Jkq2i2rV3e3bEqXHP5iQ6D7NXBov8V77maMjsXzMBUHKLzgSE3HNhb7rXX09dK+MLT+WbRJz8TyuAh2Dnp5+vjCOyWMNNsf7/zdTbPWdu02izbXd+sK/P88xP79wfEj5dW0nny31iVvqz5fHGr13YT4lATx9cOH4H7PiEBY/9z80qpWAdRf6vPPZWggIDK/oJzbOPRZO+Z698vTis1+qLdi2mhg5XkKhqdVDuHzL2VZQfiEiLktNnz4dzZo1Q3x8PK699lps2rQpYP+FCxeiRYsWiI+PR+vWrbFs2bIQOY1ejDrtqyYpMPoUtJov0Gg9c6PkspTceze89D1mYsTyGfH1dRfeTyj2PnPjotTj+RlKnHjfvBu4r+dllxKPr4W744MIyL0s5fUoIYl7YgJ9RVvp++Cak/Aa09kW+N+mySu+4mv//i5EBziGyy7ouJ46fDTH+RTi3MJSTFu5H9NXHkBuYamkLiFyCfuZmwULFmDYsGGYMWMGrr32WkydOhU9e/bEnj17UL9+fZ/+69evx0MPPYT09HTcfvvtmDdvHvr164etW7eiVatWYZiBNA6HwOa/ziEz6xx2HM1DblEpTufb0OaiWqidGAtrjAWxMSYUl9iRaI1BNWsMYswmWMwmj/+b3b+A3G0WE8ocAgmxFsRanPtjLSaYTc4fi9n5i8VyYdtsxoV2Vx/nfhMAs9kEkwkoLrF7/eIvsztgMZsU3cfiD2uMJXinChhdo6fEXv4ME7ljaV0Ku1dyE3hQNWOVeVzK0HqWSQo9jgV/z5nxl4B4jlhmF7Cq+G2l5Pkxnn2Ly3yfcxMMud8MLPPIbhzuy0blY5cFuCyl9H1w6Xoef67kItDzguwXPHp6KZOR3LgSs4qHoGdiWHohcawoZ/P4d+l8HVsez+yGKCTsyc2UKVMwYMAAPPHEEwCAGTNmYOnSpZg5cyZGjBjh0/+9997DbbfdhuHDhwMA3nzzTaxYsQLTpk3DjBkzQurdk5IyB07n22Arc6C41I5hX2zHn8fzfPrtPObbFglYS22Ys3AsAOCqUhtssVZ34hRrNiM2xux8bTEj1mKGyeT88HElSs5tZ7JkLS3B/HnO9y7tvnFIm7kJ1hgz4mMtMJmcv1Rd/zdfiDNfSMrMJmDL7mOYf8HLlM6NUGqNh+VCggaUx7kwmYBjx8+6Y/5zU1M4LlRDdv017fpFbitzIHPvMbe/T5pNx+7sxu79rnkBgMVm85rH/379y+eXdsUPNAHhXgfAmTzZhcCfB7LdWv9r/V9c3tw7cbfYir3GmrP+8IV4geIyB4pyz3vtn7XuEMwmE4QQcAigMDffvf/9Ru8jp7AEpXaB4lI7ygoKvOexwantEEBRqd0Ze2Ht5t12KUrjrChzCJiKinzjPD4QK340mkzODzHPY2nebZegzBoPE4Dlmw+59ebedglKY60QANbvyCpfm87/Q916tWAyAcs3lff/tGsTVKtVA0IA3/56wMtvmdXq43Xm2kMwm4BzZ3Ld7XMu+RD7T10EE5wJlcll+sIbdfTYWXffb679FJtSa/u8Lx/9cgAOARz++7Tbw5weF8MRH48v1+1z953d42IgPh5mc3kRUNd79evO8vnOav0JLm9WH7sPnXC3zWw/C00vquv8t2UqTwzKHAJfrd3nNW4uYpB3Ns+rTQBenv+79hDOnMpxt33UdBoOnGyCBb/swfx5owAAH3Ztgnxbmc9c9x4+Wb5+V8zA9ssbQgi4//0KISA81v6N2pORW1SK3/dluz3N6t4cJXaHu89rNSbhTIENpfkFXr7/OlN+nHYtGweHgHt/2n3j0HfaWoy79WJc+eSDAIBTXywCEipcKisqQr377/LeX7ENCLytNqYiSjX00Ay2HkbM3Y+HuBgz6teId/tAr17O199/7/y/mu0y73p1gQhrclNSUoItW7Zg5MiR7jaz2Yzu3btjw4YNkjEbNmzAsGHDvNp69uyJxYsXS/a32Wyw2Wzu7bw8Y5KLzKwc3P8fX88tUmvgqoZJqFMtFrEWM2xlDjiEgK3MgVPnbYgxm5AQa0FRqR1lDgG7Q1z4vwNldue248IvRbtDwFZmR2GJHXExZpTaHXA4nH8tCTh/CTqEM8b1oeoQAg6H869Ff394mUxAYowJ12XtAFD+qHP7BT/FcAA26VgpEkqKvbRW7w1yPbZivBDu+CdWH0BRXLyymB/2BIxJKCnGx66+e0/hp8PSX01NKCnGnx7zGL14h6J5VNSa4BpzSxaK/jjls99zrLHf7PTZ/6LH/nHf7vIb/8ShM1h3tMBv7OgKD/LzWrtvdrjXzmf+Eg8AlJyrl95OLz1/7R9LvN9e/VfsLW+X8FvR6xvf7fKZ+xN/nsD3B3L9+y4pxghX3zUHJXUnLNvt62HpLnffim3+xvnIfSz8jaI/TiOhpBhvu9o2/oWibSeCr61r3AptALw8v/ndLiSUFONlV599p/HzX/lIKCnzWl8AGF5hrgklxXjH1eePbCzZkyM5H9d4h06cx1tL//T2tOxPL0/HzhQ6tSV8j7mwDYeAGfD6PbL971w8NGMD/ly3BgDQ451VPmucUFLss79iG4CA22pjJNdFgYYemsHWw4i5+/NwdZNa+HpgF6cRhwNYvbr8NaB+WyZhTW5Onz4Nu92OlJQUr/aUlBTs3r1bMiY7O1uyf3Z2tmT/9PR0jBs3Th/DAYiPNSPOYoY1xgxrrAXxsWY8dUNzPNGlueFjK0EIZ+LjSpgcQiDOYoa5qBCY5OyzYeTNKIlPgBDOxKnULlBqd6CkzOHeFsJ5kUGIC5cbnP85L/MU5APvOrUm398WhbHxsJXZUVzq8Pj2Rvn4nn/VOoSAubDQHf/odU1QGp8IIQTsbu9eM4IQQF1TqTvmrvaNUBKf4DyVXeGehrgYM+qby9x972zbEPmxVpcUxAU9AIgtLnKPcttVqSiKi/f6dovLi+tsj+tsgNOfcF9CsJhMSCgpLtdqlYqSCjdnxhWVj9W7dQMUx8U7zwKZTLDGmFEX5fcg3NGmAQri4t0DmgDEesT3apUKmzUBsRYT4mMtqGEvz0xvalEf4sINnWaTc38tUb6/R8sUlCUkwGI2I8HmrVkYK/1h7brcJITzkljp+XwvvdILx5LnGvS8KgWlCYkwAbDaKqyN1fneVSu1efW3WRNgNpkQ5+HL5ddaXK5xV/tGyLPEQQCILSp/UN1tV6XCFh/v9up6n11n2+IqzLcoLh5xHsfAnW0bojQhASaYYLV5v182q3ffXq1SYYtP8L7mduG9qlFW4hMb6yfW5Q1wXkauLUq8+llr1XAeG++Wt3lebrqjTQMUWxMQX8FvSXyCl9+7r26EGvExbp3brkqFqXo1VLeXr2uvCsetuHD8eerc3sZX+/Y2Dbzu17nhsmSYqlWDI7/8OOnduoHPZUOpq2bWGLPXa0eM941OVofv/opt/vS0xvh4Vaihh2aw9ZAzpl6+Y+XehGYUQUtrGsjRo0cFALF+/Xqv9uHDh4tOnTpJxsTGxop58+Z5tU2fPl3Ur19fsn9xcbHIzc11/2RlZQmwKrgv+fmu3/fO1+HUUhOvJEZu31CuiZH71caqnb9SPT3a9ZiDVl0tx5WesXL11cYZ5UkPP0aNIycmmJdQaGr1EC7fMrdz4fwbOuKrgicnJ8NiseDECe9TsCdOnEBqaqpkTGpqqqL+VqsVVqtVH8OEEEIIiXjCet4oLi4OHTp0QEZGhrvN4XAgIyMDnTt3lozp3LmzV38AWLFihd/+hBBCCKlahP3bUsOGDUNaWho6duyITp06YerUqSgoKHB/e6p///5o1KgR0tPTAQBDhw5F165dMXnyZPTp0wfz58/H5s2b8dFHH4VzGoQQQgiJEMKe3DzwwAM4deoUxowZg+zsbLRr1w7Lly933zR85MgRmM3lJ5iuv/56zJs3D6NGjcKrr76Kyy67DIsXL46oZ9xUWqSenBsuLTXxSmLk9g3lmhi5X22s2vkr1dOjXY85aNXVclzpGStXX22cUdp6+DFqHDkxWjX00NTqQU1MKNYiMdF5F47HlycCYRJCCFk9o4S8vDwkJSUhNzcXNWtqq39ECCGEkNCg5PM7IsovEEIIIYToBZMbQgghhEQVTG6Ik+JioE8f54/HA9HCoqUmXkmM3L6hXBMj96uNVTt/pXp6tOsxB626Wo4rPWPl6quNM8qTHn6MGkdOTDAvodDU6iFcvuVu33uvtL4UQZ+EE2Xk5ubKfghQlSKUD6wzIl7rw9r08qFWy8j9amPVzt/Ih/X5a9djDlp19XwQn5ZYufp6PDRPT209/Bg1TjgeXKdGU6uHcPmWua3kIX48c0MIIYSQqILJDSGEEEKiCiY3hBBCCIkqmNwQQgghJKpgckMIIYSQqCLs5RdCjRACgPNJh8SDgoLy13l5gN0ePi018Upi5PYN5ZoYuV9trNr5K9XTo12POWjV1XJc6RnrSSB9tXFGearYR40fNV708lYRpRp6aAZbDzljhsO3zG3XK9fneCCqXPmFv//+G40bNw63DUIIIYSoICsrCxdddFHAPlUuuXE4HDh27Bhq1KgBk8mkq3ZeXh4aN26MrKws1q0KMVz78MG1Dx9c+/DBtQ89QgicP38eDRs29CqoLUWVuyxlNpuDZnxaqVmzJg/2MMG1Dx9c+/DBtQ8fXPvQkpSUJKsfbygmhBBCSFTB5IYQQgghUQWTGx2xWq0YO3YsrFZruK1UObj24YNrHz649uGDax/ZVLkbigkhhBAS3fDMDSGEEEKiCiY3hBBCCIkqmNwQQgghJKpgckMIIYSQqILJjU5Mnz4dzZo1Q3x8PK699lps2rQp3JaijvT0dFxzzTWoUaMG6tevj379+mHPnj1efYqLizFo0CDUrVsX1atXxz333IMTJ06EyXH0MnHiRJhMJrzwwgvuNq69cRw9ehSPPvoo6tati4SEBLRu3RqbN2927xdCYMyYMWjQoAESEhLQvXt37Nu3L4yOowO73Y7Ro0ejefPmSEhIwCWXXII333zTq7YR1z5CEUQz8+fPF3FxcWLmzJli586dYsCAAaJWrVrixIkT4bYWVfTs2VPMmjVL7NixQ2RmZorevXuLJk2aiPz8fHef5557TjRu3FhkZGSIzZs3i+uuu05cf/31YXQdfWzatEk0a9ZMtGnTRgwdOtTdzrU3hrNnz4qmTZuKxx9/XGzcuFEcPHhQ/PDDD2L//v3uPhMnThRJSUli8eLFYvv27eLOO+8UzZs3F0VFRWF0XvkZP368qFu3rvjuu+/EoUOHxMKFC0X16tXFe++95+7DtY9MmNzoQKdOncSgQYPc23a7XTRs2FCkp6eH0VX0c/LkSQFArF69WgghRE5OjoiNjRULFy509/nzzz8FALFhw4Zw2Ywqzp8/Ly677DKxYsUK0bVrV3dyw7U3jldeeUXccMMNfvc7HA6Rmpoq/vWvf7nbcnJyhNVqFZ9//nkoLEYtffr0EU8++aRX29133y0eeeQRIQTXPpLhZSmNlJSUYMuWLejevbu7zWw2o3v37tiwYUMYnUU/ubm5AIA6deoAALZs2YLS0lKv96JFixZo0qQJ3wudGDRoEPr06eO1xgDX3ki++eYbdOzYEffddx/q16+P9u3b4+OPP3bvP3ToELKzs73WPikpCddeey3XXiPXX389MjIysHfvXgDA9u3bsXbtWvTq1QsA1z6SqXKFM/Xm9OnTsNvtSElJ8WpPSUnB7t27w+Qq+nE4HHjhhRfQpUsXtGrVCgCQnZ2NuLg41KpVy6tvSkoKsrOzw+Ayupg/fz62bt2K3377zWcf1944Dh48iA8//BDDhg3Dq6++it9++w1DhgxBXFwc0tLS3Osr9TuIa6+NESNGIC8vDy1atIDFYoHdbsf48ePxyCOPAADXPoJhckMqJYMGDcKOHTuwdu3acFupEmRlZWHo0KFYsWIF4uPjw22nSuFwONCxY0dMmDABANC+fXvs2LEDM2bMQFpaWpjdRTdffPEF5s6di3nz5uGqq65CZmYmXnjhBTRs2JBrH+HwspRGkpOTYbFYfL4VcuLECaSmpobJVXQzePBgfPfdd1i5ciUuuugid3tqaipKSkqQk5Pj1Z/vhXa2bNmCkydP4uqrr0ZMTAxiYmKwevVq/Pvf/0ZMTAxSUlK49gbRoEEDtGzZ0qvtyiuvxJEjRwDAvb78HaQ/w4cPx4gRI/Dggw+idevWeOyxx/Diiy8iPT0dANc+kmFyo5G4uDh06NABGRkZ7jaHw4GMjAx07tw5jM6iDyEEBg8ejEWLFuHnn39G8+bNvfZ36NABsbGxXu/Fnj17cOTIEb4XGrnlllvwxx9/IDMz0/3TsWNHPPLII+7XXHtj6NKli88jD/bu3YumTZsCAJo3b47U1FSvtc/Ly8PGjRu59hopLCyE2ez9MWmxWOBwOABw7SOacN/RHA3Mnz9fWK1WMXv2bLFr1y7xzDPPiFq1aons7OxwW4sq/u///k8kJSWJVatWiePHj7t/CgsL3X2ee+450aRJE/Hzzz+LzZs3i86dO4vOnTuH0XX04vltKSG49kaxadMmERMTI8aPHy/27dsn5s6dKxITE8Vnn33m7jNx4kRRq1YtsWTJEvH777+Lvn378uvIOpCWliYaNWrk/ir4119/LZKTk8XLL7/s7sO1j0yY3OjE+++/L5o0aSLi4uJEp06dxK+//hpuS1EHAMmfWbNmufsUFRWJgQMHitq1a4vExERx1113iePHj4fPdBRTMbnh2hvHt99+K1q1aiWsVqto0aKF+Oijj7z2OxwOMXr0aJGSkiKsVqu45ZZbxJ49e8LkNnrIy8sTQ4cOFU2aNBHx8fHi4osvFq+99pqw2WzuPlz7yMQkhMejFgkhhBBCKjm854YQQgghUQWTG0IIIYREFUxuCCGEEBJVMLkhhBBCSFTB5IYQQgghUQWTG0IIIYREFUxuCCGEEBJVMLkhhBBCSFTB5IYQYjjdunXDCy+8EG4buhGq+QghMGXKFDRv3hyJiYno168fcnNzA8acOXMG9evXx+HDh91tv//+O2688Ua0bdsWd911F2w2GwDgwQcfxOTJk42cAiFhgckNIRHMqVOn8H//939o0qQJrFYrUlNT0bNnT6xbt04XfX8f0np/eH/99dd48803ddOr7KSnp+Oaa65BjRo1UL9+ffTr18+nOCbgrEr94YcfYs6cOVizZg22bNmC119/PaD2+PHj0bdvXzRr1gwAUFxcjAcffBCffPIJtm/fjoYNG2Lu3LkAgFGjRmH8+PFBEyZCKhtMbgiJYO655x5s27YNc+bMwd69e/HNN9+gW7duOHPmTLityaKkpAQAUKdOHdSoUSPMbiKH1atXY9CgQfj111+xYsUKlJaW4tZbb0VBQYG7z8aNGzFlyhQsWLAA//jHP9ChQwcMGDAAy5Yt86tbWFiI//73v3jqqafcbYsXL0avXr1wxRVXAABatGiBU6dOAQBatWqFSy65BJ999plBMyUkTIS5thUhxA/nzp0TAMSqVav89rHb7eLtt98Wl1xyiYiLixONGzcWb731lnv/999/L7p06SKSkpJEnTp