From d5baf8ec8f1f9616702c2c8f23298c9ccf24c467 Mon Sep 17 00:00:00 2001 From: alexrad71 <140546242+alexrad71@users.noreply.github.com> Date: Thu, 5 Dec 2024 14:01:39 -0500 Subject: [PATCH] Add files via upload --- ...TEMPO_NO2_validation_with_Pandora_04.ipynb | 3388 ++++++++++++++++ ...maldehyde_validation_with_Pandora_04.ipynb | 3369 ++++++++++++++++ ...O_tropNO2_validation_with_Pandora_03.ipynb | 3391 +++++++++++++++++ 3 files changed, 10148 insertions(+) create mode 100644 TEMPO/L2_validation_codes/TEMPO_NO2_validation_with_Pandora_04.ipynb create mode 100644 TEMPO/L2_validation_codes/TEMPO_formaldehyde_validation_with_Pandora_04.ipynb create mode 100644 TEMPO/L2_validation_codes/TEMPO_tropNO2_validation_with_Pandora_03.ipynb diff --git a/TEMPO/L2_validation_codes/TEMPO_NO2_validation_with_Pandora_04.ipynb b/TEMPO/L2_validation_codes/TEMPO_NO2_validation_with_Pandora_04.ipynb new file mode 100644 index 0000000..f2a0634 --- /dev/null +++ b/TEMPO/L2_validation_codes/TEMPO_NO2_validation_with_Pandora_04.ipynb @@ -0,0 +1,3388 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "kjVKCytfEnRt" + }, + "source": [ + "### **TEMPO NO2 validation**\n", + "\n", + "This notebook illustrates comparison of nitrogen dioxide total column retrievals by TEMPO and Pandora ground stations.\n", + "\n", + "It allows a user to choose Pandora station of interest. Since TEMPO spatial coverage is regional and limited to North America, it is user's responsibilty to select the station within TEMPO's field of regard (FOR). If the selected station is outside FOR, no TEMPO time series will be generated.\n", + "\n", + "The user is allowed to choose the time period of interest by providing start and end dates in the form YYYYMMDD. Please be aware, that if the selecte period of interest is outside of available time span of one of the sensors, corresponding time series will not be generated.\n", + "\n", + "Data files for both sensors are downloaded on-the-fly. TEMPO data are downloaded with earthaccess library that needs to be installed first.\n", + "\n", + "TEMPO data files are read by means of netCDF library that needs to be installed first.\n", + "\n", + "Pandora data files are ASCII files with header and space separated columns. Custome made function is included to read nitrogen dioxide total column along with its total uncertainty.\n", + "\n", + "This version of the code takes into account quality flags (QFs) from both TEMPO and Pandora. This is implemented as follow. On the TEMPO side, data set \"/product/main_data_quality_flag\" is read, all pixels with non-zero QFs are discarded, however negative values of total NO2 column are NOT discarded and used for averaging/interpolationg to the point of interest. For the purpose of physical sanity, another way is also implemented, i.e., negative retrievals are not used in averaging. Therefore, TWO values are returned, total_NO2_col, and total_NO2_col_noneg. On Pandora side negative columns also occur despite high quality flags, though they are rare. So, two Pandora time series are considered - with and without negative columns.\n", + "\n", + "The resulting time series are plotted with and without uncertainty of both measurement in the end of the notebook.\n", + "\n", + "This notebook is tested on TEMPO_NO2_L2_V03 and Pandora L2_rnvs3p1-8 files." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "luJG0oPIPGjC" + }, + "source": [ + "# 1 Installing and importing necessary libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m5ru-FMpPXoE" + }, + "source": [ + "## 1.1 Installing netCDF" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5NWX4mCVQJt_", + "outputId": "3a4b63d0-ccef-4e2c-e161-c583eef4b0e9" + }, + "outputs": [], + "source": [ + "# un-comment if installation is necessary\n", + "#! pip3 install netCDF4" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cQJCMByjPp9i" + }, + "source": [ + "## 1.2 Installing earthaccess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N7Gm15VaYKW9", + "outputId": "a36d2ed4-cf5e-4bcf-d963-8ff63b8e0327" + }, + "outputs": [], + "source": [ + "# un-comment if installation is necessary\n", + "#! pip3 install earthaccess" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TxfhRi7ySyFY" + }, + "source": [ + "## 1.3 Importing necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "IAAuhYMcEkvP" + }, + "outputs": [], + "source": [ + "import earthaccess # needed to discover and download TEMPO data\n", + "import netCDF4 as nc # needed to read TEMPO data\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "import platform\n", + "from subprocess import Popen\n", + "import shutil\n", + "\n", + "from shapely.geometry import Point, Polygon # needed to search a point within a polygon\n", + "from scipy.interpolate import griddata # needed to interpolate TEMPO data to the point of interest\n", + "from scipy import stats # needed for linear regression analysis\n", + "\n", + "import requests # needed to search for and download Pandora data\n", + "import codecs # needed to read Pandora data\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt # needed to plot the resulting time series\n", + "from urllib.request import urlopen, Request # needed to search for and download Pandora data\n", + "from pathlib import Path # needed to check whether a needed data file is already downloaded\n", + "from datetime import datetime, timedelta # needed to work with time in plotting time series" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ANsfYumeXjKm" + }, + "source": [ + "# 2 Defining functions to work with Pandora and TEMPO data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OQGUPNbgXyKN" + }, + "source": [ + "# 2.1 functions to work with Pandora" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RB9IWsMvX6Kd" + }, + "source": [ + "### 2.1.1 function creating the list of available Pandora sites" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "oRBnr2vbYIkv" + }, + "outputs": [], + "source": [ + "# function read_pandora_web returns the list of available Pandora sites\n", + "def read_pandora_web():\n", + " url = 'https://data.pandonia-global-network.org/'\n", + " page = urlopen(url)\n", + " html_bytes = page.read()\n", + " html = html_bytes.decode(\"utf-8\")\n", + " html_len = len(html)\n", + "\n", + " pos1 = 0\n", + "\n", + " big_line = str(html)\n", + " lines = big_line.split('\\n')\n", + "\n", + " ref_lines = [i for i in lines if 'href' in i]\n", + " refs = []\n", + " for line in ref_lines:\n", + " pos1 = line.find('\"')\n", + " pos2 = line.rfind('\"')\n", + " if pos1 > 0 and pos2 > pos1 and line[pos2-1] =='/' and line[pos1+1] == '.':\n", + " refs.append(line[pos1+3 : pos2-1])\n", + "\n", + " return refs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ht9LHL28YUmR" + }, + "source": [ + "### 2.1.2 functions allowing user to choose a Pandora site of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "PMjSGKR8Yh_r" + }, + "outputs": [], + "source": [ + "# function check_site checks whether user entered site is in the list of available Pandora sites\n", + "def check_site(site_name, refs):\n", + " site_list = []\n", + " for line in refs:\n", + " if site_name in line:\n", + " site_list.append(line)\n", + "\n", + " return site_list\n", + "\n", + "\n", + "# function take_pandora_sites takes user input and checks whether the site is in the list of available Pandora sites\n", + "def take_pandora_sites(refs):\n", + " print('please select a Pandora site name from the list')\n", + " for ref in refs:\n", + " print(ref)\n", + "\n", + " answer = 'y'\n", + " while answer == 'y':\n", + " site_name = input('Enter a name of a Pandora site: ')\n", + " print(site_name)\n", + " site_list = check_site(site_name, refs)\n", + " site_num = len(site_list)\n", + " if site_num == 0:\n", + " print('site ', site_name, 'was not found')\n", + " continue\n", + "\n", + " if site_num > 1:\n", + " print('there are ', site_num, ' site names, select one from')\n", + " for site in site_list: print(site)\n", + "\n", + " site_name = input('Enter a name of a Pandora site: ')\n", + " if site_list.count(site_name) != 1:\n", + " print('Entered name is not the exact match of one of the following sites')\n", + " for site in site_list: print(site)\n", + " print('program terminated')\n", + " sys.exit()\n", + "\n", + " for site in site_list:\n", + " if site == site_name:\n", + " pandora_site = site_name\n", + " print('site ', site_name, 'was found and added to the list of sites ')\n", + " break\n", + "\n", + " if site_num == 1:\n", + " pandora_site = site_list[0]\n", + " print('site ', site_list[0], 'was found and added to the list of sites ')\n", + "\n", + " answer = 'n'\n", + "\n", + " return pandora_site" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F5sX6MEaZGFI" + }, + "source": [ + "### 2.1.3 function creating the list of links to NO2 data files at the selected Pandora site and downloading the data files" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "abmCNXVWZR4H" + }, + "outputs": [], + "source": [ + "# Pandora site may have several instruments. In this case each instrument has its own directory.\n", + "# However, the most recent version of the NO2 data, rnvs3p1-8, is available only in one of these directories.\n", + "# The function creates all possible links, but some of them may be non-existing. This is checked and cleared later.\n", + "def instrument_path(site):\n", + "# function instrument_path returns links to possible Pandora NO2 retrievals files\n", + " url = 'https://data.pandonia-global-network.org/' + site + '/'\n", + " page = urlopen(url)\n", + " html_bytes = page.read()\n", + " html = html_bytes.decode(\"utf-8\")\n", + " html_len = len(html)\n", + "\n", + " pos1 = 0\n", + " big_line = str(html)\n", + " lines = big_line.split('\\n')\n", + "\n", + " ref_lines = [i for i in lines if 'href' in i]\n", + " links = []\n", + " for line in ref_lines:\n", + "\n", + " pos1 = line.find('\"')\n", + " pos2 = line.rfind('\"')\n", + " if pos1 > 0 and pos2 > pos1 and line[pos2-1] =='/' and\\\n", + " line[pos1+3 : pos1 + 10] == 'Pandora':\n", + " link = url + line[pos1+3 : pos2] + 'L2/' + line[pos1+3 : pos2-1] + '_' + site + '_L2_rnvs3p1-8.txt'\n", + " print(link)\n", + " links.append(link)\n", + "\n", + " return links\n", + "\n", + "\n", + "# function downloading Pandora data file with given url\n", + "def download(url):\n", + " response = requests.get(url)\n", + " response_code = response.status_code\n", + "\n", + " file_name = url.split('/')[-1]\n", + "\n", + " if response_code == 200:\n", + " content = response.content\n", + " data_path = Path(file_name)\n", + " data_path.write_bytes(content)\n", + "\n", + " return file_name, response_code" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HLnx6gUAaMg6" + }, + "source": [ + "### 2.1.4 function reading Pandora NO2 data files rnvs3p1-8" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "LL89Ivo0Z9kU" + }, + "outputs": [], + "source": [ + "# function converting Pandora timestamp into a set of year, month, day, hour, minute, and second\n", + "# function read_timestamp converts Pandora timestamp of the format\n", + "# 'yyyymmddThhmmssZ' into a set of 6 numbers:\n", + "# integer year, month, day, hour, minute, and real second.\n", + "def read_timestamp(timestamp):\n", + "\n", + " yyyy = int(timestamp[0:4])\n", + " mm = int(timestamp[4:6])\n", + " dd = int(timestamp[6:8])\n", + " hh = int(timestamp[9:11])\n", + " mn = int(timestamp[11:13])\n", + " ss = float(timestamp[13:17])\n", + "\n", + " return yyyy, mm, dd, hh, mn, ss\n", + "\n", + "\n", + "# function reading Pandora NO2 data file rnvs3p1-8\n", + "#\n", + "# Below is the second version of function read_Pandora_NO2_rnvs3p1_8. It is to be used for the future validation efforts.\n", + "# The difference with the original version is that instead of discriminating negative values of the total NO2 column,\n", + "# it uses quality flags. It was previously found that QF == 0 does not occure often enough,\n", + "# so we will have to use QF == 10 (not-assured high quality).\n", + "#\n", + "# function read_Pandora_NO2_rnvs3p1-8 reads Pandora total NO2 column data files ending with rnvs3p1-8.\n", + "# Arguments:\n", + "# fname - name file to be read, string;\n", + "# start_date - beginning of the time interval of interest,\n", + "# integer of the form YYYYMMDD;\n", + "# end_date - end of the time interval of interest,\n", + "# integer of the form YYYYMMDD.\n", + "#\n", + "# if start_date is greater than end_date, the function returns a numpy array\n", + "# with shape (0, 8), otherwise it returns an 8-column numpy array\n", + "# with with columns being year, month, day, hour, minute, second of observation\n", + "# and retrieved total NO2 column along with its total uncertainty.\n", + "#\n", + "# NO2 column is in mol/m^2, so conversion to molecules/cm^2 is performed by\n", + "# multiplication by Avogadro constant, NA = 6.02214076E+23, and division by 1.E+4\n", + "def read_Pandora_NO2_rnvs3p1_8_v2(fname, start_date, end_date):\n", + "\n", + " conversion_coeff = 6.02214076E+19 # Avogadro constant divided by 10000\n", + "\n", + " data = np.empty([0, 8])\n", + " if start_date > end_date: return -999., -999., data\n", + "\n", + " with codecs.open(fname, 'r', encoding='utf-8', errors='ignore') as f:\n", + "\n", + " while True:\n", + "# Get next line from file\n", + " line = f.readline()\n", + "\n", + " if line.find('Short location name:') >= 0:\n", + " loc_name = line.split()[-1] # location name, to be used in the output file name\n", + " print('location name ', loc_name)\n", + "\n", + " if line.find('Location latitude [deg]:') >= 0:\n", + " lat = float(line.split()[-1]) # location latitude\n", + " print('location latitude ', lat)\n", + "\n", + " if line.find('Location longitude [deg]:') >= 0:\n", + " lon = float(line.split()[-1]) # location longitude\n", + " print('location longitude ', lon)\n", + "\n", + " if line.find('--------') >= 0: break\n", + "\n", + " while True:\n", + "# Get next line from file\n", + " line = f.readline()\n", + "\n", + " if line.find('--------') >= 0: break\n", + "\n", + " while True:\n", + "# now reading line with data\n", + " line = f.readline()\n", + "\n", + " if not line: break\n", + "\n", + " line_split = line.split()\n", + "\n", + " yyyy, mm, dd, hh, mn, ss = read_timestamp(line_split[0])\n", + " date_stamp = yyyy*10000 + mm*100 + dd\n", + " if date_stamp < start_date or date_stamp > end_date: continue\n", + "\n", + " QF = int(line_split[35]) # quality flag\n", + "\n", + " if QF == 0 or QF == 10:\n", + " column = float(line_split[38])\n", + " column_unc = float(line_split[42]) # total column uncertainty\n", + " data = np.append(data, [[yyyy, mm, dd, hh, mn, ss\\\n", + " , column*conversion_coeff\\\n", + " , column_unc*conversion_coeff]], axis = 0)\n", + "\n", + " return lat, lon, loc_name, data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ep0Fl-Kzas5x" + }, + "source": [ + "## 2.2 function reading TEMPO NO2 data file" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "2rYr3a8_aza3" + }, + "outputs": [], + "source": [ + "def read_TEMPO_NO2_L2(fn):\n", + " '''\n", + " function read_TEMPO_NO2_L2 reads the following arrays from the\n", + " TEMPO L2 NO2 product TEMPO_NO2_L2_V03:\n", + " 'main_data_quality_flag';\n", + " 'vertical_column_stratosphere';\n", + " 'vertical_column_troposphere';\n", + " 'vertical_column_troposphere_uncertainty'.\n", + " It returns these variable along with their fill values and coordinates of the pixels.\n", + "\n", + " Some pixels may have only one of stratospheric OR tropospheric columns valid with the other being filled.\n", + " In these pixels the function returns fill value in total column and its uncertainty.\n", + "\n", + " This function DO NOT WORK with V01 and V02 data files as their format is different.\n", + " If a user need to READ total column from array 'vertical_column_total', he need to change this function.\n", + " Currently, in version V03 arrays 'vertical_column_total' and 'vertical_column_total_uncertrainty' are located in 'support_data' group.\n", + "\n", + " If one requested variables cannot be read, all returned variables are zeroed\n", + " '''\n", + "\n", + " try:\n", + " ds = nc.Dataset(fn)\n", + "\n", + " prod = ds.groups['product'] # this opens group product, /product, as prod\n", + "\n", + " var = prod.variables['vertical_column_stratosphere'] # this reads variable vertical_column_stratosphere from prod (group product, /product)\n", + " strat_NO2_column = np.array(var)\n", + " fv_strat_NO2 = var.getncattr('_FillValue')\n", + "\n", + " var = prod.variables['vertical_column_troposphere'] # this reads variable 'vertical_column_troposphere' from prod (group product, /product)\n", + " trop_NO2_column = np.array(var)\n", + " fv_trop_NO2 = var.getncattr('_FillValue')\n", + " prod_unit = var.getncattr('units')\n", + "\n", + " var = prod.variables['vertical_column_troposphere_uncertainty'] # this reads 'vertical_column_troposphere_uncertainty' from prod (group product, /product)\n", + " trop_NO2_column_unc = np.array(var)\n", + " fv_trop_NO2_column_unc = var.getncattr('_FillValue')\n", + "\n", + " var = prod.variables['main_data_quality_flag'] # this reads variable 'main_data_quality_flag' from prod (group product, /product)\n", + " QF = np.array(var)\n", + " fv_QF = var.getncattr('_FillValue')\n", + "\n", + " geo = ds.groups['geolocation'] # this opens group geolocation, /geolocation, as geo\n", + "\n", + " lat = np.array(geo.variables['latitude']) # this reads variable latitude from geo (geolocation group, /geolocation) into a numpy array\n", + " lon = np.array(geo.variables['longitude']) # this reads variable longitude from geo (geolocation group, /geolocation) into a numpy array\n", + " fv_geo = geo.variables['latitude'].getncattr('_FillValue')\n", + " time = np.array(geo.variables['time'] )# this reads variable longitude from geo (geolocation group, /geolocation) into a numpy array\n", + "\n", + " ds.close()\n", + "\n", + " except:\n", + " print('variable '+var_name+' cannot be read in file '+fn)\n", + " lat = 0.\n", + " lon = 0.\n", + " time = 0.\n", + " fv_geo = 0.\n", + " trop_NO2_column = 0.\n", + " strat_NO2_column = 0.\n", + " trop_NO2_column_unc = 0.\n", + " QF = 0.\n", + " fv_trop_NO2 = 0.\n", + " fv_strat_NO2 = 0.\n", + " fv_trop_NO2_column_unc = 0.\n", + " fv_QF = -999\n", + " prod_unit = ''\n", + "\n", + " return lat, lon, fv_geo, time\\\n", + ", strat_NO2_column, fv_strat_NO2, trop_NO2_column, fv_trop_NO2\\\n", + ", trop_NO2_column_unc, fv_trop_NO2_column_unc, prod_unit, QF, fv_QF" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OkRA1M7PcIYx" + }, + "source": [ + "## 2.3 auxiliary functions to handle data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AKzFo9EjcTzx" + }, + "source": [ + "### 2.3.1 function smoothing Pandora retievals and interpolating them onto TEMPO times of observations" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "uy_JdsVsckEf" + }, + "outputs": [], + "source": [ + "# Smooth Pandora retievals and interplate them into other time series times\n", + "# Pandora timeseries has significantly more data points then TEMPO and DSCOVR. It is also very noisy.\n", + "# To make comparison easier, Pandora timeseries is interpolated to the moments of TEMPO and DSCOVR observations.\n", + "\n", + "# Interpolation is performed by the function defined below with the help of Gaussian smooting as follow:\n", + "# x_int(t) = SUM(x_p(t_i)*wt(t_i, t)),\n", + "#\n", + "# wt(t_i, t) = exp(-(t - t_i)^2/(2 * sigma^2))/SUM(exp(-(t - t_i)^2/(2 * sigma^2))),\n", + "#\n", + "# where sums are taken over times t_i falling into time interval (t-dt_max, t+dt_max).\n", + "#\n", + "# Parameters dt_max and sigma can be chosen by the user.\n", + "def gauss_interpolation(timeseries, new_times):\n", + "#\n", + "# function gauss_interpolation takes 2D array timeseries with function\n", + "# to be interpolated and 1D array new_times containing times to which\n", + "# the function is to be interpolated\n", + "# arguments:\n", + "# timeseries - array with at least 2 columns,\n", + "# 1st column - times, 2nd (3rd, ...) column(s) - function to be interpolated\n", + "# new_times - 1D array of times to which the function(s) to be interpolated\n", + "#\n", + "# parameters\n", + "# dt_max = 0.0007 # 60.48 sec expressed in days\n", + "# sigma = 0.000175 # 15.12 sec expressed in days\n", + "\n", + " dt_max = 0.0007 # 60.48 sec expressed in days\n", + " sigma = 0.000175 # 15.12 sec expressed in days\n", + "\n", + " nnt = len(new_times)\n", + " (nt, nfun) = timeseries.shape\n", + "\n", + " timeseries_smooth = np.empty([0, nfun])\n", + " data_subset = np.empty(nnt, dtype = object)\n", + " cnt = 0\n", + " for new_time in new_times:\n", + " llim = new_time - dt_max\n", + " ulim = new_time + dt_max\n", + "\n", + " timeseries_subset = timeseries[((timeseries[:, 0] < ulim)\\\n", + " & (timeseries[:, 0] > llim))]\n", + " if len(timeseries_subset) < 1: continue\n", + " t_delta = timeseries_subset[:, 0] - new_time\n", + " wt = np.exp(-t_delta**2/(2.*sigma**2))\n", + " wt = wt/np.sum(wt)\n", + " timeseries_subset = np.append(timeseries_subset, np.transpose([wt]), axis = 1)\n", + " for t in timeseries_subset: print(f'{t[0]:.6f} {t[1]:.3e} {t[2]:.2e} {t[3]:.4e}')\n", + " data_subset[cnt] = timeseries_subset\n", + " cnt += 1\n", + "\n", + " timeseries_smooth_loc = np.array([new_time])\n", + " for ifun in range(1, nfun):\n", + " timeseries_smooth_loc = np.append(timeseries_smooth_loc,\\\n", + " np.sum(timeseries_subset[:, ifun]*wt))\n", + " print(f'{timeseries_smooth_loc[0]:.6f} {timeseries_smooth_loc[1]:.3e} {timeseries_smooth_loc[2]:.2e}\\n')\n", + "\n", + " timeseries_smooth = np.append(timeseries_smooth,\\\n", + " np.array([timeseries_smooth_loc]), axis = 0)\n", + "\n", + " return timeseries_smooth, data_subset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nNoGDd0MdP9Y" + }, + "source": [ + "### 2.3.2 function computing linear regression with zero intercept" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "Qgn9InxLdLyM" + }, + "outputs": [], + "source": [ + "# custom made function regress_0intercept takes vectors x and y\n", + "# representing coordinates and function values at these coordinates\n", + "# and returns slope of regression fit y = a*x\n", + "# along with coefficient of determination\n", + "def regress_0intercept(x, y):\n", + " success = False\n", + "\n", + " if len(x) != len(y):\n", + " a = 0.\n", + " R2 = 0.\n", + "\n", + " elif len(x) == 1:\n", + " if x[0] != 0.:\n", + " a = y[0]/x[0]\n", + " R2 = 1.\n", + " success = True\n", + " else:\n", + " if y[0] != 0.:\n", + " a = np.inf\n", + " R2 = 1.\n", + " success = True\n", + " else:\n", + " a = np.inf\n", + " R2 = 0.\n", + "\n", + " else:\n", + " xy_sum = np.dot(x, y)\n", + " x2_sum = np.dot(x, x)\n", + " a = xy_sum/x2_sum\n", + "\n", + " res_y = y - a*x\n", + " res_sum_2 = np.dot(res_y, res_y)\n", + " y2_sum = np.dot(y, y)\n", + " sum_tot_2 = y2_sum - len(y)*np.mean(y)**2\n", + " R2 = 1. - res_sum_2/sum_tot_2\n", + "\n", + " success = True\n", + "\n", + " return success, a, R2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0aDnIkZVdu93" + }, + "source": [ + "# Main code begins here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9FiqnX6XSt-3" + }, + "source": [ + "# 3 Establishing access to EarthData" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LXGbiH5VTbPZ" + }, + "source": [ + "## 3.1 Logging in" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aHZmh8-xYZFe", + "outputId": "3aedc294-60a3-4f34-afa6-8073f6d44167" + }, + "outputs": [], + "source": [ + "# User needs to create an account at https://www.earthdata.nasa.gov/\n", + "# Function earthaccess.login prompts for EarthData login and password.\n", + "auth = earthaccess.login(strategy=\"interactive\", persist=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5qRdIKSXeU5e" + }, + "source": [ + "## 3.2 Creating local directory" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tizBt7IvY0lx", + "outputId": "11fb4aba-516d-4c9b-ca1f-39fc47fb73c7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved .dodsrc to: /home/jovyan/\n" + ] + } + ], + "source": [ + "homeDir = os.path.expanduser(\"~\") + os.sep\n", + "\n", + "with open(homeDir + '.dodsrc', 'w') as file:\n", + " file.write('HTTP.COOKIEJAR={}.urs_cookies\\n'.format(homeDir))\n", + " file.write('HTTP.NETRC={}.netrc'.format(homeDir))\n", + " file.close()\n", + "\n", + "print('Saved .dodsrc to:', homeDir)\n", + "\n", + "# Set appropriate permissions for Linux/macOS\n", + "if platform.system() != \"Windows\":\n", + " Popen('chmod og-rw ~/.netrc', shell=True)\n", + "else:\n", + " # Copy dodsrc to working directory in Windows\n", + " shutil.copy2(homeDir + '.dodsrc', os.getcwd())\n", + " print('Copied .dodsrc to:', os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5QaStYVXVmdN" + }, + "source": [ + "# 4 Working with Pandora data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NN7_YcCackvI" + }, + "source": [ + "## 4.1 Discovering existing Pandora stations and selecting one of them" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G-VGNGOtzqBY", + "outputId": "8b7a3214-fc64-4a27-8b51-eb36192e18c8", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gathering Pandora sites information\n", + "please select a Pandora site name from the list\n", + "Agam\n", + "AldineTX\n", + "AliceSprings\n", + "Altzomoni\n", + "ArlingtonTX\n", + "Athens-NOA\n", + "AtlantaGA-Conyers\n", + "AtlantaGA-GATech\n", + "AtlantaGA-SouthDeKalb\n", + "AtlantaGA\n", + "AustinTX\n", + "Bandung\n", + "Bangkok\n", + "Banting\n", + "BayonneNJ\n", + "Beijing-RADI\n", + "BeltsvilleMD\n", + "Berlin\n", + "BlueHillMA\n", + "BostonMA\n", + "BoulderCO-NCAR\n", + "BoulderCO\n", + "Bremen\n", + "BristolPA\n", + "BronxNY\n", + "Brussels-Uccle\n", + "Bucharest\n", + "BuenosAires\n", + "BuffaloNY\n", + "Busan\n", + "Cabauw\n", + "Calakmul\n", + "calibrationfiles\n", + "CambridgeBay\n", + "CambridgeMA\n", + "CameronLA\n", + "CapeElizabethME\n", + "Cebu\n", + "ChapelHillNC\n", + "CharlesCityVA\n", + "ChelseaMA\n", + "ChiangMai\n", + "ChicagoIL\n", + "Cologne\n", + "ComodoroRivadavia\n", + "Cordoba\n", + "CornwallCT\n", + "CorpusChristiTX\n", + "Daegu\n", + "Dalanzadgad\n", + "Davos\n", + "DearbornMI\n", + "DeBilt\n", + "Dhaka\n", + "Downsview\n", + "EastProvidenceRI\n", + "EdwardsCA\n", + "Egbert\n", + "EssexMD\n", + "Eureka-0PAL\n", + "Eureka-PEARL\n", + "FairbanksAK\n", + "Fajardo\n", + "FortMcKay\n", + "FortYatesND\n", + "Fukuoka\n", + "Gongju-KNU\n", + "Granada\n", + "GrandForksND\n", + "GreenbeltMD\n", + "Haldwani-ARIES\n", + "HamptonVA-HU\n", + "HamptonVA\n", + "Heidelberg\n", + "Helsinki\n", + "HoustonTX-SanJacinto\n", + "HoustonTX\n", + "HuntsvilleAL\n", + "Ilocos\n", + "Incheon-ESC\n", + "Innsbruck\n", + "IowaCityIA-WHS\n", + "Islamabad-NUST\n", + "Izana\n", + "Jeonju\n", + "Juelich\n", + "KenoshaWI\n", + "Kobe\n", + "Kosetice\n", + "LaPaz\n", + "LaPorteTX\n", + "LapwaiID\n", + "LibertyTX\n", + "Lindenberg\n", + "LondonderryNH\n", + "LynnMA\n", + "MadisonCT\n", + "ManhattanKS\n", + "ManhattanNY-CCNY\n", + "MaunaLoaHI\n", + "MexicoCity-UNAM\n", + "MexicoCity-Vallejo\n", + "MiamiFL-FIU\n", + "MountainViewCA\n", + "Nagoya\n", + "Nainital-ARIES\n", + "NewBrunswickNJ\n", + "NewHavenCT\n", + "NewLondonCT\n", + "NewOrleansLA-XULA\n", + "NyAlesund\n", + "OldFieldNY\n", + "operationfiles\n", + "Palau\n", + "Palawan\n", + "PhiladelphiaPA\n", + "PhnomPenh\n", + "PittsburghPA\n", + "Pontianak\n", + "Potchefstroom-METSI\n", + "QueensNY\n", + "QuezonCity\n", + "RichmondCA\n", + "Rome-IIA\n", + "Rome-ISAC\n", + "Rome-SAP\n", + "Rotterdam-Haven\n", + "SaltLakeCityUT-Hawthorne\n", + "SaltLakeCityUT\n", + "SanJoseCA\n", + "Sapporo\n", + "Seosan\n", + "Seoul-KU\n", + "Seoul-SNU\n", + "Seoul\n", + "Singapore-NUS\n", + "Songkhla\n", + "SouthJordanUT\n", + "StGeorge\n", + "StonyPlain\n", + "Suwon-USW\n", + "SWDetroitMI\n", + "Tel-Aviv\n", + "Thessaloniki\n", + "Tokyo-Sophia\n", + "Tokyo-TMU\n", + "Toronto-CNTower\n", + "Toronto-Scarborough\n", + "Toronto-West\n", + "Trollhaugen\n", + "Tsukuba-NIES-West\n", + "Tsukuba-NIES\n", + "Tsukuba\n", + "TubaCityAZ\n", + "TucsonAZ\n", + "TurlockCA\n", + "TylerTX\n", + "Ulaanbaatar\n", + "Ulsan\n", + "Vientiane\n", + "VirginiaBeachVA-CBBT\n", + "WacoTX\n", + "Wakkerstroom\n", + "WallopsIslandVA\n", + "Warsaw-UW\n", + "WashingtonDC\n", + "WestportCT\n", + "WhittierCA\n", + "Windsor-West\n", + "WrightwoodCA\n", + "Yokosuka\n", + "Yongin\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a name of a Pandora site: Bould\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bould\n", + "there are 2 site names, select one from\n", + "BoulderCO-NCAR\n", + "BoulderCO\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a name of a Pandora site: BoulderCO-NCAR\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "site BoulderCO-NCAR was found and added to the list of sites \n", + "the following sites were selected\n", + "BoulderCO-NCAR\n", + "from the list of existing Pandora sites\n", + "https://data.pandonia-global-network.org/BoulderCO-NCAR/Pandora204s1/L2/Pandora204s1_BoulderCO-NCAR_L2_rnvs3p1-8.txt\n", + "Pandora204s1_BoulderCO-NCAR_L2_rnvs3p1-8.txt does not exit in local directory, downloading from the web\n", + "https://data.pandonia-global-network.org/BoulderCO-NCAR/Pandora204s1/L2/Pandora204s1_BoulderCO-NCAR_L2_rnvs3p1-8.txt\n", + "Pandora L2 file Pandora204s1_BoulderCO-NCAR_L2_rnvs3p1-8.txt has been downloaded\n" + ] + } + ], + "source": [ + "# Discovering existing Pandora stations and selecting one of them\n", + "# Discovering available Pandora site.\n", + "# Please bear in mind that some sites do not have NO2 data files\n", + "print('gathering Pandora sites information')\n", + "refs = read_pandora_web()\n", + "\n", + "pandora_site = take_pandora_sites(refs) # create list of Pandora sites of interest\n", + "print('the following sites were selected')\n", + "print(pandora_site)\n", + "print('from the list of existing Pandora sites')\n", + "\n", + "# create a list of !AVAILABLE! Pandora files for the Pandora site\n", + "pandora_files = []\n", + "\n", + "links = instrument_path(pandora_site)\n", + "\n", + "npfiles = 0\n", + "\n", + "for link in links:\n", + " pandora_fname = link.split('/')[-1]\n", + "\n", + "# check if file exists in the local directory, if not download from Pandora site\n", + " if not os.path.exists(pandora_fname):\n", + " print(pandora_fname,' does not exit in local directory, downloading from the web')\n", + " print(link)\n", + "\n", + " pandora_fname, response_code = download(link)\n", + "\n", + " if response_code == 200:\n", + " print('Pandora L2 file ', pandora_fname, ' has been downloaded')\n", + " npfiles = npfiles + 1\n", + " pandora_files.append(pandora_fname)\n", + " else:\n", + " print('Pandora L2 file ', link, ' does not exist')\n", + "\n", + " else:\n", + " print(pandora_fname,' exits in local directory')\n", + " npfiles = npfiles + 1\n", + " pandora_files.append(pandora_fname)\n", + "\n", + "if npfiles == 0: # no files were found, STOP here\n", + " print('no files were found for Pandora site ', pandora_site, 'program terminated')\n", + " sys.exit()\n", + "if npfiles > 1: # normally there should be only one file per site. if there are more - STOP\n", + "# print('there are too many files for site ', pandora_site, '- STOP and investigate file names below. Program terminated')\n", + " print('there are more than 1 files for site ', pandora_site)\n", + "# for pandora_fname in pandora_files: print(pandora_fname)\n", + " for i, link in enumerate(links): print(i, link)\n", + " num = int(input('please enter the number for the link'))\n", + " pandora_fname, response_code = download(links[num])\n", + "# sys.exit()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4vYb5skDdNvg" + }, + "source": [ + "## 4.2 Selecting timeframe of interest common for both instruments" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R7G6LBj6z9Mi", + "outputId": "d6f1ff3b-94e7-4dda-ef9e-ce76bfab750c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "enter period of interest, start and end dates, in the form YYYYMMDD\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "enter start date of interest 20230901\n", + "enter end date of interest 20230901\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023 9 1 2023 9 1\n" + ] + } + ], + "source": [ + "print('enter period of interest, start and end dates, in the form YYYYMMDD')\n", + "datestamp_ini = input('enter start date of interest ')\n", + "datestamp_fin = input('enter end date of interest ')\n", + "\n", + "start_date = int(datestamp_ini)\n", + "end_date = int(datestamp_fin)\n", + "\n", + "yyyy_ini = start_date//10000\n", + "mm_ini = (start_date//100 - yyyy_ini*100)\n", + "dd_ini = (start_date - yyyy_ini*10000 - mm_ini*100)\n", + "\n", + "yyyy_fin = end_date//10000\n", + "mm_fin = (end_date//100 - yyyy_fin*100)\n", + "dd_fin = (end_date - yyyy_fin*10000 - mm_fin*100)\n", + "print(yyyy_ini, mm_ini, dd_ini, yyyy_fin, mm_fin, dd_fin)\n", + "\n", + "date_start = str('%4.4i-%2.2i-%2.2i 00:00:00' %(yyyy_ini, mm_ini, dd_ini))\n", + "date_end = str('%4.4i-%2.2i-%2.2i 23:59:59' %(yyyy_fin, mm_fin, dd_fin))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mTFV2Fkadj8e" + }, + "source": [ + "## 4.3 Reading Pandora file within selected timeframe and creating point of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xG9oYO3Tq5_Z", + "outputId": "55cd8d21-2623-4be1-d7bc-cf357fcfc35a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "location name BoulderCO-NCAR\n", + "location latitude 40.0375\n", + "location longitude -105.242\n", + "778 Pandora measurements found within period of interes between 2023-09-01 00:00:00 and 2023-09-01 23:59:59\n" + ] + } + ], + "source": [ + "pandora_file = pandora_files[0]\n", + "lat, lon, POI_name, Pandora_data = read_Pandora_NO2_rnvs3p1_8_v2(pandora_file, start_date, end_date)\n", + "\n", + "if lat == -999.:\n", + " print('error reading pandora file ', pandora_file, 'program terminated')\n", + " sys.exit()\n", + "\n", + "POI = np.array([lat, lon])\n", + "\n", + "# print # of points in Pandora timeseries\n", + "n_Pandora_data = len(Pandora_data)\n", + "print(n_Pandora_data,\\\n", + "' Pandora measurements found within period of interes between',\\\n", + "date_start, 'and', date_end)\n", + "if n_Pandora_data == 0:\n", + " print('program terminated')\n", + " sys.exit()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3wvhtsq5kXgZ" + }, + "source": [ + "## 4.4 Setting TEMPO name constants and writing Pandora timeseries to a file" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "fZRNP9ifkMg4" + }, + "outputs": [], + "source": [ + "short_name = 'TEMPO_NO2_L2' # collection name to search for in the EarthData\n", + "out_Q = 'NO2_tot_col' # name of the output quantity with unit\n", + "out_Q_unit = 'molecules/cm^2' # name of the output quantity with unit\n", + "\n", + "POI_name_ = POI_name.replace(' ','_')\n", + "Pandora_out = open(out_Q+'_Pandora_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", + "for line in Pandora_data:\n", + " Pandora_out.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %12.4e %12.4e\\n'\\\n", + " %(line[0], line[1], line[2], line[3], line[4], line[5], line[6], line[7])))\n", + "Pandora_out.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VGfpNlrWei36" + }, + "source": [ + "# 5 Working with TEMPO data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qO9NEF61jlcy" + }, + "source": [ + "## 5.1 Searching TEMPO data files containing the POI (position of the Pandora station)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vEUujyNZzRx0", + "outputId": "58315903-f404-43d0-f4bf-4b7eb43d9ffd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 13\n" + ] + } + ], + "source": [ + "auth = earthaccess.login()\n", + "auth.refresh_tokens()\n", + "\n", + "POI_lat = POI[0]\n", + "POI_lon = POI[1]\n", + "\n", + "version = 'V03'\n", + "POI_results = earthaccess.search_data(short_name = short_name\\\n", + " , version = version\\\n", + " , temporal = (date_start, date_end)\\\n", + " , point = (POI_lon, POI_lat))\n", + "\n", + "n_gr = len(POI_results)\n", + "if n_gr == 0:\n", + " print('program terminated')\n", + " sys.exit()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OvC5eZRhk7Xz" + }, + "source": [ + "## 5.2 Printing explicit links to the granules and downloading the files" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "6b49f9fe4d634fc897131ea05348f124", + "e485d919b1de41e6b5f584cd7517d45b", + "c60d32edd0b34beaa1c892c745bc8018", + "8c937a6e34bd44b2b5cc96efb60056c8", + "fe2af4eca97748679b6d55585ec9245d", + "92c867d203b54916808be6875b8b72f5", + "14f52f84dc3841ada4af4d4e90875f98", + "f698c62cf0654e52ae703334f7e3bdc1", + "c117564fde7049189aacabde5d5bbeb8", + "e3e7e270a8944b0da8e8d3227ec7abc6", + "1a80b34247784ebea02c4e1aa7c394fc", + "a02364dd311849e39532ab63baac9dc7", + "fd0a035988504b02b0322f207bc3b256", + "9e6e69080fa54874989647e053c7a4a8", + "8fabcdf2c64247579791bdcdfeb8ccc8", + "39059823a2794467b9390f193b356a15", + "619f0a8158724a1297a2c089a60ac849", + "cb86f693bd214ce68a78c1beda3097e6", + "df8260ca968a47348441b72764fd766e", + "fc5cb86d769644c38cc5443d2bc4cf8a", + "3278abeb210b49b1892209ab8afd6a5f", + "9a583f62a246410fa6c21ccbc03c524d", + "d5ca2d880e974dec8ba56c13540e2031", + "0dc60f8e045441a6a3f89032db63c42b", + "bec460e7f64647bd8294b121536db6f5", + "6ef6ae497479463f83b9868011a9686b", + "78fab895577c4ac1b7d6852550cd8ce6", + "206e7f2230534c668650b4e7e4d66e90", + "9ba2bcfeb2094f7f9cee0ddd17b36ac4", + "d069f1e1e5de48d59cd2da3a16e55055", + "5c91b9858a3c42ce87938f792a66fcda", + "e03880103a2842e193740aad01e01431", + "df330ff4f024424c9ff0f1fe584202e3" + ] + }, + "id": "UQnLZCmB5Oxv", + "outputId": "aa4780b4-7823-47b7-fe06-520aa952ea3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T002134Z_S018G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T005257Z_S019G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T012420Z_S020G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T144803Z_S007G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T155034Z_S008G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T165942Z_S009G09.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T182132Z_S010G09.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T200038Z_S011G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T210309Z_S012G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T220540Z_S013G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T223706Z_S015G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T230829Z_S016G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T233952Z_S017G03.nc\n", + " Getting 13 granules, approx download size: 1.31 GB\n", + "Accessing cloud dataset using dataset endpoint credentials: https://data.asdc.earthdata.nasa.gov/s3credentials\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T002134Z_S018G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T005257Z_S019G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T012420Z_S020G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T144803Z_S007G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T155034Z_S008G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T165942Z_S009G09.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T182132Z_S010G09.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T200038Z_S011G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T210309Z_S012G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T220540Z_S013G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T223706Z_S015G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T230829Z_S016G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T233952Z_S017G03.nc\n" + ] + } + ], + "source": [ + "granule_links = []\n", + "for result in POI_results: granule_links.append(result['umm']['RelatedUrls'][0]['URL'])\n", + "\n", + "for granule_link in granule_links: print(granule_link)\n", + "\n", + "# Downloading TEMPO data files\n", + "downloaded_files = earthaccess.download(\n", + " POI_results,\n", + " local_path='.')\n", + "\n", + "# Checking whether all TEMPO data files have been downloaded\n", + "for granule_link in granule_links:\n", + " TEMPO_fname = granule_link.split('/')[-1]\n", + "# check if file exists in the local directory\n", + " if not os.path.exists(TEMPO_fname):\n", + " print(TEMPO_fname, 'does not exist in local directory')\n", + "# repeat attempt to download\n", + " downloaded_files = earthaccess.download(granule_link,\n", + " local_path='.')\n", + "# if file still does not exist in the directory, remove its link from the list of links\n", + " if not os.path.exists(TEMPO_fname): granule_links.remove(granule_link)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kPyYwF-E7Qiv" + }, + "source": [ + "## 5.3 Compiling TEMPO NO2 total column time series" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ir3ClyeNueQq", + "outputId": "6d1cc7b7-62ee-4d32-a16a-9fabb3c66f70", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " TEMPO_NO2_L2_V03_20230901T002134Z_S018G03.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 3 694 40.043541 -105.212753 1.4738e+16 8.7104e+15 0\n", + " 3 695 40.023048 -105.207741 9.7241e+15 2.2060e+15 0\n", + " 4 694 40.044590 -105.270340 1.1286e+16 2.3133e+15 0\n", + " 4 695 40.024281 -105.265404 5.9626e+15 2.7523e+15 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[8.71037149e+15 2.20601518e+15 2.31331128e+15 2.75232855e+15]\n", + "[8.71037149e+15 2.20601518e+15 2.31331128e+15 2.75232855e+15]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T005257Z_S019G03.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 2 694 40.037796 -105.192413 1.1941e+16 6.4848e+15 1\n", + " 2 695 40.016869 -105.187187 1.4234e+16 6.0591e+15 1\n", + " 3 694 40.039486 -105.249710 6.5000e+15 1.3962e+15 1\n", + " 3 695 40.019146 -105.244766 1.0085e+16 3.5244e+15 1\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[]\n", + "[]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T012420Z_S020G03.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 3 694 40.037354 -105.222488 -1.0000e+30 -1.0000e+30 2\n", + " 3 695 40.016949 -105.217514 -1.0000e+30 -1.0000e+30 2\n", + " 4 694 40.038174 -105.279892 -1.0000e+30 -1.0000e+30 2\n", + " 4 695 40.018059 -105.275055 -1.0000e+30 -1.0000e+30 2\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[]\n", + "[]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T144803Z_S007G08.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 1 692 40.049271 -105.237335 4.2052e+15 1.3920e+15 0\n", + " 1 693 40.028824 -105.232361 5.1515e+15 1.6724e+15 0\n", + " 2 692 40.048901 -105.294388 5.4059e+15 1.4935e+15 0\n", + " 2 693 40.029575 -105.289955 6.2155e+15 1.3916e+15 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[1.39200032e+15 1.67242090e+15 1.49352773e+15 1.39156577e+15]\n", + "[1.39200032e+15 1.67242090e+15 1.49352773e+15 1.39156577e+15]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T155034Z_S008G08.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 1 691 40.056293 -105.231339 3.9442e+15 6.1653e+14 0\n", + " 1 692 40.035725 -105.226288 3.1724e+15 7.6719e+14 0\n", + " 2 691 40.056568 -105.288567 4.5017e+15 6.9336e+14 0\n", + " 2 692 40.036541 -105.283775 3.9150e+15 6.8650e+14 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[6.16529603e+14 7.67189829e+14 6.93356403e+14 6.86498063e+14]\n", + "[6.16529603e+14 7.67189829e+14 6.93356403e+14 6.86498063e+14]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T165942Z_S009G09.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 98 691 40.055660 -105.194344 4.6782e+15 5.5742e+14 0\n", + " 98 692 40.035091 -105.189301 4.6959e+15 5.8077e+14 0\n", + " 99 691 40.057152 -105.250603 3.1769e+15 5.3812e+14 0\n", + " 99 692 40.036758 -105.245636 4.6812e+15 7.0814e+14 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[5.57420968e+14 5.80766243e+14 5.38121510e+14 7.08142829e+14]\n", + "[5.57420968e+14 5.80766243e+14 5.38121510e+14 7.08142829e+14]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T182132Z_S010G09.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 99 692 40.051983 -105.202011 7.2100e+15 7.2846e+14 0\n", + " 99 693 40.031464 -105.196983 7.7709e+15 7.8188e+14 0\n", + " 100 692 40.052910 -105.258690 6.9115e+15 1.0297e+15 0\n", + " 100 693 40.032646 -105.253777 7.2187e+15 7.6325e+14 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[7.28461493e+14 7.81877978e+14 1.02970747e+15 7.63247016e+14]\n", + "[7.28461493e+14 7.81877978e+14 1.02970747e+15 7.63247016e+14]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T200038Z_S011G08.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 2 693 40.051800 -105.225998 6.8263e+15 6.4744e+14 0\n", + " 2 694 40.031326 -105.220970 6.4968e+15 7.1260e+14 0\n", + " 3 693 40.052639 -105.282028 4.9590e+15 7.7394e+14 0\n", + " 3 694 40.032536 -105.277184 6.8804e+15 6.2925e+14 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[6.47442572e+14 7.12598844e+14 7.73937030e+14 6.29246557e+14]\n", + "[6.47442572e+14 7.12598844e+14 7.73937030e+14 6.29246557e+14]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T210309Z_S012G08.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 2 693 40.044563 -105.207939 7.7771e+15 7.2164e+15 0\n", + " 2 694 40.024006 -105.202881 1.6293e+16 8.7282e+15 0\n", + " 3 693 40.045517 -105.264740 7.3353e+15 8.4949e+14 0\n", + " 3 694 40.025234 -105.259804 7.9467e+15 9.0780e+14 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[7.21636178e+15 8.72817134e+15 8.49489757e+14 9.07798768e+14]\n", + "[7.21636178e+15 8.72817134e+15 8.49489757e+14 9.07798768e+14]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T220540Z_S013G08.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 3 695 40.041687 -105.234406 4.3299e+15 3.9569e+15 0\n", + " 3 696 40.021320 -105.229439 2.9347e+15 4.7952e+15 0\n", + " 4 695 40.042309 -105.291039 1.1490e+16 4.4873e+15 0\n", + " 4 696 40.022667 -105.286423 -1.0000e+30 -1.0000e+30 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[3.95693655e+15 4.79522095e+15 4.48733801e+15]\n", + "[3.95693655e+15 4.48733801e+15]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T223706Z_S015G03.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 2 695 40.036613 -105.187828 4.3401e+15 3.6873e+15 0\n", + " 2 696 40.015675 -105.182579 8.7790e+15 1.0498e+16 0\n", + " 3 695 40.037987 -105.244232 5.1985e+15 2.9184e+15 0\n", + " 3 696 40.017635 -105.239273 2.3135e+16 3.8497e+16 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[3.68729136e+15 1.04982815e+16 2.91844013e+15 3.84974757e+16]\n", + "[3.68729136e+15 1.04982815e+16 2.91844013e+15 3.84974757e+16]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T230829Z_S016G03.