{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c146c642",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import datetime as datetime\n",
    "from matplotlib import dates\n",
    "from scipy import signal\n",
    "import os\n",
    "from pathlib import Path\n",
    "print(Path.cwd())\n",
    "Path = Path.cwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5077823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['me22h11_y_900.csv', 'me22h14_y_800.csv', 'me22h16_y_700.csv', 'me22h17_y_600.csv', 'me22h19_y_500.csv', 'me22h21_y_400.csv', 'me22h23_y_350.csv', 'me22h24_y_260.csv', 'me22h24_y_300.csv', 'me22h27_y_230.csv', 'me22h29_y_200.csv', 'me22h31_y_180.csv', 'me22h32_y_160.csv', 'me22h34_y_130.csv', 'me22h35_y_100.csv', 'me22h37_y_080.csv', 'me22h39_y_060.csv', 'me22h42_y_040.csv', 'me22h44_y_030.csv', 'me22h46_y_020.csv', 'me22h47_y_015.csv', 'me22h49_y_010.csv', 'me22h51_y_008.csv', 'me22h52_y_006.csv', 'me22h54_y_005.csv', 'me22h55_y_004.csv', 'me22h57_y_003.csv', 'me22h58_y_002.csv', 'me23h00_y_001.csv', 'me23h02_y_000.csv']\n",
      "30\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h\n"
     ]
    }
   ],
   "source": [
    "DIR='MERCREDI_08_FEV_23h'\n",
    "file0='me08_23h02'\n",
    "#DIR='LUNDI_13_FEV_9h'\n",
    "#file0='l13_09h21'\n",
    "#DIR='LUNDI_13_FEV_7h'\n",
    "#file0='l13_07h13'\n",
    "#DIR='DIMANCHE_12_FEV_19h'\n",
    "#file0='d12_19h18'\n",
    "#DIR='SAMEDI_11_FEV_9h'\n",
    "#file0='s11_09h37'\n",
    "#DIR='SAMEDI_11_FEV_7h'\n",
    "#file0='s11_07h27'\n",
    "#DIR='VENDREDI_10_FEV_22h'\n",
    "#file0='v10_22h42'\n",
    "#DIR='MERCREDI_08_FEV_19h'\n",
    "#file0='me08_19h28'\n",
    "Path=Path/DIR\n",
    "Files = [(file) for file in sorted(os.listdir(Path))]\n",
    "#files = [filename for filename in os.listdir('.') if filename.startswith(\"field2_13.010.1d\")]\n",
    "#files.sort()\n",
    "#print(files)\n",
    "print(Files)\n",
    "n = len(Files)\n",
    "print(n)\n",
    "print(Path)\n",
    "#    pd.read_csv(Files[0], header = 0, low_memory=False)) # pour des fichiers de longueurs égales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "937032f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h11_y_900.csv\n",
      "              u         v         w\n",
      "0      3.175030  0.268548 -1.630342\n",
      "1      3.172434  0.237701 -1.612926\n",
      "2      3.140428  0.267307 -1.648023\n",
      "3      3.097686  0.272716 -1.667816\n",
      "4      3.089924  0.306985 -1.677413\n",
      "...         ...       ...       ...\n",
      "75259  2.193981  0.372186 -1.987580\n",
      "75260  2.146729  0.436463 -1.962747\n",
      "75261  2.230078  0.346302 -1.993933\n",
      "75262  2.144710  0.475271 -1.879618\n",
      "75263  2.466701  0.099315 -2.095411\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "74160\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h14_y_800.csv\n",
      "              u         v         w\n",
      "0      2.798163  0.154330 -0.894534\n",
      "1      2.761079  0.152893 -0.900382\n",
      "2      2.768839  0.151322 -0.893151\n",
      "3      2.737705  0.148516 -0.899817\n",
      "4      2.765236  0.146002 -0.902665\n",
      "...         ...       ...       ...\n",
      "75259  3.336621  0.254351 -1.559566\n",
      "75260  3.431395  0.230313 -1.554463\n",
      "75261  3.369972  0.229474 -1.568660\n",
      "75262  3.479923  0.211218 -1.595366\n",
      "75263  3.067151  0.220028 -1.591344\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "75263\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h16_y_700.csv\n",
      "              u         v         w\n",
      "0      3.377790  0.243513 -1.608575\n",
      "1      3.297258  0.296995 -1.621248\n",
      "2      3.296415  0.332492 -1.627636\n",
      "3      3.298102  0.350164 -1.579189\n",
      "4      3.362468  0.313312 -1.582659\n",
      "...         ...       ...       ...\n",
      "75259  2.960675  0.210998 -1.677974\n",
      "75260  2.857822  0.197151 -1.747529\n",
      "75261  2.866381  0.217874 -1.755639\n",
      "75262  2.794761  0.171349 -1.830569\n",
      "75263  2.994383  0.221276 -1.629110\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "75196\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h17_y_600.csv\n",
      "              u         v         w\n",
      "0      2.994500  0.145481 -1.851277\n",
      "1      3.007118  0.116317 -1.819407\n",
      "2      3.053970  0.125718 -1.812096\n",
      "3      3.051352  0.093157 -1.805861\n",
      "4      3.071594  0.115569 -1.801444\n",
      "...         ...       ...       ...\n",
      "75259  2.739075  0.120771 -1.647681\n",
      "75260  2.797632  0.093593 -1.537940\n",
      "75261  2.784200  0.075792 -1.589888\n",
      "75262  2.827958  0.011604 -1.452315\n",
      "75263  2.633630  0.089632 -1.841575\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "75263\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h19_y_500.csv\n",
      "              u         v         w\n",
      "0      2.477297  0.346074 -1.773369\n",
      "1      2.547509  0.238264 -1.743878\n",
      "2      2.520427  0.244814 -1.747419\n",
      "3      2.579498  0.162437 -1.745476\n",
      "4      2.518415  0.204744 -1.751882\n",
      "...         ...       ...       ...\n",
      "75259  3.061328  0.049525 -1.758865\n",
      "75260  3.133412  0.028617 -1.811857\n",
      "75261  3.039335  0.054180 -1.728057\n",
      "75262  3.197895 -0.008355 -1.928465\n",
      "75263  2.665798  0.132983 -1.488234\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "75251\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h21_y_400.csv\n",
      "              u         v         w\n",
      "0      2.477537 -0.018520 -1.434899\n",
      "1      2.181128  0.015976 -1.534535\n",
      "2      2.340564  0.161986 -1.728402\n",
      "3      2.406310  0.096735 -1.478396\n",
      "4      2.396814  0.023872 -1.458281\n",
      "...         ...       ...       ...\n",
      "75259  2.304778 -0.011217 -1.598538\n",
      "75260  2.489257  0.021106 -1.488117\n",
      "75261  2.319860  0.146405 -1.470687\n",
      "75262  2.436699  0.072563 -1.386495\n",
      "75263  2.021318  0.175592 -1.598139\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "73685\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h23_y_350.csv\n",
      "              u         v         w\n",
      "0      2.278008  0.036675 -1.496939\n",
      "1      2.242594  0.065881 -1.454635\n",
      "2      2.262629  0.061914 -1.470762\n",
      "3      2.269400  0.069247 -1.419809\n",
      "4      2.283373  0.038551 -1.462309\n",
      "...         ...       ...       ...\n",
      "75259  2.659460  0.059205 -1.689693\n",
      "75260  2.690150  0.058179 -1.703421\n",
      "75261  2.647445  0.057725 -1.672082\n",
      "75262  2.785960  0.056933 -1.659177\n",
      "75263  2.386372  0.013374 -1.690532\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "75255\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h24_y_260.csv\n",
      "              u         v         w\n",
      "0      2.487056  0.156559 -1.561129\n",
      "1      2.503883  0.128649 -1.536654\n",
      "2      2.494507  0.131438 -1.562540\n",
      "3      2.449584  0.139181 -1.535006\n",
      "4      2.440698  0.170357 -1.598301\n",
      "...         ...       ...       ...\n",
      "75259  2.624114  0.161670 -1.759082\n",
      "75260  2.702660  0.108383 -1.743138\n",
      "75261  2.704958  0.046575 -1.740709\n",
      "75262  2.718580  0.017026 -1.787251\n",
      "75263  2.752659  0.033320 -1.599105\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "75130\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h24_y_300.csv\n",
      "              u         v         w\n",
      "0      2.975611  0.334280 -1.320853\n",
      "1      3.013970  0.306813 -1.454591\n",
      "2      3.003577  0.312054 -1.460631\n",
      "3      2.990639  0.327844 -1.628556\n",
      "4      2.970592  0.330661 -1.569023\n",
      "...         ...       ...       ...\n",
      "75259  2.593358 -0.097112 -1.930378\n",
      "75260  2.583063 -0.102070 -1.873749\n",
      "75261  2.578836 -0.076999 -1.970990\n",
      "75262  2.575895 -0.102350 -1.850419\n",
      "75263  2.615070  0.070054 -2.044779\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "75263\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h27_y_230.csv\n",
      "              u         v         w\n",
      "0      3.043479  0.081551 -1.862444\n",
      "1      3.018575  0.105432 -1.877693\n",
      "2      3.063472  0.071054 -1.827183\n",
      "3      3.077690  0.065197 -1.823629\n",
      "4      3.102459  0.009381 -1.830499\n",
      "...         ...       ...       ...\n",
      "75259  2.387892  0.383574 -1.429703\n",
      "75260  2.316198  0.428808 -1.466761\n",
      "75261  2.403728  0.353483 -1.437388\n",
      "75262  2.116038  0.618172 -1.514190\n",
      "75263  2.667973  0.263561 -1.482617\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "73967\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h29_y_200.csv\n",
      "              u         v         w\n",
      "0      0.000000  0.000000  0.000000\n",
      "1      1.926490  0.170417 -2.068563\n",
      "2      1.972702  0.150794 -2.105887\n",
      "3      2.064773  0.077168 -1.938129\n",
      "4      2.048542  0.039683 -1.988192\n",
      "...         ...       ...       ...\n",
      "75259  2.300193  0.175118 -1.363574\n",
      "75260  2.301719  0.163933 -1.409973\n",
      "75261  2.284710  0.122716 -1.431396\n",
      "75262  2.393010  0.100827 -1.397147\n",
      "75263  2.107049  0.064505 -1.463536\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "73502\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h31_y_180.csv\n",
      "              u         v         w\n",
      "0      2.075944  0.272350 -1.400439\n",
      "1      2.139662  0.255189 -1.397202\n",
      "2      2.153357  0.251166 -1.362969\n",
      "3      2.198391  0.147465 -1.208671\n",
      "4      2.300928  0.110140 -1.018981\n",
      "...         ...       ...       ...\n",
      "75259  2.181227 -0.038431 -1.001036\n",
      "75260  1.623115  0.045870 -1.271317\n",
      "75261  1.749951 -0.011568 -1.750712\n",
      "75262  2.003297 -0.161501 -1.865946\n",
      "75263  2.130533  0.133867 -1.837963\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "73583\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h32_y_160.csv\n",
      "              u         v         w\n",
      "0      2.725558  0.248943 -1.299525\n",
      "1      2.689294  0.247601 -1.387366\n",
      "2      2.723206  0.283080 -1.317128\n",
      "3      2.673382  0.253129 -1.385216\n",
      "4      2.716528  0.276862 -1.350137\n",
      "...         ...       ...       ...\n",
      "75259  2.532487  0.106727 -1.938116\n",
      "75260  2.592737  0.137384 -1.944903\n",
      "75261  2.505379  0.157794 -1.883442\n",
      "75262  2.607684  0.243166 -1.885149\n",
      "75263  2.226727  0.207828 -1.660157\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "73783\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h34_y_130.csv\n",
      "              u         v         w\n",
      "0      2.185393  0.195226 -1.410028\n",
      "1      2.185578  0.134806 -1.473981\n",
      "2      2.239007  0.074144 -1.424152\n",
      "3      2.259899  0.004718 -1.477622\n",
      "4      2.262482 -0.011377 -1.446224\n",
      "...         ...       ...       ...\n",
      "75259  2.853767  0.219149 -2.075111\n",
      "75260  2.967142  0.207468 -2.077115\n",
      "75261  2.919513  0.137616 -1.999097\n",
      "75262  3.138129  0.152249 -2.027824\n",
      "75263  2.632376 -0.068555 -1.897817\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "75190\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h35_y_100.csv\n",
      "              u         v         w\n",
      "0      2.519063  0.091737 -1.753596\n",
      "1      2.525770  0.089684 -1.743552\n",
      "2      2.541748  0.074541 -1.738118\n",
      "3      2.513077  0.083162 -1.754797\n",
      "4      2.514397  0.080389 -1.779650\n",
      "...         ...       ...       ...\n",
      "75259  2.304700 -0.075185 -1.854139\n",
      "75260  2.311349 -0.104134 -1.826638\n",
      "75261  2.318242 -0.103265 -1.821148\n",
      "75262  2.287661 -0.099944 -1.814370\n",
      "75263  2.279425 -0.070474 -1.835891\n",
      "\n",
      "[75264 rows x 3 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73658\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h37_y_080.csv\n",
      "              u         v         w\n",
      "0      2.395097 -0.067031 -1.912626\n",
      "1      2.363038  0.002310 -2.047069\n",
      "2      2.357891 -0.008044 -2.046653\n",
      "3      2.323112  0.067947 -2.138698\n",
      "4      2.315563  0.029147 -2.073144\n",
      "...         ...       ...       ...\n",
      "75259  2.838167  0.020524 -1.538360\n",
      "75260  2.791528  0.041681 -1.612103\n",
      "75261  2.807309  0.011125 -1.603908\n",
      "75262  2.789430 -0.021383 -1.806010\n",
      "75263  2.701747 -0.055328 -1.581501\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "73189\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h39_y_060.csv\n",
      "              u         v         w\n",
      "0      2.593797  0.120206 -1.438664\n",
      "1      2.572697  0.089660 -1.408174\n",
      "2      2.598354  0.106377 -1.406761\n",
      "3      2.562395  0.088712 -1.356617\n",
      "4      2.551418  0.109032 -1.373910\n",
      "...         ...       ...       ...\n",
      "75259  2.241316 -0.065576 -1.936942\n",
      "75260  2.326900 -0.126095 -1.898616\n",
      "75261  2.350871 -0.184296 -1.831629\n",
      "75262  2.496826 -0.331187 -1.757029\n",
      "75263  2.290697 -0.180432 -1.762221\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "73345\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h42_y_040.csv\n",
      "              u         v         w\n",
      "0      2.179298  0.204958 -1.408422\n",
      "1      2.233508  0.169196 -1.338437\n",
      "2      2.238085  0.180617 -1.313682\n",
      "3      2.261315  0.163994 -1.264852\n",
      "4      2.247736  0.183300 -1.268256\n",
      "...         ...       ...       ...\n",
      "75259  0.000000  0.000000  0.000000\n",
      "75260  0.000000  0.000000  0.000000\n",
      "75261  0.000000  0.000000  0.000000\n",
      "75262  0.000000  0.000000  0.000000\n",
      "75263  0.000000  0.000000  0.000000\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "71577\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h44_y_030.csv\n",
      "              u         v         w\n",
      "0      2.061385  0.182911 -1.011328\n",
      "1      2.013611  0.163342 -1.261857\n",
      "2      1.906398  0.227846 -1.324783\n",
      "3      1.897393  0.160440 -1.570855\n",
      "4      1.862794  0.100938 -1.555023\n",
      "...         ...       ...       ...\n",
      "75259  0.000000  0.000000  0.000000\n",
      "75260  0.000000  0.000000  0.000000\n",
      "75261  0.000000  0.000000  0.000000\n",
      "75262  0.000000  0.000000  0.000000\n",
      "75263  2.134012 -0.027700 -1.933105\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "54461\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h46_y_020.csv\n",
      "              u         v         w\n",
      "0      2.681684  0.073504 -1.916795\n",
      "1      2.584635  0.018609 -1.731989\n",
      "2      2.572712  0.039091 -1.537224\n",
      "3      2.425861  0.020574 -1.448518\n",
      "4      2.359101  0.022919 -1.327780\n",
      "...         ...       ...       ...\n",
      "75259  0.000000  0.000000  0.000000\n",
      "75260  0.000000  0.000000  0.000000\n",
      "75261  0.000000  0.000000  0.000000\n",
      "75262  0.000000  0.000000  0.000000\n",
      "75263  0.000000  0.000000  0.000000\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "46020\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h47_y_015.csv\n",
      "              u         v         w\n",
      "0      1.539127 -0.116710 -1.366622\n",
      "1      1.586793 -0.095739 -1.547841\n",
      "2      1.634124 -0.115922 -1.650525\n",
      "3      1.753626 -0.132222 -1.946884\n",
      "4      1.834634 -0.230620 -1.987365\n",
      "...         ...       ...       ...\n",
      "75259  1.777231 -0.121765 -0.878853\n",
      "75260  1.741491 -0.107721 -0.836935\n",
      "75261  1.574840 -0.049737 -0.946347\n",
      "75262  1.695834 -0.006126 -0.737871\n",
      "75263  1.563722  0.014009 -1.181698\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "44109\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h49_y_010.csv\n",
      "         u    v    w\n",
      "0      0.0  0.0  0.0\n",
      "1      0.0  0.0  0.0\n",
      "2      0.0  0.0  0.0\n",
      "3      0.0  0.0  0.0\n",
      "4      0.0  0.0  0.0\n",
      "...    ...  ...  ...\n",
      "75259  0.0  0.0  0.0\n",
      "75260  0.0  0.0  0.0\n",
      "75261  0.0  0.0  0.0\n",
      "75262  0.0  0.0  0.0\n",
      "75263  0.0  0.0  0.0\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "17249\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h51_y_008.csv\n",
      "         u    v    w\n",
      "0      0.0  0.0  0.0\n",
      "1      0.0  0.0  0.0\n",
      "2      0.0  0.0  0.0\n",
      "3      0.0  0.0  0.0\n",
      "4      0.0  0.0  0.0\n",
      "...    ...  ...  ...\n",
      "75259  0.0  0.0  0.0\n",
      "75260  0.0  0.0  0.0\n",
      "75261  0.0  0.0  0.0\n",
      "75262  0.0  0.0  0.0\n",
      "75263  0.0  0.0  0.0\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "6289\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h52_y_006.csv\n",
      "         u    v    w\n",
      "0      0.0  0.0  0.0\n",
      "1      0.0  0.0  0.0\n",
      "2      0.0  0.0  0.0\n",
      "3      0.0  0.0  0.0\n",
      "4      0.0  0.0  0.0\n",
      "...    ...  ...  ...\n",
      "75259  0.0  0.0  0.0\n",
      "75260  0.0  0.0  0.0\n",
      "75261  0.0  0.0  0.0\n",
      "75262  0.0  0.0  0.0\n",
      "75263  0.0  0.0  0.0\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "4912\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h54_y_005.csv\n",
      "         u    v    w\n",
      "0      0.0  0.0  0.0\n",
      "1      0.0  0.0  0.0\n",
      "2      0.0  0.0  0.0\n",
      "3      0.0  0.0  0.0\n",
      "4      0.0  0.0  0.0\n",
      "...    ...  ...  ...\n",
      "75259  0.0  0.0  0.0\n",
      "75260  0.0  0.0  0.0\n",
      "75261  0.0  0.0  0.0\n",
      "75262  0.0  0.0  0.0\n",
      "75263  0.0  0.0  0.0\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "4497\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h55_y_004.csv\n",
      "         u    v    w\n",
      "0      0.0  0.0  0.0\n",
      "1      0.0  0.0  0.0\n",
      "2      0.0  0.0  0.0\n",
      "3      0.0  0.0  0.0\n",
      "4      0.0  0.0  0.0\n",
      "...    ...  ...  ...\n",
      "75259  0.0  0.0  0.0\n",
      "75260  0.0  0.0  0.0\n",
      "75261  0.0  0.0  0.0\n",
      "75262  0.0  0.0  0.0\n",
      "75263  0.0  0.0  0.0\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "5137\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h57_y_003.csv\n",
      "         u    v    w\n",
      "0      0.0  0.0  0.0\n",
      "1      0.0  0.0  0.0\n",
      "2      0.0  0.0  0.0\n",
      "3      0.0  0.0  0.0\n",
      "4      0.0  0.0  0.0\n",
      "...    ...  ...  ...\n",
      "75259  0.0  0.0  0.0\n",
      "75260  0.0  0.0  0.0\n",
      "75261  0.0  0.0  0.0\n",
      "75262  0.0  0.0  0.0\n",
      "75263  0.0  0.0  0.0\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "11093\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me22h58_y_002.csv\n",
      "         u    v    w\n",
      "0      0.0  0.0  0.0\n",
      "1      0.0  0.0  0.0\n",
      "2      0.0  0.0  0.0\n",
      "3      0.0  0.0  0.0\n",
      "4      0.0  0.0  0.0\n",
      "...    ...  ...  ...\n",
      "75259  0.0  0.0  0.0\n",
      "75260  0.0  0.0  0.0\n",
      "75261  0.0  0.0  0.0\n",
      "75262  0.0  0.0  0.0\n",
      "75263  0.0  0.0  0.0\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "14690\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me23h00_y_001.csv\n",
      "         u    v    w\n",
      "0      0.0  0.0  0.0\n",
      "1      0.0  0.0  0.0\n",
      "2      0.0  0.0  0.0\n",
      "3      0.0  0.0  0.0\n",
      "4      0.0  0.0  0.0\n",
      "...    ...  ...  ...\n",
      "75259  0.0  0.0  0.0\n",
      "75260  0.0  0.0  0.0\n",
      "75261  0.0  0.0  0.0\n",
      "75262  0.0  0.0  0.0\n",
      "75263  0.0  0.0  0.0\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "30945\n",
      "/Users/cbrun/Documents/PROJETS/GC2021/COBRA_fev2023/GRAND_COLON_2023/MERCREDI_08_FEV_23h/me23h02_y_000.csv\n",
      "              u         v         w\n",
      "0      1.085107  0.021708 -0.285552\n",
      "1      0.962616  0.010083 -0.419710\n",
      "2      1.085252  0.053359 -0.292608\n",
      "3      1.117400 -0.025969 -0.352271\n",
      "4      1.134991 -0.040368 -0.323166\n",
      "...         ...       ...       ...\n",
      "75259  1.270708  0.023801 -1.377678\n",
      "75260  1.320612  0.041361 -1.242355\n",
      "75261  1.271461  0.000886 -1.351653\n",
      "75262  1.375642  0.009846 -1.090050\n",
      "75263  0.000000  0.000000  0.000000\n",
      "\n",
      "[75264 rows x 3 columns]\n",
      "46281\n",
      "beta= [10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10.\n",
      " 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10.]\n",
      "alpha= [-32.89936175 -25.17162708 -30.76513753 -31.2395347  -30.25569738\n",
      " -33.5315388  -28.96972693 -32.73006711 -29.98933224 -33.94193901\n",
      " -35.63392433 -31.56093706 -35.52761354 -33.09341548 -36.17829581\n",
      " -32.48706292 -34.31577669 -35.61111426 -39.94864085 -40.51412618\n",
      " -40.44152663 -43.2252735  -45.04484465 -44.40239678 -44.36178729\n",
      " -44.57019669 -43.19966342 -41.70674713 -39.1891481  -33.37351598]\n"
     ]
    }
   ],
   "source": [
    "#DATA GC COBRA fevrier 2023\n",
    "freq=1250\n",
    "N=75264\n",
    "datarate = np.zeros(n) \n",
    "umean = np.zeros(n)\n",
    "vmean = np.zeros(n)\n",
    "wmean = np.zeros(n)\n",
    "ukat2 = np.zeros(n)\n",
    "ukat = np.zeros(n)\n",
    "vkat = np.zeros(n)\n",
    "wkat = np.zeros(n)\n",
    "alpha = np.zeros(n)\n",
    "beta = np.zeros(n)\n",
    "urms = np.zeros(n)\n",
    "vrms = np.zeros(n)\n",
    "wrms = np.zeros(n)\n",
    "uw = np.zeros(n)\n",
    "uv = np.zeros(n)\n",
    "vw = np.zeros(n)\n",
    "#\n",
    "for j in range(n): \n",
    "    file_path = Path/Files[j]\n",
    "    print(file_path)\n",
    "    data_meteo = pd.read_csv(file_path, header=0)[0::]\n",
    "    u_x = data_meteo['u'].astype(float) #\n",
    "    u_z = data_meteo['v'].astype(float) #\n",
    "    u_y = data_meteo['w'].astype(float) #\n",
    "    print(data_meteo)\n",
    "    u = []\n",
    "    v = []\n",
    "    w = []\n",
    "    Nmax=0\n",
    "    un=np.sqrt(u_x**2+u_y**2+u_z**2)\n",
    "    for i in range(N-1):\n",
    "        if  un[i]!=0.0: \n",
    "            Nmax=Nmax+1\n",
    "            u.append(u_x[i])\n",
    "            v.append(u_y[i])\n",
    "            w.append(u_z[i])       \n",
    "    tmax=Nmax/freq\n",
    "    datarate[j]=Nmax/N\n",
    "    time=np.linspace(0, tmax, num=Nmax)\n",
    "#    time_meteo=pd.DataFrame({'year': [2023], 'month': [2] , 'day': [13], \n",
    "#                             'hour': [9],  'minute': [21+n], 'second': time })\n",
    "    print(Nmax)\n",
    "    #print(np.shape(u))\n",
    "    #print(np.shape(time))\n",
    "    umean[j]=np.mean(u)\n",
    "    vmean[j]=np.mean(v)\n",
    "    wmean[j]=np.mean(w)\n",
    "#    beta=5./180.*np.pi\n",
    "# STREAMLINES!!!\n",
    "    beta[j]=np.arctan(wmean[j]/umean[j])\n",
    "# Repere vertical Elcom\n",
    "    beta[j]=0.\n",
    "    beta[j]=10./180.*np.pi\n",
    "#    beta=np.arctan(wmean[0]/umean[0])\n",
    "#    beta=np.arctan(wmean[20]/umean[20])\n",
    "#print(beta*180/np.pi)\n",
    "    wkat[j]=-umean[j]*np.sin(beta[j])+wmean[j]*np.cos(beta[j])\n",
    "    ukat2[j]=umean[j]*np.cos(beta[j])+wmean[j]*np.sin(beta[j])\n",
    "    alpha[j]=np.arctan(vmean[j]/ukat2[j])\n",
    "#print(alpha*180/np.pi)\n",
    "#alpha=0.\n",
    "    vkat[j]=-ukat2[j]*np.sin(alpha[j])+vmean[j]*np.cos(alpha[j])\n",
    "    ukat[j]=ukat2[j]*np.cos(alpha[j])+vmean[j]*np.sin(alpha[j])\n",
    "    uk = np.zeros(Nmax)\n",
    "    uk2 = np.zeros(Nmax)\n",
    "    vk = np.zeros(Nmax)\n",
    "    wk = np.zeros(Nmax)\n",
    "    for i in range(Nmax):\n",
    "        wk[i]=-u[i]*np.sin(beta[j])+w[i]*np.cos(beta[j])\n",
    "        uk2[i]=u[i]*np.cos(beta[j])+w[i]*np.sin(beta[j])\n",
    "        vk[i]=-uk2[i]*np.sin(alpha[j])+v[i]*np.cos(alpha[j])\n",
    "        uk[i]=uk2[i]*np.cos(alpha[j])+v[i]*np.sin(alpha[j])\n",
    "    #\n",
    "    ukmean=np.mean(uk)\n",
    "    vkmean=np.mean(vk)\n",
    "    wkmean=np.mean(wk)\n",
    "    up=uk-ukmean\n",
    "    vp=vk-vkmean\n",
    "    wp=wk-wkmean\n",
    "    u2=np.mean(up**2)\n",
    "    v2=np.mean(vp**2)\n",
    "    w2=np.mean(wp**2)\n",
    "    urms[j]=np.sqrt(u2)\n",
    "    vrms[j]=np.sqrt(v2)\n",
    "    wrms[j]=np.sqrt(w2)\n",
    "    uw[j]=np.mean(up*wp)\n",
    "    uv[j]=np.mean(up*vp)\n",
    "    vw[j]=np.mean(vp*wp)\n",
    "print('beta=',beta*180/np.pi)\n",
    "print('alpha=',alpha*180/np.pi)\n",
    "#print(vmean)\n",
    "#print(wmean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f09e722",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha= [-33.37351598 -39.1891481  -41.70674713 -43.19966342 -44.57019669\n",
      " -44.36178729 -44.40239678 -45.04484465 -43.2252735  -40.44152663\n",
      " -40.51412618 -39.94864085 -35.61111426 -34.31577669 -32.48706292\n",
      " -36.17829581 -33.09341548 -35.52761354 -31.56093706 -35.63392433\n",
      " -33.94193901 -29.98933224 -32.73006711 -28.96972693 -33.5315388\n",
      " -30.25569738 -31.2395347  -30.76513753 -25.17162708 -32.89936175]\n",
      "beta= [10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10.\n",
      " 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10.]\n",
      "DR= [0.6149155  0.41115274 0.19517963 0.14738786 0.06825308 0.05974968\n",
      " 0.06526361 0.0835592  0.22917995 0.58605708 0.6114477  0.7235996\n",
      " 0.95101244 0.97450308 0.97243038 0.97866178 0.99901679 0.9803226\n",
      " 0.97766528 0.97658907 0.98276733 0.99998671 0.9982196  0.99988042\n",
      " 0.97902051 0.99982727 0.99998671 0.99909651 0.99998671 0.98533163]\n"
     ]
    }
   ],
   "source": [
    "#rangement ordre croissant depuis la paroi (si necessaire)\n",
    "datarate=np.flip(datarate)\n",
    "alpha=np.flip(alpha)\n",
    "beta=np.flip(beta)\n",
    "umean=np.flip(umean)\n",
    "vmean=np.flip(vmean)\n",
    "wmean=np.flip(wmean)\n",
    "ukat=np.flip(ukat)\n",
    "vkat=np.flip(vkat)\n",
    "wkat=np.flip(wkat)\n",
    "uw=np.flip(uw)\n",
    "vw=np.flip(vw)\n",
    "uv=np.flip(uv)\n",
    "urms=np.flip(urms)\n",
    "vrms=np.flip(vrms)\n",
    "wrms=np.flip(wrms)\n",
    "print('alpha=',alpha/np.pi*180)\n",
    "print('beta=',beta/np.pi*180)\n",
    "print('DR=',datarate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c56112f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SAUVEGARDE NETCDF\n",
    "#31 points avec z50\n",
    "zcobra = np.zeros(n)\n",
    "zcobra=[0,1,2,3,4,5,6,8,10,15,20,30,40,60,80,100,130,160,180,200,230,300,260,350,400,500,600,700,800,900]\n",
    "#22 points avec z50\n",
    "#zcobra=[0,5,10,15,20,30,40,50,60,80,100,130,160,200,250,300,400,500,600,700,800,900]\n",
    "#21 points sans z50\n",
    "#zcobra=[0,5,10,15,20,30,40,60,80,100,130,160,200,250,300,400,500,600,700,800,900]\n",
    "#20 points sans z60\n",
    "#zcobra=[0,5,10,15,20,30,40,80,100,130,160,200,250,300,400,500,600,700,800,900]\n",
    "zcobra=pd.to_numeric(zcobra)\n",
    "# zcobra en m\n",
    "#zcobra=(zcobra+2)/1000\n",
    "theta=30\n",
    "theta=theta/180*np.pi\n",
    "zcobra=(np.array(zcobra)+2)*np.cos(theta)/1000\n",
    "#\n",
    "#file0='me08_19h28'\n",
    "file_path = file0+\"_beta10.nc\"\n",
    "#file_path = file0+\"_streamline.nc\"\n",
    "#file_path = file0+\"_.nc\"\n",
    "ds = nc.Dataset(file_path, 'w', format='NETCDF4')\n",
    "ds.createDimension('z', n)\n",
    "\n",
    "zc= ds.createVariable('z', 'f8', ('z',))\n",
    "dr= ds.createVariable('dr', 'f8', ('z',))\n",
    "a= ds.createVariable('alpha', 'f8', ('z',))\n",
    "b= ds.createVariable('beta', 'f8', ('z',))\n",
    "uc = ds.createVariable('umean', 'f8', ('z',))\n",
    "vc  = ds.createVariable('vmean', 'f8', ('z',))\n",
    "wc  = ds.createVariable('wmean', 'f8', ('z',))\n",
    "ua = ds.createVariable('ukat', 'f8', ('z',))\n",
    "va = ds.createVariable('vkat', 'f8', ('z',))\n",
    "wa = ds.createVariable('wkat', 'f8', ('z',))\n",
    "ur = ds.createVariable('urms', 'f8', ('z',))\n",
    "vr = ds.createVariable('vrms', 'f8', ('z',))\n",
    "wr = ds.createVariable('wrms', 'f8', ('z',))\n",
    "uwf = ds.createVariable('uw', 'f8', ('z',))\n",
    "vwf = ds.createVariable('vw', 'f8', ('z',))\n",
    "uvf = ds.createVariable('uv', 'f8', ('z',))\n",
    "\n",
    "zc[:] = zcobra\n",
    "dr[:] = datarate\n",
    "a[:] = alpha\n",
    "b[:] = beta\n",
    "uc[:] = umean\n",
    "vc[:] = vmean\n",
    "wc[:] = wmean\n",
    "ua[:] = ukat\n",
    "va[:] = vkat\n",
    "wa[:] = wkat\n",
    "ur[:] = urms\n",
    "vr[:] = vrms\n",
    "wr[:] = wrms\n",
    "uwf[:] = uw\n",
    "vwf[:] = vw\n",
    "uvf[:] = uv\n",
    "\n",
    "ds.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76de4bea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'netCDF4._netCDF4.Dataset'>\n",
      "root group (NETCDF4 data model, file format HDF5):\n",
      "    dimensions(sizes): z(30)\n",
      "    variables(dimensions): float64 z(z), float64 dr(z), float64 alpha(z), float64 beta(z), float64 umean(z), float64 vmean(z), float64 wmean(z), float64 ukat(z), float64 vkat(z), float64 wkat(z), float64 urms(z), float64 vrms(z), float64 wrms(z), float64 uw(z), float64 vw(z), float64 uv(z)\n",
      "    groups: \n",
      "[0.00173205 0.00259808 0.0034641  0.00433013 0.00519615 0.00606218\n",
      " 0.0069282  0.00866025 0.0103923  0.01472243 0.01905256 0.02771281\n",
      " 0.03637307 0.05369358 0.07101408 0.08833459 0.11431535 0.14029612\n",
      " 0.15761662 0.17493713 0.20091789 0.26153967 0.22689866 0.30484094\n",
      " 0.34814221 0.43474475 0.52134729 0.60794983 0.69455237 0.78115491]\n",
      "[1.14741829 1.26799861 1.31473562 1.5036342  1.60745024 1.57770041\n",
      " 1.91073252 2.01085527 1.99314306 1.85219807 2.22336848 2.1857464\n",
      " 2.24019524 2.16055465 2.43708289 2.35605544 2.39186949 2.55992786\n",
      " 2.26866932 2.24747206 2.45287183 2.6694661  2.44268046 2.47070033\n",
      " 2.40108741 2.59144342 2.90532118 3.0065177  2.94691257 2.80477869]\n",
      "[1.35100777 1.6178258  1.73825774 2.04121615 2.22859646 2.17371906\n",
      " 2.64610799 2.80327512 2.69282763 2.40231664 2.88493677 2.80983725\n",
      " 2.72683409 2.58887368 2.86669301 2.8945816  2.83025659 3.11596589\n",
      " 2.64597818 2.73440498 2.92647626 3.05511262 2.87356373 2.79915109\n",
      " 2.84849375 2.97969711 3.37580207 3.48360406 3.25387354 3.32671658]\n"
     ]
    }
   ],
   "source": [
    "#file1=file0+\"_streamline\"\n",
    "file1=file0+\"_beta0\"\n",
    "file1=file0+\"_beta10\"\n",
    "file_path = file1+\".nc\"\n",
    "r3 = nc.Dataset(file_path, 'r', format='NETCDF4')\n",
    "print(r3)\n",
    "print(r3['z'][:])\n",
    "print(r3['umean'][:])\n",
    "#time_meteo=r3['time'][:]\n",
    "zcobra=r3['z'][:]\n",
    "datarate=r3['dr'][:]\n",
    "alpha=r3['alpha'][:]\n",
    "beta=r3['beta'][:]\n",
    "umean=r3['umean'][:]\n",
    "vmean=r3['vmean'][:]\n",
    "wmean=r3['wmean'][:]\n",
    "ukat=r3['ukat'][:]\n",
    "vkat=r3['vkat'][:]\n",
    "wkat=r3['wkat'][:]\n",
    "urms=r3['urms'][:]\n",
    "vrms=r3['vrms'][:]\n",
    "wrms=r3['wrms'][:]\n",
    "uw=r3['uw'][:]\n",
    "vw=r3['vw'][:]\n",
    "uv=r3['uv'][:]\n",
    "print(ukat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f6e6449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#file0='l13_07h13_mean'\n",
    "#zcobra=zcobra+2.\n",
    "#print(zcobra)\n",
    "#print(np.shape(umean))\n",
    "#beta=5./180.*np.pi\n",
    "#beta=np.arctan(wmean[0]/umean[0])\n",
    "#print(beta*180/np.pi)\n",
    "#wkat=-umean*np.sin(beta)+wmean*np.cos(beta)\n",
    "#ukat2=umean*np.cos(beta)+wmean*np.sin(beta)\n",
    "#alpha=np.arctan(vmean/ukat2)\n",
    "#print(alpha*180/np.pi)\n",
    "#alpha=0.\n",
    "#vkat=-ukat2*np.sin(alpha)+vmean*np.cos(alpha)\n",
    "#ukat=ukat2*np.cos(alpha)+vmean*np.sin(alpha)\n",
    "#beta=np.arctan(wmean/ukat)\n",
    "#gamma=np.arctan(wmean/vmean)\n",
    "#beta=0.\n",
    "#wkat=-ukat*np.sin(beta)+np.array(w)*np.cos(beta)\n",
    "#ukat2=ukat*np.cos(beta)+np.array(w)*np.sin(beta)\n",
    "file_path = file0+\"_mean.pdf\"\n",
    "fig = plt.figure()\n",
    "plt.plot(umean,zcobra,'b--', label='u')\n",
    "plt.plot(vmean,zcobra,'r--', label='v')\n",
    "plt.plot(ukat,zcobra,'bo', label='u kat')\n",
    "plt.plot(vkat,zcobra,'ro', label='v kat')\n",
    "plt.plot(wmean,zcobra,'g--', label='w')\n",
    "plt.plot(wkat,zcobra,'go', label='w kat')\n",
    "plt.xlabel('vitesse moyenne (m/s)')\n",
    "plt.ylabel('hauteur z (mm)')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "fig.savefig(file_path, bbox_inches='tight')\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f3d4eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = file0+\"_rms.pdf\"\n",
    "#file_path = 'l13_07h13_rms.pdf'\n",
    "fig = plt.figure()\n",
    "plt.plot(urms,zcobra,'bo', label='u_rms')\n",
    "plt.plot(vrms,zcobra,'ro', label='v_rms')\n",
    "plt.plot(wrms,zcobra,'go', label='w_rms')\n",
    "plt.xlabel('vitesse rms (m/s)')\n",
    "plt.ylabel('hauteur z (mm)')\n",
    "zmin=0\n",
    "plt.xlim(zmin)\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "fig.savefig(file_path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71c612ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = file0+\"_flux.pdf\"\n",
    "#file_path = 'l13_09h21_flux.pdf'\n",
    "#uw=uw/ukat\n",
    "#uw=uw*ukat\n",
    "fig = plt.figure()\n",
    "plt.plot(uw,zcobra,'bo', label='uw')\n",
    "plt.plot(uv,zcobra,'ro', label='uv')\n",
    "plt.plot(vw,zcobra,'go', label='vw')\n",
    "plt.xlabel('flux turbulents (m2/s2)')\n",
    "plt.ylabel('hauteur z (mm)')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "fig.savefig(file_path, bbox_inches='tight')\n",
    "#print(uw[21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "738b75cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "utau= 0.16429543974104893\n",
      "zo+= 2.1906058632139858\n",
      "log ks+= 1.8176855004551482\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = file0+\"_log.pdf\"\n",
    "#file_path = 'l13_09h21_log.pdf'\n",
    "nu=15.e-6\n",
    "kappa=0.38\n",
    "Clog=5.5\n",
    "utau=np.sqrt(-uw[0])\n",
    "utau=np.sqrt(-uw[3])\n",
    "#utau=0.2\n",
    "print(\"utau=\",utau)\n",
    "Uplus=ukat/utau\n",
    "zplus=zcobra*utau/nu\n",
    "fig = plt.figure()\n",
    "plt.semilogx(zplus, Uplus,'ro', label='u kat')\n",
    "plt.ylabel('vitesse U+')\n",
    "plt.xlabel('hauteur z+')\n",
    "z = np.linspace(10,8000,1000)\n",
    "x = np.linspace(0.1,20,1000)\n",
    "loilog = 1./kappa*np.log(z) + Clog\n",
    "zo=0.2e-3\n",
    "zoplus=zo*utau/nu\n",
    "print(\"zo+=\",zoplus)\n",
    "print(\"log ks+=\",np.log10(30*zoplus))\n",
    "Crug=4.5\n",
    "Crug=6\n",
    "z2=nu*z/utau\n",
    "loilog2 = 1./kappa*np.log(z2/zo)\n",
    "plt.semilogx(z,loilog,label='Log law (turbulence)')\n",
    "plt.semilogx(z,loilog-Crug,label='Rough law (turbulence)')\n",
    "plt.semilogx(z,loilog2,label='Rough law 2(zo=0.2 mm)')\n",
    "plt.semilogx(x,x, label='Linear scaling (laminar)')\n",
    "zmin=1\n",
    "zmax=2.e4\n",
    "plt.xlim(zmin,zmax)\n",
    "umin=1\n",
    "umax=30\n",
    "plt.ylim(umin,umax)\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "fig.savefig(file_path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "581f3bc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = file0+\"_rms_log.pdf\"\n",
    "uplus=urms/utau\n",
    "vplus=vrms/utau\n",
    "wplus=wrms/utau\n",
    "#zplus=zcobra*utau/nu\n",
    "fig = plt.figure()\n",
    "plt.semilogx(zplus, uplus,'ro', label='u_rms')\n",
    "plt.semilogx(zplus, vplus,'bo', label='v_rms')\n",
    "plt.semilogx(zplus, wplus,'go', label='w_rms')\n",
    "plt.ylabel('vitesse u_rms+')\n",
    "plt.xlabel('hauteur z+')\n",
    "z = np.linspace(10,8000,1000)\n",
    "x = np.linspace(0.1,20,1000)\n",
    "zmin=1\n",
    "zmax=2.e4\n",
    "plt.xlim(zmin,zmax)\n",
    "#umin=1\n",
    "#umax=30\n",
    "#plt.ylim(umin,umax)\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "fig.savefig(file_path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "90c07c85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6149154974489796\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAETCAYAAAAyK6EVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbAElEQVR4nO3df5TcdX3v8dcrv0wWQTGkHkrIbuSEa7khC8kiWAPFciJY7aEF7jFxrcIFAlfpabmXUzk3B0Mv5N5WrCkCCqtH47VrUVulYNG09Zp7Faqy6eVnlBggm2yxdZNbIGYTyZL3/eM7m8x3Mz++MzvfnZnd5+OcOTPz+X7mO++Z7+73Nd/v5/udcUQIAIAxM5pdAACgtRAMAIAUggEAkEIwAABSCAYAQMqsZhdQq5NOOim6urqaXQYAtJWtW7fuiYgFWfq2XTB0dXVpYGCg2WUAQFuxPZi1L7uSAAApBAMAIIVgAACktN0YQymHDh3S0NCQDh482OxS2tbcuXO1cOFCzZ49u9mlAGiyKREMQ0NDOv7449XV1SXbzS6n7USE9u7dq6GhIS1evLjZ5QBostx2Jdn+vO2f2366zHTb/pTtHbaftL283uc6ePCg5s+fTyjUybbmz5/PFhfK6++XurqkGTOS6/7+Zld0rGo1Nuo1ZJlPpT71TptMEZHLRdIFkpZLerrM9N+S9C1JlnSepB9mme+KFStivG3bth3ThtrxPqKkv/iLiI6OCOnopaMjaW9GLZ2dEXZyPVZDtRob9RqyzKdSn3qnNYCkgci6/s7asZ6LpK4KwXCfpDVF95+VdHK1eRIM+eF9REmdnemV1dils3Ny66i04qxWY6NeQ5b5VOpT77QGqCUYmnlU0imSdhfdHyq0HcP2WtsDtgeGh4cnpbiJuPXWW/WJT3yiYp8HHnhA27Zty+X5d+7cqS9/+cu5zBvT0K5dtbXnZd06aWQk3TYykrRXq7FRryHLfCr1qXfaJO9iamYwlBoQKPmrQRHRFxE9EdGzYEGmM7ora4H9eBMNhtHR0bLTCAY01KJF2dvz3I9facVZrcZaXkMlWeZTqU890970JmntWmlwMNmGGBxM7ue53sq6aVHPRa24Kymn/Xi33357nH766XHRRRfF6tWr44477oiIiL6+vujp6Ylly5bFZZddFvv3749HHnkkTjzxxOjq6oru7u7YsWNHyX7jrV+/Pq699tpYtWpVrFmzJl544YVYuXJlnH322XH22WfHI488EhER5557bpxwwgnR3d0dn/zkJ2N0dDRuuumm6OnpiTPPPDPuvffekq+BXUnTRLn99JX6Z/mfqbb/fPxz1jpeMH9++V0tEx1jyPqeNGOModLrroHaZIzhPUoPPv8oyzwnHAw57McbGBiIpUuXxv79++Pll1+O00477Ugw7Nmz50i/devWxac+9amIiPjQhz4UX/va145MK9ev2Pr162P58uUxMjISERH79++PAwcORETE9u3bY+y9+e53vxvvec97jjzuvvvui9tuuy0iIg4ePBgrVqyI559//pj5EwwtoNaVdq2Pq/eDUZb5l/vfmj//2OecPTtizpzSddQyn1pW7vUOXNfzXlTqU+s0u/T7YZdfXiW0RDBI+ktJP5N0SMn4wdWSrpd0fWG6Jd0j6TlJT0nqyTLfCQdDg97kYhs3boxbbrnlyP0bb7zxSDBs2bIlVq5cGUuXLo2urq647rrrIuLYYCjXr9j69evj1ltvPXL/pZdeig984AOxdOnS6O7ujnnz5kXEscFw+eWXx5IlS6K7uzu6u7ujq6srNm/efMz8CYYmm8hKO+vj8hzgLPe/VctlbKVY7n+03uCspFUG18tpUH21BENuJ7hFxJoq00PSR/J6/rIWLUr20ZVqn4By51BceeWVeuCBB9Td3a1NmzZpy5YtE+p33HHHHbm9ceNGvfnNb9YTTzyhw4cPa+7cuSUfExG66667dPHFF9f0mjDJKg2u9vY25nF5DiSX+9+qxdh4Qbn/0d7eyu9Fvc9ZS/tk27AhGVMoXsYdHUl7TqbfdyVt2JC8qcUm+CZfcMEF+sY3vqEDBw5o3759euihh45M27dvn04++WQdOnRI/UWDRccff7z27dtXtV8lL7/8sk4++WTNmDFDX/rSl/Taa6+VnPfFF1+sz3zmMzp06JAkafv27dq/f3/drxc5qXcFVcvjGjUIW0q5/63587PPY9GiXP5Hqz5nLe2TrbdX6uuTOjslO7nu62t8QBaZfsGQw5u8fPlyve9979NZZ52lyy+/XOeff/6RabfddpvOPfdcrVq1Sm9961uPtK9evVp33HGHzj77bD333HNl+1Xy4Q9/WF/84hd13nnnafv27Ue2JpYtW6ZZs2apu7tbGzdu1DXXXKMzzjhDy5cv19KlS3XddddVPKoJNWrUkTj1rqBqeVyeK91y/1t33nnsc86eLc2ZU7qOyV4RTnYQ1aO3V9q5Uzp8OLnOMRQk5Tv4nMeFE9zyw/tYh0Ye5TYZYwxj/Ru9n76aWo5KaoZWqiUnaoXB57wuBEN+eB/r0OiBy7yPSsK0VUswTIlvVwWaptEDl/UOruYxKItpa8qMMSSBiHrx/tWp1QcugTpMiWCYO3eu9u7dy8qtThHJ7zGUO9wVFbTDwCVQoymxK2nhwoUaGhpSO3zBXqsa+wU31Ghs983YF7mNHW7Jbh20Mbfbp+yenp4YGBhodhkA0FZsb42Inix9p8SuJABA4xAMAIAUggEAkEIwAABSCAYAQArBAABIIRgAACkEAwAghWAAAKQQDACAFIIBAJBCMAAAUggGAEAKwQAASCEYAAApBAMAIIVgAACkEAwAgBSCAQCQQjAAAFIIBgBACsEAAEghGAAAKQQDACAl12CwfYntZ23vsH1zielvsP2Q7SdsP2P7qjzrAQBUl1sw2J4p6R5J75Z0hqQ1ts8Y1+0jkrZFRLekCyX9me05edUEAKguzy2Gt0naERHPR8Srku6XdOm4PiHpeNuW9HpJ/0/SaI41AQCqyDMYTpG0u+j+UKGt2N2Sfk3Si5KekvQHEXF4/Ixsr7U9YHtgeHg4r3oBAMo3GFyiLcbdv1jS45J+VdJZku62fcIxD4roi4ieiOhZsGBBo+sEABTJMxiGJJ1adH+hki2DYldJ+nokdkh6QdJbc6wJAFBFnsHwmKQlthcXBpRXS3pwXJ9dki6SJNtvlvTvJD2fY00AgCpm5TXjiBi1fYOkzZJmSvp8RDxj+/rC9Hsl3SZpk+2nlOx6+mhE7MmrJgBAdbkFgyRFxMOSHh7Xdm/R7RclvSvPGgAAteHMZwBACsEAAEghGAAAKQQDACCFYAAApBAMAIAUggEAkEIwAGP6+6WuLmnGjOS6v7/ZFQFNkesJbkDb6O+X1q6VRkaS+4ODyX1J6u1tXl1AE7DFAEjSunVHQ2HMyEjSDkwzBAMgSbt21dYOTGEEAyBJixbV1g5MYQQDIEkbNkgdHem2jo6kHZhmCAZASgaY+/qkzk7JTq77+hh4xrTEUUnAmN5eggAQWwwAgHEIBgBACsEAAEghGAAAKQQDACCFYAAApBAMAIAUggEAkEIwAABSCAYAQArBAABIIRgAACkEAwAghWAAAKQQDACAlKrBYLvD9i22P1u4v8T2e/MvrYH6+6WuLmnGjOS6v7/ZFQFAy8qyxfAFSb+U9PbC/SFJt+dWUaP190tr10qDg1JEcr12LeEwlRD8QENlCYbTIuLjkg5JUkQckORcq2qkdeukkZF028hI0o72R/ADDZclGF61PU9SSJLt05RsQVRl+xLbz9reYfvmMn0utP247Wds/+/MlWe1a1dt7WgvBD/QcFl+8/lWSd+WdKrtfknvkHRVtQfZninpHkmrlOx+esz2gxGxrajPGyV9WtIlEbHL9q/U/AqqWbQo+RRZqh3tj+AHGq7qFkNE/J2kyyRdKekvJfVExHczzPttknZExPMR8aqk+yVdOq7P+yV9PSJ2FZ7r5zXUns2GDVJHR7qtoyNpR/srF/AEP1C3LEclfSci9kbE30bENyNij+3vZJj3KZJ2F90fKrQVO13Siba32N5q+4Nlalhre8D2wPDwcIanLtLbK/X1SZ2dkp1c9/Ul7Wg9tQ4kE/xAw5XdlWR7rqQOSSfZPlFHB5xPkPSrGeZdaoA6Sjz/CkkXSZon6R9t/yAitqceFNEnqU+Senp6xs+jut5egqAdjA0kj40ZjA0kS+WX31j7unXJ7qNFi5JQYHkDdas0xnCdpD9UEgJbdXRF/4qSsYNqhiSdWnR/oaQXS/TZExH7Je23/X8kdUvaLkw/lQaSK63oCX6gocruSoqIOyNisaSbIuItEbG4cOmOiLszzPsxSUtsL7Y9R9JqSQ+O6/M3ks63Pct2h6RzJf24zteCdsdAMtASqh6VFBF32V4q6QxJc4va/2eVx43avkHSZkkzJX0+Ip6xfX1h+r0R8WPb35b0pKTDkj4XEU/X/3LQ1jiCDGgJjqi8y972ekkXKgmGhyW9W9L3I+KK3KsroaenJwYGBprx1Mjb+DEGKRlI5mABYMJsb42Inix9s5zgdoWSweF/iYirlIwBvG4C9QGlcQQZ0BKynOB2ICIO2x61fYKkn0t6S851YbpiIBlouizBMFA4Q/mzSo5O+oWkH+VZFACgeSoGg21L+h8R8ZKkewsDxSdExJOTURwAYPJVHGOIZGT6gaL7OwkFAJjasgw+/8D2OblXAgBoCVnGGN4p6Trbg5L2KzkDOiJiWa6VAQCaIkswvDv3KgAALSPLmc8lTkUFAExVWcYYAADTCMEAAEghGAAAKVl+we0824/Z/oXtV22/ZvuVySgOADD5smwx3C1pjaSfKvmVtWsk3ZVnUQCA5slyuKoiYoftmRHxmqQv2H4057oAAE2SJRhGCr/A9rjtj0v6maTj8i0LANAsWXYl/V6h3w1Kznw+VdJleRYFAGieLMHwOxFxMCJeiYg/joj/LOm9eRcGAGiOLMHwoRJtVza4DgBAiyg7xmB7jaT3S1ps+8GiScdL2pt3YQCA5qg0+PyokoHmkyT9WVH7Pkn8JgMATFFlg6Hw5XmDkt4+eeUAAJqNM58BACmc+QwASOHMZwBACmc+AwBS6j3z+fI8iwIANE+mn/a0vaBw+4/zLwkA0ExltxicuNX2Hkk/kbTd9rDtj01eeQCAyVZpV9IfSnqHpHMiYn5EnCjpXEnvsH3jZBQHAJh8lYLhg5LWRMQLYw0R8bykDxSmAQCmoErBMDsi9oxvjIhhSbPzK6lN9PdLXV3SjBnJdX9/sysCgIaoNPj8ap3Tpr7+fmntWmlkJLk/OJjcl6Te3ubVBQANUGmLodv2KyUu+ySdmWXmti+x/aztHbZvrtDvnMJXbVxR6wtoinXrjobCmJGRpB0A2lylL9GbOZEZ254p6R5JqyQNSXrM9oMRsa1Evz+VtHkizzepdu2qrR0A2kiWE9zq9TZJOyLi+Yh4VdL9ki4t0e/3Jf21pJ/nWEtjLVpUWzsAtJE8g+EUSbuL7g8V2o6wfYqk35V0b6UZ2V5re8D2wPDwcMMLrdmGDVJHR7qtoyNpB4A2l2cwuERbjLv/55I+WvhyvrIioi8ieiKiZ8GCBY2qr369vVJfn9TZKdnJdV8fA88ApoRM365apyEl36s0ZqGkF8f16ZF0v20p+aW437I9GhEP5FhXY/T2EgQApqQ8g+ExSUtsL5b0z5JWK/kN6SMiYvHYbdubJH2zLUIBAKaw3IIhIkZt36DkaKOZkj4fEc/Yvr4wveK4AgCgOfLcYlBEPCzp4XFtJQMhIq7MsxYAQDZ5Dj4DANoQwQAASCEYAAApBAMAIIVgAACkEAwAgBSCAQCQQjAAAFIIBgBACsEAAEghGAAAKQQDACCFYAAApBAMAIAUggEAkEIwAABSCAYAQArBAABIIRgAACkEAwAghWAAAKQQDACAFIIBAJBCMAAAUggGAEAKwQAASCEYAAApBAMap79f6uqSZsxIrvv7m10RgDrManYBmCL6+6W1a6WRkeT+4GByX5J6e5tXF4CascWAxli37mgojBkZSdoBtBWCAY2xa1dt7QBaFsGAxli0qLZ2AC2LYEBjbNggdXSk2zo6knYAbSXXYLB9ie1nbe+wfXOJ6b22nyxcHrXdnWc9yFFvr9TXJ3V2SnZy3dfHwDPQhnILBtszJd0j6d2SzpC0xvYZ47q9IOk3ImKZpNsk9eVVT8uYyod09vZKO3dKhw8n14QC0JbyPFz1bZJ2RMTzkmT7fkmXSto21iEiHi3q/wNJC3Osp/k4pBNAG8hzV9IpknYX3R8qtJVztaRvlZpge63tAdsDw8PDDSxxknFIJ4A2kGcwuERblOxov1NJMHy01PSI6IuInojoWbBgQQNLnGQc0gmgDeQZDEOSTi26v1DSi+M72V4m6XOSLo2IvTnW03wc0gmgDeQZDI9JWmJ7se05klZLerC4g+1Fkr4u6fciYnuOtbQGDukE0AZyC4aIGJV0g6TNkn4s6asR8Yzt621fX+j2MUnzJX3a9uO2B/KqpyVwSCeANuCIkrv9W1ZPT08MDEzt/ACARrO9NSJ6svTlzGcAQArBAABIIRha2VQ+SxpAy+KHeloVZ0kDaBK2GFoVZ0kDaBKCoVVxljSAJiEYWhVnSQNoEoKhVXGWNIAmIRhaFWdJA2gSjkpqZb29BAGASccWAwAghWAAAKQQDACAFIIBAJBCMAAAUggGAEAKwYBs+KZXYNrgPAZUxze9AtMKWwyojm96BaYVgqHdTcYuHr7pFZhWCIZ2NraLZ3BQiji6iydLONQSKHzTKzCtEAztLMsunlIBUGug8E2vwLTiiGh2DTXp6emJgYGBZpfRGmbMSFbs49nS4cPHDhpLyQp93jxp795jH9fZKe3cWfq5+vuTwNm1K9lS2LCBgWegjdjeGhE9mfoSDG2sqyv5tD/e2Aq+3PRyxgIFwJRTSzCwK6mdVdvFU+vgMGMGAEQwtLdqP+ZTbkU/fz5jBgDKIhjaXW9vstvo8OHkuni/f7ktijvv5NfhAJTFmc9T2diKvtygMUEAoASCYarj50EB1IhdSQCAFIIBAJBCMAAAUggGAEAKwQAASGm7r8SwPSyphu95KOsNkl5uwHwaMb9aHpulb6U+9Uwr1X6SpD1V6pgsrbIsJ3M5Vpvejsuy0ctxIvNslWVZa3ulZdkZEQsqFXlEREzLi6S+VplfLY/N0rdSn3qmlWqXNNDsZdhqy3Iyl+NUXJaNXo5TYVnW0d6QZTmddyU91ELzq+WxWfpW6lPPtEa/V43WKstyMpdjtentuCzzqK3dl2VTlmPb7UpCa7A9EBm/qRGtjWU5dTRqWU7nLQZMTF+zC0DDsCynjoYsS7YYAAApbDEAAFIIBgBACsEAAEghGAAAKfweA+pie56kCyWNRsTfN7kcAA3EFgNqZvt1ku6R1CXpXbZ/pbkVoV62O2xfa/uKZteCiSksy6tsd050XgQD6vHrkr4VEZ+RdEjSQduvb3JNqJHtOZLulvSKpOW239LkkjAxKyS9X1K37cUTmRHnMSAT247CH8u429dK+jdJb5F0b0S80sQyUYPCJ8slEfEPtv+XpKcl/TQi7mpyaaiRbSvZgr9G0lOS3ivpgYj4q3rmxxgDMikKggskzbT9g4g4oCQQLpB0NaHQ+opDPSIGJQ0WPl1erWQPwvtsz4mIV5tZJ6obtyxD0gu2ByS9XdIJkmbZnhkRr9U8b7YYkJXtjysJgVcl/UTJJ5PDkr4dEc81szbUZizgJX0/Ig7ZPlXSf5O0MSKebG51qIXt8yXNkfRPkj4iabekLZJejoiX6pknWwzIxPaJks6U9OsRcbgwWLlC0sGxUCj+BIPWNS7gt9n+iaTHJd0cEf/KcmwfRcvykKR/lHRAUn9EjBZ2L9WFwWdUVfQHtlDSRYXbDyr5VPLGsSNaWJm0vnEBf4Gkv1eyXFdGxL82tTjUZNyyPF/SjyS9UdJNtudO5P+RYMAxxn/SiMS/Sfq4pGtsdxf2QX9P0i5Jv9mEMlGjMgH/kJJweBMB3z4qfFj7W0kLlAw+141gwDGKBpr/yPYtRZO2SNoq6T/ZXh4RI5I+Lel026dMfqWopMaA3y0CvmXVsCy/L2lIE1yWBANKsv1hSTdI+m3b/12SImK3pIcl/VTSn9vulbRRyb7qF5tVK0oj4KeOyV6WDD6jnF9IukrSNklfsf0nEXFzRDxt+wVJ2yWtkjQq6dKICNszIuJwE2vGOEUB/y+250XEf42I3bYflnSxkoC/T9I7RMC3tMlclhyuirJsd0TEiO0lkj4n6YcR8UeFaanjo23PiojRZtWK0mx/UNI/qxDwkh6NiJsL045TssthVaH7fykcukrAt6DJXJYEAzKx/WtKvh/pHyS9XtLOiOAnIdsAAT91TNayJBhQ1dhx7bZPUvKJ5SlJ57ECaT8E/NSR57IkGJCZ7dskvVPShYUTaOo63R7NQcBPHXkvS45KQi2e1tFQmEUotJei8xP+QNJjKqxIbM9sYlmoQ97LkmBAVWPHUEfEV4q2FPiU2b4I+Kkjl2XJriRgmhj/HUjsCmxfeS9LggEAkMKuJABACsEAAEghGAAAKQQDACCFYAAApBAMmNJsd9l+uoHzen8j5gW0MoIByK5LUsOCodazVG1vsn1ho54fKIdgwHQw0/ZnbT9j++9sz5Mk29fafsz2E7b/2nZHoX3T2M9cFu7/onDzTySdb/tx2zfanmn7jsI8nrR9XaH/hba/WfT4u21fWbi90/bHbH9f0n8oLrIw37HLAdu/keebApRDMGA6WCLpnoj495JeknR5of3rEXFORHRL+rGkq6vM52ZJ34uIsyJiY6H/yxFxjqRzJF1re3GGeg5GxMqIuL+4sTDfsyTdImlA0qPZXh7QWPyCG6aDFyLi8cLtrUp2CUnSUtu3S3qjkq8t3lzjfN8laVnR1sUblITQq1Ue95VyEwrfs3+HpN8s/NDKxZL+tDB5kaSVhS2YX0bEuTXWC2RCMGA6+GXR7dckzSvc3iTpdyLiicKungsL7aMqbE0XvkBwTpn5WtLvR0QqUGyvVHprfO64x+0vObPkV7i+KunaiHhRkgrz3lyYvknSpojYUqYeoCHYlYTp7HhJP7M9W1JvUftOSSsKty+VNLtwe1/hMWM2K/kR9tmSZPv0wsp9UNIZtl9n+w2SLspYzxckfSEivlfPiwEahS0GTGe3SPqhkhX5Uzq60v+spL+x/SNJ39HRT/hPShq1/YSSrY07leyW+qfClsWwki2Q3ba/Wuj/U0n/t1ohtjslXSHpdNv/sdB8TUQMTPRFArXi21UBACnsSgIApBAMAIAUggEAkEIwAABSCAYAQArBAABIIRgAACn/HwlP8Tzs339KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = file0+\"_DR.pdf\"\n",
    "#file_path = 'l13_09h21_DR.pdf'\n",
    "fig = plt.figure()\n",
    "plt.semilogx(zplus, datarate,'ro', label='data rate')\n",
    "plt.ylabel('Data rate')\n",
    "plt.xlabel('hauteur z+')\n",
    "#plt.ylim(umin,umax)\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "fig.savefig(file_path, bbox_inches='tight')\n",
    "print(datarate[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "98a701c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j9/b9q3z_ld41jcr1wz5vktrv2w0000t1/T/ipykernel_3461/1927514028.py:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  utau=np.sqrt(-uw)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1186575816375786 0.13554094632212016 0.1365091606617577\n",
      " 0.16429543974104893 0.15946798285383929 0.17051145005802357\n",
      " 0.21521403486937477 0.18148328106807232 0.15299270535603104\n",
      " 0.12226120445303482 0.168101662192684 0.156353027410277\n",
      " 0.14159270160255874 0.12791312094404014 0.13011996205616883\n",
      " 0.10676986795373702 0.12792296084292787 0.11528236004380676 --\n",
      " 0.11918971942415568 0.1259335131500652 0.1265860936003631\n",
      " 0.1250444723052357 0.1170554094211862 0.1739344107909989\n",
      " 0.09878893207831077 0.11503881803603652 0.15179412794964867\n",
      " 0.1026657909995322 0.13325180230372483]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = file0+\"_local.pdf\"\n",
    "#file_path = 'l13_09h21_local.pdf'\n",
    "nu=15.e-6\n",
    "kappa=0.38\n",
    "Clog=5.5\n",
    "#zplus=(np.array(zcobra)+2)/1000\n",
    "utau=np.sqrt(-uw)\n",
    "#zplus=zcobra*utau/nu\n",
    "#print(zplus)\n",
    "print(utau)\n",
    "#utau=0.2\n",
    "Uplus=ukat/utau\n",
    "zplus=zcobra*utau/nu\n",
    "fig = plt.figure()\n",
    "plt.semilogx(zplus, Uplus,'ro', label='u kat')\n",
    "plt.ylabel('vitesse U+')\n",
    "plt.xlabel('hauteur z+')\n",
    "z = np.linspace(10,8000,1000)\n",
    "x = np.linspace(0.1,20,1000)\n",
    "loilog = 1./kappa*np.log(z) + Clog\n",
    "plt.semilogx(z,loilog,label='Log law (turbulence)')\n",
    "plt.semilogx(z,loilog-4,label='Rough law (turbulence)')\n",
    "plt.semilogx(x,x, label='Linear scaling (laminar)')\n",
    "zmin=1\n",
    "zmax=2.e4\n",
    "plt.xlim(zmin,zmax)\n",
    "umin=1\n",
    "umax=30\n",
    "plt.ylim(umin,umax)\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "fig.savefig(file_path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d82eb3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = file0+\"_sample.pdf\"\n",
    "#file_path = 's11_07h56_y_700m.pdf'\n",
    "fig = plt.figure()\n",
    "#series = pd.Series(vitesse,index=time_meteo)\n",
    "#series = series.sort_index()\n",
    "#T1h = series.resample('1H').mean()\n",
    "#plt.plot(time_meteo, temperature_surf,'r*', label='T surf')\n",
    "plt.plot(time,u,'b--', label='u')\n",
    "plt.plot(time,v,'r--', label='v')\n",
    "plt.plot(time,w,'g--', label='w')\n",
    "plt.ylabel('vitesse (m/s)')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "fig.savefig(file_path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "062a605b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = file0+\"_streamline.pdf\"\n",
    "#file_path = 's11_07h56_y_700p.pdf'\n",
    "fig = plt.figure()\n",
    "#series = pd.Series(vitesse,index=time_meteo)\n",
    "#series = series.sort_index()\n",
    "#T1h = series.resample('1H').mean()\n",
    "#plt.plot(time_meteo, temperature_surf,'r*', label='T surf')\n",
    "plt.plot(time,uk,'b--', label='u')\n",
    "plt.plot(time,vk,'r--', label='v')\n",
    "plt.plot(time,wk,'g--', label='w')\n",
    "plt.ylabel('vitesse (m/s)')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "fig.savefig(file_path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ab99bb34",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/j9/b9q3z_ld41jcr1wz5vktrv2w0000t1/T/ipykernel_3461/3590623092.py:9: RuntimeWarning: divide by zero encountered in power\n",
      "  y=(f)**(-5/3)*0.06\n",
      "/var/folders/j9/b9q3z_ld41jcr1wz5vktrv2w0000t1/T/ipykernel_3461/3590623092.py:10: RuntimeWarning: divide by zero encountered in power\n",
      "  y2=(f)**(-3)*50\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEKCAYAAAAMzhLIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABn6klEQVR4nO2dZ3hURReA39nd9N4pCYQQSoCEFqo0AREEBBGlWSkq9l4+xQqKiihYAAVBUQEVQVAQFOnSe4dQQwkpQHrdne/H3Wx6JWFDMu/z7JPduXPnnr3iPXvmNCGlRKFQKBSKotBZWwCFQqFQVG2UolAoFApFsShFoVAoFIpiUYpCoVAoFMWiFIVCoVAoikUpCoVCoVAUi8HaAlQkQoiBwEAXF5dxjRs3trY4CoVCcVOxa9euWCmlT/5xUR3zKMLDw+XOnTutLYZCoVDcVAghdkkpw/OPV6utJyHEQCHE1/Hx8dYWRaFQKKoN1UpRSCmXSykfcXNzs7YoCoVCUW2oVopCoVAoFBVPtXRmBwcHW1sUhUJRCJmZmZw/f560tDRri1Kjsbe3x9/fHxsbm1LNV85shUJxwzh9+jQuLi54eXkhhLC2ODUSKSVxcXEkJibSoEGDPMeUM1uhUFidtLQ0pSSsjBACLy+vMll11UpRVIgzOzm54gRSKBQFUErC+pT1v0G1UhTXTXIyhIXBk09CQoK1pVEoFJWAXq+nVatWltfkyZMB+OKLLwgODkYIQWxsrGX+0aNH6dSpE3Z2dkyZMsVaYluVauXMvm6khAED4PPPYelS+OILGDzY2lIpFIoKxMHBgb179xYYv+WWWxgwYAA9evTIM+7p6cn06dNZunTpDZGvKlKtLIrr9lE4O8O0abBlC3h5wV13wZAhkJhYsYIqFIoqR+vWrQkMDCww7uvrS7t27UodIVQdqVYWhZRyObA8PDx83HUt1KED7NwJU6fC2rXg5FQxAioUCgvvLD/E4YsVu8XbrI4rbw1sXuyc1NRUWrVqZfn82muvMWzYsAqVo7pRrRRFhWJjA6+8Ai+/DELApUswZgx8/DE0L/4fokKhqLoUtfWkKBqlKEoiOzrg+HHYvh1at9YUyOuvg729dWVTKG5iSvrlr6g6KB9FaeneHY4ehREjYOJELTpq7dqKv45CoVBUMaqVoqj0ooDe3vDdd/D332A0wrffVs51FApFpZHto8h+vfrqqwBMnz4df39/zp8/T1hYGGPHjgUgKioKf39/pk6dysSJE/H39yehhoXPqxIe5SUlBTIywN0dDhzQXiNG5GxVKRSKAhw5coSQkBBri6Gg8P8WNaKExw3F0VFTEgBffgmjRkG/fnD6tFXFUigUiopGKYqK4MsvtSS9zZuhRQuYMgWysqwtlUKhUFQISlFUBHq9Vvbj8GHo3Rteegm++sraUikUCkWFUK0UhdWrxwYEaKU/li2Dceacv0OHICnJOvIoFApFBVCtFEWVaIUqBAwcCA4O2vbTnXdqCXorVlhPJoVCobgOqpWiqHIYDFo4rZMT9O8Pw4dDVJS1pVIoFIoyoRRFZdOlC+zZA+++C0uWQEgIHDtmbakUihpLUWXGR40aRZMmTWjRogWjR48mMzMTgB9//JGwsDDCwsLo3Lkz+/bts6b4VkGV8LgR2NnBhAlw773w9dfQqJE2npKihdkqFIobRlG1nkaNGsUPP/wAwMiRI5k9ezbjx4+nQYMGrF+/Hg8PD1auXMkjjzzCtm3bbrDU1kVZFDeSJk3gk09Ap9OKDDZooFka6enWlkyhqPHccccdCCEQQtC+fXvOnz8PQOfOnfHw8ACgY8eOlvGahLIorIXBAL16wVtvwcKFmqXRpYu1pVIobhwrX4WoAxW7Zq1Q6De52CkllRnPzMxk/vz5TJs2rcC5c+bMoV+/fhUm7s1ClVcUQogg4HXATUo51NryVBg+PvDTT3D//TB+PHTtCo8+qiXv6fXWlk6hqLaUVGb88ccfp1u3bnTt2jXP+Nq1a5kzZw6bNm2qZAmrHpWqKIQQ3wIDgGgpZYtc432BaYAemC2lLPIngJTyFDBGCPFrZcpqNfr1g4MHNcviyhWlJBQ1hxJ++VuDd955h5iYGGbNmpVnfP/+/YwdO5aVK1fi5eVlJemsR2X7KOYBfXMPCCH0wJdAP6AZMEII0UwIESqE+CPfy7eS5asaODtrvovsarT792stWCMjrSuXQlGDmD17NqtWrWLBggXodDmPxnPnzjFkyBDmz59P48aNrSih9ahUi0JKuUEIEZhvuD0QYbYUEEIsBAZJKT9Asz5qLtmVZ48cgVWroFkzmDQJnnhCWRoKRQWR30fRt29fJk+ezGOPPUb9+vXp1KkTAEOGDOHNN9/k3XffJS4ujscffxwAg8FApVenrmJYw0dRF8j9U/k80KGoyUIIL2AS0FoI8ZpZoRQ27xHgEYB69epVnLTWYNgwaN8eHn8cnnkGfvgBvvkGWra0tmQKxU2P0WgsdDyriEKes2fPZvbs2ZUpUpXHGuGxhTVsKLIphpQyTkr5mJSyYVFKwjzva+AdYLetrW0FiGllGjTQyn789BOcOQO/Vk8XjUKhqPpYQ1GcBwJyffYHLlbEwlWi1lNFIoTWDOnoUa1HN2jtV//+27pyKRSKGoU1FMUOoJEQooEQwhYYDiyriIWtXj22svD0BHt77f3kydCnDzzwAMTEWFcuhUJRI6hURSGEWABsAZoIIc4LIcZIKbOAJ4FVwBHgZynloYq4XrWzKArj99+1ciALF2p1o77/HqphO1uFQlF1qOyopxFFjK8AKrzuthBiIDAwODi4opeuOtjba2U/hg2DRx6BBx8EDw+ttLlCoVBUAtWq1lONsCiyad4cNm7UnNz9+2tje/aAueKlQqFQVBTVSlFUWx9FUeh0cPfd2t8rV6BHD2jbFmpYZUuFoiwUVWZ8zJgxtGzZkrCwMIYOHUqSuTPl77//TlhYGK1atSI8PLxGlvAQshrub4eHh8ualhADaP6LJ56Aixe1Ht6TJoGLi7WlUigsHDlyhJCQEKvK4OzsbFECuUlISMDV1RWA559/Hl9fX1599VWSkpJwcnJCCMH+/fu59957OXr06I0Wu8Ip7L+FEGKXlDI8/9xqZVHUeAYNgsOHNSXxxRdaZvfVq9aWSqG4KchWElJKUlNTEeZKCc7Ozpb3ycnJlvc1iSpfPbYs1Ahndkm4usL06TBqFPz1l+boBtUkSVHl+HD7hxy9UrG/zJt6NuWV9q8UO6e4MuMPP/wwK1asoFmzZnzyySeWOUuWLOG1114jOjqaP//8s0JlvhmoVhZFjXJml0SHDlpFWtCKDNarBzNmgMlkXbkUCiuTXWY8+5W7F8XcuXO5ePEiISEhLFq0yDJ+1113cfToUZYuXcqECROsIbZVqVYWhaIInJ2hVSutdtQPP2hNkpo3t7ZUihpOSb/8rYVer2fYsGF8/PHHPPzww3mOdevWjZMnTxIbG4u3t7eVJLzxVCuLQlEEQUFa2Y9587RyIK1ba7kYCoUC0PwSERERlvfLly+nadOmAERERJAd9LN7924yMjJqXE+KamVRKB9FMQihJefdcQc8/7zKt1DUWAorM/7+++/z4IMPkpCQgJSSli1bMmPGDAAWL17M999/j42NDQ4ODixatKjGObRVeGxNRUpNeaxcCYsXw0cfaTWlFIpKpCqExyo0VHisomRyN0maN0+rG7VwoaobpVAoCqAURU3n+edh1y6oX18raX7HHVr/C4VCoTBTrRRFjSvhUVG0bAlbtsBnn2n1o9assbZECoWiClGtFIXKo7gO9Hqt7erx4zB6tDb222+we7d15VIoFFanWikKRQVQp47mvzAa4X//g3bt4MUXITnZ2pIpFAoroRSFonD0eti6FcaNg08+0RL0Vq60tlQKhcIKKEWhKBp3d5g5U/NbODpqju4DB6wtlUJxXTg7O1ver1ixgkaNGnHu3DnL2K+//ooQgtwh9n379sXd3Z0BAwaU+jqBgYGEhoZaypPnZsuWLYwbN47t27dbyp23bNmSJUuWFLpWjx49sGbIf7VKuFNUEl26aE2RVqyA0FBtbOdOrfdFDUs8UlQf1qxZw1NPPcXq1aupV68eAImJiUyfPp0OHTrkmfvSSy+RkpLCrFmzynSNtWvXFlrq46+//qJv3760aNGCnTt3YjAYuHTpEi1btmTgwIEYDFXr0VytLAoV9VSJ2NnBXXdp748dg44doWdPzfmtUNxkbNy4kXHjxvHnn3/SsGFDy/iECRN4+eWXsbe3zzO/V69euFRgb5c1a9bQu3dvHB0dLUohLS2tVBnf48ePJzw8nObNm/OWufDn9u3bGTJkCKA1WnJwcCAjI4O0tDSCgoKuW96qpbauEynlcmB5eHj4OGvLUq1p1EirRPvSSxAWBm+8AS+/DLa21pZMcbPRo0fBsXvv1QpYpqRo2535eegh7RUbC0OH5j22bl2Jl0xPT2fQoEGsW7fOUs8JYM+ePURGRjJgwACmTJlSlm9RKEII+vTpgxCCRx99lEceeQSA2NhYbGxsyI7O3LZtG6NHj+bs2bPMnz+/RGti0qRJeHp6YjQa6dWrF/v376dNmzbs2bMH0JRgixYt2LFjB1lZWQWso/JQrRRFNmfjUnhs/q5Cj0mKzzz2dLKjayNvbmnojZujTWWId/Oj02lO7oED4dlnYcIELZR2+3aoYiazQpEfGxsbOnfuzJw5c5g2bRoAJpOJ5557jnnz5lXYdTZv3kydOnWIjo7mtttuo2nTpnTr1o3Vq1fTp08fy7wOHTpw6NAhjhw5woMPPki/fv0KWDS5+fnnn/n666/Jysri0qVLHD58mLCwMIKDgzly5Ajbt2/n+eefZ8OGDRiNRrp27Xrd36Va/l+dnmXkdGzR4ZzFWXf/nYxjwfZz6AS0CnCne2NfujX2JszfHb1O7cfnoVYtrezHAw9ARESOklBNkhSlpTgLwNGx+OPe3qWyIPKj0+n4+eef6d27N++//z7/+9//SExM5ODBg/QwWzhRUVHceeedLFu2rIAjujAiIyMZOHAgAI899hiPPfYYderUAcDX15e77rqL7du3061bN1auXMnzzz9fYI2QkBCcnJw4ePBgkdc8ffo0U6ZMYceOHXh4ePDQQw+RlpYGQNeuXVm5ciU2Njb07t2bhx56CKPRWCHWUbVUFI39XFj1XLdynZtlNLE38hobjsew/ngMn605zqf/HMfd0YYuwd50a+xD98Y++LkWrfFrHLm3B1asgLFj4fPPYcgQ5exWVEkcHR35448/6Nq1K35+fowZM4bY2FjL8R49ejBlypRSKQmAgIAA9u7da/mcnJyMyWTCxcWF5ORkVq9ezZtvvomUkv3791uq154+fZqAgAAMBgNnz57l2LFjBAYGFnmdhIQEnJyccHNz4/Lly6xcudKi3Lp168YDDzzAAw88gI+PD3FxcURFRdG8AnrPVEtFcT0Y9DrCAz0JD/Tk+T5NuJKcwaaIWIvi+GP/JQCa1nKhW2MfujXyITzQA3sbvZUlryLUqgV+ftre8Z13ar27AwKsLZVCUQBPT0/++usvunXrhre3N4MGDSpybteuXTl69ChJSUn4+/szZ84cbr/99iLnX758mbvMwR9ZWVmMHDmSvn37snPnTlq3bm1xWm/atInJkydjY2ODTqfjq6++KrYhUsuWLWndujXNmzcnKCiIW265xXKsQ4cOXL58mW7dtB/JYWFh+Pr6VkhJdFVmvAxIKTkalWhRGjvPXCXDaMLeRkenIC9NcTT2IcjbqcbVq89DVpZWN+rNN7XEvc8+gzFjrC2VogpQ08uMT5w4keDgYIYPH25tUcpUZvymsCiEEIOB/oAv8KWUcrWV5CCktishtV15tHtDktOz2Hoqjg3HY9hwIpa1yw8D4O/hYLE2bgn2wsW+hjnFDQat7Mfdd8P48daWRqGoMrzxxhvWFqFcVLqiEEJ8CwwAoqWULXKN9wWmAXpgtpRyclFrSCmXAkuFEB7AFMAqiiI/TnYGeoX40SvED4BzcSmsPxHDhuMx/L7nAj9tO4dBJ2hTz4NujTX/Ros6buhqilO8QYO8ZT+++QZOndIsDQcH68mlUCjKxI2wKOYBXwDfZw8IIfTAl8BtwHlghxBiGZrS+CDf+aOllNHm92+Yz6uS1PNy5H6v+tzfsT4ZWSZ2n7tqtjZimLL6OFNWH8fLyZYujbzp1siHro298XWp5k7x3Ftwhw7BtGnwyy9aaZDeva0nl0KhKDWVriiklBuEEIH5htsDEVLKUwBCiIXAICnlB2jWRx6EtuE/GVgppbwp6l7bGnR0DPKiY5AXL/dtSkxiOpsiYthwXHOM/773IgBDWtflnUHNa8b21GefaQ7uRx+F227Twmo/+UQLc1TUGKSUNduHVwUoq2/aWj6KukBkrs/ngeLSB58CegNuQohgKeXM/BOEEI8AjwCWui1VCR8XO+5q7c9drf0xmSSHLyWwfN9FZm86zY6zV5g2vDVt6nlYW8zKp2dP2L8fJk3S+nTff7+yLGoQ9vb2xMXF4eXlpZSFlZBSEhcXV2xSX35uSNST2aL4I9tHIYS4B7hdSjnW/Pl+oL2U8qnrvM5AYGBwcPC4EydOXKfUN4ZdZ6/w9IK9RCWk8fxtjXmse8Oak9h3/jz4+2vv582Drl0hV90dRfUjMzOT8+fPW5LEFNbB3t4ef39/bGzy7mQUFfVkLUXRCXhbSnm7+fNrAOatp+umssJjK4v41EzeWHqQ5fsu0jHIk0+HtaK2Ww1y9iYkaI7v1FR4+2147jmwqQFbcQpFFaMoRWGt6rE7gEZCiAZCCFtgOLDsehe9WavHujnYMH14Kz4eGsb+8/H0m7aRVYeirC3WjcPVVduO6tcPXnlF66q3Y4e1pVIoFGYqXVEIIRYAW4AmQojzQogxUsos4ElgFXAE+FlKeeh6r3Uz98wWQnBPeAB/PNWFAA9HHp2/i9eXHCA1w2ht0W4MdevC4sWwZIlWFbRbN4iOLvk8hUJR6VSrzOyb0UdRGBlZJj5ZfYxZG07RyNeZ6SNaE1Lb1dpi3TgSErSuev37a5937oRS1txRKBTlx6o+ihtNeBN/uXNWOf3iNvbg5g9u9bS/zn5aWW0rsPFEDM//vI/41Ez+168pD3YOrHmRImvWaFFRQ4fC9OlQu7a1JVIoqi01QlFkWxRta+vG7XzEucT5pUJnA651wN2sONz8wS0g71/byiupHZuUzsu/7uffo9H0bOrLx0PD8HK2q7TrVTkyMmDKFHj3XbC3hw8/1HphWEl5KxTVmXIpCiFEwaLpBUmWUpatkWwlEx4eLndu316+kzMSIf4CxEeaX+fhmvlv/HlIvAjSlPccR69ciiMgr0JxDwAnn+sqty2l5Lv/zvD+yqO4Odgw9d6WdG3kU+71CiMlI4uP/jrGLzsj8XGxI8DTkfpejtT3dKKel/a+nqcjjrZWSr05cUJL1Fu7Vkva+/1368ihUFRjyqsoLgEzgOKecqOklI2vX8SKo1LDY41ZmrLIVhzXzuW8jz+vKZeMpLzn6O3ArW5BReIeAHXDwa501s+RSwk8tWAPEdFJPNotiBf6NMHWcP2/rHecucJLv+zjTFwKA8JqI9HqVp2NSyYhLcsyz6ATfDGyNX1bWGn7R0r47jutf/eIEWAyaRZHGRKHFApF0ZRXUXwkpXy5hIVLnHOjqBLObCkh7VouRZLLMsn+mxgF2S1Z7dygzf3Qbix4Nihx+dQMI+/9eZiftp0jtK4b798VSou6ruXyXaRlGpmy6hhzNp+mrrsDHw0No3PDvOU04lMyOXslmXNXUpj2zwmMJsnfz3evGkmBM2ZotaNmzYLu3a0tjUJx01MjfBTZVPmEu6wMzSqJi4C9C+DwUjAZockd0PExCOxa4lbVXwcv8criA8SnZlLbzZ7ujX3o0cSHW4K9S1U3ave5q7z4yz5OxSQzqkM9XrsjBGe74reVVhy4xOM/7mb6iNbc2bJOWb5x5fDPP5q/4swZraveRx+BRw0og6JQVBLXpSiEEEbgY+A1aT5BCLFbStmmwiWtAKq8oshPwkXYMQd2zYWUOPBtDh0ehbB7waboDO24pHT+OXKZdcdi2HQilsT0LAw6QXigBz2a+NKjiQ9N/FzyWBtpmUY+++cEX284SS1Xez4cGlZqf4fJJOnz2Qb0QrDyma5Vo1x6cjK88w5MnaoVF5wzJyesVqFQlInrVRT7gb+A1sAwKeUVIcQeKWXrihe1/FSJrafrITMNDv4KW2fC5QPg4AFtH9K2pdz8iz/VaGLX2ausOxbDumPRHI1KBMhlbfji6WTL60sOcCI6ieHtAvhf/xBcy1i1dumeCzy7aC8z72tL3xa1Sn2eySR59bf9eDrZ8XSv4Ip3iu/ZA488ApMnQ69eFbu2QlFDuF5FsVtK2UYIcS/wFvAA8I2yKCoJKeHsf7BtBhz9ExAQMhA6joeADqWKoIqKT2P98WjWHo1hU0QsSemaU7qWqz0f3B3KrU18yyValtFE76nrcbY3sPzJLqX2jfy07Rz/W3IA0DoAThzcgh7llKFITKacsNl33tFKgzz1lNZxT6FQlMj1KgqL9SCEaA4sAOpJKd0rWtCK4KZXFLm5ehZ2zIbd30FaPNRuBR0egxZDwFC6fIpsa+NEdBJ3tqyDm8P1Fdz7eUckLy/ez9yH25VK4cQkptPrk3U0r+PGs70b8b8lBzgZk8ydLeswYUAzfFwqOC/EZIIhQ7QQ2rZttc56rauU8atQVEmuV1G0lVLuyvXZFRgspfy+mNOsRrVSFNlkJMO+hbBtFsQe03IzwkdDy+Hg0eC68jTKSqbRRI+P1+Hnasfi8Z1LtCqeWbiHlQeiWPlsVxr6OJOeZWTGupN8tfYkQT5OrHi6EvwdUhI9Zz6+b7ys1Y567jmtMq2TU8VeR6GoRhSlKIq1yYUQQ3K9r5/vcL5kAUWlYusE7cZoyuHUWs2Psf5D7eXoBXXbajkZ/m219w4VHP2TcAnWTgK9DTaeDZnYzIWJW9LZerwBnZoUHQG18YTWze/pXo1o6KPli9gZ9DzbuzH1PB15/ud9/Hs0mt7N/PKcJ6UkPjUTd0fbcon7+76LPBPhxfzfN9B1zhSt/Mfo0RASUq71FIqaTEl5FHNzfRwILM/1WUopR1eWYOXhpndml5Urp+DkWriwS3vFHMOSn+HZEPzDc5SHXygYyvfQ5dxW+PkBbevLYK/liZgxoUPnUR96vgGhQ/OclpZppO9nGxDmKCl7G32e49mWSR13e355rHOeYzPWneTDv47SrLYrtzXzo19oLZrWKl1hxNQMIz0/Wcel+DT6NPPj6wfC8zZJ+vJLuOce8K1gH4lCcZNz3XkUVTHKqSiq5dZTaUiLh4t74PxOuLAbLuyEpMvaMb0t1GkNrUaVGHZrQUrNP/LXq1pG+fCfwK8ZpFyBuJOs37qVvft2Mc7nKI5JZ+GxTeCV06Fu6upjTP83gh/GdKBLo8L7Ys/dfJp3lh9m8fhOtK3vCUB0Yho9Pl5HsK8zdgYdO89eBeCHMR24JbjgOmmZRt5YepAG3k6M6xrEjHUn+fSf43QM8mTnmatsea1Xjh/k5Elo1gycnbUaUg89dEO37RSKqkxFKIoqmzeRnxqrKPIjpZYJfmGnZnFErIHow6ULu81Mgz+fh70/QqM+MOQbcHDPMyU1w0iXD/+li18m0648Bt5N4OGVZKFjb+Q1Rnyzlf6htflseNG/L1Iysug8+V/C63sy+0Ht3+eri/ezePd5Vj/XnQbeTsQkptNv2kZa+rsx56F2ec43miSP/7iLVYc0hdi0lgtn41K4takPz9/WmN5TN/D6HSGM6xaUc9KRI1oo7aZNcOutWmZ3o0Zlvr0KRXWjqnW4U9wIhNDqSTW/C/pMhPH/wUN/QmAX2DwNPguDnx+Es1s0pZLNtUiY21dTEt1fgRGLCigJAAdbPWO7BvH7KcmSOs/D+e3M/fg5mr25iqEzt+Boa+D1/s2KFdHR1sADnQL558hlTlxO5PDFBBbtjOSBToE08NYczz4udoxoH8C/x6KJvJJiOVdKyYTfD7Lq0GXeGtiMbx4I52pKBkYpebVvCMG+LrSu587POyPJ84MoJATWr9cUxO7d0KOHVjNKoVAUSkk+iuVYNr3pBmzIfVxKeWfliVZ+lEVRCvKH3dYK08JuXfzgt0e0MiNDvoamdxS7TFJ6Fj0+XktsUjrfOn1JN+N2vmsxF/cGbejU0Is67iVvcV1JzqDz5DUMCKvDpfhUDl1MYP2Lt+LmmBPGe/FaKl0+/JdHuzfklb5NAfh6w0neX3GU8T1yxpLSs7ianEGApyPERrDglC2v/XaAJY93pnW9Qhz8ly5pFkbPnkijkd3L19O8f/cC/hSFoiZQ3qKAxVZak1KurwDZKhylKMpARjLsX6SF3cYc1ca8m8DwH8G7dNsxCWmZ6IXAKSseZnQCR294ZG3JeR6R2yEpGhr14a0/j/P91rNICW8NbMbDtxQskPjI9zvZefYqW17ryYnLSdz11WZ6h/jx1ag2BUN0j/wBi0aRcvd8QhcYeKx7EC/d3rRYcba/8RHhk15lT//htP1xBpSipe6Wk3F4O9vSyM+lxLkKRVWnvFtPowBPYLeUcn3+V6VIeh0IIQYKIb6Oj4+3tig3D7ZOWsjt41vhgd+hzyQYt6bUSgLA1d4GJzsDOHnBnZ9D9CFY+37RJyTHwZLxMOc2WDQKpobwgviBIBFFkLcT93XMH4mtcX+n+lxJzuC33Rd4ZuEePJ1s+WBIaEElISVs+BgAxz1zCK3rxrZTV4r9DtEJaTxtbMS88IG0/nMhppBmWv/ufHP2RV7jSnLONtVTC/bw7h+HATgVk8ScTaeLvY5CcTNSkqL4FmgJrBBCrBFCvCKEaHkD5CoXUsrlUspH3ErxS1CRDyEgqAd0fhLsruPXcePboc0Dmg/k7H95fR9SatVyvwiHAz9D1xdg5C9QvxOue79hje3z/Gn/BjZr34PTG7Xtr1zc0tCbIG8nJiw9yMmYZGb0tsX95DJIT8wrw6m1cGkv+LWAU+voVzuRfeevkZphLFLs91cc4YrenjpzZzH4gU+IdXDVsruffRbQalXdPfM/Bn25md5T15NpNBGblE5sUjq7zl4l02hi/tazvPfHYeJTM8t//xSKKkixCXdSyq3AVuBtIYQX0Ad4QQgRCuwB/pJS/lz5YipuKm5/H06tg7n9QGcAW2dN+QihNXrybw8Dp2mhtgCN+0DiZdi/EIdjf8F/02HTVLBz1UqVtLoP/MPR6QT3dfBnyYqVfOy3mqYrzC4zW2ct5Dd8NNQKhY1TwaU2jFwE01pxR9oKPjDezu5zVwsNr01Iy2TFgShGdqhH3xa1WNyrKwMaNGWb4wFEuGaF7z95mQuxSXRt4sfGE7HsPx9PeqameFIyjBy6mMDRS5rCik1Kv+4yKQpFVaLU1dKklHFoNZ4WgFbWA+hbSXIpbmbsXLRtrIO/aT6QjKScv12ehzYPFux57eIHtzyjvdIS4MxGOLIc9v8Mu+ZpPctNRh5OjGK0nRGZ7gY9/geBt8Den7TXzm81p3zUfi3Ky80fmg3C/8QSnER3tp2KK1RRrDoYRYbRxKBWWoZ5xyAv/j58mSsvPmPpT5722uss3voffgvmcUsEbI6IxcU+53+fbafiOBqVAGi1rbKz0MtDfGqmUjSKKkWJikII0RSoC2yTUuYu2+EjpZxUaZIpbm48g6Dbi+U7194VmvbXXv0+gkNL4MRqsHNFuNYG9/qI5oPB3rzFGNhFUwz7FmrKwqW2licC0G4s4uCvPOK1n82nCy81snz/JQI8HWgV4A5AkDks93RsMhevpbHhRAwxhlo8Hx+Fa88uTO45gmV1RhNQxxMPRxvcHW1Ztu8iV1O0LaeYxPTyfW9gb+Q1Bn+5ma/vb0uf5qUv465QVCYl1Xp6GngCOALMEUI8I6XM7mr/PlqPCoWi8rB3hbYPaq/icPSETo9rpdhNRtCb/2nX6wju9egvt/HluY6kZRrzhL7GJaWzOSKWR7sFWZzigbkUxd+HL7P68GUI7Eyr3+5l8I+fMuy77+iwYw1TH3yTJiFhtAxwZ9b6U5Y1o69DURy8oAViLNoRqRSFospQkjN7HNBWSjkY6AFMEEI8Yz6m6h4oqh5C5CiJ7M/NBhGUuBN7YyJ7zl3LM33hjkiMJsmdrXKsDX8PBww6wenYZA5fSqB7Yx++HNmGAT1DYd48jv7wGxk6A4cSJU38XHi0W0OcbDXlo9eJUlsUUkpMprzh6dlK5nRccjm+vEJROZSkKPTZ201SyjNoyqKfEGIqN0hRCCFChBAzhRC/CiHG34hrKqoZIYPQmTK5zbCHdceiLcMHL8Tz2T/Hua2ZH01y5UHYJEQy1mULCad2cP5qCh2DvOgfVhuDXvvfpemou7i0aTue4WH0bVEbz5efY4Y4Qu+mvvi52JVaUUxfE0HQ/1aQkWWyjJ0zK4gzscmUtryOQlHZlKQoooQQrbI/mJXGAMAbCC1pcSHEt0KIaCHEwXzjfYUQx4QQEUKIV4tbQ0p5REr5GHAvUCARRKEokbptwaUO97nuZfHuC2QaTaRmGHnanIvx4d1hObkYWRmwcCSvpk9nYvSTjND/S7M6BavWdm/qxy9dLtHJKR527aLbu88ze8EbtEiPIyYpnXNxKby6eD/pWTkhuUejEoiIznHzffrPcQCupeaEAZ81lygxSUjOMHIyJonYpPJvZSkUFUFJiuIB4FLuASlllpTyAbSSHiUxj3yRUUIIPfAl0A9oBowQQjQTQoQKIf7I9/I1n3MnsAlYU5ovpVDkQaeDkIGEpe8iJSmedcdimLn+JKdikpl6bys8nXKVX980FS4fZFn914iRrrQRJ2hWu5Dy5pf2w6+jYeWTsHEjfP45bNnC5x88QPelc/ll60kW7ojkiDlkdvbGU/T9bCMPfru9wFIJqVqbWiklZ+NyalldTc6g1yfruXfmloq9HwpFGSlWUUgpzwO9AIQQw/Md21zS4lLKDUD+lNj2QISU8pSUMgNYCAySUh6QUg7I94o2r7NMStkZLVO8UIQQjwghdgohdsbExJQkmqKm0exO9MZ0RjjtYOb6k8zacJL+YbXzhsseWwkbpkDoPcSHjOCQqQFhhsjCW7Xu/Un7e3Yz7P4WnnwSDh/mZMtODP3nR/YfPAvA8cuJ7Dp71ZKxfeFaKleSM/JsNyWkadFS0YnpXEnOoH0Drdz6lpNxAJyKVf4KhXUpTfXYukKIe4Ei6lGXmbpAZK7P581jhSKE6CGEmC6EmAWsKGqelPJrKWW4lDLcx8engkRVVBvqdYI6bXjNNBvnyHWYJLzaN1ftp8O/w6L7tIS9Oz4myNuJI7IeDYkskCFOVoaWWd5sMAT3hn8najWr/P35a+JM+j40nfVXBTqTkcg3JnL/tDVcik/jvo71ADhwIZ6TMTlbUAnmTO7siKdOQV4A/HUoCgAHGz0mkyRObUEprESxikII8RZaraefAE8hxJsVcM3CnOBFeu2klOuklE9LKR+VUn5Z7MKq1pOiKHR6uG8xRq8mfG0zlTdapWgVZgFSr8Gyp6F2Ky1R0MGDQG8nDpvqYyBL61Gem/2LICUOWt8HfT+EzFT49z0AGtdy4ZKr9kOl/YXDPLfqG1bPeZxHk47wbO/G1CKOF79dzcerctZMSNO2ng5eSEAI6BCkWRTHL2vbVlkmEwt3RNJ24j8ci8pXrqQEVh+KKrZ0iUJRGkraenoHbevoPuCKlPLdCrjmeSAg12d/4GIFrKtqPSmKx9ETu9HLMdg7cR8rc8a3fKG1dx3wqZa3AdRxs6dH917a8agD2t9jf8G+RZpS8G+nWRPewRB6j5ZFfuwv+u95jC/vaczDtwSi69GDoaM+Qu/qymtfvoT3mAfYmvUEa+xe5N+j0dgZtP/9si2K49GJ1PN0pLabVpr9/NVUADKNkvXHtWit1WYrozQcuZTAI/N38daygyVPViiKoTRbTxeklAuBCxV0zR1AIyFEAyGELTAcWFYRCyuLQlEijp7oQ4egO/qHVkwwKQa2fAXNh0DtMMs0IQR339YdDA6aoki5Ar88CEse0drL9p2c00K1fidIvQpr3kWcXk//uO95a2BzGvu5sNs/hH9/WgkTJ8LvS2FBCq5oPodGflqZj2wfxZWkDHxd7HC1L5gHm5yuWQV7I68BcDYumfiU4osPJpotlVMxysehuD7K0uGuzEHdQogFwBagiRDivBBijJQyC3gSWIWW8f2zlPJQWdcuVEBlUShKQ9hwyErVrIB/3oKsNLj1fwXn6fTg11yLcNr7kzZvwKdw73zwzxWp7d9e+xt9CHQ2sOVLSI6joY+W4d26oR+8/jrMeRbucAAhGFDXxIdNJSGGi8SnZvLzjkjOXUnBw9EWF/ucOk8e5uZN2dtQp2OTMZok3T9ex4NzC0ZQ5cZoTuYzqXwMxXVSmqKA5XZmSylHFDG+gmIc0+VFCDEQGBgcHFzRSyuqEwHtwSNQc0InXICuLxbdf6N+J/jvc63QYEBHrUJtfrwba3Wn0uKh5TDY8wNE7WNIm254ONkSUttFay8buRDqahncXxxfDM/PZ+UtNgx2+IEliWl00R3ALngEtgYdDjZ6UjONBPs6s+PMVUvGduTVFP48oEWsZ1sXRZGUbg67LddNUihysIYzu9JQFoWiVAgBYcM0JeHXQusLXhQ9J2hO6/QE6PBo4XN0Os1nAdDpSe3vpf042RkYEFZHS+bb8BEYM+D+pdrxga2hqQ7WpzPj0/FsjHqCqbYzaWnU/CGuDtpvuAbeTrQQpxio+w/Q/BVPL9gDQJh/wX/nGVkmS5JfUrq2NZXfoPjvZKyl0q1CURqs4cyuNJSPQlFqWt+vWQh3zQKDbdHzDHZw5xfwzH6tN0ZRtBsLHZ8A3xBwC8hxgKdc0UqlH1+lNXVq0F3rn5F8BO52hFGOuGclYvddAmxNp0Psb3BmM7VcNJm8nO14zm4579jMszi/symsFPmwr7fQe6rWfDLbR5Hfohj5zTb6frax+PujUOTCGs7sSkNZFIpS4x4AY1ZBrRYlzxUCPApvz2qhST/oa27/WitU26oCWPcB/DZOc4A37qtZHwHtYd8C7XiwAZvx9tDZlm0NWtAg5l+Y3Y8HhRaV5eZgQ2PdedxIpl39nH/XDjZ6UjKMJKVnkZSexXOL9nLPzP/Yc+4akVdSOXgh3qIoCpgUCkUZKWnr6U/tj3CSUi64QTIpFDc3tcIg9oTWrOl0rl/uwb21v4FdLUNHat2JwU7CbfY87PYax9u8CUtSGfTFZ9RJuExqSgp1TJfQC0kLz5ylGvk5k5JhpOuH/9Lmvb9ZsucCO85ctRz/afs5i6JIzzKRkpHFrPUnWVWG8FqFIpuSLIqv0YoAnhFCLBJCDDaHtFZJ1NaTokoQ0B6QmlM75gjc+gY8uhGczOVCArtof2u3JKTD7QDECQ9SsCep5cPQawD6U2n8M/txbl8+A71J8zkEu2gZ4j10ewlxy+RqcgZXUzIt5UB0mPB2sqVdoAd7z10jJUNTFJlGE4/O38UHK4/y6PxdxYoupWTNkcuqcq0iDyX1zP4d+F0I4QDcCTwIzBRCrAAWSCn/vgEylhop5XJgeXh4+Dhry2KSJpIyk0jMSCQxI5GE9ATtb0YCCRkJlnFvB2+aeDahqWdTfBx8cqqYKm5eGnQDe3f45x3tc4sh4NUw53jdttDtJc1JnqVFM12zqwOp4GhngLEjQfcPcndLms2YAXV0MMSBwU0ccPLwo9+fI4mIasOihJwOgiMbw6TzD5Ma1I9P3V/j+y1nCTEXM8wwmth4IrZUov+y8zwvL97Ph3eHMqxdvQq5HYqbn1L1zJZSpgKLgEVCiDDgOzSloS/2xJsYKSXpxnTLQz333+yHfv7x3IogKSMJWUxgokDgbONMYmZOSQZPe0+aeGhKI1t51Hetj0FX6tbm18WhuENsubiFngE9CXIPuiHXrJbobbQ2rnt/1HI2cisJ0PIzer6hvTeZwMGDgOCWfFw/jKa1XCHVD9x1OM2bBN9+CbMWg73AkB5Pv3pakp5n1mXLcr1D/Hi91QXEuUwcTyyj0YD3Sc8yERGt/dvKyDLRvI4rhy6WHOl0KT4NyMkKVyiglIpCCOGH1g9iOFAb+AV4uBLlKhfZeRQuDVwYumxooXOKe3gDZBgzLA/9TFPxma8OBgdcbF1wtXXF1dYVX0dfgt2DcbF1sYy72LrgaudqmZN9zMnGCZ3QkZiRyPGrxzl65SjHrhzj6JWj/HDkB8u17fR2NHJvZFEcTT2b0tijMY42juW6R4VxIekC03dPZ8VpLbVl2u5pdKjdgZFNR9Ldvzt6XbX9PVB5hI+BmGNw2zvFz9Pp4IHfsXXy5R7X2tqYi/lv0mVoYQNPeYEpHZJi4YUPwD2L9LAcx/bEwS1wOrTV8nnwml4s041md3QrAC4npHM5IR1fF7tC27RKKZmz6TTOdgbMvZksyXoKBZTcM3scMAJoAvwGvFya8uLWInvrybeJ77g6znWKnCeKac5n0BksD/bcSiD/g9/FxgUbfcHwxLLiYutCW7+2tPVraxnLNGVyOv60RXEcu3KMf879w+ITiy3y13OtRxOPJoR6h9KuVjuaejYt8wM9ISOB2ftn8+ORHxFCMDZ0LEOCh7Dq7CoWHl3IM2ufoY5THYY1HcaQ4CG427tf9/etMfi3hXGlbJ9Su2Xez85+2t8/X9Airuq1hzMbIfI07D8C51JwCT+LZ6d4rji6aTkX185aTrdNvcw7hnn0yvgkz7I2+sJdkltOxTHxzyMAPH9bYwCMykehyEVJFkVnYDLwj5TSVMLcKkM9l3pM7znd2mKUGxudDY09GtPYozEDGw4EtF99l1Mu5yiPq8c4HHeY1WdXA+BioymcdrXa0b52exp7NEYnCn8wZBozWXRsETP3zyQhPYGBDQfyVOunqOVUC4CxoWN5qPlDrItcx09Hf+LTXZ/y1d6vuKPBHTzX9jk87D1uyH24Gfjr9F/4OfnR2rd1xS1qZ27Lmm7eKgq5E85tAYdUmPkITP4Qp82X2H3oPt689VEcDP3g6pk8SxgLiVNJNju3LXNMkpd+2YdBn/PDKbtAYXn0RJbRxPdbznJfx/rYGkoTea+4WShJUbwmpSw2nk4IUaukOYrrRwhBLada1HKqRfeA7pbxmJQYdkTtYHvUdnZE7WDd+XUAuNq6Eu4XTvva7WlXqx3B7sEIBKvPrmba7mlEJkbSsXZHXgh/gaaeTQtcz6Az0Lt+b3rX782JqydYcHQBSyOWkpSZxNQeUyv9+6ZmpbLpwiYEAnuDPW62brTwblHlnP0f7fiIRh6NmHXbrIpbVAgtKS/D3LOi+WA4tESrXmvMgFvtES1sYHkab2/9GnHpqQKKwlYULC0+IKw2P2w9Z/k8e+MpftuTNz3qmllRlGfracGOSN794zDpWSbG92hY8gmKm4aSFMUKoE0FzFFUEj6OPtwRdAd3BN0BQFRyVB7F8W/kvwB42Hng5eBFxLUIgt2DmdF7BrfUuaVUD95GHo14s9Ob1HKqxed7PmfThU10qdul0r6TlJKXN7zMush1ecandJ/C7YG3l2qNbZe28cbmN3i9w+v0COhR4TICZJmyiEuLw3RFM7aNJiNJmUm42VVAwuezBzRFce0cOPtCcE+tNlU2Pnp42BFdooRfR0LcRTjsBiHXQC+oa5cK6ZKH9X+x1HgLc+2mENrgVX7YmuPb2hRRMBIqu3mS0SSZv/Us7g421HF3oG39kq3IbGskMa14357i5qMkRdFSCFFcqIQAqkzRGFUUEGo51WJgw4GWLasLSRfYEbWDHVE7OBN/hnc6v8OghoPK5aB+qPlDLD+5nPe3vc+SQUuw0xfSIrQC+PHIj6yLXMfTrZ+mq39X0rLSeGPzG3x78Fv61O9TonKLSo7ipfUvcTX9Ki+tf4nZt8+mpU/LYs8pD3GpcZikibi0OGJTY1lzdg3T90xn3b3rrt9/5eipvdzNIarZnfRAqyt1fodmebgKuHoaDmTA0hjw1cFAe2z842kjTvCWzXy66A7SSkTAkrFoZds0zl1JKXDZo+bGSCYpmbA0p49FjyY+9A7x476OWoZ6WqaRn7ad48HOgeh1ef97VDGjT1EBlFTrSS+ldC3m5SKlLLKN6Y1GlfAoSF3nugwOHsykLpP4sf+PDGk0pNxRTLZ6W/7X4X9EJkby7YFvK1hSjUOxh/hk1yf0COjB2NCxNPVsSivfVjzU/CEOxx1me1TxpbUzjBm8sO4FMkwZfNf3O3wdfXlizRNEJkYWe155iEnN6c1+/OpxTlw7QUJGQp7xCqNWLkXn6F3weEtb+HYqGPzg21RYkcpcORmAYJGzvSQwcbduAwayuFBMCGxMvuiodcdieH/FEfac07K/v1obwbt/HGZJrq0rk3m7qrhgEcXNyY0J0FdUGzrV6UTfwL7MPjCbAUEDCHANKHReujGd/TH7uZB0QXslan9Ts1IxSiMmaUJKiY+jDz0CetAjoAeutq68uP5FvB28mXjLxDyWw8CGA/lizxfMPTiXDrU7FHrNuNQ4Xt/0Ovtj9/Npj09p49eGmb1n0n9Jf5afXM7jrR6v0HsRnRJteX/8ynEuJ2u5DZdTLlNc1F250Olg6FywcYBthfhDArvCQ8/B0LHw2DD4aSVuKVdgqCOOIs0ybYhuE5/YzsQrK4GvswYUeblYc39uWzL51fZt3s8axdaMZtz11X/88lgnrqRoWeLHohLIMpow6HWWwHNlUVQ/VGiCosy81O4lbPQ2TNo+qdBSD9Ep0Yz4cwSjV41mwuYJzNo3ix2XdyCEwM/Rj7rOdQl0DSTIPYjolGgmb59M38V96f9bfy4lX+Kjbh8V2Oe309txX7P72HxxM8euHCtwze2XtjN0+VB2RO3grU5v0bu+VlcpwDWABm4NOBx3uNDvcuzKMabunIqpHEF92YrCXm/PsavHuJySoygqhRZDtOKDGYV0rHP21f66uMAHb8FYJ+ihbQ16JcVDgvb9vITmg/DhWp7TXzIsZIJhPh2EFiZ7zdw9L0hcIkx3mncNcy1zI6KTwGTiC5vpvL6jE19+pm2JZf9TKEpPxCSmM3/LmTJ+aUVVQFkUijLj6+jLE62e4KMdH7Hm3BrLQxngTPwZHv37Ua6lX+ODrh8Q5h1Gbafaxe7Zn004y7rIdWw8v5He9XsXGWp6T+N7+Gb/N8w9NJfJXSdbxk9cPcH4f8ZT16UuM3vPpIlnkzznNfNqxrZL2wqsJ6Xk/W3vszt6Nx1qd+CWureU6T5Ep0SjEzra+rXl+NXjxKZqzuHo5OgSzrxOshVF0wGQcBEu7s4JqQXNr2FukLTf1ICwvw/D8UzoZY9oqT3NW+kiaCki+N3uTXqmT+EJg9aNeIxhJU3T5hKUHMEJgrFDsxzSyfnvl5CaiXNmLAP0WpLfnfE/AG9aklkzTZK0TCP2Nnm3OEfP28GBC/Hc1qwWtdzsK/y2lJuTa7W+6aN+VeZQEZTKohBChAoh7jG/SlGXWVHdGdF0BI09GvPhjg9JydScogdiDvDAygdIM6bxbd9vGRA0gHqu9Up07NZ3rc+DzR9k9u2zGd50eJHz3OzcGNp4KH+d/otdl7XidhnGDF7d+CrOts7MvX1uASUBmqKISY0hJiWv72Bb1DZ2R+8GYNGxRSRmJPLWf2/x38X/SnUPYlJj8Lb3JsQrhFPXTnEl7QqQd0uqrJxLOEfvX3pzNuFs0ZOyw2Z7vQmjftH6anR5Pud4dsIe4FC/rWZZ1NXDijTG/rQMoo200x3ndzutD1lvXd5CgVNsZjHL+CaddQdxMm9bherOoMfcECk5Gb8DOdtftsLc98JsUcxYd5KmE/4qYG0euaTFveR3fn/x7wl+2nYOqzF/MET8A4eXWk+GKk5JZcbdhBDrgKXASGAUWpHAtUII18oXr2yo6rE3DoPOwBsd3yAqOYqZ+2ey+cJmxqweg5ONE/P7zae5V/NKue7DLR7G38WfsavG8vOxn/l8z+ccv3qcdzu/i5eDV6HnNPNqBpBn+0lKyYy9M/B19OW+kPtYf349L65/kd9O/MZjfz/GzH0zS5QlJiUGX0dfGns0JkvmJLNdj6JYeGwhl1Mu8+epP4ue5OBu/uupVaQdsypvvwydDkLvhYHTyNTZg6cO7nOEwfZ4X7kGs5LhcE4I6/9s8nYQ6KbTemnUFbG4k2QZbyY05SW3fMXDhlWWcVuyOHQxnn3nr+VZJzuCKpsss7M7vwKZsvo4/1tyoOjve6M4vqrkOTWUkiyK94CdQCMp5V1SysFAI2AHMKmSZSszKurpxtLatzV3Bd/F/EPzeXLNk9R3rc/8O+ZTz7Xyqo56O3jzU/+f6FSnE+9tfY95h+ZxT+N78iQh5ifEMwSByKMo9sbsZXf0bsaGjmVUyCiklPx38T/GtxxPn8A+fLn3S84lFP8r93LKZXwcfWjikWPF6IX+unwUF5MuApqlVBSn7/iQtDs+Bmefohe6+xto+xDJJrM1JwS0tEU87ghtbKC+eVsoK+ehLc2Z/Do0f4YeE24ixx8SrtN8Q3Yyb7SUDVn0n76JdcfyWmz9pm2k3aR/CohWZctI3TzFJ244JSmK3sCruct3mN//z3xMUcN5ru1zuNm50davLXNvn4u3QyGhmxWMq60rn/f8nMdaPkbnOp15MfzFYuc72jgS6BaYR1H8HvE7DgYHBjUchL+LP0MaDeGOBnfwWMvHeKXdKxiEgUXHFuVZJy0rjfj0eMsv4phUzaKo51rPklMS7B5cbotCSsm+mH0ARW49pWWlce/6p/nVuXRFIUPq+eUdsM+C/g7gpNOe2N+lwO+pkGIittEwAJzN200GjHxgM8dy6ls28/EX0aTJ0rekiUlMJ8uY9wFcUmFOq7F/kZbgqChASc7sDCllVv5BKWWWEKJgGUpFjcPD3oOVd6/EXm9/Q8tr6HV6nmj1RKnnN/Nqxo5LOwDtYbvqzCpuq3+bpQrv253ftsz1cfShV/1eLI1YypOtn8TB4ECGMYN7lt/DmYQzBLkF8VG3j4hPj8fP0Q+DzkBD94YcjjtMqE8oyyKWIaUs8/2ISY2xOMTPJJyxjKdkpnDy2km2XtpKn8A+pBnTiE4tnTJydi5mh1iiWRb/ZcDxLOwfvQQu0uLQfdKwtMApLcQZ0sjrczKVkDfxxtKDTL47LGd+VdITpnxWxMHfoMuzVhGlKlOSRWEvhGgthGiT79UWqJy0XMVNh4PBocrVYMpPM89mRKdGE5say7/n/iUpM4lBDQcVOX94k+EkZCQw96AWFvrDkR84k3CG+0Lu41zCOUb+ORIHgwN9G/TV1vdqhqutKw3dGpJhyuBC0gVm7JvB2/+9XWS5+l2Xd/Hi+hctoblHrxwFIMw7jMjESE5dO8WCowuYtG0SI1eMZPqe6Wy6sAmA5MJCZAvDYP7f1MGz4DG9gN728IgTeAhcPl1G5k+pvOngwTWdjlriaoFTuun2YczXhiarkN+bzcVp9tuNwYerLN2bt56UqQhNEZ2QRuhbq5i98VTh38VUsH7VdZN/i6+IQpo1nZIsiiigqApwqhCg4qYh26G96swq1p5bS22n2oTXCi9yflu/tvQP6s+MfTOIuBbB5gub6eHfg1fav4K3gzef7f6MCe0nEOCiJRw+0eoJhjYeakm6m3NwDr8e/xWA3vV7YzQZ6R7QndPxp7HR2eDv4s+41ePINGXyavtX8Xbw5kiclsPQxb8L+2P38+7Wdy3RXdlsvaiFpOZueFUsRvOGgKMXLzhryvyTmLi8c2rpYbQTxqu3cm3pSv70dMIn2cRT17SgkDMBdxEYuQSAkYa1RJjyJhO6kYRmnmjrNxen+dPudQB66veyKPNW5v6+GjsyeNawGP3V+uBZMHiy/ftaWfaJfx5hbNd8jbN2zoU/ntWKJQ74FMLuLd33L4kCiqJq/+CxFiWV8Oghpby1qNeNElKhuF5CvEKw19szeftktkVtY0DQgCLLsINWrXfiLRO5K/gutl3aRnPv5rzS/hUARrcYzfLBy7m3Sc7DytvBm+ZezanrrFW0+e9CTojt+H/G8+S/T3Iu4RzPrX2Ot/57C8BiacSnaw/kiGsR+Dv708CtAQDnE89b1vj6tq8x6AxsvaQpiuTMUloUWamkCkGmnROrnbWXqeXIAtO+DZqC/uPvWf6sDxm2OmSGhEUpcNFI4Jh5eeYG6y7m+WwnsjhsNxobNKX0vOFXyzFnUnAmhYf33MOPtu8z3rCcWvO7cnHhs8Rc0yKq/rR9ja9tcnpnOGTnX/wwFD4yK4yd5pIxGUnwWwV2OjZpMh+ytdE8JzsrpzTNzU5JjYvaAZHZZcSFEA8AdwNngbellFcqX0QQQjgBG4C3pJR/3IhrKqoXTjZOLL9rORHXIriadpVbA0r+nWPQGXj3lnd595Z384wLIQh0Cyz0HH8XfwAuJl/E39mfC0kXLM7bBUcXcDL+JJeSL+XJBL+apm3xxKXF4evoi6+DlmV9OeUyvev15u3Ob+Nm50agayAR1yIASMpIojCWRiwFYHDwYG0gM42hdWuRbptItg/5cu8JvHehD1/FPgTAgPSJhLl1AhsnDOZf1M5XTHDeCHNSwPl5sJdgW/SvbUeRznOGX/koaxi99Hss4w1EFB10mqUUrjsOaFFVdY7Opc++pqR5NGGD7izNOQvmHTonO/NjKeLvnAukXSt40eRYyEwF98LLyJQKYyYbHex5vJYvE2KvcO+VIra9ajglbcjNAi01UwjRDa2J0fdAPPB1SYsLIb4VQkQLIQ7mG+8rhDgmhIgQQrxaCjlfAX4uxTyFokhqOdWiS90uDGw4EGdb50q5hpONEx52WknuYPfgPDWffjjyAwApWSmWBz5ovcpDvwtlR9QOvBy88HHMCXsNcAmwlDPJbiwFkJRZuKKYsHkCEzZPyMlV6PAo52xsuCxz/CS/nVzCebtkpmYO5QP/mRyUQegEoDdgYz7vYn1XeMIZRgyCTz+Fr5LgREFfyzqvuiSZlUuIOMsQ3cY8x91FMnNsPylwHoAD6fhc3VNg3NmukKKVaZrVFdqgHiNr+8HlQ/BxQ/jMvIU1tz98N7DQ6xSLKZPZ7prD/5TN9XesrK6UpCj0uayGYcDXUsrFUsoJQGlqec8D+uYeEELogS+BfkAzYIQQopk5+/uPfC9fIURv4DBQSQV0FIqKJduqCHANINA1EIA2vnlbtvx9NufX8neHvrO897DzwMchR1FkrwXkCT0uzKJIzMjxWwz+fTCXki4R+kvB/JKZ+2Zy2vZDphvvQh/sAUjuaav9Ks+2GTINdmAv4MM3YONGsBGwOSNP67sLBj1Puep5w0dLdLxVv4+ptnkTFYuLiGqii2SxXU5PcRuyeFi/kiuJKRzLlay3aMc54jMS+cJdU5gH7O0g/nzexc5ugtMbILL46sJ5hTPBp83Zba+VEzFmi7r6De3vmc2QVMnlWG4SSlQUQojs7alewL+5jpVYJ0pKuQHIvz3VHoiQUp6SUmYAC4FBUsoDUsoB+V7RwK1AR7TM8HFCFL6xLIR4RAixUwixMyamEso8KxSlJNtPUc+lnmWLanyr8dzf7H7uaHAHDgYHFhzNyYbOXZbc08ETe0NOHaQwn5ywUi/7nMzz/BZFSmYKz619zvL5VPwpVp5ZWaycBrddzD/7EnPGO9AywB2AdLN1kKkz/28m9NClCzzqBEMdNGdvogn2ZJBsVgJnbIp+FBTWkjWbj2y+yfN5rH4Fb9nM507j39z+2QbL+CuLD/CmtyezPHIl0uZTlCbgtI0B5txW7HfOQ2YKueOoFrqa62X99znEnYR5d8CURqVfrxpTkqJYAKwXQvwOpAIbAYQQwWjbT+WhLpC7OcB581ihSClfl1I+i9Zx5ZuiendLKb+WUoZLKcN9fIrJWFUoKhmLReESQJh3GI4GR0I8Q3i53ct82O1Dnm79NPHp8fRr0K+AQ93TXgtjHRg0kAebPZinTW3uEiXJmcmW7aUPt39Ih586sC1KK3z4cIuHgbzO8MLQ21/S5iXlzEsz12FaaytIFYLI7O0YgwBns6y7M2FZGnWW+hJ4Kb3YLAp7is4wz88rNgsBqC/ybh68YlhArD7fdlR6LkVx5TTfurlyp38djtnYaA7p35/MO9+YBes/gvS80WLp+aKcUrI/Xyum1lYNpKSop0nAC2hbSF1kTpEWHfBUOa9Z2L+rElNwpJTzSnJkq1pPiqpAQ/eG6ISOILcg+jXox7/3/punbPqokFHM6TOHtzu9XaC8ebaieL/r+7zYLm/GuYttToVYozRaIp+yCxsC2OpsuS/kPgCOXS1Yjj03Q8NrA1p/8rQsLRs7+8GZZMqgW8NG3LFiuFb08blDOSd2s4U77bE7eYHFEyK49484MOb9X1gCi1yc6WyTN7y3NIwzrMjzebxhOVn5nhqnknL91pzeir32Wr7IJYMB/ngO9szPe8KhJbB2Esy5HaKyXaaS/JkZi13MvqvKyNm4iSmpKKA92rZPL+C+7G0oKeVxKeXu4s4thvNA7jAFf+BiEXPLhKr1pKgK9Avsx+KBi6ntXBshBE42TnmOCyFoX7u9JSscwEan/XLPVhSFkb+YXna1Wr3I+bW9cfhGvOy90AldoX07cmOj19b7cu+X3PuHFuqbFj7acjzNqCmPabungVuOrwQhoLUt//09m3/aujJiWRyszVuoYYu9PRO9PfnUw71YGUpLfl9Hej7LQJjvTUruyrRZuayZLHN9quhDMNNcTl6aMOazKD7yMvcGX/X69QtdjShp6+k7IBw4gOZ8Ljx8oWzsABoJIRoIIWyB4cCyClhXWRSKKoFepyfYo2x924Pdtfn2+qL7NHT174q7nTvPtHkG0EJwM4wZljLvPQN64mjjiF6nx8/Rj3Rj8VV24tJyEu9Ox58GIENf0N8QnxHP2FVj+d05R+GlC7jiZsMr4wN4/60u0Mlc/+mqCW6bzlk3LcRXV8q6TotcnAltUC9n6ycf+Vcx5ct3SDX7VF7xzVVrbP5duRYoRA6TkQL1ibKJLV7J1jRKUhTNpJT3SSlnAUOBrmVZXAixANgCNBFCnBdCjDHXjnoSWAUcAX6WUh4qbp3SoiwKxc3Ge7e8x/iW4/mg6wfcHnh7Hp9EfrwdvNk4fCO31NF+EY9bPY4+v/YhNSuVgUEDmdZzmmVuUe1ic7Mucl2ez1JKskwFH50maWJb1Dbe8PEiXcB/DvaEB9bjzf+0fhb7wuuAj4/2MF6SCkNfpXZad5ASh8Ie0Pl43duTid6aJRWnz3kkbbG342cXZ1KEwJBPVUTmc6BnFqZfzm7K9eUKcW0WYlEAHLDVlN47Xh4sdXbKyW6vwZSkKCyB04UVBywJKeUIKWVtKaWNlNJfSjnHPL5CStlYStnQ7AepEJRFobjZGBw8mMdbPU5D94ZM6T6lxCZPkHd7Ki4tjovJF3EwOOSZ08O/h+X9M22e4ateX9HIo/gIniNXjrD4xOICbWg3nM+JQAoPrMcW+0KsnheOa1tSfe2hdm16PP8l06edw+1KyY+NZS45OS3SvMVUmzgeqe3He96edAgMICAz7zrb7POWmrui0+daoyAyv89hcj2Y17+A7wNgZN1a7LWz5VdXFyb4eMHP95f4Hao7JSmKlkKIBPMrEQjLfi+ESLgRApYFZVEoagKF+THyK4qOdToCWnHDsaFj6erfld/u/K3AvNwM+0MrM55dUiSb/OVC9jdon+dzalYqZG9Z1dHT8RkdU4fXocPhJB6afAkii1YW+VP4snMZttjnjZWxy2eZNMnIe+YZ2xwF+7NLwWRKufKVvANp8RBzFGMRMVvTcvtWjq0odE5NothcCCllISmSVRchxEBgYHBw2faHFYqbCRu9DS62LnkS7Bxs8ioAJxsn1t27Dle7vGXGRQklwUvD7qtH83zOXcl2raMDyaQzt68nq9o6M/W3CzSvreOiQU96FjTIFWeUKARX84W9FtU6KMO8RZSV2BSDy9E8Ya35LYiJ3p4MS9TCZy8npLH+eAzddJJn/fywlZLwtHSevqopw8IsCoCdDlWop3cVoFrV1FUWhaKmkD+SytFQsJGRl4OXJZoqm5SslDJdx8XGpcQ5yVmaoljcdihP++XkMF30seXRsfVIttEx2Lc2upnJbN4MEVL7fdqrXl36B+StRFtUJneaEDilO5N6YRQAmULwo6sz//P2Ypd90R0Pnp08nX9+m8OPri4csrNjj70937jnPB+SS1FWvCq1z7AWJWZXKxSKqkd2R71sittSuh68HLxKLGmekpmClJK3rxQsnxGv19MxMADnFCO7Gjsy5J9rXNxnw8Let5HaoGA3OROwwcGepvm2ltY6OeKSpgOzkpnm6W45ttwlr9IE+MvJkb7JKSyw1Vyg3V0Kz+kdWbdWoeO5GV7Hj0UlzqreVCuLQjmzFTUFW33edqSlVRTDmgzDVlf6VqbF5XVkI5GsOruq2DlJjnreGuPP6FcCMelg+IIVTPr6PPbpeTebjAKeqOXLiDp+BdYQUkfh+boFeSl3mCxwJX9mN3n9IxlXOhe51mE71aOtWikKtfWkqCnkf9iXVlG80fENdt1f+mzp3GVDimPx8cWlmrcjxJnRbwex65bm+MRnkm6T98H/saeW8BZtKLjZkeBwrVTXyOZ71+K3zb5zy/HfSKPySRRHtVIUCkVNIf/Wk7NN5ZRNr+1Uu9jjPw/Qqv9nN1QqDam2Ojb1DuLRFwKROoHP1Uw+/fwcAZfTK9SJ/LGXh1YosBDWOThwLs+xmypu54ZTrRSF2npS1BRyl/8AaFurbbnWeabNMwwIGlDkcQ97j2LPLy5BsCiMCC40aY00l9sIvpBGh8NJ/PZGBGP+iMGQVT73cVLESwXGChQTNPNULR+W5M7fMNqRFlX0fSCtymUD3FCqlaJQW0+KmsKbHd/k7kZ3s+aeNawftr6AhVESIZ4hANzf7H4+6PpBkfOKKykCWt2qspKAPT7BOQ/pLS1cGPR+IzaGufDsr5dZ9HYEoSfLFp0FIDMLKrVLBgPnCtnGyk/mtXYgi/HdZCQXfawGUK0UhUJRU6jtXJu3O7+Nr6NvqRzO+Zl12yzW3ru2SAVzZ8M7aVerHQadwfL5pzt+spQG0Qs9w5oMK5fsQp/B94e/zzMW42HD80/V4+mn6+GWbGT4mqK7LAdmFOy0p1Hwcfa6j1eBENxCkTYgi1F6X7av0cpChccqFDWQ/FtK8/rO40z8Gd7e8jYAk7poYaX/ntN6lUWnRBPqE8rQRkPZdmkbP/X/iWZezQBwtXUlIaPg1kwP/x6sO7+uWDnC/cLZeXmn5fPaNq5sD3FCbw6GahSZRuPodH7xfQpTuhbK+l5sHPfXKTmstWwU/5v5pCmFwE9D0b9SM3tqK4tCoVDQ1q8tdze+GyBPkl7H2h0JcAngkbBHAOjboC+bhm+yKInscwujZ72ehY5n9xQHmNF7RoHjyQ56Epw038KDf8Uy+fNIPp+/lEcC6/DXs10LDZBNPT9S+3txaDHfsnj0zseLPDbf1RV9alyRx6s71UpRKGe2QnF9/DrwV/66+y/LZ0cbR1YMWUG7Wu0sY/mLBk7uOpmf7vgpz9iM3jMspdPzczX9quV9/szx/Lz9cF229fei56mdvPTsXTRd+hPJt03NMyc9tidZiWE0reVCVnybIla6Pha7mn0qx1dXyvpVnWqlKJQzW6G4Ppp4NsHX0bdM5zjaOBLqE8qm4TllvTvW7oidIcf/4eNQeHtiva74sNQsg2BfL3duH/0FiaGtYPx4Wm+NzTMnI07rfvDR0DCOvHsHiUfeL5P82aRdvEe7ZnKDoif9+nC51r7ZqVaKQqFQWI/selO3BtyKQWfIU39qTOiYAvOfbv00QIG+4fm51GgkZz3qkLlyFSxciMPY8XSo1YEGF9Pp6NGaNc/15cSkfoT5u+Ngq6fcjzVpS+KRyaRGji56TkYSbP8G/vu8fNe4SVHObIVCUSHY6G34ffDvliS9us459ZUKi67K9m38eMePjPhzRJHrBjZoyJkB/bUPw7RIq487TcJmTChOTlsRX++C7t3LLXd6bL5zZc52mEytg3DI16l5hbmXuZ0rKaGjkBKc7Kr3o1RZFAqFosIIcguylBPJnWNhbyiYj5HdTa+Fdwu+vf3bAseLw8PVD+e5PyKyjNCjB4wdC1eKDqktjkdDnyrymMzlOp/o5UFq7ryR5U9z69s/0/yt4utcVQeUolAoFJWOg15THkFuQZax3G1XG7o3LPLcUO/Qwg/cdhscOACvvALz5kFICJw6xR9PdSm1XGlRAwn2deabB8ILPS5lziNykasLT/j5cDZXAt+bHi/gYHNRc3LPGwAmEzGJ6cQkFt+v/GajWikKFfWkUFRNwnzCAK1kSJe62oM8K1d35ezEvmyyix76OPjQxq+YSCZHR5g8GXbu1LalGjTAwVaPuBpU9Dm5MZctv61ZwWq1ABlXuub5vMPBngEBdejrX4fLej2v+npjCJ6utUs9sxGM6bSb9A/tJv3Dsn0XOXKpepT+qFaKQkU9KRRVEx9HHw48eICe9XrSP0jzNzRwy4kuMoi8isLdzh2AXwb+UroLtGoF06eDEOijLvH3hI08sDIWvbGEulFSh8ncZtXBRs8doVoinzHND2NqAFkphVs6F2wMfO6R6zmTlVZgztML9tBv2sbSyV/FqVaKQqFQVC3ubHhngbEBQQPYe/9eAlwCLGO5LYop3afwze3f8HTrp8tVnkSaTBz0C+alRVH89O5JQs6kApCVFMyDTR8lMyEUY5pmQUgEJnMW+JH3+vLVKM3BnnL6OVLOPAGy6Efk77mKCoY2qMc2ezt+2VmwGVN1QCkKhUJRaUy8ZSL7HthXYDx//kRuRXF74O0EuQUxLmxcuYoOZvjVYczdb/LC4wH4Xs1iwTsneWHhJdIuDOOh5uNIuzAKU1p2/SdRoNXpf6/myigvRlHkZ6GrCwf++JL24gh2ZNBJd6jMsldVqndMl0KhsCpCCEQputLphA5/Z39GhxaTw1BKjCYJQrC6vRtbmzvz7C9RuMV6Ik0uGMylzRFm9SB1SJlXVfi65A7lLb2ikMDj9j+QqNOxN7UT9xg20Cf9w+v7MlUEpSgUCkWVYOXdKytkHaMp58Gf4KTn3YfqknGpL1/e0QaX40f4cuVUPh7mQ5wbmqLId75Bn6McHuvWiB+jS3fd/xzsua2eljty4PQGAFbbvQKHaoGTNyRcgrB7ruerWQ219aRQKKoV/h5aKG5WUhPL2IdDutM/rDb6vXvof3QT/05ZxbjNAWQlNC9gUQDUdrPn1X5NefiWUkZPAam6Ih6nvzwI8/rDb2PL9kXKwsl/YWYXMBkrZfkqryiEED2EEBuFEDOFED2sLY9CoajaeDjZcmJSPyZ2/IyMq+3wyOrGoEbmzO6HHoJ9+9C1COXpb1ayYOEEeohrBdbY8lovHuveEH1RD/8KIt2YjrEiHu7z74KoA7Cg6Az366FS74IQ4lshRLQQ4mC+8b5CiGNCiAghxKslLCOBJMAeOF9ZsioUiuqDjV6Hn6s96VF3EyDvz+sUb9oU1q2Db76hU0IkfssXF7mOrhzOdIBMYJGLM5O8PPjQ051IQ+HFD8N/COfBP5/MM5ZlNPHO8kNEJxYMuS2RE5WTJV7ZPop5wBeApZ2VEEIPfAnchvbg3yGEWIbW3Tx/T8bRwEYp5XohhB8wFRhVyTIrFIpqgCzgfciFTqeV/RgwANzdtbF168DWFjp3zlmjkG2p0jDPzZXpnu6Wzz+4ufL7+YsEAWlZaRyMPUh4LS0bfN+VTXnOXX88hrmbz3DxWiqz7i88Y9xCRgoc/aNcMpaFSrUopJQbgPwFWNoDEVLKU1LKDGAhMEhKeUBKOSDfK1pKaY5y5ipQZGNgIcQjQoidQoidMTExlfJ9FApFNaNWLbA316F66y3o0gUefxzM1R1yO8bLwjfurgXGBvnX4fjpNby3sC8Pr3qYyG1fWY5NW/eK5X32JbNKShYE+PZ2+G1cuWQsC9bwUdQFInN9Pm8eKxQhxBAhxCxgPpp1UihSyq+llOFSynAfn8Jr3ysUCkWR/PknPPsszJql1Y367Td8XOy4x+9L0mN6lWmpohzbd294lmVGrVNe0po3LeOzz66g8zsTSc0wWoKJS1QTUkLUfkDb6nrby5MoffH9PcqLNRRFYZt+Rd4TKeVvUspHpZTDpJTril1Y1XpSKBRmgny0zOmBYXVKmGnG2RmmToVt28DPD+6+G7F6NW/27UZGbNkURWk4YWub53Ni4CKe+fZtinKLnLh6gkNxuZL4DvxqebvFwZ7Frs684+0JWRVfkNAaiuI8EJDrsz9wsYi5ZULVelIoFNnUdXfg+MR+DGsXUPLk3ISHw44d8MMP0KcPAI1jzqEr5zZUUbzu41VgbIvDUlIyjIAkhg2kZKZYjg1ZNoThfwwn9LtQMowZ8NtY9trZst8ur8IhMapC5QTrJNztABoJIRoAF4DhwMiKWFgIMRAYGBxceK9ehUJRs7A1lPO3sMEAo8xxMzEx/PrDy5xeJ3n34bocDyjYW6MieWPhv+gdrnJGzGPSNs3Fu+zksjxzTsefpglwfx2tiOG4a7l2UcoZqVUclR0euwDYAjQRQpwXQoyRUmYBTwKrgCPAz1LKCimKoiwKhUJR4Xh78+vo1/CPyWDh2xE8/WsUdhmmks8rJ7b+P4IuA4C41LgCSgLg2wX9OG6T04nvG/dczzxR8X4KUd7wr6pILoti3IkTJ6wtjkKhqCb8vDOS6dv68MKiKO7aeI2zfrYMe7shyQ6V4zwuL11SUpkxagO4ltIvkw8hxC4pZYGY3CqfmV0WlEWhUCgqg1ub+BLvbODNMf6MeTmQv9q7WZSETWblWRdlJVUILRqqgqlWikJFPSkUisrAx8WOrGSt7tP2Zs58cbfWz6LxuVRWvXicAZuvVsoDuqzscrAHWfGKq1opCmVRKBSKyiL13CPI83fnGcs06LjobcMH31zg6yln8I+2fq/s04kV3zypWikKhUKhqEySEtvl+Xy6jh0PvB7ExPtrE3oylSWvR/DAX7FWkk7jcmrFX79aKQq19aRQKG4Ufa8a+CIqmsyEtizq5cWgDxqxOdQZl5TKKfVdWi6nVnwJo2qlKNTWk0KhqCwmDm4BQOqFEaRFDeSi3VReuPopKZeGAhDtYcOzT9dnxmBfALruS+TlHy/hmHpjFYe+Eh7r1UpRKBQKRWVxX8f6zBjVhqyElmRevYV37m5HLG7kfowuj7yIydxutdmZVEb9E8fS1yPovifhhsmZkp5V4WtWK0Whtp4UCkVl0jnYGwAXOwPBvs50bpi3DEdgVs5DetYgXx54PYgkBx1fTDvHJ1+cw/taZqXL+OeFjRW+ZrVSFGrrSaFQVCbZCcrZVTK+eaD4fhH7gh25952GTLvbl+57E+lyIKmyRSTdVI6GRyVgjVpPCoVCcVOSXRdQZ95esstXS2pExutoHRFyyDLomD3QlxUd3bnorZXduHV3Amf9bDlVt+LrRnnZeFb4mtXKolAoFIrKJNuiyG6Rmr9Vqm1wjyLPvehjC0KgN0peXnCJX988yeNLLld4ZrebwblC1wOlKBQKhaLUZFsU2eoh27LIprjCrcZ0LRrKqBeMmtCQVe1dGf97DIsnRND2WHIlSFtxVCtFoZzZCoWiMsnuwy2K0Ag6IciI61LosZQzj1veX3E18NqjATz6Qn1ssiTfTj5Ng4sVk9Vd8UXGq5miUM5shUJRmfg423F/x/p8NzonQ/vpnjn9b9rW90DKvI2EUs6NJi1qQKHr/RfqwpBJjXj5sQBO17EDoHFk2nXVjRIlN1EtM9VKUSgUCkVlIoTgvcEtaF4nd/8HQXp0H+rYhTK+e8MC5xiTG5N5tXArAyDVTseqDtp6DS+kseitCL747By1YzPKJWMjx8BynVccSlEoFArFddCxgScZcT15q930Aj6LvOT80rdLql/gaN3MLM7UsuOTYbVodzSZpa9HcN+q2DK3YPU0VPyOilIUCoVCcR10Dvbm8Lu307mhloxXx8WjxHPiE9sUGPsrYAhGveCH270ZPCmYHU2deGVBFPPeP102ZaHKjCsUCkXVw9E2JyXt77Gv81r71yyfZ97X1vyuaGvjsZaPwe2TLJ8vedvy5LP1ePHxAP5u52opC1JYKG0B/4dqXFQ8KupJoVBYGxudDSNDRlo+395ca3KEqWBynUhsxC0uL/FEqycASDrxGqZMbeuoVlJtVrV3Y/7t3mQmhNF1byLLXjtB5wOJedbISgzN81miLIpiUVFPCoWiqvBo2KN80v0ThBD4ezgA8FLYVJLPjLfMSc/y4ItB91k+yyw3kiNeYtvIbUQa8/a9TnDSk2kQzPrkLJNnRuIel6N4NgzbwADXW8yLVPx3qVaKQqFQKKoKT7Z+kj6BfQC4u40/AN0COmNKrU9mYguMabXJiOuOQZ//MWzA0caR3E98abRjXyNH7n4vmK8G+dBnRwJ/TNhLv63XAPCw96C5g9aqVVaCj0LVelIoFIpK5tnejRjfoyH2Nnp8XOyISYSU088UmLfyma642BfyWDY5kBTxEgaXw8y4609WtXfjzW+SscuU/PnkbeZJZoWjFIVCoVDcfAghsLfRA7Dl1Z4cjUqkrrsDCWl5y46H1HYtcg2Z6UXmla7Y+/3Jqbr2DL13Cu7OOt6r5QOzZ9Ns62YMnUxk2ha9RnlRW08KhUJxAzHodbSo64aHky31vZyKnBfkk1PcLzMhDIC67g6WsaZ1PFkyvqf2YetW2sxZyvtfXyDNMa9voyJQikKhUCiqIK0C3AFIvXgPpjTNx7H51Z6W4yuf6Uqgt1nRzJ7N4W8nM6+fN02LsUrKS5XfehJC6ID3AFdgp5TyOyuLpFAoFJVOdgHC3iF+DG0cjl5ffLm/a7d35fDfP1Bscng5qVSLQgjxrRAiWghxMN94XyHEMSFEhBDi1RKWGQTUBTKB85Ulq0KhUFRF+ofWoXczP25topUpv7fxvYXOE5VSN1ajsree5gF9cw8IIfTAl0A/oBkwQgjRTAgRKoT4I9/LF2gCbJFSPg+MR6FQKGowEzpN4MCDB4o8LishkaJSt56klBuEEIH5htsDEVLKUwBCiIXAICnlB0CBWrxCiPNAdhlFY1HXEkI8AjwCUK9evesXXqFQKG4iiuqRURFYw5ldF4jM9fm8eawofgNuF0J8DmwoapKU8mspZbiUMtzHx6diJFUoFAorMS50HIGugXSt27VU893t3OlatyuulRAeaw1ndmFqr0hbSUqZAowp1cJCDAQGBgcHlzhXoVAoqjJB7kEsv2t5qec39WzKV72/qhRZrGFRnAcCcn32By5WxMKq1pNCoVBUPNZQFDuARkKIBkIIW2A4sKwiFlbVYxUKhaLiqezw2AXAFqCJEOK8EGKMlDILeBJYBRwBfpZSHqqI6ymLQqFQKCqeyo56GlHE+ApgRUVfT/koFAqFouKpViU8lEWhUCgUFU+1UhTKR6FQKBQVT7VSFMqiUCgUioqnWikKhUKhUFQ8Vb56bFnIdmYDCUKIE+VYwg0oy75VaeYXN6eoY6UdL2yeNxBbgkwVRVnv1/WeX9Xu942814Vdv7LPv5H3uzRj6n5X/v2uX+jKUkr1Mr+Aryt6fnFzijpW2vHC5qGVYq+S96u63e8bea+r+/0uzZi63zf2fud+qa2nvJQ+X77084ubU9Sx0o6XVd6K5nqvr+532ajO97u0YzcSdb/NCLMmUVQThBA7pZTh1pajJqDu9Y1F3W/roSyK6sfX1hagBqHu9Y1F3W8roSwKhUKhUBSLsigUCoVCUSxKUSgUCoWiWJSiUCgUCkWxKEVRjRFCOAkhvhNCfCOEGGVteao7QoggIcQcIcSv1palJiCEGGz+t/27EKKPteWpzihFcZMhhPhWCBEthDiYb7yvEOKYECJCCPGqeXgI8KuUchxw5w0XthpQlvstpTwlpSxV215F4ZTxfi81/9t+CBhmBXFrDEpR3HzMA/rmHhBC6IEvgX5AM2CEEKIZWpvZSPM04w2UsToxj9Lfb8X1M4+y3+83zMcVlYRSFDcZUsoNwJV8w+2BCPMv2gxgITAIrT+5v3mO+m9dDsp4vxXXSVnut9D4EFgppdx9o2WtSaiHR/WgLjmWA2gKoi7wG3C3EGIG1i+HUJ0o9H4LIbyEEDOB1kKI16wjWrWkqH/fTwG9gaFCiMesIVhNoVpVj63BiELGpJQyGXj4RgtTAyjqfscB6oFV8RR1v6cD02+0MDURZVFUD84DAbk++wMXrSRLTUDd7xuLut9WRimK6sEOoJEQooEQwhYYDiyzskzVGXW/byzqflsZpShuMoQQC4AtQBMhxHkhxBgpZRbwJLAKOAL8LKU8ZE05qwvqft9Y1P2umqiigAqFQqEoFmVRKBQKhaJYlKJQKBQKRbEoRaFQKBSKYlGKQqFQKBTFohSFQqFQKIpFKQqFQqFQFItSFIpqjRDiaSHEESHEj9aWpaIQQqwzl9y+0/x5nhBiaL45ScWc7yCE2CuEyBBCeFe2vIqbH1XrSVHdeRzoJ6U8nXtQCGEwJ3LdrIySUu4sz4lSylSglRDiTMWKpKiuKItCUW0xV3INApYJIZ4TQrwthPhaCLEa+F4I4SOEWCyE2GF+3WI+z0sIsVoIsUcIMUsIcVYI4S2ECMzdUEcI8aIQ4m3z+4ZCiL+EELuEEBuFEE3N4/OEENOFEP8JIU7l/uUvhHhZCHFACLFPCDHZvMbuXMcbCSF2Xec9eNdsPewVQlwQQsy9nvUUNROlKBTVFinlY2jF426VUn5qHm4LDJJSjgSmAZ9KKdsBdwOzzXPeAjZJKVuj1RSqV4rLfQ08JaVsC7wIfJXrWG2gCzAAmAwghOgHDAY6SClbAh9JKU8C8UKIVubzHkZr5FMaPs6lEPbmugdvSilbAd2BOOCLUq6nUFhQW0+KmsYy89YLaL0MmglhqWLtKoRwAbqhtZFFSvmnEOJqcQsKIZyBzsAvudayyzVlqZTSBBwWQvjluvZcKWWK+TrZzXpmAw8LIZ5Ha+/ZvpTf6yUppaVXd24fhdCE+hFNKV6XhaKomShFoahpJOd6rwM65VIcAJgf9oUVQcsirxVun2uda+Zf7oWRnnv5XH8Lu8ZiNIvmX2CXucfF9fI2cF5KqbadFOVCbT0pajKr0aqSApBry2cDMMo81g/wMI9fBnzNPgw7tK0kpJQJwGkhxD3mc4QQomUprj1aCOFoPsfTvFYaWpXUGcB1P9iFEAOA24Cnr3ctRc1FKQpFTeZpIFwIsV8IcZic7nTvAN3MjuU+wDkAKWUm8C6wDfgDOJprrVHAGCHEPuAQJfTQllL+heb/2Gn2KbyY6/CPaNbG6uv6dhovAHWA7Wb/xbsVsKaihqHKjCsUJWAOIw2XUsbeoOu9CLhJKScUcXwd8GJ5w2NzrXOGG/i9FDcvyqJQKKoQQoglwANoEVlFcQWYl51wV45rOJitGBvAVJ41FDULZVEoFAqFoliURaFQKBSKYlGKQqFQKBTFohSFQqFQKIpFKQqFQqFQFItSFAqFQqEoFqUoFAqFQlEs/weXwymxBmoMJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = file0+\"_spectre.pdf\"\n",
    "#file_path = 's11_07h56_y_700s.pdf'\n",
    "fig = plt.figure()\n",
    "Nspec=2048\n",
    "Nspec=4096\n",
    "f, Pxx_den = signal.welch(uk, freq, nperseg=Nspec)\n",
    "f, Pyy_den = signal.welch(vk, freq, nperseg=Nspec)\n",
    "f, Pzz_den = signal.welch(wk, freq, nperseg=Nspec)\n",
    "y=(f)**(-5/3)*0.06\n",
    "y2=(f)**(-3)*50\n",
    "plt.loglog(f, Pxx_den, label = 'E11')\n",
    "plt.loglog(f, Pyy_den, label = 'E22')\n",
    "plt.loglog(f, Pzz_den, label = 'E33')\n",
    "plt.loglog(f, y, 'r--', label='K41 -5/3 law')\n",
    "#plt.loglog(f, y2, 'g--', label='-3 law')\n",
    "plt.xlabel('frequency [Hz]')\n",
    "plt.ylabel('PSD [V**2/Hz]')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "fig.savefig(file_path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "992e4ae5",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (75263,) (21,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/j9/b9q3z_ld41jcr1wz5vktrv2w0000t1/T/ipykernel_12801/1109709961.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marctan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmean\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mumean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#alpha=0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mvkat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mukat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mukmean\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mukat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Applications/anaconda3/lib/python3.9/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36m__rmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   4173\u001b[0m         \u001b[0;31m# In analogy with __rsub__ and __rdiv__, use original order:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4174\u001b[0m         \u001b[0;31m# we get here from `other * self`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4175\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4177\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__div__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Applications/anaconda3/lib/python3.9/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, a, b, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1013\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m             \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseterr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdivide\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1016\u001b[0m         \u001b[0;31m# Get the mask for the result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgetmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgetmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (75263,) (21,) "
     ]
    }
   ],
   "source": [
    "alpha=np.arctan(vmean/umean)\n",
    "#alpha=0.\n",
    "vkat=-np.array(u)*np.sin(alpha)+np.array(v)*np.cos(alpha)\n",
    "ukat=np.array(u)*np.cos(alpha)+np.array(v)*np.sin(alpha)\n",
    "ukmean=np.mean(ukat)\n",
    "vkmean=np.mean(vkat)\n",
    "beta=np.arctan(wmean/ukmean)\n",
    "gamma=np.arctan(wmean/vmean)\n",
    "#beta=0.\n",
    "wkat=-ukat*np.sin(beta)+np.array(w)*np.cos(beta)\n",
    "ukat2=ukat*np.cos(beta)+np.array(w)*np.sin(beta)\n",
    "ukmean=np.mean(ukat2)\n",
    "wkmean=np.mean(wkat)\n",
    "up=ukat2-ukmean\n",
    "vp=vkat-vkmean\n",
    "wp=wkat-wkmean\n",
    "u2=np.mean(up**2)\n",
    "v2=np.mean(vp**2)\n",
    "w2=np.mean(wp**2)\n",
    "urms=np.sqrt(u2)\n",
    "vrms=np.sqrt(v2)\n",
    "wrms=np.sqrt(w2)\n",
    "uw=np.mean(up*wp)\n",
    "uv=np.mean(up*vp)\n",
    "vw=np.mean(vp*wp)\n",
    "#uw=-0.03\n",
    "#ukmean=2.\n",
    "utau=np.sqrt(-uw)\n",
    "#vtau=np.sqrt(-vw)\n",
    "#utau=np.sqrt(utau**2+vtau**2)\n",
    "uplus=ukmean/utau\n",
    "z=900.e-3\n",
    "nu=15.e-6\n",
    "zplus=z*utau/nu\n",
    "print(alpha*180/np.pi)\n",
    "print(beta*180/np.pi)\n",
    "print(gamma*180/np.pi)\n",
    "print(ukmean)\n",
    "print(vkmean)\n",
    "print(wkmean)\n",
    "print(urms)\n",
    "print(vrms)\n",
    "print(wrms)\n",
    "print(uw)\n",
    "print(uv)\n",
    "print(vw)\n",
    "print(utau)\n",
    "print(uplus)\n",
    "print(zplus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "36ff4fca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-7.31592564e-12  2.47434138e+03 -3.56166288e+03 ... -9.56772095e+02\n",
      " -3.56166288e+03  2.47434138e+03]\n",
      "[-8.88178420e-14  5.35381044e+03  4.98513598e+02 ... -1.20847334e+04\n",
      " -4.98513598e+02 -5.35381044e+03]\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sp = np.fft.fft(up)\n",
    "f2 = np.fft.fftfreq(time.shape[-1])\n",
    "#plt.plot(f2, sp.real, f2, sp.imag)\n",
    "plt.loglog(f2, sp.real, label = 'fft real')\n",
    "tmin=0.07\n",
    "tmax=0.2\n",
    "#plt.legend()\n",
    "plt.xlim(tmin,tmax)\n",
    "plt.ylim(10,1.e3)\n",
    "plt.show()\n",
    "print(sp.real)\n",
    "print(sp.imag)\n",
    "fig.savefig('fft.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3fc3590d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-7.31592564e-12-8.88178420e-14j  2.47434138e+03+5.35381044e+03j\n",
      " -3.56166288e+03+4.98513598e+02j ... -9.56772095e+02-1.20847334e+04j\n",
      " -3.56166288e+03-4.98513598e+02j  2.47434138e+03-5.35381044e+03j]\n"
     ]
    }
   ],
   "source": [
    "tfup=sp\n",
    "print(tfup)\n",
    "#tfup(0.08)=0.\n",
    "m=8*750-7\n",
    "m=8*750-100\n",
    "for i in range(300):\n",
    "    tfup[m-150+i]=0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7215adf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "#sp = np.fft.fft(up)\n",
    "#f2 = np.fft.fftfreq(time.shape[-1])\n",
    "plt.plot(f2, tfup.real, f2, sp.imag)\n",
    "plt.loglog(f2, tfup.real, label = 'fft real')\n",
    "tmin=0.07\n",
    "tmax=0.099\n",
    "#plt.legend()\n",
    "plt.xlim(tmin,tmax)\n",
    "plt.ylim(10,1.e3)\n",
    "plt.show()\n",
    "fig.savefig('fft2.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "05a4c1b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.9/site-packages/scipy/signal/spectral.py:1816: UserWarning: Input data is complex, switching to return_onesided=False\n",
      "  warnings.warn('Input data is complex, switching to '\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "up_fil = np.fft.ifft(tfup)\n",
    "Nspec=4096\n",
    "f3, Pfilt_den = signal.welch(up_fil, freq, nperseg=Nspec)\n",
    "plt.loglog(f3, Pfilt_den, label = 'filter')\n",
    "plt.xlabel('frequency [Hz]')\n",
    "plt.ylabel('PSD [V**2/Hz]')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "fig.savefig('spec_filtre.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "208a5cf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.38560044215607286-4.76875750923997e-19j)\n",
      "[0.43114408 0.40437748 0.35726598 0.48168882 0.3517833  0.35291129\n",
      " 0.40117376 0.33639477 0.31411848 0.40553172 0.36635003 0.35302035\n",
      " 0.34743593 0.31780913 0.28472182 0.40584596 0.31398678 0.2736478\n",
      " 0.42507028 0.38167734 0.38565172]\n"
     ]
    }
   ],
   "source": [
    "urms2=np.sqrt(np.mean(up_fil**2))\n",
    "print(urms2)\n",
    "print(urms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e7b588e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.3849099041854786-4.421619147839191e-19j)\n",
      "[0.43114408 0.40437748 0.35726598 0.48168882 0.3517833  0.35291129\n",
      " 0.40117376 0.33639477 0.31411848 0.40553172 0.36635003 0.35302035\n",
      " 0.34743593 0.31780913 0.28472182 0.40584596 0.31398678 0.2736478\n",
      " 0.42507028 0.38167734 0.38565172]\n"
     ]
    }
   ],
   "source": [
    "for i in range(60000):\n",
    "    tfup[m+i]=0\n",
    "up_fil = np.fft.ifft(tfup)\n",
    "urms3=np.sqrt(np.mean(up_fil**2))\n",
    "print(urms3)\n",
    "print(urms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2e5d701c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.9/site-packages/scipy/signal/spectral.py:1816: UserWarning: Input data is complex, switching to return_onesided=False\n",
      "  warnings.warn('Input data is complex, switching to '\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "up_fil = np.sqrt(2)*np.fft.ifft(tfup)\n",
    "Nspec=4096\n",
    "f4, Pfilt_den = signal.welch(up_fil, freq, nperseg=Nspec)\n",
    "plt.loglog(f4, Pfilt_den, label = 'filter')\n",
    "plt.loglog(f, Pxx_den, label = 'E11')\n",
    "plt.xlabel('frequency [Hz]')\n",
    "plt.ylabel('PSD [V**2/Hz]')\n",
    "emin=1e-7\n",
    "emax=0.1\n",
    "#plt.legend()\n",
    "plt.ylim(emin,emax)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "fig.savefig('spec_filtre2.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50743f43",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}