1RJ8+fcT+/fuFEM6Kx6hQhPTQoUN+2+12u5gwYYJo1qyZiI+PF23atBELFy708tO1a1cxaNAgMXToUFG3bl3RrVs3d7uryGbXrl3F888/L4YPHy5q164tUlJSxNixY7108vLyxMMPPywSExNFamqqmDJlik+hzooEmqunPz3G9tyWsy7BOHnypAAgVq9e7W679957Rffu3b36zZgxQ9SpU8evzsKFC0W9evW82saMGSM++eQT9/azzz4rvvnmG/f2uHHjxA033KDILyGRDs/cEBKhVK9eHdWrV8fixYvd90hUZOTIkZg4cSJGjx6NXbt2Yd68eUhJSXHvLygowLBhw7B582ZkZGTAbDbjrrvugsPhwHvvvYfOnTtjwIABOH78OI4fP47GjRv7bU9PT8enn36KGTNmYOfOnXjxxRfx6KOPYvXq1V6e5syZg7i4OKxbtw4zZsyQ9D1nzhxUq1YNGzduxKRJk/DGG29gxYoV7v3Dhg3DunXr8M0332DFihVYs2YNtm7dGnC9As3VyLHlrksgXJeF6tSpAwCw2WxYunQp7rrrLq9+xcXFSEpK8quzZs0adOjQwautQYMG2L17NwAgMzMT69evR69evdz7O3XqhE2bNvk9xgiplIQ7uyKE+OfLL78UtWvXFvHx8eL6668XI0eOFNu3bxdCOM8wWK1W8fHHH8vWO3XqlAAg/vjjDyGE8Hs2pGJ7cXGxSExMFOvXr/fq99RTT4mHHnrIK659+/YB9bp27epzpuCaa64Rr7zyintesbGxXmc/cnJyRGJiYsAzN8HmqufYrvnIXZdA2O120adPH9GlSxd32/r16wUAER8fL6pVq+b+iYuLEz179vSr1bdvX/Hkk096teXn54vevXuLq666SnTp0kXs2rXLa//27dsFAHH48GFZfgmpDMSEN7UihATinnvuQZ8+fbBmzRr8+uuv+P777zFp0iR88sknaNmyJWw2G2655Ra/8fv27cOYMWOwceNGnD592n0W48iRI2jVqpVsH/v370dhYSF69Ojh1V5SUoL27dt7tVU8cyBFmzZtvLYbNGiAkydPAgAOHjyI0tJSdOrUyb0/KSnJfc+IP+TOVc+xlayLPwYNGoQdO3Zg7dq17ra9e/eiWrVqyMzM9Orbp08fdOnSxa9WUVER4uPjvdqqVauGpUuX+o1JSEgA4Lxfh5BogckNIRFOfHw8evTogR49emD06NF4+umnMXbsWHz33XdBY++44w40bdoUH3/8MRo2bAiHw4FWrVq5b/SVS35+PgBg6dKlaNSokdc+q9XqtV2tWrWgerGxsV7bJpPJ5/KRUuTOVc+xlayLFIMHD8Z3332HX375BRdddJG7PS8vD8nJybj00kvdbX/99Rf27duHe+65x69ecnIyzp07p2gOZ8+eBQDUq1dPURwhkQzvuSGkktGyZUsUFBTgsssuQ0JCAjIyMiT7nTlzBnv27MGoUaNwyy234Morr/T54IuLi4PdbveJrdjesmVLWK1WHDlyBJdeeqnXT+PGjXWd38UXX4zY2Fj89ttv7rbc3Fzs3bvXb4ycuRoxttp1EUJg8ODBWLRoEX7++Wc0b97ca39ycjJyc3MhhHC3jR8/Hr1790bLli396rZv3x67du2SO10AwI4dO3DRRRchOTlZURwhkQzP3BASoZw5cwb33XcfnnzySbRp0wY1atTA5s2bMWnSJPTt2xfx8fF45ZVX8PLLLyMuLg5dunTBqVOnsHPnTjz11FOoXbs26tati48++ggNGjTAkSNHMGLECK8xmjVrho0bN+Lw4cOoXr066tSpA7PZLNn+0ksv4cUXX4TD4cANN9yA3NxcrFu3DjVr1kRaWppu865RowbS0tIwfPhw1KlTB/Xr18fYsWNhNpthMpkkY+TM1Yixa9SooWpdBg0ahHnz5mHJkiWoUaMGsrOzATgvgSUkJODmm29GcXExJk6ciAcffBBz587Ft99+i02bNgX037NnT4wcORLnzp1D7dq1Zc15zZo1uPXWW2X1JaTSEO6bfggh0hQXF4sRI0aIq6++WiQlJYnExERxxRVXiFGjRonCwkIhhPNm1Lfeeks0bdpUxMbGiiZNmogJEya4NVasWCGuvPJKYbVaRZs2bcSqVasEALFo0SIhhBB79uwR1113nUhISHB/5dtfu8PhEFOnThVXXHGFiI2NFfXq1RM9e/b0+vqynBuUpfr07dtXpKWlubelvo7dqVMnMWLECL/rFWyueo7tqSNnXSqCCl+1d/3MmjXL3Wf+/PmicePGIiEhQfJr7f7o1KmTmDFjhqy+RUVFIikpSWzYsEFWf0IqCyYhPM57EkJIBFJQUIBGjRph8uTJXg+oi/ax1bB06VIMHz4cO3bsgNkc+M6DDz/8EIsWLcKPP/4YIneEhAZeliKERBzbtm3D7t270alTJ+Tm5uKNN94AAPTt2zeqx9aDPn36YN++fTh69GjQ+6FiY2Px/vvvh8gZIaGDyQ0hJCJ55513sGfPHsTFxaFDhw5Ys2ZNyG56DefYeiC3LtjTTz9trBFCwgQvSxFCCCEkquBXwQkhhBASVTC5IYQQQkhUweSGEEIIIVEFkxtCCCGERBVMbgghhBASVTC5IYQQQkhUweSGEEIIIVEFkxtCCCGERBVMbgghhBASVTC5IYQQQkhUweSGEEIIIVEFkxtCCCGERBVMbgghhBASVfw/tJ1v8SJNhZsAAAAASUVORK5CYII=",
      "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": {},
   "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",
      "Cost function after refinement: 2.0171535049901094e-10\n",
      "GonioParam(dist=0.7200081377942467, poni1=0.03288139794526324, poni2=0.004, rot1=0.0, offset=-54.39982225664511, scale=1.0, nrj=17.027082549190933)\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.03288139794526324\n",
      " 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",
      " 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",
      "Cost function after refinement: 6.550962572089796e-08\n",
      "GonioParam(dist=0.7205268320603014, poni1=0.03323636624652024, poni2=0.004, rot1=0.0, offset=-54.399817662277066, scale=1.0, nrj=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",
      " 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",
      "Cost function after refinement: 2.980133018674163e-10\n",
      "GonioParam(dist=0.7205282661392393, poni1=0.03339481351901525, poni2=0.003972951169717649, rot1=1.9483670504509448e-05, offset=-54.39981566521699, scale=0.9990259510567647, nrj=17.027082549190933)\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990259510567647\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:99: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20549\n",
      "54 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:107: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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": {},
   "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",
      "Cost function after refinement: 5.651340369404608e-11\n",
      "GonioParam(dist=0.7199985717445607, poni1=0.03180385768846998, poni2=0.004, rot1=0.0, offset=-48.599891358723, scale=1.0, nrj=17.027082549190933)\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.03180385768846998\n",
      " 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",
      " 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",
      "Cost function after refinement: 5.1983308105299717e-08\n",
      "GonioParam(dist=0.7206068542793277, poni1=0.03211384891528438, poni2=0.004, rot1=0.0, offset=-48.599887342572686, scale=1.0, nrj=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",
      " 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",
      "Cost function after refinement: 2.556084121448774e-10\n",
      "GonioParam(dist=0.7206092934239683, poni1=0.03224725723057559, poni2=0.003968935506155581, rot1=2.237548771450148e-05, offset=-48.59988566030428, scale=0.9990409081096999, nrj=17.027082549190933)\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990409081096999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:99: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18650\n",
      "54 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:107: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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[6],\n",
    "           207,\n",
    "           245,\n",
    "           266)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "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",
      "Cost function after refinement: 9.109058146293743e-11\n",
      "GonioParam(dist=0.7199860337927957, poni1=0.030492505461750096, poni2=0.004, rot1=0.0, offset=-42.79990784449265, scale=1.0, nrj=17.027082549190933)\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.030492505461750096\n",
      " 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",
      " 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",
      "Cost function after refinement: 4.121300333570348e-08\n",
      "GonioParam(dist=0.720705554602103, poni1=0.03076843090598898, poni2=0.004, rot1=0.0, offset=-42.799904173872896, scale=1.0, nrj=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",
      " 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",
      "Cost function after refinement: 1.905573974207547e-10\n",
      "GonioParam(dist=0.7207071581128385, poni1=0.030863392388519827, poni2=0.003965206217913014, rot1=2.5069661472187673e-05, offset=-42.799902973828175, scale=0.9990506059528655, nrj=17.027082549190933)\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990506059528655\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:99: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16803\n",