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 3 695 40.043655 -105.232956 6.9392e+15 1.1839e+15 0\n", + " 3 696 40.023254 -105.227974 9.0335e+15 3.1892e+15 0\n", + " 4 695 40.043983 -105.289665 5.0447e+15 1.6137e+15 0\n", + " 4 696 40.024250 -105.285011 6.4210e+15 9.4193e+14 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[1.18393566e+15 3.18923174e+15 1.61373006e+15 9.41934445e+14]\n", + "[1.18393566e+15 3.18923174e+15 1.61373006e+15 9.41934445e+14]\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T233952Z_S017G03.nc\n", + "scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF\n", + " 2 693 40.054363 -105.206375 8.8157e+15 2.7685e+15 0\n", + " 2 694 40.033916 -105.201378 5.7492e+15 1.6391e+15 0\n", + " 3 693 40.054836 -105.262619 7.5013e+15 1.4504e+16 0\n", + " 3 694 40.034592 -105.257706 1.0162e+16 5.9820e+15 0\n", + "POI BoulderCO-NCAR at -105.242 40.0375 found\n", + "[2.76848731e+15 1.63911143e+15 1.45043722e+16 5.98196174e+15]\n", + "[2.76848731e+15 1.63911143e+15 1.45043722e+16 5.98196174e+15]\n" + ] + } + ], + "source": [ + "# Important note\n", + "# NO2 total column is calculated is a sum of stratospheric and tropospheric columns.\n", + "# One of them or both may be negative even with the highest quality flag.\n", + "# The code below compiles TWO timeseries one takes all values of total NO2 column,\n", + "# while another discards negative values before interpolation to the POI is performed.\n", + "# The two timeseries will be plotted later to see the difference, if any.\n", + "# This feature may be commented out should the user be not interested in accounting positive-only retrievals.\n", + "\n", + "days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n", + "\n", + "fout_noFV = open(out_Q+'_noFV_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", + "fout_noFV.write('timeseries of '+out_Q+' at '\\\n", + "+POI_name+' '+str('%08.4fN %08.4fW' %(POI[0], -POI[1]))+'\\n')\n", + "fout_noFV.write('yyyy mm dd hh mn ss '+out_Q_unit+'\\n')\n", + "\n", + "fout_noneg = open(out_Q+'_noneg_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", + "fout_noneg.write('timeseries of '+out_Q+' at '+POI_name+' '+str('%08.4fN %08.4fW' %(POI[0], -POI[1]))+'\\n')\n", + "fout_noneg.write('yyyy mm dd hh mn ss '+out_Q_unit+'\\n')\n", + "\n", + "for granule_link in granule_links:\n", + " last_slash_ind = granule_link.rfind('/')\n", + " fname = granule_link[last_slash_ind+1 : ]\n", + " print('\\n', fname)\n", + "\n", + " lat, lon, fv_geo, time, strat_NO2_column, fv_strat_NO2, trop_NO2_column\\\n", + ", fv_trop_NO2, trop_NO2_column_unc, fv_trop_NO2_column_unc, prod_unit, QF, fv_QF\\\n", + " = read_TEMPO_NO2_L2(fname)\n", + " \n", + " if isinstance(lat, float): continue\n", + "\n", + "# polygon = TEMPO_L2_polygon(lat, lon, fv_geo)\n", + "\n", + "# coords_poly = list(polygon)\n", + "# poly = Polygon(coords_poly)\n", + "\n", + " nx = lon.shape[0]\n", + " ny = lon.shape[1]\n", + "\n", + "# getting time from the granule filename\n", + " Tind = fname.rfind('T')\n", + " yyyy= int(fname[Tind-8 : Tind-4])\n", + " mm = int(fname[Tind-4 : Tind-2])\n", + " dd = int(fname[Tind-2 : Tind])\n", + " hh = int(fname[Tind+1 : Tind+3])\n", + " mn = int(fname[Tind+3 : Tind+5])\n", + " ss = float(fname[Tind+5 : Tind+7])\n", + "\n", + " pp = np.array([POI[1], POI[0]])\n", + " p = Point(pp) # POI[0] - latitudes, POI[1] - longitudes\n", + "\n", + " POI_found = False\n", + " for ix in range(nx-1):\n", + " for iy in range(ny-1):\n", + " if lon[ix, iy] == fv_geo: continue\n", + " if lat[ix, iy] == fv_geo: continue\n", + " if lon[ix, iy+1] == fv_geo: continue\n", + " if lat[ix, iy+1] == fv_geo: continue\n", + " if lon[ix+1, iy+1] == fv_geo: continue\n", + " if lat[ix+1, iy+1] == fv_geo: continue\n", + " if lon[ix+1, iy] == fv_geo: continue\n", + " if lat[ix+1, iy] == fv_geo: continue\n", + "\n", + " coords_poly_loc = [[lon[ix, iy], lat[ix, iy]], [lon[ix, iy+1], lat[ix, iy+1]] \\\n", + ", [lon[ix+1, iy+1], lat[ix+1, iy+1]], [lon[ix+1, iy], lat[ix+1, iy]]]\n", + " poly_loc = Polygon(coords_poly_loc)\n", + "\n", + " if p.within(poly_loc):\n", + " POI_found = True\n", + " strat_NO2_column_loc = strat_NO2_column[ix : ix+2, iy : iy + 2]\n", + " trop_NO2_column_loc = trop_NO2_column[ix : ix+2, iy : iy + 2]\n", + " total_NO2_column_unc_loc = trop_NO2_column_unc[ix : ix+2, iy : iy + 2]\n", + " total_NO2_column_loc = np.full((2, 2), fv_trop_NO2)\n", + " mask_valid = (strat_NO2_column_loc != fv_strat_NO2)&\\\n", + " (trop_NO2_column_loc != fv_trop_NO2) &\\\n", + " (total_NO2_column_unc_loc != fv_trop_NO2_column_unc)\n", + " total_NO2_column_loc[mask_valid] = strat_NO2_column_loc[mask_valid]\\\n", + " + trop_NO2_column_loc[mask_valid]\n", + " QF_loc = QF[ix : ix+2, iy : iy + 2]\n", + " lat_loc = lat[ix : ix+2, iy : iy + 2]\n", + " lon_loc = lon[ix : ix+2, iy : iy + 2]\n", + "\n", + " print('scanl pixel latitude longitude NO2_tot_col NO2_tot_col_unc NO2_col_QF')\n", + " for scl in range(2):\n", + " for pix in range(2):\n", + " print(\" %3d %4d %9.6f %10.6f %11.4e %11.4e %5i\"\\\n", + " %(ix+scl, iy+pix, lat_loc[scl, pix], lon_loc[scl, pix]\\\n", + ", total_NO2_column_loc[scl, pix], total_NO2_column_unc_loc[scl, pix], QF_loc[scl, pix]))\n", + "\n", + " print('POI', POI_name, 'at', POI[1], POI[0], ' found')\n", + "\n", + " mask_noFV = mask_valid & (QF_loc == 0)\n", + " mask_noneg = (strat_NO2_column_loc > 0)&\\\n", + " (trop_NO2_column_loc > 0) &\\\n", + " (total_NO2_column_unc_loc != fv_trop_NO2_column_unc)&\\\n", + " (QF_loc == 0)\n", + " points_noFV = np.column_stack((lon_loc[mask_noFV], lat_loc[mask_noFV]))\n", + " points_noneg = np.column_stack((lon_loc[mask_noneg], lat_loc[mask_noneg]))\n", + " ff_noFV = strat_NO2_column_loc[mask_noFV] + trop_NO2_column_loc[mask_noFV]\n", + " ff_noneg = strat_NO2_column_loc[mask_noneg] + trop_NO2_column_loc[mask_noneg]\n", + " ff_unc_noFV = total_NO2_column_unc_loc[mask_noFV]\n", + " ff_unc_noneg = total_NO2_column_unc_loc[mask_noneg]\n", + " print(ff_unc_noFV)\n", + " print(ff_unc_noneg)\n", + "\n", + "# handling time first:\n", + " delta_t = (time[ix+1] + time[ix])*0.5 - time[0]\n", + " ss = ss + delta_t\n", + " if ss >= 60.:\n", + " delta_mn = int(ss/60.)\n", + " ss = ss - 60.*delta_mn\n", + " mn = mn + delta_mn\n", + " if mn >= 60:\n", + " mn = mn - 60\n", + " hh = hh + 1\n", + " if hh == 24:\n", + " hh = hh - 24\n", + " dd = dd + 1\n", + " day_month = days[mm]\n", + " if (yyyy//4)*4 == yyyy and mm == 2: day_month = day_month + 1\n", + " if dd > day_month:\n", + " dd = 1\n", + " mm = mm + 1\n", + " if mm > 12:\n", + " mm = 1\n", + " yyyy = yyyy + 1\n", + "\n", + " if ff_noFV.shape[0] == 0:\n", + " continue\n", + " elif ff_noFV.shape[0] < 4:\n", + " total_NO2_column_noFV = np.mean(ff_noFV)\n", + " total_NO2_column_unc_noFV = np.mean(ff_unc_noFV)\n", + " elif ff_noFV.shape[0] == 4:\n", + " total_NO2_column_noFV = griddata(points_noFV, ff_noFV, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + " total_NO2_column_unc_noFV = griddata(points_noFV, ff_unc_noFV, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + "\n", + " fout_noFV.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %10.3e %10.3e '\\\n", + " %(yyyy, mm, dd, hh, mn, ss, total_NO2_column_noFV, total_NO2_column_unc_noFV)))\n", + " for scl in range(2):\n", + " for pix in range(2):\n", + " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat_loc[scl, pix], -lon_loc[scl, pix],\\\n", + "trop_NO2_column_loc[scl, pix], total_NO2_column_unc_loc[scl, pix])))\n", + " fout_noFV.write('\\n')\n", + "\n", + " if ff_noneg.shape[0] == 0:\n", + " continue\n", + " elif ff_noneg.shape[0] < 4:\n", + " total_NO2_column_noneg = np.mean(ff_noneg)\n", + " total_NO2_column_unc_noneg = np.mean(ff_unc_noneg)\n", + " elif ff_noneg.shape[0] == 4:\n", + " total_NO2_column_noneg = griddata(points_noneg, ff_noneg, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + " total_NO2_column_unc_noneg = griddata(points_noneg, ff_unc_noneg, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + "\n", + " fout_noneg.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %10.3e %10.3e '\\\n", + " %(yyyy, mm, dd, hh, mn, ss, total_NO2_column_noneg, total_NO2_column_unc_noneg)))\n", + " for scl in range(2):\n", + " for pix in range(2):\n", + " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat_loc[scl, pix], -lon_loc[scl, pix],\\\n", + "trop_NO2_column_loc[scl, pix], total_NO2_column_unc_loc[scl, pix])))\n", + " fout_noneg.write('\\n')\n", + "\n", + " break\n", + "\n", + " if POI_found: break\n", + "\n", + "fout_noFV.close()\n", + "fout_noneg.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xaRNZa_4bl11" + }, + "source": [ + "# 6 Plotting the results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XglvHW3AbfFl" + }, + "source": [ + "## 6.1 Reading created data files for TEMPO, create timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6tWE__dTPyGu", + "outputId": "e8eb2b3f-cdc4-4f11-eab5-64950a86a47f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "TEMPO standard and \"no negative\" are of equal length\n", + "TEMPO standard and \"no negative\" are different\n" + ] + } + ], + "source": [ + "# reading TEMPO file that was created at the previous step\n", + "# only read POI information from the header and first 8 columns of data:\n", + "# yyyy, mm, dd, hh, mn, ss, NO2 column, and its incertainty\n", + "fout = open(out_Q+'_noneg_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", + "\n", + "header1 = fout.readline()\n", + "header2 = fout.readline()\n", + "data_lines_noneg = fout.readlines()\n", + "\n", + "fout.close()\n", + "\n", + "yyyy = yyyy_ini\n", + "mm = mm_ini\n", + "dd = dd_ini\n", + "hh = 0\n", + "mn = 0\n", + "ss = 0\n", + "dt0 = datetime(yyyy, mm, dd, hh, mn, ss)\n", + "\n", + "yyyy = yyyy_fin\n", + "mm = mm_fin\n", + "dd = dd_fin\n", + "hh = 23\n", + "mn = 59\n", + "ss = 59\n", + "dt_fin = datetime(yyyy, mm, dd, hh, mn, ss) # this is time 1 second before the end of the timeframe of interest\n", + "\n", + "time_series_TEMPO_noneg = np.empty([0, 3])\n", + "\n", + "for line in data_lines_noneg:\n", + " split = line.split()\n", + " yyyy = int(split[0])\n", + " mm = int(split[1])\n", + " dd = int(split[2])\n", + " hh = int(split[3])\n", + " mn = int(split[4])\n", + " ss = float(split[5])\n", + " us = int((ss - int(ss))*1000000) # microseconds\n", + "# dt below is time since the beginning of the period of interest in hours\n", + " dt = (datetime(yyyy, mm, dd, hh, mn, int(ss), us) - dt0).total_seconds()/86400.\n", + " time_series_TEMPO_noneg = np.append(time_series_TEMPO_noneg,\\\n", + " [[dt, float(split[6]), float(split[7])]], axis = 0)\n", + "\n", + "fout = open(out_Q+'_noFV_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", + "\n", + "header1 = fout.readline()\n", + "header2 = fout.readline()\n", + "data_lines = fout.readlines()\n", + "\n", + "fout.close()\n", + "\n", + "time_series_TEMPO = np.empty([0, 3])\n", + "\n", + "for line in data_lines:\n", + " split = line.split()\n", + " yyyy = int(split[0])\n", + " mm = int(split[1])\n", + " dd = int(split[2])\n", + " hh = int(split[3])\n", + " mn = int(split[4])\n", + " ss = float(split[5])\n", + " us = int((ss - int(ss))*1000000) # microseconds\n", + "# dt below is time since the beginning of the period of interest in hours\n", + " dt = (datetime(yyyy, mm, dd, hh, mn, int(ss), us) - dt0).total_seconds()/86400.\n", + " time_series_TEMPO = np.append(time_series_TEMPO,\\\n", + " [[dt, float(split[6]), float(split[7])]], axis = 0)\n", + "\n", + "if len(time_series_TEMPO) == len(time_series_TEMPO_noneg):\n", + " print('\\nTEMPO standard and \"no negative\" are of equal length')\n", + " nt = len(time_series_TEMPO)\n", + " equal = True\n", + " for i in range(nt):\n", + " if time_series_TEMPO[i,1] != time_series_TEMPO_noneg[i,1]:\n", + " equal = False\n", + " break\n", + "else: equal = False\n", + "\n", + "if equal: print('TEMPO standard and \"no negative\" are the same')\n", + "else: print('TEMPO standard and \"no negative\" are different')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O8JvXRRcwT9O" + }, + "source": [ + "## 6.2 creating Pandora timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "Wbv86RDhwbCA" + }, + "outputs": [], + "source": [ + "Pandora_out = open(out_Q+'_Pandora_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", + "Pandora_data_lines = Pandora_out.readlines()\n", + "Pandora_out.close()\n", + "\n", + "time_series_Pandora = np.empty([0, 3])\n", + "time_series_Pandora_noneg = np.empty([0, 3])\n", + "\n", + "for line in Pandora_data_lines:\n", + " split = line.split()\n", + " yyyy = int(split[0])\n", + " mm = int(split[1])\n", + " dd = int(split[2])\n", + " hh = int(split[3])\n", + " mn = int(split[4])\n", + " ss = float(split[5])\n", + " us = int((ss - int(ss))*1000000) # microseconds\n", + "# dt below is time since the beginning of the period of interest in hours\n", + " dt = (datetime(yyyy, mm, dd, hh, mn, int(ss), us) - dt0).total_seconds()/86400.\n", + " col = float(split[6])\n", + " unc = float(split[7])\n", + " time_series_Pandora = np.append(time_series_Pandora,\\\n", + "[[dt, col, unc]], axis = 0)\n", + " if col > 0:\n", + " time_series_Pandora_noneg = np.append(time_series_Pandora_noneg,\\\n", + "[[dt, col, unc]], axis = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NGXMMgcNcMb4" + }, + "source": [ + "## 6.3 Plotting timeseries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WEkwDqvOccOK" + }, + "source": [ + "### 6.3.1 No error bars" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "aoBISVFDcYHx", + "outputId": "0798d941-c88f-4e3d-ec03-96973c713021" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_title = 'NO$_{2}$ total column '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = out_Q+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "plt.plot(time_series_Pandora[:, 0], time_series_Pandora[:, 1],\\\n", + " label = \"Pandora\", c = 'r')\n", + "plt.plot(time_series_TEMPO[:, 0], time_series_TEMPO[:, 1],\n", + " label = \"TEMPO\", c = 'b')\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = 0.\n", + "u_lim = ((dt_fin - dt0).total_seconds() + 1.)/86400.\n", + "plt.xlim(l_lim, u_lim)\n", + "\n", + "# some research is required to set the vertical range\n", + "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", + "plt.ylabel('NO$_{2}$ tot col, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "plt.legend(loc='lower left')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QyhmWrUMXU5z" + }, + "source": [ + "### 6.3.2 Plotting TEMPO and smoothed Pandora retievals with error bars" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 977 + }, + "id": "0pa3-j-6xh2P", + "outputId": "d2561cbe-7a54-46b0-fa72-a9616d057a62", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.014759 8.066e+15 8.83e+13 8.0438e-02\n", + "0.014837 8.075e+15 8.81e+13 1.7259e-01\n", + "0.014914 8.046e+15 8.80e+13 3.0430e-01\n", + "0.014993 8.091e+15 8.81e+13 4.4266e-01\n", + "0.015100 8.073e+15 8.81e+13\n", + "\n", + "0.616126 3.816e+15 1.85e+14 1.0000e+00\n", + "0.616753 3.816e+15 1.85e+14\n", + "\n", + "0.659936 4.369e+15 2.40e+14 8.3384e-02\n", + "0.660021 4.370e+15 2.40e+14 1.4055e-01\n", + "0.660102 4.365e+15 2.40e+14 1.8628e-01\n", + "0.660183 4.385e+15 2.40e+14 1.9926e-01\n", + "0.660264 4.341e+15 2.41e+14 1.7202e-01\n", + "0.660345 4.331e+15 2.42e+14 1.1986e-01\n", + "0.660425 4.319e+15 2.43e+14 6.8061e-02\n", + "0.660507 4.318e+15 2.43e+14 3.0591e-02\n", + "0.660168 4.357e+15 2.41e+14\n", + "\n", + "0.833506 8.218e+15 2.98e+14 6.1009e-02\n", + "0.833735 8.169e+15 2.98e+14 3.6963e-01\n", + "0.833964 8.240e+15 2.98e+14 4.0307e-01\n", + "0.834116 8.127e+15 2.98e+14 1.6630e-01\n", + "0.833861 8.193e+15 2.98e+14\n", + "\n", + "0.014759 8.066e+15 8.83e+13 8.0438e-02\n", + "0.014837 8.075e+15 8.81e+13 1.7259e-01\n", + "0.014914 8.046e+15 8.80e+13 3.0430e-01\n", + "0.014993 8.091e+15 8.81e+13 4.4266e-01\n", + "0.015100 8.073e+15 8.81e+13\n", + "\n", + "0.616126 3.816e+15 1.85e+14 1.0000e+00\n", + "0.616753 3.816e+15 1.85e+14\n", + "\n", + "0.659936 4.369e+15 2.40e+14 8.3384e-02\n", + "0.660021 4.370e+15 2.40e+14 1.4055e-01\n", + "0.660102 4.365e+15 2.40e+14 1.8628e-01\n", + "0.660183 4.385e+15 2.40e+14 1.9926e-01\n", + "0.660264 4.341e+15 2.41e+14 1.7202e-01\n", + "0.660345 4.331e+15 2.42e+14 1.1986e-01\n", + "0.660425 4.319e+15 2.43e+14 6.8061e-02\n", + "0.660507 4.318e+15 2.43e+14 3.0591e-02\n", + "0.660168 4.357e+15 2.41e+14\n", + "\n", + "0.833506 8.218e+15 2.98e+14 6.1009e-02\n", + "0.833735 8.169e+15 2.98e+14 3.6963e-01\n", + "0.833964 8.240e+15 2.98e+14 4.0307e-01\n", + "0.834116 8.127e+15 2.98e+14 1.6630e-01\n", + "0.833861 8.193e+15 2.98e+14\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "timeseries_Pandora_TEMPO, data_subset = gauss_interpolation(time_series_Pandora[:, 0:3]\\\n", + " , time_series_TEMPO[:, 0])\n", + "\n", + "timeseries_Pandora_TEMPO_noneg, data_subset_noneg =\\\n", + " gauss_interpolation(time_series_Pandora_noneg[:, 0:3]\\\n", + " , time_series_TEMPO_noneg[:, 0])\n", + "\n", + "plot_title = 'NO$_{2}$ total column w unc, all '+datestamp_ini+'_'+datestamp_fin+'\\n'+POI_name\n", + "img_name = out_Q+'_unc_'+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(time_series_TEMPO[:, 0], time_series_TEMPO[:, 1],\\\n", + "yerr=time_series_TEMPO[:, 2], label = \"TEMPO\", c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.errorbar(timeseries_Pandora_TEMPO[:, 0],\\\n", + " timeseries_Pandora_TEMPO[:, 1],\\\n", + " yerr=timeseries_Pandora_TEMPO[:, 2],\\\n", + " label = \"Pandora smoothed at TEMPO times\",\\\n", + " c = 'r', ls = '', marker = \".\")\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = 0.\n", + "u_lim = ((dt_fin - dt0).total_seconds() + 1.)/86400.\n", + "plt.xlim(l_lim, u_lim)\n", + "\n", + "# some research is required to set the vertical range\n", + "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", + "plt.ylabel('NO$_{2}$ tot col, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "#plt.legend(loc='lower left')\n", + "plt.legend(loc='upper left')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)\n", + "\n", + "plot_title = 'NO$_{2}$ total column w unc, positive '+datestamp_ini+'_'+datestamp_fin+'\\n'+POI_name\n", + "img_name = out_Q+'_unc_positive'+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(time_series_TEMPO_noneg[:, 0], time_series_TEMPO_noneg[:, 1],\\\n", + "yerr=time_series_TEMPO_noneg[:, 2], label = \"TEMPO\", c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.errorbar(timeseries_Pandora_TEMPO_noneg[:, 0],\\\n", + " timeseries_Pandora_TEMPO_noneg[:, 1],\\\n", + " yerr=timeseries_Pandora_TEMPO_noneg[:, 2],\\\n", + " label = \"Pandora smoothed at TEMPO times\",\\\n", + " c = 'r', ls = '', marker = \".\")\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = int(min(time_series_TEMPO_noneg[:, 0]))\n", + "u_lim = int(max(time_series_TEMPO_noneg[:, 0])) + 1\n", + "plt.xlim(l_lim, u_lim)\n", + "\n", + "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", + "plt.ylabel('NO$_{2}$ tot col, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "plt.legend(loc='lower left')\n", + "#plt.legend(loc='upper left')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E9-sFIpHzb-3" + }, + "source": [ + "## 6.4 Plotting scatter plots along with regressions" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 977 + }, + "id": "GPbJYBXuySOb", + "outputId": "6531ebd0-f0b7-46af-e714-5308c08e25b8" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting scatter plot for all retrievals\n", + "# (negative values have not been discarded in processing)\n", + "TEMPO_Pandora_scatter = np.empty([0, 4])\n", + "for td in time_series_TEMPO:\n", + " for pd in timeseries_Pandora_TEMPO:\n", + " if td[0] == pd[0]:\n", + " TEMPO_Pandora_scatter = np.append(TEMPO_Pandora_scatter,[[td[1], pd[1], td[2], pd[2]]], axis = 0)\n", + " break\n", + "\n", + "regress = stats.linregress(TEMPO_Pandora_scatter[:, 0], TEMPO_Pandora_scatter[:, 1])\n", + "slope = regress.slope\n", + "intercept = regress.intercept\n", + "r2 = regress.rvalue**2\n", + "stderr = regress.stderr\n", + "intercept_stderr = regress.intercept_stderr\n", + "\n", + "success, slope_0intercept, r2_0intercept =\\\n", + " regress_0intercept(TEMPO_Pandora_scatter[:, 0]\\\n", + " , TEMPO_Pandora_scatter[:, 1])\n", + "\n", + "plot_title = 'NO$_{2}$ total column w unc, all '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = 'scatter_'+out_Q+'_unc_'+'_'+datestamp_ini+'_'\\\n", + "+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(TEMPO_Pandora_scatter[:, 0], TEMPO_Pandora_scatter[:, 1],\\\n", + "xerr=TEMPO_Pandora_scatter[:, 2], yerr=TEMPO_Pandora_scatter[:, 3],\\\n", + "c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.xlabel(r'TEMPO NO$_{2}$ total col, mol/cm$^{2}$', fontsize=12)\n", + "plt.ylabel(r'Pandora NO$_{2}$ total col, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "fig.text(0.15, 0.72,\\\n", + "f'# of points: {len(TEMPO_Pandora_scatter):3d}\\nslope: {slope: 6.3f} $\\pm$ {stderr:6.3f}\\nintercept: {intercept: 8.2e} $\\pm$ {intercept_stderr: 8.2e}\\nR$^{2}$ = {r2:6.3f}')\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = min(0., min(TEMPO_Pandora_scatter[:, [0,1]].flatten()))*1.05\n", + "u_lim = max(TEMPO_Pandora_scatter[:, [0,1]].flatten())*1.05\n", + "plt.xlim(l_lim, u_lim)\n", + "plt.ylim(l_lim, u_lim)\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim, u_lim],\\\n", + "c = 'g', ls = '--')\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim*slope+intercept, u_lim*slope+intercept],\\\n", + "c = 'r', ls = '--')\n", + "\n", + "if success:\n", + " plt.plot([l_lim, u_lim], [l_lim*slope_0intercept, u_lim*slope_0intercept], c = 'r', ls = '-.')\n", + " fig.text(0.6, 0.12, f'\"no-intercept\" regression:\\nslope: {slope_0intercept: 6.3f} R$^{2}$ = {r2_0intercept:6.3f}')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)\n", + "\n", + "# Plotting scatter plot for positive retrievals\n", + "TEMPO_Pandora_scatter_noneg = np.empty([0, 4])\n", + "for td in time_series_TEMPO_noneg:\n", + " for pd in timeseries_Pandora_TEMPO_noneg:\n", + " if td[0] == pd[0]:\n", + " TEMPO_Pandora_scatter_noneg = np.append(TEMPO_Pandora_scatter_noneg,[[td[1], pd[1], td[2], pd[2]]], axis = 0)\n", + " break\n", + "\n", + "regress = stats.linregress(TEMPO_Pandora_scatter_noneg[:, 0], TEMPO_Pandora_scatter_noneg[:, 1])\n", + "slope = regress.slope\n", + "intercept = regress.intercept\n", + "r2 = regress.rvalue**2\n", + "stderr = regress.stderr\n", + "intercept_stderr = regress.intercept_stderr\n", + "\n", + "success, slope_0intercept, r2_0intercept =\\\n", + " regress_0intercept(TEMPO_Pandora_scatter_noneg[:, 0]\\\n", + " , TEMPO_Pandora_scatter_noneg[:, 1])\n", + "\n", + "plot_title = 'NO$_{2}$ total column w unc, positive '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = 'scatter_'+out_Q+'_unc_noneg_'+'_'+datestamp_ini+'_'\\\n", + "+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(TEMPO_Pandora_scatter_noneg[:, 0], TEMPO_Pandora_scatter_noneg[:, 1],\\\n", + "xerr=TEMPO_Pandora_scatter_noneg[:, 2], yerr=TEMPO_Pandora_scatter_noneg[:, 3],\\\n", + "c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.xlabel(r'TEMPO NO$_{2}$ total col, mol/cm$^{2}$', fontsize=12)\n", + "plt.ylabel(r'Pandora NO$_{2}$ total col, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "fig.text(0.15, 0.72,\\\n", + "f'# of points: {len(TEMPO_Pandora_scatter_noneg):3d}\\nslope: {slope: 6.3f} $\\pm$ {stderr:6.3f}\\nintercept: {intercept: 8.2e} $\\pm$ {intercept_stderr: 8.2e}\\nR$^{2}$ = {r2:6.3f}')\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = 0.\n", + "u_lim = max(TEMPO_Pandora_scatter_noneg[:, [0,1]].flatten())*1.05\n", + "plt.xlim(l_lim, u_lim)\n", + "plt.ylim(l_lim, u_lim)\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim, u_lim],\\\n", + "c = 'g', ls = '--')\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim*slope+intercept, u_lim*slope+intercept],\\\n", + "c = 'r', ls = '--')\n", + "\n", + "if success:\n", + " plt.plot([l_lim, u_lim], [l_lim*slope_0intercept, u_lim*slope_0intercept], c = 'r', ls = '-.')\n", + " fig.text(0.6, 0.12, f'\"no-intercept\" regression:\\nslope: {slope_0intercept: 6.3f} R$^{2}$ = {r2_0intercept:6.3f}')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cKn-MLMuen1q" + }, + "source": [ + "# EXTRA. Archiving output files to make downloading easier" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "v-VbL8CNXsLL" + }, + "outputs": [], + "source": [ + "import zipfile\n", + "import zlib\n", + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "4e1JLotAX9OC" + }, + "outputs": [], + "source": [ + "list_jpg = glob.glob('*'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg')\n", + "\n", + "with zipfile.ZipFile('fig_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name+'.zip', 'w') as fig_zip:\n", + " for name in list_jpg: fig_zip.write(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "JVkprHdiYpuS" + }, + "outputs": [], + "source": [ + "list_data = glob.glob('*'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'*.txt')\n", + "\n", + "with zipfile.ZipFile('data_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name+'.zip', 'w') as data_zip:\n", + " for name in 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+ { + "cell_type": "markdown", + "metadata": { + "id": "kjVKCytfEnRt" + }, + "source": [ + "### **TEMPO formaldehyde validation**\n", + "\n", + "This notebook illustrates comparison of TEMPO formaldehyde total column retrievals with Pandora ground stations.\n", + "\n", + "It allows a user to choose Pandora station of interest. Since TEMPO spatial coverage is regional and limited to North America, it is user's responsibilty to select the station within TEMPO's field of regard (FOR). If the selected station is outside FOR, no TEMPO time series will be generated.\n", + "\n", + "The user is allowed to choose the time period of interest by providing start and end dates in the form YYYYMMDD. Please be aware, that if the selecte period of interest is outside of available time span of one of the sensors, corresponding time series will not be generated.\n", + "\n", + "Data files for both sensors are downloaded on-the-fly. TEMPO data are downloaded with earthaccess library that needs to be installed first.\n", + "\n", + "TEMPO data files are read by means of netCDF library that needs to be installed first.\n", + "\n", + "Pandora data files are ASCII files with header and space separated columns. Custome made function is included to read nitrogen dioxide total column along with its total uncertainty.\n", + "\n", + "This code takes into account quality flags (QFs) from both TEMPO and Pandora. This is implemented as follow. On the TEMPO side, data set \"/product/main_data_quality_flag\" is read, all pixels with non-zero QFs are discarded, however negative values of total HCHO column are NOT discarded and used for averaging/interpolationg to the point of interest. For the purpose of physical sanity, another way is also implemented, i.e., negative retrievals are not used in averaging. Therefore, TWO values are returned, total_HCHO_col, and total_HCHO_col_noneg. On Pandora side negative columns also occur despite high quality flags, though they are rare. So, two Pandora time series are considered - with and without negative columns.\n", + "\n", + "The resulting time series are plotted with and without uncertainty of both measurement in the end of the notebook.\n", + "\n", + "This notebook is tested on TEMPO_HCHO_L2_V03 and Pandora L2_rfus5p1-8 files." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "luJG0oPIPGjC" + }, + "source": [ + "# 1 Installing and importing necessary libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m5ru-FMpPXoE" + }, + "source": [ + "## 1.1 Installing netCDF" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5NWX4mCVQJt_", + "outputId": "de27d9a3-6dbb-49b0-8f57-65ed6ef5ccf0" + }, + "outputs": [], + "source": [ + "# un-comment if installation is necessary\n", + "#! pip3 install netCDF4" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cQJCMByjPp9i" + }, + "source": [ + "## 1.2 Installing earthaccess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N7Gm15VaYKW9", + "outputId": "07aee29d-7791-4b4d-fbfe-c12defa84971" + }, + "outputs": [], + "source": [ + "# un-comment if installation is necessary\n", + "#! pip3 install earthaccess" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TxfhRi7ySyFY" + }, + "source": [ + "## 1.3 Importing necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "IAAuhYMcEkvP" + }, + "outputs": [], + "source": [ + "import earthaccess # needed to discover and download TEMPO data\n", + "import netCDF4 as nc # needed to read TEMPO data\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "import platform\n", + "from subprocess import Popen\n", + "import shutil\n", + "\n", + "from shapely.geometry import Point, Polygon # needed to search a point within a polygon\n", + "from scipy.interpolate import griddata # needed to interpolate TEMPO data to the point of interest\n", + "from scipy import stats # needed for linear regression analysis\n", + "\n", + "import requests # needed to search for and download Pandora data\n", + "import codecs # needed to read Pandora data\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt # needed to plot the resulting time series\n", + "from urllib.request import urlopen, Request # needed to search for and download Pandora data\n", + "from pathlib import Path # needed to check whether a needed data file is already downloaded\n", + "from datetime import datetime, timedelta # needed to work with time in plotting time series" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ANsfYumeXjKm" + }, + "source": [ + "# 2 Defining functions to work with Pandora and TEMPO data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OQGUPNbgXyKN" + }, + "source": [ + "## 2.1 functions to work with Pandora" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RB9IWsMvX6Kd" + }, + "source": [ + "### 2.1.1 function creating the list of available Pandora sites" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "kB3wW0-D1RP2" + }, + "outputs": [], + "source": [ + "# function read_pandora_web returns the list of available Pandora sites\n", + "def read_pandora_web():\n", + " url = 'https://data.pandonia-global-network.org/'\n", + " page = urlopen(url)\n", + " html_bytes = page.read()\n", + " html = html_bytes.decode(\"utf-8\")\n", + " html_len = len(html)\n", + "\n", + " pos1 = 0\n", + "\n", + " big_line = str(html)\n", + " lines = big_line.split('\\n')\n", + "\n", + " ref_lines = [i for i in lines if 'href' in i]\n", + " refs = []\n", + " for line in ref_lines:\n", + " pos1 = line.find('\"')\n", + " pos2 = line.rfind('\"')\n", + " if pos1 > 0 and pos2 > pos1 and line[pos2-1] =='/' and line[pos1+1] == '.':\n", + " refs.append(line[pos1+3 : pos2-1])\n", + "\n", + " return refs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ht9LHL28YUmR" + }, + "source": [ + "### 2.1.2 functions allowing user to choose a Pandora site of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "tpF3vEOD1dsm" + }, + "outputs": [], + "source": [ + "# function check_site checks whether user entered site is in the list of available Pandora sites\n", + "def check_site(site_name, refs):\n", + " site_list = []\n", + " for line in refs:\n", + " if site_name in line:\n", + " site_list.append(line)\n", + "\n", + " return site_list\n", + "\n", + "\n", + "# function take_pandora_sites takes user input and checks whether the site is in the list of available Pandora sites\n", + "def take_pandora_sites(refs):\n", + " print('please select a Pandora site name from the list')\n", + " for ref in refs:\n", + " print(ref)\n", + "\n", + " answer = 'y'\n", + " while answer == 'y':\n", + " site_name = input('Enter a name of a Pandora site: ')\n", + " print(site_name)\n", + " site_list = check_site(site_name, refs)\n", + " site_num = len(site_list)\n", + " if site_num == 0:\n", + " print('site ', site_name, 'was not found')\n", + " continue\n", + "\n", + " if site_num > 1:\n", + " print('there are ', site_num, ' site names, select one from')\n", + " for site in site_list: print(site)\n", + "\n", + " site_name = input('Enter an exact name of a Pandora site: ')\n", + " if site_list.count(site_name) != 1:\n", + " print('Entered name is not the exact match of one of the following sites')\n", + " for site in site_list: print(site)\n", + " print('program terminated')\n", + " sys.exit()\n", + "\n", + " for site in site_list:\n", + " if site == site_name:\n", + " pandora_site = site_name\n", + " print('site ', site_name, 'was found and added to the list of sites ')\n", + " break\n", + "\n", + " if site_num == 1:\n", + " pandora_site = site_list[0]\n", + " print('site ', site_list[0], 'was found and added to the list of sites ')\n", + "\n", + " answer = 'n'\n", + "\n", + " return pandora_site" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F5sX6MEaZGFI" + }, + "source": [ + "### 2.1.3 function creating the list of links to formaldehyde data files at the selected Pandora sites and downloading the data files" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "PI8UerY82LoQ" + }, + "outputs": [], + "source": [ + "# Pandora site may have several instruments. In this case each instrument has its own directory.\n", + "# However, the most recent version of the HCHO data, rfus5p1-8, is available only in one of these directories.\n", + "# The function creates all possible links, but some of them may be non-existing. This is checked and cleared later.\n", + "def instrument_path(site):\n", + "# function instrument_path returns links to available Pandora HCHO retrievals files\n", + " url = 'https://data.pandonia-global-network.org/' + site + '/'\n", + " page = urlopen(url)\n", + " html_bytes = page.read()\n", + " html = html_bytes.decode(\"utf-8\")\n", + " html_len = len(html)\n", + "\n", + " pos1 = 0\n", + " big_line = str(html)\n", + " lines = big_line.split('\\n')\n", + "\n", + " ref_lines = [i for i in lines if 'href' in i]\n", + " links = []\n", + " for line in ref_lines:\n", + "\n", + " pos1 = line.find('\"')\n", + " pos2 = line.rfind('\"')\n", + " if pos1 > 0 and pos2 > pos1 and line[pos2-1] =='/' and\\\n", + " line[pos1+3 : pos1 + 10] == 'Pandora':\n", + " link = url + line[pos1+3 : pos2] + 'L2/' + line[pos1+3 : pos2-1] + '_' + site + '_L2_rfus5p1-8.txt'\n", + " print(link)\n", + " links.append(link)\n", + "\n", + " return links\n", + "\n", + "\n", + "# function downloading Pandora data file with given url\n", + "def download(url):\n", + " response = requests.get(url)\n", + " response_code = response.status_code\n", + "\n", + " file_name = url.split('/')[-1]\n", + "\n", + " if response_code == 200:\n", + " content = response.content\n", + " data_path = Path(file_name)\n", + " data_path.write_bytes(content)\n", + "\n", + " return file_name, response_code" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HLnx6gUAaMg6" + }, + "source": [ + "### 2.1.4 function reading Pandora NCHO data file rfus5p1-8" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "9XMu3iAp27yx" + }, + "outputs": [], + "source": [ + "# function converting Pandora timestamp into a set of year, month, day, hour, minute, and second\n", + "# function read_timestamp converts Pandora timestamp of the format\n", + "# 'yyyymmddThhmmssZ' into a set of 6 numbers:\n", + "# integer year, month, day, hour, minute, and real second.\n", + "def read_timestamp(timestamp):\n", + "\n", + " yyyy = int(timestamp[0:4])\n", + " mm = int(timestamp[4:6])\n", + " dd = int(timestamp[6:8])\n", + " hh = int(timestamp[9:11])\n", + " mn = int(timestamp[11:13])\n", + " ss = float(timestamp[13:17])\n", + "\n", + " return yyyy, mm, dd, hh, mn, ss\n", + "\n", + "\n", + "# function reading Pandora NCHO data file rfus5p1-8\n", + "#\n", + "# Below is the second version of function read_Pandora_HCHO_rfus5p1_8. It is to be used for the future validation efforts.\n", + "# The difference with the original version is that instead of discriminating negative values of the total HCHO column,\n", + "# it uses quality flags. It was previously found that QF == 0 does not occure often enough,\n", + "# so we will have to use QF == 10 (not-assured high quality).\n", + "#\n", + "# function read_Pandora_HCHO_rfus5p1_8 reads Pandora total HCHO column data files ending with rfus5p1-8.\n", + "# Arguments:\n", + "# fname - name file to be read, string;\n", + "# start_date - beginning of the time interval of interest,\n", + "# integer of the form YYYYMMDD;\n", + "# end_date - end of the time interval of interest,\n", + "# integer of the form YYYYMMDD.\n", + "#\n", + "# if start_date is greater than end_date, the function returns a numpy array\n", + "# with shape (0, 8), otherwise it returns an 8-column numpy array\n", + "# with with columns being year, month, day, hour, minute, second of observation\n", + "# and retrieved total HCHO column along with its total uncertainty.\n", + "#\n", + "# HCHO column is in mol/m^2, so conversion to molecules/cm^2 is performed by\n", + "# multiplication by Avogadro constant, NA = 6.02214076E+23, and division by 1.E+4\n", + "def read_Pandora_HCHO_rfus5p1_8_v2(fname, start_date, end_date):\n", + "\n", + " conversion_coeff = 6.02214076E+19 # Avogadro constant divided by 10000\n", + "\n", + " data = np.empty([0, 8])\n", + " if start_date > end_date: return -999., -999., 'no_name', data\n", + "\n", + " with codecs.open(fname, 'r', encoding='utf-8', errors='ignore') as f:\n", + "\n", + " while True:\n", + "# Get next line from file\n", + " line = f.readline()\n", + "\n", + " if line.find('Short location name:') >= 0:\n", + " loc_name = line.split()[-1] # location name, to be used in the output file name\n", + " print('location name ', loc_name)\n", + "\n", + " if line.find('Location latitude [deg]:') >= 0:\n", + " lat = float(line.split()[-1]) # location latitude\n", + " print('location latitude ', lat)\n", + "\n", + " if line.find('Location longitude [deg]:') >= 0:\n", + " lon = float(line.split()[-1]) # location longitude\n", + " print('location longitude ', lon)\n", + "\n", + " if line.find('--------') >= 0: break\n", + "\n", + " while True:\n", + "# Get next line from file\n", + " line = f.readline()\n", + "\n", + " if line.find('--------') >= 0: break\n", + "\n", + " while True:\n", + "# now reading line with data\n", + " line = f.readline()\n", + "\n", + " if not line: break\n", + "\n", + " line_split = line.split()\n", + "\n", + " yyyy, mm, dd, hh, mn, ss = read_timestamp(line_split[0])\n", + " date_stamp = yyyy*10000 + mm*100 + dd\n", + " if date_stamp < start_date or date_stamp > end_date: continue\n", + "\n", + " QF = int(line_split[35]) # total column uncertainty\n", + "\n", + " if QF == 0 or QF == 10:\n", + " column = float(line_split[38])\n", + " column_unc = float(line_split[42]) # total column uncertainty\n", + " data = np.append(data, [[yyyy, mm, dd, hh, mn, ss\\\n", + " , column*conversion_coeff\\\n", + " , column_unc*conversion_coeff]], axis = 0)\n", + "\n", + " return lat, lon, loc_name, data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ep0Fl-Kzas5x" + }, + "source": [ + "## 2.2 function reading TEMPO HCHO data file" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "zVJY6k5i3qat" + }, + "outputs": [], + "source": [ + "# function reading TEMPO HCHO data file\n", + "def read_TEMPO_HCHO_L2(fn):\n", + " '''\n", + " function read_TEMPO_HCHO_L2 reads the following arrays from the\n", + " TEMPO L2 HCHO product TEMPO_HCHO_L2_V03:\n", + " main_data_quality_flag\n", + " vertical_column;\n", + " vertical_column_uncertainty;\n", + " and returns respective fields along with coordinates of the pixels.\n", + "\n", + " If one requested variables cannot be read, all returned variables are zeroed\n", + " '''\n", + " var_name = 'vertical_column'\n", + " var_unc_name = 'vertical_column_uncertainty'\n", + " var_QF_name = 'main_data_quality_flag'\n", + "\n", + " try:\n", + " ds = nc.Dataset(fn)\n", + "\n", + " prod = ds.groups['product'] # this opens group product, /product, as prod\n", + "\n", + " var = prod.variables[var_name] # this reads variable vertical_column from prod (group product, /product)\n", + " total_HCHO_column = np.array(var)\n", + " fv_prod = var.getncattr('_FillValue')\n", + " prod_unit = var.getncattr('units')\n", + "\n", + " var_unc = prod.variables[var_unc_name] # this reads variable vertical_column_uncertainty from prod (group product, /product)\n", + " total_HCHO_column_unc = np.array(var_unc)\n", + "\n", + " var_QF = prod.variables[var_QF_name] # this reads variable main_data_quality_flag from prod (group product, /product)\n", + " total_HCHO_column_QF = np.array(var_QF)\n", + " fv_QF = var_QF.getncattr('_FillValue')\n", + "\n", + " geo = ds.groups['geolocation'] # this opens group geolocation, /geolocation, as geo\n", + "\n", + " lat = np.array(geo.variables['latitude']) # this reads variable latitude from geo (geolocation group, /geolocation) into a numpy array\n", + " lon = np.array(geo.variables['longitude']) # this reads variable longitude from geo (geolocation group, /geolocation) into a numpy array\n", + " fv_geo = geo.variables['latitude'].getncattr('_FillValue')\n", + " time = np.array(geo.variables['time'] )# this reads variable longitude from geo (geolocation group, /geolocation) into a numpy array\n", + "\n", + " ds.close()\n", + "\n", + " except:\n", + " print('variable '+var_name+' cannot be read in file '+fn)\n", + " lat = 0.\n", + " lon = 0.\n", + " time = 0.\n", + " fv_geo = 0.\n", + " total_HCHO_column = 0.\n", + " total_HCHO_column_unc = 0.\n", + " total_HCHO_column_QF = 0.\n", + " fv_prod = 0.\n", + " fv_QF = -999\n", + " prod_unit = ''\n", + "\n", + " return lat, lon, fv_geo, time, total_HCHO_column, total_HCHO_column_unc\\\n", + ", total_HCHO_column_QF, fv_prod, fv_QF, prod_unit" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OkRA1M7PcIYx" + }, + "source": [ + "## 2.3 auxiliary functions to handle data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AKzFo9EjcTzx" + }, + "source": [ + "### 2.3.1 function smoothing Pandora retievals and interpolating them onto TEMPO times of observations" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "Or7hNgjA4oUi" + }, + "outputs": [], + "source": [ + "# Pandora timeseries has significantly more data points then TEMPO and DSCOVR. It is also very noisy.\n", + "# To make comparison easier, Pandora timeseries is interpolated to the moments of TEMPO observations.\n", + "#\n", + "# Interpolation is performed by the function defined below with the help of Gaussian smooting as follow:\n", + "# x_int(t) = SUM(x_p(t_i)*wt(t_i, t)),\n", + "#\n", + "# wt(t_i, t) = exp(-(t - t_i)^2/(2 * sigma^2))/SUM(exp(-(t - t_i)^2/(2 * sigma^2))),\n", + "#\n", + "# where sums are taken over times t_i falling into time interval (t-dt_max, t+dt_max).\n", + "#\n", + "# Parameters dt_max and sigma can be chosen by the user.\n", + "def gauss_interpolation(timeseries, new_times):\n", + "#\n", + "# function gauss_interpolation takes 2D array timeseries with function\n", + "# to be interpolated and 1D array new_times containing times to which\n", + "# the function is to be interpolated\n", + "# arguments:\n", + "# timeseries - array with at least 2 columns,\n", + "# 1st column - times, 2nd (3rd, ...) column(s) - function to be interpolated\n", + "# new_times - 1D array of times to which the function(s) to be interpolated\n", + "#\n", + "# parameters\n", + "# dt_max = 0.00375 - 324 seconds expressed in days\n", + "# sigma = 0.00125 - 81 seconds expressed in days\n", + "\n", + " dt_max = 0.00375 # 324 seconds expressed in days\n", + " sigma = 0.00125 # 81 seconds expressed in days\n", + "\n", + " nnt = len(new_times)\n", + " (nt, nfun) = timeseries.shape\n", + "\n", + " timeseries_smooth = np.empty([0, nfun])\n", + " data_subset = np.empty(nnt, dtype = object)\n", + " cnt = 0\n", + " for new_time in new_times:\n", + " llim = new_time - dt_max\n", + " ulim = new_time + dt_max\n", + "\n", + " timeseries_subset = timeseries[((timeseries[:, 0] < ulim)\\\n", + " & (timeseries[:, 0] > llim))]\n", + " if len(timeseries_subset) < 1: continue\n", + " t_delta = timeseries_subset[:, 0] - new_time\n", + " wt = np.exp(-t_delta**2/(2.*sigma**2))\n", + " wt = wt/np.sum(wt)\n", + " timeseries_subset = np.append(timeseries_subset, np.transpose([wt]), axis = 1)\n", + " for t in timeseries_subset: print(f'{t[0]:.5f} {t[1]:.3e} {t[2]:.2e} {t[3]:.4e}')\n", + " data_subset[cnt] = timeseries_subset\n", + " cnt += 1\n", + "\n", + " timeseries_smooth_loc = np.array([new_time])\n", + " for ifun in range(1, nfun):\n", + " timeseries_smooth_loc = np.append(timeseries_smooth_loc,\\\n", + " np.sum(timeseries_subset[:, ifun]*wt))\n", + " print(f'{timeseries_smooth_loc[0]:.5f} {timeseries_smooth_loc[1]:.3e} {timeseries_smooth_loc[2]:.2e}\\n')\n", + "\n", + " timeseries_smooth = np.append(timeseries_smooth,\\\n", + " np.array([timeseries_smooth_loc]), axis = 0)\n", + "\n", + " return timeseries_smooth, data_subset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nNoGDd0MdP9Y" + }, + "source": [ + "### 2.3.2 function computing linear regression with zero intercept" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "R2-9qdRo5kih" + }, + "outputs": [], + "source": [ + "# custom made function regress_0intercept takes vectors x and y\n", + "# representing coordinates and function values at these coordinates\n", + "# and returns slope of regression fit y = a*x\n", + "# along with coefficient of determination\n", + "def regress_0intercept(x, y):\n", + " success = False\n", + "\n", + " if len(x) != len(y):\n", + " a = 0.\n", + " R2 = 0.\n", + "\n", + " elif len(x) == 1:\n", + " if x[0] != 0.:\n", + " a = y[0]/x[0]\n", + " R2 = 1.\n", + " success = True\n", + " else:\n", + " if y[0] != 0.:\n", + " a = np.inf\n", + " R2 = 1.\n", + " success = True\n", + " else:\n", + " a = np.inf\n", + " R2 = 0.