diff --git a/SST_trend.ipynb b/SST_trend.ipynb deleted file mode 100644 index 4d7e128..0000000 --- a/SST_trend.ipynb +++ /dev/null @@ -1,539 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SST long-term trend\n", - "\n", - "Compute the long-term trend in sea surface temperature from 1990 to 2009 using the dataset \"Smith and Reynolds NCDC Level 4 Historical Reconstructed SST\" from PODAAC / NASA (or any other-20 year period).\n", - "\n", - "https://podaac.jpl.nasa.gov/dataset/REYNOLDS_NCDC_L4_MONTHLY_V5\n", - "\n", - "Implement a function which performs a linear regression:\n", - "https://en.wikipedia.org/wiki/Simple_linear_regression\n", - "\n", - "\n", - "Useful Julia functions for the exercise:\n", - "* @sprintf\n", - "* Dataset from NCDatasets\n", - "* sum, mean\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PyObject" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "using Statistics\n", - "using NCDatasets\n", - "using PyPlot\n", - "using Missings\n", - "using PyCall\n", - "using PyCall: PyObject, pyimport\n", - "\n", - "# allow for plotting with missing values\n", - "function PyObject(a::Array{Union{T,Missing},N}) where {T,N}\n", - " numpy_ma = pyimport(\"numpy\").ma\n", - " pycall(numpy_ma.array, Any, coalesce.(a,zero(T)), mask=ismissing.(a))\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2020/01/ersst.v5.202001.nc\"" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2020/01/ersst.v5.202001.nc\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[31mNCDataset: https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2020/01/ersst.v5.202001.nc\u001b[39m\n", - "Group: /\n", - "\n", - "\u001b[31mDimensions\u001b[39m\n", - " lat = 89\n", - " lev = 1\n", - " lon = 180\n", - " time = 1\n", - "\n", - "\u001b[31mVariables\u001b[39m\n", - "\u001b[32m lat\u001b[39m (89)\n", - " Datatype: Float64\n", - " Dimensions: lat\n", - " Attributes:\n", - " units = \u001b[36mdegrees_north\u001b[39m\n", - " long_name = \u001b[36mLatitude\u001b[39m\n", - " standard_name = \u001b[36mLatitude\u001b[39m\n", - " axis = \u001b[36mY\u001b[39m\n", - " valid_min = \u001b[36m-88.0\u001b[39m\n", - " valid_max = \u001b[36m88.0\u001b[39m\n", - " _CoordinateAxisType = \u001b[36mLat\u001b[39m\n", - " coordinate_defines = \u001b[36mcenter\u001b[39m\n", - " comment = \u001b[36mUniform grid from -88 to 88 by 2\u001b[39m\n", - "\n", - "\u001b[32m lev\u001b[39m (1)\n", - " Datatype: Float64\n", - " Dimensions: lev\n", - " Attributes:\n", - " units = \u001b[36mmeters\u001b[39m\n", - " long_name = \u001b[36mDepth of sea surface temperature measurements\u001b[39m\n", - " standard_name = \u001b[36mdepth\u001b[39m\n", - " axis = \u001b[36mZ\u001b[39m\n", - " positive = \u001b[36mdown\u001b[39m\n", - " _CoordinateAxisType = \u001b[36mHeight\u001b[39m\n", - " comment = \u001b[36mActual measurement depth of in situ sea surface temperature varies from 0.2 to 10 m, but corrected to the nominal depth of buoy at 0.2 m\u001b[39m\n", - "\n", - "\u001b[32m lon\u001b[39m (180)\n", - " Datatype: Float64\n", - " Dimensions: lon\n", - " Attributes:\n", - " units = \u001b[36mdegrees_east\u001b[39m\n", - " long_name = \u001b[36mLongitude\u001b[39m\n", - " standard_name = \u001b[36mLongitude\u001b[39m\n", - " axis = \u001b[36mX\u001b[39m\n", - " valid_min = \u001b[36m0.0\u001b[39m\n", - " valid_max = \u001b[36m358.0\u001b[39m\n", - " _CoordinateAxisType = \u001b[36mLon\u001b[39m\n", - " coordinate_defines = \u001b[36mcenter\u001b[39m\n", - " comment = \u001b[36mUniform grid from 0 to 358 by 2\u001b[39m\n", - "\n", - "\u001b[32m time\u001b[39m (1)\n", - " Datatype: Float64\n", - " Dimensions: time\n", - " Attributes:\n", - " _CoordinateAxisType = \u001b[36mTime\u001b[39m\n", - " avg_period = \u001b[36m0000-01-00\u001b[39m\n", - " axis = \u001b[36mT\u001b[39m\n", - " calendar = \u001b[36mgregorian\u001b[39m\n", - " delta_t = \u001b[36m0000-01-00\u001b[39m\n", - " long_name = \u001b[36mTime\u001b[39m\n", - " standard_name = \u001b[36mtime\u001b[39m\n", - " units = \u001b[36mdays since 1854-01-15 00:00\u001b[39m\n", - "\n", - "\u001b[32m sst\u001b[39m (180 × 89 × 1 × 1)\n", - " Datatype: Float32\n", - " Dimensions: lon × lat × lev × time\n", - " Attributes:\n", - " long_name = \u001b[36mExtended reconstructed sea surface temperature\u001b[39m\n", - " standard_name = \u001b[36msea_surface_temperature\u001b[39m\n", - " units = \u001b[36mdegree_C\u001b[39m\n", - " add_offset = \u001b[36m0.0\u001b[39m\n", - " scale_factor = \u001b[36m1.0\u001b[39m\n", - " valid_min = \u001b[36m-3.0\u001b[39m\n", - " valid_max = \u001b[36m45.0\u001b[39m\n", - " coordinates = \u001b[36mtime lev lat lon\u001b[39m\n", - " _FillValue = \u001b[36m-999.0\u001b[39m\n", - "\n", - "\u001b[32m ssta\u001b[39m (180 × 89 × 1 × 1)\n", - " Datatype: Float32\n", - " Dimensions: lon × lat × lev × time\n", - " Attributes:\n", - " long_name = \u001b[36mExtended reconstructed SST anomalies\u001b[39m\n", - " units = \u001b[36mdegree_C\u001b[39m\n", - " add_offset = \u001b[36m0.0\u001b[39m\n", - " scale_factor = \u001b[36m1.0\u001b[39m\n", - " valid_min = \u001b[36m-12.0\u001b[39m\n", - " valid_max = \u001b[36m12.0\u001b[39m\n", - " coordinates = \u001b[36mtime lev lat lon\u001b[39m\n", - " _FillValue = \u001b[36m-999.0\u001b[39m\n", - "\n", - "\u001b[31mGlobal attributes\u001b[39m\n", - " Conventions = \u001b[36mCF-1.6, ACDD-1.3\u001b[39m\n", - " metadata_link = \u001b[36mhttps://doi.org/10.7289/V5T72FNM\u001b[39m\n", - " dataset_doi = \u001b[36mhttps://doi.org/10.7289/V5T72FNM\u001b[39m\n", - " id = \u001b[36mgov.noaa.ncdc:C00927\u001b[39m\n", - " naming_authority = \u001b[36mgov.noaa.ncei\u001b[39m\n", - " title = \u001b[36mNOAA ERSSTv5 (in situ only)\u001b[39m\n", - " summary = \u001b[36mERSSTv5 is developped based on v4 and by replacing NCEP GTS by NCEI ICOADS R3.0.2 after January 2016\u001b[39m\n", - " dataset_citation_product = \u001b[36mNOAA ERSSTv5\u001b[39m\n", - " dataset_citation_version = \u001b[36mERSSTv5\u001b[39m\n", - " dataset_citation_institution = \u001b[36mNOAA/NESDIS/NCEI/CCOG\u001b[39m\n", - " dataset_citation_url = \u001b[36mhttps://doi.org/10.7289/V5T72FNM\u001b[39m\n", - " institution = \u001b[36mNOAA/NESDIS/NCEI/CCOG\u001b[39m\n", - " creator_name = \u001b[36mBoyin Huang\u001b[39m\n", - " creator_email = \u001b[36mboyin.huang@noaa.gov\u001b[39m\n", - " creator_type = \u001b[36mgroup\u001b[39m\n", - " creator_institution = \u001b[36mNOAA/NESDIS/NCEI\u001b[39m\n", - " contributor_name = \u001b[36mHuai-min Zhang\u001b[39m\n", - " contributor_role = \u001b[36mChief, NOAA/NESDIS/NCEI/CCOG/OSB Ocean Surface Section\u001b[39m\n", - " publisher_name = \u001b[36mNCEI\u001b[39m\n", - " publisher_url = \u001b[36mhttps://www.ncdc.noaa.gov\u001b[39m\n", - " publisher_email = \u001b[36mncei.info@noaa.gov\u001b[39m\n", - " publisher_type = \u001b[36minstitution\u001b[39m\n", - " publisher_institution = \u001b[36mNCEI\u001b[39m\n", - " date_created = \u001b[36m2020/05/15\u001b[39m\n", - " date_issued = \u001b[36m2020/05/15\u001b[39m\n", - " date_modified = \u001b[36m2020/05/15\u001b[39m\n", - " necdf_version_id = \u001b[36mV4 from GrADS sdfwrite\u001b[39m\n", - " netcdf_creator_name = \u001b[36mBoyin Huang\u001b[39m\n", - " netcdf_creator_email = \u001b[36mboyin.huang@noaa.gov\u001b[39m\n", - " product_version = \u001b[36mVersion v5\u001b[39m\n", - " creator_url = \u001b[36mhttps://www.ncei.noaa.gov\u001b[39m\n", - " license = \u001b[36mNo constraints on data access or use\u001b[39m\n", - " time_coverage_start = \u001b[36m2020-01-15T00:00:00Z\u001b[39m\n", - " time_coverage_end = \u001b[36m2020-02-15T00:00:00Z\u001b[39m\n", - " time_coverage_resolution = \u001b[36mP1M\u001b[39m\n", - " time_coverage_duration = \u001b[36mP1M\u001b[39m\n", - " geospatial_lon_min = \u001b[36m0.0\u001b[39m\n", - " geospatial_lon_max = \u001b[36m358.0\u001b[39m\n", - " geospatial_lat_min = \u001b[36m-88.0\u001b[39m\n", - " geospatial_lat_max = \u001b[36m88.0\u001b[39m\n", - " geospatial_lat_units = \u001b[36mdegrees_north\u001b[39m\n", - " geospatial_lat_resolution = \u001b[36m2.0\u001b[39m\n", - " geospatial_lon_units = \u001b[36mdegrees_east\u001b[39m\n", - " geospatial_lon_resolution = \u001b[36m2.0\u001b[39m\n", - " spatial_resolution = \u001b[36m2.0 degree grid\u001b[39m\n", - " geospatial_bounds = \u001b[36m0 -88, 358 88\u001b[39m\n", - " geospatial_vertical_min = \u001b[36m0.0\u001b[39m\n", - " geospatial_vertical_max = \u001b[36m0.0\u001b[39m\n", - " geospatial_vertical_units = \u001b[36mmeters\u001b[39m\n", - " geospatial_vertical_positive = \u001b[36mdown\u001b[39m\n", - " cdm_data_type = \u001b[36mGrid\u001b[39m\n", - " processing_level = \u001b[36mNOAA Level 4\u001b[39m\n", - " grid_mapping_name = \u001b[36mlatitude_longitude\u001b[39m\n", - " standard_name_vocabulary = \u001b[36mCF Standard Name Table (v40, 25 January 2017)\u001b[39m\n", - " keywords = \u001b[36mEarth Science > Oceans > Ocean Temperature > Sea Surface Temperature\u001b[39m\n", - " keywords_vocabulary = \u001b[36mNASA Global Change Master Directory (GCMD) Science Keywords\u001b[39m\n", - " project = \u001b[36mNOAA Extended Reconstructed Sea Surface Temperature (ERSST)\u001b[39m\n", - " platform = \u001b[36mShip and Buoy SSTs from ICOADS R3.0.2 and Argo SSTs from CCOG\u001b[39m\n", - " platform_vocabulary = \u001b[36mNCEI\u001b[39m\n", - " instrument = \u001b[36mConventional thermometers\u001b[39m\n", - " instrument_vocabulary = \u001b[36mNCEI\u001b[39m\n", - " source = \u001b[36mIn situ data: ICOADS R3.0 before 2016, ICOADS R3.0.2 from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015.\u001b[39m\n", - " comment = \u001b[36mSSTs were observed by conventional thermometers in Buckets (in sulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters\u001b[39m\n", - " references = \u001b[36mHuang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1\u001b[39m\n", - " climatology = \u001b[36mClimatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.\u001b[39m\n", - " acknowledgment = \u001b[36mThe NOAA Extended Reconstructed Sea Surface Temperature (ERSST) data are provided by the NOAA National Centers for Environmental Information(NCEI)\u001b[39m\n", - " history = \u001b[36mSat Jul 3 13:12:34 2021: ncap2 -O -s time=time/(1440)+(60630) ssta.nc ssta.nc\u001b[39m\n", - "\u001b[36mSat Jul 3 13:12:34 2021: ncatted -O -a _FillValue,ssta,o,f,-999.0 ssta.nc\u001b[39m\n", - "\u001b[36mSat Jul 3 13:12:34 2021: ncatted -O -a units,time,o,c,days since 1854-01-15 00:00 ssta.nc\u001b[39m\n", - "\u001b[36mVersion v5 based on Version v4\u001b[39m\n", - " NCO = \u001b[36mnetCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)\u001b[39m\n", - " nco_openmp_thread_number = \u001b[36m1\u001b[39m\n" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = Dataset(url)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "SST = nomissing(ds[\"sst\"][:,:,1,1],NaN);" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(180, 89)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "size(SST)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "lon = nomissing(ds[\"lon\"][:])\n", - "lat = nomissing(ds[\"lat\"][:]);\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "PyObject " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#pcolor(lon,lat,SST')\n", - "pcolor(lon,lat,copy(SST'))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "closed NetCDF NCDataset" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "close(ds)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/01/ersst.v5.201601.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/02/ersst.v5.201602.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/03/ersst.v5.201603.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/04/ersst.v5.201604.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/05/ersst.v5.201605.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/06/ersst.v5.201606.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/07/ersst.v5.201607.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/08/ersst.v5.201608.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/09/ersst.v5.201609.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/10/ersst.v5.201610.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/11/ersst.v5.201611.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2016/12/ersst.v5.201612.nc\"\n" - ] - } - ], - "source": [ - "using Printf\n", - "\n", - "SST = zeros(180, 89, 12)\n", - "n = 1;\n", - "\n", - "for year = 2016:2016\n", - "#for year = 1900:2000\n", - " for month = 1:12\n", - " global n\n", - " month_str = @sprintf(\"%02d\",month)\n", - " # example \n", - " # https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2020/01/ersst.v5.202001.nc \n", - " url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/$(year)/$(month_str)/ersst.v5.$(year)$(month_str).nc\"\n", - " @show url \n", - " ds = Dataset(url)\n", - " SST[:,:,n] = nomissing(ds[\"sst\"][:,:,1,1],NaN);\n", - " n = n+1\n", - " close(ds)\n", - " end\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(180, 89, 12)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "size(SST)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "56.0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lon[29]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "29" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "findfirst(lon .== 56)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-50.0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lat[20]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1-element Vector{PyObject}:\n", - " PyObject " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot(SST[29,20,:])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "@webio": { - "lastCommId": null, - "lastKernelId": null - }, - "kernelspec": { - "display_name": "Julia 1.7.2", - "language": "julia", - "name": "julia-1.7" - }, - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Solution/SST_trend.ipynb b/Solution/SST_trend.ipynb deleted file mode 100644 index f643c2b..0000000 --- a/Solution/SST_trend.ipynb +++ /dev/null @@ -1,521 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SST long-term trend\n", - "\n", - "Compute the long-term trend in sea surface temperature from 1990 to 2009 using the dataset \"Smith and Reynolds NCDC Level 4 Historical Reconstructed SST\" from PODAAC / NASA (or any other-20 year period).\n", - "\n", - "https://podaac.jpl.nasa.gov/dataset/REYNOLDS_NCDC_L4_MONTHLY_V5\n", - "\n", - "Implement a function which performs a linear regression:\n", - "https://en.wikipedia.org/wiki/Simple_linear_regression\n", - "\n", - "\n", - "Useful Julia functions for the exercise:\n", - "* @sprintf\n", - "* Dataset from NCDatasets\n", - "* sum, mean\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PyObject" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "using Statistics\n", - "using NCDatasets\n", - "using PyPlot\n", - "using Missings\n", - "using PyCall\n", - "using PyCall: PyObject, pyimport\n", - "\n", - "function PyObject(a::Array{Union{T,Missing},N}) where {T,N}\n", - " numpy_ma = pyimport(\"numpy\").ma\n", - " pycall(numpy_ma.array, Any, coalesce.(a,zero(T)), mask=ismissing.(a))\n", - "end" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Go to this site:\n", - "\n", - "https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2020/01/ersst.v5.202001.nc.html\n", - "\n", - "We need the data URL which different for every month. For January 2020, the data URL is \n", - "https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2020/01/ersst.v5.202001.nc\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/01/ersst.v5.199901.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/02/ersst.v5.199902.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/03/ersst.v5.199903.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/04/ersst.v5.199904.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/05/ersst.v5.199905.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/06/ersst.v5.199906.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/07/ersst.v5.199907.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/08/ersst.v5.199908.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/09/ersst.v5.199909.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/10/ersst.v5.199910.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/11/ersst.v5.199911.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/12/ersst.v5.199912.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/01/ersst.v5.200001.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/02/ersst.v5.200002.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/03/ersst.v5.200003.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/04/ersst.v5.200004.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/05/ersst.v5.200005.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/06/ersst.v5.200006.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/07/ersst.v5.200007.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/08/ersst.v5.200008.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/09/ersst.v5.200009.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/10/ersst.v5.200010.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/11/ersst.v5.200011.nc\"\n", - "url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2000/12/ersst.v5.200012.nc\"\n" - ] - } - ], - "source": [ - "using Printf\n", - "using Dates\n", - "URLs = []\n", - "ssttime = []\n", - "\n", - "year_range = 1980:2000\n", - "year_range = 1999:2000 # for testing\n", - "\n", - "for year = year_range\n", - " for month = 1:12\n", - " global n\n", - " month_str = @sprintf(\"%02d\",month)\n", - " # example \n", - " # https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/2020/01/ersst.v5.202001.nc \n", - " url = \"https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/$(year)/$(month_str)/ersst.v5.$(year)$(month_str).nc\"\n", - " @show url \n", - " push!(URLs,url) \n", - " push!(ssttime,DateTime(year,month,15))\n", - " end\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "24-element Vector{Any}:\n", - " 1999-01-15T00:00:00\n", - " 1999-02-15T00:00:00\n", - " 1999-03-15T00:00:00\n", - " 1999-04-15T00:00:00\n", - " 1999-05-15T00:00:00\n", - " 1999-06-15T00:00:00\n", - " 1999-07-15T00:00:00\n", - " 1999-08-15T00:00:00\n", - " 1999-09-15T00:00:00\n", - " 1999-10-15T00:00:00\n", - " 1999-11-15T00:00:00\n", - " 1999-12-15T00:00:00\n", - " 2000-01-15T00:00:00\n", - " 2000-02-15T00:00:00\n", - " 2000-03-15T00:00:00\n", - " 2000-04-15T00:00:00\n", - " 2000-05-15T00:00:00\n", - " 2000-06-15T00:00:00\n", - " 2000-07-15T00:00:00\n", - " 2000-08-15T00:00:00\n", - " 2000-09-15T00:00:00\n", - " 2000-10-15T00:00:00\n", - " 2000-11-15T00:00:00\n", - " 2000-12-15T00:00:00" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ssttime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot the first time instance:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "ds = Dataset(URLs[1])\n", - "temp = ds[\"sst\"][:,:,1,1];\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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adZBJR/OP60AjsoMBgiAIgogSLhy4YSOaAtqIIjQYIAiCIIg8IDfBIEbqCXjqT3fPf8l1Tx1cWwmTiAoiXtfGamsBAE7ce1WZPXjQWr+QEMCCC0f8fzJcrGgfgP9qtwzkSrdrY8ZFdwMw0omLmWTGtGn5etiYYlapMOGlIEq5BkrJhactgVubrLQZA4bSxBnIf/uOjg488sgj+MMf/oDa2lpMmzYNd911F0466SRZ56qrrsLatfrfqqlTp+KFF14oyK7IDgbUh476sHGywbq9tlVjJBDi822dVB8aI4gCUQcM8eHDe60fF3PGuS4gVucNTDRfL4/7Lv7cxkQcdz6v3P3vvbKqe5gEiFHHNhgLTYimDiBFDoJq7xnHqvgyzxeAKr+uk+HreDhcltD/KGXr/D8Hrsg3IHQACSOWAAC3ClqZjCFgaAcA9D6JXXnGu9V6mePq2rFA3IFemh7IbNq0Cddeey3OPvtsZDIZLFmyBLNnz8Ybb7yBYcP8pFEXXngh1qxZI5erqwvXxUV2MEAQBEEQUcJlDtxigw4VsP369eu15TVr1mDUqFHYunUrzjvvPFmeTCbR1NRUlF3RdF4QBEEQRMRwuZugmI+IM9DV1aV9enp6et3//v37AQAjR47Uyp999lmMGjUKEydOxN/93d9hz549BR/bgH4zcN7FdyNx0Hs9Gu/xX/lvfH5JpUwiBhCl8ANf+Imvl8CSynPBlDYAQOzAEQBAz5ijAABZEX42XrwCevpff98PS6ukt938fwqcIkwQg4DW1lZt+dZbb0VbW1tofcYYFi1ahOnTp2Py5MmyfM6cOfjyl7+MsWPHYseOHfjud7+Lz372s9i6dSuSyfz1INEdDDBFK6BqBvjveCp/T5FbK5xbvI10VlsGACTiWP/6HX0ylRh8uN3d3g/up1fzWUhhHPe9qjqA9V2+305Fbs9Fgo7w9SaUW1DkZRdlQlCY4Hni1bzt4o+zY3xzm5yjfJviWUPhJ5oRsQqq8g8xo/przeGBLxw0lm2VBxjCp19OIWGYqFOzQeQb4JoRR+gBHOG39/sM41oDWUf0IdtAT1xDQyugxRkw4wqY30q3Er8DolNbzh5HXyf6XCyj93G5rox5f0qTwtjbfteuXRgxwk/R1Nsf7gULFuDVV1/F888/r5Vfeuml8vfkyZMxZcoUjB07Fr/85S8xd+7cvO2K7mCAIAiCICJEFg6yRY5sxfYjRozQBgO5uO666/DEE0/gueeew+jRo3PWbW5uxtixY7F9+/aC7KLBAEEQBEFEEMYYrrvuOjz66KN49tlnMX78+F63+fjjj7Fr1y40NzcXtC8aDBAEQRBEHpTSTZAP1157LdatW4fHH38c9fX16OzsBAA0NDSgtrYWBw8eRFtbG/76r/8azc3NeO+993DLLbfgmGOOwZe+9KWC7BqUg4FZ0273fhhzsQkCKK/fd87ob5RtXwOVcy/7AYA+5BohiDKTBUrgJsifVatWAQBmzpypla9ZswZXXXUV4vE4XnvtNfzsZz/Dvn370NzcjPPPPx8PP/ww6uvrC7IrsoMBxwUcxxJkQgikMt53jIsBHZ6QQxNZiU0SIhoG31YG3ajGU1tuLbntxMBFBHSRSYFEf1Kj6cUMpVw+7Q6r09sRfVIVEJpCQVPYpYnBHK2MGYJCptrbi5lM2U9mRFLbXiYGM0WIKmLzfGYcSMEjb3eATW7OJ8FTfw82bQmOEs18jjkXvIpnHKr9/iWEoq7Zz2J637FhmxrvGAI/MxmRFq3SFBeG7ikoLpTPf2as5/t6dtUCNKwbnDPIGMutjqytrcVTTz1Vkn1FdjBAEARBEFGi3G6CckKDAYIgCILIA0pURBBE3sw5fuAm5Kkk51zxA/n7hZ+TfoCIHqwEKYxZRFVskR0M/OanCzDr2n8CAMQUD5NIVORwH6b4ZvEYNv77d8prJDHoYFxXIv3zIkiQJUmQ9LFW5bi5a5LW9mSSGCXpjKkDCNim6RZ4mdQ06G2oEf5MP32gvViwTP7zIjQ6OR5gZrAh2/6EPWZAmiLDvEeS3gIU5aM7KHRfggtqrgAAxI49BgDgiCRX8PsKq+GZOJNcOyASDanXy5ZsqDfMoFNqvzKvtxGYStUBMFcvyyaBV++5AZ+6+h4AgJtD20D0ncgOBgiCIAgiSpCbgCAIgiCGOOXOWlhOIjsY+MzCe1GD/JMsEEQUmHPijZU2YVBw9v+3HACwZc2iClsyMBDugcHK6TfcE/rHatrie8tqy2CloMHAuHHjsHPnzkD5/Pnzce+994IxhqVLl2L16tXYu3cvpk6dinvvvReTJk3qk3HSz6SMpLI1Dl54kB4QRN8x/ayab1c4LHkCGKeaJ7nSkgTF9TLhnxcxA6qU28rQCIhkMYFEQ+glVgCg+/xN375j+H0tcQaYEQeAhWgJ9H2a7SrrTB1AyLJWJkJ+xPV9a/7qaL5FLRhbPIB+R/SvIzwdrtIPnGqhT6kGALjV3onO1njfIhmR99v7Dru2ah2xHRPLIi+cLVGREW9AxrFQk9GJuAJZfdmt5nXN/ppC2RBpiIttI4oUZNWWLVuwe/du+dm4cSMA4Mtf9jr6smXLsHz5cqxcuRJbtmxBU1MTZs2ahQMHDpTecoIgCIIoI8JNUOwnihQ0GDj22GPR1NQkP//2b/+GE044ATNmzABjDCtWrMCSJUswd+5cTJ48GWvXrsXhw4exbt26/rKfIAiCIIgi6bNmIJVK4cEHH8SiRYvgOA7effdddHZ2Yvbs2bJOMpnEjBkzsHnzZlx99dXWdnp6etDT0yOXu7q6+moSQRCDjLP+7h4/tC6AF/9p6LkIAy4GZZprrLq6zNYMbVzE4Bb5mr/Y7fuLPg8GHnvsMezbtw9XXXUVAMhsSo2NjVq9xsZGq85A0NHRgaVLlwbKf7vi2rxzPRNEMTjqXP+skUZE6AKqqvwyoQ0QPvGwGAIAmKhr6gFsmoFcGgEYPn/Tlx8L0QNo+zLas+gLAtqAmGMvR7hGwPQLW+uE+JBN2wcLpYwtYMMRgwLelxnXvjjptF/niPc7VpPxvmu9umneb1PDlRwVtfp1suHrP/Rl8xtQrrPZjyz5BoRGwI8pY7Sn1mUAKyTzT5FkmYNska/5i92+v+jzEOX+++/HnDlz0NLSopU7xo3MGAuUqdx8883Yv3+//OzatauvJhEEQRAE0Qf69GZg586dePrpp/HII4/IsqYmL2tWZ2cnmpubZfmePXsCbwtUkskkkkmaQkgQBEFEG4ozYLBmzRqMGjUKn//852XZ+PHj0dTUhI0bN+LMM88E4OkKNm3ahLvuuqs01hJECZkz7oZKm0AMYC4847u91ln/yj+WwRKiXLASZC1kEZ07W/BgwHVdrFmzBvPmzUNCycXuOA4WLlyI9vZ2TJgwARMmTEB7ezvq6upw+eWXl9Rogigl8aNHyt+ZPf8FAHBqa7yCJPfFqpoBI76AzA8g4rmrGoQqPa5AYG6/6q+PBcu8bSzlcn6+qUEwtlG2k+3n0AGY+5SxAsxltU7A/2/LTaB/u2bse00zwDcXc9DdaP4nVS5kroP4pQAAR9WHiNwZ4u1qdRVC4foB54g3Md/J8HgDPLdGWtEMpIeDr/O+5dx/NYeAmWdC9l++rPXBcLPMdsV1l/t09WVH0Qg4WSDromxk4SBbZKKhYrfvLwoeDDz99NN4//338bWvfS2wbvHixeju7sb8+fNl0KENGzagvr6+JMYSBEEQBFF6Ch4MzJ49G0wNF6XgOA7a2trQ1tZWrF0EQRAEESlcVrzP37X/+aw4kc1N0FdOXnqPtvyHW/vmFz73ci+3unrdRY71qVd6cdNV18/v1uae/zz7XM936Bzh03o+3OO1ccxIrd761+/ok71E/lw4aQmA3t9cEkSxXHjakkDZ+tfoHh+ouCXQDBS7fX8x6AYDBJEvbFgdAMXfCiAh5msLvyxfxxLKpGnp5+ZxBYRmgMcXcKtUzYCoYx96aD5404+eKy+AtMWoI7DFAwjJJZ8rzoCZh147DllmfOeKSWDOTY8F6/ob8VUR/U/KFAde+MnveT94XguEvEEtltjw4fK3U8P7Lu/LAc1ARnGw8z7sDvf0MD2f8Op2H+NdhCPK/yXZWu+bxfkxcN2Gei0C19m4ULn+gQ5cUy05Ba9jagWMb88uIEuj+pJAgwGCIAiCyAMXDtwi3ykWu31/QYMBgiAIgsgDikBIEARBEMSgZUC8GZjY7osCQ/2eAN65KSgWnNDhbbv95uIDzJz9NU84aBtBffI6bz/bfkyBbKLKnFNv8X4Mwtj3xMDhwtOWaH1w/au3a+tnTecCQ645iPVkymYbkRsSEA4gXOWIijnnbiI86Uq2Wq/j5pE4LFvjGcaGe6Idt2m8XPfsk4v7bihRMKzOu2DZOluAlhF4etMtMrqcKQ4EFKGfEGQJkaDoD2rdsD7IBXWumswlLsqMJDF9GLtYgw6FiAxtby0DAWTMgEKwDMxDttG2DwgSg/s2A9wMmKGb6yvb1r96uz/4hBJsqohnUnzyRK8tRaCaHu715Uyd93xxq7nQj5sSS/tKPdE/jxzlbX+oiS8f663P1vn2s5iI+CTaK60YMiggVH6HCQe5UlALOuQCrIxjJRclCEcc0R4dzSEKQRAEQRBlY9C9GSAIgiCI/oCVYDYBi+ibgUE1GDjh+8sRDznRJyxfrr2SfHdh7iBBfeW0RZ524LXl+WsHzrje10S88kPSHBSDDO6UVt4lZrnvNc82zvvi3agrsV0EkYvzL/CTuQ2qh/Igg7IWVhjVr2oGuDDPazapO6P++K1FOGH58sL3yc+M2v6L93sDiE9dc49Wx1bX5NfP3AwA+It5y7mdXuVMbcGmEX0k2+Cd7NRRXqCWTB339at5X1Je/+lpGq5tK/QhAOBWe9tlq4R2wCsPBPexwdfJoDtK33aNZEMF/QORywdr7Nts12pvSEAhLZBQiJjXprNBLGSdOGQlkIzwCYvvfordU3JMIaAa8MdRBINPvnVnznYyw7j/n/eHbI0XJCg9zDtpqRF+WyneTbM8r5boT+LcxVR/Ot8sPYx/N/BKw0Rl5UTzIEOM++mt10DoCcQ6ppdrOLoGQTYnfrjBaEa+ZsCRld+9PvhPXFdXFxrabwmU9weDWUAYTasIgiAIgigbA+LNAEEQBEFUGnITVIDTf/JjxJLea91YEYKL43+4PHTr8T/+gb+gVNqx4Jt93h9Rev5qRjsAfcpetobnAaj2y3776LfKaxhB9JELzl4KADhyrHjGEQMBCkdcYdykxWFlFuXws7KEXV+Qiy3/O1xg+NJP+i7yO3K0Pr+cDYgrMHA43MxFGBbftst9/Jka4Yv1K720yrumQg8i/JXCB5tVQhLIWBZGYh5rQh3XWObbSB2MOhc/VzthGP7agP/WRi7NQFgsghwJhQIxA8yERXm0o80dN/3d5jkcIDz59l29V+IcbPE7WKZO9FNvOV3vfaeO8i5qdkRa1o3XeicpntBPksv97G5G7WDel8OTDyXi3jYxrhXIZpX4GEIrILQytkcwM9aZy7Z+ZbYjtAksWCauOwPw3t/fGDSAKCn0p4ggCIIg8oDcBARBEAQxxKHBwCBk3Oq74YR46sat9V7vvTfvpnKaRFSQE+/2pnyOiKg/j/A45Xt+TI43b6OYHEMFeib3P5EdDDBHiY9tez6H+EStUziFIzWuL7OkGoubr6rqXwelmOOrzi8ncpOp9U5WttY/acLvL+L5C/aPj/Nyv8z0xav5K95ov0EOBADgwDh934z7V7X2RB8T3dOIoa45yw1fvjk3XxMIhP3HYPPBGu2a8fzV+8PUIARtyGGCmb8gx1gpcL/miEkgm5dx5/2yWFpcW76OawcK0lJEnAPHewECDhzndaZDrf7BCU1ALOmdlCquC/hE3REAwLDqlKwb4xdaiNKybsz49k94lullLq+T4VoBR4kzIPNv5PgvVvj5fe1A/gNpua3UB1g6SwSvN70ZIAiCIIghzmAeDNCMFoIgCIIY4tCbgRyc8FA7YmL6Tdx3H6jjuj/M/V6ZrSJKybh/vhMJ5JGDmiCIijNu7V1gaf9/WMcF3O4jZds/Q/FxAiLo/QAQ4cHAa1dfhxEjRuRdf9z/+r73Q/V7mX5Z4TsW5Updh9cRc3Bj8f65ZG/+I4meCuXZ9Z5oaMrXfd++zO2Q1OseGsN9/LHc12/Hdd/EuH/2YsRnm3v6ZBdzRdAAsWzxdZrx2/Nq2Fy2Te63fzs2G8KwuXqNe8WsWxC5tD4yJr0ehx5QtAJcOxDrS/yFiLN7Ov/xCe8P2TFHH5DrRtR4ZYmYd1IS/OSIZfU1c4oHrMjwvig0BPJ55uh/OL0yrgfg90hMtOsGXxQX8ko7oCGw1tFzE4h7RotxIGIPGH2ZZSv/InswuwkiOxggCIIgiCgxmAcDlR9qEQRBEARRUejNADFoGHv/MsTz7NKTHm8DUNOv9hD9y4nL7pFTPwHgj98MDyEeFS7YtJD/aq2kGUQfGcxvBgbNYIDx+ABOPKgDCIuLrc6rjSX0idsiXreoo7lVB5PzcgDR0+BfhUyd950e4V2L9Eg+Gf1Yz9+qxmoXGhFxLasS3vzto0YeCuwjxq+tuGFtflA5h5v7MF1jXnQ+c7P95VwT94247WpIAnO7XO2GdVfjvACWeyYXgXnmIbapNshEEfzYeAx8ZJXzy99XxkT+DqEhUOoM9NhQ4yZ9CACoS3gxBYZXpQJ1zD8aQriWUXz7Qk/gik7O/f9Cx6Jqn0TfFlkQzPZt1y2fP1yifwr7RDu2bWXdXDEOZBwE/i3qxLN497JbAm12dXWhYWF5hNyDeTBAbgKCIAiCGOIMmjcDBEEQBNGfMObkfpuXZxtRhN4MEAQxKBi/8gcY97++Lz9R4sr//Dqu/M+vV9oMokhcOCX55EtHRwfOPvts1NfXY9SoUbjkkkvw1ltvaXUYY2hra0NLSwtqa2sxc+ZMvP766wUfW8FvBv70pz/hpptuwpNPPonu7m5MnDgR999/P8466yxp2NKlS7F69Wrs3bsXU6dOxb333otJkyYVbFwh7Pza4kDZ8f/SHlrf9D1N/L//6P2QsQi4dkDO19Udr29c0tZHS4m+IvK6A0B6OM/BfrSnFaht8LQCI3j89upEptf2xLWNKU51caP6moFw/6fQDpg+Uxth/w3k8quyPGyI5aFfCfNRyuOPBfUVYTCLbkGcB+njFXYr8eZdOXec1zW1AkpdFud1eHx8R3y7ys5FfSFX6Ke4IKWmpW4/AKCK+/rTPF9AxvWTX5h90CShxA5weRCGapbVthG6AlufDGtXLQ/oFaz91LHa698Xwfay4ttoLqPEEMjyTij6YjzhYvuXv2O1ebCzadMmXHvttTj77LORyWSwZMkSzJ49G2+88QaGDfMS3SxbtgzLly/HAw88gIkTJ+L222/HrFmz8NZbb6G+vr6XPfgUNBjYu3cvPv3pT+P888/Hk08+iVGjRuGPf/wjjjrqKFmnVIYRBEEQRJQot4Bw/fr12vKaNWswatQobN26Feeddx4YY1ixYgWWLFmCuXPnAgDWrl2LxsZGrFu3DldffXXe+ypoMHDXXXehtbUVa9askWXjxo2Tv0tpGEEQBEFEiVJqBrq6urTyZDKJZDJp20Syf7/3ZmnkyJEAgB07dqCzsxOzZ8/W2pkxYwY2b95c0N/cgjQDTzzxBKZMmYIvf/nLGDVqFM4880z89Kc/let7M8xGT08Purq6tA9BEARBDGZaW1vR0NAgPx0dHTnrM8awaNEiTJ8+HZMnTwYAdHZ2AgAaGxu1uo2NjXJdvhT0ZuDdd9/FqlWrsGjRItxyyy343e9+h2984xtIJpP46le/mtOwnTt3Wtvs6OjA0qVLCzJ6IDPrWT83wcaZ91TQEoIY3Ix74C75+72rbiqqrZMeuQ2A7gdPxD0/fXWV9x3n2ou4ouP43YXhuiVi4FFKN8GuXbu0/Du9vRVYsGABXn31VTz//POBdY4h9mGMBcp6o6DBgOu6mDJlCtrbvQ5+5pln4vXXX8eqVavw1a9+tU+G3XzzzVi0yI8c1tXVhdbW0kTnsgWoCOPt//HdkuyT6D9SRylCvwZPIDhs5GEAwKh6L9HLUclgBjMpphLiKqa/ELOJ8Ppyw+faJp/2AqLFHILEWEgkoUICYsUMkaxtnWuIF9XjSHPRmwzCJALJGAFltDpZQ2wovjOKKE6IAatE0BkuKMso58NMyKQEFnvvmm9pA4EoMCzhJcMantCDCx3OeiGAMmqmpgIQfSQrrw8/98wIiKVuY/R/19LfMm5IHVUUaARBkiJBJyheFNc/ZrQrBKkxTWxYQOCrMlNKN8GIESPyTsZ33XXX4YknnsBzzz2H0aNHy/KmpiYA3huC5uZmWb5nz57AP+W9UZCboLm5GaeeeqpWdsopp+D9998PGKaSy7BkMilPSiEnhyAIgiDKCeNvBor5FDKYYIxhwYIFeOSRR/Cb3/wG48eP19aPHz8eTU1N2LhxoyxLpVLYtGkTpk2bVtCxFTQY+PSnPx2Y4/j2229j7NixJTeMIAiCIIYy1157LR588EGsW7cO9fX16OzsRGdnJ7q7uwF4b+EXLlyI9vZ2PProo/j973+Pq666CnV1dbj88ssL2ldBboIbbrgB06ZNQ3t7O/7mb/4Gv/vd77B69WqsXr06YNiECRMwYcIEtLe398mwqHH9y5fJ31XKvOzvn/Fwn9u87IW/BwD8yzmr+24YQRAa49YGXQQn/p/bAQDv/E1h89WnPy3ilwzvky3iHm+o6qUiMSBgCOYX6Usb+bJq1SoAwMyZM7XyNWvW4KqrrgIALF68GN3d3Zg/f76M7bNhw4aCp/IXNBg4++yz8eijj+Lmm2/GbbfdhvHjx2PFihW44oorZJ1SGTZYqYl7vu7hVT0VtmTg4TT5eoCRIzytQPNwb/bJyKS3nIh5Yi41iEvK9bq56RsVfnfVZx4L8d1qdSzbqe1rZYbj0/T1FxIUJsweG7na6EugInE+Vb1FKuuVpcQ67g8WWoJM1r8GaUNXILQDQlfgKomlRGIa/5trKRI2zYBxnCKxWJXfXpwL/Iph5HCvf6nnrsZIMiTWifMyotrvryMS3u+GhPcfXZpnX0o4vduWl96Ev+QV2gFxvbJQffG6rsDUG2iBj+J6WS5dgUiWJHUK/J8l1e4M1xGkHb09W2iwRNzF6xe39XrMlcCFA6dIMUMhEQhZHiMPx3HQ1taGtra2IqzqQwTCL3zhC/jCF74Qur5UhhEEQRAEUR4oURFBEARB5MFgTlREg4EI8K1XLgUA1Ma8141x/tpNfaX8vclPAADafn9xYPu2yY/3t4kV5VO/En7euoraQQx8Pr3Rizfw77OiNfWQGBi4zIFTxnDE5YQGA3nywzP/Rf6+4/VwN0lv/OtnfgwA+PsX5wEARlQF58UTdlqP/W//9/B9AICjqjwfrPD1C39oOub7tmtZWmtH1BU+3irFbyvK4jKJkV4XAOJGmZxfXWQS0EBymBztCbtMX3FYW/b9BdvPGklnMty3nbZoBhKO9/hIuNxnzH3IKb6cVvQXCWbEIuDXJ5uwxCQQiYpcIxGSkt1GJDxifBcid0884V3LRMK/piIo0LBqfY5/PjTWHfTaiHne7YRyTLVxr18dVaXrCXq4RkUNPjQ87mmE6viAX5xnsaxqVWzXRd3Ghuz3xvVS+6S4ln5MAkfbxo1lgnV5mdQOqJoB0/9vxDYwYxUA/n/F5pUoVpRHFA8NBgiCIAgiDxgrwWyCiA58aDBAEARBEHlAmgEilLXbvWBK8ybYEzGVCt81EQ9dt2TSv/WrDeXm8899g/+iqJREaaEYHwShQ4OBPvCJxCEAwLBY32MFrJ6yttc6Nm1CVR7zkgcbrSP2AQCOq9sny0ZVe7kIhMhS+j0to25fB6ALM6u4PzSuCDXF+RU+XFMfoJaZCP9sPv56v264316Q27evr8sac8ht6/x2g1oHXyPAY8kzoZ3g3xZb5Px67kO25TyIC79/jO8zbsQdiKt5DMT895i2rMYtMF+1JuLeNamp8q5pXZXvlU4mvDIR46Mukb924Nik18+SvK+oxyTKRvLnwfC4p/+xnVezzyRFn4RIbuSvN6+TLLdoSMx9Sc1ATFxH/xHvxxXQ61Zx4YWqO8kI3QevW+Xoy4CvnzA1A2q8AnPfPdmEti/xX3Ii7uLVi26zHneUoDcDBEEQBDHEodkEBEEQBDHEIQEhMSD44R/+CgBw/clPF93W/3prBgD/9WO1owcO/frE3/ap3Yt+ex0A/3Vrdcx3e9TwqVrJuNgXaQUIgiDKAQ0G+sD8k54BAPxqx+R+3Y/pvwZ0/7ZKWEz9gcyJ9f8FAPgEn8cttBqAf258rQD3cRu+fq9M98+KbX0NgV+3OkQzoBI3Yvtnc8QZCPj0jbgAaiyBMH+/9J0rdU1fcXA/4XnsTb2BOIfevvjx80eD0AzEc2kGjD4pzndCSegl5pybc9Ftc9KzRh1m1FWJ830kZc6PFP/29TwiHoDIA1BVwL0ynOsLkk5aOzbA7z/JGI83EPf6aY0jYgn4x5Rmuq9cUJ1DA2T2p1x9RlxDs4+nlfbN613F9HtI7Qcyzoar96+ElptAbMft4afVdcS96J8r2V9FP+BxJRyH4ZUv/GPoOYga3puBYjUDJTKmxNBggCAIgiDyYDALCIsLmUYQBEEQxICH3gyUiC3vjwMAnD3mvaLbuufN2fxXcIpOPjz8ztny96UnbunjtrnzAPzru6cDAC46/tWC2icIglCZ9HgbAEQ2bbEK459i24giNBgogqNjng87n/zwfUH4IlWEL1AI+qSugDvscvkgC6E+rudMqFIEhDVO0K58OarayyWQ4MJB4c8FgDr+W8zXboh7dUcmDobu1/TBxw19ABD0/5uaAa0u9JgEAptWw/TL23z+vn1inW5vLp1BIfoCm67Ab4/XcXT/b9bxymPMP1Y5L503EzMdnOppEXEapEbAO6/WOPauPQ6EqGOLY59rCpYpQBWxA0YkvL4zLOFrBmr4fWTGl8gHqTOICb2Bf3+J+zPN4vjWKU9JDVEVgvegvE78xJpalKz12hp9xbh+almcX8O4jBkQ3I+oK663aMfcFgDSPM5AIi60Hnr8Ca8heXDel9QKiNgcfl1xvRz5zfeT6ds/PJWC3AQEQRAEQQxa6M0AQRAEQeTDIPYT0GCAIAiCIPKhBG4CRNRNQIOBEvPb904EAHxm3DsFb7vyD5/lv8p/WfyYCbVl3zdBVIpf7ZiMo2KHtbJpY9/tUzvE4IciEBJWpo7dAQD4z53jAQApVhoxjBDrmcFyACXQCRfTiWRJQiRlEy8VQh1vzxTXqYFlihEpNtZ4iV9qY57gqy6uCL34cQsbhIixPtYt64jZC0/tODXnflR7/WMRSXdca3lfyRriME3oF1ini8HUpDRSMCbPuRCBiTr+eU8hrm0jhG25RIzinLiiP/FAOK56/EJcZ8bl4SaoArqEDPiU4d8i6ZAQBfr3gxu3/zdkBuFR2zEDaanBnqq5nabotE70KyWJmCkYFffO0VyYmos7T/+/vdYRAwEhcC2kP4n+oQo1hZ1ZeW15XXFtFWGe+cwRfTvNH+2qEDbFr3fcqCO3dYJC2rT5TFNVZq5e5spgVvxbaU8IPUVwqFSc/vREDboiBEEQBJEHg3k2AQ0GCIIgCCIfmFO8z58GA4SKSASkU/o5t6+83woAOGPMrsA6/VV732aZivYFtv2UEuGSIW0D0V+8tms0jvBX6vuyXj9LK/fm58b/viJ2EUR/QoOBEnCIVQOw+NcKxNQI+Il0fH+i0BMIjYDQDNTx5Cg1SkCV6gL0A0JrYGoPbH51GfClD/qE75/xMADgvrfO9/anBBIKHlOPVkfVKtTH9KBIpp/WFghKntc+aARsiX9koBjRnPCRas3HjHVGpBa1WdmOaNdSh1MtbfA2kr53UVdNKGTY5fI/bMJHXKVZy4M3xXhf5DaIpDaaFsFIfOOawZGUgFI2bUAYMkmO0E7wZVWvIPqKqRGQ/Vety+8NUSb6k//t3zOFJDEShA0ONr03Uf4Wx+LrQno/H2ZgIlNLoLZr6gDEObQ9k8zAVKIfaHWNbmrFrCMCVQltjqM+M0SgI11XUJUdWH+CSEBIEARBEEOdQRxngCIQEgRBEMQQh94M9BPr3pmqLQdzhtM4jCAGIiIpmXCTCX0B4E83JgYnNJuAyMlnx72lLZsDARtmIhwgGCNA1FH9n1Iz4Oi+0WrpD/X9n735xm2xA6pkAiTdF2nTDBTDUfHD3N6ULAvTP5i+U892PRFL3PBTlip2gIQF10m7nBDtgLZdL9oBQPH38y2MZc1XbOoTcukM5Lx9XQcikxsp89bNpEZCK5CVc8iDMRRcQzugxk4wEe2bfShumeNu9n9bkiAZZ0PeD5lAXVNzYt47Np2AKxNf6fdBvRK/oBBMrYDUWyB4zmzPBtWGuK3PyG29cyfiD+hJuELiC1juFXH/mNoRNZlRlveNhOPtq4qvS/PzKhJXAUAVT4CV4MdWw+MNZHL0lcgS0df8xTIArwRBEARBEKWE3gwQBEEQRB6Qm4AY8Ly1qwUAcFLrh/26n9d2jQ6Undb6Qb/ukyCigojdccH4NypsCdEvDOLZBAUNBtra2rB06VKtrLGxEZ2dnQAAxhiWLl2K1atXY+/evZg6dSruvfdeTJo0qXQWD3BMf2hM8enFjTgDMSfoIwybK11lyRfQm99cnbcfM3zvYb74XO3mM3daILQCwxQfrIgdUGPoFqQfW2lf2CfqVMtzlrcJVlwm2mf6Pi3xAHrVDgBSI+Dm4Rs140uY88FVTYmZx0DEChC+fm1/Ira92EbGjg/aZMYOyBp1XOW/Gplvgen+8HyO1USNi+/3Ob3/ixgCgJ9fQJQFNC/KuTLXmfbrehCvzIz5L2xKK8cmhINHmHf2XaP/q/e224tHNu64Ml7B4+9+0lona/QPzy57n/H1Ab3HPrFpclzjGSS0I2osAnG901wPINYJDUGSKeeb5yBxq/Q+aHtuRRsH1sAfBbcRPQq+aydNmoTdu3fLz2uvvSbXLVu2DMuXL8fKlSuxZcsWNDU1YdasWThw4EBJjSYIgiAIonQU7CZIJBJoamoKlDPGsGLFCixZsgRz584FAKxduxaNjY1Yt24drr766uKtJQiCIIhKQW4Cn+3bt6OlpQXJZBJTp05Fe3s7jj/+eOzYsQOdnZ2YPXu2rJtMJjFjxgxs3rw5dDDQ09ODnh7/VXFXV1cfDoMohN++dyL/VZ7JJCKfwNSxO/DwO2cD8NOyEsRgZPPO40GTtQrjX989Xf4WqcojBw0GPKZOnYqf/exnmDhxIv785z/j9ttvx7Rp0/D6669L3UBjY6O2TWNjI3bu3BnaZkdHR0CHMNC5/MT/7LWOiEWgzQMW/m/Dx2nTDMjcAdLHH+xhvfnwdb2CHgfephXojVx+1UBd3q4tLnxYfHjbMVZL7YCIZ18crnHKRHtxJaB41uFz5Y387TC1A4C88ePcxy180eI65vIlyznpljquoWUw66jz1s19ZJnerhY7QMYXyIRsq7brWNfZ7JXbhCipc+WSqLL4/HvTCtjm6vv2cSxd2zbvX91G5CEBgCNuFd+X15DIpSF0MKoN4hzLPqzoIcw4JRcfvy1oWAh6sjFVv5J3E/Iaq/EGXKf3ayu1PDEzR0Xw+seMmBFC6yRySoi4I0TlKGgwMGfOHPn7tNNOw7nnnosTTjgBa9euxTnnnAMAcIxOxBgLlKncfPPNWLRokVzu6upCa2traH2CIAiCqAiDOIVxUf9IDRs2DKeddhq2b98udQTiDYFgz549gbcFKslkEiNGjNA+BEEQBBE1RNbCYj9RpKjBQE9PD9588000Nzdj/PjxaGpqwsaNG+X6VCqFTZs2Ydq0aUUbOlhY987UvMIV9xcirnolCJsyRRAEofLartHWmCVE/1GQm+Bb3/oWLrroIowZMwZ79uzB7bffjq6uLsybNw+O42DhwoVob2/HhAkTMGHCBLS3t6Ourg6XX355f9k/YPH9oarPXOQZ0OOrq75yX0cQzPFukjV82inuD7XmRZDz9b32+qIV8H3c/mswMQd7n1vH29WPO2ab42zEr7dpCNRzclLrh3jvg2av3BHr+/YqLms4kqvksSn2GccrrPOP268t9AWuMa/a99er8/Z1m2XsAKtmwD6Ot+kMXKNM5HUQ/nU13kDWyE3gGr5itb9ljdedZtwCa44CR7fJGhffiMERl/1AzbsRjNOhtgstzoKYg6/PubfZF9BByNgMul9cXVcFPeaH0MGoddNyG799UyvQF0RwozDtgBaTQOgU+L+mVSwk7gKC192GjFdhnCPbtRXXTugpwvKQALqOKHKQgNDjgw8+wGWXXYaPPvoIxx57LM455xy88MILGDt2LABg8eLF6O7uxvz582XQoQ0bNqC+vr5fjCcIgiCIsjGINQMFDQYeeuihnOsdx0FbWxva2tqKsYkgCIIgiDIS2dwEf/zTRAzvMqbjQBc5jBu9u8xWEVFEuAgIghhcvMvv7eMj8qx3mPcpto0oEtnBwFDE95Xqc/11X2nu+f+5fNDmHGQ1foHZbq68Bn5dvTym2DthtJcQaeOOU/g6Hl9eaAYs88H9nAz+vk9r/QDbP2gJtUnGAcihFQjzesYtU15jIYcdt7zZyxp6AlNLAPj+WTM2gVnurdN1BDL3gfB5Kz5ucT79HAWOVkf1fZt+b9/Ha9EXGL7igC7AcjZN37vpd89FLu2A3BcTc/It+RbE/H2mawfiSh+KsaDmpK+o7ZpaAXGdhFZA6GW8396jNpcPvhjCEiP95r2T5O8aeM+RLLfXqungmDoTgV1nYW9H1XMIHZTQA9TFUtpytVI3Ju/lCP7VJM0AQRAEQQxxBrFmgOJlEgRBEEREee6553DRRRehpaUFjuPgscce09ZfddVVcBxH+4gggIVAgwGCIAiCyAdWok8BHDp0CGeccQZWrlwZWufCCy/Usgn/6le/KmwnGOBugncN4VhURCY21m73Ai9Rgp7C8K4xnTSCGMqYz3qVAwdKpwnplQpoBubMmaOlArCRTCat2YQLIbKDgRiYL1SzrC/j5S8pUpAlEsKUqt08/mD6SY3CAwuZoh2biMeWFGjc6N3WG7Y6JCiSEF3ZAgqlWQyfGvO+bK+qD3efrc/EDRGfdbuQPBquxQYpNhTnUVZR6jpC2KdvK66XHsTGFAGayXIUUaAhFExxgZopDvT2oYvXxD5tYkOzfTM4kjUAUiCpT/6DN6soVEni4y2HJzHKax9GH5MCXScYuCjumP1f2KDbBEDewyK50QG3RttWPVfpkIRd/Y0a2EhkD5XkuExhfTFruT9MUaEMbqTNAtOTsIngZmFi5KGAmaE3mUwimUz2qa1nn30Wo0aNwlFHHYUZM2bgjjvuwKhRowpqg9wEBEEQBJEPJXQTtLa2oqGhQX46Ojr6ZNKcOXPw85//HL/5zW/wgx/8AFu2bMFnP/tZ9PT0FNROZN8MEARBEESkKOFsgl27dmmJ+fr6VuDSSy+VvydPnowpU6Zg7Nix+OUvf4m5c+fm3Q4NBvqZf94uVJ2D+yXMrj/pLoI3dh3Hfw0rvzEEQRARp7+y9DY3N2Ps2LHYvn17QdsNuMHAQNUK2BLyhGHzZfYF0x9r+l5t/tqwQB82n54ZiKfKAVqP260MBJTgIpbR9NSxO/DK+61a2RljdsnfQhBqDjRs5EpMZAsu1Ns2uer6wYaYts5McgQALhPbCI2A7rdPacls7L58M2iQty6mbS/89mJZ9VEHdAWG/1/1+fvJrWL25RzJfQLBh/L4D6qQhFjFYmoHRNKlmBb4imllfrKwoH7BTAakBt0CdL2Muo8vn7C12EPpE1PH7gCAQDZA23WSmgHH1IwEdSviHJkaF9vzS2wvgjCl+DmrZuq50gOfBZ9jepuFBLkqloEQgfDjjz/Grl270NxcWGTWATcYIAiCIIiKUIHZBAcPHsQ777wjl3fs2IFt27Zh5MiRGDlyJNra2vDXf/3XaG5uxnvvvYdbbrkFxxxzDL70pS8VtB8aDBAEQRBERHnxxRdx/vnny+VFixYBAObNm4dVq1bhtddew89+9jPs27cPzc3NOP/88/Hwww8XnC2YBgP9wC/+eJayVKrJgwMD75V+fvqIt3a1AEaOeZUP/9RSGqMIgiAGKDNnzgRj4a8TnnrqqZLsJ7KDgcMsjirup9Lmq3L3kOmLjSrmPHtzvnJNLC3XieQ9ZhIf/fiZtr0gV1KPQvyyweRG4duKufOmdqA+5qL1OM/fLzUBjm6LSExS42RxUuuHOW0SPvl8hhjW5EMhfcT0+ReK3BcT89a9RVc5ZdK/WcCrQdMHavPTmwmEpP8fut5A3d7UCqQNvYHenl0rYGtX2h3QIuRxXvl5Uf3N5rGZ7etlvfeKsD4t7kVNByBjaNhjHKj3XVWMaw6Miyvu6eHxI7JsWKywaV79iUgWJp+hNk2NPK8iuZlIGqXUsSTQAvzznWLBf4TENTTPmarnCHv+yWcf0zUeh90yagZQAs1ASSwpPZEdDBAEQRBEpKBERQRBEARBDFbozUAJ2bjjFP6rrqJ2EARBEP1ABWYTlIvIDga63SokmZi3q/jHjROZl1+yzNj8g3KeshEXXfXJi/nJAR9ZkfEGQs+RLXZASE91NV+h4QgP0Q4AftwAEXsgLudi539HiNdXVU7+L7JUHYBNMzCi5X18/KE339oWH8Bsx9aG3Ifon5bzkBLCH+N6m9cY8Odap7ioUvjRxTxu1TdbzfuKH4PA81OnLXEGZCwC2P3/WS3OgN3f7+c3CPfbm+u0+AX8tznXP5cWwbRBjfVvrjPnuOv7CIkzYemDYVoBsT9XEbxm3ZixzlvuytYCAA671bJuQ7w71L5yM2G0rtHxhLz69RPHH3buvHVG3gIj30YWwesfep1UnY0RvyGnhgouDrmDO1FRuSA3AUEQBEEMcSL7ZoAgCIIgosRAiEDYVwbVYECk6BRhN8uFH1J3eFn3G2XeU9IZj+NhhcMwww33daofQRBEvzKI3QSRHQwcYlWokfNJM7K8Ss6R1WNlq3nhK8VR3C9o89HnM9dfzsHtw9CxL/EWVH+g9MM5xnIeSI9djk1Obf0TAOBdPkgQ/imbziB8PzYfrzmHPIeP35hPfXTLB6H7EnoC27YyF4HQtPCjEdfN1XyaHsJ7nOJnK83r1igx2cV8b3+Ov+kzV/MjiFgBpg4g6L8X68zcBwLbHH8zb4GvTQi/YKY/2BYfwa8bHkvB5nNWbfHWhesTwgi7R/Lp67Zt0673+BT29vDl7qx3tQ9m/CQ0nRiBn5z1z73upxL0FucDALa8P67XOtlAXynsmpqYMVnMGBCAp505zLLBjYmCiexggCAIgiAiBb0ZIAiCIIihDWkGCKII3vuguSB3QDk4tHssAGBY884KW0IMFb7079cCAB799L0VtoQggkR2MHDQrUW1y+dXOylZXhPjucL53FPTB1UJavg88Bpuk801KfODG/5gzRdZwDz6fOjt3BQbv6AvVDlA63G7rUmIxHz/4Lx/cc6CvniZ80Ce33BdQVbmEOh9ZCL0BF0fjuFtBM9lMK+6iNXu2+A6+jGJ+AppXier/JuQNZKRmCHX1b5ixpXP2a84pq7GVidMe2Dz/4flkbf5jIPbmvoCmx4iEVqnEH1Cb/bmwvSDqzqOtOPZd4TbKebBi/PclaqRdbvS/u+ByNlj3gOgawfE80PmuDD6itrf/DgVuvbEFncg7FqK86trBrI4nC2jZmAQhyOO7GCAIAiCICIFaQYIgiAIYmgzmDUDlX/HThAEQRBERSlqMNDR0QHHcbBw4UJZxhhDW1sbWlpaUFtbi5kzZ+L1118v1k5iENFp0QtUiq4Px0hdgLpslhMEQUg3QbGfCNJnN8GWLVuwevVqnH766Vr5smXLsHz5cjzwwAOYOHEibr/9dsyaNQtvvfUW6uvr827/o+xwsKwnFEnF/CQfw+AlAapx0lp9U2xUTnIl3ZEiLUMcJrVMWnFpBX2lEAgK+3MFZsk1ohSzCPxgQLm2KVxYE5P25QqGw7S6plCvN0a0vI/u3eMD+wza4gmm1KBRQlQYY3pAIluAIiEyDLtqmjiSm5Bl+jZZizjSFxvahVb25EO6ENEmRg1LgGUTHQYEf2YdpSkhREvxe9o1RGd6O70HsTHrFBI4SSQWE19qoiKBn3zLO79JLnIW30DfAolFEfWZ4hrPhrQsDwpARbKsHlYFILcw0wx45ZcHn0VVTgbd2QzKRgncBFEdDPTpzcDBgwdxxRVX4Kc//Sk+8YlPyHLGGFasWIElS5Zg7ty5mDx5MtauXYvDhw9j3bp1JTOaIAiCIIjS0afBwLXXXovPf/7z+Ku/+iutfMeOHejs7MTs2bNlWTKZxIwZM7B582ZrWz09Pejq6tI+BEEQBBE5yE3g89BDD+Gll17Cli1bAus6OzsBAI2NjVp5Y2Mjdu60B3fp6OjA0qVLCzUjJ4/88Uz5e+4JL5e0bWJwkksf0PXhGFQ5lc99QQwO5jx3PQDgyfN+WGFL8ueNXcfxX0P8PqCphR67du3C9ddfjw0bNqCmJjyIhmP4LRljgTLBzTffjEWLFsnlrq4utLa24qPMcGTS3jaH49VyfX3sCACgLuZpB0QgClugi3JxPM/K9+4HzaF1zKBDNvqaMKWv2PYnfXe5diN87vKaCp98OL6/W9cOqFSF9BFr0iEjIZEZSKhQXUBvuJqvVNgVs9qnLwufON8ql1nydIZVspwf7sAUvT5maAgAX7dRVUj/4l9Sb2ALUBRyvaTewLH5ge1aBC1Zkry24lhEe2riGx7oRwQfY6bdtmA29kA3urZB1zL4AZDigXb95Dv6eRDagdq4HyytOpYEAOxL1QbsGkiozwyhEcgaGg+hDzjC9QEAcMT1fotzJq6fv+w/t/3AV+EBr6Q9joueTDp0PZE/BQ0Gtm7dij179uCss86SZdlsFs899xxWrlyJt956C4D3hqC52f/DuGfPnsDbAkEymUQymeyL7QRBEARRNijOAOcv//Iv8dprr2Hbtm3yM2XKFFxxxRXYtm0bjj/+eDQ1NWHjxo1ym1QqhU2bNmHatGklN54gCIIgiOIp6M1AfX09Jk+erJUNGzYMRx99tCxfuHAh2tvbMWHCBEyYMAHt7e2oq6vD5ZdfXjqrCYIgBjjn/fpGAMBzf3l3hS0JZ/sHIiZINOPpE6Wj5JPzFy9ejO7ubsyfPx979+7F1KlTsWHDhoJiDADAvvQwZNLei4vDrq8ZOBDzfG7JmOcnqna8OabCv1jllHHOaQEIX1shPn+rn9YyLzdfzLnOtlnnYh6xORdbmw8v/NSGf1ok7lH9isLn7CcW6l07YMYOsOkCwvz1cr1xrLXNO+RvkbEwJv3Vvoc9a8QD8I9DTZIk/N1ZzS5bMiO3D7EeRHum7kFtXbQal7aIJEx8RR9fRcbM45aJoIIIDYJr7Cvr6NcN8PtK1jhX/n2hnCcxP53rLNwcOqAsM7QH3L+cUrZxDb9/mB5A3V7ce6ZWIJe+wDVsUftgTTzNj9vTWh1ID3zXqBkrQpwroQ8Q34CvH+gx1tk0A66h7cgVFwIAejJljDFDAsJwnn32WW3ZcRy0tbWhra2t2KYJgiAIIjIMZs0AJSoiCIIgiHyJ6B/zYqFERQRBEAQxxInsm4EDmSTSGc+vdDDr+9eqYjzWu+GLVf1zd57+f8tgYTj6CEufk18DFxNGf4hX3m8FULguwPTTSb9aHlqEeI659+b5DGzr+OtFOzE5x921lnvr9H2KOiKfg3quhDKkyshnYPPJ+zoCvyzR9A4ynSfybb1lGwk+K9/fVvFJO952//XhccENDYQN/nXqXR+Q7cO/FTLvQD5xAkr8X4ufSyI8b4LQKZjxAdT+lhLnWhbxlpz8/MJeHSUWgeGfz2feetgcd/XeSbv2dsQ1tvm2/WV7LH2V6jjPX+BGU9tkEtCDqOdKnEfo51XkH1DjDAjdl6kZ6OHnO+Mq18u4r2wxI1RSmTKKG0kzQBAEQRBDm8GsGSA3AUEQBEEMcejNQAnIFYaYIAgiF1/692uRcHQHzC+mraqQNUROyE1Qfrqz1chmPD9TzAl6KoWP6UjW8z2l3Mon0JAeaMWFFQfQetzuQF1bnngzbrvwwal+ukDcbsSM8vxf9th0AnHLuQagdeC4qdeQc9y5dkCpLPYh1lWJOsz7rnH8aAdiTr44gqo8XIFZ5iLZ/K5cDtMJqIj6bufE0DppQ19h89fHDZ9wqV+zmVoBmx6g8CgGPsLeYtoAgr7xXPkM+ta+LX9Bbq2AiJNvW9djznFXnh1mXfGcSfP7K6NpEXSfdq7YH+J+FX/0h1elQutGiZSZx8GSQyJt5CRIGdcE8LUYYVqBtKILyMh4EHbNgHmeU6awoR8hNwFBEARBEIOWyL4ZIAiCIIhIQW4CwoaI212Ig+Kl98f0jzEEQQwaLnvh7wEA/3LO6gpbQmjQYKD8pFkckL68YOzwI1nP9INpT1dwOF2NSiO0AqYv2Ybv8wzmaA/E/Eb4nOl8YrKb29p0Ab3FGSiEuOIUE7kihM6ghueUqHG872GO4juNpfg2XF/A75qYjF/gV3XBtHwDfSHW9HboupbjPgQAvMfFoerZ6YsvXOgoCvHLmVcknSPuRNj+KoGvhylOM5Arj71fR79XzPtD/R0WFz+j+bb17VMi7oAr6iq+baPMnxcfPG4Re0N8V8eyfLl0911/cIQ/k8T9m0sXETfjjih90NcTuVrdfKItBOI5KP3KZU5e/YToncgOBgiCIAgiSgxmASENBgiCIAgiH8hNMLA49bE2+fuNS9pC6/WVt3Z5WoFYGaNgEgRBlIvfvHcSAKCe3sDr0GCg/LgsJn1ypo8IADLCt8friJzWR1LlPyQ/wr2jLdsw8w5o8daNHOq2+brm/Oos0+f62uvqOdn7gs3/G9ae6get4nEExLfQCtTHu712Y4eV7by7pIb1eHZz/2IaLpq4D7/cjBvtxYh4Y5efq8CMnW6eG3Uuts1/Cuj5G3rDtp9sSC8zY0DY9l0u8sm7Yepjit9nsB3X0BOYvn2hEwAs8QVcoR3Qy/UyEYMgXDsgrneC51apiXve8uqYWO9ft2Lu01JzhGsmhM9fzw/h/ZaxRCBiifD8C1wfBAT7Qsxl2rYx139uC42QPNd8l5kIxJIZzER2MEAQBEEQUYI0AwRBEAQx1BnEboLovI8iCIIgCKIi0JsBgiAIgsgDchNUAJc5cKCLBUU54IsKY8Y7l6zb/y87RPKOuEhmI4MNCVvCpxmkTTETgoIcMzGLKooyhYJimyNMD6Si/nbNQEVGsiMVU8jlC72C10AuGy+Y1ABGUhTIxURCVCTtjSsJZXhwpf/KDsfnxv8+YFslUYNDpSDOq3ceRKAnYb9NDGdL4lQMvQUbUq9BaPIp0VaRgrWwY8sVdCjQzywJcPLbt7fPNN91XIpO/ftAiPOEnVK8meNSuAiKANVydZ0QDqayxv2VI0DPkZjX72sSnpDQzFpYacT9Ke57mzBV4Cch846lJsavpboJLxJ9xBcWV2nLANAjng28LCEE1eJaK0LCDGIFiXGLhtwEBEEQBEGUm+eeew4XXXQRWlpa4DgOHnvsMW09YwxtbW1oaWlBbW0tZs6ciddff73g/dBggCAIgiDygZXoUwCHDh3CGWecgZUrV1rXL1u2DMuXL8fKlSuxZcsWNDU1YdasWThw4EBB+4msm2CwsXHHKQCAYTT8IggiAjz8ztnashrrgJ5TdhzkcgLn30YhzJkzB3PmzLGuY4xhxYoVWLJkCebOnQsAWLt2LRobG7Fu3TpcffXVee8nsoOBRCxrHUCZAT0c7i9yyug3MpN3pLl2IB3i92vRtuW+OIvfPiygkKsF+jCSGDHdf23TDKRdXWdgbmMrk4FaLL5T17A9a/hG1URFQtORjHn+xCQPtiJsEsljAOBAtgZR5YwxuwJlYoAn/NPiuqnJosLI5YPtC3Gj/5cy8VTv+9aDzvg2FBBYKUeAIus+pTZAt0G0E2dKEB/eTph2oCqm1M2KQDp6YqGc+gLe/5m4N7luSdUvZV39+FIOD5KW4T7zuO8zL+S89RcymRO3M8bC+5OpGRLaAa2Ybx6PiWemd4+Ia1IV81MWVblZvm/9edXDNQQ9jv9nqztbDbeMfb2UdHV1acvJZBLJZLKgNnbs2IHOzk7Mnj1ba2fGjBnYvHlzQYMBGv8RBEEQRD6U0E3Q2tqKhoYG+eno6CjYnM7OTgBAY2OjVt7Y2CjX5Utk3wwQBEEQRJQo5dTCXbt2YcSIEbK80LcCWpuO/uaJMRYo6w0aDOSJSNwBFJ6841c7JqOq92oEQRD9ztrt0wAANZRorXBKOLVwxIgR2mCgLzQ1NQHw3hA0NzfL8j179gTeFvRGZAcDVU4WrvAD5jHCET7TRLz//UeH3WoAwfnQYg6tWi40AocZH/UZHUn1t/tJXPT4AjYdgEhi5OZIVBSmFcgIH1zWv/xZGdNB1I1py6pPV5YZMR8Eqs/TT9Di+ftq3TS3zWu/R0lQUhWrxUBCXFPhXzX1IICqqyjcI9dbfAAg3L+cz7alQiaqMf5limvxJtzQdfmSS0MQsEm1RcYgEDEY9Lnuqh5G9FOplTE0M65yHBlHaBH045axORSdgJgb77q6viDGfejxmH8flOvaBbQtyrKpAxDo8Sty/1U0tURaO0Kvgaxlpfji14vl3k88nsq5fjAzfvx4NDU1YePGjTjzzDMBAKlUCps2bcJdd91VUFuRHQwQBEEQROQos77z4MGDeOedd+Tyjh07sG3bNowcORJjxozBwoUL0d7ejgkTJmDChAlob29HXV0dLr/88oL2Q4MBgiAIgsiDSoQjfvHFF3H++efL5UWLFgEA5s2bhwceeACLFy9Gd3c35s+fj71792Lq1KnYsGED6uvrC9rPoB8MTHq8DQDw+sVtFbWDIAiCIApl5syZYDlcJY7joK2tDW1tbUXtp6DBwKpVq7Bq1Sq89957AIBJkybhe9/7ngyIwBjD0qVLsXr1ajlCuffeezFp0qSCDYs5LiDnA8eUcn3er1iu5jG+1TwGPenSjXWEPxgI5hXIGvH7tbgAht8/GJNd0QwY6/z2bLEI7HkHdM1A3LpO+OkzrqpF0G1P8XWiTkaxTZzjTB4x2EXM9QT3jaa4TqGH5yTojlf7dWMW/2GEOeTaNQNq7AQ/ToMet0Hg5tASmH52G9IPbtTN1W6piRm5CWKWuAPiPo1Dn+sv11vevRbiOxf3TsqinRHnPCznQT76AtE31fs1wfeZcD07M7yPx9zgsQitQCZrxiCwx6R4a+73rOWlYF+2ju9ZP796/hF7f41reiD9OufKEWA+r3LdB2F91xYXIs0YYuUUQlJuAo/Ro0fjzjvvxIsvvogXX3wRn/3sZ3HxxRfLOMilCotIEARBEFFDuAmK/USRggYDF110ET73uc9h4sSJmDhxIu644w4MHz4cL7zwQiAs4uTJk7F27VocPnwY69at6y/7CYIgCIIokj6/R89ms/jFL36BQ4cO4dxzz+1zWMSenh709PTIZTNEY6k45dGlAIA3v3RrQdv98/ZzAABHD3p1BUEQg5nFr3wZAHBc32PbEIPYTVDwn7jXXnsN5557Lo4cOYLhw4fj0UcfxamnnorNmzcDsIdF3LlzZ2h7HR0dWLp0aaBc9yEFc4gLfD+S59NLJvwY18I/l870Hiu+N/Zlh8nfWWNuv/AjmnP/1Tqmj8z0oXnthusJTMyc6bI95WWPmMsv4wuExBAAfP+/qREQOdptmoGsoR2wIX3FhnaghmsGjsR9//pA0wyYWgERf0LVDJhaETPWQ6652OY8brVub7kIwuaJ28hlQz4IW2KG71j1SYv7U/jg/fwAQV1AUHtg10XYsN5X/D4ScTcCsQNyzocXNjia/UBQD5NgImaAsDP43HH5PSNyIIjvHKH/S85HaU9lLq6B0BbpmoHe+4R5neWz2Anex4XE2zD3bT7b0q5up0Wi0W9UYjZBuShYZXTSSSdh27ZteOGFF/AP//APmDdvHt544w25vtCwiDfffDP2798vP7t2BRPCEARBEATRfxT8ZqC6uhonnngiAGDKlCnYsmULfvjDH+Kmm24CUHhYxL5kaiIIgiCIskNugnAYY+jp6SlpWMQocM+bnvbhGNIKVISLn18AAHh8+sqc9c556mYAfgpr8apSm46pvFb83YXtJbWTIKLM11+8Sv4eSQlSiocGAx633HIL5syZg9bWVhw4cAAPPfQQnn32Waxfvx6O45QsLCLg+V1Z1uu9wm8NqL5t+xz3uDIHVegHYrG+n/3/zg7n+wn6IM05/sKHbIsL4Mfx1/1e6nzoMD+dbf6u7+8UMQ+ET07VV+j7zhixBKyxAwwdgDj3aUVfYOZrF/sU8dYdi73SV8yvRTfP316bSMs6NfFM6PFGET9uA9cMZJN8ORGoY8Z4SIm8EW7QrxzmG88nJoGtTli/smlSQuta2jXtjEntgD43H1Byh3B/su9vtvQVx+6DVvtFb3Pbc92DAl9f4ISWCW1H2pKjoze/uuodFfeGsFZoBbJpfl7d3n30xXKQ98/urKdtMWOLpK19p/c+Fzeuez7ajmBb+T/j0obWKZ0tX0yNwawZKGgw8Oc//xlXXnkldu/ejYaGBpx++ulYv349Zs2aBQAlC4tIEARBEET5KGgwcP/99+dcX6qwiARBEAQROchNQBAEQRBDG4cxOL2kVM6njSgS2cHA3p4axOKej0sVgAlfNjP8daaADACSVZ4POgnve1hVChdsWoiRycMAgIaqbll3eMLLiS18mnF4WgHb/P2sMT/ZnMev5xvQ/V5h8/nV9gT5+M7NbXLFZDBzCah5HMQ5ln45rhXIGloCIBhf3czRbtMMyGPimoEE1wyIHAWAf70Shs/xk7/8bmh78ZjuLzT7hYrLHHzyl9+Fei++8oV/DK0fhhCXZlEDwNcKmD5ZwNcICH+s0AqIHA3q9Td97ia5+opJrn6Qa559rv4E6OfXvM4x4x5U+29QTxDu849JfYGYx6/HKFDLTH91LkyNhC2+gqmzEdvkytHh5/HQt1X7mSzj94qb4fvO9E+cgUv/4xoAQCLmqwZ7jD5nPq/y6Qe2fhf2nMr1/CpknXleVRsyLIbMkcj+GRtQ0FkkCIIgiHwgNwFBEARBDG0G82yC8s3JIAiCIAgiktCbASLSTHq8DVX92EtPfuQ2AMAf+jF/PEGUi/N/800AwKjaChsyWCE3Qfn57+5hcLhASxXihAkHE3GeAEdJVFTNg9hUc7FaHQ9wI4RJGSXgT1fa25cZvMcUFKmEiYtsQpyMIdBLGQI9W7s2cgm6wuxkhvBGnM+sRZAmxICmOFC1080KMZQQc4odhZrtw81zhJCwyg9Mk0jpojJhU08qoR2H1hyvm0s4KOqYgjfXcu7z4aO0Li4VgsGD/DulBB0SQkFTZGbrK6bwLiEDCTlaG2qZf011wVt+/SBY1wwgZe5Pby9QBEAPtmMem2MI/kRdLaAQFxc6xnlQA4qZAkQ/4E1QvGiSj1DXPL/+vW5J2CWC9oikXlk9YBeg3EcyMRFvhwsJHQDv/d2NoTYXysG01xfFs089FtGPhH25+oop2M5V18TWt23rwjCfA2F9M3u4fKEVyU1AEARBEMSgJbJvBgiCIAgiUpCbgCAGNyc8fIf8/cdLl2DS420AgGFJL/7E0XWH5PpPHVVOywiid055dCkAoKmhwoYMcgazmyCyg4H9+2oRSwnNQA5/sOF7Tif9xDfDkt52iZge0UP4dI9kq5QyM0GJ7kGxagYMPYDpt1XrmMl9hL9VrRvm21d927aAJuq26rky+5xcxxCoKwMHuXodkVAF6vHz0ynr8u9COjnj1y2b8LtgT8zu28+lFQiusKyXZWJZ30+u/mXj3cPHaMuiHx0R+gDFV2wGczL99b3x77P8jJ/Tn14caDesP2nXNqQ/Zd1cdcP7iiCsD9owr5djXicnWFcUxWJCM8ACdUQQK1OTEFf2Z8tkqVJIcC/1mEXCHFGWET74XDqbrLhn+IoS/2H4+NAwAMBBrp0aVp2S68S5kTqWTDBImoDJ55bQEgU1Lr09g5hlXVh/sulP/G2Mc2/UdQ+X0ds9iN8MkGaAIAiCIIY4kX0zQBAEQRBRI6qv+YuFBgMEYXD8v7Sjtq7SVnic9sSt8jV5fU2FjSEigYiNAfhTFRP0JC8PjIXPqS2kjQgS2S7E9ibBjiT5Qng94ZZLVXsPzFSNrwM4Uuf97uZlh7n/TMxXVn16wn8m/IBm8h1tn8ac/DA/q1rGQnxlqi81FtMPlBn7AYI+fWbaqTVh+idz+N5EkiFR17Vvq9YxXPB+Ha1d2BFSBPWYdRdhEOVcyTqBbxZoQ+zDEZdb7FP4mVVfdNz7nU57/eBIzLtFPj48TNbp6uExKUw9iLEM6NdOOxTp41bstGhEVA6n/ARIct9Zu//fph2RSXKkPiSYJEdu75rXMse1DdGxWK99HlIJU9vhOzNtOhD9Gopr7Cg6IVvcA31/+T+cc+lsAvem1gn1+4nxBEVONo8TkgfdB3mSLL7vRI2nGeip9R/x4vmSSot4CHHdbgX5vLL0Eb+OsZ153Nrx8295rhCsw3HM51OOZxEAuN35Xz8inMgOBgiCIAgiStBsAoIgCIIY6gzi2QQ0GCAIgogAx/9wufz97vWLAuvH/7wDAJCkvANEPxDZwUDVvhjiRyy+U93dCxEOwE1w32mNP8c7lfQOr6fG86ftq+LxzHkeA82vKuf/6v4/xyj3fkMrkz5008elrDNHgyItghv3V2Squb+b2yn8oJpPL2CX0b7Nt2/aZZxDDbFNrrrGdoF28tEMSL+wcg1M96HperS4V6XmwKijhYkQ59HUKViugajrcr9q6nA13vvqTZjwi9uD+w6bZ28TPZjz4nkfVGsK/7+M7SDmzMcNkQYQ8M+GxYlQy8y4EOaysstAX7FqRwzfbqiGRKWQ/4py6EDMOq64pqYuRKmTjxalV3Icf0Azk6NZx3x2hGhLbPR0+7oo94jXTx3+LY47w9s7mA0+Q9001wGYugXVhsCx5WGYqYeyPAdkWdbQKFnaD54jS10GZG1/J/oJx1XsKKKNKBLZwQBBEARBRIpB7CagoEMEQRAEMcShNwME0QvjVt+Nqk9U2gqCICoNzSaoANX7gXi391vzRJkn0vARuwllHnAVn0eb4LED+NEKf73q4pIh0g2/lPRXZfy6MZEi3PBlBXxblvbkYQh7q30jMrWiTNgb7DXCvxfLBPcVinkseWwTdh7UMr8yLy5kyrRFB1CQZkCuc4LrzLZi3K8urjvXCIh+AEUzENAV8GuQ6vLn+MuYDHnc1NIOMQ9e7Eux772v3gQAGPfTu/VtRf4G8f4ujx1adQBm7AjRh0xNiVIn9Ppb5o4HtAPWOBYhh5DrkBz9B7Ncf7OPSK2IVtfQHIT0Mxtmn7Regnz86mH7VLb54zeDokEVt9t7gDlpvzGhq4rxMpfrjmQ3SCsvf0VhxvuO82/xbFNjHuT00yNHWS/IdvkzVDzHtBASZn8y+6KpGegp3I4+Q0GHCIIgCGJoM5jfDJBmgCAIgiCGOPRmgCAqzAnL+fzy+sraQUST41fw/jG8snYQGNSzCSI7GEjuZ4hz/1dOP13AZ6gKAUSZ/Tsff7XYT0zRDAh/l+l7d7gvyKYZMOvKUOXKFcgmHf5tHIs6Dd7wtQXajQV/i30FfLlOsG7Al2nRDJjxC0xfbK754OZ+NHuNurk0A2G5CXwffbBuTJyPuKPvW+kzQlcgYxP4YSuChPRBDVODIK6F1KIo/t+ueO72tGtrBnvgX7b52uacbmNuu1XjYlzjsDne2j5z1A3OyTdstMWmyKkDMcpy9UHHuN4FaATyoSDtSC5tQy/ED3n9I5ZSyo7wa8ifC26KawdELAGlz5jaEfkMyerfQO+6KO13AbEIZH8SzzGxb/X56op14gGjt8GM99nZVPn+upKbgCAIgiCIQUtk3wwQBEEQRKSg2QQEQRAEMbQhNwGno6MDZ599Nurr6zFq1ChccskleOutt7Q6jDG0tbWhpaUFtbW1mDlzJl5//fWSGk0QBEEQROko6M3Apk2bcO211+Lss89GJpPBkiVLMHv2bLzxxhsYNmwYAGDZsmVYvnw5HnjgAUycOBG33347Zs2ahbfeegv19fnLpZP7XSR4wh5rEotCRDuBjYPrhVhPBiSSAh8hsvJ3KIN0uKYKSjSs1A0JyBIIkgJf2CZFhTn2HSauUWFhgjmL2NINHLfdbkAVUOoCT18kFTzxYQIqVxHoyWsQIjLUhEO9CBK10xKyb1vwKV/w17voLFcCpUAd0Z4pILQEcwrr29b+KgJpCTtt16sAUWDArhzBZ3oNDqPYHhq8JiSQjJV8BKSmsNCyzmwj17MkH0FhXs8isw/b+nQIicO6EarYLn6EN5c29hcXB6eUGcdrCgdjioDQXOcnsPLrmM8ga7Ap2Nf5Nng/VIF2LMO0b9F3rM/MmINMuoyZf2g2gcf69eu15TVr1mDUqFHYunUrzjvvPDDGsGLFCixZsgRz584FAKxduxaNjY1Yt24drr766tJZThAEQRBlhNwEIezfvx8AMHLkSADAjh070NnZidmzZ8s6yWQSM2bMwObNm61t9PT0oKurS/sQBEEQBFE++iwgZIxh0aJFmD59OiZPngwA6OzsBAA0NjZqdRsbG7Fz505rOx0dHVi6dGlfzSAIghiUnHrLPd4PCjYUHVzmfYptI4L0eTCwYMECvPrqq3j++ecD6xzDZ8wYC5QJbr75Zixa5Cfo6OrqQmtrK5L70kgkhFM3hyEi0A/Tl7kh1rq29cIH61bpwTpEuTWYSxEX1R6Yx/ArW47JD/jDy6RPlwXqSqS/WkTf4ZvGfSPcan7chr7A5ld1jH3mlSxJ7khEcTJ88lCuQULXTgQ0D8pvX+ug+0itSW2Mbe3aEf6dMNbl8Ffnas+WkMmrxIvVc2cGeMkRbEXoHVxTMyB2l0sz0EviF6sNedQN3VZZFwg2VCJ3by5ffOj1ygczCFdOI3pvJ1fgszASh/X2teBAaf07LrQDIX1Ia8fV/fXCR6+W+dfWeM5a1tm0Qv4+zee0bp+j7dtr2OFaANUuQNFDwLvv4xlDMNGflFkz0NbWFvhnubGxUf7jXUr6NBi47rrr8MQTT+C5557D6NGjZXlTUxMA7w1Bc3OzLN+zZ0/gbYEgmUwimUz2xQyCIAiCKBsOSqAZKLD+pEmT8PTTT8vleDxXWNS+U5BmgDGGBQsW4JFHHsFvfvMbjB8/Xls/fvx4NDU1YePGjbIslUph06ZNmDZtWmksJgiCIIghQiKRQFNTk/wce+yx/bOfQipfe+21WLduHR5//HHU19fLVxUNDQ2ora2F4zhYuHAh2tvbMWHCBEyYMAHt7e2oq6vD5Zdf3i8HQBAEMZg4YyHXCtRV1g7CQgkjEJpi+bC35Nu3b0dLSwuSySSmTp2K9vZ2HH/88cXZYKGgwcCqVasAADNnztTK16xZg6uuugoAsHjxYnR3d2P+/PnYu3cvpk6dig0bNhQUYwAAqvYeQSLunTSWwxfl+85zOB8t8/+1cgAswV+S8Gw2Ytn3Rat+KqOZwHxbfz/SdmMOfiF+S30uesi+hE8uyyx19XXCJlblv25i1d5vXzOh6ws0TN2CTa9hHkNYHYtug0nNQExftsUkkHoCfmy22AFh199ig2v64kX76r578//aNAhhmoEcc/wD86tVG/hvNWlNvu3mkyTInFdum0MeSMxl00GY++gllgJg12nY7FbrmOdI1wwUfs/1pvXoMwXoFkQMAWmKJUGan/CKb5PWdQDq88DUAcg5/uI7pdZ19e0NnYHZNuBrfQIaJQSfFabGSW3LyQrNQNawwSbccuBke4Ll/UQppxa2trZq5bfeeiva2tq0sqlTp+JnP/sZJk6ciD//+c+4/fbbMW3aNLz++us4+uijizPEoKDBAMtjROQ4Dtra2gIHRRAEQRCEx65duzBixAi5bHsrMGfOHPn7tNNOw7nnnosTTjgBa9eu1YT3pYByExAEQRBEPpRwNsGIESO0wUA+DBs2DKeddhq2b99epBFBaDBAEAQRAc76O64VGFZZO4hwHMY0N3Bf2+grPT09ePPNN/GZz3ymKBtsRHYw4BzqhhPjvqM85q9aAznEQraTfnzFpxXXtQKO0A7EhJbAsr3p7xK6Betc/xzthdmXT6cJxB1QHIpZ4dPlZRnugxPtJxTNANcPxKq9LpFLpxEwl+Xw6QUqGzEo1GXpn+fXIMHtlvEH/JNm5o4w8zBY4wKITUw/uNp3xL6kdkDEn1C0DXG9TiDmfY58E2KdGR9es8voy9Lnrc4oMi+PoQuIqT7YfOIAyJ3pOoCYmYdD0yIE55579gabDY1bz+u62txxsS4k7oZlX2E5Rbwyptc1roE1j0Gh879C7AvDbF/NEyK0AnEAL/3kBpz1994gwcnq10bbt7zu0OraYv7LdVJfoM/r99bxMnHdxb6zfod1MvrzLm7oraxxB8xnhey3ykG5hl6BP7cC2wo73PJpBsrNt771LVx00UUYM2YM9uzZg9tvvx1dXV2YN29eyfcV2cEAQRAEQUQKF1qypj63kScffPABLrvsMnz00Uc49thjcc455+CFF17A2LFjizQiCA0GCIIgCCIPyu0meOihh4raVyEUlaiIIAiC6D/+4qrllTaBGCJE981ATyrc518opu/KEjtAOICdLB8fCb+19FsGbZEjvKyhFbDFPODbO1IzUMCxFaKZUEed4ncmoy+L9hQ7Tc1BQWfe1C2o5hn5EIR2Qtqg+IqlM5f7CuNZoQfwvmMp3145/z8QvyFHfAQDcw41YPHXG7EPACArYjFUGfsU26jdim+XTeraAVvM+7B8AL6zX92JXtf0EWvzwcNySQg/sE0HIP3LPD686UNWtpfIuAv593FxfliVf8HcuHFebc3E9Otv5hJRD4oZeTBy56bIsc8QApqUPvzjKHQNAAAj1H6iW48H4Cg6APOaynwDwrWvxhnIhlxbsZyx6AFkHxHPOqVOiv8WzxHxjIvnoRkwNV7qs0O0J59pwpaQ9+vu4M1NUE6iOxggCIIgiChRwgiEUYMGAwRBEASRB6WMQBg1SDNAEARBEEOc6L4ZyGT1SbKCWBHjF8dwEqo+LdOfntH349j8nwG/V444AwEb7HG2i0ZtN8zHZqsrTMgWcX4tec3lL6kREHO+uX/RVY7Z0eNKyDrx4PVihuYgoOko5FRqPnMzJnvwmBJ8HnUgj4OZh0Kp41Z731mhM4jnMNDI/R7IG29ZJ/3Arv6tHYuxTm6TUbQj3CfsyLnn3C+cMfzDSnsSqdfgfUiNY2FqR/hyjKdj1fJkVOk+54A+RF1naDpYzFJXahBgr2ORrQT0BTbMWAFF3L5qvAlZJmIH8HgAMu9AKnhtRawAcU2EDgCadiSkb1tiCAS1AqJ9NY6JKBN9g9eV/cCSatd8Rtriw4TFLQnTZLmWvxP9BbkJCIIgCGJo47j2oE+FthFFyE1AEARBEEMcejNAEARBEPlAboIKEHbSbXP4ze1MHMOXafEDBx1+2WCdfPaVL/3VIWzzdQXmHH9VUyDm6so5wmbAfVvshJi+ztwGCMZ0cHVdADJOoK6Ypy7XWOLNS12BqFvIO64cXSiQ48GIoa7uKxaWb0LRtYg47SLfgqkzYLaY/GI/Rt55PXaA7u8PxJC39AMzh4bUCijzy+VvEZsicB5ynDyzfynnwTH7QSLBy0U8e8W/HNe3Z/Fge8zIYyHPsyVOADPiFvixCXLVDa8j65oxLvLRDJjSFpseROaFEBoBryDew+MCpJS5/iL+g9B6iGtsLgPBeCjyQAxdABDUAdj8+GF9QvYDixAi7P4qhnL+cR3EcQbITUAQBEEQQ5zovhkgCIIgiAhR6RTG/QkNBgiCIAgiHwaxZoDcBARBEAQxxInum4Gw0ZNN/Jervkpv4sN8KSbwkaBUtuSDaa9NDGQmL3IMwZdNOBR2ztX9STGZqZyyqK0cIQYMqatuIxM/5dGuaacZHMVGLuGUaYN5jLbgSDL/VYjoEPAFc8YxOBYbpPjPFAMWEhzGJgAzhV25gsOY50+ID22JwExRoajLhYNOWjkRQkwYj+nbKGJLX3jIv7O6UFMVZjpZQ+BnBIfSRZym2JCF1vUTHRX+n14goJQloZAQDsZ6svzbE/k6KT/IjkwWlDW/LQK9sL5sE/MZAcuYTUAcgpNLSCz3WcA56y3hVbkFhMU+uqP5YiDCgwGCIAiCiBCkGSAIgiCIoQ5DCTQDJbGk5JBmgCAIgiCGONF+MxCmD1DX5bN9qclagmnka0M+/upSIYMLGfZaEhiZPkHp97PYyY70AADcw4e9ZRHghCedidXV+e3UJO02WQLT+PZle68TlvDJto04hjA/Zczi25a25PC9mttY+mkg2I7cxHJ+c7QTwAwUYwYJUq95iP8/px84H5+uTIYTEnRGhfvyA8ftJoLbCLtcricQ61wl6FBMBGISx8vrxl2+XmnPTGZlBgtSdy2T7BjbiCBEjk0zoLcnE03lCm5lBJLSAkplhFbAu6ZON9cK9KS8CuIbAFLeb5ZKh+8ssPOQ/pUrYJmNEO2UVUMhumMez72A5iBXXyzHc1RlEM8miPZggCAIgiCiggtrNMqC24gg5CYgCIIgiCEOvRkgCIIgiDyg2QSVwFUyQuR6f5HD/+n7nuwnX9smzC/Vl/gFakKVNPf3ybnSfkKW9Xv/ydrchSP/Lnxfvc3XtfjQxHkQvn3WfcTbtOeIUon7dLl9TlWC28/9ltVVflVRJ+npARwATx1ciwtqrvC26enBU0d+rtkwK/Zl3aaE396G1LqcdQshVl0tf5s2COY0zdcLVB+q9E/rSXxYT4+swsTc/rDEPFp7PBaBct21Ojb/rdmONbGWnpBGaAdkn3YtGoe+xLbg24q+o7Ub5q+VMSCUG9c1fPmijmgvHqyLmKkdUeMM6LEI5L6MZFcAAJnEyIjt4FjqygRFIQ8d2yHn44MXl1BeN13zIONEAECaa3CEDoBrdFh3N//271uXl4Wh9jv529Rt5NCoOOZ5UOvK6xQSx8TWbh7Pq4JiGrixwmIWFMsg1gyQm4AgCIIghjjRfTNAEARBEFFiEL8ZoMEAQRAEQeQDDQZ8nnvuOdx9993YunUrdu/ejUcffRSXXHKJXM8Yw9KlS7F69Wrs3bsXU6dOxb333otJkyYVtB+WzYAx7uOyTes3/aCWEyxLTJ+u8F/a5mILhBYh4/ntWC6/lMXHtdH9RXj9Xlj/3z8teJvZ1ZcDMHyEtTUAAGacQKe2pk/76I0wHz1Q2Pko5tzlw5Od9xW8TTE6BsCiGbCVCy1Ggt+WVTluz7D8AsLHr+oDwh4+vP+7qZR9PeD7l2Vbvftz5TEpuhCpCRAxKURuAREfwabbMLQC2rmS/n+jrpkvQtm37zN37N/KvgLY7n8zj0fcohkRmHEhpCZFxIdQ8wLw+5VrBpiIJcB1Kzmvl7lb5RnHjHgjpoYgkOcDADPOmaYhMPOZCEQdW+4Tk5zP1ZB1yv5YLAbGMvZ6REEUrBk4dOgQzjjjDKxcudK6ftmyZVi+fDlWrlyJLVu2oKmpCbNmzcKBAweKNpYgCIIgKoZbok8EKfjNwJw5czBnzhzrOsYYVqxYgSVLlmDu3LkAgLVr16KxsRHr1q3D1VdfXZy1BEEQBFEhBvPUwpLOJtixYwc6Ozsxe/ZsWZZMJjFjxgxs3rzZuk1PTw+6urq0D0EQBEFEDqEZKPYTQUoqIOzs7AQANDY2auWNjY3YuXOndZuOjg4sXbo0UP7I+yvxN6OvB5Db/yn9adz3FFP8rKqPbEP6Iflb+NdV/6eqCdiYfdhqa5Qx5+oTpaWvOgahNTD9tQK13zkiDgSP7YDDrl6ngPnXeSH0ALb8876BBTdrPVbh1uX7ckWdAjQIIq6F10xIjId8MOMW5OO3tumMTFtM/7qmcbDvyz14iO9HPw9PHfk5ZsUvlcthz6RitCz+sfB+p6yTtovcCXyZWXQAgWsRFn/AakQOLYb5rLeVOw4YKyAvAxFKv8QZcAyxCGMsUCa4+eabsX//fvnZtWtXf5hEEARBEMXhstJ8IkhJ3ww0NTUB8N4QNDc3y/I9e/YE3hYIkskkksmkdR1BEARBRIZBPLWwpG8Gxo8fj6amJmzcuFGWpVIpbNq0CdOmTSvlrgiCIAYts6u+UmkTiCFGwW8GDh48iHfeeUcu79ixA9u2bcPIkSMxZswYLFy4EO3t7ZgwYQImTJiA9vZ21NXV4fLLLy/YuPVdawreJh/Iv06Ui960BlK/AiBWw9+QheU6sOSb78vcc0mpNQhm81adhF07kU877PDh0DpSV8C/3bQy9zzsOIVWosTnwbTFWkfk9TBygAB+bJNKEtATcJvUnCJqvhKvkpGTQNV6hekHcmgxAC/nSW90dXWhoeGhXuuVhlIIAKP5ZqDgwcCLL76I888/Xy4vWrQIADBv3jw88MADWLx4Mbq7uzF//nwZdGjDhg2or68vndUEQRAEUW4GsZug4MHAzJkzdUWpgeM4aGtrQ1tbWzF2EQRBEARRJig3AUEQBEHkg8tQ9Gv+oTCbgCAIOxcMn+f9ELkDhI8/1xx/giCiBXOL15j0s1anr9BggCAqiKMmIwoTDsoEMk6grlNb61URQWFEEK4QQdaAIB9hH68jg4zx5E5CtBevrpZVs4cO29vr5aHc3wmzBiKquNEUOgpxoUh4pPZBKZhURJXr9//vfrOTKBwaDBAEQRBEPpCAkCAIgiCGOKQZIAiCIKLABTVXVNqEoQu9GSAIohhYigdtEQFphE/blrPDSHTDuG/bUcWG3PUq1/H2HO47z6kZkMl3LA8lpgeBKVR7QH728hLj171PQadKbIPQrwDA+r3/VClziD5CgwGCIAiCyAeGErwZKIklJYcGAwRBEASRD4PYTUCTnAmCIAhiiENvBgiiDFByLKJUPHXk55U2YejiugCKDBrkFr79fffdh7vvvhu7d+/GpEmTsGLFCnzmM58pzg4DejNAEARBEPkg3ATFfgrg4YcfxsKFC7FkyRK8/PLL+MxnPoM5c+bg/fffL+mh0WCAIAiCICLK8uXL8T//5//E17/+dZxyyilYsWIFWltbsWrVqpLuhwYDBEEQBJEPJXwz0NXVpX16enoCu0ulUti6dStmz56tlc+ePRubN28u6aHRYIAgCIIg8sFlpfkAaG1tRUNDg/x0dHQEdvfRRx8hm82isbFRK29sbERnZ2dJD40EhARBEARRZnbt2oURI0bI5SRP5mTDMYKTMcYCZcVCgwGCIAiCyAPGXBn1s5g2AGDEiBHaYMDGMcccg3g8HngLsGfPnsDbgmIhNwFBEARB5AMrgYuggNkE1dXVOOuss7Bx40atfOPGjZg2bVpJD43eDBAEQRBEPrASZC0scGrhokWLcOWVV2LKlCk499xzsXr1arz//vu45pprirPDgAYDBEEQBBFRLr30Unz88ce47bbbsHv3bkyePBm/+tWvMHbs2JLuhwYDBEEQBJEPrgs4RUYg7IPmYP78+Zg/f35x++0FGgwQBEEQRD5UwE1QLkhASBAEQRBDHHozQBAEQRB5wFwXrEg3QbFTE/sLGgwQBEEQRD6Qm4AgCIIgiMEKvRkgCIIgiHxwGeAMzjcDNBggCIIgiHxgDECxUwujORggNwFBEARBDHHozQBBEARB5AFzGViRbgIW0TcDNBggCIIgiHxgLop3E0RzamG/uQnuu+8+jB8/HjU1NTjrrLPw29/+tr92RRAEQRD9DnNZST5RpF8GAw8//DAWLlyIJUuW4OWXX8ZnPvMZzJkzB++//35/7I4gCIIgiCJwWD84MKZOnYpPfepTWLVqlSw75ZRTcMkll6Cjo0Or29PTg56eHrm8f/9+jBkzBrt27cKIESNKbRpBEAQxiOjq6kJrayv27duHhoaGfttHQ0MDpuNzSKCqqLYySON5/Ar79++P1N+4kmsGUqkUtm7dim9/+9ta+ezZs7F58+ZA/Y6ODixdujRQ3traWmrTCIIgiEHKxx9/3G+DgerqajQ1NeH5zl+VpL2mpiZUV1eXpK1SUfLBwEcffYRsNovGxkatvLGxEZ2dnYH6N998MxYtWiSX9+3bh7Fjx+L999/vtwtbasTIdKC8zSB7+xeyt38he/uXgWaveJs8cuTIfttHTU0NduzYgVQqVZL2qqurUVNTU5K2SkW/zSZwHEdbZowFygAgmUwimUwGyhsaGgZER1QZMWLEgLKZ7O1fyN7+heztXwaavbFY/4bNqampidwf8FJS8rN3zDHHIB6PB94C7NmzJ/C2gCAIgiCIylPywUB1dTXOOussbNy4USvfuHEjpk2bVurdEQRBEARRJP3iJli0aBGuvPJKTJkyBeeeey5Wr16N999/H9dcc02v2yaTSdx6661W10FUGWg2k739C9nbv5C9/QvZOzTpl6mFgBd0aNmyZdi9ezcmT56Me+65B+edd15/7IogCIIgiCLot8EAQRAEQRADA8paSBAEQRBDHBoMEARBEMQQhwYDBEEQBDHEocEAQRAEQQxxIjcYiGrq446ODpx99tmor6/HqFGjcMkll+Ctt97S6jDG0NbWhpaWFtTW1mLmzJl4/fXXK2SxT0dHBxzHwcKFC2VZFG3905/+hL/927/F0Ucfjbq6Onzyk5/E1q1b5foo2ZzJZPCd73wH48ePR21tLY4//njcdtttcF0/V3kl7X3uuedw0UUXoaWlBY7j4LHHHtPW52NbT08PrrvuOhxzzDEYNmwYvvjFL+KDDz4ou73pdBo33XQTTjvtNAwbNgwtLS346le/ig8//DCS9ppcffXVcBwHK1asiLS9b775Jr74xS+ioaEB9fX1OOecc7RMs+W0Nx+bDx48iAULFmD06NGora3FKaecoiXHq4TNA5lIDQainPp406ZNuPbaa/HCCy9g48aNyGQymD17Ng4dOiTrLFu2DMuXL8fKlSuxZcsWNDU1YdasWThw4EDF7N6yZQtWr16N008/XSuPmq179+7Fpz/9aVRVVeHJJ5/EG2+8gR/84Ac46qijImnzXXfdhZ/85CdYuXIl3nzzTSxbtgx33303fvzjH0fC3kOHDuGMM87AypUrrevzsW3hwoV49NFH8dBDD+H555/HwYMH8YUvfAHZbLas9h4+fBgvvfQSvvvd7+Kll17CI488grfffhtf/OIXtXpRsVflsccew3/+53+ipaUlsC5K9v7xj3/E9OnTcfLJJ+PZZ5/FK6+8gu9+97ta+N1y2puPzTfccAPWr1+PBx98EG+++SZuuOEGXHfddXj88ccrZvOAhkWIv/iLv2DXXHONVnbyySezb3/72xWyKJw9e/YwAGzTpk2MMcZc12VNTU3szjvvlHWOHDnCGhoa2E9+8pOK2HjgwAE2YcIEtnHjRjZjxgx2/fXXR9bWm266iU2fPj10fdRs/vznP8++9rWvaWVz585lf/u3f8sYi5a9ANijjz4ql/Oxbd++fayqqoo99NBDss6f/vQnFovF2Pr168tqr43f/e53DADbuXMnYyya9n7wwQfsuOOOY7///e/Z2LFj2T333CPXRc3eSy+9VPZdG5W0lzG7zZMmTWK33XabVvapT32Kfec734mEzQONyLwZEKmPZ8+erZWHpT6uNPv37wcAmSlrx44d6Ozs1OxPJpOYMWNGxey/9tpr8fnPfx5/9Vd/pZVH0dYnnngCU6ZMwZe//GWMGjUKZ555Jn7605/K9VGzefr06fj1r3+Nt99+GwDwyiuv4Pnnn8fnPve5SNqrko9tW7duRTqd1uq0tLRg8uTJFbcf8O4/x3Hkm6Oo2eu6Lq688krceOONmDRpUmB9lOx1XRe//OUvMXHiRFxwwQUYNWoUpk6dqr2Wj5K9gunTp+OJJ57An/70JzDG8Mwzz+Dtt9/GBRdcEFmbo0xkBgOFpj6uJIwxLFq0CNOnT8fkyZMBQNoYFfsfeughvPTSS+jo6Aisi5qtAPDuu+9i1apVmDBhAp566ilcc801+MY3voGf/exnAKJn80033YTLLrsMJ598MqqqqnDmmWdi4cKFuOyyyyJpr0o+tnV2dqK6uhqf+MQnQutUiiNHjuDb3/42Lr/8cplVL2r23nXXXUgkEvjGN75hXR8le/fs2YODBw/izjvvxIUXXogNGzbgS1/6EubOnYtNmzZFzl7Bj370I5x66qkYPXo0qqurceGFF+K+++7D9OnTAUTT5ijTbymM+0q+qY8ryYIFC/Dqq6/i+eefD6yLgv27du3C9ddfjw0bNuRMuRkFWwWu62LKlClob28HAJx55pl4/fXXsWrVKnz1q1+V9aJi88MPP4wHH3wQ69atw6RJk7Bt2zYsXLgQLS0tmDdvnqwXFXtt9MW2StufTqfxla98Ba7r4r777uu1fiXs3bp1K374wx/ipZdeKnjflbBXiF4vvvhi3HDDDQCAT37yk9i8eTN+8pOfYMaMGaHbVrI//OhHP8ILL7yAJ554AmPHjsVzzz2H+fPno7m5OfA2VKXSfTiqRObNwEBJfXzdddfhiSeewDPPPIPRo0fL8qamJgCIhP1bt27Fnj17cNZZZyGRSCCRSGDTpk340Y9+hEQiIe2Jgq2C5uZmnHrqqVrZKaecIsWjUTq/AHDjjTfi29/+Nr7yla/gtNNOw5VXXokbbrhBvomJmr0q+djW1NSEVCqFvXv3htYpN+l0Gn/zN3+DHTt2YOPGjfKtABAte3/7299iz549GDNmjLz/du7ciW9+85sYN25c5Ow95phjkEgker3/omIvAHR3d+OWW27B8uXLcdFFF+H000/HggULcOmll+L73/9+JG2OOpEZDEQ99TFjDAsWLMAjjzyC3/zmNxg/fry2fvz48WhqatLsT6VS2LRpU9nt/8u//Eu89tpr2LZtm/xMmTIFV1xxBbZt24bjjz8+MrYKPv3pTwemar799tsYO3YsgGidX8BTuMdi+u0Tj8flf1lRs1clH9vOOussVFVVaXV2796N3//+9xWxXwwEtm/fjqeffhpHH320tj5K9l555ZV49dVXtfuvpaUFN954I5566qnI2VtdXY2zzz475/0XJXsBrz+k0+mc92DUbI48lVAthvHQQw+xqqoqdv/997M33niDLVy4kA0bNoy99957lTaN/cM//ANraGhgzz77LNu9e7f8HD58WNa58847WUNDA3vkkUfYa6+9xi677DLW3NzMurq6Kmi5hzqbgLHo2fq73/2OJRIJdscdd7Dt27ezn//856yuro49+OCDkbR53rx57LjjjmP/9m//xnbs2MEeeeQRdswxx7DFixdHwt4DBw6wl19+mb388ssMAFu+fDl7+eWXpfo+H9uuueYaNnr0aPb000+zl156iX32s59lZ5xxBstkMmW1N51Osy9+8Yts9OjRbNu2bdr919PTEzl7bZizCaJm7yOPPMKqqqrY6tWr2fbt29mPf/xjFo/H2W9/+9uK2JuPzTNmzGCTJk1izzzzDHv33XfZmjVrWE1NDbvvvvsqZvNAJlKDAcYYu/fee9nYsWNZdXU1+9SnPiWn7lUaANbPmjVrZB3Xddmtt97KmpqaWDKZZOeddx577bXXKme0gjkYiKKt//qv/8omT57MkskkO/nkk9nq1au19VGyuauri11//fVszJgxrKamhh1//PFsyZIl2h+nStr7zDPPWPvrvHnz8ratu7ubLViwgI0cOZLV1tayL3zhC+z9998vu707duwIvf+eeeaZyNlrwzYYiJq9999/PzvxxBNZTU0NO+OMM9hjjz1WMXvzsXn37t3sqquuYi0tLaympoaddNJJ7Ac/+AFzXbdiNg9kKIUxQRAEQQxxIqMZIAiCIAiiMtBggCAIgiCGODQYIAiCIIghDg0GCIIgCGKIQ4MBgiAIghji0GCAIAiCIIY4NBggCIIgiCEODQYIgiAIYohDgwGCIAiCGOLQYIAgCIIghjg0GCAIgiCIIc7/D1wgBslvN2QLAAAAAElFTkSuQmCC", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "close(ds)\n", - "pcolor(temp'); colorbar();" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(180, 89)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "size(temp)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10.866639f0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp[100,20]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download all data (this will take some time)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "load https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/01/ersst.v5.199901.nc\n", - "load https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/02/ersst.v5.199902.nc\n", - "load https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/03/ersst.v5.199903.nc\n", - "load https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/04/ersst.v5.199904.nc\n", - "load https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/05/ersst.v5.199905.nc\n", - "load https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/06/ersst.v5.199906.nc\n", - "load https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/07/ersst.v5.199907.nc\n", - "load https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/08/ersst.v5.199908.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "syntax error, unexpected WORD_WORD, expecting SCAN_ATTR or SCAN_DATASET or SCAN_ERROR\n", - "context: 503 Service Unavailable

Service Unavailable

The server is temporarily unable to service yourrequest due to maintenance downtime or capacityproblems. Please try again later.

\n" - ] - }, - { - "ename": "LoadError", - "evalue": "NetCDF error: \u001b[31mOpening path https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/08/ersst.v5.199908.nc: NetCDF: DAP server error\u001b[39m (NetCDF error code: -70)", - "output_type": "error", - "traceback": [ - "NetCDF error: \u001b[31mOpening path https://podaac-opendap.jpl.nasa.gov/opendap/allData/ersst/L4/ncei/v5/monthly/netcdf/1999/08/ersst.v5.199908.nc: NetCDF: DAP server error\u001b[39m (NetCDF error code: -70)", - "", - "Stacktrace:", - " [1] nc_open(path::String, mode::UInt16)", - " @ NCDatasets ~/.julia/dev/NCDatasets/src/netcdf_c.jl:274", - " [2] NCDataset(filename::String, mode::String; format::Symbol, share::Bool, diskless::Bool, persist::Bool, memory::Nothing, attrib::Vector{Any})", - " @ NCDatasets ~/.julia/dev/NCDatasets/src/dataset.jl:203", - " [3] NCDataset (repeats 2 times)", - " @ ~/.julia/dev/NCDatasets/src/dataset.jl:163 [inlined]", - " [4] top-level scope", - " @ ./In[8]:5", - " [5] eval", - " @ ./boot.jl:368 [inlined]", - " [6] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)", - " @ Base ./loading.jl:1428" - ] - } - ], - "source": [ - "SST = allowmissing(zeros(180, 89,length(URLs)));\n", - "\n", - "for i = 1:length(URLs)\n", - " println(\"load \",URLs[i])\n", - " ds = Dataset(URLs[i])\n", - " temp = ds[\"sst\"][:,:,1,1]\n", - " close(ds)\n", - " \n", - " SST[:,:,i] = temp;\n", - "end" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "check the size" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(180, 89, 24)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "size(SST)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1-element Vector{PyObject}:\n", - " PyObject " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot(DateTime.(ssttime),SST[100,20,:])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "linear_regression (generic function with 1 method)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# y ≈ a + b*x\n", - "function linear_regression(x,y)\n", - " xm = mean(x)\n", - " ym = mean(y)\n", - " ss_xx = sum((x .- xm).^2)\n", - " ss_yy = sum((y .- ym).^2)\n", - " ss_xy = sum((x .- xm) .* (y .- ym))\n", - " b = ss_xy / ss_xx\n", - " a = ym - b * xm\n", - " r2 = ss_xy^2 / (ss_xx*ss_yy)\n", - " return a,b,r2\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.0, 3.0, 1.0)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = [0,1,2]\n", - "y = 1 .+ 3 * x\n", - "a,b,r2 = linear_regression(x,y)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(12687.778052414913, -6.345742683590364, 0.6395232530569229)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "yeartime = Dates.value.(ssttime) / (1000 * 60 * 60 * 24 * 365.25);\n", - "\n", - "linear_regression(yeartime,SST[100,20,:])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "a = allowmissing(zeros(180, 89))\n", - "b = allowmissing(zeros(180, 89))\n", - "r2 = allowmissing(zeros(180, 89))\n", - "\n", - "for i = 1:180\n", - " for j = 1:89\n", - " a[i,j],b[i,j],r2[i,j] = linear_regression(yeartime,SST[i,j,:])\n", - " end\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pcolor(b'; cmap=\"bwr\"); colorbar(); clim(-0.15,0.15)\n", - "title(\"SST trend 1980-2000\");\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: better compute first the annual mean" - ] - } - ], - "metadata": { - "@webio": { - "lastCommId": "aa46a20d8e9a4d0694d106eb31fd3e87", - "lastKernelId": "a1e12dd1-2c01-4edc-a322-106c29df2617" - }, - "kernelspec": { - "display_name": "Julia 1.8.0", - "language": "julia", - "name": "julia-1.8" - }, - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.8.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}