      "45 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:107: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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RqUemFlW1BbougXKoyBnoepgxdxV169nWqNp+W2r27NkYO3YsPvnkEyQnJ/scl5mZicTERNcP705MREQU2arl4mbOnDl48MEH8cknn6BHjx5+x44aNQo5OTmun/379wepSiIiIgqFave21Mcff4yBAwdizpw56N27d8DxNpsNNpstCJURERFROAjp4iYvLw+7du1ybe/ZswebNm1CgwYN0KJFC4waNQoHDhzABx98AMD5VlRGRgbefPNNdO7cGdnZ2QCA+Ph4JCYmhmQOREREFF5C+rbUunXr0L59e9c9akaMGIH27du7vtZ96NAh7Nu3zzX+3XffRVlZGQYNGoTGjRu7foYOHRqS+omIiCj8hPSVm+7du8PfbXZmzpzpsb1s2TJzCyIiIqJqr9p95oZMEhUF3H57xWODubZfmY5dR/PQYO9JdLlIoiu43lr0xGgdq/ia+M1l5nHZWNn5682nYr+KORjNa+R5pTpWyxjZODNrMlqPbC0qYrTUEoycRmsIVd1atktLgS++8H6OSsLmDsXBwjsUB0fayIUAgPNT6uDb4VeHuBoiIqru9Pz9rpZfBSciIiLyhYsbIiIiiihc3JBTfr6zJ4jFoqt/h69ce1/5B/a+8g/YiguDU4ueGK1jFV8Tv7nMPC4bKzt/vflU7FcxB6N5jTyvVMZqzS8bZ1ZNKuox6zxaYgLVEoycRmsIVd1at3Xc8oWLGyIiIoooXNwQERFRROHihoiIiCIKFzdEREQUUbi4ISIioojCxQ0RERFFFLZfIKeoKKBXr4rHBnP9cFZHAIDDKrF+lqlFT4zWsYqvid9cZh6XjZWdv958KvarmIPRvEaeV6pjtYyRjTOzJqP1yNaiIkZLLcHIabSGUNWtZbu0FFiyxPs5KmH7BTIF2y8QEZFKbL9ARERENRYXN0RERBRRuLghp/x8oFYt54+CVgNbJ96GrRNvk2+/oLcWPTFaxyq+Jn5zmXlcNlZ2/nrzqdivYg5G8xp5XqmM1ZpfNs6smlTUY9Z5tMQEqiUYOY3WEKq6tW6npnrP7wU/UEwVCgqUpUooLTaWQKYWPTFaxyq8JgFzmXlcNlZ2/nrzqdivYg5G8xp5XqmM1ZpfNs6s3CrqMes8WmKM5lCR02gNMjHBuBY6/y3iKzdEREQUUbi4ISIioojCxQ0RERFFFC5uiIiIKKJwcUNEREQRhd+WIierFejWreKxwVw/N28DABAWiVwyteiJ0TpW8TXxm8vM47KxsvPXm0/FfhVzMJrXyPNKdayWMbJxZtZktB7ZWlTEaKklGDmN1hCqurVsl5UBK1d6P0clbL9ApmD7BSIiUontF4iIiKjG4uKGiIiIIgoXN+SUnw80auT8UdBqYP1bd2P9W3fLt1/QW4ueGK1jFV8Tv7nMPC4bKzt/vflU7FcxB6N5jTyvVMZqzS8bZ1ZNKuox6zxaYgLVEoycRmsIVd1at1u18p7fC36gmCocO6YsVcPCXGMJZGrRE6N1rMJrEjCXmcdlY2Xnrzefiv0q5mA0r5HnlcpYrfll48zKraIes86jJcZoDhU5jdYgExOMa6Hz3yK+ckNEREQRhYsbIiIiiihc3BAREVFE4eKGiIiIIgoXN0RERBRR+G0pcrJagY4dKx4bzLU59Vz5XDK16InROlbxNfGby8zjsrGy89ebT8V+FXMwmtfI80p1rJYxsnFm1mS0HtlaVMRoqSUYOY3WEKq6tWzb7cDGjd7PUQnbL5Ap2H6BiIhUYvsFIiIiqrG4uCEiIqKIwsUNORUUAGlpzp+CAsO5fpo6ED9NHQhbSVFwatETo3Ws4mviN5eZx2VjZeevN5+K/SrmYDSvkeeVylit+WXjzKpJRT1mnUdLTKBagpHTaA2hqlvrdps23vN7wQ8Uk5MQwF9/VTw2mKtZ7hH5XDK16InROlbxNfGby8zjsrGy89ebT8V+FXMwmtfI80p1rJYxsnFm1mS0HtlaVMRoqSUYOY3WEKq6tW5rxFduiIiIKKJwcUNEREQRhYsbIiIiiihc3BAREVFE4eKGiIiIIgq/LUVOFgvQunXFY4O5djRsIZ9LphY9MVrHKr4mfnOZeVw2Vnb+evOp2K9iDkbzGnleqY7VMkY2zsyajNYjW4uKGC21BCOn0RpCVbeWbbsd2L7d+zkqn5LtF8gMbL9AREQqsf0CERER1VghXdz8+OOPuOmmm9CkSRNYLBYsWLAgYMyyZctw6aWXwmaz4ZxzzsHMmTNNr5OIiIiqj5AubvLz83HJJZdgypQpmsbv2bMHvXv3xjXXXINNmzZh2LBhePDBB/Htt9+aXGkNUFAAXHSR80dBq4Hv3n8c373/uHz7Bb216InROlbxNfGby8zjsrGy89ebT8V+FXMwmtfI80plrNb8snFm1aSiHrPOoyUmUC3ByGm0hlDVrXW7Uyfv+b0RYQKAmD9/vt8xTz/9tLjooos89t15550iPT1d83lycnIEAJGTkyNTZuTKyxPCeaNr52NFuW6esDg4teiJ0TrWpGviNZeZx2VjZeevN5+K/SrmYDSvkeeVylit+WXjzKpJRT1mnUdLTKBagpHTaA2hqlvjdg4gtP79rlafuVm9ejV69OjhsS89PR2rV6/2GVNcXIzc3FyPHyIiIopc1Wpxk52djZSUFI99KSkpyM3NRWFhodeYzMxMJCYmun6aN28ejFLpDCFCXQEREdU01WpxI2PUqFHIyclx/ezfvz/UJREREZGJqtVN/FJTU3H48GGPfYcPH0bdunURHx/vNcZms8FmswWjPCIiIgoD1eqVmy5duiArK8tj35IlS9ClS5cQVUREREThJqSv3OTl5WHXrl2u7T179mDTpk1o0KABWrRogVGjRuHAgQP44IMPAACPPvooJk+ejKeffhoDBw7EDz/8gE8++QQLFy4M1RQih8UCtGxZ8dhgrr/rJsvnkqlFT4zWsYqvid9cZh6XjZWdv958KvarmIPRvEaeV6pjtYyRjTOzJqP1yNaiIkZLLcHIabSGUNWtZdvhADR+tCSk7ReWLVuGa665psr+jIwMzJw5E/3798fevXuxbNkyj5jhw4dj69ataNasGZ5//nn0799f8znZfiE42H6BiIhU0vP3O6Sv3HTv3h3+1lbe7j7cvXt3bNy40cSqiIiIqDqrVp+5ISIiIgqEixtyKiwELrvM+ePjnkF6cn0xazi+mDUcsSXFwalFT4zWsYqvid9cZh6XjZWdv958KvarmIPRvEaeVypjteaXjTOrJhX1mHUeLTGBaglGTqM1hKpurdvdu3vP703AexhHGLZf8MGkVgM3ZbL9gqZcbL9gbD/bL+hvW8D2C2y/wPYLRERERNUDFzdEREQUUbi4ISIioojCxQ0RERFFFC5uyFRGb+xLRESkV7VqnEkmS0pSlup4vMG7P8vUoidG61iF1yRgLjOPy8bKzl9vPhX7VczBaF4jzyuVsVrzy8aZlVtFPWadR0uM0RwqchqtQSYmGNciKcnZfuHEicDnQojbL4QC2y8EB9svEBGRSnr+fvNtKSIiIoooXNwQERFRROHihpwKC523tu7eXUmrgTmzR2LO7JHy7Rf01qInRutYxdfEby4zj8vGys5fbz4V+1XMwWheI88rlbFa88vGmVWTinrMOo+WmEC1BCOn0RpCVbfW7V69vOf3JuA9jCMM2y/4wPYLauqQzcX2C8b2s/2C/rYFbL/A9gtsv0BERERUPXBxQ0RERBGFixsiIiKKKFzcEBERUUTh4oaIiIgiCtsvUIWEBGWpCmJsxhLI1KInRutYhdckYC4zj8vGys5fbz4V+1XMwWheI88rlbFa88vGmZVbRT1mnUdLjNEcKnIarUEmJhjXIiHB+f0pjbelYPsFMgXbLxARkUpsv0BEREQ1Fhc3REREFFG4uCGnoiKgd2/nT1GR4VzT572A6fNeQGyZRPsFmVr0xGgdq/ia+M1l5nHZWNn5682nYr+KORjNa+R5pTJWa37ZOLNqUlGPWefREhOolmDkNFpDqOrWun377d7zexPwHsYRhu0XfDCp1cDNE9h+QVMutl8wtp/tF/S3LWD7BbZfYPsFIiIiouqBixsylRChroCIiGoaLm6IiIgoonBxQ0RERBGFixsiIiKKKFzcEBERUURh+wUyBdsvEBGRSmy/QERERDUWFzdEREQUUbi4IaeiIuCOO5w/CloNTFmQiSkLMhFTKtl+QW8temK0jlV8TfzmMvO4bKzs/PXmU7FfxRyM5jXyvFIZqzW/bJxZNamox6zzaIkJVEswchqtIVR1a92+/37v+b0JeA/jCMP2Cz6Y1Grgpky2X9CUi+0XjO1n+wX9bQvYfoHtF9h+gYiIiKh64OKGiIiIIgoXN0RERBRRuLghU1ksoa6AiIhqGi5uiIiIKKJwcUNEREQRhe0XyEkIoKDA+Tghwdj7SULgwv/7HADQsnkSFg/vZn4temK0jlV8TfzmMvO4bKzs/PXmU7FfxRyM5jXyvFIZC2jLLxtnVk2Vx8jUI1OLqtoCXZdAOVTkDHQ9zJi7iro1bufm5iKxSRNNf7+5uCFTsLcUERGpxN5SREREVGNxcUNOxcVA//7On2KJlgmVcr228A28tvANxJSVBKcWPTFaxyq+Jn5zmXlcNlZ2/nrzqdivYg5G8xp5XqmM1ZpfNs6smlTUY9Z5tMQEqiUYOY3WEKq6tW4/+qj3/N4EvIexySZPnixatmwpbDab6NSpk1izZo3f8W+88YY477zzRFxcnGjWrJkYNmyYKCws1Hw+tl/wge0X1NQhm4vtF4ztZ/sFfbfUNxJnVk1sv2A8p9EaQlV3pLVfmDt3LkaMGIExY8Zgw4YNuOSSS5Ceno4jR454HT979myMHDkSY8aMwR9//IH//ve/mDt3Lp555pkgV05EREThKqSLm4kTJ+Khhx7CgAED0Lp1a0ybNg0JCQmYPn261/GrVq1C165dcffddyMtLQ3XX3897rrrLqxduzbIlRMREVG4CtnipqSkBOvXr0ePHj0qirFa0aNHD6xevdprzBVXXIH169e7FjO7d+/GokWL0KtXL5/nKS4uRm5urscPERERRa7oUJ342LFjsNvtSElJ8difkpKCbdu2eY25++67cezYMVx55ZUQQqCsrAyPPvqo37elMjMzMXbsWKW1ExERUfiqVt+WWrZsGcaPH4///Oc/2LBhAz7//HMsXLgQL730ks+YUaNGIScnx/Wzf//+IFZMREREwRayV26SkpIQFRWFw4cPe+w/fPgwUlNTvcY8//zzuO+++/Dggw8CAC6++GLk5+fj4YcfxrPPPgurtepazWazwWazqZ8AERERhaWQLW5iY2PRoUMHZGVloW/fvgAAh8OBrKwsDB482GtMQUFBlQVMVFQUAEAIYWq9ES8hASj/llr57a4N5Lr0iY8AAI1i44JTi54YrWMVXxO/ucw8LhsrO3+9+VTsVzEHo3mNPK9Ux2oZIxtnZk1G65GtRUWMllqCkdNoDaGqW8v26dPA2Wd7P0clIW2/MHfuXGRkZOCdd95Bp06dMGnSJHzyySfYtm0bUlJScP/996Np06bIzMwEALzwwguYOHEi3n33XXTu3Bm7du3CY489hg4dOmDu3Lmazsn2C8HB9gtERKSSnr/fIXvlBgDuvPNOHD16FKNHj0Z2djbatWuHxYsXuz5kvG/fPo9Xap577jlYLBY899xzOHDgABo1aoSbbroJ48aNC9UUKAAjvSaJiIhksHEmORUXAyNGOB9PnAgY+ZxScTE+6HIbAGDe3cPw1ZM9AgQoqEVPjNaxiq+J31xmHpeNlZ2/3nwq9quYg9G8Rp5XKmMBbfll48yqqfIYmXpkalFVW6DrEiiHipyBrocZc1dRt8bt3JISJL7/vra/3wHvYRxh2H7BB5NaDdw8ge0XNOVi+wVj+9l+QX/bArZfYPsFtl8gIiIiqh64uCEiIqKIwsUNmUqIUFdAREQ1DRc3REREFFG4uCEiIqKIwsUNERERRZSQ3sSPwkh8PLBnT8Vjg7mufPS/AIDaMRL3hpGpRU+M1rGKr4nfXGYel42Vnb/efCr2q5iD0bxGnleqY7WMkY0zsyaj9cjWoiJGSy3ByGm0hlDVrWX79GmgbVvv56iEN/EjU7D9AhERqaTn7zffliIiIqKIwsUNOZWUAE895fwpKTGca9TS6Ri1dDqiy0qDU4ueGK1jFV8Tv7nMPC4bKzt/vflU7FcxB6N5jTyvVMZqzS8bZ1ZNKuox6zxaYgLVEoycRmsIVd1at597znt+bwLewzjCsP2CDya1Grgpk+0XNOVi+wVj+9l+QX/bArZfYPsFtl8gIiIiqh64uCEiIqKIwsUNmcpiCXUFRERU03BxQ0RERBGFixsiIiKKKFzcEBERUURh+wVyio8HtmypeGwwV8+BUwAA0bLtF/TWoidG61jF18RvLjOPy8bKzl9vPhX7VczBaF4jzyvVsVrGyMaZWZPRemRrURGjpZZg5DRaQ6jq1rKdlwdcfrn3c1TC9gtkCrZfICIildh+gYiIiGosvi1FTiUlwPjxzsfPPAPExhrKNeynjwAAS/o8EJxa9MRoHav4mvjNZeZx2VjZ+evNp2K/ijkYzWvkeaUyFtCWXzbOrJoqj5GpR6YWVbUFui6BcqjIGeh6mDF3FXVr3S4urprbl4D3MI4wbL/gA9svqKlDNhfbLxjbz/YL+tsWsP0C2y+w/QIRERFR9cDFDREREUUULm6IiIgoonBxQ0RERBFFanGTkZGBH3/8UXUtFELZOYWhLoGIiEgJqcVNTk4OevTogXPPPRfjx4/HgQMHVNdFQTZ49sZQl0BERKSE1H1uFixYgKNHj+J///sfZs2ahTFjxqBHjx544IEH0KdPH8TExKiuk0y24Ughbr5/IgDgy7g4Y8ni4ly5SmMk7g0TFwesXVvxWHWM1rEydcjmMvO4bKzs/PXmU7FfxRyM5jXyvFIdq2WMbJyZNRmtR7YWFTFaaglGTqM1hKpuLdt5ecC113o/RyVK2i9s2LABM2bMwPvvv4/atWvj3nvvxeOPP45zzz3XaGrl2H7Bu/J2CQCwd0JvZfkuSK2DxcPYfoGIiIwJavuFQ4cOYcmSJViyZAmioqLQq1cv/Pbbb2jdujXeeOMNo+mJiIiIdJF6W6q0tBRffvklZsyYge+++w5t27bFsGHDcPfdd7tWU/Pnz8fAgQMxfPhwpQWTOWLspRiw7kvnRklPw60GHl7zGQBgVe97pOLx5pvOx0OHam+/oDVG61iZOmTPaeZx2VjZ+evNp2K/ijkYzWvkeaUyFtCWXzbOrJoqj5GpR6YWVbUFui6BcqjIGeh6mDF3FXVr3S4qqprbl4D3MPaiYcOGon79+uLxxx8XGzdu9Drm5MmTIi0tTSa9qdh+wbsLhn9qSquBmyew/YKmXGy/YGw/2y/ob1vA9gtsvxDB7RekXrl54403cMcddyDOzwff6tWrhz179sikJyIiIpIm9ZmbpUuXorS0tMr+/Px8DBw40HBRRERERLKkFjezZs1CYWHVm74VFhbigw8+MFwURQ4hQl0BERHVNLrelsrNzYUQAkIInD592uNtKbvdjkWLFiE5OVl5kURERERa6Vrc1KtXDxaLBRaLBeedd16V4xaLBWPHjlVWHBEREZFeuhY3S5cuhRAC1157LT777DM0aNDAdSw2NhYtW7ZEkyZNlBdJREREpJWuxU23bt0AAHv27EGLFi1gsVhMKYqCrzg6Bv3uGg8AmKOg1UB5Lun2C0uXVjxWHCNsNtx1pr63SgGfb6TK1CGby8zjsrGy89ebT8V+FXMwmlfruYIRq2WMbJyZNRmtR7YWFTFaaglGTqM1hKpuLdv5+cA//uH9HJVobr/w66+/ok2bNrBarfj111/9jm3btq2mk4cC2y94Z1b7hfNT6uDb4eHVfkEIgVajFgEAvht+Nc5LqRPiioiIKBA9f781v3LTrl07ZGdnIzk5Ge3atYPFYoG3dZHFYoHdbtdfNREREZECmhc3e/bsQaNGjVyPKbJE28tw1+bFzo3S6wEjnd1LS3Hfhq8BAOuvv0MqHu++63z88MPaatET41YfSruorUM2l5nHZWNl5683n4r9KuZgNK/Wc5kdC2jLLxtnVk2Vx8jUI1OLqtoCXZdAOVTkDHQ9zJi7irq1bnu5BY1PAe9hHGHYfsE7s9ov3JQZfu0XHKdPu8bu+POQ2jpkc7H9grH9bL+gv20B2y+w/UIEt1+QvonfwoUVn9F4+umnUa9ePVxxxRX466+/ZFISERERKSG1uBk/fjzi4+MBAKtXr8bkyZPx6quvIikpSXcX8ClTpiAtLQ1xcXHo3Lkz1q5d63f8qVOnMGjQIDRu3Bg2mw3nnXceFi1aJDMNCgJ+oY6IiIJNqnHm/v37cc455wAAFixYgNtvvx0PP/wwunbtiu7du2vOM3fuXIwYMQLTpk1D586dMWnSJKSnp2P79u1e73RcUlKCnj17Ijk5GZ9++imaNm2Kv/76C/Xq1ZOZBhEREUUgqVduateujePHjwMAvvvuO/Ts2RMAEBcX57XnlC8TJ07EQw89hAEDBqB169aYNm0aEhISMH36dK/jp0+fjhMnTmDBggXo2rUr0tLS0K1bN1xyySUy06AaSoiKx3xhiYgo8kgtbnr27IkHH3wQDz74IHbs2IFevXoBAH7//XekpaVpylFSUoL169ejR48eFcVYrejRowdWr17tNebLL79Ely5dMGjQIKSkpKBNmzYYP36836+eFxcXIzc31+OHqJwIPISIiKoZqcXNlClT0KVLFxw9ehSfffYZGjZsCABYv3497rrrLk05jh07BrvdjpSUFI/9KSkpyM7O9hqze/dufPrpp64mnc8//zxef/11vPzyyz7Pk5mZicTERNdP8+bNNc6SiIiIqiOpz9zUq1cPkydPrrLf7KaZDocDycnJePfddxEVFYUOHTrgwIED+Pe//40xY8Z4jRk1ahRGjBjh2s7NzeUCx4uS6BgMuN15DWfYbMaS2WyuXKXREveGsdmAr7+ueKw6xq2+Uf7GytQhm8vM47KxsvPXm0/FfhVzMJpX67mCEatljGycmTUZrUe2FhUxWmoJRk6jNYSqbi3bBQXAP//p/RyVaG6/UNmpU6ewdu1aHDlyBA6HoyKhxYL77rsvYHxJSQkSEhLw6aefom/fvq79GRkZOHXqFL744osqMd26dUNMTAy+//57175vvvkGvXr1QnFxMWJjA/cxYvsF78xqv3BBah0sHhZe7RccDoGznmH7BSKi6sSU9gvuvvrqK9xzzz3Iy8tD3bp1PRpoal3cxMbGokOHDsjKynItbhwOB7KysjB48GCvMV27dsXs2bPhcDhgtTrfUduxYwcaN26saWFDwSe3dCYiIpIntbj5v//7PwwcOBDjx49HQkKC9MlHjBiBjIwMdOzYEZ06dcKkSZOQn5+PAQMGAADuv/9+NG3aFJmZmQCAxx57DJMnT8bQoUPxxBNPYOfOnRg/fjyGDBkiXQM5RdvL0HfrMueGgvYLt//mfHVta9JNUvH46CPn43vu0d5+QWuMW30B2y/orUM2l5nHZWNl5683n4r9KuZgNK/Wc5kdC2jLLxtnVk2Vx8jUI1OLqtoCXZdAOVTkDHQ9zJi7irq1bpvdfiEhIUH8+eefMqFVvP3226JFixYiNjZWdOrUSfz888+uY926dRMZGRke41etWiU6d+4sbDabOOuss8S4ceNEWVmZ5vOx/YJ3Nan9Qlku2y9oOs72C2y/wPYLbL8QDtdCov2C1Cs36enpWLduHc466yyZcA+DBw/2+TbUsmXLquzr0qULfv75Z8PnJQJ4nxsiokgktbjp3bs3nnrqKWzduhUXX3wxYiq9DHXzzTcrKY6IiIhIL6nFzUMPPQQAePHFF6scs1gsfm+qRxRORKgLICIi5aQWN+5f/SYiIiIKJ1J3KHZXVFSkog4iIiIiJaQWN3a7HS+99BKaNm2K2rVrY/fu3QCA559/Hv/973+VFkhERESkh9TbUuPGjcOsWbPw6quvuj5/AwBt2rTBpEmT8MADDygrkIKjJDoGj/cZCQD4j4JWA+W5ymTuDWOzAZ98UvFYdYxbfcMC3epebx2yucw8LhsrO3+9+VTsVzEHo3m1nisYsVrGyMaZWZPRemRrURGjpZZg5DRaQ6jq1rJdUAD07+/9HJVItV8455xz8M477+C6665DnTp1sHnzZpx11lnYtm0bunTpgpMnT+pNGTRsv+BdTWq/UGZ34JxnvwEALBl+Nc5l+wUiorCn5++31NtSBw4cwDnnnFNlv8PhQGlpqUxKIiIiIiWk3pZq3bo1VqxYgZYtW3rs//TTT9G+fXslhVFwRTnsSN+x2rlRlg5ESz01zsSXode2nwAAexv1lIrH/PnOx7fcoq0WPTFu9aHsCrV1yOYy87hsrOz89eZTsV/FHIzm1Xous2MBbfll48yqqfIYmXpkalFVW6DrEiiHipyBrocZc1dRt9btgoKquX0JeA9jLxYsWCASExPFhAkTREJCgvj3v/8tHnzwQREbGyu+++47mZRBw/YL3pnVfuHmCeHXfqE0J9c1difbL7D9gqq8bL/A9gv+amH7BfmcEu0XpN6W6tOnD7766it8//33qFWrFkaPHo0//vgDX331FXr2lPgvdaIQEaEugIiIlJN+nf2qq67CkiVLVNZCREREZJjUKzdnnXUWjh8/XmX/qVOnlDTTpJott4gfSiciInlSi5u9e/d67R9VXFyMAwcOGC6KIoeQeN/ntW+3qS/EB3YFJyKKPLrelvryyy9dj7/99lskJia6tu12O7KyspCWlqasOKqZdhzOMzU/P2dDRBTZdC1u+vbtC8DZ+TsjI8PjWExMDNLS0vD6668rK46IiIhIL12Lm/Ju4K1atcIvv/yCpKQkU4qi4CuNisaTvYYBAF6LjTWWLDbWlassWn/7hbLoGP21xMYCM2ZUPNZY3yP+xurJabQ+M4/LxsrOX28+FftVzMFoXq3nCkasljGycWbWZLQe2VpUxGipJRg5jdYQqrq1bBcWAo8/7v0clUi1X6jO2H7BO7PaL5yfUgffDtfXfuGW/6zExn2nlNVSWandgXPZfoGIqFrR8/db+qvgWVlZyMrKwpEjR1yv6JSbPn26bFoiIiIiQ6QWN2PHjsWLL76Ijh07onHjxrBY+J2T6i7KYcfVezY4NxS0X7jmz18AANlJ+ptmWtziNddSVgZ8+63zcXqAGI/8AdovaM1ptD4zj8vGys5fbz4V+1XMwWherecyOxbQll82zqyaKo+RqUemFlW1BbougXKoyBnoepgxdxV1a902u/1Camqq+OCDD2RCQ47tF7wzq/3CTZn62y/88/Ul+mvRcZv9ErZf0Hac7RfYfoHtF9h+IRyuRbDaL5SUlOCKK/z8Fy/RGTIv6vGFQCIiMkJqcfPggw9i9uzZqmshCgohQl0BERGZSepDBEVFRXj33Xfx/fffo23btoiJ8fy678SJE5UUR0RERKSX1OLm119/Rbt27QAAW7ZsUVkPEV9ZISIiQ6QWN0uXLlVdBxEREZESuhY3t956a8AxFosFn332mXRBREREREboWty4N8qkyFIaFY3nez4KAHhJQauB8lwy7RfsMTH6a4mNBSZPrnissb77A93qXmtOo/WZeVw2Vnb+evOp2K9iDkbzaj1XMGK1jJGNM7Mmo/XI1qIiRkstwchptIZQ1a1lu7AQeOop7+eohO0XCIB57RcuSK2DxcPCq/1CSZkD5z3nbL/w/YircU4y2y8QEYU7PX+/pb4KTqRVOC6dBSqKCsf6iIjIGAP3k6dIYnXY0env350b9huAqCj5ZHY7Lt/3KwAgp9HlusMtbvGaa7HbgRUrnI+vusp/jEf+rmpyGq3PzOOysbLz15tPxX4VczCaV+u5zI4FtOWXjTOrpspjZOqRqUVVbYGuS6AcKnIGuh5mzF1F3Vq38/Or5vYl4D2MIwzbL3hXk9ovFJ3KcY1l+wU/x9l+ge0X2H6B7RfC4VoEq/0CERERUbji4oaIiIgiChc3FHbYOJOIiIzg4oZqNC6kiIgiDxc3FHaECHUFRERUnXFxQ6YKx1dGuHgiIopsvM8NAQDKoqIwvvsAAMAzMfpbJniIiXHlskfpf4rZo6L11xITA7z6asVjjfXd4a89hJ6cRusz87hsrOz89eZTsV/FHIzm1XquYMRqGSMbZ2ZNRuuRrUVFjJZagpHTaA2hqlvLdlERMHq093NUwvYLBCC82i/0nbISm/afUlZLZUWldlzw/GIAwJLhV+PcFLZfICIKd2y/QERERDUW35YiAM72C20O/+ncUNB+oe2hHQCAsuR2usOjREW8rvYLGzY4H196acDb7FfkD9B+QWtOo/WZeVw2Vnb+evOp2K9iDkbzaj2X2bGAtvyycWbVVHmMTD0ytaiqLdB1CZRDRc5A18OMuauoW+t2Xl7V3L4EvIdxhGH7Be/Mar9w84Twa79QeJLtFzQdZ/sFtl9g+wW2XwiHa8H2C0T6hOO3uYiIyBguboiIiCiicHFDREREESUsFjdTpkxBWloa4uLi0LlzZ6xdu1ZT3Jw5c2CxWNC3b19zCyRpQoS6AiIiqmlCvriZO3cuRowYgTFjxmDDhg245JJLkJ6ejiNHjviN27t3L5588klcddVVQaqUgiWYn4Ph4ouIKPKEfHEzceJEPPTQQxgwYABat26NadOmISEhAdOnT/cZY7fbcc8992Ds2LE466yzglgtBQMXHEREZERI73NTUlKC9evXY9SoUa59VqsVPXr0wOrVq33Gvfjii0hOTsYDDzyAFStWBKPUiFcWFYVJXe8CAAxT0GqgPFeZxL1h7FHR+muJiQHGjKl4rLG+3oFuda81p9H6zDwuGys7f735VOxXMQejebWeKxixWsbIxplZk9F6ZGtREaOllmDkNFpDqOrWsl1cDEyY4P0clYS0/cLBgwfRtGlTrFq1Cl26dHHtf/rpp7F8+XKsWbOmSsxPP/2Efv36YdOmTUhKSkL//v1x6tQpLFiwwOs5iouLUVxc7NrOzc1F8+bN2X6hErPaL5yXUhvfDe+mKzaY7Re+H3E1zklm+wUionAXse0XTp8+jfvuuw/vvfcekpKSNMVkZmYiMTHR9dO8eXOTqyQiIqJQCunbUklJSYiKisLhw4c99h8+fBipqalVxv/555/Yu3cvbrrpJtc+h8MBAIiOjsb27dtx9tlne8SMGjUKI0aMcG2Xv3JDnizCgXOO7XduOByA1cC61+HAuUf/cuZtdKH+WtziNdficAB//OF8fOGF/mMq51eR02h9Zh6XjZWdv958KvarmIPRvFrPZXYsoC2/bJxZNVUeI1OPTC2qagt0XQLlUJEz0PUwY+4q6ta6XZ3aL3Tq1EkMHjzYtW2320XTpk1FZmZmlbGFhYXit99+8/jp06ePuPbaa8Vvv/0miouLA56P7Re8M6v9wk2Z3+gON7v9QsGJivYLu3aHX/uF3XuyDcWz/QLbL2jKz/YLbL8Qwe0XQt44c8SIEcjIyEDHjh3RqVMnTJo0Cfn5+RgwYAAA4P7770fTpk2RmZmJuLg4tGnTxiO+Xr16AFBlP1F1NXTORnw58oZQl0FEVG2FfHFz55134ujRoxg9ejSys7PRrl07LF68GCkpKQCAffv2wWrk7QCiaiY7pzjwICIi8inkixsAGDx4MAYPHuz12LJly/zGzpw5U31BpIwQoa6AiIhqGr4kQkRERBGFixuiMMMXu4iIjOHihkzFP9RERBRsYfGZGwq9sqgovNPpVgDAIwpaDZTnKovS/xSzR0XrryUmBnjyyYrHfoiYivzXBbrVvcachuvzuGZeWlZoiPd5XDZWdv5686nYr2IORvNqPVcwYrWMkY0zsyaj9cjWoiJGSy3ByGm0hlDVrWW7uBh4+23v56gkpO0XQkHP7ZtrErPaL5ybXBtLRnTTFWt2+4WCkjK0Hv0tgPBqv1B+zerYovHb2PQQV0NEFF4itv0CUU1Qo/5rg4jIBHxbigA42y80zT3q3FDQfqFZzpmWGkkJ+mtxj9fTfmHfPufjFi0C3mbfI7+KnEbrc6spp1FjqXifx2VjZeevN5+K/SrmYDSv1nOZHQtoyy8bZ1ZNlcfI1CNTi6raAl2XQDlU5Ax0PcyYu4q6tW6fPl01ty8B72EcYdh+wTuz2i/0Hh9+7RfyT5xyjQ3H9gsdRy4wFM/2C2y/oCk/2y+w/UIEt1/g21JEYcYS6gKIiKo5Lm7IVEKIUJegxO6jOrrREhFRSHFxQ6TBsDkbg3auyFgOEhGFDhc3VOPIvJh0JK9EfSFERGQKLm6IiIgoonBxQxRm+IFiIiJjeJ8bAgDYrVH4oL3zbsD3Rxt8WkRHu3LZvbUSMKOW6Gjg8ccrHmus7wp/Y93nYeQeN1rqcztXqbdzaYj3eVw2Vs81NZJPxX4VczCaV+u5ghGrZYxsnJk1Ga1HthYVMVpqCUZOozWEqm4t2yUlwPvvez9HJWy/QADMa79wdqNayPq/7rpizW6/kF9chovGlLdf6IZzkmv7HFs+j3oJMdg0+nrltXg7V21bNLaw/QIRkQe2X6CwUaNWzkREFBb4thQ5CYEGhbmux7AY+OSHEGhQkHPmsf72C57xGmsRAjh2zPk4Kcl/jEd+P+0X3MbZ4xpqKNxAfW7nKoltIBXv87hsrJ5raiSfiv0q5mA0r9ZzmR0LaMsvG2dWTZXHyNQjU4uq2gJdl0A5VOQMdD3MmLuKurVus/2Cb2y/4J1Z7RduHLdId7jZ7Rfyjp9yjdXafqHzM1/omIFEfW7HL2P7Bf372X5B3y31jcSZVRPbLxjPabSGUNXN9gtExgmZoCB+hUmqPiIicuHihoiIiCIKFzdEWvDlFCKiaoOLGwo7wV1H8JZ5RESRhosbIiIiiihc3FDYCcvXUsKyKCIi8ob3uSEAzpYHn7a5DgBwu4L2C+W57Fa59gu6a4mOBjIyKh5rrK9dgFvdl48rsyhov+CvPvdz+Wq/ECDe53HZWD3X1Eg+FftVzMFoXq3nCkasljGycWbWZLQe2VpUxGipJRg5jdYQqrq1bJeUAB9/7P0clbD9AgEwr/1Cq6RaWPpkd12xZrdfyCsuQxtX+4WrcU5yHZ9jy+eRGB+DzWPYfoGIKFTYfoFqnEM5hRg6ZyM27jsZcGx1Xc87HAK7jpyutvUTEQUL35YiJyEQX1rsemy0/UJ8SdGZx3LtFyritdUyfM5GbN5+CN+t3Y0/Xr814G32PfJrGGeJN/h/FSGAggLn44QEr7dJd9UUW8tr/EufrMWctX/j8V4X44ke52nPr+HcXo8HitM7VzP3q5iD0bxaz2V2LKAtv2ycWTVVHiNTj0wtqmoLdF0C5VCRM9D1MGPuKurWup2fXzW3LwHvYRxh2H7BO7PaL9wQpPYLV4/5UnNM7rGTrrHh2H6hY4D2CxcM/1RffrZf0DYHtl+Qq4vtF7RdF7ZfkM/J9gtERERU03FxQxFBiODGERFR+OLihkwluHrQjdeMiMgYLm4oIsh+/pnLCCKiyMPFDUUEPS92hPuCxmLkm2pERMTFDYWfYC4+wn2hQ0RE+vE+NwQAcFitWHh+VwBA7yj9LRM8REW5ctkt+nPJ1CKsUdpj3Oo71197CI95GPzvgKgo4PbbKx77OVeZt1du3I47vLVn8Jdfw7m9Hg8U54vefCr2q5iD0bxazxWMWC1jZOPMrMloPbK1qIjRUkswchqtIVR1a9kuLQW++ML7OSph+wUCYF77hZYNE7D8qWt0xcq0X+g07nscOV2sKSa3qBRtX/gOAPDd8KtxXkp4tV+oFRuF31+8wedxwJy2FERE4YztFyhshPunR2rW0p6IqGbg4oZMJbN2qOmfp+UHiomIDAp4D+MIw/YL3pnVfuH6lxfqDr9zov72C1eN1t5+Icet/cK2Pw/6Hhii9gsd/jXf73G2X2D7BV23y1cdZ1ZNbL9gPKfRGkJVN9svEBEREfnHxQ2ZSohQV1CVe03hWB8RERnDxQ2FHX7iJPzM33gg1CUQEWnGxQ2FnWB+oDYcP7sbjh8onrDoj1CXQESkGRc3VKOF49tSIhyLIiKqRsJicTNlyhSkpaUhLi4OnTt3xtq1a32Ofe+993DVVVehfv36qF+/Pnr06OF3PFU/4fe6BRERVSchb78wd+5cjBgxAtOmTUPnzp0xadIkpKenY/v27UhOTq4yftmyZbjrrrtwxRVXIC4uDq+88gquv/56/P7772jatGkIZhAZHFYrfjirIwDgWgXtF8pzOSTbL+itxWGJ0h7jVl9jf/W5jVPSfqFXr4rH/s7lo72C65r6ar/gK7+Gc3s9HqgmX/zkM22/3hxa61ZRg5HzyMZqGSMbZ2ZNRuuRrUVFjJZagpHTaA2hqlvLdmkpsGSJ93NUEvL2C507d8Zll12GyZMnAwAcDgeaN2+OJ554AiNHjgwYb7fbUb9+fUyePBn3339/wPFsv+CdWe0XWjRIwI9P62u/cPvUVVj310ldtehpv5BTWIpLxjrbLywachVaN/H9PKjp7ReCOX8iIn+qTfuFkpISrF+/Hj169HDts1qt6NGjB1avXq0pR0FBAUpLS9GgQQOzyiQDRJD6bsueJVj1ERFR8IT0baljx47BbrcjJSXFY39KSgq2bdumKce//vUvNGnSxGOB5K64uBjFxcWu7dzcXPmCKShM/7IQ1zNERBEtLD5QLGvChAmYM2cO5s+fj7i4OK9jMjMzkZiY6Ppp3rx5kKusHuJLirB14m3YOvE2ID/fWLL8fFeuuJJC3eG24kLdtcSX6Ihxq89S4Ges27j4kiIdM/CeC7VqOX+81RfoXBqO+8yv4dxej8vO308+0/brzaG1bhU1GDmPTKzW/LJxZtWkoh6zzqMlJlAtwchptIZQ1a11OzXVe34vQvrKTVJSEqKionD48GGP/YcPH0ZqgEm89tprmDBhAr7//nu0bdvW57hRo0ZhxIgRru3c3FwucHxIKC0OPEhnLplPdFlg0V2LgL76tY6tmIeCl3sKCjSdS/a43/wBzu3ruPRzwtf5zNyvN4cZebWey+xYrfll48zKraIes86jJcZoDhU5jdYgExOMa6Hn/8cI8Ss3sbGx6NChA7Kyslz7HA4HsrKy0KVLF59xr776Kl566SUsXrwYHTt29HsOm82GunXrevwQkU78fj4RVSMh/yr4iBEjkJGRgY4dO6JTp06YNGkS8vPzMWDAAADA/fffj6ZNmyIzMxMA8Morr2D06NGYPXs20tLSkJ2dDQCoXbs2ateuHbJ5kHdheLNdD1pfkAnHuwYTEZF3IV/c3HnnnTh69ChGjx6N7OxstGvXDosXL3Z9yHjfvn2wut1jY+rUqSgpKcHtt9/ukWfMmDF44YUXglk6acCb7RIRUbCFfHEDAIMHD8bgwYO9Hlu2bJnH9t69e80viEIrDF8kqU4tEX4/mIOLzq0V6jKIiEKmWn9bioiq2nPM4LfdiIiqubB45YZCz2Gx4OfmbQAAl+u51b43Vqsrl0OibYGwWHXXoidGWCvmWsdffR7zMPhyktUKdOtW8djPuezezhWoFrfjovJxDef2etwtp64PT/nJd/ryroiLjkJMpf2+xmverzeH1rpV1GDkPLKxWsbIxplZk9F6ZGtREaOllmDkNFpDqOrWsl1WBqxc6f0clYS8/UKwsf2Cd2a1X2haLx4rR16rK/bOd1ZjzZ4Tumq5bNz3OKqx/cKpghK0e9HZn+TrJ65Em6aJPseGov1CQmwUtkq0Xyg//ma/dujTTk2ftfKc9RNisHG0sfn/vPs4+r37M2KjrNgx7kYV5YXUoZxCpNSJg9Uahu+jEkWgatN+gai6qGH/DWCK5TuOAgBK7I4QV2Lcl5sPokvmD3hy3uZQl0JEXnBxQzVaOK5ZwrKmUBcQZt78fgcA4PONB0JcCRF5JWqYnJwcAUDk5OSEupSwcsHwT8Wx+LriWHxdIfLyjCXLy3Plunbs17rD738rS3ctV47+UnPMySMnXGN/237A90C3eXQatUDPFLzmEklJzh9v9bmdq93Tn/s9fsHwT/0e/2rVDt3n9nrcLWfnZ78wPNfX56/3/jvyc37N+/Xm0Fq3j/he4xZVnYvWc+mpXyZWa37ZOLNqUlGPWefREhOolmDkNFpDqOrWuJ3ToIHmv9/8QDG5NCxU11TUaC6ZeD0x5WMPBnhNonyckpv4HTum6Vy+XrkJND+/xwOc29dx1/z9R+vOp3W8rv16cxjM63UuWs+lp36ZWK35ZePMyq2iHrPOoyXGaA4VOY3WIBMTjGuh5//H4NtSdEY43YDXInGjG7PfyhFBfK8onH4X1dGR0wabnGrAt+mIwhsXNxR2zP7j7rFOCcO/UuH4mZvq5P/m8kO+RDUdFzcUEaQXRBrjuN6oPtb9dTLUJRBRiHFxQxFB+tUOrXFBXN1E6kKK77YRUbBwcUMAatYfnlAsHgpKylyPywLd5yVSVzdEREHCb0sRAEBYrdicei4A4BIF7RfKc9llPhxsteiuRVh01O9WnyXAre7Lxxltv3CisAw7z+Q6V3j5P16gc+k47rX9QseOFY+9xHo97i+nPz7y+fodZZ8uwWFvvzs/dVXZ7+v6BJp7oLp9xDu8/f9F67l0nEc6VssY2TgzazJaj2wtKmK01BKMnEZrCFXdWrbtdmDjRu/nqITtFwgAcPYzi2B3OJ8KKtsvNEmMw6pR1+mKvef9n7Fy13FdtXR8+Xscy9PWfuFEfgkufcnZfuGLQV1xSfN6PseWz6O2LRpbxqZrqsWbv08W4MpXlgIAtr6YjoTYqv9dUX4uX+0Jqnv7hVcXb8N/lv0JwLP+pduOYMDMX6rs10t1CxF/rn1tGXafaVBq9rmIyIntFyhsyNwfRuar4HqEYj2v55TC4PtSSu7JQ0RUjXFxQ6aKlBcGI2UeskydPddiRKRawHsYRxi2X/Duoic/E/vrJov9dZOFyM83liw/35Wr+wtf6w4fMHmZ7lqueP4rzTHHDp9wjd3sr/2C2zzaj5yvYwZV/f33UVeu/JNenntu52rz1Gd+j58/wkv7BbfjX6/eVeWYaNnS+ePt2vg67pZTV/sFH/len7/B6+9o2cY93n93fuqqst/X9Qk090B1+4i/Ydw3VWvWei4d55GK1ZpfNs6smlTUY9Z5tMQEqiUYOY3WEKq6NW7nNG/O9gukj0UAzXKPODeMvkohhCuXReK/+S0QumvRFeNW3wl/Y93nYfSSOCpyFXg7p9u54O3LVIFqcTu+ufI1FwL466+Kx15ivR53r0nP/H3k8/k7Er73+6qryn5f1yfQ3APVrWcuWs+l4zzSsVrGyMaZWZPRemRrURGjpZZg5DRaQ6jq1rqtEd+WIlPJrAlkPjKiJ6Zmv8FU1eEc89sVEBEFExc3FBFkP7YhNL4yFMyP3AR78fXkvMDtCrReJyKicMDFDQGo/s0aq3v9obQ9+3SoS6h2uNQjCm9c3FBE0PP1cZkXIYx/PdvY+UOtGpZMRDUYFzcEwPx7y5hN9pWbYP3R1nWfm3Bc/YRhSb7wVTwi4relCAAgLMCOhi0AAOcZ/etgsbhyCZn2C7DorkVXjHt9/sa6jQvQDSowt1xNvJ3Toyb/8QGPV16IWCxA69YVjyvx+bt3n7+eX6Ov81l8/I787PeVp8p+X9cnwNwD1u0j3uvzTeu5dJxHOlbLGNk4M2syWo9sLSpitNQSjJxGawhV3Vq27XZg+3bv56h8ShGW/5loHrZf8O68Z79ByZmGjirbLzROjMNqne0X7p++Fj/uOKqrlisys3DwzLd+AsUcOV2ETuOyAACfPXYFOrSs73OsqyVCtBU7Xq7aEkGr/ScKcNWrzvYLW8amo7bNd/sFqwXYnem7vQKgtv3CxWO+xeniMq95y3PWio3C7y/eoDmnN//+dhumLPXSfmH7EQyYoa79gsUC7PFy/VS65rVl2MP2C0RBxfYLpJ9JL+UHa+lcnVoOBPrviRr1XxtERCbg4obCTnCXKSHoM2V2fhNOoCJlsD7XVX2WuURkmoD3MI4wbL/gXdunPxfbG7YQ2xu2UNJ+oTzX1WP0t194aOpy3bVcM3ah5pjDh467xq7/Y7/vgW7zuPipz/VMoYr9+4+6cuUcP+X3XL7aK2g9/sXKHVWOidatnT9erk3HUQu8Xzu3nLraT/g436QvNno9z7KNe3ye32vd3va71Xrh/30aOIfWun3Ep4/7pmrNWs+l4zxSsVrzy8aZVZOKesw6j5aYQLUEI6fRGkJVt8btnPPPZ/sF0scigPOO73NuKGi/4Mol9VFcobsWq0VHjFt9GwPc6r58nNH2C+65chzeb5Pu91w6jv9ReU5CAFu3VjyuxOfv3v33qOfX6ON8Fl+/V+F7v9e6ve33dX0CzD1g3XrmovVcOs4jHatljGycmTUZrUe2FhUxWmoJRk6jNYSqbq3bGvFtKarRtK7jjN7nJpjMeVtKzRtTKvz69ympuKOni5Wc35ddh3kzRKJwwcUNRQSze0spXTCYvE7SnV7DtVMxf1Wfhfno531+j/sq9a/j+Yoq8G7p9qOm5ici7bi4IQDm3fhM5kOkMqXIflg1FK/HmP0qkDDhpZvq87qV74WYVeFz3Fuq6vTqHlGk4+KGTBUp/+BXp1norjVoX9cPznlCdf6adccwovDGxQ2FHZl71ugKqfTZ1GAz/Zxm5FfytlTgX5IZrzq5VxB+mYjIDPy2FAEAhAX4u24yAKCZgvYL5blk2i8AFolatMcIt/oC3ereNQ+jf83cctXycZt0v+fScbzKnCwWoGVL78fg53cvO38f57NYffyO3M7TxH0JZLHA0aIlLBbPBa/Xen3UKgAccO3X0H6hct2+rp3V6rcGv89BPeeRjdUyRjbOzJqM1iNbi4oYLbUEI6fRGkJVt5ZthwPYv9/7OSrh4oYAAMUx8bjysekAgL0JCcaSJSS4ciXH2HSHl9jidNdSHKsjxq2+uXHxmsYZ/byGSEjAVWdyrY/3ck63cwWqJdDxV+LiqhzD3r0+Q4tifFw7t5wxUTougI/z2eO8P8dEfMV5dsXHu15OPimi0f6uKWjRIAE/uo0vs3nJ4+P6uOf+XMPzokrdPuZS4uX5Vqr1eavjPNKxWsbIxplZk9F6ZGtREaOllmDkNFpDqOrWsp2bCyQm+j/XGXxbiiKCnreyZD4HpPLNEm+3uQmpEL/HYnX73blfm593HwcA7DtRIJ3brEsd6s8PEZF/XNwQgPD6x1rujazqw9zPlZh0nxsVn7nx8UvyXNyoLV7vtZ61ai/eX7HbY9/6v07gqXmbcSyv4j453j4/xA8UE4WRgPcwjjBsv+Bdu5ELxKbUc8Wm1HOFKCgwlqygwJWrq0T7hUfe+VF3LenjF2uOOXjouGvsz7/7ab/gNo/zRnymZwpV7P/7mCvXwUPH9Z9Lx/G5y7dXOSY6dnT+eLk2HZ/5wvu1c8t5wf/pmL+P8/1n0a9ez7Pqt32u/fmncl37F6/90+v4pz9YXXW/j+tTnJvn2r9x+wG/ZRfmnHaNPXrkZJW8Q6b/5Bp70yvfValhykLv89N0fQL8jnTFas0vG2dWTSrqMes8WmIC1RKMnEZrCFXdGrdz2rfX/Pebn7khAIBVCFySvdO54ZBpmeDG4XDlsgr9uazQX4tVOLTH2CvGrvU31mMexv6zXLid86DdyzkDnUvH8W2V5+RwAOvWVTyuJMoC79dOcv7r9xxDBy/n8/Ucs7idJ8/uvt/H88Dbfh+1CofdtX9TgOdFaWlZxe+opKxK3r+OVtwE0OLt+ebQ+Bz09vsI8DvSHatljGycmTUZrUe2FhUxWmoJRk6jNYSqbq3bGvFtKQJg3ts61iC93yV7llC8leAw4UM3wsBEtHxYWk/2e99fq+v87p+Xsmu4Nrq+9a/ja//uh71+IU3HeYkotLi4IVOF02d5Qsn9Q8yqP1fi71yVLd9xpMo+lXfu9cf3Z24qHmtZpFl1FOy5uNF+3X19q1bruYgotLi4IQByN87TlFciRuaPhL5vS7k/Ds5fJPc5mfFtKa3X7NH/baiyT8ura2p6S3k/j8XHt6V85tHxpPJcVAYY6/aKurdaPW/N4/84EYUWFzcEwPxv8OghU0m4/11xf7VGy1svenks2HSmD9Zbh1q4Xxstr/QE4vBYVPq/MEZfUYsK1ktgRBQQFzcEwMz7gYT5P/hBWtN5Lj6q32duVPD1VHCv3f2x+9tP7vt1vUon3F+5Mba4cT/srYJwWiQS1XT8thQBcP7Dfjy+LgCgoYJ85blk/r0XErVYLNAc454/EK3jAhGiIpfdx4oq0Ln8HRfux738kfYXa7FYfF472fkHyudrv3vpUdaK/XXsArFe9jf0ksOdQ7hfF/812308L8r3VX7BrXINVh3PQSQladsnG6s1v2ycWblV1GPWebTEGM2hIqfRGmRignEtkpKc35w6cSLwucDFDZ1REBOHDkNmAwD21qplLFmtWq5czWL9tDfwodgWr7uWIh0xjoSK+mYn+BnrNg+jHAkJrlyL4rycM9C5dBx/KT7B5zFvfF5vyfkXxnp/LtnjK67BnoQE16sfdrffx0r3tgy1arv2/x4X71rc2ONrVc3vq1a33B/G+39eCLexP5W3yHDLe35MRVsLb9dM1PJSlze1agFHjwbeZyRWyxjZODNrMlqPbC0qYrTUEoycRmsIVd1atqtb+4UpU6YgLS0NcXFx6Ny5M9au9f9V0nnz5uGCCy5AXFwcLr74YixatChIlUYus96dCdbnEPS8K1Pm9p/gwfqkkft5zPi2lMe3gpRnV/NhWfccvr6i7f41efe3ecrs3vcHoudbau6f9ykpq3q/DrvHW2NV4/m2FFH4CPkrN3PnzsWIESMwbdo0dO7cGZMmTUJ6ejq2b9+O5OTkKuNXrVqFu+66C5mZmfjHP/6B2bNno2/fvtiwYQPatGkTghl4V1xmx9JtR7Fh30ls3HcSBSV2CAHUiYvGwZxCXNayAWwxVtiioxBttSAm2opoqwXRViuirMDXvx7C1ec1QuPEOJTaHUiIjUZMlAVWiwXRZ/43ympBlMUCiwWwxUQhLjoK+cVliI6yoE5cNADnMavF+d2P8sfAmX2Win3u/5gfzi1ClNWCGKsVUVEWxEZZERNlkfr8jC1a//pZ5gO3ehYMdqM3KZTg+W0pMz5QLJ9TSzkq/nBXvgbWM6/duP++fX2upcT95n5B+EBxUWnV54hDw4edywkhwv/zZkQRLOSLm4kTJ+Khhx7CgAEDAADTpk3DwoULMX36dIwcObLK+DfffBM33HADnnrqKQDASy+9hCVLlmDy5MmYNm1aUGt3V1LmwLG8YhSV2lFQYscDs37B4dxin+P3nzgQMOe27NMqS/TLVlqMWfPGAACuLh2L4krdvMsXUjFRFuSX2NGwVixyi0qRmhh3ZkHmXARFWS2ILSnGnNnO313GHWNx7/trEB3ljAeAuJgoWCwVOa1WC6yW8ld5LFi79QDmnKllXMdUOOLiYbU4P2Aa5fYHo7jMgdgzi6e/D55wxbx1ZXOIuPgzC7eqH0D95Y+/XfXNOnsqfjvQGBZU/AEvsTtQXOaAPS/fYx7vr9jtWhQCzj/KdoeAQzj/MJZvCyFgFwJ2R8X+HXuPVJzzwndx4VkpACpqsxYVVjmX5cyi1CEECk6d9jg+ddmfzsUpgOgoK4py81zHX230BsrswlWntajII3bWqr0eH7Q9dvSU69r997q0M9fOgpiSqnHlL8QJnFl8WJy/O3HmGlgqPZc+TD8bdlschBD48uc/Xfn+16MVLGfe+lmz9YBr/wcdZqJ504awWIBVv+137Z95+QdITq4PIQS+/WUP5swe5Rx//Vmw2+IQVexZ6/Sf9sDuEPj74HHX/ndbTsaeY/muBZSAc6FbVOpAUakde/Yfqxh7wTs4Ly0ZtlLP5/K7P/4JhwB27j3qumbvXZuGvwocWLftoGtsnwaxOFRqwdHTFf8GfPZYF6TUjQMKC9Hon7cAAI5+Mh+Ij/e+zxstsYC2/LJxZtVUeYxMPTK1qKot0HUJlENFzkDXw4y5+6ghNtqK5Dpxrjpw443Ox9984/xfme2ysqrXxIeQLm5KSkqwfv16jBo1yrXParWiR48eWL16tdeY1atXY8SIER770tPTsWDBAq/ji4uLUVxc8Q9Mbm6u8cK92LT/FP75TtWa7+ncAq2b1EWUxYK4mCgUl9mRU1iKg6eK0KBWLIrL7CizC5TYHbA7BErtAqcKSvDNlmw0rRePCxvXRX5xGWrZoiAEzvzRrPjJKSxFbLTVtV1UakdecRkSYp2/WgHhunu1EM4/xAJn/lc49wkAsUV2XL5/CwAgGgKVl2V2h4AdAiV25/bx/BIAwP4ThVXmHF9S5MplFQI/7Tqm61rGC+GKH7BqLwpj4wJEVIr5YaffmPiSIswsH/vHYXzzZ47PcU+5zePlhX/omkflXK76Nh/EZ3+cqHL8Dz/nii8pwhC3468s3uYz/o8DOdjw9Vafucd8+btnrPu1+2ab69oFivM5V/d8X/3ukc+1f9EfHvunlu9fvReFsdmu/dPK9/+42218cUWer7eiMDauSq0vnpl/fEkRxpaP3XkMP/yV57tu9zp+PYQF205WyTt+0baqc1y8zVVD+b6d2blVnoO3TV1dcV1XrgAA9HxtWUX9lfb5qjFQLABN+WXjzKqp8hiZemRqUVVboOsSKIeKnIGuhxlz91XDpS3q4fPHuzoLcTiA5csrHgPy2xqFdHFz7Ngx2O12pKSkeOxPSUnBtm3bvMZkZ2d7HZ+dne11fGZmJsaOHaumYD/iY6IQE+VcwMTFRCEhNgoDu7ZCxhVppp9bifx8YLzz4e8v3gCRkIAyh0CZXaC4zI7iMseZbYdrARYTZUWZwwGHAErtDpSUOZz/VZyfB7zhzPVWv3bIi7Gh1O5cfJU5BOx2x5lFlvO/nMscwvlf/mdeBYkrKXTFP3x1KxTZ4gHh/KxM+VsHDofAD9uP4IqzklDLFo3oonxXTL9OzVBsS3At3pyvKFhcb90kW8tcY2+9tCmKYuM93pKItjp/j3XsRa5xN1/SBEW2uDOLQmemaC+vPJW/Xeh62/DMY1txgStXr4sboyw+3lkfnK+A2IorFom3tG+K/Bibx/GGlhKP4yVx8a75lToEYosq4ntf3BjFcfGuV2fcj11zQSNYatX2eM+nTmnFUraP2zyjCwtc+29sk4oiW5zrVQ+LpeLfnPLrWv7Kl6WgogdT+kUpKImLhwUWxJUUeeQriYuHAGBzq++GNqkoscVXqfuGi5zjYfEyPi7eY99NbRuj0BaPKAuQKCqu2z/aNkZhbJzr1TKHELBFWZ1v6cZYUcde7Pod/aNtYxTb4uHIq1gM9W3XFMW2uCq1tW9RD4lJ9VC7rOI6XtI8ET8frvrKrS3aCpvD6rHt8LHPGy2xlc/nK79snFk1VR4jU49MLapqq0xvDhU5A10PLedUVXdMVIg/0huwtaaJDhw4IACIVatWeex/6qmnRKdOnbzGxMTEiNmzZ3vsmzJlikhOTvY6vqioSOTk5Lh+9u/fL8Cu4FXl5QnXizl5eaHNJROvJ0br2GBeEzOPy8bKzl9vPhX7VczBaF4jzyuVsVrzy8aZVZOKesw6j5aYQLUEI6fRGkJVt8btHEBo/fsd0ldukpKSEBUVhcOHD3vsP3z4MFJTU73GpKam6hpvs9lgs9m8HiMiIqLIE9LXjWJjY9GhQwdkZWW59jkcDmRlZaFLly5eY7p06eIxHgCWLFniczwRERHVLCH/ttSIESOQkZGBjh07olOnTpg0aRLy8/Nd3566//770bRpU2RmZgIAhg4dim7duuH1119H7969MWfOHKxbtw7vvvtuKKdBREREYSLki5s777wTR48exejRo5GdnY127dph8eLFrg8N79u3D1ZrxQtMV1xxBWbPno3nnnsOzzzzDM4991wsWLAgrO5xU20lJAQeE6xcMvF6YrSODeY1MfO4bKzs/PXmU7FfxRyM5jXyvFIZqzW/bJxZuVXUY9Z5tMQYzaEip9EaZGKCcS0SEpyfwims+g1dbyxCCKFpZITIzc1FYmIicnJyULeumr5BREREZC49f7/Dov0CERERkSpc3BAREVFE4eKGnIqKgN69nT9FRYHHm5lLJl5PjNaxwbwmZh6XjZWdv958KvarmIPRvEaeVypjteaXjTOrJhX1mHUeLTGBaglGTqM1hKpurdu33+49vzcB74QTYXJycjTfBKhGCeYN68yIN3qzNlV1yOYy87hsrOz8zbxZn6/9KuZgNK/KG/EZidWaX8VN81TmVlGPWecJxY3rZHIarSFUdWvc1nMTP75yQ0RERBGFixsiIiKKKFzcEBERUUTh4oaIiIgiChc3REREFFFC3n4h2IQQAJx3OiQ3+fkVj3NzAbs9dLlk4vXEaB0bzGti5nHZWNn5682nYr+KORjNa+R5pTLWnb/8snFm1VR5jEw9MrWoqq0yvTlU5Ax0PbScMxR1a9wuf1T+d9yfGtd+4e+//0bz5s1DXQYRERFJ2L9/P5o1a+Z3TI1b3DgcDhw8eBB16tSBxWJRmjs3NxfNmzfH/v372bcqyHjtQ4fXPnR47UOH1z74hBA4ffo0mjRp4tFQ25sa97aU1WoNuOIzqm7dunyyhwivfejw2ocOr33o8NoHV2JioqZx/EAxERERRRQuboiIiCiicHGjkM1mw5gxY2Cz2UJdSo3Dax86vPahw2sfOrz24a3GfaCYiIiIIhtfuSEiIqKIwsUNERERRRQuboiIiCiicHFDREREEYWLG0WmTJmCtLQ0xMXFoXPnzli7dm2oS4o4mZmZuOyyy1CnTh0kJyejb9++2L59u8eYoqIiDBo0CA0bNkTt2rVx22234fDhwyGqOHJNmDABFosFw4YNc+3jtTfPgQMHcO+996Jhw4aIj4/HxRdfjHXr1rmOCyEwevRoNG7cGPHx8ejRowd27twZwoojg91ux/PPP49WrVohPj4eZ599Nl566SWP3ka89mFKkGFz5swRsbGxYvr06eL3338XDz30kKhXr544fPhwqEuLKOnp6WLGjBliy5YtYtOmTaJXr16iRYsWIi8vzzXm0UcfFc2bNxdZWVli3bp14vLLLxdXXHFFCKuOPGvXrhVpaWmibdu2YujQoa79vPbmOHHihGjZsqXo37+/WLNmjdi9e7f49ttvxa5du1xjJkyYIBITE8WCBQvE5s2bxc033yxatWolCgsLQ1h59Tdu3DjRsGFD8fXXX4s9e/aIefPmidq1a4s333zTNYbXPjxxcaNAp06dxKBBg1zbdrtdNGnSRGRmZoawqsh35MgRAUAsX75cCCHEqVOnRExMjJg3b55rzB9//CEAiNWrV4eqzIhy+vRpce6554olS5aIbt26uRY3vPbm+de//iWuvPJKn8cdDodITU0V//73v137Tp06JWw2m/j444+DUWLE6t27txg4cKDHvltvvVXcc889Qghe+3DGt6UMKikpwfr169GjRw/XPqvVih49emD16tUhrCzy5eTkAAAaNGgAAFi/fj1KS0s9fhcXXHABWrRowd+FIoMGDULv3r09rjHAa2+mL7/8Eh07dsQdd9yB5ORktG/fHu+9957r+J49e5Cdne1x7RMTE9G5c2dee4OuuOIKZGVlYceOHQCAzZs346effsKNN94IgNc+nNW4xpmqHTt2DHa7HSkpKR77U1JSsG3bthBVFfkcDgeGDRuGrl27ok2bNgCA7OxsxMbGol69eh5jU1JSkJ2dHYIqI8ucOXOwYcMG/PLLL1WO8dqbZ/fu3Zg6dSpGjBiBZ555Br/88guGDBmC2NhYZGRkuK6vt3+DeO2NGTlyJHJzc3HBBRcgKioKdrsd48aNwz333AMAvPZhjIsbqpYGDRqELVu24Keffgp1KTXC/v37MXToUCxZsgRxcXGhLqdGcTgc6NixI8aPHw8AaN++PbZs2YJp06YhIyMjxNVFtk8++QQfffQRZs+ejYsuugibNm3CsGHD0KRJE177MMe3pQxKSkpCVFRUlW+FHD58GKmpqSGqKrINHjwYX3/9NZYuXYpmzZq59qempqKkpASnTp3yGM/fhXHr16/HkSNHcOmllyI6OhrR0dFYvnw53nrrLURHRyMlJYXX3iSNGzdG69atPfZdeOGF2LdvHwC4ri//DVLvqaeewsiRI9GvXz9cfPHFuO+++zB8+HBkZmYC4LUPZ1zcGBQbG4sOHTogKyvLtc/hcCArKwtdunQJYWWRRwiBwYMHY/78+fjhhx/QqlUrj+MdOnRATEyMx+9i+/bt2LdvH38XBl133XX47bffsGnTJtdPx44dcc8997ge89qbo2vXrlVuebBjxw60bNkSANCqVSukpqZ6XPvc3FysWbOG196ggoICWK2efyajoqLgcDgA8NqHtVB/ojkSzJkzR9hsNjFz5kyxdetW8fDDD4t69eqJ7OzsUJcWUR577DGRmJgoli1bJg4dOuT6KSgocI159NFHRYsWLcQPP/wg1q1bJ7p06SK6dOkSwqojl/u3pYTgtTfL2rVrRXR0tBg3bpzYuXOn+Oijj0RCQoL48MMPXWMmTJgg6tWrJ7744gvx66+/ij59+vDryApkZGSIpk2bur4K/vnnn4ukpCTx9NNPu8bw2ocnLm4Uefvtt0WLFi1EbGys6NSpk/j5559DXVLEAeD1Z8aMGa4xhYWF4vHHHxf169cXCQkJ4pZbbhGHDh0KXdERrPLihtfePF999ZVo06aNsNls4oILLhDvvvuux3GHwyGef/55kZKSImw2m7juuuvE9u3bQ1Rt5MjNzRVDhw4VLVq0EHFxceKss84Szz77rCguLnaN4bUPTxYh3G61SERERFTN8TM3REREFFG4uCEiIqKIwsUNERERRRQuboiIiCiicHFDREREEYWLGyIiIoooXNwQERFRROHihoiIiCIKFzdEZLru3btj2LBhoS5DmWDNRwiBiRMnolWrVkhISEDfvn2Rk5PjN+b48eNITk7G3r17Xft+/fVXXHXVVbjkkktwyy23oLi4GADQr18/vP7662ZOgSgkuLghCmNHjx7FY489hhYtWsBmsyE1NRXp6elYuXKlkvy+/kir/uP9+eef46WXXlKWr7rLzMzEZZddhjp16iA5ORl9+/at0hwTcHalnjp1KmbNmoUVK1Zg/fr1eOGFF/zmHjduHPr06YO0tDQAQFFREfr164f3338fmzdvRpMmTfDRRx8BAJ577jmMGzcu4IKJqLrh4oYojN12223YuHEjZs2ahR07duDLL79E9+7dcfz48VCXpklJSQkAoEGDBqhTp06Iqwkfy5cvx6BBg/Dzzz9jyZIlKC0txfXXX4/8/HzXmDVr1mDixImYO3curr76anTo0AEPPfQQFi1a5DNvQUEB/vvf/+KBBx5w7VuwYAFuvPFGnH/++QCACy64AEePHgUAtGnTBmeffTY+/PBDk2ZKFCIh7m1FRD6cPHlSABDLli3zOcZut4tXXnlFnH322SI2NlY0b95cvPzyy67j33zzjejatatITEwUDRo0EL179xa7du0SQjg7HqNSE9I9e/b43G+328X48eNFWlqaiIuLE23bthXz5s3zqKdbt25i0KBBYujQoaJhw4aie/furv3lTTa7desmnnjiCfHUU0+J+vXri5SUFDFmzBiPPLm5ueLuu+8WCQkJIjU1VUycOLFKo87K/M3VvT4V53bf1nJdAjly5IgAIJYvX+7ad/vtt4sePXp4jJs2bZpo0KCBzzzz5s0TjRo18tg3evRo8f7777u2H3nkEfHll1+6tseOHSuuvPJKXfUShTu+ckMUpmrXro3atWtjwYIFrs9IVDZq1ChMmDABzz//PLZu3YrZs2cjJSXFdTw/Px8jRozAunXrkJWVBavViltuuQUOhwNvvvkmunTpgoceegiHDh3CoUOH0Lx5c5/7MzMz8cEHH2DatGn4/fffMXz4cNx7771Yvny5R02zZs1CbGwsVq5ciWnTpnmte9asWahVqxbWrFmDV199FS+++CKWLFniOj5ixAisXLkSX375JZYsWYIVK1Zgw4YNfq+Xv7maeW6t18Wf8reFGjRoAAAoLi7GwoULccstt3iMKyoqQmJios88K1asQIcOHTz2NW7cGNu2bQMAbNq0CatWrcKNN97oOt6pUyesXbvW53OMqFoK9eqKiHz79NNPRf369UVcXJy44oorxKhRo8TmzZuFEM5XGGw2m3jvvfc05zt69KgAIH777TchhPD5akjl/UVFRSIhIUGsWrXKY9wDDzwg7rrrLo+49u3b+83XrVu3Kq8UXHbZZeJf//qXa14xMTEer36cOnVKJCQk+H3lJtBcVZ67fD5ar4s/drtd9O7dW3Tt2tW1b9WqVQKAiIuLE7Vq1XL9xMbGivT0dJ+5+vTpIwYOHOixLy8vT/Tq1UtcdNFFomvXrmLr1q0exzdv3iwAiL1792qql6g6iA7t0oqI/LntttvQu3dvrFixAj///DO++eYbvPrqq3j//ffRunVrFBcX47rrrvMZv3PnTowePRpr1qzBsWPHXK9i7Nu3D23atNFcx65du1BQUICePXt67C8pKUH79u099lV+5cCbtm3bemw3btwYR44cAQDs3r0bpaWl6NSpk+t4YmKi6zMjvmidq8pz67kuvgwaNAhbtmzBTz/95Nq3Y8cO1KpVC5s2bfIY27t3b3Tt2tVnrsLCQsTFxXnsq1WrFhYuXOgzJj4+HoDz8zpEkYKLG6IwFxcXh549e6Jnz554/vnn8eCDD2LMmDH4+uuvA8bedNNNaNmyJd577z00adIEDocDbdq0cX3QV6u8vDwAwMKFC9G0aVOPYzabzWO7Vq1aAfPFxMR4bFsslipvH+mlda4qz63nungzePBgfP311/jxxx/RrFkz1/7c3FwkJSXhnHPOce3766+/sHPnTtx2220+8yUlJeHkyZO65nDixAkAQKNGjXTFEYUzfuaGqJpp3bo18vPzce655yI+Ph5ZWVlexx0/fhzbt2/Hc889h+uuuw4XXnhhlT98sbGxsNvtVWIr72/dujVsNhv27duHc845x+OnefPmSud31llnISYmBr/88otrX05ODnbs2OEzRstczTi37HURQmDw4MGYP38+fvjhB7Rq1crjeFJSEnJyciCEcO0bN24cevXqhdatW/vM2759e2zdulXrdAEAW7ZsQbNmzZCUlKQrjiic8ZUbojB1/Phx3HHHHRg4cCDatm2LOnXqYN26dXj11VfRp08fxMXF4V//+heefvppxMbGomvXrjh69Ch+//13PPDAA6hfvz4aNmyId999F40bN8a+ffswcuRIj3OkpaVhzZo12Lt3L2rXro0GDRrAarV63f/kk09i+PDhcDgcuPLKK5GTk4OVK1eibt26yMjIUDbvOnXqICMjA0899RQaNGiA5ORkjBkzBlarFRaLxWuMlrmace46depIXZdBgwZh9uzZ+OKLL1CnTh1kZ2cDcL4FFh8fj2uvvRZFRUWYMGEC+vXrh48++ghfffUV1q5d67f+9PR0jBo1CidPnkT9+vU1zXnFihW4/vrrNY0lqjZC/aEfIvKuqKhIjBw5Ulx66aUiMTFRJCQkiPPPP18899xzoqCgQAjh/DDqyy+/LFq2bCliYmJEixYtxPjx4105lixZIi688EJhs9lE27ZtxbJlywQAMX/+fCGEENu3bxeXX365iI+Pd33l29d+h8MhJk2aJM4//3wRExMjGjVqJNLT0z2+vqzlA8rexvTp00dkZGS4tr19HbtTp05i5MiRPq9XoLmqPLd7Hi3XpTJU+qp9+c+MGTNcY+bMmSOaN28u4uPjvX6t3ZdOnTqJadOmaRpbWFgoEhMTxerVqzWNJ6ouLEK4ve5JRBSG8vPz0bRpU7z++useN6iL9HPLWLhwIZ566ils2bIFVqv/Tx5MnToV8+fPx3fffRek6oiCg29LEVHY2bhxI7Zt24ZOnTohJycHL774IgCgT58+EX1uFXr37o2dO3fiwIEDAT8PFRMTg7fffjtIlREFDxc3RBSWXnvtNWzfvh2xsbHo0KEDVqxYEbQPvYby3Cpo7Qv24IMPmlsIUYjwbSkiIiKKKPwqOBEREUUULm6IiIgoonBxQ0RERBGFixsiIiKKKFzcEBERUUTh4oaIiIgiChc3REREFFG4uCEiIqKIwsUNERERRRQuboiIiCiicHFDREREEYWLGyIiIoooXNwQERFRRPl/ekWKV6jay8oAAAAASUVORK5CYII=",
      "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": {},
   "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",
      "Cost function after refinement: 1.383834659393779e-10\n",
      "GonioParam(dist=0.7199980533133431, poni1=0.0317753490052087, poni2=0.004, rot1=0.0, offset=-37.20000282728478, scale=1.0, nrj=17.027082549190933)\n",
      "maxdelta on: poni1 (1) 0.032 --> 0.0317753490052087\n",
      " 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",
      " 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",
      "Cost function after refinement: 3.226788268415189e-08\n",
      "GonioParam(dist=0.7208093285809686, poni1=0.03201295264935034, poni2=0.004, rot1=0.0, offset=-37.19999969779676, scale=1.0, nrj=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",
      " 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",
      "Cost function after refinement: 1.8511707669927584e-10\n",
      "GonioParam(dist=0.720810786218182, poni1=0.0320817564491065, poni2=0.003959633634965548, rot1=2.9090593451096456e-05, offset=-37.199998826623414, scale=0.9990588539443465, nrj=17.027082549190933)\n",
      "maxdelta on: scale (5) 1.0 --> 0.9990588539443465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:99: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.figure.show()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16875\n",
      "45 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/465879643.py:107: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(goniometers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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\"%i for i in goniometers[name].param])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the negative part of the spectum ...\n",
    "\n",
    "Until now, we used only the data where 2th >0 \n",
    "For the last modules, this thows 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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 454\n",
      "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",
      " 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",
      "Cost function after refinement: 9.802713851439468e-10\n",
      "GonioParam(dist=0.7208134358051861, poni1=0.03208491868712114, poni2=0.003961539924237118, rot1=2.7705794268123684e-05, offset=-37.19999878573076, scale=0.9989913574692074, nrj=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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 543\n",
      "Number of peaks found after re-scan: 1004\n"
     ]
    }
   ],
   "source": [
    "gonio7 = complete_gonio(module_id=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "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",
      " 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",
      "Cost function after refinement: 6.982166320635817e-10\n",
      "GonioParam(dist=0.720709214422471, poni1=0.03086624488803945, poni2=0.003966690771283648, rot1=2.39914176175178e-05, offset=-42.799902936946246, scale=0.9990066875067236, nrj=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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 626\n",
      "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",
      " 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",
      "Cost function after refinement: 6.093707047210183e-10\n",
      "GonioParam(dist=0.7206117522126192, poni1=0.03224903534974467, poni2=0.003970067520032141, rot1=2.154981768904857e-05, offset=-48.5998856370124, scale=0.999018042153128, nrj=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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 754\n",
      "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",
      " 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",
      "Cost function after refinement: 5.247777068807799e-10\n",
      "GonioParam(dist=0.7205306818871419, poni1=0.03339533863359239, poni2=0.003973636919054829, rot1=1.8979820018941428e-05, offset=-54.39981565738007, scale=0.9990218992357166, nrj=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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 864\n",
      "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",
      " 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",
      "Cost function after refinement: 4.329909092681409e-10\n",
      "GonioParam(dist=0.7206464175851856, poni1=0.032235988249254756, poni2=0.0039758195842887565, rot1=1.7416684299562826e-05, offset=-60.0000524507336, scale=0.9990197113116526, nrj=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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 978\n",
      "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",
      " 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",
      "Cost function after refinement: 6.316703213409536e-10\n",
      "GonioParam(dist=0.7204847939312276, poni1=0.033407488249092, poni2=0.003977700789209715, rot1=1.605421293874649e-05, offset=-65.79992658915752, scale=0.9990156265501503, nrj=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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 1093\n",
      "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",
      " 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",
      "Cost function after refinement: 7.771718764214187e-10\n",
      "GonioParam(dist=0.7206860334374249, poni1=0.03482262187953887, poni2=0.004042896898550854, rot1=-3.357268964891843e-05, offset=-71.5999088383266, scale=0.9989982448525314, nrj=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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 1183\n",
      "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",
      " 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",
      "Cost function after refinement: 9.683583561116745e-10\n",
      "GonioParam(dist=0.7206815883863531, poni1=0.03367666939027231, poni2=0.004058082314538765, rot1=-4.453529530230961e-05, offset=-77.19997881135367, scale=0.9989759056428863, nrj=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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 1203\n",
      "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",
      " 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",
      "Cost function after refinement: 2.5799879786031964e-06\n",
      "GonioParam(dist=0.7230969945154274, poni1=0.03206584438250582, poni2=0.003981302101258431, rot1=1.351375944293161e-05, offset=-82.79994383887343, scale=0.9993962397251773, nrj=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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of peaks previously found: 0\n",
      "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",
      " 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",
      "Cost function after refinement: 9.315197979050413e-07\n",
      "GonioParam(dist=0.7219737518819529, poni1=0.032200315169332513, poni2=0.003943811455063223, rot1=4.514209223007649e-05, offset=-82.79994226212274, scale=0.9991407871114438, nrj=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 wronly 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": {},
   "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": {},
   "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",
      " 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",
      "Cost function after refinement: 9.214244602861188e-10\n",
      "GonioParam(dist=0.7205957894276002, poni1=0.032284408312060156, poni2=0.004042525656543217, rot1=-2.0533943479941534e-05, offset=-82.79994103817624, scale=0.9989751747980284, nrj=17.027082549190933)\n",
      "maxdelta on: dist (0) 0.7219737518819529 --> 0.7205957894276002\n",
      "Number of peaks previously found: 1246\n",
      "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",
      " 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",
      "Cost function after refinement: 9.382098584456135e-10\n",
      "GonioParam(dist=0.7205957964378075, poni1=0.03228479377833516, poni2=0.004042573433038651, rot1=-2.0568399467646526e-05, offset=-82.79994103322964, scale=0.9989751863383792, nrj=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": {},
   "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",
      " 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",
      "Cost function after refinement: 1.058942401027041e-09\n",
      "GonioParam(dist=0.7208138055232528, poni1=0.032085466901616906, poni2=0.003961650091808378, rot1=2.7624888069974965e-05, offset=-37.199998778669595, scale=0.9989913173312018, nrj=17.027082549190933)\n",
      "maxdelta on: poni1 (1) 0.03208491868712114 --> 0.032085466901616906\n",
      "Number of peaks previously found: 918\n",
      "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",
      " 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",
      "Cost function after refinement: 9.797990703945884e-10\n",
      "GonioParam(dist=0.7208139925217619, poni1=0.03208500579368141, poni2=0.003961700325261782, rot1=2.7587922571998175e-05, offset=-37.19999878439213, scale=0.9989910907857577, nrj=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": {},
   "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",
      " 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",
      "Cost function after refinement: 6.580239090129342e-10\n",
      "GonioParam(dist=0.7207093528191252, poni1=0.030865644602940744, poni2=0.003966722983761047, rot1=2.3967642448708757e-05, offset=-42.7999029444283, scale=0.9990064423875449, nrj=17.027082549190933)\n",
      "maxdelta on: poni1 (1) 0.03086624488803945 --> 0.030865644602940744\n",
      "Number of peaks previously found: 985\n",
      "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",
      " 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",
      "Cost function after refinement: 6.980019785354917e-10\n",
      "GonioParam(dist=0.7207094971168743, poni1=0.03086623823197484, poni2=0.003966772613855158, rot1=2.3931290270607465e-05, offset=-42.79990293688394, scale=0.9990067735018262, nrj=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": {},
   "outputs": [
    {
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      "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 differents results\n",
    "\n",
    "We are getting to an end. Here are the first actually integrated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/3999147500.py:22: RuntimeWarning: invalid value encountered in divide\n",
      "  ax.plot(radial, summed/counted, label=\"Merged\")\n",
      "/tmp/ipykernel_367778/3999147500.py:24: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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 togeather ?\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": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize\n",
    "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",
    "        npt = 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: %s\" % 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: %s\" % 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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.603024734828662e-09\n",
      "CPU times: user 357 ms, sys: 15.9 ms, total: 373 ms\n",
      "Wall time: 371 ms\n",
      "6.139505955765795e-09\n",
      "CPU times: user 308 ms, sys: 16 ms, total: 324 ms\n",
      "Wall time: 323 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": {},
   "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",
      " 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",
      "Cost function after refinement: 6.139505955765795e-09\n",
      "CPU times: user 16 s, sys: 360 ms, total: 16.4 s\n",
      "Wall time: 16.4 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": {},
   "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": {},
   "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",
      "[ 7.20612302e-01  3.36369797e-02  4.02094516e-03 -1.74996556e-05\n",
      " -7.71999791e+01  9.99038595e-01  1.70266104e+01]\n",
      "[ 7.20636130e-01  3.47920978e-02  4.01341931e-03 -1.21330600e-05\n",
      " -7.15999090e+01  9.99038595e-01  1.70266104e+01]\n",
      "[ 7.20757808e-01  3.33850817e-02  3.95036100e-03  3.46517345e-05\n",
      " -6.57999267e+01  9.99038595e-01  1.70266104e+01]\n",
      "[ 7.20813915e-01  3.22167822e-02  3.97128822e-03  2.00055269e-05\n",
      " -6.00000525e+01  9.99038595e-01  1.70266104e+01]\n",
      "[ 7.20881596e-01  3.33801850e-02  3.97760147e-03  1.47074593e-05\n",
      " -5.43998157e+01  9.99038595e-01  1.70266104e+01]\n",
      "[ 7.21048510e-01  3.22346939e-02  4.02104962e-03 -1.69519259e-05\n",
      " -4.85998856e+01  9.99038595e-01  1.70266104e+01]\n",
      "[ 7.21074630e-01  3.08484557e-02  4.09385968e-03 -6.91378973e-05\n",
      " -4.27999030e+01  9.99038595e-01  1.70266104e+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",
      "CPU times: user 11.8 s, sys: 13.5 s, total: 25.4 s\n",
      "Wall time: 6.44 s\n"
     ]
    }
   ],
   "source": [
    "%time res = multigonio.integrate([data[ds_names[i]] for i in range(9)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/1083240546.py:2: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28454\n",
      "68 60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_367778/344732789.py:7: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total run time:  1055.6983478069305\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."
   ]
  }
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