\n", + "\n", + " else:\n", + " xy_sum = np.dot(x, y)\n", + " x2_sum = np.dot(x, x)\n", + " a = xy_sum/x2_sum\n", + "\n", + " res_y = y - a*x\n", + " res_sum_2 = np.dot(res_y, res_y)\n", + " y2_sum = np.dot(y, y)\n", + " sum_tot_2 = y2_sum - len(y)*np.mean(y)**2\n", + " R2 = 1. - res_sum_2/sum_tot_2\n", + "\n", + " success = True\n", + "\n", + " return success, a, R2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0aDnIkZVdu93" + }, + "source": [ + "# Main code begins here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vXy2ArJ93d9e" + }, + "source": [ + "# 3 Establishing access to EarthData" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8BnoNI0k3mi1" + }, + "source": [ + "## 3.1 Logging in" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ytaf8mOZ5x7q", + "outputId": "8ba5bd6a-74b6-4231-a13e-995361e30c4f" + }, + "outputs": [], + "source": [ + "# User needs to create an account at https://www.earthdata.nasa.gov/\n", + "# Function earthaccess.login prompts for EarthData login and password.\n", + "auth = earthaccess.login(strategy=\"interactive\", persist=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3FRuPwwg5x7r" + }, + "source": [ + "## 3.2 Creating local directory" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G51uzF9E5x7r", + "outputId": "b992a852-49c8-44e2-bc00-6537b6de38f7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved .dodsrc to: /home/jovyan/\n" + ] + } + ], + "source": [ + "homeDir = os.path.expanduser(\"~\") + os.sep\n", + "\n", + "with open(homeDir + '.dodsrc', 'w') as file:\n", + " file.write('HTTP.COOKIEJAR={}.urs_cookies\\n'.format(homeDir))\n", + " file.write('HTTP.NETRC={}.netrc'.format(homeDir))\n", + " file.close()\n", + "\n", + "print('Saved .dodsrc to:', homeDir)\n", + "\n", + "# Set appropriate permissions for Linux/macOS\n", + "if platform.system() != \"Windows\":\n", + " Popen('chmod og-rw ~/.netrc', shell=True)\n", + "else:\n", + " # Copy dodsrc to working directory in Windows\n", + " shutil.copy2(homeDir + '.dodsrc', os.getcwd())\n", + " print('Copied .dodsrc to:', os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "adQX_ZDp5x7r" + }, + "source": [ + "# 4 Working with Pandora data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JU9ksvT_5x7r" + }, + "source": [ + "## 4.1 Discovering existing Pandora stations and selecting one of them" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "p4ha-IPH57jp", + "outputId": "c198057f-75ca-411d-d3be-b4ab78e6dd95" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gathering Pandora sites information\n", + "please select a Pandora site name from the list\n", + "Agam\n", + "AldineTX\n", + "AliceSprings\n", + "Altzomoni\n", + "ArlingtonTX\n", + "Athens-NOA\n", + "AtlantaGA-Conyers\n", + "AtlantaGA-GATech\n", + "AtlantaGA-SouthDeKalb\n", + "AtlantaGA\n", + "AustinTX\n", + "Bandung\n", + "Bangkok\n", + "Banting\n", + "BayonneNJ\n", + "Beijing-RADI\n", + "BeltsvilleMD\n", + "Berlin\n", + "BlueHillMA\n", + "BostonMA\n", + "BoulderCO-NCAR\n", + "BoulderCO\n", + "Bremen\n", + "BristolPA\n", + "BronxNY\n", + "Brussels-Uccle\n", + "Bucharest\n", + "BuenosAires\n", + "BuffaloNY\n", + "Busan\n", + "Cabauw\n", + "Calakmul\n", + "calibrationfiles\n", + "CambridgeBay\n", + "CambridgeMA\n", + "CameronLA\n", + "CapeElizabethME\n", + "Cebu\n", + "ChapelHillNC\n", + "CharlesCityVA\n", + "ChelseaMA\n", + "ChiangMai\n", + "ChicagoIL\n", + "Cologne\n", + "ComodoroRivadavia\n", + "Cordoba\n", + "CornwallCT\n", + "CorpusChristiTX\n", + "Daegu\n", + "Dalanzadgad\n", + "Davos\n", + "DearbornMI\n", + "DeBilt\n", + "Dhaka\n", + "Downsview\n", + "EastProvidenceRI\n", + "EdwardsCA\n", + "Egbert\n", + "EssexMD\n", + "Eureka-0PAL\n", + "Eureka-PEARL\n", + "FairbanksAK\n", + "Fajardo\n", + "FortMcKay\n", + "FortYatesND\n", + "Fukuoka\n", + "Gongju-KNU\n", + "Granada\n", + "GrandForksND\n", + "GreenbeltMD\n", + "Haldwani-ARIES\n", + "HamptonVA-HU\n", + "HamptonVA\n", + "Heidelberg\n", + "Helsinki\n", + "HoustonTX-SanJacinto\n", + "HoustonTX\n", + "HuntsvilleAL\n", + "Ilocos\n", + "Incheon-ESC\n", + "Innsbruck\n", + "IowaCityIA-WHS\n", + "Islamabad-NUST\n", + "Izana\n", + "Jeonju\n", + "Juelich\n", + "KenoshaWI\n", + "Kobe\n", + "Kosetice\n", + "LaPaz\n", + "LaPorteTX\n", + "LapwaiID\n", + "LibertyTX\n", + "Lindenberg\n", + "LondonderryNH\n", + "LynnMA\n", + "MadisonCT\n", + "ManhattanKS\n", + "ManhattanNY-CCNY\n", + "MaunaLoaHI\n", + "MexicoCity-UNAM\n", + "MexicoCity-Vallejo\n", + "MiamiFL-FIU\n", + "MountainViewCA\n", + "Nagoya\n", + "Nainital-ARIES\n", + "NewBrunswickNJ\n", + "NewHavenCT\n", + "NewLondonCT\n", + "NewOrleansLA-XULA\n", + "NyAlesund\n", + "OldFieldNY\n", + "operationfiles\n", + "Palau\n", + "Palawan\n", + "PhiladelphiaPA\n", + "PhnomPenh\n", + "PittsburghPA\n", + "Pontianak\n", + "Potchefstroom-METSI\n", + "QueensNY\n", + "QuezonCity\n", + "RichmondCA\n", + "Rome-IIA\n", + "Rome-ISAC\n", + "Rome-SAP\n", + "Rotterdam-Haven\n", + "SaltLakeCityUT-Hawthorne\n", + "SaltLakeCityUT\n", + "SanJoseCA\n", + "Sapporo\n", + "Seosan\n", + "Seoul-KU\n", + "Seoul-SNU\n", + "Seoul\n", + "Singapore-NUS\n", + "Songkhla\n", + "SouthJordanUT\n", + "StGeorge\n", + "StonyPlain\n", + "Suwon-USW\n", + "SWDetroitMI\n", + "Tel-Aviv\n", + "Thessaloniki\n", + "Tokyo-Sophia\n", + "Tokyo-TMU\n", + "Toronto-CNTower\n", + "Toronto-Scarborough\n", + "Toronto-West\n", + "Trollhaugen\n", + "Tsukuba-NIES-West\n", + "Tsukuba-NIES\n", + "Tsukuba\n", + "TubaCityAZ\n", + "TucsonAZ\n", + "TurlockCA\n", + "TylerTX\n", + "Ulaanbaatar\n", + "Ulsan\n", + "Vientiane\n", + "VirginiaBeachVA-CBBT\n", + "WacoTX\n", + "Wakkerstroom\n", + "WallopsIslandVA\n", + "Warsaw-UW\n", + "WashingtonDC\n", + "WestportCT\n", + "WhittierCA\n", + "Windsor-West\n", + "WrightwoodCA\n", + "Yokosuka\n", + "Yongin\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a name of a Pandora site: Mex\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mex\n", + "there are 2 site names, select one from\n", + "MexicoCity-UNAM\n", + "MexicoCity-Vallejo\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter an exact name of a Pandora site: MexicoCity-UNAM\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "site MexicoCity-UNAM was found and added to the list of sites \n", + "the following sites were selected\n", + "MexicoCity-UNAM\n", + "from the list of existing Pandora sites\n", + "https://data.pandonia-global-network.org/MexicoCity-UNAM/Pandora142s1/L2/Pandora142s1_MexicoCity-UNAM_L2_rfus5p1-8.txt\n", + "Pandora142s1_MexicoCity-UNAM_L2_rfus5p1-8.txt does not exit in local directory, downloading from the web\n", + "https://data.pandonia-global-network.org/MexicoCity-UNAM/Pandora142s1/L2/Pandora142s1_MexicoCity-UNAM_L2_rfus5p1-8.txt\n", + "Pandora L2 file Pandora142s1_MexicoCity-UNAM_L2_rfus5p1-8.txt has been downloaded\n" + ] + } + ], + "source": [ + "# Discovering existing Pandora stations and selecting one of them\n", + "# Discovering available Pandora site.\n", + "# Please bear in mind that some sites do not have HCHO data files\n", + "print('gathering Pandora sites information')\n", + "refs = read_pandora_web()\n", + "\n", + "pandora_site = take_pandora_sites(refs) # create list of Pandora sites of interest\n", + "print('the following sites were selected')\n", + "print(pandora_site)\n", + "print('from the list of existing Pandora sites')\n", + "\n", + "# create a list of !AVAILABLE! Pandora files for the Pandora site\n", + "pandora_files = []\n", + "\n", + "links = instrument_path(pandora_site)\n", + "\n", + "npfiles = 0\n", + "\n", + "for link in links:\n", + " pandora_fname = link.split('/')[-1]\n", + "\n", + "# check if file exists in the local directory, if not download from Pandora site\n", + " if not os.path.exists(pandora_fname):\n", + " print(pandora_fname,' does not exit in local directory, downloading from the web')\n", + " print(link)\n", + "\n", + " pandora_fname, response_code = download(link)\n", + "\n", + " if response_code == 200:\n", + " print('Pandora L2 file ', pandora_fname, ' has been downloaded')\n", + " npfiles = npfiles + 1\n", + " pandora_files.append(pandora_fname)\n", + " else:\n", + " print('Pandora L2 file ', link, ' does not exist')\n", + "\n", + " else:\n", + " print(pandora_fname,' exits in local directory')\n", + " npfiles = npfiles + 1\n", + " pandora_files.append(pandora_fname)\n", + "\n", + "if npfiles == 0: # no files were found, STOP here\n", + " print('no files were found for Pandora site ', pandora_site, 'program terminated')\n", + " sys.exit()\n", + "if npfiles > 1: # normally there should be only one file per site. if there are more - STOP\n", + " print('there are too many files for site ', pandora_site, '- STOP and investigate file names below. Program terminated')\n", + " for pandora_fname in pandora_files:\n", + " print(pandora_fname)\n", + " sys.exit()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-cqNd6lm6A4Q" + }, + "source": [ + "## 4.2 Selecting timeframe of interest common for both instruments" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "keDJUzxA6rdW", + "outputId": "01a61ce0-b6cb-4520-8903-6acc38432efc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "enter period of interest, start and end dates, in the form YYYYMMDD\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "enter start date of interest 20230904\n", + "enter end date of interest 20230904\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023 9 4 2023 9 4\n" + ] + } + ], + "source": [ + "print('enter period of interest, start and end dates, in the form YYYYMMDD')\n", + "datestamp_ini = input('enter start date of interest ')\n", + "datestamp_fin = input('enter end date of interest ')\n", + "\n", + "start_date = int(datestamp_ini)\n", + "end_date = int(datestamp_fin)\n", + "\n", + "yyyy_ini = start_date//10000\n", + "mm_ini = (start_date//100 - yyyy_ini*100)\n", + "dd_ini = (start_date - yyyy_ini*10000 - mm_ini*100)\n", + "\n", + "yyyy_fin = end_date//10000\n", + "mm_fin = (end_date//100 - yyyy_fin*100)\n", + "dd_fin = (end_date - yyyy_fin*10000 - mm_fin*100)\n", + "print(yyyy_ini, mm_ini, dd_ini, yyyy_fin, mm_fin, dd_fin)\n", + "\n", + "date_start = str('%4.4i-%2.2i-%2.2i 00:00:00' %(yyyy_ini, mm_ini, dd_ini))\n", + "date_end = str('%4.4i-%2.2i-%2.2i 23:59:59' %(yyyy_fin, mm_fin, dd_fin))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mTFV2Fkadj8e" + }, + "source": [ + "## 4.3 Reading Pandora file within selected timeframe and creating point of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "x7gLpKIB7cuZ", + "outputId": "cdba7f07-0bfb-402d-ec52-591b5ff0be75" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "location name MexicoCity-UNAM\n", + "location latitude 19.3262\n", + "location longitude -99.1761\n", + "21 Pandora measurements found within period of interes between 2023-09-04 00:00:00 and 2023-09-04 23:59:59\n" + ] + } + ], + "source": [ + "pandora_file = pandora_files[0]\n", + "lat, lon, POI_name, Pandora_data = read_Pandora_HCHO_rfus5p1_8_v2(pandora_file, start_date, end_date)\n", + "\n", + "if lat == -999.:\n", + " print('error reading pandora file ', pandora_file, 'program terminated')\n", + " sys.exit()\n", + "\n", + "POI = np.array([lat, lon])\n", + "\n", + "# print # of points in Pandora timeseries\n", + "n_Pandora_data = len(Pandora_data)\n", + "print(n_Pandora_data,\\\n", + "' Pandora measurements found within period of interes between',\\\n", + "date_start, 'and', date_end)\n", + "if n_Pandora_data == 0:\n", + " print('There are no Pandora observations with quality flags 0 or 10,\\n'\\\n", + "+'user should stop here unless plotting TEMPO-only time series is needed')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3wvhtsq5kXgZ" + }, + "source": [ + "## 4.4 Setting TEMPO name constants and writing Pandora timeseries to a file" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "Za0rC_qA7oiN" + }, + "outputs": [], + "source": [ + "# Setting TEMPO name constants\n", + "short_name = 'TEMPO_HCHO_L2' # collection name to search for in the EarthData\n", + "out_Q = 'HCHO_tot_col' # name of the output quantity with unit\n", + "out_Q_unit = 'molecules/cm^2' # name of the output quantity with unit\n", + "\n", + "# write Pandora timeseries to a file\n", + "POI_name_ = POI_name.replace(' ','_')\n", + "Pandora_out = open(out_Q+'_Pandora_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", + "for line in Pandora_data:\n", + " Pandora_out.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %12.4e %12.4e\\n'\\\n", + " %(line[0], line[1], line[2], line[3], line[4], line[5], line[6], line[7])))\n", + "Pandora_out.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VGfpNlrWei36" + }, + "source": [ + "# 5 Working with TEMPO data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qO9NEF61jlcy" + }, + "source": [ + "## 5.1 Searching TEMPO data files containing the POI (position of the Pandora station)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nKcGCvxX7yad", + "outputId": "b05c7aab-7312-4cc0-d55e-0967d6702714" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 14\n" + ] + } + ], + "source": [ + "# Searching TEMPO data files within 0.5 degree range around the POI (position of the Pandora station)\n", + "POI_lat = POI[0]\n", + "POI_lon = POI[1]\n", + "\n", + "version = 'V03'\n", + "POI_results = earthaccess.search_data(short_name = short_name\\\n", + " , version = version\\\n", + " , temporal = (date_start, date_end)\\\n", + " , point = (POI_lon, POI_lat))\n", + "\n", + "n_gr = len(POI_results)\n", + "if n_gr == 0:\n", + " print('program terminated')\n", + " print('There are no TEMPO granules covering the selected\\n'\\\n", + "+'Pandora station withing selected timeframe, user should stop here\\n'\\\n", + "+'unless plotting TEMPO-only time series is needed')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OvC5eZRhk7Xz" + }, + "source": [ + "## 5.2 Printing explicit links to the granules and downloading the files" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 547, + "referenced_widgets": [ + "1ade5214d9934418b94d8048886fa34f", + "979c079b73fb4629a73eaac824c8b584", + "031bec658a8242f19fb1d45909f31351", + "ce6c51c3b89e4e12a7a8dc10e9dc8b32", + "c5e13c793fc74599ae1ffbe00144edf3", + "9b8cb87d0a774b1d836191dacc66e6bf", + "037c73558dcc40d5a7c120d7047fc4dc", + "f986d11d864b49799802d97acf922b33", + "a01cdb7d820441c79bbaee7f0cf886d9", + "3ba5e8c95a3c47108e9347846e3bacad", + "98da4c74db074ca483461d02f85f9a33", + "9906d2de4cab4d6ba4fb64fa15fbcaff", + "7dbc55809000484798185222601f6e66", + "64c4adcf79b54d3bb7d8040ae7dd5e9a", + "cde073d5694a49a49174b2971f9c1284", + "684b5de7631f4f38a6c68c8c57c6ba0d", + "df81a12dbd3b4d8a944d8fc8afa47d04", + "cd244181c0da4dff91108d820a270c88", + "ff014766ab184911a51c6ced48b2eab7", + "4ed620c5d2554fd2b4ba66e52454e647", + "0e7375c5bfce498a902df7b5953548e8", + "80daa2d21dcc4d0bae44acd1864d21d7", + "1d086d1060b04605b7d2c5b5d7bc7122", + "760f35818d0d49c6819f16475d4628b9", + "5f0031e946794f6e908fee59e96f458c", + "5a3239298d2e4a25afd08bfbfcf15469", + "763f98ba55d04481af2981a34181182a", + "9a4806e861cf4ba4b97ca8f0326f9f16", + "2bee1c0ba74444d1897c573fe5572021", + "b919a8789d9940e88f3b4d151714917b", + "c57e180b9d774fe2842e6ca78fc75697", + "a85bc2be580d45b3af2d7fab8b27175b", + "290f5a45924040959f27dba901b56bbc" + ] + }, + "id": "Y17Rm0fV8KB8", + "outputId": "dcff20e9-6b0f-47dc-8f76-a43cb1e9ff11", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T000424Z_S018G02.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T003547Z_S019G02.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T010710Z_S020G02.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T144052Z_S007G07.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T154323Z_S008G07.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T164554Z_S009G07.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T174825Z_S010G07.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T185056Z_S011G07.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T195327Z_S012G07.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T205558Z_S013G07.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T215829Z_S014G07.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T222955Z_S015G02.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T230118Z_S016G02.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_HCHO_L2_V03/2023.09.04/TEMPO_HCHO_L2_V03_20230904T233241Z_S017G02.nc\n", + " Getting 14 granules, approx download size: 1.27 GB\n", + "Accessing cloud dataset using dataset endpoint credentials: https://data.asdc.earthdata.nasa.gov/s3credentials\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T000424Z_S018G02.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T003547Z_S019G02.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T010710Z_S020G02.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T144052Z_S007G07.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T154323Z_S008G07.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T164554Z_S009G07.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T174825Z_S010G07.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T185056Z_S011G07.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T195327Z_S012G07.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T205558Z_S013G07.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T215829Z_S014G07.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T222955Z_S015G02.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T230118Z_S016G02.nc\n", + "Downloaded: TEMPO_HCHO_L2_V03_20230904T233241Z_S017G02.nc\n" + ] + } + ], + "source": [ + "granule_links = []\n", + "for result in POI_results: granule_links.append(result['umm']['RelatedUrls'][0]['URL'])\n", + "for granule_link in granule_links: print(granule_link)\n", + "\n", + "# Downloading TEMPO data files\n", + "downloaded_files = earthaccess.download(\n", + " POI_results,\n", + " local_path='.')\n", + "\n", + "# Checking whether all TEMPO data files have been downloaded\n", + "for granule_link in granule_links:\n", + " TEMPO_fname = granule_link.split('/')[-1]\n", + "# check if file exists in the local directory\n", + " if not os.path.exists(TEMPO_fname):\n", + " print(TEMPO_fname, 'does not exist in local directory')\n", + "# repeat attempt to download\n", + " downloaded_files = earthaccess.download(granule_link,\n", + " local_path='.')\n", + "# if file still does not exist in the directory, remove its link from the list of links\n", + " if not os.path.exists(TEMPO_fname): granule_links.remove(granule_link)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kPyYwF-E7Qiv" + }, + "source": [ + "## 5.3 Compiling TEMPO formaldehyde column time series" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rEaA_LVX8RWM", + "outputId": "e587ebfc-2d9b-4fab-e915-54ca33e84104", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " TEMPO_HCHO_L2_V03_20230904T000424Z_S018G02.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 56 1907 19.331041 -99.135368 -4.5987e+16 3.6250e+16 1\n", + " 56 1908 19.316168 -99.134552 2.2521e+15 6.3149e+16 1\n", + " 57 1907 19.331553 -99.178902 4.0344e+16 3.3803e+16 1\n", + " 57 1908 19.316607 -99.178047 4.0999e+16 2.7387e+16 1\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T003547Z_S019G02.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 56 1906 19.340015 -99.152893 -2.4181e+17 2.1879e+17 1\n", + " 56 1907 19.325111 -99.152069 1.1652e+17 3.7041e+17 1\n", + " 57 1906 19.340120 -99.196526 2.0689e+17 2.2333e+17 1\n", + " 57 1907 19.325216 -99.195679 2.3838e+17 2.9945e+17 1\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T010710Z_S020G02.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 56 1907 19.333797 -99.137932 -1.0000e+30 -1.0000e+30 2\n", + " 56 1908 19.318918 -99.137115 -1.0000e+30 -1.0000e+30 2\n", + " 57 1907 19.333786 -99.181824 -1.0000e+30 -1.0000e+30 2\n", + " 57 1908 19.318859 -99.180984 -1.0000e+30 -1.0000e+30 2\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T144052Z_S007G07.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 54 1904 19.340120 -99.148766 1.1150e+16 1.1319e+16 0\n", + " 54 1905 19.325218 -99.147949 5.2005e+15 1.0830e+16 0\n", + " 55 1904 19.340178 -99.192062 6.5510e+15 6.2471e+15 0\n", + " 55 1905 19.325283 -99.191238 4.4872e+15 1.4107e+16 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T154323Z_S008G07.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 54 1905 19.331827 -99.134384 3.5448e+15 4.8610e+15 0\n", + " 54 1906 19.316950 -99.133568 3.3467e+15 6.8850e+15 0\n", + " 55 1905 19.332344 -99.177673 1.5345e+16 4.4514e+15 0\n", + " 55 1906 19.317402 -99.176819 1.0476e+16 6.5003e+15 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T164554Z_S009G07.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 55 1906 19.328262 -99.163361 1.8193e+16 7.3464e+15 0\n", + " 55 1907 19.313322 -99.162506 8.1210e+15 7.0487e+15 0\n", + " 56 1906 19.327961 -99.207169 1.6200e+16 7.0643e+15 0\n", + " 56 1907 19.313017 -99.206314 1.5417e+15 7.1311e+15 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T174825Z_S010G07.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 55 1905 19.339512 -99.152359 1.2979e+16 5.0483e+15 0\n", + " 55 1906 19.324604 -99.151527 1.6198e+16 6.7681e+15 0\n", + " 56 1905 19.339188 -99.196503 1.0191e+16 5.2784e+15 0\n", + " 56 1906 19.324278 -99.195663 1.7365e+16 6.1557e+15 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T185056Z_S011G07.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 55 1905 19.340410 -99.140068 2.6087e+16 1.2474e+16 0\n", + " 55 1906 19.325529 -99.139244 2.2219e+16 6.5908e+15 0\n", + " 56 1905 19.340565 -99.183426 3.2250e+16 8.7999e+15 0\n", + " 56 1906 19.325674 -99.182594 1.6021e+16 4.8546e+15 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T195327Z_S012G07.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 56 1907 19.334900 -99.157860 1.0516e+16 1.3507e+16 0\n", + " 56 1908 19.319984 -99.157013 1.6408e+16 8.5267e+15 0\n", + " 57 1907 19.335394 -99.201218 2.2460e+16 1.5104e+16 0\n", + " 57 1908 19.320517 -99.200386 2.8571e+16 1.5747e+16 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T205558Z_S013G07.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 56 1906 19.336357 -99.176163 1.2487e+16 1.1406e+16 0\n", + " 56 1907 19.321453 -99.175323 1.6882e+16 3.7443e+16 0\n", + " 57 1906 19.335800 -99.219231 1.8217e+16 1.3786e+16 0\n", + " 57 1907 19.321054 -99.218460 1.0850e+16 9.6450e+15 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T215829Z_S014G07.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 56 1907 19.330942 -99.175720 3.2543e+16 9.5985e+16 0\n", + " 56 1908 19.316004 -99.174866 4.9395e+16 6.2333e+16 0\n", + " 57 1907 19.330992 -99.219063 2.0938e+16 2.9215e+16 0\n", + " 57 1908 19.316177 -99.218262 1.1644e+16 4.4287e+16 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T222955Z_S015G02.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 56 1907 19.329115 -99.157593 2.6057e+15 3.5937e+16 0\n", + " 56 1908 19.314196 -99.156754 7.0077e+15 4.1752e+16 0\n", + " 57 1907 19.329393 -99.201202 5.8130e+15 1.0617e+17 0\n", + " 57 1908 19.314445 -99.200333 6.0727e+15 7.6883e+16 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T230118Z_S016G02.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 56 1907 19.331305 -99.160866 4.1802e+16 2.1676e+16 0\n", + " 56 1908 19.316383 -99.160019 1.0907e+16 6.6932e+16 0\n", + " 57 1907 19.331322 -99.204094 1.6597e+16 4.8749e+16 0\n", + " 57 1908 19.316401 -99.203247 3.1492e+16 4.1537e+16 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n", + "\n", + " TEMPO_HCHO_L2_V03_20230904T233241Z_S017G02.nc\n", + "scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF\n", + " 60 1907 19.326611 -99.158417 7.3455e+15 6.6129e+16 0\n", + " 60 1908 19.311693 -99.157578 1.5122e+16 5.6931e+16 0\n", + " 61 1907 19.326572 -99.201729 1.3234e+16 2.6874e+16 0\n", + " 61 1908 19.311617 -99.200874 6.8549e+15 2.4895e+16 0\n", + "POI MexicoCity-UNAM at 19.3262 -99.1761 found\n" + ] + } + ], + "source": [ + "# Important note\n", + "# HCHO total column may be negative even with the highest quality flag.\n", + "# The code below compiles TWO timeseries one takes all values of total NO2 column,\n", + "# while another discards negative values before interpolation to the POI is performed.\n", + "# The two timeseries will be plotted later to see the difference, if any.\n", + "# This feature may be commented out should the user be not interested in accounting positive-only retrievals.\n", + "\n", + "days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n", + "\n", + "fout_noFV = open(out_Q+'_noFV_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", + "fout_noFV.write('timeseries of '+out_Q+' at '\\\n", + "+POI_name+' '+str('%08.4fN %08.4fW' %(POI[0], -POI[1]))+'\\n')\n", + "fout_noFV.write('yyyy mm dd hh mn ss '+out_Q_unit+'\\n')\n", + "\n", + "fout_noneg = open(out_Q+'_noneg_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", + "fout_noneg.write('timeseries of '+out_Q+' at '\\\n", + "+POI_name+' '+str('%08.4fN %08.4fW' %(POI[0], -POI[1]))+'\\n')\n", + "fout_noneg.write('yyyy mm dd hh mn ss '+out_Q_unit+'\\n')\n", + "\n", + "for granule_link in granule_links:\n", + " last_slash_ind = granule_link.rfind('/')\n", + " fname = granule_link[last_slash_ind+1 : ]\n", + " print('\\n', fname)\n", + "\n", + " lat, lon, fv_geo, time, total_HCHO_column, total_HCHO_column_unc,\\\n", + "total_HCHO_column_QF, fv_prod, fv_QF, prod_unit = read_TEMPO_HCHO_L2(fname)\n", + "# un-comment the line below if TEMPO granules are not needed after processing\n", + "# os.remove(fname)\n", + "\n", + " if isinstance(lat, float): continue\n", + "\n", + " nx = lon.shape[0]\n", + " ny = lon.shape[1]\n", + "\n", + "# getting time from the granule filename\n", + " Tind = fname.rfind('T')\n", + " yyyy= int(fname[Tind-8 : Tind-4])\n", + " mm = int(fname[Tind-4 : Tind-2])\n", + " dd = int(fname[Tind-2 : Tind])\n", + " hh = int(fname[Tind+1 : Tind+3])\n", + " mn = int(fname[Tind+3 : Tind+5])\n", + " ss = float(fname[Tind+5 : Tind+7])\n", + "\n", + "# check whether POI is in the granule. If not - move to the next granule\n", + " pp = np.array([POI[1], POI[0]])\n", + " p = Point(pp) # POI[0] - latitudes, POI[1] - longitudes\n", + "# if not p.within(poly):\n", + "# print('point', POI[0], POI[1], 'is not within the granule polygon' )\n", + "# continue\n", + "# print('point', POI[0], POI[1], 'is within the granule polygon' )\n", + "\n", + " POI_found = False\n", + " for ix in range(nx-1):\n", + " for iy in range(ny-1):\n", + " if lon[ix, iy] == fv_geo: continue\n", + " if lat[ix, iy] == fv_geo: continue\n", + " if lon[ix, iy+1] == fv_geo: continue\n", + " if lat[ix, iy+1] == fv_geo: continue\n", + " if lon[ix+1, iy+1] == fv_geo: continue\n", + " if lat[ix+1, iy+1] == fv_geo: continue\n", + " if lon[ix+1, iy] == fv_geo: continue\n", + " if lat[ix+1, iy] == fv_geo: continue\n", + "\n", + " coords_poly_loc = [[lon[ix, iy], lat[ix, iy]], [lon[ix, iy+1], lat[ix, iy+1]] \\\n", + " , [lon[ix+1, iy+1], lat[ix+1, iy+1]], [lon[ix+1, iy], lat[ix+1, iy]]]\n", + " poly_loc = Polygon(coords_poly_loc)\n", + "\n", + " if p.within(poly_loc):\n", + " print('scanl pixel latitude longitude totHCHO_col totHCHO_col_unc totHCHO_col_QF')\n", + " for scl in range(ix, ix+2, 1):\n", + " for pix in range(iy, iy+2, 1):\n", + " print(\" %3d %4d %9.6f %10.6f %11.4e %11.4e %5i\"\\\n", + " %(scl, pix, lat[scl, pix], lon[scl, pix]\\\n", + ", total_HCHO_column[scl, pix], total_HCHO_column_unc[scl, pix], total_HCHO_column_QF[scl, pix]))\n", + "\n", + " POI_found = True\n", + " print('POI', POI_name, 'at', POI[0], POI[1], ' found')\n", + "\n", + " total_HCHO_column_loc = np.array([total_HCHO_column[ix, iy],\\\n", + " total_HCHO_column[ix, iy+1],\\\n", + " total_HCHO_column[ix+1, iy+1],\\\n", + " total_HCHO_column[ix+1, iy]])\n", + " total_HCHO_column_unc_loc = np.array([total_HCHO_column_unc[ix, iy],\\\n", + " total_HCHO_column_unc[ix, iy+1],\\\n", + " total_HCHO_column_unc[ix+1, iy+1],\\\n", + " total_HCHO_column_unc[ix+1, iy]])\n", + " total_HCHO_column_QF_loc = np.array([total_HCHO_column_QF[ix, iy],\\\n", + " total_HCHO_column_QF[ix, iy+1],\\\n", + " total_HCHO_column_QF[ix+1, iy+1],\\\n", + " total_HCHO_column_QF[ix+1, iy]])\n", + " lat_loc = np.array([lat[ix, iy], lat[ix, iy+1],\\\n", + " lat[ix+1, iy+1], lat[ix+1, iy]])\n", + " lon_loc = np.array([lon[ix, iy], lon[ix, iy+1],\\\n", + " lon[ix+1, iy+1], lon[ix+1, iy]])\n", + " mask_noFV = (total_HCHO_column_loc != fv_prod)&\\\n", + " (total_HCHO_column_unc_loc != fv_prod)&\\\n", + " (total_HCHO_column_QF_loc == 0)\n", + " mask_noneg = (total_HCHO_column_loc > 0.)&\\\n", + " (total_HCHO_column_unc_loc != fv_prod)&\\\n", + " (total_HCHO_column_QF_loc == 0)\n", + "\n", + " points_noFV = np.column_stack((lon_loc[mask_noFV], lat_loc[mask_noFV]))\n", + " points_noneg = np.column_stack((lon_loc[mask_noneg], lat_loc[mask_noneg]))\n", + " ff_noFV = total_HCHO_column_loc[mask_noFV]\n", + " ff_noneg = total_HCHO_column_loc[mask_noneg]\n", + " ff_unc_noFV = total_HCHO_column_unc_loc[mask_noFV]\n", + " ff_unc_noneg = total_HCHO_column_unc_loc[mask_noneg]\n", + "\n", + "# handling time first:\n", + " delta_t = (time[ix+1] + time[ix])*0.5 - time[0]\n", + " ss = ss + delta_t\n", + " if ss >= 60.:\n", + " delta_mn = int(ss/60.)\n", + " ss = ss - 60.*delta_mn\n", + " mn = mn + delta_mn\n", + " if mn >= 60:\n", + " mn = mn - 60\n", + " hh = hh + 1\n", + " if hh == 24:\n", + " hh = hh - 24\n", + " dd = dd + 1\n", + " day_month = days[mm]\n", + " if (yyyy//4)*4 == yyyy and mm == 2: day_month = day_month + 1\n", + " if dd > day_month:\n", + " dd = 1\n", + " mm = mm + 1\n", + " if mm > 12:\n", + " mm = 1\n", + " yyyy = yyyy + 1\n", + "\n", + " if ff_noFV.shape[0] == 0:\n", + " continue\n", + " elif ff_noFV.shape[0] < 4:\n", + " total_HCHO_column_noFV = np.mean(ff_noFV)\n", + " total_HCHO_column_unc_noFV = np.mean(ff_unc_noFV)\n", + " elif ff_noFV.shape[0] == 4:\n", + " total_HCHO_column_noFV = griddata(points_noFV, ff_noFV, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + " total_HCHO_column_unc_noFV = griddata(points_noFV, ff_unc_noFV, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + "\n", + " fout_noFV.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %10.3e %10.3e '\\\n", + " %(yyyy, mm, dd, hh, mn, ss, total_HCHO_column_noFV, total_HCHO_column_unc_noFV)))\n", + " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix, iy], -lon[ix, iy],\\\n", + "total_HCHO_column[ix, iy], total_HCHO_column_unc[ix, iy])))\n", + " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix, iy+1], -lon[ix, iy+1],\\\n", + "total_HCHO_column[ix, iy+1], total_HCHO_column_unc[ix, iy+1])))\n", + " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix+1, iy+1], -lon[ix+1, iy+1],\\\n", + "total_HCHO_column[ix+1, iy+1], total_HCHO_column_unc[ix+1, iy+1])))\n", + " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e\\n'\\\n", + " %(lat[ix+1, iy], -lon[ix+1, iy],\\\n", + "total_HCHO_column[ix+1, iy], total_HCHO_column_unc[ix+1, iy])))\n", + "\n", + " if ff_noneg.shape[0] == 0:\n", + " continue\n", + " elif ff_noneg.shape[0] < 4:\n", + " total_HCHO_column_noneg = np.mean(ff_noneg)\n", + " total_HCHO_column_unc_noneg = np.mean(ff_unc_noneg)\n", + " elif ff_noneg.shape[0] == 4:\n", + " total_HCHO_column_noneg = griddata(points_noneg, ff_noneg, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + " total_HCHO_column_unc_noneg = griddata(points_noneg, ff_unc_noneg, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + "\n", + " fout_noneg.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %10.3e %10.3e '\\\n", + " %(yyyy, mm, dd, hh, mn, ss, total_HCHO_column_noneg, total_HCHO_column_unc_noneg)))\n", + " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix, iy], -lon[ix, iy],\\\n", + "total_HCHO_column[ix, iy], total_HCHO_column_unc[ix, iy])))\n", + " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix, iy+1], -lon[ix, iy+1],\\\n", + "total_HCHO_column[ix, iy+1], total_HCHO_column_unc[ix, iy+1])))\n", + " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix+1, iy+1], -lon[ix+1, iy+1],\\\n", + "total_HCHO_column[ix+1, iy+1], total_HCHO_column_unc[ix+1, iy+1])))\n", + " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e\\n'\\\n", + " %(lat[ix+1, iy], -lon[ix+1, iy],\\\n", + "total_HCHO_column[ix+1, iy], total_HCHO_column_unc[ix+1, iy])))\n", + "\n", + " break\n", + "\n", + " if POI_found: break\n", + "\n", + "fout_noFV.close()\n", + "fout_noneg.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xaRNZa_4bl11" + }, + "source": [ + "# 6 Plotting the results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XglvHW3AbfFl" + }, + "source": [ + "## 6.1 Reading created data files for TEMPO, create timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KoLn_l159HSJ", + "outputId": "9e2af709-c83c-4964-d9d1-d447b0ad33d5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "TEMPO standard and \"no negative\" are of equal length\n", + "TEMPO standard and \"no negative\" are the same\n" + ] + } + ], + "source": [ + "# reading TEMPO file that was created at the previous step\n", + "# only read POI information from the header and first 8 columns of data:\n", + "# yyyy, mm, dd, hh, mn, ss, HCHO column, and its incertainty\n", + "fout = open(out_Q+'_noneg_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", + "\n", + "header1 = fout.readline()\n", + "header2 = fout.readline()\n", + "data_lines_noneg = fout.readlines()\n", + "\n", + "fout.close()\n", + "\n", + "yyyy = yyyy_ini\n", + "mm = mm_ini\n", + "dd = dd_ini\n", + "hh = 0\n", + "mn = 0\n", + "ss = 0\n", + "dt0 = datetime(yyyy, mm, dd, hh, mn, ss)\n", + "\n", + "yyyy = yyyy_fin\n", + "mm = mm_fin\n", + "dd = dd_fin\n", + "hh = 23\n", + "mn = 59\n", + "ss = 59\n", + "dt_fin = datetime(yyyy, mm, dd, hh, mn, ss) # this is time 1 second before the end of the timeframe of interest\n", + "\n", + "time_series_TEMPO_noneg = np.empty([0, 3])\n", + "\n", + "for line in data_lines_noneg:\n", + " split = line.split()\n", + " yyyy = int(split[0])\n", + " mm = int(split[1])\n", + " dd = int(split[2])\n", + " hh = int(split[3])\n", + " mn = int(split[4])\n", + " ss = float(split[5])\n", + " us = int((ss - int(ss))*1000000) # microseconds\n", + "# dt below is time since the beginning of the period of interest in hours\n", + " dt = (datetime(yyyy, mm, dd, hh, mn, int(ss), us) - dt0).total_seconds()/86400.\n", + " time_series_TEMPO_noneg = np.append(time_series_TEMPO_noneg,\\\n", + " [[dt, float(split[6]), float(split[7])]], axis = 0)\n", + "\n", + "fout = open(out_Q+'_noFV_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", + "\n", + "header1 = fout.readline()\n", + "header2 = fout.readline()\n", + "data_lines = fout.readlines()\n", + "\n", + "fout.close()\n", + "\n", + "time_series_TEMPO = np.empty([0, 3])\n", + "\n", + "for line in data_lines:\n", + " split = line.split()\n", + " yyyy = int(split[0])\n", + " mm = int(split[1])\n", + " dd = int(split[2])\n", + " hh = int(split[3])\n", + " mn = int(split[4])\n", + " ss = float(split[5])\n", + " us = int((ss - int(ss))*1000000) # microseconds\n", + "# dt below is time since the beginning of the period of interest in hours\n", + " dt = (datetime(yyyy, mm, dd, hh, mn, int(ss), us) - dt0).total_seconds()/86400.\n", + " time_series_TEMPO = np.append(time_series_TEMPO,\\\n", + " [[dt, float(split[6]), float(split[7])]], axis = 0)\n", + "\n", + "if len(time_series_TEMPO) == 0:\n", + " print('Standard TEMPO time series is empty')\n", + "if len(time_series_TEMPO_noneg) == 0:\n", + " print('TEMPO time series excluding negative HCHO retrievals is empty')\n", + "\n", + "if len(time_series_TEMPO) == len(time_series_TEMPO_noneg):\n", + " print('\\nTEMPO standard and \"no negative\" are of equal length')\n", + " nt = len(time_series_TEMPO)\n", + " equal = True\n", + " for i in range(nt):\n", + " if time_series_TEMPO[i,1] != time_series_TEMPO_noneg[i,1]:\n", + " equal = False\n", + " break\n", + "else: equal = False\n", + "\n", + "if equal: print('TEMPO standard and \"no negative\" are the same')\n", + "else: print('TEMPO standard and \"no negative\" are different')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O8JvXRRcwT9O" + }, + "source": [ + "## 6.2 creating Pandora timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "lI10qQGc9U-U" + }, + "outputs": [], + "source": [ + "Pandora_out = open(out_Q+'_Pandora_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", + "Pandora_data_lines = Pandora_out.readlines()\n", + "Pandora_out.close()\n", + "\n", + "time_series_Pandora = np.empty([0, 3])\n", + "time_series_Pandora_noneg = np.empty([0, 3])\n", + "\n", + "for line in Pandora_data_lines:\n", + " split = line.split()\n", + " yyyy = int(split[0])\n", + " mm = int(split[1])\n", + " dd = int(split[2])\n", + " hh = int(split[3])\n", + " mn = int(split[4])\n", + " ss = float(split[5])\n", + " us = int((ss - int(ss))*1000000) # microseconds\n", + "# dt below is time since the beginning of the period of interest in hours\n", + " dt = (datetime(yyyy, mm, dd, hh, mn, int(ss), us) - dt0).total_seconds()/86400.\n", + " col = float(split[6])\n", + " unc = float(split[7])\n", + " time_series_Pandora = np.append(time_series_Pandora,\\\n", + "[[dt, col, unc]], axis = 0)\n", + " if col > 0:\n", + " time_series_Pandora_noneg = np.append(time_series_Pandora_noneg,\\\n", + "[[dt, col, unc]], axis = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NGXMMgcNcMb4" + }, + "source": [ + "## 6.3 Plotting timeseries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WEkwDqvOccOK" + }, + "source": [ + "### 6.3.1 No error bars" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "tJHRgiVP9n_8", + "outputId": "1fad6513-3549-4c39-8bcb-2dd5528d806d" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_title = out_Q.replace('_',' ')+'_'+datestamp_ini+'_'+datestamp_fin+'\\n'+POI_name\n", + "img_name = out_Q+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "plt.plot(time_series_Pandora[:, 0], time_series_Pandora[:, 1],\\\n", + " label = \"Pandora\", c = 'r')\n", + "plt.plot(time_series_TEMPO[:, 0], time_series_TEMPO[:, 1],\n", + " label = \"TEMPO\", c = 'b')\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = 0.\n", + "u_lim = ((dt_fin - dt0).total_seconds() + 1.)/86400.\n", + "plt.xlim(l_lim, u_lim)\n", + "\n", + "# some research is required to set the vertical range\n", + "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", + "plt.ylabel('HCHO total column, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "plt.legend(loc='lower left')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QyhmWrUMXU5z" + }, + "source": [ + "### 6.3.2 Plotting TEMPO and smoothed Pandora retievals with error bars" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 977 + }, + "id": "PxudrpFs9x_q", + "outputId": "583e3b7b-4fbf-4f46-8f0e-85a7ff383341", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.69723 5.981e+15 9.76e+14 3.8708e-02\n", + "0.70124 6.102e+15 1.01e+15 9.6129e-01\n", + "0.70049 6.097e+15 1.01e+15\n", + "\n", + "0.74181 1.802e+15 1.12e+15 3.9982e-01\n", + "0.74214 2.884e+15 1.13e+15 6.0018e-01\n", + "0.74390 2.451e+15 1.12e+15\n", + "\n", + "0.79094 1.188e+16 1.17e+15 1.0000e+00\n", + "0.78732 1.188e+16 1.17e+15\n", + "\n", + "0.83447 1.223e+16 1.04e+15 1.0000e+00\n", + "0.83077 1.223e+16 1.04e+15\n", + "\n", + "0.87404 1.060e+16 9.46e+14 1.0000e+00\n", + "0.87418 1.060e+16 9.46e+14\n", + "\n", + "0.69723 5.981e+15 9.76e+14 3.8708e-02\n", + "0.70124 6.102e+15 1.01e+15 9.6129e-01\n", + "0.70049 6.097e+15 1.01e+15\n", + "\n", + "0.74181 1.802e+15 1.12e+15 3.9982e-01\n", + "0.74214 2.884e+15 1.13e+15 6.0018e-01\n", + "0.74390 2.451e+15 1.12e+15\n", + "\n", + "0.79094 1.188e+16 1.17e+15 1.0000e+00\n", + "0.78732 1.188e+16 1.17e+15\n", + "\n", + "0.83447 1.223e+16 1.04e+15 1.0000e+00\n", + "0.83077 1.223e+16 1.04e+15\n", + "\n", + "0.87404 1.060e+16 9.46e+14 1.0000e+00\n", + "0.87418 1.060e+16 9.46e+14\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "timeseries_Pandora_TEMPO, data_subset = gauss_interpolation(time_series_Pandora[:, 0:3]\\\n", + " , time_series_TEMPO[:, 0])\n", + "\n", + "timeseries_Pandora_TEMPO_noneg, data_subset_noneg =\\\n", + " gauss_interpolation(time_series_Pandora_noneg[:, 0:3]\\\n", + " , time_series_TEMPO_noneg[:, 0])\n", + "\n", + "plot_title = out_Q.replace('_',' ')+' w unc '+datestamp_ini+'_'+datestamp_fin+'\\n'+POI_name\n", + "img_name = out_Q+'_unc_'+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(time_series_TEMPO[:, 0], time_series_TEMPO[:, 1],\\\n", + "yerr=time_series_TEMPO[:, 2], label = \"TEMPO\", c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.errorbar(timeseries_Pandora_TEMPO[:, 0],\\\n", + " timeseries_Pandora_TEMPO[:, 1],\\\n", + " yerr=timeseries_Pandora_TEMPO[:, 2],\\\n", + " label = \"Pandora smoothed at TEMPO times\",\\\n", + " c = 'r', ls = '', marker = \".\")\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = 0.\n", + "u_lim = ((dt_fin - dt0).total_seconds() + 1.)/86400.\n", + "plt.xlim(l_lim, u_lim)\n", + "\n", + "# some research is required to set the vertical range\n", + "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", + "plt.ylabel('HCHO total column, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "#plt.legend(loc='lower left')\n", + "plt.legend(loc='upper left')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)\n", + "\n", + "plot_title = out_Q.replace('_',' ')+' w unc positive '+datestamp_ini+'_'+datestamp_fin+'\\n'+POI_name\n", + "img_name = out_Q+'_unc_positive_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(time_series_TEMPO_noneg[:, 0], time_series_TEMPO_noneg[:, 1],\\\n", + "yerr=time_series_TEMPO_noneg[:, 2], label = \"TEMPO\", c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.errorbar(timeseries_Pandora_TEMPO_noneg[:, 0],\\\n", + " timeseries_Pandora_TEMPO_noneg[:, 1],\\\n", + " yerr=timeseries_Pandora_TEMPO_noneg[:, 2],\\\n", + " label = \"Pandora smoothed at TEMPO times\",\\\n", + " c = 'r', ls = '', marker = \".\")\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = int(min(time_series_TEMPO_noneg[:, 0]))\n", + "u_lim = int(max(time_series_TEMPO_noneg[:, 0])) + 1\n", + "plt.xlim(l_lim, u_lim)\n", + "\n", + "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", + "plt.ylabel('HCHO total column, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "plt.legend(loc='lower left')\n", + "#plt.legend(loc='upper left')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E9-sFIpHzb-3" + }, + "source": [ + "## 6.4 Plotting scatter plots along with regressions" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 977 + }, + "id": "Fx18I6xx-LZa", + "outputId": "f0a3c95e-566b-415a-cf15-6403bfb3fe5c" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting scatter plot for all retrievals\n", + "# (negative values have not been discarded in processing)\n", + "TEMPO_Pandora_scatter = np.empty([0, 4])\n", + "for td in time_series_TEMPO:\n", + " for pd in timeseries_Pandora_TEMPO:\n", + " if td[0] == pd[0]:\n", + " TEMPO_Pandora_scatter = np.append(TEMPO_Pandora_scatter,[[td[1], pd[1], td[2], pd[2]]], axis = 0)\n", + " break\n", + "\n", + "if len(TEMPO_Pandora_scatter) < 2:\n", + " print('TEMPO and Pandora time series has less than 2 simultaneous measurements.\\n'\\\n", + "+'Potential causes for this problem is scarcity of TEMPO pixels with QF == 0\\n'\\\n", + "+'and Pandora measurements.\\n'\\\n", + "+'The TEMPO science team recommends using only data with QF == 0.\\n'\\\n", + "+'Users may overcome the restriction on the quality flag set in section 5.3,\\n'\\\n", + "+'find \"total_HCHO_column_QF_loc == 0\" there. By doing so, users assume risks of using low quality data.\\n'\\\n", + "+'There is nothing to plot here, this block is terminated.')\n", + " sys.exit()\n", + "\n", + "regress = stats.linregress(TEMPO_Pandora_scatter[:, 0], TEMPO_Pandora_scatter[:, 1])\n", + "slope = regress.slope\n", + "intercept = regress.intercept\n", + "r2 = regress.rvalue**2\n", + "stderr = regress.stderr\n", + "intercept_stderr = regress.intercept_stderr\n", + "\n", + "success, slope_0intercept, r2_0intercept =\\\n", + " regress_0intercept(TEMPO_Pandora_scatter[:, 0]\\\n", + " , TEMPO_Pandora_scatter[:, 1])\n", + "\n", + "plot_title = out_Q.replace('_',' ')+' w unc '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = 'scatter_'+out_Q+'_unc_'+'_'+datestamp_ini+'_'\\\n", + "+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(TEMPO_Pandora_scatter[:, 0], TEMPO_Pandora_scatter[:, 1],\\\n", + "xerr=TEMPO_Pandora_scatter[:, 2], yerr=TEMPO_Pandora_scatter[:, 3],\\\n", + "c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.xlabel(r'TEMPO HCHO column, mol/cm$^{2}$', fontsize=12)\n", + "plt.ylabel(r'Pandora HCHO column, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "fig.text(0.15, 0.72,\\\n", + "f'# of points: {len(TEMPO_Pandora_scatter):3d}\\nslope: {slope: 6.3f} $\\pm$ {stderr:6.3f}\\nintercept: {intercept: 8.2e} $\\pm$ {intercept_stderr: 8.2e}\\nR$^{2}$ = {r2:6.3f}')\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = min(0., min(TEMPO_Pandora_scatter[:, [0,1]].flatten()))*1.05\n", + "u_lim = max(TEMPO_Pandora_scatter[:, [0,1]].flatten())*1.05\n", + "plt.xlim(l_lim, u_lim)\n", + "plt.ylim(l_lim, u_lim)\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim, u_lim],\\\n", + "c = 'g', ls = '--')\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim*slope+intercept, u_lim*slope+intercept],\\\n", + "c = 'r', ls = '--')\n", + "\n", + "if success:\n", + " plt.plot([l_lim, u_lim], [l_lim*slope_0intercept, u_lim*slope_0intercept], c = 'r', ls = '-.')\n", + " fig.text(0.6, 0.12, f'\"no-intercept\" regression:\\nslope: {slope_0intercept: 6.3f} R$^{2}$ = {r2_0intercept:6.3f}')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)\n", + "\n", + "# Plotting scatter plot for positive retrievals\n", + "TEMPO_Pandora_scatter_noneg = np.empty([0, 4])\n", + "for td in time_series_TEMPO_noneg:\n", + " for pd in timeseries_Pandora_TEMPO_noneg:\n", + " if td[0] == pd[0]:\n", + " TEMPO_Pandora_scatter_noneg = np.append(TEMPO_Pandora_scatter_noneg,[[td[1], pd[1], td[2], pd[2]]], axis = 0)\n", + " break\n", + "\n", + "regress = stats.linregress(TEMPO_Pandora_scatter_noneg[:, 0], TEMPO_Pandora_scatter_noneg[:, 1])\n", + "slope = regress.slope\n", + "intercept = regress.intercept\n", + "r2 = regress.rvalue**2\n", + "stderr = regress.stderr\n", + "intercept_stderr = regress.intercept_stderr\n", + "\n", + "success, slope_0intercept, r2_0intercept =\\\n", + " regress_0intercept(TEMPO_Pandora_scatter_noneg[:, 0]\\\n", + " , TEMPO_Pandora_scatter_noneg[:, 1])\n", + "\n", + "plot_title = out_Q.replace('_',' ')+' w unc noneg '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = 'scatter_'+out_Q+'_unc_noneg_'+'_'+datestamp_ini+'_'\\\n", + "+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(TEMPO_Pandora_scatter_noneg[:, 0], TEMPO_Pandora_scatter_noneg[:, 1],\\\n", + "xerr=TEMPO_Pandora_scatter_noneg[:, 2], yerr=TEMPO_Pandora_scatter_noneg[:, 3],\\\n", + "c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.xlabel(r'TEMPO HCHO column, mol/cm$^{2}$', fontsize=12)\n", + "plt.ylabel(r'Pandora HCHO column, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "fig.text(0.15, 0.72,\\\n", + "f'# of points: {len(TEMPO_Pandora_scatter_noneg):3d}\\nslope: {slope: 6.3f} $\\pm$ {stderr:6.3f}\\nintercept: {intercept: 8.2e} $\\pm$ {intercept_stderr: 8.2e}\\nR$^{2}$ = {r2:6.3f}')\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = 0.