{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e185042",
   "metadata": {},
   "source": [
    "# EELS: Acquisition and Analysis\n",
    "\n",
    "This tutorial covers how to acquire EELS data using `femtus.acquisition` and analyze it using the `eels` package.\n",
    "\n",
    "| Package | Import path | Description |\n",
    "|---|---|---|\n",
    "| <a href=\"../../interface/PyJEM.femtus.acquisition.html\">femtus.acquisition</a> | `PyJEM.femtus.acquisition` | Scan acquisition via FEMTUS |\n",
    "| <a href=\"../../interface/PyJEM.femtus.worksheet.html\">femtus.worksheet</a> | `PyJEM.femtus.worksheet` | Access acquired dataset |\n",
    "| <a href=\"../../interface/PyJEM.eels.html\">eels</a> | `PyJEM.eels` | EELS camera setup and analysis |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "52ae7ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PyJEM import eels\n",
    "from PyJEM.femtus import acquisition\n",
    "from PyJEM.femtus import worksheet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b7e4e6",
   "metadata": {},
   "source": [
    "## Flow Overview\n",
    "\n",
    "| Section | Description |\n",
    "|---|---|\n",
    "| [SI Cube](#si-cube) | EELS SI Cube acquisition and analysis (`Shape = 'Rectangle'`) |\n",
    "| [Line](#line) | EELS line-scan acquisition and analysis (`Shape = 'Line'`) |\n",
    "\n",
    "### SI Cube\n",
    "1. Acquire using `femtus.acquisition` with `setting['Region']['Shape'] = 'Rectangle'`\n",
    "2. Generate whole spectrum: `eels.create_si_cube_whole_spectrum`\n",
    "3. Get worksheet element: `worksheet.get_worksheet_element`\n",
    "4. Get image-content / graph-content\n",
    "5. Apply calibration: `eels.set_calibration_result`\n",
    "\n",
    "### Line\n",
    "1. Acquire using `femtus.acquisition` with `setting['Region']['Shape'] = 'Line'`\n",
    "2. Generate whole spectrum: `eels.create_line_whole_spectrum`\n",
    "3. Get worksheet element: `worksheet.get_worksheet_element`\n",
    "4. Get graph-content / graph-content\n",
    "5. Apply calibration: `eels.set_calibration_result`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44192289",
   "metadata": {},
   "source": [
    "(si-cube)=\n",
    "## SI Cube\n",
    "\n",
    "Acquire EELS SI Cube (spectrum image) data using `femtus.acquisition` with `Shape = 'Rectangle'`, then analyze using the `eels` package.\n",
    "\n",
    "API reference: <a href=\"../../interface/PyJEM.femtus.acquisition.html\">femtus.acquisition</a> | <a href=\"../../interface/PyJEM.eels.html\">eels</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "317f3fa6",
   "metadata": {},
   "source": [
    "### Acquisition\n",
    "\n",
    "Configure the acquisition settings and start EELS SI Cube acquisition.\n",
    "\n",
    "- Set `setting['Detectors'] = ['Image', 'EELS']` to enable both image and EELS detectors.\n",
    "- Set `setting['Region']['Shape'] = 'Rectangle'` for SI Cube (spectrum image) acquisition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5632fd27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Id': '08274fde-dd82-44c0-906c-d045552c6a43',\n",
       " 'Detectors': ['Image', 'EELS'],\n",
       " 'Region': {'Left': 229,\n",
       "  'Top': 206,\n",
       "  'Width': 32,\n",
       "  'Height': 32,\n",
       "  'Points': None,\n",
       "  'Shape': 'Rectangle'},\n",
       " 'Conditions': {'DwellTime': 100,\n",
       "  'PixelResolution': 1,\n",
       "  'IsPlayback': False,\n",
       "  'CollectionMode': {'Mode': 'Sweep', 'Count': 1}},\n",
       " 'DriftCorrection': {'Type': 'None', 'Interval': 1, 'CorrectionRegion': None}}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = acquisition.get_settings()\n",
    "if result[\"Settings\"]:\n",
    "    setting = result['Settings'][0]\n",
    "else:\n",
    "    setting = {\n",
    "        'Detectors': [],\n",
    "        'Region': {'Left': 0, 'Top': 0, 'Width': 32, 'Height': 32, 'Points': None, 'Shape': 'Rectangle'},\n",
    "        'Conditions': {'DwellTime': 100, 'PixelResolution': 1, 'IsPlayback': False,\n",
    "                       'CollectionMode': {'Mode': 'Sweep', 'Count': 1}},\n",
    "        'DriftCorrection': {'Type': 'None', 'Interval': 1, 'CorrectionRegion': None}\n",
    "    }\n",
    "\n",
    "# Select detectors\n",
    "setting['Detectors'] = ['Image', 'EELS']\n",
    "\n",
    "# Set acquisition region for SI Cube\n",
    "setting['Region']['Shape'] = 'Rectangle'\n",
    "setting['Region']['Width'] = 32\n",
    "setting['Region']['Height'] = 32\n",
    "\n",
    "# Set acquisition conditions\n",
    "setting['Conditions']['DwellTime'] = 100              # microseconds\n",
    "setting['Conditions']['CollectionMode']['Mode'] = 'Sweep'\n",
    "setting['Conditions']['CollectionMode']['Count'] = 1\n",
    "\n",
    "setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "85caf384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AcquisitionID': '6b81ab09-a1c9-4bf3-bc7a-c454369354e3',\n",
       " 'WorksheetID': '6d33305e-8d86-4867-917f-7d959c3398bb',\n",
       " 'ReferenceDetector': 'VP_ADF_1',\n",
       " 'Version': '1.0.0'}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_result = acquisition.start(setting)\n",
    "acquisition_id = start_result['AcquisitionID']\n",
    "start_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bc40875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STEM_Survey -> 2c63baa5-1c41-4aea-81a2-b186d35595fa\n",
      "STEM_HAADF_SeleArea -> 4eb6b93b-149f-4225-9e09-b5b2f211a24e\n",
      "EELS_Cube -> 9ca464f7-5e58-4fc5-b42a-9b5fe7a5221a\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Results': [{'Id': '2c63baa5-1c41-4aea-81a2-b186d35595fa',\n",
       "   'DataType': 'STEM_Survey'},\n",
       "  {'Id': '4eb6b93b-149f-4225-9e09-b5b2f211a24e',\n",
       "   'DataType': 'STEM_HAADF_SeleArea'},\n",
       "  {'Id': '9ca464f7-5e58-4fc5-b42a-9b5fe7a5221a', 'DataType': 'EELS_Cube'}]}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquisition_result = acquisition.get_acquisition_result(acquisition_id)\n",
    "\n",
    "for r in acquisition_result['Results']:\n",
    "    print(r['DataType'], '->', r['Id'])\n",
    "    if r['DataType'] == 'EELS_Cube':\n",
    "        eels_cube_id = r['Id']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5debdc28",
   "metadata": {},
   "source": [
    "### SI Cube Analysis\n",
    "\n",
    "The following cells analyze the acquired SI Cube dataset using the `eels` package.\n",
    "The following example outlines the procedure for analyzing and outputting the results of the whole spectrum."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66a4bd7",
   "metadata": {},
   "source": [
    "#### `eels.create_si_cube_whole_spectrum`\n",
    "\n",
    "Generate a whole-area integrated spectrum from the SI Cube dataset.  \n",
    "The returned object contains an `Ids` list with IDs for the generated datasets (image content and graph content)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b902b2db",
   "metadata": {},
   "outputs": [],
   "source": [
    "whole_spectrum = eels.create_si_cube_whole_spectrum(eels_cube_id)\n",
    "whole_spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3869c91d",
   "metadata": {},
   "source": [
    "#### `worksheet.get_worksheet_element`\n",
    "\n",
    "`create_si_cube_whole_spectrum` returns a dict with an `Ids` list containing two entries:\n",
    "\n",
    "| Index | ContentType | Description |\n",
    "|---|---|---|\n",
    "| 0 | `ImageContent` | Retrieved via `worksheet.get_image_content()` |\n",
    "| 1 | `GraphContent` | Retrieved via `worksheet.get_graph_content()` |\n",
    "\n",
    "Use `get_worksheet_element` to retrieve each dataset by its `Id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "60c0452d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ImageContent -> ec2b3897-8040-4dd8-8dff-9b2cb61bf72f\n",
      "GraphContent -> c283a2b5-7252-4827-ac5c-950624a5c072\n"
     ]
    }
   ],
   "source": [
    "for id in whole_spectrum['Ids']:\n",
    "    element = worksheet.get_worksheet_element(id)\n",
    "    print(element['ContentSummary']['ContentType'], '->', element['Id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caca9f4c",
   "metadata": {},
   "source": [
    "#### ImageContent and GraphContent\n",
    "\n",
    "Retrieve the image content (`Ids[0]`) and graph content (`Ids[1]`) from the worksheet elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "13400722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Id': '49858513-675f-48a5-af1d-8501e4e453d8',\n",
       " 'Children': [{'Id': '1734b7c3-dec5-4e1a-b223-5118d3f40187',\n",
       "   'IsSelected': False,\n",
       "   'Clump': {'Id': '001eb99e-db3a-4668-9ee6-dd875e1e6231',\n",
       "    'Information': {'Header': {'Version': '1.0.0',\n",
       "      'ClumpId': '001eb99e-db3a-4668-9ee6-dd875e1e6231',\n",
       "      'DataType': 'EELS_Cube',\n",
       "      'DataSubType': '',\n",
       "      'DetectorType': 1,\n",
       "      'ClumpType': 'Clump',\n",
       "      'Name': 'Cube'},\n",
       "     'DataInformation': {'TypeInfo': 2,\n",
       "      'DataBytes': 4,\n",
       "      'Channel': 1,\n",
       "      'DimensionLength': 3,\n",
       "      'Dimensions': [32, 32, 512],\n",
       "      'ChannelType': 'GrayScale',\n",
       "      'IsRangeFixed': False,\n",
       "      'MinimumPossibleIntensity': -1.7976931348623157e+308,\n",
       "      'MaximumPossibleIntensity': 1.7976931348623157e+308},\n",
       "     'MeasurementInformation': {'CalibrationCoefficients': [{'Scale': 14.6875,\n",
       "        'Offset': 0,\n",
       "        'Unit': 'Nanometer'},\n",
       "       {'Scale': 14.697265625, 'Offset': 0, 'Unit': 'Nanometer'},\n",
       "       {'Scale': 2, 'Offset': 0, 'Unit': 'Nanometer'}]},\n",
       "     'Tags': {'General': {'Instrument': 'JEM-ARM200F',\n",
       "       'Manufacturer': 'JEOL Ltd.',\n",
       "       'Comment': '',\n",
       "       'DateTime': '20260402153944435',\n",
       "       'ProductName': 'FEMTUS',\n",
       "       'ProductVersion': '2.0.0.0+8cc42b3fb348c2458c76e358a38bce3fb5cd1063',\n",
       "       'UpdateDateTime': '20260402153944435',\n",
       "       'UpdateProductName': 'FEMTUS',\n",
       "       'UpdateProductVersion': '2.0.0.0+8cc42b3fb348c2458c76e358a38bce3fb5cd1063',\n",
       "       'AnalysisId': '75f87d81-1242-418e-9d6e-eda2d7417f83',\n",
       "       'AnalysisDateTime': '20260402153943687',\n",
       "       'AcquisitionId': 'af9842a2-ec66-476d-9173-79dd2cbec977',\n",
       "       'AcquisitionDateTime': '20260402153943687'},\n",
       "      'EELS': {'FilterMode': 'STEM-EELS',\n",
       "       'FilterSubmode': 'align',\n",
       "       'DataType': 'Image',\n",
       "       'Dispersion': 0,\n",
       "       'CameraLengthCoefficient': 1,\n",
       "       'MagnificationValue': 1,\n",
       "       'EnergyLossOffset': 0,\n",
       "       'DriftTubeVoltage': 0,\n",
       "       'HighTensionOffset': 0,\n",
       "       'SectorOffset': 0,\n",
       "       'SlitMode': 'Out',\n",
       "       'SlitInserted': False,\n",
       "       'SlitWidth': 0,\n",
       "       'EntranceApertureHoleString': 'Out',\n",
       "       'EntranceApertureInserted': False},\n",
       "      'ScanGenerator': {},\n",
       "      'HT': {'GunType': 'Cfeg',\n",
       "       'AccelerationVoltage': 200000,\n",
       "       'EnergyShift': 0},\n",
       "      'EOS': {'OperationMode': 'Scanning',\n",
       "       'SpotSizeNumber': 0,\n",
       "       'ConvergenceAngleAlphaNumber': 0,\n",
       "       'ImageFormingMode': 'MAG',\n",
       "       'RockingAngle': 0,\n",
       "       'MagnificationValue': 20000,\n",
       "       'MagnificationString': 'x20k',\n",
       "       'CameraLength': 15,\n",
       "       'CameraLengthString': '1.5cm'},\n",
       "      'Stage': {'X': {'Position': 0},\n",
       "       'Y': {'Position': 0},\n",
       "       'Z': {'Position': 0},\n",
       "       'TX': {'Position': 0},\n",
       "       'TY': {'Position': 0},\n",
       "       'TZ': {'Position': 0},\n",
       "       'PX': {'Position': 0},\n",
       "       'PY': {'Position': 0},\n",
       "       'PZ': {'Position': 0}},\n",
       "      'Aperture': {'CL': {'ApertureHoleString': '0'},\n",
       "       'CL2': {'ApertureHoleString': '0'},\n",
       "       'OL': {'ApertureHoleString': '0'},\n",
       "       'SA': {'ApertureHoleString': '0'}},\n",
       "      'Detector': {'DetectorKind': 'Test Camera Name 0',\n",
       "       'Manufacturer': 'Test Vendor',\n",
       "       'ModelCode': 'Test Camera Name 0',\n",
       "       'ModelDisplayName': 'Test Camera Name 0',\n",
       "       'PixelsPerMeter': {'Horizontal': 200000, 'Vertical': 200000},\n",
       "       'ImagingArea': {'X': 256, 'Y': 128, 'Width': 512, 'Height': 512},\n",
       "       'BinningSize': {'X': 2, 'Y': 2},\n",
       "       'SoftwareCroppingArea': {'X': 0, 'Y': 0, 'Width': 512, 'Height': 512},\n",
       "       'SoftwareBinningSize': {'X': 1, 'Y': 1},\n",
       "       'ExposureTimeValue': 100,\n",
       "       'FrameIntegration': 1}}},\n",
       "    'ViewInformation': {'Header': {'Version': '2.0.0',\n",
       "      'ClumpId': '001eb99e-db3a-4668-9ee6-dd875e1e6231'},\n",
       "     'Tags': {}}}}]}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ids[0]: ImageContent — retrieve with get_image_content\n",
    "element = worksheet.get_worksheet_element(whole_spectrum['Ids'][0])\n",
    "image_content = worksheet.get_image_content(element['ContentSummary']['Id'])\n",
    "image_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6dac3227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Id': '68a11875-7cd3-41c0-9d4c-947d18603ada',\n",
       " 'Children': [{'Id': '80f1c410-8884-46e6-bd54-1e668764ae94',\n",
       "   'IsSelected': False,\n",
       "   'Clump': {'Id': '744b96a9-914e-4aea-abd3-464ac6b4d7a9',\n",
       "    'Information': {'Header': {'Version': '1.0.0',\n",
       "      'ClumpId': '744b96a9-914e-4aea-abd3-464ac6b4d7a9',\n",
       "      'DataType': 'EELS_Cube_Spectrum',\n",
       "      'DataSubType': '',\n",
       "      'DetectorType': 1,\n",
       "      'ClumpType': 'Clump',\n",
       "      'Name': 'Spectrum'},\n",
       "     'DataInformation': {'TypeInfo': 2,\n",
       "      'DataBytes': 8,\n",
       "      'Channel': 1,\n",
       "      'DimensionLength': 1,\n",
       "      'Dimensions': [512],\n",
       "      'ChannelType': 'GrayScale',\n",
       "      'IsRangeFixed': False,\n",
       "      'MinimumPossibleIntensity': -1.7976931348623157e+308,\n",
       "      'MaximumPossibleIntensity': 1.7976931348623157e+308},\n",
       "     'MeasurementInformation': {'CalibrationCoefficients': [{'Scale': 2,\n",
       "        'Offset': 0,\n",
       "        'Unit': 'eV'}]},\n",
       "     'Tags': {'General': {'Instrument': 'JEM-ARM200F',\n",
       "       'Manufacturer': 'JEOL Ltd.',\n",
       "       'Comment': '',\n",
       "       'DateTime': '20260402153944435',\n",
       "       'ProductName': 'FEMTUS',\n",
       "       'ProductVersion': '2.0.0.0+8cc42b3fb348c2458c76e358a38bce3fb5cd1063',\n",
       "       'UpdateDateTime': '20260402153944435',\n",
       "       'UpdateProductName': 'FEMTUS',\n",
       "       'UpdateProductVersion': '2.0.0.0+8cc42b3fb348c2458c76e358a38bce3fb5cd1063',\n",
       "       'AnalysisId': '2e127b69-0af7-4f1c-8051-e223dcd35342',\n",
       "       'AnalysisDateTime': '20260402164653468',\n",
       "       'AcquisitionId': 'af9842a2-ec66-476d-9173-79dd2cbec977',\n",
       "       'AcquisitionDateTime': '20260402153943687'},\n",
       "      'EELS': {'FilterMode': 'STEM-EELS',\n",
       "       'FilterSubmode': 'align',\n",
       "       'DataType': 'Image',\n",
       "       'Dispersion': 0,\n",
       "       'CameraLengthCoefficient': 1,\n",
       "       'MagnificationValue': 1,\n",
       "       'EnergyLossOffset': 0,\n",
       "       'DriftTubeVoltage': 0,\n",
       "       'HighTensionOffset': 0,\n",
       "       'SectorOffset': 0,\n",
       "       'SlitMode': 'Out',\n",
       "       'SlitInserted': False,\n",
       "       'SlitWidth': 0,\n",
       "       'EntranceApertureHoleString': 'Out',\n",
       "       'EntranceApertureInserted': False},\n",
       "      'ScanGenerator': {},\n",
       "      'HT': {'GunType': 'Cfeg',\n",
       "       'AccelerationVoltage': 200000,\n",
       "       'EnergyShift': 0},\n",
       "      'EOS': {'OperationMode': 'Scanning',\n",
       "       'SpotSizeNumber': 0,\n",
       "       'ConvergenceAngleAlphaNumber': 0,\n",
       "       'ImageFormingMode': 'MAG',\n",
       "       'RockingAngle': 0,\n",
       "       'MagnificationValue': 20000,\n",
       "       'MagnificationString': 'x20k',\n",
       "       'CameraLength': 15,\n",
       "       'CameraLengthString': '1.5cm'},\n",
       "      'Stage': {'X': {'Position': 0},\n",
       "       'Y': {'Position': 0},\n",
       "       'Z': {'Position': 0},\n",
       "       'TX': {'Position': 0},\n",
       "       'TY': {'Position': 0},\n",
       "       'TZ': {'Position': 0},\n",
       "       'PX': {'Position': 0},\n",
       "       'PY': {'Position': 0},\n",
       "       'PZ': {'Position': 0}},\n",
       "      'Aperture': {'CL': {'ApertureHoleString': '0'},\n",
       "       'CL2': {'ApertureHoleString': '0'},\n",
       "       'OL': {'ApertureHoleString': '0'},\n",
       "       'SA': {'ApertureHoleString': '0'}},\n",
       "      'Detector': {'DetectorKind': 'Test Camera Name 0',\n",
       "       'Manufacturer': 'Test Vendor',\n",
       "       'ModelCode': 'Test Camera Name 0',\n",
       "       'ModelDisplayName': 'Test Camera Name 0',\n",
       "       'PixelsPerMeter': {'Horizontal': 200000, 'Vertical': 200000},\n",
       "       'ImagingArea': {'X': 256, 'Y': 128, 'Width': 512, 'Height': 512},\n",
       "       'BinningSize': {'X': 2, 'Y': 2},\n",
       "       'SoftwareCroppingArea': {'X': 0, 'Y': 0, 'Width': 512, 'Height': 512},\n",
       "       'SoftwareBinningSize': {'X': 1, 'Y': 1},\n",
       "       'ExposureTimeValue': 100,\n",
       "       'FrameIntegration': 1},\n",
       "      'Processings': [{'Parents': ['001eb99e-db3a-4668-9ee6-dd875e1e6231'],\n",
       "        'ProcessType': 'ExtractSpectrum',\n",
       "        'Parameters': [{'ExtractRoi': {'ShapeType': 'Rectangle',\n",
       "           'Shape': {'X': 0, 'Y': 0, 'Width': 32, 'Height': 32}}}]}]}},\n",
       "    'ViewInformation': {'Header': {'Version': '2.0.0',\n",
       "      'ClumpId': '744b96a9-914e-4aea-abd3-464ac6b4d7a9'},\n",
       "     'Tags': {}}}},\n",
       "  {'Id': '7a4a064c-4df0-4dda-8d52-c56090f3a1bf',\n",
       "   'IsSelected': False,\n",
       "   'Clump': {'Id': '63d2b3a0-db89-433c-9430-4b1ed550d39e',\n",
       "    'Information': {'Header': {'Version': '1.0.0',\n",
       "      'ClumpId': '63d2b3a0-db89-433c-9430-4b1ed550d39e',\n",
       "      'DataType': 'EELS_Cube_Spectrum',\n",
       "      'DataSubType': '',\n",
       "      'DetectorType': 0,\n",
       "      'ClumpType': 'Clump',\n",
       "      'Name': 'H-K Background'},\n",
       "     'DataInformation': {'TypeInfo': 2,\n",
       "      'DataBytes': 8,\n",
       "      'Channel': 1,\n",
       "      'DimensionLength': 1,\n",
       "      'Dimensions': [30],\n",
       "      'ChannelType': '',\n",
       "      'IsRangeFixed': False,\n",
       "      'MinimumPossibleIntensity': -1.7976931348623157e+308,\n",
       "      'MaximumPossibleIntensity': 1.7976931348623157e+308},\n",
       "     'MeasurementInformation': {'CalibrationCoefficients': [{'Scale': 2,\n",
       "        'Offset': 0,\n",
       "        'Unit': 'eV'}]},\n",
       "     'Tags': {'General': {'AnalysisId': '9d7668b6-bbee-4789-9e10-3eab1346a470',\n",
       "       'AnalysisDateTime': '20260402171632000'}}},\n",
       "    'ViewInformation': {'Header': {'Version': '2.0.0',\n",
       "      'ClumpId': '63d2b3a0-db89-433c-9430-4b1ed550d39e'},\n",
       "     'Tags': {'ImagePseudoColor': {'PseudoColor': {'ColorType': 'SINGLECOLOR',\n",
       "        'R': 0,\n",
       "        'G': 255,\n",
       "        'B': 255}}}}}},\n",
       "  {'Id': '4829260f-2d8d-46ed-8701-3d876ccb1d20',\n",
       "   'IsSelected': False,\n",
       "   'Clump': {'Id': '3b7d85d1-52aa-4413-9ddd-4b3fc0238d0d',\n",
       "    'Information': {'Header': {'Version': '1.0.0',\n",
       "      'ClumpId': '3b7d85d1-52aa-4413-9ddd-4b3fc0238d0d',\n",
       "      'DataType': 'EELS_Cube_Spectrum',\n",
       "      'DataSubType': '',\n",
       "      'DetectorType': 0,\n",
       "      'ClumpType': 'Clump',\n",
       "      'Name': 'H-K Signal'},\n",
       "     'DataInformation': {'TypeInfo': 2,\n",
       "      'DataBytes': 8,\n",
       "      'Channel': 1,\n",
       "      'DimensionLength': 1,\n",
       "      'Dimensions': [30],\n",
       "      'ChannelType': '',\n",
       "      'IsRangeFixed': False,\n",
       "      'MinimumPossibleIntensity': -1.7976931348623157e+308,\n",
       "      'MaximumPossibleIntensity': 1.7976931348623157e+308},\n",
       "     'MeasurementInformation': {'CalibrationCoefficients': [{'Scale': 2,\n",
       "        'Offset': 0,\n",
       "        'Unit': 'eV'}]},\n",
       "     'Tags': {'General': {'AnalysisId': '79dfcdc3-758d-4069-993d-036f26bc9e9a',\n",
       "       'AnalysisDateTime': '20260402171632001'}}},\n",
       "    'ViewInformation': {'Header': {'Version': '2.0.0',\n",
       "      'ClumpId': '3b7d85d1-52aa-4413-9ddd-4b3fc0238d0d'},\n",
       "     'Tags': {'ImagePseudoColor': {'PseudoColor': {'ColorType': 'SINGLECOLOR',\n",
       "        'R': 255,\n",
       "        'G': 0,\n",
       "        'B': 0}}}}}}]}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ids[1]: GraphContent — retrieve with get_graph_content\n",
    "element = worksheet.get_worksheet_element(whole_spectrum['Ids'][1])\n",
    "graph_content = worksheet.get_graph_content(element['ContentSummary']['Id'])\n",
    "graph_content"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25519b78",
   "metadata": {},
   "source": [
    "#### `worksheet.get_clump_num_array_accessor`\n",
    "\n",
    "The following cell generates a spectrum plot from `graph_content` (spectrum data) using these steps:\n",
    "\n",
    "1. Retrieve the `ClumpId` via `get_data_set`\n",
    "2. Retrieve `Dimensions` / `ElementType` via `get_clump_num_array`\n",
    "3. Retrieve the energy-axis `Offset` / `Scale` / `Unit` from `MeasurementInformation.CalibrationCoefficients`\n",
    "4. Retrieve the raw byte array via `get_clump_num_array_accessor`, infer `dtype` from the byte count, and convert to a numpy array\n",
    "5. Build the energy axis (`Energy Loss [eV]`) and plot the spectrum with `plt.plot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b7e099d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions: [1, 512]\n",
      "ElementType: Double\n",
      "Calibration: offset=0, scale=2, unit=eV\n",
      "dtype: <class 'numpy.float64'>   shape: (1, 512)\n"
     ]
    },
    {
     "data": {
      "image/png": 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u07b37rvvJgDI7t27teX8TGyEEHL11VeTnJwcbZl9CVaDVIdx0EEHYb/99sP06dMBAIsXL0ZhYaFLVD3qqKPc1cKsWbMQi8WkxMrBgwejc+fO7u9evXqhQYMG+PPPPwEktCvffvstRowYgU6dOrnlWrVqhQsuuADTp09HQUGBtI2LFi3C8uXLccEFF2D79u2uBqKwsBDHH388pk2b5pooTjjhBMyaNQunnnoqFi9ejMcffxxDhw5FmzZt8Pnnn3vqPvLII3HooYe6v9u1a4fTTjsN33zzDWKxGAgh+Pjjj3HKKaeAEMJpQIYOHYr8/HwsWLAAAPDxxx+jadOmUiJ4EELjP/7xD+OyKlx88cWcOaB///4uSZlF//79sXbt2sDaHCBxv5MmTeL+jRs3zlPu6quv9pSbNGkSunfvHuh6l19+OSZOnIhBgwZh+vTpeOihh3D00Ueja9eu7gpZBdq36tevH+iapmjdujXn6NCgQQNcfPHFWLhwoaux+Oijj3D00UejcePGXD8aPHgwYrGY1LRrinfffRevvvoqbr31VnTt2tXdT03nWVlZnnOys7M507qIadOm4YEHHsA555zDmXb27NkDANi9ezemTJmCSy+9FJdeeikmT54MQgin/SkuLlZem20fADz66KMoLy/Hv/71L+29BqlTxHXXXYdff/0VL7zwAmdyTCfatWuHb775Btdccw1OOeUU3HjjjVi4cCGaNWuGW2+9VXrO119/ja+++gpPPvkk2rVrh8LCQu74lVdeiXA4jHPOOQczZ87EypUrMXr0aNcDWXfPQEJDFYlEpE4VQdG4cWMUFxdrSfT7EqwXWyUxbdo0PPHEE5g/f77rETNixIhAdXz44Yd45JFH8Mcff6BZs2a47rrrcNttt1VNgxk4joOjjjrKFTJmzJiB5s2bo0uXLgASAtILL7wAAK6gJBOQ2rVr59nXuHFj7Ny5EwCwdetWFBUV4cADD/SUO+iggxCPx7F27VopZ2D58uUAgEsuuUR5H/n5+WjcuDEAoF+/fpgwYQLKysqwePFifPLJJ3j66adx1llnYdGiRdzEzE4mFAcccACKioqwdetWhEIh7Nq1Cy+99BJeeukl6bW3bNkCIMGROfDAAys18EYiEc4DKFWI76Nhw4YAEnwDcX88Hkd+fr7HbOqHY445xshM0bVrVyn3IRUMHToUQ4cORVFREebPn48PPvgAL774Ik4++WT89ttvSi4SNcfs3r07Le0Q0aVLF48QfMABBwBIuH23bNkSy5cvx88//+zy0ETQfhQUP/74I6644goMHToU//73v7lj1Cwj45mVlJRIzTYA8Ntvv+H0009Hz5498corr0jrHDBgANef2rVrh4EDB3LCak5OjvLabF2rV6/GE088gTFjxvhO4qZ1injiiSfw8ssv46GHHpJ6VVYlmjRpgssuuwyPPvoo1q1b5/nGqafwsGHDcNppp6Fnz56oV6+eG1+uV69eePfdd3HNNde4/M+WLVvimWeewT/+8Y+0CD6mIIQACLboq82wAlIlUVhYiEMOOQSXX345xycxxddff40LL7wQzz//PIYMGYJly5bhqquuQk5OTloDMKowcOBAfPHFF1iyZInHzfmoo47CbbfdhvXr12P69Olo3bo1pwGiUPE66MdUGVDt0BNPPIHevXtLy8gGiMzMTPTr1w/9+vXDAQccgMsuuwwfffQR7rvvvsDXvuiii5QCWq9evYzr80NWVpaUMKkajGKxmPTZq95HVb6nvYnc3FwcffTROProo9G0aVM88MAD+Prrr5XvqFu3bgASMbJMFi+6550q4vE4TjjhBNx+++3S41SgCoLFixfj1FNPRc+ePTF+/HiPcN6qVSsAwMaNGz3C8caNG13eDou1a9diyJAhaNiwIb766iuP1o26nrdo0cJzbvPmzTleU6tWrbB+/XpPORpbidZ17733ok2bNhg0aJBLgKeat61bt2L16tVo164dQqGQcZ0sXn/9ddxxxx245pprcPfdd3uO7w3Q579jxw7tIqhz587o06cP3nnnHW78P+uss1zNeCwWQ9++fd14an59Z7/99kM0GsXu3bsrrUXduXMncnNzlYLovgYrIFUSw4YNw7Bhw5THS0tLcdddd+G9997Drl270LNnTzz22GOuJ8pbb72FESNG4JprrgEAdOrUCaNGjcJjjz2GkSNHVrmkzsZDmjFjBm666Sb32KGHHoqsrCx8//33mDNnTsorr2bNmiE3Nxe///6759hvv/2GUCjkGcApqOmuQYMGKWsiKElVDHpHtVMs/vjjD+Tm5ror/fr16yMWi/leu3PnzpgzZw7Ky8uV3i6pvsvGjRtLCdB//fWXVGCtS1C9WxYDBw5E48aN8d577+Ff//qXL1GbaiN37drFeUT+9ddf0vIrVqwAIYR7v3/88QeApFdS586dsWfPnrRp01auXIkTTzwRzZs3x1dffSVdJNAFxbx58zhhaMOGDVi3bh3nCQYkTDFDhgxBaWkppkyZ4gpYLA4++GBkZGRIhZQNGzZwGrLevXtj6tSpKCgo4EjV1CmDtm/NmjVYsWKFtC9fe+21ABITc6NGjYzrpPjss89w5ZVX4owzzsCYMWM89e8tULqBSoPIori4WKolo4s+ismTJwOAb5+iC4RVq1ZVekG3atUqHHTQQZWqozbBcpCqGNdddx1mzZqF999/Hz///DPOPvtsnHjiie7kXFpa6trPKXJycrBu3TrlgJxOHHbYYcjOzsY777yD9evXcxqkrKws9O3bF2PGjEFhYaEysJsfwuEwhgwZgs8++4xzkd68eTPeffddDBw40OOVQnHooYeic+fO+M9//uPyH1hs3brV3Z46dapUG/LVV18BgMfEN2vWLJdDBCRWz5999hmGDBmCcDiMcDiMM888Ex9//LHUM4699plnnolt27a5JkkWtE3U60QV8ViFzp07Y/bs2ZzL8Jdffil1k95XMWXKFOl+1btlkZubizvuuAPLli3DHXfcIe0jb7/9tusRSYVylhdUWFioDC64YcMGLiJ9QUEB3nzzTfTu3dsNsHjOOedg1qxZ+Oabbzzn79q1KxAPbNOmTRgyZAhCoRC++eYb5aTbo0cPdOvWDS+99BKn/Ro7diwcx8FZZ53F3d/w4cOxfv16fPXVV1LzM5BYMAwfPhwzZ87Eb7/95u5ftmwZZs6ciRNOOMHdd9ZZZyEWi3Hm6dLSUowbNw79+/d3F0UPP/wwPvnkE+7fQw89BAC4/fbb8cknnyAvLy9QnUDi/Z133nk45phj8M477xi7s1cG7JhAsX79erz22mvo1auXK3RGo1GXgsBi7ty5WLJkicfzUMTy5ctd87KfBokGqJw3b57pbSixYMGCWhFMNV2wGqQqxJo1azBu3DisWbPGVf3+85//xMSJEzFu3Dg88sgjGDp0KG6++WZceumlOO6447BixQo8+eSTABKrYlVI+3SBrkp+/PFHZGVlcaRlIGFmo+1JVUACEoPgpEmTMHDgQFx77bWIRCL43//+h9LSUq1bbygUwiuvvIJhw4ahR48euOyyy9CmTRusX78eU6dORYMGDfDFF18AAK6//noUFRXh9NNPR7du3VBWVoaZM2figw8+QIcOHVwXWoqePXti6NChuOGGG5CVlYX//ve/AOBGuwYS5NGpU6eif//+uOqqq9C9e3fs2LEDCxYswOTJk7Fjxw4ACWL0m2++iVtuuQVz587F0UcfjcLCQkyePBnXXnstTjvtNOTk5KB79+744IMPcMABB6BJkybo2bOnb5qNK6+8EuPHj8eJJ56Ic845BytXrsTbb7/NEeP3NsaPHy/VWpxwwgmc+WXBggV4++23PeU6d+7MRRbeunUrHn74YU+5jh074sILL8Rpp52Gjh074pRTTkHnzp3dZ/vFF1+gX79+OOWUU7Ttve222/DLL7/gySefxNSpU3HWWWehZcuW2LRpEz799FPMnTvX5c8MGTIE7dq1wxVXXIHbbrsN4XAYr732Gpo1a4Y1a9Z46j7ggANwxRVX4KeffkKLFi3w2muvYfPmzRxp/bbbbsPnn3+Ok08+GZdeeikOPfRQFBYWYsmSJRg/fjxWr15t7Hp+4okn4s8//8Ttt9+O6dOnu04WQML0xQopTzzxBE499VQMGTIE5513HpYuXYoXXngBV155JacJuPDCCzF37lxcfvnlWLZsGRcnqF69epxp8pFHHsGUKVPwt7/9DTfccAMA4LnnnkOTJk04knX//v1x9tlnY9SoUdiyZQu6dOmCN954A6tXr8arr77qlpONK1Rz169fP+7apnX+9ddfOPXUU11BUEzL0atXr0DalJ9//tl19FixYgXy8/Pd/nrIIYe4/e/222/HypUrcfzxx6N169ZYvXo1/ve//6GwsBDPPvusW9+ePXvQtm1bnHvuuejRowfy8vKwZMkSjBs3Dg0bNsQ999zDXb979+44++yz0a5dO6xatQpjx45FkyZN8OKLL/q2vVOnTujZsycmT57scdSYNm2auxDYunUrCgsL3fs65phjcMwxx7hl58+fjx07duC0004zfm61HtXkPbdPAoIr8ZdffkkAkLy8PO5fJBIh55xzDiEk4QJ+++23k+zsbBIOh0njxo3J/fffTwCQ2bNn75V2jxo1igAgRx11lOcYdeutX78+iUajnuNQRNKWuaYvWLCADB06lNSrV4/k5uaS4447jsycOZMro4qkvXDhQnLGGWeQ/fbbj2RlZZH27duTc845h3NB/vrrr8nll19OunXrRurVq0cyMzNJly5dyPXXX++Jtkzb/fbbb5OuXbuSrKws0qdPH2mU4s2bN5ORI0eStm3bkoyMDNKyZUty/PHHk5deeokrV1RURO666y7SsWNHt9xZZ53FhTaYOXMmOfTQQ0lmZibn8n/JJZeQvLw8z7UpnnzySdKmTRuSlZVFBgwYQObNm6d08xddo6l7+k8//cTtN42KLTtH9Y8+Pz83f7ZvHHvsscpyxx9/PCGEkPfee4+cd955pHPnziQnJ4dkZ2eT7t27k7vuuosUFBQYt3/8+PFkyJAhpEmTJiQSiZBWrVqRc889l3z//fdcufnz55P+/fuTzMxM0q5dO/LUU08p3fxPOukk8s0335BevXqRrKws0q1bN887IISQ3bt3k1GjRpEuXbqQzMxM0rRpU3LUUUeR//znP9JQFCronqvMTfuTTz4hvXv3JllZWWT//fcnd999t+d6NFyB7J/MpX3+/Plk8ODBJC8vj9SvX5+cdtppnhAGhCSiXP/zn/8kLVu2JFlZWaRfv35k4sSJvveo6sumddLzVf9Mo9lT0Hfv15ffffddcswxx5BmzZqRSCRCmjZtSk4//XQyf/58rr7S0lJy4403kl69epEGDRqQjIwM0r59e3LFFVdw/YvivPPOI23btiWZmZmkdevW5JprrgmUHeCpp54i9erVc8OSUOi+Z/EZ3XHHHaRdu3bSSPT7KhxCahlDswaDJn+lK54PPvgAF154IX755RcP76FevXpcfqNYLIZNmzahWbNmmDJlCoYPH44tW7YY2awtgsNxHIwcOVJqErOwsLDYl5Cfn49OnTrh8ccfxxVXXBH4/NLSUnTo0AF33nknbrzxxipoYc2ENbFVIfr06YNYLIYtW7YoQ+dThMNhtGnTBgDw3nvv4cgjj7TCkYWFhYVFpdGwYUPcfvvteOKJJ3DZZZcF5mONGzcOGRkZrjNRXYHVIFUSe/bswYoVKwAkBKKnnnoKxx13HJo0aYJ27drhoosuwowZM/Dkk0+iT58+2Lp1K6ZMmYJevXrhpJNOwrZt2zB+/HgMGjQIJSUlGDduHF566SX88MMPUjdci/TAapB45Ofn+wacYzWeFunHjh07tLm7wuGwXTSlGTScgAo5OTluHDGLOojqtfDVfqhs3dQuXVZWRu69917SoUMHkpGRQVq1akVOP/108vPPPxNCEuknjjjiCJKXl0dyc3PJ8ccfv9e4R3UZUHCn6ipoqhPdP4uqhY6LBQUXyKJy8OvzshQ/FnUHVoNkYWHhJiHVIV0xfCzkmD9/vtT1myInJ8eNpGyRHtBYQiq0bt06cFoci30HVkCysLCwsLCwsBBgA0VaWFhYWFhYWAiwXmwpIh6PY8OGDahfv36dSdxnYWFhYWFR20EIwe7du9G6dWutR58VkFLEhg0blPnDLCwsLCwsLGo21q5dq00ebAWkFEGzIq9du1aZR8zCwsLCwsKiZqGgoABt27Z153EVrICUIqhZrUGDBlZAsrCwsLCwqGXwo8dYkraFhYWFhYWFhQArIFlYWFhYWFhYCLACkoWFhYWFhYWFACsgWVhYWFhYWFgIsAKShYWFhYWFhYUAKyBZWFhYWFhYWAiwApKFhYWFhYWFhQArIFlYWFhYWFhYCLACkoWFhYWFhYWFACsgWVhYWFhYWFgIsAKShYWFhYWFhYUAKyBZWFhYWFhYWAiwApKFhYVFDUVJeQzxOKnuZlhY1ElYAcnCwsKiBiK/uBzd752IM1+cWd1NsbCok7ACkoWFhUUNxLQ/tiJOgIVrdlV3Uyws6iSsgGRhYWFhYWFhIcAKSBYWFhY1EI5T3S2wsKjbsAKShYWFRQ2EAyshWVhUJ6yAZGFhYWFhYWEhwApIFhYWFhYWFhYCrIBkYWFhYWFhYSHACkgWFrUYhBBMX74NW3aXVHdTLNIMS9K2sKheRKq7ARYWFqlj8rItuOrNecgIO1j+7+HV3RwLCwuLfQZWg2RhUYsx7Y+tAIDymE1HYbHvY0tBCUrKY9XdDIs6AisgWVhYWNRAWAsbj1XbCnH4I1Mw+KkfqrspFnUEVkCysLCwsKjxmPTrJgDAup3F1dwSi7oCKyBZWNRiEFjTmoWFhUVVwApIFhYWFjUQ1ovNwqJ6YQUkCwsLCwsLCwsBVkCysLCwsLDYh0AIwWvTV2HOn9uruym1GjYOkoWFhUWNRNLGRgiBY21uFob4/vetePDLXwEAqx89qZpbU3thNUgWFhYWNRzEcvHtMwiA1dsLq7sJ+wSsgGRhUYthJ426AfuaLYLA6hrTAysgWVhYWNRAsBa1uJWELQLAmmPTAysgWVjUYthpc98FO8VZ+cgiCKx8lB5YAcnCwsKihsMGBLUIAisfpQdWQLKwsLCogWDNJFaDZLWlgWBVSGmBFZAsLCwsajisgGQRBFY8Sg+sgGQBQgj+2l6IeNyOwrUNduKsG7AmNosgsAqk9MAKSBZ4feZqHPvE97jns6XV3RQLCwsJ7NrFLgaCwLE6pLTACkgW+M83vwMA3pmzpppbYmFhQcF7sVnpwMIcVoOUHlgBycLGzLCwqOGw4pGd9IPAPqr0wApIFhYWFjUcVoFkn0EQWGEyPbACkoX9mCwsaiDY71I0sa3bWYSVW/fs5RZZ1BZYDlJ6UK0C0ujRo9GvXz/Ur18fzZs3x4gRI/D7778bn//+++/DcRyMGDGC208Iwb333otWrVohJycHgwcPxvLly7ky//73v3HUUUchNzcXjRo1SsPd1F7YT6k2wy6r6wJE7cnAx6bi+Cd/QH5RefU0yKJmQyNcW5ijWgWkH374ASNHjsTs2bMxadIklJeXY8iQISgs9M9EvHr1avzzn//E0Ucf7Tn2+OOP47nnnsOLL76IOXPmIC8vD0OHDkVJSYlbpqysDGeffTb+8Y9/pPWeLCwsLNINdopjw3FsKijxFt5HYUMdmINd9FoPyNQRqc6LT5w4kfv9+uuvo3nz5pg/fz6OOeYY5XmxWAwXXnghHnjgAfz444/YtWuXe4wQgmeeeQZ33303TjvtNADAm2++iRYtWuDTTz/FeeedBwB44IEH3GvWdViStoVFzYMqWW15PO5uh0N189slhNhxSwP22cQJQdjaCVJCjeIg5efnAwCaNGmiLffggw+iefPmuOKKKzzHVq1ahU2bNmHw4MHuvoYNG6J///6YNWtWym0rLS1FQUEB98/CwsKiqsBaRtjtaCz5I1JnBaTqbkHNBq9Bsg8rVdQYASkej+Omm27CgAED0LNnT2W56dOn49VXX8XLL78sPb5p0yYAQIsWLbj9LVq0cI+lgtGjR6Nhw4buv7Zt26ZcV02DXYhZWNQ8cAISY15iBaQ6q0Gq7gbUcHDax7i6nIUeNUZAGjlyJJYuXYr3339fWWb37t34+9//jpdffhlNmzbdi60DRo0ahfz8fPff2rVr9+r1qxJ1c4jdN2AXh/su2FfLvmdrYrNaET+ozLMWwVCtHCSK6667Dl9++SWmTZuG/fffX1lu5cqVWL16NU455RR3X7xisIhEIvj999/RsmVLAMDmzZvRqlUrt9zmzZvRu3fvlNuYlZWFrKyslM+vybC2fAuLmgfW+0hlYqtLUx/7DGrTpF8ajeG16asx6MBmOKhVg71yzRAzpsdq0bOqaahWDRIhBNdddx0++eQTfPfdd+jYsaO2fLdu3bBkyRIsWrTI/XfqqafiuOOOw6JFi9C2bVt07NgRLVu2xJQpU9zzCgoKMGfOHBx55JFVfUu1ElY8srCo2WBNbOWxpAaprrpw16bbfuXHVXhs4m8Y9uyP1XJ9Yk1sKaNaNUgjR47Eu+++i88++wz169d3OUINGzZETk4OAODiiy9GmzZtMHr0aGRnZ3v4STSGEbv/pptuwsMPP4yuXbuiY8eOuOeee9C6dWsuXtKaNWuwY8cOrFmzBrFYDIsWLQIAdOnSBfXq1au6m7awqCJYz559C5xrP6tBiss1S3UJtem+l67Pr9brWw1S6qhWAWns2LEAgEGDBnH7x40bh0svvRRAQpAJhYIpum6//XYUFhbi6quvxq5duzBw4EBMnDgR2dnZbpl7770Xb7zxhvu7T58+AICpU6d62rOvw86p+wbiBAjbd7nPgPdiY0naViVQm0xs1T2+1qZnVdNQrQKSiXr4+++/1x6XxTFyHAcPPvggHnzwQe15NgYShZ1V9wXYeCf7GuSaovKY1SDVptuu7rQfcRspMmXUGC82i+pDda9wLFJHbSWuWvhDFQeJ4yDVKlEhfahNfb06xld+XNj7199XYAUkC6tz2EdQi+YMCwNwbv5sHKQ4S9Leiw2qQahNxOPq4AWyAqTlIKUOKyBZWOwjqE2ragt/qDVIddPNn0Vt0pxVxwKU0yBZFVLKsAKShTWx7SOw8tG+BVYIiHMkbZaDVHdeOnuvdM4nhKCwNFpNLTJDtZjY2O2600XSDisgWVQ7idAiPbAapH0LfKqRJNhI2nX1jVNh6ao356PHfd9g1bbCam6RGqEabGIrjcYw9fctKC6L7Y1m1TpYAcnCapD2EVhN+r4FlRYgWke92GTE48nLNgMA3pu7BiXlMdzywSJ8vWRjNbROjWoZXg2dNx784ldcNu4n3PTBwr3QqNoHKyBZWP1RLQZB3TS31AXw71MVB6nuvHN2ASD2dQfAuBmrMWHhevzjnQV7t2F+qIYBlhWKdBykd+asAQB888vmKm9TbYQVkCws9hFYDdK+C/bdltfRSNrcYkA86ABbd5fu1faYojooDKoo7BbBYAUkC5ueohbDxkGSo6isZhN3TaDyYovGahcHae6qHXjwi18rzXOJ+/T1mjqMhao5DlJMIyFFqqNxtQhWQLKwqMWw3ipeTFy6Cd3v/Qb//X5FdTelUuA1Jiovtr3apJRwzv9m4bUZqyr9PmRebBQOnGoRRExQHYIbZ2LTdJJwTX1oNQRWQLKosSsvC3+wg5/lICVw2/jFAIDHJ/5ezS3xYsWW3Rj99TLsLCzzLcvHsklu815steed/1lJTzO/vl5TNeHVb2KzGqRUUa252CwsLCoHm1LAi5osJw55ehriBFi3oxhjLuyrLcu7+ddeDVK6wJO0+WOOAzg19FlUT6oRtbaNRSQcAmBd/FWwGiSLWqNBiscJbnx/IZ745rfqbkqNgakqvS6hJj8HOlktWZ/vW1ZlPuVysdXcW007dH3dQQ3WIFV7LjarQUoVVkCyqDWBIn9avQOfLdqAMVNXVndTagz8iKt1EbXhOWSE/b85QuSaoijrxVaLTGyVhYq0DlRokGroMFYdghunQdKRtA36YV2GFZAsag025pdI9z8z+Q8MfXoa8ovLASQ8mOoKHyeumETrMmqDqTEj7D/0KpPV1lENEvHRltZUZUh1NItfOKnLRUJWBNDBPh2LGrvyErFdQWx9ZvJy/L55N96Z8xdWbNmN7vd+g+vfqxuRYf0mjTqJWvAYTAQkFqpktXUJHAdJOOZU/FcTUd252HRu/taLTQ8rIFnU0GHFi+179IHgHDgYN2M1AODLn2tWuoGqAuvdZOWjBGqDoGhiYlOli4jG66YGSefFVqNNbNXhxWbo3aoysX2ycB2WGvDk9nVYAcmixpIbRWzfo3eNzsmoe93ZkrS90D0HQgiWb97NEZ2rA2YmNnnkaM6LrTaoy9IEncemSNKuSSb2ag8UGZCkPXPFNtz8wWKc/Pz0qmharULdm1EsPKgd4hGwvTCpQaIDIDsQZmeE93qbqhumXIO6BN1z+HjBepzw9DT8v7fm770GSWAkIClIyeV11s1fc98Or6fRmZX2NqqFpA124aQuJ+Mg/bZpd1U0qVbCCkgWtQYsB4kOkCXlSU1AXRSQTFXpFgm8On0VAOC737ZUazuMvNjYbZWJLZ2NquHw05aycki0BglI1QFu4WS92FKGFZAsao0KiTWx0QGykMm5lRWpe92ZnzSqsSG1BDVFiAysQWL28xqkmnE/ewO62D4OgBAjIdUsDdLev6ZpHCRL0taj7s0oFh7Ulk+EJWnT8a+wNOrZV5cQMxwILWoWMgyEeY6DxMZBqmXJatMF30jazO9oDfL0C1WDhGS6cJJxkGoJJXWvwApIFrWCpE0IQSGTDdzVIJUm97Gmh7oCVTBBCzlqyjPKDKhB4r3Y6uY79+vrIWayr0ljQXWPrtbNP3VYAcmi2j7gkvIY/u/njW6ARx1UE0FRGatBqkOzRQWsF1swVKfXFzvBB+cgJbd5D7za884rO8749XWWa5MuDhIhBP/6ZAmemfxHynWw608dHyidiHNCtPqaQeNx1TXYp2NRbXjkq2UY+e4CXP76T75lxU88yUFKapCq2XO7WrCvx0Eqje47iTRZ7lDEZGJiNSbMF1Bbc7FVtql+gSJjCi1bZbB6exHenbMGz0xennJoCFZDv7cWMVygSEMOUk3ibdUUWAHJotpszp8sWA8AmP/XTt+y4sBCv+UiloNUBz/wfVmDNH7+OnS/9xt8vSR9QT+r8xGVMROskYlN8YOPgxQctfU70Sardfj7iqWJg8RydHYoIvn7gR1fdcJKOqGLGcWCvb/qjg1WE2EFJIvqC9Ef4LJeASnxew8jIO2twacmwdRbpTbinx8tRixO8I93FqStzup8QqXlSW2YSRZ11SRXXgkO0qgJSzDwse9QUOJv1q5p0CarBf/9l6eJg8ReZ+tufSR/FdjxdW99opwwqXPzZ+IgWQHJCysgWVSbBinIZcWBhVR8y0WciY1UqwdGSXkMb8/+C+t3Fe+1a1o3/2CoTrf4soATEFGY2PhktcHu5725a7Ahv8TV3u5NmH6aZdE4Pl+8AVt288mpdTG/HIc3safLXMQKXSkLSKwGaS99pOxVtG7+YVaDZAcQEVZAsqg2hAJ4UHgEJHjjIMUJqdaElU9++zvu/nQpTnrux712TX7w27cGuAbZkbTXWb0apOQMbjJPqkjalTWxVRdM2/riDytxw3sLcerzM7j9flHjOU+/NE32rECTugYpib3GQWKuoxPK2LZRDZL1a0vCCkgW1YYg8UFE76MkB4nXIHFl9rJKZdof2wAAu4r2nvliX0410rxBdvorrSEcJJOJUmU+Ld/Hk9VOXrYZALCpgNcg6ZPVOtz3ny43f05A8kmWrQI7zu2t6AM6c6SqXFnU27jayldLF6yAZFFtSR6DrFTE71QWSVsUkOoCJ4kYcg1qI5rXz3K394V7YzVIJt+ZSjeYjmS1NTkCtyoum8jJEu+BF5BqkAaJdfPfa15sZs4bnOAtMQHXhTFUBysgWXCCyt78HoIEqFSRtPlI2uoBc2+gOvhP+7IGab96SQFpZ1FqHkQs1mwvwi6DmFtVhbJYUttp8qo4AYAlabMT2T72zgFAFSJK1CCx/d1xqsbExtaZqoDEIojAUVwWk2p1TMA+G901VUmQ3XP3tUElIKyAZFEtKxwACBLE1cNBqviti4PEriJ/2ZCPBWt2Bm1ijYfO7FDbwXp6ieaWoFi/qxjHPDE1ZVftdIDnIAV7V8pI2pVv1l6D6eeuMr1zfR36BVGVmNhSFJBS8TQtLovhoHsnYsBj36Xhmupyfhqkfc0zNiisgGTBYW8uGIJoXMTJn/4s0mmQKlZEhBCc9Nx0nPHfmdhZjRNkVWBf1iCx73NzJQWkBQaxtqoapbFgJG0WqlxsqU5g1dFVTK+pct6IC4IGe+8OHO53VXixbUuRg6TKqafDb5sKAFRGKDMzvbOHZF6WVoNkUedRfRqk1L3YaDtLGRV0LE54d+iKVSQXyyTFQa6mQuUKvi+AHZu37amcYFsT0g3yHCT/8goLGxfaItXPtSYrBlSaZS8HKfk74eZfBSY2ps5UYyux7TQVOCo7DrNn6+ti7q9iLHUUpHJCSJ2LlWQFJItqCWQGBBOQVBykMkFAksVC2ZejTe/LcZDSaT6szvAPFGUB4xfJiLbb9pRiC6NV2MdeOQB1AlUxDhKvQeJN7OkiabP1pGq1EzVfJogJgklQmLr5cwFIZRwkpp5Lxv2Efv+ezAXn3ddhBSSLagmFHxRiq2gzRdfpGDOKRV0BKXleVaqMg5DOU8H8v3bgwldm4/dNu919qQy+tQVkHxP+2EjaQd386fbitbuEMlXzYErKY2n3HEwrB4moPVsB3gxZGZgmfdWBE3QNmyXea1DEJX1HBvaeqHZIZaqc9sdW7Coqx49/bA3eoFoKKyBZVEsgMwAIBeh9phqkqETN7jfYEEJw4/sLcdtHi80bVA04c+wszFixHTe8t9Ddty+TtPe1RLxlATlIRPLLIyBVulVe5BeXo8+Dk3DhK3PSWq8xB0kpILHbZO+4+adDSE9hEcMKZqmMyZxZT+vmn9ym/TPmc+3yfWG1YggrIFlwIHvRxFw5DlLiLycgESL1ZPHzIlm3sxifLdqAj+avQ0l56tnj95YRh3V55+6tku9uR2EZZq7YVmMELd5rqZImtuq3sFWKg0S79ZL1+UKh1NqiO+3737eguDyGWX9uT63ySkLFQdJpkByHFwTSRtKupKAC8M/aVENfWecLU1qBzIvNj+we21vRLmsArIBkwc0etYWkTSdxlqQdFzRI9OP2WwXmV2NsnFTQODfT3dYNhI9+/RvemLnauN4TnvoBF7wyB1/8vLHSbUwH0umhVwPko0pxkGjx3SVRZZkg0F2fFeSqA2oOUnJb1CA5cHhCdbpMbGnQIKViposZCjhBr69DuatBYtohOTddBPjaACsgWXDYmwJSsEjaookt8Zf3Yku69gMsB0k/2LCTTm1wa22Um+Fui7FhKP7YvBsv/rAS933+i3G92ytCIHzzy6ZKtzEd4AMl7lsapMAcJHiFfbFMulAa9WpRpy/fhlETlnCBWYPC9BWouHzeOEjsOfy3mz4NUnI7dQ6SvD4deKEqhWsaCnacBilqNl7WhjEyXbACkgX3Be5VknaQOEjib5eDxBNfZRok4jPI6aJx10RwGiTFvbGmwqCr6VgNWSGqhD/tOXth8C4pj2He6h3GE0V+cTnOeXEWxs1c5e4L2kp6KfH+qkZA8vaXi16dg/fmrsF/v1+R/gsiYdZ7a/ZfAICwQaoR0YsNECb7mmRiS4GDxF43FS2hqfMGe4iGMYj7CJoiv2vOn9tx/+e/oKhs3/NuswLSPoxYnOCWDxfh9RmrtOXY7l5j3fzFRLQVP1mzRcLNX+bFpl9NsW6rlTGvV6WWgh18GuclBSTVSjEjnPy0g/Kq0kVwrSy4Qd7UTLAX+BH/eHs+znpxFv43baVR+f/9sBJzV+/gkhgbkbQlBHyPBsmoBcEgxhZjsXFX6gE7dW29dNxPuOfTpVi0dpfSeUP8jkVNB6dBSpOJjRdUUgMr4OiE6hkrtuG8l2bhz617Kk0O5z3n1BXwGqQKE5svB4nf98LUFXh95mr88Pu+591mBaR9GN/+sgkTFqzH/V/8qi1XXXGCgsgTXpI21SDxJG1eg2RGOtxTCzRI25lAiTkZYXdbtVJk03QUBxSQagoJMxUNknoCSp/0OrViInh9xmqj8jKzVFATG4U471cFoZ4VkETtIyt4VwX+2l5olKyWEO9vVvFZJV5sKdZpaim+8JU5mP3nDtzw/kJh4VNZDZJZObrY5DRIkmuLz7a4InDp7n0wPpIVkPZhmKZnkHnL7A0EImmLTs8kEetE1DLIoun6aSI4DlIlJpyq1CBtZ1KkqARaLt4Ta24rCybw1BQNUioUJFmwO6Bq3k1lhGkzkjZbvuKaookt5eurj7EcJLEvZEZSnzJMXkFpNK40sYn93sNJqgI3/8pygRLn6QUOEZsLSnnuUwrrFVM3f7YD0WvKNEh84Em+QbS8zDRb22EFpH0YbCJXHVIxZaQDQSYtWVA48YMU4yDJPm7ZwLm7hDV91AzhQMR2JkWKSiji7pMRFIrKg63saoqXSiqaTVZArmpidmXIqsHd/BUmtlQnbY1oxZLJxYCLQTVIQTVcpdG4kZt/QkBir1NVJO3Ka9fZs4zjIFVaq292vuw67CunshBbhTiG0mdUWokQKTUVVkDah2FKmuO5Dom/f20vxNodRVXRLBdBIk/LktWWiQISEeMgeTVIsoGzgBWQKsNBqkJnctbEpgoOqSKDFhsKyhQ1xUsllYkhXVncTVCZ52RkYpO4+Xuvmf53xRP8K6dB4oQYRRm2D5eWx4yS1QLwBFNkhcd0ufmnI1Ak910aVOKg8gJSnBNy1OdzwpuEs0nvn9MqCX2CLqgqE0OupiJS3Q2wqDoUBZwYgcSHUFQWxbFPfA8AWPnIcGVcksoiSLUyDZKYfVoVB0kWDI1FQXHN5yDtYIJDcis8xUDKPoegHKS9KWToIGoITMBqvxI8FQLHcaqEq2M6YcoWAmYkbWa74i/t0+GQk0jOXAXdleXkid9LZjjYWGDy3FmhL6FBUnCQmO1EHCT+uJ/3VSqobERr8TzTZnH9OIVr8nn81OVkwpDsOarGFvaYNbFZ1CoUlZqa2PhBYHNB0pxTldmbg3CQxGEiTohEg8Tbx2VebLKBkw0UWVO0JyLKmXtVea6pTKVBV3Y15RmkQlQV205/pqMbF5fF8MqPf7q/KyNIBuUgJc0fSQFJLJMusKTyaIzP4B7UxMbFKlKUYSfc0vKYxs2f6Q9xryY1ppnEUwVbT+rmzCRMv63Kmvb8tOaycu63IvnuiKY++oysBslir2Li0o1Yv6sEVwzsmNL5hcYmNnabcN4gValRqZwGybtiiccJt/KKSVKNyAZO1sRWQxVIStdbIw1SLSVpm5hnRMj4EeGQk5b4Xk988zteY0JmVOYxGTVHokKi7zgSclBmWk/A6xcyC6vyeJzTQGYENrH5N5ATkKJxzs0/HicY+8NK/Lh8K2cqJhAFJIGkXSWRtFN72OL46gfHqbxgZnxNTuhMUYNEOUj7oAbJCkg1GNe8vQAAcESnJujRumHg801NbF4VcPpXYlIEiYPk+cglGiTRi81Qg1TAapAqMZGa3M705dvw8P/9isfP6oVe+zcyrptX9Sf3qwZC9j51Jra/theiRYNsZDOhA2qKBimVjObixJhcAVf+nmYLuckq49BgxkFit1UapBQnbc2xPYIGidUMRAKa200eO/vORBNbNE7wxDe/e87xkLSBKtEgpYOkzT5t0/GF1U6mlqxWPl6I4DVIFX1MIjTxBHj+G9uXNUjVamIbPXo0+vXrh/r166N58+YYMWIEfv/d+zGo8P7778NxHIwYMYLbTwjBvffei1atWiEnJweDBw/G8uXLuTI7duzAhRdeiAYNGqBRo0a44oorsGfPnnTcVtqxZXepfyEJuAjRpkQ9ImhhqtCjKchY6+EbEHg4SFFBQEqufvgyIgpK9h4H6aJX5+C3TbtxyWtzA52nys2kdPM3EJDmrd6BY5/4Hmf8dya3vyrNqkEgDt5L1+dj2cYC7TkqfkQ6hD5RAK7MJBz0VFqe1SABVaPxZDXP5bE4FyaCfY5rdxThmrfmY/5fO5V1BdUgFZfFBAFJ0ReFhVwiUCRTZ5rGLVmgyK+WbMQZ/51h7MTCEaYNm8WS41PpZuwpOqFMSshmc7FRXpJmDHW92PZBDVK1Ckg//PADRo4cidmzZ2PSpEkoLy/HkCFDUFhY6Hvu6tWr8c9//hNHH32059jjjz+O5557Di+++CLmzJmDvLw8DB06FCUlybhAF154IX755RdMmjQJX375JaZNm4arr746rfdXGRCJFB8U7MRoGgvjlOenY82O5POvytQjgSJpC+2Ix4nHrVRMNVLuxkFSr34AoMhQkPRDkLV10AS5JsRsFU+gWGFqHT9/HQDgV0HoqDEaJGHiPPn56Rj27I/SXGEU4sSYFCwq3550hg0waQ6vHUz8TWqQQsb1BEWhQNIuicrHkZs+WISJv2zCmWN5AZuFSfvYd7anLMo5haiEUJmbf5XEQRK+L0IIrn1nARas2YW7Pl1qVIdpVGsW7DiVypgki8IuL5fc1pnYVNpp9rfVIKUZEydOxKWXXooePXrgkEMOweuvv441a9Zg/vz52vNisRguvPBCPPDAA+jUqRN3jBCCZ555BnfffTdOO+009OrVC2+++SY2bNiATz/9FACwbNkyTJw4Ea+88gr69++PgQMH4vnnn8f777+PDRs2VNXtBkJ5TN0hTcGqyvVEPV6oGDVhSaWvbYLKRdL2apBEE1uSg+QVmrjzUvAy2dtQmQllKnLAjIMkmihl51Yn2Pth+7KY0Z6FqHGISQb9mgAzkjbxbNP7SWqQUrsvetpf2wtx+es/YQ5jPuRMbHHCcX/Y52iiQTHTICXfWVFplBNEVRrsOBGEF4g8vXSlGuF/s7ezkwneqgNRfKMqOHC4cSqVV2xO0vaOJfLYSOoxlI4XVoNUxcjPzwcANGnSRFvuwQcfRPPmzXHFFVd4jq1atQqbNm3C4MGD3X0NGzZE//79MWvWLADArFmz0KhRIxx22GFumcGDByMUCmHOnDnSa5aWlqKgoID7V5VgzRypmn1YLzZtwkLh904mZ1RVTpaBImkT8beXg5TQIMm82JJlZIMFO5buLe1JkBhQgKDq51bO8pWiiYmtRKGJqTEaJKYZrFZBJdgBEi82yqGoYez7oKlGRE1YJKC7faI+7zUf+WoZvvttC859aTaABB+ohAkUWR7jSdrseGDi0WYSBZrVIBWWxrh4YqrxZ0dhKd6ds4a5EBFI2unXIIm/TcdltlQqXmyp8MzYM0w/55jkW6HTEGdiU/D8rAapChGPx3HTTTdhwIAB6Nmzp7Lc9OnT8eqrr+Lll1+WHt+0aRMAoEWLFtz+Fi1auMc2bdqE5s2bc8cjkQiaNGnilhExevRoNGzY0P3Xtm1b43tLBayAlColhOUSmK4iRFRpZvcgHCQx1QgkgSLjPA9B54GxbU8p3pr9FwpKypX8nsCowtDNqijBvAZJXl41cLERk1mo0nWkCyXlMYya8DOmLNusLady89cJSGLb6Xnp4JalGghU1i1MFBxci4WVfCocJN4pjk5qfENKhGcrkrRZIcQkPlpQDtKe0qhRctf/fPsH3pz1F3OdqiFpq0y24rYOQeMgOQ4vhKRyK9w1Dcd++r35mdhEjiJtq9iX9gXUGAFp5MiRWLp0Kd5//31lmd27d+Pvf/87Xn75ZTRt2nQvtg4YNWoU8vPz3X9r166t0uux5qNUSbOsalwnIOnGsKrlIJmXFZtfGo3h66W8MJsQkNg0CfSDZ8skflz86lzc8+lS3Pnxz2nyVFFjV1GZJ6p50KlWTcyWb7PvTRVJWzRRuudWcaDI12aswntz1+KKN+Zpy6kGeVW7AQk/QjLop4r0cpCCaZDoJr2fVLzYZH27W8v67vb2PaWeMtF4nBOQ2H5l4tFm0jpW61tYFhXCcpj1RQLRgzX9kbQBuUBh0LjA50QrOyZxgpxOQEpuJ6Nm8+eu3VHEPQcZtQHYN01sNcLN/7rrrnOJ0vvvv7+y3MqVK7F69Wqccsop7r54xYcQiUTw+++/o2XLlgCAzZs3o1WrVm65zZs3o3fv3gCAli1bYsuWLVzd0WgUO3bscM8XkZWVhaysrJTuLxWwK+FUVJel0Zg0qrQMWgGpCifLICty8SN/bsoKLFq7i9s3Z9UO7rdOg0SJyV8t4YWsysyjsrspKClH7wcnITsjhN8eGpZy3TIhjhAiNcMkyiffm8rEptIgVTUHafU2fycMQD54A+p2A4m4PSxEcnNlkE79oElzZORe+jdCSdpBNEiSfWx4h182FOAQIfREWZRwmgF2bjQx84k8IRlEE5uOEKyCSNJOlxbUkxzYUPDgzmG2zd38vYJYNBbH2p3F6Ng0z+Ca7GJJU07GQWKuPfrrZVi7oxgXH9ne3VcWlS9CbC62NIMQguuuuw6ffPIJvvvuO3TsqA+I2K1bNyxZsgSLFi1y/5166qk47rjjsGjRIrRt2xYdO3ZEy5YtMWXKFPe8goICzJkzB0ceeSQA4Mgjj8SuXbs4Mvh3332HeDyO/v37V83NBgRrRkhFQBK1BroPU7eqESfLkvIYtuwuUZQOhlCA3ic2kRWOVAvZqKseTu5bs70Ig5/6QXmddPNvflmfEMQqq36WqellvCwKdtJRCkjVxEHSmchYqEwTOi820SQsC/XA4q1Zq3HduwvSFljQFPRdlcfiuPXDxa5HIV/Iu+nRIAURkIh3m33GyzYWSDVIbP9hj4cNPmATjQs7xhSWRjmBwlRYJ+DHONM8lH7QaZBMPxPTmEQsohI3/2veno/j/vM9Plu03vd8PrSA2eJYFvNo7Y5iAODMmaJFw2qQqggjR47Eu+++i88++wz169d3+T8NGzZETk4OAODiiy9GmzZtMHr0aGRnZ3v4SY0aNQIAbv9NN92Ehx9+GF27dkXHjh1xzz33oHXr1m68pIMOOggnnngirrrqKrz44osoLy/Hddddh/POOw+tW7eu+hs3ANsJRV6A2fnCh62pQvfNipPlMY9PxZbdpZh+x3HYv3GucXvemfMXPvxpLV69tB+a1kto4liSdjxOlEkqAb0Ql5MRRqHEjES1KOwA8f5PetNounN2qeoLaq6Rx3cyW90qOUgqLzbJkjMaiyMSMM2ECqare9ngDeiFTVWMFtUkcc9nvwAATujeAqf1bqNvUBptbLSZP6/Lx8cL1mHBmp0461Bee862mBBeY0i1N0F6q8wcx/ar3SVRTwmRg8SWzzDQIPHvUF5G1Haaar7F67Dz9h6Np2MQiBokU24Pf466PhVkgSInL0tYPV6bvsq3r/KJjjUCErMtI2nLION+ApaknXaMHTsW+fn5GDRoEFq1auX+++CDD9wya9aswcaNGwPVe/vtt+P666/H1VdfjX79+mHPnj2YOHEisrOz3TLvvPMOunXrhuOPPx7Dhw/HwIED8dJLL6Xt3ioLthMGzcYOeG3wOpu8lqQtfNA0aOWPy7cFas9dnyzF4nX5eGrSH/Lr+HyU4tG8zKRpIIfZZiGLpO2HdGtPZFFpU6pHsiIUq1O5+asiqqvd/Pn94+evwwF3f43Jv3pJ1WXROGau2BZocDRdaapCG+iuJbZdpimRodAgb2E6TWx00qLaMNm7EEnq7DMIp+DmLyvKeSwR4nlOohebrA068CY2eVtFgbmE6a+m3mhEaPvu0jRpkEQTG7OdionNzM0fWjd/k6tWxs3fr09xHtbxZDwqq0FKM0w+7u+//157/PXXX/fscxwHDz74IB588EHleU2aNMG7777re/3qAq9BSkFACqJB0ryGdPNR8pkQAqwGKRYnyJDLOQC8wkWz+lko3J6Iw6L6MP3MK9LrVOJ2ZQoGbjUcYDL7Y/NurNpWiKE9Epw4mUeX1wU5uc0HigymQRKfwT8/WgwAuPLNeVj96EncsQe++AXvzFmDM/q0wVPn9lbfEANTpwMV6Vz3PahI2n6XTJNyzBi0ma4jgaRviLtkBOlAGiRJYS6FTdwrIP20egcaZGck2xD3tsH0mqpvS5X8VHZMeR2hbLo0SOL4R7iFilkdqXCqYhINkuq3/Jps+eT2d79txuptRbi8Ir+nX8wjGTgPawNNdW1GjfFis+DBriB0pFQVPKYGQ08GEenWqLAeEOz46vfRi81gzXGqwIFJDpL5PaTbi03mMgv4E9SHPD0N/49J48BrohJ/xaaqNC4qPkY6VnzvVMSimbDQnxdBYcxBYiejuNn3oJpM/N6rSVyqVC1ssndNhMlIthAhXHn+ebjamwDdVW5iY7eJp099OG8dXpm+KlmGE9LSw0FScVqAAF5shI+DtGV3KR79+jfsMAzmqEK64yCZDi/RSgaKhITgDwCXvz4PD375KxZXcDhlZnk/pR07boi5L/c2l6+qYQWkGorKkrRF7zO9oKM+lm4Bif2AHEGDpIM4uJuo3lPTIKV+v7L5U5Y8Nwho7jGOg+QOZMLqViEgbVdMEjqyc1WCj/Glfibc/TDFdBokj9AoeH+p6g9XYQwrWdX0yvRZyNvHlidS4SSImz9P0qYCGh9vx3ehwmqQgnKQDDVIqphfftcRv4cXf1iJh7781eh8FTyBRzmBz6wOvh+bnaRz8zepguM9SU7YtqfUU5dp1Hnd97uvmdmsgFRDwZnYUhCQVAHzZNB9cKoBKtXphG0Xp0Hy+a7ENpqsVKgQFUTIS7dAyGl12Hdi+ADpGdKAboYmtt0lUWkfqq7BjNUi6rRJqkFeR9IWX59KmAT4PmXCp0mvmz+vOZK2j0vGKuf/BJHnZZoMMUiqX/fnSdrJ6UOlHTLRuKhSV4jbOojPh8IvubF/vepvzFiDFPAcx3EEkra6PvU15YsLChmHze0TvgKSWuDb18xsVkCqoSjjBKQUTGzioKPRuOg+2nRzkMpVGiQ/krYoIDHturB/O+k5slxsfqiMhU1mppFlxk4FslW1mMaBrV58b1sryPUsTE1d6QZ7XZ0WS+UxZHpO4jy631uW3aXzoKQImh5GB9ou+j3IItbzGh9Be5MSB8lb2mti09fI9mFWqFR5JsaFe5DW6dEgmWkYWRB4+VMA0Dg30+h8FTwk7VRMbKyAZOrFFlNfx6QG/rlXCOPMy6bCrWxRFcSLTey3qkXXa9NX4fbxi2tcTkQ/WAGphqLSJG1BJRMkFxt3nkqDlOJcoY7erP9wxPbT8uce1hZnHioPLlqegokt7SZFLjdccIEkHid4atIfmPr71uQ+Qv+qtYQq78OaANYrylSDxHuxqc8RJ3iXgyR5r2ydIcdfK1kVGiTTSUk0sYVS0CDJujbHkSNeDpKuPEvSVn3XWwqSMdNUY5DX4zZZzpTQTxQapMZ5GZLS5hAvz2uQzOoQNYEm4ANFCvWZkLSZbVmcIvruZI4QQUxssjh5Mjz45a/4cN46TF8RzPu5umHkxda3b99AlTqOg88//xxt2vjEFbFQorImNlU8GBl0H0RVapCCqJ7Fo7SeFg2zlfyRmMZDSIV0k7RVhEbTyfbLnzfgp9U7uX0q4rHOW0amQVKdW9VgPYx0Zj7Val0XsVdFXJcJIOzEXFAcRb9/T8bfurXAk+ccom68pI2V0SzpzMCi+YN+pyEn2X8CJTIl3k0xDIVf/2fHA1aDVFoeQ70sfjp5bfoqPMhwgFRVi9ptXeZ4HehpeZnJuGiV1SDpFiGm34xK0Gch1iWLpJ387X9N2VjALkZoTDMiOcevD7DCsFjWz2xfmKbwC3sLRgLSokWLcOutt6JevXq+ZQkhePTRR1FaWnNWrLUR5Uw49+I0mNi0kbQ19aQ71QjvnaGe0EV4I/wmfocdR8kfSSUOUrpJ2uyglEqG8XU7iz371HGQktteAYmPfu7NyB24aSmDjVFDzWUzV2zDrxsLcMXAjq7AkUocJA8HSdMH2G9swoJ12FlUjo8XrAskIEXjBBlhB3tKo3j4y19x6iGtcVQX/zyRtD00NYpUQGK3SVKDFA45rgY3GAfJW1j0svTrByqSsmxifFAgSFelBomtg71Ko9zKaZDEBWJqkbTl5/P1CtetZLJazjxb8Zd/R6SinPcb8xumuDHNUINUW2EcB+m2225D8+bNjco++eSTKTfIIoFS5gNJJceNOOiknostuZ0O+zG7+pCpgVXwrLBidLLg4ymxSHKQzNuXbkGBHUxSEb7knnGJvypzEuDPQSoS+lRVpxehKI3GBA/NxPYFr8wBAHRv3QBHdU4IGJzA5zMZU6hItbK+y/ZFI5K2pEh5LI6McAiPff0b3v9pLd7/aa0nVpS8nYm/rgbJgINH31HIcdzQAUHemshpEq8rCxQJAMcc0AxHd2mKf3+1TOlhZsJnU2qQRCEkBQGJ8xZlOkuQfI8ypCMXG+dyrzhHFwvKy0Hyvy6RqAtZ7h59rDKTYRATm1i2TnqxrVq1Cs2aNTOu9Ndff0X79u1TbpQFUF5JN3+V2nryr5tx8weLOFWnPhebPChYquBNbP4DR7KsvF2hUHo1SJURFMQJdNW2Qkz9PZkUmR30TK0ystYkVeH8fp2HzdY9vIAkBo9MheemAiEEb81ajYVrdnqOiQH8ymJxbpBlA4mqSNq670ElNMrmWbYvmrisyyZbqoUSEyf7wSXOUsI98ZuMk32T1SAFkf5lJbkkyAqS9gWHt0VehflM5XRgMjGKfXL9rmI8PekPbM4XtJvxYPUC/HhXbjhmFZfFfBMne0nayW3TBaP4HkWs31WMs/83i9sX1ZixTF65LBdbmcTcLxuD/bX56vhdsm+TMxX7N71GwUiDFFTYadu2bUqNsUiC5yBVPlAk/ZivfHMeAKBFg2zcOawbAD8Tm3zFqMKoCUuwfPNuvH/1EdLcXaxZIwjhUSUMJExs8nPcGDOBNEjp+4SP+8/33O90aWmUudiYN0knjMa5GdhZVI4tBYIGSRCQet3/baXbtXZHEdo2ycUvGwpwz2e/4KBWDfD1jUdzZfYIHITS8jh2FiXjNDXOS3JG2NuTkbS37i5Fs/pZXH3iE9aa2FgBKUjmZLaOiplo+x41pUAmeq3cWog7xv+M9k2T+QxjhCDElObyaSGZaiTsMCa2AG2VPQPRZCTr/nlZEeQXl3vKxzhBxl/AFuu+4OXZ+KsiGj4LnoMUXINkKsQMeeYHrN1RjE+uPQp92jWW16vlIBk1Tes8AQB3jP/ZDdwIJBZPWpK2wTX5vpNAqUSbLTPLm4yBZdE4cjLDHgqGTKDdW9rpqkDgUWHixImYPn26+3vMmDHo3bs3LrjgAuzc6V0xWqQGEy+2h778FbePXyxd9YkcE/FDX78ryW0xdfM3iQT93tw1mPfXTsxdtUNRX3pMbBThkKM0sdHJuDpJ2iyCRNJOlvNCTdL2lmmYk+BgFAsrO78JLRXS9tGPT8XMFdvcyOYFxUlt0LOTl2PwUz9w/Y62g/Ww4xIYKzSMJdEYXvnxT/T792Q8P2U5V58quaifgGSk0VOY2AB1ME4dPpi3Fp8v2uD+1mkrCEneQyjEmNgCvCZZ/xADkMo+w9zMiPteKmNiE9+BTDgCBA6SqQZJMX7oxhWaqX7i0k3KMtpktYYPny0l+65+2+SN1aSLpG1y3TjXdxI/RAEpv6icTzVjqEHiy/L7ZRqkdDv67E0EFpBuu+02FBQkXuiSJUtw6623Yvjw4Vi1ahVuueWWtDewrqKM+UDkQf5ieHX6Knw4b510oCkXOqUu1ojuexPdgHVgP36VZw87kAYxsam+sZCEpN2mUQ6ApDknUKqRKjShp2slRavxDJwsD6PiRmi8E120YhlSHdTenbtG6lb89OQ/sGLLHoyZuoIrXxaNcwKSagLivdjiePj/lgEAnhSSH3s0jS7Xwns/ZYw2k+1Bqv4i69FUI5oq92Ibo3nSJUYlhLiTUZhxY3tv7hpO+6CDLMO7yDGUPae8rLBrgoxJJlTA7P5p6U35JRj27I/KcuzYZOrFxi68ju6aJMkbdWONcOzVIKmPqcBrQr3Ht+3hhWtC+PvxvBMzFRJzfuIvuyj6fdNuHPLgt5zDhO5bUVUvcl1laYDqlIC0atUqdO/eHQDw8ccf4+STT8YjjzyCMWPG4Ouvv057A+sqRBObOGjv9nGV9ks1ossWzYIjC/p0dLYdGQpOh0p1nC4N0qVHdcBTFV5I9BkF+T4rw7NitUJSrZ5hQ/yec5BktVRA8gpT+jak4nFHr0sHzTKJlkrkIJVG43ysHG4C5uul0PGlVG7ZfhwkFqr3JJP5VfF/TKFLZsxpkCBwkCr2/7ZpN04bM8PsYsS7KZp/pAKSgQbJxMRG637im9+1Ea7ZZ2L6fNn++tQ5vT3X1EGlgQb8AkUaNY2TZ8y0P8SHpG1yTWacrfjLLk7fm7tGet3EX//6k/HF+P1FZVHcMf5nvDsnWb8sCGptQWABKTMzE0VFCY3F5MmTMWTIEABAkyZNXM2SReUhqqxFIYidaAolyUjFlZdOg6DzigjCQWI1XRkKYhCnQYJ53aqjIYf3QMqKhFC/Ivt4QUlwE1u64gHJqmGFVp1Jx6+9qhxzMq5DRqRCgyTU6RstN8WJPxpPcmVkdXhNfaIGKXlMmYtNGyiS/60Lfqe6R1PeC+BdQWdnBBtSuYjJnjbyiwn6fhMcpODeWbKJWpyIZd0iNzPsfmNsv+EEGSMTW+KvnzBVGS82xwGa1c/CDcd35fbroHuSOu8y07EiaPTtWJxUKlltaTSGPaXJZywzsUnHpwAmNnq+2P/n/bUTH8xbi399sgQLKpw0oobWipqIwALSwIEDccstt+Chhx7C3LlzcdJJCZfWP/74A/vvL49obBEc4sAg/mY1SLJs9roki4CeBKiqhx0cZUIVO3Gx4zdv+pFf19/EJj8uerElBKSE78GeUkos1VbNIRW5gJAK7x/2niXtNa2bfc6yiZAe1gWKpM85M+yNKZRoi/6hpJqVOx5Prn5lk6aMC8WGIOBzg/H1Uui0D2K/dJPVyjhIikk9SAqW8ijhFgb75WVpSnvBR1pXa5DihEg1SEEgi+HjjYMk0SBlRdxgrJyGj9MgeZ+Z6FxK+2emyquiAqnFQUoKj+xfE41wEA0SnwLEqGnacU72ncWYRYbsHD/BbMCjU7lvhBaXkbRZ0EuaCUjy74qdi8Z+vxKAXkta0xFYQHrhhRcQiUQwfvx4jB071o2W/fXXX+PEE09MewPrKrwCktARS5ME2N0lie0l6/Jx7TvzsXpboZekLXyHOjdS/jx2QFTXB/AaJJOEk8FMbPL9YYc3sWUyAlJJeRzlMa95UoegJO14nODMsTNx/suzhXq8ZdnJUDfB+Zm/ZC66gJz0Lsu5JDvX29bUBjJWgxQn3gmguIz/neAgydNR+Hn/yODVqlWcLxOQFOY8lWZJJpaUxeKcgJeXFTZqp3tdTZwxj4nNJWmnluqHq08yGcaIvN9mRUJuahMxbhIF5Z68PmMVXpu+KtFOoZG0uEq77NbLCUhm750N+wHA9Ww1ccUPos0t13GDBGzfU4oNu4q1qUY2SyLcxwjhYw2J367mmrE44XhtifLeBYvssegWEyJcDZLwftjwITRkB+/oU7viJBkHiqRo164dvvzyS8/+p59+Oi0NskiAJZAmfvtrkE55IeFduHzzHpx/eDuuvC5are5zUEn/sgmnhAtEpp7c4nGCUMgJ5BFiqkHKCIe4lAd7SqJV6sW2ZkcRFqzZBQDotX9DbT3mk7zZszAysVEBKSBJO4iZSaxX5JCw4R7EoKel0ThHUqXtJIK5x2zQ9mpAksKktzyrQWLvVzUpyybSaCzOTUhB5UpdzB4imtgYLUkqGiS2dpekzX6DcXkcJMdxDDRIMfy1vRD3f5GInn1B/3ae50XfjV/MqaCmO4AJHFtx0ZAk15gKOnOlToPkV/WhD08GAPRs08DdJ36HG3d5I+XHYgQxh72OMH5qLiwzX9Iuxh6TWQDcSNoGnVjl8cbSPWjfihp8WzUVgTVI4XAYW7Zs8ezfvn07wuFgqycLNcQJ6u+vzsEnC9e5v3kBqZwr++e2Ql8TmypuCAA8dFoPphwzgGv5EvzqgQ4k0Vjc6+lQMejxGg++LlNhIswM3kDC/BAJh5CTkeiLu0uigTzTgkYLZ+MJcdF8JfWYamX81NCpkLQ979/nGqkOZNF4nOsz4gQnEqxLy+Nc0FJ6Dx4ukYFJ8LQxM/D4xN+5/UQxkAO8poj93lSTsjySNuFNF5Lr6JrO3qe4Guc1PknNXCiUIgdJsiAR3bxVbZVxkMRAkXP+TIb2KIvFPW2kdQfRIJly4VjzIwCGVO5/rpaDJDyPVEzPf25NBqPUmaTcfaVRLmyEV/urvpbMi4wWL/PhIOlCYqjqFMeRYsl4qAoVUxsQWEBSqeZLS0uRmVm5xIAWSYgC0vIte3DzB4vd33tKWBObhKTt8WLjj9MPXfY+/35kB1zYv53nPG5wlHR0loMUixMsXLMTB9//LV6e9idXzuWhSCZ0ANiwqxh9H5qEG99f6Kv2DYccsDH+6ABJzWy7S8sDaYWCygXF5cyKiTUNSCZZdjLSTXDEZwxO8keE8yTvJzPi9T6SnSsiVQ6SSDAVhQ1R8CqLxaSDalCBbvG6fPy8Lt/bHommJNkWRihiIzEHuPfyWFzJ06Mw7X86byXC1J2yBol4t8VvWuctCvBCHLtdGo27pFwgsZgS20jrVnm4JutlBNeAcZCoMtnVeBlpkNTHRIFXFNjY57VhVzHu//wXrN5WKOUDAqktBIMkq5V5eJqStFPxYhMXfawGSbY4SXVcqS4Ym9iee+45AImB/ZVXXuES18ZiMUybNg3dunVLfwvrKPxUyzqStgOva6X4obMpDmSI0BVjnBd63Pr8TGyE4J7PlqK4PIbnvuNj31CuksrE9vO6fOQXl+OzRRtwcJuGuPLoTsoJXTSxRRgBacvuUuwuiQYMppceDZJskk2XBkklRHy+aANm/7kDYy7o6+/mXwkN0uMTf0MkHMItJxzgOcZykAD/+Dil5XHuGarSqPi9FtV7U/G1AP4bYydipQZJwUEKMf1PNuGZ9il9oEjifgNsHKQg4E2WFX8DapCUsamiccz/KykgRePEI3ikxkEKpkGi78LlTJlwkDQPU/xmRS1feYy4i5Cr35qHpesL8PXSjZh15/HMOWrekglpOYjSRebhSS/hlxcyCEkbkv4DCONhxTXYd1jbYiIZC0iUY0QIwYsvvsiZ0zIzM9GhQwe8+OKL6W9hHYWfaplN2VAgmNgIJIEiFSY2HbcHUCdNlH1ELL8kFo8jKyI3uVINEluDSq0+Z9UOXHl0JyVPKiwEiqR8l3oVrv67A3KQgqqA2XALfoKBad3+uZDkwm1hWQyrthXi5g8WoWXDbABMoMjAJjZ5/9u6uxT/rfBOuebYTt62SThIOpRG4yiS8BaCet2pjhLNoF+u0BoFCXEQjRFEQ34rc7O6vIEik78JYUjaTLJaFR784leEHODuk7vL64NXs8Z6sXVqmoeLj2yPfh2buNcU28ib6Qk2MTnVYnHiaSOt21dAIux7MXt4Xi82b10q6PIUiwKAzLs4syKUxtL1Cc+xzQWlQkgFdlu/cJW2IcD4Jc2FBqpBSh6TCkhxghvfX+imldG3KfFXFHj4BSP9y2qQ9lEBadWqVQCA4447DhMmTEDjxvLcNRbpgZizSkSBj5u/qMoUOzL90FXdNSLjHPiYEtjVSzRGXB6QiKKKGB18nJvkNvuRy7RNLMIheDhIANCAcfUPIvMEXeCwwqmfezKfakQN9rnIJms/s2NZLO6ZjIIOzKqJxW81KGqQVNqYBtkRFJREURqNca7/ShObn9CoOJ4knnqPqYQilVlHZoqJEeLLsTCd4DzPXND4uCRtNlmtBOt2FuG1GYnx+tYhByInM/EdSk1snAk92dZwyMGlAzq6x5IaJL5N7rlx4uF0iYKHS9LWSSRIlYMkerHxpPI9pVGcPmYG/tatOUYNP4g7V/csxXciLjxVE77qnaemQSLa3yxkAhJtMstPkn0uRWUxfMakvtHBFbDFhTi7oJZwkGqbBikwB2nq1KlWOKpiPD3pDyys8IySYUdhGab9sdX9LZK0Hcg9x1j4aZDCFcQe1lQn6/wsxIkuWyEgba6InMytshTaF/ejVnxXjqhBEjhIu4rKsamgRHquDEG92HYrNEiyla+xBolbQUsELR8yZTjkuAM3jYMUlPSsEjjYyUQWIdfjxaYQNhrlJviKhaUxaVR38fJ+70V1NKltk2mQFCa2ABqkWDzOTZJyDpJpXaIGid0WSNqaelhNDnsvvFajog+J33TFT9FFnyp9VBqkOOGfZyxOlG7+fkJBKl5s7rOpuKQjcJA+mrcWy7fswf8EPiRbVgYPNUHoG6q+ouqvQThIB7RI0Fg8crPm8ck017Tvc31BUkewcTLxV9d+l6fE8db2UQ4SRSwWw+uvv44pU6Zgy5YtiAvkkO+++y5tjaureFZIwCnihvcWckk/RQ0SgX8kbXpc9bHRAVFtYvOeI8ZBys2UC0gb8osr6kvuY7dZUx3lNSmFASGqMBWWqKv/AxVux6YI6sVWoBCQ7vlsqaesMQfJZ4Jw+SOK6iIhx8NB8vNq1LVBdm1AzbPivNhicekzpUl0dxbxeahS1SCpbsd1R5ZxkNJA0o7GCEKO3ostdQ4SLzgmSdp6rQcbl0nFf1GRtGkTxPr9TGzRWJzrG9E48ahJieQ8GdjHNfvP7dqyYltcE5vLQUoc171Tx0m8N8fxCkseDZJHMy+vV/XKxb6gexY5mYkxTHTJ12U+kJvYEuA93CqnydF5h1K4bv6aYKg1HYEFpBtvvBGvv/46TjrpJPTs2TMld1MLPfZvnIN1O73xMSimr9jG/Zaa2IQPN0h8GIDRIClXjN4TS8r5wVhlYltP701hvmNXQdTDSfVdiYlqIxXtpulGgiJopNfdnIkt2e65q3Z4ypoGSWObIBvY2VhBMkTCjnsfNNWILiSADOz7CDkM54DVukgqkWmQZM+UBlQsFEzJrsZHuG1fDZLPil12v+VMrDETN395/fqox4m2mdfFncfVQXgTm0aHtJHRIPlFrhfbTveL2p+Iz3ggalKi8binDpXnkw6mSYBpnY7LQeI1SLpLlpbHMeg/3+Pg/RtizAV9uWOqhaX7OyqvWK1BCiAgZdAYZvx+XX/SkrQ1wSeDQuT2hZlFmXsNamJjNUjxOMqicXz322Yc1qEJmtYLFnl+byOwgPT+++/jww8/xPDhw6uiPRYAOuyX5wpIXZvXw/Ite7TlRRMbIPG+UPwWVyN0TPPjIMkFJD4OUkZEPohT7RdbA+cRI9EgqVZNIVFAqjAp5Sm0V34IOnCwwqnfxGqcasTHVOc36EdCIff9phoo0pPuxPVI4bUGIqLxOGd6K4vGpdeiGj6Ra2cS40kG1WGZKcltWyzZz1Qu/7K6WMQIH9agUhwkzU0SCCRtzbp0Y35yccUL2Oz3670mJyAJ5Av6WxUHSez70ZjXi033LioLTxwkwYtN9w6+/30L1uwowpodRRhzgbxeCrHPi+FUKFS3KC4WdO2iTi668A8iZIEik27+epJ2EIgCUm5m2LNQl+V2K4vGccbYGVi6vgBn9G3DJRauiUgpWW2XLl2qoi0WFaCd99nzerveSBQyFWphmXefN9WIKCDROEj8eXeflPB6cVXUisFfGgdJiKStWl1RAUnlFceuGumKSKlB8qx0E7+zUxWQNIN3aTSG//2wEn9s3u3uY4VTP48bToMUgBjqaSORR42miIQc9z6SudjEOvzaymuQKFgtmVy7xQvjpQoBKbfCfFBYyvfdpPkwmEDnR4oN5MWmEHRldYgaM1kzU+YgEX6bFQJ0AtIGVoOkjPzsfS6JOEiJbS8HyS+Stne8EZtIu46pBomNiO8HkYPkphpxOWh8eVbjqPtuveOmqFEKxkESSd26RRN95kE4SDqSNtuvg3rreuus0A5V1JOX6X1Xsvc9d/VO19tv0dpdlWrD3kBgAenWW2/Fs88+GzhejIU5WE8S0SWW5Rec0TeRB0++kperO93jMe/Kasqtx+KKgR3dawMJoWDqb1uQX1zun2qkjOcgqQiMG6gGialCjKniqVPR38SVbosGCYFSZd7zg05wGDN1JUZ//RuGPD0Nl7w2Fyu27OZWTSo+QvK42Tdj8m1F48ST+JUiHHKSyWojCjf/AAIHa0aPxvQTSyKSNrNijMWl960ysSVX/EJ7UuUgaTQIqvQi6knPuy8a4zlXlYqDpNEUsNqdxLepMbEx/ETaJ3eXlOO735IZEGiT+bhGyd9i7bLkr3wkbb4vykxs7nmG34FoPtfBEwdJNLFpNKi6b55/5hJTokK4UlUpCt86szu9fW/71O2VmthcN389STsI6Om0D+RKchAmSdrJ6xYwIQRUYWBqEgKb2KZPn46pU6fi66+/Ro8ePZCRwXM9JkyYkLbG1VXQbzfkOJ7M1zSxZ5tGObjnpO6YsGA94kSy0hFXKsIX4WqQmH1tG+e621QT89XSTfh00QZcdEQ7nNijVbKNMg0SF0k77hlMcjPDKCqLYcvuUpRF48pktexgmyRpey4HIDlwv/T3Q7FhVzF6tmkIIHUBSae9mbsqSRj94Y+tWDluDxrnJqPH+2uQDCdKA1PcKc9Px2+bdkuPRcKOO/AmA0UKArNPU9j+w2uQ9IJELE44s4PKxEY1SHvKBAcDd8Wv778iVEfv/+JXnHJIa18ByWS/TNDxcEokZVI1sXEkbST7hb+JLalBon3ymcnL8WpFElm2TSoNksgtlQVeZLXLYoqLhIlNxUEyszX7RdxmwZof2b8qgZt9T7qFCz2fOj6I46pqEXjduwuk+8Xy+jFBrv3lFpZxggVrduKgVg2QlxWRmth2Fpbjg5/WcDkPK6vgiMbimPzrZmyrWLDLNEj03lReiQUG8ZaqG4EFpEaNGuH000+virZYVIC4H3uSZEuxYVdi8GtaL5NL+ii62K4XkiCqVlAsGZYdz9wVU0WH3rq7VOAjedstmtjE1VLz+lnYXFCK4vIY1u0sUpvYmMG2PEYQjcV9UyAM6dGS25+ToolNN24UC5PAup3FvjFdWJjGQTIRpFTCEZAg2ItebEE1MpwGiWktF31aqkEiHg6SbELMk8TmATQTWsUOhg6lbK+Isd+vlAqEKlOaar+sjhghAFNc1k9TNrGx2wQCSVsNVqtJx4Utu8UM77Rt/DcYZ8YeFrJktWIuNvFeVBwk04VCRFQPa0D7nOjFJtOUsfv92sN+R6XRuDe+nGJR9OPybdL9ovCtWlNlhB33HXi92JJ4/6e1+NcnS9C/YxN88P+OlGqQ9pRGccfHS5R1pIJXflyFD+atdX/LPJZlAnGZoE0qiyYDbdZEBBaQxo0bVxXtsGBAv1dHokGiBMym9bI485u4CqJh/7MiFR+2x3ZeISAxnwo7nonq7Vic8PwDP5K2REAKhxx0aJqHZRsLsHp7IXfstvE/o2/7xujcrJ4nn1BJNK6cZFRelKoYTH7QDZYlEq6XzIOQRcemeejXoTE+nLfO2MRWWQJlBufmL0+54J/OJLnNdgX2HUs1SDHRiy0mTROTq+CXqDhIbJBBqaZOczuFZTFfDhILlVZAxUEijr5MqrnY2HsihBiTtGVCrDixq73Y4F6DhTRZrUIzAFSkGhHaRYsbC0gBNEhJL7bE75BgEtRpJHUaLdpU2haRwB8kJATgFSRVC5VIKMSYCflj7L28OWs1gETGAUDOQZKhsmPMlz/zASXzJN8zbXdUYb7eXRrFQfdOxJuXH44BXZpWqj1VhZorutVhsK62mYInGFWfN6vPC0iq6L9UUFBNiOxudlAUBSQxQrJskCsWktWKg0ckFEKnpnkAEhmuxSY9PvE3lEXjHuJuSXlMHQdJocGpCg6SLBGk34AUCTnSkAna4HSVHLxYDpIqknYQLzbWU1D0VBQh9pPyGNFqkET4ebGpeC262DAdm+ZKn6lKEFJpkGTaoWiFhpNtpye5qLJl3rr485K/CWHSaYQ0z4Hw3D/aNo9wLhFWEia25NjDIiwxsfG8QX8O0tbdpVi0dpfxQiEV7WxI0CD5aSQB/rmrNO1Um/XVko3c8fWacCwyyARJGSLhZBJuXX8S+7BMgyRDJTnannbLBCT67LjxQKJpvPtTb8y4moLAGqSOHTtqB/c///yzUg2yYDRIgFeD5JrYslxvFkLU7qZUfalaqfBk3OR+cXASvXXkqUZMNEgJntOqbYWeSS0WB45/6nus3cEPOjohRPRio5BpkA7v0ARzV3vjE7HQmZ6KJRokWSwgrn0hRxp0U4fKephEwowXm+L9+3EQ2PLsEy7htBMSDRLx5mKT3U+OhLMAMKZfxYQWCTkoFU+CnreVlxWRc5AUglAQknaMeBO8xkkyFxhgzvfwuHOzGiSIcZDkELViUckkxV6L3R0nrPaar1cmIIneitx1JW7+ADBizAwc3VWuLRBj6UR8craxcINouqlGEvuJQuDm0hwJ95QpST5MNbFrdhRx9cz6czvO6dfWuJ1i31IthjLCoWQ0cM+3m9wWx1jZIk6GynKQxHbnSsZb2u5yYTwQQdNC1UQEbtlNN93E/S4vL8fChQsxceJE3HbbbelqV90GXcWFvIkdKUm7cV6CHJwRDqEsGleaC7KoF5NiAmHP4iNS89eNxni3cpkgUSqkGvFokMIOOjZNhM9fta3QM7GURmMe4QjQa5BUNAWZBumAlvXwwGk9MOzZH+UnQb+ykglqfqHzI2HHXX2axn+p7OpOGgdJs4KWgZ38VBokmSBRFo27qWQASM27QLJfilCZ2GRtYaG7nziR9381Sdt/MZG8riTUASEIMyKMISfZ00bey5PXkqgWqbJkqrL9MnMX6ymniqTNasjY9pm4+VNs2CXXumSGQyiOJ/tXahqkxG9HMLHp+r/4DFZs2Y2P5q3D/zu2s0fwEjFz5TYQ4iWkq0AFmjl/bsf6XcXKfhsJOdJnDvDbooAkkuVVqOwYI7Y7EnaQEebN324cJIW3KEWTvEzPvpqClCJpyzBmzBjMmzev0g2yEDhIwkRCsyXT/RkhB2VQT9RJAUkfr8O7YuR/ixGDZR82O0jG4sQzaIYcBx0ZE1ue4BraIEce/bqkPK4kTytNbJneCTjkOGjdMEdeEW231sQmmwy11SESCrltZAUF3Xga1MSWGQlxA2Uk5Lh1UA2kx83f5xKsMMdNhIKWUAbWnVzmxeY4ag8lvyCYqvetDbJIiFQ4VXKQApC0Zc8gFidg5XPT9ymaIkUTm0myV08qjJhcg0R/iZwi+ktlYgPkZkTxmZXHzYUGioywA9axSVwcmsBNVut6sSX26zhInEkyTnD2i7Ows6gca3cWuc9NjMl0QIt6WL2tCJsLSrFuZzHaNsmFCei1zn1pNgDghO4tpOUywiFXwIwTImgTvfVRmGqQKmvGF7t9QlPOC0imkdOb5NXcaNpp4yANGzYMH3/8cbqqq9NgOUjiIEFj32RUDATUy021GqaxJorLY5gu86ygwpiw26NBise5j8pvspBzkBx0bVEPkZCDTQUlWLWNJ2qrVj/F5TFfN38RMhNbyHHg+PR4neo5SAoKikjISUYlN1QlBDWxiR4kMg6Sx80/AAeJfe/FPhokETIBKRJSa0B0cYsA9fsWBcBuLetzdQbiIKlMbDJhKEY8SXuDRgFXnSea2NhYPyrZw8txkXOQVMlqlRwk5ncsTjzPW+QgxeJxpXZX9e4zhbg4QUjaFCIHSdWf2E9RXNjtLEpIaROXbqqoE2ggpC7KyYy4CbGLJKZ3FUSz7prtRdJyEcaLzdN+jYmt1JCkrZOPju7aFAe1amBUD0U45CBDeOGmgUEb5NRcE1vaBKTx48ejSZMm6aquTiNJSIVHg0R5MHQAoOYbpYmtIp/Ph/PW4aJX53DHlm/erVwxyjhIfsHV2H2yQJHhkIMG2Rk4svN+3H1SqOJilJTHjFONUMhMbI6jJrdSVJb/IyIccpgYMmbnBF3diTFIQo7jTtpUU1MeI3jlxz+xqyI5bJBI2uwkWiKEYPBDWczrQcZ66Hiuq/A6olBpkETh5eRerXByr0TcLgK5ZlAl4JVF41ixZTfGTF3B8c6UqUYUxF4K82S1ye2VW/fgJ4YvRwjDs3HUudjEb65MpUGSrO5jOhMbMwzFCfGYDaVxkLTBCLwQza7ihGsCKsiJXmweE5vCTMWPcYm/DXIyPP0u7MhjQ/lBJXyL4E1sPM+N0yCJHCRDE5sOz5/fB3efdFCgcyIhB2FBoJWlGpGhKlLPpAuBRbc+ffpwKwBCCDZt2oStW7fiv//9b1obV1fBruJEkrarQarYTydAVTwO8XwWJzw9zc2qruIcUJh4sXExUuJxT6oRuiIc2qOlNE5IgZBTLjMcQlksrjexKSZaWRykkMOvymRI97eaEQ5JNUi6ZugUTWziWArxXuOMSzgbR+vh/1uG2X9uxyuX9DNw85drC1lNgakGSRQgEoO/vHySgyQ/rhKQxGs4jpPUIhD5ilkXKHLwU9MAALuKynBXRfodOQfJq53ykrZNBaRke45/8gfumCdZrakGiXqxCfdKiNxjizZBlawWSDxrUegrlQRAVLWRPo/zD2+L9+YmY+mIi8FUNEj0mv4aJPk72b7H6wLQMCfDow2LhNjv2nzQKI3yMd1UCz+OpE349rPni/3e1M1fh0g4pPzOVAgxmnIK+lz8xglV7sOagMAC0ogRI7jfoVAIzZo1w6BBg9CtW7d0tatOg13FiYMG/QBoB6aCkmplkuXj7p5fobUR1d6yzs4FlZNqkJLbcg1Soq0HMuYPFqIGqWFuBrbuTgSWVA1oKk1EtiSMfchAg7R6WyHO+O8MXHNsZ0/wyVRAbfNAAC82zYQaCYc8k6DoMs9qNUQBeUoFP8jXxBaXv2t2hepHUAeoic1L1le9B18Tm2TgnrFiG/73w0puX0IYTpQlhEgnMVWuQHZAX7hml7sta5JMWBCfren8qXuchDkeknixUaKwx4uNxkGSmNhkiVNVgSJZASEWJ57YBTIOkl+qETHVhMhLCzpJs+eIaTpUXpEiVm4t9OxrmJPhuZdQyKulMoHoUKM6NSMc4u6BE5AUdZfH4sYcJB0ywk7gZx8Jyc9RfXssTMaR6kJgAem+++6rinZYMEia2LwcpHLBdBJxNUh6krYfPBwkYbBKaJCYNirIqexxj5t/RZWqgTNfFJByEgJSwsQmh0oLLzO9+QXYA4B5FQE2r35rPlY/epK+sAF4DlLyLlKNgyTTmHk0SIy2T+w/9Gy/MUm1YmU5SCYrv7JY3KPdjIRDnvdAPWCS3Bh5fbJB+MJX5nj2OU5SmxBXCUgGcZDYdsomwljMK2jocqrpoJtoPSY24THESeL78nixVTxIr4nNu0+XaoTtd/G4LupURV2xuFJLGhXGMApxMZgSSZua2ITAlqIXmOpZr9y6x7OvYU6GZAEZcsdeU24hkHg/JppXdhGRIOgnj9Gmi9qi4vKYsRebDhkaE7gKYceBLPK5GB5GhqDBNvcmUmJHxWIxfPrpp1i2bBkAoEePHjj11FMRDtf85HO1AWzCSFUYdqqNyfTTIBkKSGYcJLmtnkJU/ao0SKrFSaFAdqTmv9IUAkXK4DhqzUVVgecgmU2VOu2O7HbFiSTGTH7iJCQm8VRBZU7lA0UaapCEa7H8iuS+EMpjMYYzonjfhu8v5CRTpMi8rgAdSZsRZFl3fQUHyRNjKFUOkuZ5xj0mNv45JK7hSOMRAXI3f5lpUJlqhI0NJHhVyRCNE6UdmbZFHNtEbWcqGiTRiy2ZaiRZRmZepPhru1eD1CAnw5NUORRyPJ5yJogTfpFBW9EgO4ICJip/Rii5iBCdDKh4KmrcS8pjnkVmKggptEE6hBmBkUWMEOk40addI+wpiWL5lj2+seSqE4FF9BUrVuCggw7CxRdfjAkTJmDChAm46KKL0KNHD6xcudK/Agt/UA1SyFG6Q0cEDdLfX50rLWeaMdnj5u/hIPHeSKrJwi0f82qQIq76m6/7wdN6SNvUqEJAKi6PBeYgyaAzsfklxkyVSMhzkAwnSp2AJBm4xMlyD8Plqi9437BaFR2i3LtO7mcnYJOVX6nMi01iYqP9mDZL1TwVKd9TjuGbsdoXFmoNUnICY5spUxTIvDVlpisTxNx7lyw+mHpDjtfERh+xKg6SjKQtTZ3imvf5KziM1krlFcgiqjGxuQJSWDSxiRqkFAQkgYOUjKTNj10qDdKeUm/qoIY5GZ7nzZqUTJPvUrDCFhUeLhvQET/dNThZfzgpBBPwOTNp00VhaHN+qSfnXqoIMq4CifbKhKp4XE4taJiTgUsHdACgDthaExBYQLrhhhvQuXNnrF27FgsWLMCCBQuwZs0adOzYETfccENVtLHOgV3FqUwxdNL1U0ObJgL0mNhEDVKMcLFz5DmnktuyrNLUbCfWLcYYoWiYm5jcEyRt+YBmOmECepK233NM1bYfZlKNsCslXat1cpRs0hHviQ6c4ZDjCQFQHiM453+zfHPIuVwgoTGlnJu/gYlNStIOedosCpGqCdg0eCCrLWS9gL6+8Wj02r+h2zYZ2PmOfd7SVCNxiQZJKGYeKJIGdfRe5/9+3oh3Zv8FoCJGmfAYqFZBlcpCfAdE0S6qcZI9ZldjIvHc894LUX5r9BoeDZJI0k7Bi801sWm82OIS8yKFrE80knGQHG9CXFOwQhi9XiTkoFn9ZDygiAEHSXRqWbRuV7CGaBD00YccL0kbSLRd5kDE0kdMuZnVgcAmth9++AGzZ8/mXPr3228/PProoxgwYEBaG1dXQfuL4zhKwYAOHn6usKbqfdWKniIa54PtSfOFKrgqFHSAFed4lYBEY4+UlMeUq9GgGiQ/gVOFILFOxHpp3aZkRC0HScGtYkEFpPrZEWn5uat2YO6qHZ79LFSmLva9iuacelkR9GjdwE2cCVSkGhE5SBITEU0r4ScgmZpIQw64XFa077JRqFUCHqsR8OMgxSUcCy9J21RzSP/Ky1MTTIKkLZrYEn89GqQo9WLztkmnVZM9ZxpfS+Uxy0Ln5u86EAgCkUgHSEccpGTUb16DpBJqZLw6GQepLEYqoUFieXwVz1v4TjNCfCRtGSdwj5CzchHjUJAqRC9AUyTGOQkHSSFMhxw2BMk+pEHKysrC7t27Pfv37NmDzMyaGzK8NoENFKmCSNJWwTi4oVCNeG0xOJwfSfubXzZ7jqtMbPUUuXhosMfSaDxwHCQZHHd16T3mp0EqKk1RQAonOUgmGhfAx8QmabtXg5SYSOtlRVLmXNH3K06irBebqCV04PWalGmQwhI3/wxhQlM9AtNJ02H+H2dMbIlErxVtUwzM7GviTGySNkXjcY/gKyNEm4B+96q8ihQykrZSQFJokFRalGhcPfYkwyb4eybF4nGlQwQVKDJ9SNpBUo1Q0FPoX1luP5XJFUiaVzs1y3P3JbzY+HLl0bjbvqBxy1gTG11kiAJJVkZSg0QIHwfJdZ8XxvZFa3cGaocMdMEZ1MQWCjnSbzMucFeTSGqQ9ikB6eSTT8bVV1+NOXPmgJCEi+vs2bNxzTXX4NRTT62KNtY50O8t5KgHV9HNX4WDK8wJftDFPQG8pgR5oEj9NZICipmJjd5aIsSAvM4gKx1R/c7Cb+ItLNObpFQIM/FS2IFAN/7oBly5iY3fR8mbCQEpSGuTiEomFoAnacuC0olE27Jo3HM/GeGQR7B1NUiSFT8LYw1SiOcgsRqkkON9Hyz4eFV6E5vMSydVDpLrku8jSMuS1dJreEnalIMkxkGS84hcYU9nYourOTwUulQjdKEg8iPFvhMkWa3bRroI8+Egqd4JbRsbSbp+ttfEVhaLu9cw0aixYMcSuoClz/b2Ew9Es/pZuPPEg5g4SEIeTJJ4f2L/pSEKWjXMDtQeFiHh+ZlC5eYfU2gcQ45/kOOagMA98LnnnkPnzp1x5JFHIjs7G9nZ2RgwYAC6dOmCZ599tiraWOfAapB0gcQSf9Udeeo/B7meYH4QB3+xs8dFE5uMg2SwqkzUze8Phxyptx0bxyZdJG22Xha+GqQUBSR24DBdKVVWQKKakQbZ3gjAplBFwS3hTGyCVs1JRm5n22KiQaICKi2q1CBVkoMkkrdlYAd0fw2SV5sifkumwz995n4mG1m4CnoNTxwkFQeJ0SCxdek0SCFGYxIkjpYKfm79GSn03ZCgAZH1J137qcDSqWlSg1RcHvNwcspjVaNBunZQF8z91/Fot18u41Ah9zhUeX+ZjvkypKpBCodUHCQ5xyjkOMiMBKMeVAcCc5AaNWqEzz77DCtWrHDd/A866CB06dIl7Y2rq2ADRYo50ShMNEiNc80nSHFAEy8bFU1sUg2S/6oS8PKAQo6DvKwISqNl7r4vrx+ISb8mzHQxkgzGFw45Qjwh9fXEsnSAl53jJyBt31OmPa5CJOxINUg6mrZuvJBykBRNr5cdCZwwlEIVsJE3sXkbmiXRIInai4ywl4NEuXR+gSLNvdjAmShoP5C5yItQxatSRdIWV8ji+zOdQOm9+2kkEl5swgKm4hqegI1uJG2vVouNlVUmcJWkJG1XK2PgBRnzBtAUwQpIIcerxU1Fg+S6+QsaJCJoYPxI2hnhEIYf3BIzVmzHcQc2w9Tft3jK0XRGQUnGMgcJ9rsWNe2EeIXzWJwovb9Ex4wgCAvPL8h5Ui82QqQCfyiU1CDtM5G0CwoKUK9ePYRCIXTp0sUViuLxOAoKCtCgQQOfGixM4JK04WBI9xY4pG0jLF67iyuT5CCpB5EgcX88q3wZB8lHg+Srdo/KCaCOA+RlhbGjIgRJ33aN0LNNQzcrfCyeXCGLQo/uQ55449E4+3+zsKuIRguH9PqAv0vxVkkKAhOEHVaDZDhRap6j7HWqJnxquhSfmQnoJO/JucVojcSgdA68WoGyqDdQZDikdvN3A0UqnoHpytaBA1mqBlkUahGcUO3I97P7xAnAz6tNhaihgLS9sNSjJVBxkGhdukCRGSEHVPzXapAYE5u/m7/XtCqCNalFmNQa7r7KcJCogCTpT7pAkfT5hUMOxlzQF2WxOLIiYc/zKI/FmUCRQTVIXj6jPLBtsr0y4r9K05ibmXryV9FEGeQ82SJTZWJzGA5STdYgGYvon3zyCQ477DCUlJR4jhUXF6Nfv3744osv0tq4ugqXgxRKEJU/GzkA5xy2P1eGapZ0E7tK7QkkApPdNLir+1sczMTBSoyDJBvD/cYJNxu50CQHDnIzkh81XVmw+ZTcIHlCu3QTZtcW9fGvYcmki3SQk53i51K8NcX4IgQIbmLTPEgTLzYKmm08FSsb1froNUjegV40lZZJ4iCxaRQokl5sid9+3Ds/JDwWE9tiCg2/RQO7WGBLytokd/PXm9xUoOf5mdjW7Sz2CnkqAamiLrFO1ouNNXnSyUr2iOj8p+PwJK+r5g1SsHkCIyHH8y3LeIF+rz+ZrDbxW0b6jxO1az5r8nIcx+VJeUjasWScp2iM4OslG/UNYyAz18vGMdZELD7uaJwoF1yyPJSmcDVIQU1sjlyDtH5XMb791euw4zj7mBfb2LFjcfvttyM3N9dzLC8vD3fccQdeeOGFtDauroJNVkshSuduHCTNxB52HOVK4Jubj8GNxycFJBlPhIWolpZN4n4TQblidRpygIxIcl8yn1JyFUgvJwp8fisd9j503JMMn3hRqQZgIwQpkLTVx0ziIFFQ78BUzGwuB8kjIGk0SI7j0SCVxswiaXu92BQaJGMBiUnVgOQzTXwT+nODmtjEicrLFzETkOh1/Uw2W3aXeDqQiqRdFiUVgR35OgiS3zCr0dNpkOgiQiYUUtD3HzMxsYV5AckjNEtelMqhg4K+L5ZQDsBDD/DVIIlabkk5+l1/tWQj/vHOAm27gGTflQWjlBkCVMlqAXmAUorKmNiSqVqCncdSCVhc8po8gDEbB0kl6I2asAQDHv0Ony5cH6wxaYTxY1i6dCkGDRqkPH7MMcdgyZIlgS4+evRo9OvXD/Xr10fz5s0xYsQI/P7779pzJkyYgMMOOwyNGjVCXl4eevfujbfeeosrs3nzZlx66aVo3bo1cnNzceKJJ2L58uVcmZUrV+L0009Hs2bN0KBBA5xzzjnYvNkr6VYHZOH+PQKSgZt/KKRONcKaIACv0CCbh9jBlw48f2zejbNfnImZK7YZJyUUhZrEaoJVt1M7eOJ3IvcT8ZxrMlmyz0cbNkFSFzvAp6pBihPiavtMvV10pkrZLavui8aRCroaBBgTm9AWdvKWa5Akbv4eE5s3F5vLGfFx8zcVkBwn+axYvo2J2ZmLg8TsVwlIspxmLMwDRSbOU018XZrXQ2Y4hH8NP8ibrLbir0yDJBO4WDMTmw4nGQfJe30q/Mi0ghR0ck4k8ZUW8dQHyE1sMu14Ax8CMh0zkoRyYFN+CVZuSeZY05K0FWOUxxEiGnf74uw/t2vbREEFQjFtSaLdMieVZHs9npEK0xVQWQ5Sxd8USNqyb1MVP85xkmOzqr9v3V2C9buKPTnn9iaMBaSdO3ciGlV78pSXl2Pnzp2BLv7DDz9g5MiRmD17NiZNmoTy8nIMGTIEhYXefDgUTZo0wV133YVZs2bh559/xmWXXYbLLrsM33zzDYDEhz9ixAj8+eef+Oyzz7Bw4UK0b98egwcPdustLCzEkCFD4DgOvvvuO8yYMQNlZWU45ZRTEA8aFrUK4HKQOA2SaJ+nJjb1Kww5Dnq0bqg4pm+DbBJh48bQyeLKN+bhp9U7ccErc3xV6moCqMNpwkQNEqtiZj9Ck49YRn6UQfYc2ZVNyiY2QtxBRxZ3pzwWx5Vv/ITnpiQFeH0uNu89OA5wZt/9PfvpajsVE1sykra6jOjm7zjyyO3iAJchSTWS4ZrY0qNBYgUhNlBlpUjaqlQjEvOV7rffdVUT33XHdcGSB4bgqM5NJclqE+eUCV5R0Zhc28NqhEOO49G4yJhalJRcVBZVCkjZESog+XOQ2MWbbIKVCQ1+GiTRiw0Ajhg9Bb9tSsbuY/lXIpJu9/x+b6DIpIBkSkGiHp5igEdApUFKtlfUxsWIN9clRaU4SPT5BeUgOU4gUn3IcVyBUaUxpQty0TN2b8L4SXbo0AHz5s1Dt27dpMfnzZuH9u3bB7r4xIkTud+vv/46mjdvjvnz5+OYY46RniNqsW688Ua88cYbmD59OoYOHYrly5dj9uzZWLp0KXr0SOT4Gjt2LFq2bIn33nsPV155JWbMmIHVq1dj4cKFLrH8jTfeQOPGjfHdd99h8ODB4mX3KmSBIsXORwUmLQfJ4cPXc/ATkCQfSJlEg7RhV7G+IgZ0IvGorwUTmxhQMsYMEJzJzOC7iXACVfJ6nnKS51geiyMjnCA4p65BSg704kqJEIKvlmzE5GVbMHnZFtxQYfJMJZL2wyN6IiPs4P2f1rr7XQEpBQkp6iOoAHINkkxAEleROpI2vZxKSAySrJb2cXYADjvqdDMUbHm2rDRukFSDxJcx9QJPuvnLT8iKhFwNnV8k7dzMMHaXRBPZ4yWSXYL4m9hmhRO6KJB9W1QzUVwWU6YwymE0SH6CA1tHhsTEJhvb/CZu1wNMU05nYhMFTAovBynu0Xr6ISE8lks1SDpSPBumgqLKNEhh/+cnPU/Dd5UhoUGqGBcV3nhUQBJz9u1NGItmZ5xxBu666y6pGWrTpk24++67ceaZZ1aqMfn5+QDApTHRgRCCKVOm4Pfff3cFqtLSxESWnZ0MlhUKhZCVlYXp06e7ZRzHQVZWUnjIzs5GKBRyy4goLS1FQUEB96+q4JK0NSY2Ezd/XSf38+WRTUScgOQzmAMJkvCUW491f9MPWubmz96H6GrKcijYdplpkBhXYs3zEIPUAYn7vemDRTjy0e+wPoAgyCJOCMNBSj4rQgiue3chbnx/keccnalSpv0IOQky/5AeLbj9SZJ2cAGJCgO6toh8Fwdyk65XgyTLxSZqkOTXTIWDxGmEQr5rA2UYCbmJzWtC9JC0DSMhUU2Xyqsnm4lSLr5SImiQWFOXmOol0UY++a1L0o5TkrZEg5RJNUgxpWaRtjEW8ydys99cWJbAOIBDglsPNRFpBSS1AE7HM1F7JTexJcqYkvCpQCgLOitrr5usVsZB0nixZWdUQkCSaOCMzlOY2FRIjPkV46LiPlwNkmE+0aqA8ZXvvPNO1K9fH127dsW1116LZ599Fs8++yz+8Y9/4IADDkC9evVw5513ptyQeDyOm266CQMGDEDPnj21ZfPz81GvXj1kZmbipJNOwvPPP48TTjgBANCtWze0a9cOo0aNws6dO1FWVobHHnsM69atw8aNCU+DI444wiWWFxUVobCwEP/85z8Ri8XcMiJGjx6Nhg0buv/atm2b8r36wY2DxAzlYlh+KlCo1JpsZ330jIM9x/36v6yvswKSSXb7C/u3R+dm9dzfKn6DA4GDVDHwsNFw4zINksFHzA6yOtOKTINUFovjs0UbUtYeAVSD5LW1EwL8n8LzRa9B8u5Leufx90BJ2qkEi5SlaBAhi4NkqkFSuXT7uvmnwEHyapACmNiYb1DW5WVu/l6XbKMm+2uQGFODeAf0DBpTJq/CzFIuCdQJeGNDJfOKqczgSaGrqDym1JrkZFCzSTwQBykjJHHzl3R2U2qArlw87h8JXLy0qFHLjIRcjbSpmz+d6KUkbY3zhSx3XDSWNLGJmra8ypC0hcWpKQJrkABfkjadb0wTrlcFjK9cv359zJgxAxdddBE++OAD3Hzzzbj55pvxwQcf4KKLLsL06dNRv379lBsycuRILF26FO+//75RWxYtWoSffvoJ//73v3HLLbfg+++/BwBkZGRgwoQJ+OOPP9CkSRPk5uZi6tSpGDZsGEIVvbxZs2b46KOP8MUXX6BevXpo2LAhdu3ahb59+7plRIwaNQr5+fnuv7Vr10rLpQNsoEgKcbCgHVgUnNzjzMnn9muL8w/nBTq/SUKmbWFNKiZjQrZgO1Z5yLAun4DX1TRGCBcHSddGESFOoFKXkw3GxnnsNEhwkLwCkk4ICurFJnruUNBJMhUOkhg/pn5WBE3y+FyLomaIdYtmIQpICQ4SX4Z6EdLrqVONmLWf1SCxGhk2Wa0KUYkGSbUgYEnatG2pphpxA0Wq+D0aDZKbx63iXl1Tl4KDRJhzOAFJk6yWcpCKy6KK/FrJNkYNYiVlejhI/HHphOs3brkCkrqcjoMk1pO8LP/7jcsOdzVIQTlIcpK2zsQm57VRzWX9bJ64Xik3f4PnJ0MkFNJykM49zDv/sLxD2fdF8+JVp4AUiM3VsGFD/Pe//8WYMWOwbds2EELQrFmzlKP1Ulx33XX48ssvMW3aNOy/v5dsKoIGqgSA3r17Y9myZRg9erTLTzr00EOxaNEi5Ofno6ysDM2aNUP//v1x2GGHuXUMGTIEK1euxLZt2xCJRNCoUSO0bNkSnTp1kl4zKyuLM8lVJZJxkJLPVeXmr+qU7CtxHAfdmdxCgL+ZwY+kbbJqyhFUvSovNtHEFnFXMYnfNOdfYp9XkNIhwglI6vIyLzaRBOk45nwSCsJpkJIn6x6fPlmteqWpIj7rvs/ebRthkRCEFEhO1jFGWG/XJBc7CpMRxU01SMXl/IQg4yDR509vXfUIqDnIr/s5TvK+OU5RyF/I4gNF8potEdF4MhdbZiSEknKvh5dpn0kGilQIHxFWQOJvgl5DNLGVx+JSLyHWsy/EPivXkUJmYktMF0VlMWW0d/rNxwy82CIhx/2mIuGQhJeWugZJb2LzF950HKR3r+qP/p32w8cL1ukbI4AuHmSBIuUCUuKvLG8e6+ZfPzvCfZeyRYopUo2knYiMrT7nxsFd8cG8tXx5ZlFcHo8jK8S3u1aZ2Fg4joNmzZqhefPmlRKOCCG47rrr8Mknn+C7775Dx44dU6onHo+73CMWDRs2RLNmzbB8+XLMmzcPp512mqdM06ZN0ahRI3z33XfYsmVLjUi4K+MgZQoaFvrcVZ3S08ElWhsd/DhIJqtiupq8/cQDAQCPnJ4w9cmaLOMg0XtkB9ugJjZZHCQZZFwuUYOk8qDRDQwsB4mdOGUaEjcGkFZAku2rWPUJt0C1cjo+wXn92mLUMK/jRTJZbVIw3U/QIImCgANe0KTXLxY0SJGQdzIUU0Mo+5dj9t4T6Tj4ewGCm9hoJapXwpJlKacmZS82Vwvkb2LztEPQIFFPpnJFzKJ4nI+DFJYIkyJYkrZKiM9hBDMTISTMjGMmbv7GHCRNOZnJStY21XWTgWyDTZ9ZGg6STBvOJ6vlj8VJMv4W5RoCCSE9FacMtx0GJkoZIqGQVqjK84ydDsdBkxHOa42JrW/fvoFc+AcOHIj16/2DO40cORJvv/023n33XdSvXx+bNm3Cpk2bUFycJMRefPHFGDVqlPt79OjRmDRpEv78808sW7YMTz75JN566y1cdNFFbpmPPvoI33//vevqf8IJJ2DEiBEYMmSIW2bcuHGYPXs2Vq5cibfffhtnn302br75Zhx44IHG91lVkHmxZTHaGHZCVnUecYDwykv6L8CRVCvzYtOBmtiuHdQFvz10Io7q0rSiLV5hTRoHifFic80BLEk7oBebnoMk03zwE3uDbHkMFh0pMk7kg7ps7thUUIIXvluOjQXeaPUUupWmJ8p4yH+wC4UcHNjSaxp3NUhxeg3HY2Lzax99LuJzpJoDbh8jXLw5azXm/7XTUx9th5mAlHzurEYmEfNHfo6sWrpLr0GiA3nifj9esB4nPjMNa7YXac8VsWZ7EZ745jdsypc7BOhJ2om/9BvNy6ImNgUHCTxJO8lBUpvYXAGpPKa8p2xGg+Q3RERCyck8EpZE0tbEBlIhuVjQCUj+nmdiv3Mkx4KmiqMCkuzSMoHONe9KTGyxeLJf189KjktZEa8DRBCwi9Mg9YR9NEgiLyrk8OVlWs6kBqn6vNiMTGyLFi3C4sWLjb3LFi1aJNXoiBg7diwAr+v+uHHjcOmllwIA1qxZw/GCCgsLce2112LdunXIyclBt27d8Pbbb+Pcc891y2zcuBG33HILNm/ejFatWuHiiy/GPffcw13j999/x6hRo7Bjxw506NABd911F26++Waj+6tqyDhI7OAoIzSL8ARjFIxqqWiQSgUN0q4ifQJXts3stsy+L+UgMV5s6YiDpJtYZVwukSug0iBlZ4SgStVGQKQEcNkEc+Ub8/DLBr13pEzIU7k2036imyzCCk6OyEEKhRzsV09vYnYc/nlTV3OPBinsXeXS9//j8m34cfk2rn0xxgvMcf+nBzvAl8eS35PjeBO9um0IhSRm1URZ1XzK5pqiE+D//Zwg39/3+VKMu+xw40CRS9bnY8n6fOXxbGYxpLqHMkGDFI3JvZ0ISZoGI+Gk0FiujFXGe7ElE906nMYr2yVp+0fSFjVI4jVl343fwk7kHMqENFluM0/bNBwkl94QUIOk4wbpOEiyOEjReJwzsVEkBKTUJSSRwhA3DG4b1nCQMiOhikCgrHXEqbCEJPaVxwj+2Lwb2ZEw2u2XyNZRVgNMbMYcpOOPP97YndHU7GZSHyVfUzz88MN4+OGHtefccMMNuOGGG7RlHn30UTz66KO+168OxJlORMHyedhOrHIh9ovj4feG/DhI2/aU4fBHpmjrUEn+4rgSEjVIUi+2xLGgJG12EAtK0t4jZN1mByIWuhUOIfKVlWxV7yccASpvF4f765YNyfdz5yo4OW6KBoanIprYpO3jBKQIgFIjkrZqsgmHHIA5nXVJ18FBsn9QAYG+B9XjyAg7EAP/qojXFCwHSRzIxUCalUUQkrbLQZKEIQAqXMepic1hTGwKniAA5GZ4TWyZ4RDKY8mHlsNpkPTjeyTkcMKGeE25m3/imnQseu+qI3D+y7O54xSqCd5Eg6SLpG3yXcmQk6Gebv05SPyxeDyZuoklaWdKuFxB4F1Qms357LsUQftiyHEYrWXFwrhiUbKrqAxDnp4GAFj5yHCEQ47rFFTjBaRVq1YFrtiEbG3hBSs0sh2d9QhjtS2qlZAPBclgJebdJ3Jy/Ly8VCsmjwYJijhIroktuRoNmmrEVIMk4yCJ7rgqAUn01qNomJOBmwcfIB2Mg2YAP+WQ1vjHsZ3x769+9RxzTWziBOOmbNEISAqNiujmHzYysfGxULIzkhoHFrpAkSI8pg7HnINEIRKPVednRkIoFNpKS6ome9Y0IJq7Rc+8yiKL0yDx8JK0kxokeSRthqQdchhh0qu9psh1SdpRt1xGJARWqqTPoDzmb2JjrxuRxUGSkrQdtG6UjdUV5kuxa7MLCNUEr0s1IqtHvI5JmicZcjLVE73ei83b3hghboBFToOUEa6ciU0iCJqANdOKoII1u9ah8w9dlGzbk7RGrNy6B52a5rn9p8Z7sQWNkG2ROiT8UAD86pHtiKqJ1s9NlVbBqj115wNyryUdshUdW8ZBYk1cyUjaid+EdfNnTg1K0tYVlxFCd3s0SME4SAvuOQHhkONyUVj4JSQVcePxXdGleT25F5uCa0S1Mrr7pmpuEWKST8dxsF89Aw0SUxnLWWGREfbmYlOtPmXcOSMBKZTsH24CUh9OlkxIds0ciq7PhjoQz0965vmbmkwEZlZgUGuQEn/dQJEKDhLrycVrkLz8RwrexEZj8PD3TLWpMZ9UI0lP1eRf8b3IPEsdB3jp4sPwz48W4+YTDvCMa47BBM8Khyp4NPDMbzHSvylEr16ufo3pPMFB4o/FGM1lA5akHdaTpf3ALoyDBItUJasFkv2GFVhp1Yk+HeMWGkvW5aNNoxz3d3VykKpPNLOQIq7SIDGdhDVHqEzEOi8MIMlhyFCZNmQmtqACkmJAkGm3IhINkixQpKnJTKwrUb5yGqR6Sg2S/D7dwT/gSlNXlyoXm+wYXeHq7jvsyNksYsDGhBdbMA4S9VIROUhyDZK8H4r7EzwibTMqyrHRoXmCv+p5yAS3pBeb/ENjJy7RFECfv5/so5s4VfCkGqn468ZBqqizLEakYQMIGAJ+yEvSlj0hVuCl54oR6OkziMaJ1joj9meZZ6NKg3RAi/r4/LqBOO7A5p73xZnCFO+ZEPW46dYj0VyK1wgSGBHQv2ediU0WliDhxUY1SMmFW0bEbAFh0o4g3nAhR52LLS/LG9GfbtNxl11oLFmfz801Nd6LzWLvgf0OWE8yVj3LqnZVAdu8AhEPd1IN4NGTLgHJcXithWhic1eXrokNcpK2EQfJ+1HKzmKrovnrRJJ2UBObrA0ymIxnYXdAkQ2k8olffI7yaytI2tTdnuEgNTHQIHVsmudu04FN5sWmioMkQiwXcsxNbKIXGx3wVeZlx/G+KyqImOTb8pjYwtRrSX9uU4PnKkK8BXoN+t6y3VQj3rhMifLJe2KJ+qpgroBKg8SXcwUkn1QjYqyzCGNuo9Cl36DwmNiYV6Ca4E1MbGI/kLv5BxSQNElkpV5sFX+J1IuNSEnahAQTbDztkCQNN4GOg5TDmNgo6CbtP6x1Ys6qHXhr9l9uGyqjEassrIBUw6DSIGVxGqTkfpWpxkOEFn67Kk6FhCRz81Rlj1ZBt2Jiq3YcfqClKxE+Fxvh9gHBTWy674wV0A5u0xAAsFsQkJRu/j4qYL8PXJdPj4K+JpnQqeQgpcHERp9BKOSNgyTCAdC8QTY+GzkAU249Vrnyk3ssqUja4jXk7ZW1hZajZqekC7P8HBmPgl3Fs2jXJNdzvsfE5oYu0Le1qY93oAlo8+h7y2KiFKtMbFRwzIiE3Occ1XixUQ5ScVksyUESNUiGbv4izzAii64uXQyIe0RNj//3bkTS1ghiYQNunww5moWUVINUsY/A+yxZ70lWg5QIKxKoWXw7JJoyo/M0gkwuZ2KrqDvEa5BYAWnZxgI8NekPANVL0AasgFTjwH63bH9TcZDyFKsSj5uqOJCAah3UbRE/kKDEYp1mha3bgTftAMCb2OiVg5K0WW2bjpjes01DjBrWDa9ecpjbblMvNr/kkH62fJNBgN6rTOhMRg/m94s8D3m9co1ajBD8vG4XLhv3k3sN0ySYh7RthM7N6qkFpLA375ZsMgS8AnwQDRIbaJTuo3XIkMgn5TXpAfx3ed8p3THusn6edokr6AzXxKb/bqjGMgjE50c/TTrx02cfj6vc/JPmuMyw14tNmqw2gzWx8dehSJrY9Bwk8RuXaRXlcZBEgUher7jNIiUOEqdBSlFACujm73KQ4noNEjvOEkLSZmILEudJzMXGfs9UsOasBnSB7mqQvNHFgeo1rwEpCEiXXHIJpk2bVhVtsYCGg8R8BOy3fW6/tji8gzc+lc6Gzv7W5c+pjKoW0JPrRJs+OxiKpqE4m2qEXckZtM9UgxQJOfh/x3bG8Qe1cHkVHg6SMg6Sj4Dkw0ESeRzSOiqehWyQpQOpuHo2ylWnNLHBXcWx1x93WT/ceHxXaV1iNVkqXpFktakkaXs0n2Zu/glBKrFNs4XT5qgmEJFDxZal32VmOITLBnREiwbZXLkMSWwnN/ilz2TcPBUBSfhNlxBuVG+GCyRz848T4ia2ZdterjGxuclqGS82cfyg33w0pk81EhbMVIlUI3wZEw2SWII9rnrPRnGQPJpL9lhqApJunJAnoU78Zcc/iuLymKt1Yd9BLF45AUlmSjRB4ptmPa2T2y5JW6L9p2OfKiRGrdMg5efnY/DgwejatSseeeQRo4jZFuZgBSS2n7MfF0u6zM4I460rD/fUowt0BiQ/eG2MnMrJR9rUCKxGSzSxid5GbGqAMEfSNtAgGX7k7CCToRCQ8rIiUvNMZThIpjZ2OrjIBCTXxMYObsz96CYqVVyhuJCiglZ93IHNcclRHXzbC6hXf7LJUGlik2gMTOKssd5uVEAISwRJ8VoeDlLFT/osHMmzBhLvRdxHeVV+eleVdyRFuya5eO3Sw7h9Xg5S4i8ryNF2qzhI5cwE64mDJHlEtO+VlMfdc8UAq1SIKo0G82JLlYPk5SSZaJCCm9jY0hGmzUGQq+EgSb1TXeEcnmCj/++t+fht024APH8vTkhK43afdo0AAOcf3i55/QDSQSjkcNp69tnQPsF+H3Rrn9Mgffrpp1i/fj3+8Y9/4IMPPkCHDh0wbNgwjB8/HuXl5VXRxjoFdixjPxpWIhdzNclMOH6BIk08MYK4edLrNcnLxDPn9saYC/oam2QcONyH4LrRciY2wh1jr6ltF3MPuiFRlr5FNLHVy4pIBzI/N1StictxjDhIrgZJa2KTD1C6+1ZxkEQTiYnpQjTjKtPghLwTgsrE5k35YBgoktUgUTd/16tPfk7I8U7SdCgXnQTEduVkhD37kiRtfVv94uk8PKIn/tathaRVSdB3FY17NUjlUgEpaaLJCDNxkGK8IMgilxHO91TkExP7LvX01KUjAVQcJPnzY6HTGCV+e7UUIuJxEw0Sf67sW0inm79foEidQJcR4RdDplpzFu9ffQS+u/VYHNetebJNMuK4oj6RpM1++1SwdiTvJsNXg1R9Lv5AihykZs2a4ZZbbsHixYsxZ84cdOnSBX//+9/RunVr3HzzzVi+fHm621lnwKpSVX1b5BSYCAqqVCN+QQRNwfI8RvRpg5N6tdK3h6naCQnJakWSNhMHJHCqEWby0XkThWUCkqBBys2UB2HTBYDza2coZPb+XA2ShqTNXoYTkDT3nXjH3uvHiTyrPWBuWlCZDhNmPX6fKvGnTNA3DhRZUa5c0CCpzg+F1BokMT+iWC4nM6zNL6dDKl6OoubkpOem46slG92Jn52gZN6nccIIU6wGydWUeS+aHUn2/50V2eNFIYZyIo1J2q42RqZVVGtVKMRxjXdTl19bFldI1T72HPFYVQeKpPcmM7GxYPtPjBDu3ZlmtciKhNGpWT1un8xEr0uOzt4DZ2KTeLHRbRpmRqlBCprwLs2o1NU3btyISZMmYdKkSQiHwxg+fDiWLFmC7t274+mnn05XG+sUVBokFp4s6lJJX69Bose1AlIKulrTUzgBCfyHJwZiiylysZl8+7ygoCnHDHZ01ZJfzGtEczLD0ndSGS+2kMSso6tDx0HiNEiGA4vOi40TkAwEU3G3SoMkC/ao0iDJSLlmAlKyL1KX9KSbv/octRdb8vq0LrYeuQYpOcHp4PeuZJGqZPcw6dfN0rQnMgGJgLj7ExykxH6R0M4iFHLQpXliEv15Xb57LguVI4MIbxwkQ5K2whvXPc78VsdB0mtk2PYlz/EeC6pB0nOQ1O+YQC/QZXg4SMljlaFJmFgm2P0RBQcp2xWQ2EGfH7NUIWRqnYmtvLwcH3/8MU4++WS0b98eH330EW666SZs2LABb7zxBiZPnowPP/wQDz74YFW0d58Hp0FSdG7RxCaDeKpONapCKh9XKjErHMfh1MTi6jLGJL4MGgeJLaMbZNjn0LJBgjQrCkghycQO+JO0HUfNMwo7jtFKlA5Wcjd/74BtKhiqNDIeAYmdeEw1SCoBCd5rqrhi4rMxnZR0HCSlBknGQWJW8eK5YtRwDwfJ0M1fRlrn70WyT1KuLBqXapBkq/M448WWweTvooR2VXMO2b8RALj8F5FEm50RNhL4aQk2mKoqjhd3ns/7ZwV51aSb4PT4CEgiB4n5iGhfTScHSZdjMaHx0pjYBL4ha5bSadj8ICurEjrDIgeJW3Am2icT3NwApYo5LV1pelKFcbJailatWiEej+P888/H3Llz0bt3b0+Z4447Do0aNUpD8+oe2JWqajAwcbcXT1XFB9Fmek9Jg2R2TkhoD6tKFb3YCBNJlj1PFotGBPtB6zkRyet3FFTN7PVkj0RHRk/WL08nkYhibO7mn6shaSsFJA0LKRSSR9KOxQkXbdjExCbuVXEuWH6Q214VB8nTkc3IoxwHyfVi89EgSQQV18TGBMx02xZyXI1NdkZYkuA08dcvUGRGOISIon8AClO75CZKozFXM8IKLjJ+h8hBovfNZluX4ZC2jfDR/HVM270TcE5G2BNDTNV8zs1fDFNhYmLzaJCSO3YWyTmxJm7+4rvkvYvlZfygTTUiqYs+D1myWhbsc4oTgoNa1cfwg1uief1svDd3DVdWNQ5J2yTpA2LuPbdeYXGRKdEgyThIdJcsFAUQPDhxuhFYg/T0009jw4YNGDNmjFQ4AoBGjRqllODWIjmY6gSN8oABGwExMKN8IvWc4yPsNMr1et+Yej5w7VEkq6WXjzG52NgB+dpBXXyvww48Jl41ANCJiQYNAMN6tsSS+4cgOyMs52YYkNF1Ky+VeYlNDmsWB4kZgAw1SGEJHwhIPHN2lc32OVWXuWxAR+53y4bZ0nKy0AJqN3+vBiloHCTTZLUhRxZ3KbmKB9REeBk/LelZpm9rog9oPhxDDVJpNM7wisLMfu+EloiDlHTzl3kLykA1SBSieTAcctwo3hRPnNXLU4/bZyuuI001Io2DJK9HdlyM4E4RNzGxCfVyOTIVPDQ/6Lxd9RokPQcpUzCxOY6D/154KO4/tYfX7B2A0yNbCKk0vToOEr1vGXk8aWKT31/Q/J/pRmABaerUqVJvtcLCQlx++eVpaVRdRtxdwanLBE10mqgvWSFbtZ+HlQxn9t0f953SHZ0lmpYgJpDkNgQXUf6DisWTz6Vv+8a46Ih2eObc3mi3n78GiYUpB6l1oxxuIGmYk+G6YsvuzkRAUidjVZtXGjMCKC0iE5Dc1TirGWSer58Xm+ydiW7+pYwWQoxFNKDLfvh05ABcMZAXkNiEk2xCTXoqe1kVD0fMFejAnINES7m52FzuCF+OIqx5F1S4ViVDlXGQ6OPz1yA5eODUHsrjphyk0nK5iU22Ct9eWIbtDNFaJojK0KIhH7NJFOyoBonFiT1b4sL+7bh9tHrOzd9ASAuiQVKBmJjYNBokVRk/6LhmslhptK/9tnE3bnx/kaZex11MHdV5P74Ooe8EIZbLtFpiWAe2rTwHKVmOmvzY6ui9uWZwhQapVCHk7i0EFpDeeOMNFBcXe/YXFxfjzTffTEuj6jJkA7GIoBGtE/Ult1WmGBGqQxf0b4fLBnSUnptqkLJMiQYp6cWWXEFlhB08POJgjOjTJvA1dM9W5DaxwhfHpZFpWxQfN1e/0o1d/Q5YDRJts7g6BxjNiIKf5e/F5kWMEG4FLmoh2MEwJyOM3m0beQbU1oyA1Ji7l4q/TFlVLjYZB8mki8k0TaEQPygDvHAbkvDB6LNLmnjZtrGuzN4QEFTv6c9BCuHMQ/fH8+f30RdkIBOaSqMxThikfUDlQv3F4g0AaIJToX7FMxYFVnHCjEgEpJDj4N+nH4zD2jfm9rF/I2GvBknWN8U94nMwGX+MNEghsR/4l/GDrrQuFxsVZFXICIfw2cgBuG3ogXjgtJ7cMbGJJiFFkm3y7tMJeey3I9MgcYt0d1FXISApOEjVrUEy5iAVFBSAVExUu3fvRnZ2Un0ei8Xw1VdfoXnz5poaLEwgG4hTgXZlxa6adQKSilhcsV/2sZm2WxTYZMlqZV5scsaMGbQaJGHg77BfHlZs2QPAXy1twiHSaZBUausmktxnOg6SLBAboNcghUIAkYxBsTjBbiYOlKiFCIUAVMhMKmGejRDNC/Xe8upcbF6Ngckk6Dhec687fjOnZ0VCKKrgVDiOZGKs+EtlYFWcnZyMsGcVHCf+2iMgObHs3zhHelx2u7J9rEBLid+xOFG6UFNkGgonbFvdcyNeDZIoxMsEeNdMFWY1SPy15Pet1yCZCC2JEBb6MmI9skVpkDhxgN7rVh4Hyaz+jFAITZtkYeRxXsqBJ6VPgIlFamLTaKDY9rL9gnr5yhbpLk9Q8UKqm4NkLCA1atQINPP3AQcc4DnuOA4eeOCBtDauLiIdgoAUcvkopThIdGC4cXBXTF+xzegcTXO8kbSFZJBxJlBkigoqAP6mJhbsZKXyxnr63EOwaM0unNijJRzHP2K1ar9q0OnTrjG++WUzt0/KQZJoRrjL+XCQZENQLE5QyBBtxZUcOzmoJgpW6ClhJm+XvMyVVdThEZDMvoxEQElRu5FoT5aHQJqgDIRDDsJxueZA5iQgcpD2lPIPOh4nRitg2i7V6l52v7J9haXMMw4l86v5tUFm3lINC6qEvBRhx/EkZZVpDEXHgkjYG6RTHl1a/1vWFfdvnINN+SWudi1ukGpEvHY6TGy6nmtyryrohBaxWtPwH6o2iRpE1bXYfpEl0yAJ11DRRmqNBmnq1KkghOBvf/sbPv74YzRp0sQ9lpmZifbt26N169ZV0si6hKQXSXrrVU2eWg6Skjib+NuvQxN8dM2ROPvFWUb1qeAACg1S4jebasQ08JkMdJCTrerFibgVQzDmBCTm1KE9WuL0Pvu75+vCL+gCrKmO7ZeXiZl3/o0TiuQcJLpCT+4z5SCFQg5CkudRHotz9+MRkFiysgGvoZjxfJG9Q9XAKw7opm8/FPJeh17ipF6t8dx3KxLt4gQ3LwcpLprYmOaw952TGXY1URQvTF2B12b4O6u4WhQNv8O7z1tO1CDRfuU3yWREvBwk1XfmJyCFQo7HnV30WGK3tRwkyfW9kzb/Wzb+tG6Yg0k3H4vzXp6NxWt3pRYHyaCMH3RDl2lIg5DjNdnqzGZiDSqHEBlk95cR0Ql58uskOUgOU5ZfBCs1SCk4JKUTxgLSscceCwBYtWoV2rVrV6mJykIN2Uo1FXgiZzPbplGRVU1gz2mcy5uBTPuF6PIp82JzTWycm79R9VKYRtIGeA8slQZJfI46AUnNQVK7+YdDDsfjAVQcJG97OAFJd9+Og6iQF4/1cKIQzTQqby4V8rIiKKwQIGTFTTVIQcJIiCVpmw9sWR93DT8Iz323HMN6tsR7c9cmzgk5iAhnuSY2ift7k7xMrNuZ4GMmSNredohCEwB8cu1ReGfOGoyvcJenk4lSgyS5ZZk2gtX4hUOO2+f8iK4Zktx4Su1xyOG0pbIJ18tB8tYpRiSX5eeTNcE/UKT3pJ5tGiInM+yGPqhsHCS3TBoFJKmbv6R8XmbEE0JBJ/SI43FmgNQdQbzYKq6WLCcLFMmZWPm/KgHpiE5NpPv3Foz0bT///DPiFUv4/Px8LFmyBD///LP0n0XlkCQSe4/RKLX1FVnlWegGDvZQuya8SzsLpWs6N9AJx0zd/IXVJGdiE1YX8XhyoqqM4Ni9VUPlMXFyaslka+c4SMzl2bboVM+AemAJOeoBTjZAad38FVpCrQbJ4UnPKmHHk/8vJB8MRbz090PRplEOnj+/D3rt3xCZ4RD6d0p42rBzjnEuNsPXH3K8fYX9fdUxnbD43iHo064xd44orBKS4EGc/eJMTx3N6iU5VjmZ3jhIKvRp1xi3DT3Q/U2vqepD0lolO1lNEauZ9NMgyTlI6vIZnLeSt82iV6dcg5T4cWbf/XFY+8Y4umtTzwRqkiFALMHexztX9sd5/dri1iEHcPdkRNIW+qPMDyNdJjalpl5y/7lZag2yvA7+dxANktTEZmjOY8dMeaBIfoyXkbRbN8zGc+f1MW5vVcBIg9S7d29s2rQJzZs3R+/eveE4jlSidhwHsVj1uuXVdrgrVclH8/7VR+Dxib9zg6spZIMTANx5YjeUlMdwRl+vV5iSN6PwltKd42kP1zY+kjbtWa6bP+PFlop49N2tx2LdzmIcvL9aQBIfd6uGSc2NGC1Ydo5KQ6SqP7lf7VouNUVpiPHse+E1SJp2hbycNJNI7aYapCE9WmJIj5YAgE+uHYDyWFwaFkElQHpMOMYdwEv69fTVED9lhR1ZIlyC+X/tlLrsN2NI6DkZ3kjaOvDCNdWiqPqBZJ9P/Yl7oQKSvwZJvIbWHBR23FiBsv4o5hxLcpC8i7RhB7fCsIMTeRtXbi30HBfh1TKpBbsBXZpiQJemzLGKBRfx9wT2xkFKrwaJ1cKZpu4BEppYoDTANb0CZUaY/8YPaCEPjBtUg+Rw5ZK/ZKlG6GYygrv3+d4xrBuaN5DHUttbMBKQVq1ahWbNmrnbFlUHXaDIHq0b4o3LDzeqRzxbZv8HgIa5GXj63N7SOpRebApTTqJuQwFJKMeuSsVovpwXWwoapE7N6nkSMfq1p3mD5OTHmUmY79g0XELiuNqMpuQnpfgsAd4UoYuknTCZ8JqwEiltW90209gqCXOiXMWvy/HEwvT9hyQaCF0wPlq32AxC+PxiG3aVuNucgKTI06eCTAOnfo7+mhT+GLjEu74k7bAjCRSprl+VtZ1C1HLSutgqZa9bpmEQ4XXr54+bOJ0kvLGVxRJlhduSyVN6c5P6+gDQu20jLFyzS3otWXmKegbWA74O776McAjlFYqMmwZ39QR41V0/Q5MbTcV7TLr5e8vSU6ISE1vQ51sVMHra7du3l25bpB8mgSJTgWz15gdVG3T50Ew1uDrVryzvWjLLuFn9lQWr5dhRJI9DwgqQfh+zTo2udnH3a2WyDt2+IJG0/TRhbv0B7t0EKv6N14tN3gfOP7wdl1ZBFi9JJvCLk7aMpM0+P5Y0KmqQgqSeYItSwUhpYguoQXJN1C4HKRUTm/oK7LtiTSn0nnK42FJMmzULK7bdibLya3s0SAHiINFDj3y1DA1zvFkAVG0BVBwkbRXe6wP4dOQATFiwDmf23R+njZkBQP39yG5FDPPR0lfDItMCJfcdc0Az5bOQ3Z8uTADbXlbgyXLd/Nn3X3GNin0yE1uQoJZVhZQCRf7f//2f+/v2229Ho0aNcNRRR+Gvv/5Ka+PqIkwCRaYC1UClg8mqPlUTm877JMk3Sh7XZRmvavjxiwB/dbvuWao93Mw+T9lA5hgKSKLbvGn6hKAkbT+oblUWKFJ2P9cc24mL5C0LFOmnQRLTJQCJZ6fiq7AcJFmyWh1CkuenWp3LatVdKpnfLFGfiYlNfP5aDhLT4VivJlkwUxX3USr0Od66dGVk9WifS8XBzQWl+GPzHnVByAVlb5lg06fjJDRHD57Wk4txpjO/i8hjPAQfOq0Hvr9tkO81RWRwQq36gQWNg8Q6trAmPLr4DUk6QNLEJtMg1UIB6ZFHHkFOToKfMWvWLLzwwgt4/PHH0bRpU9x8881pb2BdQ7wSXBsOGpW56TiuGqTYD8dklR70WkmXaokGKaXaU8OTZx+CgV2a8ik0FA3wW+2oQyao4yCZDhCViaEimthMeRWckJyGlZ4ylpIk1YgMIcE85jhm5hdeg+QVVgnUOfxYDVK2wotNBc5E6ZMd3tTNP1kfPyGpImlTZIRlcZDUF5ClBWKvl5OhEJB8Fmnc+1Nc22+BpDexaU/lyontk5nYggeKZJ4b8wxpGiNvO7z15zDCZ5O8LN80RyoTG4XuHmTX1zlkNK+fjQ+uPgJfXj+Q80qj9y3LxUbHeJkXWyohY9KNYAZNAGvXrkWXLomInZ9++inOOussXH311RgwYAAGDRqU7vbVOfhl004VJvZ93Tn8fmZy9AysZu3RNaFBxYDB1k3TeexNs/SZh+6PMw/d36is38es5hnp+UkyzPnX8Xjgi1/w1ZJNAFSTDatB0rv5c+YeU61VAP6VCXRu5SblxGMJAUnQIEkFJGbQDnlDLujydokcpCBaXz6xZ4UGKVCgSM3K3xE1SAZxkHw0M1x5Scwytk2sGUgUQJPbknYrnAxYiOeZ5pDzO6ZqB0U6SNqqc3u0biAtI6uei0dvcHlZP2GFM10dsvvzyypAPVRlAo/MxEovITWx1QAOUuAW1KtXD9u3bwcAfPvttzjhhBMAANnZ2dIcbRbBkK5AkeLp3OrNsA5dBnrZtuy3CrJS/zn7ENzwty44pG0jT130A0qH6bFSdShkDdEMd/NgPtq8joNkSlCmaNEgG0d1Zr1zZPUmt3UOOwkvNvnKVgfexFb5gUyleRTbo+IgOY53ABbLSQUktg2O11xJwCftffSMg91tVkBywJvc/MC2ja7K1d6MZvsoxEj0ZX4mtpAsBpFGg6T6/is2xfx2wmHPftk1lRwkkZMmHtdykEwXht5y6cjFxoL9Znq2kXvXytpbHmW5Pf7fnayJmaYmNpkGyfCeZZ6w/CKdv75MoKoJHKTAGqQTTjgBV155Jfr06YM//vgDw4cPBwD88ssv6NChQ7rbV+dQVRykVExsfqlGgGArOL493n1nCdoatq7qMLEFAR0sszNC+P6fx6FFgyzpcRE6E5tpGhi/yUbnxSYKEmHHQZtGOVi/K7HYefSMg3HnhCW45+TuyralQ4OkuldZqhEZRA+0xH35903xOXp4JQwHqWvzejjv8GRW+tzMCE46uBW27ilFh/3ylAFFKa47rgtOOSSRbSCIBi5o2iFXg1TRr0p8NUiyZ6Uuz5pZ2L4rM7HxZjX5tuyaSi82jyCnrkNXvw57W4PUvZVcgyQTGspicfy/Yzrhlw0FOOaAZr7XkX0v3DvTdFnZosU0VYksAjYvLPNCvCzVSE3gIAUWkMaMGYO7774ba9euxccff4z99kuo1ObPn4/zzz8/7Q2sa5ClNEgHRHdmo3NU7qeKlBZB6jYZ9NlBpDpJ2iagg44DhyMrUii1RBLei1unZoAIc+/Ae5zdpfViCwmaFgd49rzeOPel2WjdKBvn9muL03q34bgP9Dy3nUHdeSRQ3aoosKieSMjxmnC8HCTveX4cJDbNjewdjrmwr7u9f+NcResS5/6TCw5prrWTa5B0fUPUIPlxkEKBTJlcUFfm/dBvOkvJQdIL9SptEwtvhgD+dyq5JYGEWZCG85BpTi7s3x5fL92E/h2TkZ0rIyCx2p9eivhse4SI2UDCI3HU8INSvi4g8saCaZBMA03KhDsZH43+lZnYaiUHqVGjRnjhhRc8+22i2vRAltIgFXhWWoptHUx4IeJHZKoVNbk99vugH1y1y0eK64vEWNVxEQkvtmAcJMDf48e0/0QEkrYD4LAOTfD9PwchKyMEx3E8wpFYf1VykES3YtWlHDEwpOOtU/Y8RbW/LAdXTBObTMQph7TGF4s3aK8DJN5Z03qZ2F0SdTl3QaB75K6AZNgHMqWpRtTlVRwkusmacFSmfVmf1Tl/qNrl1ShpBCSNHJ+bGXEFJJnmZGDXpph+x3GcW31l+n12RhhPnNUL4ZCjDIYoE5CC5iaT3TPrLanVuEkOmgotcg4SU7fD91HZfelyzO0tBBaQAGDXrl2YO3cutmzZ4qYgARKd8+9//3vaGlcXoQsUWRmovEl0MOHGeN2DTTVIBmWcZN6nGqNBUmhjqJCj9vxTROMO6TRIZkHZpKtxLlCkHDkZYUTCIenE1baJWhsC6HloqUApjHs4SPJyJhwk+TX4+/B4sTEkbZP7fOKsXthVVIYfl2/zbfd7Vx2BorJYRXRkNaQaJB1JW9Ag+SFDEgcpFQ4SrSMzIu+bKmFJelxxfT8yuemiQkS9rDC2VXj+q75HUUOYqscuxdmHtdUelxGi/bSBImT9JIMTRHX9yLvPdPwtj+o1QrQa+gxlgSJrpQbpiy++wIUXXog9e/agQYMGnpWsFZAqB6pBSrccwA9OhkKMAQfJ48VmStI2vMGw4yBKSFKDZHTW3gf9mFXtUy2Gwo7aTd7UZVlO0k7uVHmx1auIEO03cfm1LUh+J5P6WIjkd7UGybtCNTG/cBxjx/G8C9bN36RrZ2eEcUSn/TwCkuzcri3q+1cI+feq+3zofZqSXCNhM74WhSyxdEVDPcdVvCK/Pqt61h4Nkk9kbVX9InKZ+EKmY1hVc2SGH9wKE5duwlFd9sO9n/0CwD+mlQjZLRvHQZJqps2u62diS3qxJf7KI5VX/2gfWId166234vLLL8eePXuwa9cu7Ny50/23Y8eOqmhjnUK64iCJ5/OTh1kdqvFVHyjSrG5T0MEqFq8a8nq64HKQFM1TJ6t1lIEodROcn0u0SaBImrYgJe0iKySnw4vNoK8BUDfQoy3y1un3nMIh76BMSNKLzVz49+6rjOZTrkFSI6lBMnsvCQ6Sef2RsPz7p1uqCVhmYmHBaxgS28cdyBOR/QJF6t38lYcCp/Dwu1Y6kBkJ4cW/H4qLj+zg7guqQZLHMvIXRAF5fzcdf+UkbbYe/+vXBA1S4JFt/fr1uOGGG5Cbq1fBW6SGqoqDxKUaMaxbPqHo+S/Gbv4BJ+LyuPkqPigC1akUgKiAFEwblIi9E1yD5OeVyJ6qMrHRSYE93VS7GIRkbALHkZsavZG05ed7TC9wPAO8PwdJEQeJpr4xNh/LrpPm71k3sbj8DrO6pKlGNDMDK+xHJEJNBsdBYgUofZ/NjnhTlLx6ST88cVYvZr/4nnmkGgcpLyt5bVMhpDo0HCaJpFnIWlgZDZJpN5YK9ZJ5w1RTWV0I3IKhQ4di3rx5VdEWC6SPg+RRmafQ16QkPcOVnx+C8qCqUoOUjsmLTqyBSdqOozRR6QZglelCtk9pYqOrZsnKzg/pdvMHvHmmEnWLXmzya4l7nZD3XcjaKWo1vJG0Cehi2FSDJCtWmSckfyeaiS2wBslrYtN9ZxkKDRLdZD20HEXfktWfywgp7kJRIDH7aQV1t6zr22zsJlMzVmU5SKnAL+inB5Imsu/PxFTLV2d2z8+d1wdN62XiybMPcffJaAG6Z1gTNEiB9YonnXQSbrvtNvz66684+OCDkZHBe2CceuqpaWtcXUSVcZCYjm0qLEl5AgqhKYZggp05DyrxN1alGqQAlaoCRbomNrWmSIZE/q/gXmxcLCofroBqzUlXzUHj7IhtS4ebf6I9ERSU8J47osCi5CAJnCNpHCQfk4EsaCebi81Ug6TSvKaKlDlIhh9LOORIIuKbrexlLuMqDpIsWSkLNs9YSXlSSGGLis0SfwdNneEeYxpUkzVIQTlIso8/YqhBkn0vprfcp11j/HTXYGVoBzGStgw1gYMUWEC66qqrAAAPPvig55jjOIjFAr5ACw7xKvNiS26nYkZx98km4xCAGD1u1p5UNBVBzguCdMScou0MqkFK1YvNb7Ix4SDlZFIOkvw8HdLt5g9A6s1l+v4d4VjI8S6epSYDZjvkyDhIwbzYVG2sjLYhKAeJ3oNpjjwxyCbgM3GpOEgVmyqNKP+svWWyM5L9vYgVkDT90xMXKUUOEtseXeR5FtVhAgrKQZLBNJK2n8bVD7oFCt3UCbTpWnhVBoEFpLgk665F+pCuQJFit1OpurV1yAh+PkJTOt38xbpVbaosgiadlCH5XNSaIhlCjprDY+rF5pf4U2Viy8nwhibQ5W3j22bWziDIk5nYArj5swg5kgSs0hUxP9mLyT8JiDR5sg6ViUslrc/wGu61qIAU4JpBIuKzTgX8u6/QIDEmNparKyPpslAJ9dqQFqIGSfvNpCY8qZAO782gCBoHSQZZ7CoZ/Ez3QcGfW7GgrOEmtkpNwyUlJelqh0UF0kXS9qqimZWeaR2SfX6TTLrd/FNNZaK9tniNQMsi+e7KcJBSIWkHcZnWxUECUuPHsNqtdE0UrKu17DqAuq0mQokf6dRxHE9+qzgJbt6VFfNL8qmtL0UNUhDNnp/piqufed88STvxl73XmBAnT7bt2zZmW7wlr2CsqSdF85uuvr0lJLVtkgMA6NehiU9Jf2RwHLFgAkpl7lbGQdL2s9ooIMViMTz00ENo06YN6tWrhz///BMAcM899+DVV19NewPrGqosFxuzXRkhQ+oJZLgiUbVHez2PSj39CPQ4fDlI8uMqwTEUcpSqei0HiX3mPkJrk7xMaR3ZFRqb1NLQsJqX9HGQRMhSYJhwcmSBIv0C34UcICuDL5SKF5vstTWtL38HZvC/X/76FRqkABOM9zvTaJAUcZDoJnuczbGV6hikS1Hi95s/pruGcXM47C0z23tXHYGRx3XGC+f3qXRdGYbjtVRASpMGSYykbXr9vY3Ab/ff//43Xn/9dTz++OPIzEx+9D179sQrr7yS1sbVRbgk7TTXyw0cpkKMoYo1pajKhsX8VozpQDo+xCQHSV5XKhok3QpK5Nvojv/v74eiw37esBy5GV4vNlOwC2cxHUiqYF2t3bo9Jjb5uQ68mgaP0OSzIg6HHM7VPIEUvNgk5ZrWy5KUNIPcHKUuHzRQJCATNNRl2X7Jm2u8glmMFZBS0GInzpNvy+rRvaNUQwDosLcEpP0b5+K2od2UaUlUkK3nQpJ3JkPanQ0k73Gfc/N/88038dJLL+HCCy9EOJwcTA455BD89ttvaW1cXQQJELWXhV/HTUE+ksJvFW6erNYMIWEwbt8kz/BMc6TDbOfGQVIcTyXViKmJTfbM2X3dWjbAZ9cN9JTJyaQcpOQ+cw6SioeSOkw1SDLIIkGbmdj45yjTIAX1YpOVamYoID17Xm8c3KYh/jW8m7a+yqQaGdG7NR4942C+PgNhkiKi0CDJzmCTkPoJ9YDcXKvTPHm0hClqkFLtwjVhEg8KmVArgyzsRioer7JruV5shjzL6kJgkvb69evRpUsXz/54PI7y8vK0NKouI9VktQ741YI3BL/ZRyHWKcIv/LyxAsmwDewAfGTn/dAwN3hiz3S1RQc6aajqUgtB6kFCHwdJ/z5NvJIqw0Fi54W0ufmbxEFyEsRpEeI9OI6Eg+TjlRN2HGRFRJJ2Kl5sEg1SfTMB6bTebXBa7zaYuHSTtj7dS/NLVtusfhbO7dcWGeEQDmyZSHcSRIPEHmPfj6yd5SwHiatDfoHsjDDKY3yoB/b78GqQzNvtx0GKhBzOJGiC6iBqVxbc89R8uqcc0hob8ovxvx/+TJ5bidtl+2NSg6QuXxOyJgQe2bp3744ff/zRs3/8+PHo06dPWhpVl5FqoEhPZ9J2vKCtSsIvw3NlVtkysPUN7dHS8KxgCDS/KxqejKQtP67kIDneGDTJdmkGdKbNbLG/dWsOALjkqA6e64igHlupaNCqIlCkmZu/+jmK5cRmySNps4Im72oOJPhHQVONyJ5nUBMb5/ElOa5rievmr9BaJpJAOzjz0P3Rs03DirJmzzlxjGmnz7fDKiS5KhXVy7UWzPV8NEhBTGxtGuUw9XgJ+iao6RokmUbY1Ou4SV4mRg07iNvHFj+6a1P0bdcIH/6/I43awo9Zei1nTUFgDdK9996LSy65BOvXr0c8HseECRPw+++/480338SXX35ZFW2sU0g1UKRfcW7VEDBIIwu/SSbdkbTZdrfzyTCfKtIRKDJVDpIsOCGFqdcNu/3KxYdhZ1EZ9hMmZFm7qNdYKgKz6B6fDuRkeCdHz8QNef91HJknVjANUigk0SBxJG1d6+V1UjQz1CBRyOIL8dfw7xsqDpJs0vRqHM36Hhco0kdWMNFiy/qALuaXR0AyNLFddEQ7HN+tBS57/Sf3WGYkhMKyYHH8/DRIqWilqhqpLGgp2HdxcJuGuP3EbprS6nPpZk3QEukQWPw97bTT8MUXX2Dy5MnIy8vDvffei2XLluGLL77ACSecUBVtrFNINVCkLwcpQFkdpMEjUyBpGwerZBor0zCkA2nhIIW9fB4WOlf+VK4fkgw2QGKSF4UjVbv2b5xYQafCK2AFvnStosUYRECFCZK9vwDPymTyFOsWNUgEwb9JqRdbvWBebNz7lQmEmnOpYKTqczKamTcAoxrsMb+kyfw1ktuqYULWB9jz/AJFak2DzMHT++zPaatCEvOqCfz6fjryFKYbqrHDBJxJOuDCSPYd13AFUnANEgAcffTRmDRpUrrbYoGqCxSZipZHBr9cbOYcpODlUsm4bYJ0eKn7kbR1ROxUNDBBhQaxzD0nd8chbRsBSFGDlIrnog9E4QQA3JQhbvgL+bmy/SYcJPaNhRxINEjBvdhk35cpSTtZh3xbt4/Cz4Vaps8wJcOLF+fiIKnP8JRQ1V+VJja2bCTkIE74d5+ZgonN75yMUAglqFnBlU1J2iIcp3LziEwwS0eQ3qpE4B7RqVMnbN++3bN/165d6NSpU1oaVZeRaqBIPy0AN+AGbRQDEzNFOsFeT+YGngrECSIdGiQ/Ext7H6wZIWFiC349U1fdZJnkdp92jXDFwI7u71Runx3Y0kVUla3gwyFHmCDl55oQ1f0cDMIhOQ8laBwkWalGualrkOTXUB9PcpDM34uf6UpVNkg/5HhViqI5EgFJFxRVtxDU1RMJO4JW3UlJQKqVGqQUFrRAov87wu9A15VoRdMx9lYlAveI1atXS/OtlZaWYv369WlpVF1G3NDN2gOhn1HypXvYYHDyqRKAvEOnlGrEsBxrvq8yDVIaPlJXSFBUxb7WRownXsKElIoGKdggp7sGO+yZdr+wIh9XZSATTrzu+vJrOfD2KY9nllS451fEWYKJJx0k7XMO278KOEia61ecGyyStrkGSSmc+VxOZyqjyMnwfuO8aU7fTlM3/4xwyBOAcj9FQFUdZIsDdvFR3fnEZJ9zujRIQW9NNmale0GdbhjPOJ9//rm7/c0336Bhw+QEHIvFMGXKFHTo0CGtjauLSEeqkev/1gXXHNuZ2ydmOk8VfhwkYxOb4fVKmaSVslQUqeCliw/FlW/MQ1EFITMd32gy1Yi8ssKypOtyg+wMbMwvcctX1sRmImzqNIiV1SDpkuoGgRiDCKjoW9wEKT/X5BnIY3gxxx2vkEaYVCOmEwL7OJrWy8TjZx1idiIDP2FCd7d+yWplQrBXmDRrm2mbEsf9xwmZBokTyHw0XTIzbfKavFAvcqIeO7MXrn1nAa4Z1FlythwyDdK1gzrj1emrEtepaRoSYbwJ0ryE92Pyd1DhRqZ5rOHykbkGacSIERgxYgQcx8Ell1zi/h4xYgTOO+88TJo0CU8++WSgi48ePRr9+vVD/fr10bx5c/z/9u49vokq7x/4J0nb9N7SewttKRdb5FaggBTlIlhuKpVVWJcFiqC4tEJl14WKqCyX4vo8uiur4LpaWRQQtIDLD/HBcpO1ILAtUMVSBQS0LatAgYIFmvP7ozTMTCbJzGQmmbTf9+vFizSZnDlzMpn55lyzs7NRWVnp8D3FxcXIyMhAeHg4goKCkJ6ejtWrV/O2qa2tRU5ODhISEhAYGIiRI0eiqqqKt01NTQ0mTZqEuLg4BAUFoXfv3vjoo49k5V8LFqUTRXIe/z4r1aZDs5ITUXQmbSej2CQ3Q0jMzy+cAElJFbiYzI5ROPriCOvfatSAOOuDVN9wO0Dy9eEHq8oCJHlBqcNh27L3Lhjmr1Izgu0s1rZNbHYXqxV5Tso6frybtkgTm5KlRrhpKu3vx2+OEN2JXc7mQRKbR0p4n1eyZIesJjY7BxDopJO22HQOjv6295qv0cj/DhkNaB8VhK2z78GDPRPspiEktsaeFiM8VcOYIECSnj9Tc39Azt9yiDXttZgmNovFAovFgqSkJJw7d876t8ViQUNDAyorK3H//ffL2vnu3buRm5uLffv2Yfv27bhx4waysrJQX19v9z0RERGYP38+SktLceTIEUydOhVTp07Fp59+CqCpU2V2djZOnDiBzZs3o6ysDMnJyRg+fDgv3cmTJ6OyshIff/wxjh49inHjxmH8+PEoKyuTdQxqU9wHyWn1tjonoti9kPtrU+1hm1dvyBt2K5Wc0Tdy0rOX1pWG28chvIC6HiC5ln9X36/WPEgp0bazpAub2OzXINk+Z9MHyUn/OYPgBgA0BRNy10cU1kwo4bSJzYWZtMXYBB4OtrUbpMq4BtnLWvd2YTbPSS1PZ33huO+17YPk8K0O9qnvAEmstlBpngSVubLT4Zfxreul3gJIAdk/yU+ePImoqChVdr5t2zbk5OSga9eu6NmzJ959912cPn0ahw4dsvueIUOG4KGHHkKXLl3QsWNHzJ49Gz169MDevXsBAFVVVdi3bx9WrFiBvn37IjU1FStWrMC1a9ewdu1aazpffPEFnnrqKfTr1w8dOnTAc889h/DwcIf7dgeli9W+PaUvAnxNePnhHqKvc89DsV+QYsRyINrEpuCCIPWmfE3mvCRKqBHUOVus9gqnBkl4kVFSDS826ZpSSt7OvfCq1c8iPiwA62cM4E3gZzRI6z8nZd1AsUCOu4nY59DUxHbrdQXnttLPxllti6Nkm4/DXuAqaZi/i/3inLGX/qP9kvDHkanYODNTNF3HS2M4boLnz98kbGJT9jn5itRqc2enHt4lFgB/UkpPU35O8idflZuOaB8kfcdHyob5l5SUoKSkxFqTxPXOO+8ozkxdXR2AploiKRhj2LFjByorK/HSSy8BaOosDgD+/rcX9TMajTCbzdi7dy+mT58OAMjMzMQHH3yAMWPGIDw8HOvXr8cvv/yCIUOGiO6roaHBmjYAXLp0SfbxSXF7qRF57xvYKQoVC0fYn3RQpeVvRSeK1HCYf8NN7YfIqtkHyd6FXzhr7+19GxT9inJlLhMhJTdDbpCtVg0SAPRLiUDXhFD8cPEaAPEmNmF2m/+293wz8ZFujs/d/SfPY//J89a8SOGov5dUzj4TR682n4smTuDqbMJC4aE5OlQpN0aDwTYQk3LOmowGzBzCX8pKSoAMiC9VY4+Pyej0s5dCWGvVr30EL92UqEDsf3YYwgLUXyJJKaXfV4NBWi2gPWLBle76aAnIDpAWLlyIP/3pT8jIyEB8fLxqTSoWiwX5+fkYOHAgunXr5nDburo6tG3bFg0NDTCZTHjjjTesk1SmpaUhKSkJBQUFePPNNxEUFIRXX30VZ8+eRXV1tTWN9evXY8KECYiMjISPjw8CAwOxceNG0XXmgKb+UgsXLlTlWB1ROlEk4PgC7mjNHftvkrYP3ig2vf8kEOGOPki5QzviSsMNPNAjAS9tu72os9FgbxFVx/uzN9TaXbj3WjUDJMC2xsBZDYK9vQt/FLg6RYX0iSK5gYCyshFbt4rLUbrNcZFwMs+bFvu1scJz0NHwdSm1eD5GA240MsHrtx/Lqm2SOMAk0Mko10bOSSvsN6e0VoXbB2nlb/tgSGo0byQyAxAb6i/yTg9R+IMMaPp+CKfFkEPsnNb7TNqyA6SVK1fi3XffxaRJk1TNSG5uLioqKqxNZY6EhISgvLwcV65cQUlJCebMmYMOHTpgyJAh8PX1RXFxMaZNm4aIiAiYTCYMHz4co0aN4k2xv2DBAly8eBGfffYZoqKisGnTJowfPx6ff/45unfvbrPPgoICzJkzx/r3pUuXkJiYqM7BczCVJooU4l4AlM4kIEzH+pyC/jx6+mKos1htUxr2PrcQf18szu5usz97fZCc5UiNX7+u4J5DavezsJmZXVAjIzx/7a/PJvjbyQADZ+eu1JZEbipKv8fOOmk7bGJrrkHiBiwmA3BrLXGxpUakLMviKD+AoI+P0YgbgulgDHa2dUZqs46zmfa5gYuwk7bSawA3kExPDIe/r4k3sESPlP6gMTVVIVn/lhtoCWvOAc/30XJGdoB0/fp1ZGZmOt9Qhry8PGzZsgV79uxBu3btnG5vNBqtNT3p6ek4duwYCgsLrc1jffr0QXl5Oerq6nD9+nVER0ejf//+yMjIAAB89913+Nvf/oaKigp07doVANCzZ098/vnneP3117Fy5UqbfZrNZpjN8uYyUcK6FptKTWLNlKQmlgfxtdjEH6udH62oMQjrdg2S88SEF3xn8/OIpyE/KJVCav807nZqB7u8fkFG4USR0oNJKXPkcJ9xFgBJH6Hp+mfDe5tYDZKD9zYfh5wFhaV0aG+WFh8q+jw3z9yAzLoPhQMjpDaxBTuZSJYbIIkN81eCGyA1p6Gj3352RiwqPyd5NUgu9EFqfijMSnZ6AjaV/4jubW0763uC7N8306dPx5o1a1TZOWMMeXl52LhxI3bs2IGUlBRF6TSPpBMKCwtDdHQ0qqqqcPDgQYwdOxYAcPXqVQBNgRaXyWSy6VPlbszaSVvddF0f6dT0v/OlRqTeRFzKjqrUGcXmeC02Lu4t314nbTk1SB4pSw3X37RpYhOM6LLXt8h2XS7ntSJyghnpE0Xaz4NUvFFsSjtpcyJ/bkd6KUuNOLqJDuochZcf7oF/5d3Ne17YCdomz3b/cExqvxdnnbQbOZd2X5PzplspuH2Qbs/to6OLmwjlQbvBZloMefu1zYMwL0PTYvDZnEHY8OQARXlUm+wapF9++QV///vf8dlnn6FHjx7w9eV3PnvllVckp5Wbm4s1a9Zg8+bNCAkJQU1NDYCmwCYgoKlT6+TJk9G2bVsUFhYCaOoLlJGRgY4dO6KhoQFbt27F6tWrsWLFCmu6GzZsQHR0NJKSknD06FHMnj0b2dnZyMrKAtDUT6lTp06YMWMG/ud//geRkZHYtGkTtm/fji1btsgtElXd7qSt3a9yJXyMRlxvtLjcTKGU2HwjaumaIP6LWI6e7cLQNjzAOmrFEWG/F7FmGKdLTbhwE1ajxkfL9ckd1SCJZt3e4dgJpOztS06tnSNiq5bL5TwAtp+wtYlNVg0S/3VH2xsMBjySYdu9gF8bJ3JSKzxn+Z+//fc566TNrUGymfRQ4efEDUJNdm76euNKE5tLNUgiU1cI7ycmowGdYkIU5U8LsgOkI0eOID09HQBQUVHBe03uhbc5qGluGmtWVFSEnJwcAMDp06d5NT319fWYOXMmzp49i4CAAKSlpeG9997DhAkTrNtUV1djzpw5qK2tRXx8PCZPnowFCxZYX/f19cXWrVsxb948PPDAA7hy5Qo6deqEVatWYfTo0bKOQW1KJ4p0hvvRKOmDZDIagEbnNUhqD/Nv5miGXKW2PHU3/t/RauQOFe+YL0dMqD/2zh0q6TsgnFhT9ELjJBmlzRXOSD03xPqxaJEH4eR0cla2l9JsJGeSU+nntnj6cvCH+dty3Afp1v8G8e+l+DB/YRoK8u2kBklpvzmptXzOOmlbBKP4eGWstKZPJG966lYj9llndmqapudOO02l9gin3JDbv07sR53Nd1RnwaXsAGnnzp2q7VzKRXbXrl28vxcvXozFixc7fM+sWbMwa9Ysh9t07txZFzNnC6mx1IgYJX2aeH0Kbp3JYssXmBRc+OTmRmwJAld1axtms2adK5ROJKikkzaXpztpa8loVNbHrWlb4a9T2224WzhLW8koNqWfjbOaLUfJOqtBEuuXYluDJP3uFxHkh/P113FvagwnD47zLOd6JPXzd7ZWY6PNSev6jwzeD5VbRcb9vNz1PZHK12hAWIAvjv1ppOyVCYQTqcotM7H7hDAg0tsoaG1W/ySKKZ0o0hlXz7vmwEh8SDr3IqFuvsf0iMf/O6JOLY9eCJc4EO+kLT09T4wIdDCljqqabrTcqnlHTT/8v20DJMezHjs7dyW38qpQg8Q7ZrFXRdI1Gpo+F+swfzt9kMQIAxo5tQNbZ92DL777Cff3uL1Eh9is1kqbtCQP83fWxOagBklxE5uC2nNPSQjzR+G4ppG0Sn5wGo3CZlR5x8v/7Joe24ye9NYapHHjxknarri4WHFmiPKJIp1RchPlvsNagySSMe61V8mvbEf+MiEdeUM7IS1OP+3SruL1sbHTxObsF7acmg8tSB3tpizt24wS+oo0vy58yabZyEkfJOdrick/t5WvxXb7sdQW2ABfE+qvN1oDQW6nZV4NkoQmNjk1SHFh/hjXmz/6WCwgU1ouBidl0SzISSdtYVCvxmhDrZq6tfBFwTCX3i+ck0xuMCP2PbadikNp7rQhOUAKC9PHsLuWzpWJIh3hnohKbm2O1hrjj2KTmKDE7XxNRnSR2Vaud5LmQXJSPtzPUM0aJKnnhruaDkxGg9NgoZmwycCmRkmsiU3koj0hIxEfHDwjmhcp1KiZcNrvSiTdAD+fpgDp1msxIbenJeH1QRLZn/B77WrQ7WwUm7waUu5jBzVITob5C5vYeJ+TwgP28aIAyVU2P1ZcqEGyNw+S3spQcoBUVFSkZT7ILVoN81dUg8R5i4+1X4PtdvzFGXX2E0CHhBdmOXP7tAbcvokmA39osaPzeELfRBT/5wfcm9bUF0b2MP9bry95qBuSIgPx8qeVvG0lj2JToW+Lwe4ftvtoFuDHX2IkhjODs7Mle2zXKXPtDEyJCsI3NZf5TyqssZE6zN9ZHyRhExv/c5KcHR5n/c20HMwghZq7NxrkDWoQe//tx3aa2HTWTEl9kHSm+YTW00zTgOMVwrlfmoRwadPq6+vo3IvbfGGvk7bTYf6q50peuu668BsFE/o5yl+gnw/+9dTtuXmkTBQpdtH2MRlFO+8rmwdJ0ltE0uAGhbavi9Yg+TbVoDQfJzdgOF9/3fpYfIV37mPXz65F2d3g52PEb/olWZ/jpiqvsz33sf039mgX7jAdi+DA5TSv2qNkBK+3EtYgye6DJDLM32b+LZ3d9yhA0hmt+iApwf2F1VyVLHYh4VZdd4wOlpS23qpS3YkbRArnFrFyUjzxYf7olxIBs49R1iKdatEyPBKmrXSeLdv+DWI1deK/iMX2omQmbaU/dKQGhVwJ4QE4XnsF0SG2M/5zAyTx/dl+110RFWzGX3/di/ec4j5IsL2xcn3+x6H46UoDUqKCHKbTKKhEU9ppnMvH6Ppn7S2MglFscg9XykzaNIqNOKRVHyRXOapB+uHCNevjGJGLsxidHZ5kb03JwOOrDuLFB7sqTiMpItD62GinOcNZ8RgMBnzwxF3Wx2qRGvh4ql+YeIdl8eMXlovYZKP2anvE+9rJz6Ma13vR80Mk3efG3IkpmVcxqHO0kxQdD/PX6iYltbO1o/eJfS6JEYFI5Hyn7BHWIKnSmd5JWelslL9LDAb1RrHZm0lbb/c9CpB0hmk0UaQSYtWpYl+K73+u57xHBxnX0F0dInH4hSyXbiLci7m9Wgkp6SuunVD0Lr7xGYmob7iJuzpEqpCagM1oo9uPXbmA+vuK1LTxbtqOfx0rmwdJmxuvWFAYEeSHTjHOa3BFm9hUrkESw29ik1GDpNLnr0UTm95u6FpydRSb2GALKXOVeRIFSDpjcUMfJCX9R3wczIN04eoNm+ec8ebriqu/sJMjOQGSnbT0XjwmowHT7+ngln2pdRMyi8zGLlwX7/bzttw5kzb3XWLfV7Fkxb6b4YG+uCjh+8n/MaTNXYo/JF76+9ToTA0AjTbzILmebkvvd8RlNAqCSjVqkIz2t9EDncVrRL9NbLaz8zYb0bVp/bGHerWVnJ6Smb1bisQ2twOkKw03RbfxVE2cp0fdALZzLKlWg+RjW4Nkt0O1WAAi8YZgcJKO3DTEPhHRpkaRq/kdIutaiX3EzhaaVZvSmbRd+VrY1CDx9qEsYb11KtZCRnIbAMCj/ZJ4n4Xc4FCsiVVYfnoLOClA0hk9ddLmsnbSFsnYn3/VE3+ZkI6lD3WXnF4ruK7YFcQZXVRT94voNq24eGw4X7hVGvHZncXTFp97SGqA5HrNBPeYmcgIfWdBTrP/Hd8TGclt8OakPo73p/IoNjHK+yCpkx/hIqhqLCqstxu6FlZP64+NMzNvjUhU3nwsVoMk/Gz1VjFATWw6o9U8SEqI9kESyVhYoC+yZdQeEeD+HvHY+c053N8zXvR1TZtYNUtZnjcn9UHu+//B/47vyXteePN39ktfalHZW55DbE9i9z3Jo9h46Sv7HGNCzOjXPgJGIxAaYHuZdtaPqFliRCA+/F0m/70iZ4C9hW3VpLRvlsHBX3L8bnBH3Gy04L47m2q81aiZ1HuApEaNcICfCb2SmmqRXJnCghuEN7+1xUwUSdyj+XTW3YnioAbJlfRaq+WP9kLDTYt4x2HoI0DW2oiucfhm0Uin64TxO1Krmwd7Q8jFgimpHUjV6KRtMBjwwQx5oxSl7kp8qRHtm9h4LY8ydqFWDWKAnwl/HJmmarrc/oRidNBirSrhKgCy3itS+yRMQm8BJzWx6UzzbK96GA02+I6m4cJ+JqPDGiQlFtx/J6KCzXh2dJrzjVsgg8FgNzjyJHdfz8WCI+FNRY3OtPYYRH7VAuqNYnPl6yJcPZ1LrBZIav7Elxq5/dgdTWyyAkeNLoVqJNujXTj+/HAPrH38LhVS0z81BiBw36v3UWw6yw7RUx+kEV3j8M/H+mHv3KGcxWrVSbtDdDAOzB+GJwZ1VCfBFkYHH79uCJtYlv2qh2o3cX5QZBB9vpmSTtrurAl2pUyMLtQMKNmHrFFsvL5h6lHaJ0pofEYiBnTUYLoLHRLrdqEsoab/hC0Sems5oQBJZ/Q0is1gMGDQHdGICfXnjGJT75TRQy2ZXunh8/cUYe3Iz5xZoE1GA9ITw/H1n0ZYn5NSUmKTRAKC2h7O86I1SIoCJElvkU28k7by9Lg3Ox+Rzuxqk/PdV2NCR1fzIFdsaNOEuUNvrQvYUrjSxMbV/Fa9N7FRHySdccdEkUraxdWuQSJ8BgP/c9EyPtLXJcg57jIZybcm2fSRGaj7+Yhvb68WQbQPkoImNncGulJv+OJ9kG4/1moeJKWjxrQqQWejFl2x+5mhuHj1BuLCpK1NqRW1m8y5pST33BarWRV+p/T2w5BudzrjjokilTCZxNuMiTqEpdqaS1l4A589rDM6RAfhszmDrLU4csvHXoDEr0Fy3MQm9dx3Fmh5mrP+S1pVICntv6LVNUetvmJi/H1NHg+OtKBWf0C7w/ypBok4oqcmNq4OtxaC7BDteEFIooxBUIWkxxurpzx93x14+r47eM/JLR57TWz2iE4JoGgUm6zdSubS6CixKQK4TWxa1SBxHivtg6Qm+obJp1YfpOZ0hGnobeJNCpB05nYNknb7EPsF6czTw+/AhL6JaNfG+aKQRD7hx/1geoJH8qEHUs5OuQGkr4/49vZqEUQ7aUtuYhNPX01KvsOOqNb51uE+xGvrZKWhVmagvx+h3sCV5mOxJlbhqeYvshyQJ1GApDN3dYiAyQh0bxvm6azwGI0GCo40xL3WrJjYG8O6xHomI144b4uUYMleDZK9t4o9Lz1w4NxE9HW9t8s9o9huP/bEPEhCFB/Jxy0yNUZNCr+7epv6hAIknRmb3hZj02lW6tam6Rd1U3Qyqrv47NqthRaT6/mJrMMG2L/5iv06lto/gh8IaFSD5EIZic+DpF1/HLGEZc2krVUTm877iqlB7e+SWqPYmt8pTMNsp6+gp+grN4S0Vi3z+qwbdkex8R47/hD0NIrNpS5IIndNkxvyzCtrpTVIKn5RWvOC2Uq5MseX2Ag4YYylt0CVAqRWqKVNf98S6Ouy4Gnqn6B+doZm2R/mb7utklFsOhuUA0C8dHkzims2ik1hDZIWmYGgpk+jfbQ0rjSxiX2/uOeB3mqPAAqQCNEFvfxwaqmxs/15kG4XfGzo7WHZYrULykaxadXEpu4n5Y48K525WrsmNp186TSkdmd+bmouTaRtrUG6nYi976gnUR8kQnTAndX9erwQccm990spOUfD/D+ZfQ+u3WhERJDf7TRd6qTNyZuX3IPdUZsi1sQi6X0a9Y9S2mm8NbNwvpxqzFnETYJqkAghotzRFLP0oe5IigjE4uxu2u/MBf1SImRtLyWechQUdokPRe+kNrznxG7gkvsgcT5MrQLfrglhiFc4EaH4MiXcIERfNUhaaQ01SKrjnDty5ywS+y5wf3SY7Qyk8CSqQSJEB9xxsf5N/yT8pn+S5vtx1WN3pyDE3xeZKi4A6it7okjb55SMYtMq8PXzMeLzPw7FMx8ewcayH2S919koNq3ybFChGa9dmwC1ssNDHbalsXADJBVOFAM1sRFCnNHL5Vntvi1K+JqMqgdyci++Yp+H1F/M3JutlpMR+sgM+jpEB+HEf+vxYE/bSUjd0dzEb2KT994v5t2LX240IjzQz/nGRDPcPk2urMUmRo9NbBQgtUKevwUSG3qJkLyQlKKTe/F1pQ8SrwZJ42u+nID2X3l349TP9bgzPtTmNZMbmgX5taTy9pEQrk3NUbOW2tqm9u8dV2qQnG1NNUiEEFF6uT631H4ZctdiE/tEpBaNwQ39eZrJuf8FmX3QNUF8hn5e85dG9yl3ND0SbXEDcrU/Q/nfUe3pL0eEtEJ6CUz00MSmBbl9kMQu/lJ/MbtzHiQtPi53dNLW2zpo+sqNfnFPN7XPE7PO1mEDKEAiRBd0dr9oMSJvDd0f0S1O1vvELv5KZtLWuvOvFuGsdsP8tRmuT9xHyx9QeqxBoia2Vqil1hJ4M7pfaGPHH4bg7IWrdpuW7BH7PJSMYvNG7pgoUnc1SPrKjmrUvtK7cutw3kmbhvkTQkTopYnNKzkourAAX4QFyAuOADudtBWMYtOaFj923DFzNZ3u3sniwulmctK5jTppE0JE6eV+QXWLTcSXGpEYILlhXbNmWlQGu2exWr2c8c30lh99ciUgH5IajV5J4ejeVvwHCwVIhBBRurtftHKuDfP37g9Tq9y7YzJKoi1X4nFfkxEbZw60+7oe50HSX46I5qiWQH/094u6dXPl43DnJ6n2YqRAKx3Fpq/s6FbnmGDN0qYaJEKIKPpFrS9iQYKvSckoNm1p08SmfpqAsIlNm30oFR7g6+ksaELt86NDdDDWPN4fMSFmdRMGddImhNihl7WgvHGAoxYlJ0xz7eN3Sb6Au/Pmr808SOqn2ZSu+6Y/kOrPD/fAt+euyF4guTXL7BilSbpUg0QIEaW3X9StHffzyEhugwEyFs51a4CkRRObZkuN3H6slxrT8RmJns4CuYX6IBF98MJagpZOJ/cLcgu/Q7G8T4fXxKb1UiNaNLFpdFfgLVarlwipxfOei33HaO36NylFNUiE6AB10tYXV/rLePsnqdW56M6+WcR7rHm8P46ercOIrrGezooNCpAIIV5Nkxu6CyOu3DlCy6uWGjFwH1OIRJpkdozSrF+Tq6iJjRAdoPuFvnD74chtcnLnZ/lAzwQAQHJkoGppumepEU12QQRGdG1ag7BDVJCHc+KdqAaJEB2gAElfeLUdMutU3Fk78kCPeLRrE4BOKs5P456lRuiEd4fnxtyJnu3CMTQtxtNZ8UoUILVC3tNtr/XQy7BnLUZFeSN+R2sPZsQJg8GA3kltVE3THUuNUA2SewT4mTC+L43UU4qa2AjRAT3fhFsj+jjUZ3BhZCAhnkABUiukxQrgxDV0u1BOkz7arfgDcc9itZrsghBVUYBEiA7o5Rc1xc5NuE2ervSX0cnHKotWzV/8Yf5eWDCk1aEAiRA9oPuFvhhEH7YKWk3iyBvFRnce4gU8epoWFhaib9++CAkJQUxMDLKzs1FZWenwPcXFxcjIyEB4eDiCgoKQnp6O1atX87apra1FTk4OEhISEBgYiJEjR6Kqqsr6+qlTp2AwGET/bdiwQZNjJcSR1nYT1jtujOCNtUBKPDm4I+JC/fH4PR00SZ/fSbuVFCrxah4NkHbv3o3c3Fzs27cP27dvx40bN5CVlYX6+nq774mIiMD8+fNRWlqKI0eOYOrUqZg6dSo+/fRTAE39a7Kzs3HixAls3rwZZWVlSE5OxvDhw63pJiYmorq6mvdv4cKFCA4OxqhRo9xy7IRw0bBnfeEvrOpCOl4U+s4blYbSgnsRrcFK7YB6ZUqIu3h0mP+2bdt4f7/77ruIiYnBoUOHMGjQINH3DBkyhPf37NmzsWrVKuzduxcjRoxAVVUV9u3bh4qKCnTt2hUAsGLFCsTFxWHt2rWYPn06TCYT4uLieOls3LgR48ePR3Cw/taDIS2fXm4Y3tgHSYuy43co1sunoz0tj5Vm0ibeRlctwXV1dQCaaomkYIyhpKQElZWV1oCqoaEBAODv72/dzmg0wmw2Y+/evaLpHDp0COXl5Zg2bZrdfTU0NODSpUu8f97KC++BLV7bNgGezgLhMLTiPkhaoXmQiLfRTYBksViQn5+PgQMHolu3bg63raurQ3BwMPz8/DBmzBgsX74c9913HwAgLS0NSUlJKCgowIULF3D9+nW89NJLOHv2LKqrq0XTe/vtt9GlSxdkZmba3WdhYSHCwsKs/xITafItop5l43rgvjtj8f70/h7NB/2wb8IfxeZCOlSeVtzO31SDRLyBbgKk3NxcVFRUYN26dU63DQkJQXl5OQ4cOIAlS5Zgzpw52LVrFwDA19cXxcXFOH78OCIiIhAYGIidO3di1KhRMIoMnbh27RrWrFnjsPYIAAoKClBXV2f9d+bMGUXHSYiYuDB/vDU5AwM76XPRxtaGf/+mm7kaqAaJeBtdLDWSl5eHLVu2YM+ePWjXrp3T7Y1GIzp16gQASE9Px7Fjx1BYWGjtn9SnTx+Ul5ejrq4O169fR3R0NPr374+MjAybtD788ENcvXoVkydPdrhPs9kMs1mbzouE6IU39kHSAlVwqI+/WC0VMNE/j9YgMcaQl5eHjRs3YseOHUhJSVGUjsVisfY94goLC0N0dDSqqqpw8OBBjB071mabt99+Gw8++CCio6MV7dsb0U2QtCRaNNeo1sSmQl5aCv5itR7MCCESebQGKTc3F2vWrMHmzZsREhKCmpoaAE2BTUBAU6fVyZMno23btigsLATQ1BcoIyMDHTt2RENDA7Zu3YrVq1djxYoV1nQ3bNiA6OhoJCUl4ejRo5g9ezays7ORlZXF2/+3336LPXv2YOvWrW46YkKIN6BO2uprrSMDiffyaIDUHNQIh+4XFRUhJycHAHD69Gle36H6+nrMnDkTZ8+eRUBAANLS0vDee+9hwoQJ1m2qq6sxZ84c1NbWIj4+HpMnT8aCBQts9v/OO++gXbt2NoETIaR1o3XD1MdfrNaDGSFEIo8GSFIWTW3ufN1s8eLFWLx4scP3zJo1C7NmzXKa9tKlS7F06VKn27UUEUF+OF9/Hfd0po7AhDhC64apz0h9kIiX0UUnbeIeW566G9u/rsUjGc47whPiLTSZKJLu36rj9evyYD4IkUo3w/yJ9hLCAzAlsz0C/SguJny9k8IBAA/3oeAZEDQHuXCVpEDrNppJm3gbulMSQvDPaf3xn+8vILNjpKezojvUxKY+6oNEvAEFSIQQBJt9MOiO1jPVhSx0M1cd1SARb0BNbIQQ4oArt3IKBMRRDRLxBhQgEUK8GsUg3ocCR+INKEAihBAH6GauDu6sLlSDRLwBBUiEEOKAS01squWiZaGgk3gDCpAIIcQBuperg+F2FRLVIBFvQAESIYQ4QPdy9dFM2sQbUIBECPFyOr7Z6jhrhBDHKEAihBAHXOkvc1cHmnizGb+TNkWORP9ookhCCHFAya28tOBeVNVeoYWh7aA+SMQbUIBECCGOKLiZx4cFID4sQP28tBA0io14A2piI4R4Na3vtbQWmzo4LWxUg0S8AgVIhBDiAFV2qI9qkIg3oACJEEIcoFu5Ohi3lzYhXoACJEIIcYAqOwhpnShAIoQQB6gPkjpS40IAAGEBvh7OCSHS0Cg2QohX0zp8oRokdQT6+eCrhSPgY6ICJd6BAiRCCCFuEWSmWw7xHtTERgghDlANEiGtEwVIhBDiEEVIhLRGFCARQogDVINESOtEARIhxKtpP5M2IaQ1ogCJEEIIIUSAAiRCCHHASG1shLRKFCARQogDFB8R0jpRgEQI8Wpaz3RN8REhrRMFSIQQ4gCtPE9I60QBEiGEEEKIAAVIhBBCCCECFCARQogD1MJGSOtEARIhxKtpP1EkRUiEtEYUIBFCiANUg0RI60QBEiGEEEKIAAVIhBDiAFUgEdI6UYBECCEOUBMbIa0TBUiEEK+mdfxCa7ER0jpRgEQIIQ6YjBQgEdIaUYBECPFqTKN0Zw3rjOTIQEy/p4NGeyCE6JmPpzNACCF6NOe+OzDnvjs8nQ1CiIdQDRIhxKtRAxghRAsUIBFCCCGECFCARAghhBAiQAESIYQQQogABUiEEK/m72vydBYIIS0QBUiEEK/0t9/0QnJkIJb/ppens0IIaYFomD8hxCvd3yMB9/dI8HQ2CCEtFNUgEUIIIYQIUIBECCGEECLg0QCpsLAQffv2RUhICGJiYpCdnY3KykqH7ykuLkZGRgbCw8MRFBSE9PR0rF69mrdNbW0tcnJykJCQgMDAQIwcORJVVVU2aZWWluLee+9FUFAQQkNDMWjQIFy7dk3VYySEEEKI9/FogLR7927k5uZi37592L59O27cuIGsrCzU19fbfU9ERATmz5+P0tJSHDlyBFOnTsXUqVPx6aefAgAYY8jOzsaJEyewefNmlJWVITk5GcOHD+elW1paipEjRyIrKwtffvklDhw4gLy8PBiNVKlGCCGEtHYGxphWaz3K9t///hcxMTHYvXs3Bg0aJPl9vXv3xpgxY7Bo0SIcP34cqampqKioQNeuXQEAFosFcXFxWLp0KaZPnw4AuOuuu3Dfffdh0aJFivJ66dIlhIWFoa6uDqGhoYrSIIQQQoh7Sb1/66q6pK6uDkBTLZEUjDGUlJSgsrLSGlA1NDQAAPz9/a3bGY1GmM1m7N27FwBw7tw57N+/HzExMcjMzERsbCwGDx5sfV1MQ0MDLl26xPtHCCGEkJZJNwGSxWJBfn4+Bg4ciG7dujnctq6uDsHBwfDz88OYMWOwfPly3HfffQCAtLQ0JCUloaCgABcuXMD169fx0ksv4ezZs6iurgYAnDhxAgDw4osv4vHHH8e2bdvQu3dvDBs2TLSvEtDUXyosLMz6LzExUcWjJ4QQQoie6CZAys3NRUVFBdatW+d025CQEJSXl+PAgQNYsmQJ5syZg127dgEAfH19UVxcjOPHjyMiIgKBgYHYuXMnRo0aZe1fZLFYAAAzZszA1KlT0atXL7z66qtITU3FO++8I7rPgoIC1NXVWf+dOXNGnQMnhBBCiO7oYqLIvLw8bNmyBXv27EG7du2cbm80GtGpUycAQHp6Oo4dO4bCwkIMGTIEANCnTx+Ul5ejrq4O169fR3R0NPr374+MjAwAQHx8PADgzjvv5KXbpUsXnD59WnSfZrMZZrNZ6SESQgghxIt4tAaJMYa8vDxs3LgRO3bsQEpKiqJ0LBaLte8RV1hYGKKjo1FVVYWDBw9i7NixAID27dsjISHBZkqB48ePIzk5WVEeCCGEENJyeLQGKTc3F2vWrMHmzZsREhKCmpoaAE2BTUBAAABg8uTJaNu2LQoLCwE09QXKyMhAx44d0dDQgK1bt2L16tVYsWKFNd0NGzYgOjoaSUlJOHr0KGbPno3s7GxkZWUBAAwGA5555hm88MIL6NmzJ9LT07Fq1Sp88803+PDDD91cCoQQQgjRG48GSM1BTXPTWLOioiLk5OQAAE6fPs2bm6i+vh4zZ87E2bNnERAQgLS0NLz33nuYMGGCdZvq6mrMmTMHtbW1iI+Px+TJk7FgwQLePvLz8/HLL7/g6aefxvnz59GzZ09s374dHTt21OZgCSGEEOI1dDUPkjeheZAIIYQQ7+OV8yARQgghhOiBLkaxeaPmijeaMJIQQgjxHs33bWcNaBQgKXT58mUAoAkjCSGEEC90+fJlhIWF2X2d+iApZLFY8OOPPyIkJAQGg0G1dC9duoTExEScOXOG+jZphMpYW1S+2qLy1RaVr/Y8XcaMMVy+fBkJCQkOF6inGiSFjEajpEktlQoNDaUvp8aojLVF5astKl9tUflqz5Nl7KjmqBl10iaEEEIIEaAAiRBCCCFEgAIknTGbzXjhhRdo3TcNURlri8pXW1S+2qLy1Z63lDF10iaEEEIIEaAaJEIIIYQQAQqQCCGEEEIEKEAihBBCCBGgAIkQQgghRIACJJ15/fXX0b59e/j7+6N///748ssvPZ0lr1BYWIi+ffsiJCQEMTExyM7ORmVlJW+bX375Bbm5uYiMjERwcDB+9atfoba2lrfN6dOnMWbMGAQGBiImJgbPPPMMbt686c5D0b1ly5bBYDAgPz/f+hyVret++OEH/Pa3v0VkZCQCAgLQvXt3HDx40Po6YwzPP/884uPjERAQgOHDh6OqqoqXxvnz5zFx4kSEhoYiPDwc06ZNw5UrV9x9KLrT2NiIBQsWICUlBQEBAejYsSMWLVrEW4uLyleePXv24IEHHkBCQgIMBgM2bdrEe12t8jxy5Ajuuece+Pv7IzExEX/+85+1PjTeQRCdWLduHfPz82PvvPMO++qrr9jjjz/OwsPDWW1traezpnsjRoxgRUVFrKKigpWXl7PRo0ezpKQkduXKFes2Tz75JEtMTGQlJSXs4MGD7K677mKZmZnW12/evMm6devGhg8fzsrKytjWrVtZVFQUKygo8MQh6dKXX37J2rdvz3r06MFmz55tfZ7K1jXnz59nycnJLCcnh+3fv5+dOHGCffrpp+zbb7+1brNs2TIWFhbGNm3axA4fPswefPBBlpKSwq5du2bdZuTIkaxnz55s37597PPPP2edOnVijz76qCcOSVeWLFnCIiMj2ZYtW9jJkyfZhg0bWHBwMPvrX/9q3YbKV56tW7ey+fPns+LiYgaAbdy4kfe6GuVZV1fHYmNj2cSJE1lFRQVbu3YtCwgIYG+++aZbjpECJB3p168fy83Ntf7d2NjIEhISWGFhoQdz5Z3OnTvHALDdu3czxhi7ePEi8/X1ZRs2bLBuc+zYMQaAlZaWMsaavvBGo5HV1NRYt1mxYgULDQ1lDQ0N7j0AHbp8+TLr3Lkz2759Oxs8eLA1QKKydd3cuXPZ3Xffbfd1i8XC4uLi2Msvv2x97uLFi8xsNrO1a9cyxhj7+uuvGQB24MAB6zaffPIJMxgM7IcfftAu815gzJgx7LHHHuM9N27cODZx4kTGGJWvq4QBklrl+cYbb7A2bdrwrhFz585lqampGh9RE2pi04nr16/j0KFDGD58uPU5o9GI4cOHo7S01IM58051dXUAgIiICADAoUOHcOPGDV75pqWlISkpyVq+paWl6N69O2JjY63bjBgxApcuXcJXX33lxtzrU25uLsaMGcMrQ4DKVg0ff/wxMjIy8MgjjyAmJga9evXCW2+9ZX395MmTqKmp4ZVxWFgY+vfvzyvj8PBwZGRkWLcZPnw4jEYj9u/f776D0aHMzEyUlJTg+PHjAIDDhw9j7969GDVqFAAqX7WpVZ6lpaUYNGgQ/Pz8rNuMGDEClZWVuHDhgubHQYvV6sRPP/2ExsZG3g0EAGJjY/HNN994KFfeyWKxID8/HwMHDkS3bt0AADU1NfDz80N4eDhv29jYWNTU1Fi3ESv/5tdas3Xr1uE///kPDhw4YPMala3rTpw4gRUrVmDOnDl49tlnceDAAcyaNQt+fn6YMmWKtYzEypBbxjExMbzXfXx8EBER0erLeN68ebh06RLS0tJgMpnQ2NiIJUuWYOLEiQBA5asytcqzpqYGKSkpNmk0v9amTRtN8m/Nj6apE+IBubm5qKiowN69ez2dlRbhzJkzmD17NrZv3w5/f39PZ6dFslgsyMjIwNKlSwEAvXr1QkVFBVauXIkpU6Z4OHfeb/369Xj//fexZs0adO3aFeXl5cjPz0dCQgKVL7GLmth0IioqCiaTyWbkT21tLeLi4jyUK++Tl5eHLVu2YOfOnWjXrp31+bi4OFy/fh0XL17kbc8t37i4ONHyb36ttTp06BDOnTuH3r17w8fHBz4+Pti9ezdee+01+Pj4IDY2lsrWRfHx8bjzzjt5z3Xp0gWnT58GcLuMHF0f4uLicO7cOd7rN2/exPnz51t9GT/zzDOYN28efv3rX6N79+6YNGkSnn76aRQWFgKg8lWbWuXp6esGBUg64efnhz59+qCkpMT6nMViQUlJCQYMGODBnHkHxhjy8vKwceNG7Nixw6Zatk+fPvD19eWVb2VlJU6fPm0t3wEDBuDo0aO8L+327dsRGhpqc/NqTYYNG4ajR4+ivLzc+i8jIwMTJ060Pqaydc3AgQNtpqU4fvw4kpOTAQApKSmIi4vjlfGlS5ewf/9+XhlfvHgRhw4dsm6zY8cOWCwW9O/f3w1HoV9Xr16F0ci/3ZlMJlgsFgBUvmpTqzwHDBiAPXv24MaNG9Zttm/fjtTUVM2b1wDQMH89WbduHTObzezdd99lX3/9NXviiSdYeHg4b+QPEfe73/2OhYWFsV27drHq6mrrv6tXr1q3efLJJ1lSUhLbsWMHO3jwIBswYAAbMGCA9fXmoehZWVmsvLycbdu2jUVHR9NQdBHcUWyMUdm66ssvv2Q+Pj5syZIlrKqqir3//vssMDCQvffee9Ztli1bxsLDw9nmzZvZkSNH2NixY0WHTffq1Yvt37+f7d27l3Xu3LnVDkPnmjJlCmvbtq11mH9xcTGLiopif/zjH63bUPnKc/nyZVZWVsbKysoYAPbKK6+wsrIy9v333zPG1CnPixcvstjYWDZp0iRWUVHB1q1bxwIDA2mYf2u1fPlylpSUxPz8/Fi/fv3Yvn37PJ0lrwBA9F9RUZF1m2vXrrGZM2eyNm3asMDAQPbQQw+x6upqXjqnTp1io0aNYgEBASwqKor9/ve/Zzdu3HDz0eifMECisnXdv/71L9atWzdmNptZWloa+/vf/8573WKxsAULFrDY2FhmNpvZsGHDWGVlJW+bn3/+mT366KMsODiYhYaGsqlTp7LLly+78zB06dKlS2z27NksKSmJ+fv7sw4dOrD58+fzho9T+cqzc+dO0WvulClTGGPqlefhw4fZ3XffzcxmM2vbti1btmyZuw6RGRjjTCVKCCGEEEKoDxIhhBBCiBAFSIQQQgghAhQgEUIIIYQIUIBECCGEECJAARIhhBBCiAAFSIQQQgghAhQgEUIIIYQIUIBECCE6curUKRgMBhgMBqSnp6ue/osvvmhN/y9/+Yvq6RPSUlCARAiRLScnx3qT5f4bOXKkp7OmWE5ODrKzsz2dDavPPvuMt5aVI7W1tfD19cW6detEX582bRp69+4NAPjDH/6A6upq3mLOhBBbFCARQhQZOXIkqquref/Wrl2r6T6vX7+uafp6EhkZicjISEnbxsbGYsyYMXjnnXdsXquvr8f69esxbdo0AEBwcDDi4uJgMplUzS8hLQ0FSIQQRcxmM+Li4nj/uCtsGwwG/OMf/8BDDz2EwMBAdO7cGR9//DEvjYqKCowaNQrBwcGIjY3FpEmT8NNPP1lfHzJkCPLy8pCfn4+oqCiMGDECAPDxxx+jc+fO8Pf3x9ChQ7Fq1SoYDAZcvHgR9fX1CA0NxYcffsjb16ZNmxAUFITLly8rOt7du3ejX79+MJvNiI+Px7x583Dz5k3r6x9++CG6d++OgIAAREZGYvjw4aivrwcA7Nq1C/369UNQUBDCw8MxcOBAfP/997Lz8I9//ANdunSBv78/0tLS8MYbb1hfmzZtGkpKSnD69GneezZs2ICbN29i4sSJio6bkNaKAiRCiGYWLlyI8ePH48iRIxg9ejQmTpyI8+fPAwAuXryIe++9F7169cLBgwexbds21NbWYvz48bw0Vq1aBT8/P/z73//GypUrcfLkSTz88MPIzs7G4cOHMWPGDMyfP9+6fVBQEH7961+jqKiIl05RUREefvhhhISEyD6OH374AaNHj0bfvn1x+PBhrFixAm+//TYWL14MAKiursajjz6Kxx57DMeOHcOuXbswbtw4MMZw8+ZNZGdnY/DgwThy5AhKS0vxxBNPwGAwyMrD+++/j+effx5LlizBsWPHsHTpUixYsACrVq0CAIwePRqxsbF4q2+hfQAABjtJREFU9913bY573LhxCA8Pl33chLRqblsWlxDSYkyZMoWZTCYWFBTE+7dkyRLrNgDYc889Z/37ypUrDAD75JNPGGOMLVq0iGVlZfHSPXPmDANgXfV78ODBrFevXrxt5s6dy7p168Z7bv78+QwAu3DhAmOMsf379zOTycR+/PFHxhhjtbW1zMfHh+3atcvhMY0dO1b0tWeffZalpqYyi8Vife71119nwcHBrLGxkR06dIgBYKdOnbJ5788//8wAONw318mTJxkAVlZWxnu+Y8eObM2aNbznFi1axAYMGGD9e968eSwlJcWaz2+//ZYZDAb22Wef2ewnOTmZvfrqq5LyREhrRDVIhBBFhg4divLyct6/J598krdNjx49rI+DgoIQGhqKc+fOAQAOHz6MnTt3Ijg42PovLS0NAPDdd99Z39enTx9empWVlejbty/vuX79+tn83bVrV2vtynvvvYfk5GQMGjRI0bEeO3YMAwYM4NX6DBw4EFeuXMHZs2fRs2dPDBs2DN27d8cjjzyCt956CxcuXAAAREREICcnByNGjMADDzyAv/71r6iurpa1//r6enz33XeYNm0ar7wWL17MK6vHHnsMJ0+exM6dOwE01R61b98e9957r6LjJqQ1owCJEKJIUFAQOnXqxPsXERHB28bX15f3t8FggMViAQBcuXIFDzzwgE2QVVVVxQtkgoKCFOVv+vTp1uamoqIiTJ06VXazllQmkwnbt2/HJ598gjvvvBPLly9HamoqTp48ad1/aWkpMjMz8cEHH+COO+7Avn37JKd/5coVAMBbb73FK6uKigpeOp07d8Y999yDoqIiWCwW/POf/9T0uAlpyShAIoR4RO/evfHVV1+hffv2NoGWo6AoNTUVBw8e5D134MABm+1++9vf4vvvv8drr72Gr7/+GlOmTFGc1y5duqC0tBSMMetz//73vxESEmIdLm8wGDBw4EAsXLgQZWVl8PPzw8aNG63b9+rVCwUFBfjiiy/QrVs3rFmzRvL+Y2NjkZCQgBMnTtiUVUpKCm/badOm4aOPPsJHH32EH374ATk5OYqPm5DWjAIkQogiDQ0NqKmp4f3jjkBzJjc3F+fPn8ejjz6KAwcO4LvvvsOnn36KqVOnorGx0e77ZsyYgW+++QZz587F8ePHsX79emtNEbempE2bNhg3bhyeeeYZZGVlSZr3p66uzqZG68yZM5g5cybOnDmDp556Ct988w02b96MF154AXPmzIHRaMT+/fuxdOlSHDx4EKdPn0ZxcTH++9//okuXLjh58iQKCgpQWlqK77//Hv/3f/+HqqoqdOnSRXJZAU0d3gsLC/Haa6/h+PHjOHr0KIqKivDKK6/wtnvkkUfg6+uLGTNmICsrC4mJibL2Qwi5xdOdoAgh3mfKlCkMgM2/1NRU6zYA2MaNG3nvCwsLY0VFRda/jx8/zh566CEWHh7OAgICWFpaGsvPz7d2Mh48eDCbPXu2zf43b97MOnXqxMxmMxsyZAhbsWIFA8CuXbvG266kpIQBYOvXr1d8TNOmTWOMMbZr1y7Wt29f5ufnx+Li4tjcuXPZjRs3GGOMff3112zEiBEsOjqamc1mdscdd7Dly5czxhirqalh2dnZLD4+nvn5+bHk5GT2/PPPs8bGRtF82OukzRhj77//PktPT2d+fn6sTZs2bNCgQay4uNhmuyeeeMLpcVMnbUIcMzDGqTMmhBAvtGTJEqxcuRJnzpzhPb969Wo8/fTT+PHHH+Hn5+eh3Mlz6tQppKSkoKysTJOlRpq1b98e+fn5yM/P12wfhHgzamIjhHidN954AwcOHMCJEyewevVqvPzyy7w+RlevXsV3332HZcuWYcaMGV4THHFlZmYiMzNT9XSXLl2K4OBgmwklCSF8VINECPE6Tz/9ND744AOcP38eSUlJmDRpEgoKCuDj4wOgaUHWJUuWYNCgQdi8eTOCg4M9nGPpbt68iVOnTgFomq1c7T5E58+ft07WGR0djbCwMFXTJ6SloACJEEIIIUSAmtgIIYQQQgQoQCKEEEIIEaAAiRBCCCFEgAIkQgghhBABCpAIIYQQQgQoQCKEEEIIEaAAiRBCCCFEgAIkQgghhBABCpAIIYQQQgT+P8k9I/v0FtdrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get ClumpId from graph_content Children via get_data_set\n",
    "dataset = worksheet.get_data_set(graph_content['Children'][0]['Id'])\n",
    "clump_id = dataset['Clump']['Id']\n",
    "\n",
    "# Get array dimensions and element type\n",
    "array_info = worksheet.get_clump_num_array(clump_id)\n",
    "print('Dimensions:', array_info['Dimensions'])\n",
    "print('ElementType:', array_info['ElementType'])\n",
    "\n",
    "# Get calibration coefficients for energy axis\n",
    "calib = dataset['Clump']['Information']['MeasurementInformation']['CalibrationCoefficients'][0]\n",
    "offset = calib['Offset']\n",
    "scale = calib['Scale']\n",
    "unit_map = {'ElectronVolt': 'eV', 'Millielectronvolt': 'meV',\n",
    "            'Nanometer': 'nm', 'Angstrom': 'Å', 'Meter': 'm'}\n",
    "unit = unit_map.get(calib['Unit'], calib['Unit'])\n",
    "print(f\"Calibration: offset={offset}, scale={scale}, unit={calib['Unit']}\")\n",
    "\n",
    "# Get raw byte array and infer dtype from byte count\n",
    "raw_bytes = worksheet.get_clump_num_array_accessor(clump_id)\n",
    "total_elements = 1\n",
    "for d in array_info['Dimensions']:\n",
    "    total_elements *= d\n",
    "bytes_per_element = len(raw_bytes) // total_elements\n",
    "dtype_map = {1: np.uint8, 2: np.uint16, 4: np.float32, 8: np.float64}\n",
    "dtype = dtype_map.get(bytes_per_element, np.float32)\n",
    "narray = np.frombuffer(raw_bytes, dtype=dtype).reshape(array_info['Dimensions'])\n",
    "print('dtype:', dtype, '  shape:', narray.shape)\n",
    "\n",
    "# Build energy axis and plot spectrum\n",
    "spectrum = narray.ravel()\n",
    "energy_axis = offset + scale * np.arange(len(spectrum))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(energy_axis, spectrum)\n",
    "plt.xlabel(f'Energy Loss [{unit}]')\n",
    "plt.ylabel('Intensity [Counts]')\n",
    "plt.title(element['Title'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b28ad76",
   "metadata": {},
   "source": [
    "#### `eels.set_calibration_result`\n",
    "\n",
    "Apply energy-axis calibration to the spectrum dataset (graph content).  \n",
    "Specify the dispersion and the first/second energy loss values in eV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a1bcda33",
   "metadata": {},
   "outputs": [],
   "source": [
    "calibration_param = {\n",
    "    'Dispersion': 2,\n",
    "    'FirstEnergyLoss': -512,\n",
    "    'SecondEnergyLoss': 510\n",
    "}\n",
    "eels.set_calibration_result(graph_content['Children'][0]['Id'], calibration_param)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd67f06b",
   "metadata": {},
   "source": [
    "## Line\n",
    "\n",
    "Acquire EELS line-scan data using `femtus.acquisition` with `Shape = 'Line'`, then analyze using the `eels` package.\n",
    "\n",
    "API reference: <a href=\"../../interface/PyJEM.femtus.acquisition.html\">femtus.acquisition</a> | <a href=\"../../interface/PyJEM.eels.html\">eels</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce68a1ce",
   "metadata": {},
   "source": [
    "### Acquisition\n",
    "\n",
    "Configure the acquisition settings and start EELS line-scan acquisition.\n",
    "\n",
    "- Set `setting['Detectors'] = ['Image', 'EELS']` to enable both image and EELS detectors.\n",
    "- Set `setting['Region']['Shape'] = 'Line'` for line-scan acquisition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1729394b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Id': 'dd37ad22-765a-4df9-94dc-7c27bba80cd5',\n",
       " 'Detectors': ['Image', 'EELS'],\n",
       " 'Region': {'Left': 320,\n",
       "  'Top': 257,\n",
       "  'Width': 32,\n",
       "  'Height': 1,\n",
       "  'Points': None,\n",
       "  'Shape': 'Line'},\n",
       " 'Conditions': {'DwellTime': 100,\n",
       "  'PixelResolution': 1,\n",
       "  'IsPlayback': False,\n",
       "  'CollectionMode': {'Mode': 'Sweep', 'Count': 1}},\n",
       " 'DriftCorrection': {'Type': 'None', 'Interval': 1, 'CorrectionRegion': None}}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = acquisition.get_settings()\n",
    "if result[\"Settings\"]:\n",
    "    setting = result['Settings'][0]\n",
    "else:\n",
    "    setting = {\n",
    "        'Detectors': [],\n",
    "        'Region': {'Left': 0, 'Top': 0, 'Width': 32, 'Height': 1, 'Points': None, 'Shape': 'Line'},\n",
    "        'Conditions': {'DwellTime': 100, 'PixelResolution': 1, 'IsPlayback': False,\n",
    "                       'CollectionMode': {'Mode': 'Sweep', 'Count': 1}},\n",
    "        'DriftCorrection': {'Type': 'None', 'Interval': 1, 'CorrectionRegion': None}\n",
    "    }\n",
    "\n",
    "# Select detectors\n",
    "setting['Detectors'] = ['Image', 'EELS']\n",
    "\n",
    "# Set acquisition region for line scan\n",
    "setting['Region']['Shape'] = 'Line'\n",
    "setting['Region']['Width'] = 32\n",
    "setting['Region']['Height'] = 1\n",
    "\n",
    "# Set acquisition conditions\n",
    "setting['Conditions']['DwellTime'] = 100              # microseconds\n",
    "setting['Conditions']['CollectionMode']['Mode'] = 'Sweep'\n",
    "setting['Conditions']['CollectionMode']['Count'] = 1\n",
    "\n",
    "setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "16a35647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AcquisitionID: 48c95a79-0d84-4846-a16b-3132969b78ba\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'AcquisitionID': '48c95a79-0d84-4846-a16b-3132969b78ba',\n",
       " 'WorksheetID': '6d33305e-8d86-4867-917f-7d959c3398bb',\n",
       " 'ReferenceDetector': 'VP_ADF_1',\n",
       " 'Version': '1.0.0'}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_result = acquisition.start(setting)\n",
    "acquisition_id = start_result['AcquisitionID']\n",
    "print('AcquisitionID:', acquisition_id)\n",
    "start_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "4114329b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STEM_Survey -> 4620d2a7-077c-4673-84c7-25f2ad6eedca\n",
      "STEM_HAADF_SeleArea -> e4a35256-447a-4047-97f8-822e493777a8\n",
      "EELS_Line -> d9247652-eb4b-44e1-96b2-2fbea49cd2d7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Results': [{'Id': '4620d2a7-077c-4673-84c7-25f2ad6eedca',\n",
       "   'DataType': 'STEM_Survey'},\n",
       "  {'Id': 'e4a35256-447a-4047-97f8-822e493777a8',\n",
       "   'DataType': 'STEM_HAADF_SeleArea'},\n",
       "  {'Id': 'd9247652-eb4b-44e1-96b2-2fbea49cd2d7', 'DataType': 'EELS_Line'}]}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquisition_result = acquisition.get_acquisition_result(acquisition_id)\n",
    "\n",
    "for r in acquisition_result['Results']:\n",
    "    print(r['DataType'], '->', r['Id'])\n",
    "    if r['DataType'] == 'EELS_Line':\n",
    "        eels_line_id = r['Id']\n",
    "\n",
    "acquisition_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c4c582",
   "metadata": {},
   "source": [
    "### Line Analysis\n",
    "\n",
    "The following cells analyze the acquired line-scan dataset using the `eels` package."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b07a65",
   "metadata": {},
   "source": [
    "#### `eels.create_line_whole_spectrum`\n",
    "\n",
    "Generate a whole-range integrated spectrum from the line-scan dataset.  \n",
    "The returned object contains an `Ids` list with IDs for the generated datasets (image content and graph content)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a6a34ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ids': ['80051f5e-2480-4612-ae21-b1f9e2c1957a',\n",
       "  'f54e6008-d36e-4f9a-a2ce-377fa9848b76']}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whole_spectrum = eels.create_line_whole_spectrum(eels_line_id)\n",
    "whole_spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ac742d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GraphContent -> 80051f5e-2480-4612-ae21-b1f9e2c1957a\n",
      "GraphContent -> f54e6008-d36e-4f9a-a2ce-377fa9848b76\n"
     ]
    }
   ],
   "source": [
    "for id in whole_spectrum['Ids']:\n",
    "    element = worksheet.get_worksheet_element(id)\n",
    "    print(element['ContentSummary']['ContentType'], '->', element['Id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84605600",
   "metadata": {},
   "source": [
    "#### ImageContent and GraphContent\n",
    "\n",
    "Retrieve the graph content (`Ids[0]`, `Ids[1]`) from the worksheet elements."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb28d8a5",
   "metadata": {},
   "source": [
    "#### `worksheet.get_worksheet_element`\n",
    "\n",
    "`create_line_whole_spectrum` returns a dict with an `Ids` list containing two entries:\n",
    "\n",
    "| Index | ContentType | Description |\n",
    "|---|---|---|\n",
    "| 0 | `GraphContent` | Retrieved via `worksheet.get_graph_content()` |\n",
    "| 1 | `GraphContent` | Retrieved via `worksheet.get_graph_content()` |\n",
    "\n",
    "Use `get_worksheet_element` to retrieve each dataset by its `Id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18bf5945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Id': '020cf41c-4e26-4f0c-9fde-e7f907cc9802',\n",
       " 'Children': [{'Id': 'd9247652-eb4b-44e1-96b2-2fbea49cd2d7',\n",
       "   'IsSelected': False,\n",
       "   'Clump': {'Id': '770ae6e5-119e-43e6-9430-54e26af2e1ca',\n",
       "    'Information': {'Header': {'Version': '1.0.0',\n",
       "      'ClumpId': '770ae6e5-119e-43e6-9430-54e26af2e1ca',\n",
       "      'DataType': 'EELS_Line',\n",
       "      'DataSubType': '',\n",
       "      'DetectorType': 1,\n",
       "      'ClumpType': 'Clump',\n",
       "      'Name': 'Line'},\n",
       "     'DataInformation': {'TypeInfo': 2,\n",
       "      'DataBytes': 4,\n",
       "      'Channel': 1,\n",
       "      'DimensionLength': 2,\n",
       "      'Dimensions': [33, 512],\n",
       "      'ChannelType': 'GrayScale',\n",
       "      'IsRangeFixed': False,\n",
       "      'MinimumPossibleIntensity': -1.7976931348623157e+308,\n",
       "      'MaximumPossibleIntensity': 1.7976931348623157e+308},\n",
       "     'MeasurementInformation': {'CalibrationCoefficients': [{'Scale': 44.2212586825585,\n",
       "        'Offset': 0,\n",
       "        'Unit': 'Nanometer'},\n",
       "       {'Scale': 200000, 'Offset': 0, 'Unit': 'Nanometer'}]},\n",
       "     'Tags': {'General': {'Instrument': 'JEM-ARM200F',\n",
       "       'Manufacturer': 'JEOL Ltd.',\n",
       "       'Comment': '',\n",
       "       'DateTime': '20260403141159941',\n",
       "       'ProductName': 'FEMTUS',\n",
       "       'ProductVersion': '2.0.0.0+8cc42b3fb348c2458c76e358a38bce3fb5cd1063',\n",
       "       'UpdateDateTime': '20260403141159941',\n",
       "       'UpdateProductName': 'FEMTUS',\n",
       "       'UpdateProductVersion': '2.0.0.0+8cc42b3fb348c2458c76e358a38bce3fb5cd1063',\n",
       "       'AnalysisId': 'caa769a6-f67d-48a8-b6f2-57df48977f67',\n",
       "       'AnalysisDateTime': '20260403141158914',\n",
       "       'AcquisitionId': '48c95a79-0d84-4846-a16b-3132969b78ba',\n",
       "       'AcquisitionDateTime': '20260403141158914'},\n",
       "      'EELS': {'FilterMode': 'STEM-EELS',\n",
       "       'FilterSubmode': 'align',\n",
       "       'DataType': 'Image',\n",
       "       'Dispersion': 0,\n",
       "       'CameraLengthCoefficient': 1,\n",
       "       'MagnificationValue': 1,\n",
       "       'EnergyLossOffset': 0,\n",
       "       'DriftTubeVoltage': 0,\n",
       "       'HighTensionOffset': 0,\n",
       "       'SectorOffset': 0,\n",
       "       'SlitMode': 'Out',\n",
       "       'SlitInserted': False,\n",
       "       'SlitWidth': 0,\n",
       "       'EntranceApertureHoleString': 'Out',\n",
       "       'EntranceApertureInserted': False},\n",
       "      'ScanGenerator': {},\n",
       "      'HT': {'GunType': 'Cfeg',\n",
       "       'AccelerationVoltage': 200000,\n",
       "       'EnergyShift': 0},\n",
       "      'EOS': {'OperationMode': 'Scanning',\n",
       "       'SpotSizeNumber': 0,\n",
       "       'ConvergenceAngleAlphaNumber': 0,\n",
       "       'ImageFormingMode': 'MAG',\n",
       "       'RockingAngle': 0,\n",
       "       'MagnificationValue': 20000,\n",
       "       'MagnificationString': 'x20k',\n",
       "       'CameraLength': 15,\n",
       "       'CameraLengthString': '1.5cm'},\n",
       "      'Stage': {'X': {'Position': 0},\n",
       "       'Y': {'Position': 0},\n",
       "       'Z': {'Position': 0},\n",
       "       'TX': {'Position': 0},\n",
       "       'TY': {'Position': 0},\n",
       "       'TZ': {'Position': 0},\n",
       "       'PX': {'Position': 0},\n",
       "       'PY': {'Position': 0},\n",
       "       'PZ': {'Position': 0}},\n",
       "      'Aperture': {'CL': {'ApertureHoleString': '0'},\n",
       "       'CL2': {'ApertureHoleString': '0'},\n",
       "       'OL': {'ApertureHoleString': '0'},\n",
       "       'SA': {'ApertureHoleString': '0'}},\n",
       "      'Detector': {'DetectorKind': 'Test Camera Name 0',\n",
       "       'Manufacturer': 'Test Vendor',\n",
       "       'ModelCode': 'Test Camera Name 0',\n",
       "       'ModelDisplayName': 'Test Camera Name 0',\n",
       "       'PixelsPerMeter': {'Horizontal': 200000, 'Vertical': 200000},\n",
       "       'ImagingArea': {'X': 256, 'Y': 128, 'Width': 512, 'Height': 512},\n",
       "       'BinningSize': {'X': 2, 'Y': 2},\n",
       "       'SoftwareCroppingArea': {'X': 0, 'Y': 0, 'Width': 512, 'Height': 512},\n",
       "       'SoftwareBinningSize': {'X': 1, 'Y': 1},\n",
       "       'ExposureTimeValue': 100,\n",
       "       'FrameIntegration': 1},\n",
       "      'ScanDataAcquisition': {'AcquiredFrames': 1}}},\n",
       "    'ViewInformation': {'Header': {'Version': '2.0.0',\n",
       "      'ClumpId': '770ae6e5-119e-43e6-9430-54e26af2e1ca'},\n",
       "     'Tags': {'GraphDisplay': {'IsLegendAreaVisible': True}}}}}]}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ids[0]: GraphContent — retrieve with get_image_content\n",
    "element = worksheet.get_worksheet_element(whole_spectrum['Ids'][0])\n",
    "graph_content1 = worksheet.get_graph_content(element['ContentSummary']['Id'])\n",
    "graph_content1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8d582dd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Id': '7ff78347-0ee0-4829-9c88-22a4b119b375',\n",
       " 'Children': [{'Id': '2ac3a4c6-9c76-412b-92f0-0180a2a40b28',\n",
       "   'IsSelected': False,\n",
       "   'Clump': {'Id': '092084a4-e896-4118-bea5-90082f91a1d5',\n",
       "    'Information': {'Header': {'Version': '1.0.0',\n",
       "      'ClumpId': '092084a4-e896-4118-bea5-90082f91a1d5',\n",
       "      'DataType': 'EELS_Line_Spectrum',\n",
       "      'DataSubType': '',\n",
       "      'DetectorType': 1,\n",
       "      'ClumpType': 'Clump',\n",
       "      'Name': 'Spectrum'},\n",
       "     'DataInformation': {'TypeInfo': 2,\n",
       "      'DataBytes': 8,\n",
       "      'Channel': 1,\n",
       "      'DimensionLength': 1,\n",
       "      'Dimensions': [512],\n",
       "      'ChannelType': 'GrayScale',\n",
       "      'IsRangeFixed': False,\n",
       "      'MinimumPossibleIntensity': -1.7976931348623157e+308,\n",
       "      'MaximumPossibleIntensity': 1.7976931348623157e+308},\n",
       "     'MeasurementInformation': {'CalibrationCoefficients': [{'Scale': 200000,\n",
       "        'Offset': 0,\n",
       "        'Unit': 'eV'}]},\n",
       "     'Tags': {'General': {'Instrument': 'JEM-ARM200F',\n",
       "       'Manufacturer': 'JEOL Ltd.',\n",
       "       'Comment': '',\n",
       "       'DateTime': '20260403141159941',\n",
       "       'ProductName': 'FEMTUS',\n",
       "       'ProductVersion': '2.0.0.0+8cc42b3fb348c2458c76e358a38bce3fb5cd1063',\n",
       "       'UpdateDateTime': '20260403141159941',\n",
       "       'UpdateProductName': 'FEMTUS',\n",
       "       'UpdateProductVersion': '2.0.0.0+8cc42b3fb348c2458c76e358a38bce3fb5cd1063',\n",
       "       'AnalysisId': '718d54b6-52b4-4849-8fe9-47d78b53f770',\n",
       "       'AnalysisDateTime': '20260403141213159',\n",
       "       'AcquisitionId': '48c95a79-0d84-4846-a16b-3132969b78ba',\n",
       "       'AcquisitionDateTime': '20260403141158914'},\n",
       "      'EELS': {'FilterMode': 'STEM-EELS',\n",
       "       'FilterSubmode': 'align',\n",
       "       'DataType': 'Image',\n",
       "       'Dispersion': 0,\n",
       "       'CameraLengthCoefficient': 1,\n",
       "       'MagnificationValue': 1,\n",
       "       'EnergyLossOffset': 0,\n",
       "       'DriftTubeVoltage': 0,\n",
       "       'HighTensionOffset': 0,\n",
       "       'SectorOffset': 0,\n",
       "       'SlitMode': 'Out',\n",
       "       'SlitInserted': False,\n",
       "       'SlitWidth': 0,\n",
       "       'EntranceApertureHoleString': 'Out',\n",
       "       'EntranceApertureInserted': False},\n",
       "      'ScanGenerator': {},\n",
       "      'HT': {'GunType': 'Cfeg',\n",
       "       'AccelerationVoltage': 200000,\n",
       "       'EnergyShift': 0},\n",
       "      'EOS': {'OperationMode': 'Scanning',\n",
       "       'SpotSizeNumber': 0,\n",
       "       'ConvergenceAngleAlphaNumber': 0,\n",
       "       'ImageFormingMode': 'MAG',\n",
       "       'RockingAngle': 0,\n",
       "       'MagnificationValue': 20000,\n",
       "       'MagnificationString': 'x20k',\n",
       "       'CameraLength': 15,\n",
       "       'CameraLengthString': '1.5cm'},\n",
       "      'Stage': {'X': {'Position': 0},\n",
       "       'Y': {'Position': 0},\n",
       "       'Z': {'Position': 0},\n",
       "       'TX': {'Position': 0},\n",
       "       'TY': {'Position': 0},\n",
       "       'TZ': {'Position': 0},\n",
       "       'PX': {'Position': 0},\n",
       "       'PY': {'Position': 0},\n",
       "       'PZ': {'Position': 0}},\n",
       "      'Aperture': {'CL': {'ApertureHoleString': '0'},\n",
       "       'CL2': {'ApertureHoleString': '0'},\n",
       "       'OL': {'ApertureHoleString': '0'},\n",
       "       'SA': {'ApertureHoleString': '0'}},\n",
       "      'Detector': {'DetectorKind': 'Test Camera Name 0',\n",
       "       'Manufacturer': 'Test Vendor',\n",
       "       'ModelCode': 'Test Camera Name 0',\n",
       "       'ModelDisplayName': 'Test Camera Name 0',\n",
       "       'PixelsPerMeter': {'Horizontal': 200000, 'Vertical': 200000},\n",
       "       'ImagingArea': {'X': 256, 'Y': 128, 'Width': 512, 'Height': 512},\n",
       "       'BinningSize': {'X': 2, 'Y': 2},\n",
       "       'SoftwareCroppingArea': {'X': 0, 'Y': 0, 'Width': 512, 'Height': 512},\n",
       "       'SoftwareBinningSize': {'X': 1, 'Y': 1},\n",
       "       'ExposureTimeValue': 100,\n",
       "       'FrameIntegration': 1},\n",
       "      'ScanDataAcquisition': {'AcquiredFrames': 1},\n",
       "      'Processings': [{'Parents': ['770ae6e5-119e-43e6-9430-54e26af2e1ca'],\n",
       "        'ProcessType': 'ExtractSpectrum',\n",
       "        'Parameters': [{'ExtractRoi': {'ShapeType': 'Rectangle',\n",
       "           'Shape': {'X': 0, 'Y': 0, 'Width': 33, 'Height': 512}}}]}]}},\n",
       "    'ViewInformation': {'Header': {'Version': '2.0.0',\n",
       "      'ClumpId': '092084a4-e896-4118-bea5-90082f91a1d5'},\n",
       "     'Tags': {}}}}]}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ids[1]: GraphContent — retrieve with get_graph_content\n",
    "element = worksheet.get_worksheet_element(whole_spectrum['Ids'][1])\n",
    "graph_content2 = worksheet.get_graph_content(element['ContentSummary']['Id'])\n",
    "graph_content2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b26401",
   "metadata": {},
   "source": [
    "#### `worksheet.get_clump_num_array_accessor`\n",
    "\n",
    "The following cells generate plots from `graph_content1` (line-scan data) and `graph_content2` (spectrum data) using these steps:\n",
    "\n",
    "1. Retrieve the `ClumpId` via `get_data_set`\n",
    "2. Retrieve `Dimensions` / `ElementType` via `get_clump_num_array`\n",
    "3. Retrieve the axis `Offset` / `Scale` / `Unit` from `MeasurementInformation.CalibrationCoefficients`\n",
    "4. Retrieve the raw byte array via `get_clump_num_array_accessor`, infer `dtype` from the byte count, and convert to a numpy array\n",
    "5. For line-scan data: integrate over the spectral axis and plot as a distance profile (`Distance [nm]`)\n",
    "6. For spectrum data: build the energy axis (`Energy Loss [eV]`) and plot with `plt.plot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "e15961bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions: [1, 33, 512]\n",
      "ElementType: Single\n",
      "Calibration: offset=0, scale=44.2212586825585, unit=Nanometer\n",
      "dtype: <class 'numpy.float32'>   shape: (1, 33, 512)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get ClumpId from graph_content1 Children via get_data_set\n",
    "dataset = worksheet.get_data_set(graph_content1['Children'][0]['Id'])\n",
    "clump_id = dataset['Clump']['Id']\n",
    "\n",
    "# Get array dimensions and element type\n",
    "array_info = worksheet.get_clump_num_array(clump_id)\n",
    "print('Dimensions:', array_info['Dimensions'])\n",
    "print('ElementType:', array_info['ElementType'])\n",
    "\n",
    "# Get spatial calibration (distance axis)\n",
    "calib = dataset['Clump']['Information']['MeasurementInformation']['CalibrationCoefficients'][0]\n",
    "offset = calib['Offset']\n",
    "scale = calib['Scale']\n",
    "unit_map = {'ElectronVolt': 'eV', 'Millielectronvolt': 'meV',\n",
    "            'Nanometer': 'nm', 'Angstrom': 'Å', 'Meter': 'm'}\n",
    "unit = unit_map.get(calib['Unit'], calib['Unit'])\n",
    "print(f\"Calibration: offset={offset}, scale={scale}, unit={calib['Unit']}\")\n",
    "\n",
    "# Get raw byte array and infer dtype from byte count\n",
    "raw_bytes = worksheet.get_clump_num_array_accessor(clump_id)\n",
    "total_elements = 1\n",
    "for d in array_info['Dimensions']:\n",
    "    total_elements *= d\n",
    "bytes_per_element = len(raw_bytes) // total_elements\n",
    "dtype_map = {1: np.uint8, 2: np.uint16, 4: np.float32, 8: np.float64}\n",
    "dtype = dtype_map.get(bytes_per_element, np.float32)\n",
    "narray = np.frombuffer(raw_bytes, dtype=dtype).reshape(array_info['Dimensions'])\n",
    "print('dtype:', dtype, '  shape:', narray.shape)\n",
    "\n",
    "# Squeeze singleton dims → (N_points, N_channels) or (N_points,)\n",
    "data = narray.squeeze()\n",
    "\n",
    "# Integrate over spectral (last) axis to get intensity per scan point\n",
    "if data.ndim >= 2:\n",
    "    profile = data.sum(axis=-1)\n",
    "else:\n",
    "    profile = data\n",
    "\n",
    "dist_axis = offset + scale * np.arange(len(profile))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(dist_axis, profile)\n",
    "plt.xlabel(f'Distance [{unit}]')\n",
    "plt.ylabel('Intensity [Counts]')\n",
    "plt.title(element['Title'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "05ad097e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions: [1, 512]\n",
      "ElementType: Double\n",
      "Calibration: offset=0, scale=200000, unit=eV\n",
      "dtype: <class 'numpy.float64'>   shape: (1, 512)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get ClumpId from graph_content Children via get_data_set\n",
    "dataset = worksheet.get_data_set(graph_content2['Children'][0]['Id'])\n",
    "clump_id = dataset['Clump']['Id']\n",
    "\n",
    "# Get array dimensions and element type\n",
    "array_info = worksheet.get_clump_num_array(clump_id)\n",
    "print('Dimensions:', array_info['Dimensions'])\n",
    "print('ElementType:', array_info['ElementType'])\n",
    "\n",
    "# Get calibration coefficients for energy axis\n",
    "calib = dataset['Clump']['Information']['MeasurementInformation']['CalibrationCoefficients'][0]\n",
    "offset = calib['Offset']\n",
    "scale = calib['Scale']\n",
    "unit_map = {'ElectronVolt': 'eV', 'Millielectronvolt': 'meV',\n",
    "            'Nanometer': 'nm', 'Angstrom': 'Å', 'Meter': 'm'}\n",
    "unit = unit_map.get(calib['Unit'], calib['Unit'])\n",
    "print(f\"Calibration: offset={offset}, scale={scale}, unit={calib['Unit']}\")\n",
    "\n",
    "# Get raw byte array and infer dtype from byte count\n",
    "raw_bytes = worksheet.get_clump_num_array_accessor(clump_id)\n",
    "total_elements = 1\n",
    "for d in array_info['Dimensions']:\n",
    "    total_elements *= d\n",
    "bytes_per_element = len(raw_bytes) // total_elements\n",
    "dtype_map = {1: np.uint8, 2: np.uint16, 4: np.float32, 8: np.float64}\n",
    "dtype = dtype_map.get(bytes_per_element, np.float32)\n",
    "narray = np.frombuffer(raw_bytes, dtype=dtype).reshape(array_info['Dimensions'])\n",
    "print('dtype:', dtype, '  shape:', narray.shape)\n",
    "\n",
    "# Build energy axis and plot spectrum\n",
    "spectrum = narray.ravel()\n",
    "energy_axis = offset + scale * np.arange(len(spectrum))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(energy_axis, spectrum)\n",
    "plt.xlabel(f'Energy Loss [{unit}]')\n",
    "plt.ylabel('Intensity [Counts]')\n",
    "plt.title(element['Title'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a02f4f13",
   "metadata": {},
   "source": [
    "#### `eels.set_calibration_result`\n",
    "\n",
    "Apply energy-axis calibration to the spectrum dataset (graph content).  \n",
    "Specify the dispersion and the first/second energy loss values in eV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd50c31a",
   "metadata": {},
   "outputs": [],
   "source": [
    "calibration_param = {\n",
    "    'Dispersion': 2,\n",
    "    'FirstEnergyLoss': -512,\n",
    "    'SecondEnergyLoss': 510\n",
    "}\n",
    "eels.set_calibration_result(graph_content['Children'][0]['Id'], calibration_param)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vjem310",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}