{ "cells": [ { "cell_type": "markdown", "id": "23f0ea38", "metadata": {}, "source": [ "# Constant Energy Q-plane\n", "\n", "After one has gotten an overview of the data measured through the Viewer3D (explained in QuickView3D.html the next step is to plot only as single plane of constant energy as function of H, K, and L or $Q_x$ and $Q_y$ and depending on the boolean state of the “rlu” key word argument. Two different binning methods are currently provided: Polar and XY. What is done is that the points measured are binned either. For explanation of units and size of bins, see below\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "4eb04ebf", "metadata": { "execution": { "iopub.execute_input": "2023-07-25T11:36:36.586224Z", "iopub.status.busy": "2023-07-25T11:36:36.586224Z", "iopub.status.idle": "2023-07-25T11:36:38.881349Z", "shell.execute_reply": "2023-07-25T11:36:38.880541Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "\n", "from MJOLNIR.Data import DataSet\n", "from MJOLNIR import _tools # Usefull tools useful across MJOLNIR\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n" ] }, { "cell_type": "markdown", "id": "7960ed08", "metadata": {}, "source": [ "Next, loading of the data files and converting using binning 8" ] }, { "cell_type": "code", "execution_count": 2, "id": "02d0ab87", "metadata": { "execution": { "iopub.execute_input": "2023-07-25T11:36:38.883576Z", "iopub.status.busy": "2023-07-25T11:36:38.883576Z", "iopub.status.idle": "2023-07-25T11:36:47.461133Z", "shell.execute_reply": "2023-07-25T11:36:47.461133Z" } }, "outputs": [], "source": [ "numbers = '483-489,494-500' # String of data numbers\n", "fileList = _tools.fileListGenerator(numbers,r'C:\\Users\\lass_j\\Documents\\CAMEA2018',2018) # Create file list from 2018 in specified folder\n", "\n", "\n", "# Create the data set\n", "ds = DataSet.DataSet(fileList)\n", "ds.convertDataFile(binning=8)" ] }, { "cell_type": "markdown", "id": "82a280fe", "metadata": {}, "source": [ "Setting up the constant energy cut, one needs to provide the energy range over which the data are integrated as well as the size of the pixels. First example utilizes polar binning where x corresponds to the radial direction and y the tangental, i.e. angular, direction." ] }, { "cell_type": "code", "execution_count": 3, "id": "a0faf237", "metadata": { "execution": { "iopub.execute_input": "2023-07-25T11:36:47.465208Z", "iopub.status.busy": "2023-07-25T11:36:47.464209Z", "iopub.status.idle": "2023-07-25T11:36:49.272208Z", "shell.execute_reply": "2023-07-25T11:36:49.271442Z" } }, "outputs": [ { "data": { "image/png": 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BV2vDUlm56ETXQwDYQnkGFtxCB97k4DDbRgIvAJ1T6YUFtfBhd7M22lnxFXSZouP3fnqs/dYeeWRqj7n65QfG3PObp/aYcJC1mKd3UkIvLKBeBd7thF0O4fiT/f9+ee7dD3c9BGAMQi8sEGGXoVi55uquh7BwvvJtV4213+X3znYce/EhNoZOTy8siF4HXgBYcCq90DFhF4BxtCSjpqd3UkIvdEjghfFM8wNq07b2e3843o7feuNsB7KH7W0Lfu6wrIRe6Ig3HkhO/MkjGV160YH7tTmMZdaOP3Xw/+Li239r5uPQu8uyEnphzoRdlsHq5/80x77FVF6HtXLJJWPtN3riiRmPBIZH6IU5EngBmFxl1MxBMCnPHMyJwHuAcS9nDIxNKwN8jUovzJiwewBhd+kde+DRA/dZncM4ZunJv/HKrocAS0/ohRkSePexW9itFRe5GLi1qy7ese74k093MJL5OnZuCB/FYxGMXIZ4YkIvzIjAuweV3aWx9of3Ja96RdfD6JWVq5811n4+yAaH590HZkDg3UWtjBd4hWKYCv28sJVKL0yRsLsHQZYjOnbZZVk7e7brYQA9JvTClAi8U6S3d6m0Bx7seggzd/FnvnLgPvXI47MfCL3WWrLmMsQTE3rhiITdMbSRau+SGl107MB9Dt5jOTzwf33hWPtd9XMfmfFIYJiEXjgCgZdltnLhhQfuo14PLAqhFyYg7E5AtRfmxofYYCehFw5J4J0xvbwAe3IZ4skJvXAIAu8R7VXtFXSX2rnvOpnjv/HxrocxU7Xqexy6JvTCGITdGRF2B+/C3/+TPP1tL+p6GL2wevHBn8q/5qc/PIeRwDAJvXAAgXfKBN3BGJ07l+PPuabrYSwVvbowOaEX9iHwArAoWioj8/ROTOiFXQi7wLTUsWNZu//T++7z5b/3yjmNBpaX0AvbCLwwXbWmpeUg1XauU9CD6RJ6YROBF6bg4hNdj2AQdgvCwOSEXtgg8MIEBFyYq1H8CWBSQi9LT9gFgOETellqAi8siO98Wdo993Y9iploa2tdDwGI0MsSE3gXSK2Yv3fALvzSY1m7/OKuh7HQnvernz9wn/f+8U/NYSQwXEIvS0ngXSC7XZaY3lj9kzM5fvLFXQ8DlkJLzNN7BN5tWDoC74Kola2BV/gFYIZUelkqAu8CEG4B6IDQy9IQeDs2RthdufDCjM6dm8NgmLeVp84lK/t/Dyzrx72OX/+irodAj4yawsGkhF6WgsDbsXEC7wV+HA1Ju+BY10PojdXPfPbAfe4a3TaHkcCw+XWBwRN4F9vKBce3BN6VCy/scDRMYvWP7k+74NiWG8Ci6Sz0VtUrq+r0LutXquqnq+ojVXW6ql6827oOhkwPCbwLYpfpyLaHXQCYpU7ecarqx5P8QJLHd9n8uiQnWmuvqqobk9ya5Od2WffaeY2XfhJ4F5Ogy16Ofcs3Z+0P7+t6GLC4Wpmy7Ai6qvR+Ksnr99j26iTvS5LW2t1JbthjHexJ4F1AbTR+4DXDwzB9dW3/G8AMdVJyaa39clVdv8fmK5I8sml5bbd1VXW8tba6/eAzZ87k5MmTSZLRaJRbbrklN99883QGzsTOnj2b06dPz+Wx3va9b5/L4zB9o6/ueEnTE2v/+Y9SN7x8331WHn96TqMZnnn9/OzS5veJO+64I3feeef5Tdd0NSaGZRH/zvhokss3La/stm63wJsk1113Xe65554k6z8kTp06NaNhchjzOhcqvIttdO7crh9U2zXsujQxPGMZ3ss2v0+cOnUqt956a5Kkqh7ocFgMyCL+DfFDSV6TJBv9u/fusQ62EHj7Z/TVVdVdgDG1JKNUr29dWohKb1VdneRdrbXXJ7k9yU1V9eEkleSNSe7bZR08Q+Dtj9G5c3p2l9Tasy7JsUee6HoYwJLqLPS21j6T5MaNrx/KxgfbWmujJG/e5ZDd1rHkhN2eaqP9g6+2hkFoF5qvdxpcmAKmYyEqvTAJgbfndgu+wm6vCbnAIhN66SWBd2CEXYCxmKd3chrr6B2Bd0DaSOBli2Mvf2nXQwAGSqWXXhF4oedW1Fo2068L8+OnD70h8MKC+/DHMzpe+94AuqLSSy8IvAAsuxY9vUeh0svCE3iHr45f0PUQABg4oZeFJvAOn8DLstLPC/OlvYGFJfAO2/awW8cvSFv9akejYV7WLr0oK19d63oYwBISellIAu+wqe4O18pv/Kfkxld0PQwYLD29kxN6WTgC73AdFHbr2LG0NVVAAKZPTy8LZdCBd/sld5fMgdVdF6lgw+gv/rmuhwAMkEovC2MpAm+tCHfbbXs+VHuHb3ThsYwuPNb1MKB3Wkp7wxEsd+mJhbEUgXev5SWx64fU/AIwTHXADaADKr10bqkCL+uEXQDmzDsyzIrAu0Nb/eqBgVdrAwCzoNJLpwZb5d0v8Kpy7krYZbODPvjY9zmdXZiCSY30CE1M6KUzAu9yamtrqWPHdqxjubTj3riB+fL3Vzoh8JKsh12Bd4A+8rtZeXp13xvAvAm9zN1gA2+yd7AVeLeqFWEXgLnS3sBcDTrwntdGWyu+Au8W53s16/gFve/LZHaefM135tJf/8M9t6895nuHJdRchvgohF7mZikC73YC7zN2+2CS4Lu8Lvr8ozn7zc/qehjAEhF6mYu3fe/bux7CfAm7Wxx4CWIGZ/Sx38/xl76k62EAPEPoZeaWssJLEmEXgMUh9ALTVys7piXbjdYGJrXyipdl9Lt/0PUwYK5a9PQehdDLTKnyLqGND/HtNh/vecIu42jf+IKuhzATLkwB3RB6mRmBdwltm6d41wtRCLxsWL3YrJnA/Ai9zITAu2T2uyjHBmF3+ax+4pM5/sLruh4GQBKhlxkQeJfMQYG3jVyIgrk7/tznZPXPvtz1MGDq9PROTuhlqgTeJXNA4K2V9R/OdeyY4MuhnX3xFftuv+ILz5nTSIAh0FDF1Ai8nFcr9UzgfWbdGLM5sFyu+rVPptay5w1gmlR6mQqBd0ltv+RysiPssuQef7LrEcBgtJT2hiMQejkygZdE2AVgsQm9wJHUsWNjXXZZTy/T9tQrXrTv9hMfb3tua19d3ffYes6z99y2+slP7T8wYCEJvRyJKu9ye+Yyw7WyZ/AVdpnUpZ99PA9/22V7br/w0TkOZpNjVz9r3+3ve+CdcxoJcBhCLxMTeJfbM4H3mRU7g6/Ay0Euu/13ku98WdfDgN5oenonJvQyEYF3ue0IvNsIu5y3+uCDZu4AFoLQy6EJvMttv8Ar7LJIzr3shXtue/rq/X9xu/w/PzDt4QAdE3qBse0beF1mmDl76urjefqqvaebf9Ynnpr4vr/4l5+357bHn7/3tvv+0Y9O/JjAbAm9HIoq73I6sJ1B4OUIjn3hwT23Hf+mS+c4Elh8o+jpnZTQy9gE3uWkustRaXsBFoHLEDMWgXegav8fAQIvAEOh0gvL6nzg3WWqMWGXvlg5t/cFKD7/PSf23PbkC/a/OMXxy/a+fPKx+y8+eGDAwhF6OZAq78DsVt3dFHwFXhbJ5b/0O1n5lhfvuf2B77p6jqNZ9/XffWbujwlJ0loyMk/vxIRe9iXwDox2BhbQ6uf/1Fy+wMwJvexJ4B2QA8LuXpcQToRdAIbBB9nYlcA7IIcIvNsDrsALwFCo9MLQtdHuwXef6m4i8NIP19zz8N4b61l7bjr21N5vf5d9oSXZu93ixD0X7f2Yf7D3JpiGpqd3YkIvO6jyDtD24KudgR5pn/qTPbfVi180x5EAfSb0soXAuwQOqPBCF/a7gIW6FjANQi/PEHgHTtgFYIkJvQAM0jW/eO/eG/eZIm3tkUf23Hb8pS85ypDgiMo8vUcg9JJElRdYXKMn9746Wu79xJ6bjl122dTHsvqJT+657a7RbVN/PGB6TFmGwAsADJ5K75ITeAGgP0xZNjmVXgAABk+ld4mp8gJDtnb2bNdDABaISu+SEngBgGWi0gsA0AMtMWXZEaj0LiFVXgBg2Qi9S0bgBQCWkdALAMDg6eldIqq8ANBjLWmt60H0l0rvkhB4AYBlJvQuAYEXAFh2c29vqKqVJO9I8ookTyf5odba/Zu2/90kf3dj8USS70jydUk+kOTRjfWfbq29cT4jBgCg77ro6X1dkhOttVdV1Y1Jbk3y2vMbW2s/m+Rnk6Sq3p7kPUmeSlKttVNzHmvvqfICzN5do9u6HgJLYhTz9E6qi9D76iTvS5LW2t1VdcNuO22s/9bW2luq6pVJLqmq92d9zG9trd2923FnzpzJyZMnkySj0Si33HJLbr755ln8Pxbe27737V0PAWApnD59uush9N7Zs2efeR7vuOOO3Hnnnec3XdPVmBiWLkLvFUke2bS8VlXHW2ur2/Z7a5J/tvH1E0l+Ksm7krwkyXur6uQux+S6667