\n", + "u_lim = max(TEMPO_Pandora_scatter_noneg[:, [0,1]].flatten())*1.05\n", + "plt.xlim(l_lim, u_lim)\n", + "plt.ylim(l_lim, u_lim)\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim, u_lim],\\\n", + "c = 'g', ls = '--')\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim*slope+intercept, u_lim*slope+intercept],\\\n", + "c = 'r', ls = '--')\n", + "\n", + "if success:\n", + " plt.plot([l_lim, u_lim], [l_lim*slope_0intercept, u_lim*slope_0intercept], c = 'r', ls = '-.')\n", + " fig.text(0.6, 0.12, f'\"no-intercept\" regression:\\nslope: {slope_0intercept: 6.3f} R$^{2}$ = {r2_0intercept:6.3f}')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cKn-MLMuen1q" + }, + "source": [ + "# EXTRA. Archiving output files to make downloading easier" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "v-VbL8CNXsLL" + }, + "outputs": [], + "source": [ + "import zipfile\n", + "import zlib\n", + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "id": "4e1JLotAX9OC" + }, + "outputs": [], + "source": [ + "list_jpg = glob.glob('*'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg')\n", + "\n", + "with zipfile.ZipFile('fig_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name+'.zip', 'w') as fig_zip:\n", + " for name in list_jpg: fig_zip.write(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "id": "JVkprHdiYpuS" + }, + "outputs": [], + "source": [ + "list_data = glob.glob('*'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'*.txt')\n", + "\n", + "with zipfile.ZipFile('data_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name+'.zip', 'w') as data_zip:\n", + " for name in 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"cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "kjVKCytfEnRt" + }, + "source": [ + "### **TEMPO NO2 validation**\n", + "\n", + "This notebook illustrates comparison of nitrogen dioxide tropospheric column retrievals by TEMPO and Pandora ground stations.\n", + "\n", + "It allows a user to choose Pandora station of interest. Since TEMPO spatial coverage is regional and limited to North America, it is user's responsibilty to select the station within TEMPO's field of regard (FOR). If the selected station is outside FOR, no TEMPO time series will be generated.\n", + "\n", + "The user is allowed to choose the time period of interest by providing start and end dates in the form YYYYMMDD. Please be aware, that if the selecte period of interest is outside of available time span of one of the sensors, corresponding time series will not be generated.\n", + "\n", + "Data files for both sensors are downloaded on-the-fly. TEMPO data are downloaded with earthaccess library that needs to be installed first.\n", + "\n", + "TEMPO data files are read by means of netCDF library that needs to be installed first.\n", + "\n", + "Pandora data files are ASCII files with header and space separated columns. Custome made function is included to read nitrogen dioxide total column along with its total uncertainty.\n", + "\n", + "This code takes into account quality flags (QFs) from both TEMPO and Pandora. This is implemented as follow. On the TEMPO side, data set \"/product/main_data_quality_flag\" is read, all pixels with non-zero QFs are discarded. However, negative values of tropospheric NO2 column are NOT discarded and used for averaging/interpolationg to the point of interest. For the purpose of physical sanity, another way is also implemented, i.e., negative retrievals are not used in averaging. Therefore, TWO values are returned, trop_NO2_col, and trop_NO2_col_noneg. On Pandora side negative columns also occur despite high quality flags, though they are rare. So, two Pandora time series are considered - with and without negative columns.\n", + "\n", + "The resulting time series are plotted with and without uncertainty of both measurement in the end of the notebook.\n", + "\n", + "This notebook is tested on TEMPO_NO2_L2_V03 and Pandora L2_rnvh3p1-8 files." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "luJG0oPIPGjC" + }, + "source": [ + "# 1 Installing and importing necessary libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m5ru-FMpPXoE" + }, + "source": [ + "## 1.1 Installing netCDF" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5NWX4mCVQJt_", + "outputId": "1aabf824-17d4-4698-f231-7c5845aa4121" + }, + "outputs": [], + "source": [ + "# un-comment if installation is necessary\n", + "#! pip3 install netCDF4" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cQJCMByjPp9i" + }, + "source": [ + "## 1.2 Installing earthaccess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N7Gm15VaYKW9", + "outputId": "2ca3f0a0-60c6-40f0-a1df-89d27c8e241d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: earthaccess in /srv/conda/envs/notebook/lib/python3.10/site-packages (0.9.0)\n", + "Requirement already satisfied: fsspec>=2022.11 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from earthaccess) (2024.3.1)\n", + "Requirement already satisfied: multimethod>=1.8 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from earthaccess) (1.11)\n", + "Requirement already satisfied: pqdm>=0.1 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from earthaccess) (0.2.0)\n", + "Requirement already satisfied: python-cmr>=0.9.0 in /srv/conda/envs/notebook/lib/python3.10/site-packages (from earthaccess) (0.9.0)\n", + 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botocore<1.34.52,>=1.34.41->aiobotocore<3.0.0,>=2.5.4->s3fs>=2022.11->earthaccess) (1.0.1)\n" + ] + } + ], + "source": [ + "# un-comment if installation is necessary\n", + "! pip3 install earthaccess" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TxfhRi7ySyFY" + }, + "source": [ + "## 1.3 Importing necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "IAAuhYMcEkvP" + }, + "outputs": [], + "source": [ + "import earthaccess # needed to discover and download TEMPO data\n", + "import netCDF4 as nc # needed to read TEMPO data\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "import platform\n", + "from subprocess import Popen\n", + "import shutil\n", + "\n", + "from shapely.geometry import Point, Polygon # needed to search a point within a polygon\n", + "from scipy.interpolate import griddata # needed to interpolate TEMPO data to the point of interest\n", + "from scipy import stats # needed for linear regression analysis\n", + "\n", + "import requests # needed to search for and download Pandora data\n", + "import codecs # needed to read Pandora data\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt # needed to plot the resulting time series\n", + "from urllib.request import urlopen, Request # needed to search for and download Pandora data\n", + "from pathlib import Path # needed to check whether a needed data file is already downloaded\n", + "from datetime import datetime, timedelta # needed to work with time in plotting time series" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ANsfYumeXjKm" + }, + "source": [ + "# 2 Defining functions to work with Pandora and TEMPO data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OQGUPNbgXyKN" + }, + "source": [ + "# 2.1 functions to work with Pandora" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RB9IWsMvX6Kd" + }, + "source": [ + "### 2.1.1 function creating the list of available Pandora sites" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "YEwbJTAFsGT1" + }, + "outputs": [], + "source": [ + "# function read_pandora_web returns the list of available Pandora sites\n", + "def read_pandora_web():\n", + " url = 'https://data.pandonia-global-network.org/'\n", + " page = urlopen(url)\n", + " html_bytes = page.read()\n", + " html = html_bytes.decode(\"utf-8\")\n", + " html_len = len(html)\n", + "\n", + " pos1 = 0\n", + "\n", + " big_line = str(html)\n", + " lines = big_line.split('\\n')\n", + "\n", + " ref_lines = [i for i in lines if 'href' in i]\n", + " refs = []\n", + " for line in ref_lines:\n", + " pos1 = line.find('\"')\n", + " pos2 = line.rfind('\"')\n", + " if pos1 > 0 and pos2 > pos1 and line[pos2-1] =='/' and line[pos1+1] == '.':\n", + " refs.append(line[pos1+3 : pos2-1])\n", + "\n", + " return refs\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ht9LHL28YUmR" + }, + "source": [ + "### 2.1.2 functions allowing user to choose a Pandora site of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "YUOuYq-8sclr" + }, + "outputs": [], + "source": [ + "# function check_site checks whether user entered site is in the list of available Pandora sites\n", + "def check_site(site_name, refs):\n", + " site_list = []\n", + " for line in refs:\n", + " if site_name in line:\n", + " site_list.append(line)\n", + "\n", + " return site_list\n", + "\n", + "\n", + "# function take_pandora_sites takes user input and checks whether the site is in the list of available Pandora sites\n", + "def take_pandora_sites(refs):\n", + " print('please select a Pandora site name from the list')\n", + " for ref in refs:\n", + " print(ref)\n", + "\n", + " answer = 'y'\n", + " while answer == 'y':\n", + " site_name = input('Enter a name of a Pandora site: ')\n", + " print(site_name)\n", + " site_list = check_site(site_name, refs)\n", + " site_num = len(site_list)\n", + " if site_num == 0:\n", + " print('site ', site_name, 'was not found')\n", + " continue\n", + "\n", + " if site_num > 1:\n", + " print('there are ', site_num, ' site names, select one from')\n", + " for site in site_list: print(site)\n", + "\n", + " site_name = input('Enter a name of a Pandora site: ')\n", + " if site_list.count(site_name) != 1:\n", + " print('Entered name is not the exact match of one of the following sites')\n", + " for site in site_list: print(site)\n", + " print('program terminated')\n", + " sys.exit()\n", + "\n", + " for site in site_list:\n", + " if site == site_name:\n", + " pandora_site = site_name\n", + " print('site ', site_name, 'was found and added to the list of sites ')\n", + " break\n", + "\n", + " if site_num == 1:\n", + " pandora_site = site_list[0]\n", + " print('site ', site_list[0], 'was found and added to the list of sites ')\n", + "\n", + " answer = 'n'\n", + "\n", + " return pandora_site" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F5sX6MEaZGFI" + }, + "source": [ + "### 2.1.3 function creating the list of links to tropospheric NO2 data files at the selected Pandora sites and downloading the data files" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "AViZrctjsVWN" + }, + "outputs": [], + "source": [ + "# Pandora site may have several instruments. In this case each instrument has its own directory.\n", + "# However, the most recent version of the troposperic NO2 data, rnvh3p1-8, is available only in one of these directories.\n", + "# The function creates all possible links, but some of them may be non-existing. This is checked and cleared later.\n", + "def instrument_path(site):\n", + "# function instrument_path returns links to possible Pandora NO2 retrievals files\n", + " url = 'https://data.pandonia-global-network.org/' + site + '/'\n", + " page = urlopen(url)\n", + " html_bytes = page.read()\n", + " html = html_bytes.decode(\"utf-8\")\n", + " html_len = len(html)\n", + "\n", + " pos1 = 0\n", + " big_line = str(html)\n", + " lines = big_line.split('\\n')\n", + "\n", + " ref_lines = [i for i in lines if 'href' in i]\n", + " links = []\n", + " for line in ref_lines:\n", + "\n", + " pos1 = line.find('\"')\n", + " pos2 = line.rfind('\"')\n", + " if pos1 > 0 and pos2 > pos1 and line[pos2-1] =='/' and\\\n", + " line[pos1+3 : pos1 + 10] == 'Pandora':\n", + " link = url + line[pos1+3 : pos2] + 'L2/' + line[pos1+3 : pos2-1] + '_' + site + '_L2_rnvh3p1-8.txt'\n", + " print(link)\n", + " links.append(link)\n", + "\n", + " return links\n", + "\n", + "\n", + "# function downloading Pandora data file with given url\n", + "def download(url):\n", + " response = requests.get(url)\n", + " response_code = response.status_code\n", + "\n", + " file_name = url.split('/')[-1]\n", + "\n", + " if response_code == 200:\n", + " content = response.content\n", + " data_path = Path(file_name)\n", + " data_path.write_bytes(content)\n", + "\n", + " return file_name, response_code" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HLnx6gUAaMg6" + }, + "source": [ + "### 2.1.4 function reading Pandora NO2 data files rnvh3p1-8" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "1NYdVqlBuKBT" + }, + "outputs": [], + "source": [ + "# function converting Pandora timestamp into a set of year, month, day, hour, minute, and second\n", + "# function read_timestamp converts Pandora timestamp of the format\n", + "# 'yyyymmddThhmmssZ' into a set of 6 numbers:\n", + "# integer year, month, day, hour, minute, and real second.\n", + "def read_timestamp(timestamp):\n", + "\n", + " yyyy = int(timestamp[0:4])\n", + " mm = int(timestamp[4:6])\n", + " dd = int(timestamp[6:8])\n", + " hh = int(timestamp[9:11])\n", + " mn = int(timestamp[11:13])\n", + " ss = float(timestamp[13:17])\n", + "\n", + " return yyyy, mm, dd, hh, mn, ss\n", + "\n", + "\n", + "# function reading Pandora NO2 data file rnvh3p1-8\n", + "#\n", + "# Below is the second version of function read_Pandora_NO2_rnvs3p1_8. It is to be used for the future validation efforts.\n", + "# The difference with the original version is that instead of discriminating negative values of the total NO2 column,\n", + "# it uses quality flags. It was previously found that QF == 0 does not occure often enough,\n", + "# so we will have to use QF == 10 (not-assured high quality).\n", + "#\n", + "# function read_Pandora_NO2_rnvh3p1_8 reads Pandora total NO2 column data files ending with rnvh3p1-8.\n", + "# Arguments:\n", + "# fname - name file to be read, string;\n", + "# start_date - beginning of the time interval of interest,\n", + "# integer of the form YYYYMMDD;\n", + "# end_date - end of the time interval of interest,\n", + "# integer of the form YYYYMMDD.\n", + "#\n", + "# if start_date is greater than end_date, the function returns a numpy array\n", + "# with shape (0, 8), otherwise it returns an 8-column numpy array\n", + "# with with columns being year, month, day, hour, minute, second of observation\n", + "# and retrieved total NO2 column along with its total uncertainty.\n", + "#\n", + "# NO2 column is in mol/m^2, so conversion to molecules/cm^2 is performed by\n", + "# multiplication by Avogadro constant, NA = 6.02214076E+23, and division by 1.E+4\n", + "def read_Pandora_NO2_rnvh3p1_8(fname, start_date, end_date):\n", + "\n", + " conversion_coeff = 6.02214076E+19 # Avogadro constant divided by 10000\n", + "\n", + " data = np.empty([0, 8])\n", + " if start_date > end_date: return -999., -999., data\n", + "\n", + " with codecs.open(fname, 'r', encoding='utf-8', errors='ignore') as f:\n", + "\n", + " while True:\n", + "# Get next line from file\n", + " line = f.readline()\n", + "\n", + " if line.find('Short location name:') >= 0:\n", + " loc_name = line.split()[-1] # location name, to be used in the output file name\n", + " print('location name ', loc_name)\n", + "\n", + " if line.find('Location latitude [deg]:') >= 0:\n", + " lat = float(line.split()[-1]) # location latitude\n", + " print('location latitude ', lat)\n", + "\n", + " if line.find('Location longitude [deg]:') >= 0:\n", + " lon = float(line.split()[-1]) # location longitude\n", + " print('location longitude ', lon)\n", + "\n", + " if line.find('--------') >= 0: break\n", + "\n", + " while True:\n", + "# Get next line from file\n", + " line = f.readline()\n", + "\n", + " if line.find('--------') >= 0: break\n", + "\n", + " while True:\n", + "# now reading line with data\n", + " line = f.readline()\n", + "\n", + " if not line: break\n", + "\n", + " line_split = line.split()\n", + "\n", + " yyyy, mm, dd, hh, mn, ss = read_timestamp(line_split[0])\n", + " date_stamp = yyyy*10000 + mm*100 + dd\n", + " if date_stamp < start_date or date_stamp > end_date: continue\n", + "\n", + " QF = int(line_split[52]) # total column uncertainty\n", + "\n", + " if QF == 0 or QF == 10:\n", + " column = float(line_split[61]) # Nitrogen dioxide tropospheric vertical column amount [moles per square meter]\n", + " column_unc = float(line_split[62]) # Independent uncertainty of nitrogen dioxide tropospheric vertical column amount [moles per square meter]\n", + " data = np.append(data, [[yyyy, mm, dd, hh, mn, ss\\\n", + " , column*conversion_coeff\\\n", + " , column_unc*conversion_coeff]], axis = 0)\n", + "\n", + " return lat, lon, loc_name, data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ep0Fl-Kzas5x" + }, + "source": [ + "## 2.2 unction reading TEMPO NO2 data file" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "GedTZnASz9o_" + }, + "outputs": [], + "source": [ + "# function reading TEMPO NO2 data file for tropospheric column\n", + "def read_TEMPO_tropNO2_L2(fn):\n", + " '''\n", + " function read_TEMPO_tropNO2_L2 reads the following arrays from the\n", + " TEMPO L2 NO2 product TEMPO_NO2_L2_V03:\n", + " vertical_column_troposphere;\n", + " vertical_column_troposphere_uncertainty;\n", + " and returns respective fields along with coordinates of the pixels.\n", + "\n", + " If one requested variables cannot be read, all returned variables are zeroed\n", + " '''\n", + " var_name = 'vertical_column_troposphere'\n", + " var_unc_name = 'vertical_column_troposphere_uncertainty'\n", + " var_QF_name = 'main_data_quality_flag'\n", + "\n", + " try:\n", + " ds = nc.Dataset(fn)\n", + "\n", + " prod = ds.groups['product'] # this opens group product, /product, as prod\n", + "\n", + " var = prod.variables[var_name] # this reads variable column_amount_o3 from prod (group product, /product)\n", + " trop_NO2_column = np.array(var)\n", + " fv_prod = var.getncattr('_FillValue')\n", + " prod_unit = var.getncattr('units')\n", + "\n", + " var_unc = prod.variables[var_unc_name] # this reads variable column_amount_o3 from prod (group product, /product)\n", + " trop_NO2_column_unc = np.array(var_unc)\n", + "\n", + " var_QF = prod.variables[var_QF_name] # this reads variable column_amount_o3 from prod (group product, /product)\n", + " trop_NO2_column_QF = np.array(var_QF)\n", + " fv_QF = var_QF.getncattr('_FillValue')\n", + "\n", + " geo = ds.groups['geolocation'] # this opens group geolocation, /geolocation, as geo\n", + "\n", + " lat = np.array(geo.variables['latitude']) # this reads variable latitude from geo (geolocation group, /geolocation) into a numpy array\n", + " lon = np.array(geo.variables['longitude']) # this reads variable longitude from geo (geolocation group, /geolocation) into a numpy array\n", + " fv_geo = geo.variables['latitude'].getncattr('_FillValue')\n", + " time = np.array(geo.variables['time'] )# this reads variable longitude from geo (geolocation group, /geolocation) into a numpy array\n", + "\n", + " ds.close()\n", + "\n", + " except:\n", + " print('variable '+var_name+' cannot be read in file '+fn)\n", + " lat = 0.\n", + " lon = 0.\n", + " time = 0.\n", + " fv_geo = 0.\n", + " trop_NO2_column = 0.\n", + " trop_NO2_column_unc = 0.\n", + " trop_NO2_column_QF = 0.\n", + " fv_prod = 0.\n", + " fv_QF = -999\n", + " prod_unit = ''\n", + "\n", + " return lat, lon, fv_geo, time, trop_NO2_column, trop_NO2_column_unc\\\n", + ", trop_NO2_column_QF, fv_prod, fv_QF, prod_unit" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OkRA1M7PcIYx" + }, + "source": [ + "## 2.3 auxiliary functions to handle data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AKzFo9EjcTzx" + }, + "source": [ + "### 2.3.1 function smoothing Pandora retievals and interpolating them onto TEMPO times of observations" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "XLSYQezN28Bq" + }, + "outputs": [], + "source": [ + "# Smooth Pandora retievals and interplate them into other time series times\n", + "# Pandora timeseries has significantly more data points then TEMPO and DSCOVR. It is also very noisy.\n", + "# To make comparison easier, Pandora timeseries is interpolated to the moments of TEMPO and DSCOVR observations.\n", + "\n", + "# Interpolation is performed by the function defined below with the help of Gaussian smooting as follow:\n", + "# x_int(t) = SUM(x_p(t_i)*wt(t_i, t)),\n", + "#\n", + "# wt(t_i, t) = exp(-(t - t_i)^2/(2 * sigma^2))/SUM(exp(-(t - t_i)^2/(2 * sigma^2))),\n", + "#\n", + "# where sums are taken over times t_i falling into time interval (t-dt_max, t+dt_max).\n", + "#\n", + "# Parameters dt_max and sigma can be chosen by the user.\n", + "def gauss_interpolation(timeseries, new_times):\n", + "#\n", + "# function gauss_interpolation takes 2D array timeseries with function\n", + "# to be interpolated and 1D array new_times containing times to which\n", + "# the function is to be interpolated\n", + "# arguments:\n", + "# timeseries - array with at least 2 columns,\n", + "# 1st column - times, 2nd (3rd, ...) column(s) - function to be interpolated\n", + "# new_times - 1D array of times to which the function(s) to be interpolated\n", + "#\n", + "# parameters\n", + "# dt_max = 0.0046875 # 6 minutes and 45 sec (405.00 sec) expressed in days\n", + "# sigma = 0.00140625 # 2 minutes and 1.5 sec (121.5 sec) expressed in days\n", + "\n", + " dt_max = 0.0046875 # 6 minutes and 45 sec (405.00 sec) expressed in days\n", + " sigma = 0.00140625 # 2 minutes and 1.5 sec (121.5 sec) expressed in days\n", + "\n", + " nnt = len(new_times)\n", + " (nt, nfun) = timeseries.shape\n", + "\n", + " timeseries_smooth = np.empty([0, nfun])\n", + " data_subset = np.empty(nnt, dtype = object)\n", + " cnt = 0\n", + " for new_time in new_times:\n", + " llim = new_time - dt_max\n", + " ulim = new_time + dt_max\n", + "\n", + " timeseries_subset = timeseries[((timeseries[:, 0] < ulim)\\\n", + " & (timeseries[:, 0] > llim))]\n", + " if len(timeseries_subset) < 1: continue\n", + " t_delta = timeseries_subset[:, 0] - new_time\n", + " wt = np.exp(-t_delta**2/(2.*sigma**2))\n", + " wt = wt/np.sum(wt)\n", + " timeseries_subset = np.append(timeseries_subset, np.transpose([wt]), axis = 1)\n", + " for t in timeseries_subset: print(f'{t[0]:.5f} {t[1]:.3e} {t[2]:.2e} {t[3]:.4e}')\n", + " data_subset[cnt] = timeseries_subset\n", + " cnt += 1\n", + "\n", + " timeseries_smooth_loc = np.array([new_time])\n", + " for ifun in range(1, nfun):\n", + " timeseries_smooth_loc = np.append(timeseries_smooth_loc,\\\n", + " np.sum(timeseries_subset[:, ifun]*wt))\n", + " print(f'{timeseries_smooth_loc[0]:.5f} {timeseries_smooth_loc[1]:.3e} {timeseries_smooth_loc[2]:.2e}\\n')\n", + "\n", + " timeseries_smooth = np.append(timeseries_smooth,\\\n", + " np.array([timeseries_smooth_loc]), axis = 0)\n", + "\n", + " return timeseries_smooth, data_subset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nNoGDd0MdP9Y" + }, + "source": [ + "### 2.3.2 function computing linear regression with zero intercept" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "WjLz_BRH3MO7" + }, + "outputs": [], + "source": [ + "# custom made function regress_0intercept takes vectors x and y\n", + "# representing coordinates and function values at these coordinates\n", + "# and returns slope of regression fit y = a*x\n", + "# along with coefficient of determination\n", + "def regress_0intercept(x, y):\n", + " success = False\n", + "\n", + " if len(x) != len(y):\n", + " a = 0.\n", + " R2 = 0.\n", + "\n", + " elif len(x) == 1:\n", + " if x[0] != 0.:\n", + " a = y[0]/x[0]\n", + " R2 = 1.\n", + " success = True\n", + " else:\n", + " if y[0] != 0.:\n", + " a = np.inf\n", + " R2 = 1.\n", + " success = True\n", + " else:\n", + " a = np.inf\n", + " R2 = 0.\n", + "\n", + " else:\n", + " xy_sum = np.dot(x, y)\n", + " x2_sum = np.dot(x, x)\n", + " a = xy_sum/x2_sum\n", + "\n", + " res_y = y - a*x\n", + " res_sum_2 = np.dot(res_y, res_y)\n", + " y2_sum = np.dot(y, y)\n", + " sum_tot_2 = y2_sum - len(y)*np.mean(y)**2\n", + " R2 = 1. - res_sum_2/sum_tot_2\n", + "\n", + " success = True\n", + "\n", + " return success, a, R2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0aDnIkZVdu93" + }, + "source": [ + "# Main code begins here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vXy2ArJ93d9e" + }, + "source": [ + "# 3 Establishing access to EarthData" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8BnoNI0k3mi1" + }, + "source": [ + "## 3.1 Logging in" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aHZmh8-xYZFe", + "outputId": "be033ca5-d430-4630-f195-7b2fde01ce55" + }, + "outputs": [], + "source": [ + "#User needs to create an account at https://www.earthdata.nasa.gov/\n", + "#Function earthaccess.login prompts for EarthData login and password.\n", + "auth = earthaccess.login(strategy=\"interactive\", persist=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AR7UGlswVGHj" + }, + "source": [ + "## 3.2 Creating local directory" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tizBt7IvY0lx", + "outputId": "a12ab611-9048-46e7-f3ab-95af7f5edd51" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved .dodsrc to: /home/jovyan/\n" + ] + } + ], + "source": [ + "homeDir = os.path.expanduser(\"~\") + os.sep\n", + "\n", + "with open(homeDir + '.dodsrc', 'w') as file:\n", + " file.write('HTTP.COOKIEJAR={}.urs_cookies\\n'.format(homeDir))\n", + " file.write('HTTP.NETRC={}.netrc'.format(homeDir))\n", + " file.close()\n", + "\n", + "print('Saved .dodsrc to:', homeDir)\n", + "\n", + "# Set appropriate permissions for Linux/macOS\n", + "if platform.system() != \"Windows\":\n", + " Popen('chmod og-rw ~/.netrc', shell=True)\n", + "else:\n", + " # Copy dodsrc to working directory in Windows\n", + " shutil.copy2(homeDir + '.dodsrc', os.getcwd())\n", + " print('Copied .dodsrc to:', os.getcwd())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5QaStYVXVmdN" + }, + "source": [ + "# 4 Working with Pandora data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NN7_YcCackvI" + }, + "source": [ + "## 4.1 Discovering existing Pandora stations and selecting one of them" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9bAQvpKS4ZUZ", + "outputId": "397ff070-4826-4d24-9f31-8ca22ce55688" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gathering Pandora sites information\n", + "please select a Pandora site name from the list\n", + "Agam\n", + "AldineTX\n", + "AliceSprings\n", + "Altzomoni\n", + "ArlingtonTX\n", + "Athens-NOA\n", + "AtlantaGA-Conyers\n", + "AtlantaGA-GATech\n", + "AtlantaGA-SouthDeKalb\n", + "AtlantaGA\n", + "AustinTX\n", + "Bandung\n", + "Bangkok\n", + "Banting\n", + "BayonneNJ\n", + "Beijing-RADI\n", + "BeltsvilleMD\n", + "Berlin\n", + "BlueHillMA\n", + "BostonMA\n", + "BoulderCO-NCAR\n", + "BoulderCO\n", + "Bremen\n", + "BristolPA\n", + "BronxNY\n", + "Brussels-Uccle\n", + "Bucharest\n", + "BuenosAires\n", + "BuffaloNY\n", + "Busan\n", + "Cabauw\n", + "Calakmul\n", + "calibrationfiles\n", + "CambridgeBay\n", + "CambridgeMA\n", + "CameronLA\n", + "CapeElizabethME\n", + "Cebu\n", + "ChapelHillNC\n", + "CharlesCityVA\n", + "ChelseaMA\n", + "ChiangMai\n", + "ChicagoIL\n", + "Cologne\n", + "ComodoroRivadavia\n", + "Cordoba\n", + "CornwallCT\n", + "CorpusChristiTX\n", + "Daegu\n", + "Dalanzadgad\n", + "Davos\n", + "DearbornMI\n", + "DeBilt\n", + "Dhaka\n", + "Downsview\n", + "EastProvidenceRI\n", + "EdwardsCA\n", + "Egbert\n", + "EssexMD\n", + "Eureka-0PAL\n", + "Eureka-PEARL\n", + "FairbanksAK\n", + "Fajardo\n", + "FortMcKay\n", + "FortYatesND\n", + "Fukuoka\n", + "Gongju-KNU\n", + "Granada\n", + "GrandForksND\n", + "GreenbeltMD\n", + "Haldwani-ARIES\n", + "HamptonVA-HU\n", + "HamptonVA\n", + "Heidelberg\n", + "Helsinki\n", + "HoustonTX-SanJacinto\n", + "HoustonTX\n", + "HuntsvilleAL\n", + "Ilocos\n", + "Incheon-ESC\n", + "Innsbruck\n", + "IowaCityIA-WHS\n", + "Islamabad-NUST\n", + "Izana\n", + "Jeonju\n", + "Juelich\n", + "KenoshaWI\n", + "Kobe\n", + "Kosetice\n", + "LaPaz\n", + "LaPorteTX\n", + "LapwaiID\n", + "LibertyTX\n", + "Lindenberg\n", + "LondonderryNH\n", + "LynnMA\n", + "MadisonCT\n", + "ManhattanKS\n", + "ManhattanNY-CCNY\n", + "MaunaLoaHI\n", + "MexicoCity-UNAM\n", + "MexicoCity-Vallejo\n", + "MiamiFL-FIU\n", + "MountainViewCA\n", + "Nagoya\n", + "Nainital-ARIES\n", + "NewBrunswickNJ\n", + "NewHavenCT\n", + "NewLondonCT\n", + "NewOrleansLA-XULA\n", + "NyAlesund\n", + "OldFieldNY\n", + "operationfiles\n", + "Palau\n", + "Palawan\n", + "PhiladelphiaPA\n", + "PhnomPenh\n", + "PittsburghPA\n", + "Pontianak\n", + "Potchefstroom-METSI\n", + "QueensNY\n", + "QuezonCity\n", + "RichmondCA\n", + "Rome-IIA\n", + "Rome-ISAC\n", + "Rome-SAP\n", + "Rotterdam-Haven\n", + "SaltLakeCityUT-Hawthorne\n", + "SaltLakeCityUT\n", + "SanJoseCA\n", + "Sapporo\n", + "Seosan\n", + "Seoul-KU\n", + "Seoul-SNU\n", + "Seoul\n", + "Singapore-NUS\n", + "Songkhla\n", + "SouthJordanUT\n", + "StGeorge\n", + "StonyPlain\n", + "Suwon-USW\n", + "SWDetroitMI\n", + "Tel-Aviv\n", + "Thessaloniki\n", + "Tokyo-Sophia\n", + "Tokyo-TMU\n", + "Toronto-CNTower\n", + "Toronto-Scarborough\n", + "Toronto-West\n", + "Trollhaugen\n", + "Tsukuba-NIES-West\n", + "Tsukuba-NIES\n", + "Tsukuba\n", + "TubaCityAZ\n", + "TucsonAZ\n", + "TurlockCA\n", + "TylerTX\n", + "Ulaanbaatar\n", + "Ulsan\n", + "Vientiane\n", + "VirginiaBeachVA-CBBT\n", + "WacoTX\n", + "Wakkerstroom\n", + "WallopsIslandVA\n", + "Warsaw-UW\n", + "WashingtonDC\n", + "WestportCT\n", + "WhittierCA\n", + "Windsor-West\n", + "WrightwoodCA\n", + "Yokosuka\n", + "Yongin\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a name of a Pandora site: Bould\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bould\n", + "there are 2 site names, select one from\n", + "BoulderCO-NCAR\n", + "BoulderCO\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a name of a Pandora site: BoulderCO-NCAR\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "site BoulderCO-NCAR was found and added to the list of sites \n", + "the following sites were selected\n", + "BoulderCO-NCAR\n", + "from the list of existing Pandora sites\n", + "https://data.pandonia-global-network.org/BoulderCO-NCAR/Pandora204s1/L2/Pandora204s1_BoulderCO-NCAR_L2_rnvh3p1-8.txt\n", + "Pandora204s1_BoulderCO-NCAR_L2_rnvh3p1-8.txt does not exit in local directory, downloading from the web\n", + "https://data.pandonia-global-network.org/BoulderCO-NCAR/Pandora204s1/L2/Pandora204s1_BoulderCO-NCAR_L2_rnvh3p1-8.txt\n", + "Pandora L2 file Pandora204s1_BoulderCO-NCAR_L2_rnvh3p1-8.txt has been downloaded\n" + ] + } + ], + "source": [ + "# Discovering existing Pandora stations and selecting one of them\n", + "# Discovering available Pandora site.\n", + "# Please bear in mind that some sites do not have tropospheric NO2 data files\n", + "print('gathering Pandora sites information')\n", + "refs = read_pandora_web()\n", + "\n", + "pandora_site = take_pandora_sites(refs) # create list of Pandora sites of interest\n", + "print('the following sites were selected')\n", + "print(pandora_site)\n", + "print('from the list of existing Pandora sites')\n", + "\n", + "# create a list of !AVAILABLE! Pandora files for the Pandora site\n", + "pandora_files = []\n", + "\n", + "links = instrument_path(pandora_site)\n", + "\n", + "npfiles = 0\n", + "\n", + "for link in links:\n", + " pandora_fname = link.split('/')[-1]\n", + "\n", + "# check if file exists in the local directory, if not download from Pandora site\n", + " if not os.path.exists(pandora_fname):\n", + " print(pandora_fname,' does not exit in local directory, downloading from the web')\n", + " print(link)\n", + "\n", + " pandora_fname, response_code = download(link)\n", + "\n", + " if response_code == 200:\n", + " print('Pandora L2 file ', pandora_fname, ' has been downloaded')\n", + " npfiles = npfiles + 1\n", + " pandora_files.append(pandora_fname)\n", + " else:\n", + " print('Pandora L2 file ', link, ' does not exist')\n", + "\n", + " else:\n", + " print(pandora_fname,' exits in local directory')\n", + " npfiles = npfiles + 1\n", + " pandora_files.append(pandora_fname)\n", + "\n", + "if npfiles == 0: # no files were found, STOP here\n", + " print('no files were found for Pandora site ', pandora_site, 'program terminated')\n", + " sys.exit()\n", + "if npfiles > 1: # normally there should be only one file per site. if there are more - STOP\n", + " print('there are too many files for site ', pandora_site, '- STOP and investigate file names below. Program terminated')\n", + " for pandora_fname in pandora_files:\n", + " print(pandora_fname)\n", + " sys.exit()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4vYb5skDdNvg" + }, + "source": [ + "## 4.2 Selecting timeframe of interest common for both instruments" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JEqqz73v5OIo", + "outputId": "a761e1cf-4f2f-4b48-d78f-73d7df6c033a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "enter period of interest, start and end dates, in the form YYYYMMDD\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "enter start date of interest 20230901\n", + "enter end date of interest 20230901\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023 9 1 2023 9 1\n" + ] + } + ], + "source": [ + "print('enter period of interest, start and end dates, in the form YYYYMMDD')\n", + "datestamp_ini = input('enter start date of interest ')\n", + "datestamp_fin = input('enter end date of interest ')\n", + "\n", + "start_date = int(datestamp_ini)\n", + "end_date = int(datestamp_fin)\n", + "\n", + "yyyy_ini = start_date//10000\n", + "mm_ini = (start_date//100 - yyyy_ini*100)\n", + "dd_ini = (start_date - yyyy_ini*10000 - mm_ini*100)\n", + "\n", + "yyyy_fin = end_date//10000\n", + "mm_fin = (end_date//100 - yyyy_fin*100)\n", + "dd_fin = (end_date - yyyy_fin*10000 - mm_fin*100)\n", + "print(yyyy_ini, mm_ini, dd_ini, yyyy_fin, mm_fin, dd_fin)\n", + "\n", + "date_start = str('%4.4i-%2.2i-%2.2i 00:00:00' %(yyyy_ini, mm_ini, dd_ini))\n", + "date_end = str('%4.4i-%2.2i-%2.2i 23:59:59' %(yyyy_fin, mm_fin, dd_fin))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mTFV2Fkadj8e" + }, + "source": [ + "## 4.3 Reading Pandora file within selected timeframe and creating point of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "elO1qjkY5cyd", + "outputId": "851c7890-c7d6-4ffb-803a-6cf0ba4a3ac3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "location name BoulderCO-NCAR\n", + "location latitude 40.0375\n", + "location longitude -105.242\n", + "32 Pandora measurements found within period of interest between 2023-09-01 00:00:00 and 2023-09-01 23:59:59\n" + ] + } + ], + "source": [ + "pandora_file = pandora_files[0]\n", + "lat, lon, POI_name, Pandora_data = read_Pandora_NO2_rnvh3p1_8(pandora_file, start_date, end_date)\n", + "\n", + "if lat == -999.:\n", + " print('error reading pandora file ', pandora_file, 'program terminated')\n", + " sys.exit()\n", + "\n", + "POI = np.array([lat, lon])\n", + "\n", + "# print # of points in Pandora timeseries\n", + "n_Pandora_data = len(Pandora_data)\n", + "print(n_Pandora_data,\\\n", + "' Pandora measurements found within period of interest between',\\\n", + "date_start, 'and', date_end)\n", + "if n_Pandora_data == 0:\n", + " print('program terminated')\n", + " sys.exit()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3wvhtsq5kXgZ" + }, + "source": [ + "## 4.4 Setting TEMPO name constants and writing Pandora timeseries to a file" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "uEaqIobZ7F81" + }, + "outputs": [], + "source": [ + "# Setting TEMPO name constants\n", + "short_name = 'TEMPO_NO2_L2' # collection name to search for in the EarthData\n", + "out_Q = 'NO2_trop_col' # name of the output quantity with unit\n", + "out_Q_unit = 'molecules/cm^2' # name of the output quantity with unit\n", + "\n", + "# write Pandora timeseries to a file\n", + "POI_name_ = POI_name.replace(' ','_')\n", + "Pandora_out = open(out_Q+'_Pandora_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", + "for line in Pandora_data:\n", + " Pandora_out.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %12.4e %12.4e\\n'\\\n", + " %(line[0], line[1], line[2], line[3], line[4], line[5], line[6], line[7])))\n", + "Pandora_out.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VGfpNlrWei36" + }, + "source": [ + "# 5 Working with TEMPO data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qO9NEF61jlcy" + }, + "source": [ + "## 5.1 Searching TEMPO data files containing the POI (position of the Pandora station)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iysK7xDc7eV4", + "outputId": "100262f1-cf51-4092-aab2-99958711413b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Granules found: 13\n" + ] + } + ], + "source": [ + "POI_lat = POI[0]\n", + "POI_lon = POI[1]\n", + "\n", + "version = 'V03'\n", + "POI_results = earthaccess.search_data(short_name = short_name\\\n", + " , version = version\\\n", + " , temporal = (date_start, date_end)\\\n", + " , point = (POI_lon, POI_lat))\n", + "\n", + "n_gr = len(POI_results)\n", + "if n_gr == 0:\n", + " print('program terminated')\n", + " sys.exit()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OvC5eZRhk7Xz" + }, + "source": [ + "## 5.2 Printing explicit links to the granules and downloading the files" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359, + "referenced_widgets": [ + "81a3dde3d96d436ba9d568932081d1e8", + "716eef3312884d01bf605d834e16e966", + "47483e59bc7e4b55879037a9e20d8b75", + "0154eba9abfc4ce9b67599ec542581ab", + "2f82bebe7f824fea90c653fb0007cde6", + "3668af5205244cc481c26d20bbcffe34", + "884729e65ae74e23a71728eb15555e99", + "4e5dc7e46a74417da29a75a85c8e0757", + "f2a29f4bea8d45ca977d928c98cec0ba", + "8feaff5762ed4e3fabd2f257b65a30fc", + "436212e52ff349739ac61acaca466e47", + "d5739de67d2a4964a080710b66c0d42d", + "8ac37128147a42b1adb0cc5311ef7685", + "8e855327fe0546658ff5f705a3890d4b", + "2c5de8de95884e4c8b829aea66b5b90c", + "dd8f79285fe2403a9edfb6ac23ac3c87", + "ec91094eb8004fc888d455c60ecb91b4", + "b1eb4aa51b294eed965638218b0c10b6", + "6dfee462007944458f6f2608ad2492aa", + "a4991eee48624c818134b8c05608300e", + "afe04c8caefa4720a8b220682bc05512", + "eccd441db95a4056953df3b4f9149004", + "fefeeaaeebc7438c813eb74eb15413c1", + "a8480fa660224b64895cce2c9b916859", + "b4ad09eded994284b5c5a581f8473dd7", + "8e9cfdaa72c64a28a19085e8bf217268", + "06fae94c546d4cafbda21cb99bfa2e1c", + "7fb6b8d5cb3d48eb91014bc2fc1ed6b4", + "c773904f1f90425f94ab9c7c5a6d8300", + "dbe9fa4070644cdf8968b0acf759bafb", + "c8454424afbf47249aa3accca871bfb0", + "28bfe5e502d4404ea73b0e7a33aa3ec4", + "83bb042eb04342e994accd7173b3ec29" + ] + }, + "id": "_lPBPwOG7pUX", + "outputId": "51510073-377c-477c-a74d-0369c7e428d1", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T002134Z_S018G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T005257Z_S019G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T012420Z_S020G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T144803Z_S007G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T155034Z_S008G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T165942Z_S009G09.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T182132Z_S010G09.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T200038Z_S011G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T210309Z_S012G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T220540Z_S013G08.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T223706Z_S015G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T230829Z_S016G03.nc\n", + "https://data.asdc.earthdata.nasa.gov/asdc-prod-protected/TEMPO/TEMPO_NO2_L2_V03/2023.09.01/TEMPO_NO2_L2_V03_20230901T233952Z_S017G03.nc\n", + " Getting 13 granules, approx download size: 1.31 GB\n", + "Accessing cloud dataset using dataset endpoint credentials: https://data.asdc.earthdata.nasa.gov/s3credentials\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T002134Z_S018G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T005257Z_S019G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T012420Z_S020G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T144803Z_S007G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T155034Z_S008G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T165942Z_S009G09.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T182132Z_S010G09.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T200038Z_S011G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T210309Z_S012G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T220540Z_S013G08.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T223706Z_S015G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T230829Z_S016G03.nc\n", + "Downloaded: TEMPO_NO2_L2_V03_20230901T233952Z_S017G03.nc\n" + ] + } + ], + "source": [ + "granule_links = []\n", + "for result in POI_results: granule_links.append(result['umm']['RelatedUrls'][0]['URL'])\n", + "for granule_link in granule_links: print(granule_link)\n", + "\n", + "# Downloading TEMPO data files\n", + "downloaded_files = earthaccess.download(\n", + " POI_results,\n", + " local_path='.')\n", + "\n", + "# Checking whether all TEMPO data files have been downloaded\n", + "for granule_link in granule_links:\n", + " TEMPO_fname = granule_link.split('/')[-1]\n", + "# check if file exists in the local directory\n", + " if not os.path.exists(TEMPO_fname):\n", + " print(TEMPO_fname, 'does not exist in local directory')\n", + "# repeat attempt to download\n", + " downloaded_files = earthaccess.download(granule_link,\n", + " local_path='.')\n", + "# if file still does not exist in the directory, remove its link from the list of links\n", + " if not os.path.exists(TEMPO_fname): granule_links.remove(granule_link)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kPyYwF-E7Qiv" + }, + "source": [ + "## 5.3 Compiling TEMPO NO2 tropospheric column time series" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VjGkJjYH8Ssu", + "outputId": "3e7f218a-79bb-494b-e32f-90e87a12a0af", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " TEMPO_NO2_L2_V03_20230901T002134Z_S018G03.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 3 694 40.043541 -105.212753 1.1262e+16 8.7104e+15 0\n", + " 3 695 40.023048 -105.207741 6.2484e+15 2.2060e+15 0\n", + " 4 694 40.044590 -105.270340 7.8105e+15 2.3133e+15 0\n", + " 4 695 40.024281 -105.265404 2.4875e+15 2.7523e+15 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T005257Z_S019G03.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 2 694 40.037796 -105.192413 8.4342e+15 6.4848e+15 1\n", + " 2 695 40.016869 -105.187187 1.0726e+16 6.0591e+15 1\n", + " 3 694 40.039486 -105.249710 2.9969e+15 1.3962e+15 1\n", + " 3 695 40.019146 -105.244766 6.5816e+15 3.5244e+15 1\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T012420Z_S020G03.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 3 694 40.037354 -105.222488 -1.0000e+30 -1.0000e+30 2\n", + " 3 695 40.016949 -105.217514 -1.0000e+30 -1.0000e+30 2\n", + " 4 694 40.038174 -105.279892 -1.0000e+30 -1.0000e+30 2\n", + " 4 695 40.018059 -105.275055 -1.0000e+30 -1.0000e+30 2\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T144803Z_S007G08.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 1 692 40.049271 -105.237335 1.6345e+15 1.3920e+15 0\n", + " 1 693 40.028824 -105.232361 2.5808e+15 1.6724e+15 0\n", + " 2 692 40.048901 -105.294388 2.8346e+15 1.4935e+15 0\n", + " 2 693 40.029575 -105.289955 3.6443e+15 1.3916e+15 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T155034Z_S008G08.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 1 691 40.056293 -105.231339 1.2180e+15 6.1653e+14 0\n", + " 1 692 40.035725 -105.226288 4.4623e+14 7.6719e+14 0\n", + " 2 691 40.056568 -105.288567 1.7754e+15 6.9336e+14 0\n", + " 2 692 40.036541 -105.283775 1.1887e+15 6.8650e+14 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T165942Z_S009G09.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 98 691 40.055660 -105.194344 1.8203e+15 5.5742e+14 0\n", + " 98 692 40.035091 -105.189301 1.8379e+15 5.8077e+14 0\n", + " 99 691 40.057152 -105.250603 3.1892e+14 5.3812e+14 0\n", + " 99 692 40.036758 -105.245636 1.8232e+15 7.0814e+14 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T182132Z_S010G09.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 99 692 40.051983 -105.202011 4.0699e+15 7.2846e+14 0\n", + " 99 693 40.031464 -105.196983 4.6310e+15 7.8188e+14 0\n", + " 100 692 40.052910 -105.258690 3.7701e+15 1.0297e+15 0\n", + " 100 693 40.032646 -105.253777 4.0773e+15 7.6325e+14 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T200038Z_S011G08.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 2 693 40.051800 -105.225998 3.5499e+15 6.4744e+14 0\n", + " 2 694 40.031326 -105.220970 3.2204e+15 7.1260e+14 0\n", + " 3 693 40.052639 -105.282028 1.6830e+15 7.7394e+14 0\n", + " 3 694 40.032536 -105.277184 3.6042e+15 6.2925e+14 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T210309Z_S012G08.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 2 693 40.044563 -105.207939 4.4134e+15 7.2164e+15 0\n", + " 2 694 40.024006 -105.202881 1.2929e+16 8.7282e+15 0\n", + " 3 693 40.045517 -105.264740 3.9721e+15 8.4949e+14 0\n", + " 3 694 40.025234 -105.259804 4.5835e+15 9.0780e+14 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T220540Z_S013G08.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 3 695 40.041687 -105.234406 9.0491e+14 3.9569e+15 0\n", + " 3 696 40.021320 -105.229439 -4.9033e+14 4.7952e+15 0\n", + " 4 695 40.042309 -105.291039 8.0656e+15 4.4873e+15 0\n", + " 4 696 40.022667 -105.286423 2.4297e+15 -1.0000e+30 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T223706Z_S015G03.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 2 695 40.036613 -105.187828 8.6153e+14 3.6873e+15 0\n", + " 2 696 40.015675 -105.182579 5.3004e+15 1.0498e+16 0\n", + " 3 695 40.037987 -105.244232 1.7207e+15 2.9184e+15 0\n", + " 3 696 40.017635 -105.239273 1.9657e+16 3.8497e+16 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T230829Z_S016G03.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 3 695 40.043655 -105.232956 3.4547e+15 1.1839e+15 0\n", + " 3 696 40.023254 -105.227974 5.5489e+15 3.1892e+15 0\n", + " 4 695 40.043983 -105.289665 1.5610e+15 1.6137e+15 0\n", + " 4 696 40.024250 -105.285011 2.9371e+15 9.4193e+14 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n", + "\n", + " TEMPO_NO2_L2_V03_20230901T233952Z_S017G03.nc\n", + "scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF\n", + " 2 693 40.054363 -105.206375 5.2080e+15 2.7685e+15 0\n", + " 2 694 40.033916 -105.201378 2.1412e+15 1.6391e+15 0\n", + " 3 693 40.054836 -105.262619 3.8946e+15 1.4504e+16 0\n", + " 3 694 40.034592 -105.257706 6.5556e+15 5.9820e+15 0\n", + "POI BoulderCO-NCAR at 40.0375 -105.242 found\n" + ] + } + ], + "source": [ + "# Important note\n", + "# NO2 tropospheric column may be negative even with the highest quality flag.\n", + "# The code below compiles TWO timeseries one takes all values of total NO2 column,\n", + "# while another discards negative values before interpolation to the POI is performed.\n", + "# The two timeseries will be plotted later to see the difference, if any.\n", + "# This feature may be commented out should the user be not interested in accounting positive-only retrievals.\n", + "\n", + "days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n", + "\n", + "fout_noFV = open(out_Q+'_noFV_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", + "fout_noFV.write('timeseries of '+out_Q+' at '+POI_name+' '+str('%08.4fN %08.4fW' %(POI[0], -POI[1]))+'\\n')\n", + "fout_noFV.write('yyyy mm dd hh mn ss '+out_Q_unit+'\\n')\n", + "\n", + "fout_noneg = open(out_Q+'_noneg_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'w')\n", + "fout_noneg.write('timeseries of '+out_Q+' at '+POI_name+' '+str('%08.4fN %08.4fW' %(POI[0], -POI[1]))+'\\n')\n", + "fout_noneg.write('yyyy mm dd hh mn ss '+out_Q_unit+'\\n')\n", + "\n", + "for granule_link in granule_links:\n", + " last_slash_ind = granule_link.rfind('/')\n", + " fname = granule_link[last_slash_ind+1 : ]\n", + " print('\\n', fname)\n", + "\n", + " lat, lon, fv_geo, time, trop_NO2_column, trop_NO2_column_unc,\\\n", + "trop_NO2_column_QF, fv_prod, fv_QF, prod_unit = read_TEMPO_tropNO2_L2(fname)\n", + "# un-comment the line below if TEMPO granules are not needed after processing\n", + "# os.remove(fname)\n", + "\n", + " if isinstance(lat, float): continue\n", + "\n", + " nx = lon.shape[0]\n", + " ny = lon.shape[1]\n", + "\n", + "# getting time from the granule filename\n", + " Tind = fname.rfind('T')\n", + " yyyy= int(fname[Tind-8 : Tind-4])\n", + " mm = int(fname[Tind-4 : Tind-2])\n", + " dd = int(fname[Tind-2 : Tind])\n", + " hh = int(fname[Tind+1 : Tind+3])\n", + " mn = int(fname[Tind+3 : Tind+5])\n", + " ss = float(fname[Tind+5 : Tind+7])\n", + "\n", + " POI_found = False\n", + " for ix in range(nx-1):\n", + " for iy in range(ny-1):\n", + " if lon[ix, iy] == fv_geo: continue\n", + " if lat[ix, iy] == fv_geo: continue\n", + " if lon[ix, iy+1] == fv_geo: continue\n", + " if lat[ix, iy+1] == fv_geo: continue\n", + " if lon[ix+1, iy+1] == fv_geo: continue\n", + " if lat[ix+1, iy+1] == fv_geo: continue\n", + " if lon[ix+1, iy] == fv_geo: continue\n", + " if lat[ix+1, iy] == fv_geo: continue\n", + "\n", + " coords_poly_loc = [[lon[ix, iy], lat[ix, iy]], [lon[ix, iy+1], lat[ix, iy+1]] \\\n", + " , [lon[ix+1, iy+1], lat[ix+1, iy+1]], [lon[ix+1, iy], lat[ix+1, iy]]]\n", + " poly_loc = Polygon(coords_poly_loc)\n", + "\n", + " if p.within(poly_loc):\n", + " print('scanl pixel latitude longitude trNO2_col trNO2_col_unc NO2_col_QF')\n", + " for scl in range(ix, ix+2, 1):\n", + " for pix in range(iy, iy+2, 1):\n", + " print(\" %3d %4d %9.6f %10.6f %11.4e %11.4e %5i\"\\\n", + " %(scl, pix, lat[scl, pix], lon[scl, pix]\\\n", + ", trop_NO2_column[scl, pix], trop_NO2_column_unc[scl, pix], trop_NO2_column_QF[scl, pix]))\n", + "\n", + " POI_found = True\n", + " print('POI', POI_name, 'at', POI[0], POI[1], 'found')\n", + "\n", + " trop_NO2_column_loc = np.array([trop_NO2_column[ix, iy],\\\n", + " trop_NO2_column[ix, iy+1],\\\n", + " trop_NO2_column[ix+1, iy+1],\\\n", + " trop_NO2_column[ix+1, iy]])\n", + " trop_NO2_column_unc_loc = np.array([trop_NO2_column_unc[ix, iy],\\\n", + " trop_NO2_column_unc[ix, iy+1],\\\n", + " trop_NO2_column_unc[ix+1, iy+1],\\\n", + " trop_NO2_column_unc[ix+1, iy]])\n", + " trop_NO2_column_QF_loc = np.array([trop_NO2_column_QF[ix, iy],\\\n", + " trop_NO2_column_QF[ix, iy+1],\\\n", + " trop_NO2_column_QF[ix+1, iy+1],\\\n", + " trop_NO2_column_QF[ix+1, iy]])\n", + " lat_loc = np.array([lat[ix, iy], lat[ix, iy+1],\\\n", + " lat[ix+1, iy+1], lat[ix+1, iy]])\n", + " lon_loc = np.array([lon[ix, iy], lon[ix, iy+1],\\\n", + " lon[ix+1, iy+1], lon[ix+1, iy]])\n", + " mask_noFV = (trop_NO2_column_loc != fv_prod)&\\\n", + " (trop_NO2_column_unc_loc != fv_prod)&\\\n", + " (trop_NO2_column_QF_loc == 0)\n", + " mask_noneg = (trop_NO2_column_loc > 0.)&\\\n", + " (trop_NO2_column_unc_loc != fv_prod)&\\\n", + " (trop_NO2_column_QF_loc == 0)\n", + "\n", + " points_noFV = np.column_stack((lon_loc[mask_noFV], lat_loc[mask_noFV]))\n", + " points_noneg = np.column_stack((lon_loc[mask_noneg], lat_loc[mask_noneg]))\n", + " ff_noFV = trop_NO2_column_loc[mask_noFV]\n", + " ff_noneg = trop_NO2_column_loc[mask_noneg]\n", + " ff_unc_noFV = trop_NO2_column_unc_loc[mask_noFV]\n", + " ff_unc_noneg = trop_NO2_column_unc_loc[mask_noneg]\n", + "\n", + "# handling time first:\n", + " delta_t = (time[ix+1] + time[ix])*0.5 - time[0]\n", + " ss = ss + delta_t\n", + " if ss >= 60.:\n", + " delta_mn = int(ss/60.)\n", + " ss = ss - 60.*delta_mn\n", + " mn = mn + delta_mn\n", + " if mn >= 60:\n", + " mn = mn - 60\n", + " hh = hh + 1\n", + " if hh == 24:\n", + " hh = hh - 24\n", + " dd = dd + 1\n", + " day_month = days[mm]\n", + " if (yyyy//4)*4 == yyyy and mm == 2: day_month = day_month + 1\n", + " if dd > day_month:\n", + " dd = 1\n", + " mm = mm + 1\n", + " if mm > 12:\n", + " mm = 1\n", + " yyyy = yyyy + 1\n", + "\n", + " if ff_noFV.shape[0] == 0:\n", + " continue\n", + " elif ff_noFV.shape[0] < 4:\n", + " trop_NO2_column_noFV = np.mean(ff_noFV)\n", + " trop_NO2_column_unc_noFV = np.mean(ff_unc_noFV)\n", + " elif ff_noFV.shape[0] == 4:\n", + " trop_NO2_column_noFV = griddata(points_noFV, ff_noFV, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + " trop_NO2_column_unc_noFV = griddata(points_noFV, ff_unc_noFV, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + "\n", + " fout_noFV.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %10.3e %10.3e '\\\n", + " %(yyyy, mm, dd, hh, mn, ss, trop_NO2_column_noFV, trop_NO2_column_unc_noFV)))\n", + " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix, iy], -lon[ix, iy],\\\n", + "trop_NO2_column[ix, iy], trop_NO2_column_unc[ix, iy])))\n", + " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix, iy+1], -lon[ix, iy+1],\\\n", + "trop_NO2_column[ix, iy+1], trop_NO2_column_unc[ix, iy+1])))\n", + " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix+1, iy+1], -lon[ix+1, iy+1],\\\n", + "trop_NO2_column[ix+1, iy+1], trop_NO2_column_unc[ix+1, iy+1])))\n", + " fout_noFV.write(str('%9.4fN %9.4fW %10.3e %10.3e\\n'\\\n", + " %(lat[ix+1, iy], -lon[ix+1, iy],\\\n", + "trop_NO2_column[ix+1, iy], trop_NO2_column_unc[ix+1, iy])))\n", + "\n", + " if ff_noneg.shape[0] == 0:\n", + " continue\n", + " elif ff_noneg.shape[0] < 4:\n", + " trop_NO2_column_noneg = np.mean(ff_noneg)\n", + " trop_NO2_column_unc_noneg = np.mean(ff_unc_noneg)\n", + " elif ff_noneg.shape[0] == 4:\n", + " trop_NO2_column_noneg = griddata(points_noneg, ff_noneg, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + " trop_NO2_column_unc_noneg = griddata(points_noneg, ff_unc_noneg, pp,\\\n", + "method='linear', fill_value=-1., rescale=False)[0]\n", + "\n", + " fout_noneg.write(str('%4.4i %2.2i %2.2i %2.2i %2.2i %4.1f %10.3e %10.3e '\\\n", + " %(yyyy, mm, dd, hh, mn, ss, trop_NO2_column_noneg, trop_NO2_column_unc_noneg)))\n", + " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix, iy], -lon[ix, iy],\\\n", + "trop_NO2_column[ix, iy], trop_NO2_column_unc[ix, iy])))\n", + " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix, iy+1], -lon[ix, iy+1],\\\n", + "trop_NO2_column[ix, iy+1], trop_NO2_column_unc[ix, iy+1])))\n", + " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e '\\\n", + " %(lat[ix+1, iy+1], -lon[ix+1, iy+1],\\\n", + "trop_NO2_column[ix+1, iy+1], trop_NO2_column_unc[ix+1, iy+1])))\n", + " fout_noneg.write(str('%9.4fN %9.4fW %10.3e %10.3e\\n'\\\n", + " %(lat[ix+1, iy], -lon[ix+1, iy],\\\n", + "trop_NO2_column[ix+1, iy], trop_NO2_column_unc[ix+1, iy])))\n", + "\n", + " break\n", + "\n", + " if POI_found: break\n", + "\n", + "fout_noFV.close()\n", + "fout_noneg.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xaRNZa_4bl11" + }, + "source": [ + "# 6 Plotting the results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XglvHW3AbfFl" + }, + "source": [ + "## 6.1 Reading created data files for TEMPO, create timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yAg1TWrF80yk", + "outputId": "654b81cc-660a-43f5-bc8c-8e7835da28a6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "TEMPO standard and \"no negative\" are of equal length\n", + "TEMPO standard and \"no negative\" are different\n" + ] + } + ], + "source": [ + "# reading TEMPO file that was created at the previous step\n", + "# only read POI information from the header and first 8 columns of data:\n", + "# yyyy, mm, dd, hh, mn, ss, NO2 column, and its incertainty\n", + "fout = open(out_Q+'_noneg_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", + "\n", + "header1 = fout.readline()\n", + "header2 = fout.readline()\n", + "data_lines_noneg = fout.readlines()\n", + "\n", + "fout.close()\n", + "\n", + "yyyy = yyyy_ini\n", + "mm = mm_ini\n", + "dd = dd_ini\n", + "hh = 0\n", + "mn = 0\n", + "ss = 0\n", + "dt0 = datetime(yyyy, mm, dd, hh, mn, ss)\n", + "\n", + "yyyy = yyyy_fin\n", + "mm = mm_fin\n", + "dd = dd_fin\n", + "hh = 23\n", + "mn = 59\n", + "ss = 59\n", + "dt_fin = datetime(yyyy, mm, dd, hh, mn, ss) # this is time 1 second before the end of the timeframe of interest\n", + "\n", + "time_series_TEMPO_noneg = np.empty([0, 3])\n", + "\n", + "for line in data_lines_noneg:\n", + " split = line.split()\n", + " yyyy = int(split[0])\n", + " mm = int(split[1])\n", + " dd = int(split[2])\n", + " hh = int(split[3])\n", + " mn = int(split[4])\n", + " ss = float(split[5])\n", + " us = int((ss - int(ss))*1000000) # microseconds\n", + "# dt below is time since the beginning of the period of interest in hours\n", + " dt = (datetime(yyyy, mm, dd, hh, mn, int(ss), us) - dt0).total_seconds()/86400.\n", + " time_series_TEMPO_noneg = np.append(time_series_TEMPO_noneg,\\\n", + " [[dt, float(split[6]), float(split[7])]], axis = 0)\n", + "\n", + "fout = open(out_Q+'_noFV_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", + "\n", + "header1 = fout.readline()\n", + "header2 = fout.readline()\n", + "data_lines = fout.readlines()\n", + "\n", + "fout.close()\n", + "\n", + "time_series_TEMPO = np.empty([0, 3])\n", + "\n", + "for line in data_lines:\n", + " split = line.split()\n", + " yyyy = int(split[0])\n", + " mm = int(split[1])\n", + " dd = int(split[2])\n", + " hh = int(split[3])\n", + " mn = int(split[4])\n", + " ss = float(split[5])\n", + " us = int((ss - int(ss))*1000000) # microseconds\n", + "# dt below is time since the beginning of the period of interest in hours\n", + " dt = (datetime(yyyy, mm, dd, hh, mn, int(ss), us) - dt0).total_seconds()/86400.\n", + " time_series_TEMPO = np.append(time_series_TEMPO,\\\n", + " [[dt, float(split[6]), float(split[7])]], axis = 0)\n", + "\n", + "if len(time_series_TEMPO) == len(time_series_TEMPO_noneg):\n", + " print('\\nTEMPO standard and \"no negative\" are of equal length')\n", + " nt = len(time_series_TEMPO)\n", + " equal = True\n", + " for i in range(nt):\n", + " if time_series_TEMPO[i,1] != time_series_TEMPO_noneg[i,1]:\n", + " equal = False\n", + " break\n", + "else: equal = False\n", + "\n", + "if equal: print('TEMPO standard and \"no negative\" are the same')\n", + "else: print('TEMPO standard and \"no negative\" are different')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O8JvXRRcwT9O" + }, + "source": [ + "## 6.2 creating Pandora timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "6cHK-fIs9KCs" + }, + "outputs": [], + "source": [ + "Pandora_out = open(out_Q+'_Pandora_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name_+'_'+str('%08.4fN_%08.4fW.txt' %(POI[0], -POI[1])), 'r')\n", + "Pandora_data_lines = Pandora_out.readlines()\n", + "Pandora_out.close()\n", + "\n", + "time_series_Pandora = np.empty([0, 3])\n", + "time_series_Pandora_noneg = np.empty([0, 3])\n", + "\n", + "for line in Pandora_data_lines:\n", + " split = line.split()\n", + " yyyy = int(split[0])\n", + " mm = int(split[1])\n", + " dd = int(split[2])\n", + " hh = int(split[3])\n", + " mn = int(split[4])\n", + " ss = float(split[5])\n", + " us = int((ss - int(ss))*1000000) # microseconds\n", + "# dt below is time since the beginning of the period of interest in hours\n", + " dt = (datetime(yyyy, mm, dd, hh, mn, int(ss), us) - dt0).total_seconds()/86400.\n", + " col = float(split[6])\n", + " unc = float(split[7])\n", + " time_series_Pandora = np.append(time_series_Pandora,\\\n", + "[[dt, col, unc]], axis = 0)\n", + " if col > 0:\n", + " time_series_Pandora_noneg = np.append(time_series_Pandora_noneg,\\\n", + "[[dt, col, unc]], axis = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NGXMMgcNcMb4" + }, + "source": [ + "## 6.3 Plotting timeseries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WEkwDqvOccOK" + }, + "source": [ + "### 6.3.1 No error bars" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "up8Rj77x9bMs", + "outputId": "6d9820d3-ae88-49d6-95f8-d74eb80f5752" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_title = 'NO$_{2}$ tropospheric column'+' '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = out_Q+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "plt.plot(time_series_Pandora[:, 0], time_series_Pandora[:, 1],\\\n", + " label = \"Pandora\", c = 'r')\n", + "plt.plot(time_series_TEMPO[:, 0], time_series_TEMPO[:, 1],\n", + " label = \"TEMPO\", c = 'b')\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = 0.\n", + "u_lim = ((dt_fin - dt0).total_seconds() + 1.)/86400.\n", + "plt.xlim(l_lim, u_lim)\n", + "\n", + "# some research is required to set the vertical range\n", + "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", + "plt.ylabel('NO$_{2}$ trop column, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "plt.legend(loc='lower left')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QyhmWrUMXU5z" + }, + "source": [ + "### 6.3.2 Plotting TEMPO and smoothed Pandora retievals with error bars" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 977 + }, + "id": "4jIPncJ3_Vzb", + "outputId": "5fc260b6-5991-430e-bcad-bfee9d1397bf", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.66171 6.851e+14 7.65e+13 1.0000e+00\n", + "0.66017 6.851e+14 7.65e+13\n", + "\n", + "0.70692 9.231e+14 8.83e+13 1.0000e+00\n", + "0.71158 9.231e+14 8.83e+13\n", + "\n", + "0.83532 3.867e+15 9.07e+13 1.0000e+00\n", + "0.83386 3.867e+15 9.07e+13\n", + "\n", + "0.88098 3.409e+15 9.24e+13 1.0000e+00\n", + "0.87728 3.409e+15 9.24e+13\n", + "\n", + "0.93919 3.547e+15 8.14e+13 1.0000e+00\n", + "0.94252 3.547e+15 8.14e+13\n", + "\n", + "0.96144 3.966e+15 9.09e+13 1.0000e+00\n", + "0.96435 3.966e+15 9.09e+13\n", + "\n", + "0.98959 3.613e+15 9.04e+13 1.0000e+00\n", + "0.98611 3.613e+15 9.04e+13\n", + "\n", + "7\n", + "0.66171 6.851e+14 7.65e+13 1.0000e+00\n", + "0.66017 6.851e+14 7.65e+13\n", + "\n", + "0.70692 9.231e+14 8.83e+13 1.0000e+00\n", + "0.71158 9.231e+14 8.83e+13\n", + "\n", + "0.83532 3.867e+15 9.07e+13 1.0000e+00\n", + "0.83386 3.867e+15 9.07e+13\n", + "\n", + "0.88098 3.409e+15 9.24e+13 1.0000e+00\n", + "0.87728 3.409e+15 9.24e+13\n", + "\n", + "0.93919 3.547e+15 8.14e+13 1.0000e+00\n", + "0.94252 3.547e+15 8.14e+13\n", + "\n", + "0.96144 3.966e+15 9.09e+13 1.0000e+00\n", + "0.96435 3.966e+15 9.09e+13\n", + "\n", + "0.98959 3.613e+15 9.04e+13 1.0000e+00\n", + "0.98611 3.613e+15 9.04e+13\n", + "\n", + "7\n" + ] + }, + { + "data": { + "image/png": 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qVAkRERF4+fKlIi0xMRFHjx7NVVsLQ6tWrXDixAkEBASgdevWAABHR0dUrFgRc+fORVJSUo4CK6n3OT/nODMymUzl5uBDhw7h+fPnkpSf7ttvv4W3tzdmz56NefPmZZqvS5cuiImJwZ49e5TSt2zZAmtrazRq1CjXZWYmMDAQGhoaqFKlSq7rTqejo4OaNWvCzs4OT58+xc6dOzFs2DCl3sy8lJtXJeE457Z+oPgd54KkmX0WKkxLliyBs7MzIiIilJ7YaNKkCVatWoWJEyeiadOmGDt2LCpWrIinT59izZo1uHjxIlatWoXGjRsrlZeUlISePXuievXqOforpVatWgCAH374AQMHDoSWlhaqVq0KIyOjLLfz8fFB69at0aJFC0ydOhXa2trw9fXFP//8gx07dqj01uzduxeamppo3bo1bt26hTlz5qBOnTro2bNnrsqMiopCixYt0LdvX1SrVg1GRka4fPkyjhw5gq5du+a6ztz44Ycf0LRpUzRr1gyjRo1CpUqV8P79e9y/fx8HDx7M9CnPrKxYsQJNmzZFo0aNMGPGDNjb2+Ply5c4cOAA1q1bl+V5yMm2uT1PGfXq1Qtz585F79698fXXXyM+Ph4//vhjnp9CLUju7u7w9fXF69evsWrVKqX0TZs2oWzZstlOtQAUzD7n5hzLZDK4ubkppjxRp0OHDti8eTOqVauG2rVrIzg4GMuWLZN0uHH58uWYO3cu2rRpg/bt26vcP/j5558rfm7bti1at26NUaNGITo6Gvb29tixYweOHDmCbdu2KXrUc1Pm8OHDYWxsjIYNG8LCwgKvX7/G7t27sXPnTnz99deK+9VyWjeQ9oTZnj174OLiAh0dHdy4cQOLFy+Gg4MDvv32W6W25Kbcw4cPIzY2Fu/fvweQ9vTwH3/8AQBo166d0rQDJfU456b+4nicC1xR3jn/Kcv4VODH+vbtKwAoTRCaLigoSHTv3l1YWFgITU1NYW5uLrp27SrOnz+vkjclJUX07t1bdOrUSWVS0azMnDlTWFtbCw0NDQFABAYGCiGynzzzzJkzomXLlsLAwEDo6emJzz//XBw8eFApT3oZwcHBomPHjsLQ0FAYGRmJPn36qJ3ILrsy4+PjxciRI0Xt2rWFsbGx0NPTE1WrVhXz5s1TTEiXmzoz28fMnp569OiR8PLyEhUqVBBaWlqifPnyonHjxmLhwoXZlplZuSEhIaJHjx7C1NRUaGtri4oVK4pBgwaJ+Ph4tcc9o5xsm5PzlNn++vv7i7p16wo9PT1RpUoVsXr16kyfCvx4fwcOHCgMDAxU2vzxZLhZlZHTp9jevn0rNDQ0hIGBgUhMTFSkpz8J+vFTiFnJyT7nts05OU/v37/P0dPAb9++FUOGDBHm5uZCX19fNG3aVJw5c0a4ubkJNze3LNuR0+Pp5uameEpM3fKx9+/fi/HjxwtLS0uhra0tateuLXbs2JHnMjdu3CiaNWsmzMzMhKampihTpoxwc3MTv/32W57qFkKIO3fuiC+++EKUK1dOaGtrC3t7ezF79mwRExOj9hjktFw7O7tM96k0Heec1l8cj3NBkwnx/4+SUakzbNgw3Lt3D0eOHFG5YbioeHt7Y/78+Xj16lWW9/OU9DqJ8svf3x8dOnTAjRs3FD3JRFT88R6rUurJkyf49ddfcfHiRZiZmcHQ0BCGhoY4c+ZMUTeNiHIgMDAQvXv3ZlBFVMLwHqtSys7ODuyMJCq5li1bVtRNIKI84FAgERERkUQ4FEhEREQkEQZWRERERBJhYEVEREQkEQZWVOg2b94MmUymtJQvXx7NmzfHn3/+WWj1P378ONu8zZs3z9PM7DkRHR2N7777Di4uLjA2NoaOjg4qVaoELy8vlVnjAeDChQvo0aMHrKysoK2tDUtLS3Tv3h1BQUG5qrd58+aQyWRo06aNyrrHjx9DJpOpfeHqw4cPMXbsWDg6OkJPTw/6+vqoUaMGZs+eneks3127doVMJsv0vWQnT55U+h7I5XKUL18eHTt2xJUrV3K1X+r8/vvvqFu3LnR1dWFtbY2JEyeqvNXgY7/++itkMlmmbym4evUqWrVqBUNDQ5QpUwZdu3ZVetVHdo4fPw5XV1fo6+vDzMwMgwYNUnmp9LNnz9ClSxdUqVIFBgYGMDExQb169bB69WqV131UqlRJ5fcpffl4mpXM8o4cOVIpX/rviK6urtoXZDdv3jxHrxe5desWRo8eDVdXVxgYGEAmk2U52WlOztfH35mMy8eTX6qzd+9e9OnTB/b29tDT00OlSpXQr1+/bN+jGhcXB0dHR7W/H8HBwRgzZgxq1aoFIyMjWFhYKGb/V+fhw4fo2rUrypQpA0NDQ5U3RaSkpKBMmTJKLyxPt3LlSshkMvTp00dl3bfffguZTIa///472+NABYeBFRWZTZs2ISgoCOfPn8f69eshl8vRsWNHHDx4sKibVuAePHiAevXqYfHixWjRogV27NiBY8eOYf78+Xj58iWcnZ0RFRWlyP/TTz+hSZMmCA0NxdKlS3H8+HF8//33eP78OZo2bYrVq1fnug1Hjx7N8ezwf/75J2rXro0///wTw4cPx59//qn4+eDBg+jQoYPKNhEREYpA2c/PL9MXLQPAokWLEBQUhJMnT2LOnDk4f/483Nzc8vXScD8/P/Tp0wcNGjTA4cOHMW/ePGzevFllRv6Mnj9/jqlTpypeSv2xf//9F82bN0diYiJ27dqFjRs34u7du2jWrJnSC2ozc+rUKbRt2xYWFhb43//+hx9++AHHjx+Hu7u70jsJY2NjYWxsjDlz5uDAgQP4/fff0bRpU4wbN04lCNq3bx+CgoKUlp07dwJIe0XIx5o0aaKSf/r06Wrbm5CQgNmzZ2e7X5m5cuUK9u/fj3LlysHd3T3LvLk9X+nfmYxLToK9JUuW4MOHD5g1axaOHDmChQsX4tq1a6hfvz5u3bqV6XZz5sxBbGys2nU7duzApUuX4OXlhf/973/49ddfoaOjA3d3d2zdulUp76tXr9CsWTPcvXsXGzduxK5duxAfH4/mzZvjzp07ANLeD9usWTOcPXtWJZA+efIkDAwMEBgYqNKOkydPwtTUlFN0FLUinZ6UPkmZzTr/4cMHoaOjI/r06VMo9edkdt6PZ6/Oj9TUVPHhwweRnJwsatWqJYyNjcXNmzfV5vX391fMHH/27FmhoaEhOnTooDKDflJSkujQoYPQ0NAQZ8+ezVE73NzchKOjo6hSpYpwdnYWqampinWPHj0SAMSyZcsUaQ8fPhQGBgaiXr164t27d2r3a8+ePSrpy5YtEwBE+/btBQDh5+enkicwMFAAELt371ZK37JliwAg5s6dm6N9+lhycrKwsrISHh4eSunpM6/7+/ur3a5Dhw6iY8eOmc4S36NHD2FmZiaioqIUaY8fPxZaWlpi2rRp2barQYMGonr16krn8dy5cwKA8PX1zXb7nj17Ck1NzWxn4ff29hYAxPHjx5XS7ezsRPv27bOtJ/13pE2bNkJDQ0Ncv35dab262fLVSUlJUfy8e/dupTc5ZJSb85XZdyan1L3h4fnz50JLS0sMGTJE7TYXL14U2train3I+PuRWZnJycmidu3a4rPPPlNK//rrr4WWlpZ4/PixIi0qKkqYmZmJnj17KtKWL18uAIigoCBFWkpKiihbtqyYOnWqACBCQkIU6xISEoSenp7o1q1bNkeAChp7rKjY0NXVhba2NrS0tJTS37x5g9GjR6NChQrQ1tZGlSpVMGvWLKW/8NOHsDZv3qxSrkwmg7e3d5Z1CyGwdOlS2NnZQVdXF/Xr18fhw4fV5o2OjsbUqVNRuXJlaGtro0KFCpg4caLKX7PpQ2A///wznJycoKOjgy1btmD//v24efMmZs6cmelf2G3btlW868rHxwcymQxr166Fpqby1HOamprw9fWFTCbD4sWLs9zHjLS0tPDdd98hODhY0buRmRUrViA2Nha+vr4wMTFRWS+TydT2KmzcuBEWFhbYsmUL9PT0sHHjxhy3z8XFBQCUXn6cGxcuXEBYWBgGDx6slN6jRw8YGhpi3759Ktts27YNp06dgq+vr9oyk5OT8eeff6Jbt24wNjZWpNvZ2aFFixZqy8zo+fPnuHz5Mvr37690Hhs3bgxHR8dstweA8uXLQ0NDQ+mdaR8TQmDTpk2oUqUKWrZsmW2ZWZk2bRpMTU0z7dHKjoZGzi4xeTlfeWVubq6SZm1tDRsbGzx79kxlXWJiIry8vDBmzBjF9zInZcrlcjg7O6uUuW/fPrRs2RJ2dnaKNGNjY3Tt2hUHDx5U9FC1aNECAJSGTm/cuIG3b99i+PDhsLKyUuq1unjxIuLi4hTbUdFhYEVFJiUlBcnJyUhKSkJoaKgiOOnbt68iT3x8PFq0aIGtW7di8uTJOHToEL766issXbo0yyGd3Jo/fz6mT5+O1q1bY//+/Rg1ahSGDRum6JpP9+HDB7i5uWHLli0YP348Dh8+jOnTp2Pz5s3o1KmTyqSs+/fvx9q1azF37lwcPXoUzZo1w7FjxwAAnTt3zrZdKSkpCAwMhIuLS6Yv1bW1tYWzszNOnDiRq5cD9+rVC87Ozpg9ezaSkpIyzXfs2DFYWFgovaw1O+fPn8ft27cxYMAAmJqaolu3bjhx4gQePXqUo+3T8zk6Oua4zoz++ecfAEDt2rWV0rW0tFCtWjXF+nQRERGYOHEiFi9enOlxfvDgAeLi4lTKTK/n/v37WQ53Ztam9LSP2wSkBUnJycl4+/Ytdu7cic2bN2PKlCkqAXZGx48fx5MnT+Dl5aX2pdqnT5+GkZERtLS0UL16dSxfvjzT742RkRFmz56dq2HjvMjt+QKAMWPGQFNTE8bGxvD09MTZs2fzXP/Dhw/x5MkTpRffp1uwYAFiY2NVXhicneTkZJw5c0apzLi4ODx48CDT70BcXJzifr06deqgbNmySsFTYGAgrKys4ODggC+++EIp6ErPx8Cq6HHmdSoyH1+odXR0sHr1anh6eirStmzZgr///hu7du1Cjx49AACtW7eGoaEhpk+fjoCAALRu3Tpf7Xj37h2WLFmCLl264Ndff1Wk16hRA02aNEHVqlUVaT/++CP+/vtvXLx4UfHXq7u7OypUqIDu3bvjyJEjSjecxsTE4ObNmyhbtqwi7enTpwCAypUrZ9u2169f48OHD9nmrVy5Mi5duoTIyEi1fz2rI5PJsGTJErRq1Qrr1q3L9Abzp0+fom7dujkqM92GDRsAAF5eXgCAIUOGYNu2bdi0aRMWLFigkj81NVURZF+7dg1TpkxB9erVFdvnVmRkJACgXLlyKuvKlSun8uDC6NGjUbVqVYwaNSrPZQoh8PbtW1hZWeVp+/T1GS1ZsgQzZ84EkHa+vvnmGyxcuDDTNgJpx14ul2PQoEEq69q3bw8XFxd89tlnePv2LXbv3o2pU6fi+vXr+O2339SWN3LkSPzwww+YPn06Ll26pDZYy6/cnC8TExNMmDABzZs3h6mpKe7fv49ly5ahefPmOHTokNL/HzmRnJyMIUOGwNDQEJMmTVJad/36dSxduhQHDx6EgYFBju6jS+ft7Y379+9j//79irS3b99CCJHpfgL/HQsNDQ24ubkhICAAycnJ0NTUxMmTJ+Hm5gYAcHNzw7x58yCEUDwUYG5ujurVq+dq/0l67LGiIrN161ZcvnwZly9fxuHDhzFw4ECMGTNG6UbsEydOwMDAAN27d1faNv2i8ddff+W7HUFBQYiPj0e/fv2U0hs3bqzUXQ+k3cRds2ZN1K1bF8nJyYrF09NT7RNPLVu2VAqqCkp6T1n6RS89UElfMuuRcHd3h4eHBxYsWID3799L0paYmBjs2rULjRs3RrVq1QCkXQQ+++wzbN68GampqSrb9OrVC1paWtDX10eTJk0QHR2NQ4cOoUyZMvlqS2ZBQMb0PXv24ODBg/jll19yFDRklSc/26tLHzRoEC5fvoyjR49i2rRpWLZsGcaNG5dp2W/evMH+/fvRpk0bVKhQQWX9mjVrMHjwYHzxxRf48ssvsW3bNowdOxbbtm3DtWvX1Japra2NhQsX4sqVK9i1a1e2+5cfOTk29erVw6pVq9C5c2c0a9YMgwcPxvnz52FlZYVp06blqj4hBIYMGYIzZ85g69atsLW1VaxLTk6Gl5cXevXqletg7ddff8V3332HKVOm4Msvv8xyf7Ja16JFC8TGxuLy5ctITU3FmTNnFE8pu7m54dWrV7h16xYSEhJw4cIF9lYVEwysqMg4OTnBxcUFLi4uaNOmDdatWwcPDw9MmzYN7969A5D215ulpaXKf0Tm5ubQ1NRU+1d+bqWXYWlpqbLu47SXL1/i77//hpaWltJiZGQEIQRev36tlF9d70XFihUBIEfDYmZmZtDX18827+PHj6Gvr6/4q9fLy0upfVk9kbVkyRK8fv1a7RQL6e3N6RAeAOzcuRMxMTHo2bMn3r17h3fv3iEqKgo9e/bEs2fPEBAQoLYNly9fxqlTpzBr1iy8fPkSnTt3VrqPLjdMTU0BQO33482bN4rjFBMTgzFjxmDcuHGwtrZWtDcxMRFAWm9m+r1z2ZUpk8myDARz2qaMLC0t4eLiAg8PDyxevBgLFizA6tWrMw2Ctm3bhoSEBAwdOjTTdnzsq6++AoAspyro3bs36tevj1mzZmU5bJxXeTk2GZUpUwYdOnTA33//jbi4uBzVKYTA0KFDsW3bNmzevFklAFq1ahUePnyIefPmKb4X0dHRANJuUXj37p3aP1g2bdqEESNGYPjw4SrveyxbtixkMlmm+wko99qlB0qBgYG4du0a3r17p+ixql69OsqXL4+TJ0/iwoULvL+qGGFgRcVK+n0Gd+/eBZD2H+7Lly9V7l2KiIhAcnIyzMzMAEAxX8/HF+KcBF7p/6mHh4errPs4zczMDLVq1VL0tH28zJkzRym/ur9M0//6zThEkBm5XI4WLVrgypUrCA0NVZsnNDQUwcHBaNmypeKmZm9vb6V2rVu3LtM66tatiz59+mDFihVqbxb39PTEy5cvczRHEPDfMODEiRNRtmxZxeLj46O0PqMqVarAxcUFX3zxBRYuXIgFCxbgxo0b+Omnn3JU58fSHze/efOmUnpycjL+/fdfxUMDr1+/xsuXL7F8+XKltu7YsQOxsbEoW7asoifzs88+g56enkqZ6fXY29urzBuVUXqdmW2fk6kCGjZsCACK34+PbdiwARYWFmqnv8hM+u9WVjeapw8bP3jwAOvXr89x2TmV0/OVlY97bbPLO3ToUGzatAm//vqrIrjM6J9//kFUVBQcHBwU34s6deoASJt6oWzZsirt3bRpE4YOHYqBAwfi559/VmmLnp4e7O3tM/0O6OnpoUqVKoq0mjVrKoKnkydPwsLCQtELDABffPEFAgMDFT3lDKyKiSJ4EpE+cZlNtyCEEK1btxYAxMOHD4UQQqxbt04AEHv37lXKl/4of0BAgBAi7ZF/XV1dMXr0aKV8GzZsEADEvHnzVOpPn27hzZs3QldXV3Tp0kVp2/TH4DNOt7Bw4UKhr6+vaF9WAIgxY8aopOdkuoUjR46oTLfQsWNHkZycrFJW+nQL586dy7ZNQqh/VP7hw4dCW1tbtG3bNk/TLaSfn5CQEAFAdOvWTQQGBqos7u7uQltbW7x+/VoIkfmj84mJicLe3l6YmpqK6OjoHO1XRumP77dp00YpfceOHQKAOHz4sBBCiLi4OLXt9PT0FLq6uiIwMFDpHPXs2VOYm5srtenJkydCW1tbTJ8+Pdt2NWzYUNSsWVPpPAYFBQkAYu3atdluP2fOHAFAXLlyRWXd5cuXBYAcTfuQ0ahRowQApSkVMvsdbd26tTA3NxfOzs45mm4ho5xMt5Dd+crMmzdvRIUKFUTdunWzbUdqaqoYMmSIkMlkYv369Znmu337tsr3Ir09I0eOFIGBgeL9+/eK/Js2bRIaGhpiwIABStNMfGzatGlCW1tbPH36VJEWHR0typcvL3r16qWSv3v37sLAwEB4enoqTccghBA//vijMDU1FW5ubsLa2jrbfafCwcCKCl36f9qbNm0SQUFBIigoSPz555/Cy8tLAFAKcOLi4kTt2rWFkZGRWLFihQgICBDz5s0TWlpaol27dkrlDh06VOjq6orly5eL48ePi0WLFomaNWtmG1gJIcTs2bMFADFkyBBx5MgR8csvv4gKFSoIS0tLpcAqJiZG1KtXT9jY2Ijly5eLgIAAcfToUfHLL7+IHj16iAsXLijyZhZYCSHE/fv3RZUqVYShoaH4+uuvhb+/vzh16pTYunWr6NSpk5DJZEpBzI8//ig0NDTE559/LrZt2yZOnz4ttm3bJlxdXYWGhob48ccfc3z8M5uDaMKECQKA2nl6Dh48KPT19UWlSpXE999/L/766y/x119/iZ9++knUq1dPcUGbMmWKACAuXryotu4DBw4IAGLVqlVCiKznJNq1a5cAIL799ltFWnr+jOczM7/99psAIIYPHy4CAwPF+vXrRZkyZUTr1q2z3Tazeaxu374tDA0NxRdffCH8/f3F3r17Rc2aNYW1tbWIiIhQyiuXy0XLli2V0gIDA4Wmpqbo0qWLCAgIEH5+fsLW1lbUrFlTaW6quXPnihEjRgg/Pz9x8uRJsX//fjFy5Eghl8tFjx491LZ55MiRAoC4c+eO2vV+fn6iW7duYuPGjeKvv/4Se/bsEb179xYAxKBBg5TyZhZYXb16VchkMgEgR4FVbGys2L17t9i9e7fiu+Ht7S12796tMpdYTs9Xnz59xPTp08Xu3bsV+apWrSo0NTUVf2il8/LyEnK5XGnOqLFjxwoAwsvLS/H/T/py9erVLPdH3TxvQqR9VzU0NET9+vXFuXPnVMrNeG4jIiKElZWVqFWrlti3b5/w9/cXX3zxhTAyMhK3b99WqXPNmjUCgJDJZGLNmjVK627cuKFY169fvyzbToWHgRUVuvT/tDMuJiYmom7dumLFihUqkx9GRkaKkSNHCisrK6GpqSns7OzEzJkzVfJFRUWJoUOHCgsLC2FgYCA6duwoHj9+nKPAKjU1Vfj4+AhbW1uhra0tateuLQ4ePKh2gtCYmBgxe/ZsUbVqVaGtrS1MTExErVq1xKRJk0R4eLgiX1aBlRBCvHv3Tnz77beifv36wtDQUGhpaYmKFSuKr776Sm3vU1BQkOjevbuwsLAQmpqawtzcXHTt2lWcP38+B0f9P5kFVq9evRLGxsZqLxxCCPHgwQMxevRoYW9vL3R0dISenp6oXr26mDx5snj06JFITEwU5ubmWfYaJCcnCxsbG1GrVi0hRPaTPTZq1EiULVtWEWQePHhQABA///xzjvZ1+/btonbt2kJbW1tYWlqK8ePHK/UyZCazwEoIIa5cuSLc3d2Fvr6+MDY2Fp07dxb3799Xyfdxb2e6Y8eOic8//1zo6uqKcuXKiQEDBqhMMHngwAHRqlUrxbk2NDQUDRs2FD/++KPKJLFCpE2ua2JiIr744otM9ykoKEi4u7sLS0tLoaWlJfT19UWDBg2Er6+vSg9LVr3Kffv2zXFglR6IqFvs7OxU8ufkfPn4+Ii6desKExMTIZfLRfny5UWXLl3EpUuXVMobOHCgyu+6nZ1drtqkbn8+/v1Iryez5ePJiO/fvy86d+4sjI2Nhb6+vnB3dxfBwcFq60zvBQYg/vnnH6V1qampoly5cgKA+OWXX7JsOxUemRAf3bxCRFRMTZs2DTt27MC9e/eyvJ+JiKio8OZ1IioxAgMDMWfOHAZVRFRssceKiIiISCLssSIiIiKSCAMrIiIiIokwsCIiIiKSCAMrIiIiIokwsCIiIiKSCAMrIiIiIokwsCIiIiKSCAMrCZ0+fRodO3aEtbU1ZDIZ9u/fn6vt4+PjMWjQINSqVQuampro3Lmz2nwJCQmYNWsW7OzsoKOjg88++wwbN27M/w4QERFRvmgWdQNKk9jYWNSpUweDBw9Gt27dcr19SkoK9PT0MH78eOzZsyfTfD179sTLly+xYcMG2NvbIyIiAsnJyflpOhEREUmAgZWE2rZti7Zt22a6PjExEbNnz4afnx/evXuHmjVrYsmSJWjevDkAwMDAAGvXrgUAnDt3Du/evVMp48iRIzh16hQePnyIcuXKAQAqVaok9a4QERFRHnAosBANHjwY586dw++//46///4bPXr0QJs2bXDv3r0cl3HgwAG4uLhg6dKlqFChAhwdHTF16lTExcUVYMuJiIgoJ9hjVUgePHiAHTt2IDQ0FNbW1gCAqVOn4siRI9i0aRMWLVqUo3IePnyIs2fPQldXF/v27cPr168xevRovHnzhvdZERERFTEGVoXk6tWrEELA0dFRKT0hIQGmpqY5Lic1NRUymQx+fn4wMTEBAKxYsQLdu3fHmjVroKenJ2m7iYiIKOcYWBWS1NRUyOVyBAcHQy6XK60zNDTMcTlWVlaoUKGCIqgCACcnJwghEBoaCgcHB8naTERERLnDwKqQ1KtXDykpKYiIiECzZs3yXE6TJk2we/duxMTEKAKyu3fvQkNDAzY2NlI1l4iIiPKAN69LKCYmBtevX8f169cBAI8ePcL169fx9OlTODo6ol+/fhgwYAD27t2LR48e4fLly1iyZAn8/f0VZYSEhOD69et48+YNoqKilMoDgL59+8LU1BSDBw9GSEgITp8+ja+//hpeXl4cBiQiIipiMiGEKOpGlBYnT55EixYtVNIHDhyIzZs3IykpCQsXLsTWrVvx/PlzmJqawtXVFfPnz0etWrUApE2d8OTJE5UyMp6mf//9F+PGjcO5c+dgamqKnj17YuHChQysiIiIihgDKyIiIiKJcCiQiIiISCK8eV0CqampePHiBYyMjCCTyYq6OURERJQDQgi8f/8e1tbW0NCQpq+JgZUEXrx4AVtb26JuBhEREeXBs2fPJHuynoGVBIyMjACknRhjY+Mibg0RERHlRHR0NGxtbRXXcSkwsJJA+vCfsbExAysiIqISRsrbeHjzOhEREZFEGFgRERERSYSBFREREZFEeI9VIUpJSUFSUlJRN4OIPnFaWloqL4MnImkwsCoEQgiEh4fj3bt3Rd0UIiIAQJkyZWBpacm594gkxsCqEKQHVebm5tDX1+d/ZERUZIQQ+PDhAyIiIgAAVlZWRdwiotKFgVUBS0lJUQRVpqamRd0cIiLFC9sjIiJgbm7OYUEiCfHm9QKWfk+Vvr5+EbeEiOg/6f8n8b5PImkxsCokHP4jouKE/ycRFQwGViVEbCwgk6UtsbFF3RoiIiJSh4EVERERkUQYWJEKmUyW5TJo0KAs8/3+++8AgJMnT0Imk6Fs2bKIj49XquPSpUuK/OnS86cv5cuXR9u2bXHjxg2lbW/duoWePXuifPny0NHRgYODA+bMmYMPHz4U7IEhIiLKBgMrUhEWFqZYVq1aBWNjY6W0H374QZF306ZNSuvCwsLQuXNnpfKMjIywb98+pbSNGzeiYsWKauu/c+cOwsLCcOjQIbx9+xZt2rRBVFQUAODChQto1KgREhMTcejQIdy9exeLFi3Cli1b0Lp1ayQmJkp7MIiIiHKBgVUJ9Px5wZZvaWmpWExMTCCTyVTS0qVPMphx0dXVVSpv4MCB2Lhxo+JzXFwcfv/9dwwcOFBt/ebm5rC0tETDhg2xfPlyhIeH48KFCxBCYMiQIXBycsLevXvRsGFD2NnZoUePHjh48CCCgoKwcuXKgjkoREREOcDAqgDFxgLVqgFPngApKfkra8uW/352cgI2bMhfeYWpf//+OHPmDJ4+fQoA2LNnDypVqoT69etnu236fDtJSUm4fv06QkJCMHnyZGhoKH9169Spg1atWmHHjh3S7wAREVEOMbAqAUJDgXHj/vucmgqMGJGWXtT69OkDQ0NDpeXhw4dKeczNzdG2bVts3rwZQNowoJeXV7ZlR0ZGYv78+TAyMkLDhg1x9+5dAICTk5Pa/E5OToo8RERERYEzr5cA9+6lBVMZpaQA9+8DNjZF06Z0K1euRKtWrZTSbG1tVfJ5eXlhwoQJ+OqrrxAUFITdu3fjzJkzasu0+f+dio2NhYODA3bv3g1zc/Ns2yKE4Nw8RERUpBhYlQAODoCGhnJwJZcD9vZF16Z0lpaWsM9BQ9q1a4cRI0ZgyJAh6NixY5av9zlz5gyMjY1Rvnx5GBsbK9IdHR0BACEhIahbt67Kdv/++y8cHBxyvxNEREQS4VBgCWBjA/z003+f5XJg3bqi763KDblcjv79++PkyZPZDgNWrlwZn332mVJQBQB169ZFtWrVsHLlSqR+1IV348YNHD9+HH369JG87URERDnFwKqEyPgAXUgIMGRI0bUlo3fv3iE8PFxpic1kavhvv/0Wr169gqenZ57qkslk+PXXXxESEoJu3brh0qVLePr0KXbv3o2OHTvC1dUVEydOzMfeEBER5Q8DqxKoQoWibsF/Bg8eDCsrK6Xlp4zdaxloa2vDzMwsX/dBNWnSBBcuXIBcLke7du1gb2+PmTNnYuDAgQgICICOjk6eyyYiIsovmRBCFHUjSrro6GiYmJggKipKafgqNhaoUSMeP//8CM2aVYaBgW4WpWQtNhYwNEz7OSYGMDDIb6uJ6FMWHx+PR48eoXLlyipzzxF9KjK7fucHe6yIiIiIJMKnAksIAwOAfYtERETFG3usiIiIiCTCwIqIiIhIIgysiIiIiCTCwIqIiIhIIgysiIiIiCTCwIqIiIhIIgysSorYWEAmS1syeWUMERERFS0GVlSseHt7o27dukXdjGLv5MmTkMlkePfuneRly2Qy7N+/X/JyP2WDBg1C586di7oZRFQIGFiRWoMGDYJMJoNMJoOWlhaqVKmCqVOnZvqCZSo4zZs3L1Evl378+DFkMhmuX7+eaR5vb2/F9yuz5fHjx5nmq1atmqKs5s2bQyaTYfHixSr1tGvXDjKZDN7e3ir5ZTIZdHR04OjoiEWLFiElJUWRJyUlBStXrkTt2rWhq6uLMmXKoG3btjh37lye9v2HH37A5s2bs9yWiEoHBlaUqTZt2iAsLAwPHz7EwoUL4evri6lTpxZ1s7KVlJRU1E2gbEydOhVhYWGKxcbGBgsWLFBKs7W1BQDUqFFDKT0sLAxnz55VKs/W1habNm1SSnvx4gVOnDgBKysrlfqHDRuGsLAw3LlzB+PHj8fs2bPx/fffAwCEEOjduzcWLFiA8ePH4/bt2zh16hRsbW3RvHnzPPXmmZiYoEyZMrnejohKHgZWJdHz54VSjY6ODiwtLWFra4u+ffuiX79+iovKtm3b4OLiAiMjI1haWqJv376IiIhQbJs+VPXXX3/BxcUF+vr6aNy4Me7cuaNUx+LFi2FhYQEjIyMMGTIE8fHxSutTU1OxYMEC2NjYQEdHB3Xr1sWRI0cU69N7CHbt2oXmzZtDV1cX27ZtQ2RkJPr06QMbGxvo6+ujVq1a2LFjR5b7++TJE3Ts2BFly5aFgYEBatSoAX9/f6X9OXr0KOrVqwc9PT20bNkSEREROHz4MJycnGBsbIw+ffrgw4cPijITEhIwfvx4mJubQ1dXF02bNsXly5eV6j116hQaNmwIHR0dWFlZYcaMGUhOTgaQ1nN46tQp/PDDD0o9OemCg4OzPL4HDx6Es7MzdHV1UaVKFcyfP19RNgDcu3cPX3zxBXR1dVG9enUEBARkeYwA4MiRI2jatCnKlCkDU1NTdOjQAQ8ePFCsr1y5MgCgXr16kMlkaN68uUoZhoaGsLS0VCxyuVzxXcqYBgCamppK6ZaWljAzM1Mqr0OHDoiMjFTqUdq8eTM8PDxgbm6uUr++vj4sLS1RqVIljB07Fu7u7orv9q5du/DHH39g69atGDp0KCpXrow6depg/fr16NSpE4YOHZppz21m+/7xUGDz5s0xbtw4TJw4EWXLloWFhQXWr1+P2NhYDB48GEZGRvjss89w+PBhpfJDQkLQrl07GBoawsLCAv3798fr168V6//44w/UqlULenp6MDU1RatWrdjLTFTISl1gdfr0aXTs2BHW1tY5uldk7969aN26NcqXLw9jY2O4urri6NGjhdPY3Niy5b+fnZyADRsKvQl6enqK3qDExER8++23uHHjBvbv349Hjx5h0KBBKtvMmjULy5cvx5UrV6CpqQkvLy/Ful27dmHevHn47rvvcOXKFVhZWcHX11dp+x9++AHLly/H999/j7///huenp7o1KkT7t27p5Rv+vTpit4FT09PxMfHw9nZGX/++Sf++ecfDB8+HP3798fFixcz3b8xY8YgISEBp0+fxs2bN7FkyRIYGhoq5fH29sbq1atx/vx5PHv2DD179sSqVauwfft2HDp0CAEBAfjpp58U+adNm4Y9e/Zgy5YtuHr1Kuzt7eHp6Yk3b94AAJ4/f4527dqhQYMGuHHjBtauXYsNGzZg4cKFiv13dXVV9LBk7MnJ7vgePXoUX331FcaPH4+QkBCsW7cOmzdvxnfffQcgLWjt2rUr5HI5Lly4gJ9//hnTp0/P9Piki42NxeTJk3H58mX89ddf0NDQQJcuXZCamgoAuHTpEgDg+PHjCAsLw969e7MtM7+0tbXRr18/pV6rzZs3Kx2PrGT8bm/fvh2Ojo7o2LGjSr4pU6YgMjIy0wA0N/u+ZcsWmJmZ4dKlSxg3bhxGjRqFHj16oHHjxrh69So8PT3Rv39/RaAeFhYGNzc31K1bF1euXMGRI0fw8uVL9OzZU7G+T58+8PLywu3bt3Hy5El07doVgi8ZJSpcopTx9/cXs2bNEnv27BEAxL59+7LMP2HCBLFkyRJx6dIlcffuXTFz5kyhpaUlrl69muM6o6KiBAARFRWllB4TI4SdXZw4fDhExMTE5WV30jx7JoSGhhBp72FOW+TytPQCMnDgQPHll18qPl+8eFGYmpqKnj17qs1/6dIlAUC8f/9eCCFEYGCgACCOHz+uyHPo0CEBQMTFpR0LV1dXMXLkSKVyGjVqJOrUqaP4bG1tLb777julPA0aNBCjR48WQgjx6NEjAUCsWrUq231q166dmDJlSqbra9WqJby9vdWuU7c/Pj4+AoB48OCBIm3EiBHC09NTCCFETEyM0NLSEn5+for1iYmJwtraWixdulQIIcQ333wjqlatKlJTUxV51qxZIwwNDUVKSooQQgg3NzcxYcKEbNvz8fFt1qyZWLRokdJ2v/32m7CyshJCCHH06FEhl8vFswzfo8OHD+fo9yajiIgIAUDcvHlTCPHfObl27VqOy7CzsxMrV65USZ83b57Q0NAQBgYGSsuQIUMUedKPz40bN4SRkZGIiYkRp06dEubm5iIxMVHUqVNHzJs3TyW/EEKkpKSIw4cPC21tbTFt2jQhhBDVqlVT+u5n9ObNGwFALFmyRO36zPb9498nNzc30bRpU8Xn5ORkYWBgIPr3769ICwsLEwBEUFCQEEKIOXPmCA8PD6Vynz17JgCIO3fuiODgYAFAPH78WG3bPhYXFydCQkIU3xeiT1Fm1+/80CzkOK7AtW3bFm3bts1x/lWrVil9XrRoEf73v//h4MGDqFevnsSty6N794D/7w1QSEkB7t8HbGwKrNo///wThoaGSE5ORlJSEr788ktFb8y1a9fg7e2N69ev482bN4reiqdPn6J69eqKMmrXrq34Of1el4iICFSsWBG3b9/GyJEjlep0dXVFYGAgACA6OhovXrxAkyZNlPI0adIEN27cUEpzcXFR+pySkoLFixdj586deP78ORISEpCQkAADA4NM93f8+PEYNWoUjh07hlatWqFbt25K7f94fywsLKCvr48qVaoopaX3Wjx48ABJSUlK7dfS0kLDhg1x+/ZtAMDt27fh6uoKmUymtH8xMTEIDQ1FxYoVM23vx+35+PgGBwfj8uXLih6q9OMSHx+PDx8+4Pbt26hYsSJsMnyHXF1ds6wvfb/mzJmDCxcu4PXr10rnvmbNmtlun1tVq1bFgQMHlNKMjIxU8tWuXRsODg74448/EBgYiP79+0NLS0ttmb6+vvj111+RmJgIAOjfvz/mzZuX4zZlPF95lfHcyeVymJqaolatWoo0CwsLAFAMsQcHByMwMFClFxVIOyceHh5wd3dHrVq14OnpCQ8PD3Tv3h1ly5bNd1uJKOdKXWCVX6mpqXj//j3KlSuXaZ70i3S66Ojogm2UgwOgoaEcXMnlgL19gVbbokULrF27FlpaWrC2tlZcpGJjY+Hh4QEPDw9s27YN5cuXx9OnT+Hp6am4UKXLeGFLvxilfhwkZuPji5gQQiXt44Bp+fLlWLlyJVatWoVatWrBwMAAEydOVGlfRkOHDoWnpycOHTqEY8eOwcfHB8uXL8e4ceMy3Z+PL9wymUyxf+L/h2Cyar+6fclsO3WyOr6pqamYP38+unbtqrKdrq6u2iGinNTZsWNH2Nra4pdffoG1tTVSU1NRs2bNLI9tfmhra8M+h991Ly8vrFmzBiEhIYoAV51+/fph1qxZ0NHRgbW1teJ+LgBwdHRESEiI2u3SA2IHB4dc7IF66r472Z3Pjh07YsmSJSplWVlZQS6XIyAgAOfPn8exY8fw008/YdasWbh48aLi3i8iKnil7h6r/Fq+fDliY2MV9y2o4+PjAxMTE8WS8Z6XAmFjA2S4bwdyObBuXYH2VgFpwYq9vT3s7OyU/sP/999/8fr1ayxevBjNmjVDtWrVlG5czyknJydcuHBBKS3jZ2NjY1hbW6s8AXb+/Hk4OTllWfaZM2fw5Zdf4quvvkKdOnVQpUoVlfuy1LG1tcXIkSOxd+9eTJkyBb/88ksu9kiZvb09tLW1ldqflJSEK1euKNpfvXp1nD9/XinIOX/+PIyMjFChQgUAaYFFxqkAcqp+/fq4c+cO7O3tVRYNDQ1Ur14dT58+xYsXLxTbBAUFZVlmZGQkbt++jdmzZ8Pd3R1OTk54+/atUh5tbW0AyFOb86tv3764efMmatasqdRz+jETExPY29vD1tZWKagCgN69e+PevXs4ePCgynbLly+HqakpWrdurbbcgtz3+vXr49atW6hUqZLK+Uz/w0Imk6FJkyaYP38+rl27Bm1tbezbt0/ythBR5hhYZbBjxw54e3tj586dap8kSjdz5kxERUUplmfPnhV84wYO/O/nkBBgyJCCrzMTFStWhLa2Nn766Sc8fPgQBw4cwLfffpvrciZMmICNGzdi48aNuHv3LubNm4dbt24p5fn666+xZMkS7Ny5E3fu3MGMGTNw/fp1TJgwIcuy7e3tFX+93759GyNGjEB4eHiW20ycOBFHjx7Fo0ePcPXqVZw4cSLbAC4rBgYGGDVqFL7++mscOXIEISEhGDZsGD58+IAh/3/+Ro8ejWfPnmHcuHH4999/8b///Q/z5s3D5MmToaGR9utZqVIlXLx4EY8fP1YaesvO3LlzsXXrVnh7e+PWrVu4ffs2du7cidmzZwMAWrVqhapVq2LAgAG4ceMGzpw5g1mzZmVZZtmyZWFqaor169fj/v37OHHiBCZPnqyUx9zcHHp6eoqbq6OionJ76JQkJycjPDxcaXn58mWm7QsLC8Nff/2V5/p69+6NLl26YODAgdiwYQMeP36Mv//+GyNGjMCBAwfw66+/ZjqkLPW+ZzRmzBi8efMGffr0waVLl/Dw4UMcO3YMXl5eSElJwcWLF7Fo0SJcuXIFT58+xd69e/Hq1at8fYeJKPcYWP2/nTt3YsiQIdi1axdatWqVZV4dHR0YGxsrLYXq/3syikr58uWxefNm7N69G9WrV8fixYsVcwDlRq9evTB37lxMnz4dzs7OePLkCUaNGqWUZ/z48ZgyZQqmTJmCWrVq4ciRIzhw4EC2QzFz5sxB/fr14enpiebNm8PS0jLbma9TUlIwZswYODk5oU2bNqhatarKU4q5tXjxYnTr1g39+/dH/fr1cf/+fRw9elRx30uFChXg7++PS5cuoU6dOhg5ciSGDBmiCH6AtDmf5HI5qlevrhh2zQlPT0/8+eefCAgIQIMGDfD5559jxYoVsLOzAwBoaGhg3759SEhIQMOGDTF06FCl+7HU0dDQwO+//47g4GDUrFkTkyZNwrJly5TyaGpq4scff8S6detgbW2NL7/8MjeHTMWtW7dgZWWltKTvgzplypTJ8l667KRP3zFr1iysXLkS1apVQ7NmzfDkyRMEBgZm+T2Set8zsra2xrlz55CSkgJPT0/UrFkTEyZMgImJCTQ0NGBsbIzTp0+jXbt2cHR0xOzZs7F8+fJc3XNKRPknE+putCglZDIZ9u3bl+0FdceOHfDy8sKOHTvy9NqJ6OhomJiYICoqSinIio0FatSIx88/P0KzZpVhYKCb67KVCku/aTUmBsjHhYOIKD4+Ho8ePULlypWhq5uP/5uISrDMrt/5UepuXo+JicH9+/cVnx89eoTr16+jXLlyqFixImbOnInnz59j69atANKCqgEDBuCHH37A559/rhgu0tPTg4mJSZHsAxEREZVMpW4o8MqVK6hXr55iqoTJkyejXr16mDt3LoC0SfQyDqWsW7cOycnJGDNmjNJQQ3b38BQ6A4P/ZrFibxUREVGxVOp6rJo3b57lTMMfvwj15MmTBdsgIiIi+mSUuh4rIiIioqLCwKqA/fcOmlL7jAARlUD8P4moYDCwKmCRkVpITATi4j4UdVOIiBTSX+6c2Wt/iChvSt09VsVNbKwcBw6UgYNDBDQ0AH19fUneM0ZElBdCCHz48AEREREoU6aMyszzRJQ/pXoeq8KS1TxWhoaATCbw6FE4Pnx4V3SNJCLKoEyZMrC0tOQfevRJ4zxWJZQQMpiZWUFX1xxJSUlF3Rwi+sRpaWmxp4qogDCwKkRyuZz/mREREZVivHmdiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISqXYWEAmS1tiYwunTgZWRERERBJhYEVEREQkkVwHVvHx8bh58yY+fPigsu7cuXOSNIqIiIioJMpVYBUUFARbW1s0b94c5cuXx+LFi5XWt23bVtLGEREREZUkuQqspkyZguXLlyMyMhLBwcHYu3cvvLy8kJqaCgAQQhRII4mIiIhKglwFViEhIRgwYAAAoFq1ajh16hQiIiLQvXt3JCYmFkgDiYiIiEqKXAVWxsbGeP78ueKznp4e9u/fD11dXbRp00bRc0VERET0KcpVYNWqVSts2rRJKU1TUxN+fn747LPPEBcXJ2njiIiIiEoSmcjFjVGJiYlITk6Gvr6+2vVPnz5FxYoVJWtcSREdHQ0TExNERUXB2NhYkR4bCxgapv0cEwMYGBRRA4mIiD5B2V2HM7t+50eueqy0tbUzDaoAFIug6vTp0+jYsSOsra0hk8mwf//+bLc5deoUnJ2doauriypVquDnn38u+IYSERFRqaMpVUH79++Hn58fnjx5gvj4eKV1MpkMN27ckKqqLMXGxqJOnToYPHgwunXrlm3+R48eoV27dhg2bBi2bduGc+fOYfTo0ShfvnyOticiIiJKJ0lgtWzZMkyfPh3ly5eHvb09DIpwzKtt27a5mk/r559/RsWKFbFq1SoAgJOTE65cuYLvv/+egRURERHliiSBla+vL7y8vLBu3TrI5XIpiiw0QUFB8PDwUErz9PTEhg0bkJSUBC0tLZVtEhISkJCQoPgcHR1d4O0kIiKi4k+SdwVGRkaib9++JS6oAoDw8HBYWFgopVlYWCA5ORmvX79Wu42Pjw9MTEwUi62tbWE0lYiIiIo5SQKrJk2a4Pbt21IUVSRkMpnS5/QHJT9OTzdz5kxERUUplmfPnhV4G4mIiKj4k2QocNWqVejSpQtsbW3Rpk0baGtrS1FsobC0tER4eLhSWkREBDQ1NWFqaqp2Gx0dHejo6BRG84iIiKgEkSSwsre3R6tWrdClSxfIZDKVKRlkMhmioqKkqEpyrq6uOHjwoFLasWPH4OLiovb+KiIiIqLMSBJYTZs2DatXr0bdunXh5ORUpD1WMTExuH//vuLzo0ePcP36dZQrVw4VK1bEzJkz8fz5c2zduhUAMHLkSKxevRqTJ0/GsGHDEBQUhA0bNmDHjh1FtQtERERUQkkSWG3evBnTp0+Hj4+PFMXly5UrV9CiRQvF58mTJwMABg4ciM2bNyMsLAxPnz5VrK9cuTL8/f0xadIkrFmzBtbW1vjxxx851QIRERHlWq5eaZOZMmXKYO/evWjZsqUUbSpx+EobIiKi4qfYv9ImMx4eHrhw4YIURRERERGVWJIMBc6ZMwe9evWCgYEB2rdvj3LlyqnkUZdGREREVJpIMhSooZHW8ZXZvE8AkJKSkt9qii0OBRIRERU/RTEUKEmP1dy5c7MMqoiIiIg+BZIEVt7e3lIUQ0RERFSiSXLzOhERERFJFFglJSUhNjZW7brY2FgkJSVJUQ0RERFRsSZJYDVs2DAMHTpU7brhw4dj1KhRUlRDREREVKxJElgFBgaiU6dOatd17NgRf/31lxTVEBERERVrkgRWL1++hJWVldp1lpaWCA8Pl6IaIiIiomJNksCqTJkySi8+zuj+/fswMjKSohoiIiKiYk2SwKpFixbw8fHBmzdvlNLfvHmDxYsXf7LvECQiIqJPi2TzWDVo0AAODg7o1asXKlSogNDQUOzevRtJSUmYP3++FNUQERERFWuSBFZVq1bFmTNnMHnyZPzyyy9ISUmBXC6Hm5sbVqxYgapVq0pRDREREVGxlufA6ocffkC3bt1gY2MDAKhTpw7++usvxMXF4e3btyhXrhx0dXUlaygRERFRcZfne6wWLVoEOzs7NGrUCMuWLcODBw8AAHp6erC2tmZQRURERJ+cPAdWYWFhOH78OBo0aIBVq1bB0dERdevWxbfffouQkBAp20hERERUIsiEEEKKgs6dO4c//vgD+/btw7Nnz+Do6Ihu3bqhW7duqFevnhRVFFvR0dEwMTFBVFQUjI2NFemxsYChYdrPMTGAgUERNZCIiOgTlN11OLPrd35I9hLmJk2aYOXKlXj8+DGCgoLQqVMn7Ny5Ey4uLqhSpQqmTZsmVVVERERExZJkgVVGDRs2xJIlS3Dv3j0EBwejX79+OHToUEFURURERFRsSDYU+CnjUCAREVHxUxRDgXmebuHp06e5yl+xYsW8VkVERERUIuQ5sKpUqRJkMlmO86ekpOS1KiIiIqISIc+B1caNG3MVWBERERGVdnkOrAYNGiRhM4iIiIhKPkneFZjR3bt3ERkZCTMzMzg4OEhdPBEREVGxJdl0C7t374adnR2cnJzQtGlTVKtWDXZ2dvjjjz+kqoKIiIioWJMksPL390fv3r1hYmKCxYsXY+vWrfDx8YGJiQl69+6Nw4cPS1ENERERUbEmyTxWTZo0gbGxMQ4dOgQNjf9iNSEE2rZti/fv3+PcuXP5rabY4jxWRERExU+JfaXN9evXMXr0aKWgCgBkMhlGjx6NGzduSFENERERUbEmSWAll8uRmJiodl1SUpJKwEVERERUGkkS8TRo0ABLly5FXFycUnpCQgK+//57NGrUSIpqiIiIiIo1SaZbmD9/Ptzd3VGlShX06NEDlpaWCAsLw969exEZGYkTJ05IUQ0RERFRsSZJYNW0aVMcO3YMM2bMwJo1ayCEgIaGBho1aoQdO3agcePGUlRDREREVKxJNkGom5sbgoKC8OHDB7x9+xZly5aFvr6+VMUTERERFXuS31Wur6+PChUqFGlQ5evri8qVK0NXVxfOzs44c+ZMlvn9/PxQp04d6Ovrw8rKCoMHD0ZkZGQhtZaIiIhKC8kCq/3796NHjx5o2LAhateurbTUqVNHqmqytXPnTkycOBGzZs3CtWvX0KxZM7Rt2xZPnz5Vm//s2bMYMGAAhgwZglu3bmH37t24fPkyhg4dWmhtJiIiotJBksBq2bJl6Nq1K06fPg0tLS2YmpoqLeXKlZOimhxZsWIFhgwZgqFDh8LJyQmrVq2Cra0t1q5dqzb/hQsXUKlSJYwfPx6VK1dG06ZNMWLECFy5ciXTOhISEhAdHa20EBEREUlyj5Wvry+8vLywbt06yOVyKYrMk8TERAQHB2PGjBlK6R4eHjh//rzabRo3boxZs2bB398fbdu2RUREBP744w+0b98+03p8fHwwf/58SdtOREREJZ8kPVaRkZHo27dvkQZVAPD69WukpKTAwsJCKd3CwgLh4eFqt2ncuDH8/PzQq1cvaGtrw9LSEmXKlMFPP/2UaT0zZ85EVFSUYnn27Jmk+0FEREQlkySBVZMmTXD79m0pipKETCZT+iyEUElLFxISgvHjx2Pu3LkIDg7GkSNH8OjRI4wcOTLT8nV0dGBsbKy0EBEREUkyFLhq1Sp06dIFtra2aNOmDbS1taUoNtfMzMwgl8tVeqciIiJUerHS+fj4oEmTJvj6668BALVr14aBgQGaNWuGhQsXwsrKqsDbTURERKWDJD1W9vb2aNWqFbp06QJ9fX2V3hwTExMpqsmWtrY2nJ2dERAQoJQeEBCQ6SSlHz58UHmXYfqQphCiYBpKREREpZIkPVbTpk3D6tWrUbduXTg5ORVZjxUATJ48Gf3794eLiwtcXV2xfv16PH36VDG0N3PmTDx//hxbt24FAHTs2BHDhg3D2rVr4enpibCwMEycOBENGzaEtbW1ZO16/hxwdJSsOCIiIiqGJAmsNm/ejOnTp8PHx0eK4vKlV69eiIyMxIIFCxAWFoaaNWvC398fdnZ2AICwsDClOa0GDRqE9+/fY/Xq1ZgyZQrKlCmDli1bYsmSJfluy5Yt//3s5ASsXw8MGZLvYomIiKiYkgkJxrvKlCmDvXv3omXLllK0qcSJjo6GiYkJoqKiFDeyh4YCdnZAaup/+eRy4PFjwMamaNpJRET0KYmNBQwN036OiQEMDJTXq7t+55ck91h5eHjgwoULUhRVaty7pxxUAUBKCnD/ftG0h4iIiAqeJEOBc+bMQa9evWBgYID27durnWm9MGdfLw4cHAANDdUeK3v7omsTERERFSxJhgLTn6rLbK4oAEhJSclvNcVWZl2Jvr7AmDFpP8vlwLp1vMeKiIiKn+yGzEqqohgKlKTHau7cuVkGVZ+qgQP/C6xCQvhUIBERUWknSWDl7e0tRTGlWoUKRd0CIiIiKmiS3LxORERERAysiIiIiCTDwIqIiIhIIgysiIiIiCTCwIqIiIhIIgysiIiIqEDExgIyWdoSG1vUrSkcDKyIiIiIJFLggVXLli3x1VdfISQkpKCrIiIiIipSBR5YnTx5Etu3b0ft2rXRv3//gq6OiIiIqMgUeGCVmpqK9+/f48CBA7Cysiro6oiIiIiKjCSvtMmOgYEB2rVrh3bt2hVGdURERERFQvLA6tWrV4iLi1NJr1ixotRVERERERUrkgRW79+/x6RJk7Bjxw7Ex8erzZOSkiJFVURERETFliSB1cSJE7F9+3YMGTIEtWvXho6OjhTFEhEREZUokgRWhw4dwuLFizFhwgQpiiMiIiIqkSR5KjA+Ph61atWSoigiIiKiEkuSwKpdu3Y4c+aMFEURERERlViSDAXOnj0b3bt3h5GRETp27AhTU1OVPOXKlZOiKiIiIqJiSyaEEPktREMjreNLJpNlmqc0PxUYHR0NExMTREVFwdjYWJEeGwsYGqb9HBMDGBgUUQOJiIiyUFDXq6K+DmZXf2bX7/yQpMdq7ty5WQZVRERERJ8CSQIrb29vKYohIiIiKtEK/F2BRERERJ8KBlZEREREEpHsXYH79++Hn58fnjx5ovJaG5lMhhs3bkhVFREREVGxJElgtWzZMkyfPh3ly5eHvb09DPj4GxEREX2CJAmsfH194eXlhXXr1kEul0tRJBEREVGJI8k9VpGRkejbty+DKiIiIvqkSRJYNWnSBLdv35aiKCIiIqISS5KhwFWrVqFLly6wtbVFmzZtoK2tLUWxRERERCWKJD1W9vb2aNWqFbp06QJ9fX0YGxsrLSYmJlJUk2O+vr6oXLkydHV14ezsnO0LohMSEjBr1izY2dlBR0cHn332GTZu3FhIrSUiIqLSQpIeq2nTpmH16tWoW7cunJycirTHaufOnZg4cSJ8fX3RpEkTrFu3Dm3btkVISAgqVqyodpuePXvi5cuX2LBhA+zt7REREYHk5ORCbjkRERGVdJK8hNnU1BTDhw+Hj4+PFG3Kl0aNGqF+/fpYu3atIs3JyQmdO3dW274jR46gd+/eePjwIcqVK5enOvkSZiIiKsn4EmbpXsIsyVBgSkoKWrduLUVR+ZKYmIjg4GB4eHgopXt4eOD8+fNqtzlw4ABcXFywdOlSVKhQAY6Ojpg6dSri4uIyrSchIQHR0dFKCxEREZEkgZWHhwcuXLggRVH58vr1a6SkpMDCwkIp3cLCAuHh4Wq3efjwIc6ePYt//vkH+/btw6pVq/DHH39gzJgxmdbj4+MDExMTxWJrayvpfhAREVHJJMk9VnPmzEGvXr1gYGCA9u3bqx1Sy+swW17IZDKlz0IIlbR0qampkMlk8PPzU9xkv2LFCnTv3h1r1qyBnp6eyjYzZ87E5MmTFZ+jo6MZXBEREZE0gVWdOnUAAJMnT1YKODJKSUmRoqosmZmZQS6Xq/RORUREqPRipbOyskKFChWUnlx0cnKCEAKhoaFwcHBQ2UZHRwc6OjrSNp6IiIhKPEkCq7lz52baI1SYtLW14ezsjICAAHTp0kWRHhAQgC+//FLtNk2aNMHu3bsRExMDw/+/w+3u3bvQ0NCAjY1NobSbiIiICtbz54CjY8HXI0lg5e3tLUUxkpg8eTL69+8PFxcXuLq6Yv369Xj69ClGjhwJIG0Y7/nz59i6dSsAoG/fvvj2228xePBgzJ8/H69fv8bXX38NLy8vtcOAREREVDJs2fLfz05OwPr1wJAhBVunJIFVcdKrVy9ERkZiwYIFCAsLQ82aNeHv7w87OzsAQFhYGJ4+farIb2hoiICAAIwbNw4uLi4wNTVFz549sXDhwqLaBSIiok9KQUzLEBoKjBv33+fUVGDECMDTEyjIASlJ5rHS0NDIdiiwMO6xKiqcx4qIiEqyop7HqiDqDwwEWrZUn968edrPBTGPVYHdY/Xq1SscO3YMKSkpGDBggBTVEBEREeWIgwOgoZHWU5VOLgfs7Qu23gK9xyoxMRGenp4wNzeXohoiIiKiHLGxAX76CUifllIuB9atK9hhQECiCUIzo62tjXHjxmHFihUFWQ0RERGRioED//s5JKTgb1wHCjiwAgA9PT2EhYUVdDVEREREmapQoXDqKdDA6tWrV1i2bBmqVq1akNUQERERFQuS3GNVuXJllZvXExISEBERAQ0NDRw4cECKaoiIiIiKNUkCKzc3N5XASldXF5UqVUKvXr1QqVIlKaohIiIiKtYkCaw2b94sRTFEREREJVqB37xORERE9KnIc49VxtfC5ETFihXzWhURERFRiZDnwKpSpUrZvsYmo9L8ShsiIiIiIB+B1caNG3MVWBERERGVdnkOrAYNGiRhM4iIiIhKPkmeCszo7t27iIyMhJmZGRwcHKQunoiIiKjYkuypwN27d8POzg5OTk5o2rQpqlWrBjs7O/zxxx9SVUFERERUrEkSWPn7+6N3794wMTHB4sWLsXXrVvj4+MDExAS9e/fG4cOHpaiGiIiIPnGxsYBMlrbExhZ1a1TJhBAiv4U0adIExsbGOHToEDQ0/ovVhBBo27Yt3r9/j3PnzuW3mmIrOjoaJiYmiIqKgrGxsSI9NhYwNEz7OSYGMDAoogYSERFloaCuVzktNzf1S5k3s+t3fkjSY3X9+nWMHj1aKagCAJlMhtGjR+PGjRtSVENERERUrEkSWMnlciQmJqpdl5SUpBJwEREREZVGkkQ8DRo0wNKlSxEXF6eUnpCQgO+//x6NGjWSohoiIiKiYk2S6Rbmz58Pd3d3VKlSBT169IClpSXCwsKwd+9eREZG4sSJE1JUQ0REVKR47yxlR5LAqmnTpjh27BhmzJiBNWvWQAgBDQ0NNGrUCDt27EDjxo2lqIaIiIioWJNsglA3NzcEBQXhw4cPePv2LcqWLQt9fX2piiciIiIq9iQJrJKSkpCYmAgDAwPo6+srBVSxsbHQ1taGlpaWFFURERERFVuS3Lw+bNgwDB06VO264cOHY9SoUVJUQ0RERFSsSRJYBQYGolOnTmrXdezYEX/99ZcU1RAREREVa5IEVi9fvoSVlZXadZaWlggPD5eiGiIiIqJiTZLAqkyZMrh//77adffv34eRkZEU1RAREVEBe/68qFtQskkSWLVo0QI+Pj548+aNUvqbN2+wePFitGzZUopqiIiIqABs2fLfz05OwIYNRdeWkk6SpwK9vb3RoEEDODg4oFevXqhQoQJCQ0Oxe/duJCUlYf78+VJUQ0RERBILDQXGjfvvc2oqMGIE4OkJ2NgUXbtKKkkCq6pVq+LMmTOYPHkyfvnlF6SkpEAul8PNzQ0rVqxA1apVpaiGiIiIJHbvXlowlVFKCnD/PgOrvJBsgtA6dergr7/+QlxcHN6+fYty5cpBV1dXquKJiIioADg4ABoaysGVXA7Y2xddm0oySe6xykhPTw/W1tYMqoiIiEoAGxvgp5/++yyXA+vWsbcqryQPrIiIiKhkGTjwv59DQoAhQ4quLSUdAysiIqI8KK3TElSoUNQtKNlKZWDl6+uLypUrQ1dXF87Ozjhz5kyOtjt37hw0NTVRt27dgm0gERGVSJyWgLJT6gKrnTt3YuLEiZg1axauXbuGZs2aoW3btnj69GmW20VFRWHAgAFwd3cvpJYSEVFJktm0BKGhhduO2FhAJktbYmMLt+7iogJC0RyBkD0v5IOfA6UusFqxYgWGDBmCoUOHwsnJCatWrYKtrS3Wrl2b5XYjRoxA37594erqmm0dCQkJiI6OVlqIiKh0y2paAio8mut9EQpbBKIl9Jzsil23oWSBVUpKCnbs2IHhw4ejW7duGD58OHbs2IHk5GSpqshWYmIigoOD4eHhoZTu4eGB8+fPZ7rdpk2b8ODBA8ybNy9H9fj4+MDExESx2Nra5qvdRERU/KVPS5ARpyUoZKGh0J76X7ehrKi6DbMgSWD1+vVrNGrUCP369cPmzZtx/vx5bN68Gf369UOjRo3w+vVrKarJUTtSUlJgYWGhlG5hYZHpi6Dv3buHGTNmwM/PD5qaOZvWa+bMmYiKilIsz549y3fbiYioeCs20xLExkJABgEZZPfuFnLlRezevbRgKqNi1m0oSWA1adIk3LlzB35+foiLi0NYWBji4uKwbds23Lt3D5MmTZKimhyTyWRKn4UQKmlAWi9b3759MX/+fDg6Oua4fB0dHRgbGystRERU+hWHaQk0/f67g17P+RO7g97BAaKYdxtKElgdPHgQCxcuRJ8+fSCXywEAcrkcffv2xYIFC3Dw4EEpqsmWmZkZ5HK5Su9URESESi8WALx//x5XrlzB2LFjoampCU1NTSxYsAA3btyApqYmTpw4USjtJiKikqdIpiUoAUNhBcrGBok/rUcy0mINUQxnM5UksBJCoEaNGmrX1axZE0IIKarJlra2NpydnREQEKCUHhAQgMaNG6vkNzY2xs2bN3H9+nXFMnLkSFStWhXXr19Ho0aNCqXdRERFiU+ZlSAlYCisoCUPHIJKeIzmCERcyONiN5upJO8KbNWqFY4fP45WrVqprAsICEDz5s2lqCZHJk+ejP79+8PFxQWurq5Yv349nj59ipEjRwJIuz/q+fPn2Lp1KzQ0NFCzZk2l7c3NzaGrq6uSTkREVOT+fyhM9om/2O85bPAcNhDFcDJTSQKrOXPmoGvXrop7liwtLREeHg4/Pz/s3bsXe/fuxZs3bxT5y5UrJ0W1avXq1QuRkZFYsGABwsLCULNmTfj7+8POzg4AEBYWlu2cVkRERMXS/w+FyceMgCZSIORyyIrZUFhhev4cyO4W6QoIhQPuQfbcAXAs+OMkExKM02lkuJEs403i6UV/fON4SkpKfqssVqKjo2FiYoKoqCilG9ljYwFDw7SfY2IAA4MiaiARURb4f1XOFYdjFRsLVDUMhT3uw/+OPfQlCBYKar9yWm5u6vf1BcaMSftZQwNYvz7z0cCElb7QmZyWWWhoQPZR5syu3/khSY/V3Llz1T51R0RERNIrzkNheZWTnqXMZr/39FTTaZfZjf5qM0tHksDK29tbimKIiIjoE5Q2m/r/9yw5Zd4NldXs9yqxUlY3+hdgYCX5K23u3r2LoKAg3Lt3T+qiiYiIKIPnz4u6BRLIxRQSuZr9vojmvJIssNq9ezfs7Ozg5OSEpk2bolq1arCzs8Mff/whVRVERESfvC3/zQ8KJwnnBy2yFxvnYgqJXM1+X0RzXkly87q/vz86duyIGjVqoH///rC2tsbz58+xbds2hISE4ODBg2jbtq0U7S2WePM6EZVk/L8q54r6WIWGAnZ2ysNhcjnw+HH+4oXsbvLOqxwdr9BQCDs71SkkMtmpjGXeuZP1U4HZ3ehfEDevSxJYNWnSBMbGxjh06JDSE4JCCLRt2xbv37/HuXPn8ltNscXAiohKMv5flXNFfawCA4GWLdWn53nKyFwGNrmR0+OV4LtBdQqJTAK73JyD7PIWRGAlyVDg9evXMXr0aKWgCkibZmH06NG4ceOGFNUQERF90nJ1j1FOFYPZ3Iv7bOq5IUlgJZfLkZiYqHZdUlKSSsBFREREuZere4xyqpi82Pg5bHAKzSEqlOzJTiWJeBo0aIClS5ciLi5OKT0hIQHff/8937lHRESlgoEBIETaUlRDpgMH/vdzSIgEnTsl4MXGJYkk81jNnz8f7u7uqFKlCnr06AFLS0uEhYVh7969iIyMxIkTJ6SohoiIiDKoINEEockDh+CzMZ5pN3mHSDOb+6dKksCqadOmOHbsGGbMmIE1a9ZACAENDQ00atQIO3bsQOPGjaWohoiIiApIaZzNvShIElgBgJubG4KCgvDhwwe8ffsWZcuWhb6+vlTFExERERV7+b7HKi4uDhUqVMDBgwcBAPr6+qhQoQKDKiIiIvrk5Duw0tPTQ1xcHAw48QkRERF94iR5KtDd3R3Hjx+XoigiIiKiEkuSe6y++eYbdOvWDbq6uujatSusrKwgk8mU8pQrV06KqoiIiIiKLUkCK2dnZwCAt7c35s+frzZPSkqKFFURERFRCfT8edbv9SstJAms5s6dq9JDRURERJ+2LVv++9nJCZDo3c7FmiSBlbe3txTFEBERUSkRGgqMG/ff59RUYMQIwNOzdE/qLsnN66dPn0ZMTIzadTExMTh9+rQU1RAREVEJce9eWjCVUSG/27lISBJYtWjRAiEhIWrX3blzBy1atJCiGiIiok9ecXhfYU44OADF4N3OhU6SwEoIkem6pKQkaHx8ZImIiKhUs7EBfvrpv8+fyrud83yPVXR0NN69e6f4HB4ejqdPnyrliYuLw5YtW2BpaZnnBhIREVHJNHAgMGZM2s8hIXwqMEsrV67EggULAAAymQxdunRRm08IgW+++Sav1RAREVEpUOETeblzngMrDw8PGBoaQgiBadOmYdy4cahYsaJSHh0dHdSqVQtubm75bigRERFRcZfnwMrV1RWurq4AgNjYWAwbNgzW1taSNYyIiIiopJFkHqt58+ZJUQwREVGuxMYChoZpP8fEFO+n5OjTwMf1iIiIiCTCwIqIiBSePy/qFhCVbAysiIg+cR+/z23DhqJrC1FJx8CKiOgTltn73EJDi65NRCUZAysiok/Yp/o+N6KCUiCBVXx8PG7evIkPHz6orDt37lxBVFkslZT3ORHRp+tTfZ8bUUGRPLAKCgqCra0tmjdvjvLly2Px4sVK69u2bSt1lURElEef6vvcqPjK7gGK4t5pIXlgNWXKFCxfvhyRkZEIDg7G3r174eXlhdT/72vO6oXNUvH19UXlypWhq6sLZ2dnnDlzJtO8e/fuRevWrVG+fHkYGxvD1dUVR48eLfA2EhEVFwMH/vdzSAgwZEjRtYU+TaXpAQrJA6uQkBAMGDAAAFCtWjWcOnUKERER6N69OxITE6WuTsXOnTsxceJEzJo1C9euXUOzZs3Qtm1blRdEpzt9+jRat24Nf39/BAcHo0WLFujYsSOuXbtW4G0lIipuPpX3uVHxUdoeoJA8sDI2NsbzDP14enp62L9/P3R1ddGmTRtFz1VBWbFiBYYMGYKhQ4fCyckJq1atgq2tLdauXas2/6pVqzBt2jQ0aNAADg4OWLRoERwcHHDw4MECbScRERGVvgcoJA+sWrVqhU2bNimlaWpqws/PD5999hni4uKkrlIhMTERwcHB8PDwUEr38PDA+fPnc1RGamoq3r9/j3LlymWaJyEhAdHR0UoLERER5V5pe4BC8sDq559/xuTJk1XSZTIZfvnlFzx+/FjqKhVev36NlJQUWFhYKKVbWFggPDw8R2UsX74csbGx6NmzZ6Z5fHx8YGJiolhsbW3z1W4iIqJPVWl7gEKywEoIgdevX+P9+/fQ09PLNF/FihWlqjJTMplM6bMQQiVNnR07dsDb2xs7d+6Eubl5pvlmzpyJqKgoxfLs2bN8t5mIiPKHr+MpuUrTAxT5DqyCgoLw5ZdfwtjYGBYWFjA3N4exsTE6d+6MixcvStHGHDMzM4NcLlfpnYqIiFDpxfrYzp07MWTIEOzatQutWrXKMq+Ojg6MjY2VFiIiKnyl6WkySlPSH6DIV2Dl6+uLL774Av7+/qhRowZ69uyJHj16oEaNGjh06BCaNm0KX19fqdqaLW1tbTg7OyMgIEApPSAgAI0bN850ux07dmDQoEHYvn072rdvX9DNJCIiCZS2p8modNDM64YXLlzA+PHj0a5dO/j6+sLmo8HQ0NBQjBo1ChMmTICLiwsaNmyY78bmxOTJk9G/f3+4uLjA1dUV69evx9OnTzFy5EgAacN4z58/x9atWwGkBVUDBgzADz/8gM8//1zR26WnpwcTE5NCaTMREeVeVk+TldT7c6jky3OP1fLly9GoUSPs379fJagCABsbG/zvf/9Dw4YNsWzZsnw1Mjd69eqFVatWYcGCBahbty5Onz4Nf39/2NnZAQDCwsKU5rRat24dkpOTMWbMGFhZWSmWCRMmFFqbiYgo90rb02RUOshEHqdCt7KywvLly9G3b98s8/n5+WHq1KkICwvLUwNLgujoaJiYmCAqKor3WxFRiRMbCxgapv0cE1M8XxOSGV9fYMyYtJ/TnyYryTc+F5WC+g7ktNyiqr8grt957rF68+ZNjp7ws7Ozw5s3b/JaDRERUaZK09NkVDrkObAyNTXFkydPss339OlTmJqa5rUaIiKiHCnpT5NR6ZDnwCr9ib+sXlGTmpqK1atXo1mzZnmthoiIiKjEyHNgNXnyZFy8eBFdu3ZVe//Uixcv0LVrV1y+fBlTpkzJVyOJiIiISoI8T7fw+eefY+XKlZg0aRL8/f3h4uKCypUrAwAePXqEK1euIDU1FatWrSq0qRaIiIiIilKeAysAGDduHOrXrw8fHx+cPHkSFy5cAADo6+vD09MTM2fOzHJiTiIiIqLSJF+BFQA0adIEf/75J1JTU/H69WsAaa+W0fh4chEiIvpklOQpHIjyI9+BVToNDY0sX1xMREREVNrlObCqXbt2jvPKZDLcuHEjr1URERERlQh5DqzKlSsHmUyWZZ6YmBgEBwdnm4+IiIioNMhzYHXy5MlM1yUnJ2P9+vVYsGABZDJZtq+9ISIiIioNJL/DfPfu3ahevTrGjRuHOnXqIDg4GL/99pvU1RAREREVO5IFVidPnkSjRo3Qq1cvGBsb49ixYzh69Cjq1q0rVRVERERUAAwMACHSFj7BmT/5Dqxu3ryJdu3awd3dHZGRkdi+fTuuXLkCd3d3KdpHRESUKQYEVNzkObB69uwZBg4ciPr16yM4OBirVq3C7du30bt3bynbR0RERFRi5PnmdUdHRyQmJqJNmzaYNm0ajIyMcPPmzUzz169fP69VERERZS40FLh3D3BwAGxsiro19InLc2CVkJAAADh8+DCOHDmSaT4hBGQyGVJSUvJaFRERkXq+vsCYMWk/a2gA69cDQ4YUbZvok5bnwGrTpk1StoOIiCh3QkOBceP++5yaCowYAXh6sueKikyeA6uBAwdK2Q4iIqLcuXcvLZjKKCUFuH+fgRUVGcneFUhERFSoHBzShv8yBldyOWBvX3RtomIl/anRwiT5BKFERFSyFNSUBRUQiuYIxMvgUOkKzcjGJu2eKrk87bNcDqxbx94qKlIMrIiISHKXvH7GE9ghEC1h52aHM4M2FExFQ4YAjx8DgYFp//LGdSpiDKyIiEhSYZdD8cWuMZAjbYhOjlS4bhmBsMsF2HPVvDl7qqhYYGBFRESSCj9zTxFUpdNECl6eu19ELSIqPAysiIhIUpbNHJDy0eUlGXJYNOFN5VT6MbAiIiJJWTWwwel+65GMtJvKkyFH0MB1sGrAoToq/RhYERGR5BquG4JKeIzmCMTTU4/RbDNvKqdPA+exIiKiAvEcNngOG1g4F3VLiAoPe6yIiIiIJMLAioiIiEgiDKyIiIiIJMLAioiIiEgiDKyIiIiIJMLAioiIiEgiDKyIiIiIJFIqAytfX19UrlwZurq6cHZ2xpkzZ7LMf+rUKTg7O0NXVxdVqlTBzz//XEgtJSIiotKk1AVWO3fuxMSJEzFr1ixcu3YNzZo1Q9u2bfH06VO1+R89eoR27dqhWbNmuHbtGr755huMHz8ee/bsKeSWExERUUknE0KIom6ElBo1aoT69etj7dq1ijQnJyd07twZPj4+KvmnT5+OAwcO4Pbt24q0kSNH4saNGwgKCspRndHR0TAxMUFUVBSMjY3zvxNERCVcbCxgaJj2c0wMYGBQtO2hopHT70FRfV8K4vpdqnqsEhMTERwcDA8PD6V0Dw8PnD9/Xu02QUFBKvk9PT1x5coVJCUlqd0mISEB0dHRSgsRERFRqQqsXr9+jZSUFFhYWCilW1hYIDw8XO024eHhavMnJyfj9evXarfx8fGBiYmJYrG1tZVmB4iISgkDA0CItIW9VfQpKVWBVTqZTKb0WQihkpZdfnXp6WbOnImoqCjF8uzZs3y2mIiIiEoDzaJugJTMzMwgl8tVeqciIiJUeqXSWVpaqs2vqakJU1NTtdvo6OhAR0dHmkYTERFRqVGqeqy0tbXh7OyMgIAApfSAgAA0btxY7Taurq4q+Y8dOwYXFxdoaWkVWFuJiIio9ClVgRUATJ48Gb/++is2btyI27dvY9KkSXj69ClGjhwJIG0Yb8CAAYr8I0eOxJMnTzB58mTcvn0bGzduxIYNGzB16tSi2gUiIiIqoUrVUCAA9OrVC5GRkViwYAHCwsJQs2ZN+Pv7w87ODgAQFhamNKdV5cqV4e/vj0mTJmHNmjWwtrbGjz/+iG7duhXVLhAREVEJVermsSoKnMeKiIhIFeexIiIiIqI8Y2BFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQS0SzqBhAREVHpZGAACFHUrShcDKyIiIioSJWmAIxDgUREREQSYWBFREREJBEGVkREREQSKVWB1du3b9G/f3+YmJjAxMQE/fv3x7t37zLNn5SUhOnTp6NWrVowMDCAtbU1BgwYgBcvXhReo4mIiKjUKFWBVd++fXH9+nUcOXIER44cwfXr19G/f/9M83/48AFXr17FnDlzcPXqVezduxd3795Fp06dCrHVREREVFrIhCgd9+Hfvn0b1atXx4ULF9CoUSMAwIULF+Dq6op///0XVatWzVE5ly9fRsOGDfHkyRNUrFgxR9tER0fDxMQEUVFRMDY2zvM+EBERUeEpiOt3qemxCgoKgomJiSKoAoDPP/8cJiYmOH/+fI7LiYqKgkwmQ5kyZTLNk5CQgOjoaKWFiIiIqNQEVuHh4TA3N1dJNzc3R3h4eI7KiI+Px4wZM9C3b98sI1cfHx/FfVwmJiawtbXNc7uJiIio9Cj2gZW3tzdkMlmWy5UrVwAAMplMZXshhNr0jyUlJaF3795ITU2Fr69vlnlnzpyJqKgoxfLs2bO87RwRERGVKsV+5vWxY8eid+/eWeapVKkS/v77b7x8+VJl3atXr2BhYZHl9klJSejZsycePXqEEydOZDvOqqOjAx0dnewbT0RERJ+UYh9YmZmZwczMLNt8rq6uiIqKwqVLl9CwYUMAwMWLFxEVFYXGjRtnul16UHXv3j0EBgbC1NRUsrYTERHRp6XYDwXmlJOTE9q0aYNhw4bhwoULuHDhAoYNG4YOHTooPRFYrVo17Nu3DwCQnJyM7t2748qVK/Dz80NKSgrCw8MRHh6OxMTEotoVIiIiKqFKTWAFAH5+fqhVqxY8PDzg4eGB2rVr47ffflPKc+fOHURFRQEAQkNDceDAAYSGhqJu3bqwsrJSLLl5kpCIiIgIKEXzWBUlzmNFRERU8nAeKyIiIqJirNjfvF4SpHf6caJQIiKikiP9ui3l4B0DKwlERkYCACcKJSIiKoEiIyNhYmIiSVkMrCRQrlw5AMDTp08lOzGUN9HR0bC1tcWzZ894v1sR47koXng+ig+ei+IjKioKFStWVFzHpcDASgIaGmm3qpmYmPCXpJgwNjbmuSgmeC6KF56P4oPnovhIv45LUpZkJRERERF94hhYEREREUmEgZUEdHR0MG/ePL4/sBjguSg+eC6KF56P4oPnovgoiHPBCUKJiIiIJMIeKyIiIiKJMLAiIiIikggDKyIiIiKJMLAiIiIikggDqxzy9fVF5cqVoaurC2dnZ5w5cybL/KdOnYKzszN0dXVRpUoV/Pzzz4XU0tIvN+di7969aN26NcqXLw9jY2O4urri6NGjhdja0i23vxfpzp07B01NTdStW7dgG/gJye25SEhIwKxZs2BnZwcdHR189tln2LhxYyG1tvTL7fnw8/NDnTp1oK+vDysrKwwePFjxujTKm9OnT6Njx46wtraGTCbD/v37s91Gkmu3oGz9/vvvQktLS/zyyy8iJCRETJgwQRgYGIgnT56ozf/w4UOhr68vJkyYIEJCQsQvv/witLS0xB9//FHILS99cnsuJkyYIJYsWSIuXbok7t69K2bOnCm0tLTE1atXC7nlpU9uz0W6d+/eiSpVqggPDw9Rp06dwmlsKZeXc9GpUyfRqFEjERAQIB49eiQuXrwozp07V4itLr1yez7OnDkjNDQ0xA8//CAePnwozpw5I2rUqCE6d+5cyC0vXfz9/cWsWbPEnj17BACxb9++LPNLde1mYJUDDRs2FCNHjlRKq1atmpgxY4ba/NOmTRPVqlVTShsxYoT4/PPPC6yNn4rcngt1qlevLubPny910z45eT0XvXr1ErNnzxbz5s1jYCWR3J6Lw4cPCxMTExEZGVkYzfvk5PZ8LFu2TFSpUkUp7ccffxQ2NjYF1sZPTU4CK6mu3RwKzEZiYiKCg4Ph4eGhlO7h4YHz58+r3SYoKEglv6enJ65cuYKkpKQCa2tpl5dz8bHU1FS8f/9e0hduforyei42bdqEBw8eYN68eQXdxE9GXs7FgQMH4OLigqVLl6JChQpwdHTE1KlTERcXVxhNLtXycj4aN26M0NBQ+Pv7QwiBly9f4o8//kD79u0Lo8n0/6S6dvMlzNl4/fo1UlJSYGFhoZRuYWGB8PBwtduEh4erzZ+cnIzXr1/DysqqwNpbmuXlXHxs+fLliI2NRc+ePQuiiZ+MvJyLe/fuYcaMGThz5gw0Nflfj1Tyci4ePnyIs2fPQldXF/v27cPr168xevRovHnzhvdZ5VNezkfjxo3h5+eHXr16IT4+HsnJyejUqRN++umnwmgy/T+prt3sscohmUym9FkIoZKWXX516ZR7uT0X6Xbs2AFvb2/s3LkT5ubmBdW8T0pOz0VKSgr69u2L+fPnw9HRsbCa90nJze9FamoqZDIZ/Pz80LBhQ7Rr1w4rVqzA5s2b2Wslkdycj5CQEIwfPx5z585FcHAwjhw5gkePHmHkyJGF0VTKQIprN/9szIaZmRnkcrnKXxoREREqkW06S0tLtfk1NTVhampaYG0t7fJyLtLt3LkTQ4YMwe7du9GqVauCbOYnIbfn4v3797hy5QquXbuGsWPHAki7uAshoKmpiWPHjqFly5aF0vbSJi+/F1ZWVqhQoQJMTEwUaU5OThBCIDQ0FA4ODgXa5tIsL+fDx8cHTZo0wddffw0AqF27NgwMDNCsWTMsXLiQoxyFRKprN3ussqGtrQ1nZ2cEBAQopQcEBKBx48Zqt3F1dVXJf+zYMbi4uEBLS6vA2lra5eVcAGk9VYMGDcL27dt5z4JEcnsujI2NcfPmTVy/fl2xjBw5ElWrVsX169fRqFGjwmp6qZOX34smTZrgxYsXiImJUaTdvXsXGhoasLGxKdD2lnZ5OR8fPnyAhoby5VgulwP4r8eECp5k1+5c3er+iUp/dHbDhg0iJCRETJw4URgYGIjHjx8LIYSYMWOG6N+/vyJ/+iObkyZNEiEhIWLDhg2cbkEiuT0X27dvF5qammLNmjUiLCxMsbx7966odqHUyO25+BifCpRObs/F+/fvhY2Njejevbu4deuWOHXqlHBwcBBDhw4tql0oVXJ7PjZt2iQ0NTWFr6+vePDggTh79qxwcXERDRs2LKpdKBXev38vrl27Jq5duyYAiBUrVohr164ppr0oqGs3A6scWrNmjbCzsxPa2tqifv364tSpU4p1AwcOFG5ubkr5T548KerVqye0tbVFpUqVxNq1awu5xaVXbs6Fm5ubAKCyDBw4sPAbXgrl9vciIwZW0srtubh9+7Zo1aqV0NPTEzY2NmLy5Mniw4cPhdzq0iu35+PHH38U1atXF3p6esLKykr069dPhIaGFnKrS5fAwMAs//8vqGu3TAj2MxIRERFJgfdYEREREUmEgRURERGRRBhYEREREUmEgRURERGRRBhYEREREUmEgRURERGRRBhYEREREUmEgRURERGRRBhYUYny999/Y8iQIfjss8+gp6cHPT09ODg4YMSIEbhy5YpSXm9vb8hkMmhoaODhw4cqZcXGxsLY2BgymQyDBg0CADRv3hwymSzbxdvbW5L92bx5M2QyGR4/fixJebmxc+dO1KhRA3p6epDJZLh+/XqhtyGn0s/l69evi6R+Kc95Zk6ePAmZTIaTJ08WaD35de3aNbi5ucHExAQymQyrVq1Smy8sLAyzZ8+Gq6srzMzMYGxsDGdnZ6xfvx4pKSkq+WNiYjBx4kRYW1tDV1cXdevWxe+//66UJyUlBStWrECbNm1gY2MDfX19ODk5YcaMGXj37p1S3tjYWPTu3RtVq1aFkZERDAwMUKNGDSxcuBCxsbEq9UdERGDQoEEwMzODvr4+XF1d8ddff6nkS0xMxNy5c1G5cmVoa2vDzs4OM2fORFxcnErepKQkzJ8/H5UqVYKOjg6qVauGn376SSXfrVu3MHr0aLi6usLAwKBEfA8oc5pF3QCinFq3bh3Gjh2LqlWrYsKECahRowZkMhlu376NHTt2oEGDBrh//z4+++wzpe0MDQ2xadMmfPvtt0rpu3fvRlJSktLLNX19fREdHa34fOjQISxcuBCbNm1CtWrVFOkl/UW1r169Qv/+/dGmTRv4+vpCR0cHjo6ORd2sYisoKKjAz3n9+vURFBSE6tWrF2g9+eXl5YXY2Fj8/vvvKFu2LCpVqqQ2X3BwMLZu3YoBAwZgzpw50NLSwuHDhzFq1ChcuHABGzduVMrftWtXXL58GYsXL4ajoyO2b9+OPn36IDU1FX379gUAxMXFwdvbG3369MHQoUNhZmaGq1evYuHChTh48CCuXLkCPT09AGlBjRACkydPRuXKlaGhoYHTp09jwYIFOHnyJI4fP66oOyEhAe7u7nj37h1++OEHmJubY82aNWjTpg2OHz8ONzc3Rd4+ffrA398fc+fORYMGDRAUFISFCxfi1q1bOHDggNI+jR49Gr/99hu+/fZbNGjQAEePHsWECRPw/v17fPPNN4p8V65cwf79+1GvXj24u7vj4MGD+TpHVMTy+y4eosJw9uxZoaGhITp27CgSEhLU5tm1a5d4/vy54vO8efMEADF06FBha2srUlJSlPI3bdpU9OnTRxgYGGT67sBNmzYJAOLy5cuS7Yu68h89elQg5Wfm7NmzAoDYuXNntnljY2MLoUVZSz+Xr169KuqmfPI0NTXFqFGjss335s0bkZiYqJI+ZswYAUA8ffpUkXbo0CEBQGzfvl0pb+vWrYW1tbVITk4WQgiRnJwsXr9+rVLm7t27BQDx22+/ZduuadOmCQDiwYMHirQ1a9YIAOL8+fOKtKSkJFG9enWlFyEHBQUJAGL58uVKZS5atEgAEMeOHVOk/fPPP0Imk4lFixYp5R02bJjQ09MTkZGRirSM/zel70tgYGC2+0LFE4cCqURYtGgR5HI51q1bB21tbbV5evToAWtra5V0Ly8vPHv2DAEBAYq0u3fv4uzZs/Dy8iqwNn/swoULaNKkCXR1dWFtbY2ZM2ciKSlJJd/OnTvh4eEBKysr6OnpKYY6Mg5f/Pbbb5DJZAgKClLZfsGCBdDS0sKLFy/UtmPQoEFo2rQpAKBXr16QyWRo3ry5Yp2hoSFu3rwJDw8PGBkZwd3dHQDw5s0bjB49GhUqVIC2tjaqVKmCWbNmISEhQal8mUyGsWPHYtOmTahatSr09PTg4uKCCxcuQAiBZcuWoXLlyjA0NETLli1x//79HB/DZ8+eoWvXrjA2NoaJiQm++uorvHr1Su0xTB9WMTQ0hKenJ65du6aS75dffoGjoyN0dHRQvXp1bN++HYMGDVLphfl4KDB9CDcwMBCjRo2CmZkZTE1N0bVrV5XjXqlSJXTo0AFHjhxB/fr1oaenh2rVqqn02KgbCkw/H/fv30e7du1gaGgIW1tbTJkyReW4h4aGonv37jAyMkKZMmXQr18/XL58GTKZDJs3b8722P7zzz/48ssvUbZsWcVQ3JYtW1T2OTk5GWvXrlUMi2embNmySr3B6Ro2bKhob7p9+/bB0NAQPXr0UMo7ePBgvHjxAhcvXgQAyOVymJqaZlrms2fPst3P8uXLAwA0Nf8bsNm3bx+qVq0KV1dXRZqmpia++uorXLp0Cc+fPwcAnDt3DgDQrl07pTI7dOgAANizZ48ibf/+/RBCYPDgwSr7FBcXhyNHjijSNDR4KS5NeDap2EtJSUFgYCBcXFxgZWWV6+0dHBzQrFkzpQvZxo0bUalSJUXQUNBCQkIUQw2bN2/Gzz//jGvXrmHhwoUqee/du4d27dphw4YNOHLkCCZOnIhdu3ahY8eOijy9evWCpaUl1qxZo7RtcnIy1q1bhy5duqgNMgFgzpw5iu0WLVqEoKAg+Pr6KtYnJiaiU6dOaNmyJf73v/9h/vz5iI+PR4sWLbB161ZMnjwZhw4dwldffYWlS5eia9euKnX8+eef+PXXX7F48WLs2LED79+/R/v27TFlyhScO3cOq1evxvr16xESEoJu3bpB5PBd8F26dIG9vT3++OMPeHt7Y//+/fD09FQKUBctWoQ+ffqgevXq2LVrF3777Te8f/8ezZo1Q0hIiCLf+vXrMXz4cNSuXRt79+7F7NmzMX/+/Fzd2zJ06FBoaWlh+/btWLp0KU6ePImvvvpKJd+NGzcwZcoUTJo0Cf/73/9Qu3ZtDBkyBKdPn862jqSkJHTq1Anu7u743//+By8vL6xcuRJLlixR5ImNjUWLFi0QGBiIJUuWYNeuXbCwsECvXr1ytB937txB48aNcevWLfz444/Yu3cvqlevjkGDBmHp0qUAgPbt2ysC+e7duyMoKEhtYJ+dEydOQFNTU2no+Z9//oGTk5NSsAMAtWvXVqzPrkwAqFGjhso6IQSSk5MRHR2NI0eOYPny5ejTpw8qVqyoVH96Xerqv3XrFoC03w0A0NHRUcqX/vnvv/9WKrN8+fKwtLTM0z5RCVa0HWZE2QsPDxcARO/evVXWJScni6SkJMWSmpqqWJdx+GjTpk1CR0dHREZGiuTkZGFlZSW8vb2FEKJQhgJ79eol9PT0RHh4uFLbq1WrluVQYGpqqkhKShKnTp0SAMSNGzeU9k9bW1u8fPlSkbZz504BQJw6dSrL9gQGBgoAYvfu3UrpAwcOFADExo0bldJ//vlnAUDs2rVLKX3JkiUqQyAAhKWlpYiJiVGk7d+/XwAQdevWVTpHq1atEgDE33//nWV708/lpEmTlNL9/PwEALFt2zYhhBBPnz4VmpqaYty4cUr53r9/LywtLUXPnj2FEGlDL5aWlqJRo0ZK+Z48eSK0tLSEnZ2dUjoAMW/ePMXn9O/F6NGjlfItXbpUABBhYWGKNDs7O6GrqyuePHmiSIuLixPlypUTI0aMUKSln5OMQ0Dp5+Pj496uXTtRtWpVxef0oazDhw8r5RsxYoQAIDZt2iSy0rt3b6Gjo6M0PCeEEG3bthX6+vri3bt3SsdizJgxWZaXmaNHjwoNDQ2V8+jg4CA8PT1V8r948UIAUBlOyyg0NFRYWFgIFxcXleF+IYTYsWOHAKBYBg8eLJKSkpTyaGlpKZ2LdOfPn1caokz/Hn885LhhwwYBQDg6OirSWrdurXSOMtLW1hbDhw9Xu45DgSUfe6yoRHN2doaWlpZiWb58udp8PXr0gLa2Nvz8/ODv74/w8HDFk4CFITAwEO7u7rCwsFCkyeVytT0KDx8+RN++fWFpaQm5XA4tLS3FzbO3b99W5Bs1ahSAtOGsdKtXr0atWrXwxRdf5Ku93bp1U/p84sQJGBgYoHv37krp6cfw46enWrRoAQMDA8VnJycnAEDbtm2Vho/S0588eZKjdvXr10/pc8+ePaGpqYnAwEAAwNGjR5GcnIwBAwYgOTlZsejq6sLNzU3RG3Xnzh2Eh4ejZ8+eSuVVrFgRTZo0yVFbAKBTp05Kn9N7Iz7en7p16yr1kOjq6sLR0TFH+y2TyZR6K9PrybjtqVOnYGRkhDZt2ijl69OnT47248SJE3B3d4etra1S+qBBg/Dhw4c89Ux97OrVq+jZsyc+//xz+Pj4qKzPalgxs3Vv3rxBu3btIITAzp071Q6peXp64vLlyzhx4gS+++477NmzB926dUNqamqu62/bti3s7e0xffp0BAQE4N27dzhy5Ai++eYbyOVylfrzsk9U8vGpQCr2zMzMoKenp/YitH37dnz48AFhYWEqF7mMDAwM0KtXL2zcuBF2dnZo1aoV7OzsCrLZSiIjI1WGBACopMXExKBZs2bQ1dXFwoUL4ejoCH19fcW9RRkf6U4f6lm3bh1mzJiBW7du4cyZM1i3bl2+2qqvrw9jY2O17f/4YmBubg5NTU1ERkYqpZcrV07pc/p9cZmlx8fH56htHx8vTU1NmJqaKup/+fIlAKBBgwZqt0+/8KXnzxjoprOwsMCjR49y1J6P7/dJHxL6+NF7dfcF6ejoqH1E/2P6+vrQ1dVV2TbjMYuMjMx0X3IiMjJS7TB7+nDyx+c3t65du4bWrVvDwcEB/v7+KkNpGc9hRv/X3v3H1PT/cQB/9sO9uRepK+sHhe5iS1qJfvij9fNTSCLDRlFG2ugPM2tSrn6M+iOLjIatX4uYFfqhlB8pNBINpenXlm6mH/phym3v7x/tnjn33ijyI9/XY7v/nPO+5/1+n3PqvM4579f7dnd3A1A/bwCgp6cHXl5eaG9vR3l5ORYtWqSxbgMDAzg4OAAYDfgtLS2xZcsW5OfnIyAgYEL1CwQCFBUVYfv27fD29gYw+r8lISEBsbGxMDMz4/VJ0xQmg4ODGB4e1tgn8m+gwIr89XR0dODu7o6SkhJ0dHTwLgDK1PTxzAMVEhKC8+fP48WLF8jOzv5VzdVIIpFALperLVddVl5ejnfv3uHu3bu8FG/VOXqUIiIikJmZifz8fBQXF3ODln+GpjtpiUSCx48fgzHGW//+/XsoFArMmTPnp+ocL7lczrt4KRQKdHV1cYGLsh1Xr179ZuCsLK8MxFTrmGokEgmqq6vVlo+3LxKJBB0dHWrLlQPxf+b4Pnv2jLuRKSkpgb6+vloZGxsb5OTkQKFQ8MZZ1dXVAQCWLl3KK9/T0wNPT080NzejrKxM4/iosSgHur9584ZXv7Kur2mqXyqV4uHDh2hvb0d3dzcsLS3x8eNHRERE8J4U29jY4NKlS5DL5bwbgrH6RP4d9CqQTAmRkZEYGRlBWFiYxky68XB2dkZISAgCAgK4O9Xfxc3NDWVlZbwL+cjICC5fvswrpwxaVO/ox3oKtXz5cri4uODEiRPIzs7Gjh07eK/gJouHhwcGBgaQl5fHW56RkcGt/x1UA+Lc3FwoFAouq/G///6Drq4u3r59CwcHB40fAFi8eDGMjY2Rm5vL215bWxuqqqp+S18mk6urK/r7+1FUVMRbrjrB5lg8PDy4oP5rGRkZEIlEcHJy+qF21dbWwtPTE/PmzUNpaSkMDAw0lgsICMDAwAAvqw4A0tPTYWpqCkdHR26ZMqhqampCSUkJ7OzsJtQm5WtjqVTKq7++vp7LPgRGg/asrCw4OjpqTAQxMzODjY0NRCIRkpKSIBaLERoayq339/eHlpYWL7MSGM2unD59utprW/LvoCdWZEpYtWoVUlNTsW/fPtjb22P37t2wtraGtrY2Ojo6uH/Iqq+wVF24cGFS23X06FHIZDLcuXOHu7hrEhUVhevXr8Pd3R3R0dEQiURITU1VmwHaxcUFBgYGCAsLQ0xMDKZNm4bs7Gw8f/58zG1HRERw0yaEh4dPVtd4goKCkJqaiuDgYLS0tMDGxgYPHjxAQkICVq9eDU9Pz19Sr6pr165BV1cXXl5eePnyJY4cOQJbW1turNSCBQtw7NgxHD58GE1NTfDx8YGBgQE6OztRXV0NsVgMmUwGbW1tyGQy7NmzB4GBgQgJCUFvby9kMhlMTEymXPp7cHAwkpOTsW3bNsTFxUEqlaKoqAi3bt0C8P10/piYGNy8eRNubm6Ijo6GoaEhsrOzUVBQgMTERI1Pmb6noaGBOy/i4+PR2NiIxsZGbr2lpSU39YGvry+8vLywd+9e9PX1QSqVIicnB8XFxcjKyoKOjg6A0VesyqkzTp48CYVCgUePHnHbNDIy4iYIPnfuHCoqKuDt7Y358+djcHAQFRUVOHXqFFxcXODv7899LyQkBKmpqdi0aROOHz+OuXPn4syZM2hoaOBNJAoAiYmJMDY2hrm5OTo7O5Gbm4u8vDxkZmbynqZaW1sjNDQUMTEx0NHRwYoVK1BSUoK0tDTExcXxXgV++vQJhYWFAMD15969e/jw4QPEYjF8fX0nvP/JH/SnR88TMhG1tbVs586dbOHChUwoFDI9PT0mlUpZUFAQKysr45Ud76SSP5MVeODAAaalpcVev3793bZXVlYyJycnJhQKmbGxMTt48CBLS0tTywqsqqpizs7OTCQSMSMjI7Zr1y5WU1MzZnbX0NAQEwqFzMfH57ttUPpWVqBYLNb4na6uLhYWFsZMTEyYrq4us7CwYJGRkezz58+8ctCQNdbc3MwAsKSkpHG1Q5XyWD59+pT5+fmxGTNmsJkzZ7KtW7fysiKV8vLymJubG5s1axYTCoXMwsKCBQYGstu3b/PKpaWlMalUygQCAbOysmIXL15k/v7+zM7OTq1PmrICVc8LTZl9FhYWbM2aNWptdHV1Za6urt/87ljHQ7k/vtbW1sY2bNjA7ZuNGzeywsJCBoDl5+erbUNVXV0d8/PzY/r6+kwgEDBbW1uN55um46uJch+N9VHddn9/P9u/fz8zNjZmAoGALVu2jOXk5PDKKM+jsT5f/x1XVlaytWvXMlNTUyYQCJhIJGK2trYsNjZW46S3crmcBQUFMUNDQ6anp8ecnJxYaWmpWjmZTMYsLS2ZUChks2fPZj4+Puz+/fsa98Hw8DCLiYlh5ubm3DmWkpKiVu5b/VLNUCV/Py3GxjmBDCFEzcqVK2FhYYErV678sTbcuHED69atQ0FBgdrEhWRient7YWVlhfXr1yMtLe1PN+enJSQkICoqCm1tbVP+Z5gImSoosCLkB/X19cHIyAi1tbXctAG/06tXr9Da2oqIiAiIxWLU1NRQCvcEyOVyxMfHw83NDRKJBK2trUhOTkZ9fT2ePHmicbLJv9np06cBAEuWLMGXL19QXl6OlJQUbN68mRsLRwj59WiMFSE/aNasWWo/K/I7hYeHo7KyEvb29khPT6egaoKEQiFaWloQHh6O7u5ubpD22bNnp1xQBYxOy5CcnIyWlhYMDQ3B3Nwchw4dQlRU1J9uGiH/V+iJFSGEEELIJJlaqS+EEEIIIX8xCqwIIYQQQiYJBVaEEEIIIZOEAitCCCGEkElCgRUhhBBCyCShwIoQQgghZJJQYEUIIYQQMkkosCKEEEIImST/A71zttcu6B3LAAAAAElFTkSuQmCC", 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SFBQkAIjffvtNJb19+/ZqUx5oW2ZWBg4cKHR0dJRj7HJTtyaPHz8WFhYWauPi8lJuXsf+lITjnNv631ccjnNxozpcn0qchQsXws3NDdHR0SpPhTRv3hxLlizBhAkT0KJFC3zxxReoUKECwsPDsXz5cpw/fx5LlixBs2bNVMpLSUlBnz59ULNmTa3+EqpTpw4A4Oeff8bgwYOhp6eH6tWrw8zMLNvt/P390a5dO7Ru3RpTpkyBvr4+AgIC8M8//2Dbtm1qvVy7d++Grq4u2rVrh5s3b2LWrFmoV68e+vTpk209P/74I1q0aIEmTZrgq6++gouLC54/f44///wTK1euhJmZWa7bklnfvn0xe/Zs9OvXD19++SXevXuHX375Jc9PWhY0Ly8vBAQE4MWLF1iyZIlK+vr161G2bNksx2ooFNQ+a3OugIweUE9PTxw/fjzHMrX53mhz/mNjY9G6dWsMGDAANWrUgJmZGS5evIiDBw+iR48eWdafl5+PMmXKoHv37tiwYQNev36NKVOmqIxd+fnnn9GiRQu0bNkSo0aNQsWKFfHmzRvcu3cP+/bty/JpYYXFixdj9uzZaN++PTp16qQ2Jqtp06bK/3fo0AHt2rXDqFGjEBcXBxcXF2zbtg0HDx7Eli1blL3duSnz888/h7m5ORo3bgxbW1u8ePECO3fuxPbt2/Hll18qx9hpWzeQ8YTZrl274O7uDgMDA1y/fh0LFixA1apV8c0336i0JTflHjhwAAkJCXjz5g0AICwsDL///jsAoGPHjjA2Ni7xxzk39RfH41wsFXVkR9rJqsdKCCEGDBggAKj1cAghRGhoqOjVq5ewtbUVurq6wsbGRvTo0UPjLLlpaWmiX79+okuXLmpzj2Rn+vTpwsHBQejo6GicxyqrHgjF3EEmJibCyMhING3aVOzbt08lT+a5sDp37ixMTU2FmZmZ6N+/v8bJ8jQJCwsTvXv3FlZWVkJfX19UqFBBDBkyROM8Vtm1JasehqCgIFG/fn1hZGQkKleuLJYtW5blU4FF3WP16tUroaOjI0xMTERycrIyXdGT8/6TiFnRZp/z0uacztWbN2+0euI1t9+bnM7/u3fvxMiRI0XdunWFubm5MDIyEtWrVxdz5sxRmSxR035l9fOR3Xk7fPiw8kmuO3fuqK1/+PCh8PX1FeXLlxd6enqiXLlyolmzZlr1Mnt6emqcX0uxvO/Nmzdi3Lhxws7OTujr64u6deuKbdu25bnMdevWiZYtWwpra2uhq6srypQpIzw9PTX2PmtTtxBC3L59W3z00UfC0tJS6OvrCxcXFzFz5kwRHx+v8RhoW66zs3OW+5TTz1tJOs7a1l8cj3NxJBPi/x8Now/eZ599hrt37+LgwYNqg6mLip+fH+bOnYt///0327FO9GEICgrCxx9/jOvXryt7gzTh94aIigoHrxMA4PHjx1izZg3Onz8Pa2trmJqawtTUFKdOnSrqphEphYSEoF+/ftkGVURERYljrAgA4OzsDHZeUnH3/fffF3UTiIiyxVuBRERERBLhrUAiIiIiiTCwIiIiIpIIAysiIiIiiTCwokK3YcMGyGQylaVcuXJo1aoV/vrrr0Kr/9GjRznmbdWqFVq1alUg7YiLi8O3334Ld3d3mJubw8DAABUrVoSvry+uXLmilv/cuXPo3bs37O3toa+vDzs7O/Tq1QuhoaG5qrdVq1aQyWRo37692rpHjx5BJpNpfEnrgwcP8MUXX6BatWowMjKCsbExatWqhZkzZ+Lp06ca6+rRowdkMlmW7zI7fvy4yvdALpejXLly6Ny5My5dupSr/dLkt99+Q/369WFoaAgHBwdMmDAB8fHx2W6zZs0ayGQylZcyZ3blyhW0bdsWpqamKFOmDHr06KHyepCcHDlyBB4eHjA2Noa1tTWGDBmi9gLtJ0+eoHv37qhcuTJMTExgYWGBBg0aYNmyZWqvCKlYsaLaz5NieX/alKzyjhw5UiWf4mfE0NBQ48vAW7VqpdUrSW7evInRo0fDw8MDJiYmkMlk2U7sqs35ev87k3nR5gXUu3fvRv/+/eHi4gIjIyNUrFgRn3zySY7vRU1MTES1atU0/nxcvnwZY8aMQZ06dWBmZgZbW1u0bds2y8laHzx4gB49eqBMmTIwNTVFu3btVH7m09LSUKZMGZWXsyv89NNPkMlk6N+/v9q6b775BjKZDH///XeOx4EKDgMrKjLr169HaGgozp49i1WrVkEul6Nz587Yt29fUTetwN2/fx8NGjTAggUL0Lp1a2zbtg2HDx/G3Llz8fz5c7i5uSE2NlaZf+nSpWjevDkiIiKwaNEiHDlyBD/88AOePn2KFi1aYNmyZbluw6FDh3KcpVvhr7/+Qt26dfHXX3/h888/x19//aX8/759+/Dxxx+rbRMdHa0MlAMDA7N8qTQAfPfddwgNDcXx48cxa9YsnD17Fp6envl6CXhgYCD69++PRo0a4cCBA5gzZw42bNiQ7UzpT58+xZQpU5QvYH7f//73P7Rq1QrJycnYsWMH1q1bhzt37qBly5YqL7XNyokTJ9ChQwfY2trijz/+wM8//4wjR47Ay8tL5f2LCQkJMDc3x6xZs/Dnn3/it99+Q4sWLTB27Fi1IGjPnj0IDQ1VWbZv3w4A6N69u1obmjdvrpZ/2rRpGtublJSEmTNn5rhfWbl06RL27t0LS0tLeHl5ZZs3t+dL8Z3JvGgT7C1cuBBv377FjBkzcPDgQcyfPx9Xr15Fw4YNcfPmzSy3mzVrFhISEjSu27ZtGy5cuABfX1/88ccfWLNmDQwMDODl5YVNmzap5P3333/RsmVL3LlzB+vWrcOOHTvw7t07tGrVCrdv3waQ8b7Xli1b4vTp02qB9PHjx2FiYoKQkBC1dhw/fhxWVlacjqSoFen0pPRBymoW+bdv3woDAwPRv3//Qqlfmxl9PT09haenpyT1pqeni7dv34rU1FRRp04dYW5uLm7cuKExb1BQkHJGb8V7Hz/++GO1GfFTUlKU7308ffq0Vu3w9PQU1apVE5UrVxZubm4q78J7+PChACC+//57ZdqDBw+EiYmJaNCggXj9+rXG/dq1a5da+vfffy8AiE6dOgkAIjAwUC1PSEiIACB27typkr5x40YBQMyePVurfXpfamqqsLe3F97e3irpihnmg4KCNG738ccfi86dO2c5I37v3r2FtbW1iI2NVaY9evRI6OnpialTp+bYrkaNGomaNWuqnMczZ84IACIgICDH7fv06SN0dXVV3hqgiZ+fnwAgjhw5opLu7OwsOnXqlGM9ip+R9u3bCx0dHXHt2jWV9ZreDKBJ5ncc7ty5U2Xm+cxyc76y+s5oS9PM+0+fPhV6enpi2LBhGrc5f/680NfXV+5D5p+PrMpMTU0VdevWFVWqVFFJ//LLL4Wenp549OiRMi02NlZYW1uLPn36KNMWL14sAIjQ0FBlWlpamihbtqyYMmWK2jsok5KShJGRkejZs2cOR4AKGnusqNgwNDSEvr4+9PT0VNJfvnyJ0aNHo3z58tDX10flypUxY8YMlb/wFbewNmzYoFauTCaDn59ftnULIbBo0SI4OzvD0NAQDRs2xIEDBzTmjYuLw5QpU1CpUiXo6+ujfPnymDBhgtpfs4pbYL/++itcXV1hYGCAjRs3Yu/evbhx4wamT5+e5V/YHTp0UL4fy9/fHzKZDCtWrICururUc7q6uggICIBMJsOCBQuy3cfM9PT08O233+Ly5cvK3o2s/Pjjj0hISEBAQAAsLCzU1stkMo29CuvWrYOtrS02btwIIyMjrFu3Tuv2ubu7AwCeP3+u9TaZnTt3DpGRkRg6dKhKeu/evWFqaoo9e/aobbNlyxacOHECAQEBGstMTU3FX3/9hZ49e8Lc3FyZ7uzsjNatW2ssM7OnT5/i4sWLGDhwoMp5bNasGapVq5bj9gBQrlw56OjoqLxn7X1CCKxfvx6VK1dGmzZtciwzO1OnToWVlVWWPVo5yfyOw+zk5XzllY2NjVqag4MDHB0d8eTJE7V1ycnJ8PX1xZgxY5TfS23KlMvlcHNzUytzz549aNOmDZydnZVp5ubm6NGjB/bt26fsoWrdujUAqNw6vX79Ol69eoXPP/8c9vb2Kr1W58+fR2JionI7KjoMrKjIpKWlITU1FSkpKYiIiFAGJwMGDFDmeffuHVq3bo1NmzZh0qRJ2L9/Pz799FMsWrQo21s6uTV37lxMmzYN7dq1w969ezFq1Ch89tlnyq55hbdv38LT0xMbN27EuHHjcODAAUybNg0bNmxAly5d1CZZ3bt3L1asWIHZs2fj0KFDaNmyJQ4fPgwA6NatW47tSktLQ0hICNzd3eHo6Kgxj5OTE9zc3HDs2LFcvQi5b9++cHNzw8yZM5GSkpJlvsOHD8PW1lblBa85OXv2LG7duoVBgwbBysoKPXv2xLFjx/Dw4UOttlfkq1atmtZ1ZvbPP/8AAOrWrauSrqenhxo1aijXK0RHR2PChAlYsGBBlsf5/v37SExMVCtTUc+9e/eyvd2ZVZsUae+3CcgIklJTU/Hq1Sts374dGzZswOTJk9UC7MyOHDmCx48fw9fXV+MLxE+ePAkzMzPo6emhZs2aWLx4cZbfGzMzM8ycOTNXt43zIrfnCwDGjBkDXV1dmJubw8fHB6dPn85z/Q8ePMDjx49VXmSvMG/ePCQkJKi9ZDgnqampOHXqlEqZiYmJuH//fpbfgcTEROV4vXr16qFs2bIqwVNISAjs7e1RtWpVfPTRRypBlyIfA6uix5nXqci8f6E2MDDAsmXL4OPjo0zbuHEj/v77b+zYsQO9e/cGALRr1w6mpqaYNm0agoOD0a5du3y14/Xr11i4cCG6d++ONWvWKNNr1aqF5s2bo3r16sq0X375BX///TfOnz+v/OvVy8sL5cuXR69evXDw4EGVAafx8fG4ceMGypYtq0wLDw8HAFSqVCnHtr148QJv377NMW+lSpVw4cIFxMTEaPzrWROZTIaFCxeibdu2WLlyZZYDzMPDw1G/fn2tylRYu3YtAMDX1xcAMGzYMGzZsgXr16/HvHnz1PKnp6crg+yrV69i8uTJqFmzpnL73IqJiQEAWFpaqq2ztLRUe3Bh9OjRqF69OkaNGpXnMoUQePXqFezt7fO0vWJ9ZgsXLsT06dMBZJyvr7/+GvPnz8+yjUDGsZfL5RgyZIjauk6dOsHd3R1VqlTBq1evsHPnTkyZMgXXrl3D5s2bNZY3cuRI/Pzzz5g2bRouXLigMVjLr9ycLwsLC4wfPx6tWrWClZUV7t27h++//x6tWrXC/v37VX5/aCM1NRXDhg2DqakpJk6cqLLu2rVrWLRoEfbt2wcTExOtxtEp+Pn54d69e9i7d68y7dWrVxBCZLmfwH/HQkdHB56enggODkZqaip0dXVx/PhxeHp6AgA8PT0xZ84cCCGUDwXY2NigZs2audp/kh57rKjIbNq0CRcvXsTFixdx4MABDB48GGPGjFEZiH3s2DGYmJigV69eKtsqLhpHjx7NdztCQ0Px7t07fPLJJyrpzZo1U+muBzIGcdeuXRv169dHamqqcvHx8dH4xFObNm1UgqqCougpU1z0FIGKYsmqR8LLywve3t6YN28e3rx5I0lb4uPjsWPHDjRr1gw1atQAkHERqFKlCjZs2ID09HS1bfr27Qs9PT0YGxujefPmiIuLw/79+1GmTJl8tSWrICBz+q5du7Bv3z6sXr1aq6Ahuzz52V5T+pAhQ3Dx4kUcOnQIU6dOxffff4+xY8dmWfbLly+xd+9etG/fHuXLl1dbv3z5cgwdOhQfffQRunbtii1btuCLL77Ali1bcPXqVY1l6uvrY/78+bh06RJ27NiR4/7lhzbHpkGDBliyZAm6deuGli1bYujQoTh79izs7e0xderUXNUnhMCwYcNw6tQpbNq0CU5OTsp1qamp8PX1Rd++fXMdrK1ZswbffvstJk+ejK5du2a7P9mta926NRISEnDx4kWkp6fj1KlTyqeUPT098e+//+LmzZtISkrCuXPn2FtVTDCwoiLj6uoKd3d3uLu7o3379li5ciW8vb0xdepUvH79GkDGX292dnZqv4hsbGygq6ur8a/83FKUYWdnp7bu/bTnz5/j77//hp6enspiZmYGIQRevHihkl9T70WFChUAQKvbYtbW1jA2Ns4x76NHj2BsbKz8q9fX11elfdk9kbVw4UK8ePFC4xQLivZqewsPALZv3474+Hj06dMHr1+/xuvXrxEbG4s+ffrgyZMnCA4O1tiGixcv4sSJE5gxYwaeP3+Obt26qYyjyw0rKysA0Pj9ePnypfI4xcfHY8yYMRg7diwcHByU7U1OTgaQ0ZupGDuXU5kymSzbQFDbNmVmZ2cHd3d3eHt7Y8GCBZg3bx6WLVuWZRC0ZcsWJCUlYfjw4Vm2432ffvopAGQ7VUG/fv3QsGFDzJgxI9vbxnmVl2OTWZkyZfDxxx/j77//RmJiolZ1CiEwfPhwbNmyBRs2bFALgJYsWYIHDx5gzpw5yu9FXFwcgIwhCq9fv9b4B8v69esxYsQIfP7552rvtixbtixkMlmW+wmo9topAqWQkBBcvXoVr1+/VvZY1axZE+XKlcPx48dx7tw5jq8qRhhYUbGiGGdw584dABm/cJ8/f642dik6OhqpqamwtrYGAOV8Pe9fiLUJvBS/1KOiotTWvZ9mbW2NOnXqKHva3l9mzZqlkl/TX6aKv34z3yLIilwuR+vWrXHp0iVERERozBMREYHLly+jTZs2ykHNfn5+Ku1auXJllnXUr18f/fv3x48//qhxsLiPjw+eP3+u1RxBwH+3ASdMmICyZcsqF39/f5X1mVWuXBnu7u746KOPMH/+fMybNw/Xr1/H0qVLtarzfYrHzW/cuKGSnpqaiv/973/KhwZevHiB58+fY/HixSpt3bZtGxISElC2bFllT2aVKlVgZGSkVqaiHhcXF7V5ozJT1JnV9tpMFdC4cWMAUP58vG/t2rWwtbXVOP1FVhQ/W9kNNFfcNr5//z5WrVqlddna0vZ8Zef9Xtuc8g4fPhzr16/HmjVrlMFlZv/88w9iY2NRtWpV5feiXr16ADKmXihbtqxae9evX4/hw4dj8ODB+PXXX9XaYmRkBBcXlyy/A0ZGRqhcubIyrXbt2srg6fjx47C1tVX2AgPARx99hJCQEGVPOQOrYqIInkSkD1xW0y0IIUS7du0EAPHgwQMhhBArV64UAMTu3btV8ike5Q8ODhZCZDzyb2hoKEaPHq2Sb+3atQKAmDNnjlr9iukWXr58KQwNDUX37t1VtlU8Bp95uoX58+cLY2NjZfuyA0CMGTNGLV2b6RYOHjyoNt1C586dRWpqqlpZiukWzpw5k2ObhND8qPyDBw+Evr6+6NChQ56mW1Ccn7CwMAFA9OzZU4SEhKgtXl5eQl9fX7x48UIIkfWj88nJycLFxUVYWVmJuLg4rfYrM8Xj++3bt1dJ37ZtmwAgDhw4IIQQIjExUWM7fXx8hKGhoQgJCVE5R3369BE2NjYqbXr8+LHQ19cX06ZNy7FdjRs3FrVr11Y5j6GhoQKAWLFiRY7bz5o1SwAQly5dUlt38eJFAUCraR8yGzVqlACgMqVCVj+j7dq1EzY2NsLNzU2r6RYy02a6hZzOV1ZevnwpypcvL+rXr59jO9LT08WwYcOETCYTq1atyjLfrVu31L4XivaMHDlShISEiDdv3ijzr1+/Xujo6IhBgwapTDPxvqlTpwp9fX0RHh6uTIuLixPlypUTffv2Vcvfq1cvYWJiInx8fFSmYxBCiF9++UVYWVkJT09P4eDgkOO+U+FgYEWFTvFLe/369SI0NFSEhoaKv/76S/j6+goAKgFOYmKiqFu3rjAzMxM//vijCA4OFnPmzBF6enqiY8eOKuUOHz5cGBoaisWLF4sjR46I7777TtSuXTvHwEoIIWbOnCkAiGHDhomDBw+K1atXi/Llyws7OzuVwCo+Pl40aNBAODo6isWLF4vg4GBx6NAhsXr1atG7d29x7tw5Zd6sAishhLh3756oXLmyMDU1FV9++aUICgoSJ06cEJs2bRJdunQRMplMJYj55ZdfhI6OjmjatKnYsmWLOHnypNiyZYvw8PAQOjo64pdfftH6+Gc1B9H48eMFAI3z9Ozbt08YGxuLihUrih9++EEcPXpUHD16VCxdulQ0aNBAeUGbPHmyACDOnz+vse4///xTABBLliwRQmQ/J9GOHTsEAPHNN98o0xT5M5/PrGzevFkAEJ9//rkICQkRq1atEmXKlBHt2rXLcdus5rG6deuWMDU1FR999JEICgoSu3fvFrVr1xYODg4iOjpaJa9cLhdt2rRRSQsJCRG6urqie/fuIjg4WAQGBgonJydRu3ZtlbmpZs+eLUaMGCECAwPF8ePHxd69e8XIkSOFXC4XvXv31tjmkSNHCgDi9u3bGtcHBgaKnj17inXr1omjR4+KXbt2iX79+gkAYsiQISp5swqsrly5ImQymQCgVWCVkJAgdu7cKXbu3Kn8bvj5+YmdO3eqzSWm7fnq37+/mDZtmti5c6cyX/Xq1YWurq7yDy0FX19fIZfLVeaM+uKLLwQA4evrq/z9o1iuXLmS7f5omudNiIzvqo6OjmjYsKE4c+aMWrmZz210dLSwt7cXderUEXv27BFBQUHio48+EmZmZuLWrVtqdS5fvlwAEDKZTCxfvlxl3fXr15XrPvnkk2zbToWHgRUVOsUv7cyLhYWFqF+/vvjxxx/VJj+MiYkRI0eOFPb29kJXV1c4OzuL6dOnq+WLjY0Vw4cPF7a2tsLExER07txZPHr0SKvAKj09Xfj7+wsnJyehr68v6tatK/bt26dxgtD4+Hgxc+ZMUb16daGvry8sLCxEnTp1xMSJE0VUVJQyX3aBlRBCvH79WnzzzTeiYcOGwtTUVOjp6YkKFSqITz/9VGPvU2hoqOjVq5ewtbUVurq6wsbGRvTo0UOcPXtWi6P+n6wCq3///VeYm5trvHAIIcT9+/fF6NGjhYuLizAwMBBGRkaiZs2aYtKkSeLhw4ciOTlZ2NjYZNtrkJqaKhwdHUWdOnWEEDlP9tikSRNRtmxZZZC5b98+AUD8+uuvWu3r1q1bRd26dYW+vr6ws7MT48aNU+llyEpWgZUQQly6dEl4eXkJY2NjYW5uLrp16ybu3bunlu/93k6Fw4cPi6ZNmwpDQ0NhaWkpBg0apDbB5J9//inatm2rPNempqaicePG4pdfflGbJFaIjMl1LSwsxEcffZTlPoWGhgovLy9hZ2cn9PT0hLGxsWjUqJEICAhQ62HJrld5wIABWgdWikBE0+Ls7KyWX5vz5e/vL+rXry8sLCyEXC4X5cqVE927dxcXLlxQK2/w4MFqP+vOzs65apOm/Xn/50NRT1bL+5MR37t3T3Tr1k2Ym5sLY2Nj4eXlJS5fvqyxTkUvMADxzz//qKxLT08XlpaWAoBYvXp1tm2nwiMT4r3BK0RExdTUqVOxbds23L17N9vxTERERYWD14moxAgJCcGsWbMYVBFRscUeKyIiIiKJsMeKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIMrCR08uRJdO7cGQ4ODpDJZNi7d2+utn/37h2GDBmCOnXqQFdXF926ddOYLykpCTNmzICzszMMDAxQpUoVrFu3Lv87QERERPmiW9QNKE0SEhJQr149DB06FD179sz19mlpaTAyMsK4ceOwa9euLPP16dMHz58/x9q1a+Hi4oLo6Gikpqbmp+lEREQkAQZWEurQoQM6dOiQ5frk5GTMnDkTgYGBeP36NWrXro2FCxeiVatWAAATExOsWLECAHDmzBm8fv1arYyDBw/ixIkTePDgASwtLQEAFStWlHpXiIiIKA94K7AQDR06FGfOnMFvv/2Gv//+G71790b79u1x9+5drcv4888/4e7ujkWLFqF8+fKoVq0apkyZgsTExAJsOREREWmDPVaF5P79+9i2bRsiIiLg4OAAAJgyZQoOHjyI9evX47vvvtOqnAcPHuD06dMwNDTEnj178OLFC4wePRovX77kOCsiIqIixsCqkFy5cgVCCFSrVk0lPSkpCVZWVlqXk56eDplMhsDAQFhYWAAAfvzxR/Tq1QvLly+HkZGRpO0mIiIi7TGwKiTp6emQy+W4fPky5HK5yjpTU1Oty7G3t0f58uWVQRUAuLq6QgiBiIgIVK1aVbI2ExERUe4wsCokDRo0QFpaGqKjo9GyZcs8l9O8eXPs3LkT8fHxyoDszp070NHRgaOjo1TNJSIiojzg4HUJxcfH49q1a7h27RoA4OHDh7h27RrCw8NRrVo1fPLJJxg0aBB2796Nhw8f4uLFi1i4cCGCgoKUZYSFheHatWt4+fIlYmNjVcoDgAEDBsDKygpDhw5FWFgYTp48iS+//BK+vr68DUhERFTEZEIIUdSNKC2OHz+O1q1bq6UPHjwYGzZsQEpKCubPn49Nmzbh6dOnsLKygoeHB+bOnYs6deoAyJg64fHjx2plZD5N//vf/zB27FicOXMGVlZW6NOnD+bPn8/AioiIqIgxsCIiIiKSCG8FEhEREUmEg9clkJ6ejmfPnsHMzAwymayom0NERERaEELgzZs3cHBwgI6ONH1NDKwk8OzZMzg5ORV1M4iIiCgPnjx5ItmT9QysJGBmZgYg48SYm5sXcWuIiIhIG3FxcXByclJex6XAwEoCitt/5ubmDKyIiIhKGCmH8XDwOhEREZFEGFgRERERSYSBFREREZFEGFgRERERSYSBFREREZFEGFgRERERSYSBFREREZFEGFgRERERSYSBFREREZFEGFgRERERSYSBFREREZFEGFgRERERSYSBFREREZFEGFgRERERSYSBVQFKSABksowlIaGoW0NEREQFjYEVERERkUQYWBERERFJhIEVERERkUQYWBERERFJhIEVERERkUQYWBERERFJhIEVERERkUQYWBERERFJhIEVERERkUQYWBERERFJhIEVERERkUQYWBERERFJhIEVERERkUQYWBERERFJhIEVERERkUQYWBERERFJhIEVERERkUQYWBERERFJhIEVERERkUQYWBERERFJpNQFVidPnkTnzp3h4OAAmUyGvXv3Zpt/9+7daNeuHcqVKwdzc3N4eHjg0KFDhdNYIiIiKlVKXWCVkJCAevXqYdmyZVrlP3nyJNq1a4egoCBcvnwZrVu3RufOnXH16tUCbikRERGVNjIhhCjqRhQUmUyGPXv2oFu3brnarlatWujbty9mz56tVf64uDhYWFggNjYW5ubmyvSEBMDUNOP/8fGAiUmumkFEREQFKKvrd37oSlJKKZKeno43b97A0tIyyzxJSUlISkpSfo6LiyuMphEREVExV+puBebX4sWLkZCQgD59+mSZx9/fHxYWFsrFycmpEFtIRERExRUDq0y2bdsGPz8/bN++HTY2Nlnmmz59OmJjY5XLkydPCrGVREREVFzxVuD/2759O4YNG4adO3eibdu22eY1MDCAgYFBIbWMiIiISgr2WCGjp2rIkCHYunUrOnXqVNTNISIiohKq1PVYxcfH4969e8rPDx8+xLVr12BpaYkKFSpg+vTpePr0KTZt2gQgI6gaNGgQfv75ZzRt2hRRUVEAACMjI1hYWBTJPhAREVHJVOp6rC5duoQGDRqgQYMGAIBJkyahQYMGyqkTIiMjER4ersy/cuVKpKamYsyYMbC3t1cu48ePL5L2ExERUclVquexKiycx4qIiKjkKYh5rEpdjxURERFRUWFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERCQRBlZEREREEmFgRURERKVSQgIgk2UsCQmFUycDKyIiIiKJMLAiIiIikggDKyIiIiKJMLAiIiIikggDKyIiIiKJMLAiIiIikggDKyIiIiKJMLAiIiIikggDKyIiIiKJlLrA6uTJk+jcuTMcHBwgk8mwd+/eHLc5ceIE3NzcYGhoiMqVK+PXX38t+IYSERFRqVPqAquEhATUq1cPy5Yt0yr/w4cP0bFjR7Rs2RJXr17F119/jXHjxmHXrl0F3FIiIiIqbXSLugFS69ChAzp06KB1/l9//RUVKlTAkiVLAACurq64dOkSfvjhB/Ts2bOAWklERESlUanrscqt0NBQeHt7q6T5+Pjg0qVLSElJ0bhNUlIS4uLiVBYiIiKiDz6wioqKgq2trUqara0tUlNT8eLFC43b+Pv7w8LCQrk4OTkVRlOJiIiomPvgAysAkMlkKp+FEBrTFaZPn47Y2Fjl8uTJkwJvIxERERV/pW6MVW7Z2dkhKipKJS06Ohq6urqwsrLSuI2BgQEMDAwKo3lERERUgnzwPVYeHh4IDg5WSTt8+DDc3d2hp6dXRK0iIiKikqjUBVbx8fG4du0arl27BiBjOoVr164hPDwcQMZtvEGDBinzjxw5Eo8fP8akSZNw69YtrFu3DmvXrsWUKVOKovlERERUgpW6W4GXLl1C69atlZ8nTZoEABg8eDA2bNiAyMhIZZAFAJUqVUJQUBAmTpyI5cuXw8HBAb/88gunWiAiIqJckwnFSG3Ks7i4OFhYWCA2Nhbm5ubK9IQEwNQ04//x8YCJSRE1kIiI6AOU03U4q+t3fuT6VuC7d+9w48YNvH37Vm3dmTNnJGkUERERUUmUq8AqNDQUTk5OaNWqFcqVK4cFCxaorM/NjOdEREREpU2uAqvJkydj8eLFiImJweXLl7F79274+voiPT0dwH/zPxERERF9iHIVWIWFhSmfqKtRowZOnDiB6Oho9OrVC8nJyQXSQCIiIqKSIleBlbm5OZ4+far8bGRkhL1798LQ0BDt27dX9lwRERERfYhyFVi1bdsW69evV0nT1dVFYGAgqlSpgsTEREkbR0RERFSS5Gq6heTkZKSmpsLY2Fjj+vDwcFSoUEGyxpUUnG6BiIio+CmK6RZyNUGovr4+9PX1s1z/IQZVRERERAql7pU2REREREVFslfa7N27F4GBgXj8+DHevXunsk4mk+H69etSVUVERERULEkSWH3//feYNm0aypUrBxcXF5hwMBERERF9gCQJrAICAuDr64uVK1dCLpdLUSQRERFRiSPJGKuYmBgMGDCAQRURERF90CQJrJo3b45bt25JURQRERFRiSXJrcAlS5age/fucHJyQvv27bOdkoGIiIiotJIksHJxcUHbtm3RvXt3yGQytQlEZTIZYmNjpaiKiIiIqNiSJLCaOnUqli1bhvr168PV1ZU9VkRERPRBkiSw2rBhA6ZNmwZ/f38piiMiIiIqkSQZvJ6WloZ27dpJURQRERFRiSVJYOXt7Y1z585JURQRERFRiSXJrcBZs2ahb9++MDExQadOnWBpaamWR1MaERERUWkiE0KI/Baio5PR8SWTybLMk5aWlt9qiq24uDhYWFggNjYW5ubmyvSEBMDUNOP/8fEA3/RDRERUeHK6Dmd1/c4PSXqsZs+enW1QRURERB+eD7GDQZLAys/PT4piiIiIiEo0SQavp6SkICEhQeO6hIQEpKSkSFENERERUbEmSY/V8OHDkZycjG3btqmt+/zzz2FkZIQ1a9ZIURURERFRsSVJj9Xx48fRpUsXjes6d+6Mo0ePSlENERERUbEmSWD1/Plz2Nvba1xnZ2eHqKgoKaohIiIiKtYkCazKlCmDe/fuaVx37949mJmZSVENERERUbEmSWDVunVr+Pv74+XLlyrpL1++xIIFC9CmTRspqiEiIiIq1iSbbqFRo0aoWrUq+vbti/LlyyMiIgI7d+5ESkoK5s6dK0U1RERERMWaJIFV9erVcerUKUyaNAmrV69GWloa5HI5PD098eOPP6J69epSVENERERUrOU5sPr555/Rs2dPODo6AgDq1auHo0ePIjExEa9evYKlpSUMDQ0laygRERFRcZfnMVbfffcdnJ2d0aRJE3z//fe4f/8+AMDIyAgODg4MqoiIiOiDk+fAKjIyEkeOHEGjRo2wZMkSVKtWDfXr18c333yDsLAwKdtIREREVCLkObDS0dFB69atsWzZMjx9+hQnT55E69atsXbtWtSpUweurq6YOXMmrl69KmV7tRIQEIBKlSrB0NAQbm5uOHXqVLb5AwMDUa9ePRgbG8Pe3h5Dhw5FTExMIbWWiIiISgtJplsAgObNm+Onn37Co0ePEBoaii5dumD79u1wd3dH5cqVMXXqVKmqytb27dsxYcIEzJgxA1evXkXLli3RoUMHhIeHa8x/+vRpDBo0CMOGDcPNmzexc+dOXLx4EcOHDy+U9hIREVHpIRNCiIKs4Nq1a9i1axd2796NmzdvFmRVAIAmTZqgYcOGWLFihTLN1dUV3bp1g7+/v1r+H374AStWrFCOEQOApUuXYtGiRXjy5InGOpKSkpCUlKT8HBcXBycnJ8TGxsLc3FyZnpAAmJpm/D8+HjAxye/eERERlRxFfR3Mqf64uDhYWFioXb/zQ7Ieq6woxl0VRlCVnJyMy5cvw9vbWyXd29sbZ8+e1bhNs2bNEBERgaCgIAgh8Pz5c/z+++/o1KlTlvX4+/vDwsJCuTg5OUm6H0RERFQy5Xm6haxurWWlQoUKea1Kay9evEBaWhpsbW1V0m1tbbN8X2GzZs0QGBiIvn374t27d0hNTUWXLl2wdOnSLOuZPn06Jk2apPys6LEiIiKiD1ueA6uKFStCJpNpnT8tLS2vVeXa++0SQmTZ1rCwMIwbNw6zZ8+Gj48PIiMj8eWXX2LkyJFYu3atxm0MDAxgYGAgebuJiIioZMtzYLVu3bpcBVaFwdraGnK5XK13Kjo6Wq0XS8Hf3x/NmzfHl19+CQCoW7cuTExM0LJlS8yfPx/29vYF3m4iIiIqHfIcWA0ZMkTCZkhDX18fbm5uCA4ORvfu3ZXpwcHB6Nq1q8Zt3r59C11d1cMgl8sBZPR0EREREWlL8sHrd+7cQWhoKO7evSt10VqZNGkS1qxZg3Xr1uHWrVuYOHEiwsPDMXLkSAAZ46MGDRqkzN+5c2fs3r0bK1aswIMHD3DmzBmMGzcOjRs3hoODg2TtevpUsqKIiIiomJLkJcwAsHPnTkyZMgURERHKNEdHRyxevBi9evWSqpoc9e3bFzExMZg3bx4iIyNRu3ZtBAUFwdnZGUDGjPGZB94PGTIEb968wbJlyzB58mSUKVMGbdq0wcKFC/Pdlo0b//u/qyuwahUwbFi+iyUiIqJiSpJ5rIKCgtC5c2fUqlULAwcOhIODA54+fYotW7YgLCwM+/btQ4cOHaRob7GkaR6MiAjA2RlIT/8vn1wOPHoE/P97q4mIiEq1D3EeK0kCq+bNm8Pc3Bz79++Hjs5/dxeFEOjQoQPevHmDM2fO5LeaYkvTiQkJAdq0Uc8bEgK0alW47SMiIioKH2JgJckYq2vXrmH06NEqQRWQMe3B6NGjcf36dSmqKVGqVgXeOxyQywEXl6JpDxERERU8SQIruVyO5ORkjetSUlLUAq4PgaMjkHmOUbkcWLmStwGJiKj4SUgAZLKMJSGhqFtTskkS8TRq1AiLFi1CYmKiSnpSUhJ++OEHNGnSRIpqSpzBg//7f1gYB64TERGVdpI8FTh37lx4eXmhcuXK6N27N+zs7BAZGYndu3cjJiYGx44dk6KaEq18+aJuARERERU0SQKrFi1a4PDhw/jqq6+wfPlyCCGgo6ODJk2aYNu2bWjWrJkU1RAREREVa5LNY+Xp6YnQ0FC8ffsWr169QtmyZWFsbCxV8URERETFnmSBlYKxsTEDKiIiIvogfXiP6xEREREVEMl6rPbu3YvAwEA8fvwY7969U1knk8k+yLmsiIiI6MMiSWD1/fffY9q0aShXrhxcXFxgUthTqxIREREVA5IEVgEBAfD19cXKlSshl8ulKJKIiIioxJFkjFVMTAwGDBjAoIqIiIg+aJIEVs2bN8etW7ekKIqIiIioxJLkVuCSJUvQvXt3ODk5oX379tDX15eiWCIiIqISRZLAysXFBW3btkX37t0hk8nU5rGSyWSIjY2VoioiIiKiYkuSwGrq1KlYtmwZ6tevD1dXV/ZYERER0QdJksBqw4YNmDZtGvz9/aUojoiIiKhEkmTwelpaGtq1aydFUUREREQlliSBlbe3N86dOydFUUREREQlliS3AmfNmoW+ffvCxMQEnTp1gqWlpVoeTWlEREREpYlMCCHyW4iOTkbHl0wmyzJPWlpafqsptuLi4mBhYYHY2FiYm5sr0xMSAFPTjP/HxwN80w8RERVHBXW9KurrYE71Z3X9zg9Jeqxmz56dbVBFRERE9CGQJLDy8/OTohgiIiKiEk2SwetERERExMCKiIiISDIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIiISCIFHli1adMGn376KcLCwgq6KiIiIqIiVeCB1fHjx7F161bUrVsXAwcOLOjqiIiIiIpMgQdW6enpePPmDf7880/Y29sXdHVERERERUaSmddzYmJigo4dO6Jjx46FUR0RERFRkSiVg9cDAgJQqVIlGBoaws3NDadOnco2f1JSEmbMmAFnZ2cYGBigSpUqWLduXSG1loiIiEqLPPdYhYeH5yp/hQoV8lpVrmzfvh0TJkxAQEAAmjdvjpUrV6JDhw4ICwvLsg19+vTB8+fPsXbtWri4uCA6OhqpqamF0l4iIiIqPWRCCJGXDXV0dCCTybTOn5aWlpdqcq1JkyZo2LAhVqxYoUxzdXVFt27d4O/vr5b/4MGD6NevHx48eABLS8s81RkXFwcLCwvExsbC3NxcmZ6QAJiaZvw/Ph4wMclT8URERAWqoK5XRX0dzKn+rK7f+ZHnHqt169blKrAqDMnJybh8+TK++uorlXRvb2+cPXtW4zZ//vkn3N3dsWjRImzevBkmJibo0qULvvnmGxgZGWncJikpCUlJScrPcXFx0u0EERERlVh5DqyGDBkiYTOk8eLFC6SlpcHW1lYl3dbWFlFRURq3efDgAU6fPg1DQ0Ps2bMHL168wOjRo/Hy5cssx1n5+/tj7ty5krefiIiISjbJB6/fuXMHoaGhuHv3rtRFa+39njQhRJa9a+np6ZDJZAgMDETjxo3RsWNH/Pjjj9iwYQMSExM1bjN9+nTExsYqlydPnki+D0RERFTySBZY7dy5E87OznB1dUWLFi1Qo0YNODs74/fff5eqihxZW1tDLper9U5FR0er9WIp2Nvbo3z58rCwsFCmubq6QgiBiIgIjdsYGBjA3NxcZSEiIiKSJLAKCgpCv379YGFhgQULFmDTpk3w9/eHhYUF+vXrhwMHDkhRTY709fXh5uaG4OBglfTg4GA0a9ZM4zbNmzfHs2fPEB8fr0y7c+cOdHR04OjoWKDtJSIiosLx9Gnh1JPnpwIza968OczNzbF//37o6PwXqwkh0KFDB7x58wZnzpzJbzVa2b59OwYOHIhff/0VHh4eWLVqFVavXo2bN2/C2dkZ06dPx9OnT7Fp0yYAQHx8PFxdXdG0aVPMnTsXL168wPDhw+Hp6YnVq1drVSefCiQiopKstD4VGBAAjBmT8X8dHWDVKmDYsP/WF6unAjO7du0afvvtN5WgCsgY6zR69GgMGDBAimq00rdvX8TExGDevHmIjIxE7dq1ERQUBGdnZwBAZGSkyhxcpqamCA4OxtixY+Hu7g4rKyv06dMH8+fPL7Q2ExERfcgKIgCLiADGjv3vc3o6MGIE4OMDFOQNKUkCK7lcjuTkZI3rUlJS1AKugjZ69GiMHj1a47oNGzaopdWoUUPt9iERERGVXHfvZgRTmaWlAffuFWxgJUnE06hRIyxatEjtKbqkpCT88MMPaNKkiRTVEBEREWmlatWM23+ZyeWAi0vB1itJj9XcuXPh5eWFypUro3fv3rCzs0NkZCR2796NmJgYHDt2TIpqiIiIiLTi6AgsXfrfGCu5HFi5smB7qwCJAqsWLVrg8OHD+Oqrr7B8+XIIIaCjo4MmTZpg27ZtWT6RR0RERFRQBg/+L7AKCwOqVSv4OiUJrADA09MToaGhePv2LV69eoWyZcvC2NhYquKJiIiI8qx8+cKpR5LAKiUlBcnJyTAxMYGxsbFKQJWQkAB9fX3o6elJURURERFRsSXJ4PXPPvsMw4cP17ju888/x6hRo6SohoiIiKhYkySwCgkJQZcuXTSu69y5M44ePSpFNURERETFmiSB1fPnz2Fvb69xnZ2dndq7+4iIiIhKI0kCqzJlyuDevXsa1927dw9mZmZSVENERERUrEkSWLVu3Rr+/v54+fKlSvrLly+xYMECtGnTRopqiIiIiIo1SZ4K9PPzQ6NGjVC1alX07dsX5cuXR0REBHbu3ImUlBTMnTtXimqIiIiIijVJAqvq1avj1KlTmDRpElavXo20tDTI5XJ4enrixx9/RPXq1aWohoiIiKhYk2yC0Hr16uHo0aNITEzEq1evYGlpCUNDQ6mKJyIiIir2JAusFIyMjGBkZCR1sURERETFniSD14mIiIiIgRURERGVIAkJgEyWsSQkFHVr1DGwIiIiIpIIAysiIiIiiTCwIiIiIpKIZE8FpqWlYceOHQgJCUFMTAysrKzQunVr9O7dG7q6kj98SERERFTsSBLxvHjxAu3bt8eVK1egq6sLKysrxMTEYM2aNfjhhx9w6NAhWFtbS1EVERFRkUlIAExNM/4fHw+YmBRte6j4keRW4MSJE3H79m0EBgYiMTERkZGRSExMxJYtW3D37l1MnDhRimqIiIiIijVJeqz27duH+fPno3///so0uVyOAQMGIDo6Gn5+flJUQ0RERFSsSdJjJYRArVq1NK6rXbs2hBBSVENERERUrEkSWLVt2xZHjhzRuC44OBitWrWSohoiIiIqoZ4+LeoWFA5JbgXOmjULPXr0QFpaGgYMGAA7OztERUUhMDAQu3fvxu7du/Hy5UtlfktLSymqJSIiomJs48b//u/qCqxaBQwbVnTtKQwyIcF9Oh2d/zq+ZDKZ8v+KojOnARlTM5QmcXFxsLCwQGxsLMzNzZXpfHqEiKh0Ka2/1wtivyIiAGdnID39vzS5HHj0CHB0zHv9UubN6vqdH5L0WM2ePVsteCIiIqKS5+lToFq1/Jdz965qUAUAaWnAvXvqgVVpIklgxaf+iIiISq6CuGVXtSqgo6PeY+Xikr9yizvJX2lz584dhIaG4u7du1IXTURERBKLiADGjv3vc3o6MGJERnp+ODoCS5f+91kuB1auLN29VYCEgdXOnTvh7OwMV1dXtGjRAjVq1ICzszN+//13qaogIiIiiWV3yy6/Bg/+7/9hYaV/4DogUWAVFBSEfv36wcLCAgsWLMCmTZvg7+8PCwsL9OvXDwcOHJCiGiIiIpKY4pZdZgVxy658eWnLK64keSqwefPmMDc3x/79+1WeEBRCoEOHDnjz5g3OnDmT32qKLT4VSET0YSitv9cDAoAxYzL+r7hlJ0XvkrbHqzQ9FShJj9W1a9cwevRolaAKyJhmYfTo0bh+/boU1RAREVEB+BBv2RUUSQIruVyO5ORkjetSUlLUAi4iIqKSrrTOJP6h3LIrKJJEPI0aNcKiRYuQmJiokp6UlIQffvgBTZo0kaIarQUEBKBSpUowNDSEm5sbTp06pdV2Z86cga6uLurXr1+wDSQiohLp/WkJ1q4turZQ8STJPFZz586Fl5cXKleujN69e8POzg6RkZHYvXs3YmJicOzYMSmq0cr27dsxYcIEBAQEoHnz5li5ciU6dOiAsLAwVKhQIcvtYmNjMWjQIHh5eeH58+eF1l4iIioZspqWwMencKcQKK3jvHKjPCJQFXche1oVqFa85m+QpMeqRYsWOHz4MCpWrIjly5dj5syZWLFiBSpWrIjDhw+jWbNmUlSjlR9//BHDhg3D8OHD4erqiiVLlsDJyQkrVqzIdrsRI0ZgwIAB8PDwyLGOpKQkxMXFqSxERFS6FeS0BKQ93VUBiIATQtAGRq7Oxa7bULLBT56enggNDcWbN2/w5MkTxMXF4cyZM/joo4+kqiJHycnJuHz5Mry9vVXSvb29cfbs2Sy3W79+Pe7fv485c+ZoVY9iKgnF4uTklK92ExFR8VdY0xJQNiIioD/lv25DmVSzmUoo34FVYmIiypcvj3379gEAjI2NUb58eRgbG+e7cbn14sULpKWlwdbWViXd1tYWUVFRGre5e/cuvvrqKwQGBkJXV7s7o9OnT0dsbKxyefLkSb7bTkRExVuxmUk8IQECMgjIILt7p5ArL2J372YEU5kVs27DfAdWRkZGSExMhEkxusn7/guhhRAaXxKdlpaGAQMGYO7cuaiWizdOGhgYwNzcXGUhIqLSrzhMS6Ab+N8IeiO3D2wEfdWqEMW821CSW4FeXl44cuSIFEXli7W1NeRyuVrvVHR0tFovFgC8efMGly5dwhdffAFdXV3o6upi3rx5uH79OnR1dQt10D0REZUsRTItQQm4FVagHB2RvHQVUiEHAIhi+AJCSZ4K/Prrr9GzZ08YGhqiR48esLe3V+shsrS0lKKqbOnr68PNzQ3BwcHo3r27Mj04OBhdu3ZVy29ubo4bN26opAUEBODYsWP4/fffUalSpQJvMxFRUeNTZiVIdrfCilFwUZBSBw9DlTE+cME9BIW5wLiYPRUoSWDl5uYGAPDz88PcuXM15klLS5OiqhxNmjQJAwcOhLu7Ozw8PLBq1SqEh4dj5MiRADLGRz19+hSbNm2Cjo4OateurbK9jY0NDA0N1dKJiIiK3P/fClMJrorZrbDC8BSOeApHiGI4makkgdXs2bM1jmEqCn379kVMTAzmzZuHyMhI1K5dG0FBQXB2dgYAREZGIjw8vIhbSURElAf/fytMPmYEdJEGIZdDVsxuhRWmp0+BnIZIF/acV5K8hPlDx5cwE1FJxt9V2isOxyohAahuGpFxK+y2NLfCCmq/CuIlzJlfGK2jA6xalfVDBEk/BcBgUkZmoaMD2XuZi+1LmE+ePIn4+HiN6+Lj43Hy5EkpqiEiIiJk3Ao7gVYQ5UtPT1V5RKAVQiB7mvVA/Kxmv9c4dr+IBvpLEli1bt0aYWFhGtfdvn0brVu3lqIaIiIiKoW0nU09V7PfF9GcV5IEVtndTUxJSYHO+3NOEBERUb49fVrULZBALnqWcjX7fRHNeZXniCcuLg7h4eHKgeBRUVHKz4rl9u3b2LhxI+zs7CRrMBER0Yds43/zg8JVwvlBtbkVVyBy0bOUq9nvi2jOqzwPXp87dy7mzZuXYz4hBL7++mvMnz8/L9WUCBy8TkQlGX9Xaa+oj1VEBODsrHo7TC4HHj3KX7yQ0yDvvNLqeEVEQDg7q08hkcVOZS7z9u3snwrMaaB/QQxez/N0C97e3jA1NYUQAlOnTsXYsWNRoUIFlTwGBgaoU6cOPD09891QIiKiD112Y4zyHFhldSvOx6dwpnHIxxQS2sx+X9hzXuU5sPLw8ICHhwcAICEhAZ999hkcHBwkaxgRERGpUowxknR+0GIwm3txn009NyQZVT5nzhwGVURERAUsV2OMtFVMXmxcWqaQ4ON6REREWjIxAYTIWIpqLNrgwf/9PyxMgqFQJeDFxiUJAysiIqISSpsxRtpIHTwMFfEIrRCCxLBHkgxc/1BJ8q5AIiIiKtmK84uNSxL2WBERERFJhIEVERERkUQKJLB69+4dbty4gbdv36qtO3PmTEFUSURERFTkJA+sQkND4eTkhFatWqFcuXJYsGCByvoOHTpIXSURERFRsSB5YDV58mQsXrwYMTExuHz5Mnbv3g1fX1+k///kY3l8gw4RERFRsSd5YBUWFoZBgwYBAGrUqIETJ04gOjoavXr1QnJystTVERERERUbkgdW5ubmePr0qfKzkZER9u7dC0NDQ7Rv317Zc0VERERU2kgeWLVt2xbr169XSdPV1UVgYCCqVKmCxMREqaskIiIiKhYknyD0119/RWpqqlq6TCbD6tWrMWvWLKmrJCIiIioWJAushBCIiYmBTCaDpaVllvkqVKggVZVEREQfHMX7Cql4yvetwNDQUHTt2hXm5uawtbWFjY0NzM3N0a1bN5w/f16KNhIRERGVCPnqsQoICMD48eMBAG5ubqhUqRKEEHj06BH279+P/fv34+eff8bo0aMlaSwRERFRcZbnwOrcuXMYN24cOnbsiICAADg6Oqqsj4iIwKhRozB+/Hi4u7ujcePG+W4sERERUXGW51uBixcvRpMmTbB37161oAoAHB0d8ccff6Bx48b4/vvv89VIIiIiopIgz4HV6dOnMWbMGOjoZF2Ejo4ORo8ejdOnT+e1GiIiIqISI8+B1cuXL7V6ws/Z2RkvX77MazVERERZSkgAZLKMJSGhqFtDlI/AysrKCo8fP84xX3h4OKysrPJaDREREVGJkefAqkWLFggICMj2FTXp6elYtmwZWrZsmddqiIioEGV6IxkR5UGeA6tJkybh/Pnz6NGjByIjI9XWP3v2DD169MDFixcxefLkfDWSiIgKzsaN//3f1RVYu7bo2kJU0uV5uoWmTZvip59+wsSJExEUFAR3d3dUqlQJAPDw4UNcunQJ6enpWLJkCadaICIqpiIigLFj//ucng6MGAH4+AAaHvgmohzka4LQsWPHomHDhvD398fx48dx7tw5AICxsTF8fHwwffp0NGvWTJKGEhGR9O7ezQimMktLA+7dY2BFlBf5fldg8+bN8ddffyE9PR0vXrwAAFhbW2c7DcOHgu9zIqLirmpVQEdHNbiSywEXl6JrE1FJJln0o6OjAxsbG9jY2DCoIiIqIRwdgaVL//sslwMrV7K3iopOTg9QKDothMj4f3GT5wiobt26Wi/16tWTss05CggIQKVKlWBoaAg3NzecOnUqy7y7d+9Gu3btUK5cOZibm8PDwwOHDh0qxNYSERWtwYP/+39YGDBsWNG1hT5MpekBijwHVpaWlrCyssp2MTAwwD///IN//vlHyjZna/v27ZgwYQJmzJiBq1evomXLlujQoQPCw8M15j958iTatWuHoKAgXL58Ga1bt0bnzp1x9erVQmszEVFxUb58UbeAPjRZPUAREVF0bcoPmRDSjwJKTU3FqlWrMG/ePPz7778YMGAANm/eLHU1GjVp0gQNGzbEihUrlGmurq7o1q0b/P39tSqjVq1a6Nu3L2bPnq1V/ri4OFhYWCA2Nhbm5uZ5ajcRUVFJSABMTTP+Hx9fPG+vZKUkt704KajjqE25ISFAmzaa01u1Ktj6C+L6LflgqJ07d6JmzZoYO3Ys6tWrh8uXLxdaUJWcnIzLly/D29tbJd3b2xtnz57Vqoz09HS8efMGlpaWWeZJSkpCXFycykJERES5p3iAIrOS/ACFZIHV8ePH0aRJE/Tt2xfm5uY4fPgwDh06hPr160tVRY5evHiBtLQ02NraqqTb2toiKipKqzIWL16MhIQE9OnTJ8s8/v7+sLCwUC5OTk75ajcREdGHqrQ9QJHvwOrGjRvo2LEjvLy8EBMTg61bt+LSpUvw8vKSon15IpPJVD4LIdTSNNm2bRv8/Pywfft22NjYZJlv+vTpiI2NVS5PnjzJd5uJiCh/+Dqekqs0PUCR58DqyZMnGDx4MBo2bIjLly9jyZIluHXrFvr16ydl+3LF2toacrlcrXcqOjparRfrfdu3b8ewYcOwY8cOtG3bNtu8BgYGMDc3V1mIiKjwlaanyShDSX+AIs8ThFarVg3Jyclo3749pk6dCjMzM9y4cSPL/A0bNsxrVVrT19eHm5sbgoOD0b17d2V6cHAwunbtmuV227Ztg6+vL7Zt24ZOnToVeDuJiCj/+DoeKo7yHFglJSUBAA4cOICDBw9mmU9xGy4tLS2vVeXKpEmTMHDgQLi7u8PDwwOrVq1CeHg4Ro4cCSDjNt7Tp0+xadMmABlB1aBBg/Dzzz+jadOmyt4uIyMjWFhYFEqbiYgo9/g6HiqO8hxYrV+/Xsp2SKZv376IiYnBvHnzEBkZidq1ayMoKAjOzs4AgMjISJU5rVauXInU1FSMGTMGY8aMUaYPHjwYGzZsKOzmExGRlvg6HiqOCmQeqw8N57EiopKsJM8FFRAAKP4mVjxNVpIHPheVopzHqijrLxHzWBERERWW0vQ0GZUODKyIiKhUKOlPk1HpwMCKiIiISCIMrIiIiIgkwsCKiIiISCIMrIiIiIgkwsCKiIgkl5AAyGQZS0JCUbeGqPAwsCIiIiKSCAMrIiIiIokwsCIiIiKSCAMrIiIiIokwsCIiIiKSiG5RN4CIiIiKlokJIERRt6J0YGBFREQlFgMCKm54K5CIiIhIIgysiIioZIuIAEJCMv4lKmIMrIiIqOQKCACcnIA2bQBnZ2Dt2qJuEX3gGFgREVHJFBEBjB373+f0dGDECPZcUZFiYEVERCXT3bsZwVRmaWnAvXtF0x4i8KlAIiIqqapWBXR0VIMruRxwcSm6NlGxUhRPjbLHiojoA6e4+AiR8X+plEcEWiEEzy8X0K05R0dg1aqMYArI+Hflyox0oiLCwIqIiCR3wfdXPIYzQtAGzp7OODWkgAaVDxsGPHqU8VTgo0cZn4mKEAMrIiKSVOTFCHy0YwzkyLhFJ0c6PDaOQOTFAuy5atWKPVVULDCwIiIiSUWduqsMqhR0kYbnZzionEo/BlZERCQpu5ZVkfbe5SUVctg256ByKv0YWBERkaTsGzni5CerkIqMQeWpkCN08ErYN+KtOir9GFgREZHkGq8chop4hFYIQfiJR2i5gYPK6cPAeayIiKhAPIUjnsIRtm5F3RKiwsMeKyIiIiKJMLAiIiIikggDKyIiIiKJMLAiIiIikggDKyIiIiKJ8KlAIiIiKhCKF3x/SNhjRURERCSRUhlYBQQEoFKlSjA0NISbmxtOnTqVbf4TJ07Azc0NhoaGqFy5Mn799ddCaikRERGVJqUusNq+fTsmTJiAGTNm4OrVq2jZsiU6dOiA8PBwjfkfPnyIjh07omXLlrh69Sq+/vprjBs3Drt27SrklhMREVFJJxOidN39bNKkCRo2bIgVK1Yo01xdXdGtWzf4+/ur5Z82bRr+/PNP3Lp1S5k2cuRIXL9+HaGhoVrVGRcXBwsLC8TGxsLc3Dz/O0FEVMIlJACmphn/j4/PGGtDlJWi+r4UxPW7VPVYJScn4/Lly/D29lZJ9/b2xtmzZzVuExoaqpbfx8cHly5dQkpKisZtkpKSEBcXp7IQERERlaqnAl+8eIG0tDTY2tqqpNva2iIqKkrjNlFRURrzp6am4sWLF7C3t1fbxt/fH3Pnzs11+9LS0rIM1oiICouenh7kcnmB1vEhPg1GBJSywEpBJpOpfBZCqKXllF9TusL06dMxadIk5ee4uDg4OTllWb4QAlFRUXj9+nVOTSciKhRlypSBnZ1dtr8biSj3SlVgZW1tDblcrtY7FR0drdYrpWBnZ6cxv66uLqysrDRuY2BgAAMDA63bpQiqbGxsYGxszF9kRFRkhBB4+/YtoqOjAUBjrzwR5V2pCqz09fXh5uaG4OBgdO/eXZkeHByMrl27atzGw8MD+/btU0k7fPgw3N3doaenl+82paWlKYOqrAI1IqLCZGRkBCDjj0gbG5sCvy1I9CEpVYPXAWDSpElYs2YN1q1bh1u3bmHixIkIDw/HyJEjAWTcxhs0aJAy/8iRI/H48WNMmjQJt27dwrp167B27VpMmTJFkvYoxlQZGxtLUh4RkRQUv5M47pNIWqWqxwoA+vbti5iYGMybNw+RkZGoXbs2goKC4OzsDACIjIxUmdOqUqVKCAoKwsSJE7F8+XI4ODjgl19+Qc+ePSVtV35v//HRZSKSEockEBWMUjePVVHIbh6Md+/e4eHDh8qZ4POKgRURSUmq301EUuA8VkRERESkhoEVqZHJZNkuQ4YMyTbfb7/9BgA4fvw4ZDIZypYti3fv3qnUceHCBWV+BUV+xVKuXDl06NAB169fV9n25s2b6NOnD8qVKwcDAwNUrVoVs2bNwtu3bwv2wBAREeWAgVUJ9PRpwZYfGRmpXJYsWQJzc3OVtJ9//lmZd/369SrrIiMj0a1bN5XyzMzMsGfPHpW0devWoUKFChrrv337NiIjI7F//368evUK7du3R2xsLADg3LlzaNKkCZKTk7F//37cuXMH3333HTZu3Ih27dohOTlZ2oNBRESUCwysSoiNG//7v6srsHZtwdVlZ2enXCwsLCCTydTSFBSTDGZe3h+vMXjwYKxbt075OTExEb/99hsGDx6ssX4bGxvY2dmhcePGWLx4MaKionDu3DkIITBs2DC4urpi9+7daNy4MZydndG7d2/s27cPoaGh+OmnnwrmoBAREWmBgVUJEBEBjB373+f0dGDEiIz0kmDgwIE4deqU8mnMXbt2oWLFimjYsGGO2yrm20lJScG1a9cQFhaGSZMmQUdH9atbr149tG3bFtu2bZN+B4iIiLTEwKoEuHs3I5jKLC0NuHevaNqTWf/+/WFqaqqyPHjwQCWPjY0NOnTogA0bNgDIuA3o6+ubY9kxMTGYO3cuzMzM0LhxY9y5cwcA4OrqqjG/q6urMg8REVFRKHXzWJVGVasCOjqqwZVcDri4FF2bFH766Se0bdtWJU3TexN9fX0xfvx4fPrppwgNDcXOnTtx6tQpjWU6OjoCABISElC1alXs3LkTNjY2ObYlp3dCEhERFTQGViWAoyOwdCkwZkzGZ7kcWLkyI72o2dnZwUWLCK9jx44YMWIEhg0bhs6dO2f7ep9Tp07B3Nwc5cqVU5lXpFq1agCAsLAw1K9fX227//3vf6hatWrud4KIiEgivBVYQmQe5x0WBgwbVnRtyQu5XI6BAwfi+PHjOd4GrFSpEqpUqaI2WVv9+vVRo0YN/PTTT0h/797o9evXceTIEfTv31/ythMREWmLgVUJVL58UbfgP69fv0ZUVJTKkpCQoDHvN998g3///Rc+Pj55qksmk2HNmjUICwtDz549ceHCBYSHh2Pnzp3o3LkzPDw8MGHChHzsDRERUf4wsKJ8GTp0KOzt7VWWpUuXasyrr68Pa2vrfI2Dat68Oc6dOwe5XI6OHTvCxcUF06dPx+DBgxEcHAwDA4M8l01ERJRffFegBPiuQCIqafiuQCpOStO7Ajl4vYQwMQEYAhMRERVvvBVIREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBFREREJBEGVkREREQSYWBVUiQkADJZxpLFzOZERERUtBhYEREREUmEgRUVK35+fqhfv35RN6PYO378OGQyGV6/fi152TKZDHv37pW83A/ZkCFD0K1bt6JuBhEVAgZWpNGQIUMgk8kgk8mgp6eHypUrY8qUKVm+YJkKTqtWrUrUy6UfPXoEmUyGa9euZZnHz89P+f3Kann06FGW+WrUqKEsq1WrVpDJZFiwYIFaPR07doRMJoOfn59afplMBgMDA1SrVg3fffcd0tLSlHnS0tLw008/oW7dujA0NESZMmXQoUMHnDlzJk/7/vPPP2PDhg3ZbktEpQMDq5Lo6dNCqaZ9+/aIjIzEgwcPMH/+fAQEBGDKlCmFUnd+pKSkFHUTKAdTpkxBZGSkcnF0dMS8efNU0pycnAAAtWrVUkmPjIzE6dOnVcpzcnLC+vXrVdKePXuGY8eOwd7eXq3+zz77DJGRkbh9+zbGjRuHmTNn4ocffgAACCHQr18/zJs3D+PGjcOtW7dw4sQJODk5oVWrVnnqzbOwsECZMmVyvR0RlTwMrEqKjRv/+7+rK7B2bYFXaWBgADs7Ozg5OWHAgAH45JNPlBeVLVu2wN3dHWZmZrCzs8OAAQMQHR2t3FZxq+ro0aNwd3eHsbExmjVrhtu3b6vUsWDBAtja2sLMzAzDhg3Du3fvVNanp6dj3rx5cHR0hIGBAerXr4+DBw8q1yt6CHbs2IFWrVrB0NAQW7ZsQUxMDPr37w9HR0cYGxujTp062LZtW7b7+/jxY3Tu3Blly5aFiYkJatWqhaCgIJX9OXToEBo0aAAjIyO0adMG0dHROHDgAFxdXWFubo7+/fvj7du3yjKTkpIwbtw42NjYwNDQEC1atMDFixdV6j1x4gQaN24MAwMD2Nvb46uvvkJqaiqAjJ7DEydO4Oeff1bpyVG4fPlytsd33759cHNzg6GhISpXroy5c+cqywaAu3fv4qOPPoKhoSFq1qyJ4ODgbI8RABw8eBAtWrRAmTJlYGVlhY8//hj3799Xrq9UqRIAoEGDBpDJZGjVqpVaGaamprCzs1Mucrlc+V3KnAYAurq6Kul2dnawtrZWKe/jjz9GTEyMSo/Shg0b4O3tDRsbG7X6jY2NYWdnh4oVK+KLL76Al5eX8ru9Y8cO/P7779i0aROGDx+OSpUqoV69eli1ahW6dOmC4cOHZ9lzm9W+v38rsFWrVhg7diwmTJiAsmXLwtbWFqtWrUJCQgKGDh0KMzMzVKlSBQcOHFApPywsDB07doSpqSlsbW0xcOBAvHjxQrn+999/R506dWBkZAQrKyu0bduWvcxEhYyBVUkQEQGMHfvf5/R0YMSIjPRCZGRkpOwNSk5OxjfffIPr169j7969ePjwIYYMGaK2zYwZM7B48WJcunQJurq68PX1Va7bsWMH5syZg2+//RaXLl2Cvb09AgICVLb/+eefsXjxYvzwww/4+++/4ePjgy5duuDu3bsq+aZNm6bsXfDx8cG7d+/g5uaGv/76C//88w8+//xzDBw4EOfPn89y/8aMGYOkpCScPHkSN27cwMKFC2GqeN36//Pz88OyZctw9uxZPHnyBH369MGSJUuwdetW7N+/H8HBwVi6dKky/9SpU7Fr1y5s3LgRV65cgYuLC3x8fPDy5UsAwNOnT9GxY0c0atQI169fx4oVK7B27VrMnz9fuf8eHh7KHpbMPTk5Hd9Dhw7h008/xbhx4xAWFoaVK1diw4YN+PbbbwFkBK09evSAXC7HuXPn8Ouvv2LatGlZHh+FhIQETJo0CRcvXsTRo0eho6OD7t27Iz09HQBw4cIFAMCRI0cQGRmJ3bt351hmfunr6+OTTz5R6bXasGGDyvHITubv9tatW1GtWjV07txZLd/kyZMRExOTZQCam33fuHEjrK2tceHCBYwdOxajRo1C79690axZM1y5cgU+Pj4YOHCgMlCPjIyEp6cn6tevj0uXLuHgwYN4/vw5+vTpo1zfv39/+Pr64tatWzh+/Dh69OgBwbe3ExUuQfkWGxsrAIjY2Fi1dYmJiSIsLEwkJibmvYJjx4QA1JeQkLyXmYPBgweLrl27Kj+fP39eWFlZiT59+mjMf+HCBQFAvHnzRgghREhIiAAgjhw5osyzf/9+AUB5LDw8PMTIkSNVymnSpImoV6+e8rODg4P49ttvVfI0atRIjB49WgghxMOHDwUAsWTJkhz3qWPHjmLy5MlZrq9Tp47w8/PTuE7T/vj7+wsA4v79+8q0ESNGCB8fHyGEEPHx8UJPT08EBgYq1ycnJwsHBwexaNEiIYQQX3/9tahevbpIT09X5lm+fLkwNTUVaWlpQgghPD09xfjx43Nsz/vHt2XLluK7775T2W7z5s3C3t5eCCHEoUOHhFwuF0+ePFGuP3DggAAg9uzZk+Vxel90dLQAIG7cuCGE+O+cXL16VesynJ2dxU8//aSWPmfOHKGjoyNMTExUlmHDhinzKI7P9evXhZmZmYiPjxcnTpwQNjY2Ijk5WdSrV0/MmTNHLb8QQqSlpYkDBw4IfX19MXXqVCGEEDVq1FD57mf28uVLAUAsXLhQ4/qs9v39nydPT0/RokUL5efU1FRhYmIiBg4cqEyLjIwUAERoaKgQQohZs2YJb29vlXKfPHkiAIjbt2+Ly5cvCwDi0aNHGtv2Pkl+NxFJJD7+v0tbfHzh1Zvd9TuvdAs7kKM8qFoV0NHJ6KlSkMsBF5cCrfavv/6CqakpUlNTkZKSgq5duyp7Y65evQo/Pz9cu3YNL1++VPZWhIeHo2bNmsoy6tatq/y/YqxLdHQ0KlSogFu3bmHkyJEqdXp4eCAkJAQAEBcXh2fPnqF58+YqeZo3b47r16+rpLm7u6t8TktLw4IFC7B9+3Y8ffoUSUlJSEpKgomJSZb7O27cOIwaNQqHDx9G27Zt0bNnT5X2v78/tra2MDY2RuXKlVXSFL0W9+/fR0pKikr79fT00LhxY9y6dQsAcOvWLXh4eEAmk6nsX3x8PCIiIlChQoUs2/t+e94/vpcvX8bFixeVPVSK4/Lu3Tu8ffsWt27dQoUKFeDo6Khc7+HhkW19iv2aNWsWzp07hxcvXqic+9q1a+e4fW5Vr14df/75p0qamZmZWr66deuiatWq+P333xESEoKBAwdCT09PY5kBAQFYs2YNkpOTAQADBw7EnDlztG5T5vOVV5nPnVwuh5WVFerUqaNMs7W1BQDlLfbLly8jJCRErRcVyDgn3t7e8PLyQp06deDj4wNvb2/06tULZcuWzXdbiUh7DKxKAkdHYOlSYMyYjM9yObByZUZ6AWrdujVWrFgBPT09ODg4KC9SCQkJ8Pb2hre3N7Zs2YJy5cohPDwcPj4+yguVQuYLm+JilJ45QNTC+xcxIYRa2vsB0+LFi/HTTz9hyZIlqFOnDkxMTDBhwgS19mU2fPhw+Pj4YP/+/Th8+DD8/f2xePFijM10G/b9/Xn/wi2TyZT7J/7/Fkx27de0L1ltp0l2xzc9PR1z585Fjx491LYzNDTUeItImzo7d+4MJycnrF69Gg4ODkhPT0ft2rWzPbb5oa+vDxct/4jw9fXF8uXLERYWpgxwNfnkk08wY8YMGBgYwMHBQTmeCwCqVauGsLAwjdspAuKqVavmYg800/Tdyel8du7cGQsXLlQry97eHnK5HMHBwTh79iwOHz6MpUuXYsaMGTh//rxy7BcRFTyOsSopBg/+7/9hYcCwYQVepYmJCVxcXODs7KzyC/9///sfXrx4gQULFqBly5aoUaOGysB1bbm6uuLcuXMqaZk/m5ubw8HBQe0JsLNnz8LV1TXbsk+dOoWuXbvi008/Rb169VC5cmW1cVmaODk5YeTIkdi9ezcmT56M1atX52KPVLm4uEBfX1+l/SkpKbh06ZKy/TVr1sTZs2dVgpyzZ8/CzMwM5cuXB5ARWGSeCkBbDRs2xO3bt+Hi4qK26OjooGbNmggPD8ezZ8+U24SGhmZbZkxMDG7duoWZM2fCy8sLrq6uePXqlUoefX19AMhTm/NrwIABuHHjBmrXrq3Sc/o+CwsLuLi4wMnJSSWoAoB+/frh7t272Ldvn9p2ixcvhpWVFdq1a6ex3ILc94YNG+LmzZuoWLGi2vlU/GEhk8nQvHlzzJ07F1evXoW+vj727NkjeVuIKGvssSqJ/v+CW1QqVKgAfX19LF26FCNHjsQ///yDb775JtfljB8/HoMHD4a7uztatGiBwMBA3Lx5U+XW2pdffok5c+agSpUqqF+/PtavX49r164hMDAw27JdXFywa9cunD17FmXLlsWPP/6IqKiobAOyCRMmoEOHDqhWrRpevXqFY8eO5RjAZcfExASjRo3Cl19+CUtLS1SoUAGLFi3C27dvMez/A+PRo0djyZIlGDt2LL744gvcvn0bc+bMwaRJk6Cjk/F3T8WKFXH+/Hk8evQIpqamsLS01Kr+2bNn4+OPP4aTkxN69+4NHR0d/P3337hx4wbmz5+Ptm3bonr16hg0aBAWL16MuLg4zJgxI9syy5YtCysrK6xatQr29vYIDw/HV199pZLHxsYGRkZGOHjwIBwdHWFoaAgLC4s8HMEMqampiIqKUkmTyWTKW2Xvty8yMjLLW4Da6NevH3bu3InBgwfj+++/h5eXF+Li4rB8+XL8+eef2LlzZ5a3lKXe98zGjBmD1atXo3///vjyyy9hbW2Ne/fu4bfffsPq1atx6dIlHD16VPkk5Pnz5/Hvv//m6ztMVFhMTDJGWJUG7LGiXCtXrhw2bNiAnTt3ombNmliwYIFyDqDc6Nu3L2bPno1p06bBzc0Njx8/xqhRo1TyjBs3DpMnT8bkyZNRp04dHDx4EH/++WeOt2JmzZqFhg0bwsfHB61atYKdnV2OM1+npaVhzJgxcHV1Rfv27VG9enW1pxRza8GCBejZsycGDhyIhg0b4t69ezh06JBy3Ev58uURFBSECxcuoF69ehg5ciSGDRuGmTNnKsuYMmUK5HI5atasqbztqg0fHx/89ddfCA4ORqNGjdC0aVP8+OOPcHZ2BgDo6Ohgz549SEpKQuPGjTF8+HCV8Via6Ojo4LfffsPly5dRu3ZtTJw4Ed9//71KHl1dXfzyyy9YuXIlHBwc0LVr19wcMjU3b96Evb29yqLYB03KlCmT7Vi6nCim75gxYwZ++ukn1KhRAy1btsTjx48REhKS7fdI6n3PzMHBAWfOnEFaWhp8fHxQu3ZtjB8/HhYWFtDR0YG5uTlOnjyJjh07olq1apg5cyYWL16MDh06SNYGIsqZTGgaaEG5EhcXBwsLC8TGxsLc3Fxl3bt37/Dw4UNUqlQJhoaGea8kIQFQDFqNj88I74mI8kiy301EJVh21++84q3AkqI09ZMSERGVUqXqVuCrV68wcOBAWFhYwMLCAgMHDsz2JbUpKSmYNm2a8qkxBwcHDBo0SGUwLxEREZG2SlVgNWDAAFy7dg0HDx7EwYMHce3aNQwcODDL/G/fvsWVK1cwa9YsXLlyBbt378adO3fQpUuXQmw1ERERlRal5lbgrVu3cPDgQZw7dw5NmjQBAKxevRoeHh64ffs2qlevrraNhYWF2qspli5disaNGyM8PDzHyRmJiIiIMis1PVahoaGwsLBQBlUA0LRpU1hYWODs2bNalxMbGwuZTJbtm+iTkpIQFxensuSEzwgQUXHC30lEBaPUBFZRUVEa32JvY2OjNgdOVt69e4evvvoKAwYMyPbpAH9/f+U4LgsLC5WX4r5PMZ+O4kWqRETFgeJ3Un7m/CIidcX+VqCfnx/mzp2bbZ6LFy8C0Pw6Dk2vDNEkJSUF/fr1Q3p6eo5zF02fPh2TJk1Sfo6Li8syuJLL5ShTpoxyZnJjY2NJ3jNGRJQXQgi8ffsW0dHRKFOmjNrM80SUP8U+sPriiy/Qr1+/bPNUrFgRf//9N54/f6627t9//9U4Q3NmKSkp6NOnDx4+fIhjx47lOJeFgYEBDAwMcm78/7OzswOAPL32hYioIJQpU0b5u4mIpFPsAytra2tYW1vnmM/DwwOxsbG4cOECGjduDAA4f/48YmNj0axZsyy3UwRVd+/eRUhICKysrCRru4JMJoO9vT1sbGyQkpIieflERLmhp6fHniqiAlKqZl7v0KEDnj17hpUrVwIAPv/8czg7O6u8TLVGjRrw9/dH9+7dkZqaip49e+LKlSv466+/VHq2LC0tlS9UzUlBzNxKREREBasgrt+lZvA6AAQGBqJOnTrw9vaGt7c36tati82bN6vkuX37NmJjYwEAERER+PPPPxEREYH69eurvIssN08SEhEREQGlrMeqqLDHioiIqORhjxURERFRMVbsB6+XBIpOP20mCiUiIqLiQXHdlvLmHQMrCcTExABAthOFEhERUfEUExMDCwsLScpiYCUBS0tLAEB4eLhkJ4byRjFZ65MnTzjerYjxXBQvPB/FB89F8REbG4sKFSoor+NSYGAlAR2djKFqFhYW/CEpJszNzXkuigmei+KF56P44LkoPhTXcUnKkqwkIiIiog8cAysiIiIiiTCwkoCBgQHmzJmTq/cHUsHguSg+eC6KF56P4oPnovgoiHPBCUKJiIiIJMIeKyIiIiKJMLAiIiIikggDKyIiIiKJMLAiIiIikggDKy0FBASgUqVKMDQ0hJubG06dOpVt/hMnTsDNzQ2GhoaoXLkyfv3110JqaemXm3Oxe/dutGvXDuXKlYO5uTk8PDxw6NChQmxt6ZbbnwuFM2fOQFdXF/Xr1y/YBn5AcnsukpKSMGPGDDg7O8PAwABVqlTBunXrCqm1pV9uz0dgYCDq1asHY2Nj2NvbY+jQocrXpVHenDx5Ep07d4aDgwNkMhn27t2b4zaSXLsF5ei3334Tenp6YvXq1SIsLEyMHz9emJiYiMePH2vM/+DBA2FsbCzGjx8vwsLCxOrVq4Wenp74/fffC7nlpU9uz8X48ePFwoULxYULF8SdO3fE9OnThZ6enrhy5Uoht7z0ye25UHj9+rWoXLmy8Pb2FvXq1SucxpZyeTkXXbp0EU2aNBHBwcHi4cOH4vz58+LMmTOF2OrSK7fn49SpU0JHR0f8/PPP4sGDB+LUqVOiVq1aolu3boXc8tIlKChIzJgxQ+zatUsAEHv27Mk2v1TXbgZWWmjcuLEYOXKkSlqNGjXEV199pTH/1KlTRY0aNVTSRowYIZo2bVpgbfxQ5PZcaFKzZk0xd+5cqZv2wcnruejbt6+YOXOmmDNnDgMrieT2XBw4cEBYWFiImJiYwmjeBye35+P7778XlStXVkn75ZdfhKOjY4G18UOjTWAl1bWbtwJzkJycjMuXL8Pb21sl3dvbG2fPntW4TWhoqFp+Hx8fXLp0CSkpKQXW1tIuL+fifenp6Xjz5o2kL9z8EOX1XKxfvx7379/HnDlzCrqJH4y8nIs///wT7u7uWLRoEcqXL49q1aphypQpSExMLIwml2p5OR/NmjVDREQEgoKCIITA8+fP8fvvv6NTp06F0WT6f1Jdu/kS5hy8ePECaWlpsLW1VUm3tbVFVFSUxm2ioqI05k9NTcWLFy9gb29fYO0tzfJyLt63ePFiJCQkoE+fPgXRxA9GXs7F3bt38dVXX+HUqVPQ1eWvHqnk5Vw8ePAAp0+fhqGhIfbs2YMXL15g9OjRePnyJcdZ5VNezkezZs0QGBiIvn374t27d0hNTUWXLl2wdOnSwmgy/T+prt3ssdKSTCZT+SyEUEvLKb+mdMq93J4LhW3btsHPzw/bt2+HjY1NQTXvg6LtuUhLS8OAAQMwd+5cVKtWrbCa90HJzc9Feno6ZDIZAgMD0bhxY3Ts2BE//vgjNmzYwF4rieTmfISFhWHcuHGYPXs2Ll++jIMHD+Lhw4cYOXJkYTSVMpHi2s0/G3NgbW0NuVyu9pdGdHS0WmSrYGdnpzG/rq4urKysCqytpV1ezoXC9u3bMWzYMOzcuRNt27YtyGZ+EHJ7Lt68eYNLly7h6tWr+OKLLwBkXNyFENDV1cXhw4fRpk2bQml7aZOXnwt7e3uUL18eFhYWyjRXV1cIIRAREYGqVasWaJtLs7ycD39/fzRv3hxffvklAKBu3bowMTFBy5YtMX/+fN7lKCRSXbvZY5UDfX19uLm5ITg4WCU9ODgYzZo107iNh4eHWv7Dhw/D3d0denp6BdbW0i4v5wLI6KkaMmQItm7dyjELEsntuTA3N8eNGzdw7do15TJy5EhUr14d165dQ5MmTQqr6aVOXn4umjdvjmfPniE+Pl6ZdufOHejo6MDR0bFA21va5eV8vH37Fjo6qpdjuVwO4L8eEyp4kl27czXU/QOleHR27dq1IiwsTEyYMEGYmJiIR48eCSGE+Oqrr8TAgQOV+RWPbE6cOFGEhYWJtWvXcroFieT2XGzdulXo6uqK5cuXi8jISOXy+vXrotqFUiO35+J9fCpQOrk9F2/evBGOjo6iV69e4ubNm+LEiROiatWqYvjw4UW1C6VKbs/H+vXrha6urggICBD3798Xp0+fFu7u7qJx48ZFtQulwps3b8TVq1fF1atXBQDx448/iqtXryqnvSioazcDKy0tX75cODs7C319fdGwYUNx4sQJ5brBgwcLT09PlfzHjx8XDRo0EPr6+qJixYpixYoVhdzi0is358LT01MAUFsGDx5c+A0vhXL7c5EZAytp5fZc3Lp1S7Rt21YYGRkJR0dHMWnSJPH27dtCbnXpldvz8csvv4iaNWsKIyMjYW9vLz755BMRERFRyK0uXUJCQrL9/V9Q126ZEOxnJCIiIpICx1gRERERSYSBFREREZFEGFgRERERSYSBFREREZFEGFgRERERSYSBFREREZFEGFgRERERSYSBFREREZFEGFhRifL3339j2LBhqFKlCoyMjGBkZISqVatixIgRuHTpkkpePz8/yGQy6Ojo4MGDB2plJSQkwNzcHDKZDEOGDAEAtGrVCjKZLMfFz89Pkv3ZsGEDZDIZHj16JEl5ubF9+3bUqlULRkZGkMlkuHbtWqG3QVuKc/nixYsiqV/Kc56V48ePQyaT4fjx4wVaT35dvXoVnp6esLCwgEwmw5IlSzTmi4yMxMyZM+Hh4QFra2uYm5vDzc0Nq1atQlpamlr++Ph4TJgwAQ4ODjA0NET9+vXx22+/qeRJS0vDjz/+iPbt28PR0RHGxsZwdXXFV199hdevX6vkTUhIQL9+/VC9enWYmZnBxMQEtWrVwvz585GQkKBWf3R0NIYMGQJra2sYGxvDw8MDR48eVcuXnJyM2bNno1KlStDX14ezszOmT5+OxMREtbwpKSmYO3cuKlasCAMDA9SoUQNLly5Vy3fz5k2MHj0aHh4eMDExKRHfA8qablE3gEhbK1euxBdffIHq1atj/PjxqFWrFmQyGW7duoVt27ahUaNGuHfvHqpUqaKynampKdavX49vvvlGJX3nzp1ISUlReblmQEAA4uLilJ/379+P+fPnY/369ahRo4YyvaS/qPbff//FwIED0b59ewQEBMDAwADVqlUr6mYVW6GhoQV+zhs2bIjQ0FDUrFmzQOvJL19fXyQkJOC3335D2bJlUbFiRY35Ll++jE2bNmHQoEGYNWsW9PT0cODAAYwaNQrnzp3DunXrVPL36NEDFy9exIIFC1CtWjVs3boV/fv3R3p6OgYMGAAASExMhJ+fH/r374/hw4fD2toaV65cwfz587Fv3z5cunQJRkZGADKCGiEEJk2ahEqVKkFHRwcnT57EvHnzcPz4cRw5ckRZd1JSEry8vPD69Wv8/PPPsLGxwfLly9G+fXscOXIEnp6eyrz9+/dHUFAQZs+ejUaNGiE0NBTz58/HzZs38eeff6rs0+jRo7F582Z88803aNSoEQ4dOoTx48fjzZs3+Prrr5X5Ll26hL1796JBgwbw8vLCvn378nWOqIjl9108RIXh9OnTQkdHR3Tu3FkkJSVpzLNjxw7x9OlT5ec5c+YIAGL48OHCyclJpKWlqeRv0aKF6N+/vzAxMcny3YHr168XAMTFixcl2xdN5T98+LBAys/K6dOnBQCxffv2HPMmJCQUQouypziX//77b1E35YOnq6srRo0alWO+ly9fiuTkZLX0MWPGCAAiPDxcmbZ//34BQGzdulUlb7t27YSDg4NITU0VQgiRmpoqXrx4oVbmzp07BQCxefPmHNs1depUAUDcv39fmbZ8+XIBQJw9e1aZlpKSImrWrKnyIuTQ0FABQCxevFilzO+++04AEIcPH1am/fPPP0Imk4nvvvtOJe9nn30mjIyMRExMjDIt8+8mxb6EhITkuC9UPPFWIJUI3333HeRyOVauXAl9fX2NeXr37g0HBwe1dF9fXzx58gTBwcHKtDt37uD06dPw9fUtsDa/79y5c2jevDkMDQ3h4OCA6dOnIyUlRS3f9u3b4e3tDXt7exgZGSlvdWS+fbF582bIZDKEhoaqbT9v3jzo6enh2bNnGtsxZMgQtGjRAgDQt29fyGQytGrVSrnO1NQUN27cgLe3N8zMzODl5QUAePnyJUaPHo3y5ctDX18flStXxowZM5CUlKRSvkwmwxdffIH169ejevXqMDIygru7O86dOwchBL7//ntUqlQJpqamaNOmDe7du6f1MXzy5Al69OgBc3NzWFhY4NNPP8W///6r8RgqbquYmprCx8cHV69eVcu3evVqVKtWDQYGBqhZsya2bt2KIUOGqPXCvH8rUHELNyQkBKNGjYK1tTWsrKzQo0cPteNesWJFfPzxxzh48CAaNmwIIyMj1KhRQ63HRtOtQMX5uHfvHjp27AhTU1M4OTlh8uTJasc9IiICvXr1gpmZGcqUKYNPPvkEFy9ehEwmw4YNG3I8tv/88w+6du2KsmXLKm/Fbdy4UW2fU1NTsWLFCuVt8ayULVtWpTdYoXHjxsr2KuzZswempqbo3bu3St6hQ4fi2bNnOH/+PABALpfDysoqyzKfPHmS436WK1cOAKCr+98Nmz179qB69erw8PBQpunq6uLTTz/FhQsX8PTpUwDAmTNnAAAdO3ZUKfPjjz8GAOzatUuZtnfvXgghMHToULV9SkxMxMGDB5VpOjq8FJcmPJtU7KWlpSEkJATu7u6wt7fP9fZVq1ZFy5YtVS5k69atQ8WKFZVBQ0ELCwtT3mrYsGEDfv31V1y9ehXz589Xy3v37l107NgRa9euxcGDBzFhwgTs2LEDnTt3Vubp27cv7OzssHz5cpVtU1NTsXLlSnTv3l1jkAkAs2bNUm733XffITQ0FAEBAcr1ycnJ6NKlC9q0aYM//vgDc+fOxbt379C6dWts2rQJkyZNwv79+/Hpp59i0aJF6NGjh1odf/31F9asWYMFCxZg27ZtePPmDTp16oTJkyfjzJkzWLZsGVatWoWwsDD07NkTQst3wXfv3h0uLi74/fff4efnh71798LHx0clQP3uu+/Qv39/1KxZEzt27MDmzZvx5s0btGzZEmFhYcp8q1atwueff466deti9+7dmDlzJubOnZursS3Dhw+Hnp4etm7dikWLFuH48eP49NNP1fJdv34dkydPxsSJE/HHH3+gbt26GDZsGE6ePJljHSkpKejSpQu8vLzwxx9/wNfXFz/99BMWLlyozJOQkIDWrVsjJCQECxcuxI4dO2Bra4u+fftqtR+3b99Gs2bNcPPmTfzyyy/YvXs3atasiSFDhmDRokUAgE6dOikD+V69eiE0NFRjYJ+TY8eOQVdXV+XW8z///ANXV1eVYAcA6tatq1yfU5kAUKtWLbV1QgikpqYiLi4OBw8exOLFi9G/f39UqFBBpX5FXZrqv3nzJoCMnw0AMDAwUMmn+Pz333+rlFmuXDnY2dnlaZ+oBCvaDjOinEVFRQkAol+/fmrrUlNTRUpKinJJT09Xrst8+2j9+vXCwMBAxMTEiNTUVGFvby/8/PyEEKJQbgX27dtXGBkZiaioKJW216hRI9tbgenp6SIlJUWcOHFCABDXr19X2T99fX3x/PlzZdr27dsFAHHixIls2xMSEiIAiJ07d6qkDx48WAAQ69atU0n/9ddfBQCxY8cOlfSFCxeq3QIBIOzs7ER8fLwybe/evQKAqF+/vso5WrJkiQAg/v7772zbqziXEydOVEkPDAwUAMSWLVuEEEKEh4cLXV1dMXbsWJV8b968EXZ2dqJPnz5CiIxbL3Z2dqJJkyYq+R4/fiz09PSEs7OzSjoAMWfOHOVnxfdi9OjRKvkWLVokAIjIyEhlmrOzszA0NBSPHz9WpiUmJgpLS0sxYsQIZZrinGS+BaQ4H+8f944dO4rq1asrPytuZR04cEAl34gRIwQAsX79epGdfv36CQMDA5Xbc0II0aFDB2FsbCxev36tcizGjBmTbXlZOXTokNDR0VE7j1WrVhU+Pj5q+Z89eyYAqN1OyywiIkLY2toKd3d3tdv9Qgixbds2AUC5DB06VKSkpKjk0dPTUzkXCmfPnlW5Ran4Hr9/y3Ht2rUCgKhWrZoyrV27dirnKDN9fX3x+eefa1zHW4ElH3usqERzc3ODnp6eclm8eLHGfL1794a+vj4CAwMRFBSEqKgo5ZOAhSEkJAReXl6wtbVVpsnlco09Cg8ePMCAAQNgZ2cHuVwOPT095eDZW7duKfONGjUKQMbtLIVly5ahTp06+Oijj/LV3p49e6p8PnbsGExMTNCrVy+VdMUxfP/pqdatW8PExET52dXVFQDQoUMHldtHivTHjx9r1a5PPvlE5XOfPn2gq6uLkJAQAMChQ4eQmpqKQYMGITU1VbkYGhrC09NT2Rt1+/ZtREVFoU+fPirlVahQAc2bN9eqLQDQpUsXlc+K3oj396d+/foqPSSGhoaoVq2aVvstk8lUeisV9WTe9sSJEzAzM0P79u1V8vXv31+r/Th27Bi8vLzg5OSkkj5kyBC8ffs2Tz1T77ty5Qr69OmDpk2bwt/fX219drcVs1r38uVLdOzYEUIIbN++XeMtNR8fH1y8eBHHjh3Dt99+i127dqFnz55IT0/Pdf0dOnSAi4sLpk2bhuDgYLx+/RoHDx7E119/DblcrlZ/XvaJSj4+FUjFnrW1NYyMjDRehLZu3Yq3b98iMjJS7SKXmYmJCfr27Yt169bB2dkZbdu2hbOzc0E2W0VMTIzaLQEAamnx8fFo2bIlDA0NMX/+fFSrVg3GxsbKsUWZH+lW3OpZuXIlvvrqK9y8eROnTp3CypUr89VWY2NjmJuba2z/+xcDGxsb6OrqIiYmRiXd0tJS5bNiXFxW6e/evdOqbe8fL11dXVhZWSnrf/78OQCgUaNGGrdXXPgU+TMHugq2trZ4+PChVu15f7yP4pbQ+4/eaxoXZGBgoPER/fcZGxvD0NBQbdvMxywmJibLfdFGTEyMxtvsitvJ75/f3Lp69SratWuHqlWrIigoSO1WWuZzmNnLly8BqH9vAODVq1do164dnj59imPHjqFy5coa6y5btizc3d0BZAT8VapUQb9+/fDHH3+ge/fuuapfX18fBw4cwMCBA+Ht7Q0g43fLd999h2+++Qbly5dX2SdNU5gkJCQgOTlZ4z5R6cDAioo9uVyONm3a4PDhw4iMjFS5ACgeTddmHihfX1+sWbMGf//9NwIDAwuquRpZWVkhKipKLf39tGPHjuHZs2c4fvy4yiPe78/RozB+/Hhs3rwZf/zxBw4ePKgctJwfmv6StrKywvnz5yGEUFkfHR2N1NRUWFtb56tObUVFRalcvFJTUxETE6MMXBTt+P3337MNnBX5FYHY+3WUNFZWVrhw4YJaurb7YmVlhcjISLV0xUD8/Jzfq1evKv+QOXz4MCwsLNTy1KlTB9u2bUNqaqrKOKsbN24AAGrXrq2S/9WrV2jbti0ePnyIo0ePahwflRXFQPc7d+6o1K+oKzNN9bu4uCA0NBRPnz7Fy5cvUaVKFcTGxmL8+PEqPcV16tTBb7/9hqioKJU/CLLaJyo9eCuQSoTp06cjLS0NI0eO1PgknTY8PDzg6+uL7t27K/9SLSytW7fG0aNHVS7kaWlp2L59u0o+RdDy/l/0WfVCubm5oVmzZli4cCECAwMxZMgQlVtwUvHy8kJ8fDz27t2rkr5p0ybl+sLwfkC8Y8cOpKamKp9q9PHxga6uLu7fvw93d3eNCwBUr14ddnZ22LFjh0p54eHhOHv2bKHsi5Q8PT3x5s0bHDhwQCX9/Qk2s+Ll5aUM6jPbtGkTjI2N0bRp0zy169q1a2jbti0cHR0RHByMsmXLaszXvXt3xMfHqzxVBwAbN26Eg4MDmjRpokxTBFUPHjzA4cOH0aBBg1y1SXHb2MXFRaX+//3vf8qnD4GMoH3Lli1o0qSJxgdBypcvjzp16sDY2Bjff/89TExMMGzYMOX6rl27QiaTqTxZCWQ8XWlkZKR225ZKD/ZYUYnQvHlzLF++HGPHjkXDhg3x+eefo1atWtDR0UFkZKTyF/L7t7Det3btWknb5efnh7lz5yIkJER5cddk5syZ+PPPP9GmTRvMnj0bxsbGWL58udoM0M2aNUPZsmUxcuRIzJkzB3p6eggMDMT169ezLHv8+PHKaRNGjx4t1a6pGDRoEJYvX47Bgwfj0aNHqFOnDk6fPo3vvvsOHTt2RNu2bQuk3vft3r0buv/X3v2FNPWGcQD/mrIjLv9kLGbQljgkCBwGiXojuzAmpUYpIoiSCqYXdSERoTbmnxC7WFhG7UIQDaFujPL/H1BZRKQIUiZCqVcKJWIUacLTRezQ3NSs/VL5fT9wrvaew/u+5x08nPM+zwkKQmpqKt68eYOqqiqYzWZ1r9Tx48dRXV2NiooKvH//HlarFYcOHcLi4iJevXoFrVYLu92OAwcOwG63o6SkBFlZWSgsLMTy8jLsdjuioqL2Xfp7QUEBHA4H8vLyUFtbC5PJhO7ubvT29gLYPp3fZrPh+fPnsFgsuHnzJiIjI/Ho0SN0dnaioaHB51Om7UxPT6vroq6uDjMzM5iZmVF/j4mJUUsfpKWlITU1FaWlpVhZWYHJZEJ7ezt6enrQ1taGwMBAAD9fsbpLZ9y5cwfr6+t4+fKlek2dTqcWCH748CFGR0dx5swZHDt2DF++fMHo6Cju3r2L5ORkZGZmqucVFhaiqakJ2dnZqK+vx5EjR3D//n1MT097FBIFgIaGBuj1ehgMBiwuLuLx48fo6OhAa2urx9PUkydPoqioCDabDYGBgTh9+jT6+vrgdDpRW1vr8Srw69ev6OrqAgB1PMPDw/j48SO0Wi3S0tJ2PP+0i3Z79zzRTkxMTMilS5ckOjpaFEWR4OBgMZlMkp+fL4ODgx5tf7eo5N9kBZaXl0tAQIBMTU1t23eXyyWJiYmiKIro9Xq5du2aOJ1Or6zAFy9eSFJSkoSEhIhOp5Pi4mIZHx/fNLtrdXVVFEURq9W6bR/ctsoK1Gq1Ps/59OmTXL58WaKioiQoKEiMRqPcuHFDvn375tEOPrLGPnz4IADk9u3bv9WPjdz3cmxsTNLT0+XgwYMSGhoqubm5HlmRbh0dHWKxWCQsLEwURRGj0ShZWVkyMDDg0c7pdIrJZBKNRiOxsbHS3NwsmZmZEh8f7zUmX1mBG9eFr8w+o9EoZ8+e9epjSkqKpKSkbHnuZvfDPR+/mp+flwsXLqhzc/HiRenq6hIA8vTpU69rbDQ5OSnp6ekSHh4uGo1GzGazz/Xm6/764p6jzY6N1/78+bNcuXJF9Hq9aDQaiYuLk/b2do827nW02fHr/9jlcsm5c+fk6NGjotFoJCQkRMxms9TU1PgseruwsCD5+fkSGRkpwcHBkpiYKP39/V7t7Ha7xMTEiKIoEhERIVarVUZGRnzOwdramthsNjEYDOoaa2xs9Gq31bg2ZqjS3hcg8psFZIjIS0JCAoxGI548ebJrfXj27BkyMjLQ2dnpVbiQdmZ5eRmxsbE4f/48nE7nbnfnr926dQuVlZWYn5/f959hItovGFgR/aGVlRXodDpMTEyoZQP+pbdv32Jubg5Xr16FVqvF+Pg4U7h3YGFhAXV1dbBYLDh8+DDm5ubgcDjw7t07vH792mexyb3s3r17AIATJ07g+/fvGBoaQmNjI3JyctS9cET03+MeK6I/FBYW5vVZkX+prKwMLpcLp06dQktLC4OqHVIUBbOzsygrK8PS0pK6SfvBgwf7LqgCfpZlcDgcmJ2dxerqKgwGA65fv47Kysrd7hrR/wqfWBERERH5yf5KfSEiIiLawxhYEREREfkJAysiIiIiP2FgRUREROQnDKyIiIiI/ISBFREREZGfMLAiIiIi8hMGVkRERER+8gOS+1ZJUN2mBAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting smoothed Pandora retievals with error bars\n", + "timeseries_Pandora_TEMPO, data_subset = gauss_interpolation(time_series_Pandora[:, 0:3]\\\n", + " , time_series_TEMPO[:, 0])\n", + "print(len(timeseries_Pandora_TEMPO))\n", + "timeseries_Pandora_TEMPO_noneg, data_subset_noneg =\\\n", + " gauss_interpolation(time_series_Pandora_noneg[:, 0:3]\\\n", + " , time_series_TEMPO_noneg[:, 0])\n", + "print(len(timeseries_Pandora_TEMPO_noneg))\n", + "#if len(timeseries_Pandora_TEMPO) == 0: sys.exit()\n", + "#sys.exit()\n", + "\n", + "plot_title = 'NO$_{2}$ tropospheric column w unc, all '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = out_Q+'_unc_'+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(time_series_TEMPO[:, 0], time_series_TEMPO[:, 1],\\\n", + "yerr=time_series_TEMPO[:, 2], label = \"TEMPO\", c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.errorbar(timeseries_Pandora_TEMPO[:, 0],\\\n", + " timeseries_Pandora_TEMPO[:, 1],\\\n", + " yerr=timeseries_Pandora_TEMPO[:, 2],\\\n", + " label = \"Pandora smoothed at TEMPO times\",\\\n", + " c = 'r', ls = '', marker = \".\")\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = 0.\n", + "u_lim = ((dt_fin - dt0).total_seconds() + 1.)/86400.\n", + "plt.xlim(l_lim, u_lim)\n", + "\n", + "# some research is required to set the vertical range\n", + "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", + "plt.ylabel('NO$_{2}$ trop column, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "#plt.legend(loc='lower left')\n", + "plt.legend(loc='upper left')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)\n", + "\n", + "plot_title = 'NO$_{2}$ trop column w unc, positive '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = out_Q+'_unc_positive'+'_'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(time_series_TEMPO_noneg[:, 0], time_series_TEMPO_noneg[:, 1],\\\n", + "yerr=time_series_TEMPO_noneg[:, 2], label = \"TEMPO\", c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.errorbar(timeseries_Pandora_TEMPO_noneg[:, 0],\\\n", + " timeseries_Pandora_TEMPO_noneg[:, 1],\\\n", + " yerr=timeseries_Pandora_TEMPO_noneg[:, 2],\\\n", + " label = \"Pandora smoothed at TEMPO times\",\\\n", + " c = 'r', ls = '', marker = \".\")\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = int(min(time_series_TEMPO_noneg[:, 0]))\n", + "u_lim = int(max(time_series_TEMPO_noneg[:, 0])) + 1\n", + "plt.xlim(l_lim, u_lim)\n", + "\n", + "plt.xlabel(r'GMT, day from beginning of '+datestamp_ini, fontsize=12)\n", + "plt.ylabel('NO$_{2}$ trop col, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "plt.legend(loc='lower left')\n", + "#plt.legend(loc='upper left')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E9-sFIpHzb-3" + }, + "source": [ + "## 6.4 Plotting scatter plots along with regressions" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "-itPCr4RBpr-", + "outputId": "143e7b79-3cd1-470c-fb15-598922a7dc83" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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6NQPAbt++nWUZOXnNOYk5L+9xRpTxN5XR68rq/47M3rfIyEimoaHBZsyYwdzc3FiZMmWk+ljI+zeWlcWLFzMAUr8HGTl79iwDwA4fPiy1vW3btjLDoOUtMzMDBgxgKioqkn5fOak7Ix8+fGD6+voy/bRyU25u+6LQdS6Y65xGumt1MbBixQrUr18fERERUj3aHRwcsH79ekyaNAlNmzbFuHHjUL58eYSFheHvv//GrVu3sH79ejRp0kSqvNTUVPTs2RM1atSQK2uuXbs2AGDDhg0YNGgQ1NTUYG1tDV1d3SzP8/T0RKtWrdCiRQtMmzYNAoEAW7ZswZMnT3Do0CGZVg9fX1+oqqqiVatWePr0KebNm4c6deqgZ8+eOSozOjoaLVq0QN++fVGtWjXo6uoiJCQE58+fh6ura47rzIkNGzagadOmaNasGcaMGYMKFSogNjYWr1+/xqlTpzIdvZeVtWvXomnTpmjUqBFmzpwJKysrfPv2DSdPnsT27duzfB/kOTen71N6vXr1wvz589G7d29Mnz4dSUlJ2LhxY65HF+YnZ2dnbNmyBT9+/MD69eultnt5ecHAwCDTfgPp5cdrzsl7zOPx4OjoiICAgGzLVcbfVHq5+b+jVKlS6NatG/bs2YOoqChMmzZNqi9FXv/G1qxZg/nz56Nt27bo0KGDTB+hxo0bS35u164dWrVqhTFjxiAmJgZWVlY4dOgQzp8/D29vb0mLek7KHDlyJPT09NCwYUOYmprix48f8PHxwZEjRzB9+nRJvy956wa4kUPHjh2DnZ0d1NXV8fDhQyxfvhxVqlTBX3/9JRVLTso9d+4c4uPjERsbC4Abzfrvv/8CANq3bw8tLS26zkq+zlJylTIVApm1/DDGWN++fRkAmW/DjDEWFBTE3NzcmKmpKVNVVWUmJibM1dU1wxknRSIR6927N+vcubPM/ANZmTVrFrOwsGAqKioZzvOT2TfWtPlDtLW1maamJmvcuDE7deqU1DHp57zp1KkT09HRYbq6uqxPnz4ZTmqVXZlJSUls9OjRzMbGhunp6TFNTU1mbW3NFixYIJmcKid15rTF4d27d2zo0KGsTJkyTE1NjRkbG7MmTZpItbLlpOWHMW7ES48ePZiRkRETCASsfPnybPDgwXLNASPPufK8T5m93rNnzzJbW1umqanJKlWqxDZv3pzpaC9ltvxERkYyFRUVpq2tzVJSUiTb01pD/hxdlhV5XnNOY5bnfYqNjZVrlKcy/qYye12Z/d+R1ft28eJFyQidly9fyuyX528sM46OjhnOP5T2+FNsbCybMGECMzMzYwKBgNnY2LBDhw7luszdu3ezZs2asdKlSzNVVVVWqlQp5ujoKNUal5O6GWPsxYsXrHnz5szQ0JAJBAJmZWXF5s6dy+Li4jK8BvKWa2lpmelryu7vja5zwVzn9HiM/TfEicgYMWIEXr16hfPnz8t0MlWWhQsXYtGiRfj+/XuW/UuKep2E5NXZs2fRsWNHPHz4UNKqkhH6/Sak5Ck2HZ4V7cOHD9i1axdu3bqF0qVLQ0dHBzo6Orh27ZqyQyOEyMHf3x+9e/fOMvEhhJRMxa7Pj6JYWlqCGsUIKbpWrVql7BAIIYUU3fYihBBCSIlCt70IIYQQUqJQ8kMIIYSQEoWSH0IIIYSUKJT8EBl79uwBj8eTehgbG8PJyQmnT58usPrfv3+f7bFOTk5wcnLKlzhiYmKwdOlS2NnZQU9PD+rq6qhQoQKGDh2Ke/fuyRwfHByMHj16wNzcHAKBAGZmZnBzc0NQUFCO6nVycgKPx0Pbtm1l9r1//x48Hk9msUEAePv2LcaNG4eqVatCU1MTWlpaqFmzJubOnYvPnz9nWJerqyt4PF6m6/0EBARI/R7w+XwYGxujU6dOuHPnTo5eV0YOHz4MW1tbaGhowMLCApMmTUJcXFyW5+zatQs8Hk9qcdH07t27BxcXF+jo6KBUqVJwdXWVTM8vj0uXLsHe3h5aWlooXbo0Bg8eLLMQ7MePH9GtWzdUqlQJ2tra0NfXR926dbF582aZKforVKgg8/eU9vhzCo3Mjh09erTUcWl/IxoaGhkuauvk5CTXkgBPnz7F2LFjYW9vD21tbfB4vCwnhJTn/frzdyb9Q56FVH19fdGnTx9YWVlBU1MTFSpUQL9+/bJdVzExMRFVq1bN8O/j7t27cHd3R+3ataGrqwtTU1O4uLhkOsnj27dv4erqilKlSkFHRwetWrWS+psXiUQoVaqU1CLDadatWwcej4c+ffrI7Pvrr7/A4/Hw6NGjbK8DyV+U/JBMeXl5ISgoCDdv3sSOHTvA5/PRqVMnnDp1Stmh5bs3b96gbt26WL58OVq0aIFDhw7h4sWLWLRoEb59+4b69esjOjpacvymTZvg4OCAT58+YeXKlbh06RJWr16Nz58/o2nTpti8eXOOY7hw4YLcs1yfPn0aNjY2OH36NEaOHInTp09Lfj516pTMCvEAEBERIUlmDxw4kOniqACwbNkyBAUFISAgAPPmzcPNmzfh6OiYp4V+Dxw4gD59+qBBgwY4d+4cFixYgD179mQ5C/Lnz58xbdo0yUKif3r+/DmcnJyQkpKCo0ePYvfu3Xj58iWaNWsmWZwxK4GBgWjXrh1MTU3xv//9Dxs2bMClS5fg7OwstUZZfHw89PT0MG/ePJw8eRKHDx9G06ZNMX78eJlE5fjx4wgKCpJ6pK3E3q1bN5kYHBwcZI738PDIMN7k5GTMnTs329eVmTt37uDEiRMwNDSEs7Nzlsfm9P1K+51J/5AnIVuxYgUSEhIwZ84cnD9/HkuWLMH9+/dRr149PH36NNPz5s2bh/j4+Az3HTp0CLdv38bQoUPxv//9D7t27YK6ujqcnZ2xb98+qWO/f/+OZs2a4eXLl9i9ezeOHj2KpKQkODk54cWLFwC49SKbNWuG69evyyS7AQEB0NbWhr+/v0wcAQEBMDIyoukXCgO5p0MkJUZms2cnJCQwdXV11qdPnwKpX57ZOh0dHZmjo6NC6hWLxSwhIYEJhUJWu3Ztpqenxx4/fpzhsWfPnpXM1pu2blzHjh1lZgJPTU2VrBt3/fp1ueJwdHRkVatWZZUqVWL169eXWi/q3bt3DABbtWqVZNvbt2+ZtrY2q1u3LouKisrwdR07dkxm+6pVqxgA1qFDBwaAHThwQOYYf39/BoD5+PhIbd+7dy8DwObPny/Xa/qTUChk5ubmrHXr1lLb02aQPnv2bIbndezYkXXq1CnT2a579OjBSpcuzaKjoyXb3r9/z9TU1NiMGTOyjatBgwasRo0aUu/jjRs3GAC2ZcuWbM/v2bMnU1VVzXY28YULFzIA7NKlS1LbLS0tWYcOHbKtJ+1vpG3btkxFRYU9ePBAan9Gs35nJP06YD4+PlKzSqeXk/crs98ZeWU0q/bnz5+ZmpoaGzZsWIbn3Lp1iwkEAslrSP/3kVmZQqGQ2djYsMqVK0ttnz59OlNTU2Pv37+XbIuOjmalS5dmPXv2lGxbs2YNA8CCgoIk20QiETMwMGDTpk2TWactOTmZaWpqsu7du2dzBUhBoJYfIjcNDQ0IBAKoqalJbf/16xfGjh2LMmXKQCAQoFKlSpgzZ47UN+W02zV79uyRKZfH42HhwoVZ1s0Yw8qVK2FpaQkNDQ3Uq1cP586dy/DYmJgYTJs2DRUrVoRAIECZMmUwadIkmW+Fabd7tm3bhurVq0NdXR179+7FiRMn8PjxY8yaNSvTb6rt2rWTrCHj6ekJHo+HrVu3QlVVeuosVVVVbNmyBTweD8uXL8/yNaanpqaGpUuX4u7du5JWgsysXbsW8fHx2LJlC/T19WX283i8DL+d7969G6ampti7dy80NTWxe/duueOzs7MDAHz79k3uc9ILDg6WWoE8TY8ePaCjo4Pjx4/LnOPt7Y3AwEBs2bIlwzKFQiFOnz6N7t27Q09PT7Ld0tISLVq0yLDM9D5//oyQkBAMGDBA6n1s0qQJqlatmu35AGBsbAwVFRWptYj+xBiDl5cXKlWqhJYtW2ZbZlZmzJgBIyOjTFuGspN+HbCs5Ob9yi0TExOZbRYWFihbtiw+fvwosy8lJQVDhw6Fu7u75PdSnjL5fD7q168vU+bx48fRsmVLWFpaSrbp6enB1dUVp06dkrT0tGjRAgCkbhM+fPgQkZGRGDlyJMzNzaVaf27duoXExETJeUS5KPkhmRKJRBAKhUhNTcWnT58kCUTfvn0lxyQlJaFFixbYt28fpkyZgjNnzqB///5YuXJllrcvcmrRokXw8PBAq1atcOLECYwZMwYjRoyQNEOnSUhIgKOjI/bu3YsJEybg3Llz8PDwwJ49e9C5c2eZiStPnDiBrVu3Yv78+bhw4QKaNWuGixcvAgC6du2abVwikQj+/v6ws7ND2bJlMzymXLlyqF+/Pq5cuZKjBT179eqF+vXrY+7cuUhNTc30uIsXL8LU1FRqocLs3Lx5E8+ePcPAgQNhZGSE7t2748qVK3j37p1c56cdV7VqVbnrTO/JkycAABsbG6ntampqqFatmmR/moiICEyaNAnLly/P9Dq/efMGiYmJMmWm1fP69essb+1lFlPatj9jArhERigUIjIyEkeOHMGePXswdepUmSQ4vUuXLuHDhw8YOnRohgvhXr16Fbq6ulBTU0ONGjWwZs2aTH9vdHV1MXfu3BzdIs2NnL5fAODu7g5VVVXo6emhTZs2uH79eq7rf/v2LT58+CC1WHWaxYsXIz4+XmaxzOwIhUJcu3ZNqszExES8efMm09+BxMRESf+xOnXqwMDAQCrB8ff3h7m5OapUqYLmzZtLJUZpx1HyUzjQDM8kU39+mKqrq2Pz5s1o06aNZNvevXvx6NEjHD16FD169AAAtGrVCjo6OvDw8ICfnx9atWqVpziioqKwYsUKdOvWDbt27ZJsr1mzJhwcHGBtbS3ZtnHjRjx69Ai3bt2SfAt0dnZGmTJl4ObmhvPnz0t1UoyLi8Pjx49hYGAg2RYWFgYAqFixYrax/fjxAwkJCdkeW7FiRdy+fRs/f/7M8FtoRng8HlasWAEXFxds3749007JYWFhsLW1lavMNP/88w8AYOjQoQCAYcOGwdvbG15eXli8eLHM8WKxWJII379/H1OnTkWNGjUk5+fUz58/AQCGhoYy+wwNDWU6u48dOxbW1tYYM2ZMrstkjCEyMhLm5ua5Oj9tf3orVqzArFmzAHDv1+zZs7FkyZJMYwS4a8/n8zF48GCZfR06dICdnR0qV66MyMhI+Pj4YNq0aXjw4AH279+fYXmjR4/Ghg0b4OHhgdu3b2eYUOVVTt4vfX19TJw4EU5OTjAyMsLr16+xatUqODk54cyZM1L/f8hDKBRi2LBh0NHRweTJk6X2PXjwACtXrsSpU6egra0tV7+uNAsXLsTr169x4sQJybbIyEgwxjJ9ncDva6GiogJHR0f4+flBKBRCVVUVAQEBcHR0BAA4OjpiwYIFYIxJOpKbmJigRo0aOXr9JH9Qyw/J1L59+xASEoKQkBCcO3cOgwYNgru7u1Tn3StXrkBbWxtubm5S56b9x3758uU8xxEUFISkpCT069dPanuTJk2kmqYBruNvrVq1YGtrC6FQKHm0adMmw5EsLVu2lEp88ktai1PaB1NaMpH2yOybvbOzM1q3bo3FixcjNjZWIbHExcXh6NGjaNKkCapVqwaA+4+6cuXK2LNnD8Riscw5vXr1gpqaGrS0tODg4ICYmBicOXMGpUqVylMsmX1Qp99+7NgxnDp1Cjt37pTrgz2rY/JyfkbbBw8ejJCQEFy4cAEzZszAqlWrMH78+EzL/vXrF06cOIG2bduiTJkyMvv//vtvDBkyBM2bN0eXLl3g7e2NcePGwdvbG/fv38+wTIFAgCVLluDOnTs4evRotq8vL+S5NnXr1sX69evRtWtXNGvWDEOGDMHNmzdhbm6OGTNm5Kg+xhiGDRuGa9euYd++fShXrpxkn1AoxNChQ9GrV68cJ1S7du3C0qVLMXXqVHTp0iXL15PVvhYtWiA+Ph4hISEQi8W4du2aZPSpo6Mjvn//jqdPnyI5ORnBwcHU6lOIUPJDMlW9enXY2dnBzs4Obdu2xfbt29G6dWvMmDEDUVFRALhvQWZmZjL/WZiYmEBVVTXDb8s5lVaGmZmZzL4/t3379g2PHj2Cmpqa1ENXVxeMMfz48UPq+IxaAcqXLw8Act0CKl26NLS0tLI99v3799DS0pJ8exw6dKhUfFmNtFmxYgV+/PiR4fD2tHjlvV0FAEeOHEFcXBx69uyJqKgoREVFITo6Gj179sTHjx/h5+eXYQwhISEIDAzEnDlz8O3bN3Tt2lWqX1dOGBkZAUCGvx+/fv2SXKe4uDi4u7tj/PjxsLCwkMSbkpICgGsVTOvLlV2ZPB4vy2RN3pjSMzMzg52dHVq3bo3ly5dj8eLF2Lx5c6aJire3N5KTkzF8+PBM4/hT//79ASDLYeK9e/dGvXr1MGfOnCxvkeZWbq5NeqVKlULHjh3x6NEjJCYmylUnYwzDhw+Ht7c39uzZI5OkrF+/Hm/fvsWCBQskvxcxMTEAuNvxUVFRGX6p8PLywqhRozBy5EiZ9d8MDAzA4/EyfZ2AdOtXWjLj7++P+/fvIyoqStLyU6NGDRgbGyMgIADBwcHU36eQoeSH5Ejafe+XL18C4P5T/Pbtm0xfmoiICAiFQpQuXRoAJPOZ/PlhKU9ylPYf79evX2X2/bmtdOnSqF27tqTF6s/HvHnzpI7P6Bte2rfI9M3hmeHz+WjRogXu3LmDT58+ZXjMp0+fcPfuXbRs2VLSEXbhwoVScW3fvj3TOmxtbdGnTx+sXbs2ww7Gbdq0wbdv3+SaQwX4fctr0qRJMDAwkDw8PT2l9qdXqVIl2NnZoXnz5liyZAkWL16Mhw8fYtOmTXLV+ae0ob6PHz+W2i4UCvH8+XNJR/MfP37g27dvWLNmjVSshw4dQnx8PAwMDCQtgpUrV4ampqZMmWn1WFlZycyrk15anZmdL88w7YYNGwKA5O/jT//88w9MTU0znHogM2l/W1l1Tk67RfrmzRvs2LFD7rLlJe/7lZU/Wz+zO3b48OHw8vLCrl27JAlgek+ePEF0dDSqVKki+b2oU6cOAG7Yu4GBgUy8Xl5eGD58OAYNGoRt27bJxKKpqQkrK6tMfwc0NTVRqVIlybZatWpJEpyAgACYmppKWlMBoHnz5vD395e0OFPyU4goYYQZKeQyG+rOGGOtWrViANjbt28ZY4xt376dAWC+vr5Sx6UNo/bz82OMccOtNTQ02NixY6WO++effxgAtmDBApn604a6//r1i2loaLBu3bpJnZs2BDn9UPclS5YwLS0tSXxZAcDc3d1ltssz1P38+fMyQ907derEhEKhTFlpQ91v3LiRbUyMZTxM+e3bt0wgELB27drlaqh72vsTGhrKALDu3bszf39/mYezszMTCATsx48fjLHMhy2npKQwKysrZmRkxGJiYuR6XemlDZ1u27at1PZDhw4xAOzcuXOMMcYSExMzjLNNmzZMQ0OD+fv7S71HPXv2ZCYmJlIxffjwgQkEAubh4ZFtXA0bNmS1atWSeh+DgoIYALZ169Zsz583bx4DwO7cuSOzLyQkhAGQa8h9emPGjGEApIazZ/Y32qpVK2ZiYsLq168v11D39OQZ6p7d+5WZX79+sTJlyjBbW9ts4xCLxWzYsGGMx+OxHTt2ZHrcs2fPZH4v0uIZPXo08/f3Z7GxsZLjvby8mIqKChs4cKDUEP8/zZgxgwkEAhYWFibZFhMTw4yNjVmvXr1kjndzc2Pa2tqsTZs2UkPhGWNs48aNzMjIiDk6OjILC4tsXzspOJT8EBlp/7F6eXmxoKAgFhQUxE6fPs2GDh3KAEglIYmJiczGxobp6uqytWvXMj8/P7ZgwQKmpqbG2rdvL1Xu8OHDmYaGBluzZg27dOkSW7ZsGatVq1a2yQ9jjM2dO5cBYMOGDWPnz59nO3fuZGXKlGFmZmZSyU9cXByrW7cuK1u2LFuzZg3z8/NjFy5cYDt37mQ9evRgwcHBkmMzS34YY+z169esUqVKTEdHh02fPp2dPXuWBQYGsn379rHOnTszHo8nlWhs3LiRqaiosMaNGzNvb2929epV5u3tzezt7ZmKigrbuHGj3Nc/szlaJk6cyABkOI/JqVOnmJaWFqtQoQJbvXo1u3z5Mrt8+TLbtGkTq1u3ruRDZ+rUqQwAu3XrVoZ1nzx5kgFg69evZ4xlPWfL0aNHGQD2119/SbalHZ/+/czM/v37GQA2cuRI5u/vz3bs2MFKlSrFWrVqle25mc3z8+zZM6ajo8OaN2/Ozp49y3x9fVmtWrWYhYUFi4iIkDqWz+ezli1bSm3z9/dnqqqqrFu3bszPz48dOHCAlStXjtWqVUtq7p758+ezUaNGsQMHDrCAgAB24sQJNnr0aMbn81mPHj0yjHn06NEMAHvx4kWG+w8cOMC6d+/Odu/ezS5fvsyOHTvGevfuzQCwwYMHSx2bWfJz7949xuPxGAC5kp/4+Hjm4+PDfHx8JL8bCxcuZD4+PjJzLcn7fvXp04d5eHgwHx8fyXHW1tZMVVVV8mUozdChQxmfz5eaU2fcuHEMABs6dKjk/5+0x71797J8PRnNg8UY97uqoqLC6tWrx27cuCFTbvr3NiIigpmbm7PatWuz48ePs7Nnz7LmzZszXV1d9uzZM5k6//77bwaA8Xg89vfff0vte/jwoWRfv379soydFCxKfoiMtP9Y0z/09fWZra0tW7t2rcwEbj9//mSjR49m5ubmTFVVlVlaWrJZs2bJHBcdHc2GDx/OTE1Nmba2NuvUqRN7//69XMmPWCxmnp6erFy5ckwgEDAbGxt26tSpDCc5jIuLY3PnzmXW1tZMIBAwfX19Vrt2bTZ58mT29etXyXFZJT+MMRYVFcX++usvVq9ePaajo8PU1NRY+fLlWf/+/TNsxQkKCmJubm7M1NSUqaqqMhMTE+bq6spu3rwpx1X/LbPk5/v370xPTy/D/9wZY+zNmzds7NixzMrKiqmrqzNNTU1Wo0YNNmXKFPbu3TuWkpLCTExMsvz2LRQKWdmyZVnt2rUZY9lPWNeoUSNmYGAgSQRPnTrFALBt27bJ9VoPHjzIbGxsmEAgYGZmZmzChAlS39Yzk1nywxhjd+7cYc7OzkxLS4vp6emxrl27stevX8sc92erYZqLFy+yxo0bMw0NDWZoaMgGDhwoM0neyZMnmYuLi+S91tHRYQ0bNmQbN26UmeiSMW6CUH19fda8efNMX1NQUBBzdnZmZmZmTE1NjWlpabEGDRqwLVu2yLRUZNU627dvX7mTn7RkIaOHpaWlzPHyvF+enp7M1taW6evrMz6fz4yNjVm3bt3Y7du3ZcobNGiQzN+6paVljmLK6PX8+feRVk9mjz8nVH39+jXr2rUr09PTY1paWszZ2ZndvXs3wzrTWlMBsCdPnkjtE4vFzNDQkAFgO3fuzDJ2UrB4jP3RWYMQQnJpxowZOHToEF69epVl/xpCCFEm6vBMCFEYf39/zJs3jxIfQkihRi0/hBBCCClRqOWHEEIIISUKJT+EEEIIKVEo+SGEEEJIiULJDyGEEEJKFEp+CCGEEFKiUPJDCCGEkBKFkh9CCCGElCglKvm5evUqOnXqBAsLC/B4PLlW7U4vKSkJgwcPRu3ataGqqoquXbvKHBMQEAAejyfzeP78uWJeBCGEEELypEQlP/Hx8ahTpw42b96cq/NFIhE0NTUxYcIEuLi4ZHnsixcvEB4eLnlUqVIlV3USQgghRLFUlR1AQWrXrh3atWuX6f6UlBTMnTsXBw4cQFRUFGrVqoUVK1bAyckJAKCtrY2tW7cCAG7cuIGoqKhMyzIxMUGpUqUUGD0hhBBCFKFEtfxkZ8iQIbhx4wYOHz6MR48eoUePHmjbti1evXqV47Lq1q0Lc3NzODs7w9/fPx+iJYQQQkhuUPLznzdv3uDQoUPw8fFBs2bNULlyZUybNg1NmzaFl5eX3OWYm5tjx44dOHbsGHx9fWFtbQ1nZ2dcvXo1H6MnhBBCiLxK1G2vrNy7dw+MMVStWlVqe3JyMoyMjOQux9raGtbW1pLn9vb2+PjxI1avXo3mzZsrLF5CCCGE5A4lP/8Ri8Xg8/m4e/cu+Hy+1D4dHZ08ld24cWN4e3vnqQxCCCGEKAYlP/+pW7cuRCIRIiIi0KxZM4WWff/+fZibmyu0TEIIIYTkTolKfuLi4vD69WvJ83fv3uHBgwcwNDRE1apV0a9fPwwcOBBr1qxB3bp18ePHD1y5cgW1a9dG+/btAQChoaFISUnBr1+/EBsbiwcPHgAAbG1tAQDr169HhQoVULNmTaSkpMDb2xvHjh3DsWPHCvrlEkIIISQDPMYYU3YQBSUgIAAtWrSQ2T5o0CDs2bMHqampWLJkCfbt24fPnz/DyMgI9vb2WLRoEWrXrg0AqFChAj58+CBTRtplXLlyJXbs2IHPnz9DU1MTNWvWxKxZsyTJEyGEEEKUq9AnP58/f4aHhwfOnTuHxMREVK1aFf/88w/q16+v7NAIIYQQUgQV6ttekZGRcHBwQIsWLXDu3DmYmJjgzZs3NHkgIYQQQnKtULf8zJw5Ezdu3MC1a9dyXYZYLMaXL1+gq6sLHo+nwOgIIYQQkl8YY4iNjYWFhQVUVBQ7LWGhTn5q1KiBNm3a4NOnTwgMDESZMmUwduxYjBgxItNzkpOTkZycLHn++fNn1KhRoyDCJYQQQoiCffz4EWXLllVomYU6+dHQ0AAATJkyBT169MDt27cxadIkbN++HQMHDszwnIULF2LRokUy2z9+/Ag9Pb18jZcQQgghucMYw42wG2hq2RQAEBMTg3LlyiEqKgr6+voKratQJz8CgQB2dna4efOmZNuECRMQEhKCoKCgDM/5s+Un7eJFR0dT8kMIIYQUQr8Sf2Ho/4bify/+h+O9jqNrta6IiYmBvr5+vnx+F+oOz+bm5jK3rKpXr57lnDnq6upQV1fP79AIIYQQogDBn4LR699eCIsOg4AvwI+EH/leZ6Fe2NTBwQEvXryQ2vby5UtYWloqKaL89fz5czRu3BgaGhqSSRPzw549e2jEHCGEEKUSMzFW31yNZl7NEBYdhsoGlRE0LAjD6w3P97oLdfIzefJkBAcHY9myZXj9+jUOHjyIHTt2wN3dXalxff/+HWpqakhISIBQKIS2tjbCwsLyXO6CBQugra2NFy9e4PLlywqINGO9evXCy5cvc3SOk5MTJk2alD8BZWHhwoXg8XgyD21t7QKPhRBCiGL8SPiBzoc6Y7rfdAjFQvSq2Qv3Rt1DPfN6BVJ/ob7t1aBBAxw/fhyzZs3C4sWLUbFiRaxfvx79+vVTalxBQUGwtbWFlpYWbt26BUNDQ5QvXz7P5b558wYdOnTI95YtTU1NaGpq5msdijJt2jSMHj1aapuzszMaNGigpIgIIYTk1bUP13Dm1Rmo89Wxoe0GjKw/skCnoynULT8A0LFjRzx+/BhJSUl49uxZlsPcC8rNmzfh4OAAALh+/brk56yIxWIsXrwYZcuWhbq6OmxtbXH+/HnJfh6Ph7t372Lx4sXg8XhYuHBhhuU4OTlh3LhxGDduHEqVKgUjIyPMnTsX6futR0ZGYuDAgTAwMICWlhbatWuHV69eSfb/edtr4cKFsLW1xf79+1GhQgXo6+ujd+/eiI2NBQAMHjwYgYGB2LBhg6Tl5f3794iMjES/fv1gbGwMTU1NVKlSBV5eXjm5lNnS0dGBmZmZ5PHt2zeEhoZi2LBhCq2HEEJIwelWvRuWtlyKW8NvYZTdqIKfh48Vc9HR0QwAi46OzlM5Hz58YPr6+kxfX5+pqakxDQ0Npq+vzwQCAVNXV2f6+vpszJgxmZ6/du1apqenxw4dOsSeP3/OZsyYwdTU1NjLly8ZY4yFh4ezmjVrsqlTp7Lw8HAWGxubYTmOjo5MR0eHTZw4kT1//px5e3szLS0ttmPHDskxnTt3ZtWrV2dXr15lDx48YG3atGFWVlYsJSWFMcaYl5cX09fXlxy/YMECpqOjw1xdXdnjx4/Z1atXmZmZGZs9ezZjjLGoqChmb2/PRowYwcLDw1l4eDgTCoXM3d2d2draspCQEPbu3Tvm5+fHTp48KRXroEGDcnvJMzRu3DhWtWpVhZZJCCEkf32L+8b6HuvLvsR8kfscRX1+Z6RQ3/YqTCwsLPDgwQPExMTAzs4OwcHB0NHRga2tLc6cOYPy5ctDR0cn0/NXr14NDw8P9O7dGwCwYsUK+Pv7Y/369fj7779hZmYGVVVVSUtHVsqVK4d169aBx+PB2toajx8/xrp16zBixAi8evUKJ0+exI0bN9CkSRMAwIEDB1CuXDmcOHECPXr0yLBMsViMPXv2QFdXFwAwYMAAXL58GUuXLoW+vj4EAgG0tLSkYgsLC0PdunVhZ2cHgFv0Nb3y5cvD3Nw86wubA8nJyThw4ABmzpypsDIJIYTkr4D3Aeh7rC/C48IRlRSFM33PKDukwn/bq7BQVVVFhQoV8Pz5czRo0AB16tTB169fYWpqiubNm6NChQooXbp0hufGxMTgy5cvMrfHHBwc8OzZsxzH0rhxY6kmQnt7e7x69QoikQjPnj2DqqoqGjVqJNlvZGQEa2vrLOuqUKGCJPEBuGkGIiIisoxjzJgxOHz4MGxtbTFjxgyp+ZgAYN++ffD09Mzpy8uUr68vYmNjM53gkhBCSOEhEouwOHAxnPc5IzwuHNVLV8cKlxXKDgtAIe/wXJjUrFkTHz58QGpqKsRiMXR0dCAUCiEUCqGjowNLS0s8ffo0yzL+vKfJGFP4fU6WyZyV2dWlpqYm9ZzH40EsFmdZV7t27fDhwwecOXMGly5dgrOzM9zd3bF69eqcBy6HXbt2oWPHjtm2jBFCCFGur3Ff0c+3H668uwIAGGw7GJvbbYa2oHCM1KWWHzmdPXsWDx48gJmZGby9vfHgwQPUqlUL69evx4MHD3D27NlMz9XT04OFhQWuX78utf3mzZuoXr16jmMJDg6WeV6lShXw+XzUqFEDQqEQt27dkuz/+fMnXr58mau60ggEAohEIpntxsbGGDx4MLy9vbF+/Xrs2LEj13Vk5d27d/D396eOzoQQUsg9/PoQdbbVwZV3V6ClpoW9XffCq4tXoUl8AGr5kZulpSW+fv2Kb9++oUuXLlBRUUFoaChcXV1hYWGR7fnTp0/HggULULlyZdja2sLLywsPHjzAgQMHchzLx48fMWXKFIwaNQr37t3Dpk2bsGbNGgBAlSpV0KVLF4wYMQLbt2+Hrq4uZs6ciTJlyqBLly45ritNhQoVcOvWLbx//x46OjowNDTEwoULUb9+fdSsWRPJyck4ffq0VII1cOBAlClTRiG3vnbv3g1zc3O0a9cuz2URQgjJP5UNK8NQ0xAm2ibw6eGDaqWrKTskGZT85EBAQAAaNGgADQ0NXLt2DWXKlJEr8QG4NcliYmIwdepUREREoEaNGjh58iSqVKmS4zgGDhyIxMRENGzYEHw+H+PHj8fIkSMl+728vDBx4kR07NgRKSkpaN68Oc6ePStzaysnpk2bhkGDBqFGjRpITEzEu3fvIBAIMGvWLLx//x6amppo1qwZDh8+LDknLCwMKip5b1xM64w9ePBg8Pn8PJdHCCFEsSLiI1BaqzRUeCrQEejgXL9zMNU2haZa4ZxTrlAvbKoI+bkwmjI4OTnB1tYW69evV3YohBBCCM6/Po8BxwfAw8ED05pMU1i5+fn5TX1+CCGEEJJjqaJUzLw0E+0OtMOPhB/wCfWBUCxUdlhyodtehBBCCMmRj9Ef0ftYb9z8yE1x4t7AHatbr4aqStFIK4pGlEQiICBA2SEQQggpwU69OIXB/xuMX4m/oKeuh386/wO3Gm7KDitHKPkhhBBCiFw+x3yGm48bUkQpsLOwwxG3I6hkUEnZYeUYJT+EEEIIkUsZvTJY6bIS76LeYYXLCqirqis7pFyh5IcQQgghmTr+7DgqGlSErZktAGBi44nKDUgBaLSXgjg5OWHSpEnKDoMQQghRiGRhMiacmwDXo67o6dMTscmxyg5JYSj5KaG2bNmCihUrQkNDA/Xr18e1a9eyPefz58/o378/jIyMoKWlBVtbW9y9e1ey/+rVq+jUqRMsLCzA4/Fw4sQJhdWdH3ISh6enJxo0aABdXV2YmJiga9euePHihdQxCxcuBI/Hk3pktQ6Zp6cneDweJc2EkELnza83cNjtgE23NwEAulh3gYaqhpKjUhxKfkqgI0eOYNKkSZgzZw7u37+PZs2aoV27dggLC8v0nMjISDg4OEBNTQ3nzp1DaGgo1qxZg1KlSkmOiY+PR506dbB582aF1v0nJycn7NmzR+7jFRFHYGAg3N3dERwcDD8/PwiFQrRu3Rrx8fFSx9WsWRPh4eGSx+PHjzMsLyQkBDt27ICNjU2eXgchhCja0adHUXd7XdwNvwsjTSOc7nMaq1qvgho/96sEFDqsmIuOjmYAWHR0dJ7L8vHxYbVq1WIaGhrM0NCQOTs7s7i4OMYYY46OjmzixImSY5OSktj48eOZsbExU1dXZw4ODuz27duS/Y6Ojszd3Z25u7szfX19ZmhoyObMmcPEYrHkGLFYzFasWMEqVqzINDQ0mI2NDfPx8cnz62jYsCEbPXq01LZq1aqxmTNnZnqOh4cHa9q0qdx1AGDHjx9XSN1/cnR0ZF5eXnIfn5G8xhEREcEAsMDAQMm2BQsWsDp16mR7bmxsLKtSpQrz8/OT+b0hhBBlSRYms9GnRjMsBMNCMId/HNjH6I9Ki0eRn99/opYfOYWHh6NPnz4YOnQonj17hoCAALi6uoJlsjrIjBkzcOzYMezduxf37t2DlZUV2rRpg1+/fkmO2bt3L1RVVXHr1i1s3LgR69atw65duyT7586dCy8vL2zduhVPnz7F5MmT0b9/fwQGBgIA9uzZAx6Pl6PXkZKSgrt376J169ZS21u3bo2bN29met7JkydhZ2eHHj16wMTEBHXr1sXOnTsLpG5FU0Qc0dHRAABDQ0Op7a9evYKFhQUqVqyI3r174+3btzLnuru7o0OHDnBxccnlKyCEEMVTVVHFm8g3AIBZTWchYHAAyuqVVXJU+UTh6VQho6jM8e7duwwAe//+fYb703+Dj4uLY2pqauzAgQOS/SkpKczCwoKtXLlScnz16tWlWno8PDxY9erVJWVoaGiwmzdvStUzbNgw1qdPH8YYY76+vsza2jpHr+Pz588MALtx44bU9qVLl7KqVatmep66ujpTV1dns2bNYvfu3WPbtm1jGhoabO/evRkejwxafnJb99KlS5m2trbkoaKiwtTV1aW2Xb16NZtXnvc40ojFYtapUyeZlrCzZ8+yf//9lz169EjSqmNqasp+/PghOebQoUOsVq1aLDExkTEm22JICCEFTSgSSn7+GvuVXXh9QYnR/JafLT801F1OderUgbOzM2rXro02bdqgdevWcHNzg4GBgcyxb968QWpqKhwcHCTb1NTU0LBhQzx79kyyrXHjxlItN/b29lizZg1EIhFCQ0ORlJSEVq1aSZWdkpKCunXrAgC6deuGbt265er1/NlixBjLshVJLBbDzs4Oy5YtAwDUrVsXT58+xdatWzFw4MB8rXv06NHo2bOn5Hm/fv3QvXt3uLq6SraVKVMmRzHkJo4048aNw6NHj3D9+nWp7e3atZP8XLt2bdjb26Ny5crYu3cvpkyZgo8fP2LixIm4ePEiNDSKT8dBQkjRlJCagAnnJoDP42N7p+0AAFMdU7TWaZ3NmUUfJT9y4vP58PPzw82bN3Hx4kVs2rQJc+bMwa1bt1CxYkWpY9l/t8Jy++EKcMkGAJw5c0bmg11dPfeTSpUuXRp8Ph9fv36V2h4REQFTU9NMzzM3N0eNGjWktlWvXh3Hjh3L97oNDQ2lbi9pamrCxMQEVlZWctetiDgAYPz48Th58iSuXr2KsmWzbg7W1tZG7dq18erVKwDA3bt3ERERgfr160uOEYlEuHr1KjZv3ozk5GTw+fxcvSZCCMmJ0O+h6OnTE0+/PwUPPIxvNB61TGopO6wCQ31+coDH48HBwQGLFi3C/fv3IRAIcPz4cZnjrKysIBAIpFoGUlNTcefOHVSvXl2yLTg4WOq84OBgVKlSBXw+HzVq1IC6ujrCwsJgZWUl9ShXrlyuX4NAIED9+vXh5+cntd3Pzw9NmjTJ9DwHBweZod0vX76EpaVlvtetaLmJgzGGcePGwdfXF1euXJFJeDOSnJyMZ8+ewdzcHADg7OyMx48f48GDB5KHnZ0d+vXrhwcPHlDiQwgpEHse7EGDnQ3w9PtTmGqbwm+AX4lKfABQnx95BQcHs6VLl7KQkBD24cMHdvToUSYQCNjZs2cZY7J9NyZOnMgsLCzYuXPn2NOnT9mgQYOYgYEB+/Xrl+R4HR0dNnnyZPb8+XN28OBBpq2tzbZt2yYpY86cOczIyIjt2bOHvX79mt27d49t3ryZ7dmzhzGWuz4/jDF2+PBhpqamxv755x8WGhrKJk2axLS1taX6M23atIm1bNlS8vz27dtMVVWVLV26lL169YodOHCAaWlpMW9vb8kxsbGx7P79++z+/fsMAFu7di27f/8++/DhQ47q/lNsbCwLDw/P8pGcnKzQa/Dn6x8zZgzT19dnAQEBUvUmJCRIjpk6dSoLCAhgb9++ZcHBwaxjx45MV1c3y9dGfX4IIQUlNjmWDTw+UDKay3mvMwuPDVd2WJnKzz4/lPzIKTQ0lLVp00YydL1q1aps06ZNkv1/foglJiay8ePHs9KlS2c61H3s2LFs9OjRTE9PjxkYGLCZM2fKDHXfsGEDs7a2ZmpqaszY2Ji1adNGMrzay8uL5TZ//fvvv5mlpSUTCASsXr16UkO2GeOGbVtaWkptO3XqFKtVqxZTV1dn1apVYzt27JDa7+/vzwDIPAYNGpSjuv+0YMGCDMtN//D391foNfjz9WdWb/oh97169WLm5uZMTU2NWVhYMFdXV/b06dMsY6DkhxBSEMRiMWu6uynDQjCVRSrsr8C/pDo6F0b5mfzwGMtkrHYxERMTA319fURHR0NPT0/Z4Ug4OTnB1tYW69evV3YohBBCSoAzL89g1OlROOB6AI4VHJUdTrby8/ObOjwTQgghxVBsciye/XiGhmUaAgA6VO2Al+NfQktNS8mRKR91eCaEEEKKmQdfH6D+jvpo690WH6I+SLZT4sOhlh8lCQgIUHYIhBBCihnGGLbd2YbJFyYjWZSMsnpl8SPhByxLyT8ytySg5IcQQggpBqKTojHi1Aj4hPoAADpW7Yg9XfbASMtIyZEVPpT8EEIIIUXcnS930OvfXngb+RaqKqpY4bICkxtPzvH6jyUFJT+EEEJIEbf3wV68jXwLS31LHHE7gkZlGyk7pEKNkh9CCCGkiFvVehU0VDUwu9lsGGjKrjlJpNFoL0IIIaSIufXpFob8bwhEYhEAQENVA6tar6LER07U8kMIIYQUEWImxrqgdZh5eSaEYiHqmNbBpMaTlB1WkUMtPzng5OSESZMmKTsMQgghJdDPhJ/ofKgzpvlNg1AsRI8aPTDEdoiywyqSKPnJAV9fX/z1119yH18UkyVFxhwbG4tJkybB0tISmpqaaNKkCUJCQrI97/Pnz+jfvz+MjIygpaUFW1tb3L17V7Lf09MTDRo0gK6uLkxMTNC1a1eZFecV7erVq+jUqRMsLCzA4/Fw4sSJLI/39PQEj8cr8PdfKBRi7ty5qFixIjQ1NVGpUiUsXrwYYrE4z+dk974oWlG55oQUhBthN2C73RZnXp2BOl8dWztsxRG3I9DX0Fd2aEUSJT85YGhoCF1d3QKvNyUlpcDrVIThw4fDz88P+/fvx+PHj9G6dWu4uLjg8+fPmZ4TGRkJBwcHqKmp4dy5cwgNDcWaNWtQqlQpyTGBgYFwd3dHcHAw/Pz8IBQK0bp1a8THx+c6VicnJ+zZsyfT/fHx8ahTpw42b96cbVkhISHYsWMHbGxsFB5HdlasWIFt27Zh8+bNePbsGVauXIlVq1Zh06ZNeTpHnvclN7J6vQV1zQkp7Hbf3w3HPY74FPMJVQyrIHh4MEbbjaZh7Hmh8KVSCxlFrgqbfgVuR0dHNn78eDZ9+nRmYGDATE1N2YIFCyTHDho0SGYF8Hfv3jHGuNV1V6xYwSpWrMg0NDSYjY0N8/HxkarH3d2dTZ48mRkZGbHmzZszxhgTiURs+fLlrHLlykwgELBy5cqxJUuWyFVm+nLd3d2Zvr4+MzQ0ZHPmzJGsJJ9VzDmVkJDA+Hw+O336tNT2OnXqsDlz5mR6noeHB2vatGmO6oqIiGAApFZll+d6pOfo6Ci1QntWALDjx49nuC82NpZVqVKF+fn55WrF9pzEkZEOHTqwoUOHSm1zdXVl/fv3z9M58rwvOb3mjMn/evPzmhNS2D38+pBpLNFgfY/1ZTFJMcoOp8Dk56ru1PKTB3v37oW2tjZu3bqFlStXYvHixfDz8wMAbNiwAfb29hgxYgTCw8MRHh6OcuXKAQDmzp0LLy8vbN26FU+fPsXkyZPRv39/BAYGSpWtqqqKGzduYPv27QCAWbNmYcWKFZg3bx5CQ0Nx8OBBmJqayl1m+nJv3bqFjRs3Yt26ddi1a1eWMe/ZsyfH3zCEQiFEIhE0NDSktmtqauL69euZnnfy5EnY2dmhR48eMDExQd26dbFz584s64qOjgbAtcylkfd6KJq7uzs6dOgAFxeXfK0nM02bNsXly5fx8uVLAMDDhw9x/fp1tG/fPk/nyPO+lNRrTkh+CI8Nl/xsY2qDB6MewLubN3TVC/7uQ7Gk8HSqkMnPlp8/vwk3aNCAeXh4ZHh8mri4OKahocFu3rwptX3YsGGsT58+kvNsbW2l9sfExDB1dXW2c+dOmbjkKTOt3OrVq0taehjjvtFXr149y5h9fX2ZtbW1TL3Zsbe3Z46Ojuzz589MKBSy/fv3Mx6Px6pWrZrpOerq6kxdXZ3NmjWL3bt3j23bto1paGiwvXv3Zni8WCxmnTp1knov5L0e6Smi5efQoUOsVq1aLDExUVJmdq0QS5cuZdra2pKHiooKU1dXl9p29epVueJijLseM2fOZDwej6mqqjIej8eWLVuW53Oye19yc80Zy3vLT26uOSGFmVAkZIsDFjP1v9RZ8MdgZYejVPnZ8kND3fPgz/4F5ubmiIiIyPKc0NBQJCUloVWrVlLbU1JSULduXclzOzs7qf3Pnj1DcnIynJ2dc10mADRu3FiqFcfe3h5r1qyBSCQCn8/PMOZu3bqhW7duWb6ujOzfvx9Dhw5FmTJlwOfzUa9ePfTt2xf37t3L9ByxWAw7OzssW7YMAFC3bl08ffoUW7duxcCBA2WOHzduHB49eiTVmiTP9Vi2bJmkDgBITExEcHAwxo0bJ9l27tw5NGvWTK7X+vHjR0ycOBEXL16Uae3KyujRo9GzZ0/J8379+qF79+5wdXWVbCtTpozc5R05cgTe3t44ePAgatasiQcPHmDSpEmwsLDAoEGDcn1Odu+LvL+Dirzuub3mhBRWX+O+or9vf1x+dxkA8L8X/6OZmvMJJT95oKamJvWcx+NlOaoGgGT/mTNnZD7U1NXVJT9ra2tL7dPU1MxzmQWtcuXKCAwMRHx8PGJiYmBubo5evXqhYsWKmZ5jbm6OGjVqSG2rXr06jh07JnPs+PHjcfLkSVy9ehVly5aVbJfneig66bh79y4iIiJQv359yTaRSISrV69i8+bNSE5OzjC5NDQ0lLpdp6mpCRMTE1hZWcldd3rTp0/HzJkz0bt3bwBA7dq18eHDB3h6emaa/MhzTnbvi7y/g4q87rm95oQURpffXkY/3374Fv8NWmpa2NphKwbWkf3CRxSDkp98JBAIIBKJpLbVqFED6urqCAsLg6Ojo9xlValSBZqamrh8+TKGDx+e6zKDg4NlnlepUkXyIZFRzHmlra0NbW1tREZG4sKFC1i5cmWmxzo4OMgMW3/58iUsLS0lzxljGD9+PI4fP46AgACZZEqe66HopMPZ2RmPHz+W2jZkyBBUq1YNHh4eBfYhnJCQABUV6a58fD4/y6RcnnOye1/k/R1U5HUvLNeckLwQioVYHLgYS64uAQNDLZNaOOp2FNWNqys7tGKNkp98VKFCBdy6dQvv37+Hjo6OZKj8tGnTMHnyZIjFYjRt2hQxMTG4efMmdHR0Mv12rqGhAQ8PD8yYMQMCgQAODg74/v07nj59imHDhsld5sePHzFlyhSMGjUK9+7dw6ZNm7BmzZosY/7f//6HWbNm4fnz5zl6/RcuXABjDNbW1nj9+jWmT58Oa2trDBnye1KuzZs34/jx47h8mWvmnTx5Mpo0aYJly5ahZ8+euH37Nnbs2IEdO3ZIznF3d8fBgwfxv//9D7q6uvj69SsAQF9fH5qamrm+xlmJi4vD69evJc/fvXuHBw8ewNDQEOXLl4euri5q1aoldY62tjaMjIxktv9ZblxcnOT54cOHAUDymgAuYRAIBHLF2alTJyxduhTly5dHzZo1cf/+faxduxZDhw6VHPPnNZfnnOzel6J0zQkpTHye+uCvq9z8cSPqjcCGthugqZZ5S3+Jkc1dlDxTeC+iQiY/Ozz/2bGyS5cubNCgQZLnL168YI0bN2aampoyQ903bNjArK2tmZqaGjM2NmZt2rSRDNXOrNOmSCRiS5YsYZaWlkxNTY2VL19e0jE1uzLTyh07diwbPXo009PTYwYGBmzmzJlSHaAzitnLy4vl5lflyJEjrFKlSkwgEDAzMzPm7u7OoqKipI5ZsGABs7S0lNp26tQpVqtWLaaurs6qVavGduzYIbUffwzHT3uk7zgrz/VIL7uOt/7+/hnWmf79zqjM7DrfLliwINPXk/bw9/fPsoz0YmJi2MSJE1n58uWZhoYGq1SpEpszZw5LTk6WqjP9NZfnHMayf19yes3TrlFm1z2/rjkhhYlYLGZ9j/VlBx4dUHYohcfZs4xVrcqiQ0LyrcMzjzHG8je9Uq6YmBjo6+sjOjoaenp6yg5HqZycnGBra4v169crOxRCCCmRhGIh1gWtw2i70TRs/U/fvwOTJgEHDwIAYtzcoP/vv/ny+U3z/BBCCCEF4GP0RzjtccKMSzMw5swYZYdTeDAG7NsHVK/OJT4qKsCUKcDGjflWJfX5IYQQQvLZ6ZenMejEIPxK/AU9dT10tu6s7JAKh7dvgdGjgf8mCEadOsCuXYCdHRATk2/VUvJTggQEBCg7BEIIKVFSRCmYfXk21gRxA0vqm9fHEbcjqGxYWcmRKZlQCKxfD8yfDyQmAhoawMKFXIvPH9PI5Icc3/ZKSkrC48ePkZCQILPvxo0bCgmKEEIIKeo+Rn9Ec6/mksRnQsMJuDH0BiU+9+4BjRoB06dziU+LFsDjx4CHR4EkPkAOk5+goCCUK1cOTk5OMDY2xvLly6X2t2vXTqHBAcDChQvB4/GkHmZmZgqvhxBCCFEkNb4a3ke9RymNUjje6zg2tNsAdVXlTTyrdAkJwIwZQMOGXAJkYADs3g1cvgzkco613MrRba+pU6dizZo1GDhwIJ4/f46BAwfi5cuX2LVrF1RUVJBfA8dq1qyJS5cuSZ7T5GWEEEIKI6FYCFUV7qPVTMcMx3sdh7muOSqUqqDcwAqDhw+BVau4n3v1AjZsAP5bnLug5ajlJzQ0VLK+UrVq1RAYGIiIiAi4ubkhJSUlXwIEAFVVVZiZmUkexsbG+VYXIYQQkhtvfr1B412NceTJEck2+3L2JTvxEQp//2xvz/XrOXUKOHxYaYkPkMPkR09PD58/f5Y819TUxIkTJ6ChoYG2bdtmu65Vbr169QoWFhaoWLEievfujbdv32Z6bHJyMmJiYqQehBBCSH7yeeqDejvq4W74Xcy6PAupolRlh6RcjAGHDnG3s9LN1I4FC4COHZUX139ylPy4uLjAy8tLapuqqioOHDiAypUrIzExUaHBAUCjRo2wb98+XLhwATt37sTXr1/RpEkT/Pz5M8PjPT09oa+vL3mUK1dO4TERQgghAJAkTMLYM2PR89+eiEmOgUM5B1wdchVq/ILpuFuo/fMP8OHD71tdhUiOZnhOSUmBUCiElpZWhvvDwsJQvnx5hQWXkfj4eFSuXBkzZszAlClTZPYnJycjOTlZ8jwmJgblypUrkBmeP378iAEDBiAiIgKqqqqYN28eevToka91EkIIUY6XP1+ip09PPPz2EAAwq+ksLG6xWNLnp8QRiYCUFEDzv7XJ3rzhbm9Nnw7IuT5hevm5QkORXN6iVatWsLKywtatW7M9tiCXtwgPD8e3b99ga2uLiIgI1KtXDy9evIC2tna+1ksIIaRgfY37iqqbqiI2JRaltUrDu5s32li1UXZYyvP4MTB8OFC/PrBli0KKzM/P7yK3vEVycjKePXsGc3NzZYciw9zcHLa2tgAAExMTGBoa4tevX8oNihBCiMKZ6ZhhRL0RcLR0xMPRD0tu4pOUBMyZA9SrB9y+zfXz+fFD2VFlS2HJz4kTJ9CjRw80bNgQNjY2Uo86derkutxp06YhMDAQ7969w61bt+Dm5oaYmBgMGjRIUaHLbfDgwZK5hlRVVVG+fHmMGTMGkZGRMsfeuXMHYrFYKX2OtmzZgooVK0JDQwP169fHtWvXsjxenrmUtm7dChsbG+jp6UFPTw/29vY4d+5cjsshhJCi6tn3ZwiLDpM8X+6yHJcGXoKFroUSo1KiwEDAxgZYtowb1eXqCjx9CpQurezIsqWQG5OrVq2Ch4cHjI2NYWVlpdDbPJ8+fUKfPn3w48cPGBsbo3HjxggODoalpaXC6siJtm3bwsvLC0KhEKGhoRg6dCiioqJw6NAhyTE/f/7EwIEDsWvXrgKP78iRI5g0aRK2bNkCBwcHbN++He3atUNoaGiW/bGym0upbNmyWL58Oaz+m4hq79696NKlC+7fv4+aNWvKXQ4hhBRF+x7uw5gzY1DHtA4CBwdCja9Wcjs1R0ZykxWmfcaZmwN//w1066bcuHKCKUCFChXYsGHDmFAoVERxChUdHc0AsOjo6DyXNWjQINalSxepbVOmTGGGhoaS50lJSaxZs2Zs3759ea4vNxo2bMhGjx4tta1atWps5syZmZ6zYMECVqdOnRzXZWBgwHbt2pXncgghpLCKS45jg08MZlgIhoVgznud2a+EX8oOSznEYsZ8fBgzNWWMG8zO2OjRjEVF5Ut1ivz8/pNCbnv9/PkTffv2LXHf8t++fYvz589D7b+1SBhjGDx4MFq2bIkBAwZke/6yZcugo6OT5SO7W1bppaSk4O7du2jdurXU9tatW+PmzZtZnpuTuZREIhEOHz6M+Ph42Nvb57ocQggpzJ5EPEGDnQ2w58EeqPBUsNhpMS70vwADTQNlh1bwPn0CunYFevQAvn0DqlUDrl4Ftm4F9PWVHV2OKeS2l4ODA549e4aWLVsqorhC7fTp09DR0YFIJEJSUhIAYO3atQC4hV2PHDkCGxsbnDhxAgCwf/9+1K5dO8OyRo8ejZ49e2ZZX5kyZeSO7cePHxCJRDD9Y9ZMU1NTfP36NdPz0uZSqlq1Kr59+4YlS5agSZMmePr0KYyMjCTHPX78GPb29khKSoKOjg6OHz+OGjVq5LgcQggpzBhj2H1/N8afG49EYSIsdC1w0PUgHCs4Kju0gicWA9u2ATNnArGx3MKjs2YBs2cD6kV3nTKFJD/r169Ht27dUK5cObRt2xaCXIznLypatGiBrVu3IiEhAbt27cLLly8xfvx4AEDTpk1zNMu1oaEhDA0NFR4jj8eTes4Yk9mWXvoFaWvXrg17e3tUrlwZe/fulZpLydraGg8ePEBUVBSOHTuGQYMGITAwUJIAyVsOIYQUZqniVGy8vRGJwkS0qdwG+7vth7F2CV5W6dAhLvGxtwd27gTS9fMsqhRy28vKygouLi7o1q0btLS0JCOC0h76RbBJLDPa2tqwsrKCjY0NNm7ciOTkZCxatChXZSn6tlfp0qXB5/NlWnkiIiJkWoOyoq2tjdq1a+PVq1dS2wUCAaysrGBnZwdPT0/UqVMHGzZsyHE5hBBSmAn4Ahx1O4oVLitwtt/Zkpf4JCdzK7ADgIoKl/Bs3gxcv14sEh9AQS0/M2bMwObNm2Fra4vq1asX65afPy1YsADt2rXDmDFjYGGRs+GOir7tJRAIUL9+ffj5+aFbul73fn5+6NKli9zlpM2l1KxZsyyPY4xJzaad23IIIUSZGGPYfnc7YpJjMMNhBgDAurQ1ZpSeoeTIlCA4GBg6FGjTBli3jttWrRr3KE4U0Wva0NAwy9FEypTfo70YY6x+/frM3d09z+UrwuHDh5mamhr7559/WGhoKJs0aRLT1tZm79+/lxyzadMm1rJlS8nzqVOnsoCAAPb27VsWHBzMOnbsyHR1daXOmTVrFrt69Sp79+4de/ToEZs9ezZTUVFhFy9ezFE5hBBSmEQlRrGePj0ZFoKpLFJh98PvKzsk5Tp3jhvFZWHBWEyMUkPJz9FeCmn5EYlEaNWqlSKKKpKmTJmCIUOGwMPDQ+kLqfbq1Qs/f/7E4sWLER4ejlq1auHs2bNS8yL9+PEDb968kTyXZy6lb9++YcCAAQgPD4e+vj5sbGxw/vx5qfe9sM3JRAghWbn75S56/tsTbyPfQlVFFStcVqCOae4n5S2yPn4E0j672rbl5u9xdQV0dZUbVz5SyNpePXv2hK2tLWbPnq2ImBSqINf2IoQQUvgxxrD59mZM85uGFFEKLPUtccTtCBqVbaTs0ApWeDgwfjxw8SLw7BmQg24WBSE/P78V0vIzb9489OrVC9ra2ujQoUOGI5jyY1QTIYQQklP9j/fHwccHAQBdq3XF7s67S9bcPWIx17ozYwYQHQ2oqnJLVfTtq+zICoxCWn5UVLhBY1kNpxaJRHmtJleo5YcQQkh62+9sx/hz47G69WqMbzg+y8+uYufFC2DkSG6CQgBo0IAbzZWHNTjzS6Fv+Zk/f37J+uUhhBBSZDDGEB4XLlmAdGT9kXCu5AwrQyslR1aAUlKAVauAv/7ihrJraQFLl3K3vUrY6gyAglp+CjNq+SGEkJLrZ8JPDP7fYDyJeIL7o+6jlEYpZYdU8G7dAoYPB5484Z63bcstS1GhglLDyk5+fn4rZJLD1NRUxMfHZ7gvPj4eqampiqiGEEIIkdvNjzdRd3tdnH55GuGx4bj16ZayQypYsbHAhAnczMxPngClSwMHDgBnzxb6xCe/KeS21/Dhw5GSkoJDhw7J7Bs5ciQ0NTWxa9cuRVRFCCGEZEnMxFh1YxXmXJkDEROhimEVHO1xFLZmtsoOreBcusRNVvjxI/d84EBgzRouASKKafkJCAhA586dM9zXqVMnXL58WRHVEEIIIVn6Hv8dHQ52wMzLMyFiIvSt3Rd3R94tWYkPAMTHc4lPxYrcUPa9e4tW4hMayi2omk8U0vLz7ds3mJubZ7jPzMwsyxXFCSGEEEWZeWkmzr8+Dw1VDWxutxlD6w4tGQNyGAPevAGs/uvE3aULl/C4uXGdm4uK69eB5cuBM2fytRqFtPyUKlUKr1+/znDf69evoVuMZ4kkhBBSeKxstRKtK7dGyIgQDKs3rGQkPj9+AK1aAQ0bAt++/d4+cGDRSnwAwNeXS3x4PKBjx3yrRiHJT4sWLeDp6Ylfv35Jbf/16xeWL1+Oli1bKqIaQgghRMq3uG9Yc3MN0gYuG2kZ4UL/C6hlUkvJkRWgUqWAyEggKQkICVF2NPKLiwM2buRGo6WZNAkYM4abj+jAgXyrWiFD3V+8eIEGDRpATU0NvXr1QpkyZfDp0yf4+PggNTUVt2/fhrW1tSLizTEa6k4IIcXT5beX0c+3H77Ff8PuzrsxpO4QZYdUcO7fB6pXBzQ0uOehoYC6OlC5snLjkkd4OLB5MzfcPjIS6NQJOHlS5rBCP8mhtbU1rl27hilTpmDnzp0QiUTg8/lwdHTE2rVrlZb4EEIIKX5EYhEWBy7GX1f/AgNDLZNaaFy2sbLDKhjx8cD8+cD69cCcOcDixdz2GjWUGpZcQkO5EWfe3tykiwDXR6lDB67PUgHeolRI8gMAderUweXLl5GYmIjIyEgYGhpCIy0jJYQQQhTgS+wX9PPth4D3AQCA4XWHY0O7DdBSK2J9W3Lj4kVg1Cjg/XvueVhYgScNuTZuHPD337+fN2kCTJ/OtfooYYbpXPf52bBhAz59+iSzXVNTExYWFpT4EEIIUajLby/DdpstAt4HQEeggwOuB7Cz887in/j8+MF1Xm7Thkt8ypfnOgXv2VN4Ex+hEEg/wXHNmlysrq7AjRvco2tXpS2tkevkZ9myZbC0tESjRo2watUqvHnzRpFxEUIIIVIYGH4k/EAd0zq4O/Iu+tYu5quQM8Z1+q1eHdi/n0seJk4Enj4F2rdXdnQZS+vEXKUKF3OaQYO4TszHjnGtPkqW6w7PYrEYgYGBOHbsGI4fP46vX7+idu3acHNzg6urK2oUkvuP1OGZEEKKLqFYCFWV3z00Tr88DZdKLtBQLeZ3F96/B0aPBi5c4J7Xrg3s2sUNZy+MwsOBTZu4TsxRUdy2Zs1+rx6fC/n5+a2whU1v3LiBf//9F8ePH8fHjx9RtWpVdO/eHd27d0fdunUVUUWuUPJDCCFF05mXZzD+3Hj4DfBDZcMiMIpJEUQiruVk7lwgIYEbwTV/Ptc/Rk1N2dHJyqgTc5UqwJQpXGuPpmauiy4SyU96t2/fxrFjx+Dr64s3b96gQoUKcHNzw8qVKxVdVbYo+SGEkKIlVZSK2ZdnY3XQagDAUNuh+KfLP0qOqgB8/sz1g7lzh3vu6Ajs2AFUrarUsLLUpg3XERtQeCfmIpf8pPfgwQNJIvT06dP8rCpDlPwQQkjR8SHqA3of643gT8EAgAkNJ2Blq5VQV1VXcmQFICUFqF+fW5Nr9WpuYVIVhcxFrBhCIddnp1kzwMKC23bpErBlCzBtmsL78hTK5CcsLCxHx5cvXz431eQZJT+EEFI0nHh+AkP+NwRRSVEopVEKXl280LVaV2WHlb9u3OD68aTd0goNBQwMgEzWy1SKuDhg925g3TquL9LMmYCnZ75XWygnOaxQoUKO1kwRiUS5rYoQQkgxd+L5CXQ70g0A0KhMIxx2O4wKpSooN6j8NmMGsGoVsHQpMHs2t62QDBYCIDsTM8CtDG9srNy4FCDXyc/u3btLxoJxhBBC8l37Ku3RqEwjNC3fFMucl0HAFyg7pPxXpw43fP3nT2VHImvCBGD7dulOzFOncvMN5aETc2GR6+Rn8ODBCgyDEEJISeP3xg9OFZygxleDgC9A4ODA4t235+NH7rZRs2bc8759ARsbbhi7sv05U7RYzCU+Dg5cfx4lzcScXxTek+rly5cICgrCq1evFF00IYSQYiBJmAT3M+5o7d0a8/3nS7YX28RHJOLmwKlRA+jR4/ctJB5P+YmPUAgcOcL1OwoO/r19xgzg5k3g+nWlzsScXxSW/Pj4+MDS0hLVq1dH06ZNUa1aNVhaWuLff/9VVBWEEEKKuFc/X6HJP02w5c4WAACPx0M+DzpWridPgKZNudtIcXHcQp4xMcqOiotlwwYunt69ueH169f/3l++PGBvr7Tw8ptCFjY9e/YsevfujZo1a2LcuHGwsLDA58+f4e3tjd69e+PUqVNo166dIqoihBBSRB1+chgjTo1AXEocSmuVxv5u+9HWqq2yw8ofSUlcR+YVK7g1rnR1gZUrgZEjlTt8PaOZmI2NuYVHx4xRXlwFTCHz/Dg4OEBPTw9nzpyBSro3lTGGdu3aITY2Fjdu3MhrNblCQ90JIUS5ElMTMen8JOy4twMA4GjpiIPdD8JC10LJkeWTq1e5JOfFC+55ly7ciuZlyig3Lsa4dcLS4irknZjz8/NbIenngwcPMHbsWKnEB+CaM8eOHYuHDx8qohpCCCFF0MeYjzjw+AB44GFe83m4NPBS8Ux8oqKAUaO4mZlfvADMzIB//wWOH1dO4sMYEBj4e8QWj8e17jg4cDE9f87FWwgTn/ymkNtefD4fKWkX9w+pqakySREhhJCSo6pRVXh18YKBpgFcKrkoO5z84evL3ToKD+eejxjB3eYqVargYxEKuaRr9Wrg7l1g3z5gwABu3/jx3MrwJZxCspIGDRpg5cqVSExMlNqenJyM1atXo1GjRoqohhBCSBEQnxKP4SeH4+qH3yt696jZo/gmPsOGAd27c4lP1apAQAC3JldBJz7pOzH36cMlPhoavxMyoHAtl6FECmn5WbRoEZydnVGpUiX06NEDZmZmCA8Ph6+vL37+/IkrV64oohpCCCGF3NOIp+j5b0+Efg/FxTcX8Wr8q+I7hD2NgwPXujJzJjBnDpdwFCSRCJg3L/NOzMVgRmZFU0jy07RpU1y8eBEzZ87E33//DcYYVFRU0KhRIxw6dAhNFLzYGSGEkMKFMYbd93dj/LnxSBQmwlzHHPu77S+eic/z50BEBNC8Ofd8yBBu4sIqVZQTD58PBAVxiU8h78RcWCh8VfeEhARERkbCwMAAWlpaiiw6V2i0FyGE5K/Y5FiMOTMGBx4fAAC0qdwG+7rtg4m2iZIjyweXLwPt23OtKaGhQEF/rqR1Yl6/nru1ZvLfNb55k0vIOncuNre2CuXCppnR0tIqFEkPIYSQ/Pcj4Qccdjvg5c+X4PP4WNJyCWY4zIAKr3h8AMuwtwfKlQOsrYHExIJLfv7sxAxwa4MtWsT9THdYckThyQ8hhJCSw0jTCHVM6yAhNQGHuh9C0/JNlR2SYsXGAtu2AVOmcLeXtLS4VhZjY+m1sPJLXBzwzz/AunXAhw/cNg0N7lZb//75X38xpbDk58SJEzhw4AA+fPiApKQkqX08Ho/m+iGEkGIiJjkGjDHoa+iDx+NhZ6edEIqFMNIyUnZoinXqFDB2LPDpE6Cuzi1RAfy+1ZTfkpO50WNpo7VKl+Y6MY8dS52Y80ghyc+qVavg4eEBY2NjWFlZQVtbWxHFEkIIKWTuhd9DT5+esDWzhU8PH/B4POhr6Cs7LMX6+pWbC+foUe555coFtwDp+/dAhQrcz+rq3Grq/v7UiVnBFJL8bNmyBUOHDsX27dvBL2YrvxJCCOFGc/0d8jemXpyKFFEKhGIhvsV/g5mOmbJDUxzGgN27gWnTuJFTfD738/z53O2u/Kw3MJDrz3PmDNenp149bt/q1YC2drHpxFxYKCT5+fnzJ/r27UuJDyGEFENRSVEYdnIYfJ/5AgC6VuuK3Z13w0DTQMmRKdCrV9x6XAEB3PP69YFduwBb2/yrM6NOzDwetzZYWvKjq5t/9ZdgCkklHRwc8OzZM0UURQghpBC5/fk26m6vC99nvlBTUcOGthvg29O3+CQ+qamApyd3WysggGvhWbMGCA7Ov8QnOVl2JmZNTa4vz8uXwKRJ+VMvkVBIy8/69evRrVs3lCtXDm3btoVAIFBEsYQQQpQoVZSKPsf64H3Ue1QyqIQjbkdgZ2Gn7LAU5/Ztbg2uR4+4561bcyO7KlbM33p5PGDVKuDz598zMY8dy3VoJgVCIcmPlZUVXFxc0K1bN/B4PJl5fng8HqKjoxVRFSGEkAKixlfD3q578XfI39jWYVvx6th84gS3HpdYDBgZcZMG9uuXP8PXQ0O5vkTLlwOqqoBAACxdCiQlUSdmJVFI8jNjxgxs3rwZtra2qF69er61/Hh6emL27NmYOHEi1q9fny91EEJISXbz4018jvmMHjV7AACalm9a/ObuAQAXF26ywmbNgLVrFT90PK0T86pVwNmz3LYGDYBevbifBw1SbH0kRxSS/OzZswceHh7w9PRURHEZCgkJwY4dO2BjY5NvdRBCSEGLjwd0dLif4+K4gT3KIGZirL65GrMvz4a6qjpqmdRCdePqygkmP3z/zt3SmjOHGzmlowPcvw8YKLjvUmadmLt14+bsIYWCQpIfkUiEVq1aKaKoDMXFxaFfv37YuXMnlixZkm/1EEJISfQ9/jsGnRiEc6/PAQC6WHdBWb2ySo5KgVJTuVaXDx+4CQpHjeK2Kzrx+f79dz0AdztryBBg8mSuczMpNBQy2qt169YIDg5WRFEZcnd3R4cOHeDi4pLtscnJyYiJiZF6EEJIUfD5c8HXefXDVdhut8W51+egoaqBnZ124oDrAeiqF6Mh1mpqXAJSpw43hF2REhJ+/2xsDJQpw/27aBEQFgb8/TclPoWQQlp+5s2bh169ekFbWxsdOnSAoaGhzDEZbZPH4cOHce/ePYSEhMh1vKenJxalLfRGSAkQH6/sCEhe7Njx++fq1YFNmwquO8jqW55YfH0uxEyMqobVsK/TUdQyri31eV4kCYVQ/Xs9xLb1IXZswW0bMg4YNJZLhBTwN8MLfQq1TWugevoEEh684jpNA+Dt8IZWJTPqxFzI8RhjLK+FqPw38yQvi17yIpEox+V+/PgRdnZ2uHjxIurUqQMAcHJygq2tbaYdnpOTk5GcnCx5HhMTg3LlyiE6Ohp6BbX6LiEFqCDWViTFVMu5QPOlwIOBwNm/gRQdZUeUZ3VxDzsxAvVxD69RGbXxGElQVCLC4IQATMNqdMBZydZh2IXdGPb7qDx/qhKA+/zW19fPl89vhbT8zJ8/P8vEJ7fu3r2LiIgI1E/XTCkSiXD16lVs3rwZycnJMrNKq6urQ11dXeGxEEJIsaCSCojVuJ8DFgKfGgEvOyk1JEXQRAIWYiGmYC1UIcIvGGAp5iAJGnkumw8h3PAvpmE17MB1YhaDB1+4Yg2mIhj2ea6DFCyFtPzkl9jYWHxI6zj2nyFDhqBatWrw8PBArVq1si0jPzNHQgoDuu1VOMTHA6am3M/fvsk3auvzZ+5Wl1j8exufz00LU6aMYuMTiUVYHvQX/N6dw4XeV6GuWny+JKr4X4L6hFFQefcWACDs3gvJKzf8fkPyiBf+BZo1KoCXmgqmqQlh/yFIHTcZrHLGfXlobW/FKPQtP/lFV1dXJsHR1taGkZGRXIkPISUB/Udb+Ghry/e+VK3K9fFxd+ee8/nA9u2KHxEdHhuOvr59EfA+AABw8eNx9K7VW7GVKMPPn9xq53v3cs/LlgW2boVqx455+3ALD+fm5hn2360sKwtgwgRATw+8sWOhVro01PIaO1GqQp38EEJIUfT5s/wJzKBBv5Of0FDFJz4X31xEf9/++J7wHToCHWzvuL3oJz6MAYcOcWtgff/OdXwbN46bNTkvC4E+fcqt6+Xt/Xt4fNrccqtXKyR0UjgUueQnIG3FXUJIkVUcb9XldtRW+mtRqpTiro1QLMSSGwuw5pYnGBhqG9fBvk5HUcWwaq7rKBStjB8+AGPGAOe4OYlQsya3+nrjxrkrjzFuQdPVq3/PxAwADg7cAqSkWCrUfX4Ugfr8EFL40Ai1AtB+HNDwb+7nkNHAhXWAMG+df5X+aZGQAFSowLX2CATAvHnAjBncz7nx9i3Qs6f0TMyurtytNHvqxKxsJbbPDyGEkFwKmgJYnwQurgae9lR2NIqhpQVMmwacOcM1tVlb57wMxn5n3xYWwKdPNBNzCUQtP4SQAlfcbnsV5KitzKSKUhEYdgUuFdtItqWIUiDgK26h6QK/7ZWUBPz1F9CuHdD0v8VVRSIueVHJ4QIF4eHcvcjLl4GbN7k3CACuXePevNKlFRs7ybMi3fLTsmVLWFhYYPbs2ahRo0Z+V0cIKQIKRd8RBSqoUVuZCYsOQ+9/eyP4UzDO9z+P1pVbAwC0objERykWLQKWLwd8fYFHj7jZmf+Y2y1bf3ZiBoDz54EOHbifmzVTbMykSFDI2l5ZCQgIwMGDB2FjY4MBAwbkd3WEEKIU6Ts3h4b+HiWd306+OAnbbbYI+hQEPXU9JAuLUSfd6dOBevWAZcu4xEdejAH+/kD79kCtWoCXF5f4NG0KnDjBtSSREi3fW37EYjHi4+MRGBhII7UIISVCQdzqShGlwMPPA+tvrQcANCzTEIe7H0ZFg4r5X3l+YAw4dozrz7N7N3dry9AQuHMn5z3kQ0KAli25n9M6MU+blvsRYaTYoT4/hBCiAPHxgM5/S2PFxeXvrb13ke/Q699eCPnCLfg81X4qljkvU2j/ngL16RN3z/DkSe75sWNcwiKv2Fjg/n2geXPuOWNAixbcMHjqxFxkFek+P4QQUhJoaxfcUPDrYdcR8iUEhpqG2NNlDzpZF9G1ucRiYNs2YOZMLoFRUwNmzfrdHyc7X75wna22bQOEQuDjR26yJB4PuHIl552iSYmR6+QnLCwsR8eXL18+t1URQghJZ0CdAQiPC0fvWr1RXr+I/t8aGgqMGMGNvAK4eXV27uRaa7KTUSfmKlWA9+8BW1vuOSU+JAu5vu2loqKSo5XcRSJRbqrJM7rtRQgp6l79fIUpF6fAq4sXSmsV8SHZycmApyfXiTk1lbtXuHw5N2tzdgnL8+fAlCm/Z3cGuJmYp08HOnWihKeYKZS3vXbv3p2j5IcQQkjOHX5yGCNOjUBcShwmnZ8Eb1dvZYeUezducK09z55xzzt1Av7+GyhXTr7zNTWBixdpJmaSZ7lOfgYPHqzAMAghhKSXmJqISecnYcc9btGw5pbNscJlhZKjyqXoaK4vz9at3HNTU66vjptb5iO5YmOBf/4BXr4EtmzhtllacrfGmjWjTsxKVpAd/PODwjs8v3z5Ej9//kTp0qVRpUoVRRdPCCHF3vMfz9HTpyceRzwGDzzMaTYHC5wWQFWliI5RmTfvd+IzbBiwahVgYJDxseHhwMaNXCfmqChu24QJQLVq3M9DhuR7uKT4U9hfko+PD6ZNm4ZPnz5JtpUtWxZr1qyBm5uboqohhJBiLeB9ADoe7Ij41HiYaJvggOsBuFRyUXZYeTN/Pjdfz9Kl3BD0jISGciur/9mJeepUrsWHEAVSSPJz9uxZ9O7dGzVr1sS4ceNgYWGBz58/w9vbG71798apU6fQjmbUJISQbNU1qwtTHVNUKFUB3t28Ya5rruyQckYs5m5X3bjBzazM43HrZqWN6srIqVNA586/nzdtyk1KSJ2Yi4TPnwtuKRdFUcgkhw4ODtDT08OZM2egku4XlTGGdu3aITY2Fjdu3MhrNblCo70yV9wWlySkqAqL/oByeuUlg0g+xXyEuY4F+Co5XMeqEOC9fgXNBjXBS01F0onzELm0kT1IKATv00ewCv/NRp2QAK2aFSFq0gypE6dB3JBmYi7sduzgBt4BXH66aZP0Ei+KEBMTAwuL/Pn8Vkjyo62tjcOHD6NTJ9mJtk6ePIm+ffsiLi4ur9XkCiU/maPBeoQoGwPqegHtxwEXVwMhY5UdUC4xAL//Q5kJTyRCE5swHmL8TuB0EIuh2I3JWIcEaKEWnoD9t8SkDmIRB92CDpwUajEA8ufzWyHtiXw+HykpKRnuS01NlWoNIoQQAkAQB3QbCHQZBqglApUvgEsiipaGuIW7qA9b3JdsW45Z2IBJksTHHF+wDLMQhvLYgEmogA8ojR+oiHeScyjxIQVJIS0/zs7OiIuLQ0BAADQ1NSXbk5OT4eTkBB0dHfj5+eW1mlyhlp/M0W2vwiM+nhv9C3BLFNFAyeLtyfdHGHCyB15FvgSfx8e8pn9hSkMPqPCK0BfF2FgIFs2B6vbN4DEGoUsbJJ84L3UI7/UrqK3xhOphb/D+68QsrlIVqeOnQthnADdvDylyPn8Gqlfnunel4fO5PuuKXNQ3P297KaTD86JFi+Ds7IxKlSqhR48eMDMzQ3h4OHx9ffHz509cuXJFEdXkSXw89+YQUhgdOPD75/r18+f+OVE+xhi8Hu3E9CsTkCxKhoVOGezpeBhNyjZVdmg5wj9/BoJJY6Dy6SMAILXvQKQsWyNzHC/sPdT2ewEARPZNkTpxGkTtqRNzUVemDDcwL63PD5/PzU5Qpoxi5/vJ14UhmIIEBASwxo0bMxUVFcbj8Rifz2dNmjRhgYGBiqoiV6KjoxkABkQzbtlBetCDHvRQ0qN0KMN8PsNCMPTtwKD1Xfkx5eBhgq/sIHpLNrxBReaCiwxgjI9U1hsH2ShsTXeOmK3GFNYIQUqPnR4F88iPz+/o6GjFFswYU8htr/QSEhIQGRkJAwMDaGlpKbLoXEm77QVEA6DbXoQQJXNYCTAVIGgK92+RwDAYe7AGU2GISIiggrWYgoVYCBWIMQz/YDLWwRJhiEQplEcY9eEpoRSZURTKtb3SS01NRUpKCrS1taGlpSWV9MTHx0MgEEBNTU0RVeXaly8AdfkhhVFB3T8nBY8xhh0PtsCxfEtUM6r+39YZSo0pp3hv30B9wijwAy4DAER16iJl8064m5lj4rYlUPtnG3j/zcTMShtDe/R4fHUHKPchhZlCkp8RI0YgOTkZhw4dktk3cuRIaGpqYteuXYqoKte0tYve2iOkZKhalevj4+7OPefzge3bi96kYURaVFIUhp0cBt9nvqhlUgshI0Kgoaqh7LDkJxQCa9cCCxYASUlc5+RFi8CfPBmaR44ALe1/z8RctSowdSp4AwZAoKkJgXIjJyRbCmlz9ff3R+f0s3Om06lTJ1y+fFkR1RBSbKXv3Bwayi1/RIqu259vo+72uvB95gs1FTUMrzsc6nx1ZYeVM8+eAbNnc4mPiwsQHAxMnw6oqgKNG3O9UZs2BU6c4I4dOTJHo7fi47m5xng8GnlKCp5CWn6+ffsGc/OMp2A3MzPD169fFVENISUC3eoquhhjWB+8Hh6XPJAqTkXFUhVxxO0IGpRpoOzQ5CMW/x6JVbs21+rz5Qtw6xaweDHw77/cvsqVgefPaU4GUmQppOWnVKlSeP36dYb7Xr9+DV1duvlLSFa0tX+Pl6Dbs0VTbHIsuh7piikXpyBVnIru1bvj3qh7RSfxuXiR63z25AkQGwusXw/s2sWtrn7/PnDhAhAZ+ft4BSY+nz8rrChC5KKQlp8WLVrA09MTrq6uMDQ0lGz/9esXli9fjpYtWyqiGlIEUXM2KSnEYk18j/sFAV+A5U7rMMJ2DHgiXpH5G1DftAWqL19C3N0NvIhvUp2YU0ePR+rwMYDAAFDQ69mx4/fP1avT3FZEVn7+7ShkqPuLFy/QoEEDqKmpoVevXihTpgw+ffoEHx8fpKam4vbt27C2tlZEvDlGMzwrF60fRoo1nhjgiQDxf6NZ9T4BWt+Br3WVG5dcGNSRjGRwnbAt8Bn7MADO8AcAvEBVrMFU7McAJIFmYibKkH9reylsnp+HDx9iypQpuHr1KkQiEfh8PhwdHbF27VrY2NgooopcoeRHuSj5IcWW1neg2yDghzVwYZ2yo8kRS7zHNowGHyJ4Yhb8wbXO6yAWR9AL2zEKp9BJsugoIcpRBJKfNImJiYiMjIShoSE0NJQ/rJOSH+UqKk3+hOTEjU/XMPh0b4THfYGmqiYeDHuJMrpllR1W9kQiqG5eB8GiueClJHObrKsj6c7TAv2mQnNbEXkU+rW90tPU1JRa3JSUbNR5lxQnYiaG5zVPzA+YDzETw9rIGkd7HEVV0yKQ+Ny8CfTuDXz8+Hubujr4Li2hzU8q0EVGaW4rIo/8XNtL4S0/hQ21/BBCFOFb3DcMOD4Afm/9AAADbAZgS4ct0BHoKDmybCQmAm5uwNmzv7fp6HBz9ri7A0ZGSgkrPp4LAwBevKDEh8gq9MtbEEJIcSYSi+C01wnPfzyHpqomtnTYgsG2g5UdVtYYA/z9gVGjgLSpSHR1gfnzuaSnELXQ060uUtAo+SGEkGzwVfhY7LQYiwIX4WiPo6hhXEPZIWUsLeFZtgz4+RN48IDbXqYMMHw4l/ioUCdmQui2FyGEZCA8Nhwfoj+gcdnGkm2polSo8ZW7SHOGhELAxwdYtYqbkDANjweMGQN4etLKzqTIodtehBBSgC6+uYj+vv0BAA9HP4S5Lrd8T6FLfGJjgX/+AdatA8LCuG0qKtwwKisrYO9eoEkT5cZISCGksORHJBLh6NGj8Pf3x8+fP2FkZIQWLVqgR48eUFWlHIsQUvgJxUIs8F8Az+ueYGCwMbVBQmqCssPK3OzZwObN3M/GxsD48UCHDsCZM8CMGYB6EVtMlZACopDbXj9+/EDbtm1x7949qKqqwsjICD9//oRQKETdunVx4cIFlC5dWhHx5hjd9iKEyONTzCf0PdYX18KuAQBG1R+FdW3WQVOt8HQMxtOngEDwe12tM2e4kVz16wN+foWqEzMheZWfn98K6fk2efJkvHjxAgcOHEBiYiLCw8ORmJgIb29vvHr1CpMnT1ZENYQQki/OvjoL2222uBZ2DboCXRzufhjbOm4rHIkPY8CVK0D79kCtWtxK62n09YGkJODxYyAmRnkxElLEKOR+1KlTp7BkyRL06dNHso3P56Nv376IiIjAwoULFVENIYTki6NPj+Jn4k/UM6+HI25HYGVopeyQfndiXr0auHeP28bjcRPkiMVc356mTbkVQtu3B0xNlRsvIUWIQpIfxhhq1qyZ4b5atWqhmA8oI4QUcZvbb0Zlg8qY4TAD6qqFoJ/M3r3csPS0TsyamkC/fkBcHHDyJPDuHVC5MrdvxAjlxUlIEaWQ214uLi64dOlShvv8/Pzg5OSkiGoIIUQhTr44iQHHB0DMuMWldAQ6mOc4r3AkPgAQEcElPsbGwOLFwNatXP+ew4eBhATp2ZoJITmmkJafefPmwdXVFSKRCH379oWZmRm+fv2KAwcOwNfXF76+vvj165fkeENDQ0VUSwghOZIiSoGHnwfW31oPAHCp6IJBtoOUG9TTp9ytrY4dge7duW0jRwKlSgEtW3LLUMyfz22vWpW7zeXoqLRwCSkOFDLaSyXdjKG8dCsDpxXN+2O1YFF+rlb2BxrtRQgBgHeR79Dr314I+RICAJjceDKWuyyHgC8o+GDSZmJevRo4d47bZmcH3L7N9esRi7kkx8OD68isqgrMnAnMmQNoaBR8vIQoQaGf5HD+/PkyCQ4hhBQWx0KPYdjJYYhOjoaBhgH2dN2DztadCz6QjDoxq6gArq7A1Klc4vPsGdfyc/06t79RI2DnTqB27YKPl5Biipa3IIQUa0uvLsVc/7kAAPuy9jjsdhjl9csrJ5gePYB//+V+1tQEhg4FJk/mOi+npADLlwNLl3I/a2tza3S5uwN8vnLiJUSJCv08P+m9fPkSQUFBePXqlaKLJoSQHGtduTUEfAFmNJmBwMGBBZv4fPkCREf/ft6vH2BiAvz1F/DxIzc7c+XKQEgIUK8eN4dPSgo3S3NoKDBhAiU+hOQDhSU/Pj4+sLS0RPXq1dG0aVNUq1YNlpaW+DftWw4hhBSQt5FvJT83KNMAr8e/xopWKwpuba4nT4AhQ4AKFX4vPwEAnTsDHz4Ac+cCRka/t8fEcB2fTUy4EV2nTgHlldQ6RUgJoJDk5+zZs+jduzf09fWxfPly7Nu3D56entDX10fv3r1xLq1DHyGE5KPE1ESMOjUKNf6ugQdfH0i2l9Mvl/+Vp83E3K4d1z9nzx4gNRV48DsOqKj87rCcNocPADg7A7t3c/19evXi+v4QQvKNQvr8ODg4QE9PD2fOnJEa+cUYQ7t27RAbG4sbN27ktZpcoT4/hJQML368QM9/e+LRt0fggYcNbTdgfKPxBVO5jw/XX+fPTszTpnEdltOLi+P6+pw9y7X2WFoWTIyEFDGFvs/PgwcPMHbsWKnEB+CGuI8dOxYPHz7Mddlbt26FjY0N9PT0oKenB3t7e2pJIoRI8X7kjfo76uPRt0cw0TbBhf4XCi7xAbhE5t49rhOzuzvw8iWXEP2Z+ACAlhYQHs6tyRUYWHAxEkIkFDLUnc/nIyUlJcN9qampMklRTpQtWxbLly+HlRW31s7evXvRpUsX3L9/P9MlNQghJUNCagLGnR0HrwdeAIAWFVrggOsBmOua51+lX74AGzdynZfThp9PmwZUqgSMHSvdlyfNq1eAuTmgo8O1Cv3zD7dGV926+RcnISRTCrnt5ezsjLi4OAQEBEBT8/cqyMnJyXBycoKOjg78/PzyWo2EoaEhVq1ahWHDhmV7LN32IqT42hKyBe5n3cEDDwscF2Bu87ngq+TT6KgnT4A1a4ADB7i+PIMGcf16spKays3ps2gRMGYMsG5d/sRGSDFU6Cc5XLRoEZydnVGpUiX06NEDZmZmCA8Ph6+vL37+/IkrV64oohqIRCL4+PggPj4e9vb2GR6TnJyM5ORkyfOYmBiF1E0IKXxG1R+FoE9BGGo7FC0qtlB8BWkzMa9aBZw//3t7s2aAm1vW54aEAMOHA48ecc9fvABEIhq6TkhhwBQkICCANW7cmKmoqDAej8f4fD5r0qQJCwwMzHPZjx49Ytra2ozP5zN9fX125syZTI9dsGABAyDziI6OznMchBDlik2OZXMuz2EJKQkFU2GHDoxxKRBjKiqMubkxFhycTZCxjE2axB0PMGZkxNj+/YyJxQUTMyHFRHR0dL59fit8hueEhARERkbCwMAAWlpaCikzJSUFYWFhiIqKwrFjx7Br1y4EBgaiRo0aMsdm1PJTrlw5uu1FSBH36Nsj9PTpiRc/X2BU/VHY1nGb4iuJjeU6JKe1zixbxs24PHQoMGkSNyFhVs6fB0aP5ubyAYD+/YG1a7nV2QkhOZKft73ynPwkJibCysoK27ZtQ6dOnRQVV5ZcXFxQuXJlbN++Pdtjqc8PIUUbYww77+3EhHMTkCxKRhndMjjU/RCaWTZTXCVpnZi3beP68XTtym2PieH67WTUiTm979+55OjgQe55hQpcWW3aKC5GQkqYQt3nR1NTE4mJidDW1lZEPHJhjEm17hBCiqeY5BiMOj0Kh58cBgC0s2qHfd32obRWacVU8GcnZoAbop6W/GT3Hy5jwP79wJQpwM+f3EiuSZOAxYu5tbkIIYWSQjo8Ozs749KlS2jZsqUiipMye/ZstGvXDuXKlUNsbCwOHz6MgIAAnE/f+ZAQUuw8iXiCbke64fWv1+Dz+PB09sTUJlOhwsvj9GRpMzGvXi3biXnaNKBjR/nK+fUL6N0bSBvJWqcOsGsXYGeXt/gIIflOIcnP7Nmz0b17d2hoaMDV1RXm5ubg/TE9u6GhYa7K/vbtGwYMGIDw8HDo6+vDxsYG58+fR6tWrRQROiGkkNJX18evxF8or18eh7sfhn25jEd45sqsWdxorKxmYs42QH1u0VINDWDhQq71R62A1g4jhOSJQjo8p5/E8M+kJ41IJMprNblCfX4IKTqShclQV1WXPA/+FIyqRlVhqJm7L08AuE7Mu3cDgwdzCQsAnDgBXLoETJ6cfSfm9B48AKytuZmcAeD5c0BVFfhvElZCiOIU6j4/ADB//vxMkx5CCJFHyOcQ9D7WG+varENn684AgMZlG+e+wPSdmKOjgZQUYPp0bl/Xrr/79chr1SquxWj6dMDTk9tWrVru4yOEKI1Ckp+FCxcqohhCSAnEGMOGWxsww28GUsWpWBy4GJ2qdsr9F6qMOjFbWwPly+ct0KpVuUkKP33i+g3RFz5CiiyFLGx69epVxMXFZbgvLi4OV69eVUQ1hJBi5lfiL3Q90hWTL0xGqjgV3at3x6WBl3KX+IjFQOfO3Hpbe/ZwiU+zZsD//geEhgK9euWsvJ8/gfT/d3XpAty+zY3uosSHkCJNIclPixYtEBoamuG+Fy9eoEWLfJh2nhBSpAV9DELd7XVx8sVJCPgCbG63GT49fFBKo5T8hYjFv39WUfm9cKibGxAczCUvnTtz2+TFGDdfT/Xq3K2xb99+72vQQP5yCCGFlkKSn6z6TOd1VXdCSPHz6ucrNN/THGHRYahsUBlBw4Lg3tBd/haf2FhukdBKlbhOx2mWLAFevuTm6snp6C2Am5m5Qwduxfbv3wELC+DHj5yXQwgp1HLd5ycmJgZRUVGS51+/fkVYWJjUMYmJidi7dy/MzMxyHSAhpPipYlQFw+sOR2RSJHZ02gE9dTlHcvzZiRkAtm4FNmzgfq5UKXcBiUTA5s3AnDlAfDwgEADz5gEzZnA/E0KKlVwnP+vWrcPixYsBcMPbu3XrluFxjDHMnj07t9UQQoqJ62HXYWVoBTMd7svQpvabwOfx5WvtyawT89Sp3PpZefHoETBiBNefB+D6Ce3YQSO5CCnGcp38tG7dGjo6OmCMYcaMGRg/fjzK/zGaQl1dHbVr14ajo2OeAyWEFE1iJsby68sx338+HCs44mL/i+Cr8KGqIud/PykpQIsWv28/pZ+JOS+31JOSgL/+AlauBIRCbg6glSuB4cPzVi4hpNDLdfJjb28Pe3tuxtX4+HiMGDECFhYWCguMEFL0RcRHoL9vf/i95ZaAsNC1QIooBZoqmpmflJoKnD7Nja5SUeFuO40fz7X+TJ2au748fwoIAEaOBF694p67ugKbNnF9fAghxZ5CZnguzGiGZ0KUw/+dP/r69sXXuK/QVNXE3+3/xmDbwZnf5oqN5dbGWr8eCAvjEqAOHbh9ipxXZ9YsYPly7mdzc+Dvv4FMbtsTQpSn0M/wTAghaURiEZZcXYLFVxdDzMSoYVwDR92OoqZJzYxP+PyZa3VJ34nZxOT3z4Bi59WpV4/7d/RoLglKW/KCEFJiUPJDCFGoJGESDj05BDETY4jtEGxqtwnaAm3ZA+PjAXd3bk6dPzsxDxjALRiqCJ8+Aa9fA05O3HM3N+DxY6BWLcWUTwgpcij5IYQolLZAG0d7HMXDrw8xoM6AzA/U0uIWCk2biXn6dO42lyI7G9+9y3WWVlcHnj0DSpfmWpEo8SGkRKPkhxCSJ0KxEIsCFsFQ0xCT7ScDAGxMbWBjavP7oNRUbuLBnTuBkycBXV0uCdm4kUtMFNGJOSO1awOWllx9sbFc8kMIKfEo+SGE5NqnmE/oe6wvroVdg6qKKrpW64qKBhV/H/BnJ2YA+OcfYNIk7ufmzRUbUHIyN0fP6NGAmho3UuziRcDUlIavE0Ik8uV/g6SkJDx+/BgJCQky+27cuJEfVRJCCti5V+dgu80W18KuQVegi/3d9v9OfL58ATw8gHLlgClTuMTHxISbV2dAFrfC8uLGDaBuXWDCBGDVqt/bzc0p8SGESFF4y09QUBA6d+4MsViMpKQkzJs3DzNnzpTsb9euHWJiYhRdLSGkgKSKUjH3ylysvLkSAFDXrC6OuB1BFaMq3AE/fwKVK3OTCAJcJ+Zp07iZmBXViTm96Ghu+PrWrdxzU1OuTkIIyYTCvw5NnToVa9aswc+fP3H37l34+vpi6NChEP+3+nIxn1aIkGJNzMRotb+VJPFxb+COm0NvoMqXpN8HGRlxsy83b8717wkN5WZNzo/E58QJoEaN34nPsGFcx+bu3RVfFyGk2FB48hMaGoqBAwcCAKpVq4bAwEBERETAzc0NKSkpiq6OEFKAVHgq6GzdGfrq+jjW9TA2RzWBRiMHoE4dbjh5mv37gcBAoFOn/Lnl9OULl+B068b9XKUK4O/P9S8yMFB8fYSQYkXh/yvp6enh8+fPkueampo4ceIENDQ00LZtW0kLECGkaEgRpSAsOkzyfHLN4fjAJsK103SgXz/g/n1AUxO4d+/3SfnRygMAYjHXoblGDcDXF1BV5W55PXz4ex4fQgjJhsKTHxcXF3h5eUltU1VVxYEDB1C5cmUkJiYqukpCSD55F/kOzbyaodX+Voj99hHw8ACvfHnoz14MfPz4uxNzWBjQs2f+BvPiBTdnz6hRXD+fBg24eXyWLeOSL0IIkZPC1/ZKSUmBUCiElpZWhvvDwsJkVn/PT7S2FyG54/vMF0P/NxTRydEopVEKfl19YdfYFYiKyv9OzH8KDATatOGGsmtrA0uWcIud8vn5XzchRCmKxNpejDH8/PkTPB4PhoaGmR5XkIkPISTnkoXJmHZhKkKP/o2lzwDvkY1w2O0ILEtZAqtXc609ip6JOTuNGgEVKgCVKnGdmy0tC65uQkixk+f/vYKCgtClSxfo6enB1NQUJiYm0NPTQ9euXXHr1i1FxEgIKSCvvz3D4tHVMGT437i8D3APAa6VXcAlPgA3miq/OjGnFxsLrFgBCIXccw0N4No14MwZSnwIIXmWp5afLVu2YOLEiQCA+vXro2LFimCM4f379zhz5gzOnDmDDRs2YOzYsQoJlhCST/6biVl/2Rws/cH1yxNqqkN12AioVq9RsLGIxdxaXw8fcre1pk3jthsbF2wchJBiK9fJT3BwMCZMmID27dtjy5YtKFu2rNT+T58+YcyYMZg4cSLs7OzQsGHDPAdLCMkHL18CDRsC0dEwBhClrw7e+PHQnzSTm7OnoKmocLM0L1kC2NoWfP2EkGIv1x2ee/TogS9fvuDatWtQyaQJXCwWo1mzZrCwsICPj0+eAs0t6vBMSAZ+/QIMDfHixwucCPWFxxhvQCQCpk7llp8oiE7MaRgD9u7lZmZu1+73tsREbuV3QkiJVCg7PF+/fh1r1qzJNPEBABUVFYwdOxbT0pqtCSHKwxhw5QrXafnOHRw6vRwjLk9EfGo8am7cio4tRhb8Glhv3nBD1y9fBsqU4WaD1tPjVnynxIcQkk9ynfz8+vVLrpFblpaW+PXrV26rIYTkVWoq4OPDJT337wMAxCo87Ns0HPFVAKcKTqjfsEvBJj5CIbB2LbBgAbcGmKYmMHEiJTyEkAKR6+THyMgIHz58QNOmTbM8LiwsDEbK6DdASEn3XydmrF/PTUIIQKyliUMNNDC/TiTeGfKwwHE+5jWfB75KAc6Xc/cut9bXgwfccxcXYNs2bjFUQggpALlOfpo2bYotW7agT58+Wfb52bx5M5o1a5brAAkhufTlCzBlCveziQke9myODnqn8VkQCTMdM1xyPYCWFVsWXDzx8cD8+VwyJhYDhoZc68/AgdxtLkIIKSC5bueeMmUKbt26BVdXV4SHh8vs//LlC1xdXRESEoKpU6fmKUhCiByePAG2b//93NoamDQJ2LkT+PABYeMG4rMgCS6VXPBg1IOCTXwuXgRq1eKSHbEY6NOHW3190CBKfAghBS5Py1ts2rQJkydPhoqKCuzs7FCxYkUAwLt373Dnzh2IxWKsX78e48aNU1jAOUWjvUixlr4T8/nz3Lw4b95IJgJMEiZBQ/X3yK1Lby+hZcWWUOEVUP+eHz+AyZMBb2/uefny3AzN7dsXTP2EkP+3d+dxMW//H8Bf07QvUikVCRFFIrmW7KFrj4tcIeu9dsJF+JI1+7VHIbmyFz/7rixdpGQrFBEpZGvfZs7vj8+d0WjRMjVTvZ+Pxzxun2XOec9Hdz7vzucsFVZZ3r9LvbbXrVu34OHhgcDAQKSlpQEA1NXV0aVLF7i5uaFdu3ZSCbSkKPkhlVI+nZihoAD89hvg4QFWvz52he3CkqAlCB4bjDraMlhWJieHa316+ZJr3Zk+nVsEVVOz/GMhhFQ4cp38iAiFQiQmJgIAatSoUegQ+PJEyQ+pdO7dAwYO5FZVB7gRUmPGcI+4zMyQlJmEP0//iUOPDwEA5trNxapuq2QTq6cn99q1i5tIkRBCiqhCJD/yipIfUikIBN9XMP/2DTAx4YaHT50KTJwonon5fvx9DDk2BNGfo8Hn8bHSfiVmt5tdPo+5cnKAzZu5vj09enD7hEIudiWlsq+fEFKpyOUkh4SQcvDoEbB+PRAVBdy8yT0+0tYGLl0CrK3FMzEzxuB5zxOuF1yRJciCSTUTHBp0CO1MyvGx88aNwF9/cf2NnjwBNDS4R3Fy0gpMCCEiJU5+mjVrVuRzeTweHjx4UNKqCKlafuzELBIWBrRsyf3curXEW7zDvDH57GQAQF/zvvDp7wM99XKeX2vCBMDPD5g8mWuVIoQQOVXi5EdXVxe8nwxRTUlJQWho6E/PI4Sg8E7Ms2Z9T3zyMaLZCHiHeWNY02GY0WZG+fw/d/UqsG8fsGcPF6emJjeBIbX0EELkXImTn8DAwAKP5eTkwMvLC0uXLgWPx8OwYcNKWg0hVcf584CzM/ezqBOzqytQv36eUxljOBZxDAMtBoKvwIeakhr+HfsvFBXK4Un258/c4609e7jtjh25WAFKfAghFYLUv6mOHj0KS0tLTJ06FdbW1ggNDcU///wj7WoIqfji4rjWE5FevYAOHYDly7nlKLZsyTfx+Zz+GQMOD8CQY0PgcdNDvL/MEx/GgMOHAQsLLvHh8YBJk4BBg8q2XkIIkTKpfVsGBgZi7ty5CAkJgY2NDS5evAh7e3tpFU9I5SHqxHzgALfEw6tXXMdlPh+4fr3Qt95+extOx5wQ+y0Wynxl6Krplk/MsbFconPmDLdtacnNHC3jebwIIaQkSt3y8+jRI/Tq1Qv29vb49OkTDhw4gHv37lHiQ0hujAGXLwO//go0awb4+nJ9fBo1At6//+nbhUyIdcHr0MGnA2K/xcJMxwzBY4IxqdWkso1bIOBaoJo04RIfZWVgyRKu8zUlPoSQCqrELT9v3rzBwoULceDAAejq6mLjxo2YMGEClGg+D0IkBQdzI6BEq5iLOjHPnl2kif8S0xIx6sQonIniWl2GNBkC777eqKZSxvNWPX4MjB8P3L7NbdvZca09FhZlWy8hhJSxEic/5ubmyMrKwq+//oo5c+ZAS0sLjx49KvB8GxubklZFSMVWrRqX+KirA2PHcjMx59OXpyDvkt/h8svLUOGrYNOvm/BHyz/KdjRXRgawciWwahXXOqWlBaxZA/zxB3VoJoRUCiWe4Tn38hWFfREzxsDj8SAQCEpSTanRDM+kXMXFcbMcp6dz/xXx8+MeeemVbO6dw48Po3GNxrA2tJZSoIVYvBhYupT72dER2LoVqFWr7OslhJBc5HJ5C19f32Kd7+LiUpJqSo2SH1IuHj3i5uc5cIBb5kFJievIbGxc7KI+pH7AuJPjsLDjQvxSSwbrYX37BnTrBri5cWuIEUKIDMjl8haySmYIkRuMAVeucEnPhQvf93fsyPXnMTQsdpGBrwIxzH8Y4lPi8eLLCzya+Kjs1+UKCACOH+cmLBQtn3H3LvczIYRUQrS2FyEltXMnt6gowPWFGTSIm4m5BKuXC4QCrLixAkuClkDIhLCoYYEjg46UfeITH89NrJiRAfTuDQwdyu2nxIcQUolR8kNIUSUlccPSGzbktgcP5vrHODkVuxNzbgkpCXAOcMbVGG7Cw9HNR2NLzy3QUNaQUuA/YOx7cmNkBKxYAXz5wvXvIYSQKkCuh254eHigVatW0NLSgoGBARwdHfHs2TNZh0Wqmrg4YM4cwMQEGD36+349PeDNG65jcwkTn1dfX8F6hzWuxlyFupI69jnuw57+e8ou8YmMBDp3Bm7d+r5v5kxg2TLxCvGEEFLZyXXyExQUhMmTJ+P27du4dOkScnJy0KNHD6Smpso6NFIVPHoEuLgAdesCa9dyLT+JidzaViLKyqWqwlTbFL/U+gVWBlYI/SMUI6xHlC7mgmRlcSO4mjfnZpF2deVagAghpAoq8WgvWfj48SMMDAwQFBSEjh07Fuk9NNqLFNudO4C7O7fQqIioE3Pv3qWe6yYuKQ7aqtrQVNYEAHzN+AoVvgrUlNRKVW6B/v2Xm6zwyRNuu3dvYPt2oE6dsqmPEEKkoCzv33Ld8vOjb9++AQB0dQtezygzMxNJSUkSL0KK5fVrLvFRUOD69dy5AwQFAX37ljrxOR99Hs13NsekM5Mg+rujumr1skl8kpKAKVO4mZmfPAEMDIBDh4BTpyjxIYRUaVLr8BwVFYWdO3ciMjIS6enpEsd4PB6uXLlSqvIZY5g5cybat2+Ppk2bFnieh4cHlixZUqq6SBWSnMwt2aCtzc2+DHBz2yxcyPXvKWFfnh9lC7Lxv2v/w+pbqwEAjz48QnJWctktUXHqFLcQ6du33Pbo0dyQ/EL+cCCEkKpCKo+9Hj9+jDZt2qBWrVqIjo5Gs2bNkJiYiLi4OJiYmMDMzAxXr14tVR2TJ0/GmTNncPPmTdSuXbvA8zIzM5GZmSneTkpKgomJCT32IpJEMzHv3MlN6lerFvDyZan78OTnzbc3GOo/FMFvggEAk2wnYb3DeqgqlkEH44QEYNo04OhRbtvMjPuMtNAwIaSCkfvHXvPnz4eDgwOePHkCxhh2796NN2/e4NSpU8jIyMDy5ctLVf7UqVNx8uRJXLt2rdDEBwBUVFRQrVo1iRchYrk7Ma9ZwyU+jRtzfXzKwKlnp9B8Z3MEvwlGNZVqODr4KLb13ib9xIcxYPdubtHRo0cBPh+YOxd4+JASH0II+YFUHnuFhYVh+/bt4vW+hEIhAKB3796YPXs23NzcEBQUVOxyGWOYOnUqjh8/jsDAQNSrV08a4ZKqavVqYN6879tS7MScn9SsVPxx+g98Tv8MW2NbHB50GPV1pPMYLY9Xr7jHXFlZQMuW3KO8Fi3Kpi5CCKngpPKN/+XLF+jq6kJBQQFKSkr48uWL+JitrS3CwsJKVO7kyZOxf/9+HDhwAFpaWkhISEBCQkKePkWE5Cs7m5u8T6R7dy7JGTJEqp2YC6KhrIF/BvyD6a2n4+bom9JPfHI/sa5Xj5uscN064PZtSnwIIaQQUvnWr1WrFhITEwEADRo0wPXr18XHHj58CE1NzRKV6+npiW/fvqFz584wMjISvw4fPiyNsElllZQEbNjA9XeZO/f7fhsbblLCw4dLtARFUQREBuB45HHxdrf63bDx141QUVSRbkUhIVwLT0jI932zZ3PLayjSxO2EEFIYqXxLtm/fHsHBwXB0dISzszMWL16M+Ph4KCsrY+/evRg+fHiJyq1AUxAReRAXB2zaxHXwFU1xcPEi9yhI1JG5BKusF0VmTiZmX5yNrSFboaWshRZGLVC3et0yqQsAsGULcP8+9xivlCMpCSGkqpFK8rNgwQK8e/cOADB37lwkJCTAz88PPB4PQ4YMwbp166RRDSH5e/yYm4H5wAEgJ4fb17gx1xLi7FwmI7hyi/4cDadjTgiL5x7v/tnyT9TSqiX9inIncRs2ABoa3KzNhBBCiqVCzfBcEjTDcxWwePH3JKBTJy7p6dWrzPry5HbkyRGMOzkOyVnJ0FPTg6+jL3qb95ZuJR8/cgunpqUBAQG04johpEqQ66Hu6enpqFWrFk6dOiWNeAgpXHY24OcH3Ljxfd+UKcDvv3OdmAMDgT59yjzxYYxh0plJcDrmhOSsZNiZ2CF8Qrh0Ex/GAF9frhXrwAHg5EmulYsQQkiplPqxl5qaGtLT06GhUUarUBMCcH14vL2BjRu5WYs7dwauXeOO6etzyUE54vF4UOFznZjd2rthaZelUFSQYkfjly+BP/8ELl/mtq2tgV27ACsr6dVBCCFVlFS+re3t7XH58mV07dpVGsUR8t3bt99nYhZ1Yq5ZE+jWDRAKy+XRVm7p2enidbhWd1+NgRYD0cG0g/QqyMnhErxFi4D0dEBVlZuAceZMQElJevUQQkgVJpXkZ/78+fjtt9+gqqqKgQMHwsjICLwf+iUUthgpIflatozry5NfJ2bVMlgaohBp2WmYdm4aniY+xTWXa1DiK0GZryzdxCcsjFt9XTQvVteuXNLXoIH06iCEECKdDs8Kuf76/jHpEREIBKWtpkSow3MFwhggEHyfp2b/fmDEiHLvxPyjyI+RGHJsCB5/eAweeLg44iK61e8mvQrS0rjWnQ0buM+vowOsXw+MGkWdmwkhVVZZ3r+l0vKzaNGiApMeQn4qO5ubeHDdOu6GP2MGt9/JCWjUCGjVSmah+Yb7YtLZSUjLTkNNjZrwG+gH+/pSXCvr8mWub8/Ll9y2kxM3V1HNmtKrgxBCiAQa6k5k58dOzABgacmNaJJxMp2alYpJZydh34N9AAD7evbYP3A/DDUNpVvR4MHAsWNA7dqApyc3Uo0QQoj8t/zk9vz5c3z69Ak1atRAw4YNpV08qQzym4m5Zk1g6lRg4kSZJz4AMPr/RuNoxFEo8BSwpPMSuLV3A1+BX/qCGQMyMgA1rtM0tmzhVphftAjQ0ip9+YQQQn5Kai0/R48exezZs/FW9Bc8gNq1a2P9+vUYNGiQNKooEWr5kUNOTsCRI9zPMuzEXJiniU/R/1B/ePXxQqe6naRTaGwsMGECl+TQ+nSEEFKosrx/SyX5OXv2LPr27YsmTZpgxIgRMDY2RlxcHPbv34+IiAicOnUKPXv2lEa8xUbJj4wxxq091aAB18IBcJMRzpkD/PWXzDox/yg5MxlXY66if+P+4n0CoUA6rT0iYWHcgqp8PhARwS28SgghJF9yn/zY2dmhWrVqOHPmjMTIL8YYevbsieTkZNy6dau01ZQIJT8ykp3Nte6sWweEhwOTJwNbt8o6qnyFJ4RjyNEhePHlBQJdAqU7fP3TJ0BP7/u2tzfQoQPX4kUIIaRAcr28BQCEh4dj0qRJEokPwA17nzRpEh48eCCNakhFkJTEDdOuXx8YPpxLfNTV5bI/C2MMniGeaLOrDaI+R8FYy1h6szRnZAALFgB16kguSTF+PCU+hBAiY1L5pufz+cjKysr3WHZ2dp6kiFRSy5dzq6vn7sQ8bRrXz0XOJrn8lvEN40+Nx9GIowCAPuZ9sLf/Xuip6/3knUUQGAj88QcQFcVtHzkCNG1a+nIJIYRIhVSyklatWmHNmjVIT0+X2J+ZmYl169ahdevW0qiGyLvUVC7xadyYW4fq1Stg/ny5S3zuvbsHGy8bHI04CkUFRazvsR4nh54sfeLz5QvXstOlC5f4GBlxq7CLVpwnhBAiF6TS8rNkyRLY29ujfv36GDx4MAwNDREfH4+AgAB8+vQJV69elUY1RF6IOjGvWwfMmgV0787tnz4dsLOTm07MBQmJC8HLLy9hqm2Kw4MOo3XtUibnjAH+/tzq8u/fc/smTABWrQK0tUsfMCGEEKmSSvLTvn17XLx4EfPmzcO2bdvAGIOCggJat26NgwcPol27dtKohsjaj52YRUTJj6FhhZikb4LtBGTkZGBU81HQUdMpXWFv33KduU+e5LYbNwa8vLhOzYQQQuSS1Gd4TktLw5cvX6CjowN1dXVpFl0iNNpLCvKbiVldHRg7FnB1BerVk2l4P3Pn7R24XXFDgFMAqqtWl06hQiE3I7ObG5CczK247ubGPeZTUZFOHYQQUoVVqBme1dXV5SLpIVL066/Av/9yP8txJ+YfCZkQf//7N+ZdmYccYQ4WXVuEzT03l77gp0+5xC84mNtu25ZLDps0KX3ZhBBCypzUkx9SCTx8yE1KKEpix43jOvPK4UzMBfmU9gkuJ1xwJuoMAGBIkyFY1mWZdAr/9o1LBjU1uX49EyfKdR8nQgghkkr82EtBQaFYK7kLBIKSVFNq9NiriESdmNeuBS5eBLZtAyZN4o7l5HA39wpyg78VewtD/YfibdJbqPBVsPHXjfiz5Z/F+n3NIz6eG70lsmcP19fJxKT0ARNCCMlDLh97LVq0SOJm4uPjg5SUFPTt21c82uv06dPQ0NDAmDFjpBIsKQP5dWJWUABevvx+jmLFaSD0j/CH0zEnCJgADXUb4sjgI2hu2LzkBWZlATNmAHv3AvfvA40acfvpd5oQQiqsEt/V3N3dxT+vX78ehoaGuHz5MjQ1NcX7k5OT0a1bN+oDJI8Y4zow//038OYNt68CdWIuSKe6nWCoaYhOdTthR+8d0FIp5czSSkpcIpieDpw9+z35IYQQUmFJZbSXmZkZ1q5di4EDB+Y55u/vj9mzZyMmJqa01ZQIPfYqRP/+3BDtCtSJOT9PE5+icY3vS0a8T3kPAw2Dkj/mio/n+jXp/DcM/tUrICaGm7yQEEJIuZD7tb3i4uKgWMCjEUVFRSQkJEijGlIaDx8Co0YBr19/37dwoVzPxPwzAqEAy4KWocn2JvAN9xXvr6lZs2SJj1DIzdFjYcGtOC9Sty4lPoQQUolIJfmxsLDAhg0bkJ2dLbE/KysL69evR2NayFE2GAMuXwYcHABra8DXF9i06fvxVq24x1wVYPTWjxJSEuCw3wGLAhdByIS4E3endAU+e8YlOH/+yY3mevSIW5yUEEJIpSOVnqzLly+Ho6Mj6tevj4EDB8LQ0BAJCQkICAhAQkICTpw4IY1qSFFlZwOHD3OdmB884PYpKACDB3ND1Su4Ky+vwDnAGe9T30NdSR3be22HS3OXkhWWlcWNcFu2DMjMBDQ0uAVap04F+HzpBk4IIUQuSCX56d27N86fP48FCxZg27ZtEAqF4PF4+OWXX+Dj44Nu3bpJoxpSFEIh0KIF8OQJt62uzs3TM2NGhe3ELCIQCrAkaAmWX18OBoamBk1xZNARWOhblKzAO3e4a/P4Mbfdsyc3a7OpqfSCJoQQInekNobZ3t4e9vb2cre8RZWQkMB1WubxuBaeX38FEhMrdCfm/NyNuytOfMbbjMemXzdBTUmt+AUlJwMLFgBbt3KPBmvU4B4H/v47dw0JIYRUalJf2+vjx49IT0/Ps79OnTrSrKbIKvVorwcPgPXrgYMHub49nTpx+79949aXqoB9eX5mxfUVqKdTD8OshpWsgDNnuBmZRcP7R47krmGNGtILkhBCSKnJ5SSHuSUnJ8PV1RUHDx5ERgGdRGU1w3OlI+rEvG4dNxOzyIUL35MfbW3ZxCZlOcIcLAtahpHWI2GmawYAWNBxQckKS03lHnEdOsRt16sH7Nz5fUV6QgghVYZUkp8ZM2bgwIEDGDt2LJo1awYVWtVa+gQCroUnv07Ms2ZxI7cqkTff3uB3/99x680tnI0+i9tjb4OvUIoOyOrqwPv33DWbNQtwd/++dhkhhJAqRSrJz5kzZ7Bq1SpMnz5dGsWRgixdCkRFVapOzPk58/wMRp4Yic/pn1FNpRrmtJtTssTnxQtAXx+oVo3ry+PtzT0StLGRftCEEEIqDKnM85ORkQErKytpFEVE3r4FFi36PtcMnw8sWQKsWMH1V9m0qdIlPlmCLMy+OBt9DvbB5/TPaGnUEmF/hGFwk8HFL2zPHqBpU27yRhEzM0p8CCGESKflp1evXrhx4wa6du0qjeKqttydmHNygDp1uFYegBuNVEm9T3mP/of6iycrnPbLNKzpvgYqiiV8hGpqyiWOz59z17ECLc5KCCGkbEnljrBw4UIMGjQIWlpa6Nu3L/T09PKco1tJhluXiYI6MXfqBDRoILu4ypGOmg4ETIDqqtXh098Hjo0di1dAaioQFgZ06MBt29sDN24AdnY0fJ0QQogEqQx1V1Dgnp4Vtp6SrEZ7yf1Q97Q07gYdHs5tV+JOzD/KzMkEX4EPRQUuB4/5EgMej4e61esWr6CLF7llKT5+BCIiuNYyQgghFZrcD3VftGhRyVfQroqysgBlZe5ndXXA0JBbVmHs2ErbiflHLz6/gNMxJziYOWCF/QoAQD2dYn7uxERg5kzgn3+47Tp1uBXZKfkhhBBSCKlPcihv5Krl5+1brqPy3r1cS0+tWtz+Fy8AHZ1KMxPzzxx9chTjTo1DUmYSaqjXwPMpz6GjplP0AhgD/PwAV1cuAeLxgOnTufW5NDXLLnBCCCHlpizv31IZ7UV+4sEDbibhevW4fj2JicD+/d+Pm5lVicQnIycDk85MwpBjQ5CUmQQ7EzuE/RFWvMQnJoZbg2vECO46WlkBt28Df/9NiQ8hhJAikdoQmKioKOzcuRORkZF5lrfg8Xi4cuWKtKqqGArrxPzXX9wNvAqJ+hSFIceGIDwhHADg1t4NSzovgRJfqWgF5OQAmzcD//sf109KRYWbCuCvvwClIpZBCCGEQErJz+PHj9GmTRvUqlUL0dHRaNasGRITExEXFwcTExOYmZlJo5qK5etXwNGRu1FXoU7M+UnPTkfHvR2RkJKAGuo1sH/Afjg0cCh6AQ8ecMP9793jtjt1Ary8AHPzsgmYEEJIpSaVx17z58+Hg4MDnjx5AsYYdu/ejTdv3uDUqVPIyMjA8uXLpVGNfEtK+t7xFuD68EybBkydCkRHc2tKVcHEBwDUlNSwyn4VOpl2woMJD4qX+Pz9N9CyJZf4VK/OzdJ89SolPoQQQkpMKi0/YWFh2L59u3jIu1AoBAD07t0bs2fPhpubG4KCgqRRlfwRdWL28uISIDMzoF077piHh2xjk6HIj5FIykxC69qtAQAuzV0wwnoEFHjFzLcbN+bWNRs8mHvsZWhYBtESQgipSqTS8vPlyxfo6upCQUEBSkpK+PLli/iYra0twsLCpFGNfHn4ULITc1ISd6NOS5N1ZDLnG+4LW29b/HbkNySmJYr3Fynx+fwZCAz8vt2zJxASAhw5QokPIYQQqZBK8lOrVi0kJnI3uQYNGuD69eviYw8fPoRmZRqF8/494OAAWFtzj7lycrg+KKdPA0+eAN26yTpCmUnNSsWoE6Mw6v9GIS07DY1rNIaQCYtewPPngIUF0L8/8O7d9/22ttIPlhBCSJUllcde7du3R3BwMBwdHeHs7IzFixcjPj4eysrK2Lt3L4YPHy6NauSDnh53k67inZh/9PjDYww5OgSRiZFQ4CnAvZM75neYX7zV2M3MgLp1geRk4NMnwNi4zOIlhBBSdUllksMXL17g3bt36NChAwQCAVxdXeHn5wcej4c+ffpg8+bNMptgsFSTJCUlcR1sjx0DgoK+z8p8/TpgYlIlZmL+GcYY9tzfgynnpiAjJwPGWsY4MPAAOtXt9PM3CwTchI/DhgFqaty+uDigRg1uKDshhJAqqywnOaQZnvPzYydmgHvEVZlasKSEMYYhx4bgWMQxOJg54J8B/0BfQ//nb3z8mBu+fucOMG9ele4cTgghJC+5XdsrPT0dJ06cwOvXr6Gvr49+/fpBX78INz559eABsH49cPAg15cH4PqgzJ7NPeIiefB4POzquwudTDthUqtJP+/UnJEBrFgBrFrFXWMtLWpBI4QQUq5K3PLz7t07dOzYETExMRAVoa2tjXPnzqFNmzZSC/D69etYu3YtQkNDER8fj+PHj8PR0bHI7y9y5hgdDTRs+H0790zMCrQKiAhjDDtDdyL4TTB8HX2Lt6Dt9evAH38Az55x246OwNat39c4I4QQQv4jl2t7LVy4EHFxcVi4cCHOnDmDjRs3QllZGRMnTpRmfEhNTYW1tTW2bt0q1XKRnc2tCSXSoAHQvTvg5ATcvcsNt+7dmxKfXL5lfMNQ/6GYeGYi/nn4D04/P120N379Cvz5J5dQPnvGDVn39weOH6fEhxBCSLkr8WOvS5cuYf78+fjf//4HAOjZsyfMzMzQr18/vH//HjVr1pRKgD179kTPYqyDlZmZiczMTPF2kqjPzvcdXF+eTZuAjx+B168BUaxnzwKKUlvurFIJfReKIceG4OWXl1BUUMTqbqvRx7zPz98YEABMmQLEx3Pbf/wBrF7NzdZMCCGEyECJmzUSEhLQsWNHiX2dO3cGYwzv378vdWAl5eHhAW1tbfHLxMSEOxAXxz3GMjHh/vv2LXcDfvr0+5sp8cmDMYYtd7ag3Z52ePnlJUy1TXFz9E3MbDuz8EdecXHAgAHAb79xiY+5OTdibufOCp/4pKYCPB73Sk2VdTSEEEKKq8R3e4FAADXR8OT/qKqqAgByRJ2FZcDNzQ0zZ84UbyclJXEJkJUVN7Qa+N6J2dmZhlT/xJSzU7D93nYAgGNjR+zptwc6ajqFvyk8nHvElZTEJZTz5gELFgD//X4QQgghslSqpo5nz55BMVdrieC/5OJp7taU/9jY2JSmqiJTUVGBSn4JjUAAdO7MJT3UibnInJo6wSfcB6u6rcLUX6YWrYNz06bchIXKytw8SVZWZR+ojMTF0RqrhBBS0ZR4tJeCgkK+N0LGmMR+0bYoMSoNHo9X8tFe166hWufOpY6hsmOMITIxEpb6luJ9H1I/wEDDoOA3ZWUBO3ZwnZr/SzzTXiaA1dAH+MWY4bmC8PICRI2LCgrAli2Ai4tsYypLGhqyjoAQUhXJ5Tw/Pj4+0oyj7JVTy1NF9intE0b93yhcjbmKe+PvwULfAgAKT3wAoFcv4MoVbkmKJUsAABpmVWMRUqEQmDyZe1VWlXsaVEJIVVTi5MelnP7UTUlJQXR0tHg7JiYG4eHh0NXVRZ06dcolhqog+E0whh4bijdJb6DCV8GjD4/Eyc9P/fkn8OgR97iLEEIIkXNyv7xFYGAgunTpkme/i4sL9u7d+9P3l2WzWWUgZEKsvbUWC64ugIAJ0FC3IY4MPoLmhs0LftOpU1wfKtHjR8a4xUhzXd/KOgoqLo7rLy/MtVg9nw9ERFTeKYvosRchRBbk8rFXeRENnyfS9zH1I0aeGInz0ecBAMOshmFH7x3QUtHK/w0JCcC0acDRo9zio+3bc//l8SQSH6Dy3jDNzbk+PqLHXHw+N3qfOj0TQkjFQUOeqjDvMG+cjz4PVUVVePf1xv4B+/NPfBgDdu/mmjyOHuXu+GPHAurq5R+0HMj9xDcigrsUhBBCKg65b/khZWeO3RxEf46GaxtXWNUsYDh6VBQ3K3NgILfdsiWwaxfQvHl5hSnXKuujLkIIqcyo5acKeZ/yHjPOz0CWIAsAoKigiD399+Sf+GRnAx4e3Bw9gYFcK8/69dx6aJT4EEIIqcCo5aeKuBpzFcP8h+F96nso85Wxpvuagk++excYPx54+JDb7tGDm8enXr3yCVbOaWjQ8G9CCKnIqOWnkhMIBVh8bTG67euG96nv0dSgKUY3H53/ySkpgKsr0LYtl/jo6QH//AOcP0+JDyGEkEqDWn4qsXfJ7+Ac4IzAV4EAgHEtxmFTz01QV8qnozJj3PIfoaHc9vDhwIYNgL5+ucVLCCGElAdq+amkbsbeRPMdzRH4KhCayprwG+gH737e+Sc+ADdcffp0oG5drqXnn38o8SGEEFIpUctPJZCaCmhqcj+npHB9Uow0jZApyIR1TWscGXwE5no/TETDGJfgVK8O9OvH7Rs+HPjttyo7hJ0QQkjVQMlPZaKUCoCbXdBM1wyXR1yGVU0rqCqq5j3Xx4eboMbQEOjQAdDR4Vp/KPEhhBBSydFjr8qi7hVg4HAcuX1dvKtVrVb5Jz4AMGwYN2R9xozvzUaEEEJIFVBlWn5SU7mJiSubbEE2Bsw/Drz+DXhljzFHhEjfKjkLMQAohIdBcccWZG31BhQVAagCQfe4i5IF7lXBVNYlNAghhJQtuV/YtLREC6MB3wBUsoVNtV8DPacCh48DLP/MTg1pcIc7ZmIDFCGAKzZgI1zLOdCyUbl/cwkhpGory4VN6bFXRdXo/4AJzQGV5AITH3tcxiNYYQ7WQhECHIITDmBY+cZJCCGEyJkq89jr3bs8C49XWPfi76KznyMAwNpCE48UGIRCnvg4n88Q1W8m6h3fCAAQ1qqNrI2e6NuzD/rKIF5CCCFEnlSZ5EdDo/L0Eelo1gojrUdCX10fK+1XYpcWD5Mnc8f4CkLsVJuJesc3caO3pkyBwooVUNXKZ7V2QgghpAqqMn1+yuKZYXk68fQEOtTpAD11PQCAkAmhwOOeWqamAuaabzEVWzAcfqiNOKBJE2719TZtZBk2IYQQUiLU56cKy8jJwOQzkzHg8AC4nHCBkAkBQJz4QCCAhvdGxGk0xjysQW3lj8CyZUBYGCU+hBBCSD4o+ZFjUZ+i0HZ3W2y/tx0A0KxmM3HyI7Z1K7cYaWoq0LEjtyDpwoWAsrIMIiaEVBZ169bFxo0bZR0GKSeBgYHg8Xj4+vWrrEMpF5T8yKmDjw7CxssG4QnhqKFeA+edz2Ol/UooKvzQTWv8eMDGBti5E7h2DWjUSDYBE0LKzKhRo+Du7g4A4PF4ePXqVZnXGRISgj/++KPI51fEm2fnzp0xY8YMiX2vXr0Cj8cNIHF3d8eoUaPKPzAZaNeuHeLj4/+bGqbyqzIdniuK9Ox0TD8/Hd5h3gCATqadcOC3AzDWMuZOCAzk+vL4+nITFKqrAyEhgALlsYQQ6dGX0cLGjDEIBAIoKla825NAIACPx4NCGXwfZ2dnQ0lJSerliigrK8PQ0LDMypc3dMeUM5mCTFx+eRk88PC/jv/D5ZGXvyc+374B/fsDfn5cAiRCiQ8hVZKoteXKlSuwtbWFuro62rVrh2fPnkmc5+npCTMzMygrK6NRo0b4559/flr2j4+9eDwedu3ahQEDBkBdXR0NGzbEyZMnAXCtJV26dAEA6OjogMfjiVtMGGNYs2YN6tevDzU1NVhbW+PYsWN5PsOFCxdga2sLFRUV3LhxA0KhEKtXr0aDBg2goqKCOnXqYMWKFeL3xcXFwcnJCTo6OtDT00P//v0lWsRGjRoFR0dHLFmyBAYGBqhWrRr+/PNPZGVliY8HBQVh06ZN4PF4JWpR27t3L6pXr47Tp0/D0tISKioqeP36NbKysjBnzhzUqlULGhoaaN26NQIDAyXe6+3tDRMTE6irq2PAgAHYsGEDqlevLj7u7u6O5s2bY8+ePahfvz5UVFTAGMO3b9/wxx9/iD9T165d8eDBA/H7Hjx4gC5dukBLSwvVqlVDy5Ytce/ePQDA69ev0bdvX+jo6EBDQwNNmjTB2bNnJf4dcrfc+fv7o0mTJlBRUUHdunWxfv16ic9Qt25drFy5EmPGjIGWlhbq1KkDLy+vYl1DmWGV3Ldv3xgA9u3bN1mHUiihUCj+OSQuhF16cSn/Ez09GZswgbGvX8spMkKIrLm4uLDFixczxhgDwGJiYhhjjF27do0BYK1bt2aBgYHsyZMnrEOHDqxdu3bi9wYEBDAlJSW2bds29uzZM7Z+/XrG5/PZ1atXC63T1NSU/f333+JtAKx27drswIEDLCoqik2bNo1pamqyT58+sZycHObv788AsGfPnrH4+Hj29b/vqPnz57PGjRuz8+fPsxcvXjAfHx+moqLCAgMDJT5Ds2bN2MWLF1l0dDRLTExkc+bMYTo6Omzv3r0sOjqa3bhxg3l7ezPGGEtNTWUNGzZkY8aMYQ8fPmQRERFs2LBhrFGjRiwzM1N8zTQ1NZmTkxN7/PgxO336NNPX12fz589njDH29etX1rZtWzZ+/HgWHx/P4uPjWU5ODouJiWGiW+PixYuZi4tLgdfIx8eHKSkpsXbt2rFbt26xp0+fspSUFDZs2DDWrl07dv36dRYdHc3Wrl3LVFRU2PPnzxljjN28eZMpKCiwtWvXsmfPnrFt27YxXV1dpq2tLS578eLFTENDgzk4OLCwsDD24MEDJhQKmZ2dHevbty8LCQlhz58/Z7NmzWJ6enrs06dPjDHGmjRpwoYPH84iIyPZ8+fP2ZEjR1h4eDhjjLHevXuz7t27s4cPH7IXL16wU6dOsaCgIIl/hy9fvjDGGLt37x5TUFBgS5cuZc+ePWM+Pj5MTU2N+fj4SPyO6Orqsm3btrGoqCjm4eHBFBQUWGRkpPicTp06FXoNC1OW929KfmQsJTOFjToxinmGeOY9+OYNY/36MXb+fPkHRgiRe6Ib1uXLl8X7zpw5wwCw9PR0xhhj7dq1Y+PHj5d43+DBg1mvXr0KLTu/5GfhwoXi7ZSUFMbj8di5c+ckYhHdPEXnqKqqsuDgYImyx44dy37//XeJ9504cUJ8PCkpiamoqIiTnR/t3r2bNWrUSOKPxszMTKampsYuXLjAGOOSH11dXZaamio+x9PTk2lqajKBQMAY427M06dPL/Q6FMbHx4cBECcXjDEWHR3NeDwei4uLkzjX3t6eubm5McYYc3JyYr1795Y47uzsnCf5UVJSYh8+fBDvu3LlCqtWrRrLyMiQeK+ZmRnbuXMnY4wxLS0ttnfv3nzjtbKyYu7u7vke+/Hfb9iwYax79+4S5/z111/M0tJSvG1qasqGDx8u3hYKhczAwIB5en6/n40YMYLNmzcv3zp/pizv3/S8RIaefHiCX3b9gr3hezHr4iwkpiVyB4RCYNs2wNISOHkSmDYNEAhkGywhRG41a9ZM/LORkREA4MOHDwCAyMhI2NnZSZxvZ2eHyMhIAICfnx80NTXFrxs3bhSpHg0NDWhpaYnryU9ERAQyMjLQvXt3iTr27duHFy9eSJxra2sr/jkyMhKZmZmwt7fPt9zQ0FBER0dDS0tLXKauri4yMjIkyrW2toa6urp4u23btkhJScGbN28KjLm4lJWVJa5LWFgYGGMwNzeX+MxBQUHi2J49e4ZffvlFopwftwHA1NRUou9VaGgoUlJSoKenJ1F2TEyMuOyZM2di3Lhx6NatG1atWiVxPaZNm4bly5fDzs4OixcvxsOHDwv8XAX93kRFRUGQ636U+7PzeDwYGhpK/E7s27cPHh4eBdYjKxWvR1klwBiDT7gPppydgvScdBhpGuHAbwdQQ70GEBHBjeAKDuZObtsW8PaunEvSE0KkIndHWNFIJaFQmGefCGNMvK9fv35o3bq1+FitWrWKVI+o3Nz1/Eh07MyZM3nKVVFRkdjWyDUFv5qaWoFlispt2bIl/Pz88hwrSkftH69HaaipqUmUJxQKwefzERoaCv4P39uampoAJK+/CMtnvmGNH5YlEAqFMDIyytN/CIC4v5C7uzuGDRuGM2fO4Ny5c1i8eDEOHTqEAQMGYNy4cXBwcMCZM2dw8eJFeHh4YP369Zg6dWqe8ooaY3F/J+QFJT/lLCUrBRNOT4DfI+5/WgczB+wbsA8GitqAuzuwciWQnQ1oaQEeHsDEidShmRBSYhYWFrh58yZGjhwp3hccHAwLCwsAgJaWFrSksPyN8n9zi+VuFRB1Ao6NjUWnTp2KXFbDhg2hpqaGK1euYNy4cXmO29jY4PDhw+JOvwV58OAB0tPTxcnU7du3oampidq1a4tjFki5Vb1FixYQCAT48OEDOnTokO85jRs3xt27dyX2iTolF8bGxgYJCQlQVFRE3bp1CzzP3Nwc5ubmcHV1xe+//w4fHx8MGDAAAGBiYoIJEyZgwoQJcHNzg7e3d77Jj6WlJW7evCmxLzg4GObm5nmSuoqI7qrlKDMnE613tYbfIz/weXx42HvgrPNZGIRHAS1aAEuWcIlP375cC9DkyZT4EEJK5a+//sLevXuxY8cOREVFYcOGDQgICMDs2bOlWo+pqSl4PB5Onz6Njx8/IiUlBVpaWpg9ezZcXV3h6+uLFy9e4P79+9i2bRt8fX0LLEtVVRVz587FnDlzxI/Ibt++jd27dwMAnJ2dUaNGDfTv3x83btxATEwMgoKCMH36dLx9+1ZcTlZWFsaOHYuIiAhxK8iUKVPEQ9Hr1q2LO3fu4NWrV0hMTJRKi4W5uTmcnZ0xcuRIBAQEICYmBiEhIVi9erV4ZNXUqVNx9uxZbNiwAVFRUdi5cyfOnTv30xapbt26oW3btnB0dMSFCxfw6tUrBAcHY+HChbh37x7S09MxZcoUBAYG4vXr17h16xZCQkLEie6MGTNw4cIFxMTEICwsDFevXhUf+9GsWbNw5coVLFu2DM+fP4evry+2bt1a7N+bkSNHws3NrVjvKRdS70UkZ+Stw/OyoGWs9oba7MbrG9yIrYkTGQO4V82ajB05wliuTnyEEFKQ/DoZ379/X2JEGGOMbd++ndWvX58pKSkxc3Nztm/fvp+WnV+H5+PHj0uco62tLTH6Z+nSpczQ0JDxeDzxCB+hUMg2bdrEGjVqxJSUlJi+vj5zcHAocJSRiEAgYMuXL2empqZMSUmJ1alTh61cuVJ8PD4+no0cOZLVqFGDqaiosPr167Px48eLv+tdXFxY//792aJFi5ienh7T1NRk48aNk+gs/OzZM9amTRumpqaW55oVhY+Pj0QnZZGsrCy2aNEiVrduXaakpMQMDQ3ZgAED2MOHD8XneHl5sVq1ajE1NTXm6OjIli9fzgwNDcXHFy9ezKytrfOUnZSUxKZOncqMjY2ZkpISMzExYc7Oziw2NpZlZmayoUOHMhMTE6asrMyMjY3ZlClTxJ3fp0yZwszMzJiKigrT19dnI0aMYImJiQX+Oxw7doxZWlqKr//atWslYvnxd4QxxqytrcUjExmT39FetLBpWdefmYTP6Z9Rt3pdAIBAKEBSZhJ0Ll4HJk0C3r3jThw7Fli7FtDRKfcYCSGkshk1ahS+fv2KEydOyDqUIhk/fjyePn1aaIfzqqYs79/U56cMhcWHYcjRIVBXUsedcXegpqQGvgIfOsnZwLBhQFoa0LAh4OUFdO4s63AJIYSUk3Xr1qF79+7Q0NDAuXPn4Ovri+3bt8s6rCqDkp8ywBjDtpBtmHVxFrIEWTDVNsWbb7Ewr/HfulsGBsCqVUBCArcI6U9GNhBCCKlc7t69izVr1iA5ORn169fH5s2b8+3cTcoGPfaSsq8ZXzH25FgERAYAABwbO2JvYzdoT5/DjeaiFh5CCCHkp8ry/k1DiaTobtxdtNjZAgGRAVBSUMKmXzchYEgAtHf9AwQFATNncl2bCSGEECIz9NhLShhjmHd5Hl59fYX6OvVx2NEPtnXacAeXLweSkrih7FKcXIsQQgghxUePvaTozbc3WHVuATZcV4PKi1fA+fOU7BBCCCElQKO95FTwm2Bcf30d89rPAwCY3HyIbXMCAdG6MTdvAgXM8EkIIYQQ2aA+PyUgZEKsubUGHX06wu2KG67ePgj8/jvQpw+X+NSrB1y8SIkPIYRUAW/evEHnzp1haWmJZs2a4ejRo7IOifwEtfwU08fUj3A54YJz0ecABuxIbI0uvSYBX75yS1HMmsWN6sq1kjAhhJDKS1FRERs3bkTz5s3x4cMH2NjYoFevXnkWJiXyg5KfYrj++jp+9/8d75LfwfKbMi7eMkOtu3e4gy1aALt2ATY2sg2SEFLldO7cGc2bN8fGjRtlHUqVZGRkBCMjIwCAgYEBdHV18fnzZ0p+5Bg99iqiv//9G118u+DD13dYd18fjzx5qHU3kpugcM0a4O5dSnwIIaQItm/fjnr16kFVVRUtW7b86ZIOOTk5WLhwIerVqwc1NTXUr18fS5culViI1N3dHTweT+JlaGiYp6y4uDgMHz4cenp6UFdXR/PmzREaGlpg3aNGjRKXp6ioiDp16mDixIn48uVLvuffu3cPQqEQJiYmRbwa0lHca5qbh4cHeDweZsyYkWd/q1atoKWlBQMDAzg6OuLZs2cS5xT1ussbavkpImMtYzSPEyLgki5MYz5yO7t1A3bsAMzMZBscIYRUEIcPH8aMGTOwfft22NnZYefOnejZsyciIiJQp06dfN+zevVq7NixA76+vmjSpAnu3buH0aNHQ1tbG9OnTxef16RJE1y+fFm8zefzJcr58uUL7Ozs0KVLF5w7dw4GBgZ48eIFqlevXmjMv/76K3x8fJCTk4OIiAiMGTMGX79+xcGDByXO+/TpE0aOHIldu3YV86qUTkmuqUhISAi8vLzQrFmzPMeCgoIwefJktGrVCjk5OViwYAF69OiBiIgIiVatn113uST1pVLlTGlWhU3KSJLYfu/Uh1t9XVeXsb17afV1Qki5OXr0KGvatClTVVVlurq6zN7enqWkpDDGuJWzp0+fLj43IyODTZ06lenr6zMVFRVmZ2fH7t69Kz7eqVMnNnnyZDZ58mSmra3NdHV12YIFC5gw13eaUChkq1evZvXq1WOqqqqsWbNm7OjRo6X+HL/88gubMGGCxL7GjRuzefPmFfie3r17szFjxkjsGzhwIBs+fLh4u6BV0HObO3cua9++fbHiFa0On9vMmTOZrq6uxL6MjAzWoUMHtm/fvmKVLw0luaaMMZacnMwaNmzILl26lOd3KD8fPnxgAFhQUJB4X1Gue0mV5aru9NgrHwKhAO6B7mi0tRESPseK9xts9wUmTAAiIwEXF5rDhxBSLuLj4/H7779jzJgxiIyMRGBgIAYOHAhWwDRtc+bMgb+/P3x9fREWFoYGDRrAwcEBnz9/Fp/j6+sLRUVF3LlzB5s3b8bff/8t0WKxcOFC+Pj4wNPTE0+ePIGrqyuGDx+OoKAgAMDevXvBK+Z3YFZWFkJDQ9GjRw+J/T169EBwcHCB72vfvj2uXLmC58+fAwAePHiAmzdvolevXhLnRUVFwdjYGPXq1cPQoUPx8uVLieMnT56Era0tBg8eDAMDA7Ro0QLe3t7F+gwvX77E+fPnoaSkJN7HGMOoUaPQtWtXjBgx4qdlrFy5EpqamoW+ivrYqqTXFAAmT56M3r17o1u3bkWq69u3bwAAXV1dif0/u+5ySerplJwpbub4Lukd67y3M9P7C8y3GdjLdpbUwkMIkanQ0FAGgL169Srf47n/ak9JSWFKSkrMz89PfDwrK4sZGxuzNWvWiM+3sLCQaOmZO3cus7CwEJehqqrKgoODJeoZO3Ys+/333xljjAUEBLBGjRoV63PExcUxAOzWrVsS+1esWMHMzc0LfJ9QKGTz5s1jPB6PKSoqMh6Px1auXClxztmzZ9mxY8fYw4cPxS0ZNWvWZImJieJzVFRUmIqKCnNzc2NhYWFsx44dTFVVlfn6+hZYt4uLC+Pz+UxDQ4OpqqoyAAwA27Bhg/icGzduMB6Px6ytrcWvhw8fFljmp0+fWFRUVKGvtLS0At+fW0mv6cGDB1nTpk1Zeno6Yyxv6+GPhEIh69u3b56Ws6Jc95Iqy5Yf6vOTy8UXFzE8YDg+pn1Ei2w1OEdmg58TCYSHc6O5CCFEBqytrWFvbw8rKys4ODigR48eGDRoEHR0dPKc++LFC2RnZ8POzk68T0lJCb/88gsiIyPF+9q0aSPRctO2bVusX78eAoEAERERyMjIQPfu3SXKzsrKQov/vgsHDBiAAQMGlOjz/NhixBgrtBXp8OHD2L9/Pw4cOIAmTZogPDwcM2bMgLGxMVxcXAAAPXv2FJ9vZWWFtm3bwszMDL6+vpg5cyYAQCgUwtbWFitXrgQAtGjRAk+ePIGnpydGjhxZYP1dunSBp6cn0tLSsGvXLjx//hxTp04VH2/fvr1E5+uf0dXVzdN6UlrFuaZv3rzB9OnTcfHiRaiqqhap/ClTpuDhw4e4efOmxP6iXHd5RI+9AOQIc7DgygIM2eWAj2kfYV3TGocWhoPvuQP4919KfAghMsXn83Hp0iWcO3cOlpaW2LJlCxo1aoSYmJg857L/HoUVN8HITXQjP3PmDMLDw8WviIgIHDt2rMSfo0aNGuDz+UhISJDY/+HDB9SsWbPA9/3111+YN28ehg4dCisrK4wYMQKurq7w8PAo8D0aGhqwsrJCVFSUeJ+RkREsLS0lzrOwsEBsbOyPb89TVoMGDdCsWTNs3rwZmZmZWLJkSaHvKYw0H3uV5JqGhobiw4cPaNmyJRQVFaGoqIigoCBs3rwZioqKEAgEEudPnToVJ0+exLVr11C7du1C48nvussjSn4ArA3yQPqalYjdAKzSGojb427DXM8cGDsWaN1a1uERQgh4PB7s7OywZMkS3L9/H8rKyjh+/Hie8xo0aABlZWWJv9Czs7Nx7949WFhYiPfdvn1b4n23b99Gw4YNwefzYWlpCRUVFcTGxqJBgwYSr9IM4VZWVkbLli1x6dIlif2XLl1Cu3btCnxfWloaFBQkb1d8Pr/Q1pbMzExERkaK598BADs7uzxDtZ8/fw5TU9PifAwsXrwY69atw7t374r1PpEJEyZIJJX5vWxtbYtUVkmuqb29PR49epSnPmdnZ4SHh4tHazHGMGXKFAQEBODq1auoV6/eT+PJ77rLJak/SJMzP31mGB7OclracKO4AMZ+6DFPCCGydvv2bbZixQoWEhLCXr9+zY4cOcKUlZXZ2bNnGWN5+2tMnz6dGRsbs3PnzrEnT54wFxcXpqOjwz5//iw+X1NTk7m6urKnT5+yAwcOMA0NDbZjxw5xGQsWLGB6enps7969LDo6moWFhbGtW7eyvXv3MsZK1ueHMcYOHTrElJSU2O7du1lERASbMWMG09DQkOjPtGXLFta1a1fxtouLC6tVqxY7ffo0i4mJYQEBAaxGjRpszpw54nNmzZrFAgMD2cuXL9nt27dZnz59mJaWlkS5d+/eZYqKimzFihUsKiqK+fn5MXV1dbZ///4C481vtBdjjLVs2ZJNnjy52J+/LJTkmv4ovz4/EydOZNra2iwwMJDFx8eLX7n7IxXlupdUWfb5qZLJT1ZOFvMN3sGEc+cyxuczBjBh9eqM7dpFnZsJIXInIiKCOTg4iIeum5ubsy1btoiP/3jjSk9PZ1OnTmU1atQocKj7pEmT2IQJE1i1atWYjo4OmzdvXp6h7ps2bWKNGjViSkpKTF9fnzk4OIiHOfv4+LCS/v28bds2ZmpqypSVlZmNjY3E0GnGuOHTpqam4u2kpCQ2ffp0VqdOHaaqqsrq16/PFixYwDIzM8XnODk5MSMjI6akpMSMjY3ZwIED2ZMnT/LUferUKda0aVOmoqLCGjduzLy8vAqNtaDkx8/PjykrK7PY2NjiffgyUtxr+qP8kh/817n7x5ePj4/4nKJe95Ioy+SHx1gBYyUriaSkJGhra+Pbt2+oVq0aYr/FYu2ynpjmE4GGolGfgwcDmzcDFWBWSkIIKS1aDoNUBD/ev6WpQvT5Kc203bmdu+OHG/YNsWU9l/ik19QF/u//gCNHKPEhhBBCqgi5T35E03YvWLAA9+/fR4cOHdCzZ8+f9s7/0QH332BjPxzOoVkQ8oCkscOh9jwG6NevjCInhBBCiDyS+8derVu3ho2NDTw9PcX7LCws4OjoWOgwRxFxsxmAagAS6uhCb38AlDp0KrugCSGEEFIqZfnYS64nORRN2z1v3jyJ/YVN252ZmYnMzEzxtmg67k88IGr8IDRcvh3pKipIT0oqu8AJIYQQUipJ/92ny6KNRq6Tn8TERAgEgjwTNdWsWTPPhE4iHh4e+U4+VZ8B8DrGvQghhBBSISQnJ0NbW1uqZcp18iNSnJlK3dzcJKbU/vr1K0xNTREbGyv1i1eVJCUlwcTEBG/evJF682NVQ9dSOug6Sg9dS+mg6yg9omsZEREBY2NjqZcv18lPSabtVlFRgYqKSp792tra9MsoBdWqVaPrKCV0LaWDrqP00LWUDrqO0lOrVq08s3tLg1yP9irpVOiEEEIIIQWR65YfAJg5cyZGjBgBW1tbtG3bFl5eXoiNjcWECRNkHRohhBBCKiC5T36cnJzw6dMnLF26FPHx8WjatCnOnj1b5IXoVFRUsHjx4nwfhZGio+soPXQtpYOuo/TQtZQOuo7SU9bXUu7n+SGEEEIIkSa57vNDCCGEECJtlPwQQgghpEqh5IcQQgghVQolP4QQQgipUip18rN9+3bUq1cPqqqqaNmyJW7cuCHrkCqk69evo2/fvjA2NgaPx8OJEydkHVKF4+HhgVatWkFLSwsGBgZwdHTEs2fPZB1WheTp6YlmzZqJJ5Jr27Ytzp07J+uwKjwPDw/weDzMmDFD1qFUOO7u7uDxeBIvQ0NDWYdVIcXFxWH48OHQ09ODuro6mjdvjtDQUKnXU2mTn8OHD2PGjBlYsGAB7t+/jw4dOqBnz56IjY2VdWgVTmpqKqytrbF161ZZh1JhBQUFYfLkybh9+zYuXbqEnJwc9OjRA6mpqbIOrcKpXbs2Vq1ahXv37uHevXvo2rUr+vfvjydPnsg6tAorJCQEXl5eaNasmaxDqbCaNGmC+Ph48evRo0eyDqnC+fLlC+zs7KCkpIRz584hIiIC69evR/Xq1aVeV6Ud6t66dWvY2NjA09NTvM/CwgKOjo7w8PCQYWQVG4/Hw/Hjx+Ho6CjrUCq0jx8/wsDAAEFBQejYsaOsw6nwdHV1sXbtWowdO1bWoVQ4KSkpsLGxwfbt27F8+XI0b94cGzdulHVYFYq7uztOnDiB8PBwWYdSoc2bNw+3bt0ql6c0lbLlJysrC6GhoejRo4fE/h49eiA4OFhGURHy3bdv3wBwN21ScgKBAIcOHUJqairatm0r63AqpMmTJ6N3797o1q2brEOp0KKiomBsbIx69eph6NChePnypaxDqnBOnjwJW1tbDB48GAYGBmjRogW8vb3LpK5KmfwkJiZCIBDkWfy0Zs2aeRZJJaS8McYwc+ZMtG/fHk2bNpV1OBXSo0ePoKmpCRUVFUyYMAHHjx+HpaWlrMOqcA4dOoSwsDBqDS+l1q1bY9++fbhw4QK8vb2RkJCAdu3a4dOnT7IOrUJ5+fIlPD090bBhQ1y4cAETJkzAtGnTsG/fPqnXJffLW5QGj8eT2GaM5dlHSHmbMmUKHj58iJs3b8o6lAqrUaNGCA8Px9evX+Hv7w8XFxcEBQVRAlQMb968wfTp03Hx4kWoqqrKOpwKrWfPnuKfrays0LZtW5iZmcHX1xczZ86UYWQVi1AohK2tLVauXAkAaNGiBZ48eQJPT0+MHDlSqnVVypafGjVqgM/n52nl+fDhQ57WIELK09SpU3Hy5Elcu3YNtWvXlnU4FZaysjIaNGgAW1tbeHh4wNraGps2bZJ1WBVKaGgoPnz4gJYtW0JRURGKiooICgrC5s2boaioCIFAIOsQKywNDQ1YWVkhKipK1qFUKEZGRnn+gLGwsCiTgUqVMvlRVlZGy5YtcenSJYn9ly5dQrt27WQUFanKGGOYMmUKAgICcPXqVdSrV0/WIVUqjDFkZmbKOowKxd7eHo8ePUJ4eLj4ZWtrC2dnZ4SHh4PP58s6xAorMzMTkZGRMDIyknUoFYqdnV2eKUCeP39e5IXMi6PSPvaaOXMmRowYAVtbW7Rt2xZeXl6IjY3FhAkTZB1ahZOSkoLo6GjxdkxMDMLDw6Grq4s6derIMLKKY/LkyThw4AD+7//+D1paWuJWSW1tbaipqck4uopl/vz56NmzJ0xMTJCcnIxDhw4hMDAQ58+fl3VoFYqWllaePmcaGhrQ09OjvmjFNHv2bPTt2xd16tTBhw8fsHz5ciQlJcHFxUXWoVUorq6uaNeuHVauXIkhQ4bg7t278PLygpeXl/QrY5XYtm3bmKmpKVNWVmY2NjYsKChI1iFVSNeuXWMA8rxcXFxkHVqFkd/1A8B8fHxkHVqFM2bMGPH/1/r6+sze3p5dvHhR1mFVCp06dWLTp0+XdRgVjpOTEzMyMmJKSkrM2NiYDRw4kD158kTWYVVIp06dYk2bNmUqKiqscePGzMvLq0zqqbTz/BBCCCGE5KdS9vkhhBBCCCkIJT+EEEIIqVIo+SGEEEJIlULJDyGEEEKqFEp+CCGEEFKlUPJDCCGEkCqFkh9CCCGEVCmU/BBCCCFEwvXr19G3b18YGxuDx+PhxIkTxXp/RkYGRo0aBSsrKygqKsLR0THPOYGBgeDxeHleT58+lc6HKAQlP4QQQgiRkJqaCmtra2zdurVE7xcIBFBTU8O0adPQrVu3Qs999uwZ4uPjxa+GDRuWqM7iqLRrexFCCCGkZHr27ImePXsWeDwrKwsLFy6En58fvn79iqZNm2L16tXo3LkzAG6dOE9PTwDArVu38PXr1wLLMjAwQPXq1aUY/c9Ryw8hhBC5kZmZidGjR8PExATVqlVDmzZtEBwcLOuwyA9Gjx6NW7du4dChQ3j48CEGDx6MX3/9FVFRUcUuq0WLFjAyMoK9vT2uXbtWBtHmRckPIYQQuZGTk4N69eqJWwsmTpyIfv36IS0tTdahkf+8ePECBw8exNGjR9GhQweYmZlh9uzZaN++PXx8fIpcjpGREby8vODv74+AgAA0atQI9vb2uH79ehlGz6HHXoQQQuSGhoYGFi1aJN52cXGBq6sroqKiYG1tLcPIiEhYWBgYYzA3N5fYn5mZCT09vSKX06hRIzRq1Ei83bZtW7x58wbr1q1Dx44dpRZvfqjlh1Ra+Y0iyO8VGBiIvXv3/vQcABLnifblxhhDgwYNwOPxxM++RX6sQ1FREbVr18bo0aMRFxeXp6zbt29j8ODBMDIygrKyMgwNDTFo0CD8+++/Rfr8ovpUVVXx+vXrPMc7d+6Mpk2b5vve0tb9M8HBwXB3dy+0H0BlIfp3ePXqlaxDKVBhMQoEAhgYGODvv/8u/8AAPH36FOnp6TAzM5NJ/SQvoVAIPp+P0NBQhIeHi1+RkZHYtGlTqcpu06ZNiR6dFRe1/JBK68cb9bJly3Dt2jVcvXpVYr+lpaX4S9/HxweNGzfOU5alpaXEtpaWFnbv3p0nwQkKCsKLFy+gpaVVYFyiOtLT03H9+nV4eHggKCgIjx49goaGBgBgy5YtmDFjBn755ResWbMGpqamiI2NxbZt29C+fXts2rQJU6ZMKdJ1yMzMxMKFC/HPP/8U6Xxp1l2Q4OBgLFmyBKNGjSr3jo6keK5fv46PHz9i4MCB5V53WloaRowYgYULF0JTU7Pc6yf5a9GiBQQCAT58+IAOHTpItez79+/DyMhIqmXmh5IfUmm1adNGYltfXx8KCgp59ufWtGlT2Nra/rRsJycn+Pn5Ydu2bahWrZp4/+7du9G2bVskJSUVqY4uXbpAIBBg2bJlOHHiBJydnXHr1i3MmDEDvXr1wvHjx6Go+P1/06FDh2LAgAGYPn06WrRoATs7u5/G+uuvv+LAgQOYPXv2Tx8bSLtuaUlLS4O6unq51Ue+O3bsGGxtbWFqalqu9WZnZ2PIkCGwtLTE/Pnzy7VuAqSkpCA6Olq8HRMTg/DwcOjq6sLc3BzOzs4YOXIk1q9fjxYtWiAxMRFXr16FlZUVevXqBQCIiIhAVlYWPn/+jOTkZISHhwMAmjdvDgDYuHEj6tatiyZNmiArKwv79++Hv78//P39y/4DMkKqCBcXF6ahoZHvMR8fHwaAhYSEFFqG6LwrV64wNTU1tmPHDvGxr1+/MjU1Nebt7c2aNGnCOnXqVKQ6zpw5wwCwFStWMMYY6927N+Pz+ezNmzf5xhAbG8v4fD7r06dPkWK9evUq09fXZw4ODhLHO3XqxJo0aSKxT1p1F2bx4sUMQJ7XtWvXJI6Hhoay3377jVWvXp0ZGhoyxhi7ceMG69q1K9PU1GRqamqsbdu27PTp0wXWERYWxgYMGMC0tLRYtWrVmLOzM/vw4cNPY4yMjGRDhw5lBgYGTFlZmZmYmLARI0awjIwMifOKEo/o3yEmJqbE1+rBgwds0KBBrFq1akxHR4e5urqy7Oxs9vTpU+bg4MA0NTWZqakpW716dZ4yShOjUChkRkZGzMPDo1jXp7RxCwQCNnToUNavXz+WnZ1d7OtGSu/atWv5/n/q4uLCGGMsKyuLLVq0iNWtW5cpKSkxQ0NDNmDAAPbw4UNxGaampvmWIbJ69WpmZmbGVFVVmY6ODmvfvj07c+ZMuXw+6vNDSC4CgQA5OTkSL4FAkOe8atWqYdCgQdizZ49438GDB6GgoAAnJ6di1Sn660pfXx8CgQDXrl2Dra0tateune/5JiYmaNmyJa5evZpvbD/S0tLCwoULceHChTyP/HIri7rzM27cOEydOhUAEBAQgH///Rf//vsvbGxsJM4bOHAgGjRogKNHj2LHjh0ICgpC165d8e3bN+zevRsHDx6ElpYW+vbti8OHD+db14ABA9CgQQMcO3YM7u7uOHHiBBwcHJCdnV1gfA8ePECrVq1w+/ZtLF26FOfOnYOHhwcyMzORlZUlPq8k8ZTUkCFDYG1tDX9/f4wfPx5///03XF1d4ejoiN69e+P48ePo2rUr5s6di4CAAKnFGBwcjPj4ePz222/Fvj6lifvPP/9EfHw8Dh8+LNH6SMpP586dwRjL89q7dy8AQElJCUuWLEFMTAyysrIQHx+PgIAAWFlZict49epVvmWIzJkzB9HR0UhPT8fnz59x48YNcatRmSuXFIsQOVCUlp/8Xnw+P895ISEh4r+MHj9+zBhjrFWrVmzUqFGMMVZoy8/t27dZdnY2S05OZqdPn2b6+vpMS0uLJSQksISEBAaADR06tNDP4uTkxACw9+/fF3hO7lgzMzNZ/fr1ma2tLRMKhYyxvC0/0qz7Z9auXVtga4io1WDRokUS+9u0acMMDAxYcnKyeF9OTg5r2rQpq127tvhz5S7D1dVVogw/Pz8GgO3fv7/A2Lp27cqqV6/+0xaiosYjjZaf9evXS+xv3rw5A8ACAgLE+7Kzs5m+vj4bOHCg1GKcMWMGs7KykthXlOtTmrhfvXrFADBVVVWmoaEhfl2/fr2wS0VIsVDLDyG57Nu3DyEhIRKvO3fu5Htup06dYGZmhj179uDRo0cICQnBmDFjflpHmzZtoKSkBC0tLfTp0weGhoY4d+4catasWeQ42X9/PfF4vCKdr6ysjOXLl+PevXs4cuRIkev5Wd1lOSFd7taG1NRU3LlzB4MGDZLo+Mrn8zFixAi8ffsWz549y1OGs7OzxPaQIUOgqKhY4ERqaWlpCAoKwpAhQ6Cvr19gbCWNp6T69OkjsW1hYQEejycxA6+ioiIaNGggHtknjRgDAgIk/h2Ken1KE7epqSkYY0hPT0dKSor4Je2OtaRqo/ZEQnKxsLAoUodngLv5jx49Gps3b0ZGRgbMzc2L9AW9b98+WFhYQFFRETVr1pQY2VCjRg2oq6sjJiam0DJevXoFdXV16OrqFilWgOuwvG7dOixYsCDfkTslqTsjI0M8IV3t2rXxzz//oF+/foiNjS11B+Xc1+XLly9gjOU7CsTY2BgA8OnTpzzHDA0NJbYVFRWhp6eX77miegQCQYGP/UobT0n9+O+srKwMdXV1qKqq5tkv6mxf2hjv3r2L2NhYieSnqNenNHETUh6o5YeQUhg1ahQSExOxY8cOjB49ukjvESVYzZs3z3Nj4vP56NKlC+7du4e3b9/m+/63b98iNDQUXbt2BZ/PL3KsPB4Pq1evxosXL+Dl5ZXneEnqFk1IV6dOHSgoKMDFxQVCoVAq83TkbtXS0dGBgoIC4uPj85z37t07AFzy9qOEhASJ7ZycHHz69KnAidh0dXXB5/ML/Pyljac8lTZGf39/mJubS8wFVdTrQ4i8o+SHkFKoVasW/vrrL/Tt2xcuLi5SKdPNzQ2MMUyaNClPp2KBQICJEyeCMQY3N7dil92tWzd0794dS5cuRUpKitTrLuqEdCoqKgCA9PT0IsWtoaGB1q1bIyAgQOI9QqEQ+/fvR+3atfPMNgsAfn5+EttHjhxBTk5OnvmZRNTU1NCpUyccPXoUiYmJUo+nPJU2Rn9/f4lWH6Do14cQeUePvQjJ5fHjx8jJycmz38zMrMA+DqtWrZJqDHZ2dti4cSNmzJiB9u3bY8qUKahTp454osE7d+5g48aNaNeuXYnKX716NVq2bIkPHz6gSZMmUqu7OBPSiUaEbNq0CS4uLlBSUkKjRo0KnRzSw8MD3bt3R5cuXTB79mwoKytj+/btePz4MQ4ePJhv/6eAgAAoKiqie/fuePLkCf73v//B2toaQ4YMKbCeDRs2oH379mjdujXmzZuHBg0a4P379zh58iR27twpjrEk8YjweDx06tQp31nCpamkMYaHh+PFixd5kh+g6NeHEHlGyQ8huRT06Mrb2xvjxo0rtzimTp2KVq1aYf369Zg1axY+ffoEXV1dtG/fHjdv3kTbtm1LXHaLFi3w+++/48CBA1Kru7gT0nXu3Blubm7w9fWFt7c3hEIhrl27VmCLDMB1ML969SoWL16MUaNGQSgUwtraGidPnszTsVYkICAA7u7u8PT0BI/HQ9++fbFx40YoKysXWI+1tTXu3r2LxYsXw83NDcnJyTA0NETXrl0l3leSeACIW9zKYxbbksbo7+8PU1NTtGzZMs+xol4fQuQZj7Fcg+4JIaSYhEIhnJ2dkZaWBn9/f7mYl8Xd3R1LlizBx48fZd735kdnz55Fnz598ODBA4k5UeSJpaUlevbsifXr18s6FELKhOy/pQghFZpoQrrz58/LReIj765du4ahQ4fKbeIDcMsSEFKZ0TcVIaTEXr9+jV27dkFVVVWiheXcuXM0L0sB1q5dK+sQCKny6LEXIYQQQqoUGupOCCGEkCqFkh9CCCGEVCmU/BBCCCGkSqHkhxBCCCFVCiU/hBBCCKlSKPkhhBBCSJVCyQ8hhBBCqhRKfgghhBBSpVDyQwghhJAqhZIfQgghhFQplPwQQgghpEr5f0K+sD6VIxTlAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting scatter plot for all retrievals\n", + "# (negative values have not been discarded in processing)\n", + "TEMPO_Pandora_scatter = np.empty([0, 4])\n", + "for td in time_series_TEMPO:\n", + " for pd in timeseries_Pandora_TEMPO:\n", + " if td[0] == pd[0]:\n", + " TEMPO_Pandora_scatter = np.append(TEMPO_Pandora_scatter,[[td[1], pd[1], td[2], pd[2]]], axis = 0)\n", + " break\n", + "\n", + "if len(TEMPO_Pandora_scatter) == 0:\n", + " print('TEMPO and Pandora time series has less than 2 simultaneous measurements.\\n'\\\n", + "+'Potential causes for this problem is scarcity of TEMPO pixels with QF == 0\\n'\\\n", + "+'and Pandora measurements.\\n'\\\n", + "+'The TEMPO science team recommends using only data with QF == 0.\\n'\\\n", + "+'Users may overcome the restriction on the quality flag set in section 5.3,\\n'\\\n", + "+'find \"trop_NO2_column_QF_loc == 0\" there. By doing so users assume risks of using low quality data.\\n'\\\n", + "+'There is nothing to plot here, this block is terminated.')\n", + " sys.exit()\n", + "\n", + "regress = stats.linregress(TEMPO_Pandora_scatter[:, 0], TEMPO_Pandora_scatter[:, 1])\n", + "slope = regress.slope\n", + "intercept = regress.intercept\n", + "r2 = regress.rvalue**2\n", + "stderr = regress.stderr\n", + "intercept_stderr = regress.intercept_stderr\n", + "\n", + "success, slope_0intercept, r2_0intercept =\\\n", + " regress_0intercept(TEMPO_Pandora_scatter[:, 0]\\\n", + " , TEMPO_Pandora_scatter[:, 1])\n", + "\n", + "plot_title = 'NO$_{2}$ tropospheric column w unc, all '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = 'scatter_'+out_Q+'_unc_'+datestamp_ini+'_'\\\n", + "+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(TEMPO_Pandora_scatter[:, 0], TEMPO_Pandora_scatter[:, 1],\\\n", + "xerr=TEMPO_Pandora_scatter[:, 2], yerr=TEMPO_Pandora_scatter[:, 3],\\\n", + "c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.xlabel(r'TEMPO NO$_{2}$ trop col, mol/cm$^{2}$', fontsize=12)\n", + "plt.ylabel(r'Pandora NO$_{2}$ trop col, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "fig.text(0.15, 0.72,\\\n", + "f'# of points: {len(TEMPO_Pandora_scatter):3d}\\nslope: {slope: 6.3f} $\\pm$ {stderr:6.3f}\\nintercept: {intercept: 8.2e} $\\pm$ {intercept_stderr: 8.2e}\\nR$^{2}$ = {r2:6.3f}')\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = min(0., min(TEMPO_Pandora_scatter[:, [0,1]].flatten()))*1.05\n", + "u_lim = max(TEMPO_Pandora_scatter[:, [0,1]].flatten())*1.05\n", + "plt.xlim(l_lim, u_lim)\n", + "plt.ylim(l_lim, u_lim)\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim, u_lim],\\\n", + "c = 'g', ls = '--')\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim*slope+intercept, u_lim*slope+intercept],\\\n", + "c = 'r', ls = '--')\n", + "\n", + "if success:\n", + " plt.plot([l_lim, u_lim], [l_lim*slope_0intercept, u_lim*slope_0intercept], c = 'r', ls = '-.')\n", + " fig.text(0.6, 0.12, f'\"no-intercept\" regression:\\nslope: {slope_0intercept: 6.3f} R$^{2}$ = {r2_0intercept:6.3f}')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)\n", + "\n", + "# Plotting scatter plot for positive retrievals\n", + "TEMPO_Pandora_scatter_noneg = np.empty([0, 4])\n", + "for td in time_series_TEMPO_noneg:\n", + " for pd in timeseries_Pandora_TEMPO_noneg:\n", + " if td[0] == pd[0]:\n", + " TEMPO_Pandora_scatter_noneg = np.append(TEMPO_Pandora_scatter_noneg,[[td[1], pd[1], td[2], pd[2]]], axis = 0)\n", + " break\n", + "\n", + "regress = stats.linregress(TEMPO_Pandora_scatter_noneg[:, 0], TEMPO_Pandora_scatter_noneg[:, 1])\n", + "slope = regress.slope\n", + "intercept = regress.intercept\n", + "r2 = regress.rvalue**2\n", + "stderr = regress.stderr\n", + "intercept_stderr = regress.intercept_stderr\n", + "\n", + "success, slope_0intercept, r2_0intercept =\\\n", + " regress_0intercept(TEMPO_Pandora_scatter_noneg[:, 0]\\\n", + " , TEMPO_Pandora_scatter_noneg[:, 1])\n", + "\n", + "plot_title = 'NO$_{2}$ tropospheric column w unc, positive '+datestamp_ini+' '+datestamp_fin+'\\n'+POI_name\n", + "img_name = 'scatter_'+out_Q+'_unc_noneg_'+datestamp_ini+'_'\\\n", + "+datestamp_fin+'_'+POI_name+'.jpg'\n", + "\n", + "fig = plt.figure()\n", + "\n", + "plt.errorbar(TEMPO_Pandora_scatter_noneg[:, 0], TEMPO_Pandora_scatter_noneg[:, 1],\\\n", + "xerr=TEMPO_Pandora_scatter_noneg[:, 2], yerr=TEMPO_Pandora_scatter_noneg[:, 3],\\\n", + "c = 'b', ls = '', marker = \".\")\n", + "\n", + "plt.xlabel(r'TEMPO NO$_{2}$ trop col, mol/cm$^{2}$', fontsize=12)\n", + "plt.ylabel(r'Pandora NO$_{2}$ trop col, mol/cm$^{2}$', fontsize=12)\n", + "\n", + "fig.text(0.15, 0.72,\\\n", + "f'# of points: {len(TEMPO_Pandora_scatter_noneg):3d}\\nslope: {slope: 6.3f} $\\pm$ {stderr:6.3f}\\nintercept: {intercept: 8.2e} $\\pm$ {intercept_stderr: 8.2e}\\nR$^{2}$ = {r2:6.3f}')\n", + "\n", + "# Set the range of x-axis\n", + "l_lim = 0.\n", + "u_lim = max(TEMPO_Pandora_scatter_noneg[:, [0,1]].flatten())*1.05\n", + "plt.xlim(l_lim, u_lim)\n", + "plt.ylim(l_lim, u_lim)\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim, u_lim],\\\n", + "c = 'g', ls = '--')\n", + "\n", + "plt.plot([l_lim, u_lim], [l_lim*slope+intercept, u_lim*slope+intercept],\\\n", + "c = 'r', ls = '--')\n", + "\n", + "if success:\n", + " plt.plot([l_lim, u_lim], [l_lim*slope_0intercept, u_lim*slope_0intercept], c = 'r', ls = '-.')\n", + " fig.text(0.6, 0.12, f'\"no-intercept\" regression:\\nslope: {slope_0intercept: 6.3f} R$^{2}$ = {r2_0intercept:6.3f}')\n", + "\n", + "plt.title(plot_title+str(', %08.4fN %08.4fW' %(POI[0], -POI[1])))\n", + "plt.savefig(img_name, format='jpg', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cKn-MLMuen1q" + }, + "source": [ + "# EXTRA. Archiving output files to make downloading easier" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "v-VbL8CNXsLL" + }, + "outputs": [], + "source": [ + "import zipfile\n", + "import zlib\n", + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "id": "4e1JLotAX9OC" + }, + "outputs": [], + "source": [ + "list_jpg = glob.glob('*'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'.jpg')\n", + "\n", + "with zipfile.ZipFile('fig_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name+'.zip', 'w') as fig_zip:\n", + " for name in list_jpg: fig_zip.write(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "id": "JVkprHdiYpuS" + }, + "outputs": [], + "source": [ + "list_data = glob.glob('*'+datestamp_ini+'_'+datestamp_fin+'_'+POI_name+'*.txt')\n", + "\n", + "with zipfile.ZipFile('data_'+datestamp_ini+'_'+datestamp_fin+'_'\\\n", + "+POI_name+'.zip', 'w') as data_zip:\n", + " for name in 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