LPffck2T9h9CpU6emPPz+eFuEXoB5WOb3mmnZ/J596tSp3HrrrUmSqnqgw2ExIF2E3keTXL5peWV7eK2qq5KcbK39+saq+5Lc31prSe6rqgeTPD/J5+Yw3l7S1gAA8DVdhN4PJbk5yS9u9PTeu8s+35PkP25aflOSlyf54aq6NuvV4i/MeqB9JfACwPC0JK3p6Z1UF1OW3Z7kqar6cJJ/neRHq+rqqvoPm/Y5meSPNy2/O8lVVfXBJL+Q5E27tTYAAMBu5l7pba2Nkrx5l02v37TPv9x2zLkk3z/joQ2CKi8AwE4uQzwgAi8ADFllpL1hYq7IBgDA4Am9A6HKCwCwN6EXAIDB09M7AKq8ALAcWut6BP2l0ttzAi8AwMGEXgA4grtGt3U9BGAMQm+PqfICAIxHTy8AQE+4DPHkVHp7SpUXAGB8Qm8PCbwAAIcj9AIAMHh6entGlRcAllNrenqPQqUXAIDBE3p7RJUXAGAyQm9PCLwAi8eFKaA/9PQCAPTESE/vxFR6e0CVFwDgaIReAAAGT+hdcKq8AABHp6d3gQm8AMBmrXU9gv5S6QUAYPCE3gWlygsAMD3aGwAAesJliCen0ruAVHkBAKZL6AUAYPCE3gWjygsAMH16egEAeqCl9PQegUrvAlHlBQCYDZVeAJjQbsWKu0a3dTAS4CAqvQtClRcAYHZUegEAesJViCen0rsAVHkBAGZL6AUAYPCE3o6p8gIMhw+xweLS0wsA0Act5uk9ApXeDqnyAgDMh9ALAMDgCb0dUeUFAJgfPb0AAH1hot6JqfR2QJUXAGC+hF4AAAZP6J0zVV4AgPnT0wsAU+DCFMyDeXonp9I7R6q8AADdEHoBABg87Q1zosoLABxVM2XZxFR6AQAYPKF3DlR5AQC6JfQCADB4enoBAHqgxZRlR6HSO2NaGwAAuif0AgAweNobZkiVF2B5bP+Z7wptsFiEXgCAPmhJ9PROTHvDjKjyAgAsDqEXAIDBE3pnQJUXAOBwqmqlqn66qj5SVaer6sXbtv/1qrq7qn6rqt5RVYfq9dDTCwBT5kNszEprXY9gpl6X5ERr7VVVdWOSW5O8Nkmq6vIk/zLJqdbaA1X140muSfLlce9cpXfKVHkBACby6iTvS5LW2t1Jbti07buT3Jvk1qr6zSRfaq2NHXgTlV4AAObnmqq6Z9PyO1tr79z4+ookj2zatlZVx1trq1mv6v6lJN+R5GyS36yqj7TW7hv3gYVeAADm5YHW2g17bHs0yeWbllc2Am+SPJjkd1prX0ySqvqNrAfgsUOv9oYp0toAAMxU6/ltfx9K8pok2ejpvXfTto8l+baquqaqjie5MckfjPOUnafSCwDAIrg9yU1V9eEkleSNVfVjSe5vrf1KVf3DJL+6se8vttb+82HufO6ht6pWkrwjySuSPJ3kh1pr92/b52NZL3EnyaeT/OBBx3RNlRcAYHKttVGSN29b/YlN2/99kn8/6f13Uel9XfaYjiJJqupEkmqtndq07vX7HQMAMHyV5jLEE+uip3e/6SiS9WruJVX1/qr6wEbIPegYAADYUxeV3v2mo0iSJ5L8VJJ3JXlJkvcm+fUDjnnGmTNncvLkySTJaDTKLbfckptvvnkG/42vedv3vn2m9w9Av2xvefuJD7ylo5H0x9mzZ3P69OkkyR133JE777zz/KZruhoTw9JF6N1vOopkfeqJ+1trLcl9VfVgkmMHHPOM6667Lvfcsz792+nTp3Pq1Klpjn1Xb4vQC8De5vFe1Heb37NPnTqVW2+9NUlSVQ90OCwGpIv2hv2mo0iSN2W9ZzdVdW3WK8PvO+AYAIDh63rKsdlOWTZTXVR6d5uO4uok72qtvT7Ju5P8bFV9MOtPz5uS3J3kL20+poNx78qsDQAAi2/uoXeP6SiS5PUb288l+f5dtu92DAAAHMgV2QBghu4a3db1EIC4ItuRaG0AAOamxTy9R6DSCwDA4Am9E1LlBQDoD6EXAIDB09MLADO0+S+DPtTGkXU8122fqfROQGsDAEC/CL0AAAye0AsAwODp6T0krQ0AQHfM0zsplV4AmAMfYoNuCb0AAAye0HsIWhsAAPpJTy8AQF+Yp3diQu+YVHkBOAoXqYBuaW8AAGDwVHoBAPpCe8PEVHrHoLUBAKDfhF4AAAZP6AUAYPD09B5AawMA02YmBybSkjSXIZ6USi8AAIMn9AIAMHhC7z60NgAwS1obYH709AIA9EQzT+/EVHoBoAOqvDBfQi8AAIMn9O5BPy8AwHDo6QUA6As9vRNT6QWAOdPPC/Mn9AIAMHhC7y708wIADIueXgCAvmjV9Qh6S+gFgBnY3LfrL4jQPaEXAGbMB9ege0LvNn4bB+i/w4TM06dP59SpU2Pt6z0C+kvoBaC35l1B3e/xBGLmoczTOzGhF4CF14f2gD6MEZaZ0LuJ39IBFocQCUyT0AvAQhBy4QAtLkN8BEIvAJ0RdIF5EXoBmCtBF+iC0LtBPy/A7Ai6QNeEXgBmRtiFaSqXIT4CoReAqRJ0gUW00vUAABgOgRdYVCq90c8LcFTCLrDohF4AJiLoQgfM0zsx7Q0AHJrAC/SNSi8AYxN2gb5a+tCrnxdgf4IuMAR7ht6quvCgg1tr56Y7HAAWicALC0ZP78T2q/Q+kuQLSbbPgtw21j03yaUzGhcAHRN4gSHZL/T+Wmvt5r02VtUdMxgPAAtA4AWGZr/Q+5aqeuHG1y3Jk621B85v3C8Q94V+XoCthF1gqPYLvb+wbfmyjT7fv9Na+60ZjgmADgi80AN6eie2Z+htrb1q+7qq+qYkP5Pke2Y5KADmS+AFhu5QF6dorX0qfscAGBSBF1gGh5qnt6qOJblyRmOZK/28wLITdqFnWpK2fVItxrXfPL3/3bZVFyW5Jcn/PssBATB7Ai+wbPar9D5/2/KTSf5Fa+3XZjgeAGZM4AWW0X4fZPtnu62vqr/VWts+swMAPSDwAsvqUD29G/6H7JzODIAFJuzCMJTpBCY2SejtfQe1D7EBy0TgBYaiqv5dkv+c5PeS3NtaOzPusZOEXr9jAPSEwAsMzP+Z5NuT/HCSb6+qy7I1BL9zrwP3m73hC1kPuJWvBd1KcvVRRlpVK0nekeQVSZ5O8kOttfs3bb8gyXuSXJ/1GSP+eWvtV6rqY0ke3djt0621Nx5lHABDJ/ACQ1FV/+2mxT9K8okktyW5KskVSR5K8p373cd+H2TbPnvD+Qc9anvD65KcaK29qqpuTHJrktdu2v63kzzYWvuBqro6ycer6v1JqrV26oiPDbAUBF4YqOX9e/s/S/K/Ze9n4O+11l603x3sV+m9MskPJnk4yc+11kZV9fIk/ybJd0823iTJq5O8L0laa3dX1Q3btt+W5JfODyPJatarwpdshN/jSd7aWrv7CGMAGCyBFxig/7m19j/vtbGq/uygO9ivp/e2JPdkvVT8gqr6UpJ/kuR/POQgt7siySOblteq6nhrbTVJWmtnk6SqLs96+P1HSZ5I8lNJ3pXkJUneW1Unzx+z2ZkzZ3Ly5MkkyWg0yi233JKbb775iEMGWHw/8YG3JElOnz7d7UB65uzZs56zBbD5PNxxxx258847z2+6pqsxsTjOB96qOpXk5qx3Cjwvyf2ttcf2C8Tn7Rd6L2+tvXWjneGPknwmyXe01g5M0gd4NMnlm5ZXtofXqnpBktuTvKO19vNVdVHW/1MtyX1V9WDWL57xue13ft111+Wee+5Jsv6D/9SpU1u2m7kBGKrtP+8Yz27vFczf5vNw6tSp3HrrrUmSqnqgw2GxeP5fSf7fSf6nrGfFf5Dkb41z4Mo+255Kko2g+WSSW6YQeJPkQ0lekyQbPb33bt5YVc9L8v4kf7+19p6N1W/KeqJPVV2b9WrxF6YwFoBB0NIALIkHWmv/Juuf9frfk1w57oH7hd7NjcIPttaemnBw292e5Kmq+nCSf53kR6vq6qr6Dxvb35rkWUl+sqpOV9XpJD+f5Kqq+mDWL4zxpt1aGwCWkcALLJFHNlocHquq5ye5dtwD92tv+C82gmkledmmr1trbeIPsrXWRknevMum129s/5EkP7LL9u+f9DEBhkrgBZbMy5L8TNaLszdloxNgHPuF3m8/4qAAmCGBF1hC39pa++okB+4Xev9Na+2v7bWxqt6333YAAKarlnee3vO+f/slI1prPzfOgfuF3u+tqj/dY1tlve+2V8zcAAyFKi+wpE5u/Luy8fXVSY4WeltrFx59XABMm8ALLKvW2ls3L1fVb4x77H6VXgAWjMALLLOq+m+3rfrZ8+sOanMQemHIatushG3UzTiYCoEXSKuD9xm2v5X1a0n8dpJXJ7ksyYfHOVDohSVy7LLLdqxbO3u2g5EAwEQuaq295vxCVf3a9paHvex3cQpgCRy7+lk5dnXvPpcKwHK6tKq+Lkmq6jlJLh/3wD0rvVX160ku2r46R7w4RVfM3MDS2d7acIDzwXftoYdnMRqOSGsDkJat18tdTv8gyW9U1eNZvwTxfz/ugfu1N/yDJP82yd9I4pK/sCSOf+P1W5ZX//gznYyDrxF4Ada11k4n+eaqenZr7cHDHLvflGW/VVX/Lsm3t9ZuP+IYgRk6/uJv3LK8ev8fj3fghRccfN8vvO5r9/snZw41Lo5O4AX4mqp6Vmvt4cMG3uSAD7K11v7l5MMCunLuv/zzO9Zd+P+758j3ez4AC78AdOTfJvmbkxxo9gZYEo/93165ZfmqX7l3xz511ZVj3Vcd31ohbqsTXQadA6jyAjvo6X1TVf2tJCfOr5jGZYiBHtje2jCuB/+rlydJnn3HJ6Y5HKZE4AXY1a8k+XSSL2wsjz1xsdALC+rYt37zjnVrv3/fgcc9cfKaQz3Ogze/NInwC0AvrLTW3jjJgYcOvVX1V5Kc2/j0HDBHT/6NrS0KF9/+W2Md9/SVxw7c56H/8qU71j37Q1/YsW7tT7801mMCwAx8rKr+QpKPZqPZo7X29DgHTlLp/c4kv19V17XWfJoFOvT437xxy/KVH/+zsY5bO3jShiTJg69+/pblZ5/+3HgHciRaG4C9lJ7eU0lu2bRcSb5hnAPHCr1VtdJaGyVJa+1fHHZ0wHycu/bKHH9s64fKVi8fM+GO4eG/cF2S67asu+K2j07t/hF4AfbTWvuOSY/d74ps35jkXyW5IclqVa0kuTfJj7bWDm4sBKbq7DdPdqngSz7x5VyyafnhV37ddAa0of35b9264sMfn+r9LxOBF2B/5+fpneTY/Sq970ryD1trzzQNVtWNSX4myV+Y5MGAndqrv2PHuvrgx2f2eM/69U/vWPfAX9nlL0Njfx52q+PXfX2SZPXM5ye7AwDY20zm6T2xOfAmSWvt7qoJ3wk7dNPKG7oeAhzK2f96W6/uvQ/N9PGuef/WIPzgXx6rPWpfwi/ADCx5T29rbaLAm+wfen+3qt6T5H1JHklyeZLXJPm9SR8MmEw7cWEu/ZPHt6x7/IWXzuzxnv2RnTM0PPjdz9ux7vLPPjWzMSwbrQ0Ae6uqf9ta+3uTbk/2D70/nOR1SV6d5Iokjya5M8nthx8qMG2X/dofbFl+9K9+6x57TsdVf7Qeuh/9xksO2BMApu6/qqoX77GtZX12sclCb2utZT3gCrnQA5f+0t071o2+5zun/jhX/PETSYTfaVLlBTjQjQfvsj9XZIMF8/h1Jw7eaUwrv/Gfsrpp+fg3Hb1X95n7erLlyedetGXdxX821vzgbCLwAoeypD2905g5TOiFOZn0ssLTtPqpnTM3HH/+9KYwe/zaE8m1L3hm+cq7XcwCgMUw+MsQm7mBRXbur3/XluULtl1YYi9132enNobVL3xxy/I0Q/DatVenrr16y7r22/dO7f4BYFwuQwwL5PjDT+TKh5/Ysu6Rl1+9x96zsT0EJ8nxF3/j1O6/jm+9QlxbHS/oD5HWBuAwqrkM8VG4DDEsuMv+/d0ZbVpe+S++be5jWL3/j5P7v7Zcr/z2uY8BAI7CZYihZ0Yf/c9dDyHH7v98Lr9/67rH/i97zSQDAN1zGWJgKi58ZHXL8lPPvmDnTh/7xJxGs/h+4gNv6XoIAEtlKS5DDPO2csnWOWxHTzyxx54DVsnZF33tqnGXffbxfXYGYCxNDpvUoC9D/LbvfXvXQ4AkyejUn9vSl5skF3z5bCdj6dSfe+nWZZVfAOZkZZ9tP5zkjiSvTPI3k7wq65ch/uE5jAsGb+2Ki3PhV849c+uzp7/9+omOW9aZG8zaADB/LkMMC+LC9/5O1roexJhWH3zwma+PP/vZHY4EAMbjimzAkbSnn86Fv/NHW9ad+66THY0GYODM0zsxoReYuuOPn8tlj3+tZWPzB9qWndYGgG4IvcDMrXy15Ylrt85occmfLuGMFgB0RugFOnHsgceSb7x+y7rVP/5MJ2MBYPiEXpiy4y96wZblJ08+b8c+x85tn8CMZaC1ATiq0tM7MaEXOnDBRz+5ZXntscc6GsnRrZ3dOt/wscsu62gkALA3oReYqra2lrr3/q0rr/32bgYDABuEXmDmLnxkdcvyuSuX70eP1gaAbi3fOw/QuYseOpdz1z1ry7oLPvbJLa0R29smAIh5eo9gv8sQAwDAIKj0wqRql98Zm1kZ2ElrA0D3hF6YopXvfFm2x96Lvrz1IgxPP+eSDNnoySe3LK9cfHFHIwGArxF6Yc4ueP89Wet6EHO08vzn5aLPPrhl3dmXb527+MKHzwWAAzTz9B6F0At07sLPfyV53nO2rvRBNgCmyAfZAAAYPJVeAIC+0N4wMZVegBkycwPAYlDpBWbm2GWXpf3ZA1vWPf2ql+b4E8v0UT4AFoHQCxM6dunWqcfWXv5NO6YrO/7A1g9jrX7yUzMeVT+NLts6rdnod/+go5EAMFRCLwBAX+jpnZieXgAABk/oBQBg8LQ3ADOz/ZLESXL88dUty6uX+jEEwOx5twE6dfyxr2Z08QVb1q1ceOGW5dG5fl6m2HRlwLS5DPHktDcAADB4Kr0wgWOXX75jXTtWO3d8+uk5jAYAOIhKLwAAg6fSC8xMW9t65bWVV7ws9fjW/tzRJVv7eQFgFoReYPEcO9b1CAAYGO0NAAAM3txDb1WtVNVPV9VHqup0Vb34oO0HHQMAAPvpor3hdUlOtNZeVVU3Jrk1yWsP2P5zBxwDADB85umdWBeh99VJ3pckrbW7q+qGMbZ/6oBjnnHmzJmcPHkySfLCfPv0Rw97qLVdfhLVLtOYsUU7vvMPTjWQnt7Tp0/vue3s2bP7bmd+nIvFsPk83HHHHbnzzjvPb7qmqzExLF2E3iuSPLJpea2qjrfWVvfaPsYxz7juuutyzz33JEluWnnDVAcOcBinTp3ac9vp06f33c78OBeLYfN5OHXqVG699dYkSVU90OGwGJAuPsj2aJLNM/uvbAuvO7aPcQwAAOypi9D7oSSvSZKN/tx7x9h+0DEAAMPWkur5rUtdtDfcnuSmqvpwkkryxqq6Osm7Wmuv3217kvt2WQcAAGOZe+htrY2SvHmXTa8/YPtu64AFVse3Xm2t/f4nk+946ZZ1K0/v7FRqX9W9BMB0uSIbAEBfmLJsYkIvTGDtscdy7LLLtqyrtVFHowEADuIyxAAADJ7QCyyeNtpyW7nwwq5HBEDPaW8AZqYuvGDnyratIW0tqae/unWXGY5pnm5aeUPuGt3W9TCAIRnKD8gOqPQCADB4Qi8AAIMn9AIAMHh6emFC2y+gUL/7yeRbv2nrPldcumX52MtfmrV7PzHzsfVNPXlu6/ILvj6rf/zZjkYDsJgq3V/Kt8+EXmBmRk88kePPe+6WdasXHtuxnz85ATBr3msAZuimlTd0PQQAIvQCALAEtDcAi6m5rDPADnp6Jyb0AnO18tRXd6wbXXFxVj7/5Q5GA8CyEHphzo5deeWOdWuPPNLBSOajXX3VluV6ajXthB89AMyXdx6YopWHzu5Yt/p1V21d8S3X59gffmYu4+nCysUXb1lun/lc6voXdDQaAFjng2wwodHTT+24rX7G3LLsZAYHYCra+jy9fb51SegFAGDwtDcAc7fy6JM71o0eeWzrPhedyOjpp+Y1JAAGTqUXmLl2yYVbbqOrLj34oIHR4gBMRev5rUMqvcCRrFxyyc51z3/elmUz7gLQNaEXOlDPf+6W5ePPf25WP/HJjkYDAMOnvQGmrH3lkS23Y5/4bNrx2nL76jXL9+f9g+w2G8bQaHEA6I5KL9CJ9uDDW5aPXXVV1r7ylW4GA9AXLkM8MaEXmLl66tzOlV//3OTzfzb/wQCwlIRe4EhWvv75u64fXX5iy3I9/dV5DGfh3bTyhtw1uq3rYQAsHaEXOJRjl1++Zbl98c9SX/fcPfYGgMUg9MKCOH7t7hXT1T/9wpxHsr+1xx7bEXwBmI+uL+XbZ2ZvgDk4/vATO27tWGX1WZd87fai5auWjh57bMutjh3rekhzYRYHgPlT6YUpW3vk0Z0rH3k0x1724vkPZoHUQzufl2PPfU7W/uzLHYwGgGUz6ErvT3zgLV0PgWXUXH8sSerc6pZbe/aVXQ8JgCWm0gtDULv8/jrH8D264uJd15uxYW9v+96359ToVNfDAPpGT+/EhF7omZULL9x1/eirq/MbxIuu3bJYT3817aILDjysPfvK1IOP7L5tbW0qQwOA3Qi9MAtttHv1dQLHr/v6HetGM+yDPf68XT5Qd/G2Su7Zp9IuO7FzvwkIuwDMg9ALHTr2+NYrlbVrrsraJTsrucfPPDCbx79yZ59te+rp1ImLDn1f41Z7t39wrY4dW9rg60IVAPMj9MK8nPnS7uu3tQrM0m4hd5bqSw/uHMPVz8raQw/PdRwAg9Cip/cIhF6YlW0fJFt75JGphc6V5z7nwBaH4899zu7Devrcruv39eSTO1scdhvXI09sXXHlFcluU7gBwJwJvbBgjj1xbtcWh4Mc/4YX7lx59vEpjGgPKzt7ltslF6WeeHp2jzlAWhwA5kPohZ6qb/6GLcstmWngHF156c4xtKP9nW1Ze3m3E3wBZk/ohTmatMVh7eufvev6lcdnE3JHX797a0TWptNMJuwCTKYG3NNbVStJ3pHkFUmeTvJDrbX7d9nn/0zyf7TWfvow9y/0wgJaeXLnRR3aiQlerpddOlaLQ/um6w5/30la1YHV3tUvz2bmiaFR7QXI65KcaK29qqpuTHJrktdu2+efJ3nWJHc++NB71+i23LTyhq6HAXs788UdqypJe8HzZ/Jw7Rt3zvub1pKqqdz/6E+3/n9WLrkkoyee2GNvAJbMNVV1z6bld7bW3rnx9auTvC9JWmt3V9UNmw+sqr+ZZHR+n8MafOiFRbP2yM4rkh27eqJfWscyesHzdlk5vUsU1xe3VnLrskszmuUH6ACWWf/bGx5ord2wx7Yrkmx+k1yrquOttdWq+rYk35/kbyb5x5M8sNALAzJ61mUzvf+VR3YJs5dckhxQyVXtHY8WB2DJPZrk8k3LK6211Y2v/06Sr0/ygSTXJzlXVZ9prY1d9RV6YQg2+mrrqXNpJw4/3dlBVh59cv2L48eS1YM/hLai2gvA4X0oyc1JfnGjp/fe8xtaaz9+/uuq+qdJvniYwJsIvbAQ1h56eLIWh0mnDFtZGavF4ZmwCwCzd3uSm6rqw1n/eMsbq+rHktzfWvuVo9650As9cezM7rMgtKum09JQ51Z3rGsnLkg9tXMmiXFoZ5iMFgdgP0Oesqy1Nkry5m2rP7HLfv90kvtfitBrBgd66b5Pb1kcJVkZoxo8bovDyhM7L0fcLjh28Lj2aXFYPeDSyIxH8AWYvqUIvdAHaw89vGPdysUXT+W+Vx4+u/uGC6bzI0DYBWDRCb3Qc/WVs3u2OKz86aaWiItPTO0x1z53ZusYjh1zlbUpU+0FmC6hF3pk9NDDY7U4bAm7h1BfXdvR4jD63J/u3O/4BWmrk/X6Mj7BF9hhwD29syb0wgIbPfnkWC0O7f4/2bqcZOXqqw5+gK+u7tni0D61cZ8rlYwO/imr2jsbgi/AdAi90DOrn99ZeU2SlYsOaF948qmxWhyeCbsAMCBLE3rN4EBfjZ6c7Vy5o0/vEnKPjTGLAwD0yNKEXlg2o4e+sqPFYXW3gJtk5aBZHHZpcdDKMD9aHIAk671renonttL1AIDZW/3CF7P6hS8m7eCrsCVJ9gm0o6efyujpp6Y0MsblL1UAR6PSCwO2+oUvTu2+BF0A+kylFwbifAV28+1I9/fkk1tudE+1F2ByQi8sm31aHEbnzj1zYzEJvrC8agC3Li1V6PVBENidoNsvgi/A4S1V6AU2tNHWG70j+AIcjtALAMDgCb0APaXaC0uo9fzWIaEXoMcEX4DxzHWe3qpaSfKOJK9I8nSSH2qt3b9tnwuSvCfJ9UkuSvLPW2u/UlUfS/Loxm6fbq29cW4DBwCg1+Z9cYrXJTnRWntVVd2Y5NYkr922z99O8mBr7Qeq6uokH6+q9yep1tqpow7grtFtKiPAYJiVBpZLuQzxxObd3vDqJO9Lktba3Ulu2GWf25L85MbXlWQ165XhS6rq/VX1gY3ADLDUBF6A8c2s0ltVP5jkR7et/lKSuzYtr1XV8dba6vkVrbWzG8dfnuSXkvyjJE8k+akk70rykiTvraqTm48778yZMzl58mSSZDQa5ZZbbsnNN988vf8YwAL4iQ+8JadPn+56GINw9uxZz+UC2Hwe7rjjjtx5553nN13T1ZgYlpmF3tbau5O8e/O6qvpXSS7ftGplt+BaVS9IcnuSd7TWfr6qLkpyf2utJbmvqh5M8vwkn9t+7HXXXZd77rknSXL69OmcOnVqx9jelrdP+t8C6JwK73Tt9V7BfG0+D6dOncqtt96aJKmqBzocFgMy7/aGDyV5TZJstCjcu32Hqnpekvcn+futtfdsrH5T1vt/U1XXJrkiyRfmMWCARSLwwpLresoxU5aN7fYkT1XVh5P862y0P1TV1VX1Hzb2eWuSZyX5yao6XVWnk/x8kquq6oNJfiHJm3arEAMMmcALMLm5zt7QWhslefMu6x9K8vqNr38kyY/scvj3T2scZnAA+uimlTcIvgATcnEKgB7xCzvAZOY9Ty8AAJMyT+/EVHoBekR7A8BkhF6AnhB4ASa3tKHXmwcAwPJY2tAL0Dc+xAZLriXV81uXhF4AAAZP6AXoCW1ZAJNb6tDrDQToCz+vAI7GPL0AC0zYBbYwT+/ElrrSCwDAchB6ARaUKi/A9Cx96PWmAiwiP5uA3XQ95ZgpywCYKnPyAkyX0AuwoARfgOkRegEAGDyhN3rnAICeaD2/dUjoBVhQfiEHmB6hF2ABCbwA0yX0AiwYgRdg+oTeDd5kgEXgZxGwn67n2TVPLwBHJvACzI7QC7AABF6A2RJ6ATom8ALMntC7iTceYN783AHG1vUcu+bpBQCAxSb0AnToppU3dD0EgKUg9G7jT43AvAm+ALN3vOsBAAAwpo77YvtMpRegY/7CBDB7Qi9AhwRegPkQenfhTQiYBz9rAOZHTy9AB37iA2/peghAz1SS0tM7MZVegDlT4QWYP6EXYI4EXoBuCL178MYETJufK8CRdX0ZYZchBgCAxSX0AsyBKi9At4RegBkTeAG6J/TuwxsVcFR+jgDTVK31+tYloRdgRgRegMUh9ALMgMALsFiE3gN44wImcdPKG7oeAgCbCL0AMyL4AlPV9Ry7PZ+n93i3D7+8tleQvTnCMN208gZ/MQJYACq9Y/CGBQDQb0JvR1R2AQDmR+hdEKrJMExe28A0Vev3rUtC75i8cQGH5ecGwOIQejukxQGGS+AFWCxmbziE3d7Ephlc7xrdJgjDAAi8AItH6AWYIoEXmKmO+2L7THvDER31DU5lF4ZD4AVYXELvgvGmCQAwfdobAKbAL6zAPHQ97VefqfROgTc7WG5+BgAsPpXeDhz0BmkWB+gPgRegH4TeOfLmCMPiNQ3QH0LvlOxVnfWmCMPktQ10Qk/vxPT0zshdo9uO9KboDRUWl9cnQP+o9E6RN0IAgMWk0gtwCH65BegnoXeBeXOFxeI1CXSqrc/T2+dbl4TenjlqrzAwGa87gH7T07vgvNFC97wOAfpvrpXeqlqpqp+uqo9U1emqevEe+31sY/vpqvqZcY9bJt6EYT681gCGYd6V3tclOdFae1VV3Zjk1iSv3bxDVZ1IUq21U5vWvf6g4wCmTeAFFo55eic2757eVyd5X5K01u5OcsMu+7wiySVV9f6q+sBGyB3nuKXjDRkAYDwzq/RW1Q8m+dFtq7+U5K5Ny2tVdby1trpp3RNJfirJu5K8JMl7k/x6kkcOOC5JcubMmZw8eTJJMhqNcsstt+Tmm28+8v8HWC4/8YG35PTp0zO7/7Nnz870/hmfc7EYNp+HO+64I3feeef5Tdd0NSaGZWaht7X27iTv3ryuqv5Vkss3rVrZJbjel+T+1lpLcl9VPZjk2BjHJUmuu+663HPPPUmS06dP59SpU0f6fyy6t+XtXQ8BBmcef0VZhp9PfeFcLIbN5+HUqVO59dZbkyRV9UCHw2JA5t3e8KEkr0mSjbaFe3fZ501Z79lNVV2b5IqstzYcdNxS0uIA0+U1BSyqSvfz7PZ5nt55f5Dt9iQ3VdWHs37u3pgkVXV1kne11l6f9erwz1bVB7Perv2mJHcn+UvbjwOYJoEXYLjmGnpba6Mkb95l/UNJXr/x9bkk37/L4TuOY91do9ty08obuh4G9JrACzBsrsgGLD2BF2D4hN6B8KYNAEugtX7fOiT0AkvNL4wAy0HoHRBv3nA4XjMAy2PeszcALASBF+ijrqf96jOV3oHxRg4H8zoBWD5CL7BUBF6A5ST0DpA3ddid1wbA8tLTCwDQB23jxkRUegdKRQu28poAWG5CLzB4Ai8AQu+AeaMHrwMA1unpBQZL4AWGpkZdj6C/VHoHzps+y8r3PgCbCb3A4Ai8AGwn9C4BAQAAWHZ6eoFB8UseMGjm6Z2YSu+SEARYBr7PAdiL0AsMgsALwH6E3iUiFDBUvrcBOIieXqDXBF5gmZSe3omp9C4ZAYEh8f0MwLiEXgAABk/oXUKqYwyB72MADkNPL9A7Ai+wlFqSpql3Uiq9S0pooK987wIwCaEXAIDBE3qXmIoZfeN7Flh21fp965LQu+SECPrC9yoARyH0AgAweEIvKmgsPN+jAByVKcuAhSbwAmxixrKJqfSSRLBgMfm+BGBahF5gIQm8AEyT0MszhAwWhe9FAKZNTy8wF+eD7E0rbxhrPwC2qnQ/122fqfSyhcDBLGz+vvI9BkAXhF5gpnYLuXsFX4EYgFkRetlB8GAetn+f+b4DYJb09AIzc1CQFXQBDqG19RsTUellV8IIR+V7CIBFIvSyJ6GFSfneAWDRCL3AVAm8ACwioZd9CTAchu8XgNmq1u9bl4ReDiTIMA7fJwAsMqEXAIDBE3oZiyoe+/H9ATAnree3Dgm9jE2wYTe+LwDoA6EXmJjAC0BfCL0cipDDeb4XAOgTlyEGDk3gBehG19N+9ZlKL4cm8Cw35x+APhJ6mYjgs5ycdwD6SuiFJXXX6LZDhViBF4A+E3qZmBDUX5vP3Tjn0bkGWAAtyaj1+9YhoReWzG4BVqgFYOiEXo5EWBqOvc6lcwzAEAi9HJlQ1B8/8YG37Lt9+7l0bgEYCvP0wpK4a3RbTp8+PdZ+ACwo8/ROTKWXqRCUFpvzA8CyE3qZGsFqMTkvACD0MmUC1mJxPgBgnZ5eGCiBF2B4Sk/vxFR6mTphq3vOAQBsJfQyE0JXdzz3ALDTXENvVa1U1U9X1Ueq6nRVvXiXff7uxrbTVXV3VT1VVVdV1cc2rf+ZeY6byQhf8+c5B4Ddzbun93VJTrTWXlVVNya5NclrN+/QWvvZJD+bJFX19iTvSfJUkmqtnZrjWJmCu0a35aaVN3Q9DAAYhqapd1LzDr2vTvK+JGmt3V1VN+y148a2b22tvaWqXpnkkqp6f9bH/NbW2t27HXfmzJmcPHkySTIajXLLLbfk5ptvnvb/A2bq/JXT3va9bz/UMQddfOLs2bNjXaCC2XMuFodzsRg2n4c77rgjd9555/lN13Q1JoZlZqG3qn4wyY9uW/2lJHdtWl6rquOttdVd7uKtSf7ZxtdPJPmpJO9K8pIk762qk7sdd9111+Wee+5Jkpw+fTqnTp060v+Dozs1OqXaewibWxTGfe7GbWvwmlgczsXicC4Ww+bzcOrUqdx6661Jkqp6oMNhMSAz6+ltrb27tfZtm29JfjfJ5Zsff7fgWlVXJTnZWvv1jVX3Jfn/tHX3JXkwyfNnNXam73zlkr3dNbpt1/B6UKDVxwuwPKr1+9alec/e8KEkr0mSjZ7ee/fY73uS/MdNy2/Kev9vquraJFck+cLshsksCGd7mzTYek4BYDzzDr23J3mqqj6c5F9no/2hqq6uqv+wab+TSf540/K7k1xVVR9M8gtJ3rRHSwT0zrjBdft+Ai8AjG+uH2RrrY2SvHmX9Q8lef2m5X+5bfu5JN8/8wEyc2Zz2OqwwVXQBYDJuDgFcye4rfM8AHAobQC3Dgm9dGLZA9+y//8BYN6EXpgzgRcA5k/opTN9D399Hz8ALBOhl071NTieH7cPogEwL5WkWuv1rUtCL53rUxDc7QISk045BgDMj9ALY9ovtLpqGgAsNqGXhTDrUHjU+x/neFdNA4DFJfSyMGYVDiftv91+/CT7CrwATNWo57cOCb0slGmHxKOG0EnGc77vV+AFgMUh9LJwphUWj9puILQCwHAIvQzSUT5YpkoLAMMj9LKQjtJ/e5RKrrALwCLrep5d8/TCDMyr//YoxwMA/SD0stDm0X+rnQEAhk/oZeG58AMAcFRCL73gwg8ALL02gFuHhF56w4UfAIBJHe96AHAYgi4AMAmhFwCgF1rS8bRffaa9AQCAzlXVSlX9dFV9pKpOV9WLt23/0ar6rY3bPzns/Qu9AAAsgtclOdFae1WSf5Dk1vMbquobk/w3Sb47yY1J/kpVffth7lzoBQBgEbw6yfuSpLV2d5IbNm37XJK/1lpba621JBckeeowd66nFwCgJ6r/Lb3XVNU9m5bf2Vp758bXVyR5ZNO2tao63lpbba19NckDVVVJ/mWS/9Rau+8wDyz0AgAwLw+01m7YY9ujSS7ftLzSWls9v1BVJ5K8J8ljSX74sA+svQEAgEXwoSSvSZKqujHJvec3bFR4/48kv9ta+7+31tYOe+cqvQAALILbk9xUVR9OUkneWFU/luT+JMeS/MUkF1XVX9/Y/x+21j4y7p0LvQAAfTHgeXpba6Mkb962+hObvj5xlPvX3gAAwOAJvQAADJ7QCwDA4OnpBQDog5bUqOtB9JdKLwAAgyf0AgAweEIvAACDp6cXAKAvBjxP76yp9AIAMHhCLwAAg6e9AQCgL3Q3TEylFwCAwRN6AQAYPKEXAIDB09MLANATZcqyiQ260nvHHXd0PQQ2OBeLwXlYHM7F4nAuFoPzwKwNOvTeeeedXQ+BDc7FYnAeFodzsTici8XgPDBrgw69AACQDLCn96Mf/egDVfXZjcVrquqBTgfEec7FYnAeFodzsTici8Ww13l40dxHssj09E5scKG3tfacrscAAMBi0d4AAMDgCb0AAAze4NobAAAGqSUZdT2I/lLpBQBg8AYTeqvqgqr6d1X1m1X121V1yy77rFTVT1fVR6rqdFW9uIuxLouqemVVnd5n+8c2zsPpqvqZOQ5t6ex3LrwuZm/c59hrYnYOOgdeB/MzznPttcAsDKm94W8nebC19gNVdXWSjyf5lW37vC7Jidbaq6rqxiS3JnntXEe5JKrqx5P8QJLH99h+Ikm11k7Nc1zL6KBzEa+LeXhdDniOvSZm7nXZ/xwctJ3peV32ea69FpiVwVR6k9yW5Cc3vq4kq7vs8+ok70uS1trdSW6Yz9CW0qeSvH6f7a9IcklVvb+qPrDxg4/ZOOhceF3M3jjPsdfEbB10DrwO5ueg59prYQ+Vlmr9vnWpl5XeqvrBJD+6bfUbW2u/U1WXJ/mlJP9ol0OvSPLIpuW1qjreWtstIDOGfc7FL1fV9fsc+kSSn0ryriQvSfLeqjrpXEzuCOfC62KK9jgPX0py16bl3Z5jr4nZOuj73Otgfg56rr0WmIleht7W2ruTvHv7+qp6QZLbk7yjtfbzuxz6aJLLNy2veBEdzV7nYgz3Jbm/tdaS3FdVDyZ5fpLPTXN8y+QI58LrYop2Ow9V9a9y8HPsNTFbB32fex3Mz0HPtdcCMzGY9oaqel6S9yf5+6219+yx24eSvGZj/xuT3Dun4bHTm7Lex5Wqujbrv/l/odMRLS+vi9kb5zn2mpitg86B18H8HPRcey0wE4MJvUnemuRZSX5y0yc+L66qq6vqP2zsc3uSp6rqw0n+dXb+CZIZ2nYu3p3kqqr6YJJfSPImVZX58bqYu12fY6+JudpxDrwOOnPQufBa2E9r/b51qFrHAwAA4GBXXnptu/Fb/ruuh3Ek7//oP/toa62TD4oOqdILAAC76uUH2QAAlpK/0E9MpRcAgMETegEAGDyhFwCAwdPTCwDQBy3JqOtB9JdKLzBVVfXKqjo9xn7/pKp+u6o+XFV//ij7VtW5qnp/VV1fVY9umqv77qr6tap61sZ+X9zl2H9aVW/etu7ujfu6s6qeqqoTB/7HAVhoQi8wNVX140nelWTfkFhVfy7JX0zyyiT/dZK3H3Hfh1prf2Xj6z9orZ3auN2Y5HeS/OBh/y9J0lr7viQ7gjIA/SP0AtP0qSSvH2O/Vyd5f1v3J0mOV9VzprDvFlVVSV6Q5OFx9gdguPT0AlPTWvvlqrp+jF2vSPLgpuXHklyZ5MtH3DdJXrbRXnF1kouT/H+T/NwYY9rOZJjAwinz9E5MpRfowqNJLt+0fHmSr0xh32SjvSHr7RCfTfKl1trqPvs/meSibesu21gPwEAIvUAXPpTkr1bVSlW9MMlKa+2BKez7jNbak0n+myT/uKpesc+uH0tyS1UdT5Kq+qYkF7XW/uww/yEAFpv2BmCmqurvJklr7WfPr2utfbSqfjPJR7L+y/dbDrvvOFprX6qq/zHJv6mq707y7Kq6Z9Mut7bW/reqenWSj1bVo0kqyd85/P8UgEVWTW8IMENV9e1JbmitvWea+2477outta+bdIwH3Pdnkry0tfbULO4fYFxXXnJte9VLJpqMZmH86u/984+21m7o4rG1NwCz9lCSn5nBvptdXVXvn+C4fVXVnUlmEqYBmC/tDcBMtdbOzGLfbcddOMlxY9zv983ifgGYP5VeAAAGT6UXAKAXWuKzWBNT6QUAYPCEXgAABk97AwBAH7RobzgClV4AAAZP6AUAYPCEXgAABk9PLwBAX4y6HkB/qfQCADB4Qi8AAIMn9AIAMHh6egEAeqLM0zsxlV4AAAZP6AUAYPCEXgAABk9PLwBAX+jpnZhKLwAAgyf0AgAweEIvAACDp6cXAKAPWpKRnt5JqfQCADB4Qi8AAIMn9AIAMHh6egEAeqGZp/cIVHoBABg8oRcAgMHT3gAA0BfaGyam0gsAwOAJvQAADJ7QCwDA4OnpBQDoCz29E1PpBQBg8IReAAAGT+gFAGDw9PQCAPRBSzLS0zsplV4AAAZP6AUAYPCEXgAABk9PLwBAL7SkjboeRG+p9AIAMHhCLwAAgyf0AgAweHp6AQD6opmnd1IqvQAADJ7QCwDA4Am9AAAMnp5eAIA+aElGenonpdILAMDgCb0AAAye9gYAgL4wZdnEVHoBABg8oRcAgMETegEAGDw9vQAAfaGnd2IqvQAADJ7QCwDA4Am9AAAMnp5eAIBeaHp6j0ClFwCAwRN6AQAYPKEXAIDB09MLANAHLclo1PUoekulFwCAwRN6AQAYPKEXAIDB09MLANAX5umdmEovAACDJ/QCADB42hsAAPpCe8PEVHoBABg8oRcAgMETegEAGDw9vQAAvdCSkZ7eSan0AgAweEIvAACDJ/QCADB4enoBAPqgJa2Nuh5Fb6n0AgAweEIvAACDJ/QCADB4enoBAPrCPL0TU+kFAGDwhF4AAAZP6AUAYPD09AIA9EXT0zsplV4AAAZP6AUAYPCEXgAABk9PLwBAH7SWjEZdj6K3VHoBABg8oRcAgMHT3gAA0BemLJuYSi8AAIMn9AIAMHhCLwAAg6enFwCgJ5opyyam0gsAwOAJvQAADJ7QCwDA4OnpBQDohWae3iNQ6QUAYPCEXgAABk/oBQBg8PT0AgD0QUsy0tM7KZVeAAAGT+gFAGDwhF4AAAZPTy8AQF+0Udcj6C2VXgAABk/oBQBg8IReAAAGT08vAEAPtCTNPL0TU+kFAGDwhF4AAAZPewMAQB+0ZsqyI1DpBQBg8IReAAAGT+gFAGDw9PQCAPSEKcsmp9ILAMDgCb0AAAye0AsAwODp6QUA6Avz9E5MpRcAgMETegEAGDyhFwCAwavWzPcGALDoqup9Sa7pehxH9EBr7a918cBCLwAAg6e9AQCAwRN6AQAYPKEXAIDBE3oBABg8oRcAgMH7/wO1KZflVr3f0gAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "# Choose energy limits for binning\n", "EMin = 3.5\n", "EMax = 4.0\n", "# Generate a figure making use of binning in polar coordinates\n", "Data,ax = ds.plotQPlane(EMin=EMin, EMax=EMax,xBinTolerance=0.03,yBinTolerance=0.03,\n", " binning='polar',vmin=2e-7,vmax=1e-5, colorbar= True)\n", "\n", "#The above figure is a bit too far zoomed out. Luckily, the axis has a method to zoom to include a given RLU point\n", "ax.set_xlim(-2.6,0.68)\n", "ax.set_ylim(-1.76,2.58)\n", "ax.get_figure().set_size_inches(12,18)" ] }, { "cell_type": "markdown", "id": "21380e5b", "metadata": {}, "source": [ "Next, generating the same figure as above but utilizing pixels as defined in $Q_x$ and $Q_y$" ] }, { "cell_type": "code", "execution_count": 4, "id": "1d0f7714", "metadata": { "execution": { "iopub.execute_input": "2023-07-25T11:36:49.274409Z", "iopub.status.busy": "2023-07-25T11:36:49.274409Z", "iopub.status.idle": "2023-07-25T11:36:50.302528Z", "shell.execute_reply": "2023-07-25T11:36:50.301669Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "Data2,ax2 = ds.plotQPlane(EMin=EMin, EMax=EMax,xBinTolerance=0.03,yBinTolerance=0.03,\n", " binning='xy',vmin=2e-7,vmax=1e-5,colorbar=True)\n", "\n", "ax2.get_figure().set_size_inches(12,18)" ] }, { "cell_type": "markdown", "id": "65d08870", "metadata": {}, "source": [ "## Binnings explained\n", "\n", "The bin sizes depends on the other parameters provided to the method. The tables below seeks to show all of the possibilities:\n", "\n", "| Binning (rlu=False) | Parameter | Unit | Limits | Comment |\n", "|---------------------|---------------|------|--------------|---------------------|\n", "| XY | xBinTolerance | 1/AA | (0,$\\infty$) | Binning along $Q_x$ |\n", "| XY | yBinTolerance | 1/AA | (0,$\\infty$) | Binning along $Q_y$ |\n", "| Ploar | xBinTolerance | rad | (0,2$\\pi$] | Angular Binning |\n", "| Ploar | yBinTolerance | 1/AA | (0,$\\infty$) | Radial Binning |\n", "\n", "With rlu true\n", "\n", "| Binning (rlu=True) | Parameter | Unit | Limits | Comment |\n", "|--------------------|---------------|------|--------------|----------------------------------------|\n", "| XY | xBinTolerance | RLU | (0,$\\infty$) | Binning along first projection vector |\n", "| XY | yBinTolerance | RLU | (0,$\\infty$) | Binning along second projection vector |\n", "| Ploar | xBinTolerance | rad | (0,2$\\pi$] | Angular Binning |\n", "| Ploar | yBinTolerance | RLU | (0,$\\infty$) | Radial Binning |\n", "\n", "\n", "For further explanation of the RLU axis see [Reciprocal Lattice Unit Axis](ReciprocalLatticeUnitAxis.